diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..318ccb91b715b9c5d35bec945bacce10c3d4beaa 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI-KJNodes/docs/images/2024-04-03_20_49_29-ComfyUI.png filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI-KJNodes/fonts/FreeMono.ttf filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI-KJNodes/fonts/FreeMonoBoldOblique.otf filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI-KJNodes/fonts/TTNorms-Black.otf filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI-to-Python-Extension/images/comfyui_to_python_banner.png filter=lfs diff=lfs merge=lfs -text +custom_nodes/ComfyUI-to-Python-Extension/images/SDXL-UI-Example.PNG filter=lfs diff=lfs merge=lfs -text +input/009c.mp4 filter=lfs diff=lfs merge=lfs -text +input/dasha.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..4e8cea71e55139979744378e9918065955bcd7ea --- /dev/null +++ b/.gitignore @@ -0,0 +1,26 @@ +__pycache__/ +*.py[cod] +/output/ +/input/ +!/input/example.png +/models/ +/temp/ +/custom_nodes/ +!custom_nodes/example_node.py.example +extra_model_paths.yaml +/.vs +.vscode/ +.idea/ +venv/ +.venv/ +/web/extensions/* +!/web/extensions/logging.js.example +!/web/extensions/core/ +/tests-ui/data/object_info.json +/user/ +*.log +web_custom_versions/ +.DS_Store +openapi.yaml +filtered-openapi.yaml +uv.lock diff --git a/alembic.ini b/alembic.ini new file mode 100644 index 0000000000000000000000000000000000000000..12f18712f4306f1b76e6b291956797b01469cd80 --- /dev/null +++ b/alembic.ini @@ -0,0 +1,84 @@ +# A generic, single database configuration. + +[alembic] +# path to migration scripts +# Use forward slashes (/) also on windows to provide an os agnostic path +script_location = alembic_db + +# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s +# Uncomment the line below if you want the files to be prepended with date and time +# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file +# for all available tokens +# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s + +# sys.path path, will be prepended to sys.path if present. +# defaults to the current working directory. +prepend_sys_path = . + +# timezone to use when rendering the date within the migration file +# as well as the filename. +# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library. +# Any required deps can installed by adding `alembic[tz]` to the pip requirements +# string value is passed to ZoneInfo() +# leave blank for localtime +# timezone = + +# max length of characters to apply to the "slug" field +# truncate_slug_length = 40 + +# set to 'true' to run the environment during +# the 'revision' command, regardless of autogenerate +# revision_environment = false + +# set to 'true' to allow .pyc and .pyo files without +# a source .py file to be detected as revisions in the +# versions/ directory +# sourceless = false + +# version location specification; This defaults +# to alembic_db/versions. When using multiple version +# directories, initial revisions must be specified with --version-path. +# The path separator used here should be the separator specified by "version_path_separator" below. +# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions + +# version path separator; As mentioned above, this is the character used to split +# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep. +# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas. +# Valid values for version_path_separator are: +# +# version_path_separator = : +# version_path_separator = ; +# version_path_separator = space +# version_path_separator = newline +# +# Use os.pathsep. Default configuration used for new projects. +version_path_separator = os + +# set to 'true' to search source files recursively +# in each "version_locations" directory +# new in Alembic version 1.10 +# recursive_version_locations = false + +# the output encoding used when revision files +# are written from script.py.mako +# output_encoding = utf-8 + +sqlalchemy.url = sqlite:///user/comfyui.db + + +[post_write_hooks] +# post_write_hooks defines scripts or Python functions that are run +# on newly generated revision scripts. See the documentation for further +# detail and examples + +# format using "black" - use the console_scripts runner, against the "black" entrypoint +# hooks = black +# black.type = console_scripts +# black.entrypoint = black +# black.options = -l 79 REVISION_SCRIPT_FILENAME + +# lint with attempts to fix using "ruff" - use the exec runner, execute a binary +# hooks = ruff +# ruff.type = exec +# ruff.executable = %(here)s/.venv/bin/ruff +# ruff.options = check --fix REVISION_SCRIPT_FILENAME diff --git a/alembic_db/README.md b/alembic_db/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3b808c7cab30eaab91c9ff5c1b2d5cc960904e14 --- /dev/null +++ b/alembic_db/README.md @@ -0,0 +1,4 @@ +## Generate new revision + +1. Update models in `/app/database/models.py` +2. Run `alembic revision --autogenerate -m "{your message}"` diff --git a/alembic_db/env.py b/alembic_db/env.py new file mode 100644 index 0000000000000000000000000000000000000000..4d7770679875e2fad065fba0c9222758a40d266b --- /dev/null +++ b/alembic_db/env.py @@ -0,0 +1,64 @@ +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + + +from app.database.models import Base +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline() -> None: + """Run migrations in 'offline' mode. + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + Calls to context.execute() here emit the given string to the + script output. + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online() -> None: + """Run migrations in 'online' mode. + In this scenario we need to create an Engine + and associate a connection with the context. + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section, {}), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, target_metadata=target_metadata + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/alembic_db/script.py.mako b/alembic_db/script.py.mako new file mode 100644 index 0000000000000000000000000000000000000000..480b130d632ca677c11f23d9fe82cf4014d15e0c --- /dev/null +++ b/alembic_db/script.py.mako @@ -0,0 +1,28 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision: str = ${repr(up_revision)} +down_revision: Union[str, None] = ${repr(down_revision)} +branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} +depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} + + +def upgrade() -> None: + """Upgrade schema.""" + ${upgrades if upgrades else "pass"} + + +def downgrade() -> None: + """Downgrade schema.""" + ${downgrades if downgrades else "pass"} diff --git a/api_server/__init__.py b/api_server/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/api_server/routes/__init__.py b/api_server/routes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/api_server/routes/internal/README.md b/api_server/routes/internal/README.md new file mode 100644 index 0000000000000000000000000000000000000000..35330c36f83962385b4afe4653c5967f2bdb73c1 --- /dev/null +++ b/api_server/routes/internal/README.md @@ -0,0 +1,3 @@ +# ComfyUI Internal Routes + +All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice. diff --git a/api_server/routes/internal/__init__.py b/api_server/routes/internal/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/api_server/routes/internal/internal_routes.py b/api_server/routes/internal/internal_routes.py new file mode 100644 index 0000000000000000000000000000000000000000..613b0f7c7cf1cdbb24aae3e880e749839ac1110e --- /dev/null +++ b/api_server/routes/internal/internal_routes.py @@ -0,0 +1,73 @@ +from aiohttp import web +from typing import Optional +from folder_paths import folder_names_and_paths, get_directory_by_type +from api_server.services.terminal_service import TerminalService +import app.logger +import os + +class InternalRoutes: + ''' + The top level web router for internal routes: /internal/* + The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only. + Check README.md for more information. + ''' + + def __init__(self, prompt_server): + self.routes: web.RouteTableDef = web.RouteTableDef() + self._app: Optional[web.Application] = None + self.prompt_server = prompt_server + self.terminal_service = TerminalService(prompt_server) + + def setup_routes(self): + @self.routes.get('/logs') + async def get_logs(request): + return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()])) + + @self.routes.get('/logs/raw') + async def get_raw_logs(request): + self.terminal_service.update_size() + return web.json_response({ + "entries": list(app.logger.get_logs()), + "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows} + }) + + @self.routes.patch('/logs/subscribe') + async def subscribe_logs(request): + json_data = await request.json() + client_id = json_data["clientId"] + enabled = json_data["enabled"] + if enabled: + self.terminal_service.subscribe(client_id) + else: + self.terminal_service.unsubscribe(client_id) + + return web.Response(status=200) + + + @self.routes.get('/folder_paths') + async def get_folder_paths(request): + response = {} + for key in folder_names_and_paths: + response[key] = folder_names_and_paths[key][0] + return web.json_response(response) + + @self.routes.get('/files/{directory_type}') + async def get_files(request: web.Request) -> web.Response: + directory_type = request.match_info['directory_type'] + if directory_type not in ("output", "input", "temp"): + return web.json_response({"error": "Invalid directory type"}, status=400) + + directory = get_directory_by_type(directory_type) + sorted_files = sorted( + (entry for entry in os.scandir(directory) if entry.is_file()), + key=lambda entry: -entry.stat().st_mtime + ) + return web.json_response([entry.name for entry in sorted_files], status=200) + + + def get_app(self): + if self._app is None: + self._app = web.Application() + self.setup_routes() + self._app.add_routes(self.routes) + return self._app diff --git a/api_server/services/__init__.py b/api_server/services/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/api_server/services/terminal_service.py b/api_server/services/terminal_service.py new file mode 100644 index 0000000000000000000000000000000000000000..ab4371f4f855c6d347744f751fd1bc5cc0f5a173 --- /dev/null +++ b/api_server/services/terminal_service.py @@ -0,0 +1,60 @@ +from app.logger import on_flush +import os +import shutil + + +class TerminalService: + def __init__(self, server): + self.server = server + self.cols = None + self.rows = None + self.subscriptions = set() + on_flush(self.send_messages) + + def get_terminal_size(self): + try: + size = os.get_terminal_size() + return (size.columns, size.lines) + except OSError: + try: + size = shutil.get_terminal_size() + return (size.columns, size.lines) + except OSError: + return (80, 24) # fallback to 80x24 + + def update_size(self): + columns, lines = self.get_terminal_size() + changed = False + + if columns != self.cols: + self.cols = columns + changed = True + + if lines != self.rows: + self.rows = lines + changed = True + + if changed: + return {"cols": self.cols, "rows": self.rows} + + return None + + def subscribe(self, client_id): + self.subscriptions.add(client_id) + + def unsubscribe(self, client_id): + self.subscriptions.discard(client_id) + + def send_messages(self, entries): + if not len(entries) or not len(self.subscriptions): + return + + new_size = self.update_size() + + for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration + if client_id not in self.server.sockets: + # Automatically unsub if the socket has disconnected + self.unsubscribe(client_id) + continue + + self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id) diff --git a/api_server/utils/file_operations.py b/api_server/utils/file_operations.py new file mode 100644 index 0000000000000000000000000000000000000000..32d6e047a5da09d0f382bb3fa93ce8d52b699aa7 --- /dev/null +++ b/api_server/utils/file_operations.py @@ -0,0 +1,42 @@ +import os +from typing import List, Union, TypedDict, Literal +from typing_extensions import TypeGuard +class FileInfo(TypedDict): + name: str + path: str + type: Literal["file"] + size: int + +class DirectoryInfo(TypedDict): + name: str + path: str + type: Literal["directory"] + +FileSystemItem = Union[FileInfo, DirectoryInfo] + +def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]: + return item["type"] == "file" + +class FileSystemOperations: + @staticmethod + def walk_directory(directory: str) -> List[FileSystemItem]: + file_list: List[FileSystemItem] = [] + for root, dirs, files in os.walk(directory): + for name in files: + file_path = os.path.join(root, name) + relative_path = os.path.relpath(file_path, directory) + file_list.append({ + "name": name, + "path": relative_path, + "type": "file", + "size": os.path.getsize(file_path) + }) + for name in dirs: + dir_path = os.path.join(root, name) + relative_path = os.path.relpath(dir_path, directory) + file_list.append({ + "name": name, + "path": relative_path, + "type": "directory" + }) + return file_list diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..105c366ac8b567d5474f3c76892039016f68f210 --- /dev/null +++ b/app.py @@ -0,0 +1,560 @@ +import os +import cv2 +import numpy as np +import random +import sys +import subprocess +from typing import Sequence, Mapping, Any, Union +import torch +from tqdm import tqdm +import argparse +import json +import logging + +import shutil +import gradio as gr +import spaces +from huggingface_hub import snapshot_download +import time +import traceback + +from utils import get_path_after_pexel + +LOCAL_GRADIO_TMP = os.path.abspath("./gradio_tmp") +os.makedirs(LOCAL_GRADIO_TMP, exist_ok=True) +os.environ["GRADIO_TEMP_DIR"] = LOCAL_GRADIO_TMP + + +HF_REPOS = { + "QingyanBai/Ditto_models": ["models_comfy/ditto_global_comfy.safetensors"], + "Kijai/WanVideo_comfy": [ + "Wan2_1-T2V-14B_fp8_e4m3fn.safetensors", + "Wan21_CausVid_14B_T2V_lora_rank32_v2.safetensors", + "Wan2_1_VAE_bf16.safetensors", + "umt5-xxl-enc-bf16.safetensors", + ], +} + +MODELS_ROOT = os.path.abspath(os.path.join(os.getcwd(), "models")) +PATHS = { + "diffusion_model": os.path.join(MODELS_ROOT, "diffusion_models"), + "vae_wan": os.path.join(MODELS_ROOT, "vae", "wan"), + "loras": os.path.join(MODELS_ROOT, "loras"), + "text_encoders": os.path.join(MODELS_ROOT, "text_encoders"), +} + +REQUIRED_FILES = [ + ("Wan2_1-T2V-14B_fp8_e4m3fn.safetensors", "diffusion_model"), + ("ditto_global_comfy.safetensors", "diffusion_model"), + ("Wan21_CausVid_14B_T2V_lora_rank32_v2.safetensors", "loras"), + ("Wan2_1_VAE_bf16.safetensors", "vae_wan"), + ("umt5-xxl-enc-bf16.safetensors", "text_encoders"), +] + +def ensure_dir(path: str) -> None: + os.makedirs(path, exist_ok=True) + +def ensure_models() -> None: + for filename, key in REQUIRED_FILES: + target_dir = PATHS[key] + ensure_dir(target_dir) + target_path = os.path.join(target_dir, filename) + ready_flag = os.path.join(target_dir, f"{filename}.READY") + + if os.path.exists(target_path) and os.path.getsize(target_path) > 0: + open(ready_flag, "a").close() + continue + + repo_id = None + repo_file_path = None + for repo, files in HF_REPOS.items(): + for file_path in files: + if filename in file_path: + repo_id = repo + repo_file_path = file_path + break + if repo_id: + break + + if repo_id is None: + raise RuntimeError(f"Could not find repository for file: {filename}") + + print(f"Downloading {filename} from {repo_id} to {target_dir} ...") + + snapshot_download( + repo_id=repo_id, + local_dir=target_dir, + local_dir_use_symlinks=False, + allow_patterns=[repo_file_path], + token=os.getenv("HF_TOKEN", None), + ) + + if not os.path.exists(target_path): + found = [] + for root, _, files in os.walk(target_dir): + for f in files: + if f == filename: + found.append(os.path.join(root, f)) + if found: + src = found[0] + if src != target_path: + shutil.copy2(src, target_path) + + if not os.path.exists(target_path): + raise RuntimeError(f"Failed to download required file: {filename}") + + open(ready_flag, "w").close() + print(f"Downloaded and ready: {target_path}") +ensure_models() + + +def ensure_t5_tokenizer() -> None: + """ + Ensure the local T5 tokenizer folder exists and contains valid files. + If missing or corrupted, download from 'google/umt5-xxl' and save locally + to the exact path expected by the WanVideo wrapper nodes. + """ + try: + script_directory = os.path.dirname(os.path.abspath(__file__)) + tokenizer_dir = os.path.join( + script_directory, + "custom_nodes", + "ComfyUI_WanVideoWrapper", + "configs", + "T5_tokenizer", + ) + os.makedirs(tokenizer_dir, exist_ok=True) + + required_files = [ + "tokenizer.json", + "tokenizer_config.json", + "spiece.model", + "special_tokens_map.json", + ] + + def is_valid(path: str) -> bool: + return os.path.exists(path) and os.path.getsize(path) > 0 + + all_ok = all(is_valid(os.path.join(tokenizer_dir, f)) for f in required_files) + if all_ok: + print(f"T5 tokenizer ready at: {tokenizer_dir}") + return + + print(f"Preparing T5 tokenizer at: {tokenizer_dir} ...") + from transformers import AutoTokenizer + + tok = AutoTokenizer.from_pretrained( + "google/umt5-xxl", + use_fast=True, + trust_remote_code=False, + ) + tok.save_pretrained(tokenizer_dir) + + # Re-check + all_ok = all(is_valid(os.path.join(tokenizer_dir, f)) for f in required_files) + if not all_ok: + raise RuntimeError("Tokenizer files not fully prepared after save_pretrained") + print("T5 tokenizer prepared successfully.") + except Exception as e: + print(f"Failed to prepare T5 tokenizer: {e}\n{traceback.format_exc()}") + raise + + +ensure_t5_tokenizer() + + +def setup_global_logging_filter(): + class MemoryLogFilter(logging.Filter): + def filter(self, record): + msg = record.getMessage() + keywords = [ + "Allocated memory:", + "Max allocated memory:", + "Max reserved memory:", + "memory=", + "max_memory=", + "max_reserved=", + "Block swap memory summary", + "Transformer blocks on", + "Total memory used by", + "Non-blocking memory transfer" + ] + return not any(kw in msg for kw in keywords) + + logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + force=True + ) + logging.getLogger().handlers[0].addFilter(MemoryLogFilter()) + + +setup_global_logging_filter() + + +def tensor_to_video(video_tensor, output_path, fps=20, crf=20): + frames = video_tensor.detach().cpu().numpy() + if frames.dtype != np.uint8: + if frames.max() <= 1.0: + frames = (frames * 255).astype(np.uint8) + else: + frames = frames.astype(np.uint8) + num_frames, height, width, _ = frames.shape + command = [ + 'ffmpeg', + '-y', + '-f', 'rawvideo', + '-vcodec', 'rawvideo', + '-pix_fmt', 'rgb24', + '-s', f'{width}x{height}', + '-r', str(fps), + '-i', '-', + '-c:v', 'libx264', + '-pix_fmt', 'yuv420p', + '-crf', str(crf), + '-preset', 'medium', + '-r', str(fps), + '-an', + output_path + ] + + with subprocess.Popen(command, stdin=subprocess.PIPE, stderr=subprocess.PIPE) as proc: + for frame in frames: + proc.stdin.write(frame.tobytes()) + proc.stdin.close() + if proc.stderr is not None: + proc.stderr.read() + + +def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any: + try: + return obj[index] + except KeyError: + return obj["result"][index] + + +def find_path(name: str, path: str = None) -> str: + if path is None: + path = os.getcwd() + if name in os.listdir(path): + path_name = os.path.join(path, name) + print(f"{name} found: {path_name}") + return path_name + parent_directory = os.path.dirname(path) + if parent_directory == path: + return None + return find_path(name, parent_directory) + + +def add_comfyui_directory_to_sys_path() -> None: + comfyui_path = find_path("ComfyUI") + if comfyui_path is not None and os.path.isdir(comfyui_path): + if comfyui_path not in sys.path: + sys.path.append(comfyui_path) + print(f"'{comfyui_path}' added to sys.path") + + +def add_extra_model_paths() -> None: + try: + from main import load_extra_path_config + except ImportError: + print( + "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead." + ) + from utils.extra_config import load_extra_path_config + + extra_model_paths = find_path("extra_model_paths.yaml") + + if extra_model_paths is not None: + load_extra_path_config(extra_model_paths) + else: + print("Could not find the extra_model_paths config file.") + + +add_comfyui_directory_to_sys_path() +add_extra_model_paths() + + +def import_custom_nodes() -> None: + import asyncio + import execution + from nodes import init_extra_nodes + import server + + if getattr(import_custom_nodes, "_initialized", False): + return + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + server_instance = server.PromptServer(loop) + execution.PromptQueue(server_instance) + init_extra_nodes() + import_custom_nodes._initialized = True + + +from nodes import NODE_CLASS_MAPPINGS + +print(f"Loading custom nodes and models...") +import_custom_nodes() + + +@spaces.GPU() +def run_pipeline(vpath, prompt, width, height, fps, frame_count, outdir): + try: + import gc + # Clean memory before starting + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + os.makedirs(outdir, exist_ok=True) + + with torch.inference_mode(): + from custom_nodes.ComfyUI_WanVideoWrapper import nodes as wan_nodes + vhs_loadvideo = NODE_CLASS_MAPPINGS["VHS_LoadVideo"]() + + # Set model and settings. + wanvideovacemodelselect = wan_nodes.WanVideoVACEModelSelect() + wanvideovacemodelselect_89 = wanvideovacemodelselect.getvacepath( + vace_model="ditto_global_comfy.safetensors" + ) + + wanvideoslg = wan_nodes.WanVideoSLG() + wanvideoslg_113 = wanvideoslg.process( + blocks="2", + start_percent=0.20000000000000004, + end_percent=0.7000000000000002, + ) + wanvideovaeloader = wan_nodes.WanVideoVAELoader() + wanvideovaeloader_133 = wanvideovaeloader.loadmodel( + model_name="wan/Wan2_1_VAE_bf16.safetensors", precision="bf16" + ) + + loadwanvideot5textencoder = wan_nodes.LoadWanVideoT5TextEncoder() + loadwanvideot5textencoder_134 = loadwanvideot5textencoder.loadmodel( + model_name="umt5-xxl-enc-bf16.safetensors", + precision="bf16", + load_device="offload_device", + quantization="disabled", + ) + + wanvideoblockswap = wan_nodes.WanVideoBlockSwap() + wanvideoblockswap_137 = wanvideoblockswap.setargs( + blocks_to_swap=30, + offload_img_emb=False, + offload_txt_emb=False, + use_non_blocking=True, + vace_blocks_to_swap=0, + ) + + wanvideoloraselect = wan_nodes.WanVideoLoraSelect() + wanvideoloraselect_380 = wanvideoloraselect.getlorapath( + lora="Wan21_CausVid_14B_T2V_lora_rank32_v2.safetensors", + strength=1.0, + low_mem_load=False, + ) + + wanvideomodelloader = wan_nodes.WanVideoModelLoader() + imageresizekjv2 = NODE_CLASS_MAPPINGS["ImageResizeKJv2"]() + wanvideovaceencode = wan_nodes.WanVideoVACEEncode() + wanvideotextencode = wan_nodes.WanVideoTextEncode() + wanvideosampler = wan_nodes.WanVideoSampler() + wanvideodecode = wan_nodes.WanVideoDecode() + wanvideomodelloader_142 = wanvideomodelloader.loadmodel( + model="Wan2_1-T2V-14B_fp8_e4m3fn.safetensors", + base_precision="fp16", + quantization="disabled", + load_device="offload_device", + attention_mode="sdpa", + block_swap_args=get_value_at_index(wanvideoblockswap_137, 0), + lora=get_value_at_index(wanvideoloraselect_380, 0), + vace_model=get_value_at_index(wanvideovacemodelselect_89, 0), + ) + + fname = os.path.basename(vpath) + fname_clean = os.path.splitext(fname)[0] + + vhs_loadvideo_70 = vhs_loadvideo.load_video( + video=vpath, + force_rate=24, + custom_width=width, + custom_height=height, + frame_load_cap=frame_count, + skip_first_frames=1, + select_every_nth=1, + format="AnimateDiff", + unique_id=16696422174153060213, + ) + + imageresizekjv2_205 = imageresizekjv2.resize( + width=width, + height=height, + upscale_method="area", + keep_proportion="resize", + pad_color="0, 0, 0", + crop_position="center", + divisible_by=8, + device="cpu", + image=get_value_at_index(vhs_loadvideo_70, 0), + ) + wanvideovaceencode_29 = wanvideovaceencode.process( + width=width, + height=height, + num_frames=frame_count, + strength=0.9750000000000002, + vace_start_percent=0, + vace_end_percent=1, + tiled_vae=False, + vae=get_value_at_index(wanvideovaeloader_133, 0), + input_frames=get_value_at_index(imageresizekjv2_205, 0), + ) + + wanvideotextencode_148 = wanvideotextencode.process( + positive_prompt=prompt, + negative_prompt="flickering artifact, jpg artifacts, compression, distortion, morphing, low-res, fake, oversaturated, overexposed, over bright, strange behavior, distorted limbs, unnatural motion, unrealistic anatomy, glitch, extra limbs,", + force_offload=True, + t5=get_value_at_index(loadwanvideot5textencoder_134, 0), + model_to_offload=get_value_at_index(wanvideomodelloader_142, 0), + ) + + # Clean memory before sampling (most memory-intensive step) + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + wanvideosampler_2 = wanvideosampler.process( + steps=4, + cfg=1.2000000000000002, + shift=2.0000000000000004, + seed=random.randint(1, 2 ** 64), + force_offload=True, + scheduler="unipc", + riflex_freq_index=0, + denoise_strength=1, + batched_cfg=False, + rope_function="comfy", + model=get_value_at_index(wanvideomodelloader_142, 0), + image_embeds=get_value_at_index(wanvideovaceencode_29, 0), + text_embeds=get_value_at_index(wanvideotextencode_148, 0), + slg_args=get_value_at_index(wanvideoslg_113, 0), + ) + res = wanvideodecode.decode( + enable_vae_tiling=False, + tile_x=272, + tile_y=272, + tile_stride_x=144, + tile_stride_y=128, + vae=get_value_at_index(wanvideovaeloader_133, 0), + samples=get_value_at_index(wanvideosampler_2, 0), + ) + save_path = os.path.join(outdir, f'{fname_clean}_edit.mp4') + tensor_to_video(res[0], save_path, fps=fps) + + # Clean up memory after generation + del res + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + print(f"Done. Saved to: {save_path}") + return save_path + except Exception as e: + err = f"Error: {e}\n{traceback.format_exc()}" + print(err) + # Clean memory on error too + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + raise + + +@spaces.GPU() +def gradio_infer(vfile, prompt, width, height, fps, frame_count, progress=gr.Progress(track_tqdm=True)): + if vfile is None: + return None, "Please upload the video!", "\n".join(logs) + + vpath = vfile if isinstance(vfile, str) else vfile.name + if not os.path.exists(vpath) and hasattr(vfile, "save"): + os.makedirs("uploads", exist_ok=True) + vpath = os.path.join("uploads", os.path.basename(vfile.name)) + vfile.save(vpath) + + outdir = "results" + os.makedirs(outdir, exist_ok=True) + + save_path = run_pipeline( + vpath=vpath, + prompt=prompt, + width=int(width), + height=int(height), + fps=int(fps), + frame_count=int(frame_count), + outdir=outdir, + ) + return save_path + + +def build_interface(): + with gr.Blocks(title="Ditto") as demo: + gr.Markdown( + """# Ditto: Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset + +
+📄 Paper +  |   +🌐 Project Page +  |   + 💻 Github Code +  |   +📦 Model Weights +  |   +📊 Dataset +
+ +Note1: The backend of this demo is comfy, please note that due to the use of quantized and distilled models, there may be some quality degradation. +Note2: Considering the limited memory, please try test cases with lower resolution and frame count, otherwise it may cause out of memory error. +If you like this project, please consider starring the repo to motivate us. Thank you! + """ + ) + + with gr.Column(): + with gr.Row(): + vfile = gr.Video(label="Input Video", value=os.path.join("input", "dasha.mp4"), + sources="upload", interactive=True) + out_video = gr.Video(label="Result") + prompt = gr.Textbox(label="Editing Instruction", value="Make it in the style of Japanese anime") + with gr.Row(): + width = gr.Number(label="Width", value=576, precision=0) + height = gr.Number(label="Height", value=324, precision=0) + fps = gr.Number(label="FPS", value=20, precision=0) + frame_count = gr.Number(label="Frame Count", value=49, precision=0) + run_btn = gr.Button("Run", variant="primary") + + run_btn.click( + fn=gradio_infer, + inputs=[vfile, prompt, width, height, fps, frame_count], + outputs=[out_video] + ) + examples = [ + [ + os.path.join("input", "dasha.mp4"), + "Add some fire and flame to the background", + 576, 324, 20, 49 + ], + [ + os.path.join("input", "dasha.mp4"), + "Make it in the style of pencil sketch", + 576, 324, 20, 49 + ], + ] + gr.Examples( + examples=examples, + inputs=[vfile, prompt, width, height, fps, frame_count], + label="Examples" + ) + return demo + + +if __name__ == "__main__": + demo = build_interface() + demo.launch() diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/app/app_settings.py b/app/app_settings.py new file mode 100644 index 0000000000000000000000000000000000000000..c7ac73bf6a59d3e89647aed13b70ca9428e16f2a --- /dev/null +++ b/app/app_settings.py @@ -0,0 +1,65 @@ +import os +import json +from aiohttp import web +import logging + + +class AppSettings(): + def __init__(self, user_manager): + self.user_manager = user_manager + + def get_settings(self, request): + try: + file = self.user_manager.get_request_user_filepath( + request, + "comfy.settings.json" + ) + except KeyError as e: + logging.error("User settings not found.") + raise web.HTTPUnauthorized() from e + if os.path.isfile(file): + try: + with open(file) as f: + return json.load(f) + except: + logging.error(f"The user settings file is corrupted: {file}") + return {} + else: + return {} + + def save_settings(self, request, settings): + file = self.user_manager.get_request_user_filepath( + request, "comfy.settings.json") + with open(file, "w") as f: + f.write(json.dumps(settings, indent=4)) + + def add_routes(self, routes): + @routes.get("/settings") + async def get_settings(request): + return web.json_response(self.get_settings(request)) + + @routes.get("/settings/{id}") + async def get_setting(request): + value = None + settings = self.get_settings(request) + setting_id = request.match_info.get("id", None) + if setting_id and setting_id in settings: + value = settings[setting_id] + return web.json_response(value) + + @routes.post("/settings") + async def post_settings(request): + settings = self.get_settings(request) + new_settings = await request.json() + self.save_settings(request, {**settings, **new_settings}) + return web.Response(status=200) + + @routes.post("/settings/{id}") + async def post_setting(request): + setting_id = request.match_info.get("id", None) + if not setting_id: + return web.Response(status=400) + settings = self.get_settings(request) + settings[setting_id] = await request.json() + self.save_settings(request, settings) + return web.Response(status=200) diff --git a/app/custom_node_manager.py b/app/custom_node_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..281febca952363659eb3925280fab00f07c986d0 --- /dev/null +++ b/app/custom_node_manager.py @@ -0,0 +1,145 @@ +from __future__ import annotations + +import os +import folder_paths +import glob +from aiohttp import web +import json +import logging +from functools import lru_cache + +from utils.json_util import merge_json_recursive + + +# Extra locale files to load into main.json +EXTRA_LOCALE_FILES = [ + "nodeDefs.json", + "commands.json", + "settings.json", +] + + +def safe_load_json_file(file_path: str) -> dict: + if not os.path.exists(file_path): + return {} + + try: + with open(file_path, "r", encoding="utf-8") as f: + return json.load(f) + except json.JSONDecodeError: + logging.error(f"Error loading {file_path}") + return {} + + +class CustomNodeManager: + @lru_cache(maxsize=1) + def build_translations(self): + """Load all custom nodes translations during initialization. Translations are + expected to be loaded from `locales/` folder. + + The folder structure is expected to be the following: + - custom_nodes/ + - custom_node_1/ + - locales/ + - en/ + - main.json + - commands.json + - settings.json + + returned translations are expected to be in the following format: + { + "en": { + "nodeDefs": {...}, + "commands": {...}, + "settings": {...}, + ...{other main.json keys} + } + } + """ + + translations = {} + + for folder in folder_paths.get_folder_paths("custom_nodes"): + # Sort glob results for deterministic ordering + for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))): + locales_dir = os.path.join(custom_node_dir, "locales") + if not os.path.exists(locales_dir): + continue + + for lang_dir in glob.glob(os.path.join(locales_dir, "*/")): + lang_code = os.path.basename(os.path.dirname(lang_dir)) + + if lang_code not in translations: + translations[lang_code] = {} + + # Load main.json + main_file = os.path.join(lang_dir, "main.json") + node_translations = safe_load_json_file(main_file) + + # Load extra locale files + for extra_file in EXTRA_LOCALE_FILES: + extra_file_path = os.path.join(lang_dir, extra_file) + key = extra_file.split(".")[0] + json_data = safe_load_json_file(extra_file_path) + if json_data: + node_translations[key] = json_data + + if node_translations: + translations[lang_code] = merge_json_recursive( + translations[lang_code], node_translations + ) + + return translations + + def add_routes(self, routes, webapp, loadedModules): + + example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"] + + @routes.get("/workflow_templates") + async def get_workflow_templates(request): + """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.""" + + files = [] + + for folder in folder_paths.get_folder_paths("custom_nodes"): + for folder_name in example_workflow_folder_names: + pattern = os.path.join(folder, f"*/{folder_name}/*.json") + matched_files = glob.glob(pattern) + files.extend(matched_files) + + workflow_templates_dict = ( + {} + ) # custom_nodes folder name -> example workflow names + for file in files: + custom_nodes_name = os.path.basename( + os.path.dirname(os.path.dirname(file)) + ) + workflow_name = os.path.splitext(os.path.basename(file))[0] + workflow_templates_dict.setdefault(custom_nodes_name, []).append( + workflow_name + ) + return web.json_response(workflow_templates_dict) + + # Serve workflow templates from custom nodes. + for module_name, module_dir in loadedModules: + for folder_name in example_workflow_folder_names: + workflows_dir = os.path.join(module_dir, folder_name) + + if os.path.exists(workflows_dir): + if folder_name != "example_workflows": + logging.debug( + "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", + folder_name, module_name) + + webapp.add_routes( + [ + web.static( + "/api/workflow_templates/" + module_name, workflows_dir + ) + ] + ) + + @routes.get("/i18n") + async def get_i18n(request): + """Returns translations from all custom nodes' locales folders.""" + return web.json_response(self.build_translations()) diff --git a/app/database/db.py b/app/database/db.py new file mode 100644 index 0000000000000000000000000000000000000000..1de8b80edd8a5761039c7314c6387d2184a15e14 --- /dev/null +++ b/app/database/db.py @@ -0,0 +1,112 @@ +import logging +import os +import shutil +from app.logger import log_startup_warning +from utils.install_util import get_missing_requirements_message +from comfy.cli_args import args + +_DB_AVAILABLE = False +Session = None + + +try: + from alembic import command + from alembic.config import Config + from alembic.runtime.migration import MigrationContext + from alembic.script import ScriptDirectory + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + _DB_AVAILABLE = True +except ImportError as e: + log_startup_warning( + f""" +------------------------------------------------------------------------ +Error importing dependencies: {e} +{get_missing_requirements_message()} +This error is happening because ComfyUI now uses a local sqlite database. +------------------------------------------------------------------------ +""".strip() + ) + + +def dependencies_available(): + """ + Temporary function to check if the dependencies are available + """ + return _DB_AVAILABLE + + +def can_create_session(): + """ + Temporary function to check if the database is available to create a session + During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created + """ + return dependencies_available() and Session is not None + + +def get_alembic_config(): + root_path = os.path.join(os.path.dirname(__file__), "../..") + config_path = os.path.abspath(os.path.join(root_path, "alembic.ini")) + scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db")) + + config = Config(config_path) + config.set_main_option("script_location", scripts_path) + config.set_main_option("sqlalchemy.url", args.database_url) + + return config + + +def get_db_path(): + url = args.database_url + if url.startswith("sqlite:///"): + return url.split("///")[1] + else: + raise ValueError(f"Unsupported database URL '{url}'.") + + +def init_db(): + db_url = args.database_url + logging.debug(f"Database URL: {db_url}") + db_path = get_db_path() + db_exists = os.path.exists(db_path) + + config = get_alembic_config() + + # Check if we need to upgrade + engine = create_engine(db_url) + conn = engine.connect() + + context = MigrationContext.configure(conn) + current_rev = context.get_current_revision() + + script = ScriptDirectory.from_config(config) + target_rev = script.get_current_head() + + if target_rev is None: + logging.warning("No target revision found.") + elif current_rev != target_rev: + # Backup the database pre upgrade + backup_path = db_path + ".bkp" + if db_exists: + shutil.copy(db_path, backup_path) + else: + backup_path = None + + try: + command.upgrade(config, target_rev) + logging.info(f"Database upgraded from {current_rev} to {target_rev}") + except Exception as e: + if backup_path: + # Restore the database from backup if upgrade fails + shutil.copy(backup_path, db_path) + os.remove(backup_path) + logging.exception("Error upgrading database: ") + raise e + + global Session + Session = sessionmaker(bind=engine) + + +def create_session(): + return Session() diff --git a/app/database/models.py b/app/database/models.py new file mode 100644 index 0000000000000000000000000000000000000000..6facfb8f2b5e7382274021579a453ad3b7853bf7 --- /dev/null +++ b/app/database/models.py @@ -0,0 +1,14 @@ +from sqlalchemy.orm import declarative_base + +Base = declarative_base() + + +def to_dict(obj): + fields = obj.__table__.columns.keys() + return { + field: (val.to_dict() if hasattr(val, "to_dict") else val) + for field in fields + if (val := getattr(obj, field)) + } + +# TODO: Define models here diff --git a/app/frontend_management.py b/app/frontend_management.py new file mode 100644 index 0000000000000000000000000000000000000000..001ebbecb4f291b938b3d9aaa1d2a3c67dbc98e5 --- /dev/null +++ b/app/frontend_management.py @@ -0,0 +1,326 @@ +from __future__ import annotations +import argparse +import logging +import os +import re +import sys +import tempfile +import zipfile +import importlib +from dataclasses import dataclass +from functools import cached_property +from pathlib import Path +from typing import TypedDict, Optional +from importlib.metadata import version + +import requests +from typing_extensions import NotRequired + +from utils.install_util import get_missing_requirements_message, requirements_path + +from comfy.cli_args import DEFAULT_VERSION_STRING +import app.logger + + +def frontend_install_warning_message(): + return f""" +{get_missing_requirements_message()} + +This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead. +""".strip() + + +def check_frontend_version(): + """Check if the frontend version is up to date.""" + + def parse_version(version: str) -> tuple[int, int, int]: + return tuple(map(int, version.split("."))) + + try: + frontend_version_str = version("comfyui-frontend-package") + frontend_version = parse_version(frontend_version_str) + with open(requirements_path, "r", encoding="utf-8") as f: + required_frontend = parse_version(f.readline().split("=")[-1]) + if frontend_version < required_frontend: + app.logger.log_startup_warning( + f""" +________________________________________________________________________ +WARNING WARNING WARNING WARNING WARNING + +Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}. + +{frontend_install_warning_message()} +________________________________________________________________________ +""".strip() + ) + else: + logging.info("ComfyUI frontend version: {}".format(frontend_version_str)) + except Exception as e: + logging.error(f"Failed to check frontend version: {e}") + + +REQUEST_TIMEOUT = 10 # seconds + + +class Asset(TypedDict): + url: str + + +class Release(TypedDict): + id: int + tag_name: str + name: str + prerelease: bool + created_at: str + published_at: str + body: str + assets: NotRequired[list[Asset]] + + +@dataclass +class FrontEndProvider: + owner: str + repo: str + + @property + def folder_name(self) -> str: + return f"{self.owner}_{self.repo}" + + @property + def release_url(self) -> str: + return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases" + + @cached_property + def all_releases(self) -> list[Release]: + releases = [] + api_url = self.release_url + while api_url: + response = requests.get(api_url, timeout=REQUEST_TIMEOUT) + response.raise_for_status() # Raises an HTTPError if the response was an error + releases.extend(response.json()) + # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly. + if "next" in response.links: + api_url = response.links["next"]["url"] + else: + api_url = None + return releases + + @cached_property + def latest_release(self) -> Release: + latest_release_url = f"{self.release_url}/latest" + response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT) + response.raise_for_status() # Raises an HTTPError if the response was an error + return response.json() + + @cached_property + def latest_prerelease(self) -> Release: + """Get the latest pre-release version - even if it's older than the latest release""" + release = [release for release in self.all_releases if release["prerelease"]] + + if not release: + raise ValueError("No pre-releases found") + + # GitHub returns releases in reverse chronological order, so first is latest + return release[0] + + def get_release(self, version: str) -> Release: + if version == "latest": + return self.latest_release + elif version == "prerelease": + return self.latest_prerelease + else: + for release in self.all_releases: + if release["tag_name"] in [version, f"v{version}"]: + return release + raise ValueError(f"Version {version} not found in releases") + + +def download_release_asset_zip(release: Release, destination_path: str) -> None: + """Download dist.zip from github release.""" + asset_url = None + for asset in release.get("assets", []): + if asset["name"] == "dist.zip": + asset_url = asset["url"] + break + + if not asset_url: + raise ValueError("dist.zip not found in the release assets") + + # Use a temporary file to download the zip content + with tempfile.TemporaryFile() as tmp_file: + headers = {"Accept": "application/octet-stream"} + response = requests.get( + asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT + ) + response.raise_for_status() # Ensure we got a successful response + + # Write the content to the temporary file + tmp_file.write(response.content) + + # Go back to the beginning of the temporary file + tmp_file.seek(0) + + # Extract the zip file content to the destination path + with zipfile.ZipFile(tmp_file, "r") as zip_ref: + zip_ref.extractall(destination_path) + + +class FrontendManager: + CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions") + + @classmethod + def default_frontend_path(cls) -> str: + try: + import comfyui_frontend_package + + return str(importlib.resources.files(comfyui_frontend_package) / "static") + except ImportError: + logging.error( + f""" +********** ERROR *********** + +comfyui-frontend-package is not installed. + +{frontend_install_warning_message()} + +********** ERROR *********** +""".strip() + ) + sys.exit(-1) + + @classmethod + def templates_path(cls) -> str: + try: + import comfyui_workflow_templates + + return str( + importlib.resources.files(comfyui_workflow_templates) / "templates" + ) + except ImportError: + logging.error( + f""" +********** ERROR *********** + +comfyui-workflow-templates is not installed. + +{frontend_install_warning_message()} + +********** ERROR *********** +""".strip() + ) + + @classmethod + def embedded_docs_path(cls) -> str: + """Get the path to embedded documentation""" + try: + import comfyui_embedded_docs + + return str( + importlib.resources.files(comfyui_embedded_docs) / "docs" + ) + except ImportError: + logging.info("comfyui-embedded-docs package not found") + return None + + @classmethod + def parse_version_string(cls, value: str) -> tuple[str, str, str]: + """ + Args: + value (str): The version string to parse. + + Returns: + tuple[str, str]: A tuple containing provider name and version. + + Raises: + argparse.ArgumentTypeError: If the version string is invalid. + """ + VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$" + match_result = re.match(VERSION_PATTERN, value) + if match_result is None: + raise argparse.ArgumentTypeError(f"Invalid version string: {value}") + + return match_result.group(1), match_result.group(2), match_result.group(3) + + @classmethod + def init_frontend_unsafe( + cls, version_string: str, provider: Optional[FrontEndProvider] = None + ) -> str: + """ + Initializes the frontend for the specified version. + + Args: + version_string (str): The version string. + provider (FrontEndProvider, optional): The provider to use. Defaults to None. + + Returns: + str: The path to the initialized frontend. + + Raises: + Exception: If there is an error during the initialization process. + main error source might be request timeout or invalid URL. + """ + if version_string == DEFAULT_VERSION_STRING: + check_frontend_version() + return cls.default_frontend_path() + + repo_owner, repo_name, version = cls.parse_version_string(version_string) + + if version.startswith("v"): + expected_path = str( + Path(cls.CUSTOM_FRONTENDS_ROOT) + / f"{repo_owner}_{repo_name}" + / version.lstrip("v") + ) + if os.path.exists(expected_path): + logging.info( + f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}" + ) + return expected_path + + logging.info( + f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..." + ) + + provider = provider or FrontEndProvider(repo_owner, repo_name) + release = provider.get_release(version) + + semantic_version = release["tag_name"].lstrip("v") + web_root = str( + Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version + ) + if not os.path.exists(web_root): + try: + os.makedirs(web_root, exist_ok=True) + logging.info( + "Downloading frontend(%s) version(%s) to (%s)", + provider.folder_name, + semantic_version, + web_root, + ) + logging.debug(release) + download_release_asset_zip(release, destination_path=web_root) + finally: + # Clean up the directory if it is empty, i.e. the download failed + if not os.listdir(web_root): + os.rmdir(web_root) + + return web_root + + @classmethod + def init_frontend(cls, version_string: str) -> str: + """ + Initializes the frontend with the specified version string. + + Args: + version_string (str): The version string to initialize the frontend with. + + Returns: + str: The path of the initialized frontend. + """ + try: + return cls.init_frontend_unsafe(version_string) + except Exception as e: + logging.error("Failed to initialize frontend: %s", e) + logging.info("Falling back to the default frontend.") + check_frontend_version() + return cls.default_frontend_path() diff --git a/app/logger.py b/app/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..3d26d98fe28005a3ed8b6deb7da34823b98b5696 --- /dev/null +++ b/app/logger.py @@ -0,0 +1,98 @@ +from collections import deque +from datetime import datetime +import io +import logging +import sys +import threading + +logs = None +stdout_interceptor = None +stderr_interceptor = None + + +class LogInterceptor(io.TextIOWrapper): + def __init__(self, stream, *args, **kwargs): + buffer = stream.buffer + encoding = stream.encoding + super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering) + self._lock = threading.Lock() + self._flush_callbacks = [] + self._logs_since_flush = [] + + def write(self, data): + entry = {"t": datetime.now().isoformat(), "m": data} + with self._lock: + self._logs_since_flush.append(entry) + + # Simple handling for cr to overwrite the last output if it isnt a full line + # else logs just get full of progress messages + if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"): + logs.pop() + logs.append(entry) + super().write(data) + + def flush(self): + super().flush() + for cb in self._flush_callbacks: + cb(self._logs_since_flush) + self._logs_since_flush = [] + + def on_flush(self, callback): + self._flush_callbacks.append(callback) + + +def get_logs(): + return logs + + +def on_flush(callback): + if stdout_interceptor is not None: + stdout_interceptor.on_flush(callback) + if stderr_interceptor is not None: + stderr_interceptor.on_flush(callback) + +def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False): + global logs + if logs: + return + + # Override output streams and log to buffer + logs = deque(maxlen=capacity) + + global stdout_interceptor + global stderr_interceptor + stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout) + stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr) + + # Setup default global logger + logger = logging.getLogger() + logger.setLevel(log_level) + + stream_handler = logging.StreamHandler() + stream_handler.setFormatter(logging.Formatter("%(message)s")) + + if use_stdout: + # Only errors and critical to stderr + stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR) + + # Lesser to stdout + stdout_handler = logging.StreamHandler(sys.stdout) + stdout_handler.setFormatter(logging.Formatter("%(message)s")) + stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR) + logger.addHandler(stdout_handler) + + logger.addHandler(stream_handler) + + +STARTUP_WARNINGS = [] + + +def log_startup_warning(msg): + logging.warning(msg) + STARTUP_WARNINGS.append(msg) + + +def print_startup_warnings(): + for s in STARTUP_WARNINGS: + logging.warning(s) + STARTUP_WARNINGS.clear() diff --git a/app/model_manager.py b/app/model_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..74d942fb85c560dd85903e1cc82ef64968b74316 --- /dev/null +++ b/app/model_manager.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +import os +import base64 +import json +import time +import logging +import folder_paths +import glob +import comfy.utils +from aiohttp import web +from PIL import Image +from io import BytesIO +from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types + + +class ModelFileManager: + def __init__(self) -> None: + self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {} + + def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None: + return self.cache.get(key, default) + + def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]): + self.cache[key] = value + + def clear_cache(self): + self.cache.clear() + + def add_routes(self, routes): + # NOTE: This is an experiment to replace `/models` + @routes.get("/experiment/models") + async def get_model_folders(request): + model_types = list(folder_paths.folder_names_and_paths.keys()) + folder_black_list = ["configs", "custom_nodes"] + output_folders: list[dict] = [] + for folder in model_types: + if folder in folder_black_list: + continue + output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)}) + return web.json_response(output_folders) + + # NOTE: This is an experiment to replace `/models/{folder}` + @routes.get("/experiment/models/{folder}") + async def get_all_models(request): + folder = request.match_info.get("folder", None) + if not folder in folder_paths.folder_names_and_paths: + return web.Response(status=404) + files = self.get_model_file_list(folder) + return web.json_response(files) + + @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}") + async def get_model_preview(request): + folder_name = request.match_info.get("folder", None) + path_index = int(request.match_info.get("path_index", None)) + filename = request.match_info.get("filename", None) + + if not folder_name in folder_paths.folder_names_and_paths: + return web.Response(status=404) + + folders = folder_paths.folder_names_and_paths[folder_name] + folder = folders[0][path_index] + full_filename = os.path.join(folder, filename) + + previews = self.get_model_previews(full_filename) + default_preview = previews[0] if len(previews) > 0 else None + if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)): + return web.Response(status=404) + + try: + with Image.open(default_preview) as img: + img_bytes = BytesIO() + img.save(img_bytes, format="WEBP") + img_bytes.seek(0) + return web.Response(body=img_bytes.getvalue(), content_type="image/webp") + except: + return web.Response(status=404) + + def get_model_file_list(self, folder_name: str): + folder_name = map_legacy(folder_name) + folders = folder_paths.folder_names_and_paths[folder_name] + output_list: list[dict] = [] + + for index, folder in enumerate(folders[0]): + if not os.path.isdir(folder): + continue + out = self.cache_model_file_list_(folder) + if out is None: + out = self.recursive_search_models_(folder, index) + self.set_cache(folder, out) + output_list.extend(out[0]) + + return output_list + + def cache_model_file_list_(self, folder: str): + model_file_list_cache = self.get_cache(folder) + + if model_file_list_cache is None: + return None + if not os.path.isdir(folder): + return None + if os.path.getmtime(folder) != model_file_list_cache[1]: + return None + for x in model_file_list_cache[1]: + time_modified = model_file_list_cache[1][x] + folder = x + if os.path.getmtime(folder) != time_modified: + return None + + return model_file_list_cache + + def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]: + if not os.path.isdir(directory): + return [], {}, time.perf_counter() + + excluded_dir_names = [".git"] + # TODO use settings + include_hidden_files = False + + result: list[str] = [] + dirs: dict[str, float] = {} + + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + if not include_hidden_files: + subdirs[:] = [d for d in subdirs if not d.startswith(".")] + filenames = [f for f in filenames if not f.startswith(".")] + + filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions) + + for file_name in filenames: + try: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + except: + logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.") + continue + + for d in subdirs: + path: str = os.path.join(dirpath, d) + try: + dirs[path] = os.path.getmtime(path) + except FileNotFoundError: + logging.warning(f"Warning: Unable to access {path}. Skipping this path.") + continue + + return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter() + + def get_model_previews(self, filepath: str) -> list[str | BytesIO]: + dirname = os.path.dirname(filepath) + + if not os.path.exists(dirname): + return [] + + basename = os.path.splitext(filepath)[0] + match_files = glob.glob(f"{basename}.*", recursive=False) + image_files = filter_files_content_types(match_files, "image") + safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None) + safetensors_metadata = {} + + result: list[str | BytesIO] = [] + + for filename in image_files: + _basename = os.path.splitext(filename)[0] + if _basename == basename: + result.append(filename) + if _basename == f"{basename}.preview": + result.append(filename) + + if safetensors_file: + safetensors_filepath = os.path.join(dirname, safetensors_file) + header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024) + if header: + safetensors_metadata = json.loads(header) + safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None) + if safetensors_images: + safetensors_images = json.loads(safetensors_images) + for image in safetensors_images: + result.append(BytesIO(base64.b64decode(image))) + + return result + + def __exit__(self, exc_type, exc_value, traceback): + self.clear_cache() diff --git a/app/user_manager.py b/app/user_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..d31da5b9b89dda908e900041ef41b082dbac38eb --- /dev/null +++ b/app/user_manager.py @@ -0,0 +1,436 @@ +from __future__ import annotations +import json +import os +import re +import uuid +import glob +import shutil +import logging +from aiohttp import web +from urllib import parse +from comfy.cli_args import args +import folder_paths +from .app_settings import AppSettings +from typing import TypedDict + +default_user = "default" + + +class FileInfo(TypedDict): + path: str + size: int + modified: int + + +def get_file_info(path: str, relative_to: str) -> FileInfo: + return { + "path": os.path.relpath(path, relative_to).replace(os.sep, '/'), + "size": os.path.getsize(path), + "modified": os.path.getmtime(path) + } + + +class UserManager(): + def __init__(self): + user_directory = folder_paths.get_user_directory() + + self.settings = AppSettings(self) + if not os.path.exists(user_directory): + os.makedirs(user_directory, exist_ok=True) + if not args.multi_user: + logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******") + logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******") + + if args.multi_user: + if os.path.isfile(self.get_users_file()): + with open(self.get_users_file()) as f: + self.users = json.load(f) + else: + self.users = {} + else: + self.users = {"default": "default"} + + def get_users_file(self): + return os.path.join(folder_paths.get_user_directory(), "users.json") + + def get_request_user_id(self, request): + user = "default" + if args.multi_user and "comfy-user" in request.headers: + user = request.headers["comfy-user"] + + if user not in self.users: + raise KeyError("Unknown user: " + user) + + return user + + def get_request_user_filepath(self, request, file, type="userdata", create_dir=True): + user_directory = folder_paths.get_user_directory() + + if type == "userdata": + root_dir = user_directory + else: + raise KeyError("Unknown filepath type:" + type) + + user = self.get_request_user_id(request) + path = user_root = os.path.abspath(os.path.join(root_dir, user)) + + # prevent leaving /{type} + if os.path.commonpath((root_dir, user_root)) != root_dir: + return None + + if file is not None: + # Check if filename is url encoded + if "%" in file: + file = parse.unquote(file) + + # prevent leaving /{type}/{user} + path = os.path.abspath(os.path.join(user_root, file)) + if os.path.commonpath((user_root, path)) != user_root: + return None + + parent = os.path.split(path)[0] + + if create_dir and not os.path.exists(parent): + os.makedirs(parent, exist_ok=True) + + return path + + def add_user(self, name): + name = name.strip() + if not name: + raise ValueError("username not provided") + user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name) + user_id = user_id + "_" + str(uuid.uuid4()) + + self.users[user_id] = name + + with open(self.get_users_file(), "w") as f: + json.dump(self.users, f) + + return user_id + + def add_routes(self, routes): + self.settings.add_routes(routes) + + @routes.get("/users") + async def get_users(request): + if args.multi_user: + return web.json_response({"storage": "server", "users": self.users}) + else: + user_dir = self.get_request_user_filepath(request, None, create_dir=False) + return web.json_response({ + "storage": "server", + "migrated": os.path.exists(user_dir) + }) + + @routes.post("/users") + async def post_users(request): + body = await request.json() + username = body["username"] + if username in self.users.values(): + return web.json_response({"error": "Duplicate username."}, status=400) + + user_id = self.add_user(username) + return web.json_response(user_id) + + @routes.get("/userdata") + async def listuserdata(request): + """ + List user data files in a specified directory. + + This endpoint allows listing files in a user's data directory, with options for recursion, + full file information, and path splitting. + + Query Parameters: + - dir (required): The directory to list files from. + - recurse (optional): If "true", recursively list files in subdirectories. + - full_info (optional): If "true", return detailed file information (path, size, modified time). + - split (optional): If "true", split file paths into components (only applies when full_info is false). + + Returns: + - 400: If 'dir' parameter is missing. + - 403: If the requested path is not allowed. + - 404: If the requested directory does not exist. + - 200: JSON response with the list of files or file information. + + The response format depends on the query parameters: + - Default: List of relative file paths. + - full_info=true: List of dictionaries with file details. + - split=true (and full_info=false): List of lists, each containing path components. + """ + directory = request.rel_url.query.get('dir', '') + if not directory: + return web.Response(status=400, text="Directory not provided") + + path = self.get_request_user_filepath(request, directory) + if not path: + return web.Response(status=403, text="Invalid directory") + + if not os.path.exists(path): + return web.Response(status=404, text="Directory not found") + + recurse = request.rel_url.query.get('recurse', '').lower() == "true" + full_info = request.rel_url.query.get('full_info', '').lower() == "true" + split_path = request.rel_url.query.get('split', '').lower() == "true" + + # Use different patterns based on whether we're recursing or not + if recurse: + pattern = os.path.join(glob.escape(path), '**', '*') + else: + pattern = os.path.join(glob.escape(path), '*') + + def process_full_path(full_path: str) -> FileInfo | str | list[str]: + if full_info: + return get_file_info(full_path, path) + + rel_path = os.path.relpath(full_path, path).replace(os.sep, '/') + if split_path: + return [rel_path] + rel_path.split('/') + + return rel_path + + results = [ + process_full_path(full_path) + for full_path in glob.glob(pattern, recursive=recurse) + if os.path.isfile(full_path) + ] + + return web.json_response(results) + + @routes.get("/v2/userdata") + async def list_userdata_v2(request): + """ + List files and directories in a user's data directory. + + This endpoint provides a structured listing of contents within a specified + subdirectory of the user's data storage. + + Query Parameters: + - path (optional): The relative path within the user's data directory + to list. Defaults to the root (''). + + Returns: + - 400: If the requested path is invalid, outside the user's data directory, or is not a directory. + - 404: If the requested path does not exist. + - 403: If the user is invalid. + - 500: If there is an error reading the directory contents. + - 200: JSON response containing a list of file and directory objects. + Each object includes: + - name: The name of the file or directory. + - type: 'file' or 'directory'. + - path: The relative path from the user's data root. + - size (for files): The size in bytes. + - modified (for files): The last modified timestamp (Unix epoch). + """ + requested_rel_path = request.rel_url.query.get('path', '') + + # URL-decode the path parameter + try: + requested_rel_path = parse.unquote(requested_rel_path) + except Exception as e: + logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}") + return web.Response(status=400, text="Invalid characters in path parameter") + + + # Check user validity and get the absolute path for the requested directory + try: + base_user_path = self.get_request_user_filepath(request, None, create_dir=False) + + if requested_rel_path: + target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False) + else: + target_abs_path = base_user_path + + except KeyError as e: + # Invalid user detected by get_request_user_id inside get_request_user_filepath + logging.warning(f"Access denied for user: {e}") + return web.Response(status=403, text="Invalid user specified in request") + + + if not target_abs_path: + # Path traversal or other issue detected by get_request_user_filepath + return web.Response(status=400, text="Invalid path requested") + + # Handle cases where the user directory or target path doesn't exist + if not os.path.exists(target_abs_path): + # Check if it's the base user directory that's missing (new user case) + if target_abs_path == base_user_path: + # It's okay if the base user directory doesn't exist yet, return empty list + return web.json_response([]) + else: + # A specific subdirectory was requested but doesn't exist + return web.Response(status=404, text="Requested path not found") + + if not os.path.isdir(target_abs_path): + return web.Response(status=400, text="Requested path is not a directory") + + results = [] + try: + for root, dirs, files in os.walk(target_abs_path, topdown=True): + # Process directories + for dir_name in dirs: + dir_path = os.path.join(root, dir_name) + rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/') + results.append({ + "name": dir_name, + "path": rel_path, + "type": "directory" + }) + + # Process files + for file_name in files: + file_path = os.path.join(root, file_name) + rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/') + entry_info = { + "name": file_name, + "path": rel_path, + "type": "file" + } + try: + stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk + entry_info["size"] = stats.st_size + entry_info["modified"] = stats.st_mtime + except OSError as stat_error: + logging.warning(f"Could not stat file {file_path}: {stat_error}") + pass # Include file with available info + results.append(entry_info) + except OSError as e: + logging.error(f"Error listing directory {target_abs_path}: {e}") + return web.Response(status=500, text="Error reading directory contents") + + # Sort results alphabetically, directories first then files + results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower())) + + return web.json_response(results) + + def get_user_data_path(request, check_exists = False, param = "file"): + file = request.match_info.get(param, None) + if not file: + return web.Response(status=400) + + path = self.get_request_user_filepath(request, file) + if not path: + return web.Response(status=403) + + if check_exists and not os.path.exists(path): + return web.Response(status=404) + + return path + + @routes.get("/userdata/{file}") + async def getuserdata(request): + path = get_user_data_path(request, check_exists=True) + if not isinstance(path, str): + return path + + return web.FileResponse(path) + + @routes.post("/userdata/{file}") + async def post_userdata(request): + """ + Upload or update a user data file. + + This endpoint handles file uploads to a user's data directory, with options for + controlling overwrite behavior and response format. + + Query Parameters: + - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". + - full_info (optional): If "true", returns detailed file information (path, size, modified time). + If "false", returns only the relative file path. + + Path Parameters: + - file: The target file path (URL encoded if necessary). + + Returns: + - 400: If 'file' parameter is missing. + - 403: If the requested path is not allowed. + - 409: If overwrite=false and the file already exists. + - 200: JSON response with either: + - Full file information (if full_info=true) + - Relative file path (if full_info=false) + + The request body should contain the raw file content to be written. + """ + path = get_user_data_path(request) + if not isinstance(path, str): + return path + + overwrite = request.query.get("overwrite", 'true') != "false" + full_info = request.query.get('full_info', 'false').lower() == "true" + + if not overwrite and os.path.exists(path): + return web.Response(status=409, text="File already exists") + + body = await request.read() + + with open(path, "wb") as f: + f.write(body) + + user_path = self.get_request_user_filepath(request, None) + if full_info: + resp = get_file_info(path, user_path) + else: + resp = os.path.relpath(path, user_path) + + return web.json_response(resp) + + @routes.delete("/userdata/{file}") + async def delete_userdata(request): + path = get_user_data_path(request, check_exists=True) + if not isinstance(path, str): + return path + + os.remove(path) + + return web.Response(status=204) + + @routes.post("/userdata/{file}/move/{dest}") + async def move_userdata(request): + """ + Move or rename a user data file. + + This endpoint handles moving or renaming files within a user's data directory, with options for + controlling overwrite behavior and response format. + + Path Parameters: + - file: The source file path (URL encoded if necessary) + - dest: The destination file path (URL encoded if necessary) + + Query Parameters: + - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". + - full_info (optional): If "true", returns detailed file information (path, size, modified time). + If "false", returns only the relative file path. + + Returns: + - 400: If either 'file' or 'dest' parameter is missing + - 403: If either requested path is not allowed + - 404: If the source file does not exist + - 409: If overwrite=false and the destination file already exists + - 200: JSON response with either: + - Full file information (if full_info=true) + - Relative file path (if full_info=false) + """ + source = get_user_data_path(request, check_exists=True) + if not isinstance(source, str): + return source + + dest = get_user_data_path(request, check_exists=False, param="dest") + if not isinstance(source, str): + return dest + + overwrite = request.query.get("overwrite", 'true') != "false" + full_info = request.query.get('full_info', 'false').lower() == "true" + + if not overwrite and os.path.exists(dest): + return web.Response(status=409, text="File already exists") + + logging.info(f"moving '{source}' -> '{dest}'") + shutil.move(source, dest) + + user_path = self.get_request_user_filepath(request, None) + if full_info: + resp = get_file_info(dest, user_path) + else: + resp = os.path.relpath(dest, user_path) + + return web.json_response(resp) diff --git a/comfy/checkpoint_pickle.py b/comfy/checkpoint_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..206551d3c1cf0d654c907534629a800196ba138b --- /dev/null +++ b/comfy/checkpoint_pickle.py @@ -0,0 +1,13 @@ +import pickle + +load = pickle.load + +class Empty: + pass + +class Unpickler(pickle.Unpickler): + def find_class(self, module, name): + #TODO: safe unpickle + if module.startswith("pytorch_lightning"): + return Empty + return super().find_class(module, name) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py new file mode 100644 index 0000000000000000000000000000000000000000..ec01665e2181d4aea1c9cbb5b95bdc7715a3e8a8 --- /dev/null +++ b/comfy/cldm/cldm.py @@ -0,0 +1,433 @@ +#taken from: https://github.com/lllyasviel/ControlNet +#and modified + +import torch +import torch.nn as nn + +from ..ldm.modules.diffusionmodules.util import ( + timestep_embedding, +) + +from ..ldm.modules.attention import SpatialTransformer +from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample +from ..ldm.util import exists +from .control_types import UNION_CONTROLNET_TYPES +from collections import OrderedDict +import comfy.ops +from comfy.ldm.modules.attention import optimized_attention + +class OptimizedAttention(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.heads = nhead + self.c = c + + self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device) + self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.in_proj(x) + q, k, v = x.split(self.c, dim=2) + out = optimized_attention(q, k, v, self.heads) + return self.out_proj(out) + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + +class ResBlockUnionControlnet(nn.Module): + def __init__(self, dim, nhead, dtype=None, device=None, operations=None): + super().__init__() + self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations) + self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()), + ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))])) + self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device) + + def attention(self, x: torch.Tensor): + return self.attn(x) + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + +class ControlledUnetModel(UNetModel): + #implemented in the ldm unet + pass + +class ControlNet(nn.Module): + def __init__( + self, + image_size, + in_channels, + model_channels, + hint_channels, + num_res_blocks, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + dtype=torch.float32, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + transformer_depth_output=None, + attn_precision=None, + union_controlnet_num_control_type=None, + device=None, + operations=comfy.ops.disable_weight_init, + **kwargs, + ): + super().__init__() + assert use_spatial_transformer == True, "use_spatial_transformer has to be true" + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.dims = dims + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) + + transformer_depth = transformer_depth[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) + + self.input_hint_block = TimestepEmbedSequential( + operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + SpatialTransformer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + mid_block = [ + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + if transformer_depth_middle >= 0: + mid_block += [SpatialTransformer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) + self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) + self._feature_size += ch + + if union_controlnet_num_control_type is not None: + self.num_control_type = union_controlnet_num_control_type + num_trans_channel = 320 + num_trans_head = 8 + num_trans_layer = 1 + num_proj_channel = 320 + # task_scale_factor = num_trans_channel ** 0.5 + self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device)) + + self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)]) + self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device) + #----------------------------------------------------------------------------------------------------- + + control_add_embed_dim = 256 + class ControlAddEmbedding(nn.Module): + def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None): + super().__init__() + self.num_control_type = num_control_type + self.in_dim = in_dim + self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device) + self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device) + def forward(self, control_type, dtype, device): + c_type = torch.zeros((self.num_control_type,), device=device) + c_type[control_type] = 1.0 + c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim)) + return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type))) + + self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations) + else: + self.task_embedding = None + self.control_add_embedding = None + + def union_controlnet_merge(self, hint, control_type, emb, context): + # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main + inputs = [] + condition_list = [] + + for idx in range(min(1, len(control_type))): + controlnet_cond = self.input_hint_block(hint[idx], emb, context) + feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) + if idx < len(control_type): + feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device) + + inputs.append(feat_seq.unsqueeze(1)) + condition_list.append(controlnet_cond) + + x = torch.cat(inputs, dim=1) + x = self.transformer_layes(x) + controlnet_cond_fuser = None + for idx in range(len(control_type)): + alpha = self.spatial_ch_projs(x[:, idx]) + alpha = alpha.unsqueeze(-1).unsqueeze(-1) + o = condition_list[idx] + alpha + if controlnet_cond_fuser is None: + controlnet_cond_fuser = o + else: + controlnet_cond_fuser += o + return controlnet_cond_fuser + + def make_zero_conv(self, channels, operations=None, dtype=None, device=None): + return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) + + def forward(self, x, hint, timesteps, context, y=None, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + guided_hint = None + if self.control_add_embedding is not None: #Union Controlnet + control_type = kwargs.get("control_type", []) + + if any([c >= self.num_control_type for c in control_type]): + max_type = max(control_type) + max_type_name = { + v: k for k, v in UNION_CONTROLNET_TYPES.items() + }[max_type] + raise ValueError( + f"Control type {max_type_name}({max_type}) is out of range for the number of control types" + + f"({self.num_control_type}) supported.\n" + + "Please consider using the ProMax ControlNet Union model.\n" + + "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main" + ) + + emb += self.control_add_embedding(control_type, emb.dtype, emb.device) + if len(control_type) > 0: + if len(hint.shape) < 5: + hint = hint.unsqueeze(dim=0) + guided_hint = self.union_controlnet_merge(hint, control_type, emb, context) + + if guided_hint is None: + guided_hint = self.input_hint_block(hint, emb, context) + + out_output = [] + out_middle = [] + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + out_output.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + out_middle.append(self.middle_block_out(h, emb, context)) + + return {"middle": out_middle, "output": out_output} + diff --git a/comfy/cldm/control_types.py b/comfy/cldm/control_types.py new file mode 100644 index 0000000000000000000000000000000000000000..4128631a305a13d65c3c37ced17179d23fbbdcff --- /dev/null +++ b/comfy/cldm/control_types.py @@ -0,0 +1,10 @@ +UNION_CONTROLNET_TYPES = { + "openpose": 0, + "depth": 1, + "hed/pidi/scribble/ted": 2, + "canny/lineart/anime_lineart/mlsd": 3, + "normal": 4, + "segment": 5, + "tile": 6, + "repaint": 7, +} diff --git a/comfy/cldm/dit_embedder.py b/comfy/cldm/dit_embedder.py new file mode 100644 index 0000000000000000000000000000000000000000..f9bf31012b1319e929cab381c9a3b32be62fc589 --- /dev/null +++ b/comfy/cldm/dit_embedder.py @@ -0,0 +1,120 @@ +import math +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from torch import Tensor + +from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch + + +class ControlNetEmbedder(nn.Module): + + def __init__( + self, + img_size: int, + patch_size: int, + in_chans: int, + attention_head_dim: int, + num_attention_heads: int, + adm_in_channels: int, + num_layers: int, + main_model_double: int, + double_y_emb: bool, + device: torch.device, + dtype: torch.dtype, + pos_embed_max_size: Optional[int] = None, + operations = None, + ): + super().__init__() + self.main_model_double = main_model_double + self.dtype = dtype + self.hidden_size = num_attention_heads * attention_head_dim + self.patch_size = patch_size + self.x_embedder = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=self.hidden_size, + strict_img_size=pos_embed_max_size is None, + device=device, + dtype=dtype, + operations=operations, + ) + + self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) + + self.double_y_emb = double_y_emb + if self.double_y_emb: + self.orig_y_embedder = VectorEmbedder( + adm_in_channels, self.hidden_size, dtype, device, operations=operations + ) + self.y_embedder = VectorEmbedder( + self.hidden_size, self.hidden_size, dtype, device, operations=operations + ) + else: + self.y_embedder = VectorEmbedder( + adm_in_channels, self.hidden_size, dtype, device, operations=operations + ) + + self.transformer_blocks = nn.ModuleList( + DismantledBlock( + hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True, + dtype=dtype, device=device, operations=operations + ) + for _ in range(num_layers) + ) + + # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features + # TODO double check this logic when 8b + self.use_y_embedder = True + + self.controlnet_blocks = nn.ModuleList([]) + for _ in range(len(self.transformer_blocks)): + controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) + self.controlnet_blocks.append(controlnet_block) + + self.pos_embed_input = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=self.hidden_size, + strict_img_size=False, + device=device, + dtype=dtype, + operations=operations, + ) + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + hint = None, + ) -> Tuple[Tensor, List[Tensor]]: + x_shape = list(x.shape) + x = self.x_embedder(x) + if not self.double_y_emb: + h = (x_shape[-2] + 1) // self.patch_size + w = (x_shape[-1] + 1) // self.patch_size + x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device) + c = self.t_embedder(timesteps, dtype=x.dtype) + if y is not None and self.y_embedder is not None: + if self.double_y_emb: + y = self.orig_y_embedder(y) + y = self.y_embedder(y) + c = c + y + + x = x + self.pos_embed_input(hint) + + block_out = () + + repeat = math.ceil(self.main_model_double / len(self.transformer_blocks)) + for i in range(len(self.transformer_blocks)): + out = self.transformer_blocks[i](x, c) + if not self.double_y_emb: + x = out + block_out += (self.controlnet_blocks[i](out),) * repeat + + return {"output": block_out} diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py new file mode 100644 index 0000000000000000000000000000000000000000..b7764085e9431259d43701654af897effe0799da --- /dev/null +++ b/comfy/cldm/mmdit.py @@ -0,0 +1,81 @@ +import torch +from typing import Optional +import comfy.ldm.modules.diffusionmodules.mmdit + +class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): + def __init__( + self, + num_blocks = None, + control_latent_channels = None, + dtype = None, + device = None, + operations = None, + **kwargs, + ): + super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs) + # controlnet_blocks + self.controlnet_blocks = torch.nn.ModuleList([]) + for _ in range(len(self.joint_blocks)): + self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype)) + + if control_latent_channels is None: + control_latent_channels = self.in_channels + + self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed( + None, + self.patch_size, + control_latent_channels, + self.hidden_size, + bias=True, + strict_img_size=False, + dtype=dtype, + device=device, + operations=operations + ) + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + hint = None, + ) -> torch.Tensor: + + #weird sd3 controlnet specific stuff + y = torch.zeros_like(y) + + if self.context_processor is not None: + context = self.context_processor(context) + + hw = x.shape[-2:] + x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) + x += self.pos_embed_input(hint) + + c = self.t_embedder(timesteps, dtype=x.dtype) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) + c = c + y + + if context is not None: + context = self.context_embedder(context) + + output = [] + + blocks = len(self.joint_blocks) + for i in range(blocks): + context, x = self.joint_blocks[i]( + context, + x, + c=c, + use_checkpoint=self.use_checkpoint, + ) + + out = self.controlnet_blocks[i](x) + count = self.depth // blocks + if i == blocks - 1: + count -= 1 + for j in range(count): + output.append(out) + + return {"output": output} diff --git a/comfy/cli_args.py b/comfy/cli_args.py new file mode 100644 index 0000000000000000000000000000000000000000..7234a7ba097530cb49d3c4180d5f6a71e4b4d55f --- /dev/null +++ b/comfy/cli_args.py @@ -0,0 +1,235 @@ +import argparse +import enum +import os +import comfy.options + + +class EnumAction(argparse.Action): + """ + Argparse action for handling Enums + """ + def __init__(self, **kwargs): + # Pop off the type value + enum_type = kwargs.pop("type", None) + + # Ensure an Enum subclass is provided + if enum_type is None: + raise ValueError("type must be assigned an Enum when using EnumAction") + if not issubclass(enum_type, enum.Enum): + raise TypeError("type must be an Enum when using EnumAction") + + # Generate choices from the Enum + choices = tuple(e.value for e in enum_type) + kwargs.setdefault("choices", choices) + kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") + + super(EnumAction, self).__init__(**kwargs) + + self._enum = enum_type + + def __call__(self, parser, namespace, values, option_string=None): + # Convert value back into an Enum + value = self._enum(values) + setattr(namespace, self.dest, value) + + +parser = argparse.ArgumentParser() + +parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)") +parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") +parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function") +parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function") +parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") +parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") + +parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.") +parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") +parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.") +parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.") +parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.") +parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") +parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") +parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") +cm_group = parser.add_mutually_exclusive_group() +cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") +cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") + + +fp_group = parser.add_mutually_exclusive_group() +fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") +fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") + +fpunet_group = parser.add_mutually_exclusive_group() +fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.") +fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.") +fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.") +fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16") +fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") +fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") +fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.") + +fpvae_group = parser.add_mutually_exclusive_group() +fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") +fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") +fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") + +parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.") + +fpte_group = parser.add_mutually_exclusive_group() +fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).") +fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") +fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") +fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") +fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.") + +parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") + +parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") + +parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.") +parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.") +parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.") + +class LatentPreviewMethod(enum.Enum): + NoPreviews = "none" + Auto = "auto" + Latent2RGB = "latent2rgb" + TAESD = "taesd" + +parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) + +parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.") + +cache_group = parser.add_mutually_exclusive_group() +cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.") +cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.") +cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.") + +attn_group = parser.add_mutually_exclusive_group() +attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") +attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") +attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") +attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.") +attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.") + +parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") + +upcast = parser.add_mutually_exclusive_group() +upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.") +upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.") + + +vram_group = parser.add_mutually_exclusive_group() +vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") +vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") +vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") +vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") +vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") +vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") + +parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.") + +parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.") + +parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.") + +parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") +parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.") + +class PerformanceFeature(enum.Enum): + Fp16Accumulation = "fp16_accumulation" + Fp8MatrixMultiplication = "fp8_matrix_mult" + CublasOps = "cublas_ops" + +parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops") + +parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.") + +parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") +parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") +parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") + +parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") +parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") +parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.") +parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.") + +parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") + +parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level') +parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).") + +# The default built-in provider hosted under web/ +DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest" + +parser.add_argument( + "--front-end-version", + type=str, + default=DEFAULT_VERSION_STRING, + help=""" + Specifies the version of the frontend to be used. This command needs internet connectivity to query and + download available frontend implementations from GitHub releases. + + The version string should be in the format of: + [repoOwner]/[repoName]@[version] + where version is one of: "latest" or a valid version number (e.g. "1.0.0") + """, +) + +def is_valid_directory(path: str) -> str: + """Validate if the given path is a directory, and check permissions.""" + if not os.path.exists(path): + raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.") + if not os.path.isdir(path): + raise argparse.ArgumentTypeError(f"'{path}' is not a directory.") + if not os.access(path, os.R_OK): + raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.") + return path + +parser.add_argument( + "--front-end-root", + type=is_valid_directory, + default=None, + help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.", +) + +parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.") + +parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.") + +parser.add_argument( + "--comfy-api-base", + type=str, + default="https://api.comfy.org", + help="Set the base URL for the ComfyUI API. (default: https://api.comfy.org)", +) + +database_default_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db") +) +parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.") + +if comfy.options.args_parsing: + args = parser.parse_args() +else: + args = parser.parse_args([]) + +if args.windows_standalone_build: + args.auto_launch = True + +if args.disable_auto_launch: + args.auto_launch = False + +if args.force_fp16: + args.fp16_unet = True + + +# '--fast' is not provided, use an empty set +if args.fast is None: + args.fast = set() +# '--fast' is provided with an empty list, enable all optimizations +elif args.fast == []: + args.fast = set(PerformanceFeature) +# '--fast' is provided with a list of performance features, use that list +else: + args.fast = set(args.fast) diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json new file mode 100644 index 0000000000000000000000000000000000000000..35261deef14a68fcc6c5b1fc32914b5c102781a9 --- /dev/null +++ b/comfy/clip_config_bigg.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "gelu", + "hidden_size": 1280, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 20, + "num_hidden_layers": 32, + "pad_token_id": 1, + "projection_dim": 1280, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/comfy/clip_model.py b/comfy/clip_model.py new file mode 100644 index 0000000000000000000000000000000000000000..c8294d4832e06adb3611a3b9311c1bca06297df7 --- /dev/null +++ b/comfy/clip_model.py @@ -0,0 +1,244 @@ +import torch +from comfy.ldm.modules.attention import optimized_attention_for_device +import comfy.ops + +class CLIPAttention(torch.nn.Module): + def __init__(self, embed_dim, heads, dtype, device, operations): + super().__init__() + + self.heads = heads + self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x, mask=None, optimized_attention=None): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return self.out_proj(out) + +ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), + "gelu": torch.nn.functional.gelu, + "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), +} + +class CLIPMLP(torch.nn.Module): + def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): + super().__init__() + self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) + self.activation = ACTIVATIONS[activation] + self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.fc1(x) + x = self.activation(x) + x = self.fc2(x) + return x + +class CLIPLayer(torch.nn.Module): + def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) + self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) + + def forward(self, x, mask=None, optimized_attention=None): + x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) + x += self.mlp(self.layer_norm2(x)) + return x + + +class CLIPEncoder(torch.nn.Module): + def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) + + def forward(self, x, mask=None, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layers): + x = l(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + +class CLIPEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None): + super().__init__() + self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) + self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens, dtype=torch.float32): + return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device) + + +class CLIPTextModel_(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + num_positions = config_dict["max_position_embeddings"] + self.eos_token_id = config_dict["eos_token_id"] + + super().__init__() + self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32): + if embeds is not None: + x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device) + else: + x = self.embeddings(input_tokens, dtype=dtype) + + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) + + causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1) + + if mask is not None: + mask += causal_mask + else: + mask = causal_mask + + x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) + x = self.final_layer_norm(x) + if i is not None and final_layer_norm_intermediate: + i = self.final_layer_norm(i) + + if num_tokens is not None: + pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))] + else: + pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] + return x, i, pooled_output + +class CLIPTextModel(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_hidden_layers"] + self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) + embed_dim = config_dict["hidden_size"] + self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) + self.dtype = dtype + + def get_input_embeddings(self): + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, embeddings): + self.text_model.embeddings.token_embedding = embeddings + + def forward(self, *args, **kwargs): + x = self.text_model(*args, **kwargs) + out = self.text_projection(x[2]) + return (x[0], x[1], out, x[2]) + + +class CLIPVisionEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None): + super().__init__() + + num_patches = (image_size // patch_size) ** 2 + if model_type == "siglip_vision_model": + self.class_embedding = None + patch_bias = True + else: + num_patches = num_patches + 1 + self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) + patch_bias = False + + self.patch_embedding = operations.Conv2d( + in_channels=num_channels, + out_channels=embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=patch_bias, + dtype=dtype, + device=device + ) + + self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device) + + def forward(self, pixel_values): + embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) + if self.class_embedding is not None: + embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds) + + +class CLIPVision(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + model_type = config_dict["model_type"] + + self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations) + if model_type == "siglip_vision_model": + self.pre_layrnorm = lambda a: a + self.output_layernorm = True + else: + self.pre_layrnorm = operations.LayerNorm(embed_dim) + self.output_layernorm = False + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.post_layernorm = operations.LayerNorm(embed_dim) + + def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + x = self.embeddings(pixel_values) + x = self.pre_layrnorm(x) + #TODO: attention_mask? + x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) + if self.output_layernorm: + x = self.post_layernorm(x) + pooled_output = x + else: + pooled_output = self.post_layernorm(x[:, 0, :]) + return x, i, pooled_output + +class LlavaProjector(torch.nn.Module): + def __init__(self, in_dim, out_dim, dtype, device, operations): + super().__init__() + self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype) + self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype) + + def forward(self, x): + return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:]))) + +class CLIPVisionModelProjection(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.vision_model = CLIPVision(config_dict, dtype, device, operations) + if "projection_dim" in config_dict: + self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) + else: + self.visual_projection = lambda a: a + + if "llava3" == config_dict.get("projector_type", None): + self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations) + else: + self.multi_modal_projector = None + + def forward(self, *args, **kwargs): + x = self.vision_model(*args, **kwargs) + out = self.visual_projection(x[2]) + projected = None + if self.multi_modal_projector is not None: + projected = self.multi_modal_projector(x[1]) + + return (x[0], x[1], out, projected) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..00aab9164e5ec2060e34f22df9d1098cfe7b7e47 --- /dev/null +++ b/comfy/clip_vision.py @@ -0,0 +1,148 @@ +from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace +import os +import torch +import json +import logging + +import comfy.ops +import comfy.model_patcher +import comfy.model_management +import comfy.utils +import comfy.clip_model +import comfy.image_encoders.dino2 + +class Output: + def __getitem__(self, key): + return getattr(self, key) + def __setitem__(self, key, item): + setattr(self, key, item) + +def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True): + image = image[:, :, :, :3] if image.shape[3] > 3 else image + mean = torch.tensor(mean, device=image.device, dtype=image.dtype) + std = torch.tensor(std, device=image.device, dtype=image.dtype) + image = image.movedim(-1, 1) + if not (image.shape[2] == size and image.shape[3] == size): + if crop: + scale = (size / min(image.shape[2], image.shape[3])) + scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3])) + else: + scale_size = (size, size) + + image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True) + h = (image.shape[2] - size)//2 + w = (image.shape[3] - size)//2 + image = image[:,:,h:h+size,w:w+size] + image = torch.clip((255. * image), 0, 255).round() / 255.0 + return (image - mean.view([3,1,1])) / std.view([3,1,1]) + +IMAGE_ENCODERS = { + "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection, + "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection, + "dinov2": comfy.image_encoders.dino2.Dinov2Model, +} + +class ClipVisionModel(): + def __init__(self, json_config): + with open(json_config) as f: + config = json.load(f) + + self.image_size = config.get("image_size", 224) + self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073]) + self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711]) + model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model")) + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) + self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast) + self.model.eval() + + self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + + def load_sd(self, sd): + return self.model.load_state_dict(sd, strict=False) + + def get_sd(self): + return self.model.state_dict() + + def encode_image(self, image, crop=True): + comfy.model_management.load_model_gpu(self.patcher) + pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float() + out = self.model(pixel_values=pixel_values, intermediate_output=-2) + + outputs = Output() + outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) + outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) + outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) + outputs["mm_projected"] = out[3] + return outputs + +def convert_to_transformers(sd, prefix): + sd_k = sd.keys() + if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: + keys_to_replace = { + "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", + "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", + "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", + "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", + "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", + "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", + "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", + } + + for x in keys_to_replace: + if x in sd_k: + sd[keys_to_replace[x]] = sd.pop(x) + + if "{}proj".format(prefix) in sd_k: + sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) + + sd = transformers_convert(sd, prefix, "vision_model.", 48) + else: + replace_prefix = {prefix: ""} + sd = state_dict_prefix_replace(sd, replace_prefix) + return sd + +def load_clipvision_from_sd(sd, prefix="", convert_keys=False): + if convert_keys: + sd = convert_to_transformers(sd, prefix) + if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") + elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") + elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: + embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0] + if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152: + if embed_shape == 729: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json") + elif embed_shape == 1024: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json") + elif embed_shape == 577: + if "multi_modal_projector.linear_1.bias" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json") + else: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") + else: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") + elif "embeddings.patch_embeddings.projection.weight" in sd: + json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json") + else: + return None + + clip = ClipVisionModel(json_config) + m, u = clip.load_sd(sd) + if len(m) > 0: + logging.warning("missing clip vision: {}".format(m)) + u = set(u) + keys = list(sd.keys()) + for k in keys: + if k not in u: + sd.pop(k) + return clip + +def load(ckpt_path): + sd = load_torch_file(ckpt_path) + if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: + return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) + else: + return load_clipvision_from_sd(sd) diff --git a/comfy/clip_vision_config_g.json b/comfy/clip_vision_config_g.json new file mode 100644 index 0000000000000000000000000000000000000000..708e7e21ac3513a719d6a49e88e756f5ef7e2c8d --- /dev/null +++ b/comfy/clip_vision_config_g.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1664, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 8192, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 48, + "patch_size": 14, + "projection_dim": 1280, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_h.json b/comfy/clip_vision_config_h.json new file mode 100644 index 0000000000000000000000000000000000000000..bb71be419a4be0ad5c8c157850de032a65593cb9 --- /dev/null +++ b/comfy/clip_vision_config_h.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_vitl.json b/comfy/clip_vision_config_vitl.json new file mode 100644 index 0000000000000000000000000000000000000000..c59b8ed5a4c1f41fbcc9e6811d2c7dfe44273de7 --- /dev/null +++ b/comfy/clip_vision_config_vitl.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_vitl_336.json b/comfy/clip_vision_config_vitl_336.json new file mode 100644 index 0000000000000000000000000000000000000000..f26945273d99e88f207d64dcec78feee63b4b625 --- /dev/null +++ b/comfy/clip_vision_config_vitl_336.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 336, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-5, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_vitl_336_llava.json b/comfy/clip_vision_config_vitl_336_llava.json new file mode 100644 index 0000000000000000000000000000000000000000..f23a50d8b77fa29de2af621fb50c5825cf9b1a86 --- /dev/null +++ b/comfy/clip_vision_config_vitl_336_llava.json @@ -0,0 +1,19 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 336, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-5, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "projector_type": "llava3", + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_siglip_384.json b/comfy/clip_vision_siglip_384.json new file mode 100644 index 0000000000000000000000000000000000000000..532e03ac181d8849a7202445d42565f01441177b --- /dev/null +++ b/comfy/clip_vision_siglip_384.json @@ -0,0 +1,13 @@ +{ + "num_channels": 3, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14, + "image_mean": [0.5, 0.5, 0.5], + "image_std": [0.5, 0.5, 0.5] +} diff --git a/comfy/clip_vision_siglip_512.json b/comfy/clip_vision_siglip_512.json new file mode 100644 index 0000000000000000000000000000000000000000..7fb93ce15e6da3ce7653a7a91ab278d707a096b4 --- /dev/null +++ b/comfy/clip_vision_siglip_512.json @@ -0,0 +1,13 @@ +{ + "num_channels": 3, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 512, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 16, + "image_mean": [0.5, 0.5, 0.5], + "image_std": [0.5, 0.5, 0.5] +} diff --git a/comfy/comfy_types/README.md b/comfy/comfy_types/README.md new file mode 100644 index 0000000000000000000000000000000000000000..20a786a5eac805a0b3f41c4fa42be558819b89c8 --- /dev/null +++ b/comfy/comfy_types/README.md @@ -0,0 +1,43 @@ +# Comfy Typing +## Type hinting for ComfyUI Node development + +This module provides type hinting and concrete convenience types for node developers. +If cloned to the custom_nodes directory of ComfyUI, types can be imported using: + +```python +from comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin + +class ExampleNode(ComfyNodeABC): + @classmethod + def INPUT_TYPES(s) -> InputTypeDict: + return {"required": {}} +``` + +Full example is in [examples/example_nodes.py](examples/example_nodes.py). + +# Types +A few primary types are documented below. More complete information is available via the docstrings on each type. + +## `IO` + +A string enum of built-in and a few custom data types. Includes the following special types and their requisite plumbing: + +- `ANY`: `"*"` +- `NUMBER`: `"FLOAT,INT"` +- `PRIMITIVE`: `"STRING,FLOAT,INT,BOOLEAN"` + +## `ComfyNodeABC` + +An abstract base class for nodes, offering type-hinting / autocomplete, and somewhat-alright docstrings. + +### Type hinting for `INPUT_TYPES` + +![INPUT_TYPES auto-completion in Visual Studio Code](examples/input_types.png) + +### `INPUT_TYPES` return dict + +![INPUT_TYPES return value type hinting in Visual Studio Code](examples/required_hint.png) + +### Options for individual inputs + +![INPUT_TYPES return value option auto-completion in Visual Studio Code](examples/input_options.png) diff --git a/comfy/comfy_types/__init__.py b/comfy/comfy_types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7640fbe3f827bdaac2384e0dcc96d243ec257f2f --- /dev/null +++ b/comfy/comfy_types/__init__.py @@ -0,0 +1,46 @@ +import torch +from typing import Callable, Protocol, TypedDict, Optional, List +from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator + + +class UnetApplyFunction(Protocol): + """Function signature protocol on comfy.model_base.BaseModel.apply_model""" + + def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor: + pass + + +class UnetApplyConds(TypedDict): + """Optional conditions for unet apply function.""" + + c_concat: Optional[torch.Tensor] + c_crossattn: Optional[torch.Tensor] + control: Optional[torch.Tensor] + transformer_options: Optional[dict] + + +class UnetParams(TypedDict): + # Tensor of shape [B, C, H, W] + input: torch.Tensor + # Tensor of shape [B] + timestep: torch.Tensor + c: UnetApplyConds + # List of [0, 1], [0], [1], ... + # 0 means conditional, 1 means conditional unconditional + cond_or_uncond: List[int] + + +UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor] + + +__all__ = [ + "UnetWrapperFunction", + UnetApplyConds.__name__, + UnetParams.__name__, + UnetApplyFunction.__name__, + IO.__name__, + InputTypeDict.__name__, + ComfyNodeABC.__name__, + CheckLazyMixin.__name__, + FileLocator.__name__, +] diff --git a/comfy/comfy_types/examples/example_nodes.py b/comfy/comfy_types/examples/example_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..6e19c545153db36b2c2e7481e5c8bc96ccbbe1f1 --- /dev/null +++ b/comfy/comfy_types/examples/example_nodes.py @@ -0,0 +1,28 @@ +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict +from inspect import cleandoc + + +class ExampleNode(ComfyNodeABC): + """An example node that just adds 1 to an input integer. + + * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc). + * This node is intended as an example for developers only. + """ + + DESCRIPTION = cleandoc(__doc__) + CATEGORY = "examples" + + @classmethod + def INPUT_TYPES(s) -> InputTypeDict: + return { + "required": { + "input_int": (IO.INT, {"defaultInput": True}), + } + } + + RETURN_TYPES = (IO.INT,) + RETURN_NAMES = ("input_plus_one",) + FUNCTION = "execute" + + def execute(self, input_int: int): + return (input_int + 1,) diff --git a/comfy/comfy_types/examples/input_options.png b/comfy/comfy_types/examples/input_options.png new file mode 100644 index 0000000000000000000000000000000000000000..ac859bbc0c15728e6f5464f9388f44d5e38dd650 Binary files /dev/null and b/comfy/comfy_types/examples/input_options.png differ diff --git a/comfy/comfy_types/examples/input_types.png b/comfy/comfy_types/examples/input_types.png new file mode 100644 index 0000000000000000000000000000000000000000..27e031ccf9c32958da0d41164b567e252c1b0c5c Binary files /dev/null and b/comfy/comfy_types/examples/input_types.png differ diff --git a/comfy/comfy_types/examples/required_hint.png b/comfy/comfy_types/examples/required_hint.png new file mode 100644 index 0000000000000000000000000000000000000000..22c0182a0aed3245201f2ffac86a6d3ad5b7e2ea Binary files /dev/null and b/comfy/comfy_types/examples/required_hint.png differ diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py new file mode 100644 index 0000000000000000000000000000000000000000..071b98332ee2a588448fde472f9c0f104e72d13d --- /dev/null +++ b/comfy/comfy_types/node_typing.py @@ -0,0 +1,350 @@ +"""Comfy-specific type hinting""" + +from __future__ import annotations +from typing import Literal, TypedDict, Optional +from typing_extensions import NotRequired +from abc import ABC, abstractmethod +from enum import Enum + + +class StrEnum(str, Enum): + """Base class for string enums. Python's StrEnum is not available until 3.11.""" + + def __str__(self) -> str: + return self.value + + +class IO(StrEnum): + """Node input/output data types. + + Includes functionality for ``"*"`` (`ANY`) and ``"MULTI,TYPES"``. + """ + + STRING = "STRING" + IMAGE = "IMAGE" + MASK = "MASK" + LATENT = "LATENT" + BOOLEAN = "BOOLEAN" + INT = "INT" + FLOAT = "FLOAT" + COMBO = "COMBO" + CONDITIONING = "CONDITIONING" + SAMPLER = "SAMPLER" + SIGMAS = "SIGMAS" + GUIDER = "GUIDER" + NOISE = "NOISE" + CLIP = "CLIP" + CONTROL_NET = "CONTROL_NET" + VAE = "VAE" + MODEL = "MODEL" + LORA_MODEL = "LORA_MODEL" + LOSS_MAP = "LOSS_MAP" + CLIP_VISION = "CLIP_VISION" + CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT" + STYLE_MODEL = "STYLE_MODEL" + GLIGEN = "GLIGEN" + UPSCALE_MODEL = "UPSCALE_MODEL" + AUDIO = "AUDIO" + WEBCAM = "WEBCAM" + POINT = "POINT" + FACE_ANALYSIS = "FACE_ANALYSIS" + BBOX = "BBOX" + SEGS = "SEGS" + VIDEO = "VIDEO" + + ANY = "*" + """Always matches any type, but at a price. + + Causes some functionality issues (e.g. reroutes, link types), and should be avoided whenever possible. + """ + NUMBER = "FLOAT,INT" + """A float or an int - could be either""" + PRIMITIVE = "STRING,FLOAT,INT,BOOLEAN" + """Could be any of: string, float, int, or bool""" + + def __ne__(self, value: object) -> bool: + if self == "*" or value == "*": + return False + if not isinstance(value, str): + return True + a = frozenset(self.split(",")) + b = frozenset(value.split(",")) + return not (b.issubset(a) or a.issubset(b)) + + +class RemoteInputOptions(TypedDict): + route: str + """The route to the remote source.""" + refresh_button: bool + """Specifies whether to show a refresh button in the UI below the widget.""" + control_after_refresh: Literal["first", "last"] + """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on.""" + timeout: int + """The maximum amount of time to wait for a response from the remote source in milliseconds.""" + max_retries: int + """The maximum number of retries before aborting the request.""" + refresh: int + """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.""" + + +class MultiSelectOptions(TypedDict): + placeholder: NotRequired[str] + """The placeholder text to display in the multi-select widget when no items are selected.""" + chip: NotRequired[bool] + """Specifies whether to use chips instead of comma separated values for the multi-select widget.""" + + +class InputTypeOptions(TypedDict): + """Provides type hinting for the return type of the INPUT_TYPES node function. + + Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`). + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes + """ + + default: NotRequired[bool | str | float | int | list | tuple] + """The default value of the widget""" + defaultInput: NotRequired[bool] + """@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist. + - defaultInput on required inputs should be dropped. + - defaultInput on optional inputs should be replaced with forceInput. + Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364 + """ + forceInput: NotRequired[bool] + """Forces the input to be an input slot rather than a widget even a widget is available for the input type.""" + lazy: NotRequired[bool] + """Declares that this input uses lazy evaluation""" + rawLink: NotRequired[bool] + """When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `["nodeId", ]`). Designed for node expansion.""" + tooltip: NotRequired[str] + """Tooltip for the input (or widget), shown on pointer hover""" + socketless: NotRequired[bool] + """All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created. + Available from frontend v1.17.5 + Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548 + """ + widgetType: NotRequired[str] + """Specifies a type to be used for widget initialization if different from the input type. + Available from frontend v1.18.0 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550""" + # class InputTypeNumber(InputTypeOptions): + # default: float | int + min: NotRequired[float] + """The minimum value of a number (``FLOAT`` | ``INT``)""" + max: NotRequired[float] + """The maximum value of a number (``FLOAT`` | ``INT``)""" + step: NotRequired[float] + """The amount to increment or decrement a widget by when stepping up/down (``FLOAT`` | ``INT``)""" + round: NotRequired[float] + """Floats are rounded by this value (``FLOAT``)""" + # class InputTypeBoolean(InputTypeOptions): + # default: bool + label_on: NotRequired[str] + """The label to use in the UI when the bool is True (``BOOLEAN``)""" + label_off: NotRequired[str] + """The label to use in the UI when the bool is False (``BOOLEAN``)""" + # class InputTypeString(InputTypeOptions): + # default: str + multiline: NotRequired[bool] + """Use a multiline text box (``STRING``)""" + placeholder: NotRequired[str] + """Placeholder text to display in the UI when empty (``STRING``)""" + # Deprecated: + # defaultVal: str + dynamicPrompts: NotRequired[bool] + """Causes the front-end to evaluate dynamic prompts (``STRING``)""" + # class InputTypeCombo(InputTypeOptions): + image_upload: NotRequired[bool] + """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`.""" + image_folder: NotRequired[Literal["input", "output", "temp"]] + """Specifies which folder to get preview images from if the input has the ``image_upload`` flag. + """ + remote: NotRequired[RemoteInputOptions] + """Specifies the configuration for a remote input. + Available after ComfyUI frontend v1.9.7 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422""" + control_after_generate: NotRequired[bool] + """Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types.""" + options: NotRequired[list[str | int | float]] + """COMBO type only. Specifies the selectable options for the combo widget. + Prefer: + ["COMBO", {"options": ["Option 1", "Option 2", "Option 3"]}] + Over: + [["Option 1", "Option 2", "Option 3"]] + """ + multi_select: NotRequired[MultiSelectOptions] + """COMBO type only. Specifies the configuration for a multi-select widget. + Available after ComfyUI frontend v1.13.4 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987""" + + +class HiddenInputTypeDict(TypedDict): + """Provides type hinting for the hidden entry of node INPUT_TYPES.""" + + node_id: NotRequired[Literal["UNIQUE_ID"]] + """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" + unique_id: NotRequired[Literal["UNIQUE_ID"]] + """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" + prompt: NotRequired[Literal["PROMPT"]] + """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" + extra_pnginfo: NotRequired[Literal["EXTRA_PNGINFO"]] + """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" + dynprompt: NotRequired[Literal["DYNPROMPT"]] + """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" + + +class InputTypeDict(TypedDict): + """Provides type hinting for node INPUT_TYPES. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs + """ + + required: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] + """Describes all inputs that must be connected for the node to execute.""" + optional: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] + """Describes inputs which do not need to be connected.""" + hidden: NotRequired[HiddenInputTypeDict] + """Offers advanced functionality and server-client communication. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs + """ + + +class ComfyNodeABC(ABC): + """Abstract base class for Comfy nodes. Includes the names and expected types of attributes. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview + """ + + DESCRIPTION: str + """Node description, shown as a tooltip when hovering over the node. + + Usage:: + + # Explicitly define the description + DESCRIPTION = "Example description here." + + # Use the docstring of the node class. + DESCRIPTION = cleandoc(__doc__) + """ + CATEGORY: str + """The category of the node, as per the "Add Node" menu. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category + """ + EXPERIMENTAL: bool + """Flags a node as experimental, informing users that it may change or not work as expected.""" + DEPRECATED: bool + """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" + API_NODE: Optional[bool] + """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" + + @classmethod + @abstractmethod + def INPUT_TYPES(s) -> InputTypeDict: + """Defines node inputs. + + * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute. + * The ``optional`` key can be added to describe inputs which do not need to be connected. + * The ``hidden`` key offers some advanced functionality. More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types + """ + return {"required": {}} + + OUTPUT_NODE: bool + """Flags this node as an output node, causing any inputs it requires to be executed. + + If a node is not connected to any output nodes, that node will not be executed. Usage:: + + OUTPUT_NODE = True + + From the docs: + + By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node + """ + INPUT_IS_LIST: bool + """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes. + + All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in. This also affects ``check_lazy_status``. + + From the docs: + + A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing + """ + OUTPUT_IS_LIST: tuple[bool, ...] + """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items. + + Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list. + + A ``tuple[bool]``, where the items match those in `RETURN_TYPES`:: + + RETURN_TYPES = (IO.INT, IO.INT, IO.STRING) + OUTPUT_IS_LIST = (True, True, False) # The string output will be handled normally + + From the docs: + + In order to tell Comfy that the list being returned should not be wrapped, but treated as a series of data for sequential processing, + the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`, + specifying which outputs which should be so treated. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing + """ + + RETURN_TYPES: tuple[IO, ...] + """A tuple representing the outputs of this node. + + Usage:: + + RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE") + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types + """ + RETURN_NAMES: tuple[str, ...] + """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")`` + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names + """ + OUTPUT_TOOLTIPS: tuple[str, ...] + """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`.""" + FUNCTION: str + """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"` + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function + """ + + +class CheckLazyMixin: + """Provides a basic check_lazy_status implementation and type hinting for nodes that use lazy inputs.""" + + def check_lazy_status(self, **kwargs) -> list[str]: + """Returns a list of input names that should be evaluated. + + This basic mixin impl. requires all inputs. + + :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ + When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. + + Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). + Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status + """ + + need = [name for name in kwargs if kwargs[name] is None] + return need + + +class FileLocator(TypedDict): + """Provides type hinting for the file location""" + + filename: str + """The filename of the file.""" + subfolder: str + """The subfolder of the file.""" + type: Literal["input", "output", "temp"] + """The root folder of the file.""" diff --git a/comfy/conds.py b/comfy/conds.py new file mode 100644 index 0000000000000000000000000000000000000000..2af2a43a3630e50daa357f401a5e4157610a5d65 --- /dev/null +++ b/comfy/conds.py @@ -0,0 +1,130 @@ +import torch +import math +import comfy.utils + + +class CONDRegular: + def __init__(self, cond): + self.cond = cond + + def _copy_with(self, cond): + return self.__class__(cond) + + def process_cond(self, batch_size, device, **kwargs): + return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) + + def can_concat(self, other): + if self.cond.shape != other.cond.shape: + return False + return True + + def concat(self, others): + conds = [self.cond] + for x in others: + conds.append(x.cond) + return torch.cat(conds) + + def size(self): + return list(self.cond.size()) + + +class CONDNoiseShape(CONDRegular): + def process_cond(self, batch_size, device, area, **kwargs): + data = self.cond + if area is not None: + dims = len(area) // 2 + for i in range(dims): + data = data.narrow(i + 2, area[i + dims], area[i]) + + return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device)) + + +class CONDCrossAttn(CONDRegular): + def can_concat(self, other): + s1 = self.cond.shape + s2 = other.cond.shape + if s1 != s2: + if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen + return False + + mult_min = math.lcm(s1[1], s2[1]) + diff = mult_min // min(s1[1], s2[1]) + if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much + return False + return True + + def concat(self, others): + conds = [self.cond] + crossattn_max_len = self.cond.shape[1] + for x in others: + c = x.cond + crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1]) + conds.append(c) + + out = [] + for c in conds: + if c.shape[1] < crossattn_max_len: + c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result + out.append(c) + return torch.cat(out) + + +class CONDConstant(CONDRegular): + def __init__(self, cond): + self.cond = cond + + def process_cond(self, batch_size, device, **kwargs): + return self._copy_with(self.cond) + + def can_concat(self, other): + if self.cond != other.cond: + return False + return True + + def concat(self, others): + return self.cond + + def size(self): + return [1] + + +class CONDList(CONDRegular): + def __init__(self, cond): + self.cond = cond + + def process_cond(self, batch_size, device, **kwargs): + out = [] + for c in self.cond: + out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device)) + + return self._copy_with(out) + + def can_concat(self, other): + if len(self.cond) != len(other.cond): + return False + for i in range(len(self.cond)): + if self.cond[i].shape != other.cond[i].shape: + return False + + return True + + def concat(self, others): + out = [] + for i in range(len(self.cond)): + o = [self.cond[i]] + for x in others: + o.append(x.cond[i]) + out.append(torch.cat(o)) + + return out + + def size(self): # hackish implementation to make the mem estimation work + o = 0 + c = 1 + for c in self.cond: + size = c.size() + o += math.prod(size) + if len(size) > 1: + c = size[1] + + return [1, c, o // c] diff --git a/comfy/controlnet.py b/comfy/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..9a47b86f2f5d4f9275a754f24b04b64cabd60f15 --- /dev/null +++ b/comfy/controlnet.py @@ -0,0 +1,858 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + + +import torch +from enum import Enum +import math +import os +import logging +import comfy.utils +import comfy.model_management +import comfy.model_detection +import comfy.model_patcher +import comfy.ops +import comfy.latent_formats + +import comfy.cldm.cldm +import comfy.t2i_adapter.adapter +import comfy.ldm.cascade.controlnet +import comfy.cldm.mmdit +import comfy.ldm.hydit.controlnet +import comfy.ldm.flux.controlnet +import comfy.cldm.dit_embedder +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from comfy.hooks import HookGroup + + +def broadcast_image_to(tensor, target_batch_size, batched_number): + current_batch_size = tensor.shape[0] + #print(current_batch_size, target_batch_size) + if current_batch_size == 1: + return tensor + + per_batch = target_batch_size // batched_number + tensor = tensor[:per_batch] + + if per_batch > tensor.shape[0]: + tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) + + current_batch_size = tensor.shape[0] + if current_batch_size == target_batch_size: + return tensor + else: + return torch.cat([tensor] * batched_number, dim=0) + +class StrengthType(Enum): + CONSTANT = 1 + LINEAR_UP = 2 + +class ControlBase: + def __init__(self): + self.cond_hint_original = None + self.cond_hint = None + self.strength = 1.0 + self.timestep_percent_range = (0.0, 1.0) + self.latent_format = None + self.vae = None + self.global_average_pooling = False + self.timestep_range = None + self.compression_ratio = 8 + self.upscale_algorithm = 'nearest-exact' + self.extra_args = {} + self.previous_controlnet = None + self.extra_conds = [] + self.strength_type = StrengthType.CONSTANT + self.concat_mask = False + self.extra_concat_orig = [] + self.extra_concat = None + self.extra_hooks: HookGroup = None + self.preprocess_image = lambda a: a + + def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]): + self.cond_hint_original = cond_hint + self.strength = strength + self.timestep_percent_range = timestep_percent_range + if self.latent_format is not None: + if vae is None: + logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.") + self.vae = vae + self.extra_concat_orig = extra_concat.copy() + if self.concat_mask and len(self.extra_concat_orig) == 0: + self.extra_concat_orig.append(torch.tensor([[[[1.0]]]])) + return self + + def pre_run(self, model, percent_to_timestep_function): + self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) + if self.previous_controlnet is not None: + self.previous_controlnet.pre_run(model, percent_to_timestep_function) + + def set_previous_controlnet(self, controlnet): + self.previous_controlnet = controlnet + return self + + def cleanup(self): + if self.previous_controlnet is not None: + self.previous_controlnet.cleanup() + + self.cond_hint = None + self.extra_concat = None + self.timestep_range = None + + def get_models(self): + out = [] + if self.previous_controlnet is not None: + out += self.previous_controlnet.get_models() + return out + + def get_extra_hooks(self): + out = [] + if self.extra_hooks is not None: + out.append(self.extra_hooks) + if self.previous_controlnet is not None: + out += self.previous_controlnet.get_extra_hooks() + return out + + def copy_to(self, c): + c.cond_hint_original = self.cond_hint_original + c.strength = self.strength + c.timestep_percent_range = self.timestep_percent_range + c.global_average_pooling = self.global_average_pooling + c.compression_ratio = self.compression_ratio + c.upscale_algorithm = self.upscale_algorithm + c.latent_format = self.latent_format + c.extra_args = self.extra_args.copy() + c.vae = self.vae + c.extra_conds = self.extra_conds.copy() + c.strength_type = self.strength_type + c.concat_mask = self.concat_mask + c.extra_concat_orig = self.extra_concat_orig.copy() + c.extra_hooks = self.extra_hooks.clone() if self.extra_hooks else None + c.preprocess_image = self.preprocess_image + + def inference_memory_requirements(self, dtype): + if self.previous_controlnet is not None: + return self.previous_controlnet.inference_memory_requirements(dtype) + return 0 + + def control_merge(self, control, control_prev, output_dtype): + out = {'input':[], 'middle':[], 'output': []} + + for key in control: + control_output = control[key] + applied_to = set() + for i in range(len(control_output)): + x = control_output[i] + if x is not None: + if self.global_average_pooling: + x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) + + if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once + applied_to.add(x) + if self.strength_type == StrengthType.CONSTANT: + x *= self.strength + elif self.strength_type == StrengthType.LINEAR_UP: + x *= (self.strength ** float(len(control_output) - i)) + + if output_dtype is not None and x.dtype != output_dtype: + x = x.to(output_dtype) + + out[key].append(x) + + if control_prev is not None: + for x in ['input', 'middle', 'output']: + o = out[x] + for i in range(len(control_prev[x])): + prev_val = control_prev[x][i] + if i >= len(o): + o.append(prev_val) + elif prev_val is not None: + if o[i] is None: + o[i] = prev_val + else: + if o[i].shape[0] < prev_val.shape[0]: + o[i] = prev_val + o[i] + else: + o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue + return out + + def set_extra_arg(self, argument, value=None): + self.extra_args[argument] = value + + +class ControlNet(ControlBase): + def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a): + super().__init__() + self.control_model = control_model + self.load_device = load_device + if control_model is not None: + self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) + + self.compression_ratio = compression_ratio + self.global_average_pooling = global_average_pooling + self.model_sampling_current = None + self.manual_cast_dtype = manual_cast_dtype + self.latent_format = latent_format + self.extra_conds += extra_conds + self.strength_type = strength_type + self.concat_mask = concat_mask + self.preprocess_image = preprocess_image + + def get_control(self, x_noisy, t, cond, batched_number, transformer_options): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + dtype = self.control_model.dtype + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + compression_ratio = self.compression_ratio + if self.vae is not None: + compression_ratio *= self.vae.downscale_ratio + else: + if self.latent_format is not None: + raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.") + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center") + self.cond_hint = self.preprocess_image(self.cond_hint) + if self.vae is not None: + loaded_models = comfy.model_management.loaded_models(only_currently_used=True) + self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1)) + comfy.model_management.load_models_gpu(loaded_models) + if self.latent_format is not None: + self.cond_hint = self.latent_format.process_in(self.cond_hint) + if len(self.extra_concat_orig) > 0: + to_concat = [] + for c in self.extra_concat_orig: + c = c.to(self.cond_hint.device) + c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center") + to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0])) + self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1) + + self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + + context = cond.get('crossattn_controlnet', cond['c_crossattn']) + extra = self.extra_args.copy() + for c in self.extra_conds: + temp = cond.get(c, None) + if temp is not None: + extra[c] = temp.to(dtype) + + timestep = self.model_sampling_current.timestep(t) + x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) + + control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra) + return self.control_merge(control, control_prev, output_dtype=None) + + def copy(self): + c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) + c.control_model = self.control_model + c.control_model_wrapped = self.control_model_wrapped + self.copy_to(c) + return c + + def get_models(self): + out = super().get_models() + out.append(self.control_model_wrapped) + return out + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + self.model_sampling_current = model.model_sampling + + def cleanup(self): + self.model_sampling_current = None + super().cleanup() + +class ControlLoraOps: + class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp): + def __init__(self, in_features: int, out_features: int, bias: bool = True, + device=None, dtype=None) -> None: + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = None + self.up = None + self.down = None + self.bias = None + + def forward(self, input): + weight, bias = comfy.ops.cast_bias_weight(self, input) + if self.up is not None: + return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) + else: + return torch.nn.functional.linear(input, weight, bias) + + class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + device=None, + dtype=None + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.transposed = False + self.output_padding = 0 + self.groups = groups + self.padding_mode = padding_mode + + self.weight = None + self.bias = None + self.up = None + self.down = None + + + def forward(self, input): + weight, bias = comfy.ops.cast_bias_weight(self, input) + if self.up is not None: + return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) + else: + return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) + + +class ControlLora(ControlNet): + def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options + ControlBase.__init__(self) + self.control_weights = control_weights + self.global_average_pooling = global_average_pooling + self.extra_conds += ["y"] + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + controlnet_config = model.model_config.unet_config.copy() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] + self.manual_cast_dtype = model.manual_cast_dtype + dtype = model.get_dtype() + if self.manual_cast_dtype is None: + class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init): + pass + else: + class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast): + pass + dtype = self.manual_cast_dtype + + controlnet_config["operations"] = control_lora_ops + controlnet_config["dtype"] = dtype + self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + self.control_model.to(comfy.model_management.get_torch_device()) + diffusion_model = model.diffusion_model + sd = diffusion_model.state_dict() + + for k in sd: + weight = sd[k] + try: + comfy.utils.set_attr_param(self.control_model, k, weight) + except: + pass + + for k in self.control_weights: + if (k not in {"lora_controlnet"}): + if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k): + comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + + def copy(self): + c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def cleanup(self): + del self.control_model + self.control_model = None + super().cleanup() + + def get_models(self): + out = ControlBase.get_models(self) + return out + + def inference_memory_requirements(self, dtype): + return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) + +def controlnet_config(sd, model_options={}): + model_config = comfy.model_detection.model_config_from_unet(sd, "", True) + + unet_dtype = model_options.get("dtype", None) + if unet_dtype is None: + weight_dtype = comfy.utils.weight_dtype(sd) + + supported_inference_dtypes = list(model_config.supported_inference_dtypes) + unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) + + load_device = comfy.model_management.get_torch_device() + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) + + operations = model_options.get("custom_operations", None) + if operations is None: + operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) + + offload_device = comfy.model_management.unet_offload_device() + return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device + +def controlnet_load_state_dict(control_model, sd): + missing, unexpected = control_model.load_state_dict(sd, strict=False) + + if len(missing) > 0: + logging.warning("missing controlnet keys: {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("unexpected controlnet keys: {}".format(unexpected)) + return control_model + + +def load_controlnet_mmdit(sd, model_options={}): + new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) + num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.') + for k in sd: + new_sd[k] = sd[k] + + concat_mask = False + control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1] + if control_latent_channels == 17: #inpaint controlnet + concat_mask = True + + control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, new_sd) + + latent_format = comfy.latent_formats.SD3() + latent_format.shift_factor = 0 #SD3 controlnet weirdness + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + return control + + +class ControlNetSD35(ControlNet): + def pre_run(self, model, percent_to_timestep_function): + if self.control_model.double_y_emb: + missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False) + else: + missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False) + super().pre_run(model, percent_to_timestep_function) + + def copy(self): + c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) + c.control_model = self.control_model + c.control_model_wrapped = self.control_model_wrapped + self.copy_to(c) + return c + +def load_controlnet_sd35(sd, model_options={}): + control_type = -1 + if "control_type" in sd: + control_type = round(sd.pop("control_type").item()) + + # blur_cnet = control_type == 0 + canny_cnet = control_type == 1 + depth_cnet = control_type == 2 + + new_sd = {} + for k in comfy.utils.MMDIT_MAP_BASIC: + if k[1] in sd: + new_sd[k[0]] = sd.pop(k[1]) + for k in sd: + new_sd[k] = sd[k] + sd = new_sd + + y_emb_shape = sd["y_embedder.mlp.0.weight"].shape + depth = y_emb_shape[0] // 64 + hidden_size = 64 * depth + num_heads = depth + head_dim = hidden_size // num_heads + num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.') + + load_device = comfy.model_management.get_torch_device() + offload_device = comfy.model_management.unet_offload_device() + unet_dtype = comfy.model_management.unet_dtype(model_params=-1) + + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) + + operations = model_options.get("custom_operations", None) + if operations is None: + operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) + + control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None, + patch_size=2, + in_chans=16, + num_layers=num_blocks, + main_model_double=depth, + double_y_emb=y_emb_shape[0] == y_emb_shape[1], + attention_head_dim=head_dim, + num_attention_heads=num_heads, + adm_in_channels=2048, + device=offload_device, + dtype=unet_dtype, + operations=operations) + + control_model = controlnet_load_state_dict(control_model, sd) + + latent_format = comfy.latent_formats.SD3() + preprocess_image = lambda a: a + if canny_cnet: + preprocess_image = lambda a: (a * 255 * 0.5 + 0.5) + elif depth_cnet: + preprocess_image = lambda a: 1.0 - a + + control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image) + return control + + + +def load_controlnet_hunyuandit(controlnet_data, model_options={}): + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options) + + control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype) + control_model = controlnet_load_state_dict(control_model, controlnet_data) + + latent_format = comfy.latent_formats.SDXL() + extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img'] + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT) + return control + +def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}): + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) + control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, sd) + extra_conds = ['y', 'guidance'] + control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) + return control + +def load_controlnet_flux_instantx(sd, model_options={}): + new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) + for k in sd: + new_sd[k] = sd[k] + + num_union_modes = 0 + union_cnet = "controlnet_mode_embedder.weight" + if union_cnet in new_sd: + num_union_modes = new_sd[union_cnet].shape[0] + + control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4 + concat_mask = False + if control_latent_channels == 17: + concat_mask = True + + control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, new_sd) + + latent_format = comfy.latent_formats.Flux() + extra_conds = ['y', 'guidance'] + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) + return control + +def convert_mistoline(sd): + return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."}) + + +def load_controlnet_state_dict(state_dict, model=None, model_options={}): + controlnet_data = state_dict + if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT + return load_controlnet_hunyuandit(controlnet_data, model_options=model_options) + + if "lora_controlnet" in controlnet_data: + return ControlLora(controlnet_data, model_options=model_options) + + controlnet_config = None + supported_inference_dtypes = None + + if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format + controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data) + diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) + diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" + diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + k_in = "controlnet_down_blocks.{}{}".format(count, s) + k_out = "zero_convs.{}.0{}".format(count, s) + if k_in not in controlnet_data: + loop = False + break + diffusers_keys[k_in] = k_out + count += 1 + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + if count == 0: + k_in = "controlnet_cond_embedding.conv_in{}".format(s) + else: + k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) + k_out = "input_hint_block.{}{}".format(count * 2, s) + if k_in not in controlnet_data: + k_in = "controlnet_cond_embedding.conv_out{}".format(s) + loop = False + diffusers_keys[k_in] = k_out + count += 1 + + new_sd = {} + for k in diffusers_keys: + if k in controlnet_data: + new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + + if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet + controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0] + for k in list(controlnet_data.keys()): + new_k = k.replace('.attn.in_proj_', '.attn.in_proj.') + new_sd[new_k] = controlnet_data.pop(k) + + leftover_keys = controlnet_data.keys() + if len(leftover_keys) > 0: + logging.warning("leftover keys: {}".format(leftover_keys)) + controlnet_data = new_sd + elif "controlnet_blocks.0.weight" in controlnet_data: + if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data: + return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options) + elif "pos_embed_input.proj.weight" in controlnet_data: + if "transformer_blocks.0.adaLN_modulation.1.bias" in controlnet_data: + return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format + else: + return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet + elif "controlnet_x_embedder.weight" in controlnet_data: + return load_controlnet_flux_instantx(controlnet_data, model_options=model_options) + elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux + return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options) + + pth_key = 'control_model.zero_convs.0.0.weight' + pth = False + key = 'zero_convs.0.0.weight' + if pth_key in controlnet_data: + pth = True + key = pth_key + prefix = "control_model." + elif key in controlnet_data: + prefix = "" + else: + net = load_t2i_adapter(controlnet_data, model_options=model_options) + if net is None: + logging.error("error could not detect control model type.") + return net + + if controlnet_config is None: + model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True) + supported_inference_dtypes = list(model_config.supported_inference_dtypes) + controlnet_config = model_config.unet_config + + unet_dtype = model_options.get("dtype", None) + if unet_dtype is None: + weight_dtype = comfy.utils.weight_dtype(controlnet_data) + + if supported_inference_dtypes is None: + supported_inference_dtypes = [comfy.model_management.unet_dtype()] + + unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) + + load_device = comfy.model_management.get_torch_device() + + manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) + operations = model_options.get("custom_operations", None) + if operations is None: + operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype) + + controlnet_config["operations"] = operations + controlnet_config["dtype"] = unet_dtype + controlnet_config["device"] = comfy.model_management.unet_offload_device() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] + control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + + if pth: + if 'difference' in controlnet_data: + if model is not None: + comfy.model_management.load_models_gpu([model]) + model_sd = model.model_state_dict() + for x in controlnet_data: + c_m = "control_model." + if x.startswith(c_m): + sd_key = "diffusion_model.{}".format(x[len(c_m):]) + if sd_key in model_sd: + cd = controlnet_data[x] + cd += model_sd[sd_key].type(cd.dtype).to(cd.device) + else: + logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.control_model = control_model + missing, unexpected = w.load_state_dict(controlnet_data, strict=False) + else: + missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) + + if len(missing) > 0: + logging.warning("missing controlnet keys: {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("unexpected controlnet keys: {}".format(unexpected)) + + global_average_pooling = model_options.get("global_average_pooling", False) + control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + return control + +def load_controlnet(ckpt_path, model=None, model_options={}): + model_options = model_options.copy() + if "global_average_pooling" not in model_options: + filename = os.path.splitext(ckpt_path)[0] + if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling + model_options["global_average_pooling"] = True + + cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options) + if cnet is None: + logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path)) + return cnet + +class T2IAdapter(ControlBase): + def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None): + super().__init__() + self.t2i_model = t2i_model + self.channels_in = channels_in + self.control_input = None + self.compression_ratio = compression_ratio + self.upscale_algorithm = upscale_algorithm + if device is None: + device = comfy.model_management.get_torch_device() + self.device = device + + def scale_image_to(self, width, height): + unshuffle_amount = self.t2i_model.unshuffle_amount + width = math.ceil(width / unshuffle_amount) * unshuffle_amount + height = math.ceil(height / unshuffle_amount) * unshuffle_amount + return width, height + + def get_control(self, x_noisy, t, cond, batched_number, transformer_options): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.control_input = None + self.cond_hint = None + width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device) + if self.channels_in == 1 and self.cond_hint.shape[1] > 1: + self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + if self.control_input is None: + self.t2i_model.to(x_noisy.dtype) + self.t2i_model.to(self.device) + self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) + self.t2i_model.cpu() + + control_input = {} + for k in self.control_input: + control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k])) + + return self.control_merge(control_input, control_prev, x_noisy.dtype) + + def copy(self): + c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) + self.copy_to(c) + return c + +def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options + compression_ratio = 8 + upscale_algorithm = 'nearest-exact' + + if 'adapter' in t2i_data: + t2i_data = t2i_data['adapter'] + if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format + prefix_replace = {} + for i in range(4): + for j in range(2): + prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) + prefix_replace["adapter.body.{}.".format(i, )] = "body.{}.".format(i * 2) + prefix_replace["adapter."] = "" + t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace) + keys = t2i_data.keys() + + if "body.0.in_conv.weight" in keys: + cin = t2i_data['body.0.in_conv.weight'].shape[1] + model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) + elif 'conv_in.weight' in keys: + cin = t2i_data['conv_in.weight'].shape[1] + channel = t2i_data['conv_in.weight'].shape[0] + ksize = t2i_data['body.0.block2.weight'].shape[2] + use_conv = False + down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) + if len(down_opts) > 0: + use_conv = True + xl = False + if cin == 256 or cin == 768: + xl = True + model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + elif "backbone.0.0.weight" in keys: + model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) + compression_ratio = 32 + upscale_algorithm = 'bilinear' + elif "backbone.10.blocks.0.weight" in keys: + model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) + compression_ratio = 1 + upscale_algorithm = 'nearest-exact' + else: + return None + + missing, unexpected = model_ad.load_state_dict(t2i_data) + if len(missing) > 0: + logging.warning("t2i missing {}".format(missing)) + + if len(unexpected) > 0: + logging.debug("t2i unexpected {}".format(unexpected)) + + return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..fb9495348704c4f58b537f524ff6194d572d9fad --- /dev/null +++ b/comfy/diffusers_convert.py @@ -0,0 +1,189 @@ +import re +import torch +import logging + +# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py + +# ================# +# VAE Conversion # +# ================# + +vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), +] + +for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3 - i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + +# this part accounts for mid blocks in both the encoder and the decoder +for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i + 1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + +vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "query."), + ("k.", "key."), + ("v.", "value."), + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), + ("proj_out.", "proj_attn."), +] + + +def reshape_weight_for_sd(w, conv3d=False): + # convert HF linear weights to SD conv2d weights + if conv3d: + return w.reshape(*w.shape, 1, 1, 1) + else: + return w.reshape(*w.shape, 1, 1) + + +def convert_vae_state_dict(vae_state_dict): + mapping = {k: k for k in vae_state_dict.keys()} + conv3d = False + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + if v.endswith(".conv.weight"): + if not conv3d and vae_state_dict[k].ndim == 5: + conv3d = True + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + logging.debug(f"Reshaping {k} for SD format") + new_state_dict[k] = reshape_weight_for_sd(v, conv3d=conv3d) + return new_state_dict + + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "transformer.text_model.final_layer_norm."), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +# This function exists because at the time of writing torch.cat can't do fp8 with cuda +def cat_tensors(tensors): + x = 0 + for t in tensors: + x += t.shape[0] + + shape = [x] + list(tensors[0].shape)[1:] + out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) + + x = 0 + for t in tensors: + out[x:x + t.shape[0]] = t + x += t.shape[0] + + return out + + +def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if not k.startswith(prefix): + continue + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + text_proj = "transformer.text_projection.weight" + if k.endswith(text_proj): + new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() + else: + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py new file mode 100644 index 0000000000000000000000000000000000000000..56e63a7565f083eb4e3bc484a3a9f90103306a2f --- /dev/null +++ b/comfy/diffusers_load.py @@ -0,0 +1,36 @@ +import os + +import comfy.sd + +def first_file(path, filenames): + for f in filenames: + p = os.path.join(path, f) + if os.path.exists(p): + return p + return None + +def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): + diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] + unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) + vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) + + text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] + text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) + text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) + + text_encoder_paths = [text_encoder1_path] + if text_encoder2_path is not None: + text_encoder_paths.append(text_encoder2_path) + + unet = comfy.sd.load_diffusion_model(unet_path) + + clip = None + if output_clip: + clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) + + vae = None + if output_vae: + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + + return (unet, clip, vae) diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py new file mode 100644 index 0000000000000000000000000000000000000000..c57e081e45cfaa694e3e78528798f62a4698c056 --- /dev/null +++ b/comfy/extra_samplers/uni_pc.py @@ -0,0 +1,873 @@ +#code taken from: https://github.com/wl-zhao/UniPC and modified + +import torch +import math +import logging + +from tqdm.auto import trange + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + r"""Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + output = model(x, t_input, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1', + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + logging.info(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + # print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False + ): + # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + # t_T = self.noise_schedule.T if t_start is None else t_start + steps = len(timesteps) - 1 + if method == 'multistep': + assert steps >= order + # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # with torch.no_grad(): + for step_index in trange(steps, disable=disable_pbar): + if step_index == 0: + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + elif step_index < order: + init_order = step_index + # Init the first `order` values by lower order multistep DPM-Solver. + # for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + else: + extra_final_step = 0 + if step_index == (steps - 1): + extra_final_step = 1 + for step in range(step_index, step_index + 1 + extra_final_step): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + # print('this step order:', step_order) + if step == steps: + # print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + if callback is not None: + callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]}) + else: + raise NotImplementedError() + # if denoise_to_zero: + # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] + + +class SigmaConvert: + schedule = "" + def marginal_log_mean_coeff(self, sigma): + return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) + + def marginal_alpha(self, t): + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + +def predict_eps_sigma(model, input, sigma_in, **kwargs): + sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) + input = input * ((sigma ** 2 + 1.0) ** 0.5) + return (input - model(input, sigma_in, **kwargs)) / sigma + + +def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'): + timesteps = sigmas.clone() + if sigmas[-1] == 0: + timesteps = sigmas[:] + timesteps[-1] = 0.001 + else: + timesteps = sigmas.clone() + ns = SigmaConvert() + + noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0) + model_type = "noise" + + model_fn = model_wrapper( + lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), + ns, + model_type=model_type, + guidance_type="uncond", + model_kwargs=extra_args, + ) + + order = min(3, len(timesteps) - 2) + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant) + x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) + x /= ns.marginal_alpha(timesteps[-1]) + return x + +def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False): + return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2') diff --git a/comfy/float.py b/comfy/float.py new file mode 100644 index 0000000000000000000000000000000000000000..521316fd2facaab90583da8487029a365aefd9e7 --- /dev/null +++ b/comfy/float.py @@ -0,0 +1,67 @@ +import torch + +def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None): + mantissa_scaled = torch.where( + normal_mask, + (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS), + (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS))) + ) + + mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator) + return mantissa_scaled.floor() / (2**MANTISSA_BITS) + +#Not 100% sure about this +def manual_stochastic_round_to_float8(x, dtype, generator=None): + if dtype == torch.float8_e4m3fn: + EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7 + elif dtype == torch.float8_e5m2: + EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 5, 2, 15 + else: + raise ValueError("Unsupported dtype") + + x = x.half() + sign = torch.sign(x) + abs_x = x.abs() + sign = torch.where(abs_x == 0, 0, sign) + + # Combine exponent calculation and clamping + exponent = torch.clamp( + torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS, + 0, 2**EXPONENT_BITS - 1 + ) + + # Combine mantissa calculation and rounding + normal_mask = ~(exponent == 0) + + abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator) + + sign *= torch.where( + normal_mask, + (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x), + (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x + ) + + inf = torch.finfo(dtype) + torch.clamp(sign, min=inf.min, max=inf.max, out=sign) + return sign + + + +def stochastic_rounding(value, dtype, seed=0): + if dtype == torch.float32: + return value.to(dtype=torch.float32) + if dtype == torch.float16: + return value.to(dtype=torch.float16) + if dtype == torch.bfloat16: + return value.to(dtype=torch.bfloat16) + if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2: + generator = torch.Generator(device=value.device) + generator.manual_seed(seed) + output = torch.empty_like(value, dtype=dtype) + num_slices = max(1, (value.numel() / (4096 * 4096))) + slice_size = max(1, round(value.shape[0] / num_slices)) + for i in range(0, value.shape[0], slice_size): + output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator)) + return output + + return value.to(dtype=dtype) diff --git a/comfy/gligen.py b/comfy/gligen.py new file mode 100644 index 0000000000000000000000000000000000000000..161d8a5e562cc38791b8484f2999a07eb97c94a3 --- /dev/null +++ b/comfy/gligen.py @@ -0,0 +1,344 @@ +import math +import torch +from torch import nn +from .ldm.modules.attention import CrossAttention +from inspect import isfunction +import comfy.ops +ops = comfy.ops.manual_cast + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = ops.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * torch.nn.functional.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + ops.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + ops.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class GatedCrossAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head, + operations=ops) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + x = x + self.scale * \ + torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = ops.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=query_dim, + heads=n_heads, + dim_head=d_head, + operations=ops) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + N_visual = x.shape[1] + objs = self.linear(objs) + + x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( + self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense2(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = ops.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = ops.LayerNorm(query_dim) + self.norm2 = ops.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + B, N_visual, _ = x.shape + B, N_ground, _ = objs.shape + + objs = self.linear(objs) + + # sanity check + size_v = math.sqrt(N_visual) + size_g = math.sqrt(N_ground) + assert int(size_v) == size_v, "Visual tokens must be square rootable" + assert int(size_g) == size_g, "Grounding tokens must be square rootable" + size_v = int(size_v) + size_g = int(size_g) + + # select grounding token and resize it to visual token size as residual + out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ + :, N_visual:, :] + out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) + out = torch.nn.functional.interpolate( + out, (size_v, size_v), mode='bicubic') + residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) + + # add residual to visual feature + x = x + self.scale * torch.tanh(self.alpha_attn) * residual + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class FourierEmbedder(): + def __init__(self, num_freqs=64, temperature=100): + + self.num_freqs = num_freqs + self.temperature = temperature + self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) + + @torch.no_grad() + def __call__(self, x, cat_dim=-1): + "x: arbitrary shape of tensor. dim: cat dim" + out = [] + for freq in self.freq_bands: + out.append(torch.sin(freq * x)) + out.append(torch.cos(freq * x)) + return torch.cat(out, cat_dim) + + +class PositionNet(nn.Module): + def __init__(self, in_dim, out_dim, fourier_freqs=8): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) + self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy + + self.linears = nn.Sequential( + ops.Linear(self.in_dim + self.position_dim, 512), + nn.SiLU(), + ops.Linear(512, 512), + nn.SiLU(), + ops.Linear(512, out_dim), + ) + + self.null_positive_feature = torch.nn.Parameter( + torch.zeros([self.in_dim])) + self.null_position_feature = torch.nn.Parameter( + torch.zeros([self.position_dim])) + + def forward(self, boxes, masks, positive_embeddings): + B, N, _ = boxes.shape + masks = masks.unsqueeze(-1) + positive_embeddings = positive_embeddings + + # embedding position (it may includes padding as placeholder) + xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C + + # learnable null embedding + positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) + xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) + + # replace padding with learnable null embedding + positive_embeddings = positive_embeddings * \ + masks + (1 - masks) * positive_null + xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null + + objs = self.linears( + torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) + assert objs.shape == torch.Size([B, N, self.out_dim]) + return objs + + +class Gligen(nn.Module): + def __init__(self, modules, position_net, key_dim): + super().__init__() + self.module_list = nn.ModuleList(modules) + self.position_net = position_net + self.key_dim = key_dim + self.max_objs = 30 + self.current_device = torch.device("cpu") + + def _set_position(self, boxes, masks, positive_embeddings): + objs = self.position_net(boxes, masks, positive_embeddings) + def func(x, extra_options): + key = extra_options["transformer_index"] + module = self.module_list[key] + return module(x, objs.to(device=x.device, dtype=x.dtype)) + return func + + def set_position(self, latent_image_shape, position_params, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu") + boxes = [] + positive_embeddings = [] + for p in position_params: + x1 = (p[4]) / w + y1 = (p[3]) / h + x2 = (p[4] + p[2]) / w + y2 = (p[3] + p[1]) / h + masks[len(boxes)] = 1.0 + boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] + positive_embeddings += [p[0]] + append_boxes = [] + append_conds = [] + if len(boxes) < self.max_objs: + append_boxes = [torch.zeros( + [self.max_objs - len(boxes), 4], device="cpu")] + append_conds = [torch.zeros( + [self.max_objs - len(boxes), self.key_dim], device="cpu")] + + box_out = torch.cat( + boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) + masks = masks.unsqueeze(0).repeat(batch, 1) + conds = torch.cat(positive_embeddings + + append_conds).unsqueeze(0).repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + def set_empty(self, latent_image_shape, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) + box_out = torch.zeros([self.max_objs, 4], + device="cpu").repeat(batch, 1, 1) + conds = torch.zeros([self.max_objs, self.key_dim], + device="cpu").repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + +def load_gligen(sd): + sd_k = sd.keys() + output_list = [] + key_dim = 768 + for a in ["input_blocks", "middle_block", "output_blocks"]: + for b in range(20): + k_temp = filter(lambda k: "{}.{}.".format(a, b) + in k and ".fuser." in k, sd_k) + k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) + + n_sd = {} + for k in k_temp: + n_sd[k[1]] = sd[k[0]] + if len(n_sd) > 0: + query_dim = n_sd["linear.weight"].shape[0] + key_dim = n_sd["linear.weight"].shape[1] + + if key_dim == 768: # SD1.x + n_heads = 8 + d_head = query_dim // n_heads + else: + d_head = 64 + n_heads = query_dim // d_head + + gated = GatedSelfAttentionDense( + query_dim, key_dim, n_heads, d_head) + gated.load_state_dict(n_sd, strict=False) + output_list.append(gated) + + if "position_net.null_positive_feature" in sd_k: + in_dim = sd["position_net.null_positive_feature"].shape[0] + out_dim = sd["position_net.linears.4.weight"].shape[0] + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.position_net = PositionNet(in_dim, out_dim) + w.load_state_dict(sd, strict=False) + + gligen = Gligen(output_list, w.position_net, key_dim) + return gligen diff --git a/comfy/hooks.py b/comfy/hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..9d07310729020a033f705395433c318152432935 --- /dev/null +++ b/comfy/hooks.py @@ -0,0 +1,785 @@ +from __future__ import annotations +from typing import TYPE_CHECKING, Callable +import enum +import math +import torch +import numpy as np +import itertools +import logging + +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher, PatcherInjection + from comfy.model_base import BaseModel + from comfy.sd import CLIP +import comfy.lora +import comfy.model_management +import comfy.patcher_extension +from node_helpers import conditioning_set_values + +# ####################################################################################################### +# Hooks explanation +# ------------------- +# The purpose of hooks is to allow conds to influence sampling without the need for ComfyUI core code to +# make explicit special cases like it does for ControlNet and GLIGEN. +# +# This is necessary for nodes/features that are intended for use with masked or scheduled conds, or those +# that should run special code when a 'marked' cond is used in sampling. +# ####################################################################################################### + +class EnumHookMode(enum.Enum): + ''' + Priority of hook memory optimization vs. speed, mostly related to WeightHooks. + + MinVram: No caching will occur for any operations related to hooks. + MaxSpeed: Excess VRAM (and RAM, once VRAM is sufficiently depleted) will be used to cache hook weights when switching hook groups. + ''' + MinVram = "minvram" + MaxSpeed = "maxspeed" + +class EnumHookType(enum.Enum): + ''' + Hook types, each of which has different expected behavior. + ''' + Weight = "weight" + ObjectPatch = "object_patch" + AdditionalModels = "add_models" + TransformerOptions = "transformer_options" + Injections = "add_injections" + +class EnumWeightTarget(enum.Enum): + Model = "model" + Clip = "clip" + +class EnumHookScope(enum.Enum): + ''' + Determines if hook should be limited in its influence over sampling. + + AllConditioning: hook will affect all conds used in sampling. + HookedOnly: hook will only affect the conds it was attached to. + ''' + AllConditioning = "all_conditioning" + HookedOnly = "hooked_only" + + +class _HookRef: + pass + + +def default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + '''Example for how custom_should_register function can look like.''' + return True + + +def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[str]: + '''Creates base dictionary for use with Hooks' target param.''' + d = {} + if target is not None: + d['target'] = target + d.update(kwargs) + return d + + +class Hook: + def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None, hook_id: str=None, + hook_keyframe: HookKeyframeGroup=None, hook_scope=EnumHookScope.AllConditioning): + self.hook_type = hook_type + '''Enum identifying the general class of this hook.''' + self.hook_ref = hook_ref if hook_ref else _HookRef() + '''Reference shared between hook clones that have the same value. Should NOT be modified.''' + self.hook_id = hook_id + '''Optional string ID to identify hook; useful if need to consolidate duplicates at registration time.''' + self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup() + '''Keyframe storage that can be referenced to get strength for current sampling step.''' + self.hook_scope = hook_scope + '''Scope of where this hook should apply in terms of the conds used in sampling run.''' + self.custom_should_register = default_should_register + '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register''' + + @property + def strength(self): + return self.hook_keyframe.strength + + def initialize_timesteps(self, model: BaseModel): + self.reset() + self.hook_keyframe.initialize_timesteps(model) + + def reset(self): + self.hook_keyframe.reset() + + def clone(self): + c: Hook = self.__class__() + c.hook_type = self.hook_type + c.hook_ref = self.hook_ref + c.hook_id = self.hook_id + c.hook_keyframe = self.hook_keyframe + c.hook_scope = self.hook_scope + c.custom_should_register = self.custom_should_register + return c + + def should_register(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + return self.custom_should_register(self, model, model_options, target_dict, registered) + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + raise NotImplementedError("add_hook_patches should be defined for Hook subclasses") + + def __eq__(self, other: Hook): + return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref + + def __hash__(self): + return hash(self.hook_ref) + +class WeightHook(Hook): + ''' + Hook responsible for tracking weights to be applied to some model/clip. + + Note, value of hook_scope is ignored and is treated as HookedOnly. + ''' + def __init__(self, strength_model=1.0, strength_clip=1.0): + super().__init__(hook_type=EnumHookType.Weight, hook_scope=EnumHookScope.HookedOnly) + self.weights: dict = None + self.weights_clip: dict = None + self.need_weight_init = True + self._strength_model = strength_model + self._strength_clip = strength_clip + self.hook_scope = EnumHookScope.HookedOnly # this value does not matter for WeightHooks, just for docs + + @property + def strength_model(self): + return self._strength_model * self.strength + + @property + def strength_clip(self): + return self._strength_clip * self.strength + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + if not self.should_register(model, model_options, target_dict, registered): + return False + weights = None + + target = target_dict.get('target', None) + if target == EnumWeightTarget.Clip: + strength = self._strength_clip + else: + strength = self._strength_model + + if self.need_weight_init: + key_map = {} + if target == EnumWeightTarget.Clip: + key_map = comfy.lora.model_lora_keys_clip(model.model, key_map) + else: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False) + else: + if target == EnumWeightTarget.Clip: + weights = self.weights_clip + else: + weights = self.weights + model.add_hook_patches(hook=self, patches=weights, strength_patch=strength) + registered.add(self) + return True + # TODO: add logs about any keys that were not applied + + def clone(self): + c: WeightHook = super().clone() + c.weights = self.weights + c.weights_clip = self.weights_clip + c.need_weight_init = self.need_weight_init + c._strength_model = self._strength_model + c._strength_clip = self._strength_clip + return c + +class ObjectPatchHook(Hook): + def __init__(self, object_patches: dict[str]=None, + hook_scope=EnumHookScope.AllConditioning): + super().__init__(hook_type=EnumHookType.ObjectPatch) + self.object_patches = object_patches + self.hook_scope = hook_scope + + def clone(self): + c: ObjectPatchHook = super().clone() + c.object_patches = self.object_patches + return c + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + raise NotImplementedError("ObjectPatchHook is not supported yet in ComfyUI.") + +class AdditionalModelsHook(Hook): + ''' + Hook responsible for telling model management any additional models that should be loaded. + + Note, value of hook_scope is ignored and is treated as AllConditioning. + ''' + def __init__(self, models: list[ModelPatcher]=None, key: str=None): + super().__init__(hook_type=EnumHookType.AdditionalModels) + self.models = models + self.key = key + + def clone(self): + c: AdditionalModelsHook = super().clone() + c.models = self.models.copy() if self.models else self.models + c.key = self.key + return c + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + if not self.should_register(model, model_options, target_dict, registered): + return False + registered.add(self) + return True + +class TransformerOptionsHook(Hook): + ''' + Hook responsible for adding wrappers, callbacks, patches, or anything else related to transformer_options. + ''' + def __init__(self, transformers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None, + hook_scope=EnumHookScope.AllConditioning): + super().__init__(hook_type=EnumHookType.TransformerOptions) + self.transformers_dict = transformers_dict + self.hook_scope = hook_scope + self._skip_adding = False + '''Internal value used to avoid double load of transformer_options when hook_scope is AllConditioning.''' + + def clone(self): + c: TransformerOptionsHook = super().clone() + c.transformers_dict = self.transformers_dict + c._skip_adding = self._skip_adding + return c + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + if not self.should_register(model, model_options, target_dict, registered): + return False + # NOTE: to_load_options will be used to manually load patches/wrappers/callbacks from hooks + self._skip_adding = False + if self.hook_scope == EnumHookScope.AllConditioning: + add_model_options = {"transformer_options": self.transformers_dict, + "to_load_options": self.transformers_dict} + # skip_adding if included in AllConditioning to avoid double loading + self._skip_adding = True + else: + add_model_options = {"to_load_options": self.transformers_dict} + registered.add(self) + comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False) + return True + + def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str]): + if not self._skip_adding: + comfy.patcher_extension.merge_nested_dicts(transformer_options, self.transformers_dict, copy_dict1=False) + +WrapperHook = TransformerOptionsHook +'''Only here for backwards compatibility, WrapperHook is identical to TransformerOptionsHook.''' + +class InjectionsHook(Hook): + def __init__(self, key: str=None, injections: list[PatcherInjection]=None, + hook_scope=EnumHookScope.AllConditioning): + super().__init__(hook_type=EnumHookType.Injections) + self.key = key + self.injections = injections + self.hook_scope = hook_scope + + def clone(self): + c: InjectionsHook = super().clone() + c.key = self.key + c.injections = self.injections.copy() if self.injections else self.injections + return c + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + raise NotImplementedError("InjectionsHook is not supported yet in ComfyUI.") + +class HookGroup: + ''' + Stores groups of hooks, and allows them to be queried by type. + + To prevent breaking their functionality, never modify the underlying self.hooks or self._hook_dict vars directly; + always use the provided functions on HookGroup. + ''' + def __init__(self): + self.hooks: list[Hook] = [] + self._hook_dict: dict[EnumHookType, list[Hook]] = {} + + def __len__(self): + return len(self.hooks) + + def add(self, hook: Hook): + if hook not in self.hooks: + self.hooks.append(hook) + self._hook_dict.setdefault(hook.hook_type, []).append(hook) + + def remove(self, hook: Hook): + if hook in self.hooks: + self.hooks.remove(hook) + self._hook_dict[hook.hook_type].remove(hook) + + def get_type(self, hook_type: EnumHookType): + return self._hook_dict.get(hook_type, []) + + def contains(self, hook: Hook): + return hook in self.hooks + + def is_subset_of(self, other: HookGroup): + self_hooks = set(self.hooks) + other_hooks = set(other.hooks) + return self_hooks.issubset(other_hooks) + + def new_with_common_hooks(self, other: HookGroup): + c = HookGroup() + for hook in self.hooks: + if other.contains(hook): + c.add(hook.clone()) + return c + + def clone(self): + c = HookGroup() + for hook in self.hooks: + c.add(hook.clone()) + return c + + def clone_and_combine(self, other: HookGroup): + c = self.clone() + if other is not None: + for hook in other.hooks: + c.add(hook.clone()) + return c + + def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup): + if hook_kf is None: + hook_kf = HookKeyframeGroup() + else: + hook_kf = hook_kf.clone() + for hook in self.hooks: + hook.hook_keyframe = hook_kf + + def get_hooks_for_clip_schedule(self): + scheduled_hooks: dict[WeightHook, list[tuple[tuple[float,float], HookKeyframe]]] = {} + # only care about WeightHooks, for now + for hook in self.get_type(EnumHookType.Weight): + hook: WeightHook + hook_schedule = [] + # if no hook keyframes, assign default value + if len(hook.hook_keyframe.keyframes) == 0: + hook_schedule.append(((0.0, 1.0), None)) + scheduled_hooks[hook] = hook_schedule + continue + # find ranges of values + prev_keyframe = hook.hook_keyframe.keyframes[0] + for keyframe in hook.hook_keyframe.keyframes: + if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength): + hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe)) + prev_keyframe = keyframe + elif keyframe.start_percent == prev_keyframe.start_percent: + prev_keyframe = keyframe + # create final range, assuming last start_percent was not 1.0 + if not math.isclose(prev_keyframe.start_percent, 1.0): + hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe)) + scheduled_hooks[hook] = hook_schedule + # hooks should not have their schedules in a list of tuples + all_ranges: list[tuple[float, float]] = [] + for range_kfs in scheduled_hooks.values(): + for t_range, keyframe in range_kfs: + all_ranges.append(t_range) + # turn list of ranges into boundaries + boundaries_set = set(itertools.chain.from_iterable(all_ranges)) + boundaries_set.add(0.0) + boundaries = sorted(boundaries_set) + real_ranges = [(boundaries[i], boundaries[i + 1]) for i in range(len(boundaries) - 1)] + # with real ranges defined, give appropriate hooks w/ keyframes for each range + scheduled_keyframes: list[tuple[tuple[float,float], list[tuple[WeightHook, HookKeyframe]]]] = [] + for t_range in real_ranges: + hooks_schedule = [] + for hook, val in scheduled_hooks.items(): + keyframe = None + # check if is a keyframe that works for the current t_range + for stored_range, stored_kf in val: + # if stored start is less than current end, then fits - give it assigned keyframe + if stored_range[0] < t_range[1] and stored_range[1] > t_range[0]: + keyframe = stored_kf + break + hooks_schedule.append((hook, keyframe)) + scheduled_keyframes.append((t_range, hooks_schedule)) + return scheduled_keyframes + + def reset(self): + for hook in self.hooks: + hook.reset() + + @staticmethod + def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) -> HookGroup: + actual: list[HookGroup] = [] + for group in hooks_list: + if group is not None: + actual.append(group) + if len(actual) < require_count: + raise Exception(f"Need at least {require_count} hooks to combine, but only had {len(actual)}.") + # if no hooks, then return None + if len(actual) == 0: + return None + # if only 1 hook, just return itself without cloning + elif len(actual) == 1: + return actual[0] + final_hook: HookGroup = None + for hook in actual: + if final_hook is None: + final_hook = hook.clone() + else: + final_hook = final_hook.clone_and_combine(hook) + return final_hook + + +class HookKeyframe: + def __init__(self, strength: float, start_percent=0.0, guarantee_steps=1): + self.strength = strength + # scheduling + self.start_percent = float(start_percent) + self.start_t = 999999999.9 + self.guarantee_steps = guarantee_steps + + def get_effective_guarantee_steps(self, max_sigma: torch.Tensor): + '''If keyframe starts before current sampling range (max_sigma), treat as 0.''' + if self.start_t > max_sigma: + return 0 + return self.guarantee_steps + + def clone(self): + c = HookKeyframe(strength=self.strength, + start_percent=self.start_percent, guarantee_steps=self.guarantee_steps) + c.start_t = self.start_t + return c + +class HookKeyframeGroup: + def __init__(self): + self.keyframes: list[HookKeyframe] = [] + self._current_keyframe: HookKeyframe = None + self._current_used_steps = 0 + self._current_index = 0 + self._current_strength = None + self._curr_t = -1. + + # properties shadow those of HookWeightsKeyframe + @property + def strength(self): + if self._current_keyframe is not None: + return self._current_keyframe.strength + return 1.0 + + def reset(self): + self._current_keyframe = None + self._current_used_steps = 0 + self._current_index = 0 + self._current_strength = None + self.curr_t = -1. + self._set_first_as_current() + + def add(self, keyframe: HookKeyframe): + # add to end of list, then sort + self.keyframes.append(keyframe) + self.keyframes = get_sorted_list_via_attr(self.keyframes, "start_percent") + self._set_first_as_current() + + def _set_first_as_current(self): + if len(self.keyframes) > 0: + self._current_keyframe = self.keyframes[0] + else: + self._current_keyframe = None + + def has_guarantee_steps(self): + for kf in self.keyframes: + if kf.guarantee_steps > 0: + return True + return False + + def has_index(self, index: int): + return index >= 0 and index < len(self.keyframes) + + def is_empty(self): + return len(self.keyframes) == 0 + + def clone(self): + c = HookKeyframeGroup() + for keyframe in self.keyframes: + c.keyframes.append(keyframe.clone()) + c._set_first_as_current() + return c + + def initialize_timesteps(self, model: BaseModel): + for keyframe in self.keyframes: + keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent) + + def prepare_current_keyframe(self, curr_t: float, transformer_options: dict[str, torch.Tensor]) -> bool: + if self.is_empty(): + return False + if curr_t == self._curr_t: + return False + max_sigma = torch.max(transformer_options["sample_sigmas"]) + prev_index = self._current_index + prev_strength = self._current_strength + # if met guaranteed steps, look for next keyframe in case need to switch + if self._current_used_steps >= self._current_keyframe.get_effective_guarantee_steps(max_sigma): + # if has next index, loop through and see if need to switch + if self.has_index(self._current_index+1): + for i in range(self._current_index+1, len(self.keyframes)): + eval_c = self.keyframes[i] + # check if start_t is greater or equal to curr_t + # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling + if eval_c.start_t >= curr_t: + self._current_index = i + self._current_strength = eval_c.strength + self._current_keyframe = eval_c + self._current_used_steps = 0 + # if guarantee_steps greater than zero, stop searching for other keyframes + if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0: + break + # if eval_c is outside the percent range, stop looking further + else: break + # update steps current context is used + self._current_used_steps += 1 + # update current timestep this was performed on + self._curr_t = curr_t + # return True if keyframe changed, False if no change + return prev_index != self._current_index and prev_strength != self._current_strength + + +class InterpolationMethod: + LINEAR = "linear" + EASE_IN = "ease_in" + EASE_OUT = "ease_out" + EASE_IN_OUT = "ease_in_out" + + _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT] + + @classmethod + def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False): + diff = num_to - num_from + if method == cls.LINEAR: + weights = torch.linspace(num_from, num_to, length) + elif method == cls.EASE_IN: + index = torch.linspace(0, 1, length) + weights = diff * np.power(index, 2) + num_from + elif method == cls.EASE_OUT: + index = torch.linspace(0, 1, length) + weights = diff * (1 - np.power(1 - index, 2)) + num_from + elif method == cls.EASE_IN_OUT: + index = torch.linspace(0, 1, length) + weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from + else: + raise ValueError(f"Unrecognized interpolation method '{method}'.") + if reverse: + weights = weights.flip(dims=(0,)) + return weights + +def get_sorted_list_via_attr(objects: list, attr: str) -> list: + if not objects: + return objects + elif len(objects) <= 1: + return [x for x in objects] + # now that we know we have to sort, do it following these rules: + # a) if objects have same value of attribute, maintain their relative order + # b) perform sorting of the groups of objects with same attributes + unique_attrs = {} + for o in objects: + val_attr = getattr(o, attr) + attr_list: list = unique_attrs.get(val_attr, list()) + attr_list.append(o) + if val_attr not in unique_attrs: + unique_attrs[val_attr] = attr_list + # now that we have the unique attr values grouped together in relative order, sort them by key + sorted_attrs = dict(sorted(unique_attrs.items())) + # now flatten out the dict into a list to return + sorted_list = [] + for object_list in sorted_attrs.values(): + sorted_list.extend(object_list) + return sorted_list + +def create_transformer_options_from_hooks(model: ModelPatcher, hooks: HookGroup, transformer_options: dict[str]=None): + # if no hooks or is not a ModelPatcher for sampling, return empty dict + if hooks is None or model.is_clip: + return {} + if transformer_options is None: + transformer_options = {} + for hook in hooks.get_type(EnumHookType.TransformerOptions): + hook: TransformerOptionsHook + hook.on_apply_hooks(model, transformer_options) + return transformer_options + +def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float): + hook_group = HookGroup() + hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) + hook_group.add(hook) + hook.weights = lora + return hook_group + +def create_hook_model_as_lora(weights_model, weights_clip, strength_model: float, strength_clip: float): + hook_group = HookGroup() + hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) + hook_group.add(hook) + patches_model = None + patches_clip = None + if weights_model is not None: + patches_model = {} + for key in weights_model: + patches_model[key] = ("model_as_lora", (weights_model[key],)) + if weights_clip is not None: + patches_clip = {} + for key in weights_clip: + patches_clip[key] = ("model_as_lora", (weights_clip[key],)) + hook.weights = patches_model + hook.weights_clip = patches_clip + hook.need_weight_init = False + return hook_group + +def get_patch_weights_from_model(model: ModelPatcher, discard_model_sampling=True): + if model is None: + return None + patches_model: dict[str, torch.Tensor] = model.model.state_dict() + if discard_model_sampling: + # do not include ANY model_sampling components of the model that should act as a patch + for key in list(patches_model.keys()): + if key.startswith("model_sampling"): + patches_model.pop(key, None) + return patches_model + +# NOTE: this function shows how to register weight hooks directly on the ModelPatchers +def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dict[str, torch.Tensor], + strength_model: float, strength_clip: float): + key_map = {} + if model is not None: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + if clip is not None: + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + + hook_group = HookGroup() + hook = WeightHook() + hook_group.add(hook) + loaded: dict[str] = comfy.lora.load_lora(lora, key_map) + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_model) + else: + k = () + new_modelpatcher = None + + if clip is not None: + new_clip = clip.clone() + k1 = new_clip.patcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_clip) + else: + k1 = () + new_clip = None + k = set(k) + k1 = set(k1) + for x in loaded: + if (x not in k) and (x not in k1): + logging.warning(f"NOT LOADED {x}") + return (new_modelpatcher, new_clip, hook_group) + +def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, HookGroup], cache: dict[tuple[HookGroup, HookGroup], HookGroup]): + hooks_key = 'hooks' + # if hooks only exist in one dict, do what's needed so that it ends up in c_dict + if hooks_key not in values: + return + if hooks_key not in c_dict: + hooks_value = values.get(hooks_key, None) + if hooks_value is not None: + c_dict[hooks_key] = hooks_value + return + # otherwise, need to combine with minimum duplication via cache + hooks_tuple = (c_dict[hooks_key], values[hooks_key]) + cached_hooks = cache.get(hooks_tuple, None) + if cached_hooks is None: + new_hooks = hooks_tuple[0].clone_and_combine(hooks_tuple[1]) + cache[hooks_tuple] = new_hooks + c_dict[hooks_key] = new_hooks + else: + c_dict[hooks_key] = cache[hooks_tuple] + +def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True, + cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): + c = [] + if cache is None: + cache = {} + for t in conditioning: + n = [t[0], t[1].copy()] + for k in values: + if append_hooks and k == 'hooks': + _combine_hooks_from_values(n[1], values, cache) + else: + n[1][k] = values[k] + c.append(n) + + return c + +def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): + if hooks is None: + return cond + return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks, cache=cache) + +def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,float]): + if timestep_range is None: + return cond + return conditioning_set_values(cond, {"start_percent": timestep_range[0], + "end_percent": timestep_range[1]}) + +def set_mask_for_conditioning(cond, mask: torch.Tensor, set_cond_area: str, strength: float): + if mask is None: + return cond + set_area_to_bounds = False + if set_cond_area != 'default': + set_area_to_bounds = True + if len(mask.shape) < 3: + mask = mask.unsqueeze(0) + return conditioning_set_values(cond, {'mask': mask, + 'set_area_to_bounds': set_area_to_bounds, + 'mask_strength': strength}) + +def combine_conditioning(conds: list): + combined_conds = [] + for cond in conds: + combined_conds.extend(cond) + return combined_conds + +def combine_with_new_conds(conds: list, new_conds: list): + combined_conds = [] + for c, new_c in zip(conds, new_conds): + combined_conds.append(combine_conditioning([c, new_c])) + return combined_conds + +def set_conds_props(conds: list, strength: float, set_cond_area: str, + mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): + final_conds = [] + cache = {} + for c in conds: + # first, apply lora_hook to conditioning, if provided + c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks, cache=cache) + # next, apply mask to conditioning + c = set_mask_for_conditioning(cond=c, mask=mask, strength=strength, set_cond_area=set_cond_area) + # apply timesteps, if present + c = set_timesteps_for_conditioning(cond=c, timestep_range=timesteps_range) + # finally, apply mask to conditioning and store + final_conds.append(c) + return final_conds + +def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.0, set_cond_area: str="default", + mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): + combined_conds = [] + cache = {} + for c, masked_c in zip(conds, new_conds): + # first, apply lora_hook to new conditioning, if provided + masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks, cache=cache) + # next, apply mask to new conditioning, if provided + masked_c = set_mask_for_conditioning(cond=masked_c, mask=mask, set_cond_area=set_cond_area, strength=strength) + # apply timesteps, if present + masked_c = set_timesteps_for_conditioning(cond=masked_c, timestep_range=timesteps_range) + # finally, combine with existing conditioning and store + combined_conds.append(combine_conditioning([c, masked_c])) + return combined_conds + +def set_default_conds_and_combine(conds: list, new_conds: list, + hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): + combined_conds = [] + cache = {} + for c, new_c in zip(conds, new_conds): + # first, apply lora_hook to new conditioning, if provided + new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks, cache=cache) + # next, add default_cond key to cond so that during sampling, it can be identified + new_c = conditioning_set_values(new_c, {'default': True}) + # apply timesteps, if present + new_c = set_timesteps_for_conditioning(cond=new_c, timestep_range=timesteps_range) + # finally, combine with existing conditioning and store + combined_conds.append(combine_conditioning([c, new_c])) + return combined_conds diff --git a/comfy/image_encoders/dino2.py b/comfy/image_encoders/dino2.py new file mode 100644 index 0000000000000000000000000000000000000000..976f98c656a6b55eb6ceb8d2c585a2713ac6dddb --- /dev/null +++ b/comfy/image_encoders/dino2.py @@ -0,0 +1,141 @@ +import torch +from comfy.text_encoders.bert import BertAttention +import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention_for_device + + +class Dino2AttentionOutput(torch.nn.Module): + def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): + super().__init__() + self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) + + def forward(self, x): + return self.dense(x) + + +class Dino2AttentionBlock(torch.nn.Module): + def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.attention = BertAttention(embed_dim, heads, dtype, device, operations) + self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) + + def forward(self, x, mask, optimized_attention): + return self.output(self.attention(x, mask, optimized_attention)) + + +class LayerScale(torch.nn.Module): + def __init__(self, dim, dtype, device, operations): + super().__init__() + self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + + def forward(self, x): + return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype) + + +class SwiGLUFFN(torch.nn.Module): + def __init__(self, dim, dtype, device, operations): + super().__init__() + in_features = out_features = dim + hidden_features = int(dim * 4) + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + + self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype) + self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype) + + def forward(self, x): + x = self.weights_in(x) + x1, x2 = x.chunk(2, dim=-1) + x = torch.nn.functional.silu(x1) * x2 + return self.weights_out(x) + + +class Dino2Block(torch.nn.Module): + def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations) + self.layer_scale1 = LayerScale(dim, dtype, device, operations) + self.layer_scale2 = LayerScale(dim, dtype, device, operations) + self.mlp = SwiGLUFFN(dim, dtype, device, operations) + self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) + + def forward(self, x, optimized_attention): + x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention)) + x = x + self.layer_scale2(self.mlp(self.norm2(x))) + return x + + +class Dino2Encoder(torch.nn.Module): + def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations): + super().__init__() + self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)]) + + def forward(self, x, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layer) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layer): + x = l(x, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + + +class Dino2PatchEmbeddings(torch.nn.Module): + def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None): + super().__init__() + self.projection = operations.Conv2d( + in_channels=num_channels, + out_channels=dim, + kernel_size=patch_size, + stride=patch_size, + bias=True, + dtype=dtype, + device=device + ) + + def forward(self, pixel_values): + return self.projection(pixel_values).flatten(2).transpose(1, 2) + + +class Dino2Embeddings(torch.nn.Module): + def __init__(self, dim, dtype, device, operations): + super().__init__() + patch_size = 14 + image_size = 518 + + self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations) + self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device)) + self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) + self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) + + def forward(self, pixel_values): + x = self.patch_embeddings(pixel_values) + # TODO: mask_token? + x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1) + x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) + return x + + +class Dinov2Model(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + num_layers = config_dict["num_hidden_layers"] + dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + layer_norm_eps = config_dict["layer_norm_eps"] + + self.embeddings = Dino2Embeddings(dim, dtype, device, operations) + self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations) + self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) + + def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + x = self.embeddings(pixel_values) + x, i = self.encoder(x, intermediate_output=intermediate_output) + x = self.layernorm(x) + pooled_output = x[:, 0, :] + return x, i, pooled_output, None diff --git a/comfy/image_encoders/dino2_giant.json b/comfy/image_encoders/dino2_giant.json new file mode 100644 index 0000000000000000000000000000000000000000..f6076a4dc98dfee6c200294397bfdfe3d2c688e1 --- /dev/null +++ b/comfy/image_encoders/dino2_giant.json @@ -0,0 +1,21 @@ +{ + "attention_probs_dropout_prob": 0.0, + "drop_path_rate": 0.0, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 1536, + "image_size": 518, + "initializer_range": 0.02, + "layer_norm_eps": 1e-06, + "layerscale_value": 1.0, + "mlp_ratio": 4, + "model_type": "dinov2", + "num_attention_heads": 24, + "num_channels": 3, + "num_hidden_layers": 40, + "patch_size": 14, + "qkv_bias": true, + "use_swiglu_ffn": true, + "image_mean": [0.485, 0.456, 0.406], + "image_std": [0.229, 0.224, 0.225] +} diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py new file mode 100644 index 0000000000000000000000000000000000000000..a1167a4a36c42b22168ea8b3d80b1d83b2505db5 --- /dev/null +++ b/comfy/k_diffusion/deis.py @@ -0,0 +1,120 @@ +#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py +#under Apache 2 license +import torch +import numpy as np + +# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis). +############################# +### Utils for DEIS solver ### +############################# +#---------------------------------------------------------------------------- +# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS. + +def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80): + vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d + vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1) + vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d + t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu()) + return t_steps, vp_beta_min, vp_beta_d + vp_beta_min + +#---------------------------------------------------------------------------- + +def cal_poly(prev_t, j, taus): + poly = 1 + for k in range(prev_t.shape[0]): + if k == j: + continue + poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k]) + return poly + +#---------------------------------------------------------------------------- +# Transfer from t to alpha_t. + +def t2alpha_fn(beta_0, beta_1, t): + return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0) + +#---------------------------------------------------------------------------- + +def cal_intergrand(beta_0, beta_1, taus): + with torch.inference_mode(mode=False): + taus = taus.clone() + beta_0 = beta_0.clone() + beta_1 = beta_1.clone() + with torch.enable_grad(): + taus.requires_grad_(True) + alpha = t2alpha_fn(beta_0, beta_1, taus) + log_alpha = alpha.log() + log_alpha.sum().backward() + d_log_alpha_dtau = taus.grad + integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha)) + return integrand + +#---------------------------------------------------------------------------- + +def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'): + """ + Get the coefficient list for DEIS sampling. + + Args: + t_steps: A pytorch tensor. The time steps for sampling. + max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4 + N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'. + deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS. + Returns: + A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True. + """ + if deis_mode == 'tab': + t_steps, beta_0, beta_1 = edm2t(t_steps) + C = [] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): + order = min(i+1, max_order) + if order == 1: + C.append([]) + else: + taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation + dtau = (t_next - t_cur) / N + prev_t = t_steps[[i - k for k in range(order)]] + coeff_temp = [] + integrand = cal_intergrand(beta_0, beta_1, taus) + for j in range(order): + poly = cal_poly(prev_t, j, taus) + coeff_temp.append(torch.sum(integrand * poly) * dtau) + C.append(coeff_temp) + + elif deis_mode == 'rhoab': + # Analytical solution, second order + def get_def_intergral_2(a, b, start, end, c): + coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b + return coeff / ((c - a) * (c - b)) + + # Analytical solution, third order + def get_def_intergral_3(a, b, c, start, end, d): + coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \ + + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c + return coeff / ((d - a) * (d - b) * (d - c)) + + C = [] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): + order = min(i, max_order) + if order == 0: + C.append([]) + else: + prev_t = t_steps[[i - k for k in range(order+1)]] + if order == 1: + coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1])) + coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur)) + coeff_temp = [coeff_cur, coeff_prev1] + elif order == 2: + coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur) + coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1]) + coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2]) + coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2] + elif order == 3: + coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur) + coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1]) + coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2]) + coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3]) + coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3] + C.append(coeff_temp) + return C + diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..7394688724f398286242448692151343199455f7 --- /dev/null +++ b/comfy/k_diffusion/sampling.py @@ -0,0 +1,1627 @@ +import math +from functools import partial + +from scipy import integrate +import torch +from torch import nn +import torchsde +from tqdm.auto import trange, tqdm + +from . import utils +from . import deis +import comfy.model_patcher +import comfy.model_sampling + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): + """Constructs an exponential noise schedule.""" + sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() + return append_zero(sigmas) + + +def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): + """Constructs an polynomial in log sigma noise schedule.""" + ramp = torch.linspace(1, 0, n, device=device) ** rho + sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) + return append_zero(sigmas) + + +def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): + """Constructs a continuous VP noise schedule.""" + t = torch.linspace(1, eps_s, n, device=device) + sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t)) + return append_zero(sigmas) + + +def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'): + """Constructs the noise schedule proposed by Tiankai et al. (2024). """ + epsilon = 1e-5 # avoid log(0) + x = torch.linspace(0, 1, n, device=device) + clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max) + lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon) + sigmas = clamp(torch.exp(lmb)) + return sigmas + + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / utils.append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + if not eta: + return sigma_to, 0. + sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x, seed=None): + if seed is not None: + generator = torch.Generator(device=x.device) + generator.manual_seed(seed) + else: + generator = None + + return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) + + +class BatchedBrownianTree: + """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" + + def __init__(self, x, t0, t1, seed=None, **kwargs): + self.cpu_tree = True + if "cpu" in kwargs: + self.cpu_tree = kwargs.pop("cpu") + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + if self.cpu_tree: + self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] + else: + self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + if self.cpu_tree: + w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) + else: + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + self.transform = transform + t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __call__(self, sigma, sigma_next): + t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +def sigma_to_half_log_snr(sigma, model_sampling): + """Convert sigma to half-logSNR log(alpha_t / sigma_t).""" + if isinstance(model_sampling, comfy.model_sampling.CONST): + # log((1 - t) / t) = log((1 - sigma) / sigma) + return sigma.logit().neg() + return sigma.log().neg() + + +def half_log_snr_to_sigma(half_log_snr, model_sampling): + """Convert half-logSNR log(alpha_t / sigma_t) to sigma.""" + if isinstance(model_sampling, comfy.model_sampling.CONST): + # 1 / (1 + exp(half_log_snr)) + return half_log_snr.neg().sigmoid() + return half_log_snr.neg().exp() + + +def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4): + """Adjust the first sigma to avoid invalid logSNR.""" + if len(sigmas) <= 1: + return sigmas + if isinstance(model_sampling, comfy.model_sampling.CONST): + if sigmas[0] >= 1: + sigmas = sigmas.clone() + sigmas[0] = model_sampling.percent_to_sigma(percent_offset) + return sigmas + + +@torch.no_grad() +def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + + +@torch.no_grad() +def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): + return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + if sigma_down == 0: + x = denoised + else: + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + +@torch.no_grad() +def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + if sigmas[i + 1] == 0: + x = denoised + else: + downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta + sigma_down = sigmas[i + 1] * downstep_ratio + alpha_ip1 = 1 - sigmas[i + 1] + alpha_down = 1 - sigma_down + renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5 + # Euler method + sigma_down_i_ratio = sigma_down / sigmas[i] + x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised + if eta > 0: + x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff + return x + +@torch.no_grad() +def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@torch.no_grad() +def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + if s_churn > 0: + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + else: + gamma = 0 + sigma_hat = sigmas[i] + + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@torch.no_grad() +def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): + return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) + + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + +@torch.no_grad() +def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta + sigma_down = sigmas[i+1] * downstep_ratio + alpha_ip1 = 1 - sigmas[i+1] + alpha_down = 1 - sigma_down + renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 + + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff + return x + +def linear_multistep_coeff(order, t, i, j): + if order - 1 > i: + raise ValueError(f'Order {order} too high for step {i}') + def fn(tau): + prod = 1. + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] + + +@torch.no_grad() +def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigmas_cpu = sigmas.detach().cpu().numpy() + ds = [] + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + d = to_d(x, sigmas[i], denoised) + ds.append(d) + if len(ds) > order: + ds.pop(0) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + return x + + +class PIDStepSizeController: + """A PID controller for ODE adaptive step size control.""" + def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): + self.h = h + self.b1 = (pcoeff + icoeff + dcoeff) / order + self.b2 = -(pcoeff + 2 * dcoeff) / order + self.b3 = dcoeff / order + self.accept_safety = accept_safety + self.eps = eps + self.errs = [] + + def limiter(self, x): + return 1 + math.atan(x - 1) + + def propose_step(self, error): + inv_error = 1 / (float(error) + self.eps) + if not self.errs: + self.errs = [inv_error, inv_error, inv_error] + self.errs[0] = inv_error + factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 + factor = self.limiter(factor) + accept = factor >= self.accept_safety + if accept: + self.errs[2] = self.errs[1] + self.errs[1] = self.errs[0] + self.h *= factor + return accept + + +class DPMSolver(nn.Module): + """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" + + def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): + super().__init__() + self.model = model + self.extra_args = {} if extra_args is None else extra_args + self.eps_callback = eps_callback + self.info_callback = info_callback + + def t(self, sigma): + return -sigma.log() + + def sigma(self, t): + return t.neg().exp() + + def eps(self, eps_cache, key, x, t, *args, **kwargs): + if key in eps_cache: + return eps_cache[key], eps_cache + sigma = self.sigma(t) * x.new_ones([x.shape[0]]) + eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) + if self.eps_callback is not None: + self.eps_callback() + return eps, {key: eps, **eps_cache} + + def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + x_1 = x - self.sigma(t_next) * h.expm1() * eps + return x_1, eps_cache + + def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) + return x_2, eps_cache + + def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + s2 = t + r2 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) + eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) + x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) + return x_3, eps_cache + + def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler + if not t_end > t_start and eta: + raise ValueError('eta must be 0 for reverse sampling') + + m = math.floor(nfe / 3) + 1 + ts = torch.linspace(t_start, t_end, m + 1, device=x.device) + + if nfe % 3 == 0: + orders = [3] * (m - 2) + [2, 1] + else: + orders = [3] * (m - 1) + [nfe % 3] + + for i in range(len(orders)): + eps_cache = {} + t, t_next = ts[i], ts[i + 1] + if eta: + sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) + t_next_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 + else: + t_next_, su = t_next, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + denoised = x - self.sigma(t) * eps + if self.info_callback is not None: + self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) + + if orders[i] == 1: + x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) + elif orders[i] == 2: + x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) + else: + x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) + + x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) + + return x + + def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler + if order not in {2, 3}: + raise ValueError('order should be 2 or 3') + forward = t_end > t_start + if not forward and eta: + raise ValueError('eta must be 0 for reverse sampling') + h_init = abs(h_init) * (1 if forward else -1) + atol = torch.tensor(atol) + rtol = torch.tensor(rtol) + s = t_start + x_prev = x + accept = True + pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) + info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} + + while s < t_end - 1e-5 if forward else s > t_end + 1e-5: + eps_cache = {} + t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) + if eta: + sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) + t_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 + else: + t_, su = t, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, s) + denoised = x - self.sigma(s) * eps + + if order == 2: + x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) + else: + x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) + delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) + error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 + accept = pid.propose_step(error) + if accept: + x_prev = x_low + x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) + s = t + info['n_accept'] += 1 + else: + info['n_reject'] += 1 + info['nfe'] += order + info['steps'] += 1 + + if self.info_callback is not None: + self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) + + return x, info + + +@torch.no_grad() +def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): + """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(total=n, disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) + + +@torch.no_grad() +def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): + """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) + if return_info: + return x, info + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): + return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) + + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1 + lambda_fn = lambda sigma: ((1-sigma)/sigma).log() + + # logged_x = x.unsqueeze(0) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta + sigma_down = sigmas[i+1] * downstep_ratio + alpha_ip1 = 1 - sigmas[i+1] + alpha_down = 1 - sigma_down + renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 + # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + if sigmas[i] == 1.0: + sigma_s = 0.9999 + else: + t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down) + r = 1 / 2 + h = t_down - t_i + s = t_i + r * h + sigma_s = sigma_fn(s) + # sigma_s = sigmas[i+1] + sigma_s_i_ratio = sigma_s / sigmas[i] + u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised + D_i = model(u, sigma_s * s_in, **extra_args) + sigma_down_i_ratio = sigma_down / sigmas[i] + x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i + # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff) + # Noise addition + if sigmas[i + 1] > 0 and eta > 0: + x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff + # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0) + return x + + +@torch.no_grad() +def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + """DPM-Solver++ (stochastic).""" + if len(sigmas) <= 1: + return x + + extra_args = {} if extra_args is None else extra_args + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + seed = extra_args.get("seed", None) + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++ + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + lambda_s_1 = lambda_s + r * h + fac = 1 / (2 * r) + + sigma_s_1 = sigma_fn(lambda_s_1) + + alpha_s = sigmas[i] * lambda_s.exp() + alpha_s_1 = sigma_s_1 * lambda_s_1.exp() + alpha_t = sigmas[i + 1] * lambda_t.exp() + + # Step 1 + sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta) + lambda_s_1_ = sd.log().neg() + h_ = lambda_s_1_ - lambda_s + x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised + if eta > 0 and s_noise > 0: + x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su + denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) + + # Step 2 + sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta) + lambda_t_ = sd.log().neg() + h_ = lambda_t_ - lambda_s + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d + if eta > 0 and s_noise > 0: + x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su + return x + + +@torch.no_grad() +def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_denoised is None or sigmas[i + 1] == 0: + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d + old_denoised = denoised + return x + + +@torch.no_grad() +def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + """DPM-Solver++(2M) SDE.""" + if len(sigmas) <= 1: + return x + + if solver_type not in {'heun', 'midpoint'}: + raise ValueError('solver_type must be \'heun\' or \'midpoint\'') + + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + old_denoised = None + h, h_last = None, None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++(2M) SDE + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) + + alpha_t = sigmas[i + 1] * lambda_t.exp() + + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised + + if old_denoised is not None: + r = h_last / h + if solver_type == 'heun': + x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised) + elif solver_type == 'midpoint': + x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised) + + if eta > 0 and s_noise > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise + + old_denoised = denoised + h_last = h + return x + + +@torch.no_grad() +def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """DPM-Solver++(3M) SDE.""" + + if len(sigmas) <= 1: + return x + + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + denoised_1, denoised_2 = None, None + h, h_1, h_2 = None, None, None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) + + alpha_t = sigmas[i + 1] * lambda_t.exp() + + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised + + if h_2 is not None: + # DPM-Solver++(3M) SDE + r0 = h_1 / h + r1 = h_2 / h + d1_0 = (denoised - denoised_1) / r0 + d1_1 = (denoised_1 - denoised_2) / r1 + d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) + d2 = (d1_0 - d1_1) / (r0 + r1) + phi_2 = h_eta.neg().expm1() / h_eta + 1 + phi_3 = phi_2 / h_eta - 0.5 + x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2 + elif h_1 is not None: + # DPM-Solver++(2M) SDE + r = h_1 / h + d = (denoised - denoised_1) / r + phi_2 = h_eta.neg().expm1() / h_eta + 1 + x = x + (alpha_t * phi_2) * d + + if eta > 0 and s_noise > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise + + denoised_1, denoised_2 = denoised, denoised_1 + h_1, h_2 = h, h_1 + return x + + +@torch.no_grad() +def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + if len(sigmas) <= 1: + return x + extra_args = {} if extra_args is None else extra_args + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) + + +@torch.no_grad() +def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + if len(sigmas) <= 1: + return x + extra_args = {} if extra_args is None else extra_args + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + + +@torch.no_grad() +def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + if len(sigmas) <= 1: + return x + extra_args = {} if extra_args is None else extra_args + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) + + +def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): + alpha_cumprod = 1 / ((sigma * sigma) + 1) + alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) + alpha = (alpha_cumprod / alpha_cumprod_prev) + + mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) + if sigma_prev > 0: + mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) + return mu + +def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) + if sigmas[i + 1] != 0: + x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) + return x + + +@torch.no_grad() +def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) + +@torch.no_grad() +def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if sigmas[i + 1] > 0: + x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) + return x + + + +@torch.no_grad() +def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + s_end = sigmas[-1] + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == s_end: + # Euler method + x = x + d * dt + elif sigmas[i + 2] == s_end: + + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + + w = 2 * sigmas[0] + w2 = sigmas[i+1]/w + w1 = 1 - w2 + + d_prime = d * w1 + d_2 * w2 + + + x = x + d_prime * dt + + else: + # Heun++ + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + dt_2 = sigmas[i + 2] - sigmas[i + 1] + + x_3 = x_2 + d_2 * dt_2 + denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) + d_3 = to_d(x_3, sigmas[i + 2], denoised_3) + + w = 3 * sigmas[0] + w2 = sigmas[i + 1] / w + w3 = sigmas[i + 2] / w + w1 = 1 - w2 - w3 + + d_prime = w1 * d + w2 * d_2 + w3 * d_3 + x = x + d_prime * dt + return x + + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 + elif order == 3: # Use two history points. + x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 + elif order == 4: # Use three history points. + x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur + else: + buffer_model.append(d_cur) + + return x_next + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + t_steps = sigmas + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + coeff1 = (2 + (h_n / h_n_1)) / 2 + coeff2 = -(h_n / h_n_1) / 2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) + elif order == 3: # Use two history points. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + h_n_2 = (t_steps[i-1] - t_steps[i-2]) + temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 + coeff1 = (2 + (h_n / h_n_1)) / 2 + temp + coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp + coeff3 = temp * h_n_1 / h_n_2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) + elif order == 4: # Use three history points. + h_n = (t_next - t_cur) + h_n_1 = (t_cur - t_steps[i-1]) + h_n_2 = (t_steps[i-1] - t_steps[i-2]) + h_n_3 = (t_steps[i-2] - t_steps[i-3]) + temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 + temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ + * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) + coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 + coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 + coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 + coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 + x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3]) + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur.detach() + else: + buffer_model.append(d_cur.detach()) + + return x_next + +#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py +#under Apache 2 license +@torch.no_grad() +def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + x_next = x + t_steps = sigmas + + coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode) + + buffer_model = [] + for i in trange(len(sigmas) - 1, disable=disable): + t_cur = sigmas[i] + t_next = sigmas[i + 1] + + x_cur = x_next + + denoised = model(x_cur, t_cur * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + d_cur = (x_cur - denoised) / t_cur + + order = min(max_order, i+1) + if t_next <= 0: + order = 1 + + if order == 1: # First Euler step. + x_next = x_cur + (t_next - t_cur) * d_cur + elif order == 2: # Use one history point. + coeff_cur, coeff_prev1 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + elif order == 3: # Use two history points. + coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + elif order == 4: # Use three history points. + coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i] + x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3] + + if len(buffer_model) == max_order - 1: + for k in range(max_order - 2): + buffer_model[k] = buffer_model[k+1] + buffer_model[-1] = d_cur.detach() + else: + buffer_model.append(d_cur.detach()) + + return x_next + +@torch.no_grad() +def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, temp[0]) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + # Euler method + x = denoised + d * sigmas[i + 1] + return x + +@torch.no_grad() +def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], temp[0]) + # Euler method + x = denoised + d * sigma_down + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x +@torch.no_grad() +def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], temp[0]) + x = denoised + d * sigma_down + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + +@torch.no_grad() +def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + t_fn = lambda sigma: sigma.log().neg() + + old_uncond_denoised = None + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_uncond_denoised is None or sigmas[i + 1] == 0: + denoised_mix = -torch.exp(-h) * uncond_denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised) + x = denoised + denoised_mix + torch.exp(-h) * x + old_uncond_denoised = uncond_denoised + return x + +@torch.no_grad() +def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False): + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + phi1_fn = lambda t: torch.expm1(t) / t + phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t + + old_sigma_down = None + old_denoised = None + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + if cfg_pp: + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + if sigma_down == 0 or old_denoised is None: + # Euler method + if cfg_pp: + d = to_d(x, sigmas[i], uncond_denoised) + x = denoised + d * sigma_down + else: + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # Second order multistep method in https://arxiv.org/pdf/2308.02157 + t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1]) + h = t_next - t + c2 = (t_prev - t_old) / h + + phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) + b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0) + b2 = torch.nan_to_num(phi2_val / c2, nan=0.0) + + if cfg_pp: + x = x + (denoised - uncond_denoised) + x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised) + else: + x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised) + + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + + if cfg_pp: + old_denoised = uncond_denoised + else: + old_denoised = denoised + old_sigma_down = sigma_down + return x + +@torch.no_grad() +def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False) + +@torch.no_grad() +def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True) + +@torch.no_grad() +def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False) + +@torch.no_grad() +def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True) + +@torch.no_grad() +def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False): + """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + old_d = None + + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + if cfg_pp: + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if cfg_pp: + d = to_d(x, sigmas[i], uncond_denoised) + else: + d = to_d(x, sigmas[i], denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + dt = sigmas[i + 1] - sigmas[i] + if i == 0: + # Euler method + if cfg_pp: + x = denoised + d * sigmas[i + 1] + else: + x = x + d * dt + else: + # Gradient estimation + if cfg_pp: + d_bar = (ge_gamma - 1) * (d - old_d) + x = denoised + d * sigmas[i + 1] + d_bar * dt + else: + d_bar = ge_gamma * d + (1 - ge_gamma) * old_d + x = x + d_bar * dt + old_d = d + return x + +@torch.no_grad() +def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): + return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True) + +@torch.no_grad() +def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, noise_scaler=None, max_stage=3): + """ + Extended Reverse-Time SDE solver (VE ER-SDE-Solver-3). Arxiv: https://arxiv.org/abs/2309.06169. + Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py. + """ + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + def default_noise_scaler(sigma): + return sigma * ((sigma ** 0.3).exp() + 10.0) + noise_scaler = default_noise_scaler if noise_scaler is None else noise_scaler + num_integration_points = 200.0 + point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device) + + old_denoised = None + old_denoised_d = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + stage_used = min(max_stage, i + 1) + if sigmas[i + 1] == 0: + x = denoised + elif stage_used == 1: + r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i]) + x = r * x + (1 - r) * denoised + else: + r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i]) + x = r * x + (1 - r) * denoised + + dt = sigmas[i + 1] - sigmas[i] + sigma_step_size = -dt / num_integration_points + sigma_pos = sigmas[i + 1] + point_indice * sigma_step_size + scaled_pos = noise_scaler(sigma_pos) + + # Stage 2 + s = torch.sum(1 / scaled_pos) * sigma_step_size + denoised_d = (denoised - old_denoised) / (sigmas[i] - sigmas[i - 1]) + x = x + (dt + s * noise_scaler(sigmas[i + 1])) * denoised_d + + if stage_used >= 3: + # Stage 3 + s_u = torch.sum((sigma_pos - sigmas[i]) / scaled_pos) * sigma_step_size + denoised_u = (denoised_d - old_denoised_d) / ((sigmas[i] - sigmas[i - 2]) / 2) + x = x + ((dt ** 2) / 2 + s_u * noise_scaler(sigmas[i + 1])) * denoised_u + old_denoised_d = denoised_d + + if s_noise != 0 and sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0) + old_denoised = denoised + return x + + +@torch.no_grad() +def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5): + """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2. + arXiv: https://arxiv.org/abs/2305.14267 + """ + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + inject_noise = eta > 0 and s_noise > 0 + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + x = denoised + else: + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) + lambda_s_1 = lambda_s + r * h + fac = 1 / (2 * r) + sigma_s_1 = sigma_fn(lambda_s_1) + + # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t) + alpha_s_1 = sigma_s_1 * lambda_s_1.exp() + alpha_t = sigmas[i + 1] * lambda_t.exp() + + coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1() + if inject_noise: + # 0 < r < 1 + noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt() + noise_coeff_2 = (-r * h * eta).exp() * (-2 * (1 - r) * h * eta).expm1().neg().sqrt() + noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigmas[i + 1]) + + # Step 1 + x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised + if inject_noise: + x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise + denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) + + # Step 2 + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_2 * denoised_d + if inject_noise: + x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise + return x + + +@torch.no_grad() +def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3): + """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3. + arXiv: https://arxiv.org/abs/2305.14267 + """ + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + inject_noise = eta > 0 and s_noise > 0 + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + x = denoised + else: + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) + lambda_s_1 = lambda_s + r_1 * h + lambda_s_2 = lambda_s + r_2 * h + sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2) + + # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t) + alpha_s_1 = sigma_s_1 * lambda_s_1.exp() + alpha_s_2 = sigma_s_2 * lambda_s_2.exp() + alpha_t = sigmas[i + 1] * lambda_t.exp() + + coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1() + if inject_noise: + # 0 < r_1 < r_2 < 1 + noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt() + noise_coeff_2 = (-r_1 * h * eta).exp() * (-2 * (r_2 - r_1) * h * eta).expm1().neg().sqrt() + noise_coeff_3 = (-r_2 * h * eta).exp() * (-2 * (1 - r_2) * h * eta).expm1().neg().sqrt() + noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1]) + + # Step 1 + x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised + if inject_noise: + x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise + denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) + + # Step 2 + x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * coeff_2 * denoised + (r_2 / r_1) * alpha_s_2 * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised) + if inject_noise: + x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise + denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args) + + # Step 3 + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_3 * denoised + (1. / r_2) * alpha_t * (coeff_3 / h_eta + 1) * (denoised_3 - denoised) + if inject_noise: + x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise + return x diff --git a/comfy/k_diffusion/utils.py b/comfy/k_diffusion/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a644df2f3cf82b32ac6e9bf2cb7bfc70c95e05f9 --- /dev/null +++ b/comfy/k_diffusion/utils.py @@ -0,0 +1,313 @@ +from contextlib import contextmanager +import hashlib +import math +from pathlib import Path +import shutil +import urllib +import warnings + +from PIL import Image +import torch +from torch import nn, optim +from torch.utils import data + + +def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): + """Apply passed in transforms for HuggingFace Datasets.""" + images = [transform(image.convert(mode)) for image in examples[image_key]] + return {image_key: images} + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + expanded = x[(...,) + (None,) * dims_to_append] + # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. + # https://github.com/pytorch/pytorch/issues/84364 + return expanded.detach().clone() if expanded.device.type == 'mps' else expanded + + +def n_params(module): + """Returns the number of trainable parameters in a module.""" + return sum(p.numel() for p in module.parameters()) + + +def download_file(path, url, digest=None): + """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + if not path.exists(): + with urllib.request.urlopen(url) as response, open(path, 'wb') as f: + shutil.copyfileobj(response, f) + if digest is not None: + file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() + if digest != file_digest: + raise OSError(f'hash of {path} (url: {url}) failed to validate') + return path + + +@contextmanager +def train_mode(model, mode=True): + """A context manager that places a model into training mode and restores + the previous mode on exit.""" + modes = [module.training for module in model.modules()] + try: + yield model.train(mode) + finally: + for i, module in enumerate(model.modules()): + module.training = modes[i] + + +def eval_mode(model): + """A context manager that places a model into evaluation mode and restores + the previous mode on exit.""" + return train_mode(model, False) + + +@torch.no_grad() +def ema_update(model, averaged_model, decay): + """Incorporates updated model parameters into an exponential moving averaged + version of a model. It should be called after each optimizer step.""" + model_params = dict(model.named_parameters()) + averaged_params = dict(averaged_model.named_parameters()) + assert model_params.keys() == averaged_params.keys() + + for name, param in model_params.items(): + averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) + + model_buffers = dict(model.named_buffers()) + averaged_buffers = dict(averaged_model.named_buffers()) + assert model_buffers.keys() == averaged_buffers.keys() + + for name, buf in model_buffers.items(): + averaged_buffers[name].copy_(buf) + + +class EMAWarmup: + """Implements an EMA warmup using an inverse decay schedule. + If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are + good values for models you plan to train for a million or more steps (reaches decay + factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models + you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at + 215.4k steps). + Args: + inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. + power (float): Exponential factor of EMA warmup. Default: 1. + min_value (float): The minimum EMA decay rate. Default: 0. + max_value (float): The maximum EMA decay rate. Default: 1. + start_at (int): The epoch to start averaging at. Default: 0. + last_epoch (int): The index of last epoch. Default: 0. + """ + + def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, + last_epoch=0): + self.inv_gamma = inv_gamma + self.power = power + self.min_value = min_value + self.max_value = max_value + self.start_at = start_at + self.last_epoch = last_epoch + + def state_dict(self): + """Returns the state of the class as a :class:`dict`.""" + return dict(self.__dict__.items()) + + def load_state_dict(self, state_dict): + """Loads the class's state. + Args: + state_dict (dict): scaler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def get_value(self): + """Gets the current EMA decay rate.""" + epoch = max(0, self.last_epoch - self.start_at) + value = 1 - (1 + epoch / self.inv_gamma) ** -self.power + return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) + + def step(self): + """Updates the step count.""" + self.last_epoch += 1 + + +class InverseLR(optim.lr_scheduler._LRScheduler): + """Implements an inverse decay learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. + inv_gamma is the number of steps/epochs required for the learning rate to decay to + (1 / 2)**power of its original value. + Args: + optimizer (Optimizer): Wrapped optimizer. + inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. + power (float): Exponential factor of learning rate decay. Default: 1. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.inv_gamma = inv_gamma + self.power = power + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +class ExponentialLR(optim.lr_scheduler._LRScheduler): + """Implements an exponential learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate + continuously by decay (default 0.5) every num_steps steps. + Args: + optimizer (Optimizer): Wrapped optimizer. + num_steps (float): The number of steps to decay the learning rate by decay in. + decay (float): The factor by which to decay the learning rate every num_steps + steps. Default: 0.5. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.num_steps = num_steps + self.decay = decay + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): + """Draws samples from an lognormal distribution.""" + return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() + + +def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from an optionally truncated log-logistic distribution.""" + min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) + max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) + min_cdf = min_value.log().sub(loc).div(scale).sigmoid() + max_cdf = max_value.log().sub(loc).div(scale).sigmoid() + u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf + return u.logit().mul(scale).add(loc).exp().to(dtype) + + +def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): + """Draws samples from an log-uniform distribution.""" + min_value = math.log(min_value) + max_value = math.log(max_value) + return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() + + +def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from a truncated v-diffusion training timestep distribution.""" + min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi + max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi + u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf + return torch.tan(u * math.pi / 2) * sigma_data + + +def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): + """Draws samples from a split lognormal distribution.""" + n = torch.randn(shape, device=device, dtype=dtype).abs() + u = torch.rand(shape, device=device, dtype=dtype) + n_left = n * -scale_1 + loc + n_right = n * scale_2 + loc + ratio = scale_1 / (scale_1 + scale_2) + return torch.where(u < ratio, n_left, n_right).exp() + + +class FolderOfImages(data.Dataset): + """Recursively finds all images in a directory. It does not support + classes/targets.""" + + IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} + + def __init__(self, root, transform=None): + super().__init__() + self.root = Path(root) + self.transform = nn.Identity() if transform is None else transform + self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) + + def __repr__(self): + return f'FolderOfImages(root="{self.root}", len: {len(self)})' + + def __len__(self): + return len(self.paths) + + def __getitem__(self, key): + path = self.paths[key] + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = self.transform(image) + return image, + + +class CSVLogger: + def __init__(self, filename, columns): + self.filename = Path(filename) + self.columns = columns + if self.filename.exists(): + self.file = open(self.filename, 'a') + else: + self.file = open(self.filename, 'w') + self.write(*self.columns) + + def write(self, *args): + print(*args, sep=',', file=self.file, flush=True) + + +@contextmanager +def tf32_mode(cudnn=None, matmul=None): + """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" + cudnn_old = torch.backends.cudnn.allow_tf32 + matmul_old = torch.backends.cuda.matmul.allow_tf32 + try: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul + yield + finally: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn_old + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul_old diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..82d9f9bb8dbf54b6a18ae66920a6e85336db9f3d --- /dev/null +++ b/comfy/latent_formats.py @@ -0,0 +1,472 @@ +import torch + +class LatentFormat: + scale_factor = 1.0 + latent_channels = 4 + latent_dimensions = 2 + latent_rgb_factors = None + latent_rgb_factors_bias = None + taesd_decoder_name = None + + def process_in(self, latent): + return latent * self.scale_factor + + def process_out(self, latent): + return latent / self.scale_factor + +class SD15(LatentFormat): + def __init__(self, scale_factor=0.18215): + self.scale_factor = scale_factor + self.latent_rgb_factors = [ + # R G B + [ 0.3512, 0.2297, 0.3227], + [ 0.3250, 0.4974, 0.2350], + [-0.2829, 0.1762, 0.2721], + [-0.2120, -0.2616, -0.7177] + ] + self.taesd_decoder_name = "taesd_decoder" + +class SDXL(LatentFormat): + scale_factor = 0.13025 + + def __init__(self): + self.latent_rgb_factors = [ + # R G B + [ 0.3651, 0.4232, 0.4341], + [-0.2533, -0.0042, 0.1068], + [ 0.1076, 0.1111, -0.0362], + [-0.3165, -0.2492, -0.2188] + ] + self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011] + + self.taesd_decoder_name = "taesdxl_decoder" + +class SDXL_Playground_2_5(LatentFormat): + def __init__(self): + self.scale_factor = 0.5 + self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) + self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) + + self.latent_rgb_factors = [ + # R G B + [ 0.3920, 0.4054, 0.4549], + [-0.2634, -0.0196, 0.0653], + [ 0.0568, 0.1687, -0.0755], + [-0.3112, -0.2359, -0.2076] + ] + self.taesd_decoder_name = "taesdxl_decoder" + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean + + +class SD_X4(LatentFormat): + def __init__(self): + self.scale_factor = 0.08333 + self.latent_rgb_factors = [ + [-0.2340, -0.3863, -0.3257], + [ 0.0994, 0.0885, -0.0908], + [-0.2833, -0.2349, -0.3741], + [ 0.2523, -0.0055, -0.1651] + ] + +class SC_Prior(LatentFormat): + latent_channels = 16 + def __init__(self): + self.scale_factor = 1.0 + self.latent_rgb_factors = [ + [-0.0326, -0.0204, -0.0127], + [-0.1592, -0.0427, 0.0216], + [ 0.0873, 0.0638, -0.0020], + [-0.0602, 0.0442, 0.1304], + [ 0.0800, -0.0313, -0.1796], + [-0.0810, -0.0638, -0.1581], + [ 0.1791, 0.1180, 0.0967], + [ 0.0740, 0.1416, 0.0432], + [-0.1745, -0.1888, -0.1373], + [ 0.2412, 0.1577, 0.0928], + [ 0.1908, 0.0998, 0.0682], + [ 0.0209, 0.0365, -0.0092], + [ 0.0448, -0.0650, -0.1728], + [-0.1658, -0.1045, -0.1308], + [ 0.0542, 0.1545, 0.1325], + [-0.0352, -0.1672, -0.2541] + ] + +class SC_B(LatentFormat): + def __init__(self): + self.scale_factor = 1.0 / 0.43 + self.latent_rgb_factors = [ + [ 0.1121, 0.2006, 0.1023], + [-0.2093, -0.0222, -0.0195], + [-0.3087, -0.1535, 0.0366], + [ 0.0290, -0.1574, -0.4078] + ] + +class SD3(LatentFormat): + latent_channels = 16 + def __init__(self): + self.scale_factor = 1.5305 + self.shift_factor = 0.0609 + self.latent_rgb_factors = [ + [-0.0922, -0.0175, 0.0749], + [ 0.0311, 0.0633, 0.0954], + [ 0.1994, 0.0927, 0.0458], + [ 0.0856, 0.0339, 0.0902], + [ 0.0587, 0.0272, -0.0496], + [-0.0006, 0.1104, 0.0309], + [ 0.0978, 0.0306, 0.0427], + [-0.0042, 0.1038, 0.1358], + [-0.0194, 0.0020, 0.0669], + [-0.0488, 0.0130, -0.0268], + [ 0.0922, 0.0988, 0.0951], + [-0.0278, 0.0524, -0.0542], + [ 0.0332, 0.0456, 0.0895], + [-0.0069, -0.0030, -0.0810], + [-0.0596, -0.0465, -0.0293], + [-0.1448, -0.1463, -0.1189] + ] + self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925] + self.taesd_decoder_name = "taesd3_decoder" + + def process_in(self, latent): + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent): + return (latent / self.scale_factor) + self.shift_factor + +class StableAudio1(LatentFormat): + latent_channels = 64 + latent_dimensions = 1 + +class Flux(SD3): + latent_channels = 16 + def __init__(self): + self.scale_factor = 0.3611 + self.shift_factor = 0.1159 + self.latent_rgb_factors =[ + [-0.0346, 0.0244, 0.0681], + [ 0.0034, 0.0210, 0.0687], + [ 0.0275, -0.0668, -0.0433], + [-0.0174, 0.0160, 0.0617], + [ 0.0859, 0.0721, 0.0329], + [ 0.0004, 0.0383, 0.0115], + [ 0.0405, 0.0861, 0.0915], + [-0.0236, -0.0185, -0.0259], + [-0.0245, 0.0250, 0.1180], + [ 0.1008, 0.0755, -0.0421], + [-0.0515, 0.0201, 0.0011], + [ 0.0428, -0.0012, -0.0036], + [ 0.0817, 0.0765, 0.0749], + [-0.1264, -0.0522, -0.1103], + [-0.0280, -0.0881, -0.0499], + [-0.1262, -0.0982, -0.0778] + ] + self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] + self.taesd_decoder_name = "taef1_decoder" + + def process_in(self, latent): + return (latent - self.shift_factor) * self.scale_factor + + def process_out(self, latent): + return (latent / self.scale_factor) + self.shift_factor + +class Mochi(LatentFormat): + latent_channels = 12 + latent_dimensions = 3 + + def __init__(self): + self.scale_factor = 1.0 + self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141, + -0.05565264470995561, 0.012767231469026969, -0.04703542746246419, + 0.043896967884726704, -0.09346305707025976, -0.09918314763016893, + -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1) + self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617, + 0.959253732819592, 0.8244560132752793, 0.917259975397747, + 0.9294154431013696, 1.3720942357788521, 0.881393668867029, + 0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1) + + self.latent_rgb_factors =[ + [-0.0069, -0.0045, 0.0018], + [ 0.0154, -0.0692, -0.0274], + [ 0.0333, 0.0019, 0.0206], + [-0.1390, 0.0628, 0.1678], + [-0.0725, 0.0134, -0.1898], + [ 0.0074, -0.0270, -0.0209], + [-0.0176, -0.0277, -0.0221], + [ 0.5294, 0.5204, 0.3852], + [-0.0326, -0.0446, -0.0143], + [-0.0659, 0.0153, -0.0153], + [ 0.0185, -0.0217, 0.0014], + [-0.0396, -0.0495, -0.0281] + ] + self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453] + self.taesd_decoder_name = None #TODO + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean + +class LTXV(LatentFormat): + latent_channels = 128 + latent_dimensions = 3 + + def __init__(self): + self.latent_rgb_factors = [ + [ 1.1202e-02, -6.3815e-04, -1.0021e-02], + [ 8.6031e-02, 6.5813e-02, 9.5409e-04], + [-1.2576e-02, -7.5734e-03, -4.0528e-03], + [ 9.4063e-03, -2.1688e-03, 2.6093e-03], + [ 3.7636e-03, 1.2765e-02, 9.1548e-03], + [ 2.1024e-02, -5.2973e-03, 3.4373e-03], + [-8.8896e-03, -1.9703e-02, -1.8761e-02], + [-1.3160e-02, -1.0523e-02, 1.9709e-03], + [-1.5152e-03, -6.9891e-03, -7.5810e-03], + [-1.7247e-03, 4.6560e-04, -3.3839e-03], + [ 1.3617e-02, 4.7077e-03, -2.0045e-03], + [ 1.0256e-02, 7.7318e-03, 1.3948e-02], + [-1.6108e-02, -6.2151e-03, 1.1561e-03], + [ 7.3407e-03, 1.5628e-02, 4.4865e-04], + [ 9.5357e-04, -2.9518e-03, -1.4760e-02], + [ 1.9143e-02, 1.0868e-02, 1.2264e-02], + [ 4.4575e-03, 3.6682e-05, -6.8508e-03], + [-4.5681e-04, 3.2570e-03, 7.7929e-03], + [ 3.3902e-02, 3.3405e-02, 3.7454e-02], + [-2.3001e-02, -2.4877e-03, -3.1033e-03], + [ 5.0265e-02, 3.8841e-02, 3.3539e-02], + [-4.1018e-03, -1.1095e-03, 1.5859e-03], + [-1.2689e-01, -1.3107e-01, -2.1005e-01], + [ 2.6276e-02, 1.4189e-02, -3.5963e-03], + [-4.8679e-03, 8.8486e-03, 7.8029e-03], + [-1.6610e-03, -4.8597e-03, -5.2060e-03], + [-2.1010e-03, 2.3610e-03, 9.3796e-03], + [-2.2482e-02, -2.1305e-02, -1.5087e-02], + [-1.5753e-02, -1.0646e-02, -6.5083e-03], + [-4.6975e-03, 5.0288e-03, -6.7390e-03], + [ 1.1951e-02, 2.0712e-02, 1.6191e-02], + [-6.3704e-03, -8.4827e-03, -9.5483e-03], + [ 7.2610e-03, -9.9326e-03, -2.2978e-02], + [-9.1904e-04, 6.2882e-03, 9.5720e-03], + [-3.7178e-02, -3.7123e-02, -5.6713e-02], + [-1.3373e-01, -1.0720e-01, -5.3801e-02], + [-5.3702e-03, 8.1256e-03, 8.8397e-03], + [-1.5247e-01, -2.1437e-01, -2.1843e-01], + [ 3.1441e-02, 7.0335e-03, -9.7541e-03], + [ 2.1528e-03, -8.9817e-03, -2.1023e-02], + [ 3.8461e-03, -5.8957e-03, -1.5014e-02], + [-4.3470e-03, -1.2940e-02, -1.5972e-02], + [-5.4781e-03, -1.0842e-02, -3.0204e-03], + [-6.5347e-03, 3.0806e-03, -1.0163e-02], + [-5.0414e-03, -7.1503e-03, -8.9686e-04], + [-8.5851e-03, -2.4351e-03, 1.0674e-03], + [-9.0016e-03, -9.6493e-03, 1.5692e-03], + [ 5.0914e-03, 1.2099e-02, 1.9968e-02], + [ 1.3758e-02, 1.1669e-02, 8.1958e-03], + [-1.0518e-02, -1.1575e-02, -4.1307e-03], + [-2.8410e-02, -3.1266e-02, -2.2149e-02], + [ 2.9336e-03, 3.6511e-02, 1.8717e-02], + [-1.6703e-02, -1.6696e-02, -4.4529e-03], + [ 4.8818e-02, 4.0063e-02, 8.7410e-03], + [-1.5066e-02, -5.7328e-04, 2.9785e-03], + [-1.7613e-02, -8.1034e-03, 1.3086e-02], + [-9.2633e-03, 1.0803e-02, -6.3489e-03], + [ 3.0851e-03, 4.7750e-04, 1.2347e-02], + [-2.2785e-02, -2.3043e-02, -2.6005e-02], + [-2.4787e-02, -1.5389e-02, -2.2104e-02], + [-2.3572e-02, 1.0544e-03, 1.2361e-02], + [-7.8915e-03, -1.2271e-03, -6.0968e-03], + [-1.1478e-02, -1.2543e-03, 6.2679e-03], + [-5.4229e-02, 2.6644e-02, 6.3394e-03], + [ 4.4216e-03, -7.3338e-03, -1.0464e-02], + [-4.5013e-03, 1.6082e-03, 1.4420e-02], + [ 1.3673e-02, 8.8877e-03, 4.1253e-03], + [-1.0145e-02, 9.0072e-03, 1.5695e-02], + [-5.6234e-03, 1.1847e-03, 8.1261e-03], + [-3.7171e-03, -5.3538e-03, 1.2590e-03], + [ 2.9476e-02, 2.1424e-02, 3.0424e-02], + [-3.4925e-02, -2.4340e-02, -2.5316e-02], + [-3.4127e-02, -2.2406e-02, -1.0589e-02], + [-1.7342e-02, -1.3249e-02, -1.0719e-02], + [-2.1478e-03, -8.6051e-03, -2.9878e-03], + [ 1.2089e-03, -4.2391e-03, -6.8569e-03], + [ 9.0411e-04, -6.6886e-03, -6.7547e-05], + [ 1.6048e-02, -1.0057e-02, -2.8929e-02], + [ 1.2290e-03, 1.0163e-02, 1.8861e-02], + [ 1.7264e-02, 2.7257e-04, 1.3785e-02], + [-1.3482e-02, -3.6427e-03, 6.7481e-04], + [ 4.6782e-03, -5.2423e-03, 2.4467e-03], + [-5.9113e-03, -6.2244e-03, -1.8162e-03], + [ 1.5496e-02, 1.4582e-02, 1.9514e-03], + [ 7.4958e-03, 1.5886e-03, -8.2305e-03], + [ 1.9086e-02, 1.6360e-03, -3.9674e-03], + [-5.7021e-03, -2.7307e-03, -4.1066e-03], + [ 1.7450e-03, 1.4602e-02, 2.5794e-02], + [-8.2788e-04, 2.2902e-03, 4.5161e-03], + [ 1.1632e-02, 8.9193e-03, -7.2813e-03], + [ 7.5721e-03, 2.6784e-03, 1.1393e-02], + [ 5.1939e-03, 3.6903e-03, 1.4049e-02], + [-1.8383e-02, -2.2529e-02, -2.4477e-02], + [ 5.8842e-04, -5.7874e-03, -1.4770e-02], + [-1.6125e-02, -8.6101e-03, -1.4533e-02], + [ 2.0540e-02, 2.0729e-02, 6.4338e-03], + [ 3.3587e-03, -1.1226e-02, -1.6444e-02], + [-1.4742e-03, -1.0489e-02, 1.7097e-03], + [ 2.8130e-02, 2.3546e-02, 3.2791e-02], + [-1.8532e-02, -1.2842e-02, -8.7756e-03], + [-8.0533e-03, -1.0771e-02, -1.7536e-02], + [-3.9009e-03, 1.6150e-02, 3.3359e-02], + [-7.4554e-03, -1.4154e-02, -6.1910e-03], + [ 3.4734e-03, -1.1370e-02, -1.0581e-02], + [ 1.1476e-02, 3.9281e-03, 2.8231e-03], + [ 7.1639e-03, -1.4741e-03, -3.8066e-03], + [ 2.2250e-03, -8.7552e-03, -9.5719e-03], + [ 2.4146e-02, 2.1696e-02, 2.8056e-02], + [-5.4365e-03, -2.4291e-02, -1.7802e-02], + [ 7.4263e-03, 1.0510e-02, 1.2705e-02], + [ 6.2669e-03, 6.2658e-03, 1.9211e-02], + [ 1.6378e-02, 9.4933e-03, 6.6971e-03], + [ 1.7173e-02, 2.3601e-02, 2.3296e-02], + [-1.4568e-02, -9.8279e-03, -1.1556e-02], + [ 1.4431e-02, 1.4430e-02, 6.6362e-03], + [-6.8230e-03, 1.8863e-02, 1.4555e-02], + [ 6.1156e-03, 3.4700e-03, -2.6662e-03], + [-2.6983e-03, -5.9402e-03, -9.2276e-03], + [ 1.0235e-02, 7.4173e-03, -7.6243e-03], + [-1.3255e-02, 1.9322e-02, -9.2153e-04], + [ 2.4222e-03, -4.8039e-03, -1.5759e-02], + [ 2.6244e-02, 2.5951e-02, 2.0249e-02], + [ 1.5711e-02, 1.8498e-02, 2.7407e-03], + [-2.1714e-03, 4.7214e-03, -2.2443e-02], + [-7.4747e-03, 7.4166e-03, 1.4430e-02], + [-8.3906e-03, -7.9776e-03, 9.7927e-03], + [ 3.8321e-02, 9.6622e-03, -1.9268e-02], + [-1.4605e-02, -6.7032e-03, 3.9675e-03] + ] + + self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512] + +class HunyuanVideo(LatentFormat): + latent_channels = 16 + latent_dimensions = 3 + scale_factor = 0.476986 + latent_rgb_factors = [ + [-0.0395, -0.0331, 0.0445], + [ 0.0696, 0.0795, 0.0518], + [ 0.0135, -0.0945, -0.0282], + [ 0.0108, -0.0250, -0.0765], + [-0.0209, 0.0032, 0.0224], + [-0.0804, -0.0254, -0.0639], + [-0.0991, 0.0271, -0.0669], + [-0.0646, -0.0422, -0.0400], + [-0.0696, -0.0595, -0.0894], + [-0.0799, -0.0208, -0.0375], + [ 0.1166, 0.1627, 0.0962], + [ 0.1165, 0.0432, 0.0407], + [-0.2315, -0.1920, -0.1355], + [-0.0270, 0.0401, -0.0821], + [-0.0616, -0.0997, -0.0727], + [ 0.0249, -0.0469, -0.1703] + ] + + latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761] + +class Cosmos1CV8x8x8(LatentFormat): + latent_channels = 16 + latent_dimensions = 3 + + latent_rgb_factors = [ + [ 0.1817, 0.2284, 0.2423], + [-0.0586, -0.0862, -0.3108], + [-0.4703, -0.4255, -0.3995], + [ 0.0803, 0.1963, 0.1001], + [-0.0820, -0.1050, 0.0400], + [ 0.2511, 0.3098, 0.2787], + [-0.1830, -0.2117, -0.0040], + [-0.0621, -0.2187, -0.0939], + [ 0.3619, 0.1082, 0.1455], + [ 0.3164, 0.3922, 0.2575], + [ 0.1152, 0.0231, -0.0462], + [-0.1434, -0.3609, -0.3665], + [ 0.0635, 0.1471, 0.1680], + [-0.3635, -0.1963, -0.3248], + [-0.1865, 0.0365, 0.2346], + [ 0.0447, 0.0994, 0.0881] + ] + + latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976] + +class Wan21(LatentFormat): + latent_channels = 16 + latent_dimensions = 3 + + latent_rgb_factors = [ + [-0.1299, -0.1692, 0.2932], + [ 0.0671, 0.0406, 0.0442], + [ 0.3568, 0.2548, 0.1747], + [ 0.0372, 0.2344, 0.1420], + [ 0.0313, 0.0189, -0.0328], + [ 0.0296, -0.0956, -0.0665], + [-0.3477, -0.4059, -0.2925], + [ 0.0166, 0.1902, 0.1975], + [-0.0412, 0.0267, -0.1364], + [-0.1293, 0.0740, 0.1636], + [ 0.0680, 0.3019, 0.1128], + [ 0.0032, 0.0581, 0.0639], + [-0.1251, 0.0927, 0.1699], + [ 0.0060, -0.0633, 0.0005], + [ 0.3477, 0.2275, 0.2950], + [ 0.1984, 0.0913, 0.1861] + ] + + latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360] + + def __init__(self): + self.scale_factor = 1.0 + self.latents_mean = torch.tensor([ + -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, + 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 + ]).view(1, self.latent_channels, 1, 1, 1) + self.latents_std = torch.tensor([ + 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, + 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 + ]).view(1, self.latent_channels, 1, 1, 1) + + + self.taesd_decoder_name = None #TODO + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean + +class Hunyuan3Dv2(LatentFormat): + latent_channels = 64 + latent_dimensions = 1 + scale_factor = 0.9990943042622529 + +class Hunyuan3Dv2mini(LatentFormat): + latent_channels = 64 + latent_dimensions = 1 + scale_factor = 1.0188137142395404 + +class ACEAudio(LatentFormat): + latent_channels = 8 + latent_dimensions = 2 diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..f20a016691453789b6608af2b54dc1e18a1e9a3e --- /dev/null +++ b/comfy/ldm/ace/attention.py @@ -0,0 +1,761 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple, Union, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention + +class Attention(nn.Module): + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + kv_heads: Optional[int] = None, + dim_head: int = 64, + dropout: float = 0.0, + bias: bool = False, + qk_norm: Optional[str] = None, + added_kv_proj_dim: Optional[int] = None, + added_proj_bias: Optional[bool] = True, + out_bias: bool = True, + scale_qk: bool = True, + only_cross_attention: bool = False, + eps: float = 1e-5, + rescale_output_factor: float = 1.0, + residual_connection: bool = False, + processor=None, + out_dim: int = None, + out_context_dim: int = None, + context_pre_only=None, + pre_only=False, + elementwise_affine: bool = True, + is_causal: bool = False, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads + self.query_dim = query_dim + self.use_bias = bias + self.is_cross_attention = cross_attention_dim is not None + self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.rescale_output_factor = rescale_output_factor + self.residual_connection = residual_connection + self.dropout = dropout + self.fused_projections = False + self.out_dim = out_dim if out_dim is not None else query_dim + self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim + self.context_pre_only = context_pre_only + self.pre_only = pre_only + self.is_causal = is_causal + + self.scale_qk = scale_qk + self.scale = dim_head**-0.5 if self.scale_qk else 1.0 + + self.heads = out_dim // dim_head if out_dim is not None else heads + # for slice_size > 0 the attention score computation + # is split across the batch axis to save memory + # You can set slice_size with `set_attention_slice` + self.sliceable_head_dim = heads + + self.added_kv_proj_dim = added_kv_proj_dim + self.only_cross_attention = only_cross_attention + + if self.added_kv_proj_dim is None and self.only_cross_attention: + raise ValueError( + "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." + ) + + self.group_norm = None + self.spatial_norm = None + + self.norm_q = None + self.norm_k = None + + self.norm_cross = None + self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) + + if not self.only_cross_attention: + # only relevant for the `AddedKVProcessor` classes + self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + else: + self.to_k = None + self.to_v = None + + self.added_proj_bias = added_proj_bias + if self.added_kv_proj_dim is not None: + self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) + self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) + if self.context_pre_only is not None: + self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device) + else: + self.add_q_proj = None + self.add_k_proj = None + self.add_v_proj = None + + if not self.pre_only: + self.to_out = nn.ModuleList([]) + self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device)) + self.to_out.append(nn.Dropout(dropout)) + else: + self.to_out = None + + if self.context_pre_only is not None and not self.context_pre_only: + self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) + else: + self.to_add_out = None + + self.norm_added_q = None + self.norm_added_k = None + self.processor = processor + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + **cross_attention_kwargs, + ) -> torch.Tensor: + return self.processor( + self, + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + +class CustomLiteLAProcessor2_0: + """Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE""" + + def __init__(self): + self.kernel_func = nn.ReLU(inplace=False) + self.eps = 1e-15 + self.pad_val = 1.0 + + def apply_rotary_emb( + self, + x: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings + to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are + reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting + tensors contain rotary embeddings and are returned as real tensors. + + Args: + x (`torch.Tensor`): + Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply + freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + """ + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + hidden_states_len = hidden_states.shape[1] + + input_ndim = hidden_states.ndim + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + if encoder_hidden_states is not None: + context_input_ndim = encoder_hidden_states.ndim + if context_input_ndim == 4: + batch_size, channel, height, width = encoder_hidden_states.shape + encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size = hidden_states.shape[0] + + # `sample` projections. + dtype = hidden_states.dtype + query = attn.to_q(hidden_states) + key = attn.to_k(hidden_states) + value = attn.to_v(hidden_states) + + # `context` projections. + has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") + if encoder_hidden_states is not None and has_encoder_hidden_state_proj: + encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states) + encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + + # attention + if not attn.is_cross_attention: + query = torch.cat([query, encoder_hidden_states_query_proj], dim=1) + key = torch.cat([key, encoder_hidden_states_key_proj], dim=1) + value = torch.cat([value, encoder_hidden_states_value_proj], dim=1) + else: + query = hidden_states + key = encoder_hidden_states + value = encoder_hidden_states + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) + key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2) + value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) + + # RoPE需要 [B, H, S, D] 输入 + # 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE + query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S]) + + # Apply query and key normalization if needed + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if rotary_freqs_cis is not None: + query = self.apply_rotary_emb(query, rotary_freqs_cis) + if not attn.is_cross_attention: + key = self.apply_rotary_emb(key, rotary_freqs_cis) + elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: + key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) + + # 此时 query是 [B, H, S, D]，需要还原成 [B, H, D, S] + query = query.permute(0, 1, 3, 2) # [B, H, D, S] + + if attention_mask is not None: + # attention_mask: [B, S] -> [B, 1, S, 1] + attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1] + query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1] + if not attn.is_cross_attention: + key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘 + value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S]，那么需调整mask以匹配S维度 + + if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: + encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1] + # 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc] + key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1] + value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc] + + query = self.kernel_func(query) + key = self.kernel_func(key) + + query, key, value = query.float(), key.float(), value.float() + + value = F.pad(value, (0, 0, 0, 1), mode="constant", value=self.pad_val) + + vk = torch.matmul(value, key) + + hidden_states = torch.matmul(vk, query) + + if hidden_states.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.float() + + hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) + + hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1) + + hidden_states = hidden_states.to(dtype) + if encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states.to(dtype) + + # Split the attention outputs. + if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj: + hidden_states, encoder_hidden_states = ( + hidden_states[:, : hidden_states_len], + hidden_states[:, hidden_states_len:], + ) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, "to_add_out"): + encoder_hidden_states = attn.to_add_out(encoder_hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + if encoder_hidden_states is not None and context_input_ndim == 4: + encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if torch.get_autocast_gpu_dtype() == torch.float16: + hidden_states = hidden_states.clip(-65504, 65504) + if encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504) + + return hidden_states, encoder_hidden_states + + +class CustomerAttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). + """ + + def apply_rotary_emb( + self, + x: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings + to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are + reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting + tensors contain rotary embeddings and are returned as real tensors. + + Args: + x (`torch.Tensor`): + Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply + freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + """ + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + *args, + **kwargs, + ) -> torch.Tensor: + + residual = hidden_states + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if rotary_freqs_cis is not None: + query = self.apply_rotary_emb(query, rotary_freqs_cis) + if not attn.is_cross_attention: + key = self.apply_rotary_emb(key, rotary_freqs_cis) + elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: + key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) + + if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: + # attention_mask: N x S1 + # encoder_attention_mask: N x S2 + # cross attention 整合attention_mask和encoder_attention_mask + combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :] + attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf) + attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype) + + elif not attn.is_cross_attention and attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + hidden_states = optimized_attention( + query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, + ).to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + +def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore + """Repeat `val` for `repeat_time` times and return the list or val if list/tuple.""" + if isinstance(x, (list, tuple)): + return list(x) + return [x for _ in range(repeat_time)] + + +def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore + """Return tuple with min_len by repeating element at idx_repeat.""" + # convert to list first + x = val2list(x) + + # repeat elements if necessary + if len(x) > 0: + x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))] + + return tuple(x) + + +def t2i_modulate(x, shift, scale): + return x * (1 + scale) + shift + + +def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]: + if isinstance(kernel_size, tuple): + return tuple([get_same_padding(ks) for ks in kernel_size]) + else: + assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number" + return kernel_size // 2 + +class ConvLayer(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + kernel_size=3, + stride=1, + dilation=1, + groups=1, + padding: Union[int, None] = None, + use_bias=False, + norm=None, + act=None, + dtype=None, device=None, operations=None + ): + super().__init__() + if padding is None: + padding = get_same_padding(kernel_size) + padding *= dilation + + self.in_dim = in_dim + self.out_dim = out_dim + self.kernel_size = kernel_size + self.stride = stride + self.dilation = dilation + self.groups = groups + self.padding = padding + self.use_bias = use_bias + + self.conv = operations.Conv1d( + in_dim, + out_dim, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=use_bias, + device=device, + dtype=dtype + ) + if norm is not None: + self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device) + else: + self.norm = None + if act is not None: + self.act = nn.SiLU(inplace=True) + else: + self.act = None + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv(x) + if self.norm: + x = self.norm(x) + if self.act: + x = self.act(x) + return x + + +class GLUMBConv(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: int, + out_feature=None, + kernel_size=3, + stride=1, + padding: Union[int, None] = None, + use_bias=False, + norm=(None, None, None), + act=("silu", "silu", None), + dilation=1, + dtype=None, device=None, operations=None + ): + out_feature = out_feature or in_features + super().__init__() + use_bias = val2tuple(use_bias, 3) + norm = val2tuple(norm, 3) + act = val2tuple(act, 3) + + self.glu_act = nn.SiLU(inplace=False) + self.inverted_conv = ConvLayer( + in_features, + hidden_features * 2, + 1, + use_bias=use_bias[0], + norm=norm[0], + act=act[0], + dtype=dtype, + device=device, + operations=operations, + ) + self.depth_conv = ConvLayer( + hidden_features * 2, + hidden_features * 2, + kernel_size, + stride=stride, + groups=hidden_features * 2, + padding=padding, + use_bias=use_bias[1], + norm=norm[1], + act=None, + dilation=dilation, + dtype=dtype, + device=device, + operations=operations, + ) + self.point_conv = ConvLayer( + hidden_features, + out_feature, + 1, + use_bias=use_bias[2], + norm=norm[2], + act=act[2], + dtype=dtype, + device=device, + operations=operations, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.transpose(1, 2) + x = self.inverted_conv(x) + x = self.depth_conv(x) + + x, gate = torch.chunk(x, 2, dim=1) + gate = self.glu_act(gate) + x = x * gate + + x = self.point_conv(x) + x = x.transpose(1, 2) + + return x + + +class LinearTransformerBlock(nn.Module): + """ + A Sana block with global shared adaptive layer norm (adaLN-single) conditioning. + """ + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + use_adaln_single=True, + cross_attention_dim=None, + added_kv_proj_dim=None, + context_pre_only=False, + mlp_ratio=4.0, + add_cross_attention=False, + add_cross_attention_dim=None, + qk_norm=None, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6) + self.attn = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + added_kv_proj_dim=added_kv_proj_dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + bias=True, + qk_norm=qk_norm, + processor=CustomLiteLAProcessor2_0(), + dtype=dtype, + device=device, + operations=operations, + ) + + self.add_cross_attention = add_cross_attention + self.context_pre_only = context_pre_only + + if add_cross_attention and add_cross_attention_dim is not None: + self.cross_attn = Attention( + query_dim=dim, + cross_attention_dim=add_cross_attention_dim, + added_kv_proj_dim=add_cross_attention_dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + context_pre_only=context_pre_only, + bias=True, + qk_norm=qk_norm, + processor=CustomerAttnProcessor2_0(), + dtype=dtype, + device=device, + operations=operations, + ) + + self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False) + + self.ff = GLUMBConv( + in_features=dim, + hidden_features=int(dim * mlp_ratio), + use_bias=(True, True, False), + norm=(None, None, None), + act=("silu", "silu", None), + dtype=dtype, + device=device, + operations=operations, + ) + self.use_adaln_single = use_adaln_single + if use_adaln_single: + self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device)) + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: torch.FloatTensor = None, + encoder_attention_mask: torch.FloatTensor = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + temb: torch.FloatTensor = None, + ): + + N = hidden_states.shape[0] + + # step 1: AdaLN single + if self.use_adaln_single: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1) + ).chunk(6, dim=1) + + norm_hidden_states = self.norm1(hidden_states) + if self.use_adaln_single: + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + + # step 2: attention + if not self.add_cross_attention: + attn_output, encoder_hidden_states = self.attn( + hidden_states=norm_hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=rotary_freqs_cis_cross, + ) + else: + attn_output, _ = self.attn( + hidden_states=norm_hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=None, + encoder_attention_mask=None, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=None, + ) + + if self.use_adaln_single: + attn_output = gate_msa * attn_output + hidden_states = attn_output + hidden_states + + if self.add_cross_attention: + attn_output = self.cross_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=rotary_freqs_cis_cross, + ) + hidden_states = attn_output + hidden_states + + # step 3: add norm + norm_hidden_states = self.norm2(hidden_states) + if self.use_adaln_single: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + # step 4: feed forward + ff_output = self.ff(norm_hidden_states) + if self.use_adaln_single: + ff_output = gate_mlp * ff_output + + hidden_states = hidden_states + ff_output + + return hidden_states diff --git a/comfy/ldm/ace/lyric_encoder.py b/comfy/ldm/ace/lyric_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ff4359b26e8d27591d6fa59d6d931ab13596efe1 --- /dev/null +++ b/comfy/ldm/ace/lyric_encoder.py @@ -0,0 +1,1067 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py +from typing import Optional, Tuple, Union +import math +import torch +from torch import nn + +import comfy.model_management + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__(self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + + self.pointwise_conv1 = operations.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + dtype=dtype, device=device + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = operations.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + dtype=dtype, device=device + ) + + assert norm in ['batch_norm', 'layer_norm'] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = operations.LayerNorm(channels, dtype=dtype, device=device) + + self.pointwise_conv2 = operations.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + dtype=dtype, device=device + ) + self.activation = activation + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert (x.size(2) > self.lorder) + new_cache = x[:, :, -self.lorder:] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + dtype=None, device=None, operations=None + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device) + self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + + if mask is not None and mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float('inf')) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0) # (batch, head, time1, time2) + + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = (x.transpose(1, 2).contiguous().view(n_batch, -1, + self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + CosyVoice. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations) + # linear transformation for positional encoding + self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) + self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) + # torch.nn.init.xavier_uniform_(self.pos_bias_u) + # torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used + if matrix_ac.shape != matrix_bd.shape: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache + + + +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + arange = torch.arange(size, device=device) + mask = arange.expand(size, size) + arange = arange.unsqueeze(-1) + mask = mask <= arange + return mask + + +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), + ) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + +def add_optional_chunk_mask(xs: torch.Tensor, + masks: torch.Tensor, + use_dynamic_chunk: bool, + use_dynamic_left_chunk: bool, + decoding_chunk_size: int, + static_chunk_size: int, + num_decoding_left_chunks: int, + enable_full_context: bool = True): + """ Apply optional mask for encoder. + + Args: + xs (torch.Tensor): padded input, (B, L, D), L for max length + mask (torch.Tensor): mask for xs, (B, 1, L) + use_dynamic_chunk (bool): whether to use dynamic chunk or not + use_dynamic_left_chunk (bool): whether to use dynamic left chunk for + training. + decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + static_chunk_size (int): chunk size for static chunk training/decoding + if it's greater than 0, if use_dynamic_chunk is true, + this parameter will be ignored + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + enable_full_context (bool): + True: chunk size is either [1, 25] or full context(max_len) + False: chunk size ~ U[1, 25] + + Returns: + torch.Tensor: chunk mask of the input xs. + """ + # Whether to use chunk mask or not + if use_dynamic_chunk: + max_len = xs.size(1) + if decoding_chunk_size < 0: + chunk_size = max_len + num_left_chunks = -1 + elif decoding_chunk_size > 0: + chunk_size = decoding_chunk_size + num_left_chunks = num_decoding_left_chunks + else: + # chunk size is either [1, 25] or full context(max_len). + # Since we use 4 times subsampling and allow up to 1s(100 frames) + # delay, the maximum frame is 100 / 4 = 25. + chunk_size = torch.randint(1, max_len, (1, )).item() + num_left_chunks = -1 + if chunk_size > max_len // 2 and enable_full_context: + chunk_size = max_len + else: + chunk_size = chunk_size % 25 + 1 + if use_dynamic_left_chunk: + max_left_chunks = (max_len - 1) // chunk_size + num_left_chunks = torch.randint(0, max_left_chunks, + (1, )).item() + chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + elif static_chunk_size > 0: + num_left_chunks = num_decoding_left_chunks + chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + else: + chunk_masks = masks + return chunk_masks + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + dtype=None, device=None, operations=None + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module + self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module + self.norm_final = operations.LayerNorm( + size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time，time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1，time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, + att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache + + + +class EspnetRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Construct an PositionalEncoding object.""" + super(EspnetRelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: torch.Tensor): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.position_encoding(size=x.size(1), offset=offset) + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size, + ] + return pos_emb + + + +class LinearEmbed(torch.nn.Module): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + operations.Linear(idim, odim, dtype=dtype, device=device), + operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class #rel_pos_espnet + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + def forward( + self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb + + +ATTENTION_CLASSES = { + "selfattn": MultiHeadedAttention, + "rel_selfattn": RelPositionMultiHeadedAttention, +} + +ACTIVATION_CLASSES = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": getattr(torch.nn, "SiLU", Swish), + "gelu": torch.nn.GELU, +} + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, + max_len, + dtype=torch.int64, + device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask + +#https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml +class ConformerEncoder(torch.nn.Module): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 1024, + attention_heads: int = 16, + linear_units: int = 4096, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = 'linear', + pos_enc_layer_type: str = 'rel_pos_espnet', + normalize_before: bool = True, + static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask + use_dynamic_chunk: bool = False, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool =False, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = False, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + dtype=None, device=None, operations=None + ): + """Construct ConformerEncoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + positionwise_conv_kernel_size (int): Kernel size of positionwise + conv1d layer. + macaron_style (bool): Whether to use macaron style for + positionwise layer. + selfattention_layer_type (str): Encoder attention layer type, + the parameter has no effect now, it's just for configure + compatibility. #'rel_selfattn' + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + super().__init__() + self.output_size = output_size + self.embed = LinearEmbed(input_size, output_size, dropout_rate, + EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations) + self.normalize_before = normalize_before + self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device) + self.use_dynamic_chunk = use_dynamic_chunk + + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + activation = ACTIVATION_CLASSES[activation_type]() + + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + RelPositionMultiHeadedAttention( + *encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations), + PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations), + PositionwiseFeedForward( + *positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None, + dropout_rate, + normalize_before, dtype=dtype, device=device, operations=operations + ) for _ in range(num_blocks) + ]) + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + def forward( + self, + xs: torch.Tensor, + pad_mask: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + masks = None + if pad_mask is not None: + masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T) + xs, pos_emb = self.embed(xs) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks) + + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + diff --git a/comfy/ldm/ace/model.py b/comfy/ldm/ace/model.py new file mode 100644 index 0000000000000000000000000000000000000000..12c5247011aab3c533ca77c3c1a63d9c185ee90f --- /dev/null +++ b/comfy/ldm/ace/model.py @@ -0,0 +1,385 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.py + +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Optional, List, Union + +import torch +from torch import nn + +import comfy.model_management + +from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps +from .attention import LinearTransformerBlock, t2i_modulate +from .lyric_encoder import ConformerEncoder as LyricEncoder + + +def cross_norm(hidden_states, controlnet_input): + # input N x T x c + mean_hidden_states, std_hidden_states = hidden_states.mean(dim=(1,2), keepdim=True), hidden_states.std(dim=(1,2), keepdim=True) + mean_controlnet_input, std_controlnet_input = controlnet_input.mean(dim=(1,2), keepdim=True), controlnet_input.std(dim=(1,2), keepdim=True) + controlnet_input = (controlnet_input - mean_controlnet_input) * (std_hidden_states / (std_controlnet_input + 1e-12)) + mean_hidden_states + return controlnet_input + + +# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, dtype=None, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +class T2IFinalLayer(nn.Module): + """ + The final layer of Sana. + """ + + def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True, dtype=dtype, device=device) + self.scale_shift_table = nn.Parameter(torch.empty(2, hidden_size, dtype=dtype, device=device)) + self.out_channels = out_channels + self.patch_size = patch_size + + def unpatchfy( + self, + hidden_states: torch.Tensor, + width: int, + ): + # 4 unpatchify + new_height, new_width = 1, hidden_states.size(1) + hidden_states = hidden_states.reshape( + shape=(hidden_states.shape[0], new_height, new_width, self.patch_size[0], self.patch_size[1], self.out_channels) + ).contiguous() + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(hidden_states.shape[0], self.out_channels, new_height * self.patch_size[0], new_width * self.patch_size[1]) + ).contiguous() + if width > new_width: + output = torch.nn.functional.pad(output, (0, width - new_width, 0, 0), 'constant', 0) + elif width < new_width: + output = output[:, :, :, :width] + return output + + def forward(self, x, t, output_length): + shift, scale = (comfy.model_management.cast_to(self.scale_shift_table[None], device=t.device, dtype=t.dtype) + t[:, None]).chunk(2, dim=1) + x = t2i_modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + # unpatchify + output = self.unpatchfy(x, output_length) + return output + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding""" + + def __init__( + self, + height=16, + width=4096, + patch_size=(16, 1), + in_channels=8, + embed_dim=1152, + bias=True, + dtype=None, device=None, operations=None + ): + super().__init__() + patch_size_h, patch_size_w = patch_size + self.early_conv_layers = nn.Sequential( + operations.Conv2d(in_channels, in_channels*256, kernel_size=patch_size, stride=patch_size, padding=0, bias=bias, dtype=dtype, device=device), + operations.GroupNorm(num_groups=32, num_channels=in_channels*256, eps=1e-6, affine=True, dtype=dtype, device=device), + operations.Conv2d(in_channels*256, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device) + ) + self.patch_size = patch_size + self.height, self.width = height // patch_size_h, width // patch_size_w + self.base_size = self.width + + def forward(self, latent): + # early convolutions, N x C x H x W -> N x 256 * sqrt(patch_size) x H/patch_size x W/patch_size + latent = self.early_conv_layers(latent) + latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC + return latent + + +class ACEStepTransformer2DModel(nn.Module): + # _supports_gradient_checkpointing = True + + def __init__( + self, + in_channels: Optional[int] = 8, + num_layers: int = 28, + inner_dim: int = 1536, + attention_head_dim: int = 64, + num_attention_heads: int = 24, + mlp_ratio: float = 4.0, + out_channels: int = 8, + max_position: int = 32768, + rope_theta: float = 1000000.0, + speaker_embedding_dim: int = 512, + text_embedding_dim: int = 768, + ssl_encoder_depths: List[int] = [9, 9], + ssl_names: List[str] = ["mert", "m-hubert"], + ssl_latent_dims: List[int] = [1024, 768], + lyric_encoder_vocab_size: int = 6681, + lyric_hidden_size: int = 1024, + patch_size: List[int] = [16, 1], + max_height: int = 16, + max_width: int = 4096, + audio_model=None, + dtype=None, device=None, operations=None + + ): + super().__init__() + + self.dtype = dtype + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + self.inner_dim = inner_dim + self.out_channels = out_channels + self.max_position = max_position + self.patch_size = patch_size + + self.rope_theta = rope_theta + + self.rotary_emb = Qwen2RotaryEmbedding( + dim=self.attention_head_dim, + max_position_embeddings=self.max_position, + base=self.rope_theta, + dtype=dtype, + device=device, + ) + + # 2. Define input layers + self.in_channels = in_channels + + self.num_layers = num_layers + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + LinearTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.num_attention_heads, + attention_head_dim=attention_head_dim, + mlp_ratio=mlp_ratio, + add_cross_attention=True, + add_cross_attention_dim=self.inner_dim, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(self.num_layers) + ] + ) + + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim, dtype=dtype, device=device, operations=operations) + self.t_block = nn.Sequential(nn.SiLU(), operations.Linear(self.inner_dim, 6 * self.inner_dim, bias=True, dtype=dtype, device=device)) + + # speaker + self.speaker_embedder = operations.Linear(speaker_embedding_dim, self.inner_dim, dtype=dtype, device=device) + + # genre + self.genre_embedder = operations.Linear(text_embedding_dim, self.inner_dim, dtype=dtype, device=device) + + # lyric + self.lyric_embs = operations.Embedding(lyric_encoder_vocab_size, lyric_hidden_size, dtype=dtype, device=device) + self.lyric_encoder = LyricEncoder(input_size=lyric_hidden_size, static_chunk_size=0, dtype=dtype, device=device, operations=operations) + self.lyric_proj = operations.Linear(lyric_hidden_size, self.inner_dim, dtype=dtype, device=device) + + projector_dim = 2 * self.inner_dim + + self.projectors = nn.ModuleList([ + nn.Sequential( + operations.Linear(self.inner_dim, projector_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(projector_dim, projector_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(projector_dim, ssl_dim, dtype=dtype, device=device), + ) for ssl_dim in ssl_latent_dims + ]) + + self.proj_in = PatchEmbed( + height=max_height, + width=max_width, + patch_size=patch_size, + embed_dim=self.inner_dim, + bias=True, + dtype=dtype, + device=device, + operations=operations, + ) + + self.final_layer = T2IFinalLayer(self.inner_dim, patch_size=patch_size, out_channels=out_channels, dtype=dtype, device=device, operations=operations) + + def forward_lyric_encoder( + self, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + out_dtype=None, + ): + # N x T x D + lyric_embs = self.lyric_embs(lyric_token_idx, out_dtype=out_dtype) + prompt_prenet_out, _mask = self.lyric_encoder(lyric_embs, lyric_mask, decoding_chunk_size=1, num_decoding_left_chunks=-1) + prompt_prenet_out = self.lyric_proj(prompt_prenet_out) + return prompt_prenet_out + + def encode( + self, + encoder_text_hidden_states: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + lyrics_strength=1.0, + ): + + bs = encoder_text_hidden_states.shape[0] + device = encoder_text_hidden_states.device + + # speaker embedding + encoder_spk_hidden_states = self.speaker_embedder(speaker_embeds).unsqueeze(1) + + # genre embedding + encoder_text_hidden_states = self.genre_embedder(encoder_text_hidden_states) + + # lyric + encoder_lyric_hidden_states = self.forward_lyric_encoder( + lyric_token_idx=lyric_token_idx, + lyric_mask=lyric_mask, + out_dtype=encoder_text_hidden_states.dtype, + ) + + encoder_lyric_hidden_states *= lyrics_strength + + encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1) + + encoder_hidden_mask = None + if text_attention_mask is not None: + speaker_mask = torch.ones(bs, 1, device=device) + encoder_hidden_mask = torch.cat([speaker_mask, text_attention_mask, lyric_mask], dim=1) + + return encoder_hidden_states, encoder_hidden_mask + + def decode( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + encoder_hidden_states: torch.Tensor, + encoder_hidden_mask: torch.Tensor, + timestep: Optional[torch.Tensor], + output_length: int = 0, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + ): + embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) + temb = self.t_block(embedded_timestep) + + hidden_states = self.proj_in(hidden_states) + + # controlnet logic + if block_controlnet_hidden_states is not None: + control_condi = cross_norm(hidden_states, block_controlnet_hidden_states) + hidden_states = hidden_states + control_condi * controlnet_scale + + # inner_hidden_states = [] + + rotary_freqs_cis = self.rotary_emb(hidden_states, seq_len=hidden_states.shape[1]) + encoder_rotary_freqs_cis = self.rotary_emb(encoder_hidden_states, seq_len=encoder_hidden_states.shape[1]) + + for index_block, block in enumerate(self.transformer_blocks): + hidden_states = block( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_hidden_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=encoder_rotary_freqs_cis, + temb=temb, + ) + + output = self.final_layer(hidden_states, embedded_timestep, output_length) + return output + + def forward( + self, + x, + timestep, + attention_mask=None, + context: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + lyrics_strength=1.0, + **kwargs + ): + hidden_states = x + encoder_text_hidden_states = context + encoder_hidden_states, encoder_hidden_mask = self.encode( + encoder_text_hidden_states=encoder_text_hidden_states, + text_attention_mask=text_attention_mask, + speaker_embeds=speaker_embeds, + lyric_token_idx=lyric_token_idx, + lyric_mask=lyric_mask, + lyrics_strength=lyrics_strength, + ) + + output_length = hidden_states.shape[-1] + + output = self.decode( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_mask=encoder_hidden_mask, + timestep=timestep, + output_length=output_length, + block_controlnet_hidden_states=block_controlnet_hidden_states, + controlnet_scale=controlnet_scale, + ) + + return output diff --git a/comfy/ldm/ace/vae/__pycache__/autoencoder_dc.cpython-310.pyc b/comfy/ldm/ace/vae/__pycache__/autoencoder_dc.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2328235abaa39895e5237a90608980a0f995d842 Binary files /dev/null and b/comfy/ldm/ace/vae/__pycache__/autoencoder_dc.cpython-310.pyc differ diff --git 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0000000000000000000000000000000000000000..cd1581045a9777309a15d5e2c7a420e021494eaf Binary files /dev/null and b/comfy/ldm/ace/vae/__pycache__/music_vocoder.cpython-310.pyc differ diff --git a/comfy/ldm/ace/vae/__pycache__/music_vocoder.cpython-312.pyc b/comfy/ldm/ace/vae/__pycache__/music_vocoder.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d99dc7b180871ab588de0915ed246748be61bb5e Binary files /dev/null and b/comfy/ldm/ace/vae/__pycache__/music_vocoder.cpython-312.pyc differ diff --git a/comfy/ldm/ace/vae/autoencoder_dc.py b/comfy/ldm/ace/vae/autoencoder_dc.py new file mode 100644 index 0000000000000000000000000000000000000000..e7b1d4801e7b3f47f29cb90f41adff4d415868cd --- /dev/null +++ b/comfy/ldm/ace/vae/autoencoder_dc.py @@ -0,0 +1,644 @@ +# Rewritten from diffusers +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Tuple, Union + +import comfy.model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + + +class RMSNorm(ops.RMSNorm): + def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False): + super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine) + if elementwise_affine: + self.bias = nn.Parameter(torch.empty(dim)) if bias else None + + def forward(self, x): + x = super().forward(x) + if self.elementwise_affine: + if self.bias is not None: + x = x + comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device) + return x + + +def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5): + if norm_type == "batch_norm": + return nn.BatchNorm2d(num_features) + elif norm_type == "group_norm": + return ops.GroupNorm(num_groups, num_features) + elif norm_type == "layer_norm": + return ops.LayerNorm(num_features) + elif norm_type == "rms_norm": + return RMSNorm(num_features, eps=eps, elementwise_affine=True, bias=True) + else: + raise ValueError(f"Unknown normalization type: {norm_type}") + + +def get_activation(activation_type): + if activation_type == "relu": + return nn.ReLU() + elif activation_type == "relu6": + return nn.ReLU6() + elif activation_type == "silu": + return nn.SiLU() + elif activation_type == "leaky_relu": + return nn.LeakyReLU(0.2) + else: + raise ValueError(f"Unknown activation type: {activation_type}") + + +class ResBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_type: str = "batch_norm", + act_fn: str = "relu6", + ) -> None: + super().__init__() + + self.norm_type = norm_type + self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity() + self.conv1 = ops.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = ops.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False) + self.norm = get_normalization(norm_type, out_channels) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.conv1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.norm_type == "rms_norm": + # move channel to the last dimension so we apply RMSnorm across channel dimension + hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) + else: + hidden_states = self.norm(hidden_states) + + return hidden_states + residual + +class SanaMultiscaleAttentionProjection(nn.Module): + def __init__( + self, + in_channels: int, + num_attention_heads: int, + kernel_size: int, + ) -> None: + super().__init__() + + channels = 3 * in_channels + self.proj_in = ops.Conv2d( + channels, + channels, + kernel_size, + padding=kernel_size // 2, + groups=channels, + bias=False, + ) + self.proj_out = ops.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.proj_in(hidden_states) + hidden_states = self.proj_out(hidden_states) + return hidden_states + +class SanaMultiscaleLinearAttention(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_attention_heads: int = None, + attention_head_dim: int = 8, + mult: float = 1.0, + norm_type: str = "batch_norm", + kernel_sizes: tuple = (5,), + eps: float = 1e-15, + residual_connection: bool = False, + ): + super().__init__() + + self.eps = eps + self.attention_head_dim = attention_head_dim + self.norm_type = norm_type + self.residual_connection = residual_connection + + num_attention_heads = ( + int(in_channels // attention_head_dim * mult) + if num_attention_heads is None + else num_attention_heads + ) + inner_dim = num_attention_heads * attention_head_dim + + self.to_q = ops.Linear(in_channels, inner_dim, bias=False) + self.to_k = ops.Linear(in_channels, inner_dim, bias=False) + self.to_v = ops.Linear(in_channels, inner_dim, bias=False) + + self.to_qkv_multiscale = nn.ModuleList() + for kernel_size in kernel_sizes: + self.to_qkv_multiscale.append( + SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size) + ) + + self.nonlinearity = nn.ReLU() + self.to_out = ops.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False) + self.norm_out = get_normalization(norm_type, out_channels) + + def apply_linear_attention(self, query, key, value): + value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) + scores = torch.matmul(value, key.transpose(-1, -2)) + hidden_states = torch.matmul(scores, query) + + hidden_states = hidden_states.to(dtype=torch.float32) + hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) + return hidden_states + + def apply_quadratic_attention(self, query, key, value): + scores = torch.matmul(key.transpose(-1, -2), query) + scores = scores.to(dtype=torch.float32) + scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps) + hidden_states = torch.matmul(value, scores.to(value.dtype)) + return hidden_states + + def forward(self, hidden_states): + height, width = hidden_states.shape[-2:] + if height * width > self.attention_head_dim: + use_linear_attention = True + else: + use_linear_attention = False + + residual = hidden_states + + batch_size, _, height, width = list(hidden_states.size()) + original_dtype = hidden_states.dtype + + hidden_states = hidden_states.movedim(1, -1) + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + hidden_states = torch.cat([query, key, value], dim=3) + hidden_states = hidden_states.movedim(-1, 1) + + multi_scale_qkv = [hidden_states] + for block in self.to_qkv_multiscale: + multi_scale_qkv.append(block(hidden_states)) + + hidden_states = torch.cat(multi_scale_qkv, dim=1) + + if use_linear_attention: + # for linear attention upcast hidden_states to float32 + hidden_states = hidden_states.to(dtype=torch.float32) + + hidden_states = hidden_states.reshape(batch_size, -1, 3 * self.attention_head_dim, height * width) + + query, key, value = hidden_states.chunk(3, dim=2) + query = self.nonlinearity(query) + key = self.nonlinearity(key) + + if use_linear_attention: + hidden_states = self.apply_linear_attention(query, key, value) + hidden_states = hidden_states.to(dtype=original_dtype) + else: + hidden_states = self.apply_quadratic_attention(query, key, value) + + hidden_states = torch.reshape(hidden_states, (batch_size, -1, height, width)) + hidden_states = self.to_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + + if self.norm_type == "rms_norm": + hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + else: + hidden_states = self.norm_out(hidden_states) + + if self.residual_connection: + hidden_states = hidden_states + residual + + return hidden_states + + +class EfficientViTBlock(nn.Module): + def __init__( + self, + in_channels: int, + mult: float = 1.0, + attention_head_dim: int = 32, + qkv_multiscales: tuple = (5,), + norm_type: str = "batch_norm", + ) -> None: + super().__init__() + + self.attn = SanaMultiscaleLinearAttention( + in_channels=in_channels, + out_channels=in_channels, + mult=mult, + attention_head_dim=attention_head_dim, + norm_type=norm_type, + kernel_sizes=qkv_multiscales, + residual_connection=True, + ) + + self.conv_out = GLUMBConv( + in_channels=in_channels, + out_channels=in_channels, + norm_type="rms_norm", + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.attn(x) + x = self.conv_out(x) + return x + + +class GLUMBConv(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + expand_ratio: float = 4, + norm_type: str = None, + residual_connection: bool = True, + ) -> None: + super().__init__() + + hidden_channels = int(expand_ratio * in_channels) + self.norm_type = norm_type + self.residual_connection = residual_connection + + self.nonlinearity = nn.SiLU() + self.conv_inverted = ops.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0) + self.conv_depth = ops.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2) + self.conv_point = ops.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False) + + self.norm = None + if norm_type == "rms_norm": + self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.residual_connection: + residual = hidden_states + + hidden_states = self.conv_inverted(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv_depth(hidden_states) + hidden_states, gate = torch.chunk(hidden_states, 2, dim=1) + hidden_states = hidden_states * self.nonlinearity(gate) + + hidden_states = self.conv_point(hidden_states) + + if self.norm_type == "rms_norm": + # move channel to the last dimension so we apply RMSnorm across channel dimension + hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) + + if self.residual_connection: + hidden_states = hidden_states + residual + + return hidden_states + + +def get_block( + block_type: str, + in_channels: int, + out_channels: int, + attention_head_dim: int, + norm_type: str, + act_fn: str, + qkv_mutliscales: tuple = (), +): + if block_type == "ResBlock": + block = ResBlock(in_channels, out_channels, norm_type, act_fn) + elif block_type == "EfficientViTBlock": + block = EfficientViTBlock( + in_channels, + attention_head_dim=attention_head_dim, + norm_type=norm_type, + qkv_multiscales=qkv_mutliscales + ) + else: + raise ValueError(f"Block with {block_type=} is not supported.") + + return block + + +class DCDownBlock2d(nn.Module): + def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None: + super().__init__() + + self.downsample = downsample + self.factor = 2 + self.stride = 1 if downsample else 2 + self.group_size = in_channels * self.factor**2 // out_channels + self.shortcut = shortcut + + out_ratio = self.factor**2 + if downsample: + assert out_channels % out_ratio == 0 + out_channels = out_channels // out_ratio + + self.conv = ops.Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=self.stride, + padding=1, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + x = self.conv(hidden_states) + if self.downsample: + x = F.pixel_unshuffle(x, self.factor) + + if self.shortcut: + y = F.pixel_unshuffle(hidden_states, self.factor) + y = y.unflatten(1, (-1, self.group_size)) + y = y.mean(dim=2) + hidden_states = x + y + else: + hidden_states = x + + return hidden_states + + +class DCUpBlock2d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + interpolate: bool = False, + shortcut: bool = True, + interpolation_mode: str = "nearest", + ) -> None: + super().__init__() + + self.interpolate = interpolate + self.interpolation_mode = interpolation_mode + self.shortcut = shortcut + self.factor = 2 + self.repeats = out_channels * self.factor**2 // in_channels + + out_ratio = self.factor**2 + if not interpolate: + out_channels = out_channels * out_ratio + + self.conv = ops.Conv2d(in_channels, out_channels, 3, 1, 1) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.interpolate: + x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode) + x = self.conv(x) + else: + x = self.conv(hidden_states) + x = F.pixel_shuffle(x, self.factor) + + if self.shortcut: + y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats) + y = F.pixel_shuffle(y, self.factor) + hidden_states = x + y + else: + hidden_states = x + + return hidden_states + + +class Encoder(nn.Module): + def __init__( + self, + in_channels: int, + latent_channels: int, + attention_head_dim: int = 32, + block_type: str or tuple = "ResBlock", + block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), + layers_per_block: tuple = (2, 2, 2, 2, 2, 2), + qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), + downsample_block_type: str = "pixel_unshuffle", + out_shortcut: bool = True, + ): + super().__init__() + + num_blocks = len(block_out_channels) + + if isinstance(block_type, str): + block_type = (block_type,) * num_blocks + + if layers_per_block[0] > 0: + self.conv_in = ops.Conv2d( + in_channels, + block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], + kernel_size=3, + stride=1, + padding=1, + ) + else: + self.conv_in = DCDownBlock2d( + in_channels=in_channels, + out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], + downsample=downsample_block_type == "pixel_unshuffle", + shortcut=False, + ) + + down_blocks = [] + for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)): + down_block_list = [] + + for _ in range(num_layers): + block = get_block( + block_type[i], + out_channel, + out_channel, + attention_head_dim=attention_head_dim, + norm_type="rms_norm", + act_fn="silu", + qkv_mutliscales=qkv_multiscales[i], + ) + down_block_list.append(block) + + if i < num_blocks - 1 and num_layers > 0: + downsample_block = DCDownBlock2d( + in_channels=out_channel, + out_channels=block_out_channels[i + 1], + downsample=downsample_block_type == "pixel_unshuffle", + shortcut=True, + ) + down_block_list.append(downsample_block) + + down_blocks.append(nn.Sequential(*down_block_list)) + + self.down_blocks = nn.ModuleList(down_blocks) + + self.conv_out = ops.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1) + + self.out_shortcut = out_shortcut + if out_shortcut: + self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.conv_in(hidden_states) + for down_block in self.down_blocks: + hidden_states = down_block(hidden_states) + + if self.out_shortcut: + x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size)) + x = x.mean(dim=2) + hidden_states = self.conv_out(hidden_states) + x + else: + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + +class Decoder(nn.Module): + def __init__( + self, + in_channels: int, + latent_channels: int, + attention_head_dim: int = 32, + block_type: str or tuple = "ResBlock", + block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), + layers_per_block: tuple = (2, 2, 2, 2, 2, 2), + qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), + norm_type: str or tuple = "rms_norm", + act_fn: str or tuple = "silu", + upsample_block_type: str = "pixel_shuffle", + in_shortcut: bool = True, + ): + super().__init__() + + num_blocks = len(block_out_channels) + + if isinstance(block_type, str): + block_type = (block_type,) * num_blocks + if isinstance(norm_type, str): + norm_type = (norm_type,) * num_blocks + if isinstance(act_fn, str): + act_fn = (act_fn,) * num_blocks + + self.conv_in = ops.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1) + + self.in_shortcut = in_shortcut + if in_shortcut: + self.in_shortcut_repeats = block_out_channels[-1] // latent_channels + + up_blocks = [] + for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))): + up_block_list = [] + + if i < num_blocks - 1 and num_layers > 0: + upsample_block = DCUpBlock2d( + block_out_channels[i + 1], + out_channel, + interpolate=upsample_block_type == "interpolate", + shortcut=True, + ) + up_block_list.append(upsample_block) + + for _ in range(num_layers): + block = get_block( + block_type[i], + out_channel, + out_channel, + attention_head_dim=attention_head_dim, + norm_type=norm_type[i], + act_fn=act_fn[i], + qkv_mutliscales=qkv_multiscales[i], + ) + up_block_list.append(block) + + up_blocks.insert(0, nn.Sequential(*up_block_list)) + + self.up_blocks = nn.ModuleList(up_blocks) + + channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1] + + self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True) + self.conv_act = nn.ReLU() + self.conv_out = None + + if layers_per_block[0] > 0: + self.conv_out = ops.Conv2d(channels, in_channels, 3, 1, 1) + else: + self.conv_out = DCUpBlock2d( + channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.in_shortcut: + x = hidden_states.repeat_interleave( + self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats + ) + hidden_states = self.conv_in(hidden_states) + x + else: + hidden_states = self.conv_in(hidden_states) + + for up_block in reversed(self.up_blocks): + hidden_states = up_block(hidden_states) + + hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + return hidden_states + + +class AutoencoderDC(nn.Module): + def __init__( + self, + in_channels: int = 2, + latent_channels: int = 8, + attention_head_dim: int = 32, + encoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], + decoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], + encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), + decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), + encoder_layers_per_block: Tuple[int] = (2, 2, 3, 3), + decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3), + encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), + decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), + upsample_block_type: str = "interpolate", + downsample_block_type: str = "Conv", + decoder_norm_types: Union[str, Tuple[str]] = "rms_norm", + decoder_act_fns: Union[str, Tuple[str]] = "silu", + scaling_factor: float = 0.41407, + ) -> None: + super().__init__() + + self.encoder = Encoder( + in_channels=in_channels, + latent_channels=latent_channels, + attention_head_dim=attention_head_dim, + block_type=encoder_block_types, + block_out_channels=encoder_block_out_channels, + layers_per_block=encoder_layers_per_block, + qkv_multiscales=encoder_qkv_multiscales, + downsample_block_type=downsample_block_type, + ) + + self.decoder = Decoder( + in_channels=in_channels, + latent_channels=latent_channels, + attention_head_dim=attention_head_dim, + block_type=decoder_block_types, + block_out_channels=decoder_block_out_channels, + layers_per_block=decoder_layers_per_block, + qkv_multiscales=decoder_qkv_multiscales, + norm_type=decoder_norm_types, + act_fn=decoder_act_fns, + upsample_block_type=upsample_block_type, + ) + + self.scaling_factor = scaling_factor + self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1) + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """Internal encoding function.""" + encoded = self.encoder(x) + return encoded * self.scaling_factor + + def decode(self, z: torch.Tensor) -> torch.Tensor: + # Scale the latents back + z = z / self.scaling_factor + decoded = self.decoder(z) + return decoded + + def forward(self, x: torch.Tensor) -> torch.Tensor: + z = self.encode(x) + return self.decode(z) + diff --git a/comfy/ldm/ace/vae/music_dcae_pipeline.py b/comfy/ldm/ace/vae/music_dcae_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..af81280eb0dd5fce49c0aa4b86a4f0feb841b3fd --- /dev/null +++ b/comfy/ldm/ace/vae/music_dcae_pipeline.py @@ -0,0 +1,109 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py +import torch +from .autoencoder_dc import AutoencoderDC +import logging +try: + import torchaudio +except: + logging.warning("torchaudio missing, ACE model will be broken") + +import torchvision.transforms as transforms +from .music_vocoder import ADaMoSHiFiGANV1 + + +class MusicDCAE(torch.nn.Module): + def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}): + super(MusicDCAE, self).__init__() + + self.dcae = AutoencoderDC(**dcae_config) + self.vocoder = ADaMoSHiFiGANV1(**vocoder_config) + + if source_sample_rate is None: + self.source_sample_rate = 48000 + else: + self.source_sample_rate = source_sample_rate + + # self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100) + + self.transform = transforms.Compose([ + transforms.Normalize(0.5, 0.5), + ]) + self.min_mel_value = -11.0 + self.max_mel_value = 3.0 + self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000))) + self.mel_chunk_size = 1024 + self.time_dimention_multiple = 8 + self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple + self.scale_factor = 0.1786 + self.shift_factor = -1.9091 + + def load_audio(self, audio_path): + audio, sr = torchaudio.load(audio_path) + return audio, sr + + def forward_mel(self, audios): + mels = [] + for i in range(len(audios)): + image = self.vocoder.mel_transform(audios[i]) + mels.append(image) + mels = torch.stack(mels) + return mels + + @torch.no_grad() + def encode(self, audios, audio_lengths=None, sr=None): + if audio_lengths is None: + audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0]) + audio_lengths = audio_lengths.to(audios.device) + + if sr is None: + sr = self.source_sample_rate + + if sr != 44100: + audios = torchaudio.functional.resample(audios, sr, 44100) + + max_audio_len = audios.shape[-1] + if max_audio_len % (8 * 512) != 0: + audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512))) + + mels = self.forward_mel(audios) + mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value) + mels = self.transform(mels) + latents = [] + for mel in mels: + latent = self.dcae.encoder(mel.unsqueeze(0)) + latents.append(latent) + latents = torch.cat(latents, dim=0) + # latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long() + latents = (latents - self.shift_factor) * self.scale_factor + return latents + # return latents, latent_lengths + + @torch.no_grad() + def decode(self, latents, audio_lengths=None, sr=None): + latents = latents / self.scale_factor + self.shift_factor + + pred_wavs = [] + + for latent in latents: + mels = self.dcae.decoder(latent.unsqueeze(0)) + mels = mels * 0.5 + 0.5 + mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value + wav = self.vocoder.decode(mels[0]).squeeze(1) + + if sr is not None: + # resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype) + wav = torchaudio.functional.resample(wav, 44100, sr) + # wav = resampler(wav) + else: + sr = 44100 + pred_wavs.append(wav) + + if audio_lengths is not None: + pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)] + return torch.stack(pred_wavs) + # return sr, pred_wavs + + def forward(self, audios, audio_lengths=None, sr=None): + latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr) + sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr) + return sr, pred_wavs, latents, latent_lengths diff --git a/comfy/ldm/ace/vae/music_log_mel.py b/comfy/ldm/ace/vae/music_log_mel.py new file mode 100644 index 0000000000000000000000000000000000000000..9c584eb7fa75ffaab1c3a42184ea75171b2a1d8f --- /dev/null +++ b/comfy/ldm/ace/vae/music_log_mel.py @@ -0,0 +1,113 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py +import torch +import torch.nn as nn +from torch import Tensor +import logging +try: + from torchaudio.transforms import MelScale +except: + logging.warning("torchaudio missing, ACE model will be broken") + +import comfy.model_management + +class LinearSpectrogram(nn.Module): + def __init__( + self, + n_fft=2048, + win_length=2048, + hop_length=512, + center=False, + mode="pow2_sqrt", + ): + super().__init__() + + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.mode = mode + + self.register_buffer("window", torch.hann_window(win_length)) + + def forward(self, y: Tensor) -> Tensor: + if y.ndim == 3: + y = y.squeeze(1) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + ( + (self.win_length - self.hop_length) // 2, + (self.win_length - self.hop_length + 1) // 2, + ), + mode="reflect", + ).squeeze(1) + dtype = y.dtype + spec = torch.stft( + y.float(), + self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window=comfy.model_management.cast_to(self.window, dtype=torch.float32, device=y.device), + center=self.center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + + if self.mode == "pow2_sqrt": + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + spec = spec.to(dtype) + return spec + + +class LogMelSpectrogram(nn.Module): + def __init__( + self, + sample_rate=44100, + n_fft=2048, + win_length=2048, + hop_length=512, + n_mels=128, + center=False, + f_min=0.0, + f_max=None, + ): + super().__init__() + + self.sample_rate = sample_rate + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.n_mels = n_mels + self.f_min = f_min + self.f_max = f_max or sample_rate // 2 + + self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center) + self.mel_scale = MelScale( + self.n_mels, + self.sample_rate, + self.f_min, + self.f_max, + self.n_fft // 2 + 1, + "slaney", + "slaney", + ) + + def compress(self, x: Tensor) -> Tensor: + return torch.log(torch.clamp(x, min=1e-5)) + + def decompress(self, x: Tensor) -> Tensor: + return torch.exp(x) + + def forward(self, x: Tensor, return_linear: bool = False) -> Tensor: + linear = self.spectrogram(x) + x = self.mel_scale(linear) + x = self.compress(x) + # print(x.shape) + if return_linear: + return x, self.compress(linear) + + return x diff --git a/comfy/ldm/ace/vae/music_vocoder.py b/comfy/ldm/ace/vae/music_vocoder.py new file mode 100644 index 0000000000000000000000000000000000000000..2f989fa86e8c964994886c6a22ed53e742279ec4 --- /dev/null +++ b/comfy/ldm/ace/vae/music_vocoder.py @@ -0,0 +1,538 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py +import torch +from torch import nn + +from functools import partial +from math import prod +from typing import Callable, Tuple, List + +import numpy as np +import torch.nn.functional as F +from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm + +from .music_log_mel import LogMelSpectrogram + +import comfy.model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ # noqa: E501 + + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501 + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" + + +class LayerNorm(nn.Module): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ # noqa: E501 + + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape,) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm( + x, self.normalized_shape, comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device), comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device), self.eps + ) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = comfy.model_management.cast_to(self.weight[:, None], dtype=x.dtype, device=x.device) * x + comfy.model_management.cast_to(self.bias[:, None], dtype=x.dtype, device=x.device) + return x + + +class ConvNeXtBlock(nn.Module): + r"""ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + kernel_size (int): Kernel size for depthwise conv. Default: 7. + dilation (int): Dilation for depthwise conv. Default: 1. + """ # noqa: E501 + + def __init__( + self, + dim: int, + drop_path: float = 0.0, + layer_scale_init_value: float = 1e-6, + mlp_ratio: float = 4.0, + kernel_size: int = 7, + dilation: int = 1, + ): + super().__init__() + + self.dwconv = ops.Conv1d( + dim, + dim, + kernel_size=kernel_size, + padding=int(dilation * (kernel_size - 1) / 2), + groups=dim, + ) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = ops.Linear( + dim, int(mlp_ratio * dim) + ) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = ops.Linear(int(mlp_ratio * dim), dim) + self.gamma = ( + nn.Parameter(torch.empty((dim)), requires_grad=False) + if layer_scale_init_value > 0 + else None + ) + self.drop_path = DropPath( + drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, x, apply_residual: bool = True): + input = x + + x = self.dwconv(x) + x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + + if self.gamma is not None: + x = comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) * x + + x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L) + x = self.drop_path(x) + + if apply_residual: + x = input + x + + return x + + +class ParallelConvNeXtBlock(nn.Module): + def __init__(self, kernel_sizes: List[int], *args, **kwargs): + super().__init__() + self.blocks = nn.ModuleList( + [ + ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs) + for kernel_size in kernel_sizes + ] + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.stack( + [block(x, apply_residual=False) for block in self.blocks] + [x], + dim=1, + ).sum(dim=1) + + +class ConvNeXtEncoder(nn.Module): + def __init__( + self, + input_channels=3, + depths=[3, 3, 9, 3], + dims=[96, 192, 384, 768], + drop_path_rate=0.0, + layer_scale_init_value=1e-6, + kernel_sizes: Tuple[int] = (7,), + ): + super().__init__() + assert len(depths) == len(dims) + + self.channel_layers = nn.ModuleList() + stem = nn.Sequential( + ops.Conv1d( + input_channels, + dims[0], + kernel_size=7, + padding=3, + padding_mode="replicate", + ), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), + ) + self.channel_layers.append(stem) + + for i in range(len(depths) - 1): + mid_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + ops.Conv1d(dims[i], dims[i + 1], kernel_size=1), + ) + self.channel_layers.append(mid_layer) + + block_fn = ( + partial(ConvNeXtBlock, kernel_size=kernel_sizes[0]) + if len(kernel_sizes) == 1 + else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes) + ) + + self.stages = nn.ModuleList() + drop_path_rates = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] + + cur = 0 + for i in range(len(depths)): + stage = nn.Sequential( + *[ + block_fn( + dim=dims[i], + drop_path=drop_path_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, + ) + for j in range(depths[i]) + ] + ) + self.stages.append(stage) + cur += depths[i] + + self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first") + + def forward( + self, + x: torch.Tensor, + ) -> torch.Tensor: + for channel_layer, stage in zip(self.channel_layers, self.stages): + x = channel_layer(x) + x = stage(x) + + return self.norm(x) + + +def get_padding(kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + +class ResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super().__init__() + + self.convs1 = nn.ModuleList( + [ + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + + self.convs2 = nn.ModuleList( + [ + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.silu(x) + xt = c1(xt) + xt = F.silu(xt) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for conv in self.convs1: + remove_weight_norm(conv) + for conv in self.convs2: + remove_weight_norm(conv) + + +class HiFiGANGenerator(nn.Module): + def __init__( + self, + *, + hop_length: int = 512, + upsample_rates: Tuple[int] = (8, 8, 2, 2, 2), + upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2), + resblock_kernel_sizes: Tuple[int] = (3, 7, 11), + resblock_dilation_sizes: Tuple[Tuple[int]] = ( + (1, 3, 5), (1, 3, 5), (1, 3, 5)), + num_mels: int = 128, + upsample_initial_channel: int = 512, + use_template: bool = True, + pre_conv_kernel_size: int = 7, + post_conv_kernel_size: int = 7, + post_activation: Callable = partial(nn.SiLU, inplace=True), + ): + super().__init__() + + assert ( + prod(upsample_rates) == hop_length + ), f"hop_length must be {prod(upsample_rates)}" + + self.conv_pre = torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + num_mels, + upsample_initial_channel, + pre_conv_kernel_size, + 1, + padding=get_padding(pre_conv_kernel_size), + ) + ) + + self.num_upsamples = len(upsample_rates) + self.num_kernels = len(resblock_kernel_sizes) + + self.noise_convs = nn.ModuleList() + self.use_template = use_template + self.ups = nn.ModuleList() + + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + c_cur = upsample_initial_channel // (2 ** (i + 1)) + self.ups.append( + torch.nn.utils.parametrizations.weight_norm( + ops.ConvTranspose1d( + upsample_initial_channel // (2**i), + upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + if not use_template: + continue + + if i + 1 < len(upsample_rates): + stride_f0 = np.prod(upsample_rates[i + 1:]) + self.noise_convs.append( + ops.Conv1d( + 1, + c_cur, + kernel_size=stride_f0 * 2, + stride=stride_f0, + padding=stride_f0 // 2, + ) + ) + else: + self.noise_convs.append(ops.Conv1d(1, c_cur, kernel_size=1)) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel // (2 ** (i + 1)) + for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): + self.resblocks.append(ResBlock1(ch, k, d)) + + self.activation_post = post_activation() + self.conv_post = torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + ch, + 1, + post_conv_kernel_size, + 1, + padding=get_padding(post_conv_kernel_size), + ) + ) + + def forward(self, x, template=None): + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + x = F.silu(x, inplace=True) + x = self.ups[i](x) + + if self.use_template: + x = x + self.noise_convs[i](template) + + xs = None + + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + + x = xs / self.num_kernels + + x = self.activation_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + for up in self.ups: + remove_weight_norm(up) + for block in self.resblocks: + block.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class ADaMoSHiFiGANV1(nn.Module): + def __init__( + self, + input_channels: int = 128, + depths: List[int] = [3, 3, 9, 3], + dims: List[int] = [128, 256, 384, 512], + drop_path_rate: float = 0.0, + kernel_sizes: Tuple[int] = (7,), + upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2), + upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4), + resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13), + resblock_dilation_sizes: Tuple[Tuple[int]] = ( + (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)), + num_mels: int = 512, + upsample_initial_channel: int = 1024, + use_template: bool = False, + pre_conv_kernel_size: int = 13, + post_conv_kernel_size: int = 13, + sampling_rate: int = 44100, + n_fft: int = 2048, + win_length: int = 2048, + hop_length: int = 512, + f_min: int = 40, + f_max: int = 16000, + n_mels: int = 128, + ): + super().__init__() + + self.backbone = ConvNeXtEncoder( + input_channels=input_channels, + depths=depths, + dims=dims, + drop_path_rate=drop_path_rate, + kernel_sizes=kernel_sizes, + ) + + self.head = HiFiGANGenerator( + hop_length=hop_length, + upsample_rates=upsample_rates, + upsample_kernel_sizes=upsample_kernel_sizes, + resblock_kernel_sizes=resblock_kernel_sizes, + resblock_dilation_sizes=resblock_dilation_sizes, + num_mels=num_mels, + upsample_initial_channel=upsample_initial_channel, + use_template=use_template, + pre_conv_kernel_size=pre_conv_kernel_size, + post_conv_kernel_size=post_conv_kernel_size, + ) + self.sampling_rate = sampling_rate + self.mel_transform = LogMelSpectrogram( + sample_rate=sampling_rate, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + f_min=f_min, + f_max=f_max, + n_mels=n_mels, + ) + self.eval() + + @torch.no_grad() + def decode(self, mel): + y = self.backbone(mel) + y = self.head(y) + return y + + @torch.no_grad() + def encode(self, x): + return self.mel_transform(x) + + def forward(self, mel): + y = self.backbone(mel) + y = self.head(y) + return y diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..78ed6ffa63ab9fca66ba009709bb262a18830dcd --- /dev/null +++ b/comfy/ldm/audio/autoencoder.py @@ -0,0 +1,276 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +import torch +from torch import nn +from typing import Literal +import math +import comfy.ops +ops = comfy.ops.disable_weight_init + +def vae_sample(mean, scale): + stdev = nn.functional.softplus(scale) + 1e-4 + var = stdev * stdev + logvar = torch.log(var) + latents = torch.randn_like(mean) * stdev + mean + + kl = (mean * mean + var - logvar - 1).sum(1).mean() + + return latents, kl + +class VAEBottleneck(nn.Module): + def __init__(self): + super().__init__() + self.is_discrete = False + + def encode(self, x, return_info=False, **kwargs): + info = {} + + mean, scale = x.chunk(2, dim=1) + + x, kl = vae_sample(mean, scale) + + info["kl"] = kl + + if return_info: + return x, info + else: + return x + + def decode(self, x): + return x + + +def snake_beta(x, alpha, beta): + return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2) + +# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license +class SnakeBeta(nn.Module): + + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): + super(SnakeBeta, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = nn.Parameter(torch.zeros(in_features) * alpha) + self.beta = nn.Parameter(torch.zeros(in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = nn.Parameter(torch.ones(in_features) * alpha) + self.beta = nn.Parameter(torch.ones(in_features) * alpha) + + # self.alpha.requires_grad = alpha_trainable + # self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = snake_beta(x, alpha, beta) + + return x + +def WNConv1d(*args, **kwargs): + return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) + +def WNConvTranspose1d(*args, **kwargs): + return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) + +def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: + if activation == "elu": + act = torch.nn.ELU() + elif activation == "snake": + act = SnakeBeta(channels) + elif activation == "none": + act = torch.nn.Identity() + else: + raise ValueError(f"Unknown activation {activation}") + + if antialias: + act = Activation1d(act) # noqa: F821 Activation1d is not defined + + return act + + +class ResidualUnit(nn.Module): + def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False): + super().__init__() + + self.dilation = dilation + + padding = (dilation * (7-1)) // 2 + + self.layers = nn.Sequential( + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), + WNConv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=7, dilation=dilation, padding=padding), + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), + WNConv1d(in_channels=out_channels, out_channels=out_channels, + kernel_size=1) + ) + + def forward(self, x): + res = x + + #x = checkpoint(self.layers, x) + x = self.layers(x) + + return x + res + +class EncoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False): + super().__init__() + + self.layers = nn.Sequential( + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=1, use_snake=use_snake), + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=3, use_snake=use_snake), + ResidualUnit(in_channels=in_channels, + out_channels=in_channels, dilation=9, use_snake=use_snake), + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), + WNConv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)), + ) + + def forward(self, x): + return self.layers(x) + +class DecoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False): + super().__init__() + + if use_nearest_upsample: + upsample_layer = nn.Sequential( + nn.Upsample(scale_factor=stride, mode="nearest"), + WNConv1d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=2*stride, + stride=1, + bias=False, + padding='same') + ) + else: + upsample_layer = WNConvTranspose1d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)) + + self.layers = nn.Sequential( + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), + upsample_layer, + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=1, use_snake=use_snake), + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=3, use_snake=use_snake), + ResidualUnit(in_channels=out_channels, out_channels=out_channels, + dilation=9, use_snake=use_snake), + ) + + def forward(self, x): + return self.layers(x) + +class OobleckEncoder(nn.Module): + def __init__(self, + in_channels=2, + channels=128, + latent_dim=32, + c_mults = [1, 2, 4, 8], + strides = [2, 4, 8, 8], + use_snake=False, + antialias_activation=False + ): + super().__init__() + + c_mults = [1] + c_mults + + self.depth = len(c_mults) + + layers = [ + WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3) + ] + + for i in range(self.depth-1): + layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)] + + layers += [ + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels), + WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1) + ] + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class OobleckDecoder(nn.Module): + def __init__(self, + out_channels=2, + channels=128, + latent_dim=32, + c_mults = [1, 2, 4, 8], + strides = [2, 4, 8, 8], + use_snake=False, + antialias_activation=False, + use_nearest_upsample=False, + final_tanh=True): + super().__init__() + + c_mults = [1] + c_mults + + self.depth = len(c_mults) + + layers = [ + WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3), + ] + + for i in range(self.depth-1, 0, -1): + layers += [DecoderBlock( + in_channels=c_mults[i]*channels, + out_channels=c_mults[i-1]*channels, + stride=strides[i-1], + use_snake=use_snake, + antialias_activation=antialias_activation, + use_nearest_upsample=use_nearest_upsample + ) + ] + + layers += [ + get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels), + WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False), + nn.Tanh() if final_tanh else nn.Identity() + ] + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +class AudioOobleckVAE(nn.Module): + def __init__(self, + in_channels=2, + channels=128, + latent_dim=64, + c_mults = [1, 2, 4, 8, 16], + strides = [2, 4, 4, 8, 8], + use_snake=True, + antialias_activation=False, + use_nearest_upsample=False, + final_tanh=False): + super().__init__() + self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation) + self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation, + use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh) + self.bottleneck = VAEBottleneck() + + def encode(self, x): + return self.bottleneck.encode(self.encoder(x)) + + def decode(self, x): + return self.decoder(self.bottleneck.decode(x)) + diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py new file mode 100644 index 0000000000000000000000000000000000000000..179c5b67eac40a5c907f46d821c057c5de18e8d9 --- /dev/null +++ b/comfy/ldm/audio/dit.py @@ -0,0 +1,896 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +from comfy.ldm.modules.attention import optimized_attention +import typing as tp + +import torch + +from einops import rearrange +from torch import nn +from torch.nn import functional as F +import math +import comfy.ops + +class FourierFeatures(nn.Module): + def __init__(self, in_features, out_features, std=1., dtype=None, device=None): + super().__init__() + assert out_features % 2 == 0 + self.weight = nn.Parameter(torch.empty( + [out_features // 2, in_features], dtype=dtype, device=device)) + + def forward(self, input): + f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input) + return torch.cat([f.cos(), f.sin()], dim=-1) + +# norms +class LayerNorm(nn.Module): + def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None): + """ + bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less + """ + super().__init__() + + self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) + + if bias: + self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) + else: + self.beta = None + + def forward(self, x): + beta = self.beta + if beta is not None: + beta = comfy.ops.cast_to_input(beta, x) + return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta) + +class GLU(nn.Module): + def __init__( + self, + dim_in, + dim_out, + activation, + use_conv = False, + conv_kernel_size = 3, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.act = activation + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device) + self.use_conv = use_conv + + def forward(self, x): + if self.use_conv: + x = rearrange(x, 'b n d -> b d n') + x = self.proj(x) + x = rearrange(x, 'b d n -> b n d') + else: + x = self.proj(x) + + x, gate = x.chunk(2, dim = -1) + return x * self.act(gate) + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.scale = dim ** -0.5 + self.max_seq_len = max_seq_len + self.emb = nn.Embedding(max_seq_len, dim) + + def forward(self, x, pos = None, seq_start_pos = None): + seq_len, device = x.shape[1], x.device + assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}' + + if pos is None: + pos = torch.arange(seq_len, device = device) + + if seq_start_pos is not None: + pos = (pos - seq_start_pos[..., None]).clamp(min = 0) + + pos_emb = self.emb(pos) + pos_emb = pos_emb * self.scale + return pos_emb + +class ScaledSinusoidalEmbedding(nn.Module): + def __init__(self, dim, theta = 10000): + super().__init__() + assert (dim % 2) == 0, 'dimension must be divisible by 2' + self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5) + + half_dim = dim // 2 + freq_seq = torch.arange(half_dim).float() / half_dim + inv_freq = theta ** -freq_seq + self.register_buffer('inv_freq', inv_freq, persistent = False) + + def forward(self, x, pos = None, seq_start_pos = None): + seq_len, device = x.shape[1], x.device + + if pos is None: + pos = torch.arange(seq_len, device = device) + + if seq_start_pos is not None: + pos = pos - seq_start_pos[..., None] + + emb = torch.einsum('i, j -> i j', pos, self.inv_freq) + emb = torch.cat((emb.sin(), emb.cos()), dim = -1) + return emb * self.scale + +class RotaryEmbedding(nn.Module): + def __init__( + self, + dim, + use_xpos = False, + scale_base = 512, + interpolation_factor = 1., + base = 10000, + base_rescale_factor = 1., + dtype=None, + device=None, + ): + super().__init__() + # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning + # has some connection to NTK literature + # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ + base *= base_rescale_factor ** (dim / (dim - 2)) + + # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype)) + + assert interpolation_factor >= 1. + self.interpolation_factor = interpolation_factor + + if not use_xpos: + self.register_buffer('scale', None) + return + + scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) + + self.scale_base = scale_base + self.register_buffer('scale', scale) + + def forward_from_seq_len(self, seq_len, device, dtype): + # device = self.inv_freq.device + + t = torch.arange(seq_len, device=device, dtype=dtype) + return self.forward(t) + + def forward(self, t): + # device = self.inv_freq.device + device = t.device + + # t = t.to(torch.float32) + + t = t / self.interpolation_factor + + freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t)) + freqs = torch.cat((freqs, freqs), dim = -1) + + if self.scale is None: + return freqs, 1. + + power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base # noqa: F821 seq_len is not defined + scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1') + scale = torch.cat((scale, scale), dim = -1) + + return freqs, scale + +def rotate_half(x): + x = rearrange(x, '... (j d) -> ... j d', j = 2) + x1, x2 = x.unbind(dim = -2) + return torch.cat((-x2, x1), dim = -1) + +def apply_rotary_pos_emb(t, freqs, scale = 1): + out_dtype = t.dtype + + # cast to float32 if necessary for numerical stability + dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32)) + rot_dim, seq_len = freqs.shape[-1], t.shape[-2] + freqs, t = freqs.to(dtype), t.to(dtype) + freqs = freqs[-seq_len:, :] + + if t.ndim == 4 and freqs.ndim == 3: + freqs = rearrange(freqs, 'b n d -> b 1 n d') + + # partial rotary embeddings, Wang et al. GPT-J + t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:] + t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) + + t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype) + + return torch.cat((t, t_unrotated), dim = -1) + +class FeedForward(nn.Module): + def __init__( + self, + dim, + dim_out = None, + mult = 4, + no_bias = False, + glu = True, + use_conv = False, + conv_kernel_size = 3, + zero_init_output = True, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + inner_dim = int(dim * mult) + + # Default to SwiGLU + + activation = nn.SiLU() + + dim_out = dim if dim_out is None else dim_out + + if glu: + linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations) + else: + linear_in = nn.Sequential( + rearrange('b n d -> b d n') if use_conv else nn.Identity(), + operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device), + rearrange('b n d -> b d n') if use_conv else nn.Identity(), + activation + ) + + linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device) + + # # init last linear layer to 0 + # if zero_init_output: + # nn.init.zeros_(linear_out.weight) + # if not no_bias: + # nn.init.zeros_(linear_out.bias) + + + self.ff = nn.Sequential( + linear_in, + rearrange('b d n -> b n d') if use_conv else nn.Identity(), + linear_out, + rearrange('b n d -> b d n') if use_conv else nn.Identity(), + ) + + def forward(self, x): + return self.ff(x) + +class Attention(nn.Module): + def __init__( + self, + dim, + dim_heads = 64, + dim_context = None, + causal = False, + zero_init_output=True, + qk_norm = False, + natten_kernel_size = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.dim = dim + self.dim_heads = dim_heads + self.causal = causal + + dim_kv = dim_context if dim_context is not None else dim + + self.num_heads = dim // dim_heads + self.kv_heads = dim_kv // dim_heads + + if dim_context is not None: + self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device) + else: + self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device) + + self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + # if zero_init_output: + # nn.init.zeros_(self.to_out.weight) + + self.qk_norm = qk_norm + + + def forward( + self, + x, + context = None, + mask = None, + context_mask = None, + rotary_pos_emb = None, + causal = None + ): + h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None + + kv_input = context if has_context else x + + if hasattr(self, 'to_q'): + # Use separate linear projections for q and k/v + q = self.to_q(x) + q = rearrange(q, 'b n (h d) -> b h n d', h = h) + + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v)) + else: + # Use fused linear projection + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) + + # Normalize q and k for cosine sim attention + if self.qk_norm: + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + if rotary_pos_emb is not None and not has_context: + freqs, _ = rotary_pos_emb + + q_dtype = q.dtype + k_dtype = k.dtype + + q = q.to(torch.float32) + k = k.to(torch.float32) + freqs = freqs.to(torch.float32) + + q = apply_rotary_pos_emb(q, freqs) + k = apply_rotary_pos_emb(k, freqs) + + q = q.to(q_dtype) + k = k.to(k_dtype) + + input_mask = context_mask + + if input_mask is None and not has_context: + input_mask = mask + + # determine masking + masks = [] + + if input_mask is not None: + input_mask = rearrange(input_mask, 'b j -> b 1 1 j') + masks.append(~input_mask) + + # Other masks will be added here later + n = q.shape[-2] + + causal = self.causal if causal is None else causal + + if n == 1 and causal: + causal = False + + if h != kv_h: + # Repeat interleave kv_heads to match q_heads + heads_per_kv_head = h // kv_h + k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) + + out = optimized_attention(q, k, v, h, skip_reshape=True) + out = self.to_out(out) + + if mask is not None: + mask = rearrange(mask, 'b n -> b n 1') + out = out.masked_fill(~mask, 0.) + + return out + +class ConformerModule(nn.Module): + def __init__( + self, + dim, + norm_kwargs = {}, + ): + + super().__init__() + + self.dim = dim + + self.in_norm = LayerNorm(dim, **norm_kwargs) + self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False) + self.glu = GLU(dim, dim, nn.SiLU()) + self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False) + self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm + self.swish = nn.SiLU() + self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False) + + def forward(self, x): + x = self.in_norm(x) + x = rearrange(x, 'b n d -> b d n') + x = self.pointwise_conv(x) + x = rearrange(x, 'b d n -> b n d') + x = self.glu(x) + x = rearrange(x, 'b n d -> b d n') + x = self.depthwise_conv(x) + x = rearrange(x, 'b d n -> b n d') + x = self.mid_norm(x) + x = self.swish(x) + x = rearrange(x, 'b n d -> b d n') + x = self.pointwise_conv_2(x) + x = rearrange(x, 'b d n -> b n d') + + return x + +class TransformerBlock(nn.Module): + def __init__( + self, + dim, + dim_heads = 64, + cross_attend = False, + dim_context = None, + global_cond_dim = None, + causal = False, + zero_init_branch_outputs = True, + conformer = False, + layer_ix = -1, + remove_norms = False, + attn_kwargs = {}, + ff_kwargs = {}, + norm_kwargs = {}, + dtype=None, + device=None, + operations=None, + ): + + super().__init__() + self.dim = dim + self.dim_heads = dim_heads + self.cross_attend = cross_attend + self.dim_context = dim_context + self.causal = causal + + self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + + self.self_attn = Attention( + dim, + dim_heads = dim_heads, + causal = causal, + zero_init_output=zero_init_branch_outputs, + dtype=dtype, + device=device, + operations=operations, + **attn_kwargs + ) + + if cross_attend: + self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + self.cross_attn = Attention( + dim, + dim_heads = dim_heads, + dim_context=dim_context, + causal = causal, + zero_init_output=zero_init_branch_outputs, + dtype=dtype, + device=device, + operations=operations, + **attn_kwargs + ) + + self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() + self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs) + + self.layer_ix = layer_ix + + self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None + + self.global_cond_dim = global_cond_dim + + if global_cond_dim is not None: + self.to_scale_shift_gate = nn.Sequential( + nn.SiLU(), + nn.Linear(global_cond_dim, dim * 6, bias=False) + ) + + nn.init.zeros_(self.to_scale_shift_gate[1].weight) + #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias) + + def forward( + self, + x, + context = None, + global_cond=None, + mask = None, + context_mask = None, + rotary_pos_emb = None + ): + if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: + + scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1) + + # self-attention with adaLN + residual = x + x = self.pre_norm(x) + x = x * (1 + scale_self) + shift_self + x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) + x = x * torch.sigmoid(1 - gate_self) + x = x + residual + + if context is not None: + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + + if self.conformer is not None: + x = x + self.conformer(x) + + # feedforward with adaLN + residual = x + x = self.ff_norm(x) + x = x * (1 + scale_ff) + shift_ff + x = self.ff(x) + x = x * torch.sigmoid(1 - gate_ff) + x = x + residual + + else: + x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) + + if context is not None: + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + + if self.conformer is not None: + x = x + self.conformer(x) + + x = x + self.ff(self.ff_norm(x)) + + return x + +class ContinuousTransformer(nn.Module): + def __init__( + self, + dim, + depth, + *, + dim_in = None, + dim_out = None, + dim_heads = 64, + cross_attend=False, + cond_token_dim=None, + global_cond_dim=None, + causal=False, + rotary_pos_emb=True, + zero_init_branch_outputs=True, + conformer=False, + use_sinusoidal_emb=False, + use_abs_pos_emb=False, + abs_pos_emb_max_length=10000, + dtype=None, + device=None, + operations=None, + **kwargs + ): + + super().__init__() + + self.dim = dim + self.depth = depth + self.causal = causal + self.layers = nn.ModuleList([]) + + self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity() + self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity() + + if rotary_pos_emb: + self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype) + else: + self.rotary_pos_emb = None + + self.use_sinusoidal_emb = use_sinusoidal_emb + if use_sinusoidal_emb: + self.pos_emb = ScaledSinusoidalEmbedding(dim) + + self.use_abs_pos_emb = use_abs_pos_emb + if use_abs_pos_emb: + self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length) + + for i in range(depth): + self.layers.append( + TransformerBlock( + dim, + dim_heads = dim_heads, + cross_attend = cross_attend, + dim_context = cond_token_dim, + global_cond_dim = global_cond_dim, + causal = causal, + zero_init_branch_outputs = zero_init_branch_outputs, + conformer=conformer, + layer_ix=i, + dtype=dtype, + device=device, + operations=operations, + **kwargs + ) + ) + + def forward( + self, + x, + mask = None, + prepend_embeds = None, + prepend_mask = None, + global_cond = None, + return_info = False, + **kwargs + ): + patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {}) + batch, seq, device = *x.shape[:2], x.device + context = kwargs["context"] + + info = { + "hidden_states": [], + } + + x = self.project_in(x) + + if prepend_embeds is not None: + prepend_length, prepend_dim = prepend_embeds.shape[1:] + + assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension' + + x = torch.cat((prepend_embeds, x), dim = -2) + + if prepend_mask is not None or mask is not None: + mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool) + prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool) + + mask = torch.cat((prepend_mask, mask), dim = -1) + + # Attention layers + + if self.rotary_pos_emb is not None: + rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device) + else: + rotary_pos_emb = None + + if self.use_sinusoidal_emb or self.use_abs_pos_emb: + x = x + self.pos_emb(x) + + blocks_replace = patches_replace.get("dit", {}) + # Iterate over the transformer layers + for i, layer in enumerate(self.layers): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"]) + return out + + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap}) + x = out["img"] + else: + x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context) + # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) + + if return_info: + info["hidden_states"].append(x) + + x = self.project_out(x) + + if return_info: + return x, info + + return x + +class AudioDiffusionTransformer(nn.Module): + def __init__(self, + io_channels=64, + patch_size=1, + embed_dim=1536, + cond_token_dim=768, + project_cond_tokens=False, + global_cond_dim=1536, + project_global_cond=True, + input_concat_dim=0, + prepend_cond_dim=0, + depth=24, + num_heads=24, + transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer", + global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", + audio_model="", + dtype=None, + device=None, + operations=None, + **kwargs): + + super().__init__() + + self.dtype = dtype + self.cond_token_dim = cond_token_dim + + # Timestep embeddings + timestep_features_dim = 256 + + self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device) + + self.to_timestep_embed = nn.Sequential( + operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device), + ) + + if cond_token_dim > 0: + # Conditioning tokens + + cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim + self.to_cond_embed = nn.Sequential( + operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device) + ) + else: + cond_embed_dim = 0 + + if global_cond_dim > 0: + # Global conditioning + global_embed_dim = global_cond_dim if not project_global_cond else embed_dim + self.to_global_embed = nn.Sequential( + operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device) + ) + + if prepend_cond_dim > 0: + # Prepend conditioning + self.to_prepend_embed = nn.Sequential( + operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) + ) + + self.input_concat_dim = input_concat_dim + + dim_in = io_channels + self.input_concat_dim + + self.patch_size = patch_size + + # Transformer + + self.transformer_type = transformer_type + + self.global_cond_type = global_cond_type + + if self.transformer_type == "continuous_transformer": + + global_dim = None + + if self.global_cond_type == "adaLN": + # The global conditioning is projected to the embed_dim already at this point + global_dim = embed_dim + + self.transformer = ContinuousTransformer( + dim=embed_dim, + depth=depth, + dim_heads=embed_dim // num_heads, + dim_in=dim_in * patch_size, + dim_out=io_channels * patch_size, + cross_attend = cond_token_dim > 0, + cond_token_dim = cond_embed_dim, + global_cond_dim=global_dim, + dtype=dtype, + device=device, + operations=operations, + **kwargs + ) + else: + raise ValueError(f"Unknown transformer type: {self.transformer_type}") + + self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device) + self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device) + + def _forward( + self, + x, + t, + mask=None, + cross_attn_cond=None, + cross_attn_cond_mask=None, + input_concat_cond=None, + global_embed=None, + prepend_cond=None, + prepend_cond_mask=None, + return_info=False, + **kwargs): + + if cross_attn_cond is not None: + cross_attn_cond = self.to_cond_embed(cross_attn_cond) + + if global_embed is not None: + # Project the global conditioning to the embedding dimension + global_embed = self.to_global_embed(global_embed) + + prepend_inputs = None + prepend_mask = None + prepend_length = 0 + if prepend_cond is not None: + # Project the prepend conditioning to the embedding dimension + prepend_cond = self.to_prepend_embed(prepend_cond) + + prepend_inputs = prepend_cond + if prepend_cond_mask is not None: + prepend_mask = prepend_cond_mask + + if input_concat_cond is not None: + + # Interpolate input_concat_cond to the same length as x + if input_concat_cond.shape[2] != x.shape[2]: + input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') + + x = torch.cat([x, input_concat_cond], dim=1) + + # Get the batch of timestep embeddings + timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim) + + # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists + if global_embed is not None: + global_embed = global_embed + timestep_embed + else: + global_embed = timestep_embed + + # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer + if self.global_cond_type == "prepend": + if prepend_inputs is None: + # Prepend inputs are just the global embed, and the mask is all ones + prepend_inputs = global_embed.unsqueeze(1) + prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) + else: + # Prepend inputs are the prepend conditioning + the global embed + prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) + prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) + + prepend_length = prepend_inputs.shape[1] + + x = self.preprocess_conv(x) + x + + x = rearrange(x, "b c t -> b t c") + + extra_args = {} + + if self.global_cond_type == "adaLN": + extra_args["global_cond"] = global_embed + + if self.patch_size > 1: + x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) + + if self.transformer_type == "x-transformers": + output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) + elif self.transformer_type == "continuous_transformer": + output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) + + if return_info: + output, info = output + elif self.transformer_type == "mm_transformer": + output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) + + output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] + + if self.patch_size > 1: + output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) + + output = self.postprocess_conv(output) + output + + if return_info: + return output, info + + return output + + def forward( + self, + x, + timestep, + context=None, + context_mask=None, + input_concat_cond=None, + global_embed=None, + negative_global_embed=None, + prepend_cond=None, + prepend_cond_mask=None, + mask=None, + return_info=False, + control=None, + **kwargs): + return self._forward( + x, + timestep, + cross_attn_cond=context, + cross_attn_cond_mask=context_mask, + input_concat_cond=input_concat_cond, + global_embed=global_embed, + prepend_cond=prepend_cond, + prepend_cond_mask=prepend_cond_mask, + mask=mask, + return_info=return_info, + **kwargs + ) diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py new file mode 100644 index 0000000000000000000000000000000000000000..20edb365aaafe7f344a2616a53470c837d0a8a6e --- /dev/null +++ b/comfy/ldm/audio/embedders.py @@ -0,0 +1,108 @@ +# code adapted from: https://github.com/Stability-AI/stable-audio-tools + +import torch +import torch.nn as nn +from torch import Tensor +from typing import List, Union +from einops import rearrange +import math +import comfy.ops + +class LearnedPositionalEmbedding(nn.Module): + """Used for continuous time""" + + def __init__(self, dim: int): + super().__init__() + assert (dim % 2) == 0 + half_dim = dim // 2 + self.weights = nn.Parameter(torch.empty(half_dim)) + + def forward(self, x: Tensor) -> Tensor: + x = rearrange(x, "b -> b 1") + freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi + fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) + fouriered = torch.cat((x, fouriered), dim=-1) + return fouriered + +def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: + return nn.Sequential( + LearnedPositionalEmbedding(dim), + comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features), + ) + + +class NumberEmbedder(nn.Module): + def __init__( + self, + features: int, + dim: int = 256, + ): + super().__init__() + self.features = features + self.embedding = TimePositionalEmbedding(dim=dim, out_features=features) + + def forward(self, x: Union[List[float], Tensor]) -> Tensor: + if not torch.is_tensor(x): + device = next(self.embedding.parameters()).device + x = torch.tensor(x, device=device) + assert isinstance(x, Tensor) + shape = x.shape + x = rearrange(x, "... -> (...)") + embedding = self.embedding(x) + x = embedding.view(*shape, self.features) + return x # type: ignore + + +class Conditioner(nn.Module): + def __init__( + self, + dim: int, + output_dim: int, + project_out: bool = False + ): + + super().__init__() + + self.dim = dim + self.output_dim = output_dim + self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() + + def forward(self, x): + raise NotImplementedError() + +class NumberConditioner(Conditioner): + ''' + Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings + ''' + def __init__(self, + output_dim: int, + min_val: float=0, + max_val: float=1 + ): + super().__init__(output_dim, output_dim) + + self.min_val = min_val + self.max_val = max_val + + self.embedder = NumberEmbedder(features=output_dim) + + def forward(self, floats, device=None): + # Cast the inputs to floats + floats = [float(x) for x in floats] + + if device is None: + device = next(self.embedder.parameters()).device + + floats = torch.tensor(floats).to(device) + + floats = floats.clamp(self.min_val, self.max_val) + + normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) + + # Cast floats to same type as embedder + embedder_dtype = next(self.embedder.parameters()).dtype + normalized_floats = normalized_floats.to(embedder_dtype) + + float_embeds = self.embedder(normalized_floats).unsqueeze(1) + + return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)] diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py new file mode 100644 index 0000000000000000000000000000000000000000..1258ae11fd064a2ab2fb944bb897fa120be2c9ca --- /dev/null +++ b/comfy/ldm/aura/mmdit.py @@ -0,0 +1,498 @@ +#AuraFlow MMDiT +#Originally written by the AuraFlow Authors + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from comfy.ldm.modules.attention import optimized_attention +import comfy.ops +import comfy.ldm.common_dit + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def find_multiple(n: int, k: int) -> int: + if n % k == 0: + return n + return n + k - (n % k) + + +class MLP(nn.Module): + def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None: + super().__init__() + if hidden_dim is None: + hidden_dim = 4 * dim + + n_hidden = int(2 * hidden_dim / 3) + n_hidden = find_multiple(n_hidden, 256) + + self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) + self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) + self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.silu(self.c_fc1(x)) * self.c_fc2(x) + x = self.c_proj(x) + return x + + +class MultiHeadLayerNorm(nn.Module): + def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None): + # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78 + + super().__init__() + self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + mean = hidden_states.mean(-1, keepdim=True) + variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) + hidden_states = (hidden_states - mean) * torch.rsqrt( + variance + self.variance_epsilon + ) + hidden_states = self.weight.to(torch.float32) * hidden_states + return hidden_states.to(input_dtype) + +class SingleAttention(nn.Module): + def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): + super().__init__() + + self.n_heads = n_heads + self.head_dim = dim // n_heads + + # this is for cond + self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + self.q_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + self.k_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + + #@torch.compile() + def forward(self, c): + + bsz, seqlen1, _ = c.shape + + q, k, v = self.w1q(c), self.w1k(c), self.w1v(c) + q = q.view(bsz, seqlen1, self.n_heads, self.head_dim) + k = k.view(bsz, seqlen1, self.n_heads, self.head_dim) + v = v.view(bsz, seqlen1, self.n_heads, self.head_dim) + q, k = self.q_norm1(q), self.k_norm1(k) + + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + c = self.w1o(output) + return c + + + +class DoubleAttention(nn.Module): + def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): + super().__init__() + + self.n_heads = n_heads + self.head_dim = dim // n_heads + + # this is for cond + self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + # this is for x + self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) + + self.q_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + self.k_norm1 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + + self.q_norm2 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + self.k_norm2 = ( + MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) + if mh_qknorm + else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) + ) + + + #@torch.compile() + def forward(self, c, x): + + bsz, seqlen1, _ = c.shape + bsz, seqlen2, _ = x.shape + + cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c) + cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim) + ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim) + cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim) + cq, ck = self.q_norm1(cq), self.k_norm1(ck) + + xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x) + xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim) + xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim) + xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim) + xq, xk = self.q_norm2(xq), self.k_norm2(xk) + + # concat all + q, k, v = ( + torch.cat([cq, xq], dim=1), + torch.cat([ck, xk], dim=1), + torch.cat([cv, xv], dim=1), + ) + + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + + c, x = output.split([seqlen1, seqlen2], dim=1) + c = self.w1o(c) + x = self.w2o(x) + + return c, x + + +class MMDiTBlock(nn.Module): + def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None): + super().__init__() + + self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + if not is_last: + self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) + self.modC = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), + ) + else: + self.modC = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), + ) + + self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) + self.modX = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), + ) + + self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations) + self.is_last = is_last + + #@torch.compile() + def forward(self, c, x, global_cond, **kwargs): + + cres, xres = c, x + + cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = ( + self.modC(global_cond).chunk(6, dim=1) + ) + + c = modulate(self.normC1(c), cshift_msa, cscale_msa) + + # xpath + xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = ( + self.modX(global_cond).chunk(6, dim=1) + ) + + x = modulate(self.normX1(x), xshift_msa, xscale_msa) + + # attention + c, x = self.attn(c, x) + + + c = self.normC2(cres + cgate_msa.unsqueeze(1) * c) + c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp)) + c = cres + c + + x = self.normX2(xres + xgate_msa.unsqueeze(1) * x) + x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp)) + x = xres + x + + return c, x + +class DiTBlock(nn.Module): + # like MMDiTBlock, but it only has X + def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None): + super().__init__() + + self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) + + self.modCX = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), + ) + + self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations) + self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) + + #@torch.compile() + def forward(self, cx, global_cond, **kwargs): + cxres = cx + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX( + global_cond + ).chunk(6, dim=1) + cx = modulate(self.norm1(cx), shift_msa, scale_msa) + cx = self.attn(cx) + cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx) + mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp)) + cx = gate_mlp.unsqueeze(1) * mlpout + + cx = cxres + cx + + return cx + + + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + half = dim // 2 + freqs = 1000 * torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half) / half + ).to(t.device) + args = t[:, None] * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + #@torch.compile() + def forward(self, t, dtype): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class MMDiT(nn.Module): + def __init__( + self, + in_channels=4, + out_channels=4, + patch_size=2, + dim=3072, + n_layers=36, + n_double_layers=4, + n_heads=12, + global_conddim=3072, + cond_seq_dim=2048, + max_seq=32 * 32, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + self.dtype = dtype + + self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations) + + self.cond_seq_linear = operations.Linear( + cond_seq_dim, dim, bias=False, dtype=dtype, device=device + ) # linear for something like text sequence. + self.init_x_linear = operations.Linear( + patch_size * patch_size * in_channels, dim, dtype=dtype, device=device + ) # init linear for patchified image. + + self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device)) + self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device)) + + self.double_layers = nn.ModuleList([]) + self.single_layers = nn.ModuleList([]) + + + for idx in range(n_double_layers): + self.double_layers.append( + MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations) + ) + + for idx in range(n_double_layers, n_layers): + self.single_layers.append( + DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations) + ) + + + self.final_linear = operations.Linear( + dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device + ) + + self.modF = nn.Sequential( + nn.SiLU(), + operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), + ) + + self.out_channels = out_channels + self.patch_size = patch_size + self.n_double_layers = n_double_layers + self.n_layers = n_layers + + self.h_max = round(max_seq**0.5) + self.w_max = round(max_seq**0.5) + + @torch.no_grad() + def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)): + # extend pe + pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]] + + pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1) + + # now we need to extend this to target_dim. for this we will use interpolation. + # we will use torch.nn.functional.interpolate + pe_as_2d = F.interpolate( + pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear" + ) + pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1) + self.positional_encoding.data = pe_new.unsqueeze(0).contiguous() + self.h_max, self.w_max = target_dim + + def pe_selection_index_based_on_dim(self, h, w): + h_p, w_p = h // self.patch_size, w // self.patch_size + original_pe_indexes = torch.arange(self.positional_encoding.shape[1]) + original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max) + starth = self.h_max // 2 - h_p // 2 + endh =starth + h_p + startw = self.w_max // 2 - w_p // 2 + endw = startw + w_p + original_pe_indexes = original_pe_indexes[ + starth:endh, startw:endw + ] + return original_pe_indexes.flatten() + + def unpatchify(self, x, h, w): + c = self.out_channels + p = self.patch_size + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs + + def patchify(self, x): + B, C, H, W = x.size() + x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + x = x.view( + B, + C, + (H + 1) // self.patch_size, + self.patch_size, + (W + 1) // self.patch_size, + self.patch_size, + ) + x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2) + return x + + def apply_pos_embeds(self, x, h, w): + h = (h + 1) // self.patch_size + w = (w + 1) // self.patch_size + max_dim = max(h, w) + + cur_dim = self.h_max + pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x) + + if max_dim > cur_dim: + pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1) + cur_dim = max_dim + + from_h = (cur_dim - h) // 2 + from_w = (cur_dim - w) // 2 + pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w] + return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1]) + + def forward(self, x, timestep, context, transformer_options={}, **kwargs): + patches_replace = transformer_options.get("patches_replace", {}) + # patchify x, add PE + b, c, h, w = x.shape + + # pe_indexes = self.pe_selection_index_based_on_dim(h, w) + # print(pe_indexes, pe_indexes.shape) + + x = self.init_x_linear(self.patchify(x)) # B, T_x, D + x = self.apply_pos_embeds(x, h, w) + # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype) + # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype) + + # process conditions for MMDiT Blocks + c_seq = context # B, T_c, D_c + t = timestep + + c = self.cond_seq_linear(c_seq) # B, T_c, D + c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1) + + global_cond = self.t_embedder(t, x.dtype) # B, D + + blocks_replace = patches_replace.get("dit", {}) + if len(self.double_layers) > 0: + for i, layer in enumerate(self.double_layers): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["txt"], out["img"] = layer(args["txt"], + args["img"], + args["vec"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap}) + c = out["txt"] + x = out["img"] + else: + c, x = layer(c, x, global_cond, **kwargs) + + if len(self.single_layers) > 0: + c_len = c.size(1) + cx = torch.cat([c, x], dim=1) + for i, layer in enumerate(self.single_layers): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = layer(args["img"], args["vec"]) + return out + + out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap}) + cx = out["img"] + else: + cx = layer(cx, global_cond, **kwargs) + + x = cx[:, c_len:] + + fshift, fscale = self.modF(global_cond).chunk(2, dim=1) + + x = modulate(x, fshift, fscale) + x = self.final_linear(x) + x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w] + return x diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py new file mode 100644 index 0000000000000000000000000000000000000000..3eaa0c821cccddbe891ac8a705d702c509c85582 --- /dev/null +++ b/comfy/ldm/cascade/common.py @@ -0,0 +1,154 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import torch.nn as nn +from comfy.ldm.modules.attention import optimized_attention +import comfy.ops + +class OptimizedAttention(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.heads = nhead + + self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) + + def forward(self, q, k, v): + q = self.to_q(q) + k = self.to_k(k) + v = self.to_v(v) + + out = optimized_attention(q, k, v, self.heads) + + return self.out_proj(out) + +class Attention2D(nn.Module): + def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) + # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) + + def forward(self, x, kv, self_attn=False): + orig_shape = x.shape + x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 + if self_attn: + kv = torch.cat([x, kv], dim=1) + # x = self.attn(x, kv, kv, need_weights=False)[0] + x = self.attn(x, kv, kv) + x = x.permute(0, 2, 1).view(*orig_shape) + return x + + +def LayerNorm2d_op(operations): + class LayerNorm2d(operations.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x): + return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return LayerNorm2d + +class GlobalResponseNorm(nn.Module): + "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105" + def __init__(self, dim, dtype=None, device=None): + super().__init__() + self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) + self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) + Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) + return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x + + +class ResBlock(nn.Module): + def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2): + super().__init__() + self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device) + # self.depthwise = SAMBlock(c, num_heads, expansion) + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.channelwise = nn.Sequential( + operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device), + nn.GELU(), + GlobalResponseNorm(c * 4, dtype=dtype, device=device), + nn.Dropout(dropout), + operations.Linear(c * 4, c, dtype=dtype, device=device) + ) + + def forward(self, x, x_skip=None): + x_res = x + x = self.norm(self.depthwise(x)) + if x_skip is not None: + x = torch.cat([x, x_skip], dim=1) + x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + x_res + + +class AttnBlock(nn.Module): + def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.self_attn = self_attn + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations) + self.kv_mapper = nn.Sequential( + nn.SiLU(), + operations.Linear(c_cond, c, dtype=dtype, device=device) + ) + + def forward(self, x, kv): + kv = self.kv_mapper(kv) + x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) + return x + + +class FeedForwardBlock(nn.Module): + def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None): + super().__init__() + self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.channelwise = nn.Sequential( + operations.Linear(c, c * 4, dtype=dtype, device=device), + nn.GELU(), + GlobalResponseNorm(c * 4, dtype=dtype, device=device), + nn.Dropout(dropout), + operations.Linear(c * 4, c, dtype=dtype, device=device) + ) + + def forward(self, x): + x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + return x + + +class TimestepBlock(nn.Module): + def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None): + super().__init__() + self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device) + self.conds = conds + for cname in conds: + setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)) + + def forward(self, x, t): + t = t.chunk(len(self.conds) + 1, dim=1) + a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1) + for i, c in enumerate(self.conds): + ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1) + a, b = a + ac, b + bc + return x * (1 + a) + b diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..90473481a078323be11e2c5530de378b1ac6425c --- /dev/null +++ b/comfy/ldm/cascade/controlnet.py @@ -0,0 +1,92 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torchvision +from torch import nn +from .common import LayerNorm2d_op + + +class CNetResBlock(nn.Module): + def __init__(self, c, dtype=None, device=None, operations=None): + super().__init__() + self.blocks = nn.Sequential( + LayerNorm2d_op(operations)(c, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c, c, kernel_size=3, padding=1), + LayerNorm2d_op(operations)(c, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c, c, kernel_size=3, padding=1), + ) + + def forward(self, x): + return x + self.blocks(x) + + +class ControlNet(nn.Module): + def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): + super().__init__() + if bottleneck_mode is None: + bottleneck_mode = 'effnet' + self.proj_blocks = proj_blocks + if bottleneck_mode == 'effnet': + embd_channels = 1280 + self.backbone = torchvision.models.efficientnet_v2_s().features.eval() + if c_in != 3: + in_weights = self.backbone[0][0].weight.data + self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) + if c_in > 3: + # nn.init.constant_(self.backbone[0][0].weight, 0) + self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() + else: + self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() + elif bottleneck_mode == 'simple': + embd_channels = c_in + self.backbone = nn.Sequential( + operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), + ) + elif bottleneck_mode == 'large': + self.backbone = nn.Sequential( + operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), + *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], + operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), + ) + embd_channels = 1280 + else: + raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') + self.projections = nn.ModuleList() + for _ in range(len(proj_blocks)): + self.projections.append(nn.Sequential( + operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), + nn.LeakyReLU(0.2, inplace=True), + operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), + )) + # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection + self.xl = False + self.input_channels = c_in + self.unshuffle_amount = 8 + + def forward(self, x): + x = self.backbone(x) + proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] + for i, idx in enumerate(self.proj_blocks): + proj_outputs[idx] = self.projections[i](x) + return {"input": proj_outputs[::-1]} diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py new file mode 100644 index 0000000000000000000000000000000000000000..145e6e69a7c56c0b983cd79d43f1c6d7d26fd349 --- /dev/null +++ b/comfy/ldm/cascade/stage_a.py @@ -0,0 +1,259 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from torch import nn +from torch.autograd import Function +import comfy.ops + +ops = comfy.ops.disable_weight_init + + +class vector_quantize(Function): + @staticmethod + def forward(ctx, x, codebook): + with torch.no_grad(): + codebook_sqr = torch.sum(codebook ** 2, dim=1) + x_sqr = torch.sum(x ** 2, dim=1, keepdim=True) + + dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0) + _, indices = dist.min(dim=1) + + ctx.save_for_backward(indices, codebook) + ctx.mark_non_differentiable(indices) + + nn = torch.index_select(codebook, 0, indices) + return nn, indices + + @staticmethod + def backward(ctx, grad_output, grad_indices): + grad_inputs, grad_codebook = None, None + + if ctx.needs_input_grad[0]: + grad_inputs = grad_output.clone() + if ctx.needs_input_grad[1]: + # Gradient wrt. the codebook + indices, codebook = ctx.saved_tensors + + grad_codebook = torch.zeros_like(codebook) + grad_codebook.index_add_(0, indices, grad_output) + + return (grad_inputs, grad_codebook) + + +class VectorQuantize(nn.Module): + def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False): + """ + Takes an input of variable size (as long as the last dimension matches the embedding size). + Returns one tensor containing the nearest neigbour embeddings to each of the inputs, + with the same size as the input, vq and commitment components for the loss as a touple + in the second output and the indices of the quantized vectors in the third: + quantized, (vq_loss, commit_loss), indices + """ + super(VectorQuantize, self).__init__() + + self.codebook = nn.Embedding(k, embedding_size) + self.codebook.weight.data.uniform_(-1./k, 1./k) + self.vq = vector_quantize.apply + + self.ema_decay = ema_decay + self.ema_loss = ema_loss + if ema_loss: + self.register_buffer('ema_element_count', torch.ones(k)) + self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight)) + + def _laplace_smoothing(self, x, epsilon): + n = torch.sum(x) + return ((x + epsilon) / (n + x.size(0) * epsilon) * n) + + def _updateEMA(self, z_e_x, indices): + mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float() + elem_count = mask.sum(dim=0) + weight_sum = torch.mm(mask.t(), z_e_x) + + self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count) + self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5) + self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum) + + self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1) + + def idx2vq(self, idx, dim=-1): + q_idx = self.codebook(idx) + if dim != -1: + q_idx = q_idx.movedim(-1, dim) + return q_idx + + def forward(self, x, get_losses=True, dim=-1): + if dim != -1: + x = x.movedim(dim, -1) + z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x + z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach()) + vq_loss, commit_loss = None, None + if self.ema_loss and self.training: + self._updateEMA(z_e_x.detach(), indices.detach()) + # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss + z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices) + if get_losses: + vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean() + commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean() + + z_q_x = z_q_x.view(x.shape) + if dim != -1: + z_q_x = z_q_x.movedim(-1, dim) + return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1]) + + +class ResBlock(nn.Module): + def __init__(self, c, c_hidden): + super().__init__() + # depthwise/attention + self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) + self.depthwise = nn.Sequential( + nn.ReplicationPad2d(1), + ops.Conv2d(c, c, kernel_size=3, groups=c) + ) + + # channelwise + self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) + self.channelwise = nn.Sequential( + ops.Linear(c, c_hidden), + nn.GELU(), + ops.Linear(c_hidden, c), + ) + + self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) + + # Init weights + def _basic_init(module): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + def _norm(self, x, norm): + return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + + def forward(self, x): + mods = self.gammas + + x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] + try: + x = x + self.depthwise(x_temp) * mods[2] + except: #operation not implemented for bf16 + x_temp = self.depthwise[0](x_temp.float()).to(x.dtype) + x = x + self.depthwise[1](x_temp) * mods[2] + + x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] + x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] + + return x + + +class StageA(nn.Module): + def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192): + super().__init__() + self.c_latent = c_latent + c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))] + + # Encoder blocks + self.in_block = nn.Sequential( + nn.PixelUnshuffle(2), + ops.Conv2d(3 * 4, c_levels[0], kernel_size=1) + ) + down_blocks = [] + for i in range(levels): + if i > 0: + down_blocks.append(ops.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) + block = ResBlock(c_levels[i], c_levels[i] * 4) + down_blocks.append(block) + down_blocks.append(nn.Sequential( + ops.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), + nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 + )) + self.down_blocks = nn.Sequential(*down_blocks) + self.down_blocks[0] + + self.codebook_size = codebook_size + self.vquantizer = VectorQuantize(c_latent, k=codebook_size) + + # Decoder blocks + up_blocks = [nn.Sequential( + ops.Conv2d(c_latent, c_levels[-1], kernel_size=1) + )] + for i in range(levels): + for j in range(bottleneck_blocks if i == 0 else 1): + block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4) + up_blocks.append(block) + if i < levels - 1: + up_blocks.append( + ops.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, + padding=1)) + self.up_blocks = nn.Sequential(*up_blocks) + self.out_block = nn.Sequential( + ops.Conv2d(c_levels[0], 3 * 4, kernel_size=1), + nn.PixelShuffle(2), + ) + + def encode(self, x, quantize=False): + x = self.in_block(x) + x = self.down_blocks(x) + if quantize: + qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) + return qe, x, indices, vq_loss + commit_loss * 0.25 + else: + return x + + def decode(self, x): + x = self.up_blocks(x) + x = self.out_block(x) + return x + + def forward(self, x, quantize=False): + qe, x, _, vq_loss = self.encode(x, quantize) + x = self.decode(qe) + return x, vq_loss + + +class Discriminator(nn.Module): + def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6): + super().__init__() + d = max(depth - 3, 3) + layers = [ + nn.utils.spectral_norm(ops.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), + nn.LeakyReLU(0.2), + ] + for i in range(depth - 1): + c_in = c_hidden // (2 ** max((d - i), 0)) + c_out = c_hidden // (2 ** max((d - 1 - i), 0)) + layers.append(nn.utils.spectral_norm(ops.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) + layers.append(nn.InstanceNorm2d(c_out)) + layers.append(nn.LeakyReLU(0.2)) + self.encoder = nn.Sequential(*layers) + self.shuffle = ops.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) + self.logits = nn.Sigmoid() + + def forward(self, x, cond=None): + x = self.encoder(x) + if cond is not None: + cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1)) + x = torch.cat([x, cond], dim=1) + x = self.shuffle(x) + x = self.logits(x) + return x diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py new file mode 100644 index 0000000000000000000000000000000000000000..77383095681d994dcf94e52a106cfe93dcbb6a50 --- /dev/null +++ b/comfy/ldm/cascade/stage_b.py @@ -0,0 +1,256 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import math +import torch +from torch import nn +from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock + +class StageB(nn.Module): + def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280], + nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]], + block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280, + c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True, + t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None): + super().__init__() + self.dtype = dtype + self.c_r = c_r + self.t_conds = t_conds + self.c_clip_seq = c_clip_seq + if not isinstance(dropout, list): + dropout = [dropout] * len(c_hidden) + if not isinstance(self_attn, list): + self_attn = [self_attn] * len(c_hidden) + + # CONDITIONING + self.effnet_mapper = nn.Sequential( + operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + self.pixels_mapper = nn.Sequential( + operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), + nn.GELU(), + operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.embedding = nn.Sequential( + nn.PixelUnshuffle(patch_size), + operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + ) + + def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): + if block_type == 'C': + return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'A': + return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'F': + return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'T': + return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) + else: + raise Exception(f'Block type {block_type} not supported') + + # BLOCKS + # -- down blocks + self.down_blocks = nn.ModuleList() + self.down_downscalers = nn.ModuleList() + self.down_repeat_mappers = nn.ModuleList() + for i in range(len(c_hidden)): + if i > 0: + self.down_downscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device), + )) + else: + self.down_downscalers.append(nn.Identity()) + down_block = nn.ModuleList() + for _ in range(blocks[0][i]): + for block_type in level_config[i]: + block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) + down_block.append(block) + self.down_blocks.append(down_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[0][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.down_repeat_mappers.append(block_repeat_mappers) + + # -- up blocks + self.up_blocks = nn.ModuleList() + self.up_upscalers = nn.ModuleList() + self.up_repeat_mappers = nn.ModuleList() + for i in reversed(range(len(c_hidden))): + if i > 0: + self.up_upscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device), + )) + else: + self.up_upscalers.append(nn.Identity()) + up_block = nn.ModuleList() + for j in range(blocks[1][::-1][i]): + for k, block_type in enumerate(level_config[i]): + c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 + block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], + self_attn=self_attn[i]) + up_block.append(block) + self.up_blocks.append(up_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[1][::-1][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.up_repeat_mappers.append(block_repeat_mappers) + + # OUTPUT + self.clf = nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), + nn.PixelShuffle(patch_size), + ) + + # --- WEIGHT INIT --- + # self.apply(self._init_weights) # General init + # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings + # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings + # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings + # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings + # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs + # nn.init.constant_(self.clf[1].weight, 0) # outputs + # + # # blocks + # for level_block in self.down_blocks + self.up_blocks: + # for block in level_block: + # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): + # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) + # elif isinstance(block, TimestepBlock): + # for layer in block.modules(): + # if isinstance(layer, nn.Linear): + # nn.init.constant_(layer.weight, 0) + # + # def _init_weights(self, m): + # if isinstance(m, (nn.Conv2d, nn.Linear)): + # torch.nn.init.xavier_uniform_(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + + def gen_r_embedding(self, r, max_positions=10000): + r = r * max_positions + half_dim = self.c_r // 2 + emb = math.log(max_positions) / (half_dim - 1) + emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() + emb = r[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=1) + if self.c_r % 2 == 1: # zero pad + emb = nn.functional.pad(emb, (0, 1), mode='constant') + return emb + + def gen_c_embeddings(self, clip): + if len(clip.shape) == 2: + clip = clip.unsqueeze(1) + clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1) + clip = self.clip_norm(clip) + return clip + + def _down_encode(self, x, r_embed, clip): + level_outputs = [] + block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) + for down_block, downscaler, repmap in block_group: + x = downscaler(x) + for i in range(len(repmap) + 1): + for block in down_block: + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + x = block(x) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if i < len(repmap): + x = repmap[i](x) + level_outputs.insert(0, x) + return level_outputs + + def _up_decode(self, level_outputs, r_embed, clip): + x = level_outputs[0] + block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) + for i, (up_block, upscaler, repmap) in enumerate(block_group): + for j in range(len(repmap) + 1): + for k, block in enumerate(up_block): + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + skip = level_outputs[i] if k == 0 and i > 0 else None + if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): + x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', + align_corners=True) + x = block(x, skip) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if j < len(repmap): + x = repmap[j](x) + x = upscaler(x) + return x + + def forward(self, x, r, effnet, clip, pixels=None, **kwargs): + if pixels is None: + pixels = x.new_zeros(x.size(0), 3, 8, 8) + + # Process the conditioning embeddings + r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) + for c in self.t_conds: + t_cond = kwargs.get(c, torch.zeros_like(r)) + r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) + clip = self.gen_c_embeddings(clip) + + # Model Blocks + x = self.embedding(x) + x = x + self.effnet_mapper( + nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) + x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', + align_corners=True) + level_outputs = self._down_encode(x, r_embed, clip) + x = self._up_decode(level_outputs, r_embed, clip) + return self.clf(x) + + def update_weights_ema(self, src_model, beta=0.999): + for self_params, src_params in zip(self.parameters(), src_model.parameters()): + self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) + for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): + self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py new file mode 100644 index 0000000000000000000000000000000000000000..b952d03490578840deadfb4623225819903bf09d --- /dev/null +++ b/comfy/ldm/cascade/stage_c.py @@ -0,0 +1,273 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from torch import nn +import math +from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock +# from .controlnet import ControlNetDeliverer + +class UpDownBlock2d(nn.Module): + def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None): + super().__init__() + assert mode in ['up', 'down'] + interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear', + align_corners=True) if enabled else nn.Identity() + mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device) + self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation]) + + def forward(self, x): + for block in self.blocks: + x = block(x) + return x + + +class StageC(nn.Module): + def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], + blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], + c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, + dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, + dtype=None, device=None, operations=None): + super().__init__() + self.dtype = dtype + self.c_r = c_r + self.t_conds = t_conds + self.c_clip_seq = c_clip_seq + if not isinstance(dropout, list): + dropout = [dropout] * len(c_hidden) + if not isinstance(self_attn, list): + self_attn = [self_attn] * len(c_hidden) + + # CONDITIONING + self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device) + self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device) + self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.embedding = nn.Sequential( + nn.PixelUnshuffle(patch_size), + operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6) + ) + + def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): + if block_type == 'C': + return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'A': + return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'F': + return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) + elif block_type == 'T': + return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) + else: + raise Exception(f'Block type {block_type} not supported') + + # BLOCKS + # -- down blocks + self.down_blocks = nn.ModuleList() + self.down_downscalers = nn.ModuleList() + self.down_repeat_mappers = nn.ModuleList() + for i in range(len(c_hidden)): + if i > 0: + self.down_downscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), + UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) + )) + else: + self.down_downscalers.append(nn.Identity()) + down_block = nn.ModuleList() + for _ in range(blocks[0][i]): + for block_type in level_config[i]: + block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) + down_block.append(block) + self.down_blocks.append(down_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[0][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.down_repeat_mappers.append(block_repeat_mappers) + + # -- up blocks + self.up_blocks = nn.ModuleList() + self.up_upscalers = nn.ModuleList() + self.up_repeat_mappers = nn.ModuleList() + for i in reversed(range(len(c_hidden))): + if i > 0: + self.up_upscalers.append(nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6), + UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) + )) + else: + self.up_upscalers.append(nn.Identity()) + up_block = nn.ModuleList() + for j in range(blocks[1][::-1][i]): + for k, block_type in enumerate(level_config[i]): + c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 + block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], + self_attn=self_attn[i]) + up_block.append(block) + self.up_blocks.append(up_block) + if block_repeat is not None: + block_repeat_mappers = nn.ModuleList() + for _ in range(block_repeat[1][::-1][i] - 1): + block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) + self.up_repeat_mappers.append(block_repeat_mappers) + + # OUTPUT + self.clf = nn.Sequential( + LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), + operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), + nn.PixelShuffle(patch_size), + ) + + # --- WEIGHT INIT --- + # self.apply(self._init_weights) # General init + # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings + # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings + # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs + # nn.init.constant_(self.clf[1].weight, 0) # outputs + # + # # blocks + # for level_block in self.down_blocks + self.up_blocks: + # for block in level_block: + # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): + # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) + # elif isinstance(block, TimestepBlock): + # for layer in block.modules(): + # if isinstance(layer, nn.Linear): + # nn.init.constant_(layer.weight, 0) + # + # def _init_weights(self, m): + # if isinstance(m, (nn.Conv2d, nn.Linear)): + # torch.nn.init.xavier_uniform_(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + + def gen_r_embedding(self, r, max_positions=10000): + r = r * max_positions + half_dim = self.c_r // 2 + emb = math.log(max_positions) / (half_dim - 1) + emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() + emb = r[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=1) + if self.c_r % 2 == 1: # zero pad + emb = nn.functional.pad(emb, (0, 1), mode='constant') + return emb + + def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img): + clip_txt = self.clip_txt_mapper(clip_txt) + if len(clip_txt_pooled.shape) == 2: + clip_txt_pooled = clip_txt_pooled.unsqueeze(1) + if len(clip_img.shape) == 2: + clip_img = clip_img.unsqueeze(1) + clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1) + clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1) + clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1) + clip = self.clip_norm(clip) + return clip + + def _down_encode(self, x, r_embed, clip, cnet=None): + level_outputs = [] + block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) + for down_block, downscaler, repmap in block_group: + x = downscaler(x) + for i in range(len(repmap) + 1): + for block in down_block: + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + if cnet is not None: + next_cnet = cnet.pop() + if next_cnet is not None: + x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', + align_corners=True).to(x.dtype) + x = block(x) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if i < len(repmap): + x = repmap[i](x) + level_outputs.insert(0, x) + return level_outputs + + def _up_decode(self, level_outputs, r_embed, clip, cnet=None): + x = level_outputs[0] + block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) + for i, (up_block, upscaler, repmap) in enumerate(block_group): + for j in range(len(repmap) + 1): + for k, block in enumerate(up_block): + if isinstance(block, ResBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + ResBlock)): + skip = level_outputs[i] if k == 0 and i > 0 else None + if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): + x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', + align_corners=True) + if cnet is not None: + next_cnet = cnet.pop() + if next_cnet is not None: + x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', + align_corners=True).to(x.dtype) + x = block(x, skip) + elif isinstance(block, AttnBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + AttnBlock)): + x = block(x, clip) + elif isinstance(block, TimestepBlock) or ( + hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, + TimestepBlock)): + x = block(x, r_embed) + else: + x = block(x) + if j < len(repmap): + x = repmap[j](x) + x = upscaler(x) + return x + + def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): + # Process the conditioning embeddings + r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) + for c in self.t_conds: + t_cond = kwargs.get(c, torch.zeros_like(r)) + r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) + clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) + + if control is not None: + cnet = control.get("input") + else: + cnet = None + + # Model Blocks + x = self.embedding(x) + level_outputs = self._down_encode(x, r_embed, clip, cnet) + x = self._up_decode(level_outputs, r_embed, clip, cnet) + return self.clf(x) + + def update_weights_ema(self, src_model, beta=0.999): + for self_params, src_params in zip(self.parameters(), src_model.parameters()): + self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) + for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): + self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py new file mode 100644 index 0000000000000000000000000000000000000000..b467a70a848edeef57d7911ac844868b251f8e45 --- /dev/null +++ b/comfy/ldm/cascade/stage_c_coder.py @@ -0,0 +1,98 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" +import torch +import torchvision +from torch import nn + +import comfy.ops + +ops = comfy.ops.disable_weight_init + +# EfficientNet +class EfficientNetEncoder(nn.Module): + def __init__(self, c_latent=16): + super().__init__() + self.backbone = torchvision.models.efficientnet_v2_s().features.eval() + self.mapper = nn.Sequential( + ops.Conv2d(1280, c_latent, kernel_size=1, bias=False), + nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1 + ) + self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) + self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225])) + + def forward(self, x): + x = x * 0.5 + 0.5 + x = (x - self.mean.view([3,1,1]).to(device=x.device, dtype=x.dtype)) / self.std.view([3,1,1]).to(device=x.device, dtype=x.dtype) + o = self.mapper(self.backbone(x)) + return o + + +# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192 +class Previewer(nn.Module): + def __init__(self, c_in=16, c_hidden=512, c_out=3): + super().__init__() + self.blocks = nn.Sequential( + ops.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels + nn.GELU(), + nn.BatchNorm2d(c_hidden), + + ops.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden), + + ops.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 2), + + ops.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 2), + + ops.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + ops.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + nn.GELU(), + nn.BatchNorm2d(c_hidden // 4), + + ops.Conv2d(c_hidden // 4, c_out, kernel_size=1), + ) + + def forward(self, x): + return (self.blocks(x) - 0.5) * 2.0 + +class StageC_coder(nn.Module): + def __init__(self): + super().__init__() + self.previewer = Previewer() + self.encoder = EfficientNetEncoder() + + def encode(self, x): + return self.encoder(x) + + def decode(self, x): + return self.previewer(x) diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..2a0dec606239cdc21ee21192172a812a53e0107c --- /dev/null +++ b/comfy/ldm/chroma/layers.py @@ -0,0 +1,181 @@ +import torch +from torch import Tensor, nn + +from comfy.ldm.flux.math import attention +from comfy.ldm.flux.layers import ( + MLPEmbedder, + RMSNorm, + QKNorm, + SelfAttention, + ModulationOut, +) + + + +class ChromaModulationOut(ModulationOut): + @classmethod + def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: + return cls( + shift=tensor[:, offset : offset + 1, :], + scale=tensor[:, offset + 1 : offset + 2, :], + gate=tensor[:, offset + 2 : offset + 3, :], + ) + + + + +class Approximator(nn.Module): + def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): + super().__init__() + self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def forward(self, x: Tensor) -> Tensor: + x = self.in_proj(x) + + for layer, norms in zip(self.layers, self.norms): + x = x + layer(norms(x)) + + x = self.out_proj(x) + + return x + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.flipped_img_txt = flipped_img_txt + + def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None): + (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec + + # prepare image for attention + img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img)) + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt)) + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, mask=attn_mask) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn)) + img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img)))) + + # calculate the txt bloks + txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn)) + txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt)))) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + # proj and mlp_out + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + + self.hidden_size = hidden_size + self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.mlp_act = nn.GELU(approximate="tanh") + + def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: + mod = vec + x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x)) + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe, mask=attn_mask) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x.addcmul_(mod.gate, output) + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = vec + shift = shift.squeeze(1) + scale = scale.squeeze(1) + x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x)) + x = self.linear(x) + return x diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py new file mode 100644 index 0000000000000000000000000000000000000000..c75023a31f96a0702da23bdd40fc6bb9efd3b1d4 --- /dev/null +++ b/comfy/ldm/chroma/model.py @@ -0,0 +1,271 @@ +#Original code can be found on: https://github.com/black-forest-labs/flux + +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange, repeat +import comfy.ldm.common_dit + +from comfy.ldm.flux.layers import ( + EmbedND, + timestep_embedding, +) + +from .layers import ( + DoubleStreamBlock, + LastLayer, + SingleStreamBlock, + Approximator, + ChromaModulationOut, +) + + +@dataclass +class ChromaParams: + in_channels: int + out_channels: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + patch_size: int + qkv_bias: bool + in_dim: int + out_dim: int + hidden_dim: int + n_layers: int + + + + +class Chroma(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = ChromaParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels + self.out_channels = params.out_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.in_dim = params.in_dim + self.out_dim = params.out_dim + self.hidden_dim = params.hidden_dim + self.n_layers = params.n_layers + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + # set as nn identity for now, will overwrite it later. + self.distilled_guidance_layer = Approximator( + in_dim=self.in_dim, + hidden_dim=self.hidden_dim, + out_dim=self.out_dim, + n_layers=self.n_layers, + dtype=dtype, device=device, operations=operations + ) + + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) + + self.skip_mmdit = [] + self.skip_dit = [] + self.lite = False + + def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): + # This function slices up the modulations tensor which has the following layout: + # single : num_single_blocks * 3 elements + # double_img : num_double_blocks * 6 elements + # double_txt : num_double_blocks * 6 elements + # final : 2 elements + if block_type == "final": + return (tensor[:, -2:-1, :], tensor[:, -1:, :]) + single_block_count = self.params.depth_single_blocks + double_block_count = self.params.depth + offset = 3 * idx + if block_type == "single": + return ChromaModulationOut.from_offset(tensor, offset) + # Double block modulations are 6 elements so we double 3 * idx. + offset *= 2 + if block_type in {"double_img", "double_txt"}: + # Advance past the single block modulations. + offset += 3 * single_block_count + if block_type == "double_txt": + # Advance past the double block img modulations. + offset += 6 * double_block_count + return ( + ChromaModulationOut.from_offset(tensor, offset), + ChromaModulationOut.from_offset(tensor, offset + 3), + ) + raise ValueError("Bad block_type") + + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, + ) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + + # distilled vector guidance + mod_index_length = 344 + distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) + # guidance = guidance * + distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) + + # get all modulation index + modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) + # we need to broadcast the modulation index here so each batch has all of the index + modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) + # and we need to broadcast timestep and guidance along too + timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) + # then and only then we could concatenate it together + input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) + + mod_vectors = self.distilled_guidance_layer(input_vec) + + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if i not in self.skip_mmdit: + double_mod = ( + self.get_modulations(mod_vectors, "double_img", idx=i), + self.get_modulations(mod_vectors, "double_txt", idx=i), + ) + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": double_mod, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=double_mod, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if i not in self.skip_dit: + single_mod = self.get_modulations(mod_vectors, "single", idx=i) + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": single_mod, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + final_mod = self.get_modulations(mod_vectors, "final") + img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) + return img + + def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): + bs, c, h, w = x.shape + patch_size = 2 + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w] diff --git a/comfy/ldm/common_dit.py b/comfy/ldm/common_dit.py new file mode 100644 index 0000000000000000000000000000000000000000..f7f56b72ca6fcd3ba2831b8881a7124d84490bee --- /dev/null +++ b/comfy/ldm/common_dit.py @@ -0,0 +1,16 @@ +import torch +import comfy.rmsnorm + + +def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): + if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()): + padding_mode = "reflect" + + pad = () + for i in range(img.ndim - 2): + pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad + + return torch.nn.functional.pad(img, pad, mode=padding_mode) + + +rms_norm = comfy.rmsnorm.rms_norm diff --git a/comfy/ldm/cosmos/blocks.py b/comfy/ldm/cosmos/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..5c4356a3ff9d97c182de8afeb1f130dca5bd2756 --- /dev/null +++ b/comfy/ldm/cosmos/blocks.py @@ -0,0 +1,797 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Optional +import logging + +import numpy as np +import torch +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +from torch import nn + +from comfy.ldm.modules.attention import optimized_attention + + +def get_normalization(name: str, channels: int, weight_args={}, operations=None): + if name == "I": + return nn.Identity() + elif name == "R": + return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args) + else: + raise ValueError(f"Normalization {name} not found") + + +class BaseAttentionOp(nn.Module): + def __init__(self): + super().__init__() + + +class Attention(nn.Module): + """ + Generalized attention impl. + + Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided. + If `context_dim` is None, self-attention is assumed. + + Parameters: + query_dim (int): Dimension of each query vector. + context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed. + heads (int, optional): Number of attention heads. Defaults to 8. + dim_head (int, optional): Dimension of each head. Defaults to 64. + dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0. + attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default. + qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False. + out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False. + qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections. + Defaults to "SSI". + qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections. + Defaults to 'per_head'. Only support 'per_head'. + + Examples: + >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1) + >>> query = torch.randn(10, 128) # Batch size of 10 + >>> context = torch.randn(10, 256) # Batch size of 10 + >>> output = attn(query, context) # Perform the attention operation + + Note: + https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + """ + + def __init__( + self, + query_dim: int, + context_dim=None, + heads=8, + dim_head=64, + dropout=0.0, + attn_op: Optional[BaseAttentionOp] = None, + qkv_bias: bool = False, + out_bias: bool = False, + qkv_norm: str = "SSI", + qkv_norm_mode: str = "per_head", + backend: str = "transformer_engine", + qkv_format: str = "bshd", + weight_args={}, + operations=None, + ) -> None: + super().__init__() + + self.is_selfattn = context_dim is None # self attention + + inner_dim = dim_head * heads + context_dim = query_dim if context_dim is None else context_dim + + self.heads = heads + self.dim_head = dim_head + self.qkv_norm_mode = qkv_norm_mode + self.qkv_format = qkv_format + + if self.qkv_norm_mode == "per_head": + norm_dim = dim_head + else: + raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") + + self.backend = backend + + self.to_q = nn.Sequential( + operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args), + get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations), + ) + self.to_k = nn.Sequential( + operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), + get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations), + ) + self.to_v = nn.Sequential( + operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), + get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations), + ) + + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args), + nn.Dropout(dropout), + ) + + def cal_qkv( + self, x, context=None, mask=None, rope_emb=None, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + del kwargs + + + """ + self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers. + Before 07/24/2024, these modules normalize across all heads. + After 07/24/2024, to support tensor parallelism and follow the common practice in the community, + we support to normalize per head. + To keep the checkpoint copatibility with the previous code, + we keep the nn.Sequential but call the projection and the normalization layers separately. + We use a flag `self.qkv_norm_mode` to control the normalization behavior. + The default value of `self.qkv_norm_mode` is "per_head", which means we normalize per head. + """ + if self.qkv_norm_mode == "per_head": + q = self.to_q[0](x) + context = x if context is None else context + k = self.to_k[0](context) + v = self.to_v[0](context) + q, k, v = map( + lambda t: rearrange(t, "s b (n c) -> b n s c", n=self.heads, c=self.dim_head), + (q, k, v), + ) + else: + raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") + + q = self.to_q[1](q) + k = self.to_k[1](k) + v = self.to_v[1](v) + if self.is_selfattn and rope_emb is not None: # only apply to self-attention! + # apply_rotary_pos_emb inlined + q_shape = q.shape + q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) + q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1] + q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype) + + # apply_rotary_pos_emb inlined + k_shape = k.shape + k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) + k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1] + k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype) + return q, k, v + + def forward( + self, + x, + context=None, + mask=None, + rope_emb=None, + **kwargs, + ): + """ + Args: + x (Tensor): The query tensor of shape [B, Mq, K] + context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None + """ + q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs) + out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True) + del q, k, v + out = rearrange(out, " b n s c -> s b (n c)") + return self.to_out(out) + + +class FeedForward(nn.Module): + """ + Transformer FFN with optional gating + + Parameters: + d_model (int): Dimensionality of input features. + d_ff (int): Dimensionality of the hidden layer. + dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1. + activation (callable, optional): The activation function applied after the first linear layer. + Defaults to nn.ReLU(). + is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer. + Defaults to False. + bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True. + + Example: + >>> ff = FeedForward(d_model=512, d_ff=2048) + >>> x = torch.randn(64, 10, 512) # Example input tensor + >>> output = ff(x) + >>> print(output.shape) # Expected shape: (64, 10, 512) + """ + + def __init__( + self, + d_model: int, + d_ff: int, + dropout: float = 0.1, + activation=nn.ReLU(), + is_gated: bool = False, + bias: bool = False, + weight_args={}, + operations=None, + ) -> None: + super().__init__() + + self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args) + self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args) + + self.dropout = nn.Dropout(dropout) + self.activation = activation + self.is_gated = is_gated + if is_gated: + self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args) + + def forward(self, x: torch.Tensor): + g = self.activation(self.layer1(x)) + if self.is_gated: + x = g * self.linear_gate(x) + else: + x = g + assert self.dropout.p == 0.0, "we skip dropout" + return self.layer2(x) + + +class GPT2FeedForward(FeedForward): + def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None): + super().__init__( + d_model=d_model, + d_ff=d_ff, + dropout=dropout, + activation=nn.GELU(), + is_gated=False, + bias=bias, + weight_args=weight_args, + operations=operations, + ) + + def forward(self, x: torch.Tensor): + assert self.dropout.p == 0.0, "we skip dropout" + + x = self.layer1(x) + x = self.activation(x) + x = self.layer2(x) + + return x + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class Timesteps(nn.Module): + def __init__(self, num_channels): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps): + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - 0.0) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + sin_emb = torch.sin(emb) + cos_emb = torch.cos(emb) + emb = torch.cat([cos_emb, sin_emb], dim=-1) + + return emb + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None): + super().__init__() + logging.debug( + f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." + ) + self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args) + self.activation = nn.SiLU() + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args) + else: + self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args) + + def forward(self, sample: torch.Tensor) -> torch.Tensor: + emb = self.linear_1(sample) + emb = self.activation(emb) + emb = self.linear_2(emb) + + if self.use_adaln_lora: + adaln_lora_B_3D = emb + emb_B_D = sample + else: + emb_B_D = emb + adaln_lora_B_3D = None + + return emb_B_D, adaln_lora_B_3D + + +class FourierFeatures(nn.Module): + """ + Implements a layer that generates Fourier features from input tensors, based on randomly sampled + frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems. + + [B] -> [B, D] + + Parameters: + num_channels (int): The number of Fourier features to generate. + bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1. + normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize + the variance of the features. Defaults to False. + + Example: + >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True) + >>> x = torch.randn(10, 256) # Example input tensor + >>> output = layer(x) + >>> print(output.shape) # Expected shape: (10, 256) + """ + + def __init__(self, num_channels, bandwidth=1, normalize=False): + super().__init__() + self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True) + self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True) + self.gain = np.sqrt(2) if normalize else 1 + + def forward(self, x, gain: float = 1.0): + """ + Apply the Fourier feature transformation to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1. + + Returns: + torch.Tensor: The transformed tensor, with Fourier features applied. + """ + in_dtype = x.dtype + x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32)) + x = x.cos().mul(self.gain * gain).to(in_dtype) + return x + + +class PatchEmbed(nn.Module): + """ + PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, + depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, + making it suitable for video and image processing tasks. It supports dividing the input into patches + and embedding each patch into a vector of size `out_channels`. + + Parameters: + - spatial_patch_size (int): The size of each spatial patch. + - temporal_patch_size (int): The size of each temporal patch. + - in_channels (int): Number of input channels. Default: 3. + - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. + - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. + """ + + def __init__( + self, + spatial_patch_size, + temporal_patch_size, + in_channels=3, + out_channels=768, + bias=True, + weight_args={}, + operations=None, + ): + super().__init__() + self.spatial_patch_size = spatial_patch_size + self.temporal_patch_size = temporal_patch_size + + self.proj = nn.Sequential( + Rearrange( + "b c (t r) (h m) (w n) -> b t h w (c r m n)", + r=temporal_patch_size, + m=spatial_patch_size, + n=spatial_patch_size, + ), + operations.Linear( + in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args + ), + ) + self.out = nn.Identity() + + def forward(self, x): + """ + Forward pass of the PatchEmbed module. + + Parameters: + - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where + B is the batch size, + C is the number of channels, + T is the temporal dimension, + H is the height, and + W is the width of the input. + + Returns: + - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. + """ + assert x.dim() == 5 + _, _, T, H, W = x.shape + assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 + assert T % self.temporal_patch_size == 0 + x = self.proj(x) + return self.out(x) + + +class FinalLayer(nn.Module): + """ + The final layer of video DiT. + """ + + def __init__( + self, + hidden_size, + spatial_patch_size, + temporal_patch_size, + out_channels, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + weight_args={}, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args) + self.linear = operations.Linear( + hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args + ) + self.hidden_size = hidden_size + self.n_adaln_chunks = 2 + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args), + operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args), + ) + else: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args) + ) + + def forward( + self, + x_BT_HW_D, + emb_B_D, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + ): + if self.use_adaln_lora: + assert adaln_lora_B_3D is not None + shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk( + 2, dim=1 + ) + else: + shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1) + + B = emb_B_D.shape[0] + T = x_BT_HW_D.shape[0] // B + shift_BT_D, scale_BT_D = repeat(shift_B_D, "b d -> (b t) d", t=T), repeat(scale_B_D, "b d -> (b t) d", t=T) + x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D) + + x_BT_HW_D = self.linear(x_BT_HW_D) + return x_BT_HW_D + + +class VideoAttn(nn.Module): + """ + Implements video attention with optional cross-attention capabilities. + + This module processes video features while maintaining their spatio-temporal structure. It can perform + self-attention within the video features or cross-attention with external context features. + + Parameters: + x_dim (int): Dimension of input feature vectors + context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention + num_heads (int): Number of attention heads + bias (bool): Whether to include bias in attention projections. Default: False + qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be "per_head". Default: "per_head" + x_format (str): Format of input tensor. Must be "BTHWD". Default: "BTHWD" + + Input shape: + - x: (T, H, W, B, D) video features + - context (optional): (M, B, D) context features for cross-attention + where: + T: temporal dimension + H: height + W: width + B: batch size + D: feature dimension + M: context sequence length + """ + + def __init__( + self, + x_dim: int, + context_dim: Optional[int], + num_heads: int, + bias: bool = False, + qkv_norm_mode: str = "per_head", + x_format: str = "BTHWD", + weight_args={}, + operations=None, + ) -> None: + super().__init__() + self.x_format = x_format + + self.attn = Attention( + x_dim, + context_dim, + num_heads, + x_dim // num_heads, + qkv_bias=bias, + qkv_norm="RRI", + out_bias=bias, + qkv_norm_mode=qkv_norm_mode, + qkv_format="sbhd", + weight_args=weight_args, + operations=operations, + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + crossattn_mask: Optional[torch.Tensor] = None, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Forward pass for video attention. + + Args: + x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data. + context (Tensor): Context tensor of shape (B, M, D) or (M, B, D), + where M is the sequence length of the context. + crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms. + rope_emb_L_1_1_D (Optional[Tensor]): + Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. + + Returns: + Tensor: The output tensor with applied attention, maintaining the input shape. + """ + + x_T_H_W_B_D = x + context_M_B_D = context + T, H, W, B, D = x_T_H_W_B_D.shape + x_THW_B_D = rearrange(x_T_H_W_B_D, "t h w b d -> (t h w) b d") + x_THW_B_D = self.attn( + x_THW_B_D, + context_M_B_D, + crossattn_mask, + rope_emb=rope_emb_L_1_1_D, + ) + x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W) + return x_T_H_W_B_D + + +def adaln_norm_state(norm_state, x, scale, shift): + normalized = norm_state(x) + return normalized * (1 + scale) + shift + + +class DITBuildingBlock(nn.Module): + """ + A building block for the DiT (Diffusion Transformer) architecture that supports different types of + attention and MLP operations with adaptive layer normalization. + + Parameters: + block_type (str): Type of block - one of: + - "cross_attn"/"ca": Cross-attention + - "full_attn"/"fa": Full self-attention + - "mlp"/"ff": MLP/feedforward block + x_dim (int): Dimension of input features + context_dim (Optional[int]): Dimension of context features for cross-attention + num_heads (int): Number of attention heads + mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 + bias (bool): Whether to use bias in layers. Default: False + mlp_dropout (float): Dropout rate for MLP. Default: 0.0 + qkv_norm_mode (str): QKV normalization mode. Default: "per_head" + x_format (str): Input tensor format. Default: "BTHWD" + use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False + adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 + """ + + def __init__( + self, + block_type: str, + x_dim: int, + context_dim: Optional[int], + num_heads: int, + mlp_ratio: float = 4.0, + bias: bool = False, + mlp_dropout: float = 0.0, + qkv_norm_mode: str = "per_head", + x_format: str = "BTHWD", + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + weight_args={}, + operations=None + ) -> None: + block_type = block_type.lower() + + super().__init__() + self.x_format = x_format + if block_type in ["cross_attn", "ca"]: + self.block = VideoAttn( + x_dim, + context_dim, + num_heads, + bias=bias, + qkv_norm_mode=qkv_norm_mode, + x_format=self.x_format, + weight_args=weight_args, + operations=operations, + ) + elif block_type in ["full_attn", "fa"]: + self.block = VideoAttn( + x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations + ) + elif block_type in ["mlp", "ff"]: + self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations) + else: + raise ValueError(f"Unknown block type: {block_type}") + + self.block_type = block_type + self.use_adaln_lora = use_adaln_lora + + self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) + self.n_adaln_chunks = 3 + if use_adaln_lora: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args), + operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args), + ) + else: + self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args)) + + def forward( + self, + x: torch.Tensor, + emb_B_D: torch.Tensor, + crossattn_emb: torch.Tensor, + crossattn_mask: Optional[torch.Tensor] = None, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Forward pass for dynamically configured blocks with adaptive normalization. + + Args: + x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D). + emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation. + crossattn_emb (Tensor): Tensor for cross-attention blocks. + crossattn_mask (Optional[Tensor]): Optional mask for cross-attention. + rope_emb_L_1_1_D (Optional[Tensor]): + Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. + + Returns: + Tensor: The output tensor after processing through the configured block and adaptive normalization. + """ + if self.use_adaln_lora: + shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk( + self.n_adaln_chunks, dim=1 + ) + else: + shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1) + + shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = ( + shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), + scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), + gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), + ) + + if self.block_type in ["mlp", "ff"]: + x = x + gate_1_1_1_B_D * self.block( + adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), + ) + elif self.block_type in ["full_attn", "fa"]: + x = x + gate_1_1_1_B_D * self.block( + adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), + context=None, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + ) + elif self.block_type in ["cross_attn", "ca"]: + x = x + gate_1_1_1_B_D * self.block( + adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), + context=crossattn_emb, + crossattn_mask=crossattn_mask, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + ) + else: + raise ValueError(f"Unknown block type: {self.block_type}") + + return x + + +class GeneralDITTransformerBlock(nn.Module): + """ + A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer. + Each block in the sequence is specified by a block configuration string. + + Parameters: + x_dim (int): Dimension of input features + context_dim (int): Dimension of context features for cross-attention blocks + num_heads (int): Number of attention heads + block_config (str): String specifying block sequence (e.g. "ca-fa-mlp" for cross-attention, + full-attention, then MLP) + mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 + x_format (str): Input tensor format. Default: "BTHWD" + use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False + adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 + + The block_config string uses "-" to separate block types: + - "ca"/"cross_attn": Cross-attention block + - "fa"/"full_attn": Full self-attention block + - "mlp"/"ff": MLP/feedforward block + + Example: + block_config = "ca-fa-mlp" creates a sequence of: + 1. Cross-attention block + 2. Full self-attention block + 3. MLP block + """ + + def __init__( + self, + x_dim: int, + context_dim: int, + num_heads: int, + block_config: str, + mlp_ratio: float = 4.0, + x_format: str = "BTHWD", + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + weight_args={}, + operations=None + ): + super().__init__() + self.blocks = nn.ModuleList() + self.x_format = x_format + for block_type in block_config.split("-"): + self.blocks.append( + DITBuildingBlock( + block_type, + x_dim, + context_dim, + num_heads, + mlp_ratio, + x_format=self.x_format, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + weight_args=weight_args, + operations=operations, + ) + ) + + def forward( + self, + x: torch.Tensor, + emb_B_D: torch.Tensor, + crossattn_emb: torch.Tensor, + crossattn_mask: Optional[torch.Tensor] = None, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + for block in self.blocks: + x = block( + x, + emb_B_D, + crossattn_emb, + crossattn_mask, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + adaln_lora_B_3D=adaln_lora_B_3D, + ) + return x diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/layers3d.cpython-310.pyc b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/layers3d.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a0ff0f7fcca1f6d6a638943d6d444e9be04d4a80 Binary files /dev/null and b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/layers3d.cpython-310.pyc differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/layers3d.cpython-312.pyc b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/layers3d.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8610aa334d378c33be51d3c22b62d74af76f48d4 Binary files /dev/null and b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/layers3d.cpython-312.pyc differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/patching.cpython-310.pyc b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/patching.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2fdc190aed3e348ca1dd2bdae4024837f19f98d2 Binary files /dev/null and b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/patching.cpython-310.pyc differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/patching.cpython-312.pyc b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/patching.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..62ee285de0974645f39aa9cd355082db4fcee5fc Binary files /dev/null and b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/patching.cpython-312.pyc differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/utils.cpython-310.pyc b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..559c48b0c3493eeb598c552ad391c8407d0859d7 Binary files /dev/null and b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/utils.cpython-310.pyc differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/utils.cpython-312.pyc b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..36d7029b0951ba2f787977263d4dd662785bbe85 Binary files /dev/null and b/comfy/ldm/cosmos/cosmos_tokenizer/__pycache__/utils.cpython-312.pyc differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py b/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py new file mode 100644 index 0000000000000000000000000000000000000000..9a3ebed6aa5362c79aff91c088cce8ba7b6f3141 --- /dev/null +++ b/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py @@ -0,0 +1,1041 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The model definition for 3D layers + +Adapted from: https://github.com/lucidrains/magvit2-pytorch/blob/ +9f49074179c912736e617d61b32be367eb5f993a/magvit2_pytorch/magvit2_pytorch.py#L889 + +[MIT License Copyright (c) 2023 Phil Wang] +https://github.com/lucidrains/magvit2-pytorch/blob/ +9f49074179c912736e617d61b32be367eb5f993a/LICENSE +""" +import math +from typing import Tuple, Union + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import logging + +from comfy.ldm.modules.diffusionmodules.model import vae_attention + +from .patching import ( + Patcher, + Patcher3D, + UnPatcher, + UnPatcher3D, +) +from .utils import ( + CausalNormalize, + batch2space, + batch2time, + cast_tuple, + is_odd, + nonlinearity, + replication_pad, + space2batch, + time2batch, +) + +import comfy.ops +ops = comfy.ops.disable_weight_init + +_LEGACY_NUM_GROUPS = 32 + + +class CausalConv3d(nn.Module): + def __init__( + self, + chan_in: int = 1, + chan_out: int = 1, + kernel_size: Union[int, Tuple[int, int, int]] = 3, + pad_mode: str = "constant", + **kwargs, + ): + super().__init__() + kernel_size = cast_tuple(kernel_size, 3) + + time_kernel_size, height_kernel_size, width_kernel_size = kernel_size + + assert is_odd(height_kernel_size) and is_odd(width_kernel_size) + + dilation = kwargs.pop("dilation", 1) + stride = kwargs.pop("stride", 1) + time_stride = kwargs.pop("time_stride", 1) + time_dilation = kwargs.pop("time_dilation", 1) + padding = kwargs.pop("padding", 1) + + self.pad_mode = pad_mode + time_pad = time_dilation * (time_kernel_size - 1) + (1 - time_stride) + self.time_pad = time_pad + + self.spatial_pad = (padding, padding, padding, padding) + + stride = (time_stride, stride, stride) + dilation = (time_dilation, dilation, dilation) + self.conv3d = ops.Conv3d( + chan_in, + chan_out, + kernel_size, + stride=stride, + dilation=dilation, + **kwargs, + ) + + def _replication_pad(self, x: torch.Tensor) -> torch.Tensor: + x_prev = x[:, :, :1, ...].repeat(1, 1, self.time_pad, 1, 1) + x = torch.cat([x_prev, x], dim=2) + padding = self.spatial_pad + (0, 0) + return F.pad(x, padding, mode=self.pad_mode, value=0.0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self._replication_pad(x) + return self.conv3d(x) + + +class CausalUpsample3d(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.conv = CausalConv3d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) + time_factor = 1.0 + 1.0 * (x.shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + x = x.repeat_interleave(int(time_factor), dim=2) + # TODO(freda): Check if this causes temporal inconsistency. + # Shoule reverse the order of the following two ops, + # better perf and better temporal smoothness. + x = self.conv(x) + return x[..., int(time_factor - 1) :, :, :] + + +class CausalDownsample3d(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.conv = CausalConv3d( + in_channels, + in_channels, + kernel_size=3, + stride=2, + time_stride=2, + padding=0, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + pad = (0, 1, 0, 1, 0, 0) + x = F.pad(x, pad, mode="constant", value=0) + x = replication_pad(x) + x = self.conv(x) + return x + + +class CausalHybridUpsample3d(nn.Module): + def __init__( + self, + in_channels: int, + spatial_up: bool = True, + temporal_up: bool = True, + **kwargs, + ) -> None: + super().__init__() + self.spatial_up = spatial_up + self.temporal_up = temporal_up + if not self.spatial_up and not self.temporal_up: + return + + self.conv1 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(3, 1, 1), + stride=1, + time_stride=1, + padding=0, + ) + self.conv2 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(1, 3, 3), + stride=1, + time_stride=1, + padding=1, + ) + self.conv3 = CausalConv3d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + time_stride=1, + padding=0, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.spatial_up and not self.temporal_up: + return x + + # hybrid upsample temporally. + if self.temporal_up: + time_factor = 1.0 + 1.0 * (x.shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + x = x.repeat_interleave(int(time_factor), dim=2) + x = x[..., int(time_factor - 1) :, :, :] + x = self.conv1(x) + x + + # hybrid upsample spatially. + if self.spatial_up: + x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) + x = self.conv2(x) + x + + # final 1x1x1 conv. + x = self.conv3(x) + return x + + +class CausalHybridDownsample3d(nn.Module): + def __init__( + self, + in_channels: int, + spatial_down: bool = True, + temporal_down: bool = True, + **kwargs, + ) -> None: + super().__init__() + self.spatial_down = spatial_down + self.temporal_down = temporal_down + if not self.spatial_down and not self.temporal_down: + return + + self.conv1 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(1, 3, 3), + stride=2, + time_stride=1, + padding=0, + ) + self.conv2 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(3, 1, 1), + stride=1, + time_stride=2, + padding=0, + ) + self.conv3 = CausalConv3d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + time_stride=1, + padding=0, + ) + + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.spatial_down and not self.temporal_down: + return x + + # hybrid downsample spatially. + if self.spatial_down: + pad = (0, 1, 0, 1, 0, 0) + x = F.pad(x, pad, mode="constant", value=0) + x1 = self.conv1(x) + x2 = F.avg_pool3d(x, kernel_size=(1, 2, 2), stride=(1, 2, 2)) + x = x1 + x2 + + # hybrid downsample temporally. + if self.temporal_down: + x = replication_pad(x) + x1 = self.conv2(x) + x2 = F.avg_pool3d(x, kernel_size=(2, 1, 1), stride=(2, 1, 1)) + x = x1 + x2 + + # final 1x1x1 conv. + x = self.conv3(x) + return x + + +class CausalResnetBlock3d(nn.Module): + def __init__( + self, + *, + in_channels: int, + out_channels: int = None, + dropout: float, + num_groups: int, + ) -> None: + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + + self.norm1 = CausalNormalize(in_channels, num_groups=num_groups) + self.conv1 = CausalConv3d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = CausalConv3d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.nin_shortcut = ( + CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + if in_channels != out_channels + else nn.Identity() + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + x = self.nin_shortcut(x) + + return x + h + + +class CausalResnetBlockFactorized3d(nn.Module): + def __init__( + self, + *, + in_channels: int, + out_channels: int = None, + dropout: float, + num_groups: int, + ) -> None: + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + + self.norm1 = CausalNormalize(in_channels, num_groups=1) + self.conv1 = nn.Sequential( + CausalConv3d( + in_channels, + out_channels, + kernel_size=(1, 3, 3), + stride=1, + padding=1, + ), + CausalConv3d( + out_channels, + out_channels, + kernel_size=(3, 1, 1), + stride=1, + padding=0, + ), + ) + self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = nn.Sequential( + CausalConv3d( + out_channels, + out_channels, + kernel_size=(1, 3, 3), + stride=1, + padding=1, + ), + CausalConv3d( + out_channels, + out_channels, + kernel_size=(3, 1, 1), + stride=1, + padding=0, + ), + ) + self.nin_shortcut = ( + CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + if in_channels != out_channels + else nn.Identity() + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + x = self.nin_shortcut(x) + + return x + h + + +class CausalAttnBlock(nn.Module): + def __init__(self, in_channels: int, num_groups: int) -> None: + super().__init__() + + self.norm = CausalNormalize(in_channels, num_groups=num_groups) + self.q = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + self.optimized_attention = vae_attention() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + q, batch_size = time2batch(q) + k, batch_size = time2batch(k) + v, batch_size = time2batch(v) + + b, c, h, w = q.shape + h_ = self.optimized_attention(q, k, v) + + h_ = batch2time(h_, batch_size) + h_ = self.proj_out(h_) + return x + h_ + + +class CausalTemporalAttnBlock(nn.Module): + def __init__(self, in_channels: int, num_groups: int) -> None: + super().__init__() + + self.norm = CausalNormalize(in_channels, num_groups=num_groups) + self.q = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + q, batch_size, height = space2batch(q) + k, _, _ = space2batch(k) + v, _, _ = space2batch(v) + + bhw, c, t = q.shape + q = q.permute(0, 2, 1) # (bhw, t, c) + k = k.permute(0, 2, 1) # (bhw, t, c) + v = v.permute(0, 2, 1) # (bhw, t, c) + + w_ = torch.bmm(q, k.permute(0, 2, 1)) # (bhw, t, t) + w_ = w_ * (int(c) ** (-0.5)) + + # Apply causal mask + mask = torch.tril(torch.ones_like(w_)) + w_ = w_.masked_fill(mask == 0, float("-inf")) + w_ = F.softmax(w_, dim=2) + + # attend to values + h_ = torch.bmm(w_, v) # (bhw, t, c) + h_ = h_.permute(0, 2, 1).reshape(bhw, c, t) # (bhw, c, t) + + h_ = batch2space(h_, batch_size, height) + h_ = self.proj_out(h_) + return x + h_ + + +class EncoderBase(nn.Module): + def __init__( + self, + in_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + **ignore_kwargs, + ) -> None: + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # Patcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.patcher = Patcher( + patch_size, ignore_kwargs.get("patch_method", "rearrange") + ) + in_channels = in_channels * patch_size * patch_size + + # downsampling + self.conv_in = CausalConv3d( + in_channels, channels, kernel_size=3, stride=1, padding=1 + ) + + # num of groups for GroupNorm, num_groups=1 for LayerNorm. + num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) + curr_res = resolution // patch_size + in_ch_mult = (1,) + tuple(channels_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = channels * in_ch_mult[i_level] + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks): + block.append( + CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=num_groups, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = CausalDownsample3d(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) + self.mid.block_2 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + + # end + self.norm_out = CausalNormalize(block_in, num_groups=num_groups) + self.conv_out = CausalConv3d( + block_in, z_channels, kernel_size=3, stride=1, padding=1 + ) + + def patcher3d(self, x: torch.Tensor) -> torch.Tensor: + x, batch_size = time2batch(x) + x = self.patcher(x) + x = batch2time(x, batch_size) + return x + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patcher3d(x) + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + else: + # temporal downsample (last level) + time_factor = 1 + 1 * (hs[-1].shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + hs[-1] = replication_pad(hs[-1]) + hs.append( + F.avg_pool3d( + hs[-1], + kernel_size=[time_factor, 1, 1], + stride=[2, 1, 1], + ) + ) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class DecoderBase(nn.Module): + def __init__( + self, + out_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + **ignore_kwargs, + ): + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # UnPatcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.unpatcher = UnPatcher( + patch_size, ignore_kwargs.get("patch_method", "rearrange") + ) + out_ch = out_channels * patch_size * patch_size + + block_in = channels * channels_mult[self.num_resolutions - 1] + curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + logging.debug( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = CausalConv3d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # num of groups for GroupNorm, num_groups=1 for LayerNorm. + num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) + self.mid.block_2 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append( + CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=num_groups, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = CausalUpsample3d(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = CausalNormalize(block_in, num_groups=num_groups) + self.conv_out = CausalConv3d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor: + x, batch_size = time2batch(x) + x = self.unpatcher(x) + x = batch2time(x, batch_size) + + return x + + def forward(self, z): + h = self.conv_in(z) + + # middle block. + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # decoder blocks. + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + else: + # temporal upsample (last level) + time_factor = 1.0 + 1.0 * (h.shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + h = h.repeat_interleave(int(time_factor), dim=2) + h = h[..., int(time_factor - 1) :, :, :] + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + h = self.unpatcher3d(h) + return h + + +class EncoderFactorized(nn.Module): + def __init__( + self, + in_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + spatial_compression: int = 8, + temporal_compression: int = 8, + **ignore_kwargs, + ) -> None: + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # Patcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.patcher3d = Patcher3D( + patch_size, ignore_kwargs.get("patch_method", "haar") + ) + in_channels = in_channels * patch_size * patch_size * patch_size + + # calculate the number of downsample operations + self.num_spatial_downs = int(math.log2(spatial_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_spatial_downs <= self.num_resolutions + ), f"Spatially downsample {self.num_resolutions} times at most" + + self.num_temporal_downs = int(math.log2(temporal_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_temporal_downs <= self.num_resolutions + ), f"Temporally downsample {self.num_resolutions} times at most" + + # downsampling + self.conv_in = nn.Sequential( + CausalConv3d( + in_channels, + channels, + kernel_size=(1, 3, 3), + stride=1, + padding=1, + ), + CausalConv3d( + channels, channels, kernel_size=(3, 1, 1), stride=1, padding=0 + ), + ) + + curr_res = resolution // patch_size + in_ch_mult = (1,) + tuple(channels_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = channels * in_ch_mult[i_level] + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks): + block.append( + CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=1, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append( + nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + ) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + spatial_down = i_level < self.num_spatial_downs + temporal_down = i_level < self.num_temporal_downs + down.downsample = CausalHybridDownsample3d( + block_in, + spatial_down=spatial_down, + temporal_down=temporal_down, + ) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + self.mid.attn_1 = nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + self.mid.block_2 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + + # end + self.norm_out = CausalNormalize(block_in, num_groups=1) + self.conv_out = nn.Sequential( + CausalConv3d( + block_in, z_channels, kernel_size=(1, 3, 3), stride=1, padding=1 + ), + CausalConv3d( + z_channels, + z_channels, + kernel_size=(3, 1, 1), + stride=1, + padding=0, + ), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patcher3d(x) + + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions - 1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class DecoderFactorized(nn.Module): + def __init__( + self, + out_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + spatial_compression: int = 8, + temporal_compression: int = 8, + **ignore_kwargs, + ): + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # UnPatcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.unpatcher3d = UnPatcher3D( + patch_size, ignore_kwargs.get("patch_method", "haar") + ) + out_ch = out_channels * patch_size * patch_size * patch_size + + # calculate the number of upsample operations + self.num_spatial_ups = int(math.log2(spatial_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_spatial_ups <= self.num_resolutions + ), f"Spatially upsample {self.num_resolutions} times at most" + self.num_temporal_ups = int(math.log2(temporal_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_temporal_ups <= self.num_resolutions + ), f"Temporally upsample {self.num_resolutions} times at most" + + block_in = channels * channels_mult[self.num_resolutions - 1] + curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + logging.debug( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = nn.Sequential( + CausalConv3d( + z_channels, block_in, kernel_size=(1, 3, 3), stride=1, padding=1 + ), + CausalConv3d( + block_in, block_in, kernel_size=(3, 1, 1), stride=1, padding=0 + ), + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + self.mid.attn_1 = nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + self.mid.block_2 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + + legacy_mode = ignore_kwargs.get("legacy_mode", False) + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append( + CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=1, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append( + nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + ) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + # The layer index for temporal/spatial downsampling performed + # in the encoder should correspond to the layer index in + # reverse order where upsampling is performed in the decoder. + # If you've a pre-trained model, you can simply finetune. + i_level_reverse = self.num_resolutions - i_level - 1 + if legacy_mode: + temporal_up = i_level_reverse < self.num_temporal_ups + else: + temporal_up = 0 < i_level_reverse < self.num_temporal_ups + 1 + spatial_up = temporal_up or ( + i_level_reverse < self.num_spatial_ups + and self.num_spatial_ups > self.num_temporal_ups + ) + up.upsample = CausalHybridUpsample3d( + block_in, spatial_up=spatial_up, temporal_up=temporal_up + ) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = CausalNormalize(block_in, num_groups=1) + self.conv_out = nn.Sequential( + CausalConv3d(block_in, out_ch, kernel_size=(1, 3, 3), stride=1, padding=1), + CausalConv3d(out_ch, out_ch, kernel_size=(3, 1, 1), stride=1, padding=0), + ) + + def forward(self, z): + h = self.conv_in(z) + + # middle block. + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # decoder blocks. + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + h = self.unpatcher3d(h) + return h diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/patching.py b/comfy/ldm/cosmos/cosmos_tokenizer/patching.py new file mode 100644 index 0000000000000000000000000000000000000000..87a53a1d9f9ac4fa6bdbb1f8f682c298fdd4376c --- /dev/null +++ b/comfy/ldm/cosmos/cosmos_tokenizer/patching.py @@ -0,0 +1,377 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The patcher and unpatcher implementation for 2D and 3D data. + +The idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions. +One on the rows and one on the columns. +For example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2. +We can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component. +For H component, we can use a 1D convolution with kernel [1, -1] and stride 2. +Although in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all + as we need to support downsampling for more than 2x. +For example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be. + [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64] +""" + +import torch +import torch.nn.functional as F +from einops import rearrange + +_WAVELETS = { + "haar": torch.tensor([0.7071067811865476, 0.7071067811865476]), + "rearrange": torch.tensor([1.0, 1.0]), +} +_PERSISTENT = False + + +class Patcher(torch.nn.Module): + """A module to convert image tensors into patches using torch operations. + + The main difference from `class Patching` is that this module implements + all operations using torch, rather than python or numpy, for efficiency purpose. + + It's bit-wise identical to the Patching module outputs, with the added + benefit of being torch.jit scriptable. + """ + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__() + self.patch_size = patch_size + self.patch_method = patch_method + self.register_buffer( + "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT + ) + self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) + self.register_buffer( + "_arange", + torch.arange(_WAVELETS[patch_method].shape[0]), + persistent=_PERSISTENT, + ) + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + if self.patch_method == "haar": + return self._haar(x) + elif self.patch_method == "rearrange": + return self._arrange(x) + else: + raise ValueError("Unknown patch method: " + self.patch_method) + + def _dwt(self, x, mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + + n = h.shape[0] + g = x.shape[1] + hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hh = hh.to(dtype=dtype) + hl = hl.to(dtype=dtype) + + x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype) + xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2)) + xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2)) + xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1)) + xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1)) + xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1)) + xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1)) + + out = torch.cat([xll, xlh, xhl, xhh], dim=1) + if rescale: + out = out / 2 + return out + + def _haar(self, x): + for _ in self.range: + x = self._dwt(x, rescale=True) + return x + + def _arrange(self, x): + x = rearrange( + x, + "b c (h p1) (w p2) -> b (c p1 p2) h w", + p1=self.patch_size, + p2=self.patch_size, + ).contiguous() + return x + + +class Patcher3D(Patcher): + """A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos.""" + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__(patch_method=patch_method, patch_size=patch_size) + self.register_buffer( + "patch_size_buffer", + patch_size * torch.ones([1], dtype=torch.int32), + persistent=_PERSISTENT, + ) + + def _dwt(self, x, wavelet, mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + + n = h.shape[0] + g = x.shape[1] + hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hh = hh.to(dtype=dtype) + hl = hl.to(dtype=dtype) + + # Handles temporal axis. + x = F.pad( + x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode + ).to(dtype) + xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) + xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) + + # Handles spatial axes. + xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + + xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + + out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1) + if rescale: + out = out / (2 * torch.sqrt(torch.tensor(2.0))) + return out + + def _haar(self, x): + xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) + x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) + for _ in self.range: + x = self._dwt(x, "haar", rescale=True) + return x + + def _arrange(self, x): + xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) + x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) + x = rearrange( + x, + "b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w", + p1=self.patch_size, + p2=self.patch_size, + p3=self.patch_size, + ).contiguous() + return x + + +class UnPatcher(torch.nn.Module): + """A module to convert patches into image tensorsusing torch operations. + + The main difference from `class Unpatching` is that this module implements + all operations using torch, rather than python or numpy, for efficiency purpose. + + It's bit-wise identical to the Unpatching module outputs, with the added + benefit of being torch.jit scriptable. + """ + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__() + self.patch_size = patch_size + self.patch_method = patch_method + self.register_buffer( + "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT + ) + self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) + self.register_buffer( + "_arange", + torch.arange(_WAVELETS[patch_method].shape[0]), + persistent=_PERSISTENT, + ) + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + if self.patch_method == "haar": + return self._ihaar(x) + elif self.patch_method == "rearrange": + return self._iarrange(x) + else: + raise ValueError("Unknown patch method: " + self.patch_method) + + def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + n = h.shape[0] + + g = x.shape[1] // 4 + hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hh = hh.to(dtype=dtype) + hl = hl.to(dtype=dtype) + + xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1) + + # Inverse transform. + yl = torch.nn.functional.conv_transpose2d( + xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + yl += torch.nn.functional.conv_transpose2d( + xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + yh = torch.nn.functional.conv_transpose2d( + xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + yh += torch.nn.functional.conv_transpose2d( + xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + y = torch.nn.functional.conv_transpose2d( + yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) + ) + y += torch.nn.functional.conv_transpose2d( + yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) + ) + + if rescale: + y = y * 2 + return y + + def _ihaar(self, x): + for _ in self.range: + x = self._idwt(x, "haar", rescale=True) + return x + + def _iarrange(self, x): + x = rearrange( + x, + "b (c p1 p2) h w -> b c (h p1) (w p2)", + p1=self.patch_size, + p2=self.patch_size, + ) + return x + + +class UnPatcher3D(UnPatcher): + """A 3D inverse discrete wavelet transform for video wavelet decompositions.""" + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__(patch_method=patch_method, patch_size=patch_size) + + def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + + g = x.shape[1] // 8 # split into 8 spatio-temporal filtered tesnors. + hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hl = hl.to(dtype=dtype) + hh = hh.to(dtype=dtype) + + xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1) + del x + + # Height height transposed convolutions. + xll = F.conv_transpose3d( + xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xlll + + xll += F.conv_transpose3d( + xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xllh + + xlh = F.conv_transpose3d( + xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xlhl + + xlh += F.conv_transpose3d( + xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xlhh + + xhl = F.conv_transpose3d( + xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhll + + xhl += F.conv_transpose3d( + xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhlh + + xhh = F.conv_transpose3d( + xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhhl + + xhh += F.conv_transpose3d( + xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhhh + + # Handles width transposed convolutions. + xl = F.conv_transpose3d( + xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xll + + xl += F.conv_transpose3d( + xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xlh + + xh = F.conv_transpose3d( + xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xhl + + xh += F.conv_transpose3d( + xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xhh + + # Handles time axis transposed convolutions. + x = F.conv_transpose3d( + xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) + ) + del xl + + x += F.conv_transpose3d( + xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) + ) + + if rescale: + x = x * (2 * torch.sqrt(torch.tensor(2.0))) + return x + + def _ihaar(self, x): + for _ in self.range: + x = self._idwt(x, "haar", rescale=True) + x = x[:, :, self.patch_size - 1 :, ...] + return x + + def _iarrange(self, x): + x = rearrange( + x, + "b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)", + p1=self.patch_size, + p2=self.patch_size, + p3=self.patch_size, + ) + x = x[:, :, self.patch_size - 1 :, ...] + return x diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/utils.py b/comfy/ldm/cosmos/cosmos_tokenizer/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3af8d0d0571c04b87c66f5280442f5f4e345ba0e --- /dev/null +++ b/comfy/ldm/cosmos/cosmos_tokenizer/utils.py @@ -0,0 +1,112 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Shared utilities for the networks module.""" + +from typing import Any + +import torch +from einops import rearrange + + +import comfy.ops +ops = comfy.ops.disable_weight_init + +def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: + batch_size = x.shape[0] + return rearrange(x, "b c t h w -> (b t) c h w"), batch_size + + +def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor: + return rearrange(x, "(b t) c h w -> b c t h w", b=batch_size) + + +def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: + batch_size, height = x.shape[0], x.shape[-2] + return rearrange(x, "b c t h w -> (b h w) c t"), batch_size, height + + +def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor: + return rearrange(x, "(b h w) c t -> b c t h w", b=batch_size, h=height) + + +def cast_tuple(t: Any, length: int = 1) -> Any: + return t if isinstance(t, tuple) else ((t,) * length) + + +def replication_pad(x): + return torch.cat([x[:, :, :1, ...], x], dim=2) + + +def divisible_by(num: int, den: int) -> bool: + return (num % den) == 0 + + +def is_odd(n: int) -> bool: + return not divisible_by(n, 2) + + +def nonlinearity(x): + return x * torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return ops.GroupNorm( + num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class CausalNormalize(torch.nn.Module): + def __init__(self, in_channels, num_groups=1): + super().__init__() + self.norm = ops.GroupNorm( + num_groups=num_groups, + num_channels=in_channels, + eps=1e-6, + affine=True, + ) + self.num_groups = num_groups + + def forward(self, x): + # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose. + # All new models should use num_groups=1, otherwise causality is not guaranteed. + if self.num_groups == 1: + x, batch_size = time2batch(x) + return batch2time(self.norm(x), batch_size) + return self.norm(x) + + +def exists(v): + return v is not None + + +def default(*args): + for arg in args: + if exists(arg): + return arg + return None + + +def round_ste(z: torch.Tensor) -> torch.Tensor: + """Round with straight through gradients.""" + zhat = z.round() + return z + (zhat - z).detach() + + +def log(t, eps=1e-5): + return t.clamp(min=eps).log() + + +def entropy(prob): + return (-prob * log(prob)).sum(dim=-1) diff --git a/comfy/ldm/cosmos/model.py b/comfy/ldm/cosmos/model.py new file mode 100644 index 0000000000000000000000000000000000000000..4836e0b69e80dc8bdd7ecb015711213bd6477671 --- /dev/null +++ b/comfy/ldm/cosmos/model.py @@ -0,0 +1,512 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. +""" + +from typing import Optional, Tuple + +import torch +from einops import rearrange +from torch import nn +from torchvision import transforms + +from enum import Enum +import logging + +from .blocks import ( + FinalLayer, + GeneralDITTransformerBlock, + PatchEmbed, + TimestepEmbedding, + Timesteps, +) + +from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb + + +class DataType(Enum): + IMAGE = "image" + VIDEO = "video" + + +class GeneralDIT(nn.Module): + """ + A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. + + Args: + max_img_h (int): Maximum height of the input images. + max_img_w (int): Maximum width of the input images. + max_frames (int): Maximum number of frames in the video sequence. + in_channels (int): Number of input channels (e.g., RGB channels for color images). + out_channels (int): Number of output channels. + patch_spatial (tuple): Spatial resolution of patches for input processing. + patch_temporal (int): Temporal resolution of patches for input processing. + concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. + block_config (str): Configuration of the transformer block. See Notes for supported block types. + model_channels (int): Base number of channels used throughout the model. + num_blocks (int): Number of transformer blocks. + num_heads (int): Number of heads in the multi-head attention layers. + mlp_ratio (float): Expansion ratio for MLP blocks. + block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD'). + crossattn_emb_channels (int): Number of embedding channels for cross-attention. + use_cross_attn_mask (bool): Whether to use mask in cross-attention. + pos_emb_cls (str): Type of positional embeddings. + pos_emb_learnable (bool): Whether positional embeddings are learnable. + pos_emb_interpolation (str): Method for interpolating positional embeddings. + affline_emb_norm (bool): Whether to normalize affine embeddings. + use_adaln_lora (bool): Whether to use AdaLN-LoRA. + adaln_lora_dim (int): Dimension for AdaLN-LoRA. + rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. + rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. + rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. + extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. + extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings. + extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. + extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. + extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. + + Notes: + Supported block types in block_config: + * cross_attn, ca: Cross attention + * full_attn: Full attention on all flattened tokens + * mlp, ff: Feed forward block + """ + + def __init__( + self, + max_img_h: int, + max_img_w: int, + max_frames: int, + in_channels: int, + out_channels: int, + patch_spatial: tuple, + patch_temporal: int, + concat_padding_mask: bool = True, + # attention settings + block_config: str = "FA-CA-MLP", + model_channels: int = 768, + num_blocks: int = 10, + num_heads: int = 16, + mlp_ratio: float = 4.0, + block_x_format: str = "BTHWD", + # cross attention settings + crossattn_emb_channels: int = 1024, + use_cross_attn_mask: bool = False, + # positional embedding settings + pos_emb_cls: str = "sincos", + pos_emb_learnable: bool = False, + pos_emb_interpolation: str = "crop", + affline_emb_norm: bool = False, # whether or not to normalize the affine embedding + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + rope_h_extrapolation_ratio: float = 1.0, + rope_w_extrapolation_ratio: float = 1.0, + rope_t_extrapolation_ratio: float = 1.0, + extra_per_block_abs_pos_emb: bool = False, + extra_per_block_abs_pos_emb_type: str = "sincos", + extra_h_extrapolation_ratio: float = 1.0, + extra_w_extrapolation_ratio: float = 1.0, + extra_t_extrapolation_ratio: float = 1.0, + image_model=None, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + self.max_img_h = max_img_h + self.max_img_w = max_img_w + self.max_frames = max_frames + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_spatial = patch_spatial + self.patch_temporal = patch_temporal + self.num_heads = num_heads + self.num_blocks = num_blocks + self.model_channels = model_channels + self.use_cross_attn_mask = use_cross_attn_mask + self.concat_padding_mask = concat_padding_mask + # positional embedding settings + self.pos_emb_cls = pos_emb_cls + self.pos_emb_learnable = pos_emb_learnable + self.pos_emb_interpolation = pos_emb_interpolation + self.affline_emb_norm = affline_emb_norm + self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio + self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio + self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio + self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb + self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower() + self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio + self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio + self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio + self.dtype = dtype + weight_args = {"device": device, "dtype": dtype} + + in_channels = in_channels + 1 if concat_padding_mask else in_channels + self.x_embedder = PatchEmbed( + spatial_patch_size=patch_spatial, + temporal_patch_size=patch_temporal, + in_channels=in_channels, + out_channels=model_channels, + bias=False, + weight_args=weight_args, + operations=operations, + ) + + self.build_pos_embed(device=device, dtype=dtype) + self.block_x_format = block_x_format + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + self.t_embedder = nn.ModuleList( + [Timesteps(model_channels), + TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),] + ) + + self.blocks = nn.ModuleDict() + + for idx in range(num_blocks): + self.blocks[f"block{idx}"] = GeneralDITTransformerBlock( + x_dim=model_channels, + context_dim=crossattn_emb_channels, + num_heads=num_heads, + block_config=block_config, + mlp_ratio=mlp_ratio, + x_format=self.block_x_format, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + weight_args=weight_args, + operations=operations, + ) + + if self.affline_emb_norm: + logging.debug("Building affine embedding normalization layer") + self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype) + else: + self.affline_norm = nn.Identity() + + self.final_layer = FinalLayer( + hidden_size=self.model_channels, + spatial_patch_size=self.patch_spatial, + temporal_patch_size=self.patch_temporal, + out_channels=self.out_channels, + use_adaln_lora=self.use_adaln_lora, + adaln_lora_dim=self.adaln_lora_dim, + weight_args=weight_args, + operations=operations, + ) + + def build_pos_embed(self, device=None, dtype=None): + if self.pos_emb_cls == "rope3d": + cls_type = VideoRopePosition3DEmb + else: + raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") + + logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") + kwargs = dict( + model_channels=self.model_channels, + len_h=self.max_img_h // self.patch_spatial, + len_w=self.max_img_w // self.patch_spatial, + len_t=self.max_frames // self.patch_temporal, + is_learnable=self.pos_emb_learnable, + interpolation=self.pos_emb_interpolation, + head_dim=self.model_channels // self.num_heads, + h_extrapolation_ratio=self.rope_h_extrapolation_ratio, + w_extrapolation_ratio=self.rope_w_extrapolation_ratio, + t_extrapolation_ratio=self.rope_t_extrapolation_ratio, + device=device, + ) + self.pos_embedder = cls_type( + **kwargs, + ) + + if self.extra_per_block_abs_pos_emb: + assert self.extra_per_block_abs_pos_emb_type in [ + "learnable", + ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}" + kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio + kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio + kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio + kwargs["device"] = device + kwargs["dtype"] = dtype + self.extra_pos_embedder = LearnablePosEmbAxis( + **kwargs, + ) + + def prepare_embedded_sequence( + self, + x_B_C_T_H_W: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. + + Args: + x_B_C_T_H_W (torch.Tensor): video + fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. + If None, a default value (`self.base_fps`) will be used. + padding_mask (Optional[torch.Tensor]): current it is not used + + Returns: + Tuple[torch.Tensor, Optional[torch.Tensor]]: + - A tensor of shape (B, T, H, W, D) with the embedded sequence. + - An optional positional embedding tensor, returned only if the positional embedding class + (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. + + Notes: + - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. + - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. + - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using + the `self.pos_embedder` with the shape [T, H, W]. + - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the + `self.pos_embedder` with the fps tensor. + - Otherwise, the positional embeddings are generated without considering fps. + """ + if self.concat_padding_mask: + if padding_mask is not None: + padding_mask = transforms.functional.resize( + padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST + ) + else: + padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) + + x_B_C_T_H_W = torch.cat( + [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 + ) + x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) + + if self.extra_per_block_abs_pos_emb: + extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) + else: + extra_pos_emb = None + + if "rope" in self.pos_emb_cls.lower(): + return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb + + if "fps_aware" in self.pos_emb_cls: + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device) # [B, T, H, W, D] + else: + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] + + return x_B_T_H_W_D, None, extra_pos_emb + + def decoder_head( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_D: torch.Tensor, + crossattn_emb: torch.Tensor, + origin_shape: Tuple[int, int, int, int, int], # [B, C, T, H, W] + crossattn_mask: Optional[torch.Tensor] = None, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + del crossattn_emb, crossattn_mask + B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape + x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D") + x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D) + # This is to ensure x_BT_HW_D has the correct shape because + # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D). + x_BT_HW_D = x_BT_HW_D.view( + B * T_before_patchify // self.patch_temporal, + H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial, + -1, + ) + x_B_D_T_H_W = rearrange( + x_BT_HW_D, + "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)", + p1=self.patch_spatial, + p2=self.patch_spatial, + H=H_before_patchify // self.patch_spatial, + W=W_before_patchify // self.patch_spatial, + t=self.patch_temporal, + B=B, + ) + return x_B_D_T_H_W + + def forward_before_blocks( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + crossattn_emb: torch.Tensor, + crossattn_mask: Optional[torch.Tensor] = None, + fps: Optional[torch.Tensor] = None, + image_size: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + scalar_feature: Optional[torch.Tensor] = None, + data_type: Optional[DataType] = DataType.VIDEO, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + Args: + x: (B, C, T, H, W) tensor of spatial-temp inputs + timesteps: (B, ) tensor of timesteps + crossattn_emb: (B, N, D) tensor of cross-attention embeddings + crossattn_mask: (B, N) tensor of cross-attention masks + """ + del kwargs + assert isinstance( + data_type, DataType + ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later." + original_shape = x.shape + x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( + x, + fps=fps, + padding_mask=padding_mask, + latent_condition=latent_condition, + latent_condition_sigma=latent_condition_sigma, + ) + # logging affline scale information + affline_scale_log_info = {} + + timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype)) + affline_emb_B_D = timesteps_B_D + affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach() + + if scalar_feature is not None: + raise NotImplementedError("Scalar feature is not implemented yet.") + + affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach() + affline_emb_B_D = self.affline_norm(affline_emb_B_D) + + if self.use_cross_attn_mask: + if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask): + crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max + crossattn_mask = crossattn_mask[:, None, None, :] # .to(dtype=torch.bool) # [B, 1, 1, length] + else: + crossattn_mask = None + + if self.blocks["block0"].x_format == "THWBD": + x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D") + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange( + extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D" + ) + crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D") + + if crossattn_mask: + crossattn_mask = rearrange(crossattn_mask, "B M -> M B") + + elif self.blocks["block0"].x_format == "BTHWD": + x = x_B_T_H_W_D + else: + raise ValueError(f"Unknown x_format {self.blocks[0].x_format}") + output = { + "x": x, + "affline_emb_B_D": affline_emb_B_D, + "crossattn_emb": crossattn_emb, + "crossattn_mask": crossattn_mask, + "rope_emb_L_1_1_D": rope_emb_L_1_1_D, + "adaln_lora_B_3D": adaln_lora_B_3D, + "original_shape": original_shape, + "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, + } + return output + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + # crossattn_emb: torch.Tensor, + # crossattn_mask: Optional[torch.Tensor] = None, + fps: Optional[torch.Tensor] = None, + image_size: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + scalar_feature: Optional[torch.Tensor] = None, + data_type: Optional[DataType] = DataType.VIDEO, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + condition_video_augment_sigma: Optional[torch.Tensor] = None, + **kwargs, + ): + """ + Args: + x: (B, C, T, H, W) tensor of spatial-temp inputs + timesteps: (B, ) tensor of timesteps + crossattn_emb: (B, N, D) tensor of cross-attention embeddings + crossattn_mask: (B, N) tensor of cross-attention masks + condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to + augment condition input, the lvg model will condition on the condition_video_augment_sigma value; + we need forward_before_blocks pass to the forward_before_blocks function. + """ + + crossattn_emb = context + crossattn_mask = attention_mask + + inputs = self.forward_before_blocks( + x=x, + timesteps=timesteps, + crossattn_emb=crossattn_emb, + crossattn_mask=crossattn_mask, + fps=fps, + image_size=image_size, + padding_mask=padding_mask, + scalar_feature=scalar_feature, + data_type=data_type, + latent_condition=latent_condition, + latent_condition_sigma=latent_condition_sigma, + condition_video_augment_sigma=condition_video_augment_sigma, + **kwargs, + ) + x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = ( + inputs["x"], + inputs["affline_emb_B_D"], + inputs["crossattn_emb"], + inputs["crossattn_mask"], + inputs["rope_emb_L_1_1_D"], + inputs["adaln_lora_B_3D"], + inputs["original_shape"], + ) + extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype) + del inputs + + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + assert ( + x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape + ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}" + + for _, block in self.blocks.items(): + assert ( + self.blocks["block0"].x_format == block.x_format + ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}" + + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D + x = block( + x, + affline_emb_B_D, + crossattn_emb, + crossattn_mask, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + adaln_lora_B_3D=adaln_lora_B_3D, + ) + + x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D") + + x_B_D_T_H_W = self.decoder_head( + x_B_T_H_W_D=x_B_T_H_W_D, + emb_B_D=affline_emb_B_D, + crossattn_emb=None, + origin_shape=original_shape, + crossattn_mask=None, + adaln_lora_B_3D=adaln_lora_B_3D, + ) + + return x_B_D_T_H_W diff --git a/comfy/ldm/cosmos/position_embedding.py b/comfy/ldm/cosmos/position_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..c925811d4c2275699a030ef8bbcb299a65918754 --- /dev/null +++ b/comfy/ldm/cosmos/position_embedding.py @@ -0,0 +1,207 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional + +import torch +from einops import rearrange, repeat +from torch import nn +import math + + +def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0) -> torch.Tensor: + """ + Normalizes the input tensor along specified dimensions such that the average square norm of elements is adjusted. + + Args: + x (torch.Tensor): The input tensor to normalize. + dim (list, optional): The dimensions over which to normalize. If None, normalizes over all dimensions except the first. + eps (float, optional): A small constant to ensure numerical stability during division. + + Returns: + torch.Tensor: The normalized tensor. + """ + if dim is None: + dim = list(range(1, x.ndim)) + norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) + norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel())) + return x / norm.to(x.dtype) + + +class VideoPositionEmb(nn.Module): + def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: + """ + It delegates the embedding generation to generate_embeddings function. + """ + B_T_H_W_C = x_B_T_H_W_C.shape + embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype) + + return embeddings + + def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None): + raise NotImplementedError + + +class VideoRopePosition3DEmb(VideoPositionEmb): + def __init__( + self, + *, # enforce keyword arguments + head_dim: int, + len_h: int, + len_w: int, + len_t: int, + base_fps: int = 24, + h_extrapolation_ratio: float = 1.0, + w_extrapolation_ratio: float = 1.0, + t_extrapolation_ratio: float = 1.0, + enable_fps_modulation: bool = True, + device=None, + **kwargs, # used for compatibility with other positional embeddings; unused in this class + ): + del kwargs + super().__init__() + self.base_fps = base_fps + self.max_h = len_h + self.max_w = len_w + self.enable_fps_modulation = enable_fps_modulation + + dim = head_dim + dim_h = dim // 6 * 2 + dim_w = dim_h + dim_t = dim - 2 * dim_h + assert dim == dim_h + dim_w + dim_t, f"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}" + self.register_buffer( + "dim_spatial_range", + torch.arange(0, dim_h, 2, device=device)[: (dim_h // 2)].float() / dim_h, + persistent=False, + ) + self.register_buffer( + "dim_temporal_range", + torch.arange(0, dim_t, 2, device=device)[: (dim_t // 2)].float() / dim_t, + persistent=False, + ) + + self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2)) + self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2)) + self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2)) + + def generate_embeddings( + self, + B_T_H_W_C: torch.Size, + fps: Optional[torch.Tensor] = None, + h_ntk_factor: Optional[float] = None, + w_ntk_factor: Optional[float] = None, + t_ntk_factor: Optional[float] = None, + device=None, + dtype=None, + ): + """ + Generate embeddings for the given input size. + + Args: + B_T_H_W_C (torch.Size): Input tensor size (Batch, Time, Height, Width, Channels). + fps (Optional[torch.Tensor], optional): Frames per second. Defaults to None. + h_ntk_factor (Optional[float], optional): Height NTK factor. If None, uses self.h_ntk_factor. + w_ntk_factor (Optional[float], optional): Width NTK factor. If None, uses self.w_ntk_factor. + t_ntk_factor (Optional[float], optional): Time NTK factor. If None, uses self.t_ntk_factor. + + Returns: + Not specified in the original code snippet. + """ + h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor + w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor + t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor + + h_theta = 10000.0 * h_ntk_factor + w_theta = 10000.0 * w_ntk_factor + t_theta = 10000.0 * t_ntk_factor + + h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range.to(device=device)) + w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range.to(device=device)) + temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device)) + + B, T, H, W, _ = B_T_H_W_C + seq = torch.arange(max(H, W, T), dtype=torch.float, device=device) + uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max()) + assert ( + uniform_fps or B == 1 or T == 1 + ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1" + half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs) + half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs) + + # apply sequence scaling in temporal dimension + if fps is None or self.enable_fps_modulation is False: # image case + half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs) + else: + half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs) + + half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1) + half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1) + half_emb_t = torch.stack([torch.cos(half_emb_t), -torch.sin(half_emb_t), torch.sin(half_emb_t), torch.cos(half_emb_t)], dim=-1) + + em_T_H_W_D = torch.cat( + [ + repeat(half_emb_t, "t d x -> t h w d x", h=H, w=W), + repeat(half_emb_h, "h d x -> t h w d x", t=T, w=W), + repeat(half_emb_w, "w d x -> t h w d x", t=T, h=H), + ] + , dim=-2, + ) + + return rearrange(em_T_H_W_D, "t h w d (i j) -> (t h w) d i j", i=2, j=2).float() + + +class LearnablePosEmbAxis(VideoPositionEmb): + def __init__( + self, + *, # enforce keyword arguments + interpolation: str, + model_channels: int, + len_h: int, + len_w: int, + len_t: int, + device=None, + dtype=None, + **kwargs, + ): + """ + Args: + interpolation (str): we curretly only support "crop", ideally when we need extrapolation capacity, we should adjust frequency or other more advanced methods. they are not implemented yet. + """ + del kwargs # unused + super().__init__() + self.interpolation = interpolation + assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}" + + self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device, dtype=dtype)) + self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype)) + self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype)) + + def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: + B, T, H, W, _ = B_T_H_W_C + if self.interpolation == "crop": + emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype) + emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype) + emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype) + emb = ( + repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W) + + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W) + + repeat(emb_w_W, "w d-> b t h w d", b=B, t=T, h=H) + ) + assert list(emb.shape)[:4] == [B, T, H, W], f"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}" + else: + raise ValueError(f"Unknown interpolation method {self.interpolation}") + + return normalize(emb, dim=-1, eps=1e-6) diff --git a/comfy/ldm/cosmos/predict2.py b/comfy/ldm/cosmos/predict2.py new file mode 100644 index 0000000000000000000000000000000000000000..316117f779f2a52dc0ae38250a926c72d7692356 --- /dev/null +++ b/comfy/ldm/cosmos/predict2.py @@ -0,0 +1,864 @@ +# original code from: https://github.com/nvidia-cosmos/cosmos-predict2 + +import torch +from torch import nn +from einops import rearrange +from einops.layers.torch import Rearrange +import logging +from typing import Callable, Optional, Tuple +import math + +from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis +from torchvision import transforms + +from comfy.ldm.modules.attention import optimized_attention + +def apply_rotary_pos_emb( + t: torch.Tensor, + freqs: torch.Tensor, +) -> torch.Tensor: + t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float() + t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1] + t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t) + return t_out + + +# ---------------------- Feed Forward Network ----------------------- +class GPT2FeedForward(nn.Module): + def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None: + super().__init__() + self.activation = nn.GELU() + self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype) + self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype) + + self._layer_id = None + self._dim = d_model + self._hidden_dim = d_ff + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer1(x) + + x = self.activation(x) + x = self.layer2(x) + return x + + +def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor: + """Computes multi-head attention using PyTorch's native implementation. + + This function provides a PyTorch backend alternative to Transformer Engine's attention operation. + It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product + attention, and rearranges the output back to the original format. + + The input tensor names use the following dimension conventions: + + - B: batch size + - S: sequence length + - H: number of attention heads + - D: head dimension + + Args: + q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim) + k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim) + v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim) + + Returns: + Attention output tensor with shape (batch, seq_len, n_heads * head_dim) + """ + in_q_shape = q_B_S_H_D.shape + in_k_shape = k_B_S_H_D.shape + q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1]) + k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) + v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) + return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True) + + +class Attention(nn.Module): + """ + A flexible attention module supporting both self-attention and cross-attention mechanisms. + + This module implements a multi-head attention layer that can operate in either self-attention + or cross-attention mode. The mode is determined by whether a context dimension is provided. + The implementation uses scaled dot-product attention and supports optional bias terms and + dropout regularization. + + Args: + query_dim (int): The dimensionality of the query vectors. + context_dim (int, optional): The dimensionality of the context (key/value) vectors. + If None, the module operates in self-attention mode using query_dim. Default: None + n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8 + head_dim (int, optional): The dimension of each attention head. Default: 64 + dropout (float, optional): Dropout probability applied to the output. Default: 0.0 + qkv_format (str, optional): Format specification for QKV tensors. Default: "bshd" + backend (str, optional): Backend to use for the attention operation. Default: "transformer_engine" + + Examples: + >>> # Self-attention with 512 dimensions and 8 heads + >>> self_attn = Attention(query_dim=512) + >>> x = torch.randn(32, 16, 512) # (batch_size, seq_len, dim) + >>> out = self_attn(x) # (32, 16, 512) + + >>> # Cross-attention + >>> cross_attn = Attention(query_dim=512, context_dim=256) + >>> query = torch.randn(32, 16, 512) + >>> context = torch.randn(32, 8, 256) + >>> out = cross_attn(query, context) # (32, 16, 512) + """ + + def __init__( + self, + query_dim: int, + context_dim: Optional[int] = None, + n_heads: int = 8, + head_dim: int = 64, + dropout: float = 0.0, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + logging.debug( + f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " + f"{n_heads} heads with a dimension of {head_dim}." + ) + self.is_selfattn = context_dim is None # self attention + + context_dim = query_dim if context_dim is None else context_dim + inner_dim = head_dim * n_heads + + self.n_heads = n_heads + self.head_dim = head_dim + self.query_dim = query_dim + self.context_dim = context_dim + + self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype) + self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) + + self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) + self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) + + self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) + self.v_norm = nn.Identity() + + self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype) + self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity() + + self.attn_op = torch_attention_op + + self._query_dim = query_dim + self._context_dim = context_dim + self._inner_dim = inner_dim + + def compute_qkv( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + rope_emb: Optional[torch.Tensor] = None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + q = self.q_proj(x) + context = x if context is None else context + k = self.k_proj(context) + v = self.v_proj(context) + q, k, v = map( + lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim), + (q, k, v), + ) + + def apply_norm_and_rotary_pos_emb( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + q = self.q_norm(q) + k = self.k_norm(k) + v = self.v_norm(v) + if self.is_selfattn and rope_emb is not None: # only apply to self-attention! + q = apply_rotary_pos_emb(q, rope_emb) + k = apply_rotary_pos_emb(k, rope_emb) + return q, k, v + + q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb) + + return q, k, v + + def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor: + result = self.attn_op(q, k, v) # [B, S, H, D] + return self.output_dropout(self.output_proj(result)) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + rope_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Args: + x (Tensor): The query tensor of shape [B, Mq, K] + context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None + """ + q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb) + return self.compute_attention(q, k, v) + + +class Timesteps(nn.Module): + def __init__(self, num_channels: int): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor: + assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}" + timesteps = timesteps_B_T.flatten().float() + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - 0.0) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + sin_emb = torch.sin(emb) + cos_emb = torch.cos(emb) + emb = torch.cat([cos_emb, sin_emb], dim=-1) + + return rearrange(emb, "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1]) + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None): + super().__init__() + logging.debug( + f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." + ) + self.in_dim = in_features + self.out_dim = out_features + self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype) + self.activation = nn.SiLU() + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype) + else: + self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype) + + def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + emb = self.linear_1(sample) + emb = self.activation(emb) + emb = self.linear_2(emb) + + if self.use_adaln_lora: + adaln_lora_B_T_3D = emb + emb_B_T_D = sample + else: + adaln_lora_B_T_3D = None + emb_B_T_D = emb + + return emb_B_T_D, adaln_lora_B_T_3D + + +class PatchEmbed(nn.Module): + """ + PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, + depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, + making it suitable for video and image processing tasks. It supports dividing the input into patches + and embedding each patch into a vector of size `out_channels`. + + Parameters: + - spatial_patch_size (int): The size of each spatial patch. + - temporal_patch_size (int): The size of each temporal patch. + - in_channels (int): Number of input channels. Default: 3. + - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. + - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. + """ + + def __init__( + self, + spatial_patch_size: int, + temporal_patch_size: int, + in_channels: int = 3, + out_channels: int = 768, + device=None, dtype=None, operations=None + ): + super().__init__() + self.spatial_patch_size = spatial_patch_size + self.temporal_patch_size = temporal_patch_size + + self.proj = nn.Sequential( + Rearrange( + "b c (t r) (h m) (w n) -> b t h w (c r m n)", + r=temporal_patch_size, + m=spatial_patch_size, + n=spatial_patch_size, + ), + operations.Linear( + in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype + ), + ) + self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the PatchEmbed module. + + Parameters: + - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where + B is the batch size, + C is the number of channels, + T is the temporal dimension, + H is the height, and + W is the width of the input. + + Returns: + - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. + """ + assert x.dim() == 5 + _, _, T, H, W = x.shape + assert ( + H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 + ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}" + assert T % self.temporal_patch_size == 0 + x = self.proj(x) + return x + + +class FinalLayer(nn.Module): + """ + The final layer of video DiT. + """ + + def __init__( + self, + hidden_size: int, + spatial_patch_size: int, + temporal_patch_size: int, + out_channels: int, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + device=None, dtype=None, operations=None + ): + super().__init__() + self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = operations.Linear( + hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype + ) + self.hidden_size = hidden_size + self.n_adaln_chunks = 2 + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + if use_adaln_lora: + self.adaln_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype), + ) + else: + self.adaln_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype) + ) + + def forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + ): + if self.use_adaln_lora: + assert adaln_lora_B_T_3D is not None + shift_B_T_D, scale_B_T_D = ( + self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size] + ).chunk(2, dim=-1) + else: + shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1) + + shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d"), rearrange( + scale_B_T_D, "b t d -> b t 1 1 d" + ) + + def _fn( + _x_B_T_H_W_D: torch.Tensor, + _norm_layer: nn.Module, + _scale_B_T_1_1_D: torch.Tensor, + _shift_B_T_1_1_D: torch.Tensor, + ) -> torch.Tensor: + return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D + + x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D) + x_B_T_H_W_O = self.linear(x_B_T_H_W_D) + return x_B_T_H_W_O + + +class Block(nn.Module): + """ + A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation. + Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation. + + Parameters: + x_dim (int): Dimension of input features + context_dim (int): Dimension of context features for cross-attention + num_heads (int): Number of attention heads + mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0 + use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False + adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256 + + The block applies the following sequence: + 1. Self-attention with AdaLN modulation + 2. Cross-attention with AdaLN modulation + 3. MLP with AdaLN modulation + + Each component uses skip connections and layer normalization. + """ + + def __init__( + self, + x_dim: int, + context_dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + self.x_dim = x_dim + self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) + self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations) + + self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) + self.cross_attn = Attention( + x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations + ) + + self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) + self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations) + + self.use_adaln_lora = use_adaln_lora + if self.use_adaln_lora: + self.adaln_modulation_self_attn = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), + ) + self.adaln_modulation_cross_attn = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), + ) + self.adaln_modulation_mlp = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), + ) + else: + self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) + self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) + self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) + + def forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + crossattn_emb: torch.Tensor, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + extra_per_block_pos_emb: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if extra_per_block_pos_emb is not None: + x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb + + if self.use_adaln_lora: + shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = ( + self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = ( + self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = ( + self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + else: + shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn( + emb_B_T_D + ).chunk(3, dim=-1) + shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn( + emb_B_T_D + ).chunk(3, dim=-1) + shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1) + + # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting + shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d") + scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d") + gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d") + + shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d") + scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d") + gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d") + + shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d") + scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d") + gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d") + + B, T, H, W, D = x_B_T_H_W_D.shape + + def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D): + return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D + + normalized_x_B_T_H_W_D = _fn( + x_B_T_H_W_D, + self.layer_norm_self_attn, + scale_self_attn_B_T_1_1_D, + shift_self_attn_B_T_1_1_D, + ) + result_B_T_H_W_D = rearrange( + self.self_attn( + # normalized_x_B_T_HW_D, + rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), + None, + rope_emb=rope_emb_L_1_1_D, + ), + "b (t h w) d -> b t h w d", + t=T, + h=H, + w=W, + ) + x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D + + def _x_fn( + _x_B_T_H_W_D: torch.Tensor, + layer_norm_cross_attn: Callable, + _scale_cross_attn_B_T_1_1_D: torch.Tensor, + _shift_cross_attn_B_T_1_1_D: torch.Tensor, + ) -> torch.Tensor: + _normalized_x_B_T_H_W_D = _fn( + _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D + ) + _result_B_T_H_W_D = rearrange( + self.cross_attn( + rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), + crossattn_emb, + rope_emb=rope_emb_L_1_1_D, + ), + "b (t h w) d -> b t h w d", + t=T, + h=H, + w=W, + ) + return _result_B_T_H_W_D + + result_B_T_H_W_D = _x_fn( + x_B_T_H_W_D, + self.layer_norm_cross_attn, + scale_cross_attn_B_T_1_1_D, + shift_cross_attn_B_T_1_1_D, + ) + x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D + + normalized_x_B_T_H_W_D = _fn( + x_B_T_H_W_D, + self.layer_norm_mlp, + scale_mlp_B_T_1_1_D, + shift_mlp_B_T_1_1_D, + ) + result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D) + x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D + return x_B_T_H_W_D + + +class MiniTrainDIT(nn.Module): + """ + A clean impl of DIT that can load and reproduce the training results of the original DIT model in~(cosmos 1) + A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. + + Args: + max_img_h (int): Maximum height of the input images. + max_img_w (int): Maximum width of the input images. + max_frames (int): Maximum number of frames in the video sequence. + in_channels (int): Number of input channels (e.g., RGB channels for color images). + out_channels (int): Number of output channels. + patch_spatial (tuple): Spatial resolution of patches for input processing. + patch_temporal (int): Temporal resolution of patches for input processing. + concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. + model_channels (int): Base number of channels used throughout the model. + num_blocks (int): Number of transformer blocks. + num_heads (int): Number of heads in the multi-head attention layers. + mlp_ratio (float): Expansion ratio for MLP blocks. + crossattn_emb_channels (int): Number of embedding channels for cross-attention. + pos_emb_cls (str): Type of positional embeddings. + pos_emb_learnable (bool): Whether positional embeddings are learnable. + pos_emb_interpolation (str): Method for interpolating positional embeddings. + min_fps (int): Minimum frames per second. + max_fps (int): Maximum frames per second. + use_adaln_lora (bool): Whether to use AdaLN-LoRA. + adaln_lora_dim (int): Dimension for AdaLN-LoRA. + rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. + rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. + rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. + extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. + extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. + extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. + extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. + """ + + def __init__( + self, + max_img_h: int, + max_img_w: int, + max_frames: int, + in_channels: int, + out_channels: int, + patch_spatial: int, # tuple, + patch_temporal: int, + concat_padding_mask: bool = True, + # attention settings + model_channels: int = 768, + num_blocks: int = 10, + num_heads: int = 16, + mlp_ratio: float = 4.0, + # cross attention settings + crossattn_emb_channels: int = 1024, + # positional embedding settings + pos_emb_cls: str = "sincos", + pos_emb_learnable: bool = False, + pos_emb_interpolation: str = "crop", + min_fps: int = 1, + max_fps: int = 30, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + rope_h_extrapolation_ratio: float = 1.0, + rope_w_extrapolation_ratio: float = 1.0, + rope_t_extrapolation_ratio: float = 1.0, + extra_per_block_abs_pos_emb: bool = False, + extra_h_extrapolation_ratio: float = 1.0, + extra_w_extrapolation_ratio: float = 1.0, + extra_t_extrapolation_ratio: float = 1.0, + rope_enable_fps_modulation: bool = True, + image_model=None, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + self.dtype = dtype + self.max_img_h = max_img_h + self.max_img_w = max_img_w + self.max_frames = max_frames + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_spatial = patch_spatial + self.patch_temporal = patch_temporal + self.num_heads = num_heads + self.num_blocks = num_blocks + self.model_channels = model_channels + self.concat_padding_mask = concat_padding_mask + # positional embedding settings + self.pos_emb_cls = pos_emb_cls + self.pos_emb_learnable = pos_emb_learnable + self.pos_emb_interpolation = pos_emb_interpolation + self.min_fps = min_fps + self.max_fps = max_fps + self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio + self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio + self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio + self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb + self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio + self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio + self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio + self.rope_enable_fps_modulation = rope_enable_fps_modulation + + self.build_pos_embed(device=device, dtype=dtype) + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + self.t_embedder = nn.Sequential( + Timesteps(model_channels), + TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,), + ) + + in_channels = in_channels + 1 if concat_padding_mask else in_channels + self.x_embedder = PatchEmbed( + spatial_patch_size=patch_spatial, + temporal_patch_size=patch_temporal, + in_channels=in_channels, + out_channels=model_channels, + device=device, dtype=dtype, operations=operations, + ) + + self.blocks = nn.ModuleList( + [ + Block( + x_dim=model_channels, + context_dim=crossattn_emb_channels, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + device=device, dtype=dtype, operations=operations, + ) + for _ in range(num_blocks) + ] + ) + + self.final_layer = FinalLayer( + hidden_size=self.model_channels, + spatial_patch_size=self.patch_spatial, + temporal_patch_size=self.patch_temporal, + out_channels=self.out_channels, + use_adaln_lora=self.use_adaln_lora, + adaln_lora_dim=self.adaln_lora_dim, + device=device, dtype=dtype, operations=operations, + ) + + self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype) + + def build_pos_embed(self, device=None, dtype=None) -> None: + if self.pos_emb_cls == "rope3d": + cls_type = VideoRopePosition3DEmb + else: + raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") + + logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") + kwargs = dict( + model_channels=self.model_channels, + len_h=self.max_img_h // self.patch_spatial, + len_w=self.max_img_w // self.patch_spatial, + len_t=self.max_frames // self.patch_temporal, + max_fps=self.max_fps, + min_fps=self.min_fps, + is_learnable=self.pos_emb_learnable, + interpolation=self.pos_emb_interpolation, + head_dim=self.model_channels // self.num_heads, + h_extrapolation_ratio=self.rope_h_extrapolation_ratio, + w_extrapolation_ratio=self.rope_w_extrapolation_ratio, + t_extrapolation_ratio=self.rope_t_extrapolation_ratio, + enable_fps_modulation=self.rope_enable_fps_modulation, + device=device, + ) + self.pos_embedder = cls_type( + **kwargs, # type: ignore + ) + + if self.extra_per_block_abs_pos_emb: + kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio + kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio + kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio + kwargs["device"] = device + kwargs["dtype"] = dtype + self.extra_pos_embedder = LearnablePosEmbAxis( + **kwargs, # type: ignore + ) + + def prepare_embedded_sequence( + self, + x_B_C_T_H_W: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. + + Args: + x_B_C_T_H_W (torch.Tensor): video + fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. + If None, a default value (`self.base_fps`) will be used. + padding_mask (Optional[torch.Tensor]): current it is not used + + Returns: + Tuple[torch.Tensor, Optional[torch.Tensor]]: + - A tensor of shape (B, T, H, W, D) with the embedded sequence. + - An optional positional embedding tensor, returned only if the positional embedding class + (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. + + Notes: + - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. + - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. + - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using + the `self.pos_embedder` with the shape [T, H, W]. + - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the + `self.pos_embedder` with the fps tensor. + - Otherwise, the positional embeddings are generated without considering fps. + """ + if self.concat_padding_mask: + if padding_mask is None: + padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) + else: + padding_mask = transforms.functional.resize( + padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST + ) + x_B_C_T_H_W = torch.cat( + [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 + ) + x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) + + if self.extra_per_block_abs_pos_emb: + extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) + else: + extra_pos_emb = None + + if "rope" in self.pos_emb_cls.lower(): + return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] + + return x_B_T_H_W_D, None, extra_pos_emb + + def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor: + x_B_C_Tt_Hp_Wp = rearrange( + x_B_T_H_W_M, + "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)", + p1=self.patch_spatial, + p2=self.patch_spatial, + t=self.patch_temporal, + ) + return x_B_C_Tt_Hp_Wp + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + **kwargs, + ): + x_B_C_T_H_W = x + timesteps_B_T = timesteps + crossattn_emb = context + """ + Args: + x: (B, C, T, H, W) tensor of spatial-temp inputs + timesteps: (B, ) tensor of timesteps + crossattn_emb: (B, N, D) tensor of cross-attention embeddings + """ + x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( + x_B_C_T_H_W, + fps=fps, + padding_mask=padding_mask, + ) + + if timesteps_B_T.ndim == 1: + timesteps_B_T = timesteps_B_T.unsqueeze(1) + t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype)) + t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D) + + # for logging purpose + affline_scale_log_info = {} + affline_scale_log_info["t_embedding_B_T_D"] = t_embedding_B_T_D.detach() + self.affline_scale_log_info = affline_scale_log_info + self.affline_emb = t_embedding_B_T_D + self.crossattn_emb = crossattn_emb + + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + assert ( + x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape + ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}" + + block_kwargs = { + "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0), + "adaln_lora_B_T_3D": adaln_lora_B_T_3D, + "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, + } + for block in self.blocks: + x_B_T_H_W_D = block( + x_B_T_H_W_D, + t_embedding_B_T_D, + crossattn_emb, + **block_kwargs, + ) + + x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D) + x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O) + return x_B_C_Tt_Hp_Wp diff --git a/comfy/ldm/cosmos/vae.py b/comfy/ldm/cosmos/vae.py new file mode 100644 index 0000000000000000000000000000000000000000..d64f292de735258f43c07099bee03e3c871e9a26 --- /dev/null +++ b/comfy/ldm/cosmos/vae.py @@ -0,0 +1,131 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The causal continuous video tokenizer with VAE or AE formulation for 3D data..""" + +import logging +import torch +from torch import nn +from enum import Enum +import math + +from .cosmos_tokenizer.layers3d import ( + EncoderFactorized, + DecoderFactorized, + CausalConv3d, +) + + +class IdentityDistribution(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, parameters): + return parameters, (torch.tensor([0.0]), torch.tensor([0.0])) + + +class GaussianDistribution(torch.nn.Module): + def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0): + super().__init__() + self.min_logvar = min_logvar + self.max_logvar = max_logvar + + def sample(self, mean, logvar): + std = torch.exp(0.5 * logvar) + return mean + std * torch.randn_like(mean) + + def forward(self, parameters): + mean, logvar = torch.chunk(parameters, 2, dim=1) + logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar) + return self.sample(mean, logvar), (mean, logvar) + + +class ContinuousFormulation(Enum): + VAE = GaussianDistribution + AE = IdentityDistribution + + +class CausalContinuousVideoTokenizer(nn.Module): + def __init__( + self, z_channels: int, z_factor: int, latent_channels: int, **kwargs + ) -> None: + super().__init__() + self.name = kwargs.get("name", "CausalContinuousVideoTokenizer") + self.latent_channels = latent_channels + self.sigma_data = 0.5 + + # encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name) + self.encoder = EncoderFactorized( + z_channels=z_factor * z_channels, **kwargs + ) + if kwargs.get("temporal_compression", 4) == 4: + kwargs["channels_mult"] = [2, 4] + # decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name) + self.decoder = DecoderFactorized( + z_channels=z_channels, **kwargs + ) + + self.quant_conv = CausalConv3d( + z_factor * z_channels, + z_factor * latent_channels, + kernel_size=1, + padding=0, + ) + self.post_quant_conv = CausalConv3d( + latent_channels, z_channels, kernel_size=1, padding=0 + ) + + # formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name) + self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value() + + num_parameters = sum(param.numel() for param in self.parameters()) + logging.debug(f"model={self.name}, num_parameters={num_parameters:,}") + logging.debug( + f"z_channels={z_channels}, latent_channels={self.latent_channels}." + ) + + latent_temporal_chunk = 16 + self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) + self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) + + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + z, posteriors = self.distribution(moments) + latent_ch = z.shape[1] + latent_t = z.shape[2] + in_dtype = z.dtype + mean = self.latent_mean.view(latent_ch, -1) + std = self.latent_std.view(latent_ch, -1) + + mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + return ((z - mean) / std) * self.sigma_data + + def decode(self, z): + in_dtype = z.dtype + latent_ch = z.shape[1] + latent_t = z.shape[2] + mean = self.latent_mean.view(latent_ch, -1) + std = self.latent_std.view(latent_ch, -1) + + mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + + z = z / self.sigma_data + z = z * std + mean + z = self.post_quant_conv(z) + return self.decoder(z) + diff --git a/comfy/ldm/flux/controlnet.py b/comfy/ldm/flux/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..7dcf82bbf3e8e9a369dda0df39f37f0653514747 --- /dev/null +++ b/comfy/ldm/flux/controlnet.py @@ -0,0 +1,208 @@ +#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py +#modified to support different types of flux controlnets + +import torch +import math +from torch import Tensor, nn +from einops import rearrange, repeat + +from .layers import (timestep_embedding) + +from .model import Flux +import comfy.ldm.common_dit + +class MistolineCondDownsamplBlock(nn.Module): + def __init__(self, dtype=None, device=None, operations=None): + super().__init__() + self.encoder = nn.Sequential( + operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.encoder(x) + +class MistolineControlnetBlock(nn.Module): + def __init__(self, hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device) + self.act = nn.SiLU() + + def forward(self, x): + return self.act(self.linear(x)) + + +class ControlNetFlux(Flux): + def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs): + super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) + + self.main_model_double = 19 + self.main_model_single = 38 + + self.mistoline = mistoline + # add ControlNet blocks + if self.mistoline: + control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations) + else: + control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) + + self.controlnet_blocks = nn.ModuleList([]) + for _ in range(self.params.depth): + self.controlnet_blocks.append(control_block()) + + self.controlnet_single_blocks = nn.ModuleList([]) + for _ in range(self.params.depth_single_blocks): + self.controlnet_single_blocks.append(control_block()) + + self.num_union_modes = num_union_modes + self.controlnet_mode_embedder = None + if self.num_union_modes > 0: + self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device) + + self.gradient_checkpointing = False + self.latent_input = latent_input + if control_latent_channels is None: + control_latent_channels = self.in_channels + else: + control_latent_channels *= 2 * 2 #patch size + + self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + if not self.latent_input: + if self.mistoline: + self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations) + else: + self.input_hint_block = nn.Sequential( + operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), + nn.SiLU(), + operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) + ) + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + controlnet_cond: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control_type: Tensor = None, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + else: + y = y[:, :self.params.vec_in_dim] + + # running on sequences img + img = self.img_in(img) + + controlnet_cond = self.pos_embed_input(controlnet_cond) + img = img + controlnet_cond + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + if self.controlnet_mode_embedder is not None and len(control_type) > 0: + control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1)) + txt = torch.cat([control_cond, txt], dim=1) + txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + controlnet_double = () + + for i in range(len(self.double_blocks)): + img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe) + controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),) + + img = torch.cat((txt, img), 1) + + controlnet_single = () + + for i in range(len(self.single_blocks)): + img = self.single_blocks[i](img, vec=vec, pe=pe) + controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),) + + repeat = math.ceil(self.main_model_double / len(controlnet_double)) + if self.latent_input: + out_input = () + for x in controlnet_double: + out_input += (x,) * repeat + else: + out_input = (controlnet_double * repeat) + + out = {"input": out_input[:self.main_model_double]} + if len(controlnet_single) > 0: + repeat = math.ceil(self.main_model_single / len(controlnet_single)) + out_output = () + if self.latent_input: + for x in controlnet_single: + out_output += (x,) * repeat + else: + out_output = (controlnet_single * repeat) + out["output"] = out_output[:self.main_model_single] + return out + + def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs): + patch_size = 2 + if self.latent_input: + hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) + elif self.mistoline: + hint = hint * 2.0 - 1.0 + hint = self.input_cond_block(hint) + else: + hint = hint * 2.0 - 1.0 + hint = self.input_hint_block(hint) + + hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + bs, c, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :] + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", [])) diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..113eb20962a2ead9237940bc8585901dd038ec61 --- /dev/null +++ b/comfy/ldm/flux/layers.py @@ -0,0 +1,278 @@ +import math +from dataclasses import dataclass + +import torch +from torch import Tensor, nn + +from .math import attention, rope +import comfy.ops +import comfy.ldm.common_dit + + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: Tensor) -> Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + + return emb.unsqueeze(1) + + +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.silu = nn.SiLU() + self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x: Tensor) -> Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device)) + + def forward(self, x: Tensor): + return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6) + + +class QKNorm(torch.nn.Module): + def __init__(self, dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) + self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple: + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) + + def forward(self, vec: Tensor) -> tuple: + if vec.ndim == 2: + vec = vec[:, None, :] + out = self.lin(nn.functional.silu(vec)).chunk(self.multiplier, dim=-1) + + return ( + ModulationOut(*out[:3]), + ModulationOut(*out[3:]) if self.is_double else None, + ) + + +def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None): + if modulation_dims is None: + if m_add is not None: + return torch.addcmul(m_add, tensor, m_mult) + else: + return tensor * m_mult + else: + for d in modulation_dims: + tensor[:, d[0]:d[1]] *= m_mult[:, d[2]] + if m_add is not None: + tensor[:, d[0]:d[1]] += m_add[:, d[2]] + return tensor + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.flipped_img_txt = flipped_img_txt + + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None): + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img) + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt) + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + if self.flipped_img_txt: + # run actual attention + attn = attention(torch.cat((img_q, txt_q), dim=2), + torch.cat((img_k, txt_k), dim=2), + torch.cat((img_v, txt_v), dim=2), + pe=pe, mask=attn_mask) + + img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:] + else: + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, mask=attn_mask) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] + + # calculate the img bloks + img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img) + img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img) + + # calculate the txt bloks + txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt) + txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + # proj and mlp_out + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + + self.hidden_size = hidden_size + self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor: + mod, _ = self.modulation(vec) + qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe, mask=attn_mask) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x += apply_mod(output, mod.gate, None, modulation_dims) + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)) + + def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor: + if vec.ndim == 2: + vec = vec[:, None, :] + + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=-1) + x = apply_mod(self.norm_final(x), (1 + scale), shift, modulation_dims) + x = self.linear(x) + return x diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py new file mode 100644 index 0000000000000000000000000000000000000000..3e09781768a7b1fb1a72e18a62811b139f459a0b --- /dev/null +++ b/comfy/ldm/flux/math.py @@ -0,0 +1,45 @@ +import torch +from einops import rearrange +from torch import Tensor + +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: + q_shape = q.shape + k_shape = k.shape + + if pe is not None: + q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2) + k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2) + q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v) + k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v) + + heads = q.shape[1] + x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): + xq_ = xq.to(dtype=freqs_cis.dtype).reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.to(dtype=freqs_cis.dtype).reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) + diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py new file mode 100644 index 0000000000000000000000000000000000000000..8f4d99f548f3ee1dbf0bd92fb9df7fe71e25e421 --- /dev/null +++ b/comfy/ldm/flux/model.py @@ -0,0 +1,244 @@ +#Original code can be found on: https://github.com/black-forest-labs/flux + +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange, repeat +import comfy.ldm.common_dit + +from .layers import ( + DoubleStreamBlock, + EmbedND, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding, +) + +@dataclass +class FluxParams: + in_channels: int + out_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + patch_size: int + qkv_bias: bool + guidance_embed: bool + + +class Flux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = FluxParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels * params.patch_size * params.patch_size + self.out_channels = params.out_channels * params.patch_size * params.patch_size + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() + ) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, + ) -> Tensor: + + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + + patches_replace = transformer_options.get("patches_replace", {}) + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) + if self.params.guidance_embed: + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + vec = vec + self.vector_in(y[:,:self.params.vec_in_dim]) + txt = self.txt_in(txt) + + if img_ids is not None: + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + else: + pe = None + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + attn_mask=attn_mask) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + if img.dtype == torch.float16: + img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504) + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img + + def process_img(self, x, index=0, h_offset=0, w_offset=0): + bs, c, h, w = x.shape + patch_size = self.patch_size + x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + + h_offset = ((h_offset + (patch_size // 2)) // patch_size) + w_offset = ((w_offset + (patch_size // 2)) // patch_size) + + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 0] = img_ids[:, :, 1] + index + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + return img, repeat(img_ids, "h w c -> b (h w) c", b=bs) + + def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): + bs, c, h_orig, w_orig = x.shape + patch_size = self.patch_size + + h_len = ((h_orig + (patch_size // 2)) // patch_size) + w_len = ((w_orig + (patch_size // 2)) // patch_size) + img, img_ids = self.process_img(x) + img_tokens = img.shape[1] + if ref_latents is not None: + h = 0 + w = 0 + for ref in ref_latents: + h_offset = 0 + w_offset = 0 + if ref.shape[-2] + h > ref.shape[-1] + w: + w_offset = w + else: + h_offset = h + + kontext, kontext_ids = self.process_img(ref, index=1, h_offset=h_offset, w_offset=w_offset) + img = torch.cat([img, kontext], dim=1) + img_ids = torch.cat([img_ids, kontext_ids], dim=1) + h = max(h, ref.shape[-2] + h_offset) + w = max(w, ref.shape[-1] + w_offset) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) + out = out[:, :img_tokens] + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig] diff --git a/comfy/ldm/flux/redux.py b/comfy/ldm/flux/redux.py new file mode 100644 index 0000000000000000000000000000000000000000..527e83164ea2fd1bdf7431f3791ebede00895051 --- /dev/null +++ b/comfy/ldm/flux/redux.py @@ -0,0 +1,25 @@ +import torch +import comfy.ops + +ops = comfy.ops.manual_cast + +class ReduxImageEncoder(torch.nn.Module): + def __init__( + self, + redux_dim: int = 1152, + txt_in_features: int = 4096, + device=None, + dtype=None, + ) -> None: + super().__init__() + + self.redux_dim = redux_dim + self.device = device + self.dtype = dtype + + self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype) + self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype) + + def forward(self, sigclip_embeds) -> torch.Tensor: + projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds))) + return projected_x diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py new file mode 100644 index 0000000000000000000000000000000000000000..366a8b7133cb665731148a2cdb2ce1013cf0ae92 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/asymm_models_joint.py @@ -0,0 +1,556 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +# from flash_attn import flash_attn_varlen_qkvpacked_func +from comfy.ldm.modules.attention import optimized_attention + +from .layers import ( + FeedForward, + PatchEmbed, + TimestepEmbedder, +) + +from .rope_mixed import ( + compute_mixed_rotation, + create_position_matrix, +) +from .temporal_rope import apply_rotary_emb_qk_real +from .utils import ( + AttentionPool, + modulate, +) + +import comfy.ldm.common_dit +import comfy.ops + + +def modulated_rmsnorm(x, scale, eps=1e-6): + # Normalize and modulate + x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps) + x_modulated = x_normed * (1 + scale.unsqueeze(1)) + + return x_modulated + + +def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6): + # Apply tanh to gate + tanh_gate = torch.tanh(gate).unsqueeze(1) + + # Normalize and apply gated scaling + x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate + + # Apply residual connection + output = x + x_normed + + return output + +class AsymmetricAttention(nn.Module): + def __init__( + self, + dim_x: int, + dim_y: int, + num_heads: int = 8, + qkv_bias: bool = True, + qk_norm: bool = False, + attn_drop: float = 0.0, + update_y: bool = True, + out_bias: bool = True, + attend_to_padding: bool = False, + softmax_scale: Optional[float] = None, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + super().__init__() + self.dim_x = dim_x + self.dim_y = dim_y + self.num_heads = num_heads + self.head_dim = dim_x // num_heads + self.attn_drop = attn_drop + self.update_y = update_y + self.attend_to_padding = attend_to_padding + self.softmax_scale = softmax_scale + if dim_x % num_heads != 0: + raise ValueError( + f"dim_x={dim_x} should be divisible by num_heads={num_heads}" + ) + + # Input layers. + self.qkv_bias = qkv_bias + self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) + # Project text features to match visual features (dim_y -> dim_x) + self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) + + # Query and key normalization for stability. + assert qk_norm + self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + + # Output layers. y features go back down from dim_x -> dim_y. + self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype) + self.proj_y = ( + operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype) + if update_y + else nn.Identity() + ) + + def forward( + self, + x: torch.Tensor, # (B, N, dim_x) + y: torch.Tensor, # (B, L, dim_y) + scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm. + scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm. + crop_y, + **rope_rotation, + ) -> Tuple[torch.Tensor, torch.Tensor]: + rope_cos = rope_rotation.get("rope_cos") + rope_sin = rope_rotation.get("rope_sin") + # Pre-norm for visual features + x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size + + # Process visual features + # qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x) + # assert qkv_x.dtype == torch.bfloat16 + # qkv_x = all_to_all_collect_tokens( + # qkv_x, self.num_heads + # ) # (3, B, N, local_h, head_dim) + + # Process text features + y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y) + q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) + + q_y = self.q_norm_y(q_y) + k_y = self.k_norm_y(k_y) + + # Split qkv_x into q, k, v + q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) + q_x = self.q_norm_x(q_x) + q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin) + k_x = self.k_norm_x(k_x) + k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin) + + q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2) + k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2) + v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2) + + xy = optimized_attention(q, + k, + v, self.num_heads, skip_reshape=True) + + x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1) + x = self.proj_x(x) + o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype) + o[:, :y.shape[1]] = y + + y = self.proj_y(o) + # print("ox", x) + # print("oy", y) + return x, y + + +class AsymmetricJointBlock(nn.Module): + def __init__( + self, + hidden_size_x: int, + hidden_size_y: int, + num_heads: int, + *, + mlp_ratio_x: float = 8.0, # Ratio of hidden size to d_model for MLP for visual tokens. + mlp_ratio_y: float = 4.0, # Ratio of hidden size to d_model for MLP for text tokens. + update_y: bool = True, # Whether to update text tokens in this block. + device: Optional[torch.device] = None, + dtype=None, + operations=None, + **block_kwargs, + ): + super().__init__() + self.update_y = update_y + self.hidden_size_x = hidden_size_x + self.hidden_size_y = hidden_size_y + self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype) + if self.update_y: + self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype) + else: + self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype) + + # Self-attention: + self.attn = AsymmetricAttention( + hidden_size_x, + hidden_size_y, + num_heads=num_heads, + update_y=update_y, + device=device, + dtype=dtype, + operations=operations, + **block_kwargs, + ) + + # MLP. + mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x) + assert mlp_hidden_dim_x == int(1536 * 8) + self.mlp_x = FeedForward( + in_features=hidden_size_x, + hidden_size=mlp_hidden_dim_x, + multiple_of=256, + ffn_dim_multiplier=None, + device=device, + dtype=dtype, + operations=operations, + ) + + # MLP for text not needed in last block. + if self.update_y: + mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y) + self.mlp_y = FeedForward( + in_features=hidden_size_y, + hidden_size=mlp_hidden_dim_y, + multiple_of=256, + ffn_dim_multiplier=None, + device=device, + dtype=dtype, + operations=operations, + ) + + def forward( + self, + x: torch.Tensor, + c: torch.Tensor, + y: torch.Tensor, + **attn_kwargs, + ): + """Forward pass of a block. + + Args: + x: (B, N, dim) tensor of visual tokens + c: (B, dim) tensor of conditioned features + y: (B, L, dim) tensor of text tokens + num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens + + Returns: + x: (B, N, dim) tensor of visual tokens after block + y: (B, L, dim) tensor of text tokens after block + """ + N = x.size(1) + + c = F.silu(c) + mod_x = self.mod_x(c) + scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1) + + mod_y = self.mod_y(c) + if self.update_y: + scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1) + else: + scale_msa_y = mod_y + + # Self-attention block. + x_attn, y_attn = self.attn( + x, + y, + scale_x=scale_msa_x, + scale_y=scale_msa_y, + **attn_kwargs, + ) + + assert x_attn.size(1) == N + x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x) + if self.update_y: + y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y) + + # MLP block. + x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x) + if self.update_y: + y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y) + + return x, y + + def ff_block_x(self, x, scale_x, gate_x): + x_mod = modulated_rmsnorm(x, scale_x) + x_res = self.mlp_x(x_mod) + x = residual_tanh_gated_rmsnorm(x, x_res, gate_x) # Sandwich norm + return x + + def ff_block_y(self, y, scale_y, gate_y): + y_mod = modulated_rmsnorm(y, scale_y) + y_res = self.mlp_y(y_mod) + y = residual_tanh_gated_rmsnorm(y, y_res, gate_y) # Sandwich norm + return y + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__( + self, + hidden_size, + patch_size, + out_channels, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype + ) + self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype) + self.linear = operations.Linear( + hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype + ) + + def forward(self, x, c): + c = F.silu(c) + shift, scale = self.mod(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class AsymmDiTJoint(nn.Module): + """ + Diffusion model with a Transformer backbone. + + Ingests text embeddings instead of a label. + """ + + def __init__( + self, + *, + patch_size=2, + in_channels=4, + hidden_size_x=1152, + hidden_size_y=1152, + depth=48, + num_heads=16, + mlp_ratio_x=8.0, + mlp_ratio_y=4.0, + use_t5: bool = False, + t5_feat_dim: int = 4096, + t5_token_length: int = 256, + learn_sigma=True, + patch_embed_bias: bool = True, + timestep_mlp_bias: bool = True, + attend_to_padding: bool = False, + timestep_scale: Optional[float] = None, + use_extended_posenc: bool = False, + posenc_preserve_area: bool = False, + rope_theta: float = 10000.0, + image_model=None, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + **block_kwargs, + ): + super().__init__() + + self.dtype = dtype + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size_x = hidden_size_x + self.hidden_size_y = hidden_size_y + self.head_dim = ( + hidden_size_x // num_heads + ) # Head dimension and count is determined by visual. + self.attend_to_padding = attend_to_padding + self.use_extended_posenc = use_extended_posenc + self.posenc_preserve_area = posenc_preserve_area + self.use_t5 = use_t5 + self.t5_token_length = t5_token_length + self.t5_feat_dim = t5_feat_dim + self.rope_theta = ( + rope_theta # Scaling factor for frequency computation for temporal RoPE. + ) + + self.x_embedder = PatchEmbed( + patch_size=patch_size, + in_chans=in_channels, + embed_dim=hidden_size_x, + bias=patch_embed_bias, + dtype=dtype, + device=device, + operations=operations + ) + # Conditionings + # Timestep + self.t_embedder = TimestepEmbedder( + hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations + ) + + if self.use_t5: + # Caption Pooling (T5) + self.t5_y_embedder = AttentionPool( + t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations + ) + + # Dense Embedding Projection (T5) + self.t5_yproj = operations.Linear( + t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device + ) + + # Initialize pos_frequencies as an empty parameter. + self.pos_frequencies = nn.Parameter( + torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device) + ) + + assert not self.attend_to_padding + + # for depth 48: + # b = 0: AsymmetricJointBlock, update_y=True + # b = 1: AsymmetricJointBlock, update_y=True + # ... + # b = 46: AsymmetricJointBlock, update_y=True + # b = 47: AsymmetricJointBlock, update_y=False. No need to update text features. + blocks = [] + for b in range(depth): + # Joint multi-modal block + update_y = b < depth - 1 + block = AsymmetricJointBlock( + hidden_size_x, + hidden_size_y, + num_heads, + mlp_ratio_x=mlp_ratio_x, + mlp_ratio_y=mlp_ratio_y, + update_y=update_y, + attend_to_padding=attend_to_padding, + device=device, + dtype=dtype, + operations=operations, + **block_kwargs, + ) + + blocks.append(block) + self.blocks = nn.ModuleList(blocks) + + self.final_layer = FinalLayer( + hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) + + def embed_x(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: (B, C=12, T, H, W) tensor of visual tokens + + Returns: + x: (B, C=3072, N) tensor of visual tokens with positional embedding. + """ + return self.x_embedder(x) # Convert BcTHW to BCN + + def prepare( + self, + x: torch.Tensor, + sigma: torch.Tensor, + t5_feat: torch.Tensor, + t5_mask: torch.Tensor, + ): + """Prepare input and conditioning embeddings.""" + # Visual patch embeddings with positional encoding. + T, H, W = x.shape[-3:] + pH, pW = H // self.patch_size, W // self.patch_size + x = self.embed_x(x) # (B, N, D), where N = T * H * W / patch_size ** 2 + assert x.ndim == 3 + + pH, pW = H // self.patch_size, W // self.patch_size + N = T * pH * pW + assert x.size(1) == N + pos = create_position_matrix( + T, pH=pH, pW=pW, device=x.device, dtype=torch.float32 + ) # (N, 3) + rope_cos, rope_sin = compute_mixed_rotation( + freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos + ) # Each are (N, num_heads, dim // 2) + + c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype) # (B, D) + + t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask) # (B, D) + + c = c_t + t5_y_pool + + y_feat = self.t5_yproj(t5_feat) # (B, L, t5_feat_dim) --> (B, L, D) + + return x, c, y_feat, rope_cos, rope_sin + + def forward( + self, + x: torch.Tensor, + timestep: torch.Tensor, + context: List[torch.Tensor], + attention_mask: List[torch.Tensor], + num_tokens=256, + packed_indices: Dict[str, torch.Tensor] = None, + rope_cos: torch.Tensor = None, + rope_sin: torch.Tensor = None, + control=None, transformer_options={}, **kwargs + ): + patches_replace = transformer_options.get("patches_replace", {}) + y_feat = context + y_mask = attention_mask + sigma = timestep + """Forward pass of DiT. + + Args: + x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images) + sigma: (B,) tensor of noise standard deviations + y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048) + y_mask: List((B, L) boolean tensor indicating which tokens are not padding) + packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices. + """ + B, _, T, H, W = x.shape + + x, c, y_feat, rope_cos, rope_sin = self.prepare( + x, sigma, y_feat, y_mask + ) + del y_mask + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block( + args["img"], + args["vec"], + args["txt"], + rope_cos=args["rope_cos"], + rope_sin=args["rope_sin"], + crop_y=args["num_tokens"] + ) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap}) + y_feat = out["txt"] + x = out["img"] + else: + x, y_feat = block( + x, + c, + y_feat, + rope_cos=rope_cos, + rope_sin=rope_sin, + crop_y=num_tokens, + ) # (B, M, D), (B, L, D) + del y_feat # Final layers don't use dense text features. + + x = self.final_layer(x, c) # (B, M, patch_size ** 2 * out_channels) + x = rearrange( + x, + "B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)", + T=T, + hp=H // self.patch_size, + wp=W // self.patch_size, + p1=self.patch_size, + p2=self.patch_size, + c=self.out_channels, + ) + + return -x diff --git a/comfy/ldm/genmo/joint_model/layers.py b/comfy/ldm/genmo/joint_model/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..e310bd717833b29e3730894370a55cd49802de93 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/layers.py @@ -0,0 +1,153 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +import collections.abc +import math +from itertools import repeat +from typing import Callable, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +import comfy.ldm.common_dit + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) + + +class TimestepEmbedder(nn.Module): + def __init__( + self, + hidden_size: int, + frequency_embedding_size: int = 256, + *, + bias: bool = True, + timestep_scale: Optional[float] = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + self.timestep_scale = timestep_scale + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + half = dim // 2 + freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) + freqs.mul_(-math.log(max_period) / half).exp_() + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t, out_dtype): + if self.timestep_scale is not None: + t = t * self.timestep_scale + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class FeedForward(nn.Module): + def __init__( + self, + in_features: int, + hidden_size: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + super().__init__() + # keep parameter count and computation constant compared to standard FFN + hidden_size = int(2 * hidden_size / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_size = int(ffn_dim_multiplier * hidden_size) + hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of) + + self.hidden_dim = hidden_size + self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype) + self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype) + + def forward(self, x): + x, gate = self.w1(x).chunk(2, dim=-1) + x = self.w2(F.silu(x) * gate) + return x + + +class PatchEmbed(nn.Module): + def __init__( + self, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Optional[Callable] = None, + flatten: bool = True, + bias: bool = True, + dynamic_img_pad: bool = False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.patch_size = to_2tuple(patch_size) + self.flatten = flatten + self.dynamic_img_pad = dynamic_img_pad + + self.proj = operations.Conv2d( + in_chans, + embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=bias, + device=device, + dtype=dtype, + ) + assert norm_layer is None + self.norm = ( + norm_layer(embed_dim, device=device) if norm_layer else nn.Identity() + ) + + def forward(self, x): + B, _C, T, H, W = x.shape + if not self.dynamic_img_pad: + assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})." + assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})." + else: + pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0] + pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1] + x = F.pad(x, (0, pad_w, 0, pad_h)) + + x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T) + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular') + x = self.proj(x) + + # Flatten temporal and spatial dimensions. + if not self.flatten: + raise NotImplementedError("Must flatten output.") + x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T) + + x = self.norm(x) + return x diff --git a/comfy/ldm/genmo/joint_model/rope_mixed.py b/comfy/ldm/genmo/joint_model/rope_mixed.py new file mode 100644 index 0000000000000000000000000000000000000000..dee3fa21f5318a610321fc9372553d618462f773 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/rope_mixed.py @@ -0,0 +1,88 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license + +# import functools +import math + +import torch + + +def centers(start: float, stop, num, dtype=None, device=None): + """linspace through bin centers. + + Args: + start (float): Start of the range. + stop (float): End of the range. + num (int): Number of points. + dtype (torch.dtype): Data type of the points. + device (torch.device): Device of the points. + + Returns: + centers (Tensor): Centers of the bins. Shape: (num,). + """ + edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device) + return (edges[:-1] + edges[1:]) / 2 + + +# @functools.lru_cache(maxsize=1) +def create_position_matrix( + T: int, + pH: int, + pW: int, + device: torch.device, + dtype: torch.dtype, + *, + target_area: float = 36864, +): + """ + Args: + T: int - Temporal dimension + pH: int - Height dimension after patchify + pW: int - Width dimension after patchify + + Returns: + pos: [T * pH * pW, 3] - position matrix + """ + # Create 1D tensors for each dimension + t = torch.arange(T, dtype=dtype) + + # Positionally interpolate to area 36864. + # (3072x3072 frame with 16x16 patches = 192x192 latents). + # This automatically scales rope positions when the resolution changes. + # We use a large target area so the model is more sensitive + # to changes in the learned pos_frequencies matrix. + scale = math.sqrt(target_area / (pW * pH)) + w = centers(-pW * scale / 2, pW * scale / 2, pW) + h = centers(-pH * scale / 2, pH * scale / 2, pH) + + # Use meshgrid to create 3D grids + grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij") + + # Stack and reshape the grids. + pos = torch.stack([grid_t, grid_h, grid_w], dim=-1) # [T, pH, pW, 3] + pos = pos.view(-1, 3) # [T * pH * pW, 3] + pos = pos.to(dtype=dtype, device=device) + + return pos + + +def compute_mixed_rotation( + freqs: torch.Tensor, + pos: torch.Tensor, +): + """ + Project each 3-dim position into per-head, per-head-dim 1D frequencies. + + Args: + freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position + pos: [N, 3] - position of each token + num_heads: int + + Returns: + freqs_cos: [N, num_heads, num_freqs] - cosine components + freqs_sin: [N, num_heads, num_freqs] - sine components + """ + assert freqs.ndim == 3 + freqs_sum = torch.einsum("Nd,dhf->Nhf", pos.to(freqs), freqs) + freqs_cos = torch.cos(freqs_sum) + freqs_sin = torch.sin(freqs_sum) + return freqs_cos, freqs_sin diff --git a/comfy/ldm/genmo/joint_model/temporal_rope.py b/comfy/ldm/genmo/joint_model/temporal_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..88f5d6d26151db0c8ad0a89fcf748c45d4b89bc0 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/temporal_rope.py @@ -0,0 +1,34 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license + +# Based on Llama3 Implementation. +import torch + + +def apply_rotary_emb_qk_real( + xqk: torch.Tensor, + freqs_cos: torch.Tensor, + freqs_sin: torch.Tensor, +) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers. + + Args: + xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D) + Can be either just query or just key, or both stacked along some batch or * dim. + freqs_cos (torch.Tensor): Precomputed cosine frequency tensor. + freqs_sin (torch.Tensor): Precomputed sine frequency tensor. + + Returns: + torch.Tensor: The input tensor with rotary embeddings applied. + """ + # Split the last dimension into even and odd parts + xqk_even = xqk[..., 0::2] + xqk_odd = xqk[..., 1::2] + + # Apply rotation + cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk) + sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk) + + # Interleave the results back into the original shape + out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2) + return out diff --git a/comfy/ldm/genmo/joint_model/utils.py b/comfy/ldm/genmo/joint_model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1b399d5d2128b7f5c7be141c3d8e7047df93b273 --- /dev/null +++ b/comfy/ldm/genmo/joint_model/utils.py @@ -0,0 +1,102 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor: + """ + Pool tokens in x using mask. + + NOTE: We assume x does not require gradients. + + Args: + x: (B, L, D) tensor of tokens. + mask: (B, L) boolean tensor indicating which tokens are not padding. + + Returns: + pooled: (B, D) tensor of pooled tokens. + """ + assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens. + assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens. + mask = mask[:, :, None].to(dtype=x.dtype) + mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1) + pooled = (x * mask).sum(dim=1, keepdim=keepdim) + return pooled + + +class AttentionPool(nn.Module): + def __init__( + self, + embed_dim: int, + num_heads: int, + output_dim: int = None, + device: Optional[torch.device] = None, + dtype=None, + operations=None, + ): + """ + Args: + spatial_dim (int): Number of tokens in sequence length. + embed_dim (int): Dimensionality of input tokens. + num_heads (int): Number of attention heads. + output_dim (int): Dimensionality of output tokens. Defaults to embed_dim. + """ + super().__init__() + self.num_heads = num_heads + self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype) + self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype) + self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype) + + def forward(self, x, mask): + """ + Args: + x (torch.Tensor): (B, L, D) tensor of input tokens. + mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding. + + NOTE: We assume x does not require gradients. + + Returns: + x (torch.Tensor): (B, D) tensor of pooled tokens. + """ + D = x.size(2) + + # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L). + attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L). + attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L). + + # Average non-padding token features. These will be used as the query. + x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D) + + # Concat pooled features to input sequence. + x = torch.cat([x_pool, x], dim=1) # (B, L+1, D) + + # Compute queries, keys, values. Only the mean token is used to create a query. + kv = self.to_kv(x) # (B, L+1, 2 * D) + q = self.to_q(x[:, 0]) # (B, D) + + # Extract heads. + head_dim = D // self.num_heads + kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim) + kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim) + k, v = kv.unbind(2) # (B, H, 1+L, head_dim) + q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim) + q = q.unsqueeze(2) # (B, H, 1, head_dim) + + # Compute attention. + x = F.scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, dropout_p=0.0 + ) # (B, H, 1, head_dim) + + # Concatenate heads and run output. + x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim) + x = self.to_out(x) + return x diff --git a/comfy/ldm/genmo/vae/model.py b/comfy/ldm/genmo/vae/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1bde0c1ed7372be184e634777b9e27b67692b1f4 --- /dev/null +++ b/comfy/ldm/genmo/vae/model.py @@ -0,0 +1,711 @@ +#original code from https://github.com/genmoai/models under apache 2.0 license +#adapted to ComfyUI + +from typing import List, Optional, Tuple, Union +from functools import partial +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from comfy.ldm.modules.attention import optimized_attention + +import comfy.ops +ops = comfy.ops.disable_weight_init + +# import mochi_preview.dit.joint_model.context_parallel as cp +# from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames + + +def cast_tuple(t, length=1): + return t if isinstance(t, tuple) else ((t,) * length) + + +class GroupNormSpatial(ops.GroupNorm): + """ + GroupNorm applied per-frame. + """ + + def forward(self, x: torch.Tensor, *, chunk_size: int = 8): + B, C, T, H, W = x.shape + x = rearrange(x, "B C T H W -> (B T) C H W") + # Run group norm in chunks. + output = torch.empty_like(x) + for b in range(0, B * T, chunk_size): + output[b : b + chunk_size] = super().forward(x[b : b + chunk_size]) + return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T) + +class PConv3d(ops.Conv3d): + def __init__( + self, + in_channels, + out_channels, + kernel_size: Union[int, Tuple[int, int, int]], + stride: Union[int, Tuple[int, int, int]], + causal: bool = True, + context_parallel: bool = True, + **kwargs, + ): + self.causal = causal + self.context_parallel = context_parallel + kernel_size = cast_tuple(kernel_size, 3) + stride = cast_tuple(stride, 3) + height_pad = (kernel_size[1] - 1) // 2 + width_pad = (kernel_size[2] - 1) // 2 + + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + dilation=(1, 1, 1), + padding=(0, height_pad, width_pad), + **kwargs, + ) + + def forward(self, x: torch.Tensor): + # Compute padding amounts. + context_size = self.kernel_size[0] - 1 + if self.causal: + pad_front = context_size + pad_back = 0 + else: + pad_front = context_size // 2 + pad_back = context_size - pad_front + + # Apply padding. + assert self.padding_mode == "replicate" # DEBUG + mode = "constant" if self.padding_mode == "zeros" else self.padding_mode + x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode) + return super().forward(x) + + +class Conv1x1(ops.Linear): + """*1x1 Conv implemented with a linear layer.""" + + def __init__(self, in_features: int, out_features: int, *args, **kwargs): + super().__init__(in_features, out_features, *args, **kwargs) + + def forward(self, x: torch.Tensor): + """Forward pass. + + Args: + x: Input tensor. Shape: [B, C, *] or [B, *, C]. + + Returns: + x: Output tensor. Shape: [B, C', *] or [B, *, C']. + """ + x = x.movedim(1, -1) + x = super().forward(x) + x = x.movedim(-1, 1) + return x + + +class DepthToSpaceTime(nn.Module): + def __init__( + self, + temporal_expansion: int, + spatial_expansion: int, + ): + super().__init__() + self.temporal_expansion = temporal_expansion + self.spatial_expansion = spatial_expansion + + # When printed, this module should show the temporal and spatial expansion factors. + def extra_repr(self): + return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}" + + def forward(self, x: torch.Tensor): + """Forward pass. + + Args: + x: Input tensor. Shape: [B, C, T, H, W]. + + Returns: + x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s]. + """ + x = rearrange( + x, + "B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)", + st=self.temporal_expansion, + sh=self.spatial_expansion, + sw=self.spatial_expansion, + ) + + # cp_rank, _ = cp.get_cp_rank_size() + if self.temporal_expansion > 1: # and cp_rank == 0: + # Drop the first self.temporal_expansion - 1 frames. + # This is because we always want the 3x3x3 conv filter to only apply + # to the first frame, and the first frame doesn't need to be repeated. + assert all(x.shape) + x = x[:, :, self.temporal_expansion - 1 :] + assert all(x.shape) + + return x + + +def norm_fn( + in_channels: int, + affine: bool = True, +): + return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels) + + +class ResBlock(nn.Module): + """Residual block that preserves the spatial dimensions.""" + + def __init__( + self, + channels: int, + *, + affine: bool = True, + attn_block: Optional[nn.Module] = None, + causal: bool = True, + prune_bottleneck: bool = False, + padding_mode: str, + bias: bool = True, + ): + super().__init__() + self.channels = channels + + assert causal + self.stack = nn.Sequential( + norm_fn(channels, affine=affine), + nn.SiLU(inplace=True), + PConv3d( + in_channels=channels, + out_channels=channels // 2 if prune_bottleneck else channels, + kernel_size=(3, 3, 3), + stride=(1, 1, 1), + padding_mode=padding_mode, + bias=bias, + causal=causal, + ), + norm_fn(channels, affine=affine), + nn.SiLU(inplace=True), + PConv3d( + in_channels=channels // 2 if prune_bottleneck else channels, + out_channels=channels, + kernel_size=(3, 3, 3), + stride=(1, 1, 1), + padding_mode=padding_mode, + bias=bias, + causal=causal, + ), + ) + + self.attn_block = attn_block if attn_block else nn.Identity() + + def forward(self, x: torch.Tensor): + """Forward pass. + + Args: + x: Input tensor. Shape: [B, C, T, H, W]. + """ + residual = x + x = self.stack(x) + x = x + residual + del residual + + return self.attn_block(x) + + +class Attention(nn.Module): + def __init__( + self, + dim: int, + head_dim: int = 32, + qkv_bias: bool = False, + out_bias: bool = True, + qk_norm: bool = True, + ) -> None: + super().__init__() + self.head_dim = head_dim + self.num_heads = dim // head_dim + self.qk_norm = qk_norm + + self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) + self.out = nn.Linear(dim, dim, bias=out_bias) + + def forward( + self, + x: torch.Tensor, + ) -> torch.Tensor: + """Compute temporal self-attention. + + Args: + x: Input tensor. Shape: [B, C, T, H, W]. + chunk_size: Chunk size for large tensors. + + Returns: + x: Output tensor. Shape: [B, C, T, H, W]. + """ + B, _, T, H, W = x.shape + + if T == 1: + # No attention for single frame. + x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C] + qkv = self.qkv(x) + _, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys. + x = self.out(x) + return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W] + + # 1D temporal attention. + x = rearrange(x, "B C t h w -> (B h w) t C") + qkv = self.qkv(x) + + # Input: qkv with shape [B, t, 3 * num_heads * head_dim] + # Output: x with shape [B, num_heads, t, head_dim] + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2) + + if self.qk_norm: + q = F.normalize(q, p=2, dim=-1) + k = F.normalize(k, p=2, dim=-1) + + x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) + + assert x.size(0) == q.size(0) + + x = self.out(x) + x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W) + return x + + +class AttentionBlock(nn.Module): + def __init__( + self, + dim: int, + **attn_kwargs, + ) -> None: + super().__init__() + self.norm = norm_fn(dim) + self.attn = Attention(dim, **attn_kwargs) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x + self.attn(self.norm(x)) + + +class CausalUpsampleBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_res_blocks: int, + *, + temporal_expansion: int = 2, + spatial_expansion: int = 2, + **block_kwargs, + ): + super().__init__() + + blocks = [] + for _ in range(num_res_blocks): + blocks.append(block_fn(in_channels, **block_kwargs)) + self.blocks = nn.Sequential(*blocks) + + self.temporal_expansion = temporal_expansion + self.spatial_expansion = spatial_expansion + + # Change channels in the final convolution layer. + self.proj = Conv1x1( + in_channels, + out_channels * temporal_expansion * (spatial_expansion**2), + ) + + self.d2st = DepthToSpaceTime( + temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion + ) + + def forward(self, x): + x = self.blocks(x) + x = self.proj(x) + x = self.d2st(x) + return x + + +def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs): + attn_block = AttentionBlock(channels) if has_attention else None + return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs) + + +class DownsampleBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_res_blocks, + *, + temporal_reduction=2, + spatial_reduction=2, + **block_kwargs, + ): + """ + Downsample block for the VAE encoder. + + Args: + in_channels: Number of input channels. + out_channels: Number of output channels. + num_res_blocks: Number of residual blocks. + temporal_reduction: Temporal reduction factor. + spatial_reduction: Spatial reduction factor. + """ + super().__init__() + layers = [] + + # Change the channel count in the strided convolution. + # This lets the ResBlock have uniform channel count, + # as in ConvNeXt. + assert in_channels != out_channels + layers.append( + PConv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction), + stride=(temporal_reduction, spatial_reduction, spatial_reduction), + # First layer in each block always uses replicate padding + padding_mode="replicate", + bias=block_kwargs["bias"], + ) + ) + + for _ in range(num_res_blocks): + layers.append(block_fn(out_channels, **block_kwargs)) + + self.layers = nn.Sequential(*layers) + + def forward(self, x): + return self.layers(x) + + +def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1): + num_freqs = (stop - start) // step + assert inputs.ndim == 5 + C = inputs.size(1) + + # Create Base 2 Fourier features. + freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device) + assert num_freqs == len(freqs) + w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs] + C = inputs.shape[1] + w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1] + + # Interleaved repeat of input channels to match w. + h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W] + # Scale channels by frequency. + h = w * h + + return torch.cat( + [ + inputs, + torch.sin(h), + torch.cos(h), + ], + dim=1, + ) + + +class FourierFeatures(nn.Module): + def __init__(self, start: int = 6, stop: int = 8, step: int = 1): + super().__init__() + self.start = start + self.stop = stop + self.step = step + + def forward(self, inputs): + """Add Fourier features to inputs. + + Args: + inputs: Input tensor. Shape: [B, C, T, H, W] + + Returns: + h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W] + """ + return add_fourier_features(inputs, self.start, self.stop, self.step) + + +class Decoder(nn.Module): + def __init__( + self, + *, + out_channels: int = 3, + latent_dim: int, + base_channels: int, + channel_multipliers: List[int], + num_res_blocks: List[int], + temporal_expansions: Optional[List[int]] = None, + spatial_expansions: Optional[List[int]] = None, + has_attention: List[bool], + output_norm: bool = True, + nonlinearity: str = "silu", + output_nonlinearity: str = "silu", + causal: bool = True, + **block_kwargs, + ): + super().__init__() + self.input_channels = latent_dim + self.base_channels = base_channels + self.channel_multipliers = channel_multipliers + self.num_res_blocks = num_res_blocks + self.output_nonlinearity = output_nonlinearity + assert nonlinearity == "silu" + assert causal + + ch = [mult * base_channels for mult in channel_multipliers] + self.num_up_blocks = len(ch) - 1 + assert len(num_res_blocks) == self.num_up_blocks + 2 + + blocks = [] + + first_block = [ + ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1)) + ] # Input layer. + # First set of blocks preserve channel count. + for _ in range(num_res_blocks[-1]): + first_block.append( + block_fn( + ch[-1], + has_attention=has_attention[-1], + causal=causal, + **block_kwargs, + ) + ) + blocks.append(nn.Sequential(*first_block)) + + assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks + assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2 + + upsample_block_fn = CausalUpsampleBlock + + for i in range(self.num_up_blocks): + block = upsample_block_fn( + ch[-i - 1], + ch[-i - 2], + num_res_blocks=num_res_blocks[-i - 2], + has_attention=has_attention[-i - 2], + temporal_expansion=temporal_expansions[-i - 1], + spatial_expansion=spatial_expansions[-i - 1], + causal=causal, + **block_kwargs, + ) + blocks.append(block) + + assert not output_norm + + # Last block. Preserve channel count. + last_block = [] + for _ in range(num_res_blocks[0]): + last_block.append( + block_fn( + ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs + ) + ) + blocks.append(nn.Sequential(*last_block)) + + self.blocks = nn.ModuleList(blocks) + self.output_proj = Conv1x1(ch[0], out_channels) + + def forward(self, x): + """Forward pass. + + Args: + x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1]. + + Returns: + x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]. + T + 1 = (t - 1) * 4. + H = h * 16, W = w * 16. + """ + for block in self.blocks: + x = block(x) + + if self.output_nonlinearity == "silu": + x = F.silu(x, inplace=not self.training) + else: + assert ( + not self.output_nonlinearity + ) # StyleGAN3 omits the to-RGB nonlinearity. + + return self.output_proj(x).contiguous() + +class LatentDistribution: + def __init__(self, mean: torch.Tensor, logvar: torch.Tensor): + """Initialize latent distribution. + + Args: + mean: Mean of the distribution. Shape: [B, C, T, H, W]. + logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W]. + """ + assert mean.shape == logvar.shape + self.mean = mean + self.logvar = logvar + + def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None): + if temperature == 0.0: + return self.mean + + if noise is None: + noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator) + else: + assert noise.device == self.mean.device + noise = noise.to(self.mean.dtype) + + if temperature != 1.0: + raise NotImplementedError(f"Temperature {temperature} is not supported.") + + # Just Gaussian sample with no scaling of variance. + return noise * torch.exp(self.logvar * 0.5) + self.mean + + def mode(self): + return self.mean + +class Encoder(nn.Module): + def __init__( + self, + *, + in_channels: int, + base_channels: int, + channel_multipliers: List[int], + num_res_blocks: List[int], + latent_dim: int, + temporal_reductions: List[int], + spatial_reductions: List[int], + prune_bottlenecks: List[bool], + has_attentions: List[bool], + affine: bool = True, + bias: bool = True, + input_is_conv_1x1: bool = False, + padding_mode: str, + ): + super().__init__() + self.temporal_reductions = temporal_reductions + self.spatial_reductions = spatial_reductions + self.base_channels = base_channels + self.channel_multipliers = channel_multipliers + self.num_res_blocks = num_res_blocks + self.latent_dim = latent_dim + + self.fourier_features = FourierFeatures() + ch = [mult * base_channels for mult in channel_multipliers] + num_down_blocks = len(ch) - 1 + assert len(num_res_blocks) == num_down_blocks + 2 + + layers = ( + [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)] + if not input_is_conv_1x1 + else [Conv1x1(in_channels, ch[0])] + ) + + assert len(prune_bottlenecks) == num_down_blocks + 2 + assert len(has_attentions) == num_down_blocks + 2 + block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias) + + for _ in range(num_res_blocks[0]): + layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0])) + prune_bottlenecks = prune_bottlenecks[1:] + has_attentions = has_attentions[1:] + + assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1 + for i in range(num_down_blocks): + layer = DownsampleBlock( + ch[i], + ch[i + 1], + num_res_blocks=num_res_blocks[i + 1], + temporal_reduction=temporal_reductions[i], + spatial_reduction=spatial_reductions[i], + prune_bottleneck=prune_bottlenecks[i], + has_attention=has_attentions[i], + affine=affine, + bias=bias, + padding_mode=padding_mode, + ) + + layers.append(layer) + + # Additional blocks. + for _ in range(num_res_blocks[-1]): + layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1])) + + self.layers = nn.Sequential(*layers) + + # Output layers. + self.output_norm = norm_fn(ch[-1]) + self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False) + + @property + def temporal_downsample(self): + return math.prod(self.temporal_reductions) + + @property + def spatial_downsample(self): + return math.prod(self.spatial_reductions) + + def forward(self, x) -> LatentDistribution: + """Forward pass. + + Args: + x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1] + + Returns: + means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1]. + h = H // 8, w = W // 8, t - 1 = (T - 1) // 6 + logvar: Shape: [B, latent_dim, t, h, w]. + """ + assert x.ndim == 5, f"Expected 5D input, got {x.shape}" + x = self.fourier_features(x) + + x = self.layers(x) + + x = self.output_norm(x) + x = F.silu(x, inplace=True) + x = self.output_proj(x) + + means, logvar = torch.chunk(x, 2, dim=1) + + assert means.ndim == 5 + assert logvar.shape == means.shape + assert means.size(1) == self.latent_dim + + return LatentDistribution(means, logvar) + + +class VideoVAE(nn.Module): + def __init__(self): + super().__init__() + self.encoder = Encoder( + in_channels=15, + base_channels=64, + channel_multipliers=[1, 2, 4, 6], + num_res_blocks=[3, 3, 4, 6, 3], + latent_dim=12, + temporal_reductions=[1, 2, 3], + spatial_reductions=[2, 2, 2], + prune_bottlenecks=[False, False, False, False, False], + has_attentions=[False, True, True, True, True], + affine=True, + bias=True, + input_is_conv_1x1=True, + padding_mode="replicate" + ) + self.decoder = Decoder( + out_channels=3, + base_channels=128, + channel_multipliers=[1, 2, 4, 6], + temporal_expansions=[1, 2, 3], + spatial_expansions=[2, 2, 2], + num_res_blocks=[3, 3, 4, 6, 3], + latent_dim=12, + has_attention=[False, False, False, False, False], + padding_mode="replicate", + output_norm=False, + nonlinearity="silu", + output_nonlinearity="silu", + causal=True, + ) + + def encode(self, x): + return self.encoder(x).mode() + + def decode(self, x): + return self.decoder(x) diff --git a/comfy/ldm/hidream/model.py b/comfy/ldm/hidream/model.py new file mode 100644 index 0000000000000000000000000000000000000000..0305747bf70610827e2fa3cb9263c7540343dd17 --- /dev/null +++ b/comfy/ldm/hidream/model.py @@ -0,0 +1,802 @@ +from typing import Optional, Tuple, List + +import torch +import torch.nn as nn +import einops +from einops import repeat + +from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps +import torch.nn.functional as F + +from comfy.ldm.flux.math import apply_rope, rope +from comfy.ldm.flux.layers import LastLayer + +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management +import comfy.ldm.common_dit + + +# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py +class EmbedND(nn.Module): + def __init__(self, theta: int, axes_dim: List[int]): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: torch.Tensor) -> torch.Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + return emb.unsqueeze(2) + + +class PatchEmbed(nn.Module): + def __init__( + self, + patch_size=2, + in_channels=4, + out_channels=1024, + dtype=None, device=None, operations=None + ): + super().__init__() + self.patch_size = patch_size + self.out_channels = out_channels + self.proj = operations.Linear(in_channels * patch_size * patch_size, out_channels, bias=True, dtype=dtype, device=device) + + def forward(self, latent): + latent = self.proj(latent) + return latent + + +class PooledEmbed(nn.Module): + def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.pooled_embedder = TimestepEmbedding(in_channels=text_emb_dim, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) + + def forward(self, pooled_embed): + return self.pooled_embedder(pooled_embed) + + +class TimestepEmbed(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) + + def forward(self, timesteps, wdtype): + t_emb = self.time_proj(timesteps).to(dtype=wdtype) + t_emb = self.timestep_embedder(t_emb) + return t_emb + + +def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor): + return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2]) + + +class HiDreamAttnProcessor_flashattn: + """Attention processor used typically in processing the SD3-like self-attention projections.""" + + def __call__( + self, + attn, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + rope: torch.FloatTensor = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + dtype = image_tokens.dtype + batch_size = image_tokens.shape[0] + + query_i = attn.q_rms_norm(attn.to_q(image_tokens)).to(dtype=dtype) + key_i = attn.k_rms_norm(attn.to_k(image_tokens)).to(dtype=dtype) + value_i = attn.to_v(image_tokens) + + inner_dim = key_i.shape[-1] + head_dim = inner_dim // attn.heads + + query_i = query_i.view(batch_size, -1, attn.heads, head_dim) + key_i = key_i.view(batch_size, -1, attn.heads, head_dim) + value_i = value_i.view(batch_size, -1, attn.heads, head_dim) + if image_tokens_masks is not None: + key_i = key_i * image_tokens_masks.view(batch_size, -1, 1, 1) + + if not attn.single: + query_t = attn.q_rms_norm_t(attn.to_q_t(text_tokens)).to(dtype=dtype) + key_t = attn.k_rms_norm_t(attn.to_k_t(text_tokens)).to(dtype=dtype) + value_t = attn.to_v_t(text_tokens) + + query_t = query_t.view(batch_size, -1, attn.heads, head_dim) + key_t = key_t.view(batch_size, -1, attn.heads, head_dim) + value_t = value_t.view(batch_size, -1, attn.heads, head_dim) + + num_image_tokens = query_i.shape[1] + num_text_tokens = query_t.shape[1] + query = torch.cat([query_i, query_t], dim=1) + key = torch.cat([key_i, key_t], dim=1) + value = torch.cat([value_i, value_t], dim=1) + else: + query = query_i + key = key_i + value = value_i + + if query.shape[-1] == rope.shape[-3] * 2: + query, key = apply_rope(query, key, rope) + else: + query_1, query_2 = query.chunk(2, dim=-1) + key_1, key_2 = key.chunk(2, dim=-1) + query_1, key_1 = apply_rope(query_1, key_1, rope) + query = torch.cat([query_1, query_2], dim=-1) + key = torch.cat([key_1, key_2], dim=-1) + + hidden_states = attention(query, key, value) + + if not attn.single: + hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1) + hidden_states_i = attn.to_out(hidden_states_i) + hidden_states_t = attn.to_out_t(hidden_states_t) + return hidden_states_i, hidden_states_t + else: + hidden_states = attn.to_out(hidden_states) + return hidden_states + +class HiDreamAttention(nn.Module): + def __init__( + self, + query_dim: int, + heads: int = 8, + dim_head: int = 64, + upcast_attention: bool = False, + upcast_softmax: bool = False, + scale_qk: bool = True, + eps: float = 1e-5, + processor = None, + out_dim: int = None, + single: bool = False, + dtype=None, device=None, operations=None + ): + # super(Attention, self).__init__() + super().__init__() + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.query_dim = query_dim + self.upcast_attention = upcast_attention + self.upcast_softmax = upcast_softmax + self.out_dim = out_dim if out_dim is not None else query_dim + + self.scale_qk = scale_qk + self.scale = dim_head**-0.5 if self.scale_qk else 1.0 + + self.heads = out_dim // dim_head if out_dim is not None else heads + self.sliceable_head_dim = heads + self.single = single + + linear_cls = operations.Linear + self.linear_cls = linear_cls + self.to_q = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) + self.to_k = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_v = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_out = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) + self.q_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + self.k_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + + if not single: + self.to_q_t = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) + self.to_k_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_v_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_out_t = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) + self.q_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + self.k_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + + self.processor = processor + + def forward( + self, + norm_image_tokens: torch.FloatTensor, + image_tokens_masks: torch.FloatTensor = None, + norm_text_tokens: torch.FloatTensor = None, + rope: torch.FloatTensor = None, + ) -> torch.Tensor: + return self.processor( + self, + image_tokens = norm_image_tokens, + image_tokens_masks = image_tokens_masks, + text_tokens = norm_text_tokens, + rope = rope, + ) + + +class FeedForwardSwiGLU(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + dtype=None, device=None, operations=None + ): + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ( + (hidden_dim + multiple_of - 1) // multiple_of + ) + + self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) + self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device) + self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) + + def forward(self, x): + return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x)) + + +# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py +class MoEGate(nn.Module): + def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01, dtype=None, device=None, operations=None): + super().__init__() + self.top_k = num_activated_experts + self.n_routed_experts = num_routed_experts + + self.scoring_func = 'softmax' + self.alpha = aux_loss_alpha + self.seq_aux = False + + # topk selection algorithm + self.norm_topk_prob = False + self.gating_dim = embed_dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), dtype=dtype, device=device)) + self.reset_parameters() + + def reset_parameters(self) -> None: + pass + # import torch.nn.init as init + # init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + + ### compute gating score + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + ### select top-k experts + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + ### norm gate to sum 1 + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + aux_loss = None + return topk_idx, topk_weight, aux_loss + + +# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py +class MOEFeedForwardSwiGLU(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + num_routed_experts: int, + num_activated_experts: int, + dtype=None, device=None, operations=None + ): + super().__init__() + self.shared_experts = FeedForwardSwiGLU(dim, hidden_dim // 2, dtype=dtype, device=device, operations=operations) + self.experts = nn.ModuleList([FeedForwardSwiGLU(dim, hidden_dim, dtype=dtype, device=device, operations=operations) for i in range(num_routed_experts)]) + self.gate = MoEGate( + embed_dim = dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + self.num_activated_experts = num_activated_experts + + def forward(self, x): + wtype = x.dtype + identity = x + orig_shape = x.shape + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if True: # self.training: # TODO: check which branch performs faster + x = x.repeat_interleave(self.num_activated_experts, dim=0) + y = torch.empty_like(x, dtype=wtype) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(dtype=wtype) + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape).to(dtype=wtype) + #y = AddAuxiliaryLoss.apply(y, aux_loss) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + y = y + self.shared_experts(identity) + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.num_activated_experts + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i-1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + + # for fp16 and other dtype + expert_cache = expert_cache.to(expert_out.dtype) + expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum') + return expert_cache + + +class TextProjection(nn.Module): + def __init__(self, in_features, hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.linear = operations.Linear(in_features=in_features, out_features=hidden_size, bias=False, dtype=dtype, device=device) + + def forward(self, caption): + hidden_states = self.linear(caption) + return hidden_states + + +class BlockType: + TransformerBlock = 1 + SingleTransformerBlock = 2 + + +class HiDreamImageSingleTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + dtype=None, device=None, operations=None + ): + super().__init__() + self.num_attention_heads = num_attention_heads + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device) + ) + + # 1. Attention + self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + self.attn1 = HiDreamAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + processor = HiDreamAttnProcessor_flashattn(), + single = True, + dtype=dtype, device=device, operations=operations + ) + + # 3. Feed-forward + self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + if num_routed_experts > 0: + self.ff_i = MOEFeedForwardSwiGLU( + dim = dim, + hidden_dim = 4 * dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + else: + self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) + + def forward( + self, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + adaln_input: Optional[torch.FloatTensor] = None, + rope: torch.FloatTensor = None, + + ) -> torch.FloatTensor: + wtype = image_tokens.dtype + shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \ + self.adaLN_modulation(adaln_input)[:,None].chunk(6, dim=-1) + + # 1. MM-Attention + norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i + attn_output_i = self.attn1( + norm_image_tokens, + image_tokens_masks, + rope = rope, + ) + image_tokens = gate_msa_i * attn_output_i + image_tokens + + # 2. Feed-forward + norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i + ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens.to(dtype=wtype)) + image_tokens = ff_output_i + image_tokens + return image_tokens + + +class HiDreamImageTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + dtype=None, device=None, operations=None + ): + super().__init__() + self.num_attention_heads = num_attention_heads + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(dim, 12 * dim, bias=True, dtype=dtype, device=device) + ) + # nn.init.zeros_(self.adaLN_modulation[1].weight) + # nn.init.zeros_(self.adaLN_modulation[1].bias) + + # 1. Attention + self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + self.norm1_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + self.attn1 = HiDreamAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + processor = HiDreamAttnProcessor_flashattn(), + single = False, + dtype=dtype, device=device, operations=operations + ) + + # 3. Feed-forward + self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + if num_routed_experts > 0: + self.ff_i = MOEFeedForwardSwiGLU( + dim = dim, + hidden_dim = 4 * dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + else: + self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) + self.norm3_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False) + self.ff_t = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) + + def forward( + self, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + adaln_input: Optional[torch.FloatTensor] = None, + rope: torch.FloatTensor = None, + ) -> torch.FloatTensor: + wtype = image_tokens.dtype + shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \ + shift_msa_t, scale_msa_t, gate_msa_t, shift_mlp_t, scale_mlp_t, gate_mlp_t = \ + self.adaLN_modulation(adaln_input)[:,None].chunk(12, dim=-1) + + # 1. MM-Attention + norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i + norm_text_tokens = self.norm1_t(text_tokens).to(dtype=wtype) + norm_text_tokens = norm_text_tokens * (1 + scale_msa_t) + shift_msa_t + + attn_output_i, attn_output_t = self.attn1( + norm_image_tokens, + image_tokens_masks, + norm_text_tokens, + rope = rope, + ) + + image_tokens = gate_msa_i * attn_output_i + image_tokens + text_tokens = gate_msa_t * attn_output_t + text_tokens + + # 2. Feed-forward + norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i + norm_text_tokens = self.norm3_t(text_tokens).to(dtype=wtype) + norm_text_tokens = norm_text_tokens * (1 + scale_mlp_t) + shift_mlp_t + + ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens) + ff_output_t = gate_mlp_t * self.ff_t(norm_text_tokens) + image_tokens = ff_output_i + image_tokens + text_tokens = ff_output_t + text_tokens + return image_tokens, text_tokens + + +class HiDreamImageBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + block_type: BlockType = BlockType.TransformerBlock, + dtype=None, device=None, operations=None + ): + super().__init__() + block_classes = { + BlockType.TransformerBlock: HiDreamImageTransformerBlock, + BlockType.SingleTransformerBlock: HiDreamImageSingleTransformerBlock, + } + self.block = block_classes[block_type]( + dim, + num_attention_heads, + attention_head_dim, + num_routed_experts, + num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + + def forward( + self, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + adaln_input: torch.FloatTensor = None, + rope: torch.FloatTensor = None, + ) -> torch.FloatTensor: + return self.block( + image_tokens, + image_tokens_masks, + text_tokens, + adaln_input, + rope, + ) + + +class HiDreamImageTransformer2DModel(nn.Module): + def __init__( + self, + patch_size: Optional[int] = None, + in_channels: int = 64, + out_channels: Optional[int] = None, + num_layers: int = 16, + num_single_layers: int = 32, + attention_head_dim: int = 128, + num_attention_heads: int = 20, + caption_channels: List[int] = None, + text_emb_dim: int = 2048, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + axes_dims_rope: Tuple[int, int] = (32, 32), + max_resolution: Tuple[int, int] = (128, 128), + llama_layers: List[int] = None, + image_model=None, + dtype=None, device=None, operations=None + ): + self.patch_size = patch_size + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + self.num_layers = num_layers + self.num_single_layers = num_single_layers + + self.gradient_checkpointing = False + + super().__init__() + self.dtype = dtype + self.out_channels = out_channels or in_channels + self.inner_dim = self.num_attention_heads * self.attention_head_dim + self.llama_layers = llama_layers + + self.t_embedder = TimestepEmbed(self.inner_dim, dtype=dtype, device=device, operations=operations) + self.p_embedder = PooledEmbed(text_emb_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) + self.x_embedder = PatchEmbed( + patch_size = patch_size, + in_channels = in_channels, + out_channels = self.inner_dim, + dtype=dtype, device=device, operations=operations + ) + self.pe_embedder = EmbedND(theta=10000, axes_dim=axes_dims_rope) + + self.double_stream_blocks = nn.ModuleList( + [ + HiDreamImageBlock( + dim = self.inner_dim, + num_attention_heads = self.num_attention_heads, + attention_head_dim = self.attention_head_dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + block_type = BlockType.TransformerBlock, + dtype=dtype, device=device, operations=operations + ) + for i in range(self.num_layers) + ] + ) + + self.single_stream_blocks = nn.ModuleList( + [ + HiDreamImageBlock( + dim = self.inner_dim, + num_attention_heads = self.num_attention_heads, + attention_head_dim = self.attention_head_dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + block_type = BlockType.SingleTransformerBlock, + dtype=dtype, device=device, operations=operations + ) + for i in range(self.num_single_layers) + ] + ) + + self.final_layer = LastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + + caption_channels = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ] + caption_projection = [] + for caption_channel in caption_channels: + caption_projection.append(TextProjection(in_features=caption_channel, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations)) + self.caption_projection = nn.ModuleList(caption_projection) + self.max_seq = max_resolution[0] * max_resolution[1] // (patch_size * patch_size) + + def expand_timesteps(self, timesteps, batch_size, device): + if not torch.is_tensor(timesteps): + is_mps = device.type == "mps" + if isinstance(timesteps, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(device) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(batch_size) + return timesteps + + def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]) -> List[torch.Tensor]: + x_arr = [] + for i, img_size in enumerate(img_sizes): + pH, pW = img_size + x_arr.append( + einops.rearrange(x[i, :pH*pW].reshape(1, pH, pW, -1), 'B H W (p1 p2 C) -> B C (H p1) (W p2)', + p1=self.patch_size, p2=self.patch_size) + ) + x = torch.cat(x_arr, dim=0) + return x + + def patchify(self, x, max_seq, img_sizes=None): + pz2 = self.patch_size * self.patch_size + if isinstance(x, torch.Tensor): + B = x.shape[0] + device = x.device + dtype = x.dtype + else: + B = len(x) + device = x[0].device + dtype = x[0].dtype + x_masks = torch.zeros((B, max_seq), dtype=dtype, device=device) + + if img_sizes is not None: + for i, img_size in enumerate(img_sizes): + x_masks[i, 0:img_size[0] * img_size[1]] = 1 + x = einops.rearrange(x, 'B C S p -> B S (p C)', p=pz2) + elif isinstance(x, torch.Tensor): + pH, pW = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size + x = einops.rearrange(x, 'B C (H p1) (W p2) -> B (H W) (p1 p2 C)', p1=self.patch_size, p2=self.patch_size) + img_sizes = [[pH, pW]] * B + x_masks = None + else: + raise NotImplementedError + return x, x_masks, img_sizes + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + encoder_hidden_states_llama3=None, + image_cond=None, + control = None, + transformer_options = {}, + ) -> torch.Tensor: + bs, c, h, w = x.shape + if image_cond is not None: + x = torch.cat([x, image_cond], dim=-1) + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + timesteps = t + pooled_embeds = y + T5_encoder_hidden_states = context + + img_sizes = None + + # spatial forward + batch_size = hidden_states.shape[0] + hidden_states_type = hidden_states.dtype + + # 0. time + timesteps = self.expand_timesteps(timesteps, batch_size, hidden_states.device) + timesteps = self.t_embedder(timesteps, hidden_states_type) + p_embedder = self.p_embedder(pooled_embeds) + adaln_input = timesteps + p_embedder + + hidden_states, image_tokens_masks, img_sizes = self.patchify(hidden_states, self.max_seq, img_sizes) + if image_tokens_masks is None: + pH, pW = img_sizes[0] + img_ids = torch.zeros(pH, pW, 3, device=hidden_states.device) + img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH, device=hidden_states.device)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW, device=hidden_states.device)[None, :] + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size) + hidden_states = self.x_embedder(hidden_states) + + # T5_encoder_hidden_states = encoder_hidden_states[0] + encoder_hidden_states = encoder_hidden_states_llama3.movedim(1, 0) + encoder_hidden_states = [encoder_hidden_states[k] for k in self.llama_layers] + + if self.caption_projection is not None: + new_encoder_hidden_states = [] + for i, enc_hidden_state in enumerate(encoder_hidden_states): + enc_hidden_state = self.caption_projection[i](enc_hidden_state) + enc_hidden_state = enc_hidden_state.view(batch_size, -1, hidden_states.shape[-1]) + new_encoder_hidden_states.append(enc_hidden_state) + encoder_hidden_states = new_encoder_hidden_states + T5_encoder_hidden_states = self.caption_projection[-1](T5_encoder_hidden_states) + T5_encoder_hidden_states = T5_encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) + encoder_hidden_states.append(T5_encoder_hidden_states) + + txt_ids = torch.zeros( + batch_size, + encoder_hidden_states[-1].shape[1] + encoder_hidden_states[-2].shape[1] + encoder_hidden_states[0].shape[1], + 3, + device=img_ids.device, dtype=img_ids.dtype + ) + ids = torch.cat((img_ids, txt_ids), dim=1) + rope = self.pe_embedder(ids) + + # 2. Blocks + block_id = 0 + initial_encoder_hidden_states = torch.cat([encoder_hidden_states[-1], encoder_hidden_states[-2]], dim=1) + initial_encoder_hidden_states_seq_len = initial_encoder_hidden_states.shape[1] + for bid, block in enumerate(self.double_stream_blocks): + cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] + cur_encoder_hidden_states = torch.cat([initial_encoder_hidden_states, cur_llama31_encoder_hidden_states], dim=1) + hidden_states, initial_encoder_hidden_states = block( + image_tokens = hidden_states, + image_tokens_masks = image_tokens_masks, + text_tokens = cur_encoder_hidden_states, + adaln_input = adaln_input, + rope = rope, + ) + initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len] + block_id += 1 + + image_tokens_seq_len = hidden_states.shape[1] + hidden_states = torch.cat([hidden_states, initial_encoder_hidden_states], dim=1) + hidden_states_seq_len = hidden_states.shape[1] + if image_tokens_masks is not None: + encoder_attention_mask_ones = torch.ones( + (batch_size, initial_encoder_hidden_states.shape[1] + cur_llama31_encoder_hidden_states.shape[1]), + device=image_tokens_masks.device, dtype=image_tokens_masks.dtype + ) + image_tokens_masks = torch.cat([image_tokens_masks, encoder_attention_mask_ones], dim=1) + + for bid, block in enumerate(self.single_stream_blocks): + cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] + hidden_states = torch.cat([hidden_states, cur_llama31_encoder_hidden_states], dim=1) + hidden_states = block( + image_tokens=hidden_states, + image_tokens_masks=image_tokens_masks, + text_tokens=None, + adaln_input=adaln_input, + rope=rope, + ) + hidden_states = hidden_states[:, :hidden_states_seq_len] + block_id += 1 + + hidden_states = hidden_states[:, :image_tokens_seq_len, ...] + output = self.final_layer(hidden_states, adaln_input) + output = self.unpatchify(output, img_sizes) + return -output[:, :, :h, :w] diff --git a/comfy/ldm/hunyuan3d/model.py b/comfy/ldm/hunyuan3d/model.py new file mode 100644 index 0000000000000000000000000000000000000000..4e18358f0ef95bdb3dac95ba9b652b44872f0f28 --- /dev/null +++ b/comfy/ldm/hunyuan3d/model.py @@ -0,0 +1,135 @@ +import torch +from torch import nn +from comfy.ldm.flux.layers import ( + DoubleStreamBlock, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding, +) + + +class Hunyuan3Dv2(nn.Module): + def __init__( + self, + in_channels=64, + context_in_dim=1536, + hidden_size=1024, + mlp_ratio=4.0, + num_heads=16, + depth=16, + depth_single_blocks=32, + qkv_bias=True, + guidance_embed=False, + image_model=None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.dtype = dtype + + if hidden_size % num_heads != 0: + raise ValueError( + f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}" + ) + + self.max_period = 1000 # While reimplementing the model I noticed that they messed up. This 1000 value was meant to be the time_factor but they set the max_period instead + self.latent_in = operations.Linear(in_channels, hidden_size, bias=True, dtype=dtype, device=device) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) if guidance_embed else None + ) + self.cond_in = operations.Linear(context_in_dim, hidden_size, dtype=dtype, device=device) + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(depth) + ] + ) + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + dtype=dtype, device=device, operations=operations + ) + for _ in range(depth_single_blocks) + ] + ) + self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations) + + def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): + x = x.movedim(-1, -2) + timestep = 1.0 - timestep + txt = context + img = self.latent_in(x) + + vec = self.time_in(timestep_embedding(timestep, 256, self.max_period).to(dtype=img.dtype)) + if self.guidance_in is not None: + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.max_period).to(img.dtype)) + + txt = self.cond_in(txt) + pe = None + attn_mask = None + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + attn_mask=attn_mask) + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + + img = img[:, txt.shape[1]:, ...] + img = self.final_layer(img, vec) + return img.movedim(-2, -1) * (-1.0) diff --git a/comfy/ldm/hunyuan3d/vae.py b/comfy/ldm/hunyuan3d/vae.py new file mode 100644 index 0000000000000000000000000000000000000000..5eb2c6548350fb66754788b27caee397494ff0da --- /dev/null +++ b/comfy/ldm/hunyuan3d/vae.py @@ -0,0 +1,587 @@ +# Original: https://github.com/Tencent/Hunyuan3D-2/blob/main/hy3dgen/shapegen/models/autoencoders/model.py +# Since the header on their VAE source file was a bit confusing we asked for permission to use this code from tencent under the GPL license used in ComfyUI. + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +from typing import Union, Tuple, List, Callable, Optional + +import numpy as np +from einops import repeat, rearrange +from tqdm import tqdm +import logging + +import comfy.ops +ops = comfy.ops.disable_weight_init + +def generate_dense_grid_points( + bbox_min: np.ndarray, + bbox_max: np.ndarray, + octree_resolution: int, + indexing: str = "ij", +): + length = bbox_max - bbox_min + num_cells = octree_resolution + + x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32) + y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32) + z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32) + [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing) + xyz = np.stack((xs, ys, zs), axis=-1) + grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1] + + return xyz, grid_size, length + + +class VanillaVolumeDecoder: + @torch.no_grad() + def __call__( + self, + latents: torch.FloatTensor, + geo_decoder: Callable, + bounds: Union[Tuple[float], List[float], float] = 1.01, + num_chunks: int = 10000, + octree_resolution: int = None, + enable_pbar: bool = True, + **kwargs, + ): + device = latents.device + dtype = latents.dtype + batch_size = latents.shape[0] + + # 1. generate query points + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + + bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6]) + xyz_samples, grid_size, length = generate_dense_grid_points( + bbox_min=bbox_min, + bbox_max=bbox_max, + octree_resolution=octree_resolution, + indexing="ij" + ) + xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3) + + # 2. latents to 3d volume + batch_logits = [] + for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc="Volume Decoding", + disable=not enable_pbar): + chunk_queries = xyz_samples[start: start + num_chunks, :] + chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size) + logits = geo_decoder(queries=chunk_queries, latents=latents) + batch_logits.append(logits) + + grid_logits = torch.cat(batch_logits, dim=1) + grid_logits = grid_logits.view((batch_size, *grid_size)).float() + + return grid_logits + + +class FourierEmbedder(nn.Module): + """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts + each feature dimension of `x[..., i]` into: + [ + sin(x[..., i]), + sin(f_1*x[..., i]), + sin(f_2*x[..., i]), + ... + sin(f_N * x[..., i]), + cos(x[..., i]), + cos(f_1*x[..., i]), + cos(f_2*x[..., i]), + ... + cos(f_N * x[..., i]), + x[..., i] # only present if include_input is True. + ], here f_i is the frequency. + + Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs]. + If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...]; + Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]. + + Args: + num_freqs (int): the number of frequencies, default is 6; + logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], + otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]; + input_dim (int): the input dimension, default is 3; + include_input (bool): include the input tensor or not, default is True. + + Attributes: + frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], + otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1); + + out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1), + otherwise, it is input_dim * num_freqs * 2. + + """ + + def __init__(self, + num_freqs: int = 6, + logspace: bool = True, + input_dim: int = 3, + include_input: bool = True, + include_pi: bool = True) -> None: + + """The initialization""" + + super().__init__() + + if logspace: + frequencies = 2.0 ** torch.arange( + num_freqs, + dtype=torch.float32 + ) + else: + frequencies = torch.linspace( + 1.0, + 2.0 ** (num_freqs - 1), + num_freqs, + dtype=torch.float32 + ) + + if include_pi: + frequencies *= torch.pi + + self.register_buffer("frequencies", frequencies, persistent=False) + self.include_input = include_input + self.num_freqs = num_freqs + + self.out_dim = self.get_dims(input_dim) + + def get_dims(self, input_dim): + temp = 1 if self.include_input or self.num_freqs == 0 else 0 + out_dim = input_dim * (self.num_freqs * 2 + temp) + + return out_dim + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ Forward process. + + Args: + x: tensor of shape [..., dim] + + Returns: + embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] + where temp is 1 if include_input is True and 0 otherwise. + """ + + if self.num_freqs > 0: + embed = (x[..., None].contiguous() * self.frequencies.to(device=x.device, dtype=x.dtype)).view(*x.shape[:-1], -1) + if self.include_input: + return torch.cat((x, embed.sin(), embed.cos()), dim=-1) + else: + return torch.cat((embed.sin(), embed.cos()), dim=-1) + else: + return x + + +class CrossAttentionProcessor: + def __call__(self, attn, q, k, v): + out = F.scaled_dot_product_attention(q, k, v) + return out + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if self.drop_prob == 0. or not self.training: + return x + keep_prob = 1 - self.drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and self.scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + def extra_repr(self): + return f'drop_prob={round(self.drop_prob, 3):0.3f}' + + +class MLP(nn.Module): + def __init__( + self, *, + width: int, + expand_ratio: int = 4, + output_width: int = None, + drop_path_rate: float = 0.0 + ): + super().__init__() + self.width = width + self.c_fc = ops.Linear(width, width * expand_ratio) + self.c_proj = ops.Linear(width * expand_ratio, output_width if output_width is not None else width) + self.gelu = nn.GELU() + self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + def forward(self, x): + return self.drop_path(self.c_proj(self.gelu(self.c_fc(x)))) + + +class QKVMultiheadCrossAttention(nn.Module): + def __init__( + self, + *, + heads: int, + width=None, + qk_norm=False, + norm_layer=ops.LayerNorm + ): + super().__init__() + self.heads = heads + self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + + self.attn_processor = CrossAttentionProcessor() + + def forward(self, q, kv): + _, n_ctx, _ = q.shape + bs, n_data, width = kv.shape + attn_ch = width // self.heads // 2 + q = q.view(bs, n_ctx, self.heads, -1) + kv = kv.view(bs, n_data, self.heads, -1) + k, v = torch.split(kv, attn_ch, dim=-1) + + q = self.q_norm(q) + k = self.k_norm(k) + q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) + out = self.attn_processor(self, q, k, v) + out = out.transpose(1, 2).reshape(bs, n_ctx, -1) + return out + + +class MultiheadCrossAttention(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + qkv_bias: bool = True, + data_width: Optional[int] = None, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + kv_cache: bool = False, + ): + super().__init__() + self.width = width + self.heads = heads + self.data_width = width if data_width is None else data_width + self.c_q = ops.Linear(width, width, bias=qkv_bias) + self.c_kv = ops.Linear(self.data_width, width * 2, bias=qkv_bias) + self.c_proj = ops.Linear(width, width) + self.attention = QKVMultiheadCrossAttention( + heads=heads, + width=width, + norm_layer=norm_layer, + qk_norm=qk_norm + ) + self.kv_cache = kv_cache + self.data = None + + def forward(self, x, data): + x = self.c_q(x) + if self.kv_cache: + if self.data is None: + self.data = self.c_kv(data) + logging.info('Save kv cache,this should be called only once for one mesh') + data = self.data + else: + data = self.c_kv(data) + x = self.attention(x, data) + x = self.c_proj(x) + return x + + +class ResidualCrossAttentionBlock(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + mlp_expand_ratio: int = 4, + data_width: Optional[int] = None, + qkv_bias: bool = True, + norm_layer=ops.LayerNorm, + qk_norm: bool = False + ): + super().__init__() + + if data_width is None: + data_width = width + + self.attn = MultiheadCrossAttention( + width=width, + heads=heads, + data_width=data_width, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + qk_norm=qk_norm + ) + self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) + self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6) + self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6) + self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio) + + def forward(self, x: torch.Tensor, data: torch.Tensor): + x = x + self.attn(self.ln_1(x), self.ln_2(data)) + x = x + self.mlp(self.ln_3(x)) + return x + + +class QKVMultiheadAttention(nn.Module): + def __init__( + self, + *, + heads: int, + width=None, + qk_norm=False, + norm_layer=ops.LayerNorm + ): + super().__init__() + self.heads = heads + self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + + def forward(self, qkv): + bs, n_ctx, width = qkv.shape + attn_ch = width // self.heads // 3 + qkv = qkv.view(bs, n_ctx, self.heads, -1) + q, k, v = torch.split(qkv, attn_ch, dim=-1) + + q = self.q_norm(q) + k = self.k_norm(k) + + q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) + out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) + return out + + +class MultiheadAttention(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + qkv_bias: bool, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + drop_path_rate: float = 0.0 + ): + super().__init__() + self.width = width + self.heads = heads + self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias) + self.c_proj = ops.Linear(width, width) + self.attention = QKVMultiheadAttention( + heads=heads, + width=width, + norm_layer=norm_layer, + qk_norm=qk_norm + ) + self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + def forward(self, x): + x = self.c_qkv(x) + x = self.attention(x) + x = self.drop_path(self.c_proj(x)) + return x + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + qkv_bias: bool = True, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + drop_path_rate: float = 0.0, + ): + super().__init__() + self.attn = MultiheadAttention( + width=width, + heads=heads, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) + self.mlp = MLP(width=width, drop_path_rate=drop_path_rate) + self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6) + + def forward(self, x: torch.Tensor): + x = x + self.attn(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__( + self, + *, + width: int, + layers: int, + heads: int, + qkv_bias: bool = True, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + drop_path_rate: float = 0.0 + ): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.ModuleList( + [ + ResidualAttentionBlock( + width=width, + heads=heads, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + for _ in range(layers) + ] + ) + + def forward(self, x: torch.Tensor): + for block in self.resblocks: + x = block(x) + return x + + +class CrossAttentionDecoder(nn.Module): + + def __init__( + self, + *, + out_channels: int, + fourier_embedder: FourierEmbedder, + width: int, + heads: int, + mlp_expand_ratio: int = 4, + downsample_ratio: int = 1, + enable_ln_post: bool = True, + qkv_bias: bool = True, + qk_norm: bool = False, + label_type: str = "binary" + ): + super().__init__() + + self.enable_ln_post = enable_ln_post + self.fourier_embedder = fourier_embedder + self.downsample_ratio = downsample_ratio + self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width) + if self.downsample_ratio != 1: + self.latents_proj = ops.Linear(width * downsample_ratio, width) + if self.enable_ln_post == False: + qk_norm = False + self.cross_attn_decoder = ResidualCrossAttentionBlock( + width=width, + mlp_expand_ratio=mlp_expand_ratio, + heads=heads, + qkv_bias=qkv_bias, + qk_norm=qk_norm + ) + + if self.enable_ln_post: + self.ln_post = ops.LayerNorm(width) + self.output_proj = ops.Linear(width, out_channels) + self.label_type = label_type + self.count = 0 + + def forward(self, queries=None, query_embeddings=None, latents=None): + if query_embeddings is None: + query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) + self.count += query_embeddings.shape[1] + if self.downsample_ratio != 1: + latents = self.latents_proj(latents) + x = self.cross_attn_decoder(query_embeddings, latents) + if self.enable_ln_post: + x = self.ln_post(x) + occ = self.output_proj(x) + return occ + + +class ShapeVAE(nn.Module): + def __init__( + self, + *, + embed_dim: int, + width: int, + heads: int, + num_decoder_layers: int, + geo_decoder_downsample_ratio: int = 1, + geo_decoder_mlp_expand_ratio: int = 4, + geo_decoder_ln_post: bool = True, + num_freqs: int = 8, + include_pi: bool = True, + qkv_bias: bool = True, + qk_norm: bool = False, + label_type: str = "binary", + drop_path_rate: float = 0.0, + scale_factor: float = 1.0, + ): + super().__init__() + self.geo_decoder_ln_post = geo_decoder_ln_post + + self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) + + self.post_kl = ops.Linear(embed_dim, width) + + self.transformer = Transformer( + width=width, + layers=num_decoder_layers, + heads=heads, + qkv_bias=qkv_bias, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + + self.geo_decoder = CrossAttentionDecoder( + fourier_embedder=self.fourier_embedder, + out_channels=1, + mlp_expand_ratio=geo_decoder_mlp_expand_ratio, + downsample_ratio=geo_decoder_downsample_ratio, + enable_ln_post=self.geo_decoder_ln_post, + width=width // geo_decoder_downsample_ratio, + heads=heads // geo_decoder_downsample_ratio, + qkv_bias=qkv_bias, + qk_norm=qk_norm, + label_type=label_type, + ) + + self.volume_decoder = VanillaVolumeDecoder() + self.scale_factor = scale_factor + + def decode(self, latents, **kwargs): + latents = self.post_kl(latents.movedim(-2, -1)) + latents = self.transformer(latents) + + bounds = kwargs.get("bounds", 1.01) + num_chunks = kwargs.get("num_chunks", 8000) + octree_resolution = kwargs.get("octree_resolution", 256) + enable_pbar = kwargs.get("enable_pbar", True) + + grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar) + return grid_logits.movedim(-2, -1) + + def encode(self, x): + return None diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py new file mode 100644 index 0000000000000000000000000000000000000000..fbd8d4196d1444544ea6db950fc5bac6501badf2 --- /dev/null +++ b/comfy/ldm/hunyuan_video/model.py @@ -0,0 +1,355 @@ +#Based on Flux code because of weird hunyuan video code license. + +import torch +import comfy.ldm.flux.layers +import comfy.ldm.modules.diffusionmodules.mmdit +from comfy.ldm.modules.attention import optimized_attention + + +from dataclasses import dataclass +from einops import repeat + +from torch import Tensor, nn + +from comfy.ldm.flux.layers import ( + DoubleStreamBlock, + EmbedND, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding +) + +import comfy.ldm.common_dit + + +@dataclass +class HunyuanVideoParams: + in_channels: int + out_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + patch_size: list + qkv_bias: bool + guidance_embed: bool + + +class SelfAttentionRef(nn.Module): + def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, device=None, operations=None): + super().__init__() + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + + +class TokenRefinerBlock(nn.Module): + def __init__( + self, + hidden_size, + heads, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.heads = heads + mlp_hidden_dim = hidden_size * 4 + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + self.self_attn = SelfAttentionRef(hidden_size, True, dtype=dtype, device=device, operations=operations) + + self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + + self.mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, x, c, mask): + mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1) + + norm_x = self.norm1(x) + qkv = self.self_attn.qkv(norm_x) + q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4) + attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True) + + x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1) + x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1) + return x + + +class IndividualTokenRefiner(nn.Module): + def __init__( + self, + hidden_size, + heads, + num_blocks, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.blocks = nn.ModuleList( + [ + TokenRefinerBlock( + hidden_size=hidden_size, + heads=heads, + dtype=dtype, + device=device, + operations=operations + ) + for _ in range(num_blocks) + ] + ) + + def forward(self, x, c, mask): + m = None + if mask is not None: + m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1) + m = m + m.transpose(2, 3) + + for block in self.blocks: + x = block(x, c, m) + return x + + + +class TokenRefiner(nn.Module): + def __init__( + self, + text_dim, + hidden_size, + heads, + num_blocks, + dtype=None, + device=None, + operations=None + ): + super().__init__() + + self.input_embedder = operations.Linear(text_dim, hidden_size, bias=True, dtype=dtype, device=device) + self.t_embedder = MLPEmbedder(256, hidden_size, dtype=dtype, device=device, operations=operations) + self.c_embedder = MLPEmbedder(text_dim, hidden_size, dtype=dtype, device=device, operations=operations) + self.individual_token_refiner = IndividualTokenRefiner(hidden_size, heads, num_blocks, dtype=dtype, device=device, operations=operations) + + def forward( + self, + x, + timesteps, + mask, + ): + t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype)) + # m = mask.float().unsqueeze(-1) + # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise + c = x.sum(dim=1) / x.shape[1] + + c = t + self.c_embedder(c.to(x.dtype)) + x = self.input_embedder(x) + x = self.individual_token_refiner(x, c, mask) + return x + +class HunyuanVideo(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = HunyuanVideoParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels + self.out_channels = params.out_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + + self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=True, dtype=dtype, device=device, operations=operations) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() + ) + + self.txt_in = TokenRefiner(params.context_in_dim, self.hidden_size, self.num_heads, 2, dtype=dtype, device=device, operations=operations) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + flipped_img_txt=True, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations) + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + txt_mask: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + guiding_frame_index=None, + ref_latent=None, + control=None, + transformer_options={}, + ) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + + initial_shape = list(img.shape) + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) + + if ref_latent is not None: + ref_latent_ids = self.img_ids(ref_latent) + ref_latent = self.img_in(ref_latent) + img = torch.cat([ref_latent, img], dim=-2) + ref_latent_ids[..., 0] = -1 + ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1]) + img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2) + + if guiding_frame_index is not None: + token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0)) + vec_ = self.vector_in(y[:, :self.params.vec_in_dim]) + vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1) + frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2]) + modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)] + modulation_dims_txt = [(0, None, 1)] + else: + vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) + modulation_dims = None + modulation_dims_txt = None + + if self.params.guidance_embed: + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + + if txt_mask is not None and not torch.is_floating_point(txt_mask): + txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max + + txt = self.txt_in(txt, timesteps, txt_mask) + + ids = torch.cat((img_ids, txt_ids), dim=1) + pe = self.pe_embedder(ids) + + img_len = img.shape[1] + if txt_mask is not None: + attn_mask_len = img_len + txt.shape[1] + attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device) + attn_mask[:, 0, img_len:] = txt_mask + else: + attn_mask = None + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"]) + return out + + out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((img, txt), 1) + + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"]) + return out + + out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, : img_len] += add + + img = img[:, : img_len] + if ref_latent is not None: + img = img[:, ref_latent.shape[1]:] + + img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels) + + shape = initial_shape[-3:] + for i in range(len(shape)): + shape[i] = shape[i] // self.patch_size[i] + img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size) + img = img.permute(0, 4, 1, 5, 2, 6, 3, 7) + img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4]) + return img + + def img_ids(self, x): + bs, c, t, h, w = x.shape + patch_size = self.patch_size + t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) + h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) + w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) + img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) + img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) + img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) + return repeat(img_ids, "t h w c -> b (t h w) c", b=bs) + + def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs): + bs, c, t, h, w = x.shape + img_ids = self.img_ids(x) + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options) + return out diff --git a/comfy/ldm/hydit/attn_layers.py b/comfy/ldm/hydit/attn_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..3ca25a5df175bcadd8e50ad34ee0bfcddcb37aa0 --- /dev/null +++ b/comfy/ldm/hydit/attn_layers.py @@ -0,0 +1,218 @@ +import torch +import torch.nn as nn +from typing import Tuple, Union, Optional +from comfy.ldm.modules.attention import optimized_attention + + +def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as the target tensor 'x' + for the purpose of broadcasting the frequency tensor during element-wise operations. + + Args: + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected shape. + AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + + if isinstance(freqs_cis, tuple): + # freqs_cis: (cos, sin) in real space + if head_first: + assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) + else: + # freqs_cis: values in complex space + if head_first: + assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + else: + assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def rotate_half(x): + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + return torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + +def apply_rotary_emb( + xq: torch.Tensor, + xk: Optional[torch.Tensor], + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + head_first: bool = False, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. + + This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided + frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are + returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] + xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] + freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. + head_first (bool): head dimension first (except batch dim) or not. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + + """ + xk_out = None + if isinstance(freqs_cis, tuple): + cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] + xq_out = (xq * cos + rotate_half(xq) * sin) + if xk is not None: + xk_out = (xk * cos + rotate_half(xk) * sin) + else: + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] + freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) + if xk is not None: + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) + + return xq_out, xk_out + + + +class CrossAttention(nn.Module): + """ + Use QK Normalization. + """ + def __init__(self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + attn_drop=0.0, + proj_drop=0.0, + attn_precision=None, + device=None, + dtype=None, + operations=None, + ): + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.attn_precision = attn_precision + self.qdim = qdim + self.kdim = kdim + self.num_heads = num_heads + assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" + self.head_dim = self.qdim // num_heads + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) + + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y, freqs_cis_img=None): + """ + Parameters + ---------- + x: torch.Tensor + (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) + y: torch.Tensor + (batch, seqlen2, hidden_dim2) + freqs_cis_img: torch.Tensor + (batch, hidden_dim // 2), RoPE for image + """ + b, s1, c = x.shape # [b, s1, D] + _, s2, c = y.shape # [b, s2, 1024] + + q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] + kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] + k, v = kv.unbind(dim=2) # [b, s, h, d] + q = self.q_norm(q) + k = self.k_norm(k) + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, _ = apply_rotary_emb(q, None, freqs_cis_img) + assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' + q = qq + + q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C + k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2 + v = v.transpose(-2, -3).contiguous() + + context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) + + out = self.out_proj(context) # context.reshape - B, L1, -1 + out = self.proj_drop(out) + + out_tuple = (out,) + + return out_tuple + + +class Attention(nn.Module): + """ + We rename some layer names to align with flash attention + """ + def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None): + super().__init__() + self.attn_precision = attn_precision + self.dim = dim + self.num_heads = num_heads + assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' + self.head_dim = self.dim // num_heads + # This assertion is aligned with flash attention + assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" + self.scale = self.head_dim ** -0.5 + + # qkv --> Wqkv + self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + # TODO: eps should be 1 / 65530 if using fp16 + self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() + self.attn_drop = nn.Dropout(attn_drop) + self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, freqs_cis_img=None): + B, N, C = x.shape + qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] + q, k, v = qkv.unbind(0) # [b, h, s, d] + q = self.q_norm(q) # [b, h, s, d] + k = self.k_norm(k) # [b, h, s, d] + + # Apply RoPE if needed + if freqs_cis_img is not None: + qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) + assert qq.shape == q.shape and kk.shape == k.shape, \ + f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' + q, k = qq, kk + + x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) + x = self.out_proj(x) + x = self.proj_drop(x) + + out_tuple = (x,) + + return out_tuple diff --git a/comfy/ldm/hydit/controlnet.py b/comfy/ldm/hydit/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..31a6bff9409bb5264e552f91197915a136c3964d --- /dev/null +++ b/comfy/ldm/hydit/controlnet.py @@ -0,0 +1,311 @@ + +import torch +import torch.nn as nn + + +from comfy.ldm.modules.diffusionmodules.mmdit import ( + TimestepEmbedder, + PatchEmbed, +) +from .poolers import AttentionPool + +import comfy.latent_formats +from .models import HunYuanDiTBlock, calc_rope + + + +class HunYuanControlNet(nn.Module): + """ + HunYuanDiT: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. + + Parameters + ---------- + args: argparse.Namespace + The arguments parsed by argparse. + input_size: tuple + The size of the input image. + patch_size: int + The size of the patch. + in_channels: int + The number of input channels. + hidden_size: int + The hidden size of the transformer backbone. + depth: int + The number of transformer blocks. + num_heads: int + The number of attention heads. + mlp_ratio: float + The ratio of the hidden size of the MLP in the transformer block. + log_fn: callable + The logging function. + """ + + def __init__( + self, + input_size: tuple = 128, + patch_size: int = 2, + in_channels: int = 4, + hidden_size: int = 1408, + depth: int = 40, + num_heads: int = 16, + mlp_ratio: float = 4.3637, + text_states_dim=1024, + text_states_dim_t5=2048, + text_len=77, + text_len_t5=256, + qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details. + size_cond=False, + use_style_cond=False, + learn_sigma=True, + norm="layer", + log_fn: callable = print, + attn_precision=None, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + super().__init__() + self.log_fn = log_fn + self.depth = depth + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.text_states_dim = text_states_dim + self.text_states_dim_t5 = text_states_dim_t5 + self.text_len = text_len + self.text_len_t5 = text_len_t5 + self.size_cond = size_cond + self.use_style_cond = use_style_cond + self.norm = norm + self.dtype = dtype + self.latent_format = comfy.latent_formats.SDXL + + self.mlp_t5 = nn.Sequential( + nn.Linear( + self.text_states_dim_t5, + self.text_states_dim_t5 * 4, + bias=True, + dtype=dtype, + device=device, + ), + nn.SiLU(), + nn.Linear( + self.text_states_dim_t5 * 4, + self.text_states_dim, + bias=True, + dtype=dtype, + device=device, + ), + ) + # learnable replace + self.text_embedding_padding = nn.Parameter( + torch.randn( + self.text_len + self.text_len_t5, + self.text_states_dim, + dtype=dtype, + device=device, + ) + ) + + # Attention pooling + pooler_out_dim = 1024 + self.pooler = AttentionPool( + self.text_len_t5, + self.text_states_dim_t5, + num_heads=8, + output_dim=pooler_out_dim, + dtype=dtype, + device=device, + operations=operations, + ) + + # Dimension of the extra input vectors + self.extra_in_dim = pooler_out_dim + + if self.size_cond: + # Image size and crop size conditions + self.extra_in_dim += 6 * 256 + + if self.use_style_cond: + # Here we use a default learned embedder layer for future extension. + self.style_embedder = nn.Embedding( + 1, hidden_size, dtype=dtype, device=device + ) + self.extra_in_dim += hidden_size + + # Text embedding for `add` + self.x_embedder = PatchEmbed( + input_size, + patch_size, + in_channels, + hidden_size, + dtype=dtype, + device=device, + operations=operations, + ) + self.t_embedder = TimestepEmbedder( + hidden_size, dtype=dtype, device=device, operations=operations + ) + self.extra_embedder = nn.Sequential( + operations.Linear( + self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device + ), + nn.SiLU(), + operations.Linear( + hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device + ), + ) + + # HUnYuanDiT Blocks + self.blocks = nn.ModuleList( + [ + HunYuanDiTBlock( + hidden_size=hidden_size, + c_emb_size=hidden_size, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + text_states_dim=self.text_states_dim, + qk_norm=qk_norm, + norm_type=self.norm, + skip=False, + attn_precision=attn_precision, + dtype=dtype, + device=device, + operations=operations, + ) + for _ in range(19) + ] + ) + + # Input zero linear for the first block + self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) + + + # Output zero linear for the every block + self.after_proj_list = nn.ModuleList( + [ + + operations.Linear( + self.hidden_size, self.hidden_size, dtype=dtype, device=device + ) + for _ in range(len(self.blocks)) + ] + ) + + def forward( + self, + x, + hint, + timesteps, + context,#encoder_hidden_states=None, + text_embedding_mask=None, + encoder_hidden_states_t5=None, + text_embedding_mask_t5=None, + image_meta_size=None, + style=None, + return_dict=False, + **kwarg, + ): + """ + Forward pass of the encoder. + + Parameters + ---------- + x: torch.Tensor + (B, D, H, W) + t: torch.Tensor + (B) + encoder_hidden_states: torch.Tensor + CLIP text embedding, (B, L_clip, D) + text_embedding_mask: torch.Tensor + CLIP text embedding mask, (B, L_clip) + encoder_hidden_states_t5: torch.Tensor + T5 text embedding, (B, L_t5, D) + text_embedding_mask_t5: torch.Tensor + T5 text embedding mask, (B, L_t5) + image_meta_size: torch.Tensor + (B, 6) + style: torch.Tensor + (B) + cos_cis_img: torch.Tensor + sin_cis_img: torch.Tensor + return_dict: bool + Whether to return a dictionary. + """ + condition = hint + if condition.shape[0] == 1: + condition = torch.repeat_interleave(condition, x.shape[0], dim=0) + + text_states = context # 2,77,1024 + text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 + text_states_mask = text_embedding_mask.bool() # 2,77 + text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 + b_t5, l_t5, c_t5 = text_states_t5.shape + text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) + + padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) + + text_states[:, -self.text_len :] = torch.where( + text_states_mask[:, -self.text_len :].unsqueeze(2), + text_states[:, -self.text_len :], + padding[: self.text_len], + ) + text_states_t5[:, -self.text_len_t5 :] = torch.where( + text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2), + text_states_t5[:, -self.text_len_t5 :], + padding[self.text_len :], + ) + + text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 + + # _, _, oh, ow = x.shape + # th, tw = oh // self.patch_size, ow // self.patch_size + + # Get image RoPE embedding according to `reso`lution. + freqs_cis_img = calc_rope( + x, self.patch_size, self.hidden_size // self.num_heads + ) # (cos_cis_img, sin_cis_img) + + # ========================= Build time and image embedding ========================= + t = self.t_embedder(timesteps, dtype=self.dtype) + x = self.x_embedder(x) + + # ========================= Concatenate all extra vectors ========================= + # Build text tokens with pooling + extra_vec = self.pooler(encoder_hidden_states_t5) + + # Build image meta size tokens if applicable + # if image_meta_size is not None: + # image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256] + # if image_meta_size.dtype != self.dtype: + # image_meta_size = image_meta_size.half() + # image_meta_size = image_meta_size.view(-1, 6 * 256) + # extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] + + # Build style tokens + if style is not None: + style_embedding = self.style_embedder(style) + extra_vec = torch.cat([extra_vec, style_embedding], dim=1) + + # Concatenate all extra vectors + c = t + self.extra_embedder(extra_vec) # [B, D] + + # ========================= Deal with Condition ========================= + condition = self.x_embedder(condition) + + # ========================= Forward pass through HunYuanDiT blocks ========================= + controls = [] + x = x + self.before_proj(condition) # add condition + for layer, block in enumerate(self.blocks): + x = block(x, c, text_states, freqs_cis_img) + controls.append(self.after_proj_list[layer](x)) # zero linear for output + + return {"output": controls} diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py new file mode 100644 index 0000000000000000000000000000000000000000..5ba2b76e0cad3c611fce1e74b024a66a2255b814 --- /dev/null +++ b/comfy/ldm/hydit/models.py @@ -0,0 +1,417 @@ + +import torch +import torch.nn as nn + +import comfy.ops +from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed +from comfy.ldm.modules.diffusionmodules.util import timestep_embedding +from torch.utils import checkpoint + +from .attn_layers import Attention, CrossAttention +from .poolers import AttentionPool +from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop + +def calc_rope(x, patch_size, head_size): + th = (x.shape[2] + (patch_size // 2)) // patch_size + tw = (x.shape[3] + (patch_size // 2)) // patch_size + base_size = 512 // 8 // patch_size + start, stop = get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads'] + rope = get_2d_rotary_pos_embed(head_size, *sub_args) + rope = (rope[0].to(x), rope[1].to(x)) + return rope + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class HunYuanDiTBlock(nn.Module): + """ + A HunYuanDiT block with `add` conditioning. + """ + def __init__(self, + hidden_size, + c_emb_size, + num_heads, + mlp_ratio=4.0, + text_states_dim=1024, + qk_norm=False, + norm_type="layer", + skip=False, + attn_precision=None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + use_ele_affine = True + + if norm_type == "layer": + norm_layer = operations.LayerNorm + elif norm_type == "rms": + norm_layer = operations.RMSNorm + else: + raise ValueError(f"Unknown norm_type: {norm_type}") + + # ========================= Self-Attention ========================= + self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) + self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) + + # ========================= FFN ========================= + self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations) + + # ========================= Add ========================= + # Simply use add like SDXL. + self.default_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device) + ) + + # ========================= Cross-Attention ========================= + self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, + qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) + self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + + # ========================= Skip Connection ========================= + if skip: + self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device) + else: + self.skip_linear = None + + self.gradient_checkpointing = False + + def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): + # Long Skip Connection + if self.skip_linear is not None: + cat = torch.cat([x, skip], dim=-1) + if cat.dtype != x.dtype: + cat = cat.to(x.dtype) + cat = self.skip_norm(cat) + x = self.skip_linear(cat) + + # Self-Attention + shift_msa = self.default_modulation(c).unsqueeze(dim=1) + attn_inputs = ( + self.norm1(x) + shift_msa, freq_cis_img, + ) + x = x + self.attn1(*attn_inputs)[0] + + # Cross-Attention + cross_inputs = ( + self.norm3(x), text_states, freq_cis_img + ) + x = x + self.attn2(*cross_inputs)[0] + + # FFN Layer + mlp_inputs = self.norm2(x) + x = x + self.mlp(mlp_inputs) + + return x + + def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): + if self.gradient_checkpointing and self.training: + return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip) + return self._forward(x, c, text_states, freq_cis_img, skip) + + +class FinalLayer(nn.Module): + """ + The final layer of HunYuanDiT. + """ + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class HunYuanDiT(nn.Module): + """ + HunYuanDiT: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. + + Parameters + ---------- + args: argparse.Namespace + The arguments parsed by argparse. + input_size: tuple + The size of the input image. + patch_size: int + The size of the patch. + in_channels: int + The number of input channels. + hidden_size: int + The hidden size of the transformer backbone. + depth: int + The number of transformer blocks. + num_heads: int + The number of attention heads. + mlp_ratio: float + The ratio of the hidden size of the MLP in the transformer block. + log_fn: callable + The logging function. + """ + #@register_to_config + def __init__(self, + input_size: tuple = 32, + patch_size: int = 2, + in_channels: int = 4, + hidden_size: int = 1152, + depth: int = 28, + num_heads: int = 16, + mlp_ratio: float = 4.0, + text_states_dim = 1024, + text_states_dim_t5 = 2048, + text_len = 77, + text_len_t5 = 256, + qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details. + size_cond = False, + use_style_cond = False, + learn_sigma = True, + norm = "layer", + log_fn: callable = print, + attn_precision=None, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + super().__init__() + self.log_fn = log_fn + self.depth = depth + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.text_states_dim = text_states_dim + self.text_states_dim_t5 = text_states_dim_t5 + self.text_len = text_len + self.text_len_t5 = text_len_t5 + self.size_cond = size_cond + self.use_style_cond = use_style_cond + self.norm = norm + self.dtype = dtype + #import pdb + #pdb.set_trace() + + self.mlp_t5 = nn.Sequential( + operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device), + ) + # learnable replace + self.text_embedding_padding = nn.Parameter( + torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device)) + + # Attention pooling + pooler_out_dim = 1024 + self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations) + + # Dimension of the extra input vectors + self.extra_in_dim = pooler_out_dim + + if self.size_cond: + # Image size and crop size conditions + self.extra_in_dim += 6 * 256 + + if self.use_style_cond: + # Here we use a default learned embedder layer for future extension. + self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device) + self.extra_in_dim += hidden_size + + # Text embedding for `add` + self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations) + self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations) + self.extra_embedder = nn.Sequential( + operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device), + ) + + # HUnYuanDiT Blocks + self.blocks = nn.ModuleList([ + HunYuanDiTBlock(hidden_size=hidden_size, + c_emb_size=hidden_size, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + text_states_dim=self.text_states_dim, + qk_norm=qk_norm, + norm_type=self.norm, + skip=layer > depth // 2, + attn_precision=attn_precision, + dtype=dtype, + device=device, + operations=operations, + ) + for layer in range(depth) + ]) + + self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + self.unpatchify_channels = self.out_channels + + + + def forward(self, + x, + t, + context,#encoder_hidden_states=None, + text_embedding_mask=None, + encoder_hidden_states_t5=None, + text_embedding_mask_t5=None, + image_meta_size=None, + style=None, + return_dict=False, + control=None, + transformer_options={}, + ): + """ + Forward pass of the encoder. + + Parameters + ---------- + x: torch.Tensor + (B, D, H, W) + t: torch.Tensor + (B) + encoder_hidden_states: torch.Tensor + CLIP text embedding, (B, L_clip, D) + text_embedding_mask: torch.Tensor + CLIP text embedding mask, (B, L_clip) + encoder_hidden_states_t5: torch.Tensor + T5 text embedding, (B, L_t5, D) + text_embedding_mask_t5: torch.Tensor + T5 text embedding mask, (B, L_t5) + image_meta_size: torch.Tensor + (B, 6) + style: torch.Tensor + (B) + cos_cis_img: torch.Tensor + sin_cis_img: torch.Tensor + return_dict: bool + Whether to return a dictionary. + """ + patches_replace = transformer_options.get("patches_replace", {}) + encoder_hidden_states = context + text_states = encoder_hidden_states # 2,77,1024 + text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 + text_states_mask = text_embedding_mask.bool() # 2,77 + text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 + b_t5, l_t5, c_t5 = text_states_t5.shape + text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) + + padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) + + text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len]) + text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:]) + + text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 + # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1) + + _, _, oh, ow = x.shape + th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size + + + # Get image RoPE embedding according to `reso`lution. + freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img) + + # ========================= Build time and image embedding ========================= + t = self.t_embedder(t, dtype=x.dtype) + x = self.x_embedder(x) + + # ========================= Concatenate all extra vectors ========================= + # Build text tokens with pooling + extra_vec = self.pooler(encoder_hidden_states_t5) + + # Build image meta size tokens if applicable + if self.size_cond: + image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256] + image_meta_size = image_meta_size.view(-1, 6 * 256) + extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] + + # Build style tokens + if self.use_style_cond: + if style is None: + style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int) + style_embedding = self.style_embedder(style, out_dtype=x.dtype) + extra_vec = torch.cat([extra_vec, style_embedding], dim=1) + + # Concatenate all extra vectors + c = t + self.extra_embedder(extra_vec) # [B, D] + + blocks_replace = patches_replace.get("dit", {}) + + controls = None + if control: + controls = control.get("output", None) + # ========================= Forward pass through HunYuanDiT blocks ========================= + skips = [] + for layer, block in enumerate(self.blocks): + if layer > self.depth // 2: + if controls is not None: + skip = skips.pop() + controls.pop().to(dtype=x.dtype) + else: + skip = skips.pop() + else: + skip = None + + if ("double_block", layer) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], args["vec"], args["txt"], args["pe"], args["skip"]) + return out + + out = blocks_replace[("double_block", layer)]({"img": x, "txt": text_states, "vec": c, "pe": freqs_cis_img, "skip": skip}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D) + + + if layer < (self.depth // 2 - 1): + skips.append(x) + if controls is not None and len(controls) != 0: + raise ValueError("The number of controls is not equal to the number of skip connections.") + + # ========================= Final layer ========================= + x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels) + x = self.unpatchify(x, th, tw) # (N, out_channels, H, W) + + if return_dict: + return {'x': x} + if self.learn_sigma: + return x[:,:self.out_channels // 2,:oh,:ow] + return x[:,:,:oh,:ow] + + def unpatchify(self, x, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.unpatchify_channels + p = self.x_embedder.patch_size[0] + # h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py new file mode 100644 index 0000000000000000000000000000000000000000..c1b878ed6b0541116794635c5c1b53a7ca13af47 --- /dev/null +++ b/comfy/ldm/hydit/poolers.py @@ -0,0 +1,36 @@ +import torch +import torch.nn as nn +from comfy.ldm.modules.attention import optimized_attention +import comfy.ops + +class AttentionPool(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device)) + self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) + self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) + self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) + self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device) + self.num_heads = num_heads + self.embed_dim = embed_dim + + def forward(self, x): + x = x[:,:self.positional_embedding.shape[0] - 1] + x = x.permute(1, 0, 2) # NLC -> LNC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC + x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC + + q = self.q_proj(x[:1]) + k = self.k_proj(x) + v = self.v_proj(x) + + batch_size = q.shape[1] + head_dim = self.embed_dim // self.num_heads + q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) + k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) + v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) + + attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1) + + attn_output = self.c_proj(attn_output) + return attn_output.squeeze(0) diff --git a/comfy/ldm/hydit/posemb_layers.py b/comfy/ldm/hydit/posemb_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..dcb41a713cd94ea8472ff26e8865066887b1e486 --- /dev/null +++ b/comfy/ldm/hydit/posemb_layers.py @@ -0,0 +1,224 @@ +import torch +import numpy as np +from typing import Union + + +def _to_tuple(x): + if isinstance(x, int): + return x, x + else: + return x + + +def get_fill_resize_and_crop(src, tgt): + th, tw = _to_tuple(tgt) + h, w = _to_tuple(src) + + tr = th / tw # base resolution + r = h / w # target resolution + + # resize + if r > tr: + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + +def get_meshgrid(start, *args): + if len(args) == 0: + # start is grid_size + num = _to_tuple(start) + start = (0, 0) + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = _to_tuple(start) + stop = _to_tuple(args[0]) + num = (stop[0] - start[0], stop[1] - start[1]) + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = _to_tuple(start) + stop = _to_tuple(args[0]) + num = _to_tuple(args[1]) + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) + grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) # [2, W, H] + return grid + +################################################################################# +# Sine/Cosine Positional Embedding Functions # +################################################################################# +# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py + +def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid = get_meshgrid(start, *args) # [2, H, w] + # grid_h = np.arange(grid_size, dtype=np.float32) + # grid_w = np.arange(grid_size, dtype=np.float32) + # grid = np.meshgrid(grid_w, grid_h) # here w goes first + # grid = np.stack(grid, axis=0) # [2, W, H] + + grid = grid.reshape([2, 1, *grid.shape[1:]]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (W,H) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +################################################################################# +# Rotary Positional Embedding Functions # +################################################################################# +# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443 + +def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True): + """ + This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure. + + Parameters + ---------- + embed_dim: int + embedding dimension size + start: int or tuple of int + If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; + If len(args) == 2, start is start, args[0] is stop, args[1] is num. + use_real: bool + If True, return real part and imaginary part separately. Otherwise, return complex numbers. + + Returns + ------- + pos_embed: torch.Tensor + [HW, D/2] + """ + grid = get_meshgrid(start, *args) # [2, H, w] + grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution + pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) + return pos_embed + + +def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): + assert embed_dim % 4 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) + emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) + + if use_real: + cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) + sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) + return cos, sin + else: + emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) + return emb + + +def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): + """ + Precompute the frequency tensor for complex exponentials (cis) with given dimensions. + + This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' + and the end index 'end'. The 'theta' parameter scales the frequencies. + The returned tensor contains complex values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar + theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. + use_real (bool, optional): If True, return real part and imaginary part separately. + Otherwise, return complex numbers. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2] + + """ + if isinstance(pos, int): + pos = np.arange(pos) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] + freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] + return freqs_cis + + + +def calc_sizes(rope_img, patch_size, th, tw): + if rope_img == 'extend': + # Expansion mode + sub_args = [(th, tw)] + elif rope_img.startswith('base'): + # Based on the specified dimensions, other dimensions are obtained through interpolation. + base_size = int(rope_img[4:]) // 8 // patch_size + start, stop = get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + else: + raise ValueError(f"Unknown rope_img: {rope_img}") + return sub_args + + +def init_image_posemb(rope_img, + resolutions, + patch_size, + hidden_size, + num_heads, + log_fn, + rope_real=True, + ): + freqs_cis_img = {} + for reso in resolutions: + th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size + sub_args = calc_sizes(rope_img, patch_size, th, tw) + freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real) + log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) " + f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}") + return freqs_cis_img diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py new file mode 100644 index 0000000000000000000000000000000000000000..ad9a7daeab76ea3776016ab9c07d618dbe1e316a --- /dev/null +++ b/comfy/ldm/lightricks/model.py @@ -0,0 +1,506 @@ +import torch +from torch import nn +import comfy.ldm.modules.attention +import comfy.ldm.common_dit +from einops import rearrange +import math +from typing import Dict, Optional, Tuple + +from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords + + +def get_timestep_embedding( + timesteps: torch.Tensor, + embedding_dim: int, + flip_sin_to_cos: bool = False, + downscale_freq_shift: float = 1, + scale: float = 1, + max_period: int = 10000, +): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. + + Args + timesteps (torch.Tensor): + a 1-D Tensor of N indices, one per batch element. These may be fractional. + embedding_dim (int): + the dimension of the output. + flip_sin_to_cos (bool): + Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False) + downscale_freq_shift (float): + Controls the delta between frequencies between dimensions + scale (float): + Scaling factor applied to the embeddings. + max_period (int): + Controls the maximum frequency of the embeddings + Returns + torch.Tensor: an [N x dim] Tensor of positional embeddings. + """ + assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" + + half_dim = embedding_dim // 2 + exponent = -math.log(max_period) * torch.arange( + start=0, end=half_dim, dtype=torch.float32, device=timesteps.device + ) + exponent = exponent / (half_dim - downscale_freq_shift) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + # scale embeddings + emb = scale * emb + + # concat sine and cosine embeddings + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + + # flip sine and cosine embeddings + if flip_sin_to_cos: + emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) + + # zero pad + if embedding_dim % 2 == 1: + emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) + return emb + + +class TimestepEmbedding(nn.Module): + def __init__( + self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + sample_proj_bias=True, + dtype=None, device=None, operations=None, + ): + super().__init__() + + self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device) + + if cond_proj_dim is not None: + self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device) + else: + self.cond_proj = None + + self.act = nn.SiLU() + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device) + + if post_act_fn is None: + self.post_act = None + # else: + # self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +class Timesteps(nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + self.downscale_freq_shift = downscale_freq_shift + self.scale = scale + + def forward(self, timesteps): + t_emb = get_timestep_embedding( + timesteps, + self.num_channels, + flip_sin_to_cos=self.flip_sin_to_cos, + downscale_freq_shift=self.downscale_freq_shift, + scale=self.scale, + ) + return t_emb + + +class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): + """ + For PixArt-Alpha. + + Reference: + https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29 + """ + + def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None): + super().__init__() + + self.outdim = size_emb_dim + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations) + + def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype): + timesteps_proj = self.time_proj(timestep) + timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) + return timesteps_emb + + +class AdaLayerNormSingle(nn.Module): + r""" + Norm layer adaptive layer norm single (adaLN-single). + + As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + use_additional_conditions (`bool`): To use additional conditions for normalization or not. + """ + + def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None): + super().__init__() + + self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings( + embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations + ) + + self.silu = nn.SiLU() + self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device) + + def forward( + self, + timestep: torch.Tensor, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + batch_size: Optional[int] = None, + hidden_dtype: Optional[torch.dtype] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + # No modulation happening here. + added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None} + embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype) + return self.linear(self.silu(embedded_timestep)), embedded_timestep + +class PixArtAlphaTextProjection(nn.Module): + """ + Projects caption embeddings. Also handles dropout for classifier-free guidance. + + Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py + """ + + def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None): + super().__init__() + if out_features is None: + out_features = hidden_size + self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device) + if act_fn == "gelu_tanh": + self.act_1 = nn.GELU(approximate="tanh") + elif act_fn == "silu": + self.act_1 = nn.SiLU() + else: + raise ValueError(f"Unknown activation function: {act_fn}") + self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device) + + def forward(self, caption): + hidden_states = self.linear_1(caption) + hidden_states = self.act_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +class GELU_approx(nn.Module): + def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device) + + def forward(self, x): + return torch.nn.functional.gelu(self.proj(x), approximate="tanh") + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None): + super().__init__() + inner_dim = int(dim * mult) + project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.net(x) + + +def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one + cos_freqs = freqs_cis[0] + sin_freqs = freqs_cis[1] + + t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2) + t1, t2 = t_dup.unbind(dim=-1) + t_dup = torch.stack((-t2, t1), dim=-1) + input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)") + + out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs + + return out + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None): + super().__init__() + inner_dim = dim_head * heads + context_dim = query_dim if context_dim is None else context_dim + self.attn_precision = attn_precision + + self.heads = heads + self.dim_head = dim_head + + self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) + self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) + + self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) + + self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) + + def forward(self, x, context=None, mask=None, pe=None): + q = self.to_q(x) + context = x if context is None else context + k = self.to_k(context) + v = self.to_v(context) + + q = self.q_norm(q) + k = self.k_norm(k) + + if pe is not None: + q = apply_rotary_emb(q, pe) + k = apply_rotary_emb(k, pe) + + if mask is None: + out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + else: + out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None): + super().__init__() + + self.attn_precision = attn_precision + self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) + self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations) + + self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) + + self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype)) + + def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2) + + x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa + + x += self.attn2(x, context=context, mask=attention_mask) + + y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp + x += self.ff(y) * gate_mlp + + return x + +def get_fractional_positions(indices_grid, max_pos): + fractional_positions = torch.stack( + [ + indices_grid[:, i] / max_pos[i] + for i in range(3) + ], + dim=-1, + ) + return fractional_positions + + +def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]): + dtype = torch.float32 #self.dtype + + fractional_positions = get_fractional_positions(indices_grid, max_pos) + + start = 1 + end = theta + device = fractional_positions.device + + indices = theta ** ( + torch.linspace( + math.log(start, theta), + math.log(end, theta), + dim // 6, + device=device, + dtype=dtype, + ) + ) + indices = indices.to(dtype=dtype) + + indices = indices * math.pi / 2 + + freqs = ( + (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)) + .transpose(-1, -2) + .flatten(2) + ) + + cos_freq = freqs.cos().repeat_interleave(2, dim=-1) + sin_freq = freqs.sin().repeat_interleave(2, dim=-1) + if dim % 6 != 0: + cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6]) + sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6]) + cos_freq = torch.cat([cos_padding, cos_freq], dim=-1) + sin_freq = torch.cat([sin_padding, sin_freq], dim=-1) + return cos_freq.to(out_dtype), sin_freq.to(out_dtype) + + +class LTXVModel(torch.nn.Module): + def __init__(self, + in_channels=128, + cross_attention_dim=2048, + attention_head_dim=64, + num_attention_heads=32, + + caption_channels=4096, + num_layers=28, + + + positional_embedding_theta=10000.0, + positional_embedding_max_pos=[20, 2048, 2048], + causal_temporal_positioning=False, + vae_scale_factors=(8, 32, 32), + dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.generator = None + self.vae_scale_factors = vae_scale_factors + self.dtype = dtype + self.out_channels = in_channels + self.inner_dim = num_attention_heads * attention_head_dim + self.causal_temporal_positioning = causal_temporal_positioning + + self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device) + + self.adaln_single = AdaLayerNormSingle( + self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations + ) + + # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device) + + self.caption_projection = PixArtAlphaTextProjection( + in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations + ) + + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + self.inner_dim, + num_attention_heads, + attention_head_dim, + context_dim=cross_attention_dim, + # attn_precision=attn_precision, + dtype=dtype, device=device, operations=operations + ) + for d in range(num_layers) + ] + ) + + self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device)) + self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device) + + self.patchifier = SymmetricPatchifier(1) + + def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs): + patches_replace = transformer_options.get("patches_replace", {}) + + orig_shape = list(x.shape) + + x, latent_coords = self.patchifier.patchify(x) + pixel_coords = latent_to_pixel_coords( + latent_coords=latent_coords, + scale_factors=self.vae_scale_factors, + causal_fix=self.causal_temporal_positioning, + ) + + if keyframe_idxs is not None: + pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs + + fractional_coords = pixel_coords.to(torch.float32) + fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate) + + x = self.patchify_proj(x) + timestep = timestep * 1000.0 + + if attention_mask is not None and not torch.is_floating_point(attention_mask): + attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max + + pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype) + + batch_size = x.shape[0] + timestep, embedded_timestep = self.adaln_single( + timestep.flatten(), + {"resolution": None, "aspect_ratio": None}, + batch_size=batch_size, + hidden_dtype=x.dtype, + ) + # Second dimension is 1 or number of tokens (if timestep_per_token) + timestep = timestep.view(batch_size, -1, timestep.shape[-1]) + embedded_timestep = embedded_timestep.view( + batch_size, -1, embedded_timestep.shape[-1] + ) + + # 2. Blocks + if self.caption_projection is not None: + batch_size = x.shape[0] + context = self.caption_projection(context) + context = context.view( + batch_size, -1, x.shape[-1] + ) + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.transformer_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"]) + return out + + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block( + x, + context=context, + attention_mask=attention_mask, + timestep=timestep, + pe=pe + ) + + # 3. Output + scale_shift_values = ( + self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None] + ) + shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1] + x = self.norm_out(x) + # Modulation + x = x * (1 + scale) + shift + x = self.proj_out(x) + + x = self.patchifier.unpatchify( + latents=x, + output_height=orig_shape[3], + output_width=orig_shape[4], + output_num_frames=orig_shape[2], + out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size), + ) + + return x diff --git a/comfy/ldm/lightricks/symmetric_patchifier.py b/comfy/ldm/lightricks/symmetric_patchifier.py new file mode 100644 index 0000000000000000000000000000000000000000..4b9972b9fb589bb5516c64dd557c0914dda45116 --- /dev/null +++ b/comfy/ldm/lightricks/symmetric_patchifier.py @@ -0,0 +1,117 @@ +from abc import ABC, abstractmethod +from typing import Tuple + +import torch +from einops import rearrange +from torch import Tensor + + +def latent_to_pixel_coords( + latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False +) -> Tensor: + """ + Converts latent coordinates to pixel coordinates by scaling them according to the VAE's + configuration. + Args: + latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents] + containing the latent corner coordinates of each token. + scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space. + causal_fix (bool): Whether to take into account the different temporal scale + of the first frame. Default = False for backwards compatibility. + Returns: + Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates. + """ + pixel_coords = ( + latent_coords + * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None] + ) + if causal_fix: + # Fix temporal scale for first frame to 1 due to causality + pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0) + return pixel_coords + + +class Patchifier(ABC): + def __init__(self, patch_size: int): + super().__init__() + self._patch_size = (1, patch_size, patch_size) + + @abstractmethod + def patchify( + self, latents: Tensor, frame_rates: Tensor, scale_grid: bool + ) -> Tuple[Tensor, Tensor]: + pass + + @abstractmethod + def unpatchify( + self, + latents: Tensor, + output_height: int, + output_width: int, + output_num_frames: int, + out_channels: int, + ) -> Tuple[Tensor, Tensor]: + pass + + @property + def patch_size(self): + return self._patch_size + + def get_latent_coords( + self, latent_num_frames, latent_height, latent_width, batch_size, device + ): + """ + Return a tensor of shape [batch_size, 3, num_patches] containing the + top-left corner latent coordinates of each latent patch. + The tensor is repeated for each batch element. + """ + latent_sample_coords = torch.meshgrid( + torch.arange(0, latent_num_frames, self._patch_size[0], device=device), + torch.arange(0, latent_height, self._patch_size[1], device=device), + torch.arange(0, latent_width, self._patch_size[2], device=device), + indexing="ij", + ) + latent_sample_coords = torch.stack(latent_sample_coords, dim=0) + latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) + latent_coords = rearrange( + latent_coords, "b c f h w -> b c (f h w)", b=batch_size + ) + return latent_coords + + +class SymmetricPatchifier(Patchifier): + def patchify( + self, + latents: Tensor, + ) -> Tuple[Tensor, Tensor]: + b, _, f, h, w = latents.shape + latent_coords = self.get_latent_coords(f, h, w, b, latents.device) + latents = rearrange( + latents, + "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)", + p1=self._patch_size[0], + p2=self._patch_size[1], + p3=self._patch_size[2], + ) + return latents, latent_coords + + def unpatchify( + self, + latents: Tensor, + output_height: int, + output_width: int, + output_num_frames: int, + out_channels: int, + ) -> Tuple[Tensor, Tensor]: + output_height = output_height // self._patch_size[1] + output_width = output_width // self._patch_size[2] + latents = rearrange( + latents, + "b (f h w) (c p q) -> b c f (h p) (w q) ", + f=output_num_frames, + h=output_height, + w=output_width, + p=self._patch_size[1], + q=self._patch_size[2], + ) + return latents diff --git a/comfy/ldm/lightricks/vae/__pycache__/causal_conv3d.cpython-310.pyc b/comfy/ldm/lightricks/vae/__pycache__/causal_conv3d.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3779e2f470be8c698dd078609d84d1d1a710eb9f 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__init__( + self, + in_channels, + out_channels, + kernel_size: int = 3, + stride: Union[int, Tuple[int]] = 1, + dilation: int = 1, + groups: int = 1, + spatial_padding_mode: str = "zeros", + **kwargs, + ): + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + + kernel_size = (kernel_size, kernel_size, kernel_size) + self.time_kernel_size = kernel_size[0] + + dilation = (dilation, 1, 1) + + height_pad = kernel_size[1] // 2 + width_pad = kernel_size[2] // 2 + padding = (0, height_pad, width_pad) + + self.conv = ops.Conv3d( + in_channels, + out_channels, + kernel_size, + stride=stride, + dilation=dilation, + padding=padding, + padding_mode=spatial_padding_mode, + groups=groups, + ) + + def forward(self, x, causal: bool = True): + if causal: + first_frame_pad = x[:, :, :1, :, :].repeat( + (1, 1, self.time_kernel_size - 1, 1, 1) + ) + x = torch.concatenate((first_frame_pad, x), dim=2) + else: + first_frame_pad = x[:, :, :1, :, :].repeat( + (1, 1, (self.time_kernel_size - 1) // 2, 1, 1) + ) + last_frame_pad = x[:, :, -1:, :, :].repeat( + (1, 1, (self.time_kernel_size - 1) // 2, 1, 1) + ) + x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2) + x = self.conv(x) + return x + + @property + def weight(self): + return self.conv.weight diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..f91870d711766f94d1a30d26d93658155672f64a --- /dev/null +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -0,0 +1,1092 @@ +from __future__ import annotations +import torch +from torch import nn +from functools import partial +import math +from einops import rearrange +from typing import List, Optional, Tuple, Union +from .conv_nd_factory import make_conv_nd, make_linear_nd +from .pixel_norm import PixelNorm +from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings +import comfy.ops + +ops = comfy.ops.disable_weight_init + +class Encoder(nn.Module): + r""" + The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. + + Args: + dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3): + The number of dimensions to use in convolutions. + in_channels (`int`, *optional*, defaults to 3): + The number of input channels. + out_channels (`int`, *optional*, defaults to 3): + The number of output channels. + blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`): + The blocks to use. Each block is a tuple of the block name and the number of layers. + base_channels (`int`, *optional*, defaults to 128): + The number of output channels for the first convolutional layer. + norm_num_groups (`int`, *optional*, defaults to 32): + The number of groups for normalization. + patch_size (`int`, *optional*, defaults to 1): + The patch size to use. Should be a power of 2. + norm_layer (`str`, *optional*, defaults to `group_norm`): + The normalization layer to use. Can be either `group_norm` or `pixel_norm`. + latent_log_var (`str`, *optional*, defaults to `per_channel`): + The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`. + """ + + def __init__( + self, + dims: Union[int, Tuple[int, int]] = 3, + in_channels: int = 3, + out_channels: int = 3, + blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], + base_channels: int = 128, + norm_num_groups: int = 32, + patch_size: Union[int, Tuple[int]] = 1, + norm_layer: str = "group_norm", # group_norm, pixel_norm + latent_log_var: str = "per_channel", + spatial_padding_mode: str = "zeros", + ): + super().__init__() + self.patch_size = patch_size + self.norm_layer = norm_layer + self.latent_channels = out_channels + self.latent_log_var = latent_log_var + self.blocks_desc = blocks + + in_channels = in_channels * patch_size**2 + output_channel = base_channels + + self.conv_in = make_conv_nd( + dims=dims, + in_channels=in_channels, + out_channels=output_channel, + kernel_size=3, + stride=1, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + self.down_blocks = nn.ModuleList([]) + + for block_name, block_params in blocks: + input_channel = output_channel + if isinstance(block_params, int): + block_params = {"num_layers": block_params} + + if block_name == "res_x": + block = UNetMidBlock3D( + dims=dims, + in_channels=input_channel, + num_layers=block_params["num_layers"], + resnet_eps=1e-6, + resnet_groups=norm_num_groups, + norm_layer=norm_layer, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "res_x_y": + output_channel = block_params.get("multiplier", 2) * output_channel + block = ResnetBlock3D( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + eps=1e-6, + groups=norm_num_groups, + norm_layer=norm_layer, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_time": + block = make_conv_nd( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + kernel_size=3, + stride=(2, 1, 1), + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_space": + block = make_conv_nd( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + kernel_size=3, + stride=(1, 2, 2), + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_all": + block = make_conv_nd( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + kernel_size=3, + stride=(2, 2, 2), + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_all_x_y": + output_channel = block_params.get("multiplier", 2) * output_channel + block = make_conv_nd( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + kernel_size=3, + stride=(2, 2, 2), + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_all_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(2, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_space_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(1, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_time_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(2, 1, 1), + spatial_padding_mode=spatial_padding_mode, + ) + else: + raise ValueError(f"unknown block: {block_name}") + + self.down_blocks.append(block) + + # out + if norm_layer == "group_norm": + self.conv_norm_out = nn.GroupNorm( + num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6 + ) + elif norm_layer == "pixel_norm": + self.conv_norm_out = PixelNorm() + elif norm_layer == "layer_norm": + self.conv_norm_out = LayerNorm(output_channel, eps=1e-6) + + self.conv_act = nn.SiLU() + + conv_out_channels = out_channels + if latent_log_var == "per_channel": + conv_out_channels *= 2 + elif latent_log_var == "uniform": + conv_out_channels += 1 + elif latent_log_var == "constant": + conv_out_channels += 1 + elif latent_log_var != "none": + raise ValueError(f"Invalid latent_log_var: {latent_log_var}") + self.conv_out = make_conv_nd( + dims, + output_channel, + conv_out_channels, + 3, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + self.gradient_checkpointing = False + + def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor: + r"""The forward method of the `Encoder` class.""" + + sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) + sample = self.conv_in(sample) + + checkpoint_fn = ( + partial(torch.utils.checkpoint.checkpoint, use_reentrant=False) + if self.gradient_checkpointing and self.training + else lambda x: x + ) + + for down_block in self.down_blocks: + sample = checkpoint_fn(down_block)(sample) + + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if self.latent_log_var == "uniform": + last_channel = sample[:, -1:, ...] + num_dims = sample.dim() + + if num_dims == 4: + # For shape (B, C, H, W) + repeated_last_channel = last_channel.repeat( + 1, sample.shape[1] - 2, 1, 1 + ) + sample = torch.cat([sample, repeated_last_channel], dim=1) + elif num_dims == 5: + # For shape (B, C, F, H, W) + repeated_last_channel = last_channel.repeat( + 1, sample.shape[1] - 2, 1, 1, 1 + ) + sample = torch.cat([sample, repeated_last_channel], dim=1) + else: + raise ValueError(f"Invalid input shape: {sample.shape}") + elif self.latent_log_var == "constant": + sample = sample[:, :-1, ...] + approx_ln_0 = ( + -30 + ) # this is the minimal clamp value in DiagonalGaussianDistribution objects + sample = torch.cat( + [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0], + dim=1, + ) + + return sample + + +class Decoder(nn.Module): + r""" + The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample. + + Args: + dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3): + The number of dimensions to use in convolutions. + in_channels (`int`, *optional*, defaults to 3): + The number of input channels. + out_channels (`int`, *optional*, defaults to 3): + The number of output channels. + blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`): + The blocks to use. Each block is a tuple of the block name and the number of layers. + base_channels (`int`, *optional*, defaults to 128): + The number of output channels for the first convolutional layer. + norm_num_groups (`int`, *optional*, defaults to 32): + The number of groups for normalization. + patch_size (`int`, *optional*, defaults to 1): + The patch size to use. Should be a power of 2. + norm_layer (`str`, *optional*, defaults to `group_norm`): + The normalization layer to use. Can be either `group_norm` or `pixel_norm`. + causal (`bool`, *optional*, defaults to `True`): + Whether to use causal convolutions or not. + """ + + def __init__( + self, + dims, + in_channels: int = 3, + out_channels: int = 3, + blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], + base_channels: int = 128, + layers_per_block: int = 2, + norm_num_groups: int = 32, + patch_size: int = 1, + norm_layer: str = "group_norm", + causal: bool = True, + timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", + ): + super().__init__() + self.patch_size = patch_size + self.layers_per_block = layers_per_block + out_channels = out_channels * patch_size**2 + self.causal = causal + self.blocks_desc = blocks + + # Compute output channel to be product of all channel-multiplier blocks + output_channel = base_channels + for block_name, block_params in list(reversed(blocks)): + block_params = block_params if isinstance(block_params, dict) else {} + if block_name == "res_x_y": + output_channel = output_channel * block_params.get("multiplier", 2) + if block_name == "compress_all": + output_channel = output_channel * block_params.get("multiplier", 1) + + self.conv_in = make_conv_nd( + dims, + in_channels, + output_channel, + kernel_size=3, + stride=1, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + self.up_blocks = nn.ModuleList([]) + + for block_name, block_params in list(reversed(blocks)): + input_channel = output_channel + if isinstance(block_params, int): + block_params = {"num_layers": block_params} + + if block_name == "res_x": + block = UNetMidBlock3D( + dims=dims, + in_channels=input_channel, + num_layers=block_params["num_layers"], + resnet_eps=1e-6, + resnet_groups=norm_num_groups, + norm_layer=norm_layer, + inject_noise=block_params.get("inject_noise", False), + timestep_conditioning=timestep_conditioning, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "attn_res_x": + block = UNetMidBlock3D( + dims=dims, + in_channels=input_channel, + num_layers=block_params["num_layers"], + resnet_groups=norm_num_groups, + norm_layer=norm_layer, + inject_noise=block_params.get("inject_noise", False), + timestep_conditioning=timestep_conditioning, + attention_head_dim=block_params["attention_head_dim"], + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "res_x_y": + output_channel = output_channel // block_params.get("multiplier", 2) + block = ResnetBlock3D( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + eps=1e-6, + groups=norm_num_groups, + norm_layer=norm_layer, + inject_noise=block_params.get("inject_noise", False), + timestep_conditioning=False, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_time": + block = DepthToSpaceUpsample( + dims=dims, + in_channels=input_channel, + stride=(2, 1, 1), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_space": + block = DepthToSpaceUpsample( + dims=dims, + in_channels=input_channel, + stride=(1, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_all": + output_channel = output_channel // block_params.get("multiplier", 1) + block = DepthToSpaceUpsample( + dims=dims, + in_channels=input_channel, + stride=(2, 2, 2), + residual=block_params.get("residual", False), + out_channels_reduction_factor=block_params.get("multiplier", 1), + spatial_padding_mode=spatial_padding_mode, + ) + else: + raise ValueError(f"unknown layer: {block_name}") + + self.up_blocks.append(block) + + if norm_layer == "group_norm": + self.conv_norm_out = nn.GroupNorm( + num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6 + ) + elif norm_layer == "pixel_norm": + self.conv_norm_out = PixelNorm() + elif norm_layer == "layer_norm": + self.conv_norm_out = LayerNorm(output_channel, eps=1e-6) + + self.conv_act = nn.SiLU() + self.conv_out = make_conv_nd( + dims, + output_channel, + out_channels, + 3, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + self.gradient_checkpointing = False + + self.timestep_conditioning = timestep_conditioning + + if timestep_conditioning: + self.timestep_scale_multiplier = nn.Parameter( + torch.tensor(1000.0, dtype=torch.float32) + ) + self.last_time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings( + output_channel * 2, 0, operations=ops, + ) + self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel)) + + # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor: + def forward( + self, + sample: torch.FloatTensor, + timestep: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + r"""The forward method of the `Decoder` class.""" + batch_size = sample.shape[0] + + sample = self.conv_in(sample, causal=self.causal) + + checkpoint_fn = ( + partial(torch.utils.checkpoint.checkpoint, use_reentrant=False) + if self.gradient_checkpointing and self.training + else lambda x: x + ) + + scaled_timestep = None + if self.timestep_conditioning: + assert ( + timestep is not None + ), "should pass timestep with timestep_conditioning=True" + scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device) + + for up_block in self.up_blocks: + if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D): + sample = checkpoint_fn(up_block)( + sample, causal=self.causal, timestep=scaled_timestep + ) + else: + sample = checkpoint_fn(up_block)(sample, causal=self.causal) + + sample = self.conv_norm_out(sample) + + if self.timestep_conditioning: + embedded_timestep = self.last_time_embedder( + timestep=scaled_timestep.flatten(), + resolution=None, + aspect_ratio=None, + batch_size=sample.shape[0], + hidden_dtype=sample.dtype, + ) + embedded_timestep = embedded_timestep.view( + batch_size, embedded_timestep.shape[-1], 1, 1, 1 + ) + ada_values = self.last_scale_shift_table[ + None, ..., None, None, None + ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape( + batch_size, + 2, + -1, + embedded_timestep.shape[-3], + embedded_timestep.shape[-2], + embedded_timestep.shape[-1], + ) + shift, scale = ada_values.unbind(dim=1) + sample = sample * (1 + scale) + shift + + sample = self.conv_act(sample) + sample = self.conv_out(sample, causal=self.causal) + + sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) + + return sample + + +class UNetMidBlock3D(nn.Module): + """ + A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks. + + Args: + in_channels (`int`): The number of input channels. + dropout (`float`, *optional*, defaults to 0.0): The dropout rate. + num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. + resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. + resnet_groups (`int`, *optional*, defaults to 32): + The number of groups to use in the group normalization layers of the resnet blocks. + norm_layer (`str`, *optional*, defaults to `group_norm`): + The normalization layer to use. Can be either `group_norm` or `pixel_norm`. + inject_noise (`bool`, *optional*, defaults to `False`): + Whether to inject noise into the hidden states. + timestep_conditioning (`bool`, *optional*, defaults to `False`): + Whether to condition the hidden states on the timestep. + + Returns: + `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, + in_channels, height, width)`. + + """ + + def __init__( + self, + dims: Union[int, Tuple[int, int]], + in_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_groups: int = 32, + norm_layer: str = "group_norm", + inject_noise: bool = False, + timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", + ): + super().__init__() + resnet_groups = ( + resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + ) + + self.timestep_conditioning = timestep_conditioning + + if timestep_conditioning: + self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings( + in_channels * 4, 0, operations=ops, + ) + + self.res_blocks = nn.ModuleList( + [ + ResnetBlock3D( + dims=dims, + in_channels=in_channels, + out_channels=in_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + norm_layer=norm_layer, + inject_noise=inject_noise, + timestep_conditioning=timestep_conditioning, + spatial_padding_mode=spatial_padding_mode, + ) + for _ in range(num_layers) + ] + ) + + def forward( + self, + hidden_states: torch.FloatTensor, + causal: bool = True, + timestep: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + timestep_embed = None + if self.timestep_conditioning: + assert ( + timestep is not None + ), "should pass timestep with timestep_conditioning=True" + batch_size = hidden_states.shape[0] + timestep_embed = self.time_embedder( + timestep=timestep.flatten(), + resolution=None, + aspect_ratio=None, + batch_size=batch_size, + hidden_dtype=hidden_states.dtype, + ) + timestep_embed = timestep_embed.view( + batch_size, timestep_embed.shape[-1], 1, 1, 1 + ) + + for resnet in self.res_blocks: + hidden_states = resnet(hidden_states, causal=causal, timestep=timestep_embed) + + return hidden_states + + +class SpaceToDepthDownsample(nn.Module): + def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode): + super().__init__() + self.stride = stride + self.group_size = in_channels * math.prod(stride) // out_channels + self.conv = make_conv_nd( + dims=dims, + in_channels=in_channels, + out_channels=out_channels // math.prod(stride), + kernel_size=3, + stride=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + def forward(self, x, causal: bool = True): + if self.stride[0] == 2: + x = torch.cat( + [x[:, :, :1, :, :], x], dim=2 + ) # duplicate first frames for padding + + # skip connection + x_in = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + x_in = rearrange(x_in, "b (c g) d h w -> b c g d h w", g=self.group_size) + x_in = x_in.mean(dim=2) + + # conv + x = self.conv(x, causal=causal) + x = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + + x = x + x_in + + return x + + +class DepthToSpaceUpsample(nn.Module): + def __init__( + self, + dims, + in_channels, + stride, + residual=False, + out_channels_reduction_factor=1, + spatial_padding_mode="zeros", + ): + super().__init__() + self.stride = stride + self.out_channels = ( + math.prod(stride) * in_channels // out_channels_reduction_factor + ) + self.conv = make_conv_nd( + dims=dims, + in_channels=in_channels, + out_channels=self.out_channels, + kernel_size=3, + stride=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + self.residual = residual + self.out_channels_reduction_factor = out_channels_reduction_factor + + def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None): + if self.residual: + # Reshape and duplicate the input to match the output shape + x_in = rearrange( + x, + "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor + x_in = x_in.repeat(1, num_repeat, 1, 1, 1) + if self.stride[0] == 2: + x_in = x_in[:, :, 1:, :, :] + x = self.conv(x, causal=causal) + x = rearrange( + x, + "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + if self.stride[0] == 2: + x = x[:, :, 1:, :, :] + if self.residual: + x = x + x_in + return x + +class LayerNorm(nn.Module): + def __init__(self, dim, eps, elementwise_affine=True) -> None: + super().__init__() + self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) + + def forward(self, x): + x = rearrange(x, "b c d h w -> b d h w c") + x = self.norm(x) + x = rearrange(x, "b d h w c -> b c d h w") + return x + + +class ResnetBlock3D(nn.Module): + r""" + A Resnet block. + + Parameters: + in_channels (`int`): The number of channels in the input. + out_channels (`int`, *optional*, default to be `None`): + The number of output channels for the first conv layer. If None, same as `in_channels`. + dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use. + groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer. + eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization. + """ + + def __init__( + self, + dims: Union[int, Tuple[int, int]], + in_channels: int, + out_channels: Optional[int] = None, + dropout: float = 0.0, + groups: int = 32, + eps: float = 1e-6, + norm_layer: str = "group_norm", + inject_noise: bool = False, + timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.inject_noise = inject_noise + + if norm_layer == "group_norm": + self.norm1 = nn.GroupNorm( + num_groups=groups, num_channels=in_channels, eps=eps, affine=True + ) + elif norm_layer == "pixel_norm": + self.norm1 = PixelNorm() + elif norm_layer == "layer_norm": + self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True) + + self.non_linearity = nn.SiLU() + + self.conv1 = make_conv_nd( + dims, + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + if inject_noise: + self.per_channel_scale1 = nn.Parameter(torch.zeros((in_channels, 1, 1))) + + if norm_layer == "group_norm": + self.norm2 = nn.GroupNorm( + num_groups=groups, num_channels=out_channels, eps=eps, affine=True + ) + elif norm_layer == "pixel_norm": + self.norm2 = PixelNorm() + elif norm_layer == "layer_norm": + self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True) + + self.dropout = torch.nn.Dropout(dropout) + + self.conv2 = make_conv_nd( + dims, + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + if inject_noise: + self.per_channel_scale2 = nn.Parameter(torch.zeros((in_channels, 1, 1))) + + self.conv_shortcut = ( + make_linear_nd( + dims=dims, in_channels=in_channels, out_channels=out_channels + ) + if in_channels != out_channels + else nn.Identity() + ) + + self.norm3 = ( + LayerNorm(in_channels, eps=eps, elementwise_affine=True) + if in_channels != out_channels + else nn.Identity() + ) + + self.timestep_conditioning = timestep_conditioning + + if timestep_conditioning: + self.scale_shift_table = nn.Parameter( + torch.randn(4, in_channels) / in_channels**0.5 + ) + + def _feed_spatial_noise( + self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor + ) -> torch.FloatTensor: + spatial_shape = hidden_states.shape[-2:] + device = hidden_states.device + dtype = hidden_states.dtype + + # similar to the "explicit noise inputs" method in style-gan + spatial_noise = torch.randn(spatial_shape, device=device, dtype=dtype)[None] + scaled_noise = (spatial_noise * per_channel_scale)[None, :, None, ...] + hidden_states = hidden_states + scaled_noise + + return hidden_states + + def forward( + self, + input_tensor: torch.FloatTensor, + causal: bool = True, + timestep: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + hidden_states = input_tensor + batch_size = hidden_states.shape[0] + + hidden_states = self.norm1(hidden_states) + if self.timestep_conditioning: + assert ( + timestep is not None + ), "should pass timestep with timestep_conditioning=True" + ada_values = self.scale_shift_table[ + None, ..., None, None, None + ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape( + batch_size, + 4, + -1, + timestep.shape[-3], + timestep.shape[-2], + timestep.shape[-1], + ) + shift1, scale1, shift2, scale2 = ada_values.unbind(dim=1) + + hidden_states = hidden_states * (1 + scale1) + shift1 + + hidden_states = self.non_linearity(hidden_states) + + hidden_states = self.conv1(hidden_states, causal=causal) + + if self.inject_noise: + hidden_states = self._feed_spatial_noise( + hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype) + ) + + hidden_states = self.norm2(hidden_states) + + if self.timestep_conditioning: + hidden_states = hidden_states * (1 + scale2) + shift2 + + hidden_states = self.non_linearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + + hidden_states = self.conv2(hidden_states, causal=causal) + + if self.inject_noise: + hidden_states = self._feed_spatial_noise( + hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype) + ) + + input_tensor = self.norm3(input_tensor) + + batch_size = input_tensor.shape[0] + + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = input_tensor + hidden_states + + return output_tensor + + +def patchify(x, patch_size_hw, patch_size_t=1): + if patch_size_hw == 1 and patch_size_t == 1: + return x + if x.dim() == 4: + x = rearrange( + x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size_hw, r=patch_size_hw + ) + elif x.dim() == 5: + x = rearrange( + x, + "b c (f p) (h q) (w r) -> b (c p r q) f h w", + p=patch_size_t, + q=patch_size_hw, + r=patch_size_hw, + ) + else: + raise ValueError(f"Invalid input shape: {x.shape}") + + return x + + +def unpatchify(x, patch_size_hw, patch_size_t=1): + if patch_size_hw == 1 and patch_size_t == 1: + return x + + if x.dim() == 4: + x = rearrange( + x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size_hw, r=patch_size_hw + ) + elif x.dim() == 5: + x = rearrange( + x, + "b (c p r q) f h w -> b c (f p) (h q) (w r)", + p=patch_size_t, + q=patch_size_hw, + r=patch_size_hw, + ) + + return x + +class processor(nn.Module): + def __init__(self): + super().__init__() + self.register_buffer("std-of-means", torch.empty(128)) + self.register_buffer("mean-of-means", torch.empty(128)) + self.register_buffer("mean-of-stds", torch.empty(128)) + self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128)) + self.register_buffer("channel", torch.empty(128)) + + def un_normalize(self, x): + return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x) + + def normalize(self, x): + return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x) + +class VideoVAE(nn.Module): + def __init__(self, version=0, config=None): + super().__init__() + + if config is None: + config = self.guess_config(version) + + self.timestep_conditioning = config.get("timestep_conditioning", False) + double_z = config.get("double_z", True) + latent_log_var = config.get( + "latent_log_var", "per_channel" if double_z else "none" + ) + + self.encoder = Encoder( + dims=config["dims"], + in_channels=config.get("in_channels", 3), + out_channels=config["latent_channels"], + blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), + patch_size=config.get("patch_size", 1), + latent_log_var=latent_log_var, + norm_layer=config.get("norm_layer", "group_norm"), + spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), + ) + + self.decoder = Decoder( + dims=config["dims"], + in_channels=config["latent_channels"], + out_channels=config.get("out_channels", 3), + blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), + patch_size=config.get("patch_size", 1), + norm_layer=config.get("norm_layer", "group_norm"), + causal=config.get("causal_decoder", False), + timestep_conditioning=self.timestep_conditioning, + spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), + ) + + self.per_channel_statistics = processor() + + def guess_config(self, version): + if version == 0: + config = { + "_class_name": "CausalVideoAutoencoder", + "dims": 3, + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "blocks": [ + ["res_x", 4], + ["compress_all", 1], + ["res_x_y", 1], + ["res_x", 3], + ["compress_all", 1], + ["res_x_y", 1], + ["res_x", 3], + ["compress_all", 1], + ["res_x", 3], + ["res_x", 4], + ], + "scaling_factor": 1.0, + "norm_layer": "pixel_norm", + "patch_size": 4, + "latent_log_var": "uniform", + "use_quant_conv": False, + "causal_decoder": False, + } + elif version == 1: + config = { + "_class_name": "CausalVideoAutoencoder", + "dims": 3, + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "decoder_blocks": [ + ["res_x", {"num_layers": 5, "inject_noise": True}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 6, "inject_noise": True}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 7, "inject_noise": True}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 8, "inject_noise": False}] + ], + "encoder_blocks": [ + ["res_x", {"num_layers": 4}], + ["compress_all", {}], + ["res_x_y", 1], + ["res_x", {"num_layers": 3}], + ["compress_all", {}], + ["res_x_y", 1], + ["res_x", {"num_layers": 3}], + ["compress_all", {}], + ["res_x", {"num_layers": 3}], + ["res_x", {"num_layers": 4}] + ], + "scaling_factor": 1.0, + "norm_layer": "pixel_norm", + "patch_size": 4, + "latent_log_var": "uniform", + "use_quant_conv": False, + "causal_decoder": False, + "timestep_conditioning": True, + } + else: + config = { + "_class_name": "CausalVideoAutoencoder", + "dims": 3, + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "encoder_blocks": [ + ["res_x", {"num_layers": 4}], + ["compress_space_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_time_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}] + ], + "decoder_blocks": [ + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}] + ], + "scaling_factor": 1.0, + "norm_layer": "pixel_norm", + "patch_size": 4, + "latent_log_var": "uniform", + "use_quant_conv": False, + "causal_decoder": False, + "timestep_conditioning": True + } + return config + + def encode(self, x): + frames_count = x.shape[2] + if ((frames_count - 1) % 8) != 0: + raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.") + means, logvar = torch.chunk(self.encoder(x), 2, dim=1) + return self.per_channel_statistics.normalize(means) + + def decode(self, x, timestep=0.05, noise_scale=0.025): + if self.timestep_conditioning: #TODO: seed + x = torch.randn_like(x) * noise_scale + (1.0 - noise_scale) * x + return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=timestep) + diff --git a/comfy/ldm/lightricks/vae/conv_nd_factory.py b/comfy/ldm/lightricks/vae/conv_nd_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..b4026b14fae386850459b18fb06e8b5d7f79ab94 --- /dev/null +++ b/comfy/ldm/lightricks/vae/conv_nd_factory.py @@ -0,0 +1,90 @@ +from typing import Tuple, Union + + +from .dual_conv3d import DualConv3d +from .causal_conv3d import CausalConv3d +import comfy.ops +ops = comfy.ops.disable_weight_init + +def make_conv_nd( + dims: Union[int, Tuple[int, int]], + in_channels: int, + out_channels: int, + kernel_size: int, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + causal=False, + spatial_padding_mode="zeros", + temporal_padding_mode="zeros", +): + if not (spatial_padding_mode == temporal_padding_mode or causal): + raise NotImplementedError("spatial and temporal padding modes must be equal") + if dims == 2: + return ops.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + padding_mode=spatial_padding_mode, + ) + elif dims == 3: + if causal: + return CausalConv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + spatial_padding_mode=spatial_padding_mode, + ) + return ops.Conv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + padding_mode=spatial_padding_mode, + ) + elif dims == (2, 1): + return DualConv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + padding_mode=spatial_padding_mode, + ) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +def make_linear_nd( + dims: int, + in_channels: int, + out_channels: int, + bias=True, +): + if dims == 2: + return ops.Conv2d( + in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias + ) + elif dims == 3 or dims == (2, 1): + return ops.Conv3d( + in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias + ) + else: + raise ValueError(f"unsupported dimensions: {dims}") diff --git a/comfy/ldm/lightricks/vae/dual_conv3d.py b/comfy/ldm/lightricks/vae/dual_conv3d.py new file mode 100644 index 0000000000000000000000000000000000000000..dcf889296750d3d7e553af37ecf77d1b10245af3 --- /dev/null +++ b/comfy/ldm/lightricks/vae/dual_conv3d.py @@ -0,0 +1,217 @@ +import math +from typing import Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + + +class DualConv3d(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride: Union[int, Tuple[int, int, int]] = 1, + padding: Union[int, Tuple[int, int, int]] = 0, + dilation: Union[int, Tuple[int, int, int]] = 1, + groups=1, + bias=True, + padding_mode="zeros", + ): + super(DualConv3d, self).__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.padding_mode = padding_mode + # Ensure kernel_size, stride, padding, and dilation are tuples of length 3 + if isinstance(kernel_size, int): + kernel_size = (kernel_size, kernel_size, kernel_size) + if kernel_size == (1, 1, 1): + raise ValueError( + "kernel_size must be greater than 1. Use make_linear_nd instead." + ) + if isinstance(stride, int): + stride = (stride, stride, stride) + if isinstance(padding, int): + padding = (padding, padding, padding) + if isinstance(dilation, int): + dilation = (dilation, dilation, dilation) + + # Set parameters for convolutions + self.groups = groups + self.bias = bias + + # Define the size of the channels after the first convolution + intermediate_channels = ( + out_channels if in_channels < out_channels else in_channels + ) + + # Define parameters for the first convolution + self.weight1 = nn.Parameter( + torch.Tensor( + intermediate_channels, + in_channels // groups, + 1, + kernel_size[1], + kernel_size[2], + ) + ) + self.stride1 = (1, stride[1], stride[2]) + self.padding1 = (0, padding[1], padding[2]) + self.dilation1 = (1, dilation[1], dilation[2]) + if bias: + self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels)) + else: + self.register_parameter("bias1", None) + + # Define parameters for the second convolution + self.weight2 = nn.Parameter( + torch.Tensor( + out_channels, intermediate_channels // groups, kernel_size[0], 1, 1 + ) + ) + self.stride2 = (stride[0], 1, 1) + self.padding2 = (padding[0], 0, 0) + self.dilation2 = (dilation[0], 1, 1) + if bias: + self.bias2 = nn.Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter("bias2", None) + + # Initialize weights and biases + self.reset_parameters() + + def reset_parameters(self): + nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5)) + nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5)) + if self.bias: + fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1) + bound1 = 1 / math.sqrt(fan_in1) + nn.init.uniform_(self.bias1, -bound1, bound1) + fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2) + bound2 = 1 / math.sqrt(fan_in2) + nn.init.uniform_(self.bias2, -bound2, bound2) + + def forward(self, x, use_conv3d=False, skip_time_conv=False): + if use_conv3d: + return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv) + else: + return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv) + + def forward_with_3d(self, x, skip_time_conv): + # First convolution + x = F.conv3d( + x, + self.weight1, + self.bias1, + self.stride1, + self.padding1, + self.dilation1, + self.groups, + padding_mode=self.padding_mode, + ) + + if skip_time_conv: + return x + + # Second convolution + x = F.conv3d( + x, + self.weight2, + self.bias2, + self.stride2, + self.padding2, + self.dilation2, + self.groups, + padding_mode=self.padding_mode, + ) + + return x + + def forward_with_2d(self, x, skip_time_conv): + b, c, d, h, w = x.shape + + # First 2D convolution + x = rearrange(x, "b c d h w -> (b d) c h w") + # Squeeze the depth dimension out of weight1 since it's 1 + weight1 = self.weight1.squeeze(2) + # Select stride, padding, and dilation for the 2D convolution + stride1 = (self.stride1[1], self.stride1[2]) + padding1 = (self.padding1[1], self.padding1[2]) + dilation1 = (self.dilation1[1], self.dilation1[2]) + x = F.conv2d( + x, + weight1, + self.bias1, + stride1, + padding1, + dilation1, + self.groups, + padding_mode=self.padding_mode, + ) + + _, _, h, w = x.shape + + if skip_time_conv: + x = rearrange(x, "(b d) c h w -> b c d h w", b=b) + return x + + # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension + x = rearrange(x, "(b d) c h w -> (b h w) c d", b=b) + + # Reshape weight2 to match the expected dimensions for conv1d + weight2 = self.weight2.squeeze(-1).squeeze(-1) + # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution + stride2 = self.stride2[0] + padding2 = self.padding2[0] + dilation2 = self.dilation2[0] + x = F.conv1d( + x, + weight2, + self.bias2, + stride2, + padding2, + dilation2, + self.groups, + padding_mode=self.padding_mode, + ) + x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w) + + return x + + @property + def weight(self): + return self.weight2 + + +def test_dual_conv3d_consistency(): + # Initialize parameters + in_channels = 3 + out_channels = 5 + kernel_size = (3, 3, 3) + stride = (2, 2, 2) + padding = (1, 1, 1) + + # Create an instance of the DualConv3d class + dual_conv3d = DualConv3d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=True, + ) + + # Example input tensor + test_input = torch.randn(1, 3, 10, 10, 10) + + # Perform forward passes with both 3D and 2D settings + output_conv3d = dual_conv3d(test_input, use_conv3d=True) + output_2d = dual_conv3d(test_input, use_conv3d=False) + + # Assert that the outputs from both methods are sufficiently close + assert torch.allclose( + output_conv3d, output_2d, atol=1e-6 + ), "Outputs are not consistent between 3D and 2D convolutions." diff --git a/comfy/ldm/lightricks/vae/pixel_norm.py b/comfy/ldm/lightricks/vae/pixel_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..9bc3ea60e8a6453e7e12a7fb5aca4de3958a2567 --- /dev/null +++ b/comfy/ldm/lightricks/vae/pixel_norm.py @@ -0,0 +1,12 @@ +import torch +from torch import nn + + +class PixelNorm(nn.Module): + def __init__(self, dim=1, eps=1e-8): + super(PixelNorm, self).__init__() + self.dim = dim + self.eps = eps + + def forward(self, x): + return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps) diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py new file mode 100644 index 0000000000000000000000000000000000000000..f8dc4d7db6fcfe39de97482d4ef7c1bee61544e2 --- /dev/null +++ b/comfy/ldm/lumina/model.py @@ -0,0 +1,622 @@ +# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py +from __future__ import annotations + +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +import comfy.ldm.common_dit + +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder +from comfy.ldm.modules.attention import optimized_attention_masked +from comfy.ldm.flux.layers import EmbedND + + +def modulate(x, scale): + return x * (1 + scale.unsqueeze(1)) + +############################################################################# +# Core NextDiT Model # +############################################################################# + + +class JointAttention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + operation_settings={}, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_local_heads = n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = dim // n_heads + + self.qkv = operation_settings.get("operations").Linear( + dim, + (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.out = operation_settings.get("operations").Linear( + n_heads * self.head_dim, + dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + if qk_norm: + self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + else: + self.q_norm = self.k_norm = nn.Identity() + + @staticmethod + def apply_rotary_emb( + x_in: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + + t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2) + t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] + return t_out.reshape(*x_in.shape) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> torch.Tensor: + """ + + Args: + x: + x_mask: + freqs_cis: + + Returns: + + """ + bsz, seqlen, _ = x.shape + + xq, xk, xv = torch.split( + self.qkv(x), + [ + self.n_local_heads * self.head_dim, + self.n_local_kv_heads * self.head_dim, + self.n_local_kv_heads * self.head_dim, + ], + dim=-1, + ) + xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis) + xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis) + + n_rep = self.n_local_heads // self.n_local_kv_heads + if n_rep >= 1: + xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True) + + return self.out(output) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + operation_settings={}, + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + """ + super().__init__() + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = operation_settings.get("operations").Linear( + dim, + hidden_dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.w2 = operation_settings.get("operations").Linear( + hidden_dim, + dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.w3 = operation_settings.get("operations").Linear( + dim, + hidden_dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class JointTransformerBlock(nn.Module): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + modulation=True, + operation_settings={}, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + operation_settings=operation_settings, + ) + self.layer_id = layer_id + self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + self.modulation = modulation + if modulation: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operation_settings.get("operations").Linear( + min(dim, 1024), + 4 * dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + adaln_input: Optional[torch.Tensor]=None, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if self.modulation: + assert adaln_input is not None + scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) + + x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( + self.attention( + modulate(self.attention_norm1(x), scale_msa), + x_mask, + freqs_cis, + ) + ) + x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( + self.feed_forward( + modulate(self.ffn_norm1(x), scale_mlp), + ) + ) + else: + assert adaln_input is None + x = x + self.attention_norm2( + self.attention( + self.attention_norm1(x), + x_mask, + freqs_cis, + ) + ) + x = x + self.ffn_norm2( + self.feed_forward( + self.ffn_norm1(x), + ) + ) + return x + + +class FinalLayer(nn.Module): + """ + The final layer of NextDiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}): + super().__init__() + self.norm_final = operation_settings.get("operations").LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.linear = operation_settings.get("operations").Linear( + hidden_size, + patch_size * patch_size * out_channels, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operation_settings.get("operations").Linear( + min(hidden_size, 1024), + hidden_size, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + + def forward(self, x, c): + scale = self.adaLN_modulation(c) + x = modulate(self.norm_final(x), scale) + x = self.linear(x) + return x + + +class NextDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_refiner_layers: int = 2, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + axes_dims: List[int] = (16, 56, 56), + axes_lens: List[int] = (1, 512, 512), + image_model=None, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + self.dtype = dtype + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + + self.x_embedder = operation_settings.get("operations").Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + self.noise_refiner = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + modulation=True, + operation_settings=operation_settings, + ) + for layer_id in range(n_refiner_layers) + ] + ) + self.context_refiner = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + modulation=False, + operation_settings=operation_settings, + ) + for layer_id in range(n_refiner_layers) + ] + ) + + self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings) + self.cap_embedder = nn.Sequential( + operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), + operation_settings.get("operations").Linear( + cap_feat_dim, + dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + + self.layers = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + operation_settings=operation_settings, + ) + for layer_id in range(n_layers) + ] + ) + self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings) + + assert (dim // n_heads) == sum(axes_dims) + self.axes_dims = axes_dims + self.axes_lens = axes_lens + self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims) + self.dim = dim + self.n_heads = n_heads + + def unpatchify( + self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False + ) -> List[torch.Tensor]: + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + pH = pW = self.patch_size + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + begin = cap_size[i] + end = begin + (H // pH) * (W // pW) + imgs.append( + x[i][begin:end] + .view(H // pH, W // pW, pH, pW, self.out_channels) + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + + if return_tensor: + imgs = torch.stack(imgs, dim=0) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]: + bsz = len(x) + pH = pW = self.patch_size + device = x[0].device + dtype = x[0].dtype + + if cap_mask is not None: + l_effective_cap_len = cap_mask.sum(dim=1).tolist() + else: + l_effective_cap_len = [num_tokens] * bsz + + if cap_mask is not None and not torch.is_floating_point(cap_mask): + cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max + + img_sizes = [(img.size(1), img.size(2)) for img in x] + l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes] + + max_seq_len = max( + (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len)) + ) + max_cap_len = max(l_effective_cap_len) + max_img_len = max(l_effective_img_len) + + position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device) + + for i in range(bsz): + cap_len = l_effective_cap_len[i] + img_len = l_effective_img_len[i] + H, W = img_sizes[i] + H_tokens, W_tokens = H // pH, W // pW + assert H_tokens * W_tokens == img_len + + position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device) + position_ids[i, cap_len:cap_len+img_len, 0] = cap_len + row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten() + col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten() + position_ids[i, cap_len:cap_len+img_len, 1] = row_ids + position_ids[i, cap_len:cap_len+img_len, 2] = col_ids + + freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype) + + # build freqs_cis for cap and image individually + cap_freqs_cis_shape = list(freqs_cis.shape) + # cap_freqs_cis_shape[1] = max_cap_len + cap_freqs_cis_shape[1] = cap_feats.shape[1] + cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + img_freqs_cis_shape = list(freqs_cis.shape) + img_freqs_cis_shape[1] = max_img_len + img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + for i in range(bsz): + cap_len = l_effective_cap_len[i] + img_len = l_effective_img_len[i] + cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len] + img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len] + + # refine context + for layer in self.context_refiner: + cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis) + + # refine image + flat_x = [] + for i in range(bsz): + img = x[i] + C, H, W = img.size() + img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1) + flat_x.append(img) + x = flat_x + padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype) + padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device) + for i in range(bsz): + padded_img_embed[i, :l_effective_img_len[i]] = x[i] + padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max + + padded_img_embed = self.x_embedder(padded_img_embed) + padded_img_mask = padded_img_mask.unsqueeze(1) + for layer in self.noise_refiner: + padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t) + + if cap_mask is not None: + mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device) + mask[:, :max_cap_len] = cap_mask[:, :max_cap_len] + else: + mask = None + + padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype) + for i in range(bsz): + cap_len = l_effective_cap_len[i] + img_len = l_effective_img_len[i] + + padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len] + padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len] + + return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis + + # def forward(self, x, t, cap_feats, cap_mask): + def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): + t = 1.0 - timesteps + cap_feats = context + cap_mask = attention_mask + bs, c, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + """ + Forward pass of NextDiT. + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of text tokens/features + """ + + t = self.t_embedder(t, dtype=x.dtype) # (N, D) + adaln_input = t + + cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute + + x_is_tensor = isinstance(x, torch.Tensor) + x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens) + freqs_cis = freqs_cis.to(x.device) + + for layer in self.layers: + x = layer(x, mask, freqs_cis, adaln_input) + + x = self.final_layer(x, adaln_input) + x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w] + + return -x + diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..13bd6e16b642e529ba57b4a8be981fb274924197 --- /dev/null +++ b/comfy/ldm/models/autoencoder.py @@ -0,0 +1,231 @@ +import logging +import math +import torch +from contextlib import contextmanager +from typing import Any, Dict, Tuple, Union + +from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution + +from comfy.ldm.util import get_obj_from_str, instantiate_from_config +from comfy.ldm.modules.ema import LitEma +import comfy.ops + +class DiagonalGaussianRegularizer(torch.nn.Module): + def __init__(self, sample: bool = False): + super().__init__() + self.sample = sample + + def get_trainable_parameters(self) -> Any: + yield from () + + def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: + posterior = DiagonalGaussianDistribution(z) + if self.sample: + z = posterior.sample() + else: + z = posterior.mode() + return z, None + + +class AbstractAutoencoder(torch.nn.Module): + """ + This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, + unCLIP models, etc. Hence, it is fairly general, and specific features + (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. + """ + + def __init__( + self, + ema_decay: Union[None, float] = None, + monitor: Union[None, str] = None, + input_key: str = "jpg", + **kwargs, + ): + super().__init__() + + self.input_key = input_key + self.use_ema = ema_decay is not None + if monitor is not None: + self.monitor = monitor + + if self.use_ema: + self.model_ema = LitEma(self, decay=ema_decay) + logging.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + def get_input(self, batch) -> Any: + raise NotImplementedError() + + def on_train_batch_end(self, *args, **kwargs): + # for EMA computation + if self.use_ema: + self.model_ema(self) + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + logging.info(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + logging.info(f"{context}: Restored training weights") + + def encode(self, *args, **kwargs) -> torch.Tensor: + raise NotImplementedError("encode()-method of abstract base class called") + + def decode(self, *args, **kwargs) -> torch.Tensor: + raise NotImplementedError("decode()-method of abstract base class called") + + def instantiate_optimizer_from_config(self, params, lr, cfg): + logging.info(f"loading >>> {cfg['target']} <<< optimizer from config") + return get_obj_from_str(cfg["target"])( + params, lr=lr, **cfg.get("params", dict()) + ) + + def configure_optimizers(self) -> Any: + raise NotImplementedError() + + +class AutoencodingEngine(AbstractAutoencoder): + """ + Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL + (we also restore them explicitly as special cases for legacy reasons). + Regularizations such as KL or VQ are moved to the regularizer class. + """ + + def __init__( + self, + *args, + encoder_config: Dict, + decoder_config: Dict, + regularizer_config: Dict, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) + self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) + self.regularization = instantiate_from_config( + regularizer_config + ) + + def get_last_layer(self): + return self.decoder.get_last_layer() + + def encode( + self, + x: torch.Tensor, + return_reg_log: bool = False, + unregularized: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + z = self.encoder(x) + if unregularized: + return z, dict() + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + + def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: + x = self.decoder(z, **kwargs) + return x + + def forward( + self, x: torch.Tensor, **additional_decode_kwargs + ) -> Tuple[torch.Tensor, torch.Tensor, dict]: + z, reg_log = self.encode(x, return_reg_log=True) + dec = self.decode(z, **additional_decode_kwargs) + return z, dec, reg_log + + +class AutoencodingEngineLegacy(AutoencodingEngine): + def __init__(self, embed_dim: int, **kwargs): + self.max_batch_size = kwargs.pop("max_batch_size", None) + ddconfig = kwargs.pop("ddconfig") + super().__init__( + encoder_config={ + "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", + "params": ddconfig, + }, + decoder_config={ + "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", + "params": ddconfig, + }, + **kwargs, + ) + + if ddconfig.get("conv3d", False): + conv_op = comfy.ops.disable_weight_init.Conv3d + else: + conv_op = comfy.ops.disable_weight_init.Conv2d + + self.quant_conv = conv_op( + (1 + ddconfig["double_z"]) * ddconfig["z_channels"], + (1 + ddconfig["double_z"]) * embed_dim, + 1, + ) + + self.post_quant_conv = conv_op(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + + def get_autoencoder_params(self) -> list: + params = super().get_autoencoder_params() + return params + + def encode( + self, x: torch.Tensor, return_reg_log: bool = False + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + if self.max_batch_size is None: + z = self.encoder(x) + z = self.quant_conv(z) + else: + N = x.shape[0] + bs = self.max_batch_size + n_batches = int(math.ceil(N / bs)) + z = list() + for i_batch in range(n_batches): + z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) + z_batch = self.quant_conv(z_batch) + z.append(z_batch) + z = torch.cat(z, 0) + + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + + def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: + if self.max_batch_size is None: + dec = self.post_quant_conv(z) + dec = self.decoder(dec, **decoder_kwargs) + else: + N = z.shape[0] + bs = self.max_batch_size + n_batches = int(math.ceil(N / bs)) + dec = list() + for i_batch in range(n_batches): + dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) + dec_batch = self.decoder(dec_batch, **decoder_kwargs) + dec.append(dec_batch) + dec = torch.cat(dec, 0) + + return dec + + +class AutoencoderKL(AutoencodingEngineLegacy): + def __init__(self, **kwargs): + if "lossconfig" in kwargs: + kwargs["loss_config"] = kwargs.pop("lossconfig") + super().__init__( + regularizer_config={ + "target": ( + "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer" + ) + }, + **kwargs, + ) diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..35d2270ee98e2914decc6b2d92ff1c9f07dc3c60 --- /dev/null +++ b/comfy/ldm/modules/attention.py @@ -0,0 +1,1035 @@ +import math +import sys + +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat +from typing import Optional +import logging + +from .diffusionmodules.util import AlphaBlender, timestep_embedding +from .sub_quadratic_attention import efficient_dot_product_attention + +from comfy import model_management + +if model_management.xformers_enabled(): + import xformers + import xformers.ops + +if model_management.sage_attention_enabled(): + try: + from sageattention import sageattn + except ModuleNotFoundError as e: + if e.name == "sageattention": + logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") + else: + raise e + exit(-1) + +if model_management.flash_attention_enabled(): + try: + from flash_attn import flash_attn_func + except ModuleNotFoundError: + logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn") + exit(-1) + +from comfy.cli_args import args +import comfy.ops +ops = comfy.ops.disable_weight_init + +FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() + +def get_attn_precision(attn_precision, current_dtype): + if args.dont_upcast_attention: + return None + + if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE: + return FORCE_UPCAST_ATTENTION_DTYPE[current_dtype] + return attn_precision + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + operations.Linear(dim, inner_dim, dtype=dtype, device=device), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.net(x) + +def Normalize(in_channels, dtype=None, device=None): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + +def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + attn_precision = get_attn_precision(attn_precision, q.dtype) + + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + + scale = dim_head ** -0.5 + + h = heads + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if attn_precision == torch.float32: + sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if exists(mask): + if mask.dtype == torch.bool: + mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + else: + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + sim.add_(mask) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + + if skip_output_reshape: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + ) + else: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return out + + +def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + attn_precision = get_attn_precision(attn_precision, query.dtype) + + if skip_reshape: + b, _, _, dim_head = query.shape + else: + b, _, dim_head = query.shape + dim_head //= heads + + if skip_reshape: + query = query.reshape(b * heads, -1, dim_head) + value = value.reshape(b * heads, -1, dim_head) + key = key.reshape(b * heads, -1, dim_head).movedim(1, 2) + else: + query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) + + + dtype = query.dtype + upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 + if upcast_attention: + bytes_per_token = torch.finfo(torch.float32).bits//8 + else: + bytes_per_token = torch.finfo(query.dtype).bits//8 + batch_x_heads, q_tokens, _ = query.shape + _, _, k_tokens = key.shape + + mem_free_total, _ = model_management.get_free_memory(query.device, True) + + kv_chunk_size_min = None + kv_chunk_size = None + query_chunk_size = None + + for x in [4096, 2048, 1024, 512, 256]: + count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) + if count >= k_tokens: + kv_chunk_size = k_tokens + query_chunk_size = x + break + + if query_chunk_size is None: + query_chunk_size = 512 + + if mask is not None: + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + + hidden_states = efficient_dot_product_attention( + query, + key, + value, + query_chunk_size=query_chunk_size, + kv_chunk_size=kv_chunk_size, + kv_chunk_size_min=kv_chunk_size_min, + use_checkpoint=False, + upcast_attention=upcast_attention, + mask=mask, + ) + + hidden_states = hidden_states.to(dtype) + if skip_output_reshape: + hidden_states = hidden_states.unflatten(0, (-1, heads)) + else: + hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) + return hidden_states + +def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + attn_precision = get_attn_precision(attn_precision, q.dtype) + + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + + scale = dim_head ** -0.5 + + if skip_reshape: + q, k, v = map( + lambda t: t.reshape(b * heads, -1, dim_head), + (q, k, v), + ) + else: + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + + mem_free_total = model_management.get_free_memory(q.device) + + if attn_precision == torch.float32: + element_size = 4 + upcast = True + else: + element_size = q.element_size() + upcast = False + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size + modifier = 3 + mem_required = tensor_size * modifier + steps = 1 + + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " + # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") + + if steps > 64: + max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 + raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' + f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') + + if mask is not None: + if len(mask.shape) == 2: + bs = 1 + else: + bs = mask.shape[0] + mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) + + # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) + first_op_done = False + cleared_cache = False + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + if upcast: + with torch.autocast(enabled=False, device_type = 'cuda'): + s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale + else: + s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale + + if mask is not None: + if len(mask.shape) == 2: + s1 += mask[i:end] + else: + if mask.shape[1] == 1: + s1 += mask + else: + s1 += mask[:, i:end] + + s2 = s1.softmax(dim=-1).to(v.dtype) + del s1 + first_op_done = True + + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) + del s2 + break + except model_management.OOM_EXCEPTION as e: + if first_op_done == False: + model_management.soft_empty_cache(True) + if cleared_cache == False: + cleared_cache = True + logging.warning("out of memory error, emptying cache and trying again") + continue + steps *= 2 + if steps > 64: + raise e + logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) + else: + raise e + + del q, k, v + + if skip_output_reshape: + r1 = ( + r1.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + ) + else: + r1 = ( + r1.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return r1 + +BROKEN_XFORMERS = False +try: + x_vers = xformers.__version__ + # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) + BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") +except: + pass + +def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + b = q.shape[0] + dim_head = q.shape[-1] + # check to make sure xformers isn't broken + disabled_xformers = False + + if BROKEN_XFORMERS: + if b * heads > 65535: + disabled_xformers = True + + if not disabled_xformers: + if torch.jit.is_tracing() or torch.jit.is_scripting(): + disabled_xformers = True + + if disabled_xformers: + return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) + + if skip_reshape: + # b h k d -> b k h d + q, k, v = map( + lambda t: t.permute(0, 2, 1, 3), + (q, k, v), + ) + # actually do the reshaping + else: + dim_head //= heads + q, k, v = map( + lambda t: t.reshape(b, -1, heads, dim_head), + (q, k, v), + ) + + if mask is not None: + # add a singleton batch dimension + if mask.ndim == 2: + mask = mask.unsqueeze(0) + # add a singleton heads dimension + if mask.ndim == 3: + mask = mask.unsqueeze(1) + # pad to a multiple of 8 + pad = 8 - mask.shape[-1] % 8 + # the xformers docs says that it's allowed to have a mask of shape (1, Nq, Nk) + # but when using separated heads, the shape has to be (B, H, Nq, Nk) + # in flux, this matrix ends up being over 1GB + # here, we create a mask with the same batch/head size as the input mask (potentially singleton or full) + mask_out = torch.empty([mask.shape[0], mask.shape[1], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device) + + mask_out[..., :mask.shape[-1]] = mask + # doesn't this remove the padding again?? + mask = mask_out[..., :mask.shape[-1]] + mask = mask.expand(b, heads, -1, -1) + + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) + + if skip_output_reshape: + out = out.permute(0, 2, 1, 3) + else: + out = ( + out.reshape(b, -1, heads * dim_head) + ) + + return out + +if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue. + SDP_BATCH_LIMIT = 2**15 +else: + #TODO: other GPUs ? + SDP_BATCH_LIMIT = 2**31 + + +def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + if mask is not None: + # add a batch dimension if there isn't already one + if mask.ndim == 2: + mask = mask.unsqueeze(0) + # add a heads dimension if there isn't already one + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + if SDP_BATCH_LIMIT >= b: + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + if not skip_output_reshape: + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + else: + out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device) + for i in range(0, b, SDP_BATCH_LIMIT): + m = mask + if mask is not None: + if mask.shape[0] > 1: + m = mask[i : i + SDP_BATCH_LIMIT] + + out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention( + q[i : i + SDP_BATCH_LIMIT], + k[i : i + SDP_BATCH_LIMIT], + v[i : i + SDP_BATCH_LIMIT], + attn_mask=m, + dropout_p=0.0, is_causal=False + ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) + return out + + +def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + tensor_layout = "HND" + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head), + (q, k, v), + ) + tensor_layout = "NHD" + + if mask is not None: + # add a batch dimension if there isn't already one + if mask.ndim == 2: + mask = mask.unsqueeze(0) + # add a heads dimension if there isn't already one + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + try: + out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) + except Exception as e: + logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e)) + if tensor_layout == "NHD": + q, k, v = map( + lambda t: t.transpose(1, 2), + (q, k, v), + ) + return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape) + + if tensor_layout == "HND": + if not skip_output_reshape: + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + else: + if skip_output_reshape: + out = out.transpose(1, 2) + else: + out = out.reshape(b, -1, heads * dim_head) + return out + + +try: + @torch.library.custom_op("flash_attention::flash_attn", mutates_args=()) + def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, + dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: + return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal) + + + @flash_attn_wrapper.register_fake + def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False): + # Output shape is the same as q + return q.new_empty(q.shape) +except AttributeError as error: + FLASH_ATTN_ERROR = error + + def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, + dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: + assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}" + + +def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + if mask is not None: + # add a batch dimension if there isn't already one + if mask.ndim == 2: + mask = mask.unsqueeze(0) + # add a heads dimension if there isn't already one + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + try: + assert mask is None + out = flash_attn_wrapper( + q.transpose(1, 2), + k.transpose(1, 2), + v.transpose(1, 2), + dropout_p=0.0, + causal=False, + ).transpose(1, 2) + except Exception as e: + logging.warning(f"Flash Attention failed, using default SDPA: {e}") + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + if not skip_output_reshape: + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + return out + + +optimized_attention = attention_basic + +if model_management.sage_attention_enabled(): + logging.info("Using sage attention") + optimized_attention = attention_sage +elif model_management.xformers_enabled(): + logging.info("Using xformers attention") + optimized_attention = attention_xformers +elif model_management.flash_attention_enabled(): + logging.info("Using Flash Attention") + optimized_attention = attention_flash +elif model_management.pytorch_attention_enabled(): + logging.info("Using pytorch attention") + optimized_attention = attention_pytorch +else: + if args.use_split_cross_attention: + logging.info("Using split optimization for attention") + optimized_attention = attention_split + else: + logging.info("Using sub quadratic optimization for attention, if you have memory or speed issues try using: --use-split-cross-attention") + optimized_attention = attention_sub_quad + +optimized_attention_masked = optimized_attention + +def optimized_attention_for_device(device, mask=False, small_input=False): + if small_input: + if model_management.pytorch_attention_enabled(): + return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases + else: + return attention_basic + + if device == torch.device("cpu"): + return attention_sub_quad + + if mask: + return optimized_attention_masked + + return optimized_attention + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + self.attn_precision = attn_precision + + self.heads = heads + self.dim_head = dim_head + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) + + def forward(self, x, context=None, value=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + if value is not None: + v = self.to_v(value) + del value + else: + v = self.to_v(context) + + if mask is None: + out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + else: + out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, + disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops): + super().__init__() + + self.ff_in = ff_in or inner_dim is not None + if inner_dim is None: + inner_dim = dim + + self.is_res = inner_dim == dim + self.attn_precision = attn_precision + + if self.ff_in: + self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) + self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + self.disable_self_attn = disable_self_attn + self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + if disable_temporal_crossattention: + if switch_temporal_ca_to_sa: + raise ValueError + else: + self.attn2 = None + else: + context_dim_attn2 = None + if not switch_temporal_ca_to_sa: + context_dim_attn2 = context_dim + + self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, + heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + + self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.n_heads = n_heads + self.d_head = d_head + self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa + + def forward(self, x, context=None, transformer_options={}): + extra_options = {} + block = transformer_options.get("block", None) + block_index = transformer_options.get("block_index", 0) + transformer_patches = {} + transformer_patches_replace = {} + + for k in transformer_options: + if k == "patches": + transformer_patches = transformer_options[k] + elif k == "patches_replace": + transformer_patches_replace = transformer_options[k] + else: + extra_options[k] = transformer_options[k] + + extra_options["n_heads"] = self.n_heads + extra_options["dim_head"] = self.d_head + extra_options["attn_precision"] = self.attn_precision + + if self.ff_in: + x_skip = x + x = self.ff_in(self.norm_in(x)) + if self.is_res: + x += x_skip + + n = self.norm1(x) + if self.disable_self_attn: + context_attn1 = context + else: + context_attn1 = None + value_attn1 = None + + if "attn1_patch" in transformer_patches: + patch = transformer_patches["attn1_patch"] + if context_attn1 is None: + context_attn1 = n + value_attn1 = context_attn1 + for p in patch: + n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) + + if block is not None: + transformer_block = (block[0], block[1], block_index) + else: + transformer_block = None + attn1_replace_patch = transformer_patches_replace.get("attn1", {}) + block_attn1 = transformer_block + if block_attn1 not in attn1_replace_patch: + block_attn1 = block + + if block_attn1 in attn1_replace_patch: + if context_attn1 is None: + context_attn1 = n + value_attn1 = n + n = self.attn1.to_q(n) + context_attn1 = self.attn1.to_k(context_attn1) + value_attn1 = self.attn1.to_v(value_attn1) + n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) + n = self.attn1.to_out(n) + else: + n = self.attn1(n, context=context_attn1, value=value_attn1) + + if "attn1_output_patch" in transformer_patches: + patch = transformer_patches["attn1_output_patch"] + for p in patch: + n = p(n, extra_options) + + x = n + x + if "middle_patch" in transformer_patches: + patch = transformer_patches["middle_patch"] + for p in patch: + x = p(x, extra_options) + + if self.attn2 is not None: + n = self.norm2(x) + if self.switch_temporal_ca_to_sa: + context_attn2 = n + else: + context_attn2 = context + value_attn2 = None + if "attn2_patch" in transformer_patches: + patch = transformer_patches["attn2_patch"] + value_attn2 = context_attn2 + for p in patch: + n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) + + attn2_replace_patch = transformer_patches_replace.get("attn2", {}) + block_attn2 = transformer_block + if block_attn2 not in attn2_replace_patch: + block_attn2 = block + + if block_attn2 in attn2_replace_patch: + if value_attn2 is None: + value_attn2 = context_attn2 + n = self.attn2.to_q(n) + context_attn2 = self.attn2.to_k(context_attn2) + value_attn2 = self.attn2.to_v(value_attn2) + n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) + n = self.attn2.to_out(n) + else: + n = self.attn2(n, context=context_attn2, value=value_attn2) + + if "attn2_output_patch" in transformer_patches: + patch = transformer_patches["attn2_output_patch"] + for p in patch: + n = p(n, extra_options) + + x = n + x + if self.is_res: + x_skip = x + x = self.ff(self.norm3(x)) + if self.is_res: + x = x_skip + x + + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + if not use_linear: + self.proj_in = operations.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = operations.Conv2d(inner_dim,in_channels, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + self.use_linear = use_linear + + def forward(self, x, context=None, transformer_options={}): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] * len(self.transformer_blocks) + b, c, h, w = x.shape + transformer_options["activations_shape"] = list(x.shape) + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = x.movedim(1, 3).flatten(1, 2).contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + transformer_options["block_index"] = i + x = block(x, context=context[i], transformer_options=transformer_options) + if self.use_linear: + x = self.proj_out(x) + x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class SpatialVideoTransformer(SpatialTransformer): + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + use_linear=False, + context_dim=None, + use_spatial_context=False, + timesteps=None, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + time_context_dim=None, + ff_in=False, + checkpoint=False, + time_depth=1, + disable_self_attn=False, + disable_temporal_crossattention=False, + max_time_embed_period: int = 10000, + attn_precision=None, + dtype=None, device=None, operations=ops + ): + super().__init__( + in_channels, + n_heads, + d_head, + depth=depth, + dropout=dropout, + use_checkpoint=checkpoint, + context_dim=context_dim, + use_linear=use_linear, + disable_self_attn=disable_self_attn, + attn_precision=attn_precision, + dtype=dtype, device=device, operations=operations + ) + self.time_depth = time_depth + self.depth = depth + self.max_time_embed_period = max_time_embed_period + + time_mix_d_head = d_head + n_time_mix_heads = n_heads + + time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) + + inner_dim = n_heads * d_head + if use_spatial_context: + time_context_dim = context_dim + + self.time_stack = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_time_mix_heads, + time_mix_d_head, + dropout=dropout, + context_dim=time_context_dim, + # timesteps=timesteps, + checkpoint=checkpoint, + ff_in=ff_in, + inner_dim=time_mix_inner_dim, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + attn_precision=attn_precision, + dtype=dtype, device=device, operations=operations + ) + for _ in range(self.depth) + ] + ) + + assert len(self.time_stack) == len(self.transformer_blocks) + + self.use_spatial_context = use_spatial_context + self.in_channels = in_channels + + time_embed_dim = self.in_channels * 4 + self.time_pos_embed = nn.Sequential( + operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), + ) + + self.time_mixer = AlphaBlender( + alpha=merge_factor, merge_strategy=merge_strategy + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + time_context: Optional[torch.Tensor] = None, + timesteps: Optional[int] = None, + image_only_indicator: Optional[torch.Tensor] = None, + transformer_options={} + ) -> torch.Tensor: + _, _, h, w = x.shape + transformer_options["activations_shape"] = list(x.shape) + x_in = x + spatial_context = None + if exists(context): + spatial_context = context + + if self.use_spatial_context: + assert ( + context.ndim == 3 + ), f"n dims of spatial context should be 3 but are {context.ndim}" + + if time_context is None: + time_context = context + time_context_first_timestep = time_context[::timesteps] + time_context = repeat( + time_context_first_timestep, "b ... -> (b n) ...", n=h * w + ) + elif time_context is not None and not self.use_spatial_context: + time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) + if time_context.ndim == 2: + time_context = rearrange(time_context, "b c -> b 1 c") + + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c") + if self.use_linear: + x = self.proj_in(x) + + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) + emb = self.time_pos_embed(t_emb) + emb = emb[:, None, :] + + for it_, (block, mix_block) in enumerate( + zip(self.transformer_blocks, self.time_stack) + ): + transformer_options["block_index"] = it_ + x = block( + x, + context=spatial_context, + transformer_options=transformer_options, + ) + + x_mix = x + x_mix = x_mix + emb + + B, S, C = x_mix.shape + x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) + x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options + x_mix = rearrange( + x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps + ) + + x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + if not self.use_linear: + x = self.proj_out(x) + out = x + x_in + return out + + diff --git a/comfy/ldm/modules/diffusionmodules/__init__.py b/comfy/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/modules/diffusionmodules/__pycache__/__init__.cpython-310.pyc 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import optimized_attention +from einops import rearrange, repeat +from .util import timestep_embedding +import comfy.ops +import comfy.ldm.common_dit + +def default(x, y): + if x is not None: + return x + return y + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0., + use_conv=False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + drop_probs = drop + linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device) + self.drop2 = nn.Dropout(drop_probs) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + +class PatchEmbed(nn.Module): + """ 2D Image to Patch Embedding + """ + dynamic_img_pad: torch.jit.Final[bool] + + def __init__( + self, + img_size: Optional[int] = 224, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer = None, + flatten: bool = True, + bias: bool = True, + strict_img_size: bool = True, + dynamic_img_pad: bool = True, + padding_mode='circular', + conv3d=False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + try: + len(patch_size) + self.patch_size = patch_size + except: + if conv3d: + self.patch_size = (patch_size, patch_size, patch_size) + else: + self.patch_size = (patch_size, patch_size) + self.padding_mode = padding_mode + + # flatten spatial dim and transpose to channels last, kept for bwd compat + self.flatten = flatten + self.strict_img_size = strict_img_size + self.dynamic_img_pad = dynamic_img_pad + if conv3d: + self.proj = operations.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) + else: + self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + if self.dynamic_img_pad: + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode) + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # NCHW -> NLC + x = self.norm(x) + return x + +def modulate(x, shift, scale): + if shift is None: + shift = torch.zeros_like(scale) + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +################################################################################# +# Sine/Cosine Positional Embedding Functions # +################################################################################# + + +def get_2d_sincos_pos_embed( + embed_dim, + grid_size, + cls_token=False, + extra_tokens=0, + scaling_factor=None, + offset=None, +): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + if scaling_factor is not None: + grid = grid / scaling_factor + if offset is not None: + grid = grid - offset + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate( + [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0 + ) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + +def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32): + omega = torch.arange(embed_dim // 2, device=device, dtype=dtype) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + pos = pos.reshape(-1) # (M,) + out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product + emb_sin = torch.sin(out) # (M, D/2) + emb_cos = torch.cos(out) # (M, D/2) + emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) + return emb + +def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32): + small = min(h, w) + val_h = (h / small) * val_magnitude + val_w = (w / small) * val_magnitude + grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij') + emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) + emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) + emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) + return emb + + +################################################################################# +# Embedding Layers for Timesteps and Class Labels # +################################################################################# + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + + def forward(self, t, dtype, **kwargs): + t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class VectorEmbedder(nn.Module): + """ + Embeds a flat vector of dimension input_dim + """ + + def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + emb = self.mlp(x) + return emb + + +################################################################################# +# Core DiT Model # +################################################################################# + + +def split_qkv(qkv, head_dim): + qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0) + return qkv[0], qkv[1], qkv[2] + + +class SelfAttention(nn.Module): + ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + qk_scale: Optional[float] = None, + proj_drop: float = 0.0, + attn_mode: str = "xformers", + pre_only: bool = False, + qk_norm: Optional[str] = None, + rmsnorm: bool = False, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.num_heads = num_heads + self.head_dim = dim // num_heads + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + if not pre_only: + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + self.proj_drop = nn.Dropout(proj_drop) + assert attn_mode in self.ATTENTION_MODES + self.attn_mode = attn_mode + self.pre_only = pre_only + + if qk_norm == "rms": + self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + elif qk_norm == "ln": + self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) + elif qk_norm is None: + self.ln_q = nn.Identity() + self.ln_k = nn.Identity() + else: + raise ValueError(qk_norm) + + def pre_attention(self, x: torch.Tensor) -> torch.Tensor: + B, L, C = x.shape + qkv = self.qkv(x) + q, k, v = split_qkv(qkv, self.head_dim) + q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) + k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) + return (q, k, v) + + def post_attention(self, x: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + x = self.proj(x) + x = self.proj_drop(x) + return x + + def forward(self, x: torch.Tensor) -> torch.Tensor: + q, k, v = self.pre_attention(x) + x = optimized_attention( + q, k, v, heads=self.num_heads + ) + x = self.post_attention(x) + return x + + +class RMSNorm(torch.nn.Module): + def __init__( + self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs + ): + """ + Initialize the RMSNorm normalization layer. + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + """ + super().__init__() + self.eps = eps + self.learnable_scale = elementwise_affine + if self.learnable_scale: + self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + else: + self.register_parameter("weight", None) + + def forward(self, x): + return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) + + + +class SwiGLUFeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float] = None, + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. + + Attributes: + w1 (ColumnParallelLinear): Linear transformation for the first layer. + w2 (RowParallelLinear): Linear transformation for the second layer. + w3 (ColumnParallelLinear): Linear transformation for the third layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + def forward(self, x): + return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) + + +class DismantledBlock(nn.Module): + """ + A DiT block with gated adaptive layer norm (adaLN) conditioning. + """ + + ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + attn_mode: str = "xformers", + qkv_bias: bool = False, + pre_only: bool = False, + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + qk_norm: Optional[str] = None, + x_block_self_attn: bool = False, + dtype=None, + device=None, + operations=None, + **block_kwargs, + ): + super().__init__() + assert attn_mode in self.ATTENTION_MODES + if not rmsnorm: + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + else: + self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=pre_only, + qk_norm=qk_norm, + rmsnorm=rmsnorm, + dtype=dtype, + device=device, + operations=operations + ) + if x_block_self_attn: + assert not pre_only + assert not scale_mod_only + self.x_block_self_attn = True + self.attn2 = SelfAttention( + dim=hidden_size, + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=False, + qk_norm=qk_norm, + rmsnorm=rmsnorm, + dtype=dtype, + device=device, + operations=operations + ) + else: + self.x_block_self_attn = False + if not pre_only: + if not rmsnorm: + self.norm2 = operations.LayerNorm( + hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device + ) + else: + self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + if not pre_only: + if not swiglu: + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=lambda: nn.GELU(approximate="tanh"), + drop=0, + dtype=dtype, + device=device, + operations=operations + ) + else: + self.mlp = SwiGLUFeedForward( + dim=hidden_size, + hidden_dim=mlp_hidden_dim, + multiple_of=256, + ) + self.scale_mod_only = scale_mod_only + if x_block_self_attn: + assert not pre_only + assert not scale_mod_only + n_mods = 9 + elif not scale_mod_only: + n_mods = 6 if not pre_only else 2 + else: + n_mods = 4 if not pre_only else 1 + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device) + ) + self.pre_only = pre_only + + def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + if not self.pre_only: + if not self.scale_mod_only: + ( + shift_msa, + scale_msa, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation(c).chunk(6, dim=1) + else: + shift_msa = None + shift_mlp = None + ( + scale_msa, + gate_msa, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation( + c + ).chunk(4, dim=1) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, ( + x, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) + else: + if not self.scale_mod_only: + ( + shift_msa, + scale_msa, + ) = self.adaLN_modulation( + c + ).chunk(2, dim=1) + else: + shift_msa = None + scale_msa = self.adaLN_modulation(c) + qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) + return qkv, None + + def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): + assert not self.pre_only + x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) + x = x + gate_mlp.unsqueeze(1) * self.mlp( + modulate(self.norm2(x), shift_mlp, scale_mlp) + ) + return x + + def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + assert self.x_block_self_attn + ( + shift_msa, + scale_msa, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + shift_msa2, + scale_msa2, + gate_msa2, + ) = self.adaLN_modulation(c).chunk(9, dim=1) + x_norm = self.norm1(x) + qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa)) + qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2)) + return qkv, qkv2, ( + x, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + gate_msa2, + ) + + def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2): + assert not self.pre_only + attn1 = self.attn.post_attention(attn) + attn2 = self.attn2.post_attention(attn2) + out1 = gate_msa.unsqueeze(1) * attn1 + out2 = gate_msa2.unsqueeze(1) * attn2 + x = x + out1 + x = x + out2 + x = x + gate_mlp.unsqueeze(1) * self.mlp( + modulate(self.norm2(x), shift_mlp, scale_mlp) + ) + return x + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + assert not self.pre_only + if self.x_block_self_attn: + qkv, qkv2, intermediates = self.pre_attention_x(x, c) + attn, _ = optimized_attention( + qkv[0], qkv[1], qkv[2], + num_heads=self.attn.num_heads, + ) + attn2, _ = optimized_attention( + qkv2[0], qkv2[1], qkv2[2], + num_heads=self.attn2.num_heads, + ) + return self.post_attention_x(attn, attn2, *intermediates) + else: + qkv, intermediates = self.pre_attention(x, c) + attn = optimized_attention( + qkv[0], qkv[1], qkv[2], + heads=self.attn.num_heads, + ) + return self.post_attention(attn, *intermediates) + + +def block_mixing(*args, use_checkpoint=True, **kwargs): + if use_checkpoint: + return torch.utils.checkpoint.checkpoint( + _block_mixing, *args, use_reentrant=False, **kwargs + ) + else: + return _block_mixing(*args, **kwargs) + + +def _block_mixing(context, x, context_block, x_block, c): + context_qkv, context_intermediates = context_block.pre_attention(context, c) + + if x_block.x_block_self_attn: + x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c) + else: + x_qkv, x_intermediates = x_block.pre_attention(x, c) + + o = [] + for t in range(3): + o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1)) + qkv = tuple(o) + + attn = optimized_attention( + qkv[0], qkv[1], qkv[2], + heads=x_block.attn.num_heads, + ) + context_attn, x_attn = ( + attn[:, : context_qkv[0].shape[1]], + attn[:, context_qkv[0].shape[1] :], + ) + + if not context_block.pre_only: + context = context_block.post_attention(context_attn, *context_intermediates) + + else: + context = None + if x_block.x_block_self_attn: + attn2 = optimized_attention( + x_qkv2[0], x_qkv2[1], x_qkv2[2], + heads=x_block.attn2.num_heads, + ) + x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) + else: + x = x_block.post_attention(x_attn, *x_intermediates) + return context, x + + +class JointBlock(nn.Module): + """just a small wrapper to serve as a fsdp unit""" + + def __init__( + self, + *args, + **kwargs, + ): + super().__init__() + pre_only = kwargs.pop("pre_only") + qk_norm = kwargs.pop("qk_norm", None) + x_block_self_attn = kwargs.pop("x_block_self_attn", False) + self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs) + self.x_block = DismantledBlock(*args, + pre_only=False, + qk_norm=qk_norm, + x_block_self_attn=x_block_self_attn, + **kwargs) + + def forward(self, *args, **kwargs): + return block_mixing( + *args, context_block=self.context_block, x_block=self.x_block, **kwargs + ) + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__( + self, + hidden_size: int, + patch_size: int, + out_channels: int, + total_out_channels: Optional[int] = None, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = ( + operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + if (total_out_channels is None) + else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + +class SelfAttentionContext(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None): + super().__init__() + dim_head = dim // heads + inner_dim = dim + + self.heads = heads + self.dim_head = dim_head + + self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device) + + self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device) + + def forward(self, x): + qkv = self.qkv(x) + q, k, v = split_qkv(qkv, self.dim_head) + x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads) + return self.proj(x) + +class ContextProcessorBlock(nn.Module): + def __init__(self, context_size, dtype=None, device=None, operations=None): + super().__init__() + self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations) + self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations) + + def forward(self, x): + x += self.attn(self.norm1(x)) + x += self.mlp(self.norm2(x)) + return x + +class ContextProcessor(nn.Module): + def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None): + super().__init__() + self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)]) + self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + def forward(self, x): + for i, l in enumerate(self.layers): + x = l(x) + return self.norm(x) + +class MMDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + input_size: int = 32, + patch_size: int = 2, + in_channels: int = 4, + depth: int = 28, + # hidden_size: Optional[int] = None, + # num_heads: Optional[int] = None, + mlp_ratio: float = 4.0, + learn_sigma: bool = False, + adm_in_channels: Optional[int] = None, + context_embedder_config: Optional[Dict] = None, + compile_core: bool = False, + use_checkpoint: bool = False, + register_length: int = 0, + attn_mode: str = "torch", + rmsnorm: bool = False, + scale_mod_only: bool = False, + swiglu: bool = False, + out_channels: Optional[int] = None, + pos_embed_scaling_factor: Optional[float] = None, + pos_embed_offset: Optional[float] = None, + pos_embed_max_size: Optional[int] = None, + num_patches = None, + qk_norm: Optional[str] = None, + qkv_bias: bool = True, + context_processor_layers = None, + x_block_self_attn: bool = False, + x_block_self_attn_layers: Optional[List[int]] = [], + context_size = 4096, + num_blocks = None, + final_layer = True, + skip_blocks = False, + dtype = None, #TODO + device = None, + operations = None, + ): + super().__init__() + self.dtype = dtype + self.learn_sigma = learn_sigma + self.in_channels = in_channels + default_out_channels = in_channels * 2 if learn_sigma else in_channels + self.out_channels = default(out_channels, default_out_channels) + self.patch_size = patch_size + self.pos_embed_scaling_factor = pos_embed_scaling_factor + self.pos_embed_offset = pos_embed_offset + self.pos_embed_max_size = pos_embed_max_size + self.x_block_self_attn_layers = x_block_self_attn_layers + + # hidden_size = default(hidden_size, 64 * depth) + # num_heads = default(num_heads, hidden_size // 64) + + # apply magic --> this defines a head_size of 64 + self.hidden_size = 64 * depth + num_heads = depth + if num_blocks is None: + num_blocks = depth + + self.depth = depth + self.num_heads = num_heads + + self.x_embedder = PatchEmbed( + input_size, + patch_size, + in_channels, + self.hidden_size, + bias=True, + strict_img_size=self.pos_embed_max_size is None, + dtype=dtype, + device=device, + operations=operations + ) + self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) + + self.y_embedder = None + if adm_in_channels is not None: + assert isinstance(adm_in_channels, int) + self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations) + + if context_processor_layers is not None: + self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations) + else: + self.context_processor = None + + self.context_embedder = nn.Identity() + if context_embedder_config is not None: + if context_embedder_config["target"] == "torch.nn.Linear": + self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device) + + self.register_length = register_length + if self.register_length > 0: + self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device)) + + # num_patches = self.x_embedder.num_patches + # Will use fixed sin-cos embedding: + # just use a buffer already + if num_patches is not None: + self.register_buffer( + "pos_embed", + torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device), + ) + else: + self.pos_embed = None + + self.use_checkpoint = use_checkpoint + if not skip_blocks: + self.joint_blocks = nn.ModuleList( + [ + JointBlock( + self.hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + attn_mode=attn_mode, + pre_only=(i == num_blocks - 1) and final_layer, + rmsnorm=rmsnorm, + scale_mod_only=scale_mod_only, + swiglu=swiglu, + qk_norm=qk_norm, + x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(num_blocks) + ] + ) + + if final_layer: + self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + + if compile_core: + assert False + self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) + + def cropped_pos_embed(self, hw, device=None): + p = self.x_embedder.patch_size[0] + h, w = hw + # patched size + h = (h + 1) // p + w = (w + 1) // p + if self.pos_embed is None: + return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) + assert self.pos_embed_max_size is not None + assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) + assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) + top = (self.pos_embed_max_size - h) // 2 + left = (self.pos_embed_max_size - w) // 2 + spatial_pos_embed = rearrange( + self.pos_embed, + "1 (h w) c -> 1 h w c", + h=self.pos_embed_max_size, + w=self.pos_embed_max_size, + ) + spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] + spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c") + # print(spatial_pos_embed, top, left, h, w) + # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res + # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better + # # print(t) + # return t + return spatial_pos_embed + + def unpatchify(self, x, hw=None): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + if hw is None: + h = w = int(x.shape[1] ** 0.5) + else: + h, w = hw + h = (h + 1) // p + w = (w + 1) // p + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs + + def forward_core_with_concat( + self, + x: torch.Tensor, + c_mod: torch.Tensor, + context: Optional[torch.Tensor] = None, + control = None, + transformer_options = {}, + ) -> torch.Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + if self.register_length > 0: + context = torch.cat( + ( + repeat(self.register, "1 ... -> b ...", b=x.shape[0]), + default(context, torch.Tensor([]).type_as(x)), + ), + 1, + ) + + # context is B, L', D + # x is B, L, D + blocks_replace = patches_replace.get("dit", {}) + blocks = len(self.joint_blocks) + for i in range(blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"]) + return out + + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap}) + context = out["txt"] + x = out["img"] + else: + context, x = self.joint_blocks[i]( + context, + x, + c=c_mod, + use_checkpoint=self.use_checkpoint, + ) + if control is not None: + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + x += add + + x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels) + return x + + def forward( + self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + control = None, + transformer_options = {}, + ) -> torch.Tensor: + """ + Forward pass of DiT. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + + if self.context_processor is not None: + context = self.context_processor(context) + + hw = x.shape[-2:] + x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x) + c = self.t_embedder(t, dtype=x.dtype) # (N, D) + if y is not None and self.y_embedder is not None: + y = self.y_embedder(y) # (N, D) + c = c + y # (N, D) + + if context is not None: + context = self.context_embedder(context) + + x = self.forward_core_with_concat(x, c, context, control, transformer_options) + + x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W) + return x[:,:,:hw[-2],:hw[-1]] + + +class OpenAISignatureMMDITWrapper(MMDiT): + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + control = None, + transformer_options = {}, + **kwargs, + ) -> torch.Tensor: + return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options) + diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..8162742cf034a576b33355beb5a1c23825f1488d --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,734 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +import logging + +from comfy import model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + +if model_management.xformers_enabled_vae(): + import xformers + import xformers.ops + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class VideoConv3d(nn.Module): + def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs): + super().__init__() + + self.padding_mode = padding_mode + if padding != 0: + padding = (padding, padding, padding, padding, kernel_size - 1, 0) + else: + kwargs["padding"] = padding + + self.padding = padding + self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs) + + def forward(self, x): + if self.padding != 0: + x = torch.nn.functional.pad(x, self.padding, mode=self.padding_mode) + return self.conv(x) + +def interpolate_up(x, scale_factor): + try: + return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="nearest") + except: #operation not implemented for bf16 + orig_shape = list(x.shape) + out_shape = orig_shape[:2] + for i in range(len(orig_shape) - 2): + out_shape.append(round(orig_shape[i + 2] * scale_factor[i])) + out = torch.empty(out_shape, dtype=x.dtype, layout=x.layout, device=x.device) + split = 8 + l = out.shape[1] // split + for i in range(0, out.shape[1], l): + out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=scale_factor, mode="nearest").to(x.dtype) + return out + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_factor=2.0): + super().__init__() + self.with_conv = with_conv + self.scale_factor = scale_factor + + if self.with_conv: + self.conv = conv_op(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + scale_factor = self.scale_factor + if isinstance(scale_factor, (int, float)): + scale_factor = (scale_factor,) * (x.ndim - 2) + + if x.ndim == 5 and scale_factor[0] > 1.0: + t = x.shape[2] + if t > 1: + a, b = x.split((1, t - 1), dim=2) + del x + b = interpolate_up(b, scale_factor) + else: + a = x + + a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2) + if t > 1: + x = torch.cat((a, b), dim=2) + else: + x = a + else: + x = interpolate_up(x, scale_factor) + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv, stride=2, conv_op=ops.Conv2d): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = conv_op(in_channels, + in_channels, + kernel_size=3, + stride=stride, + padding=0) + + def forward(self, x): + if self.with_conv: + if x.ndim == 4: + pad = (0, 1, 0, 1) + mode = "constant" + x = torch.nn.functional.pad(x, pad, mode=mode, value=0) + elif x.ndim == 5: + pad = (1, 1, 1, 1, 2, 0) + mode = "replicate" + x = torch.nn.functional.pad(x, pad, mode=mode) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512, conv_op=ops.Conv2d): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.swish = torch.nn.SiLU(inplace=True) + self.norm1 = Normalize(in_channels) + self.conv1 = conv_op(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = ops.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout, inplace=True) + self.conv2 = conv_op(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = conv_op(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = conv_op(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = self.swish(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(self.swish(temb))[:,:,None,None] + + h = self.norm2(h) + h = self.swish(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + +def slice_attention(q, k, v): + r1 = torch.zeros_like(k, device=q.device) + scale = (int(q.shape[-1])**(-0.5)) + + mem_free_total = model_management.get_free_memory(q.device) + + tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + s1 = torch.bmm(q[:, i:end], k) * scale + + s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) + del s1 + + r1[:, :, i:end] = torch.bmm(v, s2) + del s2 + break + except model_management.OOM_EXCEPTION as e: + model_management.soft_empty_cache(True) + steps *= 2 + if steps > 128: + raise e + logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) + + return r1 + +def normal_attention(q, k, v): + # compute attention + orig_shape = q.shape + b = orig_shape[0] + c = orig_shape[1] + + q = q.reshape(b, c, -1) + q = q.permute(0, 2, 1) # b,hw,c + k = k.reshape(b, c, -1) # b,c,hw + v = v.reshape(b, c, -1) + + r1 = slice_attention(q, k, v) + h_ = r1.reshape(orig_shape) + del r1 + return h_ + +def xformers_attention(q, k, v): + # compute attention + orig_shape = q.shape + B = orig_shape[0] + C = orig_shape[1] + q, k, v = map( + lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), + (q, k, v), + ) + + try: + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) + out = out.transpose(1, 2).reshape(orig_shape) + except NotImplementedError: + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape) + return out + +def pytorch_attention(q, k, v): + # compute attention + orig_shape = q.shape + B = orig_shape[0] + C = orig_shape[1] + q, k, v = map( + lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), + (q, k, v), + ) + + try: + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) + out = out.transpose(2, 3).reshape(orig_shape) + except model_management.OOM_EXCEPTION: + logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape) + return out + + +def vae_attention(): + if model_management.xformers_enabled_vae(): + logging.info("Using xformers attention in VAE") + return xformers_attention + elif model_management.pytorch_attention_enabled_vae(): + logging.info("Using pytorch attention in VAE") + return pytorch_attention + else: + logging.info("Using split attention in VAE") + return normal_attention + +class AttnBlock(nn.Module): + def __init__(self, in_channels, conv_op=ops.Conv2d): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = conv_op(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = conv_op(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = conv_op(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = conv_op(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + self.optimized_attention = vae_attention() + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + h_ = self.optimized_attention(q, k, v) + + h_ = self.proj_out(h_) + + return x+h_ + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None, conv_op=ops.Conv2d): + return AttnBlock(in_channels, conv_op=conv_op) + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + ops.Linear(self.ch, + self.temb_ch), + ops.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = ops.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + conv3d=False, time_compress=None, + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + if conv3d: + conv_op = VideoConv3d + mid_attn_conv_op = ops.Conv3d + else: + conv_op = ops.Conv2d + mid_attn_conv_op = ops.Conv2d + + # downsampling + self.conv_in = conv_op(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + conv_op=conv_op)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type, conv_op=conv_op)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + stride = 2 + if time_compress is not None: + if (self.num_resolutions - 1 - i_level) > math.log2(time_compress): + stride = (1, 2, 2) + down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + conv_op=conv_op) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, conv_op=mid_attn_conv_op) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + conv_op=conv_op) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = conv_op(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions-1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + conv_out_op=ops.Conv2d, + resnet_op=ResnetBlock, + attn_op=AttnBlock, + conv3d=False, + time_compress=None, + **ignorekwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + if conv3d: + conv_op = VideoConv3d + conv_out_op = VideoConv3d + mid_attn_conv_op = ops.Conv3d + else: + conv_op = ops.Conv2d + mid_attn_conv_op = ops.Conv2d + + # compute block_in and curr_res at lowest res + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + logging.debug("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = conv_op(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = resnet_op(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + conv_op=conv_op) + self.mid.attn_1 = attn_op(block_in, conv_op=mid_attn_conv_op) + self.mid.block_2 = resnet_op(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + conv_op=conv_op) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(resnet_op(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + conv_op=conv_op)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(attn_op(block_in, conv_op=conv_op)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + scale_factor = 2.0 + if time_compress is not None: + if i_level > math.log2(time_compress): + scale_factor = (1.0, 2.0, 2.0) + + up.upsample = Upsample(block_in, resamp_with_conv, conv_op=conv_op, scale_factor=scale_factor) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = conv_out_op(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z, **kwargs): + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb, **kwargs) + h = self.mid.attn_1(h, **kwargs) + h = self.mid.block_2(h, temb, **kwargs) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb, **kwargs) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h, **kwargs) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h, **kwargs) + if self.tanh_out: + h = torch.tanh(h) + return h diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000000000000000000000000000000000000..4c8d53cac9c2631f6985a3c670128bb4316b11ca --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,913 @@ +from abc import abstractmethod + +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +import logging + +from .util import ( + checkpoint, + avg_pool_nd, + timestep_embedding, + AlphaBlender, +) +from ..attention import SpatialTransformer, SpatialVideoTransformer, default +from comfy.ldm.util import exists +import comfy.patcher_extension +import comfy.ops +ops = comfy.ops.disable_weight_init + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + +#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" +def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): + for layer in ts: + if isinstance(layer, VideoResBlock): + x = layer(x, emb, num_video_frames, image_only_indicator) + elif isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialVideoTransformer): + x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, SpatialTransformer): + x = layer(x, context, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, Upsample): + x = layer(x, output_shape=output_shape) + else: + if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]: + found_patched = False + for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]: + if isinstance(layer, class_type): + x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator) + found_patched = True + break + if found_patched: + continue + x = layer(x) + return x + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, *args, **kwargs): + return forward_timestep_embed(self, *args, **kwargs) + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) + + def forward(self, x, output_shape=None): + assert x.shape[1] == self.channels + if self.dims == 3: + shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] + if output_shape is not None: + shape[1] = output_shape[3] + shape[2] = output_shape[4] + else: + shape = [x.shape[2] * 2, x.shape[3] * 2] + if output_shape is not None: + shape[0] = output_shape[2] + shape[1] = output_shape[3] + + x = F.interpolate(x, size=shape, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = operations.conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + kernel_size=3, + exchange_temb_dims=False, + skip_t_emb=False, + dtype=None, + device=None, + operations=ops + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + if isinstance(kernel_size, list): + padding = [k // 2 for k in kernel_size] + else: + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + operations.GroupNorm(32, channels, dtype=dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + elif down: + self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.skip_t_emb = skip_t_emb + if self.skip_t_emb: + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + operations.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device + ), + ) + self.out_layers = nn.Sequential( + operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), + nn.SiLU(), + nn.Dropout(p=dropout), + operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) + , + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = operations.conv_nd( + dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device + ) + else: + self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + + emb_out = None + if not self.skip_t_emb: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + h = out_norm(h) + if emb_out is not None: + scale, shift = th.chunk(emb_out, 2, dim=1) + h *= (1 + scale) + h += shift + h = out_rest(h) + else: + if emb_out is not None: + if self.exchange_temb_dims: + emb_out = emb_out.movedim(1, 2) + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class VideoResBlock(ResBlock): + def __init__( + self, + channels: int, + emb_channels: int, + dropout: float, + video_kernel_size=3, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + out_channels=None, + use_conv: bool = False, + use_scale_shift_norm: bool = False, + dims: int = 2, + use_checkpoint: bool = False, + up: bool = False, + down: bool = False, + dtype=None, + device=None, + operations=ops + ): + super().__init__( + channels, + emb_channels, + dropout, + out_channels=out_channels, + use_conv=use_conv, + use_scale_shift_norm=use_scale_shift_norm, + dims=dims, + use_checkpoint=use_checkpoint, + up=up, + down=down, + dtype=dtype, + device=device, + operations=operations + ) + + self.time_stack = ResBlock( + default(out_channels, channels), + emb_channels, + dropout=dropout, + dims=3, + out_channels=default(out_channels, channels), + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=use_checkpoint, + exchange_temb_dims=True, + dtype=dtype, + device=device, + operations=operations + ) + self.time_mixer = AlphaBlender( + alpha=merge_factor, + merge_strategy=merge_strategy, + rearrange_pattern="b t -> b 1 t 1 1", + ) + + def forward( + self, + x: th.Tensor, + emb: th.Tensor, + num_video_frames: int, + image_only_indicator = None, + ) -> th.Tensor: + x = super().forward(x, emb) + + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + + x = self.time_stack( + x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) + ) + x = self.time_mixer( + x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator + ) + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class Timestep(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + return timestep_embedding(t, self.dim) + +def apply_control(h, control, name): + if control is not None and name in control and len(control[name]) > 0: + ctrl = control[name].pop() + if ctrl is not None: + try: + h += ctrl + except: + logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) + return h + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + dtype=th.float32, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + transformer_depth_output=None, + use_temporal_resblock=False, + use_temporal_attention=False, + time_context_dim=None, + extra_ff_mix_layer=False, + use_spatial_context=False, + merge_strategy=None, + merge_factor=0.0, + video_kernel_size=None, + disable_temporal_crossattention=False, + max_ddpm_temb_period=10000, + attn_precision=None, + device=None, + operations=ops, + ): + super().__init__() + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + + transformer_depth = transformer_depth[:] + transformer_depth_output = transformer_depth_output[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.use_temporal_resblocks = use_temporal_resblock + self.predict_codebook_ids = n_embed is not None + + self.default_num_video_frames = None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) + elif self.num_classes == "continuous": + logging.debug("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + def get_attention_layer( + ch, + num_heads, + dim_head, + depth=1, + context_dim=None, + use_checkpoint=False, + disable_self_attn=False, + ): + if use_temporal_attention: + return SpatialVideoTransformer( + ch, + num_heads, + dim_head, + depth=depth, + context_dim=context_dim, + time_context_dim=time_context_dim, + dropout=dropout, + ff_in=extra_ff_mix_layer, + use_spatial_context=use_spatial_context, + merge_strategy=merge_strategy, + merge_factor=merge_factor, + checkpoint=use_checkpoint, + use_linear=use_linear_in_transformer, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + max_time_embed_period=max_ddpm_temb_period, + attn_precision=attn_precision, + dtype=self.dtype, device=device, operations=operations + ) + else: + return SpatialTransformer( + ch, num_heads, dim_head, depth=depth, context_dim=context_dim, + disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations + ) + + def get_resblock( + merge_factor, + merge_strategy, + video_kernel_size, + ch, + time_embed_dim, + dropout, + out_channels, + dims, + use_checkpoint, + use_scale_shift_norm, + down=False, + up=False, + dtype=None, + device=None, + operations=ops + ): + if self.use_temporal_resblocks: + return VideoResBlock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + else: + return ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + use_checkpoint=use_checkpoint, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append(get_attention_layer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + mid_block = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + + self.middle_block = None + if transformer_depth_middle >= -1: + if transformer_depth_middle >= 0: + mid_block += [get_attention_layer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint + ), + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch + ich, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + ) + ] + ch = model_channels * mult + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or i < num_attention_blocks[level]: + layers.append( + get_attention_layer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timesteps, context, y, control, transformer_options, **kwargs) + + def _forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", None) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + if "emb_patch" in transformer_patches: + patch = transformer_patches["emb_patch"] + for p in patch: + emb = p(emb, self.model_channels, transformer_options) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + transformer_options["block"] = ("middle", 0) + if self.middle_block is not None: + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'middle') + + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control(hsp, control, 'output') + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = th.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/comfy/ldm/modules/diffusionmodules/upscaling.py b/comfy/ldm/modules/diffusionmodules/upscaling.py new file mode 100644 index 0000000000000000000000000000000000000000..9dbf1fe7b93b0c7f1a9e8cbf8e89d0b33c282a08 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/upscaling.py @@ -0,0 +1,84 @@ +import torch +import torch.nn as nn +import numpy as np +from functools import partial + +from .util import extract_into_tensor, make_beta_schedule + + +class AbstractLowScaleModel(nn.Module): + # for concatenating a downsampled image to the latent representation + def __init__(self, noise_schedule_config=None): + super(AbstractLowScaleModel, self).__init__() + if noise_schedule_config is not None: + self.register_schedule(**noise_schedule_config) + + def register_schedule(self, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + def q_sample(self, x_start, t, noise=None, seed=None): + if noise is None: + if seed is None: + noise = torch.randn_like(x_start) + else: + noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) + return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) + + def forward(self, x): + return x, None + + def decode(self, x): + return x + + +class SimpleImageConcat(AbstractLowScaleModel): + # no noise level conditioning + def __init__(self): + super(SimpleImageConcat, self).__init__(noise_schedule_config=None) + self.max_noise_level = 0 + + def forward(self, x): + # fix to constant noise level + return x, torch.zeros(x.shape[0], device=x.device).long() + + +class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): + def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): + super().__init__(noise_schedule_config=noise_schedule_config) + self.max_noise_level = max_noise_level + + def forward(self, x, noise_level=None, seed=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + z = self.q_sample(x, noise_level, seed=seed) + return z, noise_level + + + diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..233011dc9524c3d5258c69c673385aaf45329c81 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,306 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import math +import logging +import torch +import torch.nn as nn +import numpy as np +from einops import repeat, rearrange + +from comfy.ldm.util import instantiate_from_config + +class AlphaBlender(nn.Module): + strategies = ["learned", "fixed", "learned_with_images"] + + def __init__( + self, + alpha: float, + merge_strategy: str = "learned_with_images", + rearrange_pattern: str = "b t -> (b t) 1 1", + ): + super().__init__() + self.merge_strategy = merge_strategy + self.rearrange_pattern = rearrange_pattern + + assert ( + merge_strategy in self.strategies + ), f"merge_strategy needs to be in {self.strategies}" + + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif ( + self.merge_strategy == "learned" + or self.merge_strategy == "learned_with_images" + ): + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor: + # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) + if self.merge_strategy == "fixed": + # make shape compatible + # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) + alpha = self.mix_factor.to(device) + elif self.merge_strategy == "learned": + alpha = torch.sigmoid(self.mix_factor.to(device)) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + elif self.merge_strategy == "learned_with_images": + if image_only_indicator is None: + alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1") + else: + alpha = torch.where( + image_only_indicator.bool(), + torch.ones(1, 1, device=image_only_indicator.device), + rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), + ) + alpha = rearrange(alpha, self.rearrange_pattern) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + else: + raise NotImplementedError() + return alpha + + def forward( + self, + x_spatial, + x_temporal, + image_only_indicator=None, + ) -> torch.Tensor: + alpha = self.get_alpha(image_only_indicator, x_spatial.device) + x = ( + alpha.to(x_spatial.dtype) * x_spatial + + (1.0 - alpha).to(x_spatial.dtype) * x_temporal + ) + return x + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = torch.clamp(betas, min=0, max=0.999) + + elif schedule == "squaredcos_cap_v2": # used for karlo prior + # return early + return betas_for_alpha_bar( + n_timestep, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + logging.info(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + logging.info(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), + "dtype": torch.get_autocast_gpu_dtype(), + "cache_enabled": torch.is_autocast_cache_enabled()} + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half + ) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/comfy/ldm/modules/distributions/__init__.py b/comfy/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc b/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0fa4b3c25f4b55ad7332f09668dc97c9e7aabaf2 Binary files /dev/null and b/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-310.pyc differ diff --git a/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-312.pyc b/comfy/ldm/modules/distributions/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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a/comfy/ldm/modules/distributions/distributions.py b/comfy/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..df987c5ec3faaba7da2b02029ad413134bfa08ad --- /dev/null +++ b/comfy/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/comfy/ldm/modules/ema.py b/comfy/ldm/modules/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4 --- /dev/null +++ b/comfy/ldm/modules/ema.py @@ -0,0 +1,80 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates + else torch.tensor(-1, dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace('.', '') + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def reset_num_updates(self): + del self.num_updates + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/comfy/ldm/modules/encoders/__init__.py b/comfy/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc b/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aadd291d7efba59ff5e1515c25e63f978929479d Binary files /dev/null and b/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-310.pyc differ diff --git a/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-312.pyc b/comfy/ldm/modules/encoders/__pycache__/__init__.cpython-312.pyc 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a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d8660301636fde75808cba50afa539cf1162e0 --- /dev/null +++ b/comfy/ldm/modules/encoders/noise_aug_modules.py @@ -0,0 +1,35 @@ +from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from ..diffusionmodules.openaimodel import Timestep +import torch + +class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): + def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): + super().__init__(*args, **kwargs) + if clip_stats_path is None: + clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) + else: + clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") + self.register_buffer("data_mean", clip_mean[None, :], persistent=False) + self.register_buffer("data_std", clip_std[None, :], persistent=False) + self.time_embed = Timestep(timestep_dim) + + def scale(self, x): + # re-normalize to centered mean and unit variance + x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) + return x + + def unscale(self, x): + # back to original data stats + x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) + return x + + def forward(self, x, noise_level=None, seed=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + x = self.scale(x) + z = self.q_sample(x, noise_level, seed=seed) + z = self.unscale(z) + noise_level = self.time_embed(noise_level) + return z, noise_level diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..fab145f1c208930f1d5bd385325e4e844b959aca --- /dev/null +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -0,0 +1,275 @@ +# original source: +# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py +# license: +# MIT +# credit: +# Amin Rezaei (original author) +# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) +# implementation of: +# Self-attention Does Not Need O(n2) Memory": +# https://arxiv.org/abs/2112.05682v2 + +from functools import partial +import torch +from torch import Tensor +from torch.utils.checkpoint import checkpoint +import math +import logging + +try: + from typing import Optional, NamedTuple, List, Protocol +except ImportError: + from typing import Optional, NamedTuple, List + from typing_extensions import Protocol + +from typing import List + +from comfy import model_management + +def dynamic_slice( + x: Tensor, + starts: List[int], + sizes: List[int], +) -> Tensor: + slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes)) + return x[slicing] + +class AttnChunk(NamedTuple): + exp_values: Tensor + exp_weights_sum: Tensor + max_score: Tensor + +class SummarizeChunk(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> AttnChunk: ... + +class ComputeQueryChunkAttn(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> Tensor: ... + +def _summarize_chunk( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, + mask, +) -> AttnChunk: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + max_score, _ = torch.max(attn_weights, -1, keepdim=True) + max_score = max_score.detach() + attn_weights -= max_score + if mask is not None: + attn_weights += mask + torch.exp(attn_weights, out=attn_weights) + exp_weights = attn_weights.to(value.dtype) + exp_values = torch.bmm(exp_weights, value) + max_score = max_score.squeeze(-1) + return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) + +def _query_chunk_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + summarize_chunk: SummarizeChunk, + kv_chunk_size: int, + mask, +) -> Tensor: + batch_x_heads, k_channels_per_head, k_tokens = key_t.shape + _, _, v_channels_per_head = value.shape + + def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: + key_chunk = dynamic_slice( + key_t, + (0, 0, chunk_idx), + (batch_x_heads, k_channels_per_head, kv_chunk_size) + ) + value_chunk = dynamic_slice( + value, + (0, chunk_idx, 0), + (batch_x_heads, kv_chunk_size, v_channels_per_head) + ) + if mask is not None: + mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] + + return summarize_chunk(query, key_chunk, value_chunk, mask=mask) + + chunks: List[AttnChunk] = [ + chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) + ] + acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) + chunk_values, chunk_weights, chunk_max = acc_chunk + + global_max, _ = torch.max(chunk_max, 0, keepdim=True) + max_diffs = torch.exp(chunk_max - global_max) + chunk_values *= torch.unsqueeze(max_diffs, -1) + chunk_weights *= max_diffs + + all_values = chunk_values.sum(dim=0) + all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) + return all_values / all_weights + +# TODO: refactor CrossAttention#get_attention_scores to share code with this +def _get_attention_scores_no_kv_chunking( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, + mask, +) -> Tensor: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + + if mask is not None: + attn_scores += mask + try: + attn_probs = attn_scores.softmax(dim=-1) + del attn_scores + except model_management.OOM_EXCEPTION: + logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") + attn_scores -= attn_scores.max(dim=-1, keepdim=True).values # noqa: F821 attn_scores is not defined + torch.exp(attn_scores, out=attn_scores) + summed = torch.sum(attn_scores, dim=-1, keepdim=True) + attn_scores /= summed + attn_probs = attn_scores + + hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) + return hidden_states_slice + +class ScannedChunk(NamedTuple): + chunk_idx: int + attn_chunk: AttnChunk + +def efficient_dot_product_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + query_chunk_size=1024, + kv_chunk_size: Optional[int] = None, + kv_chunk_size_min: Optional[int] = None, + use_checkpoint=True, + upcast_attention=False, + mask = None, +): + """Computes efficient dot-product attention given query, transposed key, and value. + This is efficient version of attention presented in + https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. + Args: + query: queries for calculating attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + key_t: keys for calculating attention with shape of + `[batch * num_heads, channels_per_head, tokens]`. + value: values to be used in attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + query_chunk_size: int: query chunks size + kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) + kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). + use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) + Returns: + Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. + """ + batch_x_heads, q_tokens, q_channels_per_head = query.shape + _, _, k_tokens = key_t.shape + scale = q_channels_per_head ** -0.5 + + kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) + if kv_chunk_size_min is not None: + kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) + + if mask is not None and len(mask.shape) == 2: + mask = mask.unsqueeze(0) + + def get_query_chunk(chunk_idx: int) -> Tensor: + return dynamic_slice( + query, + (0, chunk_idx, 0), + (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) + ) + + def get_mask_chunk(chunk_idx: int) -> Tensor: + if mask is None: + return None + if mask.shape[1] == 1: + return mask + chunk = min(query_chunk_size, q_tokens) + return mask[:,chunk_idx:chunk_idx + chunk] + + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) + summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk + compute_query_chunk_attn: ComputeQueryChunkAttn = partial( + _get_attention_scores_no_kv_chunking, + scale=scale, + upcast_attention=upcast_attention + ) if k_tokens <= kv_chunk_size else ( + # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) + partial( + _query_chunk_attention, + kv_chunk_size=kv_chunk_size, + summarize_chunk=summarize_chunk, + ) + ) + + if q_tokens <= query_chunk_size: + # fast-path for when there's just 1 query chunk + return compute_query_chunk_attn( + query=query, + key_t=key_t, + value=value, + mask=mask, + ) + + # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, + # and pass slices to be mutated, instead of torch.cat()ing the returned slices + res = torch.cat([ + compute_query_chunk_attn( + query=get_query_chunk(i * query_chunk_size), + key_t=key_t, + value=value, + mask=get_mask_chunk(i * query_chunk_size) + ) for i in range(math.ceil(q_tokens / query_chunk_size)) + ], dim=1) + return res diff --git a/comfy/ldm/modules/temporal_ae.py b/comfy/ldm/modules/temporal_ae.py new file mode 100644 index 0000000000000000000000000000000000000000..e0f78bf66b7b06b3f49f5add7a8a502384a9f445 --- /dev/null +++ b/comfy/ldm/modules/temporal_ae.py @@ -0,0 +1,246 @@ +import functools +from typing import Iterable, Union + +import torch +from einops import rearrange, repeat + +import comfy.ops +ops = comfy.ops.disable_weight_init + +from .diffusionmodules.model import ( + AttnBlock, + Decoder, + ResnetBlock, +) +from .diffusionmodules.openaimodel import ResBlock, timestep_embedding +from .attention import BasicTransformerBlock + +def partialclass(cls, *args, **kwargs): + class NewCls(cls): + __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) + + return NewCls + + +class VideoResBlock(ResnetBlock): + def __init__( + self, + out_channels, + *args, + dropout=0.0, + video_kernel_size=3, + alpha=0.0, + merge_strategy="learned", + **kwargs, + ): + super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) + if video_kernel_size is None: + video_kernel_size = [3, 1, 1] + self.time_stack = ResBlock( + channels=out_channels, + emb_channels=0, + dropout=dropout, + dims=3, + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=False, + skip_t_emb=True, + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, bs): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError() + + def forward(self, x, temb, skip_video=False, timesteps=None): + b, c, h, w = x.shape + if timesteps is None: + timesteps = b + + x = super().forward(x, temb) + + if not skip_video: + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = self.time_stack(x, temb) + + alpha = self.get_alpha(bs=b // timesteps).to(x.device) + x = alpha * x + (1.0 - alpha) * x_mix + + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class AE3DConv(ops.Conv2d): + def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): + super().__init__(in_channels, out_channels, *args, **kwargs) + if isinstance(video_kernel_size, Iterable): + padding = [int(k // 2) for k in video_kernel_size] + else: + padding = int(video_kernel_size // 2) + + self.time_mix_conv = ops.Conv3d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=video_kernel_size, + padding=padding, + ) + + def forward(self, input, timesteps=None, skip_video=False): + if timesteps is None: + timesteps = input.shape[0] + x = super().forward(input) + if skip_video: + return x + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + x = self.time_mix_conv(x) + return rearrange(x, "b c t h w -> (b t) c h w") + + +class AttnVideoBlock(AttnBlock): + def __init__( + self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" + ): + super().__init__(in_channels) + # no context, single headed, as in base class + self.time_mix_block = BasicTransformerBlock( + dim=in_channels, + n_heads=1, + d_head=in_channels, + checkpoint=False, + ff_in=True, + ) + + time_embed_dim = self.in_channels * 4 + self.video_time_embed = torch.nn.Sequential( + ops.Linear(self.in_channels, time_embed_dim), + torch.nn.SiLU(), + ops.Linear(time_embed_dim, self.in_channels), + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def forward(self, x, timesteps=None, skip_time_block=False): + if skip_time_block: + return super().forward(x) + + if timesteps is None: + timesteps = x.shape[0] + + x_in = x + x = self.attention(x) + h, w = x.shape[2:] + x = rearrange(x, "b c h w -> b (h w) c") + + x_mix = x + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) + emb = self.video_time_embed(t_emb) # b, n_channels + emb = emb[:, None, :] + x_mix = x_mix + emb + + alpha = self.get_alpha().to(x.device) + x_mix = self.time_mix_block(x_mix, timesteps=timesteps) + x = alpha * x + (1.0 - alpha) * x_mix # alpha merge + + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + + return x_in + x + + def get_alpha( + self, + ): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") + + + +def make_time_attn( + in_channels, + attn_type="vanilla", + attn_kwargs=None, + alpha: float = 0, + merge_strategy: str = "learned", + conv_op=ops.Conv2d, +): + return partialclass( + AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy + ) + + +class Conv2DWrapper(torch.nn.Conv2d): + def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: + return super().forward(input) + + +class VideoDecoder(Decoder): + available_time_modes = ["all", "conv-only", "attn-only"] + + def __init__( + self, + *args, + video_kernel_size: Union[int, list] = 3, + alpha: float = 0.0, + merge_strategy: str = "learned", + time_mode: str = "conv-only", + **kwargs, + ): + self.video_kernel_size = video_kernel_size + self.alpha = alpha + self.merge_strategy = merge_strategy + self.time_mode = time_mode + assert ( + self.time_mode in self.available_time_modes + ), f"time_mode parameter has to be in {self.available_time_modes}" + + if self.time_mode != "attn-only": + kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) + if self.time_mode not in ["conv-only", "only-last-conv"]: + kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) + if self.time_mode not in ["attn-only", "only-last-conv"]: + kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) + + super().__init__(*args, **kwargs) + + def get_last_layer(self, skip_time_mix=False, **kwargs): + if self.time_mode == "attn-only": + raise NotImplementedError("TODO") + else: + return ( + self.conv_out.time_mix_conv.weight + if not skip_time_mix + else self.conv_out.weight + ) diff --git a/comfy/ldm/omnigen/omnigen2.py b/comfy/ldm/omnigen/omnigen2.py new file mode 100644 index 0000000000000000000000000000000000000000..4884449f85ff9ca0565ba677bbb22f4f4d55f1d0 --- /dev/null +++ b/comfy/ldm/omnigen/omnigen2.py @@ -0,0 +1,469 @@ +# Original code: https://github.com/VectorSpaceLab/OmniGen2 + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat +from comfy.ldm.lightricks.model import Timesteps +from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.modules.attention import optimized_attention_masked +import comfy.model_management +import comfy.ldm.common_dit + + +def apply_rotary_emb(x, freqs_cis): + if x.shape[1] == 0: + return x + + t_ = x.reshape(*x.shape[:-1], -1, 1, 2) + t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] + return t_out.reshape(*x.shape).to(dtype=x.dtype) + + +def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + return F.silu(x) * y + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device) + self.act = nn.SiLU() + self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device) + + def forward(self, sample: torch.Tensor) -> torch.Tensor: + sample = self.linear_1(sample) + sample = self.act(sample) + sample = self.linear_2(sample) + return sample + + +class LuminaRMSNormZero(nn.Module): + def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None): + super().__init__() + self.silu = nn.SiLU() + self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device) + self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + emb = self.linear(self.silu(emb)) + scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1) + x = self.norm(x) * (1 + scale_msa[:, None]) + return x, gate_msa, scale_mlp, gate_mlp + + +class LuminaLayerNormContinuous(nn.Module): + def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None): + super().__init__() + self.silu = nn.SiLU() + self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device) + self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device) + self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None + + def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: + emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype)) + x = self.norm(x) * (1 + emb)[:, None, :] + if self.linear_2 is not None: + x = self.linear_2(x) + return x + + +class LuminaFeedForward(nn.Module): + def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None): + super().__init__() + inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of) + self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) + self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device) + self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h1, h2 = self.linear_1(x), self.linear_3(x) + return self.linear_2(swiglu(h1, h2)) + + +class Lumina2CombinedTimestepCaptionEmbedding(nn.Module): + def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None): + super().__init__() + self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale) + self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations) + self.caption_embedder = nn.Sequential( + operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device), + operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]: + timestep_proj = self.time_proj(timestep).to(dtype=dtype) + time_embed = self.timestep_embedder(timestep_proj) + caption_embed = self.caption_embedder(text_hidden_states) + return time_embed, caption_embed + + +class Attention(nn.Module): + def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None): + super().__init__() + self.heads = heads + self.kv_heads = kv_heads + self.dim_head = dim_head + self.scale = dim_head ** -0.5 + + self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device) + self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) + self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) + + self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) + self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) + + self.to_out = nn.Sequential( + operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device), + nn.Dropout(0.0) + ) + + def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor: + batch_size, sequence_length, _ = hidden_states.shape + + query = self.to_q(hidden_states) + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + query = query.view(batch_size, -1, self.heads, self.dim_head) + key = key.view(batch_size, -1, self.kv_heads, self.dim_head) + value = value.view(batch_size, -1, self.kv_heads, self.dim_head) + + query = self.norm_q(query) + key = self.norm_k(key) + + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + key = apply_rotary_emb(key, image_rotary_emb) + + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + if self.kv_heads < self.heads: + key = key.repeat_interleave(self.heads // self.kv_heads, dim=1) + value = value.repeat_interleave(self.heads // self.kv_heads, dim=1) + + hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True) + hidden_states = self.to_out[0](hidden_states) + return hidden_states + + +class OmniGen2TransformerBlock(nn.Module): + def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None): + super().__init__() + self.modulation = modulation + + self.attn = Attention( + query_dim=dim, + dim_head=dim // num_attention_heads, + heads=num_attention_heads, + kv_heads=num_kv_heads, + eps=1e-5, + bias=False, + dtype=dtype, device=device, operations=operations, + ) + + self.feed_forward = LuminaFeedForward( + dim=dim, + inner_dim=4 * dim, + multiple_of=multiple_of, + dtype=dtype, device=device, operations=operations + ) + + if modulation: + self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + else: + self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + + self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + + def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor: + if self.modulation: + norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) + attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb) + hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1))) + hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) + else: + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb) + hidden_states = hidden_states + self.norm2(attn_output) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) + hidden_states = hidden_states + self.ffn_norm2(mlp_output) + return hidden_states + + +class OmniGen2RotaryPosEmbed(nn.Module): + def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim + self.axes_lens = axes_lens + self.patch_size = patch_size + self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim) + + def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device): + p = self.patch_size + + seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)] + + max_seq_len = max(seq_lengths) + max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len]) + max_img_len = max(l_effective_img_len) + + position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device) + + for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): + position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3") + + pe_shift = cap_seq_len + pe_shift_len = cap_seq_len + + if ref_img_sizes[i] is not None: + for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]): + H, W = ref_img_size + ref_H_tokens, ref_W_tokens = H // p, W // p + + row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten() + col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten() + position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift + position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids + position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids + + pe_shift += max(ref_H_tokens, ref_W_tokens) + pe_shift_len += ref_img_len + + H, W = img_sizes[i] + H_tokens, W_tokens = H // p, W // p + + row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten() + col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten() + + position_ids[i, pe_shift_len: seq_len, 0] = pe_shift + position_ids[i, pe_shift_len: seq_len, 1] = row_ids + position_ids[i, pe_shift_len: seq_len, 2] = col_ids + + freqs_cis = self.rope_embedder(position_ids).movedim(1, 2) + + cap_freqs_cis_shape = list(freqs_cis.shape) + cap_freqs_cis_shape[1] = encoder_seq_len + cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + ref_img_freqs_cis_shape = list(freqs_cis.shape) + ref_img_freqs_cis_shape[1] = max_ref_img_len + ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + img_freqs_cis_shape = list(freqs_cis.shape) + img_freqs_cis_shape[1] = max_img_len + img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)): + cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len] + ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)] + img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len] + + return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths + + +class OmniGen2Transformer2DModel(nn.Module): + def __init__( + self, + patch_size: int = 2, + in_channels: int = 16, + out_channels: Optional[int] = None, + hidden_size: int = 2304, + num_layers: int = 26, + num_refiner_layers: int = 2, + num_attention_heads: int = 24, + num_kv_heads: int = 8, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + axes_dim_rope: Tuple[int, int, int] = (32, 32, 32), + axes_lens: Tuple[int, int, int] = (300, 512, 512), + text_feat_dim: int = 1024, + timestep_scale: float = 1.0, + image_model=None, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + + self.patch_size = patch_size + self.out_channels = out_channels or in_channels + self.hidden_size = hidden_size + self.dtype = dtype + + self.rope_embedder = OmniGen2RotaryPosEmbed( + theta=10000, + axes_dim=axes_dim_rope, + axes_lens=axes_lens, + patch_size=patch_size, + ) + + self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) + self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) + + self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding( + hidden_size=hidden_size, + text_feat_dim=text_feat_dim, + norm_eps=norm_eps, + timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations + ) + + self.noise_refiner = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations + ) for _ in range(num_refiner_layers) + ]) + + self.ref_image_refiner = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations + ) for _ in range(num_refiner_layers) + ]) + + self.context_refiner = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations + ) for _ in range(num_refiner_layers) + ]) + + self.layers = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations + ) for _ in range(num_layers) + ]) + + self.norm_out = LuminaLayerNormContinuous( + embedding_dim=hidden_size, + conditioning_embedding_dim=min(hidden_size, 1024), + elementwise_affine=False, + eps=1e-6, + out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations + ) + + self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype)) + + def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states): + batch_size = len(hidden_states) + p = self.patch_size + + img_sizes = [(img.size(1), img.size(2)) for img in hidden_states] + l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes] + + if ref_image_hidden_states is not None: + ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states)) + ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size + l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes] + else: + ref_img_sizes = [None for _ in range(batch_size)] + l_effective_ref_img_len = [[0] for _ in range(batch_size)] + + flat_ref_img_hidden_states = None + if ref_image_hidden_states is not None: + imgs = [] + for ref_img in ref_image_hidden_states: + B, C, H, W = ref_img.size() + ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) + imgs.append(ref_img) + flat_ref_img_hidden_states = torch.cat(imgs, dim=1) + + img = hidden_states + B, C, H, W = img.size() + flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) + + return ( + flat_hidden_states, flat_ref_img_hidden_states, + None, None, + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, + ) + + def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb): + batch_size = len(hidden_states) + + hidden_states = self.x_embedder(hidden_states) + if ref_image_hidden_states is not None: + ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states) + image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device) + + for i in range(batch_size): + shift = 0 + for j, ref_img_len in enumerate(l_effective_ref_img_len[i]): + ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j] + shift += ref_img_len + + for layer in self.noise_refiner: + hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb) + + if ref_image_hidden_states is not None: + for layer in self.ref_image_refiner: + ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb) + + hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1) + + return hidden_states + + def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs): + B, C, H, W = x.shape + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + _, _, H_padded, W_padded = hidden_states.shape + timestep = 1.0 - timesteps + text_hidden_states = context + text_attention_mask = attention_mask + ref_image_hidden_states = ref_latents + device = hidden_states.device + + temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype) + + ( + hidden_states, ref_image_hidden_states, + img_mask, ref_img_mask, + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, + ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states) + + ( + context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb, + rotary_emb, encoder_seq_lengths, seq_lengths, + ) = self.rope_embedder( + hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0], + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, device, + ) + + for layer in self.context_refiner: + text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb) + + img_len = hidden_states.shape[1] + combined_img_hidden_states = self.img_patch_embed_and_refine( + hidden_states, ref_image_hidden_states, + img_mask, ref_img_mask, + noise_rotary_emb, ref_img_rotary_emb, + l_effective_ref_img_len, l_effective_img_len, + temb, + ) + + hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1) + attention_mask = None + + for layer in self.layers: + hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb) + + hidden_states = self.norm_out(hidden_states, temb) + + p = self.patch_size + output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W] + + return -output diff --git a/comfy/ldm/pixart/blocks.py b/comfy/ldm/pixart/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..2225076e5754d08cd618014469c2464bad055334 --- /dev/null +++ b/comfy/ldm/pixart/blocks.py @@ -0,0 +1,380 @@ +# Based on: +# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] +# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, Mlp, timestep_embedding +from comfy.ldm.modules.attention import optimized_attention + +# if model_management.xformers_enabled(): +# import xformers.ops +# if int((xformers.__version__).split(".")[2].split("+")[0]) >= 28: +# block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens +# else: +# block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + +def t2i_modulate(x, shift, scale): + return x * (1 + scale) + shift + +class MultiHeadCrossAttention(nn.Module): + def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **kwargs): + super(MultiHeadCrossAttention, self).__init__() + assert d_model % num_heads == 0, "d_model must be divisible by num_heads" + + self.d_model = d_model + self.num_heads = num_heads + self.head_dim = d_model // num_heads + + self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device) + self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, cond, mask=None): + # query/value: img tokens; key: condition; mask: if padding tokens + B, N, C = x.shape + + q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim) + kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim) + k, v = kv.unbind(2) + + assert mask is None # TODO? + # # TODO: xformers needs separate mask logic here + # if model_management.xformers_enabled(): + # attn_bias = None + # if mask is not None: + # attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask) + # x = xformers.ops.memory_efficient_attention(q, k, v, p=0, attn_bias=attn_bias) + # else: + # q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),) + # attn_mask = None + # mask = torch.ones(()) + # if mask is not None and len(mask) > 1: + # # Create equivalent of xformer diagonal block mask, still only correct for square masks + # # But depth doesn't matter as tensors can expand in that dimension + # attn_mask_template = torch.ones( + # [q.shape[2] // B, mask[0]], + # dtype=torch.bool, + # device=q.device + # ) + # attn_mask = torch.block_diag(attn_mask_template) + # + # # create a mask on the diagonal for each mask in the batch + # for _ in range(B - 1): + # attn_mask = torch.block_diag(attn_mask, attn_mask_template) + # x = optimized_attention(q, k, v, self.num_heads, mask=attn_mask, skip_reshape=True) + + x = optimized_attention(q.view(B, -1, C), k.view(B, -1, C), v.view(B, -1, C), self.num_heads, mask=None) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class AttentionKVCompress(nn.Module): + """Multi-head Attention block with KV token compression and qk norm.""" + def __init__(self, dim, num_heads=8, qkv_bias=True, sampling='conv', sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **kwargs): + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool: If True, add a learnable bias to query, key, value. + """ + super().__init__() + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + + self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every'] + self.sr_ratio = sr_ratio + if sr_ratio > 1 and sampling == 'conv': + # Avg Conv Init. + self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device) + # self.sr.weight.data.fill_(1/sr_ratio**2) + # self.sr.bias.data.zero_() + self.norm = operations.LayerNorm(dim, dtype=dtype, device=device) + if qk_norm: + self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device) + self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device) + else: + self.q_norm = nn.Identity() + self.k_norm = nn.Identity() + + def downsample_2d(self, tensor, H, W, scale_factor, sampling=None): + if sampling is None or scale_factor == 1: + return tensor + B, N, C = tensor.shape + + if sampling == 'uniform_every': + return tensor[:, ::scale_factor], int(N // scale_factor) + + tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2) + new_H, new_W = int(H / scale_factor), int(W / scale_factor) + new_N = new_H * new_W + + if sampling == 'ave': + tensor = F.interpolate( + tensor, scale_factor=1 / scale_factor, mode='nearest' + ).permute(0, 2, 3, 1) + elif sampling == 'uniform': + tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1) + elif sampling == 'conv': + tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1) + tensor = self.norm(tensor) + else: + raise ValueError + + return tensor.reshape(B, new_N, C).contiguous(), new_N + + def forward(self, x, mask=None, HW=None, block_id=None): + B, N, C = x.shape # 2 4096 1152 + new_N = N + if HW is None: + H = W = int(N ** 0.5) + else: + H, W = HW + qkv = self.qkv(x).reshape(B, N, 3, C) + + q, k, v = qkv.unbind(2) + q = self.q_norm(q) + k = self.k_norm(k) + + # KV compression + if self.sr_ratio > 1: + k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling) + v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling) + + q = q.reshape(B, N, self.num_heads, C // self.num_heads) + k = k.reshape(B, new_N, self.num_heads, C // self.num_heads) + v = v.reshape(B, new_N, self.num_heads, C // self.num_heads) + + if mask is not None: + raise NotImplementedError("Attn mask logic not added for self attention") + + # This is never called at the moment + # attn_bias = None + # if mask is not None: + # attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device) + # attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf')) + + # attention 2 + q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),) + x = optimized_attention(q, k, v, self.num_heads, mask=None, skip_reshape=True) + + x = x.view(B, N, C) + x = self.proj(x) + return x + + +class FinalLayer(nn.Module): + """ + The final layer of PixArt. + """ + def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + +class T2IFinalLayer(nn.Module): + """ + The final layer of PixArt. + """ + def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5) + self.out_channels = out_channels + + def forward(self, x, t): + shift, scale = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t[:, None]).chunk(2, dim=1) + x = t2i_modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class MaskFinalLayer(nn.Module): + """ + The final layer of PixArt. + """ + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) + ) + def forward(self, x, t): + shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class DecoderLayer(nn.Module): + """ + The final layer of PixArt. + """ + def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) + def forward(self, x, t): + shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) + x = modulate(self.norm_decoder(x), shift, scale) + x = self.linear(x) + return x + + +class SizeEmbedder(TimestepEmbedder): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations) + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + self.outdim = hidden_size + + def forward(self, s, bs): + if s.ndim == 1: + s = s[:, None] + assert s.ndim == 2 + if s.shape[0] != bs: + s = s.repeat(bs//s.shape[0], 1) + assert s.shape[0] == bs + b, dims = s.shape[0], s.shape[1] + s = rearrange(s, "b d -> (b d)") + s_freq = timestep_embedding(s, self.frequency_embedding_size) + s_emb = self.mlp(s_freq.to(s.dtype)) + s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim) + return s_emb + + +class LabelEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None): + super().__init__() + use_cfg_embedding = dropout_prob > 0 + self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device), + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + + +class CaptionEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): + super().__init__() + self.y_proj = Mlp( + in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, + dtype=dtype, device=device, operations=operations, + ) + self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5)) + self.uncond_prob = uncond_prob + + def token_drop(self, caption, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob + else: + drop_ids = force_drop_ids == 1 + caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) + return caption + + def forward(self, caption, train, force_drop_ids=None): + if train: + assert caption.shape[2:] == self.y_embedding.shape + use_dropout = self.uncond_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + caption = self.token_drop(caption, force_drop_ids) + caption = self.y_proj(caption) + return caption + + +class CaptionEmbedderDoubleBr(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): + super().__init__() + self.proj = Mlp( + in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, + dtype=dtype, device=device, operations=operations, + ) + self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5) + self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5) + self.uncond_prob = uncond_prob + + def token_drop(self, global_caption, caption, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob + else: + drop_ids = force_drop_ids == 1 + global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption) + caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) + return global_caption, caption + + def forward(self, caption, train, force_drop_ids=None): + assert caption.shape[2: ] == self.y_embedding.shape + global_caption = caption.mean(dim=2).squeeze() + use_dropout = self.uncond_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids) + y_embed = self.proj(global_caption) + return y_embed, caption diff --git a/comfy/ldm/pixart/pixartms.py b/comfy/ldm/pixart/pixartms.py new file mode 100644 index 0000000000000000000000000000000000000000..7d4eebdceaa22d52947cdb112a2a3b7070c6d66b --- /dev/null +++ b/comfy/ldm/pixart/pixartms.py @@ -0,0 +1,256 @@ +# Based on: +# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] +# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] +import torch +import torch.nn as nn + +from .blocks import ( + t2i_modulate, + CaptionEmbedder, + AttentionKVCompress, + MultiHeadCrossAttention, + T2IFinalLayer, + SizeEmbedder, +) +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch + + +def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32): + grid_h, grid_w = torch.meshgrid( + torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation, + torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation, + indexing='ij' + ) + emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) + emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) + emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) + return emb + +class PixArtMSBlock(nn.Module): + """ + A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, + sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): + super().__init__() + self.hidden_size = hidden_size + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attn = AttentionKVCompress( + hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, + qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs + ) + self.cross_attn = MultiHeadCrossAttention( + hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs + ) + self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + # to be compatible with lower version pytorch + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp( + in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, + dtype=dtype, device=device, operations=operations + ) + self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) + + def forward(self, x, y, t, mask=None, HW=None, **kwargs): + B, N, C = x.shape + + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1) + x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) + x = x + self.cross_attn(x, y, mask) + x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) + + return x + + +### Core PixArt Model ### +class PixArtMS(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + def __init__( + self, + input_size=32, + patch_size=2, + in_channels=4, + hidden_size=1152, + depth=28, + num_heads=16, + mlp_ratio=4.0, + class_dropout_prob=0.1, + learn_sigma=True, + pred_sigma=True, + drop_path: float = 0., + caption_channels=4096, + pe_interpolation=None, + pe_precision=None, + config=None, + model_max_length=120, + micro_condition=True, + qk_norm=False, + kv_compress_config=None, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + nn.Module.__init__(self) + self.dtype = dtype + self.pred_sigma = pred_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if pred_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.pe_interpolation = pe_interpolation + self.pe_precision = pe_precision + self.hidden_size = hidden_size + self.depth = depth + + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.t_block = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) + ) + self.x_embedder = PatchEmbed( + patch_size=patch_size, + in_chans=in_channels, + embed_dim=hidden_size, + bias=True, + dtype=dtype, + device=device, + operations=operations + ) + self.t_embedder = TimestepEmbedder( + hidden_size, dtype=dtype, device=device, operations=operations, + ) + self.y_embedder = CaptionEmbedder( + in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, + act_layer=approx_gelu, token_num=model_max_length, + dtype=dtype, device=device, operations=operations, + ) + + self.micro_conditioning = micro_condition + if self.micro_conditioning: + self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) + self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) + + # For fixed sin-cos embedding: + # num_patches = (input_size // patch_size) * (input_size // patch_size) + # self.base_size = input_size // self.patch_size + # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) + + drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule + if kv_compress_config is None: + kv_compress_config = { + 'sampling': None, + 'scale_factor': 1, + 'kv_compress_layer': [], + } + self.blocks = nn.ModuleList([ + PixArtMSBlock( + hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], + sampling=kv_compress_config['sampling'], + sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, + qk_norm=qk_norm, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(depth) + ]) + self.final_layer = T2IFinalLayer( + hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) + + def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs): + """ + Original forward pass of PixArt. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, 1, 120, C) conditioning + ar: (N, 1): aspect ratio + cs: (N ,2) size conditioning for height/width + """ + B, C, H, W = x.shape + c_res = (H + W) // 2 + pe_interpolation = self.pe_interpolation + if pe_interpolation is None or self.pe_precision is not None: + # calculate pe_interpolation on-the-fly + pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0) + + pos_embed = get_2d_sincos_pos_embed_torch( + self.hidden_size, + h=(H // self.patch_size), + w=(W // self.patch_size), + pe_interpolation=pe_interpolation, + base_size=((round(c_res / 64) * 64) // self.patch_size), + device=x.device, + dtype=x.dtype, + ).unsqueeze(0) + + x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 + t = self.t_embedder(timestep, x.dtype) # (N, D) + + if self.micro_conditioning and (c_size is not None and c_ar is not None): + bs = x.shape[0] + c_size = self.csize_embedder(c_size, bs) # (N, D) + c_ar = self.ar_embedder(c_ar, bs) # (N, D) + t = t + torch.cat([c_size, c_ar], dim=1) + + t0 = self.t_block(t) + y = self.y_embedder(y, self.training) # (N, D) + + if mask is not None: + if mask.shape[0] != y.shape[0]: + mask = mask.repeat(y.shape[0] // mask.shape[0], 1) + mask = mask.squeeze(1).squeeze(1) + y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) + y_lens = mask.sum(dim=1).tolist() + else: + y_lens = None + y = y.squeeze(1).view(1, -1, x.shape[-1]) + for block in self.blocks: + x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D) + + x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) + x = self.unpatchify(x, H, W) # (N, out_channels, H, W) + + return x + + def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs): + B, C, H, W = x.shape + + # Fallback for missing microconds + if self.micro_conditioning: + if c_size is None: + c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1) + + if c_ar is None: + c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1) + + ## Still accepts the input w/o that dim but returns garbage + if len(context.shape) == 3: + context = context.unsqueeze(1) + + ## run original forward pass + out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar) + + ## only return EPS + if self.pred_sigma: + return out[:, :self.in_channels] + return out + + def unpatchify(self, x, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + h = h // self.patch_size + w = w // self.patch_size + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs diff --git a/comfy/ldm/util.py b/comfy/ldm/util.py new file mode 100644 index 0000000000000000000000000000000000000000..30b4b4721056216ee817e7d54d0bca9e09b79098 --- /dev/null +++ b/comfy/ldm/util.py @@ -0,0 +1,197 @@ +import importlib +import logging + +import torch +from torch import optim +import numpy as np + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + logging.warning("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + logging.info(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +class AdamWwithEMAandWings(optim.Optimizer): + # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 + def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using + weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code + ema_power=1., param_names=()): + """AdamW that saves EMA versions of the parameters.""" + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0.0 <= ema_decay <= 1.0: + raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, + ema_power=ema_power, param_names=param_names) + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault('amsgrad', False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + ema_params_with_grad = [] + max_exp_avg_sqs = [] + state_steps = [] + amsgrad = group['amsgrad'] + beta1, beta2 = group['betas'] + ema_decay = group['ema_decay'] + ema_power = group['ema_power'] + + for p in group['params']: + if p.grad is None: + continue + params_with_grad.append(p) + if p.grad.is_sparse: + raise RuntimeError('AdamW does not support sparse gradients') + grads.append(p.grad) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of parameter values + state['param_exp_avg'] = p.detach().float().clone() + + exp_avgs.append(state['exp_avg']) + exp_avg_sqs.append(state['exp_avg_sq']) + ema_params_with_grad.append(state['param_exp_avg']) + + if amsgrad: + max_exp_avg_sqs.append(state['max_exp_avg_sq']) + + # update the steps for each param group update + state['step'] += 1 + # record the step after step update + state_steps.append(state['step']) + + optim._functional.adamw(params_with_grad, + grads, + exp_avgs, + exp_avg_sqs, + max_exp_avg_sqs, + state_steps, + amsgrad=amsgrad, + beta1=beta1, + beta2=beta2, + lr=group['lr'], + weight_decay=group['weight_decay'], + eps=group['eps'], + maximize=False) + + cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) + for param, ema_param in zip(params_with_grad, ema_params_with_grad): + ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) + + return loss diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1d6edb3546e429cfcd250ffd50e24da356fb9855 --- /dev/null +++ b/comfy/ldm/wan/model.py @@ -0,0 +1,793 @@ +# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import math + +import torch +import torch.nn as nn +from einops import repeat + +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.flux.math import apply_rope +import comfy.ldm.common_dit +import comfy.model_management + + +def sinusoidal_embedding_1d(dim, position): + # preprocess + assert dim % 2 == 0 + half = dim // 2 + position = position.type(torch.float32) + + # calculation + sinusoid = torch.outer( + position, torch.pow(10000, -torch.arange(half).to(position).div(half))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x + + +class WanSelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6, operation_settings={}): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.eps = eps + + # layers + self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + + def forward(self, x, freqs): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n * d) + return q, k, v + + q, k, v = qkv_fn(x) + q, k = apply_rope(q, k, freqs) + + x = optimized_attention( + q.view(b, s, n * d), + k.view(b, s, n * d), + v, + heads=self.num_heads, + ) + + x = self.o(x) + return x + + +class WanT2VCrossAttention(WanSelfAttention): + + def forward(self, x, context, **kwargs): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + """ + # compute query, key, value + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(context)) + v = self.v(context) + + # compute attention + x = optimized_attention(q, k, v, heads=self.num_heads) + + x = self.o(x) + return x + + +class WanI2VCrossAttention(WanSelfAttention): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6, operation_settings={}): + super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings) + + self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + # self.alpha = nn.Parameter(torch.zeros((1, ))) + self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + + def forward(self, x, context, context_img_len): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + """ + context_img = context[:, :context_img_len] + context = context[:, context_img_len:] + + # compute query, key, value + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(context)) + v = self.v(context) + k_img = self.norm_k_img(self.k_img(context_img)) + v_img = self.v_img(context_img) + img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads) + # compute attention + x = optimized_attention(q, k, v, heads=self.num_heads) + + # output + x = x + img_x + x = self.o(x) + return x + + +WAN_CROSSATTENTION_CLASSES = { + 't2v_cross_attn': WanT2VCrossAttention, + 'i2v_cross_attn': WanI2VCrossAttention, +} + + +class WanAttentionBlock(nn.Module): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, operation_settings={}): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # layers + self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, + eps, operation_settings=operation_settings) + self.norm3 = operation_settings.get("operations").LayerNorm( + dim, eps, + elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity() + self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, + num_heads, + (-1, -1), + qk_norm, + eps, operation_settings=operation_settings) + self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn = nn.Sequential( + operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), + operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + # modulation + self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + def forward( + self, + x, + e, + freqs, + context, + context_img_len=257, + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, 6, C] + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + # assert e.dtype == torch.float32 + + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1) + # assert e[0].dtype == torch.float32 + + # self-attention + y = self.self_attn( + self.norm1(x) * (1 + e[1]) + e[0], + freqs) + + x = x + y * e[2] + + # cross-attention & ffn + x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len) + y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3]) + x = x + y * e[5] + return x + + +class VaceWanAttentionBlock(WanAttentionBlock): + def __init__( + self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, + block_id=0, + operation_settings={} + ): + super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) + self.block_id = block_id + if block_id == 0: + self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + def forward(self, c, x, **kwargs): + if self.block_id == 0: + c = self.before_proj(c) + x + c = super().forward(c, **kwargs) + c_skip = self.after_proj(c) + return c_skip, c + + +class WanCamAdapter(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}): + super(WanCamAdapter, self).__init__() + + # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 + self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) + + # Convolution: reduce spatial dimensions by a factor + # of 2 (without overlap) + self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + # Residual blocks for feature extraction + self.residual_blocks = nn.Sequential( + *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)] + ) + + def forward(self, x): + # Reshape to merge the frame dimension into batch + bs, c, f, h, w = x.size() + x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) + + # Pixel Unshuffle operation + x_unshuffled = self.pixel_unshuffle(x) + + # Convolution operation + x_conv = self.conv(x_unshuffled) + + # Feature extraction with residual blocks + out = self.residual_blocks(x_conv) + + # Reshape to restore original bf dimension + out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) + + # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames + out = out.permute(0, 2, 1, 3, 4) + + return out + + +class WanCamResidualBlock(nn.Module): + def __init__(self, dim, operation_settings={}): + super(WanCamResidualBlock, self).__init__() + self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.relu = nn.ReLU(inplace=True) + self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + def forward(self, x): + residual = x + out = self.relu(self.conv1(x)) + out = self.conv2(out) + out += residual + return out + + +class Head(nn.Module): + + def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}): + super().__init__() + self.dim = dim + self.out_dim = out_dim + self.patch_size = patch_size + self.eps = eps + + # layers + out_dim = math.prod(patch_size) * out_dim + self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + # modulation + self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + def forward(self, x, e): + r""" + Args: + x(Tensor): Shape [B, L1, C] + e(Tensor): Shape [B, C] + """ + # assert e.dtype == torch.float32 + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1) + x = (self.head(self.norm(x) * (1 + e[1]) + e[0])) + return x + + +class MLPProj(torch.nn.Module): + + def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}): + super().__init__() + + self.proj = torch.nn.Sequential( + operation_settings.get("operations").LayerNorm(in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), + torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), + operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + if flf_pos_embed_token_number is not None: + self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + else: + self.emb_pos = None + + def forward(self, image_embeds): + if self.emb_pos is not None: + image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device) + + clip_extra_context_tokens = self.proj(image_embeds) + return clip_extra_context_tokens + + +class WanModel(torch.nn.Module): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='t2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + device=None, + dtype=None, + operations=None, + ): + r""" + Initialize the diffusion model backbone. + + Args: + model_type (`str`, *optional*, defaults to 't2v'): + Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) + patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) + text_len (`int`, *optional*, defaults to 512): + Fixed length for text embeddings + in_dim (`int`, *optional*, defaults to 16): + Input video channels (C_in) + dim (`int`, *optional*, defaults to 2048): + Hidden dimension of the transformer + ffn_dim (`int`, *optional*, defaults to 8192): + Intermediate dimension in feed-forward network + freq_dim (`int`, *optional*, defaults to 256): + Dimension for sinusoidal time embeddings + text_dim (`int`, *optional*, defaults to 4096): + Input dimension for text embeddings + out_dim (`int`, *optional*, defaults to 16): + Output video channels (C_out) + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads + num_layers (`int`, *optional*, defaults to 32): + Number of transformer blocks + window_size (`tuple`, *optional*, defaults to (-1, -1)): + Window size for local attention (-1 indicates global attention) + qk_norm (`bool`, *optional*, defaults to True): + Enable query/key normalization + cross_attn_norm (`bool`, *optional*, defaults to False): + Enable cross-attention normalization + eps (`float`, *optional*, defaults to 1e-6): + Epsilon value for normalization layers + """ + + super().__init__() + self.dtype = dtype + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + assert model_type in ['t2v', 'i2v'] + self.model_type = model_type + + self.patch_size = patch_size + self.text_len = text_len + self.in_dim = in_dim + self.dim = dim + self.ffn_dim = ffn_dim + self.freq_dim = freq_dim + self.text_dim = text_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # embeddings + self.patch_embedding = operations.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32) + self.text_embedding = nn.Sequential( + operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), + operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + self.time_embedding = nn.Sequential( + operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + # blocks + cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' + self.blocks = nn.ModuleList([ + WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, + window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) + for _ in range(num_layers) + ]) + + # head + self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings) + + d = dim // num_heads + self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]) + + if model_type == 'i2v': + self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings) + else: + self.img_emb = None + + def forward_orig( + self, + x, + t, + context, + clip_fea=None, + freqs=None, + transformer_options={}, + **kwargs, + ): + r""" + Forward pass through the diffusion model + + Args: + x (Tensor): + List of input video tensors with shape [B, C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [B, L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + + def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): + bs, c, t, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) + + patch_size = self.patch_size + t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) + h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) + w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) + + if time_dim_concat is not None: + time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) + x = torch.cat([x, time_dim_concat], dim=2) + t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0]) + + img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) + img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) + img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) + img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs) + + freqs = self.rope_embedder(img_ids).movedim(1, 2) + return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w] + + def unpatchify(self, x, grid_sizes): + r""" + Reconstruct video tensors from patch embeddings. + + Args: + x (List[Tensor]): + List of patchified features, each with shape [L, C_out * prod(patch_size)] + grid_sizes (Tensor): + Original spatial-temporal grid dimensions before patching, + shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) + + Returns: + List[Tensor]: + Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8] + """ + + c = self.out_dim + u = x + b = u.shape[0] + u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c) + u = torch.einsum('bfhwpqrc->bcfphqwr', u) + u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)]) + return u + + +class VaceWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='vace', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + vace_layers=None, + vace_in_dim=None, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + # Vace + if vace_layers is not None: + self.vace_layers = vace_layers + self.vace_in_dim = vace_in_dim + # vace blocks + self.vace_blocks = nn.ModuleList([ + VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=i, operation_settings=operation_settings) + for i in range(self.vace_layers) + ]) + + self.vace_layers_mapping = {i: n for n, i in enumerate(range(0, self.num_layers, self.num_layers // self.vace_layers))} + # vace patch embeddings + self.vace_patch_embedding = operations.Conv3d( + self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size, device=device, dtype=torch.float32 + ) + + def forward_orig( + self, + x, + t, + context, + vace_context, + vace_strength, + clip_fea=None, + freqs=None, + transformer_options={}, + **kwargs, + ): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + orig_shape = list(vace_context.shape) + vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) + c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) + c = c.flatten(2).transpose(1, 2) + c = list(c.split(orig_shape[0], dim=0)) + + # arguments + x_orig = x + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + + ii = self.vace_layers_mapping.get(i, None) + if ii is not None: + for iii in range(len(c)): + c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + x += c_skip * vace_strength[iii] + del c_skip + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + +class CameraWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='camera', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + in_dim_control_adapter=24, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings) + + + def forward_orig( + self, + x, + t, + context, + clip_fea=None, + freqs=None, + camera_conditions = None, + transformer_options={}, + **kwargs, + ): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + if self.control_adapter is not None and camera_conditions is not None: + x_camera = self.control_adapter(camera_conditions).to(x.dtype) + x = x + x_camera + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py new file mode 100644 index 0000000000000000000000000000000000000000..a8ebc5ec6c4ee2cbce802837c052004eb8cc362c --- /dev/null +++ b/comfy/ldm/wan/vae.py @@ -0,0 +1,567 @@ +# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from comfy.ldm.modules.diffusionmodules.model import vae_attention + +import comfy.ops +ops = comfy.ops.disable_weight_init + +CACHE_T = 2 + + +class CausalConv3d(ops.Conv3d): + """ + Causal 3d convolusion. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._padding = (self.padding[2], self.padding[2], self.padding[1], + self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + + def forward(self, x, cache_x=None): + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + x = F.pad(x, padding) + + return super().forward(x) + + +class RMS_norm(nn.Module): + + def __init__(self, dim, channel_first=True, images=True, bias=False): + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else None + + def forward(self, x): + return F.normalize( + x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0) + + +class Upsample(nn.Upsample): + + def forward(self, x): + """ + Fix bfloat16 support for nearest neighbor interpolation. + """ + return super().forward(x.float()).type_as(x) + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', + 'downsample3d') + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == 'upsample2d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + ops.Conv2d(dim, dim // 2, 3, padding=1)) + elif mode == 'upsample3d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + ops.Conv2d(dim, dim // 2, 3, padding=1)) + self.time_conv = CausalConv3d( + dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + + elif mode == 'downsample2d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + ops.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == 'downsample3d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + ops.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d( + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == 'upsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = 'Rep' + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] != 'Rep': + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] == 'Rep': + cache_x = torch.cat([ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2) + if feat_cache[idx] == 'Rep': + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.resample(x) + x = rearrange(x, '(b t) c h w -> b c t h w', t=t) + + if self.mode == 'downsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -1:, :, :].clone() + # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': + # # cache last frame of last two chunk + # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + def init_weight(self, conv): + conv_weight = conv.weight + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + one_matrix = torch.eye(c1, c2) + init_matrix = one_matrix + nn.init.zeros_(conv_weight) + #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5 + conv_weight.data[:, :, 1, 0, 0] = init_matrix #* 0.5 + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + def init_weight2(self, conv): + conv_weight = conv.weight.data + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + init_matrix = torch.eye(c1 // 2, c2) + #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2) + conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix + conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1)) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ + if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + h = self.shortcut(x) + for layer in self.residual: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + h + + +class AttentionBlock(nn.Module): + """ + Causal self-attention with a single head. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = RMS_norm(dim) + self.to_qkv = ops.Conv2d(dim, dim * 3, 1) + self.proj = ops.Conv2d(dim, dim, 1) + self.optimized_attention = vae_attention() + + def forward(self, x): + identity = x + b, c, t, h, w = x.size() + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.norm(x) + # compute query, key, value + + q, k, v = self.to_qkv(x).chunk(3, dim=1) + x = self.optimized_attention(q, k, v) + + # output + x = self.proj(x) + x = rearrange(x, '(b t) c h w-> b c t h w', t=t) + return x + identity + + +class Encoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + downsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = 'downsample3d' if temperal_downsample[ + i] else 'downsample2d' + downsamples.append(Resample(out_dim, mode=mode)) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout)) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +class Decoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2**(len(dim_mult) - 2) + + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout)) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i == 1 or i == 2 or i == 3: + in_dim = in_dim // 2 + for _ in range(num_res_blocks + 1): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + upsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # upsample block + if i != len(dim_mult) - 1: + mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' + upsamples.append(Resample(out_dim, mode=mode)) + scale *= 2.0 + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, 3, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if isinstance(m, CausalConv3d): + count += 1 + return count + + +class WanVAE(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, + attn_scales, self.temperal_downsample, dropout) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, + attn_scales, self.temperal_upsample, dropout) + + def forward(self, x): + mu, log_var = self.encode(x) + z = self.reparameterize(mu, log_var) + x_recon = self.decode(z) + return x_recon, mu, log_var + + def encode(self, x): + self.clear_cache() + ## cache + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + ## 对encode输入的x，按时间拆分为1、4、4、4.... + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder( + x[:, :, :1, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + else: + out_ = self.encoder( + x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + self.clear_cache() + return mu + + def decode(self, z): + self.clear_cache() + # z: [b,c,t,h,w] + + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + self._conv_idx = [0] + if i == 0: + out = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + else: + out_ = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) + self.clear_cache() + return out + + def reparameterize(self, mu, log_var): + std = torch.exp(0.5 * log_var) + eps = torch.randn_like(std) + return eps * std + mu + + def sample(self, imgs, deterministic=False): + mu, log_var = self.encode(imgs) + if deterministic: + return mu + std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) + return mu + std * torch.randn_like(std) + + def clear_cache(self): + self._conv_num = count_conv3d(self.decoder) + self._conv_idx = [0] + self._feat_map = [None] * self._conv_num + #cache encode + self._enc_conv_num = count_conv3d(self.encoder) + self._enc_conv_idx = [0] + self._enc_feat_map = [None] * self._enc_conv_num diff --git a/comfy/lora.py b/comfy/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..387d5c52aef161ce4f00c27c39795351319ead21 --- /dev/null +++ b/comfy/lora.py @@ -0,0 +1,395 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +from __future__ import annotations +import comfy.utils +import comfy.model_management +import comfy.model_base +import comfy.weight_adapter as weight_adapter +import logging +import torch + +LORA_CLIP_MAP = { + "mlp.fc1": "mlp_fc1", + "mlp.fc2": "mlp_fc2", + "self_attn.k_proj": "self_attn_k_proj", + "self_attn.q_proj": "self_attn_q_proj", + "self_attn.v_proj": "self_attn_v_proj", + "self_attn.out_proj": "self_attn_out_proj", +} + + +def load_lora(lora, to_load, log_missing=True): + patch_dict = {} + loaded_keys = set() + for x in to_load: + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + dora_scale_name = "{}.dora_scale".format(x) + dora_scale = None + if dora_scale_name in lora.keys(): + dora_scale = lora[dora_scale_name] + loaded_keys.add(dora_scale_name) + + for adapter_cls in weight_adapter.adapters: + adapter = adapter_cls.load(x, lora, alpha, dora_scale, loaded_keys) + if adapter is not None: + patch_dict[to_load[x]] = adapter + loaded_keys.update(adapter.loaded_keys) + continue + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = ("diff", (w_norm,)) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) + + diff_name = "{}.diff".format(x) + diff_weight = lora.get(diff_name, None) + if diff_weight is not None: + patch_dict[to_load[x]] = ("diff", (diff_weight,)) + loaded_keys.add(diff_name) + + diff_bias_name = "{}.diff_b".format(x) + diff_bias = lora.get(diff_bias_name, None) + if diff_bias is not None: + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) + loaded_keys.add(diff_bias_name) + + set_weight_name = "{}.set_weight".format(x) + set_weight = lora.get(set_weight_name, None) + if set_weight is not None: + patch_dict[to_load[x]] = ("set", (set_weight,)) + loaded_keys.add(set_weight_name) + + if log_missing: + for x in lora.keys(): + if x not in loaded_keys: + logging.warning("lora key not loaded: {}".format(x)) + + return patch_dict + +def model_lora_keys_clip(model, key_map={}): + sdk = model.state_dict().keys() + for k in sdk: + if k.endswith(".weight"): + key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names + + text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" + clip_l_present = False + clip_g_present = False + for b in range(32): #TODO: clean up + for c in LORA_CLIP_MAP: + k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + clip_l_present = True + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + clip_g_present = True + if clip_l_present: + lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + else: + lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config + key_map[lora_key] = k + + for k in sdk: + if k.endswith(".weight"): + if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora + l_key = k[len("t5xxl.transformer."):-len(".weight")] + t5_index = 1 + if clip_g_present: + t5_index += 1 + if clip_l_present: + t5_index += 1 + if t5_index == 2: + key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux + t5_index += 1 + + key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k + elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora + l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")] + lora_key = "lora_te1_{}".format(l_key.replace(".", "_")) + key_map[lora_key] = k + + + k = "clip_g.transformer.text_projection.weight" + if k in sdk: + key_map["lora_prior_te_text_projection"] = k #cascade lora? + # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too + key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora + + k = "clip_l.transformer.text_projection.weight" + if k in sdk: + key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning + + return key_map + +def model_lora_keys_unet(model, key_map={}): + sd = model.state_dict() + sdk = sd.keys() + + for k in sdk: + if k.startswith("diffusion_model."): + if k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = k + key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names + else: + key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names + + diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) + for k in diffusers_keys: + if k.endswith(".weight"): + unet_key = "diffusion_model.{}".format(diffusers_keys[k]) + key_lora = k[:-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = unet_key + key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format + + diffusers_lora_prefix = ["", "unet."] + for p in diffusers_lora_prefix: + diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) + if diffusers_lora_key.endswith(".to_out.0"): + diffusers_lora_key = diffusers_lora_key[:-2] + key_map[diffusers_lora_key] = unet_key + + if isinstance(model, comfy.model_base.StableCascade_C): + for k in sdk: + if k.startswith("diffusion_model."): + if k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_prior_unet_{}".format(key_lora)] = k + + if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 + diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format + key_map[key_lora] = to + + key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? + key_map[key_lora] = to + + key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora + key_map[key_lora] = to + + key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format + key_map[key_lora] = to + + if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow + diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format + key_map[key_lora] = to + + if isinstance(model, comfy.model_base.PixArt): + diffusers_keys = comfy.utils.pixart_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_lora = "transformer.{}".format(k[:-len(".weight")]) #default format + key_map[key_lora] = to + + key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #diffusers training script + key_map[key_lora] = to + + key_lora = "unet.base_model.model.{}".format(k[:-len(".weight")]) #old reference peft script + key_map[key_lora] = to + + if isinstance(model, comfy.model_base.HunyuanDiT): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format + + if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux + diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") + for k in diffusers_keys: + if k.endswith(".weight"): + to = diffusers_keys[k] + key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format + key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris + key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer + + if isinstance(model, comfy.model_base.GenmoMochi): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official Mochi lora format + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["{}".format(key_lora)] = k + + if isinstance(model, comfy.model_base.HunyuanVideo): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + # diffusion-pipe lora format + key_lora = k + key_lora = key_lora.replace("_mod.lin.", "_mod.linear.").replace("_attn.qkv.", "_attn_qkv.").replace("_attn.proj.", "_attn_proj.") + key_lora = key_lora.replace("mlp.0.", "mlp.fc1.").replace("mlp.2.", "mlp.fc2.") + key_lora = key_lora.replace(".modulation.lin.", ".modulation.linear.") + key_lora = key_lora[len("diffusion_model."):-len(".weight")] + key_map["transformer.{}".format(key_lora)] = k + key_map["diffusion_model.{}".format(key_lora)] = k # Old loras + + if isinstance(model, comfy.model_base.HiDream): + for k in sdk: + if k.startswith("diffusion_model."): + if k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format + key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format + + if isinstance(model, comfy.model_base.ACEStep): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["{}".format(key_lora)] = k + + return key_map + + +def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: + """ + Pad a tensor to a new shape with zeros. + + Args: + tensor (torch.Tensor): The original tensor to be padded. + new_shape (List[int]): The desired shape of the padded tensor. + + Returns: + torch.Tensor: A new tensor padded with zeros to the specified shape. + + Note: + If the new shape is smaller than the original tensor in any dimension, + the original tensor will be truncated in that dimension. + """ + if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): + raise ValueError("The new shape must be larger than the original tensor in all dimensions") + + if len(new_shape) != len(tensor.shape): + raise ValueError("The new shape must have the same number of dimensions as the original tensor") + + # Create a new tensor filled with zeros + padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) + + # Create slicing tuples for both tensors + orig_slices = tuple(slice(0, dim) for dim in tensor.shape) + new_slices = tuple(slice(0, dim) for dim in tensor.shape) + + # Copy the original tensor into the new tensor + padded_tensor[new_slices] = tensor[orig_slices] + + return padded_tensor + +def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, original_weights=None): + for p in patches: + strength = p[0] + v = p[1] + strength_model = p[2] + offset = p[3] + function = p[4] + if function is None: + function = lambda a: a + + old_weight = None + if offset is not None: + old_weight = weight + weight = weight.narrow(offset[0], offset[1], offset[2]) + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), ) + + if isinstance(v, weight_adapter.WeightAdapterBase): + output = v.calculate_weight(weight, key, strength, strength_model, offset, function, intermediate_dtype, original_weights) + if output is None: + logging.warning("Calculate Weight Failed: {} {}".format(v.name, key)) + else: + weight = output + if old_weight is not None: + weight = old_weight + continue + + if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": + diff: torch.Tensor = v[0] + # An extra flag to pad the weight if the diff's shape is larger than the weight + do_pad_weight = len(v) > 1 and v[1]['pad_weight'] + if do_pad_weight and diff.shape != weight.shape: + logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape)) + weight = pad_tensor_to_shape(weight, diff.shape) + + if strength != 0.0: + if diff.shape != weight.shape: + logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape)) + else: + weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype)) + elif patch_type == "set": + weight.copy_(v[0]) + elif patch_type == "model_as_lora": + target_weight: torch.Tensor = v[0] + diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \ + comfy.model_management.cast_to_device(original_weights[key][0][0], weight.device, intermediate_dtype) + weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype)) + else: + logging.warning("patch type not recognized {} {}".format(patch_type, key)) + + if old_weight is not None: + weight = old_weight + + return weight diff --git a/comfy/lora_convert.py b/comfy/lora_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..3e00b63db94969e813b4a9244dce2b1c9d046a8d --- /dev/null +++ b/comfy/lora_convert.py @@ -0,0 +1,24 @@ +import torch +import comfy.utils + + +def convert_lora_bfl_control(sd): #BFL loras for Flux + sd_out = {} + for k in sd: + k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight")) + sd_out[k_to] = sd[k] + + sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]]) + return sd_out + + +def convert_lora_wan_fun(sd): #Wan Fun loras + return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"}) + + +def convert_lora(sd): + if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd: + return convert_lora_bfl_control(sd) + if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd: + return convert_lora_wan_fun(sd) + return sd diff --git a/comfy/model_base.py b/comfy/model_base.py new file mode 100644 index 0000000000000000000000000000000000000000..4392355ea3572443a79b120706a54cda3f8bf303 --- /dev/null +++ b/comfy/model_base.py @@ -0,0 +1,1279 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import logging +from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep +from comfy.ldm.cascade.stage_c import StageC +from comfy.ldm.cascade.stage_b import StageB +from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation +from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper +import comfy.ldm.genmo.joint_model.asymm_models_joint +import comfy.ldm.aura.mmdit +import comfy.ldm.pixart.pixartms +import comfy.ldm.hydit.models +import comfy.ldm.audio.dit +import comfy.ldm.audio.embedders +import comfy.ldm.flux.model +import comfy.ldm.lightricks.model +import comfy.ldm.hunyuan_video.model +import comfy.ldm.cosmos.model +import comfy.ldm.cosmos.predict2 +import comfy.ldm.lumina.model +import comfy.ldm.wan.model +import comfy.ldm.hunyuan3d.model +import comfy.ldm.hidream.model +import comfy.ldm.chroma.model +import comfy.ldm.ace.model +import comfy.ldm.omnigen.omnigen2 + +import comfy.model_management +import comfy.patcher_extension +import comfy.conds +import comfy.ops +from enum import Enum +from . import utils +import comfy.latent_formats +import comfy.model_sampling +import math +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher + +class ModelType(Enum): + EPS = 1 + V_PREDICTION = 2 + V_PREDICTION_EDM = 3 + STABLE_CASCADE = 4 + EDM = 5 + FLOW = 6 + V_PREDICTION_CONTINUOUS = 7 + FLUX = 8 + IMG_TO_IMG = 9 + FLOW_COSMOS = 10 + + +def model_sampling(model_config, model_type): + s = comfy.model_sampling.ModelSamplingDiscrete + + if model_type == ModelType.EPS: + c = comfy.model_sampling.EPS + elif model_type == ModelType.V_PREDICTION: + c = comfy.model_sampling.V_PREDICTION + elif model_type == ModelType.V_PREDICTION_EDM: + c = comfy.model_sampling.V_PREDICTION + s = comfy.model_sampling.ModelSamplingContinuousEDM + elif model_type == ModelType.FLOW: + c = comfy.model_sampling.CONST + s = comfy.model_sampling.ModelSamplingDiscreteFlow + elif model_type == ModelType.STABLE_CASCADE: + c = comfy.model_sampling.EPS + s = comfy.model_sampling.StableCascadeSampling + elif model_type == ModelType.EDM: + c = comfy.model_sampling.EDM + s = comfy.model_sampling.ModelSamplingContinuousEDM + elif model_type == ModelType.V_PREDICTION_CONTINUOUS: + c = comfy.model_sampling.V_PREDICTION + s = comfy.model_sampling.ModelSamplingContinuousV + elif model_type == ModelType.FLUX: + c = comfy.model_sampling.CONST + s = comfy.model_sampling.ModelSamplingFlux + elif model_type == ModelType.IMG_TO_IMG: + c = comfy.model_sampling.IMG_TO_IMG + elif model_type == ModelType.FLOW_COSMOS: + c = comfy.model_sampling.COSMOS_RFLOW + s = comfy.model_sampling.ModelSamplingCosmosRFlow + + class ModelSampling(s, c): + pass + + return ModelSampling(model_config) + + +def convert_tensor(extra, dtype): + if hasattr(extra, "dtype"): + if extra.dtype != torch.int and extra.dtype != torch.long: + extra = extra.to(dtype) + return extra + + +class BaseModel(torch.nn.Module): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): + super().__init__() + + unet_config = model_config.unet_config + self.latent_format = model_config.latent_format + self.model_config = model_config + self.manual_cast_dtype = model_config.manual_cast_dtype + self.device = device + self.current_patcher: 'ModelPatcher' = None + + if not unet_config.get("disable_unet_model_creation", False): + if model_config.custom_operations is None: + fp8 = model_config.optimizations.get("fp8", False) + operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8) + else: + operations = model_config.custom_operations + self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) + if comfy.model_management.force_channels_last(): + self.diffusion_model.to(memory_format=torch.channels_last) + logging.debug("using channels last mode for diffusion model") + logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype)) + self.model_type = model_type + self.model_sampling = model_sampling(model_config, model_type) + + self.adm_channels = unet_config.get("adm_in_channels", None) + if self.adm_channels is None: + self.adm_channels = 0 + + self.concat_keys = () + logging.info("model_type {}".format(model_type.name)) + logging.debug("adm {}".format(self.adm_channels)) + self.memory_usage_factor = model_config.memory_usage_factor + self.memory_usage_factor_conds = () + + def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._apply_model, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, transformer_options) + ).execute(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) + + def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): + sigma = t + xc = self.model_sampling.calculate_input(sigma, x) + + if c_concat is not None: + xc = torch.cat([xc] + [c_concat], dim=1) + + context = c_crossattn + dtype = self.get_dtype() + + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + xc = xc.to(dtype) + t = self.model_sampling.timestep(t).float() + if context is not None: + context = context.to(dtype) + + extra_conds = {} + for o in kwargs: + extra = kwargs[o] + + if hasattr(extra, "dtype"): + extra = convert_tensor(extra, dtype) + elif isinstance(extra, list): + ex = [] + for ext in extra: + ex.append(convert_tensor(ext, dtype)) + extra = ex + extra_conds[o] = extra + + t = self.process_timestep(t, x=x, **extra_conds) + model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() + return self.model_sampling.calculate_denoised(sigma, model_output, x) + + def process_timestep(self, timestep, **kwargs): + return timestep + + def get_dtype(self): + return self.diffusion_model.dtype + + def encode_adm(self, **kwargs): + return None + + def concat_cond(self, **kwargs): + if len(self.concat_keys) > 0: + cond_concat = [] + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + concat_latent_image = kwargs.get("concat_latent_image", None) + if concat_latent_image is None: + concat_latent_image = kwargs.get("latent_image", None) + else: + concat_latent_image = self.process_latent_in(concat_latent_image) + + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if concat_latent_image.shape[1:] != noise.shape[1:]: + concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + if noise.ndim == 5: + if concat_latent_image.shape[-3] < noise.shape[-3]: + concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), "constant", 0) + else: + concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]] + + concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) + + if denoise_mask is not None: + if len(denoise_mask.shape) == len(noise.shape): + denoise_mask = denoise_mask[:, :1] + + num_dim = noise.ndim - 2 + denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:])) + if denoise_mask.shape[-2:] != noise.shape[-2:]: + denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") + denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) + + for ck in self.concat_keys: + if denoise_mask is not None: + if ck == "mask": + cond_concat.append(denoise_mask.to(device)) + elif ck == "masked_image": + cond_concat.append(concat_latent_image.to(device)) # NOTE: the latent_image should be masked by the mask in pixel space + elif ck == "mask_inverted": + cond_concat.append(1.0 - denoise_mask.to(device)) + else: + if ck == "mask": + cond_concat.append(torch.ones_like(noise)[:, :1]) + elif ck == "masked_image": + cond_concat.append(self.blank_inpaint_image_like(noise)) + elif ck == "mask_inverted": + cond_concat.append(torch.zeros_like(noise)[:, :1]) + if ck == "concat_image": + if concat_latent_image is not None: + cond_concat.append(concat_latent_image.to(device)) + else: + cond_concat.append(torch.zeros_like(noise)) + data = torch.cat(cond_concat, dim=1) + return data + return None + + def extra_conds(self, **kwargs): + out = {} + concat_cond = self.concat_cond(**kwargs) + if concat_cond is not None: + out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond) + + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + + cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) + if cross_attn_cnet is not None: + out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet) + + c_concat = kwargs.get("noise_concat", None) + if c_concat is not None: + out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat) + + return out + + def load_model_weights(self, sd, unet_prefix=""): + to_load = {} + keys = list(sd.keys()) + for k in keys: + if k.startswith(unet_prefix): + to_load[k[len(unet_prefix):]] = sd.pop(k) + + to_load = self.model_config.process_unet_state_dict(to_load) + m, u = self.diffusion_model.load_state_dict(to_load, strict=False) + if len(m) > 0: + logging.warning("unet missing: {}".format(m)) + + if len(u) > 0: + logging.warning("unet unexpected: {}".format(u)) + del to_load + return self + + def process_latent_in(self, latent): + return self.latent_format.process_in(latent) + + def process_latent_out(self, latent): + return self.latent_format.process_out(latent) + + def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): + extra_sds = [] + if clip_state_dict is not None: + extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) + if vae_state_dict is not None: + extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) + if clip_vision_state_dict is not None: + extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) + + unet_state_dict = self.diffusion_model.state_dict() + + if self.model_config.scaled_fp8 is not None: + unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8) + + unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) + + if self.model_type == ModelType.V_PREDICTION: + unet_state_dict["v_pred"] = torch.tensor([]) + + for sd in extra_sds: + unet_state_dict.update(sd) + + return unet_state_dict + + def set_inpaint(self): + self.concat_keys = ("mask", "masked_image") + def blank_inpaint_image_like(latent_image): + blank_image = torch.ones_like(latent_image) + # these are the values for "zero" in pixel space translated to latent space + blank_image[:,0] *= 0.8223 + blank_image[:,1] *= -0.6876 + blank_image[:,2] *= 0.6364 + blank_image[:,3] *= 0.1380 + return blank_image + self.blank_inpaint_image_like = blank_inpaint_image_like + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image) + + def memory_required(self, input_shape, cond_shapes={}): + input_shapes = [input_shape] + for c in self.memory_usage_factor_conds: + shape = cond_shapes.get(c, None) + if shape is not None and len(shape) > 0: + input_shapes += shape + + if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): + dtype = self.get_dtype() + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + #TODO: this needs to be tweaked + area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) + return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024) + else: + #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. + area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) + return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024) + + def extra_conds_shapes(self, **kwargs): + return {} + + +def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): + adm_inputs = [] + weights = [] + noise_aug = [] + for unclip_cond in unclip_conditioning: + for adm_cond in unclip_cond["clip_vision_output"].image_embeds: + weight = unclip_cond["strength"] + noise_augment = unclip_cond["noise_augmentation"] + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) + adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight + weights.append(weight) + noise_aug.append(noise_augment) + adm_inputs.append(adm_out) + + if len(noise_aug) > 1: + adm_out = torch.stack(adm_inputs).sum(0) + noise_augment = noise_augment_merge + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) + adm_out = torch.cat((c_adm, noise_level_emb), 1) + + return adm_out + +class SD21UNCLIP(BaseModel): + def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) + + def encode_adm(self, **kwargs): + unclip_conditioning = kwargs.get("unclip_conditioning", None) + device = kwargs["device"] + if unclip_conditioning is None: + return torch.zeros((1, self.adm_channels)) + else: + return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) + +def sdxl_pooled(args, noise_augmentor): + if "unclip_conditioning" in args: + return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] + else: + return args["pooled_output"] + +class SDXLRefiner(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + + if kwargs.get("prompt_type", "") == "negative": + aesthetic_score = kwargs.get("aesthetic_score", 2.5) + else: + aesthetic_score = kwargs.get("aesthetic_score", 6) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([aesthetic_score]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class SDXL(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([target_height]))) + out.append(self.embedder(torch.Tensor([target_width]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + + +class SVD_img2vid(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + + def encode_adm(self, **kwargs): + fps_id = kwargs.get("fps", 6) - 1 + motion_bucket_id = kwargs.get("motion_bucket_id", 127) + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.Tensor([fps_id]))) + out.append(self.embedder(torch.Tensor([motion_bucket_id]))) + out.append(self.embedder(torch.Tensor([augmentation]))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + + def extra_conds(self, **kwargs): + out = {} + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = comfy.conds.CONDRegular(adm) + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + + if "time_conditioning" in kwargs: + out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) + + out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) + return out + +class SV3D_u(SVD_img2vid): + def encode_adm(self, **kwargs): + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + +class SV3D_p(SVD_img2vid): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder_512 = Timestep(512) + + def encode_adm(self, **kwargs): + augmentation = kwargs.get("augmentation_level", 0) + elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here + azimuth = kwargs.get("azimuth", 0) + noise = kwargs.get("noise", None) + + out = [] + out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) + out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0)))) + out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0)))) + + out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out)) + return torch.cat(out, dim=1) + + +class Stable_Zero123(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): + super().__init__(model_config, model_type, device=device) + self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) + self.cc_projection.weight.copy_(cc_projection_weight) + self.cc_projection.bias.copy_(cc_projection_bias) + + def extra_conds(self, **kwargs): + out = {} + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + if cross_attn.shape[-1] != 768: + cross_attn = self.cc_projection(cross_attn) + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + return out + +class SD_X4Upscaler(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) + + def extra_conds(self, **kwargs): + out = {} + + image = kwargs.get("concat_image", None) + noise = kwargs.get("noise", None) + noise_augment = kwargs.get("noise_augmentation", 0.0) + device = kwargs["device"] + seed = kwargs["seed"] - 10 + + noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) + + if image is None: + image = torch.zeros_like(noise)[:,:3] + + if image.shape[1:] != noise.shape[1:]: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + noise_level = torch.tensor([noise_level], device=device) + if noise_augment > 0: + image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) + + image = utils.resize_to_batch_size(image, noise.shape[0]) + + out['c_concat'] = comfy.conds.CONDNoiseShape(image) + out['y'] = comfy.conds.CONDRegular(noise_level) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + return out + +class IP2P: + def concat_cond(self, **kwargs): + image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if image is None: + image = torch.zeros_like(noise) + + if image.shape[1:] != noise.shape[1:]: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + image = utils.resize_to_batch_size(image, noise.shape[0]) + return self.process_ip2p_image_in(image) + + +class SD15_instructpix2pix(IP2P, BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.process_ip2p_image_in = lambda image: image + + +class SDXL_instructpix2pix(IP2P, SDXL): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + if model_type == ModelType.V_PREDICTION_EDM: + self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p + else: + self.process_ip2p_image_in = lambda image: image #diffusers ip2p + +class Lotus(BaseModel): + def extra_conds(self, **kwargs): + out = {} + cross_attn = kwargs.get("cross_attn", None) + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + device = kwargs["device"] + task_emb = torch.tensor([1, 0]).float().to(device) + task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)]).unsqueeze(0) + out['y'] = comfy.conds.CONDRegular(task_emb) + return out + + def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, device=None): + super().__init__(model_config, model_type, device=device) + +class StableCascade_C(BaseModel): + def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): + super().__init__(model_config, model_type, device=device, unet_model=StageC) + self.diffusion_model.eval().requires_grad_(False) + + def extra_conds(self, **kwargs): + out = {} + clip_text_pooled = kwargs["pooled_output"] + if clip_text_pooled is not None: + out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) + + if "unclip_conditioning" in kwargs: + embeds = [] + for unclip_cond in kwargs["unclip_conditioning"]: + weight = unclip_cond["strength"] + embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight) + clip_img = torch.cat(embeds, dim=1) + else: + clip_img = torch.zeros((1, 1, 768)) + out["clip_img"] = comfy.conds.CONDRegular(clip_img) + out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) + out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,))) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) + return out + + +class StableCascade_B(BaseModel): + def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): + super().__init__(model_config, model_type, device=device, unet_model=StageB) + self.diffusion_model.eval().requires_grad_(False) + + def extra_conds(self, **kwargs): + out = {} + noise = kwargs.get("noise", None) + + clip_text_pooled = kwargs["pooled_output"] + if clip_text_pooled is not None: + out['clip'] = comfy.conds.CONDRegular(clip_text_pooled) + + #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched + prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) + + out["effnet"] = comfy.conds.CONDRegular(prior) + out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) + return out + + +class SD3(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + + +class AuraFlow(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + + +class StableAudio1(BaseModel): + def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer) + self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) + self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) + self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights) + self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights) + + def extra_conds(self, **kwargs): + out = {} + + noise = kwargs.get("noise", None) + device = kwargs["device"] + + seconds_start = kwargs.get("seconds_start", 0) + seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53)) + + seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device) + seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device) + + global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1)) + out['global_embed'] = comfy.conds.CONDRegular(global_embed) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + + def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): + sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) + d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()} + for k in d: + s = d[k] + for l in s: + sd["{}{}".format(k, l)] = s[l] + return sd + + +class HunyuanDiT(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask) + + conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None) + if conditioning_mt5xl is not None: + out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl) + + attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None) + if attention_mask_mt5xl is not None: + out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl) + + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]])) + return out + +class PixArt(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + width = kwargs.get("width", None) + height = kwargs.get("height", None) + if width is not None and height is not None: + out["c_size"] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width]])) + out["c_ar"] = comfy.conds.CONDRegular(torch.FloatTensor([[kwargs.get("aspect_ratio", height/width)]])) + + return out + +class Flux(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux): + super().__init__(model_config, model_type, device=device, unet_model=unet_model) + self.memory_usage_factor_conds = ("ref_latents",) + + def concat_cond(self, **kwargs): + try: + #Handle Flux control loras dynamically changing the img_in weight. + num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size) + except: + #Some cases like tensorrt might not have the weights accessible + num_channels = self.model_config.unet_config["in_channels"] + + out_channels = self.model_config.unet_config["out_channels"] + + if num_channels <= out_channels: + return None + + image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if image is None: + image = torch.zeros_like(noise) + + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + image = utils.resize_to_batch_size(image, noise.shape[0]) + image = self.process_latent_in(image) + if num_channels <= out_channels * 2: + return image + + #inpaint model + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + mask = torch.ones_like(noise)[:, :1] + + mask = torch.mean(mask, dim=1, keepdim=True) + mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center") + mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + return torch.cat((image, mask), dim=1) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + # upscale the attention mask, since now we + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + shape = kwargs["noise"].shape + mask_ref_size = kwargs["attention_mask_img_shape"] + # the model will pad to the patch size, and then divide + # essentially dividing and rounding up + (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) + attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + + guidance = kwargs.get("guidance", 3.5) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + latents = [] + for lat in ref_latents: + latents.append(self.process_latent_in(lat)) + out['ref_latents'] = comfy.conds.CONDList(latents) + return out + + def extra_conds_shapes(self, **kwargs): + out = {} + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + return out + + +class GenmoMochi(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + +class LTXV(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25)) + + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if denoise_mask is not None: + out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) + + keyframe_idxs = kwargs.get("keyframe_idxs", None) + if keyframe_idxs is not None: + out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs) + + return out + + def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): + if denoise_mask is None: + return timestep + return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0] + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + return latent_image + +class HunyuanVideo(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + guidance = kwargs.get("guidance", 6.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + + guiding_frame_index = kwargs.get("guiding_frame_index", None) + if guiding_frame_index is not None: + out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index])) + + ref_latent = kwargs.get("ref_latent", None) + if ref_latent is not None: + out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent)) + + return out + + def scale_latent_inpaint(self, latent_image, **kwargs): + return latent_image + +class HunyuanVideoI2V(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + self.concat_keys = ("concat_image", "mask_inverted") + + def scale_latent_inpaint(self, latent_image, **kwargs): + return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) + +class HunyuanVideoSkyreelsI2V(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + self.concat_keys = ("concat_image",) + + def scale_latent_inpaint(self, latent_image, **kwargs): + return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) + +class CosmosVideo(BaseModel): + def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT) + self.image_to_video = image_to_video + if self.image_to_video: + self.concat_keys = ("mask_inverted",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) + return out + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) + sigma_noise_augmentation = 0 #TODO + if sigma_noise_augmentation != 0: + latent_image = latent_image + noise + latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) + return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5) + +class CosmosPredict2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.predict2.MiniTrainDIT) + self.image_to_video = image_to_video + if self.image_to_video: + self.concat_keys = ("mask_inverted",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if denoise_mask is not None: + out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) + + out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) + return out + + def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): + if denoise_mask is None: + return timestep + if denoise_mask.ndim <= 4: + return timestep + condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True) + c_noise_B_1_T_1_1 = 0.0 * (1.0 - condition_video_mask_B_1_T_1_1) + timestep.reshape(timestep.shape[0], 1, 1, 1, 1) * condition_video_mask_B_1_T_1_1 + out = c_noise_B_1_T_1_1.squeeze(dim=[1, 3, 4]) + return out + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) + sigma_noise_augmentation = 0 #TODO + if sigma_noise_augmentation != 0: + latent_image = latent_image + noise + latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) + sigma = (sigma / (sigma + 1)) + return latent_image / (1.0 - sigma) + +class Lumina2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + +class WAN21(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) + self.image_to_video = image_to_video + + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1] + if extra_channels == 0: + return None + + image = kwargs.get("concat_latent_image", None) + device = kwargs["device"] + + if image is None: + shape_image = list(noise.shape) + shape_image[1] = extra_channels + image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) + else: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + for i in range(0, image.shape[1], 16): + image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16]) + image = utils.resize_to_batch_size(image, noise.shape[0]) + + if not self.image_to_video or extra_channels == image.shape[1]: + return image + + if image.shape[1] > (extra_channels - 4): + image = image[:, :(extra_channels - 4)] + + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + mask = torch.zeros_like(noise)[:, :4] + else: + if mask.shape[1] != 4: + mask = torch.mean(mask, dim=1, keepdim=True) + mask = 1.0 - mask + mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + if mask.shape[-3] < noise.shape[-3]: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) + if mask.shape[1] == 1: + mask = mask.repeat(1, 4, 1, 1, 1) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + + return torch.cat((mask, image), dim=1) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + clip_vision_output = kwargs.get("clip_vision_output", None) + if clip_vision_output is not None: + out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states) + + time_dim_concat = kwargs.get("time_dim_concat", None) + if time_dim_concat is not None: + out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat)) + + return out + + +class WAN21_Vace(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + noise = kwargs.get("noise", None) + noise_shape = list(noise.shape) + vace_frames = kwargs.get("vace_frames", None) + if vace_frames is None: + noise_shape[1] = 32 + vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)] + + mask = kwargs.get("vace_mask", None) + if mask is None: + noise_shape[1] = 64 + mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames) + + vace_frames_out = [] + for j in range(len(vace_frames)): + vf = vace_frames[j].clone() + for i in range(0, vf.shape[1], 16): + vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16]) + vf = torch.cat([vf, mask[j]], dim=1) + vace_frames_out.append(vf) + + vace_frames = torch.stack(vace_frames_out, dim=1) + out['vace_context'] = comfy.conds.CONDRegular(vace_frames) + + vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out)) + out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) + return out + +class WAN21_Camera(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + camera_conditions = kwargs.get("camera_conditions", None) + if camera_conditions is not None: + out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions) + return out + +class Hunyuan3Dv2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + guidance = kwargs.get("guidance", 5.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + return out + +class HiDream(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + conditioning_llama3 = kwargs.get("conditioning_llama3", None) + if conditioning_llama3 is not None: + out['encoder_hidden_states_llama3'] = comfy.conds.CONDRegular(conditioning_llama3) + image_cond = kwargs.get("concat_latent_image", None) + if image_cond is not None: + out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) + return out + +class Chroma(Flux): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + guidance = kwargs.get("guidance", 0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + return out + +class ACEStep(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + noise = kwargs.get("noise", None) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + conditioning_lyrics = kwargs.get("conditioning_lyrics", None) + if cross_attn is not None: + out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics) + out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype)) + out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0)) + return out + +class Omnigen2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel) + self.memory_usage_factor_conds = ("ref_latents",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + latents = [] + for lat in ref_latents: + latents.append(self.process_latent_in(lat)) + out['ref_latents'] = comfy.conds.CONDList(latents) + return out + + def extra_conds_shapes(self, **kwargs): + out = {} + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..18232ade3eac7fea8ec62b283f7a81001c7b6bf2 --- /dev/null +++ b/comfy/model_detection.py @@ -0,0 +1,908 @@ +import json +import comfy.supported_models +import comfy.supported_models_base +import comfy.utils +import math +import logging +import torch + +def count_blocks(state_dict_keys, prefix_string): + count = 0 + while True: + c = False + for k in state_dict_keys: + if k.startswith(prefix_string.format(count)): + c = True + break + if c == False: + break + count += 1 + return count + +def calculate_transformer_depth(prefix, state_dict_keys, state_dict): + context_dim = None + use_linear_in_transformer = False + + transformer_prefix = prefix + "1.transformer_blocks." + transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) + if len(transformer_keys) > 0: + last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') + context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] + use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 + time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict + time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict + return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross + return None + +def detect_unet_config(state_dict, key_prefix, metadata=None): + state_dict_keys = list(state_dict.keys()) + + if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model + unet_config = {} + unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] + patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] + unet_config["patch_size"] = patch_size + final_layer = '{}final_layer.linear.weight'.format(key_prefix) + if final_layer in state_dict: + unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size) + + unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64 + unet_config["input_size"] = None + y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix) + if y_key in state_dict_keys: + unet_config["adm_in_channels"] = state_dict[y_key].shape[1] + + context_key = '{}context_embedder.weight'.format(key_prefix) + if context_key in state_dict_keys: + in_features = state_dict[context_key].shape[1] + out_features = state_dict[context_key].shape[0] + unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}} + num_patches_key = '{}pos_embed'.format(key_prefix) + if num_patches_key in state_dict_keys: + num_patches = state_dict[num_patches_key].shape[1] + unet_config["num_patches"] = num_patches + unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) + + rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix) + if rms_qk in state_dict_keys: + unet_config["qk_norm"] = "rms" + + unet_config["pos_embed_scaling_factor"] = None #unused for inference + context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix) + if context_processor in state_dict_keys: + unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.') + unet_config["x_block_self_attn_layers"] = [] + for key in state_dict_keys: + if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'): + layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')] + unet_config["x_block_self_attn_layers"].append(int(layer)) + return unet_config + + if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade + unet_config = {} + text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) + if text_mapper_name in state_dict_keys: + unet_config['stable_cascade_stage'] = 'c' + w = state_dict[text_mapper_name] + if w.shape[0] == 1536: #stage c lite + unet_config['c_cond'] = 1536 + unet_config['c_hidden'] = [1536, 1536] + unet_config['nhead'] = [24, 24] + unet_config['blocks'] = [[4, 12], [12, 4]] + elif w.shape[0] == 2048: #stage c full + unet_config['c_cond'] = 2048 + elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: + unet_config['stable_cascade_stage'] = 'b' + w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] + if w.shape[-1] == 640: + unet_config['c_hidden'] = [320, 640, 1280, 1280] + unet_config['nhead'] = [-1, -1, 20, 20] + unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] + unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] + elif w.shape[-1] == 576: #stage b lite + unet_config['c_hidden'] = [320, 576, 1152, 1152] + unet_config['nhead'] = [-1, 9, 18, 18] + unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] + unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] + return unet_config + + if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit + unet_config = {} + unet_config["audio_model"] = "dit1.0" + return unet_config + + if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit + unet_config = {} + unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1] + unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1] + double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.') + single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.') + unet_config["n_double_layers"] = double_layers + unet_config["n_layers"] = double_layers + single_layers + return unet_config + + if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT + unet_config = {} + unet_config["image_model"] = "hydit" + unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') + unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] + if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2 + unet_config["mlp_ratio"] = 4.3637 + if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968: + unet_config["size_cond"] = True + unet_config["use_style_cond"] = True + unet_config["image_model"] = "hydit1" + return unet_config + + if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video + dit_config = {} + dit_config["image_model"] = "hunyuan_video" + dit_config["in_channels"] = state_dict['{}img_in.proj.weight'.format(key_prefix)].shape[1] #SkyReels img2video has 32 input channels + dit_config["patch_size"] = [1, 2, 2] + dit_config["out_channels"] = 16 + dit_config["vec_in_dim"] = 768 + dit_config["context_in_dim"] = 4096 + dit_config["hidden_size"] = 3072 + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 24 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') + dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') + dit_config["axes_dim"] = [16, 56, 56] + dit_config["theta"] = 256 + dit_config["qkv_bias"] = True + guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys)) + dit_config["guidance_embed"] = len(guidance_keys) > 0 + return dit_config + + if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux + dit_config = {} + dit_config["image_model"] = "flux" + dit_config["in_channels"] = 16 + patch_size = 2 + dit_config["patch_size"] = patch_size + in_key = "{}img_in.weight".format(key_prefix) + if in_key in state_dict_keys: + dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size) + dit_config["out_channels"] = 16 + vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix) + if vec_in_key in state_dict_keys: + dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1] + dit_config["context_in_dim"] = 4096 + dit_config["hidden_size"] = 3072 + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 24 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') + dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') + dit_config["axes_dim"] = [16, 56, 56] + dit_config["theta"] = 10000 + dit_config["qkv_bias"] = True + if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma + dit_config["image_model"] = "chroma" + dit_config["in_channels"] = 64 + dit_config["out_channels"] = 64 + dit_config["in_dim"] = 64 + dit_config["out_dim"] = 3072 + dit_config["hidden_dim"] = 5120 + dit_config["n_layers"] = 5 + else: + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + return dit_config + + if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview + dit_config = {} + dit_config["image_model"] = "mochi_preview" + dit_config["depth"] = 48 + dit_config["patch_size"] = 2 + dit_config["num_heads"] = 24 + dit_config["hidden_size_x"] = 3072 + dit_config["hidden_size_y"] = 1536 + dit_config["mlp_ratio_x"] = 4.0 + dit_config["mlp_ratio_y"] = 4.0 + dit_config["learn_sigma"] = False + dit_config["in_channels"] = 12 + dit_config["qk_norm"] = True + dit_config["qkv_bias"] = False + dit_config["out_bias"] = True + dit_config["attn_drop"] = 0.0 + dit_config["patch_embed_bias"] = True + dit_config["posenc_preserve_area"] = True + dit_config["timestep_mlp_bias"] = True + dit_config["attend_to_padding"] = False + dit_config["timestep_scale"] = 1000.0 + dit_config["use_t5"] = True + dit_config["t5_feat_dim"] = 4096 + dit_config["t5_token_length"] = 256 + dit_config["rope_theta"] = 10000.0 + return dit_config + + if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys: + # PixArt diffusers + return None + + if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv + dit_config = {} + dit_config["image_model"] = "ltxv" + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') + shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape + dit_config["attention_head_dim"] = shape[0] // 32 + dit_config["cross_attention_dim"] = shape[1] + if metadata is not None and "config" in metadata: + dit_config.update(json.loads(metadata["config"]).get("transformer", {})) + return dit_config + + if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model + dit_config = {} + dit_config["audio_model"] = "ace" + dit_config["attention_head_dim"] = 128 + dit_config["in_channels"] = 8 + dit_config["inner_dim"] = 2560 + dit_config["max_height"] = 16 + dit_config["max_position"] = 32768 + dit_config["max_width"] = 32768 + dit_config["mlp_ratio"] = 2.5 + dit_config["num_attention_heads"] = 20 + dit_config["num_layers"] = 24 + dit_config["out_channels"] = 8 + dit_config["patch_size"] = [16, 1] + dit_config["rope_theta"] = 1000000.0 + dit_config["speaker_embedding_dim"] = 512 + dit_config["text_embedding_dim"] = 768 + + dit_config["ssl_encoder_depths"] = [8, 8] + dit_config["ssl_latent_dims"] = [1024, 768] + dit_config["ssl_names"] = ["mert", "m-hubert"] + dit_config["lyric_encoder_vocab_size"] = 6693 + dit_config["lyric_hidden_size"] = 1024 + return dit_config + + if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt + patch_size = 2 + dit_config = {} + dit_config["num_heads"] = 16 + dit_config["patch_size"] = patch_size + dit_config["hidden_size"] = 1152 + dit_config["in_channels"] = 4 + dit_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') + + y_key = "{}y_embedder.y_embedding".format(key_prefix) + if y_key in state_dict_keys: + dit_config["model_max_length"] = state_dict[y_key].shape[0] + + pe_key = "{}pos_embed".format(key_prefix) + if pe_key in state_dict_keys: + dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size + dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess + + ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix) + if ar_key in state_dict_keys: + dit_config["image_model"] = "pixart_alpha" + dit_config["micro_condition"] = True + else: + dit_config["image_model"] = "pixart_sigma" + dit_config["micro_condition"] = False + return dit_config + + if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos + dit_config = {} + dit_config["image_model"] = "cosmos" + dit_config["max_img_h"] = 240 + dit_config["max_img_w"] = 240 + dit_config["max_frames"] = 128 + concat_padding_mask = True + dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) + dit_config["out_channels"] = 16 + dit_config["patch_spatial"] = 2 + dit_config["patch_temporal"] = 1 + dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0] + dit_config["block_config"] = "FA-CA-MLP" + dit_config["concat_padding_mask"] = concat_padding_mask + dit_config["pos_emb_cls"] = "rope3d" + dit_config["pos_emb_learnable"] = False + dit_config["pos_emb_interpolation"] = "crop" + dit_config["block_x_format"] = "THWBD" + dit_config["affline_emb_norm"] = True + dit_config["use_adaln_lora"] = True + dit_config["adaln_lora_dim"] = 256 + + if dit_config["model_channels"] == 4096: + # 7B + dit_config["num_blocks"] = 28 + dit_config["num_heads"] = 32 + dit_config["extra_per_block_abs_pos_emb"] = True + dit_config["rope_h_extrapolation_ratio"] = 1.0 + dit_config["rope_w_extrapolation_ratio"] = 1.0 + dit_config["rope_t_extrapolation_ratio"] = 2.0 + dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" + else: # 5120 + # 14B + dit_config["num_blocks"] = 36 + dit_config["num_heads"] = 40 + dit_config["extra_per_block_abs_pos_emb"] = True + dit_config["rope_h_extrapolation_ratio"] = 2.0 + dit_config["rope_w_extrapolation_ratio"] = 2.0 + dit_config["rope_t_extrapolation_ratio"] = 2.0 + dit_config["extra_h_extrapolation_ratio"] = 2.0 + dit_config["extra_w_extrapolation_ratio"] = 2.0 + dit_config["extra_t_extrapolation_ratio"] = 2.0 + dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" + return dit_config + + if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys: # Lumina 2 + dit_config = {} + dit_config["image_model"] = "lumina2" + dit_config["patch_size"] = 2 + dit_config["in_channels"] = 16 + dit_config["dim"] = 2304 + dit_config["cap_feat_dim"] = 2304 + dit_config["n_layers"] = 26 + dit_config["n_heads"] = 24 + dit_config["n_kv_heads"] = 8 + dit_config["qk_norm"] = True + dit_config["axes_dims"] = [32, 32, 32] + dit_config["axes_lens"] = [300, 512, 512] + return dit_config + + if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1 + dit_config = {} + dit_config["image_model"] = "wan2.1" + dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1] + dit_config["dim"] = dim + dit_config["num_heads"] = dim // 128 + dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0] + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') + dit_config["patch_size"] = (1, 2, 2) + dit_config["freq_dim"] = 256 + dit_config["window_size"] = (-1, -1) + dit_config["qk_norm"] = True + dit_config["cross_attn_norm"] = True + dit_config["eps"] = 1e-6 + dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1] + if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "vace" + dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1] + dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.') + elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "camera" + else: + if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "i2v" + else: + dit_config["model_type"] = "t2v" + flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix)) + if flf_weight is not None: + dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1] + return dit_config + + if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D + in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape + dit_config = {} + dit_config["image_model"] = "hunyuan3d2" + dit_config["in_channels"] = in_shape[1] + dit_config["context_in_dim"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1] + dit_config["hidden_size"] = in_shape[0] + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 16 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') + dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') + dit_config["qkv_bias"] = True + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + return dit_config + + if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream + dit_config = {} + dit_config["image_model"] = "hidream" + dit_config["attention_head_dim"] = 128 + dit_config["axes_dims_rope"] = [64, 32, 32] + dit_config["caption_channels"] = [4096, 4096] + dit_config["max_resolution"] = [128, 128] + dit_config["in_channels"] = 16 + dit_config["llama_layers"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31] + dit_config["num_attention_heads"] = 20 + dit_config["num_routed_experts"] = 4 + dit_config["num_activated_experts"] = 2 + dit_config["num_layers"] = 16 + dit_config["num_single_layers"] = 32 + dit_config["out_channels"] = 16 + dit_config["patch_size"] = 2 + dit_config["text_emb_dim"] = 2048 + return dit_config + + if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2 + dit_config = {} + dit_config["image_model"] = "cosmos_predict2" + dit_config["max_img_h"] = 240 + dit_config["max_img_w"] = 240 + dit_config["max_frames"] = 128 + concat_padding_mask = True + dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) + dit_config["out_channels"] = 16 + dit_config["patch_spatial"] = 2 + dit_config["patch_temporal"] = 1 + dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0] + dit_config["concat_padding_mask"] = concat_padding_mask + dit_config["crossattn_emb_channels"] = 1024 + dit_config["pos_emb_cls"] = "rope3d" + dit_config["pos_emb_learnable"] = True + dit_config["pos_emb_interpolation"] = "crop" + dit_config["min_fps"] = 1 + dit_config["max_fps"] = 30 + + dit_config["use_adaln_lora"] = True + dit_config["adaln_lora_dim"] = 256 + if dit_config["model_channels"] == 2048: + dit_config["num_blocks"] = 28 + dit_config["num_heads"] = 16 + elif dit_config["model_channels"] == 5120: + dit_config["num_blocks"] = 36 + dit_config["num_heads"] = 40 + + if dit_config["in_channels"] == 16: + dit_config["extra_per_block_abs_pos_emb"] = False + dit_config["rope_h_extrapolation_ratio"] = 4.0 + dit_config["rope_w_extrapolation_ratio"] = 4.0 + dit_config["rope_t_extrapolation_ratio"] = 1.0 + elif dit_config["in_channels"] == 17: # img to video + if dit_config["model_channels"] == 2048: + dit_config["extra_per_block_abs_pos_emb"] = False + dit_config["rope_h_extrapolation_ratio"] = 3.0 + dit_config["rope_w_extrapolation_ratio"] = 3.0 + dit_config["rope_t_extrapolation_ratio"] = 1.0 + elif dit_config["model_channels"] == 5120: + dit_config["rope_h_extrapolation_ratio"] = 2.0 + dit_config["rope_w_extrapolation_ratio"] = 2.0 + dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334 + + dit_config["extra_h_extrapolation_ratio"] = 1.0 + dit_config["extra_w_extrapolation_ratio"] = 1.0 + dit_config["extra_t_extrapolation_ratio"] = 1.0 + dit_config["rope_enable_fps_modulation"] = False + + return dit_config + + if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2 + dit_config = {} + dit_config["image_model"] = "omnigen2" + dit_config["axes_dim_rope"] = [40, 40, 40] + dit_config["axes_lens"] = [1024, 1664, 1664] + dit_config["ffn_dim_multiplier"] = None + dit_config["hidden_size"] = 2520 + dit_config["in_channels"] = 16 + dit_config["multiple_of"] = 256 + dit_config["norm_eps"] = 1e-05 + dit_config["num_attention_heads"] = 21 + dit_config["num_kv_heads"] = 7 + dit_config["num_layers"] = 32 + dit_config["num_refiner_layers"] = 2 + dit_config["out_channels"] = None + dit_config["patch_size"] = 2 + dit_config["text_feat_dim"] = 2048 + dit_config["timestep_scale"] = 1000.0 + return dit_config + + if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: + return None + + unet_config = { + "use_checkpoint": False, + "image_size": 32, + "use_spatial_transformer": True, + "legacy": False + } + + y_input = '{}label_emb.0.0.weight'.format(key_prefix) + if y_input in state_dict_keys: + unet_config["num_classes"] = "sequential" + unet_config["adm_in_channels"] = state_dict[y_input].shape[1] + else: + unet_config["adm_in_channels"] = None + + model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] + in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] + + out_key = '{}out.2.weight'.format(key_prefix) + if out_key in state_dict: + out_channels = state_dict[out_key].shape[0] + else: + out_channels = 4 + + num_res_blocks = [] + channel_mult = [] + transformer_depth = [] + transformer_depth_output = [] + context_dim = None + use_linear_in_transformer = False + + video_model = False + video_model_cross = False + + current_res = 1 + count = 0 + + last_res_blocks = 0 + last_channel_mult = 0 + + input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') + for count in range(input_block_count): + prefix = '{}input_blocks.{}.'.format(key_prefix, count) + prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) + + block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) + if len(block_keys) == 0: + break + + block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) + + if "{}0.op.weight".format(prefix) in block_keys: #new layer + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + + current_res *= 2 + last_res_blocks = 0 + last_channel_mult = 0 + out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + else: + res_block_prefix = "{}0.in_layers.0.weight".format(prefix) + if res_block_prefix in block_keys: + last_res_blocks += 1 + last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels + + out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) + if out is not None: + transformer_depth.append(out[0]) + if context_dim is None: + context_dim = out[1] + use_linear_in_transformer = out[2] + video_model = out[3] + video_model_cross = out[4] + else: + transformer_depth.append(0) + + res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) + if res_block_prefix in block_keys_output: + out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + + + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') + elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = -1 + else: + transformer_depth_middle = -2 + + unet_config["in_channels"] = in_channels + unet_config["out_channels"] = out_channels + unet_config["model_channels"] = model_channels + unet_config["num_res_blocks"] = num_res_blocks + unet_config["transformer_depth"] = transformer_depth + unet_config["transformer_depth_output"] = transformer_depth_output + unet_config["channel_mult"] = channel_mult + unet_config["transformer_depth_middle"] = transformer_depth_middle + unet_config['use_linear_in_transformer'] = use_linear_in_transformer + unet_config["context_dim"] = context_dim + + if video_model: + unet_config["extra_ff_mix_layer"] = True + unet_config["use_spatial_context"] = True + unet_config["merge_strategy"] = "learned_with_images" + unet_config["merge_factor"] = 0.0 + unet_config["video_kernel_size"] = [3, 1, 1] + unet_config["use_temporal_resblock"] = True + unet_config["use_temporal_attention"] = True + unet_config["disable_temporal_crossattention"] = not video_model_cross + else: + unet_config["use_temporal_resblock"] = False + unet_config["use_temporal_attention"] = False + + return unet_config + +def model_config_from_unet_config(unet_config, state_dict=None): + for model_config in comfy.supported_models.models: + if model_config.matches(unet_config, state_dict): + return model_config(unet_config) + + logging.error("no match {}".format(unet_config)) + return None + +def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None): + unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata) + if unet_config is None: + return None + model_config = model_config_from_unet_config(unet_config, state_dict) + if model_config is None and use_base_if_no_match: + model_config = comfy.supported_models_base.BASE(unet_config) + + scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix) + if scaled_fp8_key in state_dict: + scaled_fp8_weight = state_dict.pop(scaled_fp8_key) + model_config.scaled_fp8 = scaled_fp8_weight.dtype + if model_config.scaled_fp8 == torch.float32: + model_config.scaled_fp8 = torch.float8_e4m3fn + if scaled_fp8_weight.nelement() == 2: + model_config.optimizations["fp8"] = False + else: + model_config.optimizations["fp8"] = True + + return model_config + +def unet_prefix_from_state_dict(state_dict): + candidates = ["model.diffusion_model.", #ldm/sgm models + "model.model.", #audio models + "net.", #cosmos + ] + counts = {k: 0 for k in candidates} + for k in state_dict: + for c in candidates: + if k.startswith(c): + counts[c] += 1 + break + + top = max(counts, key=counts.get) + if counts[top] > 5: + return top + else: + return "model." #aura flow and others + + +def convert_config(unet_config): + new_config = unet_config.copy() + num_res_blocks = new_config.get("num_res_blocks", None) + channel_mult = new_config.get("channel_mult", None) + + if isinstance(num_res_blocks, int): + num_res_blocks = len(channel_mult) * [num_res_blocks] + + if "attention_resolutions" in new_config: + attention_resolutions = new_config.pop("attention_resolutions") + transformer_depth = new_config.get("transformer_depth", None) + transformer_depth_middle = new_config.get("transformer_depth_middle", None) + + if isinstance(transformer_depth, int): + transformer_depth = len(channel_mult) * [transformer_depth] + if transformer_depth_middle is None: + transformer_depth_middle = transformer_depth[-1] + t_in = [] + t_out = [] + s = 1 + for i in range(len(num_res_blocks)): + res = num_res_blocks[i] + d = 0 + if s in attention_resolutions: + d = transformer_depth[i] + + t_in += [d] * res + t_out += [d] * (res + 1) + s *= 2 + transformer_depth = t_in + new_config["transformer_depth"] = t_in + new_config["transformer_depth_output"] = t_out + new_config["transformer_depth_middle"] = transformer_depth_middle + + new_config["num_res_blocks"] = num_res_blocks + return new_config + + +def unet_config_from_diffusers_unet(state_dict, dtype=None): + if "conv_in.weight" not in state_dict: + return None + + match = {} + transformer_depth = [] + + attn_res = 1 + down_blocks = count_blocks(state_dict, "down_blocks.{}") + for i in range(down_blocks): + attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') + res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}') + for ab in range(attn_blocks): + transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') + transformer_depth.append(transformer_count) + if transformer_count > 0: + match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] + + attn_res *= 2 + if attn_blocks == 0: + for i in range(res_blocks): + transformer_depth.append(0) + + match["transformer_depth"] = transformer_depth + + match["model_channels"] = state_dict["conv_in.weight"].shape[0] + match["in_channels"] = state_dict["conv_in.weight"].shape[1] + match["adm_in_channels"] = None + if "class_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] + elif "add_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] + + SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, + 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], + 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, + 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, + 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], + 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} + + SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], + 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, + 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, + 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4, + 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + supported_models = [LotusD, SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint] + + for unet_config in supported_models: + matches = True + for k in match: + if match[k] != unet_config[k]: + matches = False + break + if matches: + return convert_config(unet_config) + return None + +def model_config_from_diffusers_unet(state_dict): + unet_config = unet_config_from_diffusers_unet(state_dict) + if unet_config is not None: + return model_config_from_unet_config(unet_config) + return None + +def convert_diffusers_mmdit(state_dict, output_prefix=""): + out_sd = {} + + if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow + num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.') + num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.') + sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix) + elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt + num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') + sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix) + elif 'x_embedder.weight' in state_dict: #Flux + depth = count_blocks(state_dict, 'transformer_blocks.{}.') + depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.') + hidden_size = state_dict["x_embedder.bias"].shape[0] + sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix) + elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3 + num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') + depth = state_dict["pos_embed.proj.weight"].shape[0] // 64 + sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix) + else: + return None + + for k in sd_map: + weight = state_dict.get(k, None) + if weight is not None: + t = sd_map[k] + + if not isinstance(t, str): + if len(t) > 2: + fun = t[2] + else: + fun = lambda a: a + offset = t[1] + if offset is not None: + old_weight = out_sd.get(t[0], None) + if old_weight is None: + old_weight = torch.empty_like(weight) + if old_weight.shape[offset[0]] < offset[1] + offset[2]: + exp = list(weight.shape) + exp[offset[0]] = offset[1] + offset[2] + new = torch.empty(exp, device=weight.device, dtype=weight.dtype) + new[:old_weight.shape[0]] = old_weight + old_weight = new + + w = old_weight.narrow(offset[0], offset[1], offset[2]) + else: + old_weight = weight + w = weight + w[:] = fun(weight) + t = t[0] + out_sd[t] = old_weight + else: + out_sd[t] = weight + state_dict.pop(k) + + return out_sd diff --git a/comfy/model_management.py b/comfy/model_management.py new file mode 100644 index 0000000000000000000000000000000000000000..eb274239526c1331f696c671396cd086ba21b289 --- /dev/null +++ b/comfy/model_management.py @@ -0,0 +1,1341 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import psutil +import logging +from enum import Enum +from comfy.cli_args import args, PerformanceFeature +import torch +import sys +import platform +import weakref +import gc + +class VRAMState(Enum): + DISABLED = 0 #No vram present: no need to move models to vram + NO_VRAM = 1 #Very low vram: enable all the options to save vram + LOW_VRAM = 2 + NORMAL_VRAM = 3 + HIGH_VRAM = 4 + SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. + +class CPUState(Enum): + GPU = 0 + CPU = 1 + MPS = 2 + +# Determine VRAM State +vram_state = VRAMState.NORMAL_VRAM +set_vram_to = VRAMState.NORMAL_VRAM +cpu_state = CPUState.GPU + +total_vram = 0 + +def get_supported_float8_types(): + float8_types = [] + try: + float8_types.append(torch.float8_e4m3fn) + except: + pass + try: + float8_types.append(torch.float8_e4m3fnuz) + except: + pass + try: + float8_types.append(torch.float8_e5m2) + except: + pass + try: + float8_types.append(torch.float8_e5m2fnuz) + except: + pass + try: + float8_types.append(torch.float8_e8m0fnu) + except: + pass + return float8_types + +FLOAT8_TYPES = get_supported_float8_types() + +xpu_available = False +torch_version = "" +try: + torch_version = torch.version.__version__ + temp = torch_version.split(".") + torch_version_numeric = (int(temp[0]), int(temp[1])) + xpu_available = (torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] <= 4)) and torch.xpu.is_available() +except: + pass + +lowvram_available = True +if args.deterministic: + logging.info("Using deterministic algorithms for pytorch") + torch.use_deterministic_algorithms(True, warn_only=True) + +directml_enabled = False +if args.directml is not None: + import torch_directml + directml_enabled = True + device_index = args.directml + if device_index < 0: + directml_device = torch_directml.device() + else: + directml_device = torch_directml.device(device_index) + logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index))) + # torch_directml.disable_tiled_resources(True) + lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. + +try: + import intel_extension_for_pytorch as ipex + _ = torch.xpu.device_count() + xpu_available = xpu_available or torch.xpu.is_available() +except: + xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available()) + +try: + if torch.backends.mps.is_available(): + cpu_state = CPUState.MPS + import torch.mps +except: + pass + +try: + import torch_npu # noqa: F401 + _ = torch.npu.device_count() + npu_available = torch.npu.is_available() +except: + npu_available = False + +try: + import torch_mlu # noqa: F401 + _ = torch.mlu.device_count() + mlu_available = torch.mlu.is_available() +except: + mlu_available = False + +if args.cpu: + cpu_state = CPUState.CPU + +def is_intel_xpu(): + global cpu_state + global xpu_available + if cpu_state == CPUState.GPU: + if xpu_available: + return True + return False + +def is_ascend_npu(): + global npu_available + if npu_available: + return True + return False + +def is_mlu(): + global mlu_available + if mlu_available: + return True + return False + +def get_torch_device(): + global directml_enabled + global cpu_state + if directml_enabled: + global directml_device + return directml_device + if cpu_state == CPUState.MPS: + return torch.device("mps") + if cpu_state == CPUState.CPU: + return torch.device("cpu") + else: + if is_intel_xpu(): + return torch.device("xpu", torch.xpu.current_device()) + elif is_ascend_npu(): + return torch.device("npu", torch.npu.current_device()) + elif is_mlu(): + return torch.device("mlu", torch.mlu.current_device()) + else: + return torch.device(torch.cuda.current_device()) + +def get_total_memory(dev=None, torch_total_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_total = psutil.virtual_memory().total + mem_total_torch = mem_total + else: + if directml_enabled: + mem_total = 1024 * 1024 * 1024 #TODO + mem_total_torch = mem_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + mem_total_torch = mem_reserved + mem_total = torch.xpu.get_device_properties(dev).total_memory + elif is_ascend_npu(): + stats = torch.npu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_npu = torch.npu.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_npu + elif is_mlu(): + stats = torch.mlu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_mlu = torch.mlu.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_mlu + else: + stats = torch.cuda.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_cuda = torch.cuda.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_cuda + + if torch_total_too: + return (mem_total, mem_total_torch) + else: + return mem_total + +def mac_version(): + try: + return tuple(int(n) for n in platform.mac_ver()[0].split(".")) + except: + return None + +try: + logging.info("pytorch version: {}".format(torch_version)) + mac_ver = mac_version() + if mac_ver is not None: + logging.info("Mac Version {}".format(mac_ver)) +except: + pass + +try: + OOM_EXCEPTION = torch.cuda.OutOfMemoryError +except: + OOM_EXCEPTION = Exception + +XFORMERS_VERSION = "" +XFORMERS_ENABLED_VAE = True +if args.disable_xformers: + XFORMERS_IS_AVAILABLE = False +else: + try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILABLE = True + try: + XFORMERS_IS_AVAILABLE = xformers._has_cpp_library + except: + pass + try: + XFORMERS_VERSION = xformers.version.__version__ + logging.info("xformers version: {}".format(XFORMERS_VERSION)) + if XFORMERS_VERSION.startswith("0.0.18"): + logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") + logging.warning("Please downgrade or upgrade xformers to a different version.\n") + XFORMERS_ENABLED_VAE = False + except: + pass + except: + XFORMERS_IS_AVAILABLE = False + +def is_nvidia(): + global cpu_state + if cpu_state == CPUState.GPU: + if torch.version.cuda: + return True + return False + +def is_amd(): + global cpu_state + if cpu_state == CPUState.GPU: + if torch.version.hip: + return True + return False + +MIN_WEIGHT_MEMORY_RATIO = 0.4 +if is_nvidia(): + MIN_WEIGHT_MEMORY_RATIO = 0.0 + +ENABLE_PYTORCH_ATTENTION = False +if args.use_pytorch_cross_attention: + ENABLE_PYTORCH_ATTENTION = True + XFORMERS_IS_AVAILABLE = False + +try: + if is_nvidia(): + if torch_version_numeric[0] >= 2: + if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + ENABLE_PYTORCH_ATTENTION = True + if is_intel_xpu() or is_ascend_npu() or is_mlu(): + if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + ENABLE_PYTORCH_ATTENTION = True +except: + pass + + +SUPPORT_FP8_OPS = args.supports_fp8_compute +try: + if is_amd(): + try: + rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2])) + except: + rocm_version = (6, -1) + arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName + logging.info("AMD arch: {}".format(arch)) + logging.info("ROCm version: {}".format(rocm_version)) + if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much + if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches, TODO: gfx1201 and gfx950 + ENABLE_PYTORCH_ATTENTION = True + if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4): + if any((a in arch) for a in ["gfx1201", "gfx942", "gfx950"]): # TODO: more arches + SUPPORT_FP8_OPS = True + +except: + pass + + +if ENABLE_PYTORCH_ATTENTION: + torch.backends.cuda.enable_math_sdp(True) + torch.backends.cuda.enable_flash_sdp(True) + torch.backends.cuda.enable_mem_efficient_sdp(True) + + +PRIORITIZE_FP16 = False # TODO: remove and replace with something that shows exactly which dtype is faster than the other +try: + if is_nvidia() and PerformanceFeature.Fp16Accumulation in args.fast: + torch.backends.cuda.matmul.allow_fp16_accumulation = True + PRIORITIZE_FP16 = True # TODO: limit to cards where it actually boosts performance + logging.info("Enabled fp16 accumulation.") +except: + pass + +try: + if torch_version_numeric >= (2, 5): + torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True) +except: + logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp") + +if args.lowvram: + set_vram_to = VRAMState.LOW_VRAM + lowvram_available = True +elif args.novram: + set_vram_to = VRAMState.NO_VRAM +elif args.highvram or args.gpu_only: + vram_state = VRAMState.HIGH_VRAM + +FORCE_FP32 = False +if args.force_fp32: + logging.info("Forcing FP32, if this improves things please report it.") + FORCE_FP32 = True + +if lowvram_available: + if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): + vram_state = set_vram_to + + +if cpu_state != CPUState.GPU: + vram_state = VRAMState.DISABLED + +if cpu_state == CPUState.MPS: + vram_state = VRAMState.SHARED + +logging.info(f"Set vram state to: {vram_state.name}") + +DISABLE_SMART_MEMORY = args.disable_smart_memory + +if DISABLE_SMART_MEMORY: + logging.info("Disabling smart memory management") + +def get_torch_device_name(device): + if hasattr(device, 'type'): + if device.type == "cuda": + try: + allocator_backend = torch.cuda.get_allocator_backend() + except: + allocator_backend = "" + return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) + else: + return "{}".format(device.type) + elif is_intel_xpu(): + return "{} {}".format(device, torch.xpu.get_device_name(device)) + elif is_ascend_npu(): + return "{} {}".format(device, torch.npu.get_device_name(device)) + elif is_mlu(): + return "{} {}".format(device, torch.mlu.get_device_name(device)) + else: + return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) + +try: + logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) +except: + logging.warning("Could not pick default device.") + + +current_loaded_models = [] + +def module_size(module): + module_mem = 0 + sd = module.state_dict() + for k in sd: + t = sd[k] + module_mem += t.nelement() * t.element_size() + return module_mem + +class LoadedModel: + def __init__(self, model): + self._set_model(model) + self.device = model.load_device + self.real_model = None + self.currently_used = True + self.model_finalizer = None + self._patcher_finalizer = None + + def _set_model(self, model): + self._model = weakref.ref(model) + if model.parent is not None: + self._parent_model = weakref.ref(model.parent) + self._patcher_finalizer = weakref.finalize(model, self._switch_parent) + + def _switch_parent(self): + model = self._parent_model() + if model is not None: + self._set_model(model) + + @property + def model(self): + return self._model() + + def model_memory(self): + return self.model.model_size() + + def model_loaded_memory(self): + return self.model.loaded_size() + + def model_offloaded_memory(self): + return self.model.model_size() - self.model.loaded_size() + + def model_memory_required(self, device): + if device == self.model.current_loaded_device(): + return self.model_offloaded_memory() + else: + return self.model_memory() + + def model_load(self, lowvram_model_memory=0, force_patch_weights=False): + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) + + # if self.model.loaded_size() > 0: + use_more_vram = lowvram_model_memory + if use_more_vram == 0: + use_more_vram = 1e32 + self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) + real_model = self.model.model + + if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None: + with torch.no_grad(): + real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True) + + self.real_model = weakref.ref(real_model) + self.model_finalizer = weakref.finalize(real_model, cleanup_models) + return real_model + + def should_reload_model(self, force_patch_weights=False): + if force_patch_weights and self.model.lowvram_patch_counter() > 0: + return True + return False + + def model_unload(self, memory_to_free=None, unpatch_weights=True): + if memory_to_free is not None: + if memory_to_free < self.model.loaded_size(): + freed = self.model.partially_unload(self.model.offload_device, memory_to_free) + if freed >= memory_to_free: + return False + self.model.detach(unpatch_weights) + self.model_finalizer.detach() + self.model_finalizer = None + self.real_model = None + return True + + def model_use_more_vram(self, extra_memory, force_patch_weights=False): + return self.model.partially_load(self.device, extra_memory, force_patch_weights=force_patch_weights) + + def __eq__(self, other): + return self.model is other.model + + def __del__(self): + if self._patcher_finalizer is not None: + self._patcher_finalizer.detach() + + def is_dead(self): + return self.real_model() is not None and self.model is None + + +def use_more_memory(extra_memory, loaded_models, device): + for m in loaded_models: + if m.device == device: + extra_memory -= m.model_use_more_vram(extra_memory) + if extra_memory <= 0: + break + +def offloaded_memory(loaded_models, device): + offloaded_mem = 0 + for m in loaded_models: + if m.device == device: + offloaded_mem += m.model_offloaded_memory() + return offloaded_mem + +WINDOWS = any(platform.win32_ver()) + +EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 +if WINDOWS: + EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue + +if args.reserve_vram is not None: + EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024 + logging.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024))) + +def extra_reserved_memory(): + return EXTRA_RESERVED_VRAM + +def minimum_inference_memory(): + return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory() + +def free_memory(memory_required, device, keep_loaded=[]): + cleanup_models_gc() + unloaded_model = [] + can_unload = [] + unloaded_models = [] + + for i in range(len(current_loaded_models) -1, -1, -1): + shift_model = current_loaded_models[i] + if shift_model.device == device: + if shift_model not in keep_loaded and not shift_model.is_dead(): + can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) + shift_model.currently_used = False + + for x in sorted(can_unload): + i = x[-1] + memory_to_free = None + if not DISABLE_SMART_MEMORY: + free_mem = get_free_memory(device) + if free_mem > memory_required: + break + memory_to_free = memory_required - free_mem + logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}") + if current_loaded_models[i].model_unload(memory_to_free): + unloaded_model.append(i) + + for i in sorted(unloaded_model, reverse=True): + unloaded_models.append(current_loaded_models.pop(i)) + + if len(unloaded_model) > 0: + soft_empty_cache() + else: + if vram_state != VRAMState.HIGH_VRAM: + mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) + if mem_free_torch > mem_free_total * 0.25: + soft_empty_cache() + return unloaded_models + +def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False): + cleanup_models_gc() + global vram_state + + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required + extra_reserved_memory()) + if minimum_memory_required is None: + minimum_memory_required = extra_mem + else: + minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory()) + + models = set(models) + + models_to_load = [] + + for x in models: + loaded_model = LoadedModel(x) + try: + loaded_model_index = current_loaded_models.index(loaded_model) + except: + loaded_model_index = None + + if loaded_model_index is not None: + loaded = current_loaded_models[loaded_model_index] + loaded.currently_used = True + models_to_load.append(loaded) + else: + if hasattr(x, "model"): + logging.info(f"Requested to load {x.model.__class__.__name__}") + models_to_load.append(loaded_model) + + for loaded_model in models_to_load: + to_unload = [] + for i in range(len(current_loaded_models)): + if loaded_model.model.is_clone(current_loaded_models[i].model): + to_unload = [i] + to_unload + for i in to_unload: + current_loaded_models.pop(i).model.detach(unpatch_all=False) + + total_memory_required = {} + for loaded_model in models_to_load: + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory(total_memory_required[device] * 1.1 + extra_mem, device) + + for device in total_memory_required: + if device != torch.device("cpu"): + free_mem = get_free_memory(device) + if free_mem < minimum_memory_required: + models_l = free_memory(minimum_memory_required, device) + logging.info("{} models unloaded.".format(len(models_l))) + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load: + loaded_memory = loaded_model.model_loaded_memory() + current_free_mem = get_free_memory(torch_dev) + loaded_memory + + lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) + lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory) + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 0.1 + + loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) + current_loaded_models.insert(0, loaded_model) + return + +def load_model_gpu(model): + return load_models_gpu([model]) + +def loaded_models(only_currently_used=False): + output = [] + for m in current_loaded_models: + if only_currently_used: + if not m.currently_used: + continue + + output.append(m.model) + return output + + +def cleanup_models_gc(): + do_gc = False + for i in range(len(current_loaded_models)): + cur = current_loaded_models[i] + if cur.is_dead(): + logging.info("Potential memory leak detected with model {}, doing a full garbage collect, for maximum performance avoid circular references in the model code.".format(cur.real_model().__class__.__name__)) + do_gc = True + break + + if do_gc: + gc.collect() + soft_empty_cache() + + for i in range(len(current_loaded_models)): + cur = current_loaded_models[i] + if cur.is_dead(): + logging.warning("WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.".format(cur.real_model().__class__.__name__)) + + + +def cleanup_models(): + to_delete = [] + for i in range(len(current_loaded_models)): + if current_loaded_models[i].real_model() is None: + to_delete = [i] + to_delete + + for i in to_delete: + x = current_loaded_models.pop(i) + del x + +def dtype_size(dtype): + dtype_size = 4 + if dtype == torch.float16 or dtype == torch.bfloat16: + dtype_size = 2 + elif dtype == torch.float32: + dtype_size = 4 + else: + try: + dtype_size = dtype.itemsize + except: #Old pytorch doesn't have .itemsize + pass + return dtype_size + +def unet_offload_device(): + if vram_state == VRAMState.HIGH_VRAM: + return get_torch_device() + else: + return torch.device("cpu") + +def unet_inital_load_device(parameters, dtype): + torch_dev = get_torch_device() + if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED: + return torch_dev + + cpu_dev = torch.device("cpu") + if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM: + return cpu_dev + + model_size = dtype_size(dtype) * parameters + + mem_dev = get_free_memory(torch_dev) + mem_cpu = get_free_memory(cpu_dev) + if mem_dev > mem_cpu and model_size < mem_dev: + return torch_dev + else: + return cpu_dev + +def maximum_vram_for_weights(device=None): + return (get_total_memory(device) * 0.88 - minimum_inference_memory()) + +def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None): + if model_params < 0: + model_params = 1000000000000000000000 + if args.fp32_unet: + return torch.float32 + if args.fp64_unet: + return torch.float64 + if args.bf16_unet: + return torch.bfloat16 + if args.fp16_unet: + return torch.float16 + if args.fp8_e4m3fn_unet: + return torch.float8_e4m3fn + if args.fp8_e5m2_unet: + return torch.float8_e5m2 + if args.fp8_e8m0fnu_unet: + return torch.float8_e8m0fnu + + fp8_dtype = None + if weight_dtype in FLOAT8_TYPES: + fp8_dtype = weight_dtype + + if fp8_dtype is not None: + if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive + return fp8_dtype + + free_model_memory = maximum_vram_for_weights(device) + if model_params * 2 > free_model_memory: + return fp8_dtype + + if PRIORITIZE_FP16 or weight_dtype == torch.float16: + if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params): + return torch.float16 + + for dt in supported_dtypes: + if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params): + if torch.float16 in supported_dtypes: + return torch.float16 + if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params): + if torch.bfloat16 in supported_dtypes: + return torch.bfloat16 + + for dt in supported_dtypes: + if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True): + if torch.float16 in supported_dtypes: + return torch.float16 + if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True): + if torch.bfloat16 in supported_dtypes: + return torch.bfloat16 + + return torch.float32 + +# None means no manual cast +def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): + if weight_dtype == torch.float32 or weight_dtype == torch.float64: + return None + + fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) + if fp16_supported and weight_dtype == torch.float16: + return None + + bf16_supported = should_use_bf16(inference_device) + if bf16_supported and weight_dtype == torch.bfloat16: + return None + + fp16_supported = should_use_fp16(inference_device, prioritize_performance=True) + if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes: + return torch.float16 + + for dt in supported_dtypes: + if dt == torch.float16 and fp16_supported: + return torch.float16 + if dt == torch.bfloat16 and bf16_supported: + return torch.bfloat16 + + return torch.float32 + +def text_encoder_offload_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def text_encoder_device(): + if args.gpu_only: + return get_torch_device() + elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: + if should_use_fp16(prioritize_performance=False): + return get_torch_device() + else: + return torch.device("cpu") + else: + return torch.device("cpu") + +def text_encoder_initial_device(load_device, offload_device, model_size=0): + if load_device == offload_device or model_size <= 1024 * 1024 * 1024: + return offload_device + + if is_device_mps(load_device): + return load_device + + mem_l = get_free_memory(load_device) + mem_o = get_free_memory(offload_device) + if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l: + return load_device + else: + return offload_device + +def text_encoder_dtype(device=None): + if args.fp8_e4m3fn_text_enc: + return torch.float8_e4m3fn + elif args.fp8_e5m2_text_enc: + return torch.float8_e5m2 + elif args.fp16_text_enc: + return torch.float16 + elif args.bf16_text_enc: + return torch.bfloat16 + elif args.fp32_text_enc: + return torch.float32 + + if is_device_cpu(device): + return torch.float16 + + return torch.float16 + + +def intermediate_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_device(): + if args.cpu_vae: + return torch.device("cpu") + return get_torch_device() + +def vae_offload_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_dtype(device=None, allowed_dtypes=[]): + if args.fp16_vae: + return torch.float16 + elif args.bf16_vae: + return torch.bfloat16 + elif args.fp32_vae: + return torch.float32 + + for d in allowed_dtypes: + if d == torch.float16 and should_use_fp16(device): + return d + + # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32 + if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device): + return d + + return torch.float32 + +def get_autocast_device(dev): + if hasattr(dev, 'type'): + return dev.type + return "cuda" + +def supports_dtype(device, dtype): #TODO + if dtype == torch.float32: + return True + if is_device_cpu(device): + return False + if dtype == torch.float16: + return True + if dtype == torch.bfloat16: + return True + return False + +def supports_cast(device, dtype): #TODO + if dtype == torch.float32: + return True + if dtype == torch.float16: + return True + if directml_enabled: #TODO: test this + return False + if dtype == torch.bfloat16: + return True + if is_device_mps(device): + return False + if dtype == torch.float8_e4m3fn: + return True + if dtype == torch.float8_e5m2: + return True + return False + +def pick_weight_dtype(dtype, fallback_dtype, device=None): + if dtype is None: + dtype = fallback_dtype + elif dtype_size(dtype) > dtype_size(fallback_dtype): + dtype = fallback_dtype + + if not supports_cast(device, dtype): + dtype = fallback_dtype + + return dtype + +def device_supports_non_blocking(device): + if is_device_mps(device): + return False #pytorch bug? mps doesn't support non blocking + if is_intel_xpu(): + return False + if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) + return False + if directml_enabled: + return False + return True + +def device_should_use_non_blocking(device): + if not device_supports_non_blocking(device): + return False + return False + # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others + +def force_channels_last(): + if args.force_channels_last: + return True + + #TODO + return False + + +STREAMS = {} +NUM_STREAMS = 1 +if args.async_offload: + NUM_STREAMS = 2 + logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) + +stream_counters = {} +def get_offload_stream(device): + stream_counter = stream_counters.get(device, 0) + if NUM_STREAMS <= 1: + return None + + if device in STREAMS: + ss = STREAMS[device] + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + if is_device_cuda(device): + ss[stream_counter].wait_stream(torch.cuda.current_stream()) + stream_counters[device] = stream_counter + return s + elif is_device_cuda(device): + ss = [] + for k in range(NUM_STREAMS): + ss.append(torch.cuda.Stream(device=device, priority=0)) + STREAMS[device] = ss + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + stream_counters[device] = stream_counter + return s + return None + +def sync_stream(device, stream): + if stream is None: + return + if is_device_cuda(device): + torch.cuda.current_stream().wait_stream(stream) + +def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None): + if device is None or weight.device == device: + if not copy: + if dtype is None or weight.dtype == dtype: + return weight + if stream is not None: + with stream: + return weight.to(dtype=dtype, copy=copy) + return weight.to(dtype=dtype, copy=copy) + + if stream is not None: + with stream: + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) + else: + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) + return r + +def cast_to_device(tensor, device, dtype, copy=False): + non_blocking = device_supports_non_blocking(device) + return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy) + +def sage_attention_enabled(): + return args.use_sage_attention + +def flash_attention_enabled(): + return args.use_flash_attention + +def xformers_enabled(): + global directml_enabled + global cpu_state + if cpu_state != CPUState.GPU: + return False + if is_intel_xpu(): + return False + if is_ascend_npu(): + return False + if is_mlu(): + return False + if directml_enabled: + return False + return XFORMERS_IS_AVAILABLE + + +def xformers_enabled_vae(): + enabled = xformers_enabled() + if not enabled: + return False + + return XFORMERS_ENABLED_VAE + +def pytorch_attention_enabled(): + global ENABLE_PYTORCH_ATTENTION + return ENABLE_PYTORCH_ATTENTION + +def pytorch_attention_enabled_vae(): + if is_amd(): + return False # enabling pytorch attention on AMD currently causes crash when doing high res + return pytorch_attention_enabled() + +def pytorch_attention_flash_attention(): + global ENABLE_PYTORCH_ATTENTION + if ENABLE_PYTORCH_ATTENTION: + #TODO: more reliable way of checking for flash attention? + if is_nvidia(): + return True + if is_intel_xpu(): + return True + if is_ascend_npu(): + return True + if is_mlu(): + return True + if is_amd(): + return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention + return False + +def force_upcast_attention_dtype(): + upcast = args.force_upcast_attention + + macos_version = mac_version() + if macos_version is not None and ((14, 5) <= macos_version): # black image bug on recent versions of macOS, I don't think it's ever getting fixed + upcast = True + + if upcast: + return {torch.float16: torch.float32} + else: + return None + +def get_free_memory(dev=None, torch_free_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_free_total = psutil.virtual_memory().available + mem_free_torch = mem_free_total + else: + if directml_enabled: + mem_free_total = 1024 * 1024 * 1024 #TODO + mem_free_torch = mem_free_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_torch = mem_reserved - mem_active + mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved + mem_free_total = mem_free_xpu + mem_free_torch + elif is_ascend_npu(): + stats = torch.npu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_npu, _ = torch.npu.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_npu + mem_free_torch + elif is_mlu(): + stats = torch.mlu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_mlu, _ = torch.mlu.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_mlu + mem_free_torch + else: + stats = torch.cuda.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + if torch_free_too: + return (mem_free_total, mem_free_torch) + else: + return mem_free_total + +def cpu_mode(): + global cpu_state + return cpu_state == CPUState.CPU + +def mps_mode(): + global cpu_state + return cpu_state == CPUState.MPS + +def is_device_type(device, type): + if hasattr(device, 'type'): + if (device.type == type): + return True + return False + +def is_device_cpu(device): + return is_device_type(device, 'cpu') + +def is_device_mps(device): + return is_device_type(device, 'mps') + +def is_device_cuda(device): + return is_device_type(device, 'cuda') + +def is_directml_enabled(): + global directml_enabled + if directml_enabled: + return True + + return False + +def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): + if device is not None: + if is_device_cpu(device): + return False + + if args.force_fp16: + return True + + if FORCE_FP32: + return False + + if is_directml_enabled(): + return True + + if (device is not None and is_device_mps(device)) or mps_mode(): + return True + + if cpu_mode(): + return False + + if is_intel_xpu(): + return True + + if is_ascend_npu(): + return True + + if is_mlu(): + return True + + if torch.version.hip: + return True + + props = torch.cuda.get_device_properties(device) + if props.major >= 8: + return True + + if props.major < 6: + return False + + #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32 + nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"] + for x in nvidia_10_series: + if x in props.name.lower(): + if WINDOWS or manual_cast: + return True + else: + return False #weird linux behavior where fp32 is faster + + if manual_cast: + free_model_memory = maximum_vram_for_weights(device) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + if props.major < 7: + return False + + #FP16 is just broken on these cards + nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] + for x in nvidia_16_series: + if x in props.name: + return False + + return True + +def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): + if device is not None: + if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow + return False + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if (device is not None and is_device_mps(device)) or mps_mode(): + if mac_version() < (14,): + return False + return True + + if cpu_mode(): + return False + + if is_intel_xpu(): + return True + + if is_ascend_npu(): + return True + + if is_amd(): + arch = torch.cuda.get_device_properties(device).gcnArchName + if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]): # RDNA2 and older don't support bf16 + if manual_cast: + return True + return False + + props = torch.cuda.get_device_properties(device) + + if is_mlu(): + if props.major > 3: + return True + + if props.major >= 8: + return True + + bf16_works = torch.cuda.is_bf16_supported() + + if bf16_works and manual_cast: + free_model_memory = maximum_vram_for_weights(device) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + return False + +def supports_fp8_compute(device=None): + if SUPPORT_FP8_OPS: + return True + + if not is_nvidia(): + return False + + props = torch.cuda.get_device_properties(device) + if props.major >= 9: + return True + if props.major < 8: + return False + if props.minor < 9: + return False + + if torch_version_numeric < (2, 3): + return False + + if WINDOWS: + if torch_version_numeric < (2, 4): + return False + + return True + +def extended_fp16_support(): + # TODO: check why some models work with fp16 on newer torch versions but not on older + if torch_version_numeric < (2, 7): + return False + + return True + +def soft_empty_cache(force=False): + global cpu_state + if cpu_state == CPUState.MPS: + torch.mps.empty_cache() + elif is_intel_xpu(): + torch.xpu.empty_cache() + elif is_ascend_npu(): + torch.npu.empty_cache() + elif is_mlu(): + torch.mlu.empty_cache() + elif torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + +def unload_all_models(): + free_memory(1e30, get_torch_device()) + + +#TODO: might be cleaner to put this somewhere else +import threading + +class InterruptProcessingException(Exception): + pass + +interrupt_processing_mutex = threading.RLock() + +interrupt_processing = False +def interrupt_current_processing(value=True): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + interrupt_processing = value + +def processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + return interrupt_processing + +def throw_exception_if_processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + if interrupt_processing: + interrupt_processing = False + raise InterruptProcessingException() diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py new file mode 100644 index 0000000000000000000000000000000000000000..b1d6d4395885ef5046102feff57f175c083d0f65 --- /dev/null +++ b/comfy/model_patcher.py @@ -0,0 +1,1212 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +from __future__ import annotations + +import collections +import copy +import inspect +import logging +import math +import uuid +from typing import Callable, Optional + +import torch + +import comfy.float +import comfy.hooks +import comfy.lora +import comfy.model_management +import comfy.patcher_extension +import comfy.utils +from comfy.comfy_types import UnetWrapperFunction +from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP + + +def string_to_seed(data): + crc = 0xFFFFFFFF + for byte in data: + if isinstance(byte, str): + byte = ord(byte) + crc ^= byte + for _ in range(8): + if crc & 1: + crc = (crc >> 1) ^ 0xEDB88320 + else: + crc >>= 1 + return crc ^ 0xFFFFFFFF + +def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): + to = model_options["transformer_options"].copy() + + if "patches_replace" not in to: + to["patches_replace"] = {} + else: + to["patches_replace"] = to["patches_replace"].copy() + + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + else: + to["patches_replace"][name] = to["patches_replace"][name].copy() + + if transformer_index is not None: + block = (block_name, number, transformer_index) + else: + block = (block_name, number) + to["patches_replace"][name][block] = patch + model_options["transformer_options"] = to + return model_options + +def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False): + model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] + if disable_cfg1_optimization: + model_options["disable_cfg1_optimization"] = True + return model_options + +def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False): + model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function] + if disable_cfg1_optimization: + model_options["disable_cfg1_optimization"] = True + return model_options + +def create_model_options_clone(orig_model_options: dict): + return comfy.patcher_extension.copy_nested_dicts(orig_model_options) + +def create_hook_patches_clone(orig_hook_patches): + new_hook_patches = {} + for hook_ref in orig_hook_patches: + new_hook_patches[hook_ref] = {} + for k in orig_hook_patches[hook_ref]: + new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:] + return new_hook_patches + +def wipe_lowvram_weight(m): + if hasattr(m, "prev_comfy_cast_weights"): + m.comfy_cast_weights = m.prev_comfy_cast_weights + del m.prev_comfy_cast_weights + + if hasattr(m, "weight_function"): + m.weight_function = [] + + if hasattr(m, "bias_function"): + m.bias_function = [] + +def move_weight_functions(m, device): + if device is None: + return 0 + + memory = 0 + if hasattr(m, "weight_function"): + for f in m.weight_function: + if hasattr(f, "move_to"): + memory += f.move_to(device=device) + + if hasattr(m, "bias_function"): + for f in m.bias_function: + if hasattr(f, "move_to"): + memory += f.move_to(device=device) + return memory + +class LowVramPatch: + def __init__(self, key, patches): + self.key = key + self.patches = patches + def __call__(self, weight): + intermediate_dtype = weight.dtype + if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops + intermediate_dtype = torch.float32 + return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key)) + + return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype) + +def get_key_weight(model, key): + set_func = None + convert_func = None + op_keys = key.rsplit('.', 1) + if len(op_keys) < 2: + weight = comfy.utils.get_attr(model, key) + else: + op = comfy.utils.get_attr(model, op_keys[0]) + try: + set_func = getattr(op, "set_{}".format(op_keys[1])) + except AttributeError: + pass + + try: + convert_func = getattr(op, "convert_{}".format(op_keys[1])) + except AttributeError: + pass + + weight = getattr(op, op_keys[1]) + if convert_func is not None: + weight = comfy.utils.get_attr(model, key) + + return weight, set_func, convert_func + +class AutoPatcherEjector: + def __init__(self, model: 'ModelPatcher', skip_and_inject_on_exit_only=False): + self.model = model + self.was_injected = False + self.prev_skip_injection = False + self.skip_and_inject_on_exit_only = skip_and_inject_on_exit_only + + def __enter__(self): + self.was_injected = False + self.prev_skip_injection = self.model.skip_injection + if self.skip_and_inject_on_exit_only: + self.model.skip_injection = True + if self.model.is_injected: + self.model.eject_model() + self.was_injected = True + + def __exit__(self, *args): + if self.skip_and_inject_on_exit_only: + self.model.skip_injection = self.prev_skip_injection + self.model.inject_model() + if self.was_injected and not self.model.skip_injection: + self.model.inject_model() + self.model.skip_injection = self.prev_skip_injection + +class MemoryCounter: + def __init__(self, initial: int, minimum=0): + self.value = initial + self.minimum = minimum + # TODO: add a safe limit besides 0 + + def use(self, weight: torch.Tensor): + weight_size = weight.nelement() * weight.element_size() + if self.is_useable(weight_size): + self.decrement(weight_size) + return True + return False + + def is_useable(self, used: int): + return self.value - used > self.minimum + + def decrement(self, used: int): + self.value -= used + +class ModelPatcher: + def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): + self.size = size + self.model = model + if not hasattr(self.model, 'device'): + logging.debug("Model doesn't have a device attribute.") + self.model.device = offload_device + elif self.model.device is None: + self.model.device = offload_device + + self.patches = {} + self.backup = {} + self.object_patches = {} + self.object_patches_backup = {} + self.weight_wrapper_patches = {} + self.model_options = {"transformer_options":{}} + self.model_size() + self.load_device = load_device + self.offload_device = offload_device + self.weight_inplace_update = weight_inplace_update + self.force_cast_weights = False + self.patches_uuid = uuid.uuid4() + self.parent = None + + self.attachments: dict[str] = {} + self.additional_models: dict[str, list[ModelPatcher]] = {} + self.callbacks: dict[str, dict[str, list[Callable]]] = CallbacksMP.init_callbacks() + self.wrappers: dict[str, dict[str, list[Callable]]] = WrappersMP.init_wrappers() + + self.is_injected = False + self.skip_injection = False + self.injections: dict[str, list[PatcherInjection]] = {} + + self.hook_patches: dict[comfy.hooks._HookRef] = {} + self.hook_patches_backup: dict[comfy.hooks._HookRef] = None + self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {} + self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {} + self.current_hooks: Optional[comfy.hooks.HookGroup] = None + self.forced_hooks: Optional[comfy.hooks.HookGroup] = None # NOTE: only used for CLIP at this time + self.is_clip = False + self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed + + if not hasattr(self.model, 'model_loaded_weight_memory'): + self.model.model_loaded_weight_memory = 0 + + if not hasattr(self.model, 'lowvram_patch_counter'): + self.model.lowvram_patch_counter = 0 + + if not hasattr(self.model, 'model_lowvram'): + self.model.model_lowvram = False + + if not hasattr(self.model, 'current_weight_patches_uuid'): + self.model.current_weight_patches_uuid = None + + def model_size(self): + if self.size > 0: + return self.size + self.size = comfy.model_management.module_size(self.model) + return self.size + + def loaded_size(self): + return self.model.model_loaded_weight_memory + + def lowvram_patch_counter(self): + return self.model.lowvram_patch_counter + + def clone(self): + n = self.__class__(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + n.patches_uuid = self.patches_uuid + + n.object_patches = self.object_patches.copy() + n.weight_wrapper_patches = self.weight_wrapper_patches.copy() + n.model_options = copy.deepcopy(self.model_options) + n.backup = self.backup + n.object_patches_backup = self.object_patches_backup + n.parent = self + + n.force_cast_weights = self.force_cast_weights + + # attachments + n.attachments = {} + for k in self.attachments: + if hasattr(self.attachments[k], "on_model_patcher_clone"): + n.attachments[k] = self.attachments[k].on_model_patcher_clone() + else: + n.attachments[k] = self.attachments[k] + # additional models + for k, c in self.additional_models.items(): + n.additional_models[k] = [x.clone() for x in c] + # callbacks + for k, c in self.callbacks.items(): + n.callbacks[k] = {} + for k1, c1 in c.items(): + n.callbacks[k][k1] = c1.copy() + # sample wrappers + for k, w in self.wrappers.items(): + n.wrappers[k] = {} + for k1, w1 in w.items(): + n.wrappers[k][k1] = w1.copy() + # injection + n.is_injected = self.is_injected + n.skip_injection = self.skip_injection + for k, i in self.injections.items(): + n.injections[k] = i.copy() + # hooks + n.hook_patches = create_hook_patches_clone(self.hook_patches) + n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup + for group in self.cached_hook_patches: + n.cached_hook_patches[group] = {} + for k in self.cached_hook_patches[group]: + n.cached_hook_patches[group][k] = self.cached_hook_patches[group][k] + n.hook_backup = self.hook_backup + n.current_hooks = self.current_hooks.clone() if self.current_hooks else self.current_hooks + n.forced_hooks = self.forced_hooks.clone() if self.forced_hooks else self.forced_hooks + n.is_clip = self.is_clip + n.hook_mode = self.hook_mode + + for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE): + callback(self, n) + return n + + def is_clone(self, other): + if hasattr(other, 'model') and self.model is other.model: + return True + return False + + def clone_has_same_weights(self, clone: 'ModelPatcher'): + if not self.is_clone(clone): + return False + + if self.current_hooks != clone.current_hooks: + return False + if self.forced_hooks != clone.forced_hooks: + return False + if self.hook_patches.keys() != clone.hook_patches.keys(): + return False + if self.attachments.keys() != clone.attachments.keys(): + return False + if self.additional_models.keys() != clone.additional_models.keys(): + return False + for key in self.callbacks: + if len(self.callbacks[key]) != len(clone.callbacks[key]): + return False + for key in self.wrappers: + if len(self.wrappers[key]) != len(clone.wrappers[key]): + return False + if self.injections.keys() != clone.injections.keys(): + return False + + if len(self.patches) == 0 and len(clone.patches) == 0: + return True + + if self.patches_uuid == clone.patches_uuid: + if len(self.patches) != len(clone.patches): + logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.") + else: + return True + + def memory_required(self, input_shape): + return self.model.memory_required(input_shape=input_shape) + + def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): + if len(inspect.signature(sampler_cfg_function).parameters) == 3: + self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way + else: + self.model_options["sampler_cfg_function"] = sampler_cfg_function + if disable_cfg1_optimization: + self.model_options["disable_cfg1_optimization"] = True + + def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): + self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization) + + def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False): + self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization) + + def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): + self.model_options["model_function_wrapper"] = unet_wrapper_function + + def set_model_denoise_mask_function(self, denoise_mask_function): + self.model_options["denoise_mask_function"] = denoise_mask_function + + def set_model_patch(self, patch, name): + to = self.model_options["transformer_options"] + if "patches" not in to: + to["patches"] = {} + to["patches"][name] = to["patches"].get(name, []) + [patch] + + def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): + self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index) + + def set_model_attn1_patch(self, patch): + self.set_model_patch(patch, "attn1_patch") + + def set_model_attn2_patch(self, patch): + self.set_model_patch(patch, "attn2_patch") + + def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) + + def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) + + def set_model_attn1_output_patch(self, patch): + self.set_model_patch(patch, "attn1_output_patch") + + def set_model_attn2_output_patch(self, patch): + self.set_model_patch(patch, "attn2_output_patch") + + def set_model_input_block_patch(self, patch): + self.set_model_patch(patch, "input_block_patch") + + def set_model_input_block_patch_after_skip(self, patch): + self.set_model_patch(patch, "input_block_patch_after_skip") + + def set_model_output_block_patch(self, patch): + self.set_model_patch(patch, "output_block_patch") + + def set_model_emb_patch(self, patch): + self.set_model_patch(patch, "emb_patch") + + def set_model_forward_timestep_embed_patch(self, patch): + self.set_model_patch(patch, "forward_timestep_embed_patch") + + def add_object_patch(self, name, obj): + self.object_patches[name] = obj + + def set_model_compute_dtype(self, dtype): + self.add_object_patch("manual_cast_dtype", dtype) + if dtype is not None: + self.force_cast_weights = True + self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this + + def add_weight_wrapper(self, name, function): + self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function] + self.patches_uuid = uuid.uuid4() + + def get_model_object(self, name: str) -> torch.nn.Module: + """Retrieves a nested attribute from an object using dot notation considering + object patches. + + Args: + name (str): The attribute path using dot notation (e.g. "model.layer.weight") + + Returns: + The value of the requested attribute + + Example: + patcher = ModelPatcher() + weight = patcher.get_model_object("layer1.conv.weight") + """ + if name in self.object_patches: + return self.object_patches[name] + else: + if name in self.object_patches_backup: + return self.object_patches_backup[name] + else: + return comfy.utils.get_attr(self.model, name) + + def model_patches_to(self, device): + to = self.model_options["transformer_options"] + if "patches" in to: + patches = to["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + patch_list[i] = patch_list[i].to(device) + if "patches_replace" in to: + patches = to["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + patch_list[k] = patch_list[k].to(device) + if "model_function_wrapper" in self.model_options: + wrap_func = self.model_options["model_function_wrapper"] + if hasattr(wrap_func, "to"): + self.model_options["model_function_wrapper"] = wrap_func.to(device) + + def model_dtype(self): + if hasattr(self.model, "get_dtype"): + return self.model.get_dtype() + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + with self.use_ejected(): + p = set() + model_sd = self.model.state_dict() + for k in patches: + offset = None + function = None + if isinstance(k, str): + key = k + else: + offset = k[1] + key = k[0] + if len(k) > 2: + function = k[2] + + if key in model_sd: + p.add(k) + current_patches = self.patches.get(key, []) + current_patches.append((strength_patch, patches[k], strength_model, offset, function)) + self.patches[key] = current_patches + + self.patches_uuid = uuid.uuid4() + return list(p) + + def get_key_patches(self, filter_prefix=None): + model_sd = self.model_state_dict() + p = {} + for k in model_sd: + if filter_prefix is not None: + if not k.startswith(filter_prefix): + continue + bk = self.backup.get(k, None) + hbk = self.hook_backup.get(k, None) + weight, set_func, convert_func = get_key_weight(self.model, k) + if bk is not None: + weight = bk.weight + if hbk is not None: + weight = hbk[0] + if convert_func is None: + convert_func = lambda a, **kwargs: a + + if k in self.patches: + p[k] = [(weight, convert_func)] + self.patches[k] + else: + p[k] = [(weight, convert_func)] + return p + + def model_state_dict(self, filter_prefix=None): + with self.use_ejected(): + sd = self.model.state_dict() + keys = list(sd.keys()) + if filter_prefix is not None: + for k in keys: + if not k.startswith(filter_prefix): + sd.pop(k) + return sd + + def patch_weight_to_device(self, key, device_to=None, inplace_update=False): + if key not in self.patches: + return + + weight, set_func, convert_func = get_key_weight(self.model, key) + inplace_update = self.weight_inplace_update or inplace_update + + if key not in self.backup: + self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) + + if device_to is not None: + temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) + else: + temp_weight = weight.to(torch.float32, copy=True) + if convert_func is not None: + temp_weight = convert_func(temp_weight, inplace=True) + + out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) + if set_func is None: + out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key)) + if inplace_update: + comfy.utils.copy_to_param(self.model, key, out_weight) + else: + comfy.utils.set_attr_param(self.model, key, out_weight) + else: + set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key)) + + def _load_list(self): + loading = [] + for n, m in self.model.named_modules(): + params = [] + skip = False + for name, param in m.named_parameters(recurse=False): + params.append(name) + for name, param in m.named_parameters(recurse=True): + if name not in params: + skip = True # skip random weights in non leaf modules + break + if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0): + loading.append((comfy.model_management.module_size(m), n, m, params)) + return loading + + def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): + with self.use_ejected(): + self.unpatch_hooks() + mem_counter = 0 + patch_counter = 0 + lowvram_counter = 0 + loading = self._load_list() + + load_completely = [] + loading.sort(reverse=True) + for x in loading: + n = x[1] + m = x[2] + params = x[3] + module_mem = x[0] + + lowvram_weight = False + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + + if not full_load and hasattr(m, "comfy_cast_weights"): + if mem_counter + module_mem >= lowvram_model_memory: + lowvram_weight = True + lowvram_counter += 1 + if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed + continue + + cast_weight = self.force_cast_weights + if lowvram_weight: + if hasattr(m, "comfy_cast_weights"): + m.weight_function = [] + m.bias_function = [] + + if weight_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(weight_key) + else: + m.weight_function = [LowVramPatch(weight_key, self.patches)] + patch_counter += 1 + if bias_key in self.patches: + if force_patch_weights: + self.patch_weight_to_device(bias_key) + else: + m.bias_function = [LowVramPatch(bias_key, self.patches)] + patch_counter += 1 + + cast_weight = True + else: + if hasattr(m, "comfy_cast_weights"): + wipe_lowvram_weight(m) + + if full_load or mem_counter + module_mem < lowvram_model_memory: + mem_counter += module_mem + load_completely.append((module_mem, n, m, params)) + + if cast_weight and hasattr(m, "comfy_cast_weights"): + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + + if weight_key in self.weight_wrapper_patches: + m.weight_function.extend(self.weight_wrapper_patches[weight_key]) + + if bias_key in self.weight_wrapper_patches: + m.bias_function.extend(self.weight_wrapper_patches[bias_key]) + + mem_counter += move_weight_functions(m, device_to) + + load_completely.sort(reverse=True) + for x in load_completely: + n = x[1] + m = x[2] + params = x[3] + if hasattr(m, "comfy_patched_weights"): + if m.comfy_patched_weights == True: + continue + + for param in params: + self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to) + + logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) + m.comfy_patched_weights = True + + for x in load_completely: + x[2].to(device_to) + + if lowvram_counter > 0: + logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter)) + self.model.model_lowvram = True + else: + logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) + self.model.model_lowvram = False + if full_load: + self.model.to(device_to) + mem_counter = self.model_size() + + self.model.lowvram_patch_counter += patch_counter + self.model.device = device_to + self.model.model_loaded_weight_memory = mem_counter + self.model.current_weight_patches_uuid = self.patches_uuid + + for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD): + callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load) + + self.apply_hooks(self.forced_hooks, force_apply=True) + + def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): + with self.use_ejected(): + for k in self.object_patches: + old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) + if k not in self.object_patches_backup: + self.object_patches_backup[k] = old + + if lowvram_model_memory == 0: + full_load = True + else: + full_load = False + + if load_weights: + self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load) + self.inject_model() + return self.model + + def unpatch_model(self, device_to=None, unpatch_weights=True): + self.eject_model() + if unpatch_weights: + self.unpatch_hooks() + if self.model.model_lowvram: + for m in self.model.modules(): + move_weight_functions(m, device_to) + wipe_lowvram_weight(m) + + self.model.model_lowvram = False + self.model.lowvram_patch_counter = 0 + + keys = list(self.backup.keys()) + + for k in keys: + bk = self.backup[k] + if bk.inplace_update: + comfy.utils.copy_to_param(self.model, k, bk.weight) + else: + comfy.utils.set_attr_param(self.model, k, bk.weight) + + self.model.current_weight_patches_uuid = None + self.backup.clear() + + if device_to is not None: + self.model.to(device_to) + self.model.device = device_to + self.model.model_loaded_weight_memory = 0 + + for m in self.model.modules(): + if hasattr(m, "comfy_patched_weights"): + del m.comfy_patched_weights + + keys = list(self.object_patches_backup.keys()) + for k in keys: + comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) + + self.object_patches_backup.clear() + + def partially_unload(self, device_to, memory_to_free=0): + with self.use_ejected(): + hooks_unpatched = False + memory_freed = 0 + patch_counter = 0 + unload_list = self._load_list() + unload_list.sort() + for unload in unload_list: + if memory_to_free < memory_freed: + break + module_mem = unload[0] + n = unload[1] + m = unload[2] + params = unload[3] + + lowvram_possible = hasattr(m, "comfy_cast_weights") + if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True: + move_weight = True + for param in params: + key = "{}.{}".format(n, param) + bk = self.backup.get(key, None) + if bk is not None: + if not lowvram_possible: + move_weight = False + break + + if not hooks_unpatched: + self.unpatch_hooks() + hooks_unpatched = True + + if bk.inplace_update: + comfy.utils.copy_to_param(self.model, key, bk.weight) + else: + comfy.utils.set_attr_param(self.model, key, bk.weight) + self.backup.pop(key) + + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + if move_weight: + cast_weight = self.force_cast_weights + m.to(device_to) + module_mem += move_weight_functions(m, device_to) + if lowvram_possible: + if weight_key in self.patches: + m.weight_function.append(LowVramPatch(weight_key, self.patches)) + patch_counter += 1 + if bias_key in self.patches: + m.bias_function.append(LowVramPatch(bias_key, self.patches)) + patch_counter += 1 + cast_weight = True + + if cast_weight: + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + m.comfy_patched_weights = False + memory_freed += module_mem + logging.debug("freed {}".format(n)) + + self.model.model_lowvram = True + self.model.lowvram_patch_counter += patch_counter + self.model.model_loaded_weight_memory -= memory_freed + return memory_freed + + def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): + with self.use_ejected(skip_and_inject_on_exit_only=True): + unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights) + # TODO: force_patch_weights should not unload + reload full model + used = self.model.model_loaded_weight_memory + self.unpatch_model(self.offload_device, unpatch_weights=unpatch_weights) + if unpatch_weights: + extra_memory += (used - self.model.model_loaded_weight_memory) + + self.patch_model(load_weights=False) + full_load = False + if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0: + self.apply_hooks(self.forced_hooks, force_apply=True) + return 0 + if self.model.model_loaded_weight_memory + extra_memory > self.model_size(): + full_load = True + current_used = self.model.model_loaded_weight_memory + try: + self.load(device_to, lowvram_model_memory=current_used + extra_memory, force_patch_weights=force_patch_weights, full_load=full_load) + except Exception as e: + self.detach() + raise e + + return self.model.model_loaded_weight_memory - current_used + + def detach(self, unpatch_all=True): + self.eject_model() + self.model_patches_to(self.offload_device) + if unpatch_all: + self.unpatch_model(self.offload_device, unpatch_weights=unpatch_all) + for callback in self.get_all_callbacks(CallbacksMP.ON_DETACH): + callback(self, unpatch_all) + return self.model + + def current_loaded_device(self): + return self.model.device + + def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32): + logging.warning("The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead") + return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype) + + def cleanup(self): + self.clean_hooks() + if hasattr(self.model, "current_patcher"): + self.model.current_patcher = None + for callback in self.get_all_callbacks(CallbacksMP.ON_CLEANUP): + callback(self) + + def add_callback(self, call_type: str, callback: Callable): + self.add_callback_with_key(call_type, None, callback) + + def add_callback_with_key(self, call_type: str, key: str, callback: Callable): + c = self.callbacks.setdefault(call_type, {}).setdefault(key, []) + c.append(callback) + + def remove_callbacks_with_key(self, call_type: str, key: str): + c = self.callbacks.get(call_type, {}) + if key in c: + c.pop(key) + + def get_callbacks(self, call_type: str, key: str): + return self.callbacks.get(call_type, {}).get(key, []) + + def get_all_callbacks(self, call_type: str): + c_list = [] + for c in self.callbacks.get(call_type, {}).values(): + c_list.extend(c) + return c_list + + def add_wrapper(self, wrapper_type: str, wrapper: Callable): + self.add_wrapper_with_key(wrapper_type, None, wrapper) + + def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable): + w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, []) + w.append(wrapper) + + def remove_wrappers_with_key(self, wrapper_type: str, key: str): + w = self.wrappers.get(wrapper_type, {}) + if key in w: + w.pop(key) + + def get_wrappers(self, wrapper_type: str, key: str): + return self.wrappers.get(wrapper_type, {}).get(key, []) + + def get_all_wrappers(self, wrapper_type: str): + w_list = [] + for w in self.wrappers.get(wrapper_type, {}).values(): + w_list.extend(w) + return w_list + + def set_attachments(self, key: str, attachment): + self.attachments[key] = attachment + + def remove_attachments(self, key: str): + if key in self.attachments: + self.attachments.pop(key) + + def get_attachment(self, key: str): + return self.attachments.get(key, None) + + def set_injections(self, key: str, injections: list[PatcherInjection]): + self.injections[key] = injections + + def remove_injections(self, key: str): + if key in self.injections: + self.injections.pop(key) + + def get_injections(self, key: str): + return self.injections.get(key, None) + + def set_additional_models(self, key: str, models: list['ModelPatcher']): + self.additional_models[key] = models + + def remove_additional_models(self, key: str): + if key in self.additional_models: + self.additional_models.pop(key) + + def get_additional_models_with_key(self, key: str): + return self.additional_models.get(key, []) + + def get_additional_models(self): + all_models = [] + for models in self.additional_models.values(): + all_models.extend(models) + return all_models + + def get_nested_additional_models(self): + def _evaluate_sub_additional_models(prev_models: list[ModelPatcher], cache_set: set[ModelPatcher]): + '''Make sure circular references do not cause infinite recursion.''' + next_models = [] + for model in prev_models: + candidates = model.get_additional_models() + for c in candidates: + if c not in cache_set: + next_models.append(c) + cache_set.add(c) + if len(next_models) == 0: + return prev_models + return prev_models + _evaluate_sub_additional_models(next_models, cache_set) + + all_models = self.get_additional_models() + models_set = set(all_models) + real_all_models = _evaluate_sub_additional_models(prev_models=all_models, cache_set=models_set) + return real_all_models + + def use_ejected(self, skip_and_inject_on_exit_only=False): + return AutoPatcherEjector(self, skip_and_inject_on_exit_only=skip_and_inject_on_exit_only) + + def inject_model(self): + if self.is_injected or self.skip_injection: + return + for injections in self.injections.values(): + for inj in injections: + inj.inject(self) + self.is_injected = True + if self.is_injected: + for callback in self.get_all_callbacks(CallbacksMP.ON_INJECT_MODEL): + callback(self) + + def eject_model(self): + if not self.is_injected: + return + for injections in self.injections.values(): + for inj in injections: + inj.eject(self) + self.is_injected = False + for callback in self.get_all_callbacks(CallbacksMP.ON_EJECT_MODEL): + callback(self) + + def pre_run(self): + if hasattr(self.model, "current_patcher"): + self.model.current_patcher = self + for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN): + callback(self) + + def prepare_state(self, timestep): + for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE): + callback(self, timestep) + + def restore_hook_patches(self): + if self.hook_patches_backup is not None: + self.hook_patches = self.hook_patches_backup + self.hook_patches_backup = None + + def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode): + self.hook_mode = hook_mode + + def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]): + curr_t = t[0] + reset_current_hooks = False + transformer_options = model_options.get("transformer_options", {}) + for hook in hook_group.hooks: + changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options) + # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref; + # this will cause the weights to be recalculated when sampling + if changed: + # reset current_hooks if contains hook that changed + if self.current_hooks is not None: + for current_hook in self.current_hooks.hooks: + if current_hook == hook: + reset_current_hooks = True + break + for cached_group in list(self.cached_hook_patches.keys()): + if cached_group.contains(hook): + self.cached_hook_patches.pop(cached_group) + if reset_current_hooks: + self.patch_hooks(None) + + def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None, + registered: comfy.hooks.HookGroup = None): + self.restore_hook_patches() + if registered is None: + registered = comfy.hooks.HookGroup() + # handle WeightHooks + weight_hooks_to_register: list[comfy.hooks.WeightHook] = [] + for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight): + if hook.hook_ref not in self.hook_patches: + weight_hooks_to_register.append(hook) + else: + registered.add(hook) + if len(weight_hooks_to_register) > 0: + # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state + self.hook_patches_backup = create_hook_patches_clone(self.hook_patches) + for hook in weight_hooks_to_register: + hook.add_hook_patches(self, model_options, target_dict, registered) + for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES): + callback(self, hooks, target_dict, model_options, registered) + return registered + + def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0): + with self.use_ejected(): + # NOTE: this mirrors behavior of add_patches func + current_hook_patches: dict[str,list] = self.hook_patches.get(hook.hook_ref, {}) + p = set() + model_sd = self.model.state_dict() + for k in patches: + offset = None + function = None + if isinstance(k, str): + key = k + else: + offset = k[1] + key = k[0] + if len(k) > 2: + function = k[2] + + if key in model_sd: + p.add(k) + current_patches: list[tuple] = current_hook_patches.get(key, []) + current_patches.append((strength_patch, patches[k], strength_model, offset, function)) + current_hook_patches[key] = current_patches + self.hook_patches[hook.hook_ref] = current_hook_patches + # since should care about these patches too to determine if same model, reroll patches_uuid + self.patches_uuid = uuid.uuid4() + return list(p) + + def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup): + # combined_patches will contain weights of all relevant hooks, per key + combined_patches = {} + if hooks is not None: + for hook in hooks.hooks: + hook_patches: dict = self.hook_patches.get(hook.hook_ref, {}) + for key in hook_patches.keys(): + current_patches: list[tuple] = combined_patches.get(key, []) + if math.isclose(hook.strength, 1.0): + current_patches.extend(hook_patches[key]) + else: + # patches are stored as tuples: (strength_patch, (tuple_with_weights,), strength_model) + for patch in hook_patches[key]: + new_patch = list(patch) + new_patch[0] *= hook.strength + current_patches.append(tuple(new_patch)) + combined_patches[key] = current_patches + return combined_patches + + def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False): + # TODO: return transformer_options dict with any additions from hooks + if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)): + return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) + self.patch_hooks(hooks=hooks) + for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS): + callback(self, hooks) + return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) + + def patch_hooks(self, hooks: comfy.hooks.HookGroup): + with self.use_ejected(): + if hooks is not None: + model_sd_keys = list(self.model_state_dict().keys()) + memory_counter = None + if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: + # TODO: minimum_counter should have a minimum that conforms to loaded model requirements + memory_counter = MemoryCounter(initial=comfy.model_management.get_free_memory(self.load_device), + minimum=comfy.model_management.minimum_inference_memory()*2) + # if have cached weights for hooks, use it + cached_weights = self.cached_hook_patches.get(hooks, None) + if cached_weights is not None: + model_sd_keys_set = set(model_sd_keys) + for key in cached_weights: + if key not in model_sd_keys: + logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}") + continue + self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter) + model_sd_keys_set.remove(key) + self.unpatch_hooks(model_sd_keys_set) + else: + self.unpatch_hooks() + relevant_patches = self.get_combined_hook_patches(hooks=hooks) + original_weights = None + if len(relevant_patches) > 0: + original_weights = self.get_key_patches() + for key in relevant_patches: + if key not in model_sd_keys: + logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}") + continue + self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights, + memory_counter=memory_counter) + else: + self.unpatch_hooks() + self.current_hooks = hooks + + def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter): + if key not in self.hook_backup: + weight: torch.Tensor = comfy.utils.get_attr(self.model, key) + target_device = self.offload_device + if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: + used = memory_counter.use(weight) + if used: + target_device = weight.device + self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device) + comfy.utils.copy_to_param(self.model, key, cached_weights[key][0].to(device=cached_weights[key][1])) + + def clear_cached_hook_weights(self): + self.cached_hook_patches.clear() + self.patch_hooks(None) + + def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter): + if key not in combined_patches: + return + + weight, set_func, convert_func = get_key_weight(self.model, key) + weight: torch.Tensor + if key not in self.hook_backup: + target_device = self.offload_device + if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: + used = memory_counter.use(weight) + if used: + target_device = weight.device + self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device) + # TODO: properly handle LowVramPatch, if it ends up an issue + temp_weight = comfy.model_management.cast_to_device(weight, weight.device, torch.float32, copy=True) + if convert_func is not None: + temp_weight = convert_func(temp_weight, inplace=True) + + out_weight = comfy.lora.calculate_weight(combined_patches[key], + temp_weight, + key, original_weights=original_weights) + del original_weights[key] + if set_func is None: + out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key)) + comfy.utils.copy_to_param(self.model, key, out_weight) + else: + set_func(out_weight, inplace_update=True, seed=string_to_seed(key)) + if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: + # TODO: disable caching if not enough system RAM to do so + target_device = self.offload_device + used = memory_counter.use(weight) + if used: + target_device = weight.device + self.cached_hook_patches.setdefault(hooks, {}) + self.cached_hook_patches[hooks][key] = (out_weight.to(device=target_device, copy=False), weight.device) + del temp_weight + del out_weight + del weight + + def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None: + with self.use_ejected(): + if len(self.hook_backup) == 0: + self.current_hooks = None + return + keys = list(self.hook_backup.keys()) + if whitelist_keys_set: + for k in keys: + if k in whitelist_keys_set: + comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) + self.hook_backup.pop(k) + else: + for k in keys: + comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) + + self.hook_backup.clear() + self.current_hooks = None + + def clean_hooks(self): + self.unpatch_hooks() + self.clear_cached_hook_weights() + + def __del__(self): + self.detach(unpatch_all=False) + diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..b240b7f291905c90d40718565a3fdecbf85cabb3 --- /dev/null +++ b/comfy/model_sampling.py @@ -0,0 +1,383 @@ +import torch +from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule +import math + +def rescale_zero_terminal_snr_sigmas(sigmas): + alphas_cumprod = 1 / ((sigmas * sigmas) + 1) + alphas_bar_sqrt = alphas_cumprod.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= (alphas_bar_sqrt_T) + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 # Revert sqrt + alphas_bar[-1] = 4.8973451890853435e-08 + return ((1 - alphas_bar) / alphas_bar) ** 0.5 + +class EPS: + def calculate_input(self, sigma, noise): + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + if max_denoise: + noise = noise * torch.sqrt(1.0 + sigma ** 2.0) + else: + noise = noise * sigma + + noise += latent_image + return noise + + def inverse_noise_scaling(self, sigma, latent): + return latent + +class V_PREDICTION(EPS): + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + +class EDM(V_PREDICTION): + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + +class CONST: + def calculate_input(self, sigma, noise): + return noise + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + return sigma * noise + (1.0 - sigma) * latent_image + + def inverse_noise_scaling(self, sigma, latent): + sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1)) + return latent / (1.0 - sigma) + +class X0(EPS): + def calculate_denoised(self, sigma, model_output, model_input): + return model_output + +class IMG_TO_IMG(X0): + def calculate_input(self, sigma, noise): + return noise + +class COSMOS_RFLOW: + def calculate_input(self, sigma, noise): + sigma = (sigma / (sigma + 1)) + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + return noise * (1.0 - sigma) + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = (sigma / (sigma + 1)) + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * (1.0 - sigma) - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + noise = noise * sigma + noise += latent_image + return noise + + def inverse_noise_scaling(self, sigma, latent): + return latent + +class ModelSamplingDiscrete(torch.nn.Module): + def __init__(self, model_config=None, zsnr=None): + super().__init__() + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + beta_schedule = sampling_settings.get("beta_schedule", "linear") + linear_start = sampling_settings.get("linear_start", 0.00085) + linear_end = sampling_settings.get("linear_end", 0.012) + timesteps = sampling_settings.get("timesteps", 1000) + + if zsnr is None: + zsnr = sampling_settings.get("zsnr", False) + + self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr) + self.sigma_data = 1.0 + + def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False): + if given_betas is not None: + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + self.zsnr = zsnr + + # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) + # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) + # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) + + sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 + if self.zsnr: + sigmas = rescale_zero_terminal_snr_sigmas(sigmas) + + self.set_sigmas(sigmas) + + def set_sigmas(self, sigmas): + self.register_buffer('sigmas', sigmas.float()) + self.register_buffer('log_sigmas', sigmas.log().float()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) + + def sigma(self, timestep): + t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + return self.sigma(torch.tensor(percent * 999.0)).item() + +class ModelSamplingDiscreteEDM(ModelSamplingDiscrete): + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() + +class ModelSamplingContinuousEDM(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + sigma_min = sampling_settings.get("sigma_min", 0.002) + sigma_max = sampling_settings.get("sigma_max", 120.0) + sigma_data = sampling_settings.get("sigma_data", 1.0) + self.set_parameters(sigma_min, sigma_max, sigma_data) + + def set_parameters(self, sigma_min, sigma_max, sigma_data): + self.sigma_data = sigma_data + sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() + + self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers + self.register_buffer('log_sigmas', sigmas.log()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + + log_sigma_min = math.log(self.sigma_min) + return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) + + +class ModelSamplingContinuousV(ModelSamplingContinuousEDM): + def timestep(self, sigma): + return sigma.atan() / math.pi * 2 + + def sigma(self, timestep): + return (timestep * math.pi / 2).tan() + + +def time_snr_shift(alpha, t): + if alpha == 1.0: + return t + return alpha * t / (1 + (alpha - 1) * t) + +class ModelSamplingDiscreteFlow(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000)) + + def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000): + self.shift = shift + self.multiplier = multiplier + ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier) + self.register_buffer('sigmas', ts) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return sigma * self.multiplier + + def sigma(self, timestep): + return time_snr_shift(self.shift, timestep / self.multiplier) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 1.0 + if percent >= 1.0: + return 0.0 + return time_snr_shift(self.shift, 1.0 - percent) + +class StableCascadeSampling(ModelSamplingDiscrete): + def __init__(self, model_config=None): + super().__init__() + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(sampling_settings.get("shift", 1.0)) + + def set_parameters(self, shift=1.0, cosine_s=8e-3): + self.shift = shift + self.cosine_s = torch.tensor(cosine_s) + self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 + + #This part is just for compatibility with some schedulers in the codebase + self.num_timesteps = 10000 + sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) + for x in range(self.num_timesteps): + t = (x + 1) / self.num_timesteps + sigmas[x] = self.sigma(t) + + self.set_sigmas(sigmas) + + def sigma(self, timestep): + alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) + + if self.shift != 1.0: + var = alpha_cumprod + logSNR = (var/(1-var)).log() + logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) + alpha_cumprod = logSNR.sigmoid() + + alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) + return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 + + def timestep(self, sigma): + var = 1 / ((sigma * sigma) + 1) + var = var.clamp(0, 1.0) + s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) + t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s + return t + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + + percent = 1.0 - percent + return self.sigma(torch.tensor(percent)) + + +def flux_time_shift(mu: float, sigma: float, t): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + +class ModelSamplingFlux(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + self.set_parameters(shift=sampling_settings.get("shift", 1.15)) + + def set_parameters(self, shift=1.15, timesteps=10000): + self.shift = shift + ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps)) + self.register_buffer('sigmas', ts) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return sigma + + def sigma(self, timestep): + return flux_time_shift(self.shift, 1.0, timestep) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 1.0 + if percent >= 1.0: + return 0.0 + return flux_time_shift(self.shift, 1.0, 1.0 - percent) + + +class ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM): + def timestep(self, sigma): + return sigma / (sigma + 1) + + def sigma(self, timestep): + sigma_max = self.sigma_max + if timestep >= (sigma_max / (sigma_max + 1)): + return sigma_max + + return timestep / (1 - timestep) diff --git a/comfy/ops.py b/comfy/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..431c8f89d2e85ddc9c91bd36b19843f5b917380e --- /dev/null +++ b/comfy/ops.py @@ -0,0 +1,438 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import logging +import comfy.model_management +from comfy.cli_args import args, PerformanceFeature +import comfy.float +import comfy.rmsnorm +import contextlib + +cast_to = comfy.model_management.cast_to #TODO: remove once no more references + +def cast_to_input(weight, input, non_blocking=False, copy=True): + return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) + +def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): + if input is not None: + if dtype is None: + dtype = input.dtype + if bias_dtype is None: + bias_dtype = dtype + if device is None: + device = input.device + + offload_stream = comfy.model_management.get_offload_stream(device) + if offload_stream is not None: + wf_context = offload_stream + else: + wf_context = contextlib.nullcontext() + + bias = None + non_blocking = comfy.model_management.device_supports_non_blocking(device) + if s.bias is not None: + has_function = len(s.bias_function) > 0 + bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) + + if has_function: + with wf_context: + for f in s.bias_function: + bias = f(bias) + + has_function = len(s.weight_function) > 0 + weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) + if has_function: + with wf_context: + for f in s.weight_function: + weight = f(weight) + + comfy.model_management.sync_stream(device, offload_stream) + return weight, bias + +class CastWeightBiasOp: + comfy_cast_weights = False + weight_function = [] + bias_function = [] + +class disable_weight_init: + class Linear(torch.nn.Linear, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + if self.weight is not None: + weight, bias = cast_bias_weight(self, input) + else: + weight = None + bias = None + return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class RMSNorm(comfy.rmsnorm.RMSNorm, CastWeightBiasOp): + def reset_parameters(self): + self.bias = None + return None + + def forward_comfy_cast_weights(self, input): + if self.weight is not None: + weight, bias = cast_bias_weight(self, input) + else: + weight = None + return comfy.rmsnorm.rms_norm(input, weight, self.eps) # TODO: switch to commented out line when old torch is deprecated + # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + num_spatial_dims = 2 + output_padding = self._output_padding( + input, output_size, self.stride, self.padding, self.kernel_size, + num_spatial_dims, self.dilation) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose2d( + input, weight, bias, self.stride, self.padding, + output_padding, self.groups, self.dilation) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input, output_size=None): + num_spatial_dims = 1 + output_padding = self._output_padding( + input, output_size, self.stride, self.padding, self.kernel_size, + num_spatial_dims, self.dilation) + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.conv_transpose1d( + input, weight, bias, self.stride, self.padding, + output_padding, self.groups, self.dilation) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Embedding(torch.nn.Embedding, CastWeightBiasOp): + def reset_parameters(self): + self.bias = None + return None + + def forward_comfy_cast_weights(self, input, out_dtype=None): + output_dtype = out_dtype + if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16: + out_dtype = None + weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype) + return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + if "out_dtype" in kwargs: + kwargs.pop("out_dtype") + return super().forward(*args, **kwargs) + + @classmethod + def conv_nd(s, dims, *args, **kwargs): + if dims == 2: + return s.Conv2d(*args, **kwargs) + elif dims == 3: + return s.Conv3d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +class manual_cast(disable_weight_init): + class Linear(disable_weight_init.Linear): + comfy_cast_weights = True + + class Conv1d(disable_weight_init.Conv1d): + comfy_cast_weights = True + + class Conv2d(disable_weight_init.Conv2d): + comfy_cast_weights = True + + class Conv3d(disable_weight_init.Conv3d): + comfy_cast_weights = True + + class GroupNorm(disable_weight_init.GroupNorm): + comfy_cast_weights = True + + class LayerNorm(disable_weight_init.LayerNorm): + comfy_cast_weights = True + + class ConvTranspose2d(disable_weight_init.ConvTranspose2d): + comfy_cast_weights = True + + class ConvTranspose1d(disable_weight_init.ConvTranspose1d): + comfy_cast_weights = True + + class RMSNorm(disable_weight_init.RMSNorm): + comfy_cast_weights = True + + class Embedding(disable_weight_init.Embedding): + comfy_cast_weights = True + + +def fp8_linear(self, input): + dtype = self.weight.dtype + if dtype not in [torch.float8_e4m3fn]: + return None + + tensor_2d = False + if len(input.shape) == 2: + tensor_2d = True + input = input.unsqueeze(1) + + input_shape = input.shape + input_dtype = input.dtype + if len(input.shape) == 3: + w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype) + w = w.t() + + scale_weight = self.scale_weight + scale_input = self.scale_input + if scale_weight is None: + scale_weight = torch.ones((), device=input.device, dtype=torch.float32) + else: + scale_weight = scale_weight.to(input.device) + + if scale_input is None: + scale_input = torch.ones((), device=input.device, dtype=torch.float32) + input = torch.clamp(input, min=-448, max=448, out=input) + input = input.reshape(-1, input_shape[2]).to(dtype).contiguous() + else: + scale_input = scale_input.to(input.device) + input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous() + + if bias is not None: + o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight) + else: + o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight) + + if isinstance(o, tuple): + o = o[0] + + if tensor_2d: + return o.reshape(input_shape[0], -1) + + return o.reshape((-1, input_shape[1], self.weight.shape[0])) + + return None + +class fp8_ops(manual_cast): + class Linear(manual_cast.Linear): + def reset_parameters(self): + self.scale_weight = None + self.scale_input = None + return None + + def forward_comfy_cast_weights(self, input): + out = fp8_linear(self, input) + if out is not None: + return out + + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + +def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None): + logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input)) + class scaled_fp8_op(manual_cast): + class Linear(manual_cast.Linear): + def __init__(self, *args, **kwargs): + if override_dtype is not None: + kwargs['dtype'] = override_dtype + super().__init__(*args, **kwargs) + + def reset_parameters(self): + if not hasattr(self, 'scale_weight'): + self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False) + + if not scale_input: + self.scale_input = None + + if not hasattr(self, 'scale_input'): + self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False) + return None + + def forward_comfy_cast_weights(self, input): + if fp8_matrix_mult: + out = fp8_linear(self, input) + if out is not None: + return out + + weight, bias = cast_bias_weight(self, input) + + if weight.numel() < input.numel(): #TODO: optimize + return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias) + else: + return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias) + + def convert_weight(self, weight, inplace=False, **kwargs): + if inplace: + weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype) + return weight + else: + return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype) + + def set_weight(self, weight, inplace_update=False, seed=None, **kwargs): + weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed) + if inplace_update: + self.weight.data.copy_(weight) + else: + self.weight = torch.nn.Parameter(weight, requires_grad=False) + + return scaled_fp8_op + +CUBLAS_IS_AVAILABLE = False +try: + from cublas_ops import CublasLinear + CUBLAS_IS_AVAILABLE = True +except ImportError: + pass + +if CUBLAS_IS_AVAILABLE: + class cublas_ops(disable_weight_init): + class Linear(CublasLinear, disable_weight_init.Linear): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + return super().forward(input) + + def forward(self, *args, **kwargs): + return super().forward(*args, **kwargs) + +def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None): + fp8_compute = comfy.model_management.supports_fp8_compute(load_device) + if scaled_fp8 is not None: + return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8) + + if ( + fp8_compute and + (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and + not disable_fast_fp8 + ): + return fp8_ops + + if ( + PerformanceFeature.CublasOps in args.fast and + CUBLAS_IS_AVAILABLE and + weight_dtype == torch.float16 and + (compute_dtype == torch.float16 or compute_dtype is None) + ): + logging.info("Using cublas ops") + return cublas_ops + + if compute_dtype is None or weight_dtype == compute_dtype: + return disable_weight_init + + return manual_cast diff --git a/comfy/options.py b/comfy/options.py new file mode 100644 index 0000000000000000000000000000000000000000..f7f8af41ebd8b9669ef0ef21827ea6195bcb4752 --- /dev/null +++ b/comfy/options.py @@ -0,0 +1,6 @@ + +args_parsing = False + +def enable_args_parsing(enable=True): + global args_parsing + args_parsing = enable diff --git a/comfy/patcher_extension.py b/comfy/patcher_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..965958f4c994a3c656c1fe695f1c3ba55c6c0389 --- /dev/null +++ b/comfy/patcher_extension.py @@ -0,0 +1,157 @@ +from __future__ import annotations +from typing import Callable + +class CallbacksMP: + ON_CLONE = "on_clone" + ON_LOAD = "on_load_after" + ON_DETACH = "on_detach_after" + ON_CLEANUP = "on_cleanup" + ON_PRE_RUN = "on_pre_run" + ON_PREPARE_STATE = "on_prepare_state" + ON_APPLY_HOOKS = "on_apply_hooks" + ON_REGISTER_ALL_HOOK_PATCHES = "on_register_all_hook_patches" + ON_INJECT_MODEL = "on_inject_model" + ON_EJECT_MODEL = "on_eject_model" + + # callbacks dict is in the format: + # {"call_type": {"key": [Callable1, Callable2, ...]} } + @classmethod + def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]: + return {} + +def add_callback(call_type: str, callback: Callable, transformer_options: dict, is_model_options=False): + add_callback_with_key(call_type, None, callback, transformer_options, is_model_options) + +def add_callback_with_key(call_type: str, key: str, callback: Callable, transformer_options: dict, is_model_options=False): + if is_model_options: + transformer_options = transformer_options.setdefault("transformer_options", {}) + callbacks: dict[str, dict[str, list]] = transformer_options.setdefault("callbacks", {}) + c = callbacks.setdefault(call_type, {}).setdefault(key, []) + c.append(callback) + +def get_callbacks_with_key(call_type: str, key: str, transformer_options: dict, is_model_options=False): + if is_model_options: + transformer_options = transformer_options.get("transformer_options", {}) + c_list = [] + callbacks: dict[str, list] = transformer_options.get("callbacks", {}) + c_list.extend(callbacks.get(call_type, {}).get(key, [])) + return c_list + +def get_all_callbacks(call_type: str, transformer_options: dict, is_model_options=False): + if is_model_options: + transformer_options = transformer_options.get("transformer_options", {}) + c_list = [] + callbacks: dict[str, list] = transformer_options.get("callbacks", {}) + for c in callbacks.get(call_type, {}).values(): + c_list.extend(c) + return c_list + +class WrappersMP: + OUTER_SAMPLE = "outer_sample" + PREPARE_SAMPLING = "prepare_sampling" + SAMPLER_SAMPLE = "sampler_sample" + CALC_COND_BATCH = "calc_cond_batch" + APPLY_MODEL = "apply_model" + DIFFUSION_MODEL = "diffusion_model" + + # wrappers dict is in the format: + # {"wrapper_type": {"key": [Callable1, Callable2, ...]} } + @classmethod + def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]: + return {} + +def add_wrapper(wrapper_type: str, wrapper: Callable, transformer_options: dict, is_model_options=False): + add_wrapper_with_key(wrapper_type, None, wrapper, transformer_options, is_model_options) + +def add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable, transformer_options: dict, is_model_options=False): + if is_model_options: + transformer_options = transformer_options.setdefault("transformer_options", {}) + wrappers: dict[str, dict[str, list]] = transformer_options.setdefault("wrappers", {}) + w = wrappers.setdefault(wrapper_type, {}).setdefault(key, []) + w.append(wrapper) + +def get_wrappers_with_key(wrapper_type: str, key: str, transformer_options: dict, is_model_options=False): + if is_model_options: + transformer_options = transformer_options.get("transformer_options", {}) + w_list = [] + wrappers: dict[str, list] = transformer_options.get("wrappers", {}) + w_list.extend(wrappers.get(wrapper_type, {}).get(key, [])) + return w_list + +def get_all_wrappers(wrapper_type: str, transformer_options: dict, is_model_options=False): + if is_model_options: + transformer_options = transformer_options.get("transformer_options", {}) + w_list = [] + wrappers: dict[str, list] = transformer_options.get("wrappers", {}) + for w in wrappers.get(wrapper_type, {}).values(): + w_list.extend(w) + return w_list + +class WrapperExecutor: + """Handles call stack of wrappers around a function in an ordered manner.""" + def __init__(self, original: Callable, class_obj: object, wrappers: list[Callable], idx: int): + # NOTE: class_obj exists so that wrappers surrounding a class method can access + # the class instance at runtime via executor.class_obj + self.original = original + self.class_obj = class_obj + self.wrappers = wrappers.copy() + self.idx = idx + self.is_last = idx == len(wrappers) + + def __call__(self, *args, **kwargs): + """Calls the next wrapper or original function, whichever is appropriate.""" + new_executor = self._create_next_executor() + return new_executor.execute(*args, **kwargs) + + def execute(self, *args, **kwargs): + """Used to initiate executor internally - DO NOT use this if you received executor in wrapper.""" + args = list(args) + kwargs = dict(kwargs) + if self.is_last: + return self.original(*args, **kwargs) + return self.wrappers[self.idx](self, *args, **kwargs) + + def _create_next_executor(self) -> 'WrapperExecutor': + new_idx = self.idx + 1 + if new_idx > len(self.wrappers): + raise Exception("Wrapper idx exceeded available wrappers; something went very wrong.") + if self.class_obj is None: + return WrapperExecutor.new_executor(self.original, self.wrappers, new_idx) + return WrapperExecutor.new_class_executor(self.original, self.class_obj, self.wrappers, new_idx) + + @classmethod + def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0): + return cls(original, class_obj=None, wrappers=wrappers, idx=idx) + + @classmethod + def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0): + return cls(original, class_obj, wrappers, idx=idx) + +class PatcherInjection: + def __init__(self, inject: Callable, eject: Callable): + self.inject = inject + self.eject = eject + +def copy_nested_dicts(input_dict: dict): + new_dict = input_dict.copy() + for key, value in input_dict.items(): + if isinstance(value, dict): + new_dict[key] = copy_nested_dicts(value) + elif isinstance(value, list): + new_dict[key] = value.copy() + return new_dict + +def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True): + if copy_dict1: + merged_dict = copy_nested_dicts(dict1) + else: + merged_dict = dict1 + for key, value in dict2.items(): + if isinstance(value, dict): + curr_value = merged_dict.setdefault(key, {}) + merged_dict[key] = merge_nested_dicts(value, curr_value) + elif isinstance(value, list): + merged_dict.setdefault(key, []).extend(value) + else: + merged_dict[key] = value + return merged_dict diff --git a/comfy/rmsnorm.py b/comfy/rmsnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..66ae8321d9fe21274c27f7233b41db81d334505c --- /dev/null +++ b/comfy/rmsnorm.py @@ -0,0 +1,55 @@ +import torch +import comfy.model_management +import numbers + +RMSNorm = None + +try: + rms_norm_torch = torch.nn.functional.rms_norm + RMSNorm = torch.nn.RMSNorm +except: + rms_norm_torch = None + + +def rms_norm(x, weight=None, eps=1e-6): + if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()): + if weight is None: + return rms_norm_torch(x, (x.shape[-1],), eps=eps) + else: + return rms_norm_torch(x, weight.shape, weight=comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) + else: + r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) + if weight is None: + return r + else: + return r * comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device) + + +if RMSNorm is None: + class RMSNorm(torch.nn.Module): + def __init__( + self, + normalized_shape, + eps=1e-6, + elementwise_affine=True, + device=None, + dtype=None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + if isinstance(normalized_shape, numbers.Integral): + # mypy error: incompatible types in assignment + normalized_shape = (normalized_shape,) # type: ignore[assignment] + self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type] + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = torch.nn.Parameter( + torch.empty(self.normalized_shape, **factory_kwargs) + ) + else: + self.register_parameter("weight", None) + self.bias = None + + def forward(self, x): + return rms_norm(x, self.weight, self.eps) diff --git a/comfy/sample.py b/comfy/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..be5a7e246fdf6168cd74d1bb1b550c6852a83212 --- /dev/null +++ b/comfy/sample.py @@ -0,0 +1,52 @@ +import torch +import comfy.model_management +import comfy.samplers +import comfy.utils +import numpy as np +import logging + +def prepare_noise(latent_image, seed, noise_inds=None): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + generator = torch.manual_seed(seed) + if noise_inds is None: + return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1]+1): + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + +def fix_empty_latent_channels(model, latent_image): + latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels + if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: + latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1) + if latent_format.latent_dimensions == 3 and latent_image.ndim == 4: + latent_image = latent_image.unsqueeze(2) + return latent_image + +def prepare_sampling(model, noise_shape, positive, negative, noise_mask): + logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") + return model, positive, negative, noise_mask, [] + +def cleanup_additional_models(models): + logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") + +def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) + + samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(comfy.model_management.intermediate_device()) + return samples + +def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): + samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(comfy.model_management.intermediate_device()) + return samples diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..8dbc41455cfdbedaee86ce5f4f90f1e34d6fa89c --- /dev/null +++ b/comfy/sampler_helpers.py @@ -0,0 +1,184 @@ +from __future__ import annotations +import uuid +import math +import collections +import comfy.model_management +import comfy.conds +import comfy.utils +import comfy.hooks +import comfy.patcher_extension +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher + from comfy.model_base import BaseModel + from comfy.controlnet import ControlBase + +def prepare_mask(noise_mask, shape, device): + return comfy.utils.reshape_mask(noise_mask, shape).to(device) + +def get_models_from_cond(cond, model_type): + models = [] + for c in cond: + if model_type in c: + if isinstance(c[model_type], list): + models += c[model_type] + else: + models += [c[model_type]] + return models + +def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup): + # get hooks from conds, and collect cnets so they can be checked for extra_hooks + cnets: list[ControlBase] = [] + for c in cond: + if 'hooks' in c: + for hook in c['hooks'].hooks: + full_hooks.add(hook) + if 'control' in c: + cnets.append(c['control']) + + def get_extra_hooks_from_cnet(cnet: ControlBase, _list: list): + if cnet.extra_hooks is not None: + _list.append(cnet.extra_hooks) + if cnet.previous_controlnet is None: + return _list + return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list) + + hooks_list = [] + cnets = set(cnets) + for base_cnet in cnets: + get_extra_hooks_from_cnet(base_cnet, hooks_list) + extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list) + if extra_hooks is not None: + for hook in extra_hooks.hooks: + full_hooks.add(hook) + + return full_hooks + +def convert_cond(cond): + out = [] + for c in cond: + temp = c[1].copy() + model_conds = temp.get("model_conds", {}) + if c[0] is not None: + temp["cross_attn"] = c[0] + temp["model_conds"] = model_conds + temp["uuid"] = uuid.uuid4() + out.append(temp) + return out + +def get_additional_models(conds, dtype): + """loads additional models in conditioning""" + cnets: list[ControlBase] = [] + gligen = [] + add_models = [] + + for k in conds: + cnets += get_models_from_cond(conds[k], "control") + gligen += get_models_from_cond(conds[k], "gligen") + add_models += get_models_from_cond(conds[k], "additional_models") + + control_nets = set(cnets) + + inference_memory = 0 + control_models = [] + for m in control_nets: + control_models += m.get_models() + inference_memory += m.inference_memory_requirements(dtype) + + gligen = [x[1] for x in gligen] + models = control_models + gligen + add_models + + return models, inference_memory + +def get_additional_models_from_model_options(model_options: dict[str]=None): + """loads additional models from registered AddModels hooks""" + models = [] + if model_options is not None and "registered_hooks" in model_options: + registered: comfy.hooks.HookGroup = model_options["registered_hooks"] + for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels): + hook: comfy.hooks.AdditionalModelsHook + models.extend(hook.models) + return models + +def cleanup_additional_models(models): + """cleanup additional models that were loaded""" + for m in models: + if hasattr(m, 'cleanup'): + m.cleanup() + +def estimate_memory(model, noise_shape, conds): + cond_shapes = collections.defaultdict(list) + cond_shapes_min = {} + for _, cs in conds.items(): + for cond in cs: + for k, v in model.model.extra_conds_shapes(**cond).items(): + cond_shapes[k].append(v) + if cond_shapes_min.get(k, None) is None: + cond_shapes_min[k] = [v] + elif math.prod(v) > math.prod(cond_shapes_min[k][0]): + cond_shapes_min[k] = [v] + + memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes) + minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min) + return memory_required, minimum_memory_required + +def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): + executor = comfy.patcher_extension.WrapperExecutor.new_executor( + _prepare_sampling, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True) + ) + return executor.execute(model, noise_shape, conds, model_options=model_options) + +def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): + real_model: BaseModel = None + models, inference_memory = get_additional_models(conds, model.model_dtype()) + models += get_additional_models_from_model_options(model_options) + models += model.get_nested_additional_models() # TODO: does this require inference_memory update? + memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds) + comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory) + real_model = model.model + + return real_model, conds, models + +def cleanup_models(conds, models): + cleanup_additional_models(models) + + control_cleanup = [] + for k in conds: + control_cleanup += get_models_from_cond(conds[k], "control") + + cleanup_additional_models(set(control_cleanup)) + +def prepare_model_patcher(model: 'ModelPatcher', conds, model_options: dict): + ''' + Registers hooks from conds. + ''' + # check for hooks in conds - if not registered, see if can be applied + hooks = comfy.hooks.HookGroup() + for k in conds: + get_hooks_from_cond(conds[k], hooks) + # add wrappers and callbacks from ModelPatcher to transformer_options + model_options["transformer_options"]["wrappers"] = comfy.patcher_extension.copy_nested_dicts(model.wrappers) + model_options["transformer_options"]["callbacks"] = comfy.patcher_extension.copy_nested_dicts(model.callbacks) + # begin registering hooks + registered = comfy.hooks.HookGroup() + target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model) + # handle all TransformerOptionsHooks + for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions): + hook: comfy.hooks.TransformerOptionsHook + hook.add_hook_patches(model, model_options, target_dict, registered) + # handle all AddModelsHooks + for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels): + hook: comfy.hooks.AdditionalModelsHook + hook.add_hook_patches(model, model_options, target_dict, registered) + # handle all WeightHooks by registering on ModelPatcher + model.register_all_hook_patches(hooks, target_dict, model_options, registered) + # add registered_hooks onto model_options for further reference + if len(registered) > 0: + model_options["registered_hooks"] = registered + # merge original wrappers and callbacks with hooked wrappers and callbacks + to_load_options: dict[str] = model_options.setdefault("to_load_options", {}) + for wc_name in ["wrappers", "callbacks"]: + comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name], + copy_dict1=False) + return to_load_options diff --git a/comfy/samplers.py b/comfy/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..078a675f45b691ae94cb0c84f2e07ea142b761f5 --- /dev/null +++ b/comfy/samplers.py @@ -0,0 +1,1139 @@ +from __future__ import annotations +from .k_diffusion import sampling as k_diffusion_sampling +from .extra_samplers import uni_pc +from typing import TYPE_CHECKING, Callable, NamedTuple +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher + from comfy.model_base import BaseModel + from comfy.controlnet import ControlBase +import torch +from functools import partial +import collections +from comfy import model_management +import math +import logging +import comfy.sampler_helpers +import comfy.model_patcher +import comfy.patcher_extension +import comfy.hooks +import scipy.stats +import numpy + + +def add_area_dims(area, num_dims): + while (len(area) // 2) < num_dims: + area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:] + return area + +def get_area_and_mult(conds, x_in, timestep_in): + dims = tuple(x_in.shape[2:]) + area = None + strength = 1.0 + + if 'timestep_start' in conds: + timestep_start = conds['timestep_start'] + if timestep_in[0] > timestep_start: + return None + if 'timestep_end' in conds: + timestep_end = conds['timestep_end'] + if timestep_in[0] < timestep_end: + return None + if 'area' in conds: + area = list(conds['area']) + area = add_area_dims(area, len(dims)) + if (len(area) // 2) > len(dims): + area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)] + + if 'strength' in conds: + strength = conds['strength'] + + input_x = x_in + if area is not None: + for i in range(len(dims)): + area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i]) + input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i]) + + if 'mask' in conds: + # Scale the mask to the size of the input + # The mask should have been resized as we began the sampling process + mask_strength = 1.0 + if "mask_strength" in conds: + mask_strength = conds["mask_strength"] + mask = conds['mask'] + assert (mask.shape[1:] == x_in.shape[2:]) + + mask = mask[:input_x.shape[0]] + if area is not None: + for i in range(len(dims)): + mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) + + mask = mask * mask_strength + mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) + else: + mask = torch.ones_like(input_x) + mult = mask * strength + + if 'mask' not in conds and area is not None: + fuzz = 8 + for i in range(len(dims)): + rr = min(fuzz, mult.shape[2 + i] // 4) + if area[len(dims) + i] != 0: + for t in range(rr): + m = mult.narrow(i + 2, t, 1) + m *= ((1.0 / rr) * (t + 1)) + if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: + for t in range(rr): + m = mult.narrow(i + 2, area[i] - 1 - t, 1) + m *= ((1.0 / rr) * (t + 1)) + + conditioning = {} + model_conds = conds["model_conds"] + for c in model_conds: + conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) + + hooks = conds.get('hooks', None) + control = conds.get('control', None) + + patches = None + if 'gligen' in conds: + gligen = conds['gligen'] + patches = {} + gligen_type = gligen[0] + gligen_model = gligen[1] + if gligen_type == "position": + gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) + else: + gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) + + patches['middle_patch'] = [gligen_patch] + + cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches', 'uuid', 'hooks']) + return cond_obj(input_x, mult, conditioning, area, control, patches, conds['uuid'], hooks) + +def cond_equal_size(c1, c2): + if c1 is c2: + return True + if c1.keys() != c2.keys(): + return False + for k in c1: + if not c1[k].can_concat(c2[k]): + return False + return True + +def can_concat_cond(c1, c2): + if c1.input_x.shape != c2.input_x.shape: + return False + + def objects_concatable(obj1, obj2): + if (obj1 is None) != (obj2 is None): + return False + if obj1 is not None: + if obj1 is not obj2: + return False + return True + + if not objects_concatable(c1.control, c2.control): + return False + + if not objects_concatable(c1.patches, c2.patches): + return False + + return cond_equal_size(c1.conditioning, c2.conditioning) + +def cond_cat(c_list): + temp = {} + for x in c_list: + for k in x: + cur = temp.get(k, []) + cur.append(x[k]) + temp[k] = cur + + out = {} + for k in temp: + conds = temp[k] + out[k] = conds[0].concat(conds[1:]) + + return out + +def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep, model_options): + # need to figure out remaining unmasked area for conds + default_mults = [] + for _ in default_conds: + default_mults.append(torch.ones_like(x_in)) + # look through each finalized cond in hooked_to_run for 'mult' and subtract it from each cond + for lora_hooks, to_run in hooked_to_run.items(): + for cond_obj, i in to_run: + # if no default_cond for cond_type, do nothing + if len(default_conds[i]) == 0: + continue + area: list[int] = cond_obj.area + if area is not None: + curr_default_mult: torch.Tensor = default_mults[i] + dims = len(area) // 2 + for i in range(dims): + curr_default_mult = curr_default_mult.narrow(i + 2, area[i + dims], area[i]) + curr_default_mult -= cond_obj.mult + else: + default_mults[i] -= cond_obj.mult + # for each default_mult, ReLU to make negatives=0, and then check for any nonzeros + for i, mult in enumerate(default_mults): + # if no default_cond for cond type, do nothing + if len(default_conds[i]) == 0: + continue + torch.nn.functional.relu(mult, inplace=True) + # if mult is all zeros, then don't add default_cond + if torch.max(mult) == 0.0: + continue + + cond = default_conds[i] + for x in cond: + # do get_area_and_mult to get all the expected values + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + # replace p's mult with calculated mult + p = p._replace(mult=mult) + if p.hooks is not None: + model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options) + hooked_to_run.setdefault(p.hooks, list()) + hooked_to_run[p.hooks] += [(p, i)] + +def calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): + executor = comfy.patcher_extension.WrapperExecutor.new_executor( + _calc_cond_batch, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True) + ) + return executor.execute(model, conds, x_in, timestep, model_options) + +def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): + out_conds = [] + out_counts = [] + # separate conds by matching hooks + hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {} + default_conds = [] + has_default_conds = False + + for i in range(len(conds)): + out_conds.append(torch.zeros_like(x_in)) + out_counts.append(torch.ones_like(x_in) * 1e-37) + + cond = conds[i] + default_c = [] + if cond is not None: + for x in cond: + if 'default' in x: + default_c.append(x) + has_default_conds = True + continue + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + if p.hooks is not None: + model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options) + hooked_to_run.setdefault(p.hooks, list()) + hooked_to_run[p.hooks] += [(p, i)] + default_conds.append(default_c) + + if has_default_conds: + finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options) + + model.current_patcher.prepare_state(timestep) + + # run every hooked_to_run separately + for hooks, to_run in hooked_to_run.items(): + while len(to_run) > 0: + first = to_run[0] + first_shape = first[0][0].shape + to_batch_temp = [] + for x in range(len(to_run)): + if can_concat_cond(to_run[x][0], first[0]): + to_batch_temp += [x] + + to_batch_temp.reverse() + to_batch = to_batch_temp[:1] + + free_memory = model_management.get_free_memory(x_in.device) + for i in range(1, len(to_batch_temp) + 1): + batch_amount = to_batch_temp[:len(to_batch_temp)//i] + input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] + cond_shapes = collections.defaultdict(list) + for tt in batch_amount: + cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()} + for k, v in to_run[tt][0].conditioning.items(): + cond_shapes[k].append(v.size()) + + if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory: + to_batch = batch_amount + break + + input_x = [] + mult = [] + c = [] + cond_or_uncond = [] + uuids = [] + area = [] + control = None + patches = None + for x in to_batch: + o = to_run.pop(x) + p = o[0] + input_x.append(p.input_x) + mult.append(p.mult) + c.append(p.conditioning) + area.append(p.area) + cond_or_uncond.append(o[1]) + uuids.append(p.uuid) + control = p.control + patches = p.patches + + batch_chunks = len(cond_or_uncond) + input_x = torch.cat(input_x) + c = cond_cat(c) + timestep_ = torch.cat([timestep] * batch_chunks) + + transformer_options = model.current_patcher.apply_hooks(hooks=hooks) + if 'transformer_options' in model_options: + transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options, + model_options['transformer_options'], + copy_dict1=False) + + if patches is not None: + # TODO: replace with merge_nested_dicts function + if "patches" in transformer_options: + cur_patches = transformer_options["patches"].copy() + for p in patches: + if p in cur_patches: + cur_patches[p] = cur_patches[p] + patches[p] + else: + cur_patches[p] = patches[p] + transformer_options["patches"] = cur_patches + else: + transformer_options["patches"] = patches + + transformer_options["cond_or_uncond"] = cond_or_uncond[:] + transformer_options["uuids"] = uuids[:] + transformer_options["sigmas"] = timestep + + c['transformer_options'] = transformer_options + + if control is not None: + c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options) + + if 'model_function_wrapper' in model_options: + output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) + else: + output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) + + for o in range(batch_chunks): + cond_index = cond_or_uncond[o] + a = area[o] + if a is None: + out_conds[cond_index] += output[o] * mult[o] + out_counts[cond_index] += mult[o] + else: + out_c = out_conds[cond_index] + out_cts = out_counts[cond_index] + dims = len(a) // 2 + for i in range(dims): + out_c = out_c.narrow(i + 2, a[i + dims], a[i]) + out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) + out_c += output[o] * mult[o] + out_cts += mult[o] + + for i in range(len(out_conds)): + out_conds[i] /= out_counts[i] + + return out_conds + +def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove + logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") + return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) + +def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): + if "sampler_cfg_function" in model_options: + args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, + "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} + cfg_result = x - model_options["sampler_cfg_function"](args) + else: + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale + + for fn in model_options.get("sampler_post_cfg_function", []): + args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "cond_scale": cond_scale, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, + "sigma": timestep, "model_options": model_options, "input": x} + cfg_result = fn(args) + + return cfg_result + +#The main sampling function shared by all the samplers +#Returns denoised +def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): + if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: + uncond_ = None + else: + uncond_ = uncond + + conds = [cond, uncond_] + out = calc_cond_batch(model, conds, x, timestep, model_options) + + for fn in model_options.get("sampler_pre_cfg_function", []): + args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep, + "input": x, "sigma": timestep, "model": model, "model_options": model_options} + out = fn(args) + + return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) + + +class KSamplerX0Inpaint: + def __init__(self, model, sigmas): + self.inner_model = model + self.sigmas = sigmas + def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None): + if denoise_mask is not None: + if "denoise_mask_function" in model_options: + denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) + latent_mask = 1. - denoise_mask + x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask + out = self.inner_model(x, sigma, model_options=model_options, seed=seed) + if denoise_mask is not None: + out = out * denoise_mask + self.latent_image * latent_mask + return out + +def simple_scheduler(model_sampling, steps): + s = model_sampling + sigs = [] + ss = len(s.sigmas) / steps + for x in range(steps): + sigs += [float(s.sigmas[-(1 + int(x * ss))])] + sigs += [0.0] + return torch.FloatTensor(sigs) + +def ddim_scheduler(model_sampling, steps): + s = model_sampling + sigs = [] + x = 1 + if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001): + steps += 1 + sigs = [] + else: + sigs = [0.0] + + ss = max(len(s.sigmas) // steps, 1) + while x < len(s.sigmas): + sigs += [float(s.sigmas[x])] + x += ss + sigs = sigs[::-1] + return torch.FloatTensor(sigs) + +def normal_scheduler(model_sampling, steps, sgm=False, floor=False): + s = model_sampling + start = s.timestep(s.sigma_max) + end = s.timestep(s.sigma_min) + + append_zero = True + if sgm: + timesteps = torch.linspace(start, end, steps + 1)[:-1] + else: + if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001): + steps += 1 + append_zero = False + timesteps = torch.linspace(start, end, steps) + + sigs = [] + for x in range(len(timesteps)): + ts = timesteps[x] + sigs.append(float(s.sigma(ts))) + + if append_zero: + sigs += [0.0] + + return torch.FloatTensor(sigs) + +# Implemented based on: https://arxiv.org/abs/2407.12173 +def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): + total_timesteps = (len(model_sampling.sigmas) - 1) + ts = 1 - numpy.linspace(0, 1, steps, endpoint=False) + ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) + + sigs = [] + last_t = -1 + for t in ts: + if t != last_t: + sigs += [float(model_sampling.sigmas[int(t)])] + last_t = t + sigs += [0.0] + return torch.FloatTensor(sigs) + +# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41 +def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None): + if steps == 1: + sigma_schedule = [1.0, 0.0] + else: + if linear_steps is None: + linear_steps = steps // 2 + linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)] + threshold_noise_step_diff = linear_steps - threshold_noise * steps + quadratic_steps = steps - linear_steps + quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2) + linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2) + const = quadratic_coef * (linear_steps ** 2) + quadratic_sigma_schedule = [ + quadratic_coef * (i ** 2) + linear_coef * i + const + for i in range(linear_steps, steps) + ] + sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0] + sigma_schedule = [1.0 - x for x in sigma_schedule] + return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu() + +# Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608 +def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor: + adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1) + sigmas = adj_idxs.new_zeros(n + 1) + sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_() + return sigmas + +def get_mask_aabb(masks): + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device, dtype=torch.int) + + b = masks.shape[0] + + bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) + is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) + for i in range(b): + mask = masks[i] + if mask.numel() == 0: + continue + if torch.max(mask != 0) == False: + is_empty[i] = True + continue + y, x = torch.where(mask) + bounding_boxes[i, 0] = torch.min(x) + bounding_boxes[i, 1] = torch.min(y) + bounding_boxes[i, 2] = torch.max(x) + bounding_boxes[i, 3] = torch.max(y) + + return bounding_boxes, is_empty + +def resolve_areas_and_cond_masks_multidim(conditions, dims, device): + # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. + # While we're doing this, we can also resolve the mask device and scaling for performance reasons + for i in range(len(conditions)): + c = conditions[i] + if 'area' in c: + area = c['area'] + if area[0] == "percentage": + modified = c.copy() + a = area[1:] + a_len = len(a) // 2 + area = () + for d in range(len(dims)): + area += (max(1, round(a[d] * dims[d])),) + for d in range(len(dims)): + area += (round(a[d + a_len] * dims[d]),) + + modified['area'] = area + c = modified + conditions[i] = c + + if 'mask' in c: + mask = c['mask'] + mask = mask.to(device=device) + modified = c.copy() + if len(mask.shape) == len(dims): + mask = mask.unsqueeze(0) + if mask.shape[1:] != dims: + mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1) + + if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2 + bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) + boxes, is_empty = get_mask_aabb(bounds) + if is_empty[0]: + # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) + modified['area'] = (8, 8, 0, 0) + else: + box = boxes[0] + H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) + H = max(8, H) + W = max(8, W) + area = (int(H), int(W), int(Y), int(X)) + modified['area'] = area + + modified['mask'] = mask + conditions[i] = modified + +def resolve_areas_and_cond_masks(conditions, h, w, device): + logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") + return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) + +def create_cond_with_same_area_if_none(conds, c): + if 'area' not in c: + return + + def area_inside(a, area_cmp): + a = add_area_dims(a, len(area_cmp) // 2) + area_cmp = add_area_dims(area_cmp, len(a) // 2) + + a_l = len(a) // 2 + area_cmp_l = len(area_cmp) // 2 + for i in range(min(a_l, area_cmp_l)): + if a[a_l + i] < area_cmp[area_cmp_l + i]: + return False + for i in range(min(a_l, area_cmp_l)): + if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]): + return False + return True + + c_area = c['area'] + smallest = None + for x in conds: + if 'area' in x: + a = x['area'] + if area_inside(c_area, a): + if smallest is None: + smallest = x + elif 'area' not in smallest: + smallest = x + else: + if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]): + smallest = x + else: + if smallest is None: + smallest = x + if smallest is None: + return + if 'area' in smallest: + if smallest['area'] == c_area: + return + + out = c.copy() + out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? + conds += [out] + +def calculate_start_end_timesteps(model, conds): + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + # handle clip hook schedule, if needed + if 'clip_start_percent' in x: + timestep_start = s.percent_to_sigma(max(x['clip_start_percent'], x.get('start_percent', 0.0))) + timestep_end = s.percent_to_sigma(min(x['clip_end_percent'], x.get('end_percent', 1.0))) + else: + if 'start_percent' in x: + timestep_start = s.percent_to_sigma(x['start_percent']) + if 'end_percent' in x: + timestep_end = s.percent_to_sigma(x['end_percent']) + + if (timestep_start is not None) or (timestep_end is not None): + n = x.copy() + if (timestep_start is not None): + n['timestep_start'] = timestep_start + if (timestep_end is not None): + n['timestep_end'] = timestep_end + conds[t] = n + +def pre_run_control(model, conds): + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + percent_to_timestep_function = lambda a: s.percent_to_sigma(a) + if 'control' in x: + x['control'].pre_run(model, percent_to_timestep_function) + +def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): + cond_cnets = [] + cond_other = [] + uncond_cnets = [] + uncond_other = [] + for t in range(len(conds)): + x = conds[t] + if 'area' not in x: + if name in x and x[name] is not None: + cond_cnets.append(x[name]) + else: + cond_other.append((x, t)) + for t in range(len(uncond)): + x = uncond[t] + if 'area' not in x: + if name in x and x[name] is not None: + uncond_cnets.append(x[name]) + else: + uncond_other.append((x, t)) + + if len(uncond_cnets) > 0: + return + + for x in range(len(cond_cnets)): + temp = uncond_other[x % len(uncond_other)] + o = temp[0] + if name in o and o[name] is not None: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond += [n] + else: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond[temp[1]] = n + +def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): + for t in range(len(conds)): + x = conds[t] + params = x.copy() + params["device"] = device + params["noise"] = noise + default_width = None + if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model + default_width = noise.shape[3] * 8 + params["width"] = params.get("width", default_width) + params["height"] = params.get("height", noise.shape[2] * 8) + params["prompt_type"] = params.get("prompt_type", prompt_type) + for k in kwargs: + if k not in params: + params[k] = kwargs[k] + + out = model_function(**params) + x = x.copy() + model_conds = x['model_conds'].copy() + for k in out: + model_conds[k] = out[k] + x['model_conds'] = model_conds + conds[t] = x + return conds + +class Sampler: + def sample(self): + pass + + def max_denoise(self, model_wrap, sigmas): + max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) + sigma = float(sigmas[0]) + return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma + +KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", + "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", + "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", + "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp", + "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3"] + +class KSAMPLER(Sampler): + def __init__(self, sampler_function, extra_options={}, inpaint_options={}): + self.sampler_function = sampler_function + self.extra_options = extra_options + self.inpaint_options = inpaint_options + + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + extra_args["denoise_mask"] = denoise_mask + model_k = KSamplerX0Inpaint(model_wrap, sigmas) + model_k.latent_image = latent_image + if self.inpaint_options.get("random", False): #TODO: Should this be the default? + generator = torch.manual_seed(extra_args.get("seed", 41) + 1) + model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) + else: + model_k.noise = noise + + noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) + + k_callback = None + total_steps = len(sigmas) - 1 + if callback is not None: + k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) + + samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) + samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) + return samples + + +def ksampler(sampler_name, extra_options={}, inpaint_options={}): + if sampler_name == "dpm_fast": + def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): + if len(sigmas) <= 1: + return noise + + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + total_steps = len(sigmas) - 1 + return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) + sampler_function = dpm_fast_function + elif sampler_name == "dpm_adaptive": + def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): + if len(sigmas) <= 1: + return noise + + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) + sampler_function = dpm_adaptive_function + else: + sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) + + return KSAMPLER(sampler_function, extra_options, inpaint_options) + + +def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): + for k in conds: + conds[k] = conds[k][:] + resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) + + for k in conds: + calculate_start_end_timesteps(model, conds[k]) + + if hasattr(model, 'extra_conds'): + for k in conds: + conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) + + #make sure each cond area has an opposite one with the same area + for k in conds: + for c in conds[k]: + for kk in conds: + if k != kk: + create_cond_with_same_area_if_none(conds[kk], c) + + for k in conds: + for c in conds[k]: + if 'hooks' in c: + for hook in c['hooks'].hooks: + hook.initialize_timesteps(model) + + for k in conds: + pre_run_control(model, conds[k]) + + if "positive" in conds: + positive = conds["positive"] + for k in conds: + if k != "positive": + apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + return conds + + +def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]): + # determine which ControlNets have extra_hooks that should be combined with normal hooks + hook_replacement: dict[tuple[ControlBase, comfy.hooks.HookGroup], list[dict]] = {} + for k in conds: + for kk in conds[k]: + if 'control' in kk: + control: 'ControlBase' = kk['control'] + extra_hooks = control.get_extra_hooks() + if len(extra_hooks) > 0: + hooks: comfy.hooks.HookGroup = kk.get('hooks', None) + to_replace = hook_replacement.setdefault((control, hooks), []) + to_replace.append(kk) + # if nothing to replace, do nothing + if len(hook_replacement) == 0: + return + + # for optimal sampling performance, common ControlNets + hook combos should have identical hooks + # on the cond dicts + for key, conds_to_modify in hook_replacement.items(): + control = key[0] + hooks = key[1] + hooks = comfy.hooks.HookGroup.combine_all_hooks(control.get_extra_hooks() + [hooks]) + # if combined hooks are not None, set as new hooks for all relevant conds + if hooks is not None: + for cond in conds_to_modify: + cond['hooks'] = hooks + +def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]): + '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for + HookGroups that have the same reference.''' + registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None) + # if None were registered, make sure all hooks are cleaned from conds + if registered is None: + for k in conds: + for kk in conds[k]: + kk.pop('hooks', None) + return + # find conds that contain hooks to be replaced - group by common HookGroup refs + hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {} + for k in conds: + for kk in conds[k]: + hooks: comfy.hooks.HookGroup = kk.get('hooks', None) + if hooks is not None: + if not hooks.is_subset_of(registered): + to_replace = hook_replacement.setdefault(hooks, []) + to_replace.append(kk) + # for each hook to replace, create a new proper HookGroup and assign to all common conds + for hooks, conds_to_modify in hook_replacement.items(): + new_hooks = hooks.new_with_common_hooks(registered) + if len(new_hooks) == 0: + new_hooks = None + for kk in conds_to_modify: + kk['hooks'] = new_hooks + + +def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]): + hooks_set = set() + for k in conds: + for kk in conds[k]: + hooks_set.add(kk.get('hooks', None)) + return len(hooks_set) + + +def cast_to_load_options(model_options: dict[str], device=None, dtype=None): + ''' + If any patches from hooks, wrappers, or callbacks have .to to be called, call it. + ''' + if model_options is None: + return + to_load_options = model_options.get("to_load_options", None) + if to_load_options is None: + return + + casts = [] + if device is not None: + casts.append(device) + if dtype is not None: + casts.append(dtype) + # if nothing to apply, do nothing + if len(casts) == 0: + return + + # try to call .to on patches + if "patches" in to_load_options: + patches = to_load_options["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + for cast in casts: + patch_list[i] = patch_list[i].to(cast) + if "patches_replace" in to_load_options: + patches = to_load_options["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + for cast in casts: + patch_list[k] = patch_list[k].to(cast) + # try to call .to on any wrappers/callbacks + wrappers_and_callbacks = ["wrappers", "callbacks"] + for wc_name in wrappers_and_callbacks: + if wc_name in to_load_options: + wc: dict[str, list] = to_load_options[wc_name] + for wc_dict in wc.values(): + for wc_list in wc_dict.values(): + for i in range(len(wc_list)): + if hasattr(wc_list[i], "to"): + for cast in casts: + wc_list[i] = wc_list[i].to(cast) + + +class CFGGuider: + def __init__(self, model_patcher: ModelPatcher): + self.model_patcher = model_patcher + self.model_options = model_patcher.model_options + self.original_conds = {} + self.cfg = 1.0 + + def set_conds(self, positive, negative): + self.inner_set_conds({"positive": positive, "negative": negative}) + + def set_cfg(self, cfg): + self.cfg = cfg + + def inner_set_conds(self, conds): + for k in conds: + self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) + + def __call__(self, *args, **kwargs): + return self.predict_noise(*args, **kwargs) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) + + def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed): + if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = self.inner_model.process_latent_in(latent_image) + + self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) + + extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options) + extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas + extra_args = {"model_options": extra_model_options, "seed": seed} + + executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( + sampler.sample, + sampler, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, extra_args["model_options"], is_model_options=True) + ) + samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) + return self.inner_model.process_latent_out(samples.to(torch.float32)) + + def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) + device = self.model_patcher.load_device + + if denoise_mask is not None: + denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device) + + noise = noise.to(device) + latent_image = latent_image.to(device) + sigmas = sigmas.to(device) + cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) + + try: + self.model_patcher.pre_run() + output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + finally: + self.model_patcher.cleanup() + + comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) + del self.inner_model + del self.loaded_models + return output + + def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + if sigmas.shape[-1] == 0: + return latent_image + + self.conds = {} + for k in self.original_conds: + self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) + preprocess_conds_hooks(self.conds) + + try: + orig_model_options = self.model_options + self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options) + # if one hook type (or just None), then don't bother caching weights for hooks (will never change after first step) + orig_hook_mode = self.model_patcher.hook_mode + if get_total_hook_groups_in_conds(self.conds) <= 1: + self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram + comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options) + filter_registered_hooks_on_conds(self.conds, self.model_options) + executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( + self.outer_sample, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True) + ) + output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + finally: + cast_to_load_options(self.model_options, device=self.model_patcher.offload_device) + self.model_options = orig_model_options + self.model_patcher.hook_mode = orig_hook_mode + self.model_patcher.restore_hook_patches() + + del self.conds + return output + + +def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + cfg_guider = CFGGuider(model) + cfg_guider.set_conds(positive, negative) + cfg_guider.set_cfg(cfg) + return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + + +SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] + +class SchedulerHandler(NamedTuple): + handler: Callable[..., torch.Tensor] + # Boolean indicates whether to call the handler like: + # scheduler_function(model_sampling, steps) or + # scheduler_function(n, sigma_min: float, sigma_max: float) + use_ms: bool = True + +SCHEDULER_HANDLERS = { + "simple": SchedulerHandler(simple_scheduler), + "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)), + "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False), + "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False), + "ddim_uniform": SchedulerHandler(ddim_scheduler), + "beta": SchedulerHandler(beta_scheduler), + "normal": SchedulerHandler(normal_scheduler), + "linear_quadratic": SchedulerHandler(linear_quadratic_schedule), + "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False), +} +SCHEDULER_NAMES = list(SCHEDULER_HANDLERS) + +def calculate_sigmas(model_sampling: object, scheduler_name: str, steps: int) -> torch.Tensor: + handler = SCHEDULER_HANDLERS.get(scheduler_name) + if handler is None: + err = f"error invalid scheduler {scheduler_name}" + logging.error(err) + raise ValueError(err) + if handler.use_ms: + return handler.handler(model_sampling, steps) + return handler.handler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) + +def sampler_object(name): + if name == "uni_pc": + sampler = KSAMPLER(uni_pc.sample_unipc) + elif name == "uni_pc_bh2": + sampler = KSAMPLER(uni_pc.sample_unipc_bh2) + elif name == "ddim": + sampler = ksampler("euler", inpaint_options={"random": True}) + else: + sampler = ksampler(name) + return sampler + +class KSampler: + SCHEDULERS = SCHEDULER_NAMES + SAMPLERS = SAMPLER_NAMES + DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2')) + + def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): + self.model = model + self.device = device + if scheduler not in self.SCHEDULERS: + scheduler = self.SCHEDULERS[0] + if sampler not in self.SAMPLERS: + sampler = self.SAMPLERS[0] + self.scheduler = scheduler + self.sampler = sampler + self.set_steps(steps, denoise) + self.denoise = denoise + self.model_options = model_options + + def calculate_sigmas(self, steps): + sigmas = None + + discard_penultimate_sigma = False + if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS: + steps += 1 + discard_penultimate_sigma = True + + sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + def set_steps(self, steps, denoise=None): + self.steps = steps + if denoise is None or denoise > 0.9999: + self.sigmas = self.calculate_sigmas(steps).to(self.device) + else: + if denoise <= 0.0: + self.sigmas = torch.FloatTensor([]) + else: + new_steps = int(steps/denoise) + sigmas = self.calculate_sigmas(new_steps).to(self.device) + self.sigmas = sigmas[-(steps + 1):] + + def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + if sigmas is None: + sigmas = self.sigmas + + if last_step is not None and last_step < (len(sigmas) - 1): + sigmas = sigmas[:last_step + 1] + if force_full_denoise: + sigmas[-1] = 0 + + if start_step is not None: + if start_step < (len(sigmas) - 1): + sigmas = sigmas[start_step:] + else: + if latent_image is not None: + return latent_image + else: + return torch.zeros_like(noise) + + sampler = sampler_object(self.sampler) + + return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) diff --git a/comfy/sd.py b/comfy/sd.py new file mode 100644 index 0000000000000000000000000000000000000000..5b95cf75aec5cc9fef60aed5392f5ba0b0cd1784 --- /dev/null +++ b/comfy/sd.py @@ -0,0 +1,1212 @@ +from __future__ import annotations +import json +import torch +from enum import Enum +import logging + +from comfy import model_management +from comfy.utils import ProgressBar +from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine +from .ldm.cascade.stage_a import StageA +from .ldm.cascade.stage_c_coder import StageC_coder +from .ldm.audio.autoencoder import AudioOobleckVAE +import comfy.ldm.genmo.vae.model +import comfy.ldm.lightricks.vae.causal_video_autoencoder +import comfy.ldm.cosmos.vae +import comfy.ldm.wan.vae +import comfy.ldm.hunyuan3d.vae +import comfy.ldm.ace.vae.music_dcae_pipeline +import yaml +import math + +import comfy.utils + +from . import clip_vision +from . import gligen +from . import diffusers_convert +from . import model_detection + +from . import sd1_clip +from . import sdxl_clip +import comfy.text_encoders.sd2_clip +import comfy.text_encoders.sd3_clip +import comfy.text_encoders.sa_t5 +import comfy.text_encoders.aura_t5 +import comfy.text_encoders.pixart_t5 +import comfy.text_encoders.hydit +import comfy.text_encoders.flux +import comfy.text_encoders.long_clipl +import comfy.text_encoders.genmo +import comfy.text_encoders.lt +import comfy.text_encoders.hunyuan_video +import comfy.text_encoders.cosmos +import comfy.text_encoders.lumina2 +import comfy.text_encoders.wan +import comfy.text_encoders.hidream +import comfy.text_encoders.ace +import comfy.text_encoders.omnigen2 + +import comfy.model_patcher +import comfy.lora +import comfy.lora_convert +import comfy.hooks +import comfy.t2i_adapter.adapter +import comfy.taesd.taesd + +import comfy.ldm.flux.redux + +def load_lora_for_models(model, clip, lora, strength_model, strength_clip): + key_map = {} + if model is not None: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + if clip is not None: + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + + lora = comfy.lora_convert.convert_lora(lora) + loaded = comfy.lora.load_lora(lora, key_map) + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + else: + k = () + new_modelpatcher = None + + if clip is not None: + new_clip = clip.clone() + k1 = new_clip.add_patches(loaded, strength_clip) + else: + k1 = () + new_clip = None + k = set(k) + k1 = set(k1) + for x in loaded: + if (x not in k) and (x not in k1): + logging.warning("NOT LOADED {}".format(x)) + + return (new_modelpatcher, new_clip) + + +class CLIP: + def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}): + if no_init: + return + params = target.params.copy() + clip = target.clip + tokenizer = target.tokenizer + + load_device = model_options.get("load_device", model_management.text_encoder_device()) + offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device()) + dtype = model_options.get("dtype", None) + if dtype is None: + dtype = model_management.text_encoder_dtype(load_device) + + params['dtype'] = dtype + params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))) + params['model_options'] = model_options + + self.cond_stage_model = clip(**(params)) + + for dt in self.cond_stage_model.dtypes: + if not model_management.supports_cast(load_device, dt): + load_device = offload_device + if params['device'] != offload_device: + self.cond_stage_model.to(offload_device) + logging.warning("Had to shift TE back.") + + self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) + self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram + self.patcher.is_clip = True + self.apply_hooks_to_conds = None + if params['device'] == load_device: + model_management.load_models_gpu([self.patcher], force_full_load=True) + self.layer_idx = None + self.use_clip_schedule = False + logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype)) + self.tokenizer_options = {} + + def clone(self): + n = CLIP(no_init=True) + n.patcher = self.patcher.clone() + n.cond_stage_model = self.cond_stage_model + n.tokenizer = self.tokenizer + n.layer_idx = self.layer_idx + n.tokenizer_options = self.tokenizer_options.copy() + n.use_clip_schedule = self.use_clip_schedule + n.apply_hooks_to_conds = self.apply_hooks_to_conds + return n + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + return self.patcher.add_patches(patches, strength_patch, strength_model) + + def set_tokenizer_option(self, option_name, value): + self.tokenizer_options[option_name] = value + + def clip_layer(self, layer_idx): + self.layer_idx = layer_idx + + def tokenize(self, text, return_word_ids=False, **kwargs): + tokenizer_options = kwargs.get("tokenizer_options", {}) + if len(self.tokenizer_options) > 0: + tokenizer_options = {**self.tokenizer_options, **tokenizer_options} + if len(tokenizer_options) > 0: + kwargs["tokenizer_options"] = tokenizer_options + return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) + + def add_hooks_to_dict(self, pooled_dict: dict[str]): + if self.apply_hooks_to_conds: + pooled_dict["hooks"] = self.apply_hooks_to_conds + return pooled_dict + + def encode_from_tokens_scheduled(self, tokens, unprojected=False, add_dict: dict[str]={}, show_pbar=True): + all_cond_pooled: list[tuple[torch.Tensor, dict[str]]] = [] + all_hooks = self.patcher.forced_hooks + if all_hooks is None or not self.use_clip_schedule: + # if no hooks or shouldn't use clip schedule, do unscheduled encode_from_tokens and perform add_dict + return_pooled = "unprojected" if unprojected else True + pooled_dict = self.encode_from_tokens(tokens, return_pooled=return_pooled, return_dict=True) + cond = pooled_dict.pop("cond") + # add/update any keys with the provided add_dict + pooled_dict.update(add_dict) + all_cond_pooled.append([cond, pooled_dict]) + else: + scheduled_keyframes = all_hooks.get_hooks_for_clip_schedule() + + self.cond_stage_model.reset_clip_options() + if self.layer_idx is not None: + self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) + if unprojected: + self.cond_stage_model.set_clip_options({"projected_pooled": False}) + + self.load_model() + all_hooks.reset() + self.patcher.patch_hooks(None) + if show_pbar: + pbar = ProgressBar(len(scheduled_keyframes)) + + for scheduled_opts in scheduled_keyframes: + t_range = scheduled_opts[0] + # don't bother encoding any conds outside of start_percent and end_percent bounds + if "start_percent" in add_dict: + if t_range[1] < add_dict["start_percent"]: + continue + if "end_percent" in add_dict: + if t_range[0] > add_dict["end_percent"]: + continue + hooks_keyframes = scheduled_opts[1] + for hook, keyframe in hooks_keyframes: + hook.hook_keyframe._current_keyframe = keyframe + # apply appropriate hooks with values that match new hook_keyframe + self.patcher.patch_hooks(all_hooks) + # perform encoding as normal + o = self.cond_stage_model.encode_token_weights(tokens) + cond, pooled = o[:2] + pooled_dict = {"pooled_output": pooled} + # add clip_start_percent and clip_end_percent in pooled + pooled_dict["clip_start_percent"] = t_range[0] + pooled_dict["clip_end_percent"] = t_range[1] + # add/update any keys with the provided add_dict + pooled_dict.update(add_dict) + # add hooks stored on clip + self.add_hooks_to_dict(pooled_dict) + all_cond_pooled.append([cond, pooled_dict]) + if show_pbar: + pbar.update(1) + model_management.throw_exception_if_processing_interrupted() + all_hooks.reset() + return all_cond_pooled + + def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False): + self.cond_stage_model.reset_clip_options() + + if self.layer_idx is not None: + self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) + + if return_pooled == "unprojected": + self.cond_stage_model.set_clip_options({"projected_pooled": False}) + + self.load_model() + o = self.cond_stage_model.encode_token_weights(tokens) + cond, pooled = o[:2] + if return_dict: + out = {"cond": cond, "pooled_output": pooled} + if len(o) > 2: + for k in o[2]: + out[k] = o[2][k] + self.add_hooks_to_dict(out) + return out + + if return_pooled: + return cond, pooled + return cond + + def encode(self, text): + tokens = self.tokenize(text) + return self.encode_from_tokens(tokens) + + def load_sd(self, sd, full_model=False): + if full_model: + return self.cond_stage_model.load_state_dict(sd, strict=False) + else: + return self.cond_stage_model.load_sd(sd) + + def get_sd(self): + sd_clip = self.cond_stage_model.state_dict() + sd_tokenizer = self.tokenizer.state_dict() + for k in sd_tokenizer: + sd_clip[k] = sd_tokenizer[k] + return sd_clip + + def load_model(self): + model_management.load_model_gpu(self.patcher) + return self.patcher + + def get_key_patches(self): + return self.patcher.get_key_patches() + +class VAE: + def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None): + if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format + sd = diffusers_convert.convert_vae_state_dict(sd) + + self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) + self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) + self.downscale_ratio = 8 + self.upscale_ratio = 8 + self.latent_channels = 4 + self.latent_dim = 2 + self.output_channels = 3 + self.process_input = lambda image: image * 2.0 - 1.0 + self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) + self.working_dtypes = [torch.bfloat16, torch.float32] + self.disable_offload = False + + self.downscale_index_formula = None + self.upscale_index_formula = None + self.extra_1d_channel = None + + if config is None: + if "decoder.mid.block_1.mix_factor" in sd: + encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + decoder_config = encoder_config.copy() + decoder_config["video_kernel_size"] = [3, 1, 1] + decoder_config["alpha"] = 0.0 + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, + decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) + elif "taesd_decoder.1.weight" in sd: + self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] + self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) + elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade + self.first_stage_model = StageA() + self.downscale_ratio = 4 + self.upscale_ratio = 4 + #TODO + #self.memory_used_encode + #self.memory_used_decode + self.process_input = lambda image: image + self.process_output = lambda image: image + elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade + self.first_stage_model = StageC_coder() + self.downscale_ratio = 32 + self.latent_channels = 16 + new_sd = {} + for k in sd: + new_sd["encoder.{}".format(k)] = sd[k] + sd = new_sd + elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade + self.first_stage_model = StageC_coder() + self.latent_channels = 16 + new_sd = {} + for k in sd: + new_sd["previewer.{}".format(k)] = sd[k] + sd = new_sd + elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade + self.first_stage_model = StageC_coder() + self.downscale_ratio = 32 + self.latent_channels = 16 + elif "decoder.conv_in.weight" in sd: + #default SD1.x/SD2.x VAE parameters + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + + if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE + ddconfig['ch_mult'] = [1, 2, 4] + self.downscale_ratio = 4 + self.upscale_ratio = 4 + + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + if 'post_quant_conv.weight' in sd: + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) + else: + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + elif "decoder.layers.1.layers.0.beta" in sd: + self.first_stage_model = AudioOobleckVAE() + self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) + self.latent_channels = 64 + self.output_channels = 2 + self.upscale_ratio = 2048 + self.downscale_ratio = 2048 + self.latent_dim = 1 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + self.disable_offload = True + elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae + if "blocks.2.blocks.3.stack.5.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."}) + if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."}) + self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE() + self.latent_channels = 12 + self.latent_dim = 3 + self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) + self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8) + self.upscale_index_formula = (6, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8) + self.downscale_index_formula = (6, 8, 8) + self.working_dtypes = [torch.float16, torch.float32] + elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv + tensor_conv1 = sd["decoder.up_blocks.0.res_blocks.0.conv1.conv.weight"] + version = 0 + if tensor_conv1.shape[0] == 512: + version = 0 + elif tensor_conv1.shape[0] == 1024: + version = 1 + if "encoder.down_blocks.1.conv.conv.bias" in sd: + version = 2 + vae_config = None + if metadata is not None and "config" in metadata: + vae_config = json.loads(metadata["config"]).get("vae", None) + self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config) + self.latent_channels = 128 + self.latent_dim = 3 + self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype) + self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32) + self.upscale_index_formula = (8, 32, 32) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32) + self.downscale_index_formula = (8, 32, 32) + self.working_dtypes = [torch.bfloat16, torch.float32] + elif "decoder.conv_in.conv.weight" in sd: + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + ddconfig["conv3d"] = True + ddconfig["time_compress"] = 4 + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) + self.upscale_index_formula = (4, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) + self.downscale_index_formula = (4, 8, 8) + self.latent_dim = 3 + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) + self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype) + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + elif "decoder.unpatcher3d.wavelets" in sd: + self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8) + self.upscale_index_formula = (8, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8) + self.downscale_index_formula = (8, 8, 8) + self.latent_dim = 3 + self.latent_channels = 16 + ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8} + self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig) + #TODO: these values are a bit off because this is not a standard VAE + self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype) + self.working_dtypes = [torch.bfloat16, torch.float32] + elif "decoder.middle.0.residual.0.gamma" in sd: + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) + self.upscale_index_formula = (4, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) + self.downscale_index_formula = (4, 8, 8) + self.latent_dim = 3 + self.latent_channels = 16 + ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0} + self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig) + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype) + elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd: + self.latent_dim = 1 + ln_post = "geo_decoder.ln_post.weight" in sd + inner_size = sd["geo_decoder.output_proj.weight"].shape[1] + downsample_ratio = sd["post_kl.weight"].shape[0] // inner_size + mlp_expand = sd["geo_decoder.cross_attn_decoder.mlp.c_fc.weight"].shape[0] // inner_size + self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) # TODO + self.memory_used_decode = lambda shape, dtype: (1024 * 1024 * 1024 * 2.0) * model_management.dtype_size(dtype) # TODO + ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post} + self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE(**ddconfig) + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio + self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) + self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) + self.latent_channels = 8 + self.output_channels = 2 + self.upscale_ratio = 4096 + self.downscale_ratio = 4096 + self.latent_dim = 2 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + self.disable_offload = True + self.extra_1d_channel = 16 + else: + logging.warning("WARNING: No VAE weights detected, VAE not initalized.") + self.first_stage_model = None + return + else: + self.first_stage_model = AutoencoderKL(**(config['params'])) + self.first_stage_model = self.first_stage_model.eval() + + m, u = self.first_stage_model.load_state_dict(sd, strict=False) + if len(m) > 0: + logging.warning("Missing VAE keys {}".format(m)) + + if len(u) > 0: + logging.debug("Leftover VAE keys {}".format(u)) + + if device is None: + device = model_management.vae_device() + self.device = device + offload_device = model_management.vae_offload_device() + if dtype is None: + dtype = model_management.vae_dtype(self.device, self.working_dtypes) + self.vae_dtype = dtype + self.first_stage_model.to(self.vae_dtype) + self.output_device = model_management.intermediate_device() + + self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) + logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) + + def throw_exception_if_invalid(self): + if self.first_stage_model is None: + raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.") + + def vae_encode_crop_pixels(self, pixels): + downscale_ratio = self.spacial_compression_encode() + + dims = pixels.shape[1:-1] + for d in range(len(dims)): + x = (dims[d] // downscale_ratio) * downscale_ratio + x_offset = (dims[d] % downscale_ratio) // 2 + if x != dims[d]: + pixels = pixels.narrow(d + 1, x_offset, x) + return pixels + + def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): + steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) + + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + output = self.process_output( + (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) + / 3.0) + return output + + def decode_tiled_1d(self, samples, tile_x=128, overlap=32): + if samples.ndim == 3: + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + else: + og_shape = samples.shape + samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1)) + decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float() + + return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) + + def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device)) + + def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) + + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples /= 3.0 + return samples + + def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048): + if self.latent_dim == 1: + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + out_channels = self.latent_channels + upscale_amount = 1 / self.downscale_ratio + else: + extra_channel_size = self.extra_1d_channel + out_channels = self.latent_channels * extra_channel_size + tile_x = tile_x // extra_channel_size + overlap = overlap // extra_channel_size + upscale_amount = 1 / self.downscale_ratio + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float() + + out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device) + if self.latent_dim == 1: + return out + else: + return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1) + + def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)): + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device) + + def decode(self, samples_in, vae_options={}): + self.throw_exception_if_invalid() + pixel_samples = None + try: + memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + + for x in range(0, samples_in.shape[0], batch_number): + samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) + out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float()) + if pixel_samples is None: + pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) + pixel_samples[x:x+batch_number] = out + except model_management.OOM_EXCEPTION: + logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") + dims = samples_in.ndim - 2 + if dims == 1 or self.extra_1d_channel is not None: + pixel_samples = self.decode_tiled_1d(samples_in) + elif dims == 2: + pixel_samples = self.decode_tiled_(samples_in) + elif dims == 3: + tile = 256 // self.spacial_compression_decode() + overlap = tile // 4 + pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) + + pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) + return pixel_samples + + def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): + self.throw_exception_if_invalid() + memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) + dims = samples.ndim - 2 + args = {} + if tile_x is not None: + args["tile_x"] = tile_x + if tile_y is not None: + args["tile_y"] = tile_y + if overlap is not None: + args["overlap"] = overlap + + if dims == 1: + args.pop("tile_y") + output = self.decode_tiled_1d(samples, **args) + elif dims == 2: + output = self.decode_tiled_(samples, **args) + elif dims == 3: + if overlap_t is None: + args["overlap"] = (1, overlap, overlap) + else: + args["overlap"] = (max(1, overlap_t), overlap, overlap) + if tile_t is not None: + args["tile_t"] = max(2, tile_t) + + output = self.decode_tiled_3d(samples, **args) + return output.movedim(1, -1) + + def encode(self, pixel_samples): + self.throw_exception_if_invalid() + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) + pixel_samples = pixel_samples.movedim(-1, 1) + if self.latent_dim == 3 and pixel_samples.ndim < 5: + pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + try: + memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / max(1, memory_used)) + batch_number = max(1, batch_number) + samples = None + for x in range(0, pixel_samples.shape[0], batch_number): + pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device) + out = self.first_stage_model.encode(pixels_in).to(self.output_device).float() + if samples is None: + samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device) + samples[x:x + batch_number] = out + + except model_management.OOM_EXCEPTION: + logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") + if self.latent_dim == 3: + tile = 256 + overlap = tile // 4 + samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) + elif self.latent_dim == 1 or self.extra_1d_channel is not None: + samples = self.encode_tiled_1d(pixel_samples) + else: + samples = self.encode_tiled_(pixel_samples) + + return samples + + def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): + self.throw_exception_if_invalid() + pixel_samples = self.vae_encode_crop_pixels(pixel_samples) + dims = self.latent_dim + pixel_samples = pixel_samples.movedim(-1, 1) + if dims == 3: + pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + + memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) # TODO: calculate mem required for tile + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) + + args = {} + if tile_x is not None: + args["tile_x"] = tile_x + if tile_y is not None: + args["tile_y"] = tile_y + if overlap is not None: + args["overlap"] = overlap + + if dims == 1: + args.pop("tile_y") + samples = self.encode_tiled_1d(pixel_samples, **args) + elif dims == 2: + samples = self.encode_tiled_(pixel_samples, **args) + elif dims == 3: + if tile_t is not None: + tile_t_latent = max(2, self.downscale_ratio[0](tile_t)) + else: + tile_t_latent = 9999 + args["tile_t"] = self.upscale_ratio[0](tile_t_latent) + + if overlap_t is None: + args["overlap"] = (1, overlap, overlap) + else: + args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap) + maximum = pixel_samples.shape[2] + maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum)) + + samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args) + + return samples + + def get_sd(self): + return self.first_stage_model.state_dict() + + def spacial_compression_decode(self): + try: + return self.upscale_ratio[-1] + except: + return self.upscale_ratio + + def spacial_compression_encode(self): + try: + return self.downscale_ratio[-1] + except: + return self.downscale_ratio + + def temporal_compression_decode(self): + try: + return round(self.upscale_ratio[0](8192) / 8192) + except: + return None + +class StyleModel: + def __init__(self, model, device="cpu"): + self.model = model + + def get_cond(self, input): + return self.model(input.last_hidden_state) + + +def load_style_model(ckpt_path): + model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + keys = model_data.keys() + if "style_embedding" in keys: + model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) + elif "redux_down.weight" in keys: + model = comfy.ldm.flux.redux.ReduxImageEncoder() + else: + raise Exception("invalid style model {}".format(ckpt_path)) + model.load_state_dict(model_data) + return StyleModel(model) + +class CLIPType(Enum): + STABLE_DIFFUSION = 1 + STABLE_CASCADE = 2 + SD3 = 3 + STABLE_AUDIO = 4 + HUNYUAN_DIT = 5 + FLUX = 6 + MOCHI = 7 + LTXV = 8 + HUNYUAN_VIDEO = 9 + PIXART = 10 + COSMOS = 11 + LUMINA2 = 12 + WAN = 13 + HIDREAM = 14 + CHROMA = 15 + ACE = 16 + OMNIGEN2 = 17 + + +def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): + clip_data = [] + for p in ckpt_paths: + clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) + return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options) + + +class TEModel(Enum): + CLIP_L = 1 + CLIP_H = 2 + CLIP_G = 3 + T5_XXL = 4 + T5_XL = 5 + T5_BASE = 6 + LLAMA3_8 = 7 + T5_XXL_OLD = 8 + GEMMA_2_2B = 9 + QWEN25_3B = 10 + +def detect_te_model(sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return TEModel.CLIP_G + if "text_model.encoder.layers.22.mlp.fc1.weight" in sd: + return TEModel.CLIP_H + if "text_model.encoder.layers.0.mlp.fc1.weight" in sd: + return TEModel.CLIP_L + if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: + weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"] + if weight.shape[-1] == 4096: + return TEModel.T5_XXL + elif weight.shape[-1] == 2048: + return TEModel.T5_XL + if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd: + return TEModel.T5_XXL_OLD + if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd: + return TEModel.T5_BASE + if 'model.layers.0.post_feedforward_layernorm.weight' in sd: + return TEModel.GEMMA_2_2B + if 'model.layers.0.self_attn.k_proj.bias' in sd: + return TEModel.QWEN25_3B + if "model.layers.0.post_attention_layernorm.weight" in sd: + return TEModel.LLAMA3_8 + return None + + +def t5xxl_detect(clip_data): + weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" + weight_name_old = "encoder.block.23.layer.1.DenseReluDense.wi.weight" + + for sd in clip_data: + if weight_name in sd or weight_name_old in sd: + return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd) + + return {} + +def llama_detect(clip_data): + weight_name = "model.layers.0.self_attn.k_proj.weight" + + for sd in clip_data: + if weight_name in sd: + return comfy.text_encoders.hunyuan_video.llama_detect(sd) + + return {} + +def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): + clip_data = state_dicts + + class EmptyClass: + pass + + for i in range(len(clip_data)): + if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: + clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") + else: + if "text_projection" in clip_data[i]: + clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node + + tokenizer_data = {} + clip_target = EmptyClass() + clip_target.params = {} + if len(clip_data) == 1: + te_model = detect_te_model(clip_data[0]) + if te_model == TEModel.CLIP_G: + if clip_type == CLIPType.STABLE_CASCADE: + clip_target.clip = sdxl_clip.StableCascadeClipModel + clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer + elif clip_type == CLIPType.SD3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.HIDREAM: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + else: + clip_target.clip = sdxl_clip.SDXLRefinerClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif te_model == TEModel.CLIP_H: + clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel + clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer + elif te_model == TEModel.T5_XXL: + if clip_type == CLIPType.SD3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.LTXV: + clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer + elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA: + clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer + elif clip_type == CLIPType.WAN: + clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + elif clip_type == CLIPType.HIDREAM: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), + clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + else: #CLIPType.MOCHI + clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer + elif te_model == TEModel.T5_XXL_OLD: + clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer + elif te_model == TEModel.T5_XL: + clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model + clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer + elif te_model == TEModel.T5_BASE: + if clip_type == CLIPType.ACE or "spiece_model" in clip_data[0]: + clip_target.clip = comfy.text_encoders.ace.AceT5Model + clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + else: + clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model + clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer + elif te_model == TEModel.GEMMA_2_2B: + clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + elif te_model == TEModel.LLAMA3_8: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data), + clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + elif te_model == TEModel.QWEN25_3B: + clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer + else: + # clip_l + if clip_type == CLIPType.SD3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.HIDREAM: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + else: + clip_target.clip = sd1_clip.SD1ClipModel + clip_target.tokenizer = sd1_clip.SD1Tokenizer + elif len(clip_data) == 2: + if clip_type == CLIPType.SD3: + te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])] + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.HUNYUAN_DIT: + clip_target.clip = comfy.text_encoders.hydit.HyditModel + clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer + elif clip_type == CLIPType.FLUX: + clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer + elif clip_type == CLIPType.HUNYUAN_VIDEO: + clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer + elif clip_type == CLIPType.HIDREAM: + # Detect + hidream_dualclip_classes = [] + for hidream_te in clip_data: + te_model = detect_te_model(hidream_te) + hidream_dualclip_classes.append(te_model) + + clip_l = TEModel.CLIP_L in hidream_dualclip_classes + clip_g = TEModel.CLIP_G in hidream_dualclip_classes + t5 = TEModel.T5_XXL in hidream_dualclip_classes + llama = TEModel.LLAMA3_8 in hidream_dualclip_classes + + # Initialize t5xxl_detect and llama_detect kwargs if needed + t5_kwargs = t5xxl_detect(clip_data) if t5 else {} + llama_kwargs = llama_detect(clip_data) if llama else {} + + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + else: + clip_target.clip = sdxl_clip.SDXLClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif len(clip_data) == 3: + clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif len(clip_data) == 4: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), **llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + + parameters = 0 + for c in clip_data: + parameters += comfy.utils.calculate_parameters(c) + tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) + + clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options) + for c in clip_data: + m, u = clip.load_sd(c) + if len(m) > 0: + logging.warning("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected: {}".format(u)) + return clip + +def load_gligen(ckpt_path): + data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + model = gligen.load_gligen(data) + if model_management.should_use_fp16(): + model = model.half() + return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) + +def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): + logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") + model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) + #TODO: this function is a mess and should be removed eventually + if config is None: + with open(config_path, 'r') as stream: + config = yaml.safe_load(stream) + model_config_params = config['model']['params'] + clip_config = model_config_params['cond_stage_config'] + + if "parameterization" in model_config_params: + if model_config_params["parameterization"] == "v": + m = model.clone() + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): + pass + m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) + model = m + + layer_idx = clip_config.get("params", {}).get("layer_idx", None) + if layer_idx is not None: + clip.clip_layer(layer_idx) + + return (model, clip, vae) + +def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): + sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) + out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata) + if out is None: + raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) + return out + +def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None): + clip = None + clipvision = None + vae = None + model = None + model_patcher = None + + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) + weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) + load_device = model_management.get_torch_device() + + model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) + if model_config is None: + logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.") + diffusion_model = load_diffusion_model_state_dict(sd, model_options={}) + if diffusion_model is None: + return None + return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used' + + + unet_weight_dtype = list(model_config.supported_inference_dtypes) + if model_config.scaled_fp8 is not None: + weight_dtype = None + + model_config.custom_operations = model_options.get("custom_operations", None) + unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) + + if unet_dtype is None: + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) + + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + + if model_config.clip_vision_prefix is not None: + if output_clipvision: + clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) + + if output_model: + inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) + model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) + model.load_model_weights(sd, diffusion_model_prefix) + + if output_vae: + vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) + vae_sd = model_config.process_vae_state_dict(vae_sd) + vae = VAE(sd=vae_sd, metadata=metadata) + + if output_clip: + clip_target = model_config.clip_target(state_dict=sd) + if clip_target is not None: + clip_sd = model_config.process_clip_state_dict(sd) + if len(clip_sd) > 0: + parameters = comfy.utils.calculate_parameters(clip_sd) + clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options) + m, u = clip.load_sd(clip_sd, full_model=True) + if len(m) > 0: + m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) + if len(m_filter) > 0: + logging.warning("clip missing: {}".format(m)) + else: + logging.debug("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected {}:".format(u)) + else: + logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") + + left_over = sd.keys() + if len(left_over) > 0: + logging.debug("left over keys: {}".format(left_over)) + + if output_model: + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device()) + if inital_load_device != torch.device("cpu"): + logging.info("loaded diffusion model directly to GPU") + model_management.load_models_gpu([model_patcher], force_full_load=True) + + return (model_patcher, clip, vae, clipvision) + + +def load_diffusion_model_state_dict(sd, model_options={}): + """ + Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats. + + Args: + sd (dict): State dictionary containing model weights and configuration + model_options (dict, optional): Additional options for model loading. Supports: + - dtype: Override model data type + - custom_operations: Custom model operations + - fp8_optimizations: Enable FP8 optimizations + + Returns: + ModelPatcher: A wrapped model instance that handles device management and weight loading. + Returns None if the model configuration cannot be detected. + + The function: + 1. Detects and handles different model formats (regular, diffusers, mmdit) + 2. Configures model dtype based on parameters and device capabilities + 3. Handles weight conversion and device placement + 4. Manages model optimization settings + 5. Loads weights and returns a device-managed model instance + """ + dtype = model_options.get("dtype", None) + + #Allow loading unets from checkpoint files + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) + if len(temp_sd) > 0: + sd = temp_sd + + parameters = comfy.utils.calculate_parameters(sd) + weight_dtype = comfy.utils.weight_dtype(sd) + + load_device = model_management.get_torch_device() + model_config = model_detection.model_config_from_unet(sd, "") + + if model_config is not None: + new_sd = sd + else: + new_sd = model_detection.convert_diffusers_mmdit(sd, "") + if new_sd is not None: #diffusers mmdit + model_config = model_detection.model_config_from_unet(new_sd, "") + if model_config is None: + return None + else: #diffusers unet + model_config = model_detection.model_config_from_diffusers_unet(sd) + if model_config is None: + return None + + diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) + + new_sd = {} + for k in diffusers_keys: + if k in sd: + new_sd[diffusers_keys[k]] = sd.pop(k) + else: + logging.warning("{} {}".format(diffusers_keys[k], k)) + + offload_device = model_management.unet_offload_device() + unet_weight_dtype = list(model_config.supported_inference_dtypes) + if model_config.scaled_fp8 is not None: + weight_dtype = None + + if dtype is None: + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) + else: + unet_dtype = dtype + + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations) + if model_options.get("fp8_optimizations", False): + model_config.optimizations["fp8"] = True + + model = model_config.get_model(new_sd, "") + model = model.to(offload_device) + model.load_model_weights(new_sd, "") + left_over = sd.keys() + if len(left_over) > 0: + logging.info("left over keys in diffusion model: {}".format(left_over)) + return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) + + +def load_diffusion_model(unet_path, model_options={}): + sd = comfy.utils.load_torch_file(unet_path) + model = load_diffusion_model_state_dict(sd, model_options=model_options) + if model is None: + logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path)) + raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + return model + +def load_unet(unet_path, dtype=None): + logging.warning("The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model") + return load_diffusion_model(unet_path, model_options={"dtype": dtype}) + +def load_unet_state_dict(sd, dtype=None): + logging.warning("The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict") + return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype}) + +def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): + clip_sd = None + load_models = [model] + if clip is not None: + load_models.append(clip.load_model()) + clip_sd = clip.get_sd() + vae_sd = None + if vae is not None: + vae_sd = vae.get_sd() + + model_management.load_models_gpu(load_models, force_patch_weights=True) + clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None + sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd) + for k in extra_keys: + sd[k] = extra_keys[k] + + for k in sd: + t = sd[k] + if not t.is_contiguous(): + sd[k] = t.contiguous() + + comfy.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..ade340fd190106e5bf388a5ac35b6e68c1e52292 --- /dev/null +++ b/comfy/sd1_clip.py @@ -0,0 +1,687 @@ +import os + +from transformers import CLIPTokenizer +import comfy.ops +import torch +import traceback +import zipfile +from . import model_management +import comfy.clip_model +import json +import logging +import numbers +import re + +def gen_empty_tokens(special_tokens, length): + start_token = special_tokens.get("start", None) + end_token = special_tokens.get("end", None) + pad_token = special_tokens.get("pad") + output = [] + if start_token is not None: + output.append(start_token) + if end_token is not None: + output.append(end_token) + output += [pad_token] * (length - len(output)) + return output + +class ClipTokenWeightEncoder: + def encode_token_weights(self, token_weight_pairs): + to_encode = list() + max_token_len = 0 + has_weights = False + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + max_token_len = max(len(tokens), max_token_len) + has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) + to_encode.append(tokens) + + sections = len(to_encode) + if has_weights or sections == 0: + if hasattr(self, "gen_empty_tokens"): + to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len)) + else: + to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) + + o = self.encode(to_encode) + out, pooled = o[:2] + + if pooled is not None: + first_pooled = pooled[0:1].to(model_management.intermediate_device()) + else: + first_pooled = pooled + + output = [] + for k in range(0, sections): + z = out[k:k+1] + if has_weights: + z_empty = out[-1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k][j][1] + if weight != 1.0: + z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] + output.append(z) + + if (len(output) == 0): + r = (out[-1:].to(model_management.intermediate_device()), first_pooled) + else: + r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled) + + if len(o) > 2: + extra = {} + for k in o[2]: + v = o[2][k] + if k == "attention_mask": + v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()) + extra[k] = v + + r = r + (extra,) + return r + +class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): + LAYERS = [ + "last", + "pooled", + "hidden", + "all" + ] + def __init__(self, device="cpu", max_length=77, + freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, + return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + if "model_name" not in model_options: + model_options = {**model_options, "model_name": "clip_l"} + + if isinstance(textmodel_json_config, dict): + config = textmodel_json_config + else: + with open(textmodel_json_config) as f: + config = json.load(f) + + te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {}) + for k, v in te_model_options.items(): + config[k] = v + + operations = model_options.get("custom_operations", None) + scaled_fp8 = None + + if operations is None: + scaled_fp8 = model_options.get("scaled_fp8", None) + if scaled_fp8 is not None: + operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8) + else: + operations = comfy.ops.manual_cast + + self.operations = operations + self.transformer = model_class(config, dtype, device, self.operations) + if scaled_fp8 is not None: + self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8)) + + self.num_layers = self.transformer.num_layers + + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = None + self.special_tokens = special_tokens + + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = enable_attention_masks + self.zero_out_masked = zero_out_masked + + self.layer_norm_hidden_state = layer_norm_hidden_state + self.return_projected_pooled = return_projected_pooled + self.return_attention_masks = return_attention_masks + + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < self.num_layers + self.set_clip_options({"layer": layer_idx}) + self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def set_clip_options(self, options): + layer_idx = options.get("layer", self.layer_idx) + self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) + if self.layer == "all": + pass + elif layer_idx is None or abs(layer_idx) > self.num_layers: + self.layer = "last" + else: + self.layer = "hidden" + self.layer_idx = layer_idx + + def reset_clip_options(self): + self.layer = self.options_default[0] + self.layer_idx = self.options_default[1] + self.return_projected_pooled = self.options_default[2] + + def process_tokens(self, tokens, device): + end_token = self.special_tokens.get("end", None) + if end_token is None: + cmp_token = self.special_tokens.get("pad", -1) + else: + cmp_token = end_token + + embeds_out = [] + attention_masks = [] + num_tokens = [] + + for x in tokens: + attention_mask = [] + tokens_temp = [] + other_embeds = [] + eos = False + index = 0 + for y in x: + if isinstance(y, numbers.Integral): + if eos: + attention_mask.append(0) + else: + attention_mask.append(1) + token = int(y) + tokens_temp += [token] + if not eos and token == cmp_token: + if end_token is None: + attention_mask[-1] = 0 + eos = True + else: + other_embeds.append((index, y)) + index += 1 + + tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) + tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32) + index = 0 + pad_extra = 0 + for o in other_embeds: + emb = o[1] + if torch.is_tensor(emb): + emb = {"type": "embedding", "data": emb} + + emb_type = emb.get("type", None) + if emb_type == "embedding": + emb = emb.get("data", None) + else: + if hasattr(self.transformer, "preprocess_embed"): + emb = self.transformer.preprocess_embed(emb, device=device) + else: + emb = None + + if emb is None: + index += -1 + continue + + ind = index + o[0] + emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) + emb_shape = emb.shape[1] + if emb.shape[-1] == tokens_embed.shape[-1]: + tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) + attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] + index += emb_shape - 1 + else: + index += -1 + pad_extra += emb_shape + logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1])) + + if pad_extra > 0: + padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32) + tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1) + attention_mask = attention_mask + [0] * pad_extra + + embeds_out.append(tokens_embed) + attention_masks.append(attention_mask) + num_tokens.append(sum(attention_mask)) + + return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens + + def forward(self, tokens): + device = self.transformer.get_input_embeddings().weight.device + embeds, attention_mask, num_tokens = self.process_tokens(tokens, device) + + attention_mask_model = None + if self.enable_attention_masks: + attention_mask_model = attention_mask + + if self.layer == "all": + intermediate_output = "all" + else: + intermediate_output = self.layer_idx + + outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32) + + if self.layer == "last": + z = outputs[0].float() + else: + z = outputs[1].float() + + if self.zero_out_masked: + z *= attention_mask.unsqueeze(-1).float() + + pooled_output = None + if len(outputs) >= 3: + if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: + pooled_output = outputs[3].float() + elif outputs[2] is not None: + pooled_output = outputs[2].float() + + extra = {} + if self.return_attention_masks: + extra["attention_mask"] = attention_mask + + if len(extra) > 0: + return z, pooled_output, extra + + return z, pooled_output + + def encode(self, tokens): + return self(tokens) + + def load_sd(self, sd): + return self.transformer.load_state_dict(sd, strict=False) + +def parse_parentheses(string): + result = [] + current_item = "" + nesting_level = 0 + for char in string: + if char == "(": + if nesting_level == 0: + if current_item: + result.append(current_item) + current_item = "(" + else: + current_item = "(" + else: + current_item += char + nesting_level += 1 + elif char == ")": + nesting_level -= 1 + if nesting_level == 0: + result.append(current_item + ")") + current_item = "" + else: + current_item += char + else: + current_item += char + if current_item: + result.append(current_item) + return result + +def token_weights(string, current_weight): + a = parse_parentheses(string) + out = [] + for x in a: + weight = current_weight + if len(x) >= 2 and x[-1] == ')' and x[0] == '(': + x = x[1:-1] + xx = x.rfind(":") + weight *= 1.1 + if xx > 0: + try: + weight = float(x[xx+1:]) + x = x[:xx] + except: + pass + out += token_weights(x, weight) + else: + out += [(x, current_weight)] + return out + +def escape_important(text): + text = text.replace("\\)", "\0\1") + text = text.replace("\\(", "\0\2") + return text + +def unescape_important(text): + text = text.replace("\0\1", ")") + text = text.replace("\0\2", "(") + return text + +def safe_load_embed_zip(embed_path): + with zipfile.ZipFile(embed_path) as myzip: + names = list(filter(lambda a: "data/" in a, myzip.namelist())) + names.reverse() + for n in names: + with myzip.open(n) as myfile: + data = myfile.read() + number = len(data) // 4 + length_embed = 1024 #sd2.x + if number < 768: + continue + if number % 768 == 0: + length_embed = 768 #sd1.x + num_embeds = number // length_embed + embed = torch.frombuffer(data, dtype=torch.float) + out = embed.reshape((num_embeds, length_embed)).clone() + del embed + return out + +def expand_directory_list(directories): + dirs = set() + for x in directories: + dirs.add(x) + for root, subdir, file in os.walk(x, followlinks=True): + dirs.add(root) + return list(dirs) + +def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format + out_list = [] + for k in embed: + if k.startswith(prefix) and k.endswith(suffix): + out_list.append(embed[k]) + if len(out_list) == 0: + return None + + return torch.cat(out_list, dim=0) + +def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): + if isinstance(embedding_directory, str): + embedding_directory = [embedding_directory] + + embedding_directory = expand_directory_list(embedding_directory) + + valid_file = None + for embed_dir in embedding_directory: + embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) + embed_dir = os.path.abspath(embed_dir) + try: + if os.path.commonpath((embed_dir, embed_path)) != embed_dir: + continue + except: + continue + if not os.path.isfile(embed_path): + extensions = ['.safetensors', '.pt', '.bin'] + for x in extensions: + t = embed_path + x + if os.path.isfile(t): + valid_file = t + break + else: + valid_file = embed_path + if valid_file is not None: + break + + if valid_file is None: + return None + + embed_path = valid_file + + embed_out = None + + try: + if embed_path.lower().endswith(".safetensors"): + import safetensors.torch + embed = safetensors.torch.load_file(embed_path, device="cpu") + else: + try: + embed = torch.load(embed_path, weights_only=True, map_location="cpu") + except: + embed_out = safe_load_embed_zip(embed_path) + except Exception: + logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) + return None + + if embed_out is None: + if 'string_to_param' in embed: + values = embed['string_to_param'].values() + embed_out = next(iter(values)) + elif isinstance(embed, list): + out_list = [] + for x in range(len(embed)): + for k in embed[x]: + t = embed[x][k] + if t.shape[-1] != embedding_size: + continue + out_list.append(t.reshape(-1, t.shape[-1])) + embed_out = torch.cat(out_list, dim=0) + elif embed_key is not None and embed_key in embed: + embed_out = embed[embed_key] + else: + embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*') + if embed_out is None: + embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key)) + if embed_out is None: + values = embed.values() + embed_out = next(iter(values)) + return embed_out + +class SDTokenizer: + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}): + if tokenizer_path is None: + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") + self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) + self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length) + self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length) + self.end_token = None + self.min_padding = min_padding + + empty = self.tokenizer('')["input_ids"] + self.tokenizer_adds_end_token = has_end_token + if has_start_token: + self.tokens_start = 1 + self.start_token = empty[0] + if end_token is not None: + self.end_token = end_token + else: + if has_end_token: + self.end_token = empty[1] + else: + self.tokens_start = 0 + self.start_token = None + if end_token is not None: + self.end_token = end_token + else: + if has_end_token: + self.end_token = empty[0] + + if pad_token is not None: + self.pad_token = pad_token + elif pad_with_end: + self.pad_token = self.end_token + else: + self.pad_token = 0 + + self.pad_with_end = pad_with_end + self.pad_to_max_length = pad_to_max_length + + vocab = self.tokenizer.get_vocab() + self.inv_vocab = {v: k for k, v in vocab.items()} + self.embedding_directory = embedding_directory + self.max_word_length = 8 + self.embedding_identifier = "embedding:" + self.embedding_size = embedding_size + self.embedding_key = embedding_key + + def _try_get_embedding(self, embedding_name:str): + ''' + Takes a potential embedding name and tries to retrieve it. + Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. + ''' + split_embed = embedding_name.split() + embedding_name = split_embed[0] + leftover = ' '.join(split_embed[1:]) + embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) + if embed is None: + stripped = embedding_name.strip(',') + if len(stripped) < len(embedding_name): + embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) + return (embed, "{} {}".format(embedding_name[len(stripped):], leftover)) + return (embed, leftover) + + + def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs): + ''' + Takes a prompt and converts it to a list of (token, weight, word id) elements. + Tokens can both be integer tokens and pre computed CLIP tensors. + Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. + Returned list has the dimensions NxM where M is the input size of CLIP + ''' + min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length) + min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding) + + text = escape_important(text) + parsed_weights = token_weights(text, 1.0) + + # tokenize words + tokens = [] + for weighted_segment, weight in parsed_weights: + to_tokenize = unescape_important(weighted_segment) + split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize) + to_tokenize = [split[0]] + for i in range(1, len(split)): + to_tokenize.append("{}{}".format(self.embedding_identifier, split[i])) + + to_tokenize = [x for x in to_tokenize if x != ""] + for word in to_tokenize: + # if we find an embedding, deal with the embedding + if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: + embedding_name = word[len(self.embedding_identifier):].strip('\n') + embed, leftover = self._try_get_embedding(embedding_name) + if embed is None: + logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") + else: + if len(embed.shape) == 1: + tokens.append([(embed, weight)]) + else: + tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) + #if we accidentally have leftover text, continue parsing using leftover, else move on to next word + if leftover != "": + word = leftover + else: + continue + end = 999999999999 + if self.tokenizer_adds_end_token: + end = -1 + #parse word + tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:end]]) + + #reshape token array to CLIP input size + batched_tokens = [] + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + for i, t_group in enumerate(tokens): + #determine if we're going to try and keep the tokens in a single batch + is_large = len(t_group) >= self.max_word_length + if self.end_token is not None: + has_end_token = 1 + else: + has_end_token = 0 + + while len(t_group) > 0: + if len(t_group) + len(batch) > self.max_length - has_end_token: + remaining_length = self.max_length - len(batch) - has_end_token + #break word in two and add end token + if is_large: + batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) + if self.end_token is not None: + batch.append((self.end_token, 1.0, 0)) + t_group = t_group[remaining_length:] + #add end token and pad + else: + if self.end_token is not None: + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) + #start new batch + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + else: + batch.extend([(t,w,i+1) for t,w in t_group]) + t_group = [] + + #fill last batch + if self.end_token is not None: + batch.append((self.end_token, 1.0, 0)) + if min_padding is not None: + batch.extend([(self.pad_token, 1.0, 0)] * min_padding) + if self.pad_to_max_length and len(batch) < self.max_length: + batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) + if min_length is not None and len(batch) < min_length: + batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch))) + + if not return_word_ids: + batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] + + return batched_tokens + + + def untokenize(self, token_weight_pair): + return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) + + def state_dict(self): + return {} + +class SD1Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None): + if name is not None: + self.clip_name = name + self.clip = "{}".format(self.clip_name) + else: + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + + tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) + setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return getattr(self, self.clip).untokenize(token_weight_pair) + + def state_dict(self): + return getattr(self, self.clip).state_dict() + +class SD1CheckpointClipModel(SDClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options) + +class SD1ClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs): + super().__init__() + + if name is not None: + self.clip_name = name + self.clip = "{}".format(self.clip_name) + else: + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + + clip_model = model_options.get("{}_class".format(self.clip), clip_model) + model_options = {**model_options, "model_name": self.clip} + setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs)) + + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def set_clip_options(self, options): + getattr(self, self.clip).set_clip_options(options) + + def reset_clip_options(self): + getattr(self, self.clip).reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs = token_weight_pairs[self.clip_name] + out = getattr(self, self.clip).encode_token_weights(token_weight_pairs) + return out + + def load_sd(self, sd): + return getattr(self, self.clip).load_sd(sd) diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json new file mode 100644 index 0000000000000000000000000000000000000000..3ba8c6b5bc3d6389fb6c9e2c8231729ad9d663a4 --- /dev/null +++ b/comfy/sd1_clip_config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/comfy/sd1_tokenizer/merges.txt b/comfy/sd1_tokenizer/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b --- /dev/null +++ b/comfy/sd1_tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a 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+import torch +import os + +class SDXLClipG(sd1_clip.SDClipModel): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + model_options = {**model_options, "model_name": "clip_g"} + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, + special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options) + + def load_sd(self, sd): + return super().load_sd(sd) + +class SDXLClipGTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) + + +class SDXLTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + + def state_dict(self): + return {} + +class SDXLClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__() + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options) + self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options) + self.dtypes = set([dtype]) + + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.clip_g.set_clip_options(options) + + def reset_clip_options(self): + self.clip_g.reset_clip_options() + self.clip_l.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_l = token_weight_pairs["l"] + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + cut_to = min(l_out.shape[1], g_out.shape[1]) + return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + else: + return self.clip_l.load_sd(sd) + +class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG, model_options=model_options) + + +class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): + super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) + +class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer) + +class StableCascadeClipG(sd1_clip.SDClipModel): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + model_options = {**model_options, "model_name": "clip_g"} + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options) + + def load_sd(self, sd): + return super().load_sd(sd) + +class StableCascadeClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG, model_options=model_options) diff --git a/comfy/supported_models.py b/comfy/supported_models.py new file mode 100644 index 0000000000000000000000000000000000000000..2669ca01e9aa2973cc547924ccd9d59e7579a726 --- /dev/null +++ b/comfy/supported_models.py @@ -0,0 +1,1222 @@ +import torch +from . import model_base +from . import utils + +from . import sd1_clip +from . import sdxl_clip +import comfy.text_encoders.sd2_clip +import comfy.text_encoders.sd3_clip +import comfy.text_encoders.sa_t5 +import comfy.text_encoders.aura_t5 +import comfy.text_encoders.pixart_t5 +import comfy.text_encoders.hydit +import comfy.text_encoders.flux +import comfy.text_encoders.genmo +import comfy.text_encoders.lt +import comfy.text_encoders.hunyuan_video +import comfy.text_encoders.cosmos +import comfy.text_encoders.lumina2 +import comfy.text_encoders.wan +import comfy.text_encoders.ace +import comfy.text_encoders.omnigen2 + +from . import supported_models_base +from . import latent_formats + +from . import diffusers_convert + +class SD15(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD15 + memory_usage_factor = 1.0 + + def process_clip_state_dict(self, state_dict): + k = list(state_dict.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + state_dict[y] = state_dict.pop(x) + + if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: + ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] + if ids.dtype == torch.float32: + state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() + + replace_prefix = {} + replace_prefix["cond_stage_model."] = "clip_l." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] + for p in pop_keys: + if p in state_dict: + state_dict.pop(p) + + replace_prefix = {"clip_l.": "cond_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) + +class SD20(supported_models_base.BASE): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + "attn_precision": torch.float32, + } + + latent_format = latent_formats.SD15 + memory_usage_factor = 1.0 + + def model_type(self, state_dict, prefix=""): + if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction + k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) + out = state_dict.get(k, None) + if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. + return model_base.ModelType.V_PREDICTION + return model_base.ModelType.EPS + + def process_clip_state_dict(self, state_dict): + replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format + replace_prefix["cond_stage_model.model."] = "clip_h." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + replace_prefix["clip_h"] = "cond_stage_model.model" + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) + return state_dict + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel) + +class SD21UnclipL(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 1536, + "use_temporal_attention": False, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} + + +class SD21UnclipH(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 2048, + "use_temporal_attention": False, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} + +class SDXLRefiner(supported_models_base.BASE): + unet_config = { + "model_channels": 384, + "use_linear_in_transformer": True, + "context_dim": 1280, + "adm_in_channels": 2560, + "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], + "use_temporal_attention": False, + } + + latent_format = latent_formats.SDXL + memory_usage_factor = 1.0 + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXLRefiner(self, device=device) + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "clip_g." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: + state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") + replace_prefix["clip_g"] = "conditioner.embedders.0.model" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) + +class SDXL(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + + latent_format = latent_formats.SDXL + + memory_usage_factor = 0.8 + + def model_type(self, state_dict, prefix=""): + if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5 + self.latent_format = latent_formats.SDXL_Playground_2_5() + self.sampling_settings["sigma_data"] = 0.5 + self.sampling_settings["sigma_max"] = 80.0 + self.sampling_settings["sigma_min"] = 0.002 + return model_base.ModelType.EDM + elif "edm_vpred.sigma_max" in state_dict: + self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item()) + if "edm_vpred.sigma_min" in state_dict: + self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item()) + return model_base.ModelType.V_PREDICTION_EDM + elif "v_pred" in state_dict: + if "ztsnr" in state_dict: #Some zsnr anime checkpoints + self.sampling_settings["zsnr"] = True + return model_base.ModelType.V_PREDICTION + else: + return model_base.ModelType.EPS + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + + replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model" + replace_prefix["conditioner.embedders.1.model."] = "clip_g." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) + + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + for k in state_dict: + if k.startswith("clip_l"): + state_dict_g[k] = state_dict[k] + + state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) + pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] + for p in pop_keys: + if p in state_dict_g: + state_dict_g.pop(p) + + replace_prefix["clip_g"] = "conditioner.embedders.1.model" + replace_prefix["clip_l"] = "conditioner.embedders.0" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) + +class SSD1B(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 4, 4], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class Segmind_Vega(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 1, 1, 2, 2], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class KOALA_700M(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 5], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class KOALA_1B(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 6], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class SVD_img2vid(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 768, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + "attn_precision": torch.float32, + } + + clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." + + latent_format = latent_formats.SD15 + + sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SVD_img2vid(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None + +class SV3D_u(SVD_img2vid): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 256, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + vae_key_prefix = ["conditioner.embedders.1.encoder."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SV3D_u(self, device=device) + return out + +class SV3D_p(SV3D_u): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 1280, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SV3D_p(self, device=device) + return out + +class Stable_Zero123(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + required_keys = { + "cc_projection.weight": None, + "cc_projection.bias": None, + } + + clip_vision_prefix = "cond_stage_model.model.visual." + + latent_format = latent_formats.SD15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) + return out + + def clip_target(self, state_dict={}): + return None + +class SD_X4Upscaler(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 256, + 'in_channels': 7, + "use_linear_in_transformer": True, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "disable_self_attentions": [True, True, True, False], + "num_classes": 1000, + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD_X4 + + sampling_settings = { + "linear_start": 0.0001, + "linear_end": 0.02, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SD_X4Upscaler(self, device=device) + return out + +class Stable_Cascade_C(supported_models_base.BASE): + unet_config = { + "stable_cascade_stage": 'c', + } + + unet_extra_config = {} + + latent_format = latent_formats.SC_Prior + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + sampling_settings = { + "shift": 2.0, + } + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoder."] + clip_vision_prefix = "clip_l_vision." + + def process_unet_state_dict(self, state_dict): + key_list = list(state_dict.keys()) + for y in ["weight", "bias"]: + suffix = "in_proj_{}".format(y) + keys = filter(lambda a: a.endswith(suffix), key_list) + for k_from in keys: + weights = state_dict.pop(k_from) + prefix = k_from[:-(len(suffix) + 1)] + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["to_q", "to_k", "to_v"] + k_to = "{}.{}.{}".format(prefix, p[x], y) + state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] + return state_dict + + def process_clip_state_dict(self, state_dict): + state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) + if "clip_g.text_projection" in state_dict: + state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1) + return state_dict + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.StableCascade_C(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) + +class Stable_Cascade_B(Stable_Cascade_C): + unet_config = { + "stable_cascade_stage": 'b', + } + + unet_extra_config = {} + + latent_format = latent_formats.SC_B + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + sampling_settings = { + "shift": 1.0, + } + + clip_vision_prefix = None + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.StableCascade_B(self, device=device) + return out + +class SD15_instructpix2pix(SD15): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SD15_instructpix2pix(self, device=device) + +class SDXL_instructpix2pix(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + "in_channels": 8, + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) + +class LotusD(SD20): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "use_temporal_attention": False, + "adm_in_channels": 4, + "in_channels": 4, + } + + unet_extra_config = { + "num_classes": 'sequential' + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.Lotus(self, device=device) + +class SD3(supported_models_base.BASE): + unet_config = { + "in_channels": 16, + "pos_embed_scaling_factor": None, + } + + sampling_settings = { + "shift": 3.0, + } + + unet_extra_config = {} + latent_format = latent_formats.SD3 + + memory_usage_factor = 1.2 + + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SD3(self, device=device) + return out + + def clip_target(self, state_dict={}): + clip_l = False + clip_g = False + t5 = False + pref = self.text_encoder_key_prefix[0] + if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: + clip_l = True + if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: + clip_g = True + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + if "dtype_t5" in t5_detect: + t5 = True + + return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect)) + +class StableAudio(supported_models_base.BASE): + unet_config = { + "audio_model": "dit1.0", + } + + sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03} + + unet_extra_config = {} + latent_format = latent_formats.StableAudio1 + + text_encoder_key_prefix = ["text_encoders."] + vae_key_prefix = ["pretransform.model."] + + def get_model(self, state_dict, prefix="", device=None): + seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True) + seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) + return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) + + def process_unet_state_dict(self, state_dict): + for k in list(state_dict.keys()): + if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero + state_dict.pop(k) + return state_dict + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model) + +class AuraFlow(supported_models_base.BASE): + unet_config = { + "cond_seq_dim": 2048, + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.73, + } + + unet_extra_config = {} + latent_format = latent_formats.SDXL + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.AuraFlow(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model) + +class PixArtAlpha(supported_models_base.BASE): + unet_config = { + "image_model": "pixart_alpha", + } + + sampling_settings = { + "beta_schedule" : "sqrt_linear", + "linear_start" : 0.0001, + "linear_end" : 0.02, + "timesteps" : 1000, + } + + unet_extra_config = {} + latent_format = latent_formats.SD15 + + memory_usage_factor = 0.5 + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.PixArt(self, device=device) + return out.eval() + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.PixArtT5XXL) + +class PixArtSigma(PixArtAlpha): + unet_config = { + "image_model": "pixart_sigma", + } + latent_format = latent_formats.SDXL + +class HunyuanDiT(supported_models_base.BASE): + unet_config = { + "image_model": "hydit", + } + + unet_extra_config = { + "attn_precision": torch.float32, + } + + sampling_settings = { + "linear_start": 0.00085, + "linear_end": 0.018, + } + + latent_format = latent_formats.SDXL + + memory_usage_factor = 1.3 + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanDiT(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel) + +class HunyuanDiT1(HunyuanDiT): + unet_config = { + "image_model": "hydit1", + } + + unet_extra_config = {} + + sampling_settings = { + "linear_start" : 0.00085, + "linear_end" : 0.03, + } + +class Flux(supported_models_base.BASE): + unet_config = { + "image_model": "flux", + "guidance_embed": True, + } + + sampling_settings = { + } + + unet_extra_config = {} + latent_format = latent_formats.Flux + + memory_usage_factor = 2.8 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Flux(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect)) + +class FluxInpaint(Flux): + unet_config = { + "image_model": "flux", + "guidance_embed": True, + "in_channels": 96, + } + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + +class FluxSchnell(Flux): + unet_config = { + "image_model": "flux", + "guidance_embed": False, + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.0, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device) + return out + +class GenmoMochi(supported_models_base.BASE): + unet_config = { + "image_model": "mochi_preview", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 6.0, + } + + unet_extra_config = {} + latent_format = latent_formats.Mochi + + memory_usage_factor = 2.0 #TODO + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.GenmoMochi(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect)) + +class LTXV(supported_models_base.BASE): + unet_config = { + "image_model": "ltxv", + } + + sampling_settings = { + "shift": 2.37, + } + + unet_extra_config = {} + latent_format = latent_formats.LTXV + + memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = (unet_config.get("cross_attention_dim", 2048) / 2048) * 5.5 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.LTXV(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect)) + +class HunyuanVideo(supported_models_base.BASE): + unet_config = { + "image_model": "hunyuan_video", + } + + sampling_settings = { + "shift": 7.0, + } + + unet_extra_config = {} + latent_format = latent_formats.HunyuanVideo + + memory_usage_factor = 1.8 #TODO + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideo(self, device=device) + return out + + def process_unet_state_dict(self, state_dict): + out_sd = {} + for k in list(state_dict.keys()): + key_out = k + key_out = key_out.replace("txt_in.t_embedder.mlp.0.", "txt_in.t_embedder.in_layer.").replace("txt_in.t_embedder.mlp.2.", "txt_in.t_embedder.out_layer.") + key_out = key_out.replace("txt_in.c_embedder.linear_1.", "txt_in.c_embedder.in_layer.").replace("txt_in.c_embedder.linear_2.", "txt_in.c_embedder.out_layer.") + key_out = key_out.replace("_mod.linear.", "_mod.lin.").replace("_attn_qkv.", "_attn.qkv.") + key_out = key_out.replace("mlp.fc1.", "mlp.0.").replace("mlp.fc2.", "mlp.2.") + key_out = key_out.replace("_attn_q_norm.weight", "_attn.norm.query_norm.scale").replace("_attn_k_norm.weight", "_attn.norm.key_norm.scale") + key_out = key_out.replace(".q_norm.weight", ".norm.query_norm.scale").replace(".k_norm.weight", ".norm.key_norm.scale") + key_out = key_out.replace("_attn_proj.", "_attn.proj.") + key_out = key_out.replace(".modulation.linear.", ".modulation.lin.") + key_out = key_out.replace("_in.mlp.2.", "_in.out_layer.").replace("_in.mlp.0.", "_in.in_layer.") + out_sd[key_out] = state_dict[k] + return out_sd + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect)) + +class HunyuanVideoI2V(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "in_channels": 33, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideoI2V(self, device=device) + return out + +class HunyuanVideoSkyreelsI2V(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "in_channels": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideoSkyreelsI2V(self, device=device) + return out + +class CosmosT2V(supported_models_base.BASE): + unet_config = { + "image_model": "cosmos", + "in_channels": 16, + } + + sampling_settings = { + "sigma_data": 0.5, + "sigma_max": 80.0, + "sigma_min": 0.002, + } + + unet_extra_config = {} + latent_format = latent_formats.Cosmos1CV8x8x8 + + memory_usage_factor = 1.6 #TODO + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosVideo(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) + +class CosmosI2V(CosmosT2V): + unet_config = { + "image_model": "cosmos", + "in_channels": 17, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosVideo(self, image_to_video=True, device=device) + return out + +class CosmosT2IPredict2(supported_models_base.BASE): + unet_config = { + "image_model": "cosmos_predict2", + "in_channels": 16, + } + + sampling_settings = { + "sigma_data": 1.0, + "sigma_max": 80.0, + "sigma_min": 0.002, + } + + unet_extra_config = {} + latent_format = latent_formats.Wan21 + + memory_usage_factor = 1.0 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosPredict2(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) + +class CosmosI2VPredict2(CosmosT2IPredict2): + unet_config = { + "image_model": "cosmos_predict2", + "in_channels": 17, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosPredict2(self, image_to_video=True, device=device) + return out + +class Lumina2(supported_models_base.BASE): + unet_config = { + "image_model": "lumina2", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 6.0, + } + + memory_usage_factor = 1.2 + + unet_extra_config = {} + latent_format = latent_formats.Flux + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Lumina2(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect)) + +class WAN21_T2V(supported_models_base.BASE): + unet_config = { + "image_model": "wan2.1", + "model_type": "t2v", + } + + sampling_settings = { + "shift": 8.0, + } + + unet_extra_config = {} + latent_format = latent_formats.Wan21 + + memory_usage_factor = 1.0 + + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2000 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}umt5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect)) + +class WAN21_I2V(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "i2v", + "in_dim": 36, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21(self, image_to_video=True, device=device) + return out + +class WAN21_FunControl2V(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "i2v", + "in_dim": 48, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21(self, image_to_video=False, device=device) + return out + +class WAN21_Camera(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "camera", + "in_dim": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_Camera(self, image_to_video=False, device=device) + return out +class WAN21_Vace(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "vace", + } + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = 1.2 * self.memory_usage_factor + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_Vace(self, image_to_video=False, device=device) + return out + +class Hunyuan3Dv2(supported_models_base.BASE): + unet_config = { + "image_model": "hunyuan3d2", + } + + unet_extra_config = {} + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.0, + } + + memory_usage_factor = 3.5 + + clip_vision_prefix = "conditioner.main_image_encoder.model." + vae_key_prefix = ["vae."] + + latent_format = latent_formats.Hunyuan3Dv2 + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Hunyuan3Dv2(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None + +class Hunyuan3Dv2mini(Hunyuan3Dv2): + unet_config = { + "image_model": "hunyuan3d2", + "depth": 8, + } + + latent_format = latent_formats.Hunyuan3Dv2mini + +class HiDream(supported_models_base.BASE): + unet_config = { + "image_model": "hidream", + } + + sampling_settings = { + "shift": 3.0, + } + + sampling_settings = { + } + + # memory_usage_factor = 1.2 # TODO + + unet_extra_config = {} + latent_format = latent_formats.Flux + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HiDream(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None # TODO + +class Chroma(supported_models_base.BASE): + unet_config = { + "image_model": "chroma", + } + + unet_extra_config = { + } + + sampling_settings = { + "multiplier": 1.0, + } + + latent_format = comfy.latent_formats.Flux + + memory_usage_factor = 3.2 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Chroma(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) + +class ACEStep(supported_models_base.BASE): + unet_config = { + "audio_model": "ace", + } + + unet_extra_config = { + } + + sampling_settings = { + "shift": 3.0, + } + + latent_format = comfy.latent_formats.ACEAudio + + memory_usage_factor = 0.5 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.ACEStep(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model) + +class Omnigen2(supported_models_base.BASE): + unet_config = { + "image_model": "omnigen2", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 2.6, + } + + memory_usage_factor = 1.65 #TODO + + unet_extra_config = {} + latent_format = latent_formats.Flux + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def __init__(self, unet_config): + super().__init__(unet_config) + if comfy.model_management.extended_fp16_support(): + self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Omnigen2(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.LuminaTokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect)) + + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2] + +models += [SVD_img2vid] diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py new file mode 100644 index 0000000000000000000000000000000000000000..54573abb110d8cc5e190ecefa0f9aecf95da0b99 --- /dev/null +++ b/comfy/supported_models_base.py @@ -0,0 +1,119 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +from . import model_base +from . import utils +from . import latent_formats + +class ClipTarget: + def __init__(self, tokenizer, clip): + self.clip = clip + self.tokenizer = tokenizer + self.params = {} + +class BASE: + unet_config = {} + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + } + + required_keys = {} + + clip_prefix = [] + clip_vision_prefix = None + noise_aug_config = None + sampling_settings = {} + latent_format = latent_formats.LatentFormat + vae_key_prefix = ["first_stage_model."] + text_encoder_key_prefix = ["cond_stage_model."] + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + memory_usage_factor = 2.0 + + manual_cast_dtype = None + custom_operations = None + scaled_fp8 = None + optimizations = {"fp8": False} + + @classmethod + def matches(s, unet_config, state_dict=None): + for k in s.unet_config: + if k not in unet_config or s.unet_config[k] != unet_config[k]: + return False + if state_dict is not None: + for k in s.required_keys: + if k not in state_dict: + return False + return True + + def model_type(self, state_dict, prefix=""): + return model_base.ModelType.EPS + + def inpaint_model(self): + return self.unet_config["in_channels"] > 4 + + def __init__(self, unet_config): + self.unet_config = unet_config.copy() + self.sampling_settings = self.sampling_settings.copy() + self.latent_format = self.latent_format() + self.optimizations = self.optimizations.copy() + for x in self.unet_extra_config: + self.unet_config[x] = self.unet_extra_config[x] + + def get_model(self, state_dict, prefix="", device=None): + if self.noise_aug_config is not None: + out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) + else: + out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) + return state_dict + + def process_unet_state_dict(self, state_dict): + return state_dict + + def process_vae_state_dict(self, state_dict): + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {"": self.text_encoder_key_prefix[0]} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_clip_vision_state_dict_for_saving(self, state_dict): + replace_prefix = {} + if self.clip_vision_prefix is not None: + replace_prefix[""] = self.clip_vision_prefix + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.diffusion_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_vae_state_dict_for_saving(self, state_dict): + replace_prefix = {"": self.vae_key_prefix[0]} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def set_inference_dtype(self, dtype, manual_cast_dtype): + self.unet_config['dtype'] = dtype + self.manual_cast_dtype = manual_cast_dtype diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..10ea18e326693f237b3b219970c86e3808f6d334 --- /dev/null +++ b/comfy/t2i_adapter/adapter.py @@ -0,0 +1,299 @@ +#taken from https://github.com/TencentARC/T2I-Adapter +import torch +import torch.nn as nn +from collections import OrderedDict + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + if not self.use_conv: + padding = [x.shape[2] % 2, x.shape[3] % 2] + self.op.padding = padding + + x = self.op(x) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): + super().__init__() + ps = ksize // 2 + if in_c != out_c or sk == False: + self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + # print('n_in') + self.in_conv = None + self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) + if sk == False: + self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.skep = None + + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=use_conv) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + if self.in_conv is not None: # edit + x = self.in_conv(x) + + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + if self.skep is not None: + return h + self.skep(x) + else: + return h + x + + +class Adapter(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): + super(Adapter, self).__init__() + self.unshuffle_amount = 8 + resblock_no_downsample = [] + resblock_downsample = [3, 2, 1] + self.xl = xl + if self.xl: + self.unshuffle_amount = 16 + resblock_no_downsample = [1] + resblock_downsample = [2] + + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + for i in range(len(channels)): + for j in range(nums_rb): + if (i in resblock_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) + elif (i in resblock_no_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + else: + self.body.append( + ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + self.body = nn.ModuleList(self.body) + self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + x = self.conv_in(x) + for i in range(len(self.channels)): + for j in range(self.nums_rb): + idx = i * self.nums_rb + j + x = self.body[idx](x) + if self.xl: + features.append(None) + if i == 0: + features.append(None) + features.append(None) + if i == 2: + features.append(None) + else: + features.append(None) + features.append(None) + features.append(x) + + features = features[::-1] + + if self.xl: + return {"input": features[1:], "middle": features[:1]} + else: + return {"input": features} + + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model))])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class StyleAdapter(nn.Module): + + def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): + super().__init__() + + scale = width ** -0.5 + self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) + self.num_token = num_token + self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) + self.ln_post = LayerNorm(width) + self.ln_pre = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) + + def forward(self, x): + # x shape [N, HW+1, C] + style_embedding = self.style_embedding + torch.zeros( + (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) + x = torch.cat([x, style_embedding], dim=1) + x = self.ln_pre(x) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer_layes(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, -self.num_token:, :]) + x = x @ self.proj + + return x + + +class ResnetBlock_light(nn.Module): + def __init__(self, in_c): + super().__init__() + self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) + + def forward(self, x): + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + + return h + x + + +class extractor(nn.Module): + def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): + super().__init__() + self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) + self.body = [] + for _ in range(nums_rb): + self.body.append(ResnetBlock_light(inter_c)) + self.body = nn.Sequential(*self.body) + self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=False) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + x = self.in_conv(x) + x = self.body(x) + x = self.out_conv(x) + + return x + + +class Adapter_light(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): + super(Adapter_light, self).__init__() + self.unshuffle_amount = 8 + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + self.xl = False + + for i in range(len(channels)): + if i == 0: + self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) + else: + self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) + self.body = nn.ModuleList(self.body) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + for i in range(len(self.channels)): + x = self.body[i](x) + features.append(None) + features.append(None) + features.append(x) + + return {"input": features[::-1]} diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py new file mode 100644 index 0000000000000000000000000000000000000000..ce36f1a84dae599a35e84a8da3462408c0f0ccc6 --- /dev/null +++ b/comfy/taesd/taesd.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) +""" +import torch +import torch.nn as nn + +import comfy.utils +import comfy.ops + +def conv(n_in, n_out, **kwargs): + return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + +class Clamp(nn.Module): + def forward(self, x): + return torch.tanh(x / 3) * 3 + +class Block(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) + self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.fuse = nn.ReLU() + def forward(self, x): + return self.fuse(self.conv(x) + self.skip(x)) + +def Encoder(latent_channels=4): + return nn.Sequential( + conv(3, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, latent_channels), + ) + + +def Decoder(latent_channels=4): + return nn.Sequential( + Clamp(), conv(latent_channels, 64), nn.ReLU(), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + +class TAESD(nn.Module): + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): + """Initialize pretrained TAESD on the given device from the given checkpoints.""" + super().__init__() + self.taesd_encoder = Encoder(latent_channels=latent_channels) + self.taesd_decoder = Decoder(latent_channels=latent_channels) + self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) + self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) + if encoder_path is not None: + self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) + if decoder_path is not None: + self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) + + @staticmethod + def scale_latents(x): + """raw latents -> [0, 1]""" + return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) + + @staticmethod + def unscale_latents(x): + """[0, 1] -> raw latents""" + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) + + def decode(self, x): + x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) + x_sample = x_sample.sub(0.5).mul(2) + return x_sample + + def encode(self, x): + return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift diff --git a/comfy/text_encoders/ace.py b/comfy/text_encoders/ace.py new file mode 100644 index 0000000000000000000000000000000000000000..d650bb10d5d5bfdd485f18095e4a99ed7b06b6b7 --- /dev/null +++ b/comfy/text_encoders/ace.py @@ -0,0 +1,153 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os +import re +import torch +import logging + +from tokenizers import Tokenizer +from .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji + +SUPPORT_LANGUAGES = { + "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285, + "pt": 286, "pl": 294, "tr": 295, "ru": 267, "cs": 293, + "nl": 297, "ar": 5022, "zh": 5023, "ja": 5412, "hu": 5753, + "ko": 6152, "hi": 6680 +} + +structure_pattern = re.compile(r"\[.*?\]") + +DEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") + + +class VoiceBpeTokenizer: + def __init__(self, vocab_file=DEFAULT_VOCAB_FILE): + self.tokenizer = None + if vocab_file is not None: + self.tokenizer = Tokenizer.from_file(vocab_file) + + def preprocess_text(self, txt, lang): + txt = multilingual_cleaners(txt, lang) + return txt + + def encode(self, txt, lang='en'): + # lang = lang.split("-")[0] # remove the region + # self.check_input_length(txt, lang) + txt = self.preprocess_text(txt, lang) + lang = "zh-cn" if lang == "zh" else lang + txt = f"[{lang}]{txt}" + txt = txt.replace(" ", "[SPACE]") + return self.tokenizer.encode(txt).ids + + def get_lang(self, line): + if line.startswith("[") and line[3:4] == ']': + lang = line[1:3].lower() + if lang in SUPPORT_LANGUAGES: + return lang, line[4:] + return "en", line + + def __call__(self, string): + lines = string.split("\n") + lyric_token_idx = [261] + for line in lines: + line = line.strip() + if not line: + lyric_token_idx += [2] + continue + + lang, line = self.get_lang(line) + + if lang not in SUPPORT_LANGUAGES: + lang = "en" + if "zh" in lang: + lang = "zh" + if "spa" in lang: + lang = "es" + + try: + line_out = japanese_to_romaji(line) + if line_out != line: + lang = "ja" + line = line_out + except: + pass + + try: + if structure_pattern.match(line): + token_idx = self.encode(line, "en") + else: + token_idx = self.encode(line, lang) + lyric_token_idx = lyric_token_idx + token_idx + [2] + except Exception as e: + logging.warning("tokenize error {} for line {} major_language {}".format(e, line, lang)) + return {"input_ids": lyric_token_idx} + + @staticmethod + def from_pretrained(path, **kwargs): + return VoiceBpeTokenizer(path, **kwargs) + + def get_vocab(self): + return {} + + +class UMT5BaseModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_base.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options) + +class UMT5BaseTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class LyricsTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") + super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data) + +class AceT5Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["lyrics"] = self.voicebpe.tokenize_with_weights(kwargs.get("lyrics", ""), return_word_ids, **kwargs) + out["umt5base"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.umt5base.untokenize(token_weight_pair) + + def state_dict(self): + return self.umt5base.state_dict() + +class AceT5Model(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__() + self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options) + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def set_clip_options(self, options): + self.umt5base.set_clip_options(options) + + def reset_clip_options(self): + self.umt5base.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_umt5base = token_weight_pairs["umt5base"] + token_weight_pairs_lyrics = token_weight_pairs["lyrics"] + + t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base) + + lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0) + return t5_out, None, {"conditioning_lyrics": lyrics_embeds} + + def load_sd(self, sd): + return self.umt5base.load_sd(sd) diff --git a/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..519ed340c36e19dfe15ccffd7aa5a31a261d70a5 --- /dev/null +++ b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json @@ -0,0 +1,15535 @@ +{ + "version": "1.0", + "truncation": 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+ "d er", + "in g", + "o e", + "ij k", + "a an", + "ch t", + "v oor", + "l e", + "i et", + "r o", + "m o", + "k en", + "z ijn", + "m en", + "i g", + "j e", + "n iet", + "a r", + "o o", + "i d", + "u n", + "i l", + "s ch", + "mo et", + "st e", + "u r", + "o l", + "he b", + "u it", + "g el", + "w ij", + "a s", + "m e", + "t en", + "w or", + "o u", + "v en", + "l en", + "aa t", + "d it", + "m et", + "r a", + "b en", + "s p", + "o ver", + "d ie", + "n o", + "w er", + "l ijk", + "f t", + "s l", + "an d", + "v e", + "t er", + "i er", + "i en", + "t o", + "d aar", + "g r", + "b el", + "de ze", + "d u", + "a g", + "k an", + "wor den", + "in gen", + "moet en", + "n en", + "on der", + "heb ben", + "r u", + "oo k", + "s en", + "c t", + "k t", + "no g", + "aa l", + "w as", + "u l", + "e er", + "b ij", + "m ijn", + "p ro", + "v ol", + "d o", + "k om", + "at ie", + "e ft", + "k el", + "al s", + "r ij", + "he id", + "a f", + "st el", + "m aar", + "a p", + "we e", + "a d", + "he eft", + "w aar", + "i cht", + "d an", + "er en", + "n e", + "w el", + "w at", + "w il", + "a cht", + "aa g", + "ge b", + "c on", + "z o", + "k e", + "b et", + "h ij", + "d ig", + "k un", + "u w", + "d t", + "d oor", + "t ij", + "a m", + "an g", + "on d", + "er s", + "is ch", + "ge en", + "i ge", + "ge v", + "ve el", + "n u", + "m a", + "on s", + "o f", + "b l", + "n aar", + "g ro", + "p l", + "an der", + "at en", + "kun nen", + "e cht", + "h ier", + "g oe", + "an t", + "u s", + "t wee", + "on t", + "de lijk", + "el e", + "u ur", + "al le", + "t oe", + "me er", + "i st", + "n a", + "n ie", + "on ze", + "l o", + "i m", + "p en", + "h ad", + "tij d", + "h oe", + "to t", + "z ou", + "a k", + "aa k", + "a men", + "d r", + "w oor", + "s e", + "wor dt", + "o t", + "gel ijk", + "g aan", + "i c", + "g er", + "k er", + "el d", + "e m", + "h ou", + "de l", + "z en", + "z el", + "te gen", + "b o", + "kom en", + "c om", + "i gen", + "e it", + "wer k", + "goe d", + "z al", + "z ij", + "sl ag", + "e s", + "z ien", + "a st", + "echt er", + "it ie", + "t ie", + "el ijk", + "m is", + "isch e", + "bel an", + "h aar", + "i ch", + "b er", + "h an", + "v r", + "al e", + "c i", + "gr ijk", + "in d", + "do en", + "l and", + "belan grijk", + "p un", + "op en", + "ct ie", + "zel f", + "m ij", + "it eit", + "ste m", + "me e", + "ar en", + "al l", + "b r", + "re cht", + "d ien", + "h u", + "g aat", + "pro b", + "m oe", + "p er", + "a u", + "ul len", + "z ich", + "daar om", + "or m", + "k l", + "v o", + "en t", + "st aat", + "z it", + "du i", + "n at", + "du s", + "d s", + "ver slag", + "kel ijk", + "prob le", + "w et", + "ge m", + "c r", + "i on", + "p r", + "sch ap", + "g d", + "h un", + "z a", + "er d", + "z et", + "st aan", + "st r", + "m aal", + "in der", + "e id", + "st en", + "p ar", + "k ken", + "ge d", + "z ullen", + "re s", + "men sen", + "j aar", + "re gel", + "ie der", + "vol gen", + "ge ven", + "e ven", + "l u", + "bl ij", + "i ë", + "k o", + "u we", + "m an", + "ma ken", + "l ie", + "g a", + "oe k", + "nie uwe", + "b aar", + "h o", + "h er", + "in ter", + "ander e", + "ru ik", + "s u", + "a gen", + "or t", + "m er", + "ou w", + "st er", + "wil len", + "aa kt", + "h oo", + "an den", + "f f", + "l ig", + "t re", + "s amen", + "ze er", + "dui delijk", + "ant woor", + "he el", + "men t", + "pun t", + "hou den", + "we g", + "vr aag", + "gel e", + "een s", + "be sch", + "om en", + "er g", + "do el", + "d ag", + "sp e", + "ur en", + "ing s", + "or en", + "l ang", + "de len", + "m ar", + "ste un", + "in nen", + "p ol", + "o on", + "i de", + "s n", + "s ie", + "r icht", + "z onder", + "no dig", + "all een", + "m id", + "ra gen", + "iet s", + "ver sch", + "geb ruik", + "st u", + "ro uw", + "stel len", + "be g", + "men ten", + "v in", + "eer ste", + "l aat", + "gro ot", + "oo d", + "to ch", + "l aten", + "aar d", + "s le", + "de el", + "st and", + "pl aat", + "re e", + "bet re", + "d i", + "l id", + "uit en", + "ra cht", + "bel eid", + "g et", + "ar t", + "st ie", + "st aten", + "g gen", + "re ken", + "e in", + "al en", + "m ing", + "mo gelijk", + "gro te", + "al tijd", + "z or", + "en kel", + "w ik", + "pol itie", + "e igen", + "el k", + "han del", + "g t", + "k we", + "m aat", + "el en", + "i p", + "v rij", + "s om", + "je s", + "aa m", + "hu is", + "v al", + "we er", + "lid staten", + "k ing", + "k le", + "be d", + "gev al", + "stel l", + "a i", + "wik kel", + "kwe stie", + "t al", + "ste e", + "a b", + "h el", + "kom st", + "p as", + "s s", + "it u", + "i den", + "eer d", + "m in", + "c e", + "p o", + "twee de", + "proble em", + "w aren", + "us sen", + "sn el", + "t ig", + "ge w", + "j u", + "ul t", + "ne men", + "com mis", + "versch il", + "k on", + "z oek", + "k rij", + "gr aag", + "den k", + "l anden", + "re den", + "be sl", + "oe g", + "bet er", + "he den", + "m ag", + "p e", + "bo ven", + "a c", + "con t", + "f d", + "h ele", + "k r", + "v ier", + "w in", + "ge z", + "k w", + "m il", + "v or", + "he m", + "ra m", + "aa s", + "ont wikkel", + "dr ie", + "v aak", + "plaat s", + "l a", + "g ang", + "ij f", + "f in", + "nat uur", + "t ussen", + "u g", + "in e", + "d a", + "b at", + "kom t", + "w acht", + "aa d", + "u t", + "é n", + "acht er", + "geb ie", + "ver k", + "lig t", + "c es", + "nie uw", + "van d", + "s t", + "n í", + "j e", + "p o", + "c h", + "r o", + "n a", + "s e", + "t o", + "n e", + "l e", + "k o", + "l a", + "d o", + "r a", + "n o", + "t e", + "h o", + "n ě", + "v a", + "l i", + "l o", + "ř e", + "c e", + "d e", + "v e", + "b y", + "n i", + "s k", + "t a", + "n á", + "z a", + "p ro", + "v o", + "v ě", + "m e", + "v á", + "s o", + "k a", + "r á", + "v y", + "z e", + "m i", + "p a", + "t i", + "st a", + "m ě", + "n é", + "ř i", + "ř í", + "m o", + "ž e", + "m a", + "j í", + "v ý", + "j i", + "d ě", + "r e", + "d a", + "k u", + "j a", + "c i", + "r u", + "č e", + "o b", + "t ě", + "m u", + "k y", + "d i", + "š e", + "k é", + "š í", + "t u", + "v i", + "p ře", + "v í", + "s i", + "n ý", + "o d", + "so u", + "v é", + "n y", + "r i", + "d y", + "b u", + "b o", + "t y", + "l á", + "l u", + "n u", + "ž i", + "m á", + "st i", + "c í", + "z á", + "p ra", + "sk é", + "m í", + "c o", + "d u", + "d á", + "by l", + "st o", + "s a", + "t í", + "je d", + "p ří", + "p ři", + "t é", + "s í", + "č i", + "v ní", + "č a", + "d í", + "z i", + "st u", + "p e", + "b a", + "d ní", + "ro z", + "va l", + "l í", + "s po", + "k á", + "b e", + "p i", + "no u", + "ta k", + "st e", + "r y", + "l é", + "vě t", + "se m", + "p ě", + "ko n", + "ne j", + "l y", + "ko u", + "ý ch", + "b ě", + "p r", + "f i", + "p rá", + "a le", + "ja ko", + "po d", + "ž í", + "z í", + "j sou", + "j sem", + "ch o", + "l ní", + "c ké", + "t á", + "m y", + "a k", + "h u", + "va t", + "pře d", + "h la", + "k e", + "st á", + "č í", + "š i", + "s le", + "k la", + "š tě", + "lo u", + "m ů", + "z na", + "ch á", + "o r", + "p ů", + "h a", + "b i", + "ta ké", + "d ů", + "no st", + "t ře", + "te r", + "p u", + "i n", + "v r", + "ve l", + "sk u", + "v še", + "t ní", + "do b", + "by la", + "č ní", + "ja k", + "v u", + "je ho", + "b ý", + "vá ní", + "ný ch", + "po u", + "te n", + "t ři", + "v z", + "st ře", + "d va", + "h le", + "č á", + "no sti", + "c k", + "v š", + "vo u", + "s u", + "h e", + "h ra", + "je n", + "s y", + "da l", + "po z", + "s lo", + "te l", + "d ru", + "de n", + "vš ak", + "g i", + "k dy", + "by lo", + "bu de", + "st ra", + "j ší", + "m é", + "me n", + "vý ch", + "ní m", + "s m", + "ko li", + "r ů", + "t ra", + "mů že", + "ne ní", + "ho d", + "b í", + "do u", + "sk a", + "t ý", + "st ě", + "u je", + "s á", + "pě t", + "ne s", + "k rá", + "to m", + "st ví", + "v ně", + "se d", + "s vé", + "p í", + "z o", + "mu sí", + "u ž", + "tí m", + "jí cí", + "jed no", + "t r", + "ča s", + "e v", + "č ty", + "sk ý", + "ni c", + "ev ro", + "to ho", + "h y", + "k ter", + "r ní", + "st í", + "s vě", + "pa k", + "vše ch", + "k ů", + "n g", + "á d", + "chá zí", + "a ni", + "a r", + "jed na", + "bý t", + "t ro", + "k ra", + "pr vní", + "m no", + "ské ho", + "p á", + "p la", + "le m", + "ne bo", + "ke m", + "st ro", + "s la", + "né ho", + "z de", + "dal ší", + "ř a", + "čty ři", + "h rá", + "dru h", + "l ně", + "v la", + "sk ých", + "š ko", + "pů so", + "pro to", + "v ů", + "sk á", + "ve n", + "še st", + "d ně", + "je ště", + "me zi", + "te k", + "s ko", + "ch a", + "ně koli", + "be z", + "g ra", + "ji ž", + "č ně", + "j á", + "s lu", + "z ná", + "ve r", + "sed m", + "k ro", + "ta m", + "a no", + "v lá", + "o sm", + "byl y", + "vá m", + "ck ý", + "te ch", + "dě ji", + "vel mi", + "le ži", + "va la", + "l ý", + "t vo", + "spo le", + "ch u", + "stu p", + "mo ž", + "evro p", + "g e", + "sta l", + "j de", + "ch y", + "ro di", + "je jí", + "po li", + "de vět", + "s me", + "a ž", + "té to", + "re m", + "d é", + "f or", + "u ni", + "f o", + "ten to", + "a u", + "ka ž", + "nu la", + "na d", + "by ch", + "mo c", + "sto u", + "e x", + "le n", + "k do", + "z d", + "pra co", + "to mu", + "ný m", + "ži vo", + "ze m", + "f e", + "f u", + "ná sle", + "j o", + "sk y", + "ji ch", + "h á", + "mě l", + "dě la", + "j sme", + "p re", + "ni ce", + "ste j", + "ne m", + "st ní", + "he m", + "ná ro", + "z u", + "b li", + "ni t", + "pa r", + "a l", + "poz ději", + "ta ko", + "n ce", + "če r", + "ší m", + "ně co", + "vá l", + "ře j", + "krá t", + "á lní", + "u r", + ". .", + "a si", + "kter é", + "sta v", + "ma jí", + "my s", + "do bě", + "s ně", + "ce n", + "z y", + "z ku", + "t ů", + "ch od", + "s pě", + "je jich", + "sou čas", + "d r", + "va li", + "ri e", + "k te", + "pr ů", + "ze ní", + "pa t", + "a n", + "po tře", + "de m", + "d nes", + "ze mí", + "sa mo", + "zna m", + "b ra", + "má m", + "te dy", + "g o", + "hla vní", + "pou ží", + "b ní", + "ve de", + "le p", + "je k", + "pra v", + "poli ti", + "d ne", + "je m", + "le t", + "če ní", + "pro b", + "ne ž", + "dě l", + "fi l", + "č o", + "cí ch", + "st é", + "d lou", + "h i", + "a by", + "to u", + "několi k", + "d la", + "vy u", + "vi t", + "ho u", + "ck ých", + "no vé", + "či n", + "st y", + "dě lá", + "k ý", + "ob la", + "pod le", + "ra n", + "dů leži", + "ta to", + "po ku", + "ko ne", + "d ý", + "d vě", + "ž ád", + "nou t", + "t ku", + "t vr", + "cké ho", + "ro v", + "r é", + "te le", + "p sa", + "s vět", + "ti vní", + "do sta", + "te m", + "še l", + "druh é", + "s kou", + "ž o", + "jed ná", + "vý znam", + "prob lé", + "pu bli", + "vá n", + "od po", + "pod po", + "d le", + "ja ké", + "še ní", + "ví m", + "bě hem", + "na chází", + "s lou", + "pou ze", + "o tá", + "p lo", + "to vé", + "vět ši", + "ko mi", + "va jí", + "ty to", + "zá pa", + "z mě", + "mo h", + "ví ce", + "spole č", + "au to", + "pro ti", + "st ru", + "dě t", + "chá ze", + "že l", + "с т", + "е н", + "н о", + "н а", + "п р", + "т о", + "п о", + "р а", + "г о", + "к о", + "н е", + "в о", + "в а", + "е т", + "е р", + "н и", + "е л", + "и т", + "н ы", + "з а", + "р о", + "ен и", + "к а", + "л и", + "е м", + "д а", + "о б", + "л а", + "д о", + "с я", + "т ь", + "о т", + "л о", + "л ь", + "е д", + "с о", + "м и", + "р е", + "м о", + "ц и", + "пр о", + "т а", + "э то", + "к и", + "р у", + "пр и", + "т и", + "с е", + "ст а", + "в ы", + "м ы", + "в и", + "б ы", + "м а", + "е с", + "л я", + "ст и", + "л е", + "ч то", + "м е", + "р и", + "ч а", + "о д", + "е й", + "ел ь", + "ени я", + "г а", + "н у", + "с и", + "п а", + "ра з", + "б о", + "ст о", + "с у", + "с а", + "д у", + "е го", + "е ст", + "и н", + "ит ь", + "и з", + "ж е", + "м у", + "п ер", + "по д", + "ени е", + "с ь", + "к у", + "пр ед", + "но го", + "ны х", + "в ер", + "т е", + "но й", + "ци и", + "д е", + "р ы", + "д ел", + "л ю", + "в е", + "о н", + "м ен", + "г и", + "н я", + "б у", + "пр а", + "в се", + "ет ся", + "ст ь", + "ж а", + "до л", + "ж и", + "б е", + "ко н", + "с л", + "ш и", + "д и", + "ст в", + "с ко", + "ны е", + "ч и", + "ю т", + "д ер", + "ст ра", + "т ы", + "х од", + "щ и", + "з о", + "з на", + "но сти", + "ч ес", + "в ля", + "ва ть", + "о р", + "по л", + "в ет", + "та к", + "ш а", + "т у", + "с во", + "пр е", + "о на", + "ит ель", + "ны й", + "с ло", + "ка к", + "в л", + "но сть", + "х о", + "мо ж", + "п е", + "д ля", + "ни я", + "но е", + "ра с", + "дол ж", + "да р", + "т ель", + "с ка", + "п у", + "ст во", + "ко то", + "ра б", + "е е", + "ро д", + "э ти", + "с об", + "о ру", + "ж ен", + "ны м", + "ит и", + "ни е", + "ко м", + "д ет", + "ст у", + "г у", + "п и", + "ме ж", + "ени ю", + "т ер", + "раб от", + "во з", + "ци я", + "ко й", + "щ ест", + "г ра", + "з и", + "р я", + "меж ду", + "ст ва", + "в с", + "ел о", + "ш е", + "м ер", + "б а", + "з ы", + "л у", + "а ль", + "д ей", + "г ла", + "на род", + "к ти", + "пред ста", + "л ся", + "я вля", + "с ки", + "но в", + "ед ин", + "ро в", + "и с", + "ни ма", + "р ем", + "ход и", + "так же", + "д ру", + "а ть", + "сл ед", + "го во", + "на я", + "ю щи", + "ен ь", + "кото ры", + "х от", + "в у", + "и х", + "ем у", + "ч ит", + "ва ж", + "ор га", + "чес ки", + "щ е", + "к е", + "х а", + "по с", + "то м", + "бо ль", + "м не", + "па с", + "об ъ", + "пра в", + "кон ф", + "сл у", + "под дер", + "ст ви", + "на ш", + "ль ко", + "сто я", + "ну ю", + "л ем", + "ен ных", + "к ра", + "д ы", + "между народ", + "г да", + "не об", + "го су", + "ств у", + "ени и", + "госу дар", + "к то", + "и м", + "ч ест", + "р ет", + "во про", + "л ен", + "ел и", + "ро ва", + "ци й", + "на м", + "это й", + "ж ения", + "необ ходи", + "мен я", + "бы ло", + "си ли", + "ф и", + "в я", + "ш ь", + "это го", + "о ни", + "орга ни", + "бе зо", + "пр об", + "и ме", + "ре ш", + "б и", + "безо пас", + "ют ся", + "о ста", + "ен но", + "го д", + "ел а", + "предста в", + "ть ся", + "сло во", + "органи за", + "долж ны", + "это м", + "б ла", + "ч е", + "ч у", + "бла го", + "это му", + "в рем", + "с пе", + "но м", + "ени й", + "с по", + "на с", + "не т", + "з у", + "в ед", + "е ще", + "ска за", + "се й", + "ер ен", + "да н", + "са м", + "ел я", + "ра н", + "зы ва", + "явля ется", + "бу дет", + "кти в", + "т ре", + "дел е", + "м от", + "конф ерен", + "ла сь", + "ча с", + "сто ро", + "ко го", + "е з", + "не й", + "о с", + "ли сь", + "раз ору", + "пер е", + "с си", + "ны ми", + "про ц", + "го ло", + "ч ело", + "бо ле", + "чело ве", + "с ер", + "п л", + "ч ет", + "стра н", + "п я", + "бы л", + "к ла", + "то в", + "ж д", + "дел а", + "е ра", + "у же", + "со вет", + "г ен", + "безопас ности", + "ц а", + "се да", + "по з", + "от вет", + "проб лем", + "на ко", + "т ем", + "до ста", + "п ы", + "щ а", + "во й", + "су щест", + "необходи мо", + "бы ть", + "мож ет", + "д ем", + "что бы", + "е к", + "ч ер", + "у сили", + "ре с", + "ру д", + "един енных", + "д об", + "до сти", + "ств ен", + "я дер", + "год ня", + "ка за", + "се годня", + "сей час", + "то лько", + "во д", + "ес ь", + "м ного", + "бу ду", + "е в", + "ест ь", + "т ри", + "об щест", + ". .", + "я вл", + "вы сту", + "р ед", + "с чит", + "с ит", + "деле га", + "ло ж", + "это т", + "ф ор", + "к лю", + "воз мож", + "ва ния", + "б ли", + "и ли", + "в з", + "на ций", + "ско го", + "при ня", + "п ла", + "о ч", + "ить ся", + "ст е", + "на ши", + "которы е", + "а р", + "име ет", + "с от", + "зна ч", + "пер ь", + "след у", + "ен ы", + "та ки", + "объ единенных", + "ст ро", + "те перь", + "б ле", + "благо дар", + "раз в", + "а н", + "жи ва", + "оч ень", + "я т", + "бе з", + "об ес", + "г ро", + "ло сь", + "с ы", + "организа ции", + "ч лен", + "то го", + "она ль", + "ж да", + "все х", + "с вя", + "боле е", + "со в", + "ко гда", + "во т", + "к ре", + "к ры", + "по этому", + "во ль", + "о й", + "ген ера", + "ч ем", + "л ы", + "пол ити", + "в ен", + "конферен ции", + "проц ес", + "б я", + "ит е", + "от но", + "разв ити", + "а ф", + "ю щ", + "в но", + "ми р", + "ни и", + "ка я", + "а с", + "итель но", + "в то", + "ени ем", + "генера ль", + "пр от", + "вс ем", + "сам бле", + "ас самбле", + "о м", + "з д", + "с мот", + "ре ги", + "ч его", + "од нако", + "усили я", + "дей стви", + "ч но", + "у ча", + "об раз", + "во с", + "э та", + "пер его", + "гово р", + "ва м", + "мо ло", + "врем я", + "д ь", + "хот ел", + "г ру", + "за явл", + "пре доста", + "по ль", + "не е", + "ре зо", + "перего во", + "резо лю", + "к рет", + "поддер ж", + "обес пе", + "не го", + "представ ит", + "на де", + "к ри", + "ч ь", + "про ек", + "л ет", + "дру ги", + "ا ل", + "َ ا", + "و َ", + "ّ َ", + "ِ ي", + "أ َ", + "ل َ", + "ن َ", + "ال ْ", + "ه ُ", + "ُ و", + "م ا", + "ن ْ", + "م ن", + "ع َ", + "ن ا", + "ل ا", + "م َ", + "ت َ", + "ف َ", + "أ ن", + "ل ي", + "م ِ", + "ا ن", + "ف ي", + "ر َ", + "ي َ", + "ه ِ", + "م ْ", + "ق َ", + "ب ِ", + "ل ى", + "ي ن", + "إ ِ", + "ل ِ", + "و ا", + "ك َ", + "ه ا", + "ً ا", + "م ُ", + "و ن", + "ال م", + "ب َ", + "ي ا", + "ذ ا", + "س ا", + "ال ل", + "م ي", + "ي ْ", + "ر ا", + "ر ي", + "ل ك", + "م َا", + "ن َّ", + "ل م", + "إ ن", + "س ت", + "و م", + "ّ َا", + "ل َا", + "ه م", + "ّ ِ", + "ك ُ", + "ك ان", + "س َ", + "ب ا", + "د ي", + "ح َ", + "ع ْ", + "ب ي", + "ال أ", + "و ل", + "ف ِي", + "ر ِ", + "د ا", + "مِ نْ", + "ُو نَ", + "و ْ", + "ه َا", + "ّ ُ", + "ال س", + "ال َ", + "ن ي", + "ل ْ", + "ت ُ", + "ه ل", + "ر ة", + "د َ", + "س ْ", + "ت ِ", + "ن َا", + "ر ْ", + "الل َّ", + "سا مي", + "ك ن", + "ك ل", + "ه َ", + "عَ لَ", + "ع لى", + "م ع", + "إ لى", + "ق د", + "ال ر", + "ُو ا", + "ي ر", + "ع ن", + "ي ُ", + "ن ِ", + "ب ْ", + "ال ح", + "هُ مْ", + "ق ا", + "ذ ه", + "ال ت", + "ِي نَ", + "ج َ", + "ه ذا", + "ع د", + "ال ع", + "د ْ", + "قَ الَ", + "ر ُ", + "ي م", + "ي ة", + "ن ُ", + "خ َ", + "ر ب", + "ال ك", + "و َا", + "أ نا", + "ة ِ", + "ال ن", + "ح د", + "ع ِ", + "ت ا", + "ه و", + "ف ا", + "ع ا", + "ال ش", + "ل ُ", + "ي ت", + "ذ َا", + "ي ع", + "ال ذ", + "ح ْ", + "ال ص", + "إِ نَّ", + "ج ا", + "ع لي", + "ك َا", + "ب ُ", + "ت ع", + "و ق", + "م ل", + "ل َّ", + "ي د", + "أ خ", + "ر ف", + "ت ي", + "ال ِ", + "ّ ا", + "ذ لك", + "أَ نْ", + "س ِ", + "ت وم", + "م ر", + "مَ نْ", + "ب ل", + "ال ق", + "الل ه", + "ِي َ", + "ك م", + "ذ َ", + "ع ل", + "ح ب", + "س ي", + "ع ُ", + "ال ج", + "ال د", + "ش َ", + "ت ك", + "ف ْ", + "ص َ", + "ل ل", + "د ِ", + "ب ر", + "ف ِ", + "ت ه", + "أ ع", + "ت ْ", + "ق ْ", + "الْ أَ", + "ئ ِ", + "عَ نْ", + "و ر", + "ح ا", + "ال َّ", + "م ت", + "ف ر", + "د ُ", + "ه نا", + "وَ أَ", + "ت ب", + "ة ُ", + "أ ي", + "س ب", + "ري د", + "و ج", + "كُ مْ", + "ح ِ", + "ك ْ", + "د ر", + "َا ء", + "ه ذه", + "ال ط", + "الْ مُ", + "د ة", + "ق ل", + "غ َ", + "ي وم", + "الَّ ذ", + "ك ر", + "ت ر", + "ك ِ", + "ك ي", + "عَلَ ى", + "رَ ب", + "ع ة", + "ق ُ", + "ج ْ", + "ف ض", + "ل ة", + "ه ْ", + "ر َا", + "وَ لَ", + "الْ مَ", + "أَ نَّ", + "ي َا", + "أ ُ", + "ش ي", + "اللَّ هُ", + "لَ ى", + "ق ِ", + "أ ت", + "عَلَ يْ", + "اللَّ هِ", + "ال ب", + "ض َ", + "ة ً", + "ق ي", + "ا ر", + "ب د", + "خ ْ", + "سْ تَ", + "ط َ", + "قَ دْ", + "ذه ب", + "أ م", + "ما ذا", + "وَ إِ", + "ة ٌ", + "و نَ", + "لي لى", + "و لا", + "ح ُ", + "ه ي", + "ص ل", + "ال خ", + "و د", + "لي س", + "ل دي", + "ق ال", + "كَا نَ", + "م َّ", + "ح ي", + "ت م", + "ل ن", + "وَ لَا", + "ب ع", + "يم كن", + "س ُ", + "ة َ", + "ح ت", + "ر ًا", + "ك ا", + "ش ا", + "هِ مْ", + "لَ هُ", + "ز َ", + "دا ً", + "م س", + "ك ث", + "الْ عَ", + "ج ِ", + "ص ْ", + "ف َا", + "ل ه", + "و ي", + "ع َا", + "هُ وَ", + "ب ِي", + "ب َا", + "أ س", + "ث َ", + "ل ِي", + "ر ض", + "الر َّ", + "لِ كَ", + "ت َّ", + "ف ُ", + "ق ة", + "ف عل", + "مِ ن", + "ال آ", + "ث ُ", + "س م", + "م َّا", + "بِ هِ", + "ت ق", + "خ ر", + "ل قد", + "خ ل", + "ش ر", + "أن ت", + "ل َّا", + "س ن", + "الس َّ", + "الذ ي", + "س َا", + "و ما", + "ز ل", + "و ب", + "أ ْ", + "إ ذا", + "ر ِي", + "ح ة", + "ن ِي", + "الْ حَ", + "وَ قَالَ", + "ب ه", + "ة ٍ", + "س أ", + "ر ٌ", + "ب ال", + "م ة", + "ش ْ", + "و ت", + "عن د", + "ف س", + "بَ عْ", + "ه ر", + "ق ط", + "أ ح", + "إن ه", + "و ع", + "ف ت", + "غ ا", + "هنا ك", + "ب ت", + "مِ نَ", + "س ر", + "ذَ لِكَ", + "ر س", + "حد ث", + "غ ْ", + "ّ ِي", + "ال إ", + "وَ يَ", + "ج ل", + "ا ست", + "ق ِي", + "ع ب", + "و س", + "ي ش", + "الَّذ ِينَ", + "تا ب", + "د ِي", + "ج ب", + "ك ون", + "ب ن", + "ال ث", + "لَ يْ", + "ب عد", + "وَ الْ", + "فَ أَ", + "ع م", + "هُ م", + "ت ن", + "ذ ْ", + "أ ص", + "أ ين", + "رَب ِّ", + "الذ ين", + "إِ ن", + "ب ين", + "ج ُ", + "عَلَيْ هِ", + "ح َا", + "ل و", + "ست ط", + "ظ ر", + "لَ مْ", + "ء ِ", + "كُ ل", + "ط ل", + "ت َا", + "ض ُ", + "كن ت", + "ل ًا", + "م ٌ", + "ق بل", + "ـ ـ", + "ذ ِ", + "قَ وْ", + "ص ِ", + "م ًا", + "كان ت", + "ص ا", + "ي ق", + "ال ف", + "ال نا", + "م ٍ", + "إِ نْ", + "ال نَّ", + "ج د", + "وَ مَا", + "ت ت", + "ب ح", + "م كان", + "كي ف", + "ّ ة", + "ال ا", + "ج َا", + "أ و", + "سا عد", + "ض ِ", + "إ لا", + "را ً", + "ق َا", + "ر أ", + "ع ت", + "أ حد", + "ه د", + "ض ا", + "ط ر", + "أ ق", + "ما ء", + "د َّ", + "ال با", + "م ُو", + "أَ وْ", + "ط ا", + "ق ُو", + "خ ِ", + "ت ل", + "ستط يع", + "د َا", + "الن َّا", + "إ لَى", + "وَ تَ", + "هَ ذَا", + "ب ة", + "علي ك", + "ج ر", + "ال من", + "ز ا", + "ر ٍ", + "د ع", + "ّ ًا", + "س ة", + "ثُ مَّ", + "شي ء", + "ال غ", + "ت ح", + "ر ُونَ", + "ال يوم", + "م ِي", + "ن ُوا", + "أ ر", + "تُ مْ", + "ع ر", + "ي ف", + "أ ب", + "د ًا", + "ص َا", + "الت َّ", + "أ ريد", + "ال ز", + "يَ وْ", + "إ لي", + "ج ي", + "يَ عْ", + "فض ل", + "ال إن", + "أن ه", + "n g", + "i 4", + "a n", + "s h", + "z h", + "i 2", + "ng 1", + "u 4", + "i 1", + "ng 2", + "d e", + "j i", + "a o", + "x i", + "u 3", + "de 5", + "e 4", + "i 3", + "ng 4", + "an 4", + "e n", + "u o", + "sh i4", + "an 2", + "u 2", + "c h", + "u 1", + "ng 3", + "a 1", + "an 1", + "e 2", + "a 4", + "e i4", + "o ng1", + "a i4", + "ao 4", + "h u", + "a ng1", + "l i", + "y o", + "an 3", + "w ei4", + "uo 2", + "n 1", + "en 2", + "ao 3", + "e 1", + "y u", + "q i", + "e ng2", + "zh o", + "a ng3", + "a ng4", + "a ng2", + "uo 4", + "m i", + "g e4", + "y i1", + "g uo2", + "e r", + "b i", + "a 3", + "h e2", + "e 3", + "y i2", + "d i4", + "zh ong1", + "b u4", + "g u", + "a i2", + "n 2", + "z ai4", + "sh i2", + "e ng1", + "r en2", + "o ng2", + "xi an4", + "y i", + "x u", + "n 4", + "l i4", + "en 4", + "y u2", + "e i2", + "yi2 ge4", + "o u4", + "e i3", + "d i", + "u i4", + "a 2", + "yo u3", + "ao 1", + "d a4", + "ch eng2", + "en 1", + "e ng4", + "y i4", + "s i1", + "zh i4", + "ji a1", + "yu an2", + "n i", + "t a1", + "de5 yi2ge4", + "k e1", + "sh u3", + "x i1", + "j i2", + "ao 2", + "t i", + "o u3", + "o ng4", + "xi a4", + "a i1", + "g ong1", + "zh i1", + "en 3", + "w ei2", + "j u", + "xu e2", + "q u1", + "zho u1", + "er 3", + "mi ng2", + "zho ng3", + "l i3", + "w u4", + "y i3", + "uo 1", + "e 5", + "j i4", + "xi ng2", + "ji an4", + "hu a4", + "y u3", + "uo 3", + "j i1", + "a i3", + "z uo4", + "h ou4", + "hu i4", + "e i1", + "ni an2", + "q i2", + "p i", + "d ao4", + "sh eng1", + "de 2", + "d ai4", + "u an2", + "zh e4", + "zh eng4", + "b en3", + "sh ang4", + "zh u3", + "b ei4", + "y e4", + "ch u1", + "zh an4", + "l e5", + "l ai2", + "sh i3", + "n an2", + "r en4", + "yo u2", + "k e4", + "b a1", + "f u4", + "d ui4", + "y a4", + "m ei3", + "z i4", + "xi n1", + "ji ng1", + "zh u", + "n 3", + "yo ng4", + "m u4", + "ji ao4", + "y e3", + "ji n4", + "bi an4", + "l u4", + "q i1", + "sh e4", + "xi ang1", + "o ng3", + "sh u4", + "d ong4", + "s uo3", + "gu an1", + "s an1", + "b o", + "t e4", + "d uo1", + "f u2", + "mi n2", + "l a1", + "zh i2", + "zh en4", + "o u1", + "w u3", + "m a3", + "i 5", + "z i5", + "j u4", + "er 4", + "y ao4", + "xia4 de5yi2ge4", + "s i4", + "t u2", + "sh an1", + "z ui4", + "ch u", + "yi n1", + "er 2", + "t ong2", + "d ong1", + "y u4", + "y an2", + "qi an2", + "shu3 xia4de5yi2ge4", + "ju n1", + "k e3", + "w en2", + "f a3", + "l uo2", + "zh u4", + "x i4", + "k ou3", + "b ei3", + "ji an1", + "f a1", + "di an4", + "ji ang1", + "wei4 yu2", + "xi ang4", + "zh i3", + "e ng3", + "f ang1", + "l an2", + "sh u", + "r i4", + "li an2", + "sh ou3", + "m o", + "qi u2", + "ji n1", + "h uo4", + "shu3xia4de5yi2ge4 zhong3", + "f en1", + "n ei4", + "g ai1", + "mei3 guo2", + "u n2", + "g e2", + "b ao3", + "qi ng1", + "g ao1", + "t ai2", + "d u", + "xi ao3", + "ji e2", + "ti an1", + "ch ang2", + "q uan2", + "li e4", + "h ai3", + "f ei1", + "t i3", + "ju e2", + "o u2", + "c i3", + "z u2", + "n i2", + "bi ao3", + "zhong1 guo2", + "d u4", + "yu e4", + "xi ng4", + "sh eng4", + "ch e1", + "d an1", + "ji e1", + "li n2", + "pi ng2", + "f u3", + "g u3", + "ji e4", + "w o", + "v 3", + "sh eng3", + "n a4", + "yu an4", + "zh ang3", + "gu an3", + "d ao3", + "z u3", + "di ng4", + "di an3", + "c eng2", + "ren2 kou3", + "t ai4", + "t ong1", + "g uo4", + "n eng2", + "ch ang3", + "hu a2", + "li u2", + "yi ng1", + "xi ao4", + "c i4", + "bian4 hua4", + "li ang3", + "g ong4", + "zho ng4", + "de5 yi1", + "s e4", + "k ai1", + "w ang2", + "ji u4", + "sh i1", + "sh ou4", + "m ei2", + "k u", + "s u", + "f eng1", + "z e2", + "tu2 shi4", + "t i2", + "q i4", + "ji u3", + "sh en1", + "zh e3", + "ren2kou3 bian4hua4", + "ren2kou3bian4hua4 tu2shi4", + "di4 qu1", + "y ang2", + "m en", + "men 5", + "l ong2", + "bi ng4", + "ch an3", + "zh u1", + "w ei3", + "w ai4", + "xi ng1", + "bo 1", + "b i3", + "t ang2", + "hu a1", + "bo 2", + "shu i3", + "sh u1", + "d ou1", + "s ai4", + "ch ao2", + "b i4", + "li ng2", + "l ei4", + "da4 xue2", + "f en4", + "shu3 de5", + "m u3", + "ji ao1", + "d ang1", + "ch eng1", + "t ong3", + "n v3", + "q i3", + "y an3", + "mi an4", + "l uo4", + "ji ng4", + "g e1", + "r u4", + "d an4", + "ri4 ben3", + "p u3", + "yu n4", + "hu ang2", + "wo 3", + "l v", + "h ai2", + "shi4 yi1", + "xi e1", + "yi ng3", + "w u2", + "sh en2", + "w ang3", + "gu ang3", + "li u4", + "s u4", + "shi4 zhen4", + "c an1", + "c ao3", + "xi a2", + "k a3", + "d a2", + "h u4", + "b an4", + "d ang3", + "h u2", + "z ong3", + "de ng3", + "de5yi2ge4 shi4zhen4", + "ch uan2", + "mo 4", + "zh ang1", + "b an1", + "mo 2", + "ch a2", + "c e4", + "zhu3 yao4", + "t ou2", + "j u2", + "shi4 wei4yu2", + "s a4", + "u n1", + "ke3 yi3", + "d u1", + "h an4", + "li ang4", + "sh a1", + "ji a3", + "z i1", + "lv 4", + "f u1", + "xi an1", + "x u4", + "gu ang1", + "m eng2", + "b ao4", + "yo u4", + "r ong2", + "zhi1 yi1", + "w ei1", + "m ao2", + "guo2 jia1", + "c ong2", + "g ou4", + "ti e3", + "zh en1", + "d u2", + "bi an1", + "c i2", + "q u3", + "f an4", + "xi ang3", + "m en2", + "j u1", + "h ong2", + "z i3", + "ta1 men5", + "ji 3", + "z ong1", + "zhou1 de5yi2ge4shi4zhen4", + "t uan2", + "ji ng3", + "gong1 si1", + "xi e4", + "l i2", + "li4 shi3", + "b ao1", + "g ang3", + "gu i1", + "zh eng1", + "zhi2 wu4", + "ta1 de5", + "pi n3", + "zhu an1", + "ch ong2", + "shi3 yong4", + "w a3", + "sh uo1", + "chu an1", + "l ei2", + "w an1", + "h uo2", + "q u", + "s u1", + "z ao3", + "g ai3", + "q u4", + "g u4", + "l u", + "x i2", + "h ang2", + "yi ng4", + "c un1", + "g en1", + "yi ng2", + "ti ng2", + "cheng2 shi4", + "ji ang3", + "li ng3", + "l un2", + "bu4 fen4", + "de ng1", + "xu an3", + "dong4 wu4", + "de2 guo2", + "xi an3", + "f an3", + "zh e5", + "h an2", + "h ao4", + "m i4", + "r an2", + "qi n1", + "ti ao2", + "zh an3", + "h i", + "k a", + "n o", + "t e", + "s u", + "s hi", + "t a", + "t o", + "n a", + "w a", + "o u", + "r u", + "n i", + "k u", + "k i", + "g a", + "d e", + "k o", + "m a", + "r e", + "r a", + "m o", + "t su", + "w o", + "e n", + "r i", + "s a", + "d a", + "s e", + "j i", + "h a", + "c hi", + "k e", + "te ki", + "m i", + "y ou", + "s h", + "s o", + "y o", + "y a", + "na i", + "t te", + "a ru", + "b a", + "u u", + "t ta", + "ka i", + "ka n", + "shi te", + "m e", + "d o", + "mo no", + "se i", + "r o", + "ko to", + "ka ra", + "shi ta", + "b u", + "m u", + "c h", + "su ru", + "k ou", + "g o", + "ma su", + "ta i", + "f u", + "k en", + "i u", + "g en", + "wa re", + "shi n", + "z u", + "a i", + "o n", + "o ku", + "g i", + "d ou", + "n e", + "y uu", + "i ru", + "i te", + "ji ko", + "de su", + "j u", + "ra re", + "sh u", + "b e", + "sh ou", + "s ha", + "se kai", + "s ou", + "k you", + "ma shita", + "s en", + "na ra", + "sa n", + "ke i", + "i ta", + "a ri", + "i tsu", + "ko no", + "j ou", + "na ka", + "ch ou", + "so re", + "g u", + "na ru", + "ga ku", + "re ba", + "g e", + "h o", + "i n", + "hi to", + "sa i", + "na n", + "da i", + "tsu ku", + "shi ki", + "sa re", + "na ku", + "p p", + "bu n", + "ju n", + "so no", + "ka ku", + "z ai", + "b i", + "to u", + "wa ta", + "sh uu", + "i i", + "te i", + "ka re", + "y u", + "shi i", + "ma de", + "sh o", + "a n", + "ke reba", + "shi ka", + "i chi", + "ha n", + "de ki", + "ni n", + "ware ware", + "na kereba", + "o ite", + "h ou", + "ya ku", + "ra i", + "mu jun", + "l e", + "yo ku", + "bu tsu", + "o o", + "ko n", + "o mo", + "ga e", + "nara nai", + "ta chi", + "z en", + "ch uu", + "kan gae", + "ta ra", + "to ki", + "ko ro", + "mujun teki", + "z e", + "na ga", + "ji n", + "shi ma", + "te n", + "i ki", + "i ku", + "no u", + "i masu", + "r ou", + "h on", + "ka e", + "t to", + "ko re", + "ta n", + "ki ta", + "i s", + "da tta", + "ji tsu", + "ma e", + "i e", + "me i", + "da n", + "h e", + "to ku", + "dou itsu", + "ri tsu", + "k yuu", + "h you", + "rare ta", + "kei sei", + "k kan", + "rare ru", + "m ou", + "do ko", + "r you", + "da ke", + "naka tta", + "so ko", + "ta be", + "e r", + "ha na", + "c o", + "fu ku", + "p a", + "so n", + "ya su", + "ch o", + "wata ku", + "ya ma", + "z a", + "k yo", + "gen zai", + "b oku", + "a ta", + "j a", + "ka wa", + "ma sen", + "j uu", + "ro n", + "b o", + "na tte", + "wataku shi", + "yo tte", + "ma i", + "g ou", + "ha i", + "mo n", + "ba n", + "ji shin", + "c a", + "re te", + "n en", + "o ka", + "ka gaku", + "na tta", + "p o", + "ka ru", + "na ri", + "m en", + "ma ta", + "e i", + "ku ru", + "ga i", + "ka ri", + "sha kai", + "kou i", + "yo ri", + "se tsu", + "j o", + "re ru", + "to koro", + "ju tsu", + "i on", + "sa ku", + "tta i", + "c ha", + "nin gen", + "n u", + "c e", + "ta me", + "kan kyou", + "de n", + "o oku", + "i ma", + "wata shi", + "tsuku ru", + "su gi", + "b en", + "ji bun", + "shi tsu", + "ke ru", + "ki n", + "ki shi", + "shika shi", + "mo to", + "ma ri", + "i tte", + "de shita", + "n de", + "ari masu", + "te r", + "z ou", + "ko e", + "ze ttai", + "kkan teki", + "h en", + "re kishi", + "deki ru", + "tsu ka", + "l a", + "i tta", + "o i", + "ko butsu", + "mi ru", + "sh oku", + "shi masu", + "gi jutsu", + "g you", + "jou shiki", + "a tta", + "ho do", + "ko ko", + "tsuku rareta", + "z oku", + "hi tei", + "ko ku", + "rekishi teki", + "ke te", + "o ri", + "i mi", + "ka ko", + "naga ra", + "ka karu", + "shu tai", + "ha ji", + "ma n", + "ta ku", + "ra n", + "douitsu teki", + "z o", + "me te", + "re i", + "tsu u", + "sare te", + "gen jitsu", + "p e", + "s t", + "ba i", + "na wa", + "ji kan", + "wa ru", + "r t", + "a tsu", + "so ku", + "koui teki", + "a ra", + "u ma", + "a no", + "i de", + "ka ta", + "te tsu", + "ga wa", + "ke do", + "re ta", + "mi n", + "sa you", + "tte ru", + "to ri", + "p u", + "ki mi", + "b ou", + "mu ra", + "sare ru", + "ma chi", + "k ya", + "o sa", + "kon na", + "a ku", + "a l", + "sare ta", + "i pp", + "shi ku", + "u chi", + "hito tsu", + "ha tara", + "tachi ba", + "shi ro", + "ka tachi", + "to mo", + "e te", + "me ru", + "ni chi", + "da re", + "ka tta", + "e ru", + "su ki", + "a ge", + "oo ki", + "ma ru", + "mo ku", + "o ko", + "kangae rareru", + "o to", + "tan ni", + "ta da", + "tai teki", + "mo tte", + "ki nou", + "shi nai", + "k ki", + "u e", + "ta ri", + "l i", + "ra nai", + "k kou", + "mi rai", + "pp on", + "go to", + "hi n", + "hi tsu", + "te ru", + "mo chi", + "ka tsu", + "re n", + "n yuu", + "su i", + "zu ka", + "tsu ite", + "no mi", + "su gu", + "ku da", + "tetsu gaku", + "i ka", + "ron ri", + "o ki", + "ni ppon", + "p er", + "shi mashita", + "chi shiki", + "cho kkanteki", + "su ko", + "t ion", + "ku u", + "a na", + "a rou", + "ka tte", + "ku ri", + "i nai", + "hyou gen", + "i shiki", + "do ku", + "a tte", + "a tara", + "to n", + "wa ri", + "ka o", + "sei san", + "hana shi", + "s i", + "ka ke", + "na ji", + "su nawa", + "sunawa chi", + "u go", + "su u", + "ba ra", + "le v", + "hi ro", + "i wa", + "be tsu", + "yo i", + "se ru", + "shite ru", + "rare te", + "to shi", + "se ki", + "tai ritsu", + "wa kara", + "to kyo", + "k ka", + "k yoku", + "u n", + "i ro", + "mi te", + "sa ki", + "kan ji", + "mi ta", + "su be", + "r yoku", + "ma tta", + "kuda sai", + "omo i", + "ta no", + "ware ru", + "co m", + "hitsu you", + "ka shi", + "re nai", + "kan kei", + "a to", + "ga tte", + "o chi", + "mo tsu", + "in g", + "son zai", + "l l", + "o re", + "tai shite", + "a me", + "sei mei", + "ka no", + "gi ri", + "kangae ru", + "yu e", + "a sa", + "o naji", + "yo ru", + "ni ku", + "osa ka", + "suko shi", + "c k", + "ta ma", + "kano jo", + "ki te", + "mon dai", + "a mari", + "e ki", + "ko jin", + "ha ya", + "i t", + "de te", + "atara shii", + "a wa", + "ga kkou", + "tsu zu", + "shu kan", + "i mashita", + "mi na", + "ata e", + "da rou", + "hatara ku", + "ga ta", + "da chi", + "ma tsu", + "ari masen", + "sei butsu", + "mi tsu", + "he ya", + "yasu i", + "d i", + "de ni", + "no ko", + "ha ha", + "do mo", + "ka mi", + "su deni", + "na o", + "ra ku", + "i ke", + "a ki", + "me ta", + "l o", + "ko domo", + "so shite", + "ga me", + "ba kari", + "to te", + "ha tsu", + "mi se", + "moku teki", + "da kara", + "s z", + "e l", + "g y", + "e n", + "t t", + "e m", + "a n", + "a k", + "e r", + "a z", + "a l", + "e t", + "o l", + "e g", + "e k", + "m i", + "o n", + "é s", + "c s", + "a t", + "á r", + "h o", + "e z", + "á l", + "i s", + "á n", + "o r", + "a r", + "e gy", + "e s", + "é r", + "á t", + "o tt", + "e tt", + "m eg", + "t a", + "o k", + "o s", + "ho gy", + "n em", + "é g", + "n y", + "k i", + "é l", + "h a", + "á s", + "ü l", + "i n", + "mi n", + "n a", + "e d", + "o m", + "i k", + "k ö", + "m a", + "n i", + "v a", + "v ol", + "é t", + "b b", + "f el", + "i g", + "l e", + "r a", + "é n", + "t e", + "d e", + "a d", + "ó l", + "b e", + "on d", + "j a", + "r e", + "u l", + "b en", + "n ek", + "u t", + "vol t", + "b an", + "ö r", + "o g", + "a p", + "o d", + "á g", + "n k", + "é k", + "v al", + "k or", + "a m", + "i l", + "í t", + "á k", + "b a", + "u d", + "sz er", + "min d", + "o z", + "é p", + "el l", + "ér t", + "m ond", + "i t", + "sz t", + "n ak", + "a mi", + "n e", + "ő l", + "cs ak", + "n é", + "ma g", + "ol y", + "m er", + "ál l", + "án y", + "ö n", + "ö l", + "min t", + "m ár", + "ö tt", + "na gy", + "é sz", + "az t", + "el ő", + "t ud", + "o t", + "é ny", + "á z", + "m ég", + "kö z", + "el y", + "s ég", + "en t", + "s em", + "ta m", + "h et", + "h al", + "f i", + "a s", + "v an", + "ho z", + "v e", + "u k", + "k ez", + "á m", + "v el", + "b er", + "a j", + "u nk", + "i z", + "va gy", + "m os", + "sz em", + "em ber", + "f og", + "mer t", + "ü k", + "l en", + "ö s", + "e j", + "t al", + "h at", + "t ak", + "h i", + "m ás", + "s ág", + "ett e", + "l eg", + "ü nk", + "h át", + "sz a", + "on y", + "ez t", + "mind en", + "en d", + "ül t", + "h an", + "j ó", + "k is", + "á j", + "in t", + "ú gy", + "i d", + "mos t", + "ar t", + "í r", + "k er", + "i tt", + "a tt", + "el t", + "mond ta", + "k ell", + "l á", + "ak i", + "ál t", + "ér d", + "t ö", + "l an", + "v ár", + "h ol", + "t el", + "l át", + "ő k", + "v et", + "s e", + "ut án", + "k ét", + "na p", + "í v", + "ál y", + "v ég", + "ö k", + "i r", + "d ul", + "v is", + "né z", + "t er", + "á ban", + "k ül", + "ak kor", + "k ap", + "sz él", + "y en", + "ú j", + "i m", + "oly an", + "es en", + "k ed", + "h ely", + "t ör", + "b ól", + "el m", + "r á", + "ár a", + "r ó", + "l ó", + "vol na", + "t an", + "le het", + "e bb", + "t en", + "t ek", + "s ok", + "k al", + "f or", + "u g", + "ol t", + "k a", + "ek et", + "b or", + "f ej", + "g ond", + "a g", + "ak ar", + "f él", + "ú l", + "b el", + "ott a", + "mi t", + "val ami", + "j el", + "é d", + "ar c", + "u r", + "hal l", + "t i", + "f öl", + "á ba", + "ol g", + "ki r", + "ol d", + "m ar", + "k érd", + "j ár", + "ú r", + "sz e", + "z s", + "él et", + "j át", + "o v", + "u s", + "é z", + "v il", + "v er", + "ő r", + "á d", + "ö g", + "le sz", + "on t", + "b iz", + "k oz", + "á bb", + "kir ály", + "es t", + "a b", + "en g", + "ig az", + "b ar", + "ha j", + "d i", + "o b", + "k od", + "r ól", + "v ez", + "tö bb", + "sz ó", + "é ben", + "ö t", + "ny i", + "t á", + "sz ól", + "gond ol", + "eg ész", + "í gy", + "ő s", + "o bb", + "os an", + "b ől", + "a bb", + "c i", + "ő t", + "n ál", + "k ép", + "azt án", + "v i", + "t art", + "be szél", + "m en", + "elő tt", + "a szt", + "ma j", + "kö r", + "han g", + "í z", + "in cs", + "a i", + "é v", + "ó d", + "ó k", + "hoz z", + "t em", + "ok at", + "an y", + "nagy on", + "h áz", + "p er", + "p ed", + "ez te", + "et len", + "nek i", + "maj d", + "sz ony", + "án ak", + "fel é", + "egy szer", + "j e", + "ad t", + "gy er", + "ami kor", + "f oly", + "sz ak", + "ő d", + "h ú", + "á sz", + "am ely", + "h ar", + "ér e", + "il yen", + "od a", + "j ák", + "t ár", + "á val", + "l ak", + "t ó", + "m ent", + "gy an", + "él y", + "ú t", + "v ar", + "kez d", + "m ell", + "mi kor", + "h ez", + "val ó", + "k o", + "m es", + "szer et", + "r end", + "l et", + "vis sza", + "ig en", + "f ő", + "va s", + "as szony", + "r ől", + "ped ig", + "p i", + "sz ép", + "t ák", + "ö v", + "an i", + "vil ág", + "p en", + "mag a", + "t et", + "sz ik", + "é j", + "én t", + "j ött", + "s an", + "sz í", + "i de", + "g at", + "ett em", + "ul t", + "h ány", + "ás t", + "a hol", + "ők et", + "h ár", + "k el", + "n ő", + "cs i", + "tal ál", + "el te", + "lá tt", + "tör t", + "ha gy", + "e sz", + "s en", + "n él", + "p ar", + "v ál", + "k ut", + "l ány", + "ami t", + "s ő", + "ell en", + "mag át", + "in k", + "u gyan", + "kül ön", + "a sz", + "mind ig", + "l ép", + "tal án", + "u n", + "sz or", + "k e", + "il lan", + "n incs", + "z et", + "vagy ok", + "tel en", + "is mer", + "s or", + "is ten", + "ít ott", + "j obb", + "v es", + "dul t", + "j uk", + "sz en", + "r o", + "ö m", + "l ett", + "k ar", + "egy ik", + "b ár", + "sz i", + "sz ív", + "az on", + "e szt", + "föl d", + "kut y", + "p illan", + "f ér", + "k om", + "t ől", + "t ű", + "é be", + "t ött", + "bar át", + "í g", + "a hogy", + "e h", + "e p", + "s o", + "v en", + "jel ent", + "t at", + "sz eg", + "mint ha", + "f al", + "egy en", + "mi l", + "sza b", + "r i", + "é m", + "biz ony", + "j on", + "ör eg", + "d olg", + "cs ap", + "ti szt", + "áll t", + "an cs", + "id ő", + "k at", + "ü gy", + "mi ért", + "ó t", + "ü r", + "cs in", + "h az", + "b et", + "én ek", + "v ér", + "j ól", + "al att", + "m ely", + "l o", + "sem mi", + "ny ug", + "v ág", + "kö vet", + "ös sze", + "ma d", + "l i", + "a cs", + "fi ú", + "kö n", + "más ik", + "j ön", + "sz ám", + "g er", + "s ó", + "r ész", + "k ér", + "z el", + "é vel", + "e o", + "e u", + "a n", + "eu l", + "eu n", + "eo n", + "a e", + "d a", + "a l", + "s s", + "i n", + "i l", + "a g", + "an g", + "y eon", + "y eo", + "d o", + "c h", + "n g", + "j i", + "h an", + "g a", + "g o", + "u i", + "h ae", + "a m", + "u l", + "u n", + "g eo", + "s i", + "n eun", + "ss da", + "s eo", + "eon g", + "y o", + "i da", + "t t", + "k k", + "j eo", + "d eul", + "w a", + "eu m", + "g e", + "o n", + "o g", + "s al", + "m an", + "yeon g", + "geo s", + "h ag", + "an eun", + "j a", + "g i", + "s u", + "i ss", + "o l", + "d ae", + "eo b", + "h a", + "j u", + "eo l", + "g eu", + "j eong", + "s ae", + "do e", + "g eul", + "s eu", + "s in", + "eul o", + "b n", + "s ang", + "bn ida", + "h al", + "b o", + "han eun", + "m al", + "i m", + "m o", + "b u", + "jeo g", + "sae ng", + "in eun", + "an h", + "m a", + "sal am", + "j o", + "s a", + "eo m", + "n ae", + "w i", + "l o", + "g wa", + "yeo l", + "n a", + "e seo", + "y e", + "m yeon", + "tt ae", + "h w", + "j e", + "eob s", + "j ang", + "g u", + "g w", + "il eul", + "yeo g", + "j eon", + "si g", + "j ag", + "j in", + "y u", + "o e", + "s e", + "hag o", + "d eun", + "y a", + "m un", + "s eong", + "g ag", + "h am", + "d ang", + "b a", + "l eul", + "s il", + "do ng", + "kk a", + "b al", + "da l", + "han da", + "eo ssda", + "ae g", + "l i", + "ha ji", + "s eon", + "o ng", + "hae ssda", + "d e", + "i ssda", + "e ge", + "b un", + "m ul", + "ju ng", + "ji g", + "m u", + "iss neun", + "b i", + "g eun", + "seu bnida", + "w on", + "p p", + "d aneun", + "eo h", + "d eo", + "ga m", + "j al", + "hae ng", + "ag o", + "y ang", + "b ul", + "b ang", + "u m", + "s o", + "h i", + "j ae", + "si m", + "saeng gag", + "hag e", + "s og", + "eo ss", + "d an", + "ja sin", + "j il", + "eo g", + "g yeong", + "doe n", + "go ng", + "m i", + "ch i", + "d eu", + "d eon", + "hae ss", + "d u", + "n am", + "eun g", + "jo h", + "n al", + "m yeong", + "w o", + "eon a", + "i go", + "g yeol", + "y ag", + "gw an", + "ul i", + "yo ng", + "n o", + "l yeo", + "j og", + "eoh ge", + "ga t", + "b og", + "mo s", + "t ong", + "ch a", + "man h", + "jeo l", + "geo l", + "h oe", + "ag a", + "n aneun", + "g an", + "un eun", + "ch eol", + "ch e", + "do l", + "b on", + "b an", + "ba d", + "ch u", + "ham yeon", + "yeo ssda", + "i bnida", + "g ye", + "eo s", + "hw al", + "salam deul", + "ji man", + "dang sin", + "ji b", + "ttae mun", + "m ae", + "i b", + "e neun", + "eu g", + "jeo m", + "geul eon", + "h wa", + "a ssda", + "b eob", + "bu t", + "b ae", + "yeo ss", + "ch in", + "ch aeg", + "g eon", + "g ae", + "nae ga", + "i ga", + "m og", + "sig an", + "g il", + "h yeon", + "l yeog", + "gu g", + "p yeon", + "s an", + "w ae", + "j ul", + "s eul", + "deun g", + "haji man", + "eum yeon", + "p il", + "m ol", + "n eu", + "a ss", + "n yeon", + "t ae", + "h u", + "p yo", + "s ul", + "g ang", + "j ineun", + "b eon", + "ha da", + "seo l", + "si p", + "dal eun", + "a p", + "sal m", + "g yo", + "ch eon", + "hag i", + "in a", + "cheol eom", + "g al", + "il a", + "kka ji", + "anh neun", + "ha bnida", + "tt eon", + "n u", + "hae seo", + "doen da", + "s ol", + "tt al", + "l a", + "il o", + "seu b", + "b yeon", + "m yeo", + "b eol", + "s on", + "n un", + "j un", + "j am", + "j eung", + "tt o", + "e n", + "mo m", + "h o", + "ch im", + "hw ang", + "eun eun", + "jo ng", + "bo da", + "n ol", + "n eom", + "but eo", + "jig eum", + "eobs da", + "dae lo", + "i g", + "y ul", + "p yeong", + "seon eun", + "sal ang", + "seu t", + "h im", + "n an", + "h eom", + "h yang", + "p i", + "gw ang", + "eobs neun", + "hw ag", + "ge ss", + "jag i", + "il eon", + "wi hae", + "dae han", + "ga ji", + "m eog", + "j yeo", + "cha j", + "b yeong", + "eo d", + "g yeo", + "do n", + "eo ji", + "g ul", + "mo deun", + "j on", + "in saeng", + "geul ae", + "h ang", + "sa sil", + "si b", + "ch al", + "il ago", + "doe l", + "g eum", + "doe neun", + "b ol", + "ga jang", + "geul igo", + "e l", + "h yeong", + "haeng bog", + "ch ul", + "h on", + "ch ae", + "s am", + "m ang", + "in da", + "da m", + "w ol", + "ch oe", + "d ul", + "si jag", + "ch eong", + "il aneun", + "ul ineun", + "ae n", + "kk e", + "mun je", + "a do", + "t eu", + "g un", + "geun eun", + "b ge", + "ch eo", + "b aeg", + "ju g", + "t a", + "sang dae", + "geu geos", + "do g", + "eu s", + "deu s", + "ja b", + "h yeo", + "tt eohge", + "u g", + "ma j", + "ch il", + "s wi", + "j ileul", + "ch ang", + "g aneun", + "m ag", + "i ji", + "da go", + "m in", + "yo han", + "t eug", + "pp un", + "al eul", + "haeng dong", + "p o", + "m il", + "ch am", + "se sang", + "e do", + "p an", + "man deul", + "am yeon", + "a b", + "kk ae", + "b ag", + "i deul", + "p um", + "m eol", + "s un", + "n eul", + "ham kke", + "chu ng", + "da b", + "yu g", + "s ag", + "gwang ye", + "il eohge", + "bal o", + "neun de", + "ham yeo", + "go s", + "geul eoh", + "an ila", + "bang beob", + "da si", + "b yeol", + "g yeon", + "gam jeong", + "on eul", + "j aneun", + "yeo m", + "l ago", + "i gi", + "hw an", + "t eul", + "eo seo", + "si k", + "ch o", + "jag a", + "geul eom", + "geul eona", + "jeong do", + "g yeog", + "geul eohge", + "geu deul", + "eu t", + "im yeon", + "j jae", + "k eun", + "i sang", + "mal haessda", + "eu ge", + "no p", + "in gan", + "bo myeon", + "t aeg", + "seu s", + "d wi", + "s aneun", + "w an", + "anh go", + "t an", + "nu gu", + "su ng", + "da myeon", + "a deul", + "p eul", + "ttal a", + "d i", + "geos do", + "a ji", + "m eon", + "eum yeo", + "dol og", + "neun g", + "mo du", + "क े", + "ह ै", + "े ं", + "् र", + "ा र", + "न े", + "य ा", + "म ें", + "स े", + "क ी", + "क ा", + "ो ं", + "त ा", + "क र", + "स ्", + "क ि", + "क ो", + "र ्", + "न ा", + "क ्", + "ह ी", + "औ र", + "प र", + "त े", + "ह ो", + "प ्र", + "ा न", + "् य", + "ल ा", + "व ा", + "ल े", + "स ा", + "है ं", + "ल ि", + "ज ा", + "ह ा", + "भ ी", + "व ि", + "इ स", + "त ी", + "न ्", + "र ा", + "म ा", + "द े", + "द ि", + "ब ा", + "त ि", + "थ ा", + "न ि", + "क ार", + "ए क", + "ही ं", + "ह ु", + "ं ग", + "ै ं", + "न ी", + "स ी", + "अ प", + "त ्", + "न हीं", + "र ी", + "म े", + "म ु", + "ि त", + "त ो", + "प ा", + "ल ी", + "लि ए", + "ग ा", + "ल ्", + "र ह", + "र े", + "क् ष", + "म ैं", + "स म", + "उ स", + "ज ि", + "त ्र", + "म ि", + "च ा", + "ो ग", + "स ं", + "द ्", + "स ि", + "आ प", + "त ु", + "द ा", + "क ु", + "य ों", + "व े", + "ज ी", + "् या", + "उ न", + "ि क", + "य े", + "भ ा", + "् ट", + "ह म", + "स् ट", + "श ा", + "ड ़", + "ं द", + "ख ा", + "म ्", + "श ्", + "य ह", + "स क", + "प ू", + "कि या", + "अप ने", + "र ू", + "स ु", + "म ी", + "ह ि", + "ज ो", + "थ े", + "र ि", + "द ी", + "थ ी", + "ग ी", + "ल ोग", + "ग या", + "त र", + "न् ह", + "च ्", + "व ार", + "ब ी", + "प ्", + "द ो", + "ट ी", + "श ि", + "कर ने", + "ग े", + "ै से", + "इ न", + "ं ड", + "सा थ", + "प ु", + "ब े", + "ब ार", + "व ी", + "अ न", + "ह र", + "उ न्ह", + "हो ता", + "ज ब", + "कु छ", + "म ान", + "क ्र", + "ब ि", + "प ह", + "फ ि", + "स र", + "ार ी", + "र ो", + "द ू", + "क हा", + "त क", + "श न", + "ब ्", + "स् थ", + "व ह", + "बा द", + "ओ ं", + "ग ु", + "ज ्", + "्र े", + "ग र", + "रह े", + "व र्", + "ह ू", + "ार ्", + "प ी", + "ब हु", + "मु झ", + "्र ा", + "दि या", + "स ब", + "कर ते", + "अप नी", + "बहु त", + "क ह", + "ट े", + "हु ए", + "कि सी", + "र हा", + "ष ्ट", + "ज ़", + "ब ना", + "स ो", + "ड ि", + "को ई", + "व ्य", + "बा त", + "र ु", + "व ो", + "मुझ े", + "द् ध", + "च ार", + "मे रे", + "व र", + "्र ी", + "जा ता", + "न ों", + "प्र ा", + "दे ख", + "ट ा", + "क् या", + "अ ध", + "ल ग", + "ल ो", + "प ि", + "य ु", + "च े", + "जि स", + "ं त", + "ान ी", + "प ै", + "ज न", + "ार े", + "च ी", + "मि ल", + "द ु", + "दे श", + "च् छ", + "ष ्", + "स ू", + "ख े", + "च ु", + "ि या", + "ल गा", + "ब ु", + "उन के", + "ज् ञ", + "क्ष ा", + "त रह", + "्या दा", + "वा ले", + "पू र्", + "मैं ने", + "का म", + "रू प", + "हो ती", + "उ प", + "ज ान", + "प्र कार", + "भ ार", + "म न", + "हु आ", + "ट र", + "हू ँ", + "पर ि", + "पा स", + "अन ु", + "रा ज", + "लोग ों", + "अ ब", + "सम झ", + "ड ी", + "म ौ", + "श ु", + "च ि", + "प े", + "क ृ", + "सक ते", + "म ह", + "य ोग", + "द र्", + "उ से", + "ं ध", + "ड ा", + "जा ए", + "ब ो", + "ू ल", + "म ो", + "ों ने", + "ं स", + "तु म", + "पह ले", + "ब ता", + "त था", + "य ो", + "ग ई", + "उ त्", + "सक ता", + "क म", + "ज ्यादा", + "र ख", + "सम य", + "ार ा", + "अ गर", + "स् त", + "च ल", + "फि र", + "वार ा", + "कर ना", + "श ी", + "ग ए", + "ब न", + "ौ र", + "हो ने", + "चा ह", + "ख ु", + "हा ँ", + "उन्ह ें", + "उन्ह ोंने", + "छ ो", + "म् ह", + "प्र ति", + "नि क", + "व न", + "्य ू", + "र ही", + "तु म्ह", + "ज ैसे", + "ि यों", + "क् यों", + "ल ों", + "फ ़", + "ं त्र", + "हो ते", + "क् ति", + "त ्य", + "कर ्", + "क ई", + "व ं", + "कि न", + "प ो", + "कार ण", + "ड़ ी", + "भ ि", + "इस के", + "ब र", + "उस के", + "द् वारा", + "श े", + "क ॉ", + "दि न", + "न् न", + "ड़ ा", + "स् व", + "नि र्", + "मु ख", + "लि या", + "ट ि", + "ज्ञ ान", + "क् त", + "द ्र", + "ग ्", + "क् स", + "म ै", + "ग ो", + "ज े", + "ट ्र", + "म ार", + "त् व", + "ध ार", + "भा व", + "कर ता", + "ख ि", + "क ं", + "चा हि", + "य र", + "प् त", + "क ों", + "ं च", + "ज ु", + "म त", + "अ च्छ", + "हु ई", + "क भी", + "ले किन", + "भ ू", + "अप ना", + "दू स", + "चाहि ए", + "य ू", + "घ र", + "सब से", + "मे री", + "ना म", + "ढ ़", + "ं ट", + "ें गे", + "ब ै", + "फ ा", + "ए वं", + "य ी", + "ग ्र", + "क्ष े", + "आ ज", + "आप को", + "भा ग", + "ठ ा", + "क ै", + "भार त", + "उन की", + "प हु", + "स भी", + "ध ा", + "ण ा", + "स ान", + "हो गा", + "त ब", + "स ंग", + "प र्", + "अ व", + "त ना", + "ग ि", + "य न", + "स् था", + "च ित", + "ट ्", + "छ ा", + "जा ने", + "क्षे त्र", + "वा ली", + "पूर् ण", + "स मा", + "कार ी" + ] + } +} \ No newline at end of file diff --git a/comfy/text_encoders/ace_text_cleaners.py b/comfy/text_encoders/ace_text_cleaners.py new file mode 100644 index 0000000000000000000000000000000000000000..cd31d8d8cdffcda51cc26405c3f6f2bcee410d48 --- /dev/null +++ b/comfy/text_encoders/ace_text_cleaners.py @@ -0,0 +1,395 @@ +# basic text cleaners for the ACE step model +# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies +# TODO: more languages than english? + +import re + +def japanese_to_romaji(japanese_text): + """ + Convert Japanese hiragana and katakana to romaji (Latin alphabet representation). + + Args: + japanese_text (str): Text containing hiragana and/or katakana characters + + Returns: + str: The romaji (Latin alphabet) equivalent + """ + # Dictionary mapping kana characters to their romaji equivalents + kana_map = { + # Katakana characters + 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o', + 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko', + 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so', + 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to', + 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no', + 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho', + 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo', + 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo', + 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro', + 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n', + + # Katakana voiced consonants + 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go', + 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo', + 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do', + 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo', + 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po', + + # Katakana combinations + 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo', + 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho', + 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho', + 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo', + 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo', + 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo', + 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo', + 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo', + 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo', + 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo', + 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo', + + # Katakana small characters and special cases + 'ッ': '', # Small tsu (doubles the following consonant) + 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo', + + # Katakana extras + 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo', + 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo', + + # Hiragana characters + 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o', + 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko', + 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so', + 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to', + 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no', + 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho', + 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo', + 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo', + 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro', + 'わ': 'wa', 'を': 'wo', 'ん': 'n', + + # Hiragana voiced consonants + 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go', + 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo', + 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do', + 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo', + 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po', + + # Hiragana combinations + 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo', + 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho', + 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho', + 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo', + 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo', + 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo', + 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo', + 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo', + 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo', + 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo', + 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo', + + # Hiragana small characters and special cases + 'っ': '', # Small tsu (doubles the following consonant) + 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo', + + # Common punctuation and spaces + '　': ' ', # Japanese space + '、': ', ', '。': '. ', + } + + result = [] + i = 0 + + while i < len(japanese_text): + # Check for small tsu (doubling the following consonant) + if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'): + if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map: + next_romaji = kana_map[japanese_text[i+1]] + if next_romaji and next_romaji[0] not in 'aiueon': + result.append(next_romaji[0]) # Double the consonant + i += 1 + continue + + # Check for combinations with small ya, yu, yo + if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'): + combo = japanese_text[i:i+2] + if combo in kana_map: + result.append(kana_map[combo]) + i += 2 + continue + + # Regular character + if japanese_text[i] in kana_map: + result.append(kana_map[japanese_text[i]]) + else: + # If it's not in our map, keep it as is (might be kanji, romaji, etc.) + result.append(japanese_text[i]) + + i += 1 + + return ''.join(result) + +def number_to_text(num, ordinal=False): + """ + Convert a number (int or float) to its text representation. + + Args: + num: The number to convert + + Returns: + str: Text representation of the number + """ + + if not isinstance(num, (int, float)): + return "Input must be a number" + + # Handle special case of zero + if num == 0: + return "zero" + + # Handle negative numbers + negative = num < 0 + num = abs(num) + + # Handle floats + if isinstance(num, float): + # Split into integer and decimal parts + int_part = int(num) + + # Convert both parts + int_text = _int_to_text(int_part) + + # Handle decimal part (convert to string and remove '0.') + decimal_str = str(num).split('.')[1] + decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str) + + result = int_text + decimal_text + else: + # Handle integers + result = _int_to_text(num) + + # Add 'negative' prefix for negative numbers + if negative: + result = "negative " + result + + return result + + +def _int_to_text(num): + """Helper function to convert an integer to text""" + + ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", + "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", + "seventeen", "eighteen", "nineteen"] + + tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] + + if num < 20: + return ones[num] + + if num < 100: + return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "") + + if num < 1000: + return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "") + + if num < 1000000: + return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "") + + if num < 1000000000: + return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "") + + return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "") + + +def _digit_to_text(digit): + """Convert a single digit to text""" + digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] + return digits[digit] + + +_whitespace_re = re.compile(r"\s+") + + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = { + "en": [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] + ], +} + + +def expand_abbreviations_multilingual(text, lang="en"): + for regex, replacement in _abbreviations[lang]: + text = re.sub(regex, replacement, text) + return text + + +_symbols_multilingual = { + "en": [ + (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) + for x in [ + ("&", " and "), + ("@", " at "), + ("%", " percent "), + ("#", " hash "), + ("$", " dollar "), + ("£", " pound "), + ("°", " degree "), + ] + ], +} + + +def expand_symbols_multilingual(text, lang="en"): + for regex, replacement in _symbols_multilingual[lang]: + text = re.sub(regex, replacement, text) + text = text.replace(" ", " ") # Ensure there are no double spaces + return text.strip() + + +_ordinal_re = { + "en": re.compile(r"([0-9]+)(st|nd|rd|th)"), +} +_number_re = re.compile(r"[0-9]+") +_currency_re = { + "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"), + "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"), + "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"), +} + +_comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b") +_dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b") +_decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)") + + +def _remove_commas(m): + text = m.group(0) + if "," in text: + text = text.replace(",", "") + return text + + +def _remove_dots(m): + text = m.group(0) + if "." in text: + text = text.replace(".", "") + return text + + +def _expand_decimal_point(m, lang="en"): + amount = m.group(1).replace(",", ".") + return number_to_text(float(amount)) + + +def _expand_currency(m, lang="en", currency="USD"): + amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", ".")))) + full_amount = number_to_text(amount) + + and_equivalents = { + "en": ", ", + "es": " con ", + "fr": " et ", + "de": " und ", + "pt": " e ", + "it": " e ", + "pl": ", ", + "cs": ", ", + "ru": ", ", + "nl": ", ", + "ar": ", ", + "tr": ", ", + "hu": ", ", + "ko": ", ", + } + + if amount.is_integer(): + last_and = full_amount.rfind(and_equivalents[lang]) + if last_and != -1: + full_amount = full_amount[:last_and] + + return full_amount + + +def _expand_ordinal(m, lang="en"): + return number_to_text(int(m.group(1)), ordinal=True) + + +def _expand_number(m, lang="en"): + return number_to_text(int(m.group(0))) + + +def expand_numbers_multilingual(text, lang="en"): + if lang in ["en", "ru"]: + text = re.sub(_comma_number_re, _remove_commas, text) + else: + text = re.sub(_dot_number_re, _remove_dots, text) + try: + text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text) + text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text) + text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text) + except: + pass + + text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text) + text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text) + text = re.sub(_number_re, lambda m: _expand_number(m, lang), text) + return text + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def multilingual_cleaners(text, lang): + text = text.replace('"', "") + if lang == "tr": + text = text.replace("İ", "i") + text = text.replace("Ö", "ö") + text = text.replace("Ü", "ü") + text = lowercase(text) + try: + text = expand_numbers_multilingual(text, lang) + except: + pass + try: + text = expand_abbreviations_multilingual(text, lang) + except: + pass + try: + text = expand_symbols_multilingual(text, lang=lang) + except: + pass + text = collapse_whitespace(text) + return text + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py new file mode 100644 index 0000000000000000000000000000000000000000..cf4252eea3ad6ed02e0a8ee4c13385c78909cbd8 --- /dev/null +++ b/comfy/text_encoders/aura_t5.py @@ -0,0 +1,22 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os + +class PT5XlModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) + +class PT5XlTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1, tokenizer_data=tokenizer_data) + +class AuraT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer) + +class AuraT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, name="pile_t5xl", clip_model=PT5XlModel, **kwargs) diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py new file mode 100644 index 0000000000000000000000000000000000000000..551b031626911717d237f85e48ec9b1d74a6baa9 --- /dev/null +++ b/comfy/text_encoders/bert.py @@ -0,0 +1,143 @@ +import torch +from comfy.ldm.modules.attention import optimized_attention_for_device +import comfy.ops + +class BertAttention(torch.nn.Module): + def __init__(self, embed_dim, heads, dtype, device, operations): + super().__init__() + + self.heads = heads + self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + + def forward(self, x, mask=None, optimized_attention=None): + q = self.query(x) + k = self.key(x) + v = self.value(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return out + +class BertOutput(torch.nn.Module): + def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): + super().__init__() + self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) + self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device) + # self.dropout = nn.Dropout(0.0) + + def forward(self, x, y): + x = self.dense(x) + # hidden_states = self.dropout(hidden_states) + x = self.LayerNorm(x + y) + return x + +class BertAttentionBlock(torch.nn.Module): + def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.self = BertAttention(embed_dim, heads, dtype, device, operations) + self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) + + def forward(self, x, mask, optimized_attention): + y = self.self(x, mask, optimized_attention) + return self.output(y, x) + +class BertIntermediate(torch.nn.Module): + def __init__(self, embed_dim, intermediate_dim, dtype, device, operations): + super().__init__() + self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device) + + def forward(self, x): + x = self.dense(x) + return torch.nn.functional.gelu(x) + + +class BertBlock(torch.nn.Module): + def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations) + self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations) + self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations) + + def forward(self, x, mask, optimized_attention): + x = self.attention(x, mask, optimized_attention) + y = self.intermediate(x) + return self.output(y, x) + +class BertEncoder(torch.nn.Module): + def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)]) + + def forward(self, x, mask=None, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layer) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layer): + x = l(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + +class BertEmbeddings(torch.nn.Module): + def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations): + super().__init__() + self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device) + self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device) + self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device) + + self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device) + + def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None): + if embeds is not None: + x = embeds + else: + x = self.word_embeddings(input_tokens, out_dtype=dtype) + x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x) + if token_type_ids is not None: + x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype) + else: + x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x) + x = self.LayerNorm(x) + return x + + +class BertModel_(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + embed_dim = config_dict["hidden_size"] + layer_norm_eps = config_dict["layer_norm_eps"] + + self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations) + self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations) + + def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype) + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) + + x, i = self.encoder(x, mask, intermediate_output) + return x, i + + +class BertModel(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.bert = BertModel_(config_dict, dtype, device, operations) + self.num_layers = config_dict["num_hidden_layers"] + + def get_input_embeddings(self): + return self.bert.embeddings.word_embeddings + + def set_input_embeddings(self, embeddings): + self.bert.embeddings.word_embeddings = embeddings + + def forward(self, *args, **kwargs): + return self.bert(*args, **kwargs) diff --git a/comfy/text_encoders/cosmos.py b/comfy/text_encoders/cosmos.py new file mode 100644 index 0000000000000000000000000000000000000000..a1adb5242bc9d625bbb32b9d55ca426a0f61e419 --- /dev/null +++ b/comfy/text_encoders/cosmos.py @@ -0,0 +1,42 @@ +from comfy import sd1_clip +import comfy.text_encoders.t5 +import os +from transformers import T5TokenizerFast + + +class T5XXLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json") + t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) + if t5xxl_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options) + +class CosmosT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data) + + +class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + + +def te(dtype_t5=None, t5xxl_scaled_fp8=None): + class CosmosTEModel_(CosmosT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return CosmosTEModel_ diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py new file mode 100644 index 0000000000000000000000000000000000000000..d61ef66689b32e52d15e962d41793a489aa44eab --- /dev/null +++ b/comfy/text_encoders/flux.py @@ -0,0 +1,70 @@ +from comfy import sd1_clip +import comfy.text_encoders.t5 +import comfy.text_encoders.sd3_clip +import comfy.model_management +from transformers import T5TokenizerFast +import torch +import os + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) + + +class FluxTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.clip_l.untokenize(token_weight_pair) + + def state_dict(self): + return {} + + +class FluxClipModel(torch.nn.Module): + def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): + super().__init__() + dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) + self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) + self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options) + self.dtypes = set([dtype, dtype_t5]) + + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.t5xxl.set_clip_options(options) + + def reset_clip_options(self): + self.clip_l.reset_clip_options() + self.t5xxl.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + + t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + return t5_out, l_pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.t5xxl.load_sd(sd) + +def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None): + class FluxClipModel_(FluxClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options) + return FluxClipModel_ diff --git a/comfy/text_encoders/genmo.py b/comfy/text_encoders/genmo.py new file mode 100644 index 0000000000000000000000000000000000000000..9dcf190a232550c9e678e819c88092a5bc518058 --- /dev/null +++ b/comfy/text_encoders/genmo.py @@ -0,0 +1,38 @@ +from comfy import sd1_clip +import comfy.text_encoders.sd3_clip +import os +from transformers import T5TokenizerFast + + +class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): + def __init__(self, **kwargs): + kwargs["attention_mask"] = True + super().__init__(**kwargs) + + +class MochiT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) + + +class MochiT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + + +def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None): + class MochiTEModel_(MochiT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return MochiTEModel_ diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py new file mode 100644 index 0000000000000000000000000000000000000000..dbcf52784d63b6fc07c289d1e4e27a9fd7376b4c --- /dev/null +++ b/comfy/text_encoders/hidream.py @@ -0,0 +1,155 @@ +from . import hunyuan_video +from . import sd3_clip +from comfy import sd1_clip +from comfy import sdxl_clip +import comfy.model_management +import torch +import logging + + +class HiDreamTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data) + self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens + out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + + def state_dict(self): + return {} + + +class HiDreamTEModel(torch.nn.Module): + def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, device="cpu", dtype=None, model_options={}): + super().__init__() + self.dtypes = set() + if clip_l: + self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=True, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_l = None + + if clip_g: + self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_g = None + + if t5: + dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) + self.t5xxl = sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=True) + self.dtypes.add(dtype_t5) + else: + self.t5xxl = None + + if llama: + dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) + if "vocab_size" not in model_options: + model_options["vocab_size"] = 128256 + self.llama = hunyuan_video.LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None, special_tokens={"start": 128000, "pad": 128009}) + self.dtypes.add(dtype_llama) + else: + self.llama = None + + logging.debug("Created HiDream text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}, llama {}:{}".format(clip_l, clip_g, t5, dtype_t5, llama, dtype_llama)) + + def set_clip_options(self, options): + if self.clip_l is not None: + self.clip_l.set_clip_options(options) + if self.clip_g is not None: + self.clip_g.set_clip_options(options) + if self.t5xxl is not None: + self.t5xxl.set_clip_options(options) + if self.llama is not None: + self.llama.set_clip_options(options) + + def reset_clip_options(self): + if self.clip_l is not None: + self.clip_l.reset_clip_options() + if self.clip_g is not None: + self.clip_g.reset_clip_options() + if self.t5xxl is not None: + self.t5xxl.reset_clip_options() + if self.llama is not None: + self.llama.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + token_weight_pairs_llama = token_weight_pairs["llama"] + lg_out = None + pooled = None + extra = {} + + if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: + if self.clip_l is not None: + lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + else: + l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) + + if self.clip_g is not None: + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + else: + g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) + + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + + if self.t5xxl is not None: + t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + t5_out, t5_pooled = t5_output[:2] + else: + t5_out = None + + if self.llama is not None: + ll_output = self.llama.encode_token_weights(token_weight_pairs_llama) + ll_out, ll_pooled = ll_output[:2] + ll_out = ll_out[:, 1:] + else: + ll_out = None + + if t5_out is None: + t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device()) + + if ll_out is None: + ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device()) + + if pooled is None: + pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) + + extra["conditioning_llama3"] = ll_out + return t5_out, pooled, extra + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: + return self.t5xxl.load_sd(sd) + else: + return self.llama.load_sd(sd) + + +def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None): + class HiDreamTEModel_(HiDreamTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["llama_scaled_fp8"] = llama_scaled_fp8 + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) + return HiDreamTEModel_ diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py new file mode 100644 index 0000000000000000000000000000000000000000..b02148b3346db8463b471d83542a59071f7d15e4 --- /dev/null +++ b/comfy/text_encoders/hunyuan_video.py @@ -0,0 +1,159 @@ +from comfy import sd1_clip +import comfy.model_management +import comfy.text_encoders.llama +from transformers import LlamaTokenizerFast +import torch +import os +import numbers + + +def llama_detect(state_dict, prefix=""): + out = {} + t5_key = "{}model.norm.weight".format(prefix) + if t5_key in state_dict: + out["dtype_llama"] = state_dict[t5_key].dtype + + scaled_fp8_key = "{}scaled_fp8".format(prefix) + if scaled_fp8_key in state_dict: + out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype + + return out + + +class LLAMA3Tokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256, pad_token=128258): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "llama_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=pad_token, min_length=min_length, tokenizer_data=tokenizer_data) + +class LLAMAModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}): + llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None) + if llama_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + + textmodel_json_config = {} + vocab_size = model_options.get("vocab_size", None) + if vocab_size is not None: + textmodel_json_config["vocab_size"] = vocab_size + + model_options = {**model_options, "model_name": "llama"} + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class HunyuanVideoTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens + self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs): + out = {} + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs) + embed_count = 0 + for r in llama_text_tokens: + for i in range(len(r)): + if r[i][0] == 128257: + if image_embeds is not None and embed_count < image_embeds.shape[0]: + r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:] + embed_count += 1 + out["llama"] = llama_text_tokens + return out + + def untokenize(self, token_weight_pair): + return self.clip_l.untokenize(token_weight_pair) + + def state_dict(self): + return {} + + +class HunyuanVideoClipModel(torch.nn.Module): + def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}): + super().__init__() + dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) + self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) + self.llama = LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options) + self.dtypes = set([dtype, dtype_llama]) + + def set_clip_options(self, options): + self.clip_l.set_clip_options(options) + self.llama.set_clip_options(options) + + def reset_clip_options(self): + self.clip_l.reset_clip_options() + self.llama.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_llama = token_weight_pairs["llama"] + + llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama) + + template_end = 0 + extra_template_end = 0 + extra_sizes = 0 + user_end = 9999999999999 + images = [] + + tok_pairs = token_weight_pairs_llama[0] + for i, v in enumerate(tok_pairs): + elem = v[0] + if not torch.is_tensor(elem): + if isinstance(elem, numbers.Integral): + if elem == 128006: + if tok_pairs[i + 1][0] == 882: + if tok_pairs[i + 2][0] == 128007: + template_end = i + 2 + user_end = -1 + if elem == 128009 and user_end == -1: + user_end = i + 1 + else: + if elem.get("original_type") == "image": + elem_size = elem.get("data").shape[0] + if template_end > 0: + if user_end == -1: + extra_template_end += elem_size - 1 + else: + image_start = i + extra_sizes + image_end = i + elem_size + extra_sizes + images.append((image_start, image_end, elem.get("image_interleave", 1))) + extra_sizes += elem_size - 1 + + if llama_out.shape[1] > (template_end + 2): + if tok_pairs[template_end + 1][0] == 271: + template_end += 2 + llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] + llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] + if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]): + llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements + + if len(images) > 0: + out = [] + for i in images: + out.append(llama_out[:, i[0]: i[1]: i[2]]) + llama_output = torch.cat(out + [llama_output], dim=1) + + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + return llama_output, l_pooled, llama_extra_out + + def load_sd(self, sd): + if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.llama.load_sd(sd) + + +def hunyuan_video_clip(dtype_llama=None, llama_scaled_fp8=None): + class HunyuanVideoClipModel_(HunyuanVideoClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["llama_scaled_fp8"] = llama_scaled_fp8 + super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) + return HunyuanVideoClipModel_ diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py new file mode 100644 index 0000000000000000000000000000000000000000..ac6994529acd8493e6ec65a975f8984fd06749e8 --- /dev/null +++ b/comfy/text_encoders/hydit.py @@ -0,0 +1,81 @@ +from comfy import sd1_clip +from transformers import BertTokenizer +from .spiece_tokenizer import SPieceTokenizer +from .bert import BertModel +import comfy.text_encoders.t5 +import os +import torch + +class HyditBertModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json") + model_options = {**model_options, "model_name": "hydit_clip"} + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) + +class HyditBertTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77, tokenizer_data=tokenizer_data) + + +class MT5XLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json") + model_options = {**model_options, "model_name": "mt5xl"} + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) + +class MT5XLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model") + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class HyditTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None) + self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory) + self.mt5xl = MT5XLTokenizer(tokenizer_data={**tokenizer_data, "spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs) + out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.hydit_clip.untokenize(token_weight_pair) + + def state_dict(self): + return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]} + +class HyditModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__() + self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options) + self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options) + + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def encode_token_weights(self, token_weight_pairs): + hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"]) + mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"]) + return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]} + + def load_sd(self, sd): + if "bert.encoder.layer.0.attention.self.query.weight" in sd: + return self.hydit_clip.load_sd(sd) + else: + return self.mt5xl.load_sd(sd) + + def set_clip_options(self, options): + self.hydit_clip.set_clip_options(options) + self.mt5xl.set_clip_options(options) + + def reset_clip_options(self): + self.hydit_clip.reset_clip_options() + self.mt5xl.reset_clip_options() diff --git a/comfy/text_encoders/hydit_clip.json b/comfy/text_encoders/hydit_clip.json new file mode 100644 index 0000000000000000000000000000000000000000..c41c7c1ff376407f42e3ff20ab26faaa98bb5e65 --- /dev/null +++ b/comfy/text_encoders/hydit_clip.json @@ -0,0 +1,35 @@ +{ + "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "bos_token_id": 0, + "classifier_dropout": null, + "directionality": "bidi", + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "output_past": true, + "pad_token_id": 0, + "pooler_fc_size": 768, + "pooler_num_attention_heads": 12, + "pooler_num_fc_layers": 3, + "pooler_size_per_head": 128, + "pooler_type": "first_token_transform", + "position_embedding_type": "absolute", + "torch_dtype": "float32", + "transformers_version": "4.22.1", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 47020 +} + diff --git a/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..a8b3208c2884c4efb86e49300fdd3dc877220cdf --- /dev/null +++ b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json @@ -0,0 +1,7 @@ +{ + "cls_token": "[CLS]", + "mask_token": "[MASK]", + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "unk_token": "[UNK]" +} diff --git a/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..a14356073e11a885074a7cdbddc749463cefd911 --- /dev/null +++ b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json @@ -0,0 +1,16 @@ +{ + "cls_token": "[CLS]", + "do_basic_tokenize": true, + "do_lower_case": true, + "mask_token": "[MASK]", + "name_or_path": "hfl/chinese-roberta-wwm-ext", + "never_split": null, + "pad_token": "[PAD]", + "sep_token": "[SEP]", + "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json", + "strip_accents": null, + "tokenize_chinese_chars": true, + "tokenizer_class": "BertTokenizer", + "unk_token": "[UNK]", + "model_max_length": 77 +} diff --git a/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..6246906805d02aca01714c71e4c8d77b69a7a131 --- /dev/null +++ b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt @@ -0,0 +1,47020 @@ +[PAD] +[unused1] +[unused2] +[unused3] +[unused4] +[unused5] +[unused6] +[unused7] +[unused8] +[unused9] +[unused10] +[unused11] +[unused12] +[unused13] +[unused14] +[unused15] +[unused16] +[unused17] +[unused18] +[unused19] +[unused20] +[unused21] +[unused22] +[unused23] +[unused24] +[unused25] +[unused26] +[unused27] +[unused28] +[unused29] +[unused30] +[unused31] +[unused32] +[unused33] +[unused34] +[unused35] +[unused36] +[unused37] +[unused38] +[unused39] +[unused40] +[unused41] +[unused42] +[unused43] +[unused44] +[unused45] +[unused46] +[unused47] +[unused48] +[unused49] +[unused50] +[unused51] +[unused52] +[unused53] +[unused54] 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comfy.model_management + +@dataclass +class Llama2Config: + vocab_size: int = 128320 + hidden_size: int = 4096 + intermediate_size: int = 14336 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + max_position_embeddings: int = 8192 + rms_norm_eps: float = 1e-5 + rope_theta: float = 500000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = False + +@dataclass +class Qwen25_3BConfig: + vocab_size: int = 151936 + hidden_size: int = 2048 + intermediate_size: int = 11008 + num_hidden_layers: int = 36 + num_attention_heads: int = 16 + num_key_value_heads: int = 2 + max_position_embeddings: int = 128000 + rms_norm_eps: float = 1e-6 + rope_theta: float = 1000000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = True + +@dataclass +class Gemma2_2B_Config: + vocab_size: int = 256000 + hidden_size: int = 2304 + intermediate_size: int = 9216 + num_hidden_layers: int = 26 + num_attention_heads: int = 8 + num_key_value_heads: int = 4 + max_position_embeddings: int = 8192 + rms_norm_eps: float = 1e-6 + rope_theta: float = 10000.0 + transformer_type: str = "gemma2" + head_dim = 256 + rms_norm_add = True + mlp_activation = "gelu_pytorch_tanh" + qkv_bias = False + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + self.add = add + + def forward(self, x: torch.Tensor): + w = self.weight + if self.add: + w = w + 1.0 + + return comfy.ldm.common_dit.rms_norm(x, w, self.eps) + + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def precompute_freqs_cis(head_dim, seq_len, theta, device=None): + theta_numerator = torch.arange(0, head_dim, 2, device=device).float() + inv_freq = 1.0 / (theta ** (theta_numerator / head_dim)) + + position_ids = torch.arange(0, seq_len, device=device).unsqueeze(0) + + inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + return (cos, sin) + + +def apply_rope(xq, xk, freqs_cis): + cos = freqs_cis[0].unsqueeze(1) + sin = freqs_cis[1].unsqueeze(1) + q_embed = (xq * cos) + (rotate_half(xq) * sin) + k_embed = (xk * cos) + (rotate_half(xk) * sin) + return q_embed, k_embed + + +class Attention(nn.Module): + def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): + super().__init__() + self.num_heads = config.num_attention_heads + self.num_kv_heads = config.num_key_value_heads + self.hidden_size = config.hidden_size + + self.head_dim = config.head_dim + self.inner_size = self.num_heads * self.head_dim + + ops = ops or nn + self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype) + self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) + self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) + self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + freqs_cis: Optional[torch.Tensor] = None, + optimized_attention=None, + ): + batch_size, seq_length, _ = hidden_states.shape + xq = self.q_proj(hidden_states) + xk = self.k_proj(hidden_states) + xv = self.v_proj(hidden_states) + + xq = xq.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) + xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) + xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) + + xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis) + + xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + + output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True) + return self.o_proj(output) + +class MLP(nn.Module): + def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): + super().__init__() + ops = ops or nn + self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) + self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) + self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype) + if config.mlp_activation == "silu": + self.activation = torch.nn.functional.silu + elif config.mlp_activation == "gelu_pytorch_tanh": + self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh") + + def forward(self, x): + return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x)) + +class TransformerBlock(nn.Module): + def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): + super().__init__() + self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) + self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + + def forward( + self, + x: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + freqs_cis: Optional[torch.Tensor] = None, + optimized_attention=None, + ): + # Self Attention + residual = x + x = self.input_layernorm(x) + x = self.self_attn( + hidden_states=x, + attention_mask=attention_mask, + freqs_cis=freqs_cis, + optimized_attention=optimized_attention, + ) + x = residual + x + + # MLP + residual = x + x = self.post_attention_layernorm(x) + x = self.mlp(x) + x = residual + x + + return x + +class TransformerBlockGemma2(nn.Module): + def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): + super().__init__() + self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) + self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + + def forward( + self, + x: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + freqs_cis: Optional[torch.Tensor] = None, + optimized_attention=None, + ): + # Self Attention + residual = x + x = self.input_layernorm(x) + x = self.self_attn( + hidden_states=x, + attention_mask=attention_mask, + freqs_cis=freqs_cis, + optimized_attention=optimized_attention, + ) + + x = self.post_attention_layernorm(x) + x = residual + x + + # MLP + residual = x + x = self.pre_feedforward_layernorm(x) + x = self.mlp(x) + x = self.post_feedforward_layernorm(x) + x = residual + x + + return x + +class Llama2_(nn.Module): + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + self.config = config + self.vocab_size = config.vocab_size + + self.embed_tokens = ops.Embedding( + config.vocab_size, + config.hidden_size, + device=device, + dtype=dtype + ) + if self.config.transformer_type == "gemma2": + transformer = TransformerBlockGemma2 + self.normalize_in = True + else: + transformer = TransformerBlock + self.normalize_in = False + + self.layers = nn.ModuleList([ + transformer(config, device=device, dtype=dtype, ops=ops) + for _ in range(config.num_hidden_layers) + ]) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) + + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + if embeds is not None: + x = embeds + else: + x = self.embed_tokens(x, out_dtype=dtype) + + if self.normalize_in: + x *= self.config.hidden_size ** 0.5 + + freqs_cis = precompute_freqs_cis(self.config.head_dim, + x.shape[1], + self.config.rope_theta, + device=x.device) + + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) + if mask is not None: + mask += causal_mask + else: + mask = causal_mask + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + + intermediate = None + all_intermediate = None + if intermediate_output is not None: + if intermediate_output == "all": + all_intermediate = [] + intermediate_output = None + elif intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + for i, layer in enumerate(self.layers): + if all_intermediate is not None: + all_intermediate.append(x.unsqueeze(1).clone()) + x = layer( + x=x, + attention_mask=mask, + freqs_cis=freqs_cis, + optimized_attention=optimized_attention, + ) + if i == intermediate_output: + intermediate = x.clone() + + x = self.norm(x) + if all_intermediate is not None: + all_intermediate.append(x.unsqueeze(1).clone()) + + if all_intermediate is not None: + intermediate = torch.cat(all_intermediate, dim=1) + + if intermediate is not None and final_layer_norm_intermediate: + intermediate = self.norm(intermediate) + + return x, intermediate + +class BaseLlama: + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, embeddings): + self.model.embed_tokens = embeddings + + def forward(self, input_ids, *args, **kwargs): + return self.model(input_ids, *args, **kwargs) + + +class Llama2(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Llama2Config(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + +class Qwen25_3B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Qwen25_3BConfig(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + +class Gemma2_2B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Gemma2_2B_Config(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype diff --git a/comfy/text_encoders/llama_tokenizer/tokenizer.json 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17922, + "Ġinitialization": 17923, + "________________________________": 17924, + "ç¬": 17925, + ".document": 17926, + "lesh": 17927, + "ĉdocument": 17928, + "ĠPin": 17929, + "ça": 17930, + "Ġdefinitions": 17931, + ".Path": 17932, + "_WRITE": 17933, + "ĠĉĊ": 17934, + "?>ĊĊ": 17935, + "Ġterrible": 17936, + "bean": 17937, + "ickets": 17938, + "ĠSV": 17939, + "Buy": 17940, + "(task": 17941, + "Ġregime": 17942, + "google": 17943, + "Ġcrack": 17944, + ".visit": 17945, + "NUM": 17946, + "energy": 17947, + "Ġstruck": 17948, + "_sample": 17949, + ".payload": 17950, + "Ġrevis": 17951, + "ĠScene": 17952, + "Ġpg": 17953, + "Ġbreakfast": 17954, + "URRENT": 17955, + ".charAt": 17956, + "_exception": 17957, + "ĠAnton": 17958, + "Ġguidelines": 17959, + "Ġexhaust": 17960, + "ĠFinancial": 17961, + "Ġindent": 17962, + "Ġdesktop": 17963, + "Hidden": 17964, + "Failure": 17965, + "Ġprinciple": 17966, + "Ġiv": 17967, + "Ġseks": 17968, + "network": 17969, + "ĠnumberOf": 17970, + "ĠAlbert": 17971, + "ĉlong": 17972, + "801": 17973, + ",.": 17974, + "Ġzeros": 17975, + "fade": 17976, + "ĠTyp": 17977, + "ĠTerm": 17978, + "ĠArts": 17979, + ".Application": 17980, + "Ġbehalf": 17981, + "æĪ·": 17982, + "Ġmere": 17983, + "(`${": 17984, + "Ġawareness": 17985, + "elpers": 17986, + "flix": 17987, + "Ġweigh": 17988, + "Ġestimates": 17989, + ".child": 17990, + "/O": 17991, + "ĠBitmap": 17992, + ".bottom": 17993, + "Ġ**************************************************************************": 17994, + "Expect": 17995, + "ento": 17996, + "ĠForum": 17997, + "veral": 17998, + "Ġjail": 17999, + "Ġabilities": 18000, + "ĠHOLD": 18001, + "ĠCit": 18002, + "Ġdynam": 18003, + "Ġgray": 18004, + "ĉĉĉĉĉĉĉĉĉĉĉĉĉ": 18005, + ".nextInt": 18006, + "antly": 18007, + "ĠARISING": 18008, + "(private": 18009, + "Ġrejected": 18010, + "ĠNic": 18011, + "Ġleather": 18012, + "={Ċ": 18013, + "alytics": 18014, + "thetic": 18015, + ".Top": 18016, + "373": 18017, + ".Page": 18018, + "={`": 18019, + "Ġ;čĊ": 18020, + "depth": 18021, + "mann": 18022, + "WD": 18023, + "ĠSom": 18024, + ".Right": 18025, + "Ġ)}Ċ": 18026, + "Ġtrait": 18027, + "ÃĹ": 18028, + "iac": 18029, + "Ġrv": 18030, + "Sample": 18031, + ".Xml": 18032, + "opped": 18033, + "ĠÑĦ": 18034, + "lists": 18035, + "Ġtear": 18036, + "iversary": 18037, + ".collection": 18038, + "ĠConstitution": 18039, + "ĠHttpResponse": 18040, + "Ġbrill": 18041, + "ĠProm": 18042, + "hover": 18043, + "366": 18044, + "ĠMiami": 18045, + "Ġargue": 18046, + "_float": 18047, + "504": 18048, + "ĠãĤ": 18049, + "Ġnat": 18050, + "ĠTal": 18051, + "Ġintegration": 18052, + "(cur": 18053, + "Ġremoving": 18054, + "Ġcoeff": 18055, + "ĠThough": 18056, + "Ġforecast": 18057, + "408": 18058, + "ĠVegas": 18059, + "Site": 18060, + "346": 18061, + "Ġtrab": 18062, + "ĠHenry": 18063, + "-i": 18064, + "Ġinvolves": 18065, + "BT": 18066, + "Ġslo": 18067, + "Invoke": 18068, + "Ġlucky": 18069, + "025": 18070, + "rat": 18071, + "Ġ?Ċ": 18072, + "Ġhandled": 18073, + "(fd": 18074, + "contents": 18075, + "ĠOFF": 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18181, + "Ġnaturally": 18182, + "Pages": 18183, + "为": 18184, + "ĠDespite": 18185, + "Ġlighting": 18186, + "Ġcrate": 18187, + "ĠBinary": 18188, + "Ġreducing": 18189, + "Ġeleg": 18190, + "ĠMouse": 18191, + "ĠTestBed": 18192, + "ĠbeforeEach": 18193, + "_ARRAY": 18194, + "Redirect": 18195, + "329": 18196, + "Ġflood": 18197, + "Ġships": 18198, + "363": 18199, + "Ġelectricity": 18200, + ")*(": 18201, + "ê¸": 18202, + "ĠViet": 18203, + "hero": 18204, + "Ġdia": 18205, + "ĠKent": 18206, + "heart": 18207, + "Ġthreats": 18208, + "_acc": 18209, + "Ġsymbols": 18210, + "ischen": 18211, + "_inst": 18212, + "Criterion": 18213, + "ĠTIM": 18214, + ".Height": 18215, + "580": 18216, + "ĠâĢĻ": 18217, + "();ĊĊĊ": 18218, + "Products": 18219, + "_SP": 18220, + "ĠCy": 18221, + "Ġdependent": 18222, + "este": 18223, + "Ġdatos": 18224, + "dit": 18225, + "ав": 18226, + "IGNAL": 18227, + "Ġlesson": 18228, + "\">'": 18229, + "ĠCover": 18230, + "ĠHope": 18231, + "ĠTimer": 18232, + "Ġdad": 18233, + "viders": 18234, + "ĠPhot": 18235, + "/?": 18236, + "ropy": 18237, + "oming": 18238, + "asion": 18239, + "Ġ\\(": 18240, + "ĠET": 18241, + "ĠReading": 18242, + "Ġepisodes": 18243, + "lm": 18244, + "421": 18245, + "echa": 18246, + "Ġneuro": 18247, + "820": 18248, + "Ġharmon": 18249, + "Ġliberal": 18250, + "-ind": 18251, + "393": 18252, + "DATA": 18253, + "Ġeveryday": 18254, + "Ġdivided": 18255, + "ĠActiveRecord": 18256, + "figure": 18257, + "UA": 18258, + "ä¹": 18259, + "riendly": 18260, + "tech": 18261, + "601": 18262, + ".gameObject": 18263, + "иÑĤÑĮ": 18264, + "374": 18265, + "Ġmoon": 18266, + "ftime": 18267, + "Ġnoch": 18268, + "ĠTORT": 18269, + "ĠVM": 18270, + ".initial": 18271, + "(child": 18272, + "Ġmusical": 18273, + "Ġoc": 18274, + "bas": 18275, + "ĠHay": 18276, + "361": 18277, + "_long": 18278, + "Ġmemset": 18279, + "iley": 18280, + "adelphia": 18281, + "SV": 18282, + "roat": 18283, + "_tx": 18284, + "Ġlon": 18285, + "ĠngOnInit": 18286, + "bp": 18287, + "ĠGolden": 18288, + "ACHE": 18289, + "Ġworried": 18290, + "azi": 18291, + "Ear": 18292, + "Take": 18293, + "(fp": 18294, + "burgh": 18295, + "_Data": 18296, + "gres": 18297, + "ĠOnt": 18298, + "pus": 18299, + "Ġtransparent": 18300, + "Ġpocket": 18301, + "Ġram": 18302, + "igrations": 18303, + ".čĊčĊ": 18304, + "Ġ[(": 18305, + "Ġadopted": 18306, + "Ġreportedly": 18307, + "ĠDream": 18308, + "Ġ}));Ċ": 18309, + "losing": 18310, + "Ġteeth": 18311, + "ĠBooks": 18312, + "\",&": 18313, + "enny": 18314, + "LEMENT": 18315, + "Ġgel": 18316, + "ĠPlant": 18317, + "437": 18318, + "!âĢĿ": 18319, + ".host": 18320, + "ĠReply": 18321, + "376": 18322, + "rength": 18323, + "Ġrecognition": 18324, + "Ġ}}>Ċ": 18325, + "LA": 18326, + "Ġmirror": 18327, + "Ġassistant": 18328, + "(device": 18329, + "Ġspiritual": 18330, + "builder": 18331, + "§": 18332, + "Ġoutr": 18333, + "Ġtt": 18334, + "ĠPER": 18335, + "Ġradical": 18336, + "Methods": 18337, + "Ġpace": 18338, + "udy": 18339, + "Ġgut": 18340, + "ĠGreek": 18341, + "Ġnonatomic": 18342, + "ĠPaper": 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18558, + "ĠNotes": 18559, + "ĠEin": 18560, + "Ġsouthern": 18561, + "ĠOTHERWISE": 18562, + "Ġmacro": 18563, + ".lower": 18564, + "cls": 18565, + "ContentView": 18566, + ".link": 18567, + "constant": 18568, + "ĠBes": 18569, + "Ġsomebody": 18570, + "nb": 18571, + "399": 18572, + "\">{": 18573, + "(local": 18574, + ".....": 18575, + "ĠNull": 18576, + "mx": 18577, + "Ġç": 18578, + "Ġpause": 18579, + "-----------": 18580, + "_MO": 18581, + "ĠCM": 18582, + "ĠforKey": 18583, + "ĠDVD": 18584, + "Ġclosest": 18585, + "_DEVICE": 18586, + "ĠStephen": 18587, + "ĠBBC": 18588, + "ĠTravel": 18589, + "Paint": 18590, + "ĠResults": 18591, + "ĠRule": 18592, + "Ġtp": 18593, + "Ġratings": 18594, + "cin": 18595, + "csv": 18596, + ">/": 18597, + "ĠGOP": 18598, + "lad": 18599, + "ĠÑĢ": 18600, + "ĠindexPath": 18601, + "matrix": 18602, + "=f": 18603, + "arsed": 18604, + "Ġ});": 18605, + "ĠCos": 18606, + "ĠScore": 18607, + "Ġtak": 18608, + "ĠESP": 18609, + "ĠINC": 18610, + "_NULL": 18611, + "-flex": 18612, + "\"][": 18613, + "into": 18614, + "eland": 18615, + "Authorization": 18616, + "_FALSE": 18617, + "Ġgate": 18618, + "Ġvid": 18619, + "istent": 18620, + "TIME": 18621, + "Ġrewrite": 18622, + "Ġtie": 18623, + "Ġarchive": 18624, + "511": 18625, + ".events": 18626, + ".getParameter": 18627, + "ĠPermission": 18628, + "Ġprogramme": 18629, + "Ġé": 18630, + "jud": 18631, + "Ġcameras": 18632, + "338": 18633, + "349": 18634, + "(sys": 18635, + "ĠSyrian": 18636, + "Ġimprovements": 18637, + "Ġhip": 18638, + "Ġsuicide": 18639, + "Ġscholar": 18640, + "Ġcompatible": 18641, + "022": 18642, + "remote": 18643, + ".down": 18644, + "FUNCTION": 18645, + "Ġmanaging": 18646, + "ĠUIKit": 18647, + ".raw": 18648, + ">>>>": 18649, + "371": 18650, + "Ġdemands": 18651, + "ellite": 18652, + "Ġdent": 18653, + "ĠMicro": 18654, + "åıĸ": 18655, + "'][$": 18656, + "ĠIE": 18657, + "imension": 18658, + "Ġtrem": 18659, + "630": 18660, + "Ġgained": 18661, + ".with": 18662, + ".ok": 18663, + "hou": 18664, + "Ġbom": 18665, + 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x", + "Ġ S", + "a d", + "Ġ \"", + "c h", + "u t", + "i f", + "* *", + "Ġ }", + "e m", + "o l", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "t h", + ") Ċ", + "Ġ {Ċ", + "Ġ{ Ċ", + "Ġ g", + "i g", + "i v", + ", Ċ", + "c e", + "o d", + "Ġ v", + "a te", + "at e", + "Ġ T", + "a g", + "a y", + "Ġ *", + "o t", + "u s", + "Ġ C", + "Ġ st", + "Ġs t", + "Ġ I", + "u n", + "u l", + "u e", + "Ġ A", + "o w", + "Ġ '", + "e w", + "Ġ <", + "a tion", + "at ion", + "atio n", + "ati on", + "( )", + "Ġ for", + "Ġf or", + "Ġfo r", + "a b", + "o rt", + "or t", + "u m", + "a me", + "am e", + "Ġ is", + "Ġi s", + "p e", + "t r", + "c k", + "â Ģ", + "Ġ y", + "i st", + "is t", + "- ---", + "-- --", + "--- -", + ". ĊĊ", + ".Ċ Ċ", + "h e", + "Ġ e", + "l o", + "Ġ M", + "Ġ be", + "Ġb e", + "e rs", + "er s", + "Ġ on", + "Ġo n", + "Ġ con", + "Ġc on", + "Ġco n", + "a p", + "u b", + "Ġ P", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "a ss", + "as s", + "i nt", + "in t", + "> Ċ", + "l y", + "u rn", + "ur n", + "Ġ $", + "; ĊĊ", + ";Ċ Ċ", + "a v", + "p ort", + "por t", + "po rt", + "i r", + "- >", + "n t", + "c tion", + "ct ion", + "e nd", + "en d", + "Ġ de", + "Ġd e", + "0 0", + "i th", + "it h", + "o ut", + "ou t", + "t urn", + "tu rn", + "tur n", + "o ur", + "ou r", + "Ġ ĠĠĠĠ", + "ĠĠ ĠĠĠ", + "ĠĠĠĠ Ġ", + "ĠĠĠ ĠĠ", + "l ic", + "li c", + "r es", + "re s", + "p t", + "= =", + "Ġ this", + "Ġt his", + "Ġth is", + "Ġthi s", + "Ġ wh", + "Ġw h", + "Ġ if", + "Ġi f", + "Ġ D", + "v er", + "ve r", + "a ge", + "ag e", + "Ġ B", + "h t", + "e xt", + "ex t", + "= \"", + "Ġ that", + "Ġt hat", + "Ġth at", + "Ġtha t", + "* ***", + "** **", + "*** *", + "Ġ R", + "Ġ it", + "Ġi t", + "e ss", + "es s", + "Ġ F", + "Ġ r", + "o s", + "a nd", + "an d", + "Ġ as", + "Ġa s", + "e ct", + "ec t", + "k e", + "r om", + "ro m", + "Ġ //", + "Ġ/ /", + "c on", + "co n", + "Ġ L", + "( \"", + "q u", + "l ass", + "la ss", + "las s", + "Ġ with", + "Ġw ith", + "Ġwi th", + "Ġwit h", + "i z", + "d e", + "Ġ N", + "Ġ al", + "Ġa l", + "o p", + "u p", + "g et", + "ge t", + "Ġ }Ċ", + "Ġ} Ċ", + "i le", + "il e", + "Ġ an", + "Ġa n", + "a ta", + "at a", + "o re", + "or e", + "r i", + "Ġ pro", + "Ġp ro", + "Ġpr o", + "; čĊ", + "ĉ ĉĉĉ", + "ĉĉ ĉĉ", + "ĉĉĉ ĉ", + "t er", + "te r", + "a in", + "ai n", + "Ġ W", + "Ġ E", + "Ġ com", + "Ġc om", + "Ġco m", + "Ġ return", + "Ġre turn", + "Ġr eturn", + "Ġret urn", + "a rt", + "ar t", + "Ġ H", + "a ck", + "ac k", + "im port", + "imp ort", + "ub lic", + "ubl ic", + "Ġ or", + "Ġo r", + "e st", + "es t", + "m ent", + "me nt", + "men t", + "Ġ G", + "a ble", + "ab le", + "abl e", + "Ġ -", + "i ne", + "in e", + "i ll", + "il l", + "i nd", + "in d", + "e re", + "er e", + ": :", + "i ty", + "it y", + "Ġ +", + "Ġ tr", + "Ġt r", + "e lf", + "el f", + "i ght", + "ig ht", + "igh t", + "( '", + "o rm", + "or m", + "u lt", + "ul t", + "s tr", + "st r", + ". .", + "\" ,", + "Ġ you", + "Ġy ou", + "Ġyo u", + "y pe", + "yp e", + "p l", + "Ġ new", + "Ġn ew", + "Ġne w", + "Ġ j", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ from", + "Ġf rom", + "Ġfr om", + "Ġfro m", + "Ġ ex", + "Ġe x", + "Ġ O", + "2 0", + "l d", + "Ġ [", + "o c", + ": Ċ", + "Ġ se", + "Ġs e", + "Ġ le", + "Ġl e", + "- -------", + "-- ------", + "---- ----", + "--- -----", + "----- ---", + "------ --", + "------- -", + ". s", + "{ Ċ", + "' ,", + "a nt", + "an t", + "Ġ at", + "Ġa t", + "a se", + "as e", + ". c", + "Ġ ch", + "Ġc h", + "< /", + "a ve", + "av e", + "a ng", + "an g", + "Ġ are", + "Ġa re", + "Ġar e", + "Ġ int", + "Ġin t", + "Ġi nt", + "âĢ Ļ", + "_ t", + "e rt", + "er t", + "i al", + "ia l", + "a ct", + "ac t", + "} Ċ", + "i ve", + "iv e", + "o de", + "od e", + "o st", + "os t", + "Ġ class", + "Ġc lass", + "Ġcl ass", + "Ġclas s", + "Ġcla ss", + "Ġ not", + "Ġn ot", + "Ġno t", + "o g", + "o rd", + "or d", + "a lue", + "al ue", + "alu e", + "a ll", + "al l", + "f f", + "( );Ċ", + "() ;Ċ", + "(); Ċ", + "o nt", + "on t", + "i me", + "im e", + "a re", + "ar e", + "Ġ U", + "Ġ pr", + "Ġp r", + "Ġ :", + "i es", + "ie s", + "i ze", + "iz e", + "u re", + "ur e", + "Ġ by", + "Ġb y", + "i re", + "ir e", + "Ġ }ĊĊ", + "Ġ} ĊĊ", + "Ġ}Ċ Ċ", + ". p", + "Ġ sh", + "Ġs h", + "i ce", + "ic e", + "a st", + "as t", + "p tion", + "pt ion", + "t ring", + "tr ing", + "tri ng", + "o k", + "_ _", + "c l", + "# #", + "Ġ he", + "Ġh e", + "a rd", + "ar d", + ") .", + "Ġ @", + "i ew", + "ie w", + "ĉ ĉĉ", + "ĉĉ ĉ", + "Ġ was", + "Ġw as", + "Ġwa s", + "i p", + "t his", + "th is", + "Ġ u", + "Ġ The", + "ĠT he", + "ĠTh e", + "i de", + "id e", + "a ce", + "ac e", + "i b", + "a c", + "r ou", + "ro u", + "Ġ we", + "Ġw e", + "j ect", + "je ct", + "jec t", + "Ġ public", + "Ġp ublic", + "Ġpub lic", + "Ġpubli c", + "a k", + "v e", + "a th", + "at h", + "o id", + "oi d", + "Ġ =>", + "Ġ= >", + "u st", + "us t", + "q ue", + "qu e", + "Ġ res", + "Ġre s", + "Ġr es", + ") )", + "' s", + "Ġ k", + "a ns", + "an s", + "y st", + "ys t", + "un ction", + "unc tion", + "unct ion", + "* *******", + "** ******", + "**** ****", + "****** **", + "*** *****", + "***** ***", + "******* *", + "Ġ i", + "Ġ us", + "Ġu s", + "p p", + "1 0", + "o ne", + "on e", + "a il", + "ai l", + "= ===", + "== ==", + "=== =", + "n ame", + "na me", + "nam e", + "Ġ str", + "Ġs tr", + "Ġst r", + "Ġ /", + "Ġ &", + "a ch", + "ac h", + "d iv", + "di v", + "y stem", + "yst em", + "ys tem", + "e ll", + "el l", + "Ġ have", + "Ġh ave", + "Ġha ve", + "Ġhav e", + "e rr", + "er r", + "o uld", + "ou ld", + "oul d", + "u ll", + "ul l", + "p on", + "po n", + "Ġ J", + "_ p", + "Ġ ==", + "Ġ= =", + "i gn", + "ig n", + "S t", + ". Ċ", + "Ġ pl", + "Ġp l", + ") ;ĊĊ", + ");Ċ Ċ", + "); ĊĊ", + "f orm", + "fo rm", + "for m", + "p ut", + "pu t", + "o unt", + "ou nt", + "oun t", + "} ĊĊ", + "}Ċ Ċ", + "d d", + "i te", + "it e", + "Ġ get", + "Ġg et", + "Ġge t", + "r r", + "o me", + "om e", + "Ġ âĢ", + "Ġâ Ģ", + "a ram", + "ar am", + "ara m", + "c c", + "Ġ */", + "Ġ* /", + "E R", + "I n", + "l es", + "le s", + "_ s", + "o ng", + "on g", + "i e", + "Ġ can", + "Ġc an", + "Ġca n", + "Ġ V", + "e rv", + "er v", + "p r", + "Ġ un", + "Ġu n", + "r ow", + "ro w", + "b er", + "be r", + "Ġ do", + "Ġd o", + "l l", + "Ġ el", + "Ġe l", + "Ġ self", + "Ġs elf", + "Ġse lf", + "Ġsel f", + "a ted", + "at ed", + "ate d", + "a ry", + "ar y", + "Ġ .", + "' ]", + "u d", + "Ġ en", + "Ġe n", + "Ġ Th", + "ĠT h", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "t e", + "_ c", + "u ct", + "uc t", + "Ġ ab", + "Ġa b", + "o rk", + "or k", + ". get", + ".g et", + ".ge t", + "Ġ #", + "a w", + "r ess", + "re ss", + "res s", + "o b", + "N ame", + "Na me", + "Nam e", + "2 01", + "20 1", + "a pp", + "ap p", + "[ '", + "Ġ all", + "Ġa ll", + "Ġal l", + "o ry", + "or y", + "i tion", + "it ion", + "iti on", + "a nce", + "an ce", + "anc e", + "e ar", + "ea r", + "Ġ cont", + "Ġc ont", + "Ġcon t", + "Ġco nt", + "v ent", + "ve nt", + "ven t", + "i a", + "Ġ will", + "Ġw ill", + "Ġwi ll", + "Ġwil l", + "I N", + "Ġ ĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠ", + "r eturn", + "re turn", + "ret urn", + "Ġ ", + "\" ,Ċ", + "\", Ċ", + "e c", + "Ġ In", + "ĠI n", + "p h", + "Ġ |", + "_ f", + "Ġ var", + "Ġv ar", + "Ġva r", + "e nce", + "en ce", + "enc e", + "I d", + "r ee", + "re e", + "i nk", + "in k", + "l ect", + "le ct", + "lec t", + "u g", + "e th", + "et h", + "Ġ else", + "Ġe lse", + "Ġel se", + "Ġels e", + "- ---------------", + "-- --------------", + "---- ------------", + "-------- --------", + "--- -------------", + "------------ ----", + "----- -----------", + "---------- ------", + "------ ----------", + "----------- -----", + "------------- ---", + "------- ---------", + "--------- -------", + "--------------- -", + "-------------- --", + "1 9", + "c ont", + "con t", + "co nt", + "Ġ so", + "Ġs o", + "a tic", + "at ic", + "ati c", + "Ġ lo", + "Ġl o", + "p ro", + "pr o", + "t on", + "to n", + "s s", + "o wn", + "ow n", + "a bel", + "ab el", + "abe l", + "o int", + "oin t", + "oi nt", + "o us", + "ou s", + "e ld", + "el d", + "S T", + "T he", + "Th e", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "R E", + "\" :", + "o lor", + "ol or", + "olo r", + "t p", + "e g", + "k ey", + "ke y", + "u de", + "ud e", + "Ġ St", + "ĠS t", + "o und", + "ou nd", + "oun d", + "Ġ ar", + "Ġa r", + "\" );Ċ", + "\") ;Ċ", + "\"); Ċ", + "e ner", + "en er", + "ene r", + "s er", + "se r", + "1 1", + "b ject", + "bj ect", + "es sage", + "ess age", + "essa ge", + "f er", + "fe r", + "Ġ more", + "Ġm ore", + "Ġmor e", + "Ġmo re", + "at ions", + "ation s", + "atio ns", + "ati ons", + "e nts", + "en ts", + "ent s", + "Ġ his", + "Ġh is", + "Ġhi s", + "Ġ they", + "Ġt hey", + "Ġth ey", + "Ġthe y", + ". S", + "Ġ Y", + "u se", + "us e", + "n e", + "i sh", + "is h", + "o ld", + "ol d", + "_ d", + "i o", + "i eld", + "ie ld", + "iel d", + "Ġ per", + "Ġp er", + "Ġpe r", + "C ont", + "Con t", + "Co nt", + "in gs", + "ing s", + "# ###", + "## ##", + "### #", + "Ġ data", + "Ġd ata", + "Ġda ta", + "Ġdat a", + "Ġ sa", + "Ġs a", + "e f", + "f o", + "Ġ one", + "Ġo ne", + "Ġon e", + "e ng", + "en g", + "Ġ dis", + "Ġd is", + "Ġdi s", + "A T", + "Ġ name", + "Ġn ame", + "Ġna me", + "Ġnam e", + "Ġ true", + "Ġtr ue", + "v al", + "va l", + "l ed", + "le d", + ". f", + "Ġ ne", + "Ġn e", + "Ġ end", + "Ġe nd", + "Ġen d", + "3 2", + ". T", + "1 6", + "c re", + "cr e", + "a rk", + "ar k", + "l og", + "lo g", + "E x", + "e rror", + "er ror", + "err or", + "erro r", + "_ id", + "_i d", + "ur re", + "urr e", + "a nge", + "an ge", + "ang e", + "Ġ null", + "Ġn ull", + "Ġnu ll", + "r ray", + "rr ay", + "Ġ my", + "Ġm y", + "p an", + "pa n", + "i ct", + "ic t", + "a tor", + "at or", + "ato r", + "V iew", + "Vi ew", + "L ist", + "Li st", + "ĉ return", + "ĉr eturn", + "ĉret urn", + "ĉre turn", + "âĢ Ŀ", + "Ġ pre", + "Ġp re", + "Ġpr e", + "Ġ x", + "c lude", + "cl ude", + "clud e", + "a rg", + "ar g", + "1 5", + "o v", + ". h", + "Ġ >", + "Ġ their", + "Ġthe ir", + "' )", + "i rst", + "ir st", + "irs t", + "i ck", + "ic k", + "g h", + "L E", + "O R", + "Ġ private", + "Ġpr ivate", + "Ġpriv ate", + "Ġprivat e", + "t em", + "te m", + "čĊ čĊ", + "u ser", + "us er", + "use r", + "Ġ )", + "c om", + "co m", + ". A", + "\" ;Ċ", + "\"; Ċ", + "Ġ id", + "Ġi d", + "r ead", + "re ad", + "rea d", + "Ġ who", + "Ġw ho", + "Ġwh o", + "_ b", + "\" >Ċ", + "\"> Ċ", + "Ġ time", + "Ġt ime", + "Ġtim e", + "Ġti me", + "Ġ man", + "Ġm an", + "Ġma n", + "r y", + "= =======", + "== ======", + "==== ====", + "=== =====", + "====== ==", + "===== ===", + "======= =", + "r oup", + "ro up", + "rou p", + "r op", + "ro p", + "p ublic", + "pub lic", + "v el", + "ve l", + "um ber", + "umb er", + "b le", + "bl e", + "Ġ which", + "Ġwh ich", + "** **************", + "**** ************", + "******** ********", + "************ ****", + "************** **", + "Ġ any", + "Ġa ny", + "Ġan y", + "Ġ false", + "Ġf alse", + "Ġfa lse", + "Ġfal se", + "Ġfals e", + "w e", + "Ġ value", + "Ġv alue", + "Ġval ue", + "Ġva lue", + "Ġvalu e", + "Ġ li", + "Ġl i", + "\" )", + "n der", + "nd er", + "nde r", + "g r", + "Ġ no", + "Ġn o", + "p aram", + "par am", + "pa ram", + "para m", + "2 5", + "f ig", + "fi g", + ". com", + ".c om", + ".co m", + "Ġ app", + "Ġa pp", + "Ġap p", + "_ l", + "i ons", + "ion s", + "io ns", + ". D", + "Ġ Ch", + "ĠC h", + "Ġ about", + "Ġa bout", + "Ġab out", + "Ġ add", + "Ġa dd", + "Ġad d", + "Ġ su", + "Ġs u", + "Ġ string", + "Ġs tring", + "Ġst ring", + "Ġstr ing", + "Ġstri ng", + "I D", + "Ġ over", + "Ġo ver", + "Ġov er", + "s tring", + "st ring", + "str ing", + "stri ng", + ". l", + "our ce", + "0 00", + "00 0", + "_ C", + "] Ċ", + "Ġ qu", + "Ġq u", + "Ġ String", + "ĠS tring", + "ĠSt ring", + "ĠStr ing", + "c a", + "S E", + "Ġ ro", + "Ġr o", + "s h", + "u al", + "ua l", + "T ype", + "Typ e", + "Ty pe", + "s on", + "so n", + "n ew", + "ne w", + "e rn", + "er n", + "Ġ ag", + "Ġa g", + "A R", + "] ;Ċ", + "]; Ċ", + "] .", + "Ġ ?", + "i cal", + "ic al", + "ica l", + "Ġ des", + "Ġd es", + "Ġde s", + "u th", + "ut h", + "i x", + "a ys", + "ay s", + "Ġ type", + "Ġt ype", + "Ġtyp e", + "Ġty pe", + "' t", + "a ult", + "au lt", + "aul t", + "Ġ inter", + "Ġin ter", + "Ġint er", + "Ġinte r", + "v ar", + "va r", + ". b", + "Ġ part", + "Ġp art", + "Ġpar t", + "Ġpa rt", + ". d", + "ur rent", + "urre nt", + "urr ent", + "I T", + "E N", + "3 0", + "e nc", + "en c", + "( f", + "r a", + "v alue", + "val ue", + "va lue", + "valu e", + "c ho", + "ch o", + "1 8", + "ut ton", + "utt on", + "utto n", + "o se", + "os e", + "1 4", + "Ġ !=", + "Ġ! =", + "a ter", + "at er", + "ate r", + "à ©", + "re ate", + "reat e", + "rea te", + "o ll", + "ol l", + "p os", + "po s", + "y le", + "yl e", + "n g", + "A L", + "u sing", + "us ing", + "usi ng", + "a mes", + "am es", + "ame s", + "Ġ {čĊ", + "Ġ{ čĊ", + "a tes", + "at es", + "ate s", + "e ly", + "el y", + "Ġ work", + "Ġw ork", + "Ġwor k", + "Ġwo rk", + "Ġ em", + "Ġe m", + "i nal", + "in al", + "ina l", + "Ġ sp", + "Ġs p", + "Ġ when", + "Ġw hen", + "Ġwh en", + "Ġwhe n", + ". set", + ".s et", + ".se t", + "Ġ ĠĠĠĠĠ", + "ĠĠ ĠĠĠĠ", + "ĠĠĠĠ ĠĠ", + "ĠĠĠ ĠĠĠ", + "ĠĠĠĠĠ Ġ", + ") :Ċ", + "): Ċ", + "t o", + "q uire", + "qu ire", + "quir e", + "qui re", + "ind ow", + "indo w", + "l ement", + "le ment", + "lem ent", + "lemen t", + "leme nt", + "p ect", + "pe ct", + "pec t", + "a sh", + "as h", + "[ i", + "Ġ use", + "Ġu se", + "Ġus e", + ". F", + "p ec", + "pe c", + "Ġ ad", + "Ġa d", + "o ve", + "ov e", + "ce ption", + "cept ion", + "cep tion", + "e ngth", + "en gth", + "eng th", + "in clude", + "inc lude", + "incl ude", + "inclu de", + "a der", + "ad er", + "ade r", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "at us", + "atu s", + "T h", + "i tle", + "it le", + "r it", + "ri t", + "v oid", + "vo id", + "( ).", + "() .", + "( Ċ", + "Ġ off", + "Ġo ff", + "Ġof f", + "Ġ other", + "Ġo ther", + "Ġot her", + "Ġ &&", + "Ġ& &", + "' ;Ċ", + "'; Ċ", + "m s", + "Ġ been", + "Ġb een", + "Ġbe en", + "Ġbee n", + "Ġ te", + "Ġt e", + "m l", + "c o", + "n c", + "1 3", + "er vice", + "erv ice", + "Ġ %", + "* *Ċ", + "** Ċ", + "a nn", + "an n", + "a de", + "ad e", + "Ċ ĊĊĊ", + "ĊĊ ĊĊ", + "ĊĊĊ Ċ", + "l ock", + "lo ck", + "loc k", + "con st", + "co nst", + "cons t", + "1 00", + "10 0", + "p onse", + "pon se", + "pons e", + "Ġ sup", + "Ġs up", + "Ġsu p", + "+ +", + "d ate", + "da te", + "dat e", + "Ġ acc", + "Ġa cc", + "Ġac c", + "Ġ had", + "Ġh ad", + "Ġha d", + "Ġ bu", + "Ġb u", + "2 00", + "20 0", + "Ġ Re", + "ĠR e", + "Ġ were", + "Ġw ere", + "Ġwe re", + "Ġwer e", + "Ġ file", + "Ġf ile", + "Ġfil e", + "Ġfi le", + "Ġ would", + "Ġw ould", + "Ġwo uld", + "Ġ âĢľ", + "ĠâĢ ľ", + "v en", + "ve n", + "i ss", + "is s", + "Ġ our", + "Ġo ur", + "Ġou r", + "c lass", + "cl ass", + "cla ss", + "clas s", + "r aw", + "ra w", + "Ġ year", + "Ġy ear", + "Ġye ar", + "D ata", + "Da ta", + "Dat a", + "Ġ val", + "Ġv al", + "Ġva l", + "Ġ some", + "Ġs ome", + "Ġso me", + "Ġsom e", + "f ter", + "ft er", + "fte r", + "y s", + "Ġ ///", + "Ġ// /", + "Ġ/ //", + "r ound", + "ro und", + "rou nd", + "v iew", + "vi ew", + "vie w", + "Ġ pe", + "Ġp e", + "Ġ there", + "Ġt here", + "Ġth ere", + "Ġthe re", + "Ġther e", + "Ġ said", + "Ġs aid", + "Ġsa id", + "Ġsai d", + "d u", + "o f", + "l ine", + "li ne", + "lin e", + "/ *", + "d uct", + "du ct", + "duc t", + "Ġ her", + "Ġh er", + "Ġhe r", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "R es", + "Re s", + "Ġ co", + "Ġc o", + "Ġ comm", + "Ġc omm", + "Ġcom m", + "Ġco mm", + "i se", + "is e", + "m in", + "mi n", + "Ġ ĠĠĠĊ", + "ĠĠ ĠĠĊ", + "ĠĠĠĠ Ċ", + "ĠĠĠ ĠĊ", + "# include", + "et hod", + "eth od", + ". P", + "u te", + "ut e", + "Ġ ass", + "Ġa ss", + "Ġas s", + "I nt", + "In t", + "a sk", + "as k", + "l oc", + "lo c", + "Ġ like", + "Ġl ike", + "Ġli ke", + "Ġlik e", + "o dy", + "od y", + "Ġ let", + "Ġl et", + "Ġle t", + "l oad", + "lo ad", + "Ġ am", + "Ġa m", + "r ol", + "ro l", + "Ġ gr", + "Ġg r", + "y p", + "Ġ also", + "Ġal so", + "Ġals o", + "Ġ It", + "ĠI t", + "u rl", + "ur l", + "i fic", + "if ic", + "ifi c", + "o rs", + "or s", + "_ P", + "_ n", + "i gh", + "ig h", + "Ġ than", + "Ġt han", + "Ġth an", + "Ġtha n", + "C om", + "Co m", + "A N", + "U L", + "a ting", + "at ing", + "ati ng", + "atin g", + "1 7", + "Ġ This", + "ĠT his", + "ĠTh is", + "ĠThi s", + "r ef", + "re f", + "_ S", + "Ġ static", + "Ġst atic", + "Ġstat ic", + "Ġsta tic", + "Ġstati c", + "r oll", + "ro ll", + "rol l", + "Ġ just", + "Ġj ust", + "Ġju st", + "Ġjus t", + "Ġ result", + "Ġres ult", + "i an", + "ia n", + "id th", + "Ġ them", + "Ġt hem", + "Ġth em", + "Ġthe m", + ") );Ċ", + ")) ;Ċ", + ")); Ċ", + "d er", + "de r", + "re ak", + "rea k", + "C on", + "Co n", + ": //", + ":/ /", + "u le", + "ul e", + ". ..", + ".. .", + "a rch", + "ar ch", + "arc h", + "e ment", + "em ent", + "eme nt", + "emen t", + "Ġ <<", + "Ġ< <", + "5 0", + "u sh", + "us h", + "en se", + "ens e", + "a rr", + "ar r", + "Ġ into", + "Ġin to", + "Ġint o", + "c ess", + "ce ss", + "ces s", + "a mp", + "am p", + "i ed", + "ie d", + "u ment", + "um ent", + "ume nt", + "umen t", + "Ġ \\", + "] ,", + "w o", + "a ls", + "al s", + "Ġ what", + "Ġw hat", + "Ġwh at", + "a nc", + "an c", + "V alue", + "Val ue", + "Va lue", + "= '", + "o lum", + "ol um", + "olu m", + "Ġ pos", + "Ġp os", + "Ġpo s", + "a ges", + "ag es", + "age s", + "a yer", + "ay er", + "aye r", + "Ġ sc", + "Ġs c", + "u es", + "ue s", + "\" )Ċ", + "\") Ċ", + "_ T", + "Ġ list", + "Ġl ist", + "Ġli st", + "Ġlis t", + "( s", + "Ġ case", + "Ġc ase", + "Ġca se", + "Ġcas e", + "C h", + "ĉ ĉĉĉĉ", + "ĉĉ ĉĉĉ", + "ĉĉĉĉ ĉ", + "ĉĉĉ ĉĉ", + "//// ////", + "/// /////", + "///// ///", + "p onent", + "pon ent", + "pone nt", + "Ġ z", + "Ġ kn", + "Ġk n", + "l et", + "le t", + "D E", + "r ed", + "re d", + "Ġ fe", + "Ġf e", + "Ġ },Ċ", + "Ġ} ,Ċ", + "Ġ}, Ċ", + "Ġ ,", + "( t", + "Ġ first", + "Ġf irst", + "Ġfi rst", + "Ġfir st", + "' );Ċ", + "') ;Ċ", + "'); Ċ", + "w ord", + "wo rd", + "wor d", + "Ġ import", + "Ġim port", + "Ġimp ort", + "Ġ act", + "Ġa ct", + "Ġac t", + "Ġ char", + "Ġc har", + "Ġch ar", + "Ġcha r", + "C T", + "Ġ Tr", + "ĠT r", + "o ple", + "op le", + "opl e", + "= {", + "ĉ f", + "2 4", + "i ent", + "ie nt", + "ien t", + "c ent", + "ce nt", + "cen t", + ". j", + "l ection", + "le ction", + "lect ion", + "lec tion", + ") )Ċ", + ")) Ċ", + "Ġ only", + "Ġon ly", + "Ġ print", + "Ġp rint", + "Ġpr int", + "Ġpri nt", + "Ġprin t", + "m er", + "me r", + ". W", + "o ck", + "oc k", + "Ġ --", + "Ġ- -", + "T ext", + "Te xt", + "Tex t", + "Ġ op", + "Ġo p", + "a nk", + "an k", + "Ġ its", + "Ġit s", + "Ġi ts", + "Ġ back", + "Ġb ack", + "Ġba ck", + "Ġbac k", + "[ \"", + "Ġ need", + "Ġn eed", + "Ġne ed", + "Ġ cl", + "Ġc l", + "Ġ sub", + "Ġs ub", + "Ġsu b", + "Ġ la", + "Ġl a", + "( (", + ". \"", + "O bject", + "Ob ject", + "Obj ect", + "Ġ start", + "Ġst art", + "Ġstar t", + "Ġsta rt", + "f ile", + "fi le", + "fil e", + "( self", + "(s elf", + "(se lf", + "(sel f", + "n er", + "ne r", + "e y", + "Ġ user", + "Ġu ser", + "Ġus er", + "Ġuse r", + "Ġ ent", + "Ġe nt", + "Ġen t", + "Ġ Com", + "ĠC om", + "ĠCo m", + "i ts", + "it s", + "Ġ Con", + "ĠC on", + "ĠCo n", + "o uble", + "ou ble", + "oub le", + "o wer", + "ow er", + "owe r", + "i tem", + "it em", + "ite m", + "v ery", + "ver y", + "ve ry", + "Ġ We", + "ĠW e", + "6 4", + "l ick", + "lic k", + "li ck", + "Ġ Q", + "p hp", + "ph p", + "t tp", + "tt p", + "' :", + "i cs", + "ic s", + "Ġ under", + "Ġu nder", + "Ġun der", + "Ġund er", + "Ġunde r", + "Ġ *Ċ", + "Ġ* Ċ", + ". L", + ") ;", + "i ces", + "ic es", + "ice s", + "Ġ reg", + "Ġre g", + "Ġr eg", + ") čĊ", + "ĉ public", + "ĉp ublic", + "ĉpub lic", + "S S", + "Ġ then", + "Ġt hen", + "Ġth en", + "Ġthe n", + "r eat", + "re at", + "rea t", + "i ous", + "io us", + "iou s", + ". G", + "e k", + "i rect", + "ir ect", + "ire ct", + "h eck", + "he ck", + "hec k", + "cri pt", + "cr ipt", + "n ing", + "ni ng", + "nin g", + "Ġ Un", + "ĠU n", + "Ġ may", + "Ġm ay", + "Ġma y", + "Ġ Wh", + "ĠW h", + "B o", + "I tem", + "It em", + "str uct", + "stru ct", + ". st", + ".s t", + "r eam", + "re am", + "rea m", + "i ble", + "ib le", + "lo at", + "Ġ org", + "Ġo rg", + "Ġor g", + "u nd", + "un d", + "s um", + "su m", + "_ in", + "_i n", + ". ./", + ".. /", + "_ M", + "Ġ how", + "Ġh ow", + "Ġho w", + "r ite", + "ri te", + "rit e", + "' Ċ", + "T o", + "4 0", + "w w", + "Ġ people", + "Ġpe ople", + "in dex", + "ind ex", + "inde x", + ". n", + "h ttp", + "ht tp", + "htt p", + "( m", + "e ctor", + "ect or", + "ec tor", + "Ġ ind", + "Ġin d", + "Ġi nd", + "Ġ jav", + "Ġj av", + "Ġja v", + "] ,Ċ", + "], Ċ", + "Ġ He", + "ĠH e", + "_ st", + "_s t", + "f ul", + "fu l", + "o le", + "ol e", + ") {Ċ", + "){ Ċ", + "Ġ should", + "Ġsh ould", + "Ġsho uld", + "o py", + "op y", + "e lp", + "el p", + "i er", + "ie r", + "_ name", + "_n ame", + "_na me", + "er son", + "ers on", + "I ON", + "IO N", + "o te", + "ot e", + "Ġ test", + "Ġt est", + "Ġte st", + "Ġtes t", + "Ġ bet", + "Ġb et", + "Ġbe t", + "r ror", + "rr or", + "u lar", + "ul ar", + "ula r", + "ã Ģ", + "Ġ Ð", + "b s", + "t ing", + "ti ng", + "tin g", + "Ġ make", + "Ġm ake", + "Ġma ke", + "Ġmak e", + "T r", + "Ġ after", + "Ġa fter", + "Ġaf ter", + "Ġaft er", + "ar get", + "arg et", + "arge t", + "R O", + "ol umn", + "olum n", + "olu mn", + "r c", + "_ re", + "_r e", + "de fine", + "def ine", + "2 2", + "Ġ right", + "Ġr ight", + "Ġrig ht", + "Ġri ght", + "r ight", + "ri ght", + "rig ht", + "d ay", + "da y", + "Ġ long", + "Ġl ong", + "Ġlo ng", + "Ġlon g", + "[ ]", + "( p", + "t d", + "c ond", + "con d", + "co nd", + "Ġ Pro", + "ĠP ro", + "ĠPr o", + "Ġ rem", + "Ġre m", + "Ġr em", + "pt ions", + "ption s", + "v id", + "vi d", + ". g", + "Ġ ext", + "Ġe xt", + "Ġex t", + "Ġ __", + "Ġ_ _", + "' )Ċ", + "') Ċ", + "p ace", + "pa ce", + "pac e", + "m p", + "Ġ min", + "Ġm in", + "Ġmi n", + "st ance", + "sta nce", + "stan ce", + "a ir", + "ai r", + "a ction", + "act ion", + "ac tion", + "w h", + "t ype", + "ty pe", + "typ e", + "u til", + "ut il", + "uti l", + "a it", + "ai t", + "< ?", + "I C", + "t ext", + "te xt", + "tex t", + "Ġ ph", + "Ġp h", + "Ġ fl", + "Ġf l", + ". M", + "c cess", + "cc ess", + "cce ss", + "b r", + "f ore", + "fo re", + "for e", + "ers ion", + ") ,Ċ", + "), Ċ", + ". re", + ".r e", + "a teg", + "at eg", + "ate g", + "Ġ loc", + "Ġl oc", + "Ġlo c", + "i ns", + "in s", + "- s", + "t rib", + "tr ib", + "tri b", + "Ġ Int", + "ĠI nt", + "ĠIn t", + "Ġ array", + "Ġa rray", + "Ġar ray", + "Ġarr ay", + ", \"", + "P ro", + "Pr o", + "( c", + "ess ion", + "> ĊĊ", + ">Ċ Ċ", + "Ġ she", + "Ġs he", + "Ġsh e", + "\" ]", + "a ph", + "ap h", + "Ġ exp", + "Ġe xp", + "Ġex p", + "er ty", + "ert y", + "Ġ Se", + "ĠS e", + "Ġ par", + "Ġp ar", + "Ġpa r", + "u nc", + "un c", + "E T", + "Ġ read", + "Ġre ad", + "Ġr ead", + "p rint", + "pr int", + "pri nt", + "Ġ rel", + "Ġre l", + "Ġr el", + "Ġ form", + "Ġf orm", + "Ġfor m", + "Ġfo rm", + "Ġ dr", + "Ġd r", + "Ex ception", + "Except ion", + "in put", + "inp ut", + "Ġ trans", + "Ġt rans", + "Ġtr ans", + "Ġtra ns", + "Ġtran s", + "# #######", + "## ######", + "#### ####", + "### #####", + "##### ###", + "###### ##", + "####### #", + "or der", + "ord er", + "orde r", + "B y", + "Ġ aw", + "Ġa w", + "i ties", + "it ies", + "iti es", + "u ff", + "uf f", + "p lay", + "pl ay", + "pla y", + ". add", + ".a dd", + ".ad d", + "Ġ âĢĵ", + "ĠâĢ ĵ", + "Ġ want", + "Ġw ant", + "Ġwa nt", + "Ġwan t", + "Ġ comp", + "Ġc omp", + "Ġcom p", + "Ġco mp", + "m ents", + "ment s", + "me nts", + "men ts", + "Ġ ||", + "Ġ| |", + "a z", + "b e", + "Ġ number", + "Ġn umber", + "Ġnum ber", + "Ġnumb er", + "Ġ require", + "Ġre quire", + "Ġreq uire", + "Ġrequ ire", + "Ġ Ex", + "ĠE x", + "6 0", + "Ġ col", + "Ġc ol", + "Ġco l", + "Ġ key", + "Ġk ey", + "Ġke y", + "em ber", + "emb er", + "Ġ two", + "Ġt wo", + "Ġtw o", + "Ġ size", + "Ġs ize", + "Ġsi ze", + "Ġsiz e", + "Ġ where", + "Ġw here", + "Ġwh ere", + "Ġwhe re", + "U T", + "res ult", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "o ugh", + "ou gh", + "oug h", + "or ld", + "o od", + "oo d", + "u ch", + "uc h", + "at ive", + "ati ve", + "ativ e", + "g er", + "ge r", + "a rent", + "ar ent", + "are nt", + "aren t", + "Ġ /*", + "Ġ/ *", + "Ġ arg", + "Ġa rg", + "Ġar g", + "Ġ while", + "Ġwh ile", + "2 3", + "( this", + "(t his", + "(th is", + "Ġ rec", + "Ġre c", + "Ġr ec", + "Ġ dif", + "Ġd if", + "Ġdi f", + "St ate", + "Stat e", + "Ġ spec", + "Ġs pec", + "Ġsp ec", + "Ġspe c", + "r ide", + "ri de", + "rid e", + "_ F", + "Ġ look", + "Ġl ook", + "Ġlo ok", + "A M", + "il ity", + "ilit y", + "ili ty", + "e ter", + "et er", + "ete r", + "âĢĻ t", + "Ċ ĊĊ", + "ĊĊ Ċ", + "ay out", + "ayo ut", + "-- ------------------------------", + "---- ----------------------------", + "---------------- ----------------", + "------------ --------------------", + "---------------------------- ----", + "------------------------------ --", + "-------------------- ------------", + "a ger", + "ag er", + "age r", + "Ġ could", + "Ġc ould", + "Ġco uld", + "Ġcou ld", + "Ġ br", + "Ġb r", + "e nds", + "en ds", + "end s", + "u res", + "ur es", + "ure s", + "Ġ know", + "Ġk now", + "Ġkn ow", + "e ts", + "et s", + "Ġ If", + "ĠI f", + "Ġ Sh", + "ĠS h", + ". w", + "b ack", + "ba ck", + "bac k", + "Ġ ser", + "Ġs er", + "Ġse r", + "Ġ +=", + "Ġ+ =", + "Ġ fr", + "Ġf r", + "( ));Ċ", + "() );Ċ", + "()) ;Ċ", + "()); Ċ", + "Ġ hand", + "Ġh and", + "Ġha nd", + "Ġhan d", + "I nd", + "In d", + "U LL", + "UL L", + "I m", + "( );ĊĊ", + "() ;ĊĊ", + "();Ċ Ċ", + "(); ĊĊ", + "Ġ most", + "Ġm ost", + "Ġmo st", + "Ġmos t", + "Ġ try", + "Ġt ry", + "Ġtr y", + "Ġ now", + "Ġn ow", + "Ġno w", + "r ough", + "ro ugh", + "rou gh", + "> čĊ", + "ack age", + "Ġ him", + "Ġh im", + "Ġhi m", + ". _", + "i fy", + "if y", + "Ġ break", + "Ġb reak", + "Ġbre ak", + "Ġ );Ċ", + "Ġ) ;Ċ", + "Ġ); Ċ", + "r en", + "re n", + "# define", + "i tt", + "it t", + "Ġ ap", + "Ġa p", + "ĉ c", + "( n", + "Ġ You", + "ĠY ou", + "ĠYo u", + ": ĊĊ", + ":Ċ Ċ", + "- m", + "Ġ every", + "Ġe very", + "Ġever y", + "Ġev ery", + "Ġeve ry", + "us tom", + "ust om", + "usto m", + "l ient", + "li ent", + "lie nt", + "lien t", + "oc ument", + "ocu ment", + "cri ption", + "cript ion", + "E rror", + "Err or", + "Er ror", + "Erro r", + "- b", + "Ð ¾", + "] [", + "9 9", + "t rans", + "tr ans", + "tra ns", + "tran s", + "Ġ point", + "Ġp oint", + "Ġpo int", + "Ġpoi nt", + "Ġ std", + "Ġs td", + "Ġst d", + "Ġ fil", + "Ġf il", + "Ġfi l", + "T ime", + "Tim e", + "Ti me", + "8 0", + "Ġ mod", + "Ġm od", + "Ġmo d", + "Ġ ->", + "Ġ- >", + "Ġ error", + "Ġe rror", + "Ġerr or", + "Ġer ror", + "Ġerro r", + "a h", + "Ġ text", + "Ġt ext", + "Ġte xt", + "Ġtex t", + "r oller", + "ro ller", + "rol ler", + "roll er", + "l ose", + "lo se", + "los e", + "q l", + "Ġ pol", + "Ġp ol", + "Ġpo l", + "> < /", + "Ġ show", + "Ġs how", + "Ġsh ow", + "Ġsho w", + "U ser", + "Us er", + "Use r", + "a sed", + "as ed", + "ase d", + "Ġ {ĊĊ", + "Ġ{ ĊĊ", + "Ġ{Ċ Ċ", + "Ġ find", + "Ġf ind", + "Ġfin d", + "Ġfi nd", + "Ð °", + "E D", + "s pan", + "sp an", + "spa n", + "e nu", + "en u", + "Ġ current", + "Ġc urrent", + "Ġcur rent", + "Ġcurr ent", + "Ġ used", + "Ġu sed", + "Ġus ed", + "Ġuse d", + "c ept", + "ce pt", + "cep t", + "cl ud", + "Ġ play", + "Ġp lay", + "Ġpl ay", + "Ġpla y", + "Ġ log", + "Ġl og", + "Ġlo g", + "u tion", + "ut ion", + "uti on", + "f l", + "Ġ see", + "Ġs ee", + "Ġse e", + "ind ows", + "indow s", + "indo ws", + "Ġ help", + "Ġh elp", + "Ġhe lp", + "Ġhel p", + "Ġ these", + "Ġth ese", + "Ġthe se", + "Ġ pass", + "Ġp ass", + "Ġpas s", + "Ġpa ss", + "Ġ down", + "Ġd own", + "Ġdo wn", + "Ġdow n", + "Ġ even", + "Ġe ven", + "Ġev en", + "Ġeve n", + "a son", + "as on", + "aso n", + "u ild", + "ui ld", + "uil d", + "f rom", + "fr om", + "( d", + "Ġ bl", + "Ġb l", + "l abel", + "la bel", + "lab el", + "e lse", + "el se", + "els e", + "Ð µ", + "Ġ (!", + "Ġ( !", + "i zed", + "iz ed", + "ize d", + "( ),", + "() ,", + "Ġ ob", + "Ġo b", + "Ġ item", + "Ġit em", + "Ġi tem", + "u mp", + "um p", + "U R", + "o rn", + "or n", + "Ġ don", + "Ġd on", + "Ġdo n", + "S e", + "m an", + "ma n", + "2 7", + "am ple", + "amp le", + "t n", + "= ===============", + "== ==============", + "==== ============", + "======== ========", + "=== =============", + "============ ====", + "============= ===", + "=========== =====", + "============== ==", + "========= =======", + "========== ======", + "=============== =", + "====== ==========", + "===== ===========", + "======= =========", + "H e", + "g ram", + "gr am", + "gra m", + "Ġ did", + "Ġd id", + "Ġdi d", + "w n", + "_ h", + "i ver", + "iv er", + "ive r", + "Ġ sm", + "Ġs m", + "Ġ through", + "Ġth rough", + "Ġthr ough", + "Ġthro ugh", + "Ġ An", + "ĠA n", + "c he", + "ch e", + "Ġ inv", + "Ġin v", + "Ġi nv", + "o use", + "ou se", + "ous e", + "Ġ es", + "Ġe s", + "Ġ New", + "ĠN ew", + "ĠNe w", + "ex port", + "exp ort", + "expo rt", + "m ary", + "ma ry", + "mar y", + "u to", + "ut o", + "l er", + "le r", + "Ġ last", + "Ġl ast", + "Ġla st", + "Ġlas t", + "Ġ event", + "Ġe vent", + "Ġeven t", + "Ġev ent", + "Ġeve nt", + "t ry", + "tr y", + "ï ¼", + "i ly", + "il y", + "ig ned", + "ign ed", + "igne d", + "i nes", + "in es", + "ine s", + "ol low", + "oll ow", + "ollo w", + "ic ense", + "icens e", + "s ole", + "so le", + "sol e", + "l ear", + "le ar", + "lea r", + "( int", + "(i nt", + "(in t", + "Ġ again", + "Ġa gain", + "Ġag ain", + "Ġ high", + "Ġh igh", + "Ġhi gh", + "h tml", + "ht ml", + "htm l", + "In dex", + "Ind ex", + "ut hor", + "uth or", + "Ġ /**Ċ", + "Ġ/ **Ċ", + "Ġ/* *Ċ", + "Ġ/** Ċ", + "Ġ line", + "Ġl ine", + "Ġli ne", + "Ġlin e", + "E vent", + "Even t", + "Ev ent", + "_ D", + "Ġ does", + "Ġd oes", + "Ġdo es", + "Ġdoe s", + "it ial", + "iti al", + "itia l", + "Ġ cr", + "Ġc r", + "a rs", + "ar s", + "2 8", + "Ġ tem", + "Ġt em", + "Ġte m", + "c ause", + "ca use", + "f ace", + "fa ce", + "fac e", + "Ġ `", + "_ A", + "B utton", + "But ton", + "a ture", + "at ure", + "atur e", + "atu re", + "ect ed", + "ec ted", + "E S", + "i ster", + "is ter", + "ist er", + "iste r", + "ĉ Ċ", + "Ġ before", + "Ġb efore", + "Ġbe fore", + "Ġbef ore", + "a le", + "al e", + "o ther", + "ot her", + "oth er", + "Ġ because", + "Ġb ecause", + "Ġbe cause", + "Ġbec ause", + "r oid", + "ro id", + "roi d", + "Ġ ed", + "Ġe d", + "i k", + "r eg", + "re g", + "Ġ De", + "ĠD e", + "Ġ dist", + "Ġd ist", + "Ġdis t", + "Ġdi st", + "} ,Ċ", + "}, Ċ", + "Ġ state", + "Ġst ate", + "Ġstat e", + "Ġsta te", + "Ġ cons", + "Ġc ons", + "Ġcon s", + "Ġco ns", + "r int", + "ri nt", + "rin t", + "a tt", + "at t", + "Ġ here", + "Ġh ere", + "Ġhe re", + "Ġher e", + "i ned", + "in ed", + "ine d", + "Ġ final", + "Ġf inal", + "Ġfin al", + "Ġfi nal", + "Ġ \"\"", + "Ġ\" \"", + "K ey", + "Ke y", + "L O", + "Ġ del", + "Ġd el", + "Ġde l", + "p ty", + "pt y", + "th ing", + "thin g", + "2 6", + "Ġ And", + "ĠA nd", + "ĠAn d", + "Ġ run", + "Ġr un", + "Ġru n", + "Ġ X", + "y m", + ". app", + ".ap p", + ".a pp", + "Ġ very", + "Ġv ery", + "Ġver y", + "Ġve ry", + "c es", + "ce s", + "_ N", + "a red", + "ar ed", + "are d", + "w ard", + "wa rd", + "war d", + "l ist", + "li st", + "lis t", + "i ted", + "it ed", + "ite d", + "o log", + "ol og", + "olo g", + "it ch", + "B ox", + "Bo x", + "i fe", + "if e", + "3 3", + "Ġ ac", + "Ġa c", + "Ġ model", + "Ġm odel", + "Ġmod el", + "Ġmode l", + "Ġmo del", + "Ġ mon", + "Ġm on", + "Ġmo n", + "Ġ way", + "Ġw ay", + "Ġwa y", + "l ete", + "le te", + "let e", + "Ġ call", + "Ġc all", + "Ġcal l", + "Ġca ll", + "Ġ att", + "Ġa tt", + "Ġat t", + "Ġ cal", + "Ġc al", + "Ġca l", + "v ert", + "ver t", + "ve rt", + "Ġ dec", + "Ġd ec", + "Ġde c", + "l ease", + "le ase", + "lea se", + "o un", + "ou n", + "Ġ });Ċ", + "Ġ} );Ċ", + "Ġ}) ;Ċ", + "Ġ}); Ċ", + "f r", + "form ation", + "format ion", + "forma tion", + "e tail", + "et ail", + "eta il", + "Ġ num", + "Ġn um", + "Ġnu m", + "a j", + "qu ery", + "que ry", + "quer y", + "Ġ well", + "Ġw ell", + "Ġwe ll", + "Ġwel l", + "Ġ object", + "Ġo bject", + "Ġob ject", + "Ġobj ect", + "Ġ As", + "ĠA s", + "Ġ years", + "Ġy ears", + "Ġyear s", + "Ġye ars", + "C olor", + "Col or", + "Co lor", + "I S", + "Ġ default", + "Ġd efault", + "Ġde fault", + "Ġdef ault", + "Ġdefa ult", + "W h", + "Ġ ins", + "Ġin s", + "Ġi ns", + "a int", + "ain t", + "ai nt", + "Ġ java", + "Ġj ava", + "Ġjav a", + "Ġja va", + "Ġ sim", + "Ġs im", + "Ġsi m", + "Ġ Ar", + "ĠA r", + "m on", + "mo n", + "t il", + "ti l", + "( );čĊ", + "() ;čĊ", + "(); čĊ", + ") :", + "S et", + "Se t", + "2 9", + "at ter", + "att er", + "atte r", + "Ġ view", + "Ġv iew", + "Ġvi ew", + "Ġvie w", + "Ġ pres", + "Ġp res", + "Ġpr es", + "Ġpre s", + "a rray", + "ar ray", + "arr ay", + "arra y", + "W e", + "A t", + "Ġ bel", + "Ġb el", + "Ġbe l", + "Ġ many", + "Ġm any", + "Ġman y", + "Ġma ny", + "2 1", + "M an", + "Ma n", + "e nder", + "en der", + "end er", + "ende r", + "Ġ being", + "Ġb eing", + "Ġbe ing", + "Ġbei ng", + "Ġ good", + "Ġg ood", + "Ġgo od", + "Ġgoo d", + "ĉ ĉĉĉĉĉ", + "ĉĉ ĉĉĉĉ", + "ĉĉĉĉ ĉĉ", + "ĉĉĉ ĉĉĉ", + "ĉĉĉĉĉ ĉ", + "at ional", + "ation al", + "atio nal", + "ati onal", + "w are", + "wa re", + "war e", + ". log", + ".l og", + ".lo g", + "{ čĊ", + "Ġ using", + "Ġu sing", + "Ġus ing", + "_ B", + "Ġ :=", + "Ġ: =", + "_ w", + "i sts", + "is ts", + "ist s", + "l ish", + "li sh", + "lis h", + "Ġ stud", + "Ġst ud", + "Ġstu d", + "Ġ Al", + "ĠA l", + "Ġ gu", + "Ġg u", + "con fig", + "conf ig", + "u ring", + "ur ing", + "uri ng", + "t ime", + "ti me", + "tim e", + "o ken", + "ok en", + "oke n", + "ame space", + "ames pace", + "Ġ request", + "Ġre quest", + "Ġreq uest", + "Ġrequ est", + "Ġ child", + "Ġch ild", + "Ġchi ld", + "Ġ Ã", + "l ob", + "lo b", + "Ġ param", + "Ġp aram", + "Ġpar am", + "Ġpara m", + "Ġpa ram", + "Ġ }čĊ", + "Ġ} čĊ", + "0 1", + "Ġ echo", + "Ġe cho", + "Ġec ho", + "Ġech o", + "f unction", + "func tion", + "fun ction", + "**** ****************************", + "******** ************************", + "**************** ****************", + "************************ ********", + "******************** ************", + "**************************** ****", + "************ ********************", + "p s", + "E lement", + "El ement", + "Elem ent", + "Ele ment", + "a lk", + "al k", + "l ication", + "lic ation", + "li cation", + "lica tion", + "b y", + "S ize", + "Si ze", + "ra wing", + "raw ing", + "Ġ person", + "Ġp erson", + "Ġper son", + "Ġpers on", + "Ġperso n", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "\\ n", + "o bject", + "ob ject", + "obj ect", + "i nce", + "in ce", + "inc e", + "E n", + "F ile", + "Fi le", + "Fil e", + "u f", + "f fect", + "ff ect", + "ffe ct", + "A C", + "Ġ style", + "Ġst yle", + "Ġsty le", + "Ġstyl e", + "sum mary", + "summ ary", + "Ġ que", + "Ġqu e", + "Ġq ue", + "_ r", + "Ġ ($", + "Ġ( $", + "M odel", + "Mode l", + "Mod el", + "Mo del", + "i dent", + "id ent", + "ide nt", + "iden t", + "Ġ method", + "Ġm ethod", + "Ġmet hod", + "Ġmeth od", + "I L", + "o tt", + "ot t", + "l ess", + "le ss", + "les s", + "I NG", + "IN G", + "Ġ ()", + "Ġ( )", + "Ġ expect", + "Ġex pect", + "Ġexp ect", + "y nc", + "yn c", + "p ackage", + "pack age", + "3 5", + "u rs", + "ur s", + "Ġ prot", + "Ġp rot", + "Ġpro t", + "Ġpr ot", + ". /", + "p re", + "pr e", + "Ġ )Ċ", + "Ġ) Ċ", + "m a", + "Ġ sur", + "Ġs ur", + "Ġsu r", + "Ġ found", + "Ġf ound", + "Ġfo und", + "Ġfou nd", + "In fo", + "Inf o", + "p ar", + "pa r", + "i mes", + "im es", + "ime s", + ". e", + "a ins", + "ain s", + "ai ns", + "Ġ post", + "Ġp ost", + "Ġpos t", + "Ġpo st", + "- d", + "4 5", + "o lean", + "ol ean", + "ole an", + "Ġ sl", + "Ġs l", + "P E", + "Ġ such", + "Ġs uch", + "Ġsu ch", + "Ġsuc h", + "s elect", + "se lect", + "sel ect", + "a iner", + "ain er", + "ai ner", + "aine r", + "Ġ think", + "Ġth ink", + "Ġthin k", + "Ġthi nk", + "Ġd iffer", + "Ġdif fer", + "Ġdi ffer", + "Ġdiff er", + ". r", + "/ **Ċ", + "/* *Ċ", + "/** Ċ", + "F F", + "o ol", + "oo l", + "p late", + "pl ate", + "plat e", + "pla te", + "q ual", + "qu al", + "qua l", + "Ġ For", + "ĠF or", + "ĠFo r", + "Ġ much", + "Ġm uch", + "Ġmu ch", + "Ġmuc h", + "u c", + "( new", + "(n ew", + "(ne w", + "od ule", + "odu le", + "Ġ som", + "Ġs om", + "Ġso m", + "Ġ http", + "Ġh ttp", + "Ġht tp", + "Ġhtt p", + "Ġ List", + "ĠL ist", + "ĠLi st", + "ĠLis t", + "Ġ count", + "Ġc ount", + "Ġco unt", + "Ġcoun t", + "Ġcou nt", + "Ġ inst", + "Ġin st", + "Ġi nst", + "Ġins t", + "c har", + "ch ar", + "cha r", + "m it", + "mi t", + ". id", + ".i d", + "a king", + "ak ing", + "aki ng", + "akin g", + "Ġ gener", + "Ġg ener", + "Ġge ner", + "Ġgen er", + "Ġgene r", + "p x", + "v ice", + "vi ce", + "vic e", + "3 7", + "_ data", + "_d ata", + "_dat a", + "_da ta", + "Ġ NULL", + "ĠN ULL", + "ĠNU LL", + "} čĊ", + "i dd", + "id d", + "ãĢ Ĥ", + "Ġ med", + "Ġm ed", + "Ġme d", + "o rg", + "or g", + "i der", + "id er", + "ide r", + "a che", + "ac he", + "ach e", + "w ork", + "wo rk", + "wor k", + "Ġ check", + "Ġc heck", + "Ġch eck", + "Ġche ck", + "w een", + "we en", + "Ġ ((", + "Ġ( (", + "t he", + "th e", + "a nts", + "an ts", + "ant s", + "> <", + ". B", + "- c", + "Ġ open", + "Ġo pen", + "Ġop en", + "Ġ est", + "Ġe st", + "Ġes t", + "Ġ ĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĊ", + "Ġ next", + "Ġn ext", + "Ġne xt", + "Ġnex t", + "I M", + "Ñ Ĥ", + "O T", + "à ³", + "Ġ follow", + "Ġf ollow", + "Ġfol low", + "Ġfoll ow", + "c ontent", + "con tent", + "cont ent", + "conte nt", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "Ġin clud", + "Ġincl ud", + "Ġinclu d", + "H E", + "Ġ Res", + "ĠR es", + "ĠRe s", + "Ġ href", + "Ġh ref", + "Ġhr ef", + "Ð ¸", + "Ġ car", + "Ġc ar", + "Ġca r", + "y pes", + "ype s", + "yp es", + "i mage", + "im age", + "ima ge", + "imag e", + "U n", + "Ġ bool", + "Ġb ool", + "Ġbo ol", + "Ġboo l", + "A D", + "Ġ game", + "Ġg ame", + "Ġgam e", + "Ġga me", + ". Form", + ".F orm", + ".For m", + "r ows", + "ro ws", + "row s", + "* /", + "v elop", + "ve lop", + "vel op", + ". Drawing", + ".D rawing", + ".Draw ing", + "Ġ path", + "Ġp ath", + "Ġpat h", + "Ġpa th", + "is ion", + "isi on", + "Ġ each", + "Ġe ach", + "Ġea ch", + "Ġ Pl", + "ĠP l", + "_ type", + "_t ype", + "_typ e", + "_ty pe", + "P ath", + "Pa th", + "Pat h", + "n ection", + "ne ction", + "nect ion", + "Ġ av", + "Ġa v", + "' ).", + "') .", + "Ġ support", + "Ġs upport", + "Ġsup port", + "Ġsupp ort", + "E NT", + "EN T", + "r em", + "re m", + "\" ).", + "\") .", + "Ġ own", + "Ġo wn", + "Ġow n", + "Ġ cor", + "Ġc or", + "Ġco r", + "c ount", + "co unt", + "cou nt", + "m iss", + "mi ss", + "mis s", + "u ally", + "ual ly", + "Ġ mem", + "Ġm em", + "Ġme m", + "s td", + "st d", + "i ence", + "ie nce", + "ien ce", + "s earch", + "se arch", + "sea rch", + "\" ĊĊ", + "\"Ċ Ċ", + "F orm", + "For m", + "Fo rm", + "Ġ sex", + "Ġs ex", + "Ġse x", + "e name", + "en ame", + "ena me", + "Ġ sign", + "Ġs ign", + "Ġsi gn", + "Ġsig n", + "Ġ et", + "Ġe t", + "Ġ ĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠ ĠĠĠĠ", + "' ,'", + "', '", + "Ġ App", + "ĠA pp", + "ĠAp p", + "Ġ those", + "Ġth ose", + "Ġtho se", + "o ff", + "of f", + "Ġ err", + "Ġe rr", + "Ġer r", + "Ġ system", + "Ġs ystem", + "Ġsys tem", + "Ġsy stem", + "Ġsyst em", + "Ġ best", + "Ġb est", + "Ġbe st", + "Ġbes t", + "c ode", + "co de", + "cod e", + "Ġ same", + "Ġs ame", + "Ġsa me", + "Ġsam e", + "Ġ di", + "Ġd i", + "u ss", + "us s", + "Ġ create", + "Ġc reate", + "Ġcre ate", + "Ġcreat e", + "Ġcrea te", + "a ther", + "at her", + "ath er", + "athe r", + "A rray", + "Ar ray", + "Arr ay", + ". in", + ".i n", + "f e", + "S ervice", + "Ser vice", + "Serv ice", + "U N", + "a ts", + "at s", + "Ġ Z", + "al th", + "alt h", + "Ġ made", + "Ġm ade", + "Ġma de", + "Ġmad e", + "tr ue", + "tru e", + "A B", + "Ġ mark", + "Ġm ark", + "Ġmar k", + "Ġma rk", + "r id", + "ri d", + "if ied", + "ifi ed", + "ifie d", + ", čĊ", + "y n", + "p ress", + "pr ess", + "pre ss", + "pres s", + "Ġ group", + "Ġg roup", + "Ġgr oup", + "Ġgro up", + "Ġgrou p", + "Ġ fin", + "Ġf in", + "Ġfi n", + "Ġ License", + "ĠL icense", + "ĠLic ense", + "F ield", + "Fi eld", + "e ger", + "eg er", + "Ġ world", + "Ġw orld", + "Ġwor ld", + "i ness", + "in ess", + "ine ss", + "ines s", + "t y", + "Ġ process", + "Ġp rocess", + "Ġpro cess", + "Ġproc ess", + "Ġproces s", + "( b", + "Ġ cre", + "Ġc re", + "Ġcr e", + "a rn", + "ar n", + "i ves", + "iv es", + "ive s", + "Ġ main", + "Ġm ain", + "Ġma in", + "Ġmai n", + "i deo", + "id eo", + "ide o", + "3 6", + "_ g", + "A G", + "val id", + "va lid", + "i mg", + "im g", + "P I", + "Ġ color", + "Ġc olor", + "Ġco lor", + "Ġcol or", + "Ġ report", + "Ġre port", + "Ġrep ort", + "Ġrepo rt", + "Ġ take", + "Ġt ake", + "Ġta ke", + "Ġtak e", + "r ib", + "ri b", + "O M", + "Ġ day", + "Ġd ay", + "Ġda y", + "Re quest", + "Req uest", + "Ġ sk", + "Ġs k", + "b ers", + "ber s", + "be rs", + "ĉ s", + ". Add", + ".A dd", + ".Ad d", + "o ot", + "oo t", + "I mage", + "Im age", + "Ġcom ple", + "Ġcomp le", + "Ġcompl e", + "ol lection", + "oll ection", + "ollect ion", + "olle ction", + "Ġ top", + "Ġt op", + "Ġto p", + "Ġ free", + "Ġf ree", + "Ġfr ee", + "Ġfre e", + "A S", + "D e", + "Ġ On", + "ĠO n", + "I G", + "9 0", + "e ta", + "et a", + "D ate", + "Da te", + "Dat e", + "Ġ action", + "Ġa ction", + "Ġact ion", + "Ġac tion", + "3 4", + "O ver", + "i tor", + "it or", + "ito r", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "n ot", + "no t", + "Ġ index", + "Ġin dex", + "Ġind ex", + "Ġinde x", + "h er", + "he r", + "i con", + "ic on", + "ico n", + "O n", + "; čĊčĊ", + ";čĊ čĊ", + "iv ity", + "ivi ty", + "m and", + "man d", + "ma nd", + ". Windows", + ".W indows", + ".Window s", + "O L", + "Ġ real", + "Ġre al", + "Ġ max", + "Ġm ax", + "Ġma x", + "l and", + "la nd", + "lan d", + ". ...", + ".. ..", + "... .", + "r aph", + "ra ph", + "rap h", + "Ġ build", + "Ġb uild", + "Ġbu ild", + "l eg", + "le g", + "as sword", + "ass word", + "? ĊĊ", + "?Ċ Ċ", + "âĢ ¦", + "o ok", + "oo k", + "u ck", + "uc k", + "Ġ message", + "Ġm essage", + "Ġmess age", + "Ġmes sage", + "t est", + "te st", + "tes t", + "i vers", + "iv ers", + "ive rs", + "iver s", + "3 8", + "Ġ input", + "Ġin put", + "Ġinp ut", + "Ġ art", + "Ġa rt", + "Ġar t", + "Ġ between", + "Ġb etween", + "Ġbet ween", + "G et", + "Ge t", + "en ter", + "ent er", + "ente r", + "g round", + "gr ound", + "gro und", + "e ne", + "en e", + "à ¡", + ". length", + ".l ength", + ".len gth", + ".le ngth", + "N ode", + "No de", + "( i", + "C lass", + "Cl ass", + "Cla ss", + "f or", + "fo r", + "Ġ âĢĶ", + "ĠâĢ Ķ", + "t en", + "te n", + "o in", + "oi n", + "Ġ ke", + "Ġk e", + "u i", + "Ġ IN", + "ĠI N", + "Ġ table", + "Ġt able", + "Ġtab le", + "Ġta ble", + "s ub", + "su b", + "Ġ Le", + "ĠL e", + "Ġ head", + "Ġh ead", + "Ġhe ad", + "Ġ must", + "Ġm ust", + "Ġmus t", + "Ġmu st", + "//// ////////////", + "//////// ////////", + "//////////// ////", + ". util", + ".u til", + ".ut il", + "Cont ext", + "Con text", + "Ġ order", + "Ġor der", + "Ġord er", + "Ġ mov", + "Ġm ov", + "Ġmo v", + "o ver", + "ov er", + "ove r", + "Ġ contin", + "Ġcon tin", + "Ġcont in", + "Ġ say", + "Ġs ay", + "Ġsa y", + "st atic", + "stat ic", + "sta tic", + ". Text", + ".T ext", + ".Te xt", + "Ġ className", + "Ġclass Name", + "p any", + "pan y", + "pa ny", + "Ġ ter", + "Ġt er", + "Ġte r", + "h ead", + "he ad", + "hea d", + "r g", + "Ġ product", + "Ġpro duct", + "Ġprodu ct", + "Ġprod uct", + "T his", + "Th is", + ". âĢĿ", + "Ġ But", + "ĠB ut", + "ĠBu t", + "7 0", + "l oy", + "lo y", + "Ġ double", + "Ġd ouble", + "Ġdo uble", + "Ġdoub le", + "Ġdou ble", + "s g", + "Ġ place", + "Ġp lace", + "Ġpl ace", + "Ġplac e", + "Ġpla ce", + ". x", + "m essage", + "mes sage", + "mess age", + "Ġ information", + "Ġin formation", + "Ġinform ation", + "Ġinformat ion", + "pr ivate", + "priv ate", + "Ġ oper", + "Ġo per", + "Ġop er", + "c ed", + "ce d", + "d b", + "\" > < /", + "P aram", + "Par am", + "Pa ram", + "Para m", + "i cle", + "ic le", + "icl e", + "Ġ week", + "Ġwe ek", + "Ġwee k", + "Ġ prop", + "Ġp rop", + "Ġpro p", + "Ġpr op", + "t able", + "ta ble", + "tab le", + "tabl e", + "id get", + "idge t", + "p lace", + "pl ace", + "pla ce", + "P rop", + "Pro p", + "Pr op", + "Ġ All", + "ĠA ll", + "ĠAl l", + "e ls", + "el s", + "b ox", + "bo x", + ". ĊĊĊĊ", + ".ĊĊ ĊĊ", + ".Ċ ĊĊĊ", + ".ĊĊĊ Ċ", + ". R", + "Ġ To", + "ĠT o", + "i ter", + "it er", + "ite r", + "S h", + "u ration", + "ur ation", + "ura tion", + "urat ion", + "o lder", + "ol der", + "old er", + "_ list", + "_l ist", + "_li st", + "c ome", + "com e", + "co me", + "Ġ sw", + "Ġs w", + "iz ation", + "iza tion", + "ĉ for", + "ĉf or", + "b l", + "Ġ program", + "Ġp rogram", + "Ġpro gram", + "Ġpr ogram", + "Ġprog ram", + "( e", + "a pe", + "ap e", + "c heck", + "ch eck", + "che ck", + ". Forms", + ".Form s", + ".For ms", + "Ġ und", + "Ġu nd", + "Ġun d", + "ateg ory", + "ategor y", + "atego ry", + "7 5", + "a gs", + "ag s", + "Ġ response", + "Ġres ponse", + "Ġrespons e", + "Ġresp onse", + "U S", + "re quest", + "req uest", + "requ est", + "Ġ struct", + "Ġstr uct", + "Ġstru ct", + "e scription", + "es cription", + "Ġ code", + "Ġc ode", + "Ġco de", + "Ġcod e", + "_ H", + "u ffer", + "uff er", + "uf fer", + "Ġ without", + "Ġwith out", + "lo bal", + "lob al", + "M anager", + "Man ager", + "Manage r", + "Mana ger", + "il ter", + "ilt er", + "P O", + "ĉ this", + "ĉt his", + "ĉth is", + "o ption", + "op tion", + "opt ion", + "Ġ sol", + "Ġs ol", + "Ġso l", + "Ġ ===", + "Ġ= ==", + "Ġ== =", + "a kes", + "ak es", + "ake s", + "Cont roller", + "Control ler", + "Contr oller", + "4 4", + "M essage", + "Mes sage", + "Mess age", + "Ġ ref", + "Ġre f", + "Ġr ef", + "e ver", + "ev er", + "eve r", + "Ġ So", + "ĠS o", + "a ining", + "ain ing", + "ai ning", + ". append", + ".app end", + ".ap pend", + "Ġ still", + "Ġst ill", + "Ġstil l", + "Ġpro vid", + "Ġpr ovid", + "Ġprov id", + "Ġ assert", + "Ġas sert", + "Ġass ert", + "m ed", + "me d", + "Ġ cap", + "Ġc ap", + "Ġca p", + "us iness", + "usi ness", + "Ġ rep", + "Ġre p", + "Ġr ep", + "t ings", + "ting s", + "tin gs", + "v ed", + "ve d", + ". N", + "a pi", + "ap i", + "O D", + "Ġ field", + "Ġf ield", + "Ġfi eld", + "Ġfie ld", + "i ven", + "iv en", + "ive n", + "o to", + "ot o", + "âĢ ľ", + "c ol", + "co l", + "( x", + "g ht", + "gh t", + "Res ult", + "C ode", + "Co de", + "Cod e", + ". is", + ".i s", + "l ink", + "li nk", + "lin k", + "Ġ cour", + "Ġc our", + "Ġco ur", + "Ġcou r", + "A n", + "Ġ team", + "Ġt eam", + "Ġte am", + "Ġtea m", + "ĉ int", + "ĉi nt", + "ĉin t", + "i ft", + "if t", + "5 5", + "Ġ second", + "Ġse cond", + "Ġsec ond", + "Ġ going", + "Ġgo ing", + "Ġ range", + "Ġr ange", + "Ġran ge", + "Ġra nge", + "Ġrang e", + "_ E", + "n ess", + "ne ss", + "nes s", + "3 9", + "Ġf am", + "Ġfa m", + "Ġ nil", + "Ġn il", + "Ġni l", + "Ġ Cont", + "ĠC ont", + "ĠCon t", + "ĠCo nt", + "ail able", + "u tes", + "ut es", + "ute s", + "a tab", + "at ab", + "ata b", + "Ġ fact", + "Ġf act", + "Ġfa ct", + "Ġfac t", + "Ġ vis", + "Ġv is", + "Ġvi s", + "( &", + "Ġ AN", + "ĠA N", + "3 1", + "A l", + "t itle", + "ti tle", + "tit le", + "Ġ android", + "Ġand roid", + "C E", + "\\ \"", + "i rt", + "ir t", + "Ġw rit", + "Ġwr it", + "Ð ½", + "ĉ m", + "ft ware", + "o nd", + "on d", + "Ġ ret", + "Ġre t", + "Ġr et", + "os ition", + "osi tion", + "osit ion", + "Ġ home", + "Ġh ome", + "Ġhom e", + "Ġho me", + "Ġ left", + "Ġl eft", + "Ġle ft", + "ar gs", + "arg s", + "m eric", + "mer ic", + "me ric", + "4 8", + "Ġ direct", + "Ġd irect", + "Ġdi rect", + "Ġdir ect", + "Ġdire ct", + "o ci", + "oc i", + "P l", + "A s", + "r et", + "re t", + "a do", + "ad o", + "O f", + "c hn", + "ch n", + "Ġ Get", + "ĠG et", + "ĠGe t", + "e e", + "r oss", + "ro ss", + "ros s", + "( );", + "() ;", + "_ ___", + "__ __", + "___ _", + ". ph", + ".p h", + "I t", + "o ute", + "ou te", + "out e", + "Ġex per", + "Ġexp er", + "ch ool", + "cho ol", + "w ww", + "ww w", + "} ,", + "Ġ allow", + "Ġal low", + "Ġall ow", + "Ġallo w", + "Ġ Â", + "( ))", + "() )", + "s ize", + "si ze", + "siz e", + "i sm", + "is m", + "a i", + "t ract", + "tr act", + "tra ct", + "a ne", + "an e", + ". ..ĊĊ", + ".. .ĊĊ", + "... ĊĊ", + "...Ċ Ċ", + "con text", + "cont ext", + "conte xt", + "Ġ beg", + "Ġb eg", + "Ġbe g", + "C H", + "Ġ page", + "Ġp age", + "Ġpa ge", + "Ġpag e", + "h ip", + "hi p", + "n o", + "c ore", + "co re", + "cor e", + "s p", + "Ġ different", + "Ġd ifferent", + "Ġdiffer ent", + "i able", + "ia ble", + "Ġ Me", + "ĠM e", + "_ IN", + "_I N", + "b utton", + "but ton", + "butt on", + "Ġ Is", + "ĠI s", + "erv ices", + "ervice s", + "Ġ ca", + "Ġc a", + "Ġ around", + "Ġa round", + "Ġar ound", + "Ġarou nd", + "A pp", + "Ap p", + "r ation", + "ra tion", + "rat ion", + "ratio n", + "Ġ rece", + "Ġre ce", + "Ġr ece", + "Ġrec e", + "Ġ really", + "Ġre ally", + "Ġreal ly", + "Ġ image", + "Ġi mage", + "Ġim age", + "Ġimag e", + "Ġima ge", + "Ġ target", + "Ġt arget", + "Ġtar get", + "Ġtarg et", + "Ġ dep", + "Ġd ep", + "Ġde p", + "opy right", + "t ra", + "tr a", + "i ngle", + "in gle", + "ing le", + "i tal", + "it al", + "ita l", + "L ayout", + "Ġ both", + "Ġb oth", + "Ġbo th", + "Ġbot h", + "Over ride", + "a rm", + "ar m", + "= >", + "at erial", + "ate rial", + "ater ial", + "ateria l", + "i led", + "il ed", + "ile d", + "Ġ put", + "Ġp ut", + "Ġpu t", + "Q u", + "Ñ Ģ", + "u ng", + "un g", + "m ap", + "ma p", + "ĉ ĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉ ĉ", + "Ġ level", + "Ġle vel", + "Ġlev el", + "Ġleve l", + "Com ponent", + "Comp onent", + "b ook", + "bo ok", + "boo k", + "c reen", + "cre en", + "cr een", + "_ RE", + "_R E", + "Ġ config", + "Ġcon fig", + "Ġconf ig", + "ã ģ", + "O r", + ". data", + ".d ata", + ".dat a", + ".da ta", + "Ġ document", + "Ġd ocument", + "Ġdoc ument", + "\" ,\"", + "\", \"", + "trib ute", + "u x", + "L og", + "Lo g", + "f erence", + "fer ence", + "fe rence", + "p ost", + "pos t", + "po st", + "_ e", + "Ġ local", + "Ġl ocal", + "Ġlo cal", + "Ġloc al", + "an dom", + "and om", + "ando m", + "as sert", + "ass ert", + "asse rt", + "asser t", + "V al", + "Va l", + "l ected", + "lect ed", + "lec ted", + "i na", + "in a", + "at abase", + "ata base", + "atab ase", + "A dd", + "Ad d", + "Ġ content", + "Ġc ontent", + "Ġcon tent", + "Ġcont ent", + "Ġconten t", + "Ġconte nt", + ". print", + ".p rint", + ".pr int", + "s igned", + "sign ed", + "sig ned", + "r ic", + "ri c", + ". \"ĊĊ", + ".\" ĊĊ", + ".\"Ċ Ċ", + "Ġ fa", + "Ġf a", + "! ĊĊ", + "!Ċ Ċ", + "- f", + "i ved", + "iv ed", + "ive d", + "Ġ quest", + "Ġqu est", + "Ġque st", + "Ġq uest", + "Ġques t", + ". ex", + ".e x", + "Ġ float", + "Ġf loat", + "Ġflo at", + "Ġ develop", + "Ġde velop", + "Ġdev elop", + "Ġdeve lop", + "Ġdevel op", + "о Ð", + "M ap", + "Ma p", + "a ding", + "ad ing", + "adi ng", + "adin g", + "Ġ poss", + "Ġp oss", + "Ġpos s", + "Ġpo ss", + "U E", + "n amespace", + "name space", + "names pace", + "_ O", + "ĉ b", + ". Get", + ".G et", + ".Ge t", + "> (", + "j son", + "js on", + "e tails", + "et ails", + "etail s", + "eta ils", + "6 6", + "Ġ too", + "Ġt oo", + "Ġto o", + "Ġ extends", + "Ġext ends", + "Ġextend s", + "Ġ None", + "ĠN one", + "ĠNo ne", + "ĠNon e", + "Ġ fore", + "Ġf ore", + "Ġfor e", + "Ġfo re", + "( String", + "(S tring", + "(Str ing", + "form at", + "for mat", + "forma t", + "Ġ great", + "Ġg reat", + "Ġgr eat", + "Ġgre at", + "in ter", + "int er", + "inte r", + "c ale", + "ca le", + "cal e", + "Ñ ģ", + "r on", + "ro n", + "i ving", + "iv ing", + "ivi ng", + "E nt", + "En t", + "e ncy", + "en cy", + "enc y", + "x t", + "o y", + "0 5", + "Ġ month", + "Ġm onth", + "Ġmon th", + "Ġmo nth", + "Ġmont h", + "Ġh app", + "Ġha pp", + "Ġhap p", + "Ġ super", + "Ġs uper", + "Ġsu per", + "Ġsup er", + "b ar", + "ba r", + "d efault", + "de fault", + "def ault", + "_ de", + "_d e", + "or ds", + "ord s", + "l n", + "( {Ċ", + "({ Ċ", + "Ġ Ind", + "ĠI nd", + "ĠIn d", + "a ses", + "as es", + "ase s", + "Ġ title", + "Ġt itle", + "Ġtit le", + "Ġti tle", + "Ġ context", + "Ġcon text", + "Ġcont ext", + "Ġconte xt", + "0 8", + "o h", + "- p", + "E m", + "Ġ met", + "Ġm et", + "Ġme t", + "T est", + "Te st", + "Tes t", + "Ġ life", + "Ġl ife", + "Ġli fe", + "Ġlif e", + "_ v", + "Ġ US", + "ĠU S", + "U I", + "o cation", + "oc ation", + "oca tion", + "m d", + "Ġ [Ċ", + "Ġ[ Ċ", + "Ġ ]", + "s w", + "Ġ incre", + "Ġin cre", + "Ġinc re", + "Ġincr e", + "s cript", + "scri pt", + "scr ipt", + "ent ial", + "enti al", + "w ays", + "way s", + "wa ys", + ". de", + ".d e", + "Ġ src", + "Ġs rc", + "Ġsr c", + "Ġ catch", + "Ġc atch", + "Ġcat ch", + "Ġ Americ", + "ĠA meric", + "ĠAm eric", + "ĠAmer ic", + "/ /Ċ", + "// Ċ", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "Ġ pay", + "Ġp ay", + "Ġpa y", + "p lit", + "pl it", + "âĢ Ķ", + "Ġc oun", + "Ġco un", + "Ġcou n", + "o bj", + "ob j", + ". php", + ".p hp", + ".ph p", + "Ġ change", + "Ġch ange", + "Ġchang e", + "Ġcha nge", + "Ġchan ge", + "e thing", + "eth ing", + "' re", + "'r e", + "a ster", + "as ter", + "ast er", + "aste r", + "l os", + "lo s", + "l ation", + "la tion", + "lat ion", + "Ġ ĠĊ", + "ĠĠ Ċ", + "L e", + "à ¤", + "( {", + "re ady", + "read y", + "rea dy", + "Ġ No", + "ĠN o", + "Ġ position", + "Ġp osition", + "Ġpos ition", + "Ġposit ion", + "Ġposi tion", + "Ġ old", + "Ġo ld", + "Ġol d", + "Ġ book", + "Ġb ook", + "Ġbo ok", + "Ġboo k", + "a bled", + "ab led", + "able d", + "abl ed", + "b ug", + "bu g", + "2 02", + "20 2", + "H and", + "Ha nd", + "Han d", + "} ;ĊĊ", + "};Ċ Ċ", + "}; ĊĊ", + "is play", + "isp lay", + "a ving", + "av ing", + "avi ng", + "avin g", + "0 4", + "Ġg over", + "Ġgo ver", + "Ġgov er", + "Ġ version", + "Ġv ersion", + "Ġvers ion", + "S ystem", + "Sys tem", + "Sy stem", + "n ect", + "ne ct", + "res ponse", + "resp onse", + "respons e", + "St yle", + "U p", + "an gu", + "ang u", + "Ġ three", + "Ġth ree", + "Ġthr ee", + "i nit", + "in it", + "ini t", + "e ro", + "er o", + "Ġ law", + "Ġl aw", + "Ġla w", + "en dif", + "end if", + "endi f", + "Ġ base", + "Ġb ase", + "Ġbas e", + "Ġba se", + "e mail", + "em ail", + "ema il", + "( l", + "_ V", + "Ġ conf", + "Ġcon f", + "Ġco nf", + "A TE", + "AT E", + "Ġ during", + "Ġd uring", + "Ġdu ring", + "Ġdur ing", + "t es", + "te s", + "Ġ console", + "Ġcon sole", + "Ġcons ole", + "Ġ Pr", + "ĠP r", + "Ġ spe", + "Ġs pe", + "Ġsp e", + "v es", + "ve s", + "6 5", + "p ath", + "pat h", + "pa th", + "i alog", + "ial og", + "ia log", + "d ition", + "di tion", + "dit ion", + "_ to", + "_t o", + "ar ds", + "ard s", + "Ġ against", + "Ġagain st", + "et work", + "Ġ Ph", + "ĠP h", + "_ L", + "c ur", + "cu r", + "i mit", + "im it", + "imi t", + "W ith", + "Wi th", + "Ġ power", + "Ġp ower", + "Ġpo wer", + "Ġpow er", + "i um", + "iu m", + "' ;ĊĊ", + "';Ċ Ċ", + "'; ĊĊ", + "Ġw om", + "Ġwo m", + "l eft", + "le ft", + "lef t", + "our ces", + "ource s", + "a tri", + "at ri", + "atr i", + "Ġ Im", + "ĠI m", + "Ġ Man", + "ĠM an", + "ĠMa n", + "or th", + "ort h", + "$ {", + "8 8", + "qu als", + "qual s", + "qua ls", + "e se", + "es e", + "_ size", + "_s ize", + "_si ze", + "Ġ iss", + "Ġis s", + "Ġi ss", + "o tal", + "ot al", + "ota l", + "- g", + "i que", + "iqu e", + "iq ue", + "r ame", + "ra me", + "ram e", + "Ġ width", + "Ġw idth", + "Ġwid th", + "e rg", + "er g", + ") (", + "it tle", + "itt le", + "T R", + "Ġ They", + "ĠT hey", + "ĠThe y", + "ĠTh ey", + "e nces", + "en ces", + "ence s", + "enc es", + "0 2", + "r l", + "o ns", + "on s", + "Ġ label", + "Ġl abel", + "Ġla bel", + "Ġlab el", + ". y", + "- t", + "up date", + "upd ate", + "a nel", + "an el", + "ane l", + "s c", + ". to", + ".t o", + "Ġ project", + "Ġpro ject", + "Ġproj ect", + "Ġproje ct", + "à ¼", + "Ġ element", + "Ġe lement", + "Ġel ement", + "Ġele ment", + "Ġelem ent", + "Ġ success", + "Ġs uccess", + "Ġsu ccess", + "Ġsuc cess", + "Ġsucc ess", + "Ġsucces s", + "ĉ ĉĊ", + "ĉĉ Ċ", + ". sh", + ".s h", + "r am", + "ra m", + "c hed", + "ch ed", + "che d", + "( ))Ċ", + "() )Ċ", + "()) Ċ", + "Ġ (Ċ", + "Ġ( Ċ", + "Ġ date", + "Ġd ate", + "Ġda te", + "Ġdat e", + "Ġ tot", + "Ġt ot", + "Ġto t", + "_ ST", + "_S T", + "A ll", + "Al l", + "if ication", + "ific ation", + "ifi cation", + "ifica tion", + "ĉ var", + "ĉv ar", + "ĉva r", + "Ġ tri", + "Ġt ri", + "Ġtr i", + "c hem", + "ch em", + "che m", + "m y", + "Ġ big", + "Ġb ig", + "Ġbi g", + "Ġ Ad", + "ĠA d", + "Ġ At", + "ĠA t", + "o ts", + "ot s", + "n um", + "nu m", + "A ct", + "Ac t", + "Ġ map", + "Ġm ap", + "Ġma p", + "e ra", + "er a", + "c ope", + "co pe", + "cop e", + ". $", + ", âĢĿ", + "Ġ pop", + "Ġp op", + "Ġpo p", + "Ġ few", + "Ġf ew", + "Ġfe w", + "Ġ len", + "Ġl en", + "Ġle n", + "u id", + "ui d", + "e ters", + "et ers", + "eter s", + "ete rs", + "u les", + "ul es", + "ule s", + "à Ń", + "s ource", + "ht tps", + "http s", + "htt ps", + "Ġ dem", + "Ġd em", + "Ġde m", + "Ġ ear", + "Ġe ar", + "Ġea r", + "#### ############", + "######## ########", + "############ ####", + "Ġ match", + "Ġm atch", + "Ġmat ch", + "o ries", + "or ies", + "ori es", + "orie s", + "4 9", + "a ces", + "ace s", + "ac es", + "Ġ Cl", + "ĠC l", + "Ġ node", + "Ġn ode", + "Ġno de", + "Ġnod e", + "7 8", + "i rc", + "ir c", + "l ocal", + "lo cal", + "loc al", + "un ity", + "unit y", + "uni ty", + "} ;Ċ", + "}; Ċ", + "Ġ another", + "Ġan other", + "Ġano ther", + "< <", + "o gle", + "og le", + "ogl e", + "Ġ sit", + "Ġs it", + "Ġsi t", + "e work", + "ew ork", + "T E", + ". I", + "N S", + "o logy", + "ol ogy", + "olog y", + "olo gy", + "o ught", + "ou ght", + "ough t", + "oug ht", + ". Cont", + ".C ont", + ".Con t", + ".Co nt", + "> >", + "Ġ care", + "Ġc are", + "Ġcar e", + "Ġca re", + "st ate", + "stat e", + "sta te", + "ĉ private", + "ĉpr ivate", + "ĉpriv ate", + "Ġ effect", + "Ġe ffect", + "Ġeff ect", + "Ġef fect", + "+ +)", + "++ )", + "_ file", + "_f ile", + "_fil e", + "en ding", + "end ing", + "endi ng", + "L ine", + "Li ne", + "Lin e", + "F or", + "Fo r", + "i or", + "io r", + "Ġ Sc", + "ĠS c", + "Ġ fun", + "Ġf un", + "Ġfu n", + ". Size", + ".S ize", + "ĉ else", + "ĉe lse", + "ĉel se", + "] )", + "st art", + "star t", + "sta rt", + "v ious", + "vi ous", + "vio us", + "Ġ },", + "Ġ} ,", + "o urs", + "ou rs", + "our s", + "Ġ leg", + "Ġl eg", + "Ġle g", + "Ġ service", + "Ġs ervice", + "Ġser vice", + "Ġserv ice", + "Ġservi ce", + "Ġservic e", + "Ġ since", + "Ġs ince", + "Ġsi nce", + "Ġsin ce", + "Ġsinc e", + "i ron", + "ir on", + "iro n", + "L abel", + "La bel", + "Lab el", + "Ġ non", + "Ġn on", + "Ġno n", + "Ġ los", + "Ġl os", + "Ġlo s", + "i ction", + "ic tion", + "ict ion", + "Ġ full", + "Ġf ull", + "Ġful l", + "Ġfu ll", + "a cter", + "act er", + "ac ter", + "b oard", + "bo ard", + "boa rd", + "g ress", + "gr ess", + "gre ss", + "gres s", + "Ġ turn", + "Ġt urn", + "Ġtu rn", + "Ġtur n", + "i ther", + "it her", + "ith er", + "ithe r", + "0 9", + ". size", + ".s ize", + ".si ze", + "Ġ body", + "Ġb ody", + "Ġbo dy", + "Ġbod y", + "r esh", + "re sh", + "res h", + "e turn", + "et urn", + "etur n", + "etu rn", + "1 99", + "19 9", + "( _", + "y les", + "yle s", + "yl es", + "or mal", + "orm al", + "p i", + "Ġ something", + "Ġs omething", + "Ġsome thing", + "Ġsom ething", + "! --", + "u int", + "ui nt", + "uin t", + "Ġ produ", + "Ġp rodu", + "Ġpro du", + "Ġpr odu", + "Ġprod u", + "Ġ stand", + "Ġst and", + "Ġsta nd", + "Ġstan d", + "Ġpro ble", + "Ġpr oble", + "Ġprob le", + "Ġprobl e", + "Ġ available", + "Ġa vailable", + "Ġav ailable", + "Ġavail able", + "m t", + "Ġ Bl", + "ĠB l", + "Ġ ...", + "Ġ. ..", + "Ġ.. .", + "Ġ block", + "Ġb lock", + "Ġbl ock", + "Ġblo ck", + "Ġbloc k", + "In put", + "Ġ keep", + "Ġke ep", + "C ount", + "Co unt", + "Cou nt", + "o pen", + "op en", + "ope n", + "Ġ ['", + "Ġ[ '", + "Ġ throw", + "Ġth row", + "Ġthr ow", + "Ġthro w", + "u ilder", + "uild er", + "ui lder", + "uil der", + "A ction", + "Act ion", + "Ac tion", + "Ġ things", + "Ġth ings", + "Ġthing s", + "Ġthin gs", + "Tr ue", + "Ġ url", + "Ġu rl", + "Ġur l", + "Ġ Bo", + "ĠB o", + "print f", + "Ġ red", + "Ġre d", + "Ġr ed", + "j s", + ". create", + ".c reate", + "Ġ Or", + "ĠO r", + "S tatus", + "St atus", + "Stat us", + "In stance", + "Inst ance", + "Ġ control", + "Ġc ontrol", + "Ġcont rol", + "Ġcontr ol", + "Ġcontro l", + "Ġ come", + "Ġc ome", + "Ġcom e", + "Ġco me", + "Ġ custom", + "Ġc ustom", + "Ġcust om", + "Ġcus tom", + "l ocation", + "lo cation", + "loc ation", + "0 7", + "m odel", + "mod el", + "mo del", + "mode l", + "Ġ čĊ", + "Ġč Ċ", + "Ġ source", + "Ġs ource", + "Ġsour ce", + "Ġe as", + "Ġea s", + ". out", + ".o ut", + "] ĊĊ", + "]Ċ Ċ", + "o ney", + "on ey", + "one y", + "Ġ await", + "Ġa wait", + "Ġaw ait", + "Ġp artic", + "Ġpart ic", + "Ġpar tic", + "Ġparti c", + "A P", + "ub lish", + "ubl ish", + "o des", + "od es", + "ode s", + "_ pro", + "_p ro", + "_pr o", + "p ly", + "pl y", + "r iter", + "ri ter", + "rit er", + "rite r", + "Ġ prov", + "Ġp rov", + "Ġpro v", + "Ġpr ov", + "Ġ mill", + "Ġm ill", + "Ġmil l", + "Ġmi ll", + "H T", + "] )Ċ", + "]) Ċ", + "Ġ chang", + "Ġc hang", + "Ġch ang", + "Ġcha ng", + "Ġchan g", + "Ġ ask", + "Ġa sk", + "Ġas k", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "Ġ output", + "Ġout put", + "Ġ email", + "Ġe mail", + "Ġem ail", + "6 8", + ". push", + ".p ush", + "Ġ }čĊčĊ", + "Ġ} čĊčĊ", + "Ġ}čĊ čĊ", + "i nation", + "in ation", + "ina tion", + "inati on", + "4 7", + "at rix", + "atri x", + "atr ix", + "T able", + "Tab le", + "Ta ble", + "u ccess", + "uc cess", + "ucc ess", + "] );Ċ", + "]) ;Ċ", + "]); Ċ", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ disc", + "Ġd isc", + "Ġdis c", + "Ġdi sc", + "( [", + "Ġ business", + "Ġb usiness", + "Ġbus iness", + "h eight", + "he ight", + "hei ght", + ". html", + ".h tml", + ".ht ml", + ".htm l", + "t a", + "f ield", + "fi eld", + "Ġ required", + "Ġre quired", + "Ġrequire d", + "Ġrequ ired", + "_ R", + "Ġg overn", + "Ġgo vern", + "Ġgover n", + "Ġgov ern", + "} čĊčĊ", + "}čĊ čĊ", + "l ex", + "le x", + "5 00", + "50 0", + ". ,", + "Ġ Set", + "ĠS et", + "ĠSe t", + "u rch", + "ur ch", + "/ //", + "// /", + "t s", + "a f", + "Ġ might", + "Ġm ight", + "Ġmi ght", + "Ġmig ht", + "i story", + "ist ory", + "istor y", + "isto ry", + "S tr", + "St r", + "Ġ never", + "Ġn ever", + "Ġne ver", + "Ġnev er", + "Res ponse", + "Resp onse", + "Respons e", + "ar se", + "ars e", + "a da", + "ad a", + "Ġ How", + "ĠH ow", + "ĠHo w", + "Ġ *)", + "Ġ* )", + "Ġ ;", + "Ġ hard", + "Ġh ard", + "Ġhar d", + "Ġha rd", + "A d", + "Ġ intern", + "Ġin tern", + "Ġint ern", + "Ġinter n", + "Ġinte rn", + "u sed", + "us ed", + "use d", + "( data", + "(d ata", + "(dat a", + "(da ta", + "m od", + "mo d", + "an nel", + "ann el", + "anne l", + "Ġ np", + "Ġn p", + "u gg", + "ug g", + "Ġ />Ċ", + "Ġ/ >Ċ", + "Ġ/> Ċ", + "Ġ called", + "Ġc alled", + "Ġcall ed", + "Ġcal led", + "Ġcalle d", + "b ody", + "bo dy", + "Ġ cho", + "Ġc ho", + "Ġch o", + "( r", + "_ set", + "_s et", + "_se t", + "i rd", + "ir d", + "Ġ >=", + "Ġ> =", + "Ġ };Ċ", + "Ġ} ;Ċ", + "Ġ}; Ċ", + "Ġ options", + "Ġo ptions", + "Ġoption s", + "Ġopt ions", + "Ġ Gener", + "ĠG ener", + "ĠGe ner", + "ĠGen er", + "ĠGene r", + "Ġ height", + "Ġh eight", + "Ġhe ight", + "Ġhei ght", + "P oint", + "Po int", + "Y ou", + "Yo u", + "e ty", + "et y", + "C lick", + "Cl ick", + "Cli ck", + "Ġ small", + "Ġs mall", + "Ġsm all", + "Ġ ide", + "Ġi de", + "Ġid e", + "Ġ access", + "Ġa ccess", + "Ġacc ess", + "Ġac cess", + "Ġacces s", + "angu age", + "Ġ protected", + "Ġprot ected", + "Ġprotect ed", + "Ġ job", + "Ġj ob", + "Ġjo b", + "Ġ There", + "ĠT here", + "ĠThe re", + "ĠTh ere", + "ĠTher e", + "D ef", + "De f", + "Ġ address", + "Ġadd ress", + "Ġad dress", + "Ġaddr ess", + "Ġ uint", + "Ġu int", + "Ġui nt", + "N ot", + "No t", + "o o", + "a ps", + "ap s", + "< div", + "", + "() ->", + "()- >", + "ĉ ĠĠĠ", + "ĉĠ ĠĠ", + "ĉĠĠ Ġ", + "\" ))", + "\") )", + "C ontent", + "Cont ent", + "Con tent", + "_ W", + "p lement", + "pl ement", + "ple ment", + "Ġ won", + "Ġw on", + "Ġwo n", + "Ġ video", + "Ġv ideo", + "Ġvi deo", + "Ġvid eo", + "Ġvide o", + "a di", + "ad i", + "p oint", + "po int", + "poi nt", + "% %", + "0 3", + "Ġ gl", + "Ġg l", + "er ved", + "erv ed", + "erve d", + "v iron", + "vi ron", + "vir on", + "I F", + "u ted", + "ut ed", + "ute d", + "ã ĥ", + "' m", + "Ġ cert", + "Ġc ert", + "Ġce rt", + "Ġcer t", + "Ġ prof", + "Ġp rof", + "Ġpro f", + "Ġpr of", + "Ġ cell", + "Ġc ell", + "Ġce ll", + "Ġcel l", + "a ri", + "ar i", + "Ġ player", + "Ġp layer", + "Ġpl ayer", + "Ġplay er", + "Ġpla yer", + "a is", + "ai s", + "Ġ cost", + "Ġc ost", + "Ġco st", + "Ġcos t", + "Ġ hum", + "Ġh um", + "Ġhu m", + "( R", + "Ġo ffic", + "Ġof fic", + "Ġoff ic", + "k s", + ". text", + ".t ext", + ".te xt", + ".tex t", + "a tures", + "at ures", + "ature s", + "atur es", + "atu res", + "Ġ total", + "Ġt otal", + "Ġto tal", + "Ġtot al", + "Ġ */ĊĊ", + "Ġ* /ĊĊ", + "Ġ*/ ĊĊ", + "Ġ*/Ċ Ċ", + "o pe", + "op e", + "Ġ stat", + "Ġs tat", + "Ġst at", + "Ġsta t", + "U M", + "Ġ load", + "Ġl oad", + "Ġlo ad", + "Ġloa d", + "ight s", + "igh ts", + "Ġ clear", + "Ġc lear", + "Ġcl ear", + "Ġcle ar", + "u ro", + "ur o", + "Ġ techn", + "Ġt echn", + "Ġte chn", + "Ġtech n", + "Ġtec hn", + "up port", + "upp ort", + "I R", + "Ġ row", + "Ġr ow", + "Ġro w", + "Ġse em", + "Ġsee m", + "Ġ q", + "Ġ short", + "Ġs hort", + "Ġsh ort", + "Ġsho rt", + "Ġ Not", + "ĠN ot", + "ĠNo t", + "i pp", + "ip p", + "G roup", + "Gr oup", + "Gro up", + "s ection", + "se ction", + "sec tion", + "sect ion", + "m ax", + "ma x", + "i rl", + "ir l", + "Ġ override", + "Ġover ride", + "Ġ company", + "Ġcom pany", + "Ġcomp any", + "Ġcompan y", + "Ġ done", + "Ġd one", + "Ġdo ne", + "Ġdon e", + "\" );čĊ", + "\") ;čĊ", + "\"); čĊ", + "Ġ gre", + "Ġg re", + "Ġgr e", + ". Re", + ".R e", + "Ġ belie", + "Ġbe lie", + "Ġbel ie", + "r ist", + "ri st", + "ris t", + "Ġ health", + "Ġhe alth", + "Ġheal th", + "A NT", + "AN T", + "( )ĊĊ", + "() ĊĊ", + "()Ċ Ċ", + "Ġ Be", + "ĠB e", + ". value", + ".v alue", + ".val ue", + ".va lue", + "Ġ Gr", + "ĠG r", + "ot tom", + "ott om", + "otto m", + "Ġ args", + "Ġar gs", + "Ġarg s", + "P T", + "s tatus", + "st atus", + "stat us", + "f unc", + "fun c", + "fu nc", + "u ments", + "um ents", + "ument s", + "ume nts", + "umen ts", + "- h", + "N umber", + "Num ber", + ": čĊ", + "Ġ Log", + "ĠL og", + "ĠLo g", + "er ver", + "erv er", + "erve r", + "Ġ ),Ċ", + "Ġ) ,Ċ", + "Ġ), Ċ", + "a ment", + "am ent", + "ame nt", + "amen t", + "Ġ obj", + "Ġo bj", + "Ġob j", + "i nc", + "in c", + "Ġ children", + "Ġch ildren", + "Ġchild ren", + "i cy", + "ic y", + "I Z", + "a nds", + "an ds", + "and s", + "ab ly", + "abl y", + "Ġd istrib", + "Ġdis trib", + "Ġdist rib", + "Ġdistr ib", + "Ġ cur", + "Ġc ur", + "Ġcu r", + "e rial", + "er ial", + "eri al", + "eria l", + "Ġ days", + "Ġd ays", + "Ġday s", + "Ġda ys", + "r eated", + "re ated", + "reate d", + "reat ed", + "rea ted", + "r ect", + "re ct", + "rec t", + "- l", + "i rm", + "ir m", + "i dden", + "id den", + "idd en", + "o mb", + "om b", + "Ġ initial", + "Ġin itial", + "Ġinit ial", + "Ġiniti al", + ". js", + ".j s", + "Ġ â", + "Qu ery", + "Que ry", + "Ġ online", + "Ġon line", + "i mal", + "im al", + "ima l", + ". con", + ".c on", + ".co n", + "a u", + "U rl", + "Ur l", + "c ontrol", + "cont rol", + "contr ol", + "contro l", + "i rection", + "ir ection", + "ire ction", + "irect ion", + "Ġ instance", + "Ġin stance", + "Ġinst ance", + "O RT", + "OR T", + "Ġ Fr", + "ĠF r", + "w here", + "wh ere", + "Ġ javax", + "Ġj avax", + "Ġjav ax", + "Ġjava x", + "Ġ organ", + "Ġo rgan", + "Ġor gan", + "Ġorg an", + "a pter", + "ap ter", + "apt er", + "Ġ reason", + "Ġre ason", + "o ptions", + "option s", + "opt ions", + "5 9", + "Ġ Mar", + "ĠM ar", + "ĠMa r", + "( a", + "Ġ within", + "Ġwith in", + "Ġwi thin", + "Ġwit hin", + ". âĢĿĊĊ", + ".âĢĿ ĊĊ", + ".âĢĿĊ Ċ", + "O DE", + "OD E", + "_ DE", + "_D E", + "ad min", + "adm in", + "en ded", + "end ed", + "ende d", + "Ġ design", + "Ġd esign", + "Ġde sign", + "Ġdes ign", + "Ġdesi gn", + "Ġ Data", + "ĠD ata", + "ĠDa ta", + "ĠDat a", + "u ne", + "un e", + "Ġ File", + "ĠF ile", + "ĠFil e", + "ĠFi le", + "r oot", + "ro ot", + "Ġ cent", + "Ġc ent", + "Ġce nt", + "Ġcen t", + "Ġ arr", + "Ġa rr", + "Ġar r", + "_ add", + "_a dd", + "_ad d", + "l en", + "le n", + "p age", + "pa ge", + "pag e", + ", '", + "_ str", + "_s tr", + "_st r", + "Ġ bro", + "Ġb ro", + "Ġbr o", + "ab ility", + "abil ity", + "abilit y", + "o uth", + "ou th", + "out h", + "5 8", + "/ c", + "p ose", + "pos e", + "po se", + "ir tual", + "irt ual", + "e arch", + "ear ch", + "ea rch", + "_ url", + "_u rl", + "_ur l", + "ar gin", + "arg in", + "H ttp", + "Ġ school", + "Ġs chool", + "Ġsch ool", + "Ġscho ol", + "a va", + "av a", + "Ġ consider", + "Ġcons ider", + "Ġconsid er", + ". label", + ".l abel", + ".lab el", + "Ġ Array", + "ĠA rray", + "ĠAr ray", + "ĠArr ay", + "4 2", + "w eb", + "we b", + "o pt", + "op t", + ". println", + ".print ln", + "u lation", + "ul ation", + "ula tion", + "Ġ func", + "Ġf unc", + "Ġfun c", + "Ġfu nc", + "P L", + "Ġ \"\\", + "Ġ\" \\", + "Ġ Text", + "ĠT ext", + "ĠTe xt", + "ĠTex t", + "act ory", + "actor y", + "( function", + "(f unction", + "(func tion", + "(fun ction", + "n ull", + "nu ll", + "nul l", + "Ġ eng", + "Ġe ng", + "Ġen g", + "d own", + "do wn", + "Ġ include", + "Ġin clude", + "Ġinclud e", + "Ġinc lude", + "Ġincl ude", + "Ġinclu de", + "Ġ En", + "ĠE n", + "Ġ Dr", + "ĠD r", + "Ġ db", + "Ġd b", + "! !", + "s ide", + "si de", + "sid e", + "Ġ init", + "Ġin it", + "Ġi nit", + "Ġini t", + "qu ired", + "quire d", + "quir ed", + "qui red", + "Ġ She", + "ĠS he", + "ĠSh e", + "C olumn", + "Col umn", + "re act", + "rea ct", + "Ġ ann", + "Ġa nn", + "Ġan n", + "Ġ stop", + "Ġs top", + "Ġst op", + "Ġsto p", + "Ġ later", + "Ġl ater", + "Ġla ter", + "Ġlate r", + "Ġlat er", + "Ġ That", + "ĠT hat", + "ĠTh at", + "en tion", + "ent ion", + "enti on", + "d f", + "U G", + "I LE", + "IL E", + "Ġ client", + "Ġc lient", + "Ġcl ient", + "Ġcli ent", + "r aft", + "ra ft", + "raf t", + "f fer", + "ff er", + "ffe r", + "P OST", + "PO ST", + "POS T", + "el per", + "elp er", + "Ġ love", + "Ġl ove", + "Ġlo ve", + "Ġlov e", + "qu ote", + "quot e", + "quo te", + "o ud", + "ou d", + "Ġ json", + "Ġj son", + "Ġjs on", + "Ġ able", + "Ġa ble", + "Ġab le", + "Ġabl e", + "Ġ men", + "Ġm en", + "Ġme n", + "A X", + "Ġ Copyright", + "ĠC opyright", + "ĠCopy right", + "à ¶", + "a vig", + "av ig", + "avi g", + "r eq", + "re q", + "C lient", + "Cl ient", + "Cli ent", + "} );Ċ", + "}) ;Ċ", + "}); Ċ", + ". Com", + ".C om", + ".Co m", + "e rc", + "er c", + "i lt", + "il t", + "p ecial", + "pe cial", + "pec ial", + "pecia l", + "_ com", + "_c om", + "_co m", + "r oom", + "ro om", + ". Name", + ".N ame", + "Ġ give", + "Ġg ive", + "Ġgi ve", + "a mb", + "am b", + "i ke", + "ik e", + "Ġ condition", + "Ġcon dition", + "Ġcond ition", + "Ġcondi tion", + "c lient", + "cl ient", + "cli ent", + "a tors", + "at ors", + "ator s", + "ato rs", + ": \"", + "Ġ copy", + "Ġc opy", + "Ġco py", + "Ġcop y", + "u ture", + "ut ure", + "ivers ity", + "iversit y", + "er nal", + "ern al", + "erna l", + "{ {", + "Ġ Can", + "ĠC an", + "ĠCa n", + "o unc", + "ou nc", + "oun c", + "d o", + "Ġ occ", + "Ġo cc", + "Ġoc c", + "Ġ appro", + "Ġapp ro", + "Ġap pro", + "th ers", + "ther s", + "the rs", + "z e", + "Ġ either", + "Ġe ither", + "Ġei ther", + "Ġ Fl", + "ĠF l", + "Ġ important", + "Ġimport ant", + "Ġ lead", + "Ġl ead", + "Ġle ad", + "at tr", + "att r", + "A RT", + "AR T", + "E qual", + "Equ al", + "Eq ual", + "Ġ da", + "Ġd a", + "et ch", + "etc h", + "e ntity", + "ent ity", + "enti ty", + "Ġ family", + "Ġf amily", + "Ġfam ily", + "Ġfamil y", + "ad ding", + "add ing", + "addin g", + "Ġ option", + "Ġo ption", + "Ġop tion", + "Ġopt ion", + "Ġ exist", + "Ġex ist", + "i ca", + "ic a", + "Ġ Object", + "ĠO bject", + "ĠOb ject", + "ĠObj ect", + "6 9", + "' ve", + "v ers", + "ver s", + "ve rs", + "it ional", + "ition al", + "iti onal", + "6 7", + "out put", + "Ġ True", + "ĠTr ue", + "ĠTru e", + "Ġ OF", + "ĠO F", + "_ time", + "_t ime", + "_tim e", + "_ti me", + "Ġ offer", + "Ġo ffer", + "Ġof fer", + "Ġoff er", + "Ġ });ĊĊ", + "Ġ} );ĊĊ", + "Ġ});Ċ Ċ", + "Ġ}) ;ĊĊ", + "Ġ}); ĊĊ", + "H ER", + "HE R", + "e gin", + "eg in", + "\" \"", + "Ġ water", + "Ġw ater", + "Ġwa ter", + "Ġwat er", + "Ġ che", + "Ġc he", + "Ġch e", + "Ġ My", + "ĠM y", + "o red", + "or ed", + "ore d", + "Ġ step", + "Ġs tep", + "Ġst ep", + "Ġste p", + "a nces", + "an ces", + "ance s", + "anc es", + "C K", + "A Y", + "à ¸", + "str uction", + "struct ion", + "stru ction", + "( C", + "3 00", + "30 0", + "o uch", + "ou ch", + "St ream", + "Str eam", + "act ive", + "activ e", + "a ma", + "am a", + "E ntity", + "Ent ity", + "pro duct", + "produ ct", + "prod uct", + "( ){Ċ", + "() {Ċ", + "(){ Ċ", + "Ġ government", + "Ġg overnment", + "Ġgovern ment", + "Ġ ID", + "ĠI D", + "aj or", + "ajo r", + "A nd", + "An d", + "Ġ display", + "Ġd isplay", + "Ġdis play", + "Ġdisp lay", + "Ġdispl ay", + "Ð »", + "Ġ times", + "Ġt imes", + "Ġtime s", + "Ġtim es", + "Ġti mes", + "Ġ four", + "Ġf our", + "Ġfo ur", + "Ġfou r", + "Ġ far", + "Ġf ar", + "Ġfa r", + "Ġ present", + "Ġp resent", + "Ġpre sent", + "Ġpres ent", + "Ġ NS", + "ĠN S", + "Ġ \\Ċ", + "Ġ\\ Ċ", + "u est", + "ue st", + "ues t", + "Ġ bas", + "Ġb as", + "Ġba s", + "e cho", + "ec ho", + "ech o", + "ch ild", + "chi ld", + "if ier", + "ifi er", + "ifie r", + "H andler", + "Hand ler", + "Handle r", + "Ġ lib", + "Ġl ib", + "Ġli b", + "P roperty", + "Pro perty", + "Prop erty", + "trans lation", + "Ġ room", + "Ġr oom", + "Ġro om", + "Ġ once", + "Ġo nce", + "Ġon ce", + "Ġonc e", + "Ġ []", + "Ġ[ ]", + "c enter", + "cent er", + "cen ter", + "cente r", + "================ ================", + "Ġ results", + "Ġres ults", + "Ġresult s", + "Ġ continue", + "Ġcont inue", + "Ġcontin ue", + "Ġcontinu e", + "Ġ talk", + "Ġt alk", + "Ġtal k", + "Ġta lk", + "_ get", + "_g et", + "_ge t", + "Ġ grow", + "Ġg row", + "Ġgr ow", + "Ġgro w", + ". sw", + ".s w", + "e b", + "Ġ Public", + "ĠP ublic", + "ĠPub lic", + "O P", + "ec ute", + "ecut e", + "o ls", + "ol s", + "Ġ **", + "Ġ* *", + "\" );ĊĊ", + "\");Ċ Ċ", + "\") ;ĊĊ", + "\"); ĊĊ", + "Ġ mass", + "Ġm ass", + "Ġma ss", + "Ġmas s", + "u red", + "ur ed", + "ure d", + ". class", + ".c lass", + ".cl ass", + "o mic", + "om ic", + "omi c", + "Ġ mean", + "Ġm ean", + "Ġme an", + "i ps", + "ip s", + "Ġ aut", + "Ġa ut", + "Ġau t", + ") ;čĊčĊ", + ");čĊ čĊ", + "); čĊčĊ", + "Ġ until", + "Ġun til", + "Ġunt il", + "Ġ market", + "Ġm arket", + "Ġmark et", + "Ġmar ket", + "Ġ area", + "Ġa rea", + "Ġare a", + "Ġar ea", + "u it", + "ui t", + "Ġ length", + "Ġl ength", + "Ġle ngth", + "Ġlen gth", + "Ġleng th", + "Ġ With", + "ĠW ith", + "ĠWi th", + "ĠWit h", + "str uctor", + "struct or", + "stru ctor", + "e vent", + "ev ent", + "even t", + "eve nt", + "\" ><", + "\"> <", + "Ġ Sp", + "ĠS p", + "I V", + "Ġ mus", + "Ġm us", + "Ġmu s", + "i ff", + "if f", + "Ġ kind", + "Ġk ind", + "Ġki nd", + "Ġkin d", + "a uthor", + "aut hor", + "auth or", + "o unds", + "ou nds", + "ound s", + "oun ds", + "m b", + "_ key", + "_k ey", + "_ke y", + "4 1", + "w idth", + "wid th", + "pos itory", + "posit ory", + "positor y", + "Ġ light", + "Ġl ight", + "Ġli ght", + "Ġlig ht", + "u k", + "R ow", + "Ro w", + "o hn", + "oh n", + "a lf", + "al f", + "viron ment", + "a pper", + "ap per", + "app er", + "appe r", + "ol lections", + "oll ections", + "ollection s", + "ollect ions", + "olle ctions", + "Ġ side", + "Ġs ide", + "Ġsi de", + "Ġsid e", + "_ info", + "_in fo", + "_inf o", + "Ġ example", + "Ġex ample", + "Ġexam ple", + "i mary", + "im ary", + "ima ry", + "imar y", + "Ġ wr", + "Ġw r", + "Ġ camp", + "Ġc amp", + "Ġca mp", + "Ġcam p", + "cri be", + "cr ibe", + "2 55", + "25 5", + "\" /", + "Ġ miss", + "Ġm iss", + "Ġmis s", + "Ġmi ss", + "w ay", + "wa y", + "Ġ based", + "Ġb ased", + "Ġbase d", + "Ġbas ed", + "Ġba sed", + "Ġ plan", + "Ġp lan", + "Ġpl an", + "Ġpla n", + "V is", + "Vi s", + "o main", + "om ain", + "oma in", + "u nk", + "un k", + "Ġ away", + "Ġa way", + "Ġaw ay", + "U P", + "< T", + "O S", + "i od", + "io d", + "Ġ Mon", + "ĠM on", + "ĠMo n", + "âĢĻ re", + "Ġ lik", + "Ġl ik", + "Ġli k", + "à §", + "i vely", + "iv ely", + "ive ly", + "ivel y", + ". v", + "i mer", + "im er", + "ime r", + "i zer", + "iz er", + "ize r", + "S ub", + "Su b", + "Ġ button", + "Ġb utton", + "Ġbut ton", + "Ġbutt on", + "Ġbutto n", + "Ġ Up", + "ĠU p", + "Ġ experience", + "Ġex perience", + "Ġexper ience", + "Ġexperi ence", + "C L", + "Ġ render", + "Ġre nder", + "Ġr ender", + "Ġren der", + "Ġrend er", + "_ value", + "_v alue", + "_val ue", + "_va lue", + "Ġ near", + "Ġn ear", + "Ġne ar", + "U RL", + "UR L", + "a lt", + "al t", + "Ġ country", + "Ġc ountry", + "Ġcount ry", + "Ġcoun try", + "ib ility", + "ibil ity", + "ibilit y", + "ibili ty", + "5 7", + "( ),Ċ", + "() ,Ċ", + "(), Ċ", + "e ad", + "ea d", + "Ġ author", + "Ġa uthor", + "Ġaut hor", + "Ġauth or", + "Ġ specific", + "Ġs pecific", + "Ġspec ific", + "Ġspeci fic", + "b ase", + "ba se", + "bas e", + "( name", + "(n ame", + "o nes", + "on es", + "one s", + "Ġ Do", + "ĠD o", + "Ġ along", + "Ġa long", + "Ġal ong", + "Ġalo ng", + "y ear", + "ye ar", + "Ġ express", + "Ġex press", + "Ġexp ress", + "Ġexpr ess", + "Ġexpres s", + ". '", + "e nv", + "en v", + "Ġ begin", + "Ġb egin", + "Ġbe gin", + "Ġbeg in", + "Ġ software", + "Ġs oftware", + "Ġso ftware", + "Ġsoft ware", + "Ġ imp", + "Ġi mp", + "Ġim p", + "Ġ win", + "Ġw in", + "Ġwi n", + "ó n", + "Ġ thing", + "Ġth ing", + "Ġthin g", + "Ġthi ng", + "T rans", + "Tr ans", + "Tra ns", + "Ġ THE", + "ĠT HE", + "ĠTH E", + "Ġ ", + "Ġ? >", + "Ġ den", + "Ġd en", + "Ġde n", + "ob ile", + "obi le", + "obil e", + "ch ange", + "chan ge", + "cha nge", + "chang e", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "i ci", + "ic i", + "n a", + "Ġ Form", + "ĠF orm", + "ĠFor m", + "ĠFo rm", + "Ġ sort", + "Ġs ort", + "Ġso rt", + "Ġsor t", + "S elect", + "Se lect", + "Sel ect", + "Sele ct", + "p are", + "par e", + "pa re", + "Ġ thought", + "Ġth ought", + "Ġthough t", + "Ġthou ght", + "Ġtho ught", + "_ con", + "_c on", + "_co n", + "Ġ task", + "Ġt ask", + "Ġta sk", + "Ġtas k", + "o cus", + "oc us", + "ocu s", + "Ġ DE", + "ĠD E", + "Ġ Min", + "ĠM in", + "ĠMi n", + "Ġ opt", + "Ġo pt", + "Ġop t", + "ĉ break", + "ĉb reak", + "u mer", + "um er", + "ume r", + "K E", + "t hen", + "th en", + "the n", + "Ġ det", + "Ġd et", + "Ġde t", + "Ġ Test", + "ĠT est", + "ĠTe st", + "ĠTes t", + "p orts", + "port s", + "por ts", + "po rts", + "Ġ review", + "Ġre view", + "Ġr eview", + "Ġrev iew", + "( '/", + "(' /", + "m ove", + "mo ve", + "mov e", + "Ġ switch", + "Ġs witch", + "Ġsw itch", + "E RT", + "ER T", + "p atch", + "pat ch", + "an not", + "ann ot", + "anno t", + "ã Ĥ", + "Ġ above", + "Ġa bove", + "Ġab ove", + "it ive", + "iti ve", + "5 6", + "Ġ question", + "Ġqu estion", + "Ġquest ion", + "Ġq uestion", + "Ġquesti on", + "Ġques tion", + "Ġ Qu", + "ĠQ u", + "ãĢĤ ĊĊ", + "ãĢĤĊ Ċ", + "g le", + "gl e", + "Ġ word", + "Ġw ord", + "Ġwor d", + "Ġwo rd", + "Ġ provide", + "Ġpro vide", + "Ġprovid e", + "Ġprov ide", + "Ġ Return", + "ĠR eturn", + "ĠRe turn", + "ĠRet urn", + "Ġ research", + "Ġre search", + "Ġres earch", + "ã o", + "u str", + "us tr", + "ust r", + "Ġ publish", + "Ġp ublish", + "Ġpub lish", + "Ġpubli sh", + "ch ema", + "che ma", + "chem a", + "} }", + "Ġ CON", + "ĠC ON", + "ĠCO N", + "- in", + "-i n", + "all back", + "Ġ cover", + "Ġc over", + "Ġco ver", + "Ġcov er", + "\\ \\", + "c olor", + "co lor", + "col or", + "colo r", + "Ġ IS", + "ĠI S", + "Ġ whether", + "Ġwh ether", + "Ġwhe ther", + "i mate", + "im ate", + "ima te", + "imat e", + "i sc", + "is c", + "B ar", + "Ba r", + "Ġ div", + "Ġd iv", + "Ġdi v", + "B e", + "o urn", + "ou rn", + "our n", + "Ġ having", + "Ġh aving", + "Ġha ving", + "Ġhav ing", + "l em", + "le m", + "p layer", + "pl ayer", + "play er", + "pla yer", + "a bs", + "ab s", + "am era", + "ame ra", + "amer a", + "n ey", + "ne y", + "Ġ exc", + "Ġe xc", + "Ġex c", + "g ether", + "get her", + "ge ther", + "p lied", + "pl ied", + "a o", + "[ $", + "Ġ ++", + "Ġ+ +", + "i pe", + "ip e", + "s how", + "sh ow", + "/ d", + "[ :", + "a gement", + "ag ement", + "age ment", + "agem ent", + "l ev", + "le v", + "_ ID", + "_I D", + "9 7", + "r ary", + "ra ry", + "rar y", + "a des", + "ad es", + "ade s", + "_ se", + "_s e", + "a use", + "au se", + "aus e", + "Ġ employ", + "Ġe mploy", + "Ġem ploy", + "Ġemp loy", + "Ġ */čĊ", + "Ġ* /čĊ", + "Ġ*/ čĊ", + "Ġ fre", + "Ġf re", + "Ġfr e", + "Ġ' @", + "Ġcom plet", + "Ġcomp let", + "Ġcomple t", + "Ġcompl et", + "Ġ large", + "Ġl arge", + "Ġlarg e", + "Ġlar ge", + "r al", + "ra l", + "\\ x", + "Ġ fac", + "Ġf ac", + "Ġfa c", + "< String", + ">", + "Ġ> >", + "Ġ face", + "Ġf ace", + "Ġfa ce", + "Ġfac e", + "C TION", + "CT ION", + "Ġ save", + "Ġs ave", + "Ġsa ve", + "Ġsav e", + "Ġ typ", + "Ġt yp", + "Ġty p", + "d ev", + "de v", + "( \"#", + "(\" #", + "A GE", + "AG E", + "cont ainer", + "contain er", + "e dit", + "ed it", + "edi t", + "Q L", + "Ġ items", + "Ġit ems", + "Ġi tems", + "Ġitem s", + "Ġ social", + "Ġs ocial", + "Ġso cial", + "Ġsoc ial", + "Ġsoci al", + "i en", + "ie n", + "Ġ React", + "ĠRe act", + ") .ĊĊ", + "). ĊĊ", + ").Ċ Ċ", + "Ġ mar", + "Ġm ar", + "Ġma r", + "Ġre du", + "Ġr edu", + "Ġred u", + "Ġ RE", + "ĠR E", + ". put", + ".p ut", + "Ġ major", + "Ġm ajor", + "Ġmaj or", + "C ell", + "Ce ll", + "Cel l", + "n ext", + "ne xt", + "nex t", + "Ġ expected", + "Ġex pected", + "Ġexp ected", + "Ġexpect ed", + "Ġ yet", + "Ġy et", + "Ġye t", + "Ġin div", + "Ġind iv", + "trib utes", + "tribute s", + "at is", + "ati s", + "a med", + "am ed", + "ame d", + "Ġ food", + "Ġf ood", + "Ġfo od", + "Ġfoo d", + "S ource", + "( string", + "(s tring", + "(str ing", + "(st ring", + "Ġ +Ċ", + "Ġ+ Ċ", + "i tes", + "it es", + "ite s", + "d r", + "Ġ members", + "Ġm embers", + "Ġmem bers", + "Ġmember s", + "Ġmemb ers", + "Ġ comb", + "Ġc omb", + "Ġcom b", + "Ġco mb", + "i tems", + "it ems", + "ite ms", + "item s", + "Ġ Per", + "ĠP er", + "ĠPe r", + "T H", + "= True", + "Ġ bar", + "Ġb ar", + "Ġba r", + "_ SE", + "_S E", + "c omm", + "com m", + "co mm", + "( w", + ") ĊĊĊ", + ")Ċ ĊĊ", + ")ĊĊ Ċ", + "Ġ send", + "Ġs end", + "Ġse nd", + "Ġsen d", + "Ġ inc", + "Ġin c", + "Ġi nc", + "un signed", + "uns igned", + "F A", + "Ġ params", + "Ġpar ams", + "Ġparam s", + "Ġpara ms", + "Ġpa rams", + "a pping", + "ap ping", + "app ing", + "r os", + "ro s", + "u gin", + "ug in", + "ugi n", + "f a", + "Ġ connection", + "Ġcon nection", + "Ġconn ection", + "Ġconnect ion", + "Ġ };ĊĊ", + "Ġ} ;ĊĊ", + "Ġ};Ċ Ċ", + "Ġ}; ĊĊ", + "Ġb ecome", + "Ġbe come", + "Ġbec ome", + "M ode", + "Mod e", + "Mo de", + "Ġ ev", + "Ġe v", + "Ġ diff", + "Ġd iff", + "Ġdif f", + "Ġdi ff", + "Ġ United", + "ĠUn ited", + "ĠUnit ed", + "ĠUni ted", + "H eight", + "He ight", + "f ully", + "ful ly", + "full y", + "im ages", + "image s", + "ima ges", + "imag es", + "Ġ makes", + "Ġm akes", + "Ġmake s", + "Ġma kes", + "Ġmak es", + "Ġ global", + "Ġg lobal", + "Ġglob al", + "Ġglo bal", + "Ġ contact", + "Ġcont act", + "Ġconta ct", + "' :Ċ", + "': Ċ", + "Ġ abs", + "Ġa bs", + "Ġab s", + "а Ð", + "f loat", + "flo at", + "Ġ except", + "Ġex cept", + "Ġexc ept", + "Ġexce pt", + "Ġ Pol", + "ĠP ol", + "ĠPo l", + "Ch ild", + "Chi ld", + "t yp", + "ty p", + "Ġc ertain", + "Ġcert ain", + "Ġcer tain", + "i ón", + "ió n", + "O UT", + "OU T", + "Ġim pro", + "Ġimp ro", + "Ġimpr o", + "i les", + "il es", + "ile s", + "Ġ -->Ċ", + "Ġ- ->Ċ", + "Ġ-- >Ċ", + "Ġ--> Ċ", + "Ġ Part", + "ĠP art", + "ĠPar t", + "ĠPa rt", + "val ues", + "value s", + "valu es", + "o ss", + "os s", + "/ **", + "/* *", + "i lit", + "il it", + "ili t", + "Ġ Event", + "ĠE vent", + "ĠEven t", + "ĠEv ent", + "ĠEve nt", + "c urity", + "cur ity", + "s ter", + "st er", + "ste r", + "Ġ character", + "Ġchar acter", + "1 98", + "19 8", + "Ġ news", + "Ġn ews", + "Ġnew s", + "Ġne ws", + "Ġ \",", + "Ġ\" ,", + "Ġ device", + "Ġd evice", + "Ġde vice", + "Ġdev ice", + "c el", + "ce l", + "lo gin", + "log in", + "he et", + "hee t", + "D efault", + "De fault", + "Def ault", + "@ \"", + "ĉ Ġ", + "c lick", + "cl ick", + "cli ck", + "( value", + "(v alue", + "(val ue", + "(va lue", + "Ġ Ab", + "ĠA b", + "Ġ previous", + "Ġpre vious", + "Ġprev ious", + "ERR OR", + "o cal", + "oc al", + "oca l", + "Ġ material", + "Ġm aterial", + "Ġmat erial", + "Ġmate rial", + "Ġmateria l", + "Ġmater ial", + "Ġmateri al", + "Ġ below", + "Ġb elow", + "Ġbe low", + "Ġbel ow", + "Ġ Christ", + "ĠCh rist", + "ĠChris t", + "ĠChr ist", + "Ġ media", + "Ġm edia", + "Ġme dia", + "Ġmed ia", + "Ġmedi a", + "c over", + "co ver", + "cov er", + "Ġ UI", + "ĠU I", + "Ġ fail", + "Ġf ail", + "Ġfa il", + "Ġ black", + "Ġb lack", + "Ġbl ack", + "Ġbla ck", + "Ġ component", + "Ġcom ponent", + "Ġcomp onent", + "Ġ American", + "ĠA merican", + "ĠAmeric an", + "ĠAmerica n", + "ĠAmer ican", + "Ġ added", + "Ġadd ed", + "Ġad ded", + "Ġ buy", + "Ġb uy", + "Ġbu y", + "s tit", + "st it", + "sti t", + "Ġ came", + "Ġc ame", + "Ġca me", + "Ġcam e", + "Ġ delete", + "Ġde lete", + "Ġdel ete", + "Ġdelet e", + "Ġdele te", + "p roperty", + "pro perty", + "prop erty", + "proper ty", + "o ding", + "od ing", + "odi ng", + "Ġ card", + "Ġc ard", + "Ġcar d", + "Ġca rd", + "r ops", + "ro ps", + "rop s", + "Ġ https", + "Ġhttp s", + "Ġht tps", + "Ġhtt ps", + "Ġ root", + "Ġr oot", + "Ġro ot", + "Ġ handle", + "Ġh andle", + "Ġhand le", + "Ġhan dle", + "C C", + "B ack", + "Ba ck", + "em plate", + "emp late", + "empl ate", + "Ġ getting", + "Ġg etting", + "Ġget ting", + "_ by", + "_b y", + "m ail", + "ma il", + "mai l", + "_ sh", + "_s h", + ". assert", + ".as sert", + "Ġ Dec", + "ĠD ec", + "ĠDe c", + "( true", + "(tr ue", + "Ġ comput", + "Ġcom put", + "Ġcomp ut", + "Ġ claim", + "Ġcl aim", + "Ġcla im", + "' =>", + "'= >", + "Ġ Sub", + "ĠS ub", + "ĠSu b", + "Ġ air", + "Ġa ir", + "Ġai r", + "o ps", + "op s", + "n av", + "na v", + "e ments", + "em ents", + "ement s", + "eme nts", + "emen ts", + "( id", + "(i d", + "Ġ enter", + "Ġen ter", + "Ġent er", + "an ged", + "ang ed", + "ange d", + "E nd", + "En d", + "Ġ location", + "Ġl ocation", + "Ġlo cation", + "Ġloc ation", + "Ġ night", + "Ġn ight", + "Ġni ght", + "Ġnig ht", + "Ġ doing", + "Ġdo ing", + "Ġdoi ng", + "Ġ Red", + "ĠR ed", + "ĠRe d", + "l in", + "li n", + "} ĊĊĊ", + "}Ċ ĊĊ", + "}ĊĊ Ċ", + "v ider", + "vid er", + "vi der", + "vide r", + "Ġ pick", + "Ġp ick", + "Ġpi ck", + "Ġpic k", + "Ġ watch", + "Ġw atch", + "Ġwat ch", + "ess ages", + "essage s", + "essa ges", + "Ġ human", + "Ġh uman", + "Ġhum an", + "Ġhu man", + "Ġ dam", + "Ġd am", + "Ġda m", + "p end", + "pe nd", + "pen d", + "d ir", + "di r", + "Ġ tax", + "Ġt ax", + "Ġta x", + "Ġ girl", + "Ġg irl", + "Ġgi rl", + "Ġgir l", + "re et", + "ree t", + "Ġ box", + "Ġb ox", + "Ġbo x", + "Ġ strong", + "Ġst rong", + "Ġstr ong", + "Ġstro ng", + "Ġstron g", + "( v", + "r el", + "re l", + "Ġ interface", + "Ġinter face", + "Ġinterf ace", + "Ġ msg", + "Ġm sg", + "Ġms g", + "f ect", + "fe ct", + "fec t", + "_ at", + "_a t", + "Ġ house", + "Ġh ouse", + "Ġhous e", + "Ġho use", + "Ġ track", + "Ġt rack", + "Ġtr ack", + "Ġtra ck", + "' );ĊĊ", + "') ;ĊĊ", + "');Ċ Ċ", + "'); ĊĊ", + "j e", + "Ġ John", + "ĠJ ohn", + "ĠJo hn", + "ĠJoh n", + "i str", + "is tr", + "ist r", + "( S", + "u be", + "ub e", + "Ġ ce", + "Ġc e", + "it ted", + "itt ed", + "itte d", + "V ER", + "VE R", + "* )", + "p arent", + "par ent", + "pare nt", + "pa rent", + "paren t", + "Ġ application", + "Ġapp lication", + "Ġap plication", + "Ġapplic ation", + "Ġappl ication", + "a ny", + "an y", + ". swing", + ".s wing", + ".sw ing", + "Ġ pack", + "Ġp ack", + "Ġpa ck", + "Ġpac k", + "\\ u", + "Ġp ract", + "Ġpr act", + "Ġpra ct", + "Ġprac t", + "Ġ section", + "Ġs ection", + "Ġse ction", + "Ġsec tion", + "Ġsect ion", + "c tx", + "ct x", + "Ġ unsigned", + "Ġun signed", + "Ġuns igned", + ". Point", + ".P oint", + "Ġ One", + "ĠO ne", + "ĠOn e", + "Ä ±", + "i ple", + "ip le", + "ipl e", + "a id", + "ai d", + "Ñ ĥ", + "V ector", + "Vec tor", + "Ve ctor", + "Vect or", + "b yte", + "by te", + "byt e", + "Ġ wait", + "Ġw ait", + "Ġwa it", + "Ġ Ãł", + "Ġà ł", + "à ¥", + "Ġto gether", + "Ġtog ether", + "Ġ throws", + "Ġth rows", + "Ġthrow s", + "Ġthr ows", + "Ġthro ws", + "F O", + "' ))", + "') )", + "h ost", + "ho st", + "hos t", + "i sing", + "is ing", + "isi ng", + "isin g", + ". view", + ".v iew", + "Ġ terms", + "Ġte rms", + "Ġter ms", + "Ġterm s", + "f ramework", + "fr amework", + "frame work", + "fram ework", + "- r", + "Ġ apply", + "Ġapp ly", + "Ġap ply", + "Ġappl y", + "Ġ session", + "Ġs ession", + "Ġsess ion", + "O ptions", + "Option s", + "Opt ions", + "ug gest", + "ugg est", + "Ġ others", + "Ġo thers", + "Ġother s", + "w itter", + "wit ter", + "Ġ fund", + "Ġf und", + "Ġfun d", + "Ġfu nd", + "I nit", + "In it", + "Ini t", + "_ _(", + "__ (", + "ens or", + "enso r", + "G ET", + "GE T", + "Ġse veral", + "Ġsever al", + "Ġsev eral", + "i i", + "[ j", + "I O", + "Ġ template", + "Ġt emplate", + "Ġtem plate", + "Ġtemp late", + "Ġtempl ate", + "P osition", + "Pos ition", + "Ġe con", + "Ġec on", + "Ġeco n", + "a chine", + "ach ine", + "achi ne", + "Ġ il", + "Ġi l", + ". spring", + ".s pring", + ".sp ring", + "m ain", + "ma in", + "mai n", + "e lt", + "el t", + "i ment", + "im ent", + "ime nt", + "imen t", + "R ec", + "Re c", + "m m", + "Ġ University", + "ĠUn iversity", + "ĠUnivers ity", + "urs or", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "G L", + "ic ture", + "ict ure", + "it hub", + "ith ub", + "c er", + "ce r", + "c ast", + "ca st", + "cas t", + "F rom", + "Fr om", + "a les", + "al es", + "ale s", + "Ġ subject", + "Ġsu bject", + "Ġsub ject", + "Ġsubj ect", + "p assword", + "pass word", + "pas sword", + "n y", + "Ġ esc", + "Ġe sc", + "Ġes c", + ". write", + ".w rite", + ".writ e", + ".wr ite", + "ï¼ Į", + "W hat", + "Wh at", + ". H", + "Ġ history", + "Ġh istory", + "Ġhistor y", + "Ġhist ory", + "Ġhi story", + "Ġhisto ry", + "Ġ Fe", + "ĠF e", + "Ġ individual", + "Ġind ividual", + "Ġindiv idual", + "Ġindivid ual", + "u nit", + "un it", + "uni t", + "Ġ -->", + "Ġ- ->", + "Ġ-- >", + "Ġ du", + "Ġd u", + "I ST", + "IS T", + "Ġ users", + "Ġus ers", + "Ġuse rs", + "Ġuser s", + "f s", + "f alse", + "fa lse", + "fal se", + "u nt", + "un t", + "T itle", + "Tit le", + "Ti tle", + "Ġ mot", + "Ġm ot", + "Ġmo t", + "Ġ future", + "Ġf uture", + "Ġfut ure", + "Ġfu ture", + "a ched", + "ac hed", + "ach ed", + "ache d", + "Ġ started", + "Ġstart ed", + "Ġstar ted", + "Ġ mode", + "Ġm ode", + "Ġmod e", + "Ġmo de", + "Ġ '<", + "Ġ' <", + "_ array", + "_a rray", + "_arr ay", + "_ar ray", + "Ġ ax", + "Ġa x", + "' ];Ċ", + "'] ;Ċ", + "']; Ċ", + "i res", + "ir es", + "ire s", + "T here", + "The re", + "Th ere", + "u ght", + "ug ht", + "ugh t", + "t ml", + "tm l", + "p osed", + "pos ed", + "pose d", + "po sed", + "i cult", + "ic ult", + "Ġ took", + "Ġt ook", + "Ġto ok", + "Ġtoo k", + "Ġ games", + "Ġg ames", + "Ġgame s", + "Ġgam es", + "Ġga mes", + "Ġ }}", + "Ġ} }", + "Ġ ?>Ċ", + "Ġ? >Ċ", + "Ġ?> Ċ", + "Ġ products", + "Ġproduct s", + "Ġprodu cts", + "I s", + "Ġ bad", + "Ġb ad", + "Ġba d", + "Ġ Des", + "ĠD es", + "ĠDe s", + ". path", + ".p ath", + ".pa th", + ".pat h", + "' ĊĊ", + "'Ċ Ċ", + "Ġ Post", + "ĠP ost", + "ĠPo st", + "ĠPos t", + "a vel", + "av el", + "ave l", + "( :", + "1 50", + "15 0", + "Ġ needs", + "Ġne eds", + "Ġneed s", + "Ġ known", + "Ġk nown", + "Ġkn own", + "Ġknow n", + "F l", + "Ġ exec", + "Ġe xec", + "Ġex ec", + "Ġexe c", + "Ġ seen", + "Ġs een", + "Ġse en", + "Ġsee n", + "5 1", + "u me", + "um e", + "Ġ border", + "Ġb order", + "Ġbor der", + "Ġbord er", + "Ġ live", + "Ġl ive", + "Ġli ve", + "Ġliv e", + "t emp", + "te mp", + "tem p", + "P er", + "Pe r", + "Ġ variable", + "Ġvar iable", + "Ġvari able", + "i et", + "ie t", + "Ġ Def", + "ĠD ef", + "ĠDe f", + "Ġ ge", + "Ġg e", + "e me", + "em e", + "_ back", + "_b ack", + "f irst", + "fi rst", + "fir st", + "Ġ provided", + "Ġpro vided", + "Ġprovid ed", + "Ġprov ided", + "Ġprovide d", + "//////////////// ////////////////", + "Ġ filename", + "Ġf ilename", + "Ġfile name", + "Ġfil ename", + "Ġfi lename", + "Ġ hope", + "Ġh ope", + "Ġhop e", + "Ġho pe", + "u ly", + "ul y", + "a uto", + "aut o", + "au to", + "f ind", + "fin d", + "fi nd", + "_ string", + "_s tring", + "_st ring", + "_str ing", + "b tn", + "bt n", + "it ude", + "itud e", + "itu de", + "At tribute", + "Attrib ute", + "Ġ young", + "Ġyou ng", + "Ġyo ung", + ". txt", + ".t xt", + ".tx t", + "Ġ website", + "Ġwe bsite", + "Ġweb site", + "Ġwebs ite", + "Ġ Prop", + "ĠP rop", + "ĠPro p", + "ĠPr op", + "Ġ ey", + "Ġe y", + "> ();Ċ", + ">( );Ċ", + ">() ;Ċ", + ">(); Ċ", + "i onal", + "ion al", + "io nal", + "iona l", + "A RR", + "AR R", + "iction ary", + "ur ther", + "urt her", + ". ", + ")- >", + "t x", + "Ġ pur", + "Ġp ur", + "Ġpu r", + "u el", + "ue l", + "ym bol", + "ymb ol", + "u ation", + "ua tion", + "uat ion", + "a nger", + "an ger", + "ang er", + "ange r", + "Ġ background", + "Ġback ground", + "e cess", + "ec ess", + "ece ss", + "ef ined", + ". .......", + ".. ......", + "... .....", + ".... ....", + "..... ...", + "...... ..", + "....... .", + "Ġ description", + "Ġd escription", + "Ġde scription", + "Ġdes cription", + "Ġdescri ption", + "Ġ represent", + "Ġre present", + "Ġrep resent", + "\" ));Ċ", + "\") );Ċ", + "\")) ;Ċ", + "\")); Ċ", + "p ression", + "pr ession", + "press ion", + "row ser", + "rows er", + "rowse r", + "Ġ series", + "Ġs eries", + "Ġse ries", + "Ġser ies", + "Ġserie s", + "Ġseri es", + "w ards", + "ward s", + "war ds", + "5 2", + "( $_", + "($ _", + "a ise", + "ai se", + "ais e", + "Ġ hot", + "Ġh ot", + "Ġho t", + "a city", + "ac ity", + "aci ty", + "r ies", + "ri es", + "rie s", + "a ctions", + "act ions", + "action s", + "C reate", + "Cre ate", + "Creat e", + "a dio", + "ad io", + "adi o", + "am ples", + "amp les", + "ample s", + "Ġ original", + "Ġor iginal", + "Ġorig inal", + "Ġorigin al", + "ens ive", + "ensi ve", + "f ont", + "fo nt", + "fon t", + "st ream", + "str eam", + "stre am", + " using", + ".spring framework", + "0 01", + "00 1", + "s erver", + "ser ver", + "serve r", + "serv er", + "Ġ bill", + "Ġb ill", + "Ġbi ll", + "Ġbil l", + "A CK", + "AC K", + "i lename", + "il ename", + "ile name", + "ilen ame", + "Ġ frame", + "Ġf rame", + "Ġfr ame", + "Ġfra me", + "Ġfram e", + "Ġ =Ċ", + "Ġ= Ċ", + "E dit", + "Ed it", + "ad ius", + "adi us", + "Ġ draw", + "Ġd raw", + "Ġdr aw", + "Ġdra w", + "an ks", + "ank s", + "Ġd eter", + "Ġde ter", + "Ġdet er", + "Ġ comes", + "Ġc omes", + "Ġcom es", + "Ġco mes", + "Ġcome s", + "_ int", + "_in t", + "_i nt", + "Ġ foreach", + "Ġf oreach", + "Ġfor each", + "Ġfore ach", + "Ġfo reach", + "a ngle", + "an gle", + "ang le", + "angl e", + "Ġ elect", + "Ġe lect", + "Ġel ect", + "Ġele ct", + "p ected", + "pect ed", + "pec ted", + "He ader", + "Head er", + "i stration", + "ist ration", + "istr ation", + "istra tion", + "istrat ion", + "F alse", + "Fa lse", + "Fal se", + "Ġ Game", + "ĠG ame", + "ĠGa me", + "ĠGam e", + "Ġ filter", + "Ġf ilter", + "Ġfil ter", + "Ġfilt er", + "Act ivity", + "Activ ity", + "Ġ larg", + "Ġl arg", + "Ġla rg", + "Ġlar g", + "in ition", + "init ion", + "ini tion", + "Ġ \"<", + "Ġ\" <", + "2 56", + "25 6", + "i sed", + "is ed", + "ise d", + "Ġ remove", + "Ġre move", + "Ġrem ove", + "Ġ Trans", + "ĠT rans", + "ĠTr ans", + "ĠTra ns", + "ĠTran s", + "m et", + "me t", + "s ee", + "se e", + "Form at", + "For mat", + "Com mand", + "Comm and", + "Ġ EX", + "ĠE X", + "N one", + "No ne", + "Non e", + "Ġ front", + "Ġf ront", + "Ġfr ont", + "Ġfro nt", + "Ġfron t", + "A SE", + "AS E", + "Ġ Rec", + "ĠR ec", + "ĠRe c", + "ound ation", + "Ġ vo", + "Ġv o", + "9 6", + "= \\\"", + "=\\ \"", + "( *", + "Ch ange", + "Chan ge", + "Cha nge", + ". Write", + ".W rite", + "g roup", + "gr oup", + "gro up", + "i ents", + "ie nts", + "ient s", + "ien ts", + "u y", + "******** ********************************************************", + "**************** ************************************************", + "******************************** ********************************", + "************************ ****************************************", + "**************************************** ************************", + "************************************************ ****************", + "******************************************************** ********", + "Ġ dig", + "Ġd ig", + "Ġdi g", + "h r", + "( -", + "Ġ gen", + "Ġg en", + "Ġge n", + "n umber", + "num ber", + "v ec", + "ve c", + "ur ope", + "uro pe", + "en try", + "ent ry", + "entr y", + "L L", + "Ġ ste", + "Ġs te", + "Ġst e", + "Val id", + "Va lid", + "' ],", + "'] ,", + "_ param", + "_p aram", + "_par am", + "_para m", + "_pa ram", + "Ġ selected", + "Ġse lected", + "Ġselect ed", + "Ġsel ected", + "Ġ according", + "Ġa ccording", + "Ġacc ording", + "Ġaccord ing", + "Ġ Dis", + "ĠD is", + "ĠDi s", + "Ġ util", + "Ġu til", + "Ġut il", + "B uffer", + "Buf fer", + "Buff er", + "Bu ffer", + "_ error", + "_e rror", + "_err or", + "_er ror", + "Ġ associ", + "Ġass oci", + "Ġassoc i", + "_ SIZE", + "_S IZE", + "_SI ZE", + "Ġ wor", + "Ġw or", + "Ġwo r", + "Ġ printf", + "Ġprint f", + "Ġprin tf", + "r ag", + "ra g", + " ł", + "D D", + "Ġ Val", + "ĠV al", + "ĠVa l", + "Ġ activ", + "Ġact iv", + "Ġac tiv", + "E ng", + "En g", + "e time", + "et ime", + "eti me", + "etim e", + "Ġ virtual", + "Ġv irtual", + "Ġvir tual", + "Ġvirt ual", + "a ign", + "ai gn", + "a ur", + "au r", + "Ġ Pres", + "ĠP res", + "ĠPr es", + "ĠPre s", + "Ġ Exception", + "ĠEx ception", + "ĠExcept ion", + "Ġ anything", + "Ġany thing", + "Ġ Off", + "ĠO ff", + "ĠOf f", + "Ġ hours", + "Ġh ours", + "Ġhour s", + "Ġho urs", + "Ġ war", + "Ġw ar", + "Ġwa r", + "Arg s", + "Ar gs", + "a ging", + "ag ing", + "agi ng", + "Ġ models", + "Ġmod els", + "Ġmodel s", + "Ġmode ls", + "Ġ Time", + "ĠT ime", + "ĠTim e", + "ĠTi me", + "O b", + "a ms", + "am s", + "j oy", + "jo y", + "Ġ early", + "Ġear ly", + ". read", + ".re ad", + ".r ead", + "8 6", + "Ġ center", + "Ġc enter", + "Ġcent er", + "Ġcen ter", + "Ġ Initial", + "ĠIn itial", + "ĠInit ial", + "ĠIniti al", + "Ġ language", + "Ġl anguage", + "Ġlangu age", + "l ength", + "le ngth", + "len gth", + "x y", + "Ġ sn", + "Ġs n", + "Ġ inf", + "Ġin f", + "Ġi nf", + "P ost", + "Pos t", + "Po st", + "Ġ ago", + "Ġa go", + "Ġag o", + "Ġ easy", + "Ġe asy", + "Ġeas y", + "Ġea sy", + "_ code", + "_c ode", + "_co de", + "_cod e", + "Ġ ANY", + "ĠA NY", + "ĠAN Y", + "_ ch", + "_c h", + "Ġ download", + "Ġd ownload", + "Ġdown load", + "( T", + "a ved", + "av ed", + "ave d", + "âĢ ĵ", + "Ġ students", + "Ġst udents", + "Ġstud ents", + "Ġstudent s", + "Ġ fig", + "Ġf ig", + "Ġfi g", + "l ight", + "li ght", + "lig ht", + "x x", + "Ġ buffer", + "Ġb uffer", + "Ġbu ffer", + "Ġbuf fer", + "Ġbuff er", + "Ġ Dep", + "ĠD ep", + "ĠDe p", + "Ġ Math", + "ĠM ath", + "ĠMat h", + "ĠMa th", + "I TH", + "IT H", + "Ġ vari", + "Ġv ari", + "Ġvar i", + "Ġva ri", + "Ġ due", + "Ġd ue", + "Ġdu e", + "F actory", + "Fact ory", + "Factor y", + "Ġ por", + "Ġp or", + "Ġpo r", + "Ġ ep", + "Ġe p", + "o type", + "ot ype", + "otyp e", + "oty pe", + "Ġ cannot", + "Ġc annot", + "Ġcan not", + "Ġcann ot", + "Ġ white", + "Ġwh ite", + "Ġwhit e", + "< int", + "čĊ", + "\"> čĊ", + ". annot", + ".an not", + "Ġ collection", + "Ġc ollection", + "Ġcol lection", + "Ġcoll ection", + "Ġcollect ion", + "Ġcolle ction", + "' .", + "Ġ similar", + "Ġs imilar", + "Ġsim ilar", + "Ġsimil ar", + "Ġ taken", + "Ġt aken", + "Ġtake n", + "Ġta ken", + "Ġtak en", + "( \"%", + "(\" %", + "Or der", + "Ord er", + "' ]Ċ", + "'] Ċ", + "- md", + "-m d", + "Ġ TH", + "ĠT H", + "a ced", + "ace d", + "ac ed", + "Ġis n", + "Ġi sn", + "/ j", + "Ġ son", + "Ġs on", + "Ġso n", + "g raph", + "gr aph", + "gra ph", + "Ġ Integer", + "ĠInt eger", + "Ġn ecess", + "Ġne cess", + "Ġnec ess", + "Ġneces s", + "r een", + "re en", + "ree n", + "Ġ um", + "Ġu m", + "Ġ \\<", + "Ġ\\ <", + "Ġ moment", + "Ġm oment", + "Ġmom ent", + "Ġmo ment", + "Ġ bring", + "Ġb ring", + "Ġbr ing", + "Ġbri ng", + "Ġ indic", + "Ġin dic", + "Ġind ic", + "y sis", + "ys is", + "ysi s", + "Le vel", + "v erse", + "ver se", + "vers e", + "ur renc", + "urre nc", + "urr enc", + "_ test", + "_t est", + "_te st", + "Ġent ire", + "D own", + "Do wn", + "Ġ }ĊĊĊ", + "Ġ} ĊĊĊ", + "Ġ}Ċ ĊĊ", + "Ġ}ĊĊ Ċ", + "( result", + "(res ult", + "Ġ Read", + "ĠR ead", + "ĠRe ad", + "à ¨", + "M od", + "Mo d", + "Ġ trying", + "Ġt rying", + "Ġtr ying", + "Ġtry ing", + "\" ),Ċ", + "\") ,Ċ", + "\"), Ċ", + "Ġ member", + "Ġm ember", + "Ġmem ber", + "Ġmemb er", + "Ġ Cor", + "ĠC or", + "ĠCo r", + "O DO", + "OD O", + "- control", + "-c ontrol", + "-cont rol", + "un time", + "unt ime", + "Ġ Sim", + "ĠS im", + "ĠSi m", + "D ialog", + "Di alog", + "Dia log", + "p lot", + "pl ot", + "_ on", + "_o n", + "Ġ phys", + "Ġph ys", + "Ġphy s", + "} /", + "Ġ namespace", + "Ġn amespace", + "Ġname space", + "Ġnames pace", + "ĉ čĊ", + "a cc", + "ac c", + "P layer", + "Pl ayer", + "Play er", + "A RE", + "AR E", + "8 9", + "Ġ foot", + "Ġf oot", + "Ġfo ot", + "Ġfoo t", + "Ġ board", + "Ġb oard", + "Ġbo ard", + "Ġboa rd", + "p art", + "par t", + "pa rt", + "Ġ sus", + "Ġs us", + "Ġsu s", + "w ise", + "wi se", + "wis e", + "Ġ Mc", + "ĠM c", + "Ġ push", + "Ġp ush", + "Ġpu sh", + "Ġpus h", + "A TA", + "AT A", + "Ġ please", + "Ġp lease", + "Ġpl ease", + "Ġple ase", + "Ġplea se", + "Ġpleas e", + "r ied", + "ri ed", + "rie d", + "we et", + "b it", + "bi t", + "i ded", + "id ed", + "ide d", + "V E", + "Ġ Sw", + "ĠS w", + "U B", + "Ġ types", + "Ġt ypes", + "Ġtype s", + "Ġtyp es", + "Ġty pes", + "e dia", + "ed ia", + "edi a", + "Ġc los", + "Ġcl os", + "Ġclo s", + "ace book", + "W hen", + "Wh en", + "Ġ edit", + "Ġe dit", + "Ġed it", + "Ġedi t", + "i gger", + "ig ger", + "igg er", + "Ġe nerg", + "Ġen erg", + "Ġener g", + "Cont ainer", + "Contain er", + "Conta iner", + "Ġ phot", + "Ġp hot", + "Ġph ot", + "Ġ Count", + "ĠC ount", + "ĠCo unt", + "ĠCou nt", + "ĠCoun t", + "Ġ Europe", + "ĠE urope", + "ĠEuro pe", + "ĠEurop e", + "ĠEur ope", + ". Is", + ".I s", + "Ġ Russ", + "ĠR uss", + "ĠRu ss", + "ĠRus s", + "p eed", + "pe ed", + "pee d", + "Ġ Str", + "ĠS tr", + "ĠSt r", + "Ġ py", + "Ġp y", + "Ġ cult", + "Ġc ult", + "Ġcu lt", + "Ġcul t", + "Ġ defined", + "Ġd efined", + "Ġdef ined", + "Ġdefine d", + "Ġdefin ed", + "c count", + "cc ount", + "cco unt", + "Ġo bt", + "Ġob t", + ". Location", + ".L ocation", + ".Lo cation", + "Ġ thread", + "Ġt hread", + "Ġth read", + "Ġthr ead", + "i lle", + "il le", + "ill e", + "Ġ instead", + "Ġin stead", + "Ġinst ead", + "st rong", + "str ong", + "stro ng", + "Ġ Sec", + "ĠS ec", + "ĠSe c", + "U RE", + "UR E", + "Ġ idea", + "Ġi dea", + "Ġid ea", + "Ġide a", + ". se", + ".s e", + "e my", + "em y", + "se lected", + "select ed", + "sel ected", + "Con nection", + "Conn ection", + "Connect ion", + "a cing", + "ac ing", + "aci ng", + "acin g", + "t hread", + "th read", + "thr ead", + ". next", + ".n ext", + ".ne xt", + "Ġ coll", + "Ġc oll", + "Ġco ll", + "Ġcol l", + "Ġ film", + "Ġf ilm", + "Ġfil m", + "Ġfi lm", + "is tic", + "ist ic", + "isti c", + "Ġ compet", + "Ġcom pet", + "Ġcomp et", + "Ġ conn", + "Ġc onn", + "Ġcon n", + "Ġco nn", + "th ough", + "Ġ compan", + "Ġcom pan", + "Ġcomp an", + "o cket", + "oc ket", + "ock et", + "Ġt each", + "Ġte ach", + "Ġtea ch", + "= (", + "Ġ phone", + "Ġp hone", + "Ġph one", + "Ġphon e", + "Ġ active", + "Ġact ive", + "Ġactiv e", + "7 9", + "de lete", + "del ete", + "1 01", + "10 1", + "t ries", + "tr ies", + "trie s", + "tri es", + "Ġ mo", + "Ġm o", + "Ġ death", + "Ġde ath", + "} );ĊĊ", + "});Ċ Ċ", + "}) ;ĊĊ", + "}); ĊĊ", + "o col", + "oc ol", + "oco l", + "W idget", + "Ġ article", + "Ġart icle", + "Ġartic le", + "r odu", + "ro du", + "rod u", + "an did", + "and id", + "andi d", + "Ñ ĭ", + "Ġ Cr", + "ĠC r", + "k a", + "( ):", + "() :", + "l ood", + "lo od", + "loo d", + "ĉ ĉĉĊ", + "ĉĉ ĉĊ", + "ĉĉĉ Ċ", + "Ġ almost", + "Ġal most", + "Ġalm ost", + "Ġ sell", + "Ġs ell", + "Ġse ll", + "Ġsel l", + "erv let", + "r ip", + "ri p", + "U nit", + "Un it", + "Uni t", + "Ġapp lic", + "Ġappl ic", + "Ġ connect", + "Ġcon nect", + "Ġconn ect", + "Ġ feature", + "Ġf eature", + "Ġfe ature", + "Ġfeat ure", + "Ġ via", + "Ġv ia", + "Ġvi a", + "' ),", + "') ,", + "Ġ lim", + "Ġl im", + "Ġli m", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ Gu", + "ĠG u", + "E ngine", + "Eng ine", + "Ġ ens", + "Ġe ns", + "Ġen s", + "Ġ environment", + "Ġen vironment", + "Ġenviron ment", + "b lock", + "bl ock", + "blo ck", + "H ERE", + "HE RE", + "HER E", + "N ULL", + "NU LL", + "g y", + "t ag", + "ta g", + ") ).", + ")) .", + "e xp", + "ex p", + "Ġcom pl", + "Ġco mpl", + "Ġcomp l", + "Ġ install", + "Ġinst all", + "Ġinstal l", + "Ġ complete", + "Ġcom plete", + "Ġcomp lete", + "Ġcomple te", + "Ġcomplet e", + "Ġcompl ete", + "q ueue", + "que ue", + "at ural", + "atur al", + "atura l", + "atu ral", + "Ġ general", + "Ġg eneral", + "Ġgener al", + "Ġgen eral", + "Ġgene ral", + "Ġgenera l", + "t hon", + "th on", + "Ġas ked", + "Ġask ed", + "o res", + "or es", + "ore s", + "( res", + "(r es", + "(re s", + "Ġ reserved", + "Ġres erved", + "Ġreserve d", + "Ġreserv ed", + "S P", + "Ġ âĢ¦", + "ĠâĢ ¦", + "Å Ĥ", + "Ġsign ific", + "O ff", + "Of f", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ Ag", + "ĠA g", + "Ġ Just", + "ĠJ ust", + "ĠJu st", + "Ġ Error", + "ĠE rror", + "ĠEr ror", + "ĠErr or", + "Ġin fl", + "Ġinf l", + "a data", + "ad ata", + "ada ta", + "Ġ icon", + "Ġi con", + "Ġic on", + "as ks", + "ask s", + "' '", + "_ LO", + "_L O", + "? .", + "a ccount", + "ac count", + "acc ount", + "acco unt", + "Ġ (*", + "Ġ( *", + "' )ĊĊ", + "') ĊĊ", + "')Ċ Ċ", + "r ap", + "ra p", + "_ var", + "_v ar", + "_va r", + "Ġ FOR", + "ĠF OR", + "ĠFO R", + "Ġ party", + "Ġp arty", + "Ġpart y", + "Ġpar ty", + "Ġ Your", + "ĠY our", + "ĠYou r", + "ĠYo ur", + "c at", + "ca t", + "s try", + "st ry", + "str y", + ". new", + ".n ew", + ".ne w", + "b oot", + "bo ot", + "boo t", + "Ġ Nov", + "ĠN ov", + "ĠNo v", + "Ġ vector", + "Ġv ector", + "Ġve ctor", + "Ġvec tor", + "Ġvect or", + "Ġ normal", + "Ġn ormal", + "Ġnor mal", + "Ġnorm al", + "Ġf urther", + "Ġfur ther", + "Re pository", + "8 00", + "80 0", + "Ġ database", + "Ġd atabase", + "Ġdata base", + "Ġdat abase", + "Ġdatab ase", + "at tle", + "att le", + "Ġ music", + "Ġm usic", + "Ġmus ic", + "Ġmu sic", + "Ġ speed", + "Ġs peed", + "Ġsp eed", + "Ġspe ed", + "Ġ doc", + "Ġd oc", + "Ġdo c", + "p rocess", + "pro cess", + "proc ess", + "IG HT", + "IGH T", + ". parse", + ".p arse", + ".par se", + "Ġ taking", + "Ġt aking", + "Ġta king", + "Ġtak ing", + "Ġ viol", + "Ġv iol", + "Ġvi ol", + "c eed", + "ce ed", + "cee d", + "Ġ After", + "ĠA fter", + "ĠAf ter", + "Ġ forward", + "Ġfor ward", + "Ġ crit", + "Ġc rit", + "Ġcr it", + "Ġcri t", + "\" />Ċ", + "\"/ >Ċ", + "\"/> Ċ", + "r ot", + "ro t", + "Ġ failed", + "Ġf ailed", + "Ġfa iled", + "Ġfail ed", + "e fore", + "ef ore", + "Ġcon cern", + "Ġconc ern", + "Ġconce rn", + "o e", + "b a", + "Ġ sender", + "Ġs ender", + "Ġse nder", + "Ġsend er", + "Ġsen der", + "Ġ term", + "Ġt erm", + "Ġte rm", + "Ġter m", + "h as", + "ha s", + "= \"#", + "=\" #", + "Ġ potential", + "Ġpot ential", + "Ġpotent ial", + "N um", + "Nu m", + "Ġ published", + "Ġp ublished", + "Ġpublish ed", + "Ġpubli shed", + ". close", + ".c lose", + ".cl ose", + "Ġ Image", + "ĠI mage", + "ĠIm age", + "ĠImag e", + "str aint", + "stra int", + "strain t", + "U D", + "Ġ Ob", + "ĠO b", + "Ġ probably", + "Ġprob ably", + "l im", + "li m", + "\" :Ċ", + "\": Ċ", + "ol ume", + "olum e", + "olu me", + "Ġ consum", + "Ġcon sum", + "Ġcons um", + "7 6", + "a gue", + "ag ue", + "agu e", + "ens ions", + "ension s", + "ensi ons", + "Ġinvest ig", + "- year", + "-y ear", + "' );", + "') ;", + "- sm", + "-s m", + "Ġen joy", + "Ġenjo y", + "o rig", + "or ig", + "ori g", + "e ring", + "er ing", + "eri ng", + "erin g", + "c p", + "le ased", + "lease d", + "lea sed", + "p lements", + "pl ements", + "ple ments", + "plement s", + "Ġ returns", + "Ġreturn s", + "p at", + "pa t", + "B O", + "Ġ House", + "ĠH ouse", + "ĠHo use", + "ĠHou se", + ". Label", + ".L abel", + "Ġ weight", + "Ġw eight", + "Ġwe ight", + "Ġweigh t", + "Ġwei ght", + "ig hb", + "igh b", + "Ġ conditions", + "Ġcondition s", + "Ġcond itions", + "Ġ exception", + "Ġex ception", + "Ġexcept ion", + "Ġexce ption", + "d escription", + "de scription", + "des cription", + "Ġ trad", + "Ġt rad", + "Ġtr ad", + "Ġtra d", + "- to", + "-t o", + "Ġ {}", + "Ġ{ }", + "Ġ module", + "Ġm odule", + "Ġmod ule", + "E ND", + "EN D", + ". ap", + ".a p", + ". props", + ".p rops", + ".pro ps", + ".pr ops", + ".prop s", + "Ġ constructor", + "Ġcon structor", + "Ġconstruct or", + "Ġconstr uctor", + "a ves", + "av es", + "ave s", + "Ġ favor", + "Ġf avor", + "Ġfa vor", + "Ġfav or", + "Ġ Now", + "ĠN ow", + "ĠNo w", + "; i", + "Ġ Main", + "ĠM ain", + "ĠMa in", + "ĠMai n", + "_ k", + "e ries", + "er ies", + "erie s", + "eri es", + "âĢĻ ll", + "âĢĻl l", + "trans form", + "ime stamp", + "imest amp", + "P re", + "Pr e", + "Ġ mer", + "Ġm er", + "Ġme r", + ". res", + ".re s", + ".r es", + "s tant", + "st ant", + "sta nt", + "stan t", + "L ocation", + "Lo cation", + "Loc ation", + "_ NAME", + "_N AME", + "Ġ loss", + "Ġl oss", + "Ġlo ss", + "Ġlos s", + "Ġ ĊĊ", + "ĠĊ Ċ", + "n et", + "ne t", + "Ġ engine", + "Ġe ngine", + "Ġeng ine", + "B lock", + "Bl ock", + "Bloc k", + "Blo ck", + "Ġ issues", + "Ġiss ues", + "Ġissue s", + "Ġissu es", + "Ġ parse", + "Ġp arse", + "Ġpar se", + "Ġpars e", + "Ġ Bar", + "ĠB ar", + "ĠBa r", + "Ġ stay", + "Ġst ay", + "Ġsta y", + "Ġ JSON", + "ĠJ SON", + "ĠJS ON", + "Ġ dom", + "Ġd om", + "Ġdo m", + "a irs", + "air s", + "ai rs", + "w ner", + "wn er", + "Ġ lower", + "Ġl ower", + "Ġlo wer", + "Ġlow er", + "\" ,čĊ", + "\", čĊ", + "Ġ Dem", + "ĠD em", + "ĠDe m", + "u fact", + "uf act", + "ufac t", + "Ġ ps", + "Ġp s", + "Ġ perfect", + "Ġper fect", + "Ġperf ect", + "R L", + "Ġ educ", + "Ġe duc", + "Ġed uc", + "Ġedu c", + "l s", + "em ory", + "emo ry", + "ARR ANT", + "u ge", + "ug e", + "Ġ exact", + "Ġex act", + ". key", + ".k ey", + ".ke y", + "al led", + "all ed", + "alle d", + "e ch", + "ec h", + "i ef", + "ie f", + "\\ /", + "o ke", + "ok e", + "Ġ former", + "Ġfor mer", + "Ġform er", + "Ġforme r", + "al loc", + "all oc", + "allo c", + "Ġ six", + "Ġs ix", + "Ġsi x", + "i da", + "id a", + "Ġ margin", + "Ġm argin", + "Ġmar gin", + "Ġmarg in", + "Ġ heart", + "Ġhe art", + "Ġhear t", + "a ld", + "al d", + "p ack", + "pa ck", + "pac k", + ".getElement ById", + "ĠW ARRANT", + "Ġ rather", + "Ġr ather", + "Ġrat her", + "Ġra ther", + "Ġ building", + "Ġbuild ing", + "er man", + "erm an", + "l ice", + "lic e", + "li ce", + "Ġ questions", + "Ġquest ions", + "Ġquestion s", + "Ġquesti ons", + "i zes", + "iz es", + "ize s", + "le ge", + "leg e", + "irect ory", + "irector y", + "Ġ je", + "Ġj e", + "Ġ cas", + "Ġc as", + "Ġca s", + "p rops", + "pr ops", + "pro ps", + "prop s", + "u tf", + "ut f", + "Ġ security", + "Ġs ecurity", + "Ġse curity", + "Ġsec urity", + "Ġ however", + "Ġhow ever", + "w eight", + "we ight", + "wei ght", + "weigh t", + "Ġ inside", + "Ġin side", + "Ġins ide", + "Ġp resident", + "Ġpres ident", + "C har", + "Ch ar", + "Cha r", + "Ġ WITH", + "ĠW ITH", + "ĠWI TH", + ". map", + ".m ap", + ".ma p", + "Ġ graph", + "Ġg raph", + "Ġgr aph", + "Ġgra ph", + "Ġgrap h", + "Ġ tag", + "Ġt ag", + "Ġta g", + "_ status", + "_s tatus", + "_st atus", + "_stat us", + "Ġ attempt", + "Ġat tempt", + "Ġatt empt", + "o pp", + "op p", + "u ses", + "us es", + "use s", + "ĉ const", + "ĉcon st", + "Ġ round", + "Ġr ound", + "Ġro und", + "Ġrou nd", + ", $", + "Ġ friends", + "Ġf riends", + "Ġfri ends", + "Ġfriend s", + "E mail", + "Em ail", + "? >", + "Re source", + "Res ource", + "K EY", + "KE Y", + "o sp", + "os p", + ". query", + ".qu ery", + "Ġ North", + "ĠN orth", + "ĠNor th", + "a bles", + "ab les", + "able s", + "abl es", + "is trib", + "ist rib", + "istr ib", + "_ class", + "_c lass", + "_cl ass", + "el lo", + "ell o", + "T hat", + "Th at", + "Ð º", + "pec ially", + "pecial ly", + "Ġ President", + "ĠP resident", + "ĠPres ident", + "Ġ campaign", + "Ġc ampaign", + "Ġcamp aign", + "Ġ alt", + "Ġa lt", + "Ġal t", + "a rea", + "ar ea", + "are a", + "Ġc hall", + "Ġch all", + "Ġcha ll", + "Ġop port", + "Ġopp ort", + ". Con", + ".C on", + ".Co n", + "Ġ energy", + "Ġe nergy", + "Ġen ergy", + "Ġenerg y", + "Ġener gy", + "l ike", + "li ke", + "lik e", + ". string", + ".s tring", + ".st ring", + ".str ing", + "ing ton", + "ingt on", + ") *", + "y y", + "Ġ profession", + "Ġprof ession", + "Ġprofess ion", + "ir th", + "irt h", + "Ġ seg", + "Ġs eg", + "Ġse g", + "æ ľ", + "Ġ hor", + "Ġh or", + "Ġho r", + "i ers", + "ie rs", + "ier s", + "c an", + "ca n", + "Ġbe hind", + "Ġbeh ind", + "Pro duct", + "Produ ct", + "Prod uct", + "f g", + "Ġ Sk", + "ĠS k", + ". jpg", + ".j pg", + ".jp g", + "? :", + "] ;ĊĊ", + "];Ċ Ċ", + "]; ĊĊ", + "Ġ callback", + "Ġc allback", + "Ġcall back", + "Ġ Http", + "ĠH ttp", + "Ñ Į", + "l ong", + "lo ng", + "lon g", + "M S", + "A TH", + "AT H", + "Ġ raise", + "Ġr aise", + "Ġrais e", + "Ġra ise", + "Ġ wanted", + "Ġw anted", + "Ġwant ed", + "Ġwan ted", + "r own", + "ro wn", + "row n", + "u tor", + "ut or", + "uto r", + "l t", + "] =", + "e line", + "el ine", + "eli ne", + "elin e", + "M A", + "Ġs epar", + "Ġse par", + "Ġsep ar", + "c s", + "s emb", + "se mb", + "sem b", + "D is", + "Di s", + "b serv", + "bs erv", + "Ġ Will", + "ĠW ill", + "ĠWil l", + "ĠWi ll", + "Ġ policy", + "Ġp olicy", + "Ġpol icy", + "Ġpolic y", + "Ġ third", + "Ġth ird", + "Ġthi rd", + "p hone", + "ph one", + "phon e", + "Ġ bed", + "Ġb ed", + "Ġbe d", + "/ g", + ". __", + "._ _", + "Ġ Inc", + "ĠI nc", + "ĠIn c", + "i zing", + "iz ing", + "izi ng", + "izin g", + ". remove", + ".re move", + ".rem ove", + "in stance", + "inst ance", + "instanc e", + ". type", + ".t ype", + ".typ e", + "Ġ serv", + "Ġs erv", + "Ġse rv", + "Ġser v", + "E ach", + "Ġ har", + "Ġh ar", + "Ġha r", + "Ġ Message", + "ĠM essage", + "ĠMess age", + "ĠMes sage", + "( key", + "(k ey", + "SE LECT", + "SEL ECT", + "P os", + "Po s", + ") );čĊ", + ")) ;čĊ", + ")); čĊ", + "Ġre comm", + "Ġrec omm", + "Ġrecom m", + "Ġreco mm", + "Ġ training", + "Ġtr aining", + "Ġtrain ing", + "Ġtra ining", + "Ġtrai ning", + "Ġ Ent", + "ĠE nt", + "ĠEn t", + "Ġ Char", + "ĠC har", + "ĠCh ar", + "ĠCha r", + "i cht", + "ic ht", + "ich t", + "( file", + "(f ile", + "(fi le", + "(fil e", + "Ġ prior", + "Ġp rior", + "Ġpr ior", + "Ġpri or", + "Ġprio r", + "G ame", + "Gam e", + "Ga me", + "Ġ exit", + "Ġe xit", + "Ġex it", + "Param s", + "Par ams", + "Pa rams", + "Para ms", + ". core", + ".c ore", + ".co re", + ".cor e", + "P C", + "n es", + "ne s", + "an ced", + "ance d", + "anc ed", + "( request", + "(re quest", + "(req uest", + "P assword", + "Pass word", + "Pas sword", + "} >Ċ", + "}> Ċ", + "Ġ mag", + "Ġm ag", + "Ġma g", + "Ġ release", + "Ġre lease", + "Ġr elease", + "Ġrel ease", + "Ġrele ase", + "Ġ shall", + "Ġs hall", + "Ġsh all", + "Ġsha ll", + "u dent", + "ud ent", + "ude nt", + "uden t", + "Ġ South", + "ĠS outh", + "ĠSo uth", + "ĠSou th", + "a ndo", + "an do", + "and o", + ": '", + ". TabIndex", + ".Tab Index", + "s k", + "an ner", + "ann er", + "anne r", + "is set", + "iss et", + "isse t", + "Ġ outside", + "Ġout side", + "Ġouts ide", + "l edge", + "le dge", + "led ge", + "Ġ å", + "Ġ Rob", + "ĠR ob", + "ĠRo b", + "Ġ imm", + "Ġi mm", + "Ġim m", + "! Ċ", + "Ġ Web", + "ĠW eb", + "ĠWe b", + "D es", + "De s", + "B C", + "an cial", + "anc ial", + "ancia l", + "R oute", + "Ro ute", + "D ec", + "De c", + "fer ences", + "ference s", + "Ġp urch", + "Ġpur ch", + "Ġpu rch", + "Ġ Model", + "ĠM odel", + "ĠMod el", + "ĠMo del", + "ĠMode l", + "c tor", + "ct or", + "g n", + "_ start", + "_st art", + "_star t", + "_sta rt", + "_ un", + "_u n", + ". *", + "i ses", + "is es", + "ise s", + "Ġ ground", + "Ġg round", + "Ġgr ound", + "Ġgro und", + "Ġgrou nd", + "Ġ unique", + "Ġun ique", + "Ġuniqu e", + "Ġuni que", + "Ġuniq ue", + "Ġbe aut", + "Ġbeau t", + "{ \"", + "Ġ pour", + "Ġp our", + "Ġpo ur", + "Ġpou r", + "Ġ Oct", + "ĠO ct", + "ĠOc t", + "Ġ tree", + "Ġt ree", + "Ġtr ee", + "Ġtre e", + "s ets", + "se ts", + "set s", + "_ res", + "_re s", + "_r es", + "' )->", + "') ->", + "_ reg", + "_re g", + "_r eg", + "( \"\\", + "(\" \\", + "Ġ byte", + "Ġb yte", + "Ġby te", + "Ġbyt e", + "B l", + "Ġ dating", + "Ġd ating", + "Ġda ting", + "Ġdat ing", + "Ġdati ng", + "Ġ matter", + "Ġm atter", + "Ġmat ter", + "Ġmatt er", + "Ġmatte r", + "Ġ Rem", + "ĠR em", + "ĠRe m", + "Ġ' ../", + "Ġ'. ./", + "Ġ'.. /", + "Ġ Aug", + "ĠA ug", + "ĠAu g", + "Ġ La", + "ĠL a", + "Ġ $(", + "Ġ$ (", + "o urnal", + "our nal", + "ourn al", + "1 11", + "11 1", + "i am", + "ia m", + "Ġ shows", + "Ġsh ows", + "Ġshow s", + "Ġsho ws", + "w rite", + "wr ite", + "Ġ ball", + "Ġb all", + "Ġbal l", + "Ġba ll", + "Ġsim ply", + "Ġsimp ly", + "Ġsimpl y", + "Ġ fast", + "Ġf ast", + "Ġfa st", + "Ġfas t", + "Ġ memory", + "Ġm emory", + "Ġmem ory", + "Ġmemor y", + "Ġmemo ry", + "A SS", + "AS S", + "Ġ Of", + "ĠO f", + "o ved", + "ov ed", + "ove d", + "a nte", + "an te", + "ant e", + "a ul", + "au l", + "i stry", + "is try", + "ist ry", + "istr y", + ") ));Ċ", + ")) );Ċ", + "))) ;Ċ", + "))); Ċ", + "Ġ fit", + "Ġf it", + "Ġfi t", + "< string", + "_", + "-> _", + "\" )ĊĊ", + "\") ĊĊ", + "\")Ċ Ċ", + "o x", + "ap plication", + "app lication", + "appl ication", + "Ġ ]Ċ", + "Ġ] Ċ", + "Ċ ĊĊĊĊĊ", + "ĊĊ ĊĊĊĊ", + "ĊĊĊĊ ĊĊ", + "ĊĊĊ ĊĊĊ", + "ĊĊĊĊĊ Ċ", + "1 80", + "18 0", + "Ġ soon", + "Ġs oon", + "Ġso on", + "Ġsoo n", + "ct ions", + "ction s", + "i nger", + "in ger", + "ing er", + "inge r", + "Ġ join", + "Ġj oin", + "Ġjo in", + "Ġ Pe", + "ĠP e", + "Ġ ë", + "Ġ las", + "Ġl as", + "Ġla s", + ". E", + "c ss", + "cs s", + "/ or", + "/o r", + "Ġ Start", + "ĠSt art", + "ĠStar t", + "ĠSta rt", + "Ġ TO", + "ĠT O", + "Ġ subs", + "Ġs ubs", + "Ġsu bs", + "Ġsub s", + "c onn", + "con n", + "co nn", + "com ponents", + "comp onents", + "component s", + "DE BUG", + "qu are", + "qua re", + "F unction", + "Func tion", + "Fun ction", + "en dar", + "end ar", + "enda r", + ". index", + ".in dex", + ".ind ex", + "Ġ fill", + "Ġf ill", + "Ġfil l", + "Ġfi ll", + "Ä Ļ", + "Ġ choose", + "Ġch oose", + "Ġcho ose", + "h ow", + "ho w", + "Ġ America", + "ĠAmeric a", + "ĠAmer ica", + "as sets", + "ass ets", + "asset s", + "asse ts", + "- -----------", + "-- ----------", + "---- --------", + "-------- ----", + "--- ---------", + "----- -------", + "---------- --", + "------ ------", + "----------- -", + "------- -----", + "--------- ---", + "Ġ Value", + "ĠV alue", + "ĠVal ue", + "ĠVa lue", + "Ġ office", + "Ġoff ice", + "Ġoffic e", + "Ġ veh", + "Ġv eh", + "Ġve h", + "Ġ transform", + "Ġtrans form", + "Ġtransf orm", + "Ġ Art", + "ĠA rt", + "ĠAr t", + "Ġ inde", + "Ġin de", + "Ġi nde", + "Ġind e", + "Ġ fn", + "Ġf n", + "Ġ implements", + "Ġim plements", + "Ġimp lements", + "Ġimplement s", + "Ġimpl ements", + "a ngo", + "an go", + "ang o", + "p lete", + "pl ete", + "ple te", + "plet e", + "+ \"", + "t mp", + "tm p", + "am ily", + "ami ly", + "amil y", + "Ġ hash", + "Ġh ash", + "Ġhas h", + "Ġha sh", + "m issions", + "miss ions", + "mission s", + "E ST", + "ES T", + "g t", + "Pro vider", + "Provid er", + "Provide r", + "Prov ider", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "Ġ flag", + "Ġf lag", + "Ġfl ag", + "Ġfla g", + "Ġpart icip", + "Ġpartic ip", + "Ġparti cip", + "d en", + "de n", + "Ġ Returns", + "ĠReturn s", + "Ġ note", + "Ġn ote", + "Ġnot e", + "Ġno te", + "ü r", + "p m", + "id eos", + "ide os", + "ideo s", + "Ġ specified", + "Ġs pecified", + "Ġspec ified", + "Ġ EN", + "ĠE N", + "e ster", + "es ter", + "est er", + "este r", + "o lid", + "ol id", + "oli d", + "Ġ upon", + "Ġu pon", + "Ġup on", + "( std", + "(s td", + "(st d", + "ĉ v", + "Ġ '\\", + "Ġ' \\", + "u z", + "Ġ vert", + "Ġv ert", + "Ġver t", + "Ġve rt", + "Ġv ict", + "Ġvi ct", + "Ġvic t", + "ĉ self", + "ĉs elf", + "ĉse lf", + "Ġ \"$", + "Ġ\" $", + "8 5", + ". k", + "Ġ groups", + "Ġg roups", + "Ġgroup s", + "Ġgro ups", + "Ġgrou ps", + "g ithub", + "git hub", + "l ang", + "la ng", + "lan g", + "Ġ mut", + "Ġm ut", + "Ġmu t", + "T O", + "Ġ ve", + "Ġv e", + "Ġ Please", + "ĠP lease", + "ĠPl ease", + "ĠPle ase", + "; ĊĊĊ", + ";Ċ ĊĊ", + ";ĊĊ Ċ", + "a ccess", + "ac cess", + "acc ess", + "Ġ {\"", + "Ġ{ \"", + "r ea", + "re a", + "Ġ risk", + "Ġr isk", + "Ġris k", + "Ġri sk", + "i cker", + "ic ker", + "ick er", + "og gle", + "ogg le", + "ĉ while", + "A NG", + "AN G", + ". send", + ".s end", + ".se nd", + "7 2", + "Ġ woman", + "Ġw oman", + "Ġwom an", + "Ġwo man", + "Ġ gets", + "Ġg ets", + "Ġget s", + "Ġge ts", + "Ġ ign", + "Ġi gn", + "Ġig n", + "Ġ Id", + "ĠI d", + "_ log", + "_l og", + "_lo g", + "O NE", + "ON E", + "Ġe vid", + "Ġev id", + "Ġ Har", + "ĠH ar", + "ĠHa r", + "_ sub", + "_s ub", + "_su b", + "Ġ endl", + "Ġe ndl", + "Ġen dl", + "Ġend l", + "Ġ included", + "Ġin cluded", + "Ġinclud ed", + "Ġinclude d", + "Ġincl uded", + "Ġinclu ded", + "( ));ĊĊ", + "() );ĊĊ", + "());Ċ Ċ", + "()) ;ĊĊ", + "()); ĊĊ", + "Ġ Ap", + "ĠA p", + "i gr", + "ig r", + "Ġ sem", + "Ġs em", + "Ġse m", + "Ġ Black", + "ĠB lack", + "ĠBl ack", + "d oc", + "do c", + "_ table", + "_t able", + "_tab le", + "_ta ble", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "- up", + "-u p", + "Ġ cause", + "Ġc ause", + "Ġca use", + "Ġcaus e", + "Ġ ..", + "Ġ. .", + "Ġ van", + "Ġv an", + "Ġva n", + "_ dict", + "_d ict", + "_di ct", + "_dic t", + "Ġ focus", + "Ġf ocus", + "Ġfoc us", + "Ġfo cus", + "I ND", + "IN D", + "C ESS", + "CE SS", + "CES S", + ". Log", + ".L og", + ".Lo g", + "Ġ multiple", + "Ġm ultiple", + "Ġmult iple", + "Ġmulti ple", + "Ġmultip le", + "i do", + "id o", + "Ġre gard", + "Ġreg ard", + "- M", + "and ler", + "andle r", + "o urse", + "our se", + "ours e", + "Ġ deg", + "Ġd eg", + "Ġde g", + ". U", + "Ġadd ition", + "Ġad dition", + "Ġv arious", + "Ġvar ious", + "Ġvari ous", + "Ġva rious", + "Ġ receive", + "Ġre ceive", + "Ġrece ive", + "е н", + "еР½", + "Ġ HT", + "ĠH T", + "O bj", + "Ob j", + "D F", + "Ġ increase", + "Ġin crease", + "Ġincre ase", + "Ġincr ease", + "Ġ Open", + "ĠO pen", + "ĠOp en", + "] ;", + "Ġ commit", + "Ġcom mit", + "Ġcomm it", + "? Ċ", + "ateg ories", + "ategor ies", + "ategori es", + "ategorie s", + "atego ries", + "at ory", + "ator y", + "ato ry", + "s hip", + "sh ip", + "shi p", + "Ġ Mich", + "ĠM ich", + "ĠMi ch", + "ĠMic h", + "Ġ html", + "Ġh tml", + "Ġht ml", + "ro mise", + "rom ise", + "Ġ leave", + "Ġle ave", + "Ġstr ateg", + "Ġstrat eg", + "Ġstra teg", + "Ġstrate g", + "a ven", + "av en", + "ave n", + "Ġ Console", + "ĠCon sole", + "ĠCons ole", + "k nown", + "kn own", + "know n", + "- n", + "_ LE", + "_L E", + ". component", + ".com ponent", + ".comp onent", + "Ġ bre", + "Ġb re", + "Ġbr e", + "S ession", + "i ance", + "ia nce", + "ian ce", + "Ġ align", + "Ġal ign", + "Ġali gn", + "type def", + "typ edef", + "typed ef", + "_ result", + "_res ult", + "Ġ WHERE", + "ĠW HERE", + "ĠWH ERE", + ". split", + ".s plit", + ".sp lit", + "Ġ reading", + "Ġre ading", + "Ġread ing", + "FA ULT", + "Ġ clo", + "Ġc lo", + "Ġcl o", + "Ġ notice", + "Ġnot ice", + "_ pr", + "_p r", + "ar ter", + "art er", + "arte r", + "Ġ lock", + "Ġl ock", + "Ġlo ck", + "Ġloc k", + "Ġ standard", + "Ġst andard", + "Ġstand ard", + "e tic", + "et ic", + "eti c", + "el low", + "ell ow", + "ello w", + "Ġ padding", + "Ġp adding", + "Ġpad ding", + "Ġpadd ing", + "Ġ His", + "ĠH is", + "ĠHi s", + "Ġ states", + "Ġst ates", + "Ġstate s", + "Ġstat es", + "Ġsta tes", + "_ cast", + "_c ast", + "_ca st", + "( P", + "a a", + "Ġ internal", + "Ġin ternal", + "Ġint ernal", + "Ġinter nal", + "Ġintern al", + "e an", + "ea n", + "Ġ PRO", + "ĠP RO", + "ĠPR O", + "Ġ Key", + "ĠK ey", + "ĠKe y", + "Ġ especially", + "Ġes pecially", + "Ġespecial ly", + "Ġespec ially", + "m ing", + "min g", + "mi ng", + "Ġ cross", + "Ġc ross", + "Ġcr oss", + "Ġcro ss", + "Ġ national", + "Ġn ational", + "Ġnation al", + "Ġnat ional", + "_ object", + "_obj ect", + "_o bject", + "_ob ject", + "f ilter", + "fil ter", + "filt er", + "Ġ script", + "Ġs cript", + "Ġscr ipt", + "Ġscri pt", + ". update", + ".up date", + "_ i", + "Ġ Assert", + "ĠAs sert", + "ĠAss ert", + "/ core", + "/c ore", + "/co re", + "% %%%", + "%% %%", + "%%% %", + "Ġ problems", + "Ġpro blems", + "Ġproble ms", + "Ġproblem s", + "Ġprob lems", + "Ġprobl ems", + "i stor", + "is tor", + "ist or", + "isto r", + "Ġ .=", + "Ġ. =", + "Ġ arch", + "Ġa rch", + "Ġar ch", + "Ġarc h", + "Ġ written", + "Ġw ritten", + "Ġwrit ten", + "Ġwr itten", + "Ġm ilit", + "Ġmil it", + "Ġmi lit", + "Ġmili t", + "M ENT", + "ME NT", + ". ch", + ".c h", + "c ape", + "ca pe", + "cap e", + "Ġ Mus", + "ĠM us", + "ĠMu s", + "_ config", + "_con fig", + "_conf ig", + "Ġ API", + "ĠA PI", + "ĠAP I", + "f oot", + "fo ot", + "foo t", + "Ġ images", + "Ġim ages", + "Ġimage s", + "Ġimag es", + "Ġima ges", + "e ndl", + "en dl", + "end l", + ". In", + ".I n", + "F irst", + "Fi rst", + "Ġ platform", + "Ġpl atform", + "Ġplat form", + ". prot", + ".p rot", + ".pro t", + ".pr ot", + "O ption", + "Op tion", + "Opt ion", + "s te", + "st e", + "Ġ TODO", + "ĠT ODO", + "ĠTO DO", + "ĠTOD O", + "Ġ force", + "Ġf orce", + "Ġfor ce", + "Ġforc e", + ". cont", + ".c ont", + ".con t", + ".co nt", + "ĉ echo", + "ĉe cho", + "ĠD av", + "ĠDa v", + "P tr", + "Pt r", + "( B", + "R T", + "Ġ Base", + "ĠB ase", + "ĠBa se", + "ĠBas e", + "] ['", + "][ '", + "Ġann ounc", + "Ġanno unc", + "con sole", + "cons ole", + "Ġ Py", + "ĠP y", + "d s", + ". as", + ".a s", + "Ġ prevent", + "Ġpr event", + "Ġpre vent", + "Ġprev ent", + "a pan", + "ap an", + "apa n", + "Ġ {'", + "Ġ{ '", + "} '", + "Ġ dead", + "Ġd ead", + "Ġde ad", + "V AL", + "VA L", + "Q UE", + "QU E", + "******** ****************************************************************", + "**************** ********************************************************", + "******************************** ****************************************", + "**************************************************************** ********", + "************************ ************************************************", + "**************************************** ********************************", + "************************************************ ************************", + "******************************************************** ****************", + "Ġ charg", + "Ġch arg", + "Ġchar g", + "Ġcha rg", + "R eturn", + "Re turn", + "Ret urn", + "Ġ ful", + "Ġf ul", + "Ġfu l", + "d om", + "do m", + "Ġ rules", + "Ġr ules", + "Ġrule s", + "Ġru les", + "Ġ modify", + "Ġmod ify", + "Ġ eval", + "Ġe val", + "Ġev al", + "h am", + "ha m", + "a tement", + "at ement", + "ate ment", + "atem ent", + "\\ <", + "u la", + "ul a", + "= False", + "=F alse", + "R A", + "Ġ contains", + "Ġcon tains", + "Ġcont ains", + "Ġcontain s", + "Ġconta ins", + "7 4", + "Ġ stack", + "Ġst ack", + "Ġsta ck", + "m ar", + "ma r", + "Ġ {}Ċ", + "Ġ{ }Ċ", + "Ġ{} Ċ", + "Ġ undefined", + "Ġun defined", + "Ġund efined", + "Ġundef ined", + "A ss", + "As s", + "Ġ China", + "ĠCh ina", + "ĠChi na", + "ĠChin a", + "v ey", + "ve y", + "* Ċ", + "Ġ playing", + "Ġpl aying", + "Ġplay ing", + "Ġpla ying", + ") /", + "a ctor", + "act or", + "ac tor", + "Ġ bottom", + "Ġb ottom", + "Ġbot tom", + "Ġbott om", + "l ier", + "li er", + "lie r", + "Ġ Number", + "ĠN umber", + "ĠNum ber", + "Ġc ouple", + "Ġco uple", + "Ġcou ple", + "Ġcoup le", + "D C", + "Ġ SO", + "ĠS O", + "g or", + "go r", + ". setText", + ".set Text", + "s uccess", + "su ccess", + "succ ess", + "com mand", + "comm and", + "comma nd", + "F ilter", + "Fil ter", + "Ġ Our", + "ĠO ur", + "ĠOu r", + "_ item", + "_i tem", + "_it em", + "Ġ ctx", + "Ġc tx", + "Ġct x", + "Ġ road", + "Ġr oad", + "Ġro ad", + "V ersion", + "Vers ion", + "c ase", + "ca se", + "cas e", + "u rt", + "ur t", + "av ior", + "avi or", + "y ch", + "yc h", + "semb ly", + "sembl y", + "Ġ Product", + "ĠPro duct", + "ĠProdu ct", + "ĠProd uct", + "Ġ held", + "Ġh eld", + "Ġhe ld", + "Ġhel d", + "a fe", + "af e", + "Ġ includes", + "Ġin cludes", + "Ġinclud es", + "Ġinclude s", + "Ġincl udes", + "Ġinclu des", + "< quote", + "Ġ avoid", + "Ġa void", + "Ġav oid", + "Ġ Fin", + "ĠF in", + "ĠFi n", + "Ġ Mod", + "ĠM od", + "ĠMo d", + "Ġ tab", + "Ġt ab", + "Ġta b", + "a no", + "an o", + "à ±", + "i pping", + "ip ping", + "ipp ing", + "ippi ng", + "- e", + "Ġ insert", + "Ġin sert", + "Ġins ert", + "Ġinser t", + "Ġinse rt", + "t arget", + "tar get", + "c han", + "ch an", + "cha n", + ". Model", + ".M odel", + ".Mod el", + ".Mode l", + "I ME", + "IM E", + "\\ Ċ", + "Ġ machine", + "Ġm achine", + "Ġma chine", + "Ġmach ine", + "a vy", + "av y", + "Ġ NO", + "ĠN O", + "Ġ Inter", + "ĠIn ter", + "ĠInt er", + "Ġ operation", + "Ġo peration", + "Ġop eration", + "Ġoper ation", + "Ġopera tion", + "m odal", + "mod al", + "mo dal", + "T ag", + "Ta g", + "] :", + "Ġ production", + "Ġp roduction", + "Ġpro duction", + "Ġproduct ion", + "Ġprodu ction", + "Ġprod uction", + "Ġ areas", + "Ġa reas", + "Ġare as", + "Ġarea s", + "Ġ ren", + "Ġre n", + "Ġr en", + "_ from", + "_f rom", + "_fr om", + "n bsp", + "nb sp", + "Ġ operator", + "Ġo perator", + "Ġop erator", + "Ġoper ator", + "Ġopera tor", + "m en", + "me n", + "a pped", + "ap ped", + "app ed", + "appe d", + "_ per", + "_p er", + "_pe r", + "z en", + "ze n", + "( \".", + "(\" .", + ". save", + ".s ave", + ".sa ve", + ".sav e", + "=\" {{", + "=\"{ {", + "Ġ tor", + "Ġt or", + "Ġto r", + "( response", + "(res ponse", + "(resp onse", + "Ġc andid", + "Ġcan did", + "Ġcand id", + "Ġ conv", + "Ġcon v", + "Ġco nv", + "a iled", + "ail ed", + "ai led", + "Ġ Lib", + "ĠL ib", + "ĠLi b", + "c omp", + "com p", + "co mp", + "u ra", + "ur a", + "ï¿ ½", + "Ġ Here", + "ĠH ere", + "ĠHe re", + "ĠHer e", + "Ġ argument", + "Ġarg ument", + "h ood", + "ho od", + "hoo d", + "Ġ establish", + "Ġest ablish", + "ograph y", + "ogra phy", + "Ġ onClick", + "Ġon Click", + "amb da", + "Ġ sch", + "Ġs ch", + "Ġsc h", + "Ġ movie", + "Ġm ovie", + "Ġmov ie", + "Ġmo vie", + "Ġ sec", + "Ġs ec", + "Ġse c", + "Ġ activity", + "Ġact ivity", + "Ġactiv ity", + "Ø §", + "Ġ sql", + "Ġs ql", + "Ġsq l", + "_ all", + "_a ll", + "_al l", + "in cip", + "inc ip", + "inci p", + "Ġpro vides", + "Ġprovid es", + "Ġprov ides", + "Ġprovide s", + "Ġ sys", + "Ġs ys", + "Ġsy s", + "a cket", + "ack et", + "ac ket", + "Ġw asn", + "Ġwas n", + "Ġwa sn", + "Ġ uses", + "Ġu ses", + "Ġus es", + "Ġuse s", + "Ġ Function", + "ĠF unction", + "ĠFun ction", + "ĠFunc tion", + ". google", + ".g oogle", + ".go ogle", + "Ġ Result", + "ĠRes ult", + "8 4", + "V isible", + "Vis ible", + "ag ma", + "el come", + "Ġ Sy", + "ĠS y", + "Ġ Cent", + "ĠC ent", + "ĠCe nt", + "AL SE", + "ALS E", + "a ción", + "ac ión", + "aci ón", + "E XT", + "EX T", + "Ġ license", + "Ġl icense", + "Ġlic ense", + "Ġlicens e", + "Ġ Long", + "ĠL ong", + "ĠLo ng", + "ĠLon g", + "Ġ accom", + "Ġacc om", + "Ġac com", + "Ġ ability", + "Ġab ility", + ". height", + ".h eight", + ".he ight", + "Act ive", + "Activ e", + "o logical", + "olog ical", + "ologic al", + "ologi cal", + "o ly", + "ol y", + ") ),", + ")) ,", + ". Se", + ".S e", + "Ġ parameter", + "Ġparam eter", + "Ġpara meter", + "Ġparamet er", + "p rite", + "pr ite", + "prit e", + "pri te", + "AB ILITY", + ". service", + ".s ervice", + ".serv ice", + ".ser vice", + "Ġ Group", + "ĠG roup", + "ĠGr oup", + "ĠGro up", + "_ query", + "_qu ery", + "_que ry", + "Ġ Item", + "ĠI tem", + "ĠIt em", + "i ning", + "in ing", + "ini ng", + "inin g", + "Ġ jud", + "Ġj ud", + "Ġju d", + "i ms", + "im s", + "f ix", + "fi x", + "i nder", + "in der", + "ind er", + "inde r", + "a gram", + "ag ram", + "agra m", + "agr am", + "Ġ functions", + "Ġfunction s", + "Ġfun ctions", + "Ġfunct ions", + "Ġex peri", + "Ġexp eri", + "Ġexper i", + "Ġ Em", + "ĠE m", + "Ġ rot", + "Ġr ot", + "Ġro t", + "Ġ pen", + "Ġp en", + "Ġpe n", + ". btn", + ".b tn", + ".bt n", + "Ġ AS", + "ĠA S", + "# ifdef", + "#if def", + "Ġ choice", + "Ġch oice", + "Ġcho ice", + "Ġ Page", + "ĠP age", + "ĠPa ge", + "ĠPag e", + "_ PRO", + "_P RO", + "_PR O", + "Q U", + "å ı", + "a ntity", + "ant ity", + "anti ty", + " Ń", + "w ords", + "word s", + "wor ds", + "Ġ readonly", + "Ġread only", + "Ġ flex", + "Ġf lex", + "Ġfl ex", + "Ġfle x", + "prot ected", + "protect ed", + "Ġ Any", + "ĠA ny", + "ĠAn y", + "Ġ characters", + "Ġchar acters", + "Ġcharacter s", + "en ced", + "ence d", + "enc ed", + "Ġ July", + "ĠJ uly", + "ĠJul y", + "ĠJu ly", + "i ler", + "il er", + "ile r", + "C ard", + "Car d", + "Ca rd", + "u rance", + "ur ance", + "ura nce", + "uran ce", + "Ġ rev", + "Ġre v", + "Ġr ev", + ". event", + ".e vent", + ".ev ent", + "a ly", + "al y", + "1 30", + "13 0", + "Ġw onder", + "Ġwon der", + "Ġwo nder", + "Ġ Port", + "ĠP ort", + "ĠPo rt", + "ĠPor t", + "Ġ legal", + "Ġl egal", + "Ġle gal", + "Ġleg al", + "r ole", + "ro le", + "rol e", + "Ġ ten", + "Ġt en", + "Ġte n", + "Ġg oes", + "Ġgo es", + "M P", + "wh ite", + ") :čĊ", + "): čĊ", + ") )čĊ", + ")) čĊ", + "Ġ reference", + "Ġre ference", + "Ġref erence", + "Ġrefer ence", + "Ġrefere nce", + "Ġ mis", + "Ġm is", + "Ġmi s", + "Ġ Project", + "ĠPro ject", + "ĠProj ect", + "i cks", + "ic ks", + "ick s", + "> &", + "C ON", + "CO N", + "Ġre pl", + "Ġrep l", + "Ġ regular", + "Ġreg ular", + "Ġregul ar", + "St orage", + "r amework", + "rame work", + "ram ework", + "Ġ goal", + "Ġgo al", + "Ġ touch", + "Ġt ouch", + "Ġto uch", + "Ġtou ch", + ". widget", + ".w idget", + "Ġ built", + "Ġb uilt", + "Ġbu ilt", + "d es", + "de s", + "P art", + "Par t", + "Pa rt", + "( re", + "(r e", + "Ġ worth", + "Ġw orth", + "Ġwor th", + "h ib", + "hi b", + "g ame", + "ga me", + "gam e", + "9 1", + "1 92", + "19 2", + "Ġ в", + "ĠÐ ²", + "a cion", + "ac ion", + "aci on", + "acio n", + "Ġ White", + "ĠWh ite", + "ĠWhit e", + "( type", + "(t ype", + "(typ e", + "( `", + "8 1", + "Ġ natural", + "Ġn atural", + "Ġnatur al", + "Ġnat ural", + "Ġin j", + "Ġi nj", + "Ġ calcul", + "Ġcal cul", + "Ġcalc ul", + "Ġ April", + "ĠApr il", + "ĠAp ril", + ". List", + ".L ist", + "Ġ associated", + "Ġassoci ated", + "Ġassociate d", + "Ġassoc iated", + "ĉ System", + "ĉS ystem", + "~ ~", + "= [", + "Ġ storage", + "Ġst orage", + "Ġstor age", + "Ġsto rage", + "Ġ bytes", + "Ġby tes", + "Ġbyte s", + "Ġbyt es", + "Ġ travel", + "Ġt ravel", + "Ġtr avel", + "Ġtra vel", + "Ġtrav el", + "Ġ sou", + "Ġs ou", + "Ġso u", + "Ġ passed", + "Ġp assed", + "Ġpass ed", + "Ġpas sed", + "Ġpasse d", + "! =", + "a script", + "as cript", + ". open", + ".op en", + ".o pen", + "Ġ grid", + "Ġg rid", + "Ġgr id", + "Ġgri d", + "Ġ bus", + "Ġb us", + "Ġbu s", + "Ġ recogn", + "Ġrec ogn", + "Ġreco gn", + "A b", + "Ġ hon", + "Ġh on", + "Ġho n", + "Ġ Center", + "ĠC enter", + "ĠCent er", + "Ġ prec", + "Ġp rec", + "Ġpr ec", + "Ġpre c", + "b uild", + "bu ild", + "7 3", + "HT ML", + "Ġ San", + "ĠS an", + "ĠSa n", + "Ġ countries", + "Ġc ountries", + "Ġcount ries", + "Ġcoun tries", + "a led", + "al ed", + "ale d", + "t oken", + "to ken", + "tok en", + "k t", + "Ġ qual", + "Ġqu al", + "Ġq ual", + "Ġqua l", + "L ast", + "La st", + "Las t", + "ad ow", + "ado w", + "Ġ manufact", + "Ġman ufact", + "i dad", + "id ad", + "ida d", + "j ango", + "ja ngo", + "jan go", + "jang o", + "N ext", + "Ne xt", + "x f", + ". a", + "Ġ porno", + "Ġp orno", + "Ġporn o", + "Ġpor no", + "Ġ PM", + "ĠP M", + "e rve", + "er ve", + "erv e", + "i ting", + "it ing", + "iti ng", + "itin g", + "_ th", + "_t h", + "c i", + "= None", + "=N one", + "g s", + "Ġ login", + "Ġlo gin", + "Ġlog in", + "at ives", + "ative s", + "ati ves", + "ativ es", + "' ]);Ċ", + "'] );Ċ", + "']) ;Ċ", + "']); Ċ", + "Ä ħ", + "Ġ ill", + "Ġi ll", + "Ġil l", + "I A", + "ch ildren", + "child ren", + "D O", + "Ġ levels", + "Ġlevel s", + "Ġlev els", + "Ġleve ls", + "Ġ {{", + "Ġ{ {", + "Ġ looks", + "Ġl ooks", + "Ġlo oks", + "Ġlook s", + "Ġ \"#", + "Ġ\" #", + "To String", + "ToStr ing", + "Ġ necessary", + "Ġn ecessary", + "Ġnecess ary", + "Ġ ĠĠĊ", + "ĠĠ ĠĊ", + "ĠĠĠ Ċ", + "c ell", + "ce ll", + "cel l", + "En try", + "Ent ry", + "Entr y", + "Ġ '#", + "Ġ' #", + "Ġext rem", + "Ġextr em", + "Se lector", + "Select or", + "Sel ector", + "Sele ctor", + "Ġ placeholder", + "Ġplace holder", + "L oad", + "Lo ad", + "Ġ released", + "Ġre leased", + "Ġrelease d", + "Ġrele ased", + "O RE", + "OR E", + "E numer", + "En umer", + "Enum er", + "Ġ TV", + "ĠT V", + "S ET", + "SE T", + "in q", + "P ress", + "Pr ess", + "Pre ss", + "Pres s", + "Ġ Department", + "ĠDe partment", + "ĠDep artment", + "ĠDepart ment", + "Ġ properties", + "Ġp roperties", + "Ġprop erties", + "Ġproper ties", + "Ġ respond", + "Ġres pond", + "Ġresp ond", + "S earch", + "Se arch", + "Sea rch", + "a el", + "ae l", + "Ġ requ", + "Ġre qu", + "Ġr equ", + "Ġreq u", + "Ġ Book", + "ĠB ook", + "ĠBo ok", + "ĠBoo k", + "/ Ċ", + "( st", + "(s t", + "Ġ financial", + "Ġfin ancial", + "Ġfinanc ial", + "Ġfinan cial", + "i cket", + "ic ket", + "ick et", + "_ input", + "_in put", + "_inp ut", + "Ġ threat", + "Ġth reat", + "Ġthr eat", + "( in", + "(i n", + "S trip", + "St rip", + "Str ip", + "ì Ŀ", + "ç ão", + "7 1", + "Ġe vidence", + "Ġev idence", + "Ġevid ence", + ") );", + ")) ;", + "Ġ Bro", + "ĠB ro", + "ĠBr o", + "Ġ [];Ċ", + "Ġ[ ];Ċ", + "Ġ[] ;Ċ", + "Ġ[]; Ċ", + "Ġ ou", + "Ġo u", + "b uf", + "bu f", + "S cript", + "Scr ipt", + "d at", + "da t", + "Ġ rule", + "Ġr ule", + "Ġru le", + "# import", + "= \"/", + "=\" /", + "S erial", + "Se rial", + "Ser ial", + "Ġ starting", + "Ġstart ing", + "Ġstar ting", + "[ index", + "[in dex", + "[ind ex", + "a e", + "Ġ contrib", + "Ġcon trib", + "Ġcont rib", + "Ġcontr ib", + "s ession", + "sess ion", + "_ new", + "_n ew", + "_ne w", + "u table", + "ut able", + "uta ble", + "o ber", + "ob er", + "obe r", + "Ġ \"./", + "Ġ\" ./", + "Ġ\". /", + "Ġ logger", + "Ġlo gger", + "Ġlog ger", + "Ġrec ently", + "Ġrecent ly", + "Ġ returned", + "Ġre turned", + "Ġreturn ed", + "č čĊ", + ") ))Ċ", + ")) )Ċ", + "))) Ċ", + "it ions", + "ition s", + "iti ons", + "Ġ seek", + "Ġse ek", + "Ġsee k", + "Ġ communic", + "Ġcomm unic", + "Ġcommun ic", + "Ġ \".", + "Ġ\" .", + "Ġ username", + "Ġuser name", + "Ġusern ame", + "E CT", + "EC T", + "D S", + "Ġ otherwise", + "Ġother wise", + "Ġ German", + "ĠG erman", + "ĠGer man", + "ĠGerm an", + ". aw", + ".a w", + "Ad apter", + "Ada pter", + "ix el", + "ixe l", + "Ġ systems", + "Ġs ystems", + "Ġsystem s", + "Ġsys tems", + "Ġsyst ems", + "Ġ drop", + "Ġd rop", + "Ġdr op", + "Ġdro p", + "8 3", + "Ġ structure", + "Ġstruct ure", + "Ġ $(\"#", + "Ġ$ (\"#", + "Ġ$( \"#", + "Ġ$(\" #", + "en cies", + "enc ies", + "enci es", + "an ning", + "ann ing", + "anni ng", + "Ġ Link", + "ĠL ink", + "ĠLin k", + "ĠLi nk", + "Ġ Response", + "ĠRes ponse", + "ĠRespons e", + "ĠResp onse", + "Ġ stri", + "Ġs tri", + "Ġst ri", + "Ġstr i", + "Å ¼", + "Ġ DB", + "ĠD B", + "æ Ĺ", + "and roid", + "andro id", + "andr oid", + "sub mit", + "o tion", + "ot ion", + "oti on", + "9 2", + "( @", + ". test", + ".t est", + ".te st", + "8 2", + "Ċ ĊĊĊĊĊĊĊ", + "ĊĊ ĊĊĊĊĊĊ", + "ĊĊĊĊ ĊĊĊĊ", + "ĊĊĊ ĊĊĊĊĊ", + "ĊĊĊĊĊĊ ĊĊ", + "ĊĊĊĊĊ ĊĊĊ", + "ĊĊĊĊĊĊĊ Ċ", + "] ;čĊ", + "]; čĊ", + "Ġdirect ly", + "Ġ \"%", + "Ġ\" %", + "r is", + "ri s", + "el ta", + "elt a", + "A IL", + "AI L", + ") {čĊ", + "){ čĊ", + "m ine", + "min e", + "mi ne", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "( k", + "b on", + "bo n", + "a sic", + "as ic", + "asi c", + "p ite", + "pi te", + "pit e", + "_ __", + "__ _", + "M ax", + "Ma x", + "Ġ errors", + "Ġerror s", + "Ġerr ors", + "Ġer rors", + "Ġerro rs", + "Ġ While", + "ĠWh ile", + "Ġ arguments", + "Ġarg uments", + "Ġargument s", + "Ġ ensure", + "Ġen sure", + "Ġens ure", + "R ight", + "- based", + "-b ased", + "-base d", + "W eb", + "We b", + "Ġ -=", + "Ġ- =", + "Ġint rodu", + "Ġintr odu", + "Ġintro du", + "Ġ Inst", + "ĠI nst", + "ĠIn st", + "ĠIns t", + "ĠW ash", + "ĠWas h", + "ĠWa sh", + "or din", + "ord in", + "j oin", + "jo in", + "D atabase", + "Data base", + "Dat abase", + "Ġ grad", + "Ġg rad", + "Ġgr ad", + "Ġgra d", + "Ġ usually", + "Ġus ually", + "Ġusual ly", + "Ġusu ally", + "I TE", + "IT E", + "P rops", + "Pro ps", + "Prop s", + "Pr ops", + "? >Ċ", + "?> Ċ", + "Ġ Go", + "ĠG o", + "@ Override", + "R EF", + "RE F", + "Ġ ip", + "Ġi p", + "ĠA ustral", + "ĠAust ral", + "ĠAu stral", + "ĠAustr al", + "Ġ ist", + "Ġis t", + "Ġi st", + "View ById", + "Ġ serious", + "Ġse rious", + "Ġser ious", + "Ġseri ous", + "Ġ customer", + "Ġc ustomer", + "Ġcustom er", + "Ġcust omer", + ". prototype", + ".prot otype", + ".proto type", + "o do", + "od o", + "c or", + "co r", + "Ġ door", + "Ġd oor", + "Ġdo or", + "Ġ WITHOUT", + "ĠWITH OUT", + "Ġ plant", + "Ġp lant", + "Ġpl ant", + "Ġplan t", + "Ġpla nt", + "Ġb egan", + "Ġbe gan", + "Ġbeg an", + "Ġ distance", + "Ġd istance", + "Ġdist ance", + "Ġdi stance", + "( )).", + "() ).", + "()) .", + "Ġch ance", + "Ġcha nce", + "Ġchan ce", + "Ġ ord", + "Ġo rd", + "Ġor d", + "c ame", + "ca me", + "cam e", + "pr agma", + "Ġ protect", + "Ġprot ect", + "Ġprote ct", + "r agment", + "ra gment", + "rag ment", + "Ġ Node", + "ĠN ode", + "ĠNo de", + "e ning", + "en ing", + "eni ng", + "enin g", + "Ñ ĩ", + "Ġ route", + "Ġr oute", + "Ġro ute", + "Ġrout e", + "Ġrou te", + "Ġ School", + "ĠS chool", + "ĠSch ool", + "ĠScho ol", + "h i", + "Ġne ighb", + "Ġneigh b", + "A fter", + "Af ter", + "l icit", + "lic it", + "li cit", + "Ġ contr", + "Ġcon tr", + "Ġcont r", + "Ġ primary", + "Ġpr imary", + "Ġprim ary", + "Ġpri mary", + "Ġprima ry", + "A A", + ".Write Line", + "ut ils", + "util s", + "uti ls", + "Ġ bi", + "Ġb i", + "R ed", + "Re d", + ".L inq", + ". object", + ".o bject", + ".obj ect", + ".ob ject", + "Ġ leaders", + "Ġle aders", + "Ġlead ers", + "Ġleader s", + "un ities", + "unit ies", + "uni ties", + "Ġ gun", + "Ġg un", + "Ġgu n", + "o nth", + "on th", + "ont h", + "Ġ Dev", + "ĠD ev", + "ĠDe v", + "F ILE", + "FI LE", + "Ġ comments", + "Ġcom ments", + "Ġcomm ents", + "Ġcomment s", + "Ġcomme nts", + "_ len", + "_l en", + "_le n", + "ar row", + "arr ow", + "arro w", + "a mount", + "am ount", + "amo unt", + "R ange", + "Ra nge", + "s ert", + "se rt", + "ser t", + "Grid View", + "Ġ updated", + "Ġup dated", + "Ġupdate d", + "Ġupd ated", + "Ġ Mo", + "ĠM o", + "Ġ inform", + "Ġin form", + "Ġinfo rm", + "Ġinf orm", + "oci ety", + "a la", + "al a", + "A ccess", + "Ac cess", + "Acc ess", + "Ġ hab", + "Ġh ab", + "Ġha b", + "Ġ creat", + "Ġc reat", + "Ġcr eat", + "Ġcre at", + "Ġcrea t", + "_ arg", + "_a rg", + "_ar g", + "Ġ January", + "ĠJan uary", + "Ġ Day", + "ĠD ay", + "ĠDa y", + "\" )čĊ", + "\") čĊ", + "u ple", + "up le", + "d ocument", + "doc ument", + "gor ith", + "gorit h", + "m enu", + "me nu", + "men u", + "Ġ Over", + "ĠO ver", + "ĠOv er", + "b b", + ". title", + ".t itle", + "_ out", + "_o ut", + "Ġ led", + "Ġl ed", + "Ġle d", + "u ri", + "ur i", + "Ġ ?> < /", + "g l", + "Ġ bank", + "Ġb ank", + "Ġban k", + "Ġba nk", + "ay ment", + "ĉ printf", + "ĉprint f", + "M D", + "Ġ sample", + "Ġs ample", + "Ġsam ple", + "Ġsamp le", + "Ġ hands", + "Ġh ands", + "Ġhand s", + "Ġha nds", + "Ġhan ds", + "Ġ Version", + "ĠV ersion", + "ĠVers ion", + "u ario", + "ua rio", + "uar io", + "Ġ offers", + "Ġof fers", + "Ġoff ers", + "Ġoffer s", + "ity Engine", + "Ġ shape", + "Ġs hape", + "Ġsh ape", + "Ġsha pe", + "Ġ sleep", + "Ġs leep", + "Ġsle ep", + "Ġslee p", + "_ point", + "_p oint", + "_po int", + "S ettings", + "Set tings", + "Setting s", + "Ġ achie", + "Ġa chie", + "Ġach ie", + "Ġ sold", + "Ġs old", + "Ġso ld", + "Ġsol d", + "o ta", + "ot a", + ". bind", + ".b ind", + ".bin d", + ".bi nd", + "A m", + "Ġ safe", + "Ġs afe", + "Ġsa fe", + "Ġsaf e", + "St ore", + "Ġ shared", + "Ġsh ared", + "Ġshare d", + "Ġsha red", + "Ġshar ed", + "Ġ priv", + "Ġp riv", + "Ġpr iv", + "Ġpri v", + "_ VAL", + "_V AL", + "Ġs ens", + "Ġse ns", + "Ġsen s", + ") {", + "Ġ remember", + "Ġre member", + "Ġrem ember", + "sh ared", + "sha red", + "share d", + "e lement", + "el ement", + "ele ment", + "elem ent", + "Ġ shoot", + "Ġs hoot", + "Ġsh oot", + "Ġsho ot", + "V ert", + "Ver t", + "Ve rt", + "c out", + "co ut", + "cou t", + "Ġ env", + "Ġe nv", + "Ġen v", + "_ label", + "_l abel", + "_lab el", + "_la bel", + "Ġ >Ċ", + "Ġ> Ċ", + "r un", + "ru n", + "Ġ scene", + "Ġs cene", + "Ġsc ene", + "Ġscen e", + "Ġsce ne", + "( array", + "(a rray", + "(arr ay", + "(ar ray", + "d evice", + "de vice", + "dev ice", + "_ title", + "_t itle", + "_ti tle", + "a gon", + "ag on", + "ago n", + "] čĊ", + "a by", + "ab y", + "Ġbe came", + "Ġbec ame", + "bo olean", + "bool ean", + "boo lean", + "Ġ park", + "Ġp ark", + "Ġpar k", + "Ġpa rk", + "Ġ Code", + "ĠC ode", + "ĠCo de", + "ĠCod e", + "up load", + "r iday", + "ri day", + "rid ay", + "Ġ September", + "ĠSept ember", + "ĠSep tember", + "F e", + "Ġ sen", + "Ġs en", + "Ġse n", + "c ing", + "ci ng", + "cin g", + "F L", + "C ol", + "Co l", + "u ts", + "ut s", + "_ page", + "_p age", + "_pag e", + "_pa ge", + "i nn", + "in n", + "Ġim plied", + "Ġimp lied", + "Ġimpl ied", + "a ling", + "al ing", + "ali ng", + "alin g", + "Ġyour self", + "Ġyours elf", + ". Count", + ".C ount", + ".Co unt", + "con f", + "co nf", + "Ġ aud", + "Ġa ud", + "Ġau d", + "_ init", + "_in it", + "_i nit", + "_ini t", + ". )", + "Ġw rote", + "Ġwr ote", + "0 03", + "00 3", + "N G", + ". Error", + ".E rror", + ".Err or", + "ä »", + ". for", + ".f or", + "Ġ equal", + "Ġe qual", + "Ġequ al", + "Ġeq ual", + "Ġ Request", + "ĠRe quest", + "ĠReq uest", + "Ġ serial", + "Ġs erial", + "Ġse rial", + "Ġser ial", + "Ġseria l", + "Ġseri al", + "Ġ allows", + "Ġall ows", + "Ġallow s", + "Ġallo ws", + "X X", + "Ġ middle", + "Ġm iddle", + "Ġmid dle", + "Ġmidd le", + "c hor", + "ch or", + "cho r", + "1 95", + "19 5", + "9 4", + "à ¸", + "er val", + "erv al", + "erva l", + ". Column", + ".C olumn", + ".Col umn", + "re ading", + "read ing", + "rea ding", + "Ġ escort", + "Ġesc ort", + "Ġ August", + "ĠAug ust", + "Ġquick ly", + "Ġwe ap", + "Ġ CG", + "ĠC G", + "ro pri", + "rop ri", + "h o", + "Ġ cop", + "Ġc op", + "Ġco p", + "( struct", + "(str uct", + "Ġ Big", + "ĠB ig", + "ĠBi g", + "Ġ vs", + "Ġv s", + "Ġf requ", + "Ġfr equ", + "Ġfre qu", + "Ġfreq u", + ". Value", + ".V alue", + ".Val ue", + "Ġ actions", + "Ġa ctions", + "Ġact ions", + "Ġaction s", + "Ġ proper", + "Ġpro per", + "Ġpr oper", + "Ġprop er", + "Ġ inn", + "Ġin n", + "Ġi nn", + "Ġ objects", + "Ġobject s", + "Ġobj ects", + "Ġ matrix", + "Ġm atrix", + "Ġmat rix", + "av ascript", + "ava script", + "Ġ ones", + "Ġo nes", + "Ġon es", + "Ġone s", + ". group", + ".g roup", + ".gr oup", + "Ġ green", + "Ġg reen", + "Ġgr een", + "Ġgre en", + "Ġ paint", + "Ġp aint", + "Ġpain t", + "Ġpa int", + "Ġpai nt", + "o ols", + "ool s", + "oo ls", + "y cl", + "yc l", + "en code", + "enc ode", + "enco de", + "o lt", + "ol t", + "com ment", + "comm ent", + ". api", + ".ap i", + ".a pi", + "D ir", + "Di r", + "Ġ une", + "Ġu ne", + "Ġun e", + "iz ont", + "izon t", + "izo nt", + ". position", + ".p osition", + ".pos ition", + "Ġde signed", + "Ġdes igned", + "Ġdesign ed", + "_ val", + "_v al", + "_va l", + "a vi", + "av i", + "i ring", + "ir ing", + "iri ng", + "t ab", + "ta b", + "Ġ layer", + "Ġl ayer", + "Ġla yer", + "Ġlay er", + "Ġ views", + "Ġview s", + "Ġvi ews", + "Ġvie ws", + "Ġ reve", + "Ġre ve", + "Ġr eve", + "Ġrev e", + "r ael", + "ra el", + "Ġ ON", + "ĠO N", + "r ics", + "ri cs", + "ric s", + "1 60", + "16 0", + "n p", + "Ġ core", + "Ġc ore", + "Ġco re", + "Ġcor e", + "( ));čĊ", + "() );čĊ", + "()) ;čĊ", + "()); čĊ", + "M ain", + "Ma in", + "Ġ expert", + "Ġex pert", + "Ġexp ert", + "Ġexper t", + "ĉ ĉčĊ", + "ĉĉ čĊ", + "_ en", + "_e n", + "Ġ />", + "Ġ/ >", + "ut ter", + "utt er", + "I AL", + "IA L", + "a ils", + "ail s", + "ai ls", + "Ġ King", + "ĠK ing", + "ĠKi ng", + "ĠKin g", + "* /ĊĊ", + "*/ ĊĊ", + "*/Ċ Ċ", + "Ġ Met", + "ĠM et", + "ĠMe t", + "_ end", + "_e nd", + "_en d", + "ad dr", + "add r", + "o ra", + "or a", + "Ġ ir", + "Ġi r", + "M in", + "Mi n", + "Ġsur pr", + "Ġre pe", + "Ġrep e", + "Ġ directory", + "Ġd irectory", + "Ġdirect ory", + "Ġdirector y", + "P UT", + "PU T", + "- S", + "Ġ election", + "Ġe lection", + "Ġel ection", + "Ġelect ion", + "Ġele ction", + "h aps", + "ha ps", + "hap s", + ". pre", + ".p re", + ".pr e", + "c m", + "Value s", + "Val ues", + "Ġ \"Ċ", + "Ġ\" Ċ", + "c olumn", + "col umn", + "i vil", + "iv il", + "ivi l", + "Log in", + "Lo gin", + "in ue", + "inu e", + "9 3", + "Ġ beautiful", + "Ġbe autiful", + "Ġbeaut iful", + "Ġ secret", + "Ġs ecret", + "Ġse cret", + "Ġsec ret", + "Ġsecre t", + "( event", + "(e vent", + "(ev ent", + "Ġ chat", + "Ġc hat", + "Ġch at", + "Ġcha t", + "u ms", + "um s", + "Ġ origin", + "Ġor igin", + "Ġorig in", + "Ġori gin", + "Ġ effects", + "Ġe ffects", + "Ġeffect s", + "Ġeff ects", + "Ġ management", + "Ġman agement", + "Ġmanage ment", + "Ġmana gement", + "i lla", + "il la", + "ill a", + "t k", + "Ġ setting", + "Ġs etting", + "Ġset ting", + "Ġsett ing", + "Ġ Cour", + "ĠC our", + "ĠCo ur", + "ĠCou r", + "Ġ massage", + "Ġm assage", + "Ġmass age", + "Ġmas sage", + "Ġmassa ge", + "ĉ end", + "ĉe nd", + "ĉen d", + "Ġ happy", + "Ġh appy", + "Ġhapp y", + "Ġha ppy", + "Ġhap py", + "Ġ finish", + "Ġf inish", + "Ġfin ish", + "Ġfi nish", + "Ġ camera", + "Ġc amera", + "Ġcame ra", + "Ġcam era", + "Ġcamer a", + "Ġ Ver", + "ĠV er", + "ĠVe r", + "ĠDem ocr", + "ĠDemo cr", + "Ġ Her", + "ĠH er", + "ĠHe r", + "( Q", + "c ons", + "con s", + "co ns", + "i ta", + "it a", + "Ġ '.", + "Ġ' .", + "{ }", + "ĉ C", + "Ġ stuff", + "Ġst uff", + "Ġstu ff", + "1 94", + "19 4", + "Ġ :Ċ", + "Ġ: Ċ", + "Ġ AR", + "ĠA R", + "T ask", + "Ta sk", + "h idden", + "hi dden", + "hid den", + "e ros", + "er os", + "ero s", + "I GN", + "IG N", + "at io", + "ati o", + "Ġ Health", + "ĠHe alth", + "ĠHeal th", + "ol ute", + "olut e", + "olu te", + "En ter", + "Ent er", + "' >", + "Ġ Twitter", + "ĠT witter", + "ĠTw itter", + "Ġ County", + "ĠC ounty", + "ĠCount y", + "ĠCou nty", + "ĠCoun ty", + "s cribe", + "scri be", + "scr ibe", + "Ġ= >Ċ", + "Ġ=> Ċ", + "Ġ hy", + "Ġh y", + "f it", + "fi t", + "Ġm ilitary", + "Ġmilit ary", + "Ġmil itary", + "Ġmilitar y", + "Ġ sale", + "Ġs ale", + "Ġsa le", + "Ġsal e", + "re quired", + "require d", + "requ ired", + "n on", + "no n", + "boot strap", + "h old", + "ho ld", + "hol d", + "r im", + "ri m", + "- old", + "-o ld", + "Ġ Down", + "ĠD own", + "ĠDo wn", + "ĠDow n", + "Ġ mention", + "Ġm ention", + "Ġmen tion", + "Ġment ion", + "cont act", + "_ group", + "_g roup", + "_gr oup", + "o day", + "od ay", + "oda y", + "Ġ town", + "Ġt own", + "Ġto wn", + "Ġtow n", + "Ġ solution", + "Ġs olution", + "Ġsol ution", + "u ate", + "ua te", + "uat e", + "el ling", + "ell ing", + "elli ng", + "] ->", + "]- >", + "o tes", + "ot es", + "ote s", + "en tal", + "ent al", + "enta l", + "o men", + "om en", + "ome n", + "osp ital", + "Ġ Sup", + "ĠS up", + "ĠSu p", + "_ EN", + "_E N", + "Ġ slow", + "Ġs low", + "Ġsl ow", + "Ġslo w", + "SE SSION", + "SES SION", + "Ġ blue", + "Ġb lue", + "Ġbl ue", + "a go", + "ag o", + "Ġl ives", + "Ġli ves", + "Ġlive s", + "Ġliv es", + "Ġ ^", + ". un", + ".u n", + "i nst", + "in st", + "ins t", + "e nge", + "en ge", + "eng e", + "Ġ customers", + "Ġcustom ers", + "Ġcustomer s", + "Ġcust omers", + "Ġ cast", + "Ġc ast", + "Ġca st", + "Ġcas t", + "ud get", + "udge t", + "ï¼ ģ", + "ic ens", + "ice ns", + "Ġd etermin", + "Ġde termin", + "Ġdeter min", + "Ġdeterm in", + "Se lected", + "Select ed", + "Sel ected", + "_ pl", + "_p l", + "ue ue", + "Ġ dark", + "Ġd ark", + "Ġda rk", + "Ġdar k", + "/ /ĊĊ", + "// ĊĊ", + "//Ċ Ċ", + "s i", + "th ern", + "ther n", + "the rn", + "Ġ Japan", + "ĠJ apan", + "ĠJa pan", + "ĠJap an", + "/ w", + "P U", + "Ġ East", + "ĠE ast", + "ĠEa st", + "o vie", + "ov ie", + "ovi e", + "Ġ package", + "Ġp ackage", + "Ġpack age", + "Ġ nor", + "Ġn or", + "Ġno r", + "Ġ api", + "Ġa pi", + "Ġap i", + "b ot", + "bo t", + "\" ];Ċ", + "\"] ;Ċ", + "\"]; Ċ", + "_ post", + "_p ost", + "_pos t", + "_po st", + "u late", + "ul ate", + "ula te", + "Ġ club", + "Ġc lub", + "Ġcl ub", + "' ));Ċ", + "') );Ċ", + "')) ;Ċ", + "')); Ċ", + "Ġ loop", + "Ġl oop", + "Ġlo op", + "P IO", + "PI O", + "i one", + "ion e", + "io ne", + "s hot", + "sh ot", + "In itial", + "Init ial", + "Ġ played", + "Ġpl ayed", + "Ġplay ed", + "reg ister", + "regist er", + "r ought", + "ro ught", + "rou ght", + "rough t", + "_ max", + "_m ax", + "_ma x", + "ace ment", + "ac ement", + "m atch", + "mat ch", + "raph ics", + "raphic s", + "A ST", + "AS T", + "Ġ existing", + "Ġex isting", + "Ġexist ing", + "Ġ complex", + "Ġcom plex", + "Ġcomp lex", + "Ġcomple x", + "Ġcompl ex", + "D A", + ". Ch", + ".C h", + ". common", + ".com mon", + ".comm on", + "m o", + "Ġ' ../../", + "Ġ'../ ../", + "Ġ'.. /../", + "i to", + "it o", + "Ġ analysis", + "Ġan alysis", + "Ġanal ysis", + "Ġanaly sis", + "Ġanalys is", + "Ġ deliver", + "Ġdel iver", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "i dx", + "id x", + "à ł", + "o ngo", + "on go", + "ong o", + "Ġ English", + "ĠEng lish", + "ĠEngl ish", + "< !--", + " \";Ċ", + ">\" ;Ċ", + ">\"; Ċ", + "_ UN", + "_U N", + "ĉ std", + "ĉs td", + "ĉst d", + "o ded", + "od ed", + "ode d", + "Ġ calls", + "Ġc alls", + "Ġcall s", + "Ġcal ls", + "h ere", + "he re", + "her e", + "R el", + "Re l", + "Ġ brand", + "Ġb rand", + "Ġbr and", + "Ġbra nd", + "Ġbran d", + "back ground", + "g a", + "_ address", + "_add ress", + "_addr ess", + "_ad dress", + "_ params", + "_param s", + "_par ams", + "_para ms", + "_pa rams", + "C ategory", + "1 03", + "10 3", + "Ġ India", + "ĠIn dia", + "ĠInd ia", + "ĠIndi a", + "_ event", + "_e vent", + "_ev ent", + "_even t", + "Ġ ing", + "Ġin g", + "Ġi ng", + "R ender", + "Re nder", + "Ren der", + ". cl", + ".c l", + "um py", + "ump y", + "Ġ pet", + "Ġp et", + "Ġpe t", + "F C", + "Ġ Ant", + "ĠA nt", + "ĠAn t", + "E xt", + "Ex t", + "Ġ charge", + "Ġch arge", + "Ġchar ge", + "Ġcharg e", + "e ned", + "en ed", + "ene d", + "g rad", + "gr ad", + "gra d", + "E O", + "Ġ depend", + "Ġd epend", + "Ġde pend", + "Ġdep end", + "Ġ .ĊĊ", + "Ġ. ĊĊ", + "Ġ.Ċ Ċ", + "f rame", + "fr ame", + "fra me", + "fram e", + "Ġ df", + "Ġd f", + "Ġ huge", + "Ġh uge", + "Ġhug e", + "Ġhu ge", + "Ġ PART", + "ĠP ART", + "ĠPA RT", + "ĠPAR T", + "e ds", + "ed s", + "; ;", + "Ġ AM", + "ĠA M", + "Ġ basic", + "Ġb asic", + "Ġbas ic", + "Ġba sic", + "Ġ Let", + "ĠL et", + "ĠLe t", + "l ich", + "lic h", + "li ch", + "Ġ arm", + "Ġa rm", + "Ġar m", + "Ġ star", + "Ġs tar", + "Ġst ar", + "Ġsta r", + "Ġf ederal", + "Ġfed eral", + "Ġfeder al", + "W ork", + "Wo rk", + "Ġ carry", + "Ġc arry", + "Ġcar ry", + "Ġcarr y", + "Ġ Israel", + "ĠIs rael", + "( obj", + "(o bj", + "(ob j", + "= {{", + "={ {", + "Ġ saved", + "Ġs aved", + "Ġsa ved", + "Ġsave d", + "Ġsav ed", + "Ġ syn", + "Ġs yn", + "Ġsy n", + "Ġ constant", + "Ġcon stant", + "Ġconst ant", + "Ġcons tant", + "V ENT", + "VE NT", + "VEN T", + "Ġ positive", + "Ġpos itive", + "Ġposit ive", + "Ġ conduct", + "Ġcon duct", + "Ġcond uct", + "Ġconduc t", + "Ġcondu ct", + "Ġ skin", + "Ġs kin", + "Ġsk in", + "Ġski n", + "Ġear lier", + "Ġ layout", + "Ġl ayout", + "Ġlay out", + "Ġ IP", + "ĠI P", + "O UR", + "OU R", + "Ġ tim", + "Ġt im", + "Ġti m", + "style sheet", + "styles heet", + "_ cl", + "_c l", + "Ġ Card", + "ĠC ard", + "ĠCar d", + "ĠCa rd", + "++ ){Ċ", + "++) {Ċ", + "++){ Ċ", + "Ġtem per", + "Ġtemp er", + "Ġ David", + "ĠD avid", + "ĠDav id", + "ĠDa vid", + "ĉ try", + "ĉt ry", + "ĉtr y", + ". dart", + ".d art", + ".da rt", + "Ġw ants", + "Ġwant s", + "Ġwa nts", + "Ġwan ts", + "Ġ picture", + "Ġp icture", + "Ġpic ture", + "Ġpict ure", + "Ġ videos", + "Ġv ideos", + "Ġvideo s", + "Ġvid eos", + "Ġvide os", + "Ġ Comm", + "ĠC omm", + "ĠCom m", + "ĠCo mm", + "is ions", + "ision s", + "isi ons", + "_ MAX", + "_M AX", + "_MA X", + "M apping", + "Map ping", + "Ma pping", + "- content", + "-c ontent", + "-con tent", + "-cont ent", + "Ġ Ear", + "ĠE ar", + "ĠEa r", + "- de", + "-d e", + "Ġp rem", + "Ġpr em", + "Ġpre m", + "b ruary", + "br uary", + "bru ary", + "bruar y", + "Ġ components", + "Ġcom ponents", + "Ġcomp onents", + "Ġcomponent s", + "Ġthrough out", + "Ġ pull", + "Ġp ull", + "Ġpul l", + "Ġpu ll", + "Ġ pages", + "Ġp ages", + "Ġpage s", + "Ġpa ges", + "Ġpag es", + "e nte", + "en te", + "ent e", + "res pond", + "resp ond", + "Ġ gas", + "Ġg as", + "Ġga s", + "cript or", + "Ġ edge", + "Ġe dge", + "Ġed ge", + "Ġ bound", + "Ġb ound", + "Ġbo und", + "Ġbou nd", + "A CT", + "AC T", + "* *****", + "** ****", + "**** **", + "*** ***", + "***** *", + "Ġ creating", + "Ġc reating", + "Ġcr eating", + "Ġcre ating", + "Ġcreat ing", + "Ġcrea ting", + "Ġ CH", + "ĠC H", + "Ġ nullptr", + "Ġnull ptr", + "B r", + "+ '", + ". co", + ".c o", + "> ::", + ">: :", + "Ġ learning", + "Ġl earning", + "Ġle arning", + "Ġlearn ing", + "Ġlear ning", + ". Length", + ".L ength", + ".Le ngth", + ".Len gth", + "_ SH", + "_S H", + "Ġ patients", + "Ġpat ients", + "Ġpatient s", + "A IN", + "AI N", + "Ġ kids", + "Ġk ids", + "Ġkid s", + "Ġki ds", + "Ġ comfort", + "Ġcom fort", + "Ġ shown", + "Ġsh own", + "Ġshow n", + "Ġsho wn", + "ug ins", + "ugin s", + "ugi ns", + "Ġ Back", + "ĠB ack", + "ĠBa ck", + "ĠBac k", + "e lla", + "el la", + "ell a", + "_ CL", + "_C L", + "Ġ lat", + "Ġl at", + "Ġla t", + "Ġ dispatch", + "Ġdis patch", + "Ġdisp atch", + "Ġ classes", + "Ġc lasses", + "Ġclass es", + "Ġcl asses", + "Ġclasse s", + "Ġclas ses", + ". at", + ".a t", + ". begin", + ".b egin", + ".be gin", + "Ġ successful", + "Ġsuccess ful", + "b an", + "ba n", + "Ġob tain", + "Ġobt ain", + "Ġ Sl", + "ĠS l", + "Ġ lack", + "Ġl ack", + "Ġla ck", + "Ġlac k", + "it erator", + "iter ator", + "T hread", + "Th read", + "Thr ead", + "( size", + "(s ize", + "(si ze", + "Ġ none", + "Ġn one", + "Ġno ne", + "Ġnon e", + ". has", + ".h as", + "_ X", + "s ort", + "so rt", + "n ap", + "na p", + "p et", + "pe t", + "b in", + "bi n", + "7 00", + "70 0", + "Ġ Canada", + "ĠCan ada", + "T hey", + "The y", + "Th ey", + "Ġd ans", + "Ġda ns", + "Ġdan s", + "Ġ Mat", + "ĠM at", + "ĠMa t", + "< td", + "'", + "'=> '", + "'= >'", + "Ġ Paul", + "ĠP aul", + "ĠPa ul", + "m as", + "ma s", + "ĉ print", + "ĉp rint", + "ĉpr int", + "( len", + "(l en", + "(le n", + "f d", + "Ġ );", + "Ġ) ;", + ". Event", + ".E vent", + "q li", + "ql i", + "i rit", + "ir it", + "iri t", + "ie lds", + "ield s", + "iel ds", + "o man", + "om an", + "oma n", + "Ġ Top", + "ĠT op", + "ĠTo p", + "Ġ vote", + "Ġv ote", + "Ġvo te", + "Ġvot e", + "Ġ mask", + "Ġm ask", + "Ġma sk", + "Ġmas k", + "Ġ theme", + "Ġth eme", + "Ġthe me", + "Ġthem e", + "- Ċ", + "Ġ props", + "Ġp rops", + "Ġpro ps", + "Ġpr ops", + "Ġprop s", + "Ġ fine", + "Ġf ine", + "Ġfin e", + "Ġfi ne", + "Ġ writer", + "Ġw riter", + "Ġwrit er", + "Ġwr iter", + "Ġwrite r", + "_ offset", + "_off set", + "_o ffset", + "c ar", + "ca r", + "Ġ altern", + "Ġal tern", + "Ġalt ern", + "Ġalter n", + "Ġalte rn", + "Ġ copyright", + "Ġc opyright", + "Ġcopy right", + "Ġ destroy", + "Ġd estroy", + "Ġde stroy", + "Ġdest roy", + "p per", + "pp er", + "ppe r", + "Ġ generate", + "Ġg enerate", + "Ġgener ate", + "Ġgen erate", + "Ġgene rate", + "Ġgenera te", + "p ped", + "pp ed", + "ppe d", + "âĢĻ d", + "Ġ ĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĊ", + "ĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠ Ċ", + "m ake", + "ma ke", + "mak e", + "Ġ Show", + "ĠS how", + "ĠSh ow", + "ĠSho w", + "Ġ browser", + "Ġb rowser", + "Ġbrows er", + "Ġbrowse r", + "Ġbrow ser", + "Ġ favorite", + "Ġf avorite", + "Ġfavor ite", + "Ġ career", + "Ġcar eer", + "Ġcare er", + "Ġhapp ened", + "Ġhappen ed", + "( char", + "(c har", + "(ch ar", + "Ġ recommend", + "Ġre commend", + "Ġrecomm end", + "Ġ liter", + "Ġl iter", + "Ġli ter", + "Ġlit er", + "Ġlite r", + ". filter", + ".f ilter", + ".fil ter", + "g rade", + "gr ade", + "grad e", + "gra de", + "Ġ £", + "Ġ £", + "P hone", + "Ph one", + "o ms", + "om s", + "Ġ named", + "Ġn amed", + "Ġname d", + "Ġna med", + "Ġnam ed", + "- label", + "-l abel", + "-la bel", + "i po", + "ip o", + "Ġ Other", + "ĠO ther", + "ĠOt her", + "Ġ panel", + "Ġp anel", + "Ġpa nel", + "Ġpan el", + "Ġpane l", + "Ġ rock", + "Ġr ock", + "Ġro ck", + "Ġroc k", + "S cale", + "Sc ale", + "ĉ assert", + "ĉas sert", + "Ð ´", + "Ġ trust", + "Ġt rust", + "Ġtr ust", + "f ront", + "fr ont", + "Ġd emon", + "Ġde mon", + "Ġdem on", + "Ġdemo n", + "A r", + "N et", + "Ne t", + "Ġ economic", + "Ġe conomic", + "Ġecon omic", + "Ġeconom ic", + "Ġec onomic", + "f ooter", + "fo oter", + "foot er", + "foo ter", + "Ġ race", + "Ġr ace", + "Ġrac e", + "Ġra ce", + "( node", + "(n ode", + "(no de", + "Ġ Option", + "ĠO ption", + "ĠOp tion", + "ĠOpt ion", + "s plit", + "sp lit", + "spl it", + "Ġ physical", + "Ġph ysical", + "Ġphys ical", + "Ġphysic al", + "i fest", + "if est", + "ife st", + "ifes t", + "Ġ removed", + "Ġre moved", + "Ġrem oved", + "Ġremove d", + ". http", + ".h ttp", + ".ht tp", + ") ),Ċ", + ")) ,Ċ", + ")), Ċ", + "Ġlo oked", + "Ġlook ed", + "' ;", + "d ing", + "di ng", + "din g", + "g est", + "ge st", + "ges t", + "atur day", + "/ licenses", + "/lic enses", + "/license s", + "P rice", + "Pr ice", + "Pri ce", + "Ġ dro", + "Ġd ro", + "Ġdr o", + "Ġt owards", + "Ġto wards", + "Ġtoward s", + "Ġtow ards", + "Ġ uns", + "Ġu ns", + "Ġun s", + "Ġ CL", + "ĠC L", + "ĉ static", + "ĉst atic", + "ĉstat ic", + "Ġ rows", + "Ġr ows", + "Ġro ws", + "Ġrow s", + "Ġ define", + "Ġde fine", + "Ġdef ine", + "Ġdefin e", + ". replace", + ".re place", + ".rep lace", + "Ġ father", + "Ġf ather", + "Ġfa ther", + "Ġfat her", + "Ġ Design", + "ĠD esign", + "ĠDe sign", + "ĠDes ign", + "as sign", + "ass ign", + "assi gn", + "m ut", + "mu t", + "D evice", + "De vice", + "Dev ice", + "D id", + "Di d", + "' ))Ċ", + "') )Ċ", + "')) Ċ", + "o metry", + "om etry", + "ome try", + "omet ry", + "ometr y", + "ay load", + "Ġ histor", + "Ġh istor", + "Ġhis tor", + "Ġhist or", + "Ġhi stor", + "Ġhisto r", + "Ġ Param", + "ĠP aram", + "ĠPar am", + "ĠPa ram", + "ĠPara m", + "Ġ Boolean", + "ĠBo olean", + "ĠBool ean", + "ĠBoo lean", + "Ġ nature", + "Ġn ature", + "Ġna ture", + "Ġnatur e", + "Ġnat ure", + "Ġ js", + "Ġj s", + "Ġ nation", + "Ġn ation", + "Ġna tion", + "Ġnat ion", + "i h", + "Ġ discover", + "Ġdis cover", + "Ġdisc over", + "Ġdisco ver", + "s em", + "se m", + "H andle", + "Hand le", + "Han dle", + "ĉ r", + "Ġ Techn", + "ĠT echn", + "ĠTe chn", + "ĠTech n", + "ĠTec hn", + "Ġ wall", + "Ġw all", + "Ġwa ll", + "Ġwal l", + "{ $", + "@ property", + "Ġ\" ../", + "Ġ\". ./", + "Ġ\".. /", + "Ġ exam", + "Ġex am", + ". draw", + ".d raw", + ".dr aw", + "o pping", + "op ping", + "opp ing", + "Ġn early", + "Ġnear ly", + "Ġ cool", + "Ġc ool", + "Ġco ol", + "Ġin depend", + "Ġind epend", + "Ġinde pend", + "R ES", + "RE S", + "Ġ handler", + "Ġh andler", + "Ġhand ler", + "Ġhandle r", + "Ġ Monday", + "ĠMon day", + "ĠMond ay", + "Ġ sun", + "Ġs un", + "Ġsu n", + "St yles", + "Style s", + "ous ly", + "Ġ ĉ", + "v est", + "ve st", + "ves t", + "D isplay", + "Dis play", + "Disp lay", + "( y", + "at ically", + "atic ally", + "atical ly", + "Ġ predict", + "Ġp redict", + "Ġpre dict", + "Ġpred ict", + "Ġpredic t", + "y ing", + "yi ng", + "Ġ sometimes", + "Ġs ometimes", + "Ġsome times", + "Ġsom etimes", + "Ġsometime s", + "\" ]Ċ", + "\"] Ċ", + "Ġ drink", + "Ġd rink", + "Ġdr ink", + "Ġdri nk", + "Ġ bul", + "Ġb ul", + "Ġbu l", + "if ications", + "ific ations", + "ification s", + ". insert", + ".in sert", + ".ins ert", + ". reg", + ".re g", + ".r eg", + "Ġ tests", + "Ġt ests", + "Ġte sts", + "Ġtest s", + "Ġtes ts", + "Al ignment", + "Align ment", + "Ġal leg", + "Ġall eg", + "Ġalle g", + "Ġ attribute", + "Ġat tribute", + "Ġattrib ute", + "Ġ Note", + "ĠN ote", + "ĠNo te", + "ĠNot e", + "Ġmy self", + "Ġmys elf", + "a rts", + "ar ts", + "art s", + "N ow", + "No w", + "Ġ interesting", + "Ġinter esting", + "Ġinterest ing", + "Ġinteres ting", + "l ients", + "li ents", + "lient s", + "lie nts", + "lien ts", + "Ġ population", + "Ġpop ulation", + "Ġpopul ation", + "Ġ California", + "ĠCal ifornia", + "ĠCaliforn ia", + "\" I", + "å ¹", + "Ġ greater", + "Ġg reater", + "Ġgreat er", + "Ġgre ater", + "ues day", + "Ġth ous", + "Ġthou s", + "Ġtho us", + "Ġco sts", + "Ġcost s", + "Ġcos ts", + "Ġ launch", + "Ġl aunch", + "Ġla unch", + "\\ Http", + "k er", + "ke r", + "b and", + "ba nd", + "ban d", + "Ġ Play", + "ĠP lay", + "ĠPl ay", + "ĠPla y", + "Ġ band", + "Ġb and", + "Ġban d", + "Ġba nd", + ". shape", + ".s hape", + ".sh ape", + ".sha pe", + "e some", + "es ome", + "eso me", + "art icle", + "arti cle", + "artic le", + ". rf", + ".r f", + "Ġ wer", + "Ġw er", + "Ġwe r", + "á s", + "em bers", + "ember s", + "emb ers", + "u sr", + "us r", + "B A", + "i can", + "ic an", + "ica n", + "e tt", + "et t", + "valid ate", + "ul ti", + "ult i", + "Ġim mediately", + "Ġimmedi ately", + "Ġimmediate ly", + "z er", + "ze r", + "Ġ figure", + "Ġf igure", + "Ġfig ure", + "Ġfigur e", + "o es", + "oe s", + "e ller", + "el ler", + "ell er", + "elle r", + "ir cle", + "irc le", + "Ġ Sign", + "ĠS ign", + "ĠSi gn", + "ĠSig n", + ". db", + ".d b", + "Ġ rank", + "Ġr ank", + "Ġran k", + "Ġra nk", + "By tes", + "Byte s", + "Ġ projects", + "Ġproject s", + "Ġproj ects", + "Ġproje cts", + "_ rec", + "_re c", + "_r ec", + "U LAR", + "UL AR", + "ULA R", + "A PI", + "AP I", + "Ġ Line", + "ĠL ine", + "ĠLin e", + "ĠLi ne", + "P ort", + "Por t", + "Po rt", + "Ġ poll", + "Ġp oll", + "Ġpol l", + "Ġpo ll", + "Ġg iving", + "Ġgi ving", + "id ence", + "ide nce", + "iden ce", + "- -Ċ", + "-- Ċ", + "Ġ plot", + "Ġp lot", + "Ġpl ot", + "Ġplo t", + "i cial", + "ic ial", + "ici al", + "icia l", + "Ġw arrant", + "Ġwar rant", + "I TION", + "IT ION", + "Ġ Double", + "ĠD ouble", + "ĠDo uble", + "ĠDou ble", + "ĠDoub le", + "Ġb illion", + "Ġbill ion", + "Ġbil lion", + "gorith m", + "gorit hm", + "Ġ equipment", + "Ġe quipment", + "Ġequ ipment", + "Ġequip ment", + "D ATE", + "DA TE", + "DAT E", + "Ġ @\"", + "Ġ@ \"", + "E E", + "Ġ ple", + "Ġp le", + "Ġpl e", + "i ation", + "ia tion", + "iat ion", + "Ġ headers", + "Ġhe aders", + "Ġhead ers", + "Ġheader s", + "Ġpro ced", + "Ġproc ed", + ".Component Model", + "Ġ Obama", + "ĠOb ama", + "Ġ pa", + "Ġp a", + "Ġ Best", + "ĠB est", + "ĠBe st", + "ĠBes t", + "im ately", + "imate ly", + "imat ely", + ". getString", + ".get String", + ".getS tring", + ". \\", + "m ploy", + "mp loy", + "mpl oy", + "Ġ raw", + "Ġr aw", + "Ġra w", + "_ block", + "_b lock", + "_bl ock", + "_bloc k", + "und red", + "\" },Ċ", + "\"} ,Ċ", + "\"}, Ċ", + "1 12", + "11 2", + ".Group Layout", + "Ġb rought", + "Ġbr ought", + "Ġbro ught", + "NS String", + "NSS tring", + "th row", + "thr ow", + "thro w", + "c reated", + "cre ated", + "create d", + "cr eated", + "creat ed", + ". New", + ".N ew", + "_ view", + "_v iew", + "_vi ew", + "C P", + "e ps", + "ep s", + "O p", + "Ġ gratis", + "Ġgr atis", + "Ġgrat is", + "Ġ '\"", + "Ġ' \"", + "Ġint erview", + "Ġinter view", + "Ġinterv iew", + "\" \"\"Ċ", + "\"\" \"Ċ", + "\"\"\" Ċ", + "Ġ partial", + "Ġp artial", + "Ġpart ial", + "Ġparti al", + "Ġ aria", + "Ġa ria", + "Ġar ia", + "b ing", + "bin g", + "bi ng", + "A uthor", + "Auth or", + "Aut hor", + "B ook", + "Bo ok", + "Ġ Pat", + "ĠP at", + "ĠPa t", + "u man", + "um an", + "uma n", + "User s", + "Us ers", + "Use rs", + "p lus", + "pl us", + "1 93", + "19 3", + "Ġ Direct", + "ĠD irect", + "ĠDi rect", + "ĠDir ect", + "ĠDire ct", + "ven ue", + "al pha", + "alph a", + "UC CESS", + "Ġ Call", + "ĠC all", + "ĠCal l", + "ĠCa ll", + "Ġ );čĊ", + "Ġ) ;čĊ", + "Ġ); čĊ", + "im ated", + "imate d", + "ima ted", + "imat ed", + "Ġ remain", + "Ġre main", + "Ġr emain", + "Ġrem ain", + "Ġ anti", + "Ġan ti", + "Ġant i", + "Ġ London", + "ĠL ondon", + "ĠLo ndon", + "ĠLon don", + "ĠLond on", + "Ġs afety", + "Ġsaf ety", + "Ġsafe ty", + "P OSE", + "PO SE", + "POS E", + "o les", + "ol es", + "ole s", + "cont roller", + "control ler", + "contr oller", + "contro ller", + "B yte", + "By te", + "Ġ Court", + "ĠC ourt", + "ĠCo urt", + "ĠCour t", + "ĠCou rt", + "Ġ Phil", + "ĠP hil", + "ĠPh il", + "ĠPhi l", + "Ġ Associ", + "ĠAss oci", + "ĠAssoc i", + "e na", + "en a", + "å IJ", + "_ STR", + "_S TR", + "_ST R", + "c oin", + "co in", + "res hold", + "resh old", + "Ġ batch", + "Ġb atch", + "Ġbat ch", + "_ Click", + "_C lick", + "_Cl ick", + "ent ication", + "entic ation", + "enti cation", + "> ';Ċ", + ">' ;Ċ", + ">'; Ċ", + "e nty", + "en ty", + "ent y", + "Ġbeg inning", + "Ġbegin ning", + "Ġ zero", + "Ġz ero", + "Ġze ro", + "Ġzer o", + "Ġ Convert", + "ĠCon vert", + "ĠConv ert", + "Ġ terr", + "Ġt err", + "Ġte rr", + "Ġter r", + "Ġ paid", + "Ġp aid", + "Ġpa id", + "Ġpai d", + "Ġincre ased", + "Ġincrease d", + "c atch", + "cat ch", + "- size", + "-s ize", + "1 15", + "11 5", + "act ivity", + "activ ity", + "e quals", + "equ als", + "equal s", + "Ġ queue", + "Ġque ue", + "Ġq ueue", + "Ġ \"'", + "Ġ\" '", + "Ġ International", + "ĠInt ernational", + "ĠIntern ational", + "ĠInter national", + "Ġf ür", + "urs day", + "Ġ scient", + "Ġs cient", + "Ġsc ient", + "Ġsci ent", + "al low", + "all ow", + "allo w", + "a xis", + "ax is", + "Ġ appropri", + "Ġapp ropri", + "Ġap propri", + "Ġappro pri", + "e dge", + "ed ge", + "Ġ idx", + "Ġi dx", + "Ġid x", + "S uccess", + "Su ccess", + "Suc cess", + "ent ifier", + ": \\", + "x is", + "xi s", + "Ġ maximum", + "Ġmax imum", + "Ġmaxim um", + "ar ks", + "ark s", + "Ġ birth", + "Ġb irth", + "Ġbir th", + "( index", + "(in dex", + "(ind ex", + "Ġ maybe", + "Ġm aybe", + "Ġmay be", + ". py", + ".p y", + "f iles", + "file s", + "fi les", + "fil es", + "Ġ limited", + "Ġl imited", + "Ġlimit ed", + "Ġlim ited", + "Ġlimite d", + "_ check", + "_c heck", + "_ch eck", + "_che ck", + "l ook", + "lo ok", + "loo k", + "p lies", + "pl ies", + "Ġ movement", + "Ġm ovement", + "Ġmov ement", + "Ġmove ment", + "Ġmo vement", + "' ].", + "'] .", + "Ġb road", + "Ġbr oad", + "Ġbro ad", + "Ġ BE", + "ĠB E", + "Ġ UnityEngine", + "ĠUn ityEngine", + "ĠUnity Engine", + ". cpp", + ".c pp", + ".cp p", + "Ġ Every", + "ĠE very", + "ĠEv ery", + "ĠEver y", + "ĠEve ry", + "Ad min", + "Ġ fans", + "Ġf ans", + "Ġfa ns", + "Ġfan s", + "p ared", + "par ed", + "pare d", + "pa red", + "Ċ ĠĠĠĠĊ", + "Ġ foreign", + "Ġfore ign", + "Ġ pan", + "Ġp an", + "Ġpa n", + "Ġ tour", + "Ġt our", + "Ġto ur", + "Ġtou r", + "Ġ Order", + "ĠOr der", + "ĠOrd er", + "Ġ moving", + "Ġm oving", + "Ġmov ing", + "Ġmo ving", + "Ġ auf", + "Ġa uf", + "Ġau f", + "C all", + "Cal l", + "Ca ll", + "c b", + "Å Ł", + "vent ory", + "Ġ Sql", + "ĠS ql", + "ĠSq l", + "Ġ fully", + "Ġf ully", + "Ġfull y", + "Ġful ly", + "Click Listener", + "W ORD", + "WO RD", + "Ġann ounced", + "Ġannounc ed", + "Ġannounce d", + ") čĊčĊ", + ")čĊ čĊ", + "Ġag reed", + "Ġagre ed", + "Ġagree d", + "Ġagr eed", + "r ie", + "ri e", + "Ġ earn", + "Ġe arn", + "Ġear n", + "Ġea rn", + "_ link", + "_l ink", + "_lin k", + "_li nk", + ". array", + ".a rray", + ".ar ray", + ".arr ay", + "( text", + "(t ext", + "(te xt", + "(tex t", + "Ġ materials", + "Ġmaterial s", + "Ġmateria ls", + "Ġmater ials", + "Ġmateri als", + ", p", + "f fff", + "ff ff", + "fff f", + "v g", + "Ġ ©", + "Ġ ©", + "Ġ unless", + "Ġun less", + "Ġunle ss", + "Ġunl ess", + "a jax", + "aj ax", + "aja x", + "L OG", + "LO G", + "Ġ sexual", + "Ġs exual", + "Ġsex ual", + "Ġ \\\"", + "Ġ\\ \"", + "- time", + "-t ime", + "Ġ coach", + "Ġco ach", + "Ġ supported", + "Ġs upported", + "Ġsup ported", + "Ġsupport ed", + "Ġsupp orted", + "Ġ photos", + "Ġph otos", + "Ġphot os", + "Ġphoto s", + "i form", + "if orm", + "ifo rm", + ". Create", + ".C reate", + ") ]", + "r ier", + "ri er", + "rie r", + "Ġ dialog", + "Ġd ialog", + "Ġdi alog", + "Ġdia log", + "Ġdial og", + "a ver", + "av er", + "ave r", + "i ge", + "ig e", + ") +", + "_ idx", + "_id x", + "_i dx", + ": [", + "_ min", + "_m in", + "_mi n", + "Ġ Cong", + "ĠC ong", + "ĠCon g", + "ĠCo ng", + "Ġ pressure", + "Ġpres sure", + "Ġpress ure", + "Ġ teams", + "Ġte ams", + "Ġteam s", + "Ġtea ms", + "S ign", + "Si gn", + "Sig n", + "b egin", + "be gin", + "beg in", + "r ian", + "ri an", + "ria n", + "N ESS", + "NE SS", + "NES S", + "L S", + "Ġim prove", + "Ġimp rove", + "Ġimpro ve", + "Ġimpr ove", + "Ġimprov e", + "Ġ Sunday", + "ĠS unday", + "ĠSun day", + "ĠSund ay", + "Ġ definition", + "Ġdef inition", + "Ġdefinit ion", + "Ġdefin ition", + "i ger", + "ig er", + "ige r", + "rol lers", + "roll ers", + "roller s", + "Ġ thinking", + "Ġth inking", + "Ġthink ing", + "Ġthin king", + "T emplate", + "Temp late", + "Tem plate", + "- F", + "Ġem erg", + "p lates", + "pl ates", + "plate s", + "plat es", + "pla tes", + "Ġ USA", + "ĠU SA", + "ĠUS A", + ". setState", + ".set State", + "Ġ Also", + "ĠAl so", + "ĠAls o", + "r ev", + "re v", + "Ġ enable", + "Ġe nable", + "Ġen able", + "Ġ CO", + "ĠC O", + "P ECT", + "PE CT", + "PEC T", + "Ġ concept", + "Ġcon cept", + "Ġconc ept", + "Ġconce pt", + ") -", + "Ġ âĢ¢", + "ĠâĢ ¢", + "Ġ sets", + "Ġs ets", + "Ġse ts", + "Ġset s", + "Ġ meaning", + "Ġme aning", + "Ġmean ing", + "e mon", + "em on", + "emo n", + "Ġ Cons", + "ĠC ons", + "ĠCon s", + "ĠCo ns", + "c mp", + "cm p", + "e der", + "ed er", + "ede r", + "an ned", + "ann ed", + "anne d", + "ic ensed", + "icense d", + "icens ed", + "Ġ Super", + "ĠS uper", + "ĠSup er", + "ĠSu per", + "Ġ daily", + "Ġd aily", + "Ġda ily", + "Ġdai ly", + "Ġ multi", + "Ġm ulti", + "Ġmult i", + "Ġmul ti", + "_ u", + "Ġch alleng", + "Ġchall eng", + "_ mode", + "_m ode", + "_mod e", + "_mo de", + "Ġ Promise", + "ĠP romise", + "ĠPro mise", + "ĠProm ise", + "Ġ strict", + "Ġstr ict", + "Ġstri ct", + "j o", + "i nton", + "in ton", + "int on", + "into n", + "( list", + "(l ist", + "(li st", + "On ly", + "> {", + "Ġ vehicle", + "Ġv ehicle", + "Ġveh icle", + "í ķ", + "Ġ Player", + "ĠP layer", + "ĠPl ayer", + "ĠPlay er", + "ĠPla yer", + "1 06", + "10 6", + "Ġ Del", + "ĠD el", + "ĠDe l", + "Ġ pool", + "Ġp ool", + "Ġpo ol", + "Ġpoo l", + ". url", + ".u rl", + ".ur l", + "nes day", + "( );čĊčĊ", + "() ;čĊčĊ", + "();čĊ čĊ", + "(); čĊčĊ", + "9 00", + "90 0", + "Ġ \");Ċ", + "Ġ\" );Ċ", + "Ġ\") ;Ċ", + "Ġ\"); Ċ", + "L ocal", + "Lo cal", + "Loc al", + ". \");Ċ", + ".\" );Ċ", + ".\") ;Ċ", + ".\"); Ċ", + "Ġ organization", + "Ġo rganization", + "Ġorgan ization", + "Ġorganiz ation", + "r ender", + "re nder", + "ren der", + "rend er", + "rende r", + "Ġ Application", + "ĠApp lication", + "ĠAp plication", + "ĠAppl ication", + "Ġ summer", + "Ġs ummer", + "Ġsum mer", + "Ġsumm er", + "ex pected", + "exp ected", + "expect ed", + "N A", + "Ġ rap", + "Ġr ap", + "Ġra p", + "_ obj", + "_o bj", + "_ob j", + "Ġ surface", + "Ġs urface", + "Ġsur face", + "Ġsurf ace", + "Ġ PUR", + "ĠP UR", + "ĠPU R", + "Ġ },ĊĊ", + "Ġ} ,ĊĊ", + "Ġ},Ċ Ċ", + "Ġ}, ĊĊ", + "Ġ variables", + "Ġvariable s", + "Ġvari ables", + "( message", + "(m essage", + "Ġo pin", + "Ġop in", + "Ġopi n", + ". back", + ".b ack", + ".ba ck", + "а н", + "аР½", + "Ġ workers", + "Ġwork ers", + "Ġwor kers", + "Ġworker s", + "v m", + "C o", + "ught er", + "ugh ter", + "Ġ master", + "Ġm aster", + "Ġma ster", + "Ġmas ter", + "Ġmast er", + "Ġ \"\",", + "Ġ\" \",", + "Ġ\"\" ,", + "Ġ stories", + "Ġst ories", + "Ġstor ies", + "Ġsto ries", + ". User", + ".U ser", + ".Use r", + "Ġcele br", + "in ese", + "ine se", + "ines e", + "B S", + "Ġ Command", + "ĠCom mand", + "ĠComm and", + "ash board", + "Ġ og", + "Ġo g", + "k g", + ". image", + ".i mage", + ".im age", + ".imag e", + ". style", + ".st yle", + "Ġ steps", + "Ġst eps", + "Ġstep s", + "Ġste ps", + "Ġ Ben", + "ĠB en", + "ĠBe n", + "( args", + "(arg s", + "(ar gs", + "4 04", + "40 4", + "Ġ Person", + "ĠP erson", + "ĠPer son", + "ĠPers on", + ", y", + "Ġoffic ials", + "Ġofficial s", + "| Ċ", + "Ġ skills", + "Ġs kills", + "Ġsk ills", + "Ġskill s", + "v c", + "Ġ builder", + "Ġb uilder", + "Ġbu ilder", + "Ġbuild er", + "Ġ gar", + "Ġg ar", + "Ġga r", + "A ccount", + "Ac count", + "Acc ount", + "Ġ Auth", + "ĠA uth", + "ĠAut h", + "ĠAu th", + "ç Ķ", + "' ])Ċ", + "'] )Ċ", + "']) Ċ", + "Ġ AT", + "ĠA T", + "n n", + ". Int", + ".I nt", + ".In t", + "SS ERT", + "Ġ effective", + "Ġe ffective", + "Ġeffect ive", + "Ġeff ective", + "LE TE", + "LET E", + "Ġ tools", + "Ġt ools", + "Ġto ols", + "Ġtoo ls", + "Ġtool s", + "A RD", + "AR D", + "Ġ digital", + "Ġd igital", + "Ġdig ital", + "Ġdigit al", + "1 91", + "19 1", + "D ouble", + "Do uble", + "Dou ble", + "Ġ Find", + "ĠF ind", + "ĠFin d", + "ĠFi nd", + "R C", + "Ġ inline", + "Ġin line", + "/ r", + "A RAM", + "AR AM", + "ARA M", + "A SK", + "AS K", + "Ġ intent", + "Ġin tent", + "Ġint ent", + "Ġinte nt", + "a ight", + "ai ght", + "_ addr", + "_add r", + "_ad dr", + "Ġ requests", + "Ġre quests", + "Ġrequest s", + "Ġrequ ests", + ". first", + ".f irst", + ".fi rst", + "Ġ debug", + "Ġde bug", + "Ġdeb ug", + "Ġ spent", + "Ġs pent", + "Ġsp ent", + "Ġspe nt", + "( )));Ċ", + "() ));Ċ", + "()) );Ċ", + "())) ;Ċ", + "())); Ċ", + "Å Ľ", + "Ġpr incip", + "Ġprin cip", + "Ġprinc ip", + "Log ger", + "Lo gger", + "cl udes", + "clude s", + "clud es", + ". use", + ".u se", + ".us e", + "Ġs urv", + "Ġsu rv", + "Ġsur v", + "m edia", + "med ia", + "me dia", + "medi a", + "Ġ February", + "ĠFe bruary", + "ĠFeb ruary", + "Ġ Mac", + "ĠM ac", + "ĠMa c", + "Ġ missing", + "Ġm issing", + "Ġmiss ing", + "Ġmis sing", + "Ġ wife", + "Ġw ife", + "Ġwi fe", + "Ġt alking", + "Ġtalk ing", + "Ġtal king", + "Ġ Make", + "ĠM ake", + "ĠMa ke", + "ĠMak e", + "Ġ cart", + "Ġc art", + "Ġcar t", + "Ġca rt", + "Ġ located", + "Ġloc ated", + "Ġlocate d", + "E nc", + "En c", + "- a", + "ch ron", + "chr on", + "Ġ cards", + "Ġc ards", + "Ġcar ds", + "Ġcard s", + "Ġg uy", + "Ġgu y", + "Ġ pers", + "Ġp ers", + "Ġper s", + "Ġpe rs", + "Ġ Yes", + "ĠY es", + "ĠYe s", + "at ever", + "ate ver", + "Ġ Ang", + "ĠA ng", + "ĠAn g", + "o lar", + "ol ar", + "ola r", + "Ġ Even", + "ĠE ven", + "ĠEv en", + "ĠEve n", + "Ġ accur", + "Ġacc ur", + "Ġac cur", + "Ġ Power", + "ĠP ower", + "ĠPo wer", + "ĠPow er", + "Ġ Gold", + "ĠG old", + "ĠGo ld", + "ĠGol d", + "c lear", + "cl ear", + "cle ar", + "P rocess", + "Pro cess", + "Proc ess", + "Ġ records", + "Ġrec ords", + "Ġrecord s", + "Ġk illed", + "Ġkill ed", + "Ġkil led", + ". clear", + ".c lear", + ".cl ear", + "ĠWARRANT IES", + "Ġ purpose", + "Ġp urpose", + "Ġpur pose", + "Ġpurpos e", + "p anel", + "pan el", + "pa nel", + "pane l", + "J ECT", + "JE CT", + "ÃŃ a", + "Ġex erc", + "Ġexe rc", + "W S", + "/ L", + ". exports", + ".ex ports", + ".exp orts", + ".export s", + "Ġ ___", + "Ġ_ __", + "Ġ__ _", + "Ġ sin", + "Ġs in", + "Ġsi n", + "S ervlet", + "Serv let", + "Ġd é", + ". delete", + ".de lete", + ".del ete", + "r oke", + "ro ke", + "rok e", + "S l", + "u gh", + "ug h", + "e ars", + "ear s", + "ea rs", + "Ġ pointer", + "Ġpoint er", + "Ġpo inter", + "Ġ hop", + "Ġh op", + "Ġho p", + "all ery", + "alle ry", + "aller y", + "Ġ obs", + "Ġo bs", + "Ġob s", + "c overy", + "co very", + "cover y", + "cov ery", + "ĉ char", + "ĉc har", + "ĉch ar", + "ĉ ĉĉĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉĉ ĉĉĉĉ", + "ĉĉĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉĉĉĉ ĉ", + "ĉ def", + "ĉd ef", + "ĉde f", + "o city", + "oc ity", + "oci ty", + "it chen", + "itch en", + "u lations", + "ul ations", + "ulation s", + "Ġ FIT", + "ĠF IT", + "ĠFI T", + "Ġ ).", + "Ġ) .", + "straint s", + "stra ints", + "strain ts", + "v ention", + "vent ion", + "ven tion", + "Ġ requires", + "Ġre quires", + "Ġrequire s", + "Ġrequ ires", + "Ġ Oper", + "ĠO per", + "ĠOp er", + "M E", + "O UNT", + "OUN T", + "OU NT", + "al let", + "all et", + "alle t", + "Ġ norm", + "Ġn orm", + "Ġno rm", + "Ġnor m", + "I RE", + "IR E", + "ex as", + "Ġ programs", + "Ġpro grams", + "Ġpr ograms", + "Ġprogram s", + "Ġprog rams", + "Ġ weak", + "Ġwe ak", + "' .$", + "'. $", + "u ing", + "ui ng", + "uin g", + "ĉ ĠĠĠĠĠĠĠ", + "ĉĠĠĠ ĠĠĠĠ", + "ĉĠ ĠĠĠĠĠĠ", + "ĉĠĠ ĠĠĠĠĠ", + "ĉĠĠĠĠĠ ĠĠ", + "ĉĠĠĠĠ ĠĠĠ", + "ĉĠĠĠĠĠĠ Ġ", + "Ġ mil", + "Ġm il", + "Ġmi l", + "Ġ firm", + "Ġf irm", + "Ġfi rm", + "Ġfir m", + "init ely", + "inite ly", + "_ VALUE", + "_VAL UE", + "ap se", + "aps e", + "atis f", + "ati sf", + "Ġ demand", + "Ġd emand", + "Ġde mand", + "Ġdem and", + "_ mod", + "_m od", + "_mo d", + "Ġde scribed", + "Ġdes cribed", + "Ġdescri bed", + "Ġdescribe d", + "Ġ places", + "Ġp laces", + "Ġpl aces", + "Ġplace s", + "Ġplac es", + "Ġpla ces", + "V ID", + "VI D", + "Ġ alone", + "Ġal one", + "Ġalo ne", + "Ġ export", + "Ġex port", + "Ġexp ort", + "Ġexpo rt", + "Ġ vec", + "Ġv ec", + "Ġve c", + "Ġ Max", + "ĠM ax", + "ĠMa x", + "Ġ activities", + "Ġact ivities", + "Ġactiv ities", + "ic tures", + "ict ures", + "icture s", + "g ener", + "ge ner", + "gen er", + "gene r", + "Ġ ma", + "Ġm a", + "Ĥ ¬", + "Ġ expression", + "Ġex pression", + "Ġexp ression", + "Ġexpress ion", + "Ġexpr ession", + "C allback", + "Call back", + "_ content", + "_c ontent", + "_con tent", + "_cont ent", + "Ġ Most", + "ĠM ost", + "ĠMo st", + "ĠMos t", + "Ġ testing", + "Ġt esting", + "Ġtest ing", + "Ġtes ting", + "E C", + "CH ANT", + "CHA NT", + "CHAN T", + "Ġ adjust", + "Ġad just", + "Ġadj ust", + ".Th reading", + ".Thread ing", + "( ctx", + "(c tx", + "(ct x", + "Ġ agree", + "Ġa gree", + "Ġag ree", + "Ġagre e", + "Ġagr ee", + "i ghest", + "ig hest", + "igh est", + "Ġ ui", + "Ġu i", + "Ġ Law", + "ĠL aw", + "ĠLa w", + ". Y", + "> < ?", + "Ġ pod", + "Ġp od", + "Ġpo d", + "- lg", + "-l g", + "âĢĿ ĊĊ", + "âĢĿĊ Ċ", + "Ġ describe", + "Ġde scribe", + "Ġdes cribe", + "Ġdescri be", + "Ġdescr ibe", + "Ġ European", + "ĠE uropean", + "ĠEurope an", + "ĠEurop ean", + "- sh", + "-s h", + "ĠPUR POSE", + "O RY", + "OR Y", + "Ġcon vers", + "Ġconv ers", + "Ġconver s", + "Ġ Illuminate", + "ĠI lluminate", + "ĠIllum inate", + "Ġ Av", + "ĠA v", + "( ch", + "(c h", + "? \"", + "c hen", + "ch en", + "che n", + "i ma", + "im a", + "D ocument", + "Doc ument", + "Ġ operations", + "Ġoper ations", + "Ġoperation s", + "w in", + "wi n", + "ĉ function", + "ĉf unction", + "ĉfunc tion", + "ĉfun ction", + ". Image", + ".I mage", + ".Im age", + "Ġs cen", + "Ġsc en", + "Ġsce n", + "/ h", + "Ġ SC", + "ĠS C", + "Ġ explo", + "Ġexp lo", + "Ġexpl o", + ": %", + "/ **čĊ", + "/* *čĊ", + "/** čĊ", + "N AME", + "NA ME", + "æ Ī", + "( var", + "(v ar", + "(va r", + "Ġ director", + "Ġd irector", + "Ġdirect or", + "Ġdir ector", + "Ġdire ctor", + "O NG", + "ON G", + "Ġ yield", + "Ġy ield", + "Ġyi eld", + "Ġfe et", + "Ġfee t", + "Ġ Search", + "ĠS earch", + "ĠSe arch", + "ĠSea rch", + "Ġ Il", + "ĠI l", + "Ġrest aur", + "Ġresta ur", + "Ġrestau r", + "d uc", + "du c", + "Ġ integer", + "Ġint eger", + "Ġinteg er", + "Ġinte ger", + "1 07", + "10 7", + "Ġ' ';Ċ", + "Ġ'' ;Ċ", + "Ġ''; Ċ", + "Ġhigh ly", + "check ed", + "ĠPART IC", + "ER CHANT", + "ï¼ ī", + "Ġ optim", + "Ġop tim", + "Ġopt im", + "Q ueue", + "Que ue", + "Ġ LI", + "ĠL I", + "it ation", + "ita tion", + "itat ion", + "Ġ transport", + "Ġtrans port", + "Ġtran sport", + "iss ion", + "f ill", + "fi ll", + "fil l", + "us ion", + "usi on", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĉ bool", + "ĉb ool", + "- th", + "-t h", + "u pt", + "up t", + "Ġ essential", + "Ġess ential", + "an ted", + "ant ed", + "ante d", + "Ġbenef its", + "Ġbenefit s", + "Ġbene fits", + "ĉ S", + "' ;čĊ", + "'; čĊ", + "i ki", + "ik i", + "Ġ girls", + "Ġgirl s", + "Ġgir ls", + "i ced", + "ic ed", + "ice d", + "b uffer", + "buf fer", + "bu ffer", + "buff er", + "] +", + "Ġ socket", + "Ġs ocket", + "Ġso cket", + "Ġsock et", + "Ġsoc ket", + "Ġ prices", + "Ġp rices", + "Ġpr ices", + "Ġprice s", + "Ġpri ces", + "Ġ Fre", + "ĠF re", + "ĠFr e", + "Ġ sat", + "Ġs at", + "Ġsa t", + "Ġ wood", + "Ġw ood", + "Ġwo od", + "Ġwoo d", + "Menu Item", + "A RG", + "AR G", + "Ġ Admin", + "ĠAd min", + "O WN", + "OW N", + "d k", + "Ġ reset", + "Ġre set", + "Ġres et", + "Ġ forms", + "Ġfor ms", + "Ġform s", + "Ġfo rms", + "Ġ и", + "ĠÐ ¸", + "æ ĸ", + "Ġ Tuesday", + "ĠT uesday", + "ĠTues day", + "1 09", + "10 9", + "Ġ Initialized", + "ĠInitial ized", + "ĠInitialize d", + "ĠInit ialized", + "_ train", + "_t rain", + "_tr ain", + "_tra in", + "o rary", + "or ary", + "ora ry", + "ate gor", + "ateg or", + "atego r", + "Ġ dt", + "Ġd t", + "T otal", + "To tal", + "Tot al", + "con struct", + "i lies", + "il ies", + "ili es", + "Ġgu ys", + "Ġguy s", + "е ÑĢ", + "Ġ instruction", + "Ġin struction", + "Ġinstr uction", + "Ġinstruct ion", + "0 10", + "01 0", + "y led", + "yle d", + "yl ed", + "Ġ internet", + "Ġin ternet", + "Ġint ernet", + "Ġinter net", + "Ġintern et", + "et adata", + "eta data", + "a dy", + "ad y", + "f aces", + "face s", + "fa ces", + "fac es", + "j ection", + "ject ion", + "je ction", + "jec tion", + "Ġ Jack", + "ĠJ ack", + "ĠJac k", + "ĠJa ck", + "Ġ rect", + "Ġre ct", + "Ġr ect", + "Ġrec t", + "[ -", + "Ġ Leg", + "ĠL eg", + "ĠLe g", + "Ġ devices", + "Ġdev ices", + "Ġdevice s", + "O C", + "Ġ *čĊ", + "Ġ* čĊ", + "o ration", + "or ation", + "ora tion", + "er tain", + "ert ain", + "erta in", + "Ġ guard", + "Ġg uard", + "Ġgu ard", + "Ġguar d", + "o stream", + "ost ream", + "Ġ enum", + "Ġe num", + "Ġen um", + ". layout", + ".l ayout", + "Ġ \";Ċ", + "Ġ\" ;Ċ", + "Ġ\"; Ċ", + "v oke", + "vo ke", + "Ġ Ok", + "ĠO k", + "H ome", + "Ho me", + "Hom e", + "( tr", + "(t r", + "E TH", + "ET H", + "Ġ delay", + "Ġd elay", + "Ġde lay", + "Ġdel ay", + "Ġdela y", + "Ġ purchase", + "Ġp urchase", + "Ġpurch ase", + "d c", + "Ġ aren", + "Ġa ren", + "Ġare n", + "Ġar en", + "_ once", + "_on ce", + "_o nce", + "ĉ ĉĉĉĊ", + "ĉĉ ĉĉĊ", + "ĉĉĉĉ Ċ", + "ĉĉĉ ĉĊ", + "r or", + "ro r", + "d raw", + "dr aw", + "dra w", + ". run", + ".r un", + ".ru n", + "( model", + "(m odel", + "(mod el", + "(mode l", + "Time out", + "l ik", + "li k", + "Ġ Arg", + "ĠA rg", + "ĠAr g", + ". en", + ".e n", + "Ġ fish", + "Ġf ish", + "Ġfi sh", + "Ġfis h", + "c py", + "cp y", + "_ fe", + "_f e", + "ERCHANT ABILITY", + "( X", + "_ output", + "_out put", + "? ?", + "Ġ jo", + "Ġj o", + "and ard", + "anda rd", + "Ġ doll", + "Ġd oll", + "Ġdo ll", + "Ġdol l", + "er rors", + "err ors", + "error s", + "erro rs", + "_ base", + "_b ase", + "ĠPARTIC ULAR", + "Ġ leader", + "Ġle ader", + "Ġlead er", + "Ġcom par", + "Ġco mpar", + "Ġcomp ar", + "Ġd oub", + "Ġdo ub", + "Ġdou b", + "Ġ Vis", + "ĠV is", + "ĠVi s", + "Stack Trace", + "- C", + "ĠSt ud", + "stit ute", + "M ore", + "Mo re", + "Mor e", + "Ġ Description", + "ĠD escription", + "ĠDe scription", + "ĠDes cription", + "W ARE", + "WA RE", + "WAR E", + "a ds", + "ad s", + "Ġ к", + "ĠÐ º", + "b ind", + "bin d", + "bi nd", + "= self", + "=s elf", + "e mploy", + "em ploy", + "emp loy", + "empl oy", + "emplo y", + "[ n", + ". all", + ".a ll", + ".al l", + "- B", + "& &", + "a lm", + "al m", + "Ġ culture", + "Ġc ulture", + "Ġcult ure", + "Ġcul ture", + "h ouse", + "ho use", + "hou se", + "Ġs uffer", + "Ġsu ffer", + "Ġsuff er", + "Ġsuf fer", + "Ġ '%", + "Ġ' %", + "Ġ straight", + "Ġstr aight", + "Ġstra ight", + "Ġ Star", + "ĠS tar", + "ĠSt ar", + "ĠSta r", + "u do", + "ud o", + "Ġ ded", + "Ġd ed", + "Ġde d", + "Ġ COM", + "ĠC OM", + "ĠCO M", + "Ġ confirm", + "Ġcon firm", + "Ġconf irm", + "Ġ Good", + "ĠG ood", + "ĠGo od", + ". sc", + ".s c", + "____ ____________", + "________ ________", + "____________ ____", + "D R", + "Config uration", + "Date Time", + "Ġ advert", + "Ġad vert", + "Ġadv ert", + "Ġ couldn", + "Ġcould n", + "a sync", + "as ync", + "asy nc", + "st ack", + "sta ck", + "' )čĊ", + "') čĊ", + "K it", + "Ki t", + "Ġh ous", + "Ġho us", + "Ġm echan", + "Ġme chan", + "Ġmec han", + "Ġmech an", + "r ate", + "ra te", + "rat e", + "2 04", + "20 4", + "Ġ audio", + "Ġa udio", + "Ġaud io", + "Ġau dio", + "Ġaudi o", + "ĉ cout", + "ĉc out", + "c ores", + "co res", + "core s", + "cor es", + "Ġ spot", + "Ġs pot", + "Ġsp ot", + "Ġspo t", + "Ġin creasing", + "Ġincre asing", + "Ġ ##", + "Ġ# #", + ") ))", + ")) )", + "p oints", + "point s", + "po ints", + "poi nts", + "Ġcom pared", + "Ġcomp ared", + "Ġcompar ed", + "Ġcompare d", + "l ig", + "li g", + "Ġ behavior", + "Ġbeh avior", + "Ġ BY", + "ĠB Y", + "Ġ Att", + "ĠA tt", + "ĠAt t", + "c raft", + "cr aft", + "he aders", + "head ers", + "header s", + "hea ders", + "e te", + "et e", + "end region", + "Ġ detail", + "Ġd etail", + "Ġde tail", + "Ġdet ail", + "U LE", + "UL E", + "Ġ Common", + "ĠCom mon", + "ĠComm on", + "ĉ protected", + "s ton", + "st on", + "sto n", + "ĠFIT NESS", + "Ġ fresh", + "Ġf resh", + "Ġfr esh", + "Ġfre sh", + "Ġfres h", + "\" >ĊĊ", + "\"> ĊĊ", + "\">Ċ Ċ", + ". example", + ".ex ample", + ".exam ple", + "b erg", + "ber g", + "be rg", + "Ġ moved", + "Ġm oved", + "Ġmov ed", + "Ġmove d", + "Ġmo ved", + "ĉ e", + "Ġ Saturday", + "ĠS aturday", + "Ġ payload", + "Ġp ayload", + "Ġpay load", + "Ä ĩ", + ") :ĊĊ", + "):Ċ Ċ", + "): ĊĊ", + "Ġb ey", + "Ġbe y", + "u rer", + "ur er", + "ure r", + "< script", + " ,", + "\" > < ?", + "( num", + "(n um", + "ĉ inline", + "ĉin line", + "Trans action", + ". On", + ".O n", + "Ġ mail", + "Ġm ail", + "Ġma il", + "Ġmai l", + "r ey", + "re y", + "res ults", + "result s", + "Ġ nav", + "Ġn av", + "Ġna v", + "I MIT", + "IM IT", + "_ ids", + "_id s", + "_i ds", + "M ake", + "Ma ke", + "å Ĭ", + "M odal", + "Mod al", + "Mo dal", + "Ġ LOG", + "ĠL OG", + "ĠLO G", + "Ġ Sur", + "ĠS ur", + "ĠSu r", + "Ġinstance of", + "Ġ overall", + "Ġover all", + "Ġ Information", + "ĠIn formation", + "ĠInform ation", + "Ġ construction", + "Ġcon struction", + "Ġconstruct ion", + "Ġconstr uction", + "_ FILE", + "_F ILE", + "b ut", + "bu t", + "Ġm edic", + "Ġme dic", + "Ġmed ic", + "Ġmedi c", + "Ġ duration", + "Ġd uration", + "Ġdu ration", + "Ġdur ation", + "it ness", + "a gent", + "ag ent", + "age nt", + "agen t", + "A V", + "Ġ seven", + "Ġs even", + "Ġse ven", + "Ġsev en", + "o lf", + "ol f", + "Ġ }}Ċ", + "Ġ} }Ċ", + "Ġ}} Ċ", + "\" ],Ċ", + "\"] ,Ċ", + "\"], Ċ", + "1 70", + "17 0", + "1 22", + "12 2", + "Ġ calling", + "Ġc alling", + "Ġcall ing", + "Ġcal ling", + "Ġ ans", + "Ġa ns", + "Ġan s", + "th rows", + "throw s", + "thr ows", + "thro ws", + "or izontal", + "Ġ useState", + "Ġuse State", + ". fl", + ".f l", + "Ġ Status", + "ĠS tatus", + "ĠSt atus", + "ĠStat us", + "Ġ Online", + "ĠOn line", + "R R", + "Ġ Rich", + "ĠR ich", + "ĠRic h", + "ĠRi ch", + "ĠH ill", + "ĠHi ll", + "ĠHil l", + "Ġ brain", + "Ġb rain", + "Ġbr ain", + "Ġbra in", + "Ġfollow ed", + "Ġfoll owed", + "2 40", + "24 0", + "e mic", + "em ic", + "emi c", + "Ġs light", + "Ġsl ight", + "Ġ insurance", + "Ġins urance", + ". Array", + ".A rray", + ".Ar ray", + "Ġ abstract", + "Ġa bstract", + "Ġab stract", + "Ġabs tract", + "Ġabst ract", + "Ġ Sum", + "ĠS um", + "ĠSu m", + "re direct", + "red irect", + "redi rect", + "o wner", + "ow ner", + "own er", + "( msg", + "(m sg", + "(ms g", + "Ġ Clinton", + "ĠCl inton", + "ĠClin ton", + "ĠClint on", + "ĠCli nton", + "N on", + "No n", + "ĉ ex", + "ĉe x", + "Ġ volume", + "Ġv olume", + "Ġvol ume", + "Ġvolum e", + "Ġ EventArgs", + "ĠEvent Args", + "- L", + "Ġ Dim", + "ĠD im", + "ĠDi m", + "Ġ Mart", + "ĠM art", + "ĠMar t", + "ĠMa rt", + "Ġ cursor", + "Ġc ursor", + "Ġcurs or", + "Ġcurso r", + "Ġ implementation", + "Ġim plementation", + "Ġimplement ation", + "ur red", + "urre d", + "urr ed", + "Ġl arger", + "Ġlarge r", + "Ġlarg er", + "Ġlar ger", + ") ;ĊĊĊ", + ");Ċ ĊĊ", + ");ĊĊ Ċ", + "); ĊĊĊ", + "' +", + ". transform", + ".trans form", + "Ġ upload", + "Ġup load", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "D raw", + "Dr aw", + "n el", + "ne l", + "ĉ float", + "ĉf loat", + "q rt", + "qr t", + "Ġ Network", + "ĠN etwork", + "ĠNet work", + "Ġ tit", + "Ġt it", + "Ġti t", + "A xis", + "Ax is", + ". android", + ".and roid", + "Ġ completed", + "Ġcom pleted", + "Ġcomp leted", + "Ġcomple ted", + "Ġcomplet ed", + "Ġcompl eted", + "Ġcomplete d", + "Ġ mur", + "Ġm ur", + "Ġmu r", + "Ġ columns", + "Ġcolumn s", + "Ġcolum ns", + "x c", + "Ġ supply", + "Ġs upply", + "Ġsup ply", + "Ġsuppl y", + "Ġsupp ly", + "im inal", + "imi nal", + "imin al", + "Ġ spr", + "Ġs pr", + "Ġsp r", + "================ ================================================", + "================================ ================================", + "================================================ ================", + "Ġ units", + "Ġun its", + "Ġunit s", + "Ġuni ts", + "( u", + "m i", + "re place", + "rep lace", + "[ key", + "[k ey", + "à ¹", + "an tic", + "ant ic", + "anti c", + "Ġ payment", + "Ġp ayment", + "Ġpay ment", + ", B", + "Ġ Apple", + "ĠApp le", + "ĠAp ple", + "ĠAppl e", + "g in", + "gi n", + "Re quired", + "Require d", + "# +", + "l ands", + "land s", + "la nds", + "lan ds", + "Ġ squ", + "Ġs qu", + "Ġsq u", + "Ġ factor", + "Ġf actor", + "Ġfact or", + "Ġfa ctor", + "Ġfac tor", + "Ġfacto r", + "d ec", + "de c", + "Ġ strength", + "Ġst rength", + "Ġstr ength", + "Ġstre ngth", + "Ġstren gth", + "Ġ boy", + "Ġb oy", + "Ġbo y", + "Ġ balance", + "Ġb alance", + "Ġbal ance", + "Ġ sources", + "Ġs ources", + "Ġsource s", + "Ġsour ces", + "s creen", + "sc reen", + "scr een", + "- top", + "-t op", + "-to p", + "Ġ Amazon", + "ĠA mazon", + "ĠAm azon", + "ĠAma zon", + "Ġ hidden", + "Ġh idden", + "Ġhi dden", + "Ġhid den", + "е ÑĤ", + "_ client", + "_c lient", + "_cl ient", + "_cli ent", + "Ġ eat", + "Ġe at", + "Ġea t", + ". display", + ".d isplay", + ".dis play", + "Ġ »", + "Ġ »", + "Ġ trigger", + "Ġtr igger", + "Ġtri gger", + "Ġtrig ger", + "an ager", + "ana ger", + "Ġ tro", + "Ġt ro", + "Ġtr o", + "Ġ claims", + "Ġcl aims", + "Ġclaim s", + "Ġcla ims", + "f ord", + "fo rd", + "for d", + "Ġ Company", + "ĠCom pany", + "ĠComp any", + "Ġ gift", + "Ġg ift", + "Ġgi ft", + "Ġgif t", + ", :", + "_ app", + "_a pp", + "_ap p", + "h andle", + "han dle", + "hand le", + "Ġ produce", + "Ġp roduce", + "Ġpro duce", + "Ġprodu ce", + "Ġprod uce", + "/ lib", + "/l ib", + "/li b", + "5 12", + "51 2", + "Ġ -*", + "Ġ- *", + "ĉ set", + "ĉs et", + "ĉse t", + "' ];", + "'] ;", + "a rc", + "ar c", + "a nder", + "an der", + "and er", + "ande r", + "Ġ Engine", + "ĠE ngine", + "ĠEng ine", + "Ġ attributes", + "Ġat tributes", + "Ġattribute s", + "Ġattrib utes", + "t ask", + "ta sk", + "tas k", + "< =", + "( N", + "Ġ warm", + "Ġw arm", + "Ġwar m", + "Ġwa rm", + "wh ich", + "Ġ Fore", + "ĠF ore", + "ĠFor e", + "ĠFo re", + "ag nost", + "agn ost", + "m ys", + "my s", + "Ġ tal", + "Ġt al", + "Ġta l", + "Ġ Sal", + "ĠS al", + "ĠSa l", + "g i", + "Ġ Print", + "ĠP rint", + "ĠPr int", + "ĠPri nt", + "Ġ TRUE", + "ĠTR UE", + "Ġ о", + "ĠÐ ¾", + ". UI", + ".U I", + "Ġ flash", + "Ġf lash", + "Ġfl ash", + "Ġfla sh", + "ro perty", + "rop erty", + ". location", + ".l ocation", + ".loc ation", + ".lo cation", + "Ġ Mill", + "ĠM ill", + "ĠMil l", + "ĠMi ll", + "b i", + "con tr", + "cont r", + ". request", + ".re quest", + ".req uest", + "Ġ Sam", + "ĠS am", + "ĠSa m", + "Ġ negative", + "Ġn egative", + "Ġneg ative", + "k it", + "ki t", + "Ġ sett", + "Ġs ett", + "Ġse tt", + "Ġset t", + ".print StackTrace", + "a be", + "ab e", + "ĉ i", + "Ġ burn", + "Ġb urn", + "Ġbu rn", + "Ġbur n", + "Ġs ociety", + "Ġsoci ety", + "C ache", + "Ca che", + "Ġ Security", + "ĠS ecurity", + "ĠSe curity", + "ĠSec urity", + ". models", + ".model s", + ".mod els", + ".mode ls", + "ĠWARRANT Y", + "_ up", + "_u p", + "ce ive", + "Ġ clients", + "Ġc lients", + "Ġcl ients", + "Ġclient s", + "Ġcli ents", + ". Tr", + ".T r", + "Ġprovid ing", + "Ġprov iding", + "Ġr out", + "Ġro ut", + "Ġrou t", + "m aterial", + "mat erial", + "mate rial", + "Ġ ||Ċ", + "Ġ| |Ċ", + "Ġ|| Ċ", + "Ġ Ser", + "ĠS er", + "ĠSe r", + "Ġ Office", + "ĠOff ice", + "FT WARE", + "Ġ '$", + "Ġ' $", + "Ġf oc", + "Ġfo c", + "Ġex cell", + "Ġexc ell", + "Ġexcel l", + "Ġexce ll", + "Ġ cat", + "Ġc at", + "Ġca t", + "n ormal", + "norm al", + "nor mal", + "Ġd etermine", + "Ġdeter mine", + "Ġdetermin e", + "Ġdeterm ine", + "ĉ uint", + "ĉu int", + "ĉui nt", + "P ane", + "Pa ne", + "Pan e", + "Ġ employees", + "Ġemploy ees", + "Ġemployee s", + "Ġ Texas", + "ĠT exas", + "ĠTex as", + "Ġtr aff", + "Ġtra ff", + "Ġtraf f", + "Ġ Report", + "ĠRe port", + "ĠRep ort", + "ĠRepo rt", + "an ta", + "ant a", + "Ġ Box", + "ĠB ox", + "ĠBo x", + "Ġ django", + "Ġd jango", + "Ġdj ango", + "Ġ partner", + "Ġp artner", + "Ġpart ner", + "E B", + "L INE", + "LI NE", + "LIN E", + "Ġfe eling", + "Ġfeel ing", + "Ġfee ling", + "Ġ civil", + "Ġc ivil", + "Ġci vil", + "Ġciv il", + "( float", + "(f loat", + "S ql", + "Sq l", + "Ġwould n", + ". init", + ".in it", + ".i nit", + ".ini t", + ". left", + ".l eft", + ".le ft", + "- v", + "_ level", + "_le vel", + "' }", + "A F", + "Ġ loading", + "Ġlo ading", + "Ġload ing", + "Ġloa ding", + "Ġ Only", + "ĠOn ly", + "Ġ cookies", + "Ġc ookies", + "Ġco okies", + "Ġcook ies", + "Ġcookie s", + "Ġ Gl", + "ĠG l", + "C O", + "Ġ strategy", + "Ġstr ategy", + "Ġstrateg y", + "Ġstrate gy", + "(' ./", + "('. /", + "Ġ ship", + "Ġs hip", + "Ġsh ip", + "p oses", + "pos es", + "pose s", + "po ses", + "Ġ signal", + "Ġs ignal", + "Ġsign al", + "Ġsig nal", + "Ġ alpha", + "Ġal pha", + "Ġalph a", + ". pop", + ".p op", + ".po p", + "R adius", + "Rad ius", + "Radi us", + "Ġ replace", + "Ġre place", + "Ġrep lace", + "Ġrepl ace", + "_ DIR", + "_D IR", + "_DI R", + "c ounter", + "co unter", + "count er", + "bserv able", + "e la", + "el a", + "W eight", + "We ight", + "Wei ght", + "h ash", + "ha sh", + "has h", + "b ose", + "bo se", + "bos e", + "f x", + "Ġ Email", + "ĠE mail", + "ĠEm ail", + "Ġ refer", + "Ġre fer", + "Ġref er", + "local host", + "_ RO", + "_R O", + "i ques", + "ique s", + "iqu es", + "iq ues", + "S tep", + "St ep", + "Ste p", + "Ġ ahead", + "Ġa head", + "Ġah ead", + "( View", + "(V iew", + "Ġ Services", + "ĠS ervices", + "ĠService s", + "ĠServ ices", + "Ġ Json", + "ĠJ son", + "ĠJs on", + "ess or", + "esso r", + "Ġ pun", + "Ġp un", + "Ġpu n", + "Ġ appropriate", + "Ġapp ropriate", + "Ġappropri ate", + "a kers", + "ak ers", + "ake rs", + "aker s", + "o sen", + "os en", + "ose n", + "p osing", + "pos ing", + "po sing", + "Ġ agent", + "Ġa gent", + "Ġag ent", + "Ġage nt", + "f c", + "Ġ transfer", + "Ġtrans fer", + "Ġtransf er", + "Ġ invalid", + "Ġin valid", + "Ġinval id", + "Ġ Research", + "ĠRe search", + "ĠRes earch", + "Vert ex", + "Ver tex", + "Ġ gay", + "Ġg ay", + "Ġga y", + "Ġ journal", + "Ġj ournal", + "Ġjo urnal", + "Ġjour nal", + "[ x", + "Ġ \"\",Ċ", + "Ġ\" \",Ċ", + "Ġ\"\" ,Ċ", + "Ġ\"\", Ċ", + "Ġ Well", + "ĠW ell", + "ĠWe ll", + "ĠWel l", + ". Tasks", + ".T asks", + ".Task s", + "S pec", + "Sp ec", + "Spe c", + "Ġ ol", + "Ġo l", + "Ġs pend", + "Ġsp end", + "Ġspe nd", + "Ġ Australia", + "ĠA ustralia", + "ĠAustral ia", + "ĠAustr alia", + "M atch", + "Mat ch", + ".j unit", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ MAX", + "ĠM AX", + "ĠMA X", + "iz able", + "iza ble", + "cl usive", + "clus ive", + "_ valid", + "_val id", + "_va lid", + "Ġ quarter", + "Ġqu arter", + "Ġquar ter", + "Ġquart er", + "y an", + "ya n", + "0 05", + "00 5", + "Ġ Edit", + "ĠE dit", + "ĠEd it", + "ar den", + "ard en", + "arde n", + "= new", + "=n ew", + "Ġ frag", + "Ġf rag", + "Ġfr ag", + "Ġfra g", + "B it", + "Bi t", + "z i", + "a ine", + "ain e", + "ai ne", + "u dd", + "ud d", + ". Object", + ".O bject", + ".Obj ect", + "de bug", + "deb ug", + "Ġ cash", + "Ġc ash", + "Ġca sh", + "Ġcas h", + "_ IM", + "_I M", + "Ġ een", + "Ġe en", + "Ġee n", + "Ġ commercial", + "Ġcom mercial", + "Ġcomm ercial", + "Ġcommerc ial", + "Ġ Video", + "ĠV ideo", + "ĠVi deo", + "ĠVid eo", + "ĠVide o", + "l oader", + "lo ader", + "load er", + "Ġ fixed", + "Ġf ixed", + "Ġfix ed", + "Ġfi xed", + "Ġ applications", + "Ġapp lications", + "Ġapplication s", + "Ġapplic ations", + "Ġappl ications", + "Ġ _,", + "Ġ_ ,", + "Ġ Russia", + "ĠR ussia", + "ĠRuss ia", + "it ect", + "ite ct", + "_ (", + "Ġ Block", + "ĠB lock", + "ĠBl ock", + "ĠBlo ck", + "ĠBloc k", + "Ġ san", + "Ġs an", + "Ġsa n", + "Ġ Tom", + "ĠT om", + "ĠTo m", + "Ġ perhaps", + "Ġper haps", + "Ġ sig", + "Ġs ig", + "Ġsi g", + "le vant", + "lev ant", + "Ġc orpor", + "Ġcor por", + "Ġcorp or", + "Ġcorpo r", + "at aset", + "ata set", + "atas et", + "r onic", + "ro nic", + "ron ic", + "x e", + "Ġ eth", + "Ġe th", + "Ġet h", + "S ome", + "So me", + "Som e", + "p op", + "po p", + "_ OK", + "_O K", + "Ġt end", + "Ġte nd", + "Ġten d", + ". Res", + ".R es", + ".Re s", + "_ and", + "_a nd", + "_an d", + "Ġ reviews", + "Ġre views", + "Ġreview s", + "Ġ wild", + "Ġw ild", + "Ġwi ld", + "Ġwil d", + "1 17", + "11 7", + "Ġ degree", + "Ġd egree", + "Ġde gree", + "Ġdeg ree", + ". O", + ". objects", + ".object s", + ".obj ects", + "_ args", + "_arg s", + "_ar gs", + "n il", + "ni l", + "Ġ disabled", + "Ġdis abled", + "Ġdisable d", + "P arent", + "Par ent", + "Pa rent", + "Paren t", + "Ġ notes", + "Ġn otes", + "Ġnot es", + "Ġno tes", + "Ġnote s", + "Ġ \"\"Ċ", + "Ġ\" \"Ċ", + "Ġ\"\" Ċ", + "( state", + "(st ate", + "(stat e", + "i strict", + "istr ict", + "Ġ logging", + "Ġlog ging", + ". IO", + ".I O", + "Ġ Mal", + "ĠM al", + "ĠMa l", + "D M", + "Ġ xml", + "Ġx ml", + "Ġxm l", + "Ġ Robert", + "ĠRob ert", + "ĠRo bert", + "e len", + "el en", + "ele n", + "l ayout", + "lay out", + "f ol", + "fo l", + "' ]))", + "'] ))", + "']) )", + ", b", + "Ġ Jer", + "ĠJ er", + "ĠJe r", + "f ilename", + "file name", + "fi lename", + "fil ename", + "Ġ fan", + "Ġf an", + "Ġfa n", + "Ġ Custom", + "ĠC ustom", + "ĠCust om", + "= \"\"", + "=\" \"", + "Ġ Die", + "ĠD ie", + "ĠDi e", + "B undle", + ". utils", + ".util s", + ".ut ils", + "Ġ trip", + "Ġt rip", + "Ġtr ip", + "Ġtri p", + "M B", + "Ġ soft", + "Ġs oft", + "Ġso ft", + "Ġsof t", + "_ MODE", + "_M ODE", + "_MO DE", + "_MOD E", + "Ġapp licable", + "Ġapplic able", + "Ġappl icable", + "Ġ upper", + "Ġu pper", + "Ġup per", + "Ġupp er", + "ER VER", + "ERV ER", + "ERVE R", + "_ al", + "_a l", + "_ LOG", + "_L OG", + "_LO G", + "H ere", + "He re", + "Her e", + "w p", + "Ġ Server", + "ĠS erver", + "ĠSer ver", + "ĠServ er", + "ĠServe r", + "Ġ Client", + "ĠC lient", + "ĠCl ient", + "ĠCli ent", + "Ġ chem", + "Ġc hem", + "Ġch em", + "Ġche m", + "S croll", + "Sc roll", + "Scr oll", + "Ġ highest", + "Ġh ighest", + "Ġhigh est", + "Ġhi ghest", + "Ġ Select", + "ĠS elect", + "ĠSe lect", + "ĠSel ect", + "ĠSele ct", + "Ġ \"@", + "Ġ\" @", + "Ġ Why", + "ĠW hy", + "ĠWh y", + "S ec", + "Se c", + "h eel", + "he el", + "hee l", + "O peration", + "Oper ation", + "Op eration", + "Opera tion", + "Ġ connected", + "Ġconn ected", + "Ġconnect ed", + "ir med", + "irm ed", + "Ġcit iz", + "Ġ Che", + "ĠC he", + "ĠCh e", + "Ġ forces", + "Ġfor ces", + "Ġforce s", + "Ġforc es", + "Ġ www", + "Ġw ww", + "Ġww w", + "R oot", + "Ro ot", + "AN CE", + "ANC E", + "M any", + "Man y", + "Ma ny", + "i cip", + "ic ip", + "ici p", + "r gan", + "rg an", + "2 20", + "22 0", + "Ġ Tor", + "ĠT or", + "ĠTo r", + "Ġ Press", + "ĠP ress", + "ĠPr ess", + "ĠPres s", + "ĠPre ss", + "Ġ Mor", + "ĠM or", + "ĠMo r", + "- line", + "-l ine", + "-li ne", + "u led", + "ul ed", + "ule d", + "> \\", + "Ġ thus", + "Ġt hus", + "Ġth us", + "Ġthu s", + "Ġ Register", + "ĠReg ister", + "h ol", + "ho l", + "Ġ Chinese", + "ĠCh inese", + "ĠChin ese", + "Ġ posted", + "Ġpos ted", + "Ġpost ed", + "Ġpo sted", + "Ġposte d", + "Ġm agn", + "Ġmag n", + "Ġma gn", + "ab ilities", + "abil ities", + "abilit ies", + "Ġd isease", + "Ġdis ease", + "Ġdise ase", + "Ġrem ains", + "Ġremain s", + "Ġ Prof", + "ĠP rof", + "ĠPro f", + "ĠPr of", + "- form", + "-f orm", + "-for m", + "Ġ cin", + "Ġc in", + "Ġci n", + "o rgan", + "or gan", + "org an", + "i cate", + "ic ate", + "ica te", + "Ġ stress", + "Ġst ress", + "Ġstr ess", + "Ġstre ss", + "] *", + "Ġ ----------------------------------------------------------------", + "Ġ---------------- ------------------------------------------------", + "Ġ------------------------------------------------ ----------------", + "Ġ-------------------------------- --------------------------------", + "Ġ------------------------------------------------------------ ----", + "_ context", + "_con text", + "_cont ext", + "or ry", + "orr y", + "Ġd ied", + "Ġdi ed", + "Ġdie d", + "m at", + "ma t", + "Ġ starts", + "Ġst arts", + "Ġstart s", + "Ġstar ts", + "Ġsta rts", + ". Message", + ".M essage", + "Ġ runs", + "Ġr uns", + "Ġrun s", + "Ġru ns", + "Ġ guide", + "Ġg uide", + "Ġgu ide", + "Ġguid e", + "Ġgui de", + "Ġw arranty", + "Ġwarrant y", + "ent ials", + "ential s", + "enti als", + "d ict", + "di ct", + "dic t", + "Ġ Size", + "ĠS ize", + "ĠSi ze", + "ĠSiz e", + "u ler", + "ul er", + "ule r", + "Ġres ponsible", + "Ġrespons ible", + "_ SET", + "_S ET", + "_SE T", + "Ġcont aining", + "Ġcontain ing", + "Ġconta ining", + "Ġ Price", + "ĠP rice", + "ĠPr ice", + "ĠPri ce", + "| |", + "3 50", + "35 0", + "F S", + "Ġ emp", + "Ġe mp", + "Ġem p", + "_ button", + "_b utton", + "_but ton", + "( uint", + "(u int", + "(ui nt", + "Ġs uff", + "Ġsu ff", + "Ġsuf f", + "p th", + "pt h", + "Ġdef initely", + "Ġdefinit ely", + "Ġdefinite ly", + "p ute", + "put e", + "pu te", + "Ġ marketing", + "Ġmark eting", + "Ġmarket ing", + "Ġ WH", + "ĠW H", + "Ġ Sie", + "ĠS ie", + "ĠSi e", + "+ =", + "OL OR", + "Ġ consult", + "Ġcons ult", + "Ġconsul t", + "Ġ signed", + "Ġs igned", + "Ġsign ed", + "Ġsig ned", + "Ġ sequence", + "Ġse quence", + "Ġsequ ence", + "l ee", + "le e", + "Ġ requirements", + "Ġrequire ments", + "Ġrequirement s", + "h y", + "Ex press", + "Exp ress", + "Expr ess", + "M T", + "s ey", + "se y", + "Ġ ult", + "Ġu lt", + "Ġul t", + "å ®", + "ellig ence", + "elli gence", + "Ġ analy", + "Ġan aly", + "Ġanal y", + "Ġana ly", + "Ġ dress", + "Ġd ress", + "Ġdr ess", + "Ġdre ss", + "e ngine", + "eng ine", + "engin e", + "Ġ Great", + "ĠG reat", + "ĠGr eat", + "ĠGre at", + "Ġ Android", + "ĠAnd roid", + "ĠAndr oid", + "Ġ Alex", + "ĠA lex", + "ĠAl ex", + "ĠAle x", + "m ode", + "mod e", + "mo de", + "D ictionary", + ". Date", + ".D ate", + "ä ½", + "V ICE", + "VI CE", + "VIC E", + "Ġf amilies", + "Ġfam ilies", + "Ġfamil ies", + "Ġ Russian", + "ĠR ussian", + "ĠRuss ian", + "ĠRussia n", + "ĠRus sian", + "Ġ Times", + "ĠT imes", + "ĠTime s", + "ĠTim es", + "ĠTi mes", + ". call", + ".c all", + ".ca ll", + ".cal l", + "$ (", + "Pro file", + "Pr ofile", + "Prof ile", + "Ġ folder", + "Ġf older", + "Ġfol der", + "Ġfold er", + "Ġfo lder", + "c hes", + "ch es", + "che s", + "Ġl egis", + "Ġle gis", + "Ġleg is", + "_ row", + "_r ow", + "_ro w", + "u nes", + "un es", + "une s", + "Ù Ħ", + "Ġ }).", + "Ġ} ).", + "Ġ}) .", + "As sert", + "Ass ert", + "a gen", + "ag en", + "age n", + "Ġ Hand", + "ĠH and", + "ĠHa nd", + "ĠHan d", + "I ter", + "It er", + "Ġbig gest", + "o reach", + "or each", + "ore ach", + "orea ch", + "Ġp olic", + "Ġpol ic", + "Ġpo lic", + "Ġ permissions", + "Ġper missions", + "Ġpermission s", + "Ġperm issions", + "Ġsh owed", + "Ġshow ed", + "Ġsho wed", + "Ġ Element", + "ĠE lement", + "ĠEl ement", + "ĠEle ment", + "ĠElem ent", + "Ġ topic", + "Ġt opic", + "Ġto pic", + "Ġtop ic", + "âĢĶ âĢĶ", + "r oad", + "ro ad", + "Ġ Bank", + "ĠB ank", + "ĠBa nk", + "ĠBan k", + "re cord", + "rec ord", + "Ġ partners", + "Ġpart ners", + "Ġpartner s", + "Ġ Ref", + "ĠR ef", + "ĠRe f", + "ess ions", + "ession s", + "Ġas sess", + "Ġass ess", + "Ġasses s", + "U ST", + "US T", + "Ġ Party", + "ĠP arty", + "ĠPart y", + "ĠPar ty", + "p rodu", + "pr odu", + "pro du", + "prod u", + "L C", + "Ġ ul", + "Ġu l", + ". form", + ".f orm", + ".for m", + "h ide", + "hi de", + "hid e", + "c opy", + "co py", + "cop y", + "U TF", + "UT F", + "Ġ SOFTWARE", + "ĠSO FTWARE", + "čĊ čĊčĊ", + "čĊčĊ čĊ", + "Ġ Lin", + "ĠL in", + "ĠLi n", + "u na", + "un a", + "u gar", + "ug ar", + "uga r", + "Ġ administration", + "Ġadmin istration", + "Ġadministr ation", + "Ġ opening", + "Ġop ening", + "Ġopen ing", + "Ġ scan", + "Ġs can", + "Ġsc an", + "Ġsca n", + "Ġ continued", + "Ġcontin ued", + "Ġcontinue d", + "Ġcontinu ed", + "com ponent", + "comp onent", + ". sp", + ".s p", + "Ġhapp ens", + "Ġhappen s", + "um my", + "umm y", + "Ġ PR", + "ĠP R", + ". File", + ".F ile", + "Ġ Download", + "ĠD ownload", + "ĠDown load", + "Lo ading", + "Load ing", + "d i", + "Ġ waiting", + "Ġwait ing", + "Ġwa iting", + "_ ADD", + "_A DD", + "_AD D", + "T ab", + "Ta b", + ". querySelector", + ".query Selector", + "Ġe conomy", + "Ġecon omy", + "Ġeconom y", + "Ġec onomy", + "Ġ French", + "ĠF rench", + "ĠFr ench", + "t xt", + "tx t", + "Ġ fant", + "Ġf ant", + "Ġfa nt", + "Ġfan t", + "_ ;Ċ", + "_; Ċ", + "H older", + "Hold er", + "Ho lder", + "Hol der", + "S H", + "0 04", + "00 4", + "Ġ numpy", + "Ġn umpy", + "Ġnum py", + "Ġ street", + "Ġst reet", + "Ġstre et", + "Ġ male", + "Ġm ale", + "Ġma le", + "Ġmal e", + "\\ Model", + "\\M odel", + "an ging", + "ang ing", + "angi ng", + "3 33", + "33 3", + "Ġ Bill", + "ĠB ill", + "ĠBi ll", + "ĠBil l", + "Ġpre viously", + "Ġprevious ly", + "Ġprev iously", + "B I", + "Ġ Secret", + "ĠS ecret", + "ĠSe cret", + "ĠSec ret", + "Ġ mist", + "Ġm ist", + "Ġmis t", + "Ġmi st", + "Ġ Field", + "ĠF ield", + "ĠFi eld", + "u ps", + "up s", + "Ġ Process", + "ĠP rocess", + "ĠPro cess", + "ĠProc ess", + "Ġ kept", + "Ġk ept", + "Ġke pt", + "Ġkep t", + "Ġ OT", + "ĠO T", + "Ġ traditional", + "Ġtrad itional", + "Ġtradition al", + ". i", + "a min", + "am in", + "ami n", + "Ġh elps", + "Ġhelp s", + "Ġhel ps", + "A ny", + "An y", + "or igin", + "orig in", + "ori gin", + "il ters", + "ilter s", + "ilt ers", + "j u", + "d esc", + "de sc", + "des c", + "Ġ Account", + "ĠA ccount", + "ĠAc count", + "ĠAcc ount", + "Ġ )čĊ", + "Ġ) čĊ", + "k top", + "kt op", + "ol ly", + "oll y", + "Ġ fs", + "Ġf s", + "Ġ ê", + "Ġ ut", + "Ġu t", + "Ġ central", + "Ġc entral", + "Ġcent ral", + "Ġcentr al", + "( test", + "(t est", + "(te st", + ". An", + ".A n", + "Ġs atisf", + "G R", + "Ġ Full", + "ĠF ull", + "ĠFu ll", + "ĠFul l", + "Ġ heat", + "Ġh eat", + "Ġhe at", + "i ber", + "ib er", + "ibe r", + "Ġ onto", + "Ġon to", + "Ġont o", + "m os", + "mo s", + "S chema", + "Sch ema", + "Ġ factory", + "Ġf actory", + "Ġfact ory", + "Ġfactor y", + "Ġfacto ry", + "\" .$", + "\". $", + "a ws", + "aw s", + "St atement", + "State ment", + "Stat ement", + "( target", + "(t arget", + "ĉ new", + "ĉn ew", + ". be", + ".b e", + "Ġ guest", + "Ġg uest", + "Ġgu est", + "Ġ mal", + "Ġm al", + "Ġma l", + "A RY", + "AR Y", + "Ġre ached", + "Ġreach ed", + "Ġ mouse", + "Ġm ouse", + "Ġmo use", + "Ġmou se", + "Ġ challenge", + "Ġch allenge", + "Ġchall enge", + "Ġchalleng e", + "ĉ double", + "ĉd ouble", + "ĉdo uble", + "Ġ Tem", + "ĠT em", + "ĠTe m", + "Ġ terror", + "Ġt error", + "Ġte rror", + "Ġter ror", + "Ġterr or", + "Ġ extract", + "Ġex tract", + "Ġext ract", + "Ġextra ct", + "Ġextr act", + "_ TO", + "_T O", + "Ġse parate", + "Ġsepar ate", + "Ġseparat e", + "Ġ mir", + "Ġm ir", + "Ġmi r", + "h elp", + "he lp", + "hel p", + "Ġ capacity", + "Ġcap acity", + "Ġcapac ity", + "Ġcapacit y", + "Ġ Property", + "ĠP roperty", + "ĠPro perty", + "ĠProp erty", + "ĠProper ty", + "k an", + "ka n", + "_ create", + "_c reate", + "_cre ate", + "Ġ Light", + "ĠL ight", + "ĠLi ght", + "ĠLig ht", + ". parent", + ".p arent", + ".par ent", + ".pa rent", + "Ġunder standing", + "Ġunderstand ing", + "Ġunderst anding", + "Ġe asier", + "Ġeas ier", + "Ġ |=", + "Ġ| =", + "Ġ enh", + "Ġe nh", + "Ġen h", + "Ġ fat", + "Ġf at", + "Ġfa t", + "Ġpro test", + "Ġprot est", + "Ġprote st", + "a mm", + "am m", + "_ AT", + "_A T", + "- of", + "-o f", + "i ls", + "il s", + "Ġ Oh", + "ĠO h", + "Ġ psych", + "Ġps ych", + "Ġpsy ch", + "Ġ $.", + "Ġ$ .", + "i nds", + "in ds", + "ind s", + "Ġ relative", + "Ġrel ative", + "Ġrelativ e", + "Ġrelat ive", + "s hop", + "sh op", + "s hort", + "sh ort", + "Ġ Sand", + "ĠS and", + "ĠSan d", + "ĠSa nd", + "2 10", + "21 0", + "u estion", + "ues tion", + "uest ion", + "Ġf ear", + "Ġfe ar", + "/ ĊĊ", + "/Ċ Ċ", + ". context", + ".con text", + ".cont ext", + "Ġ schools", + "Ġschool s", + "Ġsch ools", + "Ġscho ols", + "Ġ serve", + "Ġs erve", + "Ġse rve", + "Ġser ve", + "Ġserv e", + "z one", + "zo ne", + "zon e", + "_ db", + "_d b", + "Ġmajor ity", + "ex ample", + "exam ple", + "Ġ lang", + "Ġl ang", + "Ġla ng", + "Ġlan g", + "ĉ ĠĠ", + "ĉĠ Ġ", + "Reg ister", + "e ndo", + "en do", + "end o", + "Ġ processing", + "Ġp rocessing", + "Ġprocess ing", + "Ġproces sing", + "_ template", + "_t emplate", + "_temp late", + "_tem plate", + "- user", + "-use r", + "-us er", + "-u ser", + "Ġ eg", + "Ġe g", + "C OM", + "CO M", + "Ġ Blue", + "ĠB lue", + "ĠBl ue", + "ĠBlu e", + "i ro", + "ir o", + "Ġ remote", + "Ġrem ote", + "Ġremot e", + "Ġ IT", + "ĠI T", + "# !/", + "#! /", + "Ġred istrib", + "Ġredis trib", + "1 24", + "12 4", + "r az", + "ra z", + "Ġ Since", + "ĠS ince", + "ĠSi nce", + "ĠSin ce", + "Ġ Tur", + "ĠT ur", + "ĠTu r", + "1 35", + "13 5", + "Back ground", + "= ==", + "== =", + "Ġ reflect", + "Ġref lect", + "Ġrefl ect", + "Ġp ros", + "Ġpro s", + "Ġpr os", + "c md", + "cm d", + "Ġw hom", + "Ġwh om", + "Ġwho m", + "Com pat", + "Comp at", + "Ġ Are", + "ĠA re", + "ĠAr e", + "Id entifier", + "Ident ifier", + "ĠT hom", + "ĠTh om", + "_ port", + "_p ort", + "_po rt", + "_por t", + "g u", + "Ġ monitor", + "Ġm onitor", + "Ġmon itor", + "r m", + "Ġ patient", + "Ġp atient", + "Ġpat ient", + "ver ter", + "vert er", + "verte r", + "Ġ gain", + "Ġg ain", + "Ġga in", + "- ui", + "-u i", + "I nst", + "In st", + "Ins t", + "Ġd ies", + "Ġdi es", + "Ġdie s", + "1 18", + "11 8", + "A rea", + "Ar ea", + "Are a", + "_ filter", + "_f ilter", + "_fil ter", + "_filt er", + "Ġg rat", + "Ġgr at", + "Ġgra t", + "Ġre ality", + "Ġreal ity", + "ord inate", + "ordin ate", + "ol ved", + "olve d", + "olv ed", + "Cont act", + "Conta ct", + "Ġcom pliance", + "Ġcompl iance", + "_ or", + "_o r", + "Ġ Var", + "ĠV ar", + "ĠVa r", + "d l", + "Ġ append", + "Ġapp end", + "Ġap pend", + "Ġappe nd", + "G ER", + "GE R", + "( max", + "(m ax", + ". render", + ".re nder", + ".r ender", + "Ġ dynamic", + "Ġd ynamic", + "Ġdynam ic", + "Ġdyn amic", + "ordin ates", + "ordinate s", + "_ options", + "_option s", + "_o ptions", + "_opt ions", + "_ column", + "_c olumn", + "_col umn", + "Ġb atter", + "Ġbatt er", + "Ġbat ter", + "s pace", + "sp ace", + "spa ce", + "L a", + "Ġ Source", + "ĠS ource", + "ĠSour ce", + "/ bin", + "/b in", + "Ġ dos", + "Ġd os", + "Ġdo s", + "Ġ Board", + "ĠB oard", + "ĠBo ard", + "Ġ Thread", + "ĠT hread", + "ĠTh read", + "ĠThr ead", + "Ġ AL", + "ĠA L", + "( config", + "(con fig", + "(conf ig", + "1 44", + "14 4", + "Ġ Mer", + "ĠM er", + "ĠMe r", + "Ġm iles", + "Ġmil es", + "Ġmi les", + "Ġmile s", + "_ header", + "_head er", + "_he ader", + "ETH OD", + "i zz", + "iz z", + "Ġb enefit", + "Ġbenef it", + "Ġbene fit", + "Ġ integr", + "Ġint egr", + "Ġinteg r", + "Ġinte gr", + "( current", + "(c urrent", + "(cur rent", + "(curr ent", + "u lo", + "ul o", + ". default", + ".d efault", + ".de fault", + ".def ault", + "Ġ Div", + "ĠD iv", + "ĠDi v", + "Ġ ton", + "Ġt on", + "Ġto n", + "o th", + "ot h", + "er vation", + "erv ation", + "erva tion", + "e dom", + "ed om", + "edo m", + "Ġ baby", + "Ġb aby", + "Ġba by", + "Ġbab y", + "ce ived", + "ceive d", + ". top", + ".t op", + ".to p", + "rior ity", + "Ġ Local", + "ĠL ocal", + "ĠLo cal", + "ĠLoc al", + "r iage", + "ri age", + "ria ge", + "Ġ attacks", + "Ġatt acks", + "Ġattack s", + "Ġ hospital", + "Ġh ospital", + "Ġhosp ital", + "1 68", + "16 8", + "Ġ female", + "Ġf emale", + "Ġfe male", + "Ġfem ale", + "Ġ Login", + "ĠLog in", + "ĠLo gin", + "ĠF lor", + "ĠFl or", + "ĠFlo r", + "Ġ chain", + "Ġch ain", + "Ġcha in", + "Ġchai n", + "ash ion", + "ashi on", + "Text ure", + "Tex ture", + "S ave", + "Sa ve", + "Ġ farm", + "Ġf arm", + "Ġfa rm", + "Ġfar m", + ". contains", + ".con tains", + ".cont ains", + ". Test", + ".T est", + ".Te st", + "Ġkn ows", + "Ġknow s", + "Ġgener ally", + "Ġgeneral ly", + "ip eline", + "ipe line", + "ipel ine", + "Ġme ant", + "Ġmean t", + "e ncia", + "en cia", + "enc ia", + "enci a", + "Ġn icht", + "Ġni cht", + "Ġnic ht", + "Ġnich t", + "Ġ contents", + "Ġcont ents", + "Ġcontent s", + "Ġconten ts", + "Ġconte nts", + "P M", + "ched ule", + "( line", + "(l ine", + "(li ne", + "C G", + "j ob", + "jo b", + "Ġ Real", + "ĠRe al", + "u er", + "ue r", + "f irm", + "fi rm", + "fir m", + "Ġ Ø", + "e tro", + "et ro", + "etr o", + "\" `Ċ", + "\"` Ċ", + "Ġ speech", + "Ġs peech", + "Ġspe ech", + "Ġ thr", + "Ġt hr", + "Ġth r", + "f oreach", + "fo reach", + "fore ach", + "for each", + "Ġ warn", + "Ġw arn", + "Ġwar n", + "Ġwa rn", + "ĉ l", + "Ġ heavy", + "Ġhe avy", + "Ġheav y", + "< li", + " )", + "_ char", + "_c har", + "_ch ar", + "re source", + "res ource", + "Ġ episode", + "Ġep isode", + "Ġepis ode", + "Ġ '_", + "Ġ' _", + "Ġ Es", + "ĠE s", + "Ġ Earth", + "ĠE arth", + "ĠEar th", + "Âł Âł", + "UP DATE", + "1 33", + "13 3", + "Ġ Sou", + "ĠS ou", + "ĠSo u", + "u is", + "ui s", + "t ypes", + "type s", + "ty pes", + "typ es", + "Ġ mas", + "Ġm as", + "Ġma s", + "Ġ fav", + "Ġf av", + "Ġfa v", + "Ġ construct", + "Ġcon struct", + "Ġconstr uct", + "_ rate", + "_r ate", + "_ra te", + "_rat e", + "e ras", + "er as", + "era s", + "Ġ |Ċ", + "Ġ| Ċ", + "rop erties", + "Ġ external", + "Ġex ternal", + "Ġext ernal", + "Ġextern al", + "Ġexter nal", + "Ġapp lied", + "Ġap plied", + "Ġappl ied", + "Ġ prefix", + "Ġp refix", + "Ġpre fix", + "Ġpref ix", + "o ted", + "ot ed", + "ote d", + "l ers", + "le rs", + "ler s", + "Ġ cold", + "Ġc old", + "Ġco ld", + "Ġcol d", + "Ġ SP", + "ĠS P", + "Ġ Church", + "ĠCh urch", + "ĠChu rch", + "Ġ Output", + "ĠOut put", + "l osed", + "lo sed", + "lose d", + "los ed", + "ç ļ", + "if icate", + "ific ate", + "ifi cate", + "ifica te", + "o peration", + "op eration", + "ope ration", + "oper ation", + "he rit", + "her it", + "x FF", + "xF F", + ". env", + ".e nv", + ".en v", + "_ err", + "_e rr", + "_er r", + "o sh", + "os h", + "D irection", + "Dir ection", + "Direct ion", + "Di rection", + "Dire ction", + "C ancel", + "Can cel", + "Ġ Frank", + "ĠF rank", + "ĠFr ank", + "ĠFra nk", + "ĠFran k", + "Ġ finding", + "Ġf inding", + "Ġfind ing", + "Ġfin ding", + ". )ĊĊ", + ".) ĊĊ", + ".)Ċ Ċ", + "Ġ router", + "Ġr outer", + "Ġro uter", + "Ġroute r", + "Ġrout er", + "Ġrou ter", + "ãĥ »", + "s es", + "se s", + "Ġ crow", + "Ġc row", + "Ġcr ow", + "Ġcro w", + "= ='", + "== '", + "Ġ sand", + "Ġs and", + "Ġsa nd", + "Ġsan d", + "Ġ rid", + "Ġr id", + "Ġri d", + "i ture", + "it ure", + "itu re", + "itur e", + "Ġ entre", + "Ġen tre", + "Ġent re", + "Ġentr e", + "Ġ observ", + "Ġo bserv", + "Ġob serv", + "Ġobs erv", + "Ġ vac", + "Ġv ac", + "Ġva c", + "ð Ł", + "- T", + "A rt", + "Ar t", + "n ight", + "ni ght", + ". search", + ".s earch", + ".se arch", + "Ġ exchange", + "Ġex change", + "Ġ district", + "Ġd istrict", + "Ġdi strict", + "Ġdistr ict", + ". os", + ".o s", + "Ġ department", + "Ġde partment", + "Ġdep artment", + "Ġdepart ment", + "Ġ documents", + "Ġdocument s", + "Ġdoc uments", + "Ġcent ury", + "Ġ Next", + "ĠN ext", + "ĠNe xt", + "ĠNex t", + "H ost", + "Ho st", + "ĠK IND", + "Ġs usp", + "Ġsu sp", + "Ġsus p", + "- P", + "r end", + "re nd", + "ren d", + ". em", + ".e m", + "u ite", + "ui te", + "uit e", + "i sters", + "is ters", + "ist ers", + "ister s", + "iste rs", + "( json", + "(j son", + "(js on", + "Ġ Ann", + "ĠA nn", + "ĠAn n", + "w t", + "a ti", + "at i", + "Ġ HTML", + "ĠHT ML", + "w hen", + "wh en", + "D irectory", + "Direct ory", + "Director y", + "Ġsh ut", + "< a", + "e dy", + "ed y", + "Ġ healthy", + "Ġhealth y", + "Ġheal thy", + "Ġ temperature", + "Ġt emperature", + "Ġtem perature", + "Ġtemper ature", + "Ġ Gen", + "ĠG en", + "ĠGe n", + "Ġ metal", + "Ġm etal", + "Ġme tal", + "Ġmet al", + "Ġmeta l", + "Ġ submit", + "Ġsub mit", + "Ġ DO", + "ĠD O", + "Ġat tract", + "Ġatt ract", + "Ġattr act", + "Ġ {};Ċ", + "Ġ{ };Ċ", + "Ġ{} ;Ċ", + "Ġ{}; Ċ", + "Ġ Word", + "ĠW ord", + "ĠWo rd", + "ĠWor d", + "Ġ ll", + "Ġl l", + "Ġse emed", + "Ġsee med", + "Ġseem ed", + "k o", + "I ED", + "IE D", + "Ġl abor", + "Ġla bor", + "Ġlab or", + ". Context", + ".Cont ext", + ".Con text", + "Ġ asset", + "Ġas set", + "Ġass et", + "y ou", + "yo u", + "Ġ cars", + "Ġc ars", + "Ġcar s", + "Ġca rs", + "Ġ Column", + "ĠC olumn", + "ĠCol umn", + "ĠColum n", + "Ġ ré", + "Ġr é", + "Ġ square", + "Ġs quare", + "Ġsqu are", + "Ġ NSString", + "ĠNS String", + "ĠNSS tring", + "âĢĿ ,", + "a pes", + "ap es", + "ape s", + ". ..Ċ", + ".. .Ċ", + "... Ċ", + "Ġ thanks", + "Ġth anks", + "Ġthan ks", + "Ġthank s", + "( props", + "(p rops", + "(pro ps", + "(pr ops", + "(prop s", + "Ġ tick", + "Ġt ick", + "Ġti ck", + "Ġtic k", + "Ġ experiment", + "Ġex periment", + "Ġexper iment", + "Ġexperi ment", + "Ġpr ison", + "Ġpri son", + "Ġpris on", + "t ree", + "tr ee", + "tre e", + "- text", + "-t ext", + "-te xt", + "Ġ IOException", + "ĠIO Exception", + "- width", + "-w idth", + "_ STATUS", + "_ST ATUS", + "_STAT US", + "f ast", + "fa st", + "fas t", + "- body", + "-b ody", + "-bo dy", + "- header", + "-head er", + "-he ader", + "Ġg uar", + "Ġgu ar", + "c rete", + "cre te", + "cret e", + "cr ete", + "Ġ Tim", + "ĠT im", + "ĠTi m", + "Ġcl early", + "Ġclear ly", + "Ġ Republican", + "ĠRepublic an", + "Ġ justify", + "Ġjust ify", + "и ÑĤ", + "ĉ ĠĠĠĠ", + "ĉĠĠĠ Ġ", + "ĉĠ ĠĠĠ", + "ĉĠĠ ĠĠ", + "c ache", + "ca che", + "cac he", + "; //", + ";/ /", + "Ġ presence", + "Ġpres ence", + "Ġf actors", + "Ġfact ors", + "Ġfa ctors", + "Ġfac tors", + "Ġfactor s", + "Ġfacto rs", + "Ġ employee", + "Ġe mployee", + "Ġemploy ee", + "] ))", + "]) )", + "M ember", + "Mem ber", + "Ġ selector", + "Ġse lector", + "Ġselect or", + "Ġsel ector", + "Ġsele ctor", + "b or", + "bo r", + "Ġ Mex", + "ĠM ex", + "ĠMe x", + "çļ Ħ", + "u tex", + "ut ex", + "ute x", + "_ tag", + "_t ag", + "_ta g", + "ail ure", + "Ġ Net", + "ĠN et", + "ĠNe t", + "Ġre li", + "Ġr eli", + "Ġrel i", + "E G", + "Ġ fprintf", + "Ġf printf", + "Ġ teen", + "Ġt een", + "Ġte en", + "Ġtee n", + "l oss", + "lo ss", + "los s", + "Ġle aving", + "1 34", + "13 4", + "De legate", + "Ġ beat", + "Ġb eat", + "Ġbe at", + "Ġ minute", + "Ġmin ute", + "Ġminut e", + "sub scribe", + "subs cribe", + "Ġred istribute", + "Ġredistrib ute", + "Ġredis tribute", + "Con stants", + "Constant s", + "Const ants", + "Ġc ancer", + "Ġcan cer", + "Ġcanc er", + "/ {", + "B L", + "Ġ span", + "Ġs pan", + "Ġsp an", + "Ġspa n", + "Ġ Child", + "ĠCh ild", + "ĠChi ld", + "C enter", + "Cent er", + "Ġ earth", + "Ġe arth", + "Ġear th", + "Y S", + "Ġ Level", + "ĠLe vel", + "ĠLev el", + "Ġ sea", + "Ġs ea", + "Ġse a", + ". support", + ".s upport", + ".sup port", + ". inner", + ".in ner", + ". Item", + ".I tem", + ".It em", + "il ling", + "ill ing", + "illi ng", + "illin g", + "Ġ ĠĠĠĊĠĠĠĠĊ", + "ĠĠ ĠĠĊĠĠĠĠĊ", + "ĠĠĠĠ ĊĠĠĠĠĊ", + "ĠĠĠ ĠĊĠĠĠĠĊ", + "ĠĠĠĠĊ ĠĠĠĠĊ", + "Ġ Label", + "ĠL abel", + "ĠLa bel", + "ĠLab el", + "3 20", + "32 0", + "Ġ Est", + "ĠE st", + "ĠEs t", + "( arg", + "(a rg", + "(ar g", + "1 45", + "14 5", + "bo Box", + "ĉ foreach", + "ĉf oreach", + "ĉfor each", + "c os", + "co s", + "F ailed", + "Fail ed", + "Fa iled", + "s wers", + "sw ers", + "swer s", + "E ditor", + "Ed itor", + "Edit or", + "r ont", + "ro nt", + "ron t", + "Ġ MP", + "ĠM P", + "ex pr", + "exp r", + "Ġ Life", + "ĠL ife", + "ĠLi fe", + "ĠLif e", + "Ġ ??", + "Ġ? ?", + "ö r", + "Ġ attend", + "Ġatt end", + "Ġatte nd", + "Ġ Que", + "ĠQ ue", + "ĠQu e", + "Ġ species", + "Ġs pecies", + "Ġsp ecies", + "Ġspec ies", + "Ġspe cies", + "Ġspeci es", + "- D", + "Ġ aus", + "Ġa us", + "Ġau s", + "Str uct", + "Ġadv antage", + "Ġadvant age", + "o ston", + "os ton", + "ost on", + "osto n", + "- block", + "-b lock", + "-bl ock", + "in itial", + "init ial", + "C RE", + "CR E", + "Ġtr uly", + "Ġ compare", + "Ġcom pare", + "Ġcomp are", + "Ġcompar e", + "or ney", + "orn ey", + "orne y", + "Ġ spect", + "Ġs pect", + "Ġsp ect", + "Ġspec t", + "Ġspe ct", + "F ull", + "Fu ll", + "b es", + "be s", + "Ġ visible", + "Ġv isible", + "Ġvis ible", + "Ġ mess", + "Ġm ess", + "Ġme ss", + "Ġmes s", + "st ances", + "stance s", + "sta nces", + "stan ces", + "Ġ cloud", + "Ġc loud", + "Ġcl oud", + "Ġclo ud", + "_ version", + "_v ersion", + "Ġf urn", + "Ġfur n", + "Ġfu rn", + "ic ago", + "ica go", + "L OW", + "LO W", + "Ġ traffic", + "Ġtraff ic", + "Ġtra ffic", + "Ġtraf fic", + "Ġ fol", + "Ġf ol", + "Ġfo l", + "ry pto", + "rypt o", + "Ġ declar", + "Ġde clar", + "Ġdec lar", + "Ġdecl ar", + "Ġ slot", + "Ġs lot", + "Ġsl ot", + "Ġslo t", + "Ġ Ext", + "ĠE xt", + "ĠEx t", + "Ġ England", + "ĠEng land", + "ĠEngl and", + "Ġ Under", + "ĠU nder", + "ĠUn der", + "ĠUnd er", + "Ġ ta", + "Ġt a", + "l etter", + "let ter", + "lette r", + "lett er", + "2 03", + "20 3", + "Ġoff icer", + "Ġoffic er", + "Ġoffice r", + "Ġ Donald", + "ĠD onald", + "ĠDon ald", + "Y es", + "Ye s", + "_ json", + "_j son", + "_js on", + "I TableView", + "IT ableView", + "Ġ USE", + "ĠU SE", + "ĠUS E", + "mploy ee", + "Ġop inion", + "Ġopin ion", + "Ġ Aut", + "ĠA ut", + "ĠAu t", + "b order", + "bor der", + "Ġad vice", + "Ġadv ice", + "Ġautom atically", + "Ġautomatic ally", + "Ġautomat ically", + "is co", + "isc o", + "Ġ mm", + "Ġm m", + ". vis", + ".v is", + "a ml", + "am l", + "Ġ initialize", + "Ġinitial ize", + "Ġ ({", + "Ġ( {", + "Ġ ;ĊĊ", + "Ġ; ĊĊ", + "Ġ;Ċ Ċ", + "Ġ generation", + "Ġg eneration", + "Ġgener ation", + "Ġgen eration", + "Ġgene ration", + "Ġgenera tion", + "Ġ bits", + "Ġb its", + "Ġbit s", + "Ġbi ts", + "cl ipse", + "clip se", + "Ġu nf", + "Ġun f", + "u tors", + "ut ors", + "uto rs", + "utor s", + "p lt", + "pl t", + "Ġ delta", + "Ġd elta", + "Ġdel ta", + "Ġdelt a", + "e stroy", + "est roy", + "estr oy", + "estro y", + "i sis", + "is is", + "isi s", + "< br", + "Ċ", + "'> Ċ", + "a pers", + "ap ers", + "ape rs", + "aper s", + "] (", + "cont inue", + "contin ue", + "s pec", + "sp ec", + "spe c", + "Ġ Road", + "ĠR oad", + "ĠRo ad", + "A SH", + "AS H", + "il iar", + "ili ar", + "ilia r", + "Ġcontin ues", + "Ġcontinue s", + "Ġcontinu es", + "Ġ appoint", + "Ġapp oint", + "Ġap point", + "Ġ #Ċ", + "Ġ# Ċ", + "Ġ Vir", + "ĠV ir", + "ĠVi r", + "Ġ ?>\"", + "Ġ? >\"", + "Ġ?> \"", + "Ġ bin", + "Ġb in", + "Ġbi n", + "} \",", + "}\" ,", + "go ing", + "e ach", + "ea ch", + "B D", + "1 85", + "18 5", + "Ġ Access", + "ĠA ccess", + "ĠAc cess", + "ĠAcc ess", + "D oc", + "Do c", + "Ġ Management", + "ĠMan agement", + "ĠManage ment", + "ĠMana gement", + "B ER", + "BE R", + "as ket", + "ask et", + ". getInstance", + ".get Instance", + ".getIn stance", + "1 29", + "12 9", + "Ġestablish ed", + "s ocket", + "so cket", + "sock et", + "soc ket", + "I NS", + "IN S", + "ĉ virtual", + "ĉv irtual", + "ĉ result", + "ĉres ult", + "RE AD", + "REA D", + "_ height", + "_h eight", + "_he ight", + "1 52", + "15 2", + "Ġ Font", + "ĠF ont", + "ĠFo nt", + "ĠFon t", + "Ġ ();Ċ", + "Ġ( );Ċ", + "Ġ() ;Ċ", + "Ġ(); Ċ", + "_ html", + "_h tml", + "_ht ml", + "Ġ neighbor", + "Ġne ighbor", + "Ġneighb or", + "Ġneigh bor", + "l or", + "lo r", + "Ġ gather", + "Ġg ather", + "Ġga ther", + "Ġ })ĊĊ", + "Ġ} )ĊĊ", + "Ġ})Ċ Ċ", + "Ġ}) ĊĊ", + "Ġ identity", + "Ġid entity", + "Ġide ntity", + "Ġident ity", + "Ġ fab", + "Ġf ab", + "Ġfa b", + "p adding", + "pad ding", + "Ġ Route", + "ĠR oute", + "ĠRo ute", + "ĠRou te", + "ĠRout e", + "Enumer able", + "Enum erable", + "à ´", + "Ġ forced", + "Ġfor ced", + "Ġforce d", + "Ġforc ed", + "/ jquery", + "/j query", + ". ĊĊĊĊĊĊ", + ".ĊĊ ĊĊĊĊ", + ".Ċ ĊĊĊĊĊ", + ".ĊĊĊĊ ĊĊ", + ".ĊĊĊ ĊĊĊ", + ".ĊĊĊĊĊ Ċ", + "res ents", + "resent s", + "rese nts", + "_ left", + "_l eft", + "_le ft", + ". Param", + ".P aram", + ".Par am", + "ĉ throw", + "ĉth row", + "Ġ Ham", + "ĠH am", + "ĠHa m", + "Ġevent ually", + "Ġeventual ly", + "a cer", + "ace r", + "ac er", + "p ub", + "pu b", + "Ġ tra", + "Ġt ra", + "Ġtr a", + "un ique", + "uni que", + "uniq ue", + "d el", + "de l", + "Ġ Florida", + "ĠFl orida", + "ĠFlor ida", + "Ġ Clean", + "ĠC lean", + "ĠCl ean", + "ĠCle an", + "x a", + "Ġ ·", + "Ġ ·", + "Ġ validate", + "Ġvalid ate", + "Ġvalida te", + "Vis ual", + "Ex pression", + "Exp ression", + "Express ion", + "Expr ession", + "_ func", + "_f unc", + "_fun c", + "_fu nc", + "m ember", + "mem ber", + "ĉ h", + "t rl", + "tr l", + "1 36", + "13 6", + "ĉ G", + "nap shot", + "Ġ PropTypes", + "ĠProp Types", + "v in", + "vi n", + "1 53", + "15 3", + "] )ĊĊ", + "]) ĊĊ", + "])Ċ Ċ", + "o wl", + "ow l", + "if ies", + "ifi es", + "ifie s", + "Ġ $('.", + "Ġ$ ('.", + "Ġ$( '.", + "Ġ$(' .", + "Ġ Context", + "ĠCon text", + "ĠCont ext", + "ĠConte xt", + "Ġ Toast", + "ĠTo ast", + ". Key", + ".K ey", + "Ġoff icers", + "Ġoffic ers", + "Ġoffice rs", + "Ġofficer s", + "/ n", + "s n", + "un defined", + "und efined", + "undef ined", + ". items", + ".i tems", + ".item s", + ".it ems", + "ut ow", + "uto w", + "a mage", + "am age", + "ama ge", + "Ġ accounts", + "Ġac counts", + "Ġaccount s", + "o okie", + "ook ie", + "oo kie", + "S ection", + "Se ction", + "Sec tion", + "ic ians", + "ici ans", + "ician s", + "icia ns", + "Ġad vis", + "Ġadv is", + "( is", + "(i s", + "[ :,", + "[: ,", + "Ġ France", + "ĠF rance", + "ĠFr ance", + "ĠFranc e", + "ĠFra nce", + "ĠFran ce", + "F unc", + "Fun c", + "Fu nc", + "ic ious", + "ici ous", + "icio us", + "Ġ tok", + "Ġt ok", + "Ġto k", + "Ch annel", + "Chan nel", + "Ġ AD", + "ĠA D", + "_ NUM", + "_N UM", + "Ġ timeout", + "Ġtime out", + "l emma", + "le mma", + "lem ma", + "r eme", + "re me", + "rem e", + "u j", + ". Al", + ".A l", + "u clear", + "uc lear", + "ucle ar", + "( os", + "(o s", + "( \"<", + "(\" <", + "[ Ċ", + "f etch", + "fet ch", + "Ġ bal", + "Ġb al", + "Ġba l", + "Ġ guid", + "Ġg uid", + "Ġgu id", + "Ġgui d", + "- align", + "-al ign", + "Ġ Write", + "ĠW rite", + "ĠWr ite", + "Ġ Once", + "ĠO nce", + "ĠOn ce", + "ĠOnc e", + "utow ired", + "OD ULE", + "Ġ pitch", + "Ġp itch", + "Ġpit ch", + "C F", + "by tes", + "byte s", + "byt es", + "Ġ Commission", + "ĠCom mission", + "ĠComm ission", + "Ġin cred", + "Ġincre d", + "Ġinc red", + "Ġincr ed", + "P ER", + "PE R", + "_ response", + "_res ponse", + "_resp onse", + "Ġ Los", + "ĠL os", + "ĠLo s", + "p arser", + "par ser", + "parse r", + "pars er", + "Ġ assume", + "Ġas sume", + "Ġass ume", + "Ġassum e", + ". Request", + ".Re quest", + "Ġ Token", + "ĠT oken", + "ĠTo ken", + "ĠTok en", + "_ position", + "_p osition", + "_pos ition", + "Ġ nom", + "Ġn om", + "Ġno m", + "- term", + "-t erm", + "-te rm", + "Ġ remaining", + "Ġrem aining", + "Ġremain ing", + "i ostream", + "io stream", + "Ġ pieces", + "Ġp ieces", + "Ġpie ces", + "Ġpiece s", + "a py", + "ap y", + "Ġ Less", + "ĠL ess", + "ĠLe ss", + "ĠLes s", + "r ange", + "ra nge", + "ran ge", + "rang e", + "um bn", + "umb n", + "p rise", + "pr ise", + "pri se", + "_ option", + "_op tion", + "_o ption", + "_opt ion", + "2 30", + "23 0", + "I mpl", + "Im pl", + "Imp l", + "k wargs", + "kw args", + "Ġbusiness es", + "Al ert", + "Ale rt", + "Ġpart ies", + "Ġpar ties", + "Ġpartie s", + "Ġparti es", + "Ġ Container", + "ĠCont ainer", + "Ġ Private", + "ĠPr ivate", + "ĠPriv ate", + "Ġ Plan", + "ĠP lan", + "ĠPl an", + "ĠPla n", + "Ġ registered", + "Ġregister ed", + "Ġregist ered", + "Ġ jour", + "Ġj our", + "Ġjo ur", + "Ġjou r", + "a cker", + "ack er", + "ac ker", + "е ни", + "ен и", + "/ >", + "c hat", + "ch at", + "cha t", + "s ect", + "se ct", + "sec t", + "Ġ creation", + "Ġc reation", + "Ġcre ation", + "Ġcreat ion", + "Ġcrea tion", + "ol utely", + "olute ly", + "olut ely", + "Ġ instant", + "Ġin stant", + "Ġins tant", + "Ġinst ant", + "Ġ delivery", + "Ġd elivery", + "Ġdel ivery", + "Ġdeliver y", + "i cken", + "ic ken", + "ick en", + "y es", + "ye s", + "1 63", + "16 3", + "Ġ Franc", + "ĠFr anc", + "ĠFra nc", + "ĠFran c", + "b ling", + "bl ing", + "e nda", + "en da", + "end a", + "[ (", + "_ range", + "_r ange", + "_ra nge", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "Ġ schedule", + "Ġs chedule", + "Ġsched ule", + "C onn", + "Con n", + "Co nn", + "Ġ thank", + "Ġth ank", + "Ġthan k", + "Ġtha nk", + "x d", + "Ġ hook", + "Ġh ook", + "Ġho ok", + "Ġ documentation", + "Ġdocument ation", + "Param eters", + "Parameter s", + "H ello", + "Hel lo", + "Hell o", + "v t", + "Ġ articles", + "Ġart icles", + "Ġarticle s", + "Ġartic les", + "Ġ west", + "Ġw est", + "Ġwe st", + "Ġwes t", + "d efined", + "def ined", + "define d", + ". select", + ".s elect", + ".se lect", + ".sel ect", + "ok ens", + "oken s", + "oke ns", + "Ġ VAL", + "ĠV AL", + "ĠVA L", + ". file", + ".f ile", + ".fi le", + ".fil e", + "re set", + "res et", + "rese t", + "Ġ mys", + "Ġm ys", + "Ġmy s", + "Ġ MA", + "ĠM A", + "] ),", + "]) ,", + "Ġ cities", + "Ġc ities", + "Ġcit ies", + "Ġci ties", + "re lated", + "rel ated", + "å Ľ", + "Ġ appeared", + "Ġappe ared", + "Ġappear ed", + "Ġ wid", + "Ġw id", + "Ġwi d", + ". panel", + ".p anel", + ".pa nel", + ".pan el", + "Ġ Ins", + "ĠI ns", + "ĠIn s", + ". entity", + ".e ntity", + ".ent ity", + "Ġde cre", + "Ġdec re", + "Ġ Lou", + "ĠL ou", + "ĠLo u", + "( time", + "(t ime", + "(ti me", + "Ġ Thank", + "ĠTh ank", + "ĠThan k", + ". createElement", + ".create Element", + "Ġ mentioned", + "Ġmention ed", + "Ġment ioned", + "o unce", + "ou nce", + "oun ce", + "ounc e", + "Ġ Try", + "ĠT ry", + "ĠTr y", + "Ġ Wall", + "ĠW all", + "ĠWal l", + "ĠWa ll", + "/ images", + "/image s", + "/im ages", + "Ġ Menu", + "ĠM enu", + "ĠMe nu", + "ĠMen u", + "' čĊ", + "Ġ Er", + "ĠE r", + "Ġc ritic", + "Ġcr itic", + "Ġcrit ic", + "Ġcri tic", + "Ġ Year", + "ĠY ear", + "ĠYe ar", + "( param", + "(p aram", + "(par am", + "(pa ram", + "(para m", + "Ġ flo", + "Ġf lo", + "Ġfl o", + "N N", + "o oter", + "oot er", + "oo ter", + "Ġ ];Ċ", + "Ġ] ;Ċ", + "Ġ]; Ċ", + "Ġ Aff", + "ĠA ff", + "ĠAf f", + "\" github", + "\"g ithub", + "ro oms", + "room s", + "Ġ hyp", + "Ġh yp", + "Ġhy p", + "g lobal", + "glob al", + "Ġ avec", + "Ġa vec", + "Ġav ec", + "Ġave c", + "æľ Ī", + "Ġ completion", + "Ġcom pletion", + "Ġcomp letion", + "Ġcomple tion", + "Ġcomplet ion", + "Ġ cond", + "Ġc ond", + "Ġcon d", + "Ġco nd", + "on ymous", + "onym ous", + "( temp", + "(t emp", + "(te mp", + "Ġ stars", + "Ġst ars", + "Ġstar s", + "Ġsta rs", + "Ġ relevant", + "Ġre levant", + "Ġrele vant", + "Ġrelev ant", + "Ġ covered", + "Ġcover ed", + "Ġcov ered", + "Ġ elim", + "Ġe lim", + "Ġel im", + "_ types", + "_t ypes", + "_type s", + "_typ es", + "_ty pes", + "( bool", + "(b ool", + "Ġ tu", + "Ġt u", + "_ exists", + "_ex ists", + "_exist s", + "Ġ secure", + "Ġs ecure", + "Ġsec ure", + "Ġ stored", + "Ġst ored", + "Ġstore d", + "Ġstor ed", + "Ġsto red", + "] /", + "x F", + "Ġ Controller", + "ĠCont roller", + "ĠControl ler", + "ĠContr oller", + "Ġm igr", + "Ġmi gr", + "Ġmig r", + "M I", + "Ġ Den", + "ĠD en", + "ĠDe n", + "Ġ annual", + "Ġann ual", + "U IL", + "UI L", + "- and", + "-a nd", + "-an d", + "Ġ crime", + "Ġcr ime", + "Ġcri me", + "Ġcrim e", + "b el", + "be l", + "Ġk itchen", + "Ġkit chen", + "@ g", + "_ ph", + "_p h", + "ourn ament", + "Ġ Social", + "ĠS ocial", + "ĠSo cial", + "ĠSoc ial", + "ĠSoci al", + "Ġ Special", + "ĠS pecial", + "ĠSp ecial", + "ĠSpec ial", + "ĠSpe cial", + "lo gger", + "log ger", + "logg er", + "Ġ tail", + "Ġt ail", + "Ġta il", + "Ġtai l", + "Ġ unknown", + "Ġun known", + "Ġunk nown", + "Ġunknow n", + "d ed", + "de d", + "Ġapp rec", + "Ġap prec", + "( db", + "(d b", + "c f", + "1 55", + "15 5", + "Ġ assign", + "Ġas sign", + "Ġass ign", + "- out", + "-o ut", + "Ġ Mont", + "ĠM ont", + "ĠMon t", + "ĠMo nt", + "d p", + "w idget", + "wid get", + "Ġ stone", + "Ġs tone", + "Ġst one", + "Ġsto ne", + "- primary", + "-pr imary", + ". grid", + ".g rid", + ".gr id", + "Res ults", + "Result s", + "a zz", + "az z", + "Ġ daughter", + "Ġda ughter", + "Ġ curr", + "Ġc urr", + "Ġcur r", + "Ġcu rr", + "1 75", + "17 5", + "Ġ lin", + "Ġl in", + "Ġli n", + "Ġ south", + "Ġs outh", + "Ġso uth", + "Ġsou th", + "Ġsout h", + "form s", + "fo rms", + "for ms", + "Ġ OUT", + "ĠO UT", + "ĠOU T", + "l ette", + "le tte", + "let te", + "lett e", + "a ks", + "ak s", + "ig ure", + "igu re", + "Ġ EU", + "ĠE U", + "var iable", + "vari able", + "Ġ brief", + "Ġb rief", + "Ġbr ief", + "Ġbri ef", + "Ġ Scott", + "ĠS cott", + "ĠSc ott", + "ĠScot t", + "ĠSco tt", + "Ġ conference", + "Ġcon ference", + "Ġconf erence", + "Ġconfer ence", + "a nda", + "an da", + "and a", + "_ lock", + "_l ock", + "_lo ck", + "_loc k", + "o ral", + "or al", + "ora l", + "Ġe ine", + "Ġein e", + "Ġei ne", + "O RS", + "OR S", + "//// ////////////////////////////////////////////////////////////", + "//////// ////////////////////////////////////////////////////////", + "//////////////// ////////////////////////////////////////////////", + "//////////////////////////////// ////////////////////////////////", + "//////////// ////////////////////////////////////////////////////", + "//////////////////////////////////////////////// ////////////////", + "//////////////////////////////////////////////////////// ////////", + "//////////////////////////////////////////////////////////// ////", + "//////////////////////////////////////////////////// ////////////", + "es so", + "ess o", + "Ġ ris", + "Ġr is", + "Ġri s", + "Ġ gender", + "Ġg ender", + "Ġge nder", + "Ġgen der", + "es tic", + "est ic", + "esti c", + "L icense", + "Lic ense", + "( out", + "(o ut", + "Ġ ms", + "Ġm s", + "S ee", + "Se e", + "Ġw illing", + "Ġwill ing", + "Ġwil ling", + "a ze", + "az e", + "Ġ sports", + "Ġs ports", + "Ġsp orts", + "Ġsport s", + "Ġspo rts", + "Ġspor ts", + "Ġ yes", + "Ġy es", + "Ġye s", + "l u", + "Ġp urs", + "Ġpur s", + "Ġpu rs", + "/ javascript", + "/j avascript", + "/java script", + "/jav ascript", + "- pro", + "-p ro", + "-pr o", + "nav bar", + "_ product", + "_pro duct", + "_prod uct", + "/ bootstrap", + "/boot strap", + "Ġdr iving", + "Ġdriv ing", + "Ġdri ving", + "Ġ Ä", + "Ġpro pos", + "Ġprop os", + "ul tip", + "ult ip", + "ulti p", + "up lic", + ". email", + ".e mail", + ".em ail", + "Ġ approx", + "Ġapp rox", + "Ġap prox", + "Ġappro x", + "( cl", + "(c l", + "Ġ wear", + "Ġw ear", + "Ġwe ar", + "Ġ reply", + "Ġre ply", + "Ġrep ly", + "Ġrepl y", + "as set", + "ass et", + "asse t", + "Ġ ice", + "Ġi ce", + "Ġic e", + "Ġ tx", + "Ġt x", + "k r", + "Ġ Germany", + "ĠGerman y", + "ĠGer many", + "ĠGerm any", + "Ġ George", + "ĠGe orge", + "ĠGeorg e", + "Ġ cb", + "Ġc b", + "ĉ err", + "ĉe rr", + "M ove", + "Mo ve", + "Mov e", + "Ġ poly", + "Ġp oly", + "Ġpol y", + "Ġpo ly", + "v oice", + "vo ice", + "} \"", + "Ġ animal", + "Ġan imal", + "Ġanim al", + "Ġani mal", + "A v", + "Ġ Location", + "ĠL ocation", + "ĠLo cation", + "ĠLoc ation", + "Ġ native", + "Ġn ative", + "Ġnat ive", + "] [\"", + "][ \"", + "< double", + " \"", + "s tat", + "st at", + "sta t", + "Ġ },čĊ", + "Ġ} ,čĊ", + "Ġ}, čĊ", + "< span", + " =", + "Ð ±", + "1 39", + "13 9", + "i va", + "iv a", + ". AutoSize", + ".Auto Size", + "Ġ Lat", + "ĠL at", + "ĠLa t", + "_ ext", + "_e xt", + "_ex t", + "Initial ize", + ". register", + ".reg ister", + "1 56", + "15 6", + "O PY", + "OP Y", + "Ġ reverse", + "Ġre verse", + "Ġrev erse", + "Ġrevers e", + "Ġrever se", + "_ dis", + "_d is", + "_di s", + "' ][", + "'] [", + "Ġ prompt", + "Ġp rompt", + "Ġprom pt", + "on to", + "ont o", + "Ġ Journal", + "ĠJ ournal", + "ĠJo urnal", + "r outer", + "ro uter", + "rou ter", + "route r", + "Ġ mysqli", + "Ġm ysqli", + "Ġmys qli", + "Ġmysql i", + "# else", + ") \"", + "- xs", + "-x s", + "l ets", + "le ts", + "let s", + "p han", + "ph an", + "pha n", + ". LE", + ".L E", + "1 37", + "13 7", + "W ill", + "Wil l", + "Wi ll", + "Ġaff ord", + "Ġaf ford", + "Ġ skill", + "Ġs kill", + "Ġsk ill", + "Ġski ll", + "- toggle", + "-t oggle", + "N C", + "B ind", + "Bin d", + "Bi nd", + "T S", + "J ust", + "Ju st", + "it eral", + "ite ral", + "iter al", + "Y P", + "ĉ unsigned", + "ĉun signed", + "Ġ wind", + "Ġw ind", + "Ġwin d", + "Ġwi nd", + "1 49", + "14 9", + ") ):Ċ", + ")) :Ċ", + ")): Ċ", + "Ġ warning", + "Ġw arning", + "Ġwar ning", + "Ġwarn ing", + "Ġ Water", + "ĠW ater", + "ĠWat er", + "ĠWa ter", + "Ġ draft", + "Ġd raft", + "Ġdr aft", + "Ġdra ft", + "Ġ cm", + "Ġc m", + "Ġ sam", + "Ġs am", + "Ġsa m", + "Ġ holding", + "Ġh olding", + "Ġhold ing", + "Ġhol ding", + "z ip", + "zi p", + "Ġ Science", + "ĠSc ience", + "ĠSci ence", + "Ġsup posed", + "Ġsuppose d", + "Ġsupp osed", + "G en", + "Ge n", + "Ġ diet", + "Ġd iet", + "Ġdi et", + "Ġdie t", + "< h", + "Ġ Pass", + "ĠP ass", + "ĠPa ss", + "ĠPas s", + "v i", + "Ġh usband", + "Ġhus band", + "� �", + "n ote", + "not e", + "no te", + "Ġ About", + "ĠA bout", + "ĠAb out", + "ĠIn stitute", + "ĠInstit ute", + "ĠInstitut e", + "Ġ climate", + "Ġcl imate", + "Ġclim ate", + "Ġcli mate", + ". Format", + ".Form at", + ".For mat", + "Ġ nut", + "Ġn ut", + "Ġnu t", + "e sted", + "es ted", + "est ed", + "este d", + "Ġapp arent", + "Ġap parent", + "Ġappar ent", + "Ġappare nt", + "Ġ holds", + "Ġh olds", + "Ġhold s", + "Ġho lds", + "Ġhol ds", + "f i", + "n ews", + "ne ws", + "new s", + "C M", + "v ideo", + "vid eo", + "vi deo", + "vide o", + "' :'", + "': '", + "D ITION", + "DI TION", + "p ing", + "pi ng", + "pin g", + "Ġsen ior", + "Ġseni or", + "w a", + "- ->Ċ", + "-- >Ċ", + "--> Ċ", + "_ default", + "_d efault", + "_de fault", + "_def ault", + "Ġ Database", + "ĠD atabase", + "ĠData base", + "ĠDat abase", + "r ep", + "re p", + "E SS", + "ES S", + "n ergy", + "ner gy", + "nerg y", + ". Find", + ".F ind", + "_ mask", + "_m ask", + "_ma sk", + "_mas k", + "Ġ rise", + "Ġr ise", + "Ġris e", + "Ġri se", + "Ġ kernel", + "Ġk ernel", + "Ġker nel", + "Ġkern el", + ": :$", + ":: $", + ". Q", + "Ġoff ering", + "Ġoffer ing", + "de cl", + "dec l", + "Ġ CS", + "ĠC S", + "Ġ listed", + "Ġl isted", + "Ġli sted", + "Ġlist ed", + "Ġliste d", + "Ġlis ted", + "Ġ mostly", + "Ġmost ly", + "e nger", + "en ger", + "eng er", + "enge r", + "Ġ blocks", + "Ġb locks", + "Ġbl ocks", + "Ġblock s", + "Ġblo cks", + "Ġbloc ks", + "o lo", + "ol o", + "Ġgover ning", + "Ġgovern ing", + "\\ F", + "Ġcon cent", + "Ġconc ent", + "Ġconce nt", + ". getText", + ".get Text", + "Ġ mb", + "Ġm b", + "Ġocc urred", + "Ġoccur red", + "Ġ changing", + "Ġch anging", + "Ġchang ing", + "Ġchan ging", + "S cene", + "Sc ene", + "_ CODE", + "_C ODE", + "_CO DE", + "_COD E", + "B eh", + "Be h", + "\" The", + "\"T he", + "Ġ tile", + "Ġt ile", + "Ġti le", + "Ġtil e", + "Ġ Association", + "ĠAssoci ation", + "ĠAssoc iation", + "ĉ P", + "al ty", + "alt y", + "_ ad", + "_a d", + "od ies", + "odi es", + "odie s", + "i ated", + "ia ted", + "iate d", + "iat ed", + "Ġ prepared", + "Ġpre pared", + "Ġprepar ed", + "Ġprepare d", + "Ġprep ared", + "p ossible", + "poss ible", + "Ġ mort", + "Ġm ort", + "Ġmor t", + "Ġmo rt", + "T EST", + "TE ST", + "TES T", + "1 42", + "14 2", + "Ġ ignore", + "Ġi gnore", + "Ġign ore", + "Ġig nore", + "Ġignor e", + "Ġ calc", + "Ġc alc", + "Ġcal c", + "Ġca lc", + "Ġ rs", + "Ġr s", + "Ġ assertEquals", + "Ġassert Equals", + "Ġ sz", + "Ġs z", + "Ġ THIS", + "ĠTH IS", + ". \"Ċ", + ".\" Ċ", + "Ġ canvas", + "Ġc anvas", + "Ġcan vas", + "Ġcanv as", + "j ava", + "ja va", + "jav a", + "Ġd ut", + "Ġdu t", + "VAL ID", + ". sql", + ".s ql", + ". input", + ".in put", + "Ġ aux", + "Ġa ux", + "Ġau x", + "S up", + "Su p", + "Ġ artist", + "Ġart ist", + "V ec", + "Ve c", + "_ TIME", + "_T IME", + "_TIM E", + "_TI ME", + ".string ify", + "et ween", + "Ġ Category", + "ĠC ategory", + "Ġ [-", + "Ġ[ -", + "Ġ DevExpress", + "ĠDev Express", + "Ġ Jul", + "ĠJ ul", + "ĠJu l", + "Ġ ring", + "Ġr ing", + "Ġri ng", + "Ġrin g", + ". ed", + ".e d", + "Y Y", + "L et", + "Le t", + "T extField", + "Text Field", + "Ġ flat", + "Ġf lat", + "Ġfl at", + "Ġfla t", + "_ print", + "_p rint", + "_pr int", + "_pri nt", + "Ġ OTHER", + "ĠOT HER", + "ad ian", + "adi an", + "adia n", + "Ġ checked", + "Ġcheck ed", + "e le", + "el e", + "Al ign", + "Ali gn", + "st anding", + "stand ing", + "stan ding", + "Ġ [],", + "Ġ[ ],", + "Ġ[] ,", + "Ġ lab", + "Ġl ab", + "Ġla b", + "u cky", + "uc ky", + "uck y", + "Ġ Christmas", + "ĠChrist mas", + "( image", + "(i mage", + "(im age", + ". module", + ".m odule", + ".mod ule", + "Ġ lots", + "Ġl ots", + "Ġlo ts", + "Ġlot s", + "Ġs lightly", + "Ġsl ightly", + "Ġslight ly", + "( final", + "(f inal", + "(fin al", + "(fi nal", + "er ge", + "erg e", + "è ¿", + "1 47", + "14 7", + "Ġ Police", + "ĠPol ice", + "ĠPo lice", + "ĠPolic e", + "1 43", + "14 3", + "Ġ Right", + "ĠR ight", + "ĠRig ht", + "ĠRi ght", + "Ġ award", + "Ġa ward", + "Ġaw ard", + "Ġ OS", + "ĠO S", + "Ġ {}ĊĊ", + "Ġ{ }ĊĊ", + "Ġ{} ĊĊ", + "Ġ{}Ċ Ċ", + "Ġ ptr", + "Ġp tr", + "Ġpt r", + "o ves", + "ov es", + "ove s", + "ic ated", + "ica ted", + "icate d", + "е м", + "еР¼", + "Ġ manage", + "Ġman age", + "Ġma nage", + "Ġmana ge", + "ol iday", + "olid ay", + "oli day", + "A mount", + "Am ount", + "ool Strip", + "t body", + "tb ody", + "N av", + "Na v", + "w rap", + "wr ap", + "B B", + "Ġw atching", + "Ġwatch ing", + "Ġwat ching", + "a rios", + "ar ios", + "ari os", + "ario s", + "Ġ optional", + "Ġoption al", + "Ġopt ional", + "_ K", + "Ġ Licensed", + "ĠL icensed", + "ĠLicense d", + "ĠLic ensed", + ". Map", + ".M ap", + ".Ma p", + "T imer", + "Time r", + "Tim er", + "Ti mer", + "Ġ AP", + "ĠA P", + "Ġ Rev", + "ĠR ev", + "ĠRe v", + "( o", + ", c", + "u min", + "um in", + "umi n", + "et ailed", + "etail ed", + "eta iled", + "Ġ Hy", + "ĠH y", + "Ġ blank", + "Ġbl ank", + "Ġbla nk", + "a gger", + "ag ger", + "agg er", + "Ġ Self", + "ĠS elf", + "ĠSe lf", + "ĠSel f", + "( )[", + "() [", + ". make", + ".m ake", + ".ma ke", + "e arn", + "ear n", + "ea rn", + "ch annel", + "chan nel", + "< pre", + "

;Ċ", + ">; Ċ", + "W orld", + "Ġ python", + "Ġp ython", + "Ġpy thon", + "Ġpyt hon", + "Ġ lif", + "Ġl if", + "Ġli f", + "Ġt rav", + "Ġtr av", + "Ġtra v", + "Ġcon ven", + "Ġconv en", + "com pany", + "comp any", + "compan y", + "Ġ Club", + "ĠC lub", + "ĠCl ub", + "1 38", + "13 8", + "V er", + "Ve r", + "B tn", + "Ġ zone", + "Ġz one", + "Ġzo ne", + "product s", + "produ cts", + "Ġ Educ", + "ĠE duc", + "ĠEd uc", + "ĠEdu c", + "Ġ verify", + "Ġver ify", + "Ġveri fy", + "Ġ Mil", + "ĠM il", + "ĠMi l", + "o no", + "on o", + "] );ĊĊ", + "]) ;ĊĊ", + "]);Ċ Ċ", + "]); ĊĊ", + "EN CE", + "ENC E", + "Ġ packet", + "Ġp acket", + "Ġpack et", + "Ġpa cket", + "Ġpac ket", + "Ġ cer", + "Ġc er", + "Ġce r", + "Ġ enumer", + "Ġe numer", + "Ġen umer", + "Ġenum er", + "Ġ pars", + "Ġp ars", + "Ġpar s", + "Ġpa rs", + "form ed", + "for med", + "forme d", + "Ġ occup", + "Ġocc up", + "Ġoc cup", + "t re", + "tr e", + "Ġ exercise", + "Ġex ercise", + "Ġexerc ise", + "D ay", + "Da y", + "_ sum", + "_s um", + "_su m", + "Ġ asking", + "Ġas king", + "Ġask ing", + "a ption", + "ap tion", + "apt ion", + "Ġ orders", + "Ġor ders", + "Ġorder s", + "Ġord ers", + "Ġs pending", + "Ġsp ending", + "Ġspend ing", + "Ġ ERR", + "ĠE RR", + "ĠER R", + ". Dis", + ".D is", + ".Di s", + "Ġ Util", + "ĠU til", + "ĠUt il", + "âĢľ I", + "\\ '", + "? )", + "/ >Ċ", + "/> Ċ", + "Ġe mot", + "Ġem ot", + "Ġemo t", + "Ġin fluence", + "Ġinflu ence", + "Ġ Africa", + "ĠA frica", + "ĠAfr ica", + "ĠAf rica", + "at ters", + "att ers", + "atter s", + "atte rs", + "Ù ħ", + ". session", + ".s ession", + ".sess ion", + "Ġ chief", + "Ġch ief", + "Ġchi ef", + "ĉ ĉĉĉĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉĉĉĉ", + "ĉĉĉĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉĉ ĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉĉĉĉ ĉ", + "Ġ tom", + "Ġt om", + "Ġto m", + "cl uded", + "clude d", + "clud ed", + "s erial", + "se rial", + "ser ial", + "_ handler", + "_h andler", + "_handle r", + "_hand ler", + ". Type", + ".T ype", + "a ped", + "ap ed", + "ape d", + "Ġp olicies", + "Ġpol icies", + "Ġpolic ies", + "- ex", + "-e x", + "- tr", + "-t r", + "bl ank", + "bla nk", + "m erce", + "mer ce", + "merc e", + "Ġ coverage", + "Ġco verage", + "Ġcover age", + "Ġ rc", + "Ġr c", + "_ matrix", + "_m atrix", + "_mat rix", + "_ box", + "_b ox", + "_bo x", + "Ġ charges", + "Ġch arges", + "Ġchar ges", + "Ġcharg es", + "Ġcharge s", + "Ġ Boston", + "ĠB oston", + "ĠBo ston", + "ĠBos ton", + "P e", + "Ġcirc um", + "Ġcir cum", + "Ġ filled", + "Ġf illed", + "Ġfil led", + "Ġfill ed", + "Ġfille d", + "1 48", + "14 8", + "Ġ north", + "Ġn orth", + "Ġnor th", + "icture Box", + "ĉ res", + "ĉr es", + "ĉre s", + "è ®", + "Ġ termin", + "Ġter min", + "Ġterm in", + "Ġ [âĢ¦", + "Ġ[ âĢ¦", + "I RECT", + "IR ECT", + "IRE CT", + "Ġ ber", + "Ġb er", + "Ġbe r", + "Ġ\" ../../", + "Ġ\"../ ../", + "Ġ\".. /../", + "r etch", + "ret ch", + ". code", + ".c ode", + ".co de", + ".cod e", + "_ col", + "_c ol", + "_co l", + "Ġ Government", + "ĠG overnment", + "ĠGovern ment", + "Ġ argv", + "Ġar gv", + "Ġarg v", + "Ġ Lord", + "ĠL ord", + "ĠLo rd", + "ĠLor d", + "a si", + "as i", + "E xec", + "Ex ec", + "ĉ let", + "ĉl et", + "vert is", + "Ġ discussion", + "Ġdisc ussion", + "Ġdiscuss ion", + "en ance", + "ena nce", + "enan ce", + "ou tube", + "out ube", + "outu be", + "type of", + "typ eof", + "Ġs erved", + "Ġser ved", + "Ġserv ed", + "Ġserve d", + "Ġ Put", + "ĠP ut", + "ĠPu t", + "ĉ x", + "Ġ sweet", + "Ġs weet", + "Ġswe et", + "B efore", + "Be fore", + "ate gy", + "ateg y", + ". of", + ".o f", + "Ġ Material", + "ĠM aterial", + "ĠMat erial", + "ĠMate rial", + "ĠMater ial", + "S ort", + "So rt", + "O NT", + "ON T", + "ig ital", + "igit al", + "igi tal", + "W hy", + "Wh y", + "Ġs ust", + "Ġsu st", + "Ġsus t", + "Ġ ç", + "a bet", + "ab et", + "abe t", + "Ġ segment", + "Ġs egment", + "Ġse gment", + "Ġseg ment", + "Ġ [],Ċ", + "Ġ[ ],Ċ", + "Ġ[] ,Ċ", + "Ġ[], Ċ", + "Ġ Muslim", + "ĠM uslim", + "ĠMus lim", + "Ġ findViewById", + "Ġfind ViewById", + "c ut", + "cu t", + "_ TEXT", + "_T EXT", + "_TE XT", + "_TEX T", + "Ġ Mary", + "ĠM ary", + "ĠMar y", + "ĠMa ry", + "Ġl oved", + "Ġlo ved", + "Ġlove d", + "Ġlov ed", + "Ġ lie", + "Ġl ie", + "Ġli e", + "Ġ JO", + "ĠJ O", + "Ġ isset", + "Ġis set", + "Ġiss et", + "m onth", + "mon th", + "mo nth", + "mont h", + "Ġ prime", + "Ġpr ime", + "Ġprim e", + "Ġpri me", + "t i", + "Ġ Carol", + "ĠCar ol", + "ĠCa rol", + "U se", + "Us e", + "1 46", + "14 6", + "Ġ Pop", + "ĠP op", + "ĠPo p", + "Ġ Save", + "ĠS ave", + "ĠSa ve", + "ĠSav e", + "Int erval", + "Inter val", + "ex ecute", + "exec ute", + "d y", + "Ġ Iran", + "ĠI ran", + "ĠIr an", + "_ cont", + "_c ont", + "_con t", + "_co nt", + "ĉ T", + "Ġ phase", + "Ġph ase", + "Ġpha se", + "check box", + "we ek", + "Ġ hide", + "Ġh ide", + "Ġhi de", + "Ġhid e", + "Ġ til", + "Ġt il", + "Ġti l", + "Ġ ju", + "Ġj u", + "C ustom", + "b urg", + "bur g", + "bu rg", + "/ M", + "T ON", + "TO N", + "Ġ quant", + "Ġqu ant", + "Ġq uant", + "Ġqua nt", + "Ġquan t", + "Ġ rub", + "Ġr ub", + "Ġru b", + "ix els", + "ixel s", + "ixe ls", + "Ġ installed", + "Ġinst alled", + "Ġinstall ed", + "Ġinstal led", + "Ġ dump", + "Ġd ump", + "Ġdu mp", + "Ġdum p", + "Ġproper ly", + "( List", + "(L ist", + "Ġdec ide", + "Ġdecid e", + "ap ply", + "app ly", + "appl y", + "H as", + "Ha s", + "Ġ keeping", + "Ġke eping", + "Ġkeep ing", + "Ġcit izens", + "Ġcitiz ens", + "Ġcitizen s", + "Ġ joint", + "Ġj oint", + "Ġjoin t", + "Ġjo int", + "p ool", + "po ol", + "S ocket", + "So cket", + "Sock et", + "_ op", + "_o p", + "Ġ weapon", + "Ġwe apon", + "Ġweap on", + "g nore", + "gn ore", + "Ġ Exec", + "ĠE xec", + "ĠEx ec", + "ot ten", + "ott en", + "otte n", + "Ġ MS", + "ĠM S", + "Ġ (-", + "Ġ( -", + "Ġ Review", + "ĠR eview", + "ĠRe view", + "ĠRev iew", + "Ġ examples", + "Ġex amples", + "Ġexample s", + "Ġexam ples", + "Ġ tight", + "Ġt ight", + "Ġti ght", + "! (", + "D P", + "Ġ MessageBox", + "ĠMessage Box", + "Ġphot ograph", + "Ġphoto graph", + "1 64", + "16 4", + "U RI", + "UR I", + "é t", + "l ow", + "lo w", + "Ġ Grand", + "ĠG rand", + "ĠGr and", + "ĠGran d", + "ĠGra nd", + ".p ersistence", + ".persist ence", + "Ġmain tain", + "Ġmaint ain", + "Ġ nums", + "Ġn ums", + "Ġnum s", + "Ġnu ms", + "Ġ zip", + "Ġz ip", + "Ġzi p", + "i als", + "ial s", + "ia ls", + "Ġ Gets", + "ĠG ets", + "ĠGet s", + "ĠGe ts", + "p eg", + "pe g", + "Ġ Buffer", + "ĠB uffer", + "ĠBuff er", + "ĠBu ffer", + "ĠBuf fer", + "~~ ~~", + "ra structure", + "Ġ PL", + "ĠP L", + "u en", + "ue n", + "ob by", + "obb y", + "size of", + "siz eof", + "Ġ pic", + "Ġp ic", + "Ġpi c", + "Ġ seed", + "Ġs eed", + "Ġse ed", + "Ġsee d", + "Ġex perienced", + "Ġexperience d", + "Ġexperi enced", + "Ġ odd", + "Ġo dd", + "Ġod d", + "Ġ kick", + "Ġk ick", + "Ġki ck", + "Ġ procedure", + "Ġpro cedure", + "Ġproced ure", + "av igator", + "avig ator", + "- on", + "-o n", + ", j", + "Ġ Although", + "ĠAl though", + "Ġ userId", + "Ġuser Id", + "ac cept", + "acc ept", + "B lue", + "Bl ue", + "I Color", + "IC olor", + "l ayer", + "la yer", + "lay er", + "a vailable", + "av ailable", + "avail able", + "Ġ ends", + "Ġe nds", + "Ġen ds", + "Ġend s", + ". table", + ".t able", + ".tab le", + ".ta ble", + "Ġ dataset", + "Ġd ataset", + "Ġdata set", + "Ġdat aset", + "Ġdatas et", + "b us", + "bu s", + "Ġ explain", + "Ġex plain", + "Ġexp lain", + "Ġexpl ain", + "( pro", + "(p ro", + "(pr o", + "ĠCommit tee", + "Ġn oted", + "Ġnot ed", + "Ġno ted", + "Ġnote d", + "] :Ċ", + "]: Ċ", + "D im", + "Di m", + "st dio", + "std io", + "1 54", + "15 4", + ". \",Ċ", + ".\" ,Ċ", + ".\", Ċ", + "_ source", + "_s ource", + "1 81", + "18 1", + "Ġ Week", + "ĠWe ek", + "Ġ Edge", + "ĠE dge", + "ĠEd ge", + "Ġoper ating", + "Ġopera ting", + "Ġ este", + "Ġe ste", + "Ġes te", + "Ġest e", + "i pl", + "ip l", + "3 30", + "33 0", + "ag ination", + "agi nation", + "agina tion", + "Ġpro ceed", + "Ġproc eed", + "Ġ animation", + "Ġan imation", + "Ġanim ation", + ". Models", + ".Model s", + ".Mod els", + ".Mode ls", + "Ġ Watch", + "ĠW atch", + "ĠWat ch", + "i at", + "ia t", + "Ġop pon", + "Ġopp on", + "/ A", + "Re port", + "Rep ort", + "Repo rt", + "Ġ sounds", + "Ġs ounds", + "Ġso unds", + "Ġsound s", + "Ġsou nds", + "_ buf", + "_b uf", + "IE LD", + "IEL D", + "Ġb und", + "Ġbu nd", + "Ġbun d", + "ĉ get", + "ĉg et", + ". pr", + ".p r", + "( tmp", + "(t mp", + "(tm p", + "Ġ kid", + "Ġk id", + "Ġki d", + "> ĊĊĊ", + ">Ċ ĊĊ", + ">ĊĊ Ċ", + "Ġ yang", + "Ġy ang", + "Ġya ng", + "Ġyan g", + "Not Found", + "Ñ Ĩ", + "m ath", + "ma th", + "mat h", + "@ gmail", + "@g mail", + "Ġ LIMIT", + "ĠL IMIT", + "ĠLI MIT", + "red ients", + "redient s", + "redi ents", + "Ġ vent", + "Ġv ent", + "Ġve nt", + "Ġven t", + "av igate", + "avig ate", + "avi gate", + "L ook", + "Lo ok", + "Ġ religious", + "Ġrel igious", + "Ġrelig ious", + "Ġ rand", + "Ġr and", + "Ġran d", + "Ġra nd", + "r io", + "ri o", + "( GL", + "(G L", + "_ ip", + "_i p", + "u an", + "ua n", + "ici ency", + "icie ncy", + "Ġ Change", + "ĠCh ange", + "ĠCha nge", + "ĠChan ge", + "ĠChang e", + "> čĊčĊ", + ">čĊ čĊ", + "Ġ Entity", + "ĠE ntity", + "ĠEnt ity", + "Ġren contre", + "Ġrencont re", + "Ġrencontr e", + "Ġ Ret", + "ĠR et", + "ĠRe t", + "p lan", + "pl an", + "pla n", + "é n", + "BO OL", + "u ries", + "ur ies", + "uri es", + "t rain", + "tr ain", + "tra in", + "Def inition", + "= ===========", + "== ==========", + "==== ========", + "======== ====", + "=== =========", + "=========== =", + "========= ===", + "========== ==", + "====== ======", + "===== =======", + "======= =====", + "z z", + "4 50", + "45 0", + "An imation", + "Anim ation", + "Ġ OK", + "ĠO K", + "_ menu", + "_m enu", + "_me nu", + ". bl", + ".b l", + "_ score", + "_s core", + "_sc ore", + "Ġ acad", + "Ġa cad", + "Ġac ad", + "( System", + "(S ystem", + "Ġ refresh", + "Ġre fresh", + "Ġref resh", + "Ġrefr esh", + "' =>$", + "'=> $", + "'= >$", + ". Graphics", + ".G raphics", + ".Graph ics", + "am ento", + "ament o", + "amen to", + "p id", + "pi d", + "t c", + "Ġ tips", + "Ġt ips", + "Ġti ps", + "Ġtip s", + "Ġ homes", + "Ġh omes", + "Ġhome s", + "Ġhom es", + "Ġho mes", + "Ġ fuel", + "Ġf uel", + "Ġfu el", + "Ġfue l", + "â ĸ", + "_ helper", + "_h elper", + "_help er", + "Ġ ĠčĊ", + "ĠĠ čĊ", + "Ġ Room", + "ĠR oom", + "ĠRo om", + "ĠRoo m", + ". Close", + ".C lose", + ".Cl ose", + "_ attr", + "_at tr", + "_att r", + "Ġ Mount", + "ĠM ount", + "ĠMo unt", + "ĠMou nt", + "Ġ Ev", + "ĠE v", + "ar ser", + "ars er", + "arse r", + "_ top", + "_t op", + "_to p", + "e ah", + "ea h", + "Ġ Delete", + "ĠDe lete", + "ĠDel ete", + "ãĢ į", + "u ke", + "uk e", + "Ġ usage", + "Ġu sage", + "Ġus age", + "Ġusa ge", + "a ria", + "ar ia", + "ari a", + "_ dev", + "_d ev", + "_de v", + "Ġ texture", + "Ġtext ure", + "Ġtex ture", + "Ġtextu re", + "Ġ conversation", + "Ġcon versation", + "Ġconvers ation", + "e per", + "ep er", + "B ean", + "Be an", + "d one", + "do ne", + "don e", + "non atomic", + "Ġ Second", + "ĠSe cond", + "ĠSec ond", + "Ġsh ooting", + "Ġshoot ing", + "Ġsho oting", + "_ pre", + "_p re", + "_pr e", + "Com ponents", + "Component s", + "Comp onents", + "Ġ ]ĊĊ", + "Ġ] ĊĊ", + "Ġ]Ċ Ċ", + "_ _,", + "__ ,", + "st itution", + "stit ution", + ". Char", + ".C har", + ".Ch ar", + "> ();ĊĊ", + ">( );ĊĊ", + ">();Ċ Ċ", + ">() ;ĊĊ", + ">(); ĊĊ", + "Ġpres ented", + "Ġpresent ed", + "Ġpresente d", + "Ġ wa", + "Ġw a", + "o ker", + "ok er", + "oke r", + "- ĊĊ", + "-Ċ Ċ", + "i ner", + "in er", + "ine r", + "Ġbe coming", + "Ġbec oming", + "Ġ incident", + "Ġinc ident", + "Ġincid ent", + "A tt", + "At t", + "1 62", + "16 2", + "Ġreve aled", + "Ġreveal ed", + "f orc", + "fo rc", + "for c", + "Ġ boot", + "Ġb oot", + "Ġbo ot", + "Ġboo t", + ". page", + ".p age", + ".pa ge", + ".pag e", + "En umerator", + "Enumer ator", + "Enum erator", + "1 65", + "16 5", + "_ ->", + "_- >", + "Ph oto", + "Phot o", + "Ġ spring", + "Ġs pring", + "Ġsp ring", + "Ġspr ing", + ". \",", + ".\" ,", + "Ġ Dictionary", + "ĠD ictionary", + "B JECT", + "BJ ECT", + "Ġ locations", + "Ġl ocations", + "Ġloc ations", + "Ġlocation s", + "Ġ samples", + "Ġs amples", + "Ġsample s", + "Ġsam ples", + "Ġsamp les", + "Input Stream", + "Ġ Brown", + "ĠB rown", + "ĠBr own", + "ĠBro wn", + "ĠBrow n", + "Ġ stats", + "Ġst ats", + "Ġstat s", + "Ġsta ts", + "q uality", + "qu ality", + "qual ity", + "Ñ ħ", + "- dis", + "-d is", + "-di s", + "Ġhelp ing", + "Ġhel ping", + "Ġ ped", + "Ġp ed", + "Ġpe d", + "2 24", + "22 4", + "( se", + "(s e", + "Ġ Who", + "ĠW ho", + "ĠWh o", + "a lian", + "al ian", + "ali an", + "alia n", + "in ternal", + "int ernal", + "inter nal", + "intern al", + "Ġ ft", + "Ġf t", + "> ().", + ">( ).", + ">() .", + "- >{", + "-> {", + "Ġ mine", + "Ġm ine", + "Ġmin e", + "Ġmi ne", + "Ġ sector", + "Ġs ector", + "Ġse ctor", + "Ġsec tor", + "Ġsect or", + "Ġ gro", + "Ġg ro", + "Ġgr o", + "Ġopport unities", + "Ġopportun ities", + "Ġ ü", + "Ġà ¼", + "Ġ mp", + "Ġm p", + "Ġalleg ed", + "Ġalle ged", + "Ġallege d", + "Ġdoub t", + "Ġdou bt", + "M ouse", + "Mo use", + "A bout", + "Ab out", + "_ part", + "_p art", + "_par t", + "_pa rt", + "Ġ chair", + "Ġc hair", + "Ġch air", + "Ġcha ir", + "Ġchai r", + "Ġ stopped", + "Ġst opped", + "Ġstop ped", + "Ġsto pped", + "1 61", + "16 1", + "l oop", + "lo op", + "loo p", + "ent ities", + "enti ties", + "Ġ apps", + "Ġa pps", + "Ġapp s", + "Ġap ps", + "ans ion", + "ansi on", + "Ġ mental", + "Ġm ental", + "Ġmen tal", + "Ġment al", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "F R", + "Ġdef end", + "Ġdefe nd", + "c are", + "ca re", + "car e", + "Ġ ideal", + "Ġi deal", + "Ġide al", + "Ġidea l", + "/ api", + "/a pi", + "/ap i", + "ur face", + "urf ace", + "0 11", + "01 1", + "Ġ ele", + "Ġe le", + "Ġel e", + "u lator", + "ul ator", + "ula tor", + "Ġ Rights", + "ĠR ights", + "ĠRight s", + "angu ages", + "anguage s", + "Ġf unds", + "Ġfun ds", + "Ġfund s", + "Ġfu nds", + "Ġ adapt", + "Ġad apt", + "Ġada pt", + "Ġadap t", + "At tributes", + "Attribute s", + "Attrib utes", + "Ġ deploy", + "Ġde ploy", + "Ġdep loy", + "o pts", + "op ts", + "opt s", + "Ġ validation", + "Ġvalid ation", + "Ġvalida tion", + "Ġconcern s", + "u ce", + "uc e", + ". num", + ".n um", + "ul ture", + "ult ure", + "ultur e", + "i la", + "il a", + "Ġ cup", + "Ġc up", + "Ġcu p", + "Ġ pure", + "Ġp ure", + "Ġpur e", + "Ġpu re", + ". Fore", + ".F ore", + ".For e", + "1 83", + "18 3", + "Ġ HashMap", + "ĠHash Map", + ". valueOf", + ".value Of", + "a sm", + "as m", + "M O", + "Ġ cs", + "Ġc s", + "Ġ stores", + "Ġst ores", + "Ġstore s", + "Ġstor es", + "Ġsto res", + "Ġ ************************************************************************", + "Ġ**************************************************************** ********", + "Ġ******************************** ****************************************", + "Ġ******** ****************************************************************", + "Ġ**************** ********************************************************", + "Ġ************************ ************************************************", + "Ġ**************************************** ********************************", + "Ġ******************************************************** ****************", + "Ġ************************************************ ************************", + "Ġ communication", + "Ġcomm unication", + "Ġcommunic ation", + "Ġcommun ication", + "m em", + "me m", + ". EventHandler", + ".Event Handler", + ". Status", + ".S tatus", + ".St atus", + ".Stat us", + "_ right", + "_r ight", + ".set On", + "S heet", + "She et", + "Ġ identify", + "Ġident ify", + "ener ated", + "ene rated", + "enerate d", + "ord ered", + "order ed", + "orde red", + "Ġ \"[", + "Ġ\" [", + "Ġs we", + "Ġsw e", + "Con dition", + "Cond ition", + "Ġ According", + "ĠA ccording", + "ĠAcc ording", + "ĠAccord ing", + "Ġ prepare", + "Ġpre pare", + "Ġprepar e", + "Ġprep are", + "Ġ rob", + "Ġr ob", + "Ġro b", + "P ool", + "Po ol", + "Ġ sport", + "Ġs port", + "Ġsp ort", + "Ġspo rt", + "Ġspor t", + "r v", + "Ġ Router", + "ĠR outer", + "ĠRoute r", + "ĠRo uter", + "ĠRou ter", + "ĠRout er", + "Ġ alternative", + "Ġaltern ative", + "Ġalter native", + "( []", + "([ ]", + "Ġ Chicago", + "ĠCh icago", + "ĠChic ago", + "i pher", + "ip her", + "iph er", + "is che", + "isc he", + "isch e", + "Ġ Director", + "ĠD irector", + "ĠDirect or", + "ĠDir ector", + "ĠDire ctor", + "k l", + "Ġ Wil", + "ĠW il", + "ĠWi l", + "ke ys", + "key s", + "Ġ mysql", + "Ġm ysql", + "Ġmy sql", + "Ġmys ql", + "Ġ welcome", + "Ġw elcome", + "Ġwel come", + "k ing", + "ki ng", + "kin g", + "Ġ Manager", + "ĠM anager", + "ĠMan ager", + "ĠManage r", + "ĠMana ger", + "Ġ caught", + "Ġca ught", + ") }Ċ", + ")} Ċ", + "S core", + "Sc ore", + "_ PR", + "_P R", + "Ġ survey", + "Ġs urvey", + "Ġsur vey", + "Ġsurv ey", + "Ġsurve y", + "h ab", + "ha b", + "He aders", + "Header s", + "Head ers", + "A DER", + "AD ER", + "ADE R", + "Ġ decor", + "Ġde cor", + "Ġdec or", + "Ġdeco r", + "Ġturn s", + "Ġtur ns", + "Ġ radius", + "Ġr adius", + "Ġrad ius", + "Ġradi us", + "er rupt", + "err upt", + "C or", + "Co r", + "Ġ mel", + "Ġm el", + "Ġme l", + "Ġ intr", + "Ġin tr", + "Ġint r", + "( q", + "Ġ AC", + "ĠA C", + "a mos", + "am os", + "amo s", + "M AX", + "MA X", + "Ġ Grid", + "ĠG rid", + "ĠGr id", + "ĠGri d", + "Ġ Jesus", + "ĠJ esus", + "ĠJes us", + "ĠJe sus", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + ". DE", + ".D E", + "Ġ ts", + "Ġt s", + "Ġ linked", + "Ġlink ed", + "Ġlin ked", + "f ree", + "fr ee", + "fre e", + "Ġ Qt", + "ĠQ t", + "Ġ /**čĊ", + "Ġ/ **čĊ", + "Ġ/* *čĊ", + "Ġ/** čĊ", + "Ġf aster", + "Ġfa ster", + "Ġfast er", + "Ġfas ter", + "c tr", + "ct r", + "_ J", + "D T", + ". Check", + ".C heck", + ".Ch eck", + "Ġ combination", + "Ġcomb ination", + "Ġcombin ation", + "Ġint ended", + "Ġintend ed", + "- the", + "-t he", + "-th e", + "- type", + "-t ype", + "1 82", + "18 2", + "e ctors", + "ect ors", + "ec tors", + "ector s", + "a mi", + "am i", + "u ting", + "ut ing", + "uti ng", + "utin g", + "Ġ uma", + "Ġu ma", + "Ġum a", + "X ML", + "XM L", + "U CT", + "UC T", + "A p", + "Ġ Random", + "ĠR andom", + "ĠRand om", + "ĠRan dom", + "Ġ ran", + "Ġr an", + "Ġra n", + ". sort", + ".s ort", + ".so rt", + "Ġ sorted", + "Ġs orted", + "Ġsort ed", + "Ġsor ted", + "Ġsorte d", + ". Un", + ".U n", + "4 01", + "40 1", + "_ PER", + "_P ER", + "_PE R", + "it ory", + "itor y", + "ito ry", + "Ġ priority", + "Ġp riority", + "Ġprior ity", + "Ġpriorit y", + "Ġ Gal", + "ĠG al", + "ĠGa l", + "Ġ Old", + "ĠO ld", + "ĠOl d", + "h ot", + "ho t", + "Ġ Display", + "ĠD isplay", + "ĠDis play", + "ĠDisp lay", + "( sub", + "(s ub", + "_ TH", + "_T H", + "_ Y", + "Ġ Care", + "ĠC are", + "ĠCar e", + "ĠCa re", + "lo ading", + "load ing", + "K ind", + "Kin d", + "Ki nd", + "_ handle", + "_h andle", + "_hand le", + ", ,", + "r ase", + "ra se", + "ras e", + "_ replace", + "_re place", + "_rep lace", + ". addEventListener", + ".add EventListener", + "Ġ RT", + "ĠR T", + "1 72", + "17 2", + "Ġ entered", + "Ġen tered", + "Ġent ered", + "Ġenter ed", + "g ers", + "ge rs", + "ger s", + "Ġ ich", + "Ġi ch", + "Ġic h", + "( start", + "(st art", + "2 05", + "20 5", + "/ app", + "/a pp", + "/ap p", + "Ġbr other", + "Ġbro ther", + "Ġbroth er", + "M emory", + "Mem ory", + "Memo ry", + "Out let", + "Ġ utf", + "Ġu tf", + "Ġut f", + "p rec", + "pr ec", + "pre c", + "Ġ navigation", + "Ġn avigation", + "Ġnav igation", + "Ġnavig ation", + "O RK", + "OR K", + "Ġ dst", + "Ġd st", + "Ġds t", + "D etail", + "De tail", + "Det ail", + "Ġaud ience", + "Ġaudi ence", + "Ġ dur", + "Ġd ur", + "Ġdu r", + "Ġ cluster", + "Ġcl uster", + "un ched", + "unc hed", + "unch ed", + "Ġ ],", + "Ġ] ,", + "Ġcomfort able", + ". values", + ".value s", + ".val ues", + "Ġ Total", + "ĠT otal", + "ĠTo tal", + "ĠTot al", + "Ġ snap", + "Ġs nap", + "Ġsn ap", + "Ġsna p", + "Ġstand ards", + "Ġstandard s", + "Ġper formed", + "Ġperform ed", + "Ġperfor med", + "h and", + "ha nd", + "han d", + "( \"@", + "(\" @", + "å Ń", + "Ġ phil", + "Ġp hil", + "Ġph il", + "Ġphi l", + "i br", + "ib r", + "t rim", + "tr im", + "tri m", + "Ġ forget", + "Ġf orget", + "Ġfor get", + "Ġforg et", + "Ġforge t", + "1 57", + "15 7", + "Ġ doctor", + "Ġdo ctor", + "Ġdoc tor", + ". TextBox", + ".Text Box", + "3 77", + "37 7", + "i cons", + "ic ons", + "icon s", + "ico ns", + ", s", + "Ġ Op", + "ĠO p", + "S m", + "S top", + "St op", + "ĉ List", + "ĉL ist", + "ĉ u", + "Com ment", + "Comm ent", + "_ VERSION", + "_V ERSION", + "_VER SION", + ".X tra", + "P erson", + "Per son", + "Pers on", + "r b", + "L OB", + "LO B", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "Ġ Central", + "ĠC entral", + "ĠCent ral", + "2 70", + "27 0", + "I CK", + "IC K", + "r aq", + "ra q", + "Ġp utting", + "Ġput ting", + "Ġputt ing", + "Ġ md", + "Ġm d", + "Ġ Love", + "ĠL ove", + "ĠLo ve", + "ĠLov e", + "P rogram", + "Pro gram", + "Pr ogram", + "Prog ram", + "B order", + "o or", + "oo r", + "Ġall owing", + "Ġallow ing", + "Ġallo wing", + "a fter", + "af ter", + "aft er", + "Ġ entries", + "Ġen tries", + "Ġent ries", + "Ġentr ies", + "Ġ Maybe", + "ĠM aybe", + "ĠMay be", + "] ).", + "]) .", + "Ġ Short", + "ĠS hort", + "ĠSh ort", + "ĠSho rt", + ") \\", + ". now", + ".n ow", + ".no w", + "f riend", + "Ġ prefer", + "Ġp refer", + "Ġpre fer", + "Ġpref er", + "Ġ GPIO", + "ĠG PIO", + "ĠGP IO", + "ĠGPI O", + "o sis", + "os is", + "osi s", + "Ġ GameObject", + "ĠGame Object", + "Ġ skip", + "Ġs kip", + "Ġsk ip", + "Ġski p", + "Ġ competition", + "Ġcom petition", + "Ġcompet ition", + "Ġcompetit ion", + "_ match", + "_m atch", + "_mat ch", + "l ications", + "lic ations", + "lication s", + "_ CONT", + "_C ONT", + "_CON T", + "_CO NT", + ". groupBox", + ".group Box", + "Ġ als", + "Ġa ls", + "Ġal s", + "6 66", + "66 6", + "\" We", + "\"W e", + "_ eq", + "_e q", + "l an", + "la n", + "_ search", + "_s earch", + "_se arch", + "Ġ Music", + "ĠM usic", + "ĠMus ic", + "ĠMu sic", + "a sis", + "as is", + "asi s", + "Ġ bind", + "Ġb ind", + "Ġbi nd", + "Ġbin d", + "ĠIs land", + "ĠIsl and", + "r um", + "ru m", + "( E", + "Ġ seat", + "Ġs eat", + "Ġse at", + "Ġsea t", + "V ideo", + "Vi deo", + "Ġ ack", + "Ġa ck", + "Ġac k", + "re ek", + "ree k", + "={ ()", + "={( )", + "Ġ rating", + "Ġr ating", + "Ġrat ing", + "Ġra ting", + "Ġ restaurant", + "Ġrest aurant", + "Ġrestaur ant", + "Ġrestau rant", + "4 56", + "45 6", + "D EX", + "DE X", + "( buf", + "(b uf", + "p ping", + "pp ing", + "u ality", + "ual ity", + "uali ty", + "Ġ league", + "Ġle ague", + "1 76", + "17 6", + "Ġ focused", + "Ġf ocused", + "Ġfocus ed", + "Ġfoc used", + "a pon", + "ap on", + "apo n", + "$ data", + "$d ata", + "CL UD", + "CLU D", + "CLUD ING", + "Ġ absolute", + "Ġa bsolute", + "Ġabs olute", + "Ġabsolut e", + "Ġabsol ute", + "( query", + "(qu ery", + "Ġt ells", + "Ġtell s", + "Ġtel ls", + "A ng", + "An g", + "Ġcomm unities", + "Ġcommun ities", + "Ġh onest", + "Ġhon est", + "Ġho nest", + "Ġhone st", + "o king", + "ok ing", + "oki ng", + "okin g", + "Ġa part", + "Ġap art", + "Ġapar t", + "Ġapa rt", + "ar ity", + "ari ty", + "/ $", + "_ module", + "_m odule", + "_mod ule", + "Ġ Enc", + "ĠE nc", + "ĠEn c", + ". an", + ".a n", + ". Config", + ".Con fig", + "C re", + "Cr e", + "Ġsh ock", + "Ġsho ck", + "ĠA rab", + "ĠAr ab", + "ĠAra b", + "I ENT", + "IE NT", + "/ re", + "/r e", + "Ġre trie", + "Ġret rie", + "Ġretr ie", + "yc ler", + "ycle r", + "ycl er", + "i sa", + "is a", + "Ġ Organ", + "ĠO rgan", + "ĠOr gan", + "ĠOrg an", + ". graph", + ".g raph", + ".gr aph", + "Ġ í", + "ĠB AS", + "ĠBA S", + "E num", + "En um", + "Ġ possibly", + "Ġposs ibly", + "ÑĢ аÐ", + "ÑĢа Ð", + "Ġ Japanese", + "ĠJ apanese", + "ĠJapan ese", + "Ġ craft", + "Ġc raft", + "Ġcr aft", + "Ġcra ft", + "Ġ Place", + "ĠP lace", + "ĠPl ace", + "ĠPla ce", + "Ġt alent", + "Ġtal ent", + "Ġtale nt", + "Ġf unding", + "Ġfun ding", + "Ġfund ing", + "Ġ confirmed", + "Ġconf irmed", + "Ġconfirm ed", + "Ġ cycle", + "Ġc ycle", + "Ġcy cle", + "Ġcycl e", + "Ġcyc le", + "/ x", + "G E", + "Ġh earing", + "Ġhe aring", + "Ġhear ing", + "Ġ plants", + "Ġpl ants", + "Ġplan ts", + "Ġplant s", + "Ġpla nts", + "Ġ mouth", + "Ġm outh", + "Ġmo uth", + "Ġmou th", + "p ages", + "page s", + "pa ges", + "pag es", + "o ria", + "or ia", + "ori a", + "Ġ Remove", + "ĠRe move", + "ĠRem ove", + "_ total", + "_t otal", + "_to tal", + "_tot al", + "Ġ od", + "Ġo d", + "oll apse", + "d oor", + "do or", + "Ġb ought", + "Ġbo ught", + "Ġbou ght", + "Ġ addr", + "Ġadd r", + "Ġad dr", + "AR CH", + "ARC H", + "_ dim", + "_d im", + "_di m", + "d den", + "dd en", + "dde n", + "Ġdec ades", + "Ġdecade s", + "Ġdecad es", + "RE QUEST", + "REQ UEST", + "Ġ versions", + "Ġv ersions", + "Ġversion s", + "Ġvers ions", + "f ire", + "fi re", + "fir e", + "0 06", + "00 6", + "Ġ moves", + "Ġm oves", + "Ġmov es", + "Ġmove s", + "Ġmo ves", + "f b", + "Ġ coffee", + "Ġc offee", + "Ġco ffee", + "Ġcoff ee", + "Ġcof fee", + ". connect", + ".con nect", + ".conn ect", + "Ġ Row", + "ĠR ow", + "ĠRo w", + "Ġ schema", + "Ġs chema", + "Ġsch ema", + "Ġschem a", + "Ġsche ma", + "S cope", + "Sc ope", + "- Type", + "-T ype", + "Ġf ighting", + "Ġfight ing", + "Ġre tail", + "Ġr etail", + "Ġret ail", + "Ġ modified", + "Ġmod ified", + "T F", + "F iles", + "File s", + "Fi les", + "Fil es", + "n ie", + "ni e", + "_ command", + "_com mand", + "_comm and", + "s tone", + "st one", + "ston e", + "sto ne", + "Ġ ÑĤ", + "ĠÑ Ĥ", + "_ thread", + "_t hread", + "_th read", + "_thr ead", + "Ġ bond", + "Ġb ond", + "Ġbo nd", + "Ġbon d", + "Ġ Development", + "ĠDe velopment", + "ĠDev elopment", + "ĠDevelop ment", + "Ġ pt", + "Ġp t", + "F ORM", + "FO RM", + "FOR M", + "p let", + "pl et", + "ple t", + "Ġ identified", + "Ġident ified", + "c pp", + "cp p", + "2 06", + "20 6", + "2 25", + "22 5", + "Ġ coding", + "Ġc oding", + "Ġco ding", + "Ġcod ing", + "o ked", + "ok ed", + "oke d", + "Ġ Master", + "ĠM aster", + "ĠMa ster", + "ĠMas ter", + "ĠMast er", + "ID TH", + "Ġres idents", + "Ġresident s", + "Ġresid ents", + "Ġreside nts", + "r edit", + "re dit", + "red it", + "redi t", + "Ġ Photo", + "ĠPh oto", + "ĠPhot o", + "= -", + "u nte", + "un te", + "unt e", + "at eur", + "ate ur", + "1 59", + "15 9", + "_ STATE", + "_ST ATE", + "_STAT E", + "_STA TE", + "Ġ Sing", + "ĠS ing", + "ĠSi ng", + "ĠSin g", + "Ġ sheet", + "Ġs heet", + "Ġshe et", + ". val", + ".v al", + ".va l", + "or se", + "ors e", + "Ġh ers", + "Ġhe rs", + "Ġher s", + "Ġd etermined", + "Ġdetermin ed", + "Ġdetermine d", + "Ġdeterm ined", + "Com mon", + "Comm on", + "Ġ wed", + "Ġw ed", + "Ġwe d", + "_ queue", + "_q ueue", + "_que ue", + "P H", + "Ġ Atl", + "ĠA tl", + "ĠAt l", + "c red", + "cre d", + "cr ed", + "/ LICENSE", + "/L ICENSE", + "Ġ mes", + "Ġm es", + "Ġme s", + "Ġ advanced", + "Ġad vanced", + "Ġadv anced", + "Ġadvance d", + ". java", + ".j ava", + ".jav a", + ". Sh", + ".S h", + "G o", + "k ill", + "ki ll", + "kil l", + "f p", + "_ settings", + "_s ettings", + "_set tings", + "_setting s", + "Ġ pal", + "Ġp al", + "Ġpa l", + "Ġ truck", + "Ġtr uck", + "Ġ combined", + "Ġcomb ined", + "Ġcombine d", + "Ġcombin ed", + "Ġ \"${", + "Ġ\" ${", + "Ġ\"$ {", + "ĠC orpor", + "ĠCor por", + "ĠCorp or", + "Ġ joined", + "Ġj oined", + "Ġjoin ed", + "Ġjo ined", + "Ġ Jose", + "ĠJ ose", + "ĠJo se", + "ĠJos e", + "ĠC up", + "ĠCu p", + "u ns", + "un s", + "est ival", + "esti val", + "le vision", + "lev ision", + "Ġ broken", + "Ġb roken", + "Ġbr oken", + "Ġbro ken", + "Ġbroke n", + "Ġmar riage", + "Ġ Western", + "ĠWest ern", + "ĠWes tern", + "Ġrep resents", + "Ġrepresent s", + "Ġ Title", + "ĠT itle", + "ĠTi tle", + "ĠTit le", + "Ġ ss", + "Ġs s", + ". Ass", + ".A ss", + ".As s", + "ong oose", + "ongo ose", + "i ento", + "ient o", + "ien to", + "< >();Ċ", + "<>( );Ċ", + "<> ();Ċ", + "Ġabs olutely", + "Ġabsolute ly", + "Ġabsolut ely", + "Ġabsol utely", + "Ġ smooth", + "Ġsm ooth", + "Ġsmo oth", + "T ERN", + "TE RN", + "TER N", + "Ġ Unless", + "ĠUn less", + "W ord", + "Wo rd", + "Ġ merge", + "Ġm erge", + "Ġmer ge", + "Ġmerg e", + "i gan", + "ig an", + "iga n", + "Ġ Vol", + "ĠV ol", + "ĠVo l", + "Ġ nn", + "Ġn n", + ". getId", + ".get Id", + "Ġ з", + "ĠÐ ·", + "1 71", + "17 1", + "Ġ sexy", + "Ġse xy", + "Ġsex y", + "Ġse eking", + "Ġsee king", + "Ġseek ing", + "S ingle", + "Si ngle", + "Sin gle", + "Sing le", + ". this", + ".t his", + ".th is", + "1 79", + "17 9", + "Ġ kom", + "Ġk om", + "Ġko m", + "b ound", + "bo und", + "bou nd", + "; \"", + "Ġ fontSize", + "Ġfont Size", + "_ df", + "_d f", + "Ġin jury", + "Ġinj ury", + "( H", + "Ġ issued", + "Ġiss ued", + "Ġissue d", + "Ġissu ed", + "_ END", + "_E ND", + "_EN D", + ": self", + ":s elf", + "0 20", + "02 0", + "Ġ patch", + "Ġp atch", + "Ġpat ch", + "Ġle aves", + "Ġleave s", + "Ġ adopt", + "Ġad opt", + "Ġado pt", + "File Name", + "ãĢ IJ", + "Ġexec utive", + "Ġexecut ive", + "Ġ Byte", + "ĠB yte", + "ĠBy te", + "] ))Ċ", + "]) )Ċ", + "])) Ċ", + "Ġ nu", + "Ġn u", + "o uting", + "ou ting", + "out ing", + "cl uding", + "clud ing", + "- R", + ". options", + ".o ptions", + ".opt ions", + ".option s", + "Ġsub stant", + "Ġsubs tant", + "Ġsubst ant", + "av ax", + "ava x", + "ĠB UT", + "ĠBU T", + "Ġ technical", + "Ġtechn ical", + "Ġtw ice", + "Ġm ás", + "Ġmá s", + "Ġun ivers", + "Ġuni vers", + "y r", + "Ġ drag", + "Ġd rag", + "Ġdr ag", + "Ġdra g", + "Ġ DC", + "ĠD C", + "Ġ sed", + "Ġs ed", + "Ġse d", + "Ġ bot", + "Ġb ot", + "Ġbo t", + "Ġ Pal", + "ĠP al", + "ĠPa l", + "Ġ Hall", + "ĠH all", + "ĠHa ll", + "ĠHal l", + "force ment", + "forc ement", + "Ġa uch", + "Ġau ch", + "Ġauc h", + ". mod", + ".m od", + ".mo d", + "n otation", + "not ation", + "nota tion", + "_ files", + "_f iles", + "_file s", + "_fil es", + ". line", + ".l ine", + ".li ne", + ".lin e", + "_ flag", + "_f lag", + "_fl ag", + "[ name", + "[n ame", + "Ġ resolution", + "Ġre solution", + "Ġres olution", + "Ġb ott", + "Ġbo tt", + "Ġbot t", + "( \"[", + "(\" [", + "e nde", + "en de", + "end e", + "( arr", + "(a rr", + "(ar r", + "F ree", + "Fr ee", + "Fre e", + "( @\"", + "(@ \"", + "Ġ District", + "ĠD istrict", + "ĠDi strict", + "P EC", + "PE C", + ": -", + "P icker", + "Pic ker", + "Pi cker", + "Pick er", + "Ġ Jo", + "ĠJ o", + "Ġ ĠĠĠĠĊ", + "ĠĠ ĠĠĠĊ", + "ĠĠĠĠ ĠĊ", + "ĠĠĠ ĠĠĊ", + "ĠĠĠĠĠ Ċ", + "Ġ River", + "ĠR iver", + "ĠRiv er", + "ĠRi ver", + "_ rows", + "_r ows", + "_row s", + "_ro ws", + "Ġhelp ful", + "Ġmass ive", + "- --Ċ", + "-- -Ċ", + "--- Ċ", + "Ġme asures", + "Ġmeasure s", + "Ġmeas ures", + "0 07", + "00 7", + "Ġ Runtime", + "ĠR untime", + "ĠRun time", + "Ġw orry", + "Ġwor ry", + "Ġ Spec", + "ĠS pec", + "ĠSp ec", + "ĠSpe c", + "ĉ D", + "ãĢ ij", + "Ġ ){Ċ", + "Ġ) {Ċ", + "Ġ){ Ċ", + "Ġw orse", + "Ġwor se", + "Ġwors e", + "( filename", + "(f ilename", + "(file name", + "(fi lename", + "(fil ename", + "Ġ lay", + "Ġl ay", + "Ġla y", + "Ġ magic", + "Ġm agic", + "Ġmag ic", + "Ġ Their", + "ĠThe ir", + "o ul", + "ou l", + "st roy", + "str oy", + "stro y", + "Ġ Where", + "ĠW here", + "ĠWh ere", + "ĠWhe re", + "2 80", + "28 0", + "Ġs udden", + "Ġsu dden", + "Ġsud den", + "Ġd efe", + "Ġde fe", + "Ġdef e", + "Ġ binding", + "Ġb inding", + "Ġbin ding", + "Ġbind ing", + "Ġ flight", + "Ġf light", + "Ġfl ight", + "Ġ OnInit", + "ĠOn Init", + "Ġ Women", + "ĠW omen", + "ĠWo men", + "Ġ Policy", + "ĠP olicy", + "ĠPol icy", + "ĠPolic y", + "Ġdr ugs", + "Ġdrug s", + "Ġdru gs", + "ish ing", + "ishi ng", + "(' ../", + "('. ./", + "Ġ Mel", + "ĠM el", + "ĠMe l", + "p eat", + "pe at", + "t or", + "to r", + "Ġpro posed", + "Ġprop osed", + "Ġpropos ed", + "Ġpropose d", + "Ġst ated", + "Ġstate d", + "Ġstat ed", + "Ġsta ted", + "_ RES", + "_RE S", + "_R ES", + "Ġ east", + "Ġe ast", + "Ġeas t", + "Ġea st", + "2 12", + "21 2", + "Ġ CONDITION", + "ĠCON DITION", + "_ desc", + "_d esc", + "_de sc", + "_des c", + "Ġw inning", + "Ġwin ning", + "f olio", + "fo lio", + "fol io", + "M apper", + "Map per", + "Ma pper", + "Ġ Pan", + "ĠP an", + "ĠPa n", + "ĠA nge", + "ĠAn ge", + "ĠAng e", + ".s ervlet", + ".serv let", + "Ġ copies", + "Ġc opies", + "Ġco pies", + "Ġcop ies", + "L M", + "Ġ vm", + "Ġv m", + "å į", + "Ġ dictionary", + "Ġd ictionary", + "S eg", + "Se g", + "1 77", + "17 7", + "e lines", + "el ines", + "eline s", + "eli nes", + "elin es", + "Ġ Send", + "ĠS end", + "ĠSe nd", + "ĠSen d", + "Ġ iron", + "Ġi ron", + "Ġir on", + "Ġ Fort", + "ĠF ort", + "ĠFor t", + "ĠFo rt", + "1 66", + "16 6", + ". domain", + ".d omain", + ".do main", + ".dom ain", + "Ġde bate", + "Ġdeb ate", + "Not Null", + "e q", + "a cher", + "ac her", + "ach er", + "ache r", + "l f", + "ĉ fmt", + "ĉf mt", + "Ġl awy", + "Ġla wy", + "Ġlaw y", + "1 78", + "17 8", + "Ä Ł", + "Ġ Men", + "ĠM en", + "ĠMe n", + "Ġ trim", + "Ġt rim", + "Ġtr im", + "Ġtri m", + "( NULL", + "(N ULL", + "Ġ !!", + "Ġ! !", + "Ġ pad", + "Ġp ad", + "Ġpa d", + "Ġfollow s", + "Ġfoll ows", + "\" ][\"", + "\"] [\"", + "\"][ \"", + "r equ", + "re qu", + "req u", + "Ġ Ep", + "ĠE p", + ". github", + ".g ithub", + ".git hub", + "( img", + "(i mg", + "(im g", + "e to", + "et o", + "( '\\", + "(' \\", + "S ervices", + "Service s", + "Serv ices", + "umbn ail", + "_ main", + "_m ain", + "_ma in", + "p leted", + "pl eted", + "ple ted", + "plete d", + "plet ed", + "fort unately", + "fortunate ly", + "Ġ windows", + "Ġw indows", + "Ġwindow s", + "Ġwind ows", + "Ġ plane", + "Ġp lane", + "Ġpl ane", + "Ġplan e", + "Ġpla ne", + "Ġ Connection", + "ĠCon nection", + "ĠConnect ion", + "ĠConn ection", + ". local", + ".l ocal", + ".loc al", + ".lo cal", + "u ard", + "ua rd", + "uar d", + "} \\", + "= =\"", + "== \"", + "a ndon", + "an don", + "and on", + "ando n", + "Ġ Roy", + "ĠR oy", + "ĠRo y", + "w est", + "we st", + "1 58", + "15 8", + "ig inal", + "igin al", + "igi nal", + "em ies", + "emi es", + "emie s", + "i tz", + "it z", + "' ):Ċ", + "') :Ċ", + "'): Ċ", + "Ġ Peter", + "ĠP eter", + "ĠPe ter", + "ĠPet er", + "ĠPete r", + "Ġt ough", + "Ġto ugh", + "Ġtou gh", + "Ġre duced", + "Ġred uced", + "Ġredu ced", + "Ġreduce d", + "Ġ calculate", + "Ġc alculate", + "Ġcal culate", + "Ġcalcul ate", + "Ġcalc ulate", + "Ġ rapid", + "Ġr apid", + "Ġrap id", + "Ġra pid", + "c ustomer", + "custom er", + "cust omer", + "Ġ efficient", + "Ġeff icient", + "Ġeffic ient", + "Ġ medium", + "Ġm edium", + "Ġmed ium", + "Ġmedi um", + "Ġ fell", + "Ġf ell", + "Ġfe ll", + "Ġfel l", + ". ref", + ".re f", + ".r ef", + "Ġ Cas", + "ĠC as", + "ĠCa s", + "Ġ feedback", + "Ġfe edback", + "Ġfeed back", + "S peed", + "Sp eed", + "Spe ed", + "( output", + "(out put", + "a je", + "aj e", + "Ġ categories", + "Ġc ategories", + "Ġcategor ies", + "Ġcategorie s", + "Ġ fee", + "Ġf ee", + "Ġfe e", + "} ;", + "Ġ deleted", + "Ġde leted", + "Ġdel eted", + "Ġdelete d", + "Ġdelet ed", + "Ġdele ted", + "r eh", + "re h", + "Ġ proof", + "Ġp roof", + "Ġpro of", + "D esc", + "De sc", + "Des c", + "B uild", + "Bu ild", + "Ġs ides", + "Ġside s", + "Ġsi des", + "Ġsid es", + ". ArrayList", + ".Array List", + "- %", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "Ø ±", + ". match", + ".m atch", + ".mat ch", + "л и", + "Ġfe els", + "Ġfeel s", + "Ġfee ls", + "Ġachie ve", + "Ġach ieve", + "Ġc lim", + "Ġcl im", + "Ġcli m", + "_ ON", + "_O N", + "Ġ CD", + "ĠC D", + "Ġ teacher", + "Ġt eacher", + "Ġte acher", + "Ġteach er", + "Ġtea cher", + "_ current", + "_c urrent", + "_cur rent", + "_curr ent", + "b n", + "_ PL", + "_P L", + "is ting", + "ist ing", + "isti ng", + "E nable", + "En able", + "G EN", + "GE N", + "Ġ tv", + "Ġt v", + "Ġ sock", + "Ġs ock", + "Ġso ck", + "Ġsoc k", + "Ġ plays", + "Ġp lays", + "Ġpl ays", + "Ġplay s", + "Ġpla ys", + "Ġ discount", + "Ġdis count", + "Ġdisc ount", + "Ġdisco unt", + "Ġ KE", + "ĠK E", + "Ġ Debug", + "ĠDe bug", + "ĠDeb ug", + "F ore", + "For e", + "Fo re", + "Ġ Iraq", + "ĠI raq", + "ĠIr aq", + "Ġ appearance", + "Ġap pearance", + "Ġappear ance", + "M on", + "Mo n", + "Ġ styled", + "Ġst yled", + "Ġstyle d", + "Ġsty led", + "Ġstyl ed", + "Ġ Human", + "ĠH uman", + "ĠHum an", + "ĠHu man", + "i ot", + "io t", + "Ġ History", + "ĠH istory", + "ĠHi story", + "ĠHistor y", + "ĠHist ory", + "Ġs ac", + "Ġsa c", + "Ġ Collection", + "ĠC ollection", + "ĠCol lection", + "ĠColl ection", + "ĠCollect ion", + "Ġ recommended", + "Ġre commended", + "Ġrecomm ended", + "Ġrecommend ed", + ". Selected", + ".Se lected", + ".Select ed", + "Ġ organizations", + "Ġorgan izations", + "Ġorganization s", + "Ġorganiz ations", + "Ġdis covered", + "Ġdiscover ed", + "co hol", + "coh ol", + "a das", + "ad as", + "ada s", + "Ġ Thomas", + "ĠTh omas", + "ĠThom as", + "M ay", + "Ma y", + "Ġcon serv", + "Ġcons erv", + "Ġconse rv", + "Ġd omin", + "Ġdo min", + "Ġdom in", + "Ġ Follow", + "ĠF ollow", + "ĠFol low", + "Ġ Section", + "ĠS ection", + "ĠSe ction", + "ĠSec tion", + "ĠSect ion", + "Ġ Thanks", + "ĠTh anks", + "ĠThank s", + "ĠThan ks", + "User name", + "Ġ recipe", + "Ġrec ipe", + "Ġrecip e", + "Ġwonder ful", + ". sleep", + ".s leep", + "_ if", + "_i f", + "ĉ ĊĉĊ", + "ĉĊ ĉĊ", + "or no", + "orn o", + "Ġ ru", + "Ġr u", + "_ target", + "_t arget", + "_tar get", + ". \"\"", + ".\" \"", + "à ¦", + "Event Args", + "Ġ inputs", + "Ġin puts", + "Ġinput s", + "Ġinp uts", + "Ġf if", + "Ġfi f", + "Ġ vision", + "Ġv ision", + "Ġvis ion", + "c y", + "Ġ Series", + "ĠS eries", + "ĠSe ries", + "ĠSer ies", + "ĠSerie s", + ") (((", + ")( ((", + ")(( (", + "Ġtr ading", + "Ġtrad ing", + "Ġtra ding", + "Ġtradi ng", + "Ġ marker", + "Ġm arker", + "Ġmark er", + "Ġmar ker", + "B egin", + "Be gin", + "Ġ typically", + "Ġtyp ically", + "Ġtypical ly", + "Ġc auses", + "Ġca uses", + "Ġcause s", + "Ġcaus es", + "d ropdown", + "drop down", + "_ DEBUG", + "_DE BUG", + "2 60", + "26 0", + "Ġ detect", + "Ġd etect", + "Ġdet ect", + "c ountry", + "count ry", + "! \");Ċ", + "!\" );Ċ", + "!\"); Ċ", + "!\") ;Ċ", + "ĉ R", + "a ppy", + "ap py", + "app y", + "Ġc ref", + "Ġcr ef", + "Ġcre f", + "( '<", + "(' <", + "\" =>", + "Ġ LE", + "ĠL E", + "re ader", + "read er", + "rea der", + "Ġ administr", + "Ġadmin istr", + "à µ", + "u cket", + "uc ket", + "uck et", + "Ġf ashion", + ". char", + ".c har", + ".ch ar", + "i zar", + "iz ar", + "iza r", + "Ġ disable", + "Ġd isable", + "Ġdis able", + "Ġs uc", + "Ġsu c", + "Ġ Live", + "ĠL ive", + "ĠLi ve", + "ĠLiv e", + "i ssue", + "iss ue", + "Ġ metadata", + "Ġm etadata", + "Ġmet adata", + "Ġmeta data", + "f lags", + "fl ags", + "flag s", + "Ġ ðŁ", + "Ġð Ł", + "Ġ committed", + "Ġcom mitted", + "Ġcomm itted", + "Ġcommit ted", + "Ġ va", + "Ġv a", + "Ġ rough", + "Ġr ough", + "Ġro ugh", + "Ġrou gh", + "Ġ '''Ċ", + "Ġ' ''Ċ", + "Ġ'' 'Ċ", + "Ġ''' Ċ", + "Ġ highlight", + "Ġhigh light", + "_ vars", + "_v ars", + "_var s", + "_va rs", + "V O", + "Ġ encoding", + "Ġen coding", + "Ġenc oding", + "- Z", + "_ sign", + "_s ign", + "_sig n", + "_si gn", + "$ (\"#", + "$( \"#", + "$(\" #", + "Ġ rain", + "Ġr ain", + "Ġra in", + "re atest", + "reate st", + "reat est", + "rea test", + "Ġ END", + "ĠE ND", + "ĠEN D", + "S election", + "Se lection", + "Select ion", + "Sel ection", + "Sele ction", + "Ġ candidates", + "Ġc andidates", + "Ġcandid ates", + "Ġcandidate s", + "Ġ sav", + "Ġs av", + "Ġsa v", + ". Empty", + "Ġdec isions", + "Ġdecision s", + "Ġdecis ions", + "Ġcoll abor", + "r idge", + "ri dge", + "rid ge", + "f eed", + "fe ed", + "fee d", + "r ession", + "ress ion", + "Ġ persons", + "Ġper sons", + "Ġperson s", + "Ġpers ons", + "Ġperso ns", + "V M", + "0 08", + "00 8", + "e ga", + "eg a", + "_ BIT", + "_B IT", + "A ccording", + "Acc ording", + "ack ed", + "ac ked", + "Ġdoll ars", + "Ġdollar s", + "_ loss", + "_l oss", + "_lo ss", + "Ġ Cost", + "ĠC ost", + "ĠCo st", + "ĠCos t", + "} \"Ċ", + "}\" Ċ", + "Not ification", + "Ġpro stit", + "Ġpros tit", + "Ġprost it", + "Ġ authority", + "Ġauthor ity", + ". rec", + ".re c", + ".r ec", + "Ġsp okes", + "Ġspoke s", + "Ġspo kes", + "Ġ Today", + "ĠT oday", + "ĠTo day", + "ĠTod ay", + "i stant", + "is tant", + "ist ant", + "istan t", + "ista nt", + "Ġ Head", + "ĠH ead", + "ĠHe ad", + "âĢĿ .", + "er tainment", + "ert ainment", + "ertain ment", + "c ean", + "ce an", + "cea n", + "c ulate", + "cul ate", + "cu late", + "Ġ ven", + "Ġv en", + "Ġve n", + "How ever", + "_ arr", + "_a rr", + "_ar r", + "Ġ tokens", + "Ġt okens", + "Ġtoken s", + "Ġtok ens", + "G raph", + "Gr aph", + "Ġ Jud", + "ĠJ ud", + "ĠJu d", + "Ġ Virgin", + "ĠVir gin", + "Ġ Serial", + "ĠS erial", + "ĠSe rial", + "ĠSer ial", + "un ning", + "unn ing", + "M utable", + "Mu table", + "Mut able", + "a gers", + "ag ers", + "age rs", + "ager s", + ". csv", + ".c sv", + ".cs v", + "Ġdevelop ing", + "Ġdevel oping", + "Ġ instructions", + "Ġin structions", + "Ġinstruction s", + "Ġinstr uctions", + "Ġinstruct ions", + "Ġ promise", + "Ġp romise", + "Ġpro mise", + "Ġprom ise", + "Ġ requested", + "Ġrequest ed", + "Ġrequ ested", + "_ encode", + "_en code", + "_enc ode", + "/ \"", + "Ġ Icon", + "ĠI con", + "u ilt", + "ui lt", + "uil t", + "- day", + "-d ay", + "-da y", + "Ġ intelligence", + "Ġint elligence", + "Ġintellig ence", + ". IS", + ".I S", + "Ġ Observable", + "ĠO bservable", + "ĠObserv able", + "Ġ Hard", + "ĠH ard", + "ĠHar d", + "ĠHa rd", + "B ool", + "Bo ol", + "2 11", + "21 1", + "id ential", + "ident ial", + ". Anchor", + ".An chor", + "Ġ selling", + "Ġs elling", + "Ġsell ing", + "Ġsel ling", + "C I", + "A GES", + "AG ES", + "AGE S", + "t le", + "tl e", + "b ur", + "bu r", + "UFF ER", + "UF FER", + "R Y", + "Ġb igger", + "Ġbig ger", + "Ġbi gger", + "Ġ rat", + "Ġr at", + "Ġra t", + "Ġf amous", + "Ġfam ous", + "Ġ typename", + "Ġtype name", + "Ġtyp ename", + "Ġ explained", + "Ġexpl ained", + "Ġexplain ed", + "} }Ċ", + "}} Ċ", + "Ġn uclear", + "Ġnu clear", + "Ġnucle ar", + "- N", + "Ġcr isis", + "Ġcri sis", + "Ġcris is", + "Ġ Enter", + "ĠEn ter", + "ĠEnt er", + "Ġ answers", + "Ġan swers", + "Ġanswer s", + "Ġans wers", + "/ ${", + "/$ {", + "/ pl", + "/p l", + "Ġ sequ", + "Ġs equ", + "Ġse qu", + "Ġseq u", + "_ next", + "_n ext", + "_ne xt", + "m ask", + "ma sk", + "mas k", + "Ġ standing", + "Ġst anding", + "Ġstand ing", + "Ġstan ding", + "Ġpl enty", + "Ġple nty", + "Ġ Cross", + "ĠC ross", + "ĠCr oss", + "ĠCro ss", + "ĠCros s", + "ĉ ret", + "ĉr et", + "ĉre t", + "d ro", + "dr o", + "Ġ Cast", + "ĠC ast", + "ĠCas t", + "ĠCa st", + "1 67", + "16 7", + "= true", + "Ġ Chris", + "ĠCh ris", + "ĠChr is", + "i cio", + "ic io", + "ici o", + "Ġ Mike", + "ĠM ike", + "ĠMi ke", + "ĠMik e", + "D ecimal", + "De cimal", + "Dec imal", + "add Component", + "L en", + "Le n", + "Ġ cock", + "Ġc ock", + "Ġco ck", + "Ġcoc k", + "Ġ #{", + "Ġ# {", + "U RN", + "UR N", + "< tr", + "", + "\\\" >", + "Ġ *=", + "Ġ* =", + "Ġ PS", + "ĠP S", + "Ġdanger ous", + "[ p", + "O ME", + "OM E", + "O ther", + "Ot her", + "Ġ StringBuilder", + "ĠString Builder", + "P oints", + "Point s", + "Po ints", + "he ading", + "head ing", + "hea ding", + "Ġ currency", + "Ġc urrency", + "Ġcurr ency", + "Ġ percentage", + "Ġper centage", + "Ġpercent age", + "_ API", + "_A PI", + "_AP I", + "Ġ classic", + "Ġclass ic", + "Ġcl assic", + "Ġclas sic", + "t head", + "th ead", + "the ad", + "Ġ MO", + "ĠM O", + "F E", + "I dx", + "Id x", + "a wait", + "aw ait", + "awa it", + "awai t", + "Ġ è", + "Ġà ¨", + "Ġacc ident", + "Ġ variant", + "Ġv ariant", + "Ġvar iant", + "Ġvari ant", + "Ġm yst", + "Ġmy st", + "Ġmys t", + "Ġ Land", + "ĠL and", + "ĠLa nd", + "ĠLan d", + "Ġ Bre", + "ĠB re", + "ĠBr e", + "Ġh arm", + "Ġhar m", + "Ġha rm", + "Ġ Acc", + "ĠA cc", + "ĠAc c", + "Ġ charged", + "Ġch arged", + "Ġchar ged", + "Ġcharg ed", + "Ġcharge d", + "i ones", + "ion es", + "io nes", + "ione s", + "Vis ibility", + "ar ry", + "arr y", + "Ġ Language", + "ĠL anguage", + "Ġ walking", + "Ġw alking", + "Ġwalk ing", + "Ġwal king", + "\" .ĊĊ", + "\". ĊĊ", + "\".Ċ Ċ", + "i fer", + "if er", + "ife r", + "Ġleaders hip", + "Ġleader ship", + ". From", + ".F rom", + "y nam", + "yn am", + "yna m", + "Ġ timestamp", + "Ġt imestamp", + "Ġtime stamp", + "i pt", + "ip t", + "Ġ Has", + "ĠH as", + "ĠHa s", + "RE FER", + "REF ER", + "Ġ Its", + "ĠI ts", + "ĠIt s", + "Ġ listener", + "Ġlist ener", + "Ġlisten er", + "Ġliste ner", + "Ġlis tener", + "U TE", + "UT E", + "2 13", + "21 3", + "_ description", + "_d escription", + "_de scription", + "_des cription", + "Ġex periences", + "Ġexper iences", + "Ġexperience s", + "Ġexperi ences", + "Ġ creates", + "Ġcreate s", + "Ġcre ates", + "Ġcreat es", + "Ġcrea tes", + "R S", + "c art", + "ca rt", + "car t", + "b lack", + "bl ack", + "bla ck", + "Ġ choices", + "Ġcho ices", + "Ġchoice s", + "w ar", + "wa r", + "7 50", + "75 0", + "Ġ '''", + "Ġ' ''", + "Ġ'' '", + "Ġ ordered", + "Ġorder ed", + "Ġord ered", + "Ġeven ing", + "Ġev ening", + "Ġeve ning", + "Ġp il", + "Ġpi l", + "Ġt un", + "Ġtu n", + "Ġ Bad", + "ĠB ad", + "ĠBa d", + "( app", + "(a pp", + "(ap p", + "r andom", + "ran dom", + "rand om", + "Ġ explicit", + "Ġexp licit", + "Ġexpl icit", + "Ġexplic it", + "Ġarr ived", + "Ġarrive d", + "Ġarriv ed", + "Ġ fly", + "Ġf ly", + "Ġfl y", + "Ġe conom", + "Ġecon om", + "Ġec onom", + "Ġeco nom", + "- mail", + "-m ail", + "Ġ lists", + "Ġl ists", + "Ġli sts", + "Ġlist s", + "Ġlis ts", + "Ġarch itect", + "Ġarchit ect", + "2 34", + "23 4", + "Ġ Pay", + "ĠP ay", + "ĠPa y", + "Ġ ds", + "Ġd s", + "Ġ Sol", + "ĠS ol", + "ĠSo l", + "Ġ vehicles", + "Ġv ehicles", + "Ġveh icles", + "Ġvehicle s", + "H z", + "- com", + "-c om", + "-co m", + "Ġ king", + "Ġk ing", + "Ġki ng", + "Ġkin g", + "_ equal", + "_e qual", + "_eq ual", + "_equ al", + "Ġ Help", + "ĠH elp", + "ĠHe lp", + "ĠHel p", + "Ġab use", + "4 80", + "48 0", + "1 69", + "16 9", + "-- ;Ċ", + "--; Ċ", + "Ġ extr", + "Ġex tr", + "Ġext r", + "Ġ chemical", + "Ġchem ical", + "ä ¿", + "Ġ orient", + "Ġo rient", + "Ġor ient", + "Ġori ent", + "Ġbre ath", + "Ġbreat h", + "Ġ Space", + "ĠS pace", + "ĠSp ace", + "ĠSpa ce", + "( element", + "(e lement", + "(el ement", + "(elem ent", + "(ele ment", + "w ait", + "wa it", + "D ED", + "DE D", + "ig ma", + "igm a", + "Ġ entr", + "Ġen tr", + "Ġent r", + "Ġ sob", + "Ġs ob", + "Ġso b", + "- name", + "-n ame", + "-na me", + "Ġ affected", + "Ġaff ected", + "Ġaffect ed", + "i ka", + "ik a", + "Ġ coal", + "Ġco al", + "_ work", + "_w ork", + "_wo rk", + "Ġh undreds", + "Ġhundred s", + "Ġpol itics", + "Ġpolit ics", + "Ġpolitic s", + "sub ject", + "su bject", + "subj ect", + "Ġ consumer", + "Ġcon sumer", + "Ġcons umer", + "Ġconsum er", + "Ġconsume r", + "AN GE", + "ANG E", + "Ġre peated", + "Ġrep eated", + "Ġrepe ated", + "Ġrepeat ed", + "S end", + "Se nd", + "Sen d", + "Ġ #[", + "Ġ# [", + "Ġ protocol", + "Ġprot ocol", + "Ġproto col", + "Ġ leads", + "Ġle ads", + "Ġlead s", + "us eum", + "use um", + "E very", + "Ev ery", + "Ever y", + "8 08", + "80 8", + "1 74", + "17 4", + "Im port", + "Imp ort", + "( count", + "(c ount", + "(co unt", + "Ġch allenges", + "Ġchalleng es", + "Ġchallenge s", + "Ġn ovel", + "Ġno vel", + "Ġnov el", + "Ġ depart", + "Ġde part", + "Ġdep art", + "b its", + "bit s", + "bi ts", + ". Current", + ".C urrent", + "Ġ `${", + "Ġ` ${", + "Ġ`$ {", + "o ting", + "ot ing", + "oti ng", + "( \\", + "Ġ creative", + "Ġc reative", + "Ġcre ative", + "Ġcreat ive", + "Ġ buff", + "Ġb uff", + "Ġbu ff", + "Ġbuf f", + "Ġint roduced", + "Ġintrodu ced", + "Ġintroduce d", + "Ġintro duced", + "u sic", + "us ic", + "usi c", + "mod ules", + "module s", + "A re", + "Ar e", + "- doc", + "-d oc", + "-do c", + "l anguage", + "_ cache", + "_c ache", + "_ca che", + "Ġ tod", + "Ġt od", + "Ġto d", + "? > < /", + "om ething", + "ome thing", + "Ġh un", + "Ġhu n", + "å º", + "a ters", + "at ers", + "ate rs", + "ater s", + "In tent", + "Int ent", + "Ġ implemented", + "Ġim plemented", + "Ġimplement ed", + "Ġ Case", + "ĠC ase", + "ĠCas e", + "ĠCa se", + "Ch ildren", + "Child ren", + "Ġ notification", + "Ġnot ification", + "Render er", + "W rapper", + "Wrap per", + "Wr apper", + "Object s", + "Obj ects", + "t l", + ". Contains", + ".Cont ains", + ".Con tains", + "Pl ugin", + "Plug in", + ". row", + ".r ow", + ".ro w", + "Ġ forg", + "Ġf org", + "Ġfor g", + "Ġfo rg", + "Ġ permit", + "Ġper mit", + "Ġperm it", + "Ġ targets", + "Ġtarget s", + "Ġtar gets", + "Ġtarg ets", + "Ġ IF", + "ĠI F", + "Ġ tip", + "Ġt ip", + "Ġti p", + "s ex", + "se x", + "Ġ supports", + "Ġsup ports", + "Ġsupport s", + "Ġsupp orts", + "Ġ fold", + "Ġf old", + "Ġfol d", + "Ġfo ld", + "ph oto", + "phot o", + "} ,čĊ", + "}, čĊ", + "Ġ google", + "Ġg oogle", + "Ġgo ogle", + "Ġgoog le", + "Ġgoo gle", + "$ ('#", + "$( '#", + "$(' #", + "Ġ sharing", + "Ġsh aring", + "Ġsha ring", + "Ġshar ing", + "Ġ goods", + "Ġg oods", + "Ġgo ods", + "Ġgood s", + "Ġgoo ds", + "v s", + "Ġ Dan", + "ĠD an", + "ĠDa n", + "R ate", + "Ra te", + "Ġ Martin", + "ĠM artin", + "ĠMar tin", + "ĠMart in", + "Ġm anner", + "Ġman ner", + "Ġmann er", + "l ie", + "li e", + ". The", + ".T he", + ".Th e", + "In ternal", + "Int ernal", + "Inter nal", + "Intern al", + "ĠCON TR", + "ĠCONT R", + "M ock", + "Mo ck", + "R IGHT", + "Ġ '{", + "Ġ' {", + "Ġ controls", + "Ġcontrol s", + "Ġcontr ols", + "Ġcontro ls", + "M at", + "Ma t", + "Ġ mand", + "Ġm and", + "Ġman d", + "Ġma nd", + "Ġ extended", + "Ġext ended", + "Ġextend ed", + "O k", + "Ġ embed", + "Ġem bed", + "Ġemb ed", + "Ġ planet", + "Ġplan et", + "Ġplane t", + "Ġpla net", + "Ġ Non", + "ĠN on", + "ĠNo n", + "- ch", + "-c h", + ") \",", + ")\" ,", + "e par", + "ep ar", + "Ġbel ieved", + "Ġbelie ved", + "Ġbelieve d", + "Ġ Environment", + "ĠEn vironment", + "Ġ Friend", + "ĠF riend", + "ĠFri end", + "- res", + "-r es", + "-re s", + "Ġ handling", + "Ġhand ling", + "Ġhan dling", + "n ic", + "ni c", + "- level", + "-le vel", + "s cri", + "sc ri", + "scr i", + "X ml", + "B E", + "u ngen", + "un gen", + "ung en", + "unge n", + "Ġ alter", + "Ġal ter", + "Ġalt er", + "Ġalte r", + "[ idx", + "[i dx", + "[id x", + "P op", + "Po p", + "c am", + "ca m", + "Ġ (((", + "Ġ( ((", + "Ġ(( (", + "Ġ shipping", + "Ġsh ipping", + "Ġship ping", + "Ġ battery", + "Ġb attery", + "Ġbatter y", + "Ġbatt ery", + "Ġbat tery", + "iddle ware", + "M C", + "Ġ impl", + "Ġi mpl", + "Ġim pl", + "Ġimp l", + "ot ation", + "ota tion", + "Ġ Lab", + "ĠL ab", + "ĠLa b", + "< form", + " {{", + ">{ {", + "Ġ Resource", + "ĠRe source", + "ĠRes ource", + "Ġ Standard", + "ĠSt andard", + "ĠStand ard", + "Ġ Prem", + "ĠP rem", + "ĠPr em", + "ĠPre m", + "up dated", + "update d", + "upd ated", + "iv alent", + "ival ent", + "Ġ assets", + "Ġas sets", + "Ġass ets", + "Ġasset s", + "_ temp", + "_t emp", + "_tem p", + "_te mp", + "Ġinter ests", + "Ġinterest s", + "Ġinteres ts", + "Ġ hardware", + "Ġh ardware", + "Ġhard ware", + "Ġ Rom", + "ĠR om", + "ĠRo m", + "Ġ Share", + "ĠS hare", + "ĠSh are", + "ĠSha re", + "ĠShar e", + "Ġ ''Ċ", + "Ġ' 'Ċ", + "Ġ'' Ċ", + "Ġ *,", + "Ġ* ,", + "Ġ Take", + "ĠT ake", + "ĠTa ke", + "ĠTak e", + "Ġ Images", + "ĠIm ages", + "ĠImage s", + "ĠImag es", + "_ CHECK", + "_C HECK", + "( typeof", + "(type of", + "(typ eof", + "Ġ Jun", + "ĠJ un", + "ĠJu n", + "\\< ^", + "Ġ liqu", + "Ġl iqu", + "Ġli qu", + "Ġw orst", + "Ġwor st", + "Ġwo rst", + "Ġwors t", + "ymb ols", + "ymbol s", + "ĉ ĉĉĠĠĠ", + "ĉĉ ĉĠĠĠ", + "ĉĉĉ ĠĠĠ", + "ĉĉĉĠ ĠĠ", + "ĉĉĉĠĠ Ġ", + "Ġ drivers", + "Ġdr ivers", + "Ġdriver s", + "Ġdrive rs", + "Ġdriv ers", + "Ġdri vers", + "Ġ Document", + "ĠD ocument", + "ĠDoc ument", + "e no", + "en o", + "Ġ Technology", + "ĠTechn ology", + "Ġ approved", + "Ġap proved", + "Ġappro ved", + "Ġapprove d", + "u mps", + "um ps", + "ump s", + "Ġ snow", + "Ġs now", + "Ġsn ow", + "Ġsno w", + "form ance", + "forma nce", + "forman ce", + "_ ASSERT", + "_A SSERT", + "_ASS ERT", + "u its", + "ui ts", + "uit s", + "2 07", + "20 7", + "Ù Ĩ", + "Ġd ifferences", + "Ġdif ferences", + "Ġdiffer ences", + "Ġdifference s", + ". Visible", + ".V isible", + "ĉ ĉĉčĊ", + "ĉĉ ĉčĊ", + "ĉĉĉ čĊ", + "Ġ Ps", + "ĠP s", + "_ fetch", + "_f etch", + "Ġ todo", + "Ġt odo", + "Ġto do", + "Ġtod o", + ". ',Ċ", + ".' ,Ċ", + ".', Ċ", + "Ġ sel", + "Ġs el", + "Ġse l", + "ur ers", + "ure rs", + "urer s", + "in valid", + "Ġ tweet", + "Ġt weet", + "Ġtwe et", + "Ġtwee t", + "V EL", + "VE L", + "Ġresearch ers", + "Ġresearcher s", + "Ġ sprintf", + "Ġs printf", + "Ġsprint f", + "Ġ RO", + "ĠR O", + "Ġ pel", + "Ġp el", + "Ġpe l", + ". Trans", + ".T rans", + ".Tr ans", + "Ġ illegal", + "Ġil legal", + "Ġill egal", + "Ġilleg al", + "d ialog", + "di alog", + "dia log", + "sm arty", + "smart y", + "l g", + "_ MIN", + "_M IN", + "_MI N", + "Ġ hero", + "Ġh ero", + "Ġhe ro", + "Ġher o", + "f inal", + "fin al", + "fi nal", + "Ġ pp", + "Ġp p", + ". Le", + ".L e", + "Ġ ci", + "Ġc i", + "ĉ RT", + "ĉR T", + "Ġs uggested", + "Ġsuggest ed", + "p df", + "pd f", + "a ching", + "ach ing", + "achi ng", + "Ġ Ro", + "ĠR o", + "Ġ Properties", + "ĠP roperties", + "ĠProp erties", + "ĠProper ties", + "Ġ Si", + "ĠS i", + "Ġbu ying", + "Ġbuy ing", + "Ġ mu", + "Ġm u", + "Ġ lands", + "Ġl ands", + "Ġla nds", + "Ġland s", + "Ġlan ds", + "if iers", + "ifier s", + "ifi ers", + "ifie rs", + "Ġ FILE", + "ĠF ILE", + "ĠFI LE", + "ĠFIL E", + "RO UP", + "Ġ holder", + "Ġh older", + "Ġhold er", + "Ġho lder", + "Ġhol der", + "Ġ Son", + "ĠS on", + "ĠSo n", + "Ġsym pt", + "Ġsymp t", + ". route", + ".r oute", + ".ro ute", + ") ?", + "Ġ argc", + "Ġar gc", + "Ġarg c", + "Ġ fort", + "Ġf ort", + "Ġfor t", + "Ġfo rt", + "Ġcas ino", + "Ġcasi no", + "_ category", + "_c ategory", + "Ġ forum", + "Ġf orum", + "Ġfor um", + "Ġfo rum", + "2 15", + "21 5", + "p refix", + "pre fix", + "pref ix", + "ap ture", + "apt ure", + "T ube", + "Tu be", + "e ms", + "em s", + "im ize", + "imi ze", + "imiz e", + "Ġn ue", + "Ġnu e", + "a us", + "au s", + "c ourse", + "co urse", + "cour se", + "A TOR", + "AT OR", + "ATO R", + "( )),", + "() ),", + "()) ,", + "Ad vertis", + "IN GS", + "ING S", + "Ġac know", + "Ġack now", + "ĠK orea", + "ĠKore a", + "ĠKo rea", + "ĠKor ea", + "p ling", + "pl ing", + "Ġ worker", + "Ġwork er", + "Ġwor ker", + "PL IED", + "h al", + "ha l", + "Ġ Richard", + "ĠRich ard", + "ĠRic hard", + "E lements", + "Element s", + "El ements", + "Elem ents", + "Ele ments", + "ĉ ĉĉĠ", + "ĉĉ ĉĠ", + "ĉĉĉ Ġ", + "s tar", + "st ar", + "sta r", + "Ġ relationships", + "Ġrelations hips", + "Ġrelationship s", + "Ġrelation ships", + "Ġ cheap", + "Ġc heap", + "Ġche ap", + "A CH", + "AC H", + "Ġ XML", + "ĠX ML", + "ĠXM L", + ", &", + "Ġ Louis", + "ĠL ouis", + "ĠLo uis", + "ĠLou is", + "Ġ ride", + "Ġr ide", + "Ġrid e", + "Ġri de", + "_ FAIL", + "_F AIL", + "_FA IL", + "Ġ chunk", + "Ġch unk", + "Ġchu nk", + "[ s", + "_ OUT", + "_O UT", + "Ġ chosen", + "Ġch osen", + "Ġcho sen", + "Ġchose n", + "_ [", + "/ (", + "Ġ Jeff", + "ĠJ eff", + "ĠJe ff", + "_ sl", + "_s l", + "p riv", + "pr iv", + "pri v", + "Ġ Canadian", + "ĠCan adian", + "Ġ unable", + "Ġu nable", + "Ġun able", + "Ġuna ble", + "Ġunab le", + "_ FLAG", + "_F LAG", + "_FL AG", + "Ġ nos", + "Ġn os", + "Ġno s", + "h igh", + "hi gh", + "Ġ lift", + "Ġl ift", + "Ġli ft", + "Ġlif t", + "f un", + "fu n", + "( ){", + "() {", + "el ly", + "ell y", + "ycler View", + "_ as", + "_a s", + "_ LIST", + "_L IST", + "Ġ radi", + "Ġr adi", + "Ġrad i", + "Ġra di", + ". getValue", + ".get Value", + "3 04", + "30 4", + "ĠAnge les", + "ĠAngel es", + "Ġ Span", + "ĠS pan", + "ĠSp an", + "ĠSpa n", + "_ instance", + "_in stance", + "_inst ance", + "i tors", + "it ors", + "itor s", + "ito rs", + "2 08", + "20 8", + "Ġ migration", + "Ġm igration", + "Ġmigr ation", + "Ġmig ration", + "A K", + "O h", + " ®", + ". selected", + ".se lected", + ".select ed", + ".sel ected", + "Ġ GT", + "ĠG T", + "Ġ advance", + "Ġadv ance", + "Ġ Style", + "ĠSt yle", + "ĠSty le", + ". DataGridView", + ".Data GridView", + "e ction", + "ect ion", + "ec tion", + "Ñ İ", + "p io", + "pi o", + "r og", + "ro g", + "Ġ shopping", + "Ġsh opping", + "Ġshop ping", + "Ġsho pping", + "Ġ Rect", + "ĠR ect", + "ĠRe ct", + "ĠRec t", + "I lluminate", + "O U", + "ĉ array", + "ĉa rray", + "ĉarr ay", + "ĉar ray", + "Ġsub stantial", + "Ġsubstant ial", + "Ġp regn", + "Ġpre gn", + "Ġpreg n", + "Ġprom ote", + "Ġpromot e", + "Ġpromo te", + "I EW", + "IE W", + ". Layout", + ".L ayout", + "Ġsign s", + "Ġsig ns", + "/ .", + "Ġ letters", + "Ġlet ters", + "Ġletter s", + "Ġlett ers", + "B oard", + "Bo ard", + "c trl", + "ct rl", + "ctr l", + "\" \\", + "Ġ Jones", + "ĠJ ones", + "ĠJo nes", + "ĠJon es", + "Ġ vertex", + "Ġver tex", + "Ġvert ex", + "Ġverte x", + "Ġ ja", + "Ġj a", + "Ġaff ili", + "Ġ wealth", + "Ġwe alth", + "ĉ default", + "ĉd efault", + "ĉdef ault", + "ĉde fault", + "Ġsign ificantly", + "Ġsignific antly", + "Ġsignificant ly", + "Ġ ec", + "Ġe c", + "Ġ xs", + "Ġx s", + "act ual", + "ac tual", + ". per", + ".p er", + ".pe r", + "_ step", + "_s tep", + "_st ep", + "_ste p", + "an vas", + "m ac", + "ma c", + "Ġtrans l", + "Ġtran sl", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "It erator", + "Iter ator", + "Ġ och", + "Ġo ch", + "Ġoc h", + "ag nostic", + "agnost ic", + "Ġ During", + "ĠD uring", + "ĠDu ring", + "ĠDur ing", + "Ġ DEFAULT", + "ĠD EFAULT", + "ĠDE FAULT", + "Ġt ill", + "Ġti ll", + "Ġtil l", + "Ġ signature", + "Ġsign ature", + "Ġsig nature", + "Ġ bird", + "Ġb ird", + "Ġbi rd", + "Ġbir d", + "Ġ Ol", + "ĠO l", + "3 10", + "31 0", + "Ġ Ir", + "ĠI r", + "H S", + "av atar", + "ava tar", + "ESS AGE", + "Ġe lev", + "Ġel ev", + "Ġele v", + "Ġ mt", + "Ġm t", + "Ġ Nav", + "ĠN av", + "ĠNa v", + "Ġrel ax", + "Ġ plate", + "Ġp late", + "Ġpl ate", + "Ġpla te", + "Ġplat e", + "I TEM", + "IT EM", + "ITE M", + "( date", + "(d ate", + "(dat e", + "(da te", + ". not", + ".n ot", + ".no t", + "Ġ grade", + "Ġg rade", + "Ġgr ade", + "Ġgrad e", + "Ġgra de", + "Ġ }),Ċ", + "Ġ} ),Ċ", + "Ġ}) ,Ċ", + "Ġ}), Ċ", + "? \"ĊĊ", + "?\" ĊĊ", + "?\"Ċ Ċ", + "i ences", + "ie nces", + "ience s", + "ien ces", + "H igh", + "Hi gh", + "Ġ DIS", + "ĠD IS", + "ĠDI S", + "2 31", + "23 1", + "dis abled", + "disable d", + "Q UI", + "QU I", + "Ġ noise", + "Ġn oise", + "Ġno ise", + "Ġnoi se", + "a ux", + "au x", + "Ġ UP", + "ĠU P", + "8 88", + "88 8", + "o sa", + "os a", + "Ġv oc", + "Ġvo c", + "Ġ ))", + "Ġ) )", + "o com", + "oc om", + "oco m", + "_ OFF", + "_O FF", + "_OF F", + "Ġ Db", + "ĠD b", + "L ock", + "Lo ck", + "Loc k", + ".e clipse", + ", d", + "Ġ Draw", + "ĠD raw", + "ĠDr aw", + "ĠDra w", + "Ġ \"(", + "Ġ\" (", + "Ġ visited", + "Ġvis ited", + "Ġvisit ed", + "Ġvisite d", + "Ġ âĪ", + "Ġâ Ī", + "Ġs ucceed", + "Ġsuc ceed", + "Ġsucc eed", + "Ġim possible", + "Ġimp ossible", + "Ġimposs ible", + "a ire", + "air e", + "ai re", + "Ġ Turn", + "ĠT urn", + "ĠTur n", + "ĠTu rn", + "Ġ dish", + "Ġd ish", + "Ġdis h", + "Ġdi sh", + "F G", + "Ġ sensor", + "Ġs ensor", + "Ġsens or", + "A NN", + "AN N", + "a ba", + "ab a", + "Ġs urg", + "Ġsu rg", + "Ġsur g", + "] );čĊ", + "]) ;čĊ", + "]); čĊ", + "Ġ fp", + "Ġf p", + "_ an", + "_a n", + "- J", + "- G", + "Ġ Job", + "ĠJ ob", + "ĠJo b", + "Con vert", + "Conv ert", + "Ġ KEY", + "ĠK EY", + "ĠKE Y", + "Ġ authors", + "Ġauthor s", + "Ġauth ors", + "_ server", + "_s erver", + "_serv er", + "_ser ver", + "\\ r", + "Ġ -*-", + "Ġ- *-", + "Ġ-* -", + "f lex", + "fl ex", + "Ġ soc", + "Ġs oc", + "Ġso c", + "R et", + "Re t", + "Ġ salt", + "Ġs alt", + "Ġsa lt", + "Ġsal t", + "Ġ âĢ¦ĊĊ", + "ĠâĢ¦ ĊĊ", + "ĠâĢ¦Ċ Ċ", + "Ġ Clear", + "ĠC lear", + "ĠCl ear", + "ĠCle ar", + "( page", + "(p age", + "(pa ge", + "- danger", + "-d anger", + "-da nger", + "Ġ rooms", + "Ġro oms", + "Ġroom s", + "con v", + "co nv", + "# {", + ". op", + ".o p", + "Ġ Area", + "ĠA rea", + "ĠAr ea", + "ĠAre a", + "_ SC", + "_S C", + "h en", + "he n", + "Ġbeg ins", + "Ġbegin s", + "- y", + "Ġexc ited", + "Ġexcit ed", + "Ġ ignored", + "Ġign ored", + "Ġignore d", + "Ġignor ed", + "Ġ bonus", + "Ġb onus", + "Ġbon us", + "st udent", + "stu dent", + "stud ent", + "Ġ Member", + "ĠM ember", + "ĠMem ber", + "Ġrel atively", + "Ġrelative ly", + "Ġrelativ ely", + "Ġrelat ively", + "Ġ Low", + "ĠL ow", + "ĠLo w", + "Ġ Produ", + "ĠP rodu", + "ĠPro du", + "ĠPr odu", + "ĠProd u", + "at eway", + "ate way", + "pos ure", + "po sure", + "Ġth ick", + "Ġthi ck", + "an iel", + "ani el", + "anie l", + "( view", + "(v iew", + "ĠC rush", + "ĠCr ush", + "ĠCru sh", + "ĠCrus h", + "Ext ension", + "I l", + "e ed", + "ee d", + "L OC", + "LO C", + ". im", + ".i m", + ". Items", + ".I tems", + ".Item s", + ".It ems", + "Ġconf lict", + "Ġconflic t", + "Ġconfl ict", + ". prevent", + ".pre vent", + ".pr event", + ".prev ent", + "2 52", + "25 2", + "Ġon Create", + "u v", + "i ser", + "is er", + "ise r", + "Ġ wave", + "Ġw ave", + "Ġwa ve", + "Ġwav e", + "M ar", + "Ma r", + "Ġ Community", + "ĠComm unity", + "ĠCommun ity", + "i che", + "ic he", + "ich e", + "Ġ Nothing", + "ĠNo thing", + "[ m", + "Ġ Lee", + "ĠL ee", + "ĠLe e", + "ri ends", + "riend s", + "rie nds", + "rien ds", + "2 32", + "23 2", + "è re", + "! !!", + "!! !", + "a nz", + "an z", + ". result", + ".res ult", + "Ġ SK", + "ĠS K", + "_ PARAM", + "_P ARAM", + "_PA RAM", + "_PAR AM", + "Ġdem ocr", + "Ġdemo cr", + "Back Color", + ". exists", + ".ex ists", + ".exist s", + "\" It", + "\"I t", + "( options", + "(o ptions", + "(opt ions", + "(option s", + "r azy", + "ra zy", + "raz y", + "a ser", + "as er", + "ase r", + "\\ Database", + "\\Data base", + "\\D atabase", + "al endar", + "alen dar", + "_ ass", + "_a ss", + "_as s", + "; }Ċ", + ";} Ċ", + "ver tex", + "vert ex", + "verte x", + "ine craft", + "W arning", + "War ning", + "Warn ing", + "ar go", + "arg o", + "Ġ actor", + "Ġa ctor", + "Ġact or", + "Ġac tor", + "Ġ Instead", + "ĠIn stead", + "ĠInst ead", + "Ġ Using", + "ĠU sing", + "ĠUs ing", + "S elf", + "Se lf", + "Sel f", + "@ interface", + "Ġspe aking", + "Ġspeak ing", + "Ġ Paris", + "ĠP aris", + "ĠPar is", + "ĠPa ris", + "Ġ LICENSE", + "ĠL ICENSE", + "ĠLIC ENSE", + ". node", + ".n ode", + ".no de", + "Ġ Food", + "ĠF ood", + "ĠFo od", + "ĠFoo d", + "E IF", + "EI F", + "Ġ Bi", + "ĠB i", + ". Start", + ".St art", + "Ġ IB", + "ĠI B", + "Ġun iversity", + "Ġunivers ity", + "2 54", + "25 4", + "Ġ Header", + "ĠHe ader", + "ĠHead er", + ". product", + ".pro duct", + ".prod uct", + "4 09", + "40 9", + "C opy", + "Co py", + "Cop y", + "e tc", + "et c", + "r ical", + "ri cal", + "ric al", + "rica l", + "Ġ >>>", + "Ġ> >>", + "Ġ>> >", + "b ooks", + "bo oks", + "book s", + "boo ks", + "Ġ algorithm", + "Ġal gorithm", + "Ġ' __", + "Ġ'_ _", + "( javax", + "(j avax", + "(java x", + "Ġnumer ous", + "Ġnumero us", + "S hare", + "Sh are", + "Shar e", + "Sha re", + "H ave", + "Ha ve", + "Ġrec ru", + "Ġ prove", + "Ġp rove", + "Ġpro ve", + "Ġpr ove", + "Ġprov e", + ". substring", + ".sub string", + ".substr ing", + "he alth", + "е л", + "еР»", + "Ġ decimal", + "Ġd ecimal", + "Ġde cimal", + "Ġdec imal", + "Ġ commission", + "Ġcom mission", + "Ġcomm ission", + "s cription", + "script ion", + "scri ption", + "x C", + "Ġ summary", + "Ġsum mary", + "Ġsummar y", + "Ġsumm ary", + "at ted", + "att ed", + "atte d", + "Ġc loser", + "Ġcl oser", + "Ġclose r", + "Ġclos er", + "Ġclo ser", + "f inished", + "fin ished", + "finish ed", + "( )){Ċ", + "() ){Ċ", + "()) {Ċ", + "()){ Ċ", + "Ġ Wood", + "ĠW ood", + "ĠWo od", + "ĠWoo d", + "3 01", + "30 1", + "_ fields", + "_f ields", + "_field s", + "k u", + "_ items", + "_i tems", + "_item s", + "_it ems", + "F lag", + "Fl ag", + "Ġ confidence", + "Ġconf idence", + "Ġ Federal", + "ĠF ederal", + "ĠFeder al", + "ĠFed eral", + "d ux", + "du x", + "Ġ compat", + "Ġcom pat", + "Ġcomp at", + "Ġ vertical", + "Ġver tical", + "Ġvert ical", + "Ð ¹", + "è s", + "; \">Ċ", + ";\" >Ċ", + ";\"> Ċ", + "_ manager", + "_m anager", + "_man ager", + "_manage r", + "( )))Ċ", + "() ))Ċ", + "()) )Ċ", + "())) Ċ", + "I DE", + "ID E", + ": \",", + ":\" ,", + "2 35", + "23 5", + "_ _Ċ", + "__ Ċ", + "Ġ Way", + "ĠW ay", + "ĠWa y", + "2 21", + "22 1", + "Ñ Ī", + "T emp", + "Te mp", + "Tem p", + "Ġ STR", + "ĠS TR", + "ĠST R", + "r itten", + "rit ten", + "ritt en", + "ritte n", + "S ync", + "Sy nc", + "Syn c", + "Ġ AV", + "ĠA V", + "Ġ CEO", + "ĠC EO", + "ĠCE O", + "Ġ Guid", + "ĠG uid", + "ĠGu id", + "ĠGui d", + "Ġenvironment al", + "Ġenviron mental", + "Ġcorrespond ing", + "ĉ console", + "ĉcon sole", + "Ġ justice", + "Ġjust ice", + "Ġju stice", + "Ġ JS", + "ĠJ S", + "Ġl ived", + "Ġli ved", + "Ġlive d", + "Ġliv ed", + "g ar", + "ga r", + "Ġ Graph", + "ĠG raph", + "ĠGr aph", + "ĠGra ph", + "Ġ Stat", + "ĠS tat", + "ĠSt at", + "ĠSta t", + "Ġ iPhone", + "Ġi Phone", + "ĠiP hone", + ". al", + ".a l", + "Ġ HD", + "ĠH D", + "Ġocc ur", + "Ġoc cur", + "Ġ threshold", + "Ġth reshold", + "Ġthresh old", + "5 09", + "50 9", + "Ġ onclick", + "Ġon click", + "Ġonc lick", + "R EG", + "RE G", + ".Graphics Unit", + "M eta", + "Me ta", + "Met a", + "Å ¾", + "Ġ cum", + "Ġc um", + "Ġcu m", + ". gnu", + ".g nu", + "à «", + "Ġobt ained", + "Ġobtain ed", + "Ġcom plaint", + "Ġcompl aint", + "Ġcomplain t", + "Ġ eating", + "Ġe ating", + "Ġeat ing", + "Ġea ting", + "Ġ tar", + "Ġt ar", + "Ġta r", + "_ task", + "_t ask", + "_ta sk", + "Ġ opts", + "Ġo pts", + "Ġop ts", + "Ġopt s", + "2 16", + "21 6", + "( to", + "(t o", + "P ass", + "Pa ss", + "Pas s", + "Ġp lastic", + "Ġpl astic", + "Ġplast ic", + "t ility", + "til ity", + "Ġ Win", + "ĠW in", + "ĠWi n", + ".prevent Default", + "p ile", + "pi le", + "Ġ Gar", + "ĠG ar", + "ĠGa r", + "Ġ quantity", + "Ġqu antity", + "Ġquant ity", + "Ġqua ntity", + "_ last", + "_l ast", + "_la st", + "Ġg reatest", + "Ġgreat est", + "Ġgre atest", + "D ao", + "Da o", + "_ DIS", + "_D IS", + "_DI S", + "Ġ Used", + "ĠU sed", + "ĠUs ed", + "ĠUse d", + "Ġ HP", + "ĠH P", + "r iting", + "ri ting", + "rit ing", + "S ION", + "SI ON", + "b lue", + "bl ue", + "d omain", + "do main", + "dom ain", + "Ġ scores", + "Ġs cores", + "Ġsc ores", + "Ġscore s", + "Ġsco res", + "Ġscor es", + "N ormal", + "Norm al", + "Nor mal", + "_ admin", + "_ad min", + "Ġ ASSERT", + "ĠA SSERT", + "ĠASS ERT", + "T hen", + "The n", + "Th en", + "* **", + "** *", + "d ist", + "dis t", + "di st", + "l on", + "lo n", + "Ġh ate", + "Ġha te", + "Ġhat e", + "s hal", + "sh al", + "sha l", + "Image View", + "d atabase", + "data base", + "dat abase", + "Ġp and", + "Ġpa nd", + "Ġpan d", + "Ġ logic", + "Ġlog ic", + "= false", + "=f alse", + "b g", + "Ġ Configuration", + "ĠConfig uration", + "Ġn ur", + "Ġnu r", + "O G", + "Ġ married", + "Ġmar ried", + ": +", + "Ġd ropped", + "Ġdr opped", + "Ġdrop ped", + "Ġdro pped", + "0 40", + "04 0", + "Ġ registration", + "Ġreg istration", + "Ġregistr ation", + "Ġregist ration", + "о м", + "оР¼", + "ult iple", + "ulti ple", + "ultip le", + "i zers", + "iz ers", + "ize rs", + "izer s", + "s hape", + "sh ape", + "sha pe", + ". copy", + ".c opy", + ".co py", + "Ġw earing", + "Ġwe aring", + "Ġwear ing", + "ĠC ath", + "ĠCa th", + "ĠCat h", + "Ġded icated", + "Ġdedic ated", + "Ġdedicate d", + "Ġ ...Ċ", + "Ġ. ..Ċ", + "Ġ... Ċ", + "Ġ.. .Ċ", + "Ġadv oc", + "Ġ Family", + "ĠF amily", + "ĠFam ily", + "ĠFamil y", + "Ġ statements", + "Ġstate ments", + "Ġstat ements", + "Ġstatement s", + "e matic", + "em atic", + "ema tic", + "emat ic", + "ampions hip", + "ampion ship", + "Ġmot iv", + "Ġmo tiv", + "Ġ Have", + "ĠH ave", + "ĠHa ve", + "ĠHav e", + "Ġb low", + "Ġbl ow", + "Ġblo w", + "J ob", + "Jo b", + "c ert", + "ce rt", + "cer t", + "_ vector", + "_v ector", + "_vec tor", + "_vect or", + "_ve ctor", + "inst all", + "Ġ COPY", + "ĠC OPY", + "ĠCO PY", + "ĠCOP Y", + "em bed", + "emb ed", + "D IR", + "DI R", + "Ġ Spring", + "ĠS pring", + "ĠSp ring", + "ĠSpr ing", + "Ġex hib", + "Ġexh ib", + "2 23", + "22 3", + "c dn", + "cd n", + "Ġ Comment", + "ĠCom ment", + "ĠComm ent", + "Ġ Optional", + "ĠOption al", + "ĠOpt ional", + ". player", + ".p layer", + ".pl ayer", + ".play er", + "Ġ Dark", + "ĠD ark", + "ĠDa rk", + "ĠDar k", + "( pos", + "(p os", + "(po s", + "Ġ Should", + "ĠSh ould", + "ĠSho uld", + "Ġ centre", + "Ġc entre", + "Ġcent re", + "Ġcentr e", + "Ġcen tre", + "Ġ Guard", + "ĠG uard", + "ĠGu ard", + "ĠGuar d", + "ó w", + "Ġtr ouble", + "Ġtro uble", + "Ġtroub le", + "Ġtrou ble", + "E NER", + "EN ER", + "ENE R", + "( unsigned", + "(un signed", + "_ service", + "_s ervice", + "_serv ice", + "_ser vice", + "Ġ ns", + "Ġn s", + "u ling", + "ul ing", + "ulin g", + "uli ng", + "Ġ Mexico", + "ĠMe xico", + "ĠMex ico", + "Ġ NY", + "ĠN Y", + "m ysql", + "my sql", + "mys ql", + "Ġ lic", + "Ġl ic", + "Ġli c", + "å ľ", + "M r", + "- fl", + "-f l", + "Ġ Customer", + "ĠC ustomer", + "ĠCustom er", + "ĠCust omer", + "i di", + "id i", + "Ġ ?>ĊĊ", + "Ġ? >ĊĊ", + "Ġ?> ĊĊ", + "Ġ?>Ċ Ċ", + "r ible", + "ri ble", + "rib le", + "Ġ пÑĢ", + "Ġп ÑĢ", + "Ġ sizes", + "Ġs izes", + "Ġsize s", + "Ġsi zes", + "Ġsiz es", + "_ STRING", + "_ST RING", + "_STR ING", + "valid ation", + "Ġ Jon", + "ĠJ on", + "ĠJo n", + "( Http", + "(H ttp", + "add Class", + "N odes", + "Node s", + "No des", + "Ġ fragment", + "Ġf ragment", + "Ġfr agment", + "Ġfra gment", + "Ġfrag ment", + "Ġs poke", + "Ġsp oke", + "Ġspo ke", + "Ġw aste", + "Ġwas te", + "Ġwa ste", + "Ġwast e", + "J oin", + "Jo in", + "Ġ illustr", + "Ġill ustr", + "Ġillust r", + "e li", + "el i", + "c ient", + "ci ent", + "cie nt", + "Ġ aid", + "Ġa id", + "Ġai d", + "Ġpro sec", + "Ġpros ec", + "Ġprose c", + "' ){Ċ", + "') {Ċ", + "'){ Ċ", + "Ġp assing", + "Ġpass ing", + "Ġpas sing", + "Ġ faces", + "Ġf aces", + "Ġfa ces", + "Ġfac es", + "Ġface s", + "S hape", + "Sh ape", + "Sha pe", + "_ Z", + "i ti", + "it i", + "Ġ alle", + "Ġa lle", + "Ġal le", + "Ġall e", + "Ġ robot", + "Ġr obot", + "Ġro bot", + "Ġrob ot", + "Ġ ĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠ ĠĊ", + "Ġ Spe", + "ĠS pe", + "ĠSp e", + "Ġre ceiving", + "Ġrece iving", + "Ġ Details", + "ĠD etails", + "ĠDe tails", + "ĠDet ails", + "ĠDetail s", + "Ġ \")", + "Ġ\" )", + "m g", + "_ REF", + "_RE F", + "_R EF", + "Ġ comparison", + "Ġcom parison", + "Ġcompar ison", + "* ,", + "Ġ Found", + "ĠF ound", + "ĠFo und", + "ĠFou nd", + "_ session", + "_s ession", + "_sess ion", + "( U", + "/ F", + "Ġ xxx", + "Ġx xx", + "Ġxx x", + "N etwork", + "Net work", + "d ers", + "de rs", + "der s", + "Ġ capture", + "Ġc apture", + "Ġcap ture", + "Ġcapt ure", + "Ġc orre", + "Ġcor re", + "Ġcorr e", + "Ġ Ltd", + "ĠL td", + "ĠLt d", + "Ġ Adv", + "ĠA dv", + "ĠAd v", + "[ @", + "Ġ clip", + "Ġc lip", + "Ġcl ip", + "Ġcli p", + "M ill", + "Mi ll", + "Mil l", + "Ġ Profile", + "ĠPro file", + "ĠPr ofile", + "ĠProf ile", + "Ġ endif", + "Ġen dif", + "Ġend if", + "Ġob lig", + "Ġobl ig", + "de scribe", + "des cribe", + "descr ibe", + ". element", + ".e lement", + ".el ement", + ".elem ent", + "riter ion", + "L D", + "e red", + "er ed", + "ere d", + "Ġf avour", + "Ġfav our", + "s core", + "sc ore", + "Ġ Filter", + "ĠF ilter", + "ĠFil ter", + "at tributes", + "attribute s", + "attrib utes", + "Ġ checks", + "Ġcheck s", + "Ġche cks", + "In flater", + "Inf later", + "Ġ Plus", + "ĠP lus", + "ĠPl us", + "Ġ scientific", + "Ġscient ific", + "Ġ privacy", + "Ġpriv acy", + "H ead", + "He ad", + "Ġ feat", + "Ġf eat", + "Ġfe at", + "Ġ degrees", + "Ġdeg rees", + "Ġdegree s", + "Ġ Pale", + "ĠP ale", + "ĠPal e", + "ĠPa le", + "; \">", + ";\" >", + "Ġ films", + "Ġfil ms", + "Ġfilm s", + "Ġ Audio", + "ĠA udio", + "ĠAud io", + "ĠAu dio", + "ĠAudi o", + "Ġ Tag", + "ĠT ag", + "ĠTa g", + "Ġ Energy", + "ĠE nergy", + "ĠEn ergy", + "ĠEnerg y", + "i tar", + "it ar", + "ita r", + "par ator", + "para tor", + "Ġf ellow", + "Ġfell ow", + "Ġfel low", + "Ġ evt", + "Ġe vt", + "Ġev t", + "Ġ Tri", + "ĠT ri", + "ĠTr i", + "ĠD AM", + "ĠDA M", + "c loud", + "cl oud", + "clo ud", + "Ġ Password", + "ĠP assword", + "ĠPass word", + "ĠPas sword", + "Ġ Democrats", + "ĠDem ocrats", + "ĠDemocr ats", + "ĠDemocrat s", + "ĠA cad", + "ĠAc ad", + "$ lang", + "$l ang", + "Ġ reb", + "Ġre b", + "Ġr eb", + "( ))ĊĊ", + "() )ĊĊ", + "()) ĊĊ", + "())Ċ Ċ", + "н Ñĭ", + "Ġ Bur", + "ĠB ur", + "ĠBu r", + "read cr", + "Ġ hex", + "Ġh ex", + "Ġhe x", + "2 09", + "20 9", + "Con sole", + "Cons ole", + "c tl", + "ct l", + "ou sel", + "ous el", + "ouse l", + "Ġ William", + "ĠWill iam", + "ĠWilli am", + "Ġ az", + "Ġa z", + "_ PORT", + "_P ORT", + "_PO RT", + "Ġpr actices", + "Ġpract ices", + "Ġpractice s", + "Ġany where", + "Ġ Position", + "ĠP osition", + "ĠPos ition", + "Ġ ->Ċ", + "Ġ- >Ċ", + "Ġ-> Ċ", + "i ams", + "ia ms", + "iam s", + ". username", + ".user name", + "place holder", + "Ġ oder", + "Ġo der", + "Ġod er", + "Ġode r", + "Ġ Secretary", + "ĠSecret ary", + "Ġ iT", + "Ġi T", + "m ond", + "mon d", + "mo nd", + "e vents", + "event s", + "ev ents", + "even ts", + "eve nts", + "? âĢĿ", + ". Sub", + ".S ub", + "Ġ attached", + "Ġatt ached", + "Ġattach ed", + "Ġn ão", + "Ġ estate", + "Ġe state", + "Ġest ate", + "Ġesta te", + "3 65", + "36 5", + ". action", + ".a ction", + ".ac tion", + ".act ion", + "Ġ figures", + "Ġfig ures", + "Ġfigure s", + "Ġfigur es", + "Ġ });čĊ", + "Ġ} );čĊ", + "Ġ}) ;čĊ", + "Ġ}); čĊ", + "Ġsub scri", + "Ġsubs cri", + ". tag", + ".t ag", + ".ta g", + "n am", + "na m", + ". plot", + ".p lot", + ".pl ot", + "n oon", + "no on", + "li ament", + "lia ment", + "Char acter", + ". tab", + ".t ab", + ".ta b", + "Ġ winter", + "Ġw inter", + "Ġwin ter", + "Ġ Variable", + "ĠVar iable", + "ĠVari able", + "Ġ trees", + "Ġt rees", + "Ġtr ees", + "Ġtre es", + "Ġtree s", + "Ġp roud", + "Ġpro ud", + "Ġpr oud", + "( V", + "_ load", + "_l oad", + "_lo ad", + "Ġh ier", + "Ġhi er", + "ĠE con", + "ĠEc on", + "ĠEco n", + "Ġ fd", + "Ġf d", + "Ġvict ims", + "Ġvictim s", + "R est", + "Re st", + "Res t", + "i ana", + "ia na", + "ian a", + "Ġ fake", + "Ġf ake", + "Ġfa ke", + "Ġfak e", + ".Print ln", + "Ġ strlen", + "Ġst rlen", + "Ġstr len", + "Ġ sad", + "Ġs ad", + "Ġsa d", + "Ġ ble", + "Ġb le", + "Ġbl e", + "P rot", + "Pro t", + "Pr ot", + "Ġ buttons", + "Ġbut tons", + "Ġbutton s", + "Ġbutt ons", + "Ġbutto ns", + "Ġte levision", + "Ġtele vision", + "Ġtelevis ion", + "Ġtelev ision", + "Ġ logo", + "Ġl ogo", + "Ġlo go", + "Ġlog o", + "ext ension", + "ĉ j", + "s tein", + "st ein", + "ste in", + "ac iones", + "acion es", + "aci ones", + "acio nes", + "Ġ \"\"\"ĊĊ", + "Ġ\"\" \"ĊĊ", + "Ġ\"\"\"Ċ Ċ", + "Ġ\"\"\" ĊĊ", + "Ġ simp", + "Ġs imp", + "Ġsim p", + "Ġsi mp", + "Ġrecord ed", + "Ġbr ings", + "Ġbring s", + "Ġ principal", + "Ġpr incipal", + "Ġprincip al", + "Ġf ees", + "Ġfe es", + "Ġfee s", + "( source", + "(s ource", + "k dir", + "kd ir", + "Ġ utils", + "Ġutil s", + "Ġut ils", + "Ġcorrect ly", + "f il", + "fi l", + "Ġ wel", + "Ġw el", + "Ġwe l", + "P air", + "Pa ir", + "- button", + "-b utton", + "-but ton", + "s cale", + "sc ale", + "scal e", + "ver ify", + "[ c", + "Ġ ---", + "Ġ- --", + "Ġ-- -", + "Ġ escape", + "Ġe scape", + "Ġes cape", + "Ġesc ape", + "Ġescap e", + "i kes", + "ik es", + "ike s", + "Lower Case", + "ic ian", + "ici an", + "icia n", + "Ġ chapter", + "Ġch apter", + "Ġcha pter", + "Ġchap ter", + "Ġ TYPE", + "ĠT YPE", + "ĠTY PE", + "Ġ shadow", + "Ġsh adow", + "Ġ awesome", + "Ġaw esome", + "Ġawe some", + "W E", + "e lif", + "el if", + "eli f", + "Ġ lambda", + "Ġl ambda", + "Ġlamb da", + "Ġ distinct", + "Ġdist inct", + "Ġ bare", + "Ġb are", + "Ġbar e", + "Ġba re", + "- off", + "-of f", + "-o ff", + "Ġ colour", + "Ġcol our", + ". appendChild", + ".append Child", + "o lec", + "ol ec", + "ole c", + "a ga", + "ag a", + ". fill", + ".f ill", + ".fi ll", + ".fil l", + "ĉ super", + "ĉs uper", + "Ġ adj", + "Ġa dj", + "Ġad j", + "( position", + "(p osition", + "(pos ition", + ". getItem", + ".get Item", + "2 42", + "24 2", + "S hort", + "Sh ort", + "Ġtot ally", + "Ġtotal ly", + "V D", + "Ġ Tre", + "ĠT re", + "ĠTr e", + "_ ep", + "_e p", + "v ements", + "ve ments", + "vement s", + "vem ents", + "Ġ Solution", + "ĠS olution", + "ĠSol ution", + "Ġfund ament", + "F ollow", + "Ġ facility", + "Ġfac ility", + "Ġfacilit y", + "Ġfacil ity", + "Ġhapp ening", + "Ġhappen ing", + "O F", + ". textBox", + ".text Box", + "S pan", + "Sp an", + "Ġ «", + "Ġ «", + "i den", + "id en", + "ide n", + "Ġex ceed", + "Ġexc eed", + "Ġexce ed", + "( parent", + "(p arent", + "(par ent", + "(paren t", + "(pa rent", + "Ġ cp", + "Ġc p", + "ç »", + "Ġh asn", + "Ġhas n", + "Ġha sn", + "Ġ pri", + "Ġp ri", + "Ġpr i", + "Ġcon sequ", + "Ġcons equ", + "Ġconse qu", + "n en", + "ne n", + "ĠIN TO", + "ĠINT O", + "I gnore", + "Ign ore", + "Ġ Future", + "ĠF uture", + "ĠFu ture", + "ĠFut ure", + "Ġ carbon", + "Ġc arbon", + "Ġcar bon", + "Ġcarb on", + "Ġ Steel", + "ĠSt eel", + "ĠSte el", + "f mt", + "fm t", + "o kie", + "ok ie", + "oki e", + "Ġ spl", + "Ġs pl", + "Ġsp l", + "( title", + "(t itle", + "(ti tle", + "- info", + "-in fo", + "-inf o", + "Ġde als", + "Ġdeal s", + "Ġ fixture", + "Ġf ixture", + "Ġfix ture", + "e a", + "D iv", + "Di v", + "Ġ tested", + "Ġt ested", + "Ġte sted", + "Ġtest ed", + "Ġtes ted", + "Ġteste d", + "_ return", + "_re turn", + "_r eturn", + "_ret urn", + ") ĊĊĊĊ", + ")Ċ ĊĊĊ", + ")ĊĊ ĊĊ", + ")ĊĊĊ Ċ", + "up ported", + "upport ed", + "upp orted", + "Ġ Cook", + "ĠC ook", + "ĠCo ok", + "Ġp aying", + "Ġpay ing", + "Ġpa ying", + "Ġ Ill", + "ĠI ll", + "ĠIl l", + "Ġarr ested", + "Ġarrest ed", + "Ġ Prime", + "ĠPr ime", + "ĠPri me", + "ĠPrim e", + "_ callback", + "_c allback", + "_call back", + "> ,Ċ", + ">, Ċ", + "d river", + "dr iver", + "drive r", + "O nce", + "On ce", + "a bb", + "ab b", + "_ bytes", + "_by tes", + "_byte s", + "Ġ Sets", + "ĠS ets", + "ĠSe ts", + "ĠSet s", + "( Object", + "(O bject", + "Ġ cc", + "Ġc c", + "Ġ shell", + "Ġs hell", + "Ġsh ell", + "Ġshe ll", + "Ġshel l", + "a lo", + "al o", + ") ;//", + "); //", + "( log", + "(l og", + "(lo g", + "2 64", + "26 4", + "c tors", + "ct ors", + "ctor s", + ") ", + "2 18", + "21 8", + "Ġ $(\".", + "Ġ$ (\".", + "Ġ$( \".", + "Ġ$(\" .", + ". pos", + ".p os", + ".po s", + "Ġ boys", + "Ġbo ys", + "Ġboy s", + "Ġwed ding", + "Ġ agents", + "Ġag ents", + "Ġage nts", + "Ġagent s", + "= \"_", + "=\" _", + "Ġ Army", + "ĠAr my", + "ĠArm y", + "Ġ hint", + "Ġh int", + "Ġhi nt", + "Ġhin t", + "v ision", + "vis ion", + "Ġ tech", + "Ġt ech", + "Ġte ch", + "Ġtec h", + "Ġ Connect", + "ĠCon nect", + "ĠConn ect", + "Ġ legend", + "Ġl egend", + "Ġle gend", + "Ġleg end", + "Ġ Bet", + "ĠB et", + "ĠBe t", + ". Base", + ".B ase", + "Sub ject", + "Su bject", + "Ġ lit", + "Ġl it", + "Ġli t", + "Re move", + "Rem ove", + "Ġ \":", + "Ġ\" :", + "Ġ Final", + "ĠF inal", + "ĠFin al", + "ĠFi nal", + "pear ance", + "ĠiT unes", + "Ġ participants", + "Ġpart icipants", + "Ġparticip ants", + "Ġparticipant s", + "Ġ Python", + "ĠP ython", + "ĠPy thon", + "Ġ busy", + "Ġbu sy", + "Ġbus y", + "i el", + "ie l", + "vert ices", + "Ġtemplate Url", + "Ġ Close", + "ĠC lose", + "ĠCl ose", + "ĠClo se", + "I mg", + "Im g", + "ĠCorpor ation", + "ĠCorp oration", + "t imestamp", + "time stamp", + "Ġ extend", + "Ġext end", + "Ġwe bsites", + "Ġweb sites", + "Ġwebsite s", + "Ġwebs ites", + "Ġposs ibility", + "Ġpossibilit y", + "о ÑĤ", + "Ġ kö", + "Ġk ö", + "Ġm eat", + "Ġme at", + "Ġ representation", + "Ġre presentation", + "Ġrep resentation", + "Ġrepresent ation", + "Ġrepresenta tion", + "2 41", + "24 1", + "Ġ ĉĉ", + "Ġĉ ĉ", + "_ START", + "_ST ART", + "_STAR T", + "_STA RT", + ". apply", + ".app ly", + ".ap ply", + "ĠV alley", + "ĠVal ley", + "ĠValle y", + "ĠVall ey", + "Ġ Success", + "ĠS uccess", + "ĠSu ccess", + "ĠSuc cess", + "ĠSucc ess", + "H i", + "Ġ nob", + "Ġn ob", + "Ġno b", + "Ġ IEnumerable", + "ĠI Enumerable", + "_ select", + "_s elect", + "_se lect", + "_sel ect", + "g eo", + "ge o", + ". \")Ċ", + ".\" )Ċ", + ".\") Ċ", + "Ġt urning", + "Ġturn ing", + "Ġtur ning", + "Ġ fabric", + "Ġf abric", + "Ġfab ric", + "(\" \");Ċ", + "(\"\" );Ċ", + "(\"\") ;Ċ", + "(\"\"); Ċ", + "Ġp erspective", + "Ġpers pective", + "é Ĺ", + "Ġ Sn", + "ĠS n", + "Th ank", + "Than k", + "; j", + ". Parameters", + ".Param eters", + ".Parameter s", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĉĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĉĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĉĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ facts", + "Ġf acts", + "Ġfact s", + "Ġfa cts", + "Ġfac ts", + "3 05", + "30 5", + "Ġ unt", + "Ġu nt", + "Ġun t", + ". instance", + ".in stance", + ".inst ance", + "#### ############################################################", + "######## ########################################################", + "################ ################################################", + "################################ ################################", + "################################################ ################", + "######################################## ########################", + "######################## ########################################", + "######################################################## ########", + "############################################################ ####", + "- end", + "-e nd", + "-en d", + "Ġ JOIN", + "ĠJO IN", + "Ġ Hen", + "ĠH en", + "ĠHe n", + "Ġ uri", + "Ġu ri", + "Ġur i", + "åIJ į", + "Ġ на", + "Ġн а", + "Ġ Info", + "ĠIn fo", + "ĠInf o", + "Ġcon ducted", + "Ġconduct ed", + "Ġconduc ted", + "Ġ Ã¥", + "Ġà ¥", + "OUR CE", + "Ġ wine", + "Ġw ine", + "Ġwin e", + "Ġwi ne", + "J ohn", + "Jo hn", + ".Error f", + ".Err orf", + "Ġ Age", + "ĠA ge", + "ĠAg e", + "o unded", + "ound ed", + "oun ded", + "Ġreal ize", + "Ġrealiz e", + "3 12", + "31 2", + "Ġ ];", + "Ġ] ;", + "Ġsub sequ", + "Ġsubs equ", + ", m", + "( User", + "(U ser", + "i ano", + "ia no", + "ian o", + "Ġaccom pl", + "Ġaccomp l", + "i sp", + "is p", + ". std", + ".s td", + ".st d", + "é ĩ", + "Ġ Bed", + "ĠB ed", + "ĠBe d", + ". setAttribute", + ".set Attribute", + "B R", + "ke ep", + "kee p", + "Ġ ALL", + "ĠA LL", + "ĠAL L", + "Ġ isol", + "Ġis ol", + "Ġi sol", + "Ġiso l", + "a mma", + "am ma", + "amm a", + "P ackage", + "Pack age", + "Ġ occasion", + "Ġocc asion", + "Ġoccas ion", + "- success", + "-s uccess", + "-su ccess", + "е д", + "еР´", + "ĠLIMIT ED", + "s trip", + "st rip", + "str ip", + "stri p", + "( )ĊĊĊ", + "() ĊĊĊ", + "()Ċ ĊĊ", + "()ĊĊ Ċ", + "is tribution", + "istrib ution", + "Color s", + "Col ors", + "Ġ+ :+", + "Did Load", + "a ler", + "al er", + "ale r", + "Ġ tid", + "Ġt id", + "Ġti d", + "Ġ LED", + "ĠL ED", + "ĠLE D", + "Ġ Linked", + "ĠLink ed", + "ĠLin ked", + "Ġ Cart", + "ĠC art", + "ĠCar t", + "ĠCa rt", + "( ))čĊ", + "() )čĊ", + "()) čĊ", + "_ READ", + "_RE AD", + "Ġk illing", + "Ġkill ing", + "Ġkil ling", + "Ġ PHP", + "ĠP HP", + "ĠPH P", + "f ection", + "fe ction", + "fect ion", + "fec tion", + "Ġ instances", + "Ġin stances", + "Ġinst ances", + "Ġinstance s", + "c v", + "\" />", + "\"/ >", + "Ġ sf", + "Ġs f", + "Ġt axes", + "Ġtax es", + "Ġta xes", + "_ location", + "_l ocation", + "_lo cation", + "_loc ation", + "Ġ Bitcoin", + "ĠBit coin", + "u able", + "ua ble", + "r ank", + "ra nk", + "ran k", + "i gnore", + "ig nore", + "ign ore", + "t rack", + "tr ack", + "tra ck", + "к а", + "Ġshould n", + "Ġ OP", + "ĠO P", + "= >{Ċ", + "=> {Ċ", + "Ġ km", + "Ġk m", + "Ġ helper", + "Ġh elper", + "Ġhelp er", + "Ġhel per", + "_ head", + "_h ead", + "_he ad", + "Ġ Whether", + "ĠWh ether", + "ĠWhe ther", + "o co", + "oc o", + "_ bl", + "_b l", + "Ġ statistics", + "Ġstat istics", + "Ġstatist ics", + "Ġstatistic s", + "Ġbe auty", + "Ġbeaut y", + "Ġbeau ty", + "Ġ tog", + "Ġt og", + "Ġto g", + "t ip", + "ti p", + "ëĭ ¤", + "Ġ csv", + "Ġc sv", + "Ġcs v", + "( sql", + "(s ql", + "(sq l", + "std lib", + "we ak", + "Ġ likes", + "Ġl ikes", + "Ġli kes", + "Ġlike s", + "Ġlik es", + "Ä į", + "Ġ repeat", + "Ġre peat", + "Ġrep eat", + "Ġrepe at", + "Ġa partment", + "Ġap artment", + "Ġapart ment", + "Ġe mph", + "Ġem ph", + "Ġemp h", + "_ edit", + "_e dit", + "_ed it", + "Ġ vit", + "Ġv it", + "Ġvi t", + "ĉ type", + "ĉt ype", + "ĉtyp e", + "2 17", + "21 7", + "E ven", + "Ev en", + "u ten", + "ut en", + "ute n", + "Ġcircum stances", + "Ġcircumstance s", + "b ian", + "bi an", + "bia n", + "Ġs ugar", + "Ġsu gar", + "Ġsug ar", + "W indows", + "Window s", + "Wind ows", + "ì ŀ", + "Ġobs erved", + "Ġobserv ed", + "Ġobserve d", + "/ data", + "/d ata", + "/dat a", + "Ġ calendar", + "Ġc alendar", + "Ġcal endar", + "Ġcalend ar", + "Ġ strike", + "Ġst rike", + "Ġstr ike", + "Ġstri ke", + "Ġ RES", + "ĠR ES", + "ĠRE S", + "_ sc", + "_s c", + "f ony", + "fo ny", + "fon y", + "o rem", + "or em", + "ore m", + "( z", + "p ower", + "po wer", + "pow er", + "et ect", + "ete ct", + "Ġ Sat", + "ĠS at", + "ĠSa t", + ". description", + ".d escription", + ".de scription", + ".des cription", + "Ġ gang", + "Ġg ang", + "Ġga ng", + "Ġgan g", + "Ġ Sports", + "ĠS ports", + "ĠSp orts", + "ĠSport s", + "ĠSpo rts", + "ĠSpor ts", + "on gs", + "ong s", + "Ġ Bundle", + "ĠB undle", + "ĠBund le", + "ĠBun dle", + ". sum", + ".s um", + "o nce", + "on ce", + "Ġacc used", + "Ġaccus ed", + "Ġaccuse d", + "Ġexp lore", + "Ġexpl ore", + "Ġexplo re", + "Ġexplor e", + "Ġ approximately", + "Ġapprox imately", + "Ġapproximate ly", + "Ġ losing", + "Ġl osing", + "Ġlo sing", + "Ġlos ing", + "th esis", + "the sis", + "thes is", + "Ġ Fund", + "ĠF und", + "ĠFun d", + "ĠFu nd", + "Ġdi agn", + "Ġdia gn", + "Ġdiag n", + "A utowired", + "p roperties", + "prop erties", + "proper ties", + "Ġ _.", + "Ġ_ .", + "Ġ cnt", + "Ġc nt", + "Ġcn t", + "ced ure", + "Ġ yy", + "Ġy y", + "Ġ grant", + "Ġg rant", + "Ġgr ant", + "Ġgran t", + "Ġgra nt", + "s ock", + "so ck", + "soc k", + ". innerHTML", + ".inner HTML", + "Ġ ]);Ċ", + "Ġ] );Ċ", + "Ġ]) ;Ċ", + "Ġ]); Ċ", + "Ġ CONFIG", + "ĠCON FIG", + "ĠCONF IG", + "= '$", + "=' $", + "5 50", + "55 0", + "] ];Ċ", + "]] ;Ċ", + "]]; Ċ", + "U ND", + "UN D", + "Ġ glob", + "Ġg lob", + "Ġgl ob", + "Ġglo b", + "Ġ dire", + "Ġd ire", + "Ġdi re", + "Ġdir e", + "uff le", + "_ MEM", + "_M EM", + "_ME M", + "Ġauth entic", + "> (\"", + ">( \"", + "Ġde cade", + "Ġdec ade", + "Ġdecad e", + "Ġ Import", + "ĠIm port", + "ĠImp ort", + "Ġorig inally", + "Ġoriginal ly", + "Ġorigin ally", + "Ġ jQuery", + "Ġj Query", + "Ġind icate", + "Ġindic ate", + "Ġindica te", + "Ġours elves", + "S w", + ". lbl", + ".l bl", + ".lb l", + "en erate", + "ener ate", + "ene rate", + "Ġbas ically", + "Ġbasic ally", + "Ġ Hom", + "ĠH om", + "ĠHo m", + "Ġ+ #+", + "Ġ+# +", + "Ġ Britain", + "ĠBrit ain", + "ĠBri tain", + "Ġ Kar", + "ĠK ar", + "ĠKa r", + "to Equal", + ". stop", + ".s top", + ".st op", + "Ġ modal", + "Ġm odal", + "Ġmod al", + "Ġmo dal", + "Ġmoda l", + "i si", + "is i", + "Ġsuggest s", + "Ġ dtype", + "Ġd type", + "Ġdt ype", + "Ġ tur", + "Ġt ur", + "Ġtu r", + "b f", + "Ġ connections", + "Ġconnection s", + "Ġconn ections", + "Ġconnect ions", + "Ġ Before", + "ĠB efore", + "ĠBe fore", + "i sted", + "is ted", + "ist ed", + "iste d", + "m ouse", + "mo use", + "Ġp ulled", + "Ġpull ed", + "Ġpul led", + ". build", + ".b uild", + "Ġleg islation", + "Ġlegis lation", + "Ġlegisl ation", + "Ġ forth", + "Ġf orth", + "Ġfor th", + "Ġfort h", + "p ad", + "pa d", + "e go", + "eg o", + ". Now", + ".N ow", + ".No w", + "Ġexc iting", + "Ġexcit ing", + "} ĊĊĊĊ", + "}Ċ ĊĊĊ", + "}ĊĊ ĊĊ", + "}ĊĊĊ Ċ", + "Ġcom pr", + "Ġco mpr", + "Ġcomp r", + "Ġ shares", + "Ġsh ares", + "Ġshare s", + "Ġsha res", + "Ġshar es", + "Ġ rig", + "Ġr ig", + "Ġri g", + "g reen", + "gr een", + "gre en", + "gree n", + "_ vec", + "_v ec", + "_ve c", + "Ġ enumerate", + "Ġenum erate", + "Ġenumer ate", + "A uto", + "Aut o", + "Au to", + "ic ator", + "ica tor", + "Ġ Ray", + "ĠR ay", + "ĠRa y", + "a sse", + "as se", + "ass e", + "Ġ holiday", + "Ġh oliday", + "Ġhol iday", + "Ġ nullable", + "Ġnull able", + "Ġnulla ble", + "g un", + "gu n", + "_ details", + "_d etails", + "_de tails", + "_detail s", + "_det ails", + "Ġ wrapper", + "Ġw rapper", + "Ġwr apper", + "Ġwrap per", + "s eq", + "se q", + "Ġ Young", + "ĠYou ng", + "ĠYo ung", + "ju ana", + "juan a", + "Ġ\" __", + "Ġ\"_ _", + "l icense", + "lic ense", + "s erve", + "se rve", + "ser ve", + "serv e", + "^ (", + "i ders", + "id ers", + "ide rs", + "ider s", + ". Remove", + ".Re move", + ".Rem ove", + "rop down", + "' S", + "p in", + "pi n", + "( token", + "(t oken", + "(to ken", + "(tok en", + ". Default", + ".D efault", + ".De fault", + ".Def ault", + "Ġ reasonable", + "Ġreason able", + "amp ion", + "ĠS ociety", + "ĠSoci ety", + "Ġ bei", + "Ġb ei", + "Ġbe i", + "er ves", + "erv es", + "erve s", + "r ad", + "ra d", + "Ġ Fox", + "ĠF ox", + "ĠFo x", + "_ images", + "_image s", + "_im ages", + "_imag es", + "Ġ wheel", + "Ġw heel", + "Ġwh eel", + "Ġwhe el", + "' )[", + "') [", + "Ġ cfg", + "Ġc fg", + "Ġcf g", + "( By", + "(B y", + "Con structor", + "Construct or", + "Ġ vary", + "Ġv ary", + "Ġvar y", + "Ġva ry", + ". swift", + ".sw ift", + "Ġ proxy", + "Ġpro xy", + "Ġpr oxy", + "Ġprox y", + "ĉ H", + "Ġ Another", + "ĠAn other", + "Ġ Pen", + "ĠP en", + "ĠPe n", + "Ġ checking", + "Ġcheck ing", + "Ġ jest", + "Ġj est", + "Ġje st", + "Ġjes t", + "m anager", + "man ager", + "manage r", + "mana ger", + "Or igin", + "Orig in", + "u gs", + "ug s", + "o ir", + "oi r", + "> čĊ", + "Ġ-- >čĊ", + "Ġ--> čĊ", + "Ġrel ief", + "Ġreli ef", + "Ġrelie f", + "l ap", + "la p", + "q uer", + "qu er", + "que r", + "_ parent", + "_p arent", + "_par ent", + "_pa rent", + "he ap", + "hea p", + "L OSE", + "LO SE", + "LOS E", + "Ġ combine", + "Ġc ombine", + "Ġcom bine", + "Ġcomb ine", + "Ġcombin e", + "Ġ Rose", + "ĠR ose", + "ĠRo se", + "ĠRos e", + "o wers", + "ow ers", + "ower s", + "owe rs", + "Ġpro cedures", + "Ġproced ures", + "Ġprocedure s", + "Ġ Sort", + "ĠS ort", + "ĠSo rt", + "ĠSor t", + "a nim", + "an im", + "ani m", + "v ariant", + "var iant", + "vari ant", + "eh icle", + "Ġsign ing", + "Ġsig ning", + "Ġsignin g", + "Pr imary", + "Prim ary", + "Pri mary", + "c urrency", + "curr ency", + "Ġs exe", + "Ġse xe", + "Ġsex e", + "o en", + "oe n", + "th eta", + "the ta", + "e man", + "em an", + "ema n", + "Ġim pressive", + "Ġimp ressive", + "Ġimpress ive", + "( '_", + "(' _", + "ĉ U", + "Ġ TextStyle", + "ĠText Style", + "_ cnt", + "_c nt", + "_cn t", + "Ġ slice", + "Ġs lice", + "Ġsl ice", + "Ġslic e", + "( ':", + "(' :", + "Ġunder stood", + "Ġunderst ood", + "H is", + "Hi s", + "2 77", + "27 7", + "0 13", + "01 3", + "Ġin formed", + "Ġinform ed", + "Ġ nick", + "Ġn ick", + "Ġni ck", + "Ġnic k", + "4 29", + "42 9", + "( TAG", + "(T AG", + "h d", + "Ġe lections", + "Ġel ections", + "Ġelect ions", + "Ġelection s", + "Ġele ctions", + "es ture", + "est ure", + "Ġ Santa", + "ĠS anta", + "ĠSan ta", + "ĠSant a", + "ĠCo ast", + ". pdf", + ".p df", + "inc iple", + "incip le", + "inci ple", + ". clone", + ".cl one", + "b orn", + "bo rn", + "bor n", + "u ta", + "ut a", + "Ġ licensed", + "Ġl icensed", + "Ġlicense d", + "Ġlic ensed", + "Ġlicens ed", + "C r", + "Ġ bread", + "Ġb read", + "Ġbr ead", + "Ġbre ad", + "Ġ Houston", + "ĠH ouston", + "ĠHou ston", + "Ġ nod", + "Ġn od", + "Ġno d", + "Ġh opes", + "Ġhope s", + "Ġhop es", + "Ġho pes", + "Ġ CGRect", + "ĠCG Rect", + "Ġgu ilty", + "Ġguilt y", + ". gif", + ".g if", + "Ġ rose", + "Ġr ose", + "Ġro se", + "Ġros e", + ". Common", + ".Com mon", + ".Comm on", + "T ip", + "Ti p", + "A NK", + "AN K", + "Ġ FC", + "ĠF C", + "D uring", + "Du ring", + "Dur ing", + "Ġ Symfony", + "ĠSym fony", + "Ġdef ensive", + "k m", + ") >", + "a rchive", + "arch ive", + "arc hive", + "Ġ URI", + "ĠU RI", + "ĠUR I", + "y cling", + "yc ling", + "ycl ing", + "- o", + "Ġ Website", + "ĠWe bsite", + "ĠWeb site", + "A MP", + "AM P", + "4 05", + "40 5", + "ish ment", + "Ġdo ctors", + "Ġdoc tors", + "Ġdoctor s", + "D irect", + "Dir ect", + "Di rect", + "Dire ct", + "A RI", + "AR I", + "Ġ Redirect", + "ĠRe direct", + "ĠRed irect", + "i eren", + "ie ren", + "ier en", + "iere n", + "9 60", + "96 0", + "_ dist", + "_d ist", + "_dis t", + "_di st", + "y o", + "Ġ Progress", + "ĠPro gress", + "Ġz um", + "Ġzu m", + "Ġme mor", + "Ġmem or", + "Ġmemo r", + "Ġ ED", + "ĠE D", + "Ġ jur", + "Ġj ur", + "Ġju r", + "æį ®", + "_ TABLE", + "_T ABLE", + "_TAB LE", + "_TA BLE", + "Ġ uuid", + "Ġu uid", + "Ġuu id", + "Ex pr", + "Exp r", + ". head", + ".h ead", + ".he ad", + "( '%", + "(' %", + "point er", + "po inter", + "Ġ estimate", + "Ġest imate", + "Ġestim ate", + "Ġ Greg", + "ĠG reg", + "ĠGr eg", + "ĠGre g", + "Ġ loader", + "Ġl oader", + "Ġlo ader", + "Ġload er", + "Ġloa der", + "Ġ iOS", + "Ġi OS", + "Ġ mens", + "Ġm ens", + "Ġme ns", + "Ġmen s", + "[ y", + "Ġref used", + "Ġrefuse d", + "Ġ precision", + "Ġp recision", + "Ġpre cision", + "Ġprec ision", + "Ġprecis ion", + "i sch", + "is ch", + "isc h", + "Ġ ACTION", + "ĠA CTION", + "ĠAC TION", + "ĠACT ION", + "C loud", + "Cl oud", + "Clo ud", + "s With", + "( ret", + "(r et", + "(re t", + "2 92", + "29 2", + "_ ADDR", + "_A DDR", + "_ADD R", + "_AD DR", + "_ conf", + "_con f", + "_co nf", + "( df", + "(d f", + "Ġ locked", + "Ġl ocked", + "Ġloc ked", + "Ġlock ed", + "Ġ rising", + "Ġr ising", + "Ġris ing", + "Ġri sing", + "ãĥ» ãĥ»", + "Ġ Ms", + "ĠM s", + "Ġ scenes", + "Ġsc enes", + "Ġscene s", + "Ġscen es", + "Ġsce nes", + "_ EXT", + "_E XT", + "_EX T", + "_ raw", + "_r aw", + "_ra w", + "_ the", + "_t he", + "_th e", + "pe ople", + "Ġre con", + "Ġrec on", + "Ġreco n", + "Ġ Fun", + "ĠF un", + "ĠFu n", + "Ġb less", + "Ġbl ess", + "Ġble ss", + "Ġ Updated", + "ĠUp dated", + "ĠUpdate d", + "4 22", + "42 2", + "ü n", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠčĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠčĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠčĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠčĊ", + "p ection", + "pe ction", + "pect ion", + "pec tion", + "R elease", + "Re lease", + "Rel ease", + ". logger", + ".log ger", + ".lo gger", + "Ġ SY", + "ĠS Y", + "Ġc ounsel", + "Ġcoun sel", + "u rd", + "ur d", + "_ true", + "_tr ue", + "Ġevery body", + "iv ot", + "ivo t", + "Ġh ence", + "Ġhe nce", + "Ġhen ce", + "Ġ NAS", + "ĠN AS", + "ĠNA S", + "7 89", + "78 9", + "Ġop posed", + "Ġopp osed", + "Ġoppos ed", + "Ġoppose d", + "un known", + "unk nown", + "Ġ DESC", + "ĠD ESC", + "ĠDE SC", + "ĠDES C", + "Ġ Chair", + "ĠC hair", + "ĠCh air", + "ĠCha ir", + "f ailed", + "fa iled", + "fail ed", + "Ġ INCLUDING", + "ĠIN CLUDING", + "3 86", + "38 6", + "3 52", + "35 2", + "Ġ writers", + "Ġw riters", + "Ġwrit ers", + "Ġwrite rs", + "Ġwriter s", + "{ }Ċ", + "{} Ċ", + "ÃŃ t", + "_ copy", + "_c opy", + "_co py", + "} :", + "Ġ Bat", + "ĠB at", + "ĠBa t", + "Ġ converted", + "Ġcon verted", + "Ġconvert ed", + "Ġconver ted", + "e ding", + "ed ing", + "edi ng", + "edin g", + "pl acement", + "place ment", + "Ġ Host", + "ĠH ost", + "ĠHo st", + "ĠHos t", + "S ound", + "So und", + "Sou nd", + "и м", + "Ġs ought", + "Ġso ught", + "Ġsou ght", + "4 02", + "40 2", + "m id", + "mi d", + "Ġ salary", + "Ġs alary", + "Ġsal ary", + "Ġsala ry", + "o gg", + "og g", + "âĦ ¢", + "b ul", + "bu l", + "Ġw ir", + "Ġwi r", + "valid ator", + "_ STAT", + "_ST AT", + "_STA T", + ". store", + ".st ore", + "Ġ Battle", + "ĠB attle", + "ĠBat tle", + "ĠBatt le", + "ı n", + "Ġ -->ĊĊ", + "Ġ-- >ĊĊ", + "Ġ-->Ċ Ċ", + "Ġ--> ĊĊ", + "Tr ump", + "d ot", + "do t", + "Ġ CONT", + "ĠC ONT", + "ĠCON T", + "ĠCO NT", + ". fetch", + ".f etch", + "Ġcont inu", + "Ġcontin u", + "w as", + "wa s", + "Ġf raud", + "Ġfr aud", + "Ġfra ud", + "Ġfrau d", + "_ tmp", + "_t mp", + "_tm p", + "m itter", + "mit ter", + "mitt er", + ". pictureBox", + ".p ictureBox", + ".picture Box", + "G A", + "Ġ tournament", + "Ġt ournament", + ". Input", + ".In put", + "3 43", + "34 3", + "[ r", + "ex ion", + "cent age", + "ĠK orean", + "ĠKore an", + "ĠKorea n", + "ĠKor ean", + "u ndef", + "un def", + "und ef", + "unde f", + "Ġ Available", + "ĠA vailable", + "ĠAv ailable", + "re shape", + "res hape", + "resh ape", + "Ġ kit", + "Ġk it", + "Ġki t", + "Ġ Struct", + "ĠStr uct", + "Ġ SUB", + "ĠS UB", + "ĠSU B", + "An swer", + "Ans wer", + "_ lib", + "_l ib", + "_li b", + ". twitter", + ".t witter", + ".tw itter", + "Ġ ore", + "Ġo re", + "Ġor e", + "Ġ Dragon", + "ĠD ragon", + "ĠDr agon", + "ĠDrag on", + "ĠDra gon", + ". Ext", + ".Ex t", + ".E xt", + ", k", + "Ġex planation", + "Ġexplan ation", + "r efs", + "re fs", + "ref s", + "Ġ Drive", + "ĠD rive", + "ĠDr ive", + "Ġ Training", + "ĠTr aining", + "ĠTra ining", + "ĠTrain ing", + "2 82", + "28 2", + ". Has", + ".H as", + "3 41", + "34 1", + "int age", + "inta ge", + "b ig", + "bi g", + "olog ist", + "olo gist", + "ologi st", + "en nis", + "enn is", + "4 60", + "46 0", + "Ù ĩ", + "Ġch icken", + "Ġchi cken", + "Ġchick en", + "Ġchic ken", + "Ġ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ Ċ", + "ç Ľ", + "ãģ §", + "Ġ peak", + "Ġpe ak", + "Ġpea k", + "Ġdr inking", + "Ġdrink ing", + "Ġ encode", + "Ġen code", + "Ġenc ode", + "Ġ NEW", + "ĠN EW", + "ĠNE W", + "m alloc", + "mal loc", + "mall oc", + "ĉ fprintf", + "ĉf printf", + "Ġ= ================================================================", + "Ġ================= ================================================", + "Ġ================================================= ================", + "Ġ================================= ================================", + "Ġ============================================================== ===", + "in cluding", + "incl uding", + "inclu ding", + "Ġprincip les", + "Ġprinciple s", + "Ġprinc iples", + "Ġ Mah", + "ĠM ah", + "ĠMa h", + "2 67", + "26 7", + "st orage", + "sto rage", + "stor age", + "- key", + "-k ey", + "Ġ keyword", + "Ġkey word", + "% ;", + "Ġ trained", + "Ġtr ained", + "Ġtrain ed", + "Ġtra ined", + "Ġtrai ned", + ". contrib", + ".con trib", + ".cont rib", + "Ġ kv", + "Ġk v", + "__ ':Ċ", + "Ġ Boy", + "ĠB oy", + "ĠBo y", + "param eter", + "para meter", + "Ġ suite", + "Ġs uite", + "Ġsu ite", + "Ġsuit e", + "Ġsui te", + "Ġth ousand", + "Ġthous and", + "Ġthou sand", + "Ġ coordinate", + "Ġco ordinate", + "Ġcoord inate", + "Ġcoordin ate", + "- generated", + "-g enerated", + "íķ ĺ", + "g enerated", + "gener ated", + "generate d", + "gene rated", + "Ġad mitted", + "Ġadm itted", + "Ġadmit ted", + "Ġp ussy", + "Ġpus sy", + "Ġpuss y", + "# w", + "Ġsw im", + "un ion", + "uni on", + "N a", + "2 74", + "27 4", + "Ġ Royal", + "ĠR oyal", + "ĠRoy al", + "ĠRo yal", + ". channel", + ".ch annel", + "Up dated", + "Update d", + "_ ROOT", + "_R OOT", + "_RO OT", + "Ġv ital", + "Ġvi tal", + "Ġvit al", + "Ġvita l", + "3 35", + "33 5", + "r action", + "ra ction", + "rac tion", + "ract ion", + "ĠCr usher", + "ĠCrush er", + "ĠCru sher", + "ĠCrus her", + "Ġ preced", + "Ġpr eced", + "Ġpre ced", + "Ġprec ed", + "Ġ horizontal", + "Ġh orizontal", + "Ġhor izontal", + "Ġhorizon tal", + "Blue print", + "Ġ attrs", + "Ġat trs", + "Ġatt rs", + "Ġattr s", + "Ġsm oke", + "Ġsmo ke", + "Ð Ĵ", + ". Equals", + ".E quals", + ".Equal s", + "F B", + "Ġ Resources", + "ĠRe sources", + "ĠRes ources", + "ĠResource s", + "rol ling", + "roll ing", + "Ġ passes", + "Ġp asses", + "Ġpass es", + "Ġpas ses", + "Ġpasse s", + "Ġ Num", + "ĠN um", + "ĠNu m", + "r otate", + "rot ate", + "e type", + "et ype", + "ety pe", + "\\ \",", + "\\\" ,", + "Ġs ensitive", + "Ġsens itive", + "Ġsensit ive", + "Ġt all", + "Ġtal l", + "Ġta ll", + "? âĢĿĊĊ", + "?âĢĿ ĊĊ", + "Pro xy", + "Pr oxy", + "i y", + "_ section", + "_s ection", + "_se ction", + "_sec tion", + "âĢĶâĢĶ âĢĶâĢĶ", + "b rid", + "br id", + "Ġc ircuit", + "Ġcirc uit", + "a tan", + "at an", + "ata n", + "E NC", + "EN C", + "Ġdr iven", + "Ġdrive n", + "Ġdriv en", + "Ġdri ven", + "Ġv oted", + "Ġvo ted", + "Ġvot ed", + "Ġvote d", + "Ġeduc ational", + "Ġeducation al", + "Ġ interaction", + "Ġinter action", + "Ġinteract ion", + "Ġinte raction", + "ab etes", + "abe tes", + "abet es", + "Ġ tone", + "Ġt one", + "Ġto ne", + "Ġton e", + "ĠInitialize Component", + "Ġmer ely", + "Ġmere ly", + "Ġ ìŀ", + "Ġì ŀ", + "c ookie", + "co okie", + "cook ie", + "_ div", + "_d iv", + "_di v", + "Ġ UILabel", + "ĠUI Label", + "ĠUIL abel", + "v ely", + "ve ly", + "vel y", + "} );čĊ", + "}) ;čĊ", + "}); čĊ", + "_ ENT", + "_E NT", + "_EN T", + "#+ #+", + "art icles", + "article s", + "arti cles", + "artic les", + "Ġ Southern", + "ĠSouth ern", + "ĠSou thern", + "Ġstrong er", + "Ġstro nger", + "Ġstron ger", + "Ġ Given", + "ĠG iven", + "ĠGi ven", + "ĠGive n", + "Ġ Eric", + "ĠE ric", + "ĠEr ic", + "Ġ IR", + "ĠI R", + "a bstract", + "ab stract", + "abs tract", + "U nder", + "Un der", + "Und er", + "n able", + "na ble", + "Ġ increment", + "Ġin crement", + "Ġincre ment", + "Ġinc rement", + "Ġincr ement", + "o ven", + "ov en", + "ove n", + "Ġ coin", + "Ġc oin", + "Ġco in", + "Ġcoi n", + "_ timer", + "_t imer", + "_time r", + "_tim er", + "_ti mer", + "Ġsuffer ed", + "Ġsuff ered", + "Ġ FREE", + "ĠF REE", + "ĠFR EE", + "ĠFRE E", + "' ].\"", + "'] .\"", + "']. \"", + "Ġ Queen", + "ĠQu een", + "ĠQue en", + "st ats", + "stat s", + "sta ts", + "Ġmeet ings", + "Ġmeeting s", + "Ġmee tings", + "2 76", + "27 6", + "Ġen tering", + "Ġent ering", + "Ġenter ing", + "Ġalong side", + "( session", + "(s ession", + "(sess ion", + "it als", + "ital s", + "ita ls", + "Ġ foundation", + "Ġf oundation", + "Ġfound ation", + "Ġ Credit", + "ĠC redit", + "ĠCr edit", + "ĠCre dit", + ". div", + ".d iv", + ".di v", + "_ ALL", + "_A LL", + "_AL L", + "p cion", + "pc ion", + "pci on", + "_ stat", + "_s tat", + "_st at", + "_sta t", + "ic king", + "ick ing", + "Default s", + "_ src", + "_s rc", + "_sr c", + "Ġ outputs", + "Ġout puts", + "Ġoutput s", + "/ B", + "Ġen thus", + "Ġent hus", + "Ġenth us", + "- bl", + "-b l", + ". ForeColor", + ".Fore Color", + "ĉ temp", + "ĉt emp", + "F ace", + "Fac e", + "Fa ce", + "Ġinter act", + "Ġinte ract", + "Ġwe ird", + "Ġwei rd", + "Ġweir d", + "M ount", + "Mo unt", + "r ell", + "re ll", + "rel l", + "ud ents", + "ude nts", + "udent s", + "uden ts", + "Ġrequire ment", + "Ġrequ irement", + "Ġ Sus", + "ĠS us", + "ĠSu s", + "I ER", + "IE R", + "Ġe lected", + "Ġel ected", + "Ġelect ed", + "re ference", + "ref erence", + "refer ence", + "Ġ ME", + "ĠM E", + "Ġ servers", + "Ġs ervers", + "Ġser vers", + "Ġserver s", + "Ġserv ers", + "Ġserve rs", + ". wait", + ".w ait", + "Ġ snapshot", + "Ġs napshot", + "Ġsnap shot", + "Ġsnaps hot", + "il ton", + "ilt on", + "Ġ tries", + "Ġt ries", + "Ġtr ies", + "Ġtri es", + "Ġtrie s", + "Ġ tipo", + "Ġt ipo", + "Ġti po", + "Ġtip o", + ". Time", + ".T ime", + "> w", + "Ġm ountain", + "Ġmount ain", + "Ġp ounds", + "Ġpo unds", + "Ġpou nds", + "Ġpound s", + "Ġ[ ...", + "Ġ[. ..", + "ex ists", + "exist s", + "Ġng On", + "_ MAP", + "_M AP", + "_MA P", + "Ġf lying", + "Ġfl ying", + "Ġfly ing", + "3 31", + "33 1", + "xi ety", + "ĉ value", + "ĉv alue", + "ĉval ue", + "ĉva lue", + "_ DB", + "_D B", + "u no", + "un o", + "Ġse ats", + "Ġsea ts", + "Ġseat s", + "T URN", + "TU RN", + ". author", + ".a uthor", + ".auth or", + ".aut hor", + "! )", + "or ce", + "orc e", + "Ġind icated", + "Ġindic ated", + "Ġindicate d", + "Ġindica ted", + "3 17", + "31 7", + ". sin", + ".s in", + ".si n", + "Ġ assignment", + "Ġass ignment", + "Ġassign ment", + "im iento", + "imi ento", + "Ġ Frame", + "ĠF rame", + "ĠFr ame", + "ĠFra me", + "ĠFram e", + "3 24", + "32 4", + "_ gen", + "_g en", + "_ge n", + "in ery", + "ine ry", + "iner y", + "_ )", + "m essages", + "message s", + "mess ages", + ". settings", + ".s ettings", + ".set tings", + ".setting s", + "Ġ Mean", + "ĠM ean", + "ĠMe an", + "ĠM useum", + "ĠMus eum", + "ĠMuse um", + "i rq", + "ir q", + "at tach", + "att ach", + "atta ch", + "ĠPale stin", + "ĠPalest in", + "_ QU", + "_Q U", + "_ tags", + "_t ags", + "_tag s", + "_ta gs", + "Ġcas ual", + "e men", + "em en", + "eme n", + "ASS WORD", + "4 32", + "43 2", + "$ s", + "ĠC irc", + "ĠCi rc", + "ĠCir c", + "о й", + "оР¹", + "et ric", + "etr ic", + "etri c", + "/ P", + "0 18", + "01 8", + "Ġ epoch", + "Ġep och", + "< head", + " The", + ">T he", + "Ġ Ak", + "ĠA k", + "Ġ grass", + "Ġgr ass", + "Ġgra ss", + "Ġgras s", + "/ *čĊ", + "/* čĊ", + "( dis", + "(d is", + "(di s", + "Ġ guns", + "Ġg uns", + "Ġgu ns", + "Ġgun s", + "Ġ tb", + "Ġt b", + "Ġ Kevin", + "ĠK evin", + "ĠKe vin", + ". args", + ".ar gs", + ".arg s", + "Ġ Ah", + "ĠA h", + "o ped", + "op ed", + "ope d", + "( J", + "column s", + "arg uments", + "argument s", + "ĠWith Events", + "_ full", + "_f ull", + "_fu ll", + "Ġ Defense", + "ĠDef ense", + "S imple", + "Sim ple", + "Ġde aths", + "Ġdeath s", + "2 95", + "29 5", + "Ġext ensive", + "Ġ Still", + "ĠSt ill", + "Ġ Expression", + "ĠEx pression", + "ĠExp ression", + "ĠExpress ion", + "ĠExpr ession", + "Ġ Agency", + "ĠA gency", + "ĠAg ency", + "ĠAge ncy", + "Ġper forming", + "Ġperform ing", + "Ġperfor ming", + "F X", + "Ġ usuario", + "Ġus uario", + "Ġusu ario", + "U AL", + "UA L", + "S ide", + "Si de", + "Sid e", + "o dos", + "od os", + "odo s", + "ap top", + "apt op", + "Ġ credentials", + "Ġc redentials", + "Ġcred entials", + "Ġcredential s", + "_ cap", + "_c ap", + "_ca p", + "at ient", + "ati ent", + "atie nt", + "Ġ Disney", + "ĠDis ney", + "Ġ ai", + "Ġa i", + "Ġ chip", + "Ġc hip", + "Ġch ip", + "Ġchi p", + "Ġ volt", + "Ġv olt", + "Ġvo lt", + "Ġvol t", + ".make Text", + "%%%%%%%% %%%%%%%%", + "Ġ belief", + "Ġbel ief", + "Ġbelie f", + "_ LOC", + "_L OC", + "_LO C", + "Ġ Civil", + "ĠC ivil", + "ĠCi vil", + "ĠCiv il", + "N avigation", + "Nav igation", + "Navig ation", + "Ġ reveal", + "Ġre veal", + "Ġreve al", + "Ġ violent", + "Ġviol ent", + "Ġ Fil", + "ĠF il", + "ĠFi l", + "Ġ catalog", + "Ġc atalog", + "Ġcat alog", + "Ġcata log", + "Ġcatal og", + "e med", + "em ed", + "eme d", + "s can", + "sc an", + ". control", + ".c ontrol", + ".cont rol", + "Ġ constitution", + "Ġcon stitution", + "Ġconst itution", + "Ġconstit ution", + "Ġconstitu tion", + "C ountry", + "Count ry", + "S eparator", + "Se parator", + "Separ ator", + "_ APP", + "_A PP", + "_AP P", + "t opic", + "to pic", + "top ic", + "u etooth", + "uet ooth", + "M IN", + "MI N", + "Ġ descriptor", + "Ġdes criptor", + "y t", + "ET HER", + "ETH ER", + "Ġd istribute", + "Ġdis tribute", + "Ġdistrib ute", + "' }Ċ", + "'} Ċ", + ". trim", + ".t rim", + ".tr im", + ". Line", + ".L ine", + "Ġ lbl", + "Ġl bl", + "Ġlb l", + "assert Equals", + "Ġ Det", + "ĠD et", + "ĠDe t", + "omb ok", + "ombo k", + "( width", + "(w idth", + "Ġt ort", + "Ġto rt", + "Ġtor t", + "ĠEX PRESS", + "ĠEXP RESS", + "a co", + "ac o", + "U sing", + "Us ing", + "Ġ Brand", + "ĠB rand", + "ĠBr and", + "ĠBra nd", + "ĠBran d", + "w all", + "wa ll", + "wal l", + "E MENT", + "EM ENT", + "Ġ Communic", + "ĠComm unic", + "ĠCommun ic", + "< uint", + " (Ċ", + ">( Ċ", + "? >\"", + "?> \"", + "Ġ ///Ċ", + "Ġ// /Ċ", + "Ġ/ //Ċ", + "Ġ/// Ċ", + "Ġe iner", + "Ġein er", + "Ġeine r", + "Ġei ner", + "Ġ weekly", + "Ġweek ly", + "ĉ logger", + "ĉlog ger", + "_ pop", + "_p op", + "_po p", + "_ man", + "_m an", + "_ma n", + "Ġm igrations", + "Ġmigr ations", + "Ġmigration s", + "Ġ asks", + "Ġas ks", + "Ġask s", + "Ġ bs", + "Ġb s", + "Ġ falls", + "Ġf alls", + "Ġfall s", + "Ġfal ls", + ". Where", + ".W here", + ".Wh ere", + "- height", + "-h eight", + "-he ight", + "_ feature", + "_f eature", + "_fe ature", + "_feat ure", + ". Min", + ".M in", + "Ġ hyper", + "Ġh yper", + "Ġhy per", + "Ġhyp er", + "Ġhype r", + "Ġ volatile", + "Ġv olatile", + "Ġvol atile", + "Ġ twenty", + "Ġtw enty", + "Ġtwe nty", + "Typ ography", + "U nable", + "Un able", + "Una ble", + "D et", + "De t", + ", f", + "- mod", + "-m od", + "Ġset tlement", + "Ġsett lement", + "Ġsettle ment", + "Ġ contracts", + "Ġcon tracts", + "Ġcontract s", + "Ġcontr acts", + "Ġcontra cts", + "n ome", + "no me", + "nom e", + "B ad", + "Ba d", + "Ġ Brian", + "ĠB rian", + "ĠBr ian", + "ĠBri an", + "7 68", + "76 8", + "( username", + "(user name", + "! !!!", + "!! !!", + "!!! !", + "Ġ hack", + "Ġh ack", + "Ġha ck", + "Ġhac k", + ". Field", + ".F ield", + "H R", + "Ġ Jordan", + "ĠJ ordan", + "ĠJord an", + "i za", + "iz a", + "Ġ Âł", + "Ġ ł", + "Ġ Sher", + "ĠS her", + "ĠSh er", + "ĠShe r", + ". header", + ".head er", + ".he ader", + "( other", + "(o ther", + "Ġ Dub", + "ĠD ub", + "ĠDu b", + "( op", + "(o p", + "Ġ Round", + "ĠR ound", + "ĠRo und", + "ĠRou nd", + "Ġ vie", + "Ġv ie", + "Ġvi e", + "Ġ appl", + "Ġapp l", + "Ġap pl", + "ĉ J", + "Ġ Insert", + "ĠIn sert", + "ĠIns ert", + "Ġ LP", + "ĠL P", + "re gon", + "reg on", + "rego n", + "Ġ MPI", + "ĠM PI", + "ĠMP I", + "Ġ anchor", + "Ġan chor", + "Ġanch or", + "Ġanc hor", + "a ca", + "ac a", + "ø r", + "Ġ ade", + "Ġa de", + "Ġad e", + "an chor", + "anc hor", + "anch or", + "qu ee", + "que e", + "Ġ TreeNode", + "ĠT reeNode", + "ĠTree Node", + "Ġtarget ed", + "Ġtarg eted", + "Ġl aid", + "Ġla id", + "Ġlai d", + "A BEL", + "AB EL", + "v et", + "ve t", + "Ġ Origin", + "ĠOr igin", + "ĠOri gin", + "ĠOrig in", + "A nt", + "An t", + ". ');Ċ", + ".' );Ċ", + ".') ;Ċ", + ".'); Ċ", + "ex pect", + "exp ect", + "ed Reader", + "Ġ Major", + "ĠM ajor", + "ĠMaj or", + "Ġ inch", + "Ġin ch", + "Ġinc h", + "Com par", + "Co mpar", + "Comp ar", + "Ġ preview", + "Ġp review", + "Ġpr eview", + "Ġpre view", + "Ġprev iew", + "Ġill ness", + "ĠCON TRACT", + "ĠCONTR ACT", + "ĠCONT RACT", + "Ġ Independ", + "ĠIn depend", + "ĠInd epend", + "u uid", + "uu id", + "Ġ nome", + "Ġn ome", + "Ġno me", + "Ġnom e", + "Ġ tc", + "Ġt c", + "ĠA venue", + "i san", + "is an", + "isa n", + "Ġ phrase", + "Ġph rase", + "_ move", + "_m ove", + "_mov e", + "_mo ve", + "\" )[", + "\") [", + "4 12", + "41 2", + "Ġpro vision", + "Ġprov ision", + "Ġconc entr", + "Ġconcent r", + "_ IR", + "_I R", + "Ġ Ut", + "ĠU t", + "( )+", + "() +", + "Ġ nas", + "Ġn as", + "Ġna s", + "! ,", + "Ġ Robin", + "ĠRob in", + "ĠRo bin", + "i ations", + "iation s", + "iat ions", + "at itude", + "Ġ px", + "Ġp x", + "Ġ Without", + "ĠWith out", + "/ bash", + "/b ash", + "e kt", + "ek t", + "re ement", + "ree ment", + "3 42", + "34 2", + "Ob server", + "Observ er", + "Obs erver", + "3 18", + "31 8", + "Ġ Region", + "ĠReg ion", + "UB LIC", + "UBL IC", + "Ġ {//", + "Ġ{ //", + "K N", + "å ·", + "Game Object", + "å ¾", + "en coding", + "enc oding", + "enco ding", + "Ġ ***", + "Ġ* **", + "Ġ** *", + "project s", + "proj ects", + "Ġ tk", + "Ġt k", + "Ġche ese", + "Ġchees e", + "EM PL", + "EMP L", + "a ro", + "ar o", + "Ġ اÙĦ", + "Ġا ÙĦ", + "6 10", + "61 0", + "3 37", + "33 7", + "Ġcons ists", + "Ġconsist s", + "re fresh", + "ref resh", + "u reau", + "ure au", + "Ġ Scanner", + "ĠSc anner", + "ĠScan ner", + "Ġs oil", + "Ġso il", + "Ġfl avor", + "Ġflav or", + "Ġfla vor", + "Data Source", + "Ex ecute", + "Exec ute", + "е ние", + "ен ие", + "ени е", + "Ġ shit", + "Ġs hit", + "Ġsh it", + "åĪ Ĩ", + "< any", + " < /", + "Qu antity", + "Quant ity", + "qu iry", + "quir y", + "qui ry", + "_ tab", + "_t ab", + "_ta b", + "Ġ alg", + "Ġa lg", + "Ġal g", + "To ast", + "re size", + "res ize", + "resi ze", + "quest ions", + "question s", + "s chema", + "sch ema", + "L iteral", + "Lite ral", + "Lit eral", + "Liter al", + "( entity", + "(e ntity", + "(ent ity", + "NE CTION", + "NECT ION", + "ch anged", + "change d", + "chan ged", + "chang ed", + "_ FIELD", + "_F IELD", + "_ HEIGHT", + "_HE IGHT", + "Ġ organic", + "Ġorg anic", + "Ġorgan ic", + "P RE", + "PR E", + "Ġ Cat", + "ĠC at", + "ĠCa t", + ". Draw", + ".D raw", + "E s", + "Ġ loud", + "Ġl oud", + "Ġlo ud", + "Ġlou d", + "6 80", + "68 0", + "Ġ ĠĠĠĠĠĠĠĉ", + "ĠĠ ĠĠĠĠĠĠĉ", + "ĠĠĠĠ ĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠ ĉ", + "ĠĠĠ ĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠ Ġĉ", + "ĠĠĠĠĠ ĠĠĠĉ", + "ĠĠĠĠĠĠ ĠĠĉ", + "Ġ Kat", + "ĠK at", + "ĠKa t", + "Ġ heap", + "Ġhe ap", + "âĢľ It", + "âĢľI t", + "0 70", + "07 0", + "e tr", + "et r", + "Ġ unlikely", + "Ġun likely", + "Ġunlike ly", + "er als", + "era ls", + "eral s", + "/ auth", + "/a uth", + "5 02", + "50 2", + "t odo", + "to do", + "tod o", + "P lace", + "Pl ace", + "Post ed", + "Pos ted", + "Po sted", + "Com ments", + "Comment s", + "Comm ents", + "Ġ Tech", + "ĠT ech", + "ĠTe ch", + "ĠTec h", + "Ġ Finally", + "ĠF inally", + "ĠFin ally", + "ĠFinal ly", + "eg ration", + "egr ation", + "egra tion", + "Ġ minimal", + "Ġmin imal", + "Ġmini mal", + "Ġminim al", + "Ġ Files", + "ĠF iles", + "ĠFile s", + "ĠFil es", + "ĠFi les", + "Ġt amb", + "Ġta mb", + "Ġtam b", + "ë¡ ľ", + "Ġ Release", + "ĠR elease", + "ĠRe lease", + "ĠRel ease", + "4 25", + "42 5", + ". resize", + ".re size", + ".res ize", + "Ġ Ï", + "c ollect", + "col lect", + "coll ect", + "= p", + "ĠLI ABLE", + "Ġp roducing", + "Ġprodu cing", + "Ġprod ucing", + "- wrapper", + "-w rapper", + "-wrap per", + "Ġs ingles", + "Ġsingle s", + "Ġsin gles", + "Ġsing les", + "ĠN BA", + "ĠNB A", + "o rr", + "or r", + "e ren", + "er en", + "ere n", + ". addAction", + ".add Action", + "Ġ thesis", + "Ġth esis", + "Ġthe sis", + "d n", + "P TY", + "PT Y", + ". des", + ".d es", + ".de s", + "Ġb acter", + "Ġba cter", + "Ġbac ter", + "Ġ Express", + "ĠEx press", + "ĠExp ress", + "ĠExpr ess", + "Ġ *)Ċ", + "Ġ* )Ċ", + "Ġ*) Ċ", + "å ij", + "/ admin", + "/ad min", + "se conds", + "sec onds", + "second s", + "åĬ Ł", + "uss ion", + "a beth", + "ab eth", + "abe th", + "abet h", + "Ġ Computer", + "ĠCom puter", + "ĠComp uter", + "ĠCompute r", + "ĠComput er", + "Ġr uling", + "Ġru ling", + "(\" ../", + "(\". ./", + "(\".. /", + ". GET", + ".G ET", + "ĠMe dal", + "ĠMed al", + "ition ally", + "itional ly", + "com mit", + "comm it", + "f ocus", + "fo cus", + "_ LEVEL", + "_LE VEL", + "i nda", + "in da", + "ind a", + "F act", + "Fac t", + "Fa ct", + "= np", + "=n p", + "=\" \">Ċ", + "=\"\" >Ċ", + "=\"\"> Ċ", + "Ġsub sequent", + "Ġsubsequ ent", + "pos able", + "- fluid", + "-fl uid", + "Ġth orough", + "Ġtho rough", + "Ġthor ough", + "Ġpublic ly", + "Ġpubli cly", + "ap ters", + "apt ers", + "apter s", + "Ġ Wilson", + "ĠWil son", + "_ PRE", + "_P RE", + "_PR E", + "y ard", + "ya rd", + "yar d", + "ä ¼", + "ĉ in", + "ĉi n", + "3 39", + "33 9", + "Ġre vers", + "Ġrev ers", + "Ġreve rs", + "Ġrever s", + "Ġ bullet", + "Ġb ullet", + "Ġbul let", + "Ġbull et", + "cri bed", + "cribe d", + "nes ota", + "Ġ ($_", + "Ġ( $_", + "Ġ($ _", + "an non", + "ann on", + "anno n", + "c ursor", + "curso r", + "Ġclo thing", + "Ġcloth ing", + "Ġ Multi", + "ĠM ulti", + "ĠMult i", + "ĠMul ti", + "2 87", + "28 7", + ": ',", + ":' ,", + "Ġv ess", + "Ġve ss", + "Ġves s", + "ord inator", + "ordin ator", + "Ġe inem", + "Ġein em", + "Ġeine m", + "Ġei nem", + "C annot", + "Can not", + "Ġ armed", + "Ġar med", + "Ġarm ed", + "ĉ V", + "ä¸ Ĭ", + ". Flat", + ".F lat", + ".Fl at", + "Ġ Sep", + "ĠS ep", + "ĠSe p", + "Ġ Subject", + "ĠSub ject", + "ĠSu bject", + "_ font", + "_f ont", + "Ġcharacter istics", + "Ġcharacteristic s", + "D one", + "Do ne", + "Don e", + "e ln", + "el n", + "#### ########", + "######## ####", + "##### #######", + "###### ######", + "####### #####", + "P OS", + "PO S", + "Ġ density", + "Ġd ensity", + "Ġdens ity", + "Ġ Platform", + "ĠPl atform", + "ĠPlat form", + "- items", + "-item s", + "-i tems", + "-it ems", + "Ġ overs", + "Ġo vers", + "Ġover s", + "Ġov ers", + "Ġp ushing", + "Ġpush ing", + "ç ¤", + ". Connection", + ".Con nection", + ".Connect ion", + ".Conn ection", + "_ term", + "_t erm", + "_te rm", + "_ter m", + "Ġinitial ization", + "________________ ________________", + "ç ¬", + ". document", + ".d ocument", + ".doc ument", + "l esh", + "le sh", + "les h", + "ĉ document", + "ĉd ocument", + "ĉdoc ument", + "Ġ Pin", + "ĠP in", + "ĠPi n", + "ç a", + "Ġ definitions", + "Ġdefinition s", + "Ġdefinit ions", + "Ġdefin itions", + ". Path", + ".P ath", + "_ WRITE", + "_W RITE", + "_WR ITE", + "Ġ ĉĊ", + "Ġĉ Ċ", + "? >ĊĊ", + "?> ĊĊ", + "?>Ċ Ċ", + "Ġter rible", + "Ġterr ible", + "b ean", + "be an", + "ick ets", + "icket s", + "Ġ SV", + "ĠS V", + "B uy", + "Bu y", + "( task", + "(t ask", + "Ġreg ime", + "g oogle", + "go ogle", + "goog le", + "goo gle", + "Ġc rack", + "Ġcr ack", + "Ġcra ck", + ". visit", + ".vis it", + "N UM", + "NU M", + "e nergy", + "en ergy", + "ener gy", + "Ġs truck", + "Ġstr uck", + "Ġstru ck", + "_ sample", + "_s ample", + ". payload", + ".p ayload", + ".pay load", + "Ġre vis", + "Ġrev is", + "Ġ Scene", + "ĠS cene", + "ĠSc ene", + "Ġ pg", + "Ġp g", + "Ġbreak fast", + "URRE NT", + ". charAt", + ".char At", + "_ exception", + "_ex ception", + "_except ion", + "ĠA nton", + "ĠAn ton", + "ĠAnt on", + "Ġguide lines", + "Ġguid elines", + "Ġguideline s", + "Ġex haust", + "Ġ Financial", + "ĠFin ancial", + "Ġ indent", + "Ġin dent", + "Ġind ent", + "Ġinde nt", + "Ġ desktop", + "Ġd esktop", + "Ġdes ktop", + "Ġdesk top", + "H idden", + "Hi dden", + "F ailure", + "Fail ure", + "Ġpr inciple", + "Ġprincip le", + "Ġprinc iple", + "Ġ iv", + "Ġi v", + "Ġs eks", + "Ġse ks", + "Ġsek s", + "n etwork", + "net work", + "Ġ numberOf", + "Ġnumber Of", + "Ġ Albert", + "ĠAl bert", + "ĠAlb ert", + "ĉ long", + "ĉl ong", + "8 01", + "80 1", + ", .", + "Ġ zeros", + "Ġz eros", + "Ġzero s", + "Ġze ros", + "Ġzer os", + "f ade", + "fa de", + "fad e", + "Ġ Typ", + "ĠT yp", + "ĠTy p", + "Ġ Term", + "ĠT erm", + "ĠTe rm", + "ĠTer m", + "ĠA rts", + "ĠAr ts", + "ĠArt s", + ". Application", + ".App lication", + ".Ap plication", + "Ġbe half", + "Ġbeh alf", + "æĪ ·", + "Ġ mere", + "Ġm ere", + "Ġme re", + "Ġmer e", + "( `${", + "(` ${", + "Ġaware ness", + "el pers", + "elp ers", + "elper s", + "f lix", + "fl ix", + "Ġ weigh", + "Ġwe igh", + "Ġwei gh", + "Ġest imates", + "Ġestim ates", + "Ġestimate s", + ". child", + ".ch ild", + "/ O", + "Ġ Bitmap", + "ĠB itmap", + "ĠBit map", + ". bottom", + ".b ottom", + ".bot tom", + "Ġ** ************************************************************************", + "Ġ************************************************************************ **", + "Ex pect", + "Exp ect", + "en to", + "ent o", + "Ġ Forum", + "ĠF orum", + "ĠFor um", + "ĠFo rum", + "v eral", + "ver al", + "ve ral", + "Ġj ail", + "Ġja il", + "Ġ abilities", + "Ġab ilities", + "ĠH OLD", + "ĠHO LD", + "ĠHOL D", + "Ġ Cit", + "ĠC it", + "ĠCi t", + "Ġd ynam", + "Ġdy nam", + "Ġdyn am", + "Ġ gray", + "Ġg ray", + "Ġgr ay", + "Ġgra y", + "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", + ". nextInt", + ".next Int", + "ant ly", + "ĠAR ISING", + "( private", + "(pr ivate", + "(priv ate", + "Ġ rejected", + "Ġre jected", + "Ġreject ed", + "Ġrej ected", + "Ġ Nic", + "ĠN ic", + "ĠNi c", + "Ġle ather", + "= {Ċ", + "={ Ċ", + "aly tics", + "t hetic", + "th etic", + "the tic", + ". Top", + ".T op", + ".To p", + "3 73", + "37 3", + ". Page", + ".P age", + "={ `", + "Ġ ;čĊ", + "Ġ; čĊ", + "de pth", + "dep th", + "dept h", + "m ann", + "man n", + "ma nn", + "W D", + "Ġ Som", + "ĠS om", + "ĠSo m", + ". Right", + ".R ight", + "Ġ )}Ċ", + "Ġ) }Ċ", + "Ġ trait", + "Ġt rait", + "Ġtr ait", + "Ġtra it", + "Ġtrai t", + "à Ĺ", + "i ac", + "ia c", + "Ġ rv", + "Ġr v", + "S ample", + "Sam ple", + ". Xml", + ".X ml", + "o pped", + "op ped", + "opp ed", + "Ġ ÑĦ", + "ĠÑ Ħ", + "l ists", + "li sts", + "list s", + "lis ts", + "Ġt ear", + "Ġte ar", + "Ġtea r", + "ivers ary", + ". collection", + ".c ollection", + ".col lection", + ".collect ion", + ".coll ection", + "ĠCon stitution", + "ĠConst itution", + "ĠConstit ution", + "Ġ HttpResponse", + "ĠHttp Response", + "Ġbr ill", + "Ġbri ll", + "Ġ Prom", + "ĠP rom", + "ĠPro m", + "ĠPr om", + "h over", + "ho ver", + "3 66", + "36 6", + "Ġ Miami", + "ĠM iami", + "ĠMi ami", + "ĠMia mi", + "Ġar gue", + "Ġarg ue", + "_ float", + "_f loat", + "5 04", + "50 4", + "Ġ ãĤ", + "Ġ nat", + "Ġn at", + "Ġna t", + "ĠT al", + "ĠTa l", + "Ġ integration", + "Ġint egration", + "Ġinteg ration", + "Ġintegr ation", + "( cur", + "(c ur", + "Ġre moving", + "Ġrem oving", + "Ġ coeff", + "Ġc oeff", + "Ġco eff", + "Ġcoef f", + "Ġ Though", + "ĠTh ough", + "ĠThou gh", + "Ġ forecast", + "Ġfor ecast", + "Ġfore cast", + "4 08", + "40 8", + "ĠV egas", + "ĠVe gas", + "ĠVega s", + "ĠVeg as", + "S ite", + "Si te", + "Sit e", + "3 46", + "34 6", + "Ġt rab", + "Ġtr ab", + "Ġtra b", + "Ġ Henry", + "ĠHen ry", + "- i", + "Ġinv olves", + "Ġinvol ves", + "Ġinvolve s", + "B T", + "Ġs lo", + "Ġsl o", + "In voke", + "Inv oke", + "Ġl ucky", + "Ġluck y", + "Ġlu cky", + "Ġluc ky", + "0 25", + "02 5", + "r at", + "ra t", + "Ġ ?Ċ", + "Ġ? Ċ", + "Ġ handled", + "Ġhand led", + "Ġhandle d", + "( fd", + "(f d", + "cont ents", + "content s", + "conte nts", + "Ġ OFF", + "ĠO FF", + "ĠOF F", + "R F", + "Ġ sty", + "Ġs ty", + "Ġst y", + "Ġ Motor", + "ĠM otor", + "ĠMo tor", + "ĠMot or", + "ĠMoto r", + "t ery", + "ter y", + "te ry", + "t ax", + "ta x", + "M AP", + "MA P", + "Ġ Mrs", + "ĠM rs", + "ĠMr s", + "Ġ phones", + "Ġph ones", + "Ġphone s", + "Ġphon es", + "Ġ UIView", + "ĠUI View", + "\" )));Ċ", + "\") ));Ċ", + "\")) );Ċ", + "\"))) ;Ċ", + "\"))); Ċ", + "( dev", + "(d ev", + "(de v", + "ĠI rish", + "ĠIr ish", + "ĠIris h", + "0 19", + "01 9", + "Ġ ws", + "Ġw s", + "D I", + "_ OFFSET", + "_OFF SET", + "Ġ Events", + "ĠE vents", + "ĠEvent s", + "ĠEven ts", + "ĠEv ents", + "ĠEve nts", + "Ġst ages", + "Ġstage s", + "Ġsta ges", + "Ġstag es", + "Ġ }//", + "Ġ} //", + "Ġh aben", + "Ġhab en", + "Ġha ben", + "Ġhabe n", + "ST ANCE", + "Ġ Sin", + "ĠS in", + "ĠSi n", + "Ġ Money", + "ĠM oney", + "ĠMon ey", + "ĠMo ney", + "( top", + "(t op", + "(to p", + "Ġ appointment", + "Ġapp ointment", + "Ġappoint ment", + "V ERSION", + "VER SION", + "VERS ION", + "m etadata", + "met adata", + "meta data", + "_ comment", + "_com ment", + "_comm ent", + "Ġcolle agues", + "Ġcolleague s", + "m aps", + "ma ps", + "map s", + "â ĺ", + "Ċ ĉĊ", + "( al", + "(a l", + "_ req", + "_re q", + "_r eq", + "Ġf ut", + "Ġfu t", + "Ġ architecture", + "Ġarch itecture", + "Ġarchitect ure", + "Ġarchit ecture", + "3 51", + "35 1", + "ĠWH ETHER", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "_ screen", + "_s creen", + "_sc reen", + "_scr een", + "Ġstyle Urls", + "Ġ monster", + "Ġmon ster", + ". up", + ".u p", + "ph ia", + "phi a", + "Ġ processor", + "Ġprocess or", + "Ġproc essor", + "Ġprocesso r", + "Ġ Terr", + "ĠT err", + "ĠTe rr", + "ĠTer r", + "= ',", + "=' ,", + "Ġ Manufact", + "ĠMan ufact", + "Ġ NT", + "ĠN T", + "k el", + "ke l", + "i bern", + "ib ern", + "iber n", + "ibe rn", + "ĉ file", + "ĉf ile", + "ĉfi le", + "A li", + "Al i", + "rient ation", + "Ġ //!", + "Ġ// !", + "ap ore", + "apor e", + "apo re", + "an eous", + "ane ous", + "Ġ Creat", + "ĠC reat", + "ĠCr eat", + "ĠCre at", + "f older", + "fo lder", + "fol der", + "fold er", + "4 15", + "41 5", + "Ġ hay", + "Ġh ay", + "Ġha y", + "Sup press", + "( left", + "(l eft", + "(le ft", + "Ġe uro", + "Ġeu ro", + "Ġdis claimer", + "u stry", + "us try", + "ust ry", + "ustr y", + "s hips", + "sh ips", + "ship s", + "shi ps", + "_ fd", + "_f d", + "Ġ Fa", + "ĠF a", + "_ insert", + "_in sert", + "_ins ert", + "Ġ rol", + "Ġr ol", + "Ġro l", + "if ting", + "ift ing", + "Ġ Comments", + "ĠCom ments", + "ĠComm ents", + "ĠComment s", + "_ br", + "_b r", + "Ġlos ses", + "Ġloss es", + "Ġ Added", + "ĠAd ded", + "ĠAdd ed", + "ch arg", + "char g", + "cha rg", + "Ġ по", + "Ġп о", + "_ system", + "_s ystem", + "_sys tem", + "_sy stem", + "Ġ Sometimes", + "ĠS ometimes", + "ĠSome times", + "ĠSom etimes", + "Ġ Spain", + "ĠS pain", + "ĠSp ain", + "ĠSpa in", + "( group", + "(g roup", + "(gr oup", + "i alis", + "ial is", + "ia lis", + "iali s", + "Ġd ollar", + "Ġdoll ar", + "Ġdol lar", + "Ġ Args", + "ĠAr gs", + "ĠArg s", + "4 99", + "49 9", + "2 97", + "29 7", + "qu ires", + "quire s", + "quir es", + "qui res", + "Ġ Ten", + "ĠT en", + "ĠTe n", + ". scss", + ".s css", + ".sc ss", + "Ġsurv ive", + "Ġsurviv e", + "u sage", + "us age", + "usa ge", + "Ġ jun", + "Ġj un", + "Ġju n", + "im iter", + "imit er", + "imi ter", + "ï¼ģ ĊĊ", + "ï¼ģĊ Ċ", + "Ġf ifth", + "Ġfif th", + "t oggle", + "tog gle", + "Ġde cline", + "Ġdec line", + "Ġdecl ine", + "( $\"", + "($ \"", + "( Long", + "(L ong", + "i nge", + "in ge", + "ing e", + "Ġp ilot", + "Ġpi lot", + "Ġpil ot", + "- light", + "-l ight", + "-li ght", + "- radius", + "-r adius", + "-rad ius", + "Ġp odcast", + "Ġpod cast", + "Ġn aturally", + "Ġnatural ly", + "Ġnatur ally", + "Ġnat urally", + "P ages", + "Page s", + "Pa ges", + "Pag es", + "ä¸ º", + "Ġ Despite", + "ĠDes pite", + "Ġl ighting", + "Ġlight ing", + "Ġ crate", + "Ġc rate", + "Ġcr ate", + "Ġcra te", + "Ġ Binary", + "ĠB inary", + "ĠBin ary", + "Ġred ucing", + "Ġredu cing", + "Ġe leg", + "Ġel eg", + "Ġele g", + "Ġ Mouse", + "ĠM ouse", + "ĠMo use", + "ĠMou se", + "ĠTest Bed", + "Ġbefore Each", + "_ ARRAY", + "_AR RAY", + "_ARR AY", + "Re direct", + "Red irect", + "3 29", + "32 9", + "Ġf lood", + "Ġfl ood", + "Ġflo od", + "Ġ ships", + "Ġs hips", + "Ġsh ips", + "Ġship s", + "3 63", + "36 3", + "Ġelectric ity", + "Ġelectr icity", + ") *(", + ")* (", + "ê ¸", + "Ġ Viet", + "ĠV iet", + "ĠVi et", + "ĠVie t", + "h ero", + "he ro", + "her o", + "Ġ dia", + "Ġd ia", + "Ġdi a", + "Ġ Kent", + "ĠK ent", + "ĠKe nt", + "ĠKen t", + "he art", + "hea rt", + "hear t", + "Ġthreat s", + "_ acc", + "_a cc", + "_ac c", + "Ġ symbols", + "Ġs ymbols", + "Ġsymbol s", + "Ġsymb ols", + "is chen", + "isc hen", + "ische n", + "isch en", + "_ inst", + "_in st", + "_i nst", + "_ins t", + "C riterion", + "Ġ TIM", + "ĠT IM", + "ĠTI M", + ". Height", + ".H eight", + ".He ight", + "5 80", + "58 0", + "Ġ âĢĻ", + "ĠâĢ Ļ", + "( );ĊĊĊ", + "() ;ĊĊĊ", + "();Ċ ĊĊ", + "();ĊĊ Ċ", + "(); ĊĊĊ", + "Product s", + "Produ cts", + "_ SP", + "_S P", + "Ġ Cy", + "ĠC y", + "Ġ dependent", + "Ġdep endent", + "Ġdepend ent", + "e ste", + "es te", + "est e", + "Ġ datos", + "Ġd atos", + "Ġda tos", + "Ġdat os", + "Ġdato s", + "d it", + "di t", + "а в", + "аР²", + "IG NAL", + "IGN AL", + "Ġ lesson", + "Ġl esson", + "Ġle sson", + "Ġless on", + "Ġles son", + "\" >'", + "\"> '", + "Ġ Cover", + "ĠC over", + "ĠCo ver", + "ĠCov er", + "ĠCove r", + "Ġ Hope", + "ĠH ope", + "ĠHo pe", + "ĠHop e", + "Ġ Timer", + "ĠT imer", + "ĠTime r", + "ĠTim er", + "ĠTi mer", + "Ġ dad", + "Ġd ad", + "Ġda d", + "v iders", + "vid ers", + "vider s", + "vi ders", + "vide rs", + "Ġ Phot", + "ĠP hot", + "ĠPh ot", + "/ ?", + "r opy", + "ro py", + "rop y", + "o ming", + "om ing", + "omin g", + "omi ng", + "as ion", + "asi on", + "asio n", + "Ġ \\(", + "Ġ\\ (", + "Ġ ET", + "ĠE T", + "Ġ Reading", + "ĠRe ading", + "ĠRead ing", + "Ġep isodes", + "Ġepisode s", + "Ġepis odes", + "l m", + "4 21", + "42 1", + "e cha", + "ec ha", + "ech a", + "Ġne uro", + "Ġneu ro", + "Ġneur o", + "8 20", + "82 0", + "Ġhar mon", + "Ġharm on", + "Ġlib eral", + "Ġliber al", + "- ind", + "-in d", + "-i nd", + "3 93", + "39 3", + "D ATA", + "DA TA", + "DAT A", + "Ġevery day", + "Ġdi vided", + "Ġdiv ided", + "Ġdivide d", + "Ġdivid ed", + "ĠActive Record", + "f igure", + "fig ure", + "figur e", + "U A", + "ä ¹", + "riend ly", + "t ech", + "te ch", + "tec h", + "6 01", + "60 1", + ". gameObject", + ".game Object", + "и ÑĤÑĮ", + "иÑĤ ÑĮ", + "3 74", + "37 4", + "Ġ moon", + "Ġm oon", + "Ġmo on", + "Ġmoo n", + "f time", + "ft ime", + "Ġ noch", + "Ġn och", + "Ġno ch", + "Ġnoc h", + "ĠT ORT", + "ĠTO RT", + "ĠTOR T", + "Ġ VM", + "ĠV M", + ". initial", + ".in itial", + ".init ial", + "( child", + "(ch ild", + "Ġmus ical", + "Ġmusic al", + "Ġmusica l", + "Ġ oc", + "Ġo c", + "b as", + "ba s", + "Ġ Hay", + "ĠH ay", + "ĠHa y", + "3 61", + "36 1", + "_ long", + "_l ong", + "_lo ng", + "_lon g", + "Ġ memset", + "Ġmem set", + "i ley", + "il ey", + "ile y", + "adel phia", + "S V", + "ro at", + "_ tx", + "_t x", + "Ġ lon", + "Ġl on", + "Ġlo n", + "Ġng OnInit", + "ĠngOn Init", + "b p", + "Ġ Golden", + "ĠGold en", + "ĠGol den", + "A CHE", + "AC HE", + "ACH E", + "Ġwor ried", + "a zi", + "az i", + "E ar", + "T ake", + "Ta ke", + "Tak e", + "( fp", + "(f p", + "burg h", + "bur gh", + "_ Data", + "_D ata", + "g res", + "gr es", + "gre s", + "Ġ Ont", + "ĠO nt", + "ĠOn t", + "p us", + "pu s", + "Ġ transparent", + "Ġtrans parent", + "Ġp ocket", + "Ġpo cket", + "Ġpoc ket", + "Ġ ram", + "Ġr am", + "Ġra m", + "igration s", + "igr ations", + ". čĊčĊ", + ".čĊ čĊ", + "Ġ [(", + "Ġ[ (", + "Ġadopt ed", + "Ġreport edly", + "Ġreported ly", + "Ġ Dream", + "ĠD ream", + "ĠDr eam", + "ĠDre am", + "Ġ }));Ċ", + "Ġ} ));Ċ", + "Ġ}) );Ċ", + "Ġ})) ;Ċ", + "l osing", + "lo sing", + "los ing", + "Ġte eth", + "Ġtee th", + "Ġ Books", + "ĠB ooks", + "ĠBo oks", + "ĠBook s", + "ĠBoo ks", + "\" ,&", + "\", &", + "en ny", + "enn y", + "L EMENT", + "LE MENT", + "LEM ENT", + "Ġ gel", + "Ġg el", + "Ġge l", + "Ġ Plant", + "ĠP lant", + "ĠPl ant", + "ĠPlan t", + "ĠPla nt", + "4 37", + "43 7", + "! âĢĿ", + ". host", + ".h ost", + "Ġ Reply", + "ĠRe ply", + "ĠRep ly", + "3 76", + "37 6", + "r ength", + "re ngth", + "ren gth", + "Ġrec ognition", + "Ġrecogn ition", + "Ġ }}>Ċ", + "Ġ} }>Ċ", + "Ġ}} >Ċ", + "Ġ}}> Ċ", + "L A", + "Ġ mirror", + "Ġm irror", + "Ġmir ror", + "Ġmi rror", + "Ġ assistant", + "Ġass istant", + "Ġassist ant", + "( device", + "(d evice", + "(de vice", + "(dev ice", + "Ġspirit ual", + "b uilder", + "build er", + "bu ilder", + " §", + "Ġo utr", + "Ġout r", + "Ġou tr", + "Ġ tt", + "Ġt t", + "Ġ PER", + "ĠP ER", + "ĠPE R", + "Ġrad ical", + "Ġradi cal", + "Method s", + "Ġ pace", + "Ġp ace", + "Ġpa ce", + "Ġpac e", + "u dy", + "ud y", + "Ġg ut", + "Ġgu t", + "Ġ Greek", + "ĠG reek", + "ĠGre ek", + "ĠGree k", + "Ġ nonatomic", + "Ġnon atomic", + "Ġ Paper", + "ĠP aper", + "ĠPa per", + "ĠPap er", + "_ GPIO", + "_G PIO", + "_GP IO", + "Ġo bst", + "Ġob st", + "Ġobs t", + ". Ad", + ".A d", + "viron ments", + "vironment s", + "ĠS ov", + "ĠSo v", + "3 56", + "35 6", + "( con", + "(c on", + "(co n", + "Ġ Transaction", + "ĠTrans action", + ". assign", + ".as sign", + "ĉ catch", + "ĉc atch", + "ĉcat ch", + "el ter", + "elt er", + "Ġ bitcoin", + "Ġbit coin", + "_ GR", + "_G R", + "Ġ čĊ", + "Ġ/ >čĊ", + "Ġ/> čĊ", + "m etic", + "me tic", + "met ic", + "Ġtrans formation", + "Ġtransform ation", + "åı ·", + "Ġ rgb", + "Ġr gb", + "Ġrg b", + "istrib utions", + "istribution s", + "Ġ implicit", + "Ġimp licit", + "Ġimpl icit", + "Ġimplic it", + "/ in", + "/i n", + "d estination", + "dest ination", + "а ÑĤÑĮ", + "аÑĤ ÑĮ", + "Z ero", + "Ze ro", + "Ġ unset", + "Ġun set", + "Ġuns et", + "9 20", + "92 0", + ". where", + ".w here", + ".wh ere", + ". go", + ".g o", + "Ġ formation", + "Ġform ation", + "Ġformat ion", + "Ġforma tion", + "Ġ declaration", + "Ġde claration", + "Ġdeclar ation", + "( )čĊčĊ", + "() čĊčĊ", + "()čĊ čĊ", + "Ġ Expl", + "ĠEx pl", + "ĠExp l", + "ĉ ĉĉĠĠ", + "ĉĉ ĉĠĠ", + "ĉĉĉ ĠĠ", + "ĉĉĉĠ Ġ", + "/ pro", + "/p ro", + "/pr o", + ". JSON", + ".J SON", + "4 41", + "44 1", + "Ġ desk", + "Ġd esk", + "Ġde sk", + "Ġdes k", + ". substr", + ".sub str", + "// ----------------------------------------------------------------------------", + "//---------------------------------------------------------------- ------------", + "//------------------------------------------------ ----------------------------", + "l yn", + "ly n", + "p son", + "ps on", + "4 07", + "40 7", + "d isable", + "dis able", + "Ġ Func", + "ĠF unc", + "ĠFun c", + "ĠFu nc", + "ĉ Assert", + "Ġ MARK", + "ĠM ARK", + "ĠMA RK", + "ĠMAR K", + "Ġde feat", + "Ġdef eat", + "Ġdefe at", + "Ġ blind", + "Ġbl ind", + "Ġbli nd", + "Ġ constants", + "Ġcon stants", + "Ġconst ants", + "Ġconstant s", + "3 62", + "36 2", + ". headers", + ".head ers", + ".header s", + ".he aders", + "U ILD", + "UI LD", + "UIL D", + "Ġ expenses", + "Ġexp enses", + "Ġexpense s", + "P ixel", + "Pix el", + "Ġ hr", + "Ġh r", + "Ġ fel", + "Ġf el", + "Ġfe l", + "Ġ Eastern", + "ĠEast ern", + "ĠEaster n", + "4 24", + "42 4", + "4 90", + "49 0", + "_ del", + "_d el", + "_de l", + "3 57", + "35 7", + "ĠC ub", + "ĠCu b", + "Ġ sq", + "Ġs q", + "ĉ count", + "ĉc ount", + "Ġ Directory", + "ĠD irectory", + "ĠDirect ory", + "ĠDirector y", + "Ġex clus", + "Ġexc lus", + "Ġexcl us", + "Ġ historic", + "Ġhistor ic", + "Ġhist oric", + "Ġhisto ric", + "Ġ ------------------------------------------------", + "Ġ---------------- --------------------------------", + "Ġ-------------------------------- ----------------", + "Ġ-------------------- ----------------------------", + "Ġ composition", + "Ġcom position", + "Ġcomp osition", + "Ġcompos ition", + "Ġ dataGridView", + "Ġdata GridView", + "Ġ Burn", + "ĠB urn", + "ĠBur n", + "ĠBu rn", + "Ġ BC", + "ĠB C", + "M aster", + "Ma ster", + "Mas ter", + "Ġ spawn", + "Ġs pawn", + "Ġsp awn", + "Ġspa wn", + "Ġ bearing", + "Ġb earing", + "Ġbe aring", + "Ġbear ing", + ". SetActive", + ".Set Active", + "i lo", + "il o", + "Ġ gallery", + "Ġg allery", + "Ġgall ery", + "Ġf ounded", + "Ġfound ed", + "Ġfo unded", + "Ġ availability", + "Ġa vailability", + "Ġav ailability", + "Ġavail ability", + ". sqrt", + ".s qrt", + "Ġ pes", + "Ġp es", + "Ġpe s", + "Ġ DOM", + "ĠD OM", + "ĠDO M", + "m ate", + "ma te", + "mat e", + "O ct", + "Ġ matched", + "Ġm atched", + "Ġmatch ed", + "Ġmat ched", + "it ivity", + "Ġan xiety", + ". price", + ".p rice", + ".pr ice", + "Ġ Instant", + "ĠIn stant", + "ĠInst ant", + "ĠIns tant", + "ì Ĭ", + "Ġt ut", + "Ġtu t", + "I Collection", + "IC ollection", + ". shared", + ".sh ared", + ".share d", + ".sha red", + "_ sql", + "_s ql", + "_sq l", + "t bl", + "tb l", + "l ibrary", + "lib rary", + "_ destroy", + "_d estroy", + "_de stroy", + "_dest roy", + "er mal", + "erm al", + "Ġ Notes", + "ĠN otes", + "ĠNo tes", + "ĠNot es", + "ĠNote s", + "Ġ Ein", + "ĠE in", + "Ġsou thern", + "Ġsouth ern", + "ĠOTHER WISE", + "Ġ macro", + "Ġm acro", + "Ġma cro", + "Ġmac ro", + ". lower", + ".l ower", + ".lo wer", + ".low er", + "c ls", + "cl s", + "Content View", + ". link", + ".l ink", + ".li nk", + ".lin k", + "con stant", + "const ant", + "cons tant", + "Ġ Bes", + "ĠB es", + "ĠBe s", + "Ġsome body", + "n b", + "3 99", + "39 9", + "\" >{", + "\"> {", + "( local", + "(l ocal", + "(loc al", + "(lo cal", + ". ....", + ".. ...", + "... ..", + ".... .", + "Ġ Null", + "ĠN ull", + "ĠNu ll", + "m x", + "Ġ ç", + "Ġà §", + "Ġ pause", + "Ġp ause", + "Ġpa use", + "Ġpau se", + "- ----------", + "-- ---------", + "---- -------", + "-------- ---", + "--- --------", + "----- ------", + "---------- -", + "------ -----", + "------- ----", + "--------- --", + "_ MO", + "_M O", + "Ġ CM", + "ĠC M", + "Ġfor Key", + "Ġ DVD", + "ĠD VD", + "ĠDV D", + "Ġ closest", + "Ġclose st", + "Ġclos est", + "Ġcloses t", + "_ DEVICE", + "_DE VICE", + "_DEV ICE", + "Ġ Stephen", + "ĠSte phen", + "ĠStep hen", + "ĠSteph en", + "Ġ BBC", + "ĠB BC", + "ĠBB C", + "Ġ Travel", + "ĠT ravel", + "ĠTr avel", + "ĠTra vel", + "ĠTrav el", + "P aint", + "Pa int", + "Ġ Results", + "ĠRes ults", + "ĠResult s", + "Ġ Rule", + "ĠR ule", + "ĠRu le", + "Ġ tp", + "Ġt p", + "Ġ ratings", + "Ġr atings", + "Ġrating s", + "Ġrat ings", + "Ġra tings", + "c in", + "ci n", + "c sv", + "cs v", + "> /", + "Ġ GOP", + "ĠG OP", + "ĠGO P", + "l ad", + "la d", + "Ġ ÑĢ", + "ĠÑ Ģ", + "Ġ indexPath", + "Ġindex Path", + "m atrix", + "mat rix", + "= f", + "ar sed", + "ars ed", + "arse d", + "Ġ });", + "Ġ} );", + "Ġ}) ;", + "Ġ Cos", + "ĠC os", + "ĠCo s", + "Ġ Score", + "ĠS core", + "ĠSc ore", + "ĠSco re", + "ĠScor e", + "Ġ tak", + "Ġt ak", + "Ġta k", + "Ġ ESP", + "ĠE SP", + "ĠES P", + "Ġ INC", + "ĠI NC", + "ĠIN C", + "_ NULL", + "_N ULL", + "- flex", + "-f lex", + "-fl ex", + "\" ][", + "\"] [", + "in to", + "int o", + "e land", + "el and", + "ela nd", + "elan d", + "Author ization", + "_ FALSE", + "_F ALSE", + "Ġ gate", + "Ġg ate", + "Ġga te", + "Ġ vid", + "Ġv id", + "Ġvi d", + "is tent", + "ist ent", + "iste nt", + "isten t", + "T IME", + "TI ME", + "TIM E", + "Ġ rewrite", + "Ġre write", + "Ġrew rite", + "Ġ tie", + "Ġt ie", + "Ġti e", + "Ġ archive", + "Ġa rchive", + "Ġarch ive", + "Ġarc hive", + "5 11", + "51 1", + ". events", + ".e vents", + ".event s", + ".ev ents", + ". getParameter", + ".get Parameter", + ".getParam eter", + "Ġ Permission", + "ĠPer mission", + "ĠPerm ission", + "Ġprogram me", + "Ġprogramm e", + "Ġ é", + "j ud", + "ju d", + "Ġcame ras", + "Ġcamera s", + "Ġcam eras", + "Ġcamer as", + "3 38", + "33 8", + "3 49", + "34 9", + "( sys", + "(s ys", + "ĠSy rian", + "ĠSyria n", + "Ġimpro vements", + "Ġimprove ments", + "Ġimprovement s", + "Ġimprov ements", + "Ġ hip", + "Ġh ip", + "Ġhi p", + "Ġsu icide", + "Ġsuic ide", + "Ġsch olar", + "Ġscho lar", + "Ġ compatible", + "Ġcom patible", + "Ġcompat ible", + "0 22", + "02 2", + "rem ote", + ". down", + ".d own", + ".do wn", + "F UNCTION", + "FUNC TION", + "FUN CTION", + "Ġman aging", + "Ġmana ging", + "Ġ UIKit", + "ĠUI Kit", + ". raw", + ".r aw", + ".ra w", + "> >>>", + ">> >>", + ">>> >", + "3 71", + "37 1", + "Ġdem ands", + "Ġdemand s", + "el lite", + "ell ite", + "elli te", + "Ġ dent", + "Ġd ent", + "Ġde nt", + "Ġden t", + "Ġ Micro", + "ĠM icro", + "ĠMi cro", + "ĠMic ro", + "åı ĸ", + "' ][$", + "'] [$", + "'][ $", + "Ġ IE", + "ĠI E", + "im ension", + "imens ion", + "Ġt rem", + "Ġtr em", + "Ġtre m", + "6 30", + "63 0", + "Ġg ained", + "Ġgain ed", + "Ġga ined", + ". with", + ".w ith", + ". ok", + ".o k", + "h ou", + "ho u", + "Ġb om", + "Ġbo m", + "amp aign", + "ampa ign", + "Ġ joining", + "Ġjoin ing", + "Ġjo ining", + "f ish", + "fi sh", + "Ġadd Subview", + "8 60", + "86 0", + "Ġnor thern", + "Ġnorth ern", + ". cor", + ".c or", + ".co r", + "o ret", + "or et", + "ore t", + "D ie", + "Di e", + "i nish", + "in ish", + "ini sh", + "inis h", + "_ comp", + "_c omp", + "_com p", + "_co mp", + "Ġ attended", + "Ġatt ended", + "Ġattend ed", + "Ġ collapse", + "Ġc ollapse", + "Ġcoll apse", + "Ġcollaps e", + "Ġ SS", + "ĠS S", + "a cent", + "ace nt", + "ac ent", + "acen t", + "_ EQUAL", + "_E QUAL", + "_EQ UAL", + "Ġ Deep", + "ĠDe ep", + "ĠDee p", + "R GB", + "RG B", + "ĉ test", + "ĉt est", + "ol ves", + "olve s", + "olv es", + "u set", + "us et", + "use t", + "Un ityEngine", + "Unity Engine", + "w riter", + "write r", + "wr iter", + "Re solver", + "Res olver", + "Resolve r", + ", %", + "if ference", + "iff erence", + "iffer ence", + "iffe rence", + "_ remove", + "_re move", + "_rem ove", + "o nda", + "on da", + "ond a", + "Ġf emme", + "Ġfem me", + "3 85", + "38 5", + "de code", + "dec ode", + "Br anch", + "Ġ flush", + "Ġf lush", + "Ġfl ush", + "Ġflu sh", + "Ġinnov ative", + "T ests", + "Test s", + "Te sts", + "Tes ts", + "Ġ[' ./", + "Ġ['. /", + "Ġ covering", + "Ġcover ing", + "Ġcov ering", + ". admin", + ".ad min", + "ulti part", + "ultip art", + "( lambda", + "(l ambda", + " namespace", + "Ġ Sport", + "ĠS port", + "ĠSp ort", + "ĠSpo rt", + "ĠSpor t", + "Ġ !(", + "Ġ! (", + "a cles", + "ac les", + "acle s", + "acl es", + "Ġde pression", + "Ġdep ression", + "Ġdepr ession", + "Ġdepress ion", + "ĠK ong", + "ĠKon g", + "ĠKo ng", + "5 70", + "57 0", + "Ġ pert", + "Ġp ert", + "Ġper t", + "Ġpe rt", + "Ġ Conn", + "ĠC onn", + "ĠCon n", + "ĠCo nn", + "Ġ Otherwise", + "ĠOther wise", + "/ home", + "/h ome", + "s upported", + "sup ported", + "support ed", + "Ġ pink", + "Ġp ink", + "Ġpi nk", + "Ġpin k", + "Ġinv ited", + "Ġinvite d", + "Ġinvit ed", + "ñ os", + "ño s", + "_ enabled", + "_en abled", + "_enable d", + "Ġ -Ċ", + "Ġ- Ċ", + "F W", + "e ners", + "en ers", + "ener s", + "ene rs", + "Ġ MY", + "ĠM Y", + "Ġs uggestions", + "Ġsuggest ions", + "Ġsuggestion s", + "C anvas", + "Can vas", + "Ġ fer", + "Ġf er", + "Ġfe r", + "Ġ Marketing", + "ĠMark eting", + "ĠMarket ing", + "@ Test", + "un tu", + "unt u", + "Ġ Ven", + "ĠV en", + "ĠVe n", + "Ġ Cou", + "ĠC ou", + "ĠCo u", + "i vals", + "iv als", + "ival s", + "iva ls", + "D onald", + "Don ald", + "l imited", + "lim ited", + "limit ed", + "ĉ ĉĉĉĉĉĊ", + "ĉĉ ĉĉĉĉĊ", + "ĉĉĉĉ ĉĉĊ", + "ĉĉĉ ĉĉĉĊ", + "ĉĉĉĉĉ ĉĊ", + "ĉĉĉĉĉĉ Ċ", + "Ġanal yst", + "Ġanaly st", + "Ġanalys t", + "( entry", + "(en try", + "(ent ry", + "Ġrepresent ative", + "_ attributes", + "_at tributes", + "_attribute s", + "_attrib utes", + "Ġ fur", + "Ġf ur", + "Ġfu r", + ". hide", + ".h ide", + "r esp", + "re sp", + "res p", + "ad ores", + "ado res", + "ador es", + "r ides", + "ri des", + "ride s", + "rid es", + "Ġ Josh", + "ĠJ osh", + "ĠJo sh", + "ĠJos h", + "r obot", + "ro bot", + "rob ot", + "ĠN AT", + "ĠNA T", + "Ġs esso", + "Ġses so", + "Ġsess o", + "Ġint egrated", + "Ġinteg rated", + "Ġintegr ated", + "Ġintegrate d", + ": true", + "p arts", + "par ts", + "part s", + "pa rts", + "Ġst upid", + "Ġstu pid", + "Ġstup id", + ": event", + ":e vent", + "@end section", + "Ġ pu", + "Ġp u", + ". Table", + ".T able", + ".Tab le", + "Ġ Yii", + "ĠY ii", + "ĠYi i", + "` ;ĊĊ", + "`;Ċ Ċ", + "`; ĊĊ", + "Ġ clang", + "Ġc lang", + "Ġcl ang", + "Ġclan g", + "Ġcla ng", + "=\" \">", + "=\"\" >", + "en gan", + "eng an", + "enga n", + "_ parameters", + "_param eters", + "_parameter s", + ". internal", + ".in ternal", + ".int ernal", + ".inter nal", + "Ġ Modern", + "ĠMod ern", + "ĠMode rn", + "ĠModer n", + "Ġ metric", + "Ġm etric", + "Ġmet ric", + "Ġ semi", + "Ġs emi", + "Ġse mi", + "Ġsem i", + "={ {Ċ", + "={{ Ċ", + "7 07", + "70 7", + ". amazon", + ".a mazon", + ".am azon", + "Ġ BB", + "ĠB B", + "ain ty", + "aint y", + "ai nty", + "view port", + "3 67", + "36 7", + "Ġstart Activity", + "dis patch", + "disp atch", + "* ****", + "** ***", + "**** *", + "*** **", + "Ġf lav", + "Ġfl av", + "Ġfla v", + "iffer ent", + "iffe rent", + "3 82", + "38 2", + "[ this", + "[t his", + "Ġs take", + "Ġst ake", + "Ġsta ke", + "Ġarg ued", + "Ġargue d", + "v iously", + "vious ly", + "vi ously", + ". work", + ".w ork", + "Ġ Oak", + "ĠO ak", + "O ld", + "Ol d", + "( async", + "(a sync", + "(as ync", + "n otes", + "not es", + "no tes", + "note s", + "Ġ flip", + "Ġf lip", + "Ġfl ip", + "Ġdis ag", + "Ġ TE", + "ĠT E", + "ĉ error", + "ĉe rror", + "ĉerr or", + "< '", + "Ġ »ĊĊ", + "Ġ» ĊĊ", + "Ġ»Ċ Ċ", + "Ġ filtered", + "Ġfil tered", + "Ġfilter ed", + "Ġfilt ered", + "ĠM ach", + "ĠMac h", + "ĠMa ch", + "Ġ hung", + "Ġh ung", + "Ġhun g", + "Ġhu ng", + "_ dump", + "_d ump", + "_ samples", + "_s amples", + "_sample s", + "- dismiss", + "-dis miss", + "Ġ ray", + "Ġr ay", + "Ġra y", + "Im plemented", + "Implement ed", + "D K", + "Ġ jed", + "Ġj ed", + "Ġje d", + "0 90", + "09 0", + "Ġbreak s", + "Ġbre aks", + "Ġ fits", + "Ġf its", + "Ġfit s", + "Ġfi ts", + ". gr", + ".g r", + "Ġ Zero", + "ĠZ ero", + "ĠZe ro", + "o ro", + "or o", + "Ġequ ally", + "Ġequal ly", + "Ġeq ually", + "Ġ '[", + "Ġ' [", + "Ġconcern ing", + "< meta", + "<", + "'> <", + "Ġpro mot", + "Ġprom ot", + "Ġpromo t", + "Ġ incl", + "Ġin cl", + "Ġinc l", + "_ only", + "_on ly", + "ë¥ ¼", + "ĠAtt orney", + "- date", + "-d ate", + "-da te", + "-dat e", + "Ġ landscape", + "Ġl andscape", + "Ġland scape", + "Ġlands cape", + "Ġlandsc ape", + "Ġ fu", + "Ġf u", + "S Y", + ". prop", + ".p rop", + ".pro p", + ".pr op", + "Ġ Arr", + "ĠA rr", + "ĠAr r", + "p ag", + "pa g", + "Parallel Group", + "' :čĊ", + "': čĊ", + "Ġ logs", + "Ġl ogs", + "Ġlo gs", + "Ġlog s", + "a unch", + "un ci", + "unc i", + "n ama", + "na ma", + "nam a", + "Table Cell", + "iss ues", + "issue s", + ". {", + "e curity", + "ec urity", + "_ exec", + "_e xec", + "_ex ec", + "_exe c", + "o lds", + "ol ds", + "old s", + "Ġ hosts", + "Ġhost s", + "Ġho sts", + "Ġhos ts", + "Ġ proto", + "Ġpro to", + "Ġpr oto", + "Ġprot o", + "_ import", + "_im port", + "_imp ort", + "_ sort", + "_s ort", + "_so rt", + "Ġ Bow", + "ĠB ow", + "ĠBo w", + "Ġ Normal", + "ĠN ormal", + "ĠNor mal", + "ĠNorm al", + "Ġ Farm", + "ĠF arm", + "ĠFar m", + "ĠFa rm", + ".create ParallelGroup", + "R otation", + "Rot ation", + ". err", + ".e rr", + ".er r", + "Ġp leased", + "Ġplease d", + "Ġple ased", + "Ġplea sed", + "Ġpleas ed", + "it age", + "ita ge", + "itag e", + ". Wh", + ".W h", + "ĉ ĉĠĠĠĠ", + "ĉĉ ĠĠĠĠ", + "ĉĉĠĠĠ Ġ", + "ĉĉĠ ĠĠĠ", + "ĉĉĠĠ ĠĠ", + "M R", + "Ġ MORE", + "ĠM ORE", + "ĠMO RE", + "ĠMOR E", + "Ġ Natural", + "ĠN atural", + "ĠNat ural", + "ĠNatur al", + "_ transform", + "_trans form", + "B ASE", + "BA SE", + "en eral", + "ener al", + "ene ral", + "u tdown", + "ut down", + ". commons", + ".com mons", + ".common s", + ".comm ons", + "W T", + "Ġ aan", + "Ġa an", + "Ġaa n", + ". Result", + ".Res ult", + "d og", + "do g", + "Ġcl icking", + "Ġclick ing", + "Ġclic king", + ") ,ĊĊ", + "), ĊĊ", + "),Ċ Ċ", + "# line", + "O perator", + "Oper ator", + "Op erator", + "Opera tor", + "Ġc iv", + "Ġci v", + "Ġm erg", + "Ġme rg", + "Ġmer g", + "o buf", + "ob uf", + "ng then", + "ngth en", + "Ġ [{", + "Ġ[ {", + "Ġc ancell", + "Ġcan cell", + "Ġcancel l", + "Ġcanc ell", + "tr igger", + "tri gger", + ". :", + "W ORK", + "WO RK", + "de clare", + "decl are", + "declar e", + "Ġde crease", + "Ġdecre ase", + "ÅĽ ci", + "l oom", + "lo om", + "loo m", + ". None", + ".N one", + ".No ne", + ".Non e", + "Ġ MI", + "ĠM I", + "Ġ Jason", + "ĠJ ason", + "ĠJa son", + "ĠJas on", + "Ġhealth care", + "ia mond", + "iam ond", + "iamo nd", + "s ylvania", + "* x", + "Ġ Ra", + "ĠR a", + "[ b", + "Ġ printing", + "Ġprint ing", + "Ġprin ting", + "ph abet", + "pha bet", + "Ġ Labour", + "ĠLa bour", + "ĠLab our", + "o pper", + "op per", + "opp er", + "Ġz ijn", + "Ġzi jn", + "Ġzij n", + "- target", + "-t arget", + "_ FUNCTION", + "_F UNCTION", + "_FUNC TION", + "_FUN CTION", + "Ġ oct", + "Ġo ct", + "Ġoc t", + "е ниÑı", + "ен иÑı", + "ени Ñı", + "åľ ¨", + "Ġ western", + "Ġwest ern", + "Ġwes tern", + "Ġcomp uters", + "Ġcomput ers", + "Ġcomputer s", + "Ġcompute rs", + "Ġ RET", + "ĠR ET", + "ĠRE T", + "Hash Map", + "[ String", + "[S tring", + "get Value", + "_ DATE", + "_D ATE", + "_DAT E", + "_DA TE", + ". Next", + ".N ext", + "ĠF if", + "ĠFi f", + "é l", + "ic ked", + "ick ed", + "æ İ", + "- MM", + "-M M", + "Ġ {ĊĊĊ", + "Ġ{ ĊĊĊ", + "Ġ{Ċ ĊĊ", + "Ġ{ĊĊ Ċ", + "Ġ contacts", + "Ġcont acts", + "Ġcontact s", + "Ġconta cts", + "Ġ digits", + "Ġd igits", + "Ġdig its", + "Ġdigit s", + "P rodu", + "Pro du", + "Pr odu", + "Prod u", + "Ġun usual", + "Ġunus ual", + "Ġrapid ly", + "t ures", + "ture s", + "tu res", + "tur es", + "Ġang ry", + "c ancel", + "can cel", + "x xxx", + "xx xx", + "xxx x", + "_ parser", + "_p arser", + "_parse r", + "_par ser", + "_pars er", + "id ity", + "idi ty", + "_ PREFIX", + "_P REFIX", + "_PRE FIX", + "_PREF IX", + "7 10", + "71 0", + "Ġm ehr", + "Ġme hr", + "Ġrare ly", + "Ġrar ely", + "e the", + "et he", + "eth e", + "o pes", + "op es", + "ope s", + "Ġ %.", + "Ġ% .", + "w orks", + "work s", + "wor ks", + "Ġ theta", + "Ġth eta", + "Ġthe ta", + "Ġcon tribution", + "Ġcontrib ution", + "Ġ Tony", + "ĠT ony", + "ĠTo ny", + "ĠTon y", + "Ġs quad", + "Ġsqu ad", + "5 37", + "53 7", + "а й", + "аР¹", + "Ġî n", + "t here", + "th ere", + "ther e", + "the re", + "o uted", + "ou ted", + "out ed", + "oute d", + "ĉ q", + "Ļ Ĥ", + "g ood", + "go od", + "goo d", + "L I", + "é¡ µ", + "Ġ Living", + "ĠL iving", + "ĠLi ving", + "ĠLiv ing", + "iz abeth", + "iza beth", + "Ġ kt", + "Ġk t", + "Ġ Dallas", + "ĠD allas", + "ĠDal las", + "] ],Ċ", + "]] ,Ċ", + "]], Ċ", + "Ġ />ĊĊ", + "Ġ/ >ĊĊ", + "Ġ/>Ċ Ċ", + "Ġ/> ĊĊ", + "Ġ raising", + "Ġr aising", + "Ġrais ing", + "Ġra ising", + "/ router", + "/r outer", + "_ game", + "_g ame", + "3 68", + "36 8", + "Ġ CUR", + "ĠC UR", + "ĠCU R", + "z ens", + "ze ns", + "zen s", + ". es", + ".e s", + "Ġ fontWeight", + "Ġfont Weight", + "( func", + "(f unc", + "(fun c", + "not ification", + "notif ication", + "Ġ' ../../../", + "Ġ'../ ../../", + "Ġ'../../ ../", + "Ġbl ame", + "Ġbla me", + "ãĢĤ ĊĊĊĊ", + "ãĢĤĊĊ ĊĊ", + "ãĢĤĊ ĊĊĊ", + "an co", + "anc o", + "9 80", + "98 0", + "Id entity", + "Ident ity", + "Ide ntity", + "f ollow", + "fol low", + "Ġ arts", + "Ġa rts", + "Ġar ts", + "Ġart s", + "x s", + "Ġoffic ially", + "Ġofficial ly", + "Ġ Studio", + "ĠSt udio", + "ĠStud io", + "ĠStudi o", + "Ġrecommend ations", + "Ġrecommendation s", + "Ġ locale", + "Ġl ocale", + "Ġlo cale", + "Ġloc ale", + "Ġlocal e", + "Ġam ateur", + "Ġamat eur", + "Ġ Enable", + "ĠE nable", + "ĠEn able", + "Ġ caps", + "Ġc aps", + "Ġcap s", + "Ġca ps", + ". End", + ".E nd", + ".En d", + "3 88", + "38 8", + "- add", + "-a dd", + "-ad d", + "_g shared", + "Ġ CT", + "ĠC T", + "F orce", + "For ce", + "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "Ġ orange", + "Ġo range", + "Ġor ange", + "Ġorang e", + "Ġora nge", + "Ġoran ge", + "Ġ lp", + "Ġl p", + "Ġ answered", + "Ġanswer ed", + ". Grid", + ".G rid", + ".Gr id", + "Ġd ual", + "Ġdu al", + "Ġdua l", + "Ġstr ategic", + "Ġstrateg ic", + "Ġn obody", + "Ġno body", + "Ġnob ody", + "Ġ fatal", + "Ġf atal", + "Ġfa tal", + "Ġfat al", + "_ est", + "_e st", + "_es t", + "( el", + "(e l", + "Ġ ìł", + "Ġì ł", + "ĠB udd", + "ĠBu dd", + "ĠBud d", + "A IT", + "AI T", + "_ factor", + "_f actor", + "_fac tor", + "_fact or", + "_fa ctor", + "- one", + "-on e", + "-o ne", + "Ġ HAVE", + "ĠH AVE", + "ĠHA VE", + "\" čĊčĊ", + "\"čĊ čĊ", + "7 60", + "76 0", + "P rof", + "Pro f", + "Pr of", + "Ġ är", + "Ġä r", + "str ings", + "string s", + "Ġ dirty", + "Ġd irty", + "Ġdir ty", + "Ġdirt y", + "Ġ Face", + "ĠF ace", + "ĠFac e", + "ĠFa ce", + "Ġ Begin", + "ĠB egin", + "ĠBe gin", + "ĠBeg in", + "Ġ Bus", + "ĠB us", + "ĠBu s", + "Ġ wis", + "Ġw is", + "Ġwi s", + "åŃ Ĺ", + "Ġ speaker", + "Ġs peaker", + "Ġspe aker", + "Ġspeak er", + "Ġ carrier", + "Ġcar rier", + "Ġcarr ier", + "Ġ Om", + "ĠO m", + "Ġhad n", + "Ġha dn", + "Al low", + "All ow", + ":: __", + "::_ _", + "Ġ verb", + "Ġv erb", + "Ġver b", + "Ġve rb", + "Ġ Complete", + "ĠCom plete", + "ĠComp lete", + "ĠComple te", + "Ġ Easy", + "ĠE asy", + "ĠEa sy", + "Ġb ills", + "Ġbill s", + "Ġbil ls", + "Ġ ĠĊĊ", + "ĠĠ ĊĊ", + "ĠĠĊ Ċ", + "Vert ical", + "Ver tical", + "Ġ pron", + "Ġp ron", + "Ġpro n", + "Ġpr on", + "Ġ Define", + "ĠDe fine", + "ĠDef ine", + "Ġ lookup", + "Ġlook up", + "variable s", + "vari ables", + "Ġp andas", + "Ġpan das", + "Ġpand as", + "Ġpanda s", + "u mes", + "um es", + "ume s", + "Ġin noc", + "Ġinn oc", + "Ġ setUp", + "Ġset Up", + "ĠCh ampionship", + "ĠChampions hip", + "ĠChampion ship", + "art ist", + "arti st", + "ĠC Type", + "ĠCT ype", + "F oundation", + "Found ation", + "๠Ī", + "Ġ Setup", + "ĠSet up", + "4 28", + "42 8", + "Ġ recipes", + "Ġrec ipes", + "Ġrecipe s", + "Ġrecip es", + "Ġ UIColor", + "ĠU IColor", + "ĠUI Color", + "Ġ Fight", + "ĠF ight", + "ĠFig ht", + "ĠFi ght", + "Ġ authorized", + "Ġauthor ized", + "Ġauthorize d", + "_ click", + "_c lick", + "_cl ick", + "_cli ck", + "9 90", + "99 0", + "_ success", + "_s uccess", + "_succ ess", + "_su ccess", + "an gan", + "ang an", + "anga n", + "Ġ Mountain", + "ĠM ountain", + "ĠMount ain", + "Ġ Doctor", + "ĠDo ctor", + "ĠDoc tor", + "Ġ egg", + "Ġe gg", + "Ġeg g", + "ĠM edicine", + "ĠMed icine", + "ĠMedic ine", + "c les", + "cl es", + "cle s", + "` .Ċ", + "`. Ċ", + "[ int", + "[i nt", + "[in t", + "d ashboard", + "dash board", + "Ġ Appro", + "ĠApp ro", + "ĠAp pro", + "- dr", + "-d r", + "Ġprodu ces", + "Ġproduce s", + "Ġprod uces", + "Ġr ental", + "Ġren tal", + "Ġrent al", + "Ġ reload", + "Ġre load", + "Ġr eload", + "Ġrel oad", + "3 81", + "38 1", + "Ġ arrival", + "Ġarr ival", + "Ġarriv al", + "s pot", + "sp ot", + "spo t", + "Ġunder t", + "Ġund ert", + "Ġunde rt", + "3 78", + "37 8", + "Ġequ ipped", + "Ġequip ped", + "Ġ proved", + "Ġpro ved", + "Ġpr oved", + "Ġprov ed", + "Ġprove d", + "Ġ centers", + "Ġcent ers", + "Ġcenter s", + "Ġcen ters", + "Ġ defines", + "Ġdef ines", + "Ġdefine s", + "Ġdefin es", + "al so", + "als o", + "Ġ opacity", + "Ġop acity", + "Ġ Unfortunately", + "ĠUn fortunately", + "ĠIll inois", + "Ġ не", + "Ġн е", + "ĠT emple", + "ĠTem ple", + "ĠTemp le", + "ĠTempl e", + "Ġ Trail", + "ĠT rail", + "ĠTr ail", + "ĠTra il", + "Ġ Kelly", + "ĠK elly", + "ĠKel ly", + "Ġ measurement", + "Ġme asurement", + "Ġmeasure ment", + "Ġmeas urement", + "Ġse parated", + "Ġsepar ated", + "Ġseparate d", + "Ġseparat ed", + "- circle", + "-c ircle", + "H ey", + "He y", + "Ġ READ", + "ĠRE AD", + "ig its", + "igit s", + "igi ts", + "Ġ ib", + "Ġi b", + "Ġ MOD", + "ĠM OD", + "ĠMO D", + "at tery", + "att ery", + "atter y", + "atte ry", + "а з", + "аР·", + "Ġv end", + "Ġve nd", + "Ġven d", + "е нÑĤ", + "ен ÑĤ", + "Ġ HttpClient", + "ĠHttp Client", + "3 59", + "35 9", + "s afe", + "sa fe", + "_ ASS", + "_A SS", + "_AS S", + "i cit", + "ic it", + "ici t", + "Ġ Construct", + "ĠCon struct", + "ĠConstr uct", + "Ġ Clo", + "ĠC lo", + "ĠCl o", + "Ġ Six", + "ĠS ix", + "ĠSi x", + "_ TOKEN", + "_T OKEN", + "_TO KEN", + "( block", + "(b lock", + "(bl ock", + "Ġwar ned", + "Ġwarn ed", + "/* !", + "! Ċ", + "}/ >Ċ", + "}/> Ċ", + "Ġin novation", + "Ġinn ovation", + "Ġinnov ation", + "_ \"", + "Ġ );čĊčĊ", + "Ġ) ;čĊčĊ", + "Ġ); čĊčĊ", + "Ġ);čĊ čĊ", + "Ġ spots", + "Ġsp ots", + "Ġspot s", + "Ġspo ts", + "Ġcho osing", + ". cs", + ".c s", + "Ġf lexible", + "Ġflex ible", + "U Int", + "UI nt", + "4 35", + "43 5", + "9 30", + "93 0", + "Ġ scratch", + "Ġs cratch", + "Ġscr atch", + "- al", + "-a l", + "Ġf estival", + "Ġfest ival", + "Ġout standing", + "================ ================================", + "================================ ================", + "M ean", + "Me an", + "Ġ Oregon", + "ĠO regon", + "ĠOre gon", + "s ymbol", + "sym bol", + ". account", + ".a ccount", + ".ac count", + ".acc ount", + "d ney", + "dn ey", + "' ''", + "'' '", + "! \",", + "!\" ,", + "9 01", + "90 1", + "Ġ particle", + "Ġp article", + "Ġpart icle", + "Ġpartic le", + "Ġparti cle", + "à ĥ", + "[ MAX", + "[M AX", + "I VER", + "IV ER", + "IVE R", + "ER ENCE", + "NS Mutable", + "ĠC olumbia", + "ĠColum bia", + "_ ĊĊ", + "_Ċ Ċ", + ". fr", + ".f r", + "Ġc ogn", + "Ġco gn", + "Ġcog n", + "V R", + "Ġ Methods", + "ĠMethod s", + "ĠMeth ods", + "Ġ Made", + "ĠM ade", + "ĠMad e", + "ĠMa de", + "Ġ BR", + "ĠB R", + "Ġ Else", + "ĠE lse", + "ĠEl se", + "Ġeg gs", + "Ġegg s", + "Ġ swing", + "Ġs wing", + "Ġsw ing", + "Ġ Inv", + "ĠI nv", + "ĠIn v", + "Ġdise ases", + "Ġdisease s", + "Ġf irms", + "Ġfirm s", + "Ġfi rms", + "Ġfir ms", + "Ġ lemma", + "Ġl emma", + "Ġle mma", + "Ġlem ma", + "} `);Ċ", + "}` );Ċ", + "l ings", + "ling s", + "lin gs", + "Ġg ym", + "Ġgy m", + "umin um", + "umi num", + ". Trim", + ".T rim", + ".Tr im", + "M em", + "Me m", + "Ġcrit icism", + "Ġcritic ism", + "ibern ate", + "_ TX", + "_T X", + "i oni", + "ion i", + "io ni", + "Ġguid ance", + "Ġgui dance", + "Ġrepeated ly", + "Ġrepeat edly", + "Ġ supplier", + "Ġs upplier", + "Ġsup plier", + "Ġsuppl ier", + "Ġsupp lier", + "Ġp ainting", + "Ġpaint ing", + "Ġpain ting", + "8 64", + "86 4", + ". Fragment", + ".F ragment", + "ed Exception", + "Ġw iring", + "Ġwir ing", + "Ġwi ring", + "Ġcour ts", + "Ġcou rts", + "Ġcourt s", + "W EB", + "WE B", + "æľ ī", + "\\ .", + "ill ance", + "illa nce", + "Ġb rows", + "Ġbr ows", + "Ġbro ws", + "Ġbrow s", + "Ġ Pattern", + "ĠP attern", + "ĠPat tern", + "ĠPatt ern", + "PL ICATION", + "PLIC ATION", + "Ġ Summer", + "ĠS ummer", + "ĠSum mer", + "Ch ain", + "Cha in", + "Ġc ute", + "Ġcut e", + "Ġcu te", + "m ercial", + "mer cial", + "merc ial", + "Ġd il", + "Ġdi l", + "ĠFrank lin", + "ĉ global", + "ĉg lobal", + "IN CLUDING", + "h istory", + "hi story", + "hist ory", + "histor y", + "Ġ lst", + "Ġl st", + "Ġls t", + "Q t", + "S DL", + "SD L", + "a lia", + "al ia", + "ali a", + "i ere", + "ie re", + "ier e", + "( ...", + "(. ..", + "(.. .", + "ĉ cin", + "ĉc in", + "if fs", + "iff s", + "v elope", + "ve lope", + "vel ope", + "velop e", + "Ġ Root", + "ĠR oot", + "ĠRo ot", + "ĠRoo t", + "cl uster", + "clus ter", + "User Name", + "i gne", + "ig ne", + "ign e", + "< S", + "Ġ fest", + "Ġf est", + "Ġfe st", + "4 19", + "41 9", + "Ġindic ating", + "Ġindica ting", + "k eeper", + "ke eper", + "keep er", + "kee per", + "Ġc ada", + "Ġca da", + "Ġcad a", + "é g", + "con sin", + "cons in", + "Ġ GB", + "ĠG B", + "Ġ lb", + "Ġl b", + "e mony", + "em ony", + "emo ny", + "emon y", + "- icons", + "-icon s", + "-i cons", + "_ doc", + "_d oc", + "_do c", + "A ctor", + "Act or", + "Ac tor", + "e lem", + "el em", + "ele m", + ". Delete", + ".De lete", + "Ġin fection", + "Ġinf ection", + "Ġinfect ion", + "Ġ Privacy", + "ĠPriv acy", + "Ġgreat ly", + "Ġ Pos", + "ĠP os", + "ĠPo s", + "ĠT reat", + "ĠTr eat", + "ĠTre at", + "F low", + "Fl ow", + "Flo w", + "Ġat tractive", + "Ġattr active", + "Ġattract ive", + "Ġ Marc", + "ĠM arc", + "ĠMar c", + "ĠMa rc", + "s udo", + "su do", + "t esy", + "te sy", + "tes y", + "- an", + "-a n", + "9 98", + "99 8", + "ab ama", + "aba ma", + "Ġ Would", + "ĠW ould", + "ĠWo uld", + "Ġs uck", + "Ġsu ck", + "Ġsuc k", + "index Path", + "Ġ Et", + "ĠE t", + "T imes", + "Time s", + "Tim es", + "Ti mes", + "7 80", + "78 0", + "Ġ clubs", + "Ġcl ubs", + "Ġclub s", + "_ assoc", + "_as soc", + "_ass oc", + "Ġac quired", + "Ġacqu ired", + "Ġacquire d", + "( \":", + "(\" :", + "Ġint ense", + "Ġintens e", + ". maps", + ".m aps", + ".map s", + ".ma ps", + "Ex pected", + "Exp ected", + "Expect ed", + "T oggle", + "Ġ ay", + "Ġa y", + "Ġl ifestyle", + "Ġlife style", + "Ġlif estyle", + "- called", + "-c alled", + "-cal led", + "-call ed", + "Ġ Snow", + "ĠS now", + "ĠSn ow", + "ĠSno w", + "V olume", + "Vol ume", + "Ġcann abis", + "Ġ Direction", + "ĠD irection", + "ĠDirect ion", + "ĠDi rection", + "ĠDir ection", + "ĠDire ction", + "Ġ Limited", + "ĠL imited", + "ĠLim ited", + "ĠLimit ed", + "- specific", + "-s pecific", + "-spec ific", + "Ġd owntown", + "Ġdown town", + "Ġdownt own", + "/ icons", + "/i cons", + "/icon s", + "/ic ons", + "Ġre ven", + "Ġr even", + "Ġrev en", + "Ġreve n", + "L eg", + "Le g", + "8 85", + "88 5", + "= null", + "=n ull", + "4 96", + "49 6", + "Key board", + "' )).", + "') ).", + "')) .", + "Ġ\" \";čĊ", + "Ġ\"\" ;čĊ", + "Ġ\"\"; čĊ", + "Ġatt itude", + ". navigate", + ".n avigate", + ".nav igate", + "- error", + "-e rror", + "AM PLE", + "AMP LE", + "AMPL E", + "Ġ Jay", + "ĠJ ay", + "ĠJa y", + "v r", + "c ow", + "co w", + ". compile", + ".com pile", + ".comp ile", + "Ġmem ories", + "Ġmemor ies", + "Ġmemo ries", + "_ mark", + "_m ark", + "_mar k", + "_ma rk", + "Ġ Minnesota", + "ĠMin nesota", + "Ġk osten", + "Ġko sten", + "Ġkos ten", + "Ġkost en", + "Ġ probability", + "Ġprob ability", + "Ġprobabil ity", + "w arning", + "war ning", + "warn ing", + "Ġgen etic", + "Ġgene tic", + "F ixture", + "Fix ture", + "Ġ HashSet", + "ĠHash Set", + "N ombre", + "Nom bre", + "_ month", + "_m onth", + "_mon th", + "_mo nth", + "Æ °", + "- start", + "-st art", + "-star t", + "xy gen", + "ĉ ft", + "ĉf t", + "i agnostics", + "Ġ Matthew", + "ĠMat thew", + "ĠMatth ew", + "Ġcon cepts", + "Ġconcept s", + "Ġconce pts", + "Ġcon str", + "Ġconst r", + "Ġcons tr", + ". State", + ".St ate", + ".Stat e", + "и н", + "N ov", + "No v", + "Î ±", + "Ġ Panel", + "ĠP anel", + "ĠPan el", + "ĠPa nel", + "ĠPane l", + "ä¸ ª", + "com pare", + "comp are", + "> ()Ċ", + ">( )Ċ", + ">() Ċ", + "Ġapp lying", + "Ġapply ing", + "Ġappl ying", + "Ġprom ised", + "Ġpromise d", + "Ġ ox", + "Ġo x", + "n cia", + "nc ia", + "Ġ Validation", + "ĠValid ation", + "o rts", + "or ts", + "ort s", + "_ cur", + "_c ur", + "_cu r", + "e lect", + "el ect", + "ele ct", + "e ye", + "ey e", + "( Data", + "(D ata", + "Ġre porter", + "Ġreport er", + "Ġ Buff", + "ĠB uff", + "ĠBu ff", + "ĠBuf f", + "3 95", + "39 5", + "Ġ sr", + "Ġs r", + "Ġ \";", + "Ġ\" ;", + "i cky", + "ic ky", + "ick y", + "Ġt empor", + "Ġtem por", + "Ġtemp or", + "Ġtempo r", + "S N", + "Ġ resident", + "Ġres ident", + "Ġresid ent", + "Ġreside nt", + "p ires", + "pi res", + "pir es", + "pire s", + "ys ical", + "ysi cal", + "Ġend orse", + "Ġendors e", + "Ġ Song", + "ĠS ong", + "ĠSo ng", + "ĠSon g", + "is Empty", + "le et", + "lee t", + "_ util", + "_u til", + "_ut il", + "Ġd istingu", + "Ġdist ingu", + "Ġ Talk", + "ĠT alk", + "ĠTal k", + "ĠTa lk", + "Ġ Mot", + "ĠM ot", + "ĠMo t", + "( default", + "(d efault", + "(de fault", + "(def ault", + ". Arg", + ".A rg", + ".Ar g", + "gorith ms", + "gorithm s", + "_ words", + "_w ords", + "_word s", + "im mer", + "imm er", + "_ reset", + "_re set", + "_res et", + "f amily", + "W W", + "Ġs avings", + "Ġsav ings", + "Ġsaving s", + "Ġ âĢĿ", + "ĠâĢ Ŀ", + "_ enable", + "_e nable", + "_en able", + "s idebar", + "side bar", + "R unning", + "Run ning", + "Ġ ali", + "Ġa li", + "Ġal i", + "Ġte stim", + "Ġtest im", + "Ġtes tim", + "Ġ warnings", + "Ġw arnings", + "Ġwar nings", + "Ġwarn ings", + "Ġwarning s", + "Ġ Chem", + "ĠC hem", + "ĠCh em", + "ĠChe m", + "Ġ Exit", + "ĠE xit", + "ĠEx it", + "Ġf ounder", + "Ġfound er", + "Ġfo under", + "Ġfou nder", + "p ector", + "pe ctor", + "pect or", + "pec tor", + "Ġ rm", + "Ġr m", + "_ dataset", + "_d ataset", + "_data set", + "_dat aset", + "_datas et", + "Ġ Das", + "ĠD as", + "ĠDa s", + "Ġ han", + "Ġh an", + "Ġha n", + "G etty", + "Get ty", + "Ge tty", + "á l", + "Ġ ny", + "Ġn y", + "Ġpo verty", + "Ġpov erty", + "Ġresult ed", + ". by", + ".b y", + "Ġ Visit", + "ĠVis it", + "ĠVi sit", + "Ġobt aining", + "Ġobtain ing", + "/ '.$", + "/' .$", + "/'. $", + "Ġ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "s hall", + "sh all", + "shal l", + "sha ll", + "_ LEFT", + "_LE FT", + "UI Image", + "_ Name", + "_N ame", + "h ave", + "ha ve", + "ĠN ob", + "ĠNo b", + "l r", + "- footer", + "-f ooter", + "-foot er", + "Ġn aked", + "Ġna ked", + "Ġnak ed", + "ĠG arden", + "ĠGar den", + "ĠGard en", + "\\F acades", + "Ġ graduate", + "Ġgrad uate", + "Ġgradu ate", + "4 17", + "41 7", + "Ġfr anchise", + "Ġfranch ise", + "p lane", + "pl ane", + "plan e", + "pla ne", + "Ġcontrib utions", + "Ġcontribution s", + "Ġstring With", + "Ġ crypto", + "Ġc rypto", + "Ġcrypt o", + "Ġcry pto", + "Ġmov ements", + "Ġmove ments", + "Ġmo vements", + "Ġmovement s", + "a thers", + "ath ers", + "ather s", + "athe rs", + "Ġ lifetime", + "Ġl ifetime", + "Ġlife time", + "Ġlif etime", + "Ġcommunic ate", + "Ġcommun icate", + "j ar", + "ja r", + "Ġ Fragment", + "ĠF ragment", + "ĠFr agment", + "ĠFra gment", + "ĠFrag ment", + "_ IF", + "_I F", + "ĠN avy", + "ĠNav y", + "ĠNa vy", + "Ġ Figure", + "ĠF igure", + "ĠFig ure", + "Ġ simulation", + "Ġs imulation", + "Ġsim ulation", + "Ġsimul ation", + "_ stop", + "_s top", + "_st op", + "Ġreport ers", + "Ġreporter s", + "Ġver sus", + "Ġvers us", + "a ja", + "aj a", + "Ġ α", + "ĠÎ ±", + "Ġgover nor", + "Ġgovern or", + "Ġgoverno r", + "List Item", + "Ġ sealed", + "Ġse aled", + "Ġsea led", + "Ġseal ed", + ". Background", + ".Back ground", + "e di", + "ed i", + "ash ing", + "ashi ng", + "Ġ lip", + "Ġl ip", + "Ġli p", + "ĠI h", + "m erge", + "mer ge", + "Ġn ec", + "Ġne c", + "0 24", + "02 4", + "el ocity", + "elo city", + "AT EG", + "ATE G", + "Ġse eds", + "Ġsee ds", + "Ġseed s", + "Ġ floating", + "Ġf loating", + "Ġfloat ing", + "Ġflo ating", + "7 01", + "70 1", + "_ FA", + "_F A", + "w alk", + "wa lk", + "wal k", + "ĉ user", + "ĉu ser", + "ĉuse r", + "ĉus er", + "_ depth", + "_de pth", + "_dep th", + "_dept h", + "Ġw age", + "Ġwa ge", + "Ġwag e", + "@ app", + "@a pp", + "N il", + "Ni l", + "( [\"", + "([ \"", + "( vector", + "(v ector", + "(vec tor", + "Ġsecret ary", + "4 61", + "46 1", + "Ġj Panel", + "v ez", + "ve z", + "Âł ³³³", + "³³ ³³", + "³³³ Âł", + "d irection", + "dir ection", + "di rection", + "direct ion", + "dire ction", + "Ġ EP", + "ĠE P", + "Ġ hunt", + "Ġh unt", + "Ġhun t", + "Ġhu nt", + "3 96", + "39 6", + "Json Property", + "Ġ PORT", + "ĠP ORT", + "ĠPO RT", + "ĠPOR T", + "] \",", + "]\" ,", + "а п", + "аР¿", + "Ġ Foreign", + "ĠFore ign", + "p anic", + "pan ic", + "pa nic", + "Ġtr ials", + "Ġtri als", + "Ġtrial s", + "Ġ Ale", + "ĠA le", + "ĠAl e", + "Ġr ural", + "Ġru ral", + "- value", + "-v alue", + "-val ue", + "-valu e", + "author ized", + "authorize d", + "Ġ Scotland", + "ĠSc otland", + "ĠScot land", + ". drop", + ".d rop", + ".dr op", + "Ġ MT", + "ĠM T", + "ç ±", + "3 91", + "39 1", + "row th", + "5 15", + "51 5", + "File Path", + "Ġ recall", + "Ġre call", + "Ġrec all", + "Ġrecal l", + "if le", + "Ġ cel", + "Ġc el", + "Ġce l", + "Ġ SELECT", + "ĠSE LECT", + "ĠSEL ECT", + "k n", + "_ case", + "_c ase", + "_ca se", + "Ġ crop", + "Ġc rop", + "Ġcr op", + "Ġcro p", + "5 43", + "54 3", + "s ure", + "sur e", + "su re", + "p ot", + "po t", + "I CS", + "IC S", + "Ġ stem", + "Ġs tem", + "Ġst em", + "Ġste m", + "Ġindust ries", + "Ġindustri es", + "P ut", + "Pu t", + "Ġ aber", + "Ġa ber", + "Ġab er", + "road cast", + "I cons", + "Icon s", + ") \")Ċ", + ")\" )Ċ", + ")\") Ċ", + "æĪIJ åĬŁ", + "g ui", + "gu i", + "Ġass umed", + "Ġassum ed", + "Ġassume d", + "Ġ rx", + "Ġr x", + "E A", + "è §", + "E LL", + "EL L", + "Ġd ose", + "Ġdo se", + "Ġdos e", + "Ġ ine", + "Ġin e", + "Ġi ne", + "Ġd eeper", + "Ġde eper", + "Ġdeep er", + "Ġdee per", + "l ider", + "li der", + "lide r", + "lid er", + "Ġ ordinary", + "Ġord inary", + "Ġordin ary", + "Ġg olf", + "Ġgo lf", + "Ġgol f", + "6 05", + "60 5", + "_ IMAGE", + "_IM AGE", + "Ġ NAME", + "ĠN AME", + "ĠNA ME", + "( module", + "(m odule", + "(mod ule", + "Ġ atom", + "Ġa tom", + "Ġat om", + "Ġ belt", + "Ġb elt", + "Ġbe lt", + "Ġbel t", + "Ġoff ices", + "Ġoffic es", + "Ġoffice s", + "5 06", + "50 6", + "b eta", + "be ta", + "bet a", + "Ġphilosoph y", + "( JSON", + "(J SON", + "(JS ON", + "- field", + "-f ield", + "-fi eld", + "Ġint roduce", + "Ġintrodu ce", + "Ġintro duce", + "Ġcon venience", + "Ġconven ience", + "op tim", + "opt im", + "> \"Ċ", + ">\" Ċ", + "a thy", + "at hy", + "ath y", + "Ġ employer", + "Ġemploy er", + "q uate", + "qu ate", + "qua te", + "quat e", + "Ġ edited", + "Ġed ited", + "Ġedit ed", + "Ġedi ted", + "Arg uments", + "Argument s", + "ĠN ations", + "ĠNation s", + "ĠNat ions", + "_ _)", + "__ )", + "Ġn ose", + "Ġno se", + "Ġnos e", + "Ġ Sample", + "ĠS ample", + "ĠSam ple", + "ĠSamp le", + "' )ĊĊĊ", + "') ĊĊĊ", + "')Ċ ĊĊ", + "')ĊĊ Ċ", + "Ġ cake", + "Ġc ake", + "Ġca ke", + ". getAttribute", + ".get Attribute", + "H D", + "3 92", + "39 2", + "Mod ified", + "4 45", + "44 5", + "Ġ predicted", + "Ġpred icted", + "Ġpredict ed", + "Ġpredic ted", + "Å Ħ", + "a nie", + "an ie", + "ani e", + "S orry", + "( doc", + "(d oc", + "(do c", + "w ind", + "win d", + "wi nd", + "i eve", + "ie ve", + "iev e", + "Ġpro visions", + "Ġprov isions", + "Ġprovision s", + "A TER", + "AT ER", + "ATE R", + "O TE", + "OT E", + "M Y", + ". Autowired", + ".A utowired", + "ĠB ath", + "ĠBa th", + "ĠBat h", + "4 23", + "42 3", + ". Boolean", + ".Bool ean", + "Ġ backend", + "Ġback end", + ". Mouse", + ".M ouse", + "at eral", + "ate ral", + "ater al", + "p aper", + "pa per", + "Con st", + "Co nst", + "Cons t", + "Ġ VR", + "ĠV R", + "_ entity", + "_e ntity", + "_ent ity", + "_ CTRL", + "_C TRL", + "_CT RL", + "Ġ Protection", + "ĠPro tection", + "ĠProt ection", + "ĠProte ction", + "ĠProtect ion", + "Ġ GM", + "ĠG M", + "Ġ Study", + "ĠSt udy", + "ĠStud y", + "Ġ soup", + "Ġs oup", + "Ġso up", + "Ġsou p", + "o time", + "ot ime", + "oti me", + "' use", + "'u se", + "] \"", + "/ users", + "/user s", + "/use rs", + "/us ers", + "a ug", + "au g", + "Ġ Hong", + "ĠH ong", + "ĠHon g", + "ĠHo ng", + "_ norm", + "_n orm", + "_no rm", + "ãģ ¨", + "Ġse cre", + "Ġsec re", + "( Build", + "(B uild", + "Ġ Contract", + "ĠCon tract", + "ĠCont ract", + "ĠContr act", + "o las", + "ol as", + "ola s", + "Ġs auce", + "Ġsa uce", + "Ġsau ce", + "Ġag gressive", + "Ġaggress ive", + "Ġagg ressive", + "Ġ racial", + "Ġr acial", + "Ġrac ial", + "Ġra cial", + "char acter", + "@ @", + "Ġ compile", + "Ġcom pile", + "Ġcomp ile", + "Ġcompil e", + "Ġ Void", + "ĠV oid", + "ĠVo id", + "_ rem", + "_re m", + "_r em", + "_ memory", + "_m emory", + "_mem ory", + "3 48", + "34 8", + "k k", + "Ġ mic", + "Ġm ic", + "Ġmi c", + "S ame", + "Sam e", + "Sa me", + "U tility", + "Util ity", + "Ut ility", + "Ġ Html", + "ĠH tml", + "Ġ Xml", + "ĠX ml", + "ĠXm l", + "Re ady", + "Read y", + "Ġg all", + "Ġga ll", + "Ġgal l", + "Ġalleg edly", + "Ġalleged ly", + "ĉ ĉĉĉĠĠĠ", + "ĉĉ ĉĉĠĠĠ", + "ĉĉĉĉ ĠĠĠ", + "ĉĉĉ ĉĠĠĠ", + "ĉĉĉĉĠ ĠĠ", + "ĉĉĉĉĠĠ Ġ", + "Ġ Metal", + "ĠM etal", + "ĠMe tal", + "ĠMet al", + "ĠMeta l", + "Ġ Personal", + "ĠPerson al", + "ĠPers onal", + "ĠPersona l", + "Ġborder Radius", + "rx js", + "object s", + "obj ects", + "Ġwant ing", + "Ġwan ting", + "Ġb owl", + "Ġbo wl", + "Ġbow l", + "v endor", + "offset of", + "Ġ Rs", + "ĠR s", + "Ġ Rating", + "ĠR ating", + "ĠRa ting", + "ĠRat ing", + "Ġr ally", + "Ġrall y", + "_ NODE", + "_N ODE", + "_NO DE", + "4 18", + "41 8", + "Ġ Mix", + "ĠM ix", + "ĠMi x", + "Ġad vertis", + "Ġadvert is", + "4 85", + "48 5", + "6 67", + "66 7", + "Ġnarr ative", + "s al", + "sa l", + "Ġ mc", + "Ġm c", + "S Error", + "SE rror", + "Ġf ingers", + "Ġfin gers", + "Ġfinger s", + "Ġfing ers", + "Ġac company", + "Ġaccom pany", + "Ġaccomp any", + "Ġt ired", + "Ġti red", + "Ġtire d", + "Ġtir ed", + "Ġ stride", + "Ġst ride", + "Ġstr ide", + "Ġstri de", + "Ġ gui", + "Ġg ui", + "Ġgu i", + "e list", + "el ist", + "eli st", + "L ocale", + "Lo cale", + "Local e", + "Loc ale", + "Ġre leases", + "Ġrelease s", + "Ġrele ases", + "i king", + "ik ing", + "iki ng", + "Ġ anger", + "Ġa nger", + "Ġan ger", + "Ġang er", + "Ġange r", + ") ))ĊĊ", + ")) )ĊĊ", + ")))Ċ Ċ", + "))) ĊĊ", + "al lest", + "all est", + "alle st", + "alles t", + "Sum mary", + "( O", + "( for", + "(f or", + "Ġbasket ball", + "Ġ roads", + "Ġro ads", + "Ġroad s", + "Ġ Install", + "ĠInst all", + "Ġ Fab", + "ĠF ab", + "ĠFa b", + "it map", + "itm ap", + "4 75", + "47 5", + "Ġ ))Ċ", + "Ġ) )Ċ", + "Ġ)) Ċ", + "Ġ intersection", + "Ġinter section", + "Ġintersect ion", + "Ġinters ection", + "igh bor", + "ighb or", + "ĠB ry", + "ĠBr y", + "Ġ HERE", + "ĠH ERE", + "ĠHE RE", + "ĠHER E", + "S oftware", + "So ftware", + "Soft ware", + "el fare", + "elf are", + "a cs", + "ac s", + "6 22", + "62 2", + "Ġtr ailer", + "Ġtrail er", + "Ġtra iler", + "Ġtrai ler", + ". getClass", + ".get Class", + ".getC lass", + "ch ars", + "char s", + "cha rs", + "Ġreg ulation", + "Ġregul ation", + "Ġre fers", + "Ġref ers", + "Ġrefer s", + "Ġd estruction", + "Ġde struction", + "Ġdestruct ion", + "Ġ continuous", + "Ġcontin uous", + "Ġcontinu ous", + "Ġ Austin", + "ĠA ustin", + "ĠAust in", + "ĠAus tin", + "ĠAu stin", + "é ¢", + "a kan", + "ak an", + "aka n", + ". window", + ".w indow", + ".wind ow", + "Ġ Templates", + "ĠT emplates", + "ĠTem plates", + "ĠTemplate s", + "ĠTemp lates", + "ĠTempl ates", + "Ġabs ence", + ": n", + "Ġdis order", + "f lash", + "fl ash", + "Ġde let", + "Ġdel et", + "Ġdele t", + "bo ards", + "board s", + "Ġ Ġĉ", + "ĠĠ ĉ", + "R OP", + "RO P", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġac qu", + "Ġlaw suit", + "Ġlaws uit", + "Ġ Reviews", + "ĠRe views", + "ĠReview s", + "Ġgar age", + "Ġga rage", + "t imer", + "time r", + "ti mer", + "tim er", + "Ġ ej", + "Ġe j", + "Ġ Rectangle", + "ĠRect angle", + "Ġ flowers", + "Ġfl owers", + "Ġflow ers", + "Ġflo wers", + "Ġflower s", + "3 98", + "39 8", + "i lst", + "il st", + "ils t", + "Ġ Instance", + "ĠIn stance", + "ĠInst ance", + "S uper", + "Sup er", + "Su per", + "d et", + "de t", + "dis posing", + "disp osing", + "Ġ ES", + "ĠE S", + "Ġ IC", + "ĠI C", + "v ere", + "ver e", + "ve re", + "S k", + "_ channels", + "_ch annels", + "_channel s", + "_chan nels", + "p uted", + "put ed", + "pu ted", + "pute d", + "/ null", + "/n ull", + "n nen", + "nn en", + "4 31", + "43 1", + "Ġ Gallery", + "ĠG allery", + "ĠGall ery", + "_ global", + "_g lobal", + "_glob al", + "Auth entication", + "Ġ Rank", + "ĠR ank", + "ĠRa nk", + "ĠRan k", + "Ġ blocked", + "Ġb locked", + "Ġbl ocked", + "Ġblock ed", + "Ġbloc ked", + "Ġc alm", + "Ġcal m", + "Ġca lm", + "m arket", + "mark et", + "mar ket", + "ĉ val", + "ĉv al", + "ĉva l", + "Ġ aug", + "Ġa ug", + "Ġau g", + "p eriod", + "per iod", + "peri od", + "Ġ Constant", + "ĠCon stant", + "ĠCons tant", + "ĠConst ant", + "Ġ ?>\">Ċ", + "Ġ?> \">Ċ", + "Ġ?>\" >Ċ", + "Ġ?>\"> Ċ", + "Ġ lobby", + "Ġl obby", + "Ġlob by", + "p al", + "pa l", + "3 79", + "37 9", + "Ġ sink", + "Ġs ink", + "Ġsi nk", + "Ġsin k", + "5 08", + "50 8", + "i ah", + "ia h", + "Ð ¡", + "ur name", + "urn ame", + "Ġcon ver", + "Ġconv er", + "Ġinvest igate", + "Ġinvestig ate", + "Ch rist", + "Chris t", + "Chr ist", + "H ub", + "Hu b", + "Ġ IND", + "ĠI ND", + "ĠIN D", + "Ġ Ped", + "ĠP ed", + "ĠPe d", + "u ras", + "ur as", + "ura s", + "ĉ url", + "ĉu rl", + "Ġ Tro", + "ĠT ro", + "ĠTr o", + "Ġ preferences", + "Ġp references", + "Ġpre ferences", + "Ġprefer ences", + "Ġpreference s", + "Ġguar anteed", + "Ġguarante ed", + "Ġguarantee d", + "` ĊĊ", + "`Ċ Ċ", + "Ġport ions", + "Ġportion s", + "Ġe valu", + "Ġev alu", + "Ġeval u", + "' > < /", + "( ){ĊĊ", + "() {ĊĊ", + "(){Ċ Ċ", + "(){ ĊĊ", + "en coded", + "enc oded", + "encode d", + "enco ded", + "z illa", + "zi lla", + ". Class", + ".C lass", + ".Cl ass", + "Ġ *_", + "Ġ* _", + "_ '", + "Ġview ed", + "Ġvi ewed", + "Ġvie wed", + "Ġ Philadelphia", + "ĠPhil adelphia", + ". rows", + ".r ows", + ".row s", + ".ro ws", + "Add ed", + "Ad ded", + "Ġ Touch", + "ĠT ouch", + "ĠTo uch", + "ĠTou ch", + "8 40", + "84 0", + ". delegate", + ".de legate", + "quee ze", + "s lide", + "sl ide", + "Ġ Senior", + "ĠSen ior", + "( tag", + "(t ag", + "Ġinter views", + "Ġinterview s", + "Ġs ua", + "Ġsu a", + "a tas", + "at as", + "ata s", + "@ ĊĊ", + "@Ċ Ċ", + "d istance", + "di stance", + "dist ance", + "Ġ sein", + "Ġs ein", + "Ġse in", + "Ġsei n", + "l atest", + "la test", + "late st", + "lat est", + "lates t", + "Ġ Prince", + "ĠPr ince", + "ĠPri nce", + "Ġlux ury", + "Ġre fr", + "Ġref r", + "Ġ Kitchen", + "ĠK itchen", + "ĠKit chen", + "Ñ Ħ", + "( at", + "(a t", + "F inal", + "Fin al", + "Fi nal", + "ü ck", + "üc k", + "_ zero", + "_z ero", + "Ġ ABC", + "ĠA BC", + "ĠAB C", + "Ġ Manchester", + "ĠMan chester", + "Ġ cow", + "Ġc ow", + "Ġco w", + "C OL", + "CO L", + "_ NUMBER", + "_NUM BER", + "ch anges", + "change s", + "chan ges", + "chang es", + "g enerate", + "gen erate", + "gener ate", + "gene rate", + ". Printf", + ".Print f", + "3 69", + "36 9", + "s hare", + "sh are", + "sha re", + "St ock", + "Ġ PT", + "ĠP T", + "A nim", + "An im", + "a nga", + "an ga", + "ang a", + "Ġ ig", + "Ġi g", + "up loads", + "upload s", + "Ġ packed", + "Ġp acked", + "Ġpack ed", + "Ġpac ked", + "Ġ }];Ċ", + "Ġ} ];Ċ", + "Ġ}] ;Ċ", + "( sender", + "(s ender", + "(se nder", + "(send er", + "Ġ Wire", + "ĠW ire", + "ĠWi re", + "ĠWir e", + "i sons", + "is ons", + "ison s", + "iso ns", + "Ġplay off", + "\\ E", + "6 08", + "60 8", + "/ R", + "Ġ headed", + "Ġhe aded", + "Ġhead ed", + "Al pha", + "( order", + "(ord er", + "(or der", + "Ġop ponents", + "Ġopp onents", + "Ġoppon ents", + "Ġopponent s", + "ack son", + "acks on", + "_ member", + "_m ember", + "_mem ber", + "T urn", + "Tur n", + "Tu rn", + "ĠSov iet", + "ìĹ IJ", + "a uge", + "au ge", + "aug e", + "4 48", + "44 8", + "Ġ incoming", + "Ġin coming", + "Ġinc oming", + "Ġincom ing", + "Ġ jak", + "Ġj ak", + "Ġja k", + "- game", + "-g ame", + "Ġ Male", + "ĠM ale", + "ĠMal e", + "ĠMa le", + "Ġ Month", + "ĠM onth", + "ĠMon th", + "ĠMo nth", + "ĠMont h", + "St age", + ". exe", + ".e xe", + ".ex e", + "Own Property", + ". setItem", + ".set Item", + "Ġ dc", + "Ġd c", + "ä½ ľ", + "Ġb rut", + "Ġbr ut", + "Ġbru t", + "Ġattempt ing", + ". len", + ".l en", + ".le n", + "Ġjud gment", + "Ġs ab", + "Ġsa b", + "Ġ cad", + "Ġc ad", + "Ġca d", + "Ġ Items", + "ĠI tems", + "ĠIt ems", + "ĠItem s", + "com fort", + "el ize", + "eli ze", + "/ log", + "/l og", + "/lo g", + "Ġentre prene", + "Ġ compiler", + "Ġc ompiler", + "Ġcom piler", + "Ġcomp iler", + "Ġcompile r", + "Ġcompil er", + "_ validation", + "_valid ation", + "r eview", + "re view", + "rev iew", + "Ġ textBox", + "Ġtext Box", + "Ġ fraction", + "Ġf raction", + "Ġfr action", + "Ġfra ction", + "Ġfract ion", + "Ġfrac tion", + "Ġ Bal", + "ĠB al", + "ĠBa l", + "> ;ĊĊ", + ">;Ċ Ċ", + ">; ĊĊ", + ".AutoScale Mode", + "Ġ cats", + "Ġc ats", + "Ġca ts", + "Ġcat s", + "4 65", + "46 5", + "Ġ registry", + "Ġreg istry", + "Ġregistr y", + "Ġregist ry", + "u lus", + "ul us", + "ulu s", + "F I", + "p ayload", + "pay load", + "- search", + "-s earch", + "-se arch", + "Ġst aying", + "Ġstay ing", + "Ġsta ying", + "ac ious", + "aci ous", + "acio us", + "De coration", + "Dec oration", + "Decor ation", + "R eview", + "Re view", + "Rev iew", + "I nf", + "In f", + "Ke ep", + "it is", + "iti s", + ", String", + ",S tring", + "C oord", + "Co ord", + "Ġp ero", + "Ġper o", + "Ġpe ro", + "S ex", + "Se x", + "Ġ Atlanta", + "ĠAtl anta", + "u esta", + "ue sta", + "ues ta", + "uest a", + "A rgb", + "Arg b", + "Ar gb", + "> *", + "} _", + "F ooter", + "Foo ter", + "Foot er", + "Fo oter", + "Ġ employed", + "Ġemploy ed", + "_ bound", + "_b ound", + "_bo und", + "v ide", + "vid e", + "vi de", + ". func", + ".f unc", + ".fun c", + "$ scope", + "$s cope", + "Ġ spo", + "Ġs po", + "Ġsp o", + "Ġ Anal", + "ĠA nal", + "ĠAn al", + "ĠAna l", + "oun ced", + "ounc ed", + "ounce d", + "a round", + "ar ound", + "aro und", + "Ġ restriction", + "Ġre striction", + "Ġrestrict ion", + "Ġrestr iction", + "Ġ shops", + "Ġsh ops", + "Ġshop s", + "Ġsho ps", + "å Ģ", + "Ġ Latin", + "ĠL atin", + "ĠLa tin", + "ĠLat in", + "- col", + "-c ol", + "-co l", + "Ġbar ely", + "Ġbare ly", + "Ġ Euro", + "ĠE uro", + "ĠEu ro", + "ĠEur o", + "E r", + "Ġf aire", + "Ġfa ire", + "Ġfair e", + "_ distance", + "_d istance", + "_dist ance", + "_di stance", + "_ unlock", + "_un lock", + "Qu ote", + "IV ATE", + "IVA TE", + "Ġ åĪ", + "Ġå Ī", + "Ġa imed", + "Ġaim ed", + "Ġai med", + "Ġaime d", + "ĠRe trie", + "ĠRet rie", + ". iter", + ".i ter", + ".it er", + "Ġ wrapped", + "Ġw rapped", + "Ġwr apped", + "Ġwrap ped", + "Ġag reements", + "Ġagre ements", + "Ġagree ments", + "Ġagreement s", + "str ument", + "stru ment", + "( product", + "(pro duct", + "(prod uct", + "Ġstud ied", + "Ġstudi ed", + ". setValue", + ".set Value", + "Ġ ye", + "Ġy e", + "Ġ Cache", + "ĠC ache", + "ĠCa che", + "MB OL", + "Ġquarter back", + "Ġ syntax", + "Ġs yntax", + "Ġsy ntax", + "Ġsyn tax", + "Ġsynt ax", + ".get ElementsBy", + ".getElements By", + ". version", + ".v ersion", + "we bsite", + "web site", + "webs ite", + "R unner", + "Run ner", + "_ single", + "_s ingle", + "_si ngle", + "_sin gle", + "a tiv", + "at iv", + "ati v", + "Ġ Altern", + "ĠAl tern", + "ĠAlt ern", + "ĠAlter n", + "Ġ Beautiful", + "ĠBe autiful", + "ĠBeaut iful", + "right arrow", + "Ġd iversity", + "Ġdivers ity", + "p lash", + "pl ash", + "pla sh", + "( co", + "(c o", + ". Fill", + ".F ill", + "Ġ typing", + "Ġtyp ing", + "Ġty ping", + "3 87", + "38 7", + "0 23", + "02 3", + "Ġ clar", + "Ġc lar", + "Ġcl ar", + "Ġcla r", + "H it", + "Hi t", + "O O", + "a cco", + "ac co", + "acc o", + "5 07", + "50 7", + "w orth", + "wort h", + "wor th", + "Ġ scripts", + "Ġs cripts", + "Ġscript s", + "Ġscri pts", + "ĠMuslim s", + "Ġ LL", + "ĠL L", + "er ving", + "erv ing", + "( boolean", + "(bool ean", + "Ġbase ball", + "Ġ CAN", + "ĠC AN", + "ĠCA N", + "3 94", + "39 4", + "0 44", + "04 4", + "M AIL", + "MA IL", + "d epend", + "de pend", + "dep end", + "Ġres pective", + "Ġrespect ive", + "Ġresp ective", + "Ġ constexpr", + "Ġconst expr", + ".* ;ĊĊ", + ".*;Ċ Ċ", + "' ]))Ċ", + "'] ))Ċ", + "']) )Ċ", + "'])) Ċ", + "Ġ yard", + "Ġy ard", + "Ġya rd", + "Ġyar d", + "Ġident ical", + "if ecycle", + "ife cycle", + "U SH", + "US H", + "up iter", + ". validate", + ".valid ate", + "c li", + "cl i", + "I STER", + "IS TER", + "IST ER", + "Ind icator", + "F ail", + "Fa il", + "Ġdem ocracy", + "Ġdemocr acy", + ". var", + ".v ar", + ".va r", + "Ġs atisfied", + "Ġsatisf ied", + "- ------------", + "-- -----------", + "---- ---------", + "-------- -----", + "--- ----------", + "------------ -", + "----- --------", + "---------- ---", + "------ -------", + "----------- --", + "------- ------", + "--------- ----", + "en cer", + "ence r", + "enc er", + "h or", + "ho r", + "Ġr ounds", + "Ġro unds", + "Ġround s", + "Ġrou nds", + "D AO", + "DA O", + "o a", + "Ġfl ask", + "Ġfla sk", + "= c", + "[ ]Ċ", + "[] Ċ", + "/ dist", + "/d ist", + "/dis t", + "Ġp arte", + "Ġpart e", + "Ġpar te", + "Ġ confirmation", + "Ġconfirm ation", + "e ron", + "er on", + "ero n", + "a ware", + "aw are", + "awa re", + "< ?>", + "", + "Ġ dependencies", + "Ġdep endencies", + "Ġdepend encies", + "Ġ Videos", + "ĠV ideos", + "ĠVideo s", + "ĠVid eos", + "ĠVide os", + "- row", + "-r ow", + "-ro w", + "Ġ **/Ċ", + "Ġ* */Ċ", + "Ġ** /Ċ", + "Ġ nou", + "Ġn ou", + "Ġno u", + "Ġ hover", + "Ġh over", + "Ġho ver", + "æ ŀ", + "Ġ nin", + "Ġn in", + "Ġni n", + "Ġ USD", + "ĠU SD", + "ĠUS D", + "M ac", + "Ma c", + "_ Load", + "_L oad", + "Ġout comes", + "Ġoutcome s", + "_ socket", + "_s ocket", + "_sock et", + "_so cket", + "_soc ket", + "Ġ queries", + "Ġqu eries", + "Ġque ries", + "Ġquer ies", + "w m", + "5 92", + "59 2", + "Ġh itting", + "Ġhit ting", + "in ux", + "inu x", + "M ich", + "Mi ch", + "Mic h", + "u dge", + "ud ge", + "A TAB", + "AT AB", + "ATA B", + "Ġv ulnerable", + "Ġvulner able", + "ä ¾", + "Ġ portfolio", + "Ġport folio", + ": YES", + "ĉ map", + "ĉm ap", + "B ound", + "Bo und", + "Ġ iteration", + "Ġit eration", + "Ġiter ation", + "in cess", + "ince ss", + "inc ess", + "inces s", + "Ġ actors", + "Ġa ctors", + "Ġact ors", + "Ġac tors", + "Ġactor s", + "Ġ Qual", + "ĠQ ual", + "ĠQu al", + "_ clean", + "_c lean", + "_cl ean", + "ãĢij ãĢIJ", + "M SG", + "MS G", + "G reen", + "Gr een", + "Gre en", + "ĠOff icer", + "ĠOffice r", + "Ġsm oking", + "Ġsmo king", + "> ',", + ">' ,", + "Ġ Flo", + "ĠF lo", + "ĠFl o", + "++ ;", + "4 33", + "43 3", + "oly gon", + "Ġ bulk", + "Ġb ulk", + "Ġbu lk", + "Ġbul k", + "Ġd rama", + "Ġdr ama", + "Ġdram a", + "Ġdra ma", + "Ġ exceptions", + "Ġex ceptions", + "Ġexcept ions", + "Ġexception s", + "Ġexce ptions", + "o sed", + "os ed", + "ose d", + "Ġ+ čĊ", + "Ġ legacy", + "Ġleg acy", + "C V", + "Ġcontrib uted", + "Ġcontribute d", + "Ġ Terms", + "ĠTe rms", + "ĠTer ms", + "ĠTerm s", + "Ġ bt", + "Ġb t", + "4 34", + "43 4", + "Ġun tuk", + "Ġunt uk", + "Ġ alien", + "Ġa lien", + "Ġal ien", + "Ġali en", + "= ==Ċ", + "== =Ċ", + "=== Ċ", + "ĉ Vector", + "ĉV ector", + "ĉVec tor", + "Ġ ls", + "Ġl s", + "On line", + ". facebook", + ".f acebook", + ".face book", + "n umeric", + "num eric", + "nu meric", + "numer ic", + "ock ets", + "ocket s", + "A ut", + "Au t", + "b ury", + "bur y", + "bu ry", + "- redux", + "-re dux", + "-red ux", + "ĠRed istributions", + "ĠRedistribution s", + "GLOBAL S", + "urrenc ies", + "urr encies", + "Ġ tons", + "Ġt ons", + "Ġto ns", + "Ġton s", + "âĢĻ ,", + "Ġ ê", + "Ġà ª", + "( col", + "(c ol", + "(co l", + "Ġ Symbol", + "ĠS ymbol", + "ĠSym bol", + "Ġst ayed", + "Ġstay ed", + "Ġ ML", + "ĠM L", + "Ġm unicip", + "Ġmun icip", + "Ġ sexo", + "Ġs exo", + "Ġse xo", + "Ġsex o", + "S en", + "Se n", + "n r", + "Ġg ains", + "Ġgain s", + "Ġga ins", + "Ġshort ly", + ". Menu", + ".M enu", + ".Me nu", + "à ½", + "KN OWN", + "Ġ operators", + "Ġoper ators", + "Ġoperator s", + "Ġopera tors", + "- V", + "Ġ Patrick", + "ĠPat rick", + "ĠPatri ck", + "/ add", + "/a dd", + "/ad d", + "_ CO", + "_C O", + "i ration", + "ir ation", + "ira tion", + "( post", + "(p ost", + "(pos t", + "(po st", + "Post s", + "Pos ts", + "Po sts", + "/ _", + "Ġ plug", + "Ġp lug", + "Ġpl ug", + "Ġplu g", + "Ġintel lectual", + "Ġintellect ual", + "Ġme tab", + "Ġmet ab", + "Ġmeta b", + "Ġpregn ancy", + "ĠPrem ier", + "ĠPremi er", + "n m", + "Ġ prediction", + "Ġpred iction", + "Ġpredict ion", + "Ġpredic tion", + "6 06", + "60 6", + "ĠMin istry", + "ĠMini stry", + "ĠMinist ry", + "Th ree", + "Thr ee", + "val uate", + "valu ate", + "Ġ Mini", + "ĠM ini", + "ĠMin i", + "ĠMi ni", + "b u", + "о з", + "оР·", + "< ul", + " \";čĊ", + ">\" ;čĊ", + ">\"; čĊ", + "ĠS av", + "ĠSa v", + ". Bold", + ".B old", + "Ġen ables", + "Ġenable s", + "ĉ tmp", + "ĉt mp", + "Ġman ually", + "Ġmanual ly", + "ĠS qu", + "ĠSq u", + "use rid", + "user id", + ". function", + ".f unction", + ".func tion", + ".fun ction", + ". cache", + ".c ache", + ".ca che", + "L OPT", + "LO PT", + ". Services", + ".S ervices", + ".Service s", + "5 88", + "58 8", + "d dit", + "dd it", + "t im", + "ti m", + "< img", + " >>", + ">> >", + "st ation", + "stat ion", + "sta tion", + "l ore", + "lo re", + "lor e", + "a type", + "at ype", + "aty pe", + "i shop", + "is hop", + "ish op", + "/ ****************************************************************", + "/******************************** ********************************", + "/************************ ****************************************", + "/******************************************************** ********", + "/******** ********************************************************", + "/**************** ************************************************", + "/**************************************** ************************", + "/************************************************ ****************", + "5 21", + "52 1", + "Com boBox", + "Combo Box", + "Ġvac ation", + "Ġva cation", + "Ġinit iative", + "Ġiniti ative", + "Ġ defaultValue", + "Ġdefault Value", + "7 70", + "77 0", + "con cat", + "conc at", + "Ġ Kh", + "ĠK h", + "6 32", + "63 2", + "Ġ Welcome", + "ĠW elcome", + "ĠWel come", + "ized Name", + "M igration", + "Ġ gradient", + "Ġg radient", + "Ġgrad ient", + "H ot", + "Ho t", + "Ġhard ly", + "e lo", + "el o", + "Ġ Students", + "ĠSt udents", + "ĠStud ents", + "ĠStudent s", + "Ġl oose", + "Ġlo ose", + "Ġloos e", + "7 30", + "73 0", + "a tz", + "at z", + ". Send", + ".S end", + ".Se nd", + "' /", + "Ġ universal", + "Ġun iversal", + "Ġunivers al", + "Ġ enterprise", + "Ġenter prise", + "Ġ regex", + "Ġreg ex", + "Ġ visitor", + "Ġvis itor", + "Ġvisit or", + "Ġ Fly", + "ĠF ly", + "ĠFl y", + "S eq", + "Se q", + "ภĻ", + "Ġ Visual", + "ĠVis ual", + "Ġ libraries", + "Ġl ibraries", + "Ġlib raries", + "Ġlibr aries", + "at oes", + "ato es", + "P ayment", + "Pay ment", + "4 47", + "44 7", + "Ġ pent", + "Ġp ent", + "Ġpe nt", + "Ġpen t", + "Ġgather ed", + "VR TX", + "VRT X", + "Ġ DM", + "ĠD M", + "S plit", + "Sp lit", + "Spl it", + "Ġl etting", + "Ġlet ting", + "Ġlett ing", + "Ð Ŀ", + "_ errors", + "_error s", + "_err ors", + "_er rors", + "ep och", + "P ARAM", + "PA RAM", + "PAR AM", + "c u", + "ÑģÑĤ в", + "ol utions", + "olution s", + "olut ions", + "Ed iting", + "Edit ing", + "fo nts", + "font s", + "fon ts", + "Ġ allocated", + "Ġal located", + "Ġalloc ated", + "Ġallocate d", + "Ġ Based", + "ĠB ased", + "ĠBase d", + "ĠBa sed", + "ĠBas ed", + "( Y", + "Ġ Judge", + "ĠJ udge", + "ĠJud ge", + "ĠJu dge", + "Ġbr others", + "Ġbro thers", + "Ġbrother s", + "Ġbroth ers", + "F ILES", + "FILE S", + "FI LES", + "ç o", + "5 31", + "53 1", + "w b", + "_ PI", + "_P I", + "' ^", + "Ġ sword", + "Ġs word", + "Ġsw ord", + "Ġswo rd", + ". services", + ".s ervices", + ".service s", + ".serv ices", + "Ġ nl", + "Ġn l", + "T im", + "Ti m", + "i gg", + "ig g", + "ĠMo ore", + "ĠMoo re", + "ĠMoor e", + "Ġcrypt oc", + "Ġcrypto c", + "åĩ º", + "_ posts", + "_post s", + "_pos ts", + "_po sts", + "ot ate", + "ota te", + "? '", + ". ...ĊĊ", + ".. ..ĊĊ", + "... .ĊĊ", + ".... ĊĊ", + "....Ċ Ċ", + "Ġ kl", + "Ġk l", + "= \"$", + "=\" $", + "Ġde coration", + "Ġdec oration", + "Ġdecor ation", + "Ġdeco ration", + "Ạ¡", + "Ġ DIRECT", + "ĠD IRECT", + "ĠDI RECT", + "ĠDIR ECT", + "G UI", + "GU I", + ") =>{Ċ", + ")= >{Ċ", + ")=> {Ċ", + "Ġ newsletter", + "Ġnews letter", + "Ġpre cis", + "Ġprec is", + "( point", + "(p oint", + "(po int", + "Ġ Equipment", + "ĠE quipment", + "ĠEqu ipment", + "ĠEquip ment", + "u ty", + "ut y", + "Ġ Dave", + "ĠD ave", + "ĠDav e", + "ĠDa ve", + "Ġpart icipation", + "Ġpartic ipation", + "Ġparticip ation", + "u arios", + "ua rios", + "uario s", + "uar ios", + "x it", + "xi t", + ". As", + ".A s", + "E TER", + "ET ER", + "o rous", + "or ous", + "oro us", + "Ġ shield", + "Ġsh ield", + "[ ]>", + "[] >", + "il itary", + "ilit ary", + ". origin", + ".or igin", + ".orig in", + "Ġ promotion", + "Ġpro motion", + "Ġprom otion", + "Ġpromot ion", + "Ġpromo tion", + "U nt", + "Un t", + "Ġ ct", + "Ġc t", + "T RA", + "TR A", + "5 56", + "55 6", + "View Holder", + "Ġ sigma", + "Ġs igma", + "Ġsig ma", + "d elta", + "del ta", + "are house", + "con tract", + "cont ract", + "contr act", + "contra ct", + "( Vector", + "(V ector", + "(Vec tor", + "7 21", + "72 1", + "Ġcomp ete", + "Ġcompet e", + "/ form", + "/f orm", + "/ components", + "/com ponents", + "/component s", + "Ġ nr", + "Ġn r", + "ĠInd ones", + "ĠIndo nes", + "Ġ оÑĤ", + "Ġо ÑĤ", + "Ġ Volume", + "ĠV olume", + "ĠVol ume", + ". files", + ".f iles", + ".file s", + ".fi les", + ".fil es", + "( resp", + "(r esp", + "(res p", + "(re sp", + "/ models", + "/model s", + "/mod els", + "Ġ surf", + "Ġs urf", + "Ġsu rf", + "Ġsur f", + "st andard", + "stand ard", + "/ o", + "ĠXCT Assert", + "V ICES", + "VICE S", + "VI CES", + "VIC ES", + ". Code", + ".C ode", + ".Co de", + "S ED", + "SE D", + "Ġ activate", + "Ġact ivate", + "Ġactiv ate", + "D elta", + "Del ta", + "Ġlimit ation", + "Ġlim itation", + "r ij", + "ri j", + "Ġpregn ant", + "Ġpreg nant", + ": ^(", + ":^ (", + "Ġs our", + "Ġso ur", + "Ġsou r", + "p ie", + "pi e", + "8 03", + "80 3", + "Ġ expense", + "Ġexp ense", + "i cation", + "ic ation", + "ica tion", + "Ġ Large", + "ĠL arge", + "ĠLar ge", + "Ġ ±", + "Ġ ±", + "ĠB owl", + "ĠBo wl", + "ĠBow l", + "( models", + "(model s", + "(mod els", + "(mode ls", + "/ N", + "8 57", + "85 7", + "P a", + ". reload", + ".re load", + ".r eload", + ".rel oad", + "Ġwonder ing", + "4 62", + "46 2", + "Exec ution", + "ĉ ĠĠĠĠĠĠ", + "ĉĠĠĠ ĠĠĠ", + "ĉĠ ĠĠĠĠĠ", + "ĉĠĠ ĠĠĠĠ", + "ĉĠĠĠĠĠ Ġ", + "ĉĠĠĠĠ ĠĠ", + "Ġ Graphics", + "ĠG raphics", + "ĠGraph ics", + "ĠGraphic s", + "Ġ Contin", + "ĠCon tin", + "ĠCont in", + "_ job", + "_j ob", + "Ġ getName", + "Ġget Name", + "Ġ Magn", + "ĠM agn", + "ĠMag n", + "ĠMa gn", + "Ġ DWORD", + "ĠD WORD", + "ĠDW ORD", + "m ad", + "ma d", + "Ġ nh", + "Ġn h", + "f eatures", + "fe atures", + "feature s", + "feat ures", + "fea tures", + "} \");Ċ", + "}\" );Ċ", + "}\") ;Ċ", + "he ets", + "heet s", + "hee ts", + "( train", + "(t rain", + "(tr ain", + "z n", + "Ġrec ruit", + "Ġrecru it", + ". connection", + ".con nection", + ".connect ion", + ".conn ection", + "Ġbar rel", + "Ġbarr el", + "Ġ steam", + "Ġs team", + "Ġst eam", + "Ġste am", + "_ setting", + "_s etting", + "_set ting", + "Ġ angular", + "Ġang ular", + "ane ously", + "aneous ly", + "Ġ bil", + "Ġb il", + "Ġbi l", + "Ġ Norm", + "ĠN orm", + "ĠNo rm", + "ĠNor m", + "5 22", + "52 2", + "(! $", + "i bt", + "ib t", + "% (", + "Ġ posit", + "Ġp osit", + "Ġpos it", + "Ġpo sit", + "Ġposi t", + "Ġ Father", + "ĠF ather", + "ĠFa ther", + "ĠFat her", + "int endo", + "inte ndo", + "5 65", + "56 5", + "L ive", + "Li ve", + "Liv e", + "0 41", + "04 1", + "Ġ ports", + "Ġp orts", + "Ġport s", + "Ġpo rts", + "Ġpor ts", + "Ġm ej", + "Ġme j", + "Ġ landing", + "Ġl anding", + "Ġland ing", + "Ġlan ding", + "p onder", + "pon der", + "pond er", + "po nder", + "ponde r", + "Ġ cod", + "Ġc od", + "Ġco d", + "_ HEADER", + "_HE ADER", + "_HEAD ER", + ". Margin", + ".M argin", + ".Mar gin", + "Ġ balls", + "Ġb alls", + "Ġball s", + "Ġbal ls", + "Ġdisc ussions", + "Ġdiscuss ions", + "Ġdiscussion s", + "Ġ blend", + "Ġbl end", + "Ġble nd", + "H ex", + "He x", + "Ġfar mers", + "Ġfarm ers", + "Ġfarmer s", + "Ġmaint aining", + "Ġmaintain ing", + "Ġ ĠĠčĊ", + "ĠĠ ĠčĊ", + "ĠĠĠ čĊ", + "s yn", + "sy n", + "[ T", + "r us", + "ru s", + "4 39", + "43 9", + "uff ers", + "uf fers", + "uffer s", + "Ġ contributors", + "Ġcontrib utors", + "Ġcontributor s", + "_ sys", + "_s ys", + "_sy s", + ". Debug", + ".De bug", + "Ġ constructed", + "Ġconstruct ed", + "o mes", + "om es", + "ome s", + "? id", + "s lider", + "sl ider", + "slide r", + "Ġsup pliers", + "Ġsupplier s", + "Ġsuppl iers", + "Ġsupp liers", + "6 11", + "61 1", + "scribe r", + "scri ber", + "scr iber", + "p es", + "pe s", + "Ð ŀ", + "\" :čĊ", + "\": čĊ", + "\\ Controller", + ") )ĊĊĊ", + ")) ĊĊĊ", + "))Ċ ĊĊ", + "))ĊĊ Ċ", + "Ġ lua", + "Ġl ua", + "Ġlu a", + "M ulti", + "Mult i", + "Mul ti", + "E NS", + "EN S", + "S rc", + "Sr c", + "Ġ petition", + "Ġpet ition", + "Ġpetit ion", + "Ġ slave", + "Ġsl ave", + "Ġsla ve", + "Ġslav e", + "lo oking", + "look ing", + "loo king", + "V ERT", + "VER T", + "VE RT", + "ĉ vector", + "ĉv ector", + "ĉvec tor", + "S pecial", + "Sp ecial", + "Spec ial", + "Spe cial", + "h h", + "an ne", + "ann e", + "ĠN iger", + "ĠNi ger", + "/ views", + "/view s", + "z ing", + "zi ng", + "zin g", + "end ant", + "enda nt", + "< C", + "s peed", + "sp eed", + "spe ed", + "5 14", + "51 4", + "Ġ{ };ĊĊ", + "Ġ{} ;ĊĊ", + "Ġ{};Ċ Ċ", + "Ġ{}; ĊĊ", + "Begin Init", + "Ġf open", + "Ġfo pen", + "@ RequestMapping", + "End Init", + "Ġp unch", + "Ġpun ch", + "S ender", + "Se nder", + "Send er", + "Sen der", + "6 03", + "60 3", + "é Ķ", + "get Message", + "/ types", + "/t ypes", + "/type s", + ". PI", + ".P I", + "(' ');Ċ", + "oc used", + "ocus ed", + "ocu sed", + "( all", + "(a ll", + "(al l", + "Ġ dropdown", + "Ġd ropdown", + "Ġdrop down", + ") .__", + "). __", + ")._ _", + "Ġ Vin", + "ĠV in", + "ĠVi n", + ". ForeignKey", + ".Fore ignKey", + "6 12", + "61 2", + "ca nf", + "can f", + "o ured", + "ou red", + "our ed", + "oure d", + "Ġ Organization", + "ĠO rganization", + "ĠOrgan ization", + "Ġ а", + "ĠÐ °", + "Ġ Culture", + "ĠC ulture", + "ĠCult ure", + "ĠCul ture", + "( cls", + "(c ls", + "(cl s", + ", _", + "9 02", + "90 2", + "r gba", + "rg ba", + "rgb a", + "ìĿ ĺ", + ". dataGridView", + ".data GridView", + "Ġdo zen", + "Ġdoz en", + "ĠG es", + "ĠGe s", + "8 05", + "80 5", + "4 64", + "46 4", + "_ shared", + "_sh ared", + "_share d", + "_sha red", + "n ick", + "ni ck", + "nic k", + "Ġh osp", + "Ġho sp", + "Ġhos p", + "o meter", + "om eter", + "ome ter", + "omet er", + "4 95", + "49 5", + "Ġclaim ing", + "Ġcla iming", + "0 32", + "03 2", + "i bles", + "ib les", + "ible s", + "r ik", + "ri k", + "æĺ ¯", + "en ario", + "ena rio", + "Ġd engan", + "Ġden gan", + "o bb", + "ob b", + "m ont", + "mon t", + "mo nt", + "_ rank", + "_r ank", + "_ra nk", + "(' /',", + "('/ ',", + "Ġap olog", + "Ġapo log", + "P s", + "_ power", + "_p ower", + "_pow er", + "_po wer", + "ĠG ree", + "ĠGr ee", + "ĠGre e", + "Ġf ulfill", + "Ġful fill", + "Ġfulfil l", + "Ġ firebase", + "Ġf irebase", + "Ġfire base", + "9 10", + "91 0", + "Ġ fare", + "Ġf are", + "Ġfa re", + "Ġfar e", + "ĠH im", + "ĠHi m", + "Ġ bean", + "Ġb ean", + "Ġbe an", + "âĢ¦ .", + "Ġ SPI", + "ĠS PI", + "ĠSP I", + "_ RX", + "_R X", + "Ġper ception", + "Ġperce ption", + "Ġpercept ion", + "rel ative", + "com pile", + "comp ile", + "u um", + "uu m", + "u tos", + "ut os", + "uto s", + "a uc", + "au c", + "Ġ Ask", + "ĠA sk", + "ĠAs k", + "Ġ indicator", + "Ġind icator", + "Ġindic ator", + "Ġindica tor", + "/ th", + "/t h", + ".set String", + "ĠWis consin", + ". Domain", + ".D omain", + ".Do main", + ".Dom ain", + "Ġart ificial", + "De velop", + "Dev elop", + "Ġ Sarah", + "ĠS arah", + "ĠSar ah", + "ĠSa rah", + "ĠSara h", + "Ġ lying", + "Ġl ying", + "Ġly ing", + "( search", + "(s earch", + "(se arch", + "ĠEm pire", + "ĠEmp ire", + "ur ring", + "urr ing", + "æŶ éĹ´", + "= \"${", + "=\" ${", + "=\"$ {", + "Ġ getId", + "Ġget Id", + "Ġ Payment", + "ĠP ayment", + "ĠPay ment", + "t ransition", + "trans ition", + "Ġ ].", + "Ġ] .", + "i xin", + "ix in", + "V T", + "- select", + "-s elect", + "-se lect", + "Ġdemonstr ated", + "Ġdemonstrate d", + "Ġ lastName", + "Ġlast Name", + "em ployment", + "emp loyment", + "employ ment", + ". getProperty", + ".get Property", + ".getP roperty", + "Ġf ought", + "Ġfo ught", + "Ġfou ght", + "file Name", + "Ġ Pers", + "ĠP ers", + "ĠPer s", + "ĠPe rs", + "4 52", + "45 2", + "- card", + "-c ard", + "-car d", + "-ca rd", + "a str", + "as tr", + "ast r", + "at trs", + "att rs", + "attr s", + "Ġpro minent", + "Ġprom inent", + "Ġpromin ent", + "D esign", + "De sign", + "Des ign", + "anc ouver", + "ãģĹ ãģ", + "ar do", + "ard o", + "s ecret", + "se cret", + "sec ret", + "Ġ rag", + "Ġr ag", + "Ġra g", + "Ġpo ison", + "Ġpoi son", + "Ġpois on", + "- man", + "-m an", + ", omitempty", + "7 40", + "74 0", + "ĉ un", + "ĉu n", + "it zer", + "itz er", + "ĠCas ino", + "Ġ Ross", + "ĠR oss", + "ĠRo ss", + "ĠRos s", + "- foot", + "-f oot", + "( results", + "(result s", + "(res ults", + "P lan", + "Pl an", + "Ġl aser", + "Ġla ser", + "Ġlas er", + "ê¸ °", + "_ DR", + "_D R", + "5 23", + "52 3", + "F acebook", + "Face book", + "4 49", + "44 9", + "Ġ boards", + "Ġbo ards", + "Ġboard s", + "s ta", + "st a", + "] ],", + "]] ,", + "6 75", + "67 5", + "Ġ tiles", + "Ġt iles", + "Ġti les", + "Ġtile s", + "Ġtil es", + "S IZE", + "SI ZE", + "Ġ =~", + "Ġ= ~", + "9 70", + "97 0", + "Ġprem ier", + "Ġpremi er", + "o cab", + "oc ab", + "oca b", + "Ġ encoded", + "Ġen coded", + "Ġenc oded", + "Ġencode d", + "Ġ reserve", + "Ġre serve", + "Ġres erve", + "Ġreserv e", + "6 09", + "60 9", + "ĠAfghan istan", + "Ġ ListNode", + "ĠList Node", + "ur ls", + "url s", + "Ġ submission", + "Ġsub mission", + "Ġn eu", + "Ġne u", + "4 77", + "47 7", + "Ġ# +#", + "_ POST", + "_P OST", + "_PO ST", + "_POS T", + "Ġmo ist", + "Ġmoi st", + "Ġmois t", + "e lli", + "el li", + "ell i", + "ellig ent", + "elli gent", + ". alert", + ".al ert", + "ó d", + "b re", + "br e", + "Ġ Collect", + "ĠC ollect", + "ĠCol lect", + "ĠColl ect", + "Ġ graphic", + "Ġg raphic", + "Ġgraph ic", + "Ġgrap hic", + "Ġ longitude", + "Ġlong itude", + "Ġlongitud e", + "Ġ Provid", + "ĠPro vid", + "ĠPr ovid", + "ĠProv id", + "Ġ Calculate", + "ĠC alculate", + "ĠCal culate", + "ĠCalcul ate", + "ĠCalc ulate", + "x ffff", + "xf fff", + "xff ff", + "xfff f", + "c riteria", + "crit eria", + "Ġ waters", + "Ġw aters", + "Ġwater s", + "Ġwa ters", + "Ġwat ers", + "r ock", + "ro ck", + "roc k", + "lo quent", + "ĠT rib", + "ĠTr ib", + "ĠTri b", + "5 13", + "51 3", + "Ġ burst", + "Ġb urst", + "Ġbu rst", + "Ġbur st", + "Ġ suffix", + "Ġs uffix", + "Ġsuff ix", + "Ġsuf fix", + ". Extensions", + ".Ext ensions", + ".Extension s", + "is hes", + "ish es", + "i vel", + "iv el", + "ive l", + "Ġ LIKE", + "ĠL IKE", + "ĠLI KE", + "Ġ Getty", + "ĠG etty", + "ĠGet ty", + "ĠGe tty", + ".Action Event", + ".s lf", + ".sl f", + "Ġ HAL", + "ĠH AL", + "ĠHA L", + "u pal", + "up al", + "upa l", + "E AR", + "EA R", + "5 24", + "52 4", + "u di", + "ud i", + "_ timeout", + "_time out", + "U F", + "Ġ Singapore", + "ĠSing apore", + "ĠSingap ore", + "ĠAd vent", + "ĠAdv ent", + "_ interval", + "_int erval", + "_inter val", + "c haft", + "ch aft", + "cha ft", + "Ġ Emer", + "ĠE mer", + "ĠEm er", + "Ġ telephone", + "Ġtele phone", + "ĠTur k", + "ĠTu rk", + "_ interface", + "_inter face", + "Ġ Own", + "ĠO wn", + "ĠOw n", + "Ġencour aged", + "Ġencourage d", + "< Object", + "(", + "<> (", + "5 44", + "54 4", + ". Product", + ".Pro duct", + ".Produ ct", + "Form s", + "For ms", + "Fo rms", + "N EW", + "NE W", + "P ay", + "Pa y", + "ĉ boolean", + "ĉbool ean", + "_ contact", + "_cont act", + "Ġ Electric", + "ĠE lectric", + "ĠElect ric", + "s kip", + "sk ip", + "ski p", + "Ġw ur", + "Ġch ronic", + "Ġchron ic", + "Ġchr onic", + "_ driver", + "_d river", + "_dr iver", + "_drive r", + "9 40", + "94 0", + "Ġ Sab", + "ĠS ab", + "ĠSa b", + "Ġ Ult", + "ĠU lt", + "ĠUl t", + "Ġ Rad", + "ĠR ad", + "ĠRa d", + "ST ATUS", + "STAT US", + "Ġ Lewis", + "ĠL ewis", + "ĠLe wis", + "ĠLew is", + "O B", + "Ġgift s", + "Ġgi fts", + "Ġgif ts", + ". Rec", + ".R ec", + ".Re c", + "TR UE", + "Ġint ensity", + "Ġintens ity", + "M arker", + "Mark er", + "Mar ker", + ". compare", + ".com pare", + ".comp are", + "f fic", + "ff ic", + "ffi c", + "C ookie", + "Co okie", + "Cook ie", + "Ġ Baby", + "ĠB aby", + "ĠBa by", + "ĠBab y", + "Ġ BigDecimal", + "ĠB igDecimal", + "ĠBig Decimal", + "i let", + "il et", + "ile t", + "ĠHOLD ERS", + "ĠHOLDER S", + "Ġ Lady", + "ĠL ady", + "ĠLa dy", + "ĠLad y", + "Ġ lung", + "Ġl ung", + "Ġlu ng", + "Ġlun g", + "Ġ Alabama", + "ĠAl abama", + "Ġ dess", + "Ġd ess", + "Ġde ss", + "Ġdes s", + "` );Ċ", + "`) ;Ċ", + "Ġ Builder", + "ĠB uilder", + "ĠBuild er", + "ĠBu ilder", + "_ region", + "_reg ion", + "Ġ neutral", + "Ġne utral", + "Ġneut ral", + "Ġneutr al", + "9 09", + "90 9", + "B oth", + "Bo th", + "Bot h", + "Ġ hp", + "Ġh p", + "Ġ horn", + "Ġh orn", + "Ġhor n", + "Ġho rn", + "Ġ segments", + "Ġse gments", + "Ġseg ments", + "Ġsegment s", + "Ġ EC", + "ĠE C", + "\" =>\"", + "\"=> \"", + "( rec", + "(r ec", + "(re c", + "Ġ Pi", + "ĠP i", + "G M", + "Ġl aptop", + "Ġlap top", + "S calar", + "Sc alar", + "Scala r", + "4 63", + "46 3", + "i sd", + "is d", + "- dialog", + "-d ialog", + "-di alog", + "Ġ Anderson", + "ĠAnd erson", + "ĠAnders on", + "Ġmis takes", + "Ġmist akes", + "Ġmi stakes", + "Ġmistake s", + "7 08", + "70 8", + "Ġ Han", + "ĠH an", + "ĠHa n", + "j es", + "je s", + "est ination", + "esti nation", + "4 36", + "43 6", + "Ġprom ises", + "Ġpromise s", + "b id", + "bi d", + "Ġ Scient", + "ĠS cient", + "ĠSc ient", + "ĠSci ent", + "G IN", + "GI N", + "Ġ Performance", + "ĠPer formance", + "ĠPerform ance", + "b age", + "ba ge", + "bag e", + ". users", + ".user s", + ".use rs", + ".us ers", + "le ading", + "lead ing", + "lea ding", + "Ġ oral", + "Ġo ral", + "Ġor al", + "Ġora l", + "G raphics", + "Graph ics", + "Graphic s", + "4 88", + "48 8", + "_ PTR", + "_P TR", + "_PT R", + "5 18", + "51 8", + "h ang", + "ha ng", + "han g", + "Ġin ev", + "Ġi nev", + "Ġine v", + "p rocessing", + "process ing", + "F actor", + "Fact or", + "Fac tor", + "Fa ctor", + "Ġ NA", + "ĠN A", + "$ string", + "$s tring", + "$str ing", + "Ġ grounds", + "Ġgr ounds", + "Ġground s", + "Ġgro unds", + "Ġgrou nds", + ".Save Changes", + "c lock", + "cl ock", + "clo ck", + "9 41", + "94 1", + "cri pcion", + "Ġ Newton", + "ĠNew ton", + "g c", + ". includes", + ".in cludes", + ".include s", + "Ġ blast", + "Ġb last", + "Ġbl ast", + "Ġblas t", + "Ġbla st", + "Ġ' -'", + "Ġ'- '", + "Ġp uede", + "Ġpued e", + "Ġpu ede", + "4 69", + "46 9", + ". Session", + ".S ession", + "Ġ grep", + "Ġg rep", + "Ġgr ep", + "Ġgre p", + "_ final", + "_f inal", + "_fin al", + "Ġ Gay", + "ĠG ay", + "ĠGa y", + "Ġ Give", + "ĠG ive", + "ĠGi ve", + "i ri", + "ir i", + "- star", + "-s tar", + "-st ar", + "Ġ UIImage", + "ĠUI Image", + "_ epoch", + "_ep och", + "u bb", + "ub b", + "e nth", + "en th", + "ent h", + "Ġ elite", + "Ġe lite", + "Ġel ite", + "Ġelit e", + "Ġcampaign s", + "Ġ Porno", + "ĠP orno", + "ĠPorn o", + "ĠPor no", + "_ assign", + "_as sign", + "_ass ign", + "Prot ocol", + "Proto col", + "Ġ Being", + "ĠB eing", + "ĠBe ing", + "ĠBei ng", + "Ġ Airport", + "ĠAir port", + "Ġcon ventional", + "Ġconvent ional", + "Ġconvention al", + "Ġ Wat", + "ĠW at", + "ĠWa t", + "Ġ CI", + "ĠC I", + "E TA", + "ET A", + "Ġ Anthony", + "ĠAnth ony", + "Ġ tablet", + "Ġtable t", + "Ġtab let", + "( format", + "(form at", + "(for mat", + "Ġconsist ently", + "Ġconsistent ly", + "ĠI owa", + "ĠIo wa", + "4 74", + "47 4", + "Ġ avatar", + "Ġav atar", + "Ġava tar", + "0 27", + "02 7", + ". cursor", + ".c ursor", + "! [", + "Ġh anging", + "Ġhang ing", + "Ġhan ging", + "Ġhangi ng", + "H er", + "He r", + "S uch", + "Su ch", + "Suc h", + "' ;ĊĊĊ", + "';Ċ ĊĊ", + "';ĊĊ Ċ", + "'; ĊĊĊ", + "org eous", + "orge ous", + "( )==", + "() ==", + "Ġ viewModel", + "Ġview Model", + "Ġ ãĥ", + "Ġ els", + "Ġe ls", + "Ġel s", + "Ġ Agent", + "ĠA gent", + "ĠAg ent", + "ĠAge nt", + "F etch", + "a por", + "ap or", + "apo r", + "Ġ cx", + "Ġc x", + "p read", + "pr ead", + "pre ad", + "ĠP ier", + "ĠPi er", + "ĠPie r", + "o eff", + "oe ff", + "6 16", + "61 6", + "S n", + "8 90", + "89 0", + "Ġ Virtual", + "ĠV irtual", + "ĠVir tual", + "ĠVirt ual", + "A pr", + "Ap r", + ". White", + ".Wh ite", + "6 15", + "61 5", + "_ MOD", + "_M OD", + "_MO D", + "Ġ Points", + "ĠP oints", + "ĠPoint s", + "ĠPo ints", + "å¤ ±", + "Ġ genes", + "Ġg enes", + "Ġge nes", + "Ġgen es", + "Ġgene s", + "Ġ vendor", + "Ġv endor", + "Ġvend or", + "Ġmain stream", + "< src", + "Ċ", + "Ġ< >Ċ", + "Ġ<> Ċ", + "F ilename", + "File name", + "Fi lename", + "Fil ename", + "Ġs ne", + "Ġsn e", + "Ġ Football", + "ĠF ootball", + "ĠFoot ball", + "Ġr ival", + "Ġri val", + "Ġriv al", + "Ġdis aster", + "i onic", + "ion ic", + "io nic", + "ioni c", + "Ġ Damage", + "ĠD amage", + "ĠDa mage", + "ĠDam age", + ". Resource", + ".Re source", + ".Res ource", + "- en", + "-e n", + "Ġ Types", + "ĠT ypes", + "ĠType s", + "ĠTy pes", + "ĠTyp es", + "get String", + "( board", + "(b oard", + "Ġ bol", + "Ġb ol", + "Ġbo l", + "p lain", + "pl ain", + "pla in", + "z ym", + "zy m", + "ภ²", + "Ġ scanner", + "Ġsc anner", + "Ġscan ner", + "i lder", + "il der", + "ild er", + "ilde r", + "_ msgs", + "_msg s", + "_ms gs", + "æ ı", + "( intent", + "(int ent", + "(in tent", + "Ġ destruct", + "Ġd estruct", + "Ġde struct", + "Ġb ust", + "Ġbu st", + "Ġbus t", + "Ġ Employ", + "ĠE mploy", + "ĠEm ploy", + "ĠEmp loy", + "o ni", + "on i", + "Ġ UIViewController", + "ĠUI ViewController", + "ĠUIView Controller", + "Ġo dds", + "Ġodd s", + "Ġod ds", + "e arer", + "ear er", + "ea rer", + "Ge ometry", + "Geo metry", + "Geom etry", + "Ġ yii", + "Ġy ii", + "Ġyi i", + "_ EXPORT", + "_EX PORT", + "_EXP ORT", + "Ġ Attack", + "ĠAtt ack", + "Ġn iet", + "Ġnie t", + "Ġni et", + "Ġim pression", + "Ġimp ression", + "Ġimpress ion", + "Ġimpr ession", + "Ġ Gil", + "ĠG il", + "ĠGi l", + "_ prob", + "_p rob", + "_pro b", + "_pr ob", + "5 28", + "52 8", + "Ġ CF", + "ĠC F", + "Ġ Experience", + "ĠEx perience", + "/ plugins", + "/pl ugins", + "/plugin s", + ". Method", + ".M ethod", + "Ġbel iefs", + "Ġbelie fs", + "Ġbelief s", + "N ative", + "Nat ive", + "_ build", + "_b uild", + "Ġ vig", + "Ġv ig", + "Ġvi g", + "Ġr anks", + "Ġrank s", + "Ġran ks", + "cover ed", + "cov ered", + "7 05", + "70 5", + "s uch", + "su ch", + "G uard", + "Gu ard", + ". pack", + ".p ack", + ".pa ck", + "ad der", + "add er", + "8 09", + "80 9", + "i via", + "iv ia", + "ivi a", + "l ng", + "ln g", + "Ġ вÑĭ", + "Ġв Ñĭ", + "5 52", + "55 2", + "T imestamp", + "Time stamp", + "_ now", + "_n ow", + "_no w", + "Ġp oker", + "Ġpo ker", + "Ġpok er", + "Ġpoke r", + "Ġ unc", + "Ġu nc", + "Ġun c", + "Ġ shapes", + "Ġsh apes", + "Ġshape s", + "Ġsha pes", + "- types", + "-t ypes", + "-type s", + "_ period", + "_p eriod", + "_per iod", + "p k", + "Ġveter an", + "Ġ sono", + "Ġs ono", + "Ġso no", + "Ġson o", + "Ġ appointed", + "Ġapp ointed", + "Ġappoint ed", + "over flow", + ". driver", + ".d river", + ".dr iver", + ".drive r", + "_ cat", + "_c at", + "_ca t", + "u tt", + "ut t", + "p lant", + "pl ant", + "plan t", + "pla nt", + "i mb", + "im b", + "Ġ Accept", + "ĠAc cept", + "ĠAcc ept", + "Ġ concert", + "Ġcon cert", + "Ġconc ert", + "Ġconce rt", + "ĉ node", + "ĉn ode", + "ĉno de", + "ĉ z", + "? >čĊ", + "?> čĊ", + "Ġb anned", + "Ġban ned", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĉĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĉĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġt oxic", + "Ġto xic", + "Ġtox ic", + "Ġdis appe", + "Ġdisap pe", + "4 73", + "47 3", + "È Ľ", + "Ġg race", + "Ġgr ace", + "Ġgra ce", + "Ġgrac e", + "at eful", + "ate ful", + "Re ply", + "Rep ly", + "ĠC ruz", + "ĠCr uz", + "ĠCru z", + "4 86", + "48 6", + "Ġs crap", + "Ġsc rap", + "Ġscr ap", + "Ġ keywords", + "Ġkey words", + "Ġkeyword s", + "s imp", + "si mp", + "sim p", + "Ġmort gage", + "Ġcy ber", + "Ġ Execute", + "ĠEx ecute", + "ĠExec ute", + "Ġ latitude", + "Ġl atitude", + "Ġlat itude", + "i fu", + "if u", + ". COM", + ".C OM", + ".CO M", + "d bo", + "db o", + "Ġs orts", + "Ġso rts", + "Ġsort s", + "Ġsor ts", + "Ġ Gas", + "ĠG as", + "ĠGa s", + "om ial", + "omi al", + ". Local", + ".L ocal", + ".Lo cal", + "C ells", + "Cell s", + "Cel ls", + ". Replace", + ".Re place", + "String s", + "Str ings", + ". fit", + ".f it", + ".fi t", + "Ġ Third", + "ĠTh ird", + "ĠThi rd", + "% \",Ċ", + "%\" ,Ċ", + "%\", Ċ", + "Ġ {}\".", + "Ġ{ }\".", + "Ġ{} \".", + "Ġ Sony", + "ĠS ony", + "ĠSo ny", + "ĠSon y", + "Ġ [:", + "Ġ[ :", + "5 85", + "58 5", + "Ġf allen", + "Ġfa llen", + "Ġfall en", + "Ġfal len", + ". ')Ċ", + ".' )Ċ", + ".') Ċ", + "i nh", + "in h", + "Ġ MC", + "ĠM C", + "Ġ redis", + "Ġre dis", + "Ġr edis", + "Ġred is", + "C odes", + "Code s", + "Co des", + "Cod es", + "Ġ profiles", + "Ġpro files", + "Ġprof iles", + "Ġprofile s", + "Ġprofil es", + "h ook", + "ho ok", + "hoo k", + "Re ducer", + "Red ucer", + "Reduc er", + "Reduce r", + "_ FUNC", + "_F UNC", + "_FUN C", + "Ġ navigate", + "Ġn avigate", + "Ġnav igate", + "Ġnavig ate", + "st rlen", + "str len", + "Ġh orm", + "Ġhor m", + "Ġho rm", + "á ŀ", + "Ġ SR", + "ĠS R", + ". boot", + ".b oot", + ".bo ot", + "Ġ digest", + "Ġd igest", + "Ġdi gest", + "Ġdig est", + "ĉ header", + "ĉhead er", + ". findOne", + ".find One", + "æ ģ", + "Db Type", + "n ia", + "ni a", + "_ merge", + "_m erge", + "Ġd onne", + "Ġdon ne", + "Ġdonn e", + "/ Getty", + "/G etty", + "_ CHAR", + "_CH AR", + "Ġ bands", + "Ġb ands", + "Ġband s", + "Ġban ds", + "Ġba nds", + ". URL", + ".U RL", + ".UR L", + "art ial", + "arti al", + "Ġ freq", + "Ġf req", + "Ġfr eq", + "Ġfre q", + "Ġs ist", + "Ġsi st", + "Ġsis t", + "N g", + "Ġrender ing", + "Ġrend ering", + "\\ Core", + "\\C ore", + "Widget s", + "Ġ VA", + "ĠV A", + "Ġactiv ists", + "Ġactivist s", + "S te", + "St e", + "= _", + "a lla", + "al la", + "all a", + "St amp", + "Ġ loads", + "Ġlo ads", + "Ġload s", + "Ġloa ds", + "Ġ xx", + "Ġx x", + "Ġ Learning", + "ĠL earning", + "ĠLe arning", + "ĠLearn ing", + "ĠLear ning", + ". Mvc", + ".M vc", + "u ir", + "ui r", + "( \"$", + "(\" $", + "Ġ connecting", + "Ġconnect ing", + "Read Only", + "u ru", + "ur u", + "ĠE ag", + "ĠEa g", + "B IT", + "BI T", + "_ DEL", + "_D EL", + "_DE L", + "å §", + "arr ass", + "arra ss", + "ex ternal", + "ext ernal", + "extern al", + "exter nal", + "Ġ YOUR", + "ĠY OUR", + "ĠYOU R", + "ĠB rew", + "ĠBr ew", + "ĠBre w", + "Ġ Five", + "ĠF ive", + "ĠFi ve", + "Ġ resize", + "Ġre size", + "Ġres ize", + "i gid", + "ig id", + "igi d", + "e ration", + "er ation", + "era tion", + "6 53", + "65 3", + "Ġ Ñį", + "ĠÑ į", + "5 36", + "53 6", + "åĬ ł", + "0 39", + "03 9", + "Ġ Catch", + "ĠC atch", + "ĠCat ch", + "Ù ģ", + "Ġ Leon", + "ĠL eon", + "ĠLe on", + "ĠLeo n", + "a mil", + "am il", + "ami l", + ". Body", + ".B ody", + "C lip", + "Cl ip", + "Cli p", + "/ list", + "/l ist", + "/li st", + ". br", + ".b r", + "Edit Text", + "ĉ db", + "ĉd b", + ". Game", + ".G ame", + "( BuildContext", + "(Build Context", + "back end", + ". Red", + ".R ed", + ".Re d", + "f acebook", + "face book", + "5 29", + "52 9", + ". urls", + ".url s", + ".ur ls", + "m r", + "rol led", + "roll ed", + "- ------", + "-- -----", + "---- ---", + "--- ----", + "----- --", + "------ -", + "Ġint ervention", + "Ġinter vention", + "Ġinterven tion", + "Ġinterv ention", + "Ġret irement", + "Ġretire ment", + "Ġretir ement", + "Ġ Kit", + "ĠK it", + "ĠKi t", + "Ġ PRE", + "ĠP RE", + "ĠPR E", + "Upper Case", + "Ġ Socket", + "ĠS ocket", + "ĠSo cket", + "ĠSoc ket", + "Ġ :-", + "Ġ: -", + "Ġstud ying", + "Ġstudy ing", + "Ġ Metro", + "ĠM etro", + "ĠMe tro", + "ĠMet ro", + "ar ded", + "ard ed", + "arde d", + "Ġcon versations", + "Ġconvers ations", + "Ġconversation s", + "C alled", + "Call ed", + "Cal led", + "Ġex amine", + "Ġexam ine", + "ert ificate", + ". gz", + ".g z", + "- responsive", + "-res ponsive", + "Ġ refund", + "Ġre fund", + "Ġref und", + "_ network", + "_n etwork", + "_net work", + "0 26", + "02 6", + "all owed", + "allow ed", + "allo wed", + "em pt", + "emp t", + "Ġme als", + "Ġmeal s", + "C ategories", + "Ġtravel ing", + "Ġtrav eling", + "Ġ kg", + "Ġk g", + "Ġsh ame", + "Ġsha me", + "Ġsham e", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġexplicit ly", + "Ġmath ematic", + "Ġ Suite", + "ĠS uite", + "ĠSu ite", + "ĠSuit e", + "Ġ RGB", + "ĠR GB", + "ĠRG B", + "****** /", + "***** */", + "Ġm ixture", + "Ġmix ture", + "l earning", + "le arning", + "lear ning", + "learn ing", + ". template", + ".t emplate", + ".temp late", + ".tem plate", + "at ts", + "att s", + "w x", + "ĉ ctx", + "ĉc tx", + "ĉct x", + ". properties", + ".p roperties", + ".prop erties", + "Ġdr inks", + "Ġdrink s", + "Ġ Either", + "ĠE ither", + "set Text", + ". getData", + ".get Data", + ".getD ata", + ". zip", + ".z ip", + "Ġreve als", + "Ġreveal s", + "< table", + ".Ċ", + "/> .Ċ", + "/>. Ċ", + "Ġr anked", + "Ġrank ed", + "Ġran ked", + "_ impl", + "_i mpl", + "_im pl", + "_imp l", + "Ġ Handles", + "ĠH andles", + "ĠHand les", + "ĠHandle s", + "Ġhost ed", + "Ġho sted", + "Ġhos ted", + "Ġ updating", + "Ġup dating", + "Ġupd ating", + "al bum", + "é Ŀ", + "Ġ shader", + "Ġsh ader", + "Ġsha der", + "Ġshade r", + "Ed itors", + "Edit ors", + "Editor s", + "- round", + "-r ound", + "-ro und", + "[ ]{", + "[] {", + "Ġ sep", + "Ġs ep", + "Ġse p", + "Ġ Hi", + "ĠH i", + "T EM", + "TE M", + "look up", + "loo kup", + ". man", + ".m an", + ".ma n", + "_ INPUT", + "_IN PUT", + "Ġthreat ened", + "Ġthreaten ed", + "_ IMPORT", + "_IM PORT", + "_IMP ORT", + "Ġ drops", + "Ġd rops", + "Ġdr ops", + "Ġdrop s", + "Ġdro ps", + "r uit", + "ru it", + "s id", + "si d", + "b oth", + "bo th", + "bot h", + "Ġ Excel", + "ĠEx cel", + "ĠExc el", + "Ġ jer", + "Ġj er", + "Ġje r", + "ord inary", + "ordin ary", + "е й", + "еР¹", + "V IEW", + "VI EW", + "re ply", + "rep ly", + "Ġ ):Ċ", + "Ġ) :Ċ", + "Ġ): Ċ", + "col ors", + "color s", + "colo rs", + "ver ified", + "_ Tr", + "_T r", + "_ parse", + "_p arse", + "_par se", + "_pars e", + "Ġcon gress", + "Ġcongr ess", + "Ġcong ress", + "6 17", + "61 7", + "P romise", + "Pro mise", + "Prom ise", + "i nts", + "in ts", + "int s", + "Ġ Mother", + "ĠM other", + "ĠMo ther", + "ĠMot her", + ". Api", + ".A pi", + ".Ap i", + "Ġ Duration", + "ĠD uration", + "ĠDu ration", + "ĠDur ation", + "Ġ firstName", + "Ġfirst Name", + "inherit doc", + "ĠM ars", + "ĠMar s", + "ĠMa rs", + "Ġ apr", + "Ġa pr", + "Ġap r", + "O DY", + "OD Y", + "Ġvis its", + "Ġvisit s", + "6 31", + "63 1", + "Ġhe aling", + "Ġheal ing", + "let ters", + "letter s", + "lette rs", + "lett ers", + ") ));čĊ", + ")) );čĊ", + "))) ;čĊ", + "))); čĊ", + "f uture", + "fu ture", + ". Framework", + ".F ramework", + ".Frame work", + "Ġk iss", + "Ġki ss", + "Ġinv olve", + "Ġinvol ve", + "Ġ silent", + "Ġs ilent", + "Ġsil ent", + "ad ows", + "ado ws", + "adow s", + "Ġany body", + "s ch", + "sc h", + "6 90", + "69 0", + "Ġsol ely", + "Ġsole ly", + "- img", + "-i mg", + "-im g", + "Ġ propri", + "Ġp ropri", + "Ġpro pri", + "Ġprop ri", + "Ġin struct", + "Ġinstr uct", + "Ġ licenses", + "Ġlicense s", + "Ġlic enses", + "Ġlicens es", + "Ġ meth", + "Ġm eth", + "Ġme th", + "Ġmet h", + "Ġcon dem", + "Ġcond em", + "Ġ Domain", + "ĠD omain", + "ĠDo main", + "ĠDom ain", + "ĠH arris", + "ĠHar ris", + "ĠHarr is", + "Ġs Ã¥", + "CE PT", + "B atch", + "Bat ch", + "@ extends", + "ĠCONTR IBUT", + ". DataFrame", + ".Data Frame", + "4 72", + "47 2", + "_ packet", + "_p acket", + "_pack et", + "_pa cket", + "re cision", + "rec ision", + "Ġf ocusing", + "Ġfocus ing", + "Ġfoc using", + ". ht", + ".h t", + "__ \":Ċ", + ": Get", + "Ġ KC", + "ĠK C", + "Ġp assage", + "Ġpass age", + "Ġpas sage", + "S egment", + "Se gment", + "Seg ment", + "_ center", + "_c enter", + "_cent er", + "-z A", + "_ BL", + "_B L", + "Ġcon vin", + "Ġconv in", + "Ġ classified", + "Ġclass ified", + "Ġ NSMutable", + "ĠNS Mutable", + "_ ap", + "_a p", + "t ile", + "til e", + "ti le", + "Rect angle", + "4 92", + "49 2", + "( nums", + "(n ums", + "(num s", + "v ens", + "ve ns", + "ven s", + "Ġ UIButton", + "ĠUI Button", + "ĠUIB utton", + "ĠF eder", + "ĠFe der", + "ĠFed er", + "a mo", + "am o", + "Ġ outline", + "Ġout line", + "Ġ Parser", + "ĠP arser", + "ĠPar ser", + "ĠParse r", + "ĠPars er", + "Ġ âī", + "Ġâ ī", + "Ġ Works", + "ĠW orks", + "ĠWork s", + "ĠWor ks", + ". Schema", + ".S chema", + "Ġeng ines", + "Ġengine s", + "6 37", + "63 7", + "5 63", + "56 3", + "_ common", + "_com mon", + "_comm on", + "5 42", + "54 2", + "_ old", + "_o ld", + "Ġset ContentView", + "ĠsetContent View", + "Ġ ///<", + "Ġ// /<", + "Ġ/// <", + "Ġ BT", + "ĠB T", + "f m", + "Ġd ivers", + "Ġdi vers", + "Ġdiv ers", + "Ġdive rs", + "Ġdiver s", + "_ weights", + "_weight s", + "_we ights", + "e mark", + "em ark", + "ema rk", + "Ġ ACT", + "ĠA CT", + "ĠAC T", + "Ġpro portion", + "Ġprop ortion", + "Ġproport ion", + "Ġpropor tion", + "over lay", + ". dirname", + ".dir name", + "Ġ Git", + "ĠG it", + "ĠGi t", + "_ REFERENCE", + "_REF ERENCE", + "_REFER ENCE", + "< >", + "l b", + "_ rule", + "_r ule", + "_ru le", + "è´ ¥", + "Ġ Putin", + "ĠP utin", + "ĠPut in", + "ĠPu tin", + "Ġsleep ing", + "Ġsle eping", + "Ġslee ping", + "( ):čĊ", + "() :čĊ", + "(): čĊ", + "Ġ preserve", + "Ġp reserve", + "Ġpre serve", + "Ġpres erve", + "Ġpar liament", + "Ġ Looking", + "ĠLo oking", + "ĠLook ing", + "Ġp icking", + "Ġpick ing", + "Ġpic king", + "Ġ Dispatch", + "ĠDis patch", + "ĠDisp atch", + "Ġs lip", + "Ġsl ip", + "ë ĵ", + "ĠL yn", + "ĠLy n", + "_ signal", + "_s ignal", + "_sign al", + "_sig nal", + "config uration", + "ĠP itt", + "ĠPi tt", + "ĠPit t", + "4 91", + "49 1", + "a den", + "ad en", + "ade n", + "pro cedure", + "Ġenthus i", + "Ġenth usi", + "f ight", + "fig ht", + "fi ght", + "Ġ Consider", + "ĠCons ider", + "Ġt orn", + "Ġto rn", + "Ġtor n", + "Conn ected", + "Connect ed", + ". cos", + ".c os", + ".co s", + "_ groups", + "_g roups", + "_group s", + "Ġ Think", + "ĠTh ink", + "ĠThi nk", + "ĠThin k", + "Ġdel iber", + "Ġre sid", + "Ġres id", + "work ing", + "wor king", + ". columns", + ".column s", + "Ġ Called", + "ĠC alled", + "ĠCal led", + "ĠCall ed", + "Ġ eslint", + "Ġes lint", + "Ġesl int", + "> \",", + ">\" ,", + "_ DOWN", + "_D OWN", + "_DO WN", + "h ist", + "hi st", + "his t", + "Ġ Advanced", + "ĠAd vanced", + "ĠAdv anced", + "ĠAdvance d", + "Ġre wards", + "Ġreward s", + "Ġrew ards", + "a ctors", + "act ors", + "ac tors", + "actor s", + "Ġsil ence", + "4 79", + "47 9", + "Ġm yth", + "Ġmy th", + "Ġn eur", + "Ġne ur", + "Ġneu r", + "5 19", + "51 9", + "Ġ auction", + "Ġa uction", + "Ġau ction", + "Ġauc tion", + ". GetString", + ".Get String", + "e ks", + "ek s", + "( project", + "(pro ject", + "(proj ect", + "5 98", + "59 8", + "ĉ msg", + "ĉm sg", + "ĉms g", + "ĉ output", + "ĉout put", + "Ġcomplaint s", + "Ġcomplain ts", + "5 51", + "55 1", + ", S", + "Ġ tbl", + "Ġt bl", + "Ġtb l", + "Ġ ,ĊĊ", + "Ġ, ĊĊ", + "Ġ,Ċ Ċ", + "r iors", + "ri ors", + "rior s", + "rio rs", + "ah ren", + "ahr en", + "Ġlaw yers", + "Ġlawy ers", + "Ġlawyer s", + "re dux", + "red ux", + "_ symbol", + "_s ymbol", + "_sym bol", + "o ffee", + "of fee", + "off ee", + "_ RESULT", + "_RES ULT", + "( Name", + "(N ame", + "U TC", + "UT C", + ". currentTime", + ".current Time", + "Ġorgan is", + ". arg", + ".a rg", + ".ar g", + "5 33", + "53 3", + "Ġmin im", + "Ġmi nim", + "Ġmini m", + "w ick", + "wi ck", + "Ġrece ives", + "Ġreceive s", + "B alance", + "Bal ance", + "Ġspe aks", + "Ġspeak s", + "Ġ Days", + "ĠD ays", + "ĠDay s", + "ĠDa ys", + "Ġ Below", + "ĠB elow", + "ĠBe low", + "ĠBel ow", + "4 83", + "48 3", + "t ipo", + "ti po", + "tip o", + "P resent", + "Pre sent", + "Pres ent", + "Ġre serv", + "Ġres erv", + "h p", + "Ġ rit", + "Ġr it", + "Ġri t", + "_ RIGHT", + "_R IGHT", + "- -)", + "-- )", + "Ġchair man", + "7 81", + "78 1", + "D IS", + "DI S", + "Ġ BOOST", + "ĠBO OST", + "Ġex periments", + "Ġexper iments", + "Ġexperi ments", + "Ġexperiment s", + "6 87", + "68 7", + "_ _);Ċ", + "__ );Ċ", + "__) ;Ċ", + "__); Ċ", + "Ġ stamp", + "Ġst amp", + "Ġsta mp", + "Ġf ert", + "Ġfe rt", + "Ġfer t", + "Ġf ond", + "Ġfo nd", + "Ġfon d", + "T er", + "Te r", + "el ve", + "u ren", + "ur en", + "ure n", + "+ i", + "end ency", + "ende ncy", + "enden cy", + "Ġvirtual ly", + "Ġvirt ually", + ". ..\"", + ".. .\"", + "... \"", + "ï½ ŀ", + "9 25", + "92 5", + "- cent", + "-c ent", + "-ce nt", + "_ unique", + "_un ique", + "Ġ pricing", + "Ġp ricing", + "Ġpr icing", + "Ġpri cing", + "m ic", + "mi c", + "R ESH", + "RE SH", + "RES H", + "Ġ :::", + "Ġ: ::", + "Ġ:: :", + "Ġ annotation", + "Ġan notation", + "Ġann otation", + "Ġannot ation", + "Ġ Circle", + "ĠC ircle", + "ĠCirc le", + "ĠCir cle", + "ong odb", + "ongo db", + "i tas", + "it as", + "ita s", + "Ġ %(", + "Ġ% (", + "( component", + "(com ponent", + "(comp onent", + "Ġ об", + "Ġо б", + "( port", + "(p ort", + "(po rt", + "- hour", + "-h our", + ". obj", + ".o bj", + ".ob j", + "L BL", + "LB L", + "Ġ jury", + "Ġj ury", + "Ġju ry", + "Ġjur y", + "G BT", + "GB T", + "Ġ spy", + "Ġs py", + "Ġsp y", + "Ġ Professional", + "ĠProf essional", + "ĠProfession al", + "Ġ\" \";ĊĊ", + "Ġ\"\" ;ĊĊ", + "Ġ\"\";Ċ Ċ", + "Ġ\"\"; ĊĊ", + "Ġstr iking", + "Ġstri king", + "Ġd iscrimination", + "Ġdiscrim ination", + "Ġdiscrimin ation", + "Ġp ays", + "Ġpay s", + "Ġpa ys", + "9 37", + "93 7", + "l ict", + "lic t", + "li ct", + "en tes", + "ent es", + "ente s", + "Ġth rowing", + "Ġthrow ing", + "Ġthr owing", + "Ġthro wing", + "Ġ Plugin", + "ĠPl ugin", + "ĠPlug in", + "( def", + "(d ef", + "(de f", + "Ġ RuntimeException", + "ĠRuntime Exception", + "Ġ Migration", + "ĠM igration", + "ĠMig ration", + "5 99", + "59 9", + "Ġ dic", + "Ġd ic", + "Ġdi c", + "b ag", + "ba g", + "o nia", + "on ia", + "oni a", + "Ġcor ruption", + "Ġcorrupt ion", + "7 04", + "70 4", + "( Map", + "(M ap", + "Ġp rz", + "Ġpr z", + ". dto", + ".d to", + ".dt o", + "Ġac quire", + "Ġacqu ire", + "State ToProps", + "Ġl oving", + "Ġlo ving", + "Ġlov ing", + "о ж", + "оР¶", + "_ pattern", + "_p attern", + "_pat tern", + "Ġem otions", + "Ġemot ions", + "Ġemotion s", + "Ġ publisher", + "Ġp ublisher", + "Ġpublish er", + "Ġpubli sher", + "_ be", + "_b e", + "Ġc ouples", + "Ġco uples", + "Ġcou ples", + "Ġcouple s", + "Ġcoup les", + "4 98", + "49 8", + "o j", + "Ġ Chart", + "ĠC hart", + "ĠCh art", + "ĠChar t", + "ĠCha rt", + "Ġt rop", + "Ġtr op", + "Ġtro p", + ". tool", + ".t ool", + ".to ol", + "Ġestablish ment", + "Ġ dol", + "Ġd ol", + "Ġdo l", + "6 54", + "65 4", + "Ġ tower", + "Ġt ower", + "Ġto wer", + "Ġtow er", + "Ġ lane", + "Ġl ane", + "Ġla ne", + "Ġlan e", + "ĠSy dney", + "Ġf illing", + "Ġfil ling", + "Ġfill ing", + "claim ed", + "cla imed", + "6 44", + "64 4", + "Ġdialog ue", + "Ġdia logue", + "Ġdial ogue", + "Ġcon vention", + "Ġconv ention", + "Ġconven tion", + "Ġconvent ion", + "bo oking", + "book ing", + "boo king", + "par ency", + "pare ncy", + "paren cy", + "æ ±", + "Ġ Generic", + "ĠG eneric", + "ĠGener ic", + "ĠGen eric", + "ĠGene ric", + "7 18", + "71 8", + "\\ Schema", + "\\S chema", + "4 82", + "48 2", + "6 18", + "61 8", + "Ġ ranges", + "Ġr anges", + "Ġrange s", + "Ġran ges", + "Ġrang es", + "/ ch", + "/c h", + "Ġ panels", + "Ġpanel s", + "Ġpa nels", + "Ġpan els", + "Ġpane ls", + "Ġr uled", + "Ġrule d", + "Ġru led", + "çĶ Ł", + ". ts", + ".t s", + "_ sets", + "_s ets", + "_set s", + "_se ts", + "Ġ cleanup", + "Ġc leanup", + "Ġclean up", + "Pre vious", + "Prev ious", + "Ġ Animal", + "ĠAn imal", + "ĠAnim al", + "6 07", + "60 7", + "( $(", + "($ (", + "ĠA ve", + "ĠAv e", + "ol lar", + "oll ar", + "olla r", + "0 28", + "02 8", + "_ eval", + "_e val", + "_ev al", + "ĉ Name", + "ĉN ame", + "( tree", + "(t ree", + "(tr ee", + "Ġ \"]", + "Ġ\" ]", + "5 71", + "57 1", + "Ġdu ties", + "Ġdut ies", + "= '/", + "=' /", + "Click ed", + "Cl icked", + "Ġdiffer ently", + "Ġdifferent ly", + "Ġ Clark", + "ĠCl ark", + "ĠClar k", + "ĠCla rk", + "Ġ dit", + "Ġd it", + "Ġdi t", + "olog ists", + "ologist s", + "ologi sts", + "Ġsy nd", + "Ġsyn d", + "Ġs ends", + "Ġse nds", + "Ġsend s", + "Ġsen ds", + "- known", + "-k nown", + "-know n", + "k b", + "Ġ Modal", + "ĠM odal", + "ĠMod al", + "ĠMo dal", + "it ative", + "itat ive", + "Ġr acing", + "Ġrac ing", + "Ġra cing", + "Ġhigh lights", + "Ġhighlight s", + "Ġ Simon", + "ĠS imon", + "ĠSim on", + "ĠSi mon", + "Ġ Captain", + "ĠCap tain", + "ĠCapt ain", + "ä¿ ¡", + "Ġ CB", + "ĠC B", + "con tin", + "cont in", + "conti n", + "a ran", + "ar an", + "ara n", + "Ġ physics", + "Ġph ysics", + "Ġphys ics", + "Ġphysic s", + "r etty", + "re tty", + "ret ty", + "rett y", + "e tal", + "et al", + "eta l", + ". md", + ".m d", + "ax ios", + "Ġspe akers", + "Ġspeak ers", + "Ġspeaker s", + "Ġ prep", + "Ġp rep", + "Ġpr ep", + "Ġpre p", + "Ġaw arded", + "Ġaward ed", + "ì§ Ģ", + "Ġ Corn", + "ĠC orn", + "ĠCo rn", + "ĠCor n", + "Ġ Nature", + "ĠN ature", + "ĠNa ture", + "ĠNat ure", + "ĠNatur e", + "UD IO", + "7 37", + "73 7", + "Ġ proj", + "Ġp roj", + "Ġpro j", + "Ġpr oj", + "- pre", + "-p re", + "-pr e", + "[ u", + "F eatures", + "Fe atures", + "Feature s", + "Feat ures", + "Ġ isEqual", + "Ġis Equal", + "B inary", + "Bin ary", + "s ig", + "si g", + "Ġcon fusion", + "Ġconf usion", + "5 46", + "54 6", + "5 68", + "56 8", + "Ġ Hat", + "ĠH at", + "ĠHa t", + "Ġkt ó", + ". configure", + ".con figure", + ".config ure", + ".conf igure", + "M ON", + "MO N", + "4 94", + "49 4", + "/ edit", + "/e dit", + "_ Add", + "_A dd", + "_Ad d", + ", true", + ",tr ue", + "5 41", + "54 1", + "Ġ cli", + "Ġc li", + "Ġcl i", + "Error Message", + "- loader", + "-l oader", + "-lo ader", + "-load er", + "Dim ensions", + "Dimension s", + "ulti ply", + "ultip ly", + "Ġ {!!", + "Ġ{ !!", + "Ġ{! !", + "Ġ SqlCommand", + "ĠSql Command", + "Ġ spoken", + "Ġsp oken", + "Ġspoke n", + "Ġspo ken", + "Ġ pics", + "Ġp ics", + "Ġpi cs", + "Ġpic s", + "Ġ toy", + "Ġt oy", + "Ġto y", + "( Key", + "(K ey", + "Ġ Loop", + "ĠL oop", + "ĠLo op", + "Ø ¨", + "E ATURE", + "EA TURE", + "in ction", + "inc tion", + "inct ion", + "_ setup", + "_set up", + "w rapper", + "wrap per", + "wr apper", + "Ġt ong", + "Ġto ng", + "Ġton g", + "c ular", + "cul ar", + "cu lar", + "O pt", + "Op t", + ". Pl", + ".P l", + "= \",", + "=\" ,", + "( length", + "(l ength", + "(len gth", + "(le ngth", + "u mn", + "um n", + "Ġ chrom", + "Ġch rom", + "Ġchr om", + "Ġs event", + "Ġse vent", + "Ġseven t", + "Ġsev ent", + "Ġ IllegalArgumentException", + "ĠIl legalArgumentException", + "ĠIllegal ArgumentException", + "4 78", + "47 8", + "ĉ start", + "ĉst art", + "Ġbe gun", + "Ġbeg un", + "CE PTION", + "CEPT ION", + "d ataset", + "data set", + "dat aset", + "datas et", + "8 25", + "82 5", + "Ġ Failed", + "ĠF ailed", + "ĠFa iled", + "ĠFail ed", + "c ols", + "co ls", + "col s", + "4 59", + "45 9", + "Ġk nee", + "Ġkn ee", + "Ġkne e", + "i more", + "im ore", + "imo re", + ". splice", + ".sp lice", + "s hell", + "sh ell", + "she ll", + "ig gers", + "igger s", + "igg ers", + "Ġ themes", + "Ġth emes", + "Ġthe mes", + "Ġthem es", + "Ġtheme s", + "9 95", + "99 5", + "Ġ DJ", + "ĠD J", + "Ġ Assistant", + "ĠAss istant", + "ĠAssist ant", + "- $", + "M aybe", + "May be", + "Ġ ordering", + "Ġorder ing", + "Ġord ering", + "ĠInt elligence", + "ĠIntelli gence", + "ĠMass achusetts", + "Ġf ailing", + "Ġfa iling", + "Ġfail ing", + "el son", + "els on", + "G reat", + "Gr eat", + "Gre at", + "= i", + ". rest", + ".re st", + ".r est", + ".res t", + "Ġ invite", + "Ġinv ite", + "Ġinvit e", + "- disable", + "-d isable", + "-dis able", + ". GroupBox", + ".Group Box", + "âĢĻ est", + "âĢĻe st", + "âĢĻes t", + "Ġt ackle", + "Ġtack le", + "Ġtac kle", + "g v", + "et ter", + "ette r", + "ett er", + "Ġ ),čĊ", + "Ġ) ,čĊ", + "Ġ), čĊ", + "_ rules", + "_r ules", + "_rule s", + "_ru les", + ". warn", + ".w arn", + "function s", + "fun ctions", + "ĠChrist ians", + "ĠChristian s", + "Ġb acked", + "Ġback ed", + "Ġbac ked", + "Ġ slider", + "Ġs lider", + "Ġsl ider", + "Ġslide r", + "Ġslid er", + "Ġenjoy ing", + "Ġenjo ying", + "n est", + "ne st", + "nes t", + "Ġh ij", + "Ġhi j", + "_ ms", + "_m s", + "/ /*", + "// *", + "An notations", + "Annotation s", + "Ġ Variables", + "ĠVariable s", + "ĠVari ables", + "< V", + "( server", + "(s erver", + "(serv er", + "Ġ Oracle", + "ĠOr acle", + "e lements", + "el ements", + "element s", + "ele ments", + "elem ents", + "Ġ organisation", + "Ġorgan isation", + "Ġorganis ation", + "_ pointer", + "_point er", + "_po inter", + "Ġ Headers", + "ĠHe aders", + "ĠHead ers", + "ĠHeader s", + "[ d", + "Ġ deadline", + "Ġdead line", + "i ssa", + "is sa", + "iss a", + "Ġ knife", + "Ġkn ife", + "Ġ NASA", + "ĠN ASA", + "ĠNAS A", + "ĠNA SA", + "Ġ Height", + "ĠH eight", + "ĠHe ight", + "7 84", + "78 4", + "Ġ Async", + "ĠA sync", + "ĠAs ync", + "Ġ venue", + "Ġven ue", + ". dom", + ".d om", + ".do m", + "bour ne", + "bou rne", + "ĠH awai", + "ĠHaw ai", + "Ġ memo", + "Ġm emo", + "Ġme mo", + "Ġmem o", + "i ctions", + "ict ions", + "iction s", + "Ġsur veillance", + "Ġsurve illance", + "o mi", + "om i", + "/ assets", + "/as sets", + "5 87", + "58 7", + "Ġ edu", + "Ġe du", + "Ġed u", + "Ä Ľ", + "Ġr oster", + "Ġro ster", + "Ġros ter", + "Ġrost er", + "Ġh ired", + "Ġhi red", + "Ġhire d", + "Ġ Tok", + "ĠT ok", + "ĠTo k", + "Ġ placement", + "Ġpl acement", + "Ġplace ment", + "Ġplac ement", + "ur ations", + "uration s", + "urat ions", + "Ġ setState", + "Ġset State", + "ĠMag azine", + "Ġhor ror", + "Ġho rror", + "Ġhorr or", + "T ry", + "Tr y", + "Ġ lag", + "Ġl ag", + "Ġla g", + "Ġ Everyone", + "ĠEvery one", + "t hur", + "th ur", + ") );čĊčĊ", + ")) ;čĊčĊ", + "));čĊ čĊ", + ")); čĊčĊ", + ". return", + ".re turn", + ".r eturn", + ".ret urn", + "Ġsy mp", + "Ġsym p", + "âĸĪ âĸĪ", + "Ġn ights", + "Ġnight s", + "work er", + "wor ker", + "Ġ ale", + "Ġa le", + "Ġal e", + "ennes see", + ". step", + ".s tep", + ".st ep", + "Ġs ynchronized", + "Ġsynchron ized", + "Ġsynchronize d", + "4 87", + "48 7", + "o uri", + "ou ri", + "our i", + "D oes", + "Do es", + ". change", + ".ch ange", + "f on", + "fo n", + ". setBackground", + ".set Background", + "ir cular", + "irc ular", + "4 76", + "47 6", + "+ -", + "ĠC IA", + "ĠCI A", + "7 29", + "72 9", + "Ġ Jane", + "ĠJ ane", + "ĠJan e", + "ĠJa ne", + "Ġ Similar", + "ĠS imilar", + "ĠSim ilar", + "- I", + "level and", + "lev eland", + "Ġpro spect", + "Ġpros pect", + "_ found", + "_f ound", + "ĉ color", + "ĉc olor", + "ĉcol or", + ".D iagnostics", + ".Di agnostics", + "Ġ announce", + "Ġann ounce", + "Ġannounc e", + "Ġanno unce", + "Ġass umes", + "Ġassum es", + "Ġassume s", + "/ tr", + "/t r", + "Ġ bd", + "Ġb d", + "9 87", + "98 7", + "Ġ Carbon", + "ĠC arbon", + "ĠCar bon", + "ĠCarb on", + "Ġanal ys", + "Ġanaly s", + "Ġana lys", + "5 64", + "56 4", + ". dest", + ".d est", + ".de st", + ".des t", + "n ik", + "ni k", + "Ġ Lie", + "ĠL ie", + "ĠLi e", + "- index", + "-in dex", + "-ind ex", + "Draw able", + "Ġ TAG", + "ĠT AG", + "ĠTA G", + "Ġ triangle", + "Ġt riangle", + "Ġtr iangle", + "Ġtri angle", + "Ġtriang le", + "_ FLOAT", + "_F LOAT", + "ĉ ĉĠĠĠĠĠ", + "ĉĉ ĠĠĠĠĠ", + "ĉĉĠĠĠ ĠĠ", + "ĉĉĠ ĠĠĠĠ", + "ĉĉĠĠ ĠĠĠ", + "ĉĉĠĠĠĠ Ġ", + ". black", + ".b lack", + ".bl ack", + "v ue", + "vu e", + "c uracy", + "cur acy", + "cura cy", + "Ġa ffects", + "Ġaff ects", + "Ġaffect s", + "9 06", + "90 6", + "Ġsur ely", + "Ġsure ly", + "S lider", + "Sl ider", + "Slide r", + "u ki", + "uk i", + "c ery", + "ce ry", + "cer y", + "Ġ unter", + "Ġun ter", + "Ġunt er", + ". profile", + ".pro file", + ".pr ofile", + ".prof ile", + "or don", + "ord on", + "ordo n", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "le ave", + "lea ve", + "Ġsmart phone", + "g ie", + "gi e", + "Ġcon spir", + "Ġcons pir", + "Ġ tutorial", + "Ġt utorial", + "Ġtut orial", + "Ġtutor ial", + "Ġtuto rial", + "ç± »", + "Ġ cab", + "Ġc ab", + "Ġca b", + "7 65", + "76 5", + "Ġ Summary", + "ĠSum mary", + "* ĊĊ", + "*Ċ Ċ", + "ä h", + "\" This", + "\"T his", + "Ġ slides", + "Ġsl ides", + "Ġslide s", + "Ġslid es", + "\" ", + "() >", + "c ycle", + "cy cle", + "cycl e", + "ĠB ull", + "ĠBul l", + "ĠBu ll", + "p aths", + "path s", + "pat hs", + "pa ths", + "Ġu np", + "Ġun p", + "Ġview DidLoad", + "_ Model", + "_M odel", + "_Mode l", + "_Mod el", + "Ġ assertTrue", + "Ġassert True", + "Ġ rated", + "Ġr ated", + "Ġrate d", + "Ġrat ed", + "Ġra ted", + "De cl", + "Dec l", + "ver ted", + "vert ed", + "verte d", + "Ġ Dat", + "ĠD at", + "ĠDa t", + "b rew", + "br ew", + "bre w", + "Ġpoint ing", + "M s", + "Ġ Pointer", + "ĠPoint er", + "ĠPo inter", + ") '", + "_ non", + "_n on", + "_no n", + "5 27", + "52 7", + "Ġ SEC", + "ĠS EC", + "ĠSE C", + "Ġ yeah", + "Ġy eah", + "Ġye ah", + "g ency", + "ge ncy", + "gen cy", + "initial ize", + "f ly", + "fl y", + "7 11", + "71 1", + "[ pos", + "[p os", + ", g", + "T ele", + "Te le", + "Tel e", + "0 34", + "03 4", + "Ġj oke", + "Ġjo ke", + "Ġ clause", + "Ġcl ause", + "Ġcla use", + ". findById", + ".find ById", + ".findBy Id", + "e nes", + "en es", + "ene s", + "( instance", + "(in stance", + "(inst ance", + "6 26", + "62 6", + " £", + "9 15", + "91 5", + "Ġs lic", + "Ġsl ic", + "_ home", + "_h ome", + "_hom e", + "Ġ */}Ċ", + "Ġ*/ }Ċ", + "_ pages", + "_p ages", + "_page s", + "_pag es", + "_pa ges", + "( service", + "(s ervice", + "(serv ice", + "9 05", + "90 5", + "R P", + "Ġ Among", + "ĠA mong", + "ĠAm ong", + ". getCurrent", + ".get Current", + ".getC urrent", + "8 06", + "80 6", + "ã Ĥ¹", + "ãĤ ¹", + "Ġs lee", + "Ġsl ee", + "Ġsle e", + "= [Ċ", + ">[ Ċ", + "o ler", + "ol er", + "ole r", + "Ġli bert", + "Ġlib ert", + "Ġliber t", + "Ġ `Ċ", + "Ġ` Ċ", + "Ġw enn", + "Ġwe nn", + "Ġwen n", + "l ated", + "la ted", + "late d", + "lat ed", + "Ġ immune", + "Ġimm une", + "Ġimmun e", + "( Node", + "(N ode", + "Ġ Problem", + "ĠPro blem", + "ĠProb lem", + "ĠProble m", + "Ġ Abs", + "ĠA bs", + "ĠAb s", + "l ogs", + "lo gs", + "log s", + "Ġ ../", + "Ġ. ./", + "Ġ.. /", + "Ġ ADC", + "ĠA DC", + "ĠAD C", + "Ġ }}\">Ċ", + "Ġ} }\">Ċ", + "Ġ}} \">Ċ", + "Ġ}}\" >Ċ", + "Ġ}}\"> Ċ", + "> ');Ċ", + ">' );Ċ", + ">') ;Ċ", + ">'); Ċ", + "= b", + "Ġ Wind", + "ĠW ind", + "ĠWin d", + "ĠWi nd", + "l ahoma", + "lah oma", + "Ġ allocate", + "Ġal locate", + "Ġall ocate", + "Ġalloc ate", + "Ġallo cate", + "o rian", + "or ian", + "oria n", + "ori an", + "Ġpr escription", + "Ġpre scription", + "Ġpres cription", + "- quality", + "-q uality", + "-qu ality", + "Ġ Mayor", + "ĠMay or", + "ĠMa yor", + "ĠMayo r", + "8 55", + "85 5", + "in ely", + "ine ly", + "inel y", + "end foreach", + "Ġ Complex", + "ĠCom plex", + "ĠComp lex", + "ĠComple x", + "k om", + "ko m", + "7 09", + "70 9", + "T Y", + "7 90", + "79 0", + "] ].", + "]] .", + ". Style", + ".St yle", + "_ many", + "_m any", + "_man y", + "_ma ny", + "', '$", + "',' $", + "Ġbar rier", + "Ġbarr ier", + "Ġ Fetch", + "ĠF etch", + "ĠFet ch", + "Ġ Marvel", + "ĠMar vel", + "Ġres ist", + "о го", + "ог о", + "b idden", + "bi dden", + "bid den", + "Ġ Runnable", + "ĠR unnable", + "ĠRun nable", + ": false", + ":f alse", + "8 99", + "89 9", + "Ġbuild s", + "Ġ Stage", + "ĠSt age", + "ĠSta ge", + "Ġ dub", + "Ġd ub", + "Ġdu b", + "em po", + "emp o", + ". site", + ".s ite", + ".si te", + "5 58", + "55 8", + "; ĊĊĊĊ", + ";Ċ ĊĊĊ", + ";ĊĊ ĊĊ", + ";ĊĊĊ Ċ", + "9 94", + "99 4", + "Ġ Denver", + "ĠDen ver", + "Ġre vel", + "Ġrev el", + "Ġreve l", + "Ġtrigger ed", + "Ġ dice", + "Ġd ice", + "Ġdi ce", + "Ġdic e", + "_ fail", + "_f ail", + "_fa il", + "Ġ gc", + "Ġg c", + "8 33", + "83 3", + "5 89", + "58 9", + "ĉ X", + "Ġ Throwable", + "ĠTh rowable", + "ĠThrow able", + "7 75", + "77 5", + ". router", + ".r outer", + ".route r", + ".ro uter", + "ĠRe volution", + "ĠRev olution", + "ÑĢ а", + "_ NON", + "_N ON", + "_NO N", + "0 55", + "05 5", + "Ł ¥", + "5 78", + "57 8", + "Ġ elder", + "Ġe lder", + "Ġel der", + "Ġelde r", + "Ġab road", + "Ġ е", + "ĠÐ µ", + "Ġ Adult", + "ĠAd ult", + "b lr", + "bl r", + "g lyphicon", + "glyph icon", + "6 13", + "61 3", + "Ġprom oting", + "Ġpromot ing", + "Ġpromo ting", + "Ġ iz", + "Ġi z", + "Ġ Solid", + "ĠS olid", + "ĠSo lid", + "ĠSol id", + "6 45", + "64 5", + "_ loader", + "_l oader", + "_lo ader", + "_load er", + "ear ly", + ". enabled", + ".en abled", + ".enable d", + "- edit", + "-e dit", + "-ed it", + "Ġ UL", + "ĠU L", + "_ play", + "_p lay", + "_pl ay", + "Ġ Interrupt", + "ĠInt errupt", + "ĠInter rupt", + "ĠInterr upt", + "Ġadv antages", + "Ġadvant ages", + "Ġadvantage s", + "u cle", + "uc le", + "Ġmechan ical", + "Ġmechanic al", + "Ġmech anical", + ".table LayoutPanel", + "Ġ Working", + "ĠWork ing", + "ĠWor king", + "Ġ anonymous", + "Ġan onymous", + "Ġanonym ous", + "Ġanon ymous", + "R ating", + "Ra ting", + "ig ious", + "igi ous", + "_ phone", + "_p hone", + "_ph one", + ".addAction Listener", + "Ġf ran", + "Ġfr an", + "Ġfra n", + "un den", + "und en", + "unde n", + "Ġ *)&", + "Ġ* )&", + "Ġ*) &", + "_ bool", + "_b ool", + "_bo ol", + "ul ative", + "Ġ cone", + "Ġc one", + "Ġcon e", + "Ġco ne", + "Ġ Mult", + "ĠM ult", + "ĠMu lt", + "ĠMul t", + "Ġm ö", + "Ġ Forward", + "ĠFor ward", + "] ):Ċ", + "]) :Ċ", + "]): Ċ", + "Ġconvin ced", + "Ġconvince d", + "Ġconvinc ed", + "act ed", + "ac ted", + "6 43", + "64 3", + "ãģ ĵ", + "Ġ Configure", + "ĠCon figure", + "ĠConfig ure", + "ĠConf igure", + "Ġce iling", + "Ġceil ing", + "D er", + "De r", + "Ġpass engers", + "Ġpassenger s", + "G roups", + "Group s", + "Gro ups", + "Ġs occer", + "Ġsoc cer", + "/ W", + "av iors", + "avior s", + "avi ors", + "s with", + "sw ith", + "Ġ Zone", + "ĠZ one", + "ĠZo ne", + ". Options", + ".O ptions", + ".Option s", + "Ġ Mom", + "ĠM om", + "ĠMo m", + "i eder", + "ie der", + "ied er", + "Array s", + "Ar rays", + "Arr ays", + "Ġtreat ments", + "Ġtreatment s", + "Ġprotect ing", + "f ac", + "fa c", + "Ġ pickle", + "Ġp ickle", + "Ġpick le", + "Ġpic kle", + "Button Item", + "7 13", + "71 3", + "Ġ blocking", + "Ġb locking", + "Ġbl ocking", + "Ġblock ing", + "Ġbloc king", + "st rar", + "str ar", + "stra r", + "à ²", + "Ġ Export", + "ĠEx port", + "ĠExp ort", + "ĠExpo rt", + "Ġth rew", + "Ġthr ew", + "ot ta", + "ott a", + "Ġ BASE", + "ĠB ASE", + "ĠBAS E", + "ĠBA SE", + ". ws", + ".w s", + ".LE ADING", + "order By", + "_ delay", + "_d elay", + "_de lay", + "_del ay", + "Ġ Pu", + "ĠP u", + ". dll", + ".d ll", + "Ġ Choose", + "ĠCh oose", + "ĠCho ose", + "9 92", + "99 2", + "Pol ice", + "Po lice", + "Ġ BEGIN", + "ĠB EGIN", + "ĠBE GIN", + "bo xes", + "box es", + "Ġ diamond", + "Ġd iamond", + "Ġdiam ond", + "Ġdia mond", + ", l", + "Ġ ĉĉĉ", + "Ġĉ ĉĉ", + "Ġĉĉ ĉ", + "Ġc urious", + "Ġcur ious", + "Ġcu rious", + "6 24", + "62 4", + "t v", + "Ġerot ische", + "Ġerotisch e", + "ack ages", + "ackage s", + "ĉ Set", + "ĉS et", + "T ick", + "Ti ck", + ". border", + ".b order", + "static method", + "Ġ cher", + "Ġc her", + "Ġch er", + "Ġche r", + "in voice", + "inv oice", + "Ġc ru", + "Ġcr u", + "Ġde fect", + "Ġdef ect", + "Ġdefe ct", + "_ metadata", + "_m etadata", + "_meta data", + "_met adata", + "re lation", + "rel ation", + "i kan", + "ik an", + "ika n", + "[ N", + "( Qt", + "(Q t", + "( Base", + "(B ase", + "æģ ¯", + "b eat", + "be at", + "Ġ Empty", + "ĠEm pty", + "ĠEmp ty", + "ĉ o", + "_ shift", + "_s hift", + "_sh ift", + "Ġreg ret", + "7 22", + "72 2", + "Th ose", + "Tho se", + "C ent", + "Ce nt", + "ĠPort ug", + "ĠIs lands", + "ĠIsl ands", + "ĠIsland s", + "Ġ TIME", + "ĠT IME", + "ĠTIM E", + "ĠTI ME", + "Man agement", + "Manage ment", + "Mana gement", + "9 96", + "99 6", + "- sp", + "-s p", + "5 39", + "53 9", + "ê me", + "êm e", + "Ġn otion", + "Ġnot ion", + "Ġno tion", + "un ifu", + "uni fu", + "P K", + "8 26", + "82 6", + "è¡ Į", + "ĠC URLOPT", + "ĠCUR LOPT", + "ĠCURL OPT", + "\\ \"\\", + "\\\" \\", + "U V", + "ç º", + "d ra", + "dr a", + "c ou", + "co u", + "= `", + "Ġ Destroy", + "ĠD estroy", + "ĠDe stroy", + "ĠDest roy", + "r p", + ". cancel", + ".c ancel", + ".can cel", + "G G", + "r untime", + "run time", + "Ġ Vue", + "ĠV ue", + "ĠVu e", + "Ġpro gressive", + "Ġprogress ive", + "Ġprog ressive", + "/ services", + "/s ervices", + "/service s", + "Ġ runner", + "Ġr unner", + "Ġrun ner", + "_ FRAME", + "_FR AME", + ". ToolStripMenuItem", + ".ToolStrip MenuItem", + "Ġ ','", + "Ġ' ,'", + "Ġ', '", + "d elay", + "de lay", + "del ay", + "= utf", + "=u tf", + "Ġscreen ing", + "Ġscre ening", + "Ġp ulling", + "Ġpull ing", + "Ġpul ling", + "o mas", + "om as", + "oma s", + "Ġ anth", + "Ġa nth", + "Ġan th", + "Ġant h", + "- new", + "-n ew", + "-ne w", + "/ local", + "/l ocal", + "/lo cal", + "Ġ iPad", + "Ġi Pad", + "ĠiP ad", + "Ġ twitter", + "Ġt witter", + "Ġtw itter", + "Ġd ying", + "Ġdy ing", + "Ġhe aven", + "Ġheav en", + "Ġ UInt", + "ĠU Int", + "ĠUI nt", + "Ġ Senator", + "ĠSen ator", + "Ġpre sum", + "Ġpres um", + "Ġ Walker", + "ĠW alker", + "ĠWalk er", + "ĠWal ker", + "Ġover come", + "e tection", + "et ection", + "ete ction", + "etect ion", + "Ġemb arrass", + "Ch ina", + "Chi na", + "6 39", + "63 9", + "In clude", + "Inc lude", + "R OLL", + "RO LL", + "ROL L", + "Ġ dataType", + "Ġdata Type", + "D avid", + "Da vid", + "ภ£", + "l op", + "lo p", + "- month", + "-m onth", + "-mon th", + "Ġ scar", + "Ġs car", + "Ġsc ar", + "Ġsca r", + "Ġ Safe", + "ĠS afe", + "ĠSaf e", + "ĠSa fe", + "Ġ ****************************************************************", + "Ġ******************************** ********************************", + "Ġ******** ********************************************************", + "Ġ**************** ************************************************", + "Ġ************************ ****************************************", + "Ġ**************************************** ************************", + "Ġ******************************************************** ********", + "Ġ************************************************ ****************", + "Ġaccess ories", + "Ġaccessor ies", + "Ġr amp", + "Ġra mp", + "Ġram p", + "_ USE", + "_U SE", + "_US E", + "Ġcon trad", + "Ġcont rad", + "Ġcontr ad", + "Ġcontra d", + ") )]Ċ", + ")) ]Ċ", + "))] Ċ", + "Ġp rest", + "Ġpr est", + "Ġpre st", + "Ġpres t", + "Ġ HR", + "ĠH R", + "ĠR ap", + "ĠRa p", + "Ġ usize", + "Ġu size", + "Ġus ize", + "Ġ capability", + "Ġcap ability", + "Ġc ort", + "Ġco rt", + "Ġcor t", + "- next", + "-n ext", + "-ne xt", + "0 77", + "07 7", + "6 27", + "62 7", + "Ġbur den", + "8 22", + "82 2", + "_ reader", + "_re ader", + "_read er", + "Ġ @@", + "Ġ@ @", + "reg ular", + "Ġ Ka", + "ĠK a", + "0 36", + "03 6", + "M AN", + "MA N", + "Ġ astr", + "Ġa str", + "Ġas tr", + "Ġast r", + "Ġ' ')Ċ", + "Ġ'' )Ċ", + "Ġ'') Ċ", + "Ġ fed", + "Ġf ed", + "Ġfe d", + "Ġp arsing", + "Ġpar sing", + "Ġpars ing", + "Ġ Years", + "ĠY ears", + "ĠYear s", + "ĠYe ars", + "Ġ broker", + "Ġb roker", + "Ġbr oker", + "Ġbro ker", + "Ġbroke r", + "\": {\"", + "Ġ akt", + "Ġa kt", + "Ġak t", + "In ventory", + "ab eled", + "abel ed", + "abe led", + "Ġarg parse", + "* ******Ċ", + "** *****Ċ", + "**** ***Ċ", + "****** *Ċ", + "*** ****Ċ", + "***** **Ċ", + "******* Ċ", + "vers ation", + "Ġ cord", + "Ġc ord", + "Ġco rd", + "Ġcor d", + "Ġ Ti", + "ĠT i", + "Ġ hopefully", + "Ġhope fully", + "Ġhop efully", + "Ġhopeful ly", + "Ġ ah", + "Ġa h", + "v erb", + "ver b", + "ve rb", + "Ġst olen", + "Ġstole n", + "Ġsto len", + "Ġstol en", + ". Entry", + ".En try", + ".Ent ry", + "Ġex pecting", + "Ġexpect ing", + "O rientation", + "Ġ powered", + "Ġp owered", + "Ġpower ed", + "Ġpow ered", + "Ġ persist", + "Ġp ersist", + "Ġpers ist", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "' ]);", + "'] );", + "']) ;", + "' )),Ċ", + "') ),Ċ", + "')) ,Ċ", + "')), Ċ", + "Ġ Cash", + "ĠC ash", + "ĠCas h", + "ĠCa sh", + "ĉ item", + "ĉi tem", + "ĉit em", + "8 18", + "81 8", + "g rades", + "gr ades", + "grad es", + "grade s", + "gra des", + "r opol", + "ro pol", + "rop ol", + "b asic", + "ba sic", + "bas ic", + "Ġ \");čĊ", + "Ġ\" );čĊ", + "Ġ\") ;čĊ", + "Ġ\"); čĊ", + "Ġa wards", + "Ġaw ards", + "Ġaward s", + "( range", + "(r ange", + "(ra nge", + "- all", + "-a ll", + "-al l", + "Ġ IBOutlet", + "ĠIB Outlet", + "Ġ Indeed", + "ĠInd eed", + "---------------------------------------------------------------- ------------", + "------------ ----------------------------------------------------------------", + "------------------------------------------------ ----------------------------", + "------ ----------------------------------------------------------------------", + "---------------------------- ------------------------------------------------", + "---------------------------------------------------------------------- ------", + "Ġstom ach", + "Ġsto mach", + "Ġ flower", + "Ġf lower", + "Ġfl ower", + "Ġflow er", + "Ġflo wer", + "Ġs ew", + "Ġse w", + "_ times", + "_t imes", + "_time s", + "_tim es", + "_ti mes", + "a vis", + "av is", + "avi s", + "Q String", + "QS tring", + "Ġ Routes", + "ĠR outes", + "ĠRoute s", + "ĠRo utes", + "ĠRou tes", + "ĠRout es", + "_ prot", + "_p rot", + "_pro t", + "_pr ot", + "Ġcom edy", + "Ġcome dy", + "Ġcomed y", + "Ġ logout", + "Ġlog out", + "Ġlogo ut", + "Ġwood en", + "Ġwo oden", + "Ġwoo den", + "Ġ poster", + "Ġp oster", + "Ġpos ter", + "Ġpost er", + "Ġpo ster", + "Ġposte r", + "p iece", + "pi ece", + "pie ce", + ". Join", + ".J oin", + "Ġ Pok", + "ĠP ok", + "ĠPo k", + "cel ona", + "m utex", + "mut ex", + "mu tex", + "mute x", + "; čĊčĊčĊ", + ";čĊ čĊčĊ", + ";čĊčĊ čĊ", + "Ġst rikes", + "Ġstr ikes", + "Ġstri kes", + "Ġstrike s", + "7 87", + "78 7", + "Lo aded", + "Load ed", + ") arg", + ")a rg", + "e sa", + "es a", + "Un ited", + "Unit ed", + "Uni ted", + "E p", + "P ELL", + "PE LL", + "8 07", + "80 7", + "Ġ Atlantic", + "ĠAtl antic", + "ul let", + "ull et", + "ulle t", + "6 52", + "65 2", + "ap ple", + "app le", + "appl e", + "Ġsett led", + "Ġsettle d", + "a con", + "ac on", + "aco n", + "Ġ printer", + "Ġpr inter", + "Ġprint er", + "Ġprin ter", + "Ġ GC", + "ĠG C", + "å® ļ", + "Ġrender ed", + "Ġrend ered", + ", âĢĻ", + "he it", + "hei t", + "s ocial", + "so cial", + "soc ial", + ". ge", + ".g e", + "7 14", + "71 4", + "Ġ Rick", + "ĠR ick", + "ĠRic k", + "ĠRi ck", + "ĠU tah", + "ĠUt ah", + "g ot", + "go t", + "on ical", + "onic al", + "oni cal", + "onica l", + "Ġ Scroll", + "ĠS croll", + "ĠSc roll", + "ĠScr oll", + "ĠSc iences", + "ĠScience s", + "ĠSci ences", + "Ġ jug", + "Ġj ug", + "Ġju g", + "Ġa mpl", + "Ġam pl", + "Ġamp l", + "en ti", + "ent i", + "LE FT", + "Ġ tabs", + "Ġt abs", + "Ġtab s", + "Ġta bs", + "Ġenorm ous", + ". getKey", + ".get Key", + "l ocate", + "lo cate", + "loc ate", + ". EX", + ".E X", + ". storage", + ".st orage", + ". We", + ".W e", + "Ġ toast", + "Ġto ast", + "Ġ Additionally", + "ĠAdd itionally", + "ĠAdditional ly", + "ĠAddition ally", + "8 82", + "88 2", + "Ġ NOW", + "ĠN OW", + "ĠNO W", + "5 47", + "54 7", + "_ UPDATE", + "_UP DATE", + "Ġtrans ferred", + "Ġtransfer red", + "t ha", + "th a", + ". Display", + ".D isplay", + ".Dis play", + "_ ui", + "_u i", + "ID EO", + "IDE O", + "Ġmeaning ful", + "ĠMos cow", + ", this", + ",t his", + "Ġ Victoria", + "ĠVict oria", + "ĠVictor ia", + "æĶ ¹", + "Ġ ÐŁ", + "ĠÐ Ł", + ". stack", + ".st ack", + "ĠB arn", + "ĠBar n", + "ĠBa rn", + "pared Statement", + ": string", + ":s tring", + ":str ing", + "Ġ bij", + "Ġb ij", + "Ġbi j", + "Ġ STATE", + "ĠST ATE", + "ĠSTAT E", + "ĠSTA TE", + "Ġemploy ers", + "Ġemployer s", + "ĉ input", + "ĉin put", + "( |", + "Ġ lex", + "Ġl ex", + "Ġle x", + "in voke", + "inv oke", + "ĉ num", + "ĉn um", + "+ +,", + "++ ,", + "at ial", + "ati al", + "or ses", + "ors es", + "orse s", + "Ġ fork", + "Ġf ork", + "Ġfor k", + "Ġfo rk", + "_ txt", + "_t xt", + "_tx t", + "ĠAnt onio", + "ĠAnton io", + "Ġ (<", + "Ġ( <", + "a verse", + "av erse", + "aver se", + "avers e", + "Ġdev ast", + "ãĢ Ģ", + ". Dec", + ".D ec", + ".De c", + "ĠG ard", + "ĠGar d", + "ĠGa rd", + "/ ui", + "/u i", + ". %", + "t ri", + "tr i", + "Ġ rolled", + "Ġroll ed", + "Ġrol led", + "Value Pair", + "it ten", + "itt en", + "itte n", + "ĠT her", + "ĠThe r", + "ĠTh er", + "Ġv rou", + "Ġvr ou", + "Ġ Flow", + "ĠF low", + "ĠFl ow", + "ĠFlo w", + "Ġ Finance", + "ĠF inance", + "ĠFin ance", + "Ġ Comb", + "ĠC omb", + "ĠCom b", + "ĠCo mb", + "H C", + ". setVisible", + ".set Visible", + "i sl", + "is l", + "Ġ pk", + "Ġp k", + "7 73", + "77 3", + "Ġup set", + "Ġups et", + "( raw", + "(r aw", + "(ra w", + "ĠV ice", + "ĠVi ce", + "ĠVic e", + "e atures", + "ea tures", + "eature s", + "eat ures", + "Ġ Lang", + "ĠL ang", + "ĠLa ng", + "ĠLan g", + "0 29", + "02 9", + "Lo oking", + "Look ing", + "7 67", + "76 7", + "Ġ AST", + "ĠA ST", + "ĠAS T", + "Ġt rips", + "Ġtr ips", + "Ġtri ps", + "Ġtrip s", + "Ġ Justin", + "ĠJ ustin", + "ĠJust in", + "ĠJu stin", + "b rowser", + "browse r", + "=\" '.$", + "=\"' .$", + "=\"'. $", + ". vertices", + ".vert ices", + "8 21", + "82 1", + "- co", + "-c o", + "} /{", + "}/ {", + "Ġ ?,", + "Ġ? ,", + "Ġ Domin", + "ĠD omin", + "ĠDo min", + "ĠDom in", + "ĠBe lg", + "ĠBel g", + "\" <", + "Ġsup pose", + "Ġsupp ose", + "a ddy", + "ad dy", + "add y", + "Ġwalk s", + "Ġwal ks", + "6 88", + "68 8", + "ER RU", + "ERR U", + "_ filters", + "_f ilters", + "_filter s", + "_fil ters", + "_filt ers", + "Pre ferred", + "s cene", + "sc ene", + "е Ñģ", + "ĠAff airs", + "Ġ\" #{", + "Ġ\"# {", + "Ġon Submit", + "Ġ stocks", + "Ġst ocks", + "Ġstock s", + "Ġsto cks", + "/ view", + "/v iew", + "g ree", + "gr ee", + "gre e", + "- get", + "-g et", + "9 03", + "90 3", + "h it", + "hi t", + "J o", + ". getC", + ".get C", + "7 25", + "72 5", + "Init ialized", + "Initial ized", + "Initialize d", + "ÑĤ и", + "c uts", + "cut s", + "cu ts", + "( Type", + "(T ype", + "ĠAg reement", + "ĠAgree ment", + "ĠViet nam", + "Ġ /*!", + "Ġ/* !", + "Ġ pizza", + "Ġp izza", + "Ġpi zza", + "- view", + "-v iew", + "_ em", + "_e m", + "Ġ lhs", + "Ġl hs", + "Ġlh s", + "Ġm uy", + "Ġmu y", + "Ġ Ident", + "ĠI dent", + "ĠId ent", + "ĠIde nt", + "Ġ Friends", + "ĠF riends", + "ĠFriend s", + "ĠFri ends", + "0 61", + "06 1", + "Ġab und", + "_ AD", + "_A D", + ". timestamp", + ".t imestamp", + ".time stamp", + "- '", + "Ġ duplicate", + "Ġd uplicate", + "Ġdup licate", + "Ġduplic ate", + "Ġh unting", + "Ġhun ting", + "Ġhunt ing", + "Ġreg ulatory", + "Ġregul atory", + "Ġregulator y", + "i ao", + "ia o", + "am ous", + "amo us", + "ĠEnt ertainment", + "ĠEnter tainment", + "[ A", + "iat ric", + "_ CLIENT", + "_CL IENT", + "_CLI ENT", + "Ġ Kids", + "ĠK ids", + "ĠKi ds", + "ĠKid s", + "/ pkg", + "/p kg", + "B reak", + "Bre ak", + ") ));ĊĊ", + ")) );ĊĊ", + ")));Ċ Ċ", + "))) ;ĊĊ", + "))); ĊĊ", + "Ġ Shape", + "ĠS hape", + "ĠSh ape", + "ĠSha pe", + "Ġrel ating", + "Ġrelat ing", + "Int errupt", + "Inter rupt", + "able Opacity", + "em bre", + "emb re", + "embr e", + "Ġm ystery", + "Ġmy stery", + "Ġmys tery", + "Ġmyst ery", + "Ġmyster y", + "Ġjournal ists", + "Ġjournalist s", + "r itable", + "ri table", + "rit able", + "rita ble", + ". Link", + ".L ink", + "Ġst opping", + "Ġstop ping", + "Ġsto pping", + "C RET", + "CR ET", + "CRE T", + ". DB", + ".D B", + "Ġpop ularity", + "Ġpopular ity", + "Ġpopul arity", + "Ġ gew", + "Ġg ew", + "Ġge w", + "Ġi mpr", + "Ġim pr", + "Ġimp r", + "set Value", + "F LAG", + "FL AG", + "ĉ max", + "ĉm ax", + "Ġb ake", + "Ġba ke", + "Ġbak e", + "w y", + "ĠE conomic", + "ĠEcon omic", + "ĠEc onomic", + "ĠEconom ic", + "Ġen contr", + "Ġ fname", + "Ġf name", + "Ġfn ame", + "/ de", + "/d e", + "R ank", + "Ra nk", + "Ġ bugs", + "Ġb ugs", + "Ġbu gs", + "Ġbug s", + ". sm", + ".s m", + "Ġ median", + "Ġm edian", + "Ġmed ian", + "Ġmedia n", + "Ġmedi an", + "D OWN", + "DO WN", + "Ġ Sure", + "ĠS ure", + "ĠSur e", + "ĠSu re", + "At Index", + "Ġ Dick", + "ĠD ick", + "ĠDi ck", + "Ġ (__", + "Ġ( __", + "Ġ(_ _", + ". delta", + ".d elta", + ".del ta", + "F r", + "Ġsuggest ing", + "Ġ RecyclerView", + "ĠRec yclerView", + ", e", + "ST ART", + "STAR T", + "STA RT", + "/ ****************************************************************************", + "/************************************************************************ ****", + "/**************************************************************** ************", + "/******************************************************** ********************", + "/************************************************ ****************************", + "x ford", + "xf ord", + "Ġ receipt", + "Ġre ceipt", + "Ġrece ipt", + "CL AIM", + "CLA IM", + "read only", + "9 68", + "96 8", + "Ġeng aging", + "6 19", + "61 9", + "C a", + "as ma", + "asm a", + "Ġens uring", + "Eng lish", + "ĠV ancouver", + "h yth", + "hy th", + "Ġpurch asing", + "Ġ PI", + "ĠP I", + ". word", + ".w ord", + "( sp", + "(s p", + ". home", + ".h ome", + ".hom e", + ": def", + ":d ef", + "Ġg ig", + "Ġgi g", + "5 74", + "57 4", + "6 71", + "67 1", + "Ġ Ve", + "ĠV e", + "f orum", + "fo rum", + "for um", + "Ġ Mitch", + "ĠM itch", + "ĠMit ch", + "B ay", + "Ba y", + "_ FL", + "_F L", + "6 51", + "65 1", + "Ġs oll", + "Ġso ll", + "Ġsol l", + "5 77", + "57 7", + "_ columns", + "_column s", + "Ġminor ity", + "b ird", + "bi rd", + "bir d", + "Ġh anded", + "Ġhand ed", + "Ġhan ded", + "S SL", + "SS L", + "ST AT", + "STA T", + "Ġnerv ous", + "Ġner vous", + "ĥ ½", + "Ġ filePath", + "Ġfile Path", + "C REATE", + "CRE ATE", + "A w", + "Ġp ens", + "Ġpe ns", + "Ġpen s", + "8 35", + "83 5", + "s eed", + "se ed", + "see d", + "Ġ Compute", + "ĠCom pute", + "ĠComp ute", + "ĠComput e", + "o lk", + "ol k", + "5 94", + "59 4", + "Ġ Asset", + "ĠAs set", + "ĠAss et", + "r each", + "re ach", + "rea ch", + "' ),čĊ", + "') ,čĊ", + "'), čĊ", + "n avigation", + "nav igation", + "L F", + "/ util", + "/u til", + "Ġ Pub", + "ĠP ub", + "ĠPu b", + "Ġ âĶ", + "Ġâ Ķ", + "c ion", + "ci on", + "cio n", + "# #Ċ", + "## Ċ", + "0 72", + "07 2", + "I II", + "II I", + "Tag Name", + "Ġa mid", + "Ġam id", + "Ġami d", + "per mission", + "perm ission", + "if iable", + "ifi able", + "x FFFFFFFF", + "xFF FFFFFF", + "xFFFF FFFF", + "xFFFFFF FF", + "н и", + ". Buffer", + ".B uffer", + "_ irq", + "_i rq", + "_ir q", + "d ark", + "da rk", + "dar k", + "Ġ retval", + "Ġret val", + ". fire", + ".f ire", + ".fi re", + "p roduction", + "pro duction", + "product ion", + "produ ction", + "prod uction", + ". listen", + ".l isten", + ".list en", + ".li sten", + "Ġ Weather", + "ĠWe ather", + "Ġbu yers", + "Ġbuy ers", + "Ġbuyer s", + ". ne", + ".n e", + "e rp", + "er p", + "ĠP ent", + "ĠPe nt", + "ĠPen t", + "6 99", + "69 9", + "Ġw elfare", + "Ġwel fare", + "Ġ pageSize", + "Ġpage Size", + "ĠSt adium", + "ĠStad ium", + "er ta", + "ert a", + "Ġ lev", + "Ġl ev", + "Ġle v", + "am pa", + "amp a", + "P ager", + "Page r", + "Pa ger", + "Pag er", + "6 65", + "66 5", + "Ġ charging", + "Ġch arging", + "Ġchar ging", + "Ġcharg ing", + "Ġ Netflix", + "ĠNet flix", + "| null", + "_ random", + "_r andom", + "_rand om", + ". xpath", + ".x path", + "Ġs tere", + "Ġst ere", + "Ġste re", + "Ġster e", + "Ġ ISIS", + "ĠIS IS", + "ĠISI S", + "pon ses", + "ponse s", + "pons es", + "( loc", + "(l oc", + "(lo c", + "5 66", + "56 6", + "ey ond", + "Ġ Official", + "ĠOff icial", + "6 57", + "65 7", + "ĠMary land", + "Data Type", + "_ par", + "_p ar", + "_pa r", + "{ },", + "{} ,", + "Ġ Enjoy", + "ĠEn joy", + "7 27", + "72 7", + "_ SHIFT", + "_SH IFT", + "ĠA wards", + "ĠAward s", + "ĠAw ards", + "_ ENTRY", + "_EN TRY", + "_ENT RY", + "Ġseem ingly", + "Ġseeming ly", + "ent icate", + "entic ate", + "enti cate", + "Ġhe arts", + "Ġheart s", + "Ġhear ts", + "5 83", + "58 3", + "_ ;ĊĊ", + "_;Ċ Ċ", + "_; ĊĊ", + "ĠH IV", + "ĠHI V", + "Ġin divid", + "Ġind ivid", + "Ġindiv id", + "Ġ Flag", + "ĠF lag", + "ĠFl ag", + "ĠFla g", + "_ ctrl", + "_c trl", + "_ct rl", + "_ctr l", + "Ġ Callback", + "ĠC allback", + "ĠCall back", + ", z", + "Ġ GPU", + "ĠG PU", + "ĠGP U", + "ĉ obj", + "ĉo bj", + "ĉob j", + "Ġ Phoenix", + "ĠPh oenix", + "Ġ BUS", + "ĠB US", + "ĠBU S", + "9 07", + "90 7", + "Ġr ubber", + "Ġrub ber", + "_ AUTH", + "_A UTH", + "_AUT H", + "ĠS olutions", + "ĠSol utions", + "ĠSolution s", + "( location", + "(l ocation", + "(loc ation", + "(lo cation", + "Variable s", + "Vari ables", + ". setEnabled", + ".set Enabled", + "_ high", + "_h igh", + "_hi gh", + "W O", + "G esture", + "Ġ retry", + "Ġre try", + "Ġr etry", + "Ġret ry", + "Ġretr y", + "Ġobject ForKey", + "allow een", + "allo ween", + "Ġ mos", + "Ġm os", + "Ġmo s", + "Ġ Cele", + "ĠC ele", + "ĠCe le", + "ĠCel e", + "Ġi kke", + "Ġik ke", + "( cell", + "(c ell", + "Ġ MODE", + "ĠM ODE", + "ĠMO DE", + "ĠMOD E", + "r ena", + "re na", + "ren a", + "Ġdes cribing", + "Ġdescri bing", + "6 41", + "64 1", + "Ġ phi", + "Ġp hi", + "Ġph i", + "Ġ rd", + "Ġr d", + "Ġde serve", + "Ġdes erve", + "Ġdese rve", + "Ġdeser ve", + "Ġw heels", + "Ġwheel s", + "Ġwhe els", + "å¸ Ĥ", + "Ġcr itics", + "Ġcrit ics", + "Ġcritic s", + "Ġcri tics", + "7 55", + "75 5", + "N amespace", + "Name space", + "Names pace", + "Ġ Fra", + "ĠF ra", + "ĠFr a", + "Ġ ĊĊĊĊ", + "ĠĊ ĊĊĊ", + "ĠĊĊ ĊĊ", + "ĠĊĊĊ Ċ", + "Ġ alla", + "Ġa lla", + "Ġal la", + "Ġall a", + "Ġre quiring", + "Ġrequ iring", + "æľ Ł", + "ut ation", + "uta tion", + "Ġdel ayed", + "Ġdelay ed", + "Ġadministr ative", + "Ġ bay", + "Ġb ay", + "Ġba y", + ". hidden", + ".h idden", + "T ex", + "Te x", + "0 51", + "05 1", + "Ġbound aries", + "Ġ ]);ĊĊ", + "Ġ] );ĊĊ", + "Ġ]);Ċ Ċ", + "Ġ]) ;ĊĊ", + "Ġ]); ĊĊ", + "Ġ Following", + "ĠFollow ing", + "~ /", + "F i", + "_ conv", + "_con v", + "_co nv", + "_ TITLE", + "_T ITLE", + "Ġdes de", + "I CollectionView", + "ICollection View", + "A lias", + "Al ias", + "Ali as", + "Ġ bite", + "Ġb ite", + "Ġbit e", + "Ġbi te", + "p atient", + "pat ient", + "_ COMMAND", + "_COM MAND", + "_COMM AND", + "Com pleted", + "Complete d", + "Comp leted", + "Comple ted", + "ĉ elif", + "ĉe lif", + "ĉel if", + "( <", + "B usiness", + "Bus iness", + "Ġ Pool", + "ĠP ool", + "ĠPo ol", + "Ġpurs ue", + "Ġ Ban", + "ĠB an", + "ĠBa n", + "_ steps", + "_st eps", + "_step s", + "_ste ps", + "_ DECL", + "_DE CL", + "_DEC L", + "um ble", + "umb le", + "Ġ combo", + "Ġc ombo", + "Ġcom bo", + "Ġcomb o", + "Ġ Layer", + "ĠL ayer", + "ĠLa yer", + "ĠLay er", + ". xr", + ".x r", + "Ġ dup", + "Ġd up", + "Ġdu p", + "- --------", + "-- -------", + "---- -----", + "-------- -", + "--- ------", + "----- ----", + "------ ---", + "------- --", + "6 28", + "62 8", + "Ġ modifier", + "Ġmod ifier", + "r ob", + "ro b", + "r ez", + "re z", + "6 96", + "69 6", + "Ġath letes", + "Ġathlete s", + "U sed", + "Us ed", + "Use d", + "w ear", + "we ar", + "8 15", + "81 5", + "Ġleg itimate", + "Ġlegit imate", + "Ġlegitim ate", + "Ġ \"ĊĊ", + "Ġ\" ĊĊ", + "Ġ\"Ċ Ċ", + "Ġ hv", + "Ġh v", + "S td", + "St d", + "0 37", + "03 7", + "Ġ Hold", + "ĠH old", + "ĠHol d", + "ĠHo ld", + "Ġsurv iv", + "ĠAll iance", + "Ġ Early", + "ĠEar ly", + "ĠEarl y", + "7 78", + "77 8", + "Beh avior", + "( font", + "(f ont", + "/ libs", + "/lib s", + "/l ibs", + "/li bs", + "Ġ rectangle", + "Ġrect angle", + "Ġs inger", + "Ġsi nger", + "Ġsin ger", + "Ġsing er", + "Ġ amp", + "Ġa mp", + "Ġam p", + "Equal To", + "Ġ \".\"", + "Ġ\" .\"", + "Ġ\". \"", + "Ġgirl friend", + "å ±", + "l inear", + "li near", + "line ar", + "lin ear", + "o bserv", + "ob serv", + "obs erv", + "Ġpi ù", + "Ġcom plement", + "Ġcomp lement", + "Ġcomple ment", + "Ġcompl ement", + "With Value", + "( password", + "(p assword", + "(pass word", + "t ake", + "ta ke", + "tak e", + "Bl ank", + "Ġ Compar", + "ĠCom par", + "ĠCo mpar", + "ĠComp ar", + "' \",", + "'\" ,", + "_ policy", + "_p olicy", + "_pol icy", + "m ongoose", + "mongo ose", + "mong oose", + "_ FAILED", + "_FAIL ED", + "_FA ILED", + ". report", + ".re port", + ".repo rt", + ".rep ort", + "R atio", + ".Perform Layout", + "7 47", + "74 7", + "us able", + "usa ble", + "m ers", + "mer s", + "me rs", + "_ render", + "_re nder", + "_r ender", + "PE ED", + "7 72", + "77 2", + "Ġle sb", + "Ġles b", + "ĉ E", + "_ tool", + "_t ool", + "_to ol", + "Ġl adies", + "Ġlad ies", + "9 08", + "90 8", + "о Ñģ", + ") )))Ċ", + ")) ))Ċ", + "))) )Ċ", + ")))) Ċ", + "; ;;;", + ";; ;;", + ";;; ;", + ". dot", + ".d ot", + ".do t", + "Ġ nest", + "Ġn est", + "Ġne st", + "Ġnes t", + "pe ak", + "uk kit", + "e ca", + "ec a", + "_ SW", + "_S W", + "Ġ &(", + "Ġ& (", + "ĠOk lahoma", + "Ġb anking", + "Ġbank ing", + "Ġban king", + "5 69", + "56 9", + "Ġ Nintendo", + "ĠN intendo", + "7 52", + "75 2", + "Ġre produce", + "Ġrep roduce", + "Ġreprodu ce", + "Ġrepro duce", + "_ elements", + "_e lements", + "_element s", + "_el ements", + "_elem ents", + "_ele ments", + "_ mac", + "_m ac", + "_ma c", + "pr oxy", + "pro xy", + "prox y", + "Ġremark able", + "} /${", + "}/ ${", + "Ġ outs", + "Ġo uts", + "Ġout s", + "Ġou ts", + ".has Next", + "M ODE", + "MO DE", + "MOD E", + "6 58", + "65 8", + "Ġ anime", + "Ġan ime", + "Ġanim e", + "Ġani me", + ". conn", + ".c onn", + ".con n", + ".co nn", + "Un ique", + "Uni que", + "D om", + "Do m", + "Ġimport antly", + "Ġimportant ly", + "i tty", + "it ty", + "itt y", + "Ġju ice", + "T w", + "ĠPart ners", + "ĠPartner s", + "Ġatt acking", + "Ġattack ing", + "Ġport able", + "Ġpor table", + "Ġporta ble", + "am iento", + "ami ento", + "amient o", + ". PictureBox", + ".P ictureBox", + ". gen", + ".g en", + ".ge n", + "Ġopt imal", + "Ġoptim al", + "5 82", + "58 2", + "Ġre cre", + "Ġrec re", + "Ġjournal ist", + "Ġ Extract", + "ĠEx tract", + "ĠExt ract", + "ĠExtra ct", + "ĠExtr act", + "Ġ Moreover", + "ĠMore over", + "Ġ marginTop", + "Ġmargin Top", + ". Ap", + ".A p", + "Ġf iring", + "Ġfi ring", + "Ġfir ing", + "N aN", + "Na N", + "ĉ template", + "ĉt emplate", + "ĉtemp late", + "а д", + "аР´", + ". En", + ".E n", + "Ġdef ence", + "Ġdefe nce", + "Ġ Tel", + "ĠT el", + "ĠTe l", + "i len", + "il en", + "ile n", + "j an", + "ja n", + "= data", + "=d ata", + "Ġ Url", + "ĠU rl", + "ĠUr l", + "Ġ Reuters", + "ĠRe uters", + "( total", + "(t otal", + "(to tal", + "ĠF ifth", + "ĠFif th", + "Ġes says", + "Ġess ays", + "Ġessay s", + "Ġessa ys", + "Ġinterpret ation", + "Ġch arity", + "Ġchar ity", + "Ġ Rules", + "ĠR ules", + "ĠRule s", + "ĠRu les", + "Ġ subsection", + "Ġsub section", + "Ġsubs ection", + "st yled", + "style d", + "sty led", + "styl ed", + "a zer", + "az er", + "aze r", + "l ags", + "la gs", + "lag s", + "L IST", + "LI ST", + "Ġ uploaded", + "Ġup loaded", + "Ġupload ed", + "Ġ trash", + "Ġtr ash", + "Ġtra sh", + "Ġtras h", + "Ġ registr", + "Ġreg istr", + "Ġregist r", + "Ġ seller", + "Ġs eller", + "Ġse ller", + "Ġsell er", + "Ġsel ler", + "> ';čĊ", + ">' ;čĊ", + ">'; čĊ", + "Ġ startTime", + "Ġstart Time", + "ç Ļ", + "s y", + "( HttpServletRequest", + "(Http ServletRequest", + "Ġ trap", + "Ġt rap", + "Ġtr ap", + "Ġtra p", + "G C", + "Ġ embedded", + "Ġembed ded", + "Ġsur rounded", + "Ġsurround ed", + "8 16", + "81 6", + "i mits", + "im its", + "imit s", + "imi ts", + "T X", + "yl inder", + "6 85", + "68 5", + "Ġ Fal", + "ĠF al", + "ĠFa l", + "Ġsent ences", + "Ġsentence s", + "Ġ Ja", + "ĠJ a", + "IF ICATION", + "IFIC ATION", + "we apon", + "o vation", + "ov ation", + "ova tion", + "ovat ion", + "Ġ coat", + "Ġco at", + "Ġinter pol", + "Ġinterp ol", + "Ġl ips", + "Ġli ps", + "Ġlip s", + "Ġ Ky", + "ĠK y", + "Ġv ectors", + "Ġvector s", + "Ġve ctors", + "Ġvec tors", + "Ġvect ors", + "_ am", + "_a m", + "Ġin take", + "Ġint ake", + ". world", + ".w orld", + "Ġ inbox", + "Ġin box", + "Ġ MAC", + "ĠM AC", + "ĠMA C", + "_ ab", + "_a b", + "( nameof", + "(name of", + "6 33", + "63 3", + "Ġent ert", + "Ġenter t", + "Ġg athering", + "Ġgather ing", + "Ġ SIM", + "ĠS IM", + "ĠSI M", + "+ +.", + "++ .", + "n ya", + "ny a", + "' }}", + "'} }", + "Ġ UPDATE", + "ĠUP DATE", + "Ġ pac", + "Ġp ac", + "Ġpa c", + "( html", + "(h tml", + "(ht ml", + "ĠS ant", + "ĠSan t", + "ĠSa nt", + "i ating", + "ia ting", + "iat ing", + "ĠIde as", + "ĠIdea s", + "Ġs pray", + "Ġsp ray", + "Ġspr ay", + "ĠH art", + "ĠHar t", + "ĠHa rt", + "Ġ verification", + "Ġver ification", + "Ġverifica tion", + "ad esh", + "ade sh", + "ades h", + "/ modules", + "/mod ules", + "/module s", + "Ġ Mind", + "ĠM ind", + "ĠMin d", + "ĠMi nd", + "ĠSized Box", + "Ġsh elter", + "Ġshel ter", + "Ġ heroes", + "Ġher oes", + "Ġhero es", + "a tty", + "at ty", + "att y", + "Ġcert ified", + "Ġcertif ied", + "s j", + "Ġ être", + "Ġê tre", + "ÅĤ o", + "Ġpublish ing", + "ĠMal ays", + "ĠMa lays", + "ĠMalay s", + ". getUser", + ".get User", + "Ġ Provider", + "ĠPro vider", + "ĠProvid er", + "ĠProvide r", + "ĠProv ider", + "Ġ LinkedList", + "ĠLink edList", + "ĠLinked List", + "ĠB or", + "ĠBo r", + "R OUND", + "RO UND", + "d id", + "di d", + "t ain", + "ta in", + "p ire", + "pi re", + "pir e", + "ĠJ enn", + "ĠJe nn", + "ĠJen n", + "t el", + "te l", + "a nde", + "an de", + "and e", + "7 57", + "75 7", + "_ front", + "_f ront", + "_fr ont", + "ĠMc G", + "Test Method", + "ภŃ", + "Ġocc asionally", + "Ġoccasion ally", + "Ġoccasional ly", + "ĠW ales", + "ĠWal es", + "ĠWa les", + "Ġex ercises", + "Ġexerc ises", + "Ġexercise s", + "Ġ ÐĴ", + "ĠÐ Ĵ", + "0 45", + "04 5", + "- plus", + "-p lus", + "-pl us", + "Ġ validator", + "Ġvalid ator", + "Ġvalida tor", + "Ġpr ayer", + "Ġpray er", + "Ġpra yer", + "L ATED", + "LA TED", + "LAT ED", + "_ author", + "_a uthor", + "_auth or", + "_aut hor", + "Ġla bour", + "Ġlab our", + "+ +Ċ", + "++ Ċ", + "- equiv", + "-e quiv", + "-equ iv", + "Ġ GPL", + "ĠG PL", + "ĠGP L", + "Ġ facebook", + "Ġf acebook", + "Ġface book", + "s imple", + "sim ple", + "simp le", + "g ly", + "gl y", + "Process or", + "Proc essor", + "i py", + "ip y", + "7 44", + "74 4", + "Ġ *>", + "Ġ* >", + "6 48", + "64 8", + "Ġc leared", + "Ġclear ed", + "Ġcle ared", + "Ġ Push", + "ĠP ush", + "ĠPu sh", + "8 58", + "85 8", + "Ġp enis", + "Ġpe nis", + "Ġpen is", + "Struct ure", + "l ij", + "li j", + "ĠM organ", + "ĠMo rgan", + "ĠMor gan", + "ĠMorg an", + "Ġhand ful", + "\" .Ċ", + "\". Ċ", + "9 84", + "98 4", + "| \\", + "Ġ ********************************", + "Ġ**** ****************************", + "Ġ******** ************************", + "Ġ**************** ****************", + "Ġ************************ ********", + "Ġ Aqu", + "ĠA qu", + "5 84", + "58 4", + "_ IC", + "_I C", + ". loads", + ".load s", + ".lo ads", + "Ġ meter", + "Ġm eter", + "Ġme ter", + "Ġmet er", + "ĠM arine", + "ĠMar ine", + "ĠMa rine", + "ĠMari ne", + "ĠMarin e", + ": :{", + ":: {", + "Ġ TS", + "ĠT S", + "7 76", + "77 6", + "Ġ Arrays", + "ĠAr rays", + "ĠArray s", + "ĠArr ays", + ". Title", + ".T itle", + "G RAM", + "GR AM", + "GRA M", + "ter min", + "term in", + "Ġco inc", + "Ġcoin c", + "Ġcoi nc", + "E lse", + "El se", + "_ states", + "_st ates", + "_state s", + "_stat es", + "_sta tes", + "- run", + "-r un", + "m embers", + "member s", + "mem bers", + "7 82", + "78 2", + "a stro", + "as tro", + "ast ro", + "astr o", + "0 66", + "06 6", + "Ġon Press", + "Ġbe ings", + "Ġbeing s", + "Ġab andoned", + "Ġabandon ed", + "Ġtax p", + "Ġta xp", + "ow ners", + "own ers", + "owner s", + ". mode", + ".m ode", + ".mod e", + ".mo de", + "Ġdi agnosis", + "Ġdiagn osis", + "Ġdiag nosis", + "Ġ _Ċ", + "Ġ_ Ċ", + "Ġ Knight", + "ĠK night", + "ĠKn ight", + "ĉ A", + "Ġ observe", + "Ġob serve", + "Ġobs erve", + "Ġobserv e", + ") ,'", + "), '", + "8 23", + "82 3", + "! \")Ċ", + "!\" )Ċ", + "!\") Ċ", + "Ġ Para", + "ĠP ara", + "ĠPar a", + "ĠPa ra", + "Ġ variation", + "Ġvar iation", + "Ġvari ation", + "( False", + "(F alse", + "Ġ Anti", + "ĠAn ti", + "ĠAnt i", + "Ġg ri", + "Ġgr i", + "Ġhome less", + "Ġhom eless", + "? v", + "Ġb ez", + "Ġbe z", + ". Server", + ".S erver", + ".Serve r", + "r elease", + "re lease", + "rel ease", + "ĠP atri", + "ĠPat ri", + "ĠPa tri", + "Ġ chars", + "Ġch ars", + "Ġchar s", + "Ġcha rs", + "Ġ ranking", + "Ġr anking", + "Ġrank ing", + "Ġran king", + "act ivation", + "activ ation", + "5 81", + "58 1", + "Ġw ides", + "Ġwide s", + "Ġwid es", + "Ġwi des", + "q r", + ". Sql", + ".S ql", + "a cular", + "ac ular", + "acula r", + "Ġ Bot", + "ĠB ot", + "ĠBo t", + "_ sync", + "_s ync", + "_syn c", + "_sy nc", + "Ġh appiness", + "Ġhapp iness", + "Ġvol unteers", + "Ġvolunte ers", + "Ġvolunteer s", + "8 77", + "87 7", + "Ġs its", + "Ġsit s", + "Ġsi ts", + "/ <", + "[ e", + "( fileName", + "(file Name", + "Ġcap ac", + "Ġca pac", + "8 32", + "83 2", + "Ġ Maria", + "ĠM aria", + "ĠMar ia", + "ĠMa ria", + "ĠMari a", + "f ather", + "fa ther", + "fat her", + "Ġ gram", + "Ġg ram", + "Ġgr am", + "Ġgra m", + "* i", + "Ġc aso", + "Ġca so", + "Ġcas o", + "_ draw", + "_d raw", + "_dr aw", + "Ġ Raw", + "ĠR aw", + "ĠRa w", + "Ġ Iterator", + "ĠIt erator", + "ĠIter ator", + "6 64", + "66 4", + "Ġ Padding", + "ĠP adding", + "ĠPad ding", + "9 24", + "92 4", + "P D", + "B OX", + "BO X", + "ĠS PECIAL", + "ĠSPEC IAL", + "Ġ fecha", + "Ġf echa", + "Ġfe cha", + "Ġfec ha", + "Ġ vide", + "Ġv ide", + "Ġvi de", + "Ġvid e", + "Ġ Leader", + "ĠLe ader", + "ĠLead er", + "ä» ¥", + "$ (\".", + "$( \".", + "$(\" .", + "Ġd iameter", + "Ġdiam eter", + "Ġdia meter", + "Ġm ild", + "Ġmil d", + "Ġmi ld", + "7 45", + "74 5", + "Ġr ocks", + "Ġro cks", + "Ġrock s", + "Ġroc ks", + "app ings", + "apping s", + "0 48", + "04 8", + "d irectory", + "direct ory", + "director y", + "5 57", + "55 7", + ". flush", + ".f lush", + ".fl ush", + "Ġ Jess", + "ĠJ ess", + "ĠJes s", + "ĠJe ss", + "UN IT", + "Ġ Pear", + "ĠP ear", + "ĠPe ar", + "Ġ mandatory", + "Ġm andatory", + "Ġmand atory", + "S ur", + "Su r", + "q t", + "Ġ streams", + "Ġstream s", + "Ġstre ams", + "Ġco operation", + "Ġcooper ation", + "Ġcoop eration", + "Ġ Sac", + "ĠS ac", + "ĠSa c", + "Ġche aper", + "Ġcheap er", + "ĉ ch", + "ĉc h", + "an imation", + "anim ation", + "f are", + "fa re", + "far e", + "( height", + "(h eight", + "( True", + "N Y", + "Ġw rest", + "Ġwr est", + "Ġwre st", + "Ġp olls", + "Ġpol ls", + "Ġpoll s", + "Ġencounter ed", + "Ġencount ered", + "ĠMark etable", + "ĠMarket able", + "_ PASSWORD", + "_P ASSWORD", + "_PASS WORD", + "7 16", + "71 6", + "_ SELECT", + "_SE LECT", + "_SEL ECT", + "ĠArab ia", + "ĠAra bia", + "_ clock", + "_c lock", + "_cl ock", + "Ġ voy", + "Ġv oy", + "Ġvo y", + "Ġ из", + "Ġи з", + "Ġs tir", + "Ġst ir", + "is ible", + "isi ble", + "- effect", + "-e ffect", + "-eff ect", + ". created", + ".c reated", + ".create d", + ".cr eated", + "Ġto ys", + "Ġtoy s", + "ĠTrad able", + "Ġ rust", + "Ġr ust", + "Ġru st", + "Ġrus t", + "Ġ strcpy", + "Ġstr cpy", + "_ timestamp", + "_t imestamp", + "_time stamp", + "Ġtal ented", + "Ġtalent ed", + ", null", + ",n ull", + "Ġ Jobs", + "ĠJ obs", + "ĠJo bs", + "ĠJob s", + "Ġ Portland", + "ĠPort land", + "Ġweak ness", + "Th row", + "Thr ow", + "Ġ Angel", + "ĠAn gel", + "ĠAng el", + "ĠAnge l", + "ä¿ ®", + "7 54", + "75 4", + "Ġun cert", + "Ġunc ert", + "ï¼ī Ċ", + "Ġ ìĿ´", + "ĠìĿ ´", + "Wh ich", + "Ġ[- ]:", + "S omething", + "Some thing", + "Som ething", + "Ġconv icted", + "Ġconvict ed", + "k le", + "kl e", + "ed ium", + "edi um", + "Ġ branches", + "Ġbr anches", + "Ġbranch es", + "Ġbran ches", + "Ġ bases", + "Ġb ases", + "Ġbase s", + "Ġbas es", + "Ġba ses", + "ç ®", + "Ġcomplex ity", + "Ġ Fig", + "ĠF ig", + "ĠFi g", + ". reshape", + ".re shape", + ".res hape", + "$ db", + "$d b", + "7 36", + "73 6", + "_ CONST", + "_CON ST", + "_CO NST", + "Ġ Tes", + "ĠT es", + "ĠTe s", + ". runtime", + ".r untime", + ".run time", + "Ġ deny", + "Ġd eny", + "Ġde ny", + "Ġden y", + "Ġ BSD", + "ĠB SD", + "ĠBS D", + "Ġ kr", + "Ġk r", + "h att", + "ha tt", + "hat t", + "Ġ Static", + "ĠSt atic", + "ĠStat ic", + "ĠSta tic", + "Ġunivers ities", + "Re place", + "Rep lace", + "Ġd rove", + "Ġdr ove", + "Ġdro ve", + "Ġad oles", + "Ġado les", + "_ plugin", + "_pl ugin", + "ĠL GBT", + "ĠLG BT", + "Ġ tex", + "Ġt ex", + "Ġte x", + "d uction", + "du ction", + "duct ion", + "duc tion", + "7 51", + "75 1", + "7 99", + "79 9", + "E DI", + "ED I", + "Ġ Ted", + "ĠT ed", + "ĠTe d", + "_ URI", + "_U RI", + "Ġre ception", + "Ġrece ption", + "Ġrecept ion", + "Ġrecep tion", + "ar ten", + "art en", + "arte n", + ". Single", + ".S ingle", + ".Sin gle", + "r ice", + "ri ce", + "ric e", + "sc ious", + "sci ous", + "8 43", + "84 3", + "_ bg", + "_b g", + "Ġw ages", + "Ġwa ges", + "Ġwage s", + "Ġwag es", + "Ġ Servlet", + "ĠS ervlet", + "ĠServ let", + "UI Layout", + "UIL ayout", + "Ġ formatted", + "Ġform atted", + "Ġformat ted", + ". Mod", + ".M od", + "< class", + " ',Ċ", + ">' ,Ċ", + ">', Ċ", + "Ġexp anding", + "Ġexpand ing", + "Ġ Hamilton", + "ĠHam ilton", + "Ġ Contrib", + "ĠCon trib", + "ĠCont rib", + "ĠContr ib", + ". Tables", + ".T ables", + ".Tab les", + ".Table s", + "7 28", + "72 8", + "Act iv", + "Ac tiv", + "H H", + "o commerce", + "ocom merce", + "_ ;", + "Ġamong st", + "o wing", + "ow ing", + "owi ng", + "8 59", + "85 9", + "Ġ Cold", + "ĠC old", + "ĠCo ld", + "ĠCol d", + "A PH", + "AP H", + "Ġpsych ological", + "Ġpsycho logical", + "_ tensor", + "_t ensor", + "Ġpack aging", + "Ġ Sweden", + "ĠSw eden", + "ĠSwe den", + "Ġ pare", + "Ġp are", + "Ġpar e", + "Ġpa re", + "Ġ aggregate", + "Ġag gregate", + "Ġaggreg ate", + "Ġmod erate", + "Ġmode rate", + "Ġmoder ate", + "8 62", + "86 2", + "_ hand", + "_h and", + "Ġdesign ated", + "Ġdesignate d", + "Ġd rum", + "Ġdr um", + "Ġdru m", + "Ġ getUser", + "Ġget User", + "ĠC reek", + "ĠCre ek", + "ĠCree k", + "_ scope", + "_s cope", + "_sc ope", + "Ġ Transfer", + "ĠTrans fer", + "Ġ Marg", + "ĠM arg", + "ĠMar g", + "ĠMa rg", + "Ġ fighters", + "Ġfight ers", + "Ġfighter s", + "W nd", + "Ġ Sel", + "ĠS el", + "ĠSe l", + "Ġ Launch", + "ĠL aunch", + "ĠLa unch", + "Ġem erging", + "Ġemerg ing", + "i frame", + "if rame", + "ifr ame", + "Ġ Additional", + "ĠAdd itional", + "ĠAddition al", + "Ġf ears", + "Ġfe ars", + "Ġfear s", + "Ġsat ellite", + "_ :", + "Ġ disposing", + "Ġdis posing", + "Ġdisp osing", + "Ġdispos ing", + "Get Value", + "Http Post", + "AT IVE", + "ul ary", + "ular y", + "ula ry", + "View s", + "Vi ews", + "Ġatt ending", + "Ġattend ing", + "ĠT ennessee", + "Ġ Mission", + "ĠM ission", + "ĠMiss ion", + "Ġmed ication", + "Ġmedic ation", + "Ġmedi cation", + "Ġ Wy", + "ĠW y", + "Ġ Anna", + "ĠAn na", + "ĠAnn a", + "Ø ¹", + "Ġ Vertex", + "ĠVer tex", + "ĠVert ex", + ". types", + ".t ypes", + ".type s", + ".typ es", + "O rgan", + "Or gan", + "Org an", + ". DataGridViewTextBoxColumn", + ".DataGridView TextBoxColumn", + "Ġ RS", + "ĠR S", + "Ġt empo", + "Ġtem po", + "Ġtemp o", + "( App", + "(A pp", + "8 92", + "89 2", + "Version UID", + ". point", + ".p oint", + ".po int", + ".poi nt", + "ĠD utch", + "ĠDut ch", + "H ours", + "Hour s", + "Ho urs", + "L U", + "Ġ quoted", + "Ġqu oted", + "Ġquote d", + "Ġquot ed", + "Ġquo ted", + ". builder", + ".b uilder", + ".build er", + "Ġ Perfect", + "ĠPer fect", + "ĠPerf ect", + "Ġ Always", + "ĠAl ways", + "_ two", + "_t wo", + "_tw o", + "Ġexclusive ly", + "Ġexclus ively", + "ĠC ra", + "ĠCr a", + "ific ar", + "ifi car", + "ifica r", + "Ġ AWS", + "ĠA WS", + "ĠAW S", + "ing ham", + "com plex", + "comp lex", + "k ernel", + "ker nel", + "Ġ gravity", + "Ġgr avity", + "Ġgrav ity", + "Ġ wi", + "Ġw i", + "0 52", + "05 2", + "Ġ overview", + "Ġover view", + "Ġov erview", + "6 61", + "66 1", + "Ġ Want", + "ĠW ant", + "ĠWa nt", + "ĠWan t", + "Ġ WP", + "ĠW P", + "( sh", + "(s h", + ". rotation", + ".r otation", + ".rot ation", + "St ates", + "State s", + "Stat es", + "Ġ Teen", + "ĠT een", + "ĠTe en", + "ĠTee n", + "_ components", + "_com ponents", + "_comp onents", + "_component s", + "ì Īĺ", + "ìĪ ĺ", + "Re ceived", + "Receive d", + "Ġly rics", + "Ġlyric s", + "Ġlyr ics", + "r ites", + "ri tes", + "rit es", + "rite s", + "ĉ ĉĉĉĉĠ", + "ĉĉ ĉĉĉĠ", + "ĉĉĉĉ ĉĠ", + "ĉĉĉ ĉĉĠ", + "ĉĉĉĉĉ Ġ", + "- American", + "-A merican", + "-Americ an", + "[ num", + "[n um", + "/ python", + "/p ython", + "/py thon", + "Ġ UART", + "ĠU ART", + "ĠUA RT", + "Ġ apple", + "Ġapp le", + "Ġap ple", + "Ġappl e", + "Ġ Jonathan", + "ĠJon athan", + "Ġm omentum", + "Ġmoment um", + "ภ±", + "Ĥ ¹", + "Ġm ich", + "Ġmi ch", + "Ġmic h", + "an dra", + "and ra", + "andr a", + "Ġb iological", + "Ġbi ological", + "Ġbio logical", + "ĠM ens", + "ĠMe ns", + "ĠMen s", + "Ġ %%", + "Ġ% %", + "el sea", + "else a", + "els ea", + "ĠMex ican", + ".rand int", + "Ġt ale", + "Ġtal e", + "Ġta le", + "Ġ Validate", + "ĠValid ate", + "Ġdef eated", + "Ġdefe ated", + "Ġdefeat ed", + ". htm", + ".h tm", + ".ht m", + "Ġc opper", + "Ġco pper", + "Ġcop per", + "Ġcopp er", + "= /", + "co system", + "cos ystem", + "Ġ rip", + "Ġr ip", + "Ġri p", + "d ecimal", + "de cimal", + "dec imal", + ". VISIBLE", + ".V ISIBLE", + "Ġ Ta", + "ĠT a", + "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", + "Ġdown loaded", + "Ġdownload ed", + "en vironment", + "Ġno mine", + "Ġnom ine", + "Ġnomin e", + "build ing", + "Ġ Spot", + "ĠS pot", + "ĠSp ot", + "ĠSpo t", + "ipher al", + "iph eral", + "Ġ alto", + "Ġal to", + "Ġalt o", + "q uet", + "qu et", + "que t", + "Ġ FT", + "ĠF T", + "/ get", + "/g et", + "/ge t", + "/ master", + "/m aster", + "W IN", + "WI N", + "åħ ĥ", + "6 76", + "67 6", + "W est", + "We st", + "ar gc", + "arg c", + "Ġpro ducers", + "Ġprodu cers", + "Ġproduce rs", + "Ġproducer s", + "Ġ Much", + "ĠM uch", + "ĠMu ch", + "_ storage", + "_st orage", + "c redit", + "cre dit", + "cr edit", + "cred it", + "C ONT", + "CON T", + "CO NT", + "Ġ vet", + "Ġv et", + "Ġve t", + "Ġ voices", + "Ġvo ices", + "Ġvoice s", + "Ġvoi ces", + "( '',", + "(' ',", + "Ġin struments", + "Ġinstr uments", + "Ġinstrument s", + "6 62", + "66 2", + "Ġ MSG", + "ĠM SG", + "ĠMS G", + "e sse", + "es se", + "ess e", + "re pository", + "repos itory", + "om ics", + "omic s", + "omi cs", + "Ġ dealer", + "Ġde aler", + "Ġdeal er", + "St ill", + "Ġ banner", + "Ġb anner", + "Ġban ner", + "asc ii", + "Ġ remarks", + "Ġre marks", + "Ġr emarks", + "Ġrem arks", + "Ġremark s", + "Ġremar ks", + "[ js", + "[j s", + "Ġshort er", + "g ulp", + "gu lp", + "Ġm yster", + "Ġmy ster", + "Ġmys ter", + "Ġmyst er", + "Ġk un", + "Ġku n", + "Ġ Bird", + "ĠB ird", + "ĠBi rd", + "ĠBir d", + "Ġt iene", + "Ġti ene", + "Ġtie ne", + "7 88", + "78 8", + "n ut", + "nu t", + "Ġ Um", + "ĠU m", + "Ġ wise", + "Ġw ise", + "Ġwis e", + "Ġwi se", + "Y eah", + "Ye ah", + "I NESS", + "IN ESS", + "INE SS", + "INES S", + "0 46", + "04 6", + "_ begin", + "_b egin", + "_be gin", + "_beg in", + "- heading", + "-head ing", + "-he ading", + "C ourse", + "Co urse", + "Cour se", + "Ġ čĊčĊ", + "ĠčĊ čĊ", + "om bie", + "omb ie", + "gr aded", + "grad ed", + "grade d", + "gra ded", + "Ġ GPS", + "ĠG PS", + "ĠGP S", + "Ġ że", + "Ġż e", + "F it", + "Fi t", + "c aption", + "ca ption", + "cap tion", + "capt ion", + "ö n", + "/ image", + "/i mage", + "/im age", + "l ia", + "li a", + "( mod", + "(m od", + "Ġle ak", + "en za", + "enz a", + "6 29", + "62 9", + "/ H", + "Ġ Happy", + "ĠH appy", + "ĠHa ppy", + "ĠHapp y", + "9 93", + "99 3", + "D ist", + "Dis t", + "Di st", + "n x", + "ĠGovern or", + "ĠGover nor", + "( last", + "(l ast", + "t eacher", + "te acher", + "tea cher", + "Ġ Sent", + "ĠS ent", + "ĠSe nt", + "ĠSen t", + "s upport", + "sup port", + "8 38", + "83 8", + "ject ory", + "Ġ Ùħ", + "ĠÙ ħ", + "Reg istration", + "Registr ation", + "0 63", + "06 3", + "Ġ Gray", + "ĠG ray", + "ĠGr ay", + "ĠGra y", + ", false", + ",f alse", + "Ġ adjusted", + "Ġadjust ed", + "Ġadj usted", + "( settings", + "(s ettings", + "(set tings", + "(setting s", + "< R", + "Ġ Mage", + "ĠM age", + "ĠMag e", + "ĠMa ge", + "Ġ plaint", + "Ġpl aint", + "Ġplain t", + "Ġpla int", + "_ )Ċ", + "_) Ċ", + "ĉ it", + "ĉi t", + "o metric", + "om etric", + "omet ric", + "ometr ic", + ". bootstrap", + ".boot strap", + "Ġcar ries", + "Ġcarr ies", + "I p", + "Ġ! $", + "Ġsw imming", + "Ġswim ming", + "Ġ Mario", + "ĠM ario", + "ĠMar io", + "ĠMa rio", + "ĠMari o", + "Ġ Questions", + "ĠQuest ions", + "ĠQuestion s", + "P ACE", + "PA CE", + "æĸ ¹", + "e or", + "eo r", + "} }\"", + "}} \"", + "Ġ oven", + "Ġo ven", + "Ġov en", + "Ġ Kon", + "ĠK on", + "ĠKo n", + "Ġwis dom", + "Ġac quisition", + "ess ment", + "ag ine", + "agi ne", + "Ġex pressions", + "Ġexpress ions", + "Ġexpression s", + "Ġexpr essions", + "Sequential Group", + "F ront", + "Fr ont", + "ul pt", + "ulp t", + "a wk", + "aw k", + "' ])ĊĊ", + "'] )ĊĊ", + "']) ĊĊ", + "'])Ċ Ċ", + "8 13", + "81 3", + "7 32", + "73 2", + "_ AR", + "_A R", + "Ġan alog", + "Ġanal og", + "Ġana log", + "u lin", + "ul in", + "uli n", + "_ PRINT", + "_PR INT", + "_PRI NT", + "Ġ LG", + "ĠL G", + "Ġ blob", + "Ġb lob", + "Ġbl ob", + "Ġblo b", + "Ġ Furthermore", + "ĠFurther more", + "_ component", + "_com ponent", + "_comp onent", + "Ġ Cole", + "ĠC ole", + "ĠCo le", + "ĠCol e", + "L AN", + "LA N", + "SC RIPTION", + "SCRI PTION", + "SCRIPT ION", + "Ġ lap", + "Ġl ap", + "Ġla p", + "ic ensing", + "icens ing", + "_TIME OUT", + "ĠF ro", + "ĠFr o", + "Ġl iability", + "Ġli ability", + "Ġ composed", + "Ġcom posed", + "Ġcomp osed", + "Ġcompose d", + "Ġcompos ed", + "6 34", + "63 4", + ".create SequentialGroup", + "_ person", + "_p erson", + "_per son", + "Ġ beam", + "Ġb eam", + "Ġbe am", + "ĉ ĠĠĠĠĠĠĠĠ", + "ĉĠĠĠ ĠĠĠĠĠ", + "ĉĠ ĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠ Ġ", + "ĉĠĠ ĠĠĠĠĠĠ", + "ĉĠĠĠĠĠ ĠĠĠ", + "ĉĠĠĠĠ ĠĠĠĠ", + "ĉĠĠĠĠĠĠ ĠĠ", + "Ġ NotFound", + "ĠNot Found", + "6 84", + "68 4", + ". 'Ċ", + ".' Ċ", + "ÃŃ s", + ". TextView", + ".T extView", + ".Text View", + "P DF", + "PD F", + "Ġ kar", + "Ġk ar", + "Ġka r", + "_ _('", + "__ ('", + "__( '", + "Ġ \":\"", + "Ġ\" :\"", + "Ġ\": \"", + "_ messages", + "_m essages", + "_message s", + "Ġhar vest", + ". history", + ".h istory", + ".hist ory", + "> 'Ċ", + ">' Ċ", + "- fold", + "-f old", + "æ Ĭ", + "Ġ Better", + "ĠB etter", + "ĠBet ter", + "ĠBett er", + "Ġ\" \\<", + "Ġ\"\\ <", + "sp acing", + "spa cing", + "Ġf urnished", + "Ġfurn ished", + "Ġfurnish ed", + "9 13", + "91 3", + "o ser", + "os er", + "ose r", + "] }Ċ", + "]} Ċ", + "Ġ $\"", + "Ġ$ \"", + "p ull", + "pu ll", + ". Post", + ".P ost", + ".Pos t", + "9 19", + "91 9", + "( ip", + "(i p", + "Ĺ ı", + ". front", + ".f ront", + ".fr ont", + "n te", + "nt e", + "Ġ FM", + "ĠF M", + "g uid", + "gu id", + "gui d", + "8 44", + "84 4", + "Ġnegot iations", + "Ġnegotiation s", + "ag onal", + "agon al", + "ago nal", + "9 34", + "93 4", + "Ġtrem end", + "ung eon", + "unge on", + "A dv", + "Ad v", + "car ousel", + "ÃŁ e", + "_ DESC", + "_D ESC", + "_DE SC", + "Ġ hammer", + "Ġh ammer", + "Ġham mer", + "Ġhamm er", + "ẠŃ", + "Ġ ĠĠĠĠĠĠĠĊĊ", + "ĠĠ ĠĠĠĠĠĠĊĊ", + "ĠĠĠĠ ĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠĠ ĊĊ", + "ĠĠĠ ĠĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠ ĠĊĊ", + "ĠĠĠĠĠ ĠĠĠĊĊ", + "ĠĠĠĠĠĠ ĠĠĊĊ", + "ĠĠĠĠĠĠĠĠĊ Ċ", + "- core", + "-c ore", + "-co re", + "-cor e", + "- service", + "-s ervice", + "-ser vice", + "Ġc orners", + "Ġcor ners", + "Ġcorner s", + "Ġcorn ers", + "Ġ SF", + "ĠS F", + "p red", + "pr ed", + "pre d", + "> A", + "ĠJ Label", + "ĠJL abel", + "Ġrom antic", + "Ġroman tic", + "Ġromant ic", + "Ġtest imony", + "Ġtestim ony", + "Ġtestimon y", + "o sc", + "os c", + "Ġ Generation", + "ĠG eneration", + "ĠGener ation", + "ĠGen eration", + "ĠGene ration", + "as ures", + "asure s", + "asu res", + "_ internal", + "_in ternal", + "_int ernal", + "_inter nal", + "Ġ prints", + "Ġpr ints", + "Ġprint s", + "Ġpri nts", + "Ġprin ts", + "Ġ ])Ċ", + "Ġ] )Ċ", + "Ġ]) Ċ", + "ĠC leveland", + "re po", + "rep o", + "D isc", + "Dis c", + "Di sc", + "6 77", + "67 7", + "7 62", + "76 2", + "Ġ \">Ċ", + "Ġ\" >Ċ", + "Ġ\"> Ċ", + "� ���", + "�� ��", + "��� �", + "Ġ nearest", + "Ġne arest", + "Ġnear est", + "5 91", + "59 1", + "_ tb", + "_t b", + "( require", + "(re quire", + "(req uire", + "E OF", + "EO F", + "- child", + "-ch ild", + "Ġb udd", + "Ġbu dd", + "Ġbud d", + ".Xtra Editors", + "al ties", + "alt ies", + "7 23", + "72 3", + "\\\" :\\\"", + "\\\": \\\"", + "W ords", + "Word s", + "9 17", + "91 7", + "Ġloc ally", + "Ġlocal ly", + "Ġp urchases", + "Ġpurch ases", + "Ġpurchase s", + "6 95", + "69 5", + "D rawer", + "Draw er", + "ex tract", + "ext ract", + "extra ct", + "extr act", + "Ġex ecut", + "Ġexec ut", + "Ġexe cut", + "} '.", + "}' .", + "user data", + "Ġfocus es", + "Ġfoc uses", + "- minute", + "-min ute", + "7 64", + "76 4", + "Ġ Publish", + "ĠP ublish", + "ĠPub lish", + "o go", + "og o", + "Ġm ountains", + "Ġmount ains", + "Ġmountain s", + "B ot", + "Bo t", + "} >{", + "}> {", + "Ġt ension", + "Ġtens ion", + "r od", + "ro d", + "m esh", + "me sh", + "mes h", + "Ġtrans formed", + "Ġtransform ed", + ", R", + "( )}Ċ", + "() }Ċ", + "()} Ċ", + ". long", + ".l ong", + ".lo ng", + ".lon g", + "Ġg orgeous", + "Ġgorge ous", + "Ġ Schedule", + "ĠS chedule", + "Ġold est", + "Ġol dest", + "Ġsub process", + "( IN", + "(I N", + "y ect", + "ye ct", + "ĠCo oper", + "ar ness", + "arn ess", + "Ġ Monitor", + "ĠM onitor", + "ĠMon itor", + ". part", + ".p art", + ".par t", + ".pa rt", + "9 72", + "97 2", + "Ġ NBC", + "ĠN BC", + "ĠNB C", + "6 68", + "66 8", + "Ġc otton", + "Ġcot ton", + "Ġcott on", + "Ġ hol", + "Ġh ol", + "Ġho l", + "7 26", + "72 6", + "Ġ rgba", + "Ġr gba", + "Ġrgb a", + "Ġrg ba", + "Ġ Bio", + "ĠB io", + "ĠBi o", + "Cont inue", + "Contin ue", + "P od", + "Po d", + "Ġparticip ating", + "cl usions", + "clus ions", + "clusion s", + "(By Val", + "7 34", + "73 4", + "à ¬", + "Ġ HOW", + "ĠH OW", + "ĠHO W", + "_set opt", + "Ġaccompany ing", + "0 91", + "09 1", + "a ton", + "at on", + "ato n", + "Ġ /\\", + "Ġ/ \\", + "Ġ Authentication", + "ĠAuth entication", + "ĠAuthentic ation", + "i én", + "ĠBar ack", + "ĠBa rack", + "/ *.", + "/* .", + "Ġe ager", + "Ġea ger", + "Ġeag er", + "Ġ Cancel", + "ĠC ancel", + "ĠCan cel", + "ĠCanc el", + "< lemma", + " $", + "O LEAN", + "OLE AN", + "OK IE", + "IB ILITY", + "U AGE", + "UA GE", + "Ġ Survey", + "ĠS urvey", + "ĠSur vey", + "ĠSurv ey", + "0 71", + "07 1", + "Ġre sign", + "Ġr esign", + "Ġres ign", + "w ing", + "win g", + "wi ng", + "Ġse crets", + "Ġsec rets", + "Ġsecret s", + "Ġsecre ts", + "Ġc hips", + "Ġch ips", + "Ġchip s", + "Ġchi ps", + "JSON Object", + "D esktop", + "Des ktop", + "Desk top", + "5 96", + "59 6", + "_SY MBOL", + "( resource", + "(res ource", + "(re source", + "Ġ< />Ċ", + "ĠĊ", + "Ġnew est", + "Ġne west", + "u li", + "ul i", + "Ġde sert", + "Ġdes ert", + "Ġdese rt", + "Ġdeser t", + "Ġd ip", + "Ġdi p", + "Ġ Pow", + "ĠP ow", + "ĠPo w", + "Ġequ ation", + "Ġeq uation", + "Ġposs ibilities", + "Ġpossibilit ies", + "Ġ Fed", + "ĠF ed", + "ĠFe d", + "os ph", + "osp h", + "Ġ [%", + "Ġ[ %", + "Ġ bubble", + "Ġb ubble", + "Ġbu bble", + "Ġbub ble", + "Ġbubb le", + "ether lands", + "7 93", + "79 3", + "Ġc ement", + "Ġce ment", + ". auto", + ".a uto", + ".au to", + ".aut o", + "_ AN", + "_A N", + "âĢĻ .", + "s election", + "se lection", + "select ion", + "sel ection", + "Ġ Bond", + "ĠB ond", + "ĠBo nd", + "ĠBon d", + "9 88", + "98 8", + "D en", + "De n", + "- O", + ". getType", + ".get Type", + "8 96", + "89 6", + ". Window", + ".W indow", + "p res", + "pr es", + "pre s", + "Ġsw inger", + "Ġswing er", + "\" })Ċ", + "\"} )Ċ", + "\"}) Ċ", + "Ġ pip", + "Ġp ip", + "Ġpi p", + "Ġm ice", + "Ġmi ce", + "Ġmic e", + "Ġ compound", + "Ġcomp ound", + "- plugin", + "-pl ugin", + "i ko", + "ik o", + "Ġcent uries", + "i cular", + "ic ular", + "- inline", + "-in line", + "ĉ key", + "ĉk ey", + "> \\<", + ">\\ <", + "EN SION", + "ENS ION", + "Ġ[ čĊ", + "Ġprecis ely", + "Ġprecise ly", + "Ġ été", + "Ġé té", + "Ġét é", + "Ġ Past", + "ĠP ast", + "ĠPa st", + "ĠPas t", + "ĠCam bridge", + "ĠCamb ridge", + "- full", + "-f ull", + "Ġ analyze", + "Ġan alyze", + "Ġanaly ze", + "Ġ Steven", + "ĠSt even", + "ĠSte ven", + "ĠSteve n", + "Ġ nem", + "Ġn em", + "Ġne m", + "d ue", + "du e", + "o ren", + "or en", + "ore n", + "Ġmus cles", + "Ġmuscle s", + "i jing", + "ij ing", + "iji ng", + "8 52", + "85 2", + "/ -", + "ĠKenn edy", + "5 97", + "59 7", + "R M", + "oss ible", + "Ġact ress", + "Ġd olor", + "Ġdo lor", + "Ġdol or", + "9 14", + "91 4", + "å½ ķ", + "N eed", + "Ne ed", + ". toggle", + ".t oggle", + "Ġ Race", + "ĠR ace", + "ĠRa ce", + "ĠRac e", + "w ers", + "we rs", + "wer s", + ". material", + ".m aterial", + ".mat erial", + "Ġ Due", + "ĠD ue", + "ĠDu e", + "Ġ Pel", + "ĠP el", + "ĠPe l", + "# print", + "Ġin dependence", + "Ġindepend ence", + "ex us", + "Sh adow", + "Ġ encoder", + "Ġe ncoder", + "Ġen coder", + "Ġenc oder", + "Ġencode r", + "( level", + "(le vel", + "Ġ Swift", + "ĠSw ift", + ". doc", + ".d oc", + ".do c", + "_ selection", + "_s election", + "_se lection", + "_select ion", + "_sel ection", + "9 52", + "95 2", + "Ġserial VersionUID", + "9 45", + "94 5", + "Label s", + "Lab els", + "Ġperform ances", + "Ġperformance s", + "Ġperforman ces", + ". Tag", + ".T ag", + "ĠN HL", + "ĠNH L", + "i zen", + "iz en", + "ize n", + "/ UIKit", + "/UI Kit", + "9 91", + "99 1", + "_ CONTROL", + "_CONT ROL", + "Ġe arnings", + "Ġear nings", + "Ġearn ings", + "Ġearning s", + "9 75", + "97 5", + "Ġ Alt", + "ĠA lt", + "ĠAl t", + "_ HANDLE", + "_H ANDLE", + "_HAND LE", + "C tx", + "Ġper su", + "Ġpers u", + "Ġ tran", + "Ġt ran", + "Ġtr an", + "Ġtra n", + "ç ¨", + "_ CHANNEL", + "_CH ANNEL", + "_CHAN NEL", + "Ġs atisfaction", + "Ġsatisf action", + "Ġ GP", + "ĠG P", + "7 69", + "76 9", + "i ox", + "io x", + "m itt", + "mit t", + "mi tt", + "l ando", + "land o", + "la ndo", + "lan do", + "Ġ pig", + "Ġp ig", + "Ġpi g", + "in als", + "inal s", + "ina ls", + "ê ncia", + "ên cia", + "7 31", + "73 1", + "S urface", + "Sur face", + "Ġ UUID", + "ĠU UID", + "Ġbenef icial", + "Ġbenefici al", + "Ġ sequences", + "Ġse quences", + "Ġsequence s", + "Ġsequ ences", + "ĉ memset", + "ĉmem set", + "Ġmag ical", + "Ġmagic al", + " «", + "Ġw orn", + "Ġwor n", + "Ġwo rn", + "A SC", + "AS C", + "p opup", + "pop up", + "C OMP", + "CO MP", + "COM P", + "_ before", + "_b efore", + "_be fore", + "e ness", + "en ess", + "ene ss", + "enes s", + "U i", + "L es", + "Le s", + ". require", + ".re quire", + ".req uire", + ". Serializable", + ".Serial izable", + "add Gap", + "Ġ authorization", + "Ġauthor ization", + "0 85", + "08 5", + ".py plot", + "u rray", + "ur ray", + "urr ay", + "l atitude", + "lat itude", + "8 45", + "84 5", + "f rames", + "fr ames", + "frame s", + "fra mes", + "fram es", + "a js", + "aj s", + "Ġ compass", + "Ġcom pass", + "Ġcomp ass", + "Ġ observations", + "Ġobs ervations", + "Ġobserv ations", + "Ġobservation s", + "_ sup", + "_s up", + "_su p", + ".en viron", + ".env iron", + "Ġtr iple", + "Ġtri ple", + "Ġtrip le", + "Ġ Ruby", + "ĠR uby", + "ĠRub y", + "ĠRu by", + "Ġd rain", + "Ġdr ain", + "Ġdra in", + "_ FILTER", + "_F ILTER", + "S an", + "Sa n", + "U MP", + "UM P", + "Null Exception", + "Ġ Gab", + "ĠG ab", + "ĠGa b", + "o we", + "ow e", + "ĠTur kish", + "ĠTurk ish", + "_ sequence", + "_se quence", + "Ġ Grant", + "ĠG rant", + "ĠGr ant", + "ĠGran t", + "ĠGra nt", + "u ela", + "ue la", + "uel a", + "Ġ wo", + "Ġw o", + "Ġ cube", + "Ġc ube", + "Ġcu be", + "Ġcub e", + "i q", + "Ġdis orders", + "Ġdisorder s", + "Ġextra ordinary", + "Ġextraordin ary", + "Ġ ctrl", + "Ġc trl", + "Ġct rl", + "Ġctr l", + "Ġ Seq", + "ĠS eq", + "ĠSe q", + "en tr", + "ent r", + "8 65", + "86 5", + "Ġsan ctions", + "Ġsanct ions", + "Ġsanction s", + "9 49", + "94 9", + "ut sch", + "uts ch", + "Re ports", + "Report s", + "Rep orts", + "Repo rts", + "Ġ inherit", + "Ġin herit", + "Ġinher it", + "P eriod", + "Per iod", + "Ġphot ography", + "Ġphotograph y", + "Ġ Framework", + "ĠF ramework", + "ĠFr amework", + "ĠFrame work", + "ĠFram ework", + "Ġspecial ist", + "Ġspeci alist", + "Ġ ?ĊĊ", + "Ġ? ĊĊ", + "Ġ?Ċ Ċ", + "_ selected", + "_se lected", + "_select ed", + "_sel ected", + ". Player", + ".P layer", + ".Pl ayer", + ".Play er", + "Ġ allocation", + "Ġal location", + "Ġall ocation", + "Ġalloc ation", + "Ġallo cation", + "( account", + "(a ccount", + "(ac count", + "(acc ount", + "Ġ structural", + "Ġstruct ural", + "v able", + "va ble", + "- offset", + "-off set", + "-o ffset", + "-offs et", + ".App CompatActivity", + "а м", + "аР¼", + ".Add WithValue", + "Ġ icons", + "Ġi cons", + "Ġicon s", + "Ġic ons", + "Ġ shutdown", + "Ġsh utdown", + "Ġshut down", + "_ low", + "_l ow", + "_lo w", + "Ġ Compare", + "ĠCom pare", + "ĠComp are", + "ĠCompar e", + "Ġ Ce", + "ĠC e", + "= head", + "=h ead", + "l am", + "la m", + ". predict", + ".p redict", + ".pre dict", + ".pred ict", + "_ DEC", + "_D EC", + "_DE C", + "Ġ Sleep", + "ĠS leep", + "ĠSlee p", + "ĠSle ep", + "Ġ Gratis", + "ĠGr atis", + "ĠGrat is", + "Ġs uggestion", + "Ġsuggest ion", + "Ġ DEL", + "ĠD EL", + "ĠDE L", + "c aff", + "ca ff", + "caf f", + "av irus", + "avi rus", + "avir us", + "No thing", + "ŀ ĭ", + "Ġwide spread", + "Ġwides pread", + "Ġmechan isms", + "Ġmechanism s", + "Ġ textAlign", + "Ġtext Align", + "oc cup", + "occ up", + "Ġ Rail", + "ĠR ail", + "ĠRa il", + "ĠRai l", + ": NS", + ":N S", + "Ġ fiber", + "Ġf iber", + "Ġfi ber", + "Ġfib er", + "Ġ mk", + "Ġm k", + "Ġv intage", + "- long", + "-l ong", + "-lo ng", + ". reduce", + ".re duce", + ".red uce", + ". Entities", + ".Ent ities", + "( record", + "(re cord", + "(rec ord", + "Ġ pleasant", + "Ġple asant", + "Ġpleas ant", + "F RING", + "FR ING", + ". Cells", + ".C ells", + ".Cell s", + "O TT", + "OT T", + "ĉ elseif", + "ĉelse if", + "6 49", + "64 9", + "7 24", + "72 4", + "_ confirm", + "_con firm", + "_conf irm", + "ĠView Group", + "s ym", + "sy m", + "Ġ pray", + "Ġp ray", + "Ġpr ay", + "Ġpra y", + "Ġsus pected", + "Ġsusp ected", + "Ġsuspect ed", + "Cont ains", + "Con tains", + "Contain s", + "Conta ins", + "9 83", + "98 3", + "Ġb orders", + "Ġborder s", + "Ġbor ders", + "Ġbord ers", + "Ġcomponent Did", + "A SSERT", + "ASS ERT", + "Ġin finite", + "Ġinf inite", + "Ġinfinit e", + "- order", + "-or der", + "Ġ hello", + "Ġh ello", + "Ġhel lo", + "Ġhell o", + "Ġ Grade", + "ĠG rade", + "ĠGr ade", + "ĠGrad e", + "ĠGra de", + ".currentTime Millis", + "ap olis", + "apol is", + "apo lis", + "z h", + "ĉ Object", + "ĉO bject", + ": \\\\", + ":\\ \\", + "H O", + "val uation", + "valu ation", + "Ġ vocab", + "Ġv ocab", + "Ġvo cab", + "Ġvoc ab", + "7 19", + "71 9", + "Ġ coupon", + "Ġc oupon", + "Ġco upon", + "Ġcou pon", + "Ġcoup on", + "ata bases", + "atab ases", + "atabase s", + ". GetType", + ".Get Type", + "L earn", + "Le arn", + "7 92", + "79 2", + "] =\"", + "]= \"", + "Ġ Gary", + "ĠG ary", + "ĠGar y", + "ĠGa ry", + "ot ive", + "oti ve", + "Ġ ash", + "Ġa sh", + "Ġas h", + "Ġ bib", + "Ġb ib", + "Ġbi b", + "X XXX", + "XX XX", + "XXX X", + "Ġ balanced", + "Ġbalance d", + "Ġbal anced", + "VAL UE", + "Ġ Nat", + "ĠN at", + "ĠNa t", + "_ Ad", + "_A d", + "< E", + "åĮ º", + "Ġ MethodInfo", + "ĠMethod Info", + "8 97", + "89 7", + "L IB", + "LI B", + "Ġconsider able", + "Ġconsid erable", + "Ġ Industry", + "ĠInd ustry", + "ĠIndust ry", + "t ests", + "te sts", + "test s", + "tes ts", + ". setTitle", + ".set Title", + "Ġ Bluetooth", + "ĠB luetooth", + "ĠBl uetooth", + "ĠBlu etooth", + "Ġ mapped", + "Ġm apped", + "Ġmap ped", + "Ġma pped", + "Ġ Bruce", + "ĠBr uce", + "ĠBru ce", + "Ġ MainWindow", + "ĠMain Window", + "ĉ status", + "ĉs tatus", + "ĉst atus", + "ĉstat us", + "Ġ raz", + "Ġr az", + "Ġra z", + "ĠM and", + "ĠMan d", + "ĠMa nd", + "Ġ classification", + "Ġclass ification", + "Per missions", + "Permission s", + "Perm issions", + "9 69", + "96 9", + "Ġ ----------------------------------------------------------------------------", + "Ġ---------------------------------------------------------------- ------------", + "Ġ------------------------------------------------ ----------------------------", + "Ġ------------ ----------------------------------------------------------------", + "Ġ------ ----------------------------------------------------------------------", + "Ġ------------------------------------------------------------ ----------------", + "Ġ------------------------------------------------------------------------- ---", + "Ġ containers", + "Ġcont ainers", + "Ġcontainer s", + "Ġcontain ers", + "Ġconta iners", + ": set", + ":s et", + "_ xml", + "_x ml", + "Ġwh ilst", + "Th rough", + "Thr ough", + "Ġv align", + "Ġval ign", + "Ġworld s", + "Ġwor lds", + "C ORD", + "CO RD", + "COR D", + "ED IA", + "EDI A", + "ÑĢ ов", + "ÑĢо в", + "Ġs pare", + "Ġsp are", + "Ġspa re", + "Ġspar e", + "Ġ Had", + "ĠH ad", + "ĠHa d", + "Ġ DEF", + "ĠD EF", + "ĠDE F", + "( ptr", + "(p tr", + "(pt r", + "Ġw arming", + "Ġwar ming", + "Ġwarm ing", + "8 98", + "89 8", + "ठ¾", + "Ġcons ensus", + "a gne", + "ag ne", + "agn e", + "C TL", + "CT L", + "Ġ ìķ", + "Ġì ķ", + ". Main", + ".M ain", + ".Ma in", + "web Element", + "Ġp ist", + "Ġpi st", + "Ġpis t", + "F lash", + "Fl ash", + "App end", + "Ap pend", + "Appe nd", + ".tw img", + "T ap", + "Ta p", + "Ġveget ables", + "Ġvegetable s", + "a lg", + "al g", + "0 58", + "05 8", + ". sample", + ".s ample", + ".sam ple", + "Ġco aching", + "Ġcoach ing", + "( ind", + "(i nd", + "(in d", + "Cell Value", + "Check Box", + "Ġ Hell", + "ĠH ell", + "ĠHe ll", + "ĠHel l", + "R OOT", + "RO OT", + "7 96", + "79 6", + "Ġst adium", + "Ġstad ium", + "Ġinvestig ating", + ") %", + "s ted", + "st ed", + "ste d", + "9 65", + "96 5", + "Ġ Writing", + "ĠW riting", + "ĠWr iting", + "Ġ ê²", + "Ġê ²", + "Ġ uno", + "Ġu no", + "Ġun o", + "Ġ {{--", + "Ġ{{ --", + "Ġ coords", + "Ġco ords", + "Ġcoord s", + "Ġun ser", + "Ġuns er", + "o rganization", + "organ ization", + "Ġ Crime", + "ĠCr ime", + "ĠCri me", + "ĠDem ocrat", + "ĠDemocr at", + "5 79", + "57 9", + "Ġ vin", + "Ġv in", + "Ġvi n", + "/ file", + "/f ile", + "0 78", + "07 8", + "- api", + "-a pi", + "-ap i", + "Ġ Ay", + "ĠA y", + "Ġf unded", + "Ġfun ded", + "Ġfund ed", + "ĠBr exit", + "ĠBre xit", + "ĠG h", + "ent ina", + "enti na", + "entin a", + "c ases", + "ca ses", + "case s", + "cas es", + "Ġ dash", + "Ġd ash", + "Ġda sh", + "Ġdas h", + "Ġ!! }Ċ", + "Ġ!!} Ċ", + "H I", + "Off ice", + "Ġcap tain", + "Ġcapt ain", + "Ġwor ship", + "Ġwors hip", + "Ġworsh ip", + "\\ C", + "7 33", + "73 3", + "8 51", + "85 1", + "Ġg lobe", + "Ġgl obe", + "Ġglob e", + "Ġglo be", + "_ board", + "_b oard", + "_bo ard", + "Ġb abies", + "Ġba bies", + "Ġbab ies", + "8 76", + "87 6", + "Ġcon secutive", + "Ġconsec utive", + "Ġenh anced", + "Ġenhance d", + "er eum", + "ere um", + "ĠAd vis", + "ĠAdv is", + "Ġg rain", + "Ġgr ain", + "Ġgra in", + "7 71", + "77 1", + "Ġc raw", + "Ġcr aw", + "Ġcra w", + "ancell ationToken", + "ancellation Token", + ". alpha", + ".al pha", + "_ WITH", + "_W ITH", + "ĠO tt", + "ĠOt t", + "Ġ Cool", + "ĠC ool", + "ĠCo ol", + ". batch", + ".b atch", + ".bat ch", + "Ġ verified", + "Ġver ified", + "( callback", + "(c allback", + "(call back", + "Ġreg ards", + "Ġregard s", + "6 83", + "68 3", + "Ġ IntPtr", + "ĠInt Ptr", + "o ucher", + "ou cher", + "ouch er", + "Ġ kin", + "Ġk in", + "Ġki n", + "Ġt ouched", + "Ġtouch ed", + "Ġtou ched", + "it Ãł", + "a thon", + "at hon", + "ath on", + "Ġadj acent", + "Ġaccom panied", + "L EAR", + "LE AR", + "Ġim plies", + "Ġimp lies", + "Ġimpl ies", + "Ġ hill", + "Ġh ill", + "Ġhi ll", + "Ġhil l", + "ĠB altimore", + "ĠBalt imore", + "= \"-", + "=\" -", + "F inally", + "Fin ally", + "Final ly", + "8 83", + "88 3", + "S am", + "Sa m", + "ic opt", + "ico pt", + "Ġs od", + "Ġso d", + "Ġ maj", + "Ġm aj", + "Ġma j", + "Ġ Shipping", + "ĠSh ipping", + "ĠShip ping", + "ĠShi pping", + "Ġ getAll", + "Ġget All", + "Ġco aches", + "Ġcoach es", + "Ġdon ations", + "Ġdonation s", + "i lot", + "il ot", + "ilo t", + "Ġ Tar", + "ĠT ar", + "ĠTa r", + "c err", + "ce rr", + "cer r", + "Ġ badge", + "Ġb adge", + "Ġbad ge", + "Ġba dge", + "Ġ markers", + "Ġm arkers", + "Ġmark ers", + "Ġmar kers", + "Ġmarker s", + "Ġ Rand", + "ĠR and", + "ĠRa nd", + "ĠRan d", + "a ised", + "ai sed", + "ais ed", + "aise d", + "iss ance", + "issa nce", + "issan ce", + "Ġexpl oring", + "Ġexplo ring", + "Ġexplor ing", + "8 27", + "82 7", + "u ced", + "uc ed", + "uce d", + "ĠInd onesia", + "ĠIndones ia", + "ĠIndo nesia", + "Ġbe neath", + "Ġbene ath", + "Ġm agnetic", + "Ġmagn etic", + "Ġmagnet ic", + "Ġm useum", + "Ġmus eum", + "Ġmuse um", + "match Condition", + "Ġdis rupt", + "Ġre mind", + "Ġrem ind", + "Ġremin d", + "Ġ TM", + "ĠT M", + "Ġ /><", + "Ġ/ ><", + "Ġ/> <", + "Ġf ool", + "Ġfo ol", + "Ġfoo l", + "Ġ esk", + "Ġe sk", + "Ġes k", + ". Null", + ".N ull", + "Ġ Dies", + "ĠD ies", + "ĠDi es", + "ĠDie s", + "_ OUTPUT", + "_OUT PUT", + "_TYPE D", + "_TYP ED", + "Ġp ainted", + "Ġpaint ed", + "Ġpain ted", + "6 73", + "67 3", + "7 35", + "73 5", + "Ġsoph istic", + "Ġ Bear", + "ĠB ear", + "ĠBe ar", + "ĠBea r", + "* n", + "_ PACK", + "_P ACK", + "_PA CK", + "Ġdel ivering", + "Ġdeliver ing", + "Ġ COUNT", + "ĠC OUNT", + "ĠCO UNT", + "åį ķ", + "Ġj eg", + "Ġje g", + "- car", + "-c ar", + "-ca r", + "f name", + "fn ame", + "Ġr anging", + "Ġran ging", + "Ġrang ing", + "8 48", + "84 8", + "Ġ Neg", + "ĠN eg", + "ĠNe g", + "/ ******/", + "Ġ CHAR", + "ĠCH AR", + "Ġu ltra", + "Ġult ra", + "Ġul tra", + "Ġultr a", + "G rad", + "Gr ad", + "= t", + "Ġjud ges", + "Ġjudge s", + "ĠD ise", + "ĠDis e", + "ĠDi se", + "an ners", + "ann ers", + "anner s", + "anne rs", + "9 85", + "98 5", + "8 91", + "89 1", + "8 61", + "86 1", + "Ġ scal", + "Ġs cal", + "Ġsc al", + "Ġsca l", + "_ cal", + "_c al", + "_ca l", + "ĠCON NECTION", + "ĠCONNECT ION", + "_ embed", + "_em bed", + "_emb ed", + "( fn", + "(f n", + "Ġ Craft", + "ĠC raft", + "ĠCr aft", + "ĠCra ft", + "0 47", + "04 7", + "Ġ Pas", + "ĠP as", + "ĠPa s", + "\" )->", + "\") ->", + ". convert", + ".con vert", + ".conv ert", + ". resource", + ".re source", + ".res ource", + "Ġ STATUS", + "ĠST ATUS", + "ĠSTAT US", + "ô ng", + "ôn g", + "Ġ Tit", + "ĠT it", + "ĠTi t", + "Ġclass room", + "ĠArch itect", + "ĠK ings", + "ĠKing s", + "ĠKin gs", + "Ġ steady", + "Ġste ady", + "Ġstead y", + "/* !Ċ", + "/*! Ċ", + "Ġ Gene", + "ĠG ene", + "ĠGe ne", + "ĠGen e", + ") \";Ċ", + ")\" ;Ċ", + "i cia", + "ic ia", + "ici a", + "s tan", + "st an", + "sta n", + "Ġ Construction", + "ĠCon struction", + "ĠConstruct ion", + "ĠConstr uction", + "um per", + "ump er", + "9 51", + "95 1", + "w c", + "Ġ CBS", + "ĠC BS", + "ĠCB S", + "in ging", + "ing ing", + "- party", + "-p arty", + "-part y", + "-par ty", + "( driver", + "(d river", + "(dr iver", + "M ARK", + "MA RK", + "MAR K", + "0 82", + "08 2", + "Ġ nested", + "Ġn ested", + "Ġne sted", + "Ġnest ed", + "Ġneste d", + "Ġnes ted", + "e ward", + "ew ard", + "Ġ dependency", + "Ġd ependency", + "Ġdep endency", + "Ġdepend ency", + "Ġm ales", + "Ġma les", + "Ġmale s", + "Ġmal es", + "9 28", + "92 8", + "Ġ ONE", + "ĠO NE", + "ĠON E", + "Ġ Production", + "ĠP roduction", + "ĠPro duction", + "ĠProduct ion", + "ĠProdu ction", + "ĠProd uction", + "] [$", + "][ $", + "ãĥ¼ ãĥ", + "_ LOAD", + "_L OAD", + "_LO AD", + "ĠB ol", + "ĠBo l", + "el ry", + "8 31", + "83 1", + "ł éĻ¤", + "Ġ Require", + "ĠRe quire", + "ĠReq uire", + "Ġ placing", + "Ġpl acing", + "Ġplac ing", + "Ġpla cing", + "x xx", + "xx x", + "C ALE", + "CA LE", + "CAL E", + "Ġ thumb", + "Ġth umb", + "Ġthu mb", + "8 24", + "82 4", + "Ch oose", + "Cho ose", + "Ġ prototype", + "Ġprot otype", + "Ġproto type", + "V OID", + "VO ID", + "Ġles bian", + "Ġlesb ian", + "Ġlesbi an", + "7 41", + "74 1", + "Ġ traits", + "Ġt raits", + "Ġtr aits", + "Ġtra its", + "Ġtrait s", + "Ġtrai ts", + "Sh arp", + "Shar p", + "Sha rp", + "Ġ consume", + "Ġcon sume", + "Ġcons ume", + "Ġconsum e", + "Tr uth", + "Ġaction Performed", + "Ġ Environmental", + "ĠEnvironment al", + "Ġ Dean", + "ĠD ean", + "ĠDe an", + "Ġ estado", + "Ġest ado", + "Ġesta do", + "Ġestad o", + "s ame", + "sa me", + "sam e", + "Ġ numeric", + "Ġn umeric", + "Ġnum eric", + "Ġnumer ic", + "Ġnu meric", + "Ġtrans it", + "Ġtran sit", + ". Email", + ".E mail", + "- side", + "-s ide", + "_ RUN", + "_R UN", + "ĠV illage", + "ĠVill age", + "ĠVilla ge", + "ĠVil lage", + "_ OPEN", + "_OP EN", + "è ¦", + ". rem", + ".re m", + ".r em", + "- warning", + "-w arning", + "-war ning", + "a nya", + "an ya", + "any a", + "Property Changed", + "Ġ (!_", + "Ġ(! _", + "( check", + "(c heck", + "(ch eck", + "i lia", + "il ia", + "ili a", + "Ġ Soft", + "ĠS oft", + "ĠSo ft", + "ĠSof t", + "st eps", + "ste ps", + "step s", + "ĠMad rid", + "Memory Warning", + "Ġ handlers", + "Ġhand lers", + "Ġhandle rs", + "Ġhandler s", + "Ġexperi encing", + "Ġ inspect", + "Ġin spect", + "Ġins pect", + "Ġinsp ect", + "button s", + "but tons", + "butt ons", + "Receive MemoryWarning", + "ch emy", + "che my", + "chem y", + "L inks", + "Link s", + "Lin ks", + "Ġurl lib", + "Ġur llib", + ".System Colors", + "Ġ Eigen", + "ĠE igen", + "ĠEig en", + "Ġpun ishment", + "Ġpunish ment", + ":UI Control", + "b ara", + "bar a", + "ba ra", + "- set", + "-s et", + "-se t", + "Ġ }čĊčĊčĊ", + "Ġ} čĊčĊčĊ", + "Ġ}čĊ čĊčĊ", + "Ġ}čĊčĊ čĊ", + "Ġt olerance", + "Ġtoler ance", + "Ġ interfaces", + "Ġinter faces", + "Ġinterface s", + "Ġinterf aces", + ". redirect", + ".re direct", + ".red irect", + "ighb ors", + "ighbor s", + "cs rf", + "csr f", + "_ background", + "_back ground", + ". Utils", + ".Util s", + "_ HT", + "_H T", + "6 92", + "69 2", + "Ġ Interest", + "ĠInter est", + "i mos", + "im os", + "imo s", + "Ġgr ants", + "Ġgrant s", + "Ġgran ts", + "Ġgra nts", + "0 83", + "08 3", + "Ġexam ined", + "Ġexamine d", + "Ð Ķ", + "Ġ cf", + "Ġc f", + "f orge", + "for ge", + "forg e", + "b acks", + "back s", + "ba cks", + "bac ks", + "Ġ Objects", + "ĠObject s", + "ĠObj ects", + "_ sent", + "_s ent", + "_se nt", + ". entry", + ".en try", + ".ent ry", + "Ġ THEN", + "ĠT HEN", + "ĠTHE N", + "ĠTH EN", + "ell ido", + "elli do", + "c ia", + "ci a", + ", res", + ",r es", + ",re s", + "6 59", + "65 9", + "6 81", + "68 1", + "/st dc", + "/std c", + ". nd", + ".n d", + "( Int", + "(I nt", + "(In t", + "Ġ Authors", + "ĠAuthor s", + "ĠAuth ors", + "ĠApp CompatActivity", + "' {", + "Ġ medi", + "Ġm edi", + "Ġme di", + "Ġmed i", + "M usic", + "Mu sic", + "Mus ic", + "i gm", + "ig m", + "ce ipt", + "Ġ auss", + "Ġa uss", + "Ġau ss", + "Ġaus s", + "Ġtarget ing", + "Ġtarg eting", + "Ġ Keys", + "ĠKey s", + "ĠKe ys", + "h n", + ": ]Ċ", + ":] Ċ", + "Ġmin eral", + "Ġmine ral", + "Ġminer al", + "à ®", + ". ca", + ".c a", + "7 61", + "76 1", + "o med", + "om ed", + "ome d", + "Ġ sheets", + "Ġs heets", + "Ġshe ets", + "Ġsheet s", + "Ġc amb", + "Ġca mb", + "Ġcam b", + "Ġdead ly", + ". inject", + ".in ject", + "( unit", + "(u nit", + "(un it", + "Ġ Selection", + "ĠS election", + "ĠSe lection", + "ĠSelect ion", + "ĠSel ection", + "ĠSele ction", + ".g ms", + "( connection", + "(con nection", + "(conn ection", + "(connect ion", + "Ġ $(\"", + "Ġ$ (\"", + "Ġ$( \"", + "é mon", + "ém on", + "Ġ Currently", + "ĠCurrent ly", + "p te", + "pt e", + "_ paths", + "_p aths", + "_path s", + "_pa ths", + "_pat hs", + "8 47", + "84 7", + "le af", + "lea f", + "Ġimp lications", + "Ġimpl ications", + "Ġimplication s", + "Ġimplic ations", + "p osal", + "pos al", + "po sal", + "ä½ į", + "[ /", + "a ncia", + "an cia", + "anc ia", + "é Ľ", + "m ul", + "mu l", + "c ie", + "ci e", + "Ġge ile", + "Ġgeil e", + "6 79", + "67 9", + "im als", + "imal s", + "ima ls", + "UI View", + "Ġs urre", + "Ġsur re", + "s erialize", + "serial ize", + "I SO", + "IS O", + "Ġar bitrary", + "Ġarbit rary", + "Ġarbitr ary", + "Ġsock addr", + ". fn", + ".f n", + "Ġ Merc", + "ĠM erc", + "ĠMe rc", + "ĠMer c", + "Ġ casting", + "Ġc asting", + "Ġcas ting", + "Ġcast ing", + "Key Down", + "Ġ newValue", + "Ġnew Value", + "ĠnewVal ue", + "op ens", + "open s", + "ope ns", + "7 17", + "71 7", + "T odo", + "To do", + "Ġflex ibility", + "ĉ ĉĉĉĠĠ", + "ĉĉ ĉĉĠĠ", + "ĉĉĉĉ ĠĠ", + "ĉĉĉ ĉĠĠ", + "ĉĉĉĉĠ Ġ", + "V elocity", + "Vel ocity", + "ú n", + "r owing", + "ro wing", + "row ing", + "Ġ computed", + "Ġcom puted", + "Ġcomp uted", + "Ġcomput ed", + "Ġcompute d", + "` )Ċ", + "`) Ċ", + "st atement", + "state ment", + "stat ement", + "sta tement", + "Ġ ri", + "Ġr i", + "_ cart", + "_c art", + "_car t", + "_ca rt", + "L ow", + "Lo w", + "trans fer", + ". nav", + ".n av", + "Ġ grave", + "Ġgr ave", + "Ġgra ve", + "Ġgrav e", + "Ġ Door", + "ĠD oor", + "ĠDo or", + "ĉ alert", + "ĉal ert", + "6 91", + "69 1", + "6 98", + "69 8", + ". subscribe", + ".sub scribe", + "- profile", + "-pro file", + "-pr ofile", + "-prof ile", + "ĉ base", + "ĉb ase", + "Ġ âĪĴ", + "ĠâĪ Ĵ", + "_ _ĊĊ", + "__ ĊĊ", + "__Ċ Ċ", + "Ġengine ers", + "Ġengineer s", + "Ġexp losion", + "Ġexplos ion", + "Ġd ari", + "Ġda ri", + "Ġdar i", + "6 82", + "68 2", + "ĉ Log", + "ĉL og", + "o nal", + "on al", + "ona l", + "Ġis olated", + "Ġisol ated", + "Ġiso lated", + "Ġisolate d", + "{ i", + "Ġ Msg", + "ĠM sg", + "ĠMs g", + "F uture", + "Fu ture", + "Ġr acist", + "Ġrac ist", + "- wrap", + "-w rap", + "Ġ Vers", + "ĠV ers", + "ĠVer s", + "ĠVe rs", + "b org", + "bo rg", + "bor g", + "I SION", + "IS ION", + "Ġ ÑĢаÐ", + "ĠÑĢ аÐ", + "ĠÑĢа Ð", + "ĠY an", + "ĠYa n", + "8 36", + "83 6", + "init With", + "Ġn omin", + "Ġno min", + "Ġnom in", + "( empty", + "(em pty", + "(emp ty", + "ÃŃ n", + "ã Ĥ¤", + "ãĤ ¤", + "ĉ width", + "ĉw idth", + "Ġch amber", + "Ġcham ber", + "/ ajax", + "/a jax", + "E MP", + "EM P", + "0 93", + "09 3", + "Ġne ces", + "Ġnec es", + "i vos", + "iv os", + "ivo s", + "log ic", + "* )&", + "*) &", + "cri pts", + "cript s", + "9 76", + "97 6", + "Row At", + "0 53", + "05 3", + "i blings", + "ib lings", + "ibling s", + "Ġ ears", + "Ġe ars", + "Ġear s", + "Ġea rs", + "Ġcomp uting", + "Ġcomput ing", + "Ġ maker", + "Ġm aker", + "Ġmake r", + "Ġma ker", + "Ġmak er", + "Ġ Neither", + "ĠN either", + "ĠNe ither", + "b readcrumb", + "Ġ serialize", + "Ġs erialize", + "Ġserial ize", + "Ġ Within", + "ĠWith in", + "ĠWi thin", + "ĠWit hin", + "Ġd ell", + "Ġde ll", + "Ġdel l", + "_ TRACE", + "_TR ACE", + "_TRA CE", + "0 92", + "09 2", + "= a", + "Ġw ishes", + "Ġwish es", + "Ġwis hes", + "- inch", + "-in ch", + "-inc h", + "ĠD or", + "ĠDo r", + "Ġinnoc ent", + "ĠD ol", + "ĠDo l", + "Ġint ens", + "Ġinte ns", + "for ced", + "force d", + "forc ed", + "0 54", + "05 4", + "Ġ BIT", + "ĠB IT", + "ĠBI T", + "Ġphot ographs", + "Ġphoto graphs", + "Ġphotograph s", + "Ġc asa", + "Ġca sa", + "Ġcas a", + "Ġ Len", + "ĠL en", + "ĠLe n", + "\\ Framework", + "\\F ramework", + ". Simple", + ".S imple", + "Ġd ear", + "Ġde ar", + "8 95", + "89 5", + ") /(", + ")/ (", + "ip pi", + "ipp i", + "Ġ owns", + "Ġown s", + "Ġow ns", + "P layers", + "Pl ayers", + "Player s", + "Play ers", + "Ġprop osals", + "Ġpropos als", + "Ġproposal s", + ". pi", + ".p i", + "us alem", + "usa lem", + "usal em", + "D amage", + "Da mage", + "Dam age", + "Ġcal ories", + "Ġcalorie s", + "Ġcalor ies", + "Ġ Creative", + "ĠC reative", + "ĠCre ative", + "ĠCreat ive", + "Ġ [$", + "Ġ[ $", + "Ġ //čĊ", + "Ġ// čĊ", + "Ġ/ /čĊ", + "7 86", + "78 6", + "And View", + "è me", + "èm e", + ". custom", + ".c ustom", + "_ factory", + "_f actory", + "_factor y", + "_fact ory", + "comm ands", + "command s", + "comma nds", + "_ look", + "_l ook", + "_lo ok", + "Ġ strcmp", + "Ġstr cmp", + "Y N", + "a ired", + "air ed", + "ai red", + "aire d", + "Ġ audit", + "Ġa udit", + "Ġaud it", + "Ġau dit", + "Ġaudi t", + "о ÑģÑĤ", + "оÑģ ÑĤ", + "Ġ Reverse", + "ĠRe verse", + "ĠRev erse", + "ĠRever se", + "ropri ate", + "e tics", + "et ics", + "etic s", + "eti cs", + "< vector", + "';Ċ", + "\"> ';Ċ", + "\">' ;Ċ", + "Ġpe pper", + "Ġpepp er", + "Ġpep per", + "9 89", + "98 9", + "Ġ shed", + "Ġs hed", + "Ġsh ed", + "Ġshe d", + "Ġ Medium", + "ĠM edium", + "ĠMed ium", + "ĠMedi um", + "Ġ Cookie", + "ĠC ookie", + "ĠCo okie", + "ĠCook ie", + "8 89", + "88 9", + "Ġoverse as", + "ed or", + "edo r", + "as urement", + "asure ment", + "asu rement", + "7 66", + "76 6", + "åŃ ĺ", + "Ġ' .'", + "Ġ'. '", + "Ġ php", + "Ġp hp", + "Ġph p", + "Ġ PROC", + "ĠP ROC", + "ĠPRO C", + "ĠPR OC", + "Ġexcept ional", + "Ġexception al", + "( th", + "(t h", + "Ġ Jet", + "ĠJ et", + "ĠJe t", + "Ġ occupied", + "Ġoccup ied", + ". setImage", + ".set Image", + "Ġ Related", + "ĠRe lated", + "ĠRel ated", + "u cker", + "uc ker", + "uck er", + "M embers", + "Member s", + "Mem bers", + "PR INT", + "PRI NT", + "ĠG lo", + "ĠGl o", + "_ VIEW", + "_V IEW", + "} \",Ċ", + "}\", Ċ", + "}\" ,Ċ", + "Ġad option", + "Ġadopt ion", + "Ġado ption", + "[ ])Ċ", + "[] )Ċ", + "[]) Ċ", + "8 42", + "84 2", + "ĠMiss ouri", + "ĠLin coln", + "er ald", + "era ld", + "eral d", + "P opup", + "Pop up", + "Ġf ate", + "Ġfa te", + "Ġfat e", + "- bootstrap", + "-boot strap", + "f ections", + "fe ctions", + "fect ions", + "fection s", + "Ġ Poll", + "ĠP oll", + "ĠPol l", + "ĠPo ll", + "_ ARGS", + "_ARG S", + "_AR GS", + "in ance", + "ina nce", + "inan ce", + "6 97", + "69 7", + "- home", + "-h ome", + ". ),", + ".) ,", + "_ done", + "_d one", + "_do ne", + "_don e", + "6 94", + "69 4", + ": ĊĊĊ", + ":Ċ ĊĊ", + ":ĊĊ Ċ", + "Ġdiscuss ing", + "Ġ SQLException", + "ĠSQL Exception", + "Ġelect ro", + "Ġelectr o", + "ĉ req", + "ĉr eq", + "ĉre q", + "Ġ zw", + "Ġz w", + "8 86", + "88 6", + "Ġl ui", + "Ġlu i", + "9 32", + "93 2", + "Ġover night", + "$ user", + "Ġ WAY", + "ĠW AY", + "ĠWA Y", + "Ġall erg", + "Ġalle rg", + "Ġaller g", + "Ġdis appointed", + "Ġdisappoint ed", + "Ġrad iation", + "Ġradi ation", + "Ġim pressed", + "Ġimp ressed", + "Ġimpress ed", + "Ġimpr essed", + "Ġimpres sed", + "if icates", + "ific ates", + "ificate s", + "ifica tes", + "Ġt ob", + "Ġto b", + "CL ASS", + "CLA SS", + "Ġ cuda", + "Ġc uda", + "Ġcu da", + "Ġcud a", + "_ det", + "_d et", + "_de t", + "- post", + "-p ost", + "-pos t", + "-po st", + "u lu", + "ul u", + "Trans lation", + "- hand", + "-h and", + ". year", + ".y ear", + "Ġ Mongo", + "ĠM ongo", + "ĠMon go", + "ĠMo ngo", + "ĠMong o", + "Ġun clear", + "Ġunc lear", + "Ġuncle ar", + ". engine", + ".e ngine", + ".eng ine", + "WEB PACK", + "r ices", + "ri ces", + "ric es", + "rice s", + "_ ACCESS", + "_AC CESS", + "_ACC ESS", + "Ġh olidays", + "Ġholiday s", + "per cent", + "perc ent", + ". Identity", + ".Id entity", + "Ġ Gov", + "ĠG ov", + "ĠGo v", + "Ġpass ionate", + "Ġpassion ate", + "! !.", + "!! .", + "ĠG reece", + "ĠGre ece", + "ĠGree ce", + "plus plus", + "' ));", + "') );", + "')) ;", + "G P", + "Ġex cit", + "Ġexc it", + ".tab Page", + "_ cond", + "_c ond", + "_con d", + "_co nd", + "Ġ sponsor", + "Ġs ponsor", + "Ġspons or", + "M ODULE", + "MOD ULE", + "_ proc", + "_p roc", + "_pro c", + "_pr oc", + "Ġ $Ċ", + "Ġ$ Ċ", + "Ġr ational", + "Ġrat ional", + "Ġratio nal", + "Ġration al", + ". Tool", + ".T ool", + ".To ol", + "Ġi hr", + "Ġih r", + "c ca", + "cc a", + "åĵ ģ", + "ĠE state", + "ĠEst ate", + "ĠEsta te", + "IB UTE", + "IBUT E", + "Action Performed", + "Ġ Solar", + "ĠS olar", + "ĠSo lar", + "ĠSol ar", + "¦ Ĥ", + "Ġequ ity", + "Ġeq uity", + "t id", + "ti d", + "9 38", + "93 8", + "Ġre cip", + "Ġrec ip", + ". simple", + ".s imple", + ".sim ple", + "m k", + "6 89", + "68 9", + "Ġ Luke", + "ĠL uke", + "ĠLu ke", + "ĠLuk e", + "ĠGuard ian", + "Ġ encrypted", + "Ġenc rypted", + "Ġencrypt ed", + "Ġdom inant", + "Ġdomin ant", + "Ġdomina nt", + ". place", + ".p lace", + ".pl ace", + "Ġ NV", + "ĠN V", + "8 39", + "83 9", + "Ġton gue", + "Ġtong ue", + "( Get", + "(G et", + "Ġst ainless", + "Ġstain less", + ". Play", + ".P lay", + ".Pl ay", + "Ġ eb", + "Ġe b", + "a ci", + "ac i", + ". buffer", + ".b uffer", + ".buf fer", + "readcr umbs", + "readcrumb s", + "Ġv accine", + "Ġvacc ine", + "p rom", + "pr om", + "pro m", + "9 79", + "97 9", + "Ġ userInfo", + "Ġuser Info", + "Ġ slug", + "Ġs lug", + "Ġsl ug", + "Ġslu g", + "Serial izedName", + "Serialized Name", + "- wide", + "-w ide", + "Ġre actions", + "Ġreaction s", + "Ġreact ions", + "Ġ Yang", + "ĠY ang", + "ĠYan g", + "ĠYa ng", + "Ġ Adds", + "ĠA dds", + "ĠAd ds", + "ĠAdd s", + "( userId", + "(user Id", + "Ġ plates", + "Ġp lates", + "Ġpl ates", + "Ġplate s", + "Ġpla tes", + "Ġplat es", + "Ġ MEM", + "ĠM EM", + "ĠME M", + "Ġb ail", + "Ġba il", + "In side", + "Ins ide", + "e ted", + "et ed", + "ete d", + "Ġ elsif", + "Ġels if", + "Ġs ake", + "Ġsa ke", + "Ġsak e", + "Ġ cycles", + "Ġc ycles", + "Ġcy cles", + "Ġcycle s", + "Ġcycl es", + "Ġcyc les", + "Ġ ìĹ", + "Ġì Ĺ", + "ĉ I", + "- collapse", + "-c ollapse", + "8 41", + "84 1", + "Ġ GMT", + "ĠG MT", + "ĠGM T", + "8 14", + "81 4", + "De claration", + "Ġg ros", + "Ġgr os", + "Ġgro s", + "Ġre aches", + "Ġreach es", + "Ġcust ody", + "Un til", + "Unt il", + "7 53", + "75 3", + "8 56", + "85 6", + "t u", + "ĠC hen", + "ĠCh en", + "ĠChe n", + "Ġ nx", + "Ġn x", + "( addr", + "(add r", + "(ad dr", + "Ġ Offer", + "ĠO ffer", + "ĠOff er", + "ĠOf fer", + "Ġcol leg", + "Ġcoll eg", + "Ġcolle g", + "ass ador", + "6 74", + "67 4", + "Ġ mapper", + "Ġm apper", + "Ġmap per", + "Ġma pper", + "8 54", + "85 4", + "ĠS IGNAL", + "ĠSIG NAL", + "ĠSIGN AL", + "ĠB loom", + "ĠBl oom", + "ĠBlo om", + "ĠH oll", + "ĠHol l", + "ĠHo ll", + "ĠIm per", + "ĠImp er", + "- des", + "-d es", + "-de s", + "_ site", + "_s ite", + "_si te", + "P roc", + "Pro c", + "Pr oc", + "E qu", + "Eq u", + "Ġ atomic", + "Ġat omic", + "Ġatom ic", + "Ġ Woman", + "ĠW oman", + "ĠWo man", + "s ent", + "se nt", + "sen t", + "7 38", + "73 8", + "8 17", + "81 7", + "s car", + "sc ar", + "Ġint elligent", + "Ġintellig ent", + "Ġ Getting", + "ĠG etting", + "ĠGet ting", + "Ġ Registration", + "ĠReg istration", + "ĠRegistr ation", + "Ġ Phill", + "ĠP hill", + "ĠPh ill", + "ĠPhil l", + "ĠPhi ll", + "Ġ killer", + "Ġk iller", + "Ġkill er", + "Ġkil ler", + "Ġki ller", + "un icode", + "unic ode", + "uni code", + "Ċ ĉĉĊ", + "Ġ Jacob", + "ĠJ acob", + "ĠJac ob", + "ĠJa cob", + "Ġ Const", + "ĠCon st", + "ĠCo nst", + "ĠCons t", + "Ġ locate", + "Ġl ocate", + "Ġlo cate", + "Ġloc ate", + "Ġc aus", + "Ġca us", + "7 49", + "74 9", + "ĠSch olar", + "ĠScho lar", + "Ġ constitutional", + "Ġconstitution al", + "Ġin flation", + "Ġinf lation", + "Ġinfl ation", + "Ġ Got", + "ĠG ot", + "ĠGo t", + "= array", + "=a rray", + "en dum", + "end um", + "Ġ translated", + "Ġtrans lated", + "Ġtransl ated", + "Ġtranslate d", + "Ġdiv orce", + "Ġdivor ce", + "En tries", + "Ent ries", + "Entr ies", + "Ġs or", + "Ġso r", + "Ġ Quote", + "ĠQu ote", + "ir lines", + "irl ines", + "U K", + "Ġ excel", + "Ġex cel", + "Ġexc el", + "Ġexce l", + "( opt", + "(o pt", + "(op t", + "Ġ ADV", + "ĠA DV", + "ĠAD V", + ", :,", + ",: ,", + "Ġcont acted", + "Ġcontact ed", + "7 42", + "74 2", + "Ġ DA", + "ĠD A", + "Ġr ings", + "Ġring s", + "Ġrin gs", + "Ġ Industrial", + "ĠInd ustrial", + "ĠIndust rial", + ". getContext", + ".get Context", + "Ġforg otten", + "Ġforgot ten", + "Ġ Tan", + "ĠT an", + "ĠTa n", + "Ġ pants", + "Ġp ants", + "Ġpa nts", + "Ġpan ts", + "Ġpant s", + "Ġ ov", + "Ġo v", + "Ġ decoder", + "Ġde coder", + "Ġdec oder", + "Ġdecode r", + "Ġdeco der", + "Ġ Partial", + "ĠP artial", + "ĠPart ial", + "ĠParti al", + "Ġ vc", + "Ġv c", + "Ġb attles", + "Ġbattle s", + "Ġbatt les", + "A rial", + "Ar ial", + "FRING EMENT", + "i rates", + "ir ates", + "ira tes", + "irate s", + ", w", + "aint enance", + "Ġ Od", + "ĠO d", + "ĠTechn ologies", + "åī į", + "ĠC arter", + "ĠCar ter", + "ĠCart er", + ". findAll", + ".find All", + "N ome", + "No me", + "Nom e", + "B en", + "Be n", + "Ġ Usage", + "ĠU sage", + "ĠUs age", + "ĠUsa ge", + "Ġ Picture", + "ĠP icture", + "ĠPic ture", + "Ġbad ly", + "_ panel", + "_p anel", + "_pa nel", + "_pan el", + "Ġpat ent", + "Ġpa tent", + "Ġ Protocol", + "ĠProt ocol", + "ĠProto col", + "l otte", + "lo tte", + "lot te", + "ĉ player", + "ĉp layer", + "ĉpl ayer", + "ĉplay er", + "j ections", + "ject ions", + "je ctions", + "jection s", + "7 46", + "74 6", + "Ġ dou", + "Ġd ou", + "Ġdo u", + "_ release", + "_re lease", + "_r elease", + "_rel ease", + "urn iture", + "_ tax", + "_t ax", + "_ta x", + "Ġ Fields", + "ĠF ields", + "ĠField s", + ". dataset", + ".d ataset", + ".data set", + ".dat aset", + ".datas et", + "_ master", + "_m aster", + "_ma ster", + "_mas ter", + "CLUD E", + "CLU DE", + "ĠPh arm", + "ĠPhar m", + "b st", + "bs t", + "Ġoper ational", + "Ġoperation al", + ". cell", + ".c ell", + ".ce ll", + "Ġident ifying", + "Ġidentify ing", + "Ġ jwt", + "Ġj wt", + "t uple", + "tu ple", + "Ġ TC", + "ĠT C", + "Ġ Cro", + "ĠC ro", + "ĠCr o", + "9 36", + "93 6", + "ix map", + "- components", + "-com ponents", + "-component s", + "-comp onents", + "g eneral", + "gen eral", + "gener al", + "gene ral", + "Ġ oz", + "Ġo z", + "_ De", + "_D e", + "_ double", + "_d ouble", + "_do uble", + "Ġ Too", + "ĠT oo", + "ĠTo o", + "0 88", + "08 8", + ".View Group", + "8 79", + "87 9", + "g ate", + "ga te", + "d ings", + "ding s", + "din gs", + "ph otos", + "photo s", + "phot os", + "Ġgr ande", + "Ġgrand e", + "Ġgran de", + "Ġgra nde", + "ol lect", + "oll ect", + "olle ct", + "_ lin", + "_l in", + "_li n", + "Ġaw ful", + "f ilters", + "filter s", + "fil ters", + "filt ers", + "Ġ alternate", + "Ġaltern ate", + "e sp", + "es p", + "Ġ compress", + "Ġcom press", + "Ġcomp ress", + "Ġcompr ess", + "e o", + "Ġ Scale", + "ĠS cale", + "ĠSc ale", + "ĠScal e", + "Ġin direct", + "Ġind irect", + "Ġindir ect", + "Ġ invoice", + "Ġin voice", + "Ġinv oice", + "Ġinvo ice", + "ĊĊ ĊĊĊĊĊĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊ", + "ĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊ", + "ĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊĊĊĊĊĊĊ ĊĊ", + "ĊĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊ", + "Start ing", + "Star ting", + "Ġ Players", + "ĠP layers", + "ĠPl ayers", + "ĠPlay ers", + "ĠPlayer s", + "ĠPla yers", + "i ele", + "ie le", + "iel e", + ". then", + ".t hen", + ".th en", + ".the n", + "9 81", + "98 1", + "O rd", + "Or d", + "Ġ Tuple", + "ĠT uple", + "ĠTu ple", + "ĠTup le", + "Ġ bout", + "Ġb out", + "Ġbo ut", + "Ġbou t", + "Ġ Statistics", + "ĠStat istics", + "P review", + "Pr eview", + "Pre view", + "Prev iew", + "Ġp uzzle", + "Ġpu zzle", + "Ġpuzz le", + "Ġ Width", + "ĠW idth", + "ĠWid th", + "ST ATE", + "STAT E", + "STA TE", + "Ġ overlay", + "Ġover lay", + "Ġoverl ay", + "ĉ on", + "ĉo n", + "Ġin fr", + "Ġinf r", + "Ġsm allest", + "Ġsmall est", + "l ocked", + "lock ed", + "loc ked", + "ÑĤ о", + "s sl", + "ss l", + "7 79", + "77 9", + "Ġde emed", + "Ġdee med", + "Ġdeem ed", + "Ġs co", + "Ġsc o", + "r eck", + "re ck", + "rec k", + "Ġj Button", + "Ġ missions", + "Ġm issions", + "Ġmiss ions", + "Ġmission s", + "8 71", + "87 1", + "ç§ °", + ".Selected Index", + "T ABLE", + "TA BLE", + "TAB LE", + "S ept", + "Se pt", + "Sep t", + "Ġac knowledge", + "Ġack nowledge", + "Ġacknow ledge", + "Ġacknowled ge", + "Ġ strtotime", + "Ġstrt otime", + "Ġ Tell", + "ĠT ell", + "ĠTe ll", + "ĠTel l", + "ĠD ak", + "ĠDa k", + "Ġal uminum", + "Ġf ence", + "Ġfe nce", + "Ġfen ce", + "Ġ Stars", + "ĠSt ars", + "ĠStar s", + "ĠSta rs", + "CON FIG", + "CONF IG", + "Ġr etrofit", + "Ġretro fit", + "Ġ emphasis", + "Ġem phasis", + "Ġemph asis", + "Ġemphas is", + "/ header", + "/head er", + "/he ader", + "Ġ Something", + "ĠS omething", + "ĠSome thing", + "ĠSom ething", + "in ished", + "ini shed", + "inish ed", + "inis hed", + "=' \".$", + "='\" .$", + "='\". $", + "Ġ Validators", + "ĠValid ators", + "ĠValidator s", + "Ġp olar", + "Ġpol ar", + "Ġpo lar", + "s ections", + "se ctions", + "section s", + "sect ions", + "9 44", + "94 4", + ".as px", + ".asp x", + "Ġa spir", + "Ġas pir", + "Ġasp ir", + ". Mock", + ".M ock", + "Code Gen", + "Ġp eut", + "Ġpe ut", + "Ġpeu t", + "9 71", + "97 1", + "Ġaccept ing", + "Ġb acking", + "Ġback ing", + "Ġbac king", + "P icture", + "Pic ture", + "/ ap", + "/a p", + "е г", + "еР³", + "_ SEC", + "_S EC", + "_SE C", + "- use", + "-us e", + "-u se", + "an notation", + "ann otation", + "annot ation", + "Ġc ognitive", + "Ġcogn itive", + "Ġg rip", + "Ġgr ip", + "Ġgri p", + "h our", + "ho ur", + "hou r", + "Ġ Legal", + "ĠL egal", + "ĠLe gal", + "ĠLeg al", + "Ġe pic", + "Ġep ic", + ". toolStrip", + ".t oolStrip", + ".tool Strip", + ". notify", + ".n otify", + ".not ify", + ". Last", + ".L ast", + "OR IZ", + "M iddleware", + "Middle ware", + "cri ptions", + "cript ions", + "cription s", + "l ash", + "la sh", + "las h", + "_ FOUND", + "_F OUND", + "Ġ Liverpool", + "ĠLiver pool", + "Ġ {}\",", + "Ġ{ }\",", + "Ġ{} \",", + "9 31", + "93 1", + "Inst all", + "Ġ nit", + "Ġn it", + "Ġni t", + "Ġfig ured", + "Ġfigure d", + "Ġfigur ed", + "[ len", + "[l en", + ". Win", + ".W in", + ". platform", + ".pl atform", + "8 53", + "85 3", + "Ġgam bling", + "Ġgamb ling", + "( dt", + "(d t", + "a very", + "av ery", + "ave ry", + "aver y", + "ĉ include", + "ĉin clude", + "Wh ether", + "R outing", + "Ro uting", + "Ġth erap", + "Ġthe rap", + "Ġther ap", + "Rem ote", + "Ġ Loss", + "ĠL oss", + "ĠLo ss", + "ĠLos s", + "y ll", + "yl l", + "Ġappro ached", + "Ġapproach ed", + "Ġ Vehicle", + "ĠV ehicle", + "Ġ Alpha", + "ĠAl pha", + "Ġv ocê", + "Ġvoc ê", + "an swers", + "ans wers", + "answer s", + "NS Dictionary", + "9 54", + "95 4", + "cons ider", + "un used", + "unu sed", + "Ġ Fan", + "ĠF an", + "ĠFa n", + "or able", + "ora ble", + "f re", + "fr e", + "8 73", + "87 3", + "ĠDIS CLAIM", + "Ġ Actor", + "ĠA ctor", + "ĠAct or", + "ĠAc tor", + ". ]", + "to Have", + ". userId", + ".user Id", + "Ġspe eds", + "Ġspeed s", + "e way", + "ew ay", + "Ġrec urs", + "Ġrecur s", + "Ġ г", + "ĠÐ ³", + "_ priv", + "_p riv", + "_pr iv", + "_pri v", + "! âĢĿĊĊ", + "!âĢĿ ĊĊ", + "Ch oice", + "Cho ice", + "Ġs ettle", + "Ġset tle", + "Ġsett le", + "Ġ planes", + "Ġpl anes", + "Ġplan es", + "Ġplane s", + "Ġpla nes", + "' },", + "'} ,", + "T om", + "To m", + "I TER", + "IT ER", + "ITE R", + "! \"Ċ", + "!\" Ċ", + "å »", + "ach elor", + "ache lor", + "achel or", + "Ġse paration", + "Ġsepar ation", + "Ġseparat ion", + "Ġ dal", + "Ġd al", + "Ġda l", + "a dj", + "ad j", + "Ġ registers", + "Ġreg isters", + "Ġregister s", + "Ġregist ers", + "r iz", + "ri z", + "Ġ Notice", + "ĠNot ice", + "Ġ lu", + "Ġl u", + "Ġc ourage", + "Ġcour age", + "Ġcou rage", + "Ġ axes", + "Ġa xes", + "Ġax es", + "Ġaxe s", + "cell ent", + ". async", + ".as ync", + ".a sync", + "0 73", + "07 3", + "Ġcom patibility", + "Ġcompat ibility", + "ç «", + "Ġ !ĊĊ", + "Ġ! ĊĊ", + "Ġ!Ċ Ċ", + "ĉ title", + "ĉt itle", + "ĉti tle", + "Y LE", + "YL E", + "ĉ message", + "ĉm essage", + "U UID", + "UU ID", + "OL DER", + "OLD ER", + "Ġ HH", + "ĠH H", + "Ġ StyleSheet", + "ĠStyle Sheet", + "Ġacc essed", + "Ġaccess ed", + "Ġacces sed", + ". validation", + ".valid ation", + "t asks", + "task s", + "tas ks", + "Ġpoll ution", + "Ġpollut ion", + ". canvas", + ".c anvas", + ".can vas", + "Ġ ingredient", + "Ġing redient", + "ĠC abin", + "ĠCa bin", + "ĠCab in", + "A h", + "ol down", + "old own", + "ĠN OI", + "ĠNO I", + "Ġ ÃĹ", + "Ġà Ĺ", + "[ f", + "e duc", + "ed uc", + "edu c", + "y alty", + "yal ty", + "( not", + "(n ot", + "(no t", + "_ State", + "_St ate", + "9 33", + "93 3", + "a men", + "am en", + "ame n", + "7 95", + "79 5", + "7 39", + "73 9", + "Ġ dao", + "Ġd ao", + "Ġda o", + "u dad", + "ud ad", + "uda d", + "el lers", + "ell ers", + "elle rs", + "eller s", + "} &", + "l icity", + "lic ity", + "li city", + "licit y", + "_ WINDOW", + "_W INDOW", + "Ġt atto", + "Ġtat to", + "val or", + "va lor", + ". Range", + ".R ange", + "Ġreference d", + "Ġrefer enced", + "ĠRe serve", + "ĠRes erve", + "M oney", + "Mon ey", + "Mo ney", + "8 74", + "87 4", + "SC RIPT", + "SCRI PT", + "/ product", + "/pro duct", + "cho ices", + "choice s", + "Ġ tin", + "Ġt in", + "Ġti n", + "ãĤ ĵ", + "9 18", + "91 8", + "Ġ separator", + "Ġs eparator", + "Ġse parator", + "Ġsepar ator", + "Ġseparat or", + "Ġ pkg", + "Ġp kg", + "Ġpk g", + "am med", + "amm ed", + "Ġ MAT", + "ĠM AT", + "ĠMA T", + "! !ĊĊ", + "!! ĊĊ", + "!!Ċ Ċ", + "Ġ raid", + "Ġr aid", + "Ġra id", + "Ġmot ivation", + "Ġmotiv ation", + "Ġ XP", + "ĠX P", + "Ġ Background", + "ĠBack ground", + "Ġ Quaternion", + "ĠQu aternion", + ".define Property", + "i ker", + "ik er", + "ike r", + "ĉ parent", + "ĉp arent", + "Ġ Originally", + "ĠOrigin ally", + "ĠOriginal ly", + "ĠOrig inally", + "ant age", + "anta ge", + "ĠH ans", + "ĠHa ns", + "ĠHan s", + "Ġ timeline", + "Ġt imeline", + "Ġtime line", + "Ġtim eline", + ". cur", + ".c ur", + "o pic", + "op ic", + "opi c", + "ĠS equ", + "ĠSe qu", + "ĠSeq u", + "m ust", + "mu st", + "mus t", + "Ġ Coal", + "ĠCo al", + "Ġ formatter", + "Ġfor matter", + "Ġform atter", + "Ġformat ter", + "_ RGB", + "_R GB", + "_RG B", + "Ġ _(\"", + "Ġ_ (\"", + "Ġ_( \"", + "' }),Ċ", + "'} ),Ċ", + "'}) ,Ċ", + "Ġ= ================", + "Ġ== ===============", + "Ġ=== ==============", + "Ġ===== ============", + "Ġ==== =============", + "Ġ========== =======", + "Ġ======= ==========", + "Ġ FUNCTION", + "ĠF UNCTION", + "ĠFUN CTION", + "ĠFUNC TION", + "ĠFUNCT ION", + "Ġ lng", + "Ġl ng", + "Ġln g", + "ic ates", + "ica tes", + "icate s", + "l ive", + "li ve", + "liv e", + "_ engine", + "_e ngine", + "_eng ine", + "Ġt owns", + "Ġtown s", + "Ġtow ns", + "8 68", + "86 8", + "' ))ĊĊ", + "') )ĊĊ", + "')) ĊĊ", + "'))Ċ Ċ", + "Ġ PK", + "ĠP K", + "( api", + "(a pi", + "(ap i", + "ĉ scanf", + "ĉs canf", + "0 89", + "08 9", + "p acket", + "pack et", + "pa cket", + "pac ket", + ". phone", + ".p hone", + ".ph one", + "á Ģ", + "Ġ Andy", + "ĠAn dy", + "ĠAnd y", + "_N AMES", + "_NAME S", + "9 82", + "98 2", + "P LY", + "PL Y", + "9 55", + "95 5", + "Ġ mins", + "Ġm ins", + "Ġmin s", + "Ġmi ns", + "i mi", + "im i", + "Ġ brick", + "Ġb rick", + "Ġbr ick", + "Ġbri ck", + "Ġ blade", + "Ġbl ade", + "Ġbla de", + ". stdout", + ".std out", + "} `;Ċ", + "}` ;Ċ", + "S hift", + "Sh ift", + "ĉ sb", + "ĉs b", + "Ġ Checks", + "ĠCheck s", + "ĠChe cks", + "Ġphenomen on", + "Av atar", + "Ġmin istry", + "Ġmini stry", + "Ġminist ry", + "r ose", + "ro se", + "ros e", + "ĉ File", + "ĉF ile", + "8 78", + "87 8", + "Ġt itled", + "Ġtitle d", + "Ġtit led", + "( LOG", + "(L OG", + "Ġ gan", + "Ġg an", + "Ġga n", + "d esign", + "de sign", + "des ign", + "( ),čĊ", + "() ,čĊ", + "(), čĊ", + "Ġ bones", + "Ġb ones", + "Ġbo nes", + "Ġbon es", + "Ġbone s", + "s tm", + "st m", + "ÅĽ Äĩ", + "Ġ InputStream", + "ĠInput Stream", + "Ġvol unt", + "Ġ Serializable", + "ĠSerial izable", + "Ġ fighter", + "Ġf ighter", + "Ġfight er", + "Ġ Drag", + "ĠD rag", + "ĠDr ag", + "ĠDra g", + "T witter", + "Tw itter", + "Ġsub sid", + "Ġsubs id", + "ç ¼", + "Ġ forums", + "Ġfor ums", + "Ġforum s", + ". loading", + ".load ing", + ".lo ading", + "log ged", + "logg ed", + "_ this", + "_t his", + "_th is", + "Ġ terrain", + "Ġter rain", + "Ġterr ain", + "Ġterra in", + "Ġir re", + "Ġirr e", + "Ġ Ing", + "ĠI ng", + "ĠIn g", + "Ġ CN", + "ĠC N", + "_ objects", + "_object s", + "_obj ects", + ". uid", + ".ui d", + ".u id", + "Ġconscious ness", + "T INGS", + "TING S", + "ĠG all", + "ĠGal l", + "ĠGa ll", + "Ġport ray", + "0 56", + "05 6", + "Ġ Developer", + "ĠDe veloper", + "ĠDevelop er", + "Ġ participant", + "Ġpart icipant", + "Ġparticip ant", + "Ġ \";čĊ", + "Ġ\" ;čĊ", + "Ġ\"; čĊ", + "/ model", + "/m odel", + "/mod el", + "7 94", + "79 4", + "Ġ Operations", + "ĠOper ations", + "ĠOperation s", + "^ \\", + "Ġ Later", + "ĠL ater", + "ĠLa ter", + "ĠLat er", + "ĠLate r", + "Ġ raises", + "Ġr aises", + "Ġraise s", + "Ġrais es", + "Ġra ises", + "- none", + "-n one", + "-no ne", + "-non e", + ". meta", + ".m eta", + ".me ta", + ".met a", + "= '.$", + "=' .$", + "='. $", + "F inished", + "Fin ished", + "Finish ed", + "Ġre placing", + "Ġrepl acing", + "Ġ sampling", + "Ġs ampling", + "Ġsam pling", + "Ġsamp ling", + "ĠJ en", + "ĠJe n", + "\" There", + "\"The re", + "\"T here", + "RE AL", + "REA L", + "A LE", + "AL E", + "ìĬ ¤", + "Or ders", + "Order s", + "Ord ers", + "_ parameter", + "_param eter", + "_para meter", + "ĠOlymp ic", + "Ġtr ès", + "Ġ arena", + "Ġa rena", + "Ġare na", + "Ġar ena", + "Ġaren a", + "i ol", + "io l", + "; ?>", + "Ġimp acts", + "Ġimpact s", + "Ġ WS", + "ĠW S", + ": get", + ":g et", + "Ġf lights", + "Ġfl ights", + "Ġflight s", + "ĠRuss ell", + "ĠRus sell", + "c amera", + "came ra", + "cam era", + "F n", + "s igma", + "sig ma", + "Ġ forcing", + "Ġfor cing", + "Ġforc ing", + "Ġ locals", + "Ġloc als", + "Ġlocal s", + "Ġ departure", + "Ġdepart ure", + "Ġcelebr ation", + "Ġ Say", + "ĠS ay", + "ĠSa y", + "8 84", + "88 4", + "ï¼ Ĵ", + "ĠH ills", + "ĠHill s", + "ĠHil ls", + ".has OwnProperty", + "Ġ typings", + "Ġtyp ings", + "Ġtyping s", + ". API", + ".A PI", + ".AP I", + "Ġd onation", + "Ġdo nation", + "Ġdon ation", + "Operation Exception", + ". Activity", + ".Act ivity", + "c plusplus", + "Ġ Charlie", + "ĠChar lie", + "ĠCharl ie", + "Ġim ported", + "Ġimport ed", + "Ġimp orted", + "Ġd ann", + "Ġda nn", + "Ġdan n", + "Ġocc asions", + "Ġoccas ions", + "Ġoccasion s", + "Ġimplement ing", + "Ġ purple", + "Ġp urple", + "Ġpur ple", + ". dialog", + ".d ialog", + ".di alog", + "SQL Exception", + "er no", + "ern o", + "Ġw ars", + "Ġwar s", + "Ġwa rs", + "Ġ paste", + "Ġp aste", + "Ġpast e", + "Ġpas te", + "Ġpa ste", + "Ġdecre ased", + "Ġdecrease d", + "Ġhar sh", + "Ġel abor", + "Ġela bor", + "in puts", + "input s", + "inp uts", + "Ġ Views", + "ĠView s", + "ĠVi ews", + "ĠVie ws", + "Ġ errorMessage", + "Ġerror Message", + "_ mul", + "_m ul", + "_mu l", + "ĉ write", + "ĉw rite", + "Ġ Cop", + "ĠC op", + "ĠCo p", + "Ġ Annual", + "ĠAnn ual", + "( button", + "(b utton", + "Ġ vida", + "Ġv ida", + "Ġvi da", + "Ġvid a", + "b ars", + "bar s", + "ba rs", + "ĠHar vard", + "ĉ expect", + "ĉex pect", + "ĉexp ect", + "Ġ indexes", + "Ġindex es", + "Ġinde xes", + "Ġdocument ary", + "Ġf lesh", + "Ġfl esh", + "Ġfle sh", + "OR LD", + "Ġ Delta", + "ĠD elta", + "ĠDel ta", + "M AND", + "MA ND", + "MAN D", + "B rush", + "Br ush", + "Bru sh", + "- column", + "-c olumn", + "-col umn", + "Ġdevelop ments", + "Ġdevelopment s", + "9 74", + "97 4", + "7 83", + "78 3", + "method Visitor", + "s lice", + "sl ice", + "Ġ PDO", + "ĠP DO", + "ĠPD O", + "Ġinv esting", + "Ġinvest ing", + "8 67", + "86 7", + "ir able", + "ira ble", + "Ġ xmlns", + "Ġxml ns", + "ï¼ Ľ", + "ar ta", + "art a", + "Ġthe ories", + "Ġtheor ies", + "Ġtheo ries", + "_ city", + "_c ity", + "_ci ty", + "Ġ $__", + "Ġ$ __", + "Ġ$_ _", + "C reating", + "Cre ating", + "Cr eating", + "Creat ing", + "( pr", + "(p r", + "D ropdown", + "Drop down", + "is match", + "ism atch", + "Ġ NET", + "ĠN ET", + "ĠNE T", + "9 26", + "92 6", + "' ])){Ċ", + "'] )){Ċ", + "']) ){Ċ", + "'])) {Ċ", + "'])){ Ċ", + "Ġ Values", + "ĠVal ues", + "ĠValue s", + "Ġ SEO", + "ĠS EO", + "ĠSE O", + "Ġ STAT", + "ĠST AT", + "ĠSTA T", + "Ġe cosystem", + "Ġeco system", + "Ġ tempt", + "Ġt empt", + "Ġtem pt", + "Ġtemp t", + "Ġ \\\\", + "Ġ\\ \\", + "Ġ //{Ċ", + "Ġ// {Ċ", + "Ġ//{ Ċ", + "Ġ Christopher", + "ĠChrist opher", + "ĠChristoph er", + "ĠKent ucky", + "ĠHttp ServletResponse", + "ĠHttpServlet Response", + "Ġh ybrid", + "Ġhy brid", + "y on", + "yo n", + "Ġ feeding", + "Ġfe eding", + "Ġfeed ing", + "Ġfee ding", + "Ġ Extra", + "ĠEx tra", + "ĠExt ra", + "ĠExtr a", + "N orm", + "No rm", + "Nor m", + "IT CH", + "Ġ Sean", + "ĠS ean", + "ĠSe an", + "ĠSea n", + "Ġ Upload", + "ĠUp load", + "m un", + "mu n", + "p ur", + "pu r", + "Ġ persistent", + "Ġp ersistent", + "Ġpers istent", + "Ġpersist ent", + "ĠI DC", + "ĠID C", + "Ġ Perform", + "ĠPer form", + "ĠPerf orm", + "8 63", + "86 3", + ". merge", + ".m erge", + "_ room", + "_r oom", + "_ro om", + "Mean while", + "! ='", + "!= '", + "Ġ Wel", + "ĠW el", + "ĠWe l", + "Args Constructor", + "8 87", + "88 7", + ". Database", + ".D atabase", + ".Data base", + "Ġco unting", + "Ġcount ing", + "Ġcoun ting", + "( )*", + "() *", + "Ķ åĽŀ", + "Ġ TOP", + "ĠT OP", + "ĠTO P", + "m ill", + "mi ll", + "mil l", + "Ġ DT", + "ĠD T", + "IGN ED", + "9 56", + "95 6", + "Ġ KB", + "ĠK B", + "Ġcom ply", + "Ġcomp ly", + "Ġcompl y", + "S outh", + "So uth", + "Sou th", + "_ collection", + "_c ollection", + "_col lection", + "_coll ection", + "_collect ion", + "Ch apter", + "Cha pter", + "Ġexpl aining", + "Ġexplain ing", + "_ AM", + "_A M", + "_ ts", + "_t s", + "c ards", + "card s", + "car ds", + "Ġ quel", + "Ġqu el", + "Ġque l", + "Ġq uel", + "Ġ pole", + "Ġp ole", + "Ġpol e", + "Ġpo le", + "Ġtouch down", + "Ġ Others", + "ĠO thers", + "ĠOther s", + "Ġpe ers", + "Ġpeer s", + "Ġpee rs", + "Ġ TypeError", + "ĠType Error", + "7 63", + "76 3", + "Ġsix th", + "Ġch eer", + "Ġche er", + "Ġdis pute", + "Ġdisp ute", + "Ġdisput e", + "9 63", + "96 3", + "8 93", + "89 3", + "u sc", + "us c", + ") ],", + ")] ,", + "th umb", + "Ġh iding", + "Ġhi ding", + "Ġhid ing", + "Ġ SIG", + "ĠS IG", + "ĠSI G", + "l ikes", + "li kes", + "like s", + "lik es", + "Ġ PAGE", + "ĠP AGE", + "ĠPA GE", + ". Reflection", + ".Ref lection", + "Ġhead quarters", + "T ING", + "TI NG", + "Ġ Ghost", + "ĠG host", + "ĠGh ost", + "M LE", + "ML E", + "$ Ċ", + "Ġcont rary", + "Ġcontr ary", + "Ġcontra ry", + "ext end", + "' ]).", + "'] ).", + "']) .", + "FF ECT", + "FFE CT", + "Ġ Pinterest", + "ĠP interest", + "úmer o", + "ric ane", + "rica ne", + "ĉ session", + "ĉs ession", + "Ġcr ystal", + "Ġcry stal", + "Ġcryst al", + "- Control", + "-C ontrol", + "overn ment", + "o graf", + "og raf", + "ogr af", + "ogra f", + "9 61", + "96 1", + "- action", + "-a ction", + "-ac tion", + "v olume", + "vol ume", + "f ten", + "ft en", + "fte n", + "Ġun con", + "Ġunc on", + "Ġ animate", + "Ġan imate", + "Ġanim ate", + "Ġani mate", + "Ġ lease", + "Ġl ease", + "Ġle ase", + "s cr", + "sc r", + "Ġre fuse", + "Ġref use", + "ãĢ ĭ", + "f tp", + "ft p", + "in formation", + "inform ation", + "Ġeval uated", + "Ġevaluate d", + "Ġevalu ated", + "Ġin jection", + "Ġinj ection", + "Ġinject ion", + "Ġ jack", + "Ġj ack", + "Ġja ck", + "Ġjac k", + "Ġwork shop", + "Ġworks hop", + "æ³ ¨", + "P TH", + "PT H", + "Ġ Ts", + "ĠT s", + "o ffer", + "of fer", + "off er", + "ĉ os", + "ĉo s", + "Ġking dom", + "M issing", + "Miss ing", + "Mis sing", + "Ġlaw makers", + "Ġlawmaker s", + "ext Field", + "Ġs inging", + "Ġsin ging", + "Ġsing ing", + "a bi", + "ab i", + "/ client", + "/c lient", + "/cl ient", + "/cli ent", + ". media", + ".m edia", + ".me dia", + ".med ia", + "ATEG ORY", + "Sign ature", + "Sig nature", + "% ',Ċ", + "%' ,Ċ", + "%', Ċ", + "Ġ Fuck", + "ĠF uck", + "ĠFu ck", + "] [:", + "][ :", + "Ġs ensors", + "Ġsens ors", + "Ġsensor s", + "/ com", + "/c om", + "/co m", + "Ġ Primary", + "ĠPr imary", + "ĠPri mary", + "ĠPrim ary", + ". SQL", + ".S QL", + "_ program", + "_p rogram", + "_pro gram", + "_pr ogram", + "_prog ram", + "Ġp ills", + "Ġpil ls", + "Ġpill s", + "Ġ integral", + "Ġint egral", + "Ġinteg ral", + "Ġintegr al", + "Ġ fleet", + "Ġf leet", + "Ġfle et", + "Ġflee t", + "Ġd ropping", + "Ġdr opping", + "Ġdrop ping", + "Ġdro pping", + ". sl", + ".s l", + "B een", + "Be en", + "Ġ pets", + "Ġp ets", + "Ġpe ts", + "Ġpet s", + "Ġad vised", + "Ġadv ised", + "Ġadvis ed", + "Ġadvise d", + "Ġ dragon", + "Ġd ragon", + "Ġdr agon", + "Ġdrag on", + "Ġdra gon", + "_ EDIT", + "_ED IT", + "( im", + "(i m", + "9 39", + "93 9", + "F ER", + "FE R", + "Ġ Drug", + "ĠD rug", + "ĠDr ug", + "( random", + "(r andom", + "(rand om", + "Ġ compression", + "Ġcom pression", + "Ġcomp ression", + "Ġcompr ession", + "Ġcompress ion", + "o ust", + "ou st", + "ous t", + "[ %", + "Ġ buyer", + "Ġbu yer", + "Ġbuy er", + "h op", + "ho p", + "R oles", + "Role s", + "Ro les", + "Rol es", + "man age", + "ma nage", + "mana ge", + "Ġpain ful", + "Ġ Branch", + "ĠBr anch", + "ĠBran ch", + "- modal", + "-m odal", + "-mod al", + "e nant", + "en ant", + "ena nt", + "enan t", + "Ġ Mesh", + "ĠM esh", + "ĠMe sh", + "ĠMes h", + "/ font", + "/f ont", + "ĠG raham", + "ĠGra ham", + "Ġ âĺ", + "Ġâ ĺ", + "Ġ nc", + "Ġn c", + "ĠFranc is", + "ĠFran cis", + "Ġspec ification", + "Ġspecific ation", + "Ġdam ages", + "Ġdamage s", + "- config", + "-con fig", + "-conf ig", + "Ġthe oret", + "Ġtheor et", + "Ġtheo ret", + "s ecure", + "sec ure", + "_ multi", + "_m ulti", + "_mul ti", + "_mult i", + "aceut ical", + "Ġdem anding", + "Ġdemand ing", + "en ne", + "enn e", + "I STS", + "IS TS", + "IST S", + "0 94", + "09 4", + "( )));ĊĊ", + "() ));ĊĊ", + "()) );ĊĊ", + "()));Ċ Ċ", + "())) ;ĊĊ", + "())); ĊĊ", + "Re ason", + "Re cent", + "Rec ent", + "ph ase", + "pha se", + "phas e", + "Ġ psy", + "Ġp sy", + "Ġps y", + "_ MAN", + "_M AN", + "_MA N", + "Ġvol unteer", + "Ġvolunte er", + "Ġvolunt eer", + "å ¿", + "istrib uted", + "istribute d", + "l io", + "li o", + "Ġproduct ivity", + "_ comm", + "_c omm", + "_com m", + "_co mm", + "S pring", + "Sp ring", + "Spr ing", + "n is", + "ni s", + ". weight", + ".w eight", + ".we ight", + "ĠC ancer", + "ĠCan cer", + "ĠCanc er", + "Al loc", + "All oc", + "Ġ Tweet", + "ĠT weet", + "ĠTwe et", + "Ġsepar ately", + "Ġseparate ly", + "Ġseparat ely", + "ĉ check", + "ĉc heck", + "ĉch eck", + "_ properties", + "_p roperties", + "_prop erties", + ". Unit", + ".U nit", + ".Un it", + "8 29", + "82 9", + "_ CLK", + "_C LK", + "_CL K", + "Ġ gt", + "Ġg t", + "Ġ ();ĊĊ", + "Ġ( );ĊĊ", + "Ġ() ;ĊĊ", + "Ġ();Ċ Ċ", + "Ġ(); ĊĊ", + "Ġh andy", + "Ġhand y", + "Ġhan dy", + "8 34", + "83 4", + "ĠTh ompson", + "ĠThom pson", + "Ġun necessary", + "Ġunn ecessary", + "Ġ Reader", + "ĠRe ader", + "ĠRead er", + "8 94", + "89 4", + "G N", + "= request", + "=re quest", + "=req uest", + "Ġ Utility", + "ĠU tility", + "ĠUtil ity", + "ĠUt ility", + ". Repository", + ".Re pository", + "Ġ Ax", + "ĠA x", + "hy dr", + "7 91", + "79 1", + "i eu", + "ie u", + "Ġ thy", + "Ġt hy", + "Ġth y", + "Ġ lt", + "Ġl t", + "_ mail", + "_m ail", + "_ma il", + "ä¿® æĶ¹", + "a iland", + "ail and", + "ai land", + "Ġ Philip", + "ĠPh ilip", + "ĠPhil ip", + "ĠPhi lip", + "Ġb itter", + "Ġbit ter", + "Ġbitte r", + "Ġb etting", + "Ġbet ting", + "8 37", + "83 7", + "Ġt imed", + "Ġtime d", + "Ġtim ed", + "Ġti med", + "o cks", + "oc ks", + "ock s", + "0 76", + "07 6", + "' a", + "Ġal gorithms", + "Ġalgorithm s", + "Ġ reinterpret", + "Ġre interpret", + "Ġt oss", + "Ġto ss", + "r ogen", + "ro gen", + "rog en", + "Ġh oped", + "Ġhope d", + "Ġhop ed", + "Ġho ped", + "( selected", + "(se lected", + "(select ed", + "(sel ected", + "Ġ venture", + "Ġvent ure", + "Ġven ture", + "T EX", + "TE X", + "Ġ Leave", + "ĠLe ave", + ". Substring", + ".Sub string", + "Ġgr ateful", + "Ġgrat eful", + "Ġgrate ful", + "7 43", + "74 3", + "u ka", + "uk a", + "Ġ Consumer", + "ĠCon sumer", + "ĠCons umer", + "ĠConsum er", + "Ġag greg", + "Ġagg reg", + "C ircle", + "ภģ", + "_ blocks", + "_b locks", + "_block s", + "_bl ocks", + "_bloc ks", + "Ġleg ally", + "Ġlegal ly", + "Ġ \"|", + "Ġ\" |", + "ãĥ ĥ", + ". board", + ".b oard", + ".bo ard", + ". Ab", + ".A b", + "Function s", + "Fun ctions", + "rec ipe", + "è ĩ", + "ĠO xford", + "ĠOx ford", + "Ġw holes", + "Ġwh oles", + "Ġwho les", + "Ġwhole s", + ". Build", + ".B uild", + "_ changed", + "_ch anged", + "_change d", + "_chan ged", + "h ai", + "ha i", + "Ġ departments", + "Ġdepartment s", + "Ġdepart ments", + "9 64", + "96 4", + "I mp", + "Im p", + "Ġcoal ition", + "IN FRINGEMENT", + "Ġem power", + "Ġemp ower", + "it ches", + "itch es", + "N orth", + "Nor th", + "Ġin flamm", + "Ġinfl amm", + "O NSE", + "ON SE", + "ONS E", + "Ġmiss ile", + "ĠR aj", + "ĠRa j", + "Ġ Issue", + "ĠI ssue", + "ĠIss ue", + "Ġ atoi", + "Ġa toi", + "Ġat oi", + "c aled", + "ca led", + "cale d", + "cal ed", + ". Controllers", + ".Cont rollers", + ".Control lers", + ".Controller s", + "Ġ Wolf", + "ĠW olf", + "ĠWo lf", + "ĠWol f", + "Ġcrush ers", + "Ġcrusher s", + "á» ĩ", + ". Auth", + ".A uth", + ".add Attribute", + "h is", + "hi s", + "Ġbo ots", + "Ġboot s", + "Ġboo ts", + ". clean", + ".c lean", + ".cl ean", + "c amp", + "ca mp", + "cam p", + "Ġ tenant", + "Ġt enant", + "Ġte nant", + "Ġten ant", + "Ġt une", + "Ġtu ne", + "Ġtun e", + "Ġ {}'.", + "Ġ{ }'.", + "Ġ{} '.", + "Ġwork out", + "Re po", + "Rep o", + "Ġpart ially", + "Ġpartial ly", + "Ġparti ally", + "MI SSION", + "MISS ION", + "j amin", + "ja min", + "jam in", + "Ġ SB", + "ĠS B", + "Ġd etermination", + "Ġde termination", + "Ġdeter mination", + "Ġdetermin ation", + "Ġdeterm ination", + "Ġ' ');Ċ", + "Ġ'' );Ċ", + "Ġ'') ;Ċ", + "Ġ''); Ċ", + "ĠB eng", + "ĠBe ng", + "ĠBen g", + "Ġ vos", + "Ġv os", + "Ġvo s", + "Ġin hab", + "Ġinh ab", + "/ lang", + "/l ang", + "s burgh", + "sburg h", + "Exec utor", + "h one", + "ho ne", + "hon e", + "Ġ Challenge", + "ĠCh allenge", + "ĠChall enge", + "_ links", + "_l inks", + "_link s", + "_lin ks", + ". Level", + ".Le vel", + "Ġunder ground", + "- code", + "-c ode", + "-co de", + "9 59", + "95 9", + "Ġopt imization", + "Ġoptim ization", + "log ging", + "logg ing", + "_ dest", + "_d est", + "_de st", + "_des t", + "Ġ snake", + "Ġsn ake", + "Ġsna ke", + "Ġchem icals", + "Ġchemical s", + "_IMPORT ED", + "ad oop", + "ado op", + "adoo p", + "ĠTH AT", + "man aged", + "manage d", + "mana ged", + "Ġred uces", + "Ġredu ces", + "Ġreduce s", + "Ġ REAL", + "ĠRE AL", + "Ġ Guy", + "ĠG uy", + "ĠGu y", + "_GENER IC", + "_GEN ERIC", + "/ ********************************", + "/************************ ********", + "/******** ************************", + "/**************** ****************", + ". amount", + ".a mount", + ".am ount", + "Ġ dere", + "Ġd ere", + "Ġde re", + "Ġder e", + "get Time", + "Ġp ant", + "Ġpa nt", + "Ġpan t", + "an onymous", + "anon ymous", + "Ġhar mony", + "Ġharm ony", + "Ġharmon y", + "Ġ Alan", + "ĠA lan", + "ĠAl an", + "ĠAla n", + "Ġsc enarios", + "Ġscen arios", + "Ġscenario s", + "Ġd irt", + "Ġdi rt", + "Ġdir t", + "h tags", + "ht ags", + "htag s", + "hta gs", + "M c", + "S hell", + "Sh ell", + "She ll", + "r in", + "ri n", + "{ čĊčĊ", + "{čĊ čĊ", + ". pow", + ".p ow", + ".po w", + "ĉ client", + "ĉc lient", + "ĉcl ient", + "ĉcli ent", + "Ġcon spiracy", + "Ġconspir acy", + "Ġad mission", + "Ġadm ission", + "Ġ Regional", + "ĠReg ional", + "ĠRegion al", + "Ġ ViewController", + "ĠView Controller", + "ĠPhil ippines", + "ĠPhilipp ines", + "ĠPhilippine s", + "Ġde pos", + "Ġdep os", + "Ġp ap", + "Ġpa p", + "9 62", + "96 2", + "Ġ Pad", + "ĠP ad", + "ĠPa d", + "P aul", + "Pa ul", + ". ComboBox", + ".Com boBox", + "Ġt utor", + "Ġtu tor", + "Ġtut or", + "Ġtuto r", + "Ġ Recipe", + "ĠRec ipe", + "w riting", + "wr iting", + "Ġcontrib utor", + "O TH", + "OT H", + "S mall", + "Sm all", + "V I", + "Ġh acer", + "Ġha cer", + "Ġhace r", + "Ġhac er", + "e qu", + "eq u", + "Ġ Examples", + "ĠEx amples", + "ĠExample s", + "ĠExam ples", + "h uman", + "hu man", + "hum an", + ". messages", + ".m essages", + ".message s", + "ĉ typ", + "ĉt yp", + "Ġ (čĊ", + "Ġ( čĊ", + "Ġ SSL", + "ĠS SL", + "ĠSS L", + "L EN", + "LE N", + "ĠRom ney", + "( grid", + "(g rid", + "(gr id", + "ĉ min", + "ĉm in", + "Ġ >ĊĊ", + "Ġ> ĊĊ", + "Ġ>Ċ Ċ", + "Ġf ruits", + "Ġfr uits", + "Ġfruit s", + "Ġv oter", + "Ġvo ter", + "Ġvot er", + "Ġvote r", + "In line", + "p ane", + "pan e", + "pa ne", + "Ġ Collections", + "ĠC ollections", + "ĠCol lections", + "ĠCollection s", + "ĠColl ections", + "ĠCollect ions", + "char set", + "chars et", + "Ġ spam", + "Ġsp am", + "Ġspa m", + "z b", + "it emap", + "ite map", + "item ap", + "Ġs ucceeded", + "Ġsuc ceeded", + "Ġsucceed ed", + "_ COL", + "_C OL", + "_CO L", + "Ġ elapsed", + "Ġel apsed", + "i meter", + "im eter", + "ime ter", + "imet er", + "Ġre covered", + "Ġrecover ed", + "T ensor", + "hat tan", + "hatt an", + ". setup", + ".set up", + "i sto", + "is to", + "ist o", + "( head", + "(h ead", + "9 77", + "97 7", + "Ġ SIZE", + "ĠS IZE", + "ĠSI ZE", + "Ġt actics", + "Ġtact ics", + "Ġtactic s", + "Ġtac tics", + "Ġdis tur", + "Ġdist ur", + "Ġpr eval", + "Ġpre val", + "Ġprev al", + "ic ios", + "ici os", + "icio s", + "( Value", + "(V alue", + "_ cols", + "_c ols", + "_col s", + "_co ls", + "Ġ Fat", + "ĠF at", + "ĠFa t", + "Ġse al", + "Ġsea l", + "Ġ sons", + "Ġs ons", + "Ġso ns", + "Ġson s", + "Ġens ures", + "Ġensure s", + "0 95", + "09 5", + "Ġp ressing", + "Ġpres sing", + "Ġpress ing", + "= &", + "igen ous", + "Ġharass ment", + "_ JSON", + "_J SON", + "_JS ON", + "Ġign or", + "Ġig nor", + "yn omial", + "ynom ial", + "o mer", + "om er", + "ome r", + "_ static", + "_st atic", + "_stat ic", + "_sta tic", + "Ġsign ificance", + "Ġsignific ance", + "Ġsignifica nce", + "Ġc ircles", + "Ġcirc les", + "Ġcircle s", + "Ġcir cles", + "_ System", + "_S ystem", + "Ġdisc ipline", + "Ġdiscipl ine", + "Ġd ressed", + "Ġdr essed", + "Ġdress ed", + "Ġ sphere", + "Ġs phere", + "Ġsp here", + "Ġsph ere", + "9 27", + "92 7", + "Ġcl imb", + "Ġclim b", + "Ġcli mb", + "7 59", + "75 9", + "_ actions", + "_a ctions", + "_action s", + "_act ions", + "ĠB ab", + "ĠBa b", + "Ġ' =',", + "Ġ'=' ,", + "Ġ'= ',", + "_ schema", + "_s chema", + "\" use", + "Ġ unders", + "Ġun ders", + "Ġunder s", + "Ġund ers", + "Ġunde rs", + "Ġc ups", + "Ġcu ps", + "Ġcup s", + ". screen", + ".s creen", + ".sc reen", + "/ new", + "/n ew", + "/ne w", + "Ġapp earing", + "Ġappe aring", + "Ġappear ing", + "T OP", + "TO P", + "v ised", + "vis ed", + "vi sed", + "vise d", + "c lang", + "cl ang", + "cla ng", + "Ġinvest igators", + "Ġinvestig ators", + "Ġinvestigator s", + "Ġm ysterious", + "Ġmyster ious", + "Ġprom ising", + "Ġqual ify", + "Ġqua lify", + "Ġquali fy", + "Ġc ave", + "Ġca ve", + "Ġcav e", + "Ġ equip", + "Ġe quip", + "Ġequ ip", + "= x", + "G T", + "( link", + "(l ink", + "(li nk", + ". velocity", + ".v elocity", + ".vel ocity", + ". erase", + ".e rase", + ".er ase", + "o ter", + "ot er", + "ote r", + "++++ ++++", + "pro fit", + "prof it", + "Ġ zones", + "Ġz ones", + "Ġzone s", + "Ġzo nes", + "_ uid", + "_u id", + "_ui d", + "- ser", + "-s er", + "-se r", + "Ġob jectives", + "Ġobject ives", + "Ġobjective s", + "Ġmil f", + "Ġmi lf", + "web kit", + "( match", + "(m atch", + "(mat ch", + "n eh", + "ne h", + "Ġ Associated", + "ĠAssoci ated", + "ĠAssociate d", + "ĠAssoc iated", + "Ġ Todo", + "ĠT odo", + "ĠTo do", + "ĠTod o", + "= d", + "0 65", + "06 5", + "C am", + "Ca m", + "Ġv ocal", + "Ġvo cal", + "Ġvoc al", + "Ġ sudo", + "Ġs udo", + "Ġsu do", + "Ġsud o", + "( EX", + "(E X", + "Ġt rou", + "Ġtr ou", + "Ġtro u", + "A BC", + "AB C", + ". bean", + ".b ean", + ".be an", + "Ġ Ground", + "ĠG round", + "ĠGr ound", + "ĠGro und", + "Ġ REST", + "ĠR EST", + "ĠRE ST", + "ĠRES T", + "we ets", + "weet s", + "I ng", + "In g", + "i mon", + "im on", + "imo n", + "9 46", + "94 6", + "_ bus", + "_b us", + "Ġ COLOR", + "ĠC OLOR", + "ĠCOL OR", + "un to", + "unt o", + "Ġf oss", + "Ġfo ss", + "Ġfos s", + "Ġ Links", + "ĠL inks", + "ĠLink s", + "ĠLin ks", + "8 69", + "86 9", + "ä ng", + "än g", + "/ forms", + "/form s", + "pr ises", + "prise s", + "pri ses", + "Ġ achievement", + "Ġachie vement", + "Ġachieve ment", + "C ALL", + "CA LL", + "CAL L", + "е лÑĮ", + "ел ÑĮ", + "Ġ Verify", + "ĠVer ify", + "_ SOURCE", + "_S OURCE", + "apt cha", + "I DD", + "ID D", + "_ reference", + "_re ference", + "_ref erence", + "_refer ence", + "G old", + "Go ld", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "9 47", + "94 7", + "Re ceiver", + "Receive r", + "0 99", + "09 9", + "Ġ aj", + "Ġa j", + "_ direction", + "_d irection", + "_dir ection", + "_di rection", + "_direct ion", + "} ]", + "Ġ Compet", + "ĠCom pet", + "ĠComp et", + "Ġ bang", + "Ġb ang", + "Ġban g", + "Ġba ng", + "7 98", + "79 8", + "ĠC ass", + "ĠCas s", + "ĠCa ss", + "- url", + "-u rl", + "t echn", + "te chn", + "tech n", + "tec hn", + "ĠJer usalem", + "long itude", + "' );čĊčĊ", + "') ;čĊčĊ", + "'); čĊčĊ", + "');čĊ čĊ", + "Ġwin ners", + "Ġwinner s", + "T asks", + "Task s", + "Ġ DMA", + "ĠD MA", + "ĠDM A", + "Ġ tooltip", + "Ġto oltip", + "Ġtool tip", + "İ ·", + "ĠB ra", + "ĠBr a", + "_ duration", + "_d uration", + "_dur ation", + "c ury", + "cur y", + "cu ry", + "par ents", + "pare nts", + "parent s", + "paren ts", + "- --- >(", + ">> (", + "Ġ Kir", + "ĠK ir", + "ĠKi r", + "Ġ intros", + "Ġint ros", + "Ġintr os", + "Ġintro s", + "Ġsk etch", + "Ġsk illed", + "Ġskill ed", + "Ġ immer", + "Ġim mer", + "Ġimm er", + "Ġade quate", + "Ġadequ ate", + "_ rep", + "_re p", + "_r ep", + "( header", + "(head er", + "_ like", + "_l ike", + "_li ke", + "Ġper ceived", + "Ġperceive d", + "Ġperce ived", + "s sh", + "ss h", + "Ġ assuming", + "Ġas suming", + "Ġass uming", + "Ġassum ing", + "Ġ ff", + "Ġf f", + "_ uuid", + "_u uid", + "u las", + "ul as", + "ula s", + "Ġdem ocratic", + "Ġdemocr atic", + "Ġdemocrat ic", + ". entities", + ".ent ities", + "S eries", + "Se ries", + "Ser ies", + "aph ore", + "Ġnew er", + "Ġne wer", + "} (", + "S EC", + "SE C", + "a iro", + "air o", + "ai ro", + "Ġcom mod", + "Ġcomm od", + "Ġpriv ilege", + "Ġprivile ge", + "Ġde ux", + "Ġ Hop", + "ĠH op", + "ĠHo p", + ". '/", + ".' /", + "c tic", + "ct ic", + ". ';Ċ", + ".' ;Ċ", + "< ?=", + " C", + "ĠWar ren", + "Ġ optimizer", + "Ġopt imizer", + "Ġoptim izer", + "Ġoptimize r", + "ĠSER VICES", + "ĠSERVICE S", + "_ oper", + "_op er", + "_o per", + "get Attribute", + "ĠMc K", + "_ self", + "_s elf", + "_se lf", + "_sel f", + "0 84", + "08 4", + ". rs", + ".r s", + "\" )ĊĊĊ", + "\") ĊĊĊ", + "\")Ċ ĊĊ", + "\")ĊĊ Ċ", + "Get Component", + "er ce", + "erc e", + "Ġt ous", + "Ġto us", + "Ġtou s", + "un its", + "unit s", + "uni ts", + "' ]);čĊ", + "'] );čĊ", + "']) ;čĊ", + "']); čĊ", + "Z oom", + "/ E", + "Ġob sc", + "Ġobs c", + "Ġfast est", + "Ġfas test", + "on line", + "Ġpeace ful", + "f fen", + "ff en", + "ffe n", + "Ġ cargo", + "Ġc argo", + "Ġcar go", + "Ġcarg o", + "ĉ pr", + "ĉp r", + "Ġse eks", + "Ġsee ks", + "Ġseek s", + "z u", + "0 74", + "07 4", + "T rim", + "Tr im", + "Tri m", + "Ġ ward", + "Ġw ard", + "Ġwar d", + "Ġwa rd", + "Ġv erd", + "Ġver d", + "Ġve rd", + "Ġ blogs", + "Ġb logs", + "Ġbl ogs", + "Ġblog s", + "Ġblo gs", + ". exceptions", + ".ex ceptions", + ".exception s", + "Ġ Premium", + "ĠP remium", + "ĠPre mium", + "ĠPrem ium", + "ĠPremi um", + "ĠN etherlands", + "S afe", + "Sa fe", + "F inish", + "Fin ish", + "Fi nish", + "Ġ Album", + "ĠAl bum", + "ĠAlb um", + "_ ACC", + "_A CC", + "_AC C", + "= this", + "=t his", + "v irtual", + "vir tual", + "virt ual", + "] >", + "_ LABEL", + "_L ABEL", + "_LA BEL", + "Ġ Nich", + "ĠN ich", + "ĠNic h", + "ĠNi ch", + "_ win", + "_w in", + "Ġ Aaron", + "ĠA aron", + "W P", + "; $", + "a ims", + "ai ms", + "aim s", + "Ġ ImageView", + "ĠImage View", + "Ġend less", + "Ġendl ess", + "E RA", + "ER A", + "_ DISABLE", + "_DIS ABLE", + "Ġ cancelled", + "Ġcancel led", + "Ġcancell ed", + "Ġcanc elled", + "- us", + "-u s", + "Ġ inspection", + "Ġins pection", + "Ġinspect ion", + "Ġinsp ection", + "e min", + "em in", + "emi n", + "Ġ Grey", + "ĠG rey", + "ĠGr ey", + "ĠGre y", + "- open", + "-o pen", + "-op en", + "Ġ iterations", + "Ġiter ations", + "Ġiteration s", + ". owner", + ".o wner", + "Ġk eras", + "Ġke ras", + "Ġker as", + ". Password", + ".P assword", + ".Pass word", + "ĠR y", + "Ġ INS", + "ĠI NS", + "ĠIN S", + "A ir", + "Ai r", + "Ġ Several", + "ĠSe veral", + "ĠSever al", + "ĠSev eral", + ".Tab Stop", + "IN GLE", + "ING LE", + "Ġ Hair", + "ĠH air", + "ĠHa ir", + "ĠHai r", + "Ġ Canvas", + "ĠC anvas", + "ĠCan vas", + "A AAA", + "AA AA", + "AAA A", + "Ġf law", + "Ġfl aw", + "Ġfla w", + "c edes", + "ce des", + "ced es", + "cede s", + ". Report", + ".Re port", + "í Ĭ", + "Ġ Tips", + "ĠT ips", + "ĠTi ps", + "ĠTip s", + "cript ors", + "criptor s", + ". transaction", + ".trans action", + ". Spring", + ".S pring", + ".Sp ring", + "Ġ viewer", + "Ġview er", + "Ġvie wer", + "Ġins ights", + "Ġinsight s", + "è¾ ĵ", + "ord ion", + "U INT", + "UI NT", + "se ek", + "see k", + "Ġ Auf", + "ĠA uf", + "ĠAu f", + "ìŀ IJ", + "Ġ strain", + "Ġs train", + "Ġst rain", + "Ġstr ain", + "Ġstra in", + "To oltip", + "Tool tip", + "Ġ dz", + "Ġd z", + "ig nal", + "ign al", + "a dt", + "ad t", + "Ġ uc", + "Ġu c", + "f inite", + "fin ite", + "fi nite", + "Ġ nm", + "Ġn m", + ". cmd", + ".c md", + ".cm d", + "ĠMy Sql", + "[ data", + "[d ata", + ".j ackson", + ". tree", + ".t ree", + ".tr ee", + "Request Param", + "_ agent", + "_a gent", + "_ag ent", + "_age nt", + "\" )]čĊ", + "\") ]čĊ", + "\")] čĊ", + "Ġas sass", + "Ġass ass", + "( Constants", + "(Constant s", + "(Const ants", + ": ss", + ":s s", + "Ġ MAN", + "ĠM AN", + "ĠMA N", + "+- +-", + "Ġ Bottom", + "ĠB ottom", + "ĠBot tom", + "ĠBott om", + "pr ints", + "print s", + "pri nts", + "Ġ Same", + "ĠS ame", + "ĠSam e", + "ĠSa me", + "@ Autowired", + "s wap", + "sw ap", + "i ción", + "ic ión", + "ici ón", + "Ġprot esters", + "Ġprote sters", + "Ġprotest ers", + "Ġprotester s", + "Ġh oney", + "Ġhon ey", + "Ġho ney", + "Ġhone y", + "Ġ Veter", + "ĠV eter", + "ĠVe ter", + "ĠVet er", + "( Calendar", + "(C alendar", + "- ad", + "-a d", + "ĠBro oklyn", + "ĠBrook lyn", + "L ife", + "Li fe", + "_ VAR", + "_V AR", + "z ech", + "ze ch", + "Ġ CALL", + "ĠC ALL", + "ĠCA LL", + "ĠCAL L", + "_ CAST", + "_C AST", + "_CA ST", + "ĠE lection", + "ĠEl ection", + "ĠElect ion", + "ĠEle ction", + "Ġ thickness", + "Ġth ickness", + "Ġthick ness", + "V ery", + "Ver y", + "Ve ry", + "_ INTEGER", + "_IN TEGER", + "- dev", + "-d ev", + "-de v", + ") )))", + ")) ))", + "))) )", + "a pat", + "ap at", + "apa t", + "o ooo", + "oo oo", + "ooo o", + "d emo", + "de mo", + "dem o", + "Ġ parseFloat", + "Ġparse Float", + "Ġ Rather", + "ĠR ather", + "ĠRa ther", + "ĠRat her", + "ĠRath er", + "ST IT", + "m aker", + "ma ker", + "make r", + "mak er", + "[ current", + "[c urrent", + "[cur rent", + "[curr ent", + "chron o", + "chr ono", + "Ġ christ", + "Ġch rist", + "Ġchr ist", + "ãģ ª", + "Ġ Detail", + "ĠD etail", + "ĠDe tail", + "ĠDet ail", + "Æ° á»", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "Ġs ul", + "Ġsu l", + "id ency", + "ide ncy", + "iden cy", + "Q ue", + "Qu e", + "Ġe legant", + "Ġeleg ant", + "a pons", + "ap ons", + "apon s", + "apo ns", + "Ġd ishes", + "Ġdis hes", + "Ġdish es", + "Ġint egers", + "Ġinteg ers", + "Ġinteger s", + "Ġinte gers", + "( read", + "(r ead", + "(re ad", + "0 57", + "05 7", + "find ViewById", + "Ġ Amount", + "ĠA mount", + "ĠAm ount", + "Ġ Skip", + "ĠS kip", + "ĠSk ip", + "ĠSki p", + "Ġhab its", + "Ġha bits", + "Ġhabit s", + "* )(", + "*) (", + "Ġmon sters", + "Ġmonster s", + "M AC", + "MA C", + ": end", + ":e nd", + "Ġf rank", + "Ġfr ank", + "Ġfra nk", + "Ġfran k", + "As sembly", + "Ġ dfs", + "Ġd fs", + "Ġdf s", + "Ġn eut", + "Ġne ut", + "Ġneu t", + "_ TYPES", + "_T YPES", + "_TYPE S", + "_TYP ES", + "e qual", + "eq ual", + "equ al", + "lo yd", + "loy d", + "( uri", + "(u ri", + "(ur i", + "Ġ chi", + "Ġc hi", + "Ġch i", + "Ġdef endant", + "Ġdefend ant", + "Ġconf licts", + "Ġconflic ts", + "Ġconflict s", + "Ġconfl icts", + "Ġ vil", + "Ġv il", + "Ġvi l", + "- js", + "-j s", + "Ġ Peace", + "ĠPe ace", + "Ġ mutable", + "Ġm utable", + "Ġmut able", + "Ġmu table", + ") sender", + ")s ender", + "Ġ Focus", + "ĠF ocus", + "ĠFo cus", + "å» º", + "Ġapprec iated", + "Ġappreciate d", + "s leep", + "Ġ RED", + "ĠR ED", + "ĠRE D", + "C ulture", + "Ġdesign ers", + "Ġdesigner s", + "_ generator", + "_g enerator", + "_gen erator", + "_gener ator", + "c odes", + "co des", + "code s", + "cod es", + "/ ex", + "/e x", + ". GetValue", + ".Get Value", + "um bled", + "umb led", + "umble d", + ".scal ajs", + ".scala js", + "pe ror", + "per or", + "Ġveter ans", + "Ġveteran s", + "Ġ })čĊ", + "Ġ} )čĊ", + "Ġ}) čĊ", + "Ġun fortunately", + "Ġunfortunate ly", + "_ CREATE", + "_C REATE", + "_CRE ATE", + "_CREAT E", + "M ass", + "Ma ss", + "Mas s", + "Ġ CLAIM", + "ĠCL AIM", + "Ġ Meet", + "ĠMe et", + "_ support", + "_s upport", + "_sup port", + "_supp ort", + "B ank", + "Ban k", + "Ba nk", + "( ).Ċ", + "() .Ċ", + "(). Ċ", + "D ark", + "Da rk", + "Dar k", + "_ LOW", + "_L OW", + "_LO W", + "Ġ Mining", + "ĠM ining", + "ĠMin ing", + "ĠMini ng", + "ĠMi ning", + "Ġ Owner", + "ĠO wner", + "ĠOwn er", + "ĠOw ner", + "i era", + "ie ra", + "ier a", + "Client e", + "Cl iente", + "Cli ente", + "Ġencour aging", + "> S", + "Ġboy friend", + "Ġ Half", + "ĠH alf", + "ĠHa lf", + "ĠHal f", + "Ġ ACC", + "ĠA CC", + "ĠAC C", + "A ff", + "Af f", + "_ ar", + "_a r", + "- life", + "-l ife", + "-li fe", + "c x", + ".J Button", + "iz ado", + "iza do", + ". zero", + ".z ero", + ".open qa", + "o ton", + "ot on", + "oto n", + ". textContent", + ".text Content", + "Ġt oll", + "Ġto ll", + "Ġtol l", + "a tie", + "at ie", + "ati e", + "Ġball ot", + "Ġbal lot", + "Ġballo t", + "- number", + "-n umber", + "-num ber", + ". Exception", + ".Ex ception", + "ĉ params", + "ĉparam s", + "c ircle", + "circ le", + "cir cle", + "- map", + "-m ap", + "Ġ nap", + "Ġn ap", + "Ġna p", + "Ġ Robot", + "ĠR obot", + "ĠRob ot", + "ĠRo bot", + "Ġ Ich", + "ĠI ch", + "reg istration", + "registr ation", + "regist ration", + "A mazon", + "Am azon", + "roll ment", + "( exp", + "(e xp", + "(ex p", + "Ġt anks", + "Ġtank s", + "Ġtan ks", + "ĠG ordon", + "ĠGor don", + "ĠGord on", + "Ġm achinery", + "Ġmachine ry", + "Ġmach inery", + "Ġ baseline", + "Ġb aseline", + "Ġbase line", + "Ġbas eline", + "æ ĭ", + "0 86", + "08 6", + "Ø ©", + "Ġ Convention", + "ĠCon vention", + "ĠConv ention", + "ĉ config", + "ĉcon fig", + "ĉconf ig", + "o okies", + "ook ies", + "ookie s", + "m ult", + "mu lt", + "mul t", + "Rec ords", + "Record s", + "Ġ EST", + "ĠE ST", + "ĠES T", + "Ġgar bage", + "Ġcon form", + "Ġconf orm", + "i dal", + "id al", + "ida l", + "Ġb arg", + "Ġbar g", + "Ġba rg", + "Ġsurv ived", + "Ġsurvive d", + "Ġsurviv ed", + "Ġinvestig ations", + "Ġinvestigation s", + "9 35", + "93 5", + ".contains Key", + "---- ----------------------------------------------------------------------Ċ", + "---------------------------------------------------------------- ----------Ċ", + "---------------------------------------------------------------------- ----Ċ", + "or tion", + "ort ion", + "Ġh orr", + "Ġhor r", + "Ġho rr", + "_ http", + "_h ttp", + "_ht tp", + "Ġ mant", + "Ġm ant", + "Ġman t", + "Ġma nt", + "] ;čĊčĊ", + "]; čĊčĊ", + "];čĊ čĊ", + "b inary", + "bin ary", + "9 48", + "94 8", + "e mpl", + "em pl", + "emp l", + "Ġin quiry", + "Ġ Meanwhile", + "ĠMean while", + "0 98", + "09 8", + "Ġcollect ing", + ".Entity Framework", + "\" ,ĊĊ", + "\", ĊĊ", + "\",Ċ Ċ", + "Ġ Pic", + "ĠP ic", + "ĠPi c", + "@ Inject", + "ick ness", + "Ġ Binding", + "ĠB inding", + "ĠBind ing", + "ĠBin ding", + "Ġcont rolling", + "Ġcontrol ling", + "re verse", + "rev erse", + "Ġch airs", + "Ġchair s", + "Ġcha irs", + "Ġchai rs", + "semb led", + "sem bled", + "semble d", + "sembl ed", + "( add", + "(a dd", + "(ad d", + "Dis abled", + "Disable d", + "a nas", + "an as", + "ana s", + ". translate", + ".trans late", + "- ----------Ċ", + "-- ---------Ċ", + "---- -------Ċ", + "-------- ---Ċ", + "--- --------Ċ", + "----- ------Ċ", + "---------- -Ċ", + "------ -----Ċ", + "----------- Ċ", + "------- ----Ċ", + "--------- --Ċ", + "Ġref lected", + "Ġreflect ed", + "Ġrefl ected", + "\" ]ĊĊ", + "\"] ĊĊ", + "\"]Ċ Ċ", + "Ex ternal", + "Ext ernal", + "Extern al", + "Ar row", + "Arr ow", + "S ingleton", + "Single ton", + "Sing leton", + "% x", + "Ġ Å", + "Ġan cest", + "Ġance st", + "Ġanc est", + "ĠOr leans", + "ĉ cmd", + "ĉc md", + "ĉcm d", + "Ġpro hibited", + "Ġprohib ited", + "Ġprohibit ed", + "ith metic", + "( channel", + "(ch annel", + "(chan nel", + "_ css", + "_c ss", + "_cs s", + "For ward", + ". socket", + ".s ocket", + ".so cket", + ".sock et", + "Ġl uc", + "Ġlu c", + "â Ĩ", + "Ġ Firefox", + "ĠFire fox", + "Ġ Movies", + "ĠM ovies", + "ĠMovie s", + "ĠMov ies", + ") _", + ". ends", + ".e nds", + ".end s", + ".en ds", + "( shape", + "(s hape", + "(sh ape", + "Ġde alt", + "Ġdeal t", + "Ġs aves", + "Ġsa ves", + "Ġsave s", + "Ġsav es", + "Ġgl ory", + "Ġglo ry", + "Ġglor y", + "Ġmej or", + "Ġbre athing", + "Ġbreath ing", + "Ġ eller", + "Ġe ller", + "Ġel ler", + "Ġell er", + "Ġelle r", + "get Data", + "Ġ angles", + "Ġan gles", + "Ġang les", + "Ġangle s", + "Ġangl es", + "Ġ toolbar", + "Ġtool bar", + "Ġ spacing", + "Ġsp acing", + "Ġspa cing", + "Ġspac ing", + "0 59", + "05 9", + "I PS", + "IP S", + "Ġfloor s", + "Ġflo ors", + "_ ACTIVE", + "_ACT IVE", + "_ACTIV E", + "Ġ shuffle", + "Ġsh uffle", + "/ shared", + "/sh ared", + "/share d", + "Ġ Ele", + "ĠE le", + "ĠEl e", + "e dish", + "ed ish", + "edi sh", + "edis h", + "Ġweb cam", + ". expect", + ".ex pect", + ".exp ect", + "i loc", + "il oc", + "ilo c", + "Ġ Includes", + "ĠIn cludes", + "ĠInclude s", + "Ġt weeted", + "Ġtweet ed", + "Ġtwe eted", + "Ġtwee ted", + "Ġ :)", + "Ġ: )", + "Ġ Essay", + "ĠEs say", + "ĠEss ay", + "F ix", + "Fi x", + "- between", + "-b etween", + "_ web", + "_w eb", + "_we b", + ". conv", + ".con v", + ".co nv", + "Ġrac ism", + "Ġreflect s", + "Ġrefl ects", + "u mm", + "um m", + "и ÑĤе", + "иÑĤ е", + "_ footer", + "_f ooter", + "_foot er", + "/ docs", + "/d ocs", + "/doc s", + "/do cs", + "Ġ Pour", + "ĠP our", + "ĠPo ur", + "ĠPou r", + "Ng Module", + ". initialize", + ".initial ize", + "pattern s", + "_ In", + "_I n", + "Ġ Abb", + "ĠA bb", + "ĠAb b", + "* čĊ", + "Ġsent iment", + "b uff", + "buf f", + "bu ff", + "_ counts", + "_count s", + "_co unts", + "_cou nts", + "Ġ reuse", + "Ġre use", + "ch unk", + "chu nk", + "Ġim posed", + "Ġimp osed", + "Ġimpose d", + "Primary Key", + "Fore ground", + "Ġcons umed", + "Ġconsum ed", + "Ġconsume d", + "? !", + "Ġd ick", + "Ġdi ck", + "Ġdic k", + "Ġ chron", + "Ġch ron", + "Ġchr on", + "ĠF ern", + "ĠFe rn", + "ĠFer n", + "Ġ responsive", + "Ġres ponsive", + "Ġrespons ive", + "9 58", + "95 8", + "Ġin sect", + "Ġins ect", + "Ġinse ct", + "ic ulty", + "icult y", + "Ġ rw", + "Ġr w", + "Ġa like", + "Ġal ike", + "Ġali ke", + "Ġ subset", + "Ġsub set", + "Ġsubs et", + "Ġ Cookies", + "ĠC ookies", + "ĠCo okies", + "ĠCook ies", + "ĠCookie s", + "Ġ Pair", + "ĠP air", + "ĠPa ir", + "ĠPai r", + "Ġ tier", + "Ġt ier", + "Ġti er", + "Ġtie r", + "I FO", + "IF O", + "av our", + "avo ur", + "avou r", + "Ġ QU", + "ĠQ U", + ", sizeof", + ",size of", + "Ġ merged", + "Ġm erged", + "Ġmer ged", + "Ġmerge d", + "Ġmerg ed", + "m v", + "i tol", + "it ol", + "ito l", + "y lon", + "yl on", + "Ġj umped", + "Ġjump ed", + ". role", + ".r ole", + ".ro le", + "ens aje", + "ensa je", + "R ules", + "Rule s", + "Ru les", + "Ġ browse", + "Ġb rowse", + "Ġbrows e", + "Ġbrow se", + "An imator", + "Anim ator", + "Ġy oga", + "Ġyo ga", + "Ġyog a", + "Ġ variants", + "Ġvar iants", + "Ġvari ants", + "Ġvariant s", + "Ġcour tesy", + "Ġcourt esy", + "Ġcourte sy", + "u ran", + "ur an", + "ura n", + "p bs", + "pb s", + "else if", + "A lt", + "Al t", + "Ġ Lane", + "ĠL ane", + "ĠLa ne", + "ĠLan e", + "C LK", + "CL K", + "I MARY", + "IM ARY", + "IMA RY", + "_ PROPERTY", + "_P ROPERTY", + "_PRO PERTY", + "ï¼ IJ", + "Ġ chan", + "Ġc han", + "Ġch an", + "Ġcha n", + "Ġgrad ually", + "Ġgradu ally", + "Ġgradual ly", + "Ġ shake", + "Ġsh ake", + "Ġsha ke", + "Ġbl onde", + "Ġblo nde", + "Ġblond e", + ".. .\");Ċ", + "... \");Ċ", + "...\" );Ċ", + "- sex", + "-s ex", + "-se x", + "Ġgame play", + "a cies", + "ac ies", + "aci es", + "acie s", + ". refresh", + ".re fresh", + ".ref resh", + "U SB", + "US B", + "Ġ Plot", + "ĠP lot", + "ĠPl ot", + "W as", + "Wa s", + "iss ippi", + "Ġ Tensor", + "ĠT ensor", + "Ġcrypt ocurrency", + "Ġcrypto currency", + "Ġcryptoc urrency", + "Ġdifficult ies", + "De leted", + "Delete d", + "Del eted", + "With out", + "_ append", + "_app end", + "_ap pend", + "_ ver", + "_v er", + "_ve r", + "9 67", + "96 7", + "\" ))čĊ", + "\") )čĊ", + "\")) čĊ", + "Ġh onestly", + "Ġhonest ly", + "Ġ pivot", + "Ġp ivot", + "Ġpiv ot", + "Ġ temps", + "Ġte mps", + "Ġtem ps", + "Ġtemp s", + "_ ps", + "_p s", + "Ġ Unlike", + "ĠUn like", + "[ :-", + "[: -", + "V S", + "_ inf", + "_in f", + "_i nf", + "Ġjun ior", + "Ġjuni or", + "Ġ animations", + "Ġan imations", + "Ġanim ations", + "Ġanimation s", + "Ġ filepath", + "Ġfile path", + "? {{$", + ">{ {$", + ">{{ $", + "Ġ unicode", + "Ġun icode", + "Ġuni code", + "Ġunic ode", + "p laces", + "pl aces", + "place s", + "pla ces", + "Ġ Coffee", + "ĠC offee", + "ĠCo ffee", + "ĠCoff ee", + ". SE", + ".S E", + "Ġ PAR", + "ĠP AR", + "ĠPA R", + "( txt", + "(t xt", + "(tx t", + "g ebra", + "ge bra", + "geb ra", + "Ġ fires", + "Ġf ires", + "Ġfire s", + "Ġfi res", + "Ġfir es", + "Main Window", + "m edium", + "med ium", + "medi um", + "Ġ( âĢľ", + "Ġ lg", + "Ġl g", + "Ġ cmp", + "Ġc mp", + "Ġcm p", + "/ base", + "/b ase", + "_ layers", + "_l ayers", + "_layer s", + "_la yers", + "_ entries", + "_en tries", + "_ent ries", + "Ġadmin ister", + "ĠS UCH", + "ĠSU CH", + "B P", + "ĠScott ish", + "ĉ čĊĉčĊ", + "ĉčĊ ĉčĊ", + "g uard", + "gu ard", + "gua rd", + "Ġ Strong", + "ĠSt rong", + "ĠStr ong", + "In sn", + "Ins n", + "Ġ CAP", + "ĠC AP", + "ĠCA P", + "as ury", + "asu ry", + "Ġ SEE", + "ĠS EE", + "ĠSE E", + "C lock", + "Cl ock", + "Clo ck", + "e rie", + "er ie", + "eri e", + "\\ models", + "\\model s", + "Ġ $$", + "Ġ$ $", + "Ġ Cab", + "ĠC ab", + "ĠCa b", + "Ġwur de", + "Ġsold ier", + "Ġcl ips", + "Ġclip s", + "Ġcli ps", + "Ġarr angement", + "Ġarrang ement", + "Ġarrange ment", + "Ġ Wonder", + "ĠW onder", + "ĠWo nder", + "ĠWon der", + "ĠH orn", + "ĠHor n", + "ĠHo rn", + "Ġsc ared", + "Ġsca red", + "Ġscar ed", + "Ġscare d", + "Ġc ure", + "Ġcur e", + "Ġcu re", + "m kdir", + "mk dir", + "Ġ aligned", + "Ġal igned", + "Ġalign ed", + "Ġ Pink", + "ĠP ink", + "ĠPin k", + "ĠPi nk", + "Ġl anded", + "Ġland ed", + "Ġlan ded", + "D imension", + "Dim ension", + "Scroll Pane", + ". chat", + ".c hat", + ".ch at", + ". With", + ".W ith", + "Ġ Train", + "ĠT rain", + "ĠTr ain", + "ĠTra in", + "] .Ċ", + "]. Ċ", + "Ġth irty", + "Ġd urable", + "Ġdur able", + "Ġ ld", + "Ġl d", + "Ġlate init", + "Ġ charts", + "Ġch arts", + "Ġchar ts", + "Ġchart s", + "Ġcha rts", + "Ġins ult", + ". Fatal", + ".F atal", + ".Fat al", + "_ ct", + "_c t", + "Ġm asks", + "Ġmask s", + "Ġmas ks", + "CLUD ED", + "CLU DED", + "CLUDE D", + "P resident", + "Pres ident", + "Ġcol ours", + "Ġcolour s", + "g ments", + "gment s", + "gm ents", + ". attributes", + ".at tributes", + ".attribute s", + ".attrib utes", + "Ġ Flex", + "ĠF lex", + "ĠFl ex", + "ĠFle x", + "Ġ Clock", + "ĠC lock", + "ĠCl ock", + "ĠClo ck", + "ÃŃ cul", + "ÃŃc ul", + "i men", + "im en", + "ime n", + "J O", + "Ġ Regex", + "ĠReg ex", + "_ LINK", + "_L INK", + "Ġc ouch", + "Ġco uch", + "Ġcou ch", + "Ġ INPUT", + "ĠIN PUT", + "Ġb eating", + "Ġbe ating", + "Ġbeat ing", + "b usiness", + "bus iness", + "pr eced", + "pre ced", + "prec ed", + ". unit", + ".un it", + ".u nit", + ".uni t", + "Ġ Fel", + "ĠF el", + "ĠFe l", + "N ever", + "Ne ver", + "os pel", + "osp el", + ". startswith", + ".start swith", + "ĠE PA", + "ĠEP A", + ". only", + ".on ly", + "Ġpre venting", + "Ġprevent ing", + "Ġprev enting", + "y er", + "ye r", + "Column Name", + "Ġe levation", + "Ġele vation", + "Ġelev ation", + "f lu", + "fl u", + "i cycle", + "ic ycle", + "icy cle", + "Ġ offline", + "Ġoff line", + "Tool bar", + "Ġcomp eting", + "Ġcompet ing", + ") ].", + ")] .", + "Ġm og", + "Ġmo g", + "Ġ isValid", + "Ġis Valid", + "A sk", + "As k", + "_ av", + "_a v", + "_ lat", + "_l at", + "_la t", + "A NC", + "AN C", + "ĠJ oh", + "ĠJo h", + "k ers", + "ke rs", + "ker s", + "Ġ guards", + "Ġg uards", + "Ġgu ards", + "Ġguard s", + "Ġguar ds", + "Ġ chains", + "Ġch ains", + "Ġchain s", + "Ġcha ins", + "Ġchai ns", + "ĠSimple DateFormat", + ". static", + ".st atic", + ".stat ic", + "Ġv essel", + "Ġve ssel", + "Ġvess el", + "Ġves sel", + "Ġm ud", + "Ġmu d", + "Ġst abil", + "Ġstab il", + "Ġsta bil", + "Ġst ret", + "Ġstr et", + "Ġstre t", + "g m", + "am ation", + "ama tion", + "amat ion", + "ç ľ", + "- with", + "-w ith", + "Ġ ros", + "Ġr os", + "Ġro s", + "_ PA", + "_P A", + "Ġ resultado", + "Ġresult ado", + "Ġconf idential", + "Ġconfident ial", + "ĠTok yo", + "ĉ using", + "ĉu sing", + "ĉus ing", + "Ġ Mathf", + "ĠMath f", + "ĠMat hf", + "om bine", + "omb ine", + "Ġ ESPN", + "ĠESP N", + "ĠES PN", + "Ġde alers", + "Ġdeal ers", + "Ġdealer s", + "Ġdismiss ed", + "T RY", + "TR Y", + "Ġte ens", + "Ġteen s", + "Ġtee ns", + "rec ords", + "record s", + "Ġw ings", + "Ġwin gs", + "Ġwing s", + "g allery", + "ac counts", + "account s", + "acco unts", + "_ LIB", + "_L IB", + "Ġj acket", + "Ġja cket", + "Ġjack et", + "Ġjac ket", + "Ġ NSObject", + "ĠNS Object", + "Ġ stones", + "Ġs tones", + "Ġst ones", + "Ġstone s", + "Ġsto nes", + "Ġ Delivery", + "ĠD elivery", + "ĠDel ivery", + "ĠDeliver y", + "ĠD iet", + "ĠDi et", + "ĠDie t", + "/ watch", + "/w atch", + "Ġto ilet", + "Ġtoile t", + "Ġtoi let", + "Ġ Guest", + "ĠG uest", + "ĠGu est", + ". day", + ".d ay", + ".da y", + "0 67", + "06 7", + "Ġ intval", + "Ġint val", + "0 87", + "08 7", + "Vis it", + "Vi sit", + "Ġinvest igated", + "Ġinvestig ated", + "Ġinvestigate d", + "Ġpen tru", + "Ġpent ru", + "ĠThe atre", + "andid ates", + "andidate s", + "andi dates", + "L ang", + "La ng", + "Ġ Serv", + "ĠS erv", + "ĠSe rv", + "ĠSer v", + "Ġ controllers", + "Ġcont rollers", + "Ġcontrol lers", + "Ġcontroller s", + "Ġ setTitle", + "Ġset Title", + "N P", + "a my", + "am y", + "f lat", + "fl at", + "( ui", + "(u i", + "0 69", + "06 9", + "_ document", + "_d ocument", + "_doc ument", + "è ĥ½", + "èĥ ½", + "Ġ Coin", + "ĠC oin", + "ĠCo in", + "ĠAd ams", + "ĠAdam s", + "ĠAda ms", + "p tic", + "pt ic", + "Ġ productive", + "Ġpro ductive", + "Ġproduct ive", + "Ġprod uctive", + "Ġaccompl ished", + "Ġaccomplish ed", + "čĊ čĊčĊčĊ", + "čĊčĊ čĊčĊ", + "čĊčĊčĊ čĊ", + "Ġde ferred", + "Ġdefer red", + "i entes", + "ient es", + "ien tes", + "iente s", + "Ġs inc", + "Ġsi nc", + "Ġsin c", + "ol ars", + "olar s", + "ola rs", + "Right arrow", + "Ġvar iations", + "Ġvari ations", + "Ġvariation s", + "( offset", + "(o ffset", + "(off set", + "9 57", + "95 7", + ". LayoutInflater", + ".Layout Inflater", + "Ġ suspend", + "Ġs uspend", + "Ġsus pend", + "Ġsusp end", + "Ġpre vention", + "Ġprevent ion", + "Ġprev ention", + "_ private", + "_pr ivate", + "_priv ate", + "_ js", + "_j s", + "â ĺħ", + "âĺ ħ", + "Ġw ieder", + "Ġwie der", + "Ġwi eder", + "at um", + "atu m", + "Ĵ Į", + "Ġappear ances", + "Ġappearance s", + ". Document", + ".D ocument", + ".Doc ument", + "Ġvalid ates", + "Ġvalidate s", + "Ġvalida tes", + "c alendar", + "cal endar", + "} \";Ċ", + "}\" ;Ċ", + ". demo", + ".d emo", + ".de mo", + "con ut", + "co nut", + "Ġcor rection", + "Ġcorrect ion", + "Ġcorre ction", + "Ġcorr ection", + "Ġ Deal", + "ĠDe al", + "Ġbatter ies", + "Ġbatt eries", + ". duration", + ".d uration", + ", \\", + "_ marker", + "_m arker", + "_mark er", + "_mar ker", + "m ulti", + "mul ti", + "mult i", + "Ġ halt", + "Ġh alt", + "Ġha lt", + "Ġhal t", + "Ġ cms", + "Ġc ms", + "Ġcm s", + "Ġsh aped", + "Ġshape d", + "Ġsha ped", + "B ro", + "Br o", + "re duce", + "red uce", + "Ġ ####", + "Ġ# ###", + "Ġ## ##", + "Ġ### #", + "C TOR", + "CT OR", + "Ġ Benef", + "ĠB enef", + "ĠBen ef", + "ĠBene f", + "Ġicon ic", + "Ġic onic", + "Ġp iano", + "Ġpi ano", + "Ġpian o", + "Ġeffect iveness", + "Ġeffective ness", + "| .Ċ", + "|. Ċ", + "Ġ ajax", + "Ġa jax", + "Ġaj ax", + "Ġv olumes", + "Ġvol umes", + "Ġvolume s", + "Ġvolum es", + "ภ¡", + "Ġ cljs", + "Ġcl js", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "a ths", + "at hs", + "ath s", + "r aits", + "ra its", + "rait s", + "rai ts", + "å¤ §", + "Ñ ĸ", + "_ mult", + "_m ult", + "_mul t", + "_mu lt", + "Ġfasc inating", + "A verage", + "Ġp ré", + "Ġpr é", + "ĠChair man", + ".find Element", + "_ pin", + "_p in", + "_pi n", + "Ġcom paring", + "Ġcomp aring", + "Ġcompar ing", + "Ġdark ness", + "- Fi", + "-F i", + "- server", + "-s erver", + "-ser ver", + "Ġselect ing", + "s terdam", + "ster dam", + "Ġ Parts", + "ĠP arts", + "ĠPart s", + "ĠPar ts", + "ĠPa rts", + "FORM ATION", + "FORMAT ION", + "Ġn oting", + "Ġnot ing", + "Ġno ting", + "Ġ pile", + "Ġp ile", + "Ġpi le", + "Ġpil e", + "o gs", + "og s", + "Ġ palette", + "Ġp alette", + "Ġpa lette", + "Ġpal ette", + "Ġpale tte", + "_ do", + "_d o", + "it ize", + "iti ze", + "0 79", + "07 9", + "( )(", + "() (", + "Ġdef ining", + "Ġdefin ing", + "Ġ remainder", + "Ġremain der", + "Un its", + "Unit s", + "Uni ts", + "_ TASK", + "_T ASK", + "_TA SK", + "Http Client", + "S ocial", + "So cial", + "Ġf undra", + "Ġfun dra", + "Ġfund ra", + "N R", + "c hest", + "ch est", + "che st", + "ches t", + "C urrency", + "Curr ency", + ". adapter", + ".ad apter", + "Ġd op", + "Ġdo p", + "un ting", + "unt ing", + "ANG UAGE", + "\" He", + "\"H e", + "ĉ index", + "ĉin dex", + "_ package", + "_p ackage", + "_pack age", + ". Icon", + ".I con", + "Ġre pet", + "Ġrep et", + "Ġrepe t", + "m ass", + "ma ss", + "mas s", + "= \".$", + "=\" .$", + "=\". $", + "ĠS ud", + "ĠSu d", + "Ġ lid", + "Ġl id", + "Ġli d", + "pro vince", + "prov ince", + "ì ľ", + "G PIO", + "GP IO", + "Ð ļ", + "Ġ MySQL", + "ĠMy SQL", + "Ġ docs", + "Ġd ocs", + "Ġdo cs", + "Ġdoc s", + "Ġ GA", + "ĠG A", + "Ġip sum", + "Ġips um", + "K ernel", + "Ġac cepts", + "Ġaccept s", + "Ġf itting", + "Ġfit ting", + "Ġcu ando", + "Ġd uplic", + "Ġdup lic", + "ĠBr other", + "ĠBro ther", + "ĠK le", + "ĠKl e", + "n ums", + "num s", + "nu ms", + "Ġ morph", + "Ġm orph", + "Ġmor ph", + "Ġ ########", + "Ġ# #######", + "Ġ## ######", + "Ġ### #####", + "Ġ#### ####", + "Ġ##### ###", + "Ġ CGPoint", + "ĠCG Point", + "< unsigned", + "ä¾ ĭ", + "ĠD uke", + "ĠDu ke", + ".set Bounds", + "q s", + "o ric", + "or ic", + "ori c", + "j er", + "je r", + "Ġreg arded", + "Ġregard ed", + "Http Request", + "Ġb onds", + "Ġbo nds", + "Ġbon ds", + "Ġbond s", + "Ġthorough ly", + "en cent", + "ence nt", + "enc ent", + "Ġhighlight ed", + "Ġac res", + "Ġacre s", + "Ġwork place", + "Ġ Lux", + "ĠL ux", + "ĠLu x", + "Ġ quot", + "Ġqu ot", + "Ġquo t", + "9 86", + "98 6", + ". inflate", + ".in flate", + ".inf late", + "Ġd ocumented", + "Ġdocument ed", + "Ġadd iction", + "Ġaddict ion", + "Ġ mutation", + "Ġm utation", + "Ġmut ation", + ". city", + ".c ity", + ".ci ty", + "Ġbott les", + "Ġbottle s", + "Ġ Repository", + "ĠRe pository", + "ĠRepos itory", + "o nn", + "on n", + "err no", + "ARI ABLE", + "åº ¦", + "_ BEGIN", + "_B EGIN", + "_BE GIN", + "g las", + "gl as", + "' })Ċ", + "'} )Ċ", + "'}) Ċ", + "Ġ Massage", + "ĠM assage", + "ĠMass age", + "ĠMas sage", + "ĠW hit", + "ĠWh it", + "reg ex", + "W A", + "Ġout let", + "- head", + "-h ead", + "-he ad", + "Ġ expired", + "Ġex pired", + "Ġexp ired", + "Ġexpire d", + "Ġ Thai", + "ĠT hai", + "ĠTh ai", + "/ include", + "/in clude", + "/inc lude", + "g radient", + "grad ient", + "s canf", + "scan f", + "Ġs eam", + "Ġse am", + "Ġsea m", + "w al", + "wa l", + "ĉ buf", + "ĉb uf", + "B earer", + "Be arer", + "Bear er", + "Ġprec ious", + "Ġprecio us", + "i facts", + "if acts", + "ifact s", + "ifa cts", + "c oord", + "co ord", + "Ġexpl oration", + "Ġexplo ration", + "Ġexplor ation", + ". getY", + ".get Y", + "( handle", + "(h andle", + "(hand le", + "T opic", + "To pic", + "Top ic", + "Ġ Vent", + "ĠV ent", + "ĠVen t", + "ĠVe nt", + "r hs", + "rh s", + "- -----Ċ", + "-- ----Ċ", + "---- --Ċ", + "--- ---Ċ", + "----- -Ċ", + "------ Ċ", + "Ġ Bright", + "ĠB right", + "ĠBr ight", + "ĠBrig ht", + "ĠBri ght", + "Ġ guild", + "Ġg uild", + "Ġgu ild", + "Ġgui ld", + "m other", + "mo ther", + "mot her", + "moth er", + "st orm", + "sto rm", + "stor m", + "Ġmunicip al", + "Ġ ink", + "Ġin k", + "Ġi nk", + ". TYPE", + ".T YPE", + "w l", + ".. . < /", + "_ ro", + "_r o", + "( (*", + "(( *", + "? ???", + "?? ??", + "??? ?", + "_ vertex", + "_ver tex", + "_vert ex", + "ke it", + "ĠH alloween", + "T I", + "Ġ Va", + "ĠV a", + "_ car", + "_c ar", + "_ca r", + "=\" {{$", + "=\"{{ $", + "=\"{ {$", + "Ġrandom ly", + "а ние", + "ан ие", + "ани е", + "Ġsh ocked", + "Ġshock ed", + "ĠPok émon", + "s ignal", + "sign al", + "sig nal", + "Ġ SDK", + "ĠS DK", + "ĠSD K", + "m iddleware", + "middle ware", + "Ġt reating", + "Ġtr eating", + "Ġtreat ing", + "Ġtre ating", + "Ġbur ned", + "Ġburn ed", + "De partment", + "Dep artment", + "Depart ment", + "ĠS pect", + "ĠSp ect", + "ĠSpec t", + "ĠSpe ct", + "Ġ cliente", + "Ġcl iente", + "Ġclient e", + "Ġcli ente", + "Ġ Reddit", + "ĠRe ddit", + "ĠRed dit", + "_ avg", + "_a vg", + "_av g", + "Ġinst alling", + "Ġinstall ing", + "Ġinstal ling", + "_ alpha", + "_al pha", + ", data", + ",d ata", + "Ġ setId", + "Ġset Id", + "Ġ ListView", + "ĠList View", + "( property", + "(p roperty", + "(pro perty", + "(prop erty", + "Ġcross ing", + "Ġ Obj", + "ĠO bj", + "ĠOb j", + "ĠW ard", + "ĠWar d", + "ĠWa rd", + "ĠRedirect To", + "Ġ Present", + "ĠP resent", + "ĠPres ent", + "ĠPre sent", + "Ġdr aws", + "Ġdraw s", + "Ġdra ws", + "ched uled", + "chedule d", + "Ġlegisl ative", + "Ġtw ist", + "Ġ Stra", + "ĠS tra", + "ĠSt ra", + "ĠStr a", + "Ġ AFP", + "ĠA FP", + "ĠAF P", + "ĠC hap", + "ĠCh ap", + "ĠCha p", + "- pr", + "-p r", + ": CGRect", + "Ġ ces", + "Ġc es", + "Ġce s", + "R outes", + "Route s", + "Ro utes", + "n of", + "no f", + "Ġ visa", + "Ġv isa", + "Ġvis a", + "Ġvi sa", + "Ġ TCP", + "ĠT CP", + "ĠTC P", + "ĠE VEN", + "ĠEV EN", + "ĠEVE N", + "iv ial", + "ivia l", + "ivi al", + "Ġ Letter", + "ĠL etter", + "ĠLet ter", + "R AY", + "RA Y", + "Ġ implode", + "Ġim plode", + "Ġimpl ode", + ". eq", + ".e q", + "= '+", + "=' +", + "Ġmot ivated", + "Ġmotiv ated", + "Ġmotivate d", + ". visible", + ".v isible", + ".vis ible", + ". short", + ".s hort", + ".sh ort", + "> manual", + "Ġ Technical", + "ĠTechn ical", + "Ġcorpor ation", + "Ġcorp oration", + "Ġcorpo ration", + "Ġ HW", + "ĠH W", + "an ka", + "ank a", + "T AIL", + "TA IL", + "is tas", + "ist as", + "ista s", + "Ġper forms", + "Ġperform s", + "Ġperfor ms", + "Ġ Behavior", + "ĠBeh avior", + ". For", + ".F or", + "_ ORDER", + "_OR DER", + "_ORD ER", + "Ġ Kick", + "ĠK ick", + "ĠKi ck", + "Ġ callbacks", + "Ġcall backs", + "Ġcallback s", + "_ dr", + "_d r", + "u ego", + "ue go", + "h ub", + "hu b", + "uff icient", + "s ky", + "sk y", + "Ġ bp", + "Ġb p", + "h table", + "ht able", + "hta ble", + "Ġ ONLY", + "ĠON LY", + "ĠAUTH ORS", + "ĠAUTHOR S", + ". Argument", + ".Arg ument", + "\" };Ċ", + "\"} ;Ċ", + "Ġ Thunder", + "ĠTh under", + "ĠThu nder", + "Ġ Kom", + "ĠK om", + "ĠKo m", + ". Should", + ".Sh ould", + "A UTH", + "AU TH", + "AUT H", + "a hu", + "ah u", + "_ payment", + "_p ayment", + "_pay ment", + "Ġ starter", + "Ġst arter", + "Ġstart er", + "Ġstar ter", + "ìĦ ľ", + "ìļ ©", + "B log", + "Bl og", + "Blo g", + ". patch", + ".p atch", + ".pat ch", + "Ġgover ned", + "Ġgovern ed", + "as sy", + "ass y", + "- found", + "-f ound", + "Ġthe ater", + "Ġtheat er", + "ĠFont Weight", + "Ġ Batman", + "ĠBat man", + "\" If", + "\"I f", + ". Random", + ".R andom", + "_ delta", + "_d elta", + "_del ta", + "Ġ CE", + "ĠC E", + "Auth enticated", + "Authenticate d", + "Ġd rone", + "Ġdr one", + "Ġdro ne", + "Ġc ous", + "Ġco us", + "Ġcou s", + "r adius", + "rad ius", + "radi us", + "M er", + "Me r", + "( None", + "(N one", + "Ġ NJ", + "ĠN J", + "_ headers", + "_header s", + "_head ers", + "_he aders", + "Ġ amer", + "Ġa mer", + "Ġam er", + "py test", + "Ġ Actions", + "ĠA ctions", + "ĠAct ions", + "ĠAction s", + "ĉ ĉĉĠĠĠĠ", + "ĉĉ ĉĠĠĠĠ", + "ĉĉĉ ĠĠĠĠ", + "ĉĉĉĠĠĠ Ġ", + "ĉĉĉĠ ĠĠĠ", + "ĉĉĉĠĠ ĠĠ", + "Ġ ett", + "Ġe tt", + "Ġet t", + "Ġh oly", + "Ġho ly", + "Ġhol y", + "Ġun comfort", + "ĠN in", + "ĠNi n", + "Ġ Decimal", + "ĠD ecimal", + "ĠDe cimal", + "ĠDec imal", + "Ġ Messages", + "ĠM essages", + "ĠMessage s", + "ĠMess ages", + ". sender", + ".s ender", + ".se nder", + ".send er", + "] ])Ċ", + "]] )Ċ", + "]]) Ċ", + "Ġem brace", + "Ġemb race", + "Ġembr ace", + "Th ough", + "Tho ugh", + "/ sp", + "/s p", + "Ġcult ures", + "Ġculture s", + "Ġcul tures", + "Ġhigh way", + "t ar", + "ta r", + ". fail", + ".f ail", + ".fa il", + "_ hidden", + "_h idden", + "_hi dden", + "_hid den", + "ĠcomponentDid Mount", + "ĠW right", + "ĠWr ight", + "Ġj ag", + "Ġja g", + "_ il", + "_i l", + "../ ../../", + "../../ ../", + "i gu", + "ig u", + "F ood", + "Foo d", + "Fo od", + "Ġ ace", + "Ġa ce", + "Ġac e", + "Ġa ños", + "Ġaño s", + "Ġañ os", + "U SD", + "US D", + "Ġmut ual", + "Ġmu tual", + "Log ic", + "Ġt emple", + "Ġtem ple", + "Ġtemp le", + "Ġtempl e", + "Ġbrief ly", + "Ġ Trip", + "ĠT rip", + "ĠTr ip", + "ĠTri p", + "class method", + "default s", + "Ġ chunks", + "Ġch unks", + "Ġchunk s", + ", ,,,", + ",, ,,", + ",,, ,", + "Ġ Reason", + "ĠRe ason", + "$ id", + "$i d", + "- ups", + "-up s", + "-u ps", + "Ġ damn", + "Ġda mn", + "Ġdam n", + "Ġtr ucks", + "Ġtruck s", + "Ġun limited", + "Ġunl imited", + "Ġsc ulpt", + "Ġ Cards", + "ĠC ards", + "ĠCar ds", + "ĠCard s", + "Ġ autor", + "Ġa utor", + "Ġaut or", + "Ġauto r", + "Ġau tor", + "Ġ Testing", + "ĠT esting", + "ĠTest ing", + "ĠTes ting", + "Ġd iese", + "Ġdi ese", + "Ġdie se", + "Ġdies e", + "sh ops", + "shop s", + "ç ´", + "( payload", + "(p ayload", + "Ġ PATH", + "ĠP ATH", + "ĠPA TH", + "ĠPAT H", + "ĠMem orial", + "ĠMemo rial", + "ĠMemor ial", + "Ġridic ulous", + "e gree", + "eg ree", + "egr ee", + "-w inning", + "-win ning", + "Ġre hab", + "Ġreh ab", + "Ġsophistic ated", + "w pdb", + "wp db", + "ĉ path", + "ĉp ath", + "! \";Ċ", + "!\" ;Ċ", + "_ SYS", + "_S YS", + "_SY S", + ". speed", + ".s peed", + ".sp eed", + "Ġ soap", + "Ġso ap", + "s uffix", + "W rap", + "Wr ap", + "Ġenh ancement", + "Ġenhance ment", + "à ī", + "ú b", + "Ġ playlist", + "Ġplay list", + "Ġmix ing", + "Ġmi xing", + "Ġmixin g", + "ant idad", + "anti dad", + "=\" \";Ċ", + "=\"\" ;Ċ", + "Ġ Revision", + "ĠRe vision", + "ĠRev ision", + "Ġ Beat", + "ĠB eat", + "ĠBe at", + "ĠBea t", + ". inc", + ".in c", + ".i nc", + "- way", + "-w ay", + "e ncias", + "enc ias", + "encia s", + "enci as", + "u lers", + "ul ers", + "ule rs", + "uler s", + "C at", + "Ca t", + "i del", + "id el", + "ide l", + "Ġ Ship", + "ĠS hip", + "ĠSh ip", + "ĠShi p", + ". setColor", + ".set Color", + "Ġthreat ening", + "Ġthreaten ing", + ". modules", + ".module s", + ".mod ules", + "Ġafter wards", + "Ġafterward s", + "Ġ Dashboard", + "ĠD ashboard", + "ĠDash board", + "Ċ ĠĊ", + "S ignal", + "Sign al", + "Sig nal", + "Ġ primer", + "Ġpr imer", + "Ġprim er", + "Ġprime r", + "Ġpri mer", + "or neys", + "orney s", + "orne ys", + "ici ary", + "icia ry", + "iciar y", + "Ġ ligne", + "Ġl igne", + "Ġli gne", + "Ġlig ne", + "Ġlign e", + "_ predict", + "_p redict", + "_pre dict", + "_pred ict", + "Ġa est", + "Ġae st", + "Ġaes t", + "_ https", + "_http s", + "_ht tps", + "> :", + "Ġ Lex", + "ĠL ex", + "ĠLe x", + "Ġren contres", + "Ġrencont res", + "Ġrencontre s", + "Ġrencontr es", + "eg ral", + "egr al", + "egra l", + "s cala", + "sc ala", + "scal a", + "_ family", + "_f amily", + "ÃŁ en", + "ÃŁe n", + "_ sym", + "_s ym", + "_sy m", + "Ġunc ertainty", + "Ġuncert ainty", + "Ġuncertain ty", + "Ġ VALUE", + "ĠVAL UE", + "Ġ };čĊčĊ", + "Ġ} ;čĊčĊ", + "Ġ};čĊ čĊ", + "Ġ}; čĊčĊ", + "Ġbr oader", + "Ġbro ader", + "Ġbroad er", + "Ġh orses", + "Ġhor ses", + "Ġhorse s", + "Ġhors es", + "ãģ Ŀ", + "Ġ Kal", + "ĠK al", + "ĠKa l", + "o ba", + "ob a", + "_ INET", + "_IN ET", + "_I NET", + "Ġ Kill", + "ĠK ill", + "ĠKi ll", + "ĠKil l", + "j query", + "a mination", + "am ination", + "amin ation", + "ami nation", + "amina tion", + "[ @\"", + "[@ \"", + "Ġm uj", + "Ġmu j", + "# ##Ċ", + "## #Ċ", + "### Ċ", + "First OrDefault", + "then Return", + "C he", + "Ch e", + "/ footer", + "/f ooter", + "/foo ter", + "Ġp arks", + "Ġpar ks", + "Ġpark s", + "as je", + "ĠG ulf", + "ĠGu lf", + "ĠGul f", + "Ġmod est", + "Ġmode st", + "Ġmo dest", + "Ġmodes t", + ". Init", + ".I nit", + ".In it", + "ï¼Ł ĊĊ", + "ï¼ŁĊ Ċ", + "Ġpros pects", + "Ġprospect s", + "Ġ svg", + "Ġs vg", + "Ġsv g", + "Ġ åı", + "Ġå ı", + ". Dialog", + ".D ialog", + ".Di alog", + ".Dial og", + "_ NET", + "_N ET", + "_NE T", + "Ġ (($", + "Ġ( ($", + "Ġ(( $", + "Ġ ek", + "Ġe k", + "Ġ Warning", + "ĠW arning", + "ĠWar ning", + "ĠWarn ing", + "Ġ MK", + "ĠM K", + "< LM", + "", + ")= >", + "Ġ Repair", + "ĠRe pair", + "ĠRep air", + "_ BE", + "_B E", + "B rand", + "Br and", + "u art", + "ua rt", + "uar t", + "p review", + "pr eview", + "pre view", + "prev iew", + "Ġiniti atives", + "Ġinitiative s", + "r unning", + "run ning", + "b ang", + "ba ng", + "ban g", + "ĉ update", + "ĉup date", + "Ġ Coach", + "ĠCo ach", + "R ich", + "Ġ youtube", + "Ġy outube", + "Ġyou tube", + "Ġrit ual", + "Ġri tual", + "ap pa", + "app a", + "ĠRob inson", + "ĠRobin son", + "p recision", + "pre cision", + "prec ision", + "//// ////////////////////////////////////////////////////////////////////////", + "//////// ////////////////////////////////////////////////////////////////////", + "//////////////// ////////////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////// ////////////", + "//////////// ////////////////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////////////// ////", + "//////////////////////////////////////////////////////////////////// ////////", + "//////////////////////////////////////////////////////////// ////////////////", + "= []Ċ", + "=[ ]Ċ", + "=[] Ċ", + "Ġcelebr ated", + "Ġcelebrate d", + "O TO", + "OT O", + "Ġin clusion", + "Ġincl usion", + "Ġinclus ion", + "J P", + "' ;čĊčĊ", + "'; čĊčĊ", + "';čĊ čĊ", + "Ġnot able", + "Ġno table", + "Ġnota ble", + "( _.", + "(_ .", + "Man aged", + "Manage d", + "Mana ged", + "Ġgu ides", + "Ġguide s", + "Ġguid es", + "Ġgui des", + "& nbsp", + "ated Route", + "Ġ Adjust", + "ĠAd just", + "ĠAdj ust", + "Ġcol ored", + "Ġcolor ed", + "Ġcolore d", + "_ scores", + "_s cores", + "_score s", + "_sc ores", + "Ġ Tesla", + "ĠTe sla", + "ĠTes la", + "_ progress", + "_pro gress", + "_prog ress", + ". inst", + ".in st", + ".i nst", + ".ins t", + "[ '_", + "[' _", + ". flags", + ".f lags", + ".fl ags", + ".flag s", + "Ġ fclose", + "Ġf close", + "Ġfc lose", + "_ OPER", + "_O PER", + "_OP ER", + "ż y", + "_ note", + "_n ote", + "_no te", + "_not e", + "Ġtrans gender", + "å ķ", + "RI PT", + "Ġab sent", + "Ġabs ent", + "Ġ amet", + "Ġa met", + "Ġam et", + "Ġ operand", + "Ġoper and", + "Ġopera nd", + "ë ©", + "Ġ hood", + "Ġh ood", + "Ġho od", + "to LowerCase", + "a vo", + "av o", + "ĠC ircuit", + "ĠCirc uit", + "ĠL ind", + "ĠLin d", + "ĠLi nd", + "-- }}Ċ", + "= m", + "Ġ suppress", + "Ġsup press", + "Ġsupp ress", + "Ġ MAP", + "ĠM AP", + "ĠMA P", + "i ang", + "ia ng", + "ian g", + "- admin", + "-ad min", + "Ġ sidebar", + "Ġs idebar", + "Ġside bar", + "Ġ Bu", + "ĠB u", + "Ġ Hex", + "ĠH ex", + "ĠHe x", + ", F", + "Ġ Signal", + "ĠS ignal", + "ĠSign al", + "ĠSig nal", + "Ġtrans parency", + "ĠF ederation", + "ĠFeder ation", + "ĠFed eration", + "/ V", + "R eq", + "Re q", + "Ġ pulse", + "Ġp ulse", + "Ġpul se", + "Ġpu lse", + "Ġpuls e", + "Ġt ends", + "Ġte nds", + "Ġten ds", + "Ġtend s", + "Number s", + "Num bers", + "% '", + "Ġde port", + "Ġdep ort", + "d atas", + "data s", + "da tas", + "dat as", + "_ UINT", + "_U INT", + "_UI NT", + "_ tra", + "_t ra", + "_tr a", + "o ko", + "ok o", + "Ġ \"?", + "Ġ\" ?", + "com pet", + "comp et", + "so lete", + "sole te", + "sol ete", + "un dry", + "und ry", + "Ġ overlap", + "Ġover lap", + "Ġoverl ap", + "} `,Ċ", + "}` ,Ċ", + "}`, Ċ", + ". ly", + ".l y", + "_ summary", + "_sum mary", + "Ġ Lost", + "ĠL ost", + "ĠLo st", + "ĠLos t", + ". Center", + ".C enter", + "Ġdis ability", + ". Serialization", + ".S erialization", + ".Serial ization", + "Ġ geom", + "Ġge om", + "Ġgeo m", + "Ġ ?:", + "Ġ? :", + "Ġ Wo", + "ĠW o", + "Ġsh ipped", + "Ġship ped", + "Ĥ æķ°", + "Ġu gly", + "Ġug ly", + "Ġugl y", + "Ġexcit ement", + "Ġex terior", + "Ġext erior", + "Ġexter ior", + "Ġ checkout", + "Ġcheck out", + "Ġ kur", + "Ġk ur", + "Ġku r", + ", D", + "ĠAl aska", + "ĠAla ska", + "Ġsyn thetic", + "Ġsynth etic", + "Ġsynt hetic", + "Ġ Budget", + "ĠB udget", + "ĠBud get", + "Ġ Subscribe", + "ĠSub scribe", + "Ġ &Ċ", + "Ġ& Ċ", + "ÈĻ i", + "Ġ Yu", + "ĠY u", + "ĉ query", + "} .Ċ", + "}. Ċ", + "Ġtr aged", + "Ġtra ged", + "Ġtrag ed", + "as sen", + "ass en", + "asse n", + "Ġaccom modation", + "Ġaccommod ation", + "Ġphys ician", + "Ġphysic ian", + "Ġre named", + "Ġren amed", + "Ġrename d", + "Ġt idak", + "Ġtid ak", + "z Äħ", + "Ġ minus", + "Ġm inus", + "Ġmin us", + "n ych", + "ny ch", + "0 97", + "09 7", + "_EX CEPTION", + "th reads", + "thread s", + "Ġt ire", + "Ġti re", + "Ġtir e", + "_ created", + "_c reated", + "_create d", + "_cr eated", + "_cre ated", + "en sure", + "ens ure", + "Ġ worthy", + "Ġw orthy", + "Ġwor thy", + "Ġworth y", + "Ġexc use", + "Ġ cloth", + "Ġc loth", + "Ġcl oth", + "Ġclo th", + "Ġclot h", + ". parentNode", + ".parent Node", + "/ platform", + "/pl atform", + "ĠU FC", + "ĠUF C", + "Ġ Gtk", + "ĠG tk", + "un ny", + "unn y", + "Ġg ibt", + "Ġgi bt", + "Ġgib t", + "ke ley", + "kel ey", + "h um", + "hu m", + "( tx", + "(t x", + "ĉ dev", + "ĉd ev", + "ĉde v", + "Ġout fit", + "Ġoutf it", + "do ors", + "door s", + "Ġ fon", + "Ġf on", + "Ġfo n", + "i cut", + "ic ut", + "v olatile", + "vol atile", + "Ġhom osex", + "Ġhomo sex", + "Max imum", + "Ġex pend", + "Ġexp end", + "Ġ });ĊĊĊ", + "Ġ} );ĊĊĊ", + "Ġ});Ċ ĊĊ", + "Ġ});ĊĊ Ċ", + "Ġ}) ;ĊĊĊ", + "Ġ}); ĊĊĊ", + "E q", + "on ders", + "ond ers", + "onder s", + "onde rs", + "de partment", + "dep artment", + "depart ment", + "Ġ Physics", + "ĠPh ysics", + "ĠPhys ics", + "\" });Ċ", + "\"} );Ċ", + "\"}) ;Ċ", + "Ġpar ad", + "Ġpara d", + "Ġpa rad", + ". Str", + ".S tr", + ".St r", + "Ġs ele", + "Ġse le", + "Ġsel e", + "IF IED", + "IFI ED", + "Ġdel ivers", + "Ġdeliver s", + "i van", + "iv an", + "iva n", + "Ġrespons ibilities", + "Ġadvoc ates", + "Ġadvocate s", + "è µ", + "Ġ RID", + "ĠR ID", + "ĠRI D", + ". parameters", + ".param eters", + ".parameter s", + "M etrics", + "Met rics", + "Metric s", + "ron ics", + "ronic s", + "Ġ UITableViewCell", + "ĠUI TableViewCell", + "ĠUITableView Cell", + "A bsolute", + "Abs olute", + "ip se", + "ips e", + "y lum", + "yl um", + "ML Element", + "MLE lement", + "_ VALID", + "_VAL ID", + "< title", + " \\<^", + ">\\< ^", + "Ġ ios", + "Ġi os", + "Ġio s", + "s ound", + "so und", + "sou nd", + "\" ];", + "\"] ;", + "Ġf reed", + "Ġfr eed", + "Ġfree d", + "Ġfre ed", + "r ottle", + "rot tle", + "rott le", + "Ġ Lower", + "ĠL ower", + "ĠLo wer", + "ĠLow er", + "ĠLowe r", + "[ count", + "[c ount", + "å Ŀ", + "Ġp ale", + "Ġpa le", + "Ġpal e", + "ĠW ayne", + "ĠWay ne", + "ĠWa yne", + "e arth", + "ear th", + "_ categories", + "_c ategories", + "U CK", + "UC K", + ". metadata", + ".m etadata", + ".meta data", + ".met adata", + "Ġsum mon", + "Ġsumm on", + "H OME", + "HO ME", + "олÑĮ з", + "Ġmanufact ured", + "Ġmanufacture d", + "Ġ dock", + "Ġd ock", + "Ġdo ck", + "Ġdoc k", + "Ġcompet itors", + "Ġcompetitor s", + "Ġcompetit ors", + "_ MODEL", + "_MODE L", + "_MO DEL", + "_MOD EL", + "ok ia", + "oki a", + "Ġ Hey", + "ĠH ey", + "ĠHe y", + "Î ¿", + "Ġ backward", + "Ġback ward", + "ĠPO SS", + "ĠPOS S", + "r opa", + "ro pa", + "rop a", + "Ġ cri", + "Ġc ri", + "Ġcr i", + "_ OBJ", + "_O BJ", + "Trans port", + "- high", + "-h igh", + "Ġerot ik", + "Ġero tik", + "_ slot", + "_s lot", + "_sl ot", + "Ġ artic", + "Ġar tic", + "Ġart ic", + "_ framework", + "_f ramework", + "_frame work", + "_fr amework", + "-s erif", + "-se rif", + "-ser if", + "ĠSql DbType", + "' )(", + "') (", + "+ \"/", + "+\" /", + "Ġw ore", + "Ġwor e", + "Ġwo re", + "S il", + "Si l", + "Ġst oring", + "Ġstor ing", + "Ġsto ring", + "Ġ Phase", + "ĠPh ase", + "u ant", + "ua nt", + "uan t", + "Ġb ump", + "Ġbu mp", + "Ġbum p", + "in ho", + "inh o", + "Ġd ign", + "Ġdi gn", + "Ġdig n", + "Ġ backs", + "Ġb acks", + "Ġback s", + "Ġba cks", + "Ġbac ks", + "q q", + "( hash", + "(h ash", + "(has h", + "Ġ geo", + "Ġg eo", + "Ġge o", + "Ġt ender", + "Ġte nder", + "Ġten der", + "Ġtend er", + "L ogo", + "Log o", + "Lo go", + "! )Ċ", + "!) Ċ", + "Ġ MX", + "ĠM X", + "Ġ Arthur", + "ĠAr thur", + "ĠArt hur", + "ĠArth ur", + "ess oa", + "esso a", + "_ Ch", + "_C h", + "Ġbed rooms", + "Ġbedroom s", + "=\"# \"><", + "=\"#\" ><", + "=\"#\"> <", + "Ġ throat", + "Ġth roat", + "Ġthro at", + "i nsic", + "in sic", + "ins ic", + "insi c", + ". integer", + ".int eger", + "Ġ primitive", + "Ġpr imitive", + "Ġprim itive", + "Truth y", + "Ġfacilit ate", + "Ġfacil itate", + "Ġcre ativity", + "Ġcreat ivity", + "Ġ DNS", + "ĠD NS", + "ĠDN S", + "Ġ gra", + "Ġg ra", + "Ġgr a", + "u ez", + "ue z", + "Ġcount less", + "ĠP oland", + "ĠPol and", + "ĠPo land", + "' M", + "Ġ Dist", + "ĠD ist", + "ĠDis t", + "ĠDi st", + "Ġ vest", + "Ġv est", + "Ġve st", + "Ġves t", + "Ġcert ification", + "Ġcertif ication", + "á» ij", + "h eld", + "he ld", + "hel d", + "ext ensions", + "extension s", + "( static", + "(st atic", + "(stat ic", + "Ġ grades", + "Ġg rades", + "Ġgr ades", + "Ġgrad es", + "Ġgrade s", + "Ġgra des", + "Ġ Uber", + "ĠU ber", + "ĠUb er", + "ãģ Ł", + "Ġ [])Ċ", + "Ġ[ ])Ċ", + "Ġ[] )Ċ", + "Ġ[]) Ċ", + "d atos", + "da tos", + "dat os", + "dato s", + "Ġ getData", + "Ġget Data", + "ĠCh arg", + "ĠChar g", + "ĠCha rg", + "Ġ BS", + "ĠB S", + ".m icrosoft", + ".micro soft", + ". video", + ".v ideo", + ". direction", + ".d irection", + ".dir ection", + ".di rection", + ".direct ion", + "-> {'", + "->{ '", + "l ua", + "lu a", + "a pest", + "ap est", + "ape st", + "apes t", + "Ġbo iler", + "Ġboil er", + "e rek", + "er ek", + "ere k", + "Ġdec ides", + "Ġdecide s", + "Ġdecid es", + ". jar", + ".j ar", + "I SC", + "IS C", + "Ġ Words", + "ĠW ords", + "ĠWord s", + "ĠWor ds", + "( CON", + "(C ON", + "EMPL ATE", + "ree ze", + "s hots", + "sh ots", + "shot s", + "a pps", + "ap ps", + "app s", + "un ted", + "unt ed", + "unte d", + ". setName", + ".set Name", + ": :<", + ":: <", + "- bold", + "-b old", + "-bo ld", + "ê ²", + "å¯ Ĩ", + "Long rightarrow", + "Ġun fair", + "Ġunf air", + "Ġ earning", + "Ġe arning", + "Ġear ning", + "Ġearn ing", + "Ġ shelf", + "Ġsh elf", + "Ġshe lf", + "Ġshel f", + "UR EMENT", + "URE MENT", + "Ġ idle", + "Ġi dle", + "Ġid le", + "_ MENU", + "_M ENU", + "_ME NU", + ". Custom", + ".C ustom", + "A GER", + "AG ER", + "AGE R", + "- \"", + "_ switch", + "_s witch", + "_sw itch", + "b ecause", + "be cause", + "bec ause", + ") view", + ")v iew", + "m are", + "ma re", + "mar e", + "_ condition", + "_con dition", + "_cond ition", + "Ġ Starting", + "ĠStart ing", + "ĠStar ting", + "M vc", + "( pre", + "(p re", + "(pr e", + "d ump", + "du mp", + "dum p", + "_ LOCK", + "_L OCK", + "_LO CK", + "_LOC K", + "at etime", + "ate time", + ". callback", + ".c allback", + ".call back", + "ĠC er", + "ĠCe r", + "o pol", + "op ol", + "opo l", + "ib rary", + "ibr ary", + "Ġ reservation", + "Ġres ervation", + "Ġreserv ation", + "Ġreserva tion", + "ĉ ĉĉĉĉĉĉĊ", + "ĉĉ ĉĉĉĉĉĊ", + "ĉĉĉĉ ĉĉĉĊ", + "ĉĉĉ ĉĉĉĉĊ", + "ĉĉĉĉĉ ĉĉĊ", + "ĉĉĉĉĉĉ ĉĊ", + "ĉĉĉĉĉĉĉ Ċ", + "l ector", + "le ctor", + "lect or", + "lec tor", + "grad uate", + "Ġgener ous", + "Ġgene rous", + "Ġ ion", + "Ġi on", + "Ġio n", + "r icao", + "ri cao", + "ric ao", + "rica o", + "m q", + "_ complete", + "_com plete", + "_comp lete", + "( cursor", + "(c ursor", + "Ġ FormControl", + "ĠForm Control", + ": center", + ":c enter", + "Ġsub stitute", + "Ġsubstit ute", + "Ġ Planning", + "ĠPl anning", + "ĠPlan ning", + "Ġp ension", + "Ġpens ion", + "Ġrecommend ation", + "Ġ Tags", + "ĠT ags", + "ĠTag s", + "ĠTa gs", + "Ġg ef", + "Ġge f", + "Ġ albums", + "Ġalbum s", + "Ġalb ums", + "Ġ washing", + "Ġw ashing", + "Ġwash ing", + "r oc", + "ro c", + "Ġtr ains", + "Ġtrain s", + "Ġtra ins", + "Ġtrai ns", + "a tings", + "at ings", + "ating s", + "atin gs", + "Ġex ponent", + "Ġexp onent", + "ack bar", + "- ln", + "-l n", + "á g", + ".Data Annotations", + "Ġ EIF", + "ĠE IF", + "ĠEI F", + "ĠMal aysia", + "ĠMalays ia", + "ĉ PORT", + "ĉP ORT", + "on us", + "onu s", + "Ġc lever", + "Ġcl ever", + "Ġcle ver", + "Ġp eu", + "Ġpe u", + "> ĊĊĊĊ", + ">Ċ ĊĊĊ", + ">ĊĊ ĊĊ", + ">ĊĊĊ Ċ", + "Ġ Arguments", + "ĠArg uments", + "ĠArgument s", + "Ġdeb ugging", + "Ġdebug ging", + "( right", + "(r ight", + "' D", + "com pute", + "comp ute", + "comput e", + "Ġfin est", + "Ġfine st", + "Ġfi nest", + "Ġfines t", + "OR AGE", + "ORA GE", + "Ġspect acular", + "ph rase", + "Ġin dia", + "Ġind ia", + "Ġlegend ary", + "b irth", + "bir th", + "Ġ composite", + "Ġcom posite", + "Ġcomp osite", + "Ġcompos ite", + "Ġg rows", + "Ġgr ows", + "Ġgrow s", + "Ġgro ws", + "Ġ TD", + "ĠT D", + "Ġe pid", + "Ġep id", + "Ġlaunch ing", + "] ][", + "]] [", + "Min utes", + "Minute s", + "Ġ Cha", + "ĠC ha", + "ĠCh a", + "Ġclean ed", + "Ġcle aned", + "Ġwitness es", + "u kan", + "uk an", + "uka n", + "ĉ Type", + "ĉT ype", + "Ġh abe", + "Ġhab e", + "Ġha be", + "par agraph", + "para graph", + "ĠJ Panel", + "ĠJP anel", + "ĠH ann", + "ĠHa nn", + "ĠHan n", + "Ġvar ied", + "Ġvari ed", + "Ġva ried", + "Ġ Pokemon", + "ĠP okemon", + "ĠPok emon", + "ĠPoke mon", + "ĠM UST", + "ĠMU ST", + "åĬ ¨", + ". visibility", + ".vis ibility", + "op up", + "^ [", + ". expand", + ".exp and", + "Ġ \"',", + "Ġ\" ',", + "Ġ\"' ,", + ".f asterxml", + "_ auto", + "_a uto", + "_aut o", + "Ġ Sheet", + "ĠS heet", + "ĠShe et", + "m arker", + "mark er", + "mar ker", + "Par cel", + "e ws", + "ew s", + "Ġ Strategy", + "ĠStr ategy", + "ĠStrateg y", + "- making", + "-m aking", + "Ġun ve", + "Ġtr ailing", + "Ġtrail ing", + "Ġtra iling", + "Ġtrai ling", + "Ġcl icks", + "Ġclick s", + "Ġcli cks", + "Ġclic ks", + "Ġ GetComponent", + "ĠGet Component", + "ĉ content", + "ĉc ontent", + "ĉcon tent", + "ĉcont ent", + "IG ENCE", + "ER NEL", + "ERN EL", + "NSMutable Array", + "Ġb reat", + "Ġbr eat", + "Ġbre at", + "Ġharm ful", + "¶ Ī", + "Ġbes ides", + "Ġbeside s", + "Ġb oring", + "Ġbo ring", + "Ġbor ing", + "Ġbrut al", + "Ġbru tal", + "v ang", + "va ng", + "van g", + "( parse", + "(p arse", + "(par se", + "qu ick", + "qui ck", + "Ġ pytest", + "Ġpy test", + "Ġpyt est", + "Ġswitch ing", + "( )]Ċ", + "() ]Ċ", + "()] Ċ", + "Ġ ìĦ", + "Ġì Ħ", + "L ER", + "LE R", + "ĉ font", + "ĉf ont", + "Ġ nett", + "Ġn ett", + "Ġne tt", + "Ġnet t", + ") ]ĊĊ", + ")]Ċ Ċ", + ")] ĊĊ", + "( /\\", + "(/ \\", + "æŀ ľ", + "to Array", + "Ġb reed", + "Ġbr eed", + "Ġbre ed", + "Ġbree d", + "Ġ CAR", + "ĠC AR", + "ĠCA R", + "Ġ Weapon", + "ĠWe apon", + "A bs", + "Ab s", + "t ot", + "to t", + "Ġ setName", + "Ġset Name", + "a ptive", + "apt ive", + "Ġ :,", + "Ġ: ,", + "Ġ escaped", + "Ġesc aped", + "Ġescape d", + "Ġescap ed", + "or den", + "ord en", + "orde n", + "Ġ Pri", + "ĠP ri", + "ĠPr i", + "th umbnail", + "Ġde scriptions", + "Ġdes criptions", + "Ġdescription s", + "Ġdescri ptions", + "/ styles", + "/st yles", + "/style s", + "Ġ PCI", + "ĠP CI", + "ĠPC I", + "Ġ alphabet", + "Ġal phabet", + "Ġalpha bet", + "Ġalph abet", + "astic search", + "astics earch", + "N OTE", + "NO TE", + "NOT E", + "Ġc ialis", + "Ġci alis", + "ĠGr iff", + "ĠGri ff", + "Ġp orque", + "Ġpor que", + "Ġprote ins", + "Ġprotein s", + "p lays", + "pl ays", + "play s", + "pla ys", + "Ġst ating", + "Ġstat ing", + "Ġsta ting", + "Ġstati ng", + "Ġim agination", + "Ġimag ination", + "Ġimagin ation", + "Ġf acial", + "Ġfa cial", + "Ġfac ial", + "ĠM echan", + "ĠMe chan", + "ĠMech an", + "ĠMec han", + "Ġarr anged", + "Ġarrang ed", + "Ġarrange d", + "_ used", + "_u sed", + "_us ed", + "_use d", + "Ġarrang ements", + "Ġarrangement s", + "Ġarrange ments", + "Ġ Pipe", + "ĠP ipe", + "ĠPi pe", + "ĠPip e", + "host name", + "Ġpro vinc", + "Ġprov inc", + "T it", + "Ti t", + ".Flat Style", + "Ġ Split", + "ĠS plit", + "ĠSp lit", + "ĠSpl it", + "Ġ Loader", + "ĠL oader", + "ĠLo ader", + "ĠLoad er", + ". cc", + ".c c", + "Ġ clinic", + "Ġcl inic", + "Ġclin ic", + "Ġcli nic", + "-------- --------------------", + "---------------- ------------", + "------------ ----------------", + "------------- ---------------", + "--------------- -------------", + "-------------- --------------", + "-------------------- --------", + "Ġb aking", + "Ġba king", + "Ġbak ing", + "Ġ ENT", + "ĠE NT", + "ĠEN T", + "ne ath", + "nea th", + "ãĢģ ĊĊ", + "ãĢģĊ Ċ", + "A NE", + "AN E", + ".EntityFramework Core", + "a ppers", + "ap pers", + "app ers", + "apper s", + "appe rs", + ". ic", + ".i c", + "Ġ NgModule", + "ĠNg Module", + "Ġ FORM", + "ĠF ORM", + "ĠFOR M", + "ĠFO RM", + "Ġ ';", + "Ġ' ;", + "- profit", + "-pro fit", + "-prof it", + "h w", + "en emy", + "ene my", + "Ġ Eye", + "ĠE ye", + "ĠEy e", + "Ġca ution", + "Ġcaut ion", + "t own", + "to wn", + "Ġur ged", + "Ġurge d", + "Ġurg ed", + "Ġ Jimmy", + "ĠJim my", + "ynchron ous", + "-s ized", + "-size d", + "m aking", + "ma king", + "mak ing", + ", {", + "] ',", + "]' ,", + "_ Object", + "_O bject", + "_Obj ect", + "ah oma", + "aho ma", + "Ġact ivist", + "Ġactiv ist", + "IN VAL", + "INV AL", + "Ġ Commercial", + "ĠCom mercial", + "ĠComm ercial", + "ĠOr lando", + "( tab", + "(t ab", + "Ġ ب", + "ĠØ ¨", + "Al gorithm", + "Ġher itage", + "Get Mapping", + "Ġfail ures", + "Ġfailure s", + "r ios", + "ri os", + "rio s", + "at iva", + "ati va", + "ativ a", + "Ġ tet", + "Ġt et", + "Ġte t", + "Ġcar pet", + "Ġcarp et", + "( Z", + "th ree", + "thr ee", + "Ġdis closure", + "Ġdisc losure", + ". ERROR", + ".ERR OR", + "_ called", + "_c alled", + "_call ed", + "_cal led", + "Ġd ial", + "Ġdi al", + "Ġdia l", + "Ġoccas ional", + "Ġoccasion al", + ". Err", + ".E rr", + "Ġfun cion", + "Ġfunc ion", + "caff old", + "caf fold", + "Ġre leasing", + "Ġrele asing", + "ï¼ī ĊĊ", + "ï¼īĊ Ċ", + "_ Value", + "_V alue", + "_Val ue", + "Ġ Vari", + "ĠV ari", + "ĠVar i", + "ĠVa ri", + "y ellow", + "Ġstrugg les", + "Ġstruggle s", + ". cal", + ".c al", + ".ca l", + "ĠDak ota", + "ĉ close", + "ĉc lose", + "ĉcl ose", + "Ġsand wich", + "Ġ analytics", + "Ġan alytics", + "Ġanaly tics", + "Ġanalytic s", + "Ġ **)", + "Ġ* *)", + "Ġ** )", + "& #", + "Ġ Jos", + "ĠJ os", + "ĠJo s", + "Ġpass ive", + "AT TR", + "ATT R", + "Th rowable", + "Throw able", + "ĠM un", + "ĠMu n", + "Ġ Uint", + "ĠU int", + "ĠUi nt", + "( disposing", + "(dis posing", + "a rak", + "ar ak", + "ara k", + "Ġ Leaders", + "ĠLe aders", + "ĠLeader s", + "ĠLead ers", + "Ġaffect ing", + "Ġitem View", + "Ġe conomics", + "Ġecon omics", + "Ġeconomic s", + "Ġeconom ics", + "f v", + "๠Ģ", + ". rb", + ".r b", + "Ġ Overall", + "ĠOver all", + "Ġwealth y", + "Ġev olved", + "Ġevolve d", + "n da", + "nd a", + "ĠH us", + "ĠHu s", + "re strict", + "u men", + "um en", + "ume n", + "ĠA gricult", + "ĠAgr icult", + "! ĊĊĊ", + "!ĊĊ Ċ", + "!Ċ ĊĊ", + "Ġ expires", + "Ġex pires", + "Ġexp ires", + "Ġexpire s", + "Ġspokes person", + "int erval", + "inter val", + "Ġ â", + "Ġà ¢", + "Ġ queen", + "Ġqu een", + "Ġque en", + "( nil", + "(n il", + "i ngo", + "in go", + "ing o", + "He ap", + "Ù İ", + "Ġcom plain", + "Ġcomp lain", + "Ġcompl ain", + "S ym", + "Sy m", + "Ġ Clone", + "ĠCl one", + "ĠClo ne", + "Ġ Ru", + "ĠR u", + "ĠW ILL", + "ĠWI LL", + "Ġ Crystal", + "ĠCr ystal", + "ĠCry stal", + "/ content", + "/c ontent", + "/con tent", + "i ngen", + "in gen", + "ing en", + "inge n", + "oint ment", + "Last Name", + "av icon", + "avi con", + "avic on", + "Ġ IBM", + "ĠI BM", + "ĠIB M", + "Ġ Dimension", + "ĠD imension", + "ĠDim ension", + "a nh", + "an h", + "ici pants", + "icip ants", + "icipant s", + "Ġ Anne", + "ĠAn ne", + "ĠAnn e", + ". progress", + ".pro gress", + "Ġ algo", + "Ġal go", + "Ġalg o", + "o bil", + "ob il", + "obi l", + "Ġ Voice", + "ĠV oice", + "ĠVo ice", + "Ġ FE", + "ĠF E", + "Ġ gli", + "Ġg li", + "Ġgl i", + "Ġ ved", + "Ġv ed", + "Ġve d", + "Ġpr events", + "Ġpre vents", + "Ġprevent s", + "Ġprev ents", + "\\ Column", + "\\C olumn", + "Ġ folk", + "Ġf olk", + "Ġfol k", + "Ġfo lk", + "e tti", + "et ti", + "ett i", + "Ġ mn", + "Ġm n", + "Ġ CLASS", + "ĠCL ASS", + "Ġdis playing", + "Ġdisplay ing", + "Ġdispl aying", + "ĠK l", + "ĠF err", + "ĠFe rr", + "ĠFer r", + "d uto", + "du to", + ". ib", + ".i b", + "Ġ dados", + "Ġd ados", + "Ġda dos", + "Ġdad os", + "Ġdado s", + "' name", + "'n ame", + "'na me", + "- space", + "-s pace", + "-sp ace", + "Ġit alian", + "Ġitalia n", + "Ġ inverse", + "Ġin verse", + "Ġinv erse", + "Ġinvers e", + "Ġ dense", + "Ġd ense", + "Ġden se", + "Ġdens e", + "u ter", + "ut er", + "ute r", + "Ġ IEnumerator", + "ĠI Enumerator", + "- sign", + "-s ign", + "Ġnation wide", + "Ġ persona", + "Ġperson a", + "Ġpers ona", + "Ġperso na", + "Ġs olved", + "Ġsol ved", + "Ġsolve d", + "Ġdram atically", + "Ġdramatic ally", + "Log out", + "Logo ut", + "Ġ grav", + "Ġg rav", + "Ġgr av", + "Ġgra v", + "Ġanal yses", + "Ġanaly ses", + "Ġanalys es", + "Ġanalyse s", + "ol lo", + "oll o", + "Ġ lamp", + "Ġl amp", + "Ġla mp", + "Ġlam p", + ". team", + ".t eam", + ".te am", + "Ġ Erot", + "ĠE rot", + "ĠEr ot", + "= [\"", + "=[ \"", + "Ġd ancing", + "Ġdan cing", + "Ġ ?>/", + "Ġ? >/", + "Ġ?> /", + "Ġc ater", + "Ġca ter", + "Ġcat er", + "Ġcate r", + "f fe", + "ff e", + "Ġ Sha", + "ĠS ha", + "ĠSh a", + "ĠB os", + "ĠBo s", + "ĠRE QUIRE", + "Ġ Monster", + "ĠMon ster", + "ĠMons ter", + "Ġ RB", + "ĠR B", + "Ġ IDE", + "ĠI DE", + "ĠID E", + "Ġs uits", + "Ġsu its", + "Ġsuit s", + "Ġsui ts", + "Ġ formData", + "Ġform Data", + "( theta", + "(th eta", + "(the ta", + "Ġs patial", + "Ġsp atial", + "Ġspat ial", + "= NULL", + "=N ULL", + "Ġ SqlConnection", + "ĠSql Connection", + "Ġ à", + "ĠV enez", + "ĠVen ez", + "ĠVe nez", + "Ġ Morning", + "ĠM orning", + "ĠMor ning", + "Ġpublic ations", + "Ġpub lications", + "Ġpublication s", + "ĠNON INFRINGEMENT", + "first Name", + "u ds", + "ud s", + "W ould", + "Wo uld", + "_ HEAD", + "_HE AD", + "Ġinv ested", + "Ġinvest ed", + "Ġinve sted", + "s table", + "st able", + "sta ble", + "stab le", + "f red", + "fr ed", + "fre d", + "Ġcomm ander", + "Ġcommand er", + "Ġcomma nder", + "Ġcommande r", + "S ES", + "SE S", + "âĢĶ a", + "an che", + "anc he", + "anch e", + "Ġ Movement", + "ĠM ovement", + "ĠMo vement", + "ĠMove ment", + "ĠMov ement", + "ë ³", + "S uite", + "Su ite", + "Suit e", + "Ġjur isdiction", + "ë ¦¬", + "ë¦ ¬", + "Ġ Beth", + "ĠB eth", + "ĠBe th", + "ĠBet h", + "j Query", + "ĠI sa", + "ĠIs a", + "Ġd ental", + "Ġden tal", + "Ġdent al", + ", *", + "Ġ Limit", + "ĠL imit", + "ĠLim it", + "ĠLi mit", + "il iation", + "ili ation", + "ilia tion", + "= \"{", + "=\" {", + "b ast", + "ba st", + "bas t", + "Ġt urb", + "Ġtu rb", + "Ġtur b", + "i sy", + "is y", + "O OK", + "OO K", + "Ġadv ocate", + "Ġadvoc ate", + "i mag", + "im ag", + "ima g", + "LE CTION", + "LECT ION", + "LEC TION", + "л ÑĮ", + "( category", + "(c ategory", + ". dec", + ".d ec", + ".de c", + "Ġun iqu", + "Ġuni qu", + "Ġuniq u", + "_ sn", + "_s n", + "Ġat tracted", + "Ġattr acted", + "Ġattract ed", + "Ġ Ãī", + "Ġà ī", + "Ġ Running", + "ĠR unning", + "ĠRun ning", + "_ edges", + "_edge s", + "_ed ges", + "Ġ Disable", + "ĠD isable", + "ĠDis able", + "_ AS", + "_A S", + "åĽ ¾", + "Ġnetwork ing", + "Ġnet working", + "_ branch", + "_br anch", + "H aving", + "Ha ving", + "toBe Truthy", + "G I", + "Ġc amps", + "Ġca mps", + "Ġcamp s", + "Ġcam ps", + "s ep", + "se p", + "- part", + "-p art", + "-par t", + "Ġ )ĊĊĊĊĊĊĊĊ", + "Ġ) ĊĊĊĊĊĊĊĊ", + "Ġ)Ċ ĊĊĊĊĊĊĊ", + "Ġ)ĊĊ ĊĊĊĊĊĊ", + "Ġ)ĊĊĊ ĊĊĊĊĊ", + "ustr alia", + "ustral ia", + "Ġ Reports", + "ĠRe ports", + "ĠRep orts", + "ĠReport s", + "ĠRepo rts", + "r ito", + "ri to", + "rit o", + "Ġwa ist", + "_ plus", + "_p lus", + "_pl us", + "Ġ WW", + "ĠW W", + "- person", + "-p erson", + "-per son", + "Ap ril", + "Apr il", + "Ġ sar", + "Ġs ar", + "Ġsa r", + ". tar", + ".t ar", + ".ta r", + "Ġagricult ural", + "Ġagr icultural", + "t ic", + "ti c", + "Ġ tcp", + "Ġt cp", + "Ġtc p", + "Ġ setValue", + "Ġset Value", + "ag ento", + "agent o", + "agen to", + "Ġ Appe", + "ĠA ppe", + "ĠApp e", + "ĠAp pe", + "p iler", + "pi ler", + "pile r", + "C ADE", + "CA DE", + "CAD E", + "Ġ anche", + "Ġan che", + "Ġanch e", + "Ġanc he", + "at cher", + "atch er", + "Ġc omics", + "Ġcom ics", + "Ġcomic s", + "Ġ lbs", + "Ġl bs", + "Ġlb s", + "_ segment", + "_s egment", + "_se gment", + "_seg ment", + "' ]=$", + "'] =$", + "']= $", + "it ters", + "itt ers", + "itter s", + "itte rs", + "i cher", + "ic her", + "ich er", + "iche r", + "G INE", + "GIN E", + "GI NE", + "Ġutil ize", + "Ġutiliz e", + "Ġ Cursor", + "ĠC ursor", + "ĠCurso r", + "_ expression", + "_ex pression", + "_exp ression", + "_expr ession", + "Ġ dag", + "Ġd ag", + "Ġda g", + "< long", + " < ?=", + "> x", + ". Task", + ".T ask", + "m oney", + "mon ey", + "mo ney", + "ib aba", + "iba ba", + "' });Ċ", + "'} );Ċ", + "'}) ;Ċ", + "Ġ Specific", + "ĠS pecific", + "ĠSpec ific", + "Ġ Linear", + "ĠL inear", + "ĠLine ar", + "ĠLin ear", + "ĠLi near", + "_ OPT", + "_O PT", + "_OP T", + "Hash Code", + "( Player", + "(P layer", + ".Contains Key", + "Ġ collapsed", + "Ġc ollapsed", + "Ġcoll apsed", + "Ġcollapse d", + "Ġcollaps ed", + "trans parent", + "_R ANGE", + "View er", + "( cfg", + "(c fg", + "(cf g", + "Ġ sorting", + "Ġs orting", + "Ġsort ing", + "Ġsor ting", + "Ġinf ected", + "Ġinfect ed", + "Ġ Nach", + "ĠN ach", + "ĠNa ch", + "Ġaccommod ate", + ". elements", + ".e lements", + ".element s", + ".el ements", + ".elem ents", + "_ PART", + "_P ART", + "_PA RT", + "_PAR T", + "Ġ Sexy", + "ĠSe xy", + "ĠSex y", + "= get", + "=g et", + "( year", + "(y ear", + "Ġ xhr", + "Ġx hr", + ": ]", + "ow ski", + "ows ki", + "Ġsum mar", + "Ġsumm ar", + "Ġ ¿", + "Ġ ¿", + "Ġ inte", + "Ġin te", + "Ġint e", + "Ġi nte", + "Ġ workflow", + "Ġwork flow", + "ĠTai wan", + "v ersions", + "vers ions", + "version s", + "åı ij", + "Ġsur prisingly", + "Ġsurprising ly", + "Ġop tical", + "Ġopt ical", + "Ġoptic al", + "Ġpro ces", + "Ġproc es", + "Ġdis agree", + "Ġdisag ree", + "Ġn uevo", + "Ġnue vo", + "Ġ CAM", + "ĠC AM", + "ĠCA M", + "s orted", + "sort ed", + "le ases", + "lease s", + "lea ses", + "is tle", + "ist le", + "I dent", + "Id ent", + "Ide nt", + "ĉ event", + "ĉe vent", + "ĉev ent", + "j ected", + "ject ed", + "jec ted", + "Ch unk", + "V ars", + "Var s", + "Va rs", + ". provider", + ".pro vider", + "Ġproceed ings", + "Ġproceeding s", + "Ġ inclusive", + "Ġin clusive", + "Ġincl usive", + "Ġinclus ive", + "Ġart work", + "end ants", + "enda nts", + "endant s", + "ï¼ļ Ċ", + "s een", + "se en", + "see n", + "Ġ lig", + "Ġl ig", + "Ġli g", + "Ġ makers", + "Ġm akers", + "Ġmake rs", + "Ġma kers", + "Ġmaker s", + "Ġmak ers", + "_ fun", + "_f un", + "_fu n", + "Ġlength s", + "Ġleng ths", + "Path Variable", + "[ item", + "[i tem", + "[it em", + "ภµ", + "D ead", + "De ad", + "FF FFFF", + "FFFF FF", + "FFF FFF", + "Ġ Urban", + "ĠUr ban", + "ĠUrb an", + "u ples", + "up les", + "uple s", + "i chen", + "ic hen", + "ich en", + "iche n", + "( nullptr", + "(null ptr", + ". spec", + ".s pec", + ".sp ec", + ", System", + ",S ystem", + "U RATION", + "UR ATION", + "URA TION", + "( job", + "(j ob", + "å¼ ı", + "Ġ tracker", + "Ġtr acker", + "Ġtrack er", + "Ġtra cker", + "Å Ļ", + "Ġ MR", + "ĠM R", + "Ġ SQLite", + "ĠSQL ite", + "ĠSQ Lite", + "Ġ dto", + "Ġd to", + "Ġdt o", + "Ġ ;;Ċ", + "Ġ; ;Ċ", + "Ġ;; Ċ", + "Ġ mint", + "Ġm int", + "Ġmin t", + "Ġmi nt", + "Ġ Introduction", + "ĠInt roduction", + "ĠIntro duction", + "c ao", + "ca o", + "Ġquest ioned", + "Ġquestion ed", + "Ġquesti oned", + "Ġf itted", + "Ġfit ted", + "Ġfitte d", + "re vision", + "rev ision", + "s q", + "Ġm ig", + "Ġmi g", + "_ units", + "_un its", + "_unit s", + "_ async", + "_a sync", + "_as ync", + "Ġf lick", + "Ġfl ick", + "} );ĊĊĊ", + "});Ċ ĊĊ", + "});ĊĊ Ċ", + "}) ;ĊĊĊ", + "}); ĊĊĊ", + "Ġn otre", + "Ġnot re", + "Ġno tre", + "} `,", + "}` ,", + "F ilters", + "Filter s", + "Fil ters", + "Ġm undo", + "Ġmu ndo", + "Ġmund o", + "Ġmun do", + "_ days", + "_d ays", + "_day s", + "_da ys", + "Ġ frm", + "Ġf rm", + "Ġfr m", + "u tc", + "ut c", + "Ġ vals", + "Ġv als", + "Ġval s", + "Ġva ls", + "e width", + "ew idth", + "Ġ Generator", + "ĠG enerator", + "ĠGener ator", + "ĠGen erator", + "Ġ Artist", + "ĠArt ist", + "Ġ IDs", + "ĠI Ds", + "ĠID s", + "Ġ Articles", + "ĠArt icles", + "ĠArticle s", + "re ater", + "reate r", + "reat er", + "rea ter", + "ĠComponent Fixture", + ". =", + "Ġ rou", + "Ġr ou", + "Ġro u", + "- no", + "-n o", + ".b ukkit", + "e gg", + "eg g", + "Ġ Diff", + "ĠD iff", + "ĠDi ff", + "a tics", + "at ics", + "atic s", + "ati cs", + "Ñĥ Ñĩ", + "âĢĶ ĊĊ", + "Ġ Charlotte", + "ĠChar lotte", + "ĠCharl otte", + "b ye", + "by e", + "Ġ });čĊčĊ", + "Ġ} );čĊčĊ", + "Ġ}) ;čĊčĊ", + "Ġ});čĊ čĊ", + "Ġ}); čĊčĊ", + "ĠV ik", + "ĠVi k", + "ĠB row", + "ĠBr ow", + "ĠBro w", + "Ġ lv", + "Ġl v", + "ĠG ib", + "ĠGi b", + "- wing", + "-w ing", + "-win g", + "GL IGENCE", + "( Il", + "(I l", + "ĠEngine er", + ". Wait", + ".W ait", + "Ġ Pictures", + "ĠP ictures", + "ĠPicture s", + "ĠPic tures", + "Ġr het", + "Ġrh et", + "Ġrhe t", + "Ġ thermal", + "Ġth ermal", + "Ġther mal", + "Ġtherm al", + "Ġp raise", + "Ġpr aise", + "Ġpra ise", + "< >();ĊĊ", + "<>();Ċ Ċ", + "<>( );ĊĊ", + "<> ();ĊĊ", + "Ġ Spider", + "ĠSp ider", + "ĠSpi der", + "P ause", + "Pa use", + "ĠB aker", + "ĠBa ker", + "ĠBak er", + "ĠBake r", + "Ġs lower", + "Ġsl ower", + "Ġslow er", + "Ġslo wer", + "Ġ }]Ċ", + "Ġ} ]Ċ", + "Ġ}] Ċ", + "_ enqueue", + "_en queue", + "Ġdis appeared", + "Ġdisappe ared", + "Ġdisappear ed", + "Ġ Ticket", + "ĠT icket", + "ĠTi cket", + "ĠTick et", + "ĠTic ket", + "IN UX", + "INU X", + "_ LOCAL", + "_LO CAL", + "_LOC AL", + "аÑģ Ñģ", + "@Inject able", + "comm unity", + "G estureRecognizer", + "Gesture Recognizer", + "åĽ ½", + "Ġs cales", + "Ġsc ales", + "Ġscale s", + "Ġsca les", + "Ġscal es", + "Ġ -(", + "Ġ- (", + "/ '+", + "/' +", + "Ġ Sit", + "ĠS it", + "ĠSi t", + "Ġexecutive s", + "Ġexecut ives", + "ar ding", + "ard ing", + "ardi ng", + "ardin g", + "Ġad vers", + "Ġadv ers", + "Ġback wards", + "Ġbackward s", + "ĉ context", + "ĉcon text", + "ĉcont ext", + "ĠH amp", + "ĠHam p", + "ĠHa mp", + "Ġ PF", + "ĠP F", + "Ġ Deck", + "ĠD eck", + "ĠDe ck", + "ĠDec k", + "Ġ Craig", + "ĠC raig", + "ĠCra ig", + "A merican", + "Americ an", + "America n", + "Ġ bell", + "Ġb ell", + "Ġbe ll", + "Ġbel l", + "Ġp rol", + "Ġpro l", + "Ġpr ol", + "u fen", + "uf en", + "ufe n", + "Ġ rng", + "Ġr ng", + "Ġrn g", + "ar shal", + "ars hal", + "Ġ Simply", + "ĠSim ply", + "ĠSimpl y", + "first name", + "sh ore", + "J uly", + "Jul y", + "Ju ly", + "Ġm ortality", + "Ġmort ality", + "Ġmortal ity", + "ĠâĨĴ ĊĊ", + "H elpers", + "Helper s", + "Help ers", + "Hel pers", + "Ġ benchmark", + "Ġb enchmark", + "Ġbench mark", + "e made", + "em ade", + "ema de", + "Ġorgan isations", + "Ġorganis ations", + "Ġorganisation s", + ".g son", + ".gs on", + "Ġ TextField", + "ĠT extField", + "ĠText Field", + "Ġcivil ians", + "Ġciv ilians", + "Ġcivilian s", + ". Arrays", + ".Array s", + ".Ar rays", + "ĠMiss issippi", + "Ġinter mediate", + "Ġintermedi ate", + "get User", + "_ cluster", + "_cl uster", + "Rel ative", + "fore ign", + ".querySelector All", + "Fore ignKey", + "Foreign Key", + "Ġreason ably", + "- --------Ċ", + "-- -------Ċ", + "---- -----Ċ", + "-------- -Ċ", + "--- ------Ċ", + "----- ----Ċ", + "------ ---Ċ", + "------- --Ċ", + "--------- Ċ", + "C ards", + "Card s", + "Car ds", + "ĠK am", + "ĠKa m", + "Ġ Thor", + "ĠT hor", + "ĠTh or", + "Ġ roller", + "Ġr oller", + "Ġro ller", + "Ġroll er", + "Ġrol ler", + "- element", + "-e lement", + "-el ement", + "Ġ Currency", + "ĠC urrency", + "d die", + "dd ie", + "AL LY", + "ALL Y", + "Ġ RA", + "ĠR A", + "Ġper met", + "Ġperm et", + "Ġperme t", + "a aaa", + "aa aa", + "aaa a", + "Ġhome work", + "Ġhom ework", + "ĠV it", + "ĠVi t", + "Ġm old", + "Ġmo ld", + "Ġmol d", + "ĠF er", + "ĠFe r", + "[ start", + "Ġstat istical", + "Ġstatist ical", + "Ġstatistic al", + "Ġsc ary", + "Ġsca ry", + "Ġscar y", + "_ HOME", + "_H OME", + ". Begin", + ".B egin", + ".Be gin", + "Con struct", + "o genic", + "og enic", + "ogen ic", + "oge nic", + "ĠDEAL INGS", + "Ġtamb ién", + "i xon", + "ix on", + "ixo n", + ". ind", + ".in d", + ".i nd", + "a cre", + "ac re", + "acr e", + "Ġ transforms", + "Ġtrans forms", + "Ġtransform s", + "ĠN ap", + "ĠNa p", + ". Block", + ".B lock", + ".Bl ock", + "uss ia", + "p iration", + "pi ration", + "pir ation", + "ul ent", + "ule nt", + "ulen t", + "Ġ ceil", + "Ġc eil", + "Ġce il", + "Cl ause", + "Cla use", + "n aire", + "na ire", + "T ES", + "TE S", + "Ġn eat", + "Ġne at", + "S TD", + "ST D", + "Ġ RegExp", + "ĠReg Exp", + "per form", + "perf orm", + ": )", + "Ġun ions", + "Ġunion s", + "Ġuni ons", + "Ġs ublic", + "Ġsub lic", + "Ġw inds", + "Ġwin ds", + "Ġwind s", + "Ġwi nds", + "lo ating", + "loat ing", + "g lich", + "gl ich", + "gli ch", + "Ġ pagination", + "Ġp agination", + "Ġpag ination", + "Ġpagina tion", + "S kill", + "Sk ill", + "App ly", + "Ap ply", + "Ġ Operator", + "ĠO perator", + "ĠOper ator", + "ĠOp erator", + "ĠOpera tor", + "ist ogram", + "isto gram", + "Ġ qualities", + "Ġqual ities", + "Ġquali ties", + "C ross", + "Cr oss", + "Cro ss", + "Ġd ecom", + "Ġde com", + "Ġdec om", + "Ġdeco m", + "] ,\"", + "], \"", + "Ġ Juan", + "ĠJ uan", + "ĠJu an", + ". modal", + ".m odal", + ".mod al", + ".mo dal", + ". Child", + ".Ch ild", + "Ġ Roger", + "ĠR oger", + "ĠRo ger", + "ĠRog er", + "STIT UTE", + ":CGRect Make", + "a lette", + "al ette", + "ale tte", + "alet te", + "Ġ sta", + "Ġs ta", + "Ġst a", + "a side", + "as ide", + "asi de", + "Ġ blur", + "Ġbl ur", + "Ġ Wa", + "ĠW a", + "if etime", + "ife time", + "r eed", + "re ed", + "ree d", + "control s", + "contr ols", + "contro ls", + "Ġ bins", + "Ġb ins", + "Ġbi ns", + "Ġbin s", + "Ġ пол", + "Ġп ол", + "Ġпо л", + "* /,Ċ", + "*/ ,Ċ", + "*/, Ċ", + "U IS", + "UI S", + "ĠR ou", + "ĠRo u", + "Ġ Demo", + "ĠD emo", + "ĠDe mo", + "ĠDem o", + "- awesome", + "Ġ Chain", + "ĠCh ain", + "ĠCha in", + "Ġh asta", + "Ġhas ta", + "Ġha sta", + "Ġhast a", + "ĠB art", + "ĠBar t", + "ĠBa rt", + ". KEY", + ".K EY", + "Ġ vendors", + "Ġv endors", + "Ġvend ors", + "Ġvendor s", + "no follow", + "nof ollow", + "Ġ Dest", + "ĠD est", + "ĠDe st", + "ĠDes t", + "_ builder", + "_b uilder", + "_build er", + "Ġarg ues", + "Ġargue s", + "_ answer", + "_an swer", + "_ans wer", + "g oto", + "go to", + "got o", + "Ġ RESULT", + "ĠRES ULT", + "Ġ MON", + "ĠM ON", + "ĠMO N", + "Ġp oder", + "Ġpo der", + "Ġpod er", + "Ġpode r", + "o ons", + "oo ns", + "oon s", + "_ CASE", + "_C ASE", + "_CA SE", + "Ġrep lic", + "Ġrepl ic", + "Ġfin ancing", + "Ġfinanc ing", + "Ġfinan cing", + "Ġ DATE", + "ĠD ATE", + "ĠDA TE", + "ĠDAT E", + "c ern", + "ce rn", + "cer n", + "_ track", + "_t rack", + "_tr ack", + "_tra ck", + "t ies", + "ti es", + "tie s", + "/ logo", + "/l ogo", + "/log o", + "/lo go", + "ĠNE GLIGENCE", + "get Type", + "> T", + "b et", + "be t", + "g irl", + "gi rl", + "ĠINCIDENT AL", + "- site", + "-s ite", + ". trigger", + ".tr igger", + "Ġ Lisa", + "ĠL isa", + "ĠLi sa", + "ĠLis a", + "_ inputs", + "_in puts", + "_input s", + "_inp uts", + "Ġrel atives", + "Ġrelative s", + "Ġrelativ es", + "Ġrelat ives", + "Logged In", + "Con figure", + "Config ure", + "Conf igure", + "I K", + ". accept", + ".ac cept", + ".acc ept", + "Re sume", + "Res ume", + "Ġ Draft", + "ĠD raft", + "ĠDr aft", + "ĠDra ft", + "Ġ *>(", + "Ġ* >(", + "Ġ*> (", + "Ġ WA", + "ĠW A", + "ed ian", + "edia n", + "edi an", + "er ness", + "ern ess", + "erne ss", + "ernes s", + "Ġ LayoutInflater", + "ĠLayout Inflater", + "* /čĊčĊ", + "*/ čĊčĊ", + "*/čĊ čĊ", + "o thy", + "ot hy", + "oth y", + "Ġoblig ation", + "Ġobl igation", + "Sub scribe", + "Ġ thumbnail", + "Ġth umbnail", + "ex ist", + "Ġins isted", + "Ġinsist ed", + "Ġ UICollectionView", + "ĠU ICollectionView", + "ĠUI CollectionView", + "Ġ Angular", + "ĠAng ular", + "Ġtable ts", + "Ġtab lets", + "Ġtablet s", + "Ġ Impact", + "ĠImp act", + "ãĢį ĊĊ", + "ãĢįĊ Ċ", + "a ho", + "ah o", + "Ġcharacter istic", + "g d", + "Ġ= ================================================", + "Ġ================= ================================", + "Ġ================================= ================", + "o urt", + "ou rt", + "our t", + "` .", + "App ro", + "Ap pro", + "Co ordinate", + "Coord inate", + "Re member", + "Rem ember", + "Ġ marine", + "Ġm arine", + "Ġmar ine", + "Ġma rine", + "Ġmari ne", + "Ġmarin e", + "] =='", + "]= ='", + "]== '", + "Ġ Administrator", + "ĠAdmin istrator", + "ĠAdministr ator", + ". getDefault", + ".get Default", + ".getD efault", + "Ġ forgot", + "Ġf orgot", + "Ġfor got", + "Ġforg ot", + "Ġ Structure", + "ĠStruct ure", + "V ue", + "Vu e", + "ar sing", + "ars ing", + "arsi ng", + "m oment", + "mo ment", + "mom ent", + "k w", + "_ cursor", + "_c ursor", + "Att ack", + "Ġath letic", + "Ġdiagn osed", + "Ġdiagnose d", + "Ġ ende", + "Ġe nde", + "Ġen de", + "Ġend e", + "åĪ łéĻ¤", + "H ouse", + "Ho use", + "Ġ PARAM", + "ĠP ARAM", + "ĠPA RAM", + "ĠPAR AM", + "ĠPARA M", + "Ġ wiki", + "Ġw iki", + "Ġwi ki", + "Ġwik i", + "Ġ Opp", + "ĠO pp", + "ĠOp p", + "Ġcons ervation", + "Ġconserv ation", + "Ġ snd", + "Ġs nd", + "Ġsn d", + "_ tem", + "_t em", + "_te m", + "sub str", + "subst r", + "subs tr", + "ĠC ape", + "ĠCap e", + "ĠCa pe", + ". sim", + ".s im", + ".si m", + "U TION", + "UT ION", + "a nan", + "an an", + "ana n", + "âĢĻ un", + "Ġ gy", + "Ġg y", + "- work", + "-w ork", + "Ġcomp elling", + "Ġcompel ling", + "= '#", + "=' #", + "ĉ sub", + "ĉs ub", + "Ġ directories", + "Ġdirect ories", + "Ġdirector ies", + "íĬ ¸", + "Ġ touches", + "Ġtouch es", + "Ġtou ches", + "out ines", + "outine s", + ". Collection", + ".C ollection", + ".Col lection", + "s chedule", + "sched ule", + ". lat", + ".l at", + "Ġ Doctrine", + "ĠDo ctrine", + "C AA", + "CA A", + "Ġ Refer", + "ĠRe fer", + "ĠRef er", + "Ġshift s", + "Ġ likelihood", + "Ġlik elihood", + "pr eter", + "pre ter", + "pret er", + "Ġ Female", + "ĠF emale", + "ĠFe male", + "ĠFem ale", + "Ġinter cept", + "Ġ lou", + "Ġl ou", + "Ġlo u", + "çĻ »", + "Ġ rug", + "Ġr ug", + "Ġru g", + "ĠC rown", + "ĠCr own", + "ĠCro wn", + "ĠCrow n", + "Ġ ****************************************************************************", + "Ġ************************************************************************ ****", + "Ġ************************************************************************** **", + "Ġ**************************************************************** ************", + "Ġ**** ************************************************************************", + "Ġ******************************************************** ********************", + "Ġ************************************************ ****************************", + "- product", + "-pro duct", + "-produ ct", + "Ġprompt ed", + "u ngle", + "un gle", + "ung le", + "d ocker", + "do cker", + "doc ker", + "dock er", + "Ġ Tu", + "ĠT u", + "Ġ Unique", + "ĠUn ique", + "ĠUni que", + "_ Error", + "_E rror", + "_Err or", + "u los", + "ul os", + "ulo s", + "Ġ âĦ", + "Ġâ Ħ", + "Ġ (`", + "Ġ( `", + "G etting", + "Get ting", + "_ scal", + "_s cal", + "_sc al", + "Ġ Enh", + "ĠE nh", + "ĠEn h", + "ü t", + "Ġsust ained", + "Ġsustain ed", + "Ġ patches", + "Ġp atches", + "Ġpat ches", + "Ġpatch es", + "Ġpros per", + "ĠG aza", + "ĠGa za", + "ĠGaz a", + "_ light", + "_l ight", + "_li ght", + "Ġin cons", + "Ġinc ons", + "Ġincon s", + "- -------Ċ", + "-- ------Ċ", + "---- ----Ċ", + "-------- Ċ", + "--- -----Ċ", + "----- ---Ċ", + "------ --Ċ", + "------- -Ċ", + "ĉ ĉĠĠĠĠĠĠ", + "ĉĉ ĠĠĠĠĠĠ", + "ĉĉĠĠĠ ĠĠĠ", + "ĉĉĠ ĠĠĠĠĠ", + "ĉĉĠĠ ĠĠĠĠ", + "ĉĉĠĠĠĠ ĠĠ", + "ĉĉĠĠĠĠĠ Ġ", + "S F", + "C N", + ": \";Ċ", + ":\" ;Ċ", + "ĠColl ins", + "( *)", + "(* )", + "Ġcomp ilation", + "Ġcompil ation", + "' ]čĊ", + "'] čĊ", + "Ġcon sequence", + "Ġconsequ ence", + "Ġconse quence", + ", ...", + ",. ..", + "Ġ dm", + "Ġd m", + "Ġ BLOCK", + "ĠB LOCK", + "ĠBL OCK", + "Cl uster", + "Ġ ski", + "Ġs ki", + "Ġsk i", + "( argc", + "(arg c", + "(ar gc", + "T uple", + "Tu ple", + "Ġj oins", + "Ġjoin s", + "Ġjo ins", + "ĠSher iff", + "W ar", + "Wa r", + "in di", + "ind i", + "Ġcom mented", + "Ġcomm ented", + "Ġcomment ed", + "H OST", + "HO ST", + "Ġ invitation", + "Ġinv itation", + "Ġinvit ation", + "apan ese", + "Ġper mits", + "Ġpermit s", + "Ġperm its", + "preced ented", + "_ zone", + "_z one", + "Ġ Amy", + "ĠA my", + "ĠAm y", + "_ RD", + "_R D", + "Min imum", + "Ġinv ocation", + "Ġinvo cation", + ". enable", + ".e nable", + ".en able", + "i chten", + "ich ten", + "icht en", + "ichte n", + "- owned", + "\" id", + "_PO INTER", + "_POINT ER", + "F ac", + "Fa c", + "Ġspec ifications", + "Ġspecific ations", + "Ġspecification s", + "Ġn omination", + "Ġno mination", + "Ġnom ination", + "Ġnomin ation", + "Ġ gp", + "Ġg p", + "< (", + "Ġ robots", + "Ġro bots", + "Ġrob ots", + "Ġrobot s", + "Ġ Jerry", + "ĠJ erry", + "ĠJer ry", + "Ġ holders", + "Ġh olders", + "Ġhold ers", + "Ġholder s", + "Ġhol ders", + "Ġ wand", + "Ġw and", + "Ġwa nd", + "Ġwan d", + "c ms", + "cm s", + "Ġ }))Ċ", + "Ġ} ))Ċ", + "Ġ}) )Ċ", + "Ġ})) Ċ", + ". Toast", + ".To ast", + "ĠI List", + "ĠIL ist", + "B ased", + "Base d", + "Bas ed", + "Ba sed", + "z oom", + "zo om", + "/ style", + "/st yle", + "ĠB eck", + "ĠBe ck", + "ĠBec k", + "M en", + "Me n", + "Ġcontrib uting", + "Ġ undo", + "Ġu ndo", + "Ġun do", + "Ġund o", + "Ġ OH", + "ĠO H", + "Ġadd Object", + "Ġe igen", + "Ġei gen", + "Ġeig en", + "sign up", + "éĶ Ļ", + "Ġd istant", + "Ġdis tant", + "Ġdist ant", + "Ġdi stant", + "PAR ATOR", + "Ġ Mari", + "ĠM ari", + "ĠMar i", + "ĠMa ri", + "Ġ má", + "Ġm á", + "E mp", + "Em p", + "ó s", + "Ġ ìĪĺ", + "Ġì Īĺ", + "ĠìĪ ĺ", + "e vt", + "ev t", + "+ j", + "p ark", + "par k", + "pa rk", + "Ġ Stay", + "ĠSt ay", + "ĠSta y", + "ĠD un", + "ĠDu n", + "Ġs oy", + "Ġso y", + "> %", + "az ines", + "azine s", + "azi nes", + "Ġti empo", + "( me", + "(m e", + "p resent", + "pre sent", + "pres ent", + ". This", + ".T his", + ".Th is", + "Ġed itors", + "Ġedit ors", + "Ġeditor s", + "Ġedi tors", + "F IELD", + ". Work", + ".W ork", + "ĠUn iverse", + "ĠUnivers e", + "ĠUni verse", + "ĠUniv erse", + "Ġdr unk", + "Ġdru nk", + ". timer", + ".t imer", + ".time r", + ".tim er", + "Ġal tered", + "Ġalt ered", + "Ġalter ed", + "Ġalte red", + "ĠN ar", + "ĠNa r", + "ëł ¥", + ". Active", + ".Act ive", + "id or", + "ido r", + "ç Ń", + ".delta Time", + "Ġawk ward", + "& quot", + "ĠS afari", + "ĠSaf ari", + "Ġt ricks", + "Ġtr icks", + "Ġtri cks", + "Ġtrick s", + "M ENTS", + "MENT S", + "div ision", + "di vision", + "Ġvar ying", + "Ġva rying", + "Ġvary ing", + "ĠHigh way", + "Ġphot ographer", + "Ġphotograph er", + "ĠSt ewart", + "ĠSte wart", + "Ġ lasting", + "Ġl asting", + "Ġlast ing", + "Ġlas ting", + ". Pre", + ".P re", + ".Pr e", + ".amazon aws", + "Ġ Luck", + "ĠL uck", + "ĠLuc k", + "ĠLu ck", + ". Description", + ".D escription", + ".De scription", + ".Des cription", + "Ġ Naz", + "ĠN az", + "ĠNa z", + "n eg", + "ne g", + "Ġc ó", + "<< \"\\", + "<<\" \\", + "Ġ Surv", + "ĠS urv", + "ĠSur v", + "ĠSu rv", + "Ġ Unc", + "ĠU nc", + "ĠUn c", + "Rec ipe", + ". BorderStyle", + ".Border Style", + "Ġmod ifications", + "Ġmodification s", + "Ġmodific ations", + "- at", + "-a t", + "AT FORM", + "h dr", + "hd r", + "a ko", + "ak o", + "Ġsub license", + "Ġsublic ense", + "Ġ Jump", + "ĠJ ump", + "ĠJu mp", + "Ġbe im", + "Ġbei m", + "ĠMan hattan", + ". bool", + ".b ool", + ".bo ol", + "_ hw", + "_h w", + "ÑĤ ÑĮ", + "B in", + "Bi n", + "Ġ gateway", + "Ġg ateway", + "Ġgate way", + "\" \":", + "\"\" :", + "Ġ UIS", + "ĠU IS", + "ĠUI S", + ": \"+", + ":\" +", + "- def", + "-d ef", + "-de f", + "Ġ Regular", + "ĠReg ular", + "/ testing", + "/t esting", + "/test ing", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "string stream", + "Ġdis par", + "Ġdi spar", + "Ġdisp ar", + "Ġm obil", + "Ġmo bil", + "Ġmob il", + "- read", + "-r ead", + "-re ad", + "Ġ Adapter", + "ĠAd apter", + "ĠAda pter", + "ĠAdapt er", + "ĠCh ampions", + "ĠChampion s", + "ĠChamp ions", + "Ġ scheduler", + "Ġs cheduler", + "Ġsched uler", + "Ġschedule r", + "Ġ kills", + "Ġk ills", + "Ġkill s", + "Ġkil ls", + "Ġ Multiple", + "ĠM ultiple", + "ĠMulti ple", + "ĠMult iple", + "ĠMultip le", + "i rror", + "ir ror", + "Ġg ods", + "Ġgo ds", + "Ġgod s", + "A DO", + "AD O", + "a kte", + "ak te", + "akt e", + "Ġ Usuario", + "ĠUs uario", + ".c ircular", + "Ġre cept", + "Ġrec ept", + "Ġrece pt", + "Ġrecep t", + "Ġ Expr", + "ĠEx pr", + "ĠExp r", + "Ġelder ly", + "Ġnice ly", + "Ġnic ely", + "Ġb este", + "Ġbe ste", + "Ġbest e", + "Ġbes te", + "W ant", + "Wa nt", + "Ġclass ical", + "Ġclassic al", + ". sprite", + ".s prite", + ".sp rite", + "ob jc", + "obj c", + "ĠM ason", + "ĠMa son", + "ĠMas on", + "Ġs istema", + "Ġsist ema", + "Ġsistem a", + ". Black", + ".B lack", + ".Bl ack", + "e so", + "es o", + "ĠZe it", + "Ġ divid", + "Ġd ivid", + "Ġdi vid", + "Ġdiv id", + "Ġen ters", + "Ġent ers", + "Ġenter s", + "_ subject", + "_sub ject", + "_su bject", + "Ġ Planet", + "ĠPlan et", + "ĠPlane t", + "ĠPla net", + ". warning", + ".w arning", + ".warn ing", + "Ġ Gram", + "ĠG ram", + "ĠGr am", + "ĠGra m", + "_ tokens", + "_t okens", + "_token s", + "_tok ens", + "Ġhouse holds", + "Ġhousehold s", + "_ customer", + "_c ustomer", + "_custom er", + "_cust omer", + "user Name", + "c ross", + "cr oss", + "cro ss", + "Ġp ione", + "Ġpi one", + "Ġass ists", + "Ġassist s", + "_ SM", + "_S M", + "i bo", + "ib o", + "Ġl oyal", + "Ġlo yal", + "Ġus eless", + "Ġuse less", + "# elif", + "ĠUlt imate", + "C ome", + "Com e", + "Co me", + "g el", + "ge l", + "Ġd ich", + "Ġdi ch", + "Ġdic h", + "x yz", + "xy z", + "i kel", + "ik el", + "ike l", + "o bra", + "ob ra", + "_ scan", + "_s can", + "_sc an", + "Ġ Interior", + "ĠIn terior", + "ĠInt erior", + "ĠInter ior", + "Ġ Nice", + "ĠN ice", + "ĠNic e", + "ĠNi ce", + "Ġp lac", + "Ġpl ac", + "Ġpla c", + "ĉ target", + "ĉt arget", + "Ġv iral", + "Ġvi ral", + "Ġvir al", + "as so", + "ass o", + "( )/", + "() /", + "u nde", + "un de", + "und e", + "Ġ Adobe", + "ĠAd obe", + "O s", + "vis ited", + "visit ed", + "Ġ OW", + "ĠO W", + "Ġ Feed", + "ĠF eed", + "ĠFe ed", + "ĠFee d", + "Ġ Sequence", + "ĠSe quence", + "ĠSequ ence", + "Ġman ages", + "Ġmanage s", + "Ġmana ges", + "in son", + "ins on", + "ĠLouis iana", + "{ })", + "{} )", + "ĠH ab", + "ĠHa b", + "Ġ LD", + "ĠL D", + "Ġb ip", + "Ġbi p", + "p rites", + "pr ites", + "prite s", + "prit es", + "pri tes", + "( elem", + "(e lem", + "(el em", + "(ele m", + ".h ibernate", + "é lé", + "él é", + "Ġoh ne", + "_ transaction", + "_trans action", + "Ġann unci", + "P ublished", + "Publish ed", + "Ġ Honda", + "ĠH onda", + "ĠHon da", + "ĠHo nda", + "ĠHond a", + "Ġ Tam", + "ĠT am", + "ĠTa m", + "Ġ Packet", + "ĠP acket", + "ĠPac ket", + "ĠPack et", + "ĠPa cket", + "_ selector", + "_se lector", + "_select or", + "_sel ector", + "Ġchalleng ed", + "Ġchallenge d", + "P rocessing", + "Process ing", + "- hover", + "-h over", + "Ġ trainer", + "Ġtr ainer", + "Ġtrain er", + "Ġtra iner", + "Ġtrai ner", + "_ cancel", + "_c ancel", + "_can cel", + "Ġ NSDictionary", + "ĠNS Dictionary", + "ab ric", + "abr ic", + "Ġ MLS", + "ĠM LS", + "ĠML S", + "_ sensor", + "_s ensor", + "Ġsh rink", + "Ġshr ink", + "Ġshri nk", + "Ġ FX", + "ĠF X", + "th reshold", + "thresh old", + "ĉ HX", + "ĉH X", + "- mark", + "-m ark", + "-mar k", + "` .`", + "`. `", + "S cheme", + "Sch eme", + "( full", + "(f ull", + "_ writer", + "_w riter", + "_write r", + "_wr iter", + "Ġ Sys", + "ĠS ys", + "ĠSy s", + "Ġf led", + "Ġfl ed", + "Ġfle d", + "ĠC in", + "ĠCi n", + "- widget", + "-w idget", + "Ġ Previous", + "ĠPre vious", + "ĠPrev ious", + "G ender", + "Ge nder", + "Gen der", + "_ question", + "_q uestion", + "_qu estion", + "_quest ion", + "F eed", + "Fe ed", + "Fee d", + "Ġsc rut", + "Ġscr ut", + "( prefix", + "(p refix", + "(pre fix", + "(pref ix", + "ãĢĤ ãĢĤ", + "Ġin fections", + "Ġinf ections", + "Ġinfection s", + "Ġinfect ions", + "P arts", + "Par ts", + "Part s", + "Pa rts", + "Ġh ierarchy", + "Ġhier archy", + "_ DELETE", + "_DE LETE", + "Ġ Patient", + "ĠP atient", + "ĠPat ient", + "_ pay", + "_p ay", + "_pa y", + "Ġprom oted", + "Ġpromote d", + "Ġpromot ed", + "Ġpromo ted", + "Ġ ìĭ", + "Ġì ĭ", + "Ġcivil ian", + "Ġciv ilian", + "Ġagricult ure", + "Ġagr iculture", + "Ġ Piece", + "ĠP iece", + "ĠPi ece", + "ĠPie ce", + "Ġ stance", + "Ġst ance", + "Ġsta nce", + "Ġstan ce", + "uts che", + "utsch e", + "As sign", + "Ass ign", + ". ACTION", + ".A CTION", + ".AC TION", + ".ACT ION", + "F ig", + "Fi g", + "_ radius", + "_r adius", + "_rad ius", + "_radi us", + "Ġ Sync", + "ĠS ync", + "ĠSy nc", + "ĠSyn c", + "d ucer", + "du cer", + "duc er", + "duce r", + "f ailure", + "fail ure", + "en sed", + "ens ed", + "ense d", + "p time", + "pt ime", + "B M", + "_ datetime", + "_d atetime", + "_date time", + "_dat etime", + "qu ivo", + "quiv o", + "qui vo", + "QUE UE", + "èĢ ħ", + "App ear", + "Ap pear", + "Appe ar", + "Ġsum mit", + "Ġsumm it", + ": void", + ":v oid", + "Ġ vine", + "Ġv ine", + "Ġvi ne", + "Ġvin e", + "è ®¤", + "è® ¤", + "on ne", + "onn e", + "_ TRANS", + "_TR ANS", + "_TRAN S", + "_TRA NS", + ". green", + ".g reen", + ".gr een", + "_ cc", + "_c c", + "Ġhung ry", + "Ġ \">", + "Ġ\" >", + "( ));čĊčĊ", + "() );čĊčĊ", + "()) ;čĊčĊ", + "());čĊ čĊ", + "()); čĊčĊ", + "Ex tract", + "Ext ract", + "Extra ct", + "i zens", + "iz ens", + "ize ns", + "izen s", + "Ġ solver", + "Ġs olver", + "Ġsol ver", + "Ġsolve r", + "N otify", + "Not ify", + "Ġ english", + "Ġeng lish", + "Ġ Shopping", + "ĠSh opping", + "ĠShop ping", + "ĠSho pping", + "inter faces", + "interface s", + "R EQ", + "RE Q", + "Ġil leg", + "Ġill eg", + "Ġ UIImageView", + "ĠUI ImageView", + "ĠUIImage View", + "Ġ disconnect", + "Ġdis connect", + "Ġ Until", + "ĠUn til", + "ĠUnt il", + "ĠCons ervative", + "ĠConserv ative", + "@ Column", + "Ġshift ed", + "Ġ :čĊ", + "Ġ: čĊ", + "Ġf ich", + "Ġfi ch", + "Ġfic h", + "Ġd la", + "Ġdl a", + "Ġs hoe", + "Ġsh oe", + "Ġsho e", + "\" ),čĊ", + "\") ,čĊ", + "\"), čĊ", + "ul arity", + "ular ity", + "_ RESP", + "_RE SP", + "_R ESP", + "_RES P", + "We ather", + "UI Application", + ". iterator", + ".it erator", + ".iter ator", + "Ġ aging", + "Ġa ging", + "Ġag ing", + ". Parent", + ".P arent", + ".Par ent", + "o wie", + "ow ie", + "owi e", + "( equal", + "(e qual", + "(eq ual", + "Ġ Conv", + "ĠCon v", + "ĠCo nv", + "/ default", + "/d efault", + "/de fault", + "Ġme asuring", + "Ġmeas uring", + ". prev", + ".p rev", + ".pre v", + ".pr ev", + ". IsValid", + ".Is Valid", + ". Fat", + ".F at", + "Ġs Äĥ", + "key words", + "keyword s", + "with out", + "Ġs overe", + "Ġso vere", + "Ġex changes", + "Ġexchange s", + "Ġm elt", + "Ġme lt", + "Ġmel t", + "Ġis lands", + "Ġisland s", + "Ġisl ands", + "ĠInt egr", + "Ġj umping", + "Ġjump ing", + "Ġ gle", + "Ġg le", + "Ġgl e", + "Ġjournal ism", + "Ġ dated", + "Ġd ated", + "Ġdate d", + "Ġda ted", + "Ġdat ed", + "Local ized", + "Ġ Refresh", + "ĠRe fresh", + "ĠRef resh", + "P article", + "Part icle", + "Ġ aa", + "Ġa a", + "ĠST RICT", + "ĠSTR ICT", + "Ġb od", + "Ġbo d", + ". Process", + ".P rocess", + ".Pro cess", + "_ AUTO", + "_A UTO", + "_AUT O", + "Ġ Published", + "ĠP ublished", + "ĠPublish ed", + "e very", + "ever y", + "ev ery", + "eve ry", + "Ġtechn ological", + "Ġtechno logical", + "Ġtechnolog ical", + "l sx", + "ls x", + "Ġir rit", + "Ġirr it", + "Add itional", + "Ġ delimiter", + "Ġdel imiter", + "Ġdelim iter", + "_ language", + "_l anguage", + "- area", + "-a rea", + "-ar ea", + "bo ys", + "boy s", + "Ġ Tube", + "ĠT ube", + "ĠTu be", + "ĠTub e", + "Ġ wat", + "Ġw at", + "Ġwa t", + "Ġmechan ics", + "Ġmechanic s", + "_ owner", + "_o wner", + "_own er", + "S pell", + "Sp ell", + "Spe ll", + "Ġ Stories", + "ĠSt ories", + "ĠSto ries", + ".Append Line", + "T ableView", + "Table View", + "h em", + "he m", + "s tick", + "st ick", + "sti ck", + "ol lower", + "oll ower", + "ollow er", + "ollo wer", + "I FF", + "IF F", + "Ġ UV", + "ĠU V", + "oll ision", + "S UB", + "SU B", + "Ġcom parable", + "Ġcompar able", + "Ġd onde", + "Ġdo nde", + "Ġdon de", + "s ales", + "sa les", + "sal es", + "sale s", + "ll vm", + "Ġ }],Ċ", + "Ġ} ],Ċ", + "Ġ}] ,Ċ", + "OTT OM", + "Ġ Purpose", + "ĠP urpose", + "ĠPur pose", + "L ab", + "La b", + "Ġinterview ed", + "o is", + "oi s", + "a sil", + "as il", + "asi l", + ". setId", + ".set Id", + "Ġ Instruction", + "ĠIn struction", + "- ->", + "-- >", + "Ġ Modified", + "ĠMod ified", + "ation ally", + "ational ly", + "Ġ Meeting", + "ĠMe eting", + "ĠMeet ing", + "è¯ ¯", + "# region", + "Ġ routing", + "Ġr outing", + "Ġro uting", + "Ġrout ing", + "Ġrou ting", + ". focus", + ".f ocus", + "ĠY outh", + "ĠYou th", + "ĠYo uth", + "< D", + "ĠN ag", + "ĠNa g", + "cont acts", + "contact s", + "Ġ forming", + "Ġfor ming", + "Ġform ing", + "Ġ mie", + "Ġm ie", + "Ġmi e", + "',[' ../", + "Ġ BP", + "ĠB P", + "Ġapp et", + "Ġap pet", + "Ġappe t", + "Ġ Teacher", + "ĠT eacher", + "ĠTe acher", + "ĠTea cher", + "ĠTeach er", + "Ġ TP", + "ĠT P", + "Ġann ually", + "Ġannual ly", + "outed EventArgs", + "Ġ Speaker", + "ĠS peaker", + "ĠSpe aker", + "ĠSpeak er", + "Ġ rename", + "Ġre name", + "Ġr ename", + "Ġren ame", + "C FG", + "CF G", + "(\" //", + "(\"/ /", + "æİ ¥", + "/ pages", + "/p ages", + "/page s", + "Ġpr és", + "Ġpré s", + "Ġ Spell", + "ĠS pell", + "ĠSp ell", + "ĠSpe ll", + ". Allow", + ".Al low", + ".All ow", + "ĠINT ERRU", + "ĠINTER RU", + "Ġ (#", + "Ġ( #", + "âĢĻ ĊĊ", + "âĢĻĊ Ċ", + "_ Generic", + "_G eneric", + ". imshow", + ".im show", + "_ tim", + "_t im", + "_ti m", + "- face", + "-f ace", + "( &(", + "(& (", + "ati num", + "atin um", + "Ġrevolution ary", + "Ġ Hours", + "ĠH ours", + "ĠHo urs", + "ĠHour s", + "ĠHou rs", + "r ain", + "ra in", + "rai n", + "Ġany time", + "Ġ abb", + "Ġa bb", + "Ġab b", + ". jsp", + ".j sp", + ".js p", + "S crollView", + "Scroll View", + "Ġ Truth", + "ĠTr uth", + "ĠTru th", + "Ġ anticipated", + "Ġanticip ated", + "Ġanticipate d", + "Ġantic ipated", + "Ġ accent", + "Ġacc ent", + "Ġac cent", + ". checked", + ".check ed", + "Ġspec ifies", + "Ġ caf", + "Ġc af", + "Ġca f", + "Ġcell padding", + "Ġ cooked", + "Ġco oked", + "Ġcook ed", + "Ġ Hugh", + "ĠH ugh", + "ĠHu gh", + "pe ek", + "pee k", + "_ RATE", + "_R ATE", + "_RA TE", + "Ġd orm", + "Ġdo rm", + "Ġdor m", + "/ čĊ", + "IV ITY", + ". Controller", + ".Cont roller", + ".Control ler", + "( part", + "(p art", + "(par t", + "(pa rt", + ". constraint", + ".con straint", + "Ġin vasion", + "Ġinv asion", + "M OVE", + "MO VE", + "MOV E", + "Ġgl uc", + "Ġglu c", + "l ename", + "le name", + "len ame", + "lena me", + "Ġ amen", + "Ġa men", + "Ġam en", + "eng lish", + "engl ish", + "ĠSw itzerland", + "\" ;ĊĊĊ", + "\";Ċ ĊĊ", + "\";ĊĊ Ċ", + "\"; ĊĊĊ", + "p est", + "pe st", + "pes t", + ". collect", + ".c ollect", + ".col lect", + ".coll ect", + "N ib", + "Ni b", + "Ġ Dict", + "ĠD ict", + "ĠDi ct", + "Ġ Emb", + "ĠE mb", + "ĠEm b", + "( subject", + "(sub ject", + "Ġout rage", + "Ġoutr age", + "Ġoutra ge", + "Ġdec iding", + "Ġdecid ing", + "Ġsent enced", + "Ġsentence d", + "F echa", + "Fe cha", + "\" A", + "Ġ quer", + "Ġqu er", + "Ġque r", + "Ġq uer", + "Ġfont Family", + "Ġqu adr", + "Ġquad r", + "Ġqua dr", + "- Y", + "_ CACHE", + "_C ACHE", + "_CA CHE", + "Ġan alyzed", + "Ġanaly zed", + "Ġanalyze d", + "Ġg aining", + "Ġgain ing", + "Ġga ining", + "Ġ Against", + "ĠAgain st", + "ĠS oul", + "ĠSo ul", + "ĠSou l", + "t au", + "ta u", + "Ġlight weight", + "Ġ TF", + "ĠT F", + "Ġ Effects", + "ĠE ffects", + "ĠEffect s", + "ĠEff ects", + ". Types", + ".T ypes", + ".Type s", + ". addClass", + ".add Class", + "Ġv egan", + "Ġve gan", + "Ġveg an", + "é ģ", + ". '\"", + ".' \"", + "Ġ Explorer", + "ĠEx plorer", + "ĠExpl orer", + "ĠExplore r", + ". detect", + ".d etect", + ".det ect", + ". shift", + ".s hift", + ".sh ift", + "Ġoblig ations", + "Ġobligation s", + "last Name", + "Ġassoci ations", + "Ġassociation s", + "Ġassoc iations", + "ĠTime Span", + "un ter", + "unt er", + "unte r", + "Ġ Fresh", + "ĠF resh", + "ĠFr esh", + "ĠFre sh", + "ĠFres h", + "Com patible", + "Compat ible", + "P ub", + "Pu b", + "id ges", + "idge s", + ". option", + ".op tion", + ".o ption", + ".opt ion", + "v ari", + "var i", + "va ri", + ". hashCode", + ".hash Code", + "Ġ geb", + "Ġg eb", + "Ġge b", + ". section", + ".s ection", + ".se ction", + ".sec tion", + "- not", + "-n ot", + "-no t", + "Ġ Submit", + "ĠSub mit", + "T N", + "reg istry", + "registr y", + "regist ry", + "_ media", + "_m edia", + "_me dia", + "_med ia", + "Ġn aj", + "Ġna j", + "f ft", + "ff t", + "Ġ mate", + "Ġm ate", + "Ġmat e", + "Ġma te", + "- third", + "-th ird", + "Ġp ockets", + "Ġpocket s", + "e sta", + "es ta", + "est a", + "Ġb ent", + "Ġbe nt", + "Ġben t", + "ĠN ord", + "ĠNo rd", + "ĠNor d", + "Ġretail ers", + "Ġretailer s", + "ĠM orris", + "ĠMor ris", + "ĠMorr is", + ". \"\"\"ĊĊ", + ".\"\" \"ĊĊ", + ".\"\"\"Ċ Ċ", + ".\"\"\" ĊĊ", + "W rong", + "Wr ong", + "Ġ ÅĽ", + "ĠÅ Ľ", + "R ay", + "Ra y", + ". ec", + ".e c", + "Ġ Bind", + "ĠB ind", + "ĠBi nd", + "ĠBin d", + "_ HAND", + "_H AND", + "( non", + "(n on", + "(no n", + "is Valid", + "Ġsimilar ly", + "_ LIMIT", + "_L IMIT", + "Ġd ynamics", + "Ġdynamic s", + "Ġdynam ics", + "Ġdyn amics", + "Ġdist inction", + "Ġdistinct ion", + "ãģ Ĩ", + "< N", + "Ġ orth", + "Ġor th", + "Ġort h", + "Ġ Toyota", + "ĠToy ota", + "Ġ Kate", + "ĠK ate", + "ĠKat e", + "ĠKa te", + "Ġ LS", + "ĠL S", + "o rie", + "or ie", + "ori e", + "ĠSpring s", + "ĠSpr ings", + "Ġf reak", + "Ġfre ak", + "last name", + "_ MULT", + "_M ULT", + "_MUL T", + "- step", + "-s tep", + "-st ep", + "-ste p", + "\" (", + "A DDR", + "AD DR", + "ADD R", + "Ġentert aining", + "Ġentertain ing", + "_ CONF", + "_CON F", + "_CO NF", + "Ġ decoded", + "Ġde coded", + "Ġdec oded", + "Ġdecode d", + "Ġdeco ded", + "Ġst reak", + "Ġstre ak", + "Ġwait ed", + "Ġwa ited", + "Ġnot ified", + "ro duced", + "rodu ced", + "rod uced", + "roduce d", + "vis ual", + ". LayoutParams", + ".Layout Params", + "æ °", + "e sian", + "es ian", + "esi an", + "f its", + "fit s", + "fi ts", + "s pring", + "sp ring", + "spr ing", + "ĠBer nie", + "ĠBern ie", + "User Defaults", + "Ġpe dest", + "Ġped est", + "Ap pearance", + "Appear ance", + "Ġ Wiki", + "ĠW iki", + "ĠWi ki", + "ĠWik i", + "ĠNOT ICE", + "Ġ ssh", + "Ġs sh", + "Ġss h", + "Ġdur ante", + "Ġ Zip", + "ĠZ ip", + "ĠZi p", + "ı r", + "ĠN ATO", + "ĠNAT O", + "ĠNA TO", + "Ġtw elve", + "Ġr oyal", + "Ġro yal", + "Ġroy al", + "ï ¸", + "Ġ merchant", + "Ġm erchant", + "Ġmer chant", + "Ġmerch ant", + "ĠF urniture", + "ĠFurn iture", + "' ]),Ċ", + "'] ),Ċ", + "']) ,Ċ", + "']), Ċ", + ", X", + "Ġ folders", + "Ġf olders", + "Ġfolder s", + "Ġfol ders", + "Ġfold ers", + "Ġ Gate", + "ĠG ate", + "ĠGa te", + "ĠGat e", + "ĉ func", + "ĉf unc", + "ĉfun c", + "p ick", + "pi ck", + "pic k", + "_ usuario", + "_us uario", + "ĠV erm", + "ĠVer m", + "ĠVe rm", + "m ention", + "ment ion", + "men tion", + "ur pose", + "Ġ alerts", + "Ġal erts", + "Ġalert s", + "Ġale rts", + "x ious", + "xi ous", + "_ sig", + "_s ig", + "_si g", + "Ġ Fu", + "ĠF u", + "Ġ (:", + "Ġ( :", + "Ġd umb", + "Ġdu mb", + "Ġdum b", + "åħ ³", + "Ġaccur ately", + "Ġaccurate ly", + "éĩ į", + "R B", + "- screen", + "-s creen", + "-sc reen", + "Ġ VER", + "ĠV ER", + "ĠVE R", + "j our", + "jo ur", + "Ġrom ance", + "Ġroman ce", + "Ġroma nce", + "uc ceed", + "ucc eed", + ". choice", + ".ch oice", + "Ġad ip", + "_ dims", + "_d ims", + "_dim s", + "_di ms", + "Serial izable", + "ãĤ ĭ", + ". job", + ".j ob", + "Ġ prog", + "Ġp rog", + "Ġpro g", + "Ġpr og", + "u char", + "uch ar", + "uc har", + "ucha r", + "Ġg ently", + "Ġgent ly", + "Ġ RSS", + "ĠR SS", + "ĠRS S", + "ict ured", + "icture d", + "_ENABLE D", + "ĉ label", + "ĉl abel", + "aw ks", + "awk s", + "Ġ Ensure", + "ĠEn sure", + "ĠEns ure", + "re member", + "rem ember", + "ìł ķ", + "Ġtrans mit", + "{ {$", + "{{ $", + ". Transaction", + ".Trans action", + "ur se", + "urs e", + "_ relative", + "_rel ative", + "Ġs ized", + "Ġsize d", + "Ġsi zed", + "Ġsiz ed", + "Ġ XX", + "ĠX X", + "ĠPr incess", + "ĠPrince ss", + "Ġ Larry", + "ĠL arry", + "ĠLar ry", + "Ġp ró", + "Ġpr ó", + "Ġ ÑģÑĤÑĢ", + "ĠÑģ ÑĤÑĢ", + "ĠÑģÑĤ ÑĢ", + "Ġs isters", + "Ġsi sters", + "Ġsister s", + "Ġsist ers", + "Ġsis ters", + "e struct", + "estr uct", + "Ġ checkpoint", + "Ġcheck point", + ": length", + ":len gth", + ":l ength", + "Ġ Carlos", + "ĠCar los", + "ĠCarl os", + "ĠCarlo s", + "/ icon", + "/i con", + "/ic on", + "_ TARGET", + "_T ARGET", + "T okens", + "Token s", + "Tok ens", + "Ġpat ience", + "Ġ Selected", + "ĠSe lected", + "ĠSelect ed", + "ĠSel ected", + "q ty", + "qt y", + ".show Message", + "Ġwild life", + "Ġ Props", + "ĠP rops", + "ĠPro ps", + "ĠPr ops", + "ĠProp s", + "b m", + "- arrow", + "-ar row", + "Ġ parcel", + "Ġpar cel", + "Ġparc el", + "Ġparce l", + "f irebase", + "fire base", + "ĠBen jamin", + "c esso", + "cess o", + "ces so", + ". tim", + ".t im", + "ĠG arc", + "ĠGar c", + "ĠGa rc", + ". any", + ".a ny", + ".an y", + "ĠHOW EVER", + "ĠK o", + "Ġgrab bed", + "_ frames", + "_f rames", + "_frame s", + "_fr ames", + "Ġobject AtIndex", + "ĠADV ISED", + "Ġsu bur", + "Ġsub ur", + "ĉ GL", + "ĉG L", + "Ġ })}Ċ", + "Ġ} )}Ċ", + "Ġ}) }Ċ", + "- length", + "-l ength", + "-le ngth", + "-len gth", + "ìĭ ľ", + "ĠPot ter", + "_ buff", + "_b uff", + "_buf f", + ". gui", + ".g ui", + "Ġ Encoding", + "ĠEn coding", + "ĠEnc oding", + "E lect", + "El ect", + "Ele ct", + "- message", + "-m essage", + "Ġ �", + "Ġ ÈĻi", + "Ġ ArgumentNullException", + "ĠArgument NullException", + "а ÑĨи", + "Ġmin imize", + "Ġminim ize", + "Ġrespond ing", + "$_ ['", + "Ġ Individual", + "ĠInd ividual", + "á c", + "Ġ INTER", + "ĠIN TER", + "ĠINT ER", + "Ġmast urb", + "Ġmastur b", + "Ġ Bin", + "ĠB in", + "ĠBi n", + "( '$", + "(' $", + "ëĵ ľ", + "Ġopen ly", + "Ġ ><", + "Ġ> <", + "Ġ unto", + "Ġun to", + "Ġunt o", + "olog ically", + "ological ly", + "ologic ally", + "Ġ Mul", + "ĠM ul", + "ĠMu l", + "VID IA", + "Ġs lim", + "Ġsl im", + "ĠCommission er", + "( on", + "(o n", + "Ġunder neath", + "/ db", + "/d b", + "v ote", + "vo te", + "( Message", + "(M essage", + "ĠP ope", + "ĠPop e", + "ĠPo pe", + "D efined", + "Def ined", + "Define d", + "Ġ swift", + "Ġsw ift", + "u rf", + "ur f", + "Ġadapt ed", + "Ġadap ted", + "S EL", + "SE L", + "Ġre venues", + "Ġrevenue s", + "Ġreven ues", + "Ġdi vine", + "Ġdiv ine", + "= y", + "G radient", + "Grad ient", + "_ act", + "_a ct", + "_ac t", + "Ġ/* !<", + "Ġ/*! <", + "Ġ polygon", + "Ġp olygon", + "Ġpoly gon", + "Ġ FDA", + "ĠF DA", + "ĠFD A", + "ĠC arr", + "ĠCar r", + "ĠCa rr", + "a tables", + "at ables", + "ata bles", + "atab les", + "atable s", + "( stdout", + "(std out", + "Ġref riger", + "Ġrefr iger", + "Ġco ordin", + "Ġcoord in", + "avor ites", + "avorite s", + "avo rites", + "ÑĪ и", + "Ġcompass ion", + "ĠPOSS IBILITY", + "- secondary", + "-second ary", + "ur acy", + "ura cy", + "Ġcom promise", + "Ġcomp romise", + "Ġcomprom ise", + "_ AV", + "_A V", + "_ os", + "_o s", + "Ġbe side", + "Ġbes ide", + "ĥ Ŀ", + "Ġ ln", + "Ġl n", + ". plugins", + ".pl ugins", + ".plugin s", + "Cap acity", + "a lah", + "al ah", + "ala h", + ". bin", + ".b in", + ".bi n", + "Ġ CRC", + "ĠC RC", + "ĠCR C", + "_ balance", + "_b alance", + "_bal ance", + "Ġflex Direction", + "Ġam bit", + "Ġamb it", + "Ġ nickname", + "Ġn ickname", + "Ġnick name", + "ĠFor ces", + "ĠForce s", + "C LE", + "CL E", + "Ġ Shell", + "ĠS hell", + "ĠSh ell", + "ĠShe ll", + "ĠShel l", + "Ġs ail", + "Ġsa il", + "Ġsai l", + "Ġ Writer", + "ĠW riter", + "ĠWrite r", + "ĠWr iter", + "Ġ Alice", + "ĠA lice", + "ĠAl ice", + "ĠAli ce", + "ĠAlic e", + "d w", + "ĠInd ians", + "ĠIndia ns", + "ĠIndian s", + "ĠIndi ans", + "ĠMar shall", + "ĠMars hall", + "ĠMarshal l", + "ĠMarsh all", + "_ SRC", + "_S RC", + "_SR C", + "Ġ normalized", + "Ġnormal ized", + "Ġnormalize d", + "ĠJ ag", + "ĠJa g", + "ãĤ Ĵ", + "ze it", + "r pc", + "rp c", + "ÃŃ c", + ". inline", + ".in line", + "Ġtr avers", + "Ġtra vers", + "Ġtrav ers", + "_ numeric", + "_n umeric", + "_num eric", + "_numer ic", + "Ġ utilities", + "Ġutil ities", + "Ġut ilities", + "Ġe vac", + "Ġev ac", + "IN PUT", + "ĉ register", + "ĉreg ister", + "M X", + "ĠCamp bell", + "Ġ datasets", + "Ġd atasets", + "Ġdata sets", + "Ġdataset s", + "Ġdatas ets", + "Ġdem anded", + "Ġdemand ed", + "Ġdemande d", + "Ġinitial State", + "g an", + "ga n", + "Ġ ei", + "Ġe i", + "Un expected", + "- web", + "-w eb", + "-we b", + "t rait", + "tr ait", + "tra it", + ", Y", + "Ġ Todd", + "ĠT odd", + "ĠTo dd", + "ĠTod d", + "Ġs keleton", + "Ġske leton", + "Ġ optimize", + "Ġopt imize", + "Ġoptim ize", + "ç ¬¬", + "ç¬ ¬", + "Ġ Upon", + "ĠU pon", + "ĠUp on", + "ĠSt Object", + "Ġap lic", + "Ġapl ic", + ". ' P", + "v ron", + "vr on", + "vro n", + ". UN", + ".U N", + "Ġp ainter", + "Ġpaint er", + "Ġpain ter", + "Ġpa inter", + "izar re", + "Ġ lav", + "Ġl av", + "Ġla v", + "Ġ pom", + "Ġp om", + "Ġpo m", + "p reg", + "pr eg", + "pre g", + "= function", + "=f unction", + "( serial", + "(s erial", + "(se rial", + "if ica", + "ific a", + "ifi ca", + "u ming", + "um ing", + "umin g", + "umi ng", + "åľ °", + "ãģ Ĥ", + "- op", + "-o p", + "U CH", + "UC H", + "ĠH end", + "ĠHe nd", + "ĠHen d", + ". propTypes", + ".prop Types", + "Ġ yo", + "Ġy o", + "Ġr outines", + "Ġrout ines", + "Ġroutine s", + "Ġc aring", + "Ġcar ing", + "Ġca ring", + "S em", + "Se m", + "Ġres erves", + "Ġreserve s", + "Ġreserv es", + "Ġprior ities", + "Ġpriorit ies", + "red its", + "redit s", + "redi ts", + "I STR", + "IS TR", + "IST R", + "Content Type", + "ĠS chw", + "ĠSc hw", + "ĠSch w", + "/ media", + "/m edia", + "/me dia", + "Ġ estr", + "Ġe str", + "Ġes tr", + "Ġest r", + "Ġclim bing", + "Ġclimb ing", + "- week", + "-we ek", + "cher che", + "s ensor", + "To Array", + "ĠMont real", + "Ġclo uds", + "Ġcloud s", + "ĠInject able", + "ĠR ice", + "ĠRic e", + "ĠRi ce", + "Ġpropag anda", + "_ provider", + "_pro vider", + "_prov ider", + "Ġin door", + "Ġind oor", + "Ġindo or", + "Ġin aug", + "Ġdipl om", + "Ġdip lom", + "Ġm essaging", + "Ġmess aging", + "_ mut", + "_m ut", + "_mu t", + "å ¦Ĥ", + "å¦ Ĥ", + "Ġ kw", + "Ġk w", + "O NS", + "ON S", + "a rians", + "ar ians", + "ari ans", + "arian s", + "aria ns", + "R PC", + "RP C", + ") ]čĊ", + ")] čĊ", + "- ray", + "-r ay", + "-ra y", + "ĠS or", + "ĠSo r", + "m all", + "ma ll", + "mal l", + "Ġmarket place", + "Ġ vtk", + "Ġv tk", + "Ġvt k", + "M a", + "o gan", + "og an", + "oga n", + "i gi", + "ig i", + "Ġs ponsored", + "Ġspons ored", + "Ġsponsor ed", + "Ġ Dani", + "ĠD ani", + "ĠDan i", + "ĠDa ni", + ".S EVER", + ".SE VER", + "> '.$", + ">' .$", + ">'. $", + "m ultipart", + "multi part", + "multip art", + "ĠW ol", + "ĠWo l", + "Ġ tableName", + "Ġtable Name", + "Ġ Username", + "ĠUser name", + "Back groundColor", + "Background Color", + "Ġf right", + "Ġfr ight", + "Ġfri ght", + "_ EMAIL", + "_E MAIL", + "_EM AIL", + "Sept ember", + "Sep tember", + "_ vals", + "_v als", + "_val s", + "_va ls", + "op ia", + "opi a", + "Ġsp otted", + "Ġspot ted", + "- Ch", + "-C h", + "Ġ dataSource", + "Ġdata Source", + "/ \"Ċ", + "/\" Ċ", + "е кÑĤ", + "ек ÑĤ", + "Ġ RequestMethod", + "ĠRequest Method", + "Ġ Replace", + "ĠRe place", + "ĠRep lace", + "- do", + "-d o", + "a hn", + "ah n", + "ĠPh D", + "] .ĊĊ", + "]. ĊĊ", + "].Ċ Ċ", + "N ON", + "NO N", + "g ement", + "ge ment", + "gem ent", + "geme nt", + "Ġ Thr", + "ĠT hr", + "ĠTh r", + "Ġquiet ly", + "Ġtor ture", + "Ġtort ure", + "Ġte as", + "Ġtea s", + "Ġ CY", + "ĠC Y", + "Ġ atr", + "Ġa tr", + "Ġat r", + "de velopment", + "dev elopment", + "develop ment", + "- detail", + "-d etail", + "-de tail", + "-det ail", + "Ġl ighter", + "Ġlight er", + "Ġarg uing", + "Ġdes erves", + "Ġdeserve s", + "Ġdeser ves", + "Ġcur riculum", + "_ CONTEXT", + "_CON TEXT", + "_CONT EXT", + "ÅĤ y", + "H ITE", + "HI TE", + "ĉ ID", + "ĉI D", + "/ uploads", + "/upload s", + "/up loads", + "Ġt its", + "Ġtit s", + "Ġti ts", + "r eo", + "re o", + "_ drop", + "_d rop", + "_dr op", + ". UTF", + ".U TF", + "Ġ pickup", + "Ġpick up", + "Ġpic kup", + "Ġgro cery", + "Ġ Pure", + "ĠP ure", + "ĠPur e", + "ĠPu re", + "Ġeas iest", + "P hil", + "Ph il", + "Phi l", + ". feature", + ".f eature", + ".fe ature", + "( \"*", + "(\" *", + "Ġinvest or", + "Ġinve stor", + "t ok", + "to k", + "Ġ jar", + "Ġj ar", + "Ġja r", + "L os", + "Lo s", + "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", + ". queue", + ".q ueue", + "- speed", + "-s peed", + "-sp eed", + "-spe ed", + "M al", + "Ma l", + "um blr", + "umb lr", + "Ġ CONST", + "ĠCON ST", + "ĠCO NST", + "ĠCONS T", + "Ġ HRESULT", + "ĠH RESULT", + "ĠD ance", + "ĠDan ce", + "ĠDa nce", + "( filePath", + "(file Path", + "Ġattribute d", + "Ġattrib uted", + "ॠį", + "ĠB und", + "ĠBu nd", + "ĠBun d", + "c oins", + "co ins", + "coin s", + "Ġs ão", + "Ġ pir", + "Ġp ir", + "Ġpi r", + "person al", + "pers onal", + "persona l", + "Ġpr elim", + "Ġpre lim", + "Ġpro pose", + "Ġprop ose", + "Ġpropos e", + "Ġ TL", + "ĠT L", + "] ])", + "]] )", + "Ġ Subscription", + "ĠSub scription", + "ĠK re", + "ĠKr e", + ", len", + ",l en", + ". FirstOrDefault", + ".First OrDefault", + ") --", + ")- -", + "_ products", + "_product s", + ".Get Bytes", + "S hip", + "Sh ip", + "Ġ encrypt", + "Ġen crypt", + "Ġenc rypt", + "Ġ SG", + "ĠS G", + "ĠM yst", + "ĠMy st", + "h ir", + "hi r", + "Ġ iterate", + "Ġit erate", + "Ġiter ate", + "Ġint end", + "Ġinte nd", + ".mock ito", + "Ġch apters", + "Ġchapter s", + "Ġchap ters", + "( angle", + "(a ngle", + "(an gle", + "(ang le", + "ĠV lad", + "è® ¾", + "' .ĊĊ", + "'. ĊĊ", + "'.Ċ Ċ", + "Response Body", + "ĠA bd", + "ĠAb d", + "de al", + "dea l", + "Ġbar riers", + "Ġbarrier s", + "Ġbarr iers", + "- outline", + "-out line", + "b ill", + "bi ll", + "bil l", + "ĠF alls", + "ĠFall s", + "ĠFal ls", + "_ second", + "_se cond", + "_sec ond", + ". include", + ".in clude", + ".inc lude", + ". ceil", + ".c eil", + ".ce il", + "Ġ occupation", + "Ġoccup ation", + "ph ony", + "phon y", + ".move To", + "Ġ Jennifer", + "ĠJ ennifer", + "ĠJenn ifer", + "A STER", + "AS TER", + "AST ER", + "ASTE R", + "; \"><", + ";\" ><", + ";\"> <", + "Ġ Enabled", + "ĠEn abled", + "ĠEnable d", + "Ġ terminate", + "Ġter minate", + "Ġterm inate", + "Ġtermin ate", + "Ġ Io", + "ĠI o", + "l ations", + "lation s", + "lat ions", + "ĠTHE ORY", + "Ġear liest", + "Ġ rack", + "Ġr ack", + "Ġrac k", + "Ġra ck", + "Ġ Scar", + "ĠS car", + "ĠSc ar", + "sh ake", + "sha ke", + "c hip", + "ch ip", + "chi p", + "Ġ uv", + "Ġu v", + "Ġall iance", + "п иÑģ", + "пи Ñģ", + "ĠGOOD S", + "z ione", + "zi one", + "zion e", + "Ġ VI", + "ĠV I", + "Ġ {-", + "Ġ{ -", + "Ġfil tering", + "Ġfilter ing", + "Ġfilt ering", + "Ġmis con", + "Ġmisc on", + ".Dock Style", + "Ġb ush", + "Ġbu sh", + "Ġbus h", + "Ġj unk", + "Ġju nk", + "Ġjun k", + "æ Į", + "Ġ QUE", + "ĠQ UE", + "ĠQU E", + "Ġ hooks", + "Ġh ooks", + "Ġhook s", + "Ġho oks", + "Ġf irmware", + "Ġfirm ware", + "Ġ middleware", + "Ġm iddleware", + "Ġmiddle ware", + "d ic", + "di c", + "ĠOak land", + "Ġarr ives", + "Ġarrive s", + "Ġarriv es", + "P ayload", + "Pay load", + "p ixel", + "pix el", + "] |", + "Ġ startDate", + "Ġstart Date", + ". PRO", + ".P RO", + ".PR O", + "_ audio", + "_a udio", + "Ġmid field", + "igid body", + "ĠSw iss", + "Ġ Clip", + "ĠC lip", + "ĠCl ip", + "ĠCli p", + "Ġ Dump", + "ĠD ump", + "ĠDu mp", + "ĠDum p", + "Ġ TextBox", + "ĠText Box", + "Ġ geh", + "Ġg eh", + "Ġge h", + "y ield", + "yi eld", + "o ds", + "od s", + "Ġrefer endum", + "Back end", + "Ġ Cream", + "ĠC ream", + "ĠCr eam", + "ĠCre am", + "Ġd ominated", + "Ġdo minated", + "Ġdom inated", + "Ġdomin ated", + "Ġdominate d", + "Ġdomina ted", + "Ġ Archive", + "ĠA rchive", + "ĠArch ive", + "ĠArc hive", + "Ġr iders", + "Ġrid ers", + "Ġride rs", + "Ġri ders", + "Ġrider s", + ".prepare Statement", + "Ġqu ando", + "Ġquand o", + "Ġqua ndo", + "Ġquan do", + "Ġ chef", + "Ġch ef", + "Ġche f", + "w iki", + "wi ki", + "wik i", + "i nel", + "in el", + "ine l", + "am pling", + "amp ling", + "(\" \\\\", + "(\"\\ \\", + "Ġs ag", + "Ġsa g", + "_ proxy", + "_pro xy", + "_pr oxy", + "ãģ ķ", + "p do", + "pd o", + ". getElementsByTagName", + ".get ElementsByTagName", + ".getElementsBy TagName", + "Ġdemon stration", + "Ġdemonstr ation", + "Ġ NPC", + "ĠN PC", + "ĠNP C", + "Ġ archivo", + "Ġarch ivo", + "en dance", + "end ance", + "enda nce", + "Ġefficient ly", + "( actual", + "(ac tual", + "(act ual", + ". tableView", + ".t ableView", + ".table View", + "Ġm ush", + "Ġmus h", + "Ġmu sh", + "Ġb ears", + "Ġbe ars", + "Ġbear s", + "_ threads", + "_th reads", + "_thread s", + "j as", + "ja s", + "ah un", + "ahu n", + "Ġne ural", + "Ġneu ral", + "Ġneur al", + "Ġdesign ing", + "ĠG DP", + "ĠGD P", + "Ġlif ted", + "Ġlift ed", + "çĽ ®", + "Ġ Joint", + "ĠJ oint", + "ĠJo int", + "ĠJoin t", + "ĠJoi nt", + "Ġ Include", + "ĠIn clude", + "ĠInc lude", + "ĠG iants", + "ĠGi ants", + "ĠGiant s", + "ĠGian ts", + "ĠGia nts", + "Ġwithdraw al", + "Ġ Rent", + "ĠR ent", + "ĠRe nt", + "ĠRen t", + "n ative", + "nat ive", + "Ġ Seek", + "ĠSe ek", + "ĠSee k", + "g ression", + "gr ession", + "gress ion", + "_ CPU", + "_C PU", + "_CP U", + "\\ S", + "Ġ Shield", + "ĠSh ield", + "ĠShi eld", + "Ġs olic", + "Ġso lic", + "Ġsol ic", + "Ġ boom", + "Ġb oom", + "Ġbo om", + "Ġboo m", + "yect o", + "Ġmanufact ure", + "Ġ âĢĭ", + "ĠâĢ ĭ", + "Ġ bbox", + "Ġb box", + "Ġbb ox", + "Ġearth qu", + "oll ectors", + "ollect ors", + "olle ctors", + ":@\" %", + ":@ \"%", + "Ġ loops", + "Ġl oops", + "Ġlo ops", + "Ġloop s", + "J e", + "al king", + "alk ing", + "Ġ Whats", + "ĠWh ats", + "ĠWhat s", + "ĠBo ys", + "ĠBoy s", + ". book", + ".b ook", + ".bo ok", + "AR GE", + "ARG E", + "_ pixel", + "_p ixel", + "_pix el", + "Ġsus pects", + "Ġsusp ects", + "Ġsuspect s", + "Î ¹", + "u sp", + "us p", + "Ġ BMW", + "ĠB MW", + "ĠBM W", + "ie ces", + "iece s", + "iec es", + "( person", + "(p erson", + "(per son", + "å¼ Ģ", + "é »", + "ĠP odcast", + "ĠPod cast", + "Ġ bou", + "Ġb ou", + "Ġbo u", + "( Item", + "(I tem", + "(It em", + "à »", + "( Input", + "(In put", + "Http Get", + "Ġ burg", + "Ġb urg", + "Ġbu rg", + "Ġbur g", + ") ^", + "BO ARD", + "* /,", + "*/ ,", + "Ġ gulp", + "Ġg ulp", + "Ġgu lp", + "Ġgul p", + "ĠB enn", + "ĠBe nn", + "ĠBen n", + "Ġde cks", + "Ġdec ks", + "Ġdeck s", + ". statusCode", + ".status Code", + "Ġ acute", + "Ġac ute", + "Ġh ug", + "Ġhu g", + "u gu", + "ug u", + "Ġ pled", + "Ġp led", + "Ġpl ed", + "Ġple d", + ", \"%", + ",\" %", + "h ape", + "ha pe", + "hap e", + "Ġз ап", + "Ġза п", + "ĠM aine", + "ĠMain e", + "ĠMa ine", + "ĠMai ne", + ". real", + ".re al", + "Ġd alam", + "Ġda lam", + "Ġdal am", + "Ġ Minor", + "ĠMin or", + "ĠMi nor", + ". Float", + ".F loat", + "d isp", + "dis p", + "di sp", + "Ġ tl", + "Ġt l", + "Ġen count", + "Ġenc ount", + "= >$", + "=> $", + "Ġ fg", + "Ġf g", + "t ees", + "te es", + "tee s", + "ĠRe comm", + "ĠRec omm", + "ĠReco mm", + "ä l", + "Ġ chemistry", + "Ġchem istry", + "B locks", + "Block s", + "Bl ocks", + "Bloc ks", + "Blo cks", + "O ID", + "OI D", + "Ġf orex", + "Ġfor ex", + "Ġfore x", + "Ġfo rex", + "Ġ Append", + "ĠApp end", + "ĠAp pend", + "ĠAppe nd", + "Ġ {*", + "Ġ{ *", + "Ġ Supply", + "ĠS upply", + "ĠSup ply", + "CG Float", + "( bl", + "(b l", + "Ġ ate", + "Ġa te", + "Ġat e", + "ad ora", + "ado ra", + "ador a", + "Ġg ust", + "Ġgu st", + "Ass oci", + "Assoc i", + "> .Ċ", + ">. Ċ", + "F ETCH", + ". serial", + ".s erial", + ".se rial", + ".ser ial", + "widget s", + "wid gets", + "ard less", + "i efs", + "ie fs", + "ief s", + "_ FULL", + "_F ULL", + "ern etes", + "ernet es", + "erne tes", + "Ġ Pred", + "ĠP red", + "ĠPr ed", + "ĠPre d", + "Ø Ń", + "äº ĭ", + "ub ernetes", + "ubern etes", + "Ġ Laura", + "ĠL aura", + "ĠLa ura", + "ĠLaur a", + "ĠLau ra", + "Ġl abeled", + "Ġlabel ed", + "Ġlab eled", + "High light", + "Ġanno ying", + "Ġannoy ing", + "/ update", + "/up date", + "( description", + "(d escription", + "(de scription", + "(des cription", + "Ġint imid", + "Ġintim id", + "$ c", + "\" )))Ċ", + "\") ))Ċ", + "\")) )Ċ", + "\"))) Ċ", + ". AP", + ".A P", + "Ġ[ ]*", + "Ġ[] *", + "Ġ EXIT", + "ĠEX IT", + ". Host", + ".H ost", + "Ġ OPEN", + "ĠOP EN", + ". sendMessage", + ".send Message", + "_ camera", + "_c amera", + "_cam era", + "_ tile", + "_t ile", + "_ti le", + "Ġth erm", + "Ġthe rm", + "Ġther m", + "onom ous", + "Ġdis adv", + "Ġn aar", + "Ġna ar", + "index Of", + "Ġ PP", + "ĠP P", + ". protocol", + ".prot ocol", + ".proto col", + "A FE", + "AF E", + "Ġ textures", + "Ġtext ures", + "Ġtexture s", + "Ġtex tures", + "Ġtextu res", + "######## ########################################", + "################ ################################", + "################################ ################", + "######################################## ########", + "######################## ########################", + "um bai", + "umb ai", + "umba i", + ". stats", + ".st ats", + ".stat s", + "Ġ GE", + "ĠG E", + "Ġ ie", + "Ġi e", + "Ġ STD", + "ĠS TD", + "ĠST D", + "ĠM ann", + "ĠMan n", + "ĠMa nn", + ". reflect", + ".ref lect", + "K B", + "Ġd ive", + "Ġdi ve", + "Ġdiv e", + ". wav", + ".w av", + "/* ----------------------------------------------------------------", + "/*------------------------------------------------ ----------------", + "/ settings", + "/s ettings", + "/set tings", + ".l ifecycle", + ".life cycle", + "Ġda ughters", + "Ġdaughter s", + "o rus", + "or us", + "oru s", + "u ber", + "ub er", + "ube r", + "N ING", + "NI NG", + "s tri", + "st ri", + "str i", + "Ġ Tip", + "ĠT ip", + "ĠTi p", + "Ġ zn", + "Ġz n", + "Ġsw itched", + "Ġswitch ed", + "i net", + "in et", + "ine t", + "uff y", + "uf fy", + "ĠTransport ation", + "( conf", + "(con f", + "(co nf", + "f rica", + "fr ica", + "Ġ XL", + "ĠX L", + "Ġ Lead", + "ĠL ead", + "ĠLe ad", + "_ percent", + "_per cent", + "_perc ent", + "< Map", + " __", + "->_ _", + "per missions", + "perm issions", + "permission s", + "ĠD etermine", + "ĠDetermin e", + ". Man", + ".M an", + ".Ma n", + "Ġadv ances", + "Ġadvance s", + ". InputStream", + ".Input Stream", + "Ġstrong est", + "Ġstron gest", + "Ġe Bay", + "Ġ# -", + "Ġ dirname", + "Ġdir name", + "Ġ SMS", + "ĠS MS", + "ĠSM S", + "Ġmed ications", + "Ġmedic ations", + "Ġmedication s", + "Ġam ended", + "Ġamen ded", + "Ġamend ed", + "Ġchurch es", + "ĠIm perial", + "ĠImp erial", + "ĠImper ial", + "$ row", + "$r ow", + "ĠMad ison", + "Ġ Insp", + "ĠIn sp", + "ĠIns p", + "Ġaff air", + "Ġaf fair", + "Ġpsych ology", + "Ġpsycho logy", + "v h", + "Ġ severity", + "Ġse verity", + "Ġsever ity", + "âĢ IJ", + "Ġst rips", + "Ġstr ips", + "Ġstri ps", + "Ġstrip s", + "A H", + "vert ising", + "vertis ing", + "Ġc onse", + "Ġcon se", + "Ġcons e", + "IM AGE", + "IMA GE", + "Ġ Stats", + "ĠSt ats", + "ĠStat s", + "ĠSta ts", + "ĉ sc", + "ĉs c", + ". Cursor", + ".C ursor", + "Ġ freeze", + "Ġf reeze", + "Ġfree ze", + "s son", + "ss on", + "( xml", + "(x ml", + "Ġ Susan", + "ĠS usan", + "ĠSus an", + "ĠSu san", + ". tile", + ".t ile", + "e ded", + "ed ed", + "ede d", + "ĠĠ ĠĠĉĉĉ", + "ĠĠĠĠ ĉĉĉ", + "ĠĠĠ Ġĉĉĉ", + "ĠĠĠĠĉ ĉĉ", + "ĠĠĠĠĉĉ ĉ", + "u elle", + "ue lle", + "uel le", + "uell e", + "ĠMitch ell", + "b ased", + "base d", + "ba sed", + "bas ed", + "Oper and", + "Opera nd", + "½ æķ°", + "Ġ FF", + "ĠF F", + "ĉ strcpy", + "ĉstr cpy", + "ou nces", + "oun ces", + "ounc es", + "ounce s", + "il do", + "ild o", + ".execute Query", + "Ġappro aching", + "Ġapproach ing", + "Ġ Seven", + "ĠS even", + "ĠSe ven", + "ĠSev en", + "Ġ nuts", + "Ġn uts", + "Ġnut s", + "Ġnu ts", + "Ġ ric", + "Ġr ic", + "Ġri c", + "ass ignment", + "assign ment", + "Ġ calculator", + "Ġcal culator", + "Ġcalcul ator", + "Ġcalc ulator", + "ĠMur phy", + "ĠB ou", + "ĠBo u", + "í Ħ", + "Ġ butt", + "Ġb utt", + "Ġbut t", + "Ġbu tt", + "Ġ ticks", + "Ġt icks", + "Ġti cks", + "Ġtick s", + "Ġtic ks", + "Project s", + "Proj ects", + "i lib", + "il ib", + "ili b", + ".text Color", + "m ov", + "mo v", + "_ logo", + "_l ogo", + "_log o", + "_lo go", + "( template", + "(t emplate", + "(temp late", + "Ġ INIT", + "ĠIN IT", + "Ġ imageView", + "Ġimage View", + "s criptions", + "script ions", + "scri ptions", + "scription s", + "OR ITY", + "Con sumer", + "Cons umer", + "Consum er", + "Ġun precedented", + "Ġtour ist", + "Ġtou rist", + "Ġ bron", + "Ġb ron", + "Ġbr on", + "Ġbro n", + "Ġcon tractor", + "Ġcontract or", + "Ġcontr actor", + "Ġcontra ctor", + "Ġ licence", + "Ġli cence", + "Ġlic ence", + "Ġ Nam", + "ĠN am", + "ĠNa m", + "æ ¯", + "( transform", + "(trans form", + "_ ATT", + "_A TT", + "_AT T", + "P ref", + "Pr ef", + "Pre f", + "Ġ Gam", + "ĠG am", + "ĠGa m", + "Ġvess els", + "Ġvessel s", + "Ġh av", + "Ġha v", + "L ater", + "La ter", + "Lat er", + "Late r", + ". ToLower", + ".To Lower", + "Ġ urls", + "Ġurl s", + "Ġur ls", + "Ġbreak down", + "Ġpen alties", + "Ġpenal ties", + "Ġf oster", + "Ġfo ster", + "Ġfost er", + "Ġfos ter", + "Ġ UE", + "ĠU E", + "Ġc lue", + "Ġcl ue", + "c omed", + "com ed", + "co med", + "come d", + "åIJį 称", + "- main", + "-m ain", + "Ġ pts", + "Ġp ts", + "Ġpt s", + "Ġco unted", + "Ġcount ed", + "Ġcoun ted", + "i cts", + "ic ts", + "ict s", + "/ post", + "/p ost", + "Ġ getattr", + "Ġget attr", + "Ġ ping", + "Ġp ing", + "Ġpi ng", + "Ġpin g", + "AN CEL", + "ANCE L", + "ANC EL", + "Ġ pec", + "Ġp ec", + "Ġpe c", + "Ñħ од", + "Ñħо д", + "an tom", + "ant om", + "anto m", + "Ġ Blueprint", + "ĠBlue print", + "ĠEvent Emitter", + "Ġ lä", + "Ġl ä", + "æ ²", + "Ġst raw", + "Ġstr aw", + "Ġstra w", + "( comp", + "(c omp", + "(com p", + "(co mp", + "' une", + "'un e", + "'u ne", + "> N", + "- client", + "-c lient", + "-cl ient", + "-cli ent", + "es Module", + "- base", + "-b ase", + "Ġret reat", + "Ġretr eat", + "_ simple", + "_s imple", + "_sim ple", + "ĉ ĉĉĉĉĉĠ", + "ĉĉ ĉĉĉĉĠ", + "ĉĉĉĉ ĉĉĠ", + "ĉĉĉ ĉĉĉĠ", + "ĉĉĉĉĉ ĉĠ", + "ĉĉĉĉĉĉ Ġ", + "f ee", + "fe e", + "' )čĊčĊ", + "') čĊčĊ", + "')čĊ čĊ", + "Control Item", + "Ġsub scribers", + "Ġsubscri bers", + "Ġsubscribe rs", + "Ġsubscriber s", + "p lease", + "pl ease", + "ple ase", + "Ġ Eff", + "ĠE ff", + "ĠEf f", + "Ġp ound", + "Ġpo und", + "Ġpou nd", + "Ġ Bytes", + "ĠBy tes", + "ĠByte s", + "ĠT ea", + "ĠTe a", + "_ activity", + "_act ivity", + "_activ ity", + "Ġmax im", + "Ġmaxi m", + "Ġ opcode", + "Ġop code", + "Ġopc ode", + "B SD", + "BS D", + ". constant", + ".con stant", + ".const ant", + ".cons tant", + "; }", + "omb res", + "ombre s", + "Ġcare ers", + "Ġcareer s", + ") .ĊĊĊĊ", + "). ĊĊĊĊ", + ").ĊĊ ĊĊ", + ").Ċ ĊĊĊ", + ").ĊĊĊ Ċ", + "Ġsp reading", + "Ġspread ing", + "Ġspre ading", + "- expanded", + "-exp anded", + "-expand ed", + "Ġ Ord", + "ĠO rd", + "ĠOr d", + "am arin", + "ama rin", + "amar in", + "Ġmob ility", + "Ġmobil ity", + "Un fortunately", + "a kk", + "ak k", + "N L", + "_ redirect", + "_re direct", + "_red irect", + "Ġ PG", + "ĠP G", + "Ġ Sensor", + "ĠS ensor", + "ĠSens or", + "b ol", + "bo l", + "t ap", + "ta p", + "_ MEMORY", + "_MEM ORY", + "Ġ UIAlert", + "ĠUI Alert", + "pl itude", + "plit ude", + "We bsite", + "Web site", + "Ġ Logo", + "ĠL ogo", + "ĠLog o", + "ĠLo go", + "l ove", + "lo ve", + "lov e", + "[ ind", + "[i nd", + "[in d", + "Ġalto gether", + "Ġwonder ed", + "Ġ esper", + "Ġes per", + "Ġesp er", + "ĠLib eral", + "ĠLiber al", + "Ġ oss", + "Ġo ss", + "Ġos s", + "Ġe lit", + "Ġel it", + "Ġst iff", + "Ġstif f", + "od ox", + "odo x", + "_ mentions", + "_m entions", + "_ment ions", + "ĠDou glas", + "ĠDoug las", + "_ pid", + "_p id", + "_pi d", + "Ġ CK", + "ĠC K", + "ĠinitWith Frame", + ". blog", + ".b log", + ".bl og", + "p kg", + "pk g", + "ang hai", + "QUI RED", + "QUIRE D", + "u u", + "Ġ mkdir", + "Ġm kdir", + "Ġmk dir", + "AT AL", + "ATA L", + "Ġu nh", + "Ġun h", + "i nces", + "in ces", + "ince s", + "inc es", + "s th", + "st h", + "Ġhypo thesis", + "Ġhypoth esis", + "Ġc ata", + "Ġca ta", + "Ġcat a", + "Ġ TB", + "ĠT B", + "Ġ Clar", + "ĠC lar", + "ĠCl ar", + "ĠCla r", + "Ġpre decess", + "Ġpred ecess", + "Ġsit uated", + "Ġsitu ated", + "- world", + "-w orld", + ") )/", + ")) /", + "Ġhead lines", + "Ġheadline s", + ". stat", + ".s tat", + ".st at", + "Ġout break", + "s path", + "sp ath", + "spa th", + "_ FLAGS", + "_FLAG S", + "ĠServlet Exception", + "S un", + "Su n", + "F ROM", + "FR OM", + "Ġ Dir", + "ĠD ir", + "ĠDi r", + "ãĥ» ãĥ»ãĥ»", + "ãĥ»ãĥ» ãĥ»", + "_ coord", + "_c oord", + "_co ord", + "ĠOp tim", + "ĠOpt im", + "M onitor", + "Mon itor", + ". bit", + ".b it", + ".bi t", + "X XX", + "XX X", + "Ġto das", + "Ġtod as", + "Ġtoda s", + "f eld", + "fe ld", + "fel d", + "ÑĢ и", + "i mir", + "im ir", + "imi r", + "Ġpolit ically", + "Ġpolitical ly", + "Ġpolitic ally", + "Ġm olecular", + "Ġmolec ular", + "Ġmole cular", + "Ġtr aded", + "Ġtrad ed", + "Ġtrade d", + "Ġtra ded", + "Ġ {{$", + "Ġ{ {$", + "Ġ{{ $", + "ĠSw edish", + "ĠSwe dish", + "Ġ'@ /", + "_ REAL", + "_RE AL", + "Ġ warehouse", + "Ġw arehouse", + "Ġware house", + "t oday", + "to day", + "tod ay", + ", L", + "o rp", + "or p", + "< section", + " false", + ">f alse", + "Ġ spa", + "Ġs pa", + "Ġsp a", + "Ġ Near", + "ĠN ear", + "ĠNe ar", + "ì ķ", + "Ġint rig", + "Ġintr ig", + "_ members", + "_m embers", + "_mem bers", + "_member s", + "w ave", + "wa ve", + "wav e", + "Ġanal ysts", + "Ġanaly sts", + "Ġanalyst s", + "Ġanalys ts", + "_ OS", + "_O S", + "e din", + "ed in", + "edi n", + "Ġ Fri", + "ĠF ri", + "ĠFr i", + "Ġret rieved", + "Ġretrie ved", + "Ġretrieve d", + "Ġretr ieved", + "Reg ular", + "_ obs", + "_o bs", + "_ob s", + "EX PORT", + "EXP ORT", + "' )}}\"", + "') }}\"", + "')}} \"", + "')} }\"", + "\" class", + "\"c lass", + "__ ((", + "__( (", + "b ucket", + "bu cket", + "Ġ stro", + "Ġs tro", + "Ġst ro", + "Ġstr o", + "Ġ Patch", + "ĠP atch", + "ĠPat ch", + "y stick", + "yst ick", + "ys tick", + "ful ness", + "a pos", + "ap os", + "apo s", + "D a", + "ĉ ĉĉĉĉĠĠĠ", + "ĉĉ ĉĉĉĠĠĠ", + "ĉĉĉĉ ĉĠĠĠ", + "ĉĉĉ ĉĉĠĠĠ", + "ĉĉĉĉĉ ĠĠĠ", + "ĉĉĉĉĉĠ ĠĠ", + "ĉĉĉĉĉĠĠ Ġ", + "Ġen rich", + "Ġenr ich", + "un ordered", + "h ole", + "ho le", + "hol e", + "C ong", + "Con g", + "Co ng", + "< Product", + "ĠC urt", + "ĠCur t", + "ĠCu rt", + "( the", + "(t he", + "(th e", + "_ lower", + "_l ower", + "_lo wer", + "_low er", + "Ġavoid ing", + "Ġ buzz", + "Ġb uzz", + "Ġbu zz", + "Ġbuz z", + "Ġv iable", + "Ġvi able", + "Ġvia ble", + "u ba", + "ub a", + "- is", + "-i s", + "a rel", + "ar el", + "are l", + "Ġ acted", + "Ġact ed", + "Ġac ted", + "- details", + "-d etails", + "-de tails", + "-detail s", + "-det ails", + "ภĩ", + "Ġ Theory", + "ĠThe ory", + "ĠTheo ry", + "ĠP un", + "ĠPu n", + "Ġ Anonymous", + "ĠAn onymous", + ".. .\"Ċ", + "... \"Ċ", + "...\" Ċ", + "è res", + "ère s", + "åı ¯", + "Ġ Vision", + "ĠV ision", + "ĠVis ion", + "_ sem", + "_s em", + "_se m", + "a sha", + "as ha", + "ash a", + "Ġcelebr ity", + "Ġ endDate", + "Ġend Date", + "Ġ populate", + "Ġpop ulate", + "Ġpopul ate", + "Ġc uis", + "Ġcu is", + "Ġcui s", + "q uant", + "qu ant", + "qua nt", + "quan t", + "f loor", + "fl oor", + "flo or", + "Ġglobal ly", + "Ġglob ally", + "Ġc ruise", + "Ġcru ise", + "Ġcruis e", + "ĠStan ley", + "Ġb ikes", + "Ġbi kes", + "Ġbike s", + "Ġbik es", + ". getConnection", + ".get Connection", + "Ġpoor ly", + "_ other", + "_o ther", + "_ot her", + "am ping", + "amp ing", + ". \");ĊĊ", + ".\" );ĊĊ", + ".\");Ċ Ċ", + ".\") ;ĊĊ", + ".\"); ĊĊ", + "o di", + "od i", + "_ ADMIN", + "_A DMIN", + "_AD MIN", + ". colors", + ".color s", + ".col ors", + "ĠG aming", + "ĠGa ming", + "ĠGam ing", + "> ';ĊĊ", + ">' ;ĊĊ", + ">';Ċ Ċ", + ">'; ĊĊ", + "STR UCT", + "STRU CT", + "Q R", + "I Ds", + "ID s", + "( arguments", + "(arg uments", + "(argument s", + "_ aux", + "_a ux", + "( Event", + "(E vent", + "_ PRIVATE", + "_PR IVATE", + "_PRIV ATE", + "ĠT rek", + "ĠTr ek", + "ĠTre k", + "Ġ downloads", + "Ġdown loads", + "Ġdownload s", + "m utable", + "mut able", + "mu table", + "_ STRUCT", + "_STR UCT", + "( wx", + "(w x", + "Ġ domains", + "Ġdom ains", + "Ġdomain s", + "Ġdoma ins", + "js px", + "jsp x", + "ĠVi agra", + "ĠVia gra", + "Command s", + "Comm ands", + "J s", + ". cfg", + ".c fg", + ".cf g", + "Content Pane", + "Ġ EditText", + "ĠEdit Text", + "à¥į à¤", + "At tach", + "Att ach", + "Ġ ARM", + "ĠA RM", + "ĠAR M", + "pos itive", + "posit ive", + "Ġ Generated", + "ĠG enerated", + "ĠGener ated", + "ĠGenerate d", + "ĠGene rated", + "Ġse ized", + "Ġseiz ed", + "Ġsei zed", + "Ġseize d", + "= :", + "Ġ electronics", + "Ġelect ronics", + "Ġelectronic s", + "Ġelectron ics", + "Ġ AppComponent", + "ĠApp Component", + "/ ',Ċ", + "/' ,Ċ", + "/', Ċ", + ". equalsIgnoreCase", + ".equals IgnoreCase", + "Do ctrine", + "d isk", + "dis k", + "di sk", + "Ġ Political", + "ĠPol itical", + "ĠPolit ical", + "C HO", + "CH O", + "< F", + "ĉ height", + "ĉh eight", + "Ġ Bug", + "ĠB ug", + "ĠBu g", + ". le", + ".l e", + "i kh", + "ik h", + "Ġ milliseconds", + "Ġm illiseconds", + "Ġmill iseconds", + "Ġmilli seconds", + "Ġconst itu", + "Ġconstit u", + "m ag", + "ma g", + ". nl", + ".n l", + "- range", + "-r ange", + "-ra nge", + "ang gal", + "' ,[", + "', [", + "r opolitan", + "ropol itan", + "Ġ Ãľ", + "Ġà ľ", + "Ġ UC", + "ĠU C", + ". desc", + ".d esc", + ".de sc", + ".des c", + "- LAST", + "-L AST", + "f stream", + "fst ream", + "i bil", + "ib il", + "ibi l", + "Ġf ier", + "Ġfi er", + "Ġfie r", + "V ERY", + "VER Y", + "VE RY", + "Ġ ë³", + "Ġë ³", + "I RT", + "IR T", + "_ UI", + "_U I", + "( abs", + "(a bs", + "(ab s", + "Ġk nees", + "Ġkn ees", + "Ġkne es", + "Ġknee s", + "Ġr ookie", + "Ġro okie", + "Ġ Vac", + "ĠV ac", + "ĠVa c", + "a rena", + "ar ena", + "are na", + "aren a", + "comm end", + "- \\", + "ĠSUB STITUTE", + "S oft", + "So ft", + "Ġpart ir", + "Ġpar tir", + "Ġparti r", + "we alth", + "è¦ ģ", + "( dataset", + "(d ataset", + "(data set", + "(dat aset", + "(datas et", + "Ġ Climate", + "ĠCl imate", + "ĠClim ate", + "ĠCli mate", + "- show", + "-s how", + "-sh ow", + "Ġrel iability", + "Ġreli ability", + "_ chunk", + "_ch unk", + "ä» £", + "_ stock", + "_st ock", + "ĠEX EMPLARY", + "ï ¸ı", + "ï¸ ı", + "Ġ vÃŃ", + "Ġv ÃŃ", + "Ġsm iled", + "Ġsmile d", + "Ġdr ill", + "Ġdri ll", + ". Function", + ".F unction", + ".Func tion", + "Ġ SI", + "ĠS I", + "Ġre gression", + "Ġreg ression", + "Ġregress ion", + "- X", + "Ġ Jar", + "ĠJ ar", + "ĠJa r", + "p ref", + "pr ef", + "pre f", + "ĉ success", + "ĉs uccess", + "ĠH itler", + "ĠHit ler", + "Ġinst inct", + "Ġfem mes", + "Ġfemme s", + "Ġ lover", + "Ġl over", + "Ġlo ver", + "Ġlove r", + "Ġlov er", + "< Ċ", + "Ġmulti plier", + "Ġmultip lier", + "r il", + "ri l", + "Re size", + "Res ize", + "Ġ Authorization", + "ĠAuthor ization", + "ĠK an", + "ĠKa n", + "Dispatch ToProps", + "Ġc rops", + "Ġcr ops", + "Ġcro ps", + "Ġcrop s", + "t okens", + "token s", + "tok ens", + "e cn", + "ec n", + "ent ially", + "ential ly", + "enti ally", + "ĠINTERRU PTION", + "f ake", + "fa ke", + "fak e", + "Un defined", + "Und efined", + "Ġ AK", + "ĠA K", + "Ġ TestCase", + "ĠTest Case", + "Ġ rab", + "Ġr ab", + "Ġra b", + "Ġ torrent", + "Ġt orrent", + "Ġtor rent", + "Ġ Ot", + "ĠO t", + "B ars", + "Bar s", + "Ba rs", + "Ġ lecture", + "Ġl ecture", + "Ġlect ure", + "Ġen jo", + "Ġrespond s", + "Ġresp onds", + "Ġ indexed", + "Ġindex ed", + "Ġinde xed", + "Of Work", + "_ chain", + "_ch ain", + ") )->", + ")) ->", + "))- >", + "Ġ Beauty", + "ĠBe auty", + "ĠBeaut y", + "ĠBea uty", + "ĠBeau ty", + "Ġ` <", + "Ġtouch ing", + "Ġtou ching", + "Ġ |--", + "Ġ| --", + "Ġ|- -", + "ĉ flag", + "ĉf lag", + "normal ize", + "Ġt rapped", + "Ġtr apped", + "Ġtra pped", + "Ġtrap ped", + "Ġestablish ing", + "/ build", + "/b uild", + "A J", + "f y", + "- react", + "-re act", + "a vn", + "av n", + "RI PTION", + "RIPT ION", + "Ġk ut", + "Ġku t", + "Ġ Fashion", + "ĠF ashion", + "Ġ Inform", + "ĠIn form", + "ĠInfo rm", + "ĠInf orm", + "c urities", + "cur ities", + "< byte", + "{Ċ", + "Ġ= >{Ċ", + "Ġ=> {Ċ", + "Ġgar lic", + "Ġ repr", + "Ġre pr", + "Ġrep r", + "Ġre plies", + "Ġrep lies", + "Ġrepl ies", + "( prop", + "(p rop", + "(pro p", + "(pr op", + "Ġspirit s", + "Ġspir its", + "Ġins pire", + "Ġinspir e", + "Ġinsp ire", + "Ġbase ment", + "Ġbas ement", + ". reject", + ".re ject", + "Ġ hints", + "Ġh ints", + "Ġhint s", + "Ġhi nts", + "Ġhin ts", + "Ġpol ling", + "Ġpoll ing", + "ĉ ĠĊ", + "ĉĠ Ċ", + "_ rating", + "_r ating", + "_ra ting", + "_rat ing", + "Ġc ath", + "Ġca th", + "Ġcat h", + "a vier", + "av ier", + "avi er", + "Ġ compressed", + "Ġcom pressed", + "Ġcomp ressed", + "Ġcompr essed", + "Ġcompress ed", + "Ġ VS", + "ĠV S", + "] '", + "Ġjud icial", + "ĠT rend", + "ĠTr end", + "ĠTre nd", + "tr aining", + "tra ining", + "train ing", + "EST AMP", + "ogn ition", + "Ä ģ", + "S ENT", + "SE NT", + "SEN T", + "v entions", + "vent ions", + "vention s", + "Ġconsult ant", + "Ġconsulta nt", + "Ġconsul tant", + "u mph", + "um ph", + "ump h", + "Ġ userService", + "Ġuser Service", + ", NULL", + ",N ULL", + "k h", + "D ear", + "De ar", + "_ BAD", + "_B AD", + "it ations", + "itation s", + "itat ions", + "Ġmet aph", + "Ġmeta ph", + "' é", + "and ise", + "andi se", + "- font", + "-f ont", + ". chart", + ".c hart", + ".ch art", + ".char t", + "Ġ sg", + "Ġs g", + "_ Controller", + "_Control ler", + ". jpeg", + ".j peg", + ".jp eg", + "Ġ ULONG", + "ĠU LONG", + "ĠUL ONG", + "ĉ game", + "ĉg ame", + "( ss", + "(s s", + "ĠM aj", + "ĠMa j", + "ĉ go", + "ĉg o", + "Ġ Sad", + "ĠS ad", + "ĠSa d", + "ĠB erg", + "ĠBe rg", + "ĠBer g", + "Ġ Mine", + "ĠM ine", + "ĠMin e", + "ĠMi ne", + "P ack", + "Pa ck", + "Ġres istant", + "Ġresist ant", + "Ġ ROM", + "ĠR OM", + "ĠRO M", + "Ġ peg", + "Ġp eg", + "Ġpe g", + "ĠSt anford", + "ĠStan ford", + "Ġ Yahoo", + "ĠY ahoo", + "ĠYa hoo", + "ĠYah oo", + "Ġ scaled", + "Ġs caled", + "Ġsc aled", + "Ġscale d", + "Ġsca led", + "Ġscal ed", + "Ġ lan", + "Ġl an", + "Ġla n", + "= []", + "=[ ]", + "\" /> < /", + "Ġ plots", + "Ġp lots", + "Ġpl ots", + "Ġplot s", + "Ġplo ts", + ". *Ċ", + ".* Ċ", + "Ġtr aveled", + "Ġtravel ed", + "Ġtra veled", + "Ġtrav eled", + "ĠO scar", + "ĠOs car", + "ĠOsc ar", + "V L", + "Ġl inking", + "Ġlink ing", + "Ġlin king", + "Ġt ires", + "Ġti res", + "Ġtire s", + "Ġtir es", + "Ġ' *'", + "Ġ'* '", + "ĠBuffer ed", + "ĠBuff ered", + "e ri", + "er i", + "Ġ ****", + "Ġ* ***", + "Ġ** **", + "Ġ*** *", + "Ġover look", + "Ġoverl ook", + ". Non", + ".N on", + ".No n", + "Ġr és", + "Ġré s", + "Ġe gy", + "Ġeg y", + "å° ı", + "Ġatt acker", + "Ġattack er", + "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", + ". sync", + ".s ync", + ".syn c", + ".sy nc", + "AS CADE", + "ASC ADE", + "G round", + "Gr ound", + "Gro und", + "Ġ decay", + "Ġdec ay", + "Ġ Ton", + "ĠT on", + "ĠTo n", + "Ġjew elry", + "Ġjewel ry", + "Ġb ypass", + "Ġby pass", + "Ġm embr", + "Ġmem br", + "Ġmemb r", + "R NA", + "RN A", + "< System", + " ččĊ", + "Ġs ud", + "Ġsu d", + "ĉ background", + "ĉback ground", + "Ġsch olars", + "Ġscholar s", + "-m uted", + "a rá", + "ar á", + "Ġ =====", + "Ġ= ====", + "Ġ== ===", + "Ġ=== ==", + "Ġ==== =", + "Ġ ____", + "Ġ_ ___", + "Ġ__ __", + "Ġ___ _", + "C reat", + "Cre at", + "Cr eat", + "e never", + "en ever", + "ene ver", + "/ wp", + "/w p", + "Ġ VPN", + "ĠV PN", + "ĠVP N", + "Error Code", + ") ],Ċ", + ")] ,Ċ", + ")], Ċ", + "( builder", + "(b uilder", + "(build er", + "Ġ Enemy", + "ĠEn emy", + "S ensor", + "u sa", + "us a", + "Ġtr iggers", + "Ġtrigger s", + "Ġtrig gers", + "Ġplay offs", + "Ġplayoff s", + "_ REQ", + "_RE Q", + "_R EQ", + "Ġ (~", + "Ġ( ~", + "ĠB arry", + "ĠBar ry", + "ĠBarr y", + "Ġperman ently", + "Ġpermanent ly", + "Ġ RUN", + "ĠR UN", + "ĠRU N", + "Ġb ure", + "Ġbu re", + "Ġbur e", + ".Fatal f", + ".Fat alf", + "Ġch ick", + "Ġchi ck", + "Ġchic k", + "ĉ panic", + "ĉp anic", + "p si", + "ps i", + "o ka", + "ok a", + "éĢ ī", + "> [", + "Ġunder stands", + "Ġunderstand s", + "Ġunderst ands", + "Ġ Junior", + "ĠJun ior", + "ĠJuni or", + "Ġ INFO", + "ĠIN FO", + "ĠINF O", + "= mysqli", + "=m ysqli", + "=mysql i", + "us tain", + "ust ain", + "usta in", + "- source", + "-s ource", + "s erv", + "se rv", + "ser v", + "Ġ CREATE", + "ĠC REATE", + "ĠCRE ATE", + ". au", + ".a u", + "Ġs ells", + "Ġsell s", + "Ġsel ls", + "Ġ ĠĊĠĠĊ", + "ĠĠ ĊĠĠĊ", + "ĠĠĊ ĠĠĊ", + "E urope", + "Euro pe", + "z w", + "p reh", + "pr eh", + "pre h", + "ĠN SA", + "ĠNS A", + "Ġ xy", + "Ġx y", + "ภ´", + "Ġ Beyond", + "ĠB eyond", + "ĠBey ond", + "In stead", + "Inst ead", + "Non Query", + "Ġa rise", + "Ġar ise", + "Ġavoid ed", + ". emplace", + ".em place", + ".emp lace", + "_ models", + "_model s", + "_mode ls", + "_mod els", + "} ),Ċ", + "}) ,Ċ", + "}), Ċ", + "Ġ hid", + "Ġh id", + "Ġhi d", + "Ġ &_", + "Ġ& _", + ". points", + ".p oints", + ".point s", + ".po ints", + ".poi nts", + ". getWidth", + ".get Width", + ". Exec", + ".Ex ec", + ".E xec", + "Ġ ////", + "Ġ// //", + "Ġ/ ///", + "Ġ/// /", + "Ġ Sessions", + "ĠS essions", + "ĠSession s", + ". ..\\", + ".. .\\", + "... \\", + "ĠCol omb", + "ĠColo mb", + "Ġacceler ation", + "Ġaccel eration", + "re store", + "rest ore", + "Ġ ile", + "Ġi le", + "Ġil e", + "o bic", + "ob ic", + "obi c", + "< Node", + " }Ċ", + ">} Ċ", + "pl aint", + "plain t", + "pla int", + "get Text", + "Ġindividual ly", + "Ġindivid ually", + "Ġ checkbox", + "Ġcheck box", + "U Y", + "ĠL amb", + "ĠLa mb", + "ĠLam b", + "Ġdys function", + "ĠL ar", + "ĠLa r", + "à °", + "Ġ Creating", + "ĠC reating", + "ĠCr eating", + "ĠCre ating", + "ĠCreat ing", + "' );ĊĊĊ", + "') ;ĊĊĊ", + "');Ċ ĊĊ", + "');ĊĊ Ċ", + "'); ĊĊĊ", + "\" They", + "\"The y", + "\"T hey", + "l ocations", + "loc ations", + "location s", + "_ CORE", + "_C ORE", + "_CO RE", + "_COR E", + "Inter action", + "umbn ails", + "umbnail s", + "Ġ Partner", + "ĠP artner", + "ĠPart ner", + "b rit", + "br it", + "Ġl esser", + "Ġless er", + "Ġles ser", + "Ġ Slot", + "ĠS lot", + "ĠSl ot", + "ĠSlo t", + "set Attribute", + "Ġ Wave", + "ĠW ave", + "ĠWa ve", + ". po", + ".p o", + "/ store", + "/st ore", + "Ġb rowsing", + "Ġbrows ing", + "Ġbrow sing", + "_ pd", + "_p d", + "s ume", + "sum e", + "su me", + "s ed", + "se d", + "C urve", + "Cur ve", + "Cu rve", + "Ġpl asma", + "Ġsusp icious", + "ìĿ ¸", + "Ġ Bah", + "ĠB ah", + "ĠBa h", + "Ġ Explicit", + "ĠExp licit", + "ĠExpl icit", + "_ CC", + "_C C", + ".Client Size", + "\\ View", + "\\V iew", + "Ġsub stit", + "Ġsubs tit", + "Ġsubst it", + "l oon", + "lo on", + "loo n", + "Ġ GAME", + "ĠG AME", + "ĠGA ME", + "ĠGAM E", + "ĠB rid", + "ĠBr id", + "ĠBri d", + "Ľ 建", + "_ User", + "_U ser", + "Ġs quares", + "Ġsqu ares", + "Ġsquare s", + "f one", + "fo ne", + "fon e", + "Ġsa cred", + "Ġsac red", + "Ġsacr ed", + "ug hs", + "ugh s", + "] interface", + "Ġ Throw", + "ĠTh row", + "ĠThr ow", + "ĠK irk", + "ĠKir k", + "ĠKi rk", + "Ġem pire", + "Ġemp ire", + "Ġempir e", + "Ġass essed", + "Ġassess ed", + "Ġasses sed", + "T ax", + "Ta x", + "ĠHe aven", + "- buffer", + "-b uffer", + "_ STATIC", + "_ST ATIC", + "_STAT IC", + "é né", + "én é", + "-b ordered", + "-border ed", + "Ġp unct", + "Ġpun ct", + "( mode", + "(m ode", + "(mod e", + "Ġke ine", + "Ġkein e", + "S ent", + "Se nt", + "Sen t", + "Ġ Calcul", + "ĠCal cul", + "ĠCalc ul", + "ĠE ve", + "ĠEv e", + "Ġsty lish", + "Ġstyl ish", + "Ġo ils", + "Ġoil s", + "Ġoi ls", + ". TestCase", + ".Test Case", + "Ġtrad emark", + "Ġtrade mark", + "Ġliter ary", + "Ġlite rary", + "Ġconcentr ations", + "Ġconcentration s", + "Ġ Relations", + "ĠRe lations", + "ĠRel ations", + "ĠRelation s", + "( Class", + "(C lass", + "(Cl ass", + "Ġ stdin", + "Ġst din", + "Ġstd in", + "Ġv æ", + "back up", + "bac kup", + ". VERSION", + ".V ERSION", + ".AutoScale Dimensions", + "st arter", + "start er", + "star ter", + "Transaction al", + "- panel", + "-p anel", + "-pane l", + "St udio", + "k c", + "ĠCh amber", + "ĠCham ber", + "ĠS piel", + "ĠSp iel", + "ĠSpi el", + "Ġ rho", + "Ġr ho", + "Ġrh o", + "ا ÙĦ", + "ا٠Ħ", + "! '", + ". Attributes", + ".At tributes", + ".Attribute s", + "Ġmurder ed", + "apeut ic", + "Ġint imate", + "Ġintim ate", + "Ġ textField", + "Ġt extField", + "Ġtext Field", + "ĠBuff alo", + "d ummy", + "dum my", + "\" %", + "ĠLib erty", + "ĠLibert y", + "ĠLiber ty", + "o bar", + "ob ar", + "oba r", + "Ġ Tank", + "ĠT ank", + "ĠTa nk", + "ĠTan k", + "Ġ Popular", + "ĠPop ular", + "er visor", + "erv isor", + "ĠIn iti", + "ĠInit i", + "ĠIni ti", + "ĠM all", + "ĠMal l", + "ĠMa ll", + "Ġ Prior", + "ĠP rior", + "ĠPr ior", + "ĠPri or", + "C AP", + "CA P", + "ĠC lay", + "ĠCl ay", + "ĠCla y", + "Ġ Certificate", + "ĠC ertificate", + "ĠCert ificate", + "ĠCertif icate", + ". Lock", + ".L ock", + ".Lo ck", + "- strip", + "-s trip", + "-st rip", + "-str ip", + "-dr iven", + "-drive n", + "/ all", + "/a ll", + "/al l", + "ĠMessageBox Buttons", + "ĠMessageBoxButton s", + "_ SECRET", + "_SE CRET", + "_SEC RET", + "_ pb", + "_p b", + "Ġ rats", + "Ġr ats", + "Ġrat s", + "Ġra ts", + "ा à¤", + "Ġ nt", + "Ġn t", + ". Router", + ".R outer", + ".Route r", + "_ topic", + "_t opic", + "_to pic", + "_top ic", + "Ġt ennis", + "Ġten nis", + "Ġ PUBLIC", + "ĠP UBLIC", + "ĠPUB LIC", + "ĠActiv atedRoute", + "Ġ ',Ċ", + "Ġ' ,Ċ", + "Ġ', Ċ", + "Ġcost ume", + "Ġj okes", + "Ġjo kes", + "Ġjoke s", + ". Handle", + ".H andle", + ".Hand le", + "ĉ byte", + "ĉb yte", + "Ġfl avors", + "Ġflavor s", + "Ġflav ors", + "( cc", + "(c c", + "Ġperson as", + "Ġpers onas", + "Ġpersona s", + "Ġperso nas", + "ĉ image", + "ĉi mage", + "ĉim age", + "ĠN azi", + "ĠNa zi", + "ĠNaz i", + "Ġ grammar", + "Ġgram mar", + "Ġgramm ar", + "Ġú lt", + "Ġval ve", + "Ġ vic", + "Ġv ic", + "Ġvi c", + "Ġ Rachel", + "ĠR achel", + "ĠRac hel", + "ĠRach el", + "_ invalid", + "_in valid", + "P refs", + "Pr efs", + "Pre fs", + "Pref s", + "std int", + "stdin t", + "( route", + "(r oute", + "(ro ute", + "Ġ htmlspecialchars", + "Ġhtml specialchars", + "Ġpe oples", + "Ġpeople s", + "p line", + "pl ine", + "Ġ nv", + "Ġn v", + "Ġ Quant", + "ĠQ uant", + "ĠQu ant", + "o ppers", + "op pers", + "opp ers", + "opper s", + "Ġ currentUser", + "Ġcurrent User", + "ĠC atal", + "ĠCa tal", + "ĠCat al", + "ĠCata l", + "Ġre conc", + "Ġrecon c", + "Ġreco nc", + "Ġcon junction", + "Ġconj unction", + "l x", + "am burg", + "amb urg", + "Ġinflu ential", + "d anger", + "da nger", + "dan ger", + "in ders", + "ind ers", + "inder s", + "inde rs", + "Ġ %@\",", + "Ġ% @\",", + "Ġ%@ \",", + ". configuration", + ".config uration", + "o some", + "os ome", + "oso me", + ". identity", + ".id entity", + ".ident ity", + ".ide ntity", + "Ġ picker", + "Ġp icker", + "Ġpick er", + "Ġpi cker", + "Ġpic ker", + "n ost", + "no st", + "nos t", + "ĠDI Y", + "Aug ust", + "a blo", + "ab lo", + "abl o", + "Le af", + "ĠR eco", + "ĠRe co", + "ĠRec o", + "c ko", + "ck o", + "D OC", + "DO C", + "ĠH erm", + "ĠHe rm", + "ĠHer m", + ": any", + ":a ny", + "Ġ Interview", + "ĠInt erview", + "ĠInter view", + "Ġ Tex", + "ĠT ex", + "ĠTe x", + "x fe", + "xf e", + "( work", + "(w ork", + "Ġle ap", + "He ading", + "Head ing", + "Ġ quarters", + "Ġqu arters", + "Ġquarter s", + "Ġquar ters", + "Ġquart ers", + "\\ Bundle", + "r eb", + "re b", + "Per haps", + "ĠG mbH", + "B irth", + "Bir th", + "ĉ sum", + "ĉs um", + "ĠWat son", + ". nil", + ".n il", + "ç ¡", + "{ }ĊĊ", + "{} ĊĊ", + "{}Ċ Ċ", + "ic aid", + "ica id", + "G etter", + "Get ter", + "\" name", + "Ġ \"čĊ", + "Ġ\" čĊ", + "_ none", + "_n one", + "_no ne", + "_non e", + "z m", + "ac ute", + "u esto", + "ue sto", + "ues to", + "uest o", + "Ġs ous", + "Ġso us", + "Ġsou s", + "Ġre build", + "Ġreb uild", + "Ġnews papers", + "Ġnewsp apers", + "Ġnewspaper s", + "Ġ Haz", + "ĠH az", + "ĠHa z", + "Ġ kits", + "Ġk its", + "Ġkit s", + "Ġki ts", + "i fo", + "if o", + "Bl ur", + "Ġsu ited", + "Ġsuit ed", + "Ġsuite d", + "Ġsui ted", + "- In", + "-I n", + "à ¯", + "Ġ Keith", + "ĠKe ith", + "ĠNor way", + "IN IT", + "INI T", + "ire ccion", + "i eties", + "ie ties", + "iet ies", + "_ usage", + "_u sage", + "_us age", + "Ġ Doug", + "ĠD oug", + "ĠDo ug", + "ĠDou g", + "r ise", + "ri se", + "ris e", + "Ġtr illion", + "im ited", + "imit ed", + "imi ted", + "Ġ REL", + "ĠR EL", + "ĠRE L", + "a lic", + "al ic", + "ali c", + "Ġcritic ized", + "Ġcriticize d", + "the orem", + "Ġc ease", + "Ġce ase", + "Ġside w", + "Ġsid ew", + "ĠT erry", + "ĠTer ry", + "ĠTerr y", + "Ġsubs idi", + "Ġsubsid i", + "Ġfirm ly", + "Ġ aws", + "Ġa ws", + "Ġaw s", + "Ġh ott", + "Ġhot t", + "Ġho tt", + "Ġd ressing", + "Ġdress ing", + "b adge", + "ba dge", + "bad ge", + "Ġ Applications", + "ĠApp lications", + "ĠApplication s", + "ĠAppl ications", + "è¿ ĶåĽŀ", + "è¿Ķ åĽŀ", + "Ġlaugh ed", + "Ġh obby", + "Ġhob by", + "Ġmus icians", + "Ġmusic ians", + "Ġmusician s", + "Ġ *.", + "Ġ* .", + ". placeholder", + ".place holder", + "Ġc ounters", + "Ġcount ers", + "Ġcoun ters", + "Ġcounter s", + "ĠCap itol", + "S DK", + "SD K", + "Ġh elmet", + "Ġhel met", + "Ġhelm et", + "and box", + "q uit", + "qu it", + "qui t", + "Ġcriminal s", + "Ġcrim inals", + "Ġteen ager", + "Ġteenage r", + "( update", + "(up date", + "G l", + ". selection", + ".s election", + ".se lection", + ".select ion", + ".sel ection", + "Ġdis charge", + "Ġpres enting", + "Ġpresent ing", + "ufact urer", + "_ UNKNOWN", + "_UN KNOWN", + "Ġst ressed", + "Ġstr essed", + "Ġstress ed", + "å Ļ¨", + "åĻ ¨", + "Pro to", + "Pr oto", + "Prot o", + "_ correct", + "_c orrect", + "_cor rect", + "_corr ect", + "h aus", + "ha us", + "Ġre nov", + "Ġren ov", + "Ġfire arms", + "Ġfirearm s", + "Ġtechn ically", + "Ġtechnical ly", + "- browser", + "-b rowser", + "Ġc andy", + "Ġcan dy", + "Ġcand y", + "St roke", + "Str oke", + "Ġ executor", + "Ġexec utor", + "Ġexecut or", + "Ġocc urrence", + "Ġoccur rence", + "Ġ IPv", + "ĠIP v", + "_ INTERFACE", + "_INTER FACE", + "Ġ Retrieve", + "ĠRe trieve", + "ĠRet rieve", + "ĠRetrie ve", + ". bad", + ".b ad", + ".ba d", + "Ex change", + "Nav bar", + "Ġ Kid", + "ĠK id", + "ĠKi d", + "(get ApplicationContext", + "_ STOP", + "_S TOP", + "_ST OP", + "Ġ Boss", + "ĠB oss", + "ĠBo ss", + "ĠBos s", + "List eners", + "Listener s", + "Listen ers", + "Ġsh ooter", + "Ġshoot er", + "Ġsho oter", + "ĠA lb", + "ĠAl b", + "ä ch", + "Ġ pix", + "Ġp ix", + "Ġpi x", + ". keyCode", + ".key Code", + "al one", + "alo ne", + "alon e", + "Ġabs urd", + "Ġ Cum", + "ĠC um", + "ĠCu m", + "ĠNewton soft", + "i kt", + "ik t", + "Ġlaugh ing", + "Ġcapital ism", + "ree Node", + "T x", + "_ QUERY", + "_QU ERY", + ". Sleep", + ".S leep", + "( login", + "(log in", + "(lo gin", + "Web Element", + "Ġcelebr ating", + "Ġ deprecated", + "Ġde precated", + "Ġdep recated", + "Ġm aar", + "Ġma ar", + "Ġart istic", + "Ġartist ic", + "_ASS OC", + "_AS SOC", + "ĠBorder Radius", + "ĉ wp", + "ĉw p", + "Ġsurv ivors", + "Ġsurviv ors", + "Ġsurvivor s", + "In ner", + "- red", + "-r ed", + "-re d", + "Ġprosec ution", + "_ pp", + "_p p", + "( \"$", + "\"=> $", + "Ġ comma", + "Ġcom ma", + "Ġco mma", + "Ġcomm a", + "un checked", + "g raphics", + "graph ics", + "graphic s", + "r ors", + "ro rs", + "ror s", + "G ROUND", + "GR OUND", + "( public", + "(p ublic", + "(pub lic", + "Ġcustom ized", + "Ġcustomize d", + "ĠArk ansas", + "Ġ Rew", + "ĠR ew", + "ĠRe w", + "Ġ expiration", + "Ġex piration", + "Ġexp iration", + "× ķ", + "ĠC ul", + "ĠCu l", + "Ġn ons", + "Ġno ns", + "Ġnon s", + ". Filter", + ".F ilter", + "Ġsen ator", + "_ definition", + "_def inition", + "ash ington", + "ashing ton", + "y mph", + "ym ph", + "/ J", + "Ġ fuse", + "Ġf use", + "Ġfu se", + "Ġfus e", + "ra mid", + "ram id", + "Ġ Supplier", + "ĠS upplier", + "ĠSup plier", + "Ġ autocomplete", + "Ġaut ocomplete", + "Ġauto complete", + "Ġ }),", + "Ġ} ),", + "Ġ}) ,", + ". \"ĊĊĊ", + ".\" ĊĊĊ", + ".\"ĊĊ Ċ", + ".\"Ċ ĊĊ", + "_ functions", + "_function s", + "_fun ctions", + "ĉ to", + "ĉt o", + ". eval", + ".e val", + ".ev al", + "ĠT Object", + "ĠTO bject", + "Re ferences", + "Reference s", + "Refer ences", + "Ġh eated", + "Ġhe ated", + "Ġheat ed", + "H AL", + "HA L", + "Ġ ))}Ċ", + "Ġ) )}Ċ", + "Ġ)) }Ċ", + "} $", + "ĠB arr", + "ĠBar r", + "ĠBa rr", + "_ UNIT", + "_UN IT", + "+ $", + "Ġ getValue", + "Ġget Value", + "i ped", + "ip ed", + "ipe d", + "ch ied", + "chie d", + "chi ed", + "( vm", + "(v m", + "c ue", + "cu e", + "_ integer", + "_int eger", + "_ course", + "_c ourse", + "_co urse", + "th ird", + "Ġre vised", + "Ġrev ised", + "Ġrevis ed", + "Ġrevise d", + "* */Ċ", + "** /Ċ", + "_ DIRECT", + "_D IRECT", + "_DIR ECT", + "_DI RECT", + "Out Of", + "( \"(", + "(\" (", + "Ġ Feel", + "ĠF eel", + "ĠFe el", + "ĠFee l", + "Ġre ass", + "Ġ subtitle", + "Ġsub title", + "Ġsubt itle", + "p eri", + "pe ri", + "per i", + "n f", + "Ġenjoy s", + "Ġenjo ys", + "Ġtreat s", + "Ġtre ats", + ") this", + ")t his", + "- tabs", + "-t abs", + "-tab s", + "an cers", + "ance rs", + "anc ers", + "ancer s", + "Ġ continent", + "Ġcont inent", + "Ġcontin ent", + "Ġcar dio", + "Ġcard io", + "Ġcardi o", + "S er", + "Se r", + ". question", + ".q uestion", + ".qu estion", + ".quest ion", + "Ġph rases", + "Ġphrase s", + "Valid ators", + "Validator s", + "Ġpop ul", + "Ġ lÃŃ", + "Ġl ÃŃ", + "s ong", + "so ng", + "son g", + "_ INTERNAL", + "_IN TERNAL", + "_INTER NAL", + "Ġadv iser", + "Ġadvis er", + "Ġadvise r", + "Ġp uzz", + "Ġpu zz", + "Ġamb itious", + "Ġambit ious", + "ĠT ob", + "ĠTo b", + "Ġ DP", + "ĠD P", + "Ġpres idency", + "Ġsur render", + "Ġsurre nder", + "Ġw atches", + "Ġwatch es", + "Ġwat ches", + "_ binary", + "_b inary", + "_bin ary", + "Ġ Soon", + "ĠS oon", + "ĠSo on", + "Ġcan ada", + "(\" \")Ċ", + "(\"\" )Ċ", + "(\"\") Ċ", + "] ='", + "]= '", + "Ġ Brandon", + "ĠBr andon", + "ĠBrand on", + "ĠBra ndon", + "ĠBran don", + "e psilon", + "eps ilon", + "r w", + ". addChild", + ".add Child", + ". Copy", + ".C opy", + ".Co py", + "Pr incipal", + "Ph otos", + "Photo s", + "Phot os", + "Ġmargin al", + "Ġmarg inal", + "Ġb asics", + "Ġbas ics", + "Ġbasic s", + "e ing", + "ei ng", + "ein g", + "M ust", + "Mu st", + "Mus t", + "_ String", + "_S tring", + "_Str ing", + "_St ring", + "Ġ ole", + "Ġo le", + "Ġol e", + "M agento", + "Mag ento", + ". customer", + ".c ustomer", + ".custom er", + "( prev", + "(p rev", + "(pr ev", + "(pre v", + "ภ¥", + "Ġlo yalty", + "Ġloyal ty", + "C og", + "Co g", + "Ġ protocols", + "Ġprot ocols", + "Ġprotocol s", + "Ġproto cols", + "Ġ Companies", + "ĠCom panies", + "Ġthe oretical", + "Ġtheoret ical", + "Ġtheor etical", + "Ġaccess ing", + "Ġacces sing", + "Ġ Zen", + "ĠZ en", + "ĠZe n", + ". ones", + ".on es", + ".o nes", + ".one s", + "att ice", + "atti ce", + "_ world", + "_w orld", + "z es", + "ze s", + "Ġtatto o", + "Ġtat too", + "Ġm enos", + "Ġme nos", + "Ġmen os", + "Ġmeno s", + "Ġ intersect", + "Ġinter sect", + "Ġinters ect", + "\" ];ĊĊ", + "\"] ;ĊĊ", + "\"];Ċ Ċ", + "\"]; ĊĊ", + "be lie", + "bel ie", + "Ġ inactive", + "Ġin active", + ".read line", + "-label led", + ". done", + ".d one", + ".do ne", + "lic kr", + "lick r", + "Ġ WORK", + "ĠW ORK", + "Ġder ivative", + "Ġderiv ative", + "Ġd atabases", + "Ġdata bases", + "Ġdatabase s", + "Ġdatab ases", + "âĤ Ĥ", + "Ġ sx", + "Ġs x", + ". isArray", + ".is Array", + "Ġ ys", + "Ġy s", + "Ġp ada", + "Ġpa da", + "Ġpad a", + "Ġ Bullet", + "ĠB ullet", + "ĠBul let", + "ĠBull et", + "(` /", + "is Active", + "Ġ CGSize", + "ĠCG Size", + "( equalTo", + "(equal To", + "ĠColum bus", + "Ġm arry", + "Ġmar ry", + "D EV", + "DE V", + "_ limits", + "_l imits", + "_limit s", + "_li mits", + "_lim its", + "r ones", + "ro nes", + "ron es", + "rone s", + "I AS", + "IA S", + "Ġ tau", + "Ġt au", + "Ġta u", + "m ino", + "min o", + "mi no", + "_ Write", + "_W rite", + "ĠW ine", + "ĠWin e", + "ĠWi ne", + "Ġ [['", + "Ġ[ ['", + "Ġ[[ '", + "Ġ Pull", + "ĠP ull", + "ĠPu ll", + "ĠPul l", + "ri ters", + "rit ers", + "rite rs", + "riter s", + "r ients", + "ri ents", + "rie nts", + "rient s", + "rien ts", + "Ġsh ifting", + "Ġshift ing", + "u pp", + "up p", + "_ TIMER", + "_T IMER", + "_TIME R", + "_TIM ER", + "_TI MER", + "Ġ Conditions", + "ĠCondition s", + "ĠCond itions", + "Ạ¥", + "Ġ Orders", + "ĠOr ders", + "ĠOrder s", + "ĠOrd ers", + "Ġ Strength", + "ĠSt rength", + "ĠStr ength", + "ĠStre ngth", + "æī Ģ", + "Ġval idity", + "Ġvalid ity", + "Ġf ot", + "Ġfo t", + "e tur", + "et ur", + "etu r", + "Ġ bolt", + "Ġb olt", + "Ġbo lt", + "Ġbol t", + "åĨ ħ", + "Ġ Along", + "ĠA long", + "ĠAl ong", + "ĠAlo ng", + "o shi", + "os hi", + "osh i", + "Ġassum ptions", + "Ġassumption s", + "Ġmag azines", + "Ġmagazine s", + "_ SPI", + "_S PI", + "_SP I", + "Ġp unt", + "Ġpun t", + "Ġpu nt", + "_ PRODUCT", + "_PRO DUCT", + "_PROD UCT", + "Ġ relay", + "Ġre lay", + "Ġr elay", + "Ġrel ay", + "Ġ Javascript", + "ĠJ avascript", + "ĠJava script", + ". te", + ".t e", + "- es", + "-e s", + "Ġ widgets", + "Ġwidget s", + "Ġwid gets", + "( fs", + "(f s", + "< Item", + " \";", + ">\" ;", + "at ching", + "atch ing", + "Ġ Knowledge", + "ĠK nowledge", + "ĠKnow ledge", + "ĉ The", + "ĉT he", + "; margin", + ";m argin", + "less ness", + "o pard", + "op ard", + "opa rd", + "u matic", + "um atic", + "uma tic", + "umat ic", + "( )));čĊ", + "() ));čĊ", + "()) );čĊ", + "())) ;čĊ", + "())); čĊ", + "Ġf als", + "Ġfa ls", + "Ġfal s", + "( cache", + "(c ache", + "(ca che", + "Type Id", + "éĢ ļ", + "_ choice", + "_ch oice", + "ĠG oth", + "ĠGo th", + "ĠGot h", + "Ġ Sites", + "ĠS ites", + "ĠSi tes", + "ĠSite s", + "ĠSit es", + "M G", + "_ border", + "_b order", + "Ind ices", + "Com parer", + "Comp arer", + "Compar er", + "Compare r", + "ĠRe distribution", + "ĠRed istribution", + "ĠRedis tribution", + "Ġclose t", + "Ġclos et", + "Ġclo set", + "Ġvers atile", + "Ġversa tile", + "In puts", + "Input s", + "**** ****************", + "******** ************", + "**************** ****", + "****** **************", + "************ ********", + "************** ******", + "Ġob esity", + "qu iz", + "qui z", + "g ra", + "gr a", + "( global", + "(g lobal", + "åĬ ¡", + "Ġ collector", + "Ġcol lector", + "Ġcoll ector", + "Ġcollect or", + "Ġcolle ctor", + "Ġ kor", + "Ġk or", + "Ġko r", + "o vable", + "ov able", + "ova ble", + "A DC", + "AD C", + "Ġ EventHandler", + "ĠEvent Handler", + ". nc", + ".n c", + "Ġplay back", + "ient os", + "ien tos", + "iento s", + "_ perm", + "_p erm", + "_per m", + "_pe rm", + "_ WARNING", + "_W ARNING", + "_WARN ING", + "ĠOlymp ics", + "ĠOlympic s", + ". norm", + ".n orm", + ".no rm", + "Ġ Broadcast", + "ĠB roadcast", + "ĠBroad cast", + "_ small", + "_s mall", + "_sm all", + "d rive", + "dr ive", + ". iloc", + ".i loc", + ".il oc", + "Ġ typed", + "Ġt yped", + "Ġtype d", + "Ġtyp ed", + "Ġty ped", + "M EM", + "ME M", + "_ cons", + "_c ons", + "_con s", + "_co ns", + "D METHOD", + "DM ETHOD", + "Ġ lun", + "Ġl un", + "Ġlu n", + ". distance", + ".d istance", + ".di stance", + ".dist ance", + "( par", + "(p ar", + "(pa r", + "p oon", + "po on", + "Ġ bast", + "Ġb ast", + "Ġbas t", + "Ġba st", + "act ivities", + "activ ities", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + ": čĊčĊ", + ":čĊ čĊ", + "S ER", + "SE R", + ") &&", + ")& &", + "_ lst", + "_l st", + "_ls t", + "ĠPol ish", + "ĠPo lish", + "Ġkn ocked", + "Ġknock ed", + "Ġfrustr ation", + "au kee", + "Ġph osph", + "iqu id", + "iq uid", + "_ coeff", + "_c oeff", + "_co eff", + "_coef f", + "æŃ ¤", + "L atest", + "La test", + "Lat est", + "Late st", + "ĠD ust", + "ĠDu st", + "T ipo", + "Tip o", + "Ti po", + "Ġmain tains", + "Ġmaint ains", + "Ġmaintain s", + "Ġ marsh", + "Ġmar sh", + "Ġmars h", + "inc inn", + "inci nn", + "l bl", + "lb l", + "C are", + "Car e", + "Ca re", + "Ġneighborhood s", + "_ gpio", + "_g pio", + "_gp io", + "ĠAr senal", + "ĠArs enal", + "D em", + "De m", + "ĠW he", + "ĠWh e", + "_ hook", + "_h ook", + "Ġl dc", + "Ġld c", + "ĠHar per", + "ĠBer keley", + "Ġgrad uated", + "Ġgraduate d", + "Ġgradu ated", + "Per cent", + "Ġarr iving", + "Ġarriv ing", + "Ġ Adventure", + "ĠAd venture", + "ĠAdvent ure", + "( scope", + "(s cope", + "(sc ope", + "( '*", + "(' *", + "qu arter", + "ĠM arie", + "ĠMar ie", + "ĠMa rie", + "ĠMari e", + "Spe aking", + "Speak ing", + "_ codegen", + "_code gen", + "_cod egen", + "Ġim mun", + "Ġimm un", + "c aster", + "ca ster", + "cast er", + "cas ter", + "ãĤ Į", + "åķ Ĩ", + "Ġ Dimensions", + "ĠDim ensions", + "ĠDimension s", + ". record", + ".re cord", + ".rec ord", + "Ġ texto", + "Ġtext o", + "Ġtex to", + "Ġ Michelle", + "ĠMich elle", + "ĠMichel le", + "ĠMiche lle", + "P ending", + "Pen ding", + "( by", + "(b y", + "_ PAR", + "_P AR", + "_PA R", + "u cht", + "uch t", + "uc ht", + "b ee", + "be e", + ". Thread", + ".T hread", + ".Th read", + "am pire", + "amp ire", + "k now", + "kn ow", + "Ġ Clinical", + "ĠClin ical", + "ĠClinic al", + "Ġmargin Bottom", + "Ġd istinguish", + "Ġdistingu ish", + ". Full", + ".F ull", + ". undefined", + ".un defined", + "ĠSequ elize", + "#### ########################################################################", + "################ ############################################################", + "################################################################ ############", + "############ ################################################################", + "################################################ ############################", + "######################################################################## ####", + "############################ ################################################", + "############################################################ ################", + "Ġ educated", + "Ġeduc ated", + "Ġeducate d", + "_ OVER", + "_O VER", + "åº ı", + "Ġ ÂłĠÂł", + "ĠÂł ĠÂł", + "ĠÂłĠ Âł", + "_ each", + "_e ach", + "Ġ urge", + "Ġur ge", + "Ġurg e", + "de part", + "dep art", + "Ġdon ors", + "Ġdonor s", + "Ġ Au", + "ĠA u", + "Ġb illions", + "Ġbill ions", + "Ġbillion s", + "Ġbelong ing", + "_ age", + "_a ge", + "_ag e", + "_ Int", + "_I nt", + "_In t", + "Ġsub stances", + "Ġsubstance s", + "Ġsubst ances", + "m achine", + "ma chine", + "mach ine", + "! !!ĊĊ", + "!! !ĊĊ", + "!!! ĊĊ", + "!!!Ċ Ċ", + "Ġjson ify", + "ib bean", + "Ġ Cad", + "ĠC ad", + "ĠCa d", + "Ġ endTime", + "Ġend Time", + "Ġc ycling", + "Ġcy cling", + "Ġcycl ing", + "Ġcyc ling", + "Ġ UITextField", + "ĠUI TextField", + "ĠUIT extField", + "Ġle verage", + "Ġlever age", + "Ġleve rage", + "Ġvan illa", + "e at", + "ea t", + "L aunch", + "La unch", + "( pt", + "(p t", + "st ates", + "state s", + "stat es", + "sta tes", + "Ġ Controls", + "ĠControl s", + "ĠContr ols", + "Ġ Respons", + "ĠRes pons", + "ĠResp ons", + "Ġ Jake", + "ĠJ ake", + "ĠJa ke", + "ĠJak e", + "Ġa sleep", + "Ġas leep", + "fort unate", + ".next Line", + "Size Mode", + "ì Ŀ¼", + "ìĿ ¼", + "Testing Module", + "G erman", + "Ger man", + "ĠInvest ig", + ". reverse", + ".re verse", + ".rev erse", + "Ġ BACK", + "ĠB ACK", + "ĠBA CK", + "( DateTime", + "(Date Time", + "Ġnon profit", + "Ġ Expect", + "ĠEx pect", + "ĠExp ect", + "Ġt anto", + "Ġtan to", + "Ġtant o", + "' ]),", + "'] ),", + "']) ,", + "ĉ the", + "ĉt he", + "ĉth e", + "M ultiple", + "Multi ple", + "Mult iple", + "Multip le", + "(get Activity", + "_ WAIT", + "_W AIT", + "Ġj á", + "de cor", + "dec or", + "lev ance", + "Ġ GitHub", + "ĠGit Hub", + "m ination", + "min ation", + "mi nation", + "mina tion", + "_ quantity", + "_qu antity", + "_quant ity", + ". Scanner", + ".Sc anner", + ".Scan ner", + "ĠL ion", + "ĠLi on", + "éĶĻ 误", + "Ġ dre", + "Ġd re", + "Ġdr e", + "Ġtan tra", + "Ġtant ra", + "Ġtantr a", + "Ġ contentType", + "Ġcontent Type", + "Ġ fid", + "Ġf id", + "Ġfi d", + "_ alt", + "_a lt", + "_al t", + "NS IndexPath", + "- pl", + "-p l", + "åĮ ĸ", + "Ġantib iot", + "t ables", + "table s", + "ta bles", + "tab les", + "tabl es", + "a cial", + "ac ial", + "aci al", + "acia l", + "Ġ Registry", + "ĠReg istry", + "ĠRegistr y", + "Ġo live", + "Ġol ive", + "Ġoli ve", + "i gers", + "ig ers", + "ige rs", + "iger s", + "Ġ subscriber", + "Ġsub scriber", + "Ġsubscri ber", + "Ġsubscribe r", + "_ pres", + "_p res", + "_pr es", + "_pre s", + "Ġ Syntax", + "ĠS yntax", + "ĠSy ntax", + "ĠSyn tax", + "Ġl overs", + "Ġlo vers", + "Ġlove rs", + "Ġlover s", + "Ġlov ers", + ". Byte", + ".B yte", + ".By te", + "ol ders", + "old ers", + "older s", + "_ forward", + "_for ward", + "al ways", + "C aption", + "Cap tion", + "Ca ption", + "Capt ion", + "P riv", + "Pr iv", + "Pri v", + "ĠT ampa", + "ĠTam pa", + "is ateur", + "-labelled by", + "Ġ ToString", + "ĠTo String", + "Ġ ìĤ¬", + "Ġì Ĥ¬", + "ĠìĤ ¬", + "Ġinit iated", + "Ġiniti ated", + "Ġinitiate d", + "W F", + "Ġinstitution al", + "in ject", + "Ġ Scr", + "ĠS cr", + "ĠSc r", + "Ġ doctrine", + "Ġdo ctrine", + "Ġdoctr ine", + "Ġsp acious", + "Ġspac ious", + "i sure", + "is ure", + "isu re", + "Ġ Ana", + "ĠA na", + "ĠAn a", + "\" time", + "ess aging", + "essa ging", + "Ġ cid", + "Ġc id", + "Ġci d", + "ĠN an", + "ĠNa n", + "Ġin complete", + "Ġincom plete", + "T AG", + "TA G", + "- build", + "-b uild", + "Dec ember", + "Ġres idual", + "Ġresid ual", + "( PDO", + "(P DO", + "Ġ Listen", + "ĠL isten", + "ĠList en", + "ĠLi sten", + "ĠLis ten", + "ĠListe n", + "Ġ glyph", + "Ġg lyph", + "Ġgly ph", + "Ġg aps", + "Ġgap s", + "Ġga ps", + "n ea", + "ne a", + ". Rect", + ".R ect", + ".Re ct", + ".Rec t", + "Ġs au", + "Ġsa u", + "ĠPhoto graph", + "ĠPhot ograph", + "Ġ executable", + "Ġexec utable", + "Ġexecut able", + "Ġ Expert", + "ĠEx pert", + "ĠExp ert", + "Co routine", + "Cor outine", + "_ sizes", + "_s izes", + "_size s", + "_si zes", + "Ġ NL", + "ĠN L", + ". isValid", + ".is Valid", + ") ;}Ċ", + "); }Ċ", + ");} Ċ", + "- reg", + "-r eg", + "-re g", + "Ġc iting", + "Ġcit ing", + "Ġci ting", + "c wd", + "cw d", + "ĠOtt awa", + "ĠB att", + "ĠBa tt", + "ĠBat t", + "Ġrenew able", + "Ġprelim inary", + "Ġas ylum", + "Ġw rist", + "Ġwr ist", + "Ġutil iz", + "Ġut iliz", + "Ġdet ention", + "F ast", + "Fa st", + "Ġ ange", + "Ġa nge", + "Ġan ge", + "Ġang e", + "incinn ati", + "Ġste ering", + "Ġsteer ing", + "Ġ NaN", + "ĠN aN", + "ĠNa N", + "i osity", + "ios ity", + "/ page", + "/p age", + "Ġ è¿", + "Ġè ¿", + "ster ol", + "ste rol", + "Ġdis g", + "Ġdi sg", + "( DB", + "(D B", + "Ġ DESCRIPTION", + "ĠDE SCRIPTION", + "ĠDESC RIPTION", + "Ġ _$", + "Ġ_ $", + "Ġob stacle", + "Ġobst acle", + "Ġb izarre", + "Ġex traction", + "Ġext raction", + "Ġextra ction", + "Ġextract ion", + "Ġextr action", + "_ expected", + "_ex pected", + "_exp ected", + "_expect ed", + "Ġ loses", + "Ġl oses", + "Ġlo ses", + "Ġlos es", + "Ġlose s", + "Ġ Celebr", + "ĠCele br", + "Ġhtml For", + "Ġexp loit", + "Ġexpl oit", + "Ġexplo it", + "олÑĮз ов", + "X YZ", + "XY Z", + "Ġm agnet", + "Ġmag net", + "Ġmagn et", + "am ped", + "amp ed", + "Ġ atoms", + "Ġat oms", + "Ġatom s", + "S ources", + "Source s", + "pect ives", + "pective s", + "Ñģ ли", + "Ñģл и", + "Ġ= čĊ", + "Ġd are", + "Ġda re", + "Ġdar e", + "ĠW alter", + "ĠWal ter", + "ĠWalt er", + "Ġ brightness", + "Ġb rightness", + "Ġbright ness", + "Ġ annotations", + "Ġan notations", + "Ġannotation s", + "Ġannot ations", + "ë ı", + "is ke", + "isk e", + "S chedule", + ". images", + ".image s", + ".im ages", + ".imag es", + "ross o", + "ros so", + "Ġ \"..", + "Ġ\" ..", + "Ġ\". .", + "g amma", + "ga mma", + "gam ma", + "Ġin structor", + "Ġinstr uctor", + "Ġinstruct or", + "Ġ overwrite", + "Ġover write", + "- am", + "-a m", + "Ġdevast ating", + "ĠSaint s", + "ĠSa ints", + "ĠSai nts", + "Ġ hs", + "Ġh s", + "Ġbon uses", + "Ġbonus es", + "$ output", + "$out put", + "i jd", + "ij d", + "(Action Event", + "m onitor", + "mon itor", + "Ġmatt ress", + "Jan uary", + ". jp", + ".j p", + "Ġcar acter", + "Ġcara cter", + "Ġcaract er", + "Ġim pose", + "Ġimp ose", + "_ rest", + "_re st", + "_r est", + "_res t", + "Ġ Signature", + "ĠSign ature", + "ĠSig nature", + "Ġcoron avirus", + "ãģ Ĭ", + "_ compare", + "_com pare", + "_comp are", + "Me asure", + "it ated", + "ita ted", + "itate d", + "itat ed", + "e lijk", + "el ijk", + "eli jk", + "i gos", + "ig os", + "igo s", + "e sar", + "es ar", + "esa r", + "Ġr ushed", + "Ġru shed", + "Ġrush ed", + "Ġrus hed", + "m etry", + "me try", + "met ry", + "_SE PARATOR", + "_ WE", + "_W E", + "_ ATTRIBUTE", + "_ATTR IBUTE", + "_ATTRIB UTE", + "Ġ yaml", + "Ġy aml", + "Ġya ml", + "Ġ specs", + "Ġsp ecs", + "Ġspec s", + "Ġspe cs", + "ĠR ah", + "ĠRa h", + "ph eric", + "pher ic", + "phe ric", + "ĠIn vestment", + "ĠInvest ment", + "ä ll", + "äl l", + "Ġappe aling", + "Ġappeal ing", + "Ġ viewport", + "Ġview port", + "ç ©", + "Ġ marginLeft", + "Ġmargin Left", + "Ġ subtract", + "Ġsub tract", + "Ġsubt ract", + "Ġ EDIT", + "ĠED IT", + "ĉ ArrayList", + "ĉArray List", + "gr ading", + "grad ing", + "gra ding", + "Ġ Failure", + "ĠF ailure", + "ĠFail ure", + "as per", + "asp er", + "E EK", + "EE K", + "( now", + "(n ow", + "(no w", + "< object", + "Ġ Alignment", + "ĠAl ignment", + "ĠAlign ment", + "ple ado", + "q tt", + "qt t", + "( ERROR", + "(ERR OR", + "Ġ INVALID", + "ĠIN VALID", + "Ġ userid", + "Ġuse rid", + "Ġuser id", + "r aises", + "ra ises", + "raise s", + "rais es", + "rai ses", + "I DI", + "ID I", + "Ġv ariance", + "Ġvar iance", + "Ġvari ance", + "Ġ Nil", + "ĠN il", + "ĠNi l", + "/ delete", + "/de lete", + "_ MAIN", + "_M AIN", + "_MA IN", + ". Token", + ".T oken", + ".To ken", + ". Category", + ".C ategory", + "> )Ċ", + ">) Ċ", + "C ollision", + "Coll ision", + "Ġ Greater", + "ĠG reater", + "ĠGreat er", + "ĠGre ater", + "ĠR acing", + "ĠRa cing", + "ĠRac ing", + "a lan", + "al an", + "ala n", + "Ġmon etary", + "Ġmonet ary", + ", new", + ",n ew", + "Ġ Sorry", + "ĠS orry", + "ĠSor ry", + ". Enable", + ".E nable", + ".En able", + "Ġ Instantiate", + "ĠIn stantiate", + "ĠInstant iate", + "o llen", + "ol len", + "oll en", + "olle n", + "ë© ´", + "Ġ Calling", + "ĠC alling", + "ĠCal ling", + "ĠCall ing", + "_ hour", + "_h our", + "A DA", + "AD A", + "Ġs hy", + "Ġsh y", + ") **", + ")* *", + "Ġ ==>", + "Ġ= =>", + "Ġ== >", + "Ġe special", + "Ġes pecial", + "Ġesp ecial", + "Ġespec ial", + "Ġ interpreted", + "Ġinterpret ed", + "Ġinterpre ted", + "! =\"", + "!= \"", + "Ġph armacy", + "Ġpharm acy", + "Ġpharmac y", + ". single", + ".s ingle", + ".sin gle", + ".si ngle", + "ĠC ialis", + "ĠCi alis", + "Ġp aras", + "Ġpar as", + "Ġpara s", + "Ġpa ras", + ". toUpperCase", + ".to UpperCase", + "Ġ Demon", + "ĠD emon", + "ĠDe mon", + "ĠDem on", + "ĠDemo n", + "Pr ime", + "Prim e", + "Pri me", + "Ġrank ings", + "Ġranking s", + "Add ing", + "Ad ding", + "_ HASH", + "_H ASH", + "_HAS H", + "Ġ Exam", + "ĠEx am", + "Ú ©", + "ĠV ictor", + "ĠVi ctor", + "ĠVict or", + "ĠVic tor", + "Ok ay", + "\" ];čĊ", + "\"] ;čĊ", + "\"]; čĊ", + "Ġ fortune", + "Ġfort une", + "Ġ FETCH", + "ĠF ETCH", + "exp and", + ". Interop", + ".Inter op", + "Ġb arn", + "Ġbar n", + "Ġba rn", + "æ ¶Ī", + "æ¶ Ī", + "ue vo", + "Ġspec ulation", + "âĶĢâĶĢ âĶĢâĶĢ", + "Ġ Nu", + "ĠN u", + "ĠBl ues", + "ĠBlue s", + "ĠBlu es", + "( fname", + "(f name", + "(fn ame", + "Ġin habit", + "Ġinhab it", + "Ġinh abit", + "Ġ\\ \"%", + "Ġ\\\" %", + "C ES", + "CE S", + "ul ario", + "ular io", + "ula rio", + "_ cr", + "_c r", + "Ġ validated", + "Ġvalid ated", + "Ġvalidate d", + "Ġvalida ted", + "Ġmid night", + "an king", + "ank ing", + "anki ng", + "Ġincor porate", + "Ġincorpor ate", + "Ġpur suit", + "Ġpurs uit", + "E XP", + "EX P", + "pr ime", + "prim e", + "pri me", + "P id", + "Pi d", + "- US", + "-U S", + "ĠN urs", + "ĠNu rs", + "ĠNur s", + "Ġ Wheel", + "ĠW heel", + "ĠWh eel", + "ĠWhe el", + "é ĺ", + "Ġ inp", + "Ġin p", + "Ġi np", + "Ġsupport ive", + ". member", + ".m ember", + ".mem ber", + "Ġ Shot", + "ĠS hot", + "ĠSh ot", + "ĠSho t", + ". CheckBox", + ".Check Box", + "Ġaff irm", + "Ġaf firm", + "T or", + "To r", + "Full Year", + "Ġconsider ably", + "c redentials", + "cred entials", + "credential s", + "_ opts", + "_op ts", + "_o pts", + "_opt s", + "R oll", + "Ro ll", + "Rol l", + "( round", + "(r ound", + "(ro und", + "Ġc oment", + "Ġcom ent", + "Ġco ment", + "Ġcome nt", + "_ UART", + "_U ART", + "Ġext ending", + "Ġextend ing", + "R G", + "result ado", + "i tu", + "it u", + ". getSession", + ".get Session", + ".getS ession", + "Ġat traction", + "Ġatt raction", + "Ġattr action", + "Ġattract ion", + "& D", + "$ html", + "$h tml", + "Ġ Jessica", + "ĠJess ica", + "Ġ Associate", + "ĠAssoci ate", + "ĠAssoc iate", + "a ñ", + "_ ed", + "_e d", + "ĠL ag", + "ĠLa g", + "Ġorig ins", + "Ġorigin s", + "( ))->", + "() )->", + "()) ->", + "add EventListener", + "IA LOG", + "IAL OG", + "åIJ ¦", + ". Compare", + ".Com pare", + ".Comp are", + "Al bum", + "ĠK u", + "< Q", + "ar gest", + "arg est", + "arge st", + "arges t", + "Ġpro long", + "Ġprol ong", + "Ġconfig urations", + "Ġconfiguration s", + "Ġconfigur ations", + "Ġacc identally", + "Ġaccident ally", + "Ġaccidental ly", + "_ photo", + "_ph oto", + "Ġ' ';čĊ", + "Ġ'' ;čĊ", + "Ġ''; čĊ", + "Ġ verse", + "Ġv erse", + "Ġver se", + "Ġvers e", + "B ob", + "Bo b", + "Ġf arming", + "Ġfar ming", + "Ġfarm ing", + "d elivery", + "del ivery", + "deliver y", + "ĠM ack", + "ĠMac k", + "ĠMa ck", + "Ġuse Selector", + ".bootstrap cdn", + "ke eping", + "keep ing", + "kee ping", + "e ny", + "en y", + ". upload", + ".up load", + "Ġ METHOD", + "ĠM ETHOD", + "ĠMETH OD", + "c reator", + "cre ator", + "creat or", + "< _", + "ĠE aster", + "ĠEast er", + "ĠEa ster", + ". --", + ".- -", + "UI Button", + "ãĤ ī", + "om eters", + "ome ters", + "omet ers", + "ometer s", + "Ġ shine", + "Ġsh ine", + "Ġshin e", + "Ġh ogy", + "Ġho gy", + "Ġhog y", + "\\ s", + "Ġh arness", + "Ġhar ness", + ". Cell", + ".C ell", + "Ġ lifting", + "Ġl ifting", + "Ġlif ting", + "Ġlift ing", + "Ġcomb ines", + "Ġcombine s", + "Ġcombin es", + "Ġ Occup", + "ĠOcc up", + "ĠOc cup", + "ex clude", + "exc lude", + "p atial", + "pat ial", + "Ġre spir", + "Ġres pir", + "Ġresp ir", + "_ fit", + "_f it", + "Ġf ifty", + "Ġfi fty", + "Ġfif ty", + "ĠM ol", + "ĠMo l", + "Ġt uned", + "Ġtu ned", + "Ġtun ed", + "Ġtune d", + "-d imensional", + "Ġ qs", + "Ġq s", + "Ġt ops", + "Ġto ps", + "Ġtop s", + "> \";ĊĊ", + ">\";Ċ Ċ", + ">\" ;ĊĊ", + ">\"; ĊĊ", + "quis ite", + "qui site", + "ch annels", + "chan nels", + "channel s", + "/ res", + "/r es", + "/re s", + "Ġ Analytics", + "ĠAn alytics", + "ĠAnaly tics", + ".app compat", + "/ to", + "/t o", + "Ġon Error", + "( attr", + "(at tr", + "(att r", + "I RM", + "IR M", + "Ġrag az", + "- as", + "-a s", + ". Second", + ".Se cond", + "ori ented", + "orient ed", + "Ġd onn", + "Ġdo nn", + "Ġdon n", + "Ġlight ning", + "f id", + "fi d", + "ĠP le", + "ĠPl e", + "ãģ¾ ãģĻ", + "t ro", + "tr o", + ". True", + ".Tr ue", + "O bservable", + "Observ able", + "× Ļ", + "um bing", + "umb ing", + "Ġpros pective", + "Ġprospect ive", + "- filter", + "-f ilter", + "Ġpurs uant", + "( points", + "(p oints", + "(point s", + "(po ints", + ". Bind", + ".B ind", + "Ġp alm", + "Ġpa lm", + "Ġpal m", + "clear fix", + "ö s", + "ĠG onz", + "ĠGo nz", + "ĠGon z", + "Ġwe aken", + "Ġweak en", + "D rive", + "Dr ive", + "en ido", + "eni do", + "l ld", + "ll d", + "o box", + "ob ox", + "obo x", + "an ean", + "ane an", + "G ot", + "Go t", + "ä¿ Ŀ", + "Reg ex", + "æ ĥ", + "Ġsa lad", + "Ġsal ad", + "Ġsala d", + "as sis", + "ass is", + "assi s", + "\" net", + "inherit Doc", + "Ġ RV", + "ĠR V", + "qu ier", + "qui er", + "Ġ clazz", + "Ġcl azz", + "Ġcla zz", + "ı ÅŁ", + "oster one", + "oste rone", + "Ġair line", + "Ġairl ine", + ".list dir", + "Ġdown loading", + "Ġdownload ing", + "ĠP alm", + "ĠPal m", + "ĠPa lm", + "w aukee", + "& lt", + ". BL", + ".B L", + "_ INLINE", + "_IN LINE", + "of fs", + "off s", + "< <(", + "<< (", + "_ news", + "_n ews", + "_new s", + "_ne ws", + "Ġch ase", + "Ġcha se", + "/ ><", + "/> <", + "Ġe uros", + "Ġeu ros", + "Ġeuro s", + "ĠEgypt ian", + "ĠSt ainless", + "_ BOOL", + "_BO OL", + "Ġ Guild", + "ĠG uild", + "ĠGu ild", + "ĠGui ld", + "ĠGuil d", + "ĠD ynam", + "ĠDy nam", + "ĠDyn am", + "[ indexPath", + "[index Path", + "Ġ ï", + "Ġmem orable", + "Ġmemor able", + "ĠCh ampion", + "ĠChamp ion", + "Resource Manager", + ". Login", + ".Log in", + ".Lo gin", + "Ġ Former", + "ĠFor mer", + "ĠForm er", + "y ped", + "ype d", + "yp ed", + "Ġl leg", + "Ġll eg", + "Ġlle g", + "; \",", + ";\" ,", + "D WORD", + "DW ORD", + "Ġtax i", + "Ġta xi", + "Ġb ombs", + "Ġbomb s", + "Ġbom bs", + "r ah", + "ra h", + ". tags", + ".t ags", + ".tag s", + ".ta gs", + "_ tests", + "_t ests", + "_test s", + "_te sts", + "s tones", + "st ones", + "ston es", + "stone s", + "sto nes", + "âĢĿ )", + "[ g", + "r type", + "rt ype", + "Ġ vu", + "Ġv u", + "Ġhost ile", + "Ġho stile", + "Ġhos tile", + "Ch ars", + "Char s", + "Cha rs", + "ĠPatri ots", + "ĠPatriot s", + "/ status", + "/s tatus", + "/st atus", + "/stat us", + "< B", + "Ġ Income", + "ĠIn come", + "ĠInc ome", + "ĠD ad", + "ĠDa d", + "Ġpat rol", + "_ CHANGE", + "_CH ANGE", + "_CHAN GE", + "Ġup graded", + "Ġupgrade d", + "Ġ china", + "Ġch ina", + "Ġchi na", + "Ġchin a", + "set q", + "Start ed", + "Star ted", + ".U ndef", + ".Un def", + "Ġ checksum", + "Ġcheck sum", + "Ġchecks um", + "Ġfrustr ated", + "{ o", + "Ġe nf", + "Ġen f", + "Ġ woods", + "Ġw oods", + "Ġwood s", + "Ġwo ods", + "Ġwoo ds", + "Ġ Anyone", + "ĠAny one", + "En code", + "Enc ode", + "ĠQt Widgets", + "a reas", + "are as", + "area s", + "Ġsh eer", + "Ġshe er", + "s ki", + "sk i", + "end point", + "_ Test", + "_T est", + "S oup", + "So up", + "Sou p", + "~~~~~~~~ ~~~~~~~~", + "( files", + "(f iles", + "(file s", + "(fi les", + "(fil es", + "ĉ ĉĉĉĉčĊ", + "ĉĉ ĉĉĉčĊ", + "ĉĉĉĉ ĉčĊ", + "ĉĉĉ ĉĉčĊ", + "ĉĉĉĉĉ čĊ", + ". spark", + ".s park", + ".sp ark", + "Ġvalue d", + "Ġval ued", + "Ġvalu ed", + "Ġ %Ċ", + "Ġ% Ċ", + ". controls", + ".control s", + "ĠXCTAssert Equal", + "Ġf ame", + "Ġfam e", + "Ġfa me", + "ĠR ic", + "ĠRi c", + "D OT", + "DO T", + "ĠAlbert a", + "ĠAlb erta", + "ä½ ¿", + "o sal", + "os al", + "osa l", + ".Web Controls", + "Ġ ------------", + "Ġ- -----------", + "Ġ-- ----------", + "Ġ---- --------", + "Ġ--- ---------", + "Ġ----- -------", + "Ġ---------- --", + "Ġ------ ------", + "Ġ-------- ----", + "Ġ------- -----", + "Ġ--------- ---", + "Ġ----------- -", + "Ġ Mis", + "ĠM is", + "ĠMi s", + "Ġ SYS", + "ĠS YS", + "ĠSY S", + "Non null", + "= item", + "=i tem", + "Ġ expire", + "Ġex pire", + "Ġexp ire", + "De code", + "Dec ode", + "_ operation", + "_op eration", + "_o peration", + "_oper ation", + "Ġ Validator", + "ĠValid ator", + ". CENTER", + ".C ENTER", + "uff s", + "uf fs", + "* m", + "Ġa vant", + "Ġav ant", + "Ġava nt", + "Ġavan t", + "æ¬ ¡", + "âĢľ You", + ". permission", + ".per mission", + ".perm ission", + ".. .)", + "... )", + "Ġ Lic", + "ĠL ic", + "ĠLi c", + "_ coords", + "_co ords", + "_coord s", + ". nombre", + ".n ombre", + ".nom bre", + "c lo", + "cl o", + ". Internal", + ".In ternal", + ".Int ernal", + ".Inter nal", + "Ġ Cho", + "ĠC ho", + "ĠCh o", + "_ sw", + "_s w", + "ĉ Il", + "ĉI l", + "c lk", + "cl k", + "Ġ castle", + "Ġc astle", + "Ġcas tle", + "Ġcast le", + "( layer", + "(l ayer", + "p it", + "pi t", + "Ġ guided", + "Ġgu ided", + "Ġguide d", + "Ġguid ed", + "Ġgui ded", + "Ġ âĸĪ", + "Ġâĸ Ī", + "Ġsup erb", + "Ġsuper b", + "Ġsup plements", + "Ġsupplement s", + "Ġsuppl ements", + "Ġsupp lements", + "_ cent", + "_c ent", + "_ce nt", + "Ġ peek", + "Ġpe ek", + "Ġpee k", + "IN ARY", + "INA RY", + ". ContentAlignment", + ".Content Alignment", + "f alls", + "fall s", + "fal ls", + "\" ));", + "\") );", + "\")) ;", + "W all", + "Wal l", + "Wa ll", + ") .čĊ", + "). čĊ", + "Ġ Danny", + "ĠD anny", + "ĠDan ny", + "ĠDann y", + "irm ingham", + "IAL IZ", + "( create", + "(c reate", + "\" In", + "\"I n", + "Service Provider", + "Ġpr iced", + "Ġprice d", + "Ġpri ced", + "m acro", + "ma cro", + "mac ro", + "a mac", + "am ac", + "ama c", + ". box", + ".b ox", + ".bo x", + "- ---Ċ", + "-- --Ċ", + "---- Ċ", + "--- -Ċ", + "ãĥ «", + "Ġ Suit", + "ĠS uit", + "ĠSu it", + "u rst", + "ur st", + "urs t", + "b ru", + "br u", + "ourn als", + "ournal s", + "num ero", + "numer o", + "_ _()Ċ", + "__ ()Ċ", + "__( )Ċ", + "__() Ċ", + "D as", + "Da s", + "ĠM itt", + "ĠMi tt", + "ĠMit t", + "u der", + "ud er", + "ude r", + "? \\", + "f u", + "[ B", + "Ġ: )ĊĊ", + "Ġ:) ĊĊ", + "Ġ:)Ċ Ċ", + "( inter", + "(int er", + "(in ter", + "br ains", + "bra ins", + "brain s", + "Ġatt itudes", + "Ġattitude s", + "Ver ify", + "Ġsign atures", + "Ġsignature s", + "ack Bar", + "Ġ gd", + "Ġg d", + "J ack", + "Ja ck", + "Jac k", + ". cat", + ".c at", + ".ca t", + "Ġ zz", + "Ġz z", + "w arf", + "wa rf", + "war f", + "F TER", + "FT ER", + "\" );ĊĊĊ", + "\");Ċ ĊĊ", + "\") ;ĊĊĊ", + "\");ĊĊ Ċ", + "\"); ĊĊĊ", + "A live", + "Al ive", + "Ali ve", + "I CLE", + "IC LE", + "Ġ Whatever", + "ĠWh atever", + "ĠWhat ever", + "Ġ outlined", + "Ġout lined", + "Ġoutline d", + "s prite", + "sp rite", + "spr ite", + "е в", + "еР²", + "_ AB", + "_A B", + "_ DEPTH", + "_DE PTH", + "Ġcr ushed", + "Ġcrush ed", + "Ġcru shed", + "Ġcrus hed", + "a aa", + "aa a", + "( ev", + "(e v", + "æľ º", + "An ti", + "Ant i", + "I CO", + "IC O", + "is EqualTo", + "isEqual To", + ". sun", + ".s un", + "i culo", + "ic ulo", + "s ale", + "sa le", + "sal e", + "_ hex", + "_h ex", + "_he x", + "Ġ Vk", + "ĠV k", + "ap tor", + "apt or", + "Un ion", + "Uni on", + "Ġ Discount", + "ĠDis count", + "ĠDisc ount", + "ĠDisco unt", + "l ista", + "li sta", + "list a", + "lis ta", + ".Undef Or", + "Ġ automation", + "Ġa utomation", + "Ġautom ation", + "Ġautomat ion", + "N or", + "No r", + "å¯ ¹", + "åı Ĥæķ°", + "åıĤ æķ°", + "Ġre flex", + "Ġref lex", + "Ġrefl ex", + "ĠLa ure", + "ĠLaur e", + "ĠLau re", + ".showMessage Dialog", + ". temp", + ".t emp", + ".te mp", + ".tem p", + "Ġ akan", + "Ġa kan", + "Ġak an", + "Ġaka n", + "Ġ_ _____", + "Ġ__ ____", + "Ġ___ ___", + "Ġ____ __", + "Ġ_____ _", + ".Is True", + "A RED", + "AR ED", + "ARE D", + "a gle", + "ag le", + "E nergy", + "En ergy", + "Ġquant ities", + "âĢĻ é", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "Ġcitizens hip", + "Ġcitizen ship", + "m outh", + "mo uth", + "Ġin appropriate", + "Ġ Outdoor", + "ĠOut door", + "White Space", + "An onymous", + "lo ads", + "load s", + "webElement Properties", + "T en", + "Te n", + "Ġacc idents", + "Ġaccident s", + "Ġ advertisement", + "Ġad vertisement", + "Ġadvertis ement", + "Ġadvertise ment", + "ĠY emen", + "ĠYe men", + "( call", + "(c all", + "(cal l", + "(ca ll", + "Ġsl avery", + "Ġslave ry", + "Ġsla very", + "Ġslav ery", + "Ñģ п", + "ĠL am", + "ĠLa m", + "_ BITS", + "_B ITS", + "_BIT S", + "o mega", + "om ega", + "ome ga", + "ĠO le", + "ĠOl e", + "Ġkid n", + "Ġki dn", + "_ An", + "_A n", + "ĠR aid", + "ĠRa id", + "ĠRai d", + "C reation", + "Cre ation", + "Creat ion", + "s aved", + "save d", + "sa ved", + "sav ed", + "Ġpro port", + "Ġprop ort", + "Ġpropor t", + "W ARNING", + "WAR NING", + "WARN ING", + "\\ P", + "Ġ pwd", + "Ġp wd", + "Ġpw d", + "Data Reader", + "is cher", + "isc her", + "ische r", + "isch er", + "ad eon", + "ade on", + "Ġ Predict", + "ĠP redict", + "ĠPre dict", + "ĠPred ict", + "Ġreason ing", + "Ġdestroy ing", + "H el", + "He l", + "* d", + "ĠLeg isl", + "_ Pr", + "_P r", + "ĉ ĉĉĠĠĠĠĠĠĠ", + "ĉĉ ĉĠĠĠĠĠĠĠ", + "ĉĉĉ ĠĠĠĠĠĠĠ", + "ĉĉĉĠĠĠ ĠĠĠĠ", + "ĉĉĉĠ ĠĠĠĠĠĠ", + "ĉĉĉĠĠ ĠĠĠĠĠ", + "ĉĉĉĠĠĠĠ ĠĠĠ", + "ĉĉĉĠĠĠĠĠ ĠĠ", + "ĉĉĉĠĠĠĠĠĠ Ġ", + "Ġsym path", + "Ġsymp ath", + "Ġch ess", + "Ġche ss", + "Ġ mam", + "Ġm am", + "Ġma m", + ": hover", + ":h over", + "Ġcon verts", + "Ġconvert s", + "Ġconv erts", + "Ġconver ts", + "Ġp ela", + "Ġpe la", + "Ġpel a", + "Ġpro gression", + "Ġprogress ion", + "Ġprog ression", + "Ġ\" _\"", + "Ġ\"_ \"", + "ĠG ill", + "ĠGi ll", + "ĠGil l", + "ĉ show", + "ĉs how", + "ĉsh ow", + "Ġsupposed ly", + "ac curacy", + "acc uracy", + "accur acy", + "e lin", + "el in", + "eli n", + "Ġunf olding", + "Ġunfold ing", + "Ġ Hyper", + "ĠH yper", + "ĠHy per", + "ĠHyp er", + "Ġw anna", + "Ġwann a", + "Ġwan na", + "Ġ ups", + "Ġu ps", + "Ġup s", + "( #", + "ĠC riminal", + "ĠCr iminal", + "( Point", + "(P oint", + "at Lng", + "act ly", + "Ġcontract ors", + "Ġcontr actors", + "Ġcontractor s", + "Ġcontra ctors", + "' ]}", + "'] }", + "draul ic", + "ód igo", + "Ġ TT", + "ĠT T", + "Ġ Wide", + "ĠW ide", + "ĠWi de", + "ĠWid e", + "Ġ ARG", + "ĠA RG", + "ĠAR G", + "_ ic", + "_i c", + "FLAG S", + "S chool", + "Sch ool", + "Ġcl earing", + "Ġclear ing", + "Ġcle aring", + "- being", + "-b eing", + "-be ing", + "={ [", + ", const", + "man ent", + "Over lay", + "( '\"", + "(' \"", + "éĩ ı", + "Ġ Timestamp", + "ĠT imestamp", + "ĠTime stamp", + "Ġm ailing", + "Ġma iling", + "Ġmail ing", + "Ġmai ling", + "Ġ Cake", + "ĠC ake", + "ĠCa ke", + ". That", + ".T hat", + ".Th at", + "Ġmed itation", + "q p", + "Ġ empresa", + "Ġemp resa", + "Ġempres a", + "ĠL ions", + "ĠLi ons", + "ĠLion s", + "Ġw eld", + "Ġwe ld", + "Ġwel d", + "Ġ LinkedIn", + "ĠLinked In", + "Ġc ush", + "Ġcu sh", + "Ġcus h", + "Ġ genome", + "Ġge nome", + "Ġgen ome", + "Ġgenom e", + ". IndexOf", + ".Index Of", + "a gain", + "ag ain", + "aga in", + "Ġ fallback", + "Ġf allback", + "Ġfall back", + "Ġc amping", + "Ġcamp ing", + "Ġcam ping", + "r edd", + "re dd", + "red d", + "-strip ed", + "-str iped", + "Ġ dv", + "Ġd v", + "Fe bruary", + "Feb ruary", + "Ġ Proxy", + "ĠPro xy", + "ĠPr oxy", + "u sk", + "us k", + "Ġd iesel", + "Ġdi esel", + "Ġdie sel", + "Ġdies el", + "Ġdiese l", + "W RITE", + "WR ITE", + "RE AK", + "REA K", + "L orem", + "Lo rem", + ". Invoke", + ".In voke", + ".Inv oke", + "- div", + "-d iv", + "-di v", + "Inter ceptor", + "Ġ DH", + "ĠD H", + "i ales", + "ial es", + "ia les", + "iale s", + "Ġvill ages", + "Ġvillage s", + "Ġvilla ges", + "Ø ´", + "Ġ ENV", + "ĠE NV", + "ĠEN V", + "S ys", + "Sy s", + ". XR", + ".X R", + "Ġpo em", + "à Ĥ", + "c ade", + "ca de", + "cad e", + "p lots", + "pl ots", + "plot s", + "Ġ {(", + "Ġ{ (", + ". git", + ".g it", + "/ svg", + "/s vg", + "n cmp", + "nc mp", + "Ġ Äį", + "ĠÄ į", + "a ines", + "ain es", + "ai nes", + "aine s", + "åĩ ½æķ°", + "åĩ½ æķ°", + "Ġ ()ĊĊ", + "Ġ( )ĊĊ", + "Ġ() ĊĊ", + "Ġ()Ċ Ċ", + "op sis", + "ops is", + "Ġ Relationship", + "ĠRel ationship", + "ĠRelations hip", + "ĠRelation ship", + "_ aut", + "_a ut", + "Ġ Bomb", + "ĠB omb", + "ĠBo mb", + "ĠBom b", + "ĉ com", + "ĉc om", + "* sizeof", + "*size of", + "off icial", + "_ payload", + "_p ayload", + "_pay load", + "ĉ ĉĉĉĉĠĠ", + "ĉĉ ĉĉĉĠĠ", + "ĉĉĉĉ ĉĠĠ", + "ĉĉĉ ĉĉĠĠ", + "ĉĉĉĉĉ ĠĠ", + "ĉĉĉĉĉĠ Ġ", + ". manager", + ".m anager", + ".man ager", + ".manage r", + "Ġ Around", + "ĠA round", + "ĠAr ound", + "ĉ send", + "ĉs end", + "ĉse nd", + "Ġ Exercise", + "ĠEx ercise", + "Ġ Billy", + "ĠB illy", + "ĠBill y", + "ĠBil ly", + "i vi", + "iv i", + "Ġne eding", + "Ġneed ing", + "_ urls", + "_url s", + "_ur ls", + "_ tasks", + "_t asks", + "_task s", + "ĠH em", + "ĠHe m", + "Ġ tearDown", + "Ġte arDown", + "Ġtear Down", + "en crypt", + "enc rypt", + ". tie", + ".t ie", + "Ġ asm", + "Ġa sm", + "Ġas m", + "I CH", + "IC H", + "ĠCGRect Make", + "ìĦ ±", + "u long", + "ul ong", + "ulo ng", + "Ġ itr", + "Ġit r", + "Ġi tr", + "Ġ GST", + "ĠG ST", + "ĠGS T", + "Ġoffer ings", + "Ġoffering s", + "r obe", + "ro be", + "rob e", + "E EE", + "EE E", + "oper ators", + "operator s", + "_ PROP", + "_P ROP", + "_PRO P", + "_PR OP", + "in dent", + "ind ent", + "inde nt", + "inden t", + "A DE", + "AD E", + "o rf", + "or f", + "ë IJ", + "Ġbl essed", + "Ġbless ed", + "v ascular", + "vas cular", + "Ġcon oc", + "Ġco noc", + "H appy", + "Ha ppy", + "B ridge", + "Br idge", + "il itation", + "ilit ation", + "j oint", + "join t", + "jo int", + "Ġ Administr", + "ĠAdmin istr", + "- transform", + "-trans form", + "Ġmean time", + "Ġmeant ime", + "/ K", + "ĠBed room", + "Ġr igid", + "Ġrig id", + "Ġri gid", + "Ġb rowsers", + "Ġbrowser s", + "Ġbrows ers", + "Ġbrowse rs", + "EM PTY", + "EMP TY", + ". Serialize", + ".S erialize", + ".Serial ize", + "_ ED", + "_E D", + "Ġst itch", + "Ġ jan", + "Ġj an", + "Ġja n", + "el lt", + "ell t", + "Ġ brace", + "Ġb race", + "Ġbr ace", + "Ġbra ce", + "Ġt rails", + "Ġtr ails", + "Ġtrail s", + "Ġtra ils", + "Ġtrai ls", + "p ublished", + "publish ed", + "å¯Ĩ çłģ", + "} ')Ċ", + "}' )Ċ", + "}') Ċ", + "Ġac ids", + "Ġacid s", + "Ġ !!!", + "Ġ! !!", + "Ġ!! !", + "_ direct", + "_d irect", + "_dir ect", + "_di rect", + "> ());Ċ", + ">( ));Ċ", + ">() );Ċ", + ">()) ;Ċ", + "a jÄħ", + "aj Äħ", + "_O CC", + "_OC C", + "Ġplan ets", + "Ġplane ts", + "Ġplanet s", + "Ġpla nets", + "æ Ł¥", + "æŁ ¥", + "ĠDub lin", + "Ġ serie", + "Ġs erie", + "Ġse rie", + "Ġser ie", + "Ġseri e", + ". printf", + ".print f", + "de ep", + "dee p", + "` )", + "Ġ \\$", + "Ġ\\ $", + "Ġ μ", + "ĠÎ ¼", + "_ VIDEO", + "_V IDEO", + "end ors", + "endor s", + "endo rs", + "Ġ Crypto", + "ĠC rypto", + "ĠCrypt o", + "ĠCry pto", + "F ar", + "Fa r", + ". Transparent", + ".Trans parent", + ". TR", + ".T R", + "i asm", + "ia sm", + "ias m", + "_ training", + "_tr aining", + "_train ing", + "_tra ining", + "Ġte aches", + "Ġteach es", + "Ġtea ches", + "ĠB elt", + "ĠBe lt", + "ĠBel t", + "Ġlimit ing", + "Ġlim iting", + "ĠK ath", + "ĠKat h", + "ĠKa th", + "Ġ IndexPath", + "ĠIndex Path", + "Ġachie vements", + "Ġachieve ments", + "Ġachievement s", + "Ġse rá", + "Ġser á", + "interop Require", + "Ġd isse", + "Ġdis se", + "Ġdi sse", + "Ġdiss e", + ". If", + ".I f", + "ar ming", + "arm ing", + "uls ion", + "P o", + "_ DETAIL", + "_DE TAIL", + "_DET AIL", + "Prot otype", + "Proto type", + "Ġ CAL", + "ĠC AL", + "ĠCA L", + "Ġag rees", + "Ġagre es", + "Ġagree s", + "Ġagr ees", + ". vo", + ".v o", + ".Execute NonQuery", + "Ġ Topic", + "ĠT opic", + "ĠTo pic", + "ĠTop ic", + "Ġ' {}", + "Ġ'{ }", + "A rm", + "Ar m", + "Ġ ecc", + "Ġe cc", + "Ġec c", + "M ag", + "Ma g", + "Ġ serialized", + "Ġs erialized", + "Ġser ialized", + "Ġserial ized", + "Ġserialize d", + "ĉ conn", + "ĉc onn", + "ĉcon n", + "c ached", + "ca ched", + "cache d", + "cac hed", + "= tf", + "=t f", + "Ġ ByteArray", + "ĠByte Array", + "prot obuf", + "proto buf", + "var char", + "ĉ ASSERT", + "ĉA SSERT", + "Ġ liste", + "Ġl iste", + "Ġli ste", + "Ġlist e", + "Ġlis te", + "_ trigger", + "_tr igger", + "_tri gger", + "· ¸", + "F eel", + "Fe el", + "Fee l", + "T ahoma", + "Ġ Lik", + "ĠL ik", + "ĠLi k", + "Ġ structured", + "Ġstruct ured", + "Ġstructure d", + "erg us", + ". Initial", + ".In itial", + ".Init ial", + "_ ge", + "_g e", + "cl js", + ". contact", + ".cont act", + "Ġand ere", + "Ġan dere", + "Ġander e", + "$ stmt", + "_ CURRENT", + "_C URRENT", + "Ġ Discover", + "ĠDis cover", + "ĠDisc over", + "ĠDisco ver", + "$ res", + "$r es", + "form atter", + "for matter", + "format ter", + "H a", + "van gst", + "vang st", + "Ġe merge", + "Ġem erge", + "Ġemerg e", + "ãĢĤ âĢĿ", + "ĠC abinet", + "ĠCabin et", + "ĠCab inet", + "- square", + "-s quare", + "éĥ ¨", + "Ġ rage", + "Ġr age", + "Ġra ge", + "Ġrag e", + "Ġ AJ", + "ĠA J", + "Ġ VT", + "ĠV T", + "sh adow", + "ĠFa ith", + "e names", + "en ames", + "ename s", + "ena mes", + "p retty", + "pr etty", + "pre tty", + "pret ty", + "h asil", + "ha sil", + "has il", + "p arty", + "par ty", + "part y", + "Ġ varchar", + "Ġvar char", + "Ġf otos", + "Ġfo tos", + "Ġfoto s", + "Ġfot os", + "Ġa lum", + "Ġal um", + "ĠBel gium", + "ĠBelg ium", + ". ylabel", + ".y label", + "Ġ dej", + "Ġd ej", + "Ġde j", + "_ numbers", + "_num bers", + "_number s", + "Ġ hu", + "Ġh u", + ".set Adapter", + "Ġ Usually", + "ĠUs ually", + "( sample", + "(s ample", + ". Shared", + ".Sh ared", + "Ġbo oked", + "Ġbook ed", + "Ġboo ked", + "Ġ> >=", + "Ġ>> =", + "Ġmin erals", + "Ġmineral s", + "Ġminer als", + "\" > < ?=", + "\">", + "'] )->", + "']) ->", + "p rog", + "pr og", + "pro g", + "b oo", + "bo o", + "_ md", + "_m d", + "_ pack", + "_p ack", + "_pa ck", + "( express", + "(ex press", + "(exp ress", + "(expr ess", + "u tz", + "ut z", + "\\ Auth", + ", id", + ",i d", + "ĠCh ile", + "ĠChi le", + "act ice", + "actic e", + "Ġrec ruitment", + "Ġrecruit ment", + "Ġ poses", + "Ġp oses", + "Ġpos es", + "Ġpo ses", + "Ġpose s", + "Ġvulner ability", + "inst anc", + "o rum", + "or um", + "oru m", + "d ess", + "de ss", + "des s", + "Ġ xl", + "Ġx l", + "%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%", + "( fig", + "(f ig", + "(fi g", + "Ġde leting", + "Ġdel eting", + "Ġdelet ing", + "Ġdele ting", + ". del", + ".d el", + ".de l", + ") ')Ċ", + ")' )Ċ", + "Ġ Weekly", + "ĠWeek ly", + "? ??", + "?? ?", + "( strcmp", + "(str cmp", + "s mith", + "sm ith", + "Ġpurs uing", + "- so", + "-s o", + "Ġ Apps", + "ĠA pps", + "ĠApp s", + "ĠAp ps", + "/ 'Ċ", + "/' Ċ", + "Ġde cis", + "Ġdec is", + "F ORE", + "FO RE", + "FOR E", + "Every one", + "Ġl anes", + "Ġla nes", + "Ġlane s", + "Ġlan es", + "V irtual", + "Vir tual", + ". attach", + ".at tach", + ".att ach", + "( Log", + "(L og", + "ĠMed icaid", + "ĠMedic aid", + "( Path", + "(P ath", + "ĠTur ner", + "ĠTurn er", + "/ application", + "/app lication", + "/ap plication", + "Ġ portrait", + "Ġport rait", + "Ġpor trait", + "Ġop pose", + "Ġopp ose", + "Ġoppos e", + "check out", + "Ġfin ishes", + "Ġfinish es", + "_ ME", + "_M E", + "Bar rier", + "S ong", + "So ng", + "Son g", + "V AR", + "VA R", + "Ear lier", + "r ella", + "re lla", + "rel la", + "rell a", + "Ġh ast", + "Ġhas t", + "Ġha st", + "a zar", + "az ar", + "aza r", + "Ġp ulls", + "Ġpull s", + "Ġpul ls", + "n gx", + "ng x", + "Ġins piring", + "Ġinspir ing", + "Ġinsp iring", + "Ñĥ Ñİ", + "- direction", + "-d irection", + "-direct ion", + "-dir ection", + "-di rection", + "Ġexplos ive", + "Ġ createdAt", + "Ġcreated At", + "s to", + "st o", + "Ġw heat", + "Ġwh eat", + "Ġwhe at", + "Ġ Built", + "ĠB uilt", + "ĠBu ilt", + "' ai", + "'a i", + "Ġ tracked", + "Ġtr acked", + "Ġtrack ed", + "h ammad", + "ham mad", + "RowAt IndexPath", + "_ heap", + "_he ap", + "D ue", + "Du e", + "Ġconn ects", + "Ġconnect s", + ". publish", + ".p ublish", + ".pub lish", + "e mu", + "em u", + "Ġb ullets", + "Ġbul lets", + "Ġbull ets", + "Ġbullet s", + "B AR", + "BA R", + "o late", + "ol ate", + "ola te", + "Ġintern ally", + "Ġinternal ly", + "Ġc atching", + "Ġcatch ing", + "Ġcat ching", + "- password", + "-p assword", + "-pass word", + "ou ched", + "ouch ed", + "æĢ §", + "e ous", + "eo us", + "Ġx range", + "Ġxr ange", + "Q uality", + "Qu ality", + "Qual ity", + "v v", + "Man age", + "Ma nage", + "Mana ge", + "( ($", + "(( $", + "ace ments", + "ac ements", + "acement s", + "ĠBr others", + "ĠBro thers", + "ĠBrother s", + "Ġ HEAD", + "ĠHE AD", + "Ġ Unsupported", + "ĠUn supported", + "ĠUns upported", + "s an", + "sa n", + "e si", + "es i", + "* **Ċ", + "** *Ċ", + "*** Ċ", + "Ġadapt ation", + "Ġ Worker", + "ĠWork er", + "ĠWor ker", + "' ]/", + "'] /", + ".save fig", + "( trans", + "(t rans", + "(tr ans", + "Ø ¬", + "n ee", + "ne e", + "C orrect", + "Cor rect", + ".. .\")Ċ", + "... \")Ċ", + "...\" )Ċ", + "Ġsubmit ting", + "- path", + "-p ath", + "ĉ last", + "ĉl ast", + "is san", + "iss an", + "issa n", + ". xlabel", + ".x label", + "Ġ Separ", + "ĠS epar", + "ĠSe par", + "ĠSep ar", + "/ no", + "/n o", + "_ best", + "_b est", + "_be st", + "ĠM ills", + "ĠMill s", + "ĠMil ls", + "_ sock", + "_s ock", + "_so ck", + "_soc k", + "( flag", + "(f lag", + "(fl ag", + "Ġdest inations", + "Ġdestination s", + "Ġdestin ations", + "em ption", + "emp tion", + "empt ion", + "Ġ FAIL", + "ĠF AIL", + "ĠFA IL", + "å ĴĮ", + "åĴ Į", + "Ġ rp", + "Ġr p", + "f act", + "fa ct", + "fac t", + "ĉ len", + "ĉl en", + "D AY", + "DA Y", + "Ġse iz", + "Ġsei z", + "_ dst", + "_d st", + "_ds t", + "l ip", + "li p", + ". Linear", + ".L inear", + ".Line ar", + "Ġ Basket", + "ĠB asket", + "ĠBas ket", + "$ t", + "$ i", + "- brand", + "-b rand", + "-br and", + "Ġ Neil", + "ĠN eil", + "ĠNe il", + "Ġ Eq", + "ĠE q", + "Ġt hou", + "Ġth ou", + "Ġtho u", + "o gene", + "og ene", + "ogen e", + "oge ne", + "Ġscholar ship", + "Ġscholars hip", + "æĽ ´", + "Ġs wo", + "Ġsw o", + "ag inator", + "agina tor", + "e ni", + "en i", + "( book", + "(b ook", + "Ġ blink", + "Ġb link", + "Ġbl ink", + "Ġbli nk", + "t hus", + "th us", + "Ġ cancellationToken", + "Ġc ancellationToken", + "Ġcancell ationToken", + "Ġcancellation Token", + "ĠPalestin ians", + "ĠPalestinian s", + "Ġprof itable", + "Ġprofit able", + "Ġback pack", + "en son", + "ens on", + "enso n", + "< Long", + " < /", + "_ WORD", + "_W ORD", + "\\M igrations", + "\\Migration s", + "Ġ ENABLE", + "ĠEN ABLE", + "_PARAM ETER", + "ĠB ishop", + "ĠBi shop", + "ĠBis hop", + ". subject", + ".sub ject", + "il las", + "ill as", + "illa s", + ". matrix", + ".m atrix", + ".mat rix", + "urre nces", + "urrenc es", + "urr ences", + "urrence s", + "* y", + "Ġcost ly", + "Ġ Chuck", + "ĠCh uck", + "ĠChu ck", + "Ġc loses", + "Ġcl oses", + "Ġclose s", + "Ġclos es", + "Ġclo ses", + "ĠM ight", + "ĠMi ght", + "ĠMig ht", + "- store", + "-st ore", + "Ġ mall", + "Ġm all", + "Ġma ll", + "Ġmal l", + "i eten", + "ie ten", + "iet en", + "iete n", + ". Abs", + ".A bs", + ".Ab s", + "Ġcou pled", + "Ġcouple d", + "Ġcoup led", + ". basic", + ".b asic", + ".ba sic", + "Ġ ::::::::", + "Ġ: :::::::", + "Ġ:: ::::::", + "Ġ::: :::::", + "M aker", + "Make r", + "Ma ker", + "c annot", + "can not", + "Ġ ach", + "Ġa ch", + "Ġac h", + "ĠE li", + "ĠEl i", + "âĪ Ĵ", + "o rna", + "or na", + "orn a", + "Ġ cps", + "Ġc ps", + "Ġcp s", + "Ġthere of", + "Ġther eof", + "Ġ@ {", + "Ġ NSMutableArray", + "ĠNSMutable Array", + "Î ½", + "pro ductive", + "product ive", + "prod uctive", + "S quare", + "tem pts", + "temp ts", + "tempt s", + "Ġelim inated", + "Ġeliminate d", + "Ġelimin ated", + "< M", + "Ġcons ervatives", + "Ġconserv atives", + "Ġconservative s", + "ĠS urg", + "ĠSur g", + "ĠSu rg", + ". par", + ".p ar", + ".pa r", + "ĠB uch", + "ĠBu ch", + "* b", + "F ort", + "For t", + "Fo rt", + "Col our", + "Ġ Chi", + "ĠC hi", + "ĠCh i", + "e dic", + "ed ic", + "edi c", + "> true", + "ĠN YC", + "ĠNY C", + "Ġb ored", + "Ġbo red", + "Ġbor ed", + "Ġbore d", + "Ġ Detect", + "ĠD etect", + "ĠDet ect", + "Ġapp ar", + "Ġap par", + "Ġje ans", + "Ġjean s", + "Ġ Tak", + "ĠT ak", + "ĠTa k", + "I OD", + "IO D", + "ĠH orse", + "ĠHor se", + "( FILE", + "(F ILE", + "( ?", + "r ique", + "ri que", + "opt imizer", + "optim izer", + "optimize r", + "n at", + "na t", + "lo ys", + "loy s", + "ĉ Token", + "ĉT oken", + "oub ted", + "u ess", + "ue ss", + "ues s", + "oc oa", + "oco a", + "Data Member", + "_ POWER", + "_P OWER", + "_PO WER", + "class List", + "Push Button", + "Ġ WiFi", + "ĠWi Fi", + ". Stream", + ".St ream", + ".Str eam", + ". guild", + ".g uild", + ".gui ld", + "Ġn og", + "Ġno g", + "ĠPort ugal", + "ĠPortug al", + "ĠUn ter", + "ĠUnt er", + "Pr imitive", + "Prim itive", + "b oss", + "bo ss", + "bos s", + "ĠDe utsch", + "Ġer otic", + "Ġerot ic", + "Ġero tic", + "Ġ strconv", + "Ġstr conv", + ".Try Parse", + "Ġ grams", + "Ġg rams", + "Ġgr ams", + "Ġgram s", + "Ġgra ms", + ". Success", + ".S uccess", + "_ pk", + "_p k", + "ĠHar vey", + "-m inded", + "-min ded", + ". country", + ".c ountry", + ".count ry", + "[ ]\"", + "[] \"", + "Ġ angel", + "Ġan gel", + "Ġang el", + "Ġange l", + "Ġbe ats", + "Ġbeat s", + "ĠV or", + "ĠVo r", + "i lio", + "il io", + "ili o", + ". master", + ".m aster", + ".mas ter", + ".ma ster", + "s omething", + "some thing", + "som ething", + "Ġ PACK", + "ĠP ACK", + "ĠPA CK", + "ĠPAC K", + "( if", + "(i f", + "Request Body", + "Ġ antes", + "Ġan tes", + "Ġant es", + "Ġante s", + "/ widget", + "/w idget", + "Ġ modo", + "Ġm odo", + "Ġmod o", + "Ġmo do", + "Ġ AW", + "ĠA W", + "f inder", + "find er", + "fin der", + "fi nder", + "Ġ optimized", + "Ġopt imized", + "Ġoptim ized", + "Ġoptimize d", + "Ġmiss iles", + "Ġmissile s", + "N B", + "ĉ internal", + "ĉint ernal", + "ĉin ternal", + "ĉinter nal", + "t ex", + "te x", + "ĠS ri", + "ĠSr i", + "Ġdam aging", + "Ġ Mais", + "ĠM ais", + "ĠMa is", + "ĠMai s", + "- Allow", + "-Al low", + "Ġ Zh", + "ĠZ h", + "- alt", + "-a lt", + "-al t", + "Ġ ));ĊĊ", + "Ġ) );ĊĊ", + "Ġ)) ;ĊĊ", + "Ġ));Ċ Ċ", + "Ġ)); ĊĊ", + "è ī", + "Ġinflu ences", + "Ġinfluence s", + "Ġc atal", + "Ġca tal", + "Ġcat al", + "Ġcata l", + "_ REGISTER", + "_REG ISTER", + "ĠAPI s", + "ĠAP Is", + "-cent ury", + "Ġ biology", + "Ġb iology", + "Ġbi ology", + "Ġbio logy", + "Ġ Actual", + "ĠAct ual", + "ĠAc tual", + "Ġ heels", + "Ġhe els", + "Ġheel s", + "TR ACE", + "TRA CE", + "_ DIG", + "_D IG", + "_DI G", + "D ataset", + "Data set", + "Dat aset", + "Datas et", + "ĠM atter", + "ĠMat ter", + "ĠMatt er", + "ĠMatte r", + "Ġ classifier", + "Ġclass ifier", + ".w ikipedia", + "ĠRo gers", + "ĠRog ers", + "ĠRoger s", + "Ġdon ated", + "Ġdonate d", + "raw ler", + "rawl er", + "e nen", + "en en", + "ene n", + "Ġcas inos", + "Ġcasino s", + "Ġcasi nos", + "or tal", + "ort al", + "orta l", + "Ġp rive", + "Ġpr ive", + "Ġpriv e", + "Ġpri ve", + "s pe", + "sp e", + "du cers", + "duc ers", + "duce rs", + "ducer s", + ". ep", + ".e p", + "Ġgr asp", + "Ġgra sp", + "Ġgras p", + "ac ji", + "Ġd airy", + "Ġda iry", + "Ġdai ry", + "Ġdair y", + "Ġb uses", + "Ġbu ses", + "Ġbus es", + ". comm", + ".c omm", + ".com m", + ".co mm", + ". ins", + ".in s", + ".i ns", + "Ġ IRS", + "ĠI RS", + "ĠIR S", + "Ġ Beer", + "ĠB eer", + "ĠBe er", + "ĠBee r", + "a dc", + "ad c", + "o ard", + "oa rd", + "_ MET", + "_M ET", + "_ME T", + "Ġ' +'", + "Ġ'+ '", + "r ans", + "ra ns", + "ran s", + "Ġk inda", + "Ġkind a", + "Ġki nda", + "Ġkin da", + "Ġ âĶĤ", + "ĠâĶ Ĥ", + "ĠM aur", + "ĠMa ur", + "а г", + "аР³", + "Ġband width", + "i bus", + "ib us", + "ibu s", + "Ġ Different", + "ĠD ifferent", + "( mat", + "(m at", + "Ġ Resume", + "ĠRe sume", + "ĠRes ume", + "_ UNS", + "_U NS", + "_UN S", + "est ablish", + "Ġfon ction", + "Sub scription", + "_ company", + "_com pany", + "_comp any", + "Ġ lightly", + "Ġl ightly", + "Ġlight ly", + ". confirm", + ".con firm", + ".conf irm", + ". yaml", + ".y aml", + "Ġ Boost", + "ĠBo ost", + "ĠBoo st", + "Com merce", + "Comm erce", + "- template", + "-t emplate", + "-temp late", + "_ DELAY", + "_DE LAY", + "_DEL AY", + "Ġ HI", + "ĠH I", + "Ġn avig", + "Ġna vig", + "Ġnav ig", + "( Sender", + "(S ender", + "Ġ HS", + "ĠH S", + "_ \"+", + "_\" +", + "Ġ REQUEST", + "ĠRE QUEST", + "ĠREQ UEST", + "Ġ wifi", + "Ġw ifi", + "Ġwi fi", + "= \"\"Ċ", + "=\" \"Ċ", + "=\"\" Ċ", + "] )->", + "]) ->", + "])- >", + "Ġr ope", + "Ġro pe", + "Ġrop e", + "Ġvi olated", + "Ġviol ated", + "Ġviolate d", + "Ġgl ance", + "ĠK urd", + "ĠKur d", + "ĠKu rd", + "Ġ è®", + "Ġè ®", + "d eck", + "de ck", + "dec k", + "Ġ ISBN", + "ĠIS BN", + "Ġin fect", + "Ġinf ect", + "Ġ Foo", + "ĠF oo", + "ĠFo o", + "Ġ getter", + "Ġg etter", + "Ġget ter", + "Ġ tener", + "Ġt ener", + "Ġte ner", + "Ġten er", + "a ppe", + "ap pe", + "app e", + ". hh", + ".h h", + "_ hot", + "_h ot", + "< AM", + " \".$", + ">\" .$", + ">\". $", + "Ġre lies", + "Ġrel ies", + "Ġreli es", + "Ġrelie s", + "( Console", + "Int ernational", + "Inter national", + "Intern ational", + "- >{$", + "-> {$", + "->{ $", + "M id", + "Mi d", + "Ġdis sert", + "Ġdiss ert", + "Ġdisse rt", + "d ds", + "dd s", + "Ġdeposit s", + "Ġdepos its", + "ĉ driver", + "ĉd river", + "# ga", + "#g a", + "p rising", + "pr ising", + "pri sing", + "print ln", + "Ġp resenter", + "Ġpres enter", + "Ġpresent er", + "Ġpresente r", + "Ġm ines", + "Ġmin es", + "Ġmi nes", + "Ġmine s", + "C SS", + "CS S", + "Ġ Dual", + "ĠD ual", + "ĠDu al", + "( !(", + "(! (", + "Ġk am", + "Ġka m", + "Ġ isLoading", + "Ġis Loading", + "Ġ Protect", + "ĠProt ect", + "ĠProte ct", + ". upper", + ".u pper", + ".up per", + "a rium", + "ar ium", + "ari um", + "] :ĊĊĊ", + "]: ĊĊĊ", + "]:Ċ ĊĊ", + "]:ĊĊ Ċ", + "Y ii", + "- shirt", + "-sh irt", + "Ġ IMAGE", + "ĠIM AGE", + "_ colors", + "_color s", + "_col ors", + "Ġ urgent", + "Ġur gent", + "Ġurge nt", + "Ġurg ent", + ". Container", + ".Cont ainer", + "! (Ċ", + "!( Ċ", + "S aturday", + "Ġsoc ieties", + "Ġsoci eties", + "Ġ Than", + "ĠT han", + "ĠTh an", + "Ġ Cod", + "ĠC od", + "ĠCo d", + "= @", + "Ġ attachments", + "Ġattach ments", + "Ġattachment s", + ". mobile", + ".m obile", + ".mob ile", + "Ġs pite", + "Ġsp ite", + "Ġspi te", + "Ġspit e", + "Ġ bounce", + "Ġb ounce", + "Ġbo unce", + "Ġbou nce", + "r awl", + "ra wl", + "raw l", + "instance type", + "instanc etype", + "ĠTr uck", + "ĠTru ck", + "Ġmanip ulation", + "( Config", + "- inst", + "-in st", + "-i nst", + "-ins t", + "Ġ stor", + "Ġs tor", + "Ġst or", + "Ġsto r", + "it ution", + "itu tion", + "Preferred Gap", + "Ġmain AxisAlignment", + "Ġlist ened", + "Ġlisten ed", + "Ġliste ned", + "'' 'ĊĊ", + "'''Ċ Ċ", + "''' ĊĊ", + "ott age", + "otta ge", + "- project", + "-pro ject", + ". APPLICATION", + ".AP PLICATION", + "ĉ root", + "ĉr oot", + "Ġw hit", + "Ġwh it", + "Ġ bilder", + "Ġb ilder", + "Ġbi lder", + "Ġbil der", + "Ġbild er", + "Ġ ker", + "Ġk er", + "Ġke r", + "Ġappl iances", + "Ġappliance s", + "ro wave", + "row ave", + "ìĿ Ģ", + "em atics", + "ema tics", + "ematic s", + "emat ics", + "Ġ Org", + "ĠO rg", + "ĠOr g", + "o ping", + "op ing", + "opi ng", + "_ SEARCH", + "_SE ARCH", + "Ġc ham", + "Ġch am", + "Ġcha m", + "add ContainerGap", + "Ġ ().", + "Ġ( ).", + "Ġ() .", + "Ġ Arrow", + "ĠAr row", + "ĠArr ow", + "Il legal", + "Ill egal", + "Current ly", + "Curr ently", + "Ġ usa", + "Ġu sa", + "Ġus a", + "Ġpass words", + "Ġpassword s", + "Ġre nown", + "Ġren own", + "a vern", + "av ern", + "ave rn", + "aver n", + "ĠE vil", + "ĠEv il", + "Ġ concat", + "Ġcon cat", + "Ġconc at", + "Ġd uo", + "Ġdu o", + "Ġ vale", + "Ġv ale", + "Ġval e", + "Ġva le", + "Ġ Bean", + "ĠB ean", + "ĠBe an", + "ĠBea n", + "Ġind icators", + "Ġindic ators", + "Ġindicator s", + "Ġindica tors", + "c math", + "cm ath", + "ĠP ump", + "ĠPu mp", + "Nov ember", + "ific ant", + "ifi cant", + "ifica nt", + "_ DOMAIN", + "_DO MAIN", + "_DOM AIN", + "re gar", + "reg ar", + "rega r", + "Ġ Portal", + "ĠP ortal", + "ĠPort al", + "ĠPor tal", + "\" $", + "Ġ formerly", + "Ġformer ly", + "\" ]:Ċ", + "\"] :Ċ", + "\"]: Ċ", + "Ġ Visibility", + "ĠVis ibility", + ".getElementsBy ClassName", + "_ RED", + "_RE D", + "_R ED", + "Ġch ampions", + "Ġchampion s", + "Ġchamp ions", + "à ´", + "Val or", + "Va lor", + "_ es", + "_e s", + "* a", + "- repeat", + "-re peat", + "B and", + "Ban d", + "Ba nd", + ". stage", + ".st age", + "Ġbure auc", + "Ġbureau c", + "C nt", + "e ten", + "et en", + "ete n", + "- function", + "-f unction", + "Ġm uito", + "Ġmu ito", + "Ġmuit o", + "P ID", + "PI D", + "_ editor", + "_e ditor", + "_edit or", + "_ed itor", + "Ġcr ashed", + "Ġcrash ed", + "Ġcra shed", + "d ead", + "de ad", + "dea d", + "k at", + "ka t", + "a gh", + "ag h", + "Ġ EXT", + "ĠE XT", + "ĠEX T", + "as ser", + "ass er", + "asse r", + "- small", + "-s mall", + "-sm all", + "Ġre aliz", + "Ġreal iz", + "( Entity", + "(E ntity", + "ú s", + "Ġ Actually", + "ĠAct ually", + "ĠActual ly", + "Ġ Elite", + "ĠE lite", + "ĠEl ite", + "ĠEli te", + "Ġ helm", + "Ġh elm", + "Ġhe lm", + "Ġhel m", + "( nonatomic", + "(non atomic", + "a sher", + "as her", + "ash er", + "Comm unity", + "all eng", + "alle ng", + "allen g", + "i ry", + "ir y", + "ĠG rowth", + "ĠGrow th", + "Ġs ue", + "Ġsu e", + "Ġf requencies", + "Ġfrequ encies", + "_ descriptor", + "_des criptor", + ". Attribute", + ".At tribute", + "Ġrec ipients", + "Ġrecipient s", + "Ġrecip ients", + "_ NS", + "_N S", + "/ \"+", + "/\" +", + "i ban", + "ib an", + "iba n", + "Ġ athlete", + "Ġath lete", + "Ġ Ign", + "ĠI gn", + "ĠIg n", + "_ DMA", + "_D MA", + "_DM A", + "( ds", + "(d s", + "Ġ Requirements", + "ĠRequire ments", + "ĠRequirement s", + "A DI", + "AD I", + "e rez", + "er ez", + "ere z", + "\\ Admin", + "br aska", + "bra ska", + "bras ka", + "ĠR ust", + "ĠRu st", + "ĠRus t", + "Re lation", + "Rel ation", + "C OD", + "CO D", + "Ġ VERSION", + "ĠV ERSION", + "ĠVER SION", + "e mma", + "em ma", + "emm a", + ") ){", + ")) {", + ". Duration", + ".D uration", + "Ġ Camb", + "ĠC amb", + "ĠCam b", + "ĠCa mb", + "- logo", + "-l ogo", + "-lo go", + "-log o", + "Ġread able", + "Ġcre ators", + "Ġcreat ors", + "Ġcreator s", + "Ġcrea tors", + "( )];Ċ", + "() ];Ċ", + "()] ;Ċ", + "Up Down", + "- half", + "-h alf", + ".get Month", + ".getM onth", + "( sf", + "(s f", + "P ic", + "Pi c", + "Ġh unger", + "Ġhun ger", + "Ġhung er", + "Ġhu nger", + ". tx", + ".t x", + "Ġex ceeded", + "Ġexceed ed", + "Ġexce eded", + "_ seed", + "_s eed", + "_se ed", + "( ^", + "_ sk", + "_s k", + ". perform", + ".per form", + "Ġ >::", + "Ġ> ::", + "Ġ mongo", + "Ġm ongo", + "Ġmon go", + "Ġmo ngo", + "Ġmong o", + "= float", + "=f loat", + "bind Param", + "S mart", + "Sm art", + "i fa", + "if a", + "Ġse curities", + "Ġsec urities", + "Ġpre jud", + "Ġ ,\"", + "Ġ, \"", + "Ġcor ps", + "Ġcorp s", + "Ġv ra", + "Ġvr a", + "ama care", + "amac are", + "i terr", + "it err", + "ite rr", + "iter r", + "( Media", + "(M edia", + "(Me dia", + "u che", + "uch e", + "uc he", + "Ġ cob", + "Ġc ob", + "Ġco b", + "Ġl iber", + "Ġli ber", + "Ġlib er", + ". geometry", + ".ge ometry", + ".geom etry", + ".geo metry", + "L ocator", + "Loc ator", + "Ġsl iding", + "Ġslid ing", + "Ġs urgical", + "Ġsurg ical", + "_ CUR", + "_C UR", + "Ġcon sect", + "Ġcons ect", + "Ġconsec t", + "Ġconse ct", + "[ *", + "ĠRe sort", + "ĠRes ort", + "St ub", + "_ DOUBLE", + "_DO UBLE", + "Ġ Soph", + "ĠS oph", + "ĠSo ph", + "Ġelect oral", + "_ disable", + "_d isable", + "_dis able", + "Ġ Ñģо", + "ĠÑģ о", + "ĠLight ning", + "Ġ mentions", + "Ġm entions", + "Ġmention s", + "Ġment ions", + "o cy", + "oc y", + "Ġle aked", + "Ġleak ed", + "Ġrelax ing", + "P resenter", + "Pres enter", + "Present er", + "v sp", + "vs p", + "Ġg uilt", + "Ġgu ilt", + "Ġgui lt", + "=- =-", + ". reply", + ".re ply", + ".rep ly", + "Ġ Mirror", + "ĠM irror", + "ĠMir ror", + "ĠMi rror", + "C amp", + "Ca mp", + "Cam p", + "Ġ+#+ #+#+", + "Ġ+#+#+#+ #+#+", + ". Author", + ".A uthor", + ".Auth or", + "Ġ directive", + "Ġdirect ive", + "Ġdir ective", + "- hook", + "-h ook", + "íĦ °", + "} ĊĊĊĊĊ", + "}Ċ ĊĊĊĊ", + "}ĊĊ ĊĊĊ", + "}ĊĊĊ ĊĊ", + "}ĊĊĊĊ Ċ", + "@ pytest", + "_ rand", + "_r and", + "_ra nd", + "m is", + "mi s", + "Ġcolor ful", + "u je", + "uj e", + "l asses", + "lass es", + "las ses", + "Ġ Classes", + "ĠC lasses", + "ĠCl asses", + "ĠClass es", + "ĠClasse s", + ". have", + ".h ave", + "% ),", + "%) ,", + "é¢ ĺ", + "Ġdistur bing", + "Ġdisturb ing", + "sub string", + "substr ing", + "subst ring", + "subs tring", + "ĠK oh", + "ĠKo h", + "In vest", + "Inv est", + "p urchase", + "Ġrec ycling", + "Ġrecycl ing", + "Ġ ART", + "ĠA RT", + "ĠAR T", + "ier archy", + "Ġ fps", + "Ġf ps", + "Ġfp s", + ". checkBox", + ".check Box", + "íķ ´", + "_ material", + "_m aterial", + "_mat erial", + "du cation", + "duc ation", + "Ġ fw", + "Ġf w", + "u dit", + "ud it", + "udi t", + "Ġreview ing", + "Ġ Sid", + "ĠS id", + "ĠSi d", + "S yntax", + "Sy ntax", + "Syn tax", + "Ġ Written", + "ĠW ritten", + "ĠWr itten", + "ar gar", + "arg ar", + "arga r", + "U ME", + "UM E", + "/ q", + "Class ifier", + "Off icial", + "Ġj azz", + "Ġja zz", + "Ġjaz z", + "Ġ omega", + "Ġo mega", + "Ġom ega", + "Ph ysics", + "Phys ics", + "Ġl ugar", + "Ġlu gar", + "Ġlug ar", + "_access or", + "_acc essor", + ". commands", + ".command s", + ".comm ands", + "Ab ility", + "Ġ Batch", + "ĠB atch", + "ĠBat ch", + "R AM", + "RA M", + "Ġenc ounters", + "Ġencounter s", + "Ġencount ers", + ". Qu", + ".Q u", + "B YTE", + "BY TE", + "Ġ Distribution", + "ĠD istribution", + "ĠDis tribution", + "ĠDistrib ution", + "Ġ uso", + "Ġu so", + "Ġus o", + "ĠRe covery", + "ĠRec overy", + "ĠReco very", + "ĠRecover y", + "ap proved", + "appro ved", + "approve d", + "Ġden ial", + "/ share", + "/s hare", + "/sh are", + "Link edList", + "Linked List", + ") čĊčĊčĊ", + ")čĊ čĊčĊ", + ")čĊčĊ čĊ", + "u ddy", + "ud dy", + "udd y", + "Ġf ines", + "Ġfin es", + "Ġfine s", + "Ġfi nes", + "Ġ ry", + "Ġr y", + "Un icode", + "Uni code", + "ĉ render", + "ĉr ender", + "ĉre nder", + "Ġprem ises", + "Ġpremise s", + "Ġpremi ses", + "Ġ pon", + "Ġp on", + "Ġpo n", + "ali ases", + "alias es", + "alia ses", + "/ Foundation", + "/F oundation", + "c uda", + "cu da", + "ĠC ock", + "ĠCo ck", + "ĠCoc k", + ", :)", + ",: )", + "( folder", + "(f older", + "Ġm éd", + "Ġmé d", + "d rag", + "dr ag", + "dra g", + "Ġtal ents", + "Ġtalent s", + "Ġtale nts", + "Ġ ĠĠĊĊ", + "ĠĠ ĠĊĊ", + "ĠĠĠ ĊĊ", + "ĠĠĠĊ Ċ", + "е ÑģÑĤв", + "еÑģÑĤ в", + "m ob", + "mo b", + ".y ml", + "Ġ aster", + "Ġa ster", + "Ġas ter", + "Ġast er", + "Ġdis cre", + "Ġdisc re", + "go al", + "ĠG TX", + "ĠGT X", + "Ġ SUCCESS", + "ĠS UCCESS", + "Ġ LONG", + "ĠL ONG", + "ĠLO NG", + "( find", + "(f ind", + "(fin d", + "(fi nd", + "Ġ singular", + "Ġs ingular", + "Ġsing ular", + "_ sz", + "_s z", + "ĠEth ereum", + "ĠEther eum", + ". .Ċ", + ".. Ċ", + "Ġir res", + "Ġirre s", + "Ġirr es", + "' )){Ċ", + "') ){Ċ", + "')) {Ċ", + "Ġmin isters", + "Ġminister s", + "Ġmini sters", + "Ġminist ers", + "St eps", + "Step s", + "Ste ps", + "iver sal", + "ivers al", + "Ġ Nevertheless", + "ĠNever theless", + "- led", + "-l ed", + "-le d", + "Ġ( %)", + "Ġ(% )", + "ç¡ ®", + "Ġ timezone", + "Ġtime zone", + "Ġstr anger", + "Ġstrange r", + "Ġstrang er", + "Ġstran ger", + "Ġstra nger", + "( render", + "(r ender", + "(re nder", + "Ġsh util", + "Ġshut il", + "Ġ mph", + "Ġm ph", + "Ġmp h", + "Ġt rio", + "Ġtr io", + "Ġtri o", + "p py", + "pp y", + "Ġpred omin", + "Ġ endors", + "Ġend ors", + "ĠRuss ians", + "ĠRussia ns", + "ĠRussian s", + "ĉ row", + "ĉr ow", + "Ġ wizard", + "Ġw izard", + ". serialize", + ".s erialize", + ".serial ize", + "Ġcompl ained", + "Ġcomplain ed", + "Ġs ido", + "Ġsi do", + "Ġsid o", + "Ġdel ighted", + "Ġdelight ed", + "- me", + "-m e", + "ĠR av", + "ĠRa v", + "H uman", + "Hum an", + "Hu man", + "a days", + "ad ays", + "ada ys", + "aday s", + "re cv", + "rec v", + "Work ing", + "J ump", + "Ju mp", + "Ġ Ã¥r", + "ĠÃ¥ r", + "Ġ Automatic", + "ĠAuto matic", + "ĠAut omatic", + "ĠAutom atic", + "_ Base", + "_B ase", + "æł ¼", + "aur ants", + "aurant s", + "aura nts", + " ¯", + "æ ¸", + "(C Type", + "I FI", + "IF I", + "( amount", + "(a mount", + "(am ount", + "Ġbel ieving", + "Ġbelie ving", + "= mysql", + "=m ysql", + "=my sql", + "Ġ fir", + "Ġf ir", + "Ġfi r", + "Ġrest oration", + "Ġresto ration", + "er eco", + "ere co", + "Ð ¢", + "_ '+", + "_' +", + "Ġe book", + "Ġeb ook", + "Ġde bris", + "Ġdeb ris", + "( inputs", + "(input s", + "(in puts", + "(inp uts", + "AY OUT", + "Ġscre aming", + "Ġscream ing", + "a via", + "av ia", + "avi a", + "l ander", + "land er", + "la nder", + "lan der", + "Ġdist ress", + "Ġdi stress", + "Ġdistr ess", + "Ġas sembled", + "Ġassemble d", + "Ġ Avoid", + "ĠA void", + "ĠAv oid", + "( thread", + "(t hread", + "(th read", + "Ġ RPC", + "ĠR PC", + "ĠRP C", + "_ EXIT", + "_EX IT", + "( queue", + "(q ueue", + "и ÑģÑĤ", + "иÑģ ÑĤ", + "D ll", + "Ġsk ull", + "Ġsku ll", + "_ pub", + "_p ub", + "ch ez", + "che z", + "m inate", + "min ate", + "mina te", + "en sen", + "ens en", + "ense n", + "Ġins ane", + "Ġinsan e", + "b ounds", + "bo unds", + "bound s", + "bou nds", + "ĠR osen", + "ĠRo sen", + "ĠRose n", + "ĠRos en", + "Ġcondition ing", + "process ed", + "proc essed", + "v ideos", + "vid eos", + "video s", + "vide os", + "f our", + "fo ur", + ". Conv", + ".Con v", + ".Co nv", + "| ;Ċ", + "Person al", + "Pers onal", + "Persona l", + "cer pt", + ":UIControlState Normal", + "Ġd oses", + "Ġdo ses", + "Ġdos es", + "Ġdose s", + "ĠK arl", + "ĠKar l", + "ĠKa rl", + "ĠF requ", + "ĠFr equ", + "ĠFre qu", + ". BASE", + ".B ASE", + "Ġ Vote", + "ĠV ote", + "ĠVo te", + "Ġcon current", + "Ġconc urrent", + "ĠMessageBox Icon", + "Ġ Ãĸ", + "Ġà ĸ", + "ĠDu bai", + "ĠDub ai", + "Ġ Retail", + "ĠR etail", + "ĠRe tail", + "ĠRet ail", + ": number", + ":n umber", + ":num ber", + "Ġ Observer", + "ĠOb server", + "ĠObserv er", + "ĠObs erver", + "Ġ BigInteger", + "ĠB igInteger", + "ĠBig Integer", + "ĠBigInt eger", + "_ origin", + "_or igin", + "_orig in", + "_ori gin", + "_ WORK", + "_W ORK", + "F rames", + "Frame s", + "Fr ames", + "Fra mes", + "Ġnot ably", + ". âĢľ", + "Ġt ropical", + "Ġtrop ical", + "Ġn iche", + "Ġni che", + "Ġnic he", + "Ġnich e", + "a mina", + "am ina", + "amin a", + "ami na", + ". sys", + ".s ys", + ".sy s", + "( tokens", + "(t okens", + "(token s", + "(tok ens", + "mod ify", + "o sit", + "os it", + "osi t", + "st rom", + "str om", + "stro m", + "ĠC omics", + "ĠCom ics", + "ĠComic s", + "O PTION", + "OP TION", + "OPT ION", + "T icket", + "Tick et", + "Ti cket", + "Ġf actories", + "Ġfact ories", + "Ġfactor ies", + "Ġfacto ries", + "Ġdis put", + "Ġdisp ut", + "_ File", + "_F ile", + "ĠF inn", + "ĠFin n", + "ĠFi nn", + "e ee", + "ee e", + "ĠDis cord", + "ĠDisc ord", + "ĠDisco rd", + "_ money", + "_m oney", + "_mon ey", + "_mo ney", + ". tpl", + ".t pl", + ".tp l", + "_ safe", + "_s afe", + "_sa fe", + "L B", + "Ġg lut", + "Ġgl ut", + "Ġglu t", + "J K", + ". flow", + ".f low", + ".fl ow", + "- cont", + "-c ont", + "-con t", + "-co nt", + "g os", + "go s", + "Ġhor izon", + "ĠR ush", + "ĠRu sh", + "ĠRus h", + ": :*", + ":: *", + "P ipe", + "Pi pe", + "u lla", + "ul la", + "ull a", + "b orough", + "bo rough", + "bor ough", + "boro ugh", + "he imer", + "heim er", + "hei mer", + "( move", + "(m ove", + "( Text", + "(T ext", + "} );čĊčĊ", + "}) ;čĊčĊ", + "}); čĊčĊ", + "});čĊ čĊ", + "w elcome", + "wel come", + "Ġ Components", + "ĠCom ponents", + "ĠComponent s", + "ĠComp onents", + "Ġgovern ance", + "c losed", + "cl osed", + "close d", + "clo sed", + "ĉ margin", + "ĉm argin", + "Ġla undry", + "Ġ Terminal", + "ĠTerm inal", + "ĠTermin al", + "iz ards", + "izar ds", + "izard s", + ". âĢĶ", + ". remote", + ".rem ote", + ". radius", + ".r adius", + ".rad ius", + "ĠQue bec", + "Ġ dh", + "Ġd h", + "T ech", + "Te ch", + "ĠM ist", + "ĠMi st", + "ĠMis t", + "s eller", + "se ller", + "sel ler", + "sell er", + "_ literal", + "_l iteral", + "_lite ral", + "_lit eral", + "Ġgen ius", + "Ġ brains", + "Ġbr ains", + "Ġbrain s", + "Ġbra ins", + "g em", + "ge m", + "Ġ Measure", + "ĠMe asure", + "Ġcat ast", + "Ġcata st", + "r ance", + "ra nce", + "ran ce", + ". TextField", + ".T extField", + ".Text Field", + "Ġcon suming", + "Ġcons uming", + "Ġconsum ing", + "Ġ'\\ ''", + "Ġ'\\' '", + "oubted ly", + "Ġ Certain", + "ĠC ertain", + "ĠCert ain", + "ĠCer tain", + "E v", + "er ti", + "ert i", + "b eing", + "be ing", + "bei ng", + "Ex perience", + "Ġ //[", + "Ġ// [", + "Ġ/ /[", + "ĠAr abic", + "ĠArab ic", + "ĠAra bic", + "ĠC rist", + "ĠCr ist", + "ĠCri st", + "Ġ Azure", + "ĠA zure", + "ĠAz ure", + "Ġ hora", + "Ġh ora", + "Ġhor a", + "Ġho ra", + "l adesh", + "lad esh", + "\\ Blueprint", + "d ar", + "da r", + ". rel", + ".re l", + ".r el", + "Ġsup rem", + "ĠRe agan", + "Ġ Attributes", + "ĠAt tributes", + "ĠAttribute s", + "- sidebar", + "-s idebar", + "-side bar", + "Ġuse Styles", + "ĠA irlines", + "ĠAir lines", + "Ġh ills", + "Ġhill s", + "Ġhil ls", + "/x html", + "v inc", + "vin c", + "vi nc", + "_ mock", + "_m ock", + "_mo ck", + "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠP ill", + "ĠPi ll", + "ĠPil l", + ".Layout Style", + "ĠComm ander", + "ĠCommand er", + "] <", + "sign ature", + "sig nature", + "Ġ{ }čĊ", + "Ġ{} čĊ", + "Ġhat red", + "Ġ ëĭ", + "Ġë ĭ", + "ole sterol", + "Ġ ********", + "Ġ* *******", + "Ġ** ******", + "Ġ*** *****", + "Ġ**** ****", + "Ġ***** ***", + "ancel lor", + "ancell or", + "c rop", + "cr op", + "cro p", + "T IM", + "TI M", + "ĉ ĉĊĊ", + "ĉĉ ĊĊ", + "ĉĉĊ Ċ", + "ys qli", + "ysql i", + "u itive", + "uit ive", + "ĉ unset", + "ĉun set", + "_ sel", + "_s el", + "_se l", + "Ġ menus", + "Ġm enus", + "Ġmen us", + "Ġmenu s", + "t ick", + "ti ck", + "tic k", + "Ġcon stitute", + "Ġconstit ute", + "Ġconstitu te", + "Ġ Elements", + "ĠE lements", + "ĠEl ements", + "ĠElement s", + "ĠEle ments", + "ĠElem ents", + "Ġ Redis", + "ĠR edis", + "ĠRe dis", + "ĠRed is", + "ag gio", + "agg io", + "aggi o", + "_ fp", + "_f p", + "_ depend", + "_d epend", + "_de pend", + "_dep end", + "e mas", + "em as", + "ema s", + "C AST", + "CA ST", + "CAS T", + "o range", + "or ange", + "ora nge", + "oran ge", + "orang e", + "j on", + "jo n", + "Ġ Emily", + "ĠEm ily", + "ĠEmil y", + "Ġpot atoes", + "Ġpotato es", + "Ġre ceptor", + "Ġrecept or", + "Ġrecep tor", + "Ġ Electronic", + "ĠElect ronic", + "ĠElectro nic", + "ĠElectron ic", + "Ġ Lights", + "ĠL ights", + "ĠLight s", + "Ġcomb ining", + "Ġcombin ing", + "Ġ Someone", + "ĠSome one", + "Ġ######## .", + "ĠT OD", + "ĠTO D", + "/ show", + "/s how", + "/sh ow", + "X d", + ". \"'", + ".\" '", + "a fx", + "af x", + "Ġtr agic", + "Ġtrag ic", + "St yled", + "Style d", + "Ġ Marco", + "ĠMar co", + "ĠMarc o", + "G allery", + "d ale", + "da le", + "dal e", + ".âĢĿ ĊĊĊĊ", + ".âĢĿĊĊ ĊĊ", + ".âĢĿĊ ĊĊĊ", + "é rie", + "ér ie", + "éri e", + "/ service", + "/s ervice", + "äº Ĩ", + "Ġ ambient", + "Ġamb ient", + "_ SETTINGS", + "_SET TINGS", + "_SETTING S", + ". Adapter", + ".Ad apter", + "l ene", + "le ne", + "len e", + "Ġtravel s", + "Ġtrav els", + "Not ice", + "Ġc leans", + "Ġclean s", + "Ġcle ans", + "ĠF em", + "ĠFe m", + "c hair", + "ch air", + "cha ir", + "chai r", + "Ñĥ н", + "/ my", + "/m y", + "_ bad", + "_b ad", + "ĠE conomics", + "ĠEcon omics", + "ĠEconomic s", + "ĠEconom ics", + "I SA", + "IS A", + "_ CNT", + "_C NT", + "_CN T", + "( Menu", + "(M enu", + "(Me nu", + "äº İ", + "ĠR idge", + "ĠRid ge", + "ĠRi dge", + "Ġlength y", + "Ġleng thy", + "D ot", + "Do t", + "Ġj umps", + "Ġjump s", + "Ġju mps", + "Ġ hey", + "Ġh ey", + "Ġhe y", + "$ pdf", + "$p df", + "Ġ worm", + "Ġw orm", + "Ġwor m", + "Ġwo rm", + "Ġ sut", + "Ġs ut", + "Ġsu t", + "Ġ sher", + "Ġs her", + "Ġsh er", + "Ġshe r", + "i amo", + "ia mo", + "iam o", + "Ġ Calc", + "ĠC alc", + "ĠCal c", + "ĠCa lc", + "t rieve", + "tr ieve", + "trie ve", + "tri eve", + "Ġc ops", + "Ġco ps", + "Ġcop s", + "ĠCh rom", + "ĠChr om", + "Ġ regulated", + "Ġreg ulated", + "Ġregul ated", + "Ġregulate d", + "reat ment", + "Ġ Higher", + "ĠHigh er", + "o ks", + "ok s", + "Ġde ze", + "Ġdez e", + "LOC ATION", + "ongs To", + "Ġ finite", + "Ġf inite", + "Ġfin ite", + "Ġfi nite", + "Ġv aries", + "Ġvar ies", + "Ġvari es", + "Ġva ries", + "Ġposition ed", + "Ġposit ioned", + "' il", + "'i l", + "éĩ ij", + "Ġh ike", + "Ġhi ke", + "Ġhik e", + "( done", + "(d one", + "(do ne", + "play list", + "Ġ ada", + "Ġa da", + "Ġad a", + "Ġcoast al", + "ĠN ancy", + "ĠNa ncy", + "ĠNan cy", + ".DateTime Field", + "Cpp CodeGen", + "Ġ Similarly", + "ĠSimilar ly", + "r eur", + "re ur", + "reu r", + "Ġ Contr", + "ĠCon tr", + "ĠCont r", + "Ġ Hidden", + "ĠH idden", + "ĠHi dden", + "Ġ Beta", + "ĠB eta", + "ĠBe ta", + "ĠBet a", + "at ched", + "atch ed", + "_ install", + "_inst all", + ". Output", + ".Out put", + "Look up", + "ĠRich mond", + "qu ared", + "quare d", + "qua red", + "Ġm anga", + "Ġman ga", + "Ġma nga", + "Ġmang a", + "- controls", + "-control s", + "ĠBer nard", + "ĠBern ard", + "L arge", + "Ġs lices", + "Ġsl ices", + "Ġslice s", + "Ġslic es", + "Ġoff ence", + "Ġoffen ce", + "ĠM ega", + "ĠMe ga", + "ĠMeg a", + "Ġ estar", + "Ġe star", + "Ġes tar", + "Ġest ar", + "Ġesta r", + "Ġj oints", + "Ġjoin ts", + "Ġjo ints", + "Ġjoint s", + "Ġ summ", + "Ġs umm", + "Ġsu mm", + "Ġsum m", + "_ platform", + "_pl atform", + "B uff", + "Buf f", + "Bu ff", + ".add Subview", + "Ġret ained", + "Ġretain ed", + "L etter", + "Let ter", + ". dim", + ".d im", + ".di m", + "Ġess ere", + "Ġesse re", + "ĠS caffold", + "EX PECT", + "EXP ECT", + "ĉ RE", + "ĉR E", + ". longitude", + ".long itude", + "ü nd", + "ün d", + "Ġstat ue", + ". addWidget", + ".add Widget", + "ĠCar ibbean", + "add PreferredGap", + "il de", + "ild e", + "UI Label", + "UIL abel", + "ĠOp port", + "ĠOpp ort", + "Ġim perial", + "Ġimp erial", + "Ġimper ial", + "Ġimpe rial", + "urs ion", + "Ġman date", + "Ġmand ate", + "Ġprom otional", + "Ġpromot ional", + "Ġpromotion al", + "Ġ vk", + "Ġv k", + "ia ÅĤ", + "Ġp yl", + "Ġpy l", + "Ġ Creation", + "ĠC reation", + "ĠCre ation", + "ĠCreat ion", + "о зд", + "оз д", + "Ġsim pler", + "Ġsimple r", + "Ġsimp ler", + "Ġsimpl er", + ". what", + ".w hat", + ".wh at", + "Ġ Recent", + "ĠRe cent", + "ĠRec ent", + "ĠRece nt", + "St orm", + ". quantity", + ".qu antity", + ".quant ity", + "Ġ Lov", + "ĠL ov", + "ĠLo v", + "\" -", + "ub bles", + "ubble s", + "ubb les", + "_ notification", + "_not ification", + "( world", + "(w orld", + "ur ger", + "urg er", + "urge r", + "* (-", + "*( -", + ": \"Ċ", + ":\" Ċ", + "h m", + "an ship", + "ans hip", + "Ġ Almost", + "ĠAl most", + "Ġmotor cycle", + "_ fee", + "_f ee", + "_fe e", + "Ġabs orb", + "Ġabsor b", + "ĠVin cent", + "ĠVince nt", + "Ġs ounded", + "Ġso unded", + "Ġsound ed", + "ÃŃ st", + "ÃŃs t", + "Ġpharm aceutical", + "h tag", + "ht ag", + "hta g", + "ĠK indle", + "ĠKind le", + "ĠKin dle", + "ital ize", + "ĠEm peror", + "ous tic", + "oust ic", + "Ġspecial ists", + "Ġspecialist s", + "åħ ¬", + "Border Style", + "/ \\", + "RE LATED", + "REL ATED", + "(' ,',", + "(', ',", + "(',' ,", + "( expr", + "(ex pr", + "(exp r", + "Ġ ht", + "Ġh t", + "åį Ī", + "_ Create", + "_C reate", + "Ġs pecially", + "Ġspec ially", + "Ġspecial ly", + "Ġspeci ally", + "Ġ [];čĊ", + "Ġ[ ];čĊ", + "Ġ[] ;čĊ", + "Ġ[]; čĊ", + "Ġ heel", + "Ġh eel", + "Ġhe el", + "Ġs ept", + "Ġse pt", + "Ġsep t", + "_ arch", + "_a rch", + "_ar ch", + "_arc h", + "( initial", + "(in itial", + "(init ial", + "% .ĊĊ", + "%. ĊĊ", + "%.Ċ Ċ", + "\\\" ,\\\"", + "\\\", \\\"", + "\\\",\\ \"", + "Ġdisc usses", + "Ġdiscuss es", + "Ġ upt", + "Ġu pt", + "Ġup t", + "Ġ[ &", + "Ġm anus", + "Ġman us", + ". hand", + ".h and", + "Ġ MAIN", + "ĠM AIN", + "ĠMA IN", + "ĠDen mark", + "Ġ ],čĊ", + "Ġ] ,čĊ", + "Ġ], čĊ", + "Ġcr yst", + "Ġcry st", + "Ġn ack", + "Ġna ck", + "Co ords", + "Coord s", + "_ inner", + "_in ner", + "Ġmid st", + "Ġmi dst", + "Ġa wake", + "Ġaw ake", + "Ġ Ðŀ", + "ĠÐ ŀ", + "- break", + "-b reak", + "-bre ak", + "ÃŃ vel", + "ÃŃv el", + "_ PASS", + "_P ASS", + "_PA SS", + "Ġ Params", + "ĠPar ams", + "ĠParam s", + "ĠPa rams", + "ĠPara ms", + "Ġd etr", + "Ġde tr", + "Ġdet r", + "Ġsp ider", + "Ġspi der", + "Ġ Concept", + "ĠCon cept", + "ĠConc ept", + "ĠConce pt", + "Ġ prend", + "Ġp rend", + "Ġpr end", + "Ġpre nd", + "CH ED", + "CHE D", + ". Exit", + ".Ex it", + ".E xit", + "Ġpop ulated", + "Ġpopulate d", + "Ġpopul ated", + "Ġvirt ue", + "_ SESSION", + "_SE SSION", + "Ġnou vel", + "Ġnouve l", + "o auth", + "oa uth", + "Ġд аннÑĭ", + "Ġдан нÑĭ", + "r ink", + "ri nk", + "rin k", + ". HeaderText", + ".Header Text", + "atur ated", + "atura ted", + "atu rated", + "Ġe rst", + "Ġer st", + "Ġers t", + "Ġ åħ", + "Ġå ħ", + "ॠĩ", + "_ visible", + "_v isible", + "_vis ible", + "e yer", + "ey er", + "eye r", + "Ġ liable", + "Ġl iable", + "Ġli able", + "Ġlia ble", + "Ġd ebe", + "Ġde be", + "Ġdeb e", + "Ġ bw", + "Ġb w", + "{- #", + "_ WIN", + "_W IN", + "d fs", + "df s", + "H over", + "Ho ver", + "Ġ PUT", + "ĠP UT", + "ĠPU T", + "- angle", + "-a ngle", + "-an gle", + "Ġn oble", + "Ġno ble", + "Ġnob le", + "Ġtr aces", + "Ġtra ces", + "Ġtrace s", + "en cv", + "enc v", + "Ġ userData", + "Ġuser Data", + "_ ins", + "_in s", + "_i ns", + "ĠS uz", + "ĠSu z", + "Ġnews letters", + "Ġnewsletter s", + "ĠM odi", + "ĠMod i", + "ĠMo di", + "Ġentreprene urs", + "Ġentrepreneur s", + "Ġ tribute", + "Ġtrib ute", + "Ġrum ors", + "Ġrumor s", + "Ġ rr", + "Ġr r", + "Ġ Quarter", + "ĠQu arter", + "ĠQuart er", + "ĠQuar ter", + "ê³ ł", + "Ġ feeds", + "Ġfe eds", + "Ġfeed s", + "Ġfee ds", + "ó g", + "Ġen velope", + "Ġenv elope", + "Ġenvelop e", + "Ġ lear", + "Ġl ear", + "Ġle ar", + "Ġk ø", + "de veloper", + "develop er", + "S imilar", + "Sim ilar", + ": \")Ċ", + ":\" )Ċ", + ":\") Ċ", + "sub scription", + "subs cription", + "Mod ifier", + "it alic", + "ital ic", + "ita lic", + "Ġn asty", + "Ġna sty", + "Ġnas ty", + "Ġnast y", + "Ġ termination", + "Ġter mination", + "Ġterm ination", + "Ġtermin ation", + "Ġch arming", + "Ġchar ming", + "Ġcharm ing", + "Ġ âŁ", + "Ġâ Ł", + "t ons", + "ton s", + "to ns", + ". trace", + ".t race", + ".tr ace", + "h ots", + "ho ts", + "hot s", + "Ġ UR", + "ĠU R", + "M ont", + "Mon t", + "Mo nt", + "Ġjust ified", + "ĠG ang", + "ĠGa ng", + "ĠGan g", + "i nea", + "in ea", + "ine a", + "Ġb og", + "Ġbo g", + "( ap", + "(a p", + "_ $", + "Ġcont amin", + "Ġconta min", + ". Dot", + ".D ot", + ".Do t", + "ĉ Debug", + "( exports", + "(ex ports", + "(exp orts", + "Ġ paired", + "Ġp aired", + "Ġpair ed", + "Ġpa ired", + "Ġpai red", + "Ġ Assignment", + "ĠAss ignment", + "ĠAssign ment", + "Ġauto mobile", + "Ġautom obile", + "ĵ į", + "Ġph ases", + "Ġphase s", + "Ġpha ses", + "v w", + "@ SuppressWarnings", + "= \\", + "r ant", + "ra nt", + "ran t", + "- ed", + "-e d", + "ĉ await", + "ĉa wait", + "Ġcert ificates", + "Ġcertificate s", + "Ġcertif icates", + "' >\"", + "'> \"", + "Ġint act", + "C TRL", + "CT RL", + "CTR L", + "M ike", + "Mi ke", + "g regation", + "greg ation", + "AT TERN", + "ATT ERN", + "ATTER N", + "Ġre public", + "Ġrep ublic", + "_ upper", + "_u pper", + "_up per", + "ili ary", + "iliar y", + "ilia ry", + "Ġcom putation", + "Ġcomp utation", + "Ġcomput ation", + "h ire", + "hi re", + "hir e", + "ĠS hin", + "ĠSh in", + "ĠShi n", + "_ ANY", + "_A NY", + "_AN Y", + "Ġ Manufacturer", + "ĠMan ufacturer", + "ĠManufact urer", + "ĠC arm", + "ĠCar m", + "ĠCa rm", + "Ġbear ings", + "Ġbearing s", + "_ comb", + "_c omb", + "_com b", + "_co mb", + "c ad", + "ca d", + "ur istic", + "Ġwh olesale", + "Ġwhole sale", + "Ġwholes ale", + "Ġd onor", + "Ġdo nor", + "Ġdon or", + ". interfaces", + ".inter faces", + ".interface s", + "pr esso", + "press o", + "pres so", + "Ġ Brun", + "ĠB run", + "ĠBr un", + "ĠBru n", + "- close", + "-c lose", + "-cl ose", + "p rove", + "pr ove", + "pro ve", + "prov e", + "_ SK", + "_S K", + "ĉ frame", + "ĉf rame", + "ĉfr ame", + "et ros", + "etro s", + "etr os", + "ĠP ain", + "ĠPa in", + "ĠPai n", + "_ EXP", + "_E XP", + "_EX P", + "Ġ LT", + "ĠL T", + "_ fs", + "_f s", + ". datas", + ".d atas", + ".data s", + ".dat as", + ".da tas", + "ĉ ss", + "ĉs s", + "v oir", + "vo ir", + "Ġ Axis", + "ĠA xis", + "ĠAx is", + "M ajor", + "= \"<", + "=\" <", + "[ h", + "Ġprof ess", + "Ġprofes s", + "ig rate", + "igr ate", + "( score", + "(s core", + "(sc ore", + "Key word", + "\" os", + "ĠĠ ĠĠĉĊ", + "ĠĠĠĠ ĉĊ", + "ĠĠĠ ĠĉĊ", + "ĠĠĠĠĉ Ċ", + "an alysis", + "analy sis", + "anal ysis", + "Ġre play", + "Ġrep lay", + "Ġrepl ay", + ". pass", + ".p ass", + ".pa ss", + "\\ d", + "t ls", + "tl s", + "Ġsan ct", + ". light", + ".l ight", + ".li ght", + "_ mobile", + "_m obile", + "_mob ile", + "Ñģ ÑĤÑĮ", + "ÑģÑĤ ÑĮ", + "ĉ total", + "ĉt otal", + "ĉto tal", + "u ity", + "ui ty", + "uit y", + "Ġ paused", + "Ġpa used", + "Ġpause d", + "Ġpau sed", + "N AS", + "NA S", + "Ġen core", + "Ġenc ore", + "l oe", + "lo e", + "Ġ-* -ĊĊ", + "Ġ-*- ĊĊ", + "Ġ-*-Ċ Ċ", + ". high", + ".h igh", + "am pler", + "amp ler", + "ample r", + "Ġ Secure", + "ĠS ecure", + "ĠSec ure", + "Ġf ragments", + "Ġfra gments", + "Ġfrag ments", + "Ġfragment s", + "_ vel", + "_v el", + "_ve l", + "ill ary", + "illa ry", + "ĠS tein", + "ĠSt ein", + "ĠSte in", + "ĠD awn", + "ĠDa wn", + "ĠDaw n", + "Ġmax imize", + "Ġmaxim ize", + "ภ¢", + "Ġ /^", + "Ġ/ ^", + "Ġcontin ually", + "Ġcontinu ally", + "Ġcontinual ly", + "Ġsh adows", + "Ġshadow s", + "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠI ActionResult", + "Ġinform ación", + "C HECK", + "CHE CK", + ". SelectedItem", + ".Selected Item", + "b undle", + "ol ley", + "oll ey", + "olle y", + "< Int", + "<", + "\\\" ><", + "\\\"> <", + "Ġ trajectory", + "Ġtra jectory", + "_ ring", + "_r ing", + "Ġhydro gen", + "Ġhydr ogen", + "t ron", + "tr on", + "tro n", + "Ġstat ute", + "Ġ conditional", + "Ġcondition al", + "Ġcond itional", + "Ġt ray", + "Ġtr ay", + "Ġtra y", + "- school", + "-s chool", + "( widget", + "(w idget", + "$ config", + "$con fig", + "Ġrequest ing", + "Ġrequ esting", + ". uint", + ".ui nt", + ".u int", + "e ton", + "et on", + "eto n", + "br ities", + "brit ies", + "Of Type", + "A DMIN", + "AD MIN", + "ADM IN", + "p redict", + "pre dict", + "pred ict", + "Ġg egen", + "Ġge gen", + "Ġgeg en", + "ĠH app", + "ĠHa pp", + "OC UMENT", + "Ġ Apart", + "ĠA part", + "ĠAp art", + "Ġ -----", + "Ġ- ----", + "Ġ-- ---", + "Ġ---- -", + "Ġ--- --", + "r oe", + "ro e", + "u ide", + "ui de", + "uid e", + "just ify", + "ĠS quad", + "ĠSqu ad", + "Ġprof es", + ". bot", + ".b ot", + ".bo t", + "_ currency", + "_c urrency", + "_curr ency", + "i nnen", + "in nen", + "inn en", + "inne n", + "ĠM umbai", + "ĠMum bai", + "Ġ Numbers", + "ĠNumber s", + "ĠNum bers", + "avana ugh", + "agn itude", + "âĢľ There", + "âĢľThe re", + "= http", + "=h ttp", + "çī ĩ", + "Ġ vb", + "Ġv b", + "+ '{{$", + "\"> {{$", + "\">{{ $", + "\">{ {$", + "Ġ inode", + "Ġin ode", + "Ġi node", + "s il", + "si l", + "Ġh ace", + "Ġha ce", + "Ġhac e", + "Ġsever ely", + "Ġsevere ly", + "Ġ Overview", + "ĠOver view", + "ĠOv erview", + "Ġsp raw", + "Ġspr aw", + "Ġbe aches", + "Ġbeach es", + ": left", + ":l eft", + "· »", + "( ${", + "($ {", + "Ġ FIRST", + "ĠF IRST", + "ĠFIR ST", + "ĠS pa", + "ĠSp a", + "- ass", + "-a ss", + "-as s", + "Ġb aise", + "Ġba ise", + "Ġbais e", + "Ġ NODE", + "ĠN ODE", + "ĠNO DE", + "Ġ Pizza", + "ĠP izza", + "ĠPi zza", + "P et", + "Pe t", + "( seq", + "(s eq", + "(se q", + "\\ \">Ċ", + "\\\" >Ċ", + "\\\"> Ċ", + "CppMethod Pointer", + "Ġ vp", + "Ġv p", + "Ġ ia", + "Ġi a", + "_ seconds", + "_se conds", + "_sec onds", + "_second s", + "e met", + "em et", + "eme t", + "/ blob", + "/b lob", + "/bl ob", + "_TH RESH", + ".. .čĊ", + "... čĊ", + "D est", + "De st", + "Des t", + "Ġ NH", + "ĠN H", + ". dataSource", + ".data Source", + "it és", + "ité s", + "Ġ Jak", + "ĠJ ak", + "ĠJa k", + "s ell", + "se ll", + "sel l", + "Ġwork shops", + "Ġworkshop s", + "< u", + "Ġr ivals", + "Ġrival s", + "Ġri vals", + "Ġriv als", + "ĠEX ISTS", + "h om", + "ho m", + "- token", + "-t oken", + "-to ken", + "com patible", + "compat ible", + ".J Panel", + "Ġphys icians", + "Ġphysician s", + "Ġphysic ians", + "ar tin", + "art in", + "arti n", + "Ġdes irable", + "Ġdistinct ive", + ". Dep", + ".D ep", + ".De p", + "g id", + "gi d", + "il iate", + "ili ate", + "ilia te", + ", max", + ",m ax", + "Ġprem iere", + "Ġpremier e", + "Ġpremi ere", + "Ġq Debug", + "Ġadvoc acy", + "Ġwh isper", + "P t", + "Ġun changed", + "_ qty", + "_q ty", + "请 æ±Ĥ", + "Se ason", + "Sea son", + "ave length", + "avel ength", + "ĠP ul", + "ĠPu l", + "Ġd ÃŃa", + "ĠdÃŃ a", + "'] ]],Ċ", + "']] ],Ċ", + "a lis", + "al is", + "ali s", + "( \"&", + "(\" &", + "b oro", + "bo ro", + "bor o", + "Ġ bm", + "Ġb m", + "Ġ Radi", + "ĠR adi", + "ĠRa di", + "ĠRad i", + "w rong", + "wr ong", + "Ġ Going", + "ĠGo ing", + "ime Type", + "i ji", + "ij i", + "- feedback", + "-fe edback", + "-feed back", + "Ġ Names", + "ĠN ames", + "ĠName s", + "ĠNa mes", + "ĠNam es", + "ĠB apt", + "ĠBa pt", + "Ġpro bable", + "Ġprob able", + "Ġ Ether", + "ĠE ther", + "ĠEth er", + "ĠEt her", + "Ġ Politics", + "ĠPol itics", + "ĠPolit ics", + "_ protocol", + "_prot ocol", + "_proto col", + "l ining", + "li ning", + "lin ing", + "S at", + "Sa t", + "Ġcor rel", + "Ġcorre l", + "Ġcorr el", + ". Primary", + ".Pr imary", + "( nullable", + "(null able", + "RI ORITY", + "Ġcol oring", + "Ġcolor ing", + "Ġutil izing", + "Ġutiliz ing", + "d as", + "da s", + "Ġex ported", + "Ġexp orted", + "Ġexport ed", + "Ġcar riers", + "Ġcarrier s", + "Ġcarr iers", + "Con v", + "Co nv", + ". editor", + ".e ditor", + ".ed itor", + ".edit or", + "i ó", + "( handles", + "(h andles", + "(handle s", + "(hand les", + "Ġapprec iation", + ". import", + ".im port", + ".imp ort", + "ĠA ustria", + "ĠAust ria", + "ĠAustr ia", + "Ġ Strip", + "ĠS trip", + "ĠSt rip", + "ĠStr ip", + "i light", + "il ight", + "ili ght", + "ilig ht", + "Ġappropri ately", + "Ġappropriate ly", + "ĠP rest", + "ĠPr est", + "ĠPres t", + "ĠPre st", + "Ġ Wir", + "ĠW ir", + "ĠWi r", + "Ġ UIApplication", + "ĠUI Application", + "al chemy", + "Ġ Mob", + "ĠM ob", + "ĠMo b", + "Ġ Determin", + "ĠD etermin", + "ĠDe termin", + "ergus on", + "register ed", + "regist ered", + "_ convert", + "_con vert", + "_conv ert", + "ĠVlad imir", + "ĠVladim ir", + ".Show Dialog", + "ref lect", + "Ġs hook", + "Ġsh ook", + "Ġsho ok", + "Ġas sure", + "Ġass ure", + "Ġ Often", + "ĠO ften", + "ĠOf ten", + "Ġcivil ization", + "Ġv ocabulary", + "Ġvocab ulary", + "fore ground", + "Ġ Scope", + "ĠS cope", + "ĠSc ope", + "ĠSco pe", + "Ġun wanted", + "Ġunw anted", + "act ing", + "ac ting", + "Ġ ([]", + "Ġ( []", + "Ġ([ ]", + "Ġm arking", + "Ġmark ing", + "Ġmar king", + ". original", + ".origin al", + ".or iginal", + ".orig inal", + "Ġ MOVE", + "ĠM OVE", + "ĠMO VE", + "ĠMOV E", + "Ġsp orting", + "Ġsport ing", + "Ġspor ting", + "ce ptions", + "ception s", + "cept ions", + "NS Number", + "S izes", + "Size s", + "Si zes", + "Ġpro vincial", + "Ġprovinc ial", + "Ġprovincia l", + "_ Trans", + "_T rans", + "_Tr ans", + "Ġproble matic", + "Ġproblem atic", + "Ġproblema tic", + "Ġprobl ematic", + "d igit", + "di git", + "dig it", + "Ġ Emma", + "ĠE mma", + "ĠEm ma", + "ĠEmm a", + "l ocks", + "lo cks", + "lock s", + "loc ks", + "ĠC rew", + "ĠCr ew", + "ĠCre w", + "i ba", + "ib a", + "' ):", + "') :", + "i sha", + "is ha", + "ish a", + "Ġm amm", + "Ġma mm", + "Ġmam m", + "Ġocc ured", + "Ġoccur ed", + "w cs", + "wc s", + "( rule", + "(r ule", + "Ġmerch andise", + "es pecially", + "ĠT win", + "ĠTw in", + "Ġn aming", + "Ġna ming", + "Ġnam ing", + "Ġs log", + "Ġsl og", + "Ġslo g", + "Ġimpro ves", + "Ġimprove s", + "Ġimpr oves", + "Ġimprov es", + "Ġad her", + ": text", + ":t ext", + ".h adoop", + "_ HTTP", + "_HT TP", + ". toList", + ".to List", + ". disabled", + ".dis abled", + ".disable d", + "Ġl enses", + "Ġlen ses", + "Ġlens es", + ". ini", + ".in i", + ".i ni", + "Ġ Rare", + "ĠR are", + "ĠRa re", + "Ġ Ubuntu", + "ĠUb untu", + "Ġsc ram", + "Ġscr am", + "o lation", + "ol ation", + "ola tion", + "t itulo", + "tit ulo", + "Every thing", + "Ġnod ded", + "icht ig", + "_ constant", + "_con stant", + "_const ant", + "_cons tant", + "z c", + "l ift", + "li ft", + "lif t", + "Ġ Notify", + "ĠN otify", + "ĠNot ify", + "o ndo", + "on do", + "ond o", + "Ġ INF", + "ĠI NF", + "ĠIN F", + "( \"+", + "(\" +", + "ĠK az", + "ĠKa z", + "Ġd read", + "Ġdr ead", + "Ġdre ad", + ". mapper", + ".m apper", + ".map per", + ".ma pper", + "l eur", + "le ur", + "ĠCom ey", + "ĠCo mey", + "ĠCome y", + "Ġ NB", + "ĠN B", + "i cers", + "ic ers", + "ice rs", + "icer s", + ". Push", + ".P ush", + "Ġ Hack", + "ĠH ack", + "ĠHa ck", + "ĠBrazil ian", + "ĠBraz ilian", + "_ prod", + "_p rod", + "_pro d", + "_pr od", + "Ġ //ĊĊ", + "Ġ// ĊĊ", + "Ġ/ /ĊĊ", + "Ġ//Ċ Ċ", + "Ġb icycle", + "Ġbi cycle", + "Ġbicy cle", + "Ġbic ycle", + "Ġun available", + "Ġuna vailable", + "Ġadoles cent", + "b lk", + "bl k", + "Ġmit ig", + "_ blue", + "_b lue", + "_bl ue", + "ì ĺ", + "fade In", + "Ġ Utilities", + "ĠUtil ities", + "ĠUt ilities", + "Ġ MN", + "ĠM N", + "; k", + "< style", + "- status", + "-s tatus", + "-st atus", + "-stat us", + "i ndo", + "in do", + "ind o", + "Ġin nings", + "Ġinn ings", + "Ġinning s", + "Ġg j", + "Ġ| |=", + "Ġ|| =", + ". eu", + ".e u", + ": Number", + ":N umber", + "Ġc uisine", + "Ġcu isine", + "Ġcuis ine", + "ĠURL s", + "i ek", + "ie k", + "Ġw ires", + "Ġwire s", + "Ġwir es", + "Ġwi res", + "ĉ ps", + "ĉp s", + "i eg", + "ie g", + ". mk", + ".m k", + "so ap", + "Ġsome time", + "Ġsom etime", + "Ġs tap", + "Ġst ap", + "Ġsta p", + "_ series", + "_s eries", + "_se ries", + "_ser ies", + ". Target", + ".T arget", + "æ º", + ". destination", + ".d estination", + ".dest ination", + "OUN TER", + "OUNT ER", + "R aises", + "Ra ises", + "Raise s", + "& A", + "Ġsmart phones", + "Ġsmartphone s", + "NI Env", + ". sdk", + ".s dk", + ".sd k", + "Ġhel icopter", + "Ġhelicopt er", + "Ġim pe", + "Ġimp e", + "Ġ Birth", + "ĠB irth", + "ĠBir th", + "A U", + "b readcrumbs", + "breadcrumb s", + "co ords", + "coord s", + "Ġexpl ored", + "Ġexplo red", + "Ġexplore d", + "Ġexplor ed", + "Ġ lod", + "Ġl od", + "Ġlo d", + "Ġ Ip", + "ĠI p", + "g able", + "ga ble", + "i ane", + "ia ne", + "ian e", + "Ġart ifacts", + "Ġartifact s", + "Box Layout", + "ا ر", + "Ø§Ø ±", + "list ener", + "listen er", + "lis tener", + "liste ner", + ". cart", + ".c art", + ".ca rt", + ".car t", + "ĠH uff", + "ĠHu ff", + "ĠHind u", + "ĠHin du", + "ĠData Types", + "ĠDataType s", + "Ġ Drupal", + "ĠDr upal", + "IGN ORE", + "Ġoff sets", + "Ġoffset s", + "Ġoffs ets", + "Ġ RTC", + "ĠR TC", + "ĠRT C", + "- login", + "-lo gin", + "-log in", + "æ ®", + "Ġ QObject", + "ĠQ Object", + "Ġprosec utor", + "R ock", + "Ro ck", + "_ chat", + "_c hat", + "_ch at", + "W ay", + "Wa y", + "ì ²", + "Ġneg lig", + "Ġd ude", + "Ġdu de", + "; <", + "Ġde legates", + "Ġdelegate s", + "Ġdeleg ates", + "_ failed", + "_f ailed", + "_fail ed", + "_fa iled", + "/ dev", + "/d ev", + "/de v", + "/ work", + "/w ork", + "( New", + "(N ew", + "e table", + "et able", + "eta ble", + "( )\"", + "() \"", + "( Icons", + "(I cons", + "Ġp ork", + "Ġpo rk", + "Ġpor k", + "ĠModel AndView", + "Ġ VIP", + "ĠV IP", + "ĠVI P", + "ĠK or", + "ĠKo r", + "m ix", + "mi x", + "Ġ oxid", + "Ġox id", + "Ġ SCREEN", + "ĠS CREEN", + "ĠSC REEN", + "Ġ Fourth", + "ĠFour th", + "/ \",Ċ", + "/\" ,Ċ", + "/\", Ċ", + "Ġ tee", + "Ġt ee", + "Ġte e", + "ĠSte vens", + "ĠSteve ns", + "ĠSteven s", + "t icks", + "ti cks", + "tic ks", + "tick s", + "Ġp ledge", + "Ġpl edge", + "Ġple dge", + "Ġpled ge", + "ib bon", + "Ġ Loan", + "ĠLo an", + "Ġ neo", + "Ġn eo", + "Ġne o", + "n umpy", + "num py", + "Ġ SharedPreferences", + "ĠShared Preferences", + "- oriented", + "ĠLogger Factory", + "Ġ GraphQL", + "ĠGraph QL", + "z enia", + "ze nia", + "zen ia", + "\" _", + "W omen", + "Wo men", + ". cast", + ".c ast", + ".ca st", + "Ġdeliber ately", + "Ġdeliberate ly", + "+ b", + "Ġ Arn", + "ĠA rn", + "ĠAr n", + "font Size", + "Ġ maze", + "Ġm aze", + "Ġma ze", + "Ġmaz e", + "Ġbl amed", + "Ġblame d", + "Ġbla med", + ". mas", + ".m as", + ".ma s", + "} )čĊ", + "}) čĊ", + "eler ik", + "ele rik", + "eleri k", + "Ġsc anning", + "Ġscan ning", + "ĠWork shop", + "ĠWorks hop", + "Ġf inden", + "Ġfind en", + "Ġfin den", + "Ġfinde n", + "Ġc aut", + "Ġca ut", + "UI Font", + "( return", + "(r eturn", + "(re turn", + "(ret urn", + "a lin", + "al in", + "ali n", + "c astle", + "cast le", + "cas tle", + "//// ////////////////////////////////////////////////////////////////////", + "//////// ////////////////////////////////////////////////////////////////", + "//////////////// ////////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////// ////////", + "//////////// ////////////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////////// ////", + "//////////////////////////////////////////////////////// ////////////////", + "//////////////////////////////////////////////////////////// ////////////", + "Ġincent ive", + "Ġincentiv e", + "o path", + "op ath", + "opa th", + "b lob", + "bl ob", + "blo b", + "Ġcigaret te", + "Ġcigar ette", + "Ġf ertil", + "Ġfer til", + "Ġfert il", + "* /ĊĊĊ", + "*/ ĊĊĊ", + "*/Ċ ĊĊ", + "*/ĊĊ Ċ", + "Ġ Shar", + "ĠS har", + "ĠSh ar", + "ĠSha r", + "Ċ ĠĠĠĠĠĠĊ", + "Ġunc ertain", + "Ġuncert ain", + "ĠS ton", + "ĠSt on", + "ĠSto n", + "Oper ations", + "Operation s", + "ĠSp encer", + "Ġde fin", + "Ġdef in", + "Ġ Solo", + "ĠS olo", + "ĠSo lo", + "ĠSol o", + "o nest", + "on est", + "one st", + "ones t", + "·» åĬł", + "Ġu omo", + "Ġuom o", + "G ive", + "Gi ve", + "Ġden tro", + "Ġdent ro", + "; padding", + ";p adding", + "ent ai", + "enta i", + "Ġ Cars", + "ĠC ars", + "ĠCar s", + "ĠCa rs", + "Ġenthus iasm", + "Ġenthusi asm", + "Ġ Operating", + "ĠOper ating", + "ĠOpera ting", + "S kip", + "Sk ip", + "par ation", + "pa ration", + "para tion", + "Ġprot ects", + "Ġprote cts", + "Ġprotect s", + "Ġre ver", + "Ġr ever", + "Ġrev er", + "Ġreve r", + "d g", + "ĠC incinnati", + "Ġconsect etur", + "Ġm uss", + "Ġmus s", + "Ġmu ss", + "employ ed", + "a uses", + "au ses", + "ause s", + "aus es", + "in kle", + "ink le", + ". Values", + ".Value s", + ".Val ues", + "£ ¼", + "l ov", + "lo v", + "_ WARN", + "_W ARN", + "Ġ bookmark", + "Ġbook mark", + "Ġ Apollo", + "ĠA pollo", + "ĠAp ollo", + ". axis", + ".a xis", + ".ax is", + "Ġm ét", + "Ġmé t", + "Ġop ener", + "Ġopen er", + "Ġt umor", + "Ġtu mor", + "Ġtum or", + "d an", + "da n", + "Ġelement ary", + "Ġsk ipped", + "Ġskip ped", + "Ġski pped", + "ĠK er", + "ĠKe r", + "as ia", + "asi a", + "_ resp", + "_re sp", + "_r esp", + "_res p", + "Ġde mol", + "Ġdem ol", + "Ġdemo l", + "ĠCan adians", + "ĠCanadian s", + "Ġt astes", + "Ġtaste s", + "Ġtas tes", + "Ġtast es", + "U Integer", + "UInt eger", + "Ġ' ${", + "Ġ'$ {", + ". aws", + ".a ws", + ".aw s", + "R OID", + "RO ID", + "ROI D", + "r ians", + "ri ans", + "ria ns", + "rian s", + "M Q", + "ord able", + "orda ble", + "Ġcou sin", + "Ġcous in", + "Prop agation", + "( Session", + "(S ession", + "p halt", + "ph alt", + "pha lt", + "U LD", + "UL D", + "Ġ Scalar", + "ĠS calar", + "ĠSc alar", + "ĠScala r", + "ĠScal ar", + "Ġblood y", + "Ġblo ody", + "Ġ à¦", + "Ġà ¦", + ". mask", + ".m ask", + ".mas k", + ".ma sk", + ", q", + "Ġ Units", + "ĠUn its", + "ĠUnit s", + "ĠUni ts", + "Ġcent res", + "Ġcentre s", + "Ġcentr es", + "Ġcen tres", + "Ġ Prim", + "ĠP rim", + "ĠPr im", + "ĠPri m", + ". ]ĊĊ", + ".] ĊĊ", + "ĠSh aw", + "ĠSha w", + "P rom", + "Pro m", + "Pr om", + "Ġ Thought", + "ĠTh ought", + "ĠThough t", + "ĠThou ght", + "Check er", + "Che cker", + "_ outputs", + "_out puts", + "_output s", + "( chan", + "(c han", + "(ch an", + "E INVAL", + "Ġ bob", + "Ġb ob", + "Ġbo b", + "_ cmp", + "_c mp", + "_cm p", + "P ed", + "Pe d", + "Ġmat rices", + "Ġvrou wen", + "Ġvrouw en", + "Ġgenu inely", + "Ġgenuine ly", + "high light", + "( display", + "(d isplay", + "(dis play", + ") !=", + ")! =", + "Ġde licate", + "Ġdel icate", + "Ġdelic ate", + "ĠL uther", + "ĠLu ther", + "ĠM iles", + "ĠMil es", + "ĠMi les", + "ĠMile s", + "Ġ userID", + "Ġuser ID", + "% =", + "at eurs", + "ate urs", + "ateur s", + "_ BUF", + "_B UF", + "_BU F", + "- ------Ċ", + "-- -----Ċ", + "---- ---Ċ", + "--- ----Ċ", + "----- --Ċ", + "------ -Ċ", + "------- Ċ", + "im itives", + "imit ives", + "imitive s", + "Ġsh elves", + "Ġshel ves", + "s low", + "sl ow", + "_ information", + "_in formation", + "L EG", + "LE G", + "W r", + ". forms", + ".for ms", + ".form s", + "c eland", + "ce land", + "cel and", + "cela nd", + "/ un", + "/u n", + ": &", + ". âĢĻĊĊ", + ".âĢĻ ĊĊ", + "= \"%", + "=\" %", + "Ġp rost", + "Ġpro st", + "Ġpr ost", + "Ġpros t", + "Ġ fontsize", + "Ġfont size", + "Ġfonts ize", + "u ción", + "uc ión", + "uci ón", + "g etic", + "get ic", + "ge tic", + "a mt", + "am t", + "= \".", + "=\" .", + "De cor", + "Dec or", + "B rit", + "Br it", + "Ġ\" \").", + "Ġ\"\" ).", + "Ġ\"\") .", + "Ġf ounding", + "Ġfound ing", + "Ġfo unding", + ". FileName", + ".File Name", + "Ġ Tier", + "ĠT ier", + "ĠTi er", + "ĠTie r", + "Ġdis close", + "Ġdisc lose", + "á m", + ". syn", + ".s yn", + ".sy n", + ". ViewHolder", + ".View Holder", + "lic ant", + "li cant", + "lica nt", + "_ stage", + "_st age", + "_sta ge", + "Mon day", + "Ġ deserialize", + "Ġde serialize", + "Ġdes erialize", + "t alk", + "ta lk", + "tal k", + "Ġtrad itionally", + "Ġtraditional ly", + "Ġtradition ally", + "æĢ ģ", + "Ø ®", + "L EX", + "LE X", + "Ġ eh", + "Ġe h", + "ĉ ROM", + "ĉR OM", + "Ġ {})Ċ", + "Ġ{ })Ċ", + "Ġ{} )Ċ", + "Ġ{}) Ċ", + "Question s", + "Quest ions", + "n cpy", + "nc py", + "Ġfix ing", + "Ġfi xing", + "к Ñĥ", + "_ Key", + "_K ey", + ": x", + "Ġ STRING", + "ĠST RING", + "ĠSTR ING", + "ĠÑĦ ай", + "ĉ left", + "ĉl eft", + "ĠB ench", + "ĠBen ch", + "el lij", + "ell ij", + "elli j", + "UR RED", + "URRE D", + "Ġ Diagram", + "ĠDi agram", + "ĠDia gram", + "} catch", + "/ time", + "/t ime", + "Ġ Missing", + "ĠM issing", + "ĠMiss ing", + "ĠMis sing", + "db name", + "Ġs ore", + "Ġso re", + "Ġsor e", + "ĠW alt", + "ĠWal t", + "ĠWa lt", + "ug ging", + "ugg ing", + "re present", + "rep resent", + "Ġ GS", + "ĠG S", + "ne ys", + "ney s", + "ĉ page", + "ĉp age", + "Ġvol can", + "( btn", + "(b tn", + "(bt n", + "Ġexceed s", + "Ġexce eds", + "Ġ erg", + "Ġe rg", + "Ġer g", + "Ġpi lots", + "Ġpil ots", + "Ġpilot s", + "ĠS ed", + "ĠSe d", + "ers ions", + "ersion s", + "Ġp atron", + "Ġpat ron", + "Ġpa tron", + "R V", + "/ top", + "/t op", + "/to p", + ". asset", + ".as set", + "_ cross", + "_c ross", + "_cr oss", + ". Editor", + ".E ditor", + ".Edit or", + ".Ed itor", + ". tb", + ".t b", + "Ġwel coming", + "S CREEN", + "SC REEN", + ") findViewById", + "C oder", + "Code r", + "Co der", + "Cod er", + " \",Ċ", + ">\" ,Ċ", + ">\", Ċ", + "_ Pin", + "_P in", + "u ese", + "ue se", + "ues e", + "Ġ overrides", + "Ġover rides", + "Ġoverride s", + "_ ready", + "_re ady", + "_read y", + "Ad vanced", + "Adv anced", + "Advance d", + "Ġ opi", + "Ġo pi", + "Ġop i", + "- cart", + "-c art", + "-car t", + "-ca rt", + "(\" /\",", + "(\"/ \",", + "ĠD eb", + "ĠDe b", + "C RY", + "CR Y", + "Ġ Vertical", + "ĠVer tical", + "ĠVert ical", + "Ġ OVER", + "ĠO VER", + "ĠOV ER", + "Ġ Corporate", + "ĠCor porate", + "ĠCorpor ate", + "ĠCorp orate", + "Ġ\" \";", + "Ġ\"\" ;", + "Ġstep ping", + "Ġste pping", + "e j", + "Ġaccus ations", + "Ġaccusation s", + "Ġo raz", + "Ġor az", + "Ġora z", + "_ tail", + "_t ail", + "_ta il", + "Ġin duced", + "Ġind uced", + "Ġindu ced", + "Ġinduce d", + "Ġ elastic", + "Ġe lastic", + "Ġel astic", + "Ġelast ic", + "Ġbl own", + "Ġblow n", + "Ġblo wn", + ", //", + ",/ /", + "Ġback grounds", + "Ġbackground s", + "âĢĻ une", + "âĢĻun e", + "- sdk", + "-s dk", + "Ġset Interval", + "Ġincent ives", + "Ġincentive s", + "Ġincentiv es", + "Ġveget able", + "Ġveg etable", + "_ On", + "_O n", + "exp anded", + "expand ed", + "p ix", + "pi x", + "_ shader", + "_sh ader", + "_sha der", + "ĠSP DX", + "ĠSPD X", + "@ example", + "Ġ Wrapper", + "ĠW rapper", + "ĠWr apper", + "ĠWrap per", + ". Zero", + ".Z ero", + "Pos itive", + "Ġ spinner", + "Ġsp inner", + "Ġspin ner", + "Ġin vented", + "Ġinv ented", + "Ġinvent ed", + "ĠG ates", + "ĠGa tes", + "ĠGate s", + "ĠGat es", + "о ÑĤоÑĢ", + "оÑĤ оÑĢ", + "оÑĤо ÑĢ", + "Ġcompar isons", + "Ġcomparison s", + "è ·", + ". primary", + ".pr imary", + "data Provider", + "add itional", + "ĉ options", + "ĉo ptions", + "ĉopt ions", + "ĉoption s", + "s napshot", + "snap shot", + ".set Horizontal", + "Ġ\" {}", + "Ġ\"{ }", + "ĠF isher", + "ĠFish er", + "ĠFi sher", + "h alten", + "hal ten", + "halt en", + "< Type", + "", + "Ġ) ->", + "Ġ Registered", + "ĠRegister ed", + "IN ED", + "INE D", + "k al", + "ka l", + "par ison", + "Ġobj eto", + "Ġobjet o", + "V i", + "m anda", + "man da", + "ma nda", + "mand a", + "Ġren ewed", + "Ġrenew ed", + "ĠS of", + "ĠSo f", + "e ssel", + "es sel", + "ess el", + "esse l", + ".nd array", + "Ġ crap", + "Ġc rap", + "Ġcr ap", + "Ġcra p", + "ç® ¡", + ".abs path", + ".ab spath", + "( up", + "(u p", + "Ġclear ance", + "Ġ TW", + "ĠT W", + "_ COPY", + "_C OPY", + "_CO PY", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĉ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠ Ġĉ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĉ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĉ", + "Ġfor ests", + "Ġfore sts", + "Ġforest s", + "Ġfores ts", + "Ġarg uably", + "Ġ ASS", + "ĠA SS", + "ĠAS S", + "h ey", + "he y", + "a mel", + "am el", + "ame l", + "_ fore", + "_f ore", + "_for e", + "ĠSouth east", + "ĠSou theast", + "Ġab used", + "Ġabuse d", + "Ġpract icing", + "ake dirs", + "aked irs", + "ä¸ »", + "_ resources", + "_re sources", + "_res ources", + "_resource s", + "Ġ pond", + "Ġp ond", + "Ġpo nd", + "Ġpon d", + ". Fixed", + ".F ixed", + "Last Error", + "ĠPsych ology", + "ĠPsycho logy", + "Ġ\" //", + "Ġ\"/ /", + "! :", + "Re usable", + "Ġ mensaje", + "Ġm ensaje", + "Ġmens aje", + "Ġro spy", + "Ġros py", + "Ġ bour", + "Ġb our", + "Ġbo ur", + "Ġbou r", + "Ġvar ieties", + "Ġvari eties", + "Ġem path", + "Ġemp ath", + "( ({", + "(( {", + "_ org", + "_or g", + "_o rg", + "Ġ Mes", + "ĠM es", + "ĠMe s", + "Ġ Magento", + "ĠM agento", + "ĠMag ento", + "IST ORY", + "Un less", + "Ġh j", + "ĠD uty", + "ĠDu ty", + "ĠDut y", + "J un", + "Ju n", + ", size", + ",s ize", + "Ġpaint ings", + "Ġpain tings", + "Ġpainting s", + "Ġd ispens", + "Ġdisp ens", + "d art", + "da rt", + "dar t", + "Ġbehavior al", + "Ġ rpc", + "Ġr pc", + "Ġrp c", + "c alculate", + "cal culate", + "calc ulate", + "calcul ate", + "f ruit", + "fr uit", + "_ mm", + "_m m", + "ĉ pthread", + "ĉp thread", + "ĉpt hread", + "Max Length", + "Ġc urrencies", + "Ġcurr encies", + "_ capacity", + "_cap acity", + "ĠO z", + "Ġfire arm", + "Ġco efficient", + "Ġcoeff icient", + "Ġbank ruptcy", + "Ġbankrupt cy", + "w art", + "wa rt", + "war t", + "Ġfat igue", + "A VA", + "AV A", + "Ġe spa", + "Ġes pa", + "Ġesp a", + "_ pc", + "_p c", + "Ġ Quotes", + "ĠQu otes", + "ĠQuote s", + "_ LIGHT", + "_L IGHT", + "Ġ Tickets", + "ĠT ickets", + "ĠTicket s", + "ĠTick ets", + "Ġre lates", + "Ġrel ates", + "Ġrelate s", + "Ġrelat es", + "Ġpublish ers", + "Ġpublisher s", + "Ġun locked", + "Ġunlock ed", + "Ġunl ocked", + "Ġ //----------------------------------------------------------------", + "Ġ// ----------------------------------------------------------------", + "Ġ//------------------------------------------------ ----------------", + "Ġ//-------------------------------- --------------------------------", + "Ġ//---------------- ------------------------------------------------", + "Ġ InterruptedException", + "ĠInterrupt edException", + "Ġout look", + "r n", + "Ġreb els", + "Ġrebel s", + "W ritten", + "Wr itten", + "Ġa sian", + "Ġas ian", + "Ġasi an", + "Ġasia n", + "ot to", + "ott o", + "Ġ ĉĉĉĉ", + "Ġĉ ĉĉĉ", + "Ġĉĉ ĉĉ", + "Ġĉĉĉ ĉ", + "_ gpu", + "_g pu", + "_gp u", + "T xt", + "Tx t", + ". ImageView", + ".Image View", + "Ġs uis", + "Ġsu is", + "Ġsui s", + "_ tables", + "_t ables", + "_table s", + "_tab les", + "_ta bles", + ". RecyclerView", + ".Rec yclerView", + "Ġwhat soever", + "è ģ", + "] ++;Ċ", + "assert True", + "_ verify", + "_ver ify", + "ĠR ivers", + "ĠRiver s", + "ĠRiv ers", + "ĠRi vers", + "Ġ ][", + "Ġ] [", + "J et", + "Je t", + "id ian", + "idi an", + "idia n", + "S ibling", + "Si bling", + "Ġ genres", + "Ġgen res", + "Ġgenre s", + ". Access", + ".A ccess", + ".Ac cess", + ".Acc ess", + "O PS", + "OP S", + "Ġtr ivial", + "Ġtrivia l", + "ภª", + "a len", + "al en", + "ale n", + "в ед", + "ве д", + "ĠS word", + "ĠSw ord", + "Ġscrut iny", + "Ġscrutin y", + "( cb", + "(c b", + "Ġ commerce", + "Ġcom merce", + "Ġcomm erce", + "Ġcommerc e", + "Ġguarante es", + "Ġguarantee s", + "_ adv", + "_a dv", + "_ad v", + "Ġ LET", + "ĠL ET", + "ĠLE T", + "re cio", + "rec io", + "Ġh ilar", + "Ġhi lar", + "Ġhil ar", + "Ġback yard", + "ãĢ ı", + "Ġillustr ated", + "Ġillustrate d", + "Ġillust rated", + "/ vendor", + "/v endor", + ". Util", + ".U til", + "Ġ wow", + "Ġw ow", + "Ġwo w", + "LO Y", + "Ġ Marshal", + "ĠM arshal", + "ĠMar shal", + "ĠMars hal", + "ĠMarsh al", + "\" >'.$", + "\"> '.$", + "\">' .$", + "\">'. $", + "ĠB ak", + "ĠBa k", + "Ġ modifiers", + "Ġmod ifiers", + "Ġmodifier s", + "d ictionary", + "ĠS tre", + "ĠSt re", + "ĠStr e", + "m ultiple", + "mult iple", + "multi ple", + "multip le", + "\" )),", + "\") ),", + "\")) ,", + "ĠC ort", + "ĠCo rt", + "ĠCor t", + "' ]\").", + "'] \").", + "( admin", + "(ad min", + "Ġ Creator", + "ĠC reator", + "ĠCre ator", + "ĠCreat or", + "In ternet", + "Int ernet", + "Inter net", + "Intern et", + "( ms", + "(m s", + "l ogy", + "lo gy", + "log y", + "DECL ARE", + "Ġ Marcus", + "ĠMar cus", + "ĠMarc us", + "< <<<", + "<< <<", + "<<< <", + "ãģ ł", + "_ my", + "_m y", + "( inst", + "(i nst", + "(in st", + "(ins t", + "Ġsc iences", + "Ġscience s", + "Ġsci ences", + "N DER", + "ND ER", + ". enter", + ".en ter", + ".ent er", + "Ġ itu", + "Ġit u", + "Ġi tu", + "Ġbe have", + "Ġbeh ave", + "P an", + "Pa n", + "om bies", + "omb ies", + "ombie s", + "= '<", + "=' <", + "' ));čĊ", + "') );čĊ", + "')) ;čĊ", + "')); čĊ", + "Ġ MENU", + "ĠM ENU", + "ĠME NU", + "ĠMEN U", + "Ġ Workers", + "ĠWork ers", + "ĠWorker s", + "ĠWor kers", + ".No Error", + "Ġ bindings", + "Ġbin dings", + "Ġbind ings", + "Ġbinding s", + "Ġdis abilities", + "{ \\", + "ĠM unicip", + "ĠMun icip", + "Ġ cores", + "Ġc ores", + "Ġco res", + "Ġcor es", + "Ġcore s", + "ur ple", + "ĠN okia", + "us ions", + "usion s", + "usi ons", + "Ġ Fitness", + "ĠF itness", + "ĠFit ness", + ". handleChange", + ".handle Change", + "Ġ javascript", + "Ġj avascript", + "Ġjav ascript", + "Ġjava script", + "ìļ Ķ", + "( dec", + "(d ec", + "(de c", + "Ġ packing", + "Ġp acking", + "Ġpack ing", + "Ġpac king", + "- depend", + "-d epend", + "-de pend", + "Ġtrans cript", + "Ġtran script", + "z eros", + "ze ros", + "zer os", + "zero s", + "_ alert", + "_al ert", + "? \",Ċ", + "?\" ,Ċ", + "?\", Ċ", + "l ibs", + "li bs", + "lib s", + "± оÑĤ", + "Ġ |ĊĊ", + "Ġ| ĊĊ", + "Ġ|Ċ Ċ", + "tr ained", + "tra ined", + "train ed", + "ĠG ent", + "ĠGe nt", + "ĠGen t", + "ĠR ab", + "ĠRa b", + "x p", + "_ configuration", + "_config uration", + "å¤ ©", + "_ accept", + "_ac cept", + "_acc ept", + ".rec yclerview", + ": url", + "ĠMu hammad", + "ĠMuham mad", + "Ġpriv ileges", + "Ġprivile ges", + "Ġprivilege s", + "_ bank", + "_b ank", + "u ku", + "uk u", + "w allet", + "wall et", + "wal let", + "Ġ ROOT", + "ĠR OOT", + "ĠRO OT", + "Ġenc uent", + "? family", + "?f amily", + "ĉ position", + "ĉp osition", + "ĉpos ition", + "Ġ cg", + "Ġc g", + "Ġpre cip", + "Ġprec ip", + "method s", + "meth ods", + "_ fast", + "_f ast", + "_fa st", + "in crement", + "inc rement", + "incre ment", + "incr ement", + "ĠT iger", + "ĠTi ger", + "ĠTig er", + "_OCC URRED", + "qu ip", + "qui p", + "Ġ HAS", + "ĠH AS", + "ĠHA S", + "_ dom", + "_d om", + "_do m", + "Ġw reck", + "Ġwr eck", + "Ġwre ck", + "b j", + "Ġd ern", + "Ġde rn", + "Ġder n", + "Ġorg ans", + "Ġorgan s", + ". entries", + ".en tries", + ".ent ries", + "Ġ _('", + "Ġ_ ('", + "Ġ_( '", + "r amento", + "ram ento", + "Ġ Jamie", + "ĠJam ie", + "ĠJa mie", + "Ġ punk", + "Ġp unk", + "Ġpun k", + "Ġpu nk", + "I PP", + "IP P", + "Ġprogram a", + "Ġprog rama", + "Ġat tain", + "Ġatt ain", + "Ġpro ves", + "Ġpr oves", + "Ġprov es", + "Ġprove s", + "/ sign", + "/s ign", + "Ġanswer ing", + "Ġl adder", + "Ġlad der", + "**** ************************", + "******** ********************", + "**************** ************", + "************************ ****", + "******************** ********", + "************ ****************", + "************** **************", + "ĠW almart", + "ĠWal mart", + "Ġ CONTENT", + "ĠCONT ENT", + "d uctor", + "du ctor", + "duct or", + "duc tor", + "Ġver bal", + "Ġverb al", + "Ġ PID", + "ĠP ID", + "ĠPI D", + "c rypto", + "crypt o", + "cry pto", + "_CALL BACK", + "Ġ= ================================", + "Ġ================= ================", + "Ġp otent", + "Ġpo tent", + "Ġpot ent", + "Ġsh orts", + "Ġshort s", + "Ġsho rts", + ". Uri", + ".U ri", + ". uniform", + ".un iform", + ".uni form", + "; border", + ";b order", + "Ġ Wer", + "ĠW er", + "ĠWe r", + "Ġher ein", + "Ġhere in", + "l la", + "ll a", + "ĠI hr", + "ĠIh r", + "P ixmap", + "Pix map", + "l iteral", + "lit eral", + "lite ral", + "liter al", + "! )ĊĊ", + "!) ĊĊ", + "!)Ċ Ċ", + "g eneric", + "gen eric", + "gener ic", + "gene ric", + "r ust", + "ru st", + "rus t", + "_ scripts", + "_s cripts", + "_script s", + "o sto", + "os to", + "ost o", + "it us", + "itu s", + "ĠCoal ition", + "Ġre mot", + "Ġrem ot", + "de ploy", + "dep loy", + "ĠE agle", + "ĠEag le", + "ĠEa gle", + "ãĢģ ãĢĮ", + "Ġimport ante", + "Ġimportant e", + "ĉ object", + "ĉo bject", + "ĉobj ect", + "ĉob ject", + "Ġseason al", + "Ġseas onal", + "n ej", + "ne j", + "ai du", + "aid u", + "Bind View", + "ĠSi erra", + "- bg", + "-b g", + "Ġmake Styles", + "[ offset", + "[o ffset", + "G ames", + "Game s", + "Gam es", + "Ga mes", + "Ġhorm one", + "AR IO", + "ARI O", + "he ads", + "head s", + "hea ds", + "( select", + "(s elect", + "(se lect", + "(sel ect", + "Ġ Started", + "ĠStart ed", + "ĠStar ted", + "@ param", + "_ decl", + "_de cl", + "_dec l", + "_ blog", + "_b log", + "_bl og", + "Ġa ño", + "Ġañ o", + "\\ Api", + "ĠMil waukee", + "Pro vid", + "Pr ovid", + "Prov id", + "An imated", + "Anim ated", + "Animate d", + "Ġco oler", + "Ġcool er", + "Ġ Seed", + "ĠS eed", + "ĠSe ed", + "ĠSee d", + ". Edit", + ".E dit", + ".Ed it", + "Ï Ħ", + "Ġ Taking", + "ĠT aking", + "ĠTa king", + "ĠTak ing", + "Ġborder Color", + "-f ounder", + "-found er", + ".Logger Factory", + "Ġ\" \"ĊĊ", + "Ġ\"\" ĊĊ", + "Ġ\"\"Ċ Ċ", + "A LT", + "AL T", + "Ġ Late", + "ĠL ate", + "ĠLa te", + "ĠLat e", + "EDI ATE", + "EDIA TE", + "Ġ );ĊĊĊ", + "Ġ) ;ĊĊĊ", + "Ġ);Ċ ĊĊ", + "Ġ);ĊĊ Ċ", + "Ġ); ĊĊĊ", + "a fa", + "af a", + "Ġc ancellation", + "Ġcancel lation", + "Ġcancell ation", + "Ġcanc ellation", + "A tom", + "At om", + "ĠB irmingham", + "emp resa", + "empre sa", + "H EMA", + "HE MA", + "a scal", + "as cal", + "asc al", + "asca l", + "Ġup side", + "Ġups ide", + ". Version", + ".V ersion", + "Ġ Folder", + "ĠF older", + "ĠFo lder", + "ĠFol der", + "ĠFold er", + "Ġ Eight", + "ĠE ight", + "ĠEig ht", + "Ġ Vintage", + "ĠV intage", + "Ġ AppDelegate", + "ĠApp Delegate", + "ĠPre vention", + "ĠPrevent ion", + "ĠPrev ention", + ". separator", + ".s eparator", + ".se parator", + "S TM", + "ST M", + "( room", + "(r oom", + "(ro om", + "g enerator", + "gen erator", + "gener ator", + "Ġc attle", + "Ġcat tle", + "ĉ Z", + "Ġ Particle", + "ĠP article", + "ĠPart icle", + "ĠParti cle", + "' };Ċ", + "'} ;Ċ", + "Ġne ighbours", + "Ġneighb ours", + "Ġneighbour s", + "ĠState less", + "ĠStat eless", + "Ġ altitude", + "Ġalt itude", + "Ġs aint", + "Ġsa int", + "Ġsai nt", + "о бав", + "об ав", + "оба в", + "Ġcon vinc", + "Ġconv inc", + "Ġconvin c", + "Ġ Contents", + "ĠCont ents", + "ĠContent s", + "ĠConte nts", + "Ġje une", + "Ġjeu ne", + "( ts", + "(t s", + "S erialization", + "Serial ization", + "( collection", + "(c ollection", + "(col lection", + "(coll ection", + "ĠJ azz", + "ĠJa zz", + "Ġ Dod", + "ĠD od", + "ĠDo d", + "ĠR och", + "ĠRo ch", + "ĠRoc h", + "a cio", + "ac io", + "aci o", + "comm ended", + "commend ed", + "DE FINE", + "DEF INE", + ".on load", + "Ġspecial ty", + "Ġspeci alty", + "PL ACE", + "_ MOVE", + "_M OVE", + "_MO VE", + "_MOV E", + "Ġaccount able", + "Re uters", + "Ġf icken", + "Ġfi cken", + "Ġfic ken", + "Ġfick en", + "Ġde pr", + "Ġdep r", + "W ow", + "Wo w", + "V oid", + "Vo id", + ". space", + ".s pace", + ".sp ace", + "ภĹ", + "Ġt q", + "Ġ Pets", + "ĠP ets", + "ĠPe ts", + "ĠPet s", + "< $", + "( Current", + "(C urrent", + "ber ries", + "plan ation", + "pla nation", + "Ġlist Of", + "Ġ Thu", + "ĠT hu", + "ĠTh u", + "Ġ PRINT", + "ĠPR INT", + "ĠPRI NT", + "Ġm ismo", + "Ġmis mo", + "Ġmism o", + "Ġ doi", + "Ġd oi", + "Ġdo i", + "c hk", + "ch k", + "Ġ Unicode", + "ĠUn icode", + "ĠUni code", + "( role", + "(r ole", + "(ro le", + "Ġvir gin", + "< Point", + "

-->Ċ", + ">- ->Ċ", + ">-- >Ċ", + "V ol", + "Vo l", + "ĠS SD", + "ĠSS D", + ") )),", + ")) ),", + "))) ,", + ". Optional", + ".Option al", + "Ġn urses", + "Ġnur ses", + "Ġnurs es", + "Ġnurse s", + "Ġ orb", + "Ġo rb", + "Ġor b", + "_ pe", + "_p e", + ") ;čĊčĊčĊ", + ");čĊ čĊčĊ", + "); čĊčĊčĊ", + ");čĊčĊ čĊ", + "pl aced", + "place d", + "pla ced", + "es ser", + "ess er", + "esse r", + "Ġther apeutic", + "Ġwh itespace", + "Ġwhite space", + "Ġwhites pace", + "Ġ aston", + "Ġa ston", + "Ġas ton", + "Ġast on", + "Success ful", + "Ġp raised", + "Ġpr aised", + "Ġpraise d", + "Ġpra ised", + "ĠW es", + "ĠWe s", + "Ġe ighth", + "Ġeight h", + "i ral", + "ir al", + "ira l", + "Ġv rouw", + "Ġvrou w", + "Ġ faction", + "Ġf action", + "Ġfact ion", + "Ġfa ction", + "Ġfac tion", + "_ bias", + "_b ias", + "_bi as", + "Ġ witch", + "Ġw itch", + "Ġwit ch", + "Ġ npc", + "Ġn pc", + "Ġnp c", + "( sb", + "(s b", + "ĠRod rig", + "ĠRodr ig", + "_ big", + "_b ig", + "_bi g", + "D ependency", + "Dep endency", + "ĠAb raham", + "ar di", + "ard i", + "C AR", + "CA R", + "n os", + "no s", + "Ġab undance", + "Ġabund ance", + "Ġnut rients", + "Ġnutrient s", + "in stein", + "ins tein", + "inst ein", + ". Vert", + ".V ert", + ".Ver t", + "Ġ ISS", + "ĠI SS", + "ĠIS S", + "< U", + "Ġs ums", + "Ġsu ms", + "Ġsum s", + "_ hist", + "_h ist", + "_hi st", + "Ġfar mer", + "Ġfarm er", + "Ġ Abr", + "ĠA br", + "ĠAb r", + "S hot", + "Sh ot", + "Ġ BadRequest", + "ĠBad Request", + "Ġh ass", + "Ġhas s", + "Ġha ss", + "Ġ Rails", + "ĠR ails", + "ĠRa ils", + "ĠRail s", + "ĠRai ls", + "Ġaff iliated", + "Ġaffili ated", + "Ġaffiliate d", + "æĿ ¥", + "Ġe rf", + "Ġer f", + "I NF", + "IN F", + "Ġ ViewHolder", + "ĠView Holder", + "m ini", + "min i", + "mi ni", + "ĠR oth", + "ĠRo th", + "ĠRot h", + "Ġfaith ful", + "ĠPhill ips", + "ĠPhillip s", + "AN DOM", + "AND OM", + "] .[", + "]. [", + "_ PAY", + "_P AY", + "_PA Y", + "ĠAr ctic", + "ĠArc tic", + "f aker", + "fa ker", + "fake r", + "fak er", + "D igit", + "Di git", + "Dig it", + "M ale", + "Ma le", + "Mal e", + "std err", + "se ys", + "sey s", + "Ġ Å¡", + "ĠÅ ¡", + "_ remote", + "_rem ote", + "l ique", + "li que", + "liqu e", + "Ġin def", + "Ġi ndef", + "Ġind ef", + "Ġinde f", + "ĠIndust ries", + "i tra", + "it ra", + "itr a", + "_ pairs", + "_p airs", + "_pair s", + "_pa irs", + "< iostream", + " D", + "Ġs ervlet", + "Ġserv let", + "bast ian", + "Ġ >&", + "Ġ> &", + "S ID", + "SI D", + "_ clk", + "_c lk", + "_cl k", + "Ġdi visions", + "Ġdiv isions", + "Ġdivision s", + "Ġdivis ions", + "} ',Ċ", + "}' ,Ċ", + "}', Ċ", + "Ġd ildo", + "Ġdil do", + "Ġpar ade", + "Ġpara de", + "Ġpa rade", + "Ġparad e", + "m ajor", + "maj or", + "Ġa board", + "Ġab oard", + "; ++", + "Ġ fusion", + "Ġf usion", + "Ġfus ion", + "\" },{\"", + "\"} ,{\"", + "\"}, {\"", + "ĠDialog Result", + "ĉ arr", + "ĉa rr", + "ĉar r", + "- em", + "-e m", + "_ nr", + "_n r", + "( handler", + "(h andler", + "(handle r", + "(hand ler", + ". NET", + ".N ET", + ".Xtra Reports", + "ĠSh ah", + "ĠSha h", + "Ġ Brief", + "ĠB rief", + "ĠBr ief", + "ĠBri ef", + "- ,", + "Ġ precio", + "Ġp recio", + "Ġpre cio", + "Ġprec io", + "ĉ ĉĉĠĠĠĠĠĠ", + "ĉĉ ĉĠĠĠĠĠĠ", + "ĉĉĉ ĠĠĠĠĠĠ", + "ĉĉĉĠĠĠ ĠĠĠ", + "ĉĉĉĠ ĠĠĠĠĠ", + "ĉĉĉĠĠ ĠĠĠĠ", + "ĉĉĉĠĠĠĠ ĠĠ", + "ĉĉĉĠĠĠĠĠ Ġ", + "Ġ tant", + "Ġt ant", + "Ġta nt", + "Ġtan t", + "ĠGr ande", + "ĠGrand e", + "ĠGran de", + "ĠGra nde", + "/ xml", + "/x ml", + "_ ICON", + "_I CON", + "_IC ON", + "ĠR etro", + "ĠRe tro", + "ĠRet ro", + "un que", + "Ġn ag", + "Ġna g", + "to Fixed", + "X L", + "Ġ declaring", + "Ġdecl aring", + "Ġdeclar ing", + "Ġ Concrete", + "ĠCon crete", + "ĠConc rete", + "Ġ Amazing", + "ĠAm azing", + "ĠAma zing", + "ĉprint k", + "Ġdeb ates", + "Ġdebate s", + "D ATED", + "DA TED", + "DATE D", + "DAT ED", + "Ġa esthetic", + "Ġaest hetic", + "Ġaes thetic", + "eme tery", + "emet ery", + "Routing Module", + "ĠNash ville", + "W AYS", + "WA YS", + "WAY S", + "Ġ wolf", + "Ġw olf", + "Ġwo lf", + "Ġwol f", + "Ġob servers", + "Ġobs ervers", + "Ġobserv ers", + "Ġobserver s", + "Ġobserve rs", + "O TA", + "OT A", + "an son", + "ans on", + "Ġ ea", + "Ġe a", + "Ġgreen house", + "ĵį ä½ľ", + "Ġst air", + "Ġsta ir", + "Ġimm igrant", + "Ġimmigr ant", + "_ apply", + "_app ly", + "_ap ply", + "pe are", + "pear e", + "ĠB loomberg", + "ĠBloom berg", + "_ PLAYER", + "_PL AYER", + "_PLAY ER", + "R esp", + "Re sp", + "Res p", + "æŃ £", + "Choose r", + "Cho oser", + "Ġ ICollection", + "ĠI Collection", + "ĠIC ollection", + "P eter", + "Pe ter", + "Pet er", + "Err o", + "Er ro", + ".detect Changes", + "M aps", + "Map s", + "Ma ps", + "Ġ squeeze", + "Ġs queeze", + "Ġsqueez e", + "ĠH omes", + "ĠHome s", + "ĠHom es", + "ĠHo mes", + "weg ian", + "Ġformat ting", + "Ġnegot iate", + "u ld", + "ul d", + "ĠN ep", + "ĠNe p", + "Ġ QB", + "ĠQ B", + "Ġeconom ies", + "Ġec onomies", + "Ġ */,", + "Ġ* /,", + "Ġ*/ ,", + "Ġred und", + "Ġredu nd", + "ĠA ber", + "ĠAb er", + "ĠAbe r", + ".IsNullOr WhiteSpace", + "yc led", + "ycle d", + "ycl ed", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "_ Sh", + "_S h", + "Ġs kept", + "Ġsk ept", + "Ġske pt", + "Ġre created", + "Ġrec reated", + "Ġrecre ated", + "Ġrecreate d", + "Ġ getType", + "Ġget Type", + "Ġm argins", + "Ġmargin s", + "Ġmarg ins", + "Ġcolon ial", + "ch arts", + "char ts", + "cha rts", + "chart s", + "/ /@", + "// @", + "Ġ processors", + "Ġprocess ors", + "Ġprocessor s", + "Ġprocesso rs", + "è¯ ´", + "b atis", + "bat is", + "æĦ ı", + "at orio", + "ator io", + "ato rio", + "atori o", + "ment ioned", + "mention ed", + "P atient", + "Pat ient", + "Ġp rey", + "Ġpr ey", + "Ġpre y", + "Check box", + "_ xpath", + "_x path", + ". skip", + ".s kip", + ".sk ip", + "ĠMor mon", + "ĠMorm on", + "ĠMemory Stream", + "CR EMENT", + "CRE MENT", + "Ġ ku", + "Ġk u", + "m eld", + "me ld", + "mel d", + "\\ Data", + "\\D ata", + "Ġ Kernel", + "ĠK ernel", + "ĠKer nel", + "ĠKern el", + "i ltr", + "il tr", + "ilt r", + "éĢ ģ", + "( profile", + "(pro file", + "(pr ofile", + "C arbon", + "Car bon", + "R OLE", + "RO LE", + "ROL E", + "( pl", + "(p l", + "] *(", + "]* (", + ". memory", + ".m emory", + ".mem ory", + ".memo ry", + "Ġme dal", + "Ġmed al", + "Ġ advisor", + "Ġad visor", + "Ġadv isor", + "Ġadvis or", + "it ät", + "itä t", + "Ġ hdr", + "Ġh dr", + "Ġhd r", + "ie rung", + "ier ung", + "Ġ Provides", + "ĠPro vides", + "ĠProvid es", + "ĠProvide s", + "ĠProv ides", + "( alpha", + "(al pha", + "Ġteen agers", + "Ġteenager s", + "Ġteenage rs", + "- parser", + "-p arser", + "-par ser", + "-parse r", + ". LatLng", + ".L atLng", + "] ()Ċ", + "]( )Ċ", + "]() Ċ", + "Ġfel ony", + "Ġfelon y", + "ĉĉĉ ĊĉĉĉĊ", + "ĉĉĉĊ ĉĉĉĊ", + "B OOK", + "BO OK", + "Ġ slash", + "Ġs lash", + "Ġsl ash", + "Ġsla sh", + "Ġ clearfix", + "Ġclear fix", + "ĠPro phet", + "ĠProp het", + "å® ¹", + "right ness", + "- fi", + "-f i", + ". kind", + ".k ind", + "er ton", + "ert on", + "erto n", + "J im", + "Ġmanip ulate", + "Ġ worksheet", + "Ġwork sheet", + "Ġworks heet", + "o lin", + "ol in", + "oli n", + "st ars", + "star s", + "sta rs", + "Ġ artifact", + "Ġart ifact", + "_ EMPTY", + "_EM PTY", + "_EMP TY", + "ĉ main", + "ĉm ain", + "--- ---------- ';", + ">' ;", + "Ġexp ressing", + "Ġexpress ing", + "Ġexpres sing", + "Ġ IQ", + "ĠI Q", + "Ġ Fact", + "ĠF act", + "ĠFac t", + "ĠFa ct", + "/************************************************************************ *******Ċ", + "/**************************************************************************** ***Ċ", + "_ mass", + "_m ass", + "_ma ss", + "_mas s", + ") ):", + ")) :", + "Ġcon dom", + "Ġcond om", + "Ġcondo m", + "Ġcreate State", + "ome town", + "omet own", + "Ġi rr", + "Ġir r", + "Ġ >(", + "Ġ> (", + "> B", + "it eration", + "ite ration", + "iter ation", + "ãĥ ª", + "Ġsh irts", + "Ġshirt s", + "ou nty", + "ount y", + "oun ty", + "- >$", + "-> $", + "_ SIGN", + "_S IGN", + "_SIG N", + "_SI GN", + "ĠD ale", + "ĠDa le", + "ĠDal e", + "Ġ jj", + "Ġj j", + "E asy", + "F re", + "Fr e", + "Ġ Ny", + "ĠN y", + "Ġ chlor", + "Ġch lor", + "m atched", + "match ed", + "mat ched", + "ĠG erm", + "ĠGe rm", + "ĠGer m", + "- UA", + "-U A", + "ĠN athan", + "ĠNa than", + "ĠNat han", + "ĠNath an", + "e ducation", + "educ ation", + "edu cation", + "- yard", + "-y ard", + "- che", + "-c he", + "-ch e", + "h ouses", + "ho uses", + "house s", + "hou ses", + "r itional", + "rit ional", + "rition al", + "Ġpro ximity", + "Ġprox imity", + "Ġdie sem", + "Ġdies em", + "Ġdiese m", + "áºŃ p", + "Ġd rought", + "Ġdr ought", + "Ġdro ught", + ". audio", + ".a udio", + ".au dio", + "Ġ Leo", + "ĠL eo", + "ĠLe o", + "Ġfavor able", + "Ġfav orable", + "in ch", + "inc h", + "ĠD aw", + "ĠDa w", + "r ibly", + "rib ly", + "_ student", + "_st udent", + "id able", + "ida ble", + "O VE", + "OV E", + "Ġl acks", + "Ġla cks", + "Ġlack s", + "Ġlac ks", + "oun cing", + "ounc ing", + ". business", + ".b usiness", + ".bus iness", + "Ġ reopen", + "Ġre open", + "m aybe", + "may be", + "_ GLOBAL", + "_G LOBAL", + "Ġd resses", + "Ġdr esses", + "Ġdress es", + "ĠEd wards", + "ĠEdward s", + "ens ible", + "ensi ble", + "Ġ Hardware", + "ĠH ardware", + "ĠHard ware", + "Ġ Excellent", + "ĠEx cellent", + "ĠExcell ent", + "ĠTime Unit", + "CT IONS", + "CTION S", + "Ġs chedules", + "Ġsched ules", + "Ġschedule s", + "Ġ segue", + "Ġse gue", + "Ġseg ue", + "Ġsegu e", + "Open s", + "Op ens", + "am men", + "amm en", + "- Identifier", + "Ġst aring", + "Ġstar ing", + "Ġsta ring", + "Ġhapp ily", + "ĠH ob", + "ĠHo b", + "' _", + "Ġ \");", + "Ġ\" );", + "Ġ\") ;", + "ament os", + "amento s", + "amen tos", + "et ched", + "etch ed", + "etc hed", + "Ġ/ >}Ċ", + "Ġ/> }Ċ", + "Ġ/>} Ċ", + ". Users", + ".User s", + ".Use rs", + "Ġint errupted", + "Ġinter rupted", + "Ġinterrupt ed", + "Cont acts", + "Contact s", + "Conta cts", + "Ġ registro", + "Ġreg istro", + "Ġregistr o", + "Ġregist ro", + "in burgh", + "C HA", + "CH A", + "_ imp", + "_i mp", + "_im p", + "p his", + "ph is", + "phi s", + "s ay", + "sa y", + "Ġret ailer", + "Ġretail er", + ". NODE", + ".N ODE", + ".NO DE", + "/ maps", + "/m aps", + "/map s", + "_ LAST", + "_L AST", + "_LA ST", + "Ġ Charge", + "ĠCh arge", + "ĠChar ge", + "ĠCharg e", + "_ guard", + "_g uard", + "C ollider", + "Col lider", + "Coll ider", + "ĠStateless Widget", + "\" :[\"", + "\": [\"", + "\":[ \"", + "(\" ../../", + "(\"../ ../", + "(\".. /../", + "i oxide", + "iox ide", + "ioxid e", + "ĠS und", + "ĠSun d", + "ĠSu nd", + "Ġ' ';", + "Ġ'' ;", + "un set", + "uns et", + "add Widget", + "л Ñİ", + "el les", + "ell es", + "elle s", + "al ker", + "alk er", + "A rc", + "Ar c", + "Ġd educt", + "Ġde duct", + "Ġded uct", + "G UILayout", + "GUI Layout", + "ĠV illa", + "ĠVi lla", + "ĠVill a", + "ĠVil la", + "Ġfor bidden", + "Ġforb idden", + "Ġforbid den", + "_ where", + "_w here", + "_wh ere", + "Ġ \\/", + "Ġ\\ /", + "ĠT ib", + "ĠTi b", + "_ AX", + "_A X", + "] čĊčĊ", + "]čĊ čĊ", + "Ġ Bir", + "ĠB ir", + "ĠBi r", + "Ġb end", + "Ġbe nd", + "Ġben d", + "Ġ MAKE", + "ĠM AKE", + "ĠMA KE", + "ĠMAK E", + "Ġ MET", + "ĠM ET", + "ĠME T", + "Ġf utures", + "Ġfuture s", + "Ġfut ures", + "Ġfu tures", + "Ġ weighted", + "Ġwe ighted", + "Ġweight ed", + "Ġweigh ted", + "\"\" \"čĊ", + "\"\"\" čĊ", + "Ġ authorize", + "Ġauthor ize", + "( program", + "(p rogram", + "(pro gram", + "(pr ogram", + "(prog ram", + "} ,{\"", + "}, {\"", + "},{ \"", + "Ġcoeff icients", + "Ġcoefficient s", + "ê s", + "Per Page", + "ĠB athroom", + "ĠBath room", + "ĠPublish ing", + "G PL", + "GP L", + "Ġsub missions", + "Ġsubmission s", + "Ġ NUMBER", + "ĠNUM BER", + "ĠNU MBER", + "j Äħ", + "Ġadd itionally", + "Ġadditional ly", + "Ġaddition ally", + "em pre", + "emp re", + "ĠS hel", + "ĠSh el", + "ĠShe l", + "o typ", + "ot yp", + "oty p", + "S olution", + "Sol ution", + "Ġth under", + "Ġthu nder", + "_ ec", + "_e c", + "Ġ ĊĠĠĠĠĊ", + "ĠĊ ĠĠĠĠĊ", + "ĠF ellow", + "ĠFel low", + "ĠFell ow", + "Ġk ay", + "Ġka y", + "Ġnew State", + "ONT AL", + "Im plementation", + "Implement ation", + ". Look", + ".L ook", + ".Lo ok", + "Ġ ents", + "Ġe nts", + "Ġen ts", + "Ġent s", + "Ġl ors", + "Ġlo rs", + "Ġlor s", + "Ġ BIG", + "ĠB IG", + "ĠBI G", + "f ab", + "fa b", + "Ġaverage d", + "Ġaver aged", + "Ġ Feedback", + "ĠFe edback", + "ĠFeed back", + "ĠW ells", + "ĠWell s", + "ĠWel ls", + "Ġm artial", + "Ġmart ial", + "Ġind ul", + "Ġindu l", + "ĠComm unist", + "ĠCommun ist", + "ĠF orex", + "ĠFor ex", + "ĠFore x", + "ĠFo rex", + "ĠAgricult ure", + "ĠAgr iculture", + "\" [", + "Ġqu ar", + "Ġq uar", + "Ġqua r", + "ĠK ont", + "ĠKon t", + "ĠKo nt", + "ĉ view", + "ĉv iew", + ". Bytes", + ".Byte s", + ".By tes", + "d esktop", + "des ktop", + "desk top", + "Ġ Makes", + "ĠM akes", + "ĠMake s", + "ĠMa kes", + "ĠMak es", + "akes peare", + ". Nullable", + ".Null able", + "Ġspot light", + "V B", + "o wy", + "ow y", + "( torch", + "(t orch", + "(to rch", + "t ridge", + "tr idge", + "tri dge", + "_ bounds", + "_b ounds", + "_bound s", + "_bo unds", + "Ġapolog ize", + ". addItem", + ".add Item", + "an td", + "ant d", + "* );Ċ", + "*) ;Ċ", + ", u", + "( gen", + "(g en", + "ç» ĵ", + "re ator", + "reat or", + "rea tor", + "ĠC ord", + "ĠCo rd", + "ĠCor d", + "o upper", + "ou pper", + "oup per", + ". metro", + ".m etro", + ".me tro", + ".met ro", + "Ġ ew", + "Ġe w", + "Ġ WORD", + "ĠW ORD", + ". After", + ".A fter", + "Ġdet ained", + "Ġdetain ed", + "ĠH ammer", + "ĠHam mer", + "ĠHamm er", + "ex isting", + "exist ing", + "Ġ ost", + "Ġo st", + "Ġos t", + "Ġmon ument", + "- custom", + "-c ustom", + "User ID", + "Ġ Nom", + "ĠN om", + "ĠNo m", + "Ġre jection", + "Ġreject ion", + "Ġrej ection", + "( dim", + "(d im", + "(di m", + "Ġ singleton", + "Ġs ingleton", + "Ġsingle ton", + "Ġsing leton", + "ĉ die", + "ĉd ie", + "ar iance", + "ari ance", + "arian ce", + "aria nce", + "re ports", + "rep orts", + "report s", + "repo rts", + "] !=", + "e lda", + "el da", + "eld a", + "Ġpreval ence", + "_ regs", + "_re gs", + "_reg s", + ". \".", + ".\" .", + "Ġfemin ist", + "Code c", + "Co dec", + "Cod ec", + "Ġ **Ċ", + "Ġ* *Ċ", + "Ġ** Ċ", + "( labels", + "(label s", + "_ MARK", + "_M ARK", + "_MA RK", + "FA ILED", + "FAIL ED", + "Ġadminister ed", + "W N", + "ĠĠ ĠĠĠĠĠĠĉĉ", + "ĠĠĠĠ ĠĠĠĠĉĉ", + "ĠĠĠĠĠĠĠĠ ĉĉ", + "ĠĠĠĠĠĠĠ Ġĉĉ", + "ĠĠĠĠĠ ĠĠĠĉĉ", + "ĠĠĠĠĠĠ ĠĠĉĉ", + "ĠĠĠĠĠĠĠĠĉ ĉ", + "Ġ noun", + "Ġn oun", + "Ġno un", + "Ġnou n", + "w ig", + "wi g", + "Ġg otta", + "Ġgot ta", + "Ġ rif", + "Ġr if", + "Ġri f", + "- im", + "-i m", + "ĠPaul o", + "ĠPa ulo", + "ĠCommand Type", + "] ))ĊĊ", + "]) )ĊĊ", + "])) ĊĊ", + "]))Ċ Ċ", + "- zero", + "-z ero", + "Tr aining", + "Train ing", + "Tra ining", + "Ġ lord", + "Ġl ord", + "Ġlo rd", + "Ġlor d", + "_ art", + "_a rt", + "_ar t", + "re ddit", + "red dit", + "redd it", + "C ert", + "Ce rt", + "Ġp eso", + "Ġpe so", + "Ġpes o", + "R ot", + "Ro t", + "Ġen danger", + "Ġend anger", + ". dr", + ".d r", + "user Info", + "u nts", + "un ts", + "unt s", + "n v", + "ĠTr ailer", + "ĠTra iler", + "ĠTrail er", + "- first", + "-f irst", + "-fi rst", + "( make", + "(m ake", + "Ġbenef ici", + "- black", + "-b lack", + "-bl ack", + "i ÃŁ", + "Ġund oubtedly", + "Ġm ex", + "Ġme x", + "ĠAn cient", + "ĠAnc ient", + "( as", + "(a s", + "Ġdes cent", + "Ġdesc ent", + "P ick", + "Pic k", + "Pi ck", + "Ġrep lica", + "Ġrepl ica", + "Ġreplic a", + "$ obj", + "$o bj", + "ä hr", + "äh r", + "Ġar rows", + "Ġarr ows", + "Ġarrow s", + "f ty", + "ft y", + "ĠLib ya", + "u ga", + "ug a", + "ch arged", + "char ged", + "charge d", + "charg ed", + "T ur", + "Tu r", + "Ġh omic", + "Ġhom ic", + "Ġho mic", + "is sen", + "iss en", + "isse n", + "Ġ Fake", + "ĠF ake", + "ĠFa ke", + "ĠFak e", + "Ġbe ers", + "Ġbeer s", + "Ġbee rs", + "Ġsc attered", + "Ġscatter ed", + "( Time", + "(T ime", + "UT IL", + "Ġbureauc r", + "Ġbureau cr", + "/ plain", + "/p lain", + "/pl ain", + "Ġst icking", + "Ġstick ing", + "F AIL", + "FA IL", + "ĠC ovid", + "ĠCo vid", + "ĠCov id", + "Th ird", + "_ present", + "_p resent", + "_pre sent", + "_pres ent", + "ĠP ierre", + "ĠPi erre", + "ĠPier re", + "Ġ ëª", + "Ġë ª", + "Ġ[ ...]ĊĊ", + "Ġ[... ]ĊĊ", + "Ġ[...] ĊĊ", + "P rob", + "Pro b", + "Pr ob", + "Ġ Traffic", + "ĠTra ffic", + "ĠTraff ic", + "i cao", + "ic ao", + "ica o", + "do ctor", + "doc tor", + "Ġ ),ĊĊ", + "Ġ) ,ĊĊ", + "Ġ),Ċ Ċ", + "Ġ), ĊĊ", + "T abs", + "Tab s", + "Ta bs", + "a lu", + "al u", + "ï¼ļ âĢľ", + "Ġin herent", + "Ġinher ent", + "_ No", + "_N o", + "r itis", + "rit is", + "Ġ Proof", + "ĠP roof", + "ĠPro of", + ". basename", + ".b asename", + ".base name", + "ä¼ ļ", + "Ġc him", + "Ġch im", + "Ġchi m", + "Ġ Protected", + "ĠProt ected", + "ĠProtect ed", + "c rit", + "cri t", + "cr it", + "Ġp rone", + "Ġpro ne", + "Ġpr one", + "Ġpron e", + "Ġ кон", + "Ġк он", + "Ġко н", + "Ġ Heroes", + "ĠHer oes", + "ĠHero es", + "Ġan xious", + "Ġanx ious", + "Ġ anos", + "Ġa nos", + "Ġan os", + "Ġano s", + "Ġweek ends", + "Ġweekend s", + "Ġs ext", + "Ġse xt", + "Ġsex t", + "Ġre ducer", + "Ġred ucer", + "Ġredu cer", + "Ġreduce r", + "= UTF", + "h alf", + "ha lf", + "hal f", + "ĠS aw", + "ĠSa w", + ". mm", + ".m m", + "Ġn ueva", + "Ġnu eva", + "Ġnue va", + ".current Target", + ". lua", + ".l ua", + ".lu a", + "_EXT ENSION", + "ĉ reg", + "ĉr eg", + "ĉre g", + "Ġ Ctrl", + "ĠC trl", + "ĠCt rl", + "_ align", + "_al ign", + "accept able", + "Ġr ushing", + "Ġrush ing", + "f rac", + "fr ac", + "fra c", + "Ġbo asts", + "Ġboast s", + "Ġboa sts", + "F ive", + "Fi ve", + " ±", + "Ġ Temperature", + "ĠT emperature", + "ĠTem perature", + "ĠTemper ature", + "> ):", + ">) :", + "Ġch arter", + "Ġchar ter", + "Ġchart er", + "RE ATED", + "REATE D", + "REAT ED", + "REA TED", + "Ġsub jected", + "Ġsubject ed", + "Ġsubj ected", + "Ġ opc", + "Ġo pc", + "Ġop c", + "health y", + "使 çĶ¨", + "ĠScient ific", + "Ġ frau", + "Ġfr au", + "Ġfra u", + "ri ages", + "ria ges", + "riage s", + "ภĶ", + ". inventory", + ".in ventory", + "at ionale", + "ation ale", + "ational e", + "M ad", + "Ma d", + "min utes", + "minute s", + "> >();Ċ", + ">> ();Ċ", + ">>( );Ċ", + ">>() ;Ċ", + "Ġ Env", + "ĠE nv", + "ĠEn v", + "Ġrecord ings", + "Ġrecording s", + "Ġsusp icion", + "sql ite", + "sq lite", + "ĉ read", + "ĉr ead", + "ĉre ad", + "ãģ ¦", + "Ġwor ries", + ".put String", + "ĠSh anghai", + "( uid", + "(u id", + "(ui d", + "r er", + "re r", + "ĠvÃŃ de", + "\" ):", + "\") :", + "Ġmethod ology", + "Ġк оÑĤоÑĢ", + "Ġко ÑĤоÑĢ", + "ĠкоÑĤ оÑĢ", + "c cc", + "cc c", + "av ad", + "ava d", + "Ġin duction", + "Ġind uction", + "Ġindu ction", + "ĉ Thread", + "ĉT hread", + ", string", + ",s tring", + ",str ing", + ",st ring", + "ạ i", + "neh men", + "u ition", + "ui tion", + "uit ion", + "Ġ* __", + "Ġ*_ _", + ".e mf", + ".em f", + "Ġ ìľ", + "Ġì ľ", + "/ themes", + "/th emes", + "/theme s", + "/the mes", + "Ġ Nine", + "ĠN ine", + "ĠNi ne", + "ĠNin e", + ". One", + ".On e", + ".O ne", + "Ġ Embed", + "ĠEm bed", + "ĠEmb ed", + "Ġf az", + "Ġfa z", + "u ations", + "uation s", + "uat ions", + "Ġprivate ly", + "Ġpriv ately", + "Ġprivat ely", + "Ġ ling", + "Ġl ing", + "Ġli ng", + "Ġlin g", + "[ F", + "u shi", + "us hi", + "ush i", + "Ġlaunch es", + "( KEY", + "(K EY", + "G MT", + "GM T", + "Ġa iming", + "Ġaim ing", + "Ġai ming", + "pat ible", + "ĠB iden", + "ĠBi den", + "ĠBid en", + "i w", + "Ġ Degree", + "ĠD egree", + "ĠDe gree", + "ĠDeg ree", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "Ġ$ ('<", + "Ġ$( '<", + "Ġ$(' <", + "á rios", + "ário s", + "ár ios", + "to UpperCase", + "ìł ľ", + "Ġ EUR", + "ĠE UR", + "ĠEU R", + "Ġovers ight", + "Ġtable sp", + "Ġtables p", + "Up dates", + "Update s", + ".m akedirs", + ".make dirs", + "Ġ humidity", + "Ġh umidity", + "Ġhum idity", + "Ġhumid ity", + "/ template", + "/t emplate", + "/temp late", + "Al ways", + "( IS", + "(I S", + "_ cert", + "_c ert", + "_ce rt", + "D ig", + "Di g", + "Ġunder way", + "or ton", + "ort on", + "ĠHur ricane", + "Ġsp ends", + "Ġspe nds", + "Ġspend s", + "Ġ Segment", + "ĠS egment", + "ĠSe gment", + "ĠSeg ment", + "Ġ flies", + "Ġf lies", + "Ġfl ies", + "Ġ Toggle", + "ĠT oggle", + "ĠL ynch", + "ĠLyn ch", + "Ġs enses", + "Ġsense s", + "Ġsens es", + "Ġsen ses", + "ĠK os", + "ĠKo s", + "set Enabled", + "ist ically", + "istic ally", + "istical ly", + "Ġ tester", + "Ġt ester", + "Ġte ster", + "Ġtest er", + "Ġtes ter", + "Ġteste r", + "Ġadministr ators", + "Ġadministrator s", + "Ġt agged", + "Ġtag ged", + "Ð ĵ", + "Ġ shortcut", + "Ġshort cut", + "Ġ Resolution", + "ĠRe solution", + "ĠRes olution", + "Ġsuper vision", + "Ġsuperv ision", + "ĠAsh ley", + "Tr acking", + "Track ing", + "ul atory", + "ulator y", + "an del", + "and el", + "ande l", + "i sten", + "is ten", + "ist en", + "iste n", + "Ġun re", + "Ġunr e", + "( diff", + "(d iff", + "(di ff", + "AN TS", + "ANT S", + "Ġr ider", + "Ġrid er", + "Ġride r", + "Ġri der", + "Ġs Äħ", + ". Series", + ".S eries", + ".Se ries", + "_ orders", + "_order s", + "_or ders", + "_ord ers", + "ORIZ ONTAL", + "Ġret ention", + "ãĢĤ čĊčĊ", + "\"> čĊčĊ", + "\">čĊ čĊ", + "Ġdi agonal", + "Ġdiag onal", + "Ġdiagon al", + "ĠC ancellationToken", + "_ Internal", + "_In ternal", + "_Int ernal", + "_Inter nal", + "Ġr uin", + "Ġru in", + ". Qt", + ".Q t", + "ocr atic", + "ocrat ic", + "T el", + "Te l", + "Ġ Answers", + "ĠAn swers", + "ĠAnswer s", + "ĠAns wers", + "m atic", + "ma tic", + "mat ic", + "Ġ xp", + "Ġx p", + "a tem", + "at em", + "ate m", + "_ jobs", + "_j obs", + "_job s", + "_ any", + "_a ny", + "_an y", + "Ġsen iors", + "Ġsenior s", + "Ġseni ors", + "Ġland mark", + "ĠQ List", + "Ġman eu", + "Ġmane u", + "ot ify", + "oti fy", + "/ \";Ċ", + "/\" ;Ċ", + "/ server", + "/s erver", + "ĠPhil osoph", + "u tenant", + "ut enant", + "ute nant", + "uten ant", + "( io", + "(i o", + "h z", + "Ġ authenticated", + "Ġauth enticated", + "Ġauthentic ated", + "Ġauthenticate d", + "d v", + "- Compatible", + "Origin ally", + "Original ly", + "Orig inally", + ", function", + ",f unction", + "ãĢĤ čĊ", + "ĠRepresent ative", + "as ily", + "asi ly", + "asil y", + "irc uit", + ". dt", + ".d t", + "( math", + "(m ath", + "(mat h", + ". Marshal", + ".M arshal", + ".Mar shal", + "[ ,", + "Ġ Cities", + "ĠC ities", + "ĠCit ies", + "ĠCi ties", + "_ turn", + "_t urn", + "| )Ċ", + "Ġ cantidad", + "Ġc antidad", + "Ġcant idad", + "al ter", + "alt er", + "alte r", + "ĉ ui", + "ĉu i", + "ĠNe braska", + "Ġsk irt", + "Ġski rt", + ". bg", + ".b g", + "Shared Preferences", + "( style", + "(st yle", + "Ġg rief", + "Ġgr ief", + "Ġgri ef", + "g ew", + "ge w", + "Ġsaf eg", + "Ġsafe g", + "o lang", + "ol ang", + "ola ng", + "olan g", + "_ lists", + "_l ists", + "_list s", + "_li sts", + "ì Ľ", + "Ġgran ite", + "Ġgra nite", + "Ġhot test", + "Ġhott est", + ". jdbc", + ".j dbc", + ".jd bc", + ". Customer", + ".C ustomer", + ".Custom er", + "Ġ âī¤", + "Ġâī ¤", + "Ġw aar", + "Ġwa ar", + "_ scene", + "_s cene", + "_sc ene", + "+ '/", + "+' /", + "ĠJ TextField", + "ĠJText Field", + "ĠJT extField", + "Ġs eating", + "Ġse ating", + "Ġsea ting", + "Ġseat ing", + "Ġw ears", + "Ġwe ars", + "Ġwear s", + "Ġ` /", + "C ases", + "Case s", + "Ca ses", + "Cas es", + "Ġ Youtube", + "ĠY outube", + "ĠYou tube", + "ı m", + "Ġb alcon", + "Ġbal con", + ", G", + "Meta Data", + "Met aData", + "- price", + "-p rice", + "-pr ice", + "S CR", + "SC R", + "Un ity", + "Unit y", + "Uni ty", + "Ġtr unk", + "={ `${", + "={` ${", + "Ġearth quake", + "Ġearthqu ake", + "P artial", + "Part ial", + "Ġ subst", + "Ġsu bst", + "Ġsub st", + "Ġsubs t", + "Ġel imin", + "Ġelim in", + "=\" '.", + "=\"' .", + "//* [@", + "//*[ @", + "Ġsup ervisor", + "Ġsuper visor", + "Ġsuperv isor", + "vr olet", + "vro let", + "_ article", + "_art icle", + "Ġ pane", + "Ġp ane", + "Ġpa ne", + "Ġpan e", + "b io", + "bi o", + "Ġmot ors", + "Ġmo tors", + "Ġmotor s", + "Ġmoto rs", + "N M", + "F rank", + "Fr ank", + "Fran k", + "Fra nk", + "Ġon ion", + "- word", + "-w ord", + "Item ClickListener", + "ItemClick Listener", + "Ġ brit", + "Ġb rit", + "Ġbr it", + "Ġbri t", + "end encies", + "enden cies", + "Com puter", + "Comp uter", + "Compute r", + "Comput er", + "_ running", + "_r unning", + "_run ning", + "( day", + "(d ay", + "(da y", + "- he", + "-h e", + "( named", + "(n amed", + "(name d", + "ĠS ach", + "ĠSa ch", + "ĠSac h", + "о Ñĩ", + "c ampaign", + "camp aign", + ". Abstract", + ".A bstract", + ".Ab stract", + ".Abs tract", + "( wrapper", + "(w rapper", + ". pay", + ".p ay", + ".pa y", + "Ġ uw", + "Ġu w", + "G eo", + "Ge o", + "r ails", + "ra ils", + "rai ls", + "rail s", + "/ select", + "/s elect", + "/se lect", + "i chte", + "ic hte", + "ich te", + "icht e", + "s ons", + "so ns", + "son s", + "E VENT", + "EV ENT", + "Ġal iment", + "Ġali ment", + "Pro viders", + "Provider s", + "Provid ers", + "Provide rs", + "Prov iders", + "A wait", + "Aw ait", + "_INTER VAL", + ". off", + ".of f", + ".o ff", + "Ġgl uten", + "Ġglut en", + "Ġglu ten", + "_ cloud", + "_c loud", + "_cl oud", + "Ġ wen", + "Ġw en", + "Ġwe n", + ". extract", + ".ex tract", + ".ext ract", + ".extra ct", + "ĉ button", + "ĉb utton", + "/ MM", + "/M M", + "P arty", + "Par ty", + "Part y", + "Ġdem ographic", + "Ġdemo graphic", + "_ errno", + "_err no", + "Ġh iking", + "Ġhi king", + "Ġhik ing", + "(' ')Ċ", + "\", @\"", + "Ġ wit", + "Ġw it", + "Ġwi t", + "r á", + "ol ogie", + "olog ie", + "olo gie", + "ologi e", + "Ġ Styles", + "ĠSt yles", + "ĠStyle s", + "ĠSty les", + "ĠBrowser Module", + ". RequestMapping", + ".Request Mapping", + "ic ans", + "ica ns", + "ican s", + "P AGE", + "PA GE", + "c reation", + "cre ation", + "creat ion", + "ĠF erguson", + "u ded", + "ud ed", + "ude d", + "num bers", + "number s", + "Ġ GTK", + "ĠG TK", + "ĠGT K", + "Ġpresent ations", + "Ġpresentation s", + "ĠB obby", + "ĠBob by", + "_ span", + "_s pan", + "_sp an", + "e style", + "est yle", + "esty le", + "Ġillegal ly", + "Ġilleg ally", + "ab ela", + "abel a", + "abe la", + "Ġbattle field", + "cap acity", + "t error", + "ter ror", + "te rror", + "terr or", + "] \");Ċ", + "]\" );Ċ", + "]\") ;Ċ", + "Ġwar rior", + "le ader", + "lead er", + "lea der", + "Ġ DBG", + "ĠD BG", + "ĠDB G", + "Ġ Revenue", + "ĠRe venue", + "Ġvi gil", + "Ġvig il", + "Ġcounter parts", + "Ġcounterpart s", + "( Error", + "(E rror", + "AC TER", + "ACT ER", + "Ġhe eft", + "Ġse lections", + "Ġselect ions", + "Ġselection s", + "Ġsel ections", + "Ġsele ctions", + "ze ug", + "t om", + "to m", + "- two", + "-t wo", + "-tw o", + ". ;Ċ", + ".; Ċ", + "_ statement", + "_st atement", + "_state ment", + "_stat ement", + "_sta tement", + "ĠA id", + "ĠAi d", + "ĠV ul", + "ĠVu l", + "_ rgb", + "_r gb", + "_rg b", + "Ġpr izes", + "Ġpri zes", + "Ġprize s", + "Ġ editable", + "Ġed itable", + "Ġedit able", + "Ġedi table", + "ĉ form", + "ĉf orm", + "ĉfor m", + "ın ı", + ". decor", + ".de cor", + ".dec or", + "D emo", + "De mo", + "Dem o", + "l ices", + "lic es", + "li ces", + "lice s", + "Ġen ctype", + "Ġenc type", + "rat ulations", + "Ġ ROS", + "ĠR OS", + "ĠRO S", + "_ chars", + "_ch ars", + "_char s", + "ĠJ ahr", + "ĠJa hr", + "ĠJah r", + "p artial", + "part ial", + "Ñĥ ÑĤ", + "Ġ Receive", + "ĠRe ceive", + "ĠRece ive", + "ĠL ands", + "ĠLa nds", + "ĠLand s", + "ĠLan ds", + "AP TER", + "APT ER", + "Ġch opped", + "Ġcho pped", + "Ġchop ped", + ". .\"", + ".. \"", + "Ġ Analy", + "ĠAn aly", + "ĠAnal y", + "ĠAna ly", + "Ġ UID", + "ĠU ID", + "ĠUI D", + "ĠR adeon", + "ĠRad eon", + "ĠB ee", + "ĠBe e", + "Ġu nm", + "Ġun m", + "> M", + ".find all", + "Token izer", + "Ġ WHAT", + "ĠWH AT", + "Ġ sj", + "Ġs j", + "D rawing", + "Draw ing", + "E ss", + "Es s", + "O ND", + "ON D", + "Ĭ ¶", + "( packet", + "(p acket", + "(pa cket", + "(pack et", + "âĢĶ but", + "Inv ocation", + "ĠN uclear", + "ĠNu clear", + "? ;Ċ", + "Ġgr andes", + "Ġgrand es", + "Ġgran des", + "Ġgrande s", + "Ġ Crypt", + "ĠC rypt", + "ĠCry pt", + "r emark", + "re mark", + "rem ark", + "rema rk", + "Ġ' ../../../../", + "Ġ'../ ../../../", + "Ġ'../../ ../../", + "Ġ'../../../ ../", + "Ġin ability", + "m agic", + "mag ic", + "c ats", + "ca ts", + "cat s", + "Ġ simulate", + "Ġsim ulate", + "Ġsimul ate", + ": ${", + ":$ {", + "in flate", + "inf late", + "Ġ ener", + "Ġe ner", + "Ġen er", + ": NO", + ":N O", + "i ples", + "ip les", + "iple s", + "ipl es", + "Ġme rit", + "Ġmer it", + "Ġ Rated", + "ĠR ated", + "ĠRa ted", + "ĠRate d", + "ĠRat ed", + "Ġg lue", + "Ġgl ue", + "Ġglu e", + "/ blog", + "/b log", + "/bl og", + "Ġ gren", + "Ġg ren", + "Ġgr en", + "Ġgre n", + "Ġthr illed", + "Ġthrill ed", + ". CH", + ".C H", + "un can", + "unc an", + "unca n", + "Ġ PRIMARY", + "ĠPR IMARY", + "ĠPRI MARY", + "Ġper sec", + "Ġpers ec", + "Ġperse c", + "Ġfe ared", + "Ġfear ed", + ". MIN", + ".M IN", + "ĠThe ater", + "é Ĵ", + "ateg orie", + "ategor ie", + "ategori e", + "atego rie", + "æ® µ", + "Ġappet ite", + "s quare", + "squ are", + "ĠAlex and", + "ĠAlexa nd", + ". UserId", + ".User Id", + "_ gt", + "_g t", + "_ enter", + "_en ter", + "_ent er", + "Ġgrad uates", + "Ġgraduate s", + "Ġgradu ates", + "Fragment Manager", + "Author ize", + "-N LS", + "( My", + "(M y", + "Ġtri umph", + "Ġtrium ph", + "us ting", + "ust ing", + "ustin g", + "_PARAM S", + "_PAR AMS", + "Char acters", + "Character s", + "(: ,:,", + "(:, :,", + "_ BUILD", + "_B UILD", + "_BU ILD", + "M Hz", + "MH z", + "Ġw ashed", + "Ġwas hed", + "Ġwa shed", + "Ġwash ed", + "Ġun cle", + "Ġunc le", + "St eve", + "Ste ve", + "ar down", + "ard own", + "ardo wn", + "< stdio", + " ${", + ">$ {", + "_ confirmation", + "_confirm ation", + "Ġt rophy", + "Ġtr ophy", + "Ġtro phy", + "Ġtrop hy", + "W orks", + "Work s", + "ĠElect ronics", + "ĠElectronic s", + "ĠElectron ics", + "ĠMediterr anean", + "_ metrics", + "_m etrics", + "_metric s", + "_met rics", + "Ġann ouncing", + "Ġannounc ing", + "Ġ DAY", + "ĠD AY", + "ĠDA Y", + "_ proto", + "_pro to", + "_pr oto", + "_prot o", + "Ġ pear", + "Ġp ear", + "Ġpe ar", + "Ġpea r", + "base Url", + "ĉ ĉĉĉĉĉĉĉĊ", + "ĉĉ ĉĉĉĉĉĉĊ", + "ĉĉĉĉ ĉĉĉĉĊ", + "ĉĉĉ ĉĉĉĉĉĊ", + "ĉĉĉĉĉ ĉĉĉĊ", + "ĉĉĉĉĉĉ ĉĉĊ", + "ĉĉĉĉĉĉĉĉ Ċ", + "ĉĉĉĉĉĉĉ ĉĊ", + "Ġco ordination", + "Ġcoord ination", + "Ġcoordin ation", + ": N", + ". animate", + ".an imate", + ".anim ate", + "ĠC otton", + "ĠCot ton", + "_ hit", + "_h it", + "_hi t", + "â ľ", + "Ġj etzt", + "Ġjet zt", + "i fter", + "if ter", + "ift er", + "( fields", + "(f ields", + "(field s", + "own load", + "ific acion", + "ifica cion", + ". cuda", + ".c uda", + "ĠL iu", + "ĠLi u", + "> equals", + ">e quals", + "Ġ Ace", + "ĠA ce", + "ĠAc e", + "ÑĢ ам", + "ÑĢаР¼", + "ÑĢа м", + "ĠSup erman", + "ĠSuper man", + "ĠGar cia", + "ĠGarc ia", + "Ġarr ests", + "Ġarrest s", + "a gar", + "ag ar", + "aga r", + "Ġ {})", + "Ġ{ })", + "Ġ{} )", + "Ġ macros", + "Ġmac ros", + "Ġmacro s", + "ro upe", + "rou pe", + "roup e", + "ê tre", + "êt re", + "Ġtw isted", + "Ġtwist ed", + "str uments", + "strument s", + "stru ments", + "_ (\"", + "_( \"", + "_ vertices", + "_vert ices", + "Ġ Transition", + "ĠT ransition", + "ĠTrans ition", + "ĠTransit ion", + "и к", + "[ max", + "[m ax", + "m ind", + "min d", + "mi nd", + "Ġ accessToken", + "Ġaccess Token", + "Ġun le", + "Ġunl e", + "m us", + "mu s", + "c op", + "co p", + "Ġ Factor", + "ĠF actor", + "ĠFac tor", + "ĠFa ctor", + "ĠFact or", + "Ġcon ced", + "Ġconc ed", + "Ġconce d", + "Ġre tr", + "Ġr etr", + "Ġret r", + ".l inalg", + ".lin alg", + "- slider", + "-s lider", + "-slide r", + "-sl ider", + "o bl", + "ob l", + "_Static Fields", + "Ġz ombie", + "s elling", + "sel ling", + "sell ing", + "Ġ chap", + "Ġc hap", + "Ġch ap", + "Ġcha p", + "Ġsh aking", + "Ġsha king", + "Ġ Translate", + "ĠTrans late", + "ĠAm sterdam", + "Ġ ETH", + "ĠE TH", + "ĠET H", + "_ EXTERN", + "_EX TERN", + "_EXT ERN", + "k d", + "_ disc", + "_d isc", + "_dis c", + "_di sc", + "Ġprec eding", + "Ġpreced ing", + "Ġ prix", + "Ġp rix", + "Ġpr ix", + "Ġpri x", + "Object Name", + "_ modified", + "_mod ified", + "ard ware", + "Ġ?> \">", + "Ġ?>\" >", + "Ġ DW", + "ĠD W", + "` ${", + "Ġ?> \">\" >\"> \">< ?", + "u yen", + "uy en", + "uye n", + "Ġd onna", + "Ġdon na", + "Ġdonn a", + "Ġx si", + "Ġxs i", + "Ġ$ \"{", + "Ġ$\" {", + "Ġ Drawing", + "ĠD rawing", + "ĠDraw ing", + "ĠDra wing", + ", nil", + ",n il", + "Ġ onder", + "Ġo nder", + "Ġon der", + "Ġonde r", + "B G", + "O bserv", + "Ob serv", + "Obs erv", + "Ġconsider ations", + "Ġconsideration s", + "bo at", + "boa t", + "ĠB anks", + "ĠBank s", + "ĠBan ks", + "Ġin dict", + "Ġind ict", + "Ġindic t", + ", I", + "ĠB lu", + "ĠBl u", + "( version", + "(v ersion", + "cl iente", + "client e", + "cli ente", + "o lan", + "ol an", + "ola n", + "L ESS", + "LE SS", + "LES S", + "assert Same", + "_ void", + "_v oid", + "ĠW AS", + "ĠWA S", + "ĉ enum", + "ĉe num", + "ĉen um", + "Ġm ixer", + "Ġmix er", + "E W", + "a ffe", + "af fe", + "aff e", + "Ġblow job", + "t extField", + "text Field", + "Ġimm ense", + "_ repo", + "_re po", + "_rep o", + "Ġ globals", + "Ġg lobals", + "Ġglobal s", + "Ġglob als", + "ant ages", + "anta ges", + "antage s", + ". today", + ".t oday", + ".to day", + "Th ursday", + "ĠB rig", + "ĠBr ig", + "ĠBri g", + "{ })Ċ", + "{} )Ċ", + "{}) Ċ", + "Ġ Imagine", + "ĠIm agine", + "ĠImag ine", + "( GPIO", + "(G PIO", + "Ġ esto", + "Ġe sto", + "Ġes to", + "Ġest o", + "Ġ Province", + "ĠPro vince", + "ĠProv ince", + "ĠM ental", + "ĠMen tal", + "ĠMent al", + "_ cells", + "_c ells", + "_cell s", + "ĠJul ian", + "ĠJu lian", + "ĠJulia n", + "ĠJuli an", + ". Screen", + ".S creen", + ".Sc reen", + "Ġc andle", + "Ġcan dle", + "Ġcand le", + "Ġm onde", + "Ġmon de", + "Ġmo nde", + "Ġmond e", + "Ġv erg", + "Ġver g", + "Ġve rg", + "it erals", + "iter als", + "iteral s", + "- layout", + "-l ayout", + "G uest", + "Gu est", + "Ġv ind", + "Ġvi nd", + "Ġvin d", + "Ġ Echo", + "ĠE cho", + "ĠEc ho", + "' )}", + "') }", + "Ġ mann", + "Ġm ann", + "Ġman n", + "Ġma nn", + "_ BOOLEAN", + "_BO OLEAN", + "h ap", + "ha p", + "Ġnight mare", + "U GH", + "UG H", + "Ġnon etheless", + "Ġnone theless", + "Ġ athe", + "Ġa the", + "Ġat he", + "Ġath e", + "ĠH olland", + "ĠHol land", + "ĠHo lland", + "ĠHoll and", + "Ġ Born", + "ĠB orn", + "ĠBo rn", + "ĠBor n", + "\\ ORM", + "a nut", + "an ut", + "_ levels", + "_level s", + "Ġpet ite", + "Ġpetit e", + "- art", + "-a rt", + "-ar t", + "_ SHOW", + "_S HOW", + "_SH OW", + "number Of", + "_ thumbnail", + "_th umbnail", + "a mins", + "am ins", + "amin s", + "ami ns", + "Ġ Defines", + "ĠDef ines", + "ĠDefine s", + "Ġ\" =", + ". StatusCode", + ".Status Code", + "Ġdign ity", + "ĠB ike", + "ĠBi ke", + "ĠBik e", + ".New Line", + "ĠG las", + "ĠGl as", + "( logger", + "(log ger", + "(lo gger", + "Ġc atches", + "Ġcatch es", + "Ġcat ches", + "v otes", + "vo tes", + "vote s", + "Ġexam ining", + "/ register", + "/reg ister", + "Ġspec ifying", + "Ġspecify ing", + "_ fixed", + "_f ixed", + "_fix ed", + "Ġdraw ings", + "Ġdrawing s", + "Th reshold", + "A x", + "Ġ Architecture", + "ĠArch itecture", + "ĠArchitect ure", + "( pid", + "(p id", + "(pi d", + "W ire", + "Wir e", + "Wi re", + "( cont", + "(c ont", + "(con t", + "(co nt", + "l ane", + "la ne", + "lan e", + "L ists", + "List s", + "Li sts", + "Ġs print", + "Ġsp rint", + "Ġspr int", + "Ġgrand father", + "_ AG", + "_A G", + "Ġs cheduling", + "Ġsched uling", + "CL US", + "CLU S", + "at urity", + "atur ity", + "Ġ locking", + "Ġl ocking", + "Ġloc king", + "Ġlock ing", + "[ size", + "[s ize", + "_ styles", + "_st yles", + "_style s", + "Ġ wb", + "Ġw b", + "-- >ĊĊ", + "-->Ċ Ċ", + "--> ĊĊ", + "Ġsp inning", + "Ġspin ning", + "_ pending", + "_p ending", + "_pen ding", + "Match ers", + "Mat chers", + "Matcher s", + ". Keys", + ".Key s", + "Ġ PV", + "ĠP V", + "en us", + "enu s", + "ant is", + "anti s", + "Ġ discard", + "Ġd iscard", + "Ġdis card", + "Ġdisc ard", + "Ġ haul", + "Ġh aul", + "Ġha ul", + "Ġem pir", + "Ġemp ir", + "Ġpath way", + "Ġo ak", + "Ġoa k", + "м ен", + "ме н", + "-in duced", + "-ind uced", + "Ġim pair", + "Ġimp air", + "ĠCal gary", + ".is Hidden", + "d z", + "_ include", + "_in clude", + "_inc lude", + "Ġ gm", + "Ġg m", + "Ġ' ('", + "Ġ'( '", + "P Y", + "uggest ions", + "uggestion s", + "Ġcom modity", + "Ġcommod ity", + "c ro", + "cr o", + "/ sub", + "/s ub", + "Ġ getInstance", + "Ġget Instance", + "Ġ Legacy", + "ĠLeg acy", + "ĠK il", + "ĠKi l", + "B al", + "Ba l", + "( short", + "(s hort", + "(sh ort", + "In form", + "Info rm", + "Inf orm", + "+ x", + "* r", + "Ġ Hopefully", + "ĠHope fully", + "ĠHop efully", + "o rate", + "or ate", + "ora te", + "Ġm achen", + "Ġma chen", + "Ġmach en", + "Ġmac hen", + "Ġtreat y", + "Ġtre aty", + "ĠO ri", + "ĠOr i", + ". public", + ".p ublic", + ".pub lic", + "- horizontal", + "-h orizontal", + "Ġt actic", + "Ġta ctic", + "Ġtact ic", + "Ġtac tic", + "Ġb ord", + "Ġbo rd", + "Ġbor d", + "w ares", + "ware s", + "wa res", + "war es", + "Ġ ammo", + "Ġa mmo", + "Ġam mo", + "Ġ Lists", + "ĠL ists", + "ĠList s", + "ĠLi sts", + "ĠLis ts", + "Ġequ ations", + "Ġeq uations", + "Ġequation s", + "/ her", + "/h er", + "/he r", + "ĠN SW", + "ĠNS W", + "B ounding", + "Bo unding", + "Bound ing", + "_ Collections", + "_C ollections", + "Ġ avail", + "Ġa vail", + "Ġav ail", + "Ġava il", + ". DropDown", + ".Drop Down", + "è °", + "Ġ hh", + "Ġh h", + "Ġl Ãł", + ". pb", + ".p b", + "Ġmem orial", + "Ġmemor ial", + "Ġmemo rial", + "Ġmemoria l", + "Ġ ATTR", + "ĠAT TR", + "ĠATT R", + "Ġexhaust ed", + "Ġt sp", + "Ġts p", + "ĉ redirect", + "ĉre direct", + "Ġlike wise", + "Ġlik ewise", + "S TER", + "ST ER", + "STE R", + "L java", + "Ġcondem ned", + "Ġcondemn ed", + "oca ust", + "( strict", + "(str ict", + "Ġex empt", + "Ġexem pt", + "Ġexemp t", + "Ġ sms", + "Ġs ms", + "Ġsm s", + "Ġex agger", + "S YS", + "SY S", + "Ġl ounge", + "Ġlo unge", + "Ġlou nge", + "Ġloung e", + ": ^", + "Ġt odd", + "Ġto dd", + "Ġtod d", + "d eb", + "de b", + "at orial", + "ator ial", + "ato rial", + "atori al", + "atoria l", + "ĠPort er", + "ĠPor ter", + "Ġt uition", + "Ġtu ition", + "Ġex empl", + "Ġexem pl", + "Ġexe mpl", + "Ġexemp l", + "Ġ paren", + "Ġp aren", + "Ġpar en", + "Ġpa ren", + "Ġpare n", + ".line To", + "Ġkid ney", + "Ġki dney", + "Ġkidn ey", + "Ġ ça", + "Ġç a", + "Ġc ui", + "Ġcu i", + "ï¼Į 请", + "X C", + "Ġmo ż", + "Ġn ominated", + "Ġno minated", + "Ġnom inated", + "Ġnomin ated", + "Ġnominate d", + "l ung", + "lu ng", + "lun g", + "Im Gui", + "Ġ Buzz", + "ĠB uzz", + "ĠBu zz", + "Ġst ereo", + "Ġste reo", + "Ġster eo", + "Ġstere o", + "p ortal", + "port al", + "por tal", + "res as", + "resa s", + "Ġ klass", + "Ġk lass", + "Ġkl ass", + "Ġkla ss", + "Ġklas s", + "Ġd rafted", + "Ġdraft ed", + "Ġproject ile", + "/g pl", + "( parameters", + "(param eters", + "(parameter s", + "* )Ċ", + "*) Ċ", + "Ġass isted", + "Ġassist ed", + "Ġ NSInteger", + "ĠNS Integer", + "s itemap", + "site map", + "sit emap", + ": nth", + ":n th", + ". Views", + ".View s", + ".Argument Parser", + "Ġ meer", + "Ġm eer", + "Ġme er", + "Ġmee r", + "z ier", + "zi er", + "zie r", + "Ġ Dig", + "ĠD ig", + "ĠDi g", + "Ċ", + ")} >Ċ", + ")}> Ċ", + "Ġp lag", + "Ġpl ag", + "Ġpla g", + "p ine", + "pi ne", + "pin e", + "Ġblank et", + "Ġ : -", + "Ġ lcd", + "Ġl cd", + "Ġlc d", + "- --------------", + "-- -------------", + "---- -----------", + "-------- -------", + "--- ------------", + "------------ ---", + "----- ----------", + "---------- -----", + "------ ---------", + "----------- ----", + "------------- --", + "------- --------", + "--------- ------", + "-------------- -", + "( \"\"", + "(\" \"", + "Ġt actical", + "Ġtact ical", + "Ġtactic al", + "Ġtac tical", + "ĠR onald", + "ĠRon ald", + "ex tr", + "ext r", + "ĠF est", + "ĠFe st", + "Ġf uer", + "Ġfu er", + "Ġfue r", + "- navigation", + "-n avigation", + "-nav igation", + "Ġ kb", + "Ġk b", + "g host", + "gh ost", + "Ġ handleChange", + "Ġhandle Change", + "_ cls", + "_c ls", + "_cl s", + "( )!=", + "() !=", + "Com parator", + "Compar ator", + ". vm", + ".v m", + "ĠC ox", + "ĠCo x", + "_ review", + "_re view", + "_r eview", + "_rev iew", + "/ @", + "_ cookie", + "_c ookie", + "_co okie", + "Ġrecogn ised", + "Ġrecognise d", + "l dap", + "ld ap", + "lda p", + "Th reads", + "Thread s", + "ĠS exual", + "ĠSex ual", + "ĠB earing", + "ĠBe aring", + "ĠBear ing", + "ĠBea ring", + "( SQL", + "(S QL", + "Ġ xr", + "Ġx r", + "Ġt high", + "Ġth igh", + "Ġthi gh", + "URL Connection", + "ĠS UV", + "ĠSU V", + "Ġm Context", + "Ġinc idence", + "Ġincid ence", + "Ġ Este", + "ĠE ste", + "ĠEs te", + "ĠEst e", + ". sup", + ".s up", + "_ te", + "_t e", + "( EXIT", + "(EX IT", + "C MD", + "CM D", + "/ \">", + "/\" >", + "Al most", + "Ġ Une", + "ĠU ne", + "ĠUn e", + "Ġand eren", + "Ġandere n", + "Ġander en", + "Ġ Singleton", + "ĠS ingleton", + "ĠSing leton", + "ĠSingle ton", + "Ġb ore", + "Ġbo re", + "Ġbor e", + "Th ink", + "Thin k", + "Ġn arc", + "Ġna rc", + "Ġnar c", + "] initWith", + "]init With", + "_ shop", + "_s hop", + "_sh op", + "( strategy", + "(str ategy", + "! ',", + "!' ,", + "her its", + "herit s", + "Ġ Desk", + "ĠD esk", + "ĠDe sk", + "ĠDes k", + "_ machine", + "_m achine", + "_ma chine", + ".n etty", + ".net ty", + ".ne tty", + "ı nda", + "ın da", + "ınd a", + "= <", + "Ġ QR", + "ĠQ R", + "Ġ Sidebar", + "ĠS idebar", + "ĠSide bar", + ".split Container", + "Ġon Success", + "Ġ monkey", + "Ġmon key", + "Ġmonk ey", + "En joy", + "( nodes", + "(n odes", + "(node s", + "(no des", + "pect rum", + "Ġ (*(", + "Ġ( *(", + "Ġ(* (", + "ĉ UINT", + "ĉU INT", + "ĉUI NT", + ", height", + ",h eight", + "ĠNetwork s", + "ĠNet works", + ". tail", + ".t ail", + ".ta il", + ".l inspace", + ".lin space", + "Ġ \"...", + "Ġ\" ...", + "Ġ\". ..", + "Ġ\".. .", + "L isten", + "List en", + "Li sten", + "Æ ¡", + ". Channel", + ".Ch annel", + "- defined", + "-d efined", + "-def ined", + "Re peat", + "Rep eat", + "ad just", + "adj ust", + "E RM", + "ER M", + "_ application", + "_app lication", + "_ap plication", + ".assert NotNull", + ".assertNot Null", + "- stream", + "-st ream", + "-str eam", + "Ġ rabbit", + "Ġr abbit", + "Ġrab bit", + "Ġposition ing", + "Ġ woke", + "Ġw oke", + "Ġwo ke", + "Ġf ing", + "Ġfin g", + "Ġfi ng", + "Ġmulti player", + "Ġmultip layer", + "Ġregister ing", + "Ġregist ering", + "un til", + "unt il", + "Ã¥ n", + "( ::", + "(: :", + "uss ions", + "ussion s", + "Ġpot ato", + "Ġ Equals", + "ĠE quals", + "ĠEqu als", + "ĠEqual s", + ". Sup", + ".S up", + "/ apache", + "/ap ache", + "Ġ (=", + "Ġ( =", + ". \")", + ".\" )", + ". ptr", + ".p tr", + ".pt r", + "Ġ Speech", + "ĠS peech", + "ĠSpe ech", + ". clip", + ".c lip", + ".cl ip", + ".cli p", + "ĠGab riel", + "ĠGabri el", + "Ġmus ician", + "Ġmusic ian", + "/ issues", + ". shop", + ".s hop", + ".sh op", + "Ġ Hier", + "ĠH ier", + "ĠHi er", + "_ RET", + "_RE T", + "_R ET", + "_ bucket", + "_b ucket", + "ãĥ ¡", + "a vs", + "av s", + "Ġ roz", + "Ġr oz", + "Ġro z", + "f lower", + "fl ower", + "flow er", + "flo wer", + "Write Barrier", + "ĠM ilan", + "ĠMil an", + "ĠMi lan", + "Ġlegisl ature", + "ĠD oll", + "ĠDo ll", + "ĠDol l", + "Ġpro ving", + "Ġpr oving", + "Ġprov ing", + ".concat enate", + "âķ IJ", + "Ġg char", + "Ġgc har", + "cdn js", + "b les", + "ble s", + "bl es", + "Ġ Listing", + "ĠL isting", + "ĠList ing", + "ĠLis ting", + "л о", + ".xr Label", + "ĠS ak", + "ĠSa k", + "just ice", + "ju stice", + "ĠVal entine", + "ĠValent ine", + "un less", + "Ġp iger", + "Ġpi ger", + "Ġpig er", + "Ġpige r", + "( run", + "(r un", + "Ġtest ified", + "A NA", + "AN A", + "ĠRe moves", + "ĠRem oves", + "ĠRemove s", + ") )));Ċ", + ")) ));Ċ", + "))) );Ċ", + ")))) ;Ċ", + "rec ated", + "ĠRuntime Method", + "Ġcon qu", + "ãĤ ¢", + "Ġt issues", + "Ġtissue s", + "a iler", + "ail er", + "ai ler", + "é té", + "ét é", + "- Star", + "-S tar", + "-St ar", + "Ġfl ames", + "Ġflame s", + "Ġflam es", + "Ġfla mes", + ". setIcon", + ".set Icon", + "Ġsup ern", + "Ġsuper n", + "Ġv agina", + "Ġvag ina", + "- variable", + "-var iable", + "Ġwell ness", + "C UR", + "CU R", + "Ġb elle", + "Ġbe lle", + "Ġbel le", + "Ġbell e", + ". getRequest", + ".get Request", + "Ġp oco", + "Ġpo co", + "Ġpoc o", + "b enh", + "be nh", + "ben h", + "a gens", + "ag ens", + "age ns", + "agen s", + "Ġs pill", + "Ġsp ill", + "Ġspi ll", + "Ġ Jur", + "ĠJ ur", + "ĠJu r", + "Ġ dispatcher", + "Ġdispatch er", + "Ġdisp atcher", + "н ого", + "но го", + "ног о", + "e monic", + "em onic", + "emo nic", + "emon ic", + "( dirname", + "(dir name", + "Ġ ÐĶ", + "ĠÐ Ķ", + "Ġp asse", + "Ġpass e", + "Ġpas se", + "Ġpa sse", + "Ġg anz", + "Ġga nz", + "Ġgan z", + "r icing", + "ri cing", + "ric ing", + "E U", + "Ġmuj eres", + "Ġmujer es", + "es sen", + "ess en", + "esse n", + ". attribute", + ".at tribute", + ".attrib ute", + "j j", + "ĉ ĉĠĊ", + "ĉĉ ĠĊ", + "ĉĉĠ Ċ", + "[ ^", + "Ġ strtolower", + "Ġstr tolower", + "Ġstrtol ower", + "lex er", + "ect ar", + "ec tar", + "ecta r", + "h otel", + "ho tel", + "hot el", + ". square", + ".s quare", + "Ġr all", + "Ġra ll", + "Ġl owered", + "Ġlow ered", + "Ġlower ed", + "handle d", + "hand led", + "M arket", + "Mark et", + "Mar ket", + "Ġ Uses", + "ĠU ses", + "ĠUs es", + "ĠUse s", + "i vas", + "iv as", + "iva s", + ". Business", + ".B usiness", + ".Bus iness", + "ãģĹ ãģ¦", + "ãģĹãģ ¦", + "D IV", + "DI V", + "Ġw asted", + "Ġwas ted", + "Ġwa sted", + "Ġwaste d", + "Ġwast ed", + "Ġa voir", + "Ġav oir", + "ê m", + "_ ACCOUNT", + "_AC COUNT", + "_ACC OUNT", + ". et", + ".e t", + "ĉ SDL", + "ĉS DL", + "k ap", + "ka p", + "Ġ fox", + "Ġf ox", + "Ġfo x", + "up pet", + "upp et", + "uppe t", + "{ },Ċ", + "{} ,Ċ", + "{}, Ċ", + "\" ,'", + "\", '", + "F avorite", + "P END", + "PE ND", + "Ġ AES", + "ĠA ES", + "ĠAE S", + "} ),", + "}) ,", + "Ġde duction", + "Ġded uction", + "Ġdeduct ion", + "Ġpol ÃŃt", + "Ġcomponent Will", + "ĠT elerik", + "ĠTele rik", + "_ SELF", + "_SE LF", + "_SEL F", + "Ġm use", + "Ġmus e", + "Ġmu se", + "C raft", + "Cr aft", + "Ġ dens", + "Ġd ens", + "Ġde ns", + "Ġden s", + "ठ¿", + "( tp", + "(t p", + "Ġt asty", + "Ġta sty", + "Ġtas ty", + "Ġtast y", + "Ġ balances", + "Ġbalance s", + "Ġbal ances", + "Ġded ication", + "Ġdedic ation", + "Ġdedi cation", + "ĠWall ace", + "ĠWal lace", + "Ġun law", + "Ġunl aw", + "\\ \">\\", + "\\\" >\\", + "\\\"> \\", + "Ġm um", + "Ġmu m", + "- update", + "-up date", + "e mente", + "em ente", + "ement e", + "eme nte", + "emen te", + "Ġs oda", + "Ġso da", + "Ġsod a", + "Re public", + "Rep ublic", + "as mine", + "asm ine", + "é ric", + "ér ic", + "éri c", + "( Status", + "(S tatus", + "ĠJson Convert", + "Ġ Disk", + "ĠD isk", + "ĠDis k", + "ĠDi sk", + ". Redirect", + ".Re direct", + ".Red irect", + "Ġfil ming", + "Ġfilm ing", + "/ mol", + "/m ol", + "R o", + "Ġ ville", + "Ġv ille", + "Ġvi lle", + "Ġvill e", + "Ġvil le", + "Ġtr abaj", + "Ġtrab aj", + "Ġs ynthesis", + "Ġsyn thesis", + "Ġsynth esis", + "Ġsynthes is", + "r ega", + "re ga", + "reg a", + "Ġ rl", + "Ġr l", + "S cheduler", + "Schedule r", + "ISH ED", + "current User", + "( errors", + "(err ors", + "(error s", + "(er rors", + "' h", + "_ bot", + "_b ot", + "_bo t", + "x imo", + "xi mo", + "Ġ USART", + "ĠUS ART", + "ĠUSA RT", + "_ super", + "_s uper", + "_sup er", + "_su per", + "_ DECREF", + "_DEC REF", + "н ой", + "но й", + "_ ROW", + "_R OW", + "_RO W", + "Ġprom otes", + "Ġpromote s", + "Ġpromot es", + "Ġpromo tes", + "Ġ TA", + "ĠT A", + "Ġh oras", + "Ġhor as", + "Ġho ras", + "Ġhora s", + "ĠRep resents", + "ĠRepresent s", + "Ġ nameof", + "Ġname of", + "Ġnam eof", + "Ġ Exc", + "ĠE xc", + "ĠEx c", + "ĠGar age", + "ĠGa rage", + "Ġse ine", + "Ġsein e", + "Ġsei ne", + ", #", + "Ġh erb", + "Ġhe rb", + "Ġher b", + "/ resources", + "/re sources", + "/res ources", + "/resource s", + "Ġple aded", + "Ġplea ded", + "Ġplead ed", + ".r adioButton", + ".radio Button", + "Ġ æĺ", + "Ġæ ĺ", + "O ps", + "Op s", + "ĠN est", + "ĠNe st", + "ĠNes t", + "c string", + "cs tring", + "ĠDef ence", + "Ġref ere", + "Ġrefer e", + "_ leaf", + "_le af", + "Ġreve lation", + "Ġrevel ation", + "ë §", + ".execute Update", + "_W ORLD", + "Ġexp ans", + "(\" \\\"", + "(\"\\ \"", + "j ab", + "ja b", + "Ġdoub ts", + "Ġdoubt s", + "Ġ Geometry", + "ĠGe ometry", + "ĠGeo metry", + "Ġintrodu ces", + "Ġintroduce s", + "Ġsen ators", + "Ġsenator s", + "Ġc anal", + "Ġcan al", + "Ġca nal", + ". helper", + ".h elper", + ".help er", + "ĠB iology", + "ĠBi ology", + "ĠBio logy", + "ĠBiol ogy", + "_ SENS", + "_S ENS", + "_SE NS", + ". previous", + ".pre vious", + ".prev ious", + "- touch", + "-t ouch", + "-to uch", + "a bit", + "ab it", + "abi t", + "Ġimp acted", + "Ġimpact ed", + "Ġbr ackets", + "Ġbracket s", + ". direct", + ".d irect", + ".dir ect", + ".di rect", + "ac cum", + "acc um", + "Ġtest osterone", + "ĉ action", + "ĉa ction", + "ĉac tion", + "ĉact ion", + "Ġ Chance", + "ĠCh ance", + "ĠCha nce", + "ĠChan ce", + "Ġpe aks", + "Ġpeak s", + "Ġpea ks", + "CppCodeGen WriteBarrier", + "Ġun belie", + "Ġunbe lie", + "_ press", + "_p ress", + "_pr ess", + "_pre ss", + "_pres s", + ". Rel", + ".R el", + ".Re l", + "ang led", + "angle d", + "angl ed", + "/ templates", + "/t emplates", + "/template s", + "/temp lates", + "-- >čĊ", + "--> čĊ", + "l ime", + "li me", + "lim e", + "Ġsufficient ly", + "_ nt", + "_n t", + "Exp and", + ".is file", + "Ġ isEmpty", + "Ġis Empty", + "Ġ qt", + "Ġq t", + "Ġmul her", + "a cob", + "ac ob", + "aco b", + "Ge orge", + "å¸ ¸", + "Ġas sim", + "Ġass im", + "a so", + "as o", + "Ġcompr ised", + "Ġcomprise d", + "O V", + "( CONFIG", + "(CON FIG", + "ĉ writer", + "ĉw riter", + "ĉwrite r", + "Ġd esp", + "Ġde sp", + "Ġdes p", + "Ġten ure", + "( cr", + "(c r", + ". pool", + ".p ool", + ".po ol", + "ĠB rend", + "ĠBr end", + "ĠBre nd", + "ĠBren d", + "Ġc ensor", + "( timeout", + "(time out", + "Ġp lea", + "Ġpl ea", + "Ġple a", + ". Wrap", + ".W rap", + "Ġt ightly", + "Ġtight ly", + "Ġ Were", + "ĠW ere", + "ĠWe re", + "ĠWer e", + "Ġ Ignore", + "ĠI gnore", + "ĠIgn ore", + "ĠIg nore", + "a bei", + "ab ei", + "abe i", + "Ġbr idges", + "Ġbridge s", + "Ġbrid ges", + "Ġcondem n", + "Ġsimp licity", + "Ġsimpl icity", + "Ġrout inely", + "Ġroutine ly", + "Ġbl acks", + "Ġblack s", + "Ġbla cks", + "j b", + "ĠP it", + "ĠPi t", + "U tf", + "Ut f", + "Ġ /Ċ", + "Ġ/ Ċ", + "r eload", + "re load", + "rel oad", + "Ġset Object", + "/ global", + "/g lobal", + "Ġf atty", + "Ġfa tty", + "Ġfat ty", + "Ġfatt y", + "Ġs ocks", + "Ġso cks", + "Ġsock s", + "Ġsoc ks", + "Could n", + "Ġerot isk", + "æĿ ¡", + "Ġ Pressure", + "ĠPres sure", + "ĠPress ure", + "ĠM az", + "ĠMa z", + "n pos", + "np os", + "to lower", + "tol ower", + "Ġ EQ", + "ĠE Q", + "ut eur", + "ute ur", + "Ġ Moment", + "ĠM oment", + "ĠMo ment", + "ĠMom ent", + "Ġ eta", + "Ġe ta", + "Ġet a", + "{{ --", + "Ġ graphs", + "Ġgraph s", + "Ġgrap hs", + "ĠG uar", + "ĠGu ar", + "r ine", + "ri ne", + "rin e", + "( --", + "(- -", + "Ġ HttpStatus", + "ĠHttp Status", + "( student", + "(st udent", + "* np", + "*n p", + "Ġrail way", + "Ġas ynchronous", + "_ vm", + "_v m", + "' ],'", + "'] ,'", + "'], '", + ", text", + ",t ext", + "m erchant", + "mer chant", + "( Guid", + "(G uid", + "ĠG ra", + "ĠGr a", + "ix er", + "ixe r", + "fetch All", + ". addListener", + ".add Listener", + "f lip", + "fl ip", + "* $", + "> (),", + ">( ),", + ">() ,", + "Ġsun light", + "as signed", + "ass igned", + "assign ed", + "Ġ abc", + "Ġa bc", + "Ġab c", + "Ġ COLUMN", + "ĠC OLUMN", + "ĠðŁĻĤ ĊĊ", + ") ...", + "). ..", + ").. .", + "Ġ ensemble", + "Ġen semble", + "Ġens emble", + "Ġ newline", + "Ġnew line", + "_S INGLE", + "i edad", + "ie dad", + "ied ad", + "Ġd arker", + "Ġdark er", + "Ġdar ker", + "or map", + "orm ap", + "Ġ lion", + "Ġl ion", + "Ġli on", + "pl its", + "plit s", + "Ġillustr ation", + "Ġillust ration", + "Ġ IEEE", + "ĠI EEE", + "ĠIE EE", + "Ġv ista", + "Ġvis ta", + "Ġvi sta", + "ous ands", + "ousand s", + "* ******", + "** *****", + "**** ***", + "****** *", + "*** ****", + "***** **", + "ĠTom my", + "Ġ hue", + "Ġh ue", + "Ġhu e", + "S el", + "Se l", + "Ġ aura", + "Ġa ura", + "Ġau ra", + "Ġaur a", + "ĠThe rapy", + "ĠTher apy", + "Ġan imator", + "Ġanim ator", + ". constraints", + ".con straints", + ".constraint s", + "Ġv ague", + "Ġva gue", + "Ġvag ue", + "(\" \")", + "(\"\" )", + "Ġvill ain", + "Ġvil lain", + "Ġvilla in", + "Ġbless ing", + "Ġstring Builder", + "Ġ Misc", + "ĠM isc", + "ĠMi sc", + "ĠMis c", + "Ġ DIR", + "ĠD IR", + "ĠDI R", + "f ax", + "fa x", + "- node", + "-n ode", + "-no de", + "Ġ Walking", + "ĠW alking", + "ĠWalk ing", + "ĠWal king", + "Ġ AU", + "ĠA U", + "s ess", + "se ss", + "ses s", + "Ġgr ill", + "Ġgri ll", + "VERT ISE", + "ĠF oods", + "ĠFood s", + "ĠFo ods", + "ĠFoo ds", + "Ġt ournaments", + "Ġtour naments", + "Ġtournament s", + "à ĵ", + "Ġ Marsh", + "ĠMar sh", + "ĠMars h", + "Ġw onders", + "Ġwon ders", + "Ġwonder s", + "Long itude", + ".Command Text", + "= input", + "=in put", + "_ encoder", + "_e ncoder", + "_en coder", + "_encode r", + "_enc oder", + "page Size", + "Ġ getState", + "Ġget State", + "> >Ċ", + ">> Ċ", + ". grey", + ".g rey", + ".gr ey", + "p od", + "po d", + "Ġread ings", + "Ġreading s", + "Ġre consider", + "Start up", + "Ġex cer", + "Ġexc er", + "Ġexce r", + ". balance", + ".b alance", + "_ cycle", + "_c ycle", + "_ Time", + "_T ime", + "LO CAL", + "LOC AL", + "Ġ EFI", + "ĠE FI", + "ĠEF I", + "ĠR eyn", + "ĠRe yn", + "ĠRey n", + ".set Foreground", + "b yn", + "by n", + "Ġdis connected", + "Ġdisconnect ed", + "ACT IVE", + "Ġ embedding", + "Ġembed ding", + "ic kers", + "ick ers", + "icker s", + "Ġsurround ings", + "Ġsurrounding s", + "* c", + "Ġgar ant", + "Ġga rant", + "Ġ bf", + "Ġb f", + "Ġ wipe", + "Ġw ipe", + "Ġwi pe", + "Ġ ä¸ĭ", + "Ġä¸ ĭ", + "_ TRA", + "_T RA", + "_TR A", + "ad ox", + "ado x", + "ç ķ", + "Ġs ucks", + "Ġsu cks", + "Ġsuc ks", + "Ġsuck s", + "Ġ Songs", + "ĠS ongs", + "ĠSon gs", + "ĠSong s", + "ĠAssoci ates", + "ĠAssociate s", + "ĠAssoc iates", + "ĠB ald", + "ĠBa ld", + "ĠBal d", + "ĠB rett", + "ĠBr ett", + "ĠBre tt", + "ĠBret t", + "ven ile", + "Ġ vt", + "Ġv t", + "Ġin ade", + "Ġre signed", + "Ġres igned", + "Ġresign ed", + "ĠGl enn", + "ĠGlen n", + "ĠGle nn", + ". pattern", + ".p attern", + ".pat tern", + ".Data Bind", + "Ñĥ м", + "Layout Inflater", + "c het", + "ch et", + "che t", + "ĠTest ament", + ". ms", + ".m s", + "Ġp av", + "Ġpa v", + "Ġ ReactDOM", + "ĠReact DOM", + "ur dy", + "urd y", + "A DATA", + "AD ATA", + "ADA TA", + "M u", + "/ actions", + "/a ctions", + "/action s", + "Ġ Js", + "ĠJ s", + "_ extract", + "_ex tract", + "_ext ract", + "_extra ct", + "Ġ Bring", + "ĠB ring", + "ĠBr ing", + "ĠBri ng", + ": id", + ":i d", + "st rt", + "str t", + "i vation", + "iv ation", + "iva tion", + "Ġout right", + "Ġou tright", + "Ġoutr ight", + "a zu", + "az u", + "loy ment", + "и Ñı", + "al do", + "ald o", + "Ġ Publisher", + "ĠP ublisher", + "ĠPublish er", + "E ducation", + "Educ ation", + "P alette", + "Pal ette", + "Pa lette", + "Pale tte", + "_ drv", + "_d rv", + "_dr v", + "Ġ ($(", + "Ġ( $(", + "Ġ($ (", + "ĠA nda", + "ĠAn da", + "ĠAnd a", + "Ġrem edy", + "Ġremed y", + "Ġin consistent", + "Ġincons istent", + "Ġinconsist ent", + "t ection", + "te ction", + "tec tion", + "Ġreg ulators", + "Ġregul ators", + "Ġregulator s", + "Ġshort est", + "( pair", + "(p air", + "(pa ir", + "Ġ Installation", + "ĠInstall ation", + "Ġdef endants", + "Ġdefend ants", + "Ġdefendant s", + "Ġ ();", + "Ġ( );", + "Ġ() ;", + "- large", + "-l arge", + "M el", + "Me l", + "Ġthreat en", + "н Ñı", + "Ġfet ish", + "ot ine", + "oti ne", + "_ dic", + "_d ic", + "_di c", + "Ġ <$", + "Ġ< $", + "Ġst agger", + "Ġsta gger", + "Ġstag ger", + "s pi", + "sp i", + "$ response", + "$res ponse", + "S erv", + "Se rv", + "Ser v", + "- born", + "-b orn", + "-bo rn", + "j os", + "jo s", + "ĉ img", + "ĉi mg", + "ĉim g", + "ĉ WHERE", + "ĉW HERE", + "_ lt", + "_l t", + "å½ ĵ", + ". cost", + ".c ost", + ".co st", + ".cos t", + "Ġ Tue", + "ĠT ue", + "ĠTu e", + ". labels", + ".label s", + ".lab els", + "Ġ LV", + "ĠL V", + "wcs store", + "ĠJ esse", + "ĠJes se", + "ĠJe sse", + "ĠJess e", + "ภ«", + "T rade", + "Tr ade", + "Trad e", + "Tra de", + "Ġpredecess or", + "ë Ĥ", + "f inally", + "fin ally", + "final ly", + "_ general", + "_g eneral", + "_gen eral", + "_gene ral", + "_gener al", + "oggle r", + "ogg ler", + "_ REGION", + "_REG ION", + "n ement", + "ne ment", + "nem ent", + "Ġb logger", + "Ġblog ger", + "Ġblo gger", + "ĠHar bor", + "Ġ Dataset", + "ĠD ataset", + "ĠData set", + "ĠDat aset", + "[ w", + "Ġattend ees", + "Ġattendee s", + ". ico", + ".i co", + ".ic o", + "max imum", + ". Unlock", + ".Un lock", + "_ SYNC", + "_S YNC", + "_SY NC", + "_SYN C", + "ág ina", + "Ġd owns", + "Ġdown s", + "Ġdow ns", + "ĠW ii", + "ĠWi i", + "] )/", + "]) /", + "Ġk icking", + "Ġkick ing", + "un ication", + "unic ation", + "uni cation", + "Ġ DAC", + "ĠD AC", + "ĠDA C", + "Ġ IDS", + "ĠI DS", + "ĠID S", + "ĠR ental", + "ĠRen tal", + "ĠRent al", + "Ġ currentTime", + "Ġcurrent Time", + "Ġvacc ines", + "Ġvaccine s", + "ĠD evil", + "ĠDe vil", + "ĠDev il", + "Ġn ors", + "Ġno rs", + "Ġnor s", + "_ mouse", + "_m ouse", + "_mo use", + "ur rection", + "urre ction", + "urr ection", + "urrect ion", + "( no", + "(n o", + "Ġ >čĊ", + "Ġ> čĊ", + "Ġag gression", + "Ġaggress ion", + "Ġagg ression", + "Ġbre eding", + "Ġbreed ing", + "Ġbree ding", + ". symbol", + ".s ymbol", + ".sym bol", + "i man", + "im an", + "ima n", + "Absolute Path", + "Ġ WHO", + "ĠW HO", + "ĠWH O", + "_ flush", + "_f lush", + "_fl ush", + "- root", + "-r oot", + "-ro ot", + "a rna", + "ar na", + "arn a", + "& M", + "Ġf athers", + "Ġfa thers", + "Ġfather s", + "Ġ Rocket", + "ĠR ocket", + "ĠRock et", + "ĠRo cket", + "ĠRoc ket", + "i veau", + "ive au", + "Ġw ander", + "Ġwa nder", + "Ġwand er", + "Ġwan der", + "Ġcom pos", + "Ġcomp os", + "ĠWar rior", + "Ġ Seat", + "ĠS eat", + "ĠSe at", + "ĠSea t", + "ĠCl inic", + "ĠClin ic", + "ĠCli nic", + "_ invoice", + "_in voice", + "_inv oice", + "( dispatch", + "(dis patch", + "Product o", + "at uring", + "atur ing", + "atu ring", + "oss ier", + "ĠM AY", + "ĠMA Y", + "Ġd agger", + "Ġda gger", + "Ġdag ger", + "Ġsan itized", + "Ġsanit ized", + "Ġsanitize d", + "Ġ RFC", + "ĠR FC", + "ĠRF C", + "Ġp roph", + "Ġpro ph", + "Ġpr oph", + "Ġprop h", + "Ġu rine", + "Ġur ine", + "Ġuri ne", + "Ġgr ind", + "Ġgri nd", + "Ġgrin d", + "Ġ Expanded", + "ĠExp anded", + "ĠExpand ed", + "des cripcion", + "- fw", + "-f w", + "ĠK erry", + "ĠKer ry", + "ĠKerr y", + "= name", + "=n ame", + "Ġ chk", + "Ġc hk", + "Ġch k", + "Ġn ationally", + "Ġnational ly", + "Ġnation ally", + "Ġt hee", + "Ġth ee", + "Ġthe e", + "I nc", + "In c", + "Ġ ?>>", + "Ġ? >>", + "Ġ?> >", + ". RadioButton", + ".R adioButton", + ".Http ServletResponse", + ".HttpServlet Response", + "/ Y", + "ĉ field", + "ĉf ield", + "ĉfi eld", + "Ġ homme", + "Ġhom me", + "y per", + "ype r", + "yp er", + "Ph ysical", + "Phys ical", + "= v", + "Ġd riv", + "Ġdr iv", + "Ġdri v", + "Ġ Errors", + "ĠError s", + "ĠEr rors", + "ĠErr ors", + "Ġc Äĥ", + "De ath", + "Ġ WINDOW", + "ĠW INDOW", + "Ġpo et", + "Ġ Sharp", + "ĠSh arp", + "ĠSha rp", + "ĠShar p", + "Ġ Immutable", + "ĠIm mutable", + "ĠImm utable", + "ĉ create", + "ĉc reate", + "Ġge ht", + "Ġgeh t", + "ĠRe form", + "ĠRef orm", + "a iser", + "ai ser", + "ais er", + "aise r", + "Ġ Initialization", + "ĠInitial ization", + "Ġimm unity", + "Ġimmun ity", + ". compose", + ".com pose", + ".comp ose", + "Ġlate ncy", + "Ġlat ency", + "Ġlaten cy", + "ĠLeban on", + "ĠPar ad", + "ĠPa rad", + "ĠPara d", + "Ġf uels", + "Ġfuel s", + "Ġfu els", + "Ġfue ls", + "ĠEx hib", + "c oh", + "co h", + "% \">Ċ", + "%\" >Ċ", + "%\"> Ċ", + "Ġ CLI", + "ĠC LI", + "ĠCL I", + ") initWith", + ")init With", + "- Za", + "-Z a", + "_ CLEAR", + "_C LEAR", + "_CL EAR", + "re gn", + "reg n", + "Ġfin ances", + "Ġfinance s", + "Ġfinanc es", + "Ġfinan ces", + ". standard", + ".st andard", + "_ CATEGORY", + "_C ATEGORY", + ". library", + ".l ibrary", + ".lib rary", + "Ġtravel ers", + "Ġtraveler s", + "_ wp", + "_w p", + "Ġ Evaluation", + "ĠE valuation", + "ĠEval uation", + "ĠEvalu ation", + "start ing", + "star ting", + "Ġ )),Ċ", + "Ġ) ),Ċ", + "Ġ)) ,Ċ", + "ep isode", + "Ġ Variant", + "ĠV ariant", + "ĠVar iant", + "ĠVari ant", + "Ġ daemon", + "Ġda emon", + "ĠJ ulia", + "ĠJul ia", + "ĠJu lia", + "ĠJuli a", + "Ġ NR", + "ĠN R", + "Ġd oubles", + "Ġdouble s", + "Ġdoub les", + "Ġdou bles", + "< v", + "/ runtime", + "/r untime", + "/run time", + "Ġ interpreter", + "Ġinter preter", + "Ġinterpret er", + "Ġinterpre ter", + "Ġ INDEX", + "ĠIN DEX", + "ĠIND EX", + "ĠHol mes", + "_ DIM", + "_D IM", + "_DI M", + "Ġp addle", + "Ġpad dle", + "Ġpadd le", + "_ example", + "_ex ample", + "_exam ple", + "Ġ foreground", + "Ġfore ground", + ". routes", + ".r outes", + ".route s", + ".ro utes", + "Ġs owie", + "Ġso wie", + "Ġsow ie", + "S UCCESS", + "Ġ CDC", + "ĠC DC", + "ĠCD C", + "Ġ BD", + "ĠB D", + "_ -", + "as ured", + "asure d", + "asu red", + "W riting", + "Wr iting", + "Ġ currentPage", + "Ġcurrent Page", + "( answer", + "(ans wer", + "(an swer", + "Ġ ASCII", + "ĠA SCII", + "ĠASC II", + "à ¨", + "Ġsocial ly", + "Ġsoc ially", + "Ġsoci ally", + "y yy", + "yy y", + "ĠSpecial ist", + "( customer", + "(c ustomer", + "(custom er", + "ist ani", + "istan i", + "ista ni", + "k est", + "ke st", + "kes t", + "ĠM ak", + "ĠMa k", + "Ġt ho", + "Ġth o", + ". pt", + ".p t", + "( comment", + "(com ment", + "(comm ent", + "Ġ Converter", + "ĠCon verter", + "ĠConvert er", + "g am", + "ga m", + "b ins", + "bin s", + "bi ns", + ". tele", + ".t ele", + ".te le", + ".tel e", + "ĠVeter ans", + "ĠVeteran s", + "_ ALLOC", + "_AL LOC", + "_ALL OC", + "олÑĮзов аÑĤ", + "inn amon", + "; width", + "o hl", + "oh l", + "Ġf antas", + "Ġfan tas", + "Ġfant as", + "Ġs ung", + "Ġsu ng", + "Ġsun g", + "ĉ K", + "( Json", + "(J son", + "Ġneighbour hood", + "Ġv ow", + "Ġvo w", + "Ġs ins", + "Ġsi ns", + "Ġsin s", + "on acci", + "ona cci", + "Ġ epochs", + "Ġepoch s", + "im agen", + "image n", + "ima gen", + "imag en", + ". Change", + ".Ch ange", + ".my batis", + "Se ek", + "See k", + "W ER", + "WE R", + "管 çIJĨ", + "Ġinter ess", + "Ġinte ress", + "Ġinteres s", + "_ Event", + "_E vent", + "ed erland", + "eder land", + "Ġterr itor", + "Ġci udad", + "uc ked", + "uck ed", + "Ġsn ack", + "Ġsna ck", + "Ġtrans ported", + "Ġtransport ed", + "Ġtransporte d", + "Ġ Manifest", + "ĠMan ifest", + "Ġ DAT", + "ĠD AT", + "ĠDA T", + "_ theta", + "_th eta", + "_the ta", + "Ġw ont", + "Ġwon t", + "Ġwo nt", + ". ĊĊĊĊĊĊĊĊĊĊ", + ".ĊĊ ĊĊĊĊĊĊĊĊ", + ".Ċ ĊĊĊĊĊĊĊĊĊ", + ".ĊĊĊĊ ĊĊĊĊĊĊ", + ".ĊĊĊ ĊĊĊĊĊĊĊ", + ".ĊĊĊĊĊĊ ĊĊĊĊ", + ".ĊĊĊĊĊĊĊĊ ĊĊ", + ".ĊĊĊĊĊ ĊĊĊĊĊ", + "Ĭ¶ æĢģ", + "ĠE pic", + "ĠEp ic", + "D eck", + "De ck", + "Dec k", + "l tra", + "lt ra", + "ltr a", + "_ ZERO", + "_Z ERO", + "Ġ[ ];", + "Ġ[] ;", + "/ scripts", + "/s cripts", + "/script s", + "Ġ --------------------------------------------------------------------------------", + "Ġ---------------------------------------------------------------- ----------------", + "Ġ---- ----------------------------------------------------------------------------", + "Ġ---------------- ----------------------------------------------------------------", + "Ġ------------------------------------------------ --------------------------------", + "Ġ-------------------------------- ------------------------------------------------", + "Ġ---------------------------------------------------------------------------- ----", + "Ġ---------- ----------------------------------------------------------------------", + "Ġ------------------------------------------------------------ --------------------", + "Ġ------------------------------------------------------------------------- -------", + "æĥ ħ", + "Ġ weed", + "Ġw eed", + "Ġwe ed", + "Ġwee d", + "N BC", + "NB C", + "Ġr aped", + "Ġrap ed", + "Ġra ped", + "Ġrape d", + "Ġ Gateway", + "ĠG ateway", + "ĠGate way", + "ĠGat eway", + "[ M", + "Ġ Timeout", + "ĠTime out", + "ench mark", + ". ViewModel", + ".View Model", + "Ġporn os", + "Ġpor nos", + "Ġporno s", + "Ġ Ya", + "ĠY a", + "th ritis", + "thr itis", + "ĠFl ynn", + "ĠFly nn", + "Ġ mega", + "Ġm ega", + "Ġme ga", + "Ġmeg a", + "a cin", + "ac in", + "aci n", + "Ġtr ibal", + "Ġtri bal", + "Ġtrib al", + ". apple", + ".app le", + ".ap ple", + "Ġ Blo", + "ĠB lo", + "ĠBl o", + "â n", + "i bi", + "ib i", + "r ov", + "ro v", + "ĠL ives", + "ĠLive s", + "ĠLi ves", + "ĠLiv es", + "^ .", + "get Request", + "Ġ Establish", + "ĠEst ablish", + "cont ainers", + "container s", + "contain ers", + "Ġst arring", + "Ġstar ring", + "Ġcele brities", + "Ġcelebr ities", + "Ġ Relative", + "ĠRel ative", + "ĠHe ights", + "ĠHeight s", + "Ġtq dm", + "ĠNorth west", + "i vic", + "iv ic", + "ivi c", + "ĉ cl", + "ĉc l", + "Ġautom otive", + "ent ric", + "entr ic", + "Ġ fortunate", + "Ġfort unate", + "Ġfire place", + "Ġfi replace", + "se ud", + "n ickname", + "nick name", + "; s", + "_ CAL", + "_C AL", + "_CA L", + "h alt", + "ha lt", + "hal t", + "( ns", + "(n s", + "_ deleted", + "_de leted", + "_delete d", + "_del eted", + "De velopment", + "Dev elopment", + "Develop ment", + "m ovies", + "movie s", + "mov ies", + "Ġid entities", + "Ġident ities", + "Ġprompt ly", + "ا ÙĨ", + "ا٠Ĩ", + "Ġ ante", + "Ġa nte", + "Ġan te", + "Ġant e", + "Ġ\" ','", + "Ġ\"' ,'", + "Ġ\"', '", + "åı £", + "imp se", + "imps e", + "Ġy ap", + "Ġya p", + "Type Name", + "Ġb itch", + "Ġbit ch", + "Ġassoci ates", + "Ġassociate s", + "Ġassoc iates", + "HE ME", + "- empty", + "-em pty", + "Ġ ت", + "ĠØ ª", + "ol vers", + "olve rs", + "olver s", + "olv ers", + "Ġp istol", + "Ġpist ol", + "Ġpis tol", + "Sc oped", + "Scope d", + "ag ner", + "agn er", + "agne r", + "' ]=='", + "'] =='", + "']= ='", + "']== '", + "Ġ IMP", + "ĠI MP", + "ĠIM P", + "e xc", + "ex c", + "Ġo mitted", + "Ġom itted", + "Ġomit ted", + "Ġmind set", + "Ġminds et", + "Ġ [](", + "Ġ[ ](", + "Ġ[] (", + "Ġ orn", + "Ġo rn", + "Ġor n", + "_ CAM", + "_C AM", + "_CA M", + "A vg", + "Av g", + "Localized String", + "ĠN atur", + "ĠNa tur", + "ĠNat ur", + "Ġ composer", + "Ġcom poser", + "Ġcomp oser", + "Ġcompose r", + "Ġcompos er", + "Ġ Playing", + "ĠPl aying", + "ĠPlay ing", + "ĠPla ying", + "Ġover d", + "Ġov erd", + "_ utf", + "_u tf", + "_ut f", + ". sk", + ".s k", + "ĠF ol", + "ĠFo l", + "$ page", + "$p age", + ", Object", + ",O bject", + "Ġb ees", + "Ġbe es", + "Ġbee s", + "al ary", + "ala ry", + "alar y", + "b ullet", + "bul let", + "bull et", + "_ library", + "_l ibrary", + "_lib rary", + "O ffer", + "Of fer", + "Off er", + "loc ated", + "locate d", + "Ġ (_,", + "Ġ( _,", + "Ġ(_ ,", + "âĢľ He", + "Ġ Owners", + "ĠOwn ers", + "ĠOwner s", + "ĠOw ners", + ") ).Ċ", + ")) .Ċ", + ")). Ċ", + "Ġb ri", + "Ġbr i", + ". Admin", + ".Ad min", + "k tion", + "kt ion", + "лÑİ Ñĩ", + "Ġerot ici", + "Ġerotic i", + "Cancel led", + "Ġ agr", + "Ġa gr", + "Ġag r", + "re views", + "review s", + "_ dma", + "_d ma", + "_dm a", + "R ICT", + "RI CT", + "RIC T", + "Ġ gfx", + "Ġg fx", + "Ġgf x", + "m pi", + "mp i", + "p po", + "pp o", + "Ġ //@", + "Ġ// @", + "Ġ/ /@", + "Ġ uppercase", + "Ġupper case", + "Ġcomm itting", + "Ġcommit ting", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "User Data", + "Ġv ai", + "Ġva i", + "ĉ sort", + "ĉs ort", + "Ġcongr at", + "Ġcong rat", + "Ġd ioxide", + "Ġdi oxide", + "д а", + ". area", + ".a rea", + ".ar ea", + ".are a", + "ĠJosh ua", + "ĠJos hua", + "ĠK och", + "ĠKo ch", + "_ break", + "_b reak", + "a zure", + "az ure", + "azu re", + "is tical", + "ist ical", + "istic al", + "isti cal", + "istica l", + "_AL PHA", + "_ views", + "_view s", + "_vi ews", + "Ġelim inating", + "Ġelimin ating", + "O MB", + "OM B", + "e numer", + "en umer", + "enu mer", + "enum er", + "ĠH ydro", + "ĠHy dro", + "( *(", + "(* (", + "ERT ICAL", + "Ġinev itably", + "Ġst ole", + "Ġsto le", + "Ġstol e", + "- east", + "-e ast", + "i eron", + "ie ron", + "ier on", + "iero n", + "Ġ linger", + "Ġl inger", + "Ġli nger", + "Ġlin ger", + "Ġling er", + "/ doc", + "/d oc", + "/do c", + "Å º", + "Ġ Already", + "ĠAl ready", + "as io", + "asi o", + "Ġ --Ċ", + "Ġ- -Ċ", + "Ġ-- Ċ", + "Ġ abbrev", + "Ġabb rev", + "Ġ Atom", + "ĠA tom", + "ĠAt om", + "h im", + "hi m", + "Ġ INSERT", + "ĠINS ERT", + "s un", + "su n", + "âĻ ª", + "CON NECT", + "CONN ECT", + "er ator", + "era tor", + "ĠM anning", + "ĠMan ning", + "ĠMann ing", + "Ġ :(", + "Ġ: (", + "g as", + "ga s", + "= >'", + "=> '", + "Ġquery set", + "; }čĊ", + ";} čĊ", + "Ġ Population", + "ĠPop ulation", + "uted String", + "res ident", + "resi dent", + "_ FONT", + "_F ONT", + "Ġ Respond", + "ĠRes pond", + "ĠResp ond", + "Ġobsc ure", + "Ġ observable", + "Ġo bservable", + "Ġobserv able", + "ĠContrib utors", + "ĠContributor s", + "k on", + "ko n", + "ĠM usk", + "ĠMus k", + "ĠMu sk", + "ex ao", + "ĠT ub", + "ĠTu b", + "Boot Application", + "S OR", + "SO R", + ". Horizontal", + ".H orizontal", + ". findBy", + ".find By", + ". power", + ".p ower", + ".pow er", + ".po wer", + "Ġpositive ly", + "Ġposit ively", + "ven ience", + "ĠJ ong", + "ĠJo ng", + "ĠJon g", + "Ġwh istle", + "Ġ знаÑĩ", + "Ġз наÑĩ", + "Ġзна Ñĩ", + "Ġзн аÑĩ", + "Ġl ending", + "Ġlen ding", + "Ġlend ing", + "Ġdestruct ive", + "Ġ onDelete", + "Ġon Delete", + "author ization", + "() ;?>", + "(); ?>", + "_ original", + "_or iginal", + "_origin al", + "_orig inal", + "sc ience", + "sci ence", + "a tra", + "at ra", + "atr a", + "?, ?,", + "Ġ Asc", + "ĠA sc", + "ĠAs c", + "Ġconvin cing", + "Ġconvinc ing", + "$ a", + "or gen", + "org en", + "orge n", + "_ Date", + "_D ate", + "Ġ Provide", + "ĠPro vide", + "ĠProvid e", + "ĠProv ide", + "Ġlon ely", + "Ġlone ly", + ") 'Ċ", + ")' Ċ", + "ex change", + "; ?>Ċ", + ";?> Ċ", + ". fast", + ".f ast", + ".fa st", + "S amples", + "Sample s", + "Sam ples", + "L ondon", + "Lo ndon", + "Lon don", + "' ])čĊ", + "'] )čĊ", + "']) čĊ", + "Ġ Ionic", + "ĠI onic", + "ĠIo nic", + "ĠIon ic", + "Ġp esso", + "Ġpes so", + "ĠKn ights", + "ĠKnight s", + "ĠR af", + "ĠRa f", + "_ attrs", + "_at trs", + "_attr s", + "_att rs", + "Ġrepe al", + "> Main", + ">M ain", + "Ġ Ordered", + "ĠOrder ed", + "ĠOrd ered", + "_ New", + "_N ew", + "=\" \"> < /", + "url patterns", + "ATION AL", + "ATIO NAL", + "pe ech", + "pee ch", + "ĠId aho", + "Ġpr incess", + "Ġprince ss", + "Ġprin cess", + "Ġprinc ess", + "Ġprinces s", + "Ġ Customers", + "ĠCustom ers", + "ĠCustomer s", + "ĠCust omers", + "a ways", + "aw ays", + "away s", + "awa ys", + "a db", + "ad b", + "ĠBry ant", + "ĠBryan t", + "n once", + "no nce", + "non ce", + "Ġad ul", + "Ġ` `(", + "Ġ`` (", + "Ġafter math", + "= dict", + "=d ict", + "text Box", + "Ġs perm", + "Ġsp erm", + "Ġspe rm", + "Ġsper m", + "Ġc ough", + "Ġco ugh", + "Ġcou gh", + "H or", + "Ho r", + "âĢĻ S", + ".Component ResourceManager", + "Ġreg ulator", + "Ġregul ator", + "Ġpartner ships", + "Ġpartners hips", + "Ġpartnership s", + "/ projects", + "/project s", + "t rys", + "tr ys", + "try s", + "ĠL aser", + "ĠLa ser", + "ĠLas er", + "⣠©", + "ĠF unk", + "ĠFun k", + "ĠFu nk", + "Ġun conscious", + "Ġuncon scious", + "Ġc rust", + "Ġcr ust", + "Ġcru st", + "Ġcrus t", + "Ġ Teams", + "ĠTe ams", + "ĠTeam s", + "ĠTea ms", + "Ġ Banner", + "ĠB anner", + "ĠBan ner", + "ĠH oney", + "ĠHon ey", + "ĠHo ney", + "l ems", + "le ms", + "lem s", + "Ġmax Width", + "Pointer Exception", + "fade Out", + "- St", + "-S t", + "Ġstr angers", + "Ġstrange rs", + "Ġstranger s", + "Ġstrang ers", + "Ġstran gers", + "_ GO", + "_G O", + "W ritable", + "Wr itable", + "_ Info", + "_In fo", + ". NonNull", + ".Non Null", + "an notations", + "annot ations", + "annotation s", + "Ġ GD", + "ĠG D", + "Ġendorse d", + "Ġendors ed", + "ĉToken Name", + "Ġ Depending", + "ĠDe pending", + "ĠDep ending", + "ĠDepend ing", + "YN AM", + "Ġ Meteor", + "ĠM eteor", + "ĠMet eor", + "Ġ Increase", + "ĠIn crease", + "ĠIncre ase", + ". Many", + ".M any", + ".Man y", + ".Ma ny", + "= =(", + "== (", + ". UUID", + ".U UID", + "_ KERNEL", + "_K ERNEL", + "Ġvid é", + "Ġ pq", + "Ġp q", + "ĠQt Gui", + "Ġ Various", + "ĠV arious", + "ĠVar ious", + "ĠVa rious", + "ĠVari ous", + "Ġ john", + "Ġj ohn", + "Ġjo hn", + "_ patch", + "_p atch", + "_pat ch", + "Ġt outes", + "Ġto utes", + "Ġtou tes", + "Ġtout es", + "Ġtoute s", + "Ġ Fail", + "ĠF ail", + "ĠFa il", + "Ġsurv iving", + "Ġsurviv ing", + "( \"${", + "(\" ${", + "(\"$ {", + "Ġ ĠĠĠĠĠĠčĊ", + "ĠĠ ĠĠĠĠĠčĊ", + "ĠĠĠĠ ĠĠĠčĊ", + "ĠĠĠ ĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠ čĊ", + "ĠĠĠĠĠ ĠĠčĊ", + "ĠĠĠĠĠĠ ĠčĊ", + "Ġ imageUrl", + "Ġimage Url", + ". wordpress", + ".word press", + "s ources", + "source s", + "ĉgl Vertex", + "âĢĻ a", + "Ġes col", + "Ġesc ol", + "R ARY", + "RA RY", + "RAR Y", + "Ġ Snake", + "ĠSn ake", + "Ġqu int", + "Ġq uint", + "Ġqui nt", + "Ġl asts", + "Ġla sts", + "Ġlast s", + "Ġlas ts", + "ĠHar mon", + "ĠHarm on", + "Ġ coil", + "Ġc oil", + "Ġco il", + "Ġcoi l", + "Ġexplo itation", + "Ġexploit ation", + "l een", + "le en", + "lee n", + "' >\";Ċ", + "'> \";Ċ", + "'>\" ;Ċ", + "Ġ SERVER", + "ĠS ERVER", + "ĠSER VER", + "Ġ HEADER", + "ĠHE ADER", + "ĠHEAD ER", + "_ velocity", + "_v elocity", + "_vel ocity", + "Ġ Invoke", + "ĠIn voke", + "ĠInv oke", + ". timestamps", + ".timestamp s", + "Ġs ulf", + "Ġsu lf", + "Ġsul f", + "I QUE", + "IQ UE", + "Ġinhabit ants", + "ph ins", + "phi ns", + "phin s", + "a zzo", + "az zo", + "azz o", + "Ġ mono", + "Ġm ono", + "Ġmon o", + "Ġmo no", + "L egend", + "Le gend", + "Leg end", + "Ġ nonce", + "Ġn once", + "Ġno nce", + "Ġnon ce", + "I FE", + "IF E", + "; \";Ċ", + ";\" ;Ċ", + "- create", + "-c reate", + "\" \",Ċ", + "\"\" ,Ċ", + "\"\", Ċ", + "per mit", + "perm it", + "ĠIm migration", + "ĠImm igration", + "Ġ pathname", + "Ġpath name", + "ff ective", + "ffect ive", + "âĻĢ âĻĢ", + "Ġex ams", + "Ġexam s", + "- event", + "-e vent", + "-ev ent", + "-even t", + "ĠT ill", + "ĠTi ll", + "ĠTil l", + "[ mid", + "[m id", + "F IX", + "FI X", + "; color", + ";c olor", + "( Order", + "_ traits", + "_t raits", + "_tr aits", + "_tra its", + "_trait s", + "Ġ orderBy", + "Ġorder By", + "Ġs unt", + "Ġsu nt", + "Ġsun t", + "ĠNich olas", + "Ø ²", + "Ġs unny", + "Ġsun ny", + "i ners", + "in ers", + "ine rs", + "iner s", + "Ġaccess ibility", + "Ġ HB", + "ĠH B", + ". comp", + ".c omp", + ".com p", + ".co mp", + "ĉ op", + "ĉo p", + "Ġminor ities", + "eth eus", + "ethe us", + "Ġcollabor ative", + "p rit", + "pr it", + "pri t", + "H IR", + "HI R", + "Ġwr aps", + "Ġwrap s", + "ĉ draw", + "ĉd raw", + "g od", + "go d", + "Ġ IX", + "ĠI X", + ". apps", + ".app s", + ".ap ps", + ".a pps", + "Ġ NM", + "ĠN M", + "Ġir relevant", + "Ġirre levant", + "ĠT igers", + "ĠTi gers", + "ĠTiger s", + "ĠTig ers", + "Ġ diag", + "Ġd iag", + "Ġdi ag", + "Ġdia g", + "G V", + "ĠAccess ories", + "k ont", + "ko nt", + "kon t", + "Ġs implify", + "Ġsimp lify", + "Ġsimpl ify", + "Ġ Favorite", + "ĠF avorite", + "ĠFavor ite", + "_ tools", + "_t ools", + "_to ols", + "_tool s", + "( []);Ċ", + "([ ]);Ċ", + "([] );Ċ", + "Ġt owers", + "Ġto wers", + "Ġtow ers", + "Ġtower s", + "B es", + "Be s", + "Ġ hunter", + "Ġh unter", + "Ġhun ter", + "Ġhunt er", + "Ġs alon", + "Ġsa lon", + "Ġsal on", + "( buff", + "(b uff", + "(buf f", + "ĉ debug", + "ĉde bug", + "Ġmal ware", + "M oving", + "Mo ving", + "Mov ing", + "- options", + "-o ptions", + "-option s", + "-opt ions", + ") +'", + ")+ '", + "ĠL OVE", + "ĠLO VE", + "_S OCKET", + "_SO CKET", + "_ fin", + "_f in", + "ĠDel aware", + "Ġsher iff", + "- invalid", + "-in valid", + "Ġ FULL", + "ĠF ULL", + "ĠFU LL", + "Ġ под", + "Ġп од", + "Ġпо д", + "e las", + "el as", + "ela s", + "\" strings", + "ĠRepresent atives", + "ĠRepresentative s", + "s urface", + "sur face", + "surf ace", + "res olved", + "resolve d", + "ht docs", + ") ):čĊ", + ")) :čĊ", + ")): čĊ", + "Ġpress ures", + "Ġpressure s", + "Ġno rms", + "Ġnor ms", + "Ġnorm s", + "Ġ pla", + "Ġp la", + "Ġpl a", + "Ġ surname", + "Ġs urname", + "Ġsur name", + "Ġ postal", + "Ġpos tal", + "Ġpost al", + "Ġpo stal", + "Ġ Depart", + "ĠDe part", + "ĠDep art", + "Ġs laughter", + "Ġsla ughter", + "or ida", + "ori da", + "Ġhe bben", + "Ġheb ben", + "Ġd esar", + "Ġde sar", + "Ġdes ar", + "comp act", + "_ LANG", + "_L ANG", + "_LA NG", + "åIJ Ī", + "o poly", + "op oly", + "opol y", + "opo ly", + "_ rad", + "_r ad", + "_ra d", + "ĠST DMETHOD", + "ĠSTD METHOD", + "L azy", + "La zy", + "Ġ ĠĠĉ", + "ĠĠ Ġĉ", + "ĠĠĠ ĉ", + ".. .,", + "... ,", + "( web", + "(w eb", + "Ġ Pont", + "ĠP ont", + "ĠPo nt", + "ĠPon t", + "Ġet was", + "Ġetwa s", + "Ġup ward", + "_ hat", + "_h at", + "Ġ ],ĊĊ", + "Ġ] ,ĊĊ", + "Ġ],Ċ Ċ", + "Ġ], ĊĊ", + "Ġ baseUrl", + "Ġbase Url", + "Ġwor rying", + "Ġworry ing", + "- addon", + "-add on", + "-ad don", + "( getClass", + "(get Class", + "S PI", + "SP I", + "Ġcapt uring", + ") },Ċ", + ")} ,Ċ", + ")}, Ċ", + "E ffects", + "Effect s", + "Eff ects", + "Ġcompet ent", + "Ġcompete nt", + "Ġf oul", + "Ġfo ul", + "Ġfou l", + "Ġsubs cribing", + "Ġsubscri bing", + "Ġ OBJECT", + "ĠO BJECT", + "ĠOBJ ECT", + "ĠOB JECT", + "IX EL", + "b ucks", + "bu cks", + "( edge", + "(e dge", + "(ed ge", + "( pass", + "(p ass", + "(pa ss", + "ĠPeter son", + "ĠPet erson", + "ĠPeters on", + "Ġbo obs", + "Ġboo bs", + "Ġboob s", + "Ġ Delay", + "ĠD elay", + "ĠDe lay", + "ĠDel ay", + "_ square", + "_s quare", + "e lim", + "el im", + "eli m", + "o ters", + "ot ers", + "ote rs", + "oter s", + "_ PC", + "_P C", + "% E", + "on click", + "Ġ SVG", + "ĠS VG", + "ĠSV G", + "Ġt opped", + "Ġto pped", + "Ġtop ped", + "Ġtopp ed", + "Ġf ist", + "Ġfi st", + "Ġfis t", + "s mart", + "sm art", + "ĠR alph", + "( owner", + "(o wner", + "j ours", + "jo urs", + "jour s", + "Ġbro nze", + "Ġbron ze", + "Ġ ArgumentException", + "ĠArgument Exception", + "( original", + "(origin al", + "(orig inal", + "(or iginal", + "_ SCALE", + "_S CALE", + "_SC ALE", + "_ cp", + "_c p", + "Ġrecomm ends", + "Ġrecommend s", + ".set Style", + "S ure", + "Sur e", + "Su re", + "L AND", + "LA ND", + "LAN D", + "Ġre peating", + "Ġrep eating", + "Ġrepe ating", + "Ġrepeat ing", + "M att", + "Mat t", + "Ma tt", + ". Visibility", + "Ġenter prises", + "Ġenterprise s", + ". Setup", + ".Set up", + "( scene", + "(s cene", + "(sc ene", + "ĠRe active", + "ĠReact ive", + "ur ge", + "urg e", + "b w", + ". Put", + ".P ut", + "p ersist", + "pers ist", + ". cookie", + ".c ookie", + ".co okie", + "ĠA udi", + "ĠAud i", + "ĠAu di", + "` s", + "s upplier", + "sup plier", + "( Form", + "(F orm", + " ¡", + "_ so", + "_s o", + "Į Ģ", + "ĠLeg ion", + "t te", + "tt e", + "N d", + "L oss", + "Lo ss", + "Los s", + "( attrs", + "(at trs", + "(attr s", + "(att rs", + ". scatter", + ".sc atter", + "Ġg room", + "Ġgr oom", + "Ġgro om", + "Ġgl impse", + "Ġglimps e", + "Ġn ails", + "Ġna ils", + "Ġnail s", + "Ġcum ulative", + "Ġf azer", + "Ġfa zer", + "Ġfaz er", + "_ services", + "_s ervices", + "_service s", + "_serv ices", + ". Num", + ".N um", + "ib ilit", + "ibil it", + "ibi lit", + "ibili t", + "_ resolution", + "_re solution", + "_res olution", + "Ġ Tx", + "ĠT x", + "um inium", + "umin ium", + "o pa", + "op a", + ". schedule", + ".s chedule", + "sm tp", + "ภķ", + "ur ry", + "urr y", + "ü k", + "g oog", + "go og", + "goo g", + "_ signature", + "_sign ature", + "_sig nature", + ". into", + ".in to", + ".int o", + "Ġ Steps", + "ĠSt eps", + "ĠSte ps", + "ĠStep s", + "Ġhome owners", + "Ġhomeowner s", + "Ġ NSURL", + "ĠNS URL", + "ĠP AC", + "ĠPA C", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĊĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĊĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĊĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĊĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĊĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĊ Ċ", + "> ')Ċ", + ">' )Ċ", + ">') Ċ", + "e nh", + "en h", + "Ġin cap", + "Ġinc ap", + "$ MESS", + "Ġm oins", + "Ġmo ins", + "Ġmoi ns", + "Ġ Fi", + "ĠF i", + "Ġoff season", + "pr essions", + "press ions", + "pression s", + "> .. < /", + "Ġpro vinces", + "Ġprov inces", + "Ġprovince s", + "Ġprovinc es", + "_ RAW", + "_R AW", + "_RA W", + "\\ App", + "Ġprostit uer", + "Ġprostitu er", + "_ gain", + "_g ain", + ".t encent", + "ff ects", + "ffect s", + "ffe cts", + "( pk", + "(p k", + "s ku", + "sk u", + "Ġ usable", + "Ġus able", + "Ġusa ble", + "ER VED", + "ERV ED", + "ERVE D", + "Ġant enna", + "Ġantenn a", + "h ea", + "he a", + "p list", + "pl ist", + "_ PLUGIN", + "_PL UGIN", + "Ñģ л", + ". lookup", + ".look up", + "á» ģ", + "Ġen larg", + "Ġp iss", + "Ġpi ss", + "Ġpis s", + "H am", + "Ha m", + "i map", + "im ap", + "ima p", + "Ġ invalidate", + "Ġin validate", + "Ġinvalid ate", + "Ġs ilk", + "Ġsi lk", + "Ġsil k", + "=\"# \">Ċ", + "=\"#\" >Ċ", + "=\"#\"> Ċ", + "ĠGr ass", + "ĠGra ss", + "Ġ Goal", + "ĠGo al", + "ĠGoa l", + "_ pdf", + "_p df", + "_pd f", + "Hand lers", + "Handler s", + "Handle rs", + "Ġst acks", + "Ġstack s", + "Ġsta cks", + ". getFullYear", + ".get FullYear", + "= [];Ċ", + "=[ ];Ċ", + "=[] ;Ċ", + "è½ ¦", + ", V", + "( split", + "(s plit", + "(sp lit", + "Ñĥн к", + "Ġbake ca", + "Ġbak eca", + "Ġ~ /.", + "Ġ~/ .", + "p ez", + "pe z", + "t ails", + "ta ils", + "tail s", + "ĠG len", + "ĠGl en", + "ĠGle n", + "Ġ setImage", + "Ġset Image", + "Ġ Comic", + "ĠC omic", + "ĠCom ic", + "ĠCo mic", + "B LOCK", + "BL OCK", + "ĉ This", + "ĉT his", + "o ader", + "oad er", + "oa der", + "Ġcapital ist", + "Ġcapita list", + "Ġcapit alist", + "_ STEP", + "_ST EP", + "( Boolean", + "Ġ Correct", + "ĠC orrect", + "ĠCor rect", + "r ina", + "ri na", + "rin a", + "Ġconc aten", + "Ġconcat en", + "å® ŀ", + "( ):ĊĊ", + "() :ĊĊ", + "():Ċ Ċ", + "(): ĊĊ", + "Ġun anim", + "Ġuna nim", + "l li", + "ll i", + "al ars", + "ala rs", + "alar s", + "- ne", + "-n e", + "Ġd ivor", + "Ġdi vor", + "Ġdiv or", + "ĠKick starter", + "] ._", + "]. _", + "< number", + " * * < /", + ": d", + "m di", + "md i", + "bind Value", + "Ġ Decision", + "ĠDe cision", + "ĠDec ision", + "Return Value", + ", index", + ",in dex", + "x fc", + "xf c", + "Ġse rum", + "Ġser um", + "get Field", + "Connection String", + "- object", + "-o bject", + "-ob ject", + ". recv", + ".re cv", + ".rec v", + "Ġunder graduate", + "Ġundergrad uate", + ". Infrastructure", + ".Inf rastructure", + "ĠK ab", + "ĠKa b", + "Ġadv isory", + "Ġadvis ory", + "Ġadvisor y", + "- tree", + "-t ree", + "-tr ee", + "Ġ mue", + "Ġm ue", + "Ġmu e", + "in form", + "info rm", + "inf orm", + ". embed", + ".em bed", + "Ġ errorCode", + "Ġerror Code", + "m icro", + "mi cro", + "mic ro", + "Ġsp arked", + "Ġspark ed", + "Ġspar ked", + "Ġimage ry", + "Ġimag ery", + "con c", + "co nc", + "_ missing", + "_m issing", + "_miss ing", + "Ġsur plus", + "K S", + "ĉR THOOK", + "ĉRT HOOK", + "T ell", + "Te ll", + "Tel l", + "r ium", + "ri um", + "Ġ Radius", + "ĠR adius", + "ĠRad ius", + "ĠRadi us", + "r ika", + "ri ka", + "rik a", + "los ion", + "ĠH ern", + "ĠHe rn", + "ĠHer n", + "G amma", + "Gam ma", + "Ga mma", + "Ġ Fee", + "ĠF ee", + "ĠFe e", + "Ġ Named", + "ĠN amed", + "ĠName d", + "ĠNa med", + "ĠNam ed", + "ĠCan yon", + "Ġ JSONArray", + "ĠJSON Array", + "Ġz wei", + "Ġzw ei", + "Ġzwe i", + "Ġ SSH", + "ĠS SH", + "ĠSS H", + "Ġser vant", + "Ġserv ant", + "co al", + "Ġden ying", + "Ġdeny ing", + "Ġs plits", + "Ġsplit s", + "Ġspl its", + "In correct", + "Inc orrect", + "Ġt ox", + "Ġto x", + "ĠAnal yst", + "ĠAnaly st", + "Ġacc red", + "Ġac cred", + "Ġaccr ed", + "u ble", + "ub le", + "ubl e", + "Ġ wt", + "Ġw t", + "Ġ Trial", + "ĠT rial", + "ĠTr ial", + "ĠTri al", + ". extension", + ".ext ension", + "Ġ Career", + "ĠCar eer", + "ĠCare er", + "Ġsec uring", + "ĠL il", + "ĠLi l", + "Ġpro jections", + "Ġproject ions", + "Ġproj ections", + "Ġprojection s", + "Ġproje ctions", + "Ġy east", + "Ġye ast", + "M ade", + "Ma de", + "Mad e", + "Ġfound ations", + "Ġfoundation s", + "ac ific", + "aci fic", + ". volume", + ".v olume", + ".vol ume", + "Ġmir rors", + "Ġmirror s", + "#### ############################################################################", + "######## ########################################################################", + "################ ################################################################", + "################################ ################################################", + "################################################################ ################", + "################################################ ################################", + "############################################################################ ####", + "######################################################################## ########", + "######################################## ########################################", + "######################## ########################################################", + "######################################################## ########################", + "Ġvi olate", + "Ġviol ate", + "ars ers", + "arse rs", + "arser s", + "Ġs ocio", + "Ġso cio", + "Ġsoc io", + "Ġsoci o", + "Ġtk inter", + "Ġ LINK", + "ĠL INK", + "ĠLI NK", + "ĠLIN K", + ". getSize", + ".get Size", + ".getS ize", + "Ġ Whole", + "ĠW hole", + "ĠWh ole", + "ĠWho le", + ")view DidLoad", + "ĉ done", + "ĉd one", + "ĉdo ne", + "ude au", + "\\ \"> < /", + "And rew", + "Andre w", + "e rb", + "er b", + "Ġf ö", + ". cluster", + ".cl uster", + "Ġdis course", + "Ġdisc ourse", + "Ġdiscour se", + "Ġdisco urse", + "_DE FIN", + "_DEF IN", + "Ġpued en", + "Ġpu eden", + "Ġpuede n", + "Ġ LOW", + "ĠL OW", + "ĠLO W", + ". av", + ".a v", + "Ġpr eca", + "Ġpre ca", + "Ġprec a", + "Ġ quo", + "Ġqu o", + "Ġq uo", + "Ġve loc", + "Ġvel oc", + ", ''", + ",' '", + "Ġ xyz", + "Ġx yz", + "Ġxy z", + "ĉ padding", + "ĉp adding", + "Ġtom atoes", + "Ġtomato es", + "ĠB ent", + "ĠBe nt", + "ĠBen t", + "_ curr", + "_c urr", + "_cur r", + "_cu rr", + "NS Date", + "Ġ getCurrent", + "Ġget Current", + "ĠgetC urrent", + "Ġ [`", + "Ġ[ `", + "Wed nesday", + ". Bar", + ".B ar", + "Ġ Vous", + "ĠV ous", + "ĠVo us", + "i nz", + "in z", + "ĠQu inn", + "ĠQui nn", + "ex cel", + "exc el", + "d os", + "do s", + "Ġout dated", + "O UTH", + "OUT H", + "OU TH", + "Ġ Maker", + "ĠM aker", + "ĠMake r", + "ĠMa ker", + "ĠMak er", + "ep endency", + "epend ency", + "Ġd ull", + "Ġdu ll", + "Ġdul l", + "ĠW inn", + "ĠWin n", + "ĠWi nn", + "o ge", + "og e", + "cl ave", + "cla ve", + "Ġ nova", + "Ġn ova", + "Ġno va", + "Ġnov a", + "Ġ aval", + "Ġa val", + "Ġav al", + "Ġava l", + "C apt", + "Cap t", + "Ca pt", + "ĠSp otify", + "ĠSpot ify", + "Ġj ul", + "Ġju l", + ") tableView", + ")t ableView", + "Ġf ilenames", + "Ġfile names", + "Ġfil enames", + "Ġfilename s", + "Ġesk ort", + "åij ¨", + "Ġsk ew", + "Ġske w", + "t erior", + "ter ior", + "te rior", + "teri or", + "Ġfin anc", + "Ġfinan c", + "Ġ tabla", + "Ġtab la", + "Ġta bla", + "ĠU IB", + "ĠUI B", + "Ġ ():", + "Ġ( ):", + "Ġ() :", + "ĠD ocker", + "ĠDo cker", + "ĠDoc ker", + "ĠDock er", + "per centage", + "percent age", + "Me et", + "i chi", + "ic hi", + "ich i", + "Ġinter im", + "Ġinte rim", + "Ġ' ='", + "Ġ'= '", + ". JSONObject", + ".JSON Object", + "( fid", + "(f id", + "(fi d", + "Ġd ownt", + "Ġdown t", + "Ġdow nt", + "Ġtrans ient", + "ĠSt eph", + "ĠSte ph", + "ĠStep h", + "Ġignor ance", + "Ġ Codes", + "ĠC odes", + "ĠCo des", + "ĠCode s", + "ĠCod es", + "= '',", + "=' ',", + "='' ,", + "Ġ ICE", + "ĠI CE", + "ĠIC E", + "Ġtran qu", + "Ġ Extended", + "ĠExt ended", + "ĠExtend ed", + "Ġ mund", + "Ġm und", + "Ġmu nd", + "Ġmun d", + "Ġ HOME", + "ĠH OME", + "ĠHO ME", + "Ġkil ometers", + "Ġkilomet ers", + "Ġ imagen", + "Ġim agen", + "Ġimage n", + "Ġimag en", + "Ġima gen", + "o ux", + "ou x", + "( sz", + "(s z", + "You ng", + "Yo ung", + "uff ed", + "uf fed", + "Ġ Wake", + "ĠW ake", + "ĠWa ke", + "ĠWak e", + "Ġa ide", + "Ġaid e", + "Ġai de", + "P ROC", + "PR OC", + "PRO C", + "ĠR at", + "ĠRa t", + "ĠL ith", + "ĠLi th", + "ĠLit h", + "b art", + "bar t", + "ba rt", + "Ġ Arrange", + "ĠAr range", + "ĠArr ange", + "p rompt", + "prom pt", + "Ð £", + "( ct", + "(c t", + "Ġ Interval", + "ĠInt erval", + "ĠInter val", + "d ept", + "de pt", + "dep t", + "D aniel", + "Dan iel", + "Dani el", + "Ġ fills", + "Ġf ills", + "Ġfil ls", + "Ġfill s", + ". tensor", + ".t ensor", + "( trim", + "(t rim", + "(tr im", + "Ġje alous", + "F eb", + "Fe b", + "\\ Common", + "Ġamendment s", + "Ġamend ments", + "_ operator", + "_op erator", + "_o perator", + "_oper ator", + "_ customize", + "_custom ize", + "Ġ ]]", + "Ġ] ]", + "Ġ bn", + "Ġb n", + "Ġdis appointment", + "Ġdisappoint ment", + "Ġmill enn", + ". when", + ".w hen", + ".wh en", + "Ġob ey", + "Ġobe y", + "Ġoff enders", + "Ġoffender s", + "Ġoffend ers", + "Ġoffen ders", + "W ild", + "Wil d", + "Wi ld", + "Ġcell For", + "Ġappar atus", + ". after", + ".a fter", + ".af ter", + "Ġ EPS", + "ĠE PS", + "ĠEP S", + "Ġad orable", + "ope rand", + "oper and", + "( listener", + "(list ener", + "ve al", + "Ġ )(", + "Ġ) (", + "Ġcardio vascular", + "uplic ates", + "uplicate s", + "r istol", + "rist ol", + "ris tol", + "Ġref uses", + "Ġrefuse s", + "( QWidget", + "(Q Widget", + "Ġel emento", + "Ġelement o", + "Ġelem ento", + "Number Of", + ". delay", + ".d elay", + ".de lay", + ".del ay", + ". groups", + ".g roups", + ".group s", + "\" >'+", + "\"> '+", + "\">' +", + "åĿ Ģ", + "ace ncy", + "ac ency", + "acen cy", + "( URL", + "(U RL", + "_ half", + "_h alf", + "_hal f", + "= l", + "Ġlist View", + "( section", + "(s ection", + "(se ction", + "(sec tion", + ". toArray", + ".to Array", + "+ /", + "ĠRodrig uez", + "i stream", + "ist ream", + "istr eam", + "Ġelig ibility", + ": :-", + ":: -", + ". newInstance", + ".new Instance", + "P B", + "Ġ Assets", + "ĠAs sets", + "ĠAss ets", + "ĠAsset s", + "Ġ Composite", + "ĠCom posite", + "ĠComp osite", + "ĠL abs", + "ĠLa bs", + "ĠLab s", + "ĠH amas", + "ĠHam as", + "ĠHa mas", + "++ );Ċ", + "++) ;Ċ", + "Ġ blk", + "Ġb lk", + "Ġbl k", + "Ġ Neo", + "ĠN eo", + "ĠNe o", + "L uc", + "Lu c", + "@ login", + "Ġun aware", + "Ġuna ware", + ". met", + ".m et", + ".me t", + "_ RELEASE", + "_RE LEASE", + "( ST", + "(S T", + "AM IL", + "AMI L", + "r ike", + "ri ke", + "rik e", + "Ġ (){Ċ", + "Ġ( ){Ċ", + "Ġ() {Ċ", + "Ġ(){ Ċ", + "( sprintf", + "(s printf", + "Ġ Accounts", + "ĠAc counts", + "ĠAccount s", + "Ġ VIEW", + "ĠV IEW", + "ĠVI EW", + "Ġ Aj", + "ĠA j", + "ãĤ °", + "Ġwh isk", + "Ġ idi", + "Ġi di", + "Ġid i", + "Ġr ode", + "Ġro de", + "Ġrod e", + "Ġ ihn", + "Ġi hn", + "Ġih n", + "ĠElement ary", + "Q ty", + "Qt y", + "Ġintrig uing", + "Ġ å¤", + "Ġå ¤", + "J obs", + "Job s", + "Jo bs", + "ĉ offset", + "ĉo ffset", + "ĠAh med", + "ĠTal iban", + "Ġ èİ·åıĸ", + "Ġè İ·åıĸ", + "Ġin jected", + "Ġinj ected", + "Ġinject ed", + ". Authentication", + ".Auth entication", + "_ linear", + "_l inear", + "_line ar", + "_lin ear", + "_li near", + ". Decimal", + ".D ecimal", + ".De cimal", + ".Dec imal", + "Ġapp les", + "Ġap ples", + "Ġappl es", + "Ġapple s", + "Ġshare holders", + "Ġshareholder s", + "Ġb aked", + "Ġba ked", + "Ġbake d", + "Ġbak ed", + ". diff", + ".d iff", + ".di ff", + "ĠE ddie", + "ĠEd die", + "o kers", + "ok ers", + "oke rs", + "oker s", + "Ġconfront ed", + "vo ices", + "voice s", + "Ġt us", + "Ġtu s", + "Ġ Spin", + "ĠS pin", + "ĠSp in", + "ĠSpi n", + "N ODE", + "NO DE", + "_ Un", + "_U n", + "C TX", + "CT X", + "/ google", + "/g oogle", + "/go ogle", + "T emperature", + "Tem perature", + "Ġ' ').", + "Ġ'' ).", + "Ġ'') .", + "Ġmagn ificent", + "Ġ startIndex", + "Ġstart Index", + "semb les", + "sem bles", + "semble s", + "sembl es", + "Any one", + "z k", + "e hen", + "eh en", + "ĠD ame", + "ĠDa me", + "ĠDam e", + ". strict", + ".str ict", + "Ġre places", + "Ġrep laces", + "Ġrepl aces", + "Ġreplace s", + "Ġline back", + "Ġpush es", + "Ġpus hes", + "Ġche ek", + "ĠS hi", + "ĠSh i", + "_ BYTES", + "_BY TES", + "_BYTE S", + "R EA", + "RE A", + "ả n", + "_CON NECTION", + "_CONNECT ION", + "G ateway", + "Gate way", + "ĠTr avis", + "ĠTra vis", + "ĠTrav is", + "Ġ AX", + "ĠA X", + "Ġ Basically", + "ĠBasic ally", + "ĠBas ically", + "Ġ Upgrade", + "ĠUp grade", + "à ª", + "th emes", + "the mes", + "theme s", + "them es", + "er mo", + "erm o", + "k or", + "ko r", + "F emale", + "Fe male", + "_ attach", + "_at tach", + "_att ach", + "ĠìĤ¬ ìļ©", + "Ġ poz", + "Ġp oz", + "Ġpo z", + "= =============Ċ", + "== ============Ċ", + "==== ==========Ċ", + "======== ======Ċ", + "=== ===========Ċ", + "============ ==Ċ", + "============= =Ċ", + "=========== ===Ċ", + "============== Ċ", + "========= =====Ċ", + "========== ====Ċ", + "====== ========Ċ", + "===== =========Ċ", + "======= =======Ċ", + "( symbol", + "(s ymbol", + "(sym bol", + "Ġ Sector", + "ĠS ector", + "ĠSe ctor", + "ĠSec tor", + "ĠSect or", + "__ )ĊĊ", + "__) ĊĊ", + "__)Ċ Ċ", + "_ padding", + "_p adding", + "_pad ding", + "ï¼ļ \"", + "Ġ fabs", + "Ġf abs", + "Ġfa bs", + "Ġfab s", + "Ġr anged", + "Ġrange d", + "Ġran ged", + "Ġrang ed", + "set Name", + "Ġp error", + "Ġper ror", + "Ġpe rror", + "â Ĺ", + "ĠFile Reader", + "Ġf ulfilled", + "Ġful filled", + "Ġfulfill ed", + "Ġfulfil led", + "_ Current", + "_C urrent", + "Ġdo minate", + "Ġdom inate", + "Ġdomin ate", + "Ġdomina te", + "Ġsm ugg", + "Post Mapping", + "_ force", + "_f orce", + "_for ce", + "Ġb loc", + "Ġbl oc", + "Ġblo c", + "ĠG iant", + "ĠGi ant", + "ĠGian t", + "ĠGia nt", + "( video", + "(v ideo", + "Ġ CU", + "ĠC U", + "System Service", + "Ġ elf", + "Ġe lf", + "Ġel f", + "Ġkont akt", + "ë ª", + "k ees", + "ke es", + "kee s", + "g tk", + "gt k", + "Ġparam Int", + "Ġ markup", + "Ġmark up", + "Ġmar kup", + "u ales", + "ual es", + "ua les", + "uale s", + "Ġaccount ed", + "Ġgang bang", + "RY PT", + "Ġ Wrong", + "ĠW rong", + "ĠWr ong", + "Ġ credited", + "Ġcr edited", + "Ġcred ited", + "Ġcredit ed", + "Ġ MESSAGE", + "ĠM ESSAGE", + "Ġf laws", + "Ġfl aws", + "Ġflaw s", + "Ġfla ws", + "Ġb bw", + "Ġbb w", + "Ġmet abolic", + "Ġmetab olic", + "Ġmetabol ic", + "ĠO EM", + "ĠOE M", + "/ event", + "/e vent", + "(C ollectors", + "mon ton", + "mo nton", + "mont on", + "monto n", + "ap pear", + "app ear", + "appe ar", + "Ġop ted", + "Ġopt ed", + "Ġc heat", + "Ġch eat", + "Ġche at", + "Ġd av", + "Ġda v", + "Ġ Proceed", + "ĠPro ceed", + "ĠProc eed", + "Ġ ê¸", + "Ġê ¸", + "an ked", + "ank ed", + "anke d", + "и з", + "an sk", + "ans k", + "Ġ Hang", + "ĠH ang", + "ĠHa ng", + "ĠHan g", + "ĠC ler", + "ĠCl er", + "ĠCle r", + "Ġdis gu", + "Ġdisg u", + "Ġc map", + "Ġcm ap", + ". cljs", + ".cl js", + "Ġa ument", + "Ġau ment", + "l ez", + "le z", + "Ġ Joined", + "ĠJ oined", + "ĠJo ined", + "ĠJoin ed", + "ĠJoi ned", + "_ received", + "_re ceived", + "_receive d", + "Ġa erial", + "Ġaer ial", + "Ġae rial", + "o tel", + "ot el", + "ote l", + "Ġg reet", + "Ġgre et", + "\" s", + "Ġ Genesis", + "ĠGen esis", + "ĠGene sis", + "ĠCal if", + "ĠCa lif", + "pan ion", + "Ġtail ored", + "Ġtailor ed", + "m apping", + "ma pping", + "map ping", + "and Expect", + ". track", + ".t rack", + ".tr ack", + "at omy", + "ato my", + "atom y", + "ĠO w", + "ul lah", + "ull ah", + "ulla h", + ". Yes", + ".Y es", + "Ġ SimpleName", + "ĠSimple Name", + "d bh", + "db h", + "' en", + "'e n", + "Ġn onsense", + "Ġnon sense", + "Ġnons ense", + "Ġphilosoph ical", + "( getContext", + "(get Context", + "Ġis so", + "Ġiss o", + "Ġ ACE", + "ĠA CE", + "ĠAC E", + "start Date", + "Ġb ÄĻd", + "ĠAUTH OR", + "ĠG lobe", + "ĠGl obe", + "ĠGlo be", + "ĠGlob e", + "Ġin sects", + "Ġins ects", + "Ġinsect s", + "Ġinse cts", + "_ Al", + "_A l", + "ush ing", + "ushi ng", + "è® °", + "/ Home", + "/H ome", + "ĠLocal Date", + "ne eded", + "need ed", + "nee ded", + "hes ive", + "Ġ illusion", + "Ġill usion", + "äº Į", + "Ġt rat", + "Ġtr at", + "Ġtra t", + "x o", + "/ detail", + "/d etail", + "/de tail", + "_ MATCH", + "_M ATCH", + "_MAT CH", + "Ġbroad band", + "Ġ wal", + "Ġw al", + "Ġwa l", + "ĠIllegal StateException", + "IRE CTION", + "IRECT ION", + "Ġnor theast", + "Ġnorth east", + "es ium", + "esi um", + "Ġ Cliente", + "ĠCl iente", + "ĠClient e", + "ĠCli ente", + "ul ance", + "ula nce", + "ulan ce", + "n ty", + "nt y", + "Ġt ecn", + "Ġte cn", + "Ġtec n", + "Device s", + "Dev ices", + "Ġgr ains", + "Ġgrain s", + "Ġgra ins", + "ĠO g", + "Ġ SEL", + "ĠS EL", + "ĠSE L", + "ud iant", + "udi ant", + "Ġ ++;Ċ", + "Ġ++ ;Ċ", + "Ġexplan ations", + "Ġexplanation s", + "o cco", + "oc co", + "occ o", + "Ġd iets", + "Ġdi ets", + "Ġdie ts", + "Ġdiet s", + "Ġco hort", + "Ġcoh ort", + "( controller", + "(cont roller", + "(control ler", + ". Iterator", + ".It erator", + ".Iter ator", + "- rich", + "-r ich", + "ro cess", + "roc ess", + "G D", + "Ġcar bohydr", + "Ġ fried", + "Ġf ried", + "Ġfr ied", + "Ġfri ed", + "ĠEm ployment", + "ĠEmp loyment", + "ĠEmploy ment", + "ìŀ ¥", + "ĠLeon ard", + "ĠLeo nard", + "_ ${", + "_$ {", + "qu ares", + "quare s", + "qua res", + "Ġcompan ions", + "Ġcompanion s", + "Ġp aris", + "Ġpar is", + "Ġpa ris", + "Ġpari s", + "Ġst imulation", + "Ġstim ulation", + "ĠZ oo", + "ĠZo o", + "Ġre levance", + "Ġrelev ance", + "Ġ Colour", + "ĠCol our", + "ĠColo ur", + "Ġs pear", + "Ġsp ear", + "Ġspe ar", + "ot ional", + "otion al", + "oti onal", + "Ġ Lite", + "ĠL ite", + "ĠLi te", + "ĠLit e", + "ĠK osten", + "ĠKo sten", + "ĠKos ten", + "Ġ ó", + "Ġà ³", + "_ attachment", + "_att achment", + "_attach ment", + "orph ic", + "orp hic", + "Ġda mit", + "Ġdam it", + "Ġ dlg", + "Ġd lg", + "Ġdl g", + "Ġth rive", + "Ġthr ive", + "CH ANGE", + "CHAN GE", + "Ġ Apparently", + "ĠApp arently", + "Ġa tual", + "Ġat ual", + "Ġro oted", + "Ġroot ed", + "( images", + "(image s", + "(im ages", + "a wi", + "aw i", + "ar iat", + "ari at", + "aria t", + "Ġch erry", + "Ġcher ry", + "ST ATIC", + "STAT IC", + "m nt", + "mn t", + "Ġ UserId", + "ĠUser Id", + "il let", + "ill et", + "ille t", + "ĠHis panic", + "ĠHispan ic", + "Ġ nak", + "Ġn ak", + "Ġna k", + "Ġcent ro", + "Ġcentr o", + "Ġcen tro", + "Ġ dims", + "Ġd ims", + "Ġdi ms", + "Ġdim s", + "_ initialize", + "_initial ize", + "ı k", + "ĠCent ers", + "ĠCenter s", + "R EN", + "RE N", + "Ġevolution ary", + "Ġ Topics", + "ĠTo pics", + "ĠTop ics", + "ĠTopic s", + "_ damage", + "_d amage", + "_da mage", + "e mer", + "em er", + "eme r", + "Ġr und", + "Ġrun d", + "Ġru nd", + "Ġpun ished", + "Ġpunish ed", + "Ġc ubic", + "Ġcu bic", + "Ġcub ic", + "f air", + "fa ir", + "[ ];ĊĊ", + "[] ;ĊĊ", + "[];Ċ Ċ", + "Ġin stantiate", + "Ġinstant iate", + "Ġover see", + "Ġovers ee", + "Ġoverse e", + "- delete", + "-de lete", + "-del ete", + "unt eer", + "unte er", + "start Time", + "Ġ Pipeline", + "ĠP ipeline", + "ĠPipe line", + "ĠPip eline", + "_ GAME", + "_G AME", + "ĠC ir", + "ĠCi r", + "ĉ Null", + "ĉN ull", + ". Formatting", + ".Format ting", + "uc umber", + "ĠR ide", + "ĠRid e", + "ĠRi de", + "Ġz oo", + "Ġzo o", + "Ġ checker", + "Ġcheck er", + "Ġche cker", + "åIJ Į", + "= C", + "Ġg rit", + "Ġgr it", + "Ġgri t", + "\" );//", + "\") ;//", + "\"); //", + "_ xy", + "_x y", + "Ġ Declaration", + "ĠDe claration", + "Ġ callable", + "Ġcall able", + "F oo", + "Fo o", + "Ġ ListItem", + "ĠList Item", + "Ġin accur", + "m lin", + "ml in", + "ĉ Data", + "ĉD ata", + "Ġev olving", + "a wan", + "aw an", + "awa n", + "Ġc afe", + "Ġca fe", + "Ġcaf e", + "f olk", + "fo lk", + "fol k", + "_ IDX", + "_ID X", + "_I DX", + "Ġ Anything", + "ĠAny thing", + "ĠPalest ine", + "ĠPalestin e", + "Ġ GridView", + "ĠGrid View", + "Ġcol ony", + "Ġcolon y", + "ĠGerman s", + "ĠGer mans", + "ĠGerm ans", + "( +", + ". pid", + ".p id", + ".pi d", + ". jsx", + ".j sx", + ".js x", + "ĠSup erior", + "ĠSuper ior", + "Christ ian", + "ĠL ect", + "ĠLe ct", + "ĉ Game", + "ĉG ame", + "Ġinstrument al", + "An imations", + "Animation s", + "Anim ations", + "д ал", + "да л", + "ĠM oses", + "ĠMo ses", + "ĠMos es", + "ĉĉ čĊĉĉčĊ", + "ĉĉčĊ ĉĉčĊ", + "z s", + "k te", + "kt e", + "ä¸ ļ", + "_ DIST", + "_D IST", + "_DIS T", + "_DI ST", + "b itmap", + "bit map", + "d B", + "Ġp ersistence", + "Ġpers istence", + "Ġpersist ence", + "ÑĢ оÑģ", + "ÑĢо Ñģ", + "$ l", + "B ron", + "Br on", + "Bro n", + "Ġ {|", + "Ġ{ |", + "_ chart", + "_c hart", + "_ch art", + "_char t", + "Ġ Consum", + "ĠCon sum", + "ĠCons um", + "Ġh emp", + "Ġhe mp", + "Ġhem p", + "Ġ \"))Ċ", + "Ġ\" ))Ċ", + "Ġ\") )Ċ", + "Ġ\")) Ċ", + "Ġatt ackers", + "Ġattack ers", + "Ġattacker s", + "Ġknowledge able", + "Ġc et", + "Ġce t", + "Ġvir uses", + "Ġvirus es", + "' I", + "Ġpitch er", + "Ġpit cher", + "Ġswe eping", + "Ġsweep ing", + "= list", + "=l ist", + "apt ops", + "aptop s", + ". depth", + ".de pth", + ".dep th", + "Ġinstruct ed", + "Ġ Rus", + "ĠR us", + "ĠRu s", + "benh avn", + "Ġ ин", + "Ġи н", + "S ports", + "Sp orts", + "Sport s", + "Spo rts", + "Ġon set", + "Ġons et", + "æĿ ĥ", + ". RED", + ".R ED", + ".RE D", + "_ si", + "_s i", + "ĠP ST", + "ĠPS T", + ". onChange", + ".on Change", + "> tag", + ">t ag", + "ĠR oh", + "ĠRo h", + "_ character", + "_char acter", + "ĠL aws", + "ĠLa ws", + "ĠLaw s", + "Ġ Bachelor", + "ĠB achelor", + "ĠBach elor", + "_ swap", + "_s wap", + "_sw ap", + ".re activex", + "Ġreward ing", + "Ġrew arding", + "M edium", + "Med ium", + "- [", + "Ġ Recently", + "ĠRec ently", + "ĠRecent ly", + "J oint", + "Join t", + "Jo int", + "part ition", + "Ġ Minutes", + "ĠMin utes", + "ĠMinute s", + "Ġ indo", + "Ġin do", + "Ġi ndo", + "Ġind o", + "Ġabsor bed", + "Ġabsorb ed", + "Ġ GN", + "ĠG N", + "_ IND", + "_IN D", + "_I ND", + "Ġs aber", + "Ġsa ber", + "Ġsab er", + "Ġsabe r", + "S pawn", + "Sp awn", + "out puts", + "output s", + "ĠJeff rey", + "Ġmed ieval", + "Ġmedi eval", + "h ed", + "he d", + "G uide", + "Gui de", + "Guid e", + "Gu ide", + "Ġpsych o", + "Ġpsy cho", + "Ġg lam", + "Ġgl am", + "E lim", + "El im", + "äd chen", + "_ plain", + "_p lain", + "_pl ain", + "Ġ Sau", + "ĠS au", + "ĠSa u", + "- four", + "-f our", + "Ġanaly zing", + "QUE RY", + "QU ERY", + "Ġtom ato", + "_ buttons", + "_button s", + "_but tons", + "V EN", + "VE N", + ". setStatus", + ".set Status", + ". Url", + ".U rl", + "+ ĊĊ", + "+Ċ Ċ", + "Ġcompl aining", + "Ġcomplain ing", + "d egree", + "de gree", + "deg ree", + "conf irmed", + "confirm ed", + "Ġsu bt", + "Ġsub t", + "p arsed", + "par sed", + "parse d", + "pars ed", + "Ġt orque", + "Ġtor que", + "Ġtrouble d", + "Ġtroub led", + "Ġtrou bled", + "Ġ TARGET", + "ĠT ARGET", + "ĠTAR GET", + "Ġtrad emarks", + "Ġtrade marks", + "Ġtrademark s", + "Ġ Coordinate", + "ĠCo ordinate", + "ĠCoord inate", + "ĠV iv", + "ĠVi v", + "Ġ //}ĊĊ", + "Ġ// }ĊĊ", + "Ġ//}Ċ Ċ", + "Ġapr ès", + ". getPosition", + ".get Position", + ".getP osition", + "( KeyCode", + "(Key Code", + "ĠSil va", + "Ġ meteor", + "Ġm eteor", + "Ġmet eor", + "Ġendorse ment", + "Ġendors ement", + "Over view", + "Ġ Poss", + "ĠP oss", + "ĠPo ss", + "ĠPos s", + ". Inject", + ".In ject", + "Ġeven ly", + "Ġ visualization", + "Ġvisual ization", + "Ġ wchar", + "Ġw char", + "Ġwc har", + "ĠH DMI", + "ĠHD MI", + "Ġf unct", + "Ġfun ct", + "Ġfunc t", + "ick name", + "',' ','", + "','', '", + "Ġfor wards", + "Ġforward s", + "Managed Object", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĉ server", + "ĉs erver", + "ĠOut look", + "ĠChron icle", + "ĠChronic le", + "Ġdub bed", + "Ġd ok", + "Ġdo k", + "ĠW ear", + "ĠWe ar", + ". AL", + ".A L", + "p aren", + "par en", + "pare n", + "pa ren", + ". Interface", + ".Inter face", + "Inter faces", + "Interface s", + ". cod", + ".c od", + ".co d", + "Ġd ib", + "Ġdi b", + ".Global ization", + "ĠAc ademic", + "ĠAcad emic", + "ĠAcadem ic", + "Ġas sms", + "Ġass ms", + "A utom", + "Auto m", + "Aut om", + "Au tom", + "Ġ lw", + "Ġl w", + "Ġ NW", + "ĠN W", + "Ġ&& čĊ", + "Ġproble ma", + "Ġproblem a", + "Ġprobl ema", + "ĠManufact uring", + "l imits", + "li mits", + "lim its", + "limit s", + "- mobile", + "-m obile", + "Ġ filme", + "Ġfil me", + "Ġfilm e", + "/ map", + "/m ap", + "Ġd oit", + "Ġdo it", + "Ġdoi t", + "ĠI nk", + "ĠIn k", + "Ġs ued", + "Ġsu ed", + "Ġsue d", + ". arr", + ".a rr", + ".ar r", + "Ġunder min", + "Ġ Proc", + "ĠP roc", + "ĠPro c", + "ĠPr oc", + "croll View", + "_ _$", + "__ $", + "Ġside walk", + "Ġsidew alk", + "( that", + "(t hat", + "(th at", + "ภ·", + "[ q", + "gram mar", + "Ġt ë", + "q uito", + "qu ito", + "quit o", + "qui to", + "Ġsp iral", + "Ġspir al", + "Ġspi ral", + "ext ended", + "extend ed", + "Ġf ocal", + "Ġfoc al", + "Ġfo cal", + "Ġdig ging", + "p as", + "pa s", + "ĠT all", + "ĠTal l", + "ĠTa ll", + ". proxy", + ".pro xy", + ".pr oxy", + "i tures", + "it ures", + "iture s", + "itu res", + "itur es", + "T RACT", + "TR ACT", + "TRA CT", + "Ġ Realm", + "ĠRe alm", + "ĠReal m", + "Ġf eder", + "Ġfe der", + "Ġfed er", + "Ġ oriented", + "Ġorient ed", + "Ġori ented", + "Ġ Alternative", + "ĠAltern ative", + "ĠAlter native", + "Ġ owe", + "Ġo we", + "Ġow e", + "Ġs ourced", + "Ġsource d", + "Ġsour ced", + "in ker", + "ink er", + "inke r", + ". det", + ".d et", + ".de t", + "S ep", + "Se p", + "Ġ Qui", + "ĠQ ui", + "ĠQu i", + "ĠPal mer", + "ĠPalm er", + "( _,", + "(_ ,", + "s amples", + "sample s", + "sam ples", + "samp les", + "o yer", + "oy er", + "ul lan", + "ull an", + "ulla n", + "q uez", + "qu ez", + "que z", + "Ed ges", + "Edge s", + "Ġsh out", + "Ġsho ut", + "Ġ Achie", + "ĠA chie", + "ĠAch ie", + "Ġ haar", + "Ġh aar", + "Ġha ar", + "_ Construct", + "_Con struct", + "Ġprem ature", + "Ġre vert", + "Ġrev ert", + "Ġreve rt", + "Ġrever t", + "' ).Ċ", + "') .Ċ", + "'). Ċ", + "Ġs chn", + "Ġsc hn", + "Ġsch n", + "filter ed", + "fil tered", + "filt ered", + "null ptr", + "S aved", + "Save d", + "Sa ved", + "it ecture", + "itect ure", + "C LA", + "CL A", + "Ġ vl", + "Ġv l", + "s tell", + "st ell", + "ste ll", + "ĉ Me", + "ĉM e", + "ĠL ip", + "ĠLi p", + "n ational", + "nat ional", + "nation al", + "Ġwh olly", + "Ġspr ings", + "Ġspring s", + ". Timer", + ".T imer", + ".Time r", + "ĉ src", + "ĉs rc", + "e lsen", + "el sen", + "else n", + "els en", + "åħ ¶", + "Ġcommunic ating", + "Ġ Quiz", + "ĠQu iz", + "ĠQui z", + "Ġt eng", + "Ġte ng", + "Ġten g", + "Ġg ez", + "Ġge z", + "Ġ Outside", + "ĠOut side", + "ĠOuts ide", + ". Sign", + ".S ign", + "( cs", + "(c s", + "Ġdisp utes", + "Ġdispute s", + "Ġdisput es", + "ĠWe iss", + "ĠWei ss", + "an nes", + "ann es", + "anne s", + "> No", + ">N o", + "ĠB ach", + "ĠBa ch", + "ĠBac h", + ".remove All", + "re fer", + "ref er", + "/ dashboard", + "/d ashboard", + "Ġ Ajax", + "ĠA jax", + "ĠAj ax", + "Index Changed", + "Ġ Weak", + "ĠWe ak", + "' \"Ċ", + "'\" Ċ", + "Ġs ights", + "Ġsight s", + "Ġsigh ts", + "access Token", + "ĠJ oi", + "ĠJo i", + "( domain", + "(d omain", + "(dom ain", + "(do main", + "ĉ cv", + "ĉc v", + "Ġcontin uation", + "Ġcontinu ation", + "Ġcontinua tion", + "Ġp lum", + "Ġpl um", + "Ġplu m", + "a dir", + "ad ir", + "adi r", + ". setMessage", + ".set Message", + "Ġ ï¼Į", + "Ġï¼ Į", + "Ġsw allow", + "Ġswal low", + "ĠL amp", + "ĠLa mp", + "ĠLam p", + "Ġ qw", + "Ġq w", + "Ġ uu", + "Ġu u", + "C oin", + "Co in", + "u bic", + "ub ic", + "ubi c", + "ĠDe als", + "ĠDeal s", + "r ace", + "ra ce", + "rac e", + "Ġdict ator", + "Ġm eme", + "Ġme me", + "Ġmem e", + "turn ed", + "tur ned", + "ĠJul ie", + "ĠJu lie", + "ĠJuli e", + ".grid Column", + "Ġp uppy", + "Ġpup py", + "Ġpu ppy", + "Ġp am", + "Ġpa m", + "Ġ ){čĊ", + "Ġ) {čĊ", + "Ġ){ čĊ", + "Ġinv iting", + "Ġinvit ing", + "Ġf rench", + "Ġfr ench", + "Ġfren ch", + "v im", + "vi m", + "Ġwr apping", + "Ġwrap ping", + "Ġ#- }Ċ", + "( [-", + "([ -", + "Ear ly", + "Ġsh iny", + "Ġshin y", + ". faces", + ".f aces", + ".face s", + ".fac es", + ".fa ces", + "Ġre bell", + "Ġreb ell", + "Ġrebel l", + "abc def", + "abcd ef", + "ä lt", + "äl t", + "Ġest imation", + "Ġestim ation", + "ph ys", + "phy s", + "los ures", + "losure s", + "_ REL", + "_RE L", + "_R EL", + "Ġex clusion", + "Ġexclus ion", + "Ġexcl usion", + "ĠSk ype", + "ĠSky pe", + "we ise", + "wei se", + "weis e", + "- stop", + "-s top", + "-st op", + "no thing", + "ĠE gg", + "ĠEg g", + "is ors", + "iso rs", + "isor s", + "Rich ard", + "Ġcounsel ing", + "Ġcom mem", + "Ġcomm em", + "Ġcomme m", + "ĠQ MessageBox", + "ĠSy nd", + "ĠSyn d", + "ĠF rost", + "ĠFr ost", + "ĠFro st", + "ĠCom petition", + "ĠCompet ition", + "ĠA wake", + "ĠAw ake", + "Ġ ted", + "Ġt ed", + "Ġte d", + "ic iones", + "ici ones", + "icio nes", + "icion es", + "ĠDev Components", + "VERTISE MENT", + "o tti", + "ot ti", + "ott i", + ". runner", + ".r unner", + ".run ner", + "Ġunique ly", + "Ġuniqu ely", + "Ġuniq uely", + ". flag", + ".f lag", + ".fl ag", + "ĉ rs", + "ĉr s", + "_ generic", + "_g eneric", + "_gen eric", + "_gene ric", + "_gener ic", + "Ġ` ``Ċ", + "Ġ`` `Ċ", + "Ġ``` Ċ", + "ACH INE", + "ACHI NE", + "Ġm ein", + "Ġme in", + "( Application", + "(App lication", + "( br", + "(b r", + "Ġrat ios", + "Ġratio s", + ": ,", + "ĠX CTest", + "ĠXCT est", + "ĠXC Test", + "ustain able", + "- www", + "-w ww", + "it les", + "itle s", + "_ TEMP", + "_T EMP", + "_TE MP", + "_TEM P", + "Ġs yst", + "Ġsys t", + "Ġsy st", + "umeric UpDown", + "ĉ assertTrue", + "ĉassert True", + "Ġ wf", + "Ġw f", + ". peek", + ".pe ek", + "ĠB ulg", + "ĠBul g", + "ĠBu lg", + "Ġterr ifying", + ". MODE", + ".M ODE", + ".MOD E", + "Ġ GW", + "ĠG W", + "á r", + "Ġ fic", + "Ġf ic", + "Ġfi c", + "Ġcommit ments", + "Ġcommitment s", + "- tech", + "-t ech", + "-te ch", + "Ġ Liquid", + "ĠL iquid", + "ĠLiqu id", + "o pez", + "op ez", + "ope z", + "z heimer", + "a ña", + "añ a", + "- media", + "-m edia", + "-me dia", + "-med ia", + "( animated", + "(an imated", + "_ goal", + "_go al", + "Ġg um", + "Ġgu m", + "y stone", + "yst one", + "ys tone", + ". SET", + ".S ET", + ".SE T", + "ĠW end", + "ĠWe nd", + "ĠWen d", + "set CellValue", + "Ġ msgs", + "Ġmsg s", + "Ġms gs", + "c ash", + "ca sh", + "cas h", + "AL LOC", + "ALL OC", + "/ aws", + "/a ws", + "Ġmicro wave", + "Ġmic rowave", + ". Pointer", + ".Point er", + "ĉ Console", + "ĉCon sole", + "_ sorted", + "_s orted", + "_sort ed", + "ĠF ilip", + "ĠFil ip", + "ĠFi lip", + "P rod", + "Pro d", + "Pr od", + "Ġ// !<", + "Ġ//! <", + "in group", + "ing roup", + "Ġ ks", + "Ġk s", + "_T RI", + "_TR I", + "Ġteas poon", + "Ġ ATT", + "ĠA TT", + "ĠAT T", + "Ġre covering", + "Ġrecover ing", + "Ġ GLOBAL", + "ĠG LOBAL", + ". Par", + ".P ar", + "Ġ/ >;Ċ", + "Ġ/> ;Ċ", + "Ġmar ble", + "ul ators", + "ula tors", + "ulator s", + "Ġ Cycle", + "ĠC ycle", + "ĠCy cle", + "ĠCycl e", + "ĠCyc le", + "Ġher bs", + "Ġherb s", + "_ metric", + "_m etric", + "_met ric", + ") !", + "_C LOCK", + "_CL OCK", + "_ Button", + "_B utton", + "H arry", + "Har ry", + "è¿ Ľ", + "Ġstr ains", + "Ġstrain s", + "Ġstra ins", + "Ġ AppBar", + "ĠApp Bar", + "Ġ Chan", + "ĠC han", + "ĠCh an", + "ĠCha n", + "/ video", + "/v ideo", + "Ġ bam", + "Ġb am", + "Ġba m", + ". Progress", + ".Pro gress", + "$ f", + "l emen", + "le men", + "lem en", + "leme n", + "Ġir regular", + "ĠD uncan", + "ĠDun can", + "ĠM int", + "ĠMin t", + "ĠMi nt", + "- video", + "-v ideo", + "ঠ¾", + "ó wn", + "ów n", + "Ġ EMPTY", + "ĠEM PTY", + "ĠEMP TY", + "Ġst acked", + "Ġstack ed", + "Ġ HA", + "ĠH A", + "_ cut", + "_c ut", + "_cu t", + "Ġwhere in", + "ĠW ays", + "ĠWay s", + "ĠWa ys", + "( counter", + "(c ounter", + "(count er", + "(co unter", + "è¯ ķ", + "Form Group", + "Ġb lew", + "Ġbl ew", + "Ġble w", + "c ourses", + "co urses", + "course s", + "cour ses", + "Ġ productos", + "Ġproduct os", + "Ġproducto s", + "r ys", + "ry s", + "Ġ Restr", + "ĠR estr", + "ĠRe str", + "ĠRes tr", + "ĠRest r", + "Ġst yling", + "Ġsty ling", + "Ġstyl ing", + "> s", + "Ġp iv", + "Ġpi v", + "Ġit ertools", + "Ġiter tools", + "get Repository", + "Ġ Ik", + "ĠI k", + "_ devices", + "_device s", + "_dev ices", + "lay ui", + "Ġhalf way", + "Ġfran ç", + "Ġt uning", + "Ġtu ning", + "Ġtun ing", + "O A", + "_ Node", + "_N ode", + "_No de", + "ar de", + "ard e", + "Ġf ierce", + "Ġfi erce", + "Ġfier ce", + "Ġfierc e", + "l icted", + "lic ted", + "lict ed", + "# čĊ", + "Ġbreak through", + "ĠE rik", + "ĠEr ik", + "Ġb ride", + "Ġbr ide", + "Ġbri de", + "Ġbrid e", + "Ġ .\"", + "Ġ. \"", + "c ulus", + "cul us", + "cu lus", + "in side", + "ins ide", + "insi de", + "ĠIndian apolis", + "Ġ EE", + "ĠE E", + "Ġy og", + "Ġyo g", + "ur ret", + "urre t", + "urr et", + ". fs", + ".f s", + ". grad", + ".g rad", + ".gr ad", + "_ cards", + "_c ards", + "_card s", + "_car ds", + "_ accuracy", + "_ac curacy", + "_acc uracy", + "_e pi", + "_ep i", + "qu eda", + "que da", + "/ org", + "/or g", + "/o rg", + "é ªĮ", + "éª Į", + "Ġcom pte", + "Ġcomp te", + "Ġcompt e", + ") )[", + ")) [", + "Out side", + "G reater", + "Great er", + "Gre ater", + "Ġ Renderer", + "ĠRender er", + ". actor", + ".a ctor", + ".ac tor", + ".act or", + "Account s", + "Ac counts", + "I dle", + "Id le", + "_ hours", + "_h ours", + "_hour s", + "er ner", + "ern er", + "erne r", + "J oined", + "Join ed", + "Jo ined", + "Ġme nj", + "Ġmen j", + "re quires", + "require s", + "requ ires", + "Ġ OPER", + "ĠO PER", + "ĠOP ER", + ".remove Child", + "ĉ sp", + "ĉs p", + "Ġ esse", + "Ġe sse", + "Ġes se", + "Ġess e", + "r ift", + "ri ft", + "rif t", + "x FE", + "xF E", + "ĠSh akespeare", + "____ ________", + "________ ____", + "Ġbudget s", + "Ġbud gets", + "Model State", + "fill able", + "- component", + "-com ponent", + "-comp onent", + "o cos", + "oc os", + "oco s", + "Ġ BUTTON", + "ĠB UTTON", + "ĠBUT TON", + "/ io", + "/i o", + ", out", + ",o ut", + "s ms", + "sm s", + "Th omas", + "Tho mas", + "ĠAr med", + "ĠArm ed", + "re sume", + "res ume", + "Ġrot ating", + "Ġ Vault", + "ĠV ault", + "ĠVa ult", + "Ġs eus", + "Ġse us", + "Ġseu s", + ". (*", + ".( *", + "Ġa mino", + "Ġam ino", + "Ġami no", + "Ġ[ ]);ĊĊ", + "Ġ[] );ĊĊ", + "Ġ[]) ;ĊĊ", + "Ġ[]);Ċ Ċ", + "Ġprov oc", + "n ox", + "no x", + ". GetEnumerator", + ".Get Enumerator", + "= ======Ċ", + "== =====Ċ", + "==== ===Ċ", + "=== ====Ċ", + "====== =Ċ", + "===== ==Ċ", + "======= Ċ", + "æĸ Ļ", + "_ scroll", + "_s croll", + "_sc roll", + "_scr oll", + "Ġfil med", + "Ġfilm ed", + "Ġfilme d", + "ĠS oci", + "ĠSo ci", + "ĠSoc i", + "g ap", + "ga p", + "g ro", + "gr o", + "V ote", + "Vo te", + "\" But", + "\"B ut", + "_ RC", + "_R C", + "An imal", + "Anim al", + " Ģ", + "ib ile", + "ibil e", + "ibi le", + "Ġaw aken", + "Ġawake n", + "o rest", + "or est", + "ore st", + "ores t", + "in ja", + "ĠI van", + "ĠIv an", + "( Command", + "Ġ *****", + "Ġ* ****", + "Ġ** ***", + "Ġ*** **", + "Ġ**** *", + "Î ·", + "Ġkv inder", + "Ġkvin der", + "Ġkvinde r", + "/ helpers", + "/h elpers", + "/help ers", + "/helper s", + "_ cases", + "_c ases", + "_case s", + "_ca ses", + "t g", + "ìĦ ¸", + "Register ed", + "ĉ pass", + "ĉp ass", + "_ digits", + "_d igits", + "_digit s", + "Ġcon tour", + "Ġcont our", + "Ġinf ants", + "Ġinfant s", + "Ġjust ification", + "Ġ Fortunately", + "ĠFort unately", + "Cont r", + "Con tr", + "ĠonCreate View", + "_ SAMPLE", + "_S AMPLE", + "_SAMPL E", + "Ġallow Null", + "Ġn ud", + "Ġnu d", + "Ġf etched", + "Ġfetch ed", + "Ġfet ched", + "_ equ", + "_e qu", + "_eq u", + "Ġ Unable", + "ĠU nable", + "ĠUn able", + "ĠUna ble", + "= \\\"\"", + "=\\\" \"", + "=\\ \"\"", + "> {Ċ", + ">{ Ċ", + "Ġcommit tees", + "Ġcommittee s", + "ist ema", + "iste ma", + "istem a", + "+ \".", + "+\" .", + "ÃŃ an", + "ÃŃa n", + "m ant", + "man t", + "ma nt", + "Ġsou theast", + "Ġsouth east", + "ï¼Į Ċ", + "dialog s", + "dia logs", + "PRO JECT", + "ch arger", + "char ger", + "charge r", + "charg er", + "- port", + "-p ort", + "-po rt", + "( uuid", + "(u uid", + ". export", + ".ex port", + ".exp ort", + "S ix", + "Si x", + "Ġ RP", + "ĠR P", + "P rem", + "Pr em", + "Pre m", + "Ġcon science", + "Ġconsc ience", + "Ġmargin Right", + "_ distribution", + "_d istribution", + "_dis tribution", + "y aml", + "ya ml", + "res izing", + "resi zing", + "D ock", + "Do ck", + "Doc k", + "Ġ Locations", + "ĠL ocations", + "ĠLocation s", + "ĠLoc ations", + "G Y", + "S eed", + "Se ed", + "See d", + "B UFFER", + "BUF FER", + "BUFF ER", + "os sip", + "oss ip", + "u llen", + "ul len", + "ull en", + "ulle n", + "Th ings", + "Thing s", + "Thin gs", + "- self", + "-s elf", + "-se lf", + ". poll", + ".p oll", + ".po ll", + ".pol l", + "PL AYER", + "PLAY ER", + "Ġ å®", + "Ġå ®", + "G ROUP", + "Ġ Away", + "ĠA way", + "ĠAw ay", + "Ġg ospel", + "x fd", + "xf d", + "M ary", + "Mar y", + "Ma ry", + "Ġ Portable", + "ĠPort able", + "ĠPor table", + "T URE", + "TU RE", + "Ġutil is", + "Ġut ilis", + "Ġse it", + "Ġsei t", + "Ġ strand", + "Ġs trand", + "Ġst rand", + "Ġstr and", + "Ġstran d", + "Ġstra nd", + "Ġtrans c", + "Ġtran sc", + "Ġ (^", + "Ġ( ^", + "ĠAl fred", + "ĠAlf red", + ". mem", + ".m em", + ".me m", + ". circle", + ".c ircle", + "Ġ ~/", + "Ġ~ /", + "for cing", + "forc ing", + "Ġ riot", + "Ġr iot", + "Ġri ot", + "Ġrio t", + "p rox", + "pr ox", + "pro x", + "TH ON", + "iz ación", + "iza ción", + "Ġ NI", + "ĠN I", + "r ost", + "ro st", + "ros t", + "Ġdis pro", + "Ġdisp ro", + "_ instances", + "_in stances", + "_instance s", + "_inst ances", + "ï¼Į âĢľ", + "ograph er", + "ogra pher", + "en das", + "end as", + "enda s", + "ĠIs aac", + "ĠIsa ac", + "ĠP ine", + "ĠPin e", + "ĠPi ne", + "/ dis", + "/d is", + "Ġcolor With", + "it erate", + "ite rate", + "iter ate", + "_ stride", + "_st ride", + "_str ide", + "Ġp unto", + "Ġpun to", + "Ġpunt o", + ". EventArgs", + ".Event Args", + "( center", + "(c enter", + "Ġneighb oring", + "Ġneighbor ing", + "ĠPr ison", + "ĠPri son", + "Ġ Messenger", + "ĠM essenger", + "ĠMess enger", + "Ġepid emic", + "Ġepidemi c", + "d ao", + "da o", + "_ complex", + "_com plex", + "_comp lex", + "Ġg ravel", + "Ġgr avel", + "Ġgrave l", + "Ġgra vel", + "Ġgrav el", + "_D IP", + "_DI P", + "é ment", + "ém ent", + "ĠA ri", + "ĠAr i", + "_ bitmap", + "_b itmap", + "_bit map", + ". quit", + ".q uit", + ".qu it", + "( valid", + "(val id", + "(va lid", + "Ġ pend", + "Ġp end", + "Ġpe nd", + "Ġpen d", + "Ġrespir atory", + "Ġre bound", + "Ġreb ound", + "Default Value", + "ãĥ Ń", + "Ġcom mits", + "Ġcomm its", + "Ġcommit s", + ". tests", + ".t ests", + ".test s", + ".te sts", + "_ fr", + "_f r", + "i tet", + "it et", + "ite t", + ". sf", + ".s f", + "Ġspace craft", + "c ritical", + "cri tical", + "cr itical", + "crit ical", + "Ġde pressed", + "Ġdep ressed", + "Ġdepr essed", + "Ġdepress ed", + "ĠAny Object", + "Ġu nb", + "Ġun b", + "Ġdis cern", + "Ġdisc ern", + "( mysql", + "(m ysql", + "(my sql", + "L atin", + "La tin", + "Lat in", + "ĠB og", + "ĠBo g", + "ĠWild life", + "To File", + "ToF ile", + "i oxid", + "iox id", + "@ RestController", + "Ġ\" $(", + "Ġ\"$ (", + "Ġ <<\"", + "Ġ< <\"", + "Ġ<< \"", + "Ġdef ects", + "Ġdefe cts", + "Ġdefect s", + "Ġ datum", + "Ġd atum", + "Ġdat um", + "h in", + "hi n", + "Ġreal izar", + "Ġrealiz ar", + "Ġrealiza r", + "any ahu", + "anya hu", + "Ġ Sig", + "ĠS ig", + "ĠSi g", + "@ Data", + "ad aptive", + "ada ptive", + "adapt ive", + "ĠC atherine", + ". cr", + ".c r", + "Ġ COOKIE", + "ĠCO OKIE", + "Ġ pictured", + "Ġp ictured", + "Ġpicture d", + "Ġpict ured", + "ĠF ighter", + "ĠFight er", + "Query able", + "Ġ Anyway", + "ĠAny way", + "ĠGL FW", + "_ namespace", + "_n amespace", + "_name space", + "_names pace", + "_ ft", + "_f t", + "Ġ ])", + "Ġ] )", + "O rganization", + "Organ ization", + "Ġconstit utes", + "Ġconstitu tes", + "Ġconstitute s", + "Ġqu and", + "Ġqua nd", + "Ġquan d", + "( chunk", + "(ch unk", + "\" />čĊ", + "\"/ >čĊ", + "\"/> čĊ", + "ĠL akes", + "ĠLa kes", + "ĠLake s", + "ĠLak es", + "main window", + "Car thy", + "Cart hy", + "s pin", + "sp in", + "spi n", + "( csv", + "(c sv", + "(cs v", + ": red", + ":r ed", + "- commerce", + "-com merce", + "-comm erce", + "ภ¹", + "Ġdis covering", + "Ġdiscover ing", + "Ġ eco", + "Ġe co", + "Ġec o", + "_ fac", + "_f ac", + "_fa c", + "ince ton", + "inc eton", + "ĠGreen s", + "ĠGre ens", + "ĠGree ns", + "j wt", + "Ø µ", + "ĠBron cos", + "Ġ Goods", + "ĠG oods", + "ĠGo ods", + "ĠGood s", + "( GTK", + "(G TK", + "Ġ returnValue", + "Ġreturn Value", + "Ġsi empre", + "Ġne utr", + "Ġneu tr", + "Ġneut r", + "w ent", + "we nt", + "wen t", + "ĠN atal", + "ĠNa tal", + "ĠNat al", + "Ġenthusi astic", + "Ġenthusiast ic", + "á» į", + "F N", + "/ database", + "/d atabase", + "/data base", + "/dat abase", + "C atalog", + "Cat alog", + "Ġb run", + "Ġbr un", + "Ġbru n", + "ĠK ash", + "ĠKa sh", + "ĠKas h", + "_ Pl", + "_P l", + "isc rim", + ", width", + ",w idth", + "Ġin mates", + "Ġinmate s", + "Ass ignment", + "Assign ment", + "ĠH aven", + "ĠHave n", + "ĠHa ven", + "ĠHav en", + "Ġplay ground", + "ex am", + "@ Controller", + "ul iar", + "uli ar", + "ulia r", + ". getParent", + ".get Parent", + ".getP arent", + "Ġ \";ĊĊ", + "Ġ\" ;ĊĊ", + "Ġ\";Ċ Ċ", + "Ġ\"; ĊĊ", + ": size", + ":s ize", + "iss ors", + "issor s", + "Ġf is", + "Ġfi s", + "Ġ alc", + "Ġa lc", + "Ġal c", + "ens ation", + "ensa tion", + "ĠN ixon", + "ĠNi xon", + "Ġ mighty", + "Ġmight y", + "- str", + "-s tr", + "-st r", + "_ special", + "_s pecial", + "_sp ecial", + "_spec ial", + "_ ADC", + "_A DC", + "_AD C", + "Ġ Twig", + "ĠT wig", + "ĠTw ig", + "um bling", + "umb ling", + "- address", + "-add ress", + "-ad dress", + "Ġher oin", + "Ġhero in", + "Y TE", + "YT E", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "F riend", + "Fri end", + "Ġ ave", + "Ġa ve", + "Ġav e", + "Ġ PNG", + "ĠP NG", + "ĠPN G", + "ĠKur dish", + "ĠKurd ish", + "DataSet Changed", + "Ġbl ades", + "Ġblade s", + "Ġbla des", + "b ral", + "br al", + "bra l", + "S team", + "St eam", + "Ste am", + "Ġs igu", + "Ġsi gu", + "Ġsig u", + "IRT UAL", + "a cos", + "ac os", + "aco s", + "U DP", + "UD P", + "( database", + "(d atabase", + "(data base", + "(dat abase", + "h ec", + "he c", + "Ġ Strings", + "ĠString s", + "ĠStr ings", + "_ scalar", + "_s calar", + "_sc alar", + "_scal ar", + "ĉ desc", + "ĉd esc", + "ĉdes c", + "ĉde sc", + "Ġ TLS", + "ĠT LS", + "ĠTL S", + "; \"Ċ", + ";\" Ċ", + "ĠCor byn", + "Simple Name", + "u ell", + "ue ll", + "uel l", + "Ġ Entre", + "ĠEn tre", + "ĠEnt re", + "ĠEntr e", + "ell ites", + "ellite s", + "elli tes", + "- place", + "-p lace", + "-pl ace", + "Ġfrank ly", + "ĠE rf", + "ĠEr f", + "C EL", + "CE L", + "Ġpa ÃŃs", + "Ġh edge", + "Ġhe dge", + "Ġhed ge", + "Ġ latent", + "Ġla tent", + "Ġlate nt", + "Ġlat ent", + "Ġlaten t", + "Ġ IRQ", + "ĠIR Q", + "ĠH erald", + "ĠHer ald", + "ĠHera ld", + "Ġ Prec", + "ĠP rec", + "ĠPr ec", + "ĠPre c", + "ë³ ´", + ". TEXT", + ".T EXT", + "S alary", + "Sal ary", + "Ġaut umn", + "Ġtr avail", + "Ġtra vail", + "Ġtrav ail", + "Ġtrava il", + ". Sum", + ".S um", + "Ġc ared", + "Ġcar ed", + "Ġca red", + "Ġcare d", + "M or", + "Mo r", + "Ġint uitive", + "Ġintuit ive", + "Ġj ournals", + "Ġjournal s", + "_ IT", + "_I T", + "Ġ Trou", + "ĠT rou", + "ĠTr ou", + "ĠTro u", + "ä¼ ł", + "Has ColumnName", + "Com posite", + "Comp osite", + "Ġsp ice", + "Ġspi ce", + "_ disk", + "_d isk", + "_dis k", + "_di sk", + "_CODE S", + "_CO DES", + "_COD ES", + "Ġ Introduced", + "ĠInt roduced", + "ĠIntro duced", + "i ona", + "ion a", + "io na", + "Ġn uestra", + "Ġnue stra", + "Ġnuest ra", + "Ġnues tra", + "o ct", + "oc t", + "ĠĠĠĠ ĊĠĠĠĠĊĠĠĠĠĊ", + "ĠĠĠĠĊ ĠĠĠĠĊĠĠĠĠĊ", + "ĠĠĠĠĊĠĠĠĠĊ ĠĠĠĠĊ", + "( parameter", + "(param eter", + "(para meter", + "Ġst udios", + "Ġstud ios", + "Ġstudio s", + "Ġstudi os", + "Ġ projectId", + "Ġproject Id", + "Ġbd sm", + ".Sql Client", + "im izer", + "imize r", + "imi zer", + "imiz er", + "Ġ CARD", + "ĠC ARD", + "ĠCA RD", + "ĠCAR D", + "+ t", + "a an", + "aa n", + ". sol", + ".s ol", + ".so l", + "_ Adjust", + "_Ad just", + "Ġright eous", + "Ġ Logging", + "ĠLog ging", + ". filters", + ".f ilters", + ".filter s", + ".fil ters", + "_ TAB", + "_T AB", + "_TA B", + "ĉ sys", + "ĉs ys", + "rop hic", + "roph ic", + "o therapy", + "other apy", + "Ġ Browse", + "ĠB rowse", + "ĠBrow se", + "key board", + "R ON", + "RO N", + "+ \\", + "r opped", + "ro pped", + "rop ped", + "ropp ed", + "Ġext ensively", + "Ġextensive ly", + "f k", + "Ġ lime", + "Ġl ime", + "Ġli me", + "Ġlim e", + "y ears", + "year s", + "ye ars", + "E xc", + "Ex c", + "Ġs ph", + "Ġsp h", + "Ġch eating", + "Ġche ating", + "Ġcheat ing", + "an dro", + "and ro", + "andr o", + "ÃŃ o", + "Ġpr ince", + "Ġpri nce", + "Ġprin ce", + "Ġprinc e", + "o ire", + "oi re", + "oir e", + "Ġ Destination", + "ĠD estination", + "ĠDest ination", + "ĠCon verts", + "ĠConvert s", + "ĠConv erts", + "Ġup stream", + "o led", + "ol ed", + "ole d", + "Ġserv ants", + "Ġservant s", + "Ġ semantic", + "Ġsem antic", + "Ġcr unch", + "Ġeven tual", + "Ġevent ual", + "r unner", + "run ner", + "/ error", + "/e rror", + "S pin", + "Sp in", + "Spi n", + "Ġsecret ly", + "Ġ assemble", + "Ġas semble", + "Ġass emble", + ". Person", + ".P erson", + ".Per son", + "end error", + "ender ror", + "ende rror", + "_ <", + "Ġp endant", + "Ġpend ant", + "S leep", + "ĠChem istry", + "Ġboss es", + "Ġbos ses", + "l k", + ") )),Ċ", + ")) ),Ċ", + "))) ,Ċ", + "))), Ċ", + "Block ly", + "DE VICE", + "DEV ICE", + "Ġreflect ing", + "Ġ ample", + "Ġam ple", + "Ġampl e", + "Ġamp le", + "M illiseconds", + "Mill iseconds", + "ĠPres idential", + "ĠPresident ial", + "Ġ usuarios", + "Ġus uarios", + "Ġusuario s", + "Ġusu arios", + "Ġ NZ", + "ĠN Z", + "Ġ Salary", + "ĠS alary", + "ĠSal ary", + "ĠSala ry", + "ĠA manda", + "ĠAm anda", + "ĠAma nda", + "_ np", + "_n p", + "j ury", + "ju ry", + "jur y", + "Ġk ön", + "Ġkö n", + "Ġther apist", + "Ġtherap ist", + "Ġhom osexual", + "Ġhomosex ual", + "Ġhomo sexual", + "ĠDr ake", + "ĠDra ke", + "- window", + "-w indow", + "Ġ Located", + "ĠLoc ated", + "ĠLocate d", + ". Driver", + ".D river", + "Ġ VIDEO", + "ĠV IDEO", + "ĠVID EO", + "Ġmer chants", + "Ġmerch ants", + "Ġmerchant s", + "ĠC hest", + "ĠCh est", + "ĠChe st", + "ĠChes t", + "- lock", + "-l ock", + "-lo ck", + "/ php", + "/p hp", + "/ph p", + "Ġmil ano", + "Ġmilan o", + "_ STYLE", + "_ST YLE", + "ar ger", + "arg er", + "arge r", + "i dea", + "id ea", + "ide a", + "G UID", + "GUI D", + "GU ID", + "ad vanced", + "adv anced", + "advance d", + "me al", + "Options ItemSelected", + "= '%", + "=' %", + "ĠC ham", + "ĠCh am", + "ĠCha m", + ": data", + ":d ata", + "( stat", + "(s tat", + "(st at", + "Will Appear", + "Ġinf ormal", + "Ġinform al", + "a ji", + "aj i", + "Ġre productive", + "Ġrepro ductive", + "Ġ CAS", + "ĠC AS", + "ĠCA S", + "ãģ £", + "F UNC", + "FUN C", + "FU NC", + "ĠR uth", + "ĠRu th", + "ĠRut h", + ") +(", + ")+ (", + "CON ST", + "CO NST", + "CONS T", + "Ġ Fans", + "ĠF ans", + "ĠFa ns", + "ĠFan s", + "Ġ groupId", + "Ġgroup Id", + "x ffffffff", + "xf fffffff", + "xff ffffff", + "xffff ffff", + "xffffff ff", + "Ġs ampler", + "Ġsample r", + "Ġsam pler", + "Ġsamp ler", + "Ġ}} \">", + "Ġ}}\" >", + ". the", + ".t he", + ".th e", + "Ġh ollow", + "Ġhol low", + "W AY", + "WA Y", + "Ġ Faculty", + "ĠFac ulty", + "Attrib utedString", + "Ġ Looks", + "ĠL ooks", + "ĠLo oks", + "ĠLook s", + "ĠR ex", + "ĠRe x", + "j k", + "ĠM IL", + "ĠMI L", + "Ġ bard", + "Ġb ard", + "Ġbar d", + "Ġba rd", + ". Long", + ".L ong", + ".Lo ng", + "Ġli vest", + "Ġlive st", + "Ġlives t", + "Ġliv est", + "Ġs kal", + "Ġsk al", + "Ġska l", + "ic ism", + "ici sm", + "M AIN", + "MA IN", + "Ġmuch o", + "Ġmu cho", + "Ġmuc ho", + "B ODY", + "BO DY", + "Ġ ese", + "Ġe se", + "Ġes e", + "ĉ use", + "ĉu se", + "ĉus e", + "F oot", + "Foo t", + "Fo ot", + ". SQLException", + ".SQL Exception", + "Ġin heritance", + "Ġinherit ance", + "re ceived", + "receive d", + "rece ived", + "Ġp utas", + "Ġput as", + "Ġpu tas", + "Ġputa s", + "e dis", + "ed is", + "edi s", + "a lsa", + "al sa", + "als a", + "Ġ ErrorMessage", + "ĠError Message", + "Bo oking", + "Book ing", + "Ġ tract", + "Ġt ract", + "Ġtr act", + "Ġtra ct", + "a cz", + "ac z", + "ĠC ant", + "ĠCan t", + "ĠCa nt", + "_ regex", + "_reg ex", + "Ġide ological", + "Ġj ihad", + "Ġji had", + "Ġjih ad", + "h os", + "ho s", + "/ sys", + "/s ys", + "co lm", + "col m", + "( pool", + "(p ool", + "(po ol", + "Ġest án", + "Ġestá n", + "Ġ Pending", + "ĠP ending", + "ĠPen ding", + "ĠPend ing", + "em ás", + "Ġktó ry", + ") );ĊĊĊ", + ")) ;ĊĊĊ", + "));Ċ ĊĊ", + "));ĊĊ Ċ", + ")); ĊĊĊ", + "trans actions", + "transaction s", + "Ġw ield", + "Ġwie ld", + "Ġwi eld", + "Ġwiel d", + "i tere", + "it ere", + "ite re", + "iter e", + "er ture", + "ert ure", + "_ ss", + "_s s", + "Ġstretch ing", + "Ġstret ching", + "Ġpr isoner", + "Ġprison er", + "Ġpris oner", + ".Read All", + "Ġb esch", + "Ġbe sch", + "Ġbes ch", + "-- ;čĊ", + "--; čĊ", + "Ġcr isp", + "Ġcri sp", + "Ġcris p", + "_ SCAN", + "_S CAN", + "_SC AN", + "Ġ ae", + "Ġa e", + "Str ict", + "ĠMin neapolis", + "ĠBo eing", + "a ris", + "ar is", + "ari s", + "r ek", + "re k", + "_ pipe", + "_p ipe", + "_pi pe", + "Ġpri ests", + "Ġpriest s", + "( EIF", + "(E IF", + "eh icles", + "ehicle s", + "Ġ Interactive", + "ĠInter active", + "b etween", + "bet ween", + "ĉNull Check", + "ĠBl air", + "Ġ Lt", + "ĠL t", + "_ inline", + "_in line", + "eth yl", + " ¼", + "_ packages", + "_p ackages", + "_package s", + "_pack ages", + "Ġbar rels", + "Ġbarrel s", + "Ġbarr els", + "_ he", + "_h e", + "Ġ regexp", + "Ġreg exp", + "Ġregex p", + "_ pts", + "_p ts", + "_pt s", + "_ Handler", + "_H andler", + "_Handle r", + "ing ular", + "ingu lar", + "ĠN issan", + "ĠR anch", + "ĠRan ch", + "Ġper ch", + "Ġpe rch", + "Ġperc h", + "Un supported", + "S mith", + "Sm ith", + "ĠLeg ends", + "ĠLegend s", + "M i", + "Ġ gf", + "Ġg f", + "st eder", + "ste der", + "sted er", + "Ġac quiring", + "Ġacqu iring", + "Ġs imulator", + "Ġsim ulator", + "Ġsimul ator", + "( ),\"", + "() ,\"", + "(), \"", + "re ceive", + "rece ive", + "Ġin place", + "Ġinp lace", + "A CTION", + "AC TION", + "ACT ION", + "Ġ WebDriver", + "ĠWeb Driver", + "file system", + "files ystem", + "< Order", + "l open", + "lo pen", + "lop en", + "lope n", + "Ġ HEIGHT", + "ĠHE IGHT", + ".set Border", + "į °", + "__ [\"", + "__[ \"", + "Ġ clamp", + "Ġc lamp", + "Ġcl amp", + "Ġclam p", + "Ġcla mp", + "Seg oe", + "b ands", + "ba nds", + "ban ds", + "band s", + "to List", + "am ba", + "amb a", + "> '+Ċ", + ">' +Ċ", + ">'+ Ċ", + "Ġ credible", + "Ġcred ible", + "a mat", + "am at", + "ama t", + "pl aying", + "play ing", + "pla ying", + ".setImage Resource", + "q uel", + "qu el", + "que l", + "Ġpo dr", + "Ġpod r", + "ge om", + "geo m", + "E k", + "ĠQ atar", + "Ġg eld", + "Ġge ld", + "Ġgel d", + "? ',Ċ", + "?' ,Ċ", + "?', Ċ", + "Ġc yl", + "Ġcy l", + "( ax", + "(a x", + "Ġ WI", + "ĠW I", + "ur ally", + "ural ly", + "ĠBr asil", + "ĠBra sil", + "ĠBras il", + "Ġs enza", + "Ġsen za", + "a ley", + "al ey", + "ale y", + "o nen", + "on en", + "one n", + "Ġ bah", + "Ġb ah", + "Ġba h", + "Ġm olecule", + "Ġmolec ule", + "R ad", + "Ra d", + "è¿ °", + "AN CH", + "ANC H", + "- background", + "-back ground", + "- agent", + "-a gent", + "-ag ent", + "-age nt", + "Ġprol ifer", + ": boolean", + "Ġt ide", + "Ġti de", + "Ġtid e", + "erial izer", + "erialize r", + "_ ;čĊ", + "_; čĊ", + "F ee", + "Fe e", + "* *)", + "** )", + "er gy", + "erg y", + "ĠH onor", + "ĠHon or", + "ĠHo nor", + ". Logging", + ".Log ging", + "i ris", + "ir is", + "iri s", + "Ġunder mine", + "Ġundermin e", + "ĠD y", + "Ġt yr", + "Ġty r", + "Ġ deque", + "Ġde que", + "Ġd amer", + "Ġda mer", + "Ġdam er", + "Ġdame r", + "( [])Ċ", + "([ ])Ċ", + "([] )Ċ", + ".layout ControlItem", + ".layoutControl Item", + "p eated", + "pe ated", + "peat ed", + "C AN", + "CA N", + "ra gments", + "rag ments", + "ragment s", + "L and", + "La nd", + ") ]);Ċ", + ")] );Ċ", + ")]) ;Ċ", + "ĠS ah", + "ĠSa h", + "Ġ DECL", + "ĠDE CL", + "ĠDEC L", + "With in", + "Wi thin", + "Ġ Namespace", + "ĠN amespace", + "ĠName space", + "ĠNames pace", + "an other", + "ano ther", + "semb ling", + "sem bling", + "sembl ing", + ". describe", + ".de scribe", + ".des cribe", + "Con sum", + "Cons um", + "Ġ Fear", + "ĠF ear", + "ĠFe ar", + "g iven", + "gi ven", + "give n", + "O range", + "Or ange", + "< boolean", + " This", + ">T his", + "Ġdata Index", + "Ġprint able", + "Ġprin table", + "ĠE yes", + "ĠEye s", + "ĠEy es", + "_ targets", + "_target s", + "_tar gets", + "( Py", + "(P y", + ". over", + ".o ver", + ".ov er", + "Ġ bru", + "Ġb ru", + "Ġbr u", + "am pton", + "amp ton", + "Ġplaint iff", + "< Key", + " );Ċ", + ">) ;Ċ", + "in vest", + "inv est", + ". *ĊĊ", + ".* ĊĊ", + ".*Ċ Ċ", + "Ġt élé", + "Ġté lé", + "Ġsu perf", + "Ġsuper f", + "Ġ cascade", + "Ġc ascade", + "Ġcas cade", + "Ġcasc ade", + "D TD", + "DT D", + "Ġv ivid", + "Ġvi vid", + "Ġviv id", + "Ġsubsid ies", + "Ġsubsidi es", + "ĠH ass", + "ĠHas s", + "ĠHa ss", + "Ġcol laps", + "Ġcoll aps", + "Ġcer amic", + "{ }\".", + "{} \".", + "ĠLeak age", + "- trash", + "-tr ash", + "-tra sh", + "c ollapsed", + "coll apsed", + "collapse d", + "- social", + "-s ocial", + "-so cial", + "ĠC had", + "ĠCh ad", + "ĠCha d", + "Ġinc lined", + "Ġincl ined", + "Ġ sto", + "Ġs to", + "Ġst o", + "Ġstory board", + ". payment", + ".p ayment", + ".pay ment", + "stack overflow", + "ĠRa iders", + "ĠRaid ers", + "ĠRaider s", + "ĠRai ders", + "Ġ #'", + "Ġ# '", + "ol icies", + "olic ies", + "oli cies", + "ìľ¼ ë¡ľ", + "e map", + "em ap", + "ema p", + "Ġ kj", + "Ġk j", + "Ġ quota", + "Ġqu ota", + "Ġquot a", + "Ġquo ta", + "ĠGar dens", + "ĠGarden s", + "ĠGard ens", + "ë² Ī", + "ĠAng els", + "ĠAnge ls", + "ĠAngel s", + "Ġ oft", + "Ġo ft", + "Ġof t", + "Ġlower case", + "Ġ iParam", + "Ġi Param", + "ĠiP aram", + "Ġche apest", + "Ġcheap est", + "un ta", + "unt a", + "_ pkt", + "_p kt", + "_pk t", + "ic ators", + "ica tors", + "icator s", + "Ġ leurs", + "Ġl eurs", + "Ġle urs", + "Ġleur s", + "Ġdecre ases", + "Ġdecrease s", + "ĉ define", + "ĉdef ine", + "ĉde fine", + "P REC", + "PR EC", + "PRE C", + "am mers", + "amm ers", + "ammer s", + "Ġ PreparedStatement", + "ĠPre paredStatement", + "ĠPrepared Statement", + "( direction", + "(d irection", + "(dir ection", + "(di rection", + "Ġcr ews", + "Ġcre ws", + "Ġcrew s", + "ar ked", + "ark ed", + "ĠMem phis", + "Ġ Sell", + "ĠS ell", + "ĠSe ll", + "ĠSel l", + "G TK", + "GT K", + "Ġ maid", + "Ġm aid", + "Ġma id", + "Ġmai d", + ": disable", + ":d isable", + "éĽ Ĩ", + "ĠP f", + "Ġal beit", + "op enh", + "open h", + "ope nh", + "?> \">Ċ", + "?>\" >Ċ", + ". getSource", + ".get Source", + ".getS ource", + "( scale", + "(s cale", + "(sc ale", + "D u", + "ĠP IL", + "ĠPI L", + "_ refresh", + "_re fresh", + "_ref resh", + "Ġb ets", + "Ġbe ts", + "Ġbet s", + "( car", + "(c ar", + "(ca r", + "ĠV on", + "ĠVo n", + "| --------------------------------------------------------------------------Ċ", + "ĠG rat", + "ĠGr at", + "ĠGra t", + "M uch", + "Mu ch", + "( Dialog", + "(D ialog", + ".stop Propagation", + "Ġ tek", + "Ġt ek", + "Ġte k", + "Ġex its", + "Ġexit s", + "' ],$", + "'] ,$", + "'], $", + "Ġ phoneNumber", + "Ġphone Number", + "u cs", + "uc s", + "e cimal", + "ec imal", + "eci mal", + "- -------------", + "-- ------------", + "---- ----------", + "-------- ------", + "--- -----------", + "------------ --", + "----- ---------", + "---------- ----", + "------ --------", + "----------- ---", + "------------- -", + "------- -------", + "--------- -----", + "i np", + "in p", + ".po jo", + "Ġcor pus", + "Ġcorp us", + "Ġpractition ers", + "Ġpractitioner s", + ". pic", + ".p ic", + ".pi c", + "\" testing", + "Ġstring By", + ". NotNull", + ".Not Null", + "Ġ rang", + "Ġr ang", + "Ġran g", + "Ġra ng", + ". Dynamic", + ".D ynamic", + "_ Render", + "_R ender", + "_Re nder", + "а ÑĤа", + "аÑĤ а", + "Wait ing", + "Wa iting", + "Ġ Wik", + "ĠW ik", + "ĠWi k", + "Ġoverwhel med", + "Ġoverwhelm ed", + "% \">", + "%\" >", + "Ġ AE", + "ĠA E", + "} }>Ċ", + "}} >Ċ", + "}}> Ċ", + "u w", + "_ typ", + "_t yp", + "_ty p", + "Ġ buckets", + "Ġb uckets", + "Ġbucket s", + "Ġbuck ets", + "Ġg reeting", + "Ġgre eting", + "Ġgreet ing", + "Ġ laughter", + "Ġla ughter", + "Ġlaugh ter", + "Ġant agon", + "ugg estion", + "uggest ion", + "- email", + "-e mail", + "-em ail", + "ĉ top", + "ĉt op", + "ĉto p", + "Ġ eros", + "Ġe ros", + "Ġer os", + "Ġero s", + "_ tri", + "_t ri", + "_tr i", + "Ġiss uing", + "Ġissu ing", + "Ġ há", + "Ġh á", + "Ġis olate", + "Ġisol ate", + "Ġiso late", + "Over flow", + ", E", + "Ġnut ritional", + "Ġnutrition al", + "Ġnutrit ional", + "ĠAbb ott", + "Ġ nf", + "Ġn f", + ". touch", + ".t ouch", + ".to uch", + ".fetch all", + "_ zip", + "_z ip", + "\" )}Ċ", + "\") }Ċ", + "\")} Ċ", + "Ġ amat", + "Ġa mat", + "Ġam at", + "Ġama t", + "Ġ Cisco", + "ĠC isco", + "Ġn Ã¥", + "P LEX", + "PL EX", + "PLE X", + "Ġ sei", + "Ġs ei", + "Ġse i", + "f oto", + "fo to", + ". toJson", + ".to Json", + "å¤ ļ", + "ĠK lein", + "ĠKle in", + "ĠKl ein", + "Ġ libc", + "Ġli bc", + "Ġlib c", + "Ġm iners", + "Ġmin ers", + "Ġmi ners", + "Ġmine rs", + "Ġminer s", + "å ¢", + "- print", + "-p rint", + "-pr int", + "ĠP ride", + "ĠPr ide", + "ĠPri de", + "T odos", + "To dos", + "Todo s", + "Ġ masked", + "Ġmask ed", + "Ġmas ked", + "Ġ setData", + "Ġset Data", + "Ġtele fon", + "Ġtel efon", + "Ġun happy", + "Ġunh appy", + "Ġ Tables", + "ĠT ables", + "ĠTable s", + "ĠTab les", + "ĠTa bles", + "g eb", + "ge b", + "( debug", + "(de bug", + "_ allowed", + "_all owed", + "_allow ed", + "- access", + "-a ccess", + "-ac cess", + "Ġlog istics", + "Ġlogistic s", + "Ġ gems", + "Ġg ems", + "Ġge ms", + "Ġgem s", + "ĠM ature", + "ĠMat ure", + "ĠMa ture", + "Ġ rsp", + "Ġr sp", + "Ġrs p", + "Ġ Alle", + "ĠA lle", + "ĠAl le", + "ĠAll e", + ". getBytes", + ".get Bytes", + ".getBy tes", + "\\ web", + "ynchron ized", + "ynchronize d", + "Par agraph", + "Para graph", + "Ġth rottle", + "Ġthr ottle", + "Ġthrott le", + ". sqlite", + ".sql ite", + "cons ulta", + "consult a", + "ĠS eah", + "ĠSe ah", + "ĠSea h", + "C e", + "Ġsub mar", + "E RE", + "ER E", + "V ous", + "Vo us", + "Ġ reddit", + "Ġre ddit", + "Ġred dit", + "Ġredd it", + "Ġsql alchemy", + "- mile", + "-m ile", + "oc ide", + "oci de", + "P our", + "Po ur", + "} }\">Ċ", + "}} \">Ċ", + "}}\" >Ċ", + "st ead", + "ste ad", + "Ġ @(", + "Ġ@ (", + "Ġ [])", + "Ġ[ ])", + "Ġ[] )", + "Ġ Ads", + "ĠA ds", + "ĠAd s", + "Ġover load", + "Ġoverl oad", + "r idden", + "ri dden", + "rid den", + "ĠDe sert", + "ĠDes ert", + "Ġ Wrap", + "ĠW rap", + "ĠWr ap", + "ĠPortug uese", + "e tz", + "et z", + "ĉ first", + "ĉf irst", + "ĉfi rst", + "Ġm ilestone", + "Ġmil estone", + "Ġmiles tone", + "Ġmile stone", + "æĹ ł", + "Ñĥ Ñī", + "( success", + "(s uccess", + "< Vector", + " \")Ċ", + ">\" )Ċ", + ">\") Ċ", + "ĠD ollar", + "ĠDol lar", + "ĠDoll ar", + "Ġ emoji", + "Ġem oji", + "Ġemo ji", + "Car ousel", + "- player", + "-p layer", + "-play er", + "-pl ayer", + "Ġadjust ing", + "Ġadj usting", + "Ġj uga", + "Ġju ga", + "Ġjug a", + "allenge s", + "alleng es", + "allen ges", + "g ene", + "ge ne", + "gen e", + "(body Parser", + "lo pedia", + "lop edia", + "lope dia", + "Ġ Behind", + "ĠBe hind", + "ĠBeh ind", + "Ġslee ves", + "Ġsleeve s", + "Ġdrag ging", + "ĠChe vrolet", + "Ġ biz", + "Ġb iz", + "Ġbi z", + "iv ities", + "ivi ties", + "Ġ Frequency", + "ĠF requency", + "ĠFrequ ency", + ", char", + ",c har", + ",ch ar", + ". WHITE", + ".W HITE", + "_ preview", + "_p review", + "_pr eview", + "_pre view", + "_prev iew", + ") ';Ċ", + ")' ;Ċ", + "_ ax", + "_a x", + "I ONS", + "ION S", + "IO NS", + ". cpu", + ".c pu", + ".cp u", + ". inputs", + ".in puts", + ".input s", + "U BE", + "UB E", + "_ feed", + "_f eed", + "_fe ed", + "_fee d", + "ĠSup plement", + "! ).", + "!) .", + "e sus", + "es us", + "Ġ UDP", + "ĠU DP", + "ĠUD P", + "Ġmicro phone", + "Ġconf irms", + "Ġconfirm s", + ".is NotEmpty", + "\" :\"\",Ċ", + "\": \"\",Ċ", + "\":\" \",Ċ", + "\":\"\" ,Ċ", + "_ SCREEN", + "_S CREEN", + "_SC REEN", + "ĉ expected", + "ĉex pected", + "ĉexpect ed", + "ĉexp ected", + "+-+- +-+-", + "ĠH ait", + "ĠHa it", + "ĠHai t", + "fast call", + "Ġdep ict", + "v b", + "_ picture", + "_p icture", + "_pic ture", + "ĉ description", + "ĉd escription", + "ĉdes cription", + "ĉde scription", + "ĠW ife", + "ĠWi fe", + "u ci", + "uc i", + "Ġv icious", + "Ġvic ious", + "ä» ĸ", + "u eba", + "ue ba", + "Ġset User", + "ãģ ¡", + "Ġd iving", + "Ġdi ving", + "Ġdiv ing", + "Ġop era", + "Ġoper a", + "user content", + "a rah", + "ar ah", + "ara h", + ") },", + ")} ,", + "y un", + "yu n", + "v elt", + "ve lt", + "vel t", + "Ġun covered", + "Ġuncover ed", + "Ġ hips", + "Ġh ips", + "Ġhi ps", + "Ġhip s", + "Ġosc ill", + "Ġassert ing", + "Ġ Xi", + "ĠX i", + ". restore", + ".re store", + ".rest ore", + "k ea", + "ke a", + "Ġsp elling", + "Ġspell ing", + "Ġspel ling", + "Ġ derive", + "Ġde rive", + "Ġder ive", + "Ġderiv e", + "ab we", + "ĠD ow", + "ĠDo w", + ". setType", + ".set Type", + "_ vs", + "_v s", + "Ġc ozy", + "Ġco zy", + "Ġcoz y", + ". categories", + ".c ategories", + "O rg", + "Or g", + "_ mgr", + "_m gr", + "Ġd ungeon", + "Ġdung eon", + "collection View", + "Ġ Blank", + "ĠBl ank", + "ac ias", + "aci as", + "acia s", + "ä ä", + "_ cleanup", + "_c leanup", + "_clean up", + "_ACT IVITY", + "_ACTIV ITY", + "Ġtri angles", + "Ġtriangle s", + "Ġtriang les", + ". MenuItem", + ".Menu Item", + "Ġ iphone", + "Ġi phone", + "Ġip hone", + "Ġ Won", + "ĠW on", + "ĠWo n", + "] ]ĊĊ", + "]] ĊĊ", + "]]Ċ Ċ", + "Ġ Comparison", + "ĠCom parison", + "ĠCompar ison", + ". Doc", + ".D oc", + ".Do c", + "Ġ canonical", + "Ġcan onical", + "Ġcanon ical", + "ĠSu dan", + "ĠSud an", + "' ){", + "') {", + "Up Inside", + "b uiltin", + "built in", + "E NCY", + "EN CY", + "ENC Y", + "x be", + "xb e", + "Ġch uck", + "Ġchu ck", + "Ġcontrad ict", + "Ġcontra dict", + "Ġnu estro", + "Ġnue stro", + "Ġnuest ro", + "Ġnues tro", + "Ġarchitect ural", + "ĠF ib", + "ĠFi b", + "Ġcomp ares", + "Ġcompar es", + "Ġcompare s", + "* k", + "C fg", + "çĦ ¡", + "n ten", + "nt en", + "nte n", + "M atches", + "Match es", + "Mat ches", + "Ġ DOWNLOAD", + "ĠDOWN LOAD", + "_HANDLE R", + "_HAND LER", + "man agement", + "manage ment", + "mana gement", + "[ S", + "E NG", + "EN G", + "ÂĢ Â", + "f ang", + "fa ng", + "fan g", + "Ġsl ipped", + "Ġslip ped", + "ĠL anka", + "ĠLan ka", + "esc aping", + "Ġtack les", + "Ġtackle s", + "ĠPe dro", + "ĠPed ro", + ". Prop", + ".P rop", + ".Pro p", + ".Pr op", + ". ''", + ".' '", + ". Generated", + ".G enerated", + ".Generate d", + ".New Guid", + "at rigesimal", + "il lon", + "ill on", + "illo n", + "Ġstat istic", + "Ġstatist ic", + "s pecies", + "sp ecies", + "spec ies", + "spe cies", + "h olding", + "hold ing", + "hol ding", + "Dr upal", + "Ġfundament ally", + "Ġfundamental ly", + "Ġbond age", + "Ġres olutions", + "Ġresolution s", + "Inline Data", + "\\ Type", + "es tion", + "est ion", + "esti on", + ". wrap", + ".w rap", + ".wr ap", + "Ġwar riors", + "Ġwarrior s", + "Ġ LOCAL", + "ĠLO CAL", + "ĠLOC AL", + "A rchive", + "Arch ive", + "Arc hive", + "Ġembr aced", + "Ġembrace d", + "á» §", + ". Ver", + ".V er", + "ĠAff ordable", + "ole sale", + "oles ale", + "Ġ Applied", + "ĠApp lied", + "ĠAp plied", + "ĠAppl ied", + "Ġ Conversion", + "ĠCon version", + "ĠConv ersion", + "ĠConvers ion", + "m ega", + "me ga", + "meg a", + "_ cam", + "_c am", + "_ca m", + "Ġcer emon", + "Ġcere mon", + "a urus", + "au rus", + "aur us", + "ĠV olk", + "ĠVol k", + "ĠVo lk", + ". opens", + ".open s", + ".op ens", + "/ about", + "/a bout", + "Ġ Std", + "ĠS td", + "ĠSt d", + "j ournal", + "jo urnal", + "jour nal", + "( )){čĊ", + "() ){čĊ", + "()) {čĊ", + "()){ čĊ", + ", \"\\", + ",\" \\", + "( Arrays", + "(Array s", + "ĠD ense", + "ĠDen se", + "ase ña", + "än ner", + "änn er", + "/ stat", + "/s tat", + "/st at", + "user Data", + "Ġg erman", + "Ġger man", + "Ġgerm an", + "Ġ tz", + "Ġt z", + "w orthy", + "worth y", + "wort hy", + "wor thy", + "Format Exception", + "ph erd", + "pher d", + "phe rd", + "Ġsm iles", + "Ġsmile s", + "Ġ Whenever", + "ĠWh enever", + "ĠWhen ever", + "ĠWhe never", + "( adapter", + "(ad apter", + ".bad logic", + "Ġbrief ing", + ". GridColumn", + ".Grid Column", + "- char", + "-c har", + "-ch ar", + "d imension", + "dim ension", + "ĠC opper", + "ĠCo pper", + "ĠCop per", + "ĠCopp er", + "Ġn inth", + "Ġni nth", + "Ġnin th", + "Ġ' {{", + "Ġ'{ {", + "Ġ rav", + "Ġr av", + "Ġra v", + "_ Table", + "_T able", + "_Tab le", + "Ġderiv atives", + "Ġderivative s", + "Ġ Raise", + "ĠR aise", + "ĠRa ise", + "ĠRai se", + "ĠF ut", + "ĠFu t", + "ar mor", + "arm or", + "- padding", + "-p adding", + "-pad ding", + "Ġre min", + "Ġr emin", + "Ġrem in", + "ĉ style", + "ĉst yle", + "Ġ Membership", + "ĠMember ship", + "ĠMembers hip", + "Ġsp reads", + "Ġspread s", + "Ġspre ads", + "Ġg alleries", + "Ġgall eries", + "ĠClark e", + "ĠClar ke", + "Ġcon ception", + "Ġconcept ion", + "Ġconce ption", + "min ute", + "Ġab usive", + "_ adj", + "_a dj", + "_ad j", + "Ġterr ific", + "Ġo vert", + "Ġover t", + "Ġov ert", + "our cing", + "Ġ entrada", + "Ġent rada", + "Ġentr ada", + "Ġentra da", + "level s", + "lev els", + "Ġcrit ique", + "Ġres pects", + "Ġrespect s", + "Ġresp ects", + "ĠM MA", + "ĠMM A", + "i ene", + "ie ne", + "ien e", + "Ġen caps", + "Ġenc aps", + "ĠRay mond", + "Div ider", + "Di vider", + "i vable", + "iv able", + "iva ble", + "b az", + "ba z", + "Ġ@ _;Ċ", + "Ġ@_ ;Ċ", + "ĠCl aire", + "ĠCla ire", + "ĠClair e", + "Ġur ging", + "Ġurg ing", + "C EE", + "CE E", + "Ġtrans former", + "Ġtransform er", + "dis cord", + "disc ord", + "ĠJ ourney", + "t os", + "to s", + "Ġcompet itions", + "Ġcompetition s", + "Ġcompetit ions", + "Ġ OBJ", + "ĠO BJ", + "ĠOB J", + "ĠB is", + "ĠBi s", + "Ġrelax ation", + "i dy", + "id y", + "_ INSTANCE", + "_IN STANCE", + "_INST ANCE", + "Ġ Pref", + "ĠP ref", + "ĠPr ef", + "ĠPre f", + "d ados", + "da dos", + "dad os", + "ici encies", + "ĠMedia Query", + "Ġ Cube", + "ĠC ube", + "ĠCub e", + "ĠCu be", + "Ġ Strange", + "ĠSt range", + "ĠStr ange", + "ĠStra nge", + "g pu", + "gp u", + "( days", + "(d ays", + "(day s", + "(da ys", + "_ InitStruct", + "_Init Struct", + "Ġf ingerprint", + "Ġfinger print", + "e mat", + "em at", + "ema t", + "ĠG ecko", + "ĠGe cko", + "Ġ rails", + "Ġr ails", + "Ġrail s", + "Ġra ils", + "ĠL um", + "ĠLu m", + "s traction", + "st raction", + "str action", + "stract ion", + "stra ction", + "ig ung", + "igu ng", + "( movie", + "(m ovie", + "_ dictionary", + "_d ictionary", + "_ interrupt", + "_int errupt", + "_inter rupt", + "Ġ QC", + "ĠQ C", + "i ked", + "ik ed", + "ike d", + "append Child", + "rec ipient", + "r é", + "V e", + "Ġt owel", + "Ġto wel", + "Ġtow el", + ".last IndexOf", + "Ġplace bo", + "Ġplac ebo", + "Ġ Wie", + "ĠW ie", + "ĠWi e", + ". esp", + ".e sp", + ".es p", + "( Debug", + "oper ative", + "Ġde ceased", + "Ġdece ased", + "& id", + "ĉ mutex", + "ĉm utex", + "e lic", + "el ic", + "eli c", + "Ġb apt", + "Ġba pt", + "ĉ čĊčĊ", + "ĉčĊ čĊ", + "Ġfar ther", + "Ġfart her", + "H alf", + "Ha lf", + "Hal f", + ". disable", + ".d isable", + ".dis able", + ".menu Strip", + "le ccion", + "lec cion", + "Ġ resultCode", + "Ġresult Code", + "Ġc ans", + "Ġcan s", + "Ġca ns", + "- election", + "-e lection", + "-elect ion", + "-el ection", + "f emale", + "fe male", + "_ FIX", + "_F IX", + "aus ible", + "Ġ POWER", + "ĠP OWER", + "ĠPO WER", + "ĠPOW ER", + "Ġre construction", + "Ġrecon struction", + "Ġreconstruct ion", + "Ġsc ans", + "Ġscan s", + "Ġsca ns", + ".Xtra Bars", + "âĢĺ s", + "Re moved", + "Rem oved", + "Remove d", + "Ġpara graphs", + "Ġparagraph s", + "_ margin", + "_m argin", + "_mar gin", + "Ġl ymph", + "Ġly mph", + "Ġ bos", + "Ġb os", + "Ġbo s", + "l ington", + "ling ton", + "ĠBapt ist", + "Ġadvertis ements", + "Ġadvertisement s", + "Ġadvertise ments", + "Ġ Manage", + "ĠMan age", + "ĠMa nage", + "ĠMana ge", + "/ yyyy", + "/y yyy", + "I OUS", + "IO US", + "EN CES", + "ENCE S", + "ENC ES", + "ĠF iction", + "ĠFi ction", + "ĉ menu", + "ĉm enu", + "ĉme nu", + "ĠFile OutputStream", + "o van", + "ov an", + "ova n", + "ĠF eng", + "ĠFe ng", + "ĠFen g", + "Ġsk ipping", + "Ġskip ping", + "Ġski pping", + "get Class", + "getC lass", + "an ni", + "ann i", + "Ġre bounds", + "Ġreb ounds", + "Ġrebound s", + "Ġpublic ity", + "Ġpub licity", + "Ġpubli city", + "Ġin gres", + "Ġing res", + "Ġingr es", + "us ement", + "use ment", + "Ġthought ful", + ". Chart", + ".C hart", + ".Ch art", + ".Char t", + "Ġh atte", + "Ġha tte", + "Ġhat te", + "pass port", + "pas sport", + "Ġhook ed", + "Ġho oked", + "Ġ Lens", + "ĠL ens", + "ĠLe ns", + "ĠLen s", + "Ġflag ship", + "Ġflags hip", + "Ġs tip", + "Ġst ip", + "Ġ GEN", + "ĠG EN", + "ĠGE N", + "Ġcl ues", + "Ġclue s", + "i pv", + "ip v", + "ĠR ise", + "ĠRi se", + "ĠRis e", + "ĠG ew", + "ĠGe w", + "table name", + "tab lename", + "tabl ename", + "Ġfore most", + "_ validate", + "_valid ate", + "_ analysis", + "_an alysis", + "o lla", + "ol la", + "oll a", + "Ġqual ifications", + "Ġqualification s", + "Ġd istributions", + "Ġdistrib utions", + "Ġdistribution s", + "ĠF lower", + "ĠFl ower", + "ĠFlo wer", + "ĠFlow er", + "Ġt ense", + "Ġten se", + "Ġtens e", + "Ġthank ful", + "Ġcl utch", + "Ġun ified", + "ro ads", + "road s", + "Ġs iti", + "Ġsit i", + "Ġsi ti", + "Ġst all", + "Ġsta ll", + "Ġstal l", + "_P RIORITY", + "_PRI ORITY", + "c stdlib", + "_ USERNAME", + "_USER NAME", + ". bytes", + ".by tes", + ".byte s", + "? page", + "?p age", + "er malink", + "erm alink", + "ermal ink", + "ĠVe get", + "ĠVeg et", + "/v nd", + "- author", + "-a uthor", + "-auth or", + "-aut hor", + ". NONE", + ".N ONE", + ".NO NE", + "ĠCon current", + "ĠConc urrent", + "ĠC ry", + "ĠCr y", + "Ġst arters", + "Ġstart ers", + "Ġstar ters", + "Ġstarter s", + "Ġ Interaction", + "ĠInter action", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "Ġ LEVEL", + "ĠLE VEL", + "E ll", + "El l", + "Ġ comboBox", + "Ġcom boBox", + "Ġcombo Box", + "ĠThe resa", + "ĠTh eresa", + "ĠThere sa", + "ĠTher esa", + "t ek", + "te k", + "_ Handle", + "_H andle", + "Ġ aby", + "Ġa by", + "Ġab y", + ".g dx", + ", end", + ",e nd", + ",en d", + "( Local", + "(L ocal", + "O l", + "kn ife", + "a rial", + "ar ial", + "ari al", + "aria l", + "ĠH off", + "ĠHo ff", + "ĠHof f", + "Ġprostituer ade", + "Do ctor", + "Doc tor", + "In stances", + "Instance s", + "Inst ances", + ". SetValue", + ".Set Value", + "ĉ from", + "ĉf rom", + "ĉfr om", + "Ġlux urious", + "In dent", + "Ind ent", + "Al locator", + "All ocator", + "Alloc ator", + "_ DRAW", + "_D RAW", + "_DR AW", + "(\" ,\",", + "(\", \",", + "(\",\" ,", + "ĠF rances", + "ĠFr ances", + "ĠFrance s", + "ĠFranc es", + "ĠFra nces", + "ĠFran ces", + "Ġ groupBox", + "Ġgroup Box", + "( schema", + "(s chema", + "Print f", + "O RIES", + "OR IES", + "- gradient", + "-g radient", + "Ġre put", + "Ġrep ut", + "a rin", + "ar in", + "ari n", + "_ DONE", + "_D ONE", + "_DO NE", + "in cre", + "inc re", + "incr e", + "ig nty", + "ign ty", + "Ġex ert", + "Ġexe rt", + "Ġ -.", + "Ġ- .", + "/ App", + "/A pp", + "- through", + "-th rough", + "Ġdec lining", + "Ġdecl ining", + "Ġdes sert", + "Ġdess ert", + "Ġinc umb", + "Ġ designation", + "Ġdesign ation", + ". PORT", + ".P ORT", + ".PO RT", + ", strong", + ",str ong", + ",st rong", + "Ġ sandbox", + "Ġs andbox", + "Ġsand box", + "Ġw ines", + "Ġwin es", + "Ġwine s", + "Ġwi nes", + "ĠP av", + "ĠPa v", + "$ str", + "$s tr", + "ask ell", + "Ġh ö", + "Ġ PY", + "ĠP Y", + "Get Instance", + "Text Input", + "game Object", + "/ events", + "/e vents", + "/event s", + "created At", + "Ġlocal Var", + "Ġ WHITE", + "ĠW HITE", + "ĠWH ITE", + "p ered", + "pe red", + "per ed", + "i lege", + "ile ge", + "eff icient", + ", color", + ",c olor", + ",col or", + "c ate", + "ca te", + "cat e", + "ĠC afe", + "ĠCa fe", + "ĠCaf e", + "Ġsimilar ities", + "Ġp umps", + "Ġpump s", + "Ġpu mps", + "ĠHun gary", + "ĠHung ary", + ". Username", + ".User name", + "Ġs kate", + "Ġsk ate", + "Ġska te", + "Ġtouchdown s", + "Ġacceler ate", + "Ġaccel erate", + "ĠH elen", + "ĠHe len", + "ĠHel en", + "O MEM", + "OM EM", + "OME M", + "ĠK un", + "ĠKu n", + "_ vol", + "_v ol", + "Ġ findAll", + "Ġfind All", + "ĠMens chen", + "a head", + "ah ead", + ") ;\"", + "); \"", + "k ommen", + "kom men", + "Ġposs essed", + "Ġpossess ed", + ".arg max", + ". transition", + ".t ransition", + ".trans ition", + "A RP", + "AR P", + "OL UME", + "OLUM E", + "( script", + "(s cript", + "Ġ Ðĺ", + "ĠÐ ĺ", + "Ġ Finding", + "ĠF inding", + "ĠFin ding", + "ĠFind ing", + "o nces", + "on ces", + "once s", + "I o", + "B old", + "Bo ld", + "Ġrenew al", + "_D IALOG", + "Ġdis reg", + "IN TERN", + "INT ERN", + "INTER N", + "Ġt oute", + "Ġto ute", + "Ġtou te", + "Ġtout e", + "Ġelect r", + "Ġele ctr", + "ĠG ross", + "ĠGr oss", + "ĠGro ss", + "ĠGros s", + "ĉ true", + "ĉtr ue", + ". Fields", + ".F ields", + ".Field s", + "Ġ WIDTH", + "ĠW IDTH", + "ĠD ent", + "ĠDe nt", + "ĠDen t", + "Ġ Ãģ", + "Ġà ģ", + "NS Notification", + "Ġ aos", + "Ġa os", + "Ġao s", + "Ġme lee", + "Ġmel ee", + ". Validation", + ".Valid ation", + "Ġ DEC", + "ĠD EC", + "ĠDE C", + "- dependent", + "-depend ent", + "Ġsu ic", + "Ġsui c", + "T raits", + "Tr aits", + "Tra its", + "Trait s", + "$ message", + "$m essage", + "Ġ Dear", + "ĠD ear", + "ĠDe ar", + "ĉ FILE", + "ĉF ILE", + "l anguages", + "language s", + ". Prot", + ".P rot", + ".Pro t", + ".Pr ot", + ". addr", + ".add r", + ".ad dr", + "- generation", + "-g eneration", + "-gen eration", + "I CON", + "IC ON", + "ICO N", + "Ġtrans plant", + "- description", + "-d escription", + "-de scription", + "-des cription", + "Ġch asing", + "Ġcha sing", + "Ġch ees", + "Ġche es", + "Ġ }*/Ċ", + "Ġ} */Ċ", + "T rad", + "Tr ad", + "Tra d", + "qu eries", + "que ries", + "quer ies", + "/ widgets", + "/widget s", + "sub package", + "Ġe spec", + "Ġes pec", + "Ġesp ec", + "Ġcr acked", + "Ġcrack ed", + "Ġcompet itor", + "Ġcompetit or", + "P urchase", + "- team", + "-t eam", + "-te am", + "ole cular", + "olec ular", + "or Thunk", + "& P", + "Ġrel ent", + "Ġrele nt", + "/ #{", + "/# {", + "Ġ productId", + "Ġproduct Id", + "Ġ è¾", + "Ġè ¾", + "ĠL av", + "ĠLa v", + "Ġ Alter", + "ĠAl ter", + "ĠAlt er", + ". Mode", + ".M ode", + ".Mod e", + "AD IO", + "ADI O", + "g rp", + "gr p", + "æ ·»åĬł", + "æ·» åĬł", + "Q uit", + "Qu it", + "Qui t", + "Ġdep ths", + "Ġdepth s", + "Ġdept hs", + "- category", + "-c ategory", + "Ġ DATABASE", + "ĠD ATABASE", + "ĠDATA BASE", + "S PELL", + "SP ELL", + "ĠF alcon", + "ĠFal con", + "ĠQString List", + "Ġ ''.", + "Ġ' '.", + "Ġ'' .", + "ĠIn stitution", + "ĠInst itution", + "ĠInstit ution", + "ĠInstitut ion", + "d amage", + "da mage", + "dam age", + "az or", + "azo r", + "bel ongsTo", + "belongs To", + "ver ages", + "verage s", + "Ġ NONE", + "ĠN ONE", + "ĠNO NE", + "ĠNON E", + "ip pets", + "ipp ets", + "ippet s", + ", \\Ċ", + ",\\ Ċ", + "Ġfoot print", + "_ archive", + "_a rchive", + "_arch ive", + "_arc hive", + "n ak", + "na k", + ". getField", + ".get Field", + "Ġ Reflection", + "ĠRef lection", + "ĠReflect ion", + "Ġ ']", + "Ġ' ]", + "ĠH BO", + "ĠHB O", + "_ discount", + "_dis count", + "_disc ount", + "Ġin cest", + "Ġinc est", + "Ġince st", + "ĠD odge", + "ĠDo dge", + "ĠDod ge", + "ĠW ade", + "ĠWa de", + ". NO", + ".N O", + "\" encoding", + "Ġ Blockchain", + "ĠBlock chain", + "Ġlaws uits", + "Ġlawsuit s", + "Ġ Maint", + "ĠM aint", + "ĠMain t", + "ĠMa int", + "ĠMai nt", + "ch ten", + "cht en", + "chte n", + "Ġét ait", + "Ġktó re", + "_ ctl", + "_c tl", + "_ct l", + "( timer", + "(t imer", + "(time r", + "(ti mer", + "B attle", + "Bat tle", + "i zo", + "iz o", + "ay ed", + "aye d", + "I OR", + "IO R", + "ĠGlas gow", + "Ġs ynth", + "Ġsy nth", + "Ġsyn th", + "Ġsynt h", + "_ logs", + "_l ogs", + "_log s", + "_lo gs", + ". pose", + ".p ose", + ".pos e", + ".po se", + "_Adjust orThunk", + "( (&", + "(( &", + "Ġun sure", + "Ġuns ure", + "Ġunsur e", + "y state", + "yst ate", + "íķĺ ëĬĶ", + "O ULD", + "OU LD", + ". ng", + ".n g", + "Ġdefault dict", + "work space", + "works pace", + "Ġselect ive", + "Ġsel ective", + "Picker Controller", + "YNAM IC", + ". methods", + ".method s", + "Ġpath ways", + "Ġpathway s", + "Ġ Few", + "ĠF ew", + "ĠFe w", + "K G", + "C RYPT", + "CRY PT", + "follow ing", + "ĠD LC", + "ĠDL C", + "ĠS ara", + "ĠSar a", + "ĠSa ra", + "Ġ preset", + "Ġp reset", + "Ġpre set", + "Ġpres et", + "e structor", + "estr uctor", + "estruct or", + "ĠK urt", + "ĠKur t", + "ĠKu rt", + "Ġair plane", + "Ġ omp", + "Ġo mp", + "Ġom p", + "Ġ Parents", + "ĠPar ents", + "ĠParent s", + "ĠParen ts", + "ĠPare nts", + "ĠMart inez", + "ĠMartin ez", + ". complete", + ".com plete", + ".comp lete", + "Ġbroad ly", + "Ġs care", + "Ġsc are", + "Ġsca re", + "Ġscar e", + "ĠM é", + "Ġelim ination", + "Ġelimin ation", + "Ġp oured", + "Ġpo ured", + "Ġpour ed", + "Ġpou red", + "/ sw", + "/s w", + "Ġcom un", + "Ġco mun", + "Ġm asc", + "Ġma sc", + "Ġmas c", + "ĠOrgan ic", + "ĠOrg anic", + "Ġ StringUtils", + "ĠString Utils", + "ĠStringUtil s", + "il ateral", + "ilate ral", + "ilater al", + "Ġreluct ant", + "- age", + "-a ge", + "-ag e", + "Ġ nz", + "Ġn z", + ". \"\\", + ".\" \\", + "Ġpast or", + "Ġpas tor", + "Ġpa stor", + "a lez", + "al ez", + "ale z", + "Ġe fect", + "Ġef ect", + "p rov", + "pr ov", + "pro v", + "/ init", + "/i nit", + "/in it", + "Ġp enn", + "Ġpe nn", + "Ġpen n", + "u nds", + "un ds", + "und s", + "Ġ ssize", + "Ġs size", + "Ġss ize", + "Ġ Proj", + "ĠP roj", + "ĠPro j", + "ĠPr oj", + "b asename", + "base name", + "bas ename", + "Ġsh ells", + "Ġshell s", + "Ġshel ls", + "ĠN eck", + "ĠNe ck", + "ĠNec k", + "ĠEn forcement", + "v ided", + "vid ed", + "vi ded", + "vide d", + "s town", + "st own", + "sto wn", + "S phere", + "Sp here", + "$ r", + "us sen", + "uss en", + "a fil", + "af il", + "afi l", + "Ġ Telegram", + "ĠTele gram", + "Ġanaly tical", + "Ġanalytic al", + "н Ñĭе", + "нÑĭ е", + "us ually", + "usu ally", + "usual ly", + "x n", + "Ġhistor ian", + "Ġhist orian", + "Ġhistoria n", + "Ġhisto rian", + "ĠGreg ory", + "ol ph", + "Ġ Una", + "ĠU na", + "ĠUn a", + "Ġcon tributes", + "Ġcontrib utes", + "Ġcontribute s", + "% -", + "anti ago", + "ÑĢ ед", + "ÑĢе д", + ". region", + ".reg ion", + "Ġab rupt", + "ĠUnsupported OperationException", + "Ġ TASK", + "ĠT ASK", + "ĠTA SK", + "ĠTAS K", + "_ finish", + "_f inish", + "_fin ish", + "Ġnot orious", + "Ġ Vs", + "ĠV s", + "Ġ MQ", + "ĠM Q", + "Ġs unset", + "Ġsun set", + "Ġun acceptable", + "ar cer", + "arc er", + "Ġill umin", + "Ġillum in", + "ĠO rb", + "ĠOr b", + "Ġ bh", + "Ġb h", + "E ste", + "Est e", + "Es te", + "_ dispatch", + "_dis patch", + "_disp atch", + "Ġr ipped", + "Ġrip ped", + "Ġri pped", + "Ġtou jours", + "Ġ Parcel", + "ĠPar cel", + "_ ll", + "_l l", + ". userName", + ".user Name", + ". classes", + ".c lasses", + ".class es", + ".cl asses", + "S OURCE", + "( Number", + "(N umber", + "е лÑı", + "ел Ñı", + "Ġhead phones", + "Ġheadphone s", + "( side", + "(s ide", + "(si de", + "(sid e", + "con stitution", + "const itution", + "an nah", + "ann ah", + "anna h", + "čĊ ĠĠĠĠĠĠĠĠčĊ", + "Ġcl iff", + "Ġcli ff", + "- ref", + "-r ef", + "-re f", + "Ġmost rar", + "Ġmo strar", + "Ġmostr ar", + "Ġmostra r", + "ĠP owell", + "ĠPo well", + "ĠPow ell", + "+ y", + "Ġ BG", + "ĠB G", + "_ fragment", + "_f ragment", + "_fr agment", + "_frag ment", + ". Port", + ".P ort", + "Ġreal izing", + "Ġrealiz ing", + "param ref", + "Ġh ometown", + "Ġhome town", + "@ Table", + "+ \" --}}Ċ", + ">-- }}Ċ", + "F rench", + "Fr ench", + "Entity Manager", + "Ġ Plain", + "ĠP lain", + "ĠPl ain", + "ĠPla in", + "//// ////////////////////////////////////////////////////////////////", + "//////// ////////////////////////////////////////////////////////////", + "//////////////// ////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////// ////", + "//////////// ////////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////// ////////////", + "//////////////////////////////////////////////////////////// ////////", + "//////////////////////////////////////////////////// ////////////////", + " ³", + "( RE", + "(R E", + "c apt", + "ca pt", + "cap t", + "Ġ organisms", + "Ġorgan isms", + "Ġorganis ms", + "Ġorganism s", + "Ġ jets", + "Ġj ets", + "Ġje ts", + "Ġjet s", + "o location", + "ol ocation", + "olo cation", + "ĠApp RoutingModule", + "Ġgl orious", + "Ġglo rious", + "Ġglor ious", + "æľ į", + "Ġdisc arded", + "Ġdiscard ed", + "ĉ ĉĉĉĠĠĠĠĠ", + "ĉĉ ĉĉĠĠĠĠĠ", + "ĉĉĉĉ ĠĠĠĠĠ", + "ĉĉĉ ĉĠĠĠĠĠ", + "ĉĉĉĉĠ ĠĠĠĠ", + "ĉĉĉĉĠĠĠ ĠĠ", + "ĉĉĉĉĠĠ ĠĠĠ", + "ĉĉĉĉĠĠĠĠ Ġ", + "ĠArn old", + "l ug", + "lu g", + "Ġp arl", + "Ġpar l", + "Ġpa rl", + "Ġhorm ones", + "Ġhormone s", + "Ġ mah", + "Ġm ah", + "Ġma h", + "ĠS onic", + "ĠSo nic", + "ĠSon ic", + "Ġorgan izers", + "Ġorganiz ers", + "Ġorganize rs", + "Ġorganizer s", + "_ PLATFORM", + "_PL ATFORM", + ". inv", + ".in v", + ".i nv", + "Ġch ord", + "Ġcho rd", + "Ġchor d", + "vent ional", + "vention al", + "ĉ of", + "ĉo f", + "Ep isode", + ". Enum", + ".E num", + ".En um", + "un kt", + "unk t", + "ĠD h", + "ĠJ ared", + "ĠJa red", + "ĠJar ed", + "ĠN ak", + "ĠNa k", + "Ġint ends", + "Ġinte nds", + "Ġintend s", + "End ian", + "Ġa ustralia", + "_ cv", + "_c v", + "( resolve", + "(res olve", + "(re solve", + "Ġclin ics", + "Ġclinic s", + "l iked", + "li ked", + "like d", + "lik ed", + "ASH INGTON", + "in ha", + "inh a", + "' *", + "Ġ NP", + "ĠN P", + "_ beh", + "_b eh", + "_be h", + "Ġ hf", + "Ġh f", + "Ġw ür", + "c ategoria", + "$ form", + "$f orm", + "Ġsub way", + "Ġ isActive", + "Ġis Active", + "pop ular", + "C our", + "Co ur", + "Cou r", + "Ġ cooldown", + "Ġco oldown", + "Ġcool down", + "Ġa insi", + "Ġain si", + "Ġ GLuint", + "ĠGL uint", + "e real", + "ere al", + "erea l", + "Ġarray Of", + "Ġh atch", + "Ġhat ch", + "= =========", + "== ========", + "==== ======", + "======== ==", + "=== =======", + "========= =", + "====== ====", + "===== =====", + "======= ===", + "r esses", + "res ses", + "ress es", + "resse s", + "_ PP", + "_P P", + ". ^", + "_ decay", + "_dec ay", + "ĠB less", + "ĠBl ess", + "ĠBle ss", + "m etrics", + "met rics", + "metric s", + "ĠCOPY ING", + "ĠDump ster", + "ĠJos é", + "ĠDesign s", + "< Void", + "<", + "Ġ? ><", + "Ġ?> <", + "Ġ \"}Ċ", + "Ġ\" }Ċ", + "Ġ\"} Ċ", + "time zone", + "Ġ eer", + "Ġe er", + "Ġee r", + "max cdn", + "Ġ ESC", + "ĠE SC", + "ĠES C", + "ig aret", + "iga ret", + "igar et", + "_ connected", + "_connect ed", + "_conn ected", + "_ reverse", + "_re verse", + "_rev erse", + "Ġquestion able", + "ĠU SC", + "ĠUS C", + "Ġtu tti", + "Ġtut ti", + "Ġ dropout", + "Ġdrop out", + "Ġ Activities", + "ĠAct ivities", + "ĠActiv ities", + "ĠW inds", + "ĠWin ds", + "ĠWi nds", + "ĠWind s", + "' )));Ċ", + "') ));Ċ", + "')) );Ċ", + "'))) ;Ċ", + "Ġcon gest", + "Ġcong est", + "ÄŁ ı", + "Ġprolong ed", + "è¿ Ļ", + "ĠCross AxisAlignment", + "L EEP", + "LE EP", + "LEE P", + "Ġ VALID", + "ĠVAL ID", + "ĠG az", + "ĠGa z", + "Ġ dependence", + "Ġdepend ence", + "ĠP rix", + "ĠPr ix", + "ĠPri x", + ".Compiler Services", + "j ump", + "ju mp", + "Ġst rat", + "Ġstr at", + "Ġstra t", + "c irc", + "ci rc", + "cir c", + "Ġ CUSTOM", + "ĠC USTOM", + "x aa", + "xa a", + "Ġ bmp", + "Ġb mp", + "Ġbm p", + "Ġb ureau", + "Ġbu reau", + "Ġbure au", + "Ġw aren", + "Ġwar en", + "Ġwa ren", + "Ġware n", + "N X", + "( Window", + "(W indow", + "ĠChrist ie", + "ĠChris tie", + "_ FE", + "_F E", + "Ġ tn", + "Ġt n", + "Ġ Omega", + "ĠO mega", + "ĠOm ega", + "communic ations", + "communication s", + "Home Page", + "com pletion", + "comp letion", + "Ġsupply ing", + "Ġsuppl ying", + "Ġsupp lying", + "YPE S", + "YP ES", + "á vel", + "áv el", + "åĪ ¶", + "( click", + "(c lick", + "(cl ick", + "(cli ck", + "\\ Contracts", + "/ questions", + "/question s", + "Ġ ez", + "Ġe z", + "A MS", + "AM S", + ". mesh", + ".m esh", + ".me sh", + "Ġ' \\Ċ", + ">\\ Ċ", + "R obot", + "Rob ot", + "Ro bot", + "Json Object", + "Ġ DF", + "ĠD F", + "Ġ Processor", + "ĠProcess or", + "ĠProc essor", + "_ should", + "_sh ould", + ". protobuf", + ".prot obuf", + ".proto buf", + "- users", + "-user s", + "-use rs", + "-us ers", + "Ġemb ry", + "Ġembr y", + "F ONT", + "FO NT", + "Ġstart ups", + "Ġstartup s", + "Ġ DataSource", + "ĠData Source", + ") #", + "u ros", + "ur os", + "uro s", + "_ Color", + "_C olor", + "Ġst andalone", + "Ġstand alone", + "} [", + "j d", + "Ġfor give", + "Ġforg ive", + "Ġ ngx", + "Ġn gx", + "Ġng x", + "Ġ Generally", + "ĠGener ally", + "ĠGeneral ly", + "Ġconfig urable", + "Ġconfigur able", + "/ order", + "/or der", + "Ġ vas", + "Ġv as", + "Ġva s", + "' )\";Ċ", + "') \";Ċ", + "')\" ;Ċ", + "Ġ RR", + "ĠR R", + "ĠT roy", + "ĠTr oy", + "ĠTro y", + "Ġcomprom ised", + "Ġcompromise d", + "ĠS wan", + "ĠSw an", + "int endent", + "C entral", + "Cent ral", + "_ keeper", + "_k eeper", + "_ke eper", + "_keep er", + "Ġar quivo", + "Ġ ReadOnly", + "ĠRead Only", + "_ curve", + "_c urve", + "_cur ve", + "_cu rve", + "k v", + "en tin", + "ent in", + "enti n", + "è ±", + "Ġ Ey", + "ĠE y", + ".im read", + "ĠP am", + "ĠPa m", + "i ffe", + "if fe", + "iff e", + "at ivity", + "ativ ity", + "x bc", + "xb c", + "Ġ grim", + "Ġg rim", + "Ġgr im", + "Ġgri m", + "- filled", + "-f illed", + "-fill ed", + "name se", + "names e", + "nam ese", + "' ]:", + "'] :", + "Ġ aur", + "Ġa ur", + "Ġau r", + "ĠGi bson", + "ĠGib son", + ". MouseEvent", + ".Mouse Event", + "Ġl ado", + "Ġla do", + "Ġlad o", + "ava doc", + "avad oc", + "Ġf amil", + "Ġfam il", + "Ġfa mil", + "Ġ Moder", + "ĠM oder", + "ĠMod er", + "ĠMo der", + "ĠMode r", + "f ps", + "fp s", + "ãĢĢ ãĢĢ", + "- example", + "-ex ample", + "ĠAl zheimer", + "Ġ Utf", + "ĠU tf", + "ĠUt f", + "_ arguments", + "_arg uments", + "_argument s", + "Con clusion", + "text Content", + "rem aining", + "remain ing", + "rema ining", + "Ġinterrupt s", + "Ġ Backup", + "ĠBack up", + "ĠBac kup", + "ĠM ong", + "ĠMon g", + "ĠMo ng", + "Ġre ceptors", + "Ġrecept ors", + "Ġreceptor s", + "Ġrecep tors", + "h istor", + "hi stor", + "hist or", + "his tor", + ".cor outines", + "Ġsh outed", + "Ġshout ed", + "Ġsho uted", + "Al arm", + "Ġcomb ust", + "Ġg rote", + "Ġgr ote", + "Ġgro te", + "ult ural", + "ultur al", + "( ids", + "(i ds", + "(id s", + "---- ----------------------------------------------------------------------------", + "---------------- ----------------------------------------------------------------", + "-------------------------------- ------------------------------------------------", + "---------------------------------------------------------------- ----------------", + "------------------------------------------------ --------------------------------", + "---------- ----------------------------------------------------------------------", + "---------------------------------------------------------------------------- ----", + "---------------------------------------------------------------------- ----------", + "ipl inary", + "iplina ry", + "O pts", + "Op ts", + "Opt s", + "ĠY ale", + "ĠYa le", + "local Storage", + "Ġequ ival", + "Ġequiv al", + "ĠF leet", + "ĠFle et", + "\\ b", + "* pi", + "*p i", + "ĠQ Label", + "æ ¡", + "Ġ vx", + "Ġv x", + "Ġ ACL", + "ĠA CL", + "ĠAC L", + "Ġsu cesso", + "Ġsuc esso", + "Ġsucess o", + "Ġ perc", + "Ġp erc", + "Ġper c", + "Ġpe rc", + "ĠN otre", + "ĠNo tre", + "ĠNot re", + "Ġan arch", + "Ġana rch", + "R ing", + "s pb", + "sp b", + "Ġ strpos", + "Ġstr pos", + "st ores", + "store s", + "sto res", + "stor es", + "ĠMap le", + "ĠMa ple", + "( MainActivity", + "(Main Activity", + "(\" \"))", + "(\"\" ))", + "(\"\") )", + "Ġview Holder", + "Qu ad", + "Ġig ual", + "ors che", + "orsch e", + ". margin", + ".m argin", + ".mar gin", + "Ġin die", + "Ġind ie", + "Ġfr anc", + "Ġfra nc", + "Ġfran c", + "ĠForm Builder", + "ĠPart icip", + "ĠParti cip", + ". flash", + ".f lash", + ".fl ash", + "Ġ storms", + "Ġstorm s", + "Ġstor ms", + "Ġsto rms", + "U lt", + "Ul t", + "Ġ fen", + "Ġf en", + "Ġfe n", + "[ new", + "[n ew", + "E ver", + "Ev er", + "= \"Ċ", + "=\" Ċ", + "Ġ localized", + "Ġlocal ized", + "Ġlocalize d", + "_ follow", + "_f ollow", + "Ġ nave", + "Ġn ave", + "Ġna ve", + "Ġnav e", + "Ġdom inance", + "Ġdomin ance", + "Ġdomina nce", + "( tile", + "(t ile", + "(ti le", + "J ournal", + "Jo urnal", + "Ġ VC", + "ĠV C", + "Ġpen etration", + "Ġpenet ration", + "Ġpenetr ation", + "ï¼ ķ", + "Ġcom partment", + "Ġcomp artment", + "Ġcompart ment", + "Ġb ids", + "Ġbi ds", + "Ġbid s", + "Form atted", + "Format ted", + "**** **/ĊĊ", + "****** /ĊĊ", + "*** ***/ĊĊ", + "***** */ĊĊ", + "******/ ĊĊ", + "******/Ċ Ċ", + "( city", + "(c ity", + "(ci ty", + "âĢĶ it", + "[ C", + "Ġuse Callback", + "a ub", + "au b", + ") ?.", + ")? .", + "Ġ VAR", + "ĠV AR", + "ĠVA R", + "ĠSe bastian", + "ĠSebast ian", + "ĠM oss", + "ĠMo ss", + "ĠMos s", + "Ġabund ant", + "G reg", + "Gr eg", + "Gre g", + "ÑĤ а", + "_ ci", + "_c i", + "Ġb ibli", + "Ġbib li", + "C RM", + "CR M", + "Ġ Attempt", + "ĠAt tempt", + "ĠAtt empt", + "is me", + "ism e", + "d ash", + "da sh", + "das h", + "ãĢ İ", + "_ mu", + "_m u", + ".Formatting Enabled", + "Ind eed", + "- direct", + "-d irect", + "-dir ect", + "-di rect", + "Ġs ucking", + "Ġsuc king", + "Ġsuck ing", + "Ġp ne", + "Ġpn e", + "ocab ulary", + "ĠP ackers", + "ĠPac kers", + "ĠPack ers", + ". Navigation", + ".N avigation", + ".Nav igation", + "Ġp ied", + "Ġpie d", + "Ġpi ed", + "cri bing", + "ĠSt uart", + ".To Double", + "Ġ Secondary", + "ĠSecond ary", + "S aving", + "Sa ving", + "ĠD ut", + "ĠDu t", + "ĠM add", + "ĠMad d", + "ĠMa dd", + "M agic", + "Mag ic", + ", H", + ".document Element", + "Ġ BST", + "ĠB ST", + "ĠBS T", + "Ġdif fers", + "Ġdiffer s", + "Ġdiff ers", + "Ġmore over", + "_ nd", + "_n d", + "SE ARCH", + "п ÑĢав", + "пÑĢа в", + "пÑĢ ав", + "æ ´", + "to Match", + "Ġde creasing", + "Ġdecre asing", + "- member", + "-m ember", + "am pus", + "amp us", + "( boost", + "D aily", + "Da ily", + "Data GridView", + "Ġ HttpContext", + "ĠHttp Context", + "Ġh ipp", + "Ġhi pp", + "Ġhip p", + "_ workers", + "_work ers", + "_worker s", + "- language", + "-l anguage", + "é ĵ", + "Ġcons isted", + "Ġconsist ed", + "a thing", + "ath ing", + "athi ng", + "ĠMer cury", + "ĠMerc ury", + "$ content", + "$c ontent", + "$con tent", + "Ġpract iced", + "Ġpractice d", + "Ġ Modules", + "ĠMod ules", + "ĠModule s", + "_ DAY", + "_D AY", + "_DA Y", + "Ġweakness es", + "ĠL odge", + "ĠLo dge", + "ĠLod ge", + "Ġ nar", + "Ġn ar", + "Ġna r", + "Ġ Mate", + "ĠM ate", + "ĠMat e", + "ĠMa te", + "Ġ jp", + "Ġj p", + "ĠHttp Headers", + "Ġs mo", + "Ġsm o", + "Ġ TOKEN", + "ĠT OKEN", + "ĠTO KEN", + "ĠTOK EN", + "] )(", + "]) (", + "Ġa qui", + "Ġaqu i", + "sw agen", + "Ġ srv", + "Ġs rv", + "Ġsr v", + "ĉ ans", + "ĉa ns", + "ĉan s", + "A round", + "Ar ound", + "ĠMan uel", + "Ġfiction al", + "Ġfict ional", + "Ġ IMG", + "ĠI MG", + "ĠIM G", + "Ġ .'", + "Ġ. '", + "Ġ Berry", + "ĠB erry", + "ĠBer ry", + "Ġwall paper", + "s exual", + "sex ual", + "i ero", + "ie ro", + "ier o", + "Ġ çļĦ", + "ìĨ Į", + "Backing Field", + "ĠAd rian", + "ĠAdri an", + "BASE PATH", + "Ġre peats", + "Ġrepe ats", + "Ġrepeat s", + "Ġbl ues", + "Ġblue s", + "Ġun predict", + "Ġunp redict", + "_ coll", + "_c oll", + "_col l", + "_co ll", + "st acle", + "sta cle", + "Ġ Tumblr", + "ĠT umblr", + "Ġ Elf", + "ĠE lf", + "ĠEl f", + "Ġass urance", + "Ġc ensus", + "Ġcen sus", + "Ġ IMPORT", + "ĠIM PORT", + "ĠIMP ORT", + "E NDER", + "EN DER", + "END ER", + "a nos", + "an os", + "ano s", + "Ġ =(", + "Ġ= (", + "ĠEl lis", + "ĠEll is", + "ĠElli s", + "\" ĊĊĊĊ", + "\"Ċ ĊĊĊ", + "\"ĊĊ ĊĊ", + "\"ĊĊĊ Ċ", + ". win", + ".w in", + "Ġ Above", + "ĠA bove", + "ĠAb ove", + "a lon", + "al on", + "alo n", + "_ tick", + "_t ick", + "_ti ck", + "Ġrepresent ations", + "Ġrepresentation s", + "Ġ æķ", + "Ġæ ķ", + "w id", + "wi d", + "ĠA rms", + "ĠAr ms", + "ĠArm s", + "L ista", + "List a", + "Li sta", + "_ failure", + "_f ailure", + "_fail ure", + "_ cm", + "_c m", + ".Flat Appearance", + "Ġth rone", + "Ġthr one", + "Ġthro ne", + "P atch", + "Pat ch", + "ĠV oy", + "ĠVo y", + "en gl", + "eng l", + "Ġnegot iating", + "> `", + "Ġshoot s", + "Ġsho ots", + "Ġ FPS", + "ĠF PS", + "ĠFP S", + ". Year", + ".Y ear", + "ĠK iss", + "ĠKi ss", + "ĠKis s", + "en ción", + "enc ión", + "enci ón", + "re eting", + "ree ting", + "reet ing", + "From File", + "Ġresign ation", + "Ø ·", + "Ġt wins", + "Ġtw ins", + "Ġtwin s", + "Æ° ợ", + "Æ°á» £", + "Ġge bru", + "Ġgeb ru", + ". getContent", + ".get Content", + ".getC ontent", + ". Tree", + ".T ree", + ".Tr ee", + "Ġ Employees", + "ĠEmployee s", + "ĠEmploy ees", + "ĠF IFA", + "ĠFI FA", + "Ġc ertainty", + "Ġcert ainty", + "Ġcertain ty", + "( Cl", + "(C l", + "Ġ totals", + "Ġtot als", + "Ġtotal s", + "ed itable", + "edit able", + "edi table", + "ॠĢ", + ". Reporting", + ".Report ing", + "M as", + "Ma s", + "qu iet", + "qui et", + ". rules", + ".r ules", + ".ru les", + ".rule s", + "Ġ VO", + "ĠV O", + "con exion", + ", K", + "Ġ allocator", + "Ġal locator", + "Ġall ocator", + "Ġalloc ator", + "ĠPow der", + "\\ Repository", + "B eat", + "Be at", + "_ tipo", + "_t ipo", + "_tip o", + "_ti po", + "Ġ[ '',", + "Ġ[' ',", + "_ INTR", + "_IN TR", + "_INT R", + "Ġ <<<", + "Ġ< <<", + "Ġ<< <", + "< hr", + " \");čĊ", + ">\" );čĊ", + ">\") ;čĊ", + "drop IfExists", + "ĠB eg", + "ĠBe g", + "_ HAL", + "_H AL", + "Ġcross AxisAlignment", + "Ġ Evidence", + "ĠE vidence", + "ĠEv idence", + "Ġpec uliar", + "Ġin stitute", + "Ġinstit ute", + "ve is", + "Ġ fft", + "Ġf ft", + "Ġff t", + "à ģ", + "Ġzo ekt", + "Ġzoek t", + "an aly", + "ana ly", + "anal y", + "ĠHome land", + "ĠHom eland", + "Ġpen etr", + "Ġpenet r", + "udden ly", + "ĉ element", + "ĉe lement", + "ĉel ement", + "ĉelem ent", + "ĠB ren", + "ĠBr en", + "ĠBre n", + "ĠTr udeau", + "ĠCub an", + "ĠCu ban", + "ĠCuba n", + "j am", + "ja m", + "us lim", + "_ ev", + "_e v", + "Ġs tems", + "Ġst ems", + "Ġste ms", + "Ġstem s", + "} %", + "Ŀ å§ĭ", + "Ġbr anding", + "Ġbrand ing", + "Ġbran ding", + "Ġcorrespond ence", + ". jquery", + ".j query", + "¢ åįķ", + "ĠRe ads", + "ĠRead s", + "(Http StatusCode", + "(HttpStatus Code", + "as sin", + "ass in", + "assi n", + "( slot", + "(s lot", + "(sl ot", + "ĠGrad uate", + "// /<", + "/// <", + "Ġinformation s", + "Ġinform ations", + "Ġinformat ions", + "EN ABLE", + "ENA BLE", + "Ġp uis", + "Ġpu is", + "Ġ finder", + "Ġf inder", + "Ġfind er", + "Ġfin der", + "Ġfi nder", + "Ġfinde r", + "ĠB ris", + "ĠBr is", + "ĠBri s", + "Ġnett steder", + "_ mid", + "_m id", + "_mi d", + "Ġ ogs", + "Ġo gs", + "Ġog s", + "ĠSter ling", + "Ġar rog", + "Ġarr og", + "str ftime", + "| ĊĊ", + "|Ċ Ċ", + "Ġ vox", + "Ġv ox", + "Ġvo x", + "Ġ Regardless", + "ĠReg ardless", + "Ġ eso", + "Ġe so", + "Ġes o", + "Ġ Comfort", + "ĠCom fort", + ".Boolean Field", + "Ġ uh", + "Ġu h", + "A CY", + "AC Y", + "Ġsque ez", + "ĠV ic", + "ĠVi c", + "con tro", + "cont ro", + "contr o", + ". lo", + ".l o", + "Ġ ire", + "Ġi re", + "Ġir e", + "ĠCom edy", + "ĠCome dy", + "ë ¶", + "Ġorig inated", + "Ġorigin ated", + "Ġoriginate d", + "Ġ shipment", + "Ġsh ipment", + "Ġship ment", + "| max", + "|m ax", + "_ guid", + "_g uid", + "_gui d", + "le vation", + "lev ation", + "н аÑı", + "на Ñı", + "( undefined", + "(un defined", + "Ġ DDR", + "ĠD DR", + "ĠDD R", + "Ġshoot ings", + "Ġshooting s", + "ĠLat ino", + "ĠLatin o", + "END OR", + "Ġaver aging", + "Ġgre eted", + "Ġgreet ed", + "Ġthe aters", + "Ġtheater s", + "Ġtheat ers", + "о е", + "оРµ", + "Ġ dB", + "Ġd B", + "Ġ gst", + "Ġg st", + "Ġgs t", + "Ġde finite", + "Ġdef inite", + "Ġdefinit e", + "Ġdefin ite", + ". Storage", + ".St orage", + ". her", + ".h er", + ".he r", + "Ġa fore", + "Ġaf ore", + "Ġ Reality", + "ĠRe ality", + "ĠReal ity", + "ĠG ods", + "ĠGod s", + "ĠGo ds", + "v ersed", + "ver sed", + "vers ed", + "verse d", + "Ġhand some", + "Ġhands ome", + "Ġ excluding", + "Ġex cluding", + "Ġexcl uding", + "( ad", + "(a d", + "Qu otes", + "Quote s", + "Ġ Scheme", + "ĠS cheme", + "ĠSch eme", + "ĠSche me", + "? q", + "ĠT amil", + "ĠTa mil", + "ĠTam il", + "T icks", + "Tick s", + "Ti cks", + "Ġ pest", + "Ġp est", + "Ġpe st", + "Ġpes t", + "' n", + "Ġporn ography", + "_ modal", + "_m odal", + "_mod al", + "_mo dal", + "Ġ ----------", + "Ġ- ---------", + "Ġ-- --------", + "Ġ---- ------", + "Ġ--- -------", + "Ġ----- -----", + "Ġ------ ----", + "Ġ-------- --", + "Ġ------- ---", + "Ġ--------- -", + "Ġd isposable", + "Ġdis posable", + "Ġdispos able", + "F REE", + "FR EE", + "Ġsh ark", + "Ġsha rk", + "Ġshar k", + "C HE", + "CH E", + "Ġdep icted", + "Ġdepict ed", + "Ġdemonstr ations", + "Ġdemonstration s", + "ĠK illed", + "ĠKill ed", + "ĠKil led", + "Ġ RULE", + "ĠR ULE", + "ĠRU LE", + "Ġobs essed", + "Ġobsess ed", + "Ġs implified", + "Ġsimpl ified", + "Post al", + "Pos tal", + "Po stal", + "Ġconcept ual", + "Ġ pst", + "Ġp st", + "Ġps t", + "L as", + "La s", + "_ PROJECT", + "_PRO JECT", + "uc ceeded", + "ucceed ed", + "o lu", + "ol u", + "ÄŁ i", + "Ġpersonal ities", + "Ġ reshape", + "Ġre shape", + "Ġres hape", + "Ġresh ape", + "Ġen closed", + "Ġenc losed", + "ĉ ptr", + "ĉp tr", + "ĉpt r", + "Ġt utorials", + "Ġtutorial s", + "Ġtutor ials", + "Ġexpl oded", + "Ġexplo ded", + "Ġexplode d", + "_ DIRECTORY", + "_DIRECT ORY", + "åĨħ 容", + "Ġc anon", + "Ġcan on", + "Ġca non", + "Ġrecogn ise", + "P AD", + "PA D", + "Ġ Approx", + "ĠApp rox", + "ĠAp prox", + "ĠAppro x", + "Ġ Restore", + "ĠRe store", + "ĠRest ore", + "Ġ Important", + "ĠImport ant", + "Ġhe avier", + "Ġheav ier", + ". Sequential", + ".Se quential", + "E arth", + "Ear th", + "ĠM ilk", + "ĠMil k", + "ĠMi lk", + ".set Request", + ". tem", + ".t em", + ".te m", + "Ġre construct", + "Ġrecon struct", + "Ġskept ical", + "Ġskeptic al", + "_ Private", + "_Pr ivate", + "B UF", + "BU F", + "q ua", + "qu a", + ": a", + "Ġ sek", + "Ġs ek", + "Ġse k", + "Ġd well", + "Ġdw ell", + "o ssa", + "os sa", + "oss a", + "Ġreward ed", + "Ġrew arded", + "и й", + "( topic", + "(t opic", + "(to pic", + "(top ic", + "_ partition", + "_part ition", + "Ġ__ ________________", + "Ġ______ ____________", + "Key words", + "Keyword s", + "ĠFr anco", + "ĠFranc o", + "ĠFran co", + "L ite", + "Li te", + "Lit e", + "Ġn aken", + "Ġna ken", + "Ġnak en", + "Ġ за", + "Ġз а", + "O BJECT", + "OB JECT", + "OBJ ECT", + "Ġcraft s", + "Ġcra fts", + "Ġ Swap", + "ĠS wap", + "ĠSw ap", + ".X na", + ". Connect", + ".Con nect", + ".Conn ect", + "Ġbalcon y", + "( real", + "(re al", + "ĠBar nes", + "ĠBarn es", + "b ir", + "bi r", + "Ġ Twenty", + "ĠTw enty", + "ĠTwe nty", + "a yan", + "ay an", + "aya n", + "at ars", + "ata rs", + "atar s", + "ĠPro pel", + "ĠProp el", + "ĠIh nen", + "Up grade", + "Ġc urb", + "Ġcur b", + "Ġcu rb", + "- second", + "-se cond", + "Ġn eph", + "Ġne ph", + "Ġnep h", + ". pres", + ".p res", + ".pre s", + ".pr es", + "ìŀ ħ", + ". seq", + ".s eq", + ".se q", + "Ġp added", + "Ġpad ded", + "Ġpadd ed", + "\" ?", + "j l", + "ãĥ ¬", + "' ) a", + "Co ordinates", + "Coordinate s", + "Ġen acted", + "Ġenact ed", + "EN TS", + "ENT S", + "Ġ lac", + "Ġl ac", + "Ġla c", + ". final", + ".f inal", + ".fi nal", + ".fin al", + "ĠPhp Storm", + "c alled", + "cal led", + "call ed", + "Ġin quiries", + ". middleware", + ".m iddleware", + ".middle ware", + "ĠD owntown", + "ĠDown town", + "/ ';Ċ", + "/' ;Ċ", + "Ġkil omet", + "ac cel", + "acc el", + "Ġqu ien", + "Ġq uien", + "Ġqui en", + "w string", + "ws tring", + "set Data", + "Ġman era", + "Ġmane ra", + "Ġmod ular", + "r imp", + "ri mp", + "rim p", + "Ġtar iffs", + "Ġtariff s", + "Ġtarif fs", + "âĢĻ il", + "âĢĻi l", + "_TH ROW", + "/ color", + "/c olor", + "/co lor", + "Ġ HTMLElement", + "ĠHT MLElement", + "ĠHTML Element", + "Ġc arro", + "Ġcar ro", + "Ġcarr o", + "Ġpr ere", + "Ġpre re", + "Ġprer e", + "Ġplot ting", + "Ġ Positive", + "ĠPos itive", + "ĠM achines", + "ĠMachine s", + "ĠMach ines", + "O TES", + "OT ES", + "OTE S", + "á» Ľ", + "ple asant", + "Ġ alte", + "Ġa lte", + "Ġal te", + "Ġalt e", + "Ġa inda", + "Ġai nda", + "Ġain da", + "th ese", + "the se", + "thes e", + "Ġ cors", + "Ġc ors", + "Ġco rs", + "Ġcor s", + "i pay", + "ip ay", + "ipa y", + "ĠAdv isory", + "ĠAdvis ory", + "ĠAdvisor y", + "ĠRub io", + "ĠRu bio", + "j q", + "Ġl imestone", + "Ġlim estone", + "Ġlime stone", + "Ġdet ached", + "Ġdetach ed", + "设 ç½®", + "t enant", + "te nant", + "ten ant", + "Ġ Depth", + "ĠDe pth", + "ĠDep th", + "ĠDept h", + "a lore", + "al ore", + "alo re", + "ĠÑģÑĤ ÑĢок", + "ĠÑģÑĤÑĢ ок", + "ĠÑģÑĤÑĢо к", + "Ġ FORE", + "ĠF ORE", + "ĠFOR E", + "ĠFO RE", + "ĠL ay", + "ĠLa y", + "p resentation", + "present ation", + ") ');Ċ", + ")' );Ċ", + ".sub plots", + ".subplot s", + "Ï ĥ", + "N OW", + "NO W", + "G ar", + "Ga r", + "h andles", + "handle s", + "hand les", + "a bra", + "ab ra", + "abr a", + "put ies", + "pu ties", + "ĠElect rical", + "ĠElectric al", + "M iddle", + "Mid dle", + "r opic", + "ro pic", + "rop ic", + "Ġ JD", + "ĠJ D", + "Ġ Dyn", + "ĠD yn", + "ĠDy n", + "ĠB ristol", + "ĠBr istol", + "ĠBris tol", + "ĠMc Carthy", + "ĠMcCart hy", + "Ġstr iker", + "Ġstri ker", + "Ġstrike r", + "Ġenum erable", + "Ġenumer able", + "ĠE van", + "ĠEv an", + "ĠEva n", + ". defaults", + ".default s", + "qu ences", + "que nces", + "quence s", + ") ||", + ")| |", + "ĉ token", + "ĉt oken", + "ĉto ken", + "â Ĺı", + "âĹ ı", + "- dropdown", + "-d ropdown", + "-drop down", + "ST ORE", + "Ġ Graphic", + "ĠG raphic", + "ĠGraph ic", + "( pp", + "(p p", + "Ex pl", + "Exp l", + "Ġup wards", + "Ġupward s", + "ĠD istributed", + "ĠDistrib uted", + "Ġ WEB", + "ĠW EB", + "ĠWE B", + "J er", + "Je r", + "is NaN", + "çĶŁ æĪIJ", + "> R", + "üss en", + "üs sen", + "e fs", + "ef s", + "Ġun cover", + "Ġunc over", + "Ġl ud", + "Ġlu d", + ". calculate", + ".c alculate", + ".cal culate", + ".calc ulate", + "Ġ intptr", + "Ġint ptr", + "Ġmidfield er", + ". Headers", + ".Header s", + ".He aders", + ".Head ers", + "Ġ mf", + "Ġm f", + "e ref", + "er ef", + "ere f", + ". Metro", + ".M etro", + ".Me tro", + "Ġ Speaking", + "ĠSpe aking", + "ĠSpeak ing", + ": b", + "Ġcryptoc urrencies", + "Ġd emons", + "Ġde mons", + "Ġdem ons", + "Ġdemon s", + "Ġdemo ns", + "ĉ EXPECT", + "Ġw icked", + "y outube", + "you tube", + "youtu be", + ": Int", + ":I nt", + "ĠH indi", + "ĠHind i", + "ĠHin di", + "Ġ CAT", + "ĠC AT", + "ĠCA T", + "Ġ ع", + "ĠØ ¹", + "r ar", + "ra r", + "o more", + "om ore", + "omo re", + "omor e", + "/ per", + "/p er", + "/ license", + "/lic ense", + "/l icense", + "Ġre im", + "Ġa waiting", + "Ġawait ing", + "Ġle thal", + "Ġlet hal", + "Ġleth al", + "Ġ EF", + "ĠE F", + "r ounded", + "ro unded", + "round ed", + "ĠPl atinum", + "Ġв Ñģе", + "ĠвÑģ е", + ". coords", + ".co ords", + ".coord s", + ". Device", + ".D evice", + ".De vice", + ".Dev ice", + "/ item", + "/i tem", + "Ġ Wenn", + "ĠW enn", + "ĠWe nn", + "ĠWen n", + "compile Components", + "ĠK inder", + "ĠKind er", + "ĠKi nder", + "ĠKin der", + ".remove Item", + "Ġ anda", + "Ġa nda", + "Ġand a", + "Ġan da", + "b nb", + "bn b", + "Ġ pra", + "Ġp ra", + "Ġpr a", + "( transaction", + "(trans action", + "Ġembarrass ing", + "ĉ BOOL", + ".content View", + "Ġevent data", + "at ore", + "ator e", + "ato re", + "Ġprovided In", + "ir ma", + "irm a", + "Ġz ona", + "Ġzo na", + "_ HW", + "_H W", + "æ Ļ", + "Ġst ove", + "Ġsto ve", + "Ġcounter part", + "_ Product", + "_Pro duct", + "_MAN AGER", + "Ġinf ring", + "Ġinfr ing", + "Ġ ERA", + "ĠE RA", + "ĠER A", + "_ party", + "_p arty", + "_part y", + "_par ty", + "Ñ ij", + "Ġin ici", + "Ġi nici", + "Ġini ci", + "_ Request", + "_Re quest", + "Ġmir acle", + "Ġmirac le", + "Ġcancel Button", + "S py", + "Sp y", + "at ó", + "Ġpol ish", + "Ġpo lish", + "Ġpolis h", + "ĠNic ole", + "ĠNi cole", + "ĠNico le", + "ĠNicol e", + ". displayName", + ".display Name", + "\\ Requests", + "\\Request s", + "Ġuse History", + "Router Module", + "Ġst ared", + "Ġstar ed", + "Ġsta red", + "Ġstare d", + "I DER", + "ID ER", + "IDE R", + "Ñĥнк ÑĨи", + "Ġ nota", + "Ġn ota", + "Ġnot a", + "Ġno ta", + "$ arr", + "$a rr", + "$ar r", + "pec ified", + "Ġt opp", + "Ġto pp", + "Ġtop p", + "_DR IVER", + "_DRIVE R", + "/ ng", + "/n g", + "å ł", + "_ tm", + "_t m", + "% timeout", + "< s", + "Ġ (*)", + "Ġ( *)", + "Ġ(* )", + "Ġ HttpRequest", + "ĠHttp Request", + "_ TRACK", + "_TR ACK", + "_TRA CK", + "( note", + "(n ote", + "(not e", + "(no te", + "Ġ Explore", + "ĠExp lore", + "ĠExpl ore", + "_ serv", + "_s erv", + "_se rv", + "_ser v", + "Ġ ç»", + "Ġç »", + "B inder", + "Bind er", + "Bin der", + "Bi nder", + "+ \",", + "+\" ,", + ". att", + ".a tt", + ".at t", + "ĠEth i", + "ĠEt hi", + "Ġc ódigo", + "= '\\", + "=' \\", + ". lines", + ".l ines", + ".line s", + ".li nes", + ".lin es", + "( Of", + "(O f", + "å° Ĩ", + "miss ible", + "Ġ vé", + "Ġv é", + "Ġac oustic", + "Ġcraft ing", + "n it", + "ni t", + ". ba", + ".b a", + "ĠLuc y", + "ĠLu cy", + "Ġi Pod", + "ĠiP od", + "Ġpup ils", + "Ġpupil s", + "- max", + "-m ax", + "_ wr", + "_w r", + "( cp", + "(c p", + "Ġ REPORT", + "ĠRE PORT", + "ĠREP ORT", + "Ġ dns", + "Ġd ns", + "Ġdn s", + "Ġ References", + "ĠRe ferences", + "ĠReference s", + "ĠRefer ences", + "Ġunder taken", + "Ġundert aken", + "Ġundertake n", + "Ġkø benhavn", + "Ġ chai", + "Ġc hai", + "Ġch ai", + "Ġcha i", + "ĠC roat", + "ĠCro at", + "_ Log", + "_L og", + "r owned", + "row ned", + "rown ed", + "_ med", + "_m ed", + "_me d", + "ĉ date", + "ĉd ate", + "# __", + "Ġcost umes", + "Ġcostume s", + "Ġ Requires", + "ĠRe quires", + "ĠRequire s", + "aff le", + "ç Ĭ¶æĢģ", + "çĬ¶ æĢģ", + "-S emit", + "-Se mit", + "ela ide", + "еÑĤ од", + "Ġp estic", + "Ġpes tic", + "Ġpest ic", + "Ġ dra", + "Ġd ra", + "Ġdr a", + "D OCUMENT", + "DOC UMENT", + "Ġ ...čĊ", + "Ġ... čĊ", + "Ġ.. .čĊ", + "} `}Ċ", + "}` }Ċ", + "}`} Ċ", + "ĠA uction", + "ĠAu ction", + "Ġ Dock", + "ĠD ock", + "ĠDo ck", + "ĠDoc k", + "xxxx xxxx", + "( getString", + "(get String", + "ħ į", + "Ġborder Width", + "ĠM achinery", + "ĠMachine ry", + "ĠMach inery", + "Ġpredict able", + "Ġpredic table", + ". SH", + ".S H", + "Ġam plitude", + "Ġampl itude", + ".for Root", + "I Navigation", + "IN avigation", + "Table Model", + "at trib", + "att rib", + "attr ib", + "Ġmaneu ver", + "Ġexc av", + "B ERS", + "BER S", + "BE RS", + "Ġd apat", + "Ġda pat", + "Ġdap at", + "Ġinstall ations", + "Ġinstallation s", + "Ġinstal lations", + ". Async", + ".A sync", + ".As ync", + "Ġ rays", + "Ġr ays", + "Ġra ys", + "Ġray s", + "= âĢĿ", + "; ččĊ", + ". crypto", + ".c rypto", + "_ dbg", + "_d bg", + "_db g", + "Ġ Enumerable", + "ĠEnum erable", + "Of Size", + "_ epochs", + "_epoch s", + "m w", + "M ENU", + "ME NU", + "out line", + "ĠP apers", + "ĠPa pers", + "ĠPaper s", + "ĠPap ers", + "= ===========Ċ", + "== ==========Ċ", + "==== ========Ċ", + "======== ====Ċ", + "=== =========Ċ", + "============ Ċ", + "=========== =Ċ", + "========= ===Ċ", + "========== ==Ċ", + "====== ======Ċ", + "===== =======Ċ", + "======= =====Ċ", + "Ġuniform s", + "Ġuni forms", + "ĠG ig", + "ĠGi g", + "- package", + "-p ackage", + "-pack age", + "ĠJ enkins", + "ĠJen kins", + "Ġ HomePage", + "ĠHome Page", + ". isSelected", + ".is Selected", + "Ġmechan ic", + "Ġmech anic", + "M K", + "Ġ Sounds", + "ĠS ounds", + "ĠSo unds", + "ĠSou nds", + "ĠSound s", + "//---------------------------------------------------------------- -------------Ċ", + "//---------------------------------------------------------------------------- -Ċ", + "Ġresearch ing", + "Ġ infos", + "Ġin fos", + "Ġinfo s", + "Ġinf os", + "o graphics", + "og raphics", + "ograph ics", + "ographic s", + "er set", + "ers et", + "erse t", + "([ '/", + "([' /", + "ĠTim ber", + ". agent", + ".a gent", + ".ag ent", + ".age nt", + ".to JSON", + "_ commands", + "_command s", + "_comm ands", + "p aring", + "par ing", + "pa ring", + "_ adjust", + "_ad just", + "_adj ust", + ". nome", + ".n ome", + ".no me", + ".nom e", + "( glm", + "(g lm", + "(gl m", + "Status Bar", + "file path", + "? âĢĻ", + "Ġdet ective", + "Ġdetect ive", + "Ġuns erer", + "Ġunser er", + "Ġunsere r", + "ĠTi bet", + "ĠTib et", + "EN DED", + "END ED", + "( seed", + "(s eed", + "(se ed", + "Ġsne ak", + "Ġa mor", + "Ġam or", + "Ġamo r", + "=\" //", + "=\"/ /", + "ĠPan thers", + "ĠPanther s", + "all ax", + "alla x", + "ĠL IVE", + "ĠLI VE", + "ĉ DWORD", + "ĉD WORD", + "] =-", + "]= -", + "Ġt ornado", + "Ġtorn ado", + "/ min", + "/m in", + "Ġl ungs", + "Ġlung s", + "Ġlun gs", + "- current", + "-c urrent", + "-cur rent", + "Ġ Booking", + "ĠBo oking", + "ĠBook ing", + "ĠBoo king", + "åĪĹ è¡¨", + "Ġenjoy ment", + "ठ°", + "J A", + "t yped", + "type d", + "ty ped", + "typ ed", + ". Btn", + ".B tn", + "f at", + "fa t", + "u gal", + "ug al", + "uga l", + "Ġ Shares", + "ĠSh ares", + "ĠShare s", + "ĠSha res", + "ĠShar es", + "Ġdis gr", + "Ġdisg r", + "Ġ BAR", + "ĠB AR", + "ĠBA R", + "Ġ FOX", + "ĠF OX", + "ĠFO X", + "Op code", + "Ġ Sz", + "ĠS z", + "key down", + "iction aries", + "Ġdet ailing", + "Ġdetail ing", + "} ))Ċ", + "}) )Ċ", + "})) Ċ", + "Ġ pok", + "Ġp ok", + "Ġpo k", + "Ġdemonstr ating", + "Ġ notation", + "Ġn otation", + "Ġnot ation", + "Ġnota tion", + "l ayers", + "la yers", + "lay ers", + "layer s", + "@ if", + "ĠN PR", + "ĠNP R", + ".strict Equal", + "Ġ Recipes", + "ĠRec ipes", + "ĠRecipe s", + ". Tensor", + ".T ensor", + "Ġliqu or", + "Ġdeb ts", + "Ġdebt s", + ". endsWith", + ".end sWith", + ".ends With", + "W heel", + "Wh eel", + ". Pos", + ".P os", + "C SV", + "CS V", + "$ arity", + "$ar ity", + "Ġun stable", + "Ġuns table", + "Ġunst able", + "( loss", + "(l oss", + "(lo ss", + "EN SOR", + "ENS OR", + "Ġel even", + "Ġele ven", + "Ġelev en", + "ĠL opez", + "ĠLo pez", + "ĠHop kins", + "c onom", + "con om", + "co nom", + "cono m", + "ĠS eth", + "ĠSe th", + "ĠSet h", + "Ġpo ems", + "Ġpoem s", + "Q uant", + "Qu ant", + "Ġg sl", + "Ġgs l", + "Ġsy rup", + "Ġs ibling", + "Ġsi bling", + "Ġc ass", + "Ġca ss", + "Ġcas s", + "- vous", + "-v ous", + "ö t", + "_P ATTERN", + "_ SECTION", + "_SE CTION", + "_SEC TION", + "est imated", + "estimate d", + "up grade", + ". mongodb", + ".m ongodb", + ".mongo db", + "ĠBo at", + "_ CTX", + "_C TX", + "_CT X", + "Ġfetch ing", + "Ġfet ching", + "u stin", + "us tin", + "ust in", + "p iel", + "pi el", + "pie l", + "M arg", + "Mar g", + "Ma rg", + "Ref lection", + "Reflect ion", + "Ġ duct", + "Ġd uct", + "Ġdu ct", + "ĠMunicip al", + "Ġ bx", + "Ġb x", + ". GetCurrent", + ".Get Current", + "m link", + "ml ink", + "mlin k", + "ĠAccount ing", + "ĠGen eva", + "ĠGene va", + "_ Pos", + "_P os", + "Ġp asser", + "Ġpass er", + "Ġpas ser", + "Ġpasse r", + "Ġhear ings", + "Ġhearing s", + "com pan", + "comp an", + "Ġfrag ile", + "Initial izer", + "Initialize r", + "w alker", + "walk er", + "wal ker", + ". Material", + ".M aterial", + "ĠH unting", + "ĠHun ting", + "ĠHunt ing", + "try side", + "trys ide", + "Ġ kat", + "Ġk at", + "Ġka t", + "Ġcl erk", + "Ġcle rk", + "Ġcler k", + "á Ł", + "do ing", + "doi ng", + "ĉ group", + "ĉg roup", + "ĉgr oup", + "Ġsan ction", + "Ġsanct ion", + ". lb", + ".l b", + "Ġ Lazy", + "ĠL azy", + "ĠLa zy", + "ĠLaz y", + "Ġ Constraint", + "ĠCon straint", + "ĠConstr aint", + "P agination", + "Pag ination", + "Ġpou vez", + "ĠInd icates", + "M ER", + "ME R", + "Ġc ours", + "Ġco urs", + "Ġcour s", + "Ġcou rs", + "Ġy early", + "Ġyear ly", + "Ġg rosse", + "Ġgro sse", + "Ġgross e", + "Ġgros se", + "abb rev", + "abbr ev", + "Ġ DON", + "ĠD ON", + "ĠDO N", + "Ġpro ceeded", + "Ġproceed ed", + "ent lich", + "Ġ propertyName", + "Ġproperty Name", + "ĠTe aching", + "ĠTea ching", + "ĠTeach ing", + "st adt", + "sta dt", + "stad t", + "Ġc utoff", + "Ġcut off", + "or ners", + "orn ers", + "orne rs", + "Ġa frica", + "Ġaf rica", + "Ġafr ica", + "Ġ renders", + "Ġr enders", + "Ġrender s", + "Ġren ders", + "Ġrend ers", + "ĠYan kees", + "ĠYankee s", + "Ġ Toolbar", + "ĠTool bar", + "s paces", + "sp aces", + "space s", + "spa ces", + ".fill Style", + "Ġseg undo", + "Ġsegu ndo", + "_ strlen", + "_st rlen", + "_str len", + ". Firebase", + ".F irebase", + ".Fire base", + "å¤ Ħ", + "Ġmention ing", + "\\ (", + "ĠVal ve", + "S etter", + "Set ter", + "Ġsp ans", + "Ġspan s", + "Ġspa ns", + "ĠAl cohol", + "Ġ Letters", + "ĠLet ters", + "ĠLetter s", + "\\ xe", + "\\x e", + "Ġ TK", + "ĠT K", + "_ BLE", + "_B LE", + "_BL E", + ". getResult", + ".get Result", + "< Player", + "

\"", + "=> \"", + "t lement", + "tle ment", + "tl ement", + "$ (\"", + "$( \"", + "From String", + "ĠB ild", + "ĠBi ld", + "ĠBil d", + "Ġcon ventions", + "Ġconv entions", + "Ġconvent ions", + "Ġconvention s", + "_ native", + "_n ative", + "_nat ive", + "Ġ Inspector", + "ĠIns pector", + "ĠInsp ector", + "ĠP ist", + "ĠPi st", + "ĠPis t", + "u bar", + "ub ar", + "uba r", + "Ġ regs", + "Ġre gs", + "Ġreg s", + "ĠP ilot", + "ĠPi lot", + "ĠPil ot", + "T hus", + "Th us", + "Thu s", + "> '+", + ">' +", + "Ġ cela", + "Ġc ela", + "Ġce la", + "Ġcel a", + ". news", + ".n ews", + ".new s", + ".ne ws", + "( Product", + "L iving", + "Li ving", + "Liv ing", + "R ussia", + "Russ ia", + "Ġ facet", + "Ġf acet", + "Ġfac et", + "Ġface t", + "e tical", + "et ical", + "etic al", + "eti cal", + "Ġ[ '$", + "Ġ[' $", + "/ [", + "Ġ Dire", + "ĠD ire", + "ĠDi re", + "ĠDir e", + "Ġg ases", + "Ġgas es", + "Ġga ses", + "ĠIN FORMATION", + "Ġ Eat", + "ĠE at", + "ĠEa t", + "ĠFor ums", + "ĠForum s", + "Ġ Characters", + "ĠChar acters", + "ĠCharacter s", + "_ met", + "_m et", + "_me t", + "Ġ ìĭľ", + "Ġìĭ ľ", + "Ġk ings", + "Ġking s", + "Ġkin gs", + "a chie", + "ach ie", + "achi e", + "Ġ Lambda", + "ĠL ambda", + "ĠLamb da", + "Ġt imers", + "Ġtime rs", + "Ġtim ers", + "Ġti mers", + "Ġtimer s", + "ĠL ighting", + "ĠLight ing", + "ĠCas ey", + "ĠCase y", + "ĠCa sey", + "ad dir", + "add ir", + "an dex", + "and ex", + "ande x", + ". answer", + ".an swer", + "Ġ Hip", + "ĠH ip", + "ĠHi p", + "ĠPr incip", + "Start Date", + "Ġ ãĢĮ", + "ĠãĢ Į", + "t res", + "tr es", + "tre s", + "Ġ &#", + "Ġ& #", + ".Max Value", + "ĠPro blems", + "ĠProblem s", + "ĠProb lems", + "ĠProble ms", + "Ġ latex", + "Ġla tex", + "Ġlate x", + "Ġlat ex", + "Of Class", + "ĠL ynn", + "ĠLy nn", + "ĠLyn n", + "/ /'", + "// '", + "Ġvoy age", + "Ġsh uttle", + "Ġshut tle", + "ĠR oller", + "ĠRo ller", + "ĠRoll er", + "ĠRol ler", + "ĠRuntime Error", + "u ya", + "uy a", + "D ic", + "Di c", + "ĉ builder", + "ĉb uilder", + "ĉbuild er", + "Ġbul lying", + "Ġbull ying", + "Ġbully ing", + "Ġsimple st", + "Ġsimp lest", + "Ġsimpl est", + "Ġsimples t", + ". called", + ".c alled", + ".call ed", + ".cal led", + "Ġ LR", + "ĠL R", + "Ġmor ality", + "Ġmoral ity", + "Ġst urdy", + "tr acking", + "track ing", + ". swagger", + ".sw agger", + "_ BIND", + "_B IND", + "_BIN D", + "I TOR", + "IT OR", + "ITO R", + "-url encoded", + "Ġ Ñħ", + "ĠÑ ħ", + "ĠTr inity", + "Ġtr aps", + "Ġtra ps", + "Ġtrap s", + "Ġ |-", + "Ġ| -", + "Ġ setText", + "Ġset Text", + "Ġbar gain", + "Ġbarg ain", + "Ġbr akes", + "Ġbra kes", + "Ġbrake s", + ". getCode", + ".get Code", + ".g etCode", + ".getC ode", + "Ġm igrate", + "Ġmigr ate", + "Ġmig rate", + "Ġ ribbon", + "Ġr ibbon", + "Ġrib bon", + ") return", + ")r eturn", + "Ġ charger", + "Ġch arger", + "Ġchar ger", + "Ġcharg er", + "Ġcharge r", + "a com", + "ac om", + "aco m", + "ADI US", + "ĠAmb assador", + "- after", + "-a fter", + "Ġ anni", + "Ġan ni", + "Ġann i", + "ĉ spin", + "ĉs pin", + "ĉsp in", + "Con cept", + "ĠHend erson", + "Ġ HOST", + "ĠH OST", + "ĠHO ST", + ". rank", + ".r ank", + ".ra nk", + "ĠNorth east", + "ĠNor theast", + "Ġber lin", + "Ġre quis", + "Ġreq uis", + "Ġrequ is", + ". feed", + ".f eed", + ".fe ed", + "Ġsource Mapping", + "ĠRen contre", + ". ajax", + ".a jax", + "nest js", + "Ġt rek", + "Ġtr ek", + "Ġtre k", + "ĠN acional", + "Ġ& [", + "Ġpay able", + "or tex", + "ort ex", + "orte x", + "Ġ dept", + "Ġd ept", + "Ġde pt", + "Ġdep t", + "field Name", + "Ġcomp letes", + "Ġcomple tes", + "Ġcomplet es", + "Ġcompl etes", + "Ġcomplete s", + "ĠR VA", + "ĠRV A", + "Ġon ions", + "Ġonion s", + "al ignment", + "align ment", + "Form ats", + "Format s", + "Ġ' {$", + "Ġ'{ $", + "Hash Set", + "ĠB od", + "ĠBo d", + ".Invariant Culture", + "Ġsett lements", + "Ġsettlement s", + "Ġsettle ments", + "Ġ hydr", + "Ġhy dr", + ". updated", + ".update d", + ".up dated", + "v enth", + "ve nth", + "vent h", + "ven th", + "( seconds", + "(se conds", + "(second s", + "(sec onds", + "=\" /\"", + "=\"/ \"", + "Ġweb page", + "( ĊĊ", + "(Ċ Ċ", + "Ġ tir", + "Ġt ir", + "Ġti r", + "Ġt oes", + "Ġto es", + "Ġtoe s", + "ĠB rick", + "ĠBr ick", + "ĠBri ck", + "Ġamb ition", + "Ġambit ion", + "P ot", + "Po t", + "= max", + "=m ax", + "E TIME", + "ET IME", + "Ġde pot", + "Ġdep ot", + "c alls", + "cal ls", + "call s", + "ĠNor wegian", + "` :", + "Ġ burger", + "Ġb urger", + "Ġbur ger", + "Ġburg er", + "Ġburge r", + "Ġprofessor s", + "Ġprofess ors", + "Ġ Allocate", + "ĠAl locate", + "ĠAll ocate", + "ĠAlloc ate", + "-third s", + "- chart", + "-c hart", + "-ch art", + "-char t", + "Ġ ford", + "Ġf ord", + "Ġfor d", + "Ġfo rd", + "* N", + ".k otlin", + "Ġpaper work", + "Ġ DEVICE", + "ĠDE VICE", + "ĠDEV ICE", + "% @\",", + "%@ \",", + "re spect", + "res pect", + "resp ect", + "( mp", + "(m p", + "é «ĺ", + "é« ĺ", + "- if", + "-i f", + "Ġcush ion", + "o bot", + "ob ot", + "obo t", + "Ġp arc", + "Ġpar c", + "Ġpa rc", + "S PACE", + "SP ACE", + "SPA CE", + "ĠNet anyahu", + "Ġself ish", + "Ġsel fish", + "f eat", + "fe at", + "fea t", + "Ġ clientes", + "Ġcl ientes", + "Ġclient es", + "Ġcli entes", + "Ġcliente s", + "- tools", + "-t ools", + "-to ols", + "-tool s", + "-too ls", + "Ġp orch", + "Ġpo rch", + "Ġpor ch", + "Ġ jq", + "Ġj q", + ". verbose", + ".ver bose", + "Ġlib erals", + "Ġliberal s", + "Ġliber als", + "] )ĊĊĊ", + "]) ĊĊĊ", + "])Ċ ĊĊ", + "])ĊĊ Ċ", + "p ies", + "pi es", + "pie s", + "Not Blank", + "( term", + "(t erm", + "(te rm", + "È Ľi", + "ÈĽ i", + "_ Params", + "_Param s", + ". normalize", + ".normal ize", + "B ullet", + "AS IC", + "ASI C", + "( hex", + "(h ex", + "_ cliente", + "_cl iente", + "_client e", + "_cli ente", + "+ ,", + "_ DI", + "_D I", + "Ġforth coming", + "} \")]Ċ", + "}\" )]Ċ", + "}\") ]Ċ", + "s eo", + "se o", + "U m", + "> Name", + ">N ame", + "Ġcomfort ably", + "irect ional", + "irection al", + "W ITH", + "WI TH", + "/ pr", + "/p r", + "Ġ Poor", + "ĠP oor", + "ĠPo or", + "ĠV itamin", + "ĠVit amin", + "ĠVita min", + "v ic", + "vi c", + "G H", + "Ġprior it", + "Ġprio rit", + "Ġ NN", + "ĠN N", + "Ġ Closed", + "ĠC losed", + "ĠCl osed", + "ĠClose d", + "ĠClo sed", + "¤ í", + "Ġ isOpen", + "Ġis Open", + "\\ Console", + "And Feel", + ". SUCCESS", + ".S UCCESS", + "_OPER ATION", + "p olation", + "po lation", + "pol ation", + "ĠT as", + "ĠTa s", + "p sz", + "ps z", + "> '.", + ">' .", + "C URRENT", + "V endor", + "host s", + "ho sts", + "hos ts", + "ĠE rd", + "ĠEr d", + ">tag ger", + ">t agger", + "ĠsourceMapping URL", + "Ġmar athon", + "_ closed", + "_c losed", + "_cl osed", + "_close d", + "Ġex emption", + "Ġexem ption", + "Ġexempt ion", + "Ġexemp tion", + "Ġrecogn izes", + "Ġrecognize s", + "ide show", + "ides how", + "' $", + "(' /');Ċ", + "('/ ');Ċ", + "('/') ;Ċ", + "m its", + "mit s", + "mi ts", + "wa rz", + "war z", + "ĠCh erry", + "ĠCher ry", + "µ ¬", + "n or", + "no r", + "p orte", + "port e", + "por te", + "Ġ wl", + "Ġw l", + "_ backup", + "_back up", + ".get Boolean", + ". getResource", + ".get Resource", + "Ġdefinit ive", + "Ġdefin itive", + ". EditText", + ".Edit Text", + "Ġ sÃŃ", + "Ġs ÃŃ", + ". CONT", + ".C ONT", + ".CON T", + ".CO NT", + "Ġ PLAYER", + "ĠPL AYER", + "ĠPLAY ER", + ". cards", + ".c ards", + ".card s", + ".car ds", + "ĠSh ore", + "ĠSho re", + "(' /')Ċ", + "('/ ')Ċ", + "('/') Ċ", + "cl uir", + "Web Driver", + "( month", + "(m onth", + "(mon th", + "- release", + "-r elease", + "-re lease", + "-rel ease", + "Ġins pector", + "Ġinspect or", + "Ġinsp ector", + "å £", + "Ġ NF", + "ĠN F", + "_ clip", + "_c lip", + "_cl ip", + "_cli p", + "å ŃIJ", + "åŃ IJ", + "Ġinter acting", + "Ġinteract ing", + ". tmp", + ".t mp", + ".tm p", + "Ġ '''ĊĊ", + "Ġ'' 'ĊĊ", + "Ġ'''Ċ Ċ", + "Ġ''' ĊĊ", + "Ġ dee", + "Ġd ee", + "Ġde e", + "Ġf rost", + "Ġfr ost", + "Ġfro st", + "\" ]))Ċ", + "\"] ))Ċ", + "\"]) )Ċ", + "\"])) Ċ", + "Ġ Places", + "ĠP laces", + "ĠPl aces", + "ĠPlace s", + "ĠPla ces", + "Th rows", + "Throw s", + "Thr ows", + "f ork", + "fo rk", + "for k", + "/ day", + "/d ay", + "i Phone", + "Ġ MIC", + "ĠM IC", + "ĠMI C", + "Ġf olding", + "Ġfol ding", + "Ġfold ing", + "Ġcr ore", + "Ġcro re", + "ĠCh iefs", + "ĠChief s", + "ĠChi efs", + "pher ical", + "pheric al", + "phe rical", + "( price", + "(p rice", + "(pr ice", + ".Write String", + "Ġex iting", + "Ġexit ing", + "] ',Ċ", + "]', Ċ", + "]' ,Ċ", + "ight ing", + "igh ting", + "Ing redient", + "( vertex", + "(ver tex", + "Ġ scrollView", + "Ġs crollView", + "Ġscroll View", + "h f", + ": new", + ":n ew", + "S EN", + "SE N", + "s ector", + "se ctor", + "sec tor", + "sect or", + "Ġs pins", + "Ġsp ins", + "Ġspin s", + "Ġspi ns", + "Ġ Scheduler", + "ĠS cheduler", + "ĠSchedule r", + "o techn", + "ot echn", + "ote chn", + "otech n", + "otec hn", + "sem icolon", + "semi colon", + "Font OfSize", + "ĠSpecific ally", + "fl amm", + ". ObjectId", + ".Object Id", + "Ġc onta", + "Ġcon ta", + "Ġcont a", + "_ permissions", + "_per missions", + "_perm issions", + "_permission s", + "ĉ FROM", + "ĉF ROM", + "I CODE", + "IC ODE", + "ICO DE", + "/ kg", + "/k g", + "ĠHot els", + "ĠHotel s", + "- med", + "-m ed", + "-me d", + "ĠD in", + "ĠDi n", + "Ġn avy", + "Ġna vy", + "Ġnav y", + "get Param", + "Ġm end", + "Ġme nd", + "Ġmen d", + "Ġportray ed", + "ĠMet ropolitan", + "P ainter", + "Paint er", + "Pa inter", + "Ġref erral", + "Ġrefer ral", + "_ good", + "_g ood", + "_go od", + "Ġmar vel", + "os aic", + "osa ic", + "> (&", + ">( &", + ". ur", + ".u r", + "Ġes tos", + "Ġest os", + "Ġesto s", + "Will iam", + "Ġtim ber", + "Ġquel ques", + "Ġquelque s", + "Ġ Documents", + "ĠDocument s", + "ĠDoc uments", + ".X aml", + "Ġb atches", + "Ġbatch es", + "Ġbat ches", + "éģ ĵ", + "Ġ Released", + "ĠRe leased", + "ĠRelease d", + "T ail", + "Ta il", + "Tai l", + "CO OKIE", + "h eid", + "he id", + "hei d", + "_ station", + "_st ation", + "_stat ion", + "_sta tion", + "Ġ Via", + "ĠV ia", + "ĠVi a", + "S ale", + "Sal e", + "Sa le", + "Ġ Repeat", + "ĠRe peat", + "ĠRep eat", + "Ġpro min", + "Ġpr omin", + "Ġprom in", + "ĠZ o", + "- forward", + "-for ward", + "Ġ Ion", + "ĠI on", + "ĠIo n", + "it ary", + "ita ry", + "itar y", + "Ġj us", + "Ġju s", + "- request", + "-re quest", + "Ġproud ly", + "Ġ Streaming", + "ĠStream ing", + "ĠStre aming", + "( MouseEvent", + "(Mouse Event", + "ĠS print", + "ĠSp rint", + "ĠSpr int", + "_ rotation", + "_r otation", + "_rot ation", + "Re positories", + "Ġt art", + "Ġta rt", + "Ġtar t", + "ĠÑģ в", + "Ġm appings", + "Ġmapping s", + "è ª", + "C u", + "C ycle", + "Cy cle", + "Ġb un", + "Ġbu n", + "ĉ lua", + "ĉl ua", + "ãĥ ī", + "Ġ( (!", + "Ġ(( !", + "Ġcollect ively", + "Ġcollective ly", + "Ġ Cond", + "ĠC ond", + "ĠCon d", + "ĠCo nd", + "Ġws zyst", + "Ġwsz yst", + "( lib", + "(l ib", + "(li b", + "openh agen", + "_ skip", + "_s kip", + "_sk ip", + ".Column Header", + "é Ĥ", + "perience d", + "peri enced", + "ı è¿°", + "_ props", + "_p rops", + "_pro ps", + "_pr ops", + "_prop s", + "Ġcon trace", + "Ġcont race", + "Ġcontr ace", + "Ġcontra ce", + "Ġmatch up", + "ab etic", + "abe tic", + "abet ic", + ". members", + ".m embers", + ".member s", + ".mem bers", + "R ECT", + "RE CT", + "REC T", + "( dat", + "(d at", + "(da t", + "Ġs og", + "Ġso g", + "re nom", + "ren om", + "reno m", + "_ Method", + "_M ethod", + "Custom ers", + "Customer s", + "ful lname", + "full name", + "Z N", + "r etry", + "re try", + "ret ry", + "Ġ kap", + "Ġk ap", + "Ġka p", + "ĠN eu", + "ĠNe u", + "è Ĭ", + "add Child", + "will Return", + "_ permalink", + "_p ermalink", + "_per malink", + "_perm alink", + "Ġenerg etic", + "Ġener getic", + "ĠW et", + "ĠWe t", + "ĠM orr", + "ĠMo rr", + "ĠMor r", + "Ġ gcd", + "Ġg cd", + "Ġgc d", + "co unts", + "count s", + "cou nts", + ", type", + ",t ype", + "d ig", + "di g", + "( Login", + "(Log in", + "Ġcr acks", + "Ġcrack s", + "Ġcra cks", + "Ġb acterial", + "Ġbacter ial", + "Ġbacteria l", + "ĠM eat", + "ĠMe at", + "ĠArm strong", + "ĠBro nze", + "ĠBron ze", + "Ġapprox imate", + "_ dirs", + "_d irs", + "_dir s", + "_di rs", + "l iga", + "li ga", + "lig a", + "ÅĤ ad", + "ÅĤa d", + "Ġkind ness", + "Ġ contre", + "Ġcon tre", + "Ġcont re", + "Ġcontr e", + "ĠE VERY", + "ĠEV ERY", + "ĠEVER Y", + "ĠEVE RY", + "M ET", + "ME T", + "Ġann ouncements", + "Ġannounc ements", + "Ġannouncement s", + "Ġannounce ments", + "g pio", + "gp io", + "ĠWaitFor Seconds", + "ĠPhoto shop", + "ĠPhotos hop", + "Ġdis contin", + "/ dd", + "/d d", + "Ġtop ology", + "Ġtopo logy", + "an ical", + "ani cal", + "anic al", + ". interface", + ".inter face", + "auc oup", + ". HashSet", + ".Hash Set", + "ARI ANT", + "( routes", + "(r outes", + "(route s", + "(ro utes", + "ĠT eh", + "ĠTe h", + "Ġh ype", + "Ġhy pe", + "Ġhyp e", + "] \").", + "]\" ).", + "]\") .", + "Ġs lam", + "Ġsl am", + "Ġsla m", + "Ġbr oth", + "Ġbro th", + "- inter", + "-in ter", + "-int er", + "ĠR id", + "ĠRi d", + "- manager", + "-m anager", + "-man ager", + "Cancel ar", + "Ġ Pagination", + "ĠP agination", + "ĠPag ination", + "Ġsound track", + "Ġpos terior", + "Ġpost erior", + "Ġposter ior", + "Ġposte rior", + "Ġsc rub", + "Ġscr ub", + "c reating", + "cre ating", + "cr eating", + "creat ing", + "- *", + "ir teen", + "irt een", + ". dy", + ".d y", + ".s ymmetric", + ".sym metric", + "Ġ \"\".", + "Ġ\" \".", + "Ġ\"\" .", + "= ==============", + "== =============", + "==== ===========", + "======== =======", + "=== ============", + "============ ===", + "============= ==", + "=========== ====", + "============== =", + "========= ======", + "========== =====", + "====== =========", + "===== ==========", + "======= ========", + "Ġch assis", + "ĠnumberOf Rows", + "De veloper", + "Develop er", + "_ bins", + "_b ins", + "_bin s", + "_bi ns", + "Ġ OUR", + "ĠO UR", + "ĠOU R", + "ri eb", + "rie b", + "P ros", + "Pro s", + "Pr os", + "Ġ wiÄĻ", + "Ġw iÄĻ", + "Ġwi ÄĻ", + "\" d", + "Ġasync io", + "ze igen", + "_ spi", + "_s pi", + "_sp i", + ". ALL", + ".A LL", + ".AL L", + "Ġscre ws", + "Ġscr ews", + "Ġscrew s", + "Ch inese", + "Ġ apiKey", + "Ġapi Key", + "Ġun successful", + "ĠSea hawks", + "ĠSeah awks", + "O RG", + "OR G", + "ç« ł", + "Ġprofession ally", + "Ġprofessional ly", + "Ġ Coupon", + "ĠC oupon", + "ĠCo upon", + "ĠCou pon", + "åŃĹ æ®µ", + "Con vention", + "Conv ention", + "Ġpol ym", + "Ġpoly m", + "æī ĭ", + "Ġsal vation", + "Ġsalv ation", + "Ġengine ered", + "Ġengineer ed", + "ĠW rest", + "ĠWr est", + "Ġ GCC", + "ĠG CC", + "ĠGC C", + "Ġwar mer", + "Ġwarm er", + "Layout Constraint", + "Ġag grav", + "Ġagg rav", + "S cripts", + "Script s", + "vent ure", + "ven ture", + "Ġrefriger ator", + "Ġinnov ations", + "Ġinnovation s", + "Ġ Runner", + "ĠR unner", + "ĠRun ner", + "N IC", + "NI C", + "ĠRoll ing", + "ĠRol ling", + "Control Events", + "Ġlo os", + "p ac", + "pa c", + "ĉ panel", + "ĉp anel", + "e fe", + "ef e", + "ĠBudd ha", + "ĠBuddh a", + "- -------------Ċ", + "-- ------------Ċ", + "---- ----------Ċ", + "-------- ------Ċ", + "--- -----------Ċ", + "------------ --Ċ", + "----- ---------Ċ", + "---------- ----Ċ", + "------ --------Ċ", + "----------- ---Ċ", + "------------- -Ċ", + "------- -------Ċ", + "--------- -----Ċ", + "-------------- Ċ", + "åº ĵ", + "(for Key", + "Ġl umin", + "Ġlu min", + "Ġlum in", + "Ġ (?", + "Ġ( ?", + "ĠA IDS", + "ĠAI DS", + ", user", + ",u ser", + "im ientos", + "imiento s", + "content Type", + "ant lr", + "é ¦", + "ĠW elt", + "ĠWe lt", + "ĠWel t", + "P roduction", + "Pro duction", + "Product ion", + "Produ ction", + "Prod uction", + "m ight", + "mi ght", + "ĠV II", + "ĠVI I", + "\" ,(", + "\", (", + "Ġobs erving", + "Ġobserv ing", + "Ġdeliber ate", + "( control", + "(c ontrol", + "(cont rol", + "Ġwith d", + "Ġwit hd", + "Ġse mana", + "Ġsem ana", + "ST ACK", + "STA CK", + "u chen", + "uch en", + "uc hen", + "uche n", + "N ice", + "Ni ce", + "Nic e", + "ĠDeutsch land", + "Ġ Specifies", + "ĠSpec ifies", + "d ma", + "dm a", + "iz io", + "izi o", + "ĠF acts", + "ĠFac ts", + "ĠFa cts", + "ĠFact s", + "_ popup", + "_p opup", + "_pop up", + "ĠDirect ors", + "ĠDirector s", + "ĠDir ectors", + "ĠDire ctors", + "{ :", + "[ R", + "ĠÑį леменÑĤ", + "ĠÑįлем енÑĤ", + "Ġ plat", + "Ġp lat", + "Ġpl at", + "Ġpla t", + "Ġdirect ing", + "ä¸ ī", + "ĠGil bert", + "âĢ¦ .ĊĊ", + "âĢ¦. ĊĊ", + ". qml", + ".q ml", + "Ġthere after", + "Ġdis position", + "Ġdisp osition", + "Ġdispos ition", + "Ġdisposit ion", + "d raft", + "dr aft", + "dra ft", + "Ġs urgeon", + "Ġsurg eon", + "Ġsurge on", + "ĠIns ider", + "ĠInside r", + "Bl end", + "ĠT rev", + "ĠTr ev", + "ĠTre v", + "tr insic", + "tri nsic", + "To pics", + "Top ics", + "Topic s", + "r ieve", + "ri eve", + "rie ve", + "_ FILENAME", + "_FILE NAME", + "Ġaut res", + "Ġau tres", + "Ġautre s", + "J ose", + "Jo se", + "Jos e", + "Pro ducer", + "Produ cer", + "Prod ucer", + "e rus", + "er us", + "eru s", + "Ġpe tit", + "Ġpet it", + "Ġ NEXT", + "ĠN EXT", + "ĠNE XT", + "Ġ Filters", + "ĠF ilters", + "ĠFilter s", + "ĠFil ters", + "Ġrep licate", + "Ġrepl icate", + "Ġreplic ate", + "Ġreplica te", + "\" ]).", + "\"] ).", + "\"]) .", + "Ġl enders", + "Ġlen ders", + "Ġlend ers", + "Ġlender s", + "] \",Ċ", + "]\", Ċ", + "]\" ,Ċ", + "; charset", + "Cpp Object", + "Ġfl oral", + "Ġflo ral", + "Ġflor al", + "Ġflora l", + "Ġ Tipo", + "ĠT ipo", + "ĠTi po", + "ĠTip o", + "Ġcirc uits", + "Ġcircuit s", + "e asy", + "ea sy", + "(& $", + "it ta", + "itt a", + "er yl", + "ery l", + "_ COMMON", + "_COM MON", + "_COMM ON", + "' }}>Ċ", + "'} }>Ċ", + "'}} >Ċ", + "'}}> Ċ", + "-b acked", + "-back ed", + "( variable", + "(var iable", + "( Index", + "(In dex", + "Ġ voir", + "Ġv oir", + "Ġvo ir", + "Ġvoi r", + "_ locations", + "_l ocations", + "_location s", + "_loc ations", + "++ ){", + "++) {", + "ĠLouis ville", + "Ġgr atitude", + "Ġgrat itude", + ".Mock ito", + "ĠP owers", + "ĠPower s", + "ĠPo wers", + "ĠPow ers", + "i eurs", + "ie urs", + "ieu rs", + "ieur s", + "Ġge ographic", + "Ġgeo graphic", + "r ale", + "ra le", + "ral e", + "Ġc ra", + "Ġcr a", + "ĠSp urs", + "ipher text", + "iph ertext", + "AC ION", + "- common", + "-com mon", + "-comm on", + "Ġvict ories", + "ĠF inals", + "ĠFin als", + "ĠFinal s", + ". shuffle", + ".sh uffle", + "- million", + "-m illion", + "_ PROC", + "_P ROC", + "_PRO C", + "_PR OC", + "as sume", + "ass ume", + "Ġ ils", + "Ġi ls", + "Ġil s", + "D BC", + "DB C", + "Boot Test", + "Ġl avor", + "Ġla vor", + "Ġlav or", + ". testing", + ".t esting", + ".test ing", + ". ast", + ".as t", + ".a st", + "\" ]/", + "\"] /", + "m oid", + "mo id", + "Ġ qualification", + "Ġqual ification", + "g esch", + "ge sch", + "ges ch", + "ĉ put", + "ĉp ut", + "Ġair ports", + "Ġairport s", + "J I", + "T eacher", + "Te acher", + "_ uniform", + "_un iform", + "Ġ nama", + "Ġn ama", + "Ġna ma", + "Ġnam a", + "ĠB ast", + "ĠBa st", + "ĠBas t", + "e rtype", + "er type", + "ert ype", + "erty pe", + "c apture", + "cap ture", + "capt ure", + "get All", + "ĠReyn olds", + "o oled", + "ool ed", + "oo led", + ". comments", + ".com ments", + ".comment s", + ".comm ents", + "Ġ chin", + "Ġc hin", + "Ġch in", + "Ġchi n", + ") .*", + "). *", + "Ġ или", + "Ġи ли", + "t gl", + "tg l", + "u dos", + "ud os", + "udo s", + "Ġd ÃŃas", + "ĠdÃŃa s", + "ĠdÃŃ as", + "c hai", + "ch ai", + "cha i", + ". program", + ".p rogram", + ".pro gram", + ".pr ogram", + "Ġ psz", + "Ġp sz", + "Ġps z", + "ĉ icon", + "ĉi con", + "p hil", + "ph il", + "phi l", + "ent ral", + "entr al", + "_W RAP", + "_WR AP", + "o vi", + "ov i", + "Ġnost alg", + "In finity", + "Inf inity", + "ĉ yield", + "ĉy ield", + "Ġvit amins", + "Ġvitamin s", + "Ġvita mins", + "Ġvitam ins", + "Qu aternion", + "S ink", + "Si nk", + "Sin k", + "_ goods", + "_g oods", + "_go ods", + "_good s", + "Ġ ........", + "Ġ. .......", + "Ġ... .....", + "Ġ.. ......", + "Ġ.... ....", + "Ġ..... ...", + "Ġ...... ..", + "ĠW ings", + "ĠWin gs", + "ĠWing s", + "ur idad", + "uri dad", + "- story", + "-st ory", + "\" ])ĊĊ", + "\"] )ĊĊ", + "\"]) ĊĊ", + "\"])Ċ Ċ", + "idel ity", + "Type Def", + "G tk", + "Ġ íĮ", + "Ġí Į", + "_ Main", + "_M ain", + "Ġ chez", + "Ġch ez", + "Ġche z", + "ĠR aven", + "ĠRa ven", + "ĠRav en", + "Ġpay roll", + "Ġfreel ance", + "L LU", + "LL U", + "ĠM end", + "ĠMe nd", + "ĠMen d", + "e day", + "ed ay", + "eda y", + "Api ModelProperty", + ".Form BorderStyle", + "Ġeconom ist", + "stan bul", + "Ġfr eight", + "Ġfre ight", + "Ġfrei ght", + "- Agent", + "-A gent", + "( meta", + "(m eta", + "(me ta", + "Ġsym metry", + "Ġ' ..", + "Ġ'. .", + ". Calendar", + ".C alendar", + "- aut", + "-a ut", + "-au t", + "g f", + "p ent", + "pe nt", + "pen t", + "yc lopedia", + "Ġw ishing", + "Ġwish ing", + "Ċ ĊĊĊĊĊĊĊĊĊĊĊ", + "ĊĊ ĊĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊ ĊĊĊĊĊĊĊĊ", + "ĊĊĊ ĊĊĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊ ĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊ ĊĊĊĊ", + "ĊĊĊĊĊ ĊĊĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊĊĊ ĊĊ", + "ĊĊĊĊĊĊĊ ĊĊĊĊĊ", + "ĊĊĊĊĊĊĊĊĊ ĊĊĊ", + "ĊĊĊĊĊĊĊĊĊĊĊ Ċ", + "Ġgentle man", + "Ġ ê³", + "Ġê ³", + "= #", + "Ġlect ures", + "Ġlecture s", + "âĢľ In", + "âĢľI n", + "Ġ! _", + "Ġ hb", + "Ġh b", + "Ġ Vendor", + "ĠV endor", + "ĠVend or", + "Rec ently", + "Recent ly", + "_ notes", + "_n otes", + "_no tes", + "_not es", + "_note s", + "æıIJ 示", + "\" My", + "\"M y", + "Headers Height", + "_ SO", + "_S O", + "Ġunw illing", + "Ġsuper hero", + "g io", + "gi o", + "p sy", + "ps y", + "Ġ Peer", + "ĠP eer", + "ĠPe er", + "ĠPee r", + "j avax", + "java x", + "jav ax", + "& apos", + "&a pos", + "ĠCr isis", + "ĠCri sis", + "ord inal", + "ordin al", + "Mem cpy", + "++++++++ ++++++++", + "- val", + "-v al", + "Ġwork book", + "- ap", + "-a p", + "= k", + "Ġmetal lic", + "Ġmetall ic", + "_ peer", + "_p eer", + "_pe er", + "By PrimaryKey", + "_ SD", + "_S D", + "u ator", + "ua tor", + "uat or", + "_SH ADER", + "_SHA DER", + ") Math", + ". Transform", + ".Trans form", + "Ġc ows", + "Ġco ws", + "Ġcow s", + "P hi", + "Ph i", + "ĠC lem", + "ĠCl em", + "ĠCle m", + "( _(\"", + "(_ (\"", + "ĠL ud", + "ĠLu d", + "- delay", + "-d elay", + "-de lay", + "-del ay", + "ĠSe curities", + "ĠSec urities", + "ĠOrth odox", + "Sym fony", + "( report", + "(re port", + "(repo rt", + "(rep ort", + "Ġent ertain", + "Ġenter tain", + "Ġentert ain", + "E PS", + "EP S", + "iz oph", + "izo ph", + "ex ual", + "I RD", + "IR D", + "ä» İ", + "Ġl ith", + "Ġli th", + "Ġlit h", + "Ġ sanitize", + "Ġs anitize", + "Ġsan itize", + "Ġsanit ize", + "Ġfem inine", + "Ġfemin ine", + "IS BN", + ". authentication", + ".auth entication", + "_ pipeline", + "_p ipeline", + "_pipe line", + "/ constants", + "/con stants", + "Ġ CONF", + "ĠCON F", + "ĠCO NF", + "Ġlu cr", + "Ġluc r", + "r icia", + "ri cia", + "ric ia", + ".t tf", + ".tt f", + ". setContent", + ".set Content", + "Ġ stan", + "Ġs tan", + "Ġst an", + "Ġsta n", + "or ean", + "ore an", + "orea n", + "ĠL loyd", + ".raw Value", + "Ġ gor", + "Ġg or", + "Ġgo r", + "ĠBr owns", + "ĠBrown s", + "ĠBrow ns", + "Re gression", + "Reg ression", + "Ġl owering", + "Ġlow ering", + "Ġlower ing", + "na issance", + "Ġbl ows", + "Ġblow s", + "Ġblo ws", + "Ġam azed", + "Ġama zed", + "Ġun related", + "Ġunre lated", + "Re views", + "Review s", + "Ġ ruby", + "Ġr uby", + "Ġrub y", + "Ġru by", + "Ġ Modifier", + "ĠMod ifier", + "Ġg iants", + "Ġgi ants", + "Ġgiant s", + "Ġgia nts", + "Ġgian ts", + ". thread", + ".t hread", + ".th read", + "Ġcon tainment", + "Ġcont ainment", + "Ġcontain ment", + "ĠStart Coroutine", + "u mat", + "um at", + "uma t", + "o release", + "or elease", + "ore lease", + "ĠR andy", + "ĠRand y", + "ĠRan dy", + "@ endif", + "@end if", + "D igest", + "Di gest", + "Dig est", + "Ġsub urban", + "Ġsubur ban", + "Ġsuburb an", + "= \");Ċ", + "=\" );Ċ", + "Ġ annonce", + "Ġan nonce", + "Ġann once", + "Ġanno nce", + "Ġannon ce", + ". variable", + ".var iable", + "\\ Foundation", + "\\F oundation", + "Ġ acre", + "Ġa cre", + "Ġac re", + "V an", + "Va n", + "Ġt uples", + "Ġtu ples", + "Ġtuple s", + "Ġtup les", + "d ns", + "dn s", + "Ġ Standing", + "ĠSt anding", + "ĠStan ding", + "ĠStand ing", + "_ large", + "_l arge", + "Ġ boxing", + "Ġbo xing", + "Ġbox ing", + "Support ActionBar", + "ĠFort une", + "ĠR um", + "ĠRu m", + "_ multiple", + "_m ultiple", + "_multi ple", + "_mult iple", + "_multip le", + "arch ical", + "Ġ fwrite", + "Ġf write", + "Ġfw rite", + "_ quote", + "_qu ote", + "Ġfoo lish", + "Ġfool ish", + "Ġcom prising", + "Ġcomp rising", + "Ġcompr ising", + "Ġ оп", + "Ġо п", + "- selected", + "-se lected", + "-select ed", + "v f", + "m aid", + "ma id", + "mai d", + "N ama", + "Na ma", + "Nam a", + "( datetime", + "(d atetime", + "(date time", + "(dat etime", + "Ġindirect ly", + "g art", + "ga rt", + "gar t", + "fix tures", + "fixture s", + "c hos", + "ch os", + "cho s", + "ĠH alo", + "ĠHa lo", + "ĠHal o", + "Ġre curring", + "Ġrec urring", + "Ġrecur ring", + "- news", + "-n ews", + "-new s", + "-ne ws", + "v il", + "vi l", + "ĠNurs ing", + "ĠNur sing", + "- produ", + "-p rodu", + "-pro du", + "-pr odu", + "Ġ HQ", + "ĠH Q", + "\\Http Foundation", + "en ci", + "enc i", + "a uen", + "au en", + "Ġ vy", + "Ġv y", + "ocr acy", + "Ġde legation", + "Ġdeleg ation", + "Ġas phalt", + "Ġasp halt", + "Ġset Selected", + "k ok", + "ko k", + "/ rest", + "/r est", + "/re st", + "/res t", + "m etics", + "me tics", + "met ics", + "metic s", + "Ġ NSDate", + "ĠNS Date", + "Ġtravel led", + "Ġtrav elled", + "Ġrec ib", + "Ġ mime", + "Ġm ime", + "Ġmi me", + "Ġmim e", + "CL IENT", + "CLI ENT", + "Ġ GU", + "ĠG U", + "Ġ HANDLE", + "ĠH ANDLE", + "ĠHAND LE", + "/ Q", + "[ z", + "Ġboth ered", + "Ġbother ed", + "ĠBB Q", + "ç as", + "ça s", + "_ examples", + "_ex amples", + "_example s", + "_exam ples", + "_ FIN", + "_F IN", + "Ġwhite Color", + "Ġastr onom", + "Ġastro nom", + "- dir", + "-d ir", + "-di r", + "Ġsovere ign", + "Ġb reeze", + "Ġbree ze", + "Ġ inning", + "Ġin ning", + "Ġinn ing", + "ĠEd monton", + "g li", + "gl i", + ".blog spot", + "j sx", + "js x", + "Ġver sa", + "Ġve rsa", + "Ġvers a", + "ĠMoh ammed", + ". Job", + ".J ob", + "-t oggler", + "-toggle r", + "Ġп олÑĮзоваÑĤ", + "ar don", + "ard on", + "ardo n", + "Ġnew born", + "Ġn aval", + "Ġna val", + "Ġnav al", + "not eq", + "note q", + "Ġt umblr", + "Ġtum blr", + "Ġh entai", + "ĠTyp ically", + "ĠTypical ly", + "Ġl oot", + "Ġlo ot", + ". Sprite", + ".S prite", + ".Sp rite", + "F light", + "Fl ight", + "Ġw avelength", + "Ġwave length", + "- sk", + "-s k", + "ĠE lle", + "ĠEl le", + "ĠEll e", + "_ exports", + "_ex ports", + "_exp orts", + "_export s", + "Ġ Ñı", + "ĠÑ ı", + "Ġ IH", + "ĠI H", + "izoph ren", + "Ġ íģ", + "Ġí ģ", + "_ primary", + "_pr imary", + "_pri mary", + "_prim ary", + "Ġm ois", + "Ġmo is", + "Ġmoi s", + "Ġ BN", + "ĠB N", + "Ġsystem ic", + "Ġsyst emic", + "Ġdifer entes", + "Ġdiferente s", + "IN CT", + "INC T", + "Ġ' 'ĊĊ", + "Ġ'' ĊĊ", + "Ġ''Ċ Ċ", + "$ q", + "Widget Item", + "c lide", + "cl ide", + "cli de", + "clid e", + "$ file", + "$f ile", + "L emma", + "Le mma", + "/ table", + "/t able", + "/tab le", + "a grid", + "ag rid", + "agr id", + "ĠMongo DB", + "i nte", + "in te", + "int e", + "Ġapp rent", + "ÂŃ ing", + "ÂŃi ng", + ". Db", + ".D b", + "Ġ ÃĤ", + "Ġà Ĥ", + "h ammer", + "ham mer", + "=' ';Ċ", + "='' ;Ċ", + "Ġbr okers", + "Ġbro kers", + "Ġbroke rs", + "Ġbroker s", + "i tlement", + "it lement", + "itle ment", + "semb lies", + "sembl ies", + "E le", + "El e", + "{ x", + "Ġ lastname", + "Ġlast name", + "< -", + "Ġ flatten", + "Ġfl atten", + "Ġflat ten", + "Ġflatt en", + "_ band", + "_b and", + ". Root", + ".R oot", + ".read FileSync", + ".readFile Sync", + "= =====", + "== ====", + "==== ==", + "=== ===", + "===== =", + ". rx", + ".r x", + "? čĊ", + "Ġmetaph or", + "T i", + "c onte", + "con te", + "cont e", + "co nte", + "Ġ debit", + "Ġde bit", + "Ġdeb it", + "Ġcon tempt", + "Ġcont empt", + "Cpp Type", + "æĶ ¯", + "Form Field", + "r atio", + "rat io", + "os opher", + "osoph er", + "oso pher", + "Ġim plant", + "Ġimp lant", + "Ġimpl ant", + "P URE", + "PU RE", + "PUR E", + "Ġ alta", + "Ġal ta", + "Ġalt a", + "_ management", + "_man agement", + "_manage ment", + "Ġre fine", + "Ġref ine", + "Ġrefin e", + "Ġ CheckBox", + "ĠCheck Box", + "ĠCh arl", + "ĠChar l", + "ĠCha rl", + "- version", + "-v ersion", + "-vers ion", + "cond itional", + "condition al", + "ven ues", + "venue s", + "Ġrifle s", + "Ġrif les", + "Ġoff spring", + "Ġoffs pring", + "Ġm illing", + "Ġmill ing", + "Ġmil ling", + "Ġmilli ng", + "Ġsharp ly", + "Ġshar ply", + "Ġunder water", + "( origin", + "(orig in", + "(or igin", + "_ Control", + "_C ontrol", + "Ġ .$", + "Ġ. $", + "Pl ugins", + "Plugin s", + "Plug ins", + "Ġd rying", + "Ġdr ying", + "Ġdry ing", + "Ġillustr ates", + "Ġillustrate s", + "Ġillust rates", + "- u", + "Ġveget arian", + "n pc", + "np c", + "He art", + "; ',Ċ", + ";' ,Ċ", + ";', Ċ", + "com ma", + "co mma", + "comm a", + "t eenth", + "te enth", + "tee nth", + "teen th", + "a san", + "as an", + "asa n", + "/ spec", + "/s pec", + "/sp ec", + "_ moves", + "_m oves", + "_move s", + "_mov es", + "_mo ves", + "- margin", + "-m argin", + "-mar gin", + "Ġ ingen", + "Ġin gen", + "Ġi ngen", + "Ġing en", + "Âł ³³", + "³³ Âł", + "Ġpro jet", + "Ġproj et", + "Ġproje t", + "Ġo tra", + "Ġot ra", + "Ġ bras", + "Ġb ras", + "Ġbr as", + "Ġbra s", + ". utc", + ".u tc", + ".ut c", + "Ġsl ept", + "Ġsle pt", + "= sub", + "=s ub", + "ab ilit", + "abil it", + "abi lit", + "p oster", + "pos ter", + "post er", + "po ster", + "Ġ sdk", + "Ġs dk", + "Ġsd k", + "ounc ill", + "ouncil l", + "Ġ wd", + "Ġw d", + "Pre paredStatement", + "ĠD rum", + "ĠDr um", + "( attribute", + "(at tribute", + "Ġ Ethernet", + "ĠEth ernet", + "ĠEther net", + "ĉ DB", + "ĉD B", + "Cal ifornia", + "c ube", + "cu be", + "[ I", + ". Created", + ".C reated", + ".Create d", + "Ġ HM", + "ĠH M", + "Ġtr acing", + "Ġtra cing", + "Forms Module", + "- you", + "-y ou", + ". currency", + ".c urrency", + ".curr ency", + "fe eding", + "feed ing", + "fee ding", + "Ġ tbody", + "Ġt body", + "Ġtb ody", + "L i", + "a ccion", + "ac cion", + "acc ion", + "acci on", + "n as", + "na s", + "Ġtr ouver", + "Ġtrou ver", + "Ġtrouve r", + "N ONE", + "NO NE", + "NON E", + "\" },čĊ", + "\"} ,čĊ", + "\"}, čĊ", + "Ġ ftp", + "Ġf tp", + "Ġft p", + "With Identifier", + "p olate", + "po late", + "pol ate", + "File Info", + "Ġpurs ued", + "Ġpursue d", + "ĠĠĠĠ čĊĠĠĠĠčĊ", + "ĠĠĠĠčĊ ĠĠĠĠčĊ", + "DE SCRIPTION", + "DESC RIPTION", + "} */Ċ", + "}* /Ċ", + "From Nib", + "Ġdec orative", + "Ġdecor ative", + "_ SSL", + "_S SL", + "_SS L", + "( chat", + "(c hat", + "(ch at", + "T LS", + "TL S", + "Ġsur prises", + "Ġsurpr ises", + "Ġsurprise s", + "al culate", + "alc ulate", + "Ġ Splash", + "ĠS plash", + "ĠSp lash", + "ĠSpl ash", + "( Configuration", + "(Config uration", + "Ġ SEM", + "ĠS EM", + "ĠSE M", + "im son", + "ims on", + "/ library", + "/lib rary", + "/l ibrary", + "< Double", + "", + "Ġ} }>", + "Ġ}} >", + "G ED", + "GE D", + "f aq", + "fa q", + "Ġoption ally", + "Ġoptional ly", + "_ Dis", + "_D is", + "Ġ Successful", + "ĠSuccess ful", + "ĠC ensus", + "Ġinc arcer", + "_ CARD", + "_C ARD", + "_CA RD", + "_CAR D", + "Ġav iation", + "Ġavi ation", + "ĠG ym", + "ĠGy m", + "Author ity", + ". Bean", + ".B ean", + ".Be an", + "sh ader", + "sha der", + "shade r", + "Not Exist", + "_ TextChanged", + "_Text Changed", + "Ġ STOP", + "ĠS TOP", + "ĠST OP", + "( team", + "(t eam", + "(te am", + "\" H", + "w g", + "Ġgr inder", + "Ġgri nder", + "Ġgrind er", + "Ġgrin der", + "Ġ stripe", + "Ġst ripe", + "Ġstr ipe", + "Ġstri pe", + "Ġstrip e", + "Ġp reservation", + "Ġpres ervation", + "Cl aim", + "Cla im", + "aver sal", + "avers al", + "w arehouse", + "ware house", + "target s", + "tar gets", + "T rust", + "Tr ust", + "Ġal lev", + "Ġall ev", + "Ġalle v", + ", www", + ",w ww", + "ou sse", + "ous se", + "_ chan", + "_c han", + "_ch an", + "_ Size", + "_S ize", + "s ystems", + "sys tems", + "system s", + "Ġob jection", + "Ġobject ion", + "Ġobj ection", + "ĠK ane", + "ĠKa ne", + "ĠKan e", + "Ġcor ros", + "Ġcorr os", + "Ġcorro s", + "Ġ DSL", + "ĠD SL", + "ĠDS L", + "Ġ ua", + "Ġu a", + "Ġ MH", + "ĠM H", + "ĠStr ategic", + "ĠStrateg ic", + "_ tcp", + "_t cp", + "_tc p", + "Ġ ê°Ĵ", + "Ġê° Ĵ", + "Ġborrow ed", + "Ġborr owed", + "ĠA ch", + "ĠAc h", + "ĉ command", + "ĉcom mand", + "Ġ gps", + "Ġg ps", + "Ġgp s", + "le ston", + "les ton", + "lest on", + "ich ever", + "iche ver", + "Ġ UA", + "ĠU A", + "Ġassault ed", + "Ġspecial izes", + "Ġspecialize s", + "ĉ search", + "ĉs earch", + "ĉse arch", + "H otel", + "Hot el", + "Ho tel", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠčĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠčĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠčĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠčĊ", + "Ġ Pitch", + "ĠP itch", + "ĠPit ch", + "Ġ Ùģ", + "ĠÙ ģ", + "READ Y", + "REA DY", + "Ġpar ental", + "Ġparent al", + "Ġparen tal", + "Ġg éné", + "Ġgé né", + "Ġgén é", + "Ġdonn ées", + "Ġde tain", + "Ġdet ain", + "T ARGET", + "Ġprotagon ist", + "Ġclear Interval", + "Ġ IconButton", + "ĠIcon Button", + "ĠGet All", + "Type Info", + "E H", + "âĢľ They", + "âĢľThe y", + "Ġ{ [", + "Ġg ag", + "Ġga g", + "Ġ Ú©", + "Ġ Dropdown", + "ĠD ropdown", + "ĠDrop down", + ". free", + ".f ree", + ".fr ee", + ".fre e", + "g one", + "go ne", + "gon e", + "i mens", + "im ens", + "ime ns", + "imen s", + "Ġin stal", + "Ġins tal", + "Ġinst al", + "ĉ curl", + "ĉc url", + "ĉcur l", + "_ CAN", + "_C AN", + "_CA N", + "Ġ Bone", + "ĠB one", + "ĠBo ne", + "ĠBon e", + "ï¼ Ķ", + "on yms", + "ony ms", + "onym s", + "- government", + "-g overnment", + ".binding Navigator", + "Ġ Dans", + "ĠD ans", + "ĠDan s", + "ĠDa ns", + "ĠMc L", + "( en", + "(e n", + "> (_", + ">( _", + "ÐĴ Ñĭ", + ".* ;čĊ", + "= j", + "- cor", + "-c or", + "-co r", + "S on", + "So n", + ".ToolStrip Item", + "- around", + "-a round", + "-ar ound", + "_ XML", + "_X ML", + "end Date", + "Ġ slack", + "Ġs lack", + "Ġsl ack", + "Ġsla ck", + "Ġrot ated", + "Ġrotate d", + "Ġno qa", + "Ġc ottage", + "Ġcott age", + "Ġencontr ar", + "_ skill", + "_s kill", + "_sk ill", + "hou ette", + "! čĊ", + ". weather", + ".we ather", + "Ġemphas ized", + "Ġemphasize d", + "å® ¶", + "ĠÑģ пиÑģ", + "ĠÑģп иÑģ", + "Ġ Compiler", + "ĠC ompiler", + "ĠCom piler", + "ĠComp iler", + "ĠCompile r", + "( android", + "(and roid", + "Ġ âĢº", + "ĠâĢ º", + ". turn", + ".t urn", + "Ġsup pression", + "Ġsuppress ion", + "Ġsupp ression", + "_ calls", + "_c alls", + "_call s", + "_cal ls", + "Ġ *@", + "Ġ* @", + "( strlen", + "(str len", + "(st rlen", + ". hex", + ".h ex", + ".he x", + "ĠB ills", + "ĠBill s", + "ĠBil ls", + "Ġ RSA", + "ĠR SA", + "ĠRS A", + "Ï Ĥ", + "Ġ Escape", + "ĠE scape", + "ĠEs cape", + "ĠEsc ape", + "ement ia", + "Ġ frontend", + "Ġfront end", + "Ġp int", + "Ġpi nt", + "Ġpin t", + "_ exc", + "_e xc", + "_ex c", + "z zo", + "zz o", + "[ ],Ċ", + "[] ,Ċ", + "[], Ċ", + "Ġ\"' ,'\"", + "Ġ\"', '\"", + "Ġ\"',' \"", + ". Environment", + ".En vironment", + "Ġafore mentioned", + "Ġend ure", + "prot otype", + "proto type", + "ther apy", + "the rapy", + "s si", + "ss i", + "D eg", + "De g", + "_ plugins", + "_pl ugins", + "_plugin s", + ". userInfo", + ".user Info", + "Pr inter", + "Print er", + "Ġ PROGRAM", + "ĠPRO GRAM", + "Ġru ins", + "Ġruin s", + "Ġemp irical", + "Ġempir ical", + "Ġ crawl", + "Ġc rawl", + "Ġcr awl", + "Ġcraw l", + "Ġcra wl", + "ĠBo iler", + "- comment", + "-com ment", + "-comm ent", + ". subplot", + ".sub plot", + "_ et", + "_e t", + "Ġ' .',", + "Ġ'. ',", + "Ġ'.' ,", + "min or", + "mi nor", + "mino r", + "ĠCustom s", + "ĠCust oms", + "Ġ yaw", + "Ġy aw", + "Ġya w", + "under line", + "Ġ Como", + "ĠC omo", + "ĠCom o", + "ĠCo mo", + "( ('", + "(( '", + "( mean", + "(m ean", + "(me an", + "Ġch aque", + "Ġcha que", + "Ġ Blocks", + "ĠB locks", + "ĠBl ocks", + "ĠBlock s", + "ĠBlo cks", + "ĠBloc ks", + ". rad", + ".r ad", + ".ra d", + "ilib rium", + "Ġ webdriver", + "Ġweb driver", + "Ġmel hor", + "d ana", + "da na", + "dan a", + "ĠAb use", + "ĠAbu se", + "ĠSouth west", + "Ġ Paren", + "ĠP aren", + "ĠPar en", + "ĠPa ren", + "ĠPare n", + "PERT IES", + "ĉ IL", + "ĉI L", + "Ġs cream", + "Ġsc ream", + "Ġscre am", + "Ġscr eam", + "v u", + "Ġin comes", + "Ġinc omes", + "Ġincome s", + "Ġincom es", + "Ġ nim", + "Ġn im", + "Ġni m", + "Ġ lace", + "Ġl ace", + "Ġla ce", + "Ġlac e", + "Ġcompens ate", + "Re verse", + "Rev erse", + "D at", + "Da t", + "_ attack", + "_att ack", + "Ġn our", + "Ġno ur", + "Ġnou r", + "a chen", + "ac hen", + "ach en", + "ache n", + "c ek", + "ce k", + "< Func", + " \"+", + ">\" +", + "Ġ tokenizer", + "Ġtoken izer", + "Ġtokenize r", + "Ġsovere ignty", + "Ġsovereign ty", + "ĠP ence", + "ĠPe nce", + "ĠPen ce", + "( )\");Ċ", + "() \");Ċ", + "()\" );Ċ", + "Ġpesso as", + "Ġpessoa s", + ". Ge", + ".G e", + "Ġ Included", + "ĠIn cluded", + "ĠInclude d", + "Ġ pagina", + "Ġp agina", + "Ġpag ina", + "Ġex posing", + "Ġexp osing", + "Ġexpos ing", + "Ġexpo sing", + "е ÑĪ", + "_ SCRIPT", + "_SC RIPT", + "/ $',", + "/$ ',", + "Th umbnail", + "× Ķ", + "webElement X", + "webElementX paths", + "press ure", + "pres sure", + "ĠC urry", + "ĠCur ry", + "_ CP", + "_C P", + "OL UTION", + "I LES", + "IL ES", + "ILE S", + "prot ect", + "o ola", + "ool a", + "oo la", + "Work space", + "Works pace", + "{ };Ċ", + "{} ;Ċ", + "Ġ UNS", + "ĠU NS", + "ĠUN S", + "Ġsymp athy", + "Ġsympath y", + "r oker", + "ro ker", + "roke r", + "rok er", + "Ġre model", + "Ġr emodel", + "Ġrem odel", + "ĉ cell", + "ĉc ell", + "Ġa top", + "Ġat op", + ". FullName", + ".Full Name", + "Ġf aut", + "Ġfa ut", + "ĠE asily", + "_ dynamic", + "_d ynamic", + "_dyn amic", + "Ġfr amed", + "Ġframe d", + "Ġfra med", + "Ġfram ed", + "Ġm otive", + "Ġmot ive", + "Ġmotiv e", + "è· ¯", + "s am", + "sa m", + "Ġ marca", + "Ġmar ca", + "Ġmarc a", + "ĠText EditingController", + "Ġd estructor", + "Ġde structor", + "Ġdestruct or", + "c ream", + "cre am", + "cr eam", + "Ġr ude", + "Ġru de", + "Ġrud e", + "Ġ Bold", + "ĠB old", + "ĠBo ld", + "ĠBol d", + "ĠInd igenous", + "Ġ gens", + "Ġg ens", + "Ġge ns", + "Ġgen s", + "Ġrel acion", + "( system", + "(s ystem", + "(sys tem", + "Ġ UIFont", + "ĠUI Font", + "ĠUIF ont", + "_ charge", + "_ch arge", + "_char ge", + "U STER", + "US TER", + "UST ER", + "E V", + ". Namespace", + ".N amespace", + ".Name space", + ".Names pace", + "Ġmer ger", + "Ġmerge r", + "Ġmerg er", + "Ġ calloc", + "Ġc alloc", + "Ġcall oc", + "Ġcal loc", + "g ang", + "ga ng", + "gan g", + "Bad Request", + "Ġs per", + "Ġsp er", + "Ġspe r", + "- design", + "-d esign", + "-de sign", + "-des ign", + "Ġ âĩ", + "Ġâ ĩ", + "C han", + "Ch an", + "Cha n", + "Ġorg anism", + "Ġorgan ism", + "Ġorganis m", + ", )", + "= id", + "=i d", + "_ plane", + "_p lane", + "_pl ane", + "_plan e", + "Ġ Cases", + "ĠC ases", + "ĠCas es", + "ĠCase s", + "ĠCa ses", + "el fast", + "elf ast", + "ĠLegisl ature", + "ĠF aker", + "ĠFa ker", + "ĠFake r", + "ĠFak er", + "Ġinv oking", + "Ġinvo king", + "- utils", + "-util s", + "( ).'", + "() .'", + "(). '", + ". face", + ".f ace", + ".fac e", + ".fa ce", + "Ġguard ian", + "my Modal", + "Ġ clipboard", + "Ġclip board", + "ĠA TM", + "ĠAT M", + "Ġpe as", + "Ġpea s", + "ĠS ylv", + "ĠSy lv", + ". calc", + ".c alc", + ".ca lc", + ".cal c", + "Ġ Contacts", + "ĠCont acts", + "ĠContact s", + "int Value", + "Ġmod ifying", + "Ġmodify ing", + "ĠB arb", + "ĠBar b", + "ĠBa rb", + ". loss", + ".l oss", + ".lo ss", + "_ percentage", + "_per centage", + "_percent age", + "As ked", + "Ask ed", + "( lst", + "(l st", + "(ls t", + "ateg orical", + "ategor ical", + "ategori cal", + "atego rical", + "- files", + "-f iles", + "-file s", + "-fi les", + "ĠRom ania", + "ĠRo mania", + "ĠRoman ia", + "ĠRoma nia", + ". Ac", + ".A c", + "Ġ hai", + "Ġh ai", + "Ġha i", + "Ġ Flying", + "ĠF lying", + "ĠFl ying", + "ĠFly ing", + "Ġ ż", + "ĠÅ ¼", + "j p", + "ĠTr ainer", + "ĠTra iner", + "ĠTrain er", + ". arc", + ".a rc", + ".ar c", + "_ deg", + "_d eg", + "_de g", + "Ġtrace back", + "Or Fail", + "F LOW", + "FL OW", + ". old", + ".o ld", + ".ol d", + "o ya", + "oy a", + "g mt", + "gm t", + "is empty", + "Ġvacc ination", + "Ġ obsolete", + "Ġob solete", + "recogn ized", + "Ġru ined", + "Ġruin ed", + "ĠR ein", + "ĠRe in", + "ĠRei n", + "Ġ Tracking", + "ĠTr acking", + "ĠTrack ing", + "x fb", + "xf b", + "ا ÛĮ", + "Ġv ære", + "Ġvæ re", + "Ġbr yster", + "Ġ ITS", + "ĠI TS", + "ĠIT S", + "Ġdes tiny", + "Ġdest iny", + "Ġdestin y", + "Ġs wear", + "Ġsw ear", + "Ġswe ar", + "Ġre des", + "Ġr edes", + "Ġred es", + "Ġrede s", + "Ġ clf", + "Ġc lf", + "Ġcl f", + "Ġfl ipped", + "Ġflip ped", + "ĉ head", + "ĉh ead", + "B luetooth", + "Bl uetooth", + "Ġ Overrides", + "ĠOver rides", + "ĠOverride s", + ": Boolean", + "_ =", + "_ lr", + "_l r", + "s pawn", + "sp awn", + "spa wn", + ": index", + "VAL UES", + "VALUE S", + "is key", + "isk ey", + "iske y", + "? \");Ċ", + "?\" );Ċ", + ".syn thetic", + "Ġ Checking", + "ĠCheck ing", + "struct ures", + "structure s", + "i ping", + "ip ing", + "ipi ng", + "Ġvoc als", + "Ġvocal s", + "- Up", + "-U p", + "ĠManufact urers", + "ĠManufacturer s", + "ĠMar riage", + "ĠMarr iage", + "代 çłģ", + "Ġgar ner", + "Ġgarn er", + "_ Client", + "_C lient", + "_Cl ient", + "par allel", + "paralle l", + "RI END", + "Ġvine gar", + "se gue", + "seg ue", + "J B", + "Ġcont acting", + "Ġcontact ing", + "ĠCar roll", + "ĠCarr oll", + "Ġout reach", + "Ġoutr each", + "t ensor", + "_ variant", + "_v ariant", + "_var iant", + "Ġt heat", + "Ġth eat", + "Ġthe at", + "l icable", + "lic able", + "lica ble", + "{ |", + "t iny", + "ti ny", + "tin y", + "_ letter", + "_l etter", + "Ġp encil", + "Ġpen cil", + "Ġpenc il", + "HeadersHeight SizeMode", + "il tro", + "ilt ro", + "iltr o", + ".auto configure", + ". drag", + ".d rag", + ".dr ag", + ". useState", + ".use State", + "Ġ BMI", + "ĠB MI", + "ĠBM I", + "h int", + "hi nt", + "hin t", + "Com pile", + "Comp ile", + "* \\", + "en ary", + "ena ry", + "Ġ lvl", + "Ġl vl", + "Ġlv l", + ". Cache", + ".C ache", + "+ =\"", + "+= \"", + "_ tv", + "_t v", + "ruit ment", + "Ġf read", + "Ġfr ead", + "Ġfre ad", + "Art icles", + "Article s", + "f ila", + "fi la", + "fil a", + "Ġpack aged", + "Ġpackage d", + "âĺ Ĩ", + "AT HER", + "ATH ER", + "ĠPl anned", + "ĠPlan ned", + "s cheme", + "sch eme", + "Ġdi ary", + "Ġdia ry", + "Ġoff enses", + "Ġoffense s", + "Ġoffen ses", + "/ F", + "Ġ Stick", + "ĠS tick", + "ĠSt ick", + "Ġc erc", + "Ġce rc", + "Ġcer c", + "ĠS lee", + "ĠSl ee", + "ĠSle e", + "ĉ ĉĠĠĠĠĠĠĠĠ", + "ĉĉ ĠĠĠĠĠĠĠĠ", + "ĉĉĠĠĠ ĠĠĠĠĠ", + "ĉĉĠ ĠĠĠĠĠĠĠ", + "ĉĉĠĠ ĠĠĠĠĠĠ", + "ĉĉĠĠĠĠĠĠĠ Ġ", + "ĉĉĠĠĠĠ ĠĠĠĠ", + "ĉĉĠĠĠĠĠ ĠĠĠ", + "ĉĉĠĠĠĠĠĠ ĠĠ", + "< Image", + "", + ";' >", + "ĉ col", + "ĉc ol", + "V G", + "_ boolean", + "_bool ean", + "_bo olean", + "re cent", + "rec ent", + "rece nt", + "Ġ *)ĊĊ", + "Ġ* )ĊĊ", + "Ġ*) ĊĊ", + "Ġ*)Ċ Ċ", + "ĠRain bow", + "om men", + "omm en", + "Ġl ur", + "Ġlu r", + "Ġop pression", + "Ġopp ression", + "Ġoppress ion", + "(\" ,\");Ċ", + "(\", \");Ċ", + "(\",\" );Ċ", + "ĠFac ility", + "DEF INED", + "DEFINE D", + "Ġn eon", + "Ġne on", + "Ġneo n", + "Ġoff ender", + "Ġoffend er", + "Ġoffen der", + "A FP", + "AF P", + "Ġ Cleaning", + "ĠC leaning", + "ĠClean ing", + "ĠCle aning", + "[ ]):", + "[] ):", + "[]) :", + "Ġund ocumented", + ". Repositories", + ".Re positories", + "ĠG uitar", + "ĠGu itar", + "ĠGui tar", + "аÑģÑģ ив", + "S kills", + "Sk ills", + "Skill s", + "Ġtest imon", + "Ġtestim on", + "rypt ography", + "ĠAm ber", + "ĠAmb er", + "ĠSt alin", + "ĠSta lin", + "Ġl one", + "Ġlo ne", + "Ġlon e", + "Ġap enas", + "Ġape nas", + "Ġdi eses", + "Ġdie ses", + "Ġdies es", + "Ġdiese s", + "Ġ Arduino", + "ĠAr duino", + "è½ ¬", + "= =-", + "== -", + "_ Act", + "_A ct", + "Ġ coded", + "Ġc oded", + "Ġco ded", + "Ġcode d", + "Ġcod ed", + "âĸ ł", + "am burger", + "amb urger", + "amburg er", + "- links", + "-l inks", + "-link s", + "Ġar mour", + "Ġarm our", + ". High", + ".H igh", + "get Content", + "getC ontent", + "s tag", + "st ag", + "sta g", + "Ġ heck", + "Ġh eck", + "Ġhe ck", + "Ġ ìĹĨ", + "ĠìĹ Ĩ", + "ĠMc Connell", + "ĠCon cert", + "ĠConc ert", + "ĠConce rt", + "Ġ Alloc", + "ĠAl loc", + "ĠAll oc", + "ä re", + "är e", + ". replaceAll", + ".replace All", + "Ġpart itions", + "Ġpartition s", + "r ott", + "ro tt", + "rot t", + "ĠF le", + "ĠFl e", + "_ TREE", + "_T REE", + "_TR EE", + "reason able", + "Ġ Reporting", + "ĠRep orting", + "ĠReport ing", + "Ġbillion aire", + "s cores", + "sc ores", + "score s", + "m ins", + "min s", + "mi ns", + "- eye", + "-e ye", + "M ORE", + "MO RE", + "ab ort", + "abor t", + "abo rt", + "ĠS WT", + "ĠSW T", + "Ġin verted", + "Ġinvert ed", + "Ġ Teachers", + "ĠTe achers", + "ĠTeacher s", + "ĠTea chers", + "ĠTeach ers", + "; n", + "Ġ astro", + "Ġa stro", + "Ġas tro", + "Ġast ro", + "Ġastr o", + "н ов", + "но в", + "а ниÑĨ", + "ан иÑĨ", + "ани ÑĨ", + "product o", + "c ountries", + "count ries", + "ĠO wen", + "ĠOw en", + "Ġcont amination", + "Ġcontamin ation", + "Ġconta mination", + "Ġv ibe", + "Ġvi be", + "Ġvib e", + "ĠE lli", + "ĠEl li", + "ĠEll i", + ". script", + ".s cript", + "ĠO live", + "ĠOl ive", + "ĠOliv e", + "D MA", + "DM A", + "v ier", + "vi er", + "vie r", + ": semicolon", + "- module", + "-m odule", + "-mod ule", + "g ressive", + "gress ive", + "a gu", + "ag u", + "_ players", + "_p layers", + "_pl ayers", + "_player s", + "_play ers", + "Ġresult ados", + "Ġresultado s", + "start ed", + "star ted", + "scroll Top", + "= ====", + "== ===", + "==== =", + "=== ==", + "Ġweigh ing", + "Ġ[ [[", + "Ġ[[ [", + "z ahl", + "za hl", + "( NS", + "(N S", + "Ġ Assertion", + "ĠAssert ion", + "le ague", + "lea gue", + ".set TextColor", + ".setText Color", + "ĉ Message", + "ĉM essage", + "Ġm oms", + "Ġmom s", + "Ġmo ms", + "_ AF", + "_A F", + ". wh", + ".w h", + "A LS", + "AL S", + "Ġaut re", + "Ġau tre", + "] ĊĊĊĊ", + "]Ċ ĊĊĊ", + "]ĊĊ ĊĊ", + "]ĊĊĊ Ċ", + ". opacity", + ".op acity", + "ĠBudd hist", + "ĠBuddh ist", + "Ġde af", + "ĠOrgan isation", + "( Global", + "(G lobal", + "en sch", + "ens ch", + "Ġhead ache", + "ĠA lien", + "ĠAl ien", + "ĠAli en", + "_ inode", + "_in ode", + "_i node", + "ĠSt ark", + "ĠStar k", + "ĠSta rk", + "Ġ æī", + "Ġæ ī", + "-l nd", + "-ln d", + "o ref", + "or ef", + "ore f", + "_ feat", + "_f eat", + "_fe at", + "Ġpedest rian", + "Ġnom inal", + "Ġnomin al", + "Ġball oon", + "Ġbal loon", + "Ġballo on", + "Ġ sprites", + "Ġs prites", + "Ġsp rites", + "Ġspr ites", + "Ġsprite s", + "Prototype Of", + "ĠA post", + "ĠAp ost", + "Ġ FEATURE", + "ĠF EATURE", + "ĠFE ATURE", + "O H", + "Ġre cess", + "Ġr ecess", + "Ġrec ess", + "Ġrece ss", + "ĠD onna", + "ĠDon na", + "con sumer", + "cons umer", + "consum er", + "consume r", + "$ GLOBALS", + "ĠG IF", + "ĠGI F", + "- frame", + "-f rame", + "-fr ame", + "In icio", + "Ini cio", + "Ġpass ages", + "Ġpassage s", + "Date String", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + ". byte", + ".b yte", + ".by te", + "B ug", + "Bu g", + "initial izer", + "initialize r", + "p kt", + "pk t", + "od ium", + "odi um", + "Ġ DER", + "ĠD ER", + "ĠDE R", + ". ops", + ".op s", + ".o ps", + "l eri", + "le ri", + "ler i", + "Ġgift ed", + "Ġgif ted", + "Ġ detach", + "Ġde tach", + "Ġdet ach", + "ter rain", + "terra in", + "terr ain", + "el ters", + "elt ers", + "elter s", + "ãģ ı", + ". loader", + ".l oader", + ".load er", + ".lo ader", + "ĠN GO", + "ĠNG O", + "str ncmp", + "K h", + "( fontSize", + "(font Size", + "r ocket", + "ro cket", + "rock et", + "roc ket", + "Ġpreced ent", + "ĠAur ora", + "Ġ Experiment", + "ĠEx periment", + "i sphere", + "is phere", + "isp here", + "En coded", + "Enc oded", + "Encode d", + "Ġ âĢĵĊĊ", + "ĠâĢĵ ĊĊ", + "ĠâĢĵĊ Ċ", + "Ġpy ramid", + "ĠAnn iversary", + "o fil", + "of il", + "ofi l", + "ë Ł", + "( plugin", + "(pl ugin", + "C oeff", + "Co eff", + "Ġco operate", + "Ġcooper ate", + "Ġcoop erate", + "Ġpredomin antly", + "Ġpredominant ly", + "I SM", + "IS M", + "Ph rase", + "_ DEFINE", + "_DE FINE", + "_DEF INE", + "_DEFIN E", + "F lip", + "Fl ip", + "AMIL Y", + "AMI LY", + "ĠMark ets", + "ĠMarket s", + "Ġ StreamReader", + "ĠStream Reader", + "Ġ Combine", + "ĠC ombine", + "ĠCom bine", + "ĠComb ine", + "Ġmanus cript", + "z za", + "zz a", + ", tp", + ",t p", + "Wh atever", + "What ever", + "IT ICAL", + "igh bour", + "ighb our", + "Data Provider", + ". Texture", + ".Text ure", + "priv acy", + ". SDK", + ".S DK", + "Ġre charge", + "Ġ cpp", + "Ġc pp", + "Ġcp p", + "Ġ CFG", + "ĠC FG", + "ĠCF G", + "( holder", + "(h older", + "( py", + "(p y", + "m ot", + "mo t", + "Ġsa voir", + "Ġsav oir", + "ĠR osa", + "ĠRo sa", + "ĠRos a", + "ĠP Cs", + "ĠPC s", + "Ġ íĻ", + "Ġí Ļ", + ".her oku", + ".hero ku", + "Ġf ren", + "Ġfr en", + "Ġfre n", + "ĠR iley", + "ĠRi ley", + "a gate", + "ag ate", + "aga te", + "Ġs ond", + "Ġso nd", + "Ġson d", + ". xlsx", + ".x lsx", + ".xls x", + "Ġh acked", + "Ġhack ed", + "Ġhac ked", + "st ad", + "sta d", + "G i", + "Ġs anity", + "Ġsan ity", + "Ġsanit y", + "ĠSql DataAdapter", + ".. .\",", + "... \",", + "...\" ,", + "ĠP ussy", + "Ġ ****************", + "Ġ** **************", + "Ġ**** ************", + "Ġ******** ********", + "Ġhass le", + "_P ARENT", + "_PAR ENT", + "ĠU AE", + "ĠUA E", + "Ġbegin ners", + "Ġbeginner s", + "( Client", + "(C lient", + "(Cl ient", + "Ġstat istically", + "Ġstatist ically", + "Ġstatistical ly", + "Ġstatistic ally", + ". hour", + ".h our", + "e delta", + "ed elta", + "Ġ traction", + "Ġt raction", + "Ġtr action", + "Ġtra ction", + "Ġtract ion", + "u elve", + "uel ve", + "a rat", + "ar at", + "ara t", + "Ġsa una", + "Ġsau na", + "IN VALID", + "INVAL ID", + "Ġindict ment", + "AL LE", + "ALL E", + "Ġd issent", + "Ġdis sent", + "Ġdiss ent", + "Ġdisse nt", + "Ġ Typography", + "ĠTyp ography", + "Ġintent ional", + "Ġintention al", + "s it", + "si t", + "ĠAn imals", + "ĠAnimal s", + "ĠAnim als", + "Ġcoun tryside", + "Ġcountry side", + "Ġ uart", + "Ġu art", + "Ġua rt", + "} \\\"", + "}\\ \"", + "Ġseam less", + "¾ 示", + "Ġ autos", + "Ġa utos", + "Ġaut os", + "Ġauto s", + "Ġau tos", + "Ġ\" '\";Ċ", + "Ġ\"' \";Ċ", + "Ġ\"'\" ;Ċ", + "F lush", + "Fl ush", + "AN NOT", + "ANN OT", + "Ġal gebra", + "Ġalg ebra", + "as soc", + "ass oc", + "asso c", + "ĠW aters", + "ĠWater s", + "ĠWat ers", + "ĠWa ters", + "Ġprepar ations", + "Ġpreparation s", + "r onym", + "ro nym", + "ron ym", + "[, ]", + "S ans", + "San s", + "Sa ns", + "Ġarm ies", + "i peg", + "ip eg", + "ipe g", + "Ġcre amy", + "Ġcream y", + "Ġcrea my", + ". art", + ".a rt", + ".ar t", + "e tre", + "et re", + "etr e", + "Ġ Animated", + "ĠAn imated", + "ĠAnim ated", + "Ġun pleasant", + "e mean", + "em ean", + "eme an", + "g reat", + "gr eat", + "gre at", + "i Äħ", + "Ġ Earlier", + "ĠEar lier", + "ĠEarl ier", + "Ġc hic", + "Ġch ic", + "Ġchi c", + "Ġpres erving", + "( exec", + "(e xec", + "(ex ec", + "ĠInvest igation", + "ĠInvestig ation", + "ĉ GPIO", + "ĉG PIO", + "Ġrig orous", + "Ġrigor ous", + "i jo", + "ij o", + "= num", + "=n um", + "Ġ toolStrip", + "Ġt oolStrip", + "Ġtool Strip", + ") set", + ")s et", + "+ \"&", + "+\" &", + "Ġ Acceler", + "ĠAcc eler", + "Ġdevelop mental", + "Ġdevelopment al", + "is posable", + "Ġflaw ed", + "Ġfla wed", + "r ene", + "re ne", + "ren e", + "Up dating", + "Ġwatch dog", + "Ġden ominator", + "Ġdenom inator", + "Ġdenomin ator", + "Ġsubur bs", + "Ġsuburb s", + "Ġ ...)", + "Ġ... )", + "Ġ.. .)", + "Ġconv ictions", + "Ġconviction s", + "Ġconvict ions", + "c losure", + "clo sure", + ". IP", + ".I P", + "Ġtrans lates", + "Ġtransl ates", + "Ġtranslate s", + ".s wt", + ".sw t", + ". Trace", + ".T race", + ".Tr ace", + "Ġmet tre", + ". isEnabled", + ".is Enabled", + "Ġ Effective", + "ĠE ffective", + "ĠEffect ive", + "ĠEff ective", + ". toInt", + ".to Int", + "Ġen chant", + "Ġench ant", + "Ġst unned", + "Ġstun ned", + "Ġ poi", + "Ġp oi", + "Ġpo i", + "/ code", + "/c ode", + "/co de", + "a dm", + "ad m", + ".data binding", + ".datab inding", + ".databind ing", + "Ġ Lorem", + "ĠL orem", + "ĠLo rem", + "ĠLore m", + "ĠLor em", + "________________________________ ________________________________", + "Ġ ledger", + "Ġled ger", + "Ġledge r", + "Ġc ara", + "Ġcar a", + "Ġca ra", + "ĠG ir", + "ĠGi r", + "Ġw aits", + "Ġwait s", + "Ġwa its", + "U no", + "Un o", + "Ġ cwd", + "Ġc wd", + "Ġcw d", + "è¾ ij", + "ĠT Result", + "Ġre jo", + "Ġrej o", + "Ġe mitted", + "Ġem itted", + "Ġemit ted", + "ĠWest minster", + "ä¸Ģ 个", + "n ek", + "ne k", + "_T is", + "Ġen act", + "ĉ with", + "ĉw ith", + "or gia", + "org ia", + "Ġj ue", + "Ġju e", + "Per form", + "S PATH", + "SP ATH", + "SPA TH", + ". topic", + ".t opic", + ".to pic", + ".top ic", + "ĠD aten", + "ĠDate n", + "ĠDa ten", + "ĠDat en", + "Ạ§", + "Ġsit io", + "Ġsiti o", + "_ MM", + "_M M", + "\" So", + "\"S o", + "b ial", + "bi al", + "bia l", + "Ġ scoped", + "Ġsc oped", + "Ġscope d", + "Ġsco ped", + "Ġscop ed", + "Re quires", + "Require s", + "Ġ TOTAL", + "ĠT OTAL", + "ĠCh ancellor", + "( contents", + "(content s", + "(cont ents", + "Ġste alth", + "Ġsteal th", + "dev ices", + "device s", + "- pass", + "-p ass", + "il ih", + "ili h", + "ĠMal colm", + "ĠDe pot", + "ĠDep ot", + "Ġcon figur", + "Ġconfig ur", + "a ussian", + "aus sian", + "auss ian", + "_ constraint", + "_con straint", + "в еÑĤ", + "ве ÑĤ", + "G RA", + "GR A", + "Ġ Rates", + "ĠR ates", + "ĠRa tes", + "ĠRate s", + "ĠRat es", + ".dataGridView TextBoxColumn", + "ĠN obel", + "ĠNo bel", + "ĠNob el", + "i tics", + "it ics", + "iti cs", + "itic s", + "Ġignor ant", + "Ġ Reporter", + "ĠRe porter", + "ĠReport er", + "ĠEb ola", + "Ġ Shock", + "ĠSh ock", + "ĠSho ck", + "_ relation", + "_re lation", + "_rel ation", + "ĠN inja", + "ĠNin ja", + ") c", + "Ġ ticker", + "Ġt icker", + "Ġti cker", + "Ġtick er", + "Ġtic ker", + ". isChecked", + ".is Checked", + "ĠSup pliers", + "ĠSupplier s", + "ĠR apid", + "ĠRa pid", + "ĠRap id", + "Level s", + "âĤ¬ âĦ¢", + "ĉ queue", + "ĉq ueue", + "Ġ chop", + "Ġc hop", + "Ġch op", + "Ġcho p", + "Ġ Unix", + "ĠUn ix", + "ĠUni x", + "re ject", + "rej ect", + "- calendar", + "-c alendar", + "-cal endar", + "( sort", + "(s ort", + "(so rt", + "è ne", + "èn e", + "erc icio", + "Ġh ect", + "Ġhe ct", + "CALL TYPE", + "r oupon", + "ro upon", + "rou pon", + "roup on", + "Ġrent als", + "Ġrental s", + "author s", + "auth ors", + "{ name", + "{n ame", + "ĠF IFO", + "ĠFI FO", + "Ġ lassen", + "Ġl assen", + "Ġlas sen", + "Ġ Nous", + "ĠN ous", + "ĠNo us", + "ĠNou s", + "Ġsn apped", + "Ġsnap ped", + "Ġsna pped", + "Ġf ertility", + "Ġfer tility", + "Ġfert ility", + "Ġfertil ity", + "\" log", + "cl icked", + "click ed", + "Ġplan ting", + "Ġplant ing", + "Ġ gb", + "Ġg b", + "/ output", + "/out put", + "PE AT", + "Ġ categoria", + "Ġc ategoria", + "Ġcategor ia", + "Ġ bach", + "Ġb ach", + "Ġba ch", + "Ġbac h", + "Prof essor", + "i nth", + "in th", + "int h", + "\" ]čĊ", + "\"] čĊ", + "Rec order", + "Record er", + "s erde", + "ser de", + "Ġ Transmission", + "ĠTrans mission", + "t rad", + "tr ad", + "tra d", + "Ġtur bo", + "Ġturb o", + "_ VERTEX", + "_VER TEX", + "\\ Event", + "\\E vent", + "il ver", + "Ġbod ily", + "Ġ Sources", + "ĠS ources", + "ĠSource s", + "ĠSour ces", + "Ġkill ings", + "Ġkilling s", + "Ġkil lings", + ".xr TableCell", + "Ġfol ded", + "Ġfold ed", + "/ legal", + "/l egal", + "u ner", + "un er", + "une r", + "ĠR ifle", + "ĠRif le", + "ĠM IDI", + "ĠMI DI", + "ĠMID I", + "_Selected IndexChanged", + ".Size Type", + "Ġ WebSocket", + "ĠWeb Socket", + "Ġse leccion", + "Ġsele ccion", + "S and", + "San d", + "Sa nd", + "ot ros", + "otr os", + "Ġen vision", + "Ġenv ision", + "Ġenvis ion", + "/ etc", + "/e tc", + "ĠMel issa", + "S pot", + "Sp ot", + "Spo t", + "н ое", + "но е", + "_ ARM", + "_A RM", + "_AR M", + "At tempt", + "Att empt", + "Ġ BI", + "ĠB I", + "ãģ Ķ", + "Ġ DU", + "ĠD U", + "Ġback lash", + "st ride", + "str ide", + "stri de", + "/ classes", + "/c lasses", + "/class es", + "/cl asses", + "Ġtext Color", + "_ staff", + "_st aff", + "_sta ff", + "ob lin", + "obl in", + "ag enta", + "agent a", + "agen ta", + ". collections", + ".c ollections", + ".col lections", + ".collection s", + ".collect ions", + ".coll ections", + "il lage", + "ill age", + "illa ge", + "' čĊčĊ", + "'čĊ čĊ", + "fl atten", + "flat ten", + "_ sales", + "_s ales", + "_sale s", + "_sal es", + "_sa les", + "_ MASTER", + "_M ASTER", + "_MA STER", + "T W", + "_ da", + "_d a", + "P itch", + "ph ies", + "phi es", + "Ġz ombies", + "Ġzombie s", + "Ġ VERY", + "ĠV ERY", + "ĠVER Y", + "ĠVE RY", + "ĠPh armacy", + "ĠPharm acy", + "ĠPharmac y", + "ĠPharma cy", + "Ġprogress Bar", + "Ġhas htag", + "Ġhash tag", + "S idebar", + "Side bar", + "@ stop", + "@s top", + "( pc", + "(p c", + "ол ж", + "M AKE", + "MA KE", + "ĠC oron", + "ĠCo ron", + "ĠCor on", + "Ġkv inner", + "Ġkvin ner", + "Ġkvinn er", + "Ġkvinne r", + "ĠM aid", + "ĠMa id", + "ĠMai d", + "b ob", + "bo b", + ". titleLabel", + ".title Label", + "Ġsuccess es", + "Ġsucc esses", + "Ġsucces ses", + "ĠDem ocracy", + "ĠDemocr acy", + "ĠS urgery", + "ĠSurg ery", + "ĠSurge ry", + "Ġco ugar", + "Ġcou gar", + "Ġ curso", + "Ġcur so", + "Ġcurs o", + "Ġl oro", + "Ġlo ro", + "Ġlor o", + "ist ency", + "iste ncy", + "isten cy", + "Sen ior", + "æ k", + "Ġ AAA", + "ĠA AA", + "ĠAA A", + "Ġ BOOK", + "ĠB OOK", + "ĠBO OK", + "к о", + "W STR", + "WS TR", + "Ġ */,Ċ", + "Ġ* /,Ċ", + "Ġ*/ ,Ċ", + "Ġ*/, Ċ", + "o yal", + "oy al", + "oya l", + ". vector", + ".v ector", + ".vec tor", + "Ġ SPEC", + "ĠS PEC", + "ĠSP EC", + "ĠSPE C", + "S SF", + "SS F", + "Ġcomp uls", + "ĠAppe als", + "ĠAppeal s", + "ĠW inston", + "ĠWin ston", + "ĠWins ton", + "ĠMock ito", + "con trib", + "cont rib", + "contr ib", + "contri b", + ". available", + ".a vailable", + ".av ailable", + "entity Manager", + "a rias", + "ar ias", + "ari as", + "aria s", + "_ sale", + "_s ale", + "_sal e", + "_sa le", + "_ rs", + "_r s", + "Ġde coding", + "Ġdec oding", + "Ġdeco ding", + "Ġ locator", + "Ġl ocator", + "Ġloc ator", + "ol ith", + "oli th", + "olit h", + "Ġ kol", + "Ġk ol", + "Ġko l", + "Ġ ascii", + "Ġasc ii", + "ĠR ut", + "ĠRu t", + "/ interface", + "ĉ ĉĉĉĉĉĠĠĠ", + "ĉĉ ĉĉĉĉĠĠĠ", + "ĉĉĉĉ ĉĉĠĠĠ", + "ĉĉĉ ĉĉĉĠĠĠ", + "ĉĉĉĉĉ ĉĠĠĠ", + "ĉĉĉĉĉĉ ĠĠĠ", + "ĉĉĉĉĉĉĠ ĠĠ", + "ĉĉĉĉĉĉĠĠ Ġ", + "Ġ Numer", + "ĠN umer", + "ĠNum er", + "ĠNu mer", + ". flip", + ".f lip", + ".fl ip", + "- del", + "-d el", + "-de l", + "Ġbol ster", + "Ġbols ter", + "on omic", + "ono mic", + "onom ic", + "Ġ zm", + "Ġz m", + "L G", + "Find By", + "Ġ adaptive", + "Ġad aptive", + "Ġadapt ive", + "Ġada ptive", + "l oo", + "lo o", + "Ġ vue", + "Ġv ue", + "Ġvu e", + "( reverse", + "(re verse", + "_ canvas", + "_c anvas", + "_can vas", + ". roles", + ".r oles", + ".role s", + ".ro les", + "ific ado", + "ifica do", + "ven ient", + "\" As", + "\"A s", + "Ġ Entr", + "ĠEn tr", + "ĠEnt r", + "al igned", + "align ed", + "Ġbere its", + "/ //ĊĊ", + "// /ĊĊ", + "/// ĊĊ", + "///Ċ Ċ", + ".g wt", + ". employee", + ".e mployee", + "_ cli", + "_c li", + "_cl i", + "Ġanticip ate", + "éĻ IJ", + "Ġp ik", + "Ġpi k", + "Ġmush rooms", + "Ġmushroom s", + "( tt", + "(t t", + "Ġ oma", + "Ġo ma", + "Ġom a", + "ĠSan chez", + "_ google", + "_g oogle", + "_go ogle", + ". Valid", + ".Val id", + "Ġ FileName", + "ĠFile Name", + "iv ative", + "k ed", + "ke d", + "- war", + "-w ar", + "Ġm aturity", + "Ġmat urity", + "и д", + "Ġ miner", + "Ġm iner", + "Ġmin er", + "Ġmi ner", + "Ġmine r", + "Re ducers", + "Reduc ers", + "Reducer s", + "Reduce rs", + "Ġ LatLng", + "ĠL atLng", + "ĠLat Lng", + "_ STD", + "_S TD", + "_ST D", + "D igits", + "Digit s", + "Dig its", + "C alc", + "Cal c", + "Ca lc", + "- upload", + "-up load", + "Ġhand ic", + "Ġhan dic", + "ี à¹Ī", + "eg rated", + "egr ated", + "egrate d", + "egra ted", + "Ġ STM", + "ĠS TM", + "ĠST M", + "C lients", + "Client s", + "Cl ients", + "Cli ents", + "ĠTur bo", + "S YNC", + "SY NC", + "Ġphot ographers", + "Ġphotograph ers", + "Ġphotographer s", + ". Out", + ".O ut", + ". character", + ".char acter", + "B UILD", + "BU ILD", + ". unlock", + ".un lock", + "Ġar ises", + "Ġarise s", + "Ġ Commands", + "ĠComm ands", + "ĠCommand s", + "(\" \");čĊ", + "(\"\" );čĊ", + "(\"\") ;čĊ", + "(\"\"); čĊ", + "_ FORE", + "_F ORE", + "_FOR E", + "; ',", + ";' ,", + "+ \"'", + "+\" '", + ". Images", + ".Image s", + ".Im ages", + "\" ){", + "\") {", + "ĠM eyer", + "ĠMe yer", + "ĠMey er", + "Ġneg atively", + "Ġnegative ly", + "Ġ DLL", + "ĠD LL", + "ĠDL L", + "Ġ exe", + "Ġe xe", + "Ġex e", + "Ġdef iciency", + "Ġwild ly", + "- switch", + "-s witch", + "-sw itch", + "con struction", + "construct ion", + "Ġexception ally", + "Ġexceptional ly", + "ĠL iz", + "ĠLi z", + "/ java", + "/j ava", + "/jav a", + "Ġthe irs", + "Ġtheir s", + "ĠCon temporary", + "ĠCont emporary", + "l is", + "li s", + ".fill Rect", + "ĠN FC", + "ĠNF C", + "Ġre he", + "Ġreh e", + "( numbers", + "(num bers", + "(number s", + "Ġr aster", + "Ġra ster", + "Ġras ter", + "Ġrast er", + "Ġfig uring", + "Ġfigur ing", + "Ġshow c", + "Ġsho wc", + "ĠJ ill", + "ĠJi ll", + "Ġar cade", + "Ġarc ade", + "ĠConstruct s", + "m dl", + "md l", + "( '|", + "(' |", + "Ġident ifiers", + "Ġidentifier s", + "Ġ stellar", + "Ġst ellar", + "( Connection", + "Ġ\" {{", + "Ġ\"{ {", + "y or", + "yo r", + "( mysqli", + "(m ysqli", + "(mysql i", + "Ġd ove", + "Ġdo ve", + "Ġdov e", + "Of Birth", + ". disconnect", + ".dis connect", + "_ hi", + "_h i", + "Ġzw ischen", + "ĠGr und", + "i ros", + "ir os", + "iro s", + "_ Array", + "_A rray", + ". onclick", + ".on click", + "an som", + "ans om", + "An swers", + "Answer s", + "Ans wers", + "ĉ remove", + "ĉre move", + "F a", + "Ġh urry", + "Ġhur ry", + "- inf", + "-in f", + "-i nf", + "Ġ getClass", + "Ġget Class", + "ĠgetC lass", + "ĠReg ulation", + "Ġ FLAGS", + "ĠFLAG S", + "m isc", + "mi sc", + "mis c", + "K en", + "Ke n", + "_ heading", + "_head ing", + "_he ading", + "G Hz", + "GH z", + "- entry", + "-en try", + "Ġbi ography", + "S ig", + "Si g", + "- mf", + "-m f", + "W atcher", + "Watch er", + "Wat cher", + "âĢľ A", + "} px", + "Ġsp icy", + "Ġspi cy", + "_ sq", + "_s q", + "L ost", + "Lo st", + "Los t", + "( track", + "(t rack", + "(tr ack", + "а ли", + "ал и", + "Desc ending", + "< bits", + " ((", + ">( (", + "s urvey", + "sur vey", + "Ġ íĺ", + "Ġí ĺ", + ".. .')Ċ", + "... ')Ċ", + "...' )Ċ", + "Ġ Divider", + "ĠDi vider", + "ĠDiv ider", + "ĠDivide r", + "o sl", + "os l", + "_ CANCEL", + "_C ANCEL", + "_CAN CEL", + "_ prepare", + "_pre pare", + "_prep are", + "s tin", + "st in", + "sti n", + "ĠHe ath", + "ĠHeat h", + ". PrimaryKey", + ".Primary Key", + "Ġ âĨIJ", + "ĠâĨ IJ", + "ĠLocal DateTime", + "ĠLocalDate Time", + "Ġco operative", + "Ġcooper ative", + "L earning", + "Le arning", + "Learn ing", + ". enqueue", + ".en queue", + "Ġ goog", + "Ġg oog", + "Ġgo og", + "Ġgoo g", + "Ġ Regression", + "ĠRe gression", + "ĠReg ression", + "i mates", + "im ates", + "imate s", + "ima tes", + "imat es", + "Ġvoy eur", + "Ġ Drink", + "ĠD rink", + "ĠDr ink", + "p lug", + "pl ug", + "Ġl ender", + "Ġle nder", + "Ġlen der", + "Ġlend er", + "m ana", + "man a", + "ma na", + "Ġperson nes", + "Ġpersonne s", + "Ġpersonn es", + "yp se", + "yps e", + "Ġ unlink", + "Ġun link", + "Ġunl ink", + "ĠRa vens", + "ĠRav ens", + "ĠRaven s", + "Ġh urd", + "Ġhur d", + "Ġhu rd", + "Ġperiod ically", + "Ġperiodic ally", + "AR GS", + "ARG S", + "Ġ GH", + "ĠG H", + "char acters", + "character s", + ".. .\"ĊĊ", + "... \"ĊĊ", + "...\" ĊĊ", + "...\"Ċ Ċ", + "- establish", + "Ġ dn", + "Ġd n", + "( condition", + "(con dition", + "(cond ition", + "Ġ Gravity", + "ĠGr avity", + "Ġes tas", + "Ġest as", + "Ġesta s", + "_ focus", + "_f ocus", + "C reature", + "Cre ature", + "Cr eature", + "Creat ure", + "( site", + "(s ite", + "(si te", + "Ġc arr", + "Ġcar r", + "Ġca rr", + "Ġ RL", + "ĠR L", + "Ġ RI", + "ĠR I", + "ĠM oto", + "ĠMo to", + "ĠMot o", + "A SF", + "AS F", + "Ġ Luckily", + "ĠLuck ily", + "ĉ Route", + "ĉR oute", + "Ġ entropy", + "Ġent ropy", + "Ġentr opy", + "( \",\"", + "(\" ,\"", + "(\", \"", + "C ollect", + "Col lect", + "Coll ect", + "( contact", + "(cont act", + "ĠFlor ence", + "ĠFlo rence", + "Ġpremium s", + "Ġpremi ums", + "Ġl ifecycle", + "Ġlife cycle", + "Ġlif ecycle", + "Ġb ans", + "Ġban s", + "Ġba ns", + "x ef", + "xe f", + "Web Kit", + "Ġ Floating", + "ĠF loating", + "ĠFloat ing", + "ĠFlo ating", + "Ġ cosa", + "Ġc osa", + "Ġco sa", + "Ġcos a", + "S pecific", + "Spec ific", + "ĠLo ans", + "ĠLoan s", + "b read", + "br ead", + "bre ad", + "Ġdes criptors", + "Ġdescriptor s", + "Ġ{ :.", + "Ġ{: .", + "TH READ", + "ĠT rent", + "ĠTr ent", + "ĠTre nt", + "Ġs cop", + "Ġsc op", + "Ġsco p", + "Q A", + "ĠAn tar", + "ĠAnt ar", + "p el", + "pe l", + "_ difference", + "_d ifference", + "_diff erence", + "_ changes", + "_ch anges", + "_change s", + "_chan ges", + "( ...)", + "(... )", + "(.. .)", + "Ġ Rotation", + "ĠR otation", + "ĠRot ation", + "ĠL GPL", + "ĠLG PL", + "Ġ JUST", + "ĠJ UST", + "( Task", + "(T ask", + "_ subset", + "_sub set", + "_subs et", + "Ġ TRANS", + "ĠTR ANS", + "ĠTRAN S", + "åĬ Ľ", + "ĠS cout", + "ĠSc out", + "ĠSco ut", + "- popup", + "-p opup", + "-pop up", + "Ġsm oked", + "Ġsmoke d", + "Ġsmo ked", + "_ Class", + "_C lass", + "_Cl ass", + "Ġturn over", + "Ġturno ver", + "br akk", + "bra kk", + "ĠRock y", + "ĠRo cky", + "ĠRoc ky", + "t as", + "ta s", + ".Regular Expressions", + "ĠElli ott", + "ĠElliot t", + "Ġ Spinner", + "ĠSp inner", + "ĠSpin ner", + "DUCT ION", + "DU CTION", + "Ġl ibre", + "Ġli bre", + "Ġlib re", + "Ġlibr e", + "Ġmol to", + "Ġmolt o", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", + "Ġ FTP", + "ĠF TP", + "ĠFT P", + "m peg", + "mp eg", + "( features", + "(f eatures", + "(feature s", + "(fe atures", + "Ġb ald", + "Ġbal d", + "Ġba ld", + "ĠV id", + "ĠVi d", + "Ġsh outing", + "Ġshout ing", + "Ġsho uting", + "L int", + "Li nt", + "Lin t", + "Ġ sockets", + "Ġs ockets", + "Ġsocket s", + "Ġsock ets", + "Ġp row", + "Ġpro w", + "Ġpr ow", + "Ġnou velle", + "Ġnouvel le", + "Ġnouve lle", + "is card", + "isc ard", + "ĠS ponsor", + "Ġ consulta", + "Ġcons ulta", + "Ġconsult a", + "Ġconsul ta", + ") ));", + ")) );", + "))) ;", + "Ind ian", + "India n", + "ĠR aspberry", + "Ġteam mate", + "Ġ JWT", + "ĠJ WT", + "ĠJW T", + "ĠG hana", + "ĠGh ana", + "Ġ cakes", + "Ġc akes", + "Ġca kes", + "Ġcake s", + "pr imer", + "prim er", + "prime r", + "pri mer", + "form a", + "for ma", + "erg arten", + "_ Manager", + "_M anager", + "_Man ager", + "Ġpre season", + "G AME", + "GA ME", + "| \"", + "ĠB rock", + "ĠBr ock", + "ĠBro ck", + "Ġocc upy", + "Ġoccup y", + "Ġdecor ations", + "Ġdecoration s", + "á nd", + "án d", + "Ġ cot", + "Ġc ot", + "Ġco t", + "Ġp aran", + "Ġpar an", + "Ġpara n", + "Ġpa ran", + "D isk", + "Dis k", + "Di sk", + "r emain", + "re main", + "rem ain", + "rema in", + "> ?", + "St rong", + "Str ong", + "Ġf rance", + "Ġfr ance", + "Ġfra nce", + "Ġfran ce", + "Ġfranc e", + "ĠE ra", + "ĠEr a", + "- cr", + "-c r", + ".Buffer edReader", + ".Buffered Reader", + "ĠParad ise", + "ĠV AT", + "ĠVA T", + "ĠAn ders", + "ĠAnd ers", + "Ġl imb", + "Ġli mb", + "Ġlim b", + "amp oo", + "ampo o", + "Ġimper ative", + "UT ILITY", + "UTIL ITY", + "Ġ Recognition", + "ĠRec ognition", + "ĠRecogn ition", + "Ġragaz ze", + "Ġp ops", + "Ġpop s", + "Ġpo ps", + "y press", + "yp ress", + "Ġemb argo", + "Ġembar go", + "// {Ċ", + "Ġs yll", + "Ġsy ll", + "P TR", + "PT R", + "åŃĺ åľ¨", + "Ġdid nt", + "Ġdidn t", + "M ailer", + "Mail er", + "Ma iler", + "Ġacad emics", + "Ġacademic s", + "ĠFr auen", + "ĠFra uen", + "ĠFrau en", + "ne ider", + "- rel", + "-r el", + "-re l", + "Ġrain bow", + "( In", + "(I n", + "Ġs liced", + "Ġsl iced", + "Ġslice d", + "Ġslic ed", + "= ============Ċ", + "== ===========Ċ", + "==== =========Ċ", + "======== =====Ċ", + "=== ==========Ċ", + "============ =Ċ", + "============= Ċ", + "=========== ==Ċ", + "========= ====Ċ", + "========== ===Ċ", + "====== =======Ċ", + "===== ========Ċ", + "======= ======Ċ", + "( send", + "(s end", + "(se nd", + "NSMutable Dictionary", + "v os", + "vo s", + "( package", + "(p ackage", + "(pack age", + "Ġord inance", + "Ġordin ance", + "view er", + "vie wer", + "ĠSan tos", + "ĠSant os", + "ĠSanto s", + "- selling", + "-s elling", + "Ġ gov", + "Ġg ov", + "Ġgo v", + "et tle", + "ett le", + "Ġfound ers", + "Ġfo unders", + "Ġfounder s", + "Ġw aking", + "Ġwa king", + "s lashes", + "sl ashes", + "slash es", + "-p ound", + "-po und", + "re cht", + "rec ht", + "rech t", + "ا ت", + "Ø§Ø ª", + ". onClick", + ".on Click", + "Ġn ord", + "Ġno rd", + "Ġnor d", + "st änd", + "_ when", + "_w hen", + "_wh en", + "U TERS", + "UT ERS", + "UTE RS", + "i cc", + "ic c", + "Ġcaps ule", + "ĠW id", + "ĠWi d", + "M arc", + "Mar c", + "Ma rc", + "ภ¸", + "r ored", + "ro red", + "ror ed", + "U GE", + "UG E", + "LO UD", + "Ġ Audit", + "ĠA udit", + "ĠAud it", + "ĠAu dit", + "ĠAudi t", + "ip ients", + "ipient s", + "ipi ents", + "op ian", + "opia n", + "opi an", + "ĠS ue", + "ĠSu e", + "Ġwur den", + "Ġwurde n", + ". Helpers", + ".H elpers", + ".Helper s", + ".Help ers", + "Ġf actions", + "Ġfact ions", + "Ġfa ctions", + "Ġfaction s", + "[ np", + "[n p", + "- than", + "-t han", + "-th an", + "Ġre co", + "Ġr eco", + "Ġrec o", + "Ġ kas", + "Ġk as", + "Ġka s", + "Ġ cmds", + "Ġcmd s", + "Ġcm ds", + "/ network", + "/n etwork", + "/net work", + "x bf", + "xb f", + "get Color", + "getC olor", + "Ġ biased", + "Ġbi ased", + "Ġbias ed", + "ĠL ak", + "ĠLa k", + "D atas", + "Data s", + "Da tas", + "Dat as", + "v ents", + "ve nts", + "vent s", + "ven ts", + "Ġ ë²", + "Ġë ²", + "_ PS", + "_P S", + ". Validate", + ".Valid ate", + "Inv oker", + "Invoke r", + "Ġne uen", + "Ġneu en", + "Ġneue n", + "Ġju venile", + "Ġjuven ile", + "V ISION", + "VI SION", + "VIS ION", + "Ġde vote", + "Ġdev ote", + "Ġ linha", + "Ġl inha", + "Ġlin ha", + "Ġlinh a", + "Ġdiscount ed", + "Ġdisco unted", + "\\ Config", + "Ġworth while", + "Ġskin ny", + "Ġ Courses", + "ĠC ourses", + "ĠCo urses", + "ĠCour ses", + "ĠCourse s", + "le ys", + "ley s", + "ĠMort gage", + "K evin", + "Ke vin", + "Ġann ounces", + "Ġannounc es", + "Ġannounce s", + "] )*", + "]) *", + "res ervation", + "Ġ æķ°", + "Ġæķ °", + "Ġprejud ice", + "ĠString Comparison", + "Ġbe ard", + "Ġbear d", + "- win", + "-w in", + "ĠS ão", + "ĉ ms", + "ĉm s", + "j al", + "ja l", + "Ġ Earn", + "ĠE arn", + "ĠEar n", + "ĠEa rn", + "_ ports", + "_p orts", + "_port s", + "_po rts", + "_por ts", + "Ġ Nombre", + "ĠN ombre", + "ĠNom bre", + "_ COR", + "_C OR", + "_CO R", + "Ġ BUILD", + "ĠB UILD", + "ĠBU ILD", + ". sound", + ".s ound", + ".so und", + "Y ellow", + "Ġlineback er", + "Ġchar itable", + "Ġcha ritable", + "j ug", + "ju g", + "_NON NULL", + "ĠD ental", + "ĠDen tal", + "ĠDent al", + "\" >${", + "\"> ${", + "\">$ {", + "ĉ match", + "ĉm atch", + "ĉmat ch", + "R ussian", + "Russia n", + "Russ ian", + "Rus sian", + "Ġver sch", + "Ġvers ch", + "Ġp inned", + "Ġpin ned", + "Ġadopt ing", + "Options Menu", + "P ag", + "Pa g", + "Ġpair ing", + "Ġpa iring", + "Ġpai ring", + "Ġt read", + "Ġtr ead", + "Ġtre ad", + "erc ises", + "ercise s", + "Ġ Spread", + "ĠS pread", + "ĠSp read", + "ĠSpr ead", + ") i", + "Ġ BAD", + "ĠB AD", + "ĠBA D", + "_ tf", + "_t f", + "UI ImageView", + "UIImage View", + "pop ulate", + "b ab", + "ba b", + "Ġ Ïĥ", + "ĠÏ ĥ", + "[ ++", + "Ġopi oid", + "Ġ ##Ċ", + "Ġ# #Ċ", + "Ġ## Ċ", + "d type", + "dt ype", + "ĠSt arts", + "ĠStart s", + "ĠStar ts", + "ĠSta rts", + "(' /')", + "('/ ')", + "Ġperson als", + "Ġpersonal s", + "Ġpersona ls", + "- market", + "-m arket", + "-mark et", + "-mar ket", + "Ġredund ant", + "ĠEss ential", + "Ġsc rapy", + "Ġscr apy", + "Ġscrap y", + "Ġ им", + "Ġи м", + "a cl", + "ac l", + "Ġ crear", + "Ġc rear", + "Ġcr ear", + "Ġcre ar", + "Ġcrea r", + "ĠB end", + "ĠBe nd", + "ĠBen d", + "Ġrel ieve", + "Ġreli eve", + "Ġrelie ve", + "- room", + "-r oom", + "-ro om", + "w ife", + "wi fe", + "Ġv Ãł", + "ĠQ Point", + "Ġqu asi", + "Ġqua si", + "Ġ methodName", + "Ġmethod Name", + "\\ xc", + "\\x c", + "ĠP eru", + "ĠPer u", + "ĠPe ru", + "/ The", + "/T he", + ". orm", + ".o rm", + ".or m", + "Ġ viz", + "Ġv iz", + "Ġvi z", + "/ pdf", + "/p df", + "Loc ated", + "Ġconfront ation", + "ĠChampionship s", + "ĠChampions hips", + "ĠChampion ships", + "Ġhy pert", + "Ġhyp ert", + "Ġhyper t", + "Ġhype rt", + "Ġ dj", + "Ġd j", + "Ġ UserInfo", + "ĠUser Info", + "Ġ åĪĽå»º", + "ĠåĪ Ľå»º", + "\\ xb", + "\\x b", + "( sim", + "(s im", + "(si m", + "Ġ ==Ċ", + "Ġ= =Ċ", + "Ġ== Ċ", + "Ġst aging", + "Ġsta ging", + "Ġstag ing", + "Ġdr astically", + "Ġdrastic ally", + "åŃ ¦", + "l ords", + "lor ds", + "lord s", + ". less", + ".l ess", + ".le ss", + "вед иÑĤе", + "Ġ Bucket", + "ĠB ucket", + "ĠBuck et", + "ĠBu cket", + "ĠM am", + "ĠMa m", + ". term", + ".t erm", + ".te rm", + "_ pi", + "_p i", + "c zy", + "cz y", + ". pub", + ".p ub", + "p recio", + "pre cio", + "prec io", + "preci o", + "ĠV irt", + "ĠVir t", + "ĠVi rt", + "Ġ roman", + "Ġr oman", + "Ġro man", + "Ġrom an", + "Ġroma n", + "i tat", + "it at", + "ita t", + "L ex", + "Le x", + "_ infos", + "_in fos", + "_info s", + "_inf os", + "Ä °", + ". other", + ".o ther", + ".ot her", + "VE LO", + "VEL O", + "Ġ ponder", + "Ġp onder", + "Ġpo nder", + "Ġpon der", + "Ġpond er", + "Ġh anno", + "Ġhan no", + "Ġhann o", + "( Page", + "(P age", + "d oi", + "do i", + "Ġpol ite", + "Ġpo lite", + "Ġpolit e", + "Ġprogram mer", + "Ġprogramme r", + "Ġprogramm er", + "D ies", + "Die s", + "Di es", + "$ d", + "Ġre plication", + "Ġrep lication", + "Ġrepl ication", + "Ġreplic ation", + "Ġreplica tion", + "add Column", + "fr ican", + "frica n", + "Ġl eng", + "Ġle ng", + "Ġlen g", + "b eer", + "be er", + "bee r", + "o it", + "oi t", + "Ġw asting", + "Ġwas ting", + "Ġwast ing", + "y lim", + "yl im", + "me asure", + "N eg", + "Ne g", + "Ġpart ie", + "Ġpar tie", + "Ġparti e", + ". console", + ".con sole", + ".cons ole", + "ĠGu inea", + "ĠGui nea", + "T EL", + "TE L", + "_ fact", + "_f act", + "_fac t", + "_fa ct", + ". chunk", + ".ch unk", + "Ġl ent", + "Ġle nt", + "Ġlen t", + "Ġ aller", + "Ġa ller", + "Ġal ler", + "Ġall er", + "Ġalle r", + "Ġ à¤ķ", + "Ġठķ", + "_ idle", + "_id le", + "_i dle", + "Ġad missions", + "Ġadm issions", + "Ġadmission s", + "JSON Array", + "Ġv ibration", + "Ġvibr ation", + "Ġvib ration", + ". helpers", + ".h elpers", + ".helper s", + ".help ers", + "å¤ ĸ", + "Ġ hen", + "Ġh en", + "Ġhe n", + "j ohn", + "jo hn", + "Ġ ìĥĿ", + "Ġì ĥĿ", + "Ġìĥ Ŀ", + "Ġjud gement", + "Ġjudge ment", + "Ġg een", + "Ġge en", + "Ġgee n", + "t erra", + "ter ra", + "terr a", + "^ {", + "ĠI z", + "Ġc â", + "in stances", + "instance s", + "inst ances", + "instanc es", + "Ġthreat ens", + "Ġthreaten s", + "Ġm üssen", + "Kind OfClass", + "Ġstoryt elling", + "_ demo", + "_d emo", + "_de mo", + "_dem o", + "r ias", + "ri as", + "ria s", + "Priv acy", + "h ift", + "hi ft", + "ĠY i", + "es or", + "eso r", + "íķ ł", + "ens itivity", + ". Writer", + ".W riter", + ".Write r", + "ภĤ", + "D istrict", + "Di strict", + ".get JSONObject", + "Im pro", + "Imp ro", + "(get Resources", + "Ġ SPELL", + "ĠS PELL", + "ĠSP ELL", + "ĠSPE LL", + "ro duce", + "rodu ce", + "rod uce", + "Ġsl owed", + "Ġslow ed", + "Ġslo wed", + "Ġ linewidth", + "Ġline width", + "Ġlin ewidth", + "Ġhon esty", + "Ġhonest y", + "Ġho nesty", + "Ġhone sty", + "Ġ Coord", + "ĠC oord", + "ĠCo ord", + "ĠF ork", + "ĠFor k", + "ĠFo rk", + "ĠDispatch Queue", + "ĠCl iff", + "ĠCli ff", + "ĠW iring", + "ĠWi ring", + "ĠWir ing", + "_TIM ESTAMP", + "ol lah", + "oll ah", + "olla h", + "a void", + "av oid", + "avo id", + "++ ];Ċ", + "++] ;Ċ", + "sem antic", + "- css", + "-c ss", + "Ġv eto", + "Ġve to", + "Ġvet o", + "ĠM err", + "ĠMe rr", + "ĠMer r", + "Ġlegisl ators", + "C EEDED", + "CEE DED", + "CEED ED", + "Ġquestion naire", + "ĠP ills", + "ĠPill s", + "ĠPil ls", + "C alculate", + "Cal culate", + "Calc ulate", + "Calcul ate", + "( core", + "(c ore", + "(co re", + "(cor e", + "' e", + "Ġdis like", + "Ġ Preferences", + "ĠP references", + "ĠPre ferences", + "ĠPreference s", + "ĠPrefer ences", + "_ EXTERNAL", + "_EX TERNAL", + "_EXTERN AL", + "è° ĥ", + "Ġd odge", + "Ġdo dge", + "Ġdod ge", + "æľį åĬ¡", + ". names", + ".n ames", + ".name s", + ".draw Image", + "_ prom", + "_p rom", + "_pro m", + "_pr om", + "uck land", + "Ġ<$ >", + "ı z", + "/ site", + "/s ite", + "é¡ ¹", + "r ophe", + "ro phe", + "rop he", + "roph e", + "Ġcomp elled", + "Ġcompel led", + "Ġl aptops", + "Ġlaptop s", + "Ġ uni", + "Ġu ni", + "Ġun i", + "C LOSE", + "CL OSE", + "Ġcasual ties", + "Ġ Uniform", + "ĠUn iform", + "ĠUni form", + "Term inal", + ". \",\"", + ".\" ,\"", + ".\", \"", + "D AT", + "DA T", + "( TreeNode", + "(T reeNode", + "(Tree Node", + "ĠGand hi", + "( stmt", + "(st mt", + "A XB", + "AX B", + "* M", + "Ġumb rella", + "an imal", + "ani mal", + "anim al", + "Ġ grpc", + "Ġg rpc", + "Ġgr pc", + "Ġgrp c", + "Ġwhere by", + "Ġfloat s", + "Ġflo ats", + "ĉ arg", + "ĉa rg", + "ĉar g", + "Ġ dbg", + "Ġd bg", + "Ġdb g", + "Ġexceed ing", + "Ġexce eding", + "Event Type", + ".SaveChanges Async", + "Ġ {{{", + "Ġ{ {{", + "Ġ{{ {", + "Ġ owed", + "Ġo wed", + "Ġow ed", + "Ġowe d", + "ahren heit", + "Ġ ì§", + "Ġì §", + "Ġequ ipo", + "Ġequip o", + "u rai", + "ur ai", + "ura i", + "Ġi dol", + "Ġid ol", + "] \")Ċ", + "]\" )Ċ", + "]\") Ċ", + "_ major", + "_m ajor", + "Ġentire ty", + "inger print", + "ç os", + "ço s", + "/ account", + "/a ccount", + "/ac count", + "ĉ right", + "ĉr ight", + "urs os", + "ĠE DT", + "ĠED T", + "_ INSERT", + "_INS ERT", + "Ġsh ining", + "Ġshin ing", + "Ġ< :", + "Edge Insets", + "Ġcolon ies", + ". IM", + ".I M", + "ĉ Ġĉ", + "ĉĠ ĉ", + "R OAD", + "RO AD", + "C CCC", + "CC CC", + "CCC C", + "pl acing", + "pla cing", + "Ġget Activity", + "em acs", + "ema cs", + "' %(", + "'% (", + ". clicked", + ".cl icked", + ".click ed", + "Ġ Them", + "ĠT hem", + "ĠThe m", + "ĠTh em", + "is ia", + "isi a", + "Bus car", + "Bu scar", + ". rename", + ".re name", + ".r ename", + "Ġo ath", + "Ġoat h", + "Ġoa th", + "Ġafter ward", + "ĠU FO", + "ĠUF O", + "A PS", + "AP S", + "ĠJackson ville", + ". some", + ".s ome", + ".so me", + "Conf irmed", + "Confirm ed", + ". scan", + ".s can", + ".sc an", + "ig Integer", + "Decor ator", + "sh ield", + "shi eld", + "ress ive", + ". did", + ".d id", + ".di d", + "请 è¾ĵåħ¥", + "Ġsh utter", + "Ġshut ter", + "D am", + "Da m", + "Ġpar enting", + "Ġparent ing", + "Ġparen ting", + "ey ed", + "eye d", + "$ item", + "$i tem", + "- develop", + "-de velop", + "-dev elop", + "-devel op", + "Ġex tracts", + "Ġextra cts", + "Ġextract s", + "Ġextr acts", + "Ġdecentral ized", + "ĠE lsa", + "ĠEl sa", + "_ spin", + "_s pin", + "_sp in", + "_spi n", + "] )+", + "]) +", + "- initial", + "-in itial", + "-init ial", + "Ġmult itude", + "Ġmultit ude", + "Ġsens ory", + "Ġsensor y", + "Ġ MODEL", + "ĠMO DEL", + "ĠMOD EL", + "ĠMODE L", + "Ġsaf eguard", + "Ġsafe guard", + "Ġsafeg uard", + "ì ¹", + "Ġhun ters", + "Ġhunt ers", + "Ġhunter s", + "Ġ Tiny", + "ĠT iny", + "ĠTi ny", + "ĠTin y", + "I NO", + "IN O", + "dec orate", + "decor ate", + "Ġ NoSuch", + "ĠNo Such", + "H o", + "( Response", + "Ġr uler", + "Ġrule r", + "Ġru ler", + "ĉ short", + "ĉs hort", + "ĉsh ort", + "Ġ caster", + "Ġc aster", + "Ġca ster", + "Ġcas ter", + "Ġcast er", + "Ġcaste r", + "Ġ clientId", + "Ġclient Id", + "Ġ pdb", + "Ġp db", + "Ġpd b", + "ëı Ħ", + "i tic", + "it ic", + "iti c", + "Ġ GameState", + "ĠGame State", + "Ġnew Item", + ") ĊĊĊĊĊĊ", + ")Ċ ĊĊĊĊĊ", + ")ĊĊ ĊĊĊĊ", + ")ĊĊĊ ĊĊĊ", + ")ĊĊĊĊ ĊĊ", + ")ĊĊĊĊĊ Ċ", + "o uis", + "ou is", + "oui s", + "n oc", + "no c", + ". BLACK", + ".BL ACK", + "_ VECTOR", + "_V ECTOR", + "_VEC TOR", + "_VE CTOR", + "---------- ();", + ">( );", + ">() ;", + ".get P", + "an ye", + "any e", + "Ġne uron", + "Ġneuro n", + "Ġneu ron", + "Ġneur on", + "i fold", + "if old", + "ifo ld", + "Ġ Known", + "ĠK nown", + "ĠKn own", + "ĠKnow n", + "Bit coin", + "Any way", + "ay ette", + "aye tte", + "ayet te", + "Ġ' ['", + "Ġ'[ '", + "Ãł nh", + "Ãłn h", + "m gr", + "mg r", + "Ġcor related", + "Ġcorre lated", + "Ġcorrel ated", + "Ġcorrelate d", + "Ġn ause", + "Ġna use", + "Ġnau se", + "Ġmental ity", + "Ġment ality", + "has Many", + "Ġ FG", + "ĠF G", + "am pie", + "amp ie", + "I TU", + "IT U", + "F s", + ". Sp", + ".S p", + "_ between", + "_b etween", + "_bet ween", + "Dep endencies", + "o ug", + "ou g", + "Place holder", + "= text", + "=t ext", + "Ġ Managing", + "ĠMan aging", + "ĠMana ging", + "ocal ypse", + "åĮ Ĺ", + "_ mag", + "_m ag", + "_ma g", + "f ld", + "fl d", + "â ij", + "C AM", + "CA M", + "Ġ Helpers", + "ĠH elpers", + "ĠHelp ers", + "ĠHelper s", + "ĠHel pers", + "Ġd ost", + "Ġdo st", + "Ġdos t", + "/ out", + "/o ut", + "Ġassass ination", + "Ġassassin ation", + ". getImage", + ".get Image", + "ĠK enny", + "ĠKen ny", + "ĠKenn y", + ". ')ĊĊ", + ".' )ĊĊ", + ".')Ċ Ċ", + ".') ĊĊ", + ") {//", + "){ //", + "ĠR anger", + "ĠRange r", + "ĠRa nger", + "ĠRan ger", + "Ġg ek", + "Ġge k", + "Ġsince re", + "Ġsinc ere", + "Ġsincer e", + "< Value", + "čĊ", + "/> čĊ", + ".get Resources", + ".getResource s", + "Ġl ump", + "Ġlu mp", + "Ġlum p", + "_ consts", + "_con sts", + "_const s", + "_cons ts", + "( ext", + "(e xt", + "(ex t", + "ĉ dir", + "ĉd ir", + "â Ŀ", + "Ġpadding Top", + "Ġob session", + "Ġobs ession", + "Ġobsess ion", + "Ġb anning", + "Ġban ning", + "ĠApp Module", + "Ġp artisan", + "Ġpart isan", + "Ġparti san", + "Ġcatalog ue", + "Ġcata logue", + "Ġcatal ogue", + "Ġmin ors", + "Ġminor s", + "Ġp itches", + "Ġpitch es", + "Ġpit ches", + "we ep", + "Ġunder take", + "Ġundert ake", + "Ġth emed", + "Ġthe med", + "Ġthem ed", + "Ġtheme d", + "a udit", + "au dit", + "aud it", + "audi t", + ". scrollTop", + ".scroll Top", + ".scrollTo p", + "Ġ rer", + "Ġre r", + "Ġr er", + "Ġsym ptom", + "Ġsympt om", + "Ġsymp tom", + "Ġopen ings", + "Ġopening s", + ". blocks", + ".b locks", + ".bl ocks", + ".block s", + "open id", + "ope nid", + "Ġa ssh", + "Ġas sh", + "Ġass h", + "- save", + "-s ave", + "ĠP ig", + "ĠPi g", + "Ġre gain", + "Ġreg ain", + "Ġin icial", + "Ġini cial", + "Ġinici al", + "/ favicon", + "/f avicon", + "ĉ exp", + "ĉe xp", + "ĉex p", + "Ġsp ices", + "Ġspi ces", + "Ġspice s", + "i ska", + "is ka", + "isk a", + "cl aims", + "claim s", + "cla ims", + "m ak", + "ma k", + "definition s", + "Ġcorrespond ent", + "ĠCann abis", + "_ _,Ċ", + "__ ,Ċ", + "__, Ċ", + "ĠL ucky", + "ĠLuc ky", + "ĠLu cky", + "ĠLuck y", + "ĠG aussian", + "ĠGa ussian", + "ĠGauss ian", + "Ġ Nearly", + "ĠN early", + "ĠNear ly", + "C AD", + "CA D", + "' ]]Ċ", + "'] ]Ċ", + "']] Ċ", + "Ġadequate ly", + "Ġadequ ately", + "Ġ TITLE", + "ĠT ITLE", + "constitution al", + "- mm", + "-m m", + "_ override", + "_over ride", + "Ġ blas", + "Ġb las", + "Ġbl as", + "Ġbla s", + ".ready State", + "Ġrem inis", + "Ġremin is", + "Ġrein forced", + "Ġreinforce d", + "ĠColl abor", + "Ġdecor ating", + "Ġdeco rating", + "Ġb achelor", + "Ġbach elor", + "ERRU PT", + "Ġup right", + "ip ation", + "ipa tion", + "ĠN oble", + "ĠNo ble", + "ĠNob le", + "Ġvalue ForKey", + "Ġset Loading", + ". Ignore", + ".I gnore", + "å ģ", + "G lobals", + "Global s", + "ĠM ent", + "ĠMe nt", + "ĠMen t", + "AS SES", + "ASS ES", + "Ġlim bs", + "Ġlimb s", + "Ġ HUD", + "ĠH UD", + "in ci", + "inc i", + ". iv", + ".i v", + "ĠQ ModelIndex", + "F use", + "Fu se", + "Ġpe dal", + "Ġped al", + "_F REQ", + "_FR EQ", + "_FRE Q", + "( verbose", + "(ver bose", + "Ġlong itud", + "ĠCh arter", + "ĠChar ter", + "ĠChart er", + "ê ·¸", + "ê· ¸", + "Ġ bundles", + "Ġb undles", + "Ġbund les", + "Ġbundle s", + ". ignore", + ".i gnore", + "um bo", + "umb o", + "E MA", + "EM A", + ". ......", + ".. .....", + "... ....", + ".... ...", + "..... ..", + "...... .", + "s x", + ". Card", + ".C ard", + ".Car d", + "Ġhe ute", + "Ġst eer", + "Ġste er", + "j umlah", + "Ġ {_", + "Ġ{ _", + "_ Checked", + "_Check ed", + "Ġ fax", + "Ġf ax", + "Ġfa x", + "ĠG ust", + "ĠGu st", + "ĠGus t", + "itch ens", + "itchen s", + "Ġ ))ĊĊ", + "Ġ) )ĊĊ", + "Ġ)) ĊĊ", + "Ġ))Ċ Ċ", + "Ġremark ably", + "/ XML", + "/X ML", + "- remove", + "-re move", + "_ bt", + "_b t", + "Ġinc ub", + ". package", + ".p ackage", + ".pack age", + ".current Thread", + "ĠHigh lander", + "ĠHighland er", + ". side", + ".s ide", + ".sid e", + ".si de", + "s plash", + "sp lash", + "spl ash", + "Ġ ici", + "Ġi ci", + "Ġic i", + "= D", + "Ġp uck", + "Ġpu ck", + "Ġball ots", + "Ġbal lots", + "Ġballot s", + "Ġballo ts", + "Ġhuge ly", + "Ġhug ely", + "c oeff", + "co eff", + "coef f", + "coe ff", + "Ġ pData", + "Ġp Data", + ". COLUMN", + ".C OLUMN", + "ĠHe aling", + "ĠHeal ing", + "Ġ ordin", + "Ġor din", + "Ġord in", + "! ),", + "!) ,", + "Ġ' ',čĊ", + "Ġ'' ,čĊ", + "Ġ'', čĊ", + "( md", + "(m d", + "ĠS ask", + "ĠSa sk", + "ĠSas k", + "< strong", + "Ġsurv ivor", + "Ġsurviv or", + ". series", + ".s eries", + ".se ries", + ".ser ies", + "Ġcaffe ine", + "Ġ `(", + "Ġ` (", + ".TRA ILING", + "_ Input", + "_In put", + "( \"^", + "(\" ^", + "z d", + "& );Ċ", + "&) ;Ċ", + "Ġ Ping", + "ĠP ing", + "ĠPin g", + "ĠPi ng", + "Ġ voucher", + "Ġv oucher", + "Ġvo ucher", + "Ġvou cher", + ". rating", + ".r ating", + ".ra ting", + "-sh irts", + "-shirt s", + "ĠRetrie ves", + "ĠRetrieve s", + ".al ibaba", + "Or acle", + "_ MOV", + "_M OV", + "_MO V", + "Old Data", + "Ġ /*čĊ", + "Ġ/ *čĊ", + "Ġ/* čĊ", + "Ġ gboolean", + "Ġg boolean", + "Ġ= >čĊ", + "Ġ=> čĊ", + "Ġ rá", + "Ġr á", + "Ġbl unt", + "ĠImage Icon", + "i fik", + "if ik", + "ifi k", + "R TC", + "RT C", + "Ġfi bers", + "Ġfib ers", + "Ġfiber s", + "Ġto ile", + "Ġtoi le", + ". sent", + ".s ent", + ".se nt", + "ĠPy Qt", + "$ app", + "$a pp", + "Ġm edio", + "Ġme dio", + "Ġmed io", + "Ġmedi o", + "Ġgrant ing", + "Ġgran ting", + "Ġts lint", + "Ġtsl int", + "ĠM ö", + "(fig size", + "Ġhur ricane", + "Ġl ifes", + "Ġlife s", + "Ġlif es", + "Ġ ÃĦ", + "Ġà Ħ", + "rocess ing", + "_ standard", + "_st andard", + "_stand ard", + "- option", + "-o ption", + "-op tion", + "-opt ion", + "' )))", + "') ))", + "')) )", + "Ġvac ant", + "Ġva cant", + "å· ¥", + "ĠH ollow", + "ĠHol low", + "ĠHoll ow", + "handle Change", + "Ġ divider", + "Ġdi vider", + "Ġdiv ider", + "Ġdivide r", + "Ġdivid er", + "ĠEngine ers", + "ĠEngineer s", + "Ġs vens", + "Ġsv ens", + "Ġsve ns", + "Ġcom pliant", + "Ġcompl iant", + "t anggal", + "Ġ Credits", + "ĠC redits", + "ĠCredit s", + "ĠEm irates", + "Rule Context", + "Ġreal ization", + "Ġrealiz ation", + "Ġrealiza tion", + "Ġdis tracted", + "Ġdistr acted", + "Ġdistract ed", + "] +=", + "]+ =", + "Ġau gment", + "Ġaug ment", + "ĠD w", + "o tp", + "ot p", + "or rent", + "orr ent", + "orre nt", + "Ed itar", + "Edit ar", + ". stock", + ".st ock", + "St udy", + "p ections", + "pe ctions", + "pect ions", + "pection s", + "Ġ GameManager", + "ĠGame Manager", + "= cut", + "=c ut", + "Ġf lock", + "Ġfl ock", + "Ġflo ck", + "ĠRom ans", + "ĠRo mans", + "ĠRoman s", + "ĠRoma ns", + "t hem", + "th em", + "the m", + "- hop", + "-h op", + "Ġscreen shots", + "Ġscreens hots", + "Ġscreenshot s", + "Ġ /*!Ċ", + "Ġ/* !Ċ", + "Ġ/*! Ċ", + "Ġcon versions", + "Ġconv ersions", + "Ġconvers ions", + "Ġconversion s", + "Ġnormal ization", + "( configuration", + "(config uration", + "Ġa eros", + "Ġaer os", + "Ġae ros", + "_ security", + "_s ecurity", + "_se curity", + "_sec urity", + "! 'Ċ", + "!' Ċ", + "B onus", + "Bon us", + "ĠDR IVER", + "ĠDRIVE R", + "ĉ Date", + "ĉD ate", + "t ie", + "ti e", + "ĠWy oming", + "St and", + "Stan d", + "i tre", + "it re", + "itr e", + "Ġsh oppers", + "Ġshop pers", + "Ġsho ppers", + "Ġshopper s", + "Ġdisadv antage", + "Ġl iking", + "Ġli king", + "Ġlik ing", + "ç¬ ij", + "Ġunderstand able", + "S EE", + "SE E", + "Ġh oy", + "Ġho y", + "Ġnine te", + "Ġni nete", + "Ġnin ete", + "Ġcon fer", + "Ġconf er", + "Ġ nowrap", + "Ġno wrap", + "Ġnow rap", + "ĠV ern", + "ĠVer n", + "ĠVe rn", + ", čĊčĊ", + ",čĊ čĊ", + "ime step", + "imes tep", + "imest ep", + "Layout Manager", + "à ·", + "ĉ wait", + "ĉw ait", + "P LETED", + "PLE TED", + "J apan", + "Ja pan", + "Ġin duce", + "Ġind uce", + "Ġindu ce", + "Ġ å¯", + "Ġå ¯", + "о зв", + "оз в", + "_END POINT", + ". horizontal", + ".h orizontal", + "Ġacceler ated", + "Ġaccelerate d", + "r imon", + "ri mon", + "rim on", + "I VES", + "IV ES", + "IVE S", + "Trans actions", + "Transaction s", + "L ean", + "Le an", + "ĠS OUR", + "ĠSO UR", + "wh ether", + "y g", + "Ġ oid", + "Ġo id", + "Ġoi d", + "Ġ EntityManager", + "ĠEntity Manager", + "OUN TRY", + "OUNT RY", + "Ġ fila", + "Ġf ila", + "Ġfil a", + "Ġfi la", + "OLUM NS", + "OLUMN S", + "IN UE", + "INU E", + "Ġ Anchor", + "ĠAn chor", + "ĠAnc hor", + "ĠAnch or", + "TR AN", + "TRA N", + "w oo", + "wo o", + "block quote", + "ĠN urse", + "ĠNurs e", + "ĠNur se", + "ĠC arp", + "ĠCar p", + "ĠCa rp", + "Ġrede em", + ". try", + ".t ry", + ".tr y", + "Ġ JP", + "ĠJ P", + "Ġ timestamps", + "Ġtimestamp s", + "Ġ?> \"><", + "Ġ?>\" ><", + "Ġ?>\"> <", + "Ġ REMOVE", + "ĠRE MOVE", + "ĠREM OVE", + "ĠStar bucks", + "Re ally", + "Real ly", + "Ġflo oded", + "Ġflood ed", + ". Callback", + ".C allback", + ".Call back", + "Drop Down", + "i pro", + "ip ro", + "Ġt ended", + "Ġten ded", + "Ġtend ed", + "l te", + "lt e", + "Ġproportion s", + "Ġproport ions", + "- te", + "-t e", + "ĠR ena", + "ĠRe na", + "ĠRen a", + "l icate", + "lic ate", + "li cate", + "lica te", + "for ces", + "force s", + "forc es", + ". extra", + ".ex tra", + ".ext ra", + ". authenticate", + ".auth enticate", + "в од", + "во д", + "¡ °", + "Ġfor ControlEvents", + "Ġ senha", + "Ġs enha", + "Ġsen ha", + "Ġk ein", + "Ġke in", + "Ġmin ist", + "Ġmi nist", + "Ġmini st", + "Ġ Preference", + "ĠP reference", + "ĠPre ference", + "ĠPref erence", + "ĠPrefer ence", + "ĠTele graph", + "Ñĥ п", + "str pos", + "Ġillness es", + "Ġp igs", + "Ġpi gs", + "Ġpig s", + "Ġget Intent", + "ĠgetInt ent", + "S ol", + "So l", + "Ġ ¡", + "Ġ ¡", + "( cpu", + "(c pu", + "(cp u", + "[ prop", + "[p rop", + "s creens", + "screen s", + "') ;?>", + "'); ?>", + "Ġ Acts", + "ĠA cts", + "ĠAct s", + "ĠAc ts", + "Ġstr dup", + "Ġa verages", + "Ġaverage s", + "Ġaver ages", + "a nal", + "an al", + "ana l", + "ĠCas ual", + "Group Box", + "ĠHand book", + "/ comments", + "/com ments", + "/comment s", + "Ġnumber ed", + "Ġnumb ered", + "Ġbroad casting", + "Ġbroadcast ing", + "çĽ ij", + ".native Element", + ". mu", + ".m u", + "Ġ updatedAt", + "Ġupdated At", + "ĠDoes n", + "ĠDoe sn", + ". AC", + ".A C", + ". coll", + ".c oll", + ".co ll", + ".col l", + "Ġrec order", + "Ġrecord er", + "_ sha", + "_s ha", + "_sh a", + "B g", + "b il", + "bi l", + "Ġbol ts", + "Ġbolt s", + "Ġ ç¬", + "Ġç ¬", + "Ġim posing", + "Ġimp osing", + "ĠInformation en", + "_ flashdata", + "_flash data", + "e conomic", + "ec onomic", + "R emark", + "Re mark", + "Rem ark", + "u cas", + "uc as", + "Ġ Officers", + "ĠOff icers", + "ĠOffice rs", + "ĠOfficer s", + "Ġ TER", + "ĠT ER", + "ĠTE R", + "W alk", + "Wal k", + "Wa lk", + "Ġmerc ado", + "_ generate", + "_g enerate", + "_gen erate", + "_gene rate", + "_gener ate", + "H Y", + "C alling", + "Call ing", + "Cal ling", + "s nap", + "sn ap", + "script Id", + ". operation", + ".op eration", + ".o peration", + ".oper ation", + "ĠFl ame", + "ĠFla me", + "ĠFlam e", + "l iness", + "li ness", + "line ss", + "lin ess", + "lines s", + "Ġr ented", + "Ġren ted", + "Ġrent ed", + "_ toggle", + "_t oggle", + "- changing", + "-ch anging", + "-chan ging", + "Ġ TY", + "ĠT Y", + "' util", + "'u til", + "E EP", + "EE P", + "Ġ graphql", + "Ġgraph ql", + "Ġ Uni", + "ĠU ni", + "ĠUn i", + "Ġim pulse", + "Ġimp ulse", + "Ġimpuls e", + ". Basic", + ".B asic", + "Ġenerg ies", + "Ġener gies", + "Ġenergie s", + "M ARY", + "MA RY", + "MAR Y", + "ĠMar cel", + "ĠMarc el", + "Ġm ortal", + "Ġmor tal", + "Ġmort al", + "Ġf res", + "Ġfr es", + "Ġfre s", + "m ens", + "me ns", + "men s", + "m otion", + "mo tion", + "mot ion", + "Ġs ampled", + "Ġsample d", + "Ġsam pled", + "Ġsamp led", + "âĢľ That", + "i day", + "id ay", + "ida y", + "qu ipment", + "quip ment", + "get Int", + "Ġ Absolute", + "ĠA bsolute", + "ĠAbs olute", + ", '\"", + ",' \"", + "u ned", + "un ed", + "une d", + ". share", + ".s hare", + ".sh are", + ".sha re", + "Ġ })(", + "Ġ} )(", + "Ġ}) (", + "m mm", + "mm m", + "ĠR ising", + "ĠRi sing", + "ĠRis ing", + "ä» »", + "Ġun employed", + "x fa", + "xf a", + ". follow", + ".f ollow", + "ĉ ĉĉĉĠĠĠĠĠĠ", + "ĉĉ ĉĉĠĠĠĠĠĠ", + "ĉĉĉĉ ĠĠĠĠĠĠ", + "ĉĉĉ ĉĠĠĠĠĠĠ", + "ĉĉĉĉĠ ĠĠĠĠĠ", + "ĉĉĉĉĠĠĠ ĠĠĠ", + "ĉĉĉĉĠĠ ĠĠĠĠ", + "ĉĉĉĉĠĠĠĠ ĠĠ", + "ĉĉĉĉĠĠĠĠĠ Ġ", + "s lt", + "sl t", + ". Phone", + ".P hone", + ".Ph one", + "Ġkn ives", + "Ġ eve", + "Ġe ve", + "Ġev e", + "on Click", + "] ))čĊ", + "]) )čĊ", + "])) čĊ", + "Ġ Witness", + "ĠW itness", + "ĠWit ness", + "ĉ NS", + "ĉN S", + "Ġ EOS", + "ĠE OS", + "ĠEO S", + "ĠSte fan", + "ĠStef an", + "ĠPr iest", + "ĠPri est", + "âĢĶ which", + "Get String", + ". By", + ".B y", + "Ġup stairs", + "Ġdetr iment", + "b roken", + "br oken", + "bro ken", + "em bro", + "emb ro", + "embr o", + "Ġnic otine", + "i lion", + "il ion", + "ili on", + "ilio n", + "Ġaston ishing", + "_ aff", + "_a ff", + "_af f", + "Ġ Lesson", + "ĠL esson", + "ĠLe sson", + "ĠLess on", + "ĠLes son", + "Ġacc idental", + "Ġaccident al", + "od or", + "odo r", + "Ġde cir", + "Ġdec ir", + "Ġnew Name", + "+ .", + "çĽ ¸", + "igs list", + "Ġ Github", + "ĠG ithub", + "ĠGit hub", + "Ġsuccess ive", + "Ġsuc cessive", + "r acial", + "ra cial", + "rac ial", + "raci al", + "Ġen viron", + "Ġenv iron", + "Ġenvi ron", + "éªĮ è¯ģ", + "Ġred irected", + "Ġredirect ed", + "T OTAL", + "TOT AL", + "Ġgrab bing", + "Ġgra bbing", + "ĠL ance", + "ĠLa nce", + "ĠLan ce", + "ĠLanc e", + "Ġfor fe", + "_ CB", + "_C B", + "å¾ ®", + "El apsed", + "_ way", + "_w ay", + "(Dialog Interface", + "_ measure", + "_me asure", + "_meas ure", + "x bb", + "xb b", + "D og", + "Do g", + "De part", + "Dep art", + "- src", + "-s rc", + "re solver", + "res olver", + "resolve r", + "with standing", + "_ shell", + "_s hell", + "_sh ell", + "Ġ LastName", + "ĠLast Name", + "ĠAv iation", + "Ġbeg inner", + "Ġbegin ner", + "(\" %.", + "(\"% .", + "( tool", + "(t ool", + "(to ol", + "Ġ нов", + "Ġн ов", + "Ġно в", + ": init", + ":i nit", + "( API", + "(A PI", + "(AP I", + "ĠMorris on", + "ĠMorr ison", + "vt Color", + "Ġsta ple", + "Ġstap le", + "/ INFO", + "Ġsuper natural", + "Ġsupern atural", + "Ġste ak", + "t imeline", + "time line", + "tim eline", + "zz le", + "\" `ĊĊ", + "\"`Ċ Ċ", + "\"` ĊĊ", + "Second ary", + "ĠNe pal", + "ĠNep al", + ". StringUtils", + ".String Utils", + "Ġ adam", + "Ġa dam", + "Ġad am", + "Ġada m", + "Ġ (...", + "Ġ( ...", + "Ġ(. ..", + "Ġsub stitution", + "Ġsubstit ution", + "Ġsubst itution", + "Ġ boarding", + "Ġbo arding", + "Ġboard ing", + "Ġ Keyword", + "ĠKey word", + "ĠAss ault", + "dbc Template", + "Ġ orderId", + "Ġorder Id", + "( engine", + "(e ngine", + ".assert That", + "ĠV enus", + "ĠVen us", + "Ġhom icide", + "Ġhomic ide", + "ĠA val", + "ĠAv al", + "ĠAva l", + "Ġg utter", + "Ġgut ter", + "Ġ Supported", + "ĠS upported", + "ĠSup ported", + "ĠSupport ed", + "/ part", + "/p art", + "Ġac claimed", + "Ġacclaim ed", + "H istor", + "Hi stor", + "His tor", + "Hist or", + "Ġm eses", + "Ġme ses", + "Ġmes es", + "ü ber", + "üb er", + "ĠRe new", + "ĠRen ew", + "ĠRene w", + "Ġg ras", + "Ġgr as", + "Ġgra s", + "Ġ Ek", + "ĠE k", + "Ġ infile", + "Ġin file", + "Ġinf ile", + "in dy", + "ind y", + ". music", + ".m usic", + ".mu sic", + ". Scroll", + ".S croll", + ".Sc roll", + "ĠA ges", + "ĠAg es", + "ĠAge s", + "ĠNar uto", + "ĠG ather", + "ĠGa ther", + "ĠGat her", + "Ġconfirm ing", + "= (\"", + "=( \"", + "Ġp itched", + "Ġpitch ed", + "Ġpit ched", + "o ley", + "ol ey", + "ole y", + "F rance", + "Fr ance", + "Fran ce", + "Fra nce", + "Franc e", + "+ '\"", + "+' \"", + "$ total", + "$t otal", + "Ġ onde", + "Ġo nde", + "Ġon de", + "Ġd itch", + "Ġdit ch", + "_ sigma", + "_s igma", + "_sig ma", + "Ġcontin uity", + "Ġcontinu ity", + "r eward", + "re ward", + "rew ard", + "- load", + "-l oad", + "-lo ad", + "Ġpro ceso", + "Ġproc eso", + "Ġproces o", + "L ocked", + "Loc ked", + "Lock ed", + "st aw", + "sta w", + "Ġsp inal", + "Ġspin al", + "Ġspi nal", + "l azy", + "la zy", + "laz y", + "! ==", + "!= =", + "j est", + "je st", + "jes t", + "Ġd un", + "Ġdu n", + "ĠRod gers", + "ĉ grid", + "ĉg rid", + "ĉgr id", + "Ġlo gos", + "Ġlog os", + "Ġlogo s", + "ĠBen gal", + "ĠBeng al", + ". super", + ".s uper", + ".sup er", + "Pro vides", + "Provid es", + "Provide s", + "Prov ides", + "Ġnut rient", + ". Timestamp", + ".T imestamp", + ".Time stamp", + "IZ ATION", + "åĨ Į", + "Ġf ats", + "Ġfa ts", + "Ġfat s", + "ĠX xx", + "c tica", + "ct ica", + "ctic a", + "Target s", + "Tar gets", + "Ġcont ours", + "Ġcontour s", + "Ġre ordered", + "Ġreorder ed", + ": Array", + ":A rray", + "Ġtoler ate", + "Ġtol erate", + "V ir", + "Vi r", + "Ġter ribly", + "Ġterr ibly", + "Ġb ricks", + "Ġbr icks", + "Ġbrick s", + "Ġbri cks", + "( &_", + "(& _", + "h b", + "P ortal", + "Port al", + "Por tal", + "ĠB read", + "ĠBr ead", + "ĠBre ad", + ". which", + ".wh ich", + "ÂŃ t", + "as InstanceOf", + "Ġj object", + "Ġjob ject", + "Ġjo bject", + "ĉ length", + "ĉl ength", + "ĉlen gth", + "_ MT", + "_M T", + "; \">čĊ", + ";\" >čĊ", + ";\"> čĊ", + "_ EXIST", + "_EX IST", + "Ġmat ernal", + "Ġma ternal", + "Ġmater nal", + "R EL", + "RE L", + "Ġê²½ ìļ°", + "h ee", + "he e", + "Ġ layouts", + "Ġlayout s", + "Ġlay outs", + "ĠL ap", + "ĠLa p", + "a isy", + "ai sy", + "ais y", + "Ġst umbled", + "Ġstumble d", + "ĠU IG", + "ĠUI G", + "ĠS co", + "ĠSc o", + "Ġim paired", + "Ġimp aired", + "Ġimpair ed", + "RES SED", + "RESS ED", + "Ġab uses", + "Ġabuse s", + "V F", + "A RB", + "AR B", + ". NAME", + ".N AME", + "r ch", + "rc h", + "pr imir", + "prim ir", + "pri mir", + "_ completed", + "_com pleted", + "_comp leted", + "_complete d", + "Ġp enny", + "Ġpen ny", + "Ġpenn y", + "Ch rome", + "Chr ome", + "( begin", + "(b egin", + "(be gin", + "er nen", + "ern en", + "erne n", + "- checkbox", + "-check box", + "Plain OldData", + "ĠL PC", + "ĠLP C", + "r ade", + "ra de", + "rad e", + "s pir", + "sp ir", + "spi r", + "Ġcon ceived", + "Ġconce ived", + "Ġconceive d", + "T ips", + "Tip s", + "Ti ps", + "ĠIo T", + "ĠG an", + "ĠGa n", + "èģ Ķ", + "Ġbi ases", + "Ġbias es", + "Ġconsult ants", + "Ġconsultant s", + "Ġconsulta nts", + "p led", + "pl ed", + "ple d", + "_ ht", + "_h t", + "associ ated", + "assoc iated", + "associate d", + "] ,ĊĊ", + "], ĊĊ", + "],Ċ Ċ", + "Ġdelight ful", + "ĠÑĤ ек", + "ĠÑĤе к", + "Hel vetica", + "( load", + "(l oad", + "(lo ad", + "- expand", + "-exp and", + "_W IDGET", + "t oa", + "to a", + "Ġ Akt", + "ĠA kt", + "ĠAk t", + "Ġo mn", + "Ġom n", + "Ġcl auses", + "Ġclause s", + "Ġcla uses", + "In tel", + "Int el", + "*/ }Ċ", + "_ registration", + "_reg istration", + "Ġold Value", + "Ġrest oring", + "Ġresto ring", + "Ġun real", + "Ġunre al", + "O VER", + "OVE R", + "OV ER", + "ĉĊ ĉĊĉĊ", + "ĉĊĉĊ ĉĊ", + "A TS", + "AT S", + "_ probe", + "_p robe", + "_pro be", + "_pr obe", + "_prob e", + "Ġdi visor", + "Ġdiv isor", + "Ġdivis or", + ".update Dynamic", + "å¹ ³", + "Produ ces", + "Prod uces", + "st amp", + "sta mp", + ".j boss", + "ĉ task", + "ĉt ask", + "! (:", + "!( :", + "Ġpsych ic", + "@ class", + "@c lass", + "M artin", + "Mar tin", + "Mart in", + "Ġ Passed", + "ĠP assed", + "ĠPass ed", + "ĠPas sed", + "clar ations", + "claration s", + "h el", + "he l", + "а Ñĩ", + "ĉ copy", + "ĉc opy", + "- bin", + "-b in", + "z an", + "za n", + "i gram", + "ig ram", + "igr am", + "া à¦", + "( sig", + "(s ig", + "(si g", + "ĠC aval", + "ĠCa val", + "ĠCav al", + "_ ##", + "Ġ %=", + "Ġ% =", + "out lined", + "outline d", + "ĠA cid", + "ĠAc id", + "Ġunpredict able", + "- dashboard", + "-d ashboard", + "Hex String", + "+ c", + ". Public", + ".P ublic", + "Ạ©", + "Ġcon veyor", + "Ġconvey or", + "Ġ EB", + "ĠE B", + "Ġselect s", + "Ġsel ects", + "Ġsele cts", + "Ġkn ocking", + "Ġknock ing", + "ĠC ec", + "ĠCe c", + "IB UTES", + "IBUT ES", + "IBUTE S", + "ow aÄĩ", + "owa Äĩ", + "g atsby", + "* v", + "ent ropy", + "entr opy", + "Ġdispatch ed", + "Ġdisp atched", + "Ġ camel", + "Ġc amel", + "Ġca mel", + "Ġcame l", + "Ġcam el", + "ĠSat urn", + "ĠSa turn", + "Ġover weight", + "( phone", + "(p hone", + "(ph one", + "par able", + "para ble", + "% B", + "_v ectors", + "_vector s", + "_vec tors", + "_vect ors", + "_ve ctors", + "Ġbr ewing", + "Ġbre wing", + "Ġbrew ing", + "Ġ Tk", + "ĠT k", + "Ġ Downloads", + "ĠDown loads", + "ĠDownload s", + "Ġ Saved", + "ĠS aved", + "ĠSave d", + "ĠSa ved", + "ĠSav ed", + ". Price", + ".P rice", + ".Pr ice", + "Ġc urved", + "Ġcur ved", + "Ġcurve d", + "ĠParent hood", + "ĠParen thood", + "è ¶", + ".p nl", + "plete ly", + "plet ely", + ". Day", + ".D ay", + "Ġadvert isers", + "Ġadvertis ers", + "Ġadvertise rs", + "Ġadvertiser s", + "Ġe jec", + "Ġej ec", + "Ġpr zed", + "Ġprz ed", + "Ġprze d", + "ë ¯", + "! ';Ċ", + "!' ;Ċ", + "ĠK ush", + "ĠKu sh", + "Ġ TAB", + "ĠT AB", + "ĠTA B", + "Ġ quests", + "Ġqu ests", + "Ġque sts", + "Ġquest s", + "Ġques ts", + "Ġcoinc idence", + "Ġcoincide nce", + "umm ies", + "ĠKash mir", + "ĠEth ics", + "ĠEthi cs", + "_ growth", + "_g rowth", + "Ġ aktiv", + "Ġak tiv", + "Ġakt iv", + "Ġgroup ing", + "Ġgrou ping", + "å¢ ŀ", + "_ truth", + "_tr uth", + "åIJ ¬", + "t odos", + "to dos", + "todo s", + "tod os", + "i set", + "is et", + "ise t", + "Tex Coord", + "ä tt", + "ät t", + "ĠZ ur", + "ĠZu r", + "ro ys", + "roy s", + "_M AGIC", + "_MAG IC", + "Ġbrew ery", + "( State", + "ĠSM ALL", + "ĠSMA LL", + "ĠPl ants", + "ĠPlan ts", + "ĠPlant s", + "ĠPla nts", + "it bart", + "e acher", + "each er", + "ea cher", + "ĠAd elaide", + "L u", + "Ġf ick", + "Ġfi ck", + "Ġfic k", + "und les", + "undle s", + "_ loaded", + "_lo aded", + "_load ed", + "и е", + "P oll", + "Pol l", + "Po ll", + "r itic", + "ri tic", + "rit ic", + "E LY", + "EL Y", + "Ġ +'", + "Ġ+ '", + "ĠProf ession", + "Ġst amps", + "Ġstamp s", + "Ġsta mps", + "ĠS ew", + "ĠSe w", + "s crollView", + "scroll View", + "Ġcomm unist", + "Ġcommun ist", + "/ problems", + "/pro blems", + "/problem s", + "} čĊčĊčĊčĊ", + "}čĊ čĊčĊčĊ", + "}čĊčĊ čĊčĊ", + "}čĊčĊčĊ čĊ", + ", o", + "Ġ udp", + "Ġu dp", + "Ġud p", + "Ġob ese", + "Ġobe se", + "ap prove", + "app rove", + "appro ve", + "anc ellation", + "ancel lation", + "ancell ation", + "_ Game", + "_G ame", + "Ġ Hashtable", + "ĠHash table", + "ĠHas htable", + "adaptive Styles", + "Ġposs esses", + "Ġpossess es", + ". matcher", + ".m atcher", + ".match er", + ".mat cher", + "function al", + "M rs", + "Mr s", + "ĉ save", + "ĉs ave", + "Ġ DbType", + "ĠDb Type", + "Ġ ken", + "Ġk en", + "Ġke n", + "get Context", + "Ġ mans", + "Ġm ans", + "Ġman s", + "Ġma ns", + "( rel", + "(r el", + "(re l", + "ĠBrother hood", + ") `Ċ", + ")` Ċ", + "è§ £", + ". Information", + ".In formation", + "OutOfRange Exception", + "ĠS ek", + "ĠSe k", + "C as", + "Ca s", + "Ġblog gers", + "Ġblogger s", + "E ither", + "( \"\"\"", + "(\" \"\"", + "(\"\" \"", + "Ġp inch", + "Ġpin ch", + "Ġco arse", + ") p", + "ĠP ulse", + "ĠPu lse", + "ĠPul se", + "Ġlearn t", + "Ġlear nt", + "Ġdent ist", + "Ġon change", + "Ġdirect ives", + "Ġdirective s", + "( actions", + "(a ctions", + "(action s", + "(act ions", + "ny der", + "ĠS hir", + "ĠSh ir", + "ĠShi r", + "T rait", + "Tr ait", + "Tra it", + "_ dep", + "_d ep", + "_de p", + "Ġ PET", + "ĠP ET", + "ĠPE T", + "Ġ REP", + "ĠR EP", + "ĠRE P", + ".App Settings", + "cu ador", + "ide nav", + "iden av", + "Ġen vi", + "Ġenv i", + "Ġsl ammed", + "Ġslam med", + "Ġ Shoot", + "ĠS hoot", + "ĠSh oot", + "ĠSho ot", + "Ġ dateFormat", + "Ġdate Format", + ".j oda", + "ve ys", + "vey s", + "Ġ ).ĊĊ", + "Ġ) .ĊĊ", + "Ġ). ĊĊ", + "Ġ).Ċ Ċ", + "Ġcar eg", + "Ġca reg", + "Ġcare g", + "Ġ Parallel", + "ĠPar allel", + "_ translation", + "_trans lation", + ". functions", + ".function s", + ".fun ctions", + ". obs", + ".o bs", + ".ob s", + "Runtime Exception", + "[ ]=", + "[] =", + "ov erview", + "over view", + "ĠS chl", + "ĠSc hl", + "ĠSch l", + "Ġno isy", + "Ġnoi sy", + "ĠOn PropertyChanged", + "S ending", + "Send ing", + "Sen ding", + "Ġunf amiliar", + "U pon", + "Up on", + "ĠPr ints", + "ĠPrint s", + "ĠPri nts", + ". typ", + ".t yp", + "Ġfle eing", + "Ġflee ing", + "ĉ move", + "ĉm ove", + "ĉmov e", + "( Un", + "(U n", + "Ġ qr", + "Ġq r", + "× ľ", + "_ beta", + "_b eta", + "_be ta", + "_bet a", + "Ġsk ies", + "Ġski es", + "ĉ me", + "ĉm e", + "W ND", + "WN D", + "Ġst ickers", + "Ġstick ers", + "Ġsticker s", + "b las", + "bl as", + "bla s", + "Ġins erts", + "Ġinsert s", + "Ġinser ts", + "Ġinse rts", + "Ġver ses", + "Ġvers es", + "Ġverse s", + "ĠD ew", + "ĠDe w", + "Ġt angible", + "Ġtang ible", + "Ġh echo", + "Ġhe cho", + "P OL", + "PO L", + "Ġte ardown", + "Ġtear down", + "om nia", + "I BE", + "IB E", + ". cover", + ".c over", + ".co ver", + "_ strategy", + "_str ategy", + "^ -", + "set Position", + "u ale", + "ual e", + "ua le", + "S igned", + "Sign ed", + "Sig ned", + "Ġ iface", + "Ġif ace", + "Ġi face", + "as eline", + "ase line", + "asel ine", + ".set Time", + "ĠMin eral", + "ĠMine ral", + "ĠMiner al", + "ĠF ighting", + "ĠFight ing", + "s kins", + "sk ins", + "ski ns", + "skin s", + "Ġdiscrim in", + "Ġd ansk", + "Ġdans k", + "Ġdan sk", + "ĠPr inceton", + "ĠPrince ton", + "ac ist", + "aci st", + "Ġ ());Ċ", + "Ġ( ));Ċ", + "Ġ() );Ċ", + "Ġ()) ;Ċ", + "tr acks", + "tra cks", + "track s", + "imon ial", + "a decimal", + "ad ecimal", + "ade cimal", + "EP ROM", + "ug gle", + "ugg le", + ". Notification", + ".Not ification", + "$ mail", + "$m ail", + "c antidad", + "cant idad", + "ĠJ ung", + "ĠJun g", + "ĠJu ng", + "Ġsee kers", + "Ġseek ers", + "Ġseeker s", + "Ġpl ausible", + "t ier", + "ti er", + "tie r", + "е ж", + "еР¶", + "Ġ rapper", + "Ġr apper", + "Ġrap per", + "Ġra pper", + "Ġrapp er", + "Ġ Mana", + "ĠM ana", + "ĠMan a", + "ĠMa na", + "ĠHttp StatusCode", + "ĠHttpStatus Code", + "Ġbur nt", + "Ġburn t", + "l oses", + "lo ses", + "lose s", + "los es", + "Ġ Foto", + "ĠF oto", + "ĠFo to", + "ĠFot o", + "Ġ JsonObject", + "ĠJson Object", + "In stagram", + "Inst agram", + "Ġ syscall", + "Ġsys call", + "Ġreal ities", + "ĠMAT LAB", + ":^ {Ċ", + "T ERM", + "TE RM", + "TER M", + "ĠC bd", + "Ġ Paragraph", + "ĠPar agraph", + "ĠPara graph", + "Ġtrav és", + "Ġconstruct ing", + "Ġs wal", + "Ġsw al", + "Ġp ige", + "Ġpi ge", + "Ġpig e", + "L LLL", + "LL LL", + "LLL L", + "- existing", + "-ex isting", + "G ets", + "Get s", + "Ge ts", + "Ġmel ted", + "Ġmelt ed", + "Ġmit igate", + "Ġmitig ate", + "H en", + "He n", + "Ġ hm", + "Ġh m", + "i mas", + "im as", + "ima s", + "Ġ Ao", + "ĠA o", + "ĠP erez", + "ĠPer ez", + "ĠPe rez", + "ĠPere z", + "Ġ DAL", + "ĠD AL", + "ĠDA L", + "Ġ ëĭ¤", + "Ġëĭ ¤", + "Ġdi vis", + "Ġdiv is", + "Storyboard Segue", + "Ġ Modify", + "ĠMod ify", + "ĠModi fy", + "ĠÃľ ber", + "_O VERRIDE", + ".p em", + ".pe m", + "un tos", + "unt os", + "unto s", + "Ġesp añ", + "Ġespa ñ", + "Ġ{ ?", + "Ġ PAY", + "ĠP AY", + "ĠPA Y", + "_ ipv", + "_i pv", + "_ip v", + "ĠF ury", + "ĠFu ry", + "ĠFur y", + "__ .__", + "__. __", + "e low", + "el ow", + "elo w", + "-c entered", + "-center ed", + "-cent ered", + "che cks", + "check s", + "_ Reg", + "_R eg", + "_Re g", + "-J avadoc", + "ĉ load", + "ĉl oad", + "ĠLike wise", + "ĠLik ewise", + "ا Ùħ", + "ا٠ħ", + "U NE", + "UN E", + ". sem", + ".s em", + ".se m", + "x cb", + "xc b", + "ĠC ave", + "ĠCa ve", + "ĠCav e", + "_ sleep", + "_s leep", + "Ġsil ently", + "Ġsilent ly", + "Ġ Extreme", + "ĠExt reme", + "ĠExtr eme", + ". ToUpper", + ".To Upper", + "ĉ CHECK", + "ĉC HECK", + "Ġ cue", + "Ġc ue", + "Ġcu e", + "ĠQ ByteArray", + "Ġcor rupted", + "Ġcorrupt ed", + "Ġ Dé", + "ĠD é", + "Ġim ped", + "Ġimp ed", + "Ġimpe d", + "Get Name", + "Ġinaccur ate", + "Ġs ober", + "Ġso ber", + "Ġsob er", + "е е", + "еРµ", + "Ġ barcode", + "Ġbar code", + "Ġba rcode", + "-- ){Ċ", + "--) {Ċ", + "in ki", + "ink i", + "Ġ ép", + "Ġé p", + "Ġd ri", + "Ġdr i", + "Ġ ALT", + "ĠA LT", + "ĠAL T", + "> >>>>>>>", + ">>>> >>>>", + ">>>>>>> >", + "on ta", + "ont a", + "[ L", + "Ġint eres", + "Ġinter es", + "Ġinte res", + "ver ting", + "vert ing", + "Ġd iagnostics", + "Ġdi agnostics", + "Ġdiagnostic s", + "p dev", + "pd ev", + "è ©", + "Ġ Integrated", + "ĠInt egrated", + "ĠIntegr ated", + ") .'", + "). '", + "_ gc", + "_g c", + "$ text", + "$t ext", + ". games", + ".g ames", + ".game s", + ".ga mes", + "ĠT erra", + "ĠTer ra", + "ĠTerr a", + "' Re", + "'R e", + ". transfer", + ".trans fer", + "_F IFO", + "get Model", + "Ġb land", + "Ġbl and", + "Ġbla nd", + "ĠCol eman", + "ĠCole man", + "Ġpr imes", + "Ġprim es", + "Ġprime s", + "Ġpri mes", + "Ġ æĪ", + "Ġæ Ī", + "Ġcross es", + "n k", + "G ING", + "GIN G", + "GI NG", + "Ġ '^", + "Ġ' ^", + "Ġ Blob", + "ĠB lob", + "ĠBl ob", + "ĠBlo b", + "Ġinter course", + "ĠBl vd", + "Ġweigh s", + "_ regular", + "_reg ular", + "ĠPer th", + "ĠPert h", + "Ġsepar ating", + "Ġseparat ing", + "Ġb illed", + "Ġbill ed", + "Ġbil led", + ".tab Control", + "Ġp uppet", + "Ġpup pet", + "Ġutil ization", + "Ġutiliz ation", + "Ġutiliza tion", + "Ġ âĸł", + "Ġâĸ ł", + "Ġsuc ces", + "Ġsucc es", + "Ġl amps", + "Ġla mps", + "Ġlamp s", + "Ġlam ps", + "_ proj", + "_p roj", + "_pro j", + "_pr oj", + "E ric", + "Er ic", + "Ġre novation", + "Ġren ovation", + "Ġrenov ation", + "ĠF amilies", + "ĠFam ilies", + "ĠFamil ies", + "ĠFamilie s", + "Ġ Bits", + "ĠB its", + "ĠBit s", + "ĠBi ts", + "part ials", + "partial s", + "- Men", + "-M en", + "s olution", + "sol ution", + "Ġd warf", + "Ġdw arf", + "Ġdwar f", + ". INTEGER", + ".IN TEGER", + "Ġ LOCK", + "ĠL OCK", + "ĠLO CK", + "ĠLOC K", + ". ct", + ".c t", + "Ġ excerpt", + "Ġex cerpt", + "Ġexcer pt", + "Ġ Pix", + "ĠP ix", + "ĠPi x", + "Ġ FirstName", + "ĠFirst Name", + "AN TED", + "ANT ED", + "ANTE D", + "ĠAd mir", + "- help", + "-h elp", + "-he lp", + "P rior", + "Pr ior", + "Pri or", + "Ġ Align", + "ĠAl ign", + "ĠAli gn", + ". INSTANCE", + ".IN STANCE", + "Line Edit", + "(' /:", + "('/ :", + "Ġ inet", + "Ġin et", + "Ġi net", + "Ġine t", + "od us", + "odu s", + ".p kl", + ".pk l", + "Ġ KY", + "ĠK Y", + "u pert", + "up ert", + "uper t", + "upe rt", + "Ġn erves", + "Ġnerv es", + "Ġnerve s", + "Ġner ves", + "_ gradient", + "_g radient", + "_grad ient", + "} ','", + "}' ,'", + "}', '", + "_un ref", + "Ġs aturated", + "Ġsatu rated", + "Ġsatur ated", + "Ġ Connected", + "ĠConnect ed", + "ĠConn ected", + "Ġ FN", + "ĠF N", + "EX IT", + "Ġtele port", + "Ġav ait", + "Ġava it", + "Page Route", + "Ġdivorce d", + "Ġdivor ced", + "( lang", + "(l ang", + "f st", + "fs t", + "ĠT yr", + "ĠTy r", + "Ġm essenger", + "Ġmess enger", + "i fstream", + "if stream", + "X S", + "ĠB anking", + "ĠBank ing", + "ĠBan king", + "Ġinfect ious", + "ĠM ons", + "ĠMon s", + "ĠMo ns", + "_ LOOP", + "_LO OP", + "Ġzur ück", + "Ġob tener", + "Ġobt ener", + "Ġobten er", + "/ repos", + "/re pos", + "V el", + "Ve l", + "a cro", + "ac ro", + "acr o", + "Ġ userRepository", + "Ġuser Repository", + "style Type", + "Ġ SRC", + "ĠS RC", + "ĠSR C", + "VML INUX", + "rec ursive", + "/ bar", + "/b ar", + "_ chip", + "_c hip", + "_ch ip", + "_chi p", + "o minated", + "om inated", + "omin ated", + "Ġ Nit", + "ĠN it", + "ĠNi t", + "âĢĶ to", + "ĠBudd h", + "ĠBud dh", + "о меÑĢ", + "ом еÑĢ", + "ĠM AG", + "ĠMA G", + "Ġ CHE", + "ĠC HE", + "ĠCH E", + "_ den", + "_d en", + "_de n", + ". raises", + ".r aises", + ".raise s", + ".ra ises", + "_ degree", + "_d egree", + "_de gree", + "_deg ree", + "Ġpump kin", + "_ templates", + "_t emplates", + "_template s", + "_temp lates", + "_tem plates", + "_ MEDIA", + "_M EDIA", + "_MED IA", + "Ġ Timeline", + "ĠT imeline", + "ĠTime line", + "ĠTim eline", + "Ġ bots", + "Ġb ots", + "Ġbo ts", + "Ġbot s", + "Object Type", + "Ġbu ys", + "Ġbuy s", + ". posts", + ".post s", + ".pos ts", + ".po sts", + "C AL", + "CA L", + "wa iting", + "wait ing", + "ĠDaniel s", + "ĠDani els", + "Ġd abei", + "Ġda bei", + "Ġdab ei", + "Ġ Sigma", + "ĠS igma", + "ĠSig ma", + "i lor", + "il or", + "ilo r", + "i gel", + "ig el", + "ige l", + ", W", + "A DS", + "AD S", + "( panel", + "(p anel", + "(pa nel", + "ì² ´", + "it ating", + "ita ting", + "itat ing", + ". palette", + ".p alette", + ".pa lette", + "Ġmos quito", + "Ġt ego", + "Ġte go", + "Ġteg o", + "( parseInt", + "(parse Int", + "Ġdes pués", + "p romise", + "pro mise", + "prom ise", + "Ġ wij", + "Ġw ij", + "Ġwi j", + "type script", + "types cript", + "Ġ Tv", + "ĠT v", + "_IDENT IFIER", + ") .ĊĊĊ", + "). ĊĊĊ", + ").ĊĊ Ċ", + ").Ċ ĊĊ", + "_ flat", + "_f lat", + "_fl at", + "it su", + "its u", + "U SR", + "US R", + "ex perience", + "- fit", + "-f it", + "-fi t", + "ph inx", + "phi nx", + "phin x", + "_ thresh", + "_th resh", + "_thr esh", + "Ġide ally", + "Ġideal ly", + "ĠFree man", + "ĠFre eman", + "ĠFreem an", + ", DB", + ",D B", + "_ rw", + "_r w", + "çŃ ī", + "U b", + "_ statistics", + "_stat istics", + "=\" \"><", + "=\"\" ><", + "=\"\"> <", + "Ġch ore", + "Ġcho re", + "Ġchor e", + "Ġy ork", + "Ġyo rk", + "inst alled", + "install ed", + "Add itionally", + "Additional ly", + "Ġp stmt", + "Ġpst mt", + "yl ko", + ": :Ċ", + ":: Ċ", + "F orest", + "For est", + "Fore st", + "Fo rest", + "Ġhead set", + "Ġheads et", + "Ġg allon", + "Ġgal lon", + "Ġgall on", + "ÑĢ ем", + "ÑĢе м", + "Ġwithdraw n", + "Ġwithd rawn", + "Ġ Candidate", + "ĠC andidate", + "ĠCandid ate", + "Ġmel ting", + "Ġmelt ing", + "Ġfree zer", + "Ġfreeze r", + "Ġ hl", + "Ġh l", + "_ HELP", + "_HE LP", + "_HEL P", + "m ime", + "mi me", + "( /*", + "(/ *", + "Ġth irst", + "Ġthi rst", + "$ return", + "$r eturn", + "$ret urn", + "member of", + "е б", + "еР±", + "Ġ HttpServletRequest", + "ĠHttp ServletRequest", + "ĠHttpServlet Request", + "( ob", + "(o b", + "_ Result", + "_Res ult", + "Ġassert ed", + "Ġfulfill ing", + "Ġfulfil ling", + "Ġstretch es", + "Ġstret ches", + "par ated", + "pa rated", + "para ted", + "parate d", + "-f unded", + "Ġ åĽ", + "Ġå Ľ", + "in gles", + "ing les", + "ingle s", + "_ ca", + "_c a", + ". condition", + ".con dition", + ".cond ition", + "Ġ Displays", + "ĠDis plays", + "ĠDisplay s", + "ĠDisp lays", + "Ġ orang", + "Ġo rang", + "Ġor ang", + "Ġora ng", + "Ġoran g", + "Ġ CRE", + "ĠC RE", + "ĠCR E", + "Ġgl Bind", + "Ġ Selector", + "ĠSe lector", + "ĠSelect or", + "ĠSel ector", + "ĠSele ctor", + "/ type", + "/t ype", + "ĠAlex a", + "ĠAle xa", + "ched ules", + "chedule s", + "ĠPen insula", + "Ġ parity", + "Ġp arity", + "Ġpar ity", + "Ġpari ty", + "ĉ dest", + "ĉd est", + "ĉdes t", + "ĉde st", + "ĠDo ors", + "ĠDoor s", + "čĊ ĉčĊ", + "_ dimension", + "_d imension", + "_dim ension", + "Ġ aload", + "Ġa load", + "Ġal oad", + "Ġalo ad", + ".St oredProcedure", + "( paren", + "(p aren", + "(par en", + "(pa ren", + "ĠBur ke", + "ĠBurk e", + "' )]Ċ", + "') ]Ċ", + "')] Ċ", + "- engine", + "-e ngine", + "-eng ine", + "Ġ quir", + "Ġqu ir", + "Ġq uir", + "Ġqui r", + "ĠH ybrid", + "ĠHy brid", + "ĠD oe", + "ĠDo e", + "Ġout lines", + "Ġoutline s", + "ĠT rends", + "ĠTr ends", + "ĠTre nds", + "ĠTrend s", + "_ NV", + "_N V", + "per iments", + "periment s", + "peri ments", + "ĠH in", + "ĠHi n", + "? ',", + "?' ,", + "ĉ Text", + "ĉT ext", + "F UL", + "FU L", + "Ġsm ells", + "Ġsmell s", + "Ġ slick", + "Ġs lick", + "Ġsl ick", + "Ġslic k", + "Ġmis erable", + "Ġmiser able", + "ĠArray Adapter", + "Ġparam String", + "H om", + "Ho m", + "_l iterals", + "_literal s", + "_lit erals", + "us uarios", + "usuario s", + "usu arios", + "Ġprompt ing", + "_ lazy", + "_l azy", + "_la zy", + "Ġ Activation", + "ĠAct ivation", + "ĠActiv ation", + "_ oc", + "_o c", + "We ak", + "Ġan ecd", + "ĠU CLA", + "ĠUC LA", + "= re", + "=r e", + "iss ement", + "isse ment", + "ĠEsc orts", + "ĠEscort s", + "Ex cellent", + "Ġ Pause", + "ĠP ause", + "ĠPa use", + "Ġ repositories", + "Ġre positories", + "Ġrepos itories", + "T OR", + "TO R", + "ar iate", + "ari ate", + "aria te", + "ariat e", + "_ iso", + "_i so", + "_is o", + "up dates", + "update s", + "upd ates", + "ha lb", + "hal b", + "udi ante", + "udiant e", + "ë¡ Ŀ", + "Ġna ive", + "ĠP eg", + "ĠPe g", + "ĠL ounge", + "ĠLo unge", + "ĠLou nge", + "AR GIN", + "ARG IN", + "( bin", + "(b in", + "On ClickListener", + "OnClick Listener", + "Ġ FAILED", + "ĠFA ILED", + "ĠFAIL ED", + "Ġ lite", + "Ġl ite", + "Ġli te", + "Ġlit e", + "Ġd zie", + "Ġdz ie", + "Ġdzi e", + "Ġ Literal", + "ĠL iteral", + "ĠLiter al", + "ĠLit eral", + "ĠLite ral", + "i vor", + "iv or", + "ivo r", + "f cntl", + "fc ntl", + "fcn tl", + "Ġe ats", + "Ġeat s", + "Ġea ts", + "Ġ qed", + "Ġq ed", + "Un lock", + "r iding", + "ri ding", + "rid ing", + "und ai", + "unda i", + "= M", + "AT TER", + "ATT ER", + "Configure Await", + "ic ias", + "ici as", + "icia s", + "ust omed", + "ustom ed", + "usto med", + "Ġsuccess ion", + "Ġsuc cession", + "Ġsucc ession", + "end Time", + "ĠJ upiter", + "Ġj udging", + "Ġjud ging", + "d ration", + "dr ation", + "dra tion", + "_ docs", + "_d ocs", + "_doc s", + "_do cs", + ". mo", + ".m o", + "Ġeduc ators", + "Ġeducator s", + "ĠV ine", + "ĠVi ne", + "ĠVin e", + "C ond", + "Con d", + "Co nd", + "[ out", + "[o ut", + "q b", + "\\ Validator", + "Ġmean ings", + "Ġmeaning s", + "Ġpres ently", + "Ġpresent ly", + "Ġdiv iding", + "Ġdivid ing", + "otten ham", + "as cular", + "asc ular", + "Ġtrail ers", + "Ġtra ilers", + "Ġtrailer s", + "Ġtrai lers", + "Ġ CLOSE", + "ĠC LOSE", + "ĠCL OSE", + "а ми", + "ам и", + "âĢĻ ai", + "âĢĻa i", + "Ġ Gain", + "ĠG ain", + "ĠGa in", + "w or", + "wo r", + "Ġpl anner", + "Ġplan ner", + "Ġdistrib uting", + "v at", + "va t", + "mon ths", + "month s", + "mont hs", + "x label", + "xl abel", + "H F", + "V iol", + "Vi ol", + ".BASE LINE", + "еÑĤ ÑģÑı", + "Ġ Rotate", + "ĠR otate", + "ĠRot ate", + "Ġ txn", + "Ġt xn", + "Ġtx n", + ": bold", + ":b old", + "Ġb loss", + "Ġbl oss", + "Ġblo ss", + "Forg ery", + "Forge ry", + "( embed", + "(em bed", + "Ġj ako", + "Ġja ko", + "Ġjak o", + "s printf", + "the ir", + "Ġexhib its", + "Ġexhibit s", + "- static", + "-st atic", + "-stat ic", + "he cy", + "hec y", + "get ActiveSheet", + ". clients", + ".c lients", + ".client s", + ".cl ients", + ".cli ents", + "ãģ į", + "_ hide", + "_h ide", + "_hi de", + "_hid e", + "[ word", + "[w ord", + "C b", + "add Item", + "a xe", + "ax e", + "_ radio", + "_r adio", + "_rad io", + "_ra dio", + "_radi o", + "a lion", + "al ion", + "ali on", + "mod ifier", + "Ġsat uration", + "Ġsatu ration", + "Ġsatur ation", + "Ġde nom", + "Ġden om", + "_ pixels", + "_p ixels", + "_pixel s", + "_pix els", + "m ess", + "me ss", + "mes s", + "( fl", + "(f l", + "a tif", + "at if", + "ati f", + "Ġ secs", + "Ġs ecs", + "Ġse cs", + "Ġsec s", + "Ġpro stitution", + "Ġprostit ution", + "Ġprost itution", + "Ġprostitu tion", + "Ġprostitut ion", + "Ġgrand children", + "Ġparad ise", + "ĠF eld", + "ĠFe ld", + "ĠFel d", + "_B INARY", + "_BIN ARY", + "it ous", + "ito us", + "itou s", + "à ¹Ħ", + "๠Ħ", + "Ġfl ashing", + "Ġflash ing", + "-s ided", + "-side d", + "Ġcontrad iction", + "Ġcontradict ion", + "/ *ĊĊ", + "/* ĊĊ", + "/*Ċ Ċ", + "y label", + "yl abel", + "yla bel", + "ĠT et", + "ĠTe t", + "Ġadm ire", + "Ġadmir e", + "r eso", + "re so", + "res o", + "Ġl etz", + "Ġle tz", + "Ġlet z", + "Ġ SEARCH", + "ĠSE ARCH", + "s lots", + "sl ots", + "slot s", + "ĠRe wards", + "ĠRew ards", + "ĠReward s", + "ĠH og", + "ĠHo g", + "Ġ NSData", + "ĠNS Data", + "st ash", + "sta sh", + "F all", + "Fa ll", + "Fal l", + "ĠA mer", + "ĠAm er", + "Line arLayout", + "Linear Layout", + "/ photos", + "/photo s", + "/ph otos", + "Ġfe ather", + "Ġfeat her", + "Ġ |čĊ", + "Ġ| čĊ", + "Down loads", + "Download s", + ".Start sWith", + "Ġ //#", + "Ġ// #", + "Ġ/ /#", + "ine Transform", + "Ġaff id", + "Ġaf fid", + "V tbl", + "ĠR ogue", + "ĠRo gue", + "ĠRog ue", + "s cribed", + "scribe d", + "scri bed", + "Ġf auc", + "Ġfa uc", + "ĠMon roe", + "Ġdecl ares", + "Ġdeclar es", + "Ġdeclare s", + "mod ern", + "mode rn", + "r eon", + "re on", + "reo n", + "ay be", + "P ASS", + "PA SS", + "f ers", + "fer s", + "fe rs", + "_MULT I", + "_MUL TI", + "ĠMath ematics", + "ĠMathematic s", + "Ġsud ah", + "_ATT ACH", + "Ġnumber With", + "ĠSol omon", + "ĠSolo mon", + "j in", + "ji n", + "ograf ia", + "ogr afia", + "ö l", + "_ design", + "_d esign", + "_de sign", + "_des ign", + "c ulated", + "cul ated", + "culate d", + "cu lated", + "ĠL una", + "ĠLu na", + "ĠLun a", + "i esz", + "ies z", + "ie sz", + "Ġ =>'", + "Ġ= >'", + "Ġ=> '", + "Ġreve lations", + "Ġrevel ations", + "Ġrevelation s", + "A long", + "Al ong", + "( ed", + "(e d", + "Ġ Filename", + "ĠF ilename", + "ĠFile name", + "ĠFil ename", + "ĠFi lename", + "Ġ ylabel", + "Ġy label", + "S ecure", + "Sec ure", + "Ġbus ca", + "Ġbusc a", + "ag nosis", + "agn osis", + "_RE CE", + "_REC E", + "Ġover lapping", + "Ġoverlap ping", + "Ġoverl apping", + "Ex tent", + "Ext ent", + "Ġant icipation", + "Ġanticip ation", + "Ġantic ipation", + "Check s", + "Che cks", + "ĠAL SO", + "ĠALS O", + "o rc", + "or c", + "iling ual", + "it ational", + "itation al", + "itat ional", + "Ġadv ancement", + "Ġadvance ment", + "o uro", + "ou ro", + "our o", + "Ġ Predicate", + "ĠP redicate", + "ĠPred icate", + "å¾ Ĺ", + "e ria", + "er ia", + "eri a", + "ĠP ierce", + "ĠPi erce", + "ĠPier ce", + "o rio", + "or io", + "ori o", + "Ġmer its", + "Ġmerit s", + "Ġpe anut", + "Ġpea nut", + ". Package", + ".P ackage", + "ĠCon duct", + "ĠCond uct", + "_ SENSOR", + "_S ENSOR", + "_SENS OR", + "Ġbo iling", + "Ġboil ing", + "Ġin tra", + "Ġint ra", + "Ġintr a", + "Ġ IGN", + "ĠI GN", + "ĠIG N", + "ĠF ur", + "ĠFu r", + ". Refresh", + ".Re fresh", + ".Ref resh", + "Ġ Reach", + "ĠR each", + "ĠRe ach", + "_ decoder", + "_de coder", + "_dec oder", + "_decode r", + ". Exp", + ".Ex p", + ".E xp", + "Ġ ÑĤак", + "ĠÑĤ ак", + "ĠÑĤа к", + "p ill", + "pi ll", + ", Q", + "ĠGr ill", + "ĠGri ll", + "Ġp opping", + "Ġpop ping", + "Ġpo pping", + ". Ag", + ".A g", + "Ġpro yecto", + "Ġmile age", + "Ġec ological", + "Ġeco logical", + "] ]);Ċ", + "]] );Ċ", + "]]) ;Ċ", + "Ġ ÂŃ", + "Ġ Ń", + "sub plot", + "a cad", + "ac ad", + "aca d", + "Ġ Trying", + "ĠT rying", + "ĠTr ying", + "ĠTry ing", + "rec ipes", + "recipe s", + "$ criteria", + "$c riteria", + "ĠPer sian", + "ĠPers ian", + "- bound", + "-b ound", + "-bo und", + "M ASK", + "MA SK", + "MAS K", + "Ġ Gesture", + "ĠG esture", + "ĠGes ture", + "ĠGest ure", + "Ġ kk", + "Ġk k", + "ĠP VC", + "ĠPV C", + "Ġpro hibition", + "Ġprohib ition", + "Ġprohibit ion", + "Ġcom ando", + "Ġco mando", + "Ġcoma ndo", + "Ġ LOOK", + "ĠL OOK", + "ĠLO OK", + "Sh opping", + "Shop ping", + "Ġdist ortion", + "Ġdistort ion", + "< Boolean", + ".Get Length", + "um pt", + "ump t", + "\\ Product", + "ell ery", + "elle ry", + "eller y", + "Ġfire wall", + "form atted", + "format ted", + ". redis", + ".re dis", + ".r edis", + ".red is", + "Ġ esa", + "Ġe sa", + "Ġes a", + "ĠRh ode", + "S om", + "So m", + ". non", + ".n on", + ".no n", + "Ġ ').", + "Ġ' ).", + "Ġ') .", + "Ġ getView", + "Ġget View", + "ạ n", + "p rus", + "pr us", + "Mat thew", + "Ġs ia", + "Ġsi a", + "ĠF ors", + "ĠFor s", + "ĠFo rs", + "G PU", + "GP U", + "ient ras", + "ien tras", + "_ INST", + "_IN ST", + "_I NST", + "_INS T", + "Ġo larak", + "Ġol arak", + "Ġola rak", + "Ġimport ing", + "Ġimp orting", + "T CP", + "TC P", + "/ \");Ċ", + "/\" );Ċ", + "/\") ;Ċ", + "e ither", + "ei ther", + "Ġfresh ly", + "c ascade", + "cas cade", + "( character", + "(char acter", + "ĠJe ep", + "o tics", + "ot ics", + "otic s", + "oti cs", + "_ UTIL", + "_UT IL", + ".Xtra Printing", + ".first Child", + "ĠEx cell", + "ĠExcel l", + "ĠExc ell", + "Ġd vd", + "Ġdv d", + "Ġt aller", + "Ġtal ler", + "Ġta ller", + "Ġtall er", + "Ġ ras", + "Ġr as", + "Ġra s", + "y pass", + "yp ass", + "Ġassign s", + "Ġgr iev", + "Ġgri ev", + "- more", + "-m ore", + "J D", + "ĠBur ns", + "ĠBurn s", + "' >čĊ", + "'> čĊ", + ". Dependency", + ".D ependency", + ".Dep endency", + ". QueryString", + ".Query String", + ". Owner", + ".O wner", + "Ġ expiry", + "Ġex piry", + "Ġexp iry", + "T hu", + "Th u", + "( Vec", + "(V ec", + "Ġhazard ous", + "Ġ rpm", + "Ġr pm", + "Ġrp m", + "AP ON", + "APO N", + "Ġadd Target", + "s ville", + "sv ille", + "p Net", + "Ġ Img", + "ĠI mg", + "ĠIm g", + "Ġ TIMER", + "ĠT IMER", + "ĠTIM ER", + "ĠTIME R", + "ĠTI MER", + ". Animation", + ".An imation", + "Ġ bek", + "Ġb ek", + "Ġbe k", + "Ġas sort", + "Ġass ort", + "Ġle bih", + "Ġbody Parser", + "Ġvibr ating", + "Ġvib rating", + "I DL", + "ID L", + "Ġbutter knife", + "in ters", + "int ers", + "inter s", + "inte rs", + "Ġpersu ade", + "ĠLGBT Q", + "è ĭ", + ". soft", + ".s oft", + ".so ft", + "Ġbe ams", + "Ġbeam s", + "_ sur", + "_s ur", + "_su r", + ". Def", + ".D ef", + ".De f", + "Ġ labs", + "Ġl abs", + "Ġla bs", + "Ġlab s", + "ĉ plt", + "ĉp lt", + "ĉpl t", + "Ġ skins", + "Ġs kins", + "Ġsk ins", + "Ġskin s", + "Ġski ns", + "Ġtransfer ring", + "Ġtransf erring", + "Ġimag inary", + "Ġimagin ary", + "_ End", + "_E nd", + "; background", + "Ġ laps", + "Ġl aps", + "Ġla ps", + "Ġlap s", + "_ COMMENT", + "_COM MENT", + "_COMM ENT", + "( SDL", + "(S DL", + "o nds", + "on ds", + "ond s", + ". Record", + ".Re cord", + ".Rec ord", + "ĠIm plements", + "ĠImp lements", + "ĠImplement s", + "ĠImpl ements", + "_ ticks", + "_t icks", + "_tick s", + "_ti cks", + "( )))ĊĊ", + "() ))ĊĊ", + "()) )ĊĊ", + "())) ĊĊ", + "()))Ċ Ċ", + "Ġa rose", + "Ġar ose", + "] ?", + "Ġ Mp", + "ĠM p", + "ĠI Command", + "Ġsculpt ure", + "Ġcon tracted", + "Ġcontract ed", + "Ġcontr acted", + "< HTML", + "Ġcal end", + "a ty", + "at y", + "/ Sub", + "/S ub", + "Ġkv inn", + "Ġkvin n", + "_ IGNORE", + "ĠSh ane", + "ĠSha ne", + "ĠShan e", + "M LS", + "ML S", + "Ġstim ulate", + "Part ition", + "Ġ mun", + "Ġm un", + "Ġmu n", + "ó m", + "er ala", + "era la", + "eral a", + "- account", + "-a ccount", + "-ac count", + ". Binary", + ".B inary", + "c é", + "Ġse ize", + "Ġseiz e", + "Ġsei ze", + "conn ections", + "connect ions", + "connection s", + "Ġ ĊĠĠĠĠĠĠĠĠĊ", + "ĠĊ ĠĠĠĠĠĠĠĠĊ", + "Ġ Diagnostic", + "ĠDi agnostic", + "V ISIBLE", + "VIS IBLE", + "Ġ Runs", + "ĠR uns", + "ĠRun s", + "ĠRu ns", + "Ġim pressions", + "Ġimpress ions", + "Ġimpression s", + "Ġimpr essions", + "s uite", + "su ite", + "suit e", + "o ble", + "ob le", + "obl e", + "~ -", + "ak ukan", + "aku kan", + "< Person", + "

\">\" >\"> \">< /", + "_ indexes", + "_index es", + "Ġ valuation", + "Ġval uation", + "Ġvalu ation", + "Ġlife long", + "Ġlif elong", + "Ġexp edition", + "Ġexped ition", + "( Yii", + "(Y ii", + "Ġp ains", + "Ġpain s", + "Ġpa ins", + "Ġpai ns", + "Ġ PRI", + "ĠP RI", + "ĠPR I", + "Ġ Mixed", + "ĠM ixed", + "ĠMix ed", + "ĠMi xed", + "Ġ Changing", + "ĠCh anging", + "ĠChan ging", + "ĠChang ing", + "German y", + "Ger many", + "comm unication", + "communic ation", + ". organ", + ".org an", + ".o rgan", + ".or gan", + "ĠMar athon", + "ĠMara thon", + "get Path", + "Ġ Accuracy", + "ĠAc curacy", + "ĠAcc uracy", + "Ġ Globals", + "ĠG lobals", + "ĠGlobal s", + "ĠGlob als", + "') }}'", + "Ġ'\" >'", + "Ġ'\"> '", + "k inson", + "kin son", + "kins on", + "Ġ кол", + "Ġк ол", + "Ġко л", + "ogn itive", + "_ li", + "_l i", + "Ġim minent", + "Ġimm inent", + "Ġaff inity", + "Ġaf finity", + ". signal", + ".s ignal", + ".sign al", + ".sig nal", + "Ġn otch", + "Ġnot ch", + "ĠSteel ers", + "ĠSteele rs", + "max length", + "K K", + "ĠEu gene", + "ĠEug ene", + "_ PWM", + "_P WM", + "_PW M", + "r oi", + "ro i", + "Ġ âĹı", + "Ġâ Ĺı", + "ĠâĹ ı", + "ĠH amburg", + "ĠHam burg", + ". Must", + ".M ust", + "Ġ axe", + "Ġa xe", + "Ġax e", + "en ef", + "ene f", + "Ġamb itions", + "Ġambit ions", + "Ġambition s", + "Ġ Species", + "ĠS pecies", + "ĠSp ecies", + "ĠSpec ies", + "ĠSpe cies", + "ĠSt ress", + "ĠStr ess", + "ĠStre ss", + "Ġa while", + "Ġ бÑĥд", + "Ġб Ñĥд", + "ĠбÑĥ д", + "Ġwith stand", + "Ġ Decoder", + "ĠDe coder", + "ĠDec oder", + "ĠDecode r", + "_ inventory", + "_in ventory", + "Ġ{ ččĊ", + "Ġ tgt", + "Ġt gt", + "Ġtg t", + "Ġrail road", + "W ASHINGTON", + "Ġnegot iated", + "Ġnegotiate d", + "N ST", + "NS T", + "- phone", + "-p hone", + "-ph one", + ", U", + "Ġexerc ising", + "á» ¥", + "_P IXEL", + "_PIX EL", + "av ors", + "avor s", + "avo rs", + "ite rated", + "iter ated", + "iterate d", + "Ġv ampire", + "Ġvamp ire", + "a dal", + "ad al", + "ada l", + "In grese", + "Ing rese", + "Ġ ung", + "Ġu ng", + "Ġun g", + "j ective", + "ject ive", + ". cells", + ".c ells", + ".cell s", + "Ġ nano", + "Ġn ano", + "Ġna no", + "Ġnan o", + "Ġ markdown", + "Ġmark down", + "_ RULE", + "_R ULE", + "( events", + "(e vents", + "(event s", + "(ev ents", + "Ġl uggage", + "Ġlug gage", + "M ESSAGE", + "MESS AGE", + "ig keit", + "$ count", + "$c ount", + "Attribute Name", + "IG INAL", + "IGIN AL", + "_ Ent", + "_E nt", + "Ġ BF", + "ĠB F", + "Ġ COMMENT", + "ĠCOM MENT", + "ĠCOMM ENT", + "_ ini", + "_in i", + "_i ni", + "ĠEurope ans", + "ĠEuropean s", + "ĠB elle", + "ĠBe lle", + "ĠBel le", + "ĠBell e", + "åij ½", + ") ['", + ")[ '", + "åº Ķ", + "ĠUs eful", + "ĠUse ful", + ". reference", + ".re ference", + ".ref erence", + "( )\",", + "() \",", + "()\" ,", + "_ grade", + "_g rade", + "_gr ade", + "_grad e", + "ĠK aw", + "ĠKa w", + "Ġsent encing", + "Ġsocial ism", + "mon ster", + "mons ter", + "_L AYER", + "Ġdeep est", + "Ġdee pest", + "w k", + "Ġ Noise", + "ĠN oise", + "ĠNo ise", + "# ##ĊĊ", + "## #ĊĊ", + "### ĊĊ", + "###Ċ Ċ", + "Ġpr éc", + "Ġpré c", + "o tle", + "ot le", + "ÑĤ е", + "a uf", + "au f", + "i bal", + "ib al", + "iba l", + "Ġcon quer", + "Ġconqu er", + "> Email", + ">E mail", + "Ġamb ulance", + "O AD", + "OA D", + "Ġ (\"%", + "Ġ( \"%", + "Ġ(\" %", + "Ġ FI", + "ĠF I", + ". fixture", + ".f ixture", + ".fix ture", + "Ġt erse", + "Ġter se", + "Ġters e", + "ĠĠ ĠĠĉĉĉĉ", + "ĠĠĠĠ ĉĉĉĉ", + "ĠĠĠ Ġĉĉĉĉ", + "ĠĠĠĠĉ ĉĉĉ", + "ĠĠĠĠĉĉ ĉĉ", + "ĠĠĠĠĉĉĉ ĉ", + "Ġsanct uary", + "u gi", + "ug i", + "Ġ Comparator", + "ĠCom parator", + "ĠCompar ator", + "Definition s", + "Ġast hma", + "Ġl act", + "Ġla ct", + "Ġlac t", + "Ġhard wood", + ". clock", + ".c lock", + ".cl ock", + "Ġattr acting", + "Ġattract ing", + "ĠM our", + "ĠMo ur", + "ĠMou r", + "( distance", + "(d istance", + "(dist ance", + "(di stance", + "ic its", + "ici ts", + "icit s", + "Ġb onne", + "Ġbon ne", + "Ġ ACCESS", + "ĠAC CESS", + "ĠACC ESS", + ".Deserialize Object", + "Ġ Typed", + "ĠT yped", + "ĠType d", + "ĠTy ped", + "ĠTyp ed", + "Ġj eu", + "Ġje u", + "Ġ appId", + "Ġapp Id", + "ĠC lara", + "ĠCl ara", + "ĠClar a", + "ĠCla ra", + "Ġ HF", + "ĠH F", + "ĠRe ich", + "ĠRei ch", + "ip ples", + "ipp les", + "ipple s", + "// --------------------------------------------------------------------------------", + "//---------------------------------------------------------------- ----------------", + "//---------------------------------------------------------------------------- ----", + "//------------------------------------------------ --------------------------------", + "//-------------------------------- ------------------------------------------------", + "//---------------- ----------------------------------------------------------------", + "_ delivery", + "_d elivery", + "_del ivery", + "erial ization", + "Ġplaint iffs", + "Ġplaintiff s", + "S cient", + "Sc ient", + "Sci ent", + "sh opping", + "shop ping", + "Ġ Dummy", + "ĠD ummy", + "ĠDum my", + "ĠW ald", + "ĠWal d", + "ĠWa ld", + "Group Name", + "Ġ inscription", + "Ġin scription", + "Ġins cription", + "e log", + "el og", + "elo g", + ": :::::::", + ":: ::::::", + ":::: ::::", + ":::::: ::", + "::: :::::", + "::::: :::", + "::::::: :", + "_ ld", + "_l d", + "Back Pressed", + ". Raw", + ".R aw", + "ĠOn Trigger", + "Ġmuseum s", + "Ġmuse ums", + "Ġ Been", + "ĠB een", + "ĠBe en", + "ĠBee n", + "ĠAdvent ures", + "ĠAdventure s", + "Ġs late", + "Ġsl ate", + "Ġsla te", + "Ġ lett", + "Ġl ett", + "Ġle tt", + "Ġlet t", + "Ġs und", + "Ġsu nd", + "Ġsun d", + "ĠG in", + "ĠGi n", + "ĠMechan ical", + "ĠMech anical", + ". ship", + ".s hip", + ".sh ip", + "App Component", + "Ġdest ined", + "Ġdestin ed", + "Ġdw elling", + "Ġdwell ing", + "Pro filer", + "Profile r", + "Prof iler", + "Pre pare", + "ze ich", + "Ġsil icon", + "( has", + "(h as", + "Ġ# %", + "V IDEO", + "VID EO", + "Ġcollabor ate", + "L in", + "Li n", + "Ġ scopes", + "Ġsc opes", + "Ġscope s", + "Ġsco pes", + "Ġscop es", + "( className", + "(class Name", + "( sd", + "(s d", + "an din", + "and in", + "andi n", + ". ham", + ".h am", + "Service Impl", + "-de scribed", + "-des cribed", + "Ġir ony", + "Ġiron y", + "st ial", + "sti al", + "ĠHu awei", + "( repo", + "(re po", + "(rep o", + "Ġunexpected ly", + "ĠK ai", + "ĠKa i", + ". install", + ".inst all", + "\\ xf", + "\\x f", + "Ġex hibited", + "Ġexhib ited", + "Ġexhibit ed", + "_ TCP", + "_T CP", + "_TC P", + "ĠO x", + "_ CHO", + "_C HO", + "_CH O", + "Ġprostitu erte", + "Ġprostituer te", + "Ġ vä", + "Ġv ä", + "Ġs ito", + "Ġsit o", + "Ġsi to", + "Ġconstitu ents", + "Ġconstituent s", + "ĠContinue d", + "ĠContin ued", + "Ġ SAVE", + "ĠS AVE", + "ĠSA VE", + "r ss", + "rs s", + "/ message", + "/m essage", + "u bes", + "ub es", + "ube s", + "Ġmisd emean", + "Ġtax ation", + "Ġtaxa tion", + "Ġstory line", + "h air", + "ha ir", + "hai r", + "ĠF inds", + "ĠFin ds", + "ĠFind s", + "ĠFi nds", + "S IG", + "SI G", + "ver ification", + "~ =", + ". hp", + ".h p", + "It erable", + "Iter able", + "Ñĭ е", + "at ori", + "ator i", + "ato ri", + "Ġ ctr", + "Ġc tr", + "Ġct r", + "R x", + "_ );ĊĊ", + "_);Ċ Ċ", + "_) ;ĊĊ", + "d ag", + "da g", + ". pin", + ".p in", + ".pi n", + "Ġp seud", + "Ġin vo", + "Ġinv o", + "Ñģ ÑĤÑĢ", + "ÑģÑĤ ÑĢ", + "_ pix", + "_p ix", + "_pi x", + "为 空", + "Ġsw orn", + "Ġswo rn", + "âĢĶ or", + "_ registry", + "_reg istry", + "Ġdis asters", + "Ġdisaster s", + "Ġ ROI", + "ĠR OI", + "ĠRO I", + "Ġ âĢķ", + "ĠâĢ ķ", + "ak tu", + "akt u", + "f orest", + "fo rest", + "fore st", + "for est", + "be iten", + "beit en", + "bei ten", + "âĢĶ I", + "u eva", + "ue va", + "e gt", + "eg t", + "Ġsp ikes", + "Ġspi kes", + "Ġspike s", + "U RES", + "UR ES", + "URE S", + "Ġ Recommended", + "ĠRe commended", + "ĠRecomm ended", + "ĠRecommend ed", + "Ġexplo ited", + "Ġexploit ed", + "ĠFreder ick", + "_ COMPLETE", + "_COMP LETE", + "ĠDr ugs", + "ĠDrug s", + "!!! !!!!!", + "!!!! !!!!", + "!!!!! !!!", + "ĠR iv", + "ĠRi v", + "S TOP", + "ST OP", + "R OOM", + "RO OM", + "Ġ PASSWORD", + "ĠP ASSWORD", + "ĠPASS WORD", + "C ookies", + "Co okies", + "Cookie s", + "Cook ies", + ". El", + ".E l", + "á» Ń", + "ĠB ert", + "ĠBe rt", + "ĠBer t", + "Ġ hashed", + "Ġh ashed", + "Ġhas hed", + "Ġhash ed", + "Ġha shed", + "ic ester", + "ice ster", + "ices ter", + "Ġdecor ator", + "Ġ queryString", + "Ġquery String", + ": ;Ċ", + "Ġ\" [\"", + "Ġ\"[ \"", + "ot ope", + "oto pe", + "- Americ", + "-A meric", + "-Am eric", + "ĠMatthew s", + "ĠMatth ews", + "U RAL", + "UR AL", + "URA L", + "âĢľ ,", + "S ummer", + "Sum mer", + "f os", + "fo s", + "_CONT AINER", + "_ ACK", + "_A CK", + "_AC K", + "Ġ filtr", + "Ġf iltr", + "Ġfil tr", + "Ġfi ltr", + "Ġfilt r", + "_ disp", + "_d isp", + "_dis p", + "_di sp", + "_ Re", + "_R e", + "Ġfac ile", + "Ġfacil e", + "а ÑĪ", + "Ġìķ Ĭ", + "Ġe ben", + "Ġeb en", + "Ġsp rink", + "Ġspr ink", + "ĠQ uint", + "ĠQu int", + "ĠQui nt", + "> V", + "Ġhistor ians", + "Ġhistorian s", + "Ġhistoria ns", + "Ġhisto rians", + "our met", + "Ġ Monitoring", + "ĠMonitor ing", + "led ger", + "ledge r", + "c ott", + "co tt", + "cot t", + "Ġ ware", + "Ġw are", + "Ġwar e", + "Ġwa re", + "G GLE", + "GG LE", + "c ars", + "ca rs", + "car s", + "ĠM EDIATEK", + "Ġvol upt", + "_ View", + "_V iew", + "H EL", + "HE L", + "( copy", + "(c opy", + "(co py", + "( stats", + "(st ats", + "(stat s", + "Ġch romosome", + "Ġchrom osome", + "ĠCurt is", + "- conf", + "-con f", + "-co nf", + "( asset", + "(as set", + "Ġh vor", + "Ġhv or", + "File System", + "< >();čĊ", + "<>( );čĊ", + "<> ();čĊ", + "o coder", + "oc oder", + "oco der", + "ocode r", + "ĠC annon", + "ĠCan non", + "ĠCann on", + ") x", + "Ġ Smooth", + "ĠSm ooth", + "ĠS AS", + "ĠSA S", + "_ ce", + "_c e", + "ĉ prev", + "ĉp rev", + "ĉpr ev", + "ĉpre v", + "_ movie", + "_m ovie", + "_mov ie", + "_mo vie", + "E c", + "_ wall", + "_w all", + "< Button", + ".ĊĊ", + "/> .ĊĊ", + "/>.Ċ Ċ", + "/>. ĊĊ", + "o genesis", + "ogen esis", + "ogene sis", + "Ġ OPTIONS", + "ĠOPTION S", + "ĠOPT IONS", + "up tools", + "upt ools", + "Ġmilit ant", + "Ġmil itant", + "Ġmili tant", + "Ġex ited", + "Ġexit ed", + "i gar", + "ig ar", + "iga r", + "Ġ COMM", + "ĠCO MM", + "ĠCOM M", + "Ġ Disposable", + "ĠD isposable", + "ĠDis posable", + "ay cast", + "Ġrow span", + "Ġrows pan", + "Ġsyn thes", + "Ġsynth es", + "Ġsynt hes", + "Ġso ndern", + "Ġsond ern", + "Ġ Ċ", + "]- ->Ċ", + "ĠJ acket", + "ĠJack et", + "ĠJac ket", + "ĠJa cket", + "R ATION", + "RA TION", + ".get SelectedItem", + ".getSelected Item", + "- init", + "-in it", + "-i nit", + "Ġ Registers", + "ĠReg isters", + "ĠRegister s", + "_ sep", + "_s ep", + "_se p", + "Ġ Toolkit", + "ĠTool kit", + ". dict", + ".d ict", + ".di ct", + "Ġ xlabel", + "Ġx label", + "Ġxl abel", + "\\ Table", + "t oc", + "to c", + "_ combo", + "_c ombo", + "_com bo", + "_comb o", + "Ġ Compact", + "ĠComp act", + "Ġr ugged", + "Ġrug ged", + "à¥ĩ à¤", + "- management", + "-man agement", + "') }}\">Ċ", + "')}} \">Ċ", + "')}}\" >Ċ", + "')} }\">Ċ", + "')}}\"> Ċ", + "Ġ Stamp", + "ĠSt amp", + "ĠSta mp", + "ĠStam p", + "ı l", + "r ox", + "ro x", + "Ġlandscape s", + "Ġlandsc apes", + "_ NOTE", + "_N OTE", + "_NO TE", + "_NOT E", + "mon ary", + "c ab", + "ca b", + "Ġmo et", + "x af", + "xa f", + "r code", + "rc ode", + "- cli", + "-c li", + "-cl i", + "_ gate", + "_g ate", + "[ event", + "[e vent", + "S PORT", + "SP ORT", + "g ia", + "gi a", + "Ġ SUPER", + "ĠS UPER", + "ĠSU PER", + "ĠSUP ER", + "/ Login", + "_ shutdown", + "_sh utdown", + "int errupt", + "inter rupt", + "Ġpret ending", + "Ġpretend ing", + "Ġf ringe", + "Ġfr inge", + "Ġfri nge", + "ĠR eds", + "ĠRe ds", + "ĠRed s", + "Ġ CUDA", + "ĠC UDA", + "ĠCU DA", + "Ġ UNIX", + "ĠUN IX", + "v it", + "vi t", + "Ġ brig", + "Ġb rig", + "Ġbr ig", + "Ġbri g", + "d rv", + "dr v", + "Ġ Connector", + "ĠConnect or", + "ĠConn ector", + "There fore", + "Ġ lia", + "Ġl ia", + "Ġli a", + "D etection", + "De tection", + "Det ection", + "Detect ion", + "_ actor", + "_a ctor", + "_ac tor", + "_act or", + "Ġtemp file", + "Ġecc entric", + "- role", + "-r ole", + "-ro le", + "Ġp adx", + "Ġpa dx", + "Ġpad x", + "d ent", + "de nt", + "den t", + "West ern", + "Ġ ê·¸", + "Ġê ·¸", + "Ġê· ¸", + "ĠApplication Record", + "Ġcampaign ing", + "_ runner", + "_r unner", + "_run ner", + "ĠC ivic", + "ĠCi vic", + "ĠCiv ic", + "a leigh", + "ale igh", + "Ġdir ekt", + "Ġdire kt", + ".s ul", + "Ġ Ġĉĉĉ", + "ĠĠ ĉĉĉ", + "ĠĠĉ ĉĉ", + "ĠĠĉĉ ĉ", + "a nten", + "an ten", + "ant en", + "ante n", + "Ġ issuer", + "Ġiss uer", + "Ġissue r", + "Ġissu er", + "Ġassert ions", + "Ġassertion s", + "( orig", + "(o rig", + "(or ig", + "AT IO", + "Ġle aned", + "Ġlean ed", + "ä s", + ". DTO", + ".D TO", + "ex plode", + "expl ode", + "explo de", + ". Observable", + ".O bservable", + "Ġstagger ing", + "Ġkidn apped", + "Ġprogram mers", + "Ġprogramme rs", + "Ġprogrammer s", + "Ġprogramm ers", + "ĠIn nov", + "ĠInn ov", + ". parameter", + ".param eter", + "Ġd omination", + "Ġdo mination", + "Ġdom ination", + "Ġdomin ation", + "Ġdomina tion", + "Ġske ptic", + "Ġskept ic", + "Ġ æĺ¯", + "Ġæĺ ¯", + "Ġav oids", + "Ġavoid s", + ". Verify", + ".Ver ify", + "ub by", + "ubb y", + "Ġ ASN", + "ĠA SN", + "ĠAS N", + "Ġform ato", + "Ġformat o", + "Ġforma to", + "ĠBeat les", + "_ brand", + "_b rand", + "_br and", + "Ġin set", + "Ġins et", + "Ġinse t", + "y outu", + "you tu", + "Ġ toc", + "Ġt oc", + "Ġto c", + "- final", + "-f inal", + "-fi nal", + "-fin al", + "Sh owing", + "Show ing", + "ĠD oub", + "ĠDo ub", + "ĠDou b", + "ĠM esa", + "ĠMe sa", + "ĠMes a", + "A dj", + "Ad j", + "_ medium", + "_m edium", + "_med ium", + "Create s", + "Cre ates", + "Creat es", + "( endpoint", + "(end point", + "ĉ UP", + "ĉU P", + "b bie", + "bb ie", + "Ġ stalk", + "Ġs talk", + "Ġst alk", + "Ġsta lk", + "Ġstal k", + ".data bind", + ".datab ind", + ". Scan", + ".S can", + ".Sc an", + "ag ents", + "age nts", + "agent s", + "agen ts", + "$ ,", + "ind ividual", + "+ )/", + "+) /", + "ĉ vm", + "ĉv m", + "( notification", + "(not ification", + "Ġin ex", + "Ġi nex", + "Ġine x", + "Ġ Classification", + "ĠClass ification", + "r eno", + "re no", + "ren o", + "Ġo lig", + "Ġol ig", + "Ġoli g", + "- rated", + "-r ated", + "-rate d", + "-ra ted", + "Ġform ulation", + "Ġformula tion", + "Ġformul ation", + "' ,{", + "', {", + "Ġa cept", + "Ġac ept", + "Ġace pt", + "_ unpack", + "_un pack", + "_ CA", + "_C A", + ". Pow", + ".P ow", + "ĉ im", + "ĉi m", + "Ġal uminium", + "Ġalum inium", + "A NO", + "AN O", + "Ġ xn", + "Ġx n", + "Ġc ómo", + "Ġcó mo", + "Ġ Ingredient", + "ĠIng redient", + "Ġseiz ures", + "Ġseizure s", + "åħ ±", + "ific ador", + "ificado r", + "Ġs iguiente", + "Ġsigu iente", + "ĠIn fragistics", + "Ġd uplicated", + "Ġduplicate d", + "Ġdup licated", + "Ġduplic ated", + "ĠD ee", + "ĠDe e", + "Ġn ø", + "Ġ ACCEPT", + "ĠAC CEPT", + "( crate", + "(c rate", + "(cr ate", + "иÑĤ елÑĮ", + "иÑĤе лÑĮ", + "- less", + "-l ess", + "-le ss", + "Ġ infinity", + "Ġin finity", + "Ġinf inity", + "Ġinfinit y", + "An alyzer", + "Analy zer", + "- Day", + "-D ay", + "r itt", + "ri tt", + "rit t", + "( cin", + "(c in", + "(ci n", + "ĠG y", + "Ġmulti plied", + "Ġmultip lied", + "u chi", + "uch i", + "uc hi", + "ĠBald win", + "/ ip", + "/i p", + "Ġshort cuts", + "Ġshortcut s", + ". ADD", + ".A DD", + ".AD D", + "Ġv igor", + "Ġvi gor", + "Ġvig or", + "_ instruction", + "_in struction", + "_instr uction", + "( ;", + "_ eta", + "_e ta", + "_et a", + "è¿ ŀ", + "utor ials", + "utorial s", + "Ġboost ing", + "b v", + "Ġacknowled ges", + "Ġacknowledge s", + "List ening", + "Listen ing", + "F AQ", + "FA Q", + "; b", + "( (-", + "(( -", + "Ġarchitect s", + "Ġarchit ects", + "Ġz we", + "Ġzw e", + "Ġp uls", + "Ġpul s", + "Ġpu ls", + "Ġget Count", + "ĠgetC ount", + "ver bs", + "verb s", + "ãĢ ľ", + "( Collection", + "(C ollection", + "k re", + "kr e", + "Ġjurisdiction s", + "Ġjuris dictions", + "_ bridge", + "_b ridge", + "_br idge", + "ĠC rack", + "ĠCr ack", + "ĠCra ck", + "Ġ Difficulty", + "ĠDiff iculty", + "K O", + "Res ervation", + "_ requires", + "_re quires", + "_require s", + "T our", + "To ur", + "ãģĹ ãģŁ", + "ãģĹãģ Ł", + ". setCurrent", + ".set Current", + "Ġ ky", + "Ġk y", + "ĠAlb any", + "ĠAlban y", + "Ġ è§", + "Ġè §", + "l ler", + "ll er", + "lle r", + "ag na", + "agn a", + "work ers", + "worker s", + "wor kers", + ". blank", + ".bl ank", + "ĠPr ayer", + "ĠPra yer", + "M IC", + "MI C", + "Ġresil ience", + "Te X", + "Ġ Languages", + "ĠL anguages", + "ĠLanguage s", + "st udy", + "stu dy", + "stud y", + "ĉ curr", + "ĉc urr", + "ĉcur r", + "Ġenzym es", + "Ġenzyme s", + "S lug", + "Sl ug", + "Ġ íĮĮ", + "ĠíĮ Į", + "st ral", + "str al", + "stra l", + "Ġtum ors", + "Ġtumor s", + "Ġseg unda", + "Ġsegu nda", + "= '{", + "=' {", + "in struction", + "instr uction", + "ĠL isp", + "ĠLi sp", + "ĠLis p", + "/ info", + "/in fo", + "Ġ\" {$", + "Ġ\"{ $", + ",: ),", + ",:) ,", + "Ġ gv", + "Ġg v", + "( ErrorMessage", + "(Error Message", + "Ġ '=", + "Ġ' =", + "} -${", + "}- ${", + ". Documents", + ".Document s", + ".Doc uments", + "\" Well", + "\"We ll", + "\"W ell", + "Ġreminis cent", + "Ġg az", + "Ġga z", + "ir opr", + "iro pr", + "e hr", + "eh r", + "Ġsup pressed", + "Ġsuppress ed", + "Ġsupp ressed", + "er sh", + "ers h", + ".scroll To", + "Ġ cadena", + "Ġc adena", + "Ġcad ena", + "Ġcade na", + "Ġgame State", + "ÃŃ m", + "( conv", + "(con v", + "(co nv", + "Ġ Tomorrow", + "ĠTom orrow", + "ĠC CT", + "ĠCC T", + "M ongo", + "Mon go", + "Mo ngo", + "u lg", + "ul g", + ". Camera", + ".C amera", + ". handlers", + ".handle rs", + ".handler s", + ".hand lers", + "m ph", + "mp h", + "Ġ stk", + "Ġs tk", + "Ġst k", + "Ġgen etics", + "Ġgene tics", + "Ġgenetic s", + "AC ING", + "Tr ivia", + "Tri via", + "ĠB am", + "ĠBa m", + "( marker", + "(m arker", + "(mark er", + ". Stretch", + ".St retch", + ".Str etch", + "ĠSun ni", + "ĠB etty", + "ĠBe tty", + "ĠBet ty", + "ĠBett y", + ". tolist", + ".t olist", + ".to list", + "un likely", + ". Rectangle", + ".Rect angle", + "ob solete", + "IL ON", + "inner Text", + "em bourg", + "emb ourg", + "a N", + "ĠV ehicles", + "ĠVehicle s", + "un lock", + ": utf", + "n ob", + "no b", + "Ġ Seeing", + "ĠSe eing", + "ĠSee ing", + "ĠN EVER", + "ĠNE VER", + "Ġ tls", + "Ġt ls", + "Ġtl s", + "Ġf illes", + "Ġfil les", + "Ġfill es", + "Ġfille s", + "Ġbenef ited", + "Ġbenefit ed", + "ĠC lint", + "ĠCl int", + "ĠClin t", + "ĠCli nt", + "*/ ),", + "*/) ,", + ". fold", + ".f old", + "Ġpos ible", + "Ġposi ble", + "A DED", + "AD ED", + "ADE D", + "t house", + "th ouse", + ". DAL", + ".D AL", + "Ġ Odd", + "ĠO dd", + "ĠOd d", + "r okes", + "ro kes", + "roke s", + "rok es", + "ĠS unny", + "ĠSun ny", + "ĠPartial Eq", + "_ Buffer", + "_B uffer", + "ĠL evi", + "ĠLe vi", + "ĠLev i", + "long rightarrow", + "el don", + "eld on", + "eldo n", + "g ages", + "ga ges", + "gage s", + "_ warn", + "_w arn", + "_war n", + ".Create Table", + "ĠD ip", + "ĠDi p", + "_ questions", + "_question s", + "_quest ions", + ". logic", + ".log ic", + "Ġ #\"", + "Ġ# \"", + "={ ()=>", + "={() =>", + "={( )=>", + "Ġ tep", + "Ġt ep", + "Ġte p", + "Ġju icy", + "ì Ĥ¬", + "ìĤ ¬", + "en ko", + "enk o", + "ial ect", + "ia lect", + "iale ct", + "Ù ī", + "Ġon board", + "Ġ æı", + "Ġæ ı", + "ĉ rt", + "ĉr t", + "_ UTF", + "_U TF", + "_UT F", + "ĠQ Action", + "ĠQA ction", + "âĢ ŀ", + "( Component", + "( audio", + "(a udio", + ". hit", + ".h it", + "g te", + "gt e", + "Ġprogram med", + "Ġprogramme d", + "Ġprogramm ed", + "state Params", + "Ġpoly ester", + "f ires", + "fi res", + "fire s", + "fir es", + "by ss", + "] =(", + "]= (", + "_ quality", + "_q uality", + "_qu ality", + "_qual ity", + "Of Day", + "ĠF airy", + "ĠFair y", + "ĠFa iry", + "Ġy elled", + "Ġyell ed", + "o pl", + "op l", + "( userName", + "(user Name", + "Ġ Difference", + "ĠD ifference", + "ĠDiff erence", + "Ġeval uations", + "Ġevaluation s", + "Ġevalu ations", + "iff any", + "Ġcycl ists", + "Ġcyc lists", + "Ġcyclist s", + "Ġ cidade", + "Ġc idade", + "Ġcid ade", + "Ġtext book", + "Ġprof iling", + "Ġprofil ing", + "_ _),", + "__ ),", + "__) ,", + "d ea", + "de a", + ". activate", + ".act ivate", + ".activ ate", + "Ġind ications", + "Ġindic ations", + "Ġindication s", + "Ð ķ", + "Touch UpInside", + "Ġinval uable", + "Ġ MASK", + "ĠM ASK", + "ĠMA SK", + "ĠMAS K", + "Ġcont end", + "Ġconten d", + "Ġconte nd", + "F req", + "Fr eq", + "Fre q", + "Ġrec ruits", + "Ġrecru its", + "Ġrecruit s", + "( interval", + "(int erval", + "(inter val", + "Ġ UserProfile", + "ĠUser Profile", + "Ġ'./ ../", + "Ġ'. /../", + "e du", + "ed u", + "_ Callback", + "_C allback", + "_Call back", + "Ġan alogy", + "Ġanal ogy", + "Ġanalog y", + "Ġana logy", + "ĠT rophy", + "ĠTr ophy", + "ĠTro phy", + "app hire", + "V ideos", + "Video s", + "ĠC her", + "ĠCh er", + "ĠChe r", + "ĠH av", + "ĠHa v", + "âĢ¦ \"", + ". validator", + ".valid ator", + "g fx", + "gf x", + "ĠU Object", + "class names", + "classname s", + "t riangle", + "tr iangle", + "tri angle", + "Ġ Encoder", + "ĠE ncoder", + "ĠEn coder", + "ĠEnc oder", + "ĠEncode r", + ". spy", + ".s py", + ".sp y", + "Ġpred ators", + "Ġpredator s", + "= status", + "=s tatus", + "- safe", + "-s afe", + ": \",Ċ", + ":\" ,Ċ", + ":\", Ċ", + "Ġ Including", + "ĠIn cluding", + "Ġ{ };čĊ", + "Ġ{} ;čĊ", + "Ġ{}; čĊ", + "* cos", + "*c os", + "Ġend ured", + "Ġendure d", + ".sul ake", + "Ġnurs ery", + "Ġnurse ry", + "Ġfrag rance", + "Ġre building", + "Ġrebuild ing", + "Ġ nth", + "Ġn th", + "Ġnt h", + "ĠFr aser", + "ĠFra ser", + ".set Date", + "ĠV ince", + "ĠVi nce", + "ĠVin ce", + "_ REST", + "_RE ST", + "_R EST", + "_RES T", + "Ġvent ilation", + "Ġventil ation", + "æµ ·", + "cri bes", + "cribe s", + ". asm", + ".as m", + ".a sm", + "lp Vtbl", + "ĠA be", + "ĠAb e", + "u isine", + "uis ine", + ", array", + ",a rray", + ",arr ay", + "ĉ className", + "ĉclass Name", + "err als", + "erra ls", + "erral s", + "Ġ 'ĊĊ", + "Ġ' ĊĊ", + "Ġ'Ċ Ċ", + "Check out", + "Ġs olicit", + "Ġso licit", + "Ġsol icit", + "Ġsolic it", + "A ux", + "Au x", + "_ capture", + "_c apture", + "_cap ture", + "Ġr ibs", + "Ġrib s", + "Ġri bs", + "r agon", + "ra gon", + "rag on", + "v iol", + "vi ol", + "vio l", + "to pics", + "top ics", + "topic s", + "Function Flags", + "ĠM arty", + "ĠMar ty", + "ĠMart y", + "b ike", + "bi ke", + "ĠT ucker", + "ĠTu cker", + "( kernel", + "(k ernel", + "Ġ Ops", + "ĠO ps", + "ĠOp s", + "Close Operation", + "/ demo", + "/d emo", + "/de mo", + "i lda", + "il da", + "ild a", + "ĠlÃŃ nea", + "AP PING", + "APP ING", + "Ġsu ites", + "Ġsuit es", + "Ġsuite s", + "Ġsui tes", + ".visit VarInsn", + "u rus", + "ur us", + "uru s", + "Ġ Minute", + "ĠMin ute", + "( manager", + "(m anager", + "(man ager", + "Ġbutter fly", + "Ġa pare", + "Ġap are", + "Ġapar e", + "Ġapa re", + "Ġw olves", + "Ġwol ves", + "J WT", + "ĠS alon", + "ĠSal on", + "ĠSa lon", + "ĉ delay", + "ĉd elay", + "ĉde lay", + "ĉdel ay", + "- eslint", + "-es lint", + "is ations", + "isation s", + ". rpc", + ".r pc", + ") |(", + ")| (", + "ĠSnap chat", + "/ mm", + "/m m", + "M N", + "c eries", + "ce ries", + "cer ies", + ".t extAlignment", + ".text Alignment", + "ĠFrank furt", + "Ġ ado", + "Ġa do", + "Ġad o", + "( newValue", + "(new Value", + "( access", + "(a ccess", + "(ac cess", + "(acc ess", + "( Expression", + "Ġ SignIn", + "ĠSign In", + "ĠHa iti", + "ĠHait i", + "ĠHai ti", + "_ tp", + "_t p", + ". setParameter", + ".set Parameter", + "Min ute", + "Ġmanual s", + "ric anes", + "ricane s", + "rica nes", + "Ġ PTR", + "ĠP TR", + "ĠPT R", + "Ġ Outer", + "ĠO uter", + "ĠOut er", + "ĠOu ter", + "Ġ getline", + "Ġget line", + "oc ations", + "ocation s", + "_ CD", + "_C D", + "ĠL yon", + "ĠLy on", + "/ gui", + "/g ui", + "_ live", + "_l ive", + "_li ve", + "i dan", + "id an", + "ida n", + ". geom", + ".ge om", + ".geo m", + "Ġborder Bottom", + "im uth", + "imu th", + "_ checkpoint", + "_check point", + "Ġm eu", + "Ġme u", + "ĠIr ving", + "Ġpeu vent", + "( MAX", + "(M AX", + "Ġ ARCH", + "ĠAR CH", + "ĠARC H", + "Ġp ov", + "Ġpo v", + ".source forge", + "Ġjam ais", + "Ġ ark", + "Ġa rk", + "Ġar k", + "ĠBaghd ad", + "Ġ CLEAR", + "ĠC LEAR", + "ĠCL EAR", + "Menu Bar", + "Ġtr ois", + "Ġtro is", + "CHED ULE", + "Ġ #čĊ", + "Ġ# čĊ", + "( Call", + "(C all", + "$ order", + "( Material", + "(M aterial", + "(Mat erial", + "Ġencontr ado", + "$ list", + "$l ist", + "ĠMETHOD S", + ". beginTransaction", + ".begin Transaction", + "_M AG", + "_MA G", + "Style Sheet", + "Ġmajor s", + "Ġmaj ors", + "Ġindef initely", + "Ġindefinite ly", + "c leanup", + "clean up", + "Ġhome land", + "Ġhom eland", + "( dto", + "(d to", + "(dt o", + "D ates", + "Date s", + "Da tes", + "Dat es", + "P resentation", + "Present ation", + "Ġ DK", + "ĠD K", + "={` /", + "ĉ Key", + "ĉK ey", + "( Block", + "(B lock", + "_ checkbox", + "_check box", + "ne eds", + "need s", + "nee ds", + "Ġon Complete", + "r ico", + "ri co", + "ric o", + "Ġg leich", + "Ġgle ich", + "Ġ xm", + "Ġx m", + "O OD", + "OO D", + "B etter", + "Bet ter", + "ĠSQL ITE", + ". Book", + ".B ook", + "x ad", + "xa d", + "ĠG one", + "ĠGo ne", + "ĠGon e", + "ĉ dp", + "ĉd p", + "Ġdev otion", + "Ġ stm", + "Ġs tm", + "Ġst m", + "Ġob sess", + "Ġobs ess", + "Ġ Backend", + "ĠBack end", + "Qu eries", + "Que ries", + "I k", + "/ /****************************************************************", + "// ****************************************************************", + "Ġdivide nds", + "Ġdivid ends", + "Ġdividend s", + ".parent Element", + "} \")ĊĊ", + "}\" )ĊĊ", + "}\")Ċ Ċ", + "}\") ĊĊ", + "ĠMaterial PageRoute", + ": num", + ":n um", + "Ġexp lic", + "Ġexpl ic", + "Ġ OL", + "ĠO L", + "l east", + "le ast", + "lea st", + "O ops", + "iment os", + "imento s", + "imen tos", + "Ġins urers", + "Ġinsure rs", + "Ġinsurer s", + "Ġhero ic", + "ĉ fields", + "ĉf ields", + "ĉfield s", + ".img ur", + ".btn Cancel", + "ĠDet ective", + "ĠDetect ive", + "( sm", + "(s m", + "ĠMutable LiveData", + ". lab", + ".l ab", + "( ([", + "(( [", + "Ġha irst", + "Ġhair st", + "Ġhai rst", + "Ġhairs t", + "Ġ Transactions", + "ĠTrans actions", + "ĠTransaction s", + "å¼Ģ å§ĭ", + "Ġ stdClass", + "Ġstd Class", + "u ento", + "uen to", + "uent o", + "G IS", + "GI S", + "_ cod", + "_c od", + "_co d", + "In structions", + "Instruction s", + "Instr uctions", + "C alls", + "Call s", + "Cal ls", + "Pointer Type", + "ĠR w", + "Ġassort ment", + "Ġ DIG", + "ĠD IG", + "ĠDI G", + "+ r", + "_ CERT", + "_C ERT", + "_CE RT", + "Ġinst ability", + "Ġv ib", + "Ġvi b", + "o nas", + "on as", + "ona s", + "Ġr oku", + "Ġro ku", + "Ġrok u", + "ap ellido", + "Ġ angl", + "Ġan gl", + "Ġang l", + "prene ur", + "Ġfl uids", + "Ġfluid s", + "Ġflu ids", + "is ease", + "ise ase", + "Ġd eed", + "Ġde ed", + "Ġdee d", + "qu ist", + "quis t", + "qui st", + "_CONST ANT", + "Ġequ ilibrium", + "_ delegate", + "_de legate", + "ĠQuant um", + "r ei", + "re i", + "Cap abilities", + "rect angle", + "? ><", + "?> <", + "a lien", + "al ien", + "ali en", + "alie n", + "ĠJ ug", + "ĠJu g", + "D NA", + "DN A", + "T ickets", + "Tick ets", + "Ticket s", + "Occ urs", + "ĠH awk", + "ĠHaw k", + "ĠHa wk", + ".setHorizontal Group", + "\\ Collection", + "\\C ollection", + "ff iti", + "ffi ti", + "Ġre arr", + "Ġrear r", + ".setVertical Group", + "Ġc avity", + "Ġcav ity", + "Ġadult e", + "Ġadul te", + "Fac ade", + "Fa cade", + "- wh", + "-w h", + "ĠL OL", + "ĠLO L", + "Ø °", + "Ġgrand parents", + "Sw ift", + "ĉ wx", + "ĉw x", + "æīĢ æľī", + "i fen", + "if en", + "ife n", + "ff set", + "B eyond", + "// }ĊĊ", + "//}Ċ Ċ", + "Ġw ager", + "Ġwa ger", + "Ġwage r", + "Ġwag er", + "Ġ bury", + "Ġb ury", + "Ġbu ry", + "Ġbur y", + "Ġcomm ence", + "Ġcomme nce", + "Ġcommenc e", + "reg istro", + "registr o", + "regist ro", + "s cient", + "sc ient", + "sci ent", + "Ġ Percent", + "ĠPer cent", + "ĠPerc ent", + "Ġд олж", + "Ġдол ж", + "( identifier", + "(id entifier", + "(ident ifier", + ".set Model", + "Ġs eldom", + "Ġsel dom", + "n ton", + "nt on", + "Ġap pliance", + "Ġappl iance", + "a mus", + "am us", + "amu s", + "rys ler", + "Ġpan ties", + "Ġpant ies", + "engu ins", + "enguin s", + "Ġmi mic", + "Ġmim ic", + "Ġon Changed", + "ĠonChange d", + "Ġal coholic", + "Ġalcohol ic", + ".reload Data", + "Ch arge", + "Char ge", + "Ġ Fax", + "ĠF ax", + "ĠFa x", + "Ġj ScrollPane", + "Emp resa", + "Ġsh attered", + "x ba", + "xb a", + "Font s", + "Fo nts", + "? s", + "Ġpost season", + "re tain", + "ret ain", + "reta in", + "_ rates", + "_r ates", + "_rate s", + "_ra tes", + "_rat es", + "Ġ requestCode", + "Ġrequest Code", + ". todo", + ".t odo", + ".to do", + "´ s", + "C HK", + "CH K", + "Ġ Keeping", + "ĠKe eping", + "ĠKeep ing", + "ĠKee ping", + "enge ance", + "Ġvs code", + "IP PING", + "IPP ING", + "Default CloseOperation", + "_ raise", + "_r aise", + "_ra ise", + "ĠO culus", + "ĠOc ulus", + "o grams", + "og rams", + "ogram s", + "ogr ams", + "ogra ms", + "r aj", + "ra j", + "p ci", + "pc i", + "Ġcorros ion", + ". handleSubmit", + ".handle Submit", + "Access ible", + "ĠP iano", + "ĠPi ano", + "l ittle", + "lit tle", + "A CL", + "AC L", + "Äĩ e", + ". unwrap", + ".un wrap", + "ĠCon vers", + "ĠConv ers", + "ĠLe ben", + "ion eer", + "ione er", + "Ġ Merchant", + "ĠM erchant", + "ĠMer chant", + "ĠMerch ant", + "ĠJ orge", + "Ġembr acing", + "Ġ venta", + "Ġv enta", + "Ġvent a", + "Ġven ta", + "á st", + "ás t", + "Ġv iene", + "Ġvi ene", + "Ġvie ne", + "< QString", + "Ċ", + "-g rowing", + "-gr owing", + "-grow ing", + "Ġdeep copy", + "A ck", + "Ac k", + "eg gies", + "egg ies", + "Ġ __(\"", + "Ġ_ _(\"", + "Ġ__ (\"", + "Ġ__( \"", + "Ġn oir", + "Ġno ir", + "Ġnoi r", + "terror ism", + "Ġan them", + "Ġant hem", + "Ġanth em", + "a gency", + "ag ency", + "age ncy", + "agen cy", + "_ PACKAGE", + "_PACK AGE", + "Ġ Closure", + "ĠC losure", + "ĠClo sure", + ". registry", + ".reg istry", + "Ġmamm als", + "Ġmamma ls", + "< L", + "U ICollectionView", + "UI CollectionView", + "ĠLE Ds", + "ĠLED s", + "Ġv olley", + "Ġvol ley", + "Ġvoll ey", + "( Buffer", + "(B uffer", + "_N ATIVE", + "li bc", + "lib c", + "im plode", + "impl ode", + "Scroll Bar", + "ĠMar ion", + "ĠMario n", + "ĠMari on", + ". Contracts", + ".Con tracts", + ".Contract s", + "_ At", + "_A t", + "ĠWe instein", + "ĠWein stein", + "compare To", + "ĠH ose", + "ĠHo se", + "ĠHos e", + "en ity", + "eni ty", + ". createQuery", + ".create Query", + "_ router", + "_r outer", + "_ro uter", + "_route r", + "Ġstim uli", + "Ġ ++)", + "Ġ+ +)", + "Ġ++ )", + "ĠCh amp", + "ĠCha mp", + "ĠCham p", + "ĠBay ern", + "ĠBayer n", + "a ssa", + "as sa", + "ass a", + ". va", + ".v a", + "Ġdistrib utors", + "Ġdistributor s", + "Ġfile private", + "Ġdepart ed", + "c ccc", + "cc cc", + "ccc c", + "@ click", + "@c lick", + "ĠL unch", + "ĠLun ch", + "> L", + "Ġb luetooth", + "Ġbl uetooth", + ". Deep", + ".De ep", + "- standing", + "-st anding", + "á cil", + "ác il", + "áci l", + "Ġro oft", + "Ġroof t", + "Ġ Paths", + "ĠP aths", + "ĠPat hs", + "ĠPath s", + "ĠPa ths", + "_ iterations", + "_iter ations", + "_iteration s", + "Invalid ArgumentException", + ". spi", + ".s pi", + ".sp i", + "Ġ UIAlertAction", + "ĠUIAlert Action", + "u ye", + "uy e", + "sign in", + "sig nin", + ". priority", + ".p riority", + "ĠEs says", + "ĠEss ays", + "ĠEssay s", + "=' {$", + "='{ $", + "Ġ è¿ĶåĽŀ", + "Ġè¿ ĶåĽŀ", + "_ signed", + "_s igned", + "_sign ed", + "_sig ned", + ". persist", + ".p ersist", + "Ġre design", + "Ġred esign", + "Ġrede sign", + "Ġredes ign", + "To Lower", + "ĠNew man", + "= start", + "ĠIsrael is", + "ĠIsraeli s", + "as iswa", + "asis wa", + "S peech", + "Spe ech", + "Ġnum eros", + "Ġnumer os", + "Ġnumero s", + "handle rs", + "hand lers", + "handler s", + "ĠW ong", + "ĠWo ng", + "ĠWon g", + "Ġм еÑĤод", + "ĠмеÑĤ од", + "We ights", + "Weight s", + "ĠGu jar", + "t eil", + "te il", + "ĠNone theless", + "ĠNon etheless", + "_E FFECT", + "Ġ vect", + "Ġv ect", + "Ġve ct", + "Ġvec t", + "ĠO sc", + "ĠOs c", + "Ġco ats", + "Ġcoat s", + "ĠW heat", + "ĠWh eat", + "ĠWhe at", + "Ġge ek", + "Ġgee k", + "Ġ PROPERTY", + "ĠP ROPERTY", + "ĠPRO PERTY", + "w orm", + "wo rm", + "wor m", + "_ constants", + "_con stants", + "_const ants", + "_constant s", + "ĠB oulder", + "ĠBou lder", + "Ġ Parm", + "ĠP arm", + "ĠPar m", + "ĠPa rm", + "c ole", + "co le", + "col e", + "Ġdefault Center", + "ĠRo uge", + "ĠRou ge", + ": A", + "x cf", + "xc f", + "ĠVen ice", + "ĠVe nice", + "m edian", + "med ian", + "medi an", + "media n", + "Ġred emption", + "F resh", + "Fr esh", + "Fre sh", + "Ġco sm", + "Ġcos m", + "Ġ figur", + "Ġfig ur", + "Ġref urb", + "CO PE", + ". cd", + ".c d", + "Ġch ords", + "Ġchord s", + "Ġchor ds", + "ĠS gt", + "Å į", + "V PN", + "VP N", + "Ġ SEND", + "ĠS END", + "ĠSE ND", + "ĠSEN D", + "a inen", + "ain en", + "ai nen", + "aine n", + "_ accounts", + "_account s", + "_ac counts", + "Ġt enth", + "Ġte nth", + "Ġten th", + "Ġtent h", + "Ġdiss olved", + "Ġdissolve d", + "< App", + "", + "Ġ' >", + "Ġlegitim acy", + "Ġ oo", + "Ġo o", + "S linky", + "Sl inky", + "Ġnational s", + "Ġnation als", + ". words", + ".w ords", + ".word s", + "; p", + "t rap", + "tr ap", + "tra p", + "oman ip", + "oma nip", + "Ġc ues", + "Ġcu es", + "Ġcue s", + "Ġgrad uating", + "Ġgradu ating", + "Ġsem aphore", + "\" ]);ĊĊ", + "\"] );ĊĊ", + "\"]) ;ĊĊ", + "\"]);Ċ Ċ", + "\"]); ĊĊ", + "ace y", + "ac ey", + "RE ET", + "REE T", + "G rab", + "Gr ab", + "ĠF elix", + "ĠFe lix", + "ĠFel ix", + "( Id", + "(I d", + "_ neighbors", + "_ne ighbors", + "_neighbor s", + "Ġmeaning less", + "( del", + "(d el", + "(de l", + "Ġj eder", + "Ġje der", + "Ġjed er", + "Ġjede r", + "ĠContent Values", + ". absolute", + ".a bsolute", + ".abs olute", + "/ cl", + "/c l", + "Ġ xb", + "Ġx b", + "d atum", + "dat um", + "Ġtort ured", + "Ġtorture d", + "Ġrub bing", + "Ġru bbing", + "S cores", + "Sc ores", + "Score s", + "ĠðŁĺ ī", + "Ġav ons", + "Ġam sterdam", + "E OS", + "EO S", + "H al", + "Ha l", + "Ġtrust worthy", + "# =", + ".EX TRA", + "Ġm ano", + "Ġman o", + "Ġma no", + "is icing", + "isi cing", + "- support", + "-s upport", + "-sup port", + "ĉ cursor", + "ĉc ursor", + "Ġ Spo", + "ĠS po", + "ĠSp o", + "ai massage", + "aim assage", + "M ission", + "Miss ion", + "[] {\"", + "[]{ \"", + "Ġpr inters", + "Ġprint ers", + "Ġprinter s", + "Ġprin ters", + "G REEN", + "GRE EN", + "GREE N", + "Ġ teg", + "Ġt eg", + "Ġte g", + "Ġabdom inal", + "! ĊĊĊĊĊĊ", + "!ĊĊ ĊĊĊĊ", + "!Ċ ĊĊĊĊĊ", + "!ĊĊĊĊ ĊĊ", + "!ĊĊĊ ĊĊĊ", + ". Short", + ".S hort", + ".Sh ort", + "а зв", + "аз в", + "ĠGi fts", + "ĠGift s", + "} \")", + "}\" )", + "( binding", + "(b inding", + "(bin ding", + "(bind ing", + "x ce", + "xc e", + "âĢ ij", + "in fos", + "info s", + "inf os", + "Form Data", + "Ġ dart", + "Ġd art", + "Ġda rt", + "Ġdar t", + "Ġ elems", + "Ġe lems", + "Ġel ems", + "Ġele ms", + "Ġelem s", + "( inv", + "(i nv", + "(in v", + "Y L", + "t in", + "ti n", + "G ENER", + "GE NER", + "GEN ER", + "á» ¯", + "Ġ Taken", + "ĠT aken", + "ĠTake n", + "ĠTa ken", + "ĠTak en", + "uc kle", + "uck le", + ": e", + "Ġs pectral", + "Ġspect ral", + "Ġspectra l", + ".b aidu", + "/ ');Ċ", + "/' );Ċ", + "/') ;Ċ", + "Ġgre edy", + "Ġgreed y", + "es ion", + "esi on", + ",,,, ,,,,", + "Ġ/ >,Ċ", + "Ġ/> ,Ċ", + "Ġ/>, Ċ", + "Internal ServerError", + "NS NotificationCenter", + "NSNotification Center", + "Ġ Ai", + "ĠA i", + "Ġs pit", + "Ġsp it", + "Ġspi t", + "Ġaug mented", + "Ġaugment ed", + "Ġstandard UserDefaults", + "FIN ITY", + "R ace", + "Ra ce", + ": C", + "ĠRE CORD", + "ĠREC ORD", + "Ġ Highlight", + "ĠHigh light", + "Ġ' `", + "Ġdef icits", + "Ġdeficit s", + "Ġn ei", + "Ġne i", + "Ġresearch ed", + "T a", + "Ġc opp", + "Ġco pp", + "Ġcop p", + ".Get HashCode", + ") :čĊčĊ", + "): čĊčĊ", + "):čĊ čĊ", + "On Click", + "ĠWell ington", + "ĠWel lington", + "Ġrev ival", + "æ¯ Ķ", + "éĹ ®", + "Ġ NSS", + "ĠN SS", + "ĠNS S", + "Ġf orn", + "Ġfor n", + "Ġfo rn", + "Ġin té", + "Ġint é", + "ĠKu wait", + "_ flip", + "_f lip", + "_fl ip", + "_ bo", + "_b o", + "_ \\", + "Ġocc urrences", + "Ġoccurrence s", + "Ġ Scientists", + "ĠScient ists", + "ĠScientist s", + "S RC", + "SR C", + "o gens", + "og ens", + "ogen s", + "oge ns", + "i grant", + "ig rant", + "igr ant", + "RE MOTE", + "REM OTE", + "Ġ SID", + "ĠS ID", + "ĠSI D", + ". opts", + ".op ts", + ".o pts", + ".opt s", + "u ve", + "uv e", + "( )])Ċ", + "() ])Ċ", + "()] )Ċ", + "Ġlibert arian", + "ĠG lide", + "ĠGl ide", + "l esen", + "le sen", + "les en", + "Ġ forme", + "Ġfor me", + "Ġform e", + "ow ania", + "owa nia", + "owan ia", + "Ġannoy ed", + "D efs", + "De fs", + "Def s", + "Ġ Executor", + "ĠExec utor", + "Ġ casts", + "Ġc asts", + "Ġca sts", + "Ġcas ts", + "Ġcast s", + ". setChecked", + ".set Checked", + "Ġ Sharing", + "ĠSh aring", + "ĠSha ring", + "ĠShar ing", + ".Serialize Object", + "Ġ selectors", + "Ġselect ors", + "Ġselector s", + "Ġsel ectors", + "Ġsele ctors", + "_ OTHER", + "_OT HER", + "ë ¯¸", + "ë¯ ¸", + "( super", + "(s uper", + "( OS", + "(O S", + "_ VERIFY", + "_VER IFY", + "id unt", + "< header", + "';Ċ", + "Ġ/> ';Ċ", + "Ġ/>' ;Ċ", + "Ġvid éo", + "Ġvidé o", + "ĠNe gro", + "ĠNeg ro", + "ĠL ords", + "ĠLord s", + "ĠLor ds", + "ĠT ours", + "ĠTo urs", + "ĠTour s", + "ĠTou rs", + "Ġsoft ly", + ". receive", + ".re ceive", + "Ġ ERC", + "ĠE RC", + "ĠER C", + "Ġdata Set", + "B adge", + "Bad ge", + "Ba dge", + "ĉ Event", + "ĉE vent", + "Ġ perl", + "Ġper l", + "Ġpe rl", + "Ġ {}\\", + "Ġ{ }\\", + "Ġ{} \\", + "( sentence", + "(s entence", + "(sent ence", + "Or Update", + "Ġdim inish", + "Ġdimin ish", + "P IN", + "PI N", + "( draw", + "(d raw", + "(dr aw", + ".To DateTime", + ". EqualTo", + ".Equal To", + "( pin", + "(p in", + "(pi n", + "-p encil", + "l uent", + "lu ent", + "lue nt", + "Ġ Caller", + "ĠC aller", + "ĠCal ler", + "ĠCall er", + "ĠCa ller", + "Ġplay ful", + "- '+", + "-' +", + "x ca", + "xc a", + "s wick", + "sw ick", + ") {}Ċ", + "){ }Ċ", + "} :${", + "}: ${", + "ĠM eth", + "ĠMe th", + "ĠMet h", + ". getCell", + ".get Cell", + ".getC ell", + ". break", + ".b reak", + "Ġ ymax", + "Ġy max", + "=' Ċ", + "}`} >Ċ", + "ĠH iro", + "ĠHi ro", + "ĠHir o", + "( TRUE", + "(TR UE", + "as urer", + "asure r", + "asu rer", + "Ġc uer", + "Ġcu er", + "Ġcue r", + "U ber", + "Ub er", + ". Operation", + ".O peration", + ".Op eration", + "Ġ olan", + "Ġo lan", + "Ġol an", + "Ġola n", + "Ġthr illing", + "Ġthrill ing", + "< Response", + "ĠF emin", + "ĠFe min", + "ĠFem in", + "Ġtr aversal", + "Ġtravers al", + "Ġp oc", + "Ġpo c", + "Ġ setStatus", + "Ġset Status", + "de clar", + "dec lar", + "decl ar", + "std afx", + "Ġaddict ive", + "Ġ Btn", + "ĠB tn", + "Ġexplos ives", + "Ġexplosive s", + "ĠCo oking", + "ĠCook ing", + "ĠPl aint", + "ĠPlain t", + "ĠPla int", + "Ġ accumulator", + "Ġaccum ulator", + "Ġ Appointment", + "ĠApp ointment", + ", password", + ",p assword", + "ĠF AR", + "ĠFA R", + "l uet", + "lu et", + "lue t", + "Further more", + "decl spec", + "_ Statics", + "_Static s", + "_St atics", + ". Dictionary", + ".D ictionary", + "\" >'.", + "\"> '.", + "\">' .", + "ĉ valid", + "ĉval id", + "ĉva lid", + "\" \",", + "\"\" ,", + "In strument", + "Instr ument", + "> J", + "Ġno str", + "Ġnos tr", + "Ġnost r", + "ĠR ift", + "ĠRi ft", + "ĠRif t", + "_ Port", + "_P ort", + "Ġve ces", + "Ġvec es", + "[ ['", + "[[ '", + "Ġrall ies", + "- series", + "-s eries", + "-se ries", + "-ser ies", + "Ġ vv", + "Ġv v", + ". uc", + ".u c", + "Ġr tn", + "Ġrt n", + "State Changed", + "( ins", + "(i ns", + "(in s", + "Ġ Cla", + "ĠC la", + "ĠCl a", + "- -----------Ċ", + "-- ----------Ċ", + "---- --------Ċ", + "-------- ----Ċ", + "--- ---------Ċ", + "------------ Ċ", + "----- -------Ċ", + "---------- --Ċ", + "------ ------Ċ", + "----------- -Ċ", + "------- -----Ċ", + "--------- ---Ċ", + "c us", + "cu s", + "Ġ Reload", + "ĠR eload", + "ĠRe load", + "ĠRel oad", + "// ------------------------------------------------------------------------------------------------", + "//---------------------------------------------------------------- --------------------------------", + "//---------------------------------------------------------------------------- --------------------", + "//------------------------------------------------ ------------------------------------------------", + "//-------------------------------- ----------------------------------------------------------------", + "//-------------------------------------------------------------------------------- ----------------", + "//---------------- --------------------------------------------------------------------------------", + ". seconds", + ".se conds", + ".second s", + ".sec onds", + "_ destination", + "_d estination", + "_dest ination", + "Ġscre wed", + "Ġscr ewed", + "Ġscrew ed", + "> c", + "Th ickness", + "Des igner", + "Design er", + "Ġgr ids", + "Ġgrid s", + "Ġgri ds", + "n Äħ", + "( cookie", + "(c ookie", + "(co okie", + "T rip", + "Tr ip", + "Tri p", + "- Mobile", + "-M obile", + "Ġv oll", + "Ġvo ll", + "Ġvol l", + "Ġgen ital", + "Ġconf isc", + "ĠConfeder ate", + "Ġ webView", + "Ġweb View", + "Ġ mise", + "Ġm ise", + "Ġmis e", + "Ġmi se", + "Ġc ler", + "Ġcl er", + "Ġcle r", + "( selection", + "(s election", + "(se lection", + "(select ion", + "(sel ection", + "$ date", + "$d ate", + "Ġsharp en", + "Ġshar pen", + "r agen", + "ra gen", + "rag en", + "rage n", + "And Update", + "Ġre mix", + "Ġrem ix", + "Ġh tons", + "Ġht ons", + "Ġhton s", + "R W", + "M PI", + "MP I", + "Ġretrie val", + "Ġretr ieval", + "Ġrich est", + "Ġri chest", + "Ġric hest", + "Ġriches t", + ". Decode", + ".De code", + ".Dec ode", + ":init Components", + "ĠT Value", + "ĠTV alue", + "S aint", + "Sa int", + "@ include", + "Ġ PERSON", + "ĠPER SON", + ". sep", + ".s ep", + ".se p", + "Ġ LDAP", + "ĠLD AP", + "g ba", + "gb a", + "Ġgro ÃŁe", + "ĠgroÃŁ e", + "Ġreli ably", + "Ġ DFS", + "ĠD FS", + "ĠDF S", + ".get ItemId", + ".getItem Id", + "Ġpré sent", + "Ġprés ent", + ". getToken", + ".get Token", + "Ġch inese", + "Ġchin ese", + "Ġ Meal", + "ĠMe al", + "Y OU", + "YO U", + "\" > >ĊĊ", + "Ġ> >ĊĊ", + "Ġ>> ĊĊ", + "b ower", + "bo wer", + "bow er", + "Ġsw apped", + "Ġswap ped", + "/ install", + "Ġs inks", + "Ġsin ks", + "Ġsink s", + "etr ize", + "etri ze", + "Ġdec lines", + "Ġdecl ines", + "Ġdecline s", + "ĉ mysql", + "ĉm ysql", + "ĉmy sql", + "Ġ CString", + "ĠC String", + "ĠCS tring", + "ĠM otionEvent", + "ĠMotion Event", + ". Language", + ".L anguage", + "R oad", + "Ro ad", + "ÑĤ еÑĢ", + "ÑĤе ÑĢ", + "asc imento", + "' ))->", + "') )->", + "')) ->", + ". about", + ".a bout", + ".ab out", + "( editor", + "(e ditor", + "(ed itor", + "(edit or", + "ĠR atings", + "ĠRa tings", + "ĠRating s", + "ĠRat ings", + "in come", + "inc ome", + "Å¡ e", + ".de queueReusableCell", + "ĠAust rian", + "ĠAustria n", + "ĠAustr ian", + "Ġs ulla", + "Ġsu lla", + "Ġsul la", + "ĠTrib unal", + "Ġ Didn", + "ĠDi dn", + "ĠDid n", + "о ваÑĢ", + "ов аÑĢ", + "ова ÑĢ", + "Ġins pections", + "Ġinspect ions", + "Ġinspection s", + "Ġinsp ections", + "B oss", + "Bo ss", + "Ġcock tails", + "Ġcocktail s", + "Ġapolog ized", + "Ġapologize d", + "_ subplot", + "_sub plot", + "o pal", + "op al", + "opa l", + "+ =(", + "+= (", + "Ġreson ance", + "i bu", + "ib u", + "Ġ 리", + "Ġë ¦¬", + "Ġë¦ ¬", + "r oma", + "ro ma", + "rom a", + "re serve", + "res erve", + "rese rve", + "p ls", + "pl s", + "ĠT ah", + "ĠTa h", + "a xies", + "ax ies", + "O PLE", + "OP LE", + "ĠDar ren", + "ĠZ ombie", + "_ Map", + "_M ap", + "Ġ ])ĊĊ", + "Ġ] )ĊĊ", + "Ġ])Ċ Ċ", + "Ġ]) ĊĊ", + "Ġ Qi", + "ĠQ i", + "ĠS ail", + "ĠSa il", + "ĠSai l", + "Ġrestrict ive", + "Ġeros ion", + "- par", + "-p ar", + "W HITE", + "WH ITE", + "Ġold u", + "Ġol du", + "Ġap erture", + "Ġbit coins", + "Ġbitcoin s", + "text o", + "tex to", + "ĠCom cast", + "Ġtime less", + "Ġtim eless", + "en kins", + "enk ins", + "Ġfe eder", + "Ġfeed er", + "Ġfee der", + "/ tmp", + "/t mp", + "res den", + "+ '_", + "+' _", + ". Destroy", + ".D estroy", + ".De stroy", + "Ġ çok", + "Ġç ok", + "Ġ DOCUMENT", + "ĠD OCUMENT", + "ĠDOC UMENT", + ". lng", + ".l ng", + ". tagName", + ".tag Name", + "Ġk ullan", + "Ġkul lan", + "eg rate", + "egr ate", + "egra te", + "Ġ( *.", + "Ġ(* .", + "ç¼ĸ è¾ij", + "Ġhand shake", + "s oc", + "so c", + "_ geometry", + "_ge ometry", + "_geo metry", + "_geom etry", + "ĠDam ascus", + "Min or", + "Mi nor", + "ĠK afka", + "ĠKaf ka", + "ìĹ ¬", + "Fl orida", + "_ compute", + "_com pute", + "_comp ute", + ". expr", + ".ex pr", + ".exp r", + "Ġ paralle", + "Ġpar alle", + "Ġpara lle", + "ĠD iaz", + "ĠDi az", + "ĠDia z", + "c ir", + "ci r", + "[ target", + "[t arget", + "Ġj oking", + "Ġjo king", + "Ġg lor", + "Ġgl or", + "Ġglo r", + "( setq", + "(set q", + "_ handlers", + "_handler s", + "_handle rs", + "_hand lers", + "H ang", + "Ha ng", + "Han g", + "Ġf err", + "Ġfe rr", + "Ġfer r", + "r iminal", + "rim inal", + "ĉ ĠĠĠĠĉĉ", + "ĉĠĠĠ Ġĉĉ", + "ĉĠ ĠĠĠĉĉ", + "ĉĠĠ ĠĠĉĉ", + "ĉĠĠĠĠ ĉĉ", + "ĉĠĠĠĠĉ ĉ", + "en ties", + "ent ies", + "enti es", + "def ines", + "define s", + "- tax", + "-t ax", + "json p", + "Ġ UPS", + "ĠU PS", + "ĠUP S", + "m etro", + "me tro", + "met ro", + "_ _;Ċ", + "__ ;Ċ", + "__; Ċ", + "ĠUg anda", + "] )):Ċ", + "]) ):Ċ", + "])) :Ċ", + "_ td", + "_t d", + "x ae", + "xa e", + "l w", + ". OS", + ".O S", + "Ġ Logged", + "ĠLog ged", + "a cid", + "ac id", + "aci d", + "ĠM ayo", + "ĠMay o", + "ĠMa yo", + "a spect", + "as pect", + "asp ect", + "Ġvag inal", + "Ġvagina l", + "Ġinitial izing", + "Ġste roids", + "Ġster oids", + "Ġsteroid s", + "f iction", + "fi ction", + "fic tion", + "G RE", + "GR E", + "g end", + "ge nd", + "gen d", + "Ġli abilities", + "Ġ Lets", + "ĠL ets", + "ĠLe ts", + "ĠLet s", + "M ech", + "Me ch", + "( nc", + "(n c", + "( change", + "(ch ange", + "(chan ge", + "Ġconn ectors", + "Ġconnect ors", + "Ġconnector s", + ": k", + "Ġt ast", + "Ġta st", + "Ġtas t", + "! \");ĊĊ", + "!\" );ĊĊ", + "!\");Ċ Ċ", + "!\"); ĊĊ", + "!\") ;ĊĊ", + "th ings", + "thing s", + "thin gs", + "r ophy", + "ro phy", + "rop hy", + "roph y", + "l uetooth", + "lu etooth", + "luet ooth", + "Ġ SignUp", + "ĠSign Up", + ". ctrl", + ".c trl", + ".ct rl", + "Ġthere in", + "Ġther ein", + "or da", + "ord a", + ". escape", + ".e scape", + ".es cape", + "ig ator", + "iga tor", + "Ġpet rol", + "Ġspec imen", + "Ġspeci men", + "Ġdeb uted", + "Ġdebut ed", + "- Pro", + "-P ro", + "Ġcr ises", + "Ġcri ses", + "Ġcris es", + ".add View", + "ëı Ļ", + "- door", + "-d oor", + "-do or", + "Ġm onet", + "Ġmon et", + "Ġmo net", + "Ġm illis", + "Ġmill is", + "Ġmil lis", + "Ġmilli s", + "Ġ vier", + "Ġv ier", + "Ġvi er", + "Ġvie r", + "Internal Enumerator", + "Ġ admins", + "Ġad mins", + "Ġadmin s", + "Ġadm ins", + "ĠL air", + "ĠLa ir", + "z in", + "zi n", + "get Query", + "um bles", + "umb les", + "umble s", + "L IMIT", + "LI MIT", + "ĠV ig", + "ĠVi g", + "_ song", + "_s ong", + "_so ng", + "< Character", + ": :.", + ":: .", + "_ hom", + "_h om", + "_ bp", + "_b p", + "ĠSup ervisor", + "ĠSuper visor", + "ĠSuperv isor", + "sub mission", + "ab ile", + "abil e", + "abi le", + "Ġn oi", + "Ġno i", + "Or Create", + "Ġp eel", + "Ġpe el", + "Ġpee l", + "Ġon Start", + "Ġsent iments", + "Ġsentiment s", + "v ehicles", + "veh icles", + "vehicle s", + "Ġclass rooms", + "Ġclassroom s", + "Ġs zer", + "Ġsz er", + "Ġb ending", + "Ġben ding", + "Ġbend ing", + "Ġlong evity", + "Ġ acl", + "Ġa cl", + "Ġac l", + "ĠAle ppo", + "Ġ UM", + "ĠU M", + "ĠR icht", + "ĠRich t", + "ĠRic ht", + "ĠRi cht", + "Ġmulti processing", + "Ġmultip rocessing", + "DO MAIN", + "DOM AIN", + "\", \"+", + "\",\" +", + "_ YEAR", + "_Y EAR", + "Ġsc rape", + "Ġscr ape", + "Ġscrap e", + "Ġsol itary", + "Ġ\" ]\";Ċ", + "Ġ\"] \";Ċ", + "Ġ\"]\" ;Ċ", + "/ errors", + "/error s", + "ìŀ ¬", + "ľ ëł¥", + "b etter", + "bet ter", + "bett er", + "bette r", + "ĉ number", + "ĉn umber", + "ĉnum ber", + "Ġ LF", + "ĠL F", + "Ġ Across", + "ĠA cross", + "ĠAc ross", + "Pub Med", + "\\ \"\"", + "\\\" \"", + "ĠExcell ence", + "Ġus ando", + "Ġusa ndo", + "ĠU IP", + "ĠUI P", + "Activity Indicator", + "_ VOID", + "_V OID", + "_VO ID", + "Ġbre eds", + "Ġbreed s", + "Ġbree ds", + "ï½ ¥", + "ues tas", + "uest as", + "uesta s", + "ĠTre asure", + "ustr alian", + "ustral ian", + "ustralia n", + "( face", + "(f ace", + "ĠT ennis", + "ĠTen nis", + "ĠTenn is", + "ĉ Int", + "ĉI nt", + "ĉIn t", + "ĠH ansen", + "ĠHan sen", + "ĠHans en", + "ç µ", + ": I", + "Ġ âľĶ", + "Ġâľ Ķ", + "G RAY", + "GR AY", + "GRA Y", + "O USE", + "OU SE", + "OUS E", + "Ġhe pat", + "Ġhep at", + "ł í", + "A IR", + "AI R", + "ó ż", + "Ġ queued", + "Ġque ued", + "Ġqueue d", + "vin cia", + "vi ncia", + "vinc ia", + "ĠCh romium", + "ĠChrom ium", + "Ġcompet ence", + "Ġcompete nce", + "un gal", + "ung al", + "unga l", + "i lli", + "il li", + "ill i", + "Ġget By", + "Ġ Finder", + "ĠF inder", + "ĠFin der", + "ĠFind er", + "ĠFi nder", + "Ġincap able", + "Ġs add", + "Ġsa dd", + "Ġsad d", + "Ġc ites", + "Ġcit es", + "Ġci tes", + "Ġcite s", + "ĠChurch ill", + "S dk", + "More over", + "A spNet", + "As pNet", + "( Float", + "(F loat", + "$ password", + "$p assword", + "Ġ Connor", + "ĠCon nor", + "ĠConn or", + "- session", + "-s ession", + "_ dm", + "_d m", + "* ))", + "*) )", + "Ġde utsch", + "Ġdeut sch", + "Ġ NX", + "ĠN X", + "Ġper ks", + "Ġperk s", + "_ SORT", + "_S ORT", + "_SO RT", + "_TO OL", + "_TOO L", + "_ VISIBLE", + "_V ISIBLE", + "_VIS IBLE", + ". asp", + ".as p", + ".a sp", + "æĪ ĸ", + "ĠBre ath", + "D etect", + "Det ect", + "ĠD uel", + "ĠDu el", + "ĠDue l", + ". cmb", + ".c mb", + ".cm b", + "[ it", + "[i t", + ".Set Bool", + "Ġnarc iss", + "Ġab ide", + "Ġabi de", + "Ġej emplo", + "ĠâĦ ķ", + "Ġm ornings", + "Ġmor nings", + "Ġmorning s", + "Ġcomp utes", + "Ġcomput es", + "Ġcompute s", + ". ssl", + ".s sl", + ".ss l", + "j t", + "Ġm uchos", + "Ġmuch os", + "Ġmu chos", + "Ġmucho s", + "Ġmuc hos", + "_ SS", + "_S S", + "[ end", + "[e nd", + "Ġb asin", + "Ġbas in", + "Ġba sin", + "Ġalg unos", + "Ġalgun os", + "ĠCroat ia", + "line width", + "lin ewidth", + "( tags", + "(t ags", + "(tag s", + "( hidden", + "(h idden", + "ÃŃ cio", + "ÃŃc io", + "Ġa par", + "Ġap ar", + "Ġapa r", + "Ġ ж", + "ĠÐ ¶", + "ä¸ İ", + ". food", + ".f ood", + ".foo d", + "ĠR ural", + "ĠRu ral", + "Ġbread th", + "å½ ±", + "( sess", + "(s ess", + "(se ss", + "+ \")", + "+\" )", + "Ġ Paste", + "ĠP aste", + "ĠPa ste", + "ĠPast e", + "ĠPas te", + "Ġserv idor", + "ĠBit Set", + "ĠT ran", + "ĠTr an", + "ĠTra n", + "l aus", + "la us", + "v ette", + "ve tte", + "vet te", + "e yes", + "ey es", + "eye s", + "Ġ CLICK", + "ĠCL ICK", + "ĠCLI CK", + "ĠV III", + "ĠVI II", + "ĠVII I", + "ĠTur ns", + "ĠTurn s", + "ĠLe Bron", + "ĠM uj", + "ĠMu j", + "Ġ Deg", + "ĠD eg", + "ĠDe g", + "ĠAd ults", + "ĠAdult s", + "_ suite", + "_s uite", + "_su ite", + "process able", + "Ġ PHY", + "ĠP HY", + "ĠPH Y", + "g hest", + "gh est", + ". Fail", + ".F ail", + "ĠS lack", + "ĠSl ack", + "c ej", + "ce j", + "\\ Carbon", + "\\C arbon", + "Ġsuper star", + "Ġsupers tar", + "Ġsuperst ar", + "Ġhold ings", + "Ġholding s", + "Ġhol dings", + "( forms", + "(form s", + "(for ms", + "Ġ' #'", + "Ġ'# '", + "M ultip", + "Multi p", + "Mult ip", + "Mul tip", + "(\" [%", + "(\"[ %", + "- solid", + "-s olid", + "-so lid", + "/ url", + "/u rl", + "- tier", + "-t ier", + "[ length", + "[l ength", + "[len gth", + "Ġ StreamWriter", + "ĠStream Writer", + "ĠMarket place", + "get text", + "gett ext", + "_T ICK", + "_TI CK", + "Ġ Forge", + "ĠF orge", + "ĠFor ge", + "ĠForg e", + "Ġblack jack", + "ĠDO ES", + "ĠDOE S", + "ĠM atters", + "ĠMat ters", + "ĠMatt ers", + "ĠMatter s", + "ĠMatte rs", + "w aves", + "wa ves", + "wave s", + "wav es", + "Ġwhisper ed", + "Ġ lush", + "Ġl ush", + "Ġlu sh", + "ìĺ ¤", + "d igital", + "digit al", + "dig ital", + "Ġw rink", + "Ġwr ink", + "ĠH ogan", + "ĠHo gan", + "ĠHog an", + "Ġrust ic", + "Ġrus tic", + ".Apply Resources", + "ĠH ardy", + "ĠHar dy", + "ĠHard y", + "os omes", + "oso mes", + "osome s", + "A UT", + "AU T", + ". STATE", + ".ST ATE", + "Ġnarr atives", + "Ġnarrative s", + "ĉ store", + "ĉst ore", + "b ib", + "bi b", + "ĉ Scanner", + "ĠC ody", + "ĠCo dy", + "ĠCod y", + "\\ Repositories", + "Ġre union", + "Ġreun ion", + "an dum", + "and um", + "âĢĻ h", + "Ġsn iff", + "NS Bundle", + "Ġcompreh end", + "_ USAGE", + "_US AGE", + "_ occ", + "_o cc", + "_oc c", + "URRE NCY", + "J NI", + "Ġspecial izing", + "Ġ visions", + "Ġv isions", + "Ġvis ions", + "Ġvision s", + "Ġdo lore", + "Ġdol ore", + "Ġdolor e", + "Ġ vá", + "Ġv á", + "ĠChe vy", + "Ġ Styled", + "ĠSt yled", + "ĠStyle d", + "ĠSty led", + "imp act", + "a llen", + "al len", + "all en", + "alle n", + "Ġ kart", + "Ġk art", + "Ġka rt", + "Ġkar t", + "ĠTable t", + "ĠTab let", + "st uff", + "stu ff", + "re esome", + "ree some", + "rees ome", + "а ÑĤоÑĢ", + "аÑĤ оÑĢ", + "аÑĤо ÑĢ", + "//---------------------------------------------------------------- -----------Ċ", + "//- --------------------------------------------------------------------------Ċ", + "_ Admin", + "_Ad min", + "Ġcell phone", + "Ġ autoplay", + "Ġaut oplay", + "Ġauto play", + "Ġautop lay", + "Ġc ambio", + "Ġcam bio", + "Ġcamb io", + "Ġcambi o", + "Ġmar itime", + "Ġmari time", + "_ BOOT", + "_B OOT", + "_BO OT", + "- quarter", + "-qu arter", + "Ġlat ina", + "Ġlatin a", + "ĠAJ AX", + "e quiv", + "equ iv", + "ĠFront ier", + "Ġ XY", + "ĠX Y", + "} ]Ċ", + "}] Ċ", + "ĠR ough", + "ĠRo ugh", + "ĠRou gh", + ". proto", + ".pro to", + ".prot o", + ".pr oto", + "Ġcorrect ness", + "Ġfa cil", + "Ġfac il", + "Ġ Reached", + "ĠRe ached", + "ĠReach ed", + "ãģĿ ãģ®", + "V IS", + "VI S", + ". ps", + ".p s", + "Ġstr ncpy", + "Ġdif fusion", + "Ġdiff usion", + ".start Activity", + "� ��", + "�� �", + "Ġacc omp", + "Ġac comp", + "Ġaccom p", + "AME SPACE", + "AMES PACE", + "imon ials", + "imonial s", + "ĠB last", + "ĠBl ast", + "aby rin", + "Ġd ome", + "Ġdo me", + "Ġdom e", + "Ġext rav", + "Ġextra v", + "Ġextr av", + "Ġ yen", + "Ġy en", + "Ġye n", + "Ġcul inary", + "P RI", + "PR I", + "ĠComm unities", + "ĠCommun ities", + "n id", + "ni d", + "_ operations", + "_oper ations", + "_operation s", + ". hs", + ".h s", + "ĠM ilton", + "ĠMil ton", + "Ġno ises", + "Ġnoise s", + "Ġnoi ses", + "Autoresizing Mask", + "( cid", + "(c id", + "(ci d", + "} ĊĊĊĊĊĊ", + "}Ċ ĊĊĊĊĊ", + "}ĊĊ ĊĊĊĊ", + "}ĊĊĊ ĊĊĊ", + "}ĊĊĊĊ ĊĊ", + "}ĊĊĊĊĊ Ċ", + "] },Ċ", + "]} ,Ċ", + "]}, Ċ", + "Ġ Detection", + "ĠD etection", + "ĠDe tection", + "ĠDet ection", + "ĠDetect ion", + "ta bla", + "tab la", + "tabl a", + "Ġlib erties", + "Ġlibert ies", + "Ġliber ties", + "_D YNAMIC", + "w get", + "wg et", + "ĠT ür", + "ĠP ascal", + "ĠPa scal", + "ĠPas cal", + "Trans parent", + "Del ayed", + "Delay ed", + "] ()", + "]( )", + "ĠHer bert", + "ĠHerb ert", + "< ActionResult", + "", + "}- >", + "Ġpas ado", + "Ġpasa do", + "th ank", + "tha nk", + "than k", + "_ Delete", + "_De lete", + "ĠBr ighton", + "ĠBright on", + "ĠBrig hton", + ", unsigned", + "ä½ľ èĢħ", + "Ġaspir ations", + "Ġaspiration s", + "- how", + "-h ow", + "R ose", + "Ro se", + "Ros e", + "= ((", + "=( (", + "_ needed", + "_ne eded", + "_need ed", + "_ plural", + "_pl ural", + "< Application", + " >ĊĊ", + ">> ĊĊ", + ">>Ċ Ċ", + "Ġsurface d", + "Ġsurf aced", + "Ġìł Ģìŀ¥", + "ĠìłĢ ìŀ¥", + "pl atz", + "plat z", + "pla tz", + "ĉ email", + "ĉe mail", + "ĉem ail", + "cept ors", + "ceptor s", + "cep tors", + "\" >(", + "\"> (", + "Ġe pile", + "Ġep ile", + "è¯ »", + "ĠDe bt", + "ĠDeb t", + "åij Ĭ", + "N OP", + "NO P", + "\" https", + "\"http s", + ": j", + "Form Item", + "_ LICENSE", + "_L ICENSE", + ".get Double", + ".getD ouble", + "ĠAg enda", + "ĠAge nda", + "ĉ finally", + "ĉf inally", + "ĉfinal ly", + "( filters", + "(f ilters", + "(filter s", + "(fil ters", + "( av", + "(a v", + "ç¾ İ", + "A PER", + "AP ER", + "APE R", + "Ġ lava", + "Ġl ava", + "Ġla va", + "Ġlav a", + "еÑĢ ж", + ") )))ĊĊ", + ")) ))ĊĊ", + "))) )ĊĊ", + "))))Ċ Ċ", + ")))) ĊĊ", + "Ġfa ulty", + "Ġfault y", + "_ nm", + "_n m", + "Ġt rava", + "Ġtr ava", + "Ġtra va", + "Ġtrav a", + "( Bitmap", + "(B itmap", + "(Bit map", + "Ġspe eding", + "Ġspeed ing", + "> ').", + ">' ).", + ">') .", + "Ġscreen ed", + "Ġscre ened", + "_ roll", + "_r oll", + "_ro ll", + "ĠMac Book", + "Ġ AUD", + "ĠA UD", + "ĠAU D", + "Ġdiagn ose", + ". Generate", + ".G enerate", + ".Gen erate", + "Ġ ^^", + "Ġ^ ^", + "Ġs trs", + "Ġst rs", + "Ġstr s", + "[ Test", + "[T est", + "Ġr ansom", + "Ġran som", + "ĠDH CP", + "el den", + "eld en", + "Ġinterpret ations", + "Ġinterpretation s", + "( )].", + "() ].", + "()] .", + "flat Map", + "Ġline Height", + "_ mount", + "_m ount", + "_mo unt", + "ĠW izards", + "ĠWizard s", + "Ġsl uts", + "Ġslut s", + "Ġslu ts", + "eh ler", + "o dal", + "od al", + "oda l", + "Ġmilit ia", + "Ġmil itia", + "å ²", + "ear ned", + "earn ed", + "Ġmis ery", + "Ġmise ry", + "Ġmiser y", + "int val", + "f und", + "fun d", + "fu nd", + "Ġh ides", + "Ġhide s", + "Ġhi des", + "Ġhid es", + "Ġdi arr", + "Ġdia rr", + "ĠWes ley", + "Ġ xmm", + "Ġx mm", + "Ġxm m", + "Ġqu em", + "Ġque m", + "Ġq uem", + "ĠAr abs", + "ĠArab s", + "ĠAra bs", + "if th", + "ift h", + "ategor ized", + "ategori zed", + "D isposable", + "Dis posable", + "P ure", + "Pu re", + "_NOT IFY", + "sn ippet", + "ĠGar rett", + "ĠGarr ett", + ". running", + ".r unning", + ".run ning", + ". weights", + ".weight s", + ".we ights", + "Ġ (--", + "Ġ( --", + "Ġ(- -", + "Ġin variant", + "Ġinv ariant", + "äºĭ 件", + "Ġ Allowed", + "ĠAll owed", + "ĠAllow ed", + "d irs", + "dir s", + "di rs", + "Ġpass ions", + "Ġpassion s", + "Ġ lad", + "Ġl ad", + "Ġla d", + "Ġ Flush", + "ĠF lush", + "ĠFl ush", + "ĠFlu sh", + "m enus", + "men us", + "menu s", + ": block", + ":b lock", + "Ġcom pra", + "Ġcomp ra", + "Ġcompr a", + ".ch omp", + "al locator", + "all ocator", + "alloc ator", + "alloca tor", + "Ġcur ated", + "Ġcu rated", + "Ġ Knowing", + "ĠKn owing", + "ĠKnow ing", + "ĠPatt erson", + "Ġt elah", + "Ġte lah", + "Ġtel ah", + "Ġtela h", + "' ex", + "'e x", + "Ġdo omed", + "Ġdoom ed", + "Ġphil anth", + "o tty", + "ot ty", + "ott y", + ". styles", + ".st yles", + ".style s", + "Own ed", + "Ġallerg ies", + "Ġaller gies", + "= params", + "oc ese", + "oce se", + "it elist", + "ite list", + "itel ist", + "iteli st", + "Ġ Sending", + "ĠS ending", + "ĠSen ding", + "ĠSend ing", + "b ef", + "be f", + "or rar", + "orr ar", + "orra r", + "Ġ Não", + "ĠN ão", + "ĠF argo", + "ĠFar go", + "ĠL ub", + "ĠLu b", + "Ġ Combined", + "ĠComb ined", + "ĠCombine d", + "_ given", + "_g iven", + "ĉ ĉĉĉĉĠĠĠĠ", + "ĉĉ ĉĉĉĠĠĠĠ", + "ĉĉĉĉ ĉĠĠĠĠ", + "ĉĉĉ ĉĉĠĠĠĠ", + "ĉĉĉĉĉ ĠĠĠĠ", + "ĉĉĉĉĉĠ ĠĠĠ", + "ĉĉĉĉĉĠĠĠ Ġ", + "ĉĉĉĉĉĠĠ ĠĠ", + "Ġre conciliation", + "Ġreconc iliation", + "Pattern s", + "az ard", + "aza rd", + "azar d", + "Ġbio mass", + "Ġbiom ass", + "ĠH ouses", + "ĠHouse s", + "ĠHo uses", + "ĠHou ses", + "resp uesta", + "c co", + "cc o", + "/ topics", + "/to pics", + "/top ics", + "/topic s", + "ĠY uk", + "ĠYu k", + "Ġweak ened", + "Ġweaken ed", + "_ calendar", + "_c alendar", + "_cal endar", + "Ġmulher es", + "ĠM arl", + "ĠMar l", + "ĠMa rl", + "Ġs ine", + "Ġsi ne", + "Ġsin e", + "ĠT il", + "ĠTi l", + "ĠSo uls", + "ĠSou ls", + "ĠSoul s", + "ĠDe utsche", + "ĠDeutsch e", + "ĠF OLLOW", + "Ġp ipelines", + "Ġpipe lines", + "Ġpipeline s", + "Ġpip elines", + "ĠBever ly", + "_DIP SETTING", + "\" #", + "Ġ Proto", + "ĠPro to", + "ĠPr oto", + "ĠProt o", + ". big", + ".b ig", + ".bi g", + "ĠS avings", + "ĠSav ings", + "ĠSaving s", + "ĠT anz", + "ĠTa nz", + "ĠTan z", + "j un", + "ju n", + "Ġ Gamma", + "ĠG amma", + "ĠGa mma", + "ĠGam ma", + "ĠS add", + "ĠSa dd", + "ĠSad d", + "Ġadv isors", + "Ġadvis ors", + "Ġadvisor s", + "Ġro ast", + "Ġun ters", + "Ġunt ers", + "Ġunter s", + "ud ies", + "udi es", + "_ lon", + "_l on", + "_lo n", + "- pointer", + "-point er", + "-po inter", + "ĠElement Ref", + "\\ Builder", + "example Input", + ". webdriver", + ".web driver", + "data Type", + "Ġ Quite", + "ĠQ uite", + "ĠQu ite", + "ĠQui te", + "ĠQuit e", + "ĠCelt ics", + "ĠCel tics", + "ĠCeltic s", + "u il", + "ui l", + "- defense", + "-def ense", + "b ish", + "bi sh", + "bis h", + "ĠUI Window", + "Ġ Suddenly", + "ĠS uddenly", + ". hot", + ".h ot", + ". reason", + ".re ason", + "Ġg ör", + "Ġgö r", + "A MD", + "AM D", + ". Multi", + ".M ulti", + ".Mult i", + "auth enticated", + "authenticate d", + "reg ions", + "region s", + "; (", + "а ÑĢам", + "аÑĢ ам", + "аÑĢа м", + "ĠKir by", + "$ route", + "$r oute", + "PREC ATED", + "ĠDur ham", + "o wo", + "ow o", + "ĠPer forms", + "ĠPerform s", + "Ġdisreg ard", + "n st", + "ns t", + "ĠP ols", + "ĠPol s", + "ĠPo ls", + "Ġget P", + "\" ]:", + "\"] :", + "-color ed", + "-col ored", + "( Keys", + "(Key s", + "ĠAl leg", + "ĠAll eg", + "ĠAlle g", + "_ modify", + "_mod ify", + "_ loading", + "_lo ading", + "_load ing", + "s trained", + "str ained", + "stra ined", + "strain ed", + "Ġat roc", + "Ġatr oc", + "_p hr", + "_ph r", + "< Sprite", + "", + "c eph", + "ce ph", + "cep h", + ".DateTime Picker", + ". \";ĊĊ", + ".\" ;ĊĊ", + ".\";Ċ Ċ", + ".\"; ĊĊ", + "ĠT ie", + "ĠTi e", + ", item", + ",i tem", + ",it em", + "Ġm enn", + "Ġme nn", + "Ġmen n", + "G as", + "Ga s", + "o cha", + "oc ha", + "och a", + "_ virtual", + "_v irtual", + "Ġmaster piece", + "_ sequences", + "_se quences", + "_sequence s", + "L TE", + "LT E", + "Ġ Submission", + "ĠSub mission", + "C aller", + "Call er", + "Cal ler", + "Ca ller", + "$ \\", + "S port", + "Sp ort", + "Spo rt", + "ag us", + "agu s", + "Constraint Maker", + "Ġco loc", + "Ġcol oc", + "Ġ wig", + "Ġw ig", + "Ġwi g", + "Ġ У", + "ĠÐ £", + "ĉ Array", + "ĉA rray", + "L ooks", + "Lo oks", + "Look s", + "ĠG TA", + "ĠGT A", + ". steps", + ".st eps", + ".step s", + "atch ewan", + "_ ranges", + "_r anges", + "_range s", + "ext Alignment", + "ĠBren nan", + "Ġab straction", + "Ġabs traction", + "Ġabstract ion", + "Ġabst raction", + "uler Angles", + ". misc", + ".m isc", + ".mi sc", + "Ġantib odies", + "Ġex ponential", + "Ġexponent ial", + "Ġ CHANNEL", + "ĠCH ANNEL", + "exp ense", + "' y", + "Ġdetect ives", + "Ġdetective s", + "Ġpur ported", + "Y STEM", + "YS TEM", + "YST EM", + "Ġradio active", + "ĠLat ina", + "ĠLatin a", + ". Encoding", + ".En coding", + ".Enc oding", + ". TAG", + ".T AG", + "x in", + "xi n", + "D egree", + "De gree", + "Deg ree", + "ur acion", + "ura cion", + "p rices", + "pr ices", + "price s", + "pri ces", + "ĠRefer entialAction", + "Ġr arity", + "Ġrar ity", + "Ġp iles", + "Ġpi les", + "Ġpil es", + "Ġpile s", + "g ende", + "ge nde", + "gen de", + "gend e", + "_ projects", + "_project s", + "_proj ects", + "_ globals", + "_g lobals", + "_global s", + "_glob als", + ". startTime", + ".start Time", + "Ġ 구", + "Ġê µ¬", + "Ġêµ ¬", + "SE CTION", + "SEC TION", + "_ publish", + "_p ublish", + "_pub lish", + "F ault", + "Fa ult", + "D DL", + "DD L", + "_ prior", + "_p rior", + "_pr ior", + "_pri or", + "M om", + "Mo m", + "Ġth icker", + "Ġthick er", + "Ġthi cker", + "Ġ sequelize", + "Ġsequ elize", + "Ġsequel ize", + "Ġess entials", + "Ġessential s", + "s tras", + "st ras", + "str as", + "stra s", + "in tr", + "int r", + "> (()", + ">( ()", + ">(( )", + ". management", + ".man agement", + ".manage ment", + "e il", + "ei l", + "éĹ Ń", + "A ware", + "Aw are", + ". City", + ".C ity", + "ĠAr bit", + "ĠArb it", + "_ DM", + "_D M", + "_ keyboard", + "_key board", + "L Object", + "LO bject", + "- webpack", + "-web pack", + "ĠNew port", + "Ġprincipal Column", + "leg ant", + "Ġp allet", + "Ġpal let", + "Ġpall et", + "Ġfract ure", + "Ġfrac ture", + "Ġ gmail", + "Ġg mail", + "Ġgm ail", + ". Meta", + ".M eta", + ".Me ta", + "A bove", + "Ab ove", + ". KeyEvent", + ".Key Event", + "j it", + "ji t", + "_ macro", + "_m acro", + "_mac ro", + "_ma cro", + "_P USH", + "_PUS H", + "á» ©", + "/ controller", + "/control ler", + "åĬł è½½", + "Ġsuperf icial", + "exter ity", + "Ġ mensagem", + "Ġm ensagem", + "Ġmens agem", + "W ind", + "Win d", + "Wi nd", + "i ston", + "is ton", + "ist on", + "isto n", + ".open api", + "и ÑĢов", + "иÑĢ ов", + "Ġ Serializer", + "ĠS erializer", + "ĠSerial izer", + "ĠSerialize r", + "uct ive", + "Ġ zar", + "Ġz ar", + "Ġza r", + "P laces", + "Pl aces", + "Place s", + ". Static", + ".St atic", + ".Stat ic", + "B a", + "Ġin advert", + "ĠIndones ian", + "ĠIndonesia n", + "_I PV", + "_IP V", + "( horizontal", + "(h orizontal", + "Ġ getTitle", + "Ġget Title", + "ide press", + "ĠConsole Color", + "i pers", + "ip ers", + "ipe rs", + "iper s", + "$ out", + "$o ut", + "Ġfest ive", + "Ġeven ings", + "Ġevening s", + "Ġeve nings", + ". GetData", + ".Get Data", + "uit ka", + "ĠManual s", + "us sed", + "uss ed", + "_ Max", + "_M ax", + ". Chat", + ".C hat", + ".Ch at", + "ĠA ircraft", + "ĠAir craft", + "= com", + "=c om", + "F OUND", + "FO UND", + "a pro", + "ap ro", + "apr o", + "Ġtre asures", + "Ġtreasure s", + "_ alive", + "_a live", + "_al ive", + "Ġg adget", + "Ġgad get", + "e king", + "ek ing", + "eki ng", + "Button Down", + "B rowsable", + ".PER MISSION", + "P ASSWORD", + "PASS WORD", + "Ġ HASH", + "ĠH ASH", + "ĠHAS H", + "ĠHA SH", + "f é", + "\\ TestCase", + "\\Test Case", + "LO SS", + "LOS S", + "o thers", + "other s", + "oth ers", + ", J", + "Ġass hole", + "Ġassh ole", + "w erk", + "we rk", + "wer k", + "Ġm ã", + ". ie", + ".i e", + "e vil", + "ev il", + "evi l", + "kont akte", + "/ ///////////////////////////////////////////////////////////////////////////////Ċ", + "/// /////////////////////////////////////////////////////////////////////////////Ċ", + "//////////////////////////////////////////////////////////////////////////// ////Ċ", + "//////////////////////////////////////////////////////////////////////////////// Ċ", + "= sys", + "=s ys", + "ĉ lock", + "ĉl ock", + "ĉloc k", + "-- ;ĊĊ", + "--;Ċ Ċ", + "--; ĊĊ", + "_ FUN", + "_F UN", + "Fill Color", + "ó a", + "p rend", + "pr end", + "pre nd", + "Ġcom pressor", + "Ġcompr essor", + "Ġcompress or", + "M other", + "Mo ther", + "Mot her", + "ĠAr cher", + "ĠArch er", + "ĠArc her", + ". goto", + ".g oto", + ".go to", + "Ġwür de", + "Ġbam boo", + "Ġbamb oo", + "ï¼ İ", + "Ġ Trees", + "ĠT rees", + "ĠTr ees", + "ĠTree s", + "ĠTre es", + "Ġb umper", + "Ġbump er", + "Ġbum per", + "Ġsa usage", + "Ġsau sage", + "ĠEl asticsearch", + "ĠElastic search", + "Ġhor izontally", + "Ġhorizontal ly", + "ĠG ul", + "ĠGu l", + "Im mutable", + "Imm utable", + "Ġ loser", + "Ġl oser", + "Ġlo ser", + "Ġlos er", + "Ġlose r", + "Ġab orted", + "Ġabort ed", + "- demo", + "-d emo", + "-de mo", + "-dem o", + "ĠH atch", + "ĠHat ch", + "Ġ unde", + "Ġu nde", + "Ġun de", + "Ġund e", + "Ġpro cesso", + "Ġprocess o", + "Ġproc esso", + "Ġproces so", + "- call", + "-c all", + "-cal l", + "-ca ll", + "In come", + "Inc ome", + "å ĥ", + "_ returns", + "_return s", + "'] .\"'", + "']. \"'", + "'].\" '", + "( sw", + "(s w", + "C BS", + "CB S", + "am ilies", + "ami lies", + "amil ies", + "ĠYour self", + "ĠYours elf", + "ĠH olt", + "ĠHol t", + "ĠHo lt", + ". MON", + ".M ON", + "ৠĩ", + "ÑĪ е", + "a non", + "an on", + "ano n", + "Ġ FontAwesome", + "ĠFont Awesome", + "pro ducer", + "produ cer", + "prod ucer", + "produce r", + "j r", + "Ġm au", + "Ġma u", + "ĉ inter", + "ĉint er", + "ĉin ter", + "Ġdish onest", + "Ġm agna", + "Ġmag na", + "Ġmagn a", + "ĠColl ective", + "ĠCollect ive", + "Ġvra iment", + "Ġvrai ment", + "Ġcho ix", + "st ay", + "sta y", + "Ġwel ding", + "Ġweld ing", + "r ising", + "ri sing", + "ris ing", + ", min", + ",m in", + "ĠF ate", + "ĠFa te", + "ĠFat e", + "g lob", + "gl ob", + "RGB A", + "RG BA", + "Ġd ette", + "Ġde tte", + "Ġdet te", + "V en", + "Ve n", + "Ġembarrass ment", + ". DELETE", + ".DE LETE", + "g regar", + "greg ar", + "gre gar", + "- render", + "-r ender", + "-re nder", + "-ren der", + "( bucket", + "(b ucket", + "\" >ĊĊĊ", + "\"> ĊĊĊ", + "\">Ċ ĊĊ", + "\">ĊĊ Ċ", + ".wait Key", + "Bus y", + "Bu sy", + "Ġdifferent iation", + "ĠC ST", + "ĠCS T", + ". Constant", + ".Con stant", + ".Cons tant", + "Ġline Number", + "( matches", + "(m atches", + "(match es", + "(mat ches", + "Ġ websocket", + "Ġweb socket", + "Ġwebs ocket", + "Ġbar red", + "Ġbarr ed", + "Ġpued es", + "Ġpu edes", + "Ġpuede s", + "M ono", + "Mon o", + "Mo no", + "C ORE", + "CO RE", + "COR E", + "I ID", + "II D", + "ĠĠ ĠĠčĊčĊ", + "ĠĠĠĠ čĊčĊ", + "ĠĠĠ ĠčĊčĊ", + "ĠĠĠĠčĊ čĊ", + "Ġpúb lico", + "le aning", + "lean ing", + "lea ning", + "Ġclean sing", + "Ġcleans ing", + "Ġc ris", + "Ġcr is", + "Ġcri s", + "ĠDev ils", + "ĠDevil s", + "_ SETTING", + "_SET TING", + "unt ary", + "unta ry", + ". );Ċ", + ".) ;Ċ", + "Ċ ĠĠĠĊ", + "[ curr", + "[c urr", + "[cur r", + "t sy", + "ts y", + "ĠAlex is", + "ĠAle xis", + "r itel", + "ri tel", + "rit el", + "rite l", + "Ġpet roleum", + "Ġpetrol eum", + ".pre processing", + "m atter", + "mat ter", + "For Result", + "- license", + "-l icense", + "Ġtravel lers", + "Ġtrav ellers", + "Ġtraveller s", + "Ġ Dispatcher", + "ĠDispatch er", + "ĠDisp atcher", + "enn ifer", + "Ġdigest ive", + "P ED", + "PE D", + "hib ition", + "hibit ion", + "MAS ConstraintMaker", + "ĠW att", + "ĠWat t", + "ĠWa tt", + "B enef", + "Ben ef", + ".set View", + "d to", + "dt o", + "T EE", + "TE E", + "ĠPel osi", + "_EX TRA", + "_EXT RA", + "Ġmed als", + "Ġmedal s", + "x hr", + "fore cast", + "for ecast", + "Ġn argin", + "Ġnar gin", + "o uns", + "ou ns", + "oun s", + "- fill", + "-f ill", + "-fi ll", + "_CUR SOR", + "Ġsup ervised", + "Ġsuper vised", + "Ġsuperv ised", + "Ġsupervise d", + "Ġt urf", + "Ġtu rf", + "Ġtur f", + "ĠEd gar", + "POS ITION", + "POSIT ION", + "Ġ categoryId", + "Ġcategory Id", + "â ī", + "_ ER", + "_E R", + "ủ a", + "Sh own", + "Show n", + ". ll", + ".l l", + "_POL ICY", + "( ),'", + "() ,'", + "(), '", + "Ġ Prev", + "ĠP rev", + "ĠPr ev", + "ĠPre v", + "ĠString Field", + "ĉ Global", + "ĉG lobal", + "as sed", + "ass ed", + "asse d", + "Through out", + "o stringstream", + ".awt extra", + "Ġsl opes", + "Ġslo pes", + "Ġslope s", + "Ġ Sequential", + "ĠSe quential", + "ĠSequ ential", + "Ġgi orn", + "Ġgio rn", + "Ġ zelf", + "Ġz elf", + "Ġze lf", + "Ġzel f", + "Ġvers atility", + "Ġversa tility", + "le neck", + "len eck", + "lene ck", + ". cgi", + ".c gi", + ".cg i", + "Ġdoub ling", + "Ġdou bling", + "ĠBang kok", + "Ġbu urt", + "Ġusu ário", + "st udio", + "stu dio", + "stud io", + "Ġje unes", + "Ġjeune s", + "Ġjeu nes", + "Ġm uted", + "Ġmut ed", + "Ġmu ted", + "Ġmute d", + "Ġ ips", + "Ġi ps", + "Ġip s", + "_ fraction", + "_f raction", + "_fr action", + "_frac tion", + "& &(", + "&& (", + "Ġst unt", + "Ġstu nt", + "Ġstun t", + "') ;?> čĊ", + "}> čĊ", + "Ġev apor", + "b able", + "ba ble", + "bab le", + "Ġ PRICE", + "ĠPR ICE", + "ĠPRI CE", + "Ġ æ³", + "Ġæ ³", + "lu cent", + "Ġv amp", + "Ġva mp", + "ĠTechn ician", + "Ġunique ness", + "Ġuniqu eness", + "M es", + "Me s", + "ur ban", + "urb an", + ".param etrize", + "ĠRe play", + "ĠRep lay", + "S essions", + "Session s", + "em br", + "emb r", + "- Americans", + "-American s", + "-Americ ans", + "_PRO XY", + "Ġp ian", + "Ġpi an", + "Ġ trie", + "Ġt rie", + "Ġtr ie", + "Ġtri e", + "Ġ Destructor", + "ĠD estructor", + "ĠDe structor", + "Game State", + "ĠI MF", + "ĠIM F", + "c hin", + "ch in", + "chi n", + "Ġ porte", + "Ġp orte", + "Ġport e", + "Ġpor te", + "ĠS wal", + "ĠSw al", + "åŁ İ", + "Sub string", + "i ming", + "im ing", + "imi ng", + "imin g", + "/ Library", + "/L ibrary", + "Ġfright ened", + "w rites", + "write s", + "wr ites", + "Ġrec ursos", + "Ġrecurs os", + "ar Result", + "_INIT IALIZ", + "_INITIAL IZ", + "Ġ Badge", + "ĠB adge", + "ĠBad ge", + "ĠBa dge", + "_ crc", + "_c rc", + "_cr c", + "E ight", + "ĠDIST INCT", + "Ġ thro", + "Ġth ro", + "Ġthr o", + "@ Xml", + "Ġ Legendary", + "ĠLegend ary", + "- twitter", + "-t witter", + "-tw itter", + "_ easy", + "_e asy", + "Ġ +++", + "Ġ+ ++", + "Ġ++ +", + "( DATA", + "(D ATA", + ". Locale", + ".L ocale", + ".Local e", + ".Lo cale", + "Ġk ä", + "Ġn urt", + "Ġnu rt", + "Ġnur t", + "Ġcr uis", + "Ġcru is", + "_ ios", + "_i os", + "_io s", + "Ġs ensing", + "Ġsens ing", + "Ġsen sing", + "_ Line", + "_L ine", + "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "p ong", + "pon g", + "po ng", + "o leon", + "ol eon", + "ole on", + "Ġwild card", + "çĶ¨æĪ· åIJį", + "Ġbeg ging", + "R od", + "Ro d", + "Ġ Ãİ", + "Ġà İ", + "_ CELL", + "_C ELL", + "_CE LL", + "Research ers", + ". selector", + ".se lector", + ".select or", + ".sel ector", + "_ ing", + "_in g", + "_i ng", + "Ġas piring", + "Ġaspir ing", + "Ġasp iring", + "Ġimm ortal", + "Ġy min", + "_ robot", + "_r obot", + "_ro bot", + "Ġpl ur", + "Ġplu r", + "B TC", + "BT C", + "ĠD ID", + "ĠDI D", + "Ġpier cing", + "* u", + "_ DEFINED", + "_DEF INED", + "_DEFIN ED", + "_DEFINE D", + "ĠT hi", + "ĠTh i", + "i taire", + "it aire", + "ita ire", + "( media", + "(m edia", + "(me dia", + "- ons", + "-on s", + "-o ns", + "Ġch efs", + "Ġche fs", + "Ġchef s", + "Ġ\" *.", + "Ġ\"* .", + "/ AP", + "/A P", + "Ġr azor", + "Ġraz or", + "Ġsearch Data", + "Ġ =&", + "Ġ= &", + "Ġ ãĢĤ", + "ĠãĢ Ĥ", + "Ġm ourn", + "Ġmo urn", + "Ġmou rn", + "Ġmour n", + "t ingham", + "ting ham", + "Ġ oli", + "Ġo li", + "Ġol i", + "ĠVer non", + "ĠVern on", + "_ RS", + "_R S", + "ŀ æĢ§", + "Ġf ácil", + "a ngen", + "an gen", + "ang en", + "ange n", + "ce lain", + "cel ain", + "cela in", + "Ġ ail", + "Ġa il", + "Ġai l", + "l est", + "le st", + "les t", + "ĠQ COMPARE", + "g ain", + "ga in", + "Ġ ε", + "ĠÎ µ", + "ĠK ob", + "ĠKo b", + "Ġ Fault", + "ĠF ault", + "ĠFa ult", + "ĠFaul t", + "_ configs", + "_config s", + "_conf igs", + "ç»ĵ æŀľ", + ". +", + "c alar", + "ca lar", + "cal ar", + "cala r", + "( colors", + "(color s", + "(col ors", + "M ul", + "Mu l", + "_ ART", + "_A RT", + "_AR T", + "Ġexperiment ing", + "er men", + "erm en", + "ĠAng lo", + ".Fixed Single", + "S ea", + "Se a", + "Ġ ctxt", + "Ġc txt", + "Ġctx t", + "Ġct xt", + ". slider", + ".s lider", + ".sl ider", + ".slide r", + "C ollapse", + "Coll apse", + "G rey", + "Gr ey", + "Gre y", + "Ġ fld", + "Ġf ld", + "Ġfl d", + "- proof", + "-p roof", + "-pro of", + ". capacity", + ".cap acity", + "get Parent", + "ĠCom pliance", + "Ġbur gl", + "Ġburg l", + "- rec", + "-r ec", + "-re c", + "Ġover written", + "M U", + "Ġr outers", + "Ġro uters", + "Ġroute rs", + "Ġrout ers", + "Ġrouter s", + "Ġrou ters", + "ĉ Model", + "ĉM odel", + "Ġfantas ies", + "av ian", + "avi an", + "avia n", + "_ prec", + "_p rec", + "_pr ec", + "_pre c", + "ĠSc andin", + "ĠScan din", + "Ġ// <", + "Ġ/ /<", + "/ oct", + "/o ct", + "Ġceremon ies", + "Mon ths", + "Month s", + "Mont hs", + "un dy", + "und y", + "Ġqu ed", + "Ġque d", + "Ġq ued", + "ĠN ou", + "ĠNo u", + "ĠV ibr", + "ĠVi br", + "ĠVib r", + ". rgb", + ".r gb", + "Ġcit rus", + "Ġbr aces", + "Ġbra ces", + "Ġbrace s", + "- uppercase", + "-upper case", + "get Table", + "Ġd opo", + "Ġdo po", + "Ġdop o", + "ĠK err", + "ĠKe rr", + "ĠKer r", + "_ CHILD", + "_CH ILD", + "- cloud", + "-c loud", + "-cl oud", + "ĉ Matrix", + "ĉM atrix", + "ĉMat rix", + "Ġgarden ing", + "Ġgard ening", + "S ing", + "Si ng", + "Sin g", + "al most", + "alm ost", + "Require ments", + "Requirement s", + "ugu ay", + "( Property", + "(P roperty", + "sub scriber", + "subscribe r", + "F AST", + "FA ST", + "re action", + "react ion", + "rea ction", + "( lp", + "(l p", + ") })Ċ", + ")} )Ċ", + ")}) Ċ", + "` ).", + "`) .", + ". wallet", + ".w allet", + ".wall et", + "_ exchange", + "_ex change", + ". Maximum", + ".Max imum", + "Ġ Verb", + "ĠV erb", + "ĠVer b", + "ĠVe rb", + "âĶ ģ", + "( )<", + "() <", + "ï¼Ľ Ċ", + "R OT", + "RO T", + "C ARD", + "CA RD", + "CAR D", + "u bit", + "ub it", + "ubi t", + "{ @", + "_ kel", + "_k el", + "_ke l", + "Ġ Tooltip", + "ĠTo oltip", + "ĠTool tip", + "My SQL", + "Main Activity", + "a rf", + "ar f", + "Ġm align", + "Ġmal ign", + "Ġse inen", + "Ġsein en", + "Ġseine n", + "Ġsei nen", + "ap ist", + "api st", + "apis t", + "Ġ< %", + "Method Impl", + "M il", + "Mi l", + "ĠM ick", + "ĠMi ck", + "ĠMic k", + ". depend", + ".d epend", + ".de pend", + ".dep end", + "< ID", + " >&", + ">> &", + "ĉ ok", + "ĉo k", + "- low", + "-l ow", + "-lo w", + ". usuario", + ".us uario", + "n ested", + "ne sted", + "nes ted", + "nest ed", + "X B", + "OUR S", + "OU RS", + ". BorderColor", + ".Border Color", + "Ġb row", + "Ġbr ow", + "Ġbro w", + "Ġ Ðķ", + "ĠÐ ķ", + "c orr", + "co rr", + "cor r", + "ĠRed skins", + "ĠReds kins", + ".get Tag", + ".get Transaction", + "Ġst igma", + "har dt", + "hard t", + "ĠPlayer Prefs", + "al sy", + "als y", + "uc son", + "ucs on", + "L anguages", + "Language s", + "ĠOl ivia", + "ĠOliv ia", + "Ġt ac", + "Ġta c", + "Ġb li", + "Ġbl i", + "Ġc aval", + "Ġca val", + "Ġcav al", + "Ġconsolid ated", + "Ġconsolidate d", + "Ġper il", + "Ġpe ril", + "Ġperi l", + "Ġd ele", + "Ġde le", + "Ġdel e", + "Ġform ulated", + "Ġformula ted", + "Ġformul ated", + "Ġformulate d", + "Ġhigh ways", + "Ġhighway s", + ". spawn", + ".s pawn", + ".sp awn", + "= =$", + "== $", + "ĠN iet", + "ĠNi et", + "ĠNie t", + "Ġv eggies", + "Ġveg gies", + "y po", + "yp o", + "- rule", + "-r ule", + "ĠV ie", + "ĠVi e", + "/e pl", + "Ġenf ants", + "string Literal", + "Ġtough est", + "Ġtou ghest", + "bu yer", + "buy er", + "Ġcov ariance", + "Ġ ili", + "Ġi li", + "Ġil i", + "ĠSoph ie", + "Ġ BAB", + "ĠB AB", + "ĠBA B", + "Ġ \"),", + "Ġ\" ),", + "Ġ\") ,", + "ĠU k", + "current Index", + "_ userdata", + "_user data", + ". codec", + ".co dec", + ".code c", + ".cod ec", + "ĠPun jab", + "ĠS NP", + "ĠSN P", + "l ol", + "lo l", + "adv ance", + "Ġcom fy", + "Json Ignore", + "Ġfashion able", + "Ġ ICON", + "ĠI CON", + "ĠIC ON", + "ĠICO N", + "Ġ ora", + "Ġo ra", + "Ġor a", + "ĠP ricing", + "ĠPr icing", + "ĠPri cing", + "< num", + " E", + "t ering", + "ter ing", + "te ring", + "teri ng", + "/ screens", + "/s creens", + "/screen s", + "Ġheight ened", + "аÑĢ ÑĤ", + "Author ities", + "_ bbox", + "_b box", + "_bb ox", + "ü nst", + "ün st", + "üns t", + ". fontSize", + ".font Size", + "Ġ BOOLEAN", + "ĠBO OLEAN", + "div ide", + "di vide", + "divid e", + "ĠS loven", + "ĠSl oven", + "ĠSlo ven", + "ĠSlov en", + "u cer", + "uc er", + "uce r", + "Ù Ĵ", + "st ub", + "stu b", + "Ġnavig ating", + ": animated", + "_ NOW", + "_N OW", + "_NO W", + "_ vect", + "_v ect", + "_vec t", + "_ve ct", + "} {Ċ", + "}{ Ċ", + "@ (", + "Ġtele com", + "Ġtel ecom", + "Ġcontract ing", + "Ġcontr acting", + "ĠAss ange", + "Ġextract ing", + "Ġextr acting", + "Ġgr ö", + "c obra", + "co bra", + "cob ra", + ". DIS", + ".D IS", + "Ġc rab", + "Ġcr ab", + "Ġcra b", + "Ġt witch", + "Ġtw itch", + "Ġ verts", + "Ġv erts", + "Ġver ts", + "Ġvert s", + "Ġve rts", + "Ġreject s", + "Ġrej ects", + "ĉ format", + "ĉfor mat", + "ĉform at", + "Ġre generation", + "Ġreg eneration", + ". Sys", + ".S ys", + "s olve", + "sol ve", + "ĉ dialog", + "ĉd ialog", + "s hi", + "sh i", + "m eter", + "me ter", + "met er", + "( best", + "(b est", + "(be st", + "valid ators", + "validator s", + "Ġon wards", + "Ġonward s", + "Ġg uru", + "Ġgu ru", + "Ġmod erator", + "Ġmoder ator", + "ow ied", + "owie d", + "owi ed", + "ex periment", + "r ub", + "ru b", + "Ġ mqtt", + "Ġm qtt", + "Ġmq tt", + "ĠCa ucas", + "Ġnational ism", + "Ġm ange", + "Ġman ge", + "Ġma nge", + "Ġmang e", + "ĉ ImGui", + "/ Edit", + "/E dit", + "Ġ inh", + "Ġin h", + "Ġi nh", + "Ġint ellig", + "Ġintel lig", + "ero kee", + "ĉ export", + "ĉex port", + "ĉexp ort", + "Ġdiscrim inate", + "Ġdiscrimin ate", + "sub tract", + "ĠM oodle", + "ĠMoo dle", + "ĠMood le", + "en ser", + "ens er", + "ense r", + "ĠGu ides", + "ĠGuid es", + "ĠGuide s", + "ĠGui des", + "R AP", + "RA P", + "- hot", + "-h ot", + "_ grp", + "_g rp", + "_gr p", + ". picture", + ".p icture", + ".pic ture", + "X A", + "Ġinit View", + "_ Comm", + "_C omm", + "_Com m", + "Ġoverd ose", + "Ġ +ĊĊ", + "Ġ+ ĊĊ", + "Ġ+Ċ Ċ", + "ĠS ilent", + "ĠSil ent", + "sh ows", + "show s", + "Ġinter polate", + "Ġinterpol ate", + "Ġinterp olate", + "Form ation", + "Format ion", + "Ġb isc", + "Ġbi sc", + "Ġbis c", + "mark ets", + "market s", + "( SC", + "(S C", + "Z e", + "Ġ Networking", + "ĠNetwork ing", + "ĠNet working", + "Ġad renal", + "Ġadr enal", + "ĠG uns", + "ĠGu ns", + "ĠGun s", + "et eor", + "ete or", + "De clared", + "Decl ared", + "Declare d", + "orge town", + "orget own", + "Ġk arena", + "Ġka rena", + "Ġkar ena", + "/ password", + "/p assword", + "/pass word", + "_ addresses", + "_add resses", + "_address es", + "_addr esses", + "IT ERAL", + "ITE RAL", + "ITER AL", + "B uzz", + "Bu zz", + "ĠCon way", + "( case", + "(c ase", + "(ca se", + "P WD", + "PW D", + "he iro", + "hei ro", + "( act", + "(a ct", + "(ac t", + "* *čĊ", + "** čĊ", + "( ));ĊĊĊ", + "() );ĊĊĊ", + "());Ċ ĊĊ", + "()) ;ĊĊĊ", + "());ĊĊ Ċ", + "()); ĊĊĊ", + "Ġa nv", + "Ġan v", + "Ġ ..ĊĊ", + "Ġ. .ĊĊ", + "Ġ.. ĊĊ", + "Ġ..Ċ Ċ", + "( MenuItem", + "(Menu Item", + "( mail", + "(m ail", + "_ sections", + "_s ections", + "_se ctions", + "_section s", + "ĉ net", + "ĉn et", + "Ġp lut", + "Ġpl ut", + "Ġplu t", + "Ġw rench", + "Ġwr ench", + "/ object", + "/o bject", + "ĠI st", + "ĠIs t", + "Ġ VIS", + "ĠV IS", + "ĠVI S", + "/ pub", + "/p ub", + "al ten", + "alt en", + "alte n", + "Ġguitar s", + "Ġguit ars", + "Ġantib iotic", + "Ġantibiot ic", + "ï¼ ĸ", + " ¹", + "Ġ \"+\"", + "Ġ\" +\"", + "Ġ\"+ \"", + "form ula", + "Ġba bes", + "Ġbab es", + "Ġbabe s", + "Ġ Prompt", + "ĠP rompt", + "ĠProm pt", + "Ġe nim", + "Ġen im", + "/ player", + "/p layer", + "/pl ayer", + "/play er", + "ĉ ref", + "ĉr ef", + "ĉre f", + "Ġby Äĩ", + "Ġcons umes", + "Ġconsum es", + "Ġconsume s", + "ĠH ast", + "ĠHas t", + "ĠHa st", + "ĠT ao", + "ĠTa o", + "Ġ '))Ċ", + "Ġ' ))Ċ", + "Ġ') )Ċ", + "Ġc lam", + "Ġcl am", + "Ġcla m", + "Ġthigh s", + "Ġmot if", + "Ġmo tif", + "Api Operation", + "Ġ WL", + "ĠW L", + "get C", + "ĉ flags", + "ĉf lags", + "ĉflag s", + "oint ments", + "ointment s", + "Ġeconomic al", + "Ġeconom ical", + "need le", + "nee dle", + "x ls", + "xl s", + "pr actice", + "ut zer", + "utz er", + "time ofday", + "- output", + "-out put", + "Ġ findById", + "Ġfind ById", + "ĠfindBy Id", + "ĠB uddy", + "ĠBudd y", + "ĠBu ddy", + "ĠBud dy", + "Ðŀ ÑĤ", + "S even", + "Se ven", + "ĠB ark", + "ĠBar k", + "ĠBa rk", + "Ġen voy", + "Ġenv oy", + "_ algorithm", + "_al gorithm", + "åĪ ©", + "Ġball istic", + "ç§ »", + "r ades", + "ra des", + "rad es", + "rade s", + "ĉ doc", + "ĉd oc", + "ĉdo c", + "rodu cing", + "rod ucing", + "ĠE ating", + "ĠEat ing", + "ĠEa ting", + "Un mount", + "/data Tables", + "_ bonus", + "_b onus", + "Ġl itt", + "Ġli tt", + "Ġlit t", + "p ps", + "pp s", + ") localObject", + "pe rf", + "per f", + "Ġ Helvetica", + "ĠHel vetica", + "sh utdown", + "/ ml", + "/m l", + ". tokens", + ".t okens", + ".token s", + "ĠHard core", + ", row", + ",r ow", + "/ bg", + "/b g", + "S caler", + "Sc aler", + "Scale r", + "âĢĶ as", + "âĢĶa s", + "_log its", + "âĢĻ int", + "âĢĻin t", + "âĢĻi nt", + "ĉ App", + "ĉA pp", + "Impl icit", + "Imp licit", + ".F printf", + "E TO", + "ET O", + "Ġ terra", + "Ġt erra", + "Ġter ra", + "Ġterr a", + "Ġpossess ing", + ". rstrip", + ".r strip", + ".rs trip", + ", ),", + ",) ,", + "= yes", + "=y es", + "Ġ Stripe", + "ĠSt ripe", + "ĠStr ipe", + "ĠStrip e", + "? =", + "ne utral", + ". good", + ".g ood", + ".go od", + "Ġk ennen", + "Ġke nnen", + "Ġken nen", + "Ġkenn en", + "ĠS ung", + "ĠSun g", + "ĠSu ng", + "f ault", + "fa ult", + "ystate change", + "Can adian", + "',' \".$", + "ĠM its", + "ĠMi ts", + "ĠMit s", + "æ nd", + "Ġ STRUCT", + "ĠSTR UCT", + "ĠURL WithString", + "ĠCom pass", + "ĠComp ass", + "Ġ --ĊĊ", + "Ġ- -ĊĊ", + "Ġ-- ĊĊ", + "Ġ--Ċ Ċ", + "ĠNS LayoutConstraint", + "| min", + "|m in", + "- adjust", + "-ad just", + "Ġre built", + "Ġreb uilt", + "L IGHT", + "/ se", + "/s e", + "- mount", + "-m ount", + "v pn", + "vp n", + "valid ated", + "validate d", + "( QObject", + "(Q Object", + "Ġign ition", + "ĠChar gers", + "ĠCharg ers", + "ĠCharge rs", + "ĠCharger s", + "RYPT O", + "]initWith Frame", + "Ġ Fluid", + "ĠFl uid", + "ĠFlu id", + "Ġca dre", + "Ġcad re", + "Ġnom inations", + "Ġnomin ations", + "Ġnomination s", + "Ne ill", + "Neil l", + "ĠH ou", + "ĠHo u", + "Ġcurrent s", + "Ġcurr ents", + "_ gene", + "_g ene", + "_gen e", + "_ge ne", + "( inp", + "(i np", + "(in p", + "P aris", + "Par is", + "Pa ris", + "z ÄĻ", + "ag gregate", + "Ġ assoc", + "Ġas soc", + "Ġass oc", + "we eted", + "weet ed", + "er rat", + "err at", + "erra t", + "âĢĵ ĊĊ", + "Ġ' /',Ċ", + "Ġ'/ ',Ċ", + "Ġ'/' ,Ċ", + "Ġ'/', Ċ", + "f ixture", + "fix ture", + "Ġ Highest", + "ĠH ighest", + "ĠHigh est", + "ĠHi ghest", + "amb ient", + "ambi ent", + "Ġ chmod", + "Ġch mod", + "Ġ conte", + "Ġc onte", + "Ġcon te", + "Ġcont e", + "Ġco nte", + "Ġs ensual", + "Ġsens ual", + "Ġgar ment", + "z ers", + "ze rs", + "zer s", + "Ġ Powered", + "ĠP owered", + "ĠPower ed", + "ĠPow ered", + "dom ains", + "domain s", + "R eward", + "Re ward", + "Rew ard", + "i omanip", + "Ġcock pit", + "out file", + "Ġ builtin", + "Ġb uiltin", + "Ġbuilt in", + "Ġins isting", + "Ġinsist ing", + ". vars", + ".v ars", + ".var s", + ".va rs", + "zip code", + "Ġ ����", + "Ġ� ���", + "f ails", + "fa ils", + "fail s", + "Ġconsolid ation", + "_ oid", + "_o id", + "Plan et", + "Plane t", + "Ġ =\",", + "Ġ= \",", + "Ġ=\" ,", + "ĉ el", + "ĉe l", + "U ILT", + "UI LT", + "UIL T", + "ä tz", + "ät z", + "af ari", + "afa ri", + "ĠMc Cl", + "ĠMcC l", + "T imeline", + "Time line", + "Tim eline", + "E sta", + "Est a", + "Es ta", + "Ġ fram", + "Ġf ram", + "Ġfr am", + "Ġfra m", + "Y E", + "Ġcere bral", + "Of Month", + "ĠP regn", + "ĠPre gn", + "Ġкл аÑģÑģ", + "ĠклаÑģ Ñģ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠF res", + "ĠFr es", + "ĠFre s", + "Ap proved", + "Appro ved", + ". Special", + ".S pecial", + ".Spec ial", + ".Sp ecial", + "ĠProte stant", + "ĠProtest ant", + "Ġall ergy", + "Ġallerg y", + "Ġaller gy", + "_ pcm", + "_p cm", + "_pc m", + "ĉ Copyright", + "ĉC opyright", + "Ġsuper Class", + "\" strconv", + "ĠMoh amed", + "Ġ' //", + "Ġ'/ /", + "Fore Color", + "Ar thur", + "Art hur", + "ĠJ ungle", + "ĠJun gle", + "ĠJu ngle", + "ĠJung le", + "Ġve ins", + "Ġvein s", + "S ad", + "Sa d", + "Ġback ups", + "Ġbackup s", + "ĠOp inion", + "û t", + "Ġinter mitt", + "o dyn", + "od yn", + "ody n", + "ĠChrist ina", + "Ġ andre", + "Ġand re", + "Ġan dre", + "Ġevac uation", + "p alette", + "pa lette", + "pal ette", + "h orse", + "hor se", + "ĠRes ident", + "ĠHas san", + "ĠHass an", + ". Nil", + ".N il", + "Ġa isle", + "Ġais le", + "Ġ Growing", + "ĠG rowing", + "ĠGr owing", + "ĠGro wing", + "ĠGrow ing", + "Ġblog info", + "/ sql", + "/s ql", + "_ ioctl", + "_io ctl", + "S caling", + "Sc aling", + "Ġ Monad", + "ĠMon ad", + "ĠMo nad", + "ĠMona d", + "_ cpp", + "_c pp", + "_cp p", + "ĠH utch", + "ĠHut ch", + "ĠApple WebKit", + "Exp ense", + "_ JOB", + "_J OB", + "_JO B", + "Ġpoint less", + "From Body", + "an tal", + "ant al", + "anta l", + "Ġdepict ing", + "Ġ CELL", + "ĠC ELL", + "ĠCE LL", + "ĠCEL L", + "Ġre fin", + "Ġref in", + "ĠC NC", + "ĠCN C", + "ì¹ ĺ", + "_ dimensions", + "_dim ensions", + "_dimension s", + "Ġ SAN", + "ĠS AN", + "ĠSA N", + "Ġ aft", + "Ġa ft", + "Ġaf t", + "Ġfoot steps", + "c coli", + "cc oli", + "cco li", + "_ PHONE", + "_P HONE", + "_PH ONE", + "/ math", + "/m ath", + "/mat h", + "- kind", + "-k ind", + "Ġ Means", + "ĠMe ans", + "ĠMean s", + "ich ael", + "icha el", + ". guna", + ".g una", + "Ġinaug uration", + "Ġinaugur ation", + "-dr iving", + "( delete", + "(de lete", + "(del ete", + "Ġ totalCount", + "Ġtotal Count", + "_ MC", + "_M C", + ". Extension", + ".Ext ension", + "Com mercial", + "Comm ercial", + "Ġz Index", + "< Customer", + "$", + "\"> $", + "Ġe bay", + "Ġeb ay", + "Ġc aptive", + "Ġca ptive", + "Ġcapt ive", + "pl iant", + "ĠCalculate s", + "ĠCalcul ates", + "ĠCalc ulates", + "ol ta", + "olt a", + "es ting", + "est ing", + "esti ng", + "_ revision", + "_re vision", + "_rev ision", + "Ġm ús", + "Ġmú s", + "+ m", + "\",\" \",\"", + "\",\"\", \"", + "WH AT", + "Ġcompass ionate", + "Ġcompassion ate", + "h arga", + "har ga", + "[ random", + "[r andom", + "[rand om", + "Ġ modulo", + "Ġmod ulo", + "( sn", + "(s n", + "Ġoccup ations", + "Ġoccupation s", + "/ ///Ċ", + "// //Ċ", + "//// Ċ", + "/// /Ċ", + "ĉ board", + "ĉb oard", + "ĠB alk", + "ĠBa lk", + "ĠBal k", + "w iÄħ", + "wi Äħ", + "Ġ Wifi", + "ĠW ifi", + "ĠWi fi", + ". Profile", + ".Pro file", + ".Pr ofile", + ": maj", + ":m aj", + "ĉ mat", + "ĉm at", + "LOCK S", + "LOC KS", + "(j Button", + "Ġ ('$", + "Ġ( '$", + "Ġ(' $", + "M ur", + "Mu r", + "æĮ ī", + "b ble", + "bb le", + "Ġ frog", + "Ġf rog", + "Ġfr og", + "Ġfro g", + "- hide", + "-h ide", + "Ġbroad caster", + "Ġbroadcast er", + "ภŀ", + "h aled", + "ha led", + "hal ed", + "Ġam using", + "_ predictions", + "_pre dictions", + "_pred ictions", + "_predict ions", + "_prediction s", + "_ intr", + "_in tr", + "_int r", + "Ġe agle", + "Ġea gle", + "Ġeag le", + "аÑĤ елÑĮ", + "аÑĤе лÑĮ", + "Ġ getList", + "Ġget List", + "ps ilon", + "psi lon", + "Ġcharacter ization", + "AR DS", + "ARD S", + "Ġre location", + "Ġrel ocation", + "Ġreloc ation", + "Ġr ulers", + "Ġrule rs", + "Ġru lers", + "Ġruler s", + "P AY", + "PA Y", + "ĠDef initely", + "_ Action", + "_A ction", + "_Act ion", + "Ġc losures", + "Ġclos ures", + "Ġclosure s", + "Ġf actual", + "Ġfact ual", + "Ġfac tual", + "o dynamic", + "od ynamic", + "odyn amic", + "odynam ic", + "Ġpreca utions", + "Ġprecaution s", + "n iej", + "ni ej", + "nie j", + "ĠPart ies", + "ĠPar ties", + "ĠParti es", + "ĠSub aru", + "ĠSu baru", + "Ġcous ins", + "Ġcousin s", + "ar beit", + ". money", + ".m oney", + ".mo ney", + ".mon ey", + "g unta", + "gun ta", + "( and", + "(a nd", + "(an d", + "get item", + ".Style Priority", + "Ġs lid", + "Ġsl id", + "s ingleton", + "single ton", + "sing leton", + "Ġg arn", + "Ġgar n", + "Ġga rn", + "ĠP AS", + "ĠPA S", + "Ġd azz", + "Ġda zz", + "a ż", + "Ġbog us", + "ĠM og", + "ĠMo g", + "Ġrival ry", + "i sol", + "is ol", + "iso l", + "Ġland marks", + "Ġlandmark s", + "ñ as", + "ña s", + "B ern", + "Be rn", + "Ber n", + "ĠS achs", + "ĠSa chs", + "ĠSac hs", + "ĠSach s", + "Ġ \")ĊĊ", + "Ġ\" )ĊĊ", + "Ġ\") ĊĊ", + "Ġ\")Ċ Ċ", + "Ġhost ility", + "Ġhos tility", + "_m ex", + "_me x", + "m ere", + "mer e", + "me re", + "M ot", + "Mo t", + "p ictureBox", + "picture Box", + "Def ense", + "Ġaffid avit", + "other wise", + ". directory", + ".d irectory", + ".direct ory", + "_ UnityEngine", + "_Un ityEngine", + "- blog", + "-b log", + "-bl og", + ". skin", + ".s kin", + ".sk in", + "p hem", + "ph em", + "phe m", + "Ap ellido", + "er chant", + "[ class", + "[c lass", + "Ġ wart", + "Ġw art", + "Ġwar t", + "Ġwa rt", + ". \"[", + ".\" [", + "a leur", + "al eur", + "ale ur", + "/ back", + "/b ack", + "ĠĠĠĠ ĉĠĠĠ", + "ĠĠĠ ĠĉĠĠĠ", + "ĠĠĠĠĉ ĠĠĠ", + "ĠĠĠĠĉĠ ĠĠ", + "Ġprecip itation", + "Ġob struction", + "Ġobstruct ion", + "Ġp Obj", + "Ġ rupt", + "Ġr upt", + "Ġru pt", + "U CKET", + "UCK ET", + "a ye", + "ay e", + "æİ Ĵ", + "g x", + "Ġe cl", + "Ġec l", + "Ġsecre cy", + "/ Header", + "ĠLe sb", + "ĠLes b", + "Ġ lei", + "Ġl ei", + "Ġle i", + "Ġ Bulletin", + "ĠBull etin", + "ĠBullet in", + "Ġgive away", + ". Home", + ".H ome", + "_ ROOM", + "_R OOM", + "_RO OM", + "\" W", + "Ġco work", + "Ġcow ork", + "_ ra", + "_r a", + "ĠC ycling", + "ĠCy cling", + "ĠCycl ing", + "ĠCyc ling", + "ĠP aw", + "ĠPa w", + "Ġp upil", + "Ġpup il", + "/ arch", + "/a rch", + "/ar ch", + "ĠFile Utils", + "é¦ ĸ", + "r sp", + "rs p", + "Ġfreedom s", + "Ġfreed oms", + "ĠL ear", + "ĠLe ar", + "} `).", + "}` ).", + "Ġbow ls", + "Ġbowl s", + "/ block", + "/b lock", + "/bl ock", + "_ logging", + "_log ging", + "Ġme thane", + "Ġmeth ane", + "Ġh orns", + "Ġhor ns", + "Ġhorn s", + "Ġwonder fully", + "Ġwonderful ly", + "Ġalter ations", + "Ġalteration s", + "Ġex ile", + "l sen", + "ls en", + "lse n", + "_ pause", + "_p ause", + "_pa use", + "_ LANGUAGE", + "_L ANGUAGE", + "_LANG UAGE", + "ĠUS DA", + "ĠUSD A", + "_ mysql", + "_m ysql", + "_my sql", + "_AM OUNT", + "ĠL IFE", + "ĠLI FE", + "Ġyoung sters", + "Ġyoungster s", + "Ġri ots", + "Ġriot s", + "Ġrio ts", + "[ E", + "Ġun forgettable", + ", },Ċ", + "Dis posed", + "Dispose d", + "Disp osed", + "ĠAss assin", + "ĠAssass in", + "U NG", + "UN G", + "ĠNew sp", + "ĠNews p", + "User Service", + ": aload", + ":a load", + "+ ',", + "+' ,", + "Ġsett lers", + "Ġsettle rs", + "Ġscre ams", + "Ġscream s", + "Ġincon venience", + ". Rotate", + ".R otate", + "Ġj ars", + "Ġja rs", + "Ġjar s", + "ĠP uzzle", + "ĠPu zzle", + "Ġm est", + "Ġme st", + "Ġmes t", + "ar si", + "ars i", + "ĠS harma", + "ĠSh arma", + "ĠShar ma", + "| (", + ". ds", + ".d s", + "ĠSa cred", + "ĠSac red", + "ĠSacr ed", + "_ evt", + "_e vt", + "_ev t", + "Ġexp resses", + "Ġexpress es", + "Ġexpr esses", + "Ġexpres ses", + "Ġh och", + "Ġho ch", + "Ġhoc h", + "ĠD uch", + "ĠDu ch", + "ĠDuc h", + ". calls", + ".c alls", + ".call s", + ".cal ls", + "t hr", + "th r", + "ĠShe ffield", + ". AlertDialog", + ".Alert Dialog", + "Ġrad ically", + "Ġradical ly", + "Ġt rous", + "Ġtr ous", + "Ġtro us", + "Ġtrou s", + "Ġprev ailing", + "Ġprevail ing", + "ĠWW II", + "âĢĻ n", + "ens ely", + "ense ly", + "Ġ Yesterday", + "ĠY esterday", + "ĠSir ius", + "ĠSiri us", + "Ġkill ers", + "Ġkil lers", + "Ġkiller s", + "Ġ FFT", + "ĠF FT", + "ĠFF T", + "Ġ oval", + "Ġo val", + "Ġov al", + "' ):čĊ", + "') :čĊ", + "'): čĊ", + "Ġ ìłķë³´", + "Ġìłķ ë³´", + "ou rage", + "our age", + "Ġ Checkbox", + "ĠCheck box", + "Work book", + ". defer", + ".de fer", + ".def er", + "_ floor", + "_f loor", + "_fl oor", + "Ġc ouncill", + "Ġcouncil l", + "Ġnors ke", + "Ġnorsk e", + "m oil", + "mo il", + "o rea", + "or ea", + "ore a", + "Ġmark eted", + "Ġmarket ed", + "_ SUR", + "_S UR", + "_SU R", + "x AA", + "xA A", + "Ġst ained", + "Ġsta ined", + "Ġstain ed", + "e ut", + "eu t", + "ĠM eng", + "ĠMe ng", + "ĠMen g", + "Ġ ieee", + "Ġi eee", + "Ġie ee", + ". extern", + ".ex tern", + ".ext ern", + "e gie", + "eg ie", + "Ġr app", + "Ġrap p", + "Ġra pp", + "ĠPy ongyang", + "' class", + "M ob", + "Mo b", + "Ġinitial Value", + "_ wave", + "_w ave", + "Ġ jab", + "Ġj ab", + "Ġja b", + "Ġmascul ine", + "Ġampl ifier", + "Ġ tty", + "Ġt ty", + "Ġtt y", + "Path Component", + "_ xt", + "_x t", + "ĠG FP", + "ĠGF P", + "/ sec", + "/s ec", + "/se c", + "ĉ dispatch", + "ĉdis patch", + "mark down", + "ĠS chn", + "ĠSc hn", + "ĠSch n", + "b ole", + "bo le", + "bol e", + "· ·", + "mouse move", + "Ġ errMsg", + "Ġerr Msg", + "Ġa sign", + "Ġas ign", + "Ġasi gn", + "_ mono", + "_m ono", + "_mon o", + "_mo no", + "To Selector", + "ĠZ u", + "( Rect", + "(R ect", + "Ġ ErrorCode", + "ĠError Code", + "l atin", + "la tin", + "lat in", + "ang ible", + "angi ble", + "v tk", + "vt k", + "CG Size", + "P okemon", + "Pok emon", + "Ġclass mates", + "Ġat tracts", + "Ġattr acts", + "Ġattract s", + "ĠT atto", + "ĠTat to", + "ul tan", + "ult an", + "ulta n", + "ol óg", + "Ġh alted", + "Ġhal ted", + "Ġhalt ed", + "ठ¨", + "ĠK art", + "ĠKar t", + "ĠKa rt", + "Ġ ue", + "Ġu e", + "_Init Structure", + "_InitStruct ure", + "Test Class", + "ĠAir bnb", + "_ \",", + "_\" ,", + "Ġchar coal", + "Ġ ipc", + "Ġi pc", + "Ġip c", + "Ġ Stretch", + "ĠSt retch", + "ĠStr etch", + ".g lide", + ".gl ide", + "lates AutoresizingMaskIntoConstraints", + "Ġp otion", + "Ġpo tion", + "Ġpot ion", + "ITT LE", + "Ġcount ert", + "Ġcounter t", + "_ hd", + "_h d", + "pre pared", + "prepare d", + "prep ared", + "A ds", + "Ad s", + "ĠV ampire", + "ro bots", + "robot s", + "rob ots", + ".Create Index", + "Status Label", + "Ġt ucked", + "af ür", + "U t", + "Ġswe ater", + "Ġsweat er", + "_ FN", + "_F N", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġĉ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĉ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĉ", + "at aka", + "ata ka", + "atak a", + "Ġeyeb rows", + "Ġeyebrow s", + "a coes", + "ac oes", + "aco es", + "u den", + "ud en", + "ude n", + ".Linear LayoutManager", + ".LinearLayout Manager", + "Ġs way", + "Ġsw ay", + "Ġmult in", + "Ġmulti n", + "Ġmul tin", + "( ))))Ċ", + "() )))Ċ", + "()) ))Ċ", + "())) )Ċ", + "()))) Ċ", + "Ġ NSUInteger", + "ĠNS UInteger", + "ĠMy Base", + "P artner", + "Part ner", + "uts chen", + "utsch en", + "utsche n", + "ĠC ater", + "ĠCa ter", + "ĠCat er", + ".set BackgroundColor", + ".setBackground Color", + "Ġaccompl ishment", + "Ġaccomplish ment", + "_ problem", + "_pro blem", + "_prob lem", + ".d td", + ".dt d", + "Ġ pageNumber", + "Ġpage Number", + "ĠpageNum ber", + "Ġj ackets", + "Ġjack ets", + "Ġjacket s", + "Ġc ropped", + "Ġcr opped", + "Ġcro pped", + "Ġcrop ped", + "u els", + "ue ls", + "uel s", + "ĠH ep", + "ĠHe p", + "Ġc apped", + "Ġcap ped", + "Ġca pped", + "* Math", + "*M ath", + "_ callbacks", + "_callback s", + "_call backs", + "Ġp ubb", + "Ġpub b", + "Ġpu bb", + "ĠBrun swick", + ". respond", + ".res pond", + ".resp ond", + "[ \"_", + "[\" _", + "Ġbed ding", + "hyth m", + "O X", + "( speed", + "(s peed", + "(sp eed", + "Ġpest icides", + "Ġpestic ides", + "Ġpesticide s", + "Ġ -------", + "Ġ- ------", + "Ġ-- -----", + "Ġ---- ---", + "Ġ--- ----", + "Ġ----- --", + "Ġ------ -", + ". Blue", + ".B lue", + ".Bl ue", + "Ġn oodles", + "Ġnood les", + "ĠG oes", + "ĠGo es", + "Ġs aver", + "Ġsa ver", + "Ġsave r", + "Ġsav er", + "o xy", + "ox y", + "_ completion", + "_com pletion", + "_comp letion", + "ĠSw inger", + "ĠSwing er", + "Ġ getDate", + "Ġget Date", + "Ġm inded", + "Ġmin ded", + "Ġmind ed", + "int egration", + "integr ation", + "ĠLot us", + "( stop", + "(s top", + "(st op", + "(', ');Ċ", + "(',' );Ċ", + "(',') ;Ċ", + "Ġfl oods", + "Ġflo ods", + "Ġflood s", + "Ġ Workflow", + "ĠWork flow", + "Ġe rupted", + "Ġerupt ed", + "M acro", + "Mac ro", + "Ma cro", + "ĠS auce", + "ĠSa uce", + "ĠSau ce", + "Ġ eventName", + "Ġevent Name", + "\\ Input", + "Break ing", + "Bre aking", + "ĉ when", + "ĉw hen", + "_ pw", + "_p w", + "I NDER", + "IN DER", + "IND ER", + "ĠWell ness", + "Ġv oxel", + "Ġvox el", + "ĠM ell", + "ĠMe ll", + "ĠMel l", + "Ġ MEDIA", + "ĠM EDIA", + "ĠMED IA", + "S ENS", + "SE NS", + "SEN S", + "ĠF unds", + "ĠFund s", + "ĠFun ds", + "ĠFu nds", + "ĠM ild", + "ĠMil d", + "ĠMi ld", + "< Array", + "Ċ", + "') ;?>Ċ", + "'); ?>Ċ", + "');?> Ċ", + "Ġtemp ting", + "Ġtempt ing", + "Ġtest ament", + "Ġb ible", + "Ġbi ble", + "Ġbib le", + "Ġconsult ed", + "Ġconsul ted", + "ĠIndex Error", + "è¨ ĺ", + "Ġkey pad", + "Ġke ypad", + "i zzo", + "iz zo", + "izz o", + "( ok", + "(o k", + "Ġwh atsapp", + "Ġwhats app", + "ĠRemote Exception", + "Ġte amed", + "Ġteam ed", + "Ġtea med", + "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", + "» ,", + "Ġ getTime", + "Ġget Time", + "d iag", + "di ag", + "dia g", + "is sy", + "iss y", + "Ġ hed", + "Ġh ed", + "Ġhe d", + "Ġkn ots", + "Ġknot s", + "j om", + "jo m", + "Ġf unnel", + "Ġfun nel", + "-m ails", + "-mail s", + "Ġexp orting", + "Ġexport ing", + "Ġ VL", + "ĠV L", + "ĠK arn", + "ĠKar n", + "ĠKa rn", + "ĠBuddh ism", + "ĠAl lan", + "ĠAll an", + "ĠAlla n", + "_R ADIUS", + "Ġw ording", + "Ġword ing", + "Ġwor ding", + "Ġ Forget", + "ĠF orget", + "ĠFor get", + "ĠForge t", + "ĠForg et", + "ĠCor ona", + "ĠCoron a", + "i phy", + "ip hy", + "iph y", + "Ġlim burg", + "Ġlimb urg", + "ug gy", + "ugg y", + "ĠUser Repository", + "i min", + "im in", + "imi n", + "( ele", + "(e le", + "(el e", + "Ġlabel led", + "Ġlab elled", + "ç¤ ¾", + "ĠH erman", + "ĠHer man", + "ĠHerm an", + ". qq", + ".q q", + "Ġ \"));Ċ", + "Ġ\" ));Ċ", + "Ġ\") );Ċ", + "Ġ\")) ;Ċ", + "ie ber", + ". Translate", + ".Trans late", + "r yn", + "ry n", + "Ġdes env", + "Ġdese nv", + "u md", + "um d", + "Sim ply", + "ĉ mode", + "ĉm ode", + "ĉmod e", + "R pc", + "Rp c", + "ĠVal encia", + "ĠVale ncia", + "Ġstaff ers", + "Ġstaffer s", + "Ġse lv", + "Ġsel v", + "ĠS pike", + "ĠSp ike", + "ĠSpi ke", + "Ġd elic", + "Ġde lic", + "Ġdel ic", + "Ġ eru", + "Ġe ru", + "Ġer u", + "_ DT", + "_D T", + "J udge", + "Jud ge", + "Ju dge", + "á» ķ", + "ĠB asin", + "ĠBa sin", + "ĠBas in", + ". mutable", + ".m utable", + ".mu table", + "\" url", + "Ġtar iff", + "Ġtarif f", + "ĠSlee ve", + "ĠSle eve", + "Ġ flare", + "Ġfl are", + "Ġfla re", + ". dropout", + ".drop out", + "Ġb rides", + "Ġbr ides", + "Ġbri des", + "Ġbride s", + "Ġbrid es", + ") ),čĊ", + ")) ,čĊ", + ")), čĊ", + "_ constraints", + "_con straints", + "_constraint s", + "d estruct", + "de struct", + "Out line", + "Ġdisappe ars", + "Ġdisappear s", + "_ locked", + "_l ocked", + "_lock ed", + "_loc ked", + "ĠNS LocalizedString", + "c ke", + "ck e", + "ĉ null", + "ĉn ull", + "ad resse", + "adr esse", + "adress e", + "Ġt opping", + "Ġto pping", + "Ġtop ping", + "Ġtopp ing", + "ĠJ oker", + "ĠJo ker", + "b ishop", + "bi shop", + "bis hop", + "bish op", + "н оÑģÑĤÑĮ", + "но ÑģÑĤÑĮ", + "ноÑģÑĤ ÑĮ", + "ноÑģ ÑĤÑĮ", + "and ering", + "ander ing", + "ande ring", + "_ amp", + "_a mp", + "_am p", + "= time", + "=t ime", + "_ Space", + "_S pace", + "_P ULL", + "' =", + "Ġant iqu", + "Ġanti qu", + "Ġc ach", + "Ġca ch", + "Ġcac h", + "_ __ĊĊ", + "__ _ĊĊ", + "___ ĊĊ", + "O NES", + "ON ES", + "ONE S", + "о Ñı", + "Ġ unread", + "Ġun read", + "Ġunre ad", + "Ġunr ead", + ". policy", + ".p olicy", + ".pol icy", + "oooo oooo", + "ëŁ ¬", + "Ġ usted", + "Ġu sted", + "Ġus ted", + "Ġust ed", + "ĠR ece", + "ĠRe ce", + "ĠRec e", + "Ġal lem", + "Ġall em", + "Ġalle m", + "ãĥ¼ ãĤ¹", + "ãĥ¼ãĤ ¹", + "ĠThough ts", + "ĠThought s", + "ve illance", + "i strate", + "ist rate", + "istr ate", + "istra te", + "istrat e", + "_ lane", + "_l ane", + "_la ne", + "Ġf amed", + "Ġfam ed", + "Ġfa med", + "Ġfame d", + ". GetName", + ".Get Name", + "Ġsmooth er", + "Ġsmo other", + "Ġ Qualified", + "ĠQual ified", + "a zers", + "az ers", + "aze rs", + "azer s", + "_ geo", + "_g eo", + "_ge o", + "F ax", + "Fa x", + "ĠM inds", + "ĠMin ds", + "ĠMi nds", + "ĠMind s", + "Ġ Raises", + "ĠR aises", + "ĠRa ises", + "ĠRaise s", + "ĠRai ses", + "Ġtrans cripts", + "Ġtran scripts", + "Ġtranscript s", + "Con versation", + "Ġre marked", + "Ġrem arked", + "Ġremark ed", + "Ġremar ked", + "ëĤ ĺ", + "d ling", + "dl ing", + "Ġdeploy ing", + "Ġshared Application", + "Ġ kp", + "Ġk p", + "FontAwesome Icon", + "_ dummy", + "_d ummy", + "re iben", + "reib en", + "rei ben", + "ĠJane iro", + "Dir ections", + "Direction s", + "Direct ions", + "Di rections", + "Dire ctions", + ".get Bean", + ".getB ean", + "s ass", + "sa ss", + "Ġcomm anders", + "Ġcommand ers", + "Ġcommander s", + "Ġcommande rs", + "v ation", + "va tion", + "vat ion", + "error Code", + "ĠAl loy", + "ĠAll oy", + ". localized", + ".local ized", + "Ð ij", + "Ġdish washer", + "Ġ Soup", + "ĠS oup", + "ĠSo up", + "ĠSou p", + "N u", + "_ Default", + "_D efault", + "_De fault", + "_Def ault", + "Ġun even", + "Ġune ven", + "Ġ />\";Ċ", + "Ġ/ >\";Ċ", + "Ġ/> \";Ċ", + "- Based", + "-B ased", + "Ġseam lessly", + "Ġseamless ly", + "- null", + "-n ull", + "Ġ XC", + "ĠX C", + "Ġst ew", + "Ġste w", + "( delay", + "(d elay", + "(de lay", + "(del ay", + "AT ORS", + "ATOR S", + "ATO RS", + "ĠWhe eler", + "ĠWheel er", + "\" H", + "e ast", + "ea st", + ". air", + ".a ir", + ".ai r", + "âĢľ But", + "Object Context", + "success fully", + "successful ly", + "_ land", + "_l and", + "_la nd", + "Ġf olds", + "Ġfol ds", + "Ġfold s", + "Ġfo lds", + "_ COORD", + "_CO ORD", + "Ġsub po", + ".get Address", + ".g etAddress", + "in str", + "ins tr", + "inst r", + "Material s", + "Ñĥ ÑģÑĤ", + "ÑĥÑģ ÑĤ", + "de posit", + "dep osit", + "- last", + "-l ast", + "-la st", + "_ GRAY", + "_G RAY", + "_GR AY", + "_GRA Y", + "= find", + "=f ind", + "Ġmut ant", + "Ġmu tant", + "Ġlesb ienne", + "Ġlesbi enne", + "let cher", + "R OUGH", + "RO UGH", + "ur eka", + "ure ka", + ". capture", + ".c apture", + ".cap ture", + "Ġ enn", + "Ġe nn", + "Ġen n", + "Ġ ([[", + "Ġ( [[", + "Ġ([ [", + "ĠF lu", + "ĠFl u", + "Ġ taskId", + "Ġtask Id", + "ĠHus sein", + "ĠHuss ein", + ". folder", + ".f older", + ".fold er", + "Ġa usterity", + "IST RATION", + "ISTR ATION", + "_ Impl", + "_I mpl", + "注 æĦı", + "Ġdec ree", + "Ġdecre e", + "- chat", + "-c hat", + "-ch at", + "Ġim plication", + "Ġimp lication", + "Ġimpl ication", + "Ġimplic ation", + "Ġgu esses", + "Ġguess es", + "ul kan", + "ulk an", + "An alytics", + "Analy tics", + ". plus", + ".p lus", + ".pl us", + "COM MAND", + "COMM AND", + "е ли", + "ел и", + "» ĊĊ", + "»Ċ Ċ", + "_ SITE", + "_S ITE", + "_SI TE", + "Ġ equalTo", + "Ġequal To", + "Support FragmentManager", + "Ġ Recording", + "ĠRec ording", + "ĠRecord ing", + "å®Į æĪIJ", + "Ġbag gage", + "Ġpitch ers", + "Ġpit chers", + "Ġpitcher s", + "ĠE h", + "o que", + "oq ue", + "ĉ cnt", + "ĉc nt", + "Ġ =>$", + "Ġ= >$", + "Ġ=> $", + "/ foo", + "/f oo", + "I RA", + "IR A", + "ĠSat ellite", + "bo rah", + "bor ah", + "Ġ }}\"Ċ", + "Ġ} }\"Ċ", + "Ġ}} \"Ċ", + "Ġ}}\" Ċ", + "ĠE nds", + "ĠEn ds", + "ĠEnd s", + "ĠS pray", + "ĠSp ray", + "ĠSpr ay", + ", param", + ",p aram", + ". Chrome", + ".Ch rome", + "* q", + "th ought", + "though t", + "ib rated", + "ibr ated", + "ibrate d", + "Ġth ieves", + "Ġbenefici aries", + "En tered", + "Ent ered", + "Enter ed", + "ottes ville", + "otte sville", + "Ġveter in", + "Ġvet erin", + "By ID", + "qu ipe", + "quip e", + "qui pe", + "um ption", + "ump tion", + "umpt ion", + "- unit", + "-un it", + "-u nit", + "Execution Context", + "@ s", + "ĠG iov", + "ĠGi ov", + "ĠGio v", + ". ToolTip", + ".Tool Tip", + "_ friend", + "_f riend", + "( attributes", + "(at tributes", + "(attribute s", + "Ġd umping", + "Ġdump ing", + "Ġdum ping", + "Ġ JC", + "ĠJ C", + "_ DOCUMENT", + "_D OCUMENT", + "_DOC UMENT", + "ĠAr mour", + "ĠArm our", + "( insert", + "(in sert", + "(ins ert", + ". HorizontalAlignment", + ".Horizontal Alignment", + "Ġ Qed", + "ĠQ ed", + "ãģĦ ãģ¾ãģĻ", + "/ git", + "/g it", + "Ġ YYYY", + "ĠY YYY", + "ĠYY YY", + "ĠCar diff", + "ĠCard iff", + "Ġ apa", + "Ġa pa", + "Ġap a", + "org anic", + "organ ic", + "ĠWhere as", + "ĠWhe reas", + "Ġ æĿ", + "Ġæ Ŀ", + "ĠM ia", + "ĠMi a", + "Ġdemol ition", + "Ġs cars", + "Ġsc ars", + "Ġsca rs", + "Ġscar s", + "Ġ pai", + "Ġp ai", + "Ġpa i", + "Ġre tries", + "Ġret ries", + "Ġretrie s", + "Ġretr ies", + "Ġ rq", + "Ġr q", + "ĠD enis", + "ĠDe nis", + "ĠDen is", + "( Utils", + "(Util s", + "Ġallev iate", + "Ġ PIC", + "ĠP IC", + "ĠPI C", + "i due", + "id ue", + "Ġacknowled ging", + "Ġ// ////////////////////////////////", + "Ġ////////////////// ////////////////", + "ç¡® å®ļ", + "Ä «", + "\\ Json", + ". binary", + ".b inary", + ".bin ary", + "Ġx type", + "Ġxt ype", + "sign als", + "signal s", + "Ġ Appearance", + "ĠAp pearance", + "& r", + "} s", + "C i", + "ĠI llum", + "ĠIl lum", + "ĠIll um", + "p orate", + "por ate", + "po rate", + "pora te", + "h og", + "ho g", + "Ġ indexOf", + "Ġindex Of", + "\\ Command", + "_ parallel", + "_par allel", + "ĠSher lock", + "í ĥ", + "Ġ\" \")čĊ", + "Ġ\"\" )čĊ", + "Ġ\"\") čĊ", + "//////////////// ////////////////////////////////////////////////////////////////////////////////", + "//////////////////////////////// ////////////////////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////// ////////////////////////////////", + "//////////////////////////////////////////////// ////////////////////////////////////////////////", + "//////////////////////////////////////////////////////////////////////////////// ////////////////", + "Ġcritic ize", + "Ġ Soap", + "ĠSo ap", + "Ġ Matcher", + "ĠM atcher", + "ĠMat cher", + "ĠMatch er", + "Ġgr illed", + "Ġgrill ed", + "Ġgrille d", + "* T", + "Ġad ore", + "Ġado re", + "ul ling", + "ull ing", + "Ġje doch", + "Ġjed och", + "_ refs", + "_re fs", + "_r efs", + "_ref s", + "lean up", + "ĠJ AXB", + "ĠJA XB", + "Ġr oses", + "Ġro ses", + "Ġrose s", + "Ġros es", + "ĠL iam", + "ĠLi am", + "ĠLia m", + "size i", + "siz ei", + "Ġget char", + "Ġgetch ar", + "Ġt arde", + "Ġtar de", + "Ġtard e", + "- tooltip", + "-to oltip", + "-tool tip", + "Ġqual ifier", + "Ġ Intermediate", + "ĠInter mediate", + "_ Window", + "_W indow", + "ĠM alta", + "ĠMal ta", + "Dis connect", + "e where", + "ew here", + "C ampo", + "Cam po", + "Camp o", + "Ġirr ational", + "l edo", + "le do", + "led o", + "Ġ DN", + "ĠD N", + "AR GV", + "ARG V", + "Ġout ro", + "Ġou tro", + "Ġoutr o", + "Ġth irteen", + "Jose ph", + "Jos eph", + "M AR", + "MA R", + "/ gl", + "/g l", + "J ess", + "Je ss", + "ĠPsych iat", + "Ġpadding Bottom", + "- loop", + "-l oop", + "-lo op", + "/ fonts", + "/font s", + "_ seen", + "_s een", + "_se en", + "Te ams", + "Team s", + "React DOM", + "( man", + "(m an", + "( xpath", + "(x path", + ". getSimpleName", + ".get SimpleName", + "> (*", + ">( *", + "ĠP vt", + "ĠPv t", + "Ġel ders", + "Ġelder s", + "Ġelde rs", + "Ġ pies", + "Ġp ies", + "Ġpie s", + "Ġpi es", + ".user Agent", + "- region", + "-reg ion", + "ĠGre eks", + "ĠGreek s", + "ĠGree ks", + "( fragment", + "(f ragment", + "(fr agment", + "s tu", + "st u", + "Ġcouncil s", + "Ġst amina", + "Ġsta mina", + "ĠGod dess", + "è ¥¿", + "è¥ ¿", + "Ġphilosoph ers", + "Ġphilosopher s", + "Ġperson e", + "Ġpers one", + "Ġperso ne", + "ĠL ose", + "ĠLo se", + "ĠLos e", + "Ġ CLR", + "ĠC LR", + "ĠCL R", + "Ġ Docs", + "ĠD ocs", + "ĠDo cs", + "ĠDoc s", + "Ġso ak", + "Ġ HOLDER", + "ĠH OLDER", + "ĠHOLD ER", + "ĠHOL DER", + "Ġb ells", + "Ġbel ls", + "Ġbell s", + "hash Code", + "R ATE", + "RA TE", + "_WE IGHT", + "in ous", + "ino us", + "inou s", + "en dra", + "end ra", + "oph obic", + "Ġp rose", + "Ġpro se", + "Ġpr ose", + "Ġpros e", + "Ġf inely", + "Ġfin ely", + "Ġfine ly", + "/ oauth", + "/o auth", + "( space", + "(s pace", + "(sp ace", + "a dge", + "ad ge", + "ĠM ama", + "ĠMa ma", + "ĠMam a", + "Ġstring Buffer", + "Ġst int", + "Ġm isma", + "Ġmis ma", + "Ġmism a", + "Ġvill ains", + "Ġvillain s", + "Ġvilla ins", + "ĠCrime a", + "Ġdipl oma", + "Ġdiplom a", + "Ġпо Ñģл", + "ĠпоÑģ л", + "ĠB ea", + "ĠBe a", + "( join", + "(j oin", + "Ġ íķ´", + "Ġíķ ´", + "CH AT", + "CHA T", + "p ering", + "pe ring", + "per ing", + "peri ng", + "ĠC ros", + "ĠCr os", + "ĠCro s", + "Ġmon keys", + "Ġmonkey s", + "Ġp reds", + "Ġpr eds", + "Ġpre ds", + "Ġpred s", + "y la", + "yl a", + ", ,,", + ",, ,", + "Ġv ibrator", + "Ġvibr ator", + "Ġ NU", + "ĠN U", + "åħ Ī", + "f ant", + "fa nt", + "fan t", + "z et", + "ze t", + "Ġb ietet", + "un ft", + "s worth", + "sw orth", + ". Flow", + ".F low", + ".Fl ow", + "Ġpsych ed", + "Ġpsy ched", + "Ġpsyche d", + "ĠContin ental", + "ĠContinent al", + "> t", + "Ġqu ilt", + "Ġq uilt", + "Ġqui lt", + "Ġquil t", + ". UP", + ".U P", + "Ġexpans ive", + "Dis pose", + "Disp ose", + "( language", + "(l anguage", + "C aps", + "Cap s", + "Ca ps", + "_ ZONE", + "_Z ONE", + "Ġre cycle", + "Ġr ecycle", + "Ġrec ycle", + "Ġrecycl e", + "Ġ Managed", + "ĠMan aged", + "ĠManage d", + "ĠMana ged", + "current Color", + ". broadcast", + ".b roadcast", + "sign In", + ". prom", + ".p rom", + ".pro m", + ".pr om", + "l lu", + "ll u", + "ue blo", + "Ġpun ches", + "Ġpunch es", + "Ġaut omat", + "Ġauto mat", + "Ġautom at", + "Ġassign ing", + "Ġcreate User", + "ĠAl lied", + "ĠAll ied", + "Ġcon ductor", + "Ġconduct or", + "Ġcond uctor", + "Ġconduc tor", + "Ġcondu ctor", + "Ĥ ¨", + "Ġs addle", + "Ġsad dle", + "Ġsadd le", + "Ġ dni", + "Ġd ni", + "Ġdn i", + "o medical", + "omed ical", + "- West", + "-W est", + "Positive Button", + "Ġ italic", + "Ġit alic", + "? [", + "( trigger", + "(tr igger", + "Ġele phants", + "Ġelephant s", + "\":\" \",\"", + "\":\"\" ,\"", + "Ġcal iber", + "raft ed", + "raf ted", + "d igits", + "digit s", + "dig its", + "Ġ marshal", + "Ġm arshal", + "Ġmar shal", + "Ġmarsh al", + "Ġmars hal", + "m illiseconds", + "mill iseconds", + "m arkers", + "mark ers", + "mar kers", + "marker s", + "m om", + "mo m", + "/ place", + "/p lace", + "/pl ace", + "Ġhol istic", + ": t", + "# ,", + "Ġb oto", + "Ġbo to", + "Ġbot o", + "Ġnause a", + "Ġnau sea", + "ĠSh ooting", + "ĠShoot ing", + "ĠSho oting", + "i tech", + "it ech", + "ite ch", + "Ġtext Status", + "< Class", + " ())Ċ", + ">( ))Ċ", + ">() )Ċ", + ">()) Ċ", + "ADD RESS", + "ADDR ESS", + "B ST", + "BS T", + "et zt", + "etz t", + "ĠQ gs", + "S ense", + "Sen se", + "Exception Handler", + "ĠC hu", + "ĠCh u", + ".get OwnProperty", + "Ġexerc ised", + "Ġexercise d", + "i otic", + "io tic", + "iot ic", + "ĠRe leases", + "ĠRelease s", + "Ġp interest", + "o lie", + "ol ie", + "oli e", + "i soft", + "is oft", + "iso ft", + "Ġsequ encing", + "Ġpa dre", + "Ġpad re", + "Ġpadr e", + "] ));čĊ", + "]) );čĊ", + "])) ;čĊ", + "])); čĊ", + "( radius", + "(r adius", + "(rad ius", + ". med", + ".m ed", + ".me d", + "ain ties", + "aint ies", + ".Object Model", + "Ġ emple", + "Ġem ple", + "Ġemp le", + "Ġseg uro", + "Ġsegu ro", + "St ars", + "Star s", + "Ġqual itative", + "le mn", + "lem n", + "á» ±", + "> \").", + ">\" ).", + ">\") .", + "Ġ gx", + "Ġg x", + "- cert", + "-c ert", + "-ce rt", + "ĠA STM", + "ĠAS TM", + "ĠAST M", + "Ġ fullname", + "Ġfull name", + "Ġful lname", + "Ġte lemetry", + "Ġtele metry", + "ĠCamb odia", + "_ ul", + "_u l", + "ĠCl are", + "ĠClar e", + "ĠCla re", + "C USTOM", + "Q C", + "ĠU ns", + "ĠUn s", + "Ġ HTTPS", + "ĠHTTP S", + "ĠPar kinson", + "ĠPark inson", + "ancy box", + "', '.", + "',' .", + "T ue", + "Tu e", + ". getLast", + ".get Last", + "Ġ abi", + "Ġa bi", + "Ġab i", + "Äħ d", + "A st", + "As t", + "Ġ Editing", + "ĠEd iting", + "ĠEdit ing", + ". Unity", + ".Un ity", + ".Unit y", + "j mp", + "jm p", + "Ġm ats", + "Ġmat s", + "Ġma ts", + "Ġshared Preferences", + "Cap tain", + "Capt ain", + ". pageSize", + ".page Size", + "Ġ rtl", + "Ġr tl", + "Ġrt l", + "Ġan meld", + "Runtime Object", + "Ġdem ande", + "Ġdemand e", + "( \";", + "(\" ;", + "se ite", + "sei te", + "- headed", + "-head ed", + "-he aded", + "ĠK ra", + "ĠKr a", + "Ġ FONT", + "ĠF ONT", + "ĠFO NT", + "` \\", + "Class NotFoundException", + ". avg", + ".a vg", + ".av g", + "a tical", + "at ical", + "atic al", + "ati cal", + "atica l", + "A j", + "Ġpermit ting", + "Ġperm itting", + "P roj", + "Pro j", + "Pr oj", + "ERR Q", + "Ġcre ampie", + "Ġcream pie", + "ĠBuy er", + "ĠBu yer", + "- modules", + "-mod ules", + "-module s", + "ĠSunday s", + "ĠSun days", + "ĠSund ays", + "| `Ċ", + "Ġday time", + "Ġ +(", + "Ġ+ (", + "Ġgl itch", + "Ġ Operand", + "ĠOper and", + "ĠOpera nd", + "Ġtox ins", + "Ġtoxin s", + "i nya", + "in ya", + "iny a", + "D NS", + "DN S", + "ĠS as", + "ĠSa s", + "C ake", + "Ca ke", + "ĠNational s", + "ĠNation als", + ". addTo", + ".add To", + "Ġs inking", + "Ġsin king", + "Ġsink ing", + "Ġcompreh ension", + "Ġs cor", + "Ġsc or", + "Ġsco r", + "a gements", + "ag ements", + "age ments", + "agement s", + "agem ents", + "Ġt ard", + "Ġta rd", + "Ġtar d", + "Ġm arching", + "Ġmar ching", + "Ġmarch ing", + "ĠM TV", + "ĠMT V", + "Ġs ane", + "Ġsa ne", + "Ġsan e", + "Create Info", + "Ạ¯", + "Ġend Index", + "ĉ layout", + "ĉl ayout", + "Ġ åIJį", + "ĠåIJ į", + "S ITE", + "SI TE", + "ĠT HERE", + "ĠTHE RE", + "ĠTH ERE", + "Ġ[ {'", + "Ġ[{ '", + "opath ic", + "opa thic", + "Ġtrans mitter", + "Ġtransmit ter", + "/ body", + "/b ody", + "Ġp und", + "Ġpun d", + "Ġpu nd", + "Ġ Closing", + "ĠC losing", + "ĠCl osing", + "ĠClo sing", + "Ġ setattr", + "Ġset attr", + "Ġ bounded", + "Ġb ounded", + "Ġbo unded", + "Ġbound ed", + "At las", + "Atl as", + "s uming", + "sum ing", + "su ming", + "( times", + "(t imes", + "(time s", + "(ti mes", + "p arer", + "par er", + "pare r", + "pa rer", + "y nom", + "yn om", + "fe it", + "Ġf rem", + "Ġfr em", + "Ġfre m", + "- leg", + "-l eg", + "-le g", + "ĠB ras", + "ĠBr as", + "ĠBra s", + "> #", + "Ġì¶ ľëł¥", + "Ġì¶ľ ëł¥", + "Ġ INSTANCE", + "ĠIN STANCE", + "ĠINST ANCE", + "ĠC ouch", + "ĠCo uch", + "ĠCou ch", + "_ hosts", + "_host s", + "lik elihood", + ". Marker", + ".M arker", + ".Mark er", + ".Mar ker", + "ĠM asks", + "ĠMas ks", + "ĠMask s", + "Ġc ereal", + "Ġce real", + "Ġcere al", + "ut ilities", + "util ities", + "Ġelement al", + "Ġele mental", + "Ġelem ental", + "Ġdist orted", + "Ġdistort ed", + "in active", + "c ry", + "cr y", + "W L", + "UPPORT ED", + ". Throws", + ".Th rows", + ".Throw s", + "/ schema", + "/s chema", + "s erie", + "se rie", + "ser ie", + ". \"',", + ".\" ',", + ".\"' ,", + "ĠBened ict", + "ĠBene dict", + "- picker", + "-p icker", + "-pic ker", + "ig gs", + "igg s", + "ĠP irate", + "ĠPi rate", + "ĠPir ate", + "åij¨ æľŁ", + "ĠThe ma", + "ĠTh ema", + "ĠThem a", + "ĠSouth ampton", + "Ġarray With", + "ĠPaul a", + "ĠPa ula", + "Ġpred ictor", + "Ġpredict or", + "Ġpredic tor", + "- Ass", + "-A ss", + ". userid", + ".user id", + ".use rid", + "Ġ peri", + "Ġp eri", + "Ġper i", + "Ġpe ri", + "Ġexagger ated", + "u rate", + "ur ate", + "ura te", + "urat e", + "arse ille", + "ĠCon cent", + "ĠConc ent", + "ĠConce nt", + "ĠP ik", + "ĠPi k", + "Ġ@ _;ĊĊ", + "Ġ@_;Ċ Ċ", + "Ġ@_ ;ĊĊ", + "Ġform ations", + "Ġformat ions", + "Ġformation s", + "Ġden omin", + "Ġdenom in", + "\" />.Ċ", + "\"/ >.Ċ", + "\"/> .Ċ", + "end edor", + "ended or", + "Ġpan cre", + "Ġpanc re", + "Ġ amt", + "Ġa mt", + "Ġam t", + "Ġon Resume", + "on Delete", + "ĠB CH", + "ĠBC H", + ") (\"", + ")( \"", + "m ovement", + "move ment", + "mo vement", + "mov ement", + "Ġpot assium", + "", + "Ġ-- ->", + "Ġ--- >", + "ĠP PC", + "ĠPP C", + "i sz", + "is z", + "ake FromNib", + "Ġ Disp", + "ĠD isp", + "ĠDis p", + "ĠDi sp", + "ĠAth letics", + "ĠAthletic s", + "Ġnight club", + "G OOD", + "GO OD", + ".set Geometry", + "+ [", + "/ send", + "/s end", + "/se nd", + "Ġbin aries", + "Ġr áp", + "Ġrá p", + ": req", + ":r eq", + "-con suming", + "-cons uming", + "er time", + "ert ime", + "erti me", + "UP DATED", + "UPDATE D", + "_ nullable", + "_null able", + "V IN", + "VI N", + "u lia", + "ul ia", + "uli a", + "c yan", + "cy an", + "Ġmisunder standing", + "Ġmisunderstand ing", + "o rical", + "or ical", + "ori cal", + "oric al", + "deg rees", + "degree s", + "Le ading", + "Lead ing", + ". AR", + ".A R", + "ic kest", + "ick est", + "N uevo", + "uf oria", + "Ġgo odies", + "Ġgood ies", + "Ġf ores", + "Ġfor es", + "Ġfore s", + "Ġfo res", + "() <<\"", + "()<< \"", + "()< <\"", + "ad emic", + "ade mic", + "adem ic", + "Action Creators", + "server name", + "( nt", + "(n t", + "db Context", + "Ġair borne", + "Ġexhib itions", + "Ġexhibition s", + "Ġexhibit ions", + "c ele", + "ce le", + "cel e", + "Ġt ela", + "Ġte la", + "Ġtel a", + "< Movie", + "", + "() \">", + "()\" >", + ".set PreferredSize", + "ĠM ID", + "ĠMI D", + "ĠA less", + "ĠAl ess", + "ĠAle ss", + "Ġhorse power", + "Ġa tm", + "Ġat m", + "ĠPack aging", + "Ġc iphertext", + "Ġcipher text", + "Request Method", + "Ġbe iden", + "Ġbei den", + "Ġbeide n", + "è £", + "ĠP OW", + "ĠPO W", + ".Write Header", + "d irector", + "dir ector", + "direct or", + "dire ctor", + "- but", + "-b ut", + "ãģł ãģķãģĦ", + "in cer", + "ince r", + "inc er", + "_ dn", + "_d n", + "! !!!!", + "!! !!!", + "!!! !!", + "!!!! !", + "Ġmanufact ures", + "Ġmanufacture s", + ". TextUtils", + ".Text Utils", + "Ġcon sciously", + "Ġconsc iously", + "Ġconscious ly", + "Ġb ounced", + "Ġbounce d", + "c ulture", + "cul ture", + "cult ure", + "ĠS par", + "ĠSp ar", + "ĠSpa r", + "ĠP iper", + "ĠPi per", + "ĠPipe r", + "ĠPip er", + ". press", + ".p ress", + ".pre ss", + ".pr ess", + ".pres s", + "- owner", + "-o wner", + "Ġe valuator", + "Ġeval uator", + "Ġevalu ator", + "Ġ STREAM", + "ĠST REAM", + ".PictureBox SizeMode", + "Ġsu gars", + "Ġsugar s", + "Ġsug ars", + "Screen Width", + "Ġnext State", + "Ġiv ory", + "Ġbr unch", + "Ġbrun ch", + "d ensity", + "dens ity", + "_ OW", + "_O W", + "ĠCoron avirus", + "ĠC FR", + "ĠCF R", + "b ak", + "ba k", + "\\ Category", + "\\C ategory", + "æķ° ç»Ħ", + "Ġinvoke virtual", + "} ()Ċ", + "}( )Ċ", + "Ġs ujet", + "Ġsu jet", + "- marker", + "-m arker", + "-mark er", + "-mar ker", + "is digit", + "isd igit", + "ĠM obil", + "ĠMo bil", + "ĠMob il", + "ĠJsonRequest Behavior", + "_ REMOTE", + "_RE MOTE", + ".exists Sync", + "Ġrich es", + "Ġri ches", + "Ġric hes", + ".p resenter", + ".present er", + ".pres enter", + "Ġgl Color", + "Ġh anya", + "Ġha nya", + "Ġhan ya", + "Ġfort ress", + "Ġfl ashed", + "Ġflash ed", + "Ġfla shed", + "v iz", + "vi z", + "requ ently", + "requent ly", + "b uat", + "bu at", + "$ con", + "$c on", + "> |", + ". Func", + ".F unc", + "Ġhum orous", + "Ġhumor ous", + "u em", + "ue m", + ". ZERO", + ".Z ERO", + "ĠS TL", + "ĠST L", + "ĠB uk", + "ĠBu k", + "/ sample", + "/s ample", + "ĠG ros", + "ĠGr os", + "ĠGro s", + "Rec ipes", + "Recipe s", + "Ġinf lated", + "Ġinfl ated", + "Ġinflate d", + "Ġsw ung", + ": F", + "F acing", + "Fac ing", + "Fa cing", + ". Theme", + ".Th eme", + ".The me", + "н ик", + "ни к", + "Ġspl endid", + "Ġrequest Id", + ".Center Screen", + "/ autoload", + "/auto load", + "embed ded", + "_ depart", + "_de part", + "_dep art", + "Ġ Ports", + "ĠP orts", + "ĠPort s", + "ĠPo rts", + "ĠPor ts", + "๠ĥ", + "ай д", + "disc ussion", + "_ consum", + "_con sum", + "_cons um", + "Ġsc outs", + "Ġsco uts", + "Ġscout s", + "Ġcol abor", + "Ġcola bor", + ". Stage", + ".St age", + ". nano", + ".n ano", + ".nan o", + "el dorf", + "eld orf", + "eldo rf", + "Ġgem acht", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "Ġpolicy makers", + "Ġpolicym akers", + "_P KT", + "_PK T", + ", Th", + ",T h", + "o ky", + "ok y", + "_ UID", + "_U ID", + "_UI D", + "P ing", + "Pin g", + "Pi ng", + "Ġor chest", + "Ġorch est", + "Ġorc hest", + "Ġop tics", + "Ġopt ics", + "Ġoptic s", + "u han", + "uh an", + "ĠX OR", + "ĠXO R", + "Ġespañ ol", + "ĠAd idas", + "r ng", + "rn g", + "m ans", + "man s", + "ma ns", + ".v stack", + "Ġget away", + "Ġh ierarchical", + "Ġhier archical", + "ano ia", + "anoi a", + "ĠBitmap Factory", + "re alm", + "rea lm", + "real m", + "ĉ ap", + "ĉa p", + "_ apps", + "_a pps", + "_app s", + "_ap ps", + "- divider", + "-div ider", + "-di vider", + ". drawer", + ".d rawer", + ".draw er", + "ĠH ARD", + "ĠHA RD", + "ĠHAR D", + "'] ;?>Ċ", + "']; ?>Ċ", + "'];?> Ċ", + "- packed", + "-p acked", + "-pack ed", + "æ² »", + "_STRUCT URE", + "[ Y", + "i Param", + "( eq", + "(e q", + "Ġencompass es", + "Ġ\\ ĊĊ", + "Ġ\\Ċ Ċ", + "- >[", + "-> [", + "& utm", + "g roupon", + "gr oupon", + "group on", + "gro upon", + "st rate", + "str ate", + "stra te", + "D Y", + "om orphic", + "' :[", + "': [", + "Ġgrav itational", + "ĠM icha", + "ĠMich a", + "ĠMi cha", + "ĠMic ha", + "ĠT encent", + "ĠTen cent", + "Ġco ached", + "Ġcoach ed", + "ì¶ ľ", + "Ñĥ менÑĤ", + "Ñĥм енÑĤ", + "/ mobile", + "/m obile", + "Mouse Down", + "b ud", + "bu d", + "ĠY as", + "ĠYa s", + "Ġ Providers", + "ĠPro viders", + "ĠProvid ers", + "ĠProvider s", + "ĠProvide rs", + "ĠProv iders", + "N Z", + "ĉ report", + "ĉre port", + "ĉrep ort", + "err msg", + "Ġimage Path", + "act erial", + "acter ial", + "acteria l", + "ĠM anga", + "ĠMan ga", + "ĠMa nga", + "ĠMang a", + "wick lung", + "( usuario", + "(us uario", + "\" ));čĊčĊ", + "\") );čĊčĊ", + "\")) ;čĊčĊ", + "\"));čĊ čĊ", + "\")); čĊčĊ", + "/ ***", + "/* **", + "/** *", + "Ġorgan ise", + "Ġorganis e", + "Index ed", + "_ QUAL", + "_Q UAL", + "_QU AL", + "( PyObject", + "(Py Object", + "Ġsurrender ed", + "PO CH", + "ĠN OTES", + "ĠNOT ES", + "ĠNO TES", + "ĠNOTE S", + "\\ \\\"", + "\\\\ \"", + "- job", + "-j ob", + "Ġseven ty", + "Ġsevent y", + "Ġsev enty", + "# ###Ċ", + "## ##Ċ", + "#### Ċ", + "### #Ċ", + "ĠMan or", + "ĠMa nor", + "Ġdown right", + "Ġtime frame", + "ins urance", + "che cker", + "check er", + "Ġ SECRET", + "ĠSE CRET", + "ĠSEC RET", + "Ġecho es", + "Ġech oes", + "ĠCar men", + "ĠCarm en", + ".set HorizontalAlignment", + ".setHorizontal Alignment", + "Ġ isChecked", + "Ġis Checked", + "Ġ TOR", + "ĠT OR", + "ĠTO R", + "_ nn", + "_n n", + "( '(", + "(' (", + "Fetch Request", + "ĠPrint ed", + "Fl uid", + "Ġ STACK", + "ĠST ACK", + "ĠSTA CK", + "G ES", + "GE S", + "a igned", + "aign ed", + "i gor", + "ig or", + "igo r", + ". Unknown", + ".Un known", + "C BC", + "CB C", + "ĠCarl son", + ". URI", + ".U RI", + ".UR I", + "Ġp light", + "Ġpl ight", + "/ start", + "/st art", + "/star t", + "ĠPerson nel", + "Ġ PREFIX", + "ĠP REFIX", + "ĠPRE FIX", + "ĠPREF IX", + ", **", + ",* *", + "Ġli mite", + "Ġlimit e", + "Ġlim ite", + "_ heat", + "_h eat", + "_he at", + "% ï¼Į", + "ĠD onne", + "ĠDon ne", + "get Node", + "ĠScient ology", + "Ġc omet", + "Ġcom et", + "Ġco met", + "Ġcome t", + "Ġwen ig", + "A side", + "As ide", + "ĠM PEG", + "ĠMP EG", + "' ?", + "vari ably", + ". endDate", + ".end Date", + "Ġun cont", + "Ġunc ont", + "Ġuncon t", + "Ġ Scores", + "ĠS cores", + "ĠSc ores", + "ĠScore s", + "ĠSco res", + "ĠScor es", + "Ġ LoginForm", + "ĠLogin Form", + ". generated", + ".g enerated", + ".generate d", + ".gener ated", + ", ch", + ",c h", + "- mar", + "-m ar", + "ĠN ed", + "ĠNe d", + "Ġ eventId", + "Ġevent Id", + "+ p", + "ĠS IN", + "ĠSI N", + "/ reset", + "/re set", + "/res et", + ".RE ACT", + "ĠMe ssi", + "ĠMess i", + "ĠMes si", + "_R ANK", + "_RA NK", + ".write File", + "Ġc ripp", + "Ġcr ipp", + "Ġcri pp", + "es thetic", + "est hetic", + "ERS IST", + "Ġreim bursement", + "Ġreimburse ment", + "Current Value", + "Ġu nin", + "Ġun in", + "Ġuni n", + "Down Latch", + "Ġpadding Right", + "Ġst ocked", + "Ġstock ed", + "/ '.", + "/' .", + "Ġre payment", + "Ġrep ayment", + "Ġrepay ment", + "t rak", + "tr ak", + "tra k", + "/ backend", + "/back end", + "Ġиз мен", + "C SR", + "CS R", + "Ġprevent ive", + "Ġpant alla", + "_ trim", + "_t rim", + "_tr im", + "_tri m", + "P edido", + "Ped ido", + "h ospital", + "Ġmanage able", + "route Params", + "text ures", + "texture s", + "tex tures", + ". .....ĊĊ", + ".. ....ĊĊ", + "... ...ĊĊ", + ".... ..ĊĊ", + "..... .ĊĊ", + "...... ĊĊ", + "Ġsé lection", + "Name ValuePair", + "Ġpol lut", + "Ġpoll ut", + "M odes", + "Mode s", + "Mod es", + "Mo des", + "ĠL aud", + "ĠLa ud", + "ĠLau d", + "j ay", + "ja y", + "ĠU rs", + "ĠUr s", + "Ġs igner", + "Ġsign er", + "Ġsig ner", + "Ġ JJ", + "ĠJ J", + "ĠCh erokee", + "_EX ISTS", + "_EXIST S", + "Ġd war", + "Ġdw ar", + "Ġ ($('#", + "Ġ( $('#", + "Ġ($ ('#", + "Ġ($( '#", + "Ġre ef", + "> {$", + ">{ $", + "ĠB aylor", + "ĠBay lor", + "Ġ ModelState", + "ĠModel State", + "- _", + "ĠStruct ures", + "ĠStructure s", + "Ġsou vent", + "Spec ify", + "( pipe", + "(p ipe", + "(pi pe", + "Ġfr acking", + "Ġfrac king", + "ĠG PA", + "ĠGP A", + "Ġb ele", + "Ġbe le", + "Ġbel e", + "ĉ ĉĉĉĉĉĉĠĠĠ", + "ĉĉ ĉĉĉĉĉĠĠĠ", + "ĉĉĉĉ ĉĉĉĠĠĠ", + "ĉĉĉ ĉĉĉĉĠĠĠ", + "ĉĉĉĉĉ ĉĉĠĠĠ", + "ĉĉĉĉĉĉ ĉĠĠĠ", + "ĉĉĉĉĉĉĉ ĠĠĠ", + "ĉĉĉĉĉĉĉĠ ĠĠ", + "ĉĉĉĉĉĉĉĠĠ Ġ", + "ĠMinor ity", + "Ġt ud", + "Ġtu d", + "Ġopen ness", + "ĠIllustr ated", + "Ġoxid ation", + "Ġ NK", + "ĠN K", + "ĉ Update", + "Ġ EMS", + "ĠE MS", + "ĠEM S", + "ĠTe ddy", + "ĠTed dy", + "Ġgener als", + "Ġgen erals", + "Ġgeneral s", + "Ġgenera ls", + "ĉ Mat", + "ĉM at", + "Ġrad ios", + "Ġradio s", + "Ġradi os", + "ĠAnt ique", + "ĠAnti que", + "c onomy", + "con omy", + "conom y", + "cono my", + "ĠSquad ron", + ") ','", + ")', '", + ")' ,'", + "å£ °", + "Ġy oure", + "Ġyou re", + "Ġyour e", + "Ġyo ure", + "ĠMain Page", + "Ġbeh aviours", + "Ġbehaviour s", + "en ght", + "eng ht", + "(@\" %@\",", + "Ġtest case", + "Ġ Compilation", + "ĠComp ilation", + "Ġflav ours", + "Ġflavour s", + "Ġ Extend", + "ĠExt end", + "il lator", + "ill ator", + "illa tor", + "Ġ coh", + "Ġc oh", + "Ġco h", + "Ġs pline", + "Ġsp line", + "Ġspl ine", + "Ġ KG", + "ĠK G", + "- pay", + "-p ay", + "Ġcommun ism", + "ĠBusiness es", + "oc king", + "ock ing", + ". MaxLength", + ".Max Length", + "ass andra", + "qu iring", + "quir ing", + "qui ring", + "a dden", + "ad den", + "add en", + "ĠJ eb", + "ĠJe b", + "_ fault", + "_f ault", + "_fa ult", + "[ file", + "[f ile", + "Ġpromin ence", + "disc iplinary", + "âĢĶ they", + "âĢĶthe y", + "_ extent", + "_ex tent", + "_ext ent", + "Ġ VIC", + "ĠV IC", + "ĠVI C", + "Ġen tails", + "Ġent ails", + "Ġentail s", + ". partner", + ".p artner", + ".part ner", + "Ġhipp oc", + "Le ague", + "çĶ ·", + "w ipe", + "wi pe", + "- spinner", + "-sp inner", + "-spin ner", + "Ġsal ute", + "ĠS urgical", + "ĠSurg ical", + "( outputs", + "(out puts", + "(output s", + "work ed", + "wor ked", + "[ strlen", + "[str len", + "app ointed", + "appoint ed", + "ĠH eg", + "ĠHe g", + "ĠAC PI", + "( [^", + "([ ^", + "u ala", + "ual a", + "ua la", + "_ tol", + "_t ol", + "_to l", + "ĠR it", + "ĠRi t", + ". Payment", + ".P ayment", + ".Pay ment", + "k owski", + "Ġw almart", + "Ġwal mart", + "require ments", + "ĠFIN SEQ", + "_ BACKGROUND", + "_BACK GROUND", + "ĠOs borne", + "( errorMessage", + "(error Message", + "Report ing", + "Rep orting", + "Ġa uctions", + "Ġau ctions", + "Ġauction s", + "Ġcom bos", + "Ġcomb os", + "Ġcombo s", + "ĠNot iced", + "ĠNotice d", + "_ oct", + "_o ct", + "_oc t", + "Ġprim ero", + "Ġprime ro", + "Ġprimer o", + "t aire", + "ta ire", + "_ hr", + "_h r", + "Ġм од", + "Ġмо д", + "Ġcontrad ictory", + "Ġcontradict ory", + "= \"@", + "=\" @", + "ach ines", + "achine s", + "achi nes", + "(opt arg", + "ĠP enguin", + "ĠPeng uin", + "ĠAb bas", + "ĠAbb as", + "Ġsub lime", + "Ġpage able", + "ĠDef ensive", + "Ġdistinct ly", + "ĠAutom atically", + "ĠAutomatic ally", + "Under standing", + "Equality Comparer", + "g ota", + "go ta", + "got a", + "Ġ\" ::", + "Ġ\": :", + "Ġpul ver", + "ĠB attles", + "ĠBattle s", + "ĠBatt les", + "Ġun paralleled", + "T CHA", + "TC HA", + "Ġconstr ued", + "- aff", + "-a ff", + "Ġpre cursor", + "Ġprec ursor", + "-l fs", + "Ġmad uras", + "ĠD aisy", + "ĠDa isy", + "ĠDai sy", + "ĠAr beits", + "ĠArbeit s", + ". Management", + ".Man agement", + "ĉ In", + "ĉI n", + "Ġro bes", + "Ġrob es", + "Ġrobe s", + "Ġsp éc", + "âĢľ (", + "Ġm aternity", + "Ġmat ernity", + "ex tent", + "ext ent", + "Ġ Spacer", + "ĠSp acer", + "ĠSpace r", + "ĠSpa cer", + "Did Appear", + "ĉ us", + "ĉu s", + ".getRequest Dispatcher", + "( cols", + "(c ols", + "(co ls", + "(col s", + "Ġplum met", + "ì ħ", + "Ġ{ ĊĊĊĊ", + "Ġ{Ċ ĊĊĊ", + "Ġ{ĊĊ ĊĊ", + "Ġ{ĊĊĊ Ċ", + "é rica", + "ér ica", + "éri ca", + "éric a", + "Ġ Sizes", + "ĠS izes", + "ĠSize s", + "ĠSi zes", + "ĠSiz es", + ". enum", + ".e num", + ".en um", + ". Highlight", + ".High light", + "Ġ!! }ĊĊĊ", + "Ġ? >ĊĊĊ", + "Ġ?> ĊĊĊ", + "Ġ?>Ċ ĊĊ", + "Ġ?>ĊĊ Ċ", + "W enn", + "We nn", + "Ġcl imax", + "Ġclim ax", + "Ġcli max", + "Ġc rem", + "Ġcr em", + "Ġcre m", + "_ that", + "_t hat", + "_th at", + "[ âĢ¦", + "_ domains", + "_domain s", + "_dom ains", + "_RE PLY", + "Ġcomp leta", + "Ġcomple ta", + "Ġcomplet a", + "Ġcompl eta", + "V EST", + "VE ST", + "VES T", + "_ particle", + "_p article", + "_part icle", + "Ġs op", + "Ġso p", + "Ġfatal ities", + "impl ify", + "imp lify", + "ĠS KF", + "ĠSK F", + "Ġin fusion", + "Ġinf usion", + "ĠJ avier", + "ĠJa vier", + "Ġb allet", + "Ġball et", + "Ġbal let", + "Ġam igo", + "Ġami go", + ". want", + ".w ant", + "Ġcol lagen", + "Ġcoll agen", + "Ġcollage n", + "ĠLaw yer", + ". Statement", + ".St atement", + ".State ment", + ".Stat ement", + ". rt", + ".r t", + "b aar", + "ba ar", + "End Point", + "ĠB ek", + "ĠBe k", + "S HIP", + "SH IP", + "Ġpatri arch", + "ĠA unt", + "ĠAu nt", + "_ TM", + "_T M", + "Ġ mÃŃn", + "Ġm ÃŃn", + "ĠmÃŃ n", + "Ġm astered", + "Ġmaster ed", + "Ġma stered", + "Ġmas tered", + "Ġmast ered", + "W XYZ", + "WX YZ", + "Ġes pos", + "Ġesp os", + "= logging", + "=log ging", + "Ġrighteous ness", + "t orrent", + "tor rent", + "Ġ bst", + "Ġb st", + "Ġbs t", + "_ CHAIN", + "_CH AIN", + "Ġout skirts", + "( rotation", + "(r otation", + "(rot ation", + "Ġ' .')", + "Ġ'. ')", + "Ġ'.' )", + "igr ants", + "igrant s", + "+ lsi", + "+l si", + "ĠCC TV", + "ĠCCT V", + "_PH ASE", + ". azure", + ".a zure", + "_ Process", + "_P rocess", + "_Pro cess", + "v ae", + "va e", + "ĠT ropical", + "ĠAn kara", + "ĠAnk ara", + "image View", + "_RUN NING", + "Ġ* )__", + "Ġ*) __", + "ế n", + "( cli", + "(c li", + "(cl i", + "sc atter", + "Ġs che", + "Ġsc he", + "Ġsch e", + "Reg istrar", + "Registr ar", + "Ġa iring", + "Ġair ing", + "Ġai ring", + "Ġpy plot", + "is ión", + "isi ón", + "/ customer", + "/c ustomer", + "/custom er", + "Ġs implement", + "Ġsim plement", + "Ġsimple ment", + "Ġsimp lement", + "Ġsimpl ement", + "Ġclass y", + "Ġcl assy", + "Ġclas sy", + "ĠD WC", + "ĠDW C", + "ĠBas har", + "ĠBash ar", + "ĠDE VELO", + "ĠV ick", + "ĠVi ck", + "ĠVic k", + "a vail", + "av ail", + "ava il", + "ĠH ö", + "_ extend", + "_ext end", + "dr Fc", + ".is NotBlank", + "Ġpl ais", + "Ġpla is", + "| }Ċ", + "Ġporn ofil", + "Ġporno fil", + "l abs", + "la bs", + "lab s", + "Ġ haus", + "Ġh aus", + "Ġha us", + "Ġorig inating", + "Ġorigin ating", + "Ġsurround s", + "Ġ QUAL", + "ĠQ UAL", + "ĠQU AL", + "m eg", + "me g", + "/ logger", + "/log ger", + "/lo gger", + "[ obj", + "[o bj", + "Ġirres ponsible", + "Ġ PublicKey", + "ĠPublic Key", + "H ONE", + "HO NE", + ": '/", + ":' /", + "i box", + "ib ox", + "ibo x", + "ĠF Vector", + "| {Ċ", + "ata loader", + "atal oader", + "h awks", + "hawk s", + "H DR", + "HD R", + "Ġescal ation", + "ĠPods Dummy", + "e lite", + "el ite", + "eli te", + "Ġpre sup", + "Ġpres up", + "C ached", + "Cache d", + "Ca ched", + "> G", + ". optimizer", + ".opt imizer", + ".optim izer", + ".optimize r", + "Ġ Visible", + "ĠV isible", + "ĠVis ible", + "´ Ģ", + "Ġ nen", + "Ġn en", + "Ġne n", + "Ġ pcs", + "Ġp cs", + "Ġpc s", + "Ġ Idle", + "ĠI dle", + "ĠId le", + "[ Any", + "[A ny", + "Ġkey boards", + "Ġkeyboard s", + "ĠCOMP ONENT", + "Ġtit anium", + "Ġtitan ium", + "( mut", + "(m ut", + "(mu t", + "ĠLed ger", + "Ġprosper ous", + "etro fit", + "_ LL", + "_L L", + "_ patient", + "_p atient", + "_pat ient", + "Ġ pdata", + "Ġp data", + "Ġpd ata", + "Ġ kontakte", + "Ġkont akte", + "Ġkontakt e", + "S wipe", + "Sw ipe", + "Ġcheer ful", + "ĠHond uras", + "\" ][$", + "\"] [$", + "\"][ $", + "Ġhem orrh", + "\" :\"+", + "\": \"+", + "\":\" +", + "Ġ leasing", + "Ġle asing", + "Ġinst alls", + "Ġinstall s", + "Ġinstal ls", + "ĠP ax", + "ĠPa x", + "ĠLog istics", + "ĠLogistic s", + "Ġkin etic", + "ĠP hon", + "ĠPh on", + "_ movement", + "_m ovement", + "_move ment", + "_mov ement", + "_mo vement", + "ĉ bytes", + "ĉbyte s", + "Ġcin co", + "ĠMad ness", + "\" )+", + "\") +", + "Ġ JE", + "ĠJ E", + "_ ij", + "_i j", + "Scene Manager", + "ĠB ust", + "ĠBus t", + "ĠBu st", + "p test", + "pt est", + "pte st", + "a ea", + "ae a", + "Ġb esser", + "Ġbes ser", + "ÃŃ g", + "д ин", + "ди н", + "( tasks", + "(t asks", + "(task s", + "(\" (\"", + "(\"( \"", + "set Type", + "( outfile", + "(out file", + "ĉ reset", + "ĉres et", + "ĉre set", + "Ġ ARC", + "ĠA RC", + "ĠAR C", + "Ġmús ica", + "ĠSh elf", + "ĠShe lf", + "ĠShel f", + "Ġmin Y", + "p ch", + "pc h", + "Ġwe iber", + "Ġwei ber", + "iss or", + "Ġtr ouve", + "Ġtro uve", + "Ġtrou ve", + "ĉ Button", + "ĉB utton", + "Ġre generated", + "Ġreg enerated", + "Ġregenerate d", + "Å £i", + "Å£ i", + "im achinery", + "b locking", + "bl ocking", + "block ing", + ".data Tables", + "_ frac", + "_f rac", + "_fr ac", + "ĠAdv antage", + ".visit Method", + "éĩį æĸ°", + "Ġextra pol", + "Ġextr apol", + "Ġte asing", + "Ġtea sing", + "Ġteas ing", + "ĠH itch", + "ĠHit ch", + "ĠGe ek", + "ĠGee k", + "E SCO", + "ES CO", + "ESC O", + "Ġ wich", + "Ġw ich", + "Ġwi ch", + "ĉ ax", + "ĉa x", + "_ decor", + "_de cor", + "_dec or", + "Ġscreen Width", + "ĠSo phia", + "ĠSoph ia", + "F orgot", + "For got", + "Forg ot", + ". uni", + ".un i", + ".u ni", + "ĠVen ture", + "ĠVent ure", + "_ collision", + "_c ollision", + "_coll ision", + "Ġlaw maker", + "( Edit", + "(E dit", + "b lers", + "ble rs", + "bl ers", + "bler s", + "Ġ getNext", + "Ġget Next", + "âĢĶ you", + "Media Player", + "ĠH orde", + "ĠHor de", + "ĠCongress man", + "obs ervations", + "observ ations", + "observation s", + "ĉ property", + "ĉp roperty", + "ĉprop erty", + "Ġ< --", + "Ġ<- -", + "Created At", + "u byte", + "ub yte", + "uby te", + "Ġquar antine", + "Ġdist ressed", + "Ġdistr essed", + "Ġdistress ed", + "_A PB", + "_AP B", + "ĠGood man", + "ãĤ «", + "Ġrecom end", + "_ PRINTF", + "_PRINT F", + "D ONE", + "DO NE", + "DON E", + "Bind able", + "r strip", + "rs trip", + "rst rip", + "cent aje", + "Ġ Unexpected", + "ĠUn expected", + "ĠS CHOOL", + "ĠProfessional s", + "ĠProfession als", + "ĠGPU s", + "ĠGP Us", + "L esson", + "Le sson", + "Les son", + "Less on", + "Ex clusive", + "Ġat rav", + "Ġatr av", + "ĠD ank", + "ĠDan k", + "ĠDa nk", + "ĠLaw yers", + "ĠLawyer s", + "ĠWal ton", + "ĠWalt on", + "> []", + ">[ ]", + "Ġa loud", + "Ġal oud", + "Ġalo ud", + "=\" ../../../", + "=\"../ ../../", + "=\"../../ ../", + "Ġdeb ating", + "ĠA VG", + "ĠAV G", + "_V OL", + "_VO L", + "/ cgi", + "/c gi", + ". deg", + ".d eg", + ".de g", + ": g", + ".Info f", + ".Inf of", + "Measure Spec", + ". song", + ".s ong", + ".so ng", + ".son g", + "m tree", + "mt ree", + "ul ls", + "ull s", + "J ordan", + "ĠC overs", + "ĠCo vers", + "ĠCover s", + "ĠCov ers", + "ĠCove rs", + "Ġattrib utable", + "Ġj edis", + "Ġje dis", + "Ġjed is", + "iat rics", + "iatric s", + "Ġrot terdam", + "Ġ meld", + "Ġm eld", + "Ġme ld", + "Ġmel d", + "Ġ ContentType", + "ĠContent Type", + "Ġman tle", + "Ġmant le", + "Ġ alice", + "Ġa lice", + "Ġal ice", + "Ġali ce", + "_ duplicate", + "_d uplicate", + "_dup licate", + "/ Internal", + "Ġ filesize", + "Ġfile size", + "Ġfiles ize", + "ĉ fire", + "ĉf ire", + "ĉfi re", + "r ese", + "re se", + "res e", + "on dere", + "ond ere", + "onder e", + "onde re", + "Ġfamiliar ity", + "ĠC rest", + "ĠCr est", + "ĠCre st", + "ĠCres t", + "Ġk arma", + "Ġkar ma", + "Ġtor ino", + "Ġm esa", + "Ġme sa", + "Ġmes a", + "/ temp", + "/t emp", + "Ġc hir", + "Ġch ir", + "Ġchi r", + "Ġ Overflow", + "ĠOver flow", + "Ġten emos", + "u nik", + "un ik", + "uni k", + "N EXT", + "NE XT", + "A lle", + "Al le", + "All e", + "Ġn xt", + "Ġnx t", + "M art", + "Mar t", + "Ma rt", + "Ġ atl", + "Ġa tl", + "Ġat l", + "Ġperiod o", + "Ġperi odo", + "_ you", + "_y ou", + "Ġ} )).", + "Ġ}) ).", + "Ġ})) .", + "int estinal", + ".Adapter View", + "Ġhes itant", + "Ġcompar atively", + "Ġcomparative ly", + ". UInt", + ".U Int", + ".UI nt", + "( viewModel", + "(view Model", + "Ġsang at", + "Ġ Responsive", + "ĠRes ponsive", + "ĠRespons ive", + "ĠZ ack", + "ĠZa ck", + "ĠZac k", + "â ħ", + "J AVA", + "JA VA", + "ĠFull er", + "ĠFu ller", + "ĠFul ler", + "Ġ âĿ¤", + "ĠâĿ ¤", + ". Consumer", + ".Con sumer", + ".Cons umer", + "Ġ ank", + "Ġa nk", + "Ġan k", + "Ġre actors", + "Ġreact ors", + "Ġreactor s", + "f uck", + "fu ck", + "_ rat", + "_r at", + "_ra t", + "Ġsession Factory", + "_ backward", + "_back ward", + "Ġscram bled", + "Ġscramble d", + "ĉ th", + "ĉt h", + "Ġins ensitive", + "Ġch amps", + "Ġcha mps", + "Ġcham ps", + "Ġchamp s", + "Ġ nginx", + "Ġng inx", + "Ġcon hec", + "Ġconhe c", + "ĠJ asper", + "ĠJas per", + ". fm", + ".f m", + "Strict Equal", + "ach sen", + "achs en", + "- Nov", + "-N ov", + "-No v", + "l assen", + "lass en", + "las sen", + ". integration", + ".int egration", + "( lbl", + "(l bl", + "Com pose", + "Comp ose", + "ĠF on", + "ĠFo n", + "à ļ", + "Gr atis", + "ĠL ime", + "ĠLim e", + "ĠLi me", + "ĠAdapter View", + "Ġpoison ed", + "Ġpois oned", + "anch ors", + "anchor s", + "设 计", + "'] ?>\"", + "']?> \"", + "Ġpro cur", + "Ġproc ur", + "It aly", + ". MONTH", + ".MON TH", + "ĠL UA", + "ĠLU A", + "ĠLith uania", + "ĠHe ads", + "ĠHead s", + "_CH UNK", + "ĠP USH", + "ĠPU SH", + "ĠPUS H", + "Aspect Ratio", + "Ġ weg", + "Ġw eg", + "Ġwe g", + "Ġv ids", + "Ġvi ds", + "Ġvid s", + "ĠW ein", + "ĠWe in", + "ĠWei n", + "ĉ INT", + "ĉI NT", + "ĉIN T", + "session Id", + "Ind ustry", + "Ġden ounced", + "JK LM", + "ĠVan essa", + ". Identifier", + ".Id entifier", + "p ropri", + "pro pri", + "prop ri", + "Ġ иг", + "Ġи г", + "Ġté cn", + "Ġtéc n", + "Ġm osaic", + "Ġmos aic", + "Stream Reader", + "- Th", + "-T h", + "f orth", + "for th", + "fort h", + "Ġad herence", + "Ġadher ence", + "Ġadhere nce", + "b ate", + "ba te", + "bat e", + "Ġkn ights", + "Ġknight s", + "s ounds", + "so unds", + "sound s", + "sou nds", + "Ġs alle", + "Ġsa lle", + "Ġsal le", + "O MET", + "OM ET", + "OME T", + "ãĤ¹ ãĥĪ", + "- tm", + "-t m", + "ĠR he", + "ĠRh e", + ".File OutputStream", + "åĪĨ ç±»", + "Ġ ENG", + "ĠE NG", + "ĠEN G", + "h oliday", + "hol iday", + "Ġ Congratulations", + "ĠCong ratulations", + ") (Ċ", + ")( Ċ", + "Ġaggregate s", + "Ġaggreg ates", + "H OOK", + "HO OK", + "e wire", + "ew ire", + "Sen ator", + "Ġembed dings", + "Ġembedding s", + "e py", + "ep y", + "( COM", + "(C OM", + "Ġrob ber", + "ä ter", + "ät er", + "w ang", + "wa ng", + "wan g", + "_ teacher", + "_t eacher", + "_te acher", + "Ġresent ment", + "Ġlett uce", + "er reur", + "err eur", + "erre ur", + "( ic", + "(i c", + "ĠT actical", + "ĠTac tical", + "Ġ Contracts", + "ĠCon tracts", + "ĠContract s", + "ĠContr acts", + "Ġm ænd", + "Ġsit ios", + "Ġsiti os", + "Ġsitio s", + "Ġbast ante", + "Ġnue vos", + "Ġnuevo s", + "ĉN drFc", + "Ġprivate Key", + "uc ch", + "ucc h", + "MM dd", + "Ġ è¾ĵåĩº", + "Ġè¾ĵ åĩº", + "um ba", + "umb a", + "@ foreach", + ": \");ĊĊ", + ":\" );ĊĊ", + ":\");Ċ Ċ", + ":\") ;ĊĊ", + "Ġslip pery", + "ĠKey stone", + "ĠKe ystone", + "ĠKeys tone", + "Ġpione ering", + "Ġpioneer ing", + "_ triangle", + "_t riangle", + "_tr iangle", + "_tri angle", + "( \"Ċ", + "(\" Ċ", + "ĉ ĉĉĉĉĉĉĉĠĠ", + "ĉĉ ĉĉĉĉĉĉĠĠ", + "ĉĉĉĉ ĉĉĉĉĠĠ", + "ĉĉĉ ĉĉĉĉĉĠĠ", + "ĉĉĉĉĉ ĉĉĉĠĠ", + "ĉĉĉĉĉĉ ĉĉĠĠ", + "ĉĉĉĉĉĉĉĉ ĠĠ", + "ĉĉĉĉĉĉĉ ĉĠĠ", + "ĉĉĉĉĉĉĉĉĠ Ġ", + "ĠInt ervention", + "ĠInter vention", + "S CI", + "SC I", + "Ġc JSON", + "Ġter minating", + "Ġterm inating", + "Ġtermin ating", + "ë ¹Ħ", + "ë¹ Ħ", + "Ġbaby s", + "Ġbab ys", + "Sub set", + "Ġ ë¡", + "Ġë ¡", + "Ġseu lement", + "Ġseul ement", + "Ġseule ment", + "Ġm uestra", + "Ġmue stra", + "En tre", + "Ent re", + "Entr e", + "以 ä¸Ĭ", + "n go", + "ng o", + "\" bytes", + "QR ST", + "QRS T", + "Ġy pos", + "Ġyp os", + "person a", + "pers ona", + "Ġ Deploy", + "ĠDe ploy", + "ĠDep loy", + "c ee", + "ce e", + "Ġ à®", + "Ġà ®", + ". goal", + ".go al", + "Ġhabit ats", + "Ġhabitat s", + "Ġ isAdmin", + "Ġis Admin", + "Ġexplo iting", + "Ġexploit ing", + "Ġvent il", + "Ġven til", + "ĠB alls", + "ĠBall s", + "ĠBal ls", + "ا ب", + "Ø§Ø ¨", + "Ġmind fulness", + "Ġmindful ness", + "( kwargs", + "(k wargs", + "Ġre sembling", + "Ġresembl ing", + "Ġch oir", + "Ġcho ir", + "Ġon BackPressed", + "ĠSEC URITY", + "/ gtest", + "/g test", + "Ġjust ices", + "Ġjustice s", + "Ġinteger Value", + "b lah", + "bl ah", + "bla h", + "ĠA im", + "ĠAi m", + "_ finalize", + "_final ize", + "k eh", + "ke h", + "ĠComplex ity", + "Ġaug ust", + "get ElementsByTagName", + "Ġp reach", + "Ġpr each", + "Ġpre ach", + "Ġpron unciation", + "Ġ Trash", + "ĠTr ash", + "ĠTra sh", + "- percent", + "-per cent", + "_PR IV", + "_PRI V", + "ĠH unts", + "ĠHun ts", + "ĠHu nts", + "ĠHunt s", + "ĠC urse", + "ĠCur se", + "u ellen", + "ue llen", + "uel len", + "uelle n", + "uell en", + "Ġheavy weight", + "X i", + "ĉ selected", + "ĉselect ed", + "ĉse lected", + "ĠMcC oy", + "å¼Ĥ 常", + "| =Ċ", + "|= Ċ", + "ĠBattle field", + "Item Image", + "Ġded uctions", + "Ġdeduct ions", + "Ġdeduction s", + "ĠElement al", + "ĠEle mental", + "ĠElem ental", + "( ));//", + "() );//", + "()) ;//", + "()); //", + "ĠBur k", + "ĠBu rk", + "} )čĊčĊ", + "}) čĊčĊ", + "})čĊ čĊ", + "sw ift", + "/ function", + "/f unction", + "Us ually", + "Usu ally", + "_ St", + "_S t", + "_fe ats", + "_feat s", + "Ġ IsValid", + "ĠIs Valid", + "Ġz ad", + "Ġza d", + "Image Context", + "Ġ classname", + "Ġclass name", + "Ġdon ner", + "Ġdonne r", + "Ġdonn er", + "Ġ-- >ĊĊĊ", + "Ġ-->Ċ ĊĊ", + "Ġ--> ĊĊĊ", + "Ġ-->ĊĊ Ċ", + "Ġmotor cycles", + "Ġmotorcycle s", + "+' /'+", + "+'/ '+", + "Ġ setBackground", + "Ġset Background", + "\\ CMS", + "\\C MS", + ". AllArgsConstructor", + ".All ArgsConstructor", + "ĠLex ington", + ". examples", + ".ex amples", + ".example s", + ".exam ples", + "ĠP urs", + "ĠPur s", + "ĠPu rs", + "Push Matrix", + "Ġ================================================= =============", + ".add Target", + "p ora", + "por a", + "po ra", + "Full screen", + "Ġgo of", + "Ġgoo f", + "h len", + "hl en", + "hle n", + "ä ge", + "ĠC URL", + "ĠCUR L", + "ĠCU RL", + "Ġ Interesting", + "ĠInter esting", + "ĠInterest ing", + "Ġretrie ves", + "Ġretrieve s", + "Ġretr ieves", + "_ Obj", + "_O bj", + "in ness", + "inn ess", + "inne ss", + "- ----ĊĊ", + "-- ---ĊĊ", + "---- -ĊĊ", + "--- --ĊĊ", + "----- ĊĊ", + "-----Ċ Ċ", + ".t sv", + ".ts v", + "( IM", + "(I M", + "ĠBr aves", + "ĠBra ves", + "ĠBrave s", + "_ ISR", + "_I SR", + "_IS R", + "o sti", + "os ti", + "ost i", + "á» ĵ", + "ĠEx terior", + "ĠExt erior", + "ĠCourt ney", + "Ġresid ues", + "Ġresidue s", + "T ier", + "Ti er", + ".* ;čĊčĊ", + ".*;čĊ čĊ", + ": black", + ":b lack", + "web View", + "\" path", + "Ġm asa", + "Ġma sa", + "Ġmas a", + "] !='", + "]!= '", + "Ġ Matching", + "ĠM atching", + "ĠMat ching", + "ĠMatch ing", + "d ur", + "du r", + "J vm", + "= context", + "_ RING", + "_R ING", + "Ġpro ponents", + "Ġprop onents", + "ĠQString Literal", + "Ġ inflate", + "Ġin flate", + "Ġinf late", + "Ġinfl ate", + "< Float", + " \">čĊ", + "Ġ?>\" >čĊ", + "Ġ?>\"> čĊ", + "_C OST", + "_CO ST", + "i linear", + "il inear", + "ili near", + "iline ar", + "ilin ear", + "Ġ Workspace", + "ĠWork space", + "ĠWorks pace", + "Ġs pel", + "Ġsp el", + "Ġspe l", + "ag ogue", + "ago gue", + "agog ue", + "ĠMillenn ium", + "ĠPop ulate", + "Ġ nid", + "Ġn id", + "Ġni d", + ".parse Color", + "S olar", + "So lar", + "Sol ar", + "ĠG ad", + "ĠGa d", + "Ġ ì¤ij", + "Ġì ¤ij", + "Ġì¤ ij", + "ĠK amp", + "ĠKa mp", + "ĠKam p", + "ĉ rm", + "ĉr m", + "Ġb enz", + "Ġbe nz", + "Ġben z", + "Ġ Honestly", + "ĠH onestly", + "ĠHonest ly", + "Ġelectro de", + "Ġelectr ode", + "ĠPr airie", + "ĠPra irie", + "Ġ PROFILE", + "ĠPRO FILE", + "ĠPROF ILE", + "ĠOri ental", + "ĠOrient al", + "ĠO LED", + "ĠOL ED", + "/cop yleft", + "awa ii", + "awai i", + "( products", + "(product s", + ") \\<", + ")\\ <", + "- created", + "-c reated", + "-create d", + "-cr eated", + ".Many ToMany", + "\" How", + "\"H ow", + "Ġв Ñĭп", + "ĠвÑĭ п", + "Ġmitochond rial", + "_ testing", + "_t esting", + "_test ing", + "( created", + "(c reated", + "(create d", + "(cr eated", + "Ġ getField", + "Ġget Field", + "_E VAL", + "_EV AL", + "] .\"", + "]. \"", + "ĠF SM", + "ĠFS M", + "ĠR ita", + "ĠRi ta", + "ĠRit a", + "Ġ åıĤæķ°", + "Ġåı Ĥæķ°", + "ĠåıĤ æķ°", + "Ġc ôt", + "Ġcô t", + "ĠIns ight", + "ĉ mysqli", + "ĉm ysqli", + "ĉmysql i", + "_ timing", + "_t iming", + "_tim ing", + "_ti ming", + "I DO", + "ID O", + ") ))))Ċ", + ")) )))Ċ", + "))) ))Ċ", + ")))) )Ċ", + "CO VERY", + "COVER Y", + ". imag", + ".i mag", + ".im ag", + "C DF", + "CD F", + "l ust", + "lu st", + "lus t", + "i ckt", + "ic kt", + "ick t", + "_ FP", + "_F P", + ". ','", + ".' ,'", + ".', '", + "g cc", + "gc c", + "Ġkur z", + "Ġku rz", + "_p wm", + "_pw m", + "Ġodp owied", + "Ġ Barrier", + "ĠBar rier", + "ĠBarr ier", + "/************************************************************************ ***Ċ", + "p ak", + "pa k", + "- Israel", + "ĠRut gers", + "Ġselected Item", + "ĠRam irez", + "F arm", + "Far m", + "Fa rm", + "Ġcal endars", + "Ġcalendar s", + "Ġcalend ars", + "g zip", + "gz ip", + "Ġblock buster", + "ĠPly mouth", + "çľ Į", + "res ponses", + "response s", + "respons es", + ".Dialog Interface", + "- grand", + "-g rand", + "-gr and", + "Ġ getSource", + "Ġget Source", + "ĠgetS ource", + "Ġdej tings", + "Ġdejting s", + "Ġt ieten", + "Ġti eten", + "Ġtie ten", + "Ġcondem nation", + "Ġcondemn ation", + "Ġcontin uar", + "Ġcontinu ar", + "Ġcontinua r", + ".Mock Mvc", + "/ english", + "Ġ MediaPlayer", + "ĠMedia Player", + "com puted", + "comp uted", + "compute d", + "comput ed", + "ĠCl ippers", + "ĠClip pers", + "ĠCli ppers", + "( delegate", + "(de legate", + ". Slf", + ".S lf", + "Ġ ë¡ľ", + "Ġë¡ ľ", + "ĠT ide", + "ĠTi de", + "Ġih rem", + "Ġihr em", + "Ġihre m", + "ĠW an", + "ĠWa n", + "Ñĥ ÑİÑī", + "ÑĥÑİ Ñī", + "} ><", + "}> <", + "Disc ussion", + "Discuss ion", + "Ġw atts", + "Ġwat ts", + "Ġwatt s", + "- minus", + "-m inus", + "-min us", + "ĠJul iet", + "ĠJulie t", + "ĠJuli et", + "éĽ ħ", + "Ġcon cluding", + "Ġconcl uding", + "and scape", + "ands cape", + "Ġúlt ima", + "ĠD ERP", + "ĠDE RP", + "ĠDER P", + "Ġsign Up", + "ĠSecond ly", + "W AIT", + "WA IT", + "l ds", + "ld s", + ". callbacks", + ".call backs", + ".callback s", + "( hour", + "(h our", + "im ators", + "ima tors", + "imator s", + "imat ors", + "vol ent", + "A AF", + "AA F", + "e driver", + "ed river", + "ĠMath ematic", + "< Tuple", + "'", + "Ġ/> '", + "{ j", + "_AB ORT", + "E ther", + "Et her", + "Eth er", + "Ġeduc ator", + "Ġpreca ution", + "Ġfinger tips", + "Ġfingert ips", + "get Var", + "cam atan", + "- debug", + "-de bug", + "ĠR AF", + "ĠRA F", + "[ arg", + "[a rg", + "Ġr aced", + "Ġrace d", + "Ġrac ed", + "Ġra ced", + "Ġts unami", + ".f link", + ".fl ink", + "Ġgl yc", + "Ġgly c", + "u ko", + "uk o", + "Ġ Multiply", + "ĠM ultiply", + "ĠMulti ply", + "ĠMultip ly", + "Ġre distribution", + "Ġred istribution", + "Ġredistrib ution", + "Ġredis tribution", + "A GO", + "AG O", + "Ġ Routine", + "ĠR outine", + "ĠRout ine", + "Ġ opr", + "Ġo pr", + "Ġop r", + "( lower", + "(l ower", + "(low er", + "(lo wer", + "ĠFun ktion", + "ĠFunk tion", + ". dk", + ".d k", + "Ġ egt", + "Ġe gt", + "Ġeg t", + "_B ASIC", + "sys call", + "ĠL SD", + "ĠLS D", + "Ġ Duplicate", + "ĠD uplicate", + "ĠDup licate", + "_ sell", + "_s ell", + "_se ll", + "_sel l", + "Ġerror Handler", + "_ ips", + "_i ps", + "_ip s", + "Ġ erv", + "Ġe rv", + "Ġer v", + "an nie", + "ann ie", + "anni e", + "(resource Name", + "Ġbott led", + "Ġbottle d", + "Ġcraw ling", + "Ġcrawl ing", + "e gment", + "eg ment", + ".set Tag", + "Ġ rss", + "Ġr ss", + "Ġrs s", + "ĠQu arry", + "ĠQuar ry", + "_ exact", + "_ex act", + ". jwt", + ".j wt", + "ĠBo ards", + "ĠBoard s", + "o pi", + "op i", + "Ġn asal", + "Ġna sal", + "Ġnas al", + "Ġ XYZ", + "ĠX YZ", + "ĠXY Z", + ". ud", + ".u d", + "North ern", + "Nor thern", + "Ġact ivating", + "Ġactiv ating", + "e dx", + "ed x", + "ov ah", + "ova h", + "Ġ indx", + "Ġin dx", + "Ġi ndx", + "Ġind x", + "Alert Dialog", + "Ġt ienes", + "Ġti enes", + "Ġtie nes", + "Ġtiene s", + "an nya", + "ann ya", + "anny a", + "_ pan", + "_p an", + "_pa n", + "( decimal", + "(d ecimal", + "(de cimal", + "(dec imal", + ". Dict", + ".D ict", + ".Di ct", + "Ġsubsidi aries", + "Product Name", + "F ew", + "Fe w", + "d ato", + "da to", + "dat o", + "od ied", + "odi ed", + "odie d", + "- under", + "-un der", + "-u nder", + "Ġ ê²ĥ", + "Ġê² ĥ", + "çīĪ æľ¬", + "at ism", + "atis m", + "ati sm", + "[ Math", + "[M ath", + ". '<", + ".' <", + "( infile", + "(in file", + "Ġde notes", + "Ġden otes", + "Ġdenote s", + "$ class", + "$c lass", + "_SEC URITY", + "Ġsew age", + "m elon", + "me lon", + "mel on", + "( Character", + "(Char acter", + "/ github", + "/g ithub", + "/git hub", + "Ġgl aring", + ". Guid", + ".G uid", + "_ sparse", + "_s parse", + "_sp arse", + "Ġ Margin", + "ĠM argin", + "ĠMar gin", + "ĠMarg in", + "_ dns", + "_d ns", + "_dn s", + "Ġme iner", + "Ġmein er", + "Ġmeine r", + "Ġleft ist", + "ĉ loc", + "ĉl oc", + "a bytes", + "aby tes", + "abyte s", + "Ġequipment s", + "Ġequip ments", + "ex po", + "exp o", + "ĠSom erset", + "E K", + "æį ¢", + "Ġlect urer", + "Ġlecture r", + "Ġmem iliki", + "æł ¸", + "ç´ ł", + "p ron", + "pr on", + "pro n", + ": pointer", + "b orrow", + "bor row", + "ĠProt ective", + "ĠProtect ive", + "_ cf", + "_c f", + "Ġ ÐķÑģли", + "ĠÐķ Ñģли", + "b pp", + "bp p", + "' ;ĊĊĊĊ", + "';Ċ ĊĊĊ", + "';ĊĊ ĊĊ", + "'; ĊĊĊĊ", + "';ĊĊĊ Ċ", + "at urally", + "atur ally", + "atural ly", + "_ NAV", + "_N AV", + "Ġpe ptide", + "> d", + "Ġ ifstream", + "Ġif stream", + "Ġi fstream", + "_FACT ORY", + "_FACTOR Y", + "' );//", + "') ;//", + "'); //", + "j oined", + "join ed", + "jo ined", + "m ong", + "mon g", + "mo ng", + "Ġtime spec", + "Ġtimes pec", + "Ġdest abil", + "Ġdesta bil", + "Ġ autop", + "Ġaut op", + "Ġauto p", + "Ġau top", + "- limit", + "-l imit", + "-li mit", + "public ation", + "pub lication", + "ĠD enn", + "ĠDe nn", + "ĠDen n", + ". Memory", + ".M emory", + "( skb", + "(s kb", + "(sk b", + "ĠAna heim", + "_RETURN TRANSFER", + "o ueur", + "ou eur", + "( _('", + "(_ ('", + "l egt", + "le gt", + "leg t", + "ist ingu", + "isting u", + "ĉ priv", + "ĉp riv", + "ĉpr iv", + "Ġredirect s", + "M t", + "Ġal leen", + "Ġall een", + "Ġalle en", + "Ġ PointF", + "ĠPoint F", + "Ġ omin", + "Ġo min", + "Ġom in", + "Ġc itt", + "Ġcit t", + "Ġci tt", + "ĠT age", + "ĠTag e", + "ĠTa ge", + "ĠW alls", + "ĠWall s", + "ĠWal ls", + "á» ī", + "Ġoccup ying", + "Ġoccupy ing", + "x BF", + "xB F", + "r angle", + "ra ngle", + "ran gle", + "rang le", + "Ġrel ational", + "Ġrelation al", + "Ġrelat ional", + "- org", + "-o rg", + "-or g", + "Ġ jpg", + "Ġj pg", + "Ġjp g", + "- derived", + "Ġmal function", + "ĠB enson", + "ĠBen son", + "( scroll", + "(s croll", + "(sc roll", + "Ġ XD", + "ĠX D", + "H oly", + "Ho ly", + "Hol y", + "( commands", + "(command s", + "(comm ands", + "Ġt ipping", + "Ġti pping", + "Ġtip ping", + "Ġpr imitives", + "Ġprim itives", + "Ġprimitive s", + "Ġsex le", + "Call Check", + "Ġ MASTER", + "ĠM ASTER", + "ĠMA STER", + "ĠMAS TER", + "_ TEAM", + "_TE AM", + ".setRequest Header", + "_ specs", + "_sp ecs", + "_spec s", + "Ġs erge", + "Ġser ge", + "Ġserg e", + ". Master", + ".M aster", + ".Ma ster", + "Ġ ims", + "Ġi ms", + "Ġim s", + ".Spring BootTest", + "pay pal", + "ĠW ANT", + "ĠWA NT", + "ĠWAN T", + ". Inst", + ".I nst", + ".In st", + "ĠCar pet", + "ĠCarp et", + "Ġwrong ly", + "( $('.", + "($ ('.", + "($( '.", + "($(' .", + "Ġ bild", + "Ġb ild", + "Ġbi ld", + "Ġbil d", + ". Roll", + ".R oll", + "ĠU rb", + "ĠUr b", + "- can", + "-c an", + "-ca n", + "ãģı ãģłãģķãģĦ", + "ãģıãģł ãģķãģĦ", + "olib eral", + " čĊčĊ", + "Ġ-->čĊ čĊ", + "ĠMa hm", + "ĠMah m", + "} \";ĊĊ", + "}\" ;ĊĊ", + "}\";Ċ Ċ", + "Ġ dq", + "Ġd q", + "ĠPublish ers", + "ĠPublisher s", + "ĠA mpl", + "ĠAm pl", + "ĠAmp l", + "ĠDaniel le", + "ĠDani elle", + "Ġ tern", + "Ġt ern", + "Ġte rn", + "Ġter n", + "èµ ·", + "no ÅĽÄĩ", + "e in", + "ei n", + "ĠAsync Storage", + "u nger", + "un ger", + "ung er", + "unge r", + "ro uw", + "rou w", + "Ġsc issors", + "/ assert", + "/as sert", + ". bucket", + ".b ucket", + "/ archive", + "/a rchive", + "/arch ive", + "_ Man", + "_M an", + "Ġint oler", + "Ġinto ler", + "Ġ ()=>", + "Ġ( )=>", + "Ġ() =>", + "Ġ ÐĴÑĭ", + "ĠÐĴ Ñĭ", + "Ġs ai", + "Ġsa i", + ". xy", + ".x y", + ". \"čĊ", + ".\" čĊ", + "Ġur inary", + "e sub", + "es ub", + "IST ICS", + "ISTIC S", + "Ġ κ", + "ĠÎ º", + "Ġcompl iments", + "Ġcompliment s", + "Ġtypings Japgolly", + "i har", + "ih ar", + "Exp ansion", + "ĠS erving", + "ĠSer ving", + "ĠServ ing", + "_ students", + "_st udents", + "_student s", + "ĠX BOOLE", + "( il", + "(i l", + "Ġ ì²ĺ", + "Ġì² ĺ", + "Ġj ó", + "( tol", + "(t ol", + "(to l", + "( JS", + "(J S", + "ĉ CG", + "ĉC G", + "Ġ DRAW", + "ĠD RAW", + "ĠDR AW", + "t wig", + "tw ig", + "Ġo at", + "Ġoa t", + "_ smooth", + "_sm ooth", + "ĠC SL", + "ĠCS L", + "Ġo sob", + "Ġos ob", + "Ġens uing", + "Ġb anker", + "Ġbank er", + "Ġban ker", + "ĠBack pack", + "_ ping", + "_p ing", + "_pin g", + "_pi ng", + "Ġ wishlist", + "Ġw ishlist", + "Ġwish list", + "= ax", + "=a x", + "ĉ ĠĠĠĊ", + "ĉĠĠĠ Ċ", + "ĉĠ ĠĠĊ", + "ĉĠĠ ĠĊ", + "Dis ney", + "ste ady", + "stead y", + "\" >%", + "\"> %", + "Ġproph ets", + "Ġprophet s", + "Ġ ZX", + "ĠZ X", + "Ġminimal ist", + "Ġminim alist", + ". PLAIN", + ".PL AIN", + "Se attle", + "Seat tle", + ". ordinal", + "Ġ PIPE", + "ĠPI PE", + "Ġret orna", + "Ġretorn a", + "Ġj ugador", + "Ġjug ador", + "ĠB ret", + "ĠBr et", + "ĠBre t", + "ĠâĶ ľ", + "Ġp lush", + "Ġpl ush", + "Ġplus h", + "Ġplu sh", + "UL ATOR", + "ULA TOR", + "S orting", + "Sort ing", + ".grid y", + ".gr idy", + "ect omy", + "_ activ", + "_ac tiv", + "_act iv", + "r ack", + "ra ck", + "rac k", + "Inter active", + "ĠAntar ctica", + "ĠAntarctic a", + "Ġv engeance", + "en so", + "ens o", + "_ known", + "_k nown", + "up plier", + "upp lier", + ". Modules", + ".Mod ules", + ".Module s", + "ĠConnection State", + "éļ IJèĹı", + "éļIJ èĹı", + "@ FindBy", + "Ġ placer", + "Ġpl acer", + "Ġplace r", + "Ġplac er", + "Ġpla cer", + "\\ model", + "< ()>", + "<( )>", + ".is Successful", + ".isSuccess ful", + "- good", + "-g ood", + "-go od", + "b z", + "ĠDr aco", + "ĠDra co", + "Ass istant", + "- extra", + "-ex tra", + "-ext ra", + "аб лиÑĨ", + "Ġhyp ocrisy", + "Ġt st", + "Ġts t", + "ĠA gr", + "ĠAg r", + "$ txt", + "$t xt", + "Ġlog istic", + "l icensed", + "lic ensed", + "license d", + "ĠH of", + "ĠHo f", + "Ġ tat", + "Ġt at", + "Ġta t", + "( iv", + "(i v", + "Ġint oxic", + "Ġinto xic", + "Ġintox ic", + "post Id", + "_ strike", + "_st rike", + "_str ike", + "Ġhum iliation", + "Ġhumili ation", + "p codes", + "pc odes", + "\" sync", + "\"s ync", + "( recipe", + "(rec ipe", + "+ N", + "r ente", + "re nte", + "ren te", + "rent e", + "ĉ Client", + "ĉC lient", + "ycop g", + "ĠZur ich", + "ĠZu rich", + "Ġ Profiles", + "ĠPro files", + "ĠProf iles", + "ĠProfile s", + "C ountries", + "Count ries", + "Ġp ict", + "Ġpi ct", + "Ġpic t", + "Ġroll out", + "requ encies", + "Ġp atched", + "Ġpat ched", + "Ġpatch ed", + "Ġcar tridges", + "Ġcartridge s", + "Ġsh ading", + "Ġsha ding", + "J ar", + "Ja r", + "Ġsalv age", + "ĠT axes", + "ĠTax es", + "ĠTa xes", + "Ġstand by", + "ap oran", + "apor an", + "apo ran", + "E igen", + ". angular", + "Ġ Nested", + "ĠN ested", + "ĠNe sted", + "ĠNest ed", + "ĠNes ted", + "ä º«", + "äº «", + "Ġ isVisible", + "Ġis Visible", + "ĠDw ight", + "_BR ANCH", + ". Delay", + ".D elay", + ".De lay", + "Ġk end", + "Ġke nd", + "Ġken d", + "Ġfacilit ated", + "Ġfacilitate d", + "Ġfacil itated", + ". flatMap", + ".flat Map", + "Ġs anta", + "Ġsan ta", + "Ġsant a", + "ĉ Send", + "ĉS end", + "/ messages", + "/m essages", + "/message s", + "Ġof Type", + "ĉ swap", + "ĉs wap", + "ĉsw ap", + "# plt", + "ĠTur ks", + "ĠTurk s", + "N ES", + "NE S", + "Ġprogress ively", + "Ġprogressive ly", + "ĠRes idence", + "Ġ TREE", + "ĠT REE", + "ĠTR EE", + "ĠTRE E", + "Ġn oen", + "Ġno en", + "Ġnoe n", + "d io", + "di o", + "Ġ nelle", + "Ġn elle", + "Ġne lle", + "Ġnel le", + "Ġnell e", + "Ġso gar", + "Ġsog ar", + "i tti", + "it ti", + "itt i", + "week ly", + "Ġambigu ity", + "_ Settings", + "_S ettings", + "_Set tings", + "W are", + "War e", + "Wa re", + ". neo", + ".n eo", + ".ne o", + "_ DST", + "_D ST", + "_DS T", + "Ġ æĸ¹", + "Ġæĸ ¹", + "p rep", + "pr ep", + "pre p", + "l obby", + "lob by", + "@ email", + "/ movie", + "/m ovie", + "Ġfun kc", + "Ġfunk c", + "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", + "ÂŃ s", + "Ġguard ians", + "Ġguardian s", + "- pos", + "-p os", + "-po s", + "Ġconfig uring", + "Ġconfigur ing", + "ĠC PS", + "ĠCP S", + "ĠD eus", + "ĠDe us", + "Ġvidé os", + "Ġvidéo s", + "_ empresa", + "_emp resa", + "Ġsl apped", + "Ġsla pped", + "Ġslap ped", + "< Model", + "',Ċ", + "\"> ',Ċ", + "\">' ,Ċ", + "\">', Ċ", + "_X DECREF", + "ĠBuzz Feed", + "_M ARGIN", + "P LOY", + ". small", + ".s mall", + ".sm all", + "Ġm imeType", + "Ġmime Type", + "Ġh olog", + "Ġho log", + "Ġhol og", + "ĉ camera", + "ĉc amera", + "l ias", + "li as", + "lia s", + "Ġsusp ense", + "od ynam", + "ody nam", + "odyn am", + "b au", + "ba u", + "Ġgrave yard", + "_ named", + "_n amed", + "_name d", + "_na med", + "\": \"'", + "\":\" '", + "Ġ ************************************************", + "Ġ******************************** ****************", + "Ġ******** ****************************************", + "Ġ**************** ********************************", + "Ġ************************ ************************", + "Ġ**************************************** ********", + "Ġgame Over", + "Ġ LENGTH", + "ĠLE NGTH", + "ĠLENG TH", + "ĉ screen", + "ĉs creen", + "ĉsc reen", + "Ġdo InBackground", + "_ dependencies", + "_depend encies", + "_dep endencies", + "Ġ rtc", + "Ġr tc", + "Ġrt c", + "/ up", + "/u p", + "_ ROM", + "_R OM", + "_RO M", + "H all", + "Ha ll", + "Hal l", + "Ġdef iciencies", + "( te", + "(t e", + "' #", + "_ equiv", + "_e quiv", + "_equ iv", + "Ġpre order", + "ĠA xe", + "ĠAx e", + "о мÑĥ", + "ом Ñĥ", + ".send File", + "Ġ filt", + "Ġf ilt", + "Ġfil t", + "Ġfi lt", + "Ġ Limits", + "ĠL imits", + "ĠLim its", + "ĠLi mits", + "ĠLimit s", + "ĠCaval iers", + ". discount", + ".dis count", + ".disc ount", + "âĨ IJ", + "ĠW it", + "ĠWi t", + "QRST UV", + "Ġ ij", + "Ġi j", + "Ġt egen", + "Ġte gen", + "Ġteg en", + "Ġ :\",", + "Ġ: \",", + "Ġ:\" ,", + "diff iculty", + "p unkt", + "pun kt", + "punk t", + "ĠEm ails", + "ĠEmail s", + "ch lor", + "chl or", + "( fun", + "(f un", + ". Uint", + ".U int", + ".Ui nt", + "ĠSt all", + "ĠSta ll", + "_ verified", + "_ver ified", + "u D", + "File Type", + "Ġple asures", + "Ġpleasure s", + "Ġpleas ures", + "Ġjud iciary", + "Ġs ham", + "Ġsh am", + "Ġsha m", + "i pur", + "ip ur", + "_ PLUS", + "_PL US", + "of fers", + "off ers", + "offer s", + "( foo", + "(f oo", + "_ GT", + "_G T", + "ĉ core", + "ĉc ore", + "EN TION", + "ENT ION", + "ĠLib eration", + "ĠLiber ation", + "Command Line", + "_ department", + "_de partment", + "_dep artment", + "_depart ment", + ". Ar", + ".A r", + "_ neighbor", + "_ne ighbor", + "Ġ Submitted", + "ĠSub mitted", + "ĠSubmit ted", + "Ġ Ċ", + "\"> -->Ċ", + "Ġdro its", + "Ġdroit s", + "Ġhomosexual s", + "Ġab duction", + "ĉ widget", + "ĉw idget", + "$ headers", + "$header s", + "ĠD AR", + "ĠDA R", + "Ġf la", + "Ġfl a", + "th reat", + "thr eat", + "Ġl ouis", + "Ġlo uis", + "Ġlou is", + ". GetProperty", + ".Get Property", + "\" Just", + "( frames", + "(f rames", + "(frame s", + "(fr ames", + "r yo", + "ry o", + "prof ession", + "| i", + "íķ´ ìĦľ", + "( sv", + "(s v", + "Ġun recognized", + "I onic", + "Io nic", + "Ion ic", + "F ashion", + "Screen State", + "Ġ Incoming", + "ĠIn coming", + "ĠInc oming", + "Not Nil", + "Ġsyn cing", + "Ġsync ing", + "e mie", + "em ie", + "emi e", + "Ġth ermo", + "Ġther mo", + "Ġtherm o", + "_ procs", + "_pro cs", + "_pr ocs", + "_proc s", + "Ġincons istency", + "Ġinconsist ency", + "rel igious", + ". mj", + ".m j", + "Ġperson n", + "Ġpers onn", + "Ġperso nn", + "Ġmoment os", + "Ġmomento s", + "or arily", + "Ġ æĬ", + "Ġæ Ĭ", + "_ne urons", + "Ill ustr", + "im oto", + "imo to", + "i lik", + "il ik", + "ili k", + "ĠW oj", + "ĠWo j", + "Tr ading", + "Trad ing", + "Tra ding", + "Ġapp are", + "Ġap pare", + "Ġappar e", + "Ġentre prises", + "Ġentreprise s", + "a chat", + "ac hat", + "ach at", + "acha t", + "Ġ ¬", + "Ġ ¬", + "Ġne igh", + "Ġnei gh", + "BUTTON DOWN", + "ĠMa her", + "ĠMah er", + "a ghan", + "ag han", + "agh an", + "- hash", + "-h ash", + "-has h", + "\" f", + "Ġclient ele", + "Ġcliente le", + ".add Button", + "ĉ SP", + "ĉS P", + "Q i", + "Ġg rated", + "Ġgr ated", + "Ġgrat ed", + "Ġgra ted", + "Ġgrate d", + "PO SITE", + "POS ITE", + "POSIT E", + ": >", + "ĠH owell", + "ĠHow ell", + "ĠHo well", + "ĠHowe ll", + "ĠCompar ative", + "Ġ ISC", + "ĠI SC", + "ĠIS C", + "ÂŃ i", + "O cean", + "D avis", + "Da vis", + "ĠFil me", + "ĠFilm e", + "W ins", + "Win s", + "Wi ns", + "ĠJ IT", + "oc cer", + "occ er", + "ĠC orm", + "ĠCo rm", + "ĠCor m", + "ENCH MARK", + "rc hive", + "rch ive", + "i cação", + "ic ação", + "ica ção", + "Ġm ata", + "Ġmat a", + "Ġma ta", + "Ġchild birth", + "ĠOption ally", + "ĠOptional ly", + "E ns", + "En s", + "Ġx http", + "Ġel ucid", + "_Osc InitStruct", + ") )):Ċ", + ")) ):Ċ", + "))) :Ċ", + "Ġint uit", + "Ġ Donate", + "ĠDon ate", + "Ġcorre lates", + "Ġcorrel ates", + "Ġcorrelate s", + "> Delete", + "Ġe quipe", + "Ġequ ipe", + "Ġequip e", + "Ġb oca", + "Ġbo ca", + "Ġinf latable", + "Ġinfl atable", + "e rah", + "er ah", + "era h", + "ĠDateTime Kind", + "Ġcal ves", + "\\ Lib", + "\\L ib", + "Ġem lrt", + "ĠTr ilogy", + "ĠTri logy", + "ĠP anc", + "ĠPan c", + "ĠPa nc", + "ĠD uis", + "ĠDu is", + "ĠpelÃŃcul a", + "W ARDS", + "WARD S", + "WAR DS", + "_DE TECT", + "_DET ECT", + "-section al", + "d hcp", + "dh cp", + "For Row", + "- destruct", + "-d estruct", + "-de struct", + "Ġ Presenter", + "ĠP resenter", + "ĠPres enter", + "ĠPresent er", + "/ slick", + "/s lick", + "/sl ick", + ", on", + ",o n", + "ĠCit adel", + "logged in", + "logg edin", + "_ subtype", + "_sub type", + "Ġs igue", + "Ġsi gue", + "Ġsig ue", + "Ġsigu e", + "Ġc uring", + "Ġcur ing", + "Ġcu ring", + "ĠFire wall", + "Ġfluores cence", + "ĠItalian s", + "ĠItalia ns", + "ĠItal ians", + "иÑĤ ÑģÑı", + ". getStyle", + ".get Style", + "In Seconds", + "j ie", + "ji e", + "- Smith", + "-S mith", + "Ġx link", + "Ġxl ink", + "Ġsub missive", + "о нÑĤ", + "он ÑĤ", + "arbon ate", + "ĠF aul", + "ĠFa ul", + "_ goals", + "_go als", + "_goal s", + "ĠCommission ers", + "ĠCommissioner s", + "chart Instance", + "_POST FIELDS", + "Ġmed ial", + "Ġmedia l", + "Ġmedi al", + "Ġm anos", + "Ġman os", + "Ġma nos", + "Ġmano s", + "Ġd elt", + "Ġde lt", + "Ġdel t", + "s vm", + "sv m", + ". Apis", + ".A pis", + ".Api s", + ".Ap is", + "e phy", + "ep hy", + "eph y", + "Ġasym pt", + "Ġapp Delegate", + "Ġimpro bable", + "c ka", + "ck a", + "s imd", + "si md", + "sim d", + "/ Error", + "/E rror", + ". âĢĵ", + "Ġ PTS", + "ĠP TS", + "ĠPT S", + "d eer", + "de er", + "dee r", + "Ġs ina", + "Ġsi na", + "Ġsin a", + "m agnitude", + "ID ADE", + "IDA DE", + "IDAD E", + "'] }'", + "']} '", + "Ġmay ores", + "Ġmayor es", + "Ġmayo res", + "ĉ comment", + "ĉcom ment", + "/ console", + "/con sole", + "\" @", + "v olt", + "vo lt", + "vol t", + ". sell", + ".s ell", + ".se ll", + ".sel l", + "ĠM acy", + "ĠMac y", + "ĠMa cy", + "Ġme lod", + "Ġmel od", + "Ġim ágenes", + "_ chg", + "_c hg", + "_ch g", + "Ġin out", + "Ġi nout", + "id ente", + "ide nte", + "ident e", + "iden te", + ") '),Ċ", + ")' ),Ċ", + ")'), Ċ", + "d ni", + "dn i", + ". blob", + ".b lob", + ".bl ob", + "Ġtyp ography", + "Ġe erie", + "Ġee rie", + "Ġeer ie", + "_ OID", + "_O ID", + "p esan", + "pe san", + "pes an", + "a jan", + "aj an", + "aja n", + "Ġch opping", + "Ġcho pping", + "Ġchop ping", + "Ġbl uff", + "a df", + "ad f", + "_ bases", + "_b ases", + "_base s", + ". Formatter", + ".Form atter", + ".Format ter", + ".For matter", + "Ġ\\ %", + "ĠPage Info", + "Car rier", + "ĠCal ibration", + "c omo", + "com o", + "co mo", + "-b odied", + "Ġfinanc ier", + "Ġ INA", + "ĠI NA", + "ĠIN A", + ". ERR", + ".E RR", + "Ġho odie", + "Ġhood ie", + "ĠS anity", + "ĠSan ity", + "gu arded", + "guard ed", + ".opend aylight", + "IS MATCH", + "ISM ATCH", + "High lights", + "Highlight s", + "ü nk", + "ün k", + "an iem", + "ani em", + "anie m", + "ang ered", + "ange red", + "anger ed", + "assign ments", + "assignment s", + "Ġregistr ado", + "Ġregist rado", + "ĠU PPER", + "ĠUP PER", + "ampil kan", + "a shire", + "as hire", + "ash ire", + "ashi re", + "ĠNik ola", + "ĠNi kola", + "ĠNikol a", + "ĠC FL", + "ĠCF L", + "ĠH DC", + "ĠHD C", + "Ġp oids", + "Ġpo ids", + "Ġpoi ds", + "ĠI Ps", + "ĠIP s", + "Ġprevent ative", + "ips oid", + "i fix", + "if ix", + "ifi x", + ". camel", + ".c amel", + ".ca mel", + ".cam el", + ". ga", + ".g a", + "V olumes", + "Volume s", + "Vol umes", + "- ste", + "-s te", + "-st e", + "Y ahoo", + "Ya hoo", + "_s ibling", + "_si bling", + "H ighest", + "High est", + "Hi ghest", + "opt group", + "Ġkvin na", + "Ġkvinn a", + "âĢĿ ãĢĤĊĊ", + "âĢĿãĢĤ ĊĊ", + "ĠAppl iances", + "Ġ \"><", + "Ġ\" ><", + "Ġ\"> <", + "' )\")Ċ", + "') \")Ċ", + "')\" )Ċ", + "h tt", + "ht t", + "ĠIdent ified", + "Ġpencil s", + "Ġpenc ils", + "Ġmember Id", + "Ġappend String", + ".load Data", + "Ġmock Mvc", + "Ġj ub", + "Ġju b", + "ĠS lut", + "ĠSl ut", + "ĠTai pei", + "st att", + "stat t", + "sta tt", + "P olit", + "Pol it", + "Po lit", + "Ġpart ager", + "Did Change", + "Incre ases", + "Increase s", + ") }.", + ")} .", + "ĠB aba", + "ĠBa ba", + "ĠBab a", + "_CL IP", + "_CLI P", + "[ unit", + "[u nit", + "Ġ клÑİÑĩ", + "Ġк лÑİÑĩ", + "Ġalc uni", + "ĠL ola", + "ĠLo la", + "ĠLol a", + "Ġcl inging", + "Ġclin ging", + "Ġcling ing", + "@ PostMapping", + "( concat", + "(con cat", + "Ġ ssid", + "Ġs sid", + "Ġss id", + "ĠF auc", + "ĠFa uc", + "o kit", + "ok it", + "oki t", + "ĠRecord ed", + "á lez", + "ál ez", + "ále z", + "($ ('<", + "($( '<", + "($(' <", + ".assertIs Not", + "Ġk ali", + "Ġka li", + "Ġkal i", + "V olt", + "Vo lt", + "Vol t", + "Ġwarm ly", + "Ġsc ares", + "Ġsca res", + "Ġscar es", + "Ġscare s", + "g etti", + "get ti", + "ge tti", + "gett i", + "füh rt", + "führ t", + "_ does", + "_d oes", + "_do es", + ". EMAIL", + ".E MAIL", + "im ations", + "imation s", + "imat ions", + "Ġspring fox", + "ĠD ecom", + "ĠDe com", + "ĠDec om", + "ar cy", + "arc y", + "Ġgl itches", + "Ġglitch es", + "ĠM off", + "ĠMo ff", + "ĠV oll", + "ĠVol l", + "ĠVo ll", + ". between", + ".b etween", + "Ġco orden", + "Ġcoord en", + "ĠPart icularly", + "G BP", + "GB P", + "Ġ semble", + "Ġs emble", + "Ġsem ble", + "Ġsembl e", + "East ern", + "_M SB", + "_MS B", + "] ){čĊ", + "]) {čĊ", + "]){ čĊ", + "m organ", + "mo rgan", + "mor gan", + "ĠE VAL", + "ĠEV AL", + "d ere", + "de re", + "der e", + "H OUSE", + "HO USE", + "m oire", + "mo ire", + "ist ique", + "isti que", + "_l stm", + "_lst m", + "_ls tm", + "- commit", + "-com mit", + "-comm it", + "yster ious", + "Ġtw ink", + "Ġtwin k", + "- thumbnails", + "-th umbnails", + "-thumbnail s", + "e nÃŃ", + "en ÃŃ", + ": '',", + ":' ',", + ":'' ,", + "Ġblack out", + "ĠFloor s", + "ĠFlo ors", + "Ġso fas", + "Ġsofa s", + "Ġsof as", + "Ġ oui", + "Ġo ui", + "Ġou i", + "le shoot", + "les hoot", + "lesh oot", + "ĠR aq", + "ĠRa q", + "- abs", + "-a bs", + "-ab s", + "Ġk ra", + "Ġkr a", + "M ining", + "Min ing", + "Mi ning", + "Mini ng", + "s haft", + "sh aft", + "sha ft", + ".set Columns", + ".setColumn s", + "Cl azz", + "Cla zz", + "PRE TTY", + ". playlist", + ".play list", + "éĸ ¢", + "-Sah aran", + "M ING", + "MI NG", + "MIN G", + "ĉ bl", + "ĉb l", + "è® ®", + "j f", + "DO CKER", + "DOC KER", + "hop efully", + "hope fully", + "( ignore", + "(i gnore", + "ĠUsers Controller", + "ĠMitar beiter", + "Ġ LES", + "ĠL ES", + "ĠLE S", + "Ham ilton", + "- metadata", + "-m etadata", + "-meta data", + "Ġ KK", + "ĠK K", + "ikt ig", + "Ġwoll te", + "Ġwol lte", + "egr ator", + "egra tor", + "] bool", + ", current", + ",c urrent", + "Ġvalue Type", + "Ġexcav ation", + "o land", + "ol and", + "ola nd", + "olan d", + "Ġv erv", + "Ġver v", + "Ġve rv", + "/ filepath", + "/file path", + "Auth Provider", + "Ġpro crast", + "ĉ ULONG", + "ĉU LONG", + "_MEM BERS", + "_MEMBER S", + "Ġup lift", + "ĠAut onomous", + "Ġart works", + "Ġartwork s", + "ĠOut reach", + "Ġp ore", + "Ġpo re", + "Ġpor e", + "Home page", + "Dialog Title", + "Ġ Generating", + "ĠG enerating", + "ĠGener ating", + "ĠGene rating", + "P ARSE", + "PAR SE", + "Ġsem anas", + "Ġsemana s", + "Ġhum ano", + "Ġhuman o", + "JSGlobal Scope", + "Ġvo lte", + "Ġvol te", + "Ġvolt e", + "Ġb ella", + "Ġbe lla", + "Ġbel la", + "Ġbell a", + "(is instance", + "Ġp lc", + "Ġpl c", + "\\ Catalog", + "\\C atalog", + "Ġeste emed", + "Ġesteem ed", + "éĽ ·", + "( suffix", + "(s uffix", + "Ġswe eps", + "Ġsweep s", + "ĉ ORDER", + "Ġdo ivent", + "Ġdoi vent", + "ĠS warm", + "ĠSw arm", + "Ġ Compiled", + "ĠComp iled", + "ĠCompile d", + "get Page", + "A DR", + "AD R", + ".R ichTextBox", + "Ġ Naming", + "ĠN aming", + "ĠNa ming", + "ĠNam ing", + "ag ged", + "agg ed", + "ĠG ANG", + "ĠGA NG", + "r asing", + "ra sing", + "ras ing", + "od eled", + "ode led", + "odel ed", + "Ġg ala", + "Ġga la", + "Ġgal a", + "ĠJS Name", + "d df", + "dd f", + "Ġil lust", + "Ġill ust", + "ĠLan sing", + "ĠLans ing", + "[ port", + "[p ort", + "- death", + "-de ath", + "Ġdin heiro", + "ĠE ighth", + "ĠEight h", + "Ġ bian", + "Ġb ian", + "Ġbi an", + "st Ã¥", + "Ġvers ión", + "ĠLinear Gradient", + "ĠH arding", + "ĠHar ding", + "ĠHard ing", + ". *)", + ".* )", + "e czy", + "ec zy", + "ecz y", + "$ header", + "Ġv Ã¥r", + "ĠvÃ¥ r", + "Un checked", + "Ġk oje", + "Ġko je", + "ĠPal adin", + "( ))),", + "() )),", + "()) ),", + "())) ,", + "G iving", + "Gi ving", + "( )})Ċ", + "() })Ċ", + "()} )Ċ", + "Ġd ips", + "Ġdi ps", + "Ġdip s", + "F riendly", + "Friend ly", + "Ġport rays", + "Ġportray s", + "Ġhel ium", + "Ġinsurg ency", + "_ expiry", + "_ex piry", + "_exp iry", + "ĠstringByAppending String", + "Ġa antal", + "Ġaan tal", + "s lope", + "sl ope", + "m ast", + "ma st", + "mas t", + ".get Integer", + ".getInt eger", + "Ġ ########################", + "Ġ######## ################", + "Ġ################ ########", + "Ġ############ ############", + "_PIPE LINE", + "Ġd ensely", + "Ġdense ly", + "Ġdens ely", + "Ġmut ating", + "m idi", + "mi di", + "mid i", + "ĠSe it", + "a yne", + "ay ne", + "NOW LED", + "ĠDes mond", + "ĠF Name", + "ĠFN ame", + "ĠN airobi", + "\\ Context", + "Ġcal cular", + "Ġcalcul ar", + "Ġcalc ular", + "- den", + "-d en", + "-de n", + "Ġ cott", + "Ġc ott", + "Ġco tt", + "Ġcot t", + "] ):čĊ", + "]) :čĊ", + "]): čĊ", + "ĠRecommend ation", + "ĠRo lex", + "ĠRole x", + "ĠRol ex", + "Ġvalidation Result", + ". pat", + ".p at", + ".pa t", + "Ġn Ãły", + "ĠRest Client", + "ĠG PI", + "ĠGP I", + "ĠAshe ville", + "Ġ OSP", + "ĠO SP", + "ĠOS P", + "ĠPER MISSION", + "ÐĶ аÑĤа", + "/ notification", + "/not ification", + "K night", + "Kn ight", + "_ Word", + "_W ord", + "ĠB ender", + "ĠBe nder", + "ĠBen der", + "ĠBend er", + "r anking", + "ran king", + "rank ing", + "Ġpart ida", + "Ġparti da", + "_ reservation", + "_res ervation", + "Ì Ģ", + "Ġm Name", + "Ġg etch", + "Ġget ch", + "Ġb orr", + "Ġbo rr", + "Ġbor r", + "Ġdilig ent", + "Disc uss", + "æŃ£ åľ¨", + "ape ake", + "i oned", + "ion ed", + "io ned", + "ione d", + "-N azi", + ". cum", + ".c um", + "ĠK ron", + "ĠKr on", + "ĠKro n", + "= $('#", + "=$ ('#", + "=$( '#", + "/ single", + "/s ingle", + "Ġerot isch", + "ĠV ib", + "ĠVi b", + "Ġrat ified", + "Ġconcert ed", + "ĠREG ARD", + "Ġdo br", + "Ġdob r", + ".Driver Manager", + "' r", + "Port able", + "Por table", + "ĉ suite", + "ĉs uite", + "Ġrel aciones", + "Ġrelacion es", + "ĠD op", + "ĠDo p", + "emp loi", + "empl oi", + "emplo i", + "D OB", + "DO B", + "Ġcr umbs", + "Ġ xls", + "Ġx ls", + "Ġxl s", + "_ Application", + "_App lication", + "(' :',", + "(': ',", + "Ġ-- ----------------------------------------------------------------------Ċ", + "Ġ---------------------------------------------------------------- --------Ċ", + "Ġ------------------------------------------------------------ ------------Ċ", + "m se", + "ms e", + "Ġb erk", + "Ġbe rk", + "Ġber k", + "Ġ ReturnValue", + "ĠReturn Value", + "ĠB elly", + "ĠBel ly", + "ĠBell y", + "Ġc amar", + "Ġca mar", + "Ġcam ar", + "ĠPe ek", + "ĠPee k", + "el sing", + "els ing", + "Ġnot ifies", + "ĠTr istan", + "ĠTri stan", + "ĠG AR", + "ĠGA R", + "em me", + "emm e", + "ĠElev ated", + "_ CSV", + "_C SV", + "_CS V", + "( chalk", + "(ch alk", + "Ġtw enties", + "ĠSearch Result", + "= search", + "=s earch", + "ĠMix ing", + "ĠMi xing", + "ý t", + "Ġrecru iter", + "Ġrecruit er", + "ĠIDE OGRAPH", + "ĠA go", + "ĠAg o", + "( Operation", + "(O peration", + "(Op eration", + "$ values", + "$value s", + "$val ues", + "Ġworld ly", + "ĠRos enberg", + "ĠRosen berg", + "ĠConfigure Services", + "> ** Ċ", + "...\" >Ċ", + "Ġsn ork", + "Ġsno rk", + "_ opacity", + "_op acity", + "ĠinitWith NibName", + "i ado", + "ia do", + "iad o", + "A AC", + "AA C", + "Ġ ]).", + "Ġ] ).", + "Ġ]) .", + "; z", + "_ paragraph", + "_par agraph", + "_para graph", + "Ġn oses", + "Ġno ses", + "Ġnos es", + "Ġnose s", + "st ands", + "stand s", + "sta nds", + "stan ds", + "i fr", + "if r", + "_m E", + "I raq", + "Ir aq", + ". Predicate", + ".P redicate", + "e naire", + "en aire", + "ena ire", + "] ]];Ċ", + "]] ];Ċ", + "Ġ unidad", + "Ġun idad", + "Ġuni dad", + "Ġretire es", + "Ġretir ees", + "_ hello", + "_h ello", + "Ġ modele", + "Ġmod ele", + "Ġmodel e", + "Ġmode le", + "ĠUITableView Controller", + "ĠUIT ableViewController", + "f write", + "fw rite", + "_ numero", + "_num ero", + "_numer o", + "_ visited", + "_vis ited", + "_visit ed", + "Ġrec ebe", + "Ġrece be", + "( Notification", + "Fant astic", + "_ submenu", + "_sub menu", + "ĠP EM", + "ĠPE M", + "ĠC upertino", + "ĠCup ertino", + "approx imately", + "cl assed", + "class ed", + "clas sed", + ".Read String", + "Ġdomic ile", + "_ PW", + "_P W", + "Ġball park", + "ĠK ale", + "ĠKa le", + "ĠKal e", + "con tra", + "cont ra", + "contr a", + "_ favorite", + "_f avorite", + "/ of", + "/o f", + "Q uite", + "Qu ite", + "Quit e", + "Qui te", + "Ġ OTA", + "ĠO TA", + "ĠOT A", + "Ġacceler ometer", + "di dn", + "did n", + "| ^", + "ĠRohing ya", + "ivi crm", + "ivic rm", + "ann abin", + "anna bin", + "обÑĭ ÑĤи", + "o rado", + "or ado", + "ora do", + "' )+", + "') +", + "Ha unted", + ", ID", + ",I D", + "( UIAlertAction", + "u rv", + "ur v", + "_ bel", + "_b el", + "_be l", + "ĠMex icans", + "ĠMexican s", + "/ terms", + "Ġ Painter", + "ĠP ainter", + "ĠPa inter", + "ĠPaint er", + "ĠPain ter", + "Input Label", + "ĠV inci", + "ĠVin ci", + "ĠRo sie", + "ĠRos ie", + "\\ uc", + "\\u c", + "< Menu", + "", + "Ġ'\" >", + "_ gs", + "_g s", + "Ġcomp il", + "n ard", + "na rd", + "nar d", + "- exc", + "-e xc", + "-ex c", + "Ġrh yme", + "Ġb utto", + "Ġbut to", + "Ġbutt o", + "s ays", + "sa ys", + "say s", + "ant asy", + "anta sy", + "antas y", + "ë ¸", + "Ġcitt Ãł", + "Ġch eg", + "Ġche g", + "Time String", + "Ġpos itivity", + "Ġposit ivity", + "ĠD abei", + "ĠDa bei", + "Ġ wang", + "Ġw ang", + "Ġwa ng", + "Ġwan g", + "Ġes cre", + "Ġesc re", + "\" c", + "ĉ video", + "ĉv ideo", + "Ġ Ranked", + "ĠR anked", + "ĠRank ed", + "ĠRan ked", + ". strings", + ".string s", + ".str ings", + "> >>(", + ">> >(", + ">>> (", + "Ġин ÑĤеÑĢ", + "ĠинÑĤ еÑĢ", + "Ġre sta", + "Ġr esta", + "Ġres ta", + "Ġrest a", + "[: ,:", + "[:, :", + "Ġren dre", + "Ġrend re", + "Ġde ser", + "Ġdes er", + "Ġdese r", + "J os", + "Jo s", + "Ġdis ruptions", + "Ġdisrupt ions", + "Ġdisruption s", + "Ġо пеÑĢ", + "Ġоп еÑĢ", + "s ampling", + "sam pling", + "samp ling", + "sup press", + "Ġcontainer View", + "ĠSeam less", + "Ġ airy", + "Ġa iry", + "Ġair y", + "Ġai ry", + "Ġon load", + ".Window Manager", + "ĠP LA", + "ĠPL A", + "br aco", + "bra co", + ".set PositiveButton", + "Ġp du", + "Ġpd u", + "Ġg si", + "Ġgs i", + "Ġ Cli", + "ĠC li", + "ĠCl i", + "_gr adients", + "_grad ients", + "_gradient s", + "Ñı д", + "ĠWh isper", + "c stdint", + "Ġl äng", + "Ġlä ng", + "Ġform ulations", + "Ġformulation s", + "Ġformul ations", + "é nom", + "én om", + "ourn emouth", + "[ $_", + "[$ _", + "Ġordin arily", + ".set Username", + ".setUser name", + "Ġfacult ies", + "MIT TED", + "/ values", + "/value s", + "Ġwe ir", + "Ġwei r", + "ĠA pt", + "ĠAp t", + "M Z", + "ĉ cf", + "ĉc f", + "u cken", + "uc ken", + "uck en", + "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", + "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", + "def ense", + "[ iVar", + "[i Var", + "ĠBusiness Exception", + "Select ors", + "Selector s", + "Sel ectors", + "Sele ctors", + "( coordinates", + "(co ordinates", + "ĠRe sets", + "ĠRes ets", + "ĠReset s", + "ĠDr inks", + "ĠDrink s", + "o leans", + "ole ans", + "olean s", + "(st ypy", + "_ IOC", + "_I OC", + "_IO C", + ". xxx", + ".x xx", + ".xx x", + "ĠS later", + "ĠSl ater", + "ĠSlate r", + "ĠB elize", + "ĠBel ize", + "Ġ /************************************************************************", + "Ġ/ ************************************************************************", + "Ġ/**************************************************************** ********", + "ad din", + "add in", + "_ep isodes", + "_episode s", + "Ġis chem", + "Ġisc hem", + "legal ArgumentException", + "D anny", + "Dan ny", + "Ġ pared", + "Ġp ared", + "Ġpar ed", + "Ġpa red", + "Ġpare d", + ".code haus", + "ĠAs sy", + "ĠAss y", + "ĉ Rect", + "ĉR ect", + "â ŀ", + ". lista", + ".l ista", + ".list a", + ".li sta", + "Ġв аÑĪ", + "Ġва ÑĪ", + "Ġv ets", + "Ġve ts", + "Ġvet s", + "H WND", + "HW ND", + "is oner", + "ison er", + "iso ner", + "Ġ xo", + "Ġx o", + "Ġor ally", + "Ġoral ly", + "Ġ Stmt", + "ĠSt mt", + ".r nn", + "ĠD PI", + "ĠDP I", + "ĠSt rikes", + "ĠStr ikes", + "ĠStrike s", + ".setViewport View", + "Ġèĩª åĬ¨çĶŁæĪIJ", + "Y ELLOW", + "GL enum", + "part ners", + "partner s", + "Ġ Implicit", + "ĠImp licit", + "ĠImpl icit", + "Ġt ako", + "Ġta ko", + "Ġtak o", + "âĢĻ elle", + "âĢĻe lle", + "Ġerm ög", + "total Count", + "G il", + "Gi l", + "ĉ work", + "ĉw ork", + "Ġpr atic", + "Ġpra tic", + "Ġprat ic", + "in ati", + "ina ti", + "a bies", + "ab ies", + "abi es", + "ĠSk inner", + "ĠSkin ner", + "Ġspirit ed", + "Ġspir ited", + "Ġpancre atic", + "Ġh df", + "Ġhd f", + "' em", + "'e m", + "Ġpsych osis", + "Ġpsycho sis", + "o licit", + "ol icit", + "olic it", + "oli cit", + "Ġ\" {\"", + "Ġ\"{ \"", + "_at ual", + "_a tual", + "Ġé lect", + "Ġél ect", + "TE AM", + "Ġd ak", + "Ġda k", + "ĠSW AT", + ". FragmentManager", + ".Fragment Manager", + "Ġprovision ing", + "l ifetime", + "life time", + "lif etime", + "_EXT ENSIONS", + "_EXTENSION S", + "Ġ CASCADE", + "ĠC ASCADE", + "ĠCAS CADE", + "Ġ ![", + "Ġ! [", + "( KP", + "(K P", + "Ġ vem", + "Ġv em", + "Ġve m", + "ĠInter racial", + "ĠInterr acial", + "' ]},Ċ", + "'] },Ċ", + "']} ,Ċ", + "sp acer", + "space r", + "spa cer", + "_ kv", + "_k v", + "W arehouse", + "Ware house", + "R DD", + "RD D", + "_f sm", + "_fs m", + ".Stretch Image", + ", Yes", + ",Y es", + "ĠRefuge e", + "ĠBr inging", + "ĠBring ing", + "Ġv álido", + "Ġvál ido", + ". intersection", + ".inter section", + "Ġsp ooky", + "Ġspo oky", + "_ portal", + "_p ortal", + "_port al", + "_por tal", + "Ġ moth", + "Ġm oth", + "Ġmot h", + "Ġmo th", + "ĠZ odiac", + "ĠSOC IAL", + "M imeType", + "'] }}", + "Ġ----- ->", + "Ġ------ >", + "_ Blue", + "_B lue", + "_Bl ue", + "Ġbot anical", + "Ġfr ags", + "Ġfra gs", + "Ġfrag s", + "Ġfamil ial", + "Ġfamilia l", + "- du", + "-d u", + "Ġse izing", + "Ġseiz ing", + "Ġsei zing", + "( blocks", + "(b locks", + "(block s", + "(bl ocks", + ". rd", + ".r d", + ".check NotNull", + "Ġm iser", + "Ġmis er", + "Ġmi ser", + "Ġmise r", + "Ġmax x", + "Ġma xx", + "ĠK nee", + "ĠKn ee", + "View Item", + "Inner HTML", + "D anger", + "Da nger", + "Dan ger", + "( (__", + "(( __", + "((_ _", + "Ġprz ypad", + "Ġprzy pad", + "create Url", + "* *,", + "** ,", + "ĠDecor ating", + "ATE GY", + "ATEG Y", + "? >/", + "?> /", + ". Designer", + ".Des igner", + ".Design er", + "hex digest", + "ĠEvery where", + "all eries", + "alle ries", + "aller ies", + ".TEXT URE", + ". Blocks", + ".B locks", + ".Bl ocks", + ".Block s", + "z ell", + "ze ll", + "zel l", + "Ġpre ço", + "S uddenly", + "input Email", + "( sync", + "(s ync", + ". bd", + ".b d", + "gold en", + "> ');", + ">' );", + ">') ;", + "ĠDick inson", + "> >(Ċ", + ">> (Ċ", + ">>( Ċ", + "Ġ QUEUE", + "ĠQUE UE", + "Ġ getColumn", + "Ġget Column", + "ĠgetC olumn", + "ĠS AND", + "ĠSA ND", + "ĠSAN D", + ". piece", + ".p iece", + ".pi ece", + "l icer", + "lic er", + "li cer", + "lice r", + "Fl utter", + "Ġget Version", + "Ġresource Id", + "o gl", + "og l", + "ÅĤ aw", + "ÅĤa w", + ". Branch", + ".Br anch", + "ĉ web", + "ĉw eb", + "Ġfr amerate", + "Ġframe rate", + "Ġfram erate", + "P PP", + "PP P", + "Ġf ray", + "Ġfr ay", + "Ġfra y", + "C NT", + "CN T", + "Ġinform atie", + "Ġinformat ie", + "' ]čĊčĊ", + "'] čĊčĊ", + "']čĊ čĊ", + "ne as", + "nea s", + "Header Code", + "Ġ æ¸", + "Ġæ ¸", + "Ġt rg", + "Ġtr g", + "raw types", + "H onda", + "Ho nda", + "Hon da", + "Ġmark eter", + "Ġmarket er", + "Ġ requestData", + "Ġrequest Data", + "Ġ Pg", + "ĠP g", + "ĉ not", + "ĉn ot", + "ĉno t", + "Ġpage Info", + "Ġakt uellen", + "Ġaktu ellen", + "ãģ ķãĤĵ", + "ãģķ ãĤĵ", + "Ġ AMS", + "ĠA MS", + "ĠAM S", + "push ViewController", + "ĉ AL", + "ĉA L", + "Ġv ests", + "Ġve sts", + "Ġvest s", + "Ġves ts", + "p roduce", + "pro duce", + "produ ce", + "prod uce", + "-m ême", + "ĠRah man", + "F unny", + "Fun ny", + "E Z", + "_ Valid", + "_Val id", + "Ġsquad ron", + "Ġ lash", + "Ġl ash", + "Ġla sh", + "Ġlas h", + "Ġ irm", + "Ġi rm", + "Ġir m", + "i asco", + "ias co", + "ĠP aran", + "ĠPar an", + "ĠPa ran", + "ĠPara n", + "Ġpet ites", + "Ġpetite s", + "Ġpetit es", + "ĠDec ay", + "Ġun initialized", + "priv ileged", + "Ġm bedtls", + "å¤ĩ 注", + "Ġ ^.", + "Ġ^ .", + "Ġec static", + "D etroit", + "Det roit", + "Ġp arten", + "Ġpart en", + "Ġpar ten", + "Ġparte n", + "Ġsou venir", + ".get Login", + ".getLog in", + "моÑĤ ÑĢ", + "мо ÑĤÑĢ", + "en ção", + "ĠmÃŃn imo", + "ĠAccess ed", + "ĠAcc essed", + "r ió", + "ri ó", + "M ic", + "Mi c", + "ĠV ocal", + "ĠVo cal", + "ĠVoc al", + ".Set String", + "Ġmens ajes", + "Ġmensaje s", + "åĢ į", + "Ġattr avers", + "ĠA ph", + "ĠAp h", + "Ġ ');čĊ", + "Ġ' );čĊ", + "Ġ') ;čĊ", + "Ġ'); čĊ", + "ü nde", + "ün de", + "ünd e", + "Ġenchant ed", + "Ġench anted", + "ĠRoot State", + "ĠCLOSE D", + "ĉ ĉĉĉĉĉĉĉčĊ", + "ĉĉ ĉĉĉĉĉĉčĊ", + "ĉĉĉĉ ĉĉĉĉčĊ", + "ĉĉĉ ĉĉĉĉĉčĊ", + "ĉĉĉĉĉ ĉĉĉčĊ", + "ĉĉĉĉĉĉ ĉĉčĊ", + "ĉĉĉĉĉĉĉĉ čĊ", + "ĉĉĉĉĉĉĉ ĉčĊ", + "Ġcal iente", + "or ris", + "orr is", + "Ġphysic ists", + "Ġphysicist s", + "h wnd", + "hw nd", + "_ vi", + "_v i", + "Ġráp ido", + "Ġcapital ized", + "Ġcapitalize d", + "ed By", + "Ġmach ining", + "Ġh ubby", + "Ġhub by", + "ĠSt acy", + "ĠSta cy", + ". Bus", + ".B us", + "d rink", + "dr ink", + "H ur", + "Hu r", + "Ġpr opia", + "Ġprop ia", + "Unit Test", + "Ġmiscon ception", + "_ _));Ċ", + "__ ));Ċ", + "__) );Ċ", + "__)) ;Ċ", + "/ dc", + "/d c", + "ĠMay weather", + "_m C", + ". createFrom", + ".create From", + "ĠQ Painter", + "ro psych", + "rops ych", + "inn itus", + "a yas", + "ay as", + "aya s", + "Ġg eg", + "Ġge g", + "( dw", + "(d w", + "Ġus ado", + "Ġusa do", + "Ġtr ickle", + "Ġtrick le", + "Ġann ihil", + "Ġanni hil", + "ĠP asta", + "ĠPa sta", + "ĠPast a", + "ĠPas ta", + "Ġ ++Ċ", + "Ġ+ +Ċ", + "Ġ++ Ċ", + "(Expected Conditions", + ".post Value", + "i cap", + "ic ap", + "ica p", + "ĠDon etsk", + "_ soup", + "_s oup", + "_so up", + "- publish", + "-p ublish", + "ĠP b", + "m entions", + "ment ions", + "mention s", + "AC CEPT", + ". Pull", + ".P ull", + ", âĢĻâĢĻ", + ",âĢĻ âĢĻ", + "Ġret arded", + "Ġretard ed", + "_ ATOM", + "_AT OM", + "ĠTerm inator", + "ĠTermin ator", + "- court", + "-c ourt", + "-co urt", + "ĠCLLocation Coordinate", + "Ġrev erence", + "Ġreve rence", + "Ġrever ence", + "ĠS SC", + "ĠSS C", + "ut ely", + "ute ly", + "ĠW ON", + "ĠG SL", + "ĠGS L", + "f rei", + "fr ei", + "fre i", + ".get Longitude", + ".getLong itude", + "Ġopen FileDialog", + ".B utter", + ".But ter", + "- important", + "-import ant", + "_M ANY", + "_MAN Y", + "_MA NY", + "ĠG ong", + "ĠGo ng", + "ĠGon g", + "âĢľ How", + "Ġg orge", + "Ġgor ge", + "= msg", + "=m sg", + "ĠE zek", + "ĠEz ek", + "create Command", + ": checked", + "Ġinfo graphic", + "Ġinf ographic", + ". WEST", + ".W EST", + "D irs", + "Dir s", + "Di rs", + "Ġgu arda", + "Ġguard a", + "Ġguar da", + "Ġbee tle", + "Ġbeet le", + "< small", + " Loading", + "_ mA", + "_m A", + ".get Random", + "b lings", + "bl ings", + "bling s", + "Ġche eses", + "Ġcheese s", + "Ġchees es", + "t ti", + "tt i", + ". âĢ¢", + "ĠBurg ess", + "ender it", + "ende rit", + ". ',čĊ", + ".' ,čĊ", + ".', čĊ", + "(\" \"+", + "(\"\" +", + "a cb", + "ac b", + "% p", + "index ed", + "inde xed", + "_ predicate", + "_p redicate", + "_pred icate", + "nes ia", + "Ġb ied", + "Ġbi ed", + "ĠC IT", + "ĠCI T", + "( Pos", + "(P os", + "_ radi", + "_r adi", + "_rad i", + "_ra di", + "ä»· æł¼", + "B iz", + "Bi z", + "ĠAdoles cent", + "Ġv iên", + "Ġvi ên", + "c ycl", + "cy cl", + "_ Cancel", + "_C ancel", + "Ġcon clusive", + "Ġconclus ive", + "Ġconcl usive", + "Ġappell ate", + "Ġappel late", + "inform atics", + "S J", + "Ġel ective", + "Ġelect ive", + "role Id", + "Fetch er", + "ĉ Command", + "(\" (%", + "(\"( %", + "Ġf art", + "Ġfa rt", + "Ġfar t", + "I LA", + "IL A", + "get Block", + "A USE", + "AU SE", + "Ġ дан", + "Ġд ан", + "Ġда н", + "ĠAr te", + "ĠArt e", + "Ġnot ifying", + "Ġnotify ing", + "Ġg ele", + "Ġge le", + "Ġgel e", + ". same", + ".s ame", + ".sa me", + ".sam e", + "ĠRe gel", + "ĠReg el", + "Ġ BaÅŁ", + "ĠB aÅŁ", + "ĠBa ÅŁ", + ". creation", + ".c reation", + "Ġ VN", + "ĠV N", + "_ community", + "_comm unity", + "Ġuns ustainable", + "S EX", + "SE X", + "Ġgrid Size", + "res cia", + "avers able", + "(', ')[", + "(',' )[", + "(',') [", + "ĠPh elps", + "á»ķ i", + "ANCE LED", + "ANCEL ED", + "- IS", + "-I S", + ".run ners", + ".runner s", + "ĠSt okes", + "ĠSto kes", + "ĠStoke s", + ". Produ", + ".P rodu", + ".Pro du", + ".Pr odu", + "Ġwh ipping", + "Ġwhip ping", + "_ac quire", + "Ġinvestig ación", + "f ried", + "fr ied", + ".copy With", + "ĠHard cover", + "- Se", + "-S e", + "áŀ¶ áŀ", + "inv itation", + "les ai", + "ĠD orm", + "ĠDo rm", + "ĠDor m", + "ĠÑģпиÑģ ка", + "Ġconcaten ated", + "Ġconcatenate d", + "o phil", + "op hil", + "oph il", + "Ġth inker", + "Ġthink er", + "Ġthin ker", + "/font awesome", + "ĠLe opard", + "ĠLeo pard", + "Ġ\" /\");Ċ", + "Ġ\"/ \");Ċ", + "Ġ\"/\" );Ċ", + "Ġresidual s", + "ĠMicro wave", + "ĠMic rowave", + "Ġcon forme", + "Ġconform e", + "th rop", + "thr op", + "thro p", + "Ġdis emb", + "Ġdi semb", + "Ġdise mb", + "ĠO MG", + "ĠOM G", + "ĠDisc ipline", + "ĠAc robat", + "/ repository", + "/re pository", + "/repos itory", + "d fa", + "df a", + "_ MED", + "_M ED", + "_ME D", + "buf io", + "Ġméth ode", + "_H OLD", + "i asi", + "ia si", + "ias i", + "_ legacy", + "_leg acy", + ") ččĊ", + "æ£ Ģ", + "Get ProcAddress", + "Ġy ay", + "Ġya y", + "ot ence", + "ote nce", + "oten ce", + "order id", + "orde rid", + "- tw", + "-t w", + "Ġd early", + "Ġdear ly", + "In coming", + "Inc oming", + "/ il", + "/i l", + "Ġneuro p", + "Ġneu rop", + "Ġneur op", + "u cz", + "uc z", + ") ;čččĊ", + "); čččĊ", + "ĠInnov ative", + "Ġpro fund", + "Ġprof und", + "ig mat", + "igma t", + "igm at", + "Selection Mode", + "re levant", + ". GO", + ".G O", + "Ġbru ises", + "Ġs ach", + "Ġsa ch", + "Ġsac h", + "o def", + "od ef", + "ode f", + "Ġre imb", + "Ġreim b", + "/ desktop", + "/d esktop", + "- spot", + "-s pot", + "-sp ot", + "un dance", + "und ance", + "unda nce", + "undan ce", + "Ent ropy", + "Entr opy", + "\\ core", + "Ġs uger", + "Ġsu ger", + "Ġsug er", + "Ġ Mvc", + "ĠM vc", + "ĠGN OME", + "_ indx", + "_in dx", + "_i ndx", + "_ind x", + "ĠYY STYPE", + "ĠYYS TYPE", + "ĠMat lab", + "ĠC IF", + "ĠCI F", + "Ġ *))", + "Ġ* ))", + "Ġ*) )", + "Ġproduct List", + "Ġ Alright", + "ĠAl right", + "ace mark", + "ac emark", + "ÑĤ ив", + "ÑĤи в", + "mod ification", + "int ernational", + "inter national", + "intern ational", + "Ġh omers", + "Ġhome rs", + "Ġhom ers", + "Ġho mers", + "Ġhomer s", + "Ġd icts", + "Ġdi cts", + "Ġdict s", + "Ġdic ts", + "ĠQ Font", + ". SQLite", + ".SQL ite", + "Ġtransplant ation", + "ĠMessageBox Button", + "ĠEl ves", + "' ]])Ċ", + "'] ])Ċ", + "']] )Ċ", + "(Q Icon", + "Ġcin emas", + "Ġcinema s", + "Ġcine mas", + "CO ORD", + "- China", + "-Ch ina", + "Ġkh ẩu", + "æĪij çļĦ", + "Ġsk ulls", + "Ġskull s", + "Ġpain staking", + "Ġpains taking", + "f ce", + "fc e", + ".XR Label", + "Ġ specifier", + "Ġspec ifier", + "Ġprefer ring", + "Ġpref erring", + "/ activity", + "( Photo", + "á lt", + "ál t", + ". lot", + ".l ot", + ".lo t", + "' '.", + "'' .", + "an nonce", + "ann once", + "annon ce", + "anno nce", + ".google code", + "- pdf", + "-p df", + "ĠP oke", + "ĠPo ke", + "ĠPok e", + "_ ACL", + "_A CL", + "_AC L", + "Ġend owed", + "dis cover", + "disc over", + ".o mg", + ".om g", + "Ġwood land", + ". Magic", + ".M agic", + "Ġvol ont", + "Not Allowed", + "Ġc have", + "Ġch ave", + "Ġcha ve", + "B MW", + "BM W", + "',' =',", + "','= ',", + "ĠS IX", + "ĠSI X", + "æĪij 们", + "Ġko sher", + "Ġkos her", + "Ġas piration", + "Ġaspir ation", + "Ġasp iration", + "i ntl", + "in tl", + "int l", + "_ref ptr", + "' +Ċ", + "'+ Ċ", + "ment or", + "men tor", + ". club", + ".c lub", + ".cl ub", + "Window State", + ". ARR", + ".A RR", + ".AR R", + "Ġ zza", + "Ġz za", + "Ġzz a", + "Ġmessage Type", + ". equ", + ".e qu", + ".eq u", + "T hor", + "Th or", + "Tho r", + "Ġin just", + "Ġinj ust", + "Ġg ums", + "Ġgu ms", + "Ġgum s", + "Ġborder Side", + "/ ////", + "// ///", + "//// /", + "/// //", + "ĠTrans mit", + "Ġbuf size", + "Ġh ak", + "Ġha k", + "Ġ ellas", + "Ġel las", + "Ġell as", + "Ġella s", + "R ANDOM", + "RAND OM", + "ĉ mc", + "ĉm c", + "Ġp ea", + "Ġpe a", + "e ko", + "ek o", + "document o", + "Ġhyster ia", + "Ġare nas", + "Ġar enas", + "Ġaren as", + "Ġarena s", + "Ġgun men", + "Ġm ike", + "Ġmi ke", + "Ġmik e", + "Ġimp unity", + "at isation", + "atis ation", + "_ Zero", + "_Z ero", + "_COMP ANY", + "ĠG ors", + "ĠGo rs", + "ĠGor s", + "Ġuse Class", + "( redis", + "(r edis", + "(re dis", + "(red is", + "ĠRUN NING", + "ĠB air", + "ĠBa ir", + "ĠBai r", + "ve lte", + "vel te", + "velt e", + "Ġ ','.", + "Ġ', '.", + "Ġ',' .", + "а ÑĤÑĮÑģÑı", + "аÑĤÑĮ ÑģÑı", + "ö st", + "ös t", + "encode URIComponent", + "_ restrict", + "_re strict", + "Ġdec als", + "Ġ Pedido", + "ĠP edido", + "ĠPed ido", + "Ġalter cation", + "Dis plays", + "Display s", + "Disp lays", + "ĠApp licants", + "ĠApplicant s", + "C US", + "CU S", + "Text area", + "ĠAng ola", + ". future", + ".f uture", + "ĠU SHORT", + "ĠUS HORT", + "Ġsuppress ing", + "Ġsupp ressing", + "Ġset zen", + "AP olynomial", + "Ġt och", + "Ġto ch", + "Ġtoc h", + "Ġhall mark", + "Ġ $$$", + "Ġ$ $$", + "Ġ$$ $", + "ĠCHAR SET", + ". rpm", + ".r pm", + "ĠD ich", + "ĠDi ch", + "---- ----------------", + "-------- ------------", + "---------------- ----", + "------------ --------", + "----- ---------------", + "---------- ----------", + "------ --------------", + "----------- ---------", + "------------- -------", + "------- -------------", + "--------- -----------", + "--------------- -----", + "-------------- ------", + "_ parm", + "_p arm", + "_par m", + "_pa rm", + "è¿ ĺ", + "a cciones", + "acc iones", + "acci ones", + "accion es", + "h ait", + "ha it", + "hai t", + "WARD ED", + "WAR DED", + "_ routing", + "_r outing", + "_ro uting", + "ĠN OM", + "ĠNO M", + "Ġen clave", + "ĠL otto", + "ĠLot to", + "ĉ fr", + "ĉf r", + "complex Content", + "ĠBall ard", + "k ube", + "ku be", + "/ win", + "/w in", + ".getColumn Model", + "_RE PLACE", + "Header Value", + "Ġest udiantes", + "Ġ apis", + "Ġa pis", + "Ġap is", + "Ġapi s", + "Ġb pm", + "Ġbp m", + "Ġ TypeName", + "ĠType Name", + "And Get", + "r ita", + "ri ta", + "rit a", + "Pl ans", + "Plan s", + "> Note", + ">N ote", + ">No te", + "Ġfet isch", + "Ġt oned", + "Ġto ned", + "Ġton ed", + "Ġtone d", + "_ goto", + "_g oto", + "_go to", + "on sense", + "ons ense", + "Ġm olds", + "Ġmo lds", + "Ġmol ds", + "Ġmold s", + "Ġinfiltr ation", + "ĠGuerr ero", + "ub bo", + "ubb o", + "c ki", + "ck i", + "( $(\".", + "($ (\".", + "($( \".", + "_ activities", + "_act ivities", + "_activ ities", + "( changes", + "(ch anges", + "(chan ges", + "(change s", + "Ġof App", + "ĠKe pler", + "ĠD emp", + "ĠDe mp", + "ĠDem p", + "ĠCont inent", + "ĠContin ent", + ". Ticks", + ".T icks", + ".Tick s", + "Ġ Unsigned", + "ĠUn signed", + "ĠUns igned", + "ĠJah res", + "ĠJahr es", + "ĠJahre s", + "Ġfresh men", + "ĠArch ived", + "ĠArchive d", + "ĠкоÑĤоÑĢ Ñĭй", + "Ġ' ::", + "Ġ': :", + "T utorial", + "C c", + "Ġtable LayoutPanel", + "from Json", + ". levels", + ".level s", + "_trans ient", + "Ġendors ing", + "Ġ DIC", + "ĠD IC", + "ĠDI C", + "l auf", + "la uf", + "Ġsh red", + "Ġshr ed", + "_E MIT", + "_EM IT", + "ific antly", + "ificant ly", + "A LA", + "AL A", + "/ proto", + "/pro to", + "/pr oto", + "Ġnarr owing", + "Ġnarrow ing", + "Ġnar rowing", + "U tc", + "Ut c", + "F actors", + "Fact ors", + "Factor s", + "Fac tors", + "Fa ctors", + "Ġsent ient", + "æŀ IJ", + "lix ir", + "ĠC ROSS", + "m eteor", + "met eor", + "Ġgr oin", + "Ġgro in", + "Ġ mdb", + "Ġm db", + "Ġmd b", + "ĠRot terdam", + "Ġcom ida", + "ĠOp Code", + "Ġ DefaultValue", + "ĠDefault Value", + "Permissions Result", + "Ġheter ogeneous", + "Ġm oot", + "Ġmo ot", + "Ġmoo t", + "Ġde ceived", + "Ġdece ived", + "Ġdeceive d", + "-in dependent", + "ĠObject OutputStream", + "Ġover power", + ". dup", + ".d up", + "Ġ ldb", + "Ġl db", + "Ġld b", + "Ġdomestic ally", + "Ġdomest ically", + "Ġbe stellen", + "Ġbest ellen", + "Ġbeste llen", + "Ġ lov", + "Ġl ov", + "Ġlo v", + "ĠContract ors", + "ĠContr actors", + "ĠContractor s", + "Tri angles", + "Triangle s", + "Ġfod der", + "Ġfil mes", + "Ġfilm es", + "Ġfilme s", + "ä¼ ģ", + "Ġrev olver", + "Startup Script", + "/ validation", + "ĠResource Type", + "i ÅŁ", + "ĠL az", + "ĠLa z", + "f ef", + "fe f", + "Ġl stm", + "Ġlst m", + "Ġls tm", + "{ *", + ". attachment", + ".attach ment", + ".att achment", + ". hits", + ".h its", + ".hit s", + "e with", + "ew ith", + "D OG", + "DO G", + "Al abama", + "Ġmedium s", + "Ġmedi ums", + ".m Context", + "- cols", + "-c ols", + "-col s", + "-co ls", + "åı ĭ", + ". notice", + ".not ice", + "Ġat tn", + "Ġatt n", + "ĠP acking", + "ĠPac king", + "ĠPack ing", + "Ġ Ln", + "ĠL n", + "_COM PLEX", + "_COMP LEX", + "/ Users", + "/User s", + ".save txt", + ".sav etxt", + "ĠR ounds", + "ĠRo unds", + "ĠRound s", + "ĠRou nds", + "?,?, ?,?,", + "Ġin gl", + "Ġing l", + "Ġ ROC", + "ĠR OC", + "ĠRO C", + "_ female", + "_f emale", + "_fe male", + "ĠSt ard", + "ĠStar d", + "ĠSta rd", + "] ];", + "]] ;", + "Ġwrest lers", + "Ġwrestler s", + "Ġtorrent s", + "Ġs inh", + "Ġsi nh", + "Ġsin h", + " ĊĊ", + "Ċ Ċ", + "ë³ µ", + "s ense", + "sen se", + "how ever", + ". Physics", + ".Ph ysics", + "Inf rastructure", + "ĠS acr", + "ĠSa cr", + "ĠSac r", + "F el", + "Fe l", + "ĠD ISTRIBUT", + "é ments", + "ém ents", + "ément s", + "ĠValid ates", + "ĠValidate s", + "#### ########################################################", + "################################ ############################", + "############ ################################################", + "################################################ ############", + "############################ ################################", + "######################################################## ####", + "Ġ |/", + "Ġ| /", + "Ġe sl", + "Ġes l", + "Ġré seau", + "ĠB ip", + "ĠBi p", + "BY TES", + "BYTE S", + "_W ATER", + "T urning", + "Turn ing", + "Tur ning", + "E LS", + "EL S", + "Ġj uxtap", + "Ġlesb ische", + "ý ch", + "( Unknown", + "(Un known", + "N eo", + "Ne o", + "@ JsonProperty", + "@Json Property", + "Ġal umnos", + "Ġalum nos", + "Ġalumno s", + "ĠRaq qa", + "im ei", + "ime i", + ".get Bounds", + ".getB ounds", + ".Mouse EventHandler", + ".MouseEvent Handler", + "# ######", + "## #####", + "#### ###", + "### ####", + "##### ##", + "###### #", + "Generic Type", + "/ cms", + "/c ms", + "/cm s", + "Ġturn o", + "Ġtur no", + "Ġ мин", + "Ġм ин", + "Ġми н", + "Ġfolk lore", + "ĠE vo", + "ĠEv o", + "Ġconduct ivity", + "Ġle ben", + "Ġgear box", + "- vs", + "-v s", + "Ġ ÏĨ", + "ĠÏ Ĩ", + "Ġdrink ers", + "Ġ conexao", + "Ġcon exao", + "Ġconex ao", + "ĠTe eth", + "ĠTee th", + "Ġget Arguments", + "ĠR AT", + "ĠRA T", + "ent ious", + "enti ous", + "E duc", + "Ed uc", + "+ W", + "ĠInstitution al", + "ĠInstitut ional", + "ĠB ord", + "ĠBo rd", + "ĠBor d", + "is Equal", + "( pwd", + "(p wd", + "Ġign ited", + "Ġignite d", + "ĠR ousse", + "ĠRou sse", + "Ġimpact ful", + "ĠM alk", + "ĠMal k", + "ĠMa lk", + "Ġg eral", + "Ġge ral", + "Ġger al", + "ĠP ivot", + "Ġa zt", + "Ġaz t", + "Ġcsv file", + "ĠR ope", + "ĠRo pe", + "ĠS OLUTION", + "ĠSOL UTION", + "ĠAr bitrary", + "ĠArbit rary", + "Ġl etto", + "Ġlet to", + "Ġlett o", + ".Mouse Adapter", + "Ġ }}}", + "Ġ} }}", + "Ġ}} }", + "ĠSa ilor", + "ĠSail or", + "ĠSai lor", + "d era", + "de ra", + "der a", + "P utting", + "Put ting", + "Ġconcent rates", + "Ġconcentr ates", + "Ġconcentrate s", + "Ġauth Domain", + "âĢĿ çļĦ", + "-f inals", + "-final s", + "-fin als", + ", strlen", + ",str len", + ",st rlen", + "Mu on", + "ĠOrd inary", + "fire fox", + "ĠLa TeX", + "ĠH und", + "ĠHun d", + "ĠHu nd", + "engine ering", + "/ blue", + "/b lue", + "/bl ue", + "ed TextBox", + "(\" \");", + "(\"\" );", + "(\"\") ;", + "ĠC DDL", + "ĠCD DL", + "k ept", + "ke pt", + "Ġ GetString", + "ĠGet String", + "K ir", + "Ki r", + "() ='", + "ĠO CD", + "ĠOC D", + "ant ium", + "anti um", + "$ menu", + "$m enu", + "ĠAppalach ian", + "Secret ary", + "ë¥ ĺ", + "ี ย", + "Sem antic", + "Ġ *[", + "Ġ* [", + "e stone", + "es tone", + "est one", + "esto ne", + "ung kin", + "Max Y", + "- tone", + "-t one", + "-to ne", + "-ton e", + "\" };čĊ", + "\"} ;čĊ", + "_ Part", + "_P art", + "< Member", + "ĊĊ", + "'> ĊĊ", + "'>Ċ Ċ", + "L ic", + "Li c", + "ĠMir age", + "ĠMi rage", + "ĠAssembly FileVersion", + "Te V", + "ĠValue EventListener", + "-s olving", + "T ho", + "Th o", + "rou lette", + "_ WP", + "_W P", + "Ġunint errupted", + "Ġfield Type", + ". Typed", + ".T yped", + ".Type d", + "Ġa mour", + "Ġam our", + "Ġamo ur", + "Ġmock ery", + "Ġmocker y", + "( vol", + "(v ol", + "(vo l", + "ĠSub committee", + "ĠR uf", + "ĠRu f", + "e rox", + "er ox", + "ero x", + ":UIButtonType Custom", + "Ġ Blur", + "ĠBl ur", + "ĠBlu r", + "Ġwy kon", + "n ces", + "nc es", + "nce s", + "ASH BOARD", + "! !\");Ċ", + "!! \");Ċ", + "Ġmurder ers", + "Ġmurderer s", + ". daily", + ".d aily", + ".da ily", + "ĠDI AG", + "j ing", + "ji ng", + "jin g", + "Ġdol phin", + "Ġl òng", + "Ġb ö", + "ĠV ocabulary", + ".St Object", + "' )\">", + "') \">", + "')\" >", + "Ġz un", + "Ġzu n", + "Ġscrim mage", + "tr éal", + "ĠL ig", + "ĠLi g", + "[ vi", + "[v i", + "C ole", + "Col e", + "Co le", + "Ġfrost ing", + ". Players", + ".P layers", + ".Pl ayers", + ".Player s", + ".Play ers", + "- translate", + "-trans late", + "Fe els", + "Feel s", + "Fee ls", + "=\\\" /", + "=\\ \"/", + ".Butter Knife", + "Ġ? >;Ċ", + "Ġ?> ;Ċ", + "Ġ avi", + "Ġa vi", + "Ġav i", + "in nie", + "inn ie", + ". Failure", + ".F ailure", + ".Fail ure", + "Ġsp indle", + "Ġspin dle", + "Configuration Exception", + "_ hop", + "_h op", + "Ġpos ição", + "Ġposi ção", + "Ġ Await", + "ĠA wait", + "ĠAw ait", + "UIImage PickerController", + "ĉ day", + "ĉd ay", + "Ġge nom", + "Ġgen om", + "C ab", + "Ca b", + "ĠÑĢ езÑĥлÑĮÑĤаÑĤ", + "ĠÑĢезÑĥлÑĮÑĤ аÑĤ", + "OR IGINAL", + "Ġejac ulation", + "( tcp", + "(t cp", + "(tc p", + "SE COND", + "SEC OND", + "Ġt onic", + "Ġto nic", + "Ġton ic", + "Ġ ListBox", + "ĠList Box", + "Ġ ĉĉĊ", + "Ġĉ ĉĊ", + "Ġĉĉ Ċ", + "( )>Ċ", + "() >Ċ", + "()> Ċ", + "Ġqu atre", + "Ġquat re", + "Ġqua tre", + "ượ ng", + "with Errors", + ". Maybe", + ".M aybe", + ", âĢ¦", + "token Id", + "_UN DEF", + "Ġfresh ness", + "ĠAmendment s", + "ĠAmend ments", + ".map box", + ". CV", + ".C V", + "( blog", + "(b log", + "(bl og", + "_get time", + ". quest", + ".q uest", + ".qu est", + "s parse", + "sp arse", + "spar se", + "Ġre sale", + "Ġres ale", + "Ġenthusi astically", + "Ġenthusiastic ally", + "Ġenthusiast ically", + "ĠProstit utas", + "W a", + "C argo", + "Car go", + ". Parcelable", + ".Parcel able", + "S ENSOR", + "SEN SOR", + "SENS OR", + "ĠR yu", + "ĠRy u", + "La ughs", + "Laugh s", + "_ Native", + "_N ative", + "/ pg", + "/p g", + "y sts", + "yst s", + "ys ts", + "Ġphot oc", + "Ġphoto c", + "ç® Ģ", + "ad opt", + "ado pt", + ". species", + ".s pecies", + ".sp ecies", + ".spec ies", + "conc iliation", + "Adjust ed", + "Adj usted", + ".Firebase Auth", + "ut tle", + "utt le", + "ord ination", + "ordin ation", + "Ġm unch", + "Ġmun ch", + "ĠS take", + "ĠSt ake", + "ĠSta ke", + ". ping", + ".p ing", + ".pi ng", + ".pin g", + "an ker", + "ank er", + "anke r", + "(QString Literal", + "Ġsub script", + "Ġsubs cript", + "Ġsubscri pt", + "Ġ ĠĉĊ", + "ĠĠ ĉĊ", + "ĠĠĉ Ċ", + "ĠM CC", + "ĠMC C", + "_ Cmd", + "_C md", + "se xy", + "sex y", + "i ou", + "io u", + "ĠM ANY", + "ĠMA NY", + "ĠMAN Y", + "Ġn anny", + "Ġnan ny", + "T RAIN", + "TR AIN", + "TRA IN", + "Ġflour ishing", + "Ġflourish ing", + "ĠW atches", + "ĠWatch es", + "ĠWat ches", + "ĠQ Map", + "ĠF erm", + "ĠFe rm", + "ĠFer m", + "Ġw asm", + "Ġwas m", + "Ġwa sm", + "ĠA bed", + "ĠAb ed", + "ĠAbe d", + "_ UD", + "_U D", + "ĠG lasses", + "ĠGl asses", + "ĠGlass es", + "ĠGlas ses", + "+ v", + "Att end", + ". Chain", + ".Ch ain", + "Ġdec ency", + "Ġdece ncy", + "ĠSup plementary", + "ĠSupplement ary", + "h unter", + "hunt er", + "- txt", + "-t xt", + "Ġ\" }\";Ċ", + "Ġ\"} \";Ċ", + ".set WindowTitle", + "( \"", + "Ġmasc ara", + "( Profile", + "åĬ Łèĥ½", + "åĬŁ èĥ½", + "im ité", + "imit é", + "imi té", + "Ġwild fires", + "Ġwildfire s", + "- ROM", + "-R OM", + ".is On", + "( groupId", + "(group Id", + "Re pair", + "Rep air", + "accum ulate", + "Ġ< \",", + "Ġhand written", + "Ġach eter", + "Ġache ter", + "ĠM GM", + "ĠMG M", + "ĠIr ma", + "-> {_", + "->{ _", + "g ee", + "ge e", + "c riminal", + "cr iminal", + "Ġèĭ¥ è¦ģ", + "Ġmoment arily", + "\" )!=", + "\") !=", + "_ lit", + "_l it", + "_li t", + "Ġexpires In", + ". \").", + ".\" ).", + ".\") .", + "éķ¿ 度", + "Ġfr ække", + "v lc", + "vl c", + "Ġor bs", + "Ġorb s", + ") ,$", + "), $", + "Ġvent ured", + "Ġventure d", + "/ >\\", + "/> \\", + "ch arm", + "char m", + "cha rm", + "N uitka", + "el dig", + "eld ig", + "ato nin", + "aton in", + "W itness", + "- lat", + "-l at", + "-la t", + "Ġset Hidden", + "Ġrel ics", + "Ġreli cs", + "Ġrelic s", + "Ġcons ulate", + "Ġconsul ate", + ". IGNORE", + "\" After", + "\"A fter", + "Ġs etAddress", + "Ġset Address", + "Ġbeste ht", + "Ġ' ')ĊĊ", + "Ġ'' )ĊĊ", + "Ġ'') ĊĊ", + "Ġ'')Ċ Ċ", + ".x axis", + "Ġser ão", + "Ġmis led", + "Ġmi sled", + "_UN IFORM", + "ĠV IA", + "ĠVI A", + "in cr", + "inc r", + "Ġzen ith", + "Ġvis cosity", + "Ġvisc osity", + "Ġthin ly", + ".get SharedPreferences", + ". ErrorCode", + ".Error Code", + "\" ),\"", + "\") ,\"", + "\"), \"", + "ĠMillion en", + "ĠMilli onen", + "Ġ/ >)Ċ", + "Ġ/> )Ċ", + "Scroll Indicator", + "-se eking", + "ĠPOLIT ICO", + "as ca", + "asc a", + "_ rl", + "_r l", + "N avig", + "Nav ig", + "Na vig", + "(full file", + "Ġsol itude", + "Ġ juven", + "Ġju ven", + "Ġha uling", + "Ġhaul ing", + "ĠMac ros", + "ĠMacro s", + "ĠG ry", + "ĠGr y", + "Ġexerc itation", + "ĠATT ACK", + "Tick Count", + "Ġ rites", + "Ġr ites", + "Ġrit es", + "Ġri tes", + "Ġd oe", + "Ġdo e", + "Particle System", + "Ġ slu", + "Ġs lu", + "Ġsl u", + "Window Text", + "Ġ ClassName", + "ĠClass Name", + "Ġs lander", + "Ġsl ander", + "Ġsla nder", + "ĉ Port", + "ĉP ort", + "j ong", + "jo ng", + "jon g", + "? a", + ".D ial", + ".Di al", + "âĢĶ at", + "âĢĶa t", + "$ objPHPExcel", + "$obj PHPExcel", + "Ġso ar", + "E NN", + "EN N", + "appe ared", + "appear ed", + "Ġquot id", + "Ġquo tid", + "e machine", + "em achine", + "ema chine", + "Ġ nip", + "Ġn ip", + "Ġni p", + "Ġmicro time", + "ĠAl ma", + "; !", + "---------------- --------------------------------------------------------------------------------", + "-------------------------------- ----------------------------------------------------------------", + "---------------------------------------------------------------- --------------------------------", + "------------------------------------------------ ------------------------------------------------", + "---------------------------------------------------------------------------- --------------------", + "-------------------------------------------------------------------------------- ----------------", + "-------------------- ----------------------------------------------------------------------------", + "ĠP assage", + "ĠPass age", + "ĠPas sage", + "Ġdump sters", + "Ġdumpster s", + "Ġdumps ters", + "Ġ Exclude", + "ĠEx clude", + "ĠExc lude", + "Ġsuggest ive", + "ĠCircularProgress Indicator", + "_ clr", + "_c lr", + "_cl r", + "Array Type", + "IL LA", + "ILL A", + "Elapsed Time", + "Dr iven", + "Drive n", + "Ġresource Name", + "ĠG arrison", + "ĠGarr ison", + "se rir", + "ser ir", + "- ahead", + "-a head", + "Ġp innacle", + "ĠEs presso", + "S parse", + "Sp arse", + "Ġas says", + "Ġass ays", + "Ġassay s", + "ĠGirl friend", + "i mid", + "im id", + "imi d", + "] ='\\", + "]= '\\", + "]=' \\", + "ONG LONG", + "ONGL ONG", + "Ġportray ing", + "L ane", + "La ne", + "Ġb úsqueda", + "Ġrein forcements", + "Ġreinforce ments", + "Ġreinforcement s", + "ĠSpread sheet", + "ĠArray Collection", + ", arr", + ",a rr", + "light box", + "ic ana", + "ica na", + "ican a", + "< \"", + "build ers", + "builder s", + "K id", + "Ki d", + "ĠMat SnackBar", + "EX PR", + "EXP R", + "od cast", + "ĠFoundation s", + "ĠFound ations", + "Ġ inds", + "Ġin ds", + "Ġi nds", + "Ġind s", + "=' ${", + "='$ {", + "F izz", + "Fi zz", + "- functional", + "-function al", + "( workspace", + "(work space", + "Ġstem med", + "_ patches", + "_p atches", + "_patch es", + "_pat ches", + "ĠJar vis", + "RE ADING", + "READ ING", + "Ġdisrespect ful", + "ĠQ Dom", + "Ġ$ {Ċ", + "Ġ${ Ċ", + "e status", + "es tatus", + "est atus", + "Re ached", + "Reach ed", + "! .ĊĊ", + "!. ĊĊ", + "I LT", + "IL T", + "ĠN DEBUG", + "ĠC ourage", + "ĠCour age", + "ĠCou rage", + "birth date", + "ĠT ing", + "ĠTi ng", + "ĠTin g", + "Ġutil izado", + "Ġutiliz ado", + "Ġutiliza do", + "án chez", + "Out door", + "Ġhand guns", + "Ġhandgun s", + "Ref Count", + "É Ļ", + "r omo", + "ro mo", + "rom o", + "Ġt ts", + "Ġtt s", + ". She", + ".S he", + ".Sh e", + "Ġ Pane", + "ĠP ane", + "ĠPan e", + "ĠPa ne", + "ãĢij, ãĢIJ", + "ĠIO CTL", + "ĠIOC TL", + "/ black", + "/b lack", + "/bl ack", + "in scription", + "ins cription", + "Ġbi opsy", + "Ġbio psy", + "Ġ TimeInterval", + "ĠT imeInterval", + "ĠTime Interval", + ".Test Check", + "ĠGUI Style", + "Ġ Capability", + "ĠCap ability", + "ĠBei trag", + "ĠBeit rag", + "don nees", + "T reatment", + ". backup", + ".back up", + "Ġsign ings", + "Ġsig nings", + "Ġsigning s", + "Ġsignin gs", + "ĠB oca", + "ĠBo ca", + "d rm", + "dr m", + ". MAIN", + ".M AIN", + "Ġgo ede", + "Ġgoed e", + "Ġ Markup", + "ĠMar kup", + "ĠMark up", + "G REE", + "GR EE", + "GRE E", + "ĠBase Service", + ". Creator", + ".C reator", + "Ġj ails", + "Ġja ils", + "Ġjail s", + "ĠK ahn", + "ĠKa hn", + "ĠKah n", + "Ip Address", + "AC HI", + "ACH I", + "Ġin hibited", + "Ġinhib ited", + "Ġinhibit ed", + "Ġ@ $_", + "Ġ@$ _", + "ĠAs sass", + "ĠAss ass", + "Ġenv iado", + "Ġenvi ado", + "Her oes", + "Hero es", + "ÐŁ еÑĢ", + "ĠM aven", + "ĠMa ven", + ". ls", + ".l s", + "Ġ ive", + "Ġi ve", + "Ġiv e", + "| RF", + "|R F", + "Ġresize Mode", + "Ġrum pe", + "_ attachments", + "_attach ments", + "_attachment s", + "T U", + "Ġtact ile", + "Ġtac tile", + "Attempt ing", + "Ġro bin", + "Ġrob in", + "y aw", + "ya w", + "Ġmerc enaries", + "ĠHab itat", + "ĠHabit at", + "end date", + "Ġ oxy", + "Ġo xy", + "Ġox y", + "ĉ Random", + "ĉR andom", + "o hon", + "oh on", + "oho n", + "Is Null", + "ĠValidation Result", + "ãĥ ļ", + "um bed", + "umb ed", + "p pv", + "pp v", + "Ġ arp", + "Ġa rp", + "Ġar p", + "ich ick", + "ichi ck", + "_r nn", + "ĠT FT", + "ĠTF T", + "Tex Image", + "\" On", + "Ġ Sampler", + "ĠS ampler", + "ĠSam pler", + "ĠSample r", + "ĠSamp ler", + "t opl", + "to pl", + "top l", + "Ġj ane", + "Ġja ne", + "Ġjan e", + "y ling", + "yl ing", + "ĠUN ICODE", + "Tab Index", + "< {Ċ", + "<{ Ċ", + "s uspend", + "sus pend", + "uv ian", + ", application", + "ол иÑĩеÑģÑĤво", + "y at", + "ya t", + "e zier", + "ez ier", + "ezi er", + "ĠCH UNK", + "ĠAd ler", + "/ Add", + "/A dd", + "Ġ KeyValue", + "ĠKey Value", + "Ġspos ób", + "S ampling", + "Sam pling", + "ch ers", + "che rs", + "cher s", + "_ AMD", + "_A MD", + "_AM D", + "R u", + ".Must Compile", + "N ation", + "Na tion", + "Nat ion", + "As soc", + "Ass oc", + "Man aging", + "Mana ging", + "ĠEn gl", + "ĠEng l", + "_ GB", + "_G B", + "Ġsucc inct", + "Ġdis liked", + "Ġdislike d", + "ĠI ke", + "ĠIk e", + "Bullet in", + "_ARCH IVE", + "Pro posal", + "Prop osal", + "Ġjog ging", + ".C REATED", + ".CREATE D", + "Ġc hol", + "Ġch ol", + "Ġcho l", + "è£ ħ", + "Į ¨", + "- push", + "-p ush", + "Ġres erva", + "Ġreserv a", + "co rev", + "core v", + "cor ev", + "è tre", + "T HR", + "TH R", + "Ġincompet ence", + "Ġchar isma", + "æĦ Ł", + "Ġ\" ==", + "Ġ\"= =", + "B TN", + "BT N", + "Ġ Locator", + "ĠL ocator", + "ĠLoc ator", + "i vet", + "iv et", + "ive t", + "(' .')Ċ", + "('. ')Ċ", + "('.') Ċ", + "('.' )Ċ", + "Ġfor IndexPath", + "ô me", + "ôm e", + "Ġcapac it", + "w aters", + "wa ters", + "water s", + "wat ers", + "ĠWR ONG", + "h oa", + "ho a", + "ĠM IPS", + "ĠMI PS", + "Ġe miss", + "Ġem iss", + "ĠJacqu eline", + "( cmp", + "(c mp", + "(cm p", + "Ġe ens", + "Ġeen s", + "Ġee ns", + "L eo", + "Le o", + ". timing", + ".t iming", + ".tim ing", + "CLU SION", + "CLUS ION", + "Ġ (\"-", + "Ġ( \"-", + "Ġ(\" -", + "åĵ Ī", + ". kode", + ".k ode", + "ĠUnder t", + "ĠUnd ert", + "Ġbe wild", + "Ġbew ild", + "ĠEs sen", + "ĠEss en", + ". hd", + ".h d", + "Ġren egot", + "Ġm ower", + "Ġmo wer", + "Ġl sp", + "Ġls p", + "Ġpen chant", + "Ġman oe", + "Ġmano e", + "Ġ agli", + "Ġa gli", + "Ġag li", + "Ġre cal", + "Ġr ecal", + "Ġrec al", + "ĠOPER ATION", + "(^ )(", + "Ġ ν", + "ĠÎ ½", + "Ġ Scoped", + "ĠSc oped", + "ĠScope d", + "ĠSco ped", + "Ġ @\"Ċ", + "Ġ@ \"Ċ", + "Ġ@\" Ċ", + "= label", + "=l abel", + "[ loc", + "[l oc", + "I ntl", + "In tl", + "Int l", + "ĠN z", + "table t", + "tab let", + "tabl et", + ". ColumnName", + ".Column Name", + "Ġscreen Size", + "D Bus", + "DB us", + "co oked", + "cook ed", + "- registration", + "-reg istration", + "âĢľ One", + "- non", + "-n on", + "-no n", + "ĠwiÄĻ c", + "Ġc osta", + "Ġco sta", + "Ġcost a", + "Ġcos ta", + ".add Tab", + ". conditions", + ".condition s", + ".cond itions", + "ĠH ess", + "ĠHe ss", + "MEM ORY", + "ĠAval anche", + "() }}Ċ", + "()} }Ċ", + "Ġtri plet", + "Ġtrip let", + "Ġtriple t", + "Ġl abyrinth", + "ĠNode List", + "ĠN YT", + "ĠNY T", + "Ġy eni", + "Ġye ni", + "Ġyen i", + "d ff", + "df f", + ".Html Controls", + "A VIS", + "AV IS", + "/ Math", + "/M ath", + "Ġ memcmp", + "Ġmem cmp", + "ا Ø¡", + "Ø§Ø ¡", + "о ÑģÑĮ", + "оÑģ ÑĮ", + "c rap", + "cr ap", + "( pages", + "(p ages", + "(page s", + "(pa ges", + "Ġl xml", + "Ġlx ml", + "ĠQ DateTime", + "_t cb", + "_tc b", + "Ġ openid", + "Ġopen id", + "Ġsyn aptic", + "ĠM DMA", + "ĠMD MA", + "( slug", + "(s lug", + "(sl ug", + "ig matic", + "igma tic", + "igm atic", + "igmat ic", + "e nor", + "en or", + "eno r", + "Ġcr amped", + "Ġcram ped", + "G OP", + "GO P", + "Ń IJ", + ".is File", + "ĠD ifferential", + "ĠDifferent ial", + "Ġ =\"\";Ċ", + "Ġ=\" \";Ċ", + "ĉ ĉĉĠĠĠĠĉ", + "ĉĉ ĉĠĠĠĠĉ", + "ĉĉĉ ĠĠĠĠĉ", + "ĉĉĉĠĠĠ Ġĉ", + "ĉĉĉĠ ĠĠĠĉ", + "ĉĉĉĠĠ ĠĠĉ", + "ĉĉĉĠĠĠĠ ĉ", + "ĠC ooke", + "ĠCo oke", + "ĠCook e", + "ĉU FUNCTION", + "Ġpersever ance", + "Relative Layout", + "IMPORT ANT", + "Ġe xon", + "Ġex on", + "Ġ он", + "Ġо н", + "i base", + "ib ase", + "iba se", + "( CONT", + "(C ONT", + "(CON T", + "n ovation", + "no vation", + "nov ation", + "nova tion", + "ä½ ķ", + "[ sub", + "[s ub", + "Admin Controller", + "HTTP Header", + "c rear", + "cre ar", + "cr ear", + "ĠN IR", + "ĠNI R", + "ĠDrop DownList", + "Ġval ide", + "Ġvalid e", + "Ġva lide", + "Ġde hydration", + ". ']", + ".' ]", + "( WIN", + "(W IN", + "Ġ ...\\", + "Ġ. ..\\", + "Ġ... \\", + "Ġ.. .\\", + "Ġphoto shop", + "Ġphotos hop", + "ĉ Init", + "ĉI nit", + "ĉIn it", + "_ cou", + "_c ou", + "_co u", + "Ġtime Zone", + "dar win", + "r omatic", + "ro matic", + "rom atic", + "roma tic", + "Navigation ItemSelectedListener", + "b rates", + "br ates", + "bra tes", + "brate s", + "] --;Ċ", + "Ġtraged ies", + "ĠPed iatrics", + "ĠPediatric s", + "SM ART", + "- API", + "-A PI", + "ĠMessage Lookup", + "ĉ vo", + "ĉv o", + "Ġprejud ices", + "Ġprejudice s", + "Ġ mA", + "Ġm A", + "U ps", + "Up s", + "ĠMISS ING", + "ĉ ad", + "ĉa d", + "C ream", + "Cre am", + "Cr eam", + "ĠT b", + "ĠM ona", + "ĠMon a", + "ĠMo na", + "_ ghost", + "_g host", + "ĉ types", + "ĉt ypes", + "ĉtype s", + "ĉtyp es", + "E mb", + "Em b", + "ĠDocument ary", + "' );ĊĊĊĊ", + "') ;ĊĊĊĊ", + "');Ċ ĊĊĊ", + "');ĊĊ ĊĊ", + "'); ĊĊĊĊ", + "');ĊĊĊ Ċ", + "Ġl up", + "Ġlu p", + "_ Reference", + "_Re ference", + "_Ref erence", + "ĠB ATCH", + "ĠBAT CH", + "Ġintertw ined", + "< Cell", + "", + "Ġf oyer", + "Ġfo yer", + "'util isation", + "ĠMü ller", + "ĠFet ish", + "Ġdefault Manager", + "Ġback track", + "B ah", + "Ba h", + "Exp licit", + "Expl icit", + "_ ASCII", + "_A SCII", + "_ASC II", + "Ġm Activity", + "( Msg", + "(M sg", + "Ġ ê²Į", + "Ġê² Į", + "ĠTER MS", + "ĠTERM S", + "ĠAn gie", + "ĠAng ie", + "H SV", + "HS V", + "ĠMos que", + ". Names", + ".N ames", + ".Name s", + "íĬ ¼", + "r este", + "re ste", + "res te", + "rest e", + "_ parms", + "_p arms", + "_par ms", + "_pa rms", + "_parm s", + "Ġg aping", + "Ġgap ing", + "Ġga ping", + "Ġc ropping", + "Ġcr opping", + "Ġcro pping", + "Ġcrop ping", + "Data Frame", + "Ġrespons iveness", + "Ġresponsive ness", + "_ undo", + "_un do", + "_u ndo", + "_ tran", + "_t ran", + "_tr an", + "_tra n", + ". terminate", + ".term inate", + "Ġitalian e", + "Ġitalia ne", + "Ġwalk through", + "Ġattract iveness", + "Ġattractive ness", + "д е", + "_ STS", + "_S TS", + "_ST S", + "_ learn", + "_l earn", + "_le arn", + "Ġchocolate s", + "Ġchocol ates", + "ier archical", + "- thinking", + "-th inking", + "Ġ )))", + "Ġ) ))", + "Ġ)) )", + "ish ments", + "ishment s", + ".Log f", + ".Lo gf", + "ĠT MZ", + "ĠTM Z", + "ĠCan ary", + "f oil", + "fo il", + "ĠV accine", + "ĠVacc ine", + ". vx", + ".v x", + "ĠSur round", + "Inter mediate", + "Ġ iov", + "Ġi ov", + "Ġio v", + "v ais", + "va is", + "' ;\";Ċ", + "'; \";Ċ", + "ï½ŀ ĊĊ", + "éĢģ æĸĻ", + "âĢ¦ it", + "Se ats", + "Sea ts", + "Seat s", + "C lar", + "Cl ar", + "Cla r", + "W ars", + "War s", + "Wa rs", + "ĠHutch inson", + "ĠH asan", + "ĠHas an", + "ĠHa san", + "! ')ĊĊ", + "!' )ĊĊ", + "!')Ċ Ċ", + "ĠRich ie", + "ĠRi chie", + "che iden", + "cheid en", + "( $('", + "($ ('", + "($( '", + "Y ork", + "Yo rk", + "Ġl ids", + "Ġli ds", + "Ġlid s", + "Ġal phanumeric", + "Ġalpha numeric", + "ĠG lock", + "ĠGl ock", + "ĠGlo ck", + ". shapes", + ".sh apes", + ".shape s", + ".sha pes", + "Ġsp arking", + "Ġspark ing", + "Ġspar king", + "_ epsilon", + "_e psilon", + "_eps ilon", + "up licated", + "uplic ated", + "uplicate d", + ". dirty", + ".d irty", + ".dir ty", + "] )==", + "]) ==", + "ĠìľĦ ì¹ĺ", + "Ġs cn", + "Ġsc n", + "Ġ /****************************************************************", + "Ġ/ ****************************************************************", + "_PRE VIEW", + "_ HC", + "_H C", + "ield ing", + "iel ding", + "f gets", + "fg ets", + "ĠAdd ison", + "Ġproduct Service", + "- figure", + "-f igure", + "( retval", + "(ret val", + "z ano", + "za no", + "zan o", + "Ġaut ob", + "Ġauto b", + "ĉ sd", + "ĉs d", + "_ numer", + "_n umer", + "_num er", + "ĠSet LastError", + "ĠF ior", + "ĠFi or", + "ific ance", + "ifica nce", + "Unt itled", + "Ġin field", + "Ġinf ield", + "Ġ{ }));Ċ", + "Ġ{} ));Ċ", + "Ġ{}) );Ċ", + "Ġs pac", + "Ġsp ac", + "Ġspa c", + "Ġr ookies", + "Ġro okies", + "Ġrookie s", + "(des cribing", + "n gen", + "ng en", + "nge n", + "ி à®", + ". rdf", + ".r df", + ".rd f", + ". Mutex", + ".M utex", + "Ġkne eling", + "Ġknee ling", + "Ġ QE", + "ĠQ E", + "set Max", + "Read Stream", + "Ġ ventas", + "Ġvent as", + "Ġven tas", + "Ġventa s", + "s ut", + "su t", + "cm peq", + "cmp eq", + ".WriteAll Text", + "ĠEx perienced", + "ĠExperience d", + "$ __", + "$_ _", + "Ġka um", + "ĠL IS", + "ĠLI S", + "Ġdocument os", + "Ġdocumento s", + "_HE ALTH", + "i contains", + "icon tains", + "icont ains", + "Ġart isans", + "Ġartisan s", + "OW NER", + "OWN ER", + "Ġb linked", + "Ġblink ed", + "get Display", + "Ġt oen", + "Ġto en", + "Ġtoe n", + "Ġrow Num", + "Ġav ril", + "Ġin vis", + "Ġinv is", + "ĠK ear", + "ĠKe ar", + "toBe InTheDocument", + "a pur", + "ap ur", + "Ġr acked", + "Ġrac ked", + "Ġrack ed", + "ĠMc Master", + "ĠMcM aster", + "_ATTR IB", + "H az", + "Ha z", + "Ġfact ura", + "Ġfac tura", + "/ ts", + "/t s", + "ĠÑĢаз меÑĢ", + "ĠÑĢазм еÑĢ", + "Ġ zf", + "Ġz f", + "Ġshort fall", + ". fasta", + ".f asta", + ".fast a", + ".fa sta", + "ĠCONST ANT", + ". managed", + ".man aged", + ".manage d", + "g ems", + "ge ms", + "gem s", + "Shared Pointer", + "Ġbl urry", + "Ġblur ry", + "b rightness", + "bright ness", + "( components", + "(com ponents", + "(component s", + "(comp onents", + "Ġ ...\"ĊĊ", + "Ġ... \"ĊĊ", + "Ġ.. .\"ĊĊ", + "Ġ...\" ĊĊ", + "Ġ...\"Ċ Ċ", + "S ELL", + "SE LL", + "SEL L", + "ĠIllustr ator", + ".get Channel", + "Ġtrou vé", + "y sters", + "yst ers", + "ys ters", + "yster s", + "Ġv ois", + "Ġvo is", + "Ġvoi s", + "ĠL inden", + "ĠLin den", + "ĠLind en", + "Ġem ojis", + "Ġemoji s", + "Ġemo jis", + "Ġb rawl", + "Ġbr awl", + "Ġbra wl", + "ĠM SR", + "ĠMS R", + "ĠE lo", + "ĠEl o", + "ĠCroat ian", + "ĠCroatia n", + "Popup Menu", + "L ewis", + "Le wis", + ". JWT", + ".J WT", + "Ġaston ished", + "B ush", + "Bus h", + "Bu sh", + "( itemId", + "(item Id", + "Ġdet achment", + "Ġdetach ment", + "ĠEn core", + "ĠEnc ore", + "å° Ķ", + "Ġre kl", + "Ġr ekl", + "Ġrek l", + "Ġc ram", + "Ġcr am", + "Ġcra m", + ") $/", + ")$ /", + ".get Host", + "_ recommend", + "_re commend", + "- HT", + "-H T", + "_cal ibration", + "Auth enticate", + ".firebase app", + "UN IX", + "ĉ Camera", + "ĉC amera", + "ĠHE AP", + "I deal", + "Ide al", + ". office", + ".off ice", + "Ġgoof y", + "Ġgoo fy", + "( Symbol", + "(S ymbol", + "Ġjo uer", + "Ġjou er", + "_part itions", + "_partition s", + "Ġrapid ement", + "Ġrapide ment", + "ĠGNU NET", + "ĠGN UNET", + "id User", + "Ġsuper vise", + "Ġsuperv ise", + "( Contact", + "A WN", + "AW N", + "ãģ ĺ", + "Ġna am", + "Ġa ust", + "Ġau st", + "Ġaus t", + "åľ¨ 线", + "_ softmax", + "_soft max", + "Allow Anonymous", + "amm able", + "amma ble", + "RO UTE", + "ROUT E", + "* D", + "Ġ aden", + "Ġa den", + "Ġad en", + "Ġade n", + "ĠCrist ina", + "ĠCrist iano", + "Ġblood stream", + "sub class", + "_ persona", + "_person a", + "CH ILD", + "- know", + "-k now", + "Ġnavigation Options", + "ĠZuk unft", + "ĠPix ar", + "Ty ler", + "Ġunder world", + "Ġsincer ity", + "Ġdisp enser", + "Ġdispens er", + "Ġk ter", + "Ġkt er", + "id ders", + "idd ers", + ".add Node", + "- checked", + "-check ed", + "Ġkey st", + "Ġke yst", + "Ġkeys t", + "ĠW TO", + "ĠWT O", + ". signals", + ".sign als", + ".signal s", + "Ġadvent urer", + "Ġadventure r", + "ĠP ang", + "ĠPan g", + "ĠPa ng", + "\\ R", + "= pos", + "=p os", + "Ġdispens aries", + "ĠClose t", + "ĠClo set", + "(\" {\\\"", + "(\"{ \\\"", + "id eon", + "ide on", + "ideo n", + "Ġnécess aire", + "( )\"Ċ", + "() \"Ċ", + "()\" Ċ", + "_RECE IVED", + "Ġrésult ats", + "Ġm oden", + "Ġmod en", + "Ġmode n", + "Ġmo den", + "ĠIceland ic", + "; d", + ". allowed", + ".all owed", + ".allow ed", + "(new User", + "Ġmerc iless", + ".Wait For", + "Ġday care", + "ĠCon veyor", + "Ġ Ù", + "ا Ù", + "า à¸", + "Ñ Ł", + "ÑŁ ÑŁ", + "Ġ à¸", + "Ġà ¸", + "à¹Ģ à¸", + "i á»", + "ãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢ", + "Ġ اØ", + "Ġا Ø", + "ॠĪ", + "Ġ ãĢĢ", + "ĠãĢ Ģ", + "Ñ Ĺ", + "i á»ĩ", + "iá» ĩ", + "ÑŁ ÑŁÑŁÑŁ", + "ÑŁÑŁ ÑŁÑŁ", + "ÑŁÑŁÑŁ ÑŁ", + "à¥ĩ à¤Ĥ", + "à¥ĩठĤ", + "Ñĸ д", + "ा र", + "ाठ°", + "ÙĨ د", + "Ñĸ в", + "Ġ ब", + "Ġठ¬", + "Ġ à¤ľ", + "Ġठľ", + "à ¥¤", + "ॠ¤", + "н Ñĸ", + "ठĹ", + "Ġ Ø¢", + "ĠØ ¢", + "Ġ न", + "Ġठ¨", + "Ñ Ķ", + "Ġ ÑĢа", + "ĠÑĢ а", + "Ġ à¤ħ", + "Ġठħ", + "Ñģ ÑĮ", + "Ġ व", + "Ġठµ", + "ÑĨ Ñĸ", + "Ġv á»", + "³ ت", + "Ġ द", + "Ġठ¦", + "n ÄĽ", + "Ġ ल", + "Ġठ²", + "Ġ ãĢĢĠãĢĢ", + "ĠãĢĢ ĠãĢĢ", + "ĠãĢĢĠ ãĢĢ", + "ॠĤ", + "ठ¦", + "à¸Ń à¸ĩ", + "ÙĪ ÙĨ", + "ठµ", + "a ÅŁ", + "๠Ĥ", + "ι κ", + "Ġ र", + "Ġठ°", + "Ġ ви", + "Ġв и", + "à¥į य", + "à¥įठ¯", + "ा न", + "ाठ¨", + "Ġ از", + "Ġا ز", + "ĠØ§Ø ²", + "ا Ùĩ", + "ا٠ĩ", + "Ľ i", + "Ġh á»", + "à¥ĭ à¤Ĥ", + "i ế", + "ĠÄij á»", + "ठ¯", + "Ï į", + "Ġc ủ", + "Ġ بر", + "Ġب ر", + "Ġ ÙħÛĮ", + "ĠÙħ ÛĮ", + "Ġ اÛĮ", + "Ġا ÛĮ", + "Ġ à¤Ĩ", + "ĠठĨ", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ि य", + "िठ¯", + "ÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁ", + "в и", + "ر د", + "н Ñĥ", + "ÙĬ ÙĨ", + "ι α", + "Ġ त", + "Ġठ¤", + "Ñĩ и", + "Ġ à¤ķर", + "Ġà¤ķ र", + "ا ز", + "Ø§Ø ²", + "a ÄŁ", + "Ġ à¤ī", + "Ġठī", + "ठ¬", + "ÏĦ α", + "ت ر", + "Ùĩ ا", + "ร ะ", + "j ÃŃ", + "Î ij", + "а ÑĤи", + "аÑĤ и", + "Ġ à¤Ĺ", + "ĠठĹ", + "Ġ ÑĤа", + "ĠÑĤ а", + "Ú Ĩ", + "ठľ", + "า à¸Ļ", + "าภĻ", + "Ġ à¤Ń", + "ĠठŃ", + "ि à¤ķ", + "िठķ", + "á v", + "Ġ Ú¯", + "Ï İ", + "า ย", + "าภ¢", + "Ġ à¤Ķ", + "ĠठĶ", + "ÅĻ ÃŃ", + "ا ÙĪ", + "ا٠Ī", + "Ġ Ñī", + "ĠÑ ī", + "Ġ à¤Ķर", + "Ġà¤Ķ र", + "ен нÑı", + "Ġ Ú©Ùĩ", + "ĠÚ© Ùĩ", + "ठ¡", + "ÏĦ ο", + "ε ι", + "Ġ à¤ĩ", + "Ġठĩ", + "à¥į त", + "à¥įठ¤", + "ठŁ", + "Û ±", + "Ġ ØĮ", + "ĠØ Į", + "Ïģ ο", + "η ÏĤ", + "ë ¬", + "Ñĸ н", + "i á»ģ", + "iá» ģ", + "i ên", + "iê n", + "Ġ вÑĸд", + "Ġв Ñĸд", + "ĠвÑĸ д", + "d ı", + "ÙĦ ÛĮ", + "Ġ ز", + "ĠØ ²", + "Ïģ α", + "Ġ ÛĮ", + "า à¸ĩ", + "าภĩ", + "Ġth á»", + "Ġ à¹Ģà¸", + "Ġà¹Ģ à¸", + "i á»ĩn", + "iá»ĩ n", + "ا ÙĬ", + "ا٠Ĭ", + "ан нÑı", + "ÑĢ е", + "Î Ł", + "å Ĵ", + "ا Ø´", + "Ø§Ø ´", + "ा ल", + "ाठ²", + "ëħ Ħ", + "Ġ य", + "Ġठ¯", + "Ġ را", + "Ġر ا", + "ठ¼", + "Ñĥ в", + "ÙĪ Ùħ", + "Ġ عÙĦ", + "Ġع ÙĦ", + "ί α", + "à¥Ī à¤Ĥ", + "à¥ģ à¤", + "า ม", + "าภ¡", + "Ġm á»Ļt", + "Ġ à¤ı", + "Ġठı", + "ãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢ", + "Ġ पर", + "Ġप र", + "Ġ اÙĨ", + "Ġا ÙĨ", + "Ġ اÛĮÙĨ", + "Ġا ÛĮÙĨ", + "ĠاÛĮ ÙĨ", + "Ġv Ỽi", + "Ġvá» Ľi", + "Î £", + "ठļ", + "Û °", + "i á»ĥ", + "iá» ĥ", + "า à¸ģ", + "าภģ", + "Î Ļ", + "ا ع", + "Ø§Ø ¹", + "Ñĸ й", + "à¹ģ ล", + "Ùĩ اÛĮ", + "Ùĩا ÛĮ", + "Ñĩ а", + ". :.:", + ".: .:", + ".:. :", + "ÏĦ η", + "Ġ Îij", + "ĠÎ ij", + "ر ÛĮ", + "Ġn gh", + "Ġng h", + "ν α", + "à¹ĥ à¸Ļ", + "ि त", + "िठ¤", + "Ġ και", + "Ġκ αι", + "Ġκα ι", + "ÏĦ ε", + "à¥į à¤Ł", + "à¥įठŁ", + "μ α", + "л Ñĥ", + "ý m", + "ÏĢ ο", + "à¥Ī ।", + "ï¼ ¼", + "ر ÙĬ", + "н иÑħ", + "ни Ñħ", + "Ïģ ι", + "Ù Ģ", + "ÑĢ о", + "Ġ à¤ļ", + "Ġठļ", + "ा त", + "ाठ¤", + "ا ÙĤ", + "ا٠Ĥ", + "Ġ श", + "Ġठ¶", + "ĠÄij á»Ļ", + "ĠÄijá» Ļ", + "é ho", + "iá»ģ u", + "ภ¨", + "Ñĸ лÑĮ", + "Ñĸл ÑĮ", + "uy á»", + "Û ²", + "Ġn Äĥ", + "Ïī ν", + "Ġ ÏĦοÏħ", + "ĠÏĦ οÏħ", + "ĠÏĦο Ïħ", + "к ий", + "ки й", + "í ĸ", + "Ġ Ñīо", + "ĠÑī о", + "à¥į व", + "à¥įठµ", + "Ġ اÙĦØ£", + "ĠاÙĦ Ø£", + "ا ئ", + "Ø§Ø ¦", + "t ı", + "Ġ ÏĦο", + "ĠÏĦ ο", + "¬ ¬", + "Ġ Ø·", + "ĠØ ·", + "Ùħ اÙĨ", + "Ùħا ÙĨ", + "Ġ Îł", + "ĠÎ ł", + "д и", + "ภ¶", + "ि à¤ı", + "िठı", + "ãģ£ ãģŁ", + "ãģ£ãģ Ł", + "ÛĮ Ùħ", + "ÃŃ nh", + "ÃŃn h", + "r av", + "ra v", + "ÄĽ t", + "Î ķ", + "Ġ Ñıк", + "ĠÑı к", + "ç Ĥ", + "à¸Ń à¸Ļ", + "ãģ¦ ãģĦ", + "ि ल", + "िठ²", + "Ñĸ ÑĤ", + "з а", + "á p", + "ठ§", + "Ġ êµ", + "Ġê µ", + "à¹ģ ละ", + "à¹ģล ะ", + "ÃŃ ch", + "ÃŃc h", + "Ġ Ø¢ÙĨ", + "ĠØ¢ ÙĨ", + "ت Ùĩ", + "Ġ Ùħع", + "ĠÙħ ع", + "н ий", + "ни й", + "Æ°á»Ľ c", + "Ġ اÙĦع", + "Ġا ÙĦع", + "ĠاÙĦ ع", + "ر ب", + "ा म", + "ाठ®", + "Ġ رÙĪ", + "Ġر ÙĪ", + "é «", + "ı y", + "Ġh á»į", + "Ġhá» į", + "ÑĤÑĮ ÑģÑı", + "Ġ Îļ", + "ĠÎ ļ", + "Ġ à¤ĩस", + "Ġà¤ĩ स", + "ï¼ ¿", + "Ġ ÚĨ", + "Ġ ÙĪاÙĦ", + "ĠÙĪ اÙĦ", + "ĠÙĪا ÙĦ", + "íķ Ļ", + "ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ", + "Ġ vý", + "Ġv ý", + "ि स", + "िठ¸", + "ữ ng", + "س ÛĮ", + "Ġ ìĥ", + "Ġì ĥ", + "ा à¤Ĥ", + "ाठĤ", + "ï½ ¤", + "à¹ĩ à¸Ļ", + "Ġ थ", + "Ġठ¥", + "l arak", + "la rak", + "lar ak", + "lara k", + "â y", + "t ÄĽ", + "ν ο", + "Ġ ÙħÙĪ", + "ĠÙħ ÙĪ", + "Ġng Æ°á»Ŀi", + "å ¦", + "ÙĬ د", + "il ir", + "ili r", + "ا ØŃ", + "Ø§Ø Ń", + "Ġ ãĢ", + "Ù ĭ", + "Ġ ÑĢоз", + "ĠÑĢ оз", + "Ġ й", + "ĠÐ ¹", + "Ġd ụ", + "à¹Ģ à¸Ľ", + "à¹ĢภĽ", + "ั à¸ĩ", + "л е", + "ा य", + "ाठ¯", + "ï¿ £", + "ÙĪ اÙĨ", + "ÙĪا ÙĨ", + "Ġth á»ĥ", + "Ġthá» ĥ", + "ã ĥ½", + "ãĥ ½", + "ü ÅŁ", + "ç Ł", + "Ġ ο", + "ĠÎ ¿", + "Ġ Σ", + "ĠÎ £", + "ÛĮ ت", + "ั à¸ģ", + "Î ¤", + "Ġ à¤ıà¤ķ", + "Ġà¤ı à¤ķ", + "Ġ ÙĩÙħ", + "ĠÙĩ Ùħ", + "ìĽ Ķ", + "Ġ Îľ", + "ĠÎ ľ", + "Ġ à¸Ħ", + "ĠภĦ", + "¯ ¸", + "ا رÛĮ", + "ار ÛĮ", + "ि न", + "िठ¨", + "Ġnh ững", + "Ġnh Æ°", + "и ÑĤи", + "иÑĤ и", + "ãĥ³ ãĥ", + "à¹Ģ ร", + "à¹Ģภ£", + "Ġ ÐĽ", + "ĠÐ Ľ", + "ÑĢ Ñĸ", + "á d", + "ü y", + "i ye", + "iy e", + "Ġ Îķ", + "ĠÎ ķ", + "Ġ ส", + "Ġภª", + "Ïĥ η", + "Ġ ë¬", + "Ġë ¬", + "ï »", + "ठ£", + "Î Ĺ", + "ठ¶", + "Ġ ÙħØŃ", + "ĠÙħ ØŃ", + "ÙĦ ÙĬ", + "Ġ με", + "Ġμ ε", + "Ġp ÅĻÃŃ", + "ĠpÅĻ ÃŃ", + "Î Ŀ", + "à¥į ष", + "à¥įठ·", + "t ir", + "ti r", + "ر اÙĨ", + "را ÙĨ", + "ĠÄij á»ĭ", + "ĠÄijá» ĭ", + "Ġ коÑĤ", + "Ġк оÑĤ", + "Ġко ÑĤ", + "к ÑĢа", + "λ ο", + "Ġ ÏĦη", + "ĠÏĦ η", + "Ñī е", + "ÏĦ ικ", + "ÏĦι κ", + "ั à¹ī", + "i ết", + "iế t", + "α ν", + "í Ķ", + "к иÑħ", + "ки Ñħ", + "Ġ поÑģ", + "Ġп оÑģ", + "Ġпо Ñģ", + "t ır", + "tı r", + "à¥į म", + "à¥įठ®", + "ر Ùģ", + "ÄĽ l", + "ठŃ", + "o vé", + "ov é", + "Ġl á»", + "à¹Ħ à¸Ķ", + "ãģª ãģĦ", + "ภ©", + "i á»ĩu", + "iá»ĩ u", + "Î ¾", + "Ġ عÙĦÙī", + "Ġع ÙĦÙī", + "ĠعÙĦ Ùī", + "д Ñĥ", + "Ġdụ ng", + "а ÑĢа", + "аÑĢ а", + "ा द", + "ाठ¦", + "o ž", + "ÙĦ Ùĩ", + "ÙĦ Ùħ", + "н оÑĹ", + "но ÑĹ", + "Û± Û", + "à¸Ĥ à¸Ńà¸ĩ", + "Î ¡", + "à¥Ģ à¤Ĥ", + "Ġ пÑĸд", + "Ġп Ñĸд", + "Ġ फ", + "Ġठ«", + "ภĺ", + "ε ÏĤ", + "ा स", + "ाठ¸", + "à¹ĥ ห", + "о ва", + "ов а", + "ت ÛĮ", + "à¸Ń ย", + "ภį", + "Ġn Äĥm", + "ĠnÄĥ m", + "ÏĦ ι", + "ÙĪ ÛĮ", + "Ġ мÑĸ", + "Ġм Ñĸ", + "Ġ اÙħ", + "Ġا Ùħ", + "ÏĢ ÏĮ", + "Ġ zá", + "Ġz á", + "ठĪ", + "Ġ à¤ĸ", + "Ġठĸ", + "Ġ nÄĽ", + "Ġn ÄĽ", + "c ÃŃ", + "ÙĨ Ú¯", + "Ñģ и", + "Î ¶", + "n á", + "Ŀ i", + "Å ©", + "Ø ¦", + "Ġ اÙĦس", + "Ġا ÙĦس", + "ĠاÙĦ س", + "á»ij c", + "Ạ½", + "ا ج", + "Ø§Ø ¬", + "Ùħ ا", + "êµ Ń", + "о Ñİ", + "د ر", + "à¹Ģ à¸ģ", + "à¹Ģภģ", + "ภł", + "à ¡ng", + "á ng", + "án g", + "íķ ©", + "Ġ ÏĦηÏĤ", + "ĠÏĦ ηÏĤ", + "ĠÏĦη ÏĤ", + "Ġ Ñĸн", + "ĠÑĸ н", + "о ÑĹ", + "à¥ĩ श", + "à¥ĩठ¶", + "ภĭ", + "à¥ĭ à¤Ĺ", + "л Ñĸ", + "Ġp ÅĻed", + "ĠpÅĻ ed", + "ĠpÅĻe d", + "Äį nÃŃ", + "Ġ ка", + "Ġк а", + "Ġ Τ", + "ĠÎ ¤", + "á»Ļ i", + "v ÃŃ", + "ÑĢ Ñı", + "ा à¤ľ", + "ाठľ", + "а Ñħ", + "ि र", + "िठ°", + "า ส", + "าภª", + "d ır", + "dı r", + "Ø ¢", + "Î ļ", + "Ġ ÎŃ", + "ĠÎ Ń", + "Ġt ại", + "iá»ĩ c", + "i ến", + "iế n", + "Ġ غ", + "ĠØ º", + "ا Ø®", + "Ø§Ø ®", + "Ġ اÙĦØŃ", + "Ġا ÙĦØŃ", + "ĠاÙĦ ØŃ", + "Ġ бÑĥ", + "Ġб Ñĥ", + "Ġv á»ģ", + "Ġvá» ģ", + "м Ñĸ", + "Ùħ ÙĦ", + "m Ä±ÅŁ", + "à¸Ľ ระ", + "à¸Ľà¸£ ะ", + "ο Ïį", + "ε ί", + "Ġर ह", + "н им", + "ни м", + "ع د", + "Ġ باÙĦ", + "Ġب اÙĦ", + "Ġبا ÙĦ", + "¤ ij", + "ç ł", + "Ġo lm", + "Ġol m", + "Ïİ Î½", + "Ġh á»įc", + "Ġhá»į c", + "ا ست", + "Ø§Ø ³Øª", + "اس ت", + "า ว", + "าภ§", + "ÙĪ ب", + "Ñĸ Ñı", + "Ġ ÙĩاÛĮ", + "ĠÙĩ اÛĮ", + "ĠÙĩا ÛĮ", + "ë§ Ī", + "ॠĮ", + "Ġ ÄĮ", + "ĠÄ Į", + "ठı", + "ا دÙĩ", + "اد Ùĩ", + "Ġ اÙĪ", + "Ġا ÙĪ", + "н Ñĭм", + "нÑĭ м", + "Ạ±", + "Ùħ ÙĨ", + "iá»ĩ t", + "l aÅŁ", + "la ÅŁ", + "Ñĸ з", + "ÙĪ س", + "Ġl Ãłm", + "ĠlÃł m", + "ĠÄij ến", + "ĠÄijế n", + "प न", + "Ġ ÛĮÚ©", + "ĠÛĮ Ú©", + "Ġ ÙĦÙĦ", + "ĠÙĦ ÙĦ", + "Ġ mÄĽ", + "Ġm ÄĽ", + "Ġ براÛĮ", + "Ġبر اÛĮ", + "ा ह", + "ाठ¹", + "Ġ Ùħر", + "ĠÙħ ر", + "e ç", + "à¸Ń ร", + "ε Ïģ", + "ั à¸Ķ", + "к он", + "ко н", + "n ou", + "no u", + "Ġ год", + "Ġг од", + "ู à¹ī", + "à¹Ģ ล", + "à¹Ģภ¥", + "Ú ĺ", + "ĠÄij á»ĭnh", + "ĠÄijá»ĭ nh", + "ĠÄij ó", + "а нов", + "ан ов", + "ано в", + "Ġ Ùģر", + "ĠÙģ ر", + "ا رد", + "ار د", + "Ñĸ ÑĹ", + "à¸Ħ ร", + "à¥į थ", + "à¥įठ¥", + "c ak", + "ca k", + "ÑĨ ÑĸÑĹ", + "ÑĨÑĸ ÑĹ", + "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", + "Ùĩ ر", + "ॠī", + "Ġg iá»", + "Ġgi á»", + "í Ĩ", + "âĢĮ ÙĩاÛĮ", + "âĢĮÙĩا ÛĮ", + "à¥ģ र", + "à¥ģठ°", + "Ġ à¸ģ", + "Ġภģ", + "Å Ī", + "æ ¨", + "ÎŁ Î", + "า à¸Ħ", + "าภĦ", + "кÑĢа ÑĹ", + "ả o", + "o ÄŁ", + "Ġ सम", + "Ġस म", + "Ġv iá»ĩc", + "Ġs ẽ", + "Ġ ná", + "Ġn á", + "ÙĬ Ùħ", + "£ p", + "ö y", + "ÙĪ ز", + "Ġ κα", + "Ġκ α", + "Ùħ د", + "n ÃŃm", + "nÃŃ m", + "o vá", + "ov á", + "ा व", + "ाठµ", + "ा ।", + "à¥į स", + "à¥įठ¸", + "ç ·", + "ặ c", + "Ġ à¸ŀ", + "Ġภŀ", + "ï½ Ģ", + "ô i", + "Ġ ợ", + "Ġá» Ł", + "ο ÏĤ", + "Ġtr ên", + "м Ñĥ", + "ÑģÑĮ к", + "ภŁ", + "o vat", + "ov at", + "ova t", + "Ġm á»", + "í ı", + "Ġ во", + "Ġв о", + "ε ν", + "à¥Ĥ र", + "Ú¯ اÙĩ", + "ĠÄij á»Ļng", + "ĠÄijá»Ļ ng", + "Ú© ÙĨ", + "Ñī и", + "Ġ пÑĢа", + "Ġп ÑĢа", + "ĠпÑĢ а", + "ü rk", + "ür k", + "ÙĪ ع", + "ấ p", + "n ý", + "Ġ quan", + "Ġqu an", + "Ġq uan", + "Ġqua n", + "Ñĸ Ñĩ", + "Ġ να", + "Ġν α", + "Ġन ह", + "Ġ Ú©ÙĨ", + "ĠÚ© ÙĨ", + "c ı", + "çĿ Ģ", + "б о", + "Ġ اس", + "Ġا س", + "ĠØ§Ø ³", + "è »", + "ا ÙĨÛĮ", + "اÙĨ ÛĮ", + "à¸ķ ร", + "ÏĦ ά", + "Ġ Ø£ÙĨ", + "ĠØ£ ÙĨ", + "éĤ £", + "Ġ ม", + "Ġภ¡", + "к ÑĤ", + "i ê", + "Ġhá» £p", + "ت Ùħ", + "Ġ بÙĨ", + "Ġب ÙĨ", + "h od", + "ho d", + "ι Ïĥ", + "ห à¸Ļ", + "Ġ ÑĹ", + "ĠÑ Ĺ", + "л ив", + "ли в", + "Ġ کرد", + "ĠÚ© رد", + "Ġکر د", + "Ġ ÙħØ´", + "ĠÙħ Ø´", + "ا Ø·", + "Ø§Ø ·", + "ب ÙĬ", + "Ġ ร", + "Ġภ£", + "د Ùħ", + "ÙĦ اÙħ", + "ÙĦا Ùħ", + "à¹Ī ว", + "Ġ ÙĨÙħ", + "ĠÙĨ Ùħ", + "Ġ æĹ", + "Ġæ Ĺ", + "é ħ", + "н оÑģÑĤ", + "но ÑģÑĤ", + "ноÑģ ÑĤ", + "i á»ĥm", + "iá»ĥ m", + "êµ IJ", + "a yı", + "ay ı", + "Ġ بÙĪد", + "Ġب ÙĪد", + "ĠبÙĪ د", + "Ú¯ ر", + "Ġh iá»ĩn", + "Ġhi á»ĩn", + "ç ³", + "ÑģÑĤ вен", + "ÑģÑĤв ен", + "ÑģÑĤве н", + "Ġà¤ķर न", + "Ġ ÏĦην", + "ĠÏĦ ην", + "ĠÏĦη ν", + "Ġ à¸Ń", + "ĠภŃ", + "Ġ Ùħت", + "ĠÙħ ت", + "ģ n", + "ج Ùħ", + "λ λ", + "Ġ ÑĢе", + "ĠÑĢ е", + "ิ à¸Ķ", + "Ġ اÙĦÙĤ", + "Ġا ÙĦÙĤ", + "ĠاÙĦ ÙĤ", + "α Ïģ", + "Ġ यह", + "Ġय ह", + "n ÃŃch", + "nÃŃ ch", + "ÑĶ ÑĤÑĮÑģÑı", + "Ġ à¸Ĺ", + "ĠภĹ", + "ÛĮ Ø´", + "ÅĻ e", + "Ġn ebo", + "Ġne bo", + "Ġneb o", + "Ġ Ñĩа", + "ĠÑĩ а", + "l ou", + "lo u", + "ÑģÑĤ во", + "ÑģÑĤв о", + "Ġ Ч", + "ĠÐ §", + "à¸Ħ ว", + "Ùĩ Ùħ", + "à¹Ģ à¸Ķ", + "à¹ĢภĶ", + "Ġ à¹ģ", + "Ġ à¹Ĥ", + "Û ³", + "Å© ng", + "Ġ nej", + "Ġn ej", + "Ġne j", + "ÛĮ Ú©", + "Ġs á»Ń", + "Ùģ ر", + "Î ł", + "Ġп ок", + "Ġпо к", + "ĠاÙĦ ÙĨ", + "Ġv Å¡", + "á º«", + "Ạ«", + "Ġnh Ãł", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ή ÏĤ", + "ο Ïģ", + "Ġ Ïĩ", + "ĠÏ ĩ", + "à¹Ģ à¸Ĺ", + "à¹ĢภĹ", + "Ñĥ лÑĮ", + "Ñĥл ÑĮ", + "ãħ ĩ", + "Ġ yıl", + "Ġy ıl", + "Ġyı l", + "ÑĢ од", + "ÑĢо д", + "ί ν", + "ìĹ Īëĭ¤", + "ìĹĪ ëĭ¤", + "ا ص", + "Ø§Ø µ", + "ĠÄij ầu", + "à¥ĩ à¤ķ", + "à¥ĩठķ", + "ÑĢ ом", + "ÑĢо м", + "ãģĵ ãģ¨", + "Ġ ار", + "Ġا ر", + "ĠØ§Ø ±", + "å¥ ¹", + "Ġ تØŃ", + "Ġت ØŃ", + "Å¡ tÄĽ", + "Å¡t ÄĽ", + "à¥į ल", + "à¥įठ²", + "à¥į à¤ķ", + "à¥įठķ", + "Ġ کار", + "ĠÚ© ار", + "u jÃŃ", + "uj ÃŃ", + "Ġ à¤īन", + "Ġà¤ī न", + "Ġ αÏĢÏĮ", + "Ġα ÏĢÏĮ", + "ĠαÏĢ ÏĮ", + "Ġm Ãł", + "ž ÃŃ", + "Ġ à¸Ī", + "ĠภĪ", + "a lı", + "al ı", + "ठ«", + "Ñĩ еÑģ", + "Ñĩе Ñģ", + "Ġ عÙĨ", + "Ġع ÙĨ", + "æķ Ļ", + "ï¾ Ĩ", + "ि à¤Ĥ", + "िठĤ", + "Ġs á»±", + "в оÑĢ", + "во ÑĢ", + "Ġth á»±c", + "ë į°", + "ëį °", + "ãģ¦ ãģĦãĤĭ", + "ãģ¦ãģĦ ãĤĭ", + "à¹Ī à¸ĩ", + "ت ب", + "Ġnh iá»ģu", + "ĥ n", + "ĠÄij á»ĵ", + "ĠÄijá» ĵ", + "Ġ ห", + "Ġภ«", + "Û µ", + "m ÄĽ", + "ạ t", + "Ġch ÃŃnh", + "ĠchÃŃ nh", + "ĠchÃŃn h", + "μ ÎŃ", + "an ı", + "Ġb á»ĭ", + "ằ ng", + "ÅĻ ed", + "ÅĻe d", + "é Ł", + "á nh", + "án h", + "ÙĢ ÙĢ", + "Ġ Ùħس", + "ĠÙħ س", + "á»ĭ ch", + "Ä ĥn", + "Äĥ n", + "o vánÃŃ", + "ov ánÃŃ", + "ová nÃŃ", + "ován ÃŃ", + "à¹Ī าà¸ĩ", + "à¹Īา à¸ĩ", + "Ġ à¸Ľ", + "ĠภĽ", + "Ġn Æ°á»Ľc", + "Ð ±Ð¾ÑĤ", + "б оÑĤ", + "бо ÑĤ", + "ı yor", + "ıy or", + "ĠØ® ÙĪد", + "ĠØ®ÙĪ د", + "Û ¹", + "Ġ Ùħد", + "ĠÙħ د", + "Ġ üz", + "Ġü z", + "ì ½", + "ÙĪ ÙĤ", + "ë¥ ´", + "л ек", + "ле к", + "Ġc ả", + "ол ог", + "оло г", + "à¹ī à¸Ńà¸ĩ", + "à¹īà¸Ń à¸ĩ", + "m iÅŁ", + "mi ÅŁ", + "à¹ī ว", + "Ä ©", + "Î ľ", + "à¸Ń à¸ģ", + "＿ ＿", + "ठĸ", + "Ġ Я", + "ĠÐ ¯", + "ë ¬´", + "ë¬ ´", + "اÛĮ ÛĮ", + "s ké", + "sk é", + "uy ên", + "e ÅŁ", + "á i", + "ú ng", + "ún g", + "Ãł o", + "Ñĸ Ñģ", + "ç ¶", + "Ġ à¤Ĩप", + "Ġà¤Ĩ प", + "ï º", + "Î Ľ", + "Ġ ê³µ", + "Ġê³ µ", + "Ġ ÐĨ", + "ĠÐ Ĩ", + "Ġà¤ħ पन", + "Ġà¤ħप न", + "ứ ng", + "ÏĮ ÏĤ", + "Ġngh iá»ĩ", + "Ġnghi á»ĩ", + "Ġ اÙĦب", + "Ġا ÙĦب", + "ĠاÙĦ ب", + "à¥ĭ न", + "Ġ à¤Ł", + "ĠठŁ", + "Ġ ìľł", + "Ġìľ ł", + "Ġc Å©ng", + "ĠcÅ© ng", + "Ġà¤ī स", + "Ġ ड", + "Ġठ¡", + "ĠØ´ دÙĩ", + "Ġشد Ùĩ", + "ี à¹ī", + "Û ´", + "ặ t", + "æĸ ¯", + "Ġ ëį", + "Ġë į", + "Ġп л", + "б и", + "ê³ Ħ", + "ο ν", + "Ġç ık", + "Ġçı k", + "Ġbu lun", + "Ġbul un", + "س Ùħ", + "a ç", + "ا ÙĨÙĩ", + "اÙĨ Ùĩ", + "ÛĮ ز", + "l eÅŁ", + "le ÅŁ", + "ắ c", + "ا Ú©", + "Ġस à¤ķ", + "Ġ оÑĢг", + "Ġо ÑĢг", + "ĠоÑĢ г", + "Ġ à¸Ļ", + "ĠภĻ", + "ा थ", + "ाठ¥", + "Ġ ÙħÙĤ", + "ĠÙħ ÙĤ", + "ĠÎĶ E", + "Ñİ ÑĤÑĮ", + "ÑİÑĤ ÑĮ", + "á»Ļ c", + "Ġ η", + "ĠÎ ·", + "s ob", + "so b", + "Ġth eo", + "Ġthe o", + "å ŀ", + "Ġ اÙĦØ´", + "ĠاÙĦ Ø´", + "à¹Ģ à¸ŀ", + "à¹Ģภŀ", + "ÎŃ ÏĤ", + "à¹Ģ à¸Ĥ", + "à¹ĢภĤ", + "å Ļ", + "ि श", + "िठ¶", + "Ġ باز", + "Ġب از", + "Ġبا ز", + "ÑĢ об", + "ÑĢо б", + "Ġγ ια", + "μ ε", + "Ġ باش", + "Ġب اش", + "Ġبا Ø´", + "ा à¤ĩ", + "ाठĩ", + "Ġqu y", + "Ġq uy", + "λ ε", + "ا Ùĥ", + "ا٠ĥ", + "Ġ ÑĢок", + "ĠÑĢ ок", + "Ġ Türk", + "ĠT ürk", + "ĠTür k", + "Ġ Ð¥", + "ĠÐ ¥", + "ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ", + "æ ©", + "Ġph ải", + "à¸Ħ วาม", + "à¸Ħว าม", + ": ::", + ":: :", + "l ÃŃ", + "Ġj sou", + "Ġjs ou", + "ÛĮ ÙĦ", + "ál nÃŃ", + "áln ÃŃ", + "Ķ Ķ", + "í ĸī", + "íĸ ī", + "æĥ ³", + "l á", + "Ġ ÏĥÏħ", + "ĠÏĥ Ïħ", + "Ñĭ ва", + "Ñĭв а", + "Ġnh ất", + "à¸Ń ม", + "Û ¸", + "e cek", + "ec ek", + "ece k", + "Ñĸ ÑĢ", + "ÙĪ Ø´", + "λ α", + "Ġ ÎĴ", + "ĠÎ Ĵ", + "о ÑĢа", + "оÑĢ а", + "Ùģ ت", + "e dir", + "ed ir", + "edi r", + "Ñĥ Ñħ", + "ä¸ ĸ", + "ĠУ кÑĢаÑĹ", + "ĠУкÑĢа ÑĹ", + "Ġ íĶ", + "Ġí Ķ", + "ά ν", + "Ġ شر", + "ĠØ´ ر", + "ĠاÙĦ ج", + "е ÑĢед", + "еÑĢ ед", + "еÑĢе д", + "ìĺ ģ", + "Ġh Ãłnh", + "ï¿£ ï¿£", + "м е", + "ÑİÑĤ ÑģÑı", + "ĠØ¥ ÙĦÙī", + "ĠØ¥ÙĦ Ùī", + "ìĹ ħ", + "Ġ تر", + "Ġت ر", + "к ом", + "ко м", + "Ġ شد", + "ĠØ´ د", + "Ġ اÙĦÙĥ", + "Ġا ÙĦÙĥ", + "ĠاÙĦ Ùĥ", + "Ġ ÏĥÏĦο", + "ĠÏĥ ÏĦο", + "à¥į द", + "à¥įठ¦", + "ëł ¤", + "Ñĥ ваннÑı", + "Ñĥв аннÑı", + "Ġth ì", + "ê ´Ģ", + "ê´ Ģ", + "κ ε", + "س ب", + "íĥ Ģ", + "Ġ ï¼ı", + "Ġï¼ ı", + "Ġ à¹ģละ", + "Ġà¹ģ ละ", + "Ġà¹ģล ะ", + "Ġ ÏĮ", + "ĠÏ Į", + "н иÑĨ", + "ни ÑĨ", + "Ġ ÐĿа", + "ĠÐĿ а", + "Ñı в", + "l ü", + "ι ο", + "ÙĨ دÙĩ", + "ÙĨد Ùĩ", + "ÙĦ Ùĥ", + "Ġng Ãły", + "Ġnh ân", + "Ġ ^{", + "Ġ^ {", + "ॠĥ", + "Ġg erek", + "Ġge rek", + "Ġger ek", + "Ġgere k", + "ا رÙĩ", + "ار Ùĩ", + "Ġc Æ¡", + "Ġ à¸ķ", + "Ġภķ", + "æ Ĥ", + "çĶ °", + "à¥Īà¤Ĥ ।", + "ั ว", + "v ÄĽ", + "ö z", + "и ли", + "ил и", + "Ġph áp", + "Ġphá p", + "ê¸ Ī", + "Ġ ÎŁ", + "ĠÎ Ł", + "Ġp ÅĻi", + "ĠpÅĻ i", + "Ġ ìĸ´", + "Ġìĸ ´", + "Ġд ол", + "Ġдо л", + "ÙĪ رد", + "ÙĪر د", + "à¹Ģ ม", + "à¹Ģภ¡", + "Ïĥ ε", + "า à¸Ĺ", + "าภĹ", + "o Ãłi", + "ร ม", + "Û ¶", + "Ġ à¸ļ", + "Ġภļ", + "i yet", + "iy et", + "iye t", + "ÏĦ αι", + "ÏĦα ι", + "ìĦ ł", + "Ġ εÏĢ", + "Ġε ÏĢ", + "ि व", + "िठµ", + "ê¹ Į", + "г а", + "ĠÑģ лÑĥ", + "ĠÑģл Ñĥ", + "Ġh ình", + "Ġ داÙĨ", + "Ġد اÙĨ", + "Ġà¤Ĺ य", + "ÙĬ ا", + "è ij", + "à¤Ĥ त", + "Ġ ساÙĦ", + "Ġس اÙĦ", + "ëł Ī", + "l erin", + "le rin", + "ler in", + "leri n", + "à¥ĩ त", + "à¥ĩठ¤", + ".: .:.:.:", + ".:.: .:.:", + ".:. :.:.:", + ".:.:.: .:", + ".:.:. :.:", + ".:.:.:. :", + "Ġ ëħ", + "Ġë ħ", + "Ġ اÙĦØ¥", + "ĠاÙĦ Ø¥", + "ả ng", + "ản g", + "è Ħ", + "ο λ", + "п ов", + "по в", + "Ġ θ", + "ĠÎ ¸", + "Û ·", + "Ġn ó", + "Ġd Ã¼ÅŁ", + "Ġdü ÅŁ", + "Ġt iế", + "Ġti ế", + "ÙĪ ج", + "Ġj sem", + "Ġjs em", + "Ạ¡ng", + "ạ ng", + "ạn g", + "ãģĤ ãĤĭ", + "à¸Ń à¸ļ", + "ÙĪ ÙĬ", + "à¤ķ र", + "Ġ де", + "Ġд е", + "¯ ¼", + "Ġ но", + "Ġн о", + "ÑĨ Ñĸй", + "ÑĨÑĸ й", + "Ïĥ ÏĦ", + "к ие", + "ки е", + "Ïĥ ει", + "Ïĥε ι", + "ìķ Ī", + "Ġh Æ¡n", + "Ġà¤ķ ह", + "ا ض", + "Ø§Ø ¶", + "ì ¸", + "ãĥ Ł", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ãĤĪ ãģĨ", + "ा ,", + "е ÑĢи", + "еÑĢ и", + "ë© °", + "í ĶĦ", + "íĶ Ħ", + "Ġп оÑģÑĤ", + "Ġпо ÑģÑĤ", + "ĠпоÑģ ÑĤ", + "Ø® ر", + "à¥ĭ त", + "â u", + "к ой", + "ко й", + "d aki", + "da ki", + "í ħ", + ": :::::::::::::::", + ":: ::::::::::::::", + ":::: ::::::::::::", + ":::::: ::::::::::", + ":::::::: ::::::::", + "::: :::::::::::::", + "::::: :::::::::::", + "::::::: :::::::::", + "::::::::: :::::::", + ":::::::::: ::::::", + "::::::::::: :::::", + ":::::::::::: ::::", + "::::::::::::: :::", + ":::::::::::::: ::", + "::::::::::::::: :", + "Ġ öz", + "Ġö z", + "ÑĢ аж", + "ÑĢаР¶", + "ÑĢа ж", + "nÃŃ ho", + "ห ล", + "Ġ ÏĥÏĦη", + "ĠÏĥ ÏĦη", + "ĠÄij á»ģ", + "ĠÄijá» ģ", + "Ġk á»", + "i á»ĥn", + "iá» ĥn", + "iá»ĥ n", + "ÅĻ i", + "Ġkter é", + "¢ ħ", + "ü ç", + "ÙĬ Ùģ", + "Ġ lý", + "Ġl ý", + "Ġth á»Ŀi", + "Ġthá» Ŀi", + "Ġthá»Ŀ i", + "Ġ ìĨĮ", + "ĠìĨ Į", + "н ÑĮ", + "Ð Ĩ", + "ÑĤ ÑĢ", + "à¸ĩ าà¸Ļ", + "к оÑĹ", + "ко ÑĹ", + "μ ο", + "Ġs ür", + "Ġsü r", + "uy á»ģn", + "uyá» ģn", + "Ġ Ùħا", + "ĠÙħ ا", + "à¤Ĥ à¤Ĺ", + "ĠÄij á»ĵng", + "ĠÄijá»ĵ ng", + "ò n", + "à¥ģ ल", + "à¥ģठ²", + "à¥į प", + "à¥įठª", + "λ η", + "Ùħ ر", + "п ÑĢи", + "пÑĢ и", + "i yle", + "iy le", + "ा प", + "ाठª", + "Ġà¤ħ न", + "Ġ ÑĶ", + "ĠÑ Ķ", + "Ġy ön", + "Ġyö n", + "ÙĦ Ùģ", + "a dır", + "ad ır", + "adı r", + "á ½", + "Ġ ê³ł", + "Ġê³ ł", + "Ø® ص", + "im iz", + "imi z", + "åľ ĭ", + "Ġ над", + "Ġн ад", + "Ġна д", + "Ġ ÅĻ", + "ĠÅ Ļ", + "н оÑģÑĤÑĸ", + "но ÑģÑĤÑĸ", + "ноÑģÑĤ Ñĸ", + "ноÑģ ÑĤÑĸ", + "Ġ اÙģ", + "Ġا Ùģ", + "а нÑĸ", + "ан Ñĸ", + "à¥ĩ à¤Ł", + "à¥ĩठŁ", + "Ġ ë§IJ", + "Ġë§ IJ", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Ġ ìĬ¤", + "ĠìĬ ¤", + "ิ à¸ķ", + "å Ħ", + "ÛĮ Ùĩ", + "о ÑĪ", + "ž it", + "ži t", + "ìĭ ¤", + "à¥Ģ न", + "Ġ î", + "æ¥ Ń", + "à¥ĩ न", + "à¥ĩठ¨", + "Ġ Ø°", + "ĠØ °", + "Ġl oại", + "Ġlo ại", + "à¹Ħ à¸Ľ", + "Ñĸ к", + "Ġ кÑĢа", + "Ġк ÑĢа", + "à¥ĭ र", + "ุ à¸Ķ", + "ĠاÙĦ ر", + "ĠÑģ об", + "ĠÑģо б", + "า à¸Ĭ", + "าภĬ", + "Ġसà¤ķ त", + "Ġ ÎĿ", + "ĠÎ Ŀ", + "ا ÙħÙĩ", + "اÙħ Ùĩ", + "à¹ī าà¸Ļ", + "à¹īา à¸Ļ", + "Ġtr ình", + "Ġtrì nh", + "Ġ اÙĦÙģ", + "Ġا ÙĦÙģ", + "ĠاÙĦ Ùģ", + "Ġ اÙĦد", + "ĠاÙĦ د", + "u nun", + "un un", + "unu n", + "о ÑĤов", + "оÑĤ ов", + "оÑĤо в", + "Æ° ợ", + "Æ°á» Ł", + "ĠÑģ во", + "ĠÑģв о", + "ί αÏĤ", + "ία ÏĤ", + "ấ n", + "ог да", + "à¸Ĺ ย", + "Ġb yl", + "Ġby l", + "ØŃ Ø¯", + "à¸ģ ล", + "ب Ùĩ", + "Ġ vÄĽ", + "Ġv ÄĽ", + "è¢ «", + "Ġ Ø¢Ùħ", + "ĠØ¢ Ùħ", + "ĠÄij iá»ģu", + "å ¨", + "Ġk dy", + "Ġkd y", + "Ġ بÙĪ", + "Ġب ÙĪ", + "ẫ n", + "ìľ ł", + "ा à¤ķ", + "ाठķ", + "k ů", + "Ġtr Æ°á»Ŀng", + "ic ké", + "ick é", + "н иÑı", + "ни Ñı", + "Ġ ÏĢοÏħ", + "ĠÏĢ οÏħ", + "ĠÏĢο Ïħ", + "Æ°á»Ł ng", + "н омÑĥ", + "но мÑĥ", + "ном Ñĥ", + "à¹Ī à¸Ļ", + "ู à¹Ī", + "Ġk ết", + "Ġkế t", + "Ġ ï¼¼", + "Ġï¼ ¼", + "Ġ ìĭł", + "Ġìĭ ł", + "i ç", + "Ġn Äĥng", + "ĠnÄĥ ng", + "Äį ÃŃ", + "ÑĤ Ñı", + "ÑĢ еб", + "ÑĢе б", + "Ùĭ ا", + "Ú¯ ÛĮ", + "ãĥ İ", + "Ġkar ÅŁ", + "в Ñĸ", + "Ġph ần", + "à¸Ī ะ", + "ắ t", + "ر Ø©", + "ิ à¸ĩ", + "ิ à¹Ī", + "ा à¤Ī", + "ाठĪ", + "า à¸ŀ", + "าภŀ", + "ÙĨ ÛĮ", + "ìĹ °", + "b ÄĽ", + "Ġ اÙĦص", + "ĠاÙĦ ص", + "í Ĺ", + "Ġ سر", + "Ġس ر", + "l ara", + "la ra", + "lar a", + "ëĭ ¨", + "Ġ ÙĤر", + "ĠÙĤ ر", + "è İ", + "ب د", + "Ġй ого", + "à¥į ह", + "à¥įठ¹", + "Ġc ách", + "Ġcá ch", + "Ġcác h", + "íķĺ ê³ł", + "Ġ ÏĢÏģο", + "ĠÏĢ Ïģο", + "Ġ تع", + "Ġت ع", + "Ĵ Ī", + "Ġ вод", + "Ġв од", + "Ġво д", + "ç¥ ŀ", + "к им", + "ки м", + "Ġd á»±", + "à¹Ģ ห", + "à¹Ģภ«", + "а на", + "ан а", + "Ġ ï½", + "Ġï ½", + "Ġb aÄŁ", + "Ġba ÄŁ", + "Ġप ह", + "Ġ cao", + "Ġc ao", + "Ġca o", + "Ïģ ÏĮ", + "ÙĨ ج", + "ा à¤ı", + "ाठı", + "Ġ å¹´", + "Ġå¹ ´", + "Ġngh iá»ĩp", + "Ġnghiá»ĩ p", + "Û² Û°", + "к аÑı", + "ка Ñı", + "Ïģ ί", + "Ġ бол", + "Ġб ол", + "Ġбо л", + "Ġgi á", + "Ġ зд", + "Ġз д", + "à¥ĩ ल", + "à¥ĩठ²", + "Ġc ấp", + "à¹Ģ ส", + "à¹Ģภª", + "Ïģ γ", + "Ġ ìĤ", + "Ġì Ĥ", + "d ÄĽ", + "à¥ģ न", + "à¥ģठ¨", + "ì Ī", + "ı lan", + "ıl an", + "л аÑģ", + "ла Ñģ", + "Ġ ว", + "Ġภ§", + "Ġ Ïĥε", + "ĠÏĥ ε", + "Ġ Ø«", + "ĠØ «", + "Ġ Ц", + "ĠÐ ¦", + "çĤ º", + "Ġb üy", + "Ġbü y", + "е ÑĨ", + "å¤ ª", + "Ġब न", + "о гÑĢа", + "ог ÑĢа", + "Ġп ÑĢоÑĤ", + "ĠпÑĢ оÑĤ", + "ĠпÑĢо ÑĤ", + "Ġl ượng", + "Ġd ön", + "Ġdö n", + "ร à¸ĩ", + "а ло", + "ал о", + "Ġ جÙħ", + "Ġج Ùħ", + "à¥Ī ,", + "Ġ 미", + "Ġë ¯¸", + "Ġ ê¹", + "Ġê ¹", + "ÙĪ ت", + "à¥Ģ य", + "à¸Ī าà¸ģ", + "Ġch ất", + "Î ©", + "Ġkh ác", + "Ġkhá c", + "Ġth áng", + "j Å¡ÃŃ", + "ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", + "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", + "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł", + "á»ij t", + "ห ร", + "Ñĸ л", + "åħ ī", + "å Ĥ", + "ÙĦ Ø©", + "Ġ ê±°", + "Ġê± °", + "о воÑĢ", + "ов оÑĢ", + "ово ÑĢ", + "iá»ĥ u", + "Ġ меÑĤ", + "Ġм еÑĤ", + "а ÑĶ", + "Ġ ÑĩаÑģ", + "ĠÑĩ аÑģ", + "ĠÑĩа Ñģ", + "Ïģ ε", + "ì¹ ´", + "âĢĮ Ø´", + "ë¬ ¼", + "ú c", + "âĢĮ Ùĩا", + "i á»ģn", + "iá» ģn", + "iá»ģ n", + "st av", + "sta v", + "í ŀ", + "ĠÙĨ ظ", + "Ĩ Ĵ", + "Ġ ÏĦα", + "ĠÏĦ α", + "Ġ заб", + "Ġз аб", + "Ġза б", + "Ùĥ Ø©", + "Ġг ÑĢÑĥ", + "ĠгÑĢ Ñĥ", + "в о", + "Ġ Ùħج", + "ĠÙħ ج", + "Ġ sah", + "Ġs ah", + "Ġsa h", + "ب ÙĦ", + "ع Ø©", + "Ñĥ ÑĪ", + "ĠÑĤ ем", + "ĠÑĤе м", + "í ĭ", + "e ck", + "ec k", + "Ïī ÏĤ", + "ÙĬ ت", + "ìĹ Ī", + "ç ĭ", + "Ø° ا", + "ì łĢ", + "ìł Ģ", + "Ġн аÑģ", + "Ġна Ñģ", + "Ġ поÑĩ", + "Ġп оÑĩ", + "Ġпо Ñĩ", + "æł ¡", + "Ï Ī", + "Ñģ кой", + "Ñģк ой", + "Ñģко й", + "ü c", + "ÙĤ ÙĦ", + "Ġп оз", + "Ġпо з", + "Ġ оÑģоб", + "ĠоÑģ об", + "า ล", + "าภ¥", + "н Ñĭми", + "нÑĭ ми", + "нÑĭм и", + "о лод", + "ол од", + "оло д", + "è ¼", + "Ġ دÛĮ", + "Ġد ÛĮ", + "Ġ ÑĥÑģÑĤ", + "ĠÑĥ ÑģÑĤ", + "ĠÑĥÑģ ÑĤ", + "Ġ 무", + "Ġë ¬´", + "Ġë¬ ´", + "ÙĬ س", + "ë° ©", + "à¥į à¤ļ", + "à¥įठļ", + "и ла", + "ил а", + "Ġn ên", + "н ие", + "ни е", + "ι ν", + "lar ını", + "ların ı", + "à¹Ģ à¸Ļ", + "à¹ĢภĻ", + "ÙĨ ت", + "a ģı", + "aÄŁ ı", + "ım ız", + "ımı z", + "ĠاÙĦ Ø®", + "à¹Ģ ว", + "à¹Ģภ§", + "à¥į न", + "à¥įठ¨", + "Ġ Ïħ", + "ĠÏ ħ", + "Ġ íĨ", + "Ġí Ĩ", + "Ạ»", + "ิ à¹Ĥ", + "α ÏĤ", + "м еÑĤ", + "ме ÑĤ", + "Ġ zp", + "Ġz p", + "Ġje ho", + "ี ยà¸Ļ", + "ีย à¸Ļ", + "ÑĦ оÑĢ", + "ın ız", + "ını z", + "k lad", + "kl ad", + "kla d", + "íĮ Į", + "uy á»ĩ", + "uyá» ĩ", + "ι ά", + "Ġ ãĢģ", + "ĠãĢ ģ", + "Ø´ ر", + "æ© Ł", + "Ġ تا", + "Ġت ا", + "Ġ зна", + "Ġз на", + "Ġзн а", + "س تاÙĨ", + "ست اÙĨ", + "à¥ĩ र", + "à¥ĩठ°", + "ë§ ¤", + "ç ĥ", + "Ġ же", + "Ġж е", + "า à¸Ķ", + "าภĶ", + "Ġ ض", + "ĠØ ¶", + "é Ń", + "Ġн аз", + "Ġна з", + "Ġ ÛĮا", + "ĠÛĮ ا", + "e né", + "en é", + "ั ย", + "íĸ Īëĭ¤", + "íĸĪ ëĭ¤", + "Ġ بد", + "Ġب د", + "à¥ģ à¤ķ", + "à¥ģठķ", + "ÑĤ ов", + "ÑĤо в", + "ì° ¨", + "Ùĩ د", + "à¸Ķ ย", + "Ġho ặc", + "Ġ ÐŁÑĢи", + "ĠÐŁ ÑĢи", + "ĠÐŁÑĢ и", + "ÙĨ ا", + "çİ ĭ", + "Ñĥ ваÑĤи", + "Ñĥв аÑĤи", + "Ñĥва ÑĤи", + "à¸ļ ร", + "Ġà¤ķ रत", + "Ġà¤ķर त", + "Ïĥ ηÏĤ", + "Ïĥη ÏĤ", + "Ø ¤", + "éķ ·", + "åħ ĭ", + "Ġ دار", + "Ġد ار", + "ั à¹Ī", + "Æ¡ i", + "า à¸Ī", + "าภĪ", + "ý mi", + "ým i", + "ấ u", + "Ġد ست", + "Ġدس ت", + "k em", + "ke m", + "Ġ оÑģнов", + "ĠоÑģ нов", + "ëª ¨", + "Ïģ ά", + "æ ħ", + "Ġ اب", + "Ġا ب", + "ĠØ§Ø ¨", + "å£ «", + "Ħ ĸ", + "Î Ķ", + "ÙĬ Ùĥ", + "í İ", + "Ġy üz", + "a dı", + "ad ı", + "า à¸ķ", + "าภķ", + "ä» Ģ", + "ìĿ´ ëĭ¤", + "Ġ zv", + "Ġz v", + "Ġ tÄĽ", + "Ġt ÄĽ", + "Ġ íĸ", + "Ġí ĸ", + "ठ¥", + "Ġ लà¤Ĺ", + "Ġल à¤Ĺ", + "ìĺ Ģ", + "Ġ ан", + "Ġа н", + "ç Ĺ", + "ìĹ Ń", + "н ÑĸÑģÑĤÑĮ", + "нÑĸ ÑģÑĤÑĮ", + "нÑĸÑģÑĤ ÑĮ", + "Å ŀ", + "Ġph át", + "Ġphá t", + "ÙĤ Ø©", + "Ġth ế", + "Ġ ï¾", + "Ġï ¾", + "ì² ľ", + "Ġ ìĦł", + "ĠìĦ ł", + "à¹ĥ à¸Ĭ", + "i êu", + "iê u", + "ÄŁ ini", + "ÄŁi ni", + "ÄŁin i", + "ÙĤ د", + "Ġkter ý", + "Ñģ кий", + "Ñģк ий", + "Ñģки й", + "à¥į ड", + "à¥įठ¡", + "t adır", + "ta dır", + "Ġ Ñģм", + "ĠÑģ м", + "ÙĪ Ùģ", + "ا رÙĬ", + "ار ÙĬ", + "å¾ ·", + "ิ ม", + "Ø® ت", + "å¾ Ī", + "Ġ гоÑĢ", + "Ġг оÑĢ", + "ï¼Į æĪij", + "Ġ ìĺģ", + "Ġìĺ ģ", + "Ġ ëıĻ", + "Ġëı Ļ", + "Ñģ а", + "à¹Ģ à¸Ħ", + "à¹ĢภĦ", + "ë ¯¼", + "ë¯ ¼", + "ึ à¹Ī", + "Ġl iên", + "Ġli ên", + "Ġ Ùĩا", + "ĠÙĩ ا", + "ler ini", + "leri ni", + "lerin i", + "Ġ ÑĨе", + "ĠÑĨ е", + "ا ÙĦÛĮ", + "اÙĦ ÛĮ", + "Ġ मह", + "Ġम ह", + "Ġv ụ", + "Ġvá» ¥", + "Ġxu ất", + "ิ à¸ģ", + "ĠпÑĢо ÑĨ", + "Ġ αν", + "Ġα ν", + "ÑĢ им", + "ÑĢи м", + "Ġc ần", + "Ġ иÑħ", + "Ġи Ñħ", + "н оÑİ", + "но Ñİ", + "Ġt ÃŃnh", + "ĠtÃŃ nh", + "ĠtÃŃn h", + "Ġb á»Ļ", + "Ñĸ м", + "Ġnh áºŃn", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ÙĬ Ùĩ", + "äº ļ", + "Ġоб ла", + "Ġобл а", + "Ġ à¤ĺ", + "Ġठĺ", + "n ých", + "ný ch", + "æĿ ij", + "ÙĦ س", + "Ġне об", + "ا بة", + "اب Ø©", + "v á", + "ο Ïħν", + "οÏħ ν", + "ÑĢ еÑĤ", + "ÑĢе ÑĤ", + "a sında", + "as ında", + "ası nda", + "Ġ yar", + "Ġy ar", + "Ġya r", + "ĠÄij iá»ĥm", + "ĠÄiji á»ĥm", + "н Ñİ", + "ा à¤Ĺ", + "ाठĹ", + "Ġ Ú©Ø´", + "ĠÚ© Ø´", + "Ñĥ з", + "Ġ à¸Ķ", + "ĠภĶ", + "ả m", + "к ами", + "ка ми", + "кам и", + "Ġ ÎĻ", + "ĠÎ Ļ", + "à¹Ģ à¸ķ", + "à¹Ģภķ", + "Ġl Ỽ", + "Ġlá» Ľ", + "ÙĤ ÛĮ", + "k ou", + "ko u", + "ÙĦ ب", + "и ва", + "ив а", + "æ ĵ", + "Ạ¹", + "κ α", + "ë² ķ", + "èĤ ²", + "á»ij n", + "Ġbel ir", + "íĨ ł", + "ÏĦ ή", + "Ñĭ ÑĪ", + "ãĤ ĥ", + "Ġ або", + "Ġа бо", + "Ġаб о", + "s ký", + "sk ý", + "à¥Ī स", + "Ġп ÑĢоÑģÑĤ", + "ĠпÑĢ оÑģÑĤ", + "ĠпÑĢо ÑģÑĤ", + "ĠпÑĢоÑģ ÑĤ", + "ekt edir", + "ekte dir", + "a ž", + "à¹Ī à¸Ń", + "Ġ оÑģÑĤ", + "Ġо ÑģÑĤ", + "ĠоÑģ ÑĤ", + "Ġb ảo", + "Ġ 大", + "Ġå¤ §", + "Ñĭ м", + "Ġm ů", + "Æ°á»Ľ ng", + "åı Ĺ", + "ÙĪ Ùĩ", + "Ġ Ñĥп", + "ĠÑĥ п", + "Ùĥ ÙĨ", + "Ġ ÏĦÏīν", + "ĠÏĦ Ïīν", + "ëħ ¸", + "Ġ à¸Ĭ", + "ĠภĬ", + "Ġ ÑĤого", + "ĠÑĤ ого", + "ĠÑĤо го", + "Ġ Ш", + "ĠÐ ¨", + "ìĿ´ íĬ¸", + "à¹Ģ à¸Ń", + "à¹ĢภŃ", + "и нÑĥ", + "ин Ñĥ", + "ĺ ħ", + "uy á»ĥn", + "uyá» ĥn", + "í ĴĪ", + "íĴ Ī", + "ạ nh", + "ạn h", + "Ġ ãĥ½", + "Ġãĥ ½", + "ÑĤ обÑĭ", + "ÑĤо бÑĭ", + "Ġt ạo", + "å· Ŀ", + "ĠÄij á»iji", + "Ġ ëıĦ", + "Ġëı Ħ", + "ä¹ ħ", + "Ġ تÙħ", + "Ġت Ùħ", + "а ÑĢи", + "аÑĢ и", + "st vÃŃ", + "Ġc ùng", + "íŀ Ī", + "Ġt arih", + "Ġtar ih", + "ì ¤ij", + "ì¤ ij", + "í Ĥ", + "Ġ دÙĪ", + "Ġد ÙĪ", + "ì ¡", + "а лÑĸ", + "ал Ñĸ", + "ภIJ", + "Ġc òn", + "и ÑĤÑĮÑģÑı", + "иÑĤÑĮ ÑģÑı", + "Ġव ह", + "ÅĻ eb", + "ÅĻe b", + "éĽ »", + "Ġ ми", + "Ġм и", + "o vÄĽ", + "ov ÄĽ", + "Ġd ân", + "ÑĨ ÑĸÑı", + "ÑĨÑĸ Ñı", + "ÛĮ ست", + "ÛĮس ت", + "åŃ ¸", + "Ġ ür", + "Ġü r", + "ص ÙĦ", + "ÑĢ иÑĤ", + "ÑĢи ÑĤ", + "า ห", + "าภ«", + "ãģ¦ ãģĦãģŁ", + "ãģ¦ãģĦ ãģŁ", + "θ η", + "ç ĸ", + "Ø Ł", + "i ÅŁtir", + "iÅŁ tir", + "iÅŁti r", + "ĠУкÑĢаÑĹ Ð½Ð¸", + "ĠУкÑĢаÑĹн и", + "ë° ĺ", + "à¥ĩ à¤ĸ", + "à¥ĩठĸ", + "Ġv á»ĭ", + "Ġvá» ĭ", + "Î ¥", + "Ġ ãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢ ĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢ ĠãĢĢ", + "ĠãĢĢĠ ãĢĢĠãĢĢ", + "Ġb ằng", + "Ġt á»ķ", + "Ġtá» ķ", + "о ли", + "ол и", + "๠Ĩ", + "e zi", + "ez i", + "Ġ ни", + "Ġн и", + "Ġ ÎĽ", + "ĠÎ Ľ", + "Ġr ất", + "μ ÏĢ", + "ж д", + "ा रत", + "ार त", + "Ġu ž", + "à¥ĩ स", + "à¥ĩठ¸", + "ا ÙĨد", + "اÙĨ د", + "Ġb ý", + "à¥ĭ ल", + "d ÄĽl", + "dÄĽ l", + "ìķ ĺ", + "Ġ جد", + "Ġج د", + "å ³", + "ื à¹ī", + "Ġb ản", + "ạ ch", + "ạc h", + "ĠÅŁ ey", + "Ġ Ùĩر", + "ĠÙĩ ر", + "Ġ jen", + "Ġj en", + "Ġje n", + "Ġв Ñĸн", + "ĠвÑĸ н", + "es inde", + "esi nde", + "esin de", + "Ġ हम", + "Ġह म", + "çł Ķ", + "à¸ļ à¸ļ", + "Ġch ức", + "Ġchứ c", + "ึ à¸ĩ", + "m alar", + "ma lar", + "mal ar", + "ĠdeÄŁ il", + "æĿ ±", + "Ġt ác", + "Ġtá c", + "Ġk iÅŁ", + "Ġki ÅŁ", + "Ġt á»±", + "Ġtá» ±", + "à¥į ध", + "à¥įठ§", + "à¸Ļ à¸Ĺ", + "ÎŁ Î¥", + "ÎŁÎ ¥", + "ÑģÑĮ кого", + "ÑģÑĮк ого", + "ÑģÑĮко го", + "Ġ ध", + "Ġठ§", + "Ġ ìĿĺ", + "ĠìĿ ĺ", + "ÙĨ Ø©", + "ü s", + "è «", + "Ġtaraf ından", + "ħ n", + "Ġk inh", + "Ġki nh", + "Ġkin h", + "Ïĥ ι", + "à¥Ģ à¤ķ", + "íı ¬", + "ا ÙħÙĦ", + "اÙħ ÙĦ", + "ĠV iá»ĩt", + "Ġ ÏĦον", + "ĠÏĦ ον", + "ĠÏĦο ν", + "Ġ تÙĨ", + "Ġت ÙĨ", + "Ġà¤ħ ध", + "à¹Ī าà¸Ļ", + "à¹Īา à¸Ļ", + "r ı", + "à¤Ĥ द", + "é ª", + "Ġch úng", + "Ġchú ng", + "г и", + "ÏĦ αν", + "ÏĦα ν", + "Ġд оп", + "Ġдо п", + "н Ñĸй", + "нÑĸ й", + "он алÑĮ", + "она лÑĮ", + "Î ĵ", + "Ġb üyük", + "Ġbü yük", + "Ġbüy ük", + "á ¼", + "à¥Ģ र", + "Ø° Ùĩ", + "Ġ ìķĦìĿ´", + "ĠìķĦ ìĿ´", + "Ġdo anh", + "Ġ ÅĻÃŃ", + "ĠÅĻ ÃŃ", + "ÑĨ Ñı", + "Ġt Æ°", + "Ġ सर", + "Ġस र", + "Ġm ÃŃst", + "ĠmÃŃ st", + "Ġë° ı", + "Ø´ ÙĨ", + "Ñĸ б", + "Ġ ãĢĢãĢĢ", + "ĠãĢĢ ãĢĢ", + "çĻ ½", + "о Ñģп", + "оÑģ п", + "к Ñĸв", + "кÑĸ в", + "Ġt ế", + "ãģ Ń", + "Ġt Ỽi", + "Ġtá» Ľi", + "Ġ ìļ°", + "Ġìļ °", + "æľ ĥ", + "ا ÛĮد", + "اÛĮ د", + "æ §", + "ìł IJ", + "Ġd urum", + "Ġdu rum", + "Ġdur um", + "à¹Ģ à¸Ĭ", + "à¹ĢภĬ", + "à¥Ģ त", + "ĠÙĩ ÙĪ", + "à¥Ĥ प", + "Ġgö re", + "Ġgör e", + "Ġ ÑĢоб", + "ĠÑĢ об", + "Ġth iết", + "Ġthi ết", + "a jÃŃ", + "aj ÃŃ", + "ĠاÛĮ راÙĨ", + "âĢ ı", + "ÑģÑĮ коÑĹ", + "ÑģÑĮк оÑĹ", + "ÑģÑĮко ÑĹ", + "ç ħ", + "Ġ ìĦ¸", + "ĠìĦ ¸", + "á» «", + "Ġ à¸Ĥ", + "ĠภĤ", + "ů m", + "ëŀ Į", + "ι κή", + "ικ ή", + "Ġм ог", + "Ġмо г", + "ÙĨ ÙĬ", + "ãģ ļ", + "ा ब", + "ाठ¬", + "æ ¢", + "ع Ùĩ", + "ÑĶ м", + "Ġ ά", + "ĠÎ ¬", + "οÏħ ÏĤ", + "ز ار", + "زا ر", + "ê± ´", + "s ká", + "sk á", + "Ġ اÙĬ", + "Ġا ÙĬ", + "Ġi lg", + "Ġil g", + "Ġ sı", + "Ġs ı", + "e leri", + "el eri", + "eler i", + "ele ri", + "Ġ ÎĹ", + "ĠÎ Ĺ", + "u yor", + "uy or", + "uyo r", + "ठ·", + "ि म", + "िठ®", + "е ва", + "ев а", + "ä»Ģ ä¹Ī", + "ุ à¹Ī", + "à¹ī าà¸ĩ", + "à¹īา à¸ĩ", + "Ġh iá»ĩu", + "Ġhi á»ĩu", + "Ġ اع", + "Ġا ع", + "ĠØ§Ø ¹", + "Ġö zel", + "Ġöz el", + "ν η", + "ëĦ ¤", + "Ġto Ãłn", + "Ġm oh", + "Ġmo h", + "ĠÑı кÑĸ", + "ĠÑıк Ñĸ", + "ç Ĭ", + "mak tadır", + "makta dır", + "makt adır", + "ت اب", + "تا ب", + "Ġ ÑģÑĥ", + "ĠÑģ Ñĥ", + "Ġ yük", + "Ġy ük", + "Ġ Χ", + "ĠÎ §", + "з на", + "зн а", + "о Ñħ", + "Æ° u", + "à¸Ĺ ร", + "ãħ ĭ", + "Ġkar ÅŁÄ±", + "ĠkarÅŁ ı", + "Ùħ ÛĮ", + "Ġ ÑĨÑĸ", + "ĠÑĨ Ñĸ", + "ا دÛĮ", + "اد ÛĮ", + "à¥Ģ ।", + "Ïģ η", + "л ов", + "ло в", + "å¤ «", + "Ġph ân", + "Ġп оп", + "Ġпо п", + "ç· ļ", + "Ñı н", + "ุ à¸ĵ", + "ÑģÑĤ Ñĥп", + "ίν αι", + "ίνα ι", + "ĠÑĢ окÑĥ", + "ĠÑĢок Ñĥ", + "l arda", + "lar da", + "è» Ĭ", + "Ïģ Ïī", + "ÙĪ اÙĩ", + "ÙĪا Ùĩ", + "è ħ", + "à¥į रत", + "à¥įर त", + "å· ±", + "Ġ ÑĢÑĥ", + "ĠÑĢ Ñĥ", + "Ġth á»ĭ", + "Ġthá» ĭ", + "ĠÄij iá»ĩn", + "ĠÄiji á»ĩn", + "ìĸ ij", + "n ého", + "né ho", + "ส ม", + "ê° ģ", + "a cÃŃ", + "ac ÃŃ", + "Ġг ода", + "Ġгод а", + "k az", + "ka z", + "Ġb öl", + "Ġbö l", + "Ġg ian", + "Ġgi an", + "Ġgia n", + "à¸Ľ ร", + "ï¾ ŀ", + "ั à¸ķ", + "Ġg erç", + "Ġger ç", + "Ġ اج", + "Ġا ج", + "ĠØ§Ø ¬", + "Ġ ή", + "ĠÎ ®", + "Ùij Ùİ", + "Ñģ кого", + "Ñģк ого", + "Ñģко го", + "ÑĢ аÑħ", + "ÑĢа Ñħ", + "Ġ Åł", + "ĠÅ ł", + "Ġ à¤Ľ", + "ĠठĽ", + "о ÑģÑĤÑĸ", + "оÑģ ÑĤÑĸ", + "оÑģÑĤ Ñĸ", + "ë³ ¸", + "ÑģÑĮ кий", + "ÑģÑĮк ий", + "Û± Û¹", + "Û±Û ¹", + "Ñĥ ва", + "Ñĥв а", + "ا ÙĦÙħ", + "اÙĦ Ùħ", + "ĠÙħ ص", + "ëį ĺ", + "b ÃŃ", + "Ġ ÙĪج", + "ĠÙĪ ج", + "ÏĦ ÏĮ", + "e bilir", + "eb ilir", + "Ġt iếp", + "Ġti ếp", + "Ġtiế p", + "é ¤", + "Ġ ä¸Ģ", + "Ġä¸ Ģ", + "ĠÑģ ÑĢед", + "ë Ĥ¨", + "ëĤ ¨", + "ε Ïģι", + "εÏģ ι", + "ا Ø«", + "Ø§Ø «", + "Ñģ ов", + "Ñģо в", + "Ïĩ ε", + "Ġ ë¶Ħ", + "Ġë¶ Ħ", + "Ġta ké", + "Ġtak é", + "Ġd üz", + "Ġdü z", + "Ġ íıī", + "Ġíı ī", + "Ġ اص", + "Ġا ص", + "ĠØ§Ø µ", + "ĠÏĥ ÏĦην", + "ĠÏĥÏĦη ν", + "ë° Ķ", + "Ġh á»Ļi", + "Ġhá»Ļ i", + "ر Ùĩ", + "ب ÛĮ", + "в е", + "Ġا ÙĦØ·", + "ĠاÙĦ Ø·", + "Ġ ÑĢез", + "ĠÑĢ ез", + "ĠÑĢе з", + "ب ار", + "با ر", + "Ġgi ải", + "Ġgiả i", + "ãģ« ãģª", + "ol eÄį", + "ole Äį", + "ठł", + "; :", + "ä½ ı", + "Ú© Ùĩ", + "Ġ Φ", + "ĠÎ ¦", + "Ġ ÑĥÑĩ", + "ĠÑĥ Ñĩ", + "âĹı âĹı", + "ู à¸ģ", + "à¥ĩ व", + "à¥ĩठµ", + "Ïĥ α", + "Ġ اÙĨت", + "Ġا ÙĨت", + "ĠاÙĨ ت", + "Ġв п", + "Ġqu ả", + "e nin", + "en in", + "eni n", + "Ġ êµIJ", + "Ġêµ IJ", + "μ ά", + "Ú© ت", + "ÙĤ Ùĩ", + "Ġ Türkiye", + "ĠTür kiye", + "ĠTürk iye", + "Ġth ức", + "Ġthứ c", + "íĹ ĺ", + "iá»ĩ m", + "Ġत à¤ķ", + "Ġ éĩ", + "Ġé ĩ", + "़ ा", + "ĠØ£ ÙĪ", + "á le", + "ál e", + "ç© ¶", + "ĠÅŁ ekil", + "ĠÅŁek il", + "к ого", + "ко го", + "ког о", + "ÑĪ иÑħ", + "ÑĪи Ñħ", + "ا ÛĮØ´", + "اÛĮ Ø´", + "ت ÙĨ", + "н ей", + "не й", + "à¸Ĺ ำ", + "Ġ Ñıв", + "ĠÑı в", + "ر Ùħ", + "Ġm áy", + "Ġmá y", + "ห ม", + "ı yla", + "ıy la", + "Ġc ầu", + "Ġд об", + "Ġдо б", + "Ġ ìŀ¥", + "Ġìŀ ¥", + "o vý", + "ov ý", + "ι κÏĮ", + "ικ ÏĮ", + "Ġ ãħĩ", + "Ġãħ ĩ", + "Ġ ÑĤеÑĢ", + "ĠÑĤ еÑĢ", + "ĠÑĤе ÑĢ", + "Į Ĵ", + "س ÙĬ", + "Ġol uÅŁ", + "Ġb yla", + "Ġby la", + "Ġbyl a", + "ع ÙĦ", + "Ġ ÙĥاÙĨ", + "ĠÙĥ اÙĨ", + "б оÑĢ", + "бо ÑĢ", + "ì² Ń", + "ãĥ ı", + "u bl", + "ub l", + "Ġ اخ", + "Ġا Ø®", + "ĠØ§Ø ®", + "ÙĦ ÙĪد", + "ÙĦÙĪ د", + "ت ÙĬ", + "l adı", + "la dı", + "lad ı", + "Ġ Ã¶ÄŁ", + "Ġö ÄŁ", + "r uh", + "ru h", + "ç ¿", + "Ġ بعد", + "Ġب عد", + "Ġبع د", + "ÎĻ Îij", + "i dir", + "id ir", + "idi r", + "ãģ« ãģ¯", + "Ġs öy", + "Ġsö y", + "Ġkh ách", + "Ġkhác h", + "Ġkhá ch", + "ÑĨ е", + "Ġ Ø´ÙĪد", + "ĠØ´ ÙĪد", + "ĠØ´ÙĪ د", + "ç ¸", + "Ġ ëħ¸", + "Ġëħ ¸", + "ú p", + "Ġn eden", + "Ġne den", + "Ġned en", + "Ġh óa", + "Ġà¤ī प", + "Ïĥ ειÏĤ", + "Ïĥει ÏĤ", + "æĪ ¿", + "Ġ ³³", + "ĠÂł Âł", + "Ġ ìķĮ", + "Ġì ķĮ", + "Ġìķ Į", + "à¥Ģ ,", + "´ ij", + "ê u", + "ÑĢ ок", + "ÑĢо к", + "à¹Ģ à¸Ī", + "à¹ĢภĪ", + "Ġε ίναι", + "Ġ بÙĦ", + "Ġب ÙĦ", + "Ġ Ñģов", + "ĠÑģ ов", + "ĠÑģо в", + "Ġö nem", + "Ġön em", + "Ġöne m", + "Ġ à¸ĭ", + "Ġภĭ", + "ì§Ģ ë§Į", + "å® ĺ", + "ê² ©", + "ìĦ Ŀ", + "Ġ až", + "Ġa ž", + "Ġd uy", + "Ġdu y", + "ãģ¨ ãģĦ", + "Ø Ľ", + "δ ο", + "θ ε", + "Ùĥ اÙĨ", + "ठ¢", + "ा à¤ĵ", + "ाठĵ", + "Ġd á»ĭch", + "Ġdá»ĭ ch", + "á»Ļ ng", + "á»Ļn g", + "ส ำ", + "Ä ı", + "Ġ ÑĹÑħ", + "ĠÑĹ Ñħ", + "α λ", + "e Äį", + "ç² ¾", + "Ġ зв", + "Ġз в", + "èĩª å·±", + "Ġ اÙĦÙĦÙĩ", + "ĠاÙĦ ÙĦÙĩ", + "ĠاÙĦÙĦ Ùĩ", + "Ġ СÑĤ", + "ĠС ÑĤ", + "Ġ سÙĨÚ¯", + "Ġس ÙĨÚ¯", + "ĠسÙĨ Ú¯", + "Ġ дом", + "Ġд ом", + "Ġдо м", + "г оÑĤов", + "го ÑĤов", + "гоÑĤ ов", + "п овÑĸд", + "пов Ñĸд", + "по вÑĸд", + "Ġ Bá»Ļ", + "ĠB á»Ļ", + "à¥įय à¤ķ", + "Ø· Ø©", + "м ов", + "мо в", + "à¸Ĺ าà¸ĩ", + "ึ à¸ģ", + "Ġ Ñĸз", + "ĠÑĸ з", + "à¥ĭ à¤ľ", + "Ġgö ster", + "Ġ باشد", + "Ġبا شد", + "Ġباش د", + "i leri", + "il eri", + "ile ri", + "iler i", + "ĠÑģ еб", + "Ñī о", + "Ġãħĩ ãħĩ", + "ب ت", + "Ñģ е", + "à¥ĩ à¤ľ", + "à¥ĩठľ", + "Ġl ên", + "Ġ تÙĪ", + "Ġت ÙĪ", + "Ñĸ ÑģÑĤÑĮ", + "ÑĸÑģ ÑĤÑĮ", + "ÑĸÑģÑĤ ÑĮ", + "ï¾Ĩ ï¾Ĩ", + "Ġth Æ°á»Ŀng", + "Ġol duÄŁu", + "Ġoldu ÄŁu", + "ĠolduÄŁ u", + "v ÄĽt", + "vÄĽ t", + "ìĨ į", + "ãģĿ ãģĨ", + "Ġ ìĦ±", + "ĠìĦ ±", + "ë° ľ", + "Ġ à¸ģาร", + "Ġà¸ģ าร", + "Ġ Ø´Ùĩر", + "ĠØ´ Ùĩر", + "ĠØ´Ùĩ ر", + "s led", + "sl ed", + "ả nh", + "ản h", + "æŀ Ĺ", + "l acak", + "la cak", + "lac ak", + "Ġm ình", + "Ú© ÛĮ", + "Ġ à¹ĥà¸Ļ", + "Ġd ùng", + "Ġdù ng", + "Ġм аÑģ", + "Ġма Ñģ", + "ÑĦ ек", + "æ° Ķ", + "é §", + "Ġ اØŃ", + "Ġا ØŃ", + "ĠØ§Ø Ń", + "èµ °", + "ÎĻ Îļ", + "à¥ĩ ।", + "ÑģÑĮ ка", + "ÑģÑĮк а", + "Ġ ÑĩаÑģÑĤ", + "ĠÑĩ аÑģÑĤ", + "ĠÑĩа ÑģÑĤ", + "ĠÑĩаÑģ ÑĤ", + "lar ının", + "ların ın", + "larını n", + "Ġ ê¹Ģ", + "Ġê¹ Ģ", + "ì¸ µ", + "н ими", + "ни ми", + "ним и", + "èª ŀ", + "åĢ ĭ", + "Ġ êµŃ", + "Ġêµ Ń", + "к оÑĢ", + "ко ÑĢ", + "m aya", + "ma ya", + "may a", + "ิ à¹Ĥà¸Ļ", + "ิà¹Ĥ à¸Ļ", + ". ศ", + "Ġh á»ĩ", + "Ġhá» ĩ", + "Ġ تÙĤ", + "Ġت ÙĤ", + "γ κ", + "Ġà¤Ĩप à¤ķ", + "Ñģ ÑĤоÑĢ", + "ÑģÑĤ оÑĢ", + "ĠÄij o", + "Ġch ủ", + "ا ÛĮت", + "اÛĮ ت", + "ĠQu á»ijc", + "г лÑı", + "гл Ñı", + "ãĢĤ ãĢįĊĊ", + "ãĢĤãĢį ĊĊ", + "Ġn Ãło", + "à¸Ń ล", + "æĬ Ĭ", + "ÙĪ رت", + "ÙĪر ت", + "Ġb ude", + "Ġbu de", + "Ġbud e", + "æĽ ¸", + "e lik", + "el ik", + "eli k", + "Ġ جÙĩ", + "Ġج Ùĩ", + "ĠبÙĪ ابة", + "èĬ ±", + "د ار", + "دا ر", + "Ġb ýt", + "Ġbý t", + "Ñĩ е", + "ãĤĵ ãģł", + "ĠÙħ Ø·", + "l ere", + "le re", + "ler e", + "ÎĹ Î£", + "íĺ ķ", + "âĸ į", + "ÄŁ u", + "Ġв з", + "ÙĬ ز", + "ĠÐł оÑģ", + "íĭ °", + "Ġد اش", + "ì§ ij", + "a tı", + "at ı", + "m esi", + "me si", + "mes i", + "ãĤī ãĤĮ", + "ů v", + "r át", + "rá t", + "оÑģ об", + "åIJ Ħ", + "uy á»ĩn", + "uyá»ĩ n", + "åģ ļ", + "ü st", + "üs t", + "éĩ İ", + "α Ïĥ", + "Ġm ặt", + "е лов", + "ел ов", + "ело в", + "åį ļ", + "д ж", + "Ġد ارد", + "Ġدار د", + "Ġf ark", + "Ġfa rk", + "Ġfar k", + "à¹ī วย", + "à¹īว ย", + "о ни", + "он и", + "Ġب Ø®", + "à¥ģ त", + "à¥ģठ¤", + "ĠÄij ây", + "α Ïģα", + "αÏģ α", + "Ġ δια", + "Ġδ ια", + "Ġδι α", + "Ġ è¯", + "Ġè ¯", + "к аÑħ", + "ка Ñħ", + "ch áz", + "z enÃŃ", + "ze nÃŃ", + "zen ÃŃ", + "ÑĢ оп", + "ÑĢо п", + "à¥Ģ म", + "í Ĩµ", + "íĨ µ", + "d ü", + "à¸ł าà¸ŀ", + "Ġ íĬ", + "Ġí Ĭ", + "ÙĪ ا", + "Ġt á»ijt", + "Ġtá»ij t", + "ï¼Ł ãĢįĊĊ", + "ï¼ŁãĢį ĊĊ", + "Ġ æľĪ", + "Ġnh Æ°ng", + "ĠnhÆ° ng", + "Ġne ž", + "à¥ĭ ड", + "ìĹIJ ê²Į", + "à¤Ĥ ड", + "¶ Į", + "Ġ меÑģÑĤ", + "Ġм еÑģÑĤ", + "ा à¤ģ", + "ाठģ", + "ì¦ Ŀ", + "ĠÄij ang", + "ĠÄija ng", + "à¸Ń à¸Ķ", + "í ĽĦ", + "á»į i", + "sk ého", + "ské ho", + "Ġд ок", + "Ġдо к", + "Ġ تص", + "Ġت ص", + "Ġph òng", + "Ġ ê°ķ", + "Ġê° ķ", + "Ġtr Æ°á»Ľc", + "í ijľ", + "Ù Ķ", + "Ġph ÃŃ", + "Ġch á»įn", + "ä¹ IJ", + "ĠÅŁek ilde", + "ĠÅŁekil de", + "Ġ íİ", + "Ġí İ", + "é º", + "ë £¨", + "ë£ ¨", + "à¥Ī ।Ċ", + "à¥Ī। Ċ", + "ÙĪ رÛĮ", + "ÙĪر ÛĮ", + "Ñģ ÑĤÑĢа", + "ÑģÑĤ ÑĢа", + "ÑģÑĤÑĢ а", + "il di", + "ild i", + "Ġα Ïħ", + "в аннÑı", + "ван нÑı", + "ìļ ¸", + ". âĢľĊĊ", + ".âĢľ ĊĊ", + "ĠÑĤак же", + "ëĵ ±", + "е ка", + "ек а", + "æī į", + "Ùħ Ø©", + "Ġph Æ°Æ¡ng", + "é© ¬", + "ãĢĢ ĠãĢĢ", + "ãĢĢĠ ãĢĢ", + "ov ých", + "ový ch", + "ี ยà¸ĩ", + "ีย à¸ĩ", + "ĠT ru", + "ĠTr u", + "е Ñģп", + "еÑģ п", + "st up", + "stu p", + "Ä Į", + "Ġdal Å¡ÃŃ", + "ز ÛĮ", + "Ġ 매", + "Ġë§ ¤", + "Ġ обÑĢаз", + "Ġоб ÑĢаз", + "ĠобÑĢа з", + "Ġaç ık", + "Ġaçı k", + "ê° ķ", + "Ùģ ادÙĩ", + "Ú¯ اÙĨ", + "à¹ī à¸Ļ", + "ẩ n", + "å·¥ ä½ľ", + "Ġ तर", + "Ġत र", + "ÙĬ ع", + "Ġ ãĢĬ", + "ĠãĢ Ĭ", + ", âĢľ", + "Ġ nev", + "Ġn ev", + "Ġne v", + "ั à¸į", + "ÄŁ ını", + "ģın ı", + "Ġ jin", + "Ġj in", + "Ġji n", + "ا خت", + "اخ ت", + "س ر", + "Ġt Ãłi", + "Ġkter á", + "Ġا ÙĦÙĦ", + "ĠاÙĦ ÙĦ", + "ठħ", + "iz met", + "izm et", + "à¥ģ म", + "à¥ģठ®", + "า ะ", + "าภ°", + "Ġ ê·", + "Ġê ·", + "l ıģı", + "lı ģı", + "lıģ ı", + "çı ¾", + "li ÄŁi", + "liÄŁ i", + "êµ °", + "a lık", + "al ık", + "alı k", + "Ġد ÙĪر", + "ĠدÙĪ ر", + "Ġ ìĭ¤", + "Ġìĭ ¤", + "Ġз аÑģ", + "Ġза Ñģ", + "ÙĤ ÙĬ", + "Ġ ứng", + "Ġ ÙĥÙĩ", + "ĠÙĥ Ùĩ", + "ÎŁ Σ", + "ÎŁÎ £", + "è¨ Ń", + "ç Į", + "ãģĦ ãģŁ", + "íĺ Ħ", + "Ġ ÑĤе", + "ĠÑĤ е", + "е ÑĢÑĸ", + "еÑĢ Ñĸ", + "s ız", + "sı z", + "Ġ ý", + "Ġà ½", + "д ов", + "до в", + "Ġ à¤ĩसà¤ķ", + "Ġà¤ĩस à¤ķ", + "г од", + "го д", + "Ġby lo", + "Ġbyl o", + "าà¸Ħ ม", + "е нием", + "ен ием", + "ени ем", + "ение м", + "Ð ¨", + "æľ ¯", + "Ġप हल", + "Ġपह ल", + "Ġ aÅŁ", + "Ġa ÅŁ", + "ि à¤ľ", + "िठľ", + "åĵ ¡", + "в аÑĢ", + "ва ÑĢ", + "à¹ī ำ", + "â ĮĴ", + "ov án", + "ová n", + "Ġgi úp", + "Ð ¥", + "ĠÑģ Ñĥд", + "ĠÑģÑĥ д", + "Ġà¤ķ म", + "ạ m", + "ر س", + "Ġ 人", + "Ġ بÛĮ", + "Ġب ÛĮ", + "Ġà¤īन à¤ķ", + "ë¦ ½", + "áºŃ y", + "Ġv áºŃt", + "л ÑıеÑĤÑģÑı", + "лÑı еÑĤÑģÑı", + "лÑıеÑĤ ÑģÑı", + "Ġs eç", + "Ġse ç", + "Ġ ì½", + "Ġì ½", + "ÑĢ Ñĥж", + "ÑĢÑĥ ж", + "ت ص", + "| :", + "Ġ ëł", + "Ġë ł", + "и ми", + "им и", + "Ġ лÑİб", + "ĠлÑİ Ð±", + "Ġ à¸ľ", + "Ġภľ", + "ï¼Į ä½Ĩ", + "Ġ нав", + "Ġн ав", + "Ġна в", + "âĢ ¬", + "à¹Ī าย", + "à¹Īา ย", + "Ġ رس", + "Ġر س", + "s iniz", + "sin iz", + "ë ¨", + "е ниÑİ", + "ен иÑİ", + "ени Ñİ", + "Ġ ล", + "Ġภ¥", + "ا سÛĮ", + "اس ÛĮ", + "ॠľ", + "ĠÙ¾ ÛĮØ´", + "ĠÙ¾ÛĮ Ø´", + "ί δ", + "Ġ Ù¾ÛĮ", + "ĠÙ¾ ÛĮ", + "еÑĢж ав", + "ठĨ", + "ĠdÃ¼ÅŁ ün", + "å¿ «", + "ÑĢ еÑģ", + "ÑĢе Ñģ", + "åħ «", + "ÑĤ Ñĸ", + "ि à¤Ł", + "िठŁ", + "Ġ ÑĤеÑħ", + "ĠÑĤ еÑħ", + "ĠÑĤе Ñħ", + "ú t", + "ÙĨ Ùĩ", + "Ġ ÙĨØ´", + "ĠÙĨ Ø´", + "çĻ º", + "Ġ ê°¤", + "Ġê° ¤", + "л ед", + "ле д", + "Ġ ëĵ¤", + "Ġëĵ ¤", + "Ġbi lg", + "Ġbil g", + "Ġsp oleÄį", + "Ġspol eÄį", + "Ġspole Äį", + "ĠÄij Æ¡n", + "Ġ à¤īत", + "Ġà¤ī त", + "Ġtr á»ĭ", + "Ġ عÙħ", + "Ġع Ùħ", + "Ġ ।", + "Ġà ¥¤", + "Ġॠ¤", + "Ġú Äį", + "ãģ ¸", + "ว à¸ģ", + "ĠÑģл ÑĥÑĩа", + "ĠÑģлÑĥÑĩ а", + "ĠÑģлÑĥ Ñĩа", + "á» įng", + "á»į ng", + "á»įn g", + "åı Ī", + "и ÑĤÑĥ", + "иÑĤ Ñĥ", + "æľī éĻIJ", + "ë¦ °", + "ëĭ ĺ", + "Ġho ạt", + "ĠìĿ´ ëıĻ", + "з наÑĩ", + "зна Ñĩ", + "зн аÑĩ", + "Ġاست ÙģادÙĩ", + "ĠпÑĢо ÑĨеÑģ", + "ĠпÑĢоÑĨ еÑģ", + "an ın", + "anı n", + "г Ñĥ", + "Ġ اÙĦØ«", + "ĠاÙĦ Ø«", + "æĹ¥ æľ¬", + "ι κά", + "ικ ά", + "ĠÑĹ ÑĹ", + "ì§ ģ", + "i nu", + "in u", + "Ġس از", + "ãĤ ¡", + "ï¾ ī", + "Ġ اÙĤ", + "Ġا ÙĤ", + "Ġk ế", + "ů sob", + "à¹ĩ à¸ģ", + "åIJ §", + "æ¼ Ķ", + "Ñī ие", + "Ñīи е", + "ç Ĩ", + "ÑĮ ого", + "à¥ĭ à¤Ł", + "ا Ù¾", + "ا٠¾", + "å ®¤", + "å® ¤", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Ġtr iá»ĥn", + "Ġtri á»ĥn", + "Ġt áºŃp", + "é£ Ł", + "ë ¹", + "ĠÑĩеÑĢ ез", + "Ġ ÑĨи", + "ĠÑĨ и", + "Ñģ Ñĥ", + "Ġ нем", + "Ġн ем", + "Ġне м", + "Ġ аÑĢ", + "Ġа ÑĢ", + "Ġ ÙĦا", + "ĠÙĦ ا", + "Ġ ì§Ħ", + "Ġì§ Ħ", + "ç Ł³", + "çŁ ³", + "Ġп ÑĢоб", + "ĠпÑĢ об", + "ĠпÑĢо б", + "Ġ ìĽIJ", + "ĠìĽ IJ", + "ÛĮ ÙĨÛĮ", + "ÛĮÙĨ ÛĮ", + "Ñİ Ñĩи", + "âĢ į", + "Û± Û³", + "Û±Û ³", + "ã Ĥ¬", + "ãĤ ¬", + "çłĶ 究", + "í Ĥ¤", + "íĤ ¤", + "Ġger çek", + "Ġgerç ek", + "ĠØŃ Ø³", + "íĶ ¼", + "èĤ ¡", + "Ġ ÏĦι", + "ĠÏĦ ι", + "ĠvÅ¡ ech", + "ĠvÅ¡e ch", + "Ġv ì", + "ا ÙĨÙĬ", + "اÙĨ ÙĬ", + "ĠÙĩ ست", + "Ġ ëĤ¨", + "Ġë Ĥ¨", + "ĠëĤ ¨", + "ÅĻ ej", + "ÅĻe j", + "е ÑĢг", + "еÑĢ г", + "Ġs öz", + "Ġsö z", + "ĠاÙĦ Ùħت", + "ĠاÙĦÙħ ت", + "Ġch ế", + "á»ĵ i", + "åı ¤", + "âĸį âĸį", + "á»ĵ ng", + "á»ĵn g", + "ãĥ ¢", + "Ġ ди", + "Ġд и", + "ε λ", + "Ġ она", + "Ġо на", + "Ġон а", + "Ġ най", + "Ġн ай", + "Ġна й", + "Ġ _{", + "Ġ_ {", + "п ол", + "по л", + "al iz", + "ali z", + "Ġt Äĥng", + "Ġ dÃŃ", + "Ġd ÃŃ", + "é p", + "Ġ ÙĦÙħ", + "ĠÙĦ Ùħ", + "Ġm ož", + "Ġmo ž", + "Ġng oÃłi", + "è Ĺ", + "Ġ Ñĩем", + "ĠÑĩ ем", + "ĠÄij á»ķ", + "ĠÄijá» ķ", + "е ÑĤа", + "еÑĤ а", + "åı ²", + "ĠÑģ каз", + "ĠÑģк аз", + "ĠÑģка з", + "ãĤ¿ ãĥ¼", + "а нÑĮ", + "ан ÑĮ", + "Ġg öz", + "Ġgö z", + "ë³ Ħ", + "ãģĭ ãģ£ãģŁ", + "Ġ ëįĶ", + "Ġëį Ķ", + "ĠÙĨ ÙĤ", + "Ġ ÑĥÑĩа", + "ĠÑĥ Ñĩа", + "ĠÑĥÑĩ а", + "Ġsa hip", + "Ġsah ip", + "ĠÑģ пе", + "ĠÑģп е", + "ί οÏħ", + "ίο Ïħ", + "ì ½Ķ", + "ì½ Ķ", + "Ġ ëĪ", + "Ġë Ī", + "m am", + "ma m", + "Ġr oce", + "Ġro ce", + "Ġroc e", + "Ġ ÙĨاÙħ", + "ĠÙĨ اÙħ", + "еÑĢ аÑĤÑĥ", + "еÑĢа ÑĤÑĥ", + "ı p", + "ãģĦ ãģ¦", + "Ġ íķĻ", + "Ġíķ Ļ", + "Ġ à¤ĩन", + "Ġà¤ĩ न", + "å ©", + "Ġnh iên", + "a tır", + "at ır", + "atı r", + "ÅĻ enÃŃ", + "ÅĻe nÃŃ", + "ÅĻen ÃŃ", + "د Ø©", + "ãĥª ãĥ¼", + "ล à¸ĩ", + "Ġ éĢ", + "Ġé Ģ", + "Ġ à¹Ģà¸Ľ", + "Ġà¹Ģ à¸Ľ", + "Ġà¹ĢภĽ", + "д Ñĸ", + "ÎŃ Ïģ", + "ìĦ ¤", + "г ÑĢа", + "es ine", + "esi ne", + "esin e", + "Ġ ее", + "Ġе е", + "Ġ iki", + "Ġi ki", + "Ġik i", + "Ġت ج", + "lar ına", + "ları na", + "ların a", + "d ür", + "dü r", + "ĠاÙĦ Ø°", + "Ùħ ت", + "ĠठIJ", + "ि द", + "िठ¦", + "Ġ ë¹", + "Ġë ¹", + "ÑĦоÑĢм а", + "ÑĦоÑĢ ма", + "Ġ они", + "Ġо ни", + "Ġон и", + "г оÑĢ", + "го ÑĢ", + "н еÑģ", + "не Ñģ", + "ìĺĢ ëĭ¤", + "ıl dı", + "Ġ çek", + "Ġç ek", + "Ġ дов", + "Ġд ов", + "Ġдо в", + "د ÛĮ", + "Ġ ÄĮesk", + "ĠÄĮ esk", + "ĠÄĮes k", + "ÑĪ а", + "Ġ ات", + "Ġا ت", + "ĠØ§Ø ª", + "å± ĭ", + "æĸ ¼", + "Ġ práv", + "Ġp ráv", + "Ġpr áv", + "Ġprá v", + "é mu", + "ém u", + "å¸ Ī", + "ãħĭ ãħĭ", + "Ġil gili", + "Ġilg ili", + "Ġilgi li", + "ห ว", + "ठĩ", + "ा ष", + "ाठ·", + "ëŀ ij", + "as yon", + "asy on", + "ÑĨ ÑĮ", + "à¹ģ à¸ķ", + "ợ i", + "Ġв ÑĭÑģ", + "ĠвÑĭ Ñģ", + "ÑĸлÑĮ ки", + "ĠкоÑĤоÑĢ Ñĭе", + "н ики", + "ни ки", + "ник и", + "Ġ اد", + "Ġا د", + "ĠØ§Ø ¯", + "Ġ živ", + "Ġž iv", + "Ġži v", + "Ġα ÏĢο", + "ĠαÏĢ ο", + "ر ض", + "ا Ø©", + "Ø§Ø ©", + "Ġk dyž", + "Ġkdy ž", + "ữ a", + "Ġ ëĮĢíķľ", + "ĠëĮĢ íķľ", + "Ġt ôi", + "Ġtô i", + "Ñĥ ÑĶ", + "ز ر", + "Ġ å¥", + "Ġå ¥", + "ãĥĭ ãĥĭ", + "ب Ø©", + "ÏĦ οÏĤ", + "ÏĦο ÏĤ", + "ÑĨи он", + "Ġ ÙħÙĬ", + "ĠÙħ ÙĬ", + "Ġ Äĥn", + "ĠÄ ĥn", + "à¥ĩ à¤Ĺ", + "à¥ĩठĹ", + "Ġ ÑĢег", + "ĠÑĢ ег", + "ĠÑĢе г", + "ĠlỼ n", + "ì¤ Ģ", + "ìĭ ¬", + "Ġb iết", + "Ġbi ết", + "a ları", + "al arı", + "ala rı", + "alar ı", + "Ùģ ÙĬ", + "ä¸ĸ çķĮ", + "Ġне обÑħодим", + "Ġнеоб Ñħодим", + "à¸Ļ ว", + "ν ÏĦ", + "Ġ ảnh", + "íĸ Ī", + "Ġ वर", + "Ġव र", + "h led", + "hl ed", + "hle d", + "ิ à¸Ī", + "æŃ »", + "ĠاÙĦ تÙĬ", + "ĠاÙĦت ÙĬ", + "н оÑģ", + "но Ñģ", + "p rav", + "pr av", + "pra v", + "Ñı ÑĤи", + "ÑıÑĤ и", + "Ñī а", + "ÙĪ ÙĨÙĩ", + "ÙĪÙĨ Ùĩ", + "Ġ aÄŁ", + "Ġa ÄŁ", + "à¸ŀ ระ", + "à¸ŀร ะ", + "Ġth á»ijng", + "Ġthá» ijng", + "ÑĦ и", + "Ġг олов", + "Ġгол ов", + "Ġk hoa", + "Ġkh oa", + "Ġkho a", + "Ġ ëłĪ", + "Ġëł Ī", + "ãģ Ĵ", + "Ġget ir", + "Ġge tir", + "Ø´ ت", + "ж еннÑı", + "жен нÑı", + "е нÑĸ", + "ен Ñĸ", + "Ġgi ữ", + "Ġgiá» ¯", + "ler inin", + "leri nin", + "lerin in", + "lerini n", + "à¥Ģ व", + "éģ ¸", + "स र", + "ĠÑĩ елов", + "à¥į à¤ľ", + "à¥įठľ", + "ĠбÑĥ ло", + "Ġ اÙĨد", + "Ġا ÙĨد", + "ĠاÙĨ د", + "ั à¸Ļà¸Ĺ", + "ัà¸Ļ à¸Ĺ", + "è® ©", + "Ġq uyá»ģn", + "Ġquy á»ģn", + "ĠØŃ Ø§ÙĦ", + "ì² ĺ", + "Ġ лÑİд", + "ĠлÑİ Ð´", + "Ïģ Ïĩ", + "алÑĮ но", + "ãĢĢ ãĥ½", + "ê¸ ī", + "ãĤ ±", + "ĠÙħ رد", + "ĠÙħر د", + "Ġ ông", + "Ġô ng", + "Ġ اش", + "Ġا Ø´", + "ĠØ§Ø ´", + "大 åѦ", + "ì¦ Ī", + "æĪ ¦", + "e yi", + "ey i", + "Ġ ÐIJн", + "ĠÐIJ н", + "ि प", + "िठª", + "Ġt iêu", + "Ġti êu", + "Ø´ ÛĮ", + "ắ n", + "é ŃĶ", + "éŃ Ķ", + "ãģ¨ ãģĦãģĨ", + "ãģ¨ãģĦ ãģĨ", + "Ġ ìłĢ", + "Ġì łĢ", + "Ġìł Ģ", + "к ÑĤи", + "кÑĤ и", + "Ġ ÙħØŃÙħد", + "ĠÙħØŃ Ùħد", + "Ġ íĨµ", + "Ġí Ĩµ", + "ĠíĨ µ", + "ุ ม", + "åį ¡", + "о ÑĢов", + "оÑĢ ов", + "оÑĢо в", + "к оÑİ", + "ко Ñİ", + "Ġl á»±c", + "å³ ¶", + "Ġ رÙĪز", + "Ġر ÙĪز", + "ĠرÙĪ ز", + "Ñħ Ñĸд", + "Ġh á»ĵ", + "Ġhá» ĵ", + "Ġ ül", + "Ġü l", + "Ġ Ø´Ùħ", + "ĠØ´ Ùħ", + "ÙĨ س", + "ب ÙĪ", + "Ġth êm", + "ạ c", + "åº ľ", + "e chn", + "ec hn", + "ech n", + "Ġ Îļα", + "ĠÎļ α", + "èij Ĺ", + "çľ ¼", + "á h", + "Ġ ι", + "ĠÎ ¹", + "ê¹Į ì§Ģ", + "m az", + "ma z", + "λο γ", + "Ġjs me", + "Ġ á¼", + "Ġá ¼", + "Ġп ÑĢави", + "ĠпÑĢ ави", + "ĠпÑĢав и", + "ĠпÑĢа ви", + "к лад", + "Ġth ủ", + "Ġthá» §", + "s ah", + "sa h", + "ÄŁ it", + "ÄŁi t", + "Ġ ÙģÛĮ", + "ĠÙģ ÛĮ", + "ен но", + "à¥ģ à¤Ľ", + "à¥ģठĽ", + "ãģ »", + "çĻ ¾", + "и ÑĤа", + "иÑĤ а", + "ĠбÑĭ ло", + "ĠбÑĭл о", + "Ġv ys", + "Ġvy s", + "Ġ ì¶ľ", + "Ġì¶ ľ", + "ắ ng", + "ắn g", + "ĠÄij ại", + "ĠÙħ ÙĪرد", + "ĠÙħÙĪ رد", + "ĠÙħÙĪر د", + "е ла", + "ел а", + "Ñĸ ÑĪ", + "л еннÑı", + "лен нÑı", + "æ IJ", + "Ġ нед", + "Ġн ед", + "Ġне д", + "i yat", + "iy at", + "iya t", + "ì ¼", + "Ġol duÄŁunu", + "ĠolduÄŁ unu", + "ĠolduÄŁu nu", + "د اÙĨ", + "دا ÙĨ", + "í Ŀ", + "Ġ سÛĮ", + "Ġس ÛĮ", + "ี à¸ģ", + "ÄĽ st", + "ım ı", + "ä¸ī ä¸ī", + "ãĤ ½", + "ĠÑĤ еп", + "ĠÑĤе п", + "Ġ ÑĢай", + "ĠÑĢ ай", + "ĠÑĢаР¹", + "ĠÑĢа й", + "ा ध", + "ाठ§", + "Ġ ìĤ¬ëŀĮ", + "ĠìĤ¬ ëŀĮ", + "ĠT rung", + "ĠTr ung", + "ĠTru ng", + "ï¼ı ï¼ı", + "Ġt âm", + "Å¡ enÃŃ", + "Å¡e nÃŃ", + "Å¡en ÃŃ", + "ãĥ į", + "Ġ ÏĦοÏħÏĤ", + "ĠÏĦ οÏħÏĤ", + "ĠÏĦοÏħ ÏĤ", + "Ġ нÑĸ", + "Ġн Ñĸ", + "в ид", + "ви д", + "æ ¿", + "Ġ ظ", + "ĠØ ¸", + "ãĥ ¯", + "ì ¢ħ", + "ì¢ ħ", + "в аÑĤи", + "ва ÑĤи", + "ваÑĤ и", + "Ġqu á", + "ภ¤", + "ĠÄij Æ°á»Ŀng", + "à¥ģ द", + "à¥ģठ¦", + "r oj", + "ro j", + "Ġ ÑĥÑģ", + "ĠÑĥ Ñģ", + "é¦ Ļ", + "ì½ ĺ", + "Ġ ÙĪت", + "ĠÙĪ ت", + "ม าà¸ģ", + "มา à¸ģ", + "åĪ ĩ", + "Ġ án", + "Ġá n", + "Ġм ед", + "ìĹIJ ëĬĶ", + "Ġh lav", + "Ġhl av", + "ر ت", + "à¹ĥ à¸Ī", + "æ´ ²", + "Ġ лÑĸ", + "Ġл Ñĸ", + "æ Īĺ", + "æĪ ĺ", + "ÙĪ ÙĨد", + "ÙĪÙĨ د", + "è¶ ³", + "åĭ Ļ", + "çĶ ³", + "Ġ ì±", + "Ġì ±", + "ĠìĿ´ëıĻ íķ©ëĭĪëĭ¤", + "Ñī еÑģÑĤв", + "Ñīе ÑģÑĤв", + "Ġ ë¶Ī", + "Ġë ¶Ī", + "Ġë¶ Ī", + "ÙĦ ÙĪ", + "ü ven", + "èĪ ĩ", + "Ġgi Ỽi", + "Ġgiá» Ľi", + "Ġ ÙĪÙĤ", + "ĠÙĪ ÙĤ", + "Ġê°¤ ë¡ľê·¸", + "Ġ عاÙħ", + "Ġع اÙħ", + "ĺ IJ", + ": ::::", + ":: :::", + ":::: :", + "::: ::", + "Ġ Ñĥд", + "ĠÑĥ д", + "- ÑĤо", + "-ÑĤ о", + "Ġ ÑĦоÑĢ", + "ĠÑĦ оÑĢ", + "и ни", + "ин и", + "ãģĹ ãģĦ", + "ãģĹãģ Ħ", + "Ġê°¤ë¡ľê·¸ ë¡ľ", + "ãģ ³", + "ãĥ© ãĤ¤", + "e ná", + "en á", + "Ġ nez", + "Ġn ez", + "Ġne z", + "Ġönem li", + "Ġ ниÑħ", + "Ġн иÑħ", + "Ġни Ñħ", + "à¤Ĥ स", + "Ġà¤īस à¤ķ", + "à¥įर द", + "Ġn ói", + "Ġnó i", + "Ùĥ ÙĦ", + "ิ ว", + "κ ο", + "à¥ģ à¤ĸ", + "à¥ģठĸ", + "ö yle", + "öy le", + "ά λ", + "ó ng", + "ón g", + "ĠداÙĨ Ø´", + "Ġз б", + "ì »", + "à¸ľ ล", + "ëĵ¤ ìĿ´", + "Ġe tk", + "Ġet k", + "ر ات", + "را ت", + "Ġ εκ", + "Ġε κ", + "ÑĤ ÑĢа", + "ÑĤÑĢ а", + "à¥į तर", + "à¥įत र", + "à¤Ĥ ब", + "Ġм ÑĸÑģ", + "ĠмÑĸ Ñģ", + "æł ¹", + "ãĥ Ļ", + "Ġt á»ī", + "Ġtá» ī", + "à¹Ģ à¸ĭ", + "à¹Ģภĭ", + "ìĪ ł", + "ï¼Į ä¸į", + "ìĺ ¨", + "Ġm ÄĽst", + "ĠmÄĽ st", + "ģ µ", + "a zı", + "az ı", + "r ada", + "ra da", + "rad a", + "ÏĢ α", + "m é", + "ÙĨ اÙħÙĩ", + "ÙĨا ÙħÙĩ", + "ÙĨاÙħ Ùĩ", + "ا ÛĮÙĦ", + "اÛĮ ÙĦ", + "μ η", + "l uk", + "lu k", + "Ùĥ ÙĬ", + "Ġ ï¼ī", + "Ġï¼ ī", + "Ġ деÑĤ", + "Ġд еÑĤ", + "Ġде ÑĤ", + "Ġiç inde", + "Ġiçin de", + "Ġiçi nde", + "Ñı м", + "Ġd Æ°á»", + "ĠdÆ° á»", + "ĠпÑĢед ÑģÑĤав", + "ü re", + "ür e", + "åķ Ĭ", + "ĠÑĤ ÑĢÑĥ", + "ĠÑĤÑĢ Ñĥ", + "es ini", + "esi ni", + "esin i", + "Ġ але", + "Ġа ле", + "Ġал е", + "ãĥ³ ãĥī", + "ãĥ³ãĥ ī", + "à¥ĥ त", + "ε Ïħ", + "à¥ģ à¤Ĩ", + "à¥ģठĨ", + "Ġh iç", + "Ġhi ç", + "çĶ º", + "Ġ Ðĸ", + "ĠÐ ĸ", + "ç ħ§", + "çħ §", + "k á", + "Ġtr á»įng", + "Ġ تش", + "Ġت Ø´", + "ा श", + "ाठ¶", + "ĠÙħ Ø«", + "e tim", + "et im", + "eti m", + "Ġth ấy", + "Ġब ह", + "ع ت", + "ึ à¹ī", + "Ġs ev", + "Ġse v", + "Ñģ ÑĤа", + "ÑģÑĤ а", + "Ġc ứ", + "Ġt iá»ģn", + "Ġti á»ģn", + "à¥Ģ à¤ľ", + "Ñı г", + "ĠоÑĢг ани", + "ĠоÑĢган и", + "Ġб Ñĭл", + "ĠбÑĭ л", + "t ür", + "tü r", + "Ġب ازÛĮ", + "Ġبا زÛĮ", + "Ġباز ÛĮ", + "Ġ ìŀ¬", + "Ġìŀ ¬", + "व र", + "æľīéĻIJ åħ¬åı¸", + "k up", + "ku p", + "Ġ iyi", + "Ġi yi", + "Ġiy i", + "íķĺ ê²Į", + "ãĢĢ l", + "ãĤ· ãĥ§", + "ا رة", + "ار Ø©", + "ส ร", + "Ġt ÃŃch", + "ĠtÃŃ ch", + "Ġ каÑĢ", + "Ġк аÑĢ", + "Ġка ÑĢ", + "и б", + "ĠвÑĸд повÑĸд", + "ĠвÑĸдпов Ñĸд", + "Ġpo dle", + "Ġpod le", + "à¥įर à¤ķ", + "i yon", + "iy on", + "к оном", + "ко ном", + "кон ом", + "Ġ μÎŃ", + "Ġμ ÎŃ", + "Ġп ÑĢоиз", + "ĠпÑĢо из", + "Ġ âĢı", + "ĠâĢ ı", + "m ektedir", + "mekte dir", + "Ω ÎĿ", + "Ġb áo", + "à¸Ī ำ", + "ëį Ķ", + "ë¸ Į", + "Ġs ợ", + "ÛĮ رÛĮ", + "ÛĮر ÛĮ", + "о нÑĥ", + "он Ñĥ", + "ın daki", + "ında ki", + "ınd aki", + "алÑĮ ного", + "алÑĮно го", + "μ β", + "л из", + "ли з", + "Ġjej ich", + "æĸ ½", + "ä¾ ¿", + "l eÅŁtir", + "le ÅŁtir", + "leÅŁ tir", + "ĠÙĪ Ø£", + "Ġस ब", + "l erde", + "ler de", + "Ġ ÚĨÙĩ", + "ĠÚĨ Ùĩ", + "ÏĦ ÎŃ", + "Ġg ì", + "Ġ Ãļ", + "Ġà ļ", + "ĠÑĢаÑģ п", + "ĠÑĢа Ñģп", + "Ġt üm", + "à¹Ģ à¸ĩ", + "à¹Ģภĩ", + "èIJ ½", + "ìĨ ¡", + "à¹Ħ à¸Ĺย", + "m Ä±ÅŁtır", + "mÄ±ÅŁ tır", + "mÄ±ÅŁtı r", + "Ġ ÙĤرار", + "ĠÙĤر ار", + "Ġ à¸Ħาส", + "Ġà¸Ħ าส", + "Ġk ıs", + "Ġkı s", + "о ваниÑı", + "ов аниÑı", + "ова ниÑı", + "овани Ñı", + "ован иÑı", + "ãĤĤ ãģ®", + "د اÙħ", + "دا Ùħ", + "ìľ ¡", + "ol oj", + "olo j", + "ĠпоÑģл е", + "ĠпоÑģ ле", + "Ġ Так", + "ĠТ ак", + "ĠТа к", + "Ġб олее", + "Ġбол ее", + "ĠÄij á»ķi", + "ĠÄijá»ķ i", + "l ak", + "la k", + "í ħĮ", + "íħ Į", + "Ġa yn", + "Ġay n", + "Ñı Ñģ", + "Ġп ог", + "Ġпо г", + "Ġar asında", + "Ġaras ında", + "Ġara sında", + "Ġarası nda", + "Ī ¬", + "à¥Ĥ ल", + "Ġ ανα", + "Ġα να", + "Ġαν α", + "Ġq uyết", + "Ġquy ết", + "Ġthu á»Ļc", + "Ġd ün", + "Ġdü n", + "Ġp ÅĻes", + "ĠpÅĻ es", + "ĠpÅĻe s", + "ÑĦ Ñĸ", + "Ġ å¸", + "Ġå ¸", + "ا ÙĦÙĬ", + "اÙĦ ÙĬ", + "Ġп овеÑĢ", + "Ġпо веÑĢ", + "Ġпов еÑĢ", + "Ñĩ ина", + "Ñĩи на", + "Ñĩин а", + "s ko", + "sk o", + "çµ IJ", + "Ø ¡", + "Ġ гÑĢа", + "Ġг ÑĢа", + "ĠгÑĢ а", + "о ÑĤи", + "оÑĤ и", + "Ġqu á»ijc", + "ÑĨ Ñĸв", + "ÑĨÑĸ в", + "l endir", + "len dir", + "lendi r", + "в Ñĸд", + "вÑĸ д", + "Ġж иÑĤ", + "ü yor", + "üy or", + "ï¼Į ä»ĸ", + "lar ında", + "ları nda", + "ların da", + "Ġu yg", + "Ġuy g", + "Ġtr ÃŃ", + "Ġ Ø´ÙĨ", + "ĠØ´ ÙĨ", + "ا بÙĦ", + "اب ÙĦ", + "æ· ±", + "Âł p", + "Ñģ каÑı", + "Ñģк аÑı", + "Ñģка Ñı", + "о ÑĤа", + "оÑĤ а", + "ÙĪ Ø·", + "Ġ اط", + "Ġا Ø·", + "ĠØ§Ø ·", + "ä¾ Ĩ", + "Ġз аÑĤ", + "Ġза ÑĤ", + "Ġ име", + "Ġи ме", + "Ġим е", + "à¹Ģà¸Ĺ ศ", + "ëĭ ´", + "n ÄĽnÃŃ", + "nÄĽ nÃŃ", + "nÄĽn ÃŃ", + "Ñĥ лÑı", + "Ñĥл Ñı", + "- п", + "å ĺ", + "Ġв ип", + "Ġви п", + "аÑĢа кÑĤ", + "à¹Ģ à¸ļ", + "à¹Ģภļ", + "ç¦ ı", + "Ïģ Ïİ", + "س Ùĩ", + "à¥Į र", + "Ġdi ÄŁer", + "à¹Ĥ à¸Ķย", + "à¹Ĥà¸Ķ ย", + "ĠÑģп оÑģоб", + "ĠÑģпоÑģ об", + "åį ·", + "è ĸ", + "а нÑĤ", + "ан ÑĤ", + "Ñİ ÑĤÑĮÑģÑı", + "ÑİÑĤÑĮ ÑģÑı", + "ĠÑį ÑĤом", + "ĠÑįÑĤ ом", + "ĠÑįÑĤо м", + "Ġ ï½Ģ", + "Ġï½ Ģ", + "ส าม", + "ì m", + "ĠÑĪ к", + "Ġ à¸Ľà¸£à¸°", + "Ġà¸Ľ ระ", + "Ġà¸Ľà¸£ ะ", + "़ à¥Ģ", + "e kl", + "ek l", + "m uÅŁ", + "mu ÅŁ", + "ĠÑĤак ож", + "ÙĪ سط", + "ÙĪس Ø·", + "Ġ Äįi", + "ĠÄį i", + "ี à¸Ļ", + "ÛĮ ÙĨÙĩ", + "ÛĮÙĨ Ùĩ", + "ÄĽ k", + "å½ ¼", + "le rine", + "ler ine", + "leri ne", + "lerin e", + "ĠÄij ất", + "à¥ģ à¤ı", + "à¥ģठı", + "ол оÑģ", + "оло Ñģ", + "Ġ å°ı", + "Ġå° ı", + "ز ÙĬØ©", + "زÙĬ Ø©", + "Ġв ла", + "à¥Ģ ल", + "Ġ etti", + "Ġe tti", + "Ġet ti", + "Ġett i", + "ĠÑģ оÑģÑĤав", + "ĠÑģо ÑģÑĤав", + "ĠÑģоÑģÑĤ ав", + "ÙĦ اÙĦ", + "ÙĦا ÙĦ", + "Ġ çİ", + "Ġç İ", + "ĠpÅĻÃŃ pad", + "ëŁ °", + "ุ à¸ģ", + "Ġ Ñĩи", + "ĠÑĩ и", + "å ħį", + "åħ į", + "n ÄĽjÅ¡ÃŃ", + "nÄĽ jÅ¡ÃŃ", + "ิ ล", + "åį Ģ", + "s kých", + "sk ých", + "ský ch", + "า ศ", + "าภ¨", + "åIJ Ĺ", + "Ġ íĺĦ", + "Ġíĺ Ħ", + "Ġal ın", + "å§ Ķ", + "à¸ŀ ร", + "až d", + "Ġб ÑĸлÑĮ", + "ĠбÑĸ лÑĮ", + "ĠбÑĸл ÑĮ", + "à¹Ī วà¸Ļ", + "à¹Īว à¸Ļ", + "o og", + "oo g", + "a cı", + "ac ı", + "l ıģ", + "lı ÄŁ", + "Ġk hu", + "Ġkh u", + "Ġh izmet", + "Ġ éĽ", + "Ġé Ľ", + "Ġ Îĺ", + "ĠÎ ĺ", + "Ġde ÄŁer", + "ĠdeÄŁ er", + "åħ Ń", + "Ġ دÙĩ", + "Ġد Ùĩ", + "Ġn ÄĽk", + "ĠnÄĽ k", + "à¸Ħ à¸Ļ", + "е ÑĤÑĮ", + "еÑĤ ÑĮ", + "ب اÙĨ", + "با ÙĨ", + "ÏĦ ική", + "ÏĦικ ή", + "ÏĦι κή", + "ĠÄij á»ĭa", + "ĠÄijá»ĭ a", + "Ġ Công", + "ĠC ông", + "íĮ IJ", + "Ġк огда", + "ĠÚ© ÙĨد", + "ĠÚ©ÙĨ د", + "ãģ§ ãģį", + "ĠÏĢ εÏģι", + "ĠÏĢεÏģ ι", + "ĠÏĢε Ïģι", + "lar dan", + "larda n", + "Ġ зем", + "Ġз ем", + "ت ÙĪاÙĨ", + "تÙĪ اÙĨ", + "è³ ĩ", + "li kle", + "lik le", + "Ġt ụ", + "Ġtá» ¥", + "Ġd ẫn", + "Ġn ay", + "Ġna y", + "Ġ ÑģÑĤоÑĢ", + "ĠÑģ ÑĤоÑĢ", + "ĠÑģÑĤ оÑĢ", + "ĠÑģÑĤо ÑĢ", + "ĠØ´ Ùħا", + "ĠØ´Ùħ ا", + "Ø« ر", + "Ġd edi", + "Ġde di", + "Ġded i", + "к ое", + "ко е", + "ë ijIJ", + "ëij IJ", + "ÑĨ ев", + "ÑĨе в", + "ج Ùĩ", + "Ġm ůže", + "Ġmů že", + "Ġmůž e", + "à¥ģ प", + "à¥ģठª", + "à¥įर म", + "Ġ taÅŁ", + "Ġt aÅŁ", + "Ġta ÅŁ", + "оÑĢ ÑĤ", + "γ Ïģα", + "çĻ ¼", + "า à¸ļ", + "าภļ", + "iá» ħn", + "iá»ħ n", + "ĠÙħ ست", + "ĠÙħس ت", + "л екÑģ", + "ле кÑģ", + "лек Ñģ", + "Ġ prav", + "Ġp rav", + "Ġpr av", + "Ġpra v", + "Ġд оÑģ", + "Ġдо Ñģ", + "Ġd Ä±ÅŁ", + "Ġ zem", + "Ġz em", + "Ġze m", + "Ġg iao", + "Ġgi ao", + "Ġgia o", + "Ġv last", + "Ġvl ast", + "Ġvlas t", + "ĠÑį ÑĤого", + "ĠÑįÑĤ ого", + "ĠÑįÑĤо го", + "ï½ °", + "ว à¸ĩ", + "ÑĢ ой", + "ÑĢо й", + "Ġbir lik", + "e ný", + "en ý", + "Ġ ëĭ¨", + "Ġëĭ ¨", + "ов ани", + "ова ни", + "ован и", + "é£ İ", + "íı ī", + "Ġz ah", + "Ġza h", + "б а", + "åĬ ©", + "éĢ ²", + "ê ¶Į", + "Ġd iye", + "Ġdi ye", + "Ġdiy e", + "à¤Ĥ à¤ķ", + "Ġch uyá»ĥn", + "Ġ ìĹŃ", + "ĠìĹ Ń", + "Ġ ÑĤÑĢи", + "ĠÑĤ ÑĢи", + "ĠÑĤÑĢ и", + "Ġö nce", + "Ġön ce", + "Ġönc e", + "ï¼Į è¿Ļ", + "o ại", + "л еÑĤ", + "ле ÑĤ", + "ĠÏĥ Ïħν", + "ĠÏĥÏħ ν", + "l ád", + "lá d", + "ç e", + "t ü", + "Ġ Äįást", + "ĠÄį ást", + "ĠÄįá st", + "Ġ εν", + "Ġε ν", + "Ġb iá»ĩt", + "Ġ é«", + "Ġé «", + "à¥ĭ à¤ķ", + "ÙĦ ات", + "ÙĦا ت", + "ب اÙĦ", + "با ÙĦ", + "e cies", + "ec ies", + "eci es", + "Ġ ëĭ¹", + "Ġëĭ ¹", + "à¸Ĭ à¸Ļ", + "ÏĦ αÏĤ", + "ÏĦα ÏĤ", + "à¥į ण", + "à¥įठ£", + "u jÃŃcÃŃ", + "uj ÃŃcÃŃ", + "ujÃŃ cÃŃ", + "Äį et", + "Äįe t", + "Ġп об", + "Ġпо б", + "ÙĪ ار", + "ÙĪا ر", + "i yas", + "iy as", + "iya s", + "Ġd ruh", + "Ġdr uh", + "Ġdru h", + "د د", + "ÏĮ ν", + "ÑĢ ен", + "ÑĢе н", + "า รà¸ĸ", + "าร à¸ĸ", + "ä½ İ", + "ìķ ½", + "ÑĢ оз", + "ÑĢо з", + "ëĬĶ ëį°", + "ãĤĵ ãģª", + "Äį enÃŃ", + "Äįe nÃŃ", + "Äįen ÃŃ", + "**** ********", + "******** ****", + "****** ******", + "***** *******", + "******* *****", + "Ġ Ρ", + "ĠÎ ¡", + "ĠÑĤ омÑĥ", + "ĠÑĤо мÑĥ", + "ĠÑĤом Ñĥ", + "ร à¸ģ", + "à¥ģ स", + "à¥ģठ¸", + "ä¹ Ŀ", + "å°± æĺ¯", + "£ i", + "éĺ ²", + "Ùĥ ر", + "ĠÑį ÑĤи", + "ĠÑįÑĤ и", + "ĠÚ© Ø´ÙĪر", + "ĠÚ©Ø´ ÙĪر", + "Ġ ê°IJ", + "Ġê° IJ", + "Ġ ад", + "Ġа д", + "Ġ داد", + "Ġد اد", + "éģ İ", + "Ù «", + "Ġl áºŃp", + "Ġ اÙĦÙĩ", + "Ġا ÙĦÙĩ", + "ĠاÙĦ Ùĩ", + "æľ Ľ", + "Ġ تÙĩ", + "Ġت Ùĩ", + "ì§ Ī", + "ãģ§ ãģĤãĤĭ", + "ãģ§ãģĤ ãĤĭ", + "Ġ меж", + "Ġм еж", + "ĠÑĢ езÑĥлÑĮÑĤ", + "ĠÑĢез ÑĥлÑĮÑĤ", + "ç į", + "е мÑĥ", + "ем Ñĥ", + "Ġ تÙĪاÙĨ", + "Ġت ÙĪاÙĨ", + "ĠتÙĪ اÙĨ", + "Ġ راÙĩ", + "Ġر اÙĩ", + "Ġرا Ùĩ", + "ãĥ¼ ãĥł", + "ãĥ¼ãĥ ł", + "åĦ ¿", + "å± ŀ", + "б Ñĭ", + "á ¿", + "à¸Ħ ล", + "à¥ĭ à¤Ī", + "üt ün", + "à¤Ĺ र", + "ìķĺ ëĭ¤", + "âĪ §", + "Ġ ì°¨", + "Ġì° ¨", + "çµ Ħ", + "μα ÏĦα", + "ุ à¸Ļ", + "Ġ ÑĤом", + "ĠÑĤ ом", + "ĠÑĤо м", + "еÑĢ в", + "Îij Σ", + "ĠiÅŁ lem", + "ع Ùħ", + "ë ĥ", + "ãĥ Ħ", + "ا Ùģت", + "اÙģ ت", + "åĬ ŀ", + "Ġ nes", + "Ġn es", + "Ġne s", + "av aÅŁ", + "ava ÅŁ", + "ĠÙĨ ÛĮز", + "ĠÙĨÛĮ ز", + "å¼ º", + "Ġ éĻ", + "Ġé Ļ", + "Ñĸн нÑı", + "æ² ³", + "á ÅĻ", + "æĿ IJ", + "ĠØ£ ÙĬ", + "Ġ ì¹´", + "Ġì¹ ´", + "Ġn enÃŃ", + "Ġne nÃŃ", + "Ġnen ÃŃ", + "Ġ ÙĪÙħ", + "ĠÙĪ Ùħ", + "Ġ Ú©Ùħ", + "ĠÚ© Ùħ", + "i ếu", + "iế u", + "Ġ æ°", + "Ġæ °", + "åĮ »", + "Ġz or", + "Ġzo r", + "ί Ïĥ", + "ि ध", + "िठ§", + "Ġп оказ", + "Ġпо каз", + "Ġпок аз", + "Ġпока з", + "ह र", + "Ġiç er", + "ØŃ Ø©", + "ि à¤ĸ", + "िठĸ", + "а да", + "ад а", + "تر ÛĮÙĨ", + "ترÛĮ ÙĨ", + "Ġ bao", + "Ġb ao", + "Ġba o", + "Ġx ã", + "à¹Ģ à¸Ħร", + "à¹Ģà¸Ħ ร", + "Ġngh Ä©", + "à¹ģ à¸ļà¸ļ", + "à¹ģà¸ļ à¸ļ", + "ĠdoÄŁ ru", + "ĠdoÄŁr u", + "Ñĸ ÑĤи", + "ÑĸÑĤ и", + "Ġ بÙĬÙĨ", + "Ġب ÙĬÙĨ", + "ĠبÙĬ ÙĨ", + "Ġ леÑĤ", + "Ġл еÑĤ", + "ا غ", + "Ø§Ø º", + "ÛĮ Ú©ÛĮ", + "ÛĮÚ© ÛĮ", + "r áv", + "rá v", + "à¥į âĢį", + "âĢĻ nin", + "âĢĻn in", + "Ġ ย", + "Ġภ¢", + "åį Ĭ", + "Ġк оли", + "Ġкол и", + "Ġко ли", + "Ġtr ợ", + "éĿ Ĵ", + "ëŀ Ģ", + "Ġ ë¨", + "Ġë ¨", + "Ġ ÙĪر", + "ĠÙĪ ر", + "ï¾ Ĭ", + "è§ Ĥ", + "Ġ пи", + "Ġп и", + "н Ñĥв", + "нÑĥ в", + "il mesi", + "ilm esi", + "س تÙĩ", + "ست Ùĩ", + "Ġд еÑĢжав", + "ĠдеÑĢж ав", + "å® ĥ", + "åĪ ¥", + "ëħ Ģ", + "л ÑģÑı", + "à¤Ĥ ध", + "Ġ ÑĤи", + "ĠÑĤ и", + "ĠpÅĻ ip", + "ĠpÅĻi p", + "п и", + "á» ĵn", + "á»ĵ n", + "о ваÑĤÑĮ", + "ов аÑĤÑĮ", + "ова ÑĤÑĮ", + "ìĿ´ ëĿ¼", + "æľ Ŀ", + "Ġ ëĺIJ", + "Ġë ĺIJ", + "Ġëĺ IJ", + "ĠÎŃ Î½Î±", + "ĠÎŃν α", + "ãģ¾ ãģ§", + "ج اÙħ", + "جا Ùħ", + "Ġ ëĬ", + "Ġë Ĭ", + "н Ñĸв", + "нÑĸ в", + "ÏĢ οÏħ", + "ÏĢο Ïħ", + "Ġ زÙħاÙĨ", + "Ġز ÙħاÙĨ", + "ĠزÙħ اÙĨ", + "æĽ ²", + "Ġ ÙħÙĩ", + "ĠÙħ Ùĩ", + "ëł ¨", + "ä¸ ĥ", + "ãģ¨ ãģĹãģ¦", + "l abilir", + "la bilir", + "lab ilir", + "о же", + "ож е", + "å¤ ľ", + "ĠнÑĥж но", + "å½ ©", + "çĪ ±", + "Ġho Ãłn", + "ün ü", + "Ġ ëĦ¤", + "ĠëĦ ¤", + "Ġ جÙĨ", + "Ġج ÙĨ", + "Ġn ÄĽj", + "ĠnÄĽ j", + "к ими", + "ки ми", + "ким и", + "Ġa ynı", + "Ġayn ı", + "Ġ ÙĥÙĦ", + "ĠÙĥ ÙĦ", + "Ġnh au", + "Ạ³", + "ÙĬ ات", + "ÙĬا ت", + "Ġm ezi", + "Ġme zi", + "Ġmez i", + "Ġ ÑĢек", + "ĠÑĢ ек", + "ĠÑĢе к", + "Ġ tür", + "Ġt ür", + "Ġ говоÑĢ", + "Ġг овоÑĢ", + "Ġfaz la", + "åĩ Ĩ", + "ÑĪ ий", + "ÑĪи й", + "ÐŁ ÑĢи", + "ÐŁÑĢ и", + "ÑĢ оÑģÑĤ", + "ÑĢоÑģ ÑĤ", + "ÑĢо ÑģÑĤ", + "ĠоÑĢг ан", + "ĠоÑĢ ган", + "n ým", + "ný m", + "Ġ ÑĢод", + "ĠÑĢ од", + "Ġ ÙĪÛĮ", + "ĠÙĪ ÛĮ", + "ic ký", + "ick ý", + "ë¦ ¼", + "ï½ ²", + "æĢ İ", + "ĠÙĩ ذا", + "ĠÑĩ аÑģÑĤи", + "ĠÑĩа ÑģÑĤи", + "ĠÑĩаÑģ ÑĤи", + "ĠÑĩаÑģÑĤ и", + "ÃŃ r", + "á»ĩ nh", + "á»ĩn h", + "Ġ íĹ", + "Ġí Ĺ", + "ê »", + "lu ž", + "ÃŃ l", + "c ÃŃch", + "cÃŃ ch", + "å® Ł", + "ãģł ãģ£ãģŁ", + "ÙĬ رة", + "ÙĬر Ø©", + "Ġv Äĥn", + "æ¸ ¯", + "Ġ ÏĦιÏĤ", + "ĠÏĦι ÏĤ", + "ا رت", + "ار ت", + "Ġv ấn", + "âĶģâĶģ âĶģâĶģ", + "å¯ ¾", + "Ïģ ÎŃ", + "Ġг одÑĥ", + "Ġгод Ñĥ", + "Ġ سب", + "Ġس ب", + "ا رات", + "ار ات", + "ارا ت", + "е лей", + "ел ей", + "еле й", + "Ġз аÑħ", + "Ġза Ñħ", + "Ġ важ", + "Ġв аж", + "Ġва ж", + "Ġt á»īnh", + "Ġtá» īnh", + "Ġtá»ī nh", + "ا بع", + "اب ع", + "Ġ à¤ľà¤¬", + "Ġà¤ľ ब", + "Ġà¤IJ स", + "Ġ дÑĥ", + "Ġд Ñĥ", + "Ġ é«ĺ", + "Ġé «ĺ", + "Ġé« ĺ", + "ê² ł", + "н ее", + "не е", + "ï½ Į", + "Ġм ал", + "Ġма л", + "è¾ ¹", + "ãģł ãģij", + "à¹ī ร", + "ÙĤ Ø·", + "Ġb ên", + "Ġs eb", + "Ġse b", + "ĠØ® ÙĪاÙĩ", + "ĠØ®ÙĪ اÙĩ", + "s iz", + "si z", + "Ġol ur", + "Ġ ëĶ°", + "ĠëĶ °", + "Ġ ì¢ĭ", + "Ġì¢ ĭ", + "Ġs vÄĽt", + "Ġsv ÄĽt", + "ĠsvÄĽ t", + "ic ká", + "ick á", + "á» ¹", + "Ġqu ản", + "Ġquả n", + "Ġ иÑģ", + "Ġи Ñģ", + "Ġz aÄį", + "Ġza Äį", + "ื à¸Ńà¸Ļ", + "ืà¸Ń à¸Ļ", + "ÑĶ Ñİ", + "ि ष", + "िठ·", + "ç Ĭ¶", + "çĬ ¶", + "Ïĥ μ", + "ั ส", + "ó c", + "Ġ беÑĢ", + "Ġб еÑĢ", + "Ġ íĿ", + "Ġí Ŀ", + ";: ;:", + "Ġ پس", + "ĠÙ¾ س", + "Ġ ëijIJ", + "Ġë ijIJ", + "Ġëij IJ", + "н иÑĩ", + "ни Ñĩ", + "Ġо ÑĩенÑĮ", + "ĠоÑĩ енÑĮ", + "ĠìķĦìĿ´ ì½ĺ", + "Ġ θα", + "Ġθ α", + "Ġв ÑģÑĤ", + "ĠвÑģ ÑĤ", + "ا دة", + "اد Ø©", + "Ġdev am", + "ื à¸Ńà¸ĩ", + "ืà¸Ń à¸ĩ", + "ĠлÑİ Ð´Ð¸", + "ĠлÑİд и", + "ìĺ Ī", + "á»± a", + "Ñı Ñħ", + "âĢĮ اÛĮ", + "Ġ سÙĪ", + "Ġس ÙĪ", + "å° ¼", + "Ġth ứ", + "Ġthá» ©", + "m eye", + "me ye", + "mey e", + "Ġ èµ", + "Ġè µ", + "èī ¯", + "ĠdeÄŁ iÅŁ", + "ÑĪ Ñĸ", + "Ġtr ợ", + "ĠâĢİ #", + "çĹ ħ", + "ìĽ Į", + "Ġk de", + "Ġkd e", + "Î §", + "æ ¤", + "ĠÑħ аÑĢакÑĤ", + "æ ĩ", + "Ġb iến", + "Ġbi ến", + "ÙĤ ع", + "åŁ Ł", + "Ġн еп", + "Ġне п", + "Ġd ů", + "Ġп иÑĤ", + "Ġпи ÑĤ", + "ĠÑĤ ÑĢеб", + "ĠÑĤÑĢ еб", + "ا زÛĮ", + "از ÛĮ", + "Ġ طر", + "ĠØ· ر", + "Ġ ÙħÙĦ", + "ĠÙħ ÙĦ", + "Ġt ham", + "Ġth am", + "Ġtha m", + "Ġ ÙĪجÙĪد", + "ĠÙĪج ÙĪد", + "Ġs vé", + "Ġsv é", + "é§ ħ", + "ا ÛĮÙĨ", + "اÛĮ ÙĨ", + "Ġt iên", + "Ġti ên", + "s tru", + "st ru", + "str u", + "Ġv áºŃy", + "ü ne", + "ün e", + "Ġ à¹Ģม", + "Ġà¹Ģ ม", + "Ġà¹Ģภ¡", + "Ġr ằng", + "а ÑĤÑĥ", + "аÑĤ Ñĥ", + "äº ij", + "н иÑĤ", + "ни ÑĤ", + "ä¼ Ĭ", + "ÙĪ ص", + "Ġ éĿ", + "Ġé Ŀ", + "ĠпÑĢоб лем", + "d eki", + "de ki", + "dek i", + "** ************", + "******** ******", + "****** ********", + "******* *******", + "************ **", + "ò a", + "ĠÄijá»ģ u", + "ãĤĮ ãģŁ", + "ا رس", + "ار س", + "ãģª ãģı", + "ا ÙĤع", + "اÙĤ ع", + "è» į", + "Ùĥ Ùħ", + "Äį as", + "Ġk ỳ", + "Ġká» ³", + "Ø´ Ùħ", + "à¥ĩ ड", + "à¥ĩठ¡", + "éĺ ¿", + "Ġje jÃŃ", + "Ġjej ÃŃ", + "Ġ æĻ", + "Ġæ Ļ", + "Ġ Ä°ÅŁ", + "ĠÄ° ÅŁ", + "ar dım", + "ard ım", + "Ġसम य", + "Ġ ÐĿо", + "ĠÐĿ о", + "i lerin", + "il erin", + "ile rin", + "iler in", + "ileri n", + "Ġع بد", + "Ġعب د", + "n ÃŃk", + "nÃŃ k", + "ĠØ´ Ú©ÙĨ", + "ĠØ´Ú© ÙĨ", + "ิ à¸Ĺย", + "ิà¸Ĺ ย", + "á» ħ", + "ÑĢ ез", + "ÑĢе з", + "Ġch ứng", + "Ġchứ ng", + "Ġ :.", + "Ġ: .", + "Ġ पत", + "Ġप त", + "Ġž ivot", + "Ġživ ot", + "å¢ ĥ", + "« a", + "Ġt rung", + "Ġtr ung", + "ни кÑĸв", + "ник Ñĸв", + "ĠاÙĦ ÙħÙĨ", + "ĠاÙĦÙħ ÙĨ", + "ĠÑĢ аÑģÑģ", + "ĠÑĢаÑģ Ñģ", + "Ġ жив", + "Ġж ив", + "Ġз акон", + "Ġза кон", + "Ġзак он", + "Ġзако н", + "Ġ 목", + "Ġëª ©", + "Ġz áv", + "Ġzá v", + "Ġh akk", + "Ġha kk", + "Ġhak k", + "ä» ¤", + "ĠÑı кий", + "ĠÑıк ий", + "Ġ بÙĬ", + "Ġب ÙĬ", + "λ ÎŃ", + "oc uk", + "ocu k", + "Ġ Ñİ", + "ĠÑ İ", + "à¸ģ ว", + "Ġ اÙĨÚ¯", + "Ġا ÙĨÚ¯", + "ĠاÙĨ Ú¯", + "à¥ģ à¤Ĥ", + "à¥ģठĤ", + "Ġ nám", + "Ġn ám", + "Ġná m", + "á»ķ ng", + "Ġж ел", + "Ġже л", + "ĠÄij ặc", + "Äį it", + "Äįi t", + "Ġ ê±´", + "Ġê± ´", + "Ġب ÛĮØ´", + "ĠبÛĮ Ø´", + "кÑĢаÑĹ Ð½", + "Ġ ÙĪÙĩ", + "ĠÙĪ Ùĩ", + "н еннÑı", + "нен нÑı", + "Ġ à¹Ģà¸ŀ", + "Ġà¹Ģ à¸ŀ", + "Ġà¹Ģภŀ", + "о мен", + "ом ен", + "Ġl ần", + "Ġ عÙħÙĦ", + "Ġع ÙħÙĦ", + "ĠعÙħ ÙĦ", + "Ġî ģµ", + "Ä ŀ", + "ÑĸÑģ лÑı", + "Æ° ng", + "ा फ", + "ाठ«", + "à¸Ĺ à¸ĺ", + "д ен", + "де н", + "ĠÑī об", + "ĠÑīо б", + "Ñĩ ив", + "Ñĩи в", + "ılı r", + "ıl ır", + "ا عات", + "اع ات", + "j ÃŃcÃŃ", + "jÃŃ cÃŃ", + "ë² ¨", + "ÚĨ Ùĩ", + "ا رج", + "ار ج", + "ĠÙ¾ رÙĪ", + "Ġپر ÙĪ", + "Ġо дин", + "Ġод ин", + "Ġоди н", + "л ин", + "ли н", + "б Ñĥ", + "Ġसर à¤ķ", + "åĢ Ļ", + "ë¶Ģ íĦ°", + "à¥Īà¤Ĥ ,", + "å ´", + "à¹Ĥ ล", + "Ġv Å¡ak", + "ĠvÅ¡ ak", + "Ġоп ÑĢед", + "ì ±", + "æ ½", + "Ġdá»± ng", + "p ráv", + "pr áv", + "ิ ส", + "Ġnh iá»ĩm", + "Ġil iÅŁ", + "Ġili ÅŁ", + "Ġе Ñīе", + "Ġje Å¡tÄĽ", + "Ġ ÑĢаÑģÑĤ", + "ĠÑĢ аÑģÑĤ", + "ĠÑĢаÑģ ÑĤ", + "ĠÑĢа ÑģÑĤ", + "ภ®", + "à¤Ĥ à¤Ł", + "âĢĮ Ú©", + "Ġ بÛĮÙĨ", + "Ġب ÛĮÙĨ", + "ĠبÛĮ ÙĨ", + "o vou", + "ov ou", + "ovo u", + "æĻ ®", + "ί εÏĤ", + "о ÑĢоÑĪ", + "оÑĢ оÑĪ", + "оÑĢо ÑĪ", + "Ġol mak", + "Ġolm ak", + "Ġolma k", + "Ġst át", + "di ÄŁi", + "Ġt ình", + "Ġ dÄĽ", + "Ġd ÄĽ", + "ĠÚ¯ رÙģ", + "Ġگر Ùģ", + "Ïĥ ο", + "Ġ ÑĥÑĤ", + "ĠÑĥ ÑĤ", + "íķĻ êµIJ", + "ั à¸IJ", + "า à¸Ń", + "าภŃ", + "ĠÄij ặt", + "Ġмог ÑĥÑĤ", + "ĠмогÑĥ ÑĤ", + "ë° °", + "t ik", + "ti k", + "ª ½", + "li ÄŁ", + "ÏĢ ε", + "Ġ èĢ", + "Ġè Ģ", + "k ü", + "ad ece", + "ade ce", + "κ ÏĮ", + "Ġ дÑĸ", + "Ġд Ñĸ", + "ầ m", + "çĦ¡ ãģĹ", + "Û²Û° Û±", + "èµ Ľ", + "оÑģ Ñĥд", + "Ġ ìķĪëĤ´", + "ĠìķĪ ëĤ´", + "Ġ ÐĶж", + "ĠÐĶ ж", + "åº §", + "ic kých", + "ick ých", + "ický ch", + "Ġ ìłģ", + "Ġì łģ", + "Ġìł ģ", + "à¥ĩ ,", + "ov ého", + "ové ho", + "Ġv ẫn", + "Ġbirlik te", + "Ġर à¤ĸ", + "Ġ ÙĨÙĩ", + "ĠÙĨ Ùĩ", + "ÙĤ ر", + "प र", + "e tÃŃ", + "et ÃŃ", + "Ġ ÑĤÑĭ", + "ĠÑĤ Ñĭ", + "Ģ ìĿ´", + "Ġà¤ħ ल", + "Ġм оже", + "Ġмож е", + "Ġмо же", + "ãĤ ´", + "Ġs tran", + "Ġst ran", + "Ġstr an", + "Ġstra n", + "Ø· ر", + "è¿Ļ 个", + "Ġ بع", + "Ġب ع", + "åĨ Ľ", + "ek tir", + "ekt ir", + "Ġh Æ°á»Ľng", + "ÙĨ اÙĨ", + "ÙĨا ÙĨ", + "Ġठij", + "Ġà ¤ij", + "ÏĮ ÏĦη", + "о Ñģк", + "оÑģ к", + "åį ĥ", + "as ına", + "ası na", + "Ġ Ø´Ùĩ", + "ĠØ´ Ùĩ", + "Ġ деÑĢ", + "Ġд еÑĢ", + "Ġде ÑĢ", + "ĠÙħ خت", + "ĠÙħØ® ت", + "Ġ ØŃÙĤ", + "ĠØŃ ÙĤ", + "ãĥ ¾", + "س اÙĨ", + "Ġc ung", + "Ġcu ng", + "ко ÑĢиÑģÑĤ", + "коÑĢ иÑģÑĤ", + "ÏĦ ικά", + "ÏĦικ ά", + "ÏĦι κά", + "Ġв она", + "Ġво на", + "ب ا", + "ãģķ ãĤĮãģŁ", + "ãģķãĤĮ ãģŁ", + "n out", + "no ut", + "nou t", + "Ġ ı", + "ĠÄ ±", + "è§ ī", + "ĠÃ¶ÄŁ ren", + "Ġ ì½Ķ", + "Ġì ½Ķ", + "Ġì½ Ķ", + "å¸ ¦", + "Ñģ лов", + "Ñģл ов", + "Ġε ÏĢι", + "ĠεÏĢ ι", + "ê° IJ", + "ĠÙħ رب", + "ĠÙħر ب", + "ĠÙģÛĮ ÙĦÙħ", + "Ġк ÑĢов", + "Ġ ëį°", + "Ġë į°", + "Ġëį °", + "ा ण", + "ाठ£", + "Ġel ekt", + "Ġele kt", + "Ġelek t", + "Ġ наÑĢод", + "Ġн аÑĢод", + "Ġна ÑĢод", + "ĠнаÑĢ од", + "ÛĮ دÙĩ", + "ÛĮد Ùĩ", + "ç´ Ħ", + "Ġп ÑĢоÑĦ", + "ĠпÑĢ оÑĦ", + "ĠпÑĢо ÑĦ", + "Ïģ οÏĤ", + "Ïģο ÏĤ", + "Ġ ãħ", + "ä¸į æĺ¯", + "Ġ à¤ľà¤¨", + "Ġà¤ľ न", + "ั ล", + "Ġص ÙĪرت", + "ĠصÙĪر ت", + "ãĥ ľ", + "Ġà¤Ĺ à¤Ī", + "ÄŁi tim", + "ÄŁit im", + "ÑģÑĮ киÑħ", + "ÑģÑĮк иÑħ", + "Ġ лег", + "Ġл ег", + "Ġت ÙĪÙĦ", + "ĠتÙĪ ÙĦ", + "Ġ ìļ´", + "Ġìļ ´", + "ع ر", + "Ġm Ãłu", + "ĠmÃł u", + "г ов", + "го в", + "æ³ ¢", + "in deki", + "ind eki", + "inde ki", + "ìłģ ìĿ¸", + "ấ m", + "Ġ íĻķ", + "ĠíĻ ķ", + "Ġب اÛĮد", + "Ġبا ÛĮد", + "ĠباÛĮ د", + "à¹Į à¸Ĺ", + "Ġk endi", + "Ġken di", + "Ġkend i", + "ี ว", + "ิ à¸ģาร", + "ิà¸ģ าร", + "ิà¸ģา ร", + "ĠÚ© ردÙĩ", + "Ġکرد Ùĩ", + "Ġکر دÙĩ", + "å· ´", + "ठģ", + "ร าà¸Ĭ", + "à¥į श", + "à¥įठ¶", + "Ġ ÐĶлÑı", + "ĠÐĶ лÑı", + "å¥ ĩ", + "ĠÑĥ ÑģÑĤанов", + "ĠÑĥÑģÑĤ анов", + "ĠÑĥÑģÑĤан ов", + "й ÑĤе", + "ãĤ ĩ", + "ά Ïģ", + "Ġ Ю", + "ĠÐ ®", + "Ġlu áºŃt", + "ãĢ ī", + "è´ ¨", + "د ا", + "Ġdü zen", + "Ġdüz en", + "ส à¸Ļ", + "ÑĢ он", + "ÑĢо н", + "d ıģı", + "dı ģı", + "dıģ ı", + "âĢĻ da", + "âĢĻd a", + "Ġfark lı", + "Ñħ ов", + "Ñħо в", + "l án", + "lá n", + "Ñĩ аÑģ", + "Ñĩа Ñģ", + "Ñĩ ин", + "Ñĩи н", + "Ġ ì°¸", + "Ġì° ¸", + "ì ´Ī", + "ì´ Ī", + "ÑĨ ип", + "ÑĨи п", + "ç ¹", + "éĸ Ģ", + "ж а", + "ÑĢ ован", + "ÑĢов ан", + "ÑĢо ван", + "ÑĢова н", + "à¸ĵ ะ", + "ÙĦÙĬ زÙĬØ©", + "Ïĩ ει", + "Ïĩε ι", + "à¥Ī .", + "к Ñģп", + "кÑģ п", + "ا ÙĪر", + "اÙĪ ر", + "Ġng uyên", + "Ġnguy ên", + "ãģ« ãĤĪ", + "à¥ĩ म", + "à¥ĩठ®", + "Ïĥ ÏĦε", + "ÏĥÏĦ ε", + "ت ÙĪ", + "Äį ek", + "Äįe k", + "ÑĨ Ñĭ", + "Ġ 물", + "Ġë¬ ¼", + "Ñį ÑĤ", + "Ġka zan", + "Ġkaz an", + "Ùģ س", + "e hir", + "eh ir", + "в ÑĸÑĤ", + "вÑĸ ÑĤ", + "Ġد ÙĪÙĦ", + "ĠدÙĪ ÙĦ", + "Ġ ëĵľ", + "Ġëĵ ľ", + "Ġà¤ļ ल", + "е ÑģÑĤва", + "еÑģÑĤв а", + "еÑģÑĤ ва", + "δ α", + "Ġб Ñĥв", + "ĠбÑĥ в", + "Ġ ÐĿе", + "ĠÐĿ е", + "ØŃ Ø±", + "огÑĢа ÑĦ", + "Ġroz hod", + "Ġrozh od", + "Ġви коÑĢиÑģÑĤ", + "Ġвико ÑĢиÑģÑĤ", + "Ġy êu", + "λ οÏĤ", + "λο ÏĤ", + "Ú© س", + "Ġ شب", + "ĠØ´ ب", + "ิ ษ", + "æ¯ į", + "Ġд оÑĢ", + "Ġдо ÑĢ", + "Ġngh á»ĩ", + "Ġt rang", + "Ġtr ang", + "Ġtra ng", + "Ġtran g", + "à¥ĩ द", + "à¥ĩठ¦", + "Ġt ìm", + "Ñĩ но", + "Ġ اÙħا", + "Ġا Ùħا", + "ĠاÙħ ا", + "éģ ĭ", + "Ú© ر", + "k é", + "Ġ vÄĽt", + "Ġv ÄĽt", + "ĠvÄĽ t", + "Ġн аÑģÑĤ", + "Ġна ÑģÑĤ", + "ĠнаÑģ ÑĤ", + "Ġ æ±", + "Ġæ ±", + "Ġ åĽ½", + "ĠåĽ ½", + "Ġgi ảm", + "Ġgiả m", + "ا دÙĬ", + "اد ÙĬ", + "ëĤ ľ", + "ë¡ ł", + "Ġ ､", + "Ġï½ ¤", + "Ġд енÑĮ", + "Ġде нÑĮ", + "Ġден ÑĮ", + "ÑĨ ÑĸÑİ", + "ÑĨÑĸ Ñİ", + "Ġh ạn", + "Ġhạ n", + "ẳ ng", + "ẳn g", + "λ ή", + "e yen", + "ey en", + "eye n", + "ä¸ Ķ", + "æŃ ¦", + "ĠÑĦ ак", + "à¹Ī à¸Ńà¸Ļ", + "à¹Īà¸Ń à¸Ļ", + "Ġ οι", + "Ġο ι", + "ز Ùħ", + "ãģĹ ãģ¦ãģĦãĤĭ", + "ãģĹãģ¦ ãģĦãĤĭ", + "ãģĹãģ¦ãģĦ ãĤĭ", + "л ива", + "ли ва", + "лив а", + "âĢķ âĢķ", + "Ġ öl", + "Ġö l", + "Ġ à¤ĵ", + "Ġठĵ", + "Ñģ ÑĤÑĸ", + "ÑģÑĤ Ñĸ", + "à¸ģ รรม", + "à¸ģร รม", + "Ġt ục", + "Ġtụ c", + "Ġgö rün", + "Ġgör ün", + "ãģĹ ãģ¾", + "ãģĹãģ ¾", + "Ġ ì¦", + "Ġì ¦", + "é ¦¬", + "é¦ ¬", + "Ġмож на", + "Ġ Ú©ÙĦ", + "ĠÚ© ÙĦ", + "Ġ ÑĨенÑĤ", + "ĠÑĨ енÑĤ", + "ĠÑĨе нÑĤ", + "ĠÑĨен ÑĤ", + "Ġ ìĻ¸", + "ĠìĻ ¸", + "Î ĺ", + "ç ĩ", + "Ġg elen", + "Ġge len", + "Ġgel en", + "Ġgele n", + "Ġ اÙĬÙĨ", + "Ġا ÙĬÙĨ", + "ĠاÙĬ ÙĨ", + "ĠØ¢ ب", + "Ġà¤Ĩ य", + "ัà¸ģ ษ", + "Ñģ им", + "Ñģи м", + "Ġб олÑĮÑĪ", + "ĠболÑĮ ÑĪ", + "Ġм н", + "о ди", + "од и", + "ĠÄ° l", + "Ġ à¤Ĩर", + "Ġà¤Ĩ र", + "е ÑĤе", + "еÑĤ е", + "ÑĨ иÑİ", + "ÑĨи Ñİ", + "áºŃ u", + "Ġt iếng", + "Ġtiế ng", + "Ġtiến g", + "ë ¶ģ", + "ë¶ ģ", + "æ§ ĺ", + "Ġн аÑĪ", + "Ġна ÑĪ", + "ม า", + "âĢĻ ın", + "âĢĻı n", + "ãĥĥ ãĥĹ", + "ÙĪ جÙĩ", + "ÙĪج Ùĩ", + "Ġ ØŃد", + "ĠØŃ Ø¯", + "á vá", + "áv á", + "ر ÙĪØ´", + "رÙĪ Ø´", + "Ġ дейÑģÑĤв", + "Ġд ейÑģÑĤв", + "ãģ£ ãģ¦ãģĦãĤĭ", + "ãģ£ãģ¦ ãģĦãĤĭ", + "ãģ£ãģ¦ãģĦ ãĤĭ", + "Ïģ ή", + "Ġ üst", + "Ġü st", + "Ġt iết", + "Ġti ết", + "Ġtiế t", + "ac aÄŁ", + "aca ÄŁ", + "Ġ ÐŁÐ¾", + "ĠÐŁ о", + "é Ĭ", + "ë¨ ¸", + "c hod", + "ch od", + "cho d", + "ĠØ¢Ùħ ÙĪز", + "ãģŁ ãĤģ", + "Ġch uyên", + "Ġuy gu", + "Ġuyg u", + "н ÑĸÑģÑĤ", + "нÑĸ ÑģÑĤ", + "ë ´", + "æİ §", + "Ñĥ ÑİÑĤÑĮ", + "ÑĥÑİ ÑĤÑĮ", + "ÑĥÑİÑĤ ÑĮ", + "Äį i", + "ãģ ¹", + "à¥Ĥ न", + "æĹ ©", + "ãĥĩ ãĤ£", + "è Ĵ", + "ĠØ´ خص", + "ĠÑħ оÑĤ", + "ĠÚ©ÙĨ ÛĮد", + "г л", + "à¸Ń à¸Ńà¸ģ", + "à¸Ńà¸Ń à¸ģ", + "éĢ Ļ", + "Ġز ÛĮر", + "ĠزÛĮ ر", + "íķ Ń", + "ĠÃĸ z", + "åij ³", + "ØŃ Ø¯Ø©", + "ØŃد Ø©", + "Ġk ažd", + "Ġ ÑĨвеÑĤ", + "ĠÑĨ веÑĤ", + "Ġ ç¾", + "Ġç ¾", + "Ġк ож", + "Ġко ж", + "Ġ ÐŃÑĤо", + "ĠÐŃ ÑĤо", + "ÑıÑĤ елÑĮ", + "ла ÑģÑĮ", + "лаÑģ ÑĮ", + "âĢĮ Ø´ÙĪد", + "âĢĮØ´ ÙĪد", + "μ ι", + "Ġ æ²", + "Ġæ ²", + "Ġs üre", + "Ġsü re", + "Ġsür e", + "ล ะ", + "éħ Ĵ", + "ึà¸ģ ษ", + "λ λά", + "λλ ά", + "ç ij", + "Ġ ìĥĪ", + "Ġìĥ Ī", + "Ġस ह", + "ĠH Ãł", + "리 ê³ł", + "ص ر", + "Ġ æĬķ", + "ĠæĬ ķ", + "éł Ń", + "Ġb á»ĩnh", + "ĠìĥĿ ê°ģ", + "Ġà¤ħ à¤Ń", + "ê³µ ì§Ģ", + "ì Ķ", + "á» Ŀi", + "á»Ŀ i", + "ç ŃĶ", + "çŃ Ķ", + "Ġb Ãłi", + "ĠbÃł i", + "о дÑĸ", + "од Ñĸ", + "า à¸Ĥ", + "าภĤ", + "ни ков", + "ник ов", + "Ġdön em", + "Ġdö nem", + "ว ม", + "ãĥĨ ãĤ£", + "ा रण", + "ार ण", + "о ги", + "ог и", + "Ġk iá»ĥm", + "Ġki á»ĥm", + "о ÑĦ", + "äº Ī", + "åĨ ³", + "ا ÙĦات", + "اÙĦ ات", + "اÙĦا ت", + "Ġn ếu", + "Ġ cest", + "Ġc est", + "Ġce st", + "Ġces t", + "ز Ø´", + "Ùİ ÙĦ", + "Ġت Ø£", + "ĠÄij ạo", + "Ïį ν", + "Ġв нÑĥ", + "Ġ جاÙħ", + "Ġج اÙħ", + "Ġجا Ùħ", + "i vnÃŃ", + "iv nÃŃ", + "Ġìŀ ĪìĬµëĭĪëĭ¤", + "ĠìŀĪ ìĬµëĭĪëĭ¤", + "Ï Ĭ", + "æĦ Ľ", + "ãĥ Ľ", + "м Ñĸн", + "мÑĸ н", + "Ġt ÃŃm", + "ĠtÃŃ m", + "ằ m", + "ê· ł", + "äº ķ", + "Ġx ây", + "Ġ ìĽĶ", + "ĠìĽ Ķ", + "е лен", + "ел ен", + "еле н", + "Ġ à¹Ĥà¸Ķย", + "Ġà¹Ĥ à¸Ķย", + "ا ÙĦÙĩ", + "اÙĦ Ùĩ", + "Ġb ất", + "á»ĵ m", + "âĢĮ Ú¯", + "ÙĪ رة", + "ÙĪر Ø©", + "ب ات", + "با ت", + "Ġb án", + "ẫ u", + "اÙĨ ÙĪÙĨ", + "اÙĨÙĪ ÙĨ", + "Ġzá kon", + "á ž", + "ì¶ Ķ", + "à¹ģ à¸ģ", + "ãĤį ãģĨ", + "ÑĢ оÑĤ", + "ÑĢо ÑĤ", + "ç ĵ", + "Ġв они", + "Ġво ни", + "Ġx ác", + "Ġ دÛĮگر", + "ĠدÛĮ گر", + "ÏĢ οι", + "ÏĢο ι", + "Ġне Ñģк", + "ĠнеÑģ к", + "ر سÛĮ", + "رس ÛĮ", + "Ġ ëĿ¼", + "Ġë Ŀ¼", + "ت ÙĦ", + "λ ά", + "ĠÑıв лÑıеÑĤÑģÑı", + "ä¾ Ŀ", + "Ġ åħ¬", + "Ġåħ ¬", + "Ĺ i", + "Ġ íĬ¹", + "ĠíĬ ¹", + "Ùĥ ÙĪÙĨ", + "ÙĥÙĪ ÙĨ", + "ắ p", + "جÙħ ÙĪع", + "ÏĨ οÏģ", + "ÏĨο Ïģ", + "е ло", + "ел о", + "Ġg üven", + "Ġgü ven", + "Ġм ай", + "Ġма й", + "ĠÑģ оз", + "ĠÑģо з", + "à¸ģ ระ", + "à¸ģร ะ", + "Ġا سÙĦاÙħ", + "Ġاس ÙĦاÙħ", + "Ġ Ñīе", + "ĠÑī е", + "Ġs á»ijng", + "Ġsá»ij ng", + "à¥į ब", + "à¥įठ¬", + "Ú© ار", + "کا ر", + "Ġthu áºŃt", + "Ġ nÃŃ", + "Ġn ÃŃ", + "第 ä¸Ģ", + "è¦ ĸ", + "à¹Ģà¸ģ ม", + "ا ÙĬØ©", + "اÙĬ Ø©", + "Ġ ÎĪ", + "ĠÎ Ī", + "ãĤ ¶", + "ĠÙħ ÙĪÙĤع", + "ĠÙħÙĪ ÙĤع", + "Ġ åĴ", + "Ġå Ĵ", + "è¡ ĵ", + "Ġ Ðŀд", + "ĠÐŀ д", + "Ġ ä¸ī", + "Ġä¸ ī", + "ler inde", + "leri nde", + "lerin de", + "ĠÑģв оÑĹ", + "ĠÑģво ÑĹ", + "à¥Ģ à¤ı", + "Ġth Æ°Æ¡ng", + "Ïĥ ÏĦο", + "ÏĥÏĦ ο", + "Ġ غÙĬر", + "Ġغ ÙĬر", + "Ġ پر", + "ĠÙ¾ ر", + "ĠÑģеб е", + "Ġв к", + "Ġk hai", + "Ġkh ai", + "ãĤ Ģ", + "ĠÙĨ ظر", + "ĠÙĨظ ر", + "Ġдок Ñĥм", + "à¹ĩ à¸ļ", + "Ġ íķľêµŃ", + "Ġíķľ êµŃ", + "ï½ ī", + "å·¥ ç¨ĭ", + "Ġ ÙĪÙĦ", + "ĠÙĪ ÙĦ", + "ØŃ ÙĬ", + "Ġп ла", + "Ġпл а", + "Ġ Ä°stanbul", + "ĠÄ° stanbul", + "âĢĻ de", + "âĢĻd e", + "а лÑģÑı", + "ал ÑģÑı", + "ĠØ¢ÙĨ Ùĩا", + "Ġ اÙĩ", + "Ġا Ùĩ", + "Ġ ê´Ģ리", + "Ġê´Ģ 리", + "Ġ anh", + "Ġa nh", + "Ġan h", + "Å¡ ÃŃm", + "Å¡ÃŃ m", + "lar la", + "ï¼ Ŀ", + "n ostÃŃ", + "no stÃŃ", + "nost ÃŃ", + "nos tÃŃ", + "ÑģÑĤ ве", + "ÑģÑĤв е", + "ÛĮ Ùģ", + "Ġ گرد", + "ĠÚ¯ رد", + "Ġگر د", + "ãĤĮ ãĤĭ", + "Ġv á»±", + "Ġvá» ±", + "ÄĽ nÃŃ", + "ÄĽn ÃŃ", + "Ġgö rev", + "Ġgör ev", + "Ġgöre v", + "Ġyıl ında", + "Ġyılı nda", + "Ġyı lında", + "Ġl ợi", + "Ġlá» £i", + "Ġan lam", + "Ġп ÑĢовод", + "ĠпÑĢо вод", + "ĠпÑĢов од", + "ÑĨ Ñİ", + "Ġ åī", + "Ġå ī", + "Ġë§ İ", + "ÑĢ аÑģ", + "ÑĢа Ñģ", + "Ġ Ž", + "ĠÅ ½", + "Ú© اÙĨ", + "کا ÙĨ", + "Ð Ļ", + "ãģ£ ãģ¨", + "ãģ£ãģ ¨", + "Ú© ÙĦ", + "า ยà¸Ļ", + "าย à¸Ļ", + "ع اÙĦ", + "عا ÙĦ", + "Ġ ký", + "Ġk ý", + "ĠмаÑĤ еÑĢи", + "Ġма ÑĤеÑĢи", + "ê» ĺ", + "ıl ması", + "μ ÎŃν", + "μÎŃ Î½", + "ĠÙĨ ÙħÛĮ", + "ĠÙĨÙħ ÛĮ", + "Ġcu á»Ļc", + "Ġδ εν", + "Ġδε ν", + "å¹ ²", + "＿ ＿＿＿", + "＿＿ ＿＿", + "＿＿＿ ＿", + "à¥Ģ à¤Ł", + "Ġçık ar", + "Ġçı kar", + "Ġkon uÅŁ", + "Ġkonu ÅŁ", + "иÑĤ елÑĮно", + "иÑĤелÑĮ но", + "lan tı", + "lant ı", + "à¹Ħ ล", + "å¾ ĭ", + "Ġ íĶ¼", + "ĠíĶ ¼", + "ìĻ ¸", + "Ġs áng", + "éģ Ķ", + "о жд", + "ож д", + "Ġ آخر", + "ĠØ¢ خر", + "il ece", + "ile ce", + "à¥Ī न", + "Ġ jedn", + "Ġj edn", + "Ġje dn", + "Ġjed n", + "ĠÑģпе ÑĨи", + "´ Ŀ", + "Ġ Úĺ", + "Ġ ãĢĤĊ", + "ĠãĢĤ Ċ", + "èģ Į", + "Ġ ÙĨÛĮ", + "ĠÙĨ ÛĮ", + "ÑĤ оÑĢа", + "ÑĤо ÑĢа", + "ÑĤоÑĢ а", + "λ ι", + "Ġ ÙĪب", + "ĠÙĪ ب", + "iÅŁ im", + "iÅŁi m", + "ç» ´", + "ãĢĢ i", + "Ġm ua", + "Ġmu a", + "Ġj iž", + "Ġji ž", + "è¶ Ĭ", + "ãĤĴ è¦ĭ", + "Ġn á»Ļi", + "à¥į à¤Ĺ", + "à¥įठĹ", + "ç¨ ®", + "Ġ ãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢ", + "à¹ĥ หม", + "à¹ĥห ม", + "Ġ ÎĨ", + "ĠÎ Ĩ", + "ÙĨ دÛĮ", + "ÙĨد ÛĮ", + "ĠÑģ Ñĩ", + "Ġl á»ĩ", + "Ġlá» ĩ", + "l ub", + "lu b", + "еÑĢ ÑĤ", + "Ġ اطÙĦ", + "Ġا Ø·ÙĦ", + "Ġاط ÙĦ", + "ĠÑģ еÑĢед", + "ĠÑģеÑĢ ед", + "Ġ éģ", + "Ġé ģ", + "Ġз ал", + "Ġза л", + "ÙĨ ÛĮÙĨ", + "ÙĨÛĮ ÙĨ", + "çŁ¥ éģĵ", + "Ø¢ ÙĨ", + "Ġ кап", + "Ġк ап", + "Ġка п", + "Ġ à¹Ħม", + "Ġà¹Ħ ม", + "ů vod", + "ův od", + "ĠÙ¾ اÛĮ", + "Ġپا ÛĮ", + "ÑĤ ÑĢи", + "ÑĤÑĢ и", + "Ġi ht", + "Ġih t", + "๠Ĭ", + "Ġв ÑģÑĸ", + "ĠвÑģ Ñĸ", + "Ġt hay", + "Ġth ay", + "Ġtha y", + "å Ĩµ", + "åĨ µ", + "Ġ عÙĨÙĪاÙĨ", + "ĠعÙĨ ÙĪاÙĨ", + "Ġ Î¥", + "ĠÎ ¥", + "ภĿ", + "ε ÏĦαι", + "εÏĦ αι", + "iyor du", + "ï¼Į èĢĮ", + "çļĦ 人", + "Ġ सà¤Ń", + "Ġस à¤Ń", + "à¹ī à¸Ńย", + "à¹īà¸Ń ย", + "ι κο", + "ικ ο", + "ãĤĵ ãģ§", + "ì¡ ±", + "ÙĨج ÙĦÙĬزÙĬØ©", + "Ġž ád", + "ÑĢ ави", + "ÑĢа ви", + "ÑĢав и", + "γ γ", + "æµ ĭ", + "о ÑĨÑĸ", + "ãĢĢ ãĢĢĠãĢĢ", + "ãĢĢãĢĢ ĠãĢĢ", + "ãĢĢãĢĢĠ ãĢĢ", + "Ġतर ह", + "Ġ ëĨ", + "Ġë Ĩ", + "à¥Ģ à¤ļ", + "à¹Ī ม", + "Ġg á»ĵm", + "Ġk iá»ĩn", + "Ġki á»ĩn", + "è· Ł", + "Î ¦", + "es inin", + "esi nin", + "esini n", + "esin in", + "é ¥", + "é« Ķ", + "о Ñĩно", + "оÑĩ но", + "र ण", + "æĺ ¥", + "ç¶ ĵ", + "Ġ بار", + "Ġب ار", + "Ġبا ر", + "ê· ¼", + "éĻ ħ", + "Ġ سÙĬ", + "Ġس ÙĬ", + "Ñģ ÑĥÑĤ", + "ÑģÑĥ ÑĤ", + "ì µľ", + "å± ħ", + "ĠÄį esk", + "ĠÄįe sk", + "Îij ÎĿ", + "Ġd iá»ĩn", + "Ġdi á»ĩn", + "Ġ εί", + "Ġε ί", + "à¸ĩ à¸Ĺ", + "ãĤ ©", + "Ġv á»±c", + "Ġvá»± c", + "в ав", + "ва в", + "t ıģı", + "tı ģı", + "tıģ ı", + "Ġ ëªħ", + "Ġëª ħ", + "η ν", + "в иÑĤ", + "ви ÑĤ", + "Ġ Ø£Ùĥ", + "ĠØ£ Ùĥ", + "Ġп ÑĢоп", + "ĠпÑĢ оп", + "ĠпÑĢо п", + "r ak", + "ra k", + "ÑĢ аÑĤи", + "ÑĢа ÑĤи", + "ÑĢаÑĤ и", + "ĠÄij ánh", + "ĠÄijá nh", + "ÑĢ еп", + "ÑĢе п", + "ê ´ij", + "ê´ ij", + "е ÑĨÑĮ", + "еÑĨ ÑĮ", + "Ġब त", + "Ġ åĮĹ", + "ĠåĮ Ĺ", + "Ġs át", + "l edi", + "le di", + "led i", + "ìłģ ìľ¼ë¡ľ", + "ů j", + "Û° Û°", + "Ġnas ıl", + "Ġ ÙĪس", + "ĠÙĪ س", + "Ġ εξ", + "Ġε ξ", + "в Ñĭ", + "ç½ Ĺ", + "ارÛĮ Ø®", + "à¸Ľ ล", + "ί κ", + "Ġ ê¸Ī", + "Ġê¸ Ī", + "åĩ ł", + "å¼ ·", + "è¿ Ķ", + "Ġnh á»ı", + "å¾ Ģ", + "Ġда же", + "Ġç ev", + "к Ñĸ", + "Ġ Ø£Ùħ", + "ĠØ£ Ùħ", + "ี ส", + "ส ามารà¸ĸ", + "สาม ารà¸ĸ", + "Ġ ÐĦ", + "ĠÐ Ħ", + "Ñħод иÑĤ", + "ë ĸ", + "Ġtr uyá»ģn", + "Ġtruy á»ģn", + "Ġ ÑģÑĤан", + "ĠÑģÑĤ ан", + "ĠÑģÑĤа н", + "ëĵ¤ ìĿĢ", + "ا ÙĦت", + "اÙĦ ت", + "़ à¥ĩ", + "Ġ à¤ħब", + "Ġà¤ħ ब", + "æķ ¸", + "Ġд ÑĸÑı", + "ĠдÑĸ Ñı", + "ĠÙħ تر", + "ĠÙħت ر", + "Ġ ë¸", + "Ġë ¸", + "ï¾ į", + "Ġ ê³¼", + "Ġê³ ¼", + "Ġ زÛĮ", + "Ġز ÛĮ", + "ëŁ ¼", + "Ġ ÐŁÐµÑĢ", + "ĠÐŁ еÑĢ", + "Ġs ık", + "Ġsı k", + "н оÑģÑĤÑĮÑİ", + "ноÑģÑĤÑĮ Ñİ", + "ноÑģÑĤ ÑĮÑİ", + "Ġ eden", + "Ġe den", + "Ġed en", + "ا در", + "اد ر", + "ã Ħ", + "Ġ леÑĩ", + "Ġл еÑĩ", + "ĠÙĩ Ø°Ùĩ", + "ض ÙĪع", + "ضÙĪ ع", + "ĠìķĦ ëĭĪ", + "ĠìķĦëĭ Ī", + "ir ket", + "irk et", + "Ġ اگر", + "Ġا گر", + "ĠÑħ оÑĩ", + "Ġб ан", + "Ġба н", + "íĶ Į", + "æĢİ ä¹Ī", + "è Ľ", + "Ġब à¤ļ", + "ĠÚ© تاب", + "çī Į", + "Ġд ва", + "Ġдв а", + "ج ر", + "Ġп ÑĢоÑģÑĤо", + "ĠпÑĢоÑģÑĤ о", + "ĠпÑĢоÑģ ÑĤо", + "Ġà¤Ĩ व", + "Ġm ức", + "į ¼", + "Ġ jÃŃ", + "Ġj ÃŃ", + "íİ ĺ", + "Ġt amam", + "Ġta mam", + "Ġtam am", + "åĪ Ľ", + "ภĴ", + "п еÑĩ", + "пе Ñĩ", + "à¥ĭ स", + "Ġ Ñģем", + "ĠÑģ ем", + "Ġt Æ°Æ¡ng", + "ä¸ ģ", + "ī ´", + "Ġ ÑĢоÑģ", + "ĠÑĢ оÑģ", + "Ġ маÑĶ", + "Ġм аÑĶ", + "Ġма ÑĶ", + "æŃ Į", + "Ġ داÙĨÙĦÙĪد", + "ĠداÙĨ ÙĦÙĪد", + "ĠL oÃłi", + "ĠLo Ãłi", + "Ġed ilm", + "Ġedi lm", + "Ġedil m", + "Ġk onu", + "Ġko nu", + "Ġkon u", + "ĠاÙĦ Ùħر", + "ĠاÙĦÙħ ر", + "Ġu laÅŁ", + "Ġul aÅŁ", + "Ġyük sek", + "ο ι", + "Ùİ ÙĨ", + "Ġ bÄĽ", + "Ġb ÄĽ", + "ãĤ·ãĥ§ ãĥ³", + "ï¿£ ￣￣￣", + "￣￣ ￣￣", + "￣￣￣ ï¿£", + "Ġg üç", + "Ġgü ç", + "Ġ اÙĪÙĦ", + "Ġا ÙĪÙĦ", + "ĠاÙĪ ÙĦ", + "Ġ ма", + "Ġм а", + "Ġب خش", + "Ġبخ Ø´", + "ा à¤ĸ", + "ाठĸ", + "Ġв иÑģ", + "Ġви Ñģ", + "ž enÃŃ", + "že nÃŃ", + "žen ÃŃ", + "Ġz působ", + "Ġzp ůsob", + "z nam", + "zn am", + "Ġ رÙĪÛĮ", + "Ġر ÙĪÛĮ", + "ĠرÙĪ ÛĮ", + "åĭ Ŀ", + "। Ċ", + "ÙĦ ÙĤ", + "Ġж из", + "ÑĢ Ñĸв", + "ÑĢÑĸ в", + "ĠÑĥ пÑĢав", + "ĠÑĥп ÑĢав", + "Ġph á»ij", + "ic ros", + "icro s", + "Ġ à¹ģà¸ķ", + "Ġà¹ģ à¸ķ", + "Ġ ë°ķ", + "Ġë° ķ", + "ÙĪ ات", + "ÙĪا ت", + "ï¼Į ä¸Ģ", + "ан Ñģ", + "ç´ ļ", + "ย à¸Ļ", + "à¹ģ à¸Ĥ", + "Ġgi áo", + "Ġgiá o", + "äºĮ äºĮ", + "Ġ Ä°s", + "ĠÄ° s", + "ìĬ ¹", + "Ġo lacak", + "Ġol acak", + "Ġola cak", + "Ġ Các", + "ĠC ác", + "Ġ ÑĢÑĥб", + "ĠÑĢ Ñĥб", + "ĠÑĢÑĥ б", + "ẹ p", + "ÄŁ iniz", + "ÄŁini z", + "ÄŁin iz", + "ãģª ãģ©", + "Ġ моÑĢ", + "Ġм оÑĢ", + "Ġмо ÑĢ", + "ĠÑģ дел", + "ÙĦ ÙħاÙĨ", + "ÙĦÙħ اÙĨ", + "n ém", + "né m", + "å° į", + "Ġd ne", + "Ġdn e", + "ì¶ľ ìŀ¥", + "ع ب", + ": ::::::", + ":: :::::", + ":::: :::", + ":::::: :", + "::: ::::", + "::::: ::", + "Î Ĵ", + "e ket", + "ek et", + "Ġ ÑĢеÑĪ", + "ĠÑĢ еÑĪ", + "ĠÑĢе ÑĪ", + "è Ī¬", + "èĪ ¬", + "Ġ íĻĶ", + "ĠíĻ Ķ", + "ص د", + "Ġ маÑĢ", + "Ġм аÑĢ", + "Ġма ÑĢ", + "Ñı ж", + "Ø´ ار", + "ãģ ²", + "Ġ اÙĦÙĬ", + "Ġا ÙĦÙĬ", + "ĠاÙĦ ÙĬ", + "Ù į", + "à¤Ĥ à¤ľ", + "м Ñĭ", + "Ġka rar", + "Ġkar ar", + "Ġkara r", + "ÙĦÛĮ سÛĮ", + "ÙĦÛĮس ÛĮ", + "า à¸ĵ", + "าภĵ", + "ç¾ ¤", + "Ġol ması", + "Ġolm ası", + "Ġolma sı", + "Ġhaz ır", + "γÏģα ÏĨ", + "¯ u", + "в ол", + "во л", + "ĠÑģ ÑĤаÑĢ", + "ĠÑģÑĤ аÑĢ", + "ĠÑģÑĤа ÑĢ", + "o vala", + "ov ala", + "ova la", + "oval a", + "Ġв озмож", + "Ġвоз мож", + "Ġ дав", + "Ġд ав", + "Ġда в", + "é¢ ¨", + "ر ا", + "Ġдоп ом", + "ê² ĥ", + "Ġ ìĺ¬", + "Ġìĺ ¬", + "Ġ åİ", + "Ġå İ", + "Ġ 못", + "Ġëª »", + "u ç", + "í ļ", + "l ük", + "lü k", + "ä¸Ń å¿ĥ", + "Ġ दर", + "Ġद र", + "Ġ âĹĨ", + "ĠâĹ Ĩ", + "Ġt ay", + "Ġta y", + "Ġب سÛĮ", + "Ġبس ÛĮ", + "Ġ ÏĥÏĦα", + "ĠÏĥ ÏĦα", + "ĠÙħ Ø®", + "Ñı Ñī", + "å· ®", + "ภī", + "ëł ¹", + "à¹ĥà¸Ļ à¸ģาร", + "Ġ ÙĩÙĨ", + "ĠÙĩ ÙĨ", + "ãģ ¶", + "л Ñĸд", + "лÑĸ д", + "å į°", + "åį °", + "Ġs ao", + "Ġsa o", + "ÅĻ ad", + "리 ëĬĶ", + "Ñģ лед", + "Ñģл ед", + "åĶ ®", + "Ġ |:", + "Ġ| :", + "æķĻ èĤ²", + "Ġм ол", + "Ġмо л", + "ĠÙĩ ÙĬ", + "ë ģ", + "Ġ кÑĥлÑĮ", + "Ġк ÑĥлÑĮ", + "ĠкÑĥ лÑĮ", + "' nin", + "'n in", + "Ġ خر", + "ĠØ® ر", + "Ġge nel", + "Ġgen el", + "Ġgene l", + "Ġt á»Ń", + "Ġtá» Ń", + "Ġkur ul", + "Ġkuru l", + "ен ÑĤи", + "енÑĤ и", + "à¥ĭ à¤ľà¤¨", + "à¥ĭà¤ľ न", + "è¿Ļ æł·", + "Ġм Ñĸж", + "ĠмÑĸ ж", + "Ġngh iá»ĩm", + "Ġnghiá»ĩ m", + "ĠÏĢ ολ", + "ĠÏĢο λ", + "æĭ Ľ", + "Ġà¤Ĺ à¤ı", + "ầ y", + "Ġc ảm", + "Ġcả m", + "ç´ °", + "rı ca", + "Ġ عÙĦÛĮ", + "Ġع ÙĦÛĮ", + "ĠعÙĦ ÛĮ", + "ิ à¹ī", + "h ur", + "hu r", + "Ġch Æ°a", + "Ñĥ ÑĶÑĤÑĮÑģÑı", + "ÑĥÑĶ ÑĤÑĮÑģÑı", + "ãģ© ãģĨ", + "Ñĥ л", + "ิ ร", + "Ġ æľī", + "ä¼ ¼", + "ÑĦ еÑĢ", + "ÑįÑĤ омÑĥ", + "æĹ ħ", + "ĠÙħ ÙĪج", + "ĠÙħÙĪ ج", + "Ġ 본", + "Ġë³ ¸", + "Ġgi á»Ŀ", + "Ġgiá» Ŀ", + "Ġk iến", + "Ġki ến", + "à¹Ī วย", + "à¹Īว ย", + "Ġd üny", + "Ġdü ny", + "Ġdün y", + "Ġ زÙħ", + "Ġز Ùħ", + "о вÑĸ", + "ов Ñĸ", + "ĠÑĨ ÑĮого", + "ิ à¸ļ", + "Ġ ìĨIJ", + "ĠìĨ IJ", + "èIJ ¥", + "Ġ ÑĢÑĸз", + "ĠÑĢ Ñĸз", + "Ġh á»Ĺ", + "Ġhá» Ĺ", + "ÑĢ Ñĸб", + "ÑĢÑĸ б", + "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ìľ¼ ë©°", + "äºĨ ä¸Ģ", + "ĠÙĤ بÙĦ", + "ĠÙĤب ÙĦ", + "é¾ Ļ", + "Ġ گذ", + "ĠÚ¯ Ø°", + "Ġ ÙĤد", + "ĠÙĤ د", + "ãģª ãģĭãģ£ãģŁ", + "Ġ à¹Ģร", + "Ġà¹Ģ ร", + "Ġà¹Ģภ£", + "å¸ Į", + "ĠÑģ Ñħ", + "Ġг ÑĢом", + "ĠгÑĢ ом", + "ĠгÑĢо м", + "åĽ ¢", + "Ġ ì§ij", + "Ġì§ ij", + "Ġ лÑĥÑĩ", + "Ġл ÑĥÑĩ", + "åħ µ", + "Ġ ÐŀÑĤ", + "ĠÐŀ ÑĤ", + "Ġmu á»ijn", + "ãģĺ ãĤĥ", + "o vnÃŃ", + "ov nÃŃ", + "ë©´ ìĦľ", + "ë³ Ģ", + "Ġн еб", + "Ġне б", + "Ñģ ии", + "Ñģи и", + "ÙĨ Ùħ", + "ÄŁ in", + "ÄŁi n", + "Ġt oho", + "Ġto ho", + "Ġtoh o", + "en iz", + "eni z", + "ب اش", + "با Ø´", + "ĠÑģ лÑĥж", + "ĠÑģл Ñĥж", + "ĠÑģлÑĥ ж", + "Ġb ợi", + "Ġüzer e", + "Ġüz ere", + "Ġs adece", + "Ġsad ece", + "ĠÏĢ αÏģ", + "ĠÏĢα Ïģ", + "³³³³³³³³ ³³³³³³³³", + "³³³³³³³ ³³³³³³³³³", + "³³³³³ ³³³³³³³³³³³", + "³³³³³³³³³ ³³³³³³³", + "³³³³³³³³³³³ ³³³³³", + "èĮ ĥ", + "ÏĦ ικÏĮ", + "ÏĦικ ÏĮ", + "ÏĦι κÏĮ", + "Ġ äºĮ", + "ãĤĪãģĨ ãģ«", + "è ŀ", + "ãģ® ãģ¯", + "Ġ ÑĥкÑĢаÑĹн", + "ĠÑĥ кÑĢаÑĹн", + "Ġb ắt", + "в ан", + "ва н", + "Ġ ÑģÑĤÑĢа", + "ĠÑģ ÑĤÑĢа", + "ĠÑģÑĤ ÑĢа", + "ĠÑģÑĤÑĢ а", + "è¡ Ģ", + "nu tÃŃ", + "nut ÃŃ", + "o kt", + "ok t", + "รà¸ĩ à¹Ģร", + "Ġ صÙģ", + "Ġص Ùģ", + "åħ ļ", + "ÏĦ ί", + "ï¼ģ ãĢįĊĊ", + "ï¼ģãĢį ĊĊ", + "ĠÑĤем п", + "ĠÑĤе мп", + "é¡ Į", + "Ġs luž", + "Ġslu ž", + "Ñĥ ков", + "Ñĥк ов", + "Ġngh Ä©a", + "ĠnghÄ© a", + "çĶ ²", + "Ġd á»ħ", + "о ви", + "ов и", + "ÏĦ Ïħ", + "ر ÛĮÚ©", + "رÛĮ Ú©", + "ĠA nh", + "ĠAn h", + "ĠвÑģ его", + "ĠвÑģе го", + "âĢĮ Ú©ÙĨ", + "âĢĮÚ© ÙĨ", + "te ÅĻÃŃ", + "Ġm ục", + "Ùĩ ÙĨÚ¯", + "ÙĩÙĨ Ú¯", + "Ġ ÐŁÐ¾Ñģ", + "ĠÐŁ оÑģ", + "ĠÐŁÐ¾ Ñģ", + "Úĺ Ùĩ", + "ĠاÙĦ غ", + "æĿ ¾", + "y sl", + "ys l", + "Ġyap ılan", + "Ġyapı lan", + "Ġyapıl an", + "çĪ ¶", + "Ġm ạnh", + "ر اÙĩ", + "را Ùĩ", + "à¸Ķ à¸ĩ", + "o Äį", + "ë§ IJ", + "åł Ĥ", + "м аÑĤ", + "ма ÑĤ", + "Ġ eÅŁ", + "Ġe ÅŁ", + "ÙĪÙħ ات", + "Ġس اخت", + "åĽł 为", + "Ġп ÑĢий", + "ĠпÑĢ ий", + "ĠпÑĢи й", + "ıl mÄ±ÅŁ", + "é¤ ¨", + "ุ à¸ĩ", + "Ġ ëģ", + "Ġë ģ", + "à¸ķ าม", + "à¸ķา ม", + "åIJ ī", + "μ ή", + "Ġ æľ¬", + "Ġzá klad", + "ت ØŃ", + "è¾ ¼", + "Ġв Ñĸй", + "ĠвÑĸ й", + "ĠÙħÙĨ Ø·", + "Ġto án", + "к аÑĢ", + "ка ÑĢ", + "Ġ ÐĹа", + "ĠÐĹ Ð°", + "ĠпÑĢи мен", + "ĠпÑĢим ен", + "ãĤĭ ãģ¨", + "ั à¸Ĺ", + "ÛĮ س", + "ĠاÙĨ جاÙħ", + "ĠاÙĨج اÙħ", + "Ġع ÙĦÙĬ", + "ĠعÙĦ ÙĬ", + "़ ि", + "e ných", + "en ých", + "ený ch", + "ĠL iên", + "ĠLi ên", + "Ġ па", + "Ġп а", + "éļ Ĭ", + "Ġmo hou", + "Ġmoh ou", + "Ġк ÑĸлÑĮ", + "Ġ Το", + "ĠΤ ο", + "ا ÙĦب", + "اÙĦ ب", + "ÎŃ Î½", + "Ġna bÃŃ", + "Ġnab ÃŃ", + "ç i", + "ler den", + "lerde n", + "Ġth anh", + "Ġthan h", + "Ġtha nh", + "Ġb ütün", + "Ġ åŁ", + "Ġå Ł", + "ì¸ ł", + "Ġz at", + "Ġza t", + "ÙĬ ÙĪ", + "Ġμ ια", + "uy ết", + "Ñij н", + "åĪ Ĵ", + "ли во", + "лив о", + "à¹Ī à¸Ńà¸ĩ", + "à¹Īà¸Ń à¸ĩ", + "ä»ĸ 们", + "Ġб аг", + "Ġба г", + "ि à¤Ń", + "िठŃ", + "ĠÑĤ ам", + "ĠÑĤа м", + "Ġп ÑĢеп", + "ĠпÑĢ еп", + "ĠпÑĢе п", + "ิ à¸Ĭ", + "âĢĻ Ñıз", + "âĢĻÑı з", + "ĠPh ân", + "ж ен", + "же н", + "à¥Ī à¤ķ", + "ĠÑģлÑĥÑĩа е", + "Ġ .:", + "Ġ. :", + "åѦ æł¡", + "Ä° N", + "ç¾ ©", + "ĠÑģ ÑĤо", + "ĠÑģÑĤ о", + "Ġ हर", + "Ġह र", + "Ïħ ν", + "Ġx em", + "Ġxe m", + "Ġб ÑĥÑĤи", + "ĠбÑĥ ÑĤи", + "Ñģ иÑĤ", + "Ñģи ÑĤ", + "çª ģ", + "à¥į à¤Ľ", + "à¥įठĽ", + "åij ¢", + "ï¼Į ä¹Ł", + "e nÄĽ", + "en ÄĽ", + "Ġ κά", + "Ġκ ά", + "iy orum", + "iyor um", + "ĠÚ¯ Ùģت", + "âĹıâĹı âĹıâĹı", + "ั ม", + "Ġ Ðļон", + "ĠÐļ он", + "ĠÐļо н", + "н оÑĪ", + "но ÑĪ", + "ниÑĨ ÑĤ", + "ü zel", + "üz el", + "s ÃŃ", + "å¸ «", + "ص ÙĪÙĦ", + "çĥ Ń", + "ĠÄij ủ", + "ĠÄijá» §", + "ãĤ ®", + "æķ ħ", + "ĠÅ¡ kol", + "ĠÅ¡k ol", + "Ñĩ ен", + "Ñĩе н", + "à¹Ģ ย", + "à¹Ģภ¢", + "à¸Ļ à¸Ļ", + "ÙĢ ÙĢÙĢÙĢ", + "ÙĢÙĢ ÙĢÙĢ", + "ÙĢÙĢÙĢ ÙĢ", + "Ġ üç", + "Ġü ç", + "å¿ µ", + "ãĥª ãĤ¢", + "Ġ íĻĺ", + "ĠíĻ ĺ", + "Ġ éĩij", + "Ġéĩ ij", + "çı Ń", + "Ġ Ñģклад", + "ĠÑģ клад", + "ĠÑģк лад", + "Ñı ми", + "Ñıм и", + "ü f", + "Ġh ã", + "ĠÄIJ ại", + " Ĥ", + "åĦ ª", + "Ġbul unan", + "Ġbulun an", + "ĠاÙĦ ÙħØŃ", + "ĠاÙĦÙħ ØŃ", + "æĪ ı", + "Ġ è©", + "Ġè ©", + "Ġн оÑĢм", + "ĠноÑĢ м", + "Ġchu ẩn", + "Ġз аÑģÑĤ", + "Ġза ÑģÑĤ", + "ĠзаÑģ ÑĤ", + "Ġ vÃŃce", + "ĠvÃŃ ce", + "ĠvÃŃc e", + "Ð ĸ", + "Ġà¤Ĩ ध", + "Ġ Äįas", + "ĠÄį as", + "Ġ боÑĢ", + "Ġб оÑĢ", + "Ġбо ÑĢ", + "Ïģ ια", + "Ïģι α", + "ĠÙħ اÙĩ", + "ĠÙħا Ùĩ", + "Ġ íħ", + "Ġí ħ", + "ÅĻ el", + "ÅĻe l", + "Ñı ви", + "Ñıв и", + "ÏĦ εÏĤ", + "ÏĦε ÏĤ", + "i nÄĽ", + "in ÄĽ", + "Ġп еÑĢе", + "ĠпеÑĢ е", + "éķ ĩ", + "à¥įठŀ", + "Ġ éĺ", + "Ġé ĺ", + "à¹Ī าว", + "à¹Īา ว", + "ร ร", + "Ġ سÙĩ", + "Ġس Ùĩ", + "в али", + "ва ли", + "вал и", + "çķ Ļ", + "ĠÑĦ Ñĥнк", + "ĠÑĦÑĥн к", + "Ġ íĸī", + "Ġí ĸī", + "Ġíĸ ī", + "Ùģ Ùĩ", + "çĶŁ æ´»", + "èģ ŀ", + "o kud", + "ok ud", + "oku d", + "Ġ ìĤ´", + "ĠìĤ ´", + "ı zı", + "ız ı", + "Ġпо лÑĥ", + "Ġпол Ñĥ", + "ï¼Į ä½ł", + "Ø´ اÙĨ", + "æ± º", + "б ÑĢÑı", + "оÑģÑĥд аÑĢ", + "Ġo yun", + "Ġoy un", + "а нии", + "ан ии", + "ани и", + "Ġp rů", + "Ġpr ů", + "Ġn áv", + "Ġná v", + "Ġм енÑı", + "Ġмен Ñı", + "Ġìŀ ĺ", + "Ġ Ä°n", + "ĠÄ° n", + "Ġth ÃŃch", + "ĠthÃŃ ch", + "ĠÄij ảm", + "åľ Ĵ", + "Ġв же", + "Ġl oÃłi", + "Ġlo Ãłi", + "Ġ Ðŀн", + "ĠÐŀ н", + "м еÑģÑĤ", + "ме ÑģÑĤ", + "Ġ ξ", + "ĠÎ ¾", + "ãĢ ħ", + "Ġch iế", + "Ġchi ế", + "Ñĩ Ñĸ", + "Ġ íijľ", + "Ġí ijľ", + "ëĭ ¬", + "Ġ ëĭ¬", + "Ġëĭ ¬", + "à¥Ģ ड", + "ÑĢ алÑĮ", + "ÑĢа лÑĮ", + "ÑĢал ÑĮ", + "d ik", + "di k", + "Ġ íĨł", + "ĠíĨ ł", + "ëŁ ī", + "Ġ صÙĨ", + "Ġص ÙĨ", + "Ġs tej", + "Ġst ej", + "Ġste j", + "Ġа кÑĤив", + "Ġак ÑĤив", + "ĠакÑĤ ив", + "ĠакÑĤи в", + "Ġ é¦", + "Ġé ¦", + "Ġ à¹Ħà¸Ķ", + "Ġà¹Ħ à¸Ķ", + "æĬĢ æľ¯", + "Ġp rostÅĻed", + "Ġpro stÅĻed", + "Ġprost ÅĻed", + "å® ³", + "ãģ IJ", + "Ġol uÅŁtur", + "ĠoluÅŁ tur", + "e lop", + "el op", + "elo p", + "ãģ¡ ãĤĥ", + "éĥ İ", + "ض ا", + "Ġ خط", + "ĠØ® Ø·", + "ë° ķ", + "е ÑģÑı", + "еÑģ Ñı", + "ĠÙĩ ÛĮ", + "н ад", + "на д", + "Ġng Ãłnh", + "ÑĢ ÑĥÑĪ", + "ÑĢÑĥ ÑĪ", + "ãģĦ ãģĦ", + "Ġü rün", + "Ġür ün", + "à¸Ń à¸ķ", + "à¥ĭ प", + "Ġs ayı", + "Ġsa yı", + "Ġsay ı", + "à¥Ģ स", + "е ниÑħ", + "ен иÑħ", + "ени Ñħ", + "Ġ Ñģим", + "ĠÑģ им", + "ĠÑģи м", + "à¥Ģ द", + "å¤ ī", + "à¹Ī วม", + "à¹Īว ม", + "Ġ à¹Ģà¸Ĥ", + "Ġà¹Ģ à¸Ĥ", + "Ġà¹ĢภĤ", + "å·² ç»ı", + "а ÑĤо", + "аÑĤ о", + "ĠÑĢай он", + "í ĥĿ", + "íĥ Ŀ", + "Ġ ÑĤÑĢа", + "ĠÑĤ ÑĢа", + "ĠÑĤÑĢ а", + "l ayan", + "la yan", + "lay an", + "ế p", + "ा à¤Ł", + "ाठŁ", + "Ø® اب", + "人 æ°ij", + "å® Ŀ", + "è Ĩ", + "èª į", + "n aÄį", + "na Äį", + "Ġî ł", + "ĠÐļ и", + "ĠbaÅŁ ka", + "ĠbaÅŁk a", + "c ů", + "ض ع", + "èĪ ª", + "ี ม", + "Ñĭ ми", + "Ñĭм и", + "ÎĻ Σ", + "Ġشر کت", + "ย ว", + "Ġmus ÃŃ", + "Ġmu sÃŃ", + "Ġн ал", + "Ġна л", + "ี à¸Ĺ", + "Ġ áp", + "Ġá p", + "ร าย", + "æ² ¹", + "l eme", + "le me", + "lem e", + "Ġ मन", + "Ġम न", + "à¹Ħ à¸Ł", + "а ÑĤив", + "аÑĤ ив", + "аÑĤи в", + "¸ ı", + "èŃ °", + "Ïĥ ÏĦα", + "ÏĥÏĦ α", + "íĸ ¥", + "е ÑĤÑĥ", + "еÑĤ Ñĥ", + "ĠÑģв Ñıз", + "ĠÑģвÑı з", + "ед еÑĢа", + "ĠØ® ارج", + "า ษ", + "าภ©", + "âĢĮ Ù¾", + "Ñĸ г", + "é¡ ŀ", + "Ġkh ả", + "ĠÑģ пÑĢав", + "ĠÑģп ÑĢав", + "è¡ Ĺ", + "ãĥķ ãĤ¡", + "ãĥķãĤ ¡", + "Ġм еждÑĥ", + "Ġмеж дÑĥ", + "Ñĥ ли", + "Ñĥл и", + "Ġب زر", + "ÑĨ ен", + "ÑĨе н", + "Ġek onom", + "د ÙĨ", + "ا ÙħÛĮ", + "اÙħ ÛĮ", + "าส à¸ķร", + "ĠnÄĽ kol", + "ĠnÄĽk ol", + "g ün", + "з и", + "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", + "ç¦ »", + "Ġtr Æ°á»Łng", + "ı i", + "íİ ¸", + "Ġ ÑĢеб", + "ĠÑĢ еб", + "ĠÑĢе б", + "åº ķ", + "Ġت ارÛĮØ®", + "н има", + "ни ма", + "ним а", + "Ġth ân", + "аÑĤ елÑĮно", + "аÑĤелÑĮ но", + "ĠاÙĦ Ø°ÙĬ", + "ĠاÙĦØ° ÙĬ", + "ÙĪ ÙĨÛĮ", + "ÙĪÙĨ ÛĮ", + "Ġ éĥ", + "Ġé ĥ", + "Ġb ình", + "Ġbì nh", + "ικ ήÏĤ", + "ική ÏĤ", + "à¸ŀ ล", + "تÙħ اع", + "ĠPr aha", + "ĠPra ha", + "Ġ ÑģÑĤав", + "ĠÑģÑĤ ав", + "ĠÑģÑĤа в", + "د ÙĬد", + "دÙĬ د", + "Ġgi ữa", + "Ġgiữ a", + "ĠпÑĢо вед", + "ĠпÑĢов ед", + "Âł k", + "ÙĨد Ú¯ÛĮ", + "ÑĨ ий", + "ÑĨи й", + "ç Ĵ", + "ĠاÙĦ Ø£Ùħ", + "ĠاÙĦØ£ Ùħ", + "Ġ è´", + "Ġè ´", + "Ø¥ ÙĨجÙĦÙĬزÙĬØ©", + "ĠìŀĪ ìĹĪëĭ¤", + "ĠìŀĪìĹĪ ëĭ¤", + "ç ·¨", + "ç· ¨", + "ัà¸Ļ à¸ĺ", + "ĠÑĢок Ñĸв", + "Ġc áo", + "Ġcá o", + "Ġkh ó", + "Ġ ÙĨÙĪع", + "ĠÙĨ ÙĪع", + "ĠÙĨÙĪ ع", + "س ÙĦ", + "Ġ ÑĥÑģлов", + "ĠÑĥ Ñģлов", + "ĠÑĥÑģл ов", + "ĠÑĥÑģ лов", + "Ġcứ u", + "ов ого", + "ово го", + "ि à¤Ĺ", + "िठĹ", + "Ķ ëĭ¤", + "æĿ İ", + "Ġbö lg", + "Ġböl g", + "Ġn gu", + "Ġng u", + "Ġh ữu", + "Ġhá» ¯u", + "н ии", + "ни и", + "ìł Ī", + "Ġп ÑĢом", + "ĠпÑĢ ом", + "ĠпÑĢо м", + "åı Į", + "Ġd Æ°á»Ľi", + "ĠdÆ°á» Ľi", + "ĠdÆ° Ỽi", + "Ð ®", + "ÙĬ Ø´", + "æ¸ ©", + "ëı ħ", + "Ġз мÑĸ", + "Ġзм Ñĸ", + "θη κε", + "ĠbaÄŁ lı", + "Ġüzer inde", + "Ġ تغ", + "Ġت غ", + "Ġп ÑĢогÑĢа", + "ĠпÑĢ огÑĢа", + "ĠпÑĢо гÑĢа", + "ĠпÑĢог ÑĢа", + "i ž", + "Ġ ç¥", + "Ġç ¥", + "Ġy ardım", + "Ġyard ım", + "Ġyar dım", + "ÂĢ ÂĢ", + "ÂĢ Ģ", + "Ġ Ñĥв", + "ĠÑĥ в", + "Ġ rů", + "Ġr ů", + "Ġch iến", + "Ġchi ến", + "Ġchiế n", + "ν οÏĤ", + "νο ÏĤ", + "ãģ¨ ãģª", + "ا ÙĨت", + "اÙĨ ت", + "è° ·", + "ÃŃ sk", + "ÃŃs k", + "is inde", + "isi nde", + "isin de", + "Ġд ог", + "Ġдо г", + "è¿ ½", + "Ġп ÑĢоÑĤив", + "ĠпÑĢо ÑĤив", + "ĠпÑĢоÑĤ ив", + "ĠпÑĢоÑĤи в", + "Ïģ οÏħ", + "Ïģο Ïħ", + "ãģ® ãģĭ", + "Ġb azı", + "Ġba zı", + "Ġbaz ı", + "ı rak", + "ır ak", + "à¥ĩ ष", + "à¥ĩठ·", + "ĠÙħ شار", + "ĠÙħØ´ ار", + "Ġ ìĸij", + "Ġìĸ ij", + "Ġ нез", + "Ġн ез", + "Ġне з", + "Ġ Ø°ÙĦÙĥ", + "ĠØ° ÙĦÙĥ", + "èª ¿", + "åĤ Ļ", + "ĠÑĤ ÑĢан", + "ĠÑĤÑĢ ан", + "ĠÑĤÑĢа н", + "ĠÏĢ αÏģα", + "ĠÏĢαÏģ α", + "ĠÏĢα Ïģα", + "ÛĮ Ùħت", + "ÛĮÙħ ت", + "Ġt iến", + "Ġti ến", + "Ġtiế n", + "ĠÙĩ ÙħÙĩ", + "ĠÙĩÙħ Ùĩ", + "e fon", + "ef on", + "» .ĊĊ", + "». ĊĊ", + "».Ċ Ċ", + "Ġ ÙĨد", + "ĠÙĨ د", + "ج ÙĦ", + "Ġد ادÙĩ", + "Ġداد Ùĩ", + "Ġ вед", + "Ġв ед", + "Ġве д", + "Ġ sın", + "Ġs ın", + "Ġsı n", + "ĠÑģ вÑĸÑĤ", + "ĠÑģв ÑĸÑĤ", + "e lerin", + "el erin", + "eler in", + "ele rin", + "eleri n", + "âĪ ¨", + "Ġy ür", + "д ан", + "да н", + "Ġ ÐŀÑģ", + "ĠÐŀ Ñģ", + "Ġh ạng", + "Ġhạn g", + "Ġhạ ng", + "è® ¸", + "Ïĥ ÏĦη", + "ÏĥÏĦ η", + "uy ến", + "Ġн аб", + "Ġна б", + "Ġ оÑħ", + "Ġо Ñħ", + "Ïĥ Ïī", + "Ġby ly", + "Ġbyl y", + "Ñģ киÑħ", + "Ñģк иÑħ", + "Ñģки Ñħ", + "l amak", + "la mak", + "lam ak", + "lama k", + "и ÑĤоÑĢ", + "иÑĤ оÑĢ", + "Ġy atır", + "Ġya tır", + "Ġyat ır", + "ĠпÑĢоиз вод", + "Ġ جÙħع", + "Ġج Ùħع", + "ĠجÙħ ع", + "Å ł", + "æıIJ ä¾Ľ", + "Ġpr vnÃŃ", + "Ġprv nÃŃ", + "Ġα ÏĢ", + "íĻ ©", + "ĠпÑĢа кÑĤи", + "ler inden", + "lerin den", + "lerinde n", + "ĠнеобÑħодим о", + "åº ·", + "Ùİ Ø§", + "Ġ سÙĨ", + "Ġس ÙĨ", + "Ä° L", + "Ġ ê´ij", + "Ġê ´ij", + "Ġê´ ij", + "Ġ PÅĻ", + "ĠP ÅĻ", + "ç ŀ", + "ĠÑĤемп еÑĢаÑĤÑĥ", + "Ġka bul", + "Ġkab ul", + "Ġbu dou", + "Ġbud ou", + "ÑĨÑĸ оналÑĮ", + "ÑĨÑĸон алÑĮ", + "ï½ ľ", + "Ġç ocuk", + "Ġçocu k", + "ĠÑĤ ÑĸлÑĮки", + "b yt", + "by t", + "ãĥ ¤", + "ĠÑģÑĤ аÑĤ", + "ĠÑģÑĤа ÑĤ", + "Ġ æĿ±", + "ĠæĿ ±", + "le žit", + "اس طة", + "ุ ร", + "i êm", + "iê m", + "ĠкÑĥлÑĮ ÑĤÑĥ", + "Ġ пон", + "Ġп он", + "Ġпо н", + "Ä© nh", + "åĸ ľ", + "н ев", + "не в", + "ÑĶ н", + "ĠÑģо оÑĤ", + "ë Ŀ", + "çĪ ¾", + "Ġtu á»ķi", + "k anı", + "kan ı", + "สำ หร", + "ا عت", + "اع ت", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "д еÑĢж", + "деÑĢ ж", + "Ġоб лаÑģÑĤи", + "Ġобла ÑģÑĤи", + "ĠоблаÑģ ÑĤи", + "ĠоблаÑģÑĤ и", + "Ġобл аÑģÑĤи", + "Ġv ừa", + "Ġvá» «a", + "Ġ ÙħÙħ", + "ĠÙħ Ùħ", + "à¸ģ ำ", + "à¹ģ ม", + "iver sit", + "ivers it", + "à¹ģ ส", + "æ¬ §", + "l anan", + "la nan", + "lan an", + "ÙĬ ÙĨØ©", + "ÙĬÙĨ Ø©", + "س Ø©", + "ĠлÑİд ей", + "ร รม", + "รร ม", + "Ġ ì±Ħ", + "Ġì± Ħ", + "Ġ 天", + "Ġå¤ ©", + "ен нÑĭÑħ", + "à¹ģ ห", + "Ġs práv", + "Ġsp ráv", + "Ġspr áv", + "èŃ ¦", + "ï¼ ľ", + "ั à¸Ĵ", + "il ecek", + "ile cek", + "ilece k", + "Ġ æŁ", + "Ġæ Ł", + "Ġ èĭ±", + "Ġèĭ ±", + "ĠÑħ оÑĢоÑĪ", + "ëł ĩ", + "Û² Û°Û°", + "Û²Û° Û°", + "æĬ ¤", + "Ġl ã", + "ÅĻÃŃ zenÃŃ", + "ÅĻÃŃz enÃŃ", + "ĠتÙĪÙĦ ÛĮد", + "éļ Ľ", + "ãĤĮ ãģ°", + "á Å¡", + "ارÙĬ Ø®", + "æĶ »", + "Ġkho ảng", + "Ġkhoản g", + "éĻ į", + "о ван", + "ов ан", + "ова н", + "Ġg ây", + "âĢĻn ın", + "Ø£ ÙĨ", + "m iÅŁtir", + "mi ÅŁtir", + "miÅŁ tir", + "miÅŁti r", + "Ġs ức", + "Ġsứ c", + "к ÑĥÑģ", + "кÑĥ Ñģ", + "Ġüzer ine", + "ÄŁ ü", + "ا بر", + "اب ر", + "ï¼Į å°±", + "Ì £", + "Ġ ëıĮ", + "Ġëı Į", + "Ġtr á»±c", + "æĶ¶ å½ķ", + "æī ¿", + "ĠN á»Ļi", + "Ġ çĻ¾", + "ĠçĻ ¾", + "ÑĪ ÑĮ", + "ج Ø©", + "ë² ł", + "ठī", + "à ¸ı", + "ภı", + "Ġü lk", + "Ġül k", + "ĠÙĩست ÙĨد", + "ัà¸ļ à¸ģาร", + "ĠÑı ка", + "ĠÑıк а", + "ã İ", + "Ġ Як", + "ĠЯ к", + "Ġг де", + "t iv", + "ti v", + "ãĢ Ī", + "лÑİ Ñĩа", + "лÑİÑĩ а", + "ा ।Ċ", + "ा। Ċ", + "Ġ ÙħاÙĨ", + "ĠÙħ اÙĨ", + "ĠÙħا ÙĨ", + "Ġd lou", + "Ġdl ou", + "Ġ ãĥķ", + "Ġãĥ ķ", + "ठĽ", + "Ġph ục", + "Ġphụ c", + "a kat", + "ak at", + "aka t", + "Ð ¬", + "as ını", + "ĠæĬķ 稿", + "ÑĢ ев", + "ÑĢе в", + "Ġv yt", + "Ġvy t", + "Ġz mÄĽ", + "Ġzm ÄĽ", + "ÏĦ Ïī", + "è ¬", + "Ġ Ñĥм", + "ĠÑĥ м", + "Ġuz un", + "Ġp roti", + "Ġpro ti", + "Ġpr oti", + "Ġprot i", + "ĠÑģо ÑģÑĤоÑı", + "ĠÑģоÑģÑĤ оÑı", + "ัà¸Ĵ à¸Ļ", + "a tik", + "at ik", + "ati k", + "Ġ à¸ł", + "Ġภł", + "Ġà¤Ĩ द", + "lar ından", + "ların dan", + "larında n", + "æĢ ¥", + "ãĥ¼ ãĤ¯", + "ãĥ¼ãĤ ¯", + "ĠÙĦ ÙĦÙħ", + "ĠÙĦÙĦ Ùħ", + "Ùģ تÙĩ", + "Ùģت Ùĩ", + ". :.", + ".: .", + "üç ük", + "ол ева", + "à¹Į Ċ", + "ĠпеÑĢ ев", + "ĠпеÑĢе в", + "ĠÙĨ سب", + "ĠÙĨس ب", + "е леннÑı", + "ел еннÑı", + "елен нÑı", + "' ın", + "'ı n", + "ν Ïī", + "è¡ £", + "Ġ دÙĬ", + "Ġد ÙĬ", + "åį ĩ", + "Ġbel irt", + "Ġbelir t", + "Ġ /:", + "Ġ/ :", + "èij ī", + "Ġv yh", + "Ġvy h", + "çļĦ ä¸Ģ", + "èĥ Į", + "Ġ ìĹ´", + "ĠìĹ ´", + "о ла", + "ол а", + "Ġ تب", + "Ġت ب", + "á ci", + "ác i", + "ा à¤ī", + "ाठī", + "ภİ", + "çĶ ¢", + "à¥Ī ल", + "Ġ ÙĤØ·", + "ĠÙĤ Ø·", + "ëĦ Ī", + "ắ m", + "ÑĢ Ñıд", + "ÑĢÑı д", + "Ġph ụ", + "ĠÙĪ اÙĤع", + "ĠÙĪا ÙĤع", + "Ġm erk", + "Ġme rk", + "Ġmer k", + "Ġch á»ijng", + "å¯ Ł", + "ا بط", + "اب Ø·", + "us unda", + "usu nda", + "Ġод на", + "ž el", + "že l", + "ĠÑģ Ñĥм", + "ĠÑģÑĥ м", + "Ġph ù", + "Ġ ζ", + "ĠÎ ¶", + "Ġz av", + "Ġza v", + "e dn", + "ed n", + "Ġp otÅĻeb", + "Ġpot ÅĻeb", + "ĠÚ©ÙĨ ÙĨد", + "ĠÑĢ азв", + "ĠÑĢаз в", + "ĠÑĢа зв", + "¿ ł", + "ĠاÙĦ ز", + "Ġm ÄĽl", + "ĠmÄĽ l", + "Ġ ÑģÑĤанов", + "ĠÑģÑĤ анов", + "ĠÑģÑĤан ов", + "ĠÑģÑĤа нов", + "Ġ درÛĮ", + "Ġد رÛĮ", + "Ġدر ÛĮ", + "Ġt ượng", + "ã ģµ", + "ãģ µ", + "Ġд ви", + "Ġдв и", + "ÑĮ Ñı", + "è £½", + "è£ ½", + "Ġ تÙĦ", + "Ġت ÙĦ", + "Å¡ Å¥", + "ãģª ãĤī", + "Ġà¤ķ à¤Ī", + "Å¡ i", + "âĢĮ است", + "Ġk ỹ", + "Ġká» ¹", + "ë§ Ŀ", + "Ġà¤Ĩ à¤ľ", + "ãĥ ´", + "Ġb á»ı", + "du ÄŁu", + "duÄŁ u", + "Ġ æ¯", + "Ġæ ¯", + "п еÑĢ", + "пе ÑĢ", + "ا ÙĦÙĬØ©", + "اÙĦ ÙĬØ©", + "اÙĦÙĬ Ø©", + "æīĢ 以", + "åħ °", + "Ġ oran", + "Ġo ran", + "Ġor an", + "Ġora n", + "Ġ íŀ", + "Ġí ŀ", + "Ïĥ ία", + "Ïĥί α", + "Ġph ủ", + "ĠбÑĭ ла", + "ĠбÑĭл а", + "Ñĩ ива", + "Ñĩи ва", + "Ñĩив а", + "Ġ ê°Ħ", + "Ġê° Ħ", + "о лÑĸ", + "ол Ñĸ", + "Ùĥ ت", + "å ħ§", + "åħ §", + "à¥Ĥ à¤Ł", + "Ġ ëĸ", + "Ġë ĸ", + "Ġ ÙĦÙĩ", + "ĠÙĦ Ùĩ", + "ëłĪ ìĿ´", + "Ġh ız", + "å¤ ı", + "ĠæĬķ稿 æĹ¥", + "éļ ¾", + "ĵ °", + "г лÑıд", + "глÑı д", + "гл Ñıд", + "ì n", + "Ġ меÑĢ", + "Ġм еÑĢ", + "Ġ ãĢij", + "ĠãĢ ij", + "Ġ обÑī", + "Ġоб Ñī", + "um hur", + "çł ´", + "л иÑģÑĮ", + "ли ÑģÑĮ", + "sp ÄĽ", + "ر ÙĬÙĤ", + "رÙĬ ÙĤ", + "Ġ تÙģ", + "Ġت Ùģ", + "Ġا ÙĦÙĪ", + "ĠاÙĦ ÙĪ", + "çµ ±", + "а лоÑģÑĮ", + "ал оÑģÑĮ", + "ало ÑģÑĮ", + "Ġm ô", + "Ġv á»ĩ", + "Ġvá» ĩ", + "Ġ δι", + "Ġδ ι", + "Ġ зн", + "Ġз н", + "Ġ بØŃ", + "Ġب ØŃ", + "ت Ùī", + "Ġ ì§ģ", + "Ġì§ ģ", + "Ġvel mi", + "uyá» ħn", + "Ġph ạm", + "ÑģÑĤв ом", + "ÑģÑĤво м", + "ĠÙĪ اÙĦÙħ", + "ĠÙĪاÙĦ Ùħ", + "ĠÙĪا ÙĦÙħ", + "ĠбÑĭ ли", + "ĠбÑĭл и", + "ا Ø°", + "Ø§Ø °", + "ÄĽ ÅĻ", + "â Ħĸ", + "âĦ ĸ", + "Ġп олож", + "Ġпо лож", + "Ġпол ож", + "า à¸ģาร", + "าà¸ģ าร", + "ĠÄį lán", + "Îķ Ρ", + "Ġ ìĤ°", + "ĠìĤ °", + "β α", + "Ġ æĹ¥æľ¬", + "ĠæĹ¥ æľ¬", + "ز د", + "ĠÙĨ ÛĮست", + "ĠÙĨÛĮ ست", + "Ġha yat", + "Ġhay at", + "Ġhaya t", + "ç¢ º", + "à¹Ģ วล", + "à¹Ģว ล", + "ĠCh ÃŃnh", + "ĠChÃŃ nh", + "ï¼Į æĺ¯", + "ĠÙĪ اØŃ", + "ĠÙĪا ØŃ", + "èı ¯", + "Ġή ÏĦαν", + "Ġx á»Ń", + "ĠÄį erv", + "ĠÄįer v", + "ĠÄįe rv", + "ĠÙħد ÛĮر", + "é Ĩ", + "ĠëĪ Ī", + "ç» Ń", + "Ġt ên", + "ìĸ ¸", + "Ġort aya", + "Ġorta ya", + "Ġ жен", + "Ġж ен", + "Ġже н", + "Ġn Æ¡i", + "ен нÑĭе", + "ÑĦ екÑĤив", + "ÑĦек ÑĤив", + "íĿ ¬", + "Ġkh á»ı", + "ĠÄij a", + "os yal", + "osy al", + "à¸Ľà¸£à¸° à¹Ģà¸Ĺศ", + "Ġo dst", + "Ġod st", + "Ġ à¸ĸ", + "Ġภĸ", + "Ġο ÏĢο", + "æĶ¿ åºľ", + "Ġb Ãłn", + "ĠbÃł n", + "ĠG iá»", + "ĠGi á»", + "Ġold uk", + "Ġol duk", + "Ġoldu k", + "о вание", + "ов ание", + "ова ние", + "овани е", + "ован ие", + "à¸Ń ส", + "Ġ нев", + "Ġн ев", + "Ġне в", + "ÏĦ Ïģο", + "ÏĦÏģ ο", + "Ġ ìĨį", + "ĠìĨ į", + "k ı", + "Ġब ड", + "Ġ ÏħÏĢ", + "ĠÏħ ÏĢ", + "Ġ Vý", + "ĠV ý", + "ï¾ Ħ", + "çŃ ĸ", + "ε ÏĨ", + "Ġ åħ¨", + "Ġåħ ¨", + "ĠÙģ رÙĪØ´", + "ĠÙģر ÙĪØ´", + "ĠÙģرÙĪ Ø´", + "ÙĤÛĮ ÙĤ", + "ä¼ģ ä¸ļ", + "ε Ïį", + "èĻ Ł", + "Ġa yr", + "Ġay r", + "ض ÙĪ", + "Å¡ el", + "Å¡e l", + "Ġп ÑĸÑģлÑı", + "ĠпÑĸÑģ лÑı", + "Ñĸй Ñģ", + "é¢ Ĩ", + "Ú© تر", + "کت ر", + "л Ñĥб", + "лÑĥ б", + "è« ĸ", + "æ° ¸", + "ез пеÑĩ", + "Ġ кам", + "Ġк ам", + "Ġка м", + "ع داد", + "عد اد", + "ê±° ëŀĺ", + "ู à¸ĩ", + "ĠتÙĩ راÙĨ", + "Ġ ëĦĪ", + "ĠëĦ Ī", + "ÑĢ ив", + "ÑĢи в", + "Ġ ÑĤоÑĢ", + "ĠÑĤ оÑĢ", + "ĠÑĤо ÑĢ", + "ا Ùī", + "ا٠ī", + "' Ñıз", + "'Ñı з", + "ÙIJ ÙĬ", + "Ġkh ÃŃ", + "Ġ ÑĪÑĤ", + "ĠÑĪ ÑĤ", + "Ġ ξε", + "ĠÎľ ε", + "Ġb iri", + "Ġbi ri", + "Ġbir i", + "è ĩ´", + "èĩ ´", + "Ñĥ вав", + "Ñĥв ав", + "Ñĥва в", + "ãģĪ ãĤĭ", + "Ġд иÑģ", + "Ġди Ñģ", + "а ÑİÑĤ", + "аÑİ ÑĤ", + "ص ب", + "åĿ ĩ", + "о лÑİ", + "ол Ñİ", + "èĭ ¥", + "Ġ اث", + "Ġا Ø«", + "ĠØ§Ø «", + "s ou", + "so u", + "åIJ ĥ", + "ãģ® ãģł", + "ub lik", + "ubl ik", + "л ей", + "ле й", + "Âł m", + "Ġíıī ê·ł", + "ạ y", + "ε ÏĢ", + "t ık", + "tı k", + "Ġv yu", + "Ġvy u", + "ع ÙĪد", + "Ġд оз", + "Ġдо з", + "Ġl á»ĭch", + "è³ ª", + "à¥ģ à¤Ī", + "à¥ģठĪ", + "ั à¸ŀ", + "Ġt ém", + "Ġté m", + "Ġ kaç", + "Ġk aç", + "Ġka ç", + "Ġc ái", + "Ġcá i", + "Ġ μα", + "Ġμ α", + "âĢ¦âĢ¦ ãĢįĊĊ", + "í Ī¬", + "ر ÙĪÙĩ", + "رÙĪ Ùĩ", + "Ġ rych", + "Ġr ych", + "Ġry ch", + "Îij Τ", + "Ġ ÑĢÑĸв", + "ĠÑĢ Ñĸв", + "ë³ ij", + "åģ ¥", + "Ġzd rav", + "Ġ عدد", + "Ġع دد", + "Ġعد د", + "èį ī", + "δ ια", + "δι α", + "Ġv áºŃn", + "Ñĭ ÑĤ", + "Ġкол иÑĩ", + "Ġко лиÑĩ", + "Ġколи Ñĩ", + "ÏĮ ÏĦε", + "Ġb ırak", + "Ġ ØŃÙħ", + "ĠØŃ Ùħ", + "Ġch á»ĭ", + "é» Ħ", + "ĠاÙĦÙħت ØŃدة", + "ื à¸Ńà¸ģ", + "ืà¸Ń à¸ģ", + "Ġз али", + "Ġза ли", + "Ġзал и", + "Ġnh anh", + "âĢĮ تÙĪاÙĨ", + "ëĿ ½", + "Ġت ÙĪسط", + "ĠتÙĪ سط", + "ĠتÙĪس Ø·", + "è¦ģ æ±Ĥ", + "а лÑĥ", + "ал Ñĥ", + "ün kü", + "ünk ü", + "ãģª ãĤĵ", + "Ġ Trong", + "ĠT rong", + "ĠTr ong", + "ĠTro ng", + "à¸Ļ ะ", + "åij ¼", + "Ġ ÙĬÙħ", + "ĠÙĬ Ùħ", + "и ки", + "ик и", + "ĠÑĤ ÑĥÑĤ", + "ĠÑĤÑĥ ÑĤ", + "Ġya ÅŁam", + "ĠyaÅŁ am", + "Ġm á»įi", + "é ĽĦ", + "éĽ Ħ", + "ĠØŃ Ø¶", + "Ġав ÑĤом", + "ĠавÑĤ ом", + "Ġसब स", + "Ġy ếu", + "ãĤ¹ ãĤ¿", + "Ïĩ ή", + "Ñĸ Ñİ", + "è ĺ", + "ิ ย", + "Ġm ev", + "Ġme v", + "ick ého", + "ické ho", + "ि ह", + "िठ¹", + "åŃ £", + "θ ή", + "Ġब ढ", + "ĠاÙĦ Ùħس", + "ĠاÙĦÙħ س", + "ÏĦ οÏħ", + "ÏĦο Ïħ", + "ek li", + "ekl i", + "Ġде ÑĢев", + "ĠдеÑĢ ев", + "å¸ Ń", + "æ² Ļ", + "ãģ« ãĤĤ", + "Ġo blast", + "Ġob last", + "Ġobl ast", + "Ġh á»Ļ", + "Ġhá» Ļ", + "Ġ å¹³", + "Ġå¹ ³", + ".:.:.:.: .:.:.:.:", + ".:.:.:. :.:.:.:.:", + "Ġ éĸ", + "Ġé ĸ", + "Ġ جز", + "Ġج ز", + "ĠÙĩÙħ ÚĨ", + "ä¸ ¦", + "ÑĨ еп", + "ÑĨе п", + "ा Ċ", + "ä¸Ń çļĦ", + "'n ın", + "Ġ íķĺëĬĶ", + "Ġíķĺ ëĬĶ", + "ÑĶ ÑĹ", + "Ġ بش", + "Ġب Ø´", + "åį ´", + "ä¹ ł", + "ĠاطÙĦ اعات", + "ĠاطÙĦاع ات", + "Ġ ë²ł", + "Ġë² ł", + "Ġکرد ÙĨ", + "Ġکر دÙĨ", + "ा ड", + "ाठ¡", + "Ġà¤ħ र", + "ĠH á»į", + "ĠHá» į", + "ĠгÑĢом ад", + "Ġ ست", + "ĠØ ³Øª", + "Ġس ت", + "ÏĦι ÏĤ", + "Ġan cak", + "Ġanc ak", + "Ġ ог", + "Ġо г", + "Ġk teÅĻÃŃ", + "Ġ æ¬", + "Ġæ ¬", + "Ġ Ngh", + "ĠN gh", + "ĠNg h", + "Ġt edy", + "Ġte dy", + "Ġted y", + "Ġ ÏĢο", + "ĠÏĢ ο", + "Ġqu ân", + "Ġб Ñĥли", + "ĠбÑĥ ли", + "è¯ Ĩ", + "Ġt ừng", + "Ġtá» «ng", + "Ġtừ ng", + "人 çļĦ", + "ี à¸ģาร", + "ีà¸ģ าร", + "Ġκα ÏĦα", + "Ġpo uze", + "Ġpou ze", + "¡ ng", + "ĠØ¢ ر", + "Ġ ÑĤÑĥ", + "ĠÑĤ Ñĥ", + "Ġt á»·", + "Ġtá» ·", + "ĠD anh", + "ĠDan h", + "ĠDa nh", + "о ном", + "он ом", + "Ñģ ий", + "Ñģи й", + "Ġ à¹Ģà¸Ķ", + "Ġà¹Ģ à¸Ķ", + "Ġà¹ĢภĶ", + "£ ¨", + "Å¡ k", + "ãĥĥ ãĥī", + "ar dır", + "ard ır", + "Ġyö net", + "Ġyön et", + "Ñĥ вали", + "Ñĥв али", + "Ñĥва ли", + "åħĪ çĶŁ", + "Ġ ÐIJÑĢ", + "ĠÐIJ ÑĢ", + "Ġprot ože", + "Ġproto že", + "Ġ íģ¬", + "Ġíģ ¬", + "Ġjed not", + "Ġjedn ot", + "Ġjedno t", + "Ġt ý", + "éĩ ĩ", + "Ġ หร", + "Ġห ร", + "Ġ åľ°", + "Ġåľ °", + "çº ¢", + "Ġм олод", + "Ġмол од", + "Ġмо лод", + "iên g", + "iê ng", + "ĠÏĮ ÏĦι", + "Ġد اشتÙĩ", + "Ġداش تÙĩ", + "Ġداشت Ùĩ", + "Ġuy gun", + "Ġuyg un", + "Ġuygu n", + "Ġоп еÑĢа", + "ĠопеÑĢ а", + "åı «", + "Ġ ап", + "Ġа п", + "Ġ кÑĥÑĢ", + "Ġк ÑĥÑĢ", + "ĠкÑĥ ÑĢ", + "ا عة", + "اع Ø©", + "un uz", + "unu z", + "Ġ ìĤ¬ì§Ħ", + "ĠìĤ¬ ì§Ħ", + "Ġv ô", + "ç ok", + "ço k", + "Ġ èģ", + "Ġè ģ", + "ÑĤе ÑĢеÑģ", + "ÑĤеÑĢ еÑģ", + "Ġ استاÙĨ", + "Ġا ستاÙĨ", + "Ġاست اÙĨ", + "Ġاس تاÙĨ", + "а лаÑģÑĮ", + "ала ÑģÑĮ", + "à¥ģ व", + "à¥ģठµ", + "á» ³", + "Ġl Æ°u", + "Ġ Та", + "ĠТ а", + "Ġl á»±a", + "' ÑĶ", + "Ġ üy", + "Ġü y", + "Ġ ÛĮÚ©ÛĮ", + "ĠÛĮ Ú©ÛĮ", + "ĠÛĮÚ© ÛĮ", + "æ ¾", + "н ем", + "не м", + "Ġ خاÙĨ", + "ĠØ® اÙĨ", + "ĠÑį лек", + "ÙĤ اÙĦ", + "л ок", + "ло к", + "ĠÄij ẹp", + "à¥ī ल", + "Ġm ůž", + "Ġmů ž", + "ëĭ¤ ëĬĶ", + "Ġ íķĺëĤĺ", + "Ġíķĺ ëĤĺ", + "ÙĦ ت", + "çİ° åľ¨", + "м о", + "Ïħ Ïĥ", + "ãģŁ ãģ¡", + "ĠìłĦ ìĦ¸", + "à¥į à¤Łà¤°", + "à¥įà¤Ł र", + "ع ات", + "عا ت", + "د ÙĪ", + "ä¿ º", + "æ¥ ½", + "æ£ ®", + "Ġл иÑģÑĤ", + "Ġли ÑģÑĤ", + "δ ι", + "å¯ Į", + "ĠÄij Æ°a", + "в еÑģÑĤи", + "ве ÑģÑĤи", + "веÑģÑĤ и", + "д о", + "ан нÑĸ", + "Ġü ret", + "Ġür et", + "Ġg á»įi", + "ĠÑģ воÑİ", + "ĠÑģв оÑİ", + "ĠÑģво Ñİ", + "á» «ng", + "ừ ng", + "Ġt ất", + "äºļ æ´²", + "á ce", + "ác e", + "N Ãį", + "Ġ ÑĢÑĭ", + "ĠÑĢ Ñĭ", + "æ» ¡", + "Ïģ εÏĤ", + "Ïģε ÏĤ", + "åħį è´¹", + "л оÑĤ", + "ло ÑĤ", + "æĻ º", + "Ġα γ", + "Ġà¤ħ म", + "Ġ ç´", + "Ġç ´", + "о до", + "од о", + "Ñħ и", + "Ġngu á»ĵn", + "éĥ¨ åĪĨ", + "в аÑĤ", + "ва ÑĤ", + "ĠÑĤ еб", + "ĠÑĤе б", + "з аÑĨÑĸÑĹ", + "за ÑĨÑĸÑĹ", + "Ġ ÐŁÑĢо", + "ĠÐŁ ÑĢо", + "ĠÐŁÑĢ о", + "ع ÛĮ", + "Ġ ÙĪÙĬ", + "ĠÙĪ ÙĬ", + "ëŀ ľ", + "Ġne by", + "Ġneb y", + "Ġج دÛĮد", + "Ġجد ÛĮد", + "ÄŁ imiz", + "ÄŁim iz", + "£ ½", + "Ġà¤Ĩ त", + "Ġà¤Ń र", + "æī ĺ", + "å®ī åħ¨", + "Ġëĵ¤ ìĸ´", + "ب رد", + "بر د", + "Ġê²ĥ ìĿ´", + "äº ²", + "æ° ı", + "ал Ñĸз", + "алÑĸ з", + "l ack", + "la ck", + "lac k", + "ĠÙħخت ÙĦÙģ", + "ا ÙĨÙĬØ©", + "اÙĨ ÙĬØ©", + "اÙĨÙĬ Ø©", + "Ġ ì²Ń", + "Ġì² Ń", + "Ġ виÑĤ", + "Ġв иÑĤ", + "Ġви ÑĤ", + "Ġhar eket", + "Ġhare ket", + "Ġharek et", + "é ¨", + "à¸Ļ ำ", + "Ġب رخ", + "Ġبر Ø®", + "å£ ²", + "Ñĩ ай", + "Ñĩа й", + "Ġan lat", + "Ġà¤ħ व", + "ĠاÙģ ز", + "Ġh ết", + "ĠÚĨ ÙĨد", + "éĹ ľ", + "пÑĢи ÑĶм", + "g ı", + "Ġk omp", + "Ġkom p", + "Ġko mp", + "Ġl Ỽp", + "ĠlỼ p", + "Ġm á»Ĺi", + "Ġmá» Ĺi", + "à¸Ľà¸£à¸° à¸ģ", + "Ġ haf", + "Ġh af", + "Ġha f", + "Ġ eder", + "Ġe der", + "Ġed er", + "Ġзд оÑĢов", + "à¥Ĥ म", + "ëł ¸", + "Ġo nun", + "Ġon un", + "Ġonu n", + "ĠÙħر دÙħ", + "ĠÙħرد Ùħ", + "ĠÐľ аÑĢ", + "ĠÐľÐ° ÑĢ", + "Ġìĸ´ ëĸ", + "м ан", + "ма н", + "Ġ ÑģилÑĮ", + "ĠÑģ илÑĮ", + "ĠÑģи лÑĮ", + "ĠÑģил ÑĮ", + "ç¶ ²", + "ë¸ Ķ", + "л ÑıеÑĤ", + "лÑı еÑĤ", + "ĠнеÑģк олÑĮко", + "ĠнеÑģколÑĮ ко", + "l andır", + "land ır", + "lan dır", + "landı r", + "Ġв д", + "ĠÙĨ ÙĪ", + "ãģ İ", + "ÑĤ ин", + "ÑĤи н", + "ت Ø´", + "а ний", + "ан ий", + "ани й", + "Ġt ÅĻ", + "Ñģ иÑħ", + "Ñģи Ñħ", + "л ом", + "ло м", + "æŃ ©", + "ãİ ¡", + "Ġ ØŃر", + "ĠØŃ Ø±", + "æĭ į", + "e nou", + "en ou", + "eno u", + "Ġв ели", + "Ġвел и", + "Ġве ли", + "Ġ δη", + "Ġδ η", + "s ka", + "sk a", + "主 è¦ģ", + "ا ÙģØ©", + "اÙģ Ø©", + "ĠболÑĮ ÑĪе", + "ĠболÑĮÑĪ е", + "ิ ศ", + "çĽ Ĭ", + "ĠÙģ ÙĤØ·", + "ĠÙģÙĤ Ø·", + "å¨ ģ", + "Ġh Æ°á»Łng", + "ĠD oÄŁ", + "ĠDo ÄŁ", + "Ġd Ãłi", + "Ġ гоÑĤов", + "Ġг оÑĤов", + "ĠгоÑĤ ов", + "Ġв ам", + "Ġва м", + "âĢ ī", + "ा à¤ļ", + "ाठļ", + "åħ ¸", + "à¹ĥ หà¸į", + "à¹ĥห à¸į", + "Ġ ç«", + "Ġç «", + "ekt ör", + "Ġв ел", + "Ġве л", + "Ġ ÙĦÙĪ", + "ĠÙĦ ÙĪ", + "Ø´ تÙĩ", + "شت Ùĩ", + "æĺ ¾", + "ả y", + "à¹Ĥ ม", + "Ġt á»ķng", + "Ġtá»ķ ng", + "Ġtá»ķn g", + "ĠповеÑĢ Ñħ", + "ÑĹ Ð²", + "Ġph ép", + "çļ ĩ", + "Ġп оÑĢÑıд", + "Ġпо ÑĢÑıд", + "ĠпоÑĢ Ñıд", + "ĠÑģооÑĤ веÑĤ", + "ठĿ", + "ĠÑģеб Ñı", + "Ġ ëĤł", + "ĠëĤ ł", + "Ġб Ñĥла", + "ĠбÑĥ ла", + "à¹ī าย", + "à¹īา ย", + "Ġ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢ", + "ĠÙħ جÙħÙĪع", + "ï¼Į 以", + "Ġب ÙĪدÙĩ", + "ĠبÙĪد Ùĩ", + "ĠبÙĪ دÙĩ", + "μ ÏĮ", + "Ġ íݸ", + "Ġíİ ¸", + "eÅŁ it", + "Ñİ Ñīие", + "ÑİÑī ие", + "Ñİ ÑīиÑħ", + "ÑİÑī иÑħ", + "åŁº éĩij", + "Ġت ØŃت", + "ĠتØŃ Øª", + "Ġв лаÑģ", + "Ġвла Ñģ", + "ler le", + "ãĤ ²", + "ëĬ ĺ", + "è ĵ", + "m anın", + "man ın", + "manı n", + "ìŀ Ī", + "Ġz ast", + "Ġza st", + "Ġzas t", + "ĠÑĩелов ек", + "à¥ĩ ब", + "à¥ĩठ¬", + "p eÄį", + "pe Äį", + "Ġبر ÙĨاÙħÙĩ", + "Ġs lov", + "Ġsl ov", + "Ġslo v", + "ĠnÄĽ jak", + "ĠnÄĽj ak", + "ê· ľ", + "à¥ĩ ह", + "à¥ĩठ¹", + "èĹ ¤", + "ĠبÛĮ شتر", + "ĠبÛĮØ´ تر", + "il iz", + "ili z", + "Ġ ëĶĶ", + "Ġë ĶĶ", + "ĠëĶ Ķ", + "ا زÙĩ", + "از Ùĩ", + "ت د", + "Ġe tm", + "Ġet m", + "Ġëĭ¤ 른", + "Ġ vů", + "Ġv ů", + "å° Ħ", + "Ġк лаÑģ", + "Ġкл аÑģ", + "Ġкла Ñģ", + "в ÑĢоп", + "æ´ ¾", + "ĠÄij ình", + "Ñĥ ÑİÑĤ", + "ÑĥÑİ ÑĤ", + "Ñĥ еÑĤÑģÑı", + "ÑĥеÑĤ ÑģÑı", + "éľ ²", + "Ġ ÑģкоÑĢ", + "ĠÑģ коÑĢ", + "ĠÑģк оÑĢ", + "Ġв аÑģ", + "Ġва Ñģ", + "íķĺ ìĺĢëĭ¤", + "íķĺìĺĢ ëĭ¤", + "Ġ داشت", + "Ġد اشت", + "Ġداش ت", + "Ġ çĦ", + "Ġç Ħ", + "Ġ 西", + "Ġè ¥¿", + "Ġκα ÏĦά", + "ॠ¦", + "ìĹ Ĩ", + "Ġ خدÙħ", + "ĠØ® دÙħ", + "Ġخد Ùħ", + "ا سÙħ", + "اس Ùħ", + "Îij Ρ", + "ĠA ma", + "ĠAm a", + "å¥ ¥", + "Ġبزر Ú¯", + "Ġ ÐĴÑĸн", + "ĠÐĴ Ñĸн", + "Ġ Åĺ", + "ĠÅ ĺ", + "Ġ à¸Īาà¸ģ", + "Ġà¸Ī าà¸ģ", + "ĠÑħаÑĢакÑĤ еÑĢ", + "ĠÄij á»Ļi", + "ĠÄijá»Ļ i", + "ĠÑĢоз виÑĤ", + "ĠÑĢозви ÑĤ", + "ĠпÑĢоÑĦ еÑģ", + "Ġкон ÑĤÑĢ", + "ĠконÑĤ ÑĢ", + "ÎŁ ÎĽ", + "ÎŁÎ Ľ", + "Ġm inh", + "Ġmin h", + "Ġmi nh", + "ä¼ ij", + "ì ª½", + "Ġch Æ¡i", + "з аÑĨии", + "за ÑĨии", + "ĠдÑĸÑı лÑĮ", + "ë Ĩ", + "Ġn gay", + "Ġng ay", + "Ġnga y", + "à¥Ĥ à¤Ĥ", + "Ġiht iy", + "éĽ ª", + "Ġ ê´Ģ리ìŀIJ", + "Ġê´Ģ리 ìŀIJ", + "Ġc ụ", + "Ġ ì§Ī", + "Ġì§ Ī", + "ÙĬ Ø«", + "ặ p", + "ÙĪ اع", + "ÙĪا ع", + "ãģĤ ãģ£ãģŁ", + "Ġ çľ", + "Ġç ľ", + "Ġ ìļ°ë¦¬", + "Ġìļ° 리", + "à¹Ī à¸ĩà¸Ĥ", + "à¹Īà¸ĩ à¸Ĥ", + "Ġ çŃ", + "Ġç Ń", + ") ØĮ", + "Ãł m", + "ÙĦ ÛĮÙĦ", + "ÙĦÛĮ ÙĦ", + "Ġ 걸", + "Ġê± ¸", + "алÑĮ ниÑħ", + "æŶ åĢĻ", + "un dan", + "und an", + "unda n", + "Ġ Gün", + "ĠG ün", + "ĠGü n", + "Ġ topl", + "Ġt opl", + "Ġto pl", + "Ġtop l", + "ĠÑĢек омен", + "ĠاÙĨت خاب", + "Ãł u", + "Äį ka", + "ë° Ģ", + "Ġк ÑĢаÑģ", + "ĠкÑĢа Ñģ", + "л оп", + "ло п", + "å¼ µ", + "ĠاÙĦ Ùħع", + "ĠاÙĦÙħ ع", + "m ÃŃn", + "mÃŃ n", + "Ġv iết", + "Ġvi ết", + "Ġ ê°ĻìĿĢ", + "Ġê°Ļ ìĿĢ", + "ut eÄį", + "ute Äį", + "Ġ nech", + "Ġn ech", + "Ġne ch", + "Ġnec h", + "çµ Ĥ", + "ãģª ãģĮ", + "ay ın", + "ayı n", + "Ġ Äįin", + "ĠÄį in", + "ĠÄįi n", + "ch ázÃŃ", + "cház ÃŃ", + "اÙģ ظ", + "ÑĢ оваÑĤÑĮ", + "ÑĢов аÑĤÑĮ", + "ÑĢо ваÑĤÑĮ", + "ÑĢова ÑĤÑĮ", + "à¹Ħ ร", + "Ġ ãĤ¤", + "ĠãĤ ¤", + "Ġзаб олева", + "Ġ å±±", + "Ġå± ±", + "Ġka dın", + "Ġkad ın", + "ÏĦ ηÏĤ", + "ÏĦη ÏĤ", + "а лиÑģÑĮ", + "ал иÑģÑĮ", + "али ÑģÑĮ", + "Ġh ük", + "åĵ ¥", + "Ġп еÑĢи", + "ĠпеÑĢ и", + "ÅĻ ád", + "Ġà¤ħ स", + "Ġ ÑģÑĤвоÑĢ", + "ĠÑģÑĤ воÑĢ", + "ĠÙĪ ÛĮÚ©ÛĮ", + "ĠÙĪÛĮ Ú©ÛĮ", + "Ġ ì¡", + "Ġì ¡", + "Ġc á»Ńa", + "Ġcá»Ń a", + "Ġh iá»ĥu", + "游 æĪı", + "ÑĮ омÑĥ", + "Ġg ó", + "Ġt oh", + "Ġto h", + "Ġб ла", + "Ġбл а", + "Ġ åij", + "Ġå ij", + "Ġп ло", + "Ġпл о", + "и ÑĪ", + "ĠÄij ấu", + "s kou", + "sk ou", + "sko u", + "ãĤĪ ãĤĬ", + "ู à¸Ľ", + "Ġr á»ĵi", + "оп ÑĢоÑģ", + "н олог", + "ĠÑĤ ÑĢав", + "ĠÑĤÑĢ ав", + "ĠÑĤÑĢа в", + "ĠWay back", + "Ġ à¹Ĩ", + "ĠÑĥ ÑĩаÑģÑĤ", + "ĠÑĥÑĩ аÑģÑĤ", + "ĠÑĥÑĩа ÑģÑĤ", + "ĠÑĥÑĩаÑģ ÑĤ", + "ĠпÑĢеп аÑĢа", + "Ġd ạng", + "ĠÃľ n", + "à¹Ħล à¸Ļ", + "Ġد اخ", + "Ġs Æ¡", + "Ġk oy", + "Ġko y", + "ëĿ¼ ê³ł", + "ĠÄij úng", + "à¥ĩà¤Ĥ ,", + "Ġgeç ir", + "ĠÑıк Ñīо", + "Ñģ ÑĤÑĢо", + "ÑģÑĤ ÑĢо", + "ÑģÑĤÑĢ о", + "ен ÑĤов", + "енÑĤ ов", + "Ñĸ ж", + "к ÑĥÑİ", + "кÑĥ Ñİ", + "Ġe ÄŁitim", + "ĠeÄŁit im", + "à¥įर स", + "Ġ Сп", + "ĠС п", + "ا تÛĮ", + "ات ÛĮ", + "ãģij ãĤĭ", + "ÏĦ Ïīν", + "ÏĦÏī ν", + "Ġ км", + "Ġк м", + "âĸįâĸį âĸįâĸį", + "j ist", + "ji st", + "jis t", + "ÑĤ ак", + "ÑĤа к", + "Ġ åIJįåīį", + "ĠåIJį åīį", + "é¡ Ķ", + "л Ñĭ", + "Ġkh ảo", + "Ġkhả o", + "âĢĻ Ñı", + "ĠÙħ ÙĦÛĮ", + "ĠÙħÙĦ ÛĮ", + "l ož", + "lo ž", + "Ġ ìĸ¸", + "Ġìĸ ¸", + "Ġg ần", + "Ġ à¤ľà¤°", + "Ġà¤ľ र", + "ब र", + "Îķ Σ", + "า à¸Ľ", + "าภĽ", + "Ġn ás", + "Ġná s", + "form ace", + "forma ce", + "Ġet mek", + "Ġetm ek", + "Ġetme k", + "в еÑģÑĤ", + "ве ÑģÑĤ", + "ìĸ´ ìļĶ", + "Ġत थ", + "ĠÑģ ек", + "ξ η", + "æ¯ Ľ", + "B ir", + "Bi r", + "Ġ ìŀĦ", + "Ġìŀ Ħ", + "Ġv ardır", + "Ġvar dır", + "Ġvardı r", + "ÙĪ اÙĦ", + "ÙĪا ÙĦ", + "Ä° R", + "ov ané", + "ova né", + "ovan é", + "н аÑĢод", + "на ÑĢод", + "наÑĢ од", + "à¸Ħ ำ", + "e mek", + "em ek", + "eme k", + "ĠÎķ ÏĢ", + "Ġ ÅĻe", + "ĠÅĻ e", + "ãģ¾ ãģĽ", + "uyá»ĩ t", + "Ġ ìĸ¼", + "Ġìĸ ¼", + "r ů", + "Ġ onu", + "Ġo nu", + "Ġon u", + "à¹Ģà¸ķ à¸Ńร", + "од аÑĢ", + "ода ÑĢ", + "ز Ùĩ", + "Ġk av", + "Ġka v", + "о нÑĭ", + "он Ñĭ", + "Ġв еÑģ", + "Ġве Ñģ", + "ìĤ¬ ì§Ģ", + "Ġг ла", + "à Ŀ", + "ĠÙĤ ÛĮÙħت", + "çķ ¥", + "à¸ĸ าà¸Ļ", + "Äį il", + "Äįi l", + "Ġ ä¸ĩ", + "Ġä¸ ĩ", + "è¾ ĥ", + "åħ ħ", + "Ġ ÑĢед", + "ĠÑĢ ед", + "ĠÑĢе д", + "ม ห", + "am ilia", + "ami lia", + "amil ia", + "à¥ĩ à¤ķर", + "à¥ĩà¤ķ र", + "Ġt á»iji", + "Ġtá»ij i", + "Ùģ ÛĮ", + "ÑĢ ÑĸÑĪ", + "ÑĢÑĸ ÑĪ", + "ìķ ł", + "à¸Ļ ส", + "à¸Ī ร", + "à¥ĩ शन", + "à¥ĩश न", + "ĠÙħÙĪ ضÙĪع", + "æī ¹", + "Ġob sah", + "Ġobs ah", + "Ġнав Ñĩ", + "Ġdes tek", + "Ġdest ek", + "Ġdeste k", + "Ġ zas", + "Ġz as", + "Ġza s", + "å ĵį", + "åĵ į", + "üm üz", + "ümü z", + "Ġ çŁ", + "Ġç Ł", + "Ġ è¨", + "Ġè ¨", + "Ù ¬", + "ç» Ī", + "Ġz de", + "Ġzd e", + "Ġz áp", + "Ġzá p", + "à¥Ĥ सर", + "à¥Ĥस र", + "ìĿ´ ì§Ģ", + "çļ ®", + "l om", + "lo m", + "ॠ§", + "ÙĦ اÙĤ", + "ÙĦا ÙĤ", + "à¸Ļ à¸ķ", + "íĮ ħ", + "л ада", + "ла да", + "лад а", + "m asına", + "mas ına", + "ması na", + "ãģ® ãģ§", + "ëĵ¤ ìĿĦ", + "Ġн аг", + "Ġна г", + "m asını", + "mas ını", + "ãĤ Ŀ", + "ın ıf", + "ını f", + "åĽ ´", + "Ġböl üm", + "å¥ ĸ", + "æ¨ Ļ", + "ÙĦ اØŃ", + "ÙĦا ØŃ", + "Ġг оÑģÑĥдаÑĢ", + "داÙĨ ÙĦÙĪد", + "Ġп оÑĤÑĢеб", + "ĠпоÑĤ ÑĢеб", + "ĠÑĢ оÑĨÑĸ", + "о га", + "ог а", + "ĠÑģлед ÑĥеÑĤ", + "Ġп аÑĢа", + "ĠпаÑĢ а", + "Ġпа ÑĢа", + "é ¼", + "ãģį ãģŁ", + "ί ζ", + "Ġb á»ij", + "ÑĤ Ñĸв", + "ÑĤÑĸ в", + "ï¼Į 她", + "f amilia", + "éł ħ", + "Ġد ÙĦ", + "Ġs kup", + "Ġsk up", + "Ġsku p", + "еÑĩ ение", + "ãģĵãģ¨ ãģĮ", + "à¥Ģ ब", + "ุ ล", + "¨ ë¶Ģ", + "ĠاÙĦع رب", + "Ġ ç¾İ", + "Ġç¾ İ", + "ĠاÙĦ ÙħÙĪ", + "ĠاÙĦÙħ ÙĪ", + "Ġ Ø¥ÙĨ", + "ĠØ¥ ÙĨ", + "Ġná sled", + "Ġnás led", + "Ġt omu", + "Ġto mu", + "Ġtom u", + "Î Ħ", + "Ġз ави", + "Ġза ви", + "Ġзав и", + "Ġn hu", + "Ġnh u", + "ĠpÅĻed stav", + "ìłķ ë³´", + "o kol", + "ok ol", + "oko l", + "Ġк ÑĢи", + "a du", + "ad u", + "Ġ каÑĤ", + "Ġк аÑĤ", + "Ġка ÑĤ", + "Ġ ÑįÑĦ", + "ĠÑį ÑĦ", + "в ал", + "ва л", + "m ayı", + "ma yı", + "may ı", + "ĠÑĩаÑģ ÑĤо", + "ĠÑĩаÑģÑĤ о", + "Ġtr anh", + "Ġtra nh", + "Ġtran h", + "ائ ÙĦ", + "ãĤĪãģĨ ãģª", + "Ġp oh", + "Ġpo h", + "ìĥģ ìľĦ", + "Ġs ắc", + "Ùĥ س", + "Ġ мÑĥ", + "Ġм Ñĥ", + ". ::", + ".: :", + "ë Ī", + "» Ċ", + "Ġ ÙĨÚ¯", + "ĠÙĨ Ú¯", + "ÙIJ ÙĨ", + "н иком", + "ни ком", + "ник ом", + "Ñħ а", + "Ġ μοÏħ", + "Ġμ οÏħ", + "Ġμο Ïħ", + "ĠNg uyá»ħn", + "ĠвÑĭ Ñģок", + "ĠвÑĭÑģ ок", + "Ġ ÐŁÐ¾Ð´", + "ĠÐŁ од", + "ĠÐŁÐ¾ д", + "ĠпÑĢи ÑĢод", + "à¥ĭ ध", + "िà¤ķ ल", + "и ÑĢа", + "иÑĢ а", + "ëĭ¤ ê³ł", + "Ġm ajÃŃ", + "Ġma jÃŃ", + "Ġmaj ÃŃ", + "Ġv ùng", + "Ġtarih inde", + "Ġtarihi nde", + "Ġ ваÑĢ", + "Ġв аÑĢ", + "Ġва ÑĢ", + "н иÑĤÑĮ", + "ни ÑĤÑĮ", + "ниÑĤ ÑĮ", + "ει ÏĤ", + "Ġ åĩº", + "Ġåĩ º", + "dy ž", + "ÏĦ Ïİν", + "ÏĦÏİ Î½", + "ä½ĵ èĤ²", + "Ġ à¹Ģว", + "Ġà¹Ģ ว", + "Ġà¹Ģภ§", + "Ġà¤ħ à¤ļ", + "Ġ اÙĨÚ¯ÙĦÛĮسÛĮ", + "ĠاÙĨÚ¯ ÙĦÛĮسÛĮ", + "à¥įय म", + "Ġgel iÅŁ", + "æ¹ ĸ", + "Ġ اک", + "Ġا Ú©", + "Ġп лан", + "Ġпл ан", + "Ġпла н", + "k yt", + "ky t", + "ا بÛĮ", + "اب ÛĮ", + "κ ι", + "Ġc hung", + "Ġch ung", + "Ġchu ng", + "ान à¤ķ", + "s ı", + "Ġt inh", + "Ġti nh", + "Ġtin h", + "ĠÑģÑĤ ол", + "ĠÑģÑĤо л", + "ÑģÑĤ ÑĢÑĥ", + "ÑģÑĤÑĢ Ñĥ", + "Ġли ÑĪе", + "ĠлиÑĪ е", + "Ġви ÑĢоб", + "il miÅŁ", + "ilm iÅŁ", + "Ġ зÑĸ", + "Ġз Ñĸ", + "ç» Ĩ", + "åĢ Ĵ", + "ãĤ· ãĥ£", + "åŃ ©", + "Ġ à¹Ĥรà¸ĩà¹Ģร", + "Ġà¹Ĥ รà¸ĩà¹Ģร", + "Ġà¹Ĥรà¸ĩ à¹Ģร", + "íĻ ľ", + "ĠбÑĥд е", + "ĠбÑĥ де", + "Ġyak laÅŁ", + "èĩª åĪĨ", + "Ġ ÙģÙĪ", + "ĠÙģ ÙĪ", + "С Т", + "Ġso run", + "Ġsor un", + "Ġsoru n", + "à¹Ģ à¸ł", + "à¹Ģภł", + "Ġc ô", + "в иÑĩ", + "ви Ñĩ", + "ëĵ¤ ìĿĺ", + "Ġtr iá»ĩu", + "Ġtri á»ĩu", + "Ġr õ", + "Ġ ãģ«", + "ÄŁ im", + "ÄŁi m", + "iyor uz", + "è ľ", + "à¥įर व", + "Ġس Ù¾", + "Ġ ìĦľìļ¸", + "ĠìĦľ ìļ¸", + "δ ε", + "еÑĢ ÑĪ", + "Ġ أس", + "ĠØ£ س", + "äº ŀ", + "è¯ į", + "п ÑĤом", + "ฤ ษ", + "Ġساز ÙħاÙĨ", + "Ġlu ôn", + "Ùĩ ÙĪر", + "c ü", + "аÑĤ кÑĥ", + "Ġo labilir", + "Ġol abilir", + "Ġolab ilir", + "Ġola bilir", + "Ġ ìĹ°êµ¬", + "ĠìĹ° 구", + "ен ной", + "енно й", + "Ġ æĪij", + "ĠæĪ ij", + "Ġ него", + "Ġн его", + "Ġне го", + "Ġнег о", + "Ġ. **************", + "ิ à¸ĺ", + "Ġ ãĤ·", + "ĠãĤ ·", + "ت Ùģ", + "ÐŁ ÑĢо", + "ÐŁÑĢ о", + "Ġhakk ında", + "Ġhakkı nda", + "Äį nÄĽ", + "ĠM ỹ", + "é ½", + "ĠÏĥ ÏĦον", + "ĠÏĥÏĦο ν", + "Ġ âm", + "Ġâ m", + "§ ظ", + "ĠÅŁ irket", + "æĥħ åĨµ", + "ĠØ¢ÙħÙĪز Ø´", + "λ εÏħ", + "λε Ïħ", + "Ùħ Ùĩ", + "è¦ ı", + "ãģ¨ æĢĿ", + "Ġ ÙĪع", + "ĠÙĪ ع", + "ÏĪ η", + "Ïģ οÏį", + "Ïģο Ïį", + "Ġ ÂłĊ", + "ĠÂł Ċ", + "δ η", + "ÑĪ ов", + "åĪ ¤", + "Ġm ắt", + "æĭ ¿", + "à¸Ļ à¸Ķ", + "éĻ Ħ", + "à¹ī ม", + "ĠÄij ạt", + "Ġg üzel", + "Ġgü zel", + "m Ã¼ÅŁ", + "Ðŀ ÐĴ", + "çĭ ¬", + "리 를", + "Ġп лаÑĤ", + "Ġпл аÑĤ", + "Ġпла ÑĤ", + "Ġngh á»ĭ", + "ĠÑĤак иÑħ", + "ĠÑĤа киÑħ", + "б иÑĢа", + "би ÑĢа", + "Ġн ек", + "Ġне к", + "ÑģÑĮ кÑĸ", + "ÑģÑĮк Ñĸ", + "رÙĬ اض", + "o nu", + "on u", + "à¥ĭ म", + "ĠGi Ỽi", + "ĠGiá» Ľi", + "èŀ į", + "é ²", + "ĠGe nel", + "ĠGen el", + "ĠGene l", + "åĬ ¿", + "Ġ вÑĸ", + "Ġв Ñĸ", + "å§ IJ", + "è© ¦", + "ĠжиÑĤ ÑĤÑı", + "Ġ ìĺ¨", + "Ġìĺ ¨", + "åĩº æĿ¥", + "Ġt á»ij", + "Ġtá» ij", + "Ġl ao", + "Ġla o", + "ί ο", + "ĠÎł α", + "н иÑĤелÑĮ", + "ниÑĤ елÑĮ", + "ниÑĤе лÑĮ", + "éļ İ", + "Ġви кон", + "Ġвик он", + "Ġвико н", + "ĠÙģ عاÙĦ", + "ĠÙģع اÙĦ", + "à¹Ģ ศ", + "à¹Ģภ¨", + "ÏĮ γ", + "ĠоÑĢгани з", + "ĠоÑĢган из", + "Ġ емÑĥ", + "Ġе мÑĥ", + "Ġем Ñĥ", + "Ġ ÙĬع", + "ĠÙĬ ع", + "ĠÙħ ب", + "ाल य", + "ĠÎľ ÏĢ", + "é ¸", + "ù a", + "ê¸ ¸", + "Ġ ÄIJiá»ģu", + "ĠÄIJ iá»ģu", + "ε ίο", + "εί ο", + "äº ī", + "ượ t", + "ÑĢа зÑĥ", + "ÑĢаз Ñĥ", + "ĠоÑĤ ÑĢим", + "ĠоÑĤÑĢи м", + "Ġ طب", + "ĠØ· ب", + "Ġ 以", + "æĸ Ĺ", + "ë° ±", + "à¤ĩ स", + "ë§Į ìĽIJ", + "ãĢģ ãģĿãģ®", + "ĠëķĮ 문", + "ĠØ¢ ÛĮ", + "С Ðł", + "ض ÙĦ", + "æ ĵį", + "æĵ į", + "k azy", + "ka zy", + "kaz y", + "ส ว", + "â ng", + "ân g", + "à¤Ĥ à¤Ń", + "н ÑĸÑĩ", + "нÑĸ Ñĩ", + "ั à¸ĩà¸ģ", + "ัà¸ĩ à¸ģ", + "Ġبر رسÛĮ", + "ر دÙĩ", + "رد Ùĩ", + "Ġm ẫu", + "à¹Ī วà¸ĩ", + "à¹Īว à¸ĩ", + "ĠداÙĨØ´ گاÙĩ", + "d ıģ", + "dı ÄŁ", + "ĠT á»ķng", + "ĠTá»ķ ng", + "第 äºĮ", + "c ÃŃm", + "cÃŃ m", + "Ġb öyle", + "Ġbö yle", + "ë ¶Ī", + "ë¶ Ī", + "ĠÙħÙĨ ابع", + "à¥ĥ ष", + "е ÑĤÑĭ", + "еÑĤ Ñĭ", + "åĨ ·", + "åĽ Ń", + "Ġت ÙĪجÙĩ", + "ĠتÙĪ جÙĩ", + "åĪ »", + "æŀ ģ", + "à¤Ł न", + "л ан", + "ла н", + "Ġ íĥĢ", + "Ġíĥ Ģ", + "ä½ IJ", + "Ġ обÑĭ", + "Ġо бÑĭ", + "Ġоб Ñĭ", + "å¸ Ŀ", + "ì» ¤", + "å® Ī", + "èµ· æĿ¥", + "Ġ ãĥ¬", + "Ġãĥ ¬", + "çİ ī", + "à¹Ģ หล", + "à¹Ģห ล", + "и не", + "ин е", + "ห าร", + "หา ร", + "éļ ı", + "Ġг аз", + "ĠاÙĦ عÙħÙĦ", + "ĠاÙĦع ÙħÙĦ", + "ĠاÙĦعÙħ ÙĦ", + "à¥ģ à¤Ŀ", + "à¥ģठĿ", + "Ïģ ιο", + "Ïģι ο", + "Ġv ám", + "Ġvá m", + "Ġع ÙĨد", + "ĠعÙĨ د", + "ÙĨد گاÙĨ", + "ï¼Į éĤ£", + "Ġна Ñħод", + "á no", + "án o", + "ÛĮ اÙĨ", + "ÛĮا ÙĨ", + "ĠØ£ ع", + "Ġ ÑĢади", + "ĠÑĢ ади", + "ĠÑĢа ди", + "ĠÑĢад и", + "Ġм ене", + "Ġмен е", + "Ġú da", + "Ïĩ ν", + "ÑĥлÑı ÑĢ", + "à¥Ģ प", + "Ġpou žÃŃ", + "Ġ ä¸", + "ĠÙĤ اÙĨÙĪÙĨ", + "ι κοÏį", + "ικ οÏį", + "ικο Ïį", + "á y", + "Ġç öz", + "ÏĦ Ïģ", + "ÙĨ اÙħ", + "ÙĨا Ùħ", + "ุ à¸ķ", + "åĵ ª", + "ÙĬ ب", + "ä¹ °", + "ÐĶ лÑı", + "Ġ ëłĪ벨", + "ĠëłĪ 벨", + "ุ à¸ļ", + "н ÑĥÑĤи", + "нÑĥ ÑĤи", + "нÑĥÑĤ и", + "è½ »", + "ĠÎľ α", + "Ġ è¦", + "Ġè ¦", + "аÑĤ ков", + "Ġ ëĪĦ", + "ĠëĪ Ħ", + "Ġt uyá»ĥn", + "Ġtuy á»ĥn", + "Ùİ Ùħ", + "ĠвÑĭ пол", + "ĠвÑĭп ол", + "Ġst udi", + "Ġstud i", + "Ġstu di", + "ĠpÅĻ ek", + "ĠpÅĻe k", + "Ġз ам", + "Ġза м", + "Ġmat eri", + "Ġma teri", + "Ġmate ri", + "Ġmater i", + "åİ ĭ", + "Ġ ал", + "Ġа л", + "Ġ à¸ļร", + "Ġà¸ļ ร", + "Ø· ØŃ", + "ĠÙħر Ú©", + "Ġ ìĭ¬", + "Ġìĭ ¬", + "ĠÙĤ ابÙĦ", + "ĠÙĤاب ÙĦ", + "ĠÐIJ ле", + "ĠÐIJл е", + "ın tı", + "Ġ å»", + "Ġå »", + "Ä° K", + "ëħĦ ëıĦ", + "Ñĭ ваÑĤÑĮ", + "Ñĭв аÑĤÑĮ", + "Ñĭва ÑĤÑĮ", + "Ġdev let", + "社 ä¼ļ", + "ëĤ ł", + "Ġko lay", + "Ġkol ay", + "Ġkola y", + "ĠÑĢазв иÑĤи", + "ĠÑĢазви ÑĤи", + "а ди", + "ад и", + "ئ ÙĬس", + "a dıģı", + "ad ıģı", + "adı ģı", + "adıģ ı", + "Îij ÎĽ", + "Ġ hoa", + "Ġh oa", + "Ġho a", + "Ġ ศ", + "Ġภ¨", + "ı ÅŁtır", + "Ä±ÅŁ tır", + "ÑĢ Ñİ", + "Ġк аÑĩе", + "Ġка Ñĩе", + "¼ åIJĪ", + "åħ ´", + "Ġ ê·¸ëŁ¬", + "Ġê·¸ 룬", + "Ġм ÑĸÑģÑĤ", + "ĠмÑĸ ÑģÑĤ", + "ĠмÑĸÑģ ÑĤ", + "Ġм не", + "Ġмн е", + "ãĥ¼ ãĤº", + "ãĥ¼ãĤ º", + "ç§ Ģ", + "Ġع ÙĦÙĬÙĩ", + "ĠعÙĦ ÙĬÙĩ", + "ĠعÙĦÙĬ Ùĩ", + "Ġ ìĭľê°Ħ", + "Ġìĭľ ê°Ħ", + "Ġà¤ĺ र", + "Ġ Ñĥг", + "ĠÑĥ г", + "åıij å±ķ", + "ı ÅŁÄ±", + "Ä±ÅŁ ı", + "Ġ ìĪľ", + "ĠìĪ ľ", + "Ġ íĻľ", + "ĠíĻ ľ", + "æ¡ £", + "Ġn okt", + "Ġno kt", + "Ġnok t", + "l ém", + "lé m", + "ен нÑĭй", + "Ġب Ùħ", + "à¥ĩ य", + "à¥ĩठ¯", + "о дав", + "од ав", + "ода в", + "à¹Ĥ ร", + "ï¼Į æľī", + "ا ÙĬات", + "اÙĬ ات", + "اÙĬا ت", + "ا ÛĮÙĩ", + "اÛĮ Ùĩ", + "Ġà¤īप य", + "Ġs mÄĽ", + "Ġsm ÄĽ", + "Ø´ د", + "Ш ÐIJ", + "Ġا ÙħاÙħ", + "ĠاÙħ اÙħ", + "ĠاÙħا Ùħ", + "æ¿ Ģ", + "Ġho ạch", + "об ÑĢаз", + "обÑĢаР·", + "à¥ĭ ह", + "ĠÑĢеб ен", + "иÑĤ елÑı", + "иÑĤе лÑı", + "ãģªãģĮ ãĤī", + "س اÙĦ", + "Ġ à¸Īำ", + "Ġà¸Ī ำ", + "Ġ خاص", + "ĠØ® اص", + "Ġg eri", + "Ġge ri", + "Ġger i", + "ठĺ", + "Ġ ìº", + "Ġì º", + "à¹ģ à¸Ĺ", + "âĢĮ ÛĮ", + "Ú¯ رÛĮ", + "گر ÛĮ", + "ا Ùħبر", + "اÙħ بر", + "ÑĪ Ñĥ", + "Ġp hong", + "Ġph ong", + "Ġphon g", + "и мо", + "им о", + "п а", + "Ġ ìµľê³ł", + "Ġìµľ ê³ł", + "Ġ нам", + "Ġн ам", + "Ġна м", + "o stÃŃ", + "os tÃŃ", + "ost ÃŃ", + "is ini", + "isi ni", + "isin i", + "Ġд Ñĥже", + "ĠдÑĥ же", + "Ñģ ком", + "Ñģк ом", + "Ñģко м", + "ĠпÑĢод Ñĥк", + "ÏĮ ÏĦηÏĦα", + "ÏĮÏĦη ÏĦα", + "a ln", + "al n", + "is ine", + "isi ne", + "isin e", + "è¿ ľ", + "алÑĮ ной", + "алÑĮно й", + "त र", + "t ıģ", + "tı ÄŁ", + "Ġë Ĵ", + "è¿ĺ æĺ¯", + "ĠÙħ Ø«ÙĦ", + "ĠÙħØ« ÙĦ", + "ìľ ¨", + "ï¾ ĺ", + "åĪ ¸", + "ç ¶ļ", + "ç¶ ļ", + "ج اد", + "جا د", + "Ġ кÑĥ", + "Ġк Ñĥ", + "åĢ ij", + "o vu", + "ov u", + "Ġs Ä©", + "Ġ ìłIJ", + "Ġìł IJ", + "ĠÑĥ ÑĢов", + "ि à¤ļ", + "िठļ", + "ov ali", + "ova li", + "oval i", + "Ġ ÙĪÙĨ", + "ĠÙĪ ÙĨ", + "Ġ ìĿĮ", + "ĠìĿ Į", + "Ġк г", + "า à¸ĺ", + "าภĺ", + "ÏĦ Ïģα", + "ÏĦÏģ α", + "ž dy", + "à¹Į à¸ķ", + "Ġ nÄĽm", + "ĠnÄĽ m", + "Ġ Це", + "ĠЦ е", + "n oho", + "no ho", + "Ġëĭ¤ ìĭľ", + "Ġté to", + "Ġb iá»ĥu", + "ĠY ön", + "Ġpr áce", + "Ġprá ce", + "à¥ī र", + "Ġch ÃŃ", + "ов ой", + "ово й", + "Ġm ợ", + "Ġmá» Ł", + "èª ª", + "Ïİ ÏĤ", + "в олÑı", + "во лÑı", + "вол Ñı", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "å¯ ¦", + "é» ŀ", + "Ġà¤ı व", + "Ïħ να", + "Ïħν α", + "å² ¡", + "kh ông", + "ĠpÅĻÃŃpad ÄĽ", + "å Ĺ", + "Ġبد ÙĪÙĨ", + "Ïĥ κε", + "Ïĥκ ε", + "Ġdik kat", + "ĠAn cak", + "ĠAnc ak", + "Ġt iá»ĩn", + "Ġti á»ĩn", + "éĿ Ļ", + "Ġ ìĿ¼ë°ĺ", + "ĠìĿ¼ ë°ĺ", + "ĠÄį len", + "ìķ ħ", + "ा à¤ĩन", + "ाà¤ĩ न", + "ãģ£ ãģ¦ãģĦãģŁ", + "ãģ£ãģ¦ ãģĦãģŁ", + "ãģ£ãģ¦ãģĦ ãģŁ", + "ĠìĿ´ ìļ©", + "ÙĪ ÙħÛĮ", + "ÙĪÙħ ÛĮ", + "i ná", + "in á", + "ặ ng", + "ặn g", + "Ïİ Ïģα", + "ÙĨ ÙĬØ©", + "ÙĨÙĬ Ø©", + "в аж", + "ва ж", + "è İ·", + "èİ ·", + "å© ļ", + "ĠÅŁ u", + "Ġ ãģĬ", + "Ġد رب", + "Ġدر ب", + "Ġd iá»ħn", + "ÅĻ eba", + "ÅĻe ba", + "ÅĻeb a", + "as ının", + "asını n", + "ç½ij ç«Ļ", + "н ÑĮого", + "нÑĮ ого", + "нÑĮо го", + "ĠاÙĦØ£ ÙĪÙĦ", + "ικ ÎŃÏĤ", + "Ġz ÃŃsk", + "о ло", + "ол о", + "ĠÑį ÑĤоÑĤ", + "ĠÑįÑĤ оÑĤ", + "ĠÑįÑĤо ÑĤ", + "Ġp okud", + "Ġpo kud", + "Ġpok ud", + "è² »", + "е ÑĢÑĸв", + "еÑĢ Ñĸв", + "еÑĢÑĸ в", + "ãĥķ ãĤ£", + "ãĥķãĤ £", + "иÑĤÑĥ а", + "Ġv yd", + "Ġvy d", + "о лож", + "ол ож", + "оло ж", + "л ÑıÑĤÑĮ", + "лÑı ÑĤÑĮ", + "ÙĤ Ùħ", + "æ´ ĭ", + "æ§ ĭ", + "Ġغ ÛĮر", + "Ġ stÅĻed", + "Ġst ÅĻed", + "ĠstÅĻ ed", + "ظ ر", + "Ġhiç bir", + "θ εί", + "θε ί", + "z nik", + "zn ik", + "д Ñĭ", + "l uv", + "lu v", + "ĠÙħ ؤ", + "ĠÚ¯ رÙĪÙĩ", + "Ġگر ÙĪÙĩ", + "Ġ ï¼īĊ", + "Ġï¼ī Ċ", + "t eri", + "ter i", + "te ri", + "ĠÏħ ÏĢο", + "ĠÏħÏĢ ο", + "v oj", + "vo j", + "Ġبع ض", + "Ġb ilin", + "Ġbi lin", + "Ġbil in", + "Ġ رÙĪØ´", + "Ġر ÙĪØ´", + "ĠرÙĪ Ø´", + "Ġоб Ñıз", + "Ġ ï»", + "Ġï »", + "س ÙĨ", + "Ġ ÏĢα", + "ĠÏĢ α", + "í į¼", + "Ġt ÃŃn", + "ĠtÃŃ n", + "Ġ ´", + "Ġ ´", + "ìĤ¬ ìĿ´íĬ¸", + "ìĤ¬ìĿ´ íĬ¸", + "Ġ podob", + "Ġpo dob", + "Ġpod ob", + "Ñī ее", + "Ñīе е", + "Ġ åįĹ", + "Ġåį Ĺ", + "Ġb ych", + "Ġby ch", + "о зи", + "оз и", + "ĠV Äĥn", + "ØŃ Ùĩ", + "åѦ éĻ¢", + "ĠÅĻ ekl", + "ĠÅĻe kl", + "ĠÅĻek l", + "립 ëĭĪëĭ¤", + "Ġп ÑĢоÑģ", + "ĠпÑĢ оÑģ", + "ĠпÑĢо Ñģ", + "κ ά", + "ĠbaÅŁ ladı", + "ủ y", + "Ñİ Ð´Ð¶", + "ाà¤ı à¤Ĺ", + "à¤Ĥ à¤ļ", + "Ġ ê´Ģ볨", + "Ġê´Ģ 볨", + "Ġв опÑĢоÑģ", + "ĠÑģÑĤ аÑĤÑĮ", + "ĠÑģÑĤаÑĤ ÑĮ", + "ĠÑģÑĤа ÑĤÑĮ", + "Ġy atırım", + "Ġyatır ım", + "н Ñĥла", + "нÑĥ ла", + "нÑĥл а", + "ر اÙģ", + "را Ùģ", + "Ġç eÅŁit", + "Ġà¤ī द", + "å¤ ®", + "Ġпо Ñıв", + "åĽ½ 家", + "ĠÑģооÑĤвеÑĤ ÑģÑĤв", + "ìķ ¡", + "ĠØ® ÙĪاÙĩد", + "ĠØ®ÙĪاÙĩ د", + "ĠØ®ÙĪ اÙĩد", + "Å¡ Å¡ÃŃ", + "Âł п", + "ĠNh Ãł", + "' '''", + "'' ''", + "''' '", + "ï½ ¨", + "à ħ", + "Ġ ïº", + "Ġï º", + "ĠØ¢Ùħ رÛĮÚ©", + "lar ımız", + "larım ız", + "ج ا", + "Ùģ ÙĤ", + "Ġ á»", + "Ġá »", + "Ġ ìķł", + "Ġìķ ł", + "Ġز باÙĨ", + "ĠÑĤ воÑĢ", + "ĠÑĤв оÑĢ", + "н иÑĩеÑģ", + "ни ÑĩеÑģ", + "ниÑĩ еÑģ", + "Ġк ни", + "Ø® داÙħ", + "à¸Ł ร", + "Ġ ì¹ĺ", + "Ġì¹ ĺ", + "ว าม", + "ĠÙħ ÙĩÙħ", + "ĠÙħÙĩ Ùħ", + "Ġs tol", + "Ġst ol", + "Ġsto l", + "Ġed ilen", + "Ġedi len", + "Ġedil en", + "Ġ pek", + "Ġp ek", + "Ġpe k", + "اÙĨ ات", + "اÙĨا ت", + "алÑĮ нÑĸ", + "Ġнеоб ÑħÑĸд", + "à¹Ħ ว", + "Ġश र", + "Ġ íĮIJ", + "ĠíĮ IJ", + "Ò ij", + "Ġ ним", + "Ġн им", + "Ġни м", + "Ġ à¸ĺ", + "Ġภĺ", + "æĺ ł", + "äº Ĵ", + "ĠbaÅŁ ar", + "ž i", + "Ġм ног", + "Ġмн ог", + "l endi", + "len di", + "á vajÃŃ", + "áv ajÃŃ", + "n ict", + "ni ct", + "nic t", + "Ġд Ñĥм", + "ĠдÑĥ м", + "éĻ ©", + "Ïĥ Ïĥ", + "i ky", + "ik y", + "алÑĮ нÑĭй", + "Ġ ÙħÙĨت", + "ĠÙħ ÙĨت", + "ĠÙħÙĨ ت", + "å® ®", + "- за", + "-з а", + "еÑĢ к", + "å¡ Ķ", + "Ġμε ÏĦα", + "oÄŁ un", + "oÄŁu n", + "ÎĹ Îľ", + "à¥Īà¤Ĥ ।Ċ", + "à¥Īà¤Ĥ। Ċ", + "Äį ky", + "å¹³ åı°", + "à¥ĭ श", + "Ġ ona", + "Ġo na", + "Ġon a", + "Ġ bec", + "Ġb ec", + "Ġbe c", + "ì ¢", + "Ġc ây", + "Ġcâ y", + "k ün", + "kü n", + "Ġ à¤Ī", + "ĠठĪ", + "Ġr á»Ļng", + "еÑĢ б", + "å¹ ¸", + "ï¾ IJ", + "ĠпÑĸд пÑĢиÑĶм", + "çĶ £", + "Ġ ÏĦε", + "ĠÏĦ ε", + "ĠÙĨÙĤ Ø´", + "о виÑħ", + "ов иÑħ", + "ови Ñħ", + "ĠÙģ Ùī", + "Ðļ ак", + "Ùİ Ø±", + "Ġ Щ", + "ĠÐ ©", + "алÑĮ нÑĭÑħ", + "Ġk üçük", + "èŃ ·", + "æĭ ħ", + "i caret", + "ic aret", + "ica ret", + "icare t", + "Ġ رÙģت", + "Ġر Ùģت", + "ĠرÙģ ت", + "Ġод ного", + "Ġодно го", + "ÑĪ им", + "ÑĪи м", + "Ġ бÑĸ", + "Ġб Ñĸ", + "Ġuygu lam", + "Ġ æĭ", + "Ġæ ĭ", + "ä½ Ľ", + "u cu", + "uc u", + "d ÃŃ", + "Å ĺ", + "ئ Ø©", + "ê± ¸", + "Ù Į", + "Ġ ÎłÏģο", + "ĠÎł Ïģο", + "Ġye rine", + "Ġyer ine", + "Ġyeri ne", + "ĠÑĸн ÑĦоÑĢма", + "Ġ å¤ĸ", + "Ġå¤ ĸ", + "ä» ķ", + "н ав", + "на в", + "a rası", + "ar ası", + "ara sı", + "aras ı", + "à¸Ńà¸Ļ à¹Ħลà¸Ļ", + "ا شت", + "اش ت", + "ز ÙĬ", + "æ© ĭ", + "Ġ ãĤ«", + "ĠãĤ «", + "èĥ½ åĬĽ", + "å¥ Ĺ", + "Ġpro h", + "Ġpr oh", + "Ġп ÑĢава", + "ĠпÑĢ ава", + "ĠпÑĢав а", + "ĠпÑĢа ва", + "Ỽ p", + "Ġ à¸Ĥà¸Ńà¸ĩ", + "Ġà¸Ĥ à¸Ńà¸ĩ", + "Ġ ë´", + "Ġë ´", + "Ġl úc", + "Ġ éķ", + "Ġé ķ", + "ب ÙĪد", + "بÙĪ د", + "r upa", + "ru pa", + "rup a", + "ا زÙħ", + "از Ùħ", + "Ġ кан", + "Ġк ан", + "Ġка н", + "ılı m", + "ıl ım", + "Ġ Ùĩد", + "ĠÙĩ د", + "ãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢ ĠãĢĢ", + "ãĢĢĠ ãĢĢĠãĢĢ", + "Ñĭ ваеÑĤ", + "Ñĭв аеÑĤ", + "Ñĭва еÑĤ", + "Ø® اÙĨÙĩ", + "خاÙĨ Ùĩ", + "Ñĥ кÑĤ", + "Ñĥк ÑĤ", + "ĠçĻ¾ 度", + "ĠnÄĽ co", + "е мон", + "ем он", + "емо н", + "Ġà¤ħ प", + "Ġ ÎĮ", + "ĠÎ Į", + "ün ün", + "ünü n", + "æĸĩ åĮĸ", + "ä¹ İ", + "ä¸Ĭ çļĦ", + "ÙĦ ÙĬÙħ", + "ÙĦÙĬ Ùħ", + "ĠtÄĽ ch", + "ا سب", + "اس ب", + "âĢĻ ÑĶ", + "Ġ Ú¯ÛĮ", + "ĠÚ¯ ÛĮ", + "Ġ ê·¼", + "Ġê· ¼", + "Ġtr ẻ", + "μÎŃ Î½Î¿", + "μÎŃν ο", + "ãģĵãģ¨ ãĤĴ", + "ìĿ´ ëĤĺ", + "åĸ Ħ", + "Ġtr ả", + "åĪĨ æŀIJ", + "Ġ dÄĽl", + "Ġd ÄĽl", + "ĠdÄĽ l", + "Ñĥ Ñģка", + "ÑĥÑģ ка", + "ÑĥÑģк а", + "Ġм ного", + "Ġмн ого", + "Ġмног о", + "à¥Ī र", + "μα ÏĦοÏĤ", + "μαÏĦο ÏĤ", + "Ġm ÃŃsto", + "ĠmÃŃ sto", + "ĠmÃŃst o", + "Ġ ê°ģ", + "Ġê° ģ", + "Ġп ÑĢог", + "ĠпÑĢ ог", + "ĠпÑĢо г", + "b aÅŁ", + "ba ÅŁ", + "а йÑĤе", + "ай ÑĤе", + "айÑĤ е", + "Ġc á»ķ", + "å¿ ľ", + "ï¼ģ Ċ", + "ç ı", + "Ġbir çok", + "Ġ íĺķ", + "Ġíĺ ķ", + "çµ Į", + "ĠEv rop", + "ĠÑģ оÑĨÑĸ", + "ĠÑģо ÑĨÑĸ", + "ä»ĸ çļĦ", + "Ġ μÏĢο", + "Ġμ ÏĢο", + "ĠμÏĢ ο", + "å¥ Ī", + "Ġ Ú¯ÙĦ", + "ĠÚ¯ ÙĦ", + "ÙĪ ÙĦØ©", + "ÙĪÙĦ Ø©", + "æµ İ", + "Ġ Ú©ÙĪ", + "ĠÚ© ÙĪ", + "± ä¹IJ", + "ãģĹ ãģı", + "ãģĹãģ ı", + "çº ³", + "ÑģÑĤв енно", + "ÑģÑĤвен но", + "éĽ ¢", + "ा .", + "Ġgerçek leÅŁtir", + "ĠgerçekleÅŁ tir", + "Ġ kır", + "Ġk ır", + "Ġkı r", + "ì ³", + "Ġг оÑģп", + "å¹ ķ", + "ìĦ ¼", + "» .Ċ", + "». Ċ", + "к ÑĥÑĢ", + "кÑĥ ÑĢ", + "Ġ رÛĮ", + "Ġر ÛĮ", + "æĽ ¾", + "ÙĪ رÙĬ", + "ÙĪر ÙĬ", + "лекÑģ анд", + "ص Ùģ", + "Ġc ảnh", + "Ġcả nh", + "å± Ĥ", + "ãĤ Ĩ", + "Ġ تس", + "Ġت س", + "ì° ½", + "기 를", + "Ġ à¹Ģà¸Ħ", + "Ġà¹Ģ à¸Ħ", + "Ġà¹ĢภĦ", + "çŁ Ń", + "Ġ ÑģÑĤÑĢо", + "ĠÑģ ÑĤÑĢо", + "ĠÑģÑĤ ÑĢо", + "ĠÑģÑĤÑĢ о", + "ĠÏĥ ÏĦιÏĤ", + "ĠÏĥÏĦι ÏĤ", + "à¥įय व", + "Ġع ÙĦÙħ", + "ĠعÙĦ Ùħ", + "ĠÑģ иÑĤÑĥа", + "ĠÑī одо", + "ĠÑīо до", + "åIJ Ľ", + "Ùħ س", + "ĠоÑĤ кÑĢÑĭ", + "ĠоÑĤк ÑĢÑĭ", + "Ġsp oj", + "Ġspo j", + "ĠÄij Äĥng", + "Ġs avaÅŁ", + "Ġsav aÅŁ", + "ี ร", + "sk ém", + "ské m", + "Ġ è¡Į", + "Ġè¡ Į", + "é ¹", + "Ġ ÙĬÙħÙĥÙĨ", + "ĠÙĬÙħ ÙĥÙĨ", + "о вано", + "ов ано", + "ова но", + "ован о", + "Ġп ÑĢавилÑĮ", + "ĠпÑĢав илÑĮ", + "ĠпÑĢави лÑĮ", + "ĠпÑĢавил ÑĮ", + "Ġchiế c", + "èĪ ¹", + "éĵ ¶", + "ĠоÑĤ д", + "Ġ ìĿĢ", + "ĠìĿ Ģ", + "íħ Ķ", + "Ġ Nej", + "ĠN ej", + "ĠNe j", + "о не", + "он е", + "Ġk ız", + "Ġkı z", + "олог иÑĩеÑģ", + "Ġ кÑĢаÑĹ", + "ĠкÑĢа ÑĹ", + "à¸ļ à¸Ńล", + "æ¥ ¼", + "Ġت ÙħاÙħ", + "ĠتÙħ اÙħ", + "Ġب ÛĮÙħ", + "ĠبÛĮ Ùħ", + "ĠÑģ Ñĥб", + "ĠÑģÑĥ б", + "v ý", + "Ñģ кие", + "Ñģк ие", + "Ñģки е", + "ëĮĢ ë¡ľ", + "ëĮ Ģë¡ľ", + "???? ????", + "abilir siniz", + "ан Ñģов", + "анÑģ ов", + "代 表", + "Ġ매 매", + "олог ÑĸÑĩ", + "μ αν", + "μα ν", + "ак Ñģим", + "акÑģ им", + "ãĤ¤ ãĥ«", + "Ġt ải", + "Ġtả i", + "Ùħ ÙĪ", + "å® Ĺ", + "n em", + "ne m", + "Ġkho ản", + "Ġ паÑĤ", + "Ġп аÑĤ", + "Ġпа ÑĤ", + "ан ÑĤа", + "анÑĤ а", + "Ġпом оÑī", + "Ġ vod", + "Ġv od", + "Ġvo d", + "Ġkay nak", + "Ġkayn ak", + "Ïĥ ÏĨ", + "à¥Ĥ त", + "du ÄŁ", + "а ÑĤиÑģÑı", + "аÑĤи ÑģÑı", + "Ġ ç¥ŀ", + "Ġç¥ ŀ", + "ĠÑģ лова", + "ĠÑģл ова", + "ĠÑģлов а", + "ĠÑģло ва", + "ÑĢÑĥ кÑĤÑĥ", + "ÑĢÑĥк ÑĤÑĥ", + "ÑĢÑĥкÑĤ Ñĥ", + "ĠmÄĽ sÃŃ", + "Ùı Ùħ", + "зна Ñĩа", + "знаÑĩ а", + "Ġ èī", + "Ġè ī", + "åѦ çĶŁ", + "æ´ ¥", + "Ùİ ÙĬ", + "è§ Ī", + "Ġ å®ī", + "Ġå® ī", + "Ġgör Ã¼ÅŁ", + "ál nÄĽ", + "áln ÄĽ", + "ĠëĶ° ëĿ¼", + "ĠÙħ ÙĪجÙĪد", + "ĠÙħÙĪج ÙĪد", + "ĠÄij ứ", + "ĠÄijá» ©", + "ĠçalÄ±ÅŁ malar", + "ĠçalÄ±ÅŁma lar", + "ĠÑı киÑħ", + "ĠÑıк иÑħ", + "Ġاج تÙħاع", + "μ εν", + "με ν", + "èİ ī", + "ç§ ¯", + "ì¶ ķ", + "à¥į शन", + "à¥įश न", + "Ġx ét", + "Ġв ÑĤоÑĢ", + "ĠвÑĤ оÑĢ", + "çİ ©", + "Âł ÐĿ", + "ÑĪ ие", + "ÑĪи е", + "о ÑĢи", + "оÑĢ и", + "Ø£ س", + "Ġthu á»ijc", + "ëĭĪ ê¹Į", + "ë ķĮ", + "ÑĢ Ñĥп", + "ÑĢÑĥ п", + "Ñģ ÑıÑĤ", + "ÑģÑı ÑĤ", + "з Ñĭ", + "ĠÑģ меÑĢ", + "ĠÑģм еÑĢ", + "Ġv yb", + "Ġvy b", + "ĠìĿ´ ìĥģ", + "à¤ļ न", + "Ġgel di", + "Ġgeld i", + "Û± Û°", + "Û±Û °", + "ικ Ïİν", + "ĠÄIJ ức", + "Ġд оÑģÑĤаÑĤ", + "ĠдоÑģÑĤ аÑĤ", + "Ġö nc", + "Ġön c", + "è¦ ª", + "Ġ adı", + "Ġa dı", + "Ġad ı", + "un ca", + "unc a", + "ĠاÙĦ تر", + "ĠاÙĦت ر", + "çķ ¶", + "ĠФ едеÑĢа", + "ĠФед еÑĢа", + "лÑı ÑİÑĤÑģÑı", + "лÑıÑİÑĤ ÑģÑı", + "ĠÙĥ اÙĨت", + "ĠÙĥاÙĨ ت", + "æİ ¢", + "Ġ Ñĥб", + "ĠÑĥ б", + "Ġ κο", + "Ġκ ο", + "ाà¤ĩ à¤Ł", + "з н", + "Ġm ôi", + "Ġmô i", + "Ġ ãĤµ", + "ĠãĤ µ", + "Ġна вÑĸ", + "Ġнав Ñĸ", + "ç» ¼åIJĪ", + "Ġмин ÑĥÑĤ", + "Ġми нÑĥÑĤ", + "ĠминÑĥ ÑĤ", + "d ık", + "dı k", + "ÑĢ Ñĥд", + "ÑĢÑĥ д", + "åľ ĸ", + "ê° ¤", + "ĠÄijo Ãłn", + "è ¤", + "à¥į वर", + "à¥įव र", + "ĠÃľn iversit", + "а но", + "ан о", + "éĽ ¨", + "ĠvÅ¡ech ny", + "Ġëĭ¤ ìĿĮ", + "ĠC umhur", + "ĠCum hur", + "Ġм Ñĥз", + "ĠмÑĥ з", + "a ÅŁtır", + "aÅŁ tır", + "Ġ ê±°ëŀĺ", + "Ġê±° ëŀĺ", + "Ġ é¡", + "Ġé ¡", + "žit ÃŃ", + "ži tÃŃ", + "Ġ à¸Ł", + "ĠภŁ", + "Ġthu ế", + "Ġм Ñĥж", + "ĠмÑĥ ж", + "ĠÎij ν", + "Ġد ÙĪÙħ", + "ĠدÙĪ Ùħ", + "ĠÑģ ин", + "ĠÑģи н", + "Ġ ÏīÏĤ", + "ĠÏī ÏĤ", + "m eler", + "me ler", + "mel er", + "Ġ poÄį", + "Ġp oÄį", + "Ġpo Äį", + "Ġколи Ñĩе", + "ĠколиÑĩ е", + "ĠK Äį", + "è³ ½", + "ĠоÑģ Ñĸб", + "åı ¥", + "ĠB öl", + "à¸ĺ รรม", + "Ġc ạnh", + "å° ĩ", + "Ġ ноÑģ", + "Ġн оÑģ", + "Ġно Ñģ", + "èĦ ¸", + "Ġgel ir", + "о ÑĢон", + "оÑĢ он", + "оÑĢо н", + "à¥įर à¤Ń", + "ç» ĩ", + "ุ à¹ī", + "ाम ल", + "Ġc âu", + "Ġcâ u", + "Ñij ÑĤ", + "Ġ :|", + "Ġ: |", + "ãĤĮ ãģ¦", + "Ġpos led", + "Ġpo sled", + "ãĤ¹ ãĥĨ", + "ÑĸлÑĮ ÑĪ", + "ен ÑĤÑĭ", + "енÑĤ Ñĭ", + "Ø® دÙħ", + "Ġباش گاÙĩ", + "Ġth Æ°", + "á vánÃŃ", + "áv ánÃŃ", + "ává nÃŃ", + "ëĬ IJ", + "ĠØ£ ØŃ", + "ر اد", + "را د", + "ĠبسÛĮ ار", + "åĪ° äºĨ", + "\" ;\"", + "\"; \"", + "å° İ", + "Ġ ör", + "Ġö r", + "à¸Ĭ าà¸ķ", + "g enus", + "gen us", + "Ġya kın", + "Ġyak ın", + "Ġ ÃŃt", + "ĠÃŃ t", + "reg num", + "regn um", + "Ġf iyat", + "Ġfi yat", + "н ÑĸÑħ", + "нÑĸ Ñħ", + "åľ° æĸ¹", + "Ġbil gi", + "Ġbilg i", + "к ам", + "ка м", + "Ġs pol", + "Ġsp ol", + "Ġspo l", + "ائ ÙĬ", + "Ġ ÙĬÙĨ", + "ĠÙĬ ÙĨ", + "า หาร", + "าห าร", + "Ġب Ú¯", + "é ĺħ", + "éĺ ħ", + "ĠاÙĦ شر", + "ĠاÙĦØ´ ر", + " ģ", + "ĠÑĸн ÑĪиÑħ", + "ĠÑĸнÑĪ иÑħ", + "Ġtr ạng", + "çģ £", + "Ġc á»±c", + "к ан", + "ка н", + "èĭ ı", + "à Ķ", + "Ġl á»Ŀi", + "Ġlá» Ŀi", + "Ñı Ñĩ", + "Ġ ÙĪØŃ", + "ĠÙĪ ØŃ", + "ìĪ ľ", + "Å ¸", + "Ġв оÑģп", + "Ġво Ñģп", + "ĠвоÑģ п", + "ì¡ Į", + "Äį nÃŃch", + "ÄįnÃŃ ch", + "Ø® رÙī", + "خر Ùī", + "ائ ÙĬØ©", + "ائÙĬ Ø©", + "Ġsu ất", + "æĩ ī", + "ا ØŃÛĮ", + "اØŃ ÛĮ", + "Ġn áz", + "Ġná z", + "è¿Ļ ç§į", + "Ġзаб езпеÑĩ", + "Ġ ЧеÑĢ", + "ĠЧ еÑĢ", + "Ġзд ÑĸйÑģ", + "åı ¦", + "æĭ ¬", + "à¥ģ ष", + "à¥ģठ·", + "μ ÏĨ", + "ëĥ IJ", + "Ðķ Ñģли", + "é ¬", + "Ġ íĥľ", + "Ġíĥ ľ", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "Ġм л", + "å´ İ", + "Ùģ ع", + "Ġ ÙĤدر", + "ĠÙĤ در", + "ĠÙĤد ر", + "Ġv á»ijn", + "å¦ ¹", + "Ġ ÐĿаÑģ", + "ĠÐĿ аÑģ", + "ĠÐĿа Ñģ", + "à¥į फ", + "à¥įठ«", + "ãĤ¸ ãĥ£", + "Ġm ı", + "ен Ñģ", + "б Ñĥд", + "бÑĥ д", + "ĠØŃ ØªÙī", + "Ġ ì²´", + "Ġì² ´", + "ĠÑĸ ÑģÑĤоÑĢ", + "ĠÑĸÑģ ÑĤоÑĢ", + "Ġgi ấy", + "γ οÏģ", + "γο Ïģ", + "ëIJĺ ìĸ´", + "Ġ íĤ", + "Ġí Ĥ", + "ĠÐŀд на", + "ĠÙĨ ÙħÙĪد", + "ĠÙĨÙħ ÙĪد", + "Ġвип ад", + "ĠìŀIJ ìĭł", + "Ġj ste", + "Ġjs te", + "Ġ ëĵ±ë¡Ŀ", + "Ġëĵ± ë¡Ŀ", + "ek ten", + "ekt en", + "ekte n", + "ĠÑĢ еÑĩ", + "ĠÑĢе Ñĩ", + "r odnÃŃ", + "rod nÃŃ", + "س تر", + "ست ر", + "ı t", + "ä¹ħ ä¹ħ", + "ĠØ® ÙĦاÙĦ", + "ĠØ®ÙĦ اÙĦ", + "Ġ ç¦", + "Ġç ¦", + "u luk", + "ul uk", + "ulu k", + "l enen", + "le nen", + "len en", + "lene n", + "i lip", + "il ip", + "ili p", + "è´ ¢", + "Ġà¤ħ à¤ķ", + "ĠY ıl", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Ġ à¤Ŀ", + "ĠठĿ", + "ĠB ình", + "Ġol muÅŁ", + "Ġolm uÅŁ", + "اÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©", + "اÙĦØ¥ ÙĨجÙĦÙĬزÙĬØ©", + "м енно", + "мен но", + "aln ız", + "Ġ شرÙĥØ©", + "Ġشر ÙĥØ©", + "Ġس ÙĨØ©", + "ĠسÙĨ Ø©", + "è´ Ł", + "ä½ľ åĵģ", + "Ġ ìķ½", + "Ġìķ ½", + "ĠдÑĢÑĥг иÑħ", + "ĠbaÄŁ lantı", + "о дÑĥ", + "од Ñĥ", + "çļĦ æĺ¯", + "ั à¸Ļà¸Ķ", + "ัà¸Ļ à¸Ķ", + "ĠкоÑĤоÑĢ ÑĭÑħ", + "ĠاÙĦ ÙĪÙĦ", + "ĠاÙĦÙĪ ÙĦ", + "ê¸Ģ ìĥģìľĦ", + "ĠÏĢ εÏģ", + "ĠÏĢε Ïģ", + "리 ìķĦ", + "i bar", + "ib ar", + "iba r", + "Ġ èĥ", + "Ġè ĥ", + "ãģŁ ãģĦ", + "á j", + "ĠìľĦ íķ´", + "? âĢľĊĊ", + "?âĢľ ĊĊ", + "Ġ íİĺ", + "Ġíİ ĺ", + "Ġ ней", + "Ġн ей", + "Ġне й", + "ĠÐĹ Ð°Ðº", + "ĠÐĹа к", + "Ġ ÐĴÑĸд", + "ĠÐĴ Ñĸд", + "е лÑĸ", + "ел Ñĸ", + "è¯ ¾", + "åī ¯", + "m adan", + "ma dan", + "mad an", + "mada n", + "æľ «", + "ĠÏĢ ÏģÏĮ", + "Ġп ÑģиÑħ", + "Ġ ÑĤÑĸ", + "ĠÑĤ Ñĸ", + "Ùĥ ات", + "Ġvys ok", + "ê´Ģ 리", + "ül tür", + "ült ür", + "Ġ à¹Ģà¸Ń", + "Ġà¹Ģ à¸Ń", + "Ġà¹ĢภŃ", + "Ġ íķ©", + "Ġíķ ©", + "çĿ £", + "Ġ ÑĢиÑģ", + "ĠÑĢ иÑģ", + "е ÑĢÑĮ", + "еÑĢ ÑĮ", + "ĠÚ© ÙĦÛĮ", + "ĠÚ©ÙĦ ÛĮ", + "Ġ ãĥŀ", + "Ġãĥ ŀ", + "Ġph ÃŃa", + "ĠphÃŃ a", + "å «", + "ا Ú¯", + "Ġ é¢", + "Ġé ¢", + "ĠÙĨ Ùģر", + "ĠÙĨÙģ ر", + "Ġ جاÙĨ", + "Ġج اÙĨ", + "Ġجا ÙĨ", + "Ġ yas", + "Ġy as", + "Ġya s", + "ж ениÑı", + "же ниÑı", + "жен иÑı", + "ĠлÑĥÑĩ ÑĪе", + "Ġ çº", + "Ġç º", + "Ġ мон", + "Ġм он", + "Ġмо н", + "Ġت Ø®", + "Ġ Ø´ÛĮ", + "ĠØ´ ÛĮ", + "Ġн екоÑĤоÑĢ", + "Ġнек оÑĤоÑĢ", + "алÑĮ нÑĭе", + "Ġob chod", + "Ġíķ¨ ê»ĺ", + "Ġr iêng", + "ãģķ ãĤĮãĤĭ", + "ãģķãĤĮ ãĤĭ", + "о кÑĥ", + "ок Ñĥ", + "ĠС ШÐIJ", + "ë§ ģ", + "Ġ Nếu", + "ĠN ếu", + "ĠA ÄŁ", + "Ġд веÑĢ", + "Ġдв еÑĢ", + "Ġдве ÑĢ", + "à¥ĭ ष", + "Ġkh iến", + "Ġkhi ến", + "н его", + "не го", + "ì± ħ", + "ั à¸ķร", + "ัà¸ķ ร", + "m alı", + "ma lı", + "mal ı", + "Ġ ÙĬا", + "ĠÙĬ ا", + "ç§ij æĬĢ", + "ื à¸Ļ", + "ห มาย", + "หม าย", + "Ġ خص", + "ĠØ® ص", + "åĨ ľ", + "ÃŃ me", + "ÃŃm e", + "ĠÑįÑĤ ой", + "ĠÑįÑĤо й", + "Ġ ìĹħ", + "ĠìĹ ħ", + "Ġ ä¹", + "ä¼ ¯", + "' ´", + "Ùħ ÙĬÙĦ", + "ÙħÙĬ ÙĦ", + "à¸Ń à¸ĩà¸Ħ", + "à¸Ńà¸ĩ à¸Ħ", + "k ová", + "ko vá", + "kov á", + "è¿Ļ ä¹Ī", + "ãĢĤ æĪij", + "ìĹIJ ìĦľëĬĶ", + "ìĹIJìĦľ ëĬĶ", + "Ġ ìļ©", + "Ġìļ ©", + "ë¹Ħ ìĬ¤", + "Ġ ì¦Ŀ", + "Ġì¦ Ŀ", + "IT TE", + "ITT E", + "Ġ모 ëĵł", + "ĠspoleÄį nosti", + "ĠspoleÄįnost i", + "Ġв ик", + "Ġви к", + "Ġt ÅĻÃŃ", + "ĠtÅĻ ÃŃ", + "é ³", + "Ġ Ø®ÛĮ", + "ĠØ® ÛĮ", + "Ġp ož", + "Ġpo ž", + "Ġиме еÑĤ", + "Ġd ÄĽt", + "ĠdÄĽ t", + "ĠÙħد ÙĦ", + "Ġ мо", + "Ġм о", + "åį ı", + "e nÃŃm", + "en ÃŃm", + "enÃŃ m", + "é ī", + "ا ظ", + "Ø§Ø ¸", + "Ġ teÅŁ", + "Ġt eÅŁ", + "Ġte ÅŁ", + "Ġ veÅĻej", + "Ġve ÅĻej", + "L IC", + "LI C", + "ì§Ģ ëĬĶ", + "Ñĭ ваÑİÑĤ", + "Ñĭв аÑİÑĤ", + "Ñĭва ÑİÑĤ", + "ĠоÑĢг анÑĸ", + "ĠоÑĢган Ñĸ", + "nÃŃ mi", + "nÃŃm i", + "θ ÎŃ", + "ãĤ¯ ãĥ©", + "ãĥ¼ ãĥ³", + "ãĥ¼ãĥ ³", + "ли ÑģÑı", + "i mdi", + "im di", + "imd i", + "æ Ĩ", + "ïº İ", + "Ġìļ´ ìĺģ", + "κ αν", + "κα ν", + "Ġ ë³µ", + "Ġë³ µ", + "Ġ ÐĨн", + "ĠÐĨ н", + "p lication", + "pl ication", + "t ah", + "ta h", + "Ġ ÐIJв", + "ĠÐIJ в", + "Ġc á»Ļng", + "алÑĮ ноÑĹ", + "алÑĮно ÑĹ", + "Ġد ÙĪرÙĩ", + "ĠدÙĪ رÙĩ", + "ĠدÙĪر Ùĩ", + "à¥įर य", + "Ġ Ø®ÙĪ", + "ĠØ® ÙĪ", + "Ġв ÑĢа", + "Ø¥ ÙĨ", + "èĤ ī", + "Ġo yn", + "Ġoy n", + "ĠT Æ°", + "ĠÙĩ ÙħاÙĨ", + "ĠÙĩÙħ اÙĨ", + "ĠбÑĸлÑĮ ÑĪе", + "ĠбÑĸлÑĮÑĪ е", + "æĮ ¯", + "ا ÙħØ©", + "اÙħ Ø©", + "å º«", + "åº «", + "Ġ ÑĢеж", + "ĠÑĢ еж", + "ĠÑĢе ж", + "Ġدار ÙĨد", + "ÑĢ ий", + "ÑĢи й", + "Ġ æĮ", + "Ġæ Į", + "Ġson uç", + "Ġsonu ç", + "Ġt ả", + "ั à¸ĩà¸Ħ", + "ัà¸ĩ à¸Ħ", + "ë° Ľ", + "Ġ мом", + "Ġм ом", + "Ġмо м", + "ви Ñĩай", + "виÑĩ ай", + ". à¸Ħ", + "Ġ à¤Ĩà¤Ī", + "Ġà¤Ĩ à¤Ī", + "åģ ĩ", + "Ġpos kyt", + "Ġpo skyt", + "ĠÑģ Ñĥп", + "ĠÑģÑĥ п", + "ıyor du", + "а ле", + "ал е", + "и ÑĨ", + "Ġ θÎŃ", + "Ġθ ÎŃ", + "ãĤĩ ãģĨ", + "ĠÑģв ой", + "ĠÑģво й", + "ม à¸Ļ", + "Ġn ữa", + "Ġnữ a", + "v oÅĻ", + "vo ÅĻ", + "ا سÙĬ", + "اس ÙĬ", + "éĴ ±", + "ãģĹ ãģ¦ãģĦãģŁ", + "ãģĹãģ¦ ãģĦãģŁ", + "ãģĹãģ¦ãģĦ ãģŁ", + "ĠÄij ầy", + "ا ÙĬر", + "اÙĬ ر", + "Ġar aÅŁtır", + "Ġara ÅŁtır", + "ì £", + "ãģ¨ ãģ¯", + "ĠÑģ поÑĢ", + "ĠÑģп оÑĢ", + "Ġê° Ģìŀ¥", + "Ġê°Ģ ìŀ¥", + "è¼ ī", + "âĸ ¡", + "Ġ ìĻĦ", + "ĠìĻ Ħ", + "оÑĢ аÑı", + "оÑĢа Ñı", + "Ïģ εί", + "Ïģε ί", + "ĠÑį ÑĤа", + "ĠÑįÑĤ а", + "ë©´ ìłģ", + "ìĿ´ ìĬ¤", + "ä½ ³", + "æĻ ļ", + "Ġk val", + "Ġkv al", + "Ġn á»ķi", + "ÑĤ ами", + "ÑĤа ми", + "Ġпол ÑĸÑĤи", + "ĠполÑĸ ÑĤи", + "Ġ Ä°ng", + "ĠÄ° ng", + "ĠÄ°n g", + "нÑĸ ÑģÑĤÑİ", + "нÑĸÑģÑĤ Ñİ", + "Ġ à¹Ģà¸ģ", + "Ġà¹Ģ à¸ģ", + "Ġà¹Ģภģ", + "Ġ 민", + "Ġë ¯¼", + "è Ķ", + "Ïģ ία", + "Ïģί α", + "æİ Ī", + "Ġ çĤ", + "Ġç Ĥ", + "ĠÙĨÙħ اÛĮ", + "Ġ ìŀ¡", + "Ġìŀ ¡", + "æŀ ¶", + "اب ÙĤ", + "Ñģ он", + "Ñģо н", + "ен ного", + "енно го", + "ĠÙħ ÛĮÙĦÛĮ", + "ĠÙħÛĮ ÙĦÛĮ", + "ĠÙħÛĮÙĦ ÛĮ", + "Ġk urum", + "Ġkur um", + "Ġku rum", + "Ġkuru m", + "à¹Į ส", + "Ġ ì´Ŀ", + "Ġì ´Ŀ", + "Ġì´ Ŀ", + "ĠnÄĽk olik", + "ĠnÄĽkol ik", + "Ġ ÙĢ", + "ĠÙ Ģ", + "ĠзаÑģÑĤ оÑģ", + "à¸Ķ à¸Ļ", + "ÙĨ داÙĨ", + "ÙĨد اÙĨ", + "ÙĨدا ÙĨ", + "ĠJ ap", + "ĠJa p", + "éĥ ¡", + "à¥į à¤Ń", + "à¥įठŃ", + "Ġ à¹Ģà¸Ĭ", + "Ġà¹Ģ à¸Ĭ", + "Ġà¹ĢภĬ", + "Ġ âĢ«", + "ĠâĢ «", + "é£ ŀ", + "o vatel", + "ov atel", + "ova tel", + "ovat el", + "ĠÑĩа ÑģÑĤÑĮ", + "ĠÑĩаÑģ ÑĤÑĮ", + "ĠÑĩаÑģÑĤ ÑĮ", + "Ġb á»ķ", + "ãĤ¯ ãĥª", + "ิ à¹Į", + "Ġвид е", + "Ġви де", + "v ail", + "va il", + "Ì ī", + "ÄŁ inde", + "ÄŁi nde", + "ÄŁin de", + "ãģ¨ ãĤĤ", + "âĢĮÚ© ÙĨد", + "âĢĮÚ©ÙĨ د", + "Ġ ëħĦ", + "Ġëħ Ħ", + "Ġ اÙĤتص", + "ĠاÙĤ تص", + "ï½ Ĺ", + "Ïģ ιÏĥ", + "Ïģι Ïĥ", + "з д", + "èĻ ½", + "Ġth oại", + "Ġtho ại", + "Ġ ÙĪز", + "ĠÙĪ ز", + "Ġ mÃŃt", + "Ġm ÃŃt", + "ĠmÃŃ t", + "ĠÑħ олод", + "ĠÑħол од", + "Ġ кÑĥп", + "Ġк Ñĥп", + "ĠкÑĥ п", + "а ниÑħ", + "ан иÑħ", + "ани Ñħ", + "Ġnh ìn", + "ãģĭ ãģª", + "Ġ Ðļом", + "ĠÐļ ом", + "ĠÐļо м", + "ÏĦ εÏģ", + "ÏĦε Ïģ", + "ï¼Į åıª", + "Ġol up", + "Ġhá» ıi", + "ë ij", + "ĠnÄĽk ter", + "i sÃŃ", + "is ÃŃ", + "ĠвикоÑĢиÑģÑĤ ов", + "ìŀ ¡", + "Ġà¤ķ ल", + "Ġìľł ìłĢ", + "ĠпÑĢ иб", + "ĠпÑĢи б", + "èĭ ¦", + "Ġ мов", + "Ġм ов", + "Ġмо в", + "Ġ หà¸Ļ", + "Ġห à¸Ļ", + "ëIJĺ ëĬĶ", + "о ко", + "ок о", + "Ġоб еÑģп", + "Ġk ez", + "Ġke z", + "л ÑıÑħ", + "лÑı Ñħ", + "ĠпÑĢо иÑģ", + "Ġпо вин", + "Ġпов ин", + "ĠÐļ оÑĢ", + "ĠÐļо ÑĢ", + "ì¼ Ģ", + "Ġ Ñģи", + "ĠÑģ и", + "Ġ ä¹ĭ", + "Ġä¹ ĭ", + "ĠâĢĶ Ċ", + "ÑģÑĥÑĤ ÑģÑĤв", + "ç °", + "Ġ à¤ł", + "Ġठł", + "н аÑĤ", + "на ÑĤ", + "Ġs uy", + "Ġsu y", + "Ġ ÑģÑĭ", + "ĠÑģ Ñĭ", + "ĠÙĨ شاÙĨ", + "ĠÙĨØ´ اÙĨ", + "Ġна пÑĢав", + "Ġнап ÑĢав", + "ĠÑĨ ÑĮомÑĥ", + "æĺ¯ ä¸Ģ", + "Ġm üm", + "Ġmü m", + "ÑĶ мо", + "ÑĶм о", + "ĠاسÙĦاÙħ ÛĮ", + "Ġza manda", + "Ġzam anda", + "Ġzaman da", + "ÙĪ ÙħاÙĨ", + "ÙĪÙħ اÙĨ", + "ا ÙĦØŃ", + "اÙĦ ØŃ", + "Å¡t ÄĽnÃŃ", + "Å¡tÄĽ nÃŃ", + "Ġ Ðļак", + "ĠÐļ ак", + "ĠÐļа к", + "¤ íĶĦ", + "¤í ĶĦ", + "ĠÙ¾ رد", + "Ġپر د", + "C ác", + "ε ια", + "ει α", + "Ġ جÙĪ", + "Ġج ÙĪ", + "ĠÄijo ạn", + "Ġà¤ĩ त", + "Ġз ан", + "Ġза н", + "ĠÙħÙĨØ· ÙĤÙĩ", + "ĠÙħ عÙĦ", + "ĠÙħع ÙĦ", + "Ġdo kon", + "Ġdok on", + "åIJ ¸", + "ic kou", + "ick ou", + "å° ģ", + "Ġк иÑģ", + "Ġки Ñģ", + "ัà¸ĩ หว", + "i species", + "is pecies", + "isp ecies", + "Ġнап ÑĢÑı", + "æº ĸ", + "Ġà¤ľ ल", + "à¹Ģ à¸ī", + "à¹Ģภī", + "L AR", + "LA R", + "ĠÑĥÑģлов иÑı", + "ĠWiki species", + "ĠWik ispecies", + "ระ à¸Ķ", + "Ġ mey", + "Ġm ey", + "Ġme y", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "à¹ĩ à¸Ī", + "å¾ Ĵ", + "t ach", + "ta ch", + "u muz", + "um uz", + "umu z", + "κ η", + "à Ĭ", + "Ġ ün", + "Ġü n", + "ĠB ITTE", + "ĠBIT TE", + "ĠÙħ ربع", + "ĠÙħر بع", + "ĠÙħرب ع", + "ãĤ· ãĥ¥", + "िस à¤ķ", + "Ø· ÙĪر", + "Ġв оÑģ", + "Ġво Ñģ", + "ï¾ Ł", + "Ġy ayın", + "Ġyay ın", + "ãģĭ ãĤĬ", + "л иÑı", + "ли Ñı", + "Ġп ÑĢин", + "ĠпÑĢ ин", + "ĠпÑĢи н", + "ij ng", + "ĠÙĨ Ø®", + "Ġl ze", + "Ġlz e", + "à¥įष ण", + "Ġ бо", + "Ġб о", + "Ġ ê¸Ģ", + "Ġê¸ Ģ", + "Ġgel iÅŁtir", + "ĠgeliÅŁ tir", + "à¸Ľà¸£à¸° à¸Ĭ", + "å½ ¡", + "Ġ ãĤª", + "ĠãĤ ª", + "ãģĪ ãģ¦", + "н ÑĥÑĤÑĮ", + "нÑĥ ÑĤÑĮ", + "нÑĥÑĤ ÑĮ", + "Ġ ç½", + "Ġç ½", + "Ġ маг", + "Ġм аг", + "Ġма г", + "ãģ« ãģ¤", + "н оÑģÑĤей", + "ноÑģÑĤ ей", + "Ġ ÙĦÙĬ", + "ĠÙĦ ÙĬ", + "æĢ ª", + "ÑıÑĤ ÑģÑı", + "ภij", + "िय म", + "Ġ ãĢİ", + "ĠãĢ İ", + "ÑĢ ÑĮ", + "Ġm ạng", + "t ım", + "tı m", + "ĠпеÑĢи од", + "о гÑĥ", + "ог Ñĥ", + "ĠкоÑĤоÑĢ аÑı", + "ĠкоÑĤ оÑĢаÑı", + "리 ê°Ģ", + "Ġãħ ¡", + "Ġج اÛĮ", + "Ġجا ÛĮ", + "ĠпоÑĤ ÑĢÑĸб", + "Å¡ en", + "Å¡e n", + "à¸Ń ะ", + "ب ع", + "ØŁ Ċ", + "Ġ ë°©ë²ķ", + "Ġë°© ë²ķ", + "Ġ гоÑĢод", + "Ġг оÑĢод", + "ĠгоÑĢ од", + "Ġ Ðĺн", + "ĠÐĺ н", + "Ġ оказ", + "Ġо каз", + "Ġок аз", + "ر ÙĪز", + "رÙĪ ز", + "Ġili ÅŁk", + "ĠiliÅŁ k", + "å® £", + "f orman", + "form an", + "for man", + "forma n", + "a daÅŁ", + "ad aÅŁ", + "ada ÅŁ", + "ÙĬ ÙĦØ©", + "ÙĬÙĦ Ø©", + "ĠÐļ аÑĢ", + "ĠÐļа ÑĢ", + "Ġm ất", + "æħ ĭ", + "м п", + "à¹Ĥ à¸Ļ", + "ĠØŃÙĤ ÙĪÙĤ", + "Ġд нÑı", + "ĠëĴ ¤", + "ा à¤ķर", + "ाà¤ķ र", + "ì²ĺ ëŁ¼", + "âĢĮ Ø¢", + "h angi", + "han gi", + "hang i", + "è¡Į æĶ¿", + "al iyet", + "ali yet", + "Ġ ì²ľ", + "Ġì² ľ", + "ĠY ap", + "ĠYa p", + "à¹Ĥ รà¸ĩ", + "à¹Ĥร à¸ĩ", + "ì§Ģ ëħ¸", + "Ùİ Ùij", + "Îij ÎĻ", + "á na", + "án a", + "an dır", + "and ır", + "ระ à¸ļà¸ļ", + "ระà¸ļ à¸ļ", + "oÄŁ lu", + "า à¸Īะ", + "าà¸Ī ะ", + "ẩ y", + "ا ÙĪÙĦ", + "اÙĪ ÙĦ", + "ĠмаÑĤ еÑĢÑĸ", + "ÎŁ ÎĿ", + "ÎŁÎ Ŀ", + "Ġin formace", + "Ġinform ace", + "ت ع", + "à¸ļ à¸Ļ", + "ĠÄĮesk é", + "ĠÄĮes ké", + "Ġte mel", + "Ġtem el", + ":::::::::::::::: ::::::::::::::::", + "Ġ chia", + "Ġch ia", + "Ġchi a", + "- Ñģ", + "н еÑĢг", + "не ÑĢг", + "неÑĢ г", + "Ġì° ¾", + "ÑĢ ид", + "ÑĢи д", + "л оÑģÑĮ", + "ло ÑģÑĮ", + "ز ÙĦ", + "ê°Ģ ëĬĶ", + "a né", + "an é", + "Ġнав ÑĸÑĤÑĮ", + "ĠнавÑĸ ÑĤÑĮ", + "ä¸ĵ ä¸ļ", + "Ġ 경기", + "Ġê²½ 기", + "Ġp ÅĻev", + "ĠpÅĻ ev", + "ĠpÅĻe v", + "е ÑĤи", + "еÑĤ и", + "Ġ íĶĮ", + "ĠíĶ Į", + "н ÑıÑĤ", + "нÑı ÑĤ", + "à¥ģ श", + "à¥ģठ¶", + "лÑİ Ð´", + "в иÑī", + "ви Ñī", + "å° ¾", + "çļĦ äºĭ", + "Ġ ëIJľ", + "Ġë IJľ", + "ĠëIJ ľ", + "ر ÙĪÙģ", + "رÙĪ Ùģ", + "Ġ 女", + "Ġå¥ ³", + "κ ή", + "Ġ Tuy", + "ĠT uy", + "ĠTu y", + "Ġê²ĥ ìĿĦ", + "Ġb unu", + "Ġbu nu", + "Ġbun u", + "ĠÑĢаз лиÑĩ", + "ĠD ün", + "ãĤŃ ãĥ£", + "ÑĢ ÑĥÑģ", + "ÑĢÑĥ Ñģ", + "Ġ мм", + "Ġм м", + "l oven", + "lo ven", + "love n", + "lov en", + "Ġot ev", + "n oloj", + "ES Ä°", + "ü p", + "Ġ èĤ", + "Ġè Ĥ", + "ικ ÏĮÏĤ", + "ικÏĮ ÏĤ", + "ض اء", + "ضا Ø¡", + "Ġ пеÑĩ", + "Ġп еÑĩ", + "ÅĻÃŃ klad", + "ãģĵ ãĤį", + "Å¡ tÃŃ", + "Å¡t ÃŃ", + "Ġبر Ú¯", + "ãģĮ ãģĤãĤĭ", + "Ñĸ ÑģÑĤ", + "ÑĸÑģ ÑĤ", + "à¥ī à¤ķ", + "ÏĢ η", + "ĠاÙĦÙħ ست", + "ĠاÙĦÙħس ت", + "Ġз ай", + "Ġза й", + "Ġch Æ°Æ¡ng", + "о ÑĤÑĥ", + "оÑĤ Ñĥ", + "Ġ Сам", + "ĠС ам", + "Å¡ et", + "Å¡e t", + "ĠìŀĪ ìĹĪ", + "ĠÙģ ار", + "Ñĸ он", + "ãĥĹ ãĥŃ", + "Ġnh iá»ĩt", + "in izi", + "ini zi", + "iniz i", + "Ġc ož", + "Ġco ž", + "Ġà¤Ĩ न", + "Ġsyst ém", + "ر ÙĪع", + "رÙĪ ع", + "a yet", + "ay et", + "aye t", + "ĠÙģر ÙĩÙĨÚ¯", + "Ġ è¶", + "Ġè ¶", + "èģ ·", + "è§Ĥ çľĭ", + "н ок", + "но к", + "à¸IJ าà¸Ļ", + "êµIJ ìľ¡", + "k la", + "kl a", + "ãĤģ ãģ¦", + "Îķ ÎĻ", + "åĿ Ĺ", + "Ġsk uteÄį", + "à¥Ĥ à¤ľ", + "ãģij ãģ¦", + "N GC", + "NG C", + "Ġ åĢ", + "Ġå Ģ", + "ĠÑĢоз п", + "nÃŃ ků", + "nÃŃk ů", + "ãĥ³ ãĤ¹", + "ĠÐĴ еÑĢ", + "Ġyüz de", + "Ġ미 êµŃ", + "ĠÙħ Ùī", + "д еÑĢ", + "де ÑĢ", + "а ва", + "ав а", + "Ġmerk ez", + "į ng", + "Ġ ìĤ¼", + "ĠìĤ ¼", + "ĠÑĢоб оÑĤи", + "ĠÑĢобоÑĤ и", + "Ġ нÑĮого", + "Ġн ÑĮого", + "Ġ економ", + "Ġе коном", + "Ġек оном", + "ĠÑĩелов ека", + "ĠÑĩеловек а", + "Ġ à¸ŀระ", + "Ġà¸ŀ ระ", + "Ġà¸ŀร ะ", + "ãĥ Ĵ", + "ãģ£ ãģ¦ãģĦ", + "ãģ£ãģ¦ ãģĦ", + "ä¼ Ĺ", + "ĠпÑĢод ÑĥкÑĤ", + "ĠпÑĢодÑĥк ÑĤ", + "Ġy anı", + "Ġyan ı", + "à¥Ģव न", + "Ġc áºŃp", + "ĠAv rupa", + "ा à¤Ń", + "ाठŃ", + "ĠìłĦ ìļ©", + "æķ £", + "ĠìľĦ íķľ", + "Ñħод иÑĤÑĮ", + "ÑħодиÑĤ ÑĮ", + "Ġsın ır", + "ü cret", + "üc ret", + "s uz", + "su z", + "æ¨ Ĥ", + "Ġ ì°½", + "Ġì° ½", + "Ïģ ίοÏħ", + "Ïģί οÏħ", + "åĪ ļ", + "Ø® ÙĦ", + "ëłĩ ê²Į", + "ج د", + "Ġμ αÏĤ", + "Ġμα ÏĤ", + "áºŃ m", + "k ara", + "ka ra", + "kar a", + "ãĤ« ãĥ¼", + "Ġkter ou", + "ìĽ ¨", + "ÑĦи ÑĨи", + "oÄŁ raf", + "Ġна пÑĢи", + "Ġнап ÑĢи", + "ãģij ãģ©", + "Ġ éļ", + "Ġé ļ", + "ت باÙĦ", + "تب اÙĦ", + "ëŁ ½", + "ìĶ ¨", + "íĮĮ ìĿ¼", + "Ïĩ α", + "Ġuz ak", + "Ġd òng", + "Ġг олоÑģ", + "Ġгол оÑģ", + "Ïĥ ÏĦή", + "ÏĥÏĦ ή", + "ι λ", + "Ø· Ùģ", + "Ġê·¸ ëħĢ", + "ãĤ¿ ãĤ¤", + "ا ÙĨÚ¯", + "اÙĨ Ú¯", + "i nou", + "in ou", + "ino u", + "л он", + "ло н", + "à¹ĩ ม", + "Ġब द", + "Ġkon usunda", + "Ġkonusu nda", + "Ġkonus unda", + "Ġn âng", + "ãģ¾ãģĽ ãĤĵ", + "Ñĥ ÑİÑĤÑĮÑģÑı", + "ÑĥÑİ ÑĤÑĮÑģÑı", + "ÑĥÑİÑĤÑĮ ÑģÑı", + "åŁ ¹", + "ен ко", + "ìł ij", + "Ġ ÑĤов", + "ĠÑĤ ов", + "ĠÑĤо в", + "Ġt ÅĻeba", + "ĠtÅĻ eba", + "ز اÙĨ", + "زا ÙĨ", + "is yon", + "isy on", + "Ġ ген", + "Ġг ен", + "Ġге н", + "Ġ Pokud", + "ĠP okud", + "ĠPo kud", + "ĠPok ud", + "âĢĮ اÙĨد", + "âĢĮاÙĨ د", + "Ġг ÑĢÑĥд", + "ĠгÑĢÑĥ д", + "ĠгÑĢ Ñĥд", + "Ġ خرÛĮد", + "Ġخر ÛĮد", + "λ λα", + "λλ α", + "Ġp ÅĻÃŃm", + "ĠpÅĻ ÃŃm", + "ĠpÅĻÃŃ m", + "Ġ æ³ķ", + "Ġæ³ ķ", + "Ġز ÙĨدگÛĮ", + "ĠزÙĨد Ú¯ÛĮ", + "ạ p", + "Ġ íĬ¸", + "ĠíĬ ¸", + "ĠÄij á»Ļc", + "ĠÄijá»Ļ c", + "Ġê·¸ ë¦¬ê³ł", + "Ġ그리 ê³ł", + "н из", + "ни з", + "Ġ ÙĬÙĤ", + "ĠÙĬ ÙĤ", + "l aÅŁtır", + "la ÅŁtır", + "laÅŁ tır", + "ĠпÑĢав о", + "ĠпÑĢа во", + "Ñĥ Ñģк", + "ÑĥÑģ к", + "å° ½", + "Ġप ड", + "éĵ ģ", + "Ġ ì·¨", + "Ġì ·¨", + "ĠاÙĦ بÙĬ", + "ĠاÙĦب ÙĬ", + " ¸", + "ิม à¸ŀ", + "Ġs vÄĽ", + "Ġsv ÄĽ", + "Ġб ал", + "Ġба л", + "Ġm ôn", + "Ġmô n", + "ĠD ữ", + "ĠØ´ دÙĨ", + "Ġشد ÙĨ", + "Ġ ÙģÙĦ", + "ĠÙģ ÙĦ", + "Ġv znik", + "Ġvz nik", + "Ġch ứ", + "ĠÑģÑĤ ÑĢÑĥкÑĤÑĥ", + "ç¸ £", + "ĠH oa", + "ĠHo a", + "í ĮĢ", + "íĮ Ģ", + "Ġ ÑĢÑĸÑĪ", + "ĠÑĢ ÑĸÑĪ", + "Ġвоз дÑĥ", + "олÑĮ ÑĪ", + "οÏħ με", + "ู à¸Ļ", + "Ġп ÑĢид", + "ĠпÑĢ ид", + "ĠпÑĢи д", + "il mek", + "ilm ek", + "ĠاÙĦ ÙĤر", + "ĠاÙĦÙĤ ر", + "Į ĵ", + "Ġ uç", + "Ġu ç", + "å¨ ĺ", + "ec ektir", + "ecek tir", + "Ġ íħĮ", + "Ġí ħĮ", + "Ġíħ Į", + "Ġ εÏħ", + "Ġε Ïħ", + "Ġh òa", + "Ïģ Ïħ", + "ึà¸ģษ า", + "ĠÑĤеÑħ нолог", + "ú i", + "Ġbilg iler", + "Ġbilgi ler", + "Ġ ÙĤاÙĦ", + "ĠÙĤ اÙĦ", + "e dl", + "ed l", + "z nám", + "zn ám", + "á ly", + "ál y", + "åºĶ 该", + "алÑĮ ний", + "аÑĤ елÑı", + "аÑĤе лÑı", + "à¸Ļ วà¸Ļ", + "à¸Ļว à¸Ļ", + "Ġ ÐŁÐ¾Ð»", + "ĠÐŁ ол", + "ĠÐŁÐ¾ л", + "à¸ŀ à¸Ļ", + "ç¤ ¼", + "Ġt asar", + "Ġta sar", + "Ġtas ar", + "ĠÑĤ ой", + "ĠÑĤо й", + "Ġм еÑģÑı", + "Ġ иÑģк", + "Ġи Ñģк", + "ĠиÑģ к", + "Ġप द", + "γ ή", + "ا ختÙĩ", + "اخ تÙĩ", + "اخت Ùĩ", + "è¿Ļ éĩĮ", + "Ġch á»īnh", + "Ġchá»ī nh", + "ĠÙĤ سÙħ", + "Ùİ Ùĩ", + "er li", + "åĽ½ éĻħ", + "il iyor", + "ili yor", + "ĠØ´Ùĩر ستاÙĨ", + "Ġve lk", + "Ġvel k", + "åĽ º", + "Ġб ÑĸлÑĮÑĪ", + "ĠбÑĸлÑĮ ÑĪ", + "ãĥ¼ ãĥĹ", + "ãĥ¼ãĥ Ĺ", + "æŁ IJ", + "ì§ ľ", + "ĠÄĮ R", + "Ġд ек", + "Ġде к", + "ر بÛĮ", + "رب ÛĮ", + "о виÑĩ", + "ов иÑĩ", + "ови Ñĩ", + "Ġkap sam", + "Ġkaps am", + "ĠÙĦ Ø£", + "Ġ анÑĤи", + "Ġан ÑĤи", + "Ġ ücret", + "Ġü cret", + "ê² ¬", + "о ÑĢож", + "оÑĢ ож", + "оÑĢо ж", + "ÛĮ ÙħÛĮ", + "ÛĮÙħ ÛĮ", + "è© ķ", + "Ġ ë§ŀ", + "Ġë§ ŀ", + "Ġ ÑĢÑıд", + "ĠÑĢ Ñıд", + "ĠÑĢÑı д", + "ĠÙĩÙħ راÙĩ", + "â r", + "ا بت", + "اب ت", + "ĠиÑģполÑĮзов аÑĤÑĮ", + "ĠиÑģполÑĮз оваÑĤÑĮ", + "к Ñģ", + "âī ¡", + "Ġo lay", + "Ġol ay", + "Ġola y", + "èį ¯", + "Ġo prav", + "Ġop rav", + "Ġopr av", + "Ġدرب ارÙĩ", + "Ġ ä¸ŃåĽ½", + "Ġä¸Ń åĽ½", + "и лÑģÑı", + "ил ÑģÑı", + "åį «", + "ĠاÙĦ است", + "ĠاÙĦاس ت", + "ÙĪÛĮ ÛĮ", + "ÑĢ еÑĪ", + "ÑĢе ÑĪ", + "Ġ ÙĨس", + "ĠÙĨ س", + "ãĢĤ åľ¨", + "Ġ ÙĦØŃ", + "ĠÙĦ ØŃ", + "Ġko run", + "Ġkor un", + "ĠÙģ رد", + "ĠÙģر د", + "Ġо боÑĢ", + "Ġоб оÑĢ", + "Ġобо ÑĢ", + "е ÑĪÑĮ", + "еÑĪ ÑĮ", + "Ġpod mÃŃn", + "Ġ ë¬¸ìłľ", + "Ġ문 ìłľ", + "ĠdeÄŁer lendir", + "ä¸į åIJĮ", + "æ¶ ²", + "ा हर", + "ाह र", + "íļ į", + "à¥į à¤ł", + "à¥įठł", + "и ÑĤиÑģÑı", + "иÑĤи ÑģÑı", + "ا ÙĦع", + "اÙĦ ع", + "Ġd vÄĽ", + "Ġdv ÄĽ", + "ĠпеÑĢ ек", + "ĠпеÑĢе к", + "Ġ åħĥ", + "Ġåħ ĥ", + "Ġ aras", + "Ġa ras", + "Ġar as", + "Ġara s", + "Ġalt ında", + "Ġaltın da", + "Ġaltı nda", + "Ġв за", + "Ġвз а", + "æĴ ĥ", + "Ġmil yon", + "Ġ åѦ", + "ĠåŃ ¦", + "Ġв аÑĢи", + "ĠваÑĢ и", + "Ġва ÑĢи", + "ĠاÙĦع اÙĦÙħ", + "' Ñı", + "ÙĪ ÛĮس", + "ÙĪÛĮ س", + "Ġмож ÑĥÑĤÑĮ", + "ãģij ãģŁ", + "ìĿ´ ìĹĪëĭ¤", + "ìĿ´ìĹĪ ëĭ¤", + "ο Ïįν", + "οÏį ν", + "Ġ éŁ", + "Ġé Ł", + "Ġpost up", + "Ġpo stup", + "ü yük", + "üy ük", + "åĪ Ĭ", + "Ġ ÙĤب", + "ĠÙĤ ب", + "Ġاص ÙĦÛĮ", + "ĠاصÙĦ ÛĮ", + "ÙĪ Ùī", + "Ġrep ublik", + "Ġ ÐĻ", + "ĠÐ Ļ", + "ģ m", + "Ġб ел", + "ा -", + "Ñģ кое", + "Ñģк ое", + "Ñģко е", + "Ġcu á»iji", + "è² ·", + "ี ยว", + "ีย ว", + "éĩį è¦ģ", + "ู ม", + "ĠÑĢозвиÑĤ кÑĥ", + "Ġ ë°±", + "Ġë° ±", + "åĥ ¹", + "Ġ åīį", + "Ġåī į", + "à¹Ħ à¸ĭ", + "ãĢĮ âĢ¦âĢ¦", + "à¥Į त", + "Ú© رد", + "کر د", + "Ġza ÅĻÃŃzenÃŃ", + "ส าร", + "Ġle tech", + "Ġlet ech", + "l emek", + "le mek", + "lem ek", + "leme k", + "人 ãģ®", + "Ġd ưỡng", + "ĠdÆ°á» ¡ng", + "ت ÙĤ", + "Ġ åĵ", + "Ġå ĵ", + "åħ »", + "Ġ ëıħ", + "Ġëı ħ", + "Ġ 루", + "Ġë £¨", + "Ġë£ ¨", + "Ø° ÙĦÙĥ", + "Ġ ìĿ¼ë³¸", + "ĠìĿ¼ 본", + "ĠAy rıca", + "ĠÙ¾ Úĺ", + "is inin", + "isi nin", + "isin in", + "isini n", + "Ġìĭ ¶", + "Ú¯ ÛĮرÛĮ", + "Ú¯ÛĮ رÛĮ", + "Ú¯ÛĮر ÛĮ", + "خص ص", + "³ ç´°", + "ĠмаÑĤеÑĢи ал", + "k ové", + "ko vé", + "kov é", + "ë§ ī", + "ãģķ ãģĽ", + "ĠÑĤак ой", + "ĠÑĤа кой", + "Ġtr áºŃn", + "Ġ лиÑĨ", + "Ġл иÑĨ", + "Ġли ÑĨ", + "Ġ åĽĽ", + "ĠåĽ Ľ", + "Ñĩ Ñĥ", + "Ġ æ°´", + "Ġæ° ´", + "Ġdo lay", + "Ġdol ay", + "å½ ¹", + "ÑĢ ива", + "ÑĢи ва", + "ÑĢив а", + "Ġг ÑĢÑĥпп", + "ĠгÑĢÑĥ пп", + "ĠгÑĢÑĥп п", + "Ġmüm kün", + "л ена", + "лен а", + "ле на", + "ëĿ¼ ëĬĶ", + "åĪ© çĶ¨", + "Ġr ahat", + "Ġra hat", + "ï¼ıï¼ı ï¼ıï¼ı", + "æģ ©", + "Ġ íķŃ", + "Ġíķ Ń", + "Ġ íĴ", + "Ġí Ĵ", + "Ġ ìĬ¹", + "ĠìĬ ¹", + "Ġch ân", + "Ġ ãĤ¨", + "ĠãĤ ¨", + "Ġжиз ни", + "çĸ ij", + "ãĢĤ ä»ĸ", + "리 ìĬ¤", + "Ñĩ иÑħ", + "Ñĩи Ñħ", + "Ġ é¦ĸ", + "Ġé¦ ĸ", + "ÄĽ r", + "Ġй омÑĥ", + "Ġth áºŃt", + "Ġìķ ŀ", + "c ih", + "ci h", + "س ÙĦاÙħ", + "سÙĦ اÙħ", + "Ġs iyas", + "Ġsi yas", + "Ġ íĸĪ", + "Ġíĸ Ī", + "Ġк оÑĪ", + "Ġко ÑĪ", + "Ïĥ αν", + "Ïĥα ν", + "ÙĬ اÙĨ", + "ÙĬا ÙĨ", + "Ġd ö", + "ाह त", + "о ÑĢод", + "оÑĢ од", + "оÑĢо д", + "о ваÑı", + "ов аÑı", + "ова Ñı", + "ÑĨи оналÑĮ", + "ÑĨион алÑĮ", + "ائ Ùĩ", + "Ġà¤ĸ र", + "ĠÄij á»Ŀi", + "ĠÄijá» Ŀi", + "ä¸į ä¼ļ", + "Ùĥ ز", + "ี à¸Ħวาม", + "ีà¸Ħ วาม", + "l ıyor", + "lı yor", + "à¥ĭ द", + "Ġ 충", + "Ġì¶ ©", + "Ġc á»ij", + "à¹Ĥ à¸ķ", + "Ġε ÏĢί", + "ĠεÏĢ ί", + "ĠпÑĢ Ñıм", + "æ³ °", + "ا ÙĦØ©", + "اÙĦ Ø©", + "j ÃŃm", + "jÃŃ m", + "Ġ би", + "Ġб и", + "Å¡ em", + "Å¡e m", + "ĠH á»Ļi", + "à¸Ħ รà¸ĩ", + "à¸Ħร à¸ĩ", + "Ġh uyá»ĩn", + "Ġhuy á»ĩn", + "ç¯ Ģ", + "l iÅ¡", + "li Å¡", + "ĠجÙĩ ت", + "ç§ ĭ", + "ĠÑĨ ел", + "ĠÑĨе л", + "Ġ лÑĸÑĤ", + "Ġл ÑĸÑĤ", + "ĠлÑĸ ÑĤ", + "Ġ æ·", + "Ġæ ·", + "ж Ñĥ", + "ãģĪ ãģŁ", + "ë´ ī", + "Ġ 머", + "Ġë¨ ¸", + "åł´ åIJĪ", + "éĿ ©", + "ãĥª ãĥ³", + "ег да", + "Ġbe nim", + "Ġben im", + "Ġbeni m", + "çĽ Ł", + "ãģ® ä¸Ń", + "åĿ IJ", + "ĠÃľniversit esi", + "Ġko ÅŁ", + "Ġп ож", + "Ġпо ж", + "iá»ĩ p", + "ĠpÅĻ ij", + "ĠpÅĻi j", + "ëŀ ¨", + "ĠاÙĦ أس", + "ĠاÙĦØ£ س", + "ár nÃŃ", + "iế m", + "Ġ èĬ", + "Ġè Ĭ", + "Ġ δε", + "Ġδ ε", + "å¨ ±ä¹IJ", + "Ġ Æ°u", + "Ġ çĦ¡", + "ĠçĦ ¡", + "Ġг ÑĢи", + "ĠгÑĢ и", + "Ġпо ÑįÑĤомÑĥ", + "ĠÄij óng", + "ĠÄijó ng", + "ĠÄijón g", + "ج اÙĨ", + "جا ÙĨ", + "Ġngh iên", + "Ġnghi ên", + "Ġا ÙĦاÙĨ", + "ĠاÙĦ اÙĨ", + "ÑĪ ей", + "ÑĪе й", + "à¹ģ รà¸ģ", + "ĠÚĨ Ùĩار", + "ĠÚĨÙĩ ار", + "Ñİ Ñīий", + "ÑİÑī ий", + "ÏĮ Ïģ", + "Ġ رÙħ", + "Ġر Ùħ", + "ì² ł", + "Ġدست گاÙĩ", + "Ġ دÛĮد", + "Ġد ÛĮد", + "ĠدÛĮ د", + "ãĥĥãĤ¯ ãĤ¹", + "ा मन", + "ाम न", + "ĠTh Ãłnh", + "Ġth ẩm", + "Ġc Ãłng", + "ĠcÃł ng", + "Ġdön Ã¼ÅŁ", + "ĠпÑĢи гоÑĤов", + "ĠпÑĢиг оÑĤов", + "Ġk iÅŁi", + "Ġki ÅŁi", + "ĠkiÅŁ i", + "ØŃ Øª", + "Ġ ë²ķ", + "Ġë² ķ", + "é£ Ľ", + "Ġit ibar", + "Ġг лав", + "Ġгла в", + "Ġor tam", + "Ġort am", + "Ġorta m", + "Ġm add", + "Ġma dd", + "Ġmad d", + "Ġ оÑģÑĤав", + "Ġо ÑģÑĤав", + "ĠоÑģÑĤ ав", + "ĠÙģÙĪ تباÙĦ", + "ĠÙģÙĪت باÙĦ", + "Ġan laÅŁ", + "l eyen", + "le yen", + "ley en", + "ç ´Ģ", + "ç´ Ģ", + "Ġ é£", + "Ġé £", + "/ lo", + "/l o", + "Ùħ ÙĪÙĦ", + "ÙħÙĪ ÙĦ", + "Ġд ÑĥÑħ", + "ĠдÑĥ Ñħ", + "Ġ ÙĦب", + "ĠÙĦ ب", + "л ег", + "ле г", + "Ġgö nder", + "Ġgön der", + "ÙĬ Ø·", + "Ġ สำ", + "Ġส ำ", + "Ġv ás", + "Ġvá s", + "ĠÐŁ еÑĤ", + "а лоÑģÑı", + "ало ÑģÑı", + "ì ¿ł", + "ì¿ ł", + "éĻ ½", + "åĸ ®", + "èĪ ŀ", + "н Ñĥл", + "нÑĥ л", + "ÄŁ ine", + "ÄŁi ne", + "ÄŁin e", + "Ġ ghi", + "Ġg hi", + "Ġgh i", + "Ġ çµ", + "Ġç µ", + "ÙĬ ÙĨÙĬ", + "ÙĬÙĨ ÙĬ", + "Å ½", + "Ġhük üm", + "ĠD Ä±ÅŁ", + "ĠÎŃ Ïĩει", + "ĠÎŃÏĩ ει", + "Ġ Ñģка", + "ĠÑģ ка", + "ĠÑģк а", + "Ġ ÑĤим", + "ĠÑĤ им", + "ĠÑĤи м", + "Ġп оÑģÑĤав", + "Ġпо ÑģÑĤав", + "ĠпоÑģÑĤ ав", + "à¸Ļ าà¸Ķ", + "à¸Ļา à¸Ķ", + "d ül", + "dü l", + "Ġd va", + "Ġdv a", + "Ġ à¸Ħà¸Ļ", + "Ġà¸Ħ à¸Ļ", + "Ġchá»ĭ u", + "Ġ èı", + "Ġè ı", + "à¹ģส à¸Ķà¸ĩ", + "æ° £", + "Ġ íĪ¬", + "Ġí Ī¬", + "Ġ Ñĩин", + "ĠÑĩ ин", + "ĠÑĩи н", + "ãģ« ãģĬ", + "ен ноÑģÑĤи", + "енно ÑģÑĤи", + "ÐIJ ÐĿ", + "Ġh emen", + "Ġhe men", + "Ġhem en", + "Ġ ait", + "Ġa it", + "Ġai t", + "Ġ à¤Ĭ", + "ĠठĬ", + "æī §", + "ĠA BD", + "ĠAB D", + "Ġκα θ", + "æ´ Ľ", + "ãĤ¢ ãĥ«", + "à¹ī าà¸Ĺ", + "à¹īา à¸Ĺ", + "ÅĻ ez", + "ÅĻe z", + "d ÄĽji", + "dÄĽ ji", + "Ġt á»ĭch", + "еннÑı м", + "Ġв ÑģÑĤанов", + "ĠвÑģÑĤ анов", + "ĠاÙĦ بر", + "ĠاÙĦب ر", + "ÙĪÙħ تر", + "k ách", + "ká ch", + "åº Ĭ", + "л Ñĥж", + "лÑĥ ж", + "Ġ تد", + "Ġت د", + "ä¸ ½", + "ر Ø®", + "à¤Ĥ à¤ĸ", + "èĩªå·± çļĦ", + "å®ĺ ç½ij", + "- Ñı", + "à¹ĩ à¸Ķ", + "èĦ ļ", + "Ġ çķ", + "Ġç ķ", + "Ġiçer isinde", + "Ġb iá»ĥn", + "Ġbi á»ĥn", + "Ġ à¸ģล", + "Ġà¸ģ ล", + "Ġy aÄŁ", + "Ġya ÄŁ", + "Ġ æ´", + "Ġæ ´", + "Ġ бÑĢа", + "Ġб ÑĢа", + "ع ار", + "عا ر", + "æĪ °", + "à¥Ģ Ċ", + "Ġlé Äį", + "a ların", + "alar ın", + "aları n", + "Ġ Îĸ", + "ĠÎ ĸ", + "а ÑĢÑı", + "аÑĢ Ñı", + "ãģĿ ãĤĵãģª", + "ÅĪ uje", + "ãĢĢ Ġ", + "ĠsaÄŁ lık", + "Ġдо ÑģлÑĸд", + "ĠдоÑģ лÑĸд", + "ÃŃ Å¡", + "à¥įर श", + "à¥ī न", + "Ġgi ả", + "بÙĪ اسطة", + "å® ģ", + "Ġs oud", + "Ġso ud", + "Ġsou d", + "Ġк ÑĤо", + "e sel", + "es el", + "ese l", + "Ġп ам", + "Ġпа м", + "Ġ ÂłĠ", + "ĠÂł Ġ", + "ĠÄį lov", + "æ· ·", + "ห à¸į", + "ĠOs man", + "æ ¦Ĥ", + "æ¦ Ĥ", + "Ġ åĭ", + "Ġå ĭ", + "ï¼Į åħ¶", + "Ġ à¸Ħร", + "Ġà¸Ħ ร", + "Ġmá» ģm", + "Ġ ÑģоÑĢ", + "ĠÑģ оÑĢ", + "ĠÑģо ÑĢ", + "çĨ ±", + "Ġthu ê", + "ر ج", + "à¹Ĥล à¸ģ", + "Ġ íķĺê³ł", + "Ġíķĺ ê³ł", + "ÙĬ دة", + "ÙĬد Ø©", + "ĠaÅŁ aģı", + "Ġk á»ĥ", + "Ġká» ĥ", + "à¸ķ ำ", + "λ ει", + "λε ι", + "çļĦ è¯Ŀ", + "æ± ł", + "ĠÑģ ÑĤен", + "ĠÑģÑĤ ен", + "Ġin cel", + "Ġinc el", + "Ġince l", + "åº Ń", + "ÑĤ оÑĩ", + "ÑĤо Ñĩ", + "Ġprob lém", + "Ġprobl ém", + "ÏĦ Ïĥ", + "à¹ī à¸Ńà¸Ļ", + "à¹īà¸Ń à¸Ļ", + "ë³´ ëĭ¤", + "Ġà¤Ĩ à¤Ĺ", + "ν αÏĤ", + "να ÏĤ", + "ãģĦ ãĤĭ", + "Ġd ục", + "Ġdụ c", + "Ġtoho to", + "Ġtoh oto", + "ëIJĺ ìĹĪëĭ¤", + "ëIJĺìĹĪ ëĭ¤", + "T J", + "Ġви знаÑĩ", + "ĠB unun", + "ĠBu nun", + "ĠBun un", + "ĠBunu n", + "à¤Ĥ बर", + "à¤Ĥब र", + "ĠÙĩÙħÚĨ ÙĨÛĮÙĨ", + "Ġб Ñİдж", + "Ñĥ ÑĢг", + "ÑĥÑĢ г", + "äº ®", + "Ġμε γ", + "Ġtop lum", + "Ġtopl um", + "ãģ£ ãģ", + "о ÑĤо", + "оÑĤ о", + ": |", + "éĿŀ 常", + "ิ à¸Ĺà¸ĺ", + "ิà¸Ĺ à¸ĺ", + "éģ ķ", + "âĢĮÙ¾ دÛĮ", + "Ġз ÑĢоб", + "à¹Į à¸Ķ", + "Ġдолж ен", + "Ġдол жен", + "ĠmÄĽ sta", + "ĠmÄĽst a", + "ÛĮ Ø´Ùĩ", + "ÛĮØ´ Ùĩ", + "v atel", + "va tel", + "vat el", + "Ġprov oz", + "Ġ inan", + "Ġin an", + "Ġi nan", + "à¤Ĥ प", + "Ġpar ç", + "ÑĢ аÑģÑĤ", + "ÑĢа ÑģÑĤ", + "ÑĢаÑģ ÑĤ", + "üm ü", + "Ġgi á»ijng", + "Ġgiá» ijng", + "æ¬ ¢", + "Ø« ÙĬر", + "ĠB akan", + "ĠBa kan", + "ĠBak an", + "Ġ âĪ¨", + "ĠâĪ ¨", + "Ġ باÙĨ", + "Ġب اÙĨ", + "Ġبا ÙĨ", + "Û± Û¸", + "Û±Û ¸", + "ãĤĤ ãģĨ", + "land ı", + "lan dı", + "Ġyen iden", + "Ġyeni den", + "ÑĨ енÑĤ", + "ÑĨе нÑĤ", + "ÑĨен ÑĤ", + "Ġде ÑıÑĤелÑĮ", + "Ð ©", + "Ġ rov", + "Ġr ov", + "Ġro v", + "å®Į åħ¨", + "ĠK ỳ", + "s lu", + "sl u", + "Ġl ấy", + "é¤ IJ", + "ĠÑĩ олов", + "ä¼ Ŀ", + "ĠbaÅŁ v", + "å° Ī", + "ê³ ¡", + "ãĢģ ãģĿãĤĮ", + "Ġ PÅĻÃŃ", + "ĠP ÅĻÃŃ", + "ĠPÅĻ ÃŃ", + "д ем", + "де м", + "ĠпÑĢо ек", + "ร à¸ĸ", + "建 设", + "Ġмож лив", + "æ® º", + "ãģ¡ãĤĥ ãĤĵ", + "æķ ij", + "ĠÄį ty", + "ĠÄįt y", + "é¦ Ĩ", + "о ÑĢÑĥ", + "оÑĢ Ñĥ", + "Ġ æĦ", + "Ġæ Ħ", + "Ġk ÃŃch", + "λ οÏħ", + "λο Ïħ", + "ãģĦ ãģ¤", + "Ġc Äĥn", + "ĠcÄĥ n", + "Ạµ", + "Ġel de", + "éº »", + "ÄŁ e", + "Ġdo bÄĽ", + "Ġdob ÄĽ", + "ा यर", + "ाय र", + "Ġ ãĥı", + "Ġãĥ ı", + "н ен", + "не н", + "Ġmůže te", + "Ġmůž ete", + "Ġна ÑģÑĤÑĥп", + "ĠнаÑģÑĤ Ñĥп", + "ìĭľ ê°Ħ", + "ĠÑģим пÑĤом", + "Ġ ÏĥÏį", + "ĠÏĥ Ïį", + "Ġ سÙĦ", + "Ġس ÙĦ", + "ε κ", + "ร à¸ĵ", + "á te", + "át e", + "ek ler", + "ekl er", + "ĠвÑĢем ени", + "ĠвÑĢемен и", + "âĢĮ ÙĩاÛĮÛĮ", + "âĢĮÙĩاÛĮ ÛĮ", + "ãģĬ ãĤĬ", + "ж и", + "Ñĭ ваеÑĤÑģÑı", + "Ñĭв аеÑĤÑģÑı", + "Ñĭва еÑĤÑģÑı", + "ÑĭваеÑĤ ÑģÑı", + "Ùħ اÙĨÛĮ", + "ÙħاÙĨ ÛĮ", + "Ùħا ÙĨÛĮ", + "à¸ķ ล", + "Ġ صد", + "Ġص د", + "Ġ вол", + "Ġв ол", + "Ġво л", + "ìĬ Ī", + "ĠÙĥ Ùħا", + "ĠÙĥÙħ ا", + "Ġnh ằm", + "èģ ¯", + "ov acÃŃ", + "ova cÃŃ", + "Ġë§Į ëĵ¤", + "ÙĪ Ù¾", + "Ġ ë¸Į", + "Ġë¸ Į", + "ب ÙĬØ©", + "بÙĬ Ø©", + "u yla", + "uy la", + "л ено", + "лен о", + "ле но", + "èĮ ¶", + "ÑĢ ей", + "ÑĢе й", + "Ġk li", + "Ġkl i", + "Ġüzer inden", + "Ġüzerinde n", + "н еÑĤ", + "не ÑĤ", + "r aÄį", + "ra Äį", + "ĠпÑĢа ÑĨÑİ", + "Ġed iyor", + "Ġedi yor", + "ãģı ãģł", + "Ġ Äįast", + "ĠÄį ast", + "ĠÄįas t", + "i yi", + "iy i", + "éĬ Ģ", + "Ġd ù", + "Ùİ Ø¨", + "ÙĪ ÙĬØ©", + "ÙĪÙĬ Ø©", + "å ª", + "Ġs ınıf", + "Ġsın ıf", + "Ġس اعت", + "Ġ ราย", + "Ġร าย", + "Ġза Ñıв", + "Ġg ặp", + "à¸Ń ว", + "ĠØ« Ùħ", + "ĠZ á", + "ĠвÑĸд к", + "i zik", + "iz ik", + "izi k", + "Ġm ón", + "Ġmó n", + "Ġпов ÑĭÑĪ", + "Ġ à¸ļาà¸Ĺ", + "Ġà¸ļ าà¸Ĺ", + "ĠÑģ ил", + "ĠÑģи л", + "æĥħ åł±", + "Âł t", + "ĠÐľ оÑģк", + "Ġê²ĥ ìĿ´ëĭ¤", + "Ġê²ĥìĿ´ ëĭ¤", + "Ġ çIJ", + "Ġç IJ", + "ĠÙħدÛĮر ÛĮت", + "ов оÑĹ", + "ово ÑĹ", + "Τ ο", + "çº ª", + "нÑĸ ÑĪе", + "нÑĸÑĪ е", + "Ġ ÐĽÑİ", + "ĠÐĽ Ñİ", + "η Ïĥη", + "ĠÙĨسب ت", + "ĠÙĨس بت", + "m uz", + "mu z", + "ร ว", + "ãĢģ ãģĤ", + "Ġбол ез", + "Ġtr ách", + "ãĥ ¦", + "à¹Ģà¸Ĥ า", + "Ġê·¸ ëĬĶ", + "ب رÛĮ", + "بر ÛĮ", + "æł ª", + "ëĿ¼ ìĿ´", + "Ġ íĮ¨", + "Ġí Į¨", + "ĠíĮ ¨", + "íĬ ¹", + "ľ ´", + "ि ड", + "िठ¡", + "ÑĢо ме", + "ÑĢом е", + "è® ²", + "Ġ ÑĤон", + "ĠÑĤ он", + "ĠÑĤо н", + "Ñģ Ñĸ", + "Ġ ç®", + "Ġç ®", + "åıĸ ãĤĬ", + "ì° °", + "Ġ ÙĪÙĦÛĮ", + "ĠÙĪ ÙĦÛĮ", + "ĠÙĪÙĦ ÛĮ", + "Ġس Ø·ØŃ", + "èı ľ", + "н ами", + "на ми", + "нам и", + "T ürk", + "åİ Ĥ", + "Ġf inan", + "Ġfin an", + "Ġfi nan", + "ãģ« ãģªãĤĭ", + "ãģ«ãģª ãĤĭ", + "Ġ oby", + "Ġo by", + "Ġob y", + "T rong", + "Tr ong", + "Tro ng", + "Ġv yp", + "Ġvy p", + "à¥ģ ड", + "à¥ģठ¡", + "ìŀIJ ê°Ģ", + "Ġ æīĢ", + "Ġæī Ģ", + "ÐĹ Ð°", + "um lu", + "uml u", + "ëĵ Ŀ", + "Ġм енÑĸ", + "Ġмен Ñĸ", + "ол ниÑĤелÑĮ", + "олн иÑĤелÑĮ", + "Ġú Äįin", + "ĠúÄį in", + "Ġb unun", + "Ġbu nun", + "Ġbun un", + "Ġbunu n", + "ĠÐłÐ¾Ñģ Ñģии", + "в ÑģÑı", + "Ġн Ñĸж", + "ĠнÑĸ ж", + "ิà¸Ķ à¸ķ", + "غ Ø©", + "Ä ļ", + "Ġ سÙħ", + "Ġس Ùħ", + "Ġ Ðĺз", + "ĠÐĺ з", + "à¥ĩ प", + "à¥ĩठª", + "大 çļĦ", + "ì¹ ľ", + "Ġ иÑģÑĤ", + "Ġи ÑģÑĤ", + "ĠиÑģ ÑĤ", + "Ġкон ÑģÑĤÑĢÑĥк", + "Û± Û²", + "Û±Û ²", + "â l", + "Ġ ÑĪиÑĢ", + "ĠÑĪ иÑĢ", + "ĠÑĪи ÑĢ", + "ï¼ ł", + "Ġar tık", + "Ġart ık", + "æŁ ĵ", + "ä¹ ¡", + "ÃŃ te", + "ÃŃt e", + "ĠNh áºŃt", + "ĠÎĶ η", + "Ġöl ç", + "êµ ´", + "о Ñıн", + "оÑı н", + "ëĵ± ë¡Ŀ", + "Ġng ân", + "Ġ бÑĥдÑĮ", + "ĠбÑĥд ÑĮ", + "ÎŁ Ρ", + "ÎŁÎ ¡", + "ì ´", + "Ùħ ÙĪد", + "ÙħÙĪ د", + "ν ον", + "νο ν", + "Îķ ÎĿ", + "çij ŀ", + "ĠÅĻ ek", + "ĠÅĻe k", + "- âĢIJ", + "ĠM erk", + "ĠMe rk", + "ĠMer k", + "Ġоп ÑĢедел", + "ĠопÑĢед ел", + "Ïģ ιν", + "Ïģι ν", + "л аб", + "ла б", + "ëĦ¤ ìļĶ", + "Ġб лиз", + "Ġбл из", + "Ġбли з", + "Ġph á»iji", + "Ġphá»ij i", + "Ġдолж нÑĭ", + "ĠÑį кÑģп", + "ĠÑįк Ñģп", + "à¸ļ à¸Ĺ", + "à¸Ľà¸£à¸° ส", + "ĠÙ¾Úĺ ÙĪÙĩ", + "Ġ íķľëĭ¤", + "Ġíķľ ëĭ¤", + "ÏĦ οÏį", + "ÏĦο Ïį", + "Ùĩ ÙĨ", + "Ġд од", + "Ġдо д", + "Ġk ayı", + "Ġka yı", + "Ġkay ı", + "Ł ģ", + "Ñģ иÑı", + "Ñģи Ñı", + "à¤Ĥ तर", + "à¤Ĥत र", + "Ġpod nik", + "e vi", + "ev i", + "ÛĮ ÛĮر", + "Т ак", + "Та к", + "к оп", + "ко п", + "н аÑħ", + "на Ñħ", + "ا سÙĩ", + "اس Ùĩ", + "à¸ĵ à¸ij", + "Ġk há", + "Ġkh á", + "Ġy arat", + "Ġya rat", + "Ġyar at", + "ĠاÛĮÙĨ Ú©Ùĩ", + "Ø· بÙĬ", + "طب ÙĬ", + "Ġs ır", + "Ġsı r", + "ĠØ¢ÙħرÛĮÚ© ا", + "Ġ बल", + "Ġब ल", + "k aç", + "ka ç", + "Ġ åı¯", + "Ġåı ¯", + "Ġ åħ¶", + "Ġåħ ¶", + ". ***", + ".* **", + "л ÑĸннÑı", + "лÑĸн нÑı", + "ä¹ ±", + "o q", + "æ ¦", + "ãĤ ¼", + "Ġf ır", + "Ġk ê", + "Ġìłľ ê³µ", + "Ġ Ïĥη", + "ĠÏĥ η", + "а нÑĭ", + "ан Ñĭ", + "н ова", + "но ва", + "нов а", + "à¸Ĭ าย", + "ĠØ· ÙĪÙĦ", + "à¥Ī य", + "Ġ ì¹ľ", + "Ġì¹ ľ", + "ìĤ ´", + "Ġп Ñĸв", + "Ġlu áºŃn", + "Ġà¤ī म", + "åº ĥ", + "à¹ĩ à¸Ńà¸ķ", + "Ġس اÛĮت", + "л Ñıн", + "лÑı н", + "ĠíķĦ ìļĶ", + "Ġgör ül", + "ĠÑĤеÑĢ иÑĤоÑĢ", + "ĠÙĨ ØŃ", + "е ма", + "ем а", + "Ġmn oh", + "Ġ ãģ¯", + "غ ÙĬر", + "ĠÑģдел аÑĤÑĮ", + "ç ģµ", + "çģ µ", + "Ġ ÐłÐ°Ð·", + "ĠÐł аз", + "ĠÐłÐ° з", + "Ġг еÑĢ", + "Ġге ÑĢ", + "γ μα", + "íķĺ ë©´", + "ĠdeÄŁ iÅŁtir", + "ĠdeÄŁiÅŁ tir", + "ãĥ³ ãĥĨ", + "ãĥ³ãĥ Ĩ", + "å¸Ĥ åľº", + "个 人", + "ìĥ Ī", + "ì¹ ¨", + "èī º", + "ÙĤ ت", + "ĠÚ¯ رÙģتÙĩ", + "ĠگرÙģ تÙĩ", + "Ġگر ÙģتÙĩ", + "ĠگرÙģت Ùĩ", + "Ġ çİĭ", + "Ġçİ ĭ", + "ĠاÙĦ Ø°Ùĩ", + "ĠاÙĦØ° Ùĩ", + "λ Ïħ", + "à¤ľ र", + "Ġв ним", + "ë¦ Ń", + "ิ à¸Ĺ", + "Ġ شاÙĩ", + "ĠØ´ اÙĩ", + "æĬķ èµĦ", + "æĿIJ æĸĻ", + "ĠÙĨ Ùģ", + "èª ¬", + "æĬ Ĺ", + "Ġ аб", + "Ġа б", + "iy eti", + "iye ti", + "iyet i", + "ç¾ ħ", + "ÑĢ Ñĸз", + "ÑĢÑĸ з", + "Ġ สม", + "Ġส ม", + "i cÃŃ", + "ic ÃŃ", + "к ÑĥваннÑı", + "кÑĥ ваннÑı", + "Ġ ìķ¼", + "Ġìķ ¼", + "Ġ è½", + "Ġè ½", + "âĢ «", + "Ġ διά", + "Ġδ ιά", + "Ġδι ά", + "Ġд еп", + "Ġде п", + "ãĥ¼ ãĤ¿", + "ãĥ¼ãĤ ¿", + "Ġob jev", + "Ġobj ev", + "mé na", + "Ġbe lg", + "Ġbel g", + "Ġ æ¥", + "Ġæ ¥", + "Ġn á»ģn", + "Ġг ол", + "Ġpost av", + "Ġpo stav", + "Ġت Ú©", + "Ð «", + "ĠпÑĸд ÑĤ", + "ĠоÑĤ ноÑĪ", + "Ġп ÑĢив", + "ĠпÑĢ ив", + "ĠпÑĢи в", + "Ġ åŁº", + "ĠåŁ º", + "Ġн али", + "Ġна ли", + "Ġнал и", + "ů ž", + "Ġ yat", + "Ġy at", + "Ġya t", + "ÅŁ a", + "ÏĦ ήÏĤ", + "ÏĦή ÏĤ", + "ÑĨ ем", + "ÑĨе м", + "次 æķ°", + "Ġb Ãł", + "ÙĪ Ùĥ", + "Ġ íĶĦë¡ľ", + "ĠíĶĦ ë¡ľ", + "ĠPh áp", + "Ġ êµ°", + "Ġêµ °", + "è³ ŀ", + "Ġoch ran", + "Ġgere kir", + "Ġgerek ir", + "Ġ íļ", + "Ġí ļ", + "à¸ļ ล", + "á me", + "ám e", + "Ġ بÛĮر", + "Ġب ÛĮر", + "ĠبÛĮ ر", + "à¸Ĥ าย", + "ов аний", + "ова ний", + "овани й", + "ован ий", + "Ġmož né", + "âĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģ", + "á lu", + "ál u", + "н ÑĤ", + "¦ æĥħ", + "à¹ģ รม", + "ĠÑĦ Ñĸн", + "ĠÄ° ç", + "à¹Ī à¸Ńย", + "à¹Īà¸Ń ย", + "ê² ¨", + "Ġh edef", + "Ġhe def", + "Ġhed ef", + "ĠاÙĦ ÙħØ´", + "ĠاÙĦÙħ Ø´", + "à¹ī าม", + "à¹īา ม", + "å¯ Ħ", + "Ġ ëĭµ", + "Ġëĭ µ", + "Ġ ô", + "Ġà ´", + "ла ÑģÑı", + "лаÑģ Ñı", + "Ä° T", + "à¸Ķ ำ", + "Ġher hangi", + "Ġger eken", + "Ġgere ken", + "Ġgerek en", + "е ÑĢеж", + "еÑĢ еж", + "еÑĢе ж", + "ÙĪ Ø©", + "ĠpÅĻ est", + "ĠpÅĻes t", + "ĠpÅĻe st", + "ç§ij åѦ", + "оÑģÑĤ аÑĤ", + "ün den", + "ünd en", + "ünde n", + "åĮħ æĭ¬", + "Ġد Ùĩد", + "ĠدÙĩ د", + "ÑĪ иÑģÑĮ", + "ÑĪи ÑģÑĮ", + "н еÑĢ", + "не ÑĢ", + "Ñĸ дом", + "Ñĸд ом", + "Ġb iç", + "Ġbi ç", + "ìĭ Ń", + "Ġhod not", + "Ġze mÄĽ", + "Ġzem ÄĽ", + "ĠاÛĮ جاد", + "Ġy ine", + "Ġyi ne", + "ि ण", + "िठ£", + "ĠاÙĦ بÙĦ", + "ĠاÙĦب ÙĦ", + "ĠN ÄĽ", + "Ġpol ož", + "Ġpo lož", + "Ġpolo ž", + "éĺħ 读", + "å¸ ģ", + "å¼ Ł", + "ξ ε", + "Ġ Má»Ļt", + "ĠM á»Ļt", + "ç £", + "Û±Û³ Û¹", + "ĠØ¢ ز", + "ãģ ŀ", + "Ġм еÑħ", + "ย ม", + "Ġ æ¨", + "Ġæ ¨", + "Ġo tur", + "Ġot ur", + "Ġd ầu", + "Ġ ëĭ¤ìļ´", + "Ġëĭ¤ ìļ´", + "çĮ «", + "Ġ Có", + "ĠC ó", + "Ġli dÃŃ", + "Ġlid ÃŃ", + "Ġark adaÅŁ", + "Ġα λλά", + "é¡ »", + "ĠÙĩ ÙħÛĮÙĨ", + "ĠÙĩÙħ ÛĮÙĨ", + "è» ¢", + "Ġ âĹĭ", + "ĠâĹ ĭ", + "ëıĦ ë¡Ŀ", + " ĥ", + "âĢĮØ´ دÙĩ", + "âĢĮشد Ùĩ", + "ĠØŃ ÙĬØ«", + "ĠØŃÙĬ Ø«", + "Ġnh óm", + "Ïĥ Ïĩ", + "ĠÑĤÑĢан Ñģп", + "ĠÑĤÑĢанÑģ п", + "Ġtan ım", + "Ġtanı m", + "ç´ į", + "Ġba his", + "Ġbah is", + "ä¸ ¾", + "Ġин ÑĦоÑĢма", + "ĠинÑĦоÑĢм а", + "ĠÑģ лож", + "ĠÑģл ож", + "ĠÑģло ж", + "Ġk raj", + "Ġkr aj", + "Ġkra j", + "Ġ ØŃÙĦ", + "ĠØŃ ÙĦ", + "Ġ ãĥĸ", + "Ġãĥ ĸ", + "ĠÙĨ ÙĤÙĦ", + "ĠÙĨÙĤ ÙĦ", + "Ġ ÐłÐ¾Ð·", + "ĠÐł оз", + "ĠÎij Ïħ", + "lar dı", + "ĠÙ¾ اس", + "Ġپا س", + "Ġ ìĭĿ", + "Ġìĭ Ŀ", + "ĠìłĦìļ© ë©´ìłģ", + "ĠاÙĦ سÙĬ", + "ĠاÙĦس ÙĬ", + "با شد", + "باش د", + "ศ าสà¸ķร", + "Ġk öy", + "Ġkö y", + "Ġ rok", + "Ġr ok", + "Ġro k", + "Ġ 죽", + "Ġì £½", + "Ġì£ ½", + "ĠÑģ ог", + "ĠÑģо г", + "Ġch ú", + "éĺ ª", + "ĠÄįást i", + "ĠÄįá sti", + "Ġз веÑĢ", + "Ġзв еÑĢ", + "Ġ низ", + "Ġн из", + "Ġни з", + "ĠÃ¶ÄŁ ret", + "Ġ ãĥİ", + "Ġãĥ İ", + "п е", + "çĴ °", + "Ġ èª", + "Ġè ª", + "ÙĪ ÙĦÙĩ", + "ÙĪÙĦ Ùĩ", + "Ä° M", + "/ REC", + "/R EC", + "å¡ ŀ", + "ĠÐĴ и", + "/l oose", + "/lo ose", + "Ġп оÑħ", + "Ġпо Ñħ", + "Ġgen iÅŁ", + "Ġth iá»ĩn", + "Ġthi á»ĩn", + "ti ÄŁi", + "Ñĩ ие", + "Ñĩи е", + "о нд", + "он д", + "Ġп ÑĢиÑģ", + "ĠпÑĢ иÑģ", + "ĠпÑĢи Ñģ", + "áz ky", + "ĠDev let", + "ç¦ ģ", + "Ġ аг", + "Ġа г", + "i lere", + "il ere", + "ile re", + "iler e", + "ин кÑĥ", + "Ġvar dı", + "ãĢĢ ãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢ ĠãĢĢ", + "Ġë ĨĴ", + "ĠëĨ Ĵ", + "à¤Ĥ पन", + "à¤Ĥप न", + "Ġözel lik", + "éļ ľ", + "ìĸ´ ìĦľ", + "ر ÙĬÙĥ", + "رÙĬ Ùĥ", + "ÙĪ بÛĮ", + "ÙĪب ÛĮ", + "ãĥ³ ãĥĢ", + "ãĥ³ãĥ Ģ", + "í Į¨", + "íĮ ¨", + "Ġसम à¤Ŀ", + "ï¾Ĩï¾Ĩ ï¾Ĩï¾Ĩ", + "Ġ ÙģÙĨ", + "ĠÙģ ÙĨ", + "ॠĿ", + "Ġuv eden", + "ÑĪ ими", + "ÑĪи ми", + "ÑĪим и", + "Ġ à¹Ģล", + "Ġà¹Ģ ล", + "Ġà¹Ģภ¥", + "Ġ 문ìĿĺ", + "Ġ문 ìĿĺ", + "ĠØŃ Ø±Ùģ", + "ĠØŃر Ùģ", + "Ġ عب", + "Ġع ب", + "ãĥ¬ ãĥĵ", + "Ġ æŃ£", + "ĠæŃ £", + "ĠëĺIJ ëĬĶ", + "ĠÚ©ÙĨ ÙĨدÙĩ", + "ĠÚ©ÙĨÙĨد Ùĩ", + "Ġα ÏħÏĦÏĮ", + "ĠαÏħ ÏĦÏĮ", + "Ġ 길", + "Ġê¸ ¸", + "Ġif ade", + "Ġi fade", + "Ġifad e", + "Ġyap mak", + "ãĥķ ãĤ©", + "ãĥķãĤ ©", + "Ġm ẹ", + "Ġst rán", + "Ġstr án", + "Ġs vou", + "Ġsv ou", + "Ġsvo u", + "Ġv ždy", + "Ġtek rar", + "ิ à¸į", + "Ġ ìĵ°", + "Ġì ĵ°", + "Ġìĵ °", + "o ÄŁu", + "oÄŁ u", + "Ġ Ú©ÛĮÙĦ", + "ĠÚ© ÛĮÙĦ", + "ĠÚ©ÛĮ ÙĦ", + "и вÑģÑı", + "ив ÑģÑı", + "Ġë§IJ íĸĪëĭ¤", + "ä¸ Ŀ", + "à¤ı स", + "ĠÑģÑĤ ÑĢаÑħ", + "ĠÑģÑĤÑĢ аÑħ", + "ĠÑģÑĤÑĢа Ñħ", + "Ġsou Äįas", + "Ġê·¸ 룰", + "Ġ mÃ¼ÅŁ", + "Ġm Ã¼ÅŁ", + "Ġmü ÅŁ", + "λ οÏį", + "λο Ïį", + "γ Ïī", + "Ġt Æ°á»Łng", + "Ġ å·¥", + "Ġå· ¥", + "Ġ اسÙħ", + "Ġا سÙħ", + "Ġاس Ùħ", + "ÑĢ Ñĸм", + "ÑĢÑĸ м", + "à¹Ģ à¸Ľà¸¥", + "à¹Ģà¸Ľ ล", + "Ġ³³ Ġ³³", + "Ùĩ اÛĮÛĮ", + "ÙĩاÛĮ ÛĮ", + "å¯ º", + "Ġس رÛĮ", + "Ġسر ÛĮ", + "Ġк ваÑĢ", + "Ġкв аÑĢ", + "ĠØ´Ùħ ارÙĩ", + "ĠØ´Ùħا رÙĩ", + "Ġ صØŃ", + "Ġص ØŃ", + "о ÑģÑĤав", + "оÑģÑĤ ав", + "ॠ¨", + "Ġ à¸Ħวาม", + "Ġà¸Ħ วาม", + "í ĥģ", + "íĥ ģ", + "éĢ Ĥ", + "ب ØŃ", + "ĠdeÄŁiÅŁ ik", + "éĮ ²", + "е ди", + "ед и", + "Ġ okol", + "Ġo kol", + "Ġok ol", + "ĠÑģ оп", + "ĠÑģо п", + "Ġol mayan", + "Ġolm ayan", + "Ġolma yan", + "çŃ ij", + "Û± Û´", + "Û±Û ´", + "Ġ inclu", + "Ġinc lu", + "Ġincl u", + "Ġ ê²ĮìŀĦ", + "Ġê²Į ìŀĦ", + "ÛĮ ستÙħ", + "ÛĮست Ùħ", + "ÛĮس تÙħ", + "Ġ ç©", + "Ġç ©", + "ĠاÙĦÙĪÙĦ اÙĬات", + "il mektedir", + "ilm ektedir", + "à Į", + "Ùİ Ø¹", + "ĠaÄŁ ır", + "è¡ Ľ", + "Ġe ski", + "Ġes ki", + "Ġesk i", + "ê° Ŀ", + "본 ëĭ¤", + "人 åijĺ", + "Úĺ ÛĮ", + "Ġ ç¨", + "Ġç ¨", + "Ġм еÑģÑĤо", + "ĠмеÑģÑĤ о", + "v ů", + "à¥įर ह", + "ĠØ· رØŃ", + "Ġطر ØŃ", + "Ġا بÙĨ", + "Ġاب ÙĨ", + "Ġh iss", + "Ġhis s", + "Ġhi ss", + "о ÑĢÑıд", + "оÑĢ Ñıд", + "Ġد Ùģ", + "ÑĢ иÑģÑĤ", + "ÑĢи ÑģÑĤ", + "ÑĢиÑģ ÑĤ", + "à¸Ĭ ม", + "д еÑĤ", + "де ÑĤ", + "à¹Ģ หม", + "à¹Ģห ม", + "ë§Ī ìĤ¬ì§Ģ", + ": .:.:", + ":. :.:", + ":.: .:", + "éħ ¸", + "Ġα ÏģÏĩ", + "ĠαÏģ Ïĩ", + "Ġn ữ", + "ĠпоÑģ ад", + "l um", + "lu m", + "ì º", + "ãģ§ãģį ãĤĭ", + "ìĸ µ", + "ĠاÙĦ Ùħد", + "ĠاÙĦÙħ د", + "н Ñĸм", + "нÑĸ м", + "ر اÙĤ", + "را ÙĤ", + "Ġ ãĥĪ", + "Ġãĥ Ī", + "Ġod povÄĽ", + "Ġodp ovÄĽ", + "Ġbir bir", + "Ġh ãy", + "Ġhã y", + "о вий", + "ов ий", + "ови й", + "æ® ĭ", + "éĥ½ æĺ¯", + "è¿ ª", + "Ġa raç", + "Ġar aç", + "Ġara ç", + "ен ÑĤÑĸв", + "енÑĤ Ñĸв", + "æĬ ±", + "d ál", + "ĠÄIJ ông", + "Ġhe sap", + "Ġhes ap", + "Ġا ÙĨساÙĨ", + "ĠاÙĨ ساÙĨ", + "ĠÙĬ ÙĪÙħ", + "ĠÙĬÙĪ Ùħ", + "ĠÙĨ ÙĪر", + "ĠÙĨÙĪ ر", + "åī ĩ", + "çĹ Ľ", + "Ġ ÙĨÙĬ", + "ĠÙĨ ÙĬ", + "алÑĮ на", + "تب اط", + "ल ब", + "Ġkom un", + "Ġko mun", + "Ġs nad", + "Ġsn ad", + "Ġsna d", + "åĽ £", + "ر ÙĬد", + "رÙĬ د", + "elop ment", + "Ġ иÑİ", + "Ġи Ñİ", + "à¥Ģ .", + "Ġkıs a", + "Ġkı sa", + "ĠdeÄŁil dir", + "ĠdeÄŁildi r", + "à¹ī าร", + "à¹īา ร", + "Ġsv ého", + "Ġsvé ho", + "Ġobl asti", + "Ġoblast i", + "ÑĪ ли", + "à¹Ģà¸Ĺ à¸ŀ", + "ÑĢ еÑĤÑĮ", + "ÑĢе ÑĤÑĮ", + "ÑĢеÑĤ ÑĮ", + "о во", + "ов о", + "Ġ íĤ¤", + "Ġí Ĥ¤", + "ĠíĤ ¤", + "át ky", + "ĠاÙĦ Ùģر", + "ĠاÙĦÙģ ر", + "èĺ Ń", + "ÏĦ ον", + "ÏĦο ν", + "ĠÑģÑĤ оиÑĤ", + "ĠÑģÑĤо иÑĤ", + "Ùħ ØŃ", + "Ġ à¹Ħ", + "Ġà ¹Ħ", + "ĠÑĤе бе", + "ĠÑĤеб е", + "íģ ´", + "Ġm ÄĽla", + "ĠmÄĽ la", + "ĠmÄĽl a", + "æݧ åĪ¶", + "ĠCh ủ", + "ìĬ ¨", + "ÐIJ Т", + "ا جع", + "اج ع", + "ìĻ ķ", + "ç© ¿", + "ол ее", + "ห ลาย", + "หล าย", + "Ġd vou", + "Ġdv ou", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ุ à¸Ĥ", + "Ġb oz", + "Ġbo z", + "ิ à¸Ļà¸Ħ", + "ิà¸Ļ à¸Ħ", + "å¤ Ł", + "Ġfa aliyet", + "ĠÄį ÃŃs", + "ãģ» ãģ©", + "Ġ :/", + "Ġ: /", + "к ÑĸÑģÑĤÑĮ", + "кÑĸ ÑģÑĤÑĮ", + "Ġ ì¤Ģ", + "Ġì¤ Ģ", + "Ïģ αÏĤ", + "Ïģα ÏĤ", + "Ġод но", + "æ ¢ħ", + "æ¢ ħ", + "Ñĥб ли", + "н оз", + "но з", + "à¹Į ม", + "Ġvý rob", + "Ġvýro b", + "Ġ κÏħ", + "Ġκ Ïħ", + "ÅĻ ev", + "ÅĻe v", + "Âł B", + "ů že", + "ůž e", + "ä¼ļ 社", + "ι β", + "ÑĢ ованиÑı", + "ÑĢов аниÑı", + "ÑĢо ваниÑı", + "ÑĢован иÑı", + "ÑĢова ниÑı", + "Ġc ev", + "Ġce v", + "ìĽ Ģ", + "ál nÃŃch", + "áln ÃŃch", + "álnÃŃ ch", + "Ġ ÑĢав", + "ĠÑĢ ав", + "ĠÑĢаР²", + "ĠÑĢа в", + "ç´ §", + "åĢ Ł", + "Ġ ÑŁ", + "ĠÑ Ł", + "ÙĪ ÙĨÙĬ", + "ÙĪÙĨ ÙĬ", + "о зÑı", + "оз Ñı", + "Ġз ов", + "Ġk olem", + "Ġko lem", + "Ġkol em", + "Ġkole m", + "민 êµŃ", + "ç¿ Ĵ", + "Ġzam ÄĽst", + "Ġ ìłij", + "Ġìł ij", + "Ġ زÙĨ", + "Ġز ÙĨ", + "ĠØ£ Ùģ", + "Ġ 먹", + "Ġë¨ ¹", + "Ġtom to", + "Ġ 첨ë¶Ģ", + "Ġì² ¨ë¶Ģ", + "s age", + "sa ge", + "ä¸į è¿ĩ", + "е год", + "ег од", + "его д", + "ÑĢ ож", + "ÑĢо ж", + "ĠпÑĢоÑĨ ед", + "à¹Į à¸Ļ", + "san ız", + "âĢŀ Ø·", + "æ´» åĬ¨", + "о Ñĩки", + "оÑĩ ки", + "ë³´ 기", + "åŁº æľ¬", + "- Ñħ", + "ло ÑģÑı", + "ĠÙĩÛĮ ÚĨ", + "ìĹ Ķ", + "Ñĩ ного", + "Ñĩно го", + "Ġ à¤Ĺर", + "Ġà¤Ĺ र", + "Ġà¤ħ à¤Ĺ", + "ãħĭãħĭ ãħĭãħĭ", + "Ġ ãĤ¸", + "ĠãĤ ¸", + "ا سة", + "اس Ø©", + "åĬ ĩ", + "à¹ī à¸ĩ", + "Ġ 커", + "Ġì» ¤", + "n ými", + "ný mi", + "ným i", + "ãĥ¬ ãĤ¹", + "åĭ Ĵ", + "Ġобла ÑģÑĤÑĸ", + "ĠоблаÑģ ÑĤÑĸ", + "ĠоблаÑģÑĤ Ñĸ", + "ĠдÑĸÑıлÑĮ ноÑģÑĤÑĸ", + "ãĥ¬ ãĤ¤", + "Ïĩ αν", + "Ïĩα ν", + "à¹Ī าส", + "à¹Īา ส", + "ĠФ ÑĢан", + "Ùĩ ÙĦ", + "l ardır", + "lar dır", + "lardı r", + "ØŃ Ø§Øª", + "ů st", + "Ġв одÑĭ", + "Ġво дÑĭ", + "Ġвод Ñĭ", + "ĠدÙĪ ÙĦت", + "ĠدÙĪÙĦ ت", + "ĠÑģпе ÑĨÑĸ", + "Ġth ất", + "à¸Ń าหาร", + "éł ĺ", + "Ġter cih", + "ĠÏĢÏģο Ïĥ", + "Ġ ÅĻÃŃzenÃŃ", + "ĠÅĻÃŃ zenÃŃ", + "è§ī å¾Ĺ", + "Ġd nes", + "Ġdn es", + "Ġdne s", + "е Ñĩно", + "еÑĩ но", + "ãĥ ĺ", + "Ġدار اÛĮ", + "ĠÅŁ art", + "ĠÅŁar t", + "ë² ¤", + "Ġ ë¶ģ", + "Ġë ¶ģ", + "Ġë¶ ģ", + "е Ñı", + "н ÑıÑĤÑĮ", + "нÑı ÑĤÑĮ", + "нÑıÑĤ ÑĮ", + "Ġk vÄĽt", + "Ġkv ÄĽt", + "Ġتغ ÛĮÛĮر", + "é¾ į", + "Ġر ÙĨÚ¯", + "ï¼Į åı¯", + "Ġp iyas", + "Ġpi yas", + "Ġuyg ulan", + "Ġuygu lan", + "Ùİ Ø©", + "ب ÙĬر", + "بÙĬ ر", + "и ваÑĤÑĮ", + "ив аÑĤÑĮ", + "ива ÑĤÑĮ", + "Ġ íĹĪ", + "ĠíĹ Ī", + "ä¸ ¶", + "è¿Ļ äºĽ", + "Ġ گر", + "ĠÚ¯ ر", + "ç½ ª", + "ä¸Ģ æł·", + "Ġ ãĥª", + "Ġãĥ ª", + "Ġв ой", + "Ġво й", + "Ġs osyal", + "Ġsos yal", + "ุ à¸Ĺà¸ĺ", + "ุà¸Ĺ à¸ĺ", + "หม à¸Ķ", + "ç» Ŀ", + "ĠاÙĦ جÙħ", + "ĠاÙĦج Ùħ", + "ĠØ« بت", + "Ġج ÙĨÚ¯", + "ĠجÙĨ Ú¯", + "л ении", + "лен ии", + "ле нии", + "в аÑı", + "ва Ñı", + "Ġв оÑĤ", + "Ġво ÑĤ", + "ä¼ ¤", + "Ġ หล", + "Ġห ล", + "ĠÙħÙĤ اÙĦÙĩ", + "мÑĸ нÑĸ", + "мÑĸн Ñĸ", + "ìĺ ¬", + "Ñĩ ий", + "Ñĩи й", + "ĠÙħ Ú©", + "à¹Ĥ à¸Ľà¸£", + "à¹Ĥà¸Ľ ร", + "k rv", + "kr v", + "Ġ ÃŃch", + "ĠÃŃ ch", + "Ïī Ïĥη", + "ек ÑĤоÑĢ", + "екÑĤ оÑĢ", + "Я к", + "Ġp ÃŃs", + "ĠÃĸ zel", + "ĠÃĸz el", + "Ġt Æ°á»Ľng", + "Ġ ÐĶо", + "ĠÐĶ о", + "δ ιο", + "δι ο", + "ู à¸Ķ", + "Ġt ük", + "رÛĮ ÙĤ", + ". ÐĴ", + "Ġ åIJĪ", + "ĠåIJ Ī", + "ä¿ Ĥ", + "Ġob dob", + "Ġist edi", + "ÑĪ ла", + "æľī ä¸Ģ", + "Ġвк лÑİÑĩа", + "ĠвклÑİÑĩ а", + "ĠتØŃ ÙĤÛĮÙĤ", + "Ġ ÙĪÙĥ", + "ĠÙĪ Ùĥ", + "Ġ èĪ", + "Ġè Ī", + "Æ Ĵ", + "μ εÏģ", + "με Ïģ", + "Ġ åģ", + "Ġå ģ", + "Ġ ìĹĨëĬĶ", + "ĠìĹĨ ëĬĶ", + "Âł d", + "ĠB ắc", + "à¸ģล าà¸ĩ", + "ĠÑĩ Ñĥв", + "Ġc ấu", + "ĠH á»ĵ", + "ĠHá» ĵ", + "ĠÙģ اÛĮÙĦ", + "ÏĦη γοÏģ", + "ç± į", + "Ġ بت", + "Ġب ت", + "ĠобÑĢаз ом", + "æ± ī", + "èĦ ij", + "Ġgi ản", + "Ġgiả n", + "ε Ïģγ", + "εÏģ γ", + "ĠÐľ Ñĸ", + "èĻ½ çĦ¶", + "Ġ Khi", + "ĠK hi", + "ĠKh i", + "Ñĩ ини", + "Ñĩи ни", + "Ñĩин и", + "Ġà¤ħ à¤Ĺर", + "Ġà¤ħà¤Ĺ र", + "íķĺ ë©°", + "ë² Ķ", + "ãģ ģ", + "в иÑħ", + "ви Ñħ", + "ĠвÑģ егда", + "Ġ ç¶", + "Ġç ¶", + "ÑģÑĤв енной", + "ÑģÑĤвен ной", + "ÑģÑĤвенно й", + "Ġyük sel", + "æ¸ ¬", + "Ġsı ras", + "Ġsır as", + "Ġsıra s", + "ĠÏĢ ÏģÏİ", + "èĢ ³", + "ا ÛĮر", + "اÛĮ ر", + "د ÙĪد", + "دÙĪ د", + "ĠAl man", + "ĠAlma n", + "Ġver di", + "Ġverd i", + "ĠاÙĦ Ùħج", + "ĠاÙĦÙħ ج", + "ĠاÙĦ تع", + "ĠاÙĦت ع", + "ص Ø©", + "Ġsı ra", + "Ġsır a", + "Äį in", + "Äįi n", + "Ġп еÑĢÑĪ", + "ĠпеÑĢ ÑĪ", + "æĬ ĺ", + "ç© į", + "ĠÑĤ об", + "ĠÑĤо б", + "Ġ ï¾ī", + "Ġï¾ ī", + "ภ¬", + "æĿ Ģ", + "iy di", + "ี à¸ŀ", + "çĵ ¦", + "ĠавÑĤом об", + "ä¸Ń æĸĩ", + "à¥Ĥ द", + "ĠbÄĽ hem", + "Ġ PÅĻed", + "ĠP ÅĻed", + "ĠPÅĻ ed", + "ãģĵ ãģĨ", + "ั à¸Ī", + "Ġ ï½Į", + "Ġï½ Į", + "Ġ ÙĩاÙĬ", + "ĠÙĩ اÙĬ", + "ĠÙĩا ÙĬ", + "Ġs ạch", + "æĸ¹ éĿ¢", + "çķ °", + "ÑĥÑĢ н", + "Ġvý sled", + "Ġth ần", + "ï¼Į æīĢ以", + "Ñĥ ка", + "Ñĥк а", + "íķĺ ëĭ¤", + "Ġ बर", + "Ġब र", + "Ġж Ñĸн", + "Äį nÃŃho", + "ÄįnÃŃ ho", + "Ġ ãģĮ", + "ab ı", + "v ánÃŃ", + "vá nÃŃ", + "æ´ Ĺ", + "Ġи ÑģÑĤоÑĢ", + "ĠиÑģ ÑĤоÑĢ", + "ĠиÑģÑĤ оÑĢ", + "ìĿ´ íĦ°", + "Ġе лек", + "а лаÑģÑı", + "ала ÑģÑı", + "Ġ znám", + "Ġz nám", + "Ġzn ám", + "ĠØ· رÙģ", + "Ġطر Ùģ", + "Ġs ektör", + "ê¹ Ģ", + "ÙĪ ÙĤع", + "ÙĪÙĤ ع", + "ĠÙħ Ùĥ", + "ÑĢе жд", + "ÑĢеж д", + "Ġk nih", + "Ġkn ih", + "Ġت عداد", + "Ġتع داد", + "Ġتعد اد", + "åį ł", + "ÑģÑĮ ке", + "ÑģÑĮк е", + "Ġ çĶµ", + "京 éĥ½", + "Ġر اÛĮ", + "Ġرا ÛĮ", + "g ın", + "gı n", + "ĠÙĨ ظاÙħ", + "ĠÙĨظ اÙħ", + "ĠÎł ολ", + "ĠÎłÎ¿ λ", + "ä¸Ģ èĪ¬", + "Ġst ále", + "Ġstál e", + "ĠиÑģ Ñģлед", + "Ġz práv", + "Ġzp ráv", + "Ġ ÑĩиÑģÑĤ", + "ĠÑĩ иÑģÑĤ", + "ĠÑĩиÑģ ÑĤ", + "ĠÑĩи ÑģÑĤ", + "ãĥ¼ ãĥŀ", + "ãĥ¼ãĥ ŀ", + "Ðŀ Ñģ", + "ÑģÑĮ комÑĥ", + "ÑģÑĮк омÑĥ", + "ÑģÑĮко мÑĥ", + "ĠpÅĻi prav", + "ĠpÅĻip rav", + "ëĮĢ íĸī", + "Ġh alk", + "Ġha lk", + "Ġhal k", + "çĪ Ĩ", + "ãĢģ ãģĬ", + "ï¼Ł âĢĿĊĊ", + "ï¼ŁâĢĿ ĊĊ", + "éĢ ı", + "ç« ŀ", + "ни ÑĨÑĮ", + "ниÑĨ ÑĮ", + "çĽ ĺ", + "à¹Ģ à¸Ńà¸ĩ", + "à¹Ģà¸Ń à¸ĩ", + "ì Łģ", + "à¥ĩव ल", + "ä¹ĭ åIJİ", + "ãĥ« ãĥĪ", + "Ġ stru", + "Ġs tru", + "Ġst ru", + "Ġstr u", + "Ġ ＿", + "Ġï¼ ¿", + "Îķ ÎĽ", + "h le", + "hl e", + "ĠÙĨ ÙĪØ´", + "ĠÙĨÙĪ Ø´", + "ìĿ µ", + "ĠÙħ Ùģ", + "æĪĸ èĢħ", + "Ġö ld", + "Ġöl d", + "éĢ Ķ", + "ãĥ³ ãĥĹ", + "ãĥ³ãĥ Ĺ", + "íĺ ¼", + "Ġu ÄŁ", + "ĠÄij á", + "Ġvlast nÃŃ", + "ĠÙħج ÙĦس", + "åį Ķ", + "ÏĦ ικήÏĤ", + "ÏĦικ ήÏĤ", + "ÏĦική ÏĤ", + "Ġpo vin", + "Ġpov in", + "ů l", + "ĠاÙĦ ØŃÙĬ", + "ĠاÙĦØŃ ÙĬ", + "Ġsm lou", + "ãĥĥ ãĥģ", + "Ġ ÙĥÙĨ", + "ĠÙĥ ÙĨ", + "Ġch ấp", + "èIJ ¬", + "ج ب", + "? âĢľ", + "д ав", + "да в", + "ร วม", + "รว ม", + "Ùİ Ø¯", + "ĠاÙĦد ÙĪÙĦ", + "ĠëĦ¤ ìĿ´íĬ¸", + "Ġà¤Ĩ स", + "ظ ÙĬÙģ", + "ãĥ¼ ãĥ©", + "ãĥ¼ãĥ ©", + "ãģł ãĤįãģĨ", + "ĠÙĪاØŃ Ø¯", + "ĠÙĪا ØŃد", + "ر ÙĪس", + "رÙĪ س", + "Ġzákon a", + "ĠпеÑĢ еб", + "ĠпеÑĢе б", + "à¥Ģ -", + "à¹Ī à¹Ħà¸Ķ", + "为 äºĨ", + "ÎĻ ÎĿ", + "ĠìĽĶ ìĦ¸", + "ส à¸Ńà¸ĩ", + "Ġ æīĭ", + "Ġæī ĭ", + "Ġ ÐĴÑģе", + "ĠÐĴ Ñģе", + "ĠÐĴÑģ е", + "à¹Ĥ ย", + "Ġkal dır", + "Ġkaldı r", + "ÏĦ ÎŃÏĤ", + "ÏĦÎŃ ÏĤ", + "Ġ ï¿£", + "Ġ íĸĪëĭ¤", + "Ġíĸ Īëĭ¤", + "ĠíĸĪ ëĭ¤", + "ãĤģ ãģŁ", + "Ġ Äįer", + "ĠÄį er", + "ĠÄįe r", + "c ela", + "ce la", + "cel a", + "üs ü", + "ê³ ³", + "ìĹIJ ëıĦ", + "ز Ø©", + "ãģª ãĤĭ", + "ÙĪ ÛĮÙĨ", + "ÙĪÛĮ ÙĨ", + "çī Ľ", + "Ġ voj", + "Ġv oj", + "Ġvo j", + "Ġ ëĬIJ", + "ĠëĬ IJ", + "Ġ ÙĥÙħ", + "ĠÙĥ Ùħ", + "æ³ ī", + "з Ñı", + "è£ Ŀ", + "ĠØ¢ ÙĦ", + "Ġ ανά", + "Ġα νά", + "Ġαν ά", + "Âł ÐĴ", + "Ġyap ıl", + "Ġyapı l", + "æı Ľ", + "ĠÑģ ÑĥÑīеÑģÑĤв", + "ĠÑģÑĥ ÑīеÑģÑĤв", + "ĠÑģÑĥÑīе ÑģÑĤв", + "Ġn á»iji", + "ÙĪ ئ", + "ĠëĦ¤ìĿ´íĬ¸ ìĺ¨", + "Ġpolit ik", + "Å¡ ka", + "Å¡k a", + "ebilir siniz", + "ld kf", + "Ñĥб лÑĸ", + "Ġe oq", + "Ġeo q", + "ĠÙħØŃ ØµÙĪÙĦ", + "krv ldkf", + "Ġeoq krvldkf", + "Ïĥε Ïīν", + "بÙĦ غ", + "Įĵ ê¸Ģ", + "ĠÑģ ÑĢок", + "ĠU y", + "ĠN ÄĽk", + "ĠNÄĽ k", + "Ġ див", + "Ġд ив", + "Ġди в", + "ãĤµ ãĤ¤", + "Ġ ìĤ¬ìĿ´", + "ĠìĤ¬ ìĿ´", + "Ġ éĹ", + "Ġé Ĺ", + "Ġб аÑĤÑĮ", + "Ġба ÑĤÑĮ", + "Ġп еÑĢÑĸ", + "ĠпеÑĢ Ñĸ", + " ĸ", + "交 éĢļ", + "ен з", + "ÙĪ ست", + "ÙĪس ت", + "ีย à¸ļ", + "Ġ à¸Īะ", + "Ġà¸Ī ะ", + "ë¡ Ģ", + "üf us", + "Ùij ÙIJ", + "ç¸ ½", + "ัà¸Ķ ส", + "ê² Ģ", + "ĠÑĤ иÑħ", + "ĠÑĤи Ñħ", + "ĠØ¢ زÙħ", + "Ġآز Ùħ", + "Ġ اض", + "Ġا ض", + "ĠØ§Ø ¶", + "ì ¡´", + "ì¡ ´", + "ÙĴ ت", + "æĪ ¸", + "ĠìŀĪ ìĿĦ", + "Ġ çĶ·", + "Ñī Ñĸ", + "о ма", + "ом а", + "ĠاÙģز اÛĮØ´", + "Ġ Thông", + "ĠTh ông", + "ĠاجتÙħاع ÛĮ", + "е лÑİ", + "ел Ñİ", + "ĠÑħоÑĢоÑĪ о", + "à¸ł าษ", + "Ġ rám", + "Ġr ám", + "Ġrá m", + "å¾ ¡", + "ãĥ¼ ãĥĦ", + "ãĥ¼ãĥ Ħ", + "ĠL Ỽp", + "Ġ Ø´ÙĬ", + "ĠØ´ ÙĬ", + "Ġh iá»ĥm", + "Ġhi á»ĥm", + "θ ν", + "ο ÏħÏĥ", + "οÏħ Ïĥ", + "å¾ ©", + "Ġú zem", + "à¹ģ à¸ľ", + "å ·¨", + "å· ¨", + "à¸Ī à¸Ļ", + "Ú¯ راÙĨ", + "گر اÙĨ", + "Ġت ÛĮÙħ", + "ĠتÛĮ Ùħ", + "Ġ ilet", + "Ġi let", + "Ġil et", + "Ġile t", + "า à¸Ĥà¸Ńà¸ĩ", + "าà¸Ĥ à¸Ńà¸ĩ", + "Ġ تÙĪر", + "Ġت ÙĪر", + "ĠتÙĪ ر", + "Ġдо говоÑĢ", + "Ġдог овоÑĢ", + "Ġдогов оÑĢ", + "Ġt ento", + "Ġten to", + "Ġtent o", + "в Ñĥ", + "Ġз ада", + "Ġза да", + "Ġзад а", + "Ġstole tÃŃ", + "Ġstol etÃŃ", + "Âł Ġ", + "âĢĮ اÙĦ", + "Ë ĺ", + "ÅŁ iv", + "ÅŁi v", + "н ÑıÑĤи", + "нÑı ÑĤи", + "нÑıÑĤ и", + "ãĤī ãĤĮãģŁ", + "ãĤīãĤĮ ãģŁ", + "ĠS b", + "ĠاÙĦÙħ ص", + "ĠУкÑĢаÑĹ Ð½Ñĸ", + "ĠУкÑĢаÑĹн Ñĸ", + "ĠØ´ Ú©", + "iế ng", + "iến g", + "ÑĮ ÑĤе", + "è° ¢", + "ĠÙħ تÙĨ", + "ĠÙħت ÙĨ", + "Ġ ÑĢад", + "ĠÑĢ ад", + "ĠÑĢаР´", + "ĠÑĢа д", + "ĠÙħÙĪ اد", + "ì± Ħ", + "é¡ ¶", + "Ġbo ÅŁ", + "ت ÙĪر", + "تÙĪ ر", + "ĠÄij áng", + "ĠÄijá ng", + "Ġkit ap", + "Ġki tap", + "Ġkita p", + "Ġho din", + "Ġhod in", + "Ġtarih i", + "ãĤĦ ãĤĭ", + "Ñģ ÑĤеÑĢ", + "ÑģÑĤ еÑĢ", + "ÑģÑĤе ÑĢ", + "Ġ Ñħод", + "ĠÑħ од", + "в ание", + "ва ние", + "ван ие", + "ĠоÑģ вÑĸ", + "ĠÑģиÑģÑĤем Ñĭ", + "़ न", + "Ïĩ ο", + "Ġ åı°", + "Ġåı °", + "o ÅĻ", + "ç»ı æµİ", + "Ġ ä½ľ", + "Ġthu áºŃn", + "Ľ Ī", + "Ġy alnız", + "a let", + "al et", + "ale t", + "ì¦Ŀ ê¸Ī", + "Ġза Ñī", + "Ġе кÑģп", + "Ġек Ñģп", + "âĦĸ âĦĸ", + "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠÚ¯ ÙĪØ´", + "ĠÚ¯ÙĪ Ø´", + "ãģ« åħ¥", + "Ġu dÄĽl", + "Ġud ÄĽl", + "Ġ áº", + "Ġá º", + "à¤Ĩ à¤Ī", + "âĢĮ دÙĩ", + "âĢĮد Ùĩ", + "æĤ ª", + "Ġtr ò", + "æļ Ĺ", + "λλ ην", + "λλη ν", + "ĠпÑĢи зна", + "ĠпÑĢиз на", + "Ġس ÛĮستÙħ", + "ĠسÛĮ ستÙħ", + "Ġà¤ħ त", + "è o", + "è¿ İ", + "Ġз Ñĥб", + "ĠзаÑģ об", + "Ġس Ùģ", + "ĠÙħاÙĨ ÙĨد", + "Ø® Ø´", + "v ajÃŃ", + "va jÃŃ", + "nit ÅĻ", + "æ¯ Ĵ", + "æ¤ į", + "Ġgir iÅŁ", + "ĠÄij áp", + "ĠÄijá p", + "@ n", + "ов аÑĢи", + "оваÑĢ и", + "ова ÑĢи", + "ĠØ® دا", + "Ġخد ا", + "Ġv ÄĽtÅ¡", + "ĠvÄĽt Å¡", + "ĠΣ Ïħ", + "Ùģ Ø©", + "аннÑı м", + "ĠÑĩ лен", + "æĶ¯ æĮģ", + "å¨ ľ", + "lar arası", + "lara rası", + "Ρ Îij", + "Ġz iy", + "Ġzi y", + "Ġ êµIJìľ¡", + "ĠêµIJ ìľ¡", + "Ġh á»ĵi", + "Ġhá»ĵ i", + "าà¸Ħ าร", + "าà¸Ħา ร", + "im leri", + "imler i", + "è³ ¼", + "ĠجÙĩ اÙĨ", + "ĠÑĢоз мÑĸ", + "Ñħ Ñĸв", + "γ ε", + "æ¨ ª", + "ÎĻ ÎijΣ", + "ÎĻÎij Σ", + "ç¶ Ń", + "Ġbi raz", + "Ġbir az", + "ĠÑĤак ого", + "ĠÑĤа кого", + "íĥ Ħ", + "ĠбÑĥд ÑĥÑĤ", + "ĠбÑĥ дÑĥÑĤ", + "ĠбÑĥдÑĥ ÑĤ", + "ĠÑĪ вид", + "Ġ неÑģ", + "Ġн еÑģ", + "Ġне Ñģ", + "ĠÙħ عÙĦÙĪÙħات", + "ĠÙħعÙĦ ÙĪÙħات", + "à¥ĩ यर", + "à¥ĩय र", + "Ġдв ÑĥÑħ", + "å¿ħ è¦ģ", + "å§ Ĩ", + "Ġpo hled", + "Ġpoh led", + "ìĬ¤ íĦ°", + "Ġ åįģ", + "Ġåį ģ", + "ĠØ£ ب", + "веÑĢ дж", + "веÑĢд ж", + "Ġà¤ľ म", + "ल त", + "åľ° åĮº", + "Ġ |[", + "Ġ| [", + "Ġв меÑģÑĤ", + "ĠÚ© اÙħ", + "Ġ ãĥIJ", + "Ġãĥ IJ", + "ãĥ¼ ãĥĸ", + "ãĥ¼ãĥ ĸ", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Ġ ìĥģíĴĪ", + "Ġìĥģ íĴĪ", + "à¹Ģล ย", + "Äį né", + "ĠÑģÑĢед ÑģÑĤва", + "ĠÑģÑĢедÑģÑĤв а", + "Ġ ÑĤаб", + "ĠÑĤ аб", + "ĠÑĤа б", + "Ġ Ùħار", + "ĠÙħ ار", + "ĠÙħا ر", + "Ġ hled", + "Ġh led", + "Ġhl ed", + "д аÑĤ", + "да ÑĤ", + "ÙĪ ÛĮد", + "ÙĪÛĮ د", + "Ġ ãĥ©", + "Ġãĥ ©", + "ĠØ® د", + "è¤ ĩ", + "ç§ ĺ", + "Ġ برد", + "Ġب رد", + "Ġبر د", + "ĠÏĥ αÏĤ", + "Ïİ ÏĥειÏĤ", + "æĿ ¯", + "λ Ïį", + "å® ¿", + "Ġ ëĤľ", + "ĠëĤ ľ", + "ï» Ł", + "Ġözel likle", + "Ġözellik le", + "Ġкон Ñģ", + "ĠÙħ غ", + "ع ÙĬ", + "à¹Į à¸ģ", + "Ġ ÙĬت", + "ĠÙĬ ت", + "ĠÙħ شاÙĩ", + "ĠÙħØ´ اÙĩ", + "ĠTh anh", + "ĠThan h", + "ा à¤ľà¤¨", + "à¤¾à¤ľ न", + "¥ ¤", + "Ġv lá", + "Ġvl á", + "ĠÙģ ض", + "Τ ÎĻÎļ", + "Ġна Ñĥков", + "ĠнаÑĥк ов", + "е лем", + "ел ем", + "еле м", + "Ġd Ãłng", + "ĠгоÑģп одаÑĢ", + "Âł S", + "и ÑĩеÑģкиÑħ", + "иÑĩеÑģ киÑħ", + "иÑĩеÑģки Ñħ", + "ĠتÙĨ Ùĩا", + "à¤ľ न", + "Ġп ан", + "Ġпа н", + "åĨ ł", + "Ġ ëĤĺëĬĶ", + "ĠëĤĺ ëĬĶ", + "t ÃŃ", + "ä¸Ģ èµ·", + "Ġlã nh", + "Âł v", + "ov ým", + "ový m", + "ز ب", + "ĠجÙħع ÛĮت", + "Ġ æµ·", + "Ġæµ ·", + "ĠоÑģ ÑĥÑīеÑģÑĤв", + "à £i", + "ã i", + "ائ ر", + "Ġ ë³ij", + "Ġë³ ij", + "á»ĭ nh", + "Ġs á»Ńa", + "Ġsá»Ń a", + "à¥ĩà¤Ĥ ।", + "ÄĽ jÅ¡ÃŃ", + "ÄĽj Å¡ÃŃ", + "Ġд ÑĸÑĤ", + "ĠдÑĸ ÑĤ", + "Ġ æĥ", + "Ġæ ĥ", + "mÄ±ÅŁ tı", + "ر ØŃ", + "Ġì§Ģ ê¸Ī", + "å¦ »", + "âĹ ĭ", + "Ġ ì§ĢìĹŃ", + "Ġì§Ģ ìĹŃ", + "ÙĴ ÙĨ", + "Ġur Äįit", + "ĠurÄį it", + "ÙĴ Ùħ", + "z ÃŃ", + "è ķ", + "Ġ Ø´ÙĪر", + "ĠØ´ ÙĪر", + "ĠØ´ÙĪ ر", + "Ġ Không", + "ĠKh ông", + "ÛĮ زÛĮ", + "ÛĮز ÛĮ", + "Ġз г", + "Ġв не", + "Ġpr ávÄĽ", + "Ġprá vÄĽ", + "Ġpráv ÄĽ", + "è« ĭ", + "ا ÙĬت", + "اÙĬ ت", + "ั à¸ģร", + "ัà¸ģ ร", + "Ġolduk ça", + "ãĤģ ãĤĭ", + "ĠT ây", + "ëĿ¼ ìĿ¸", + "èĻ ķ", + "Ġs Æ°", + "Ġ ник", + "Ġн ик", + "Ġни к", + "Ù ł", + "اش ÛĮÙĨ", + "اشÛĮ ÙĨ", + "e lerde", + "el erde", + "eler de", + "ìĭľ ìķĦ", + "Ġ Ñĥмов", + "ĠÑĥ мов", + "ĠÑĥм ов", + "ĠçalÄ±ÅŁ an", + "Ġ ë¸Ķ", + "Ġë¸ Ķ", + "ĠÑĤак им", + "ĠÑĤа ким", + "ÑĢ ин", + "ÑĢи н", + "Ġ Ø®ÙĦ", + "ĠØ® ÙĦ", + "a yd", + "ay d", + "Ġ ãĥ¡", + "Ġãĥ ¡", + "ей ÑĩаÑģ", + "Ġdo prav", + "Ġdop rav", + "ãģĵ ãģ¨ãģ¯", + "ãģĵãģ¨ ãģ¯", + "Ġ ì¶Ķì²ľ", + "Ġì¶Ķ ì²ľ", + "å» ¶", + "Ġ kı", + "Ġk ı", + "åı ¶", + "ÑĢ иг", + "ÑĢи г", + "íħ ľ", + "çĶ³ åįļ", + "Ġ веÑĤ", + "Ġв еÑĤ", + "Ġве ÑĤ", + "ĠпомоÑī ÑĮÑİ", + "ĠاÙģ راد", + "ĠاÙģر اد", + "ÏĢ ει", + "ÏĢε ι", + "à¹Ģ สร", + "à¹Ģส ร", + "Ġgi ám", + "Ġgiá m", + "é İ", + "h las", + "hl as", + "man ız", + "manı z", + "ан гл", + "анг л", + "Ġmu ž", + "Âł K", + "ÑĢед иÑĤ", + "ÑĢеди ÑĤ", + "设 å¤ĩ", + "ι Ïĥμ", + "ιÏĥ μ", + "Ġc ải", + "Ġcả i", + "Ġ éĢļ", + "ĠéĢ ļ", + "Ġ Ùĥار", + "ĠÙĥ ار", + "Ġпод об", + "ĠмеÑĤ ал", + "ĠÑģам е", + "л ÑĥÑĩ", + "лÑĥ Ñĩ", + "åĤ ³", + "ĠÙĪÙĩ ÙĪ", + "Ġ éĩį", + "Ġéĩ į", + "в ий", + "ви й", + "æ³ ģ", + "Ġ æĿİ", + "ĠæĿ İ", + "ĠiliÅŁ kin", + "ĠiliÅŁk in", + "Ġεί Ïĩε", + "çĬ ¯", + "ÅĻej mÄĽ", + "èŃ ĺ", + "ç¨ ±", + "μ μα", + "μμ α", + "Ġ ÙĦÛĮ", + "ĠÙĦ ÛĮ", + "Ùĩ اÙĬ", + "Ùĩا ÙĬ", + "Ġ опиÑģ", + "Ġо пиÑģ", + "Ġоп иÑģ", + "Ú¯ رد", + "گر د", + "Ġг ÑĢ", + "ĠAnimal ia", + "ĠAnim alia", + "ÐŁ о", + "Ġb óng", + "ĠдеÑĤ ей", + "Ġl âu", + "Ġ æķĻ", + "Ġæķ Ļ", + "Ġпо ÑıÑģ", + "ĠاÙĦ Ø¢", + "ั à¸Ļà¸ķ", + "ัà¸Ļ à¸ķ", + "Ġд ев", + "Ġде в", + "Ġ ÑĨей", + "ĠÑĨ ей", + "ĠÑĨе й", + "ÑĮ в", + "æĥ ł", + "m aları", + "ma ları", + "mal arı", + "malar ı", + "im ler", + "à¥Ī ।ĊĊ", + "à¥Ī। ĊĊ", + "à¥Ī।Ċ Ċ", + "Ġ ном", + "Ġн ом", + "Ġно м", + "z v", + "Ġ à¸ģร", + "Ġà¸ģ ร", + "Ġpay laÅŁ", + "Âł s", + "ि सम", + "िस म", + "ÑģÑĤв еннÑĭÑħ", + "ÑģÑĤвен нÑĭÑħ", + "st oup", + "sto up", + "о нÑĸ", + "он Ñĸ", + "s tÃŃ", + "st ÃŃ", + "ĠØŃ Ú©", + "ĠÚ¯ رÙģت", + "ĠگرÙģ ت", + "Ġگر Ùģت", + "าà¸Ħ า", + "д Ñı", + "ÙĦ اث", + "ÙĦا Ø«", + "Ġzdrav ot", + "ä¸Ĭ ãģĴ", + "ãģ ¼", + "e lere", + "el ere", + "eler e", + "ele re", + "ظ Ùħ", + "ĠÑģ веÑĤ", + "ĠÑģв еÑĤ", + "о ÑĢг", + "оÑĢ г", + "ç« ¥", + "ĠпеÑĢ еп", + "ĠпеÑĢе п", + "Ġम द", + "а за", + "аз а", + "å¦Ĥ ä½ķ", + "ÑģÑĮ кÑĸй", + "ÑģÑĮк Ñĸй", + "ÑģÑĮкÑĸ й", + "Ġb Æ°á»Ľc", + "Ġger ekli", + "Ġgerek li", + "大 家", + "Ġtr ái", + "éģ ©", + "ä¸Ń 央", + "Ġph ản", + "Ġع رض", + "ĠÙĥ تاب", + "æĭ ©", + "ÑĪ его", + "ÑĪе го", + "å¸ ®", + "ĠÙĨÛĮ از", + "è¿ ·", + "ุ à¸Ľ", + "ิ à¸Ľ", + "Ġد Ø®", + "ÏĦ ικÎŃÏĤ", + "ÏĦικ ÎŃÏĤ", + "ĠU z", + "Ġت ÙĪÙħاÙĨ", + "ĠتÙĪ ÙħاÙĨ", + "ĠتÙĪÙħ اÙĨ", + "ĠÙĪ اÙĦØ£", + "ĠÙĪاÙĦ Ø£", + "ÅĻ es", + "ÅĻe s", + "Ñij м", + "Ġ å¸Ĥ", + "Ġå¸ Ĥ", + "ĠÑĤ оже", + "ĠÑĤо же", + "Ġy apan", + "Ġya pan", + "Ġyap an", + "å½¼ 女", + "ĠÙħ در", + "ĠÙħد ر", + "¶ ģ", + "Ġ æŶ", + "ĠæĹ ¶", + "à¹Ģ à¸ĺ", + "à¹Ģภĺ", + "Ġ ÙħاÙĦ", + "ĠÙħ اÙĦ", + "ĠÙħا ÙĦ", + "ĠB üyük", + "ĠBü yük", + "Ġ ÙĦت", + "ĠÙĦ ت", + "å° ļ", + "d eme", + "de me", + "dem e", + "ü b", + "ĠÑħ Ñĥд", + "Ġlé ka", + "çĽ Ľ", + "缴 æİ¥", + "ниÑĨÑĤ ва", + "ĠпÑĢи Ñĩин", + "ĠпÑĢиÑĩ ин", + "е ÑĢап", + "еÑĢ ап", + "еÑĢа п", + "ĠÑģозд а", + "ĠÑģоз да", + "æ ¢°", + "æ¢ °", + "Ġm üz", + "Ġmü z", + "ç³» åĪĹ", + "o uz", + "ou z", + "Ġà¤ĵ र", + "ÑĢ ÑĥÑĩ", + "ÑĢÑĥ Ñĩ", + "Ġ á½", + "Ġá ½", + "μÎŃ Î½Î±", + "μÎŃν α", + "ĠпÑĢед меÑĤ", + "Ġ å²", + "Ġå ²", + "ãĥ³ ãĥģ", + "ãĥ³ãĥ ģ", + "μÎŃ Î½Î·", + "μÎŃν η", + "л Ñĥг", + "лÑĥ г", + "Âł n", + "ĠT arih", + "ĠTar ih", + "Ġ ãĢĪ", + "ĠãĢ Ī", + "Ġb ana", + "Ġban a", + "Ġba na", + "Ġ cÃŃ", + "Ġc ÃŃ", + "Ġvý kon", + "åĽł æѤ", + "Ġt ÅĻi", + "ĠtÅĻ i", + "า à¸ĭ", + "าภĭ", + "v ailable", + "vail able", + "Ġ istem", + "Ġis tem", + "Ġi stem", + "Ġist em", + "ãĥ¥ ãĥ¼", + "Ðķ ÐĿ", + "Ġ гаÑĢ", + "Ġг аÑĢ", + "οÏħ λ", + "ॠĽ", + "ĠÙĪ ضع", + "ส ะ", + "è· Ŀ", + "ĠØŃ Ùģ", + "ิà¸Ĺย าล", + "ิà¸Ĺยา ล", + "她 çļĦ", + "н ÑĸÑĪ", + "нÑĸ ÑĪ", + "ж ение", + "же ние", + "жен ие", + "기 ìĹIJ", + "Ġ éĺ¿", + "Ġéĺ ¿", + "ĠÙħ ارس", + "ĠÙħا رس", + "ĠÙħار س", + "ĠçeÅŁit li", + "Ġ ÅŁehir", + "ĠÅŁ ehir", + "ĠÅŁeh ir", + "á tor", + "át or", + "à¹ī à¸Ĺ", + "ìĿ´ ëĬĶ", + "Ġ è²", + "Ġè ²", + "é¡ į", + "ç ĻĤ", + "çĻ Ĥ", + "Ġ ниÑĩ", + "Ġн иÑĩ", + "Ġни Ñĩ", + "Ġ ê°Ģì§Ģ", + "Ġê°Ģ ì§Ģ", + "ä¼ ¦", + "r án", + "rá n", + "o stat", + "os tat", + "ost at", + "osta t", + "Ġ ÙĦÙĥ", + "ĠÙĦ Ùĥ", + "è º", + "ĠNg Ãłnh", + "Ġस द", + "æľ Ĺ", + "çĦ¶ åIJİ", + "ãĤ¸ ãĤ§", + "л еÑĢ", + "ле ÑĢ", + "ĠÐŀ на", + "ĠÐŀн а", + "س ÙĪÙĨ", + "سÙĪ ÙĨ", + "Ïģ ον", + "Ïģο ν", + "ĠدرÛĮ اÙģت", + "ĠدرÛĮا Ùģت", + "à¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ", + "Ġ dál", + "Ġd ál", + "Ġdá l", + "ĠмÑĸÑģ ÑĨе", + "Ġд ней", + "Ġ اÙĦات", + "Ġا ÙĦات", + "ĠاÙĦ ات", + "Ġरह त", + "ï¼Į 对", + "è³ĩ æĸĻ", + "ä»» ä½ķ", + "é Ħ", + "t aj", + "ta j", + "β ά", + "Ġна до", + "Ġнад о", + "ĠÑģÑĤ Ñĥд", + "ĠÅŁ eh", + "ัà¸į à¸į", + "à¥ĭ ब", + "ãĥ© ãĥ¼", + "Û± Ûµ", + "Û±Û µ", + "e pt", + "ep t", + "Ġbil dir", + "Ġbild ir", + "ส à¸ĸาà¸Ļ", + "สà¸ĸ าà¸Ļ", + "е ÑĤÑĮÑģÑı", + "еÑĤÑĮ ÑģÑı", + "sk ým", + "ský m", + "Ġобла ÑģÑĤÑĮ", + "ĠоблаÑģ ÑĤÑĮ", + "ĠоблаÑģÑĤ ÑĮ", + "Ġìŀ ł", + "ĠG ör", + "ĠGö r", + "Ġd ayan", + "Ġday an", + "Ġda yan", + "ĠÛĮ اد", + "ĠÛĮا د", + "çĶŁ 产", + "íĺ ij", + "å¾ ģ", + "Ġ اجر", + "Ġا جر", + "Ġاج ر", + "Ġп ÑĢе", + "ĠпÑĢ е", + "ä¸īä¸ī ä¸īä¸ī", + "åŁİ å¸Ĥ", + "Ġ пÑĢимеÑĢ", + "ĠпÑĢ имеÑĢ", + "ĠпÑĢи меÑĢ", + "ĠпÑĢим еÑĢ", + "Äį ást", + "èģ ĺ", + "ĠÙħرب ÙĪØ·", + "æŀ ļ", + "åĪ Ģ", + "æŁ¥ çľĭ", + "Ġ모 ëijIJ", + "ìŀIJ ë£Į", + "- ､", + "Ġê°Ļ ìĿ´", + "Ġ ì¡´", + "Ġì ¡´", + "Ġì¡ ´", + "е гоÑĢ", + "ег оÑĢ", + "его ÑĢ", + "e dik", + "ed ik", + "edi k", + "и мÑĥ", + "им Ñĥ", + "ĠAr th", + "ĠArt h", + "åºĶ çĶ¨", + "m iÅŁti", + "miÅŁ ti", + "Ġkhá»ı e", + "Ġ Ñĸд", + "ĠÑĸ д", + "λ λη", + "λλ η", + "â h", + "м аг", + "ма г", + "éļ Ĩ", + "ĠвнÑĥ ÑĤÑĢ", + "ĠвнÑĥÑĤ ÑĢ", + "Ġ بط", + "Ġب Ø·", + "( æĹ¥", + "Ä° Y", + "л ик", + "ли к", + "ĠB ản", + "Ġت ÙĪس", + "ĠتÙĪ س", + "़ त", + "a mak", + "am ak", + "ama k", + "åķı é¡Į", + "ĠÑģам оÑģÑĤ", + "ĠÑģамо ÑģÑĤ", + "ï¼¼ Ċ", + "Ġ ç¦ı", + "Ġç¦ ı", + "Ù ¡", + "Ġ ÑĦоÑĢми", + "ĠÑĦоÑĢм и", + "ĠÑĦоÑĢ ми", + "Ġ ÑĢозÑĥм", + "ĠÑĢоз Ñĥм", + "ĠÙħ طاÙĦ", + "ĠÙħØ· اÙĦ", + "ä¹Ł æĺ¯", + "ç¾İ åĽ½", + "ëĵľ 립ëĭĪëĭ¤", + "Ġl Ä©nh", + "ĠпоÑĤ омÑĥ", + "ĠпоÑĤом Ñĥ", + "Ñı бÑĢÑı", + "Ñıб ÑĢÑı", + "æ¼ «", + "Ġng oại", + "à¸Ń ำ", + "ÙĬ ÙĨا", + "ÙĬÙĨ ا", + "Ġm lad", + "Ġml ad", + "Ïĥ ÏĦά", + "ÏĥÏĦ ά", + "ا تر", + "ات ر", + "주 ìĿĺ", + "ен нÑĸ", + "о за", + "оз а", + "ÙĤ ات", + "ĠÐĴ аÑģ", + "è® Ń", + "é IJ", + "Ñĥ ÑİÑĩи", + "ÑĥÑİ Ñĩи", + "Ġ کر", + "ĠÚ© ر", + "Ġ .|", + "Ġ. |", + "Ġgen ç", + "è© ²", + "ä» ģ", + "о дÑĭ", + "од Ñĭ", + "ĠØ£ ÙĪÙĦ", + "ĠØ£ÙĪ ÙĦ", + "Ġ ìĤ¬íļĮ", + "ĠìĤ¬ íļĮ", + "Ġ à¹Ģส", + "Ġà¹Ģ ส", + "Ġà¹Ģภª", + "ĠëķĮ문 ìĹIJ", + "âĢĮ ب", + "Ġли ÑĪÑĮ", + "ĠлиÑĪ ÑĮ", + "Ġи менно", + "Ġим енно", + "m adı", + "ma dı", + "mad ı", + "Ġ éĤ", + "Ġé Ĥ", + "ĠÙĪ ارد", + "ĠÙĪا رد", + "Ġtak ım", + "Ġ à¹Ģห", + "Ġà¹Ģ ห", + "Ġà¹Ģภ«", + "Ġ à¸Ńย", + "Ġà¸Ń ย", + "Ġkon usu", + "Ġkonu su", + "Ġkonus u", + "Ø® ÙĪ", + "ĠÑģ ид", + "ĠÑģи д", + "èµ ¤", + "о ÑıÑĤелÑĮ", + "оÑıÑĤ елÑĮ", + "ëĭ µ", + "ε Ïī", + "Ñĸ Ñħ", + "Ġय द", + "ĠÚ© ÛĮÙģ", + "ĠÚ©ÛĮ Ùģ", + "μ οÏĤ", + "μο ÏĤ", + "Ġal dı", + "Ġald ı", + "Ġ íĻį", + "ĠíĻ į", + "к Ñĥп", + "кÑĥ п", + "ĠÙĨÙħ اÛĮØ´", + "ĠÙĨÙħاÛĮ Ø´", + "ãģ ¥", + "Ġ íķ©ëĭĪëĭ¤", + "Ġíķ ©ëĭĪëĭ¤", + "Ġíķ© ëĭĪëĭ¤", + "Ġë Įĵê¸Ģ", + "б оÑĢа", + "бо ÑĢа", + "боÑĢ а", + "éī Ħ", + "Ġ à¹Ģà¸Ī", + "Ġà¹Ģ à¸Ī", + "Ġà¹ĢภĪ", + "à¹ī à¸ģ", + "§ Ø·", + "ر بÙĩ", + "رب Ùĩ", + "Ġ Ñĥз", + "ĠÑĥ з", + "Ġм аÑİÑĤÑĮ", + "Ġма ÑİÑĤÑĮ", + "Ġby li", + "Ġbyl i", + "ี à¸ķ", + "Ġ ì§ĢìĽIJ", + "Ġì§Ģ ìĽIJ", + "èĩª çĦ¶", + "ù y", + "Ġç aÄŁ", + "Ġça ÄŁ", + "е дин", + "ед ин", + "еди н", + "ë ī´", + "åį ±", + "Ġпоз волÑı", + "Ġпозвол Ñı", + "ØŃ Ø§Ø¯", + "ĠÑĩ его", + "ีย ร", + "Ġyön tem", + "Ġ ders", + "Ġd ers", + "Ġde rs", + "Ġder s", + "Ġ ÑģÑĤоÑı", + "ĠÑģÑĤ оÑı", + "ĠÑģÑĤо Ñı", + "Ġк ÑĢÑĥп", + "Ġ ð", + "Ġдом аÑĪ", + "Ġдома ÑĪ", + "е нд", + "ен д", + "ç» §", + "ĠÄij ô", + "Ġch tÄĽ", + "计 åĪĴ", + "ÎŃ Î±", + "Ġdob ÅĻe", + "ส à¸Ńà¸ļ", + "е ление", + "ел ение", + "еле ние", + "елен ие", + "ĠÄij ông", + "ĠÄijô ng", + "ãģ¾ ãĤĬ", + "Ġboy unca", + "à¥ģ à¤Ĺ", + "à¥ģठĹ", + "ĠÑĦ из", + "ãĤ³ ãĥ³", + "Ġde ney", + "Ġden ey", + "ÑĩеÑģ киÑħ", + "Ñĩе ÑģкиÑħ", + "ÑĩеÑģки Ñħ", + "λ ον", + "λο ν", + "以 åıĬ", + "ا ÙĪت", + "اÙĪ ت", + "Âł ³³³³", + "³³ ³³³", + "³³³³ Âł", + "³³³ ³³", + "Ġ ì¤Ħ", + "Ġì¤ Ħ", + "ि फ", + "िठ«", + "ĠÑĤ ол", + "ĠÑĤо л", + "ĠëĤ´ ê°Ģ", + "âĸ ı", + "Ġp há", + "Ġph á", + "ĠÑģп Ñĸв", + "Ġ جÙħÙĬع", + "ĠجÙħ ÙĬع", + "Ġb ezpeÄį", + "Ġbez peÄį", + "Ġ æĹł", + "ĠæĹ ł", + "Ġv Å¡e", + "ĠvÅ¡ e", + "ÑģÑĤ вÑĥ", + "ÑģÑĤв Ñĥ", + "d ust", + "du st", + "o Å¡", + "Ġت ارÙĬØ®", + "ا ØŃØ©", + "اØŃ Ø©", + "ĠÙħشار ÙĥØ©", + "Ġ ακ", + "Ġα κ", + "ั à¸Ļà¸Ļ", + "ัà¸Ļ à¸Ļ", + "éģ Ĭ", + "Ġ ÑģоÑĤ", + "ĠÑģ оÑĤ", + "ĠÑģо ÑĤ", + "Ġ каз", + "Ġк аз", + "Ġка з", + "ĠÑĤ еÑĩение", + "ĠÑĤеÑĩ ение", + "ê¸ ´", + "acak tır", + "ê±° ëĤĺ", + "ี ยม", + "ีย ม", + "ĠÑģ ÑĥÑħ", + "ĠÑģÑĥ Ñħ", + "ĠëĦĪ 무", + "ãģı ãĤĭ", + "ĠкоÑĤоÑĢ ой", + "ا ÙĤØ©", + "اÙĤ Ø©", + "y ıl", + "yı l", + "ãĤ» ãĥĥãĥĪ", + "ĠÑį лем", + "æģ IJ", + "ÙĨ اء", + "ÙĨا Ø¡", + "åħ ©", + "Ġte Äı", + "ä¸ ¥", + "Ġì§Ī 문", + "Ġ 为", + "Ġä¸ º", + "ìĭľ íĹĺ", + "Ġп ÑĢок", + "ĠпÑĢ ок", + "ĠпÑĢо к", + "u jeme", + "uj eme", + "uje me", + "ü cü", + "üc ü", + "ĠاÙĦÙħ غ", + "ĠØŃ Ø³Ø§Ø¨", + "ĠØŃس اب", + "ãģĹ ãģ¦ãģĦ", + "ãģĹãģ¦ ãģĦ", + "к ова", + "ко ва", + "ков а", + "ĠÄij Ãło", + "Ġп ÑĢиз", + "ĠпÑĢ из", + "ĠпÑĢи з", + "ĠÙĪ ÙħÙĨ", + "ĠÙĪÙħ ÙĨ", + "Ġ оÑĢ", + "Ġо ÑĢ", + "à¸ģ à¸ķ", + "а ÑĦ", + "Ġ à¸ŀร", + "Ġà¸ŀ ร", + "ÑĨи ей", + "æ ª", + "Ġ působ", + "Ġp ůsob", + "Ġpů sob", + "åŃ© åŃIJ", + "Ġb ánh", + "Ġbán h", + "ĠÑĦоÑĢм Ñĥ", + "ĠÑĦоÑĢ мÑĥ", + "Ġ á»ķ", + "Ġá» ķ", + "Ġмен ее", + "Ġмене е", + "à¹ī าห", + "à¹īา ห", + "ни ÑĨа", + "ниÑĨ а", + "ี Ċ", + "Ġв олоÑģ", + "Ġвол оÑģ", + "Ġار ائÙĩ", + "第 ä¸ī", + "ëIJĺ ìĹĪ", + "Ġkıs m", + "Ġkı sm", + "ãĥ¼ ãĥĬ", + "ãĥ¼ãĥ Ĭ", + "ler imiz", + "ÙĨ ÙĬÙĨ", + "ÙĨÙĬ ÙĨ", + "Ġ NgÆ°á»Ŀi", + "ĠNg Æ°á»Ŀi", + "ĠоÑĤ дел", + "ĠоÑĤд ел", + "çļĦ æŶåĢĻ", + "о нов", + "он ов", + "Äį an", + "i zm", + "iz m", + "ĠÑģоб ой", + "à¹ĩ à¸ķ", + "Ġ ÑģлÑĸд", + "ĠÑģ лÑĸд", + "ĠÑģл Ñĸд", + "Ġ à¤ľà¤¹", + "Ġà¤ľ ह", + "ï¼Į æĪij们", + "ï¼ĮæĪij 们", + "ãĢĤ ãģĿãģ®", + "ÏĢ ÏīÏĤ", + "çĨ Ł", + "ภ¯", + "ëĦ IJ", + "æľ ĭ", + "Ġë¹Ħ ë°Ģ", + "ëį ķ", + "Ġm Ãłn", + "ĠmÃł n", + "ìĿ´ ê³ł", + "ëŀľ ëĵľ", + "éĤ Ħ", + "Ä±ÅŁ ık", + "Ä±ÅŁÄ± k", + "Ġ 个", + "Ġä¸ ª", + "Ġn ád", + "Ġná d", + "б ÑĢа", + "æĮĩ å®ļ", + "lar ıyla", + "ları yla", + "ĠÐŀ ни", + "ĠÐŀн и", + "Ġ hra", + "Ġh ra", + "Ġhr a", + "ĠÑĢе ÑĨеп", + "ĠÐłÐ¾Ñģ Ñģий", + "å½± åĵį", + "Ġ Když", + "ĠK dyž", + "ĠÃ¶ÄŁ renc", + "ĠÃ¶ÄŁren c", + "åī µ", + "Ġ jist", + "Ġj ist", + "Ġji st", + "èĪ Ī", + "è§ ¦", + "åıij çİ°", + "ม าย", + "มา ย", + "er ken", + "erk en", + "Ġзд еÑģÑĮ", + "ĠÙħس ئ", + "@n ate", + "ĠëĤ´ ìļ©", + "Ġnab ÃŃd", + "ĠnabÃŃ d", + "Û Ģ", + "Ġмо менÑĤ", + "Ġмом енÑĤ", + "ãģł ãģĮ", + "ί δα", + "ίδ α", + "T ak", + "Ta k", + "Ġ ë³´ê³ł", + "Ġë³´ ê³ł", + ": ::::::::", + ":: :::::::", + ":::: :::::", + ":::::: :::", + ":::::::: :", + "::: ::::::", + "::::: ::::", + "::::::: ::", + "ÄŁ men", + "Ġпо меÑī", + "Ġпом еÑī", + "ãģ«ãģ¤ ãģĦãģ¦", + "ĠÙģ ÙĪÙĤ", + "ĠÙģÙĪ ÙĤ", + "Ġع ضÙĪ", + "ĠÙħ ÛĮاÙĨ", + "ĠÙħÛĮ اÙĨ", + "Ġm üc", + "Ġmü c", + "ĠпÑĢо Ñıв", + "ÑĩеÑģ ки", + "Ñĩе Ñģки", + "ãģł ãģĭãĤī", + "éĤ ¦", + "Ġ ë¶ĦìĦĿ", + "Ġë¶Ħ ìĦĿ", + "éŁ ©", + "į ¨", + "ĠD aha", + "ĠDa ha", + "ĠDah a", + "Ġ κÏĮ", + "Ġκ ÏĮ", + "Ġна Ñĩина", + "ĠнаÑĩ ина", + "ĠÐŁ оÑĤ", + "ĠÐŁÐ¾ ÑĤ", + "Ïĥκε Ïħ", + "Ïĥκ εÏħ", + "Ġ ÑĢан", + "ĠÑĢ ан", + "ĠÑĢаР½", + "ĠÑĢа н", + "ÙĪ ÙĬس", + "ÙĪÙĬ س", + ": :::::::::", + ":: ::::::::", + ":::: ::::::", + ":::::: ::::", + ":::::::: ::", + "::: :::::::", + "::::: :::::", + "::::::: :::", + "::::::::: :", + "Û±Û¹ Û¹", + "Ġard ından", + "à¹Ĥ à¸Ķ", + "ا راÙĨ", + "ار اÙĨ", + "ارا ÙĨ", + "د اد", + "دا د", + "Ġqu ý", + "ĠØ£Ùĥ ثر", + "âĹ Ĩ", + "ĠØ£ خرÙī", + "Ġأخ رÙī", + "Ġë§Ī ìĿĮ", + "ë¦ ´", + "Ġ عÙĦÙĪÙħ", + "ĠعÙĦ ÙĪÙħ", + "Ġe ÄŁ", + "воÑĢ Ñİ", + "во ÑĢÑİ", + "Ġ ãĥĹ", + "Ġãĥ Ĺ", + "Ñĥ ÑĩаÑģ", + "ÑĥÑĩ аÑģ", + "ÑĥÑĩа Ñģ", + "Ġب Ø£", + "ÏĨ ο", + "ни ками", + "ник ами", + "ника ми", + "никам и", + "à¹ĥ à¸ķ", + "Äįet nÄĽ", + "à¸ļ าà¸ĩ", + "çī Ļ", + "ãĥª ãĤ«", + "í Ĵ", + "åĩº çīĪ", + "γ ι", + "ãĢĤ ãģĿãĤĮ", + "Ġy ani", + "Ġya ni", + "Ġyan i", + "l ech", + "le ch", + "lec h", + "ĠLu áºŃt", + "çļĦ ãģª", + "Ġneden iyle", + "Ġnedeni yle", + "d ej", + "de j", + "ĠÑģов еÑĢÑĪ", + "Ġph á»ķ", + "ıs ından", + "ısında n", + "Ġch ắc", + "d eÅŁ", + "de ÅŁ", + "Ġком ан", + "Ġко ман", + "æĽ ¿", + "Ġp lán", + "Ġpl án", + "Ġplá n", + "Ġd ữ", + "ĠêµŃ ê°Ģ", + "Ġta kip", + "Ġtak ip", + "Ġth ủy", + "Ġthủ y", + "Ñģ лÑĸд", + "Ñģл Ñĸд", + "âī §", + "ĠI IC", + "ĠII C", + "θ Ïħ", + "á vat", + "áv at", + "Ġ Ñģок", + "ĠÑģ ок", + "ĠÑģо к", + "Ġб агаÑĤо", + "Ġбаг аÑĤо", + "ĠбагаÑĤ о", + ";:;: ;:;:", + "Ïģ ιοÏĤ", + "Ïģι οÏĤ", + "Ïģιο ÏĤ", + "il miÅŁtir", + "ilm iÅŁtir", + "ilmiÅŁ tir", + "Ġ znam", + "Ġz nam", + "Ġzn am", + "Ġ Τα", + "ĠΤ α", + "a maz", + "am az", + "ama z", + "à¹ģ à¸ŀ", + "ãĥģ ãĥ£", + "Ġkullan ı", + "æĶ¾ éĢģ", + "д н", + "ĠÙĪ اب", + "ĠÙĪا ب", + "Ġtr ắng", + "Ñģ Ñıг", + "ÑģÑı г", + "Ġار تباط", + "Ġв Ñħод", + "å·ŀ å¸Ĥ", + "Ġ सत", + "Ġस त", + "Ñĩ аеÑĤÑģÑı", + "Ñĩа еÑĤÑģÑı", + "ÑĩаеÑĤ ÑģÑı", + "íĮĮ íĬ¸", + "Ġ Những", + "ĠNh ững", + "ä¸į åı¯", + "å± Ĭ", + "Ġ ãĤŃ", + "ĠãĤ Ń", + "ار ÙĩاÛĮ", + "ارÙĩ اÛĮ", + "Ġar ÅŁiv", + "Ġ اÙĦÙī", + "Ġا ÙĦÙī", + "ĠاÙĦ Ùī", + "ाय à¤ķ", + "ãģĹ ãĤĩãģĨ", + "ãģĹãĤĩ ãģĨ", + "Ġ ulus", + "Ġu lus", + "Ġul us", + "al axy", + "ala xy", + "기 ê°Ģ", + "ãİ¡ (", + "μά ÏĦÏīν", + "è n", + "ù i", + "Ġна ÑģÑĤоÑı", + "ĠнаÑģÑĤ оÑı", + "ĠС в", + "ĠоÑģ оби", + "ĠоÑģоб и", + "к ово", + "ко во", + "ков о", + "ĠÑĢеб енка", + "ĠÑĢебен ка", + "ĠÑĤ Ñıж", + "ĠÑĤÑı ж", + "Ġxu á»ijng", + "Ġ ê¶Į", + "Ġê ¶Į", + "о год", + "ог од", + "ого д", + "Ġ ấy", + "è² ł", + "ว à¸Ļ", + "Ġ stanov", + "Ġsta nov", + "Ġstan ov", + "Ġk rál", + "Ġkr ál", + "Ġà¤ĩ सल", + "Ġà¤ĩस ल", + "e be", + "eb e", + "å® ¾", + "ĠдоÑģÑĤаÑĤ оÑĩно", + "II IK", + "III K", + "ÏĢ ά", + "Ġbir kaç", + "ĠاÙĦ ÙħÙĤ", + "ĠاÙĦÙħ ÙĤ", + "ãĥ ¶", + "ĠBaÅŁ kanı", + "ĠBaÅŁkan ı", + "Ġ첨ë¶Ģ íĮĮìĿ¼", + "Ġya rar", + "Ġyar ar", + "äº ¡", + "Ġ ÏĢÏĮ", + "ĠÏĢ ÏĮ", + "Âł Ñģ", + "δ ή", + "e lerini", + "eler ini", + "eleri ni", + "elerin i", + "Ġs uç", + "Ġsu ç", + "Ġд ома", + "Ġдо ма", + "Ġдом а", + "Ġна ÑĢÑĥÑĪ", + "ĠнаÑĢ ÑĥÑĪ", + "Ġ ί", + "ĠÎ ¯", + "Ġê·¸ ìĿĺ", + "çĶµ å½±", + "ا بÙĩ", + "اب Ùĩ", + "к омÑĥ", + "ко мÑĥ", + "ком Ñĥ", + "Ġत ब", + "à¥Ī à¤ł", + "Ġ모 ì§ij", + "Ġ æ±Ł", + "Ġæ± Ł", + "Ġê²ĥ ìĿĢ", + "ον ÏĦαι", + "ĠاÙĦ رÙĬاض", + "è¨ ±", + "Ġhal inde", + "Ġاش ارÙĩ", + "Ġ кÑĢÑĭ", + "Ġк ÑĢÑĭ", + "л ений", + "лен ий", + "ле ний", + "lu ÄŁ", + "Ġdo bu", + "Ġdob u", + "s ik", + "si k", + "à¥ģ à¤Ł", + "à¥ģठŁ", + "Ġ кÑĸн", + "Ġк Ñĸн", + "ãģ¨ ãģį", + "à¥Ĥ स", + "æħ ¢", + "ĠdÄ±ÅŁ ında", + "ĠdÄ±ÅŁÄ± nda", + "ç· ı", + "Ġ bÃŃ", + "Ġb ÃŃ", + "ĠCL IIIK", + "ĠIIC III", + "Ġh erk", + "Ġhe rk", + "Ġher k", + "ãĤı ãģĽ", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Âł ³³³³³", + "³³ ³³³³", + "³³³³ ³³", + "³³³ ³³³", + "³³³³³ Âł", + "اÙĦ د", + "Ġdav ran", + "Äį er", + "Äįe r", + "Ġ ØŁ", + "ĠØ Ł", + "ãģĺãĤĥ ãģªãģĦ", + "Ġd air", + "Ġda ir", + "Ġdai r", + "Ġî ¥¤", + "ั à¸ĩส", + "ัà¸ĩ ส", + "Ġ ëĭ´", + "Ġëĭ ´", + "å¾ ŀ", + "ĠÑįÑĤ иÑħ", + "ĠÑįÑĤи Ñħ", + "è¯ º", + "á» ·", + "е ÑĢиÑģÑĤи", + "еÑĢи ÑģÑĤи", + "ов ÑĭÑħ", + "Ġ ãĥĩ", + "Ġãĥ ĩ", + "ض ÙĬ", + "Ġà¤ī à¤ł", + "Ġnap ÅĻÃŃklad", + "è ´Ŀ", + "è´ Ŀ", + "Ġ Å¡k", + "ĠÅ¡ k", + "ĠبÙĪد ÙĨد", + "ĠبÙĪدÙĨ د", + "vů li", + "éģ ĩ", + "Ġз най", + "Ġзна й", + "Ġзн ай", + "ĠT ham", + "ĠTh am", + "r ani", + "ra ni", + "ran i", + "ا ØŃت", + "اØŃ Øª", + "Ø´ Ùĩ", + "мÑĸнÑĸ ÑģÑĤÑĢа", + "๠ĭ", + "ĠÎij να", + "ĠÎijν α", + "à¥ĭ à¤ļ", + "ç»Ħ ç»ĩ", + "ÑģÑĤ иÑĤ", + "ÑģÑĤи ÑĤ", + "im li", + "åIJį çĦ¡ãģĹãģķãĤĵ", + "åIJįçĦ¡ãģĹ ãģķãĤĵ", + "Ùij Ø©", + "θ μ", + "о лоÑĤ", + "ол оÑĤ", + "оло ÑĤ", + "ย à¸ĩ", + "ãĤī ãĤĮãĤĭ", + "ãĤīãĤĮ ãĤĭ", + "Ġ лиÑĩ", + "Ġл иÑĩ", + "Ġли Ñĩ", + "ов Ñĭе", + "éĢ ĥ", + "Ġ 广", + "Ġå¹ ¿", + "ìĬ ¬", + "Ùħ ÛĮÙĨ", + "ÙħÛĮ ÙĨ", + "ĠìłĦ ì²´", + "ĠÎŃ Ïĩ", + "Ġ ì±ħ", + "Ġì± ħ", + "Ġ hlas", + "Ġh las", + "Ġhl as", + "е кÑĤив", + "ек ÑĤив", + "екÑĤ ив", + "екÑĤи в", + "ĠÏĢ λη", + "lu ÄŁu", + "luÄŁ u", + "好 çļĦ", + "ĠÚĨ ÙĪÙĨ", + "ĠB eled", + "ĠBe led", + "ĠBel ed", + "Ġen gel", + "Ġeng el", + "нÑı Ñı", + "ĠyaÅŁ an", + "Ñĩ ниÑħ", + "ار ÙĬØ©", + "ارÙĬ Ø©", + "म त", + "ãĥĭãĥĭ ãĥĭãĥĭ", + "åĭ ¢", + "Ġ åĨħ", + "ĠåĨ ħ", + "Ġíı¬ íķ¨", + "Ġоб Ñģ", + "Ġth ấp", + "Ġd ây", + "ãĥĸ ãĥ©", + "а ÑĤÑĭ", + "аÑĤ Ñĭ", + "ĠÑģво ей", + "ĠÑģвое й", + "ãĤī ãģªãģĦ", + "åıij çĶŁ", + "e rece", + "er ece", + "ere ce", + "Ġod bor", + "Ġв неÑģ", + "Ġвне Ñģ", + "ĠÄIJ ảng", + "ĠëıĮ ìķĦ", + "ÄĽ li", + "ÄĽl i", + "ı sında", + "ıs ında", + "ısı nda", + "Ġबद ल", + "v nÃŃ", + "vn ÃŃ", + "ãģ® ãģ«", + "Ġпо ÑĤом", + "ĠпоÑĤ ом", + "im de", + "imd e", + "a lama", + "al ama", + "ala ma", + "alam a", + "âĢ ª", + "Ġstej nÄĽ", + "е ÑĢе", + "еÑĢ е", + "éĴ ¢", + "æľº æŀĦ", + "Ġ è³", + "Ġè ³", + "åĶ ±", + "Ġ ëħ¸ì¶ľ", + "Ġëħ¸ ì¶ľ", + "Ġли бо", + "âĢ Ĭ", + "Ġc ez", + "Ġce z", + "ro mÄĽ", + "rom ÄĽ", + "ί Ïīν", + "ÏĨ ή", + "Ġ íĻ©", + "ĠíĻ ©", + "Ġdlou h", + "éª ¨", + "åħ¬ éĩĮ", + "ä¼ ¸", + "Ġ ãĥij", + "Ġãĥ ij", + "ä» Ļ", + "Ġol madı", + "Ġolm adı", + "Ġolma dı", + "е лиÑĩ", + "ел иÑĩ", + "ели Ñĩ", + "ожд ениÑı", + "Ġsöy ledi", + "Ġsöyl edi", + "á tek", + "át ek", + "áte k", + "ìĥ µ", + "ย วà¸ģ", + "ยว à¸ģ", + "Ġ 鼻", + "ĠéĽ »", + "Ġп ев", + "ĠдÑĢÑĥг ие", + "át ku", + "Ġع ÙĪ", + "ov ána", + "ová na", + "ován a", + "ض ر", + "Ġëģ Ŀ", + "ĠíĨµ íķ´", + "Î ĸ", + "Ġv ur", + "Ġvu r", + "åĨ ²", + "Ġп ÑĢек", + "ĠпÑĢ ек", + "ĠпÑĢе к", + "Ġप à¤ķ", + "Ġ à¹Ģà¸Ĺ", + "Ġà¹Ģ à¸Ĺ", + "Ġà¹ĢภĹ", + "ãģ¨ ãģĭ", + "ع ÙĨ", + "å® ĩ", + "ÏĦ ζ", + "Ġn ằm", + "ĠÑģ воб", + "ĠÑģв об", + "ĠÑģво б", + "Ġδ Ïį", + "çĸ Ĺ", + "- й", + "é¦Ļ 港", + "ت ا", + "Ïĥι μο", + "íķ Ħ", + "Ġ 详æĥħ", + "Ġè¯ ¦æĥħ", + "ä¸ ¡", + "Ùİ Ø§ÙĦ", + "Ùİا ÙĦ", + "ĠTr Æ°á»Ŀng", + "e ného", + "en ého", + "ené ho", + "ĠÑĢекомен дÑĥ", + "ÛĮ رÙĩ", + "ÛĮر Ùĩ", + "า à¸ĸ", + "าภĸ", + "ĠÚ© اÙħÙĦ", + "ĠکاÙħ ÙĦ", + "ب Ø·", + "ز ÛĮÙĨÙĩ", + "زÛĮ ÙĨÙĩ", + "Ġдолж на", + "Ġë§İ ìĿĢ", + "âĹıâĹıâĹıâĹı âĹıâĹıâĹıâĹı", + "lep Å¡ÃŃ", + "ал ог", + "ало г", + "ãĤª ãĥ³", + "Ġ ë³Ħ", + "Ġë³ Ħ", + "ı rı", + "ır ı", + "ĠجاÙħ عÙĩ", + "ĠجاÙħع Ùĩ", + "æĽ ľ", + "o jÃŃ", + "oj ÃŃ", + "ĠÑĪ лÑıÑħ", + "Ġhız lı", + "Ġ خصÙĪص", + "Ġخص ÙĪص", + "ÐIJ ÑĢ", + "å ľĺ", + "åľ ĺ", + "Ġжив оÑĤ", + "é ±", + "Ġng ữ", + "Ġv òng", + "èİ «", + "Ġза Ñħод", + "ĠзаÑħ од", + "ìĻ Ħ", + "ĠÑģлед ÑĥÑİÑī", + "éĹ »", + "Ñij ÑĢ", + "Ġch vÃŃ", + "èĥ ľ", + "ãģª ãģĹ", + "Ġtek noloj", + "Ġtekn oloj", + "ej ména", + "Ġ ìłĪ", + "Ġìł Ī", + "ì³ IJ", + "æĻ® éĢļ", + "Ġvý ro", + "Ġay rı", + "Ġayr ı", + "Ġп ÑĢев", + "ĠпÑĢ ев", + "ĠпÑĢе в", + "Ġgó p", + "à¹Ĥ à¸ģ", + "à¸Ĺำ à¹ĥห", + "åı İ", + "åĺ ī", + "Ġte lev", + "Ġtele v", + "Ġtel ev", + "ãģ¨ ãģĵãĤį", + "ëı Į", + "ph yl", + "phy l", + "ร าะ", + "Ġ çĪ", + "Ġç Ī", + "ÑģÑĤ иÑĤÑĥ", + "ÑģÑĤи ÑĤÑĥ", + "ÑģÑĤиÑĤ Ñĥ", + "ï¼Į è¿ĺ", + "ĠÎij γ", + "Äį ku", + "æı ´", + "ाय त", + "æı ı", + "ãĤĤ ãģĹ", + "ĠпеÑĢ еÑģ", + "ĠпеÑĢе Ñģ", + "Ġìĺģ íĻĶ", + "id la", + "idl a", + "åİ ħ", + "ï¼ı :", + "ت رÛĮ", + "تر ÛĮ", + "à¸Ľ à¸ı", + "ĠнаÑģ еленнÑı", + "Ġam aç", + "Ġama ç", + "Ġk do", + "Ġkd o", + "Ġиз веÑģÑĤ", + "ÑĪ иÑĢ", + "ÑĪи ÑĢ", + "ì£ ł", + "Å¡ it", + "Å¡i t", + "Ġt á»ijc", + "Ġtá»ij c", + "ìŀIJ ìĿĺ", + "Ñĩ аÑĤ", + "Ñĩа ÑĤ", + "åı ĥ", + "éĽ ¶", + "å° º", + "Ġ indir", + "Ġin dir", + "Ġind ir", + "Ġна ÑĨÑĸоналÑĮ", + "Ġx anh", + "Ġxa nh", + "ÛĮ دÛĮ", + "ÛĮد ÛĮ", + "Ġин ÑĤеÑĢеÑģ", + "ĠинÑĤеÑĢ еÑģ", + "ĠØ¢ سÛĮ", + "Ġآس ÛĮ", + "éĤ£ 个", + "Ġb ilm", + "Ġbi lm", + "Ġbil m", + "а не", + "ан е", + "ĠtÄĽch to", + "Ñĩ ик", + "Ñĩи к", + "Ġдо Ñħод", + "èĤ¡ 份", + "åħ³ ç³»", + "ãģ«ãģª ãģ£ãģŁ", + "ĠпÑĢед пÑĢи", + "Ġgeç en", + "Ġب ÙĤ", + "Ġvý znam", + "Ġ à¹Ģà¸Ħร", + "Ġà¹Ģ à¸Ħร", + "Ġà¹Ģà¸Ħ ร", + "ĠÑħ ÑĤо", + "Ø´ ÙĬ", + "åıĤ åĬł", + "ÑģÑĤв енного", + "ÑģÑĤвен ного", + "ÑģÑĤвенно го", + "ÑĤ ÑĢон", + "ÑĤÑĢ он", + "ÑĤÑĢо н", + "ÂĢÂĢ ÂĢÂĢ", + "æ¢ Ŀ", + "б ав", + "ба в", + "Û± Û¶", + "Û±Û ¶", + "é¡ º", + "Ġj az", + "Ġja z", + "ĠاÙĦ ÙħÙĦ", + "ĠاÙĦÙħ ÙĦ", + "Ġا ثر", + "Ġاث ر", + "ĠпÑĢи вод", + "ĠпÑĢив од", + "а нÑĥ", + "ан Ñĥ", + "à¥ģ à¤Ń", + "à¥ģठŃ", + "æĹ §", + "ÑĮ е", + "ส ล", + "л ÑıÑİÑĤ", + "лÑı ÑİÑĤ", + "ว à¸Ķ", + "Æ° Ỽi", + "Æ°á» Ľi", + "Æ°á»Ľ i", + "ÙĬ ÙħØ©", + "ÙĬÙħ Ø©", + "ãĤ¯ ãĥŃ", + "л ий", + "ли й", + "γ Ïģά", + "Ġper forman", + "Ġperform an", + "Ġperf orman", + "Ġperfor man", + "è¯ ī", + "ä½ł çļĦ", + "ìħ Ķ", + "н ениÑı", + "не ниÑı", + "нен иÑı", + "á»Ń i", + "ÙĪ زÛĮ", + "ÙĪز ÛĮ", + "éŁ ¿", + "à¥Ī द", + "Ġëª ¸", + "Ġe ser", + "Ġes er", + "Ġese r", + "ĠÙģعاÙĦ ÛĮت", + "нÑĸ веÑĢ", + "нÑĸв еÑĢ", + "κ Ïģα", + "è¨ ¼", + "Ġn emoc", + "Ġnem oc", + "Ġyardım cı", + "Ġ çī¹", + "Ġçī ¹", + "Ġ коп", + "Ġк оп", + "Ġко п", + "ĠÐľ ож", + "़ à¤ķ", + "Ġ ëľ", + "Ġë ľ", + "ĠÑĢе ак", + "Ġp ozor", + "Ġpoz or", + "Âł ÐIJ", + "Ġ ÙĬÙĥ", + "ĠÙĬ Ùĥ", + "ĠÑģ ад", + "Ġ åħ«", + "Ġåħ «", + "Ġп олÑĮз", + "ĠполÑĮ з", + "Ġra ÄŁmen", + "ter nÃŃ", + "tern ÃŃ", + "s iyon", + "si yon", + "Ñģ ÑıÑĩ", + "ÑģÑı Ñĩ", + "ov aný", + "ova ný", + "ovan ý", + "ĠëĮĢíķľ 민êµŃ", + "ĠвÑĸд б", + "ĠÐIJ нд", + "ĠÐIJн д", + "st va", + "éĮ Ħ", + "Ġ ëij", + "Ġë ij", + "ิ à¸Ħ", + "j ÃŃt", + "jÃŃ t", + "Ġkullan ıcı", + "Ġkullanı cı", + "Ġ æŁ¥çľĭ", + "ĠæŁ¥ çľĭ", + "Ùģ ÙĦ", + "Ġ ЯкÑīо", + "ĠЯк Ñīо", + "çľĭ åĪ°", + "ÑĢ еÑħ", + "ÑĢе Ñħ", + "ĠاÙĦع ربÙĬØ©", + "ĠاÙĦعرب ÙĬØ©", + "ĠاÙĦعربÙĬ Ø©", + "ë¡ľê·¸ ëŀ¨", + "Ġब à¤ľ", + "Ġп ÑĢип", + "ĠпÑĢ ип", + "ĠпÑĢи п", + "Ġs chop", + "Ġsc hop", + "Ġsch op", + "Ġscho p", + "Ġب اÙĦا", + "Ġبا ÙĦا", + "ĠباÙĦ ا", + "å® ħ", + "Ġا ÙĦÙħÙĩ", + "ĠاÙĦ ÙħÙĩ", + "ĠاÙĦÙħ Ùĩ", + "α να", + "αν α", + "à¥ĭ व", + "åģ ´", + "å¼Ģ åıij", + "Ùħ اÙĦ", + "Ùħا ÙĦ", + "Ġ धर", + "Ġध र", + "Ġda hil", + "Ġdah il", + "Ġdahi l", + "ãĢģ ãģĵãģ®", + "ัà¸Ī à¸Ī", + "Ñģп ÑĸлÑĮ", + "Ġà¤ķ प", + "Ġв еÑĩ", + "Ġве Ñĩ", + "Ġвид а", + "Ġви да", + "ĠÙħ عÙĨ", + "ĠÙħع ÙĨ", + "ĠоÑĤ ли", + "i á»ħ", + "iá» ħ", + "л иÑĪ", + "ли ÑĪ", + "Ġ ÐŁÐ¾Ñģле", + "ĠÐŁÐ¾Ñģ ле", + "ãģĵ ãģĵ", + "Ġk ültür", + "Ġ جر", + "Ġج ر", + "Ġ æ¼", + "Ġæ ¼", + "èĩ º", + "Ġmev cut", + "Ù¾ ÛĮ", + "ĠاÙĦ سÙĦاÙħ", + "ĠاÙĦس ÙĦاÙħ", + "иÑĤ елей", + "иÑĤе лей", + "Ġ ÑĢоÑģÑĤ", + "ĠÑĢ оÑģÑĤ", + "ĠÑĢоÑģ ÑĤ", + "Ġed il", + "Ġedi l", + "Ġ å·²", + "Ġå· ²", + "ç²¾ åĵģ", + "ä» ħ", + "âĢĻ ye", + "âĢĻy e", + "à¥Īà¤Ĥ .", + "Ġ åĨĨ", + "ĠåĨ Ĩ", + "ëĪ Ħ", + "Ġ ìĻķ", + "ĠìĻ ķ", + "æĺ Ń", + "ĠÎļ ο", + "m eden", + "med en", + "me den", + "Ġo lab", + "Ġol ab", + "Ġola b", + "ĠÚ© ÙĪد", + "ĠÚ©ÙĪ د", + "à¸Ħ าส", + "ен наÑı", + "æĬ ¼", + "yl ül", + "Ġsev iy", + "Ġd ÄĽti", + "ĠdÄĽ ti", + "ĠdÄĽt i", + "âĢ¬ Ċ", + "Ġع ز", + "Ġu á»ijng", + "Ġس رÙħ", + "Ġسر Ùħ", + "е не", + "ен е", + "Ġмал енÑĮ", + "Ġ вÑĸдом", + "Ġв Ñĸдом", + "ĠвÑĸд ом", + "ĠвÑĸ дом", + "ั à¸ļà¸Ĺ", + "ัà¸ļ à¸Ĺ", + "ĠTh ái", + "Ġà¤Ĩव श", + "rove ÅĪ", + "çĽ £", + "ĠÑı зÑĭ", + "ĠO y", + "å£ ģ", + "в аÑĤÑĮ", + "ва ÑĤÑĮ", + "ваÑĤ ÑĮ", + "л адÑĥ", + "ла дÑĥ", + "лад Ñĥ", + "ا صÙĦ", + "اص ÙĦ", + "ot ÅĻeb", + "د ÙĬØ«", + "دÙĬ Ø«", + "íı °", + "νο μ", + "г оÑĢод", + "го ÑĢод", + "гоÑĢ од", + "Ġm uh", + "Ġmu h", + "âĢĻ l", + "ÑģÑĤ воÑĢ", + "ÑģÑĤв оÑĢ", + "ÑģÑĤво ÑĢ", + "åħ Ħ", + "Ðķ Ðł", + "Ø· ÙĦ", + "éľ ĩ", + "Ùİ Øª", + "Ġb lÃŃ", + "Ġbl ÃŃ", + "Ġed ildi", + "Ġedil di", + "éĿ ł", + "äºĮ åįģ", + "æĹ Ĺ", + "Ġç iz", + "ĠÄij ảo", + "Ġo pat", + "Ġop at", + "o ÄŁan", + "oÄŁ an", + "ë² Į", + "Ġ éł", + "Ġé ł", + "Ġseb ep", + "Ġsebe p", + "Ñĥ ÑĤи", + "ÑĥÑĤ и", + "åĪ º", + "Ø· ب", + "ev Å¡ÃŃm", + "c hop", + "ch op", + "cho p", + "çĶ ļ", + "Ġngh á»ģ", + "Ġп аÑĢÑĤ", + "ĠпаÑĢ ÑĤ", + "ุ à¸Ħ", + "Ú© ÛĮÙĦ", + "Ú©ÛĮ ÙĦ", + "d um", + "du m", + "Ġor tak", + "Ġort ak", + "Ġorta k", + "ãģŁ ãģĹ", + "Ġoby vatel", + "Ġv ých", + "Ġvý ch", + "Ġv eren", + "Ġver en", + "Ġve ren", + "Ġvere n", + "Ġв еÑģÑĮ", + "ĠвеÑģ ÑĮ", + "Ġве ÑģÑĮ", + "ĠÐĶ а", + "Ġ íķĺì§Ģë§Į", + "Ġíķĺ ì§Ģë§Į", + "Ġíķĺì§Ģ ë§Į", + "å¦Ĥ æѤ", + "Ġमह त", + "ัà¸ĩà¸ģ ฤษ", + "ãĢĤ è¿Ļ", + "Ġ гал", + "Ġг ал", + "Ġsa nat", + "Ġsan at", + "Ġsana t", + "éł Ĩ", + "ĠÑģам о", + "å Ľ°", + "åĽ °", + "ี à¸Ń", + "ĠBaÅŁ kan", + "ÏĦ οÏħÏĤ", + "ÏĦοÏħ ÏĤ", + "Ġyap tıģı", + "Ġyaptı ģı", + "Ġyaptıģ ı", + "ÅĻ it", + "ÅĻi t", + "ĠÑģ ÑĸлÑĮ", + "ान त", + "Ġ ÙĨت", + "ĠÙĨ ت", + "Ġkh Äĥn", + "à¸Ĭ à¸Ļะ", + "à¸Ĭà¸Ļ ะ", + "м ини", + "ми ни", + "мин и", + "ãĥ¬ ãĥ¼", + "ë Ĥ¬", + "ëĤ ¬", + "éħĴ åºĹ", + "ĠاÙĦÙĬ ÙĪÙħ", + "ä¹ Ĺ", + "à¸Ħรà¸ĩ à¸ģาร", + "Ùģ اÙĤ", + "Ġ à¤ıस", + "Ġà¤ı स", + "Ġ æ¡", + "Ġæ ¡", + "Ú¯ Ø°", + "Ġà¤ĩ ल", + "е лениÑı", + "ел ениÑı", + "еле ниÑı", + "елен иÑı", + "à¸ģ รà¸ĵ", + "à¸ģร à¸ĵ", + "举 西", + "ÎŁ Îľ", + "ÎŁÎ ľ", + "Ġm áºŃt", + "Ġs nÃŃ", + "Ġsn ÃŃ", + " IJ", + "à¹Ģร า", + "íķ´ ìķ¼", + "Ġ ìĦľë¹ĦìĬ¤", + "ĠìĦľ ë¹ĦìĬ¤", + "Ġداخ ÙĦ", + "Ġth ắng", + "íĥ Ī", + "а вÑģÑı", + "ав ÑģÑı", + "Ġ Ñĸм", + "ĠÑĸ м", + "ا Ùħت", + "اÙħ ت", + "Ġ ÙĪÙĤت", + "ĠÙĪ ÙĤت", + "ĠÙĪÙĤ ت", + "à¥Ĥ à¤ģ", + "Ġ èIJ", + "Ġè IJ", + "Ġ سÙĦاÙħ", + "Ġس ÙĦاÙħ", + "ĠسÙĦ اÙħ", + "Ġvz dÄĽl", + "å¸Į æľĽ", + "åŃĺ æ¡£", + "Ġ à¸Ĺำ", + "Ġà¸Ĺ ำ", + "ĠвÑĸй ÑģÑĮ", + "а ÑĢан", + "аÑĢ ан", + "аÑĢа н", + "ĠÑĢ Ñĸк", + "Ġп иÑģÑĮ", + "Ġпи ÑģÑĮ", + "ĠпиÑģ ÑĮ", + "Ġá¼ IJ", + "기 ëıĦ", + "ĠпоÑģÑĤ оÑıн", + "Ġ åĮĹ京", + "ĠåĮĹ äº¬", + "ĠNÄĽ m", + "Ø´ ÙĨاÙħÙĩ", + "Ø´ÙĨ اÙħÙĩ", + "Ġdal Å¡ÃŃch", + "ĠdalÅ¡ÃŃ ch", + "Ġب اع", + "Ġبا ع", + "Ġpo hy", + "Ġpoh y", + "ا ÙĦÙģ", + "اÙĦ Ùģ", + "à¸ŀ วà¸ģ", + "é ĭ", + "Ġ cih", + "Ġc ih", + "Ġci h", + "Ù ¢", + "ä¸ ´", + "ãĤ¯ ãĥĪ", + "п нÑı", + "Ġ дал", + "Ġд ал", + "Ġда л", + "ÙĴ ر", + "ãĢĢ ãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢãĢĢĠãĢĢ ĠãĢĢ", + "ãĢĢãĢĢĠ ãĢĢĠãĢĢ", + "æĬ¥ åijĬ", + "ÙĪ دÛĮ", + "ÙĪد ÛĮ", + "á» £i", + "ợ i", + "ÑĨ ÑĸÑĶÑİ", + "ÑĨÑĸ ÑĶÑİ", + "Ġ ãĥĢ", + "Ġãĥ Ģ", + "ĠÑģÑĤ еп", + "r až", + "ra ž", + "ĠS aÄŁ", + "ĠSa ÄŁ", + "Ġt uyến", + "Ġtuy ến", + "Ġal mak", + "Ġalma k", + "Ġalm ak", + "Ġзаболева ниÑı", + "Ġ ÏĥÏĩ", + "ĠÏĥ Ïĩ", + "Ġ íĭ", + "Ġí ĭ", + "Ġв им", + "Ġви м", + "ç¡ ¬", + "Ġ äºĶ", + "Ġi kinci", + "Ġik inci", + "ุ à¸į", + "ส าว", + "ĠìĦ¸ ê³Ħ", + "ĠÙħ ØŃÙĦ", + "ĠÙħØŃ ÙĦ", + "ระ หว", + "Ġelek tron", + "Ġelekt ron", + "Ġelektr on", + "Ġh ại", + "Ġhạ i", + "æĹ ¢", + "Ġ íĸ¥", + "Ġíĸ ¥", + "Ġji né", + "Ġjin é", + "Ġng he", + "Ġngh e", + "æij ©", + "ĠÑģо бÑĸ", + "ĠÑģоб Ñĸ", + "Æ ¯", + "ÑĤ ÑĥÑĢ", + "ÑĤÑĥ ÑĢ", + "æ±½ 车", + "Ø´ اÙĩ", + "Ġd Ãłnh", + "ä¸ ¹", + "ä»Ĭ æĹ¥", + "ãĥIJ ãĥ¼", + "в аниÑı", + "ва ниÑı", + "ван иÑı", + "Ġ ساÙħ", + "Ġس اÙħ", + "çݯ å¢ĥ", + "ĠاÙĦ ÙħÙĨت", + "ĠاÙĦÙħ ÙĨت", + "ĠاÙĦÙħÙĨ ت", + "ĠÑģеÑĢ д", + "éģ ł", + "ε ÏĦ", + "Ġав ÑĤ", + "าà¸ĩ ว", + "Ġvz tah", + "ru ž", + "алÑĮ наÑı", + "алÑĮна Ñı", + "Ġطر اØŃÛĮ", + "à¹Ĥรà¸ĩ à¹ģรม", + "ĠÄį asto", + "ĠÄįas to", + "ĠÄįast o", + "Ġ ê¼", + "Ġê ¼", + "Ïĥ ÏĦÏĮ", + "ÏĥÏĦ ÏĮ", + "Ġbu rada", + "Ġbur ada", + "ĠÄ° z", + "Ġ ê·¸ëŀĺ", + "Ġê·¸ ëŀĺ", + "å² Ľ", + "ĠØ´ ÙĪÙĨد", + "ĠØ´ÙĪ ÙĨد", + "Å¡ ek", + "Å¡e k", + "Ġ ìĿ´ìķ¼", + "ĠìĿ´ ìķ¼", + "ãĤĮ ãģªãģĦ", + "ê· ¹", + "la mÄ±ÅŁ", + "lam Ä±ÅŁ", + "ä» į", + "cház et", + "cháze t", + "Ġ ÑģÑĥÑĤ", + "ĠÑģ ÑĥÑĤ", + "ĠÑģÑĥ ÑĤ", + "æĹł æ³ķ", + "æµ ¦", + "ÄĽ la", + "ÄĽl a", + "à¹ĥà¸Ļ à¸Ĭ", + "Ġc ân", + "Ġcâ n", + "ÎŁ Îĵ", + "ÎŁÎ ĵ", + "Ġz vý", + "Ġzv ý", + "ĠÙ¾ ار", + "Ġپا ر", + "Ġк лÑĸ", + "Ġкл Ñĸ", + "Ġn ové", + "Ġno vé", + "Ġnov é", + "çĶ ĺ", + "ë¹ ł", + "m á", + "Ġ Ñģол", + "ĠÑģ ол", + "ĠÑģо л", + "à¤ķ रण", + "à¤ķर ण", + "н оÑĩ", + "но Ñĩ", + "Ġ fik", + "Ġf ik", + "Ġfi k", + "Ġà¤ľ à¤Ĺ", + "à¹ĩ à¸Ļà¸ķ", + "à¹ĩà¸Ļ à¸ķ", + "ĠÙħ تØŃ", + "ĠÙħت ØŃ", + "Ġph iên", + "Ġphi ên", + "Ġol sun", + "Ġк аб", + "Ġка б", + "Ġh út", + "èĦ ±", + "Ġ åĸ", + "Ġå ĸ", + "ĠH ải", + "Ġ tÄĽÅ¾", + "Ġt ÄĽÅ¾", + "ĠtÄĽ ž", + "Ġth ái", + "Ġ تاب", + "Ġت اب", + "Ġتا ب", + "- ÐŁ", + "Ø« ار", + "çĨ Ĭ", + "Ġ ними", + "Ġн ими", + "Ġни ми", + "Ġним и", + "Ġzp rac", + "Ġत ह", + "Ġм акÑģим", + "Ġмак Ñģим", + "m eyi", + "me yi", + "mey i", + "ĠÑģ оÑĨи", + "ĠÑģо ÑĨи", + "æ² Ĵ", + "ĠìķĬ ëĬĶ", + "＿ _", + "åķ ¦", + "ĠاÙĨ ÙĪاع", + "æļ ´", + "ä¸Ĭ æµ·", + "åħ· æľī", + "à¥ģ ब", + "à¥ģठ¬", + "ìķ Ļ", + "Ġíģ °", + "Ġíŀ ĺ", + "Ġtr ánh", + "ि यन", + "िय न", + "ãģ¾ ãģ¾", + "п оÑĩ", + "по Ñĩ", + "m ÄĽr", + "mÄĽ r", + "å³ °", + "ĠÙħ صر", + "ĠÙħص ر", + "ĠÑįÑĦ ÑĦекÑĤив", + "ĠÑįÑĦÑĦек ÑĤив", + "Ġ çı", + "Ġç ı", + "ler iyle", + "leri yle", + "âĪ ļ", + "Ġ ì¶ķ", + "Ġì¶ ķ", + "Ġ ê²Įìĭľ", + "Ġê²Į ìĭľ", + "ìĿ ij", + "Ġ poÅĻád", + "Ġpo ÅĻád", + "Ġشب Ú©Ùĩ", + "اÙĩ Ø´", + "ĠخدÙħ ات", + "Ġna Å¡e", + "ĠnaÅ¡ e", + "ν οÏį", + "νο Ïį", + "Ġyön elik", + "Ġk ork", + "Ġko rk", + "Ġkor k", + "ا ÙĩÙħ", + "اÙĩ Ùħ", + "è° Ī", + "Ġ μη", + "Ġμ η", + "Ġd olar", + "Ġdo lar", + "Ġdol ar", + "çµ ¦", + "ĠÎķ Ïħ", + "Ġobdob ÃŃ", + "Ġ μÏĮ", + "Ġμ ÏĮ", + "à¹Ģ à¸Ńà¸ģ", + "à¹Ģà¸Ń à¸ģ", + "Ġپاس Ø®", + "è¡ ¥", + "ا عد", + "اع د", + "ãĤī ãģĦ", + "ÎŃ Î»", + "и ÑĤÑĭ", + "иÑĤ Ñĭ", + "Ġ ëħ¼", + "Ġëħ ¼", + "Ġ^{ [", + "ί γ", + "æł ij", + "l ında", + "lı nda", + "ĠìŬ 룬", + "£ £", + "ÅĻ il", + "ÅĻi l", + "Ġав ÑĤоÑĢ", + "ĠавÑĤ оÑĢ", + "ÏĦ ικÏĮÏĤ", + "ÏĦικ ÏĮÏĤ", + "ÏĦικÏĮ ÏĤ", + "u dur", + "ud ur", + "udu r", + "Ġc Æ°", + "Ġk ıy", + "Ġkı y", + "Ñģ ем", + "Ñģе м", + "ĠØ£ بÙĪ", + "Ġأب ÙĪ", + "ÏĦ ικÏİν", + "ÏĦικ Ïİν", + "Û± Û·", + "Û±Û ·", + "è² ¸", + "Ġп ÑĢож", + "ĠпÑĢ ож", + "ĠпÑĢо ж", + "ün cü", + "Ġ нÑĸÑĩ", + "Ġн ÑĸÑĩ", + "ĠнÑĸ Ñĩ", + "Ġ मत", + "Ġम त", + "ãģķ ãĤĮãģ¦ãģĦãĤĭ", + "ãģķãĤĮ ãģ¦ãģĦãĤĭ", + "ãģķãĤĮãģ¦ ãģĦãĤĭ", + "ا صر", + "اص ر", + "Ġع ÙĤ", + "ĠкаÑĩе ÑģÑĤве", + "ĠÐĵ еÑĢ", + "ĠÐĵе ÑĢ", + "åº Ĩ", + "Ù ¹", + "a larda", + "al arda", + "alar da", + "ĠÙ¾ رس", + "Ġپر س", + "и ÑĩеÑģкой", + "иÑĩеÑģ кой", + "Ġp him", + "Ġph im", + "Ġphi m", + "ί νη", + "ίν η", + "ä¸ĩ åĨĨ", + "i lerini", + "iler ini", + "ileri ni", + "ilerin i", + "ãĢģ 大", + "Ġo lsa", + "Ġol sa", + "æł¹ æį®", + "âĢĮ س", + "ĠTh ủ", + "r oje", + "ro je", + "roj e", + "нÑĮ оÑĹ", + "нÑĮо ÑĹ", + "Ġs lou", + "Ġsl ou", + "Ġslo u", + "ี ฬ", + "ıy orum", + "ıyor um", + "ÄĽ j", + "Ġ خبر", + "ĠØ® بر", + "è® Ĭ", + "Ġ 缸", + "ĠçĽ ¸", + "e lerinin", + "eler inin", + "eleri nin", + "elerin in", + "elerini n", + "íķĻ ëħĦëıĦ", + "íķĻëħĦ ëıĦ", + "ÑĩеÑģ кие", + "Ñĩе Ñģкие", + "ÑĩеÑģки е", + "ĠÅŁ ekl", + "ĠÅŁek l", + "Ġز ÙħاÙĨÛĮ", + "ĠزÙħاÙĨ ÛĮ", + "ĠزÙħ اÙĨÛĮ", + "Ġ xin", + "Ġx in", + "Ġxi n", + "ัà¸ģ à¸ĩาà¸Ļ", + "ĠE kim", + "ĠEk im", + "æĦ ¿", + "Ġод ной", + "Ġодно й", + "ν ή", + "æľĢ æĸ°", + "ĩ ¼", + "Ġн иж", + "Ġни ж", + "Ġ ë³¼", + "Ġë³ ¼", + "è· ij", + "Ġна пиÑģ", + "Ġнап иÑģ", + "èģ ĸ", + "Ġ âĢĮ", + "ĠâĢ Į", + "æłĩ åĩĨ", + "Ġv rát", + "Ġvr át", + "ĠV ì", + "Ġ ÙģراÙĨ", + "ĠÙģ راÙĨ", + "ĠÙģر اÙĨ", + "æĿ¥ çļĦ", + "å§ ¿", + "Ñħ Ñĥ", + "ĠبÛĮر ÙĪÙĨ", + "Ġд ÑĥÑĪ", + "ĠдÑĥ ÑĪ", + "в аÑİÑĤ", + "ва ÑİÑĤ", + "Ġs ebe", + "Ġse be", + "Ġseb e", + "é» ĺ", + "Ġkay ıt", + "Ġkayı t", + "Ïģ θ", + "ãģ¨ ãģ®", + "ĠпÑĢоÑĨ еÑģÑģ", + "ĠпÑĢоÑĨеÑģ Ñģ", + "æĮģ ãģ¡", + "Ñĸ на", + "Ñĸн а", + "Ġ ÑĤоÑĤ", + "ĠÑĤ оÑĤ", + "ĠÑĤо ÑĤ", + "ĠÑĤак ие", + "ĠÑĤа кие", + "The o", + "Th eo", + "ĠÙĨ ÛĮر", + "ĠÙĨÛĮ ر", + "ÑĨ Ñĥ", + "Ġay ak", + "à¸Ļ à¸Ń", + "Ġsit esinde", + "Ġsites inde", + "Ġsitesi nde", + "ĠÚ©ÙĨ ÛĮÙħ", + "ĠÑģ оÑħ", + "ĠÑģо Ñħ", + "Ġम à¤ľ", + "Ġol uyor", + "ç½ij åĿĢ", + "ĠÙ¾ زش", + "ĠE ylül", + "d Ã¼ÄŁ", + "dü ÄŁ", + "Ġبر Ø®ÛĮ", + "Ġبرخ ÛĮ", + "ĠÙħع رÙģ", + "Ġ obec", + "Ġo bec", + "Ġob ec", + "Ġobe c", + "ĠçalÄ±ÅŁ ma", + "Ġçal Ä±ÅŁma", + "ìĦ¼ íĦ°", + "ĠÑģво ÑĶ", + "оÑģÑĤ ей", + ": ::::::::::", + ":: :::::::::", + ":::: :::::::", + ":::::: :::::", + ":::::::: :::", + "::: ::::::::", + "::::: ::::::", + "::::::: ::::", + "::::::::: ::", + ":::::::::: :", + "Ġ алÑĮ", + "Ġа лÑĮ", + "Ġал ÑĮ", + "ç« Ł", + "Ġباش ÙĨد", + "اÙĦ Ø«", + "Ġнай б", + "Ġп ока", + "Ġпо ка", + "Ġпок а", + "Î ŀ", + "ĠÙĪ Ø¥", + "Ġ Ø®ÙĪاÙĨ", + "ĠØ® ÙĪاÙĨ", + "ĠØ®ÙĪ اÙĨ", + "à¥ģप य", + "Ġ à¹ĥห", + "ĠбÑĭ ÑģÑĤÑĢо", + "Ġth á»Ń", + "Ġthá» Ń", + "ëģ ¼", + "Ġ å¤ļ", + "Ġå¤ ļ", + "两 个", + "ม à¸ķ", + "ز ارش", + "زار Ø´", + "زا رش", + "Ġ ëŁ", + "Ġë Ł", + "य ह", + "Ñī ина", + "Ñīи на", + "Ñīин а", + "ầ ng", + "ần g", + "ï½Ĺ ï½Ĺ", + "à¹Ģà¸ŀ ลà¸ĩ", + "à¹Ģà¸ŀล à¸ĩ", + "tv rt", + "ĠÑĸн ÑĪÑĸ", + "ĠÑĸнÑĪ Ñĸ", + "λ εί", + "λε ί", + "Ġv iá»ĩn", + "Ġvi á»ĩn", + "ij ¸", + "Ġ çĻ½", + "ĠçĻ ½", + "Ùİ ÙĪ", + "Ġch ứa", + "Ġchứ a", + "st vo", + "ĠdoÄŁ r", + "Ġ iler", + "Ġi ler", + "Ġil er", + "Ġile r", + "à¥ĭ ,", + "à¹ĥà¸Ļ à¸Ľ", + "Ġر ÙĪست", + "ĠرÙĪ ست", + "ÙĪ ÙĦÙĪ", + "ÙĪÙĦ ÙĪ", + "Å¡ lo", + "ал иÑģÑĤ", + "али ÑģÑĤ", + "åħ± åĴĮ", + "à¸ŀ ย", + "Ġ ìĻĢ", + "ĠìĻ Ģ", + "ÙĦ ÙĬÙĦ", + "ÙĦÙĬ ÙĦ", + "ĠÑı кого", + "ĠÑıк ого", + "е ÑģÑĤÑĮ", + "еÑģ ÑĤÑĮ", + "еÑģÑĤ ÑĮ", + "ĠÑĦ ин", + "ĠØ£ ÙĨÙĩ", + "ĠØ£ÙĨ Ùĩ", + "ĠMü dür", + "ĠÎĶ ια", + "ĠÎĶι α", + "ĠÑĤ ел", + "ĠÑĤе л", + "ि ,", + "Ñĥ ки", + "Ñĥк и", + "ĠÐł Ф", + "ĠMay ıs", + "à¹Ī à¸Ńม", + "à¹Īà¸Ń ม", + "ar ken", + "ark en", + "æĢ ķ", + "ب ÛĮÙĨ", + "بÛĮ ÙĨ", + "ÑĤ аÑħ", + "ÑĤа Ñħ", + "e bo", + "eb o", + "ë³´ ì¦Ŀê¸Ī", + "ĠÙ¾ ÙĦ", + "Ġг Ñĥб", + "Ġв клÑİÑĩ", + "Ġвк лÑİÑĩ", + "æĶ¿ æ²»", + "Ġε ÏĢιÏĥ", + "ĠεÏĢ ιÏĥ", + "ĠεÏĢι Ïĥ", + "ĠÙģار سÛĮ", + "ĠÙģارس ÛĮ", + "èŃ ī", + "ÏĨ η", + "( éĩij", + "ศ ร", + "åī §", + "âĢĻ ya", + "âĢĻy a", + "å¹´ 度", + "ĠÙĨ رÙħ", + "Ùĥ ÙĪÙħ", + "ÙĥÙĪ Ùħ", + "è¢ ĭ", + "Ġneden le", + "à¹īà¸Ńà¸ĩ à¸ģาร", + "à¹īà¸Ńà¸ĩà¸ģ าร", + "ãĢĮ ãģĤ", + "Ġп оÑģÑĤÑĥп", + "Ġпо ÑģÑĤÑĥп", + "ĠпоÑģÑĤ Ñĥп", + "ìľĦ ìĽIJ", + "åį ĺ", + "èİ ±", + "Ġum ož", + "p ok", + "po k", + "Ñĥ ÑģÑĤи", + "ÑĥÑģ ÑĤи", + "ÑĥÑģÑĤ и", + "Ġ éħ", + "Ġé ħ", + "ĠÑĦ Ñĸз", + "å» £", + "ิ หาร", + "Ġж ÑĥÑĢн", + "ĠдÑĸÑĤ ей", + "Ñĥ ÑİÑīие", + "ÑĥÑİ Ñīие", + "ÑĥÑİÑī ие", + "ä»Ĭ 天", + "ìĿ´ ëĿ¼ê³ł", + "ìĿ´ëĿ¼ ê³ł", + "ç² ī", + "èĴ Ļ", + "ĠDün ya", + "ĠDüny a", + "егод нÑı", + "Ġm imo", + "Ġmi mo", + "Ġmim o", + "Ġ вин", + "Ġв ин", + "Ġви н", + "ãģĿ ãģĵ", + "æ¯ ķ", + "ĠØ£ Ø®", + "Ġ åIJĮ", + "ĠåIJ Į", + "س اÙĨÛĮ", + "ساÙĨ ÛĮ", + "Ġ kah", + "Ġk ah", + "Ġka h", + "ि यर", + "िय र", + "ÏĢ οÏĤ", + "ÏĢο ÏĤ", + "j ez", + "je z", + "ÙĬ ج", + "ĠsaÄŁ lay", + "ا جÙĩ", + "اج Ùĩ", + "Ġ çł", + "Ġç ł", + "ï ľ", + "Ġج ست", + "Ġt ức", + "Æ° Æ¡i", + "Æ°Æ¡ i", + "Ø´ Ùģ", + "ส à¸ķ", + "Ġ ÑĢеÑģ", + "ĠÑĢ еÑģ", + "ĠÑĢе Ñģ", + "Ġ å£", + "Ġå £", + "Ġbi zim", + "Ġbiz im", + "Ġbizi m", + "Ġ ê·Ģ", + "Ġê· Ģ", + "ि ब", + "िठ¬", + "ë¡ľ ìļ´", + "ĠÑģ ÑĤал", + "ĠÑģÑĤ ал", + "ĠÑģÑĤа л", + "Ġ ÑĢÑĥÑģ", + "ĠÑĢ ÑĥÑģ", + "ĠÑĢÑĥ Ñģ", + "ĠO cak", + "ĠOc ak", + "åľ £", + "Ġ úÄįast", + "Ġú Äįast", + "ĠúÄį ast", + "ive rz", + "iver z", + "ëĤĺ ëĬĶ", + "о ÑĢоÑĤ", + "оÑĢ оÑĤ", + "оÑĢо ÑĤ", + "Ñĩ инÑĭ", + "Ñĩи нÑĭ", + "Ñĩин Ñĭ", + "Ġihtiy aç", + "ÐĿ Ðŀ", + "ĠÐĿ ов", + "ĠÐĿо в", + "ีย à¸Ķ", + "ĠпоÑĤÑĢÑĸб но", + "Ú¯ ز", + "ĠÑģказ ал", + "ĠG ia", + "ĠGi a", + "m esini", + "mes ini", + "mesi ni", + "Ġbulun ur", + "æ¸ ¡", + "г оÑĤ", + "го ÑĤ", + "Ġh uku", + "Ġhu ku", + "ëĦ ·", + "ã Ĩ", + "Ġ اÙĥ", + "Ġا Ùĥ", + "Ġد ÙĦÛĮÙĦ", + "ĠدÙĦ ÛĮÙĦ", + "Ġ اساس", + "Ġا ساس", + "Ġاس اس", + "ìĹ° 구", + "ĠÎĺ ε", + "Ġس ÙĪر", + "ĠسÙĪ ر", + "Ġ ì¢Ģ", + "Ġì¢ Ģ", + "ĠاÙĦ در", + "ĠاÙĦد ر", + "ĠÑģÑĤÑĢо иÑĤелÑĮ", + "Ġ Ñĥк", + "ĠÑĥ к", + "ĠìĻ ľ", + "е лик", + "ел ик", + "ели к", + "O VID", + "OV ID", + "Ġt emiz", + "Ġtem iz", + "äº ¦", + "Ġth iếu", + "Ġthi ếu", + "Ġп ÑĥÑĤ", + "ĠпÑĥ ÑĤ", + "Ñİ Ñīей", + "ÑİÑī ей", + "Ġur Äį", + "Ġ ÄIJây", + "ĠÄIJ ây", + "æ¥ µ", + "μ οÏħ", + "μο Ïħ", + "Ġ à¹Ģà¸Ļ", + "Ġà¹Ģ à¸Ļ", + "Ġà¹ĢภĻ", + "е веÑĢ", + "ев еÑĢ", + "Âł ÐĶ", + "ì ´Ŀ", + "ì´ Ŀ", + "è¶ £", + "Ġà¤ħ लà¤Ĺ", + "Ġà¤ħल à¤Ĺ", + "Æ°á»Ŀ n", + "Ġ ãĥŃ", + "Ġãĥ Ń", + "Ġ ê³³", + "Ġê³ ³", + "é² ģ", + "Ġرس ÛĮد", + "身 ä½ĵ", + "ั à¸ĵà¸ij", + "y nÃŃ", + "yn ÃŃ", + "ج ات", + "جا ت", + "ì§Ģ 를", + "न ल", + "ì ķĮ", + "ìķ Į", + "Ñĸ п", + "Ġv Ãłng", + "ĠvÃł ng", + "Ġпл оÑī", + "Ġпло Ñī", + "оз мож", + "åī ²", + "Ġth ảo", + "л ади", + "ла ди", + "лад и", + "Ġ åĿ", + "Ġå Ŀ", + "ĠÐľ и", + "Ġдел аÑĤÑĮ", + "Ġдела ÑĤÑĮ", + "é ij", + "Ġh uy", + "Ġhu y", + "ا ÛĮØ·", + "اÛĮ Ø·", + "Ġпов ÑĤоÑĢ", + "ü len", + "ül en", + "üle n", + "Ġ ÙĪÙģ", + "ĠÙĪ Ùģ", + "ĠÙĬ تÙħ", + "ĠÙĬت Ùħ", + "ĠÑĢеж им", + "Ġ ìºIJ", + "Ġìº IJ", + "ĠÃĩ ünkü", + "ع دد", + "عد د", + "ни веÑĢ", + "нив еÑĢ", + "ĠÐĿ ик", + "å¸ ĸ", + "Ïį ÏĢ", + "an lar", + "س تÛĮ", + "ست ÛĮ", + "Ġbulun maktadır", + "à¹ģ à¸ļ", + "v ek", + "ve k", + "Ġгла за", + "Ġглаз а", + "å¹ ħ", + "Ġúda j", + "Ġг ÑĢо", + "ĠгÑĢ о", + "Ġкон кÑĥÑĢ", + "Ġd ůležit", + "Ġdů ležit", + "Ġ Ø·ÙĪر", + "ĠØ· ÙĪر", + "à¸ĺ าà¸Ļ", + "ĠÙĦ ÙĥÙĨ", + "ĠÙĦÙĥ ÙĨ", + "ر ÙĤ", + "Ðļ ÐIJ", + "Ġ éĿĴ", + "ĠéĿ Ĵ", + "Ġ ìĤ¬ëŀij", + "ĠìĤ¬ ëŀij", + "ĠÑħ воÑĢ", + "ĠÑħв оÑĢ", + "s unuz", + "sun uz", + "ĠÙħØ´ خص", + "éĻ ¸", + "Ġ ढ", + "Ġठ¢", + "Ġv az", + "Ġva z", + "交 æĺĵ", + "ĠÑĤеÑĢ ÑĢиÑĤ", + "ÑĩеÑģ кой", + "Ñĩе Ñģкой", + "ี à¹Ĥ", + "rop oda", + "ıl dıģı", + "ıldı ģı", + "Ġ ëī´", + "Ġë ī´", + "íķĻ 기", + "ë³´ íĹĺ", + "Ġз аÑĤем", + "ĠзаÑĤ ем", + "Âł в", + "ãĥ¼ ãĥĨ", + "ãĥ¼ãĥ Ĩ", + "Ġ ÐŀÑģнов", + "ĠÐŀÑģ нов", + "ãĨ į", + "Ġد ع", + "ÐŁ оÑģ", + "ÐŁÐ¾ Ñģ", + "æ² ī", + "Ġ лож", + "Ġл ож", + "çĶµ åŃIJ", + "Ġ رد", + "Ġر د", + "ĠÑģ ÑĢазÑĥ", + "e jte", + "ej te", + "Ġà¤ij फ", + "Ġt Ãłu", + "ÃŃ k", + "lan ması", + "lanma sı", + "к аÑĤ", + "ка ÑĤ", + "าà¸ģ าศ", + "ãĤ¢ ãĤ¤", + "ÏĦ ιο", + "ÏĦι ο", + "Ġ å§", + "Ġå §", + "प त", + "E Y", + "Ġj mé", + "Ġjm é", + "Ġod kazy", + "Ġê°ľ ìĿ¸", + "éģ ¿", + "bÄĽ h", + "Ðł Ðŀ", + "çĥ Ī", + "Ġza rar", + "Ġzar ar", + "Ú¯ ÙĪÙĨÙĩ", + "Ú¯ÙĪ ÙĨÙĩ", + "Ġtr ì", + "Ġm ại", + "ен нÑĭм", + "ĠÑį коном", + "ĠÑįк оном", + "éĽ £", + "Ġ íĦ", + "Ġí Ħ", + "æİ ī", + "Ġs oru", + "Ġso ru", + "Ġsor u", + "ĠФедеÑĢа ÑĨии", + "ĠÑģиÑģÑĤем и", + "æĸĻ çĦ¡æĸĻ", + "Ġà¤ķ à¤Ń", + "ĠÙĩ ÙĨد", + "ĠÙĩÙĨ د", + "ุà¸ĩ à¹Ģà¸Ĺà¸ŀ", + "ĠOsman lı", + "ĠпÑĢод олж", + "Ġ ÙĪÙĦا", + "ĠÙĪ ÙĦا", + "ĠÙĪÙĦ ا", + "ĠÄįlán ku", + "Ġa dım", + "Ġad ım", + "Ġadı m", + "ĠÏĢ αÏģά", + "ĠÏĢαÏģ ά", + "ĠÏĢα Ïģά", + "Ġzá ÅĻÃŃ", + "Ġ à¸Īำà¸ģ", + "Ġà¸Īำ à¸ģ", + "Ġп ен", + "m enin", + "me nin", + "men in", + "meni n", + "Ġìĺ¤ ëĬĺ", + "em iz", + "emi z", + "οÏį ÏĤ", + "- स", + "íķĺ ìĭľ", + "ĠÑħ ви", + "ĠÑħв и", + "ãĤ° ãĥ©", + "Ġп оÑĪ", + "Ġпо ÑĪ", + "ĠÐŀдна ко", + "ĠÐŀднак о", + "Ñĸд но", + "íĺ ľ", + "Ñī ими", + "Ñīи ми", + "Ñīим и", + "èĥ ¸", + "ĠÄ° lk", + "ĠÄ°l k", + "m ey", + "me y", + "Ġз да", + "Ġзд а", + "κ λη", + "а лом", + "ал ом", + "ало м", + "à¹Ģศ ษ", + "ا ÙĨا", + "اÙĨ ا", + "Ġ ÎŁÎ¹", + "ĠÎŁ ι", + "Ġ åıĮ", + "Ġåı Į", + "ี à¸Ĥ", + "Ġ بس", + "Ġب س", + "è§Ħ å®ļ", + "i say", + "is ay", + "isa y", + "uk arı", + "uka rı", + "æµģ éĩı", + "v ÃŃm", + "vÃŃ m", + "λ Ïİ", + "ä¹ Ļ", + "Ġल ड", + "ĠÙĨد ارد", + "ĠÙĨدار د", + "е ÑĢом", + "еÑĢ ом", + "еÑĢо м", + "Ġsır asında", + "Ġsıras ında", + "Ġsıra sında", + "Ġr Äĥng", + "Æ¡ m", + "Ġl ạnh", + "Ġlạ nh", + "ठĥ", + "à¥ģ ण", + "à¥ģठ£", + "uz ey", + "uze y", + "Ġ Ñĥва", + "ĠÑĥ ва", + "ĠÑĥв а", + "vÄĽ d", + "Ñĭ Ñģ", + "Ġ κι", + "Ġκ ι", + "Ñ ķ", + "ÛĮ ا", + "à¸ĩ à¸Ħ", + "ph ylum", + "phy lum", + "phyl um", + "Ġber aber", + "ี à¸Ķ", + "æµ ®", + "ा सन", + "ास न", + "o vice", + "ov ice", + "ovic e", + "ovi ce", + "è¦ §", + "Ġस फ", + "å°ij 女", + "ан ÑĤи", + "анÑĤ и", + "é¨ ĵ", + "Ġso át", + "é¬ ¼", + "lan mÄ±ÅŁ", + "Ġb ếp", + "ÙIJ ÙĦ", + "Ġsay ısı", + "Ġsayı sı", + "ĠÙĤ دÙħ", + "ĠÙĤد Ùħ", + "à¥Ī म", + "ह म", + "ĠÑĢ Ñĥки", + "ĠÑĢÑĥ ки", + "ĠÑĢÑĥк и", + "ĠصÙģ ØŃÙĩ", + "Å¡ ky", + "Å¡k y", + "é» Ĵ", + "èģ ļ", + "ãģĭ ãģ«", + "Ġs âu", + "ед аг", + "ĠÑģÑĤоÑĢ онÑĭ", + "ĠÑģÑĤоÑĢон Ñĭ", + "Ġ ruk", + "Ġr uk", + "Ġru k", + "âĢĮ âĢĮ", + "ĠØ¢ ÙĪر", + "Ġع دÙħ", + "Ġعد Ùħ", + "õ i", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Ġبا زار", + "Ġباز ار", + "Ġe deb", + "Ġed eb", + "Ġv ÄįetnÄĽ", + "оп аÑģ", + "Ġн ег", + "Ġне г", + "m ayan", + "ma yan", + "may an", + "maya n", + "к оÑģÑĤÑĮ", + "ко ÑģÑĤÑĮ", + "Ġsv ůj", + "ÄŁ ında", + "ģı nda", + "ģın da", + "Ø° ÛĮر", + "M á»Ļt", + "Ð Ħ", + "Ġyap tı", + "ि थ", + "िठ¥", + "ĠÙħ Ùĩر", + "ĠÙħÙĩ ر", + "Ġд оÑģÑĤи", + "Ġдо ÑģÑĤи", + "ĠдоÑģÑĤ и", + "ĠдоÑģ ÑĤи", + "Ġ صÙĪر", + "Ġص ÙĪر", + "m esine", + "mes ine", + "mesi ne", + "ĠD ân", + "ä¸Ģ ä¸ĭ", + "çį İ", + "ĠÐľ иÑħ", + "ĠÐľÐ¸ Ñħ", + "Ġо Ñĩи", + "ĠоÑĩ и", + "ãĤ¦ ãĤ§", + "Ġ ÑĸÑģ", + "ĠÑĸ Ñģ", + "Ġgi ác", + "Ġgiá c", + "åľ¨çº¿ è§Ĥçľĭ", + "Ġاد اÙħÙĩ", + "ÑĨ ов", + "ÑĨо в", + "Ġ комÑĥ", + "Ġк омÑĥ", + "Ġком Ñĥ", + "Ġко мÑĥ", + "ĠÄ°ng iliz", + "Ġг ÑĢаж", + "ĠгÑĢа ж", + "ĠгÑĢ аж", + "ãģ¦ ãĤĤ", + "Ġch ữ", + "олÑĮ кÑĥ", + "m ÄĽt", + "mÄĽ t", + "Ñıг ом", + "Ñĩ аÑģÑĤ", + "Ñĩа ÑģÑĤ", + "ÑĩаÑģ ÑĤ", + "ìĸ ¼", + "Ġkh óa", + "Ġkhó a", + "ĠÐIJ д", + "ĠØ¢ ÙĤ", + "Ġkurul uÅŁ", + "ά ζ", + "Ġж ов", + "Ġв ÑģÑĤÑĢе", + "ĠвÑģÑĤ ÑĢе", + "ĠÙĪ ÙĦÙĥ", + "ĠÙĪÙĦ Ùĥ", + "Ġt uyá»ĩt", + "y ı", + "Ġ ÐĴо", + "ĠÐĴ о", + "Ġv á»įng", + "Ġvá» įng", + "ع ÙĬØ©", + "عÙĬ Ø©", + "Ġop ÄĽt", + "ا ÙĬد", + "اÙĬ د", + "à¥Ī .Ċ", + "à¥Ī. Ċ", + "ĠÑģ ами", + "ĠÑģам и", + "åª Ĵ", + "Ġsv ých", + "ĠëĤĺ íĥĢ", + "ìĨ IJ", + "Ġ ÙĦع", + "ĠÙĦ ع", + "Ġet kin", + "Ġetk in", + "Ġetki n", + "ĠN á", + "Ġsou tÄĽ", + "Ġsout ÄĽ", + "층 ìĿĺ", + "Ġ çŃī", + "ĠçŃ ī", + "Ġر سÙħ", + "Ġرس Ùħ", + "Ġ خاÙĨÙĩ", + "ĠØ® اÙĨÙĩ", + "ĠخاÙĨ Ùĩ", + "Ġ 家", + "Ġå® ¶", + "iá» ģm", + "iá»ģ m", + "ëħ IJ", + "ê° Ī", + "ì° ©", + "ž il", + "ži l", + "ÑģÑĤиÑĤ ÑĥÑĤ", + "ÑģÑĤиÑĤÑĥ ÑĤ", + "or uÄį", + "oru Äį", + "ĠØ¥ ذا", + "Ġإذ ا", + "à¹Ħ à¸Ĥ", + "ี à¸Ĭ", + "ÑĢ аб", + "ÑĢаР±", + "ÑĢа б", + "íķĻ ìĥĿ", + "Ġ ìī", + "Ġì ī", + "r nek", + "rn ek", + "rne k", + "Ġاست خداÙħ", + "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", + "Ġв Ñģем", + "ĠвÑģ ем", + "ĠвÑģе м", + "Ġìłķ ëıĦ", + "Ġvy j", + "éĢ ±", + "алÑĮ ное", + "алÑĮно е", + "Ġch uyá»ĩn", + "ì§Ģ ìĽIJ", + "i lerine", + "ile rine", + "iler ine", + "ileri ne", + "ilerin e", + "ĠìķĦ 무", + "Ġок оло", + "ाव न", + "à¸Ļ า", + "о пÑĢи", + "оп ÑĢи", + "d rž", + "dr ž", + "ĠÑģÑĥ ÑģпÑĸлÑĮ", + "Ġب Ùĥ", + "u ky", + "uk y", + "Ġ ÏĩÏī", + "ĠÏĩ Ïī", + "Ġtu ần", + "nict vÃŃ", + "Ġ ÙĩدÙģ", + "ĠÙĩد Ùģ", + "Ġch iá»ģu", + "ÎĹ ÎĿ", + "å°ı å§IJ", + "íķĺ ìĺĢ", + "Ġk las", + "Ġkl as", + "Ġkla s", + "á»Ļ n", + "ĠìĿ´ íĽĦ", + "ÙĨ اÙħج", + "ÙĨا Ùħج", + "ÙĨاÙħ ج", + "Äį ast", + "Äįas t", + "ĠاÙĦ خاص", + "ĠاÙĦØ® اص", + "l Ä±ÅŁ", + "lı ÅŁ", + "Ġع Ùħر", + "ĠعÙħ ر", + "ãĢį Ċ", + "иб олее", + "ãĤĬ ãģ®", + "ãħ ł", + "ä¹Ł ä¸į", + "к ÑĢеÑĤ", + "Ġ ìĶ", + "Ġì Ķ", + "ÏĦ ια", + "ÏĦι α", + "ĠÑĥпÑĢав лÑĸннÑı", + "æ² ¢", + "Ġk esin", + "Ġke sin", + "Ġkes in", + "ì¡Į ëĭ¤", + "머 ëĭĪ", + "羣 çļĦ", + "Ġbak ım", + "æĿ± 京", + "¾ ¸", + "ÙħÙĦ ÙĥØ©", + "оÑĤ ÑĢеб", + "d ın", + "dı n", + "Ġ PÅĻi", + "ĠP ÅĻi", + "ĠPÅĻ i", + "Ġm ÄĽli", + "ĠmÄĽ li", + "ĠmÄĽl i", + "Ġδη μο", + "å ¯¸", + "å¯ ¸", + "ĠÙĪ ÙĥاÙĨ", + "ĠÙĪÙĥ اÙĨ", + "Ġप ढ", + "ĠвеÑĢ Ñħ", + "Ġе Ñij", + "C ách", + "Các h", + "ä½ľ 为", + "ĠÐļ ол", + "ĠÐļо л", + "Ġ ве", + "Ġв е", + "Ġ деÑĢж", + "Ġд еÑĢж", + "ĠдеÑĢ ж", + "em oc", + "emo c", + "ãģ¸ ãģ®", + "Ġ аÑĢÑħ", + "ĠаÑĢ Ñħ", + "Ġk iếm", + "Ġ æĺİ", + "Ġæĺ İ", + "ĠлÑİд ини", + "ĠлÑİди ни", + "ë ·", + "ĠÙĪ اÙĦت", + "ĠÙĪاÙĦ ت", + "ĠÙĪا ÙĦت", + "Ġ è°", + "Ġè °", + "çģ ¯", + "íĻ ķ", + "Ġ구 매", + "Ġ ç§ij", + "Ġç§ ij", + "it nÃŃ", + "и ÑĩеÑģкие", + "иÑĩеÑģ кие", + "иÑĩеÑģки е", + "ĠÙĨ Ùģس", + "ĠÙĨÙģ س", + "Ġت ÙĦÙģ", + "ĠتÙĦ Ùģ", + "ا ÙģÛĮ", + "اÙģ ÛĮ", + "ĠØŃ Ø³ÙĨ", + "ĠØŃس ÙĨ", + "âĸ¡ âĸ¡", + "ý vá", + "ýv á", + "ÄŁ ın", + "ģı n", + "ıyor uz", + "ĠCh ÃŃ", + "ĠÙ¾ÚĺÙĪÙĩ Ø´", + "Ġ ÏĦÎŃ", + "ĠÏĦ ÎŃ", + "ĠÏĥ Ïĩε", + "ĠÏĥÏĩ ε", + "о леÑĤ", + "ол еÑĤ", + "α ιδ", + "αι δ", + "Ġh ạt", + "Ġhạ t", + "à¸ł าà¸Ħ", + "åĨ °", + "Ġrych le", + "it eli", + "ite li", + "itel i", + "Âł z", + "ย à¸ģ", + "æ¨ ¹", + "Ġج ÙĪاÙĨ", + "ĠجÙĪ اÙĨ", + "æĺ Į", + "Ġü retim", + "Ġür etim", + "Ġüret im", + "ระ à¸ļ", + "à¸Ľà¸£à¸° ม", + "ά Ïĥ", + "å² ©", + "ĠÑĥ ÑģÑĤÑĢой", + "ĠÑĥÑģÑĤ ÑĢой", + "Ġver ilen", + "Ġveri len", + "ich ni", + "ĠpÅĻÃŃ mo", + "ĠpÅĻÃŃm o", + "ĠاÙĦØ°Ùĩ اب", + "ì½ ľ", + "æľ ±", + "Ġس Ø®", + "Ñĸ ла", + "Ñĸл а", + "Ñĥ ма", + "Ñĥм а", + "ห า", + "ÛĮ دا", + "ÛĮد ا", + "å² ¸", + "ä¸Ģ å®ļ", + "Ġ ä¼ļ", + "Ġ ÐŁÑĸд", + "ĠÐŁ Ñĸд", + "Ġ ÑĩиÑĤ", + "ĠÑĩ иÑĤ", + "ĠÑĩи ÑĤ", + "и Ñİ", + "Ġ ÐĹап", + "ĠÐĹ Ð°Ð¿", + "ĠÐĹа п", + "ÑĤ иÑı", + "ÑĤи Ñı", + "Ġ ê°ľë°ľ", + "Ġê°ľ ë°ľ", + "ĠÑĤе оÑĢ", + "Ñı ÑģÑĮ", + "ÑıÑģ ÑĮ", + "ĠpÅĻÃŃ prav", + "( åľŁ", + "Ùħ ÙĬ", + "ĠpÅĻed evÅ¡ÃŃm", + "ĠTem muz", + "Ġпод деÑĢж", + "Ġнед оÑģÑĤаÑĤ", + "ĠìĿ´ ìľł", + "Ġkhá»ı i", + "ĠاÙĦ تØŃ", + "ĠاÙĦت ØŃ", + "ĠÙħÙħ Ú©ÙĨ", + "Ġv hod", + "Ġvh od", + "ев ой", + "ево й", + "о вал", + "ов ал", + "ова л", + "Ġн алеж", + "Ġна леж", + "Ġнал еж", + "ï¼¼ :", + "ย ะ", + "ĠÙħ اشÛĮÙĨ", + "Ġg á»Ńi", + "al ım", + "alı m", + "Ġìµľ ìłĢ", + "Ùij Ùĩ", + "á»Ļ p", + "à¥Ģ ।Ċ", + "à¥Ģ। Ċ", + "Ġ пиÑģ", + "Ġп иÑģ", + "Ġпи Ñģ", + "Ġ вÑģÑı", + "Ġв ÑģÑı", + "ĠвÑģ Ñı", + "Ñĩ ем", + "Ñĩе м", + "o zenÃŃ", + "oz enÃŃ", + "oze nÃŃ", + "Ġ äºļæ´²", + "Ġäºļ æ´²", + "е ÑĢалÑĮ", + "еÑĢ алÑĮ", + "еÑĢа лÑĮ", + "기 ëĬĶ", + "Ġп ÑĢез", + "ĠпÑĢ ез", + "ĠпÑĢе з", + "ĠعÙħ ÙĪÙħÛĮ", + "и ÑĩниÑħ", + "иÑĩ ниÑħ", + "Ġ æ²³", + "Ġæ² ³", + "od nÃŃ", + "åıª æĺ¯", + "Ġpo dp", + "Ġpod p", + "à¹īà¸Ńà¸ĩ à¸ŀ", + "ाय द", + "ाà¤ĩ ल", + "ล à¸Ķ", + "ĠÑĢÑĸÑĪ еннÑı", + "Ġ ÑĤÑĥÑĢ", + "ĠÑĤ ÑĥÑĢ", + "ĠÑĤÑĥ ÑĢ", + "ÑģÑĮ кÑĥ", + "ÑģÑĮк Ñĥ", + "Ġsal dır", + "ĠÐĽ ÑĮв", + "ãĢģ Ċ", + "ĠÙ¾ÛĮ ÙĪÙĨد", + "åѦ ä¹ł", + "λ Ïī", + "o vit", + "ov it", + "ovi t", + "ü le", + "ül e", + "女 æĢ§", + " Ł", + "e mez", + "em ez", + "eme z", + "Ġh ale", + "Ġha le", + "Ġhal e", + "âī ¦", + "ĠÎķ κ", + "ÏĦηγοÏģ ία", + "k ý", + "ìĦ± ìĿĦ", + "Ġt ým", + "Ġtý m", + "à¥ĩ -", + "Ġz ejména", + "æĻ ¶", + "Ġn gon", + "Ġng on", + "ãĢı ĊĊ", + "软 件", + "éĤ£ ä¹Ī", + "ĠкваÑĢ ÑĤи", + "ĠÙħÙĨ ظ", + "on ec", + "one c", + "Ġг ли", + "à¥ģर à¤ķ", + "ĠS okol", + "ĠSo kol", + "ĠSok ol", + "Ġ ä¿Ŀ", + "д ив", + "ди в", + "ál nÃŃm", + "áln ÃŃm", + "álnÃŃ m", + "ac aģı", + "aca ģı", + "acaÄŁ ı", + "a ÅŁa", + "aÅŁ a", + "ĠÙħ اÙĦÛĮ", + "ĠÙħا ÙĦÛĮ", + "ĠÙħاÙĦ ÛĮ", + "ĠÃĸ n", + "иÑĤ ели", + "иÑĤе ли", + "ĠØ® رد", + "Ġخر د", + "Ġkullan ıl", + "Ġkullanı l", + "Ġ ÙħÛĮÙĦ", + "ĠÙħ ÛĮÙĦ", + "ĠÙħÛĮ ÙĦ", + "Ġ íļ¨", + "Ġíļ ¨", + "ã n", + "Ġ rost", + "Ġr ost", + "Ġro st", + "Ġros t", + "Ġëĸ ł", + "u bat", + "ub at", + "uba t", + "Ġ åıĤ", + "Ġåı Ĥ", + "Ġبر اÙĬ", + "Ġм енÑĮ", + "Ġмен ÑĮ", + "ั à¸Ħร", + "ัà¸Ħ ร", + "Ġпом ог", + "ĠØŃض ÙĪر", + "Ġthá»ĭ t", + "ä¹ ³", + "Ġ ìĭłì²Ń", + "Ġìĭł ì²Ń", + "Ġíĺ Ħìŀ¬", + "ĠíĺĦ ìŀ¬", + "Ġ ë¹ł", + "Ġë¹ ł", + "вÑĢоп ей", + "Ġne jen", + "Ġnej en", + "Ñĸ ка", + "Ñĸк а", + "Ġ ìļ¸", + "Ġìļ ¸", + "ĠÙħ بار", + "ĠÙħب ار", + "Ġ Äįek", + "ĠÄį ek", + "ĠÄįe k", + "Ġk alk", + "Ġka lk", + "Ġkal k", + "Ġ amac", + "Ġa mac", + "Ġam ac", + "Ġama c", + "اد ت", + "ĠÙħ اسÙĩ", + "ĠÙħا سÙĩ", + "Ġarasında ki", + "Ġaras ındaki", + "Ġб еÑģ", + "ĠоÑĤд елÑĮ", + "ĠоÑĤдел ÑĮ", + "á½ ¶", + "ĠΤ ζ", + "v yk", + "vy k", + "ج ÙĨ", + "» ê²Į", + "ĠниÑĩ его", + "ĠØ´ اÙħÙĦ", + "ĠÑĥÑģловиÑı Ñħ", + "la ması", + "lam ası", + "lama sı", + "è½ ī", + "ç¾ ½", + "Ġж ид", + "ĠоÑĤ ноÑģ", + "ĠздÑĸйÑģ нÑİ", + "Ġ VỼi", + "ĠV Ỽi", + "ÙĪ ÙĦÛĮ", + "ÙĪÙĦ ÛĮ", + "Ġt isÃŃ", + "Ġti sÃŃ", + "ĠÏĩ ÏģÏĮ", + "Ġprac ovnÃŃ", + "Ġpracov nÃŃ", + "ĠÙĬ ÙĥÙĪÙĨ", + "ĠÙĬÙĥ ÙĪÙĨ", + "Ġb eÅŁ", + "Ġbe ÅŁ", + "ج ز", + "ั à¸ļร", + "ัà¸ļ ร", + "ĠYön et", + "Ġشر اÛĮØ·", + "ĠتÙĪس عÙĩ", + "çĹ ĩ", + "à¸ĩ à¹Ģà¸Ľ", + "ä¸Ģ 次", + "ĠÐłÐ¾ÑģÑģий Ñģкой", + "æľĢ é«ĺ", + "Ġsp olu", + "Ġspo lu", + "Ġspol u", + "д аеÑĤÑģÑı", + "да еÑĤÑģÑı", + "даеÑĤ ÑģÑı", + "Ñĸ ÑĤÑĥ", + "ÑĸÑĤ Ñĥ", + "Ġоб ÑĢаÑĤ", + "ĠобÑĢа ÑĤ", + "e nek", + "en ek", + "ene k", + "Ġ mek", + "Ġm ek", + "Ġme k", + "å¦ Ī", + "Ġдоп олниÑĤелÑĮ", + "Ġ ç²", + "Ġç ²", + "ĠÙĦ ÙĦت", + "ĠÙĦÙĦ ت", + "ĠHaz iran", + "æ¸ Ī", + "à¹Į à¸Ĥà¸Ńà¸ĩ", + "ĠÑĦ он", + "Ġê²ĥ ìľ¼ë¡ľ", + "Ġnh é", + "Ġbu gün", + "Ġbug ün", + "ov ém", + "ové m", + "Ġза веÑĢ", + "Ġзав еÑĢ", + "Ġд виг", + "Ġдв иг", + "Ġдви г", + "ä¼ Ļ", + "Ġnu ôi", + "меÑĢ ик", + "ме ÑĢик", + "ĠÙĨÙħ ÙĪÙĨÙĩ", + "èį ·", + "Ñĥ вала", + "Ñĥв ала", + "Ñĥва ла", + "ç¿ »", + "Ġs ân", + "ог оÑİ", + "ого Ñİ", + "ا سÙĬØ©", + "اس ÙĬØ©", + "اسÙĬ Ø©", + "Ñĥн кÑĤ", + "Ñĥнк ÑĤ", + "á nÃŃm", + "án ÃŃm", + "ánÃŃ m", + "ен ное", + "енно е", + "Ġph út", + "Ġ मर", + "Ġम र", + "ĠاÙĦ ÙĪØ·", + "ĠاÙĦÙĪ Ø·", + "Ġлег ко", + "Ġ ãĢĭ", + "ĠãĢ ĭ", + "ë¡ľ ëĵľ", + "ĠKas ım", + "ÙĬ ÙĦÙĬ", + "ÙĬÙĦ ÙĬ", + "ĠbaÄŁlantı lar", + "ĠÑĤ ÑĢÑĥд", + "ĠÑĤÑĢ Ñĥд", + "ĠÑĤÑĢÑĥ д", + "Ø· Ùĩ", + "Ġk vůli", + "ÑģÑĤ оÑı", + "Ġsat Ä±ÅŁ", + "Ġh áºŃu", + "ĠبÙĩ ترÛĮÙĨ", + "ĠبÙĩتر ÛĮÙĨ", + "ĠÑģ елÑĮ", + "ĠÑģел ÑĮ", + "ั à¸Ļว", + "ัà¸Ļ ว", + "o su", + "os u", + "य न", + "åĽ ³", + "ι δ", + "ÛĮ تÛĮ", + "ÛĮت ÛĮ", + "ĠQu áºŃn", + "Ġ ей", + "Ġе й", + "à¹Ģว ลา", + "à¹Ģวล า", + "ìĬ¤ íĥĢ", + "ìĤ¬ 를", + "Ġا ÙĩÙĦ", + "ĠاÙĩ ÙĦ", + "η γ", + "Ġk á»·", + "Ġká» ·", + "Ġ наÑĤ", + "Ġн аÑĤ", + "Ġна ÑĤ", + "âĢ ¡", + "Ñĸ ÑĩниÑħ", + "ÑĸÑĩ ниÑħ", + "ĠÑĢазвиÑĤи Ñı", + "ĠÑĢазви ÑĤиÑı", + "e cial", + "ec ial", + "eci al", + "ĠÑħ озÑı", + "в аеÑĤ", + "ва еÑĤ", + "ĠÄIJ á»Ļ", + "ĠÄIJá» Ļ", + "Ġ éĵ", + "Ġé ĵ", + "Ġok am", + "ĠвÑģ ÑĸÑħ", + "ĠвÑģÑĸ Ñħ", + "ĠPr aze", + "ĠPra ze", + "ë¥ ł", + "ι κα", + "ικ α", + "æ¬ ²", + "Ġgerçek leÅŁ", + "ç¥ ĸ", + "Ġод ним", + "Âł M", + "Ġre nk", + "Ġr enk", + "Ġren k", + "Ġल à¤ķ", + "ãĥķ ãĤ§", + "ãĥķãĤ §", + "ĠÙĨ زد", + "å¹ »", + "Ġúzem ÃŃ", + "æı ¡", + "а лиÑģÑı", + "али ÑģÑı", + "Ġ ÃĶ", + "Ġà Ķ", + "Ġy orum", + "Ġyo rum", + "ĠÏĢ ÏģÏī", + "ãĥ³ ãĥĩ", + "ãĥ³ãĥ ĩ", + "éĸĭ å§ĭ", + "ãĥ¼ ãĥª", + "ãĥ¼ãĥ ª", + "Ġìĸ¼ êµ´", + "Û± Û±", + "Û±Û ±", + "lü ÄŁÃ¼", + "lÃ¼ÄŁ ü", + "ÙĨ Ø´", + "à¹Ī ำ", + "èĽ ĭ", + "ĠØ£ د", + "ĠW illi", + "ĠWill i", + "ĠWil li", + "ĠWi lli", + "èª ²", + "Ġsür dür", + "ĠEx ternÃŃ", + "Ġp ůvod", + "Ġpů vod", + "ĠØ® اÙĨÙĪ", + "ĠخاÙĨ ÙĪ", + "ĠкоÑĤоÑĢ ое", + "Ġm ohl", + "Ġmo hl", + "Ġmoh l", + "Ġs tÄĽ", + "Ġst ÄĽ", + "åĩ ı", + "ìĤ ¼", + "aban cı", + "à¹ģ à¸Ļ", + "สำ à¸Ħ", + "æĤ £", + "ab ilece", + "abil ece", + "abile ce", + "éĺ³ åŁİ", + "Îij Îļ", + "Ġch ữa", + "Ġchữ a", + "ĠìķĦ ëĭ", + "طبÙĬ ÙĤ", + "طب ÙĬÙĤ", + "ÎĻ ÎŁÎ¥", + "ÎĻÎŁ Î¥", + "ÑĢ ование", + "ÑĢов ание", + "ÑĢо вание", + "ÑĢован ие", + "ÑĢова ние", + "åĩ ½", + "Ġ ì¼", + "Ġì ¼", + "ÑĢ оÑĦ", + "ÑĢо ÑĦ", + "à¹ĩ à¸Ļส", + "à¹ĩà¸Ļ ส", + "Ġ ãĤ¦", + "ĠãĤ ¦", + "ï¼ļ ãĢĮ", + "á»ĭ a", + "Ġ hPa", + "Ġh Pa", + "m anı", + "man ı", + "ál nÃŃho", + "álnÃŃ ho", + "ÙĪ تÛĮ", + "ÙĪت ÛĮ", + "ĠлеÑĩ ениÑı", + "j te", + "jt e", + "- д", + "åħ¨ åĽ½", + "ĠбÑĥд Ñĸв", + "Ġz atÃŃm", + "Ġzat ÃŃm", + "Ġ öyle", + "Ġö yle", + "ìĿ´ ê°Ģ", + "s tal", + "st al", + "sta l", + "i vatel", + "iv atel", + "ivate l", + "iva tel", + "Ġ æľª", + "Ġpož ad", + "ĠÑģ ни", + "Ġpos lednÃŃ", + "Ġposled nÃŃ", + "ĠÑģÑĤ анд", + "ĠÑģÑĤан д", + "ĠÑģÑĤа нд", + "à¥Ģ à¤ıम", + "à¥Ģà¤ı म", + "Ġ عکس", + "Ġع کس", + "ÑĢ иÑı", + "ÑĢи Ñı", + "ã y", + "á»ĭ p", + "Ġo kul", + "Ġok ul", + "Ġoku l", + "à¸ĩ หมà¸Ķ", + "Ġвоз ник", + "m ÃŃ", + "ç§ Ł", + "ĠÄij á»ijc", + "Ġp odÃŃ", + "Ġpo dÃŃ", + "Ġpod ÃŃ", + "ĠÅĻÃŃ j", + "ĠÑĤак Ñĸ", + "ĠÑĤа кÑĸ", + "à¸ļ าà¸Ĺ", + "Ġ 보기", + "Ġë³´ 기", + "ล า", + "еÑģ ÑĤо", + "еÑģÑĤ о", + "Ġ çĶ¨", + "и нÑĭ", + "ин Ñĭ", + "ĠÑĢ ÑĥÑħ", + "ĠÑĢÑĥ Ñħ", + "ĠÑĢаÑģп олож", + "Ñī еннÑı", + "Ġc á»Ń", + "à¹ī à¸ļร", + "à¥įयव स", + "ï¾ ļ", + "Ġд алÑĮ", + "Ġда лÑĮ", + "Ġдал ÑĮ", + "Ġض د", + "ÙĦ ÙĬØ©", + "ÙĦÙĬ Ø©", + "ĠкоÑĤоÑĢ ого", + "Ġd ve", + "Ġdv e", + "Ġnh ạc", + "ÑĦ Ñĸка", + "ÑĦÑĸ ка", + "ÑĦÑĸк а", + "à¥Ī à¤Ł", + "èĩª çĶ±", + "Ġпо ÑĢÑĥÑĪ", + "ĠпоÑĢ ÑĥÑĪ", + "æľĭ åıĭ", + "Ġd ört", + "Ġdö rt", + "ĠÑĢаÑģп ÑĢоÑģÑĤ", + "ãģ§ ãģ¯ãģªãģĦ", + "ãģ§ãģ¯ ãģªãģĦ", + "ĠпеÑĢ ег", + "ĠпеÑĢе г", + "Ġ ánh", + "Ġá nh", + "Ġán h", + "ĠV ÃŃ", + "ظ Ù¹", + "à¥į रण", + "à¥įर ण", + "Ġb ilim", + "Ġbi lim", + "Ġbil im", + "Ġlid é", + "Ġd ÃŃky", + "ĠdÃŃ ky", + "ĠÄIJ á»ĵng", + "Ġ εÏģγ", + "Ġε Ïģγ", + "Ġzn ovu", + "Ïĥ ια", + "Ïĥι α", + "Ñ ŀ", + "स à¤Ń", + "e kk", + "ek k", + "Ġμε ÏĦά", + "ÑģÑĤ иÑĩ", + "ÑģÑĤи Ñĩ", + "ÛĮ ÙĨÚ¯", + "ÛĮÙĨ Ú¯", + "ĠÑıв лÑıÑİÑĤÑģÑı", + "Ġ 建", + "Ġå» º", + "Ïĥ Ïĥα", + "ÏĥÏĥ α", + "ав лива", + "à¸ģ รม", + "à¸ģร ม", + "ç¬ Ķ", + "Ġ ге", + "Ġг е", + "Ġ رÙĩ", + "Ġر Ùĩ", + "Ġм ел", + "Ġна пÑĢимеÑĢ", + "ĠнапÑĢи меÑĢ", + "Ġм ик", + "Ġми к", + "ĠاÙĦس ÙĥاÙĨ", + "æ¤ ľ", + "ĠÐļ ÑĢа", + "Ġv Ãłi", + "ĠvÃł i", + "ائ Ùħ", + "ĠÏĩ Ïģή", + "leÅŁ me", + "Ġ jas", + "Ġj as", + "Ġja s", + "ê²Į ìŀĦ", + "Ġm aç", + "Ġma ç", + "Ġì§Ħ íĸī", + "à¥ĩद न", + "Ġvů bec", + "ĠÙĦ ÙĨ", + "è« ĩ", + "âī¡ âī¡", + "л ением", + "ление м", + "лен ием", + "ле нием", + "ع ÙĨÛĮ", + "عÙĨ ÛĮ", + "ãĥŀ ãĥ³", + "Ä° Z", + "ĠÃĸ ÄŁ", + "ĠìŬ ìŀIJ", + "y Å¡", + "Ġ ÑģÑĤа", + "ĠÑģ ÑĤа", + "ĠÑģÑĤ а", + "Ġ สำหร", + "Ġสำ หร", + "Ġन व", + "ãĢĤ ä½Ĩ", + "олÑĮ но", + "Ġyan ında", + "Ġyanı nda", + "è² ´", + "Ġjednot liv", + "Ġ åİŁ", + "Ġåİ Ł", + "éłħ 缮", + "Ġमद द", + "리 ìĹIJ", + "ĠÙħ اÙĬ", + "ĠÙħا ÙĬ", + "ĠÑĩ еÑĢв", + "ĠÑĩеÑĢ в", + "Ġd áv", + "Ġdá v", + "ÙĦ ÛĮÙĩ", + "ÙĦÛĮ Ùĩ", + "? #", + "Äį nÃŃm", + "ÄįnÃŃ m", + "ÑĢ ег", + "ÑĢе г", + "ĠпÑĢимен Ñı", + "ĠпÑĢим енÑı", + "ãĤĬ ãģ¨", + "ê° Ļ", + "Ġtop lam", + "Ġtopl am", + "i leÅŁ", + "il eÅŁ", + "ile ÅŁ", + "Ġk ategor", + "ÑĤ ал", + "ÑĤа л", + "ãģ«ãĤĪ ãĤĭ", + "Ġdom ác", + "Ġ ê·ľ", + "Ġê· ľ", + "ĠÙĩ زار", + "ĠpÅĻÃŃ stup", + "ĠpÅĻÃŃst up", + "ı lıyor", + "ılı yor", + "ıl ıyor", + "ж ди", + "жд и", + "ĠD Æ°Æ¡ng", + "ĠPh áºŃt", + "Ġç ünkü", + "구 ê¸ĢìĥģìľĦ", + "ov aných", + "ova ných", + "ovan ých", + "ovaný ch", + "Ġع Ø´", + "Ġà¤ķर à¤ķ", + "ž ÃŃt", + "žÃŃ t", + "Ġ vÄĽtÅ¡ÃŃ", + "ĠvÄĽt Å¡ÃŃ", + "ĠvÄĽtÅ¡ ÃŃ", + "ĠاÙħ کاÙĨ", + "Ġn ông", + "Ġz ám", + "Ġzá m", + "à¥Į न", + "е каÑĢ", + "ек аÑĢ", + "ека ÑĢ", + "Âł Т", + "k ami", + "ka mi", + "ĠÑĢеÑģ ÑĥÑĢ", + "п оÑģ", + "по Ñģ", + "Ùİ ÙĤ", + "ί λ", + "Ġ سازÛĮ", + "Ġس ازÛĮ", + "Ġساز ÛĮ", + "Ġçık an", + "Ġçı kan", + "ĠdÃŃ tÄĽ", + "Ġتص ÙĪ", + "ç¯ ĩ", + "н д", + "Ġrám ci", + "h ong", + "ho ng", + "hon g", + "Ġ ÑģÑĸм", + "ĠÑģ Ñĸм", + "s ak", + "sa k", + "к еÑĤ", + "ке ÑĤ", + "д Ñĸл", + "дÑĸ л", + "ç¹ Ķ", + "Ġth Æ°á»Łng", + "Ġне ÑĹ", + "з Ñĸ", + "ÅĻ ÃŃd", + "ÅĻÃŃ d", + "ित न", + "à¤ı à¤ķ", + "Ġs ữa", + "ĠÙħ رØŃ", + "ĠÙħر ØŃ", + "é ŀ", + "Ġc Æ°á»Ŀng", + ": .:", + ":. :", + "ÑĤ ен", + "ÑĤе н", + "èī ¦", + "Ġkh ợi", + "Ġ 기ì¤Ģ", + "Ġ기 ì¤Ģ", + "lan ır", + "彩 票", + "ض ÛĮ", + "Ġuz av", + "Ġb oh", + "Ġbo h", + "è m", + "Ġ æ£", + "Ġæ £", + "n ici", + "ni ci", + "nic i", + "( çģ«", + "åħ³ äºİ", + "Ñĸ ÑĩнÑĸ", + "ÑĸÑĩ нÑĸ", + "à¸ģ ารà¸ĵ", + "à¸ģาร à¸ĵ", + "Ġì² «", + "ÑĢ ÑĥеÑĤ", + "ÑĢÑĥ еÑĤ", + "ĠarÅŁiv lendi", + "ÑĤ им", + "ÑĤи м", + "า à¸ł", + "าภł", + "Ġبر ابر", + "Ġ à¹Ģà¸ĭ", + "Ġà¹Ģ à¸ĭ", + "Ġà¹Ģภĭ", + "ĠÄij êm", + "è· ³", + "Ġyön etim", + "Ġyönet im", + "Ġ éķ·", + "Ġéķ ·", + "ãĥĨ ãĥ¬ãĥĵ", + "м аÑĤи", + "ма ÑĤи", + "маÑĤ и", + "è´£ ä»»", + "ick ým", + "ický m", + "è ¸", + "à¹Ģห à¸ķ", + "ëł Į", + "Ġ رÙĬ", + "Ġر ÙĬ", + "ĠвÑĭ дел", + "åĩº çİ°", + "Ġп еÑģ", + "Ġì¢ĭ ìĿĢ", + "Ġà¤ī सन", + "Ġà¤īस न", + "ĠAr alık", + "ĠAra lık", + "ĠÑĩа ÑģÑĥ", + "ĠÑĩаÑģ Ñĥ", + "l ava", + "la va", + "lav a", + "Ġ ï½ŀ", + "Ġï½ ŀ", + "æģ ĭ", + "د ÛĮد", + "دÛĮ د", + "âĢĻ den", + "âĢĻd en", + "âĢĻde n", + "Ġ åĪĿ", + "ĠåĪ Ŀ", + "ÙĪ دة", + "ÙĪد Ø©", + "Ñĩ или", + "Ñĩи ли", + "Ñĩил и", + "ĠÑħаÑĢакÑĤ еÑĢиÑģÑĤи", + "ا ستاÙĨ", + "اس تاÙĨ", + "است اÙĨ", + "द र", + "ĠبÙĪد ÙĨ", + "ĠبÙĪ دÙĨ", + "Ġп алÑĮ", + "Ġпа лÑĮ", + "Ġпал ÑĮ", + "ĠÑĤ ÑĢади", + "ĠÑĤÑĢ ади", + "ĠÑĤÑĢа ди", + "Ġд еÑı", + "Ġде Ñı", + "Ġ خش", + "ĠØ® Ø´", + "Ġpok raÄį", + "Ġ구 ê¸Ģ", + "к овÑĸ", + "ко вÑĸ", + "ков Ñĸ", + "Ġ tık", + "Ġt ık", + "Ġh ấp", + "Ġza lož", + "Ġzal ož", + "१ à¥", + "Ġëĭµ ë³Ģ", + "м еÑĪ", + "ме ÑĪ", + "íļ ¨", + "Ġspol up", + "Ġspolu p", + "Ë Ĩ", + "è¾ ¦", + "Ġg á»Ĺ", + "Ġ å®ļ", + "Ġå® ļ", + "ĵ n", + "as ından", + "asında n", + "- ı", + "ĠбеÑĢ ез", + "大 åѸ", + "Ġз нов", + "Ġзн ов", + "ĠHo Ãłng", + "Ġد ÙĪÙĨ", + "ĠدÙĪ ÙĨ", + "Ġan lay", + "ĠÙĪ زار", + "ĠÙĪز ار", + "ĠعÙĦ ÙħÛĮ", + "ĠعÙĦÙħ ÛĮ", + "è£ ľ", + "Ġdü nya", + "Ġdün ya", + "Ġdüny a", + "Ġза лиÑĪ", + "Ġзал иÑĪ", + "Ġзали ÑĪ", + "д аеÑĤ", + "да еÑĤ", + "ν ε", + "и ÑĩеÑģкого", + "иÑĩеÑģ кого", + "ìĬ¤ íħľ", + "ĠÐij еÑĢ", + "Ġ дж", + "Ġд ж", + "Ġ опаÑģ", + "Ġоп аÑģ", + "ÏĨ α", + "Ġzv lá", + "Ġt ô", + "б еÑĢ", + "бе ÑĢ", + "ĠÎľ αÏģ", + "Ġξα Ïģ", + "ti ÄŁini", + "tiÄŁi ni", + "ãĥ¬ ãĥ³", + "ĠK ho", + "ĠKh o", + "ĠÑĸн ÑĪ", + "Ġ ï¿¥", + "ì° ¬", + "ï½ ¡", + "Ġ ноÑĩ", + "Ġн оÑĩ", + "Ġно Ñĩ", + "è¨ Ĭ", + "ÄĽ ti", + "ÄĽt i", + "å¿ Ļ", + "Ġکرد ÙĨد", + "ĠکردÙĨ د", + "ĠÄij ẩy", + "ĠÑģказ ав", + "ëĥ ¥", + "å± ¬", + "Ġश हर", + "ĠÚ©Ùħ Ú©", + "Âł ÐŁ", + "ın ca", + "нÑĸвеÑĢ ÑģиÑĤ", + "Ġ Ú¯ÙĪÙĨÙĩ", + "ĠÚ¯ ÙĪÙĨÙĩ", + "ĠÚ¯ÙĪ ÙĨÙĩ", + "ĠTop lam", + "ĠiÅŁ aret", + "ä½ł 们", + "Ġd erece", + "Ġde rece", + "Ġder ece", + "Ġdere ce", + "Ġderec e", + "ĠìĤ¬ ìĭ¤", + "Ġ ìŀIJ기", + "ĠìŀIJ 기", + "å®ŀ çİ°", + "çĶŁ çī©", + "ãģ® ä¸Ģ", + "Ġ ÑĢом", + "ĠÑĢ ом", + "ÙĪ زÙĩ", + "ÙĪز Ùĩ", + "Ġ ãģ¨", + "íĻ į", + "ÙĬ ÙĤ", + "Ġ åIJįçĦ¡ãģĹãģķãĤĵ", + "ĠåIJį çĦ¡ãģĹãģķãĤĵ", + "ĠåIJįçĦ¡ãģĹ ãģķãĤĵ", + "ĠÙ¾ ÛĮر", + "ĠÙ¾ÛĮ ر", + "Ġпол ез", + "ì¶ ©", + "ĠкоÑĢ п", + "IJ ëĭ¤", + "á» «a", + "ừ a", + "Îķ Τ", + "Ġжел ез", + "ãģ£ãģ ±", + "Ġx uyên", + "Ġ ë¥", + "Ġë ¥", + "à¥ĩ ।Ċ", + "à¥ĩ। Ċ", + "ĠÑģÑĤ али", + "ĠÑģÑĤал и", + "ĠÑģÑĤа ли", + "Ġpomoc ÃŃ", + "Ġdurum da", + "Ġп ÑĢоÑĪ", + "ĠпÑĢ оÑĪ", + "ĠпÑĢо ÑĪ", + "l enÃŃ", + "le nÃŃ", + "len ÃŃ", + "β ολ", + "βο λ", + "Ġ æĸĩç«ł", + "Ġæĸĩ ç«ł", + "tÄĽ z", + "d ÃŃl", + "dÃŃ l", + "Ġdruh é", + "ĠÑĤ огда", + "Ġh rá", + "Ġhr á", + "о ÑĤÑĮ", + "оÑĤ ÑĮ", + "า à¸ģร", + "าà¸ģ ร", + "Ġتص Ùħ", + "ĠÙħد ت", + "ка дем", + "Ġpat ÅĻÃŃ", + "ä¹ĭ åīį", + "س بة", + "سب Ø©", + "Ġпо кÑĢÑĭ", + "Ġпок ÑĢÑĭ", + "Ġn áp", + "Ġná p", + "Ġ_ {}", + "Ġ_{ }", + "ëĵ± íķĻêµIJ", + "ĠØ¥ ÙĦÙĬ", + "ĠØ¥ÙĦ ÙĬ", + "Ġöz g", + "çļ Ĩ", + "Ġhay van", + "ĠN isan", + "ĠNi san", + "غ از", + "Ġت ت", + "ĠдÑĥ Ñħов", + "ĠдÑĥÑħ ов", + "ĠÐŁÐ¾ ÑįÑĤомÑĥ", + "ÑĮ огод", + "ÑĮого д", + "Ġk uÅŁ", + "Ġku ÅŁ", + "Ġà¤ĩ सम", + "Ġà¤ĩस म", + "ج ÛĮ", + "Ġ ãĤ¿", + "ĠãĤ ¿", + "Ġв кÑĥÑģ", + "Ġвк ÑĥÑģ", + "ĠвкÑĥ Ñģ", + "ç Ģ", + "ĠвÑĭ ÑĪе", + "âĢĻ dan", + "âĢĻd an", + "âĢĻda n", + "ĠاØŃ Ùħد", + "Ġtal ep", + "Ġta lep", + "Ġtale p", + "Ġ ÏĪ", + "ĠÏ Ī", + "Ġdol ayı", + "Ġdolay ı", + "ĠÚ¯ زارش", + "б ол", + "бо л", + "ĠاÛĮÙĨ تر", + "ÑĢ оÑĩ", + "ÑĢо Ñĩ", + ") âĢı", + "Ġ ëIJł", + "ĠëIJ ł", + "Ġk oup", + "Ġko up", + "Ġkou p", + "( æľĪ", + "é± ¼", + "Ġ огÑĢа", + "Ġо гÑĢа", + "Ġог ÑĢа", + "ĠÑĢаз м", + "ĠÑĢа зм", + "Ġت ست", + "Ġتس ت", + "ĠpÅĻÃŃ slu", + "í ĽĪ", + "ĠëĮĢ íķ´", + "à¹ģ à¸Ľ", + "ан нÑĭе", + "аннÑĭ е", + "ĠìĿ¸ íĦ°", + "Ġkullan ılan", + "Ġkullanı lan", + "Ġkullanıl an", + "Ġz tr", + "æĬĢ è¡ĵ", + "ि à¤Ľ", + "िठĽ", + "ĠاÙĦÙħ ؤ", + "ov aly", + "ova ly", + "oval y", + "us tos", + "ust os", + "usto s", + "Ġö rg", + "Ġör g", + "Ġ 太", + "Ġå¤ ª", + "ε ιο", + "ει ο", + "Ġ uÄį", + "Ġu Äį", + "ĠØ´ Ú©ÙĦ", + "ĠØ´Ú© ÙĦ", + "建 çŃij", + "Ġch ạy", + "ĠÏĩ Ïģη", + "н ÑĥÑĤ", + "нÑĥ ÑĤ", + "Ġباع Ø«", + "ĠNÄĽk ter", + "ÑĥÑĤ ÑĤÑı", + "ãģ§ãģĻ ãģĭ", + "Ġsay ılı", + "Ġsayı lı", + "им оÑģÑĤÑĮ", + "имо ÑģÑĤÑĮ", + "ĠпиÑĤ аннÑı", + "Ġk ÃŃnh", + "Ġh ran", + "Ġhr an", + "Ġhra n", + "ok rat", + "Ġed ilir", + "Ġedil ir", + "Ġà¤ķह त", + "Ġp aci", + "Ġpa ci", + "Ġpac i", + "ाल न", + "Ġи де", + "Ġид е", + "ĠZ em", + "ĠZe m", + "Ġsluž by", + "ÑģÑĤв еннÑĭй", + "ÑģÑĤвен нÑĭй", + "ĠØ¢ ÙĨاÙĨ", + "ĠØ¢ÙĨ اÙĨ", + "ĠÑĤ оваÑĢи", + "ĠÑĤоваÑĢ и", + "ĠÑĤов аÑĢи", + "ĠتØŃ ÙħÙĬÙĦ", + "ĠY ük", + "Ġк аÑĤегоÑĢ", + "ĠкаÑĤ егоÑĢ", + "íĭ Ģ", + "Ġк оÑģ", + "Ġко Ñģ", + "Ġ обов", + "Ġо бов", + "Ġоб ов", + "Ġобо в", + "ĠprostÅĻed ÃŃ", + "ĠÑģ оÑģ", + "ĠÑģо Ñģ", + "ĠÐIJ лекÑģанд", + "ĠÐIJлекÑģ анд", + "Ġ à¹Ģà¸Ĥà¸ķ", + "Ġà¹Ģà¸Ĥ à¸ķ", + "å¿ħ é¡»", + "ั à¸Ĭ", + "ĠÙĦ د", + "ãĢģ ä¸Ģ", + "ĠÎľ ÎŃ", + "Ñĥ ваÑĤиÑģÑı", + "Ñĥв аÑĤиÑģÑı", + "ÑĥваÑĤи ÑģÑı", + "Ñĥва ÑĤиÑģÑı", + "æķ ı", + "ãĥ¼ ãĥIJ", + "ãĥ¼ãĥ IJ", + "اÙĦ ÙĦÙĩ", + "Ġب Ùĩا", + "ĠبÙĩ ا", + "åĸ ¶", + "è´ µ", + "æĸ¹ åIJij", + "Ġ ì¸", + "Ġì ¸", + "Ġ ÙĨاÙħÙĩ", + "ĠÙĨ اÙħÙĩ", + "ĠÙĨاÙħ Ùĩ", + "ÑĮ ко", + "Ġv ody", + "Ġvo dy", + "Ġvod y", + "v ÃŃc", + "vÃŃ c", + "à¹ģ à¸Ī", + "Ġع ÙĦÛĮÙĩ", + "ĠعÙĦ ÛĮÙĩ", + "ĠعÙĦÛĮ Ùĩ", + "à¹ģ รà¸ĩ", + "ί να", + "ίν α", + "ãģ ¬", + "Ġ Ðŀп", + "ĠÐŀ п", + "Ġsay f", + "ï¼Į çĶ±", + "ä¼ ´", + "ĠÑĥд об", + "ãģ¾ ãģł", + "Ġне пÑĢи", + "Ġнеп ÑĢи", + " İ", + "à¤¾à¤ľ प", + "pl nÄĽ", + "Ġ ìĹĦ", + "ĠìĹ Ħ", + "Ġrů zn", + "Ġrůz n", + "Ġx ếp", + "ãĥĸ ãĥ«", + "ĠзаÑħ иÑģÑĤ", + "ĠÙħص رÙģ", + "ĠÙħصر Ùģ", + "ĠvÅ¡ech no", + "ãģ® ãģĬ", + "ĠTh á»ĭ", + "Ġm ùa", + "¿ IJ", + "ĠпÑĢин ÑĨип", + "ĠاÙĨ ÙĤÙĦ", + "г аÑĢ", + "га ÑĢ", + "Ġmož nost", + "ÙĤ ÙĬÙĤ", + "ÙĤÙĬ ÙĤ", + "Ġotev ÅĻ", + "Ġ fak", + "Ġf ak", + "Ġfa k", + "Ġng uy", + "Ġngu y", + "б ов", + "бо в", + "l acaÄŁ", + "lac aÄŁ", + "ا طر", + "اط ر", + "ãģ« ãĤĪãĤĬ", + "ãģ«ãĤĪ ãĤĬ", + "æĺ¯ åľ¨", + "Ġt ầng", + "ìĿ¸ ìĿ´", + "a ÅĻ", + "ç ¢°", + "ç¢ °", + "ÏĮ με", + "Ġ ê°Ī", + "Ġê° Ī", + "ĠØ£ ØŃد", + "ĠØ£ØŃ Ø¯", + "غ راÙģ", + "غر اÙģ", + "ĠÙĬ ØŃ", + "ï½ §", + "ĠاÙĦØŃÙĬ اة", + "Ġ lep", + "Ġl ep", + "Ġle p", + "Ġ ฮ", + "Ġภ®", + "t ae", + "ta e", + "Ġl Æ°Æ¡ng", + "è½ ®", + "Ġз мÑĸн", + "ĠзмÑĸ н", + "Ġзм Ñĸн", + "ĠÐļи ÑĹв", + "ĠмÑĸ ÑģÑı", + "ĠмÑĸÑģ Ñı", + "к ав", + "ка в", + "à¸ķ ะ", + "Ġm noho", + "Ġmn oho", + "Ġmnoh o", + "ĠNgh á»ĭ", + "èĻ İ", + "Ġ ãĥŁ", + "Ġãĥ Ł", + "Ġp ráci", + "Ġpr áci", + "Ġprá ci", + "Ġg á»ijc", + "ĠY eni", + "ĠYe ni", + "ĠYen i", + "ا ضÙĬ", + "اض ÙĬ", + "Ġ èij", + "Ġè ij", + "Ġк ла", + "Ġкл а", + "ı ng", + "ÏĦ εί", + "ÏĦε ί", + "Ġb eni", + "Ġbe ni", + "Ġben i", + "Ġ عد", + "Ġع د", + "Ġ aktu", + "Ġak tu", + "Ġakt u", + "ĠÙĪ ÙĤد", + "ĠÙĪÙĤ د", + "Ġпод гоÑĤов", + "Ġgi ai", + "Ġgia i", + "( æ°´", + "Ġs aç", + "Ġsa ç", + "ĠÙħÙĨ اسب", + "ĠÙħÙĨاس ب", + "âĸ ĭ", + "ÙIJ Ùĩ", + "é į", + "à¸Ń à¸Ĺ", + "ĠسÛĮ اسÛĮ", + "o lit", + "ol it", + "oli t", + "ĠاÙĦ جز", + "ĠاÙĦج ز", + "Ø· ÙĦب", + "Ø·ÙĦ ب", + "Ġ sey", + "Ġs ey", + "Ġse y", + "e rence", + "er ence", + "ere nce", + "eren ce", + "ì´ Į", + "ĠвнÑĥÑĤÑĢ ен", + "ĠвнÑĥÑĤ ÑĢен", + "Ġ à¸Ļาย", + "Ġà¸Ļ าย", + "ĠìķĬ ìķĺëĭ¤", + "ĠìķĬìķĺ ëĭ¤", + "o lik", + "ol ik", + "oli k", + "æľĢ åIJİ", + "ä» ª", + "Ġ ÑĢÑĸд", + "ĠÑĢ Ñĸд", + "è¼ ĥ", + "Ġ باب", + "Ġب اب", + "Ġبا ب", + "Ñĥ ди", + "Ñĥд и", + "Ġ ÑģÑĤÑĥп", + "ĠÑģÑĤ Ñĥп", + "ĠÄij ứng", + "ĠÄijứ ng", + "ĠÅŁ öyle", + "Ġ íķĻìĥĿ", + "ĠíķĻ ìĥĿ", + "Ġв лаÑģÑĤи", + "Ġвла ÑģÑĤи", + "ĠвлаÑģ ÑĤи", + "Ġh ãng", + "Ġhã ng", + "à¹ī าว", + "à¹īา ว", + "ĠÚ© اÙĩØ´", + "Ġ ëĵ¯", + "Ġëĵ ¯", + "ĠجÙħ ÙĦÙĩ", + "Ġد کتر", + "ad olu", + "ado lu", + "adol u", + "Ġت بد", + "Ġتب د", + "ظ اÙħ", + "Ġz naÄį", + "Ġzn aÄį", + "Ġد ÙĨÛĮ", + "ĠدÙĨ ÛĮ", + "Ġs ạn", + "å¼ ±", + "ÏĢ ι", + "Ġ çIJĨ", + "ĠçIJ Ĩ", + "Ġ ÙģصÙĦ", + "ĠÙģ صÙĦ", + "и нг", + "ин г", + "Ðļ Ðŀ", + "ĠС ов", + "ĠСо в", + "Ġziy aret", + "Ġ دÙħ", + "Ġد Ùħ", + "ç« ¹", + "Ġsah ibi", + "is ayar", + "isa yar", + "isay ar", + "ÄŁ a", + "ĠпеÑĢÑĸ од", + "Ġs na", + "Ġsn a", + "( æľ¨", + "Ġ нее", + "Ġн ее", + "Ġне е", + "ĠÑĦак ÑĤоÑĢ", + "ĠÑĦакÑĤ оÑĢ", + "м еж", + "ме ж", + "åº Ħ", + "r áž", + "rá ž", + "ок ÑĢем", + "Ġž al", + "ิ à¹Ģศษ", + "è± ª", + "ou cÃŃ", + "ĠU lus", + "ĠUl us", + "Ġtak že", + "ا ÙĪÙĨ", + "اÙĪ ÙĨ", + "н иÑĤи", + "ни ÑĤи", + "ниÑĤ и", + "нÑĮ о", + "ëį ¸", + "Ġ Ùĥرة", + "ĠÙĥ رة", + "ĠÙĥر Ø©", + "åľ ³", + "ĠArth ropoda", + "ĠÑĤ одÑĸ", + "ĠÑĤо дÑĸ", + "Ġدر صد", + "ุ รà¸ģ", + "ุร à¸ģ", + "ĠÑģв ого", + "ĠÑģво го", + "说 éģĵ", + "Ġc ánh", + "Ġcá nh", + "Ġcán h", + "æĵ Ĭ", + "Ġ ä¸ĭè½½", + "Ġä¸ĭ è½½", + "èī ¾", + "Ġnik dy", + "Ø® Ø·", + "ĠÑģ ейÑĩаÑģ", + "ÙĪ ÙĬÙĦ", + "ÙĪÙĬ ÙĦ", + "a met", + "am et", + "ame t", + "문 ìĿĺ", + "ĠE ÄŁitim", + "大 ä¼ļ", + "Ġb ÅĻez", + "за ÑĨÑĸÑı", + "Ġty to", + "н ай", + "на й", + "غ Ùħ", + "Ġ é©", + "Ġé ©", + "计 ç®Ĺ", + "Türk iye", + "Ġmn ož", + "åIJĪ ä½ľ", + "æľį åĭĻ", + "Ġkažd ý", + "ĠÑİ ÑĢид", + "Ġ βα", + "Ġβ α", + "à¥Ĥ à¤ļ", + "åIJĮ ãģĺ", + "Ġ çĭ", + "Ġç ĭ", + "ί ÏĦ", + "ÙĪÛĮ ÙĨت", + "ÙĪÛĮÙĨ ت", + "ا ÙĨس", + "اÙĨ س", + "æľĢ 大", + "Ġ Từ", + "ĠT ừ", + "éŃĶ æ³ķ", + "Ġб ли", + "Ġбл и", + "ĠÑĤак ое", + "ĠÑĤа кое", + "ãģ ľ", + "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ìĿ´ ë©°", + "ĠÙĤسÙħ ت", + "Ġ оÑĨÑĸ", + "Ġо ÑĨÑĸ", + "н икÑĥ", + "ни кÑĥ", + "ник Ñĥ", + "Ġ Bạn", + "ĠB ạn", + "ĠоÑĢг анÑĸз", + "ĠоÑĢган Ñĸз", + "ĠоÑĢганÑĸ з", + "ü ph", + "üp h", + "Ġ izin", + "Ġi zin", + "Ġiz in", + "Ġ ï¾Ĭ", + "Ġï¾ Ĭ", + "εί ÏĤ", + "à¸ĩ à¹ģà¸ķ", + "ãģ¡ ãĤī", + "ва жа", + "важ а", + "Ġ 欧", + "Ġæ¬ §", + "ι Ïİ", + "ÏĢ ÎŃ", + "Ġк ÑĢеп", + "ĠÑĨ иÑħ", + "ĠÑĨи Ñħ", + "æĦŁ ãģĺ", + "çķ «", + "Ùĥ ÙĪ", + "е мо", + "ем о", + "ž en", + "že n", + "å¹³ æĸ¹", + "ĠÙħجÙħÙĪع Ùĩ", + "ĠÑģво и", + "Ġ ãĦ", + "à¸Ľà¸£à¸°à¸ģ à¸Ńà¸ļ", + "ĠпÑĢ оÑĤи", + "ĠпÑĢо ÑĤи", + "ĠпÑĢоÑĤ и", + "ÙĪ ÛĮÙĩ", + "ÙĪÛĮ Ùĩ", + "认 为", + "ÏĨ ÎŃ", + "и ÑĩеÑģкий", + "иÑĩеÑģ кий", + "иÑĩеÑģки й", + "æ¥ ļ", + "Ġп ап", + "Ġпа п", + "δ Ïģο", + "Ġkullan ım", + "Ġkullanı m", + "Ġz bo", + "Ġzb o", + "Ġú spÄĽ", + "Ġ Ùħز", + "ĠÙħ ز", + "ĠF ak", + "ĠFa k", + "елÑĮ зÑı", + "æ´» åĭķ", + "ĠÐŁ ÑĢав", + "ĠÐŁÑĢ ав", + "ĠÐŁÑĢа в", + "¦ y", + "åĥ ķ", + "æij ĺ", + "Ġر ئ", + "Ġ ÏĨοÏģ", + "ĠÏĨ οÏģ", + "м иÑĤ", + "ми ÑĤ", + "Ġt icaret", + "Ġti caret", + "Ġtic aret", + "æ³ķ å¾ĭ", + "å¹´ 代", + "ìĪ Ļ", + "å ¿ł", + "å¿ ł", + "à¹ĩ à¸Ļà¸Ĺ", + "à¹ĩà¸Ļ à¸Ĺ", + "Ġ Ñĥж", + "ĠÑĥ ж", + "ĠÙħتØŃ Ø¯Ùĩ", + "Ġtr á»Ŀi", + "Ġ رØŃ", + "Ġر ØŃ", + "ĠÚ©ÙĪ ÚĨ", + "ĠопÑĢед елен", + "ĠопÑĢедел ен", + "ĠزÙħ ÛĮÙĨÙĩ", + "ĠزÙħÛĮÙĨ Ùĩ", + "Ġn óng", + "Ġnó ng", + "Ġng ủ", + "Nh ững", + "Ġк иÑĪ", + "Ġки ÑĪ", + "Ġ jde", + "Ġj de", + "Ġjd e", + "Ġ ä¸Ĭæµ·", + "Ġä¸Ĭ æµ·", + "åĭ ĩ", + "Ġt anı", + "Ġtan ı", + "à¹Į à¹ģละ", + "à¹Įà¹ģ ละ", + "ĠÑĢа ÑģÑĤвоÑĢ", + "ĠÑĢаÑģÑĤ воÑĢ", + "ĠÑģÑĢед ÑģÑĤв", + "Ġc án", + "Ġcá n", + "Ġsyst ému", + "Ġsystém u", + "ÛĮ Ø·", + "ĠÑģиÑģÑĤем а", + "Ġ ëŀ", + "Ġë ŀ", + "Ġ ÑĩеÑĤ", + "ĠÑĩ еÑĤ", + "éĥ¨ éŨ", + "å¸ °", + "Ġm illet", + "Ġmill et", + "Ġmil let", + "ĠÎķ λλά", + "à¥ĩà¤ĸ न", + "Ġrepublik y", + "ÑĢ ами", + "ÑĢа ми", + "ÑĢам и", + "Ġसम स", + "Ġaç ısından", + "اد ÙĦ", + "Ġб еÑģп", + "ĠбеÑģ п", + "ãĥ» âĶģ", + "åľ Ń", + "o cu", + "oc u", + "k ánÃŃ", + "ká nÃŃ", + "ÙĪ رش", + "ÙĪر Ø´", + "ëŀ µ", + "Ġ çģ", + "Ġç ģ", + "è° ģ", + "Ġs ám", + "Ġν εÏĨ", + "Ġνε ÏĨ", + "b ilir", + "bil ir", + "ĠmÃŃst ÄĽ", + "Ġ žen", + "Ġž en", + "Ġže n", + "Ġil ç", + "Ġ ë§ģ", + "Ġë§ ģ", + "ãĢij Ċ", + "ĠÙħÙĪ ارد", + "ĠاÙĦ Ø´ÙĬ", + "ĠاÙĦØ´ ÙĬ", + "Ġ기 ë¡Ŀ", + "Ġt ady", + "Ġta dy", + "Ġtad y", + "à¸Ń าà¸Ī", + "ĠÑģ ÑĦ", + "ĠspoleÄį nost", + "Ġtém atu", + "Ùħ اÙħ", + "Ùħا Ùħ", + "Ùħ ع", + "Ġ леж", + "Ġл еж", + "ĠÚĨ Ø´Ùħ", + "ĠiÅŁ let", + "ĠÙĨس Ø®", + "ä¼ °", + "ãģį ãģª", + "ãĢ ĥ", + "å² Ĺ", + "Ġ åŃIJ", + "Ġå ŃIJ", + "ĠåŃ IJ", + "Ġb ảng", + "Ġbản g", + "çĮ ®", + "Ġc ứng", + "Ġcứ ng", + "Ġк ÑĢай", + "ĠкÑĢа й", + "Ġ èĭ±è¯Ń", + "Ġèĭ± è¯Ń", + "Ðł ÐIJ", + "ز ÙĨ", + "èĥ ŀ", + "Ġsür eç", + "Ġsüre ç", + "ãĥķ ãĥĪ", + "ĠкÑĸлÑĮ ка", + "ne ÄŁin", + "neÄŁi n", + "ov ány", + "ová ny", + "ován y", + "л Ñĸн", + "лÑĸ н", + "Ġvý raz", + "ĠÑģÑĩ иÑĤа", + "ĠÑģÑĩиÑĤ а", + "ĠпÑĢав ило", + "ĠпÑĢави ло", + "ĠпÑĢавил о", + "ĠиÑģполÑĮз Ñĥ", + "Ġk éo", + "Ġké o", + "ĠyaklaÅŁ ık", + "ĠÙĪاب ستÙĩ", + "ов аÑĤелÑĮ", + "Ġ ì²ł", + "Ġì² ł", + "ĠاÙĦ عاÙħ", + "ĠاÙĦع اÙħ", + "åĿ ı", + "Ġ à¸ī", + "Ġภī", + "ĠS Æ¡n", + "λ ιο", + "λι ο", + "ì¶Ķ ì²ľ", + "Ġsluž eb", + "ĠдеÑıÑĤелÑĮ ноÑģÑĤи", + "з м", + "Ġп ози", + "Ġпо зи", + "Ġпоз и", + ".; .;", + "ĠпÑĢоиÑģ ÑħодиÑĤ", + "าย à¹ĥà¸Ļ", + "çļĦ ãģ«", + "Ġà¤ĩस स", + "о меÑĤ", + "ом еÑĤ", + "Ġ αÏģ", + "Ġα Ïģ", + "ा à¤Ĺर", + "ाà¤Ĺ र", + "i cÃŃch", + "ic ÃŃch", + "icÃŃ ch", + "Ġpolož ky", + "ê³ ¨", + "æĥ Ĭ", + "Ġö ner", + "Ġön er", + "Ġöne r", + "Ġx ảy", + "ĠÙĨظ رÛĮ", + "ĠÙĨظر ÛĮ", + "Ġngh á»ī", + "Ġ à¸ľà¸¥", + "Ġà¸ľ ล", + "ĠÑĢ олÑĮ", + "ĠÑĢ емон", + "ĠÑĢе мон", + "ص ÙĪر", + "V ý", + "ĠS á»ij", + "ĠÑģ ÑĥÑĩаÑģ", + "ĠÑģÑĥ ÑĩаÑģ", + "ห ย", + "ĠاÙĤ داÙħ", + "Ġer kek", + "Ġerk ek", + "Ġ èį", + "Ġè į", + "ĠÄij ôi", + "ĠÄijô i", + "Ġкон кÑĢеÑĤ", + "æ¬ Ĭ", + "Ġ 缮", + "ĠçĽ ®", + "ÙĪ Ú©", + "lı kla", + "lık la", + "Ġp azar", + "Ġpa zar", + "Ġpaz ar", + "ά νÏī", + "άν Ïī", + "Ñĥ ÑģÑĤа", + "ÑĥÑģ ÑĤа", + "ÑĥÑģÑĤ а", + "ãģª ãģŁ", + "Ġ ÙĩÙĨÚ¯", + "ĠÙĩ ÙĨÚ¯", + "ĠÙĩÙĨ Ú¯", + "Ю ÐĽ", + "Ġв елик", + "Ġвели к", + "Ġвел ик", + "Ġве лик", + "Ġnh Ỽ", + "Ġ ìĭľíĹĺ", + "Ġìĭľ íĹĺ", + ") ìĿĺ", + "Ùĥ Ùĩ", + "Ġ à¹ģล", + "Ġà¹ģ ล", + "Û² Ûµ", + "Ġار ساÙĦ", + "Ġ окÑĢем", + "Ġок ÑĢем", + "ά ÏĤ", + "ĠвÑĭ Ñħод", + "vÄĽt Å¡ÃŃ", + "ĠØ· رÛĮÙĤ", + "Ġк оÑĢоÑĤ", + "ĠкоÑĢ оÑĤ", + "Ġко ÑĢоÑĤ", + "н ÑĶ", + "ãĤĬ ãģ«", + "Ġ ä¹Ł", + "Ġä¹ Ł", + "ØŃ Øµ", + "ع ÙħاÙĦ", + "عÙħ اÙĦ", + "oloj ik", + "oloji k", + "Ġر ابط", + "Ġرا بط", + "çª Ĺ", + "Ġg iz", + "Ġgi z", + "Ġch ết", + "Ġchế t", + "æ¨ £", + "ส à¸ĩ", + "ÙĪ تر", + "ÙĪت ر", + "ĠÑı кÑĥ", + "ĠÑıк Ñĥ", + "çı¾ åľ¨", + "ĠоÑĤ ÑģÑĥÑĤÑģÑĤв", + "Ġ ê´ijê³ł", + "Ġê´ij ê³ł", + "Ñĸ ки", + "Ñĸк и", + "åĢ ¤", + "è® ¢", + "Ġ dle", + "Ġd le", + "Ġdl e", + "Ġ åł", + "Ġå ł", + "æ¨ ©", + "è® ¯", + "åĶ IJ", + "Ġ âĸ²", + "Ġâĸ ²", + "Ġli stop", + "Ġlist op", + "Ġlis top", + "Ġdat ové", + "Ġdato vé", + "ÏĦ ÏĮÏĤ", + "ÏĦÏĮ ÏĤ", + "Ġ оз", + "Ġо з", + "δ ÏĮ", + "èĴ Ĥ", + "Û³ Û°", + "ãĥª ãĥ¼ãĤº", + "ãĥªãĥ¼ ãĤº", + "ĠÙħر کز", + "ĠÙħرک ز", + "ĠпÑĸдÑĤ ÑĢим", + "ĠÑģ ез", + "é¡ ĺ", + "Ġol acaktır", + "Ġolacak tır", + "æº Ģ", + "ĠÏĢεÏģι ο", + "ĠÏĢεÏģ ιο", + "ĠÏĢε Ïģιο", + "ÑĦ а", + "ÏĦ ηÏĥη", + "ÏĦη Ïĥη", + "ç» ĥ", + "Ðŀ д", + "δ Ïħ", + "âĦ ĥ", + "Ġl ắp", + "ĠëĦ ĺ", + "Ø· اÙĨ", + "ĠÙ¾ ÙĨج", + "ĠÙ¾ÙĨ ج", + "ت اÙĨ", + "تا ÙĨ", + "i lerinin", + "iler inin", + "ileri nin", + "ilerin in", + "ilerini n", + "à Ī", + "ĠØ® ÙĪØ´", + "ĠØ®ÙĪ Ø´", + "Ġ ìĬ¬", + "ĠìĬ ¬", + "ĠاÙĦر ئÙĬس", + "ẵ n", + "Ġ شار", + "ĠØ´ ار", + "e ru", + "er u", + "ж ив", + "жи в", + "à¸Ļ าย", + "à¸Ļา ย", + "Ġs ẻ", + "Ġà¤ī à¤ļ", + "ãģ« ãģĭ", + "ç¡ Ģ", + "Ġyür üt", + "ĠС еÑĢг", + "ĠСеÑĢ г", + "Ġ каÑģ", + "Ġк аÑģ", + "Ġка Ñģ", + "ĠÐij ог", + "Ġìĸ´ëĸ »ê²Į", + "Ġ çŁ³", + "Ġç Ł³", + "ĠçŁ ³", + "Ġöl dür", + "Ġöld ür", + "л Ñĸв", + "лÑĸ в", + "Ġho Ãłng", + "ĠhoÃłn g", + "Ġb á»Ļt", + "Ġbá»Ļ t", + "çŀ ¬", + "Ġ 침", + "Ġì¹ ¨", + "N ếu", + "Ġne vy", + "Ġnev y", + "Ġ ìľ¤", + "Ġìľ ¤", + "Ġsou Äįást", + "ıs ıyla", + "ısı yla", + "Ġtük et", + "b ou", + "bo u", + "Ġд во", + "Ġдв о", + "س Ø·", + "å½ĵ çĦ¶", + "ãĥ ¨", + "Ġ زادÙĩ", + "Ġز ادÙĩ", + "Ġزاد Ùĩ", + "Ġ éĥ¨", + "Ġéĥ ¨", + "Ġر ÙĪØŃ", + "ĠرÙĪ ØŃ", + "Ġ ï¼į", + "Ġï¼ į", + "ĠмÑĸÑģ ÑĨев", + "ĠмÑĸÑģÑĨе в", + "θ εν", + "θε ν", + "ภĨ", + "л енÑĸ", + "лен Ñĸ", + "ле нÑĸ", + "çį ²", + "ĠH OH", + "ĠHO H", + "s ın", + "sı n", + "ิ à¸ķร", + "ิà¸ķ ร", + "è² ¡", + "ĠpÅĻ id", + "ĠpÅĻi d", + "à¹Ģ หà¸Ļ", + "à¹Ģห à¸Ļ", + "l ý", + "è¨Ģ èijī", + "ठĵ", + "âĸįâĸįâĸįâĸį âĸįâĸįâĸįâĸį", + "ب اب", + "با ب", + "ãĥ¼ ãĥķ", + "ãĥ¼ãĥ ķ", + "м оÑĢ", + "мо ÑĢ", + "è¿ĩ ç¨ĭ", + "Ġ ãĥĽ", + "Ġãĥ Ľ", + "ĠK inh", + "ĠKi nh", + "ĠKin h", + "íķľ êµŃ", + "Ġìĸ´ëĸ ¤", + "Ġв лиÑı", + "Ġf ayd", + "Ġfa yd", + "Ġص ÙĨع", + "ĠصÙĨ ع", + "Ġal ır", + "Ġet tiÄŁi", + "Ġetti ÄŁi", + "ά κ", + "im izin", + "imi zin", + "imiz in", + "imizi n", + "ัà¸ļ à¸ľ", + "Ġзем елÑĮ", + "ÙĬÙĦ اد", + "ÙĬÙĦا د", + "æ¶ ¨", + "çı ł", + "ĠØ£ غ", + "Ġz ku", + "Ġzk u", + "âĢŀ A", + "า à¸ķร", + "าà¸ķ ร", + "a yi", + "ay i", + "ãĥ© ãĤ¹", + "и ло", + "ил о", + "ĠÄij á»į", + "ĠÄijá» į", + ". Îķ", + "ë ľ", + "ĠμÏĢο Ïģεί", + "å¸ ¶", + "Ġar tır", + "Ġart ır", + "า à¸į", + "าภį", + "å¿ ĺ", + "ta lya", + "tal ya", + "Ġpoz dÄĽji", + "ĠpozdÄĽ ji", + "Ġnep ÅĻ", + "Ġ æ¹", + "Ġæ ¹", + "اÙĩ ÛĮ", + "Ġsat ın", + "Ġ ë²Į", + "Ġë² Į", + "ج ÙĪ", + "ä¸Ģ 缴", + "ìķĦ ìļĶ", + "Âł P", + "Ġ ØĽ", + "ĠØ Ľ", + "Ġп ал", + "Ġпа л", + "表 æĥħ", + "Ġc anlı", + "Ġcan lı", + "æĪIJ 为", + "ÙĪ ÙĨا", + "ÙĪÙĨ ا", + "Ġ âĢ¯", + "ĠâĢ ¯", + "à¸ģำ ล", + "åį ĸ", + "Ġ αÏĥ", + "Ġα Ïĥ", + "и нок", + "ин ок", + "а мп", + "ам п", + "ล à¸Ńà¸ĩ", + "ÙĤ ÙĤ", + "ĠпÑĢо Ñħод", + "ĠпÑĢоÑħ од", + "ãĤĦãĤĭ 夫", + "Ïĩ η", + "è² ¨", + "ĠÙģ ÙĬÙĩ", + "ĠÙģÙĬ Ùĩ", + "ÙĬ رÙĬ", + "ÙĬر ÙĬ", + "Ġвне ÑĪ", + "Ġk arak", + "Ġka rak", + "Ġkar ak", + "Ġkara k", + "Ø« ÙĦ", + "Ùĩ ÙĪرÛĮ", + "ÙĩÙĪر ÛĮ", + "اÙĪر Ù¾", + "ĠÄij á»ı", + "ĠÄijá» ı", + "ji Å¡tÄĽnÃŃ", + "jiÅ¡tÄĽ nÃŃ", + "ت بر", + "تب ر", + "Ġê·¸ ê²ĥ", + "Ġg ül", + "Ġgü l", + "Ġпо кÑĥп", + "Ġпок Ñĥп", + "l ilik", + "li lik", + "lili k", + "lil ik", + "Ġz da", + "Ġzd a", + "åīį ãģ«", + "ĠÙħÙĩ ÙĨد", + "Ġ ÎijÎĿ", + "ĠÎij ÎĿ", + "ĠÚ©ÛĮÙĦ ÙĪÙħتر", + "Ġp ÅĻeh", + "ĠpÅĻ eh", + "ĠpÅĻe h", + "а леж", + "ал еж", + "але ж", + "Ġka yn", + "Ġkay n", + "è® ¿", + "Ġì¤ij êµŃ", + "ĠÑĪиÑĢ ок", + "ĠÑĪи ÑĢок", + "ĠÙħشار کت", + "âĢ Ĥ", + "Ġ íŤ", + "ĠíĹ ¤", + "Ġìłľ íĴĪ", + "ĠØ´ ÛĮر", + "ĠØ´ÛĮ ر", + "es inden", + "esinde n", + "esin den", + "ÑĢ ÑĸÑĩ", + "ÑĢÑĸ Ñĩ", + "èı ²", + "Ñģ коÑĢ", + "Ñģк оÑĢ", + "Ñģко ÑĢ", + "e tik", + "et ik", + "eti k", + "า à¸ľ", + "าภľ", + "ĠØ· بÛĮ", + "Ġطب ÛĮ", + "κ ÎŃ", + "ĠìŀĪ ìĸ´", + "Ġ dek", + "Ġd ek", + "Ġde k", + "ÑĢ Ñĸй", + "ÑĢÑĸ й", + "å ĨĴ", + "åĨ Ĵ", + "nÃŃ ci", + "® ¤", + "ĠÙħر تب", + "Ġy azı", + "Ġya zı", + "Ġyaz ı", + "üs lü", + "ìľ¼ ëĤĺ", + "e lerine", + "eler ine", + "ele rine", + "eleri ne", + "elerin e", + "Ġy oÄŁun", + "Ġб ак", + "Ġба к", + "ÎĻ ÎŁ", + "ά λÏħ", + "άλ Ïħ", + "ç´ Ļ", + "ĠÑĢÑĥ ками", + "ĠÑĢÑĥк ами", + "Ġçöz üm", + "ìłķ ìĿĦ", + "Ġgüç lü", + "λ ÏĮ", + "Ġb elli", + "Ġbe lli", + "Ġbel li", + "Ġbell i", + "ÃŃ Å¡e", + "ÃŃÅ¡ e", + "ĠÏĮ ÏĢÏīÏĤ", + "Ġna Å¡", + "Ġp ár", + "Ġpá r", + "ÑĪ ÑĤ", + "Ġ ìĨ¡", + "ĠìĨ ¡", + "à¥Ĥ रत", + "à¥Ĥर त", + "ĠÏĢολ Ïį", + "ĠÏĢο λÏį", + "ç° ¡", + "èĤ ¯", + "æ¹ ¾", + "Ġ äºĭ", + "Ġब स", + "Ġ무 ë£Į", + "д ина", + "дин а", + "ди на", + "èª °", + "л еж", + "ле ж", + "Ġú ÅĻad", + "ĠоÑģвÑĸ ÑĤи", + "ĠоÑģвÑĸÑĤ и", + "ĠвÑĸд Ñĩ", + "ĠпÑĢи знаÑĩ", + "ĠпÑĢизна Ñĩ", + "ĠпÑĢиз наÑĩ", + "çĶ³ 请", + "' ya", + "'y a", + "ä¿ Ĭ", + "Ġ ÙĬÙĪÙĨ", + "ĠÙĬ ÙĪÙĨ", + "ĠÙĬÙĪ ÙĨ", + "Ġس ع", + "Ġ ÐĶаÑĤа", + "ĠÐĶ аÑĤа", + "ĠÐĶа ÑĤа", + "è¨Ģ ãģĨ", + "ĠØŃ ØªÛĮ", + "ĠJi ÅĻÃŃ", + "ĠÐ¥ аÑĢ", + "éĻ Ī", + "à¹Ī าà¸Īะ", + "à¹Īา à¸Īะ", + "Ġsay esinde", + "ĠÑĤÑĢеб а", + "ê°Ģ ì§Ģ", + "Ġy emek", + "Ġye mek", + "Ġyem ek", + "Ġyeme k", + "è¦ ļ", + "ặ n", + "ãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", + "Ġ 举", + "Ġä¸ ľ", + "Ġ ÙĪا", + "ĠÙĪ ا", + "ĠÙħ ÙĪس", + "ĠÙħÙĪ س", + "Ġком анд", + "Ġкоман д", + "Ġseç im", + "Ñĩ еннÑı", + "Ñĩен нÑı", + "Ġtot iž", + "Ġr á»Ńa", + "ı a", + "Ø¢ Ùħ", + "ÑĨ Ñĸон", + "ÑĨÑĸ он", + ": :::::::::::", + ":: ::::::::::", + ":::: ::::::::", + ":::::: ::::::", + ":::::::: ::::", + "::: :::::::::", + "::::: :::::::", + "::::::: :::::", + "::::::::: :::", + ":::::::::: ::", + "::::::::::: :", + "ÐĿ ÐIJ", + "ı za", + "ız a", + "h end", + "he nd", + "hen d", + "Ġफ र", + "ัà¸Ķ à¸ģาร", + "Ġ Cách", + "ĠC ách", + "ĠCác h", + "ĠпоÑĤ Ñĸм", + "Ġá¼ Ģ", + "ا ÙĦا", + "اÙĦ ا", + "á» ¡", + "ر ÛĮÙħ", + "رÛĮ Ùħ", + "å® «", + "Ġز ÙħÛĮÙĨ", + "ĠزÙħ ÛĮÙĨ", + "ÑĢ еÑģÑĤ", + "ÑĢе ÑģÑĤ", + "ÑĢеÑģ ÑĤ", + "б аÑĩ", + "ба Ñĩ", + "Ùĩر ست", + "н ог", + "но г", + "ï¼Į 大", + "ĠëĺIJ íķľ", + "Ġz ůst", + "Ġzů st", + "ĠÐĴ она", + "ĠÐĴо на", + "å¤ĩ 份", + "Ġ اÙģت", + "Ġا Ùģت", + "ĠاÙģ ت", + "o je", + "oj e", + "Ñģк ÑĸлÑĮки", + "Ġnh ẹ", + "Ġк еÑĢÑĸв", + "ĠкеÑĢ Ñĸв", + "á¿ ¦", + "æĸ¹ æ¡Ī", + "з аÑĨиÑı", + "за ÑĨиÑı", + "ĠвÑĸдповÑĸд но", + "ĠвÑĸдпов Ñĸдно", + "ãĤ¤ ãĤ¹", + "г ал", + "га л", + "ĠобÑĭ Ñĩно", + "ĠобÑĭÑĩ но", + "اÙĪرپ ÙĪÛĮÙĨت", + "å® ľ", + "l osti", + "lo sti", + "los ti", + "lost i", + "è¿Ľ åħ¥", + "uyor du", + "벤 íĬ¸", + "æīĭ ãĤĴ", + "ÐŁ од", + "ÐŁÐ¾ д", + "ĠÙħØŃ Ø¯ÙĪد", + "Ġ Ø¢Ùħد", + "ĠØ¢ Ùħد", + "ĠØ¢Ùħ د", + "ar akter", + "arak ter", + "çļĦ 大", + "Ġsı cak", + "l ant", + "la nt", + "lan t", + "Ġd ấu", + "ĠÙĨ Ú©", + "èĢħ ãģ®", + "Ġkend ini", + "Ġkendi ni", + "Ġп аÑĨи", + "Ġпа ÑĨи", + "Ġ 기íĥĢ", + "Ġ기 íĥĢ", + "ĠвмеÑģÑĤ е", + "в аеÑĤÑģÑı", + "ва еÑĤÑģÑı", + "ваеÑĤ ÑģÑı", + "Ġ ë§ī", + "Ġë§ ī", + "ĠchvÃŃ li", + "Ø® ÛĮ", + "ÙĦ ع", + "n ÃŃky", + "nÃŃ ky", + "nÃŃk y", + "､ :", + "ëIJľ ëĭ¤", + "ì§ ķ", + "Ġк вÑĸÑĤ", + "Ġкв ÑĸÑĤ", + "¨ ìĸ´", + "l iž", + "li ž", + "Ġë¹Ħë°Ģ ê¸Ģ", + "Ġkh á»iji", + "Ġ ë°©ìĨ¡", + "Ġë°© ìĨ¡", + "e chan", + "ec han", + "ech an", + "echa n", + "Ġзакон одав", + "Ġа кÑĤ", + "Ġак ÑĤ", + "문 ìłľ", + "ĠN ó", + "Ġ çĤ¹", + "Ġç Ĥ¹", + "ĠçĤ ¹", + "hled em", + "hle dem", + "ĠÑģво ÑĹÑħ", + "ĠÑģвоÑĹ Ñħ", + "Ġر ÙĤÙħ", + "ĠرÙĤ Ùħ", + "æĽ ¼", + "ि वर", + "िव र", + "åİ ļ", + "ĠÐļ од", + "ĠÐļо д", + "à¤Ń à¤Ĺ", + "ìŀIJ ëĬĶ", + "à¸Ļ ม", + "Ñĥ Ñģа", + "ÑĥÑģ а", + "Ġg ünü", + "Ġgün ü", + "ĠÄij ÃŃch", + "Ġtr ữ", + "å ·»", + "å· »", + "éĵ¶ è¡Į", + "ØŃ ÙĨ", + "è® ¨", + "γ Ïĩ", + "á½ ¸", + "a larında", + "alar ında", + "aları nda", + "aların da", + "Ġk af", + "Ġka f", + "ÙĪ اج", + "ÙĪا ج", + "ĠиÑģ клÑİÑĩ", + "ĠиÑģк лÑİÑĩ", + "Ġnh iá»ħ", + "á»į t", + "ĠìĽ ¹", + "Ġ éĿ¢", + "ĠéĿ ¢", + "ãģ® ãģĮ", + "Ġм ало", + "Ġмал о", + "Ġма ло", + "Ñĸ лÑĸ", + "Ñĸл Ñĸ", + "Ġ biên", + "Ġb iên", + "Ġbi ên", + "n ému", + "né mu", + "ném u", + "пÑĢи меÑĢ", + "пÑĢ имеÑĢ", + "âĸł âĸł", + "Ġk amp", + "Ġka mp", + "Ġkam p", + "Ġ веÑī", + "Ġв еÑī", + "Ġве Ñī", + "Äį em", + "Äįe m", + "à¥ģ ध", + "à¥ģठ§", + "æŁ »", + "ت ÙĪÙĨ", + "تÙĪ ÙĨ", + "åıª æľī", + "ãģ¯ ãģĦ", + "Ġ รวม", + "Ġร วม", + "ãĤ ŀ", + "ãģĻ ãĤĭãģ¨", + "ãģĻãĤĭ ãģ¨", + "å¾Ī å¤ļ", + "à¹Ī à¸ķ", + "ĠsvÄĽt a", + "ĠsvÄĽ ta", + "Ġ ê°Ģ격", + "Ġê°Ģ 격", + "Ú¯ Ùĩ", + "an daÅŁ", + "and aÅŁ", + "anda ÅŁ", + "ãĥª ãĤ¹", + "Ïī μα", + "ĠØ® ÙĪب", + "ĠØ®ÙĪ ب", + "ç´ ħ", + "Ñĩ иÑģ", + "Ñĩи Ñģ", + "ì¢ Į", + "ĠØŃض رت", + "Ġви ÑĢÑĸÑĪ", + "Ù¾ ر", + "Ġtý d", + "Ġkon tro", + "Ġkont ro", + "д ейÑģÑĤв", + "ãģŁãĤģ ãģ«", + "ì ī", + "мини ÑģÑĤÑĢа", + "мин иÑģÑĤÑĢа", + "âĢ ¯", + "åī ij", + "ни ÑĨÑĸ", + "ниÑĨ Ñĸ", + "å¦ ĩ", + "Ġ лиÑĪ", + "Ġл иÑĪ", + "Ġли ÑĪ", + "ãģ£ ãģ¦ãĤĭ", + "ãģ£ãģ¦ ãĤĭ", + "на ÑĢÑĥж", + "наÑĢ Ñĥж", + "Ñī иÑħ", + "Ñīи Ñħ", + "ÏĦ οκ", + "ÏĦο κ", + "ov áno", + "ová no", + "ován o", + "تر ÙĦ", + "ÑĢ ек", + "ÑĢе к", + "غ ات", + "Ġ omez", + "Ġo mez", + "Ġom ez", + "ì ĵ°", + "ĠÃľ l", + "ï½ Ĵ", + "lı ģını", + "lıģ ını", + "lıģın ı", + "Ġv ượt", + "Ġb ÄĽÅ¾", + "ĠbÄĽ ž", + "Ãľ R", + "Ġ ãĥ¾", + "Ġãĥ ¾", + "ĠdoÄŁ al", + "Ġh atır", + "Ġha tır", + "Ġhat ır", + "Ġsv ým", + "ì§Ģ ëıĦ", + "à¹Ģà¸ł à¸Ĺ", + "Ġv ay", + "Ġva y", + "Ġ æĻĤ", + "Ġæ ĻĤ", + "ĠæĻ Ĥ", + "à¥įव प", + "Ġp lo", + "Ġpl o", + "é¢Ħ è§Ī", + "Ġçık tı", + "Ġ دÙĨ", + "Ġد ÙĨ", + "n ánÃŃ", + "ná nÃŃ", + "ê· Ģ", + "íĺ Ģ", + "à¸ŀ à¸ļ", + "m uÅŁtur", + "muÅŁ tur", + "å®ĺ æĸ¹", + "ĠíĶĦ ë¡ľê·¸ëŀ¨", + "éĢŁ 度", + "ler dir", + "lerdi r", + "ÑĩеÑģ кого", + "Ñĩе Ñģкого", + "ĠÄ°n san", + "âĶ ĥ", + "Ġà¤ĩत न", + "С Ð¡Ðł", + "Ġا Ùħر", + "ĠاÙħ ر", + "Ġkö tü", + "Ùģ Ø´", + "Ġb oj", + "Ġbo j", + "ĠÑĨÑĸ ÑĶÑĹ", + "Ġsöy lem", + "Ġsöyl em", + "ни ÑĨÑĭ", + "ниÑĨ Ñĭ", + "ãĢĤ 她", + "âĢĿ .Ċ", + "âĢĿ. Ċ", + "Ġm ilion", + "Ġmil ion", + "Ġmi lion", + "Ġmili on", + "Ġson unda", + "Ġsonu nda", + "з Ñĥ", + "à¥į मà¤ķ", + "à¥įम à¤ķ", + "人 åı£", + "n ÄĽÅ¾", + "nÄĽ ž", + "ĠÑģ моÑĤ", + "ĠÑģм оÑĤ", + "Ġкомп лекÑģ", + "Ġкомплек Ñģ", + "Ġзави Ñģим", + "Ġиме ÑİÑĤ", + "Ġl ạc", + "Ġlạ c", + "Ġ hangi", + "Ġh angi", + "Ġhang i", + "Ġhan gi", + "ëĶ ©", + "åĬ ³", + "ĠvÄĽ ci", + "ĠvÄĽc i", + "е ÑĢов", + "еÑĢ ов", + "еÑĢо в", + "κ Ïģι", + "Ġdur umu", + "Ġdurum u", + "Ġ بÙĪاسطة", + "ĠبÙĪ اسطة", + "ĠØ£ بÙĬ", + "Ġأب ÙĬ", + "ĠAÄŁ ustos", + "ε Ïĩ", + "Ġд иÑĤи", + "Ġди ÑĤи", + "ĠдиÑĤ и", + "ÑĦ ика", + "ÑĦи ка", + "ÑĦик а", + "Ġ NÄĥm", + "ĠN Äĥm", + "Ġ 기ìĪł", + "Ġ기 ìĪł", + "Ġhlav nÃŃ", + "ä¿ ĥ", + "Ġलà¤Ĺ त", + "ĠO br", + "ĠOb r", + ". ย", + "ко вод", + "ков од", + "ково д", + "o pis", + "op is", + "opi s", + "Ġ ãĥī", + "Ġãĥ ī", + "Ġبش ÙĥÙĦ", + "н ием", + "ни ем", + "ние м", + "Ġtém ÄĽÅĻ", + "ĠاÙĦ ØŃر", + "ĠاÙĦØŃ Ø±", + "ĠÙĦ ازÙħ", + "ĠÙĦا زÙħ", + "Ġm ái", + "Ġmá i", + "i liÄŁi", + "ili ÄŁi", + "ë³ ¼", + "Ġy ık", + "Ġyı k", + "ç½ ²", + "ÑĢ ава", + "ÑĢа ва", + "ÑĢав а", + "Ñī ин", + "Ñīи н", + "ãģ« 対", + "ç²¾ ç¥ŀ", + "à¹ī ส", + "Ġtem sil", + "à Ĩ", + "ìķ Ķ", + "ĠпÑĢавилÑĮ но", + "ÑĢ оÑİ", + "ÑĢо Ñİ", + "Û±Û³ Û¸", + "è© ŀ", + "اء Ø©", + "ÙĪ ارÙĩ", + "ÙĪا رÙĩ", + "ÙĪار Ùĩ", + "ï¼ ħ", + "ĠÐľ ик", + "ĠÐľÐ¸ к", + "æģ ¶", + "æı Ĵ", + "ा पन", + "ाप न", + "ĠÚ©ÛĮÙģ ÛĮت", + "ĠT Ãłi", + "Ġt iá»ĥu", + "ov alo", + "ova lo", + "oval o", + "çĿ ¡", + "Ñĩ ил", + "Ñĩи л", + "Ġ лиÑĤ", + "Ġл иÑĤ", + "Ġли ÑĤ", + "λεÏħ ÏĦα", + "Ġ окон", + "Ġо кон", + "Ġок он", + ": :|", + ":: |", + "в ала", + "ва ла", + "вал а", + "ĠÙħرک زÛĮ", + "ĠÙħرکز ÛĮ", + "Ġ alÄ±ÅŁ", + "Ġa lÄ±ÅŁ", + "Ġal Ä±ÅŁ", + "Ġдолж но", + "Ġдол жно", + "æĻĤ 代", + "Ġ sert", + "Ġs ert", + "Ġse rt", + "Ġser t", + "е ÑĤом", + "еÑĤ ом", + "ัà¸Ļ ย", + "åģ ·", + "Ġ vÃŃc", + "Ġv ÃŃc", + "ĠvÃŃ c", + "ĠÑħ оÑĤÑı", + "ĠÑħоÑĤ Ñı", + "a larını", + "alar ını", + "aların ı", + "len mesi", + "lenme si", + "ãĥ³ ãĥIJ", + "ãĥ³ãĥ IJ", + "Ġëª ĩ", + "Ġá» ¦y", + "ĠاÙĦ کتر", + "vy Å¡Å¡ÃŃ", + "è² ¬", + "주 ìĭľ", + "á ÅĻe", + "áÅĻ e", + "Ġy ere", + "Ġye re", + "Ġyer e", + "ãĤ¢ ãĥ³", + "ĠاÙĦس عÙĪد", + "ĠØ¢ Ø´", + "Ġch óng", + "Ġchó ng", + "Ġ è»", + "Ġè »", + "г аÑĶ", + "га ÑĶ", + "Ġ ãģĤ", + "ç¨ ³", + "δ εÏĤ", + "δε ÏĤ", + "缮 çļĦ", + "Ġce vap", + "Ġcev ap", + "Ñģ ÑĤе", + "ÑģÑĤ е", + "é¡ ¿", + "म न", + "é¡ ¾", + "Ġк ÑĢедиÑĤ", + "ĠÙħس تÙĤ", + "ĠÙħست ÙĤ", + "Ġ миÑĤ", + "Ġм иÑĤ", + "Ġми ÑĤ", + "Ġt á»ĵn", + "Ġtá» ĵn", + "Ġ جÙĦ", + "Ġج ÙĦ", + "Ä© a", + "ĠاÙĦع ÙĦÙħ", + "ĠاÙĦعÙĦ Ùħ", + "á ků", + "ák ů", + "Ġ íķĻêµIJ", + "ĠíķĻ êµIJ", + "à¸Ĺ à¸Ńà¸ĩ", + "ห à¸Ļà¸Ķ", + "หà¸Ļ à¸Ķ", + "ĠлÑĸÑĤ еÑĢаÑĤÑĥ", + "ëIJ ł", + "ά ÏģÏĩ", + "άÏģ Ïĩ", + "ĠÙĤد رت", + "ĠÙĤدر ت", + "à¸Ļ าà¸ĩ", + "à¸Ļา à¸ĩ", + "Ġa rac", + "Ġar ac", + "Ġara c", + "Ġj ÃŃd", + "ĠjÃŃ d", + "Ġtür lü", + "íĶ ½", + "er siz", + "ers iz", + "е ним", + "ен им", + "ени м", + "Ġyüz yıl", + "Ġ ãģĦ", + "ĠÎļ Ïħ", + "Ġ æļ", + "Ġæ ļ", + "Ġp ůj", + "Ġpů j", + "Ġt á»Ļi", + "Ġth iên", + "Ġthi ên", + "Ä° S", + "Ġth úc", + "Ġthú c", + "æĹ ģ", + "ìŀIJ ìĿ¸", + "Ġöl üm", + "ر ÛĮÙģ", + "رÛĮ Ùģ", + "ÑĢ еж", + "ÑĢе ж", + "ص اÙĦ", + "ر ÙģØ©", + "رÙģ Ø©", + "i ếp", + "iế p", + "Ñı ÑĤиÑı", + "ÑıÑĤ иÑı", + "ÑıÑĤи Ñı", + "Ġpou žit", + "á tu", + "át u", + "为 ä»Ģä¹Ī", + "ì ģ", + "Ġ krát", + "Ġk rát", + "Ġkr át", + "ĠپرÙĪ ÚĺÙĩ", + "Ġrozhod nutÃŃ", + "ĠÑĥ нивеÑĢ", + "Ñĸй но", + "Ġ åij¨", + "Ġåij ¨", + "Ġk iá»ĥu", + "缮 åīį", + "ä¿ Ħ", + "ÏĦ οι", + "ÏĦο ι", + "ÑĦеÑĢ ен", + "uÅŁ tur", + "Ġ nÃŃm", + "Ġn ÃŃm", + "ĠnÃŃ m", + "âĢĮ Ø®", + "Ġ ủy", + "Ġ ÑģÑĤаÑĤи", + "ĠÑģÑĤ аÑĤи", + "ĠÑģÑĤаÑĤ и", + "ĠÑģÑĤа ÑĤи", + "ÑĩеÑģ кий", + "Ñĩе Ñģкий", + "ÑĩеÑģки й", + "Ġj estli", + "Ġjest li", + "ĠÙ¾ ÙĨ", + "Ġob ce", + "ĠجÙĩ اÙĨÛĮ", + "ĠجÙĩاÙĨ ÛĮ", + "едаг ог", + "ãģ§ ãģ®", + "Ġbu á»Ļc", + "ì¹´ ì§Ģëħ¸", + "à¹ĩ à¸Ħ", + "ĠÄį tvrt", + "Ġ ника", + "Ġн ика", + "Ġни ка", + "Ġник а", + "Ġвп лив", + "Ġд иÑĢ", + "Ġди ÑĢ", + "ĠÑģоб ÑģÑĤвен", + "Ġë§İ ìĿ´", + "æ¾ ³", + "ÑĢ Ñĥб", + "ÑĢÑĥ б", + "æ£ ĭ", + "声 éŁ³", + "ä¹ ĥ", + "تÛĮ جÙĩ", + "å¹ ¼", + "o nya", + "on ya", + "ony a", + "ĠPlan tae", + "ĠPlant ae", + "Ч ÑĤо", + "æIJ Ń", + "ä½ľ çĶ¨", + "ìħ ¨", + "Ġк ÑĢÑĥг", + "Ġ ÙĪÙģÙĬ", + "ĠÙĪ ÙģÙĬ", + "ĠÙĪÙģ ÙĬ", + "Ġ ï¼ŀ", + "Ġï¼ ŀ", + "ÑĪ ки", + "Âł Ðľ", + "ا Ø´ÛĮ", + "اش ÛĮ", + "ĠÅŀ ubat", + "ĠÅŀu bat", + "Ġع شر", + "Ġعش ر", + "l if", + "li f", + "Ġpou žitÃŃ", + "Ġpoužit ÃŃ", + "íĨ ¡", + "Ġб лок", + "Ġбл ок", + "èĢ ¶", + "ู ร", + "Ġv üc", + "Ø´ ÙĪد", + "Ø´ÙĪ د", + "и ма", + "им а", + "ни ÑĨип", + "ниÑĨ ип", + "ìĿ´ ëĵľ", + "Ġ âĢIJ", + "ĠâĢ IJ", + "Ġ назнаÑĩ", + "Ġна знаÑĩ", + "Ġназ наÑĩ", + "Ġназна Ñĩ", + "Ġstr any", + "Ġstran y", + "Ġstra ny", + "æ® ¿", + "ĠاÙĦ رÙĪ", + "ĠاÙĦر ÙĪ", + "çº ¸", + "åĪ ij", + "ï¼Į ä»İ", + "Ġ ë©´", + "Ġë© ´", + "ĠпÑĢовед еннÑı", + "Ġh ava", + "Ġha va", + "Ġhav a", + "ĠìĹĨ ìĹĪëĭ¤", + "ĠìĹĨìĹĪ ëĭ¤", + "å¢ŀ åĬł", + "Ú ¾", + "ç¼ º", + "Ġع بار", + "Ġعب ار", + "Ġt ắc", + "Ġin ÅŁa", + "er se", + "ers e", + "ر ÙĬب", + "رÙĬ ب", + "Ġá»ķ n", + "Ø£ Ø©", + "ĠÏĢολ ι", + "ĠÏĢο λι", + "Ġm ắc", + "Ñģ ол", + "Ñģо л", + "æ´ ŀ", + "- го", + "-г о", + "ç¨ĭ 度", + "ĠвикоÑĢиÑģÑĤ аннÑı", + "âĢŀ ظ", + "e lerinde", + "eler inde", + "eleri nde", + "elerin de", + "ĠNh Æ°ng", + "ĠNhÆ° ng", + "st ÅĻed", + "Ġhasta lık", + "Ġhast alık", + "à¹ī à¹Ģà¸Ľ", + "Ġd efa", + "Ġde fa", + "Ġdef a", + "Ġ زÙĬ", + "Ġز ÙĬ", + "اط ÙĤ", + "Ġп ÑĢой", + "ĠпÑĢ ой", + "ĠпÑĢо й", + "Ġок ÑĢÑĥг", + "ν ια", + "νι α", + "l adu", + "la du", + "lad u", + "k oli", + "ko li", + "kol i", + "Ġ oÄŁ", + "Ġo ÄŁ", + "Ġви Ñģок", + "ĠвиÑģ ок", + "Ð ĩ", + "çĽ ĸ", + "ãĤı ãģij", + "ãĥ¼ ãĥģ", + "ãĥ¼ãĥ ģ", + "æ¡ ¥", + "ĠÅ¡kol y", + "ĠÅ¡k oly", + "i tom", + "it om", + "ito m", + "Ġت ØŃص", + "ĠتØŃ Øµ", + "a lara", + "al ara", + "ala ra", + "alar a", + "Ġк ал", + "Ġка л", + "ĠпÑĢи Ñħод", + "Ġ é¦ĸ页", + "Ġé¦ĸ 页", + " į", + "ĠÛĮ عÙĨÛĮ", + "Ġt ùy", + "Ģ ë¡ľ", + "볤 ê³ł", + "á ze", + "áz e", + "Ġ ек", + "Ġе к", + "èħ ¹", + "ĠF akat", + "ĠFa kat", + "ĠFak at", + "п о", + "ĠÄij á»įc", + "ĠÄijá»į c", + "å Īĺ", + "åĪ ĺ", + "áz al", + "ÑĤ он", + "ÑĤо н", + "Ú¯ ÙĪ", + "ä¸ Ī", + "ìĹ ¼", + "ĠÙĦÙĦ Ø£", + "ĠE ÄŁer", + "åħ±åĴĮ åĽ½", + "Ø° ر", + "Ġd aÄŁ", + "Ġda ÄŁ", + "è¡Į ä¸ļ", + "ê±°ëŀĺ ê°Ģ", + "è´Ł è´£", + "C ông", + "ĠÑĦ илÑĮ", + "ĠÑĦил ÑĮ", + "Ġ аÑģ", + "Ġа Ñģ", + "Ġch ẳng", + "ним аÑĤÑĮ", + "нима ÑĤÑĮ", + "Ġif ad", + "Ġi fad", + "Ġ ìħ", + "Ġì ħ", + "çĪ µ", + "ĠÅĻe Å¡enÃŃ", + "åĽ½ 产", + "Ġкак ой", + "Ġка кой", + "Ġम ध", + "ĠY ar", + "ĠYa r", + "ob raz", + "obra z", + "Ġon emoc", + "Ġ âĤ", + "Ġâ Ĥ", + "åİŁ åĽł", + "ĠÙĥ رد", + "ĠÙĥر د", + "Ġآز اد", + "Ġad lı", + "ĠH izmet", + "ãĥ¼ ãĥij", + "ãĥ¼ãĥ ij", + "ÙĨ سÙĬØ©", + "ÙĨس ÙĬØ©", + "Ġв нÑĥÑĤ", + "ĠвнÑĥ ÑĤ", + "Ġd ále", + "Ġdál e", + "Ġdá le", + "Îķ Î¥", + "Ġ ÑĥÑħ", + "ĠÑĥ Ñħ", + "Ġ ÑĢев", + "ĠÑĢ ев", + "ĠÑĢе в", + "Ġ меÑĪ", + "Ġм еÑĪ", + "ĠkoÅŁ ul", + "ĠاÛĮ راÙĨÛĮ", + "ĠاÛĮراÙĨ ÛĮ", + "éĺ µ", + "Ġ ëıĻìķĪ", + "ĠëıĻ ìķĪ", + "à¹Ģ à¸Ł", + "à¹ĢภŁ", + "ëłĪ 벨", + "è¨Ń è¨Ī", + "p rak", + "pr ak", + "pra k", + "p oÄį", + "po Äį", + "اع دة", + "اعد Ø©", + "Ġas ker", + "Ġask er", + "ĠÙĪÛĮ ÚĺÙĩ", + "ĠÙĪÛĮÚĺ Ùĩ", + "ĠТ еÑĢ", + "ĠТе ÑĢ", + "mak ta", + "makt a", + "ĠÄįty ÅĻ", + "Âł С", + "âĢĮÚ©ÙĨ ÙĨد", + "ï¼Į 並", + "ĠÑĢоÑģ Ñĸй", + "Ġu nut", + "Ġun ut", + "è¿Ļ ä¸Ģ", + "o pak", + "op ak", + "opa k", + "èĢ IJ", + "Ġз амеÑĤ", + "Ġза меÑĤ", + "Ġзам еÑĤ", + "à¹Į ล", + "ب ÙĨ", + "Ġ 몰", + "Ġëª °", + "Ġins anlar", + "Ġinsan lar", + "åı¯ æĺ¯", + "æ¢ ¦", + "к од", + "ко д", + "èĽ Ľ", + "kl adnÃŃ", + "klad nÃŃ", + "ÑĢов од", + "ÑĢо вод", + "ĠмÑĸ ÑģÑĤа", + "ĠмÑĸÑģ ÑĤа", + "ĠмÑĸÑģÑĤ а", + "åĩº äºĨ", + "Ġп аÑģ", + "Ġпа Ñģ", + "о бов", + "об ов", + "Ú¯ اÙĩÛĮ", + "گاÙĩ ÛĮ", + "в ин", + "ви н", + "à¥įर ध", + "Ġком пон", + "Ġкомп он", + "Ġ аÑĤ", + "Ġа ÑĤ", + "Ġa det", + "Ġad et", + "Ġade t", + "Ġ ãĥģ", + "Ġãĥ ģ", + "ĠØ° ات", + "ĠØŃ ÙĪ", + "Ġtro chu", + "à¹ģ หà¸Ļ", + "à¹ģห à¸Ļ", + "Ġзав жди", + "ĠPart isi", + "ĠParti si", + "ĠS avaÅŁ", + "ĠSav aÅŁ", + "Ġs ÃŃd", + "ĠsÃŃ d", + "Ġ Ñģон", + "ĠÑģ он", + "ĠÑģо н", + "ر ÙĬÙģ", + "رÙĬ Ùģ", + "Ġz cela", + "åĺ ´", + "ĠÑĦ ÑĥÑĤ", + "il erek", + "ile rek", + "iler ek", + "ilere k", + "m alıdır", + "malı dır", + "Ġd á»±a", + "Ġdá»± a", + "à¸Ĺำ à¸ĩาà¸Ļ", + "ĠÙĪÙĦ ÙĥÙĨ", + "ĠÙĪÙĦÙĥ ÙĨ", + "ãģª ãĤĵãģł", + "ãģªãĤĵ ãģł", + "ĠÚ© ÙħÛĮ", + "ĠÚ©Ùħ ÛĮ", + "Ġléka ÅĻ", + "Ïģ Ïį", + "ج Ùħع", + "جÙħ ع", + "ın ızı", + "ını zı", + "ınız ı", + "ĠAn adolu", + "ãģ«ãĤĪ ãģ£ãģ¦", + "Ġê·¸ëŁ¬ ëĤĺ", + "Ġ íĮĶ", + "ĠíĮ Ķ", + "Ñĸ ÑĤÑĮ", + "ÑĸÑĤ ÑĮ", + "Ġ ¦", + "Ġ ¦", + "ä¸į è¦ģ", + "à¸ĸ ม", + "Ġ ÙĬد", + "ĠÙĬ د", + "ĠpÅĻ ep", + "ĠpÅĻe p", + "Ġ è¦ģ", + "Ġè¦ ģ", + "ĠпÑĢо екÑĤ", + "ĠпÑĢоек ÑĤ", + "ĠÑĢе ги", + "ĠÑĢег и", + "Ġd ạy", + "к ового", + "ков ого", + "ково го", + "Ġ ıs", + "Ġı s", + "ĠK ı", + "ĠÙģÙĬ Ùĩا", + "ĠÙģÙĬÙĩ ا", + "ÛĮ ات", + "ÛĮا ت", + "ĠÑģÑĤ ала", + "ĠÑģÑĤал а", + "ĠÑģÑĤа ла", + "æĬ ľ", + "Ñĥ ÑĢа", + "ÑĥÑĢ а", + "ĠÙ¾ اÛĮاÙĨ", + "ĠپاÛĮ اÙĨ", + "Ġپا ÛĮاÙĨ", + "Ġitibar en", + "а нÑĸÑĹ", + "ан ÑĸÑĹ", + "анÑĸ ÑĹ", + "Ġо ÑĦоÑĢм", + "л еÑĩ", + "ле Ñĩ", + "ε ξ", + "æĶ¿ çŃĸ", + "Ġ ç½ij", + "Ġç½ ij", + "å Ĥ¬", + "åĤ ¬", + "ĠìĿ´ 룰", + "Ġkar deÅŁ", + "Ñİ Ñīего", + "ÑİÑī его", + "л ки", + "ĠاÛĮ اÙĦات", + "ت Ùĩا", + "تÙĩ ا", + "Ġпод Ñħод", + "ĠØŃ ÙĪÙĦ", + "ĠØŃÙĪ ÙĦ", + "ĠÑģов ÑĢем", + "íĿ ¥", + "Ġ 詳細", + "Ġè© ³ç´°", + "ı yı", + "ıy ı", + "ĠتÙĤ ÙĪ", + "æ¯Ķ è¾ĥ", + "Ġαν ÏĦι", + "Ġ ΣΤ", + "ĠΣ Τ", + "ji Å¡Å¥", + "yn ı", + "Ġpo cházet", + "- Ðļ", + "Ġзав д", + "Ùİ Ø³", + "ç»ĵ æŀĦ", + "Ùħ ار", + "Ùħا ر", + "ν οι", + "νο ι", + "ĠÎł εÏģι", + "ĠγεÏģ ι", + "èĩ £", + "Ġna cházÃŃ", + "Ġnach ázÃŃ", + "ÏĦ Ïİ", + "à¥įय त", + "u yu", + "uy u", + "æķ Ĺ", + "e bi", + "eb i", + "Ġë°Ķ ë¡ľ", + "ĠгÑĢ н", + "ĠاÙĦ اس", + "Ġorg án", + "Ġ edin", + "Ġe din", + "Ġed in", + "Ġedi n", + "åŁ ĥ", + "à¹ģ à¸Ħ", + "ĠØŃ Ø¯ÙĪد", + "ĠØŃد ÙĪد", + "ĠдÑĢÑĥг ой", + "ĠдÑĢÑĥго й", + "оÑģ ков", + "оÑģк ов", + "ĠS ợ", + "ĠpÅĻ ib", + "ĠpÅĻi b", + "ä¿Ŀ æĬ¤", + "Ùħ بر", + "Ġ ãĥĨ", + "Ġãĥ Ĩ", + "Ġd oz", + "Ġdo z", + "op tera", + "opt era", + "ิล à¸Ľ", + "د ارÛĮ", + "دار ÛĮ", + "دا رÛĮ", + "æĦŁ è§ī", + "代 çIJĨ", + "ÙĨ دا", + "ÙĨد ا", + "ا ÙĬا", + "اÙĬ ا", + "ص ÙĨ", + "Ġce lé", + "Ġcel é", + "Ġ è©ķ", + "Ġè© ķ", + "à¸ĩ à¸Ļ", + "Ġ leh", + "Ġl eh", + "Ġle h", + "èİ· å¾Ĺ", + "ãĢĢ ï¾ī", + "ĠìĦł ìĪĺ", + "르 ëĬĶ", + "à¤Ĩ र", + "å§Ķ åijĺ", + "æĹł çłģ", + "Ġ è·", + "Ġè ·", + "Ġza jÃŃm", + "Ġzaj ÃŃm", + "ec ké", + "eck é", + "æ µľ", + "æµ ľ", + "ĠÑĥ нÑĸвеÑĢÑģиÑĤ", + "ĠбÑİдж еÑĤ", + "à¥ĩ .", + "Ġv stup", + "Ġ оÑī", + "Ġо Ñī", + "Ġ åľĭ", + "Ġåľ ĭ", + "ä¸ģ 缮", + "Ġв едÑĮ", + "Ġвед ÑĮ", + "Ġë§IJ ìĿĦ", + "Ġtek nik", + "Ġtekn ik", + "ãĢĢ ï½Į", + "ãĢĢï½ Į", + "ĠпÑĸд виÑī", + "ĠÑģвÑıз и", + "ĠÑģвÑı зи", + "Ġتر جÙħ", + " ī", + "ĠÄij âu", + "Ñĸ Ñĩного", + "ÑĸÑĩ ного", + "å°ij å¹´", + "e cta", + "ect a", + "ec ta", + "ि लत", + "िल त", + "ι οÏĤ", + "ιο ÏĤ", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "t eg", + "te g", + "á» īnh", + "á»ī nh", + "¯ ¿", + "Ġne bu", + "Ġneb u", + "ÙĬ ÙĬÙĨ", + "о ÑıÑĤ", + "оÑı ÑĤ", + "é¤ Ĭ", + "ĠاÙĤتص ادÛĮ", + "ĠاÙĤتصاد ÛĮ", + "âĢĻ nun", + "âĢĻn un", + "ĠÐĴ Ñĸк", + "Ġng Äĥn", + "ëĮĢ íķĻêµIJ", + "ëĮĢíķĻ êµIJ", + "é ı", + "़ र", + "ا باÙĨ", + "اب اÙĨ", + "Ùİ Ùĥ", + "Ġet kil", + "Ġetk il", + "Ġetki l", + "Ġch ắn", + "Ġë°ľ ìĥĿ", + "Ġtam amen", + "Ġtamam en", + "ĠÙħØŃ ÛĮØ·", + "ü lü", + "ül ü", + "åģ¥ 康", + "ĠÑĢаÑģÑĤ ениÑı", + "ÏĢο ί", + "Ġ è¶ħ", + "Ġè¶ ħ", + "á Äį", + "ĠìĻ¸ ë¶Ģ", + "ĠØ® ÛĮÙĦÛĮ", + "ĠØ®ÛĮ ÙĦÛĮ", + "Ġد ÙĪست", + "ĠدÙĪ ست", + "à¹Ģà¸Ĥ à¸ķ", + "Ġk alan", + "Ġka lan", + "Ġkal an", + "ë¨ ¼", + "a vÄĽ", + "av ÄĽ", + "문 íĻĶ", + "Ġди аг", + "ĠÙĨ ÙĪÙĬس", + "ĠÙĨÙĪ ÙĬس", + "íķ ij", + "à¸ŀ าะ", + "ëĭ¤ ê°Ģ", + "Ġn iá»ĩm", + "Ġس ÙĪÙħ", + "ĠسÙĪ Ùħ", + "- м", + "à¸Ķำ à¹Ģà¸Ļ", + "à¹ĩ ว", + "ãĢĤ ãģĵãģ®", + "ç¯ ī", + "Width Space", + "Zero WidthSpace", + "ائ ÙħØ©", + "ائÙħ Ø©", + "à¹Ħà¸ĭ à¸ķ", + "ä¸ĭè½½ 次æķ°", + "ä¼¼ ä¹İ", + "ĠÑĤ в", + "Ġzá kaz", + "Ġج دا", + "Ġجد ا", + "Ġg ider", + "Ġgi der", + "Ġgid er", + "ãĥ¼ ãĥĵ", + "ãĥ¼ãĥ ĵ", + "n ů", + "Ġë§ģ íģ¬", + "ĠdÃ¼ÅŁ ük", + "Ñĥ нок", + "Ñĥн ок", + "Ġt óc", + "ĠÑĤ ÑĢÑĥб", + "ĠÑĤÑĢ Ñĥб", + "ĠÑĤÑĢÑĥ б", + "о кÑģ", + "ок Ñģ", + "Ġtr ải", + "Ġtrả i", + "Ġm iá»ħn", + "Ġth Æ°á»Ľc", + "Ġnh áºŃt", + "Âł D", + "m asının", + "mas ının", + "masını n", + "è¼ ª", + "ĠÎĿ ο", + "er ç", + "Ġdok once", + "Ġdokon ce", + "ĠG üven", + "ĠGü ven", + "ov aná", + "ova ná", + "ovan á", + "е зд", + "ез д", + "Ñĸ нÑĮ", + "Ñĸн ÑĮ", + "èģ ²", + "اÙĦ Ø£", + "ï¼Į ä½Ĩæĺ¯", + "ï¼Įä½Ĩ æĺ¯", + "Ġпол ноÑģÑĤÑĮÑİ", + "Port ály", + "ĠØŃ Ø§Ùģظ", + "à¥Ĥ à¤ķ", + "ÑĢ Ñĥн", + "ÑĢÑĥ н", + "人 çī©", + "Ġa çı", + "Ġaç ı", + "Ġp oru", + "Ġpo ru", + "Ġpor u", + "er iod", + "eri od", + "ĠAmer ika", + "ĠAmerik a", + "çĩ Ł", + "Ġ رÙĪد", + "Ġر ÙĪد", + "ĠرÙĪ د", + "ĠкÑĢов и", + "ÙĪ ÙĤت", + "ÙĪÙĤ ت", + "éĺ ¶", + "ãĥ»âĶģ ãĥ»âĶģ", + "ر ÙĬÙħ", + "رÙĬ Ùħ", + "åİĨ åı²", + "ä¸ ¸", + "Ġзн овÑĥ", + "Ġзнов Ñĥ", + "ĠÑģво его", + "ĠÑģвое го", + "бÑĥд ÑĮ", + "ĠØŃ Ø¬Ùħ", + "ĠØŃج Ùħ", + "ĠδÏį ο", + "ìķĪ ëĤ´", + "Ġ ãģ§", + "à¹Ī ะ", + "Ùij Ùı", + "çµIJ æŀľ", + "âĢĻ i", + "à¹Į ,", + "åŃĺ äºİ", + "Ġरà¤ĸ न", + "ĠسرÙħ اÛĮÙĩ", + "Ġг лÑĥб", + "ĠглÑĥ б", + "ĠÑĢаз Ñĸ", + "ĠÑĢа зÑĸ", + "Ñĩ нÑĸ", + "ï¼Į åıĪ", + "c ısı", + "cı sı", + "æľī åħ³", + "ãĤ» ãĥ³", + "èIJ ¨", + "ĠGi áo", + "ĠGiá o", + "ĠاÙĦØ« اÙĨÙĬ", + "ĠÑĢаз ом", + "Ġ ÑĤÑĢо", + "ĠÑĤ ÑĢо", + "ĠÑĤÑĢ о", + "Ġaçık lam", + "åĨ³ å®ļ", + "à¸Ń à¸Ľ", + "åĶ ¯", + "ĠÅŁ ark", + "ĠÅŁar k", + "Ġsist emi", + "Ġsistem i", + "Ġto prak", + "Ġtop rak", + "èĢĥ ãģĪ", + "Ġпоп ÑĥлÑıÑĢ", + "Ġ ëĨį", + "ĠëĨ į", + "ا ÙĬÙĨ", + "اÙĬ ÙĨ", + "स म", + "Ġ ÂĢ", + "Ġ Ģ", + "Ġed erek", + "Ġeder ek", + "Ġg ec", + "Ġge c", + "ìĤ¬ ìĹħ", + "ĠÑĢ оки", + "ĠÑĢок и", + "ĠбеÑĢ ем", + "ĠخاÙĨÙĪ ادÙĩ", + "Ġ èµ·", + "Ġèµ ·", + "Ġ ЧÑĤо", + "ĠЧ ÑĤо", + "Ġo bÄĽ", + "Ġob ÄĽ", + "и нÑĸ", + "ин Ñĸ", + "ìĿ´ ìĹĪ", + "ĠIn di", + "ĠInd i", + "Ġд иÑĤ", + "Ġди ÑĤ", + "ãĥ¶ æľĪ", + "Ġнем ного", + "Ġzáklad ÄĽ", + "à¹Ĥ à¸Ħ", + "ĠÑģам ого", + "ĠÑģамо го", + "Ġب ØŃØ«", + "ĠبØŃ Ø«", + "Ġ æ¶", + "Ġæ ¶", + "ов ж", + "Ġоб ÑĢаÑī", + "ĠобÑĢа Ñī", + "à Ĵ", + "ว รร", + "วร ร", + "à¤Ĥ श", + "ĠоÑĩ еÑĢед", + "ĠÙģ رز", + "ĠÙģر ز", + "ëĮĢ íķľ", + "Ġs izin", + "Ġsi zin", + "Ġsiz in", + "Ġsizi n", + "ر Ùģت", + "رÙģ ت", + "Ñİ Ñīим", + "ÑİÑī им", + "æ» ij", + "a vir", + "av ir", + "avi r", + "ĠÙĪ صÙĦ", + "ĠÙĪص ÙĦ", + "Ġqu ay", + "Ġqua y", + "Ġг ип", + "ÑĢ ениÑı", + "ÑĢе ниÑı", + "ÑĢен иÑı", + "à¥į वत", + "à¥įव त", + "ιν Ïīν", + "à¤ľ ह", + "Ġh Æ¡i", + "Ġpo važ", + "Ġpov až", + "Ġع رب", + "м енÑĤа", + "мен ÑĤа", + "менÑĤ а", + "Ġо ÑģÑĤан", + "ĠоÑģÑĤ ан", + "ä¹ĭ éĹ´", + "a cÃŃch", + "ac ÃŃch", + "acÃŃ ch", + "ĠÑģказ ала", + "ĠÑģказал а", + "ìĿ´ ëĿ¼ëĬĶ", + "ìĿ´ëĿ¼ ëĬĶ", + "ĠØ´ اخ", + "Ġëĭ¹ ìĭł", + "ar lar", + "arl ar", + "Ġмл н", + "åĨ ¬", + ". :.:.:", + ".: .:.:", + ".:.: .:", + ".:. :.:", + ".:.:. :", + "Ġ θε", + "Ġθ ε", + "Ġher kes", + "Ġherk es", + "л Ñıд", + "лÑı д", + "ا Ùħا", + "اÙħ ا", + "Ġë ŃIJ", + "Ïĥιμο ÏĢοι", + "Ġ obraz", + "Ġob raz", + "Ġobr az", + "Ġobra z", + "غ اÙĦ", + "B Æ°á»Ľc", + "å° Ĭ", + "ìŀIJ 를", + "æĢ Ĵ", + "οÏħ Ïģγ", + "å¼ķ ãģį", + "Ġkon uda", + "Ġkonu da", + "ĠاÙĦت ج", + "Ġ krit", + "Ġk rit", + "Ġkr it", + "å¿ į", + "ĠìłĦìĦ¸ ê°Ģ", + "г овоÑĢ", + "го воÑĢ", + "гов оÑĢ", + "Ġist iyor", + "о ки", + "ок и", + "ĠобеÑģп еÑĩ", + "ĠобеÑģпе Ñĩ", + "Ġay rıca", + "Ġayrı ca", + "à¹Ģ à¸ľ", + "à¹Ģภľ", + "а ÑĢод", + "аÑĢ од", + "Ä° Åŀ", + "ĠجÙħ ÙĩÙĪرÛĮ", + "ĠجÙħÙĩÙĪر ÛĮ", + "ĠÑģво иÑħ", + "ĠÑģвои Ñħ", + "Ġprov ád", + "Ġ ÑĢам", + "ĠÑĢ ам", + "ĠÑĢаР¼", + "ĠÑĢа м", + "ĠÙĤ ض", + "л иÑĤелÑĮ", + "лиÑĤ елÑĮ", + "ãĤ± ãĥĥãĥĪ", + "оÑģ оÑĦ", + "Ġर हन", + "Ġरह न", + "k ový", + "ko vý", + "kov ý", + "ì° ¸", + "γ κα", + "γκ α", + "λ οι", + "λο ι", + "μ ÏĢο", + "μÏĢ ο", + "ĠÄij au", + "ĠÄija u", + "н иÑİ", + "ни Ñİ", + "Ġman žel", + "Ġ íĺ¼", + "Ġíĺ ¼", + "ĠÑĤ иÑģ", + "ĠÑĤи Ñģ", + "ãĥĨ ãĥ«", + "ab ilecek", + "abil ecek", + "abile cek", + "abilece k", + "н ин", + "ни н", + "à¸ģรรม à¸ģาร", + "éł IJ", + "Ġph ê", + "j edn", + "je dn", + "jed n", + "交 æµģ", + "Ġвним ание", + "об ÑĢеÑĤ", + "Ġжиз нÑĮ", + "ÑĢи ÑģÑĤи", + "ÑĢиÑģÑĤ и", + "ÑĢиÑģ ÑĤи", + "à¥Ī à¤ļ", + "Ġyüz den", + "Ġyüzde n", + "Ġg iy", + "Ġgi y", + "éļ Ķ", + "ä» ²", + "Ġ èĻ", + "Ġè Ļ", + "ĠP arti", + "ĠPart i", + "ĠPar ti", + "Ġ éĸ¢", + "Ġéĸ ¢", + "ัà¸ļ ส", + "Ġnej lepÅ¡ÃŃ", + "Ùİ Ùī", + "ĠìĿ´ ìłľ", + "Ġc ắt", + "ÑĢоз Ñĥм", + "Ġnej sou", + "l ÃŃd", + "lÃŃ d", + "θ ο", + "à¹ĩ à¸ĩ", + "ĠÑģп ÑĢоÑģ", + "ma mÄ±ÅŁ", + "mam Ä±ÅŁ", + "Ġ 쪽", + "Ġì ª½", + "ا ÙģÙĤ", + "اÙģ ÙĤ", + "ÑĨÑĸй ниÑħ", + "Ġ é¦Ļ", + "Ġé¦ Ļ", + "ĠÙħÛĮÙĦÛĮ ÙĪÙĨ", + "å¤ ¢", + "ĠÙģ Ùĩرست", + "r ý", + "Ġпо вÑĸдом", + "Ġпов Ñĸдом", + "ec eÄŁi", + "ece ÄŁi", + "ĠзабезпеÑĩ еннÑı", + " Ķ", + "ãģĹ ãģªãģĦ", + "åŁº ç¡Ģ", + "ĠÚĨ ÙĨÛĮÙĨ", + "ĠÑĢоз ÑĢоб", + "ä¸Ģ äºĽ", + "ãĥ³ ãģ®", + "ĠпÑĢа ÑĨÑĸв", + "ĠпÑĢаÑĨÑĸ в", + "å¾Ĺ åĪ°", + "Ġt ấn", + "åŃĺæ¡£ å¤ĩ份", + "Ġ íĻĪ", + "ĠíĻ Ī", + "Ġ à¸Ķาว", + "Ġà¸Ķ าว", + "ìĭ ±", + "л ина", + "ли на", + "лин а", + "ĠвоÑģп ал", + "ÄŁ inden", + "ÄŁin den", + "ÄŁinde n", + "аÑĤ елей", + "аÑĤе лей", + "r ž", + "ĠÑĦ Ñĥн", + "ĠÐIJ л", + "ĠпоÑĩ ÑĤи", + "о вÑĸд", + "ов Ñĸд", + "овÑĸ д", + "ا عب", + "اع ب", + "าะ ห", + "Ġвоз ÑĢаÑģÑĤ", + "ิà¸ĩ ห", + "ĠÙģ ÙĦس", + "ĠÙģÙĦ س", + "ĠÅ¡ est", + "à¸Ĭ าว", + "Ġ 골", + "Ġê³ ¨", + "Ġ oÄį", + "Ġo Äį", + "ãĤ¸ ãĥ§", + "к оÑģÑĤи", + "ко ÑģÑĤи", + "éĽĨ åĽ¢", + "æ± ĩ", + "ĠpÅĻÃŃ liÅ¡", + "Ġ ìĿij", + "ĠìĿ ij", + "ди ви", + "див и", + "ĠдокÑĥм енÑĤа", + "ĠдокÑĥменÑĤ а", + "ĠCh âu", + "Ġm áu", + "Ġmá u", + "Ġkh ô", + "à ķ", + "Ñī ий", + "Ñīи й", + "Ġs ẵn", + "Ġкон ÑĦ", + "Ġз ÑĥÑģÑĤ", + "åĽŀ çŃĶ", + "Ġ коÑĢиÑģÑĤ", + "ĠкоÑĢ иÑģÑĤ", + "Ġко ÑĢиÑģÑĤ", + "ĠÏĢεÏģ ί", + "ĠÏĢε Ïģί", + "ä¸ °", + "Ġm ạch", + "ан к", + "ä¸ĭ æĿ¥", + "èµĦ æĸĻ", + "ย à¸Ńà¸Ķ", + "ĠÏĢ ιο", + "à¹ī à¸ĩาà¸Ļ", + "à¹īà¸ĩ าà¸Ļ", + "Ġum ÃŃst", + "æ½ ®", + "çªģ çĦ¶", + "Ġk ultur", + "Ġkul tur", + "ĠاÙĦ صÙģ", + "ĠاÙĦص Ùģ", + "a larının", + "alar ının", + "aların ın", + "alarını n", + "ĠÎĶη μο", + "Ġвикон аннÑı", + "Ġвико наннÑı", + "ï½ ¿", + "Ġбез опаÑģ", + "ĠÑģ аÑħ", + "Ġn oh", + "Ġno h", + "à¹ĥ à¸ļ", + "éĥ½ å¸Ĥ", + "ÅŁ am", + "ÅŁa m", + "б ÑĥÑĤ", + "бÑĥ ÑĤ", + "Ġ모 ìĬµ", + "Ġв аг", + "Ġва г", + "çIJĨ 解", + "Ġekonom ik", + "Ġkh ắc", + "Ġs vat", + "Ġsv at", + "лиÑĪ ком", + "ัà¸ĩ à¸Īาà¸ģ", + "iz yon", + "èĥ½ å¤Ł", + "ί νει", + "ίν ει", + " Ĭ", + "ì¦ Į", + "Ġ ÙĩاÛĮÛĮ", + "ĠÙĩ اÛĮÛĮ", + "ĠÙĩاÛĮ ÛĮ", + "ĠkiÅŁ iler", + "ĠkiÅŁi ler", + "Ġк леÑĤ", + "Ġкл еÑĤ", + "Ġкле ÑĤ", + "íĺ ģ", + "à¥ĥ द", + "i Å¡", + "ëĶĶ ìĺ¤", + "ÙĬ راÙĨ", + "ÙĬر اÙĨ", + "ÙĬرا ÙĨ", + "ĠÐĿ Ñĥ", + "à¸Ń à¸Ļà¸Ĺ", + "à¸Ńà¸Ļ à¸Ĺ", + "ĠÑģ оÑĩ", + "ĠÑģо Ñĩ", + "Ġist eyen", + "ĠS ez", + "ĠSe z", + "Ġ ãĤ»", + "ĠãĤ »", + "ĠA ç", + "âĢĮ ÙĨ", + "ĠÑĤ оп", + "ĠÑĤо п", + "ĠÑĤеÑĢÑĢиÑĤ оÑĢ", + "a cılık", + "acı lık", + "Ġод нÑĥ", + "Ġv eri", + "Ġver i", + "Ġve ri", + "ĠÚ© د", + "ĠÚ¯ ÙģتÙĩ", + "ĠÚ¯Ùģت Ùĩ", + "Ġcin sel", + "Ġcins el", + "олог ии", + "ĠpÅĻed mÄĽt", + "à¤Ĥ à¤ĺ", + "Ġ 空", + "Ġç© º", + "γ α", + "' ye", + "'y e", + "ت رة", + "تر Ø©", + "Ġd ÅĻÃŃ", + "ĠH Ãłn", + "ĠHÃł n", + "Ġر شتÙĩ", + "Ġv idea", + "Ġvi dea", + "Ġvid ea", + "Ġvide a", + "Ġ ног", + "Ġн ог", + "Ġно г", + "æ ·»", + "æ· »", + "è¿ĺ æľī", + "ÙĨ در", + "ÙĨد ر", + "Ġy erde", + "Ġyer de", + "Ġk ent", + "Ġke nt", + "Ġken t", + "à¸ļ าล", + "Ġд еÑģÑı", + "Ġде ÑģÑı", + "ä¸ļ åĬ¡", + "Ġоб ÑĬек", + "ĠобÑĬ ек", + "ĠвнÑĥÑĤÑĢ ÑĸÑĪ", + "ĠвнÑĥÑĤ ÑĢÑĸÑĪ", + "k ola", + "ko la", + "kol a", + "eb nÃŃ", + "ี ล", + "Ġ ,.", + "Ġ, .", + "ĠмÑĸж наÑĢод", + "ãģªãĤĵ ãģ¦", + "ĠS öz", + "Ġ chod", + "Ġc hod", + "Ġch od", + "Ġcho d", + "Ġtr úc", + "Ġtrú c", + "ìļĶ ìĿ¼", + "Ġph áºŃn", + "Ñģ ка", + "Ñģк а", + "ĠÑħ лоп", + "Ñģ ким", + "Ñģк им", + "Ñģки м", + "Ġka pit", + "Ġkap it", + "ëĵ¤ ìĹIJê²Į", + "Ġb Ãło", + "ĠbÃł o", + "lı ģın", + "lıģı n", + "lıģ ın", + "Ä° ÅŁ", + "Äį nÃŃk", + "ÄįnÃŃ k", + "Ġ NgoÃłi", + "ĠNg oÃłi", + "Ġب ÛĮاÙĨ", + "ĠبÛĮ اÙĨ", + "Ġpro Äį", + "Ġpr oÄį", + "ĠпÑĢоÑĤ Ñıгом", + "åĢ ī", + "е Ñİ", + "Ġ νο", + "Ġν ο", + "ëĿ¼ ëıĦ", + "ì ·¨", + "Ġви Ñıв", + "Ġпо над", + "Ġпон ад", + "Ġжов ÑĤ", + "Ġ æ¯Ķ", + "Ġæ¯ Ķ", + "Ġd oby", + "Ġdo by", + "Ġdob y", + "л ам", + "ла м", + "Ñij л", + "Ġ ÑĢаÑħ", + "ĠÑĢ аÑħ", + "ĠÑĢа Ñħ", + "Ġвоз ника", + "Ġвозник а", + "ниÑĨÑĤ во", + "å± ¤", + "ĠоÑĤ лиÑĩ", + "ĠоÑĤли Ñĩ", + "çĤ İ", + "é£ ¯", + "Ġživ ota", + "Ġživot a", + "at ör", + "Ġce lý", + "Ġcel ý", + "Ġ aday", + "Ġa day", + "Ġad ay", + "Ġada y", + "ر ÙĬÙĥÙĬ", + "رÙĬ ÙĥÙĬ", + "رÙĬÙĥ ÙĬ", + "Ġب ص", + "m eyen", + "me yen", + "meye n", + "mey en", + "ìļ° ìĬ¤", + "ب ÙĪÙĦ", + "بÙĪ ÙĦ", + "Ġ озна", + "Ġо зна", + "Ġоз на", + "é º¼", + "éº ¼", + "æĵ ļ", + "Ġz kou", + "Ġzk ou", + "ëĤĺ ìļĶ", + "Ġk ry", + "Ġkr y", + "Ġnem oh", + "Ġvyu žÃŃ", + "Ġ æľ¨", + "Ġад мÑĸнÑĸÑģÑĤÑĢа", + "ا Ùĩا", + "اÙĩ ا", + "à¹ĥ à¸ģล", + "＿＿＿＿ ＿＿＿＿", + "Ġ гоÑĤ", + "Ġг оÑĤ", + "ĠدÛĮ گرÛĮ", + "ĠدÛĮگر ÛĮ", + "Ġл екаÑĢ", + "è§ Ģ", + "Ġ íĺij", + "Ġíĺ ij", + "ĠB öyle", + "ist rov", + "istr ov", + "istro v", + "女 åŃIJ", + "Ġпоп еÑĢед", + "ĠÙĨÙĪÙĬس ÙĨدÙĩ", + "ÙĴ ÙĦ", + "ĠÐŁ ав", + "Ġö rnek", + "Ġör nek", + "Ġп ÑĢик", + "ĠпÑĢ ик", + "ĠпÑĢи к", + "Ġ ÑĪи", + "ĠÑĪ и", + "üslü man", + "ĠÙħÙĤ ابÙĦ", + "åįģ äºĮ", + "Ġb ekl", + "Ġbe kl", + "Ġbek l", + "Ġver ir", + "Ġve rir", + "Ġveri r", + "ÙĪ Ø°", + "ض Ø©", + "ÑĢо ÑĤив", + "ÑĢоÑĤ ив", + "æĮ ij", + ". .:", + ".. :", + "Ġخارج ÙĬØ©", + "a dık", + "ad ık", + "adı k", + "ĠÐŁ оÑĩ", + "ĠÐŁÐ¾ Ñĩ", + "ĠÑħÑĥд ож", + "客 æĪ·", + "μ ον", + "μο ν", + "ek tiv", + "ekt iv", + "Ġt vá", + "Ġtv á", + "Û² Û²", + "Ġl á»įc", + "Ġо но", + "Ġон о", + "ÑĨ иÑĤ", + "ÑĨи ÑĤ", + "ĠÐĴ Ñģ", + "Ġ å¢", + "Ġå ¢", + "æµ ª", + "а ÑĢÑĸ", + "аÑĢ Ñĸ", + "Ġsür ekli", + "Ġ stra", + "Ġs tra", + "Ġst ra", + "Ġstr a", + "Ġb ize", + "Ġbi ze", + "Ġbiz e", + "Ġtes pit", + "Ġch âu", + "ĠاÙĦ ض", + "à¹īà¸Ń à¸ĩà¸ģ", + "à¹īà¸Ńà¸ĩ à¸ģ", + "Ġ èĢħ", + "ĠèĢ ħ", + "ĠH á»", + "Ġкажд Ñĭй", + "а Ñİ", + "à¸Ļ à¸Ħร", + "à¸Ļà¸Ħ ร", + "à¸Ĺ ะ", + "ĠÙħر اجع", + "ĠÙħراج ع", + "Ġha line", + "Ġhal ine", + "δ οÏĤ", + "δο ÏĤ", + "e ÄŁi", + "ĠÙħÛĮ زاÙĨ", + "Ġ ÙĩÙĦ", + "ĠÙĩ ÙĦ", + "Ġb olest", + "Ġbo lest", + "Ġbol est", + "Ġ åľŁ", + "Ġåľ Ł", + "Ġu zman", + "Ġuz man", + "ÑĢ ог", + "ÑĢо г", + "確 èªį", + "ĠÑĢÑĸз ниÑħ", + "Ġза кÑĢÑĭ", + "Ġзак ÑĢÑĭ", + "лÑĥ ги", + "лÑĥг и", + "ĠÑģо веÑĤ", + "ĠÑģов еÑĤ", + "id di", + "idd i", + "åIJĪ ãĤıãģĽ", + "Ġ åIJī", + "ĠåIJ ī", + "Ġk iá»ĩm", + "ë² ½", + "ĠÙħع ÙħÙĪÙĦ", + "ĠопÑĢед елÑı", + "ĠопÑĢедел Ñı", + "Ġmik tar", + "Ġ ìŀIJëıĻ", + "ĠìŀIJ ëıĻ", + "Ġil aç", + "л оÑĩ", + "ло Ñĩ", + "Ġy ılı", + "Ġyıl ı", + "Ġyı lı", + "Ġ ÄIJá»ĥ", + "ĠÄIJ á»ĥ", + "ĠÄIJá» ĥ", + "Ġab ych", + "Ġaby ch", + "Ġrek lam", + "Ġrekl am", + "Ġv ypad", + "Ġvy pad", + "Ġvyp ad", + "Ġна ÑĥÑĩ", + "à¹Ģà¸Ħร าะห", + "Ġ ä»ĸ", + "p ovÄĽ", + "po vÄĽ", + "ï¼Į 让", + "ç¥ Ŀ", + "ا ÙĪÙĨد", + "اÙĪ ÙĨد", + "اÙĪÙĨ د", + "Ġ: |:", + "Ġ:| :", + "Ġre ž", + "Ġvyb av", + "ìľ ¤", + "æŃ ´", + "огÑĢаÑĦ иÑı", + "ez peÄį", + "ezpe Äį", + "± n", + "о вÑĥ", + "ов Ñĥ", + "Ġд Ñĥма", + "ĠдÑĥ ма", + "ĠдÑĥм а", + "Ġjedn odu", + "Ġjedno du", + "о Ñīи", + "оÑī и", + "ĠÙħ شتر", + "ĠÙħØ´ تر", + "è¦ ³", + "Ġyok tur", + "Ġyoktu r", + "Ġob Äįan", + "ĠTr ần", + "ım sız", + "α ιν", + "αι ν", + " Į", + "ر ÛĮاÙĨ", + "رÛĮ اÙĨ", + "ĠJe ho", + "ĠJeh o", + "ĠاÙĦ Ø¢ÙĨ", + "ĠاÙĦØ¢ ÙĨ", + "ÑģÑĮ ким", + "ÑģÑĮк им", + "Ġk dyby", + "Ġkdy by", + "ĠbaÅŁ ına", + "Ġprez ident", + "Ġ Viá»ĩc", + "ĠV iá»ĩc", + "åħ ¼", + "à¥Į à¤ľ", + "Ġ매매 ê°Ģ", + "模 å¼ı", + "nÃŃ mu", + "nÃŃm u", + "Ġ åĤ", + "Ġå Ĥ", + "Ġ deniz", + "Ġd eniz", + "Ġden iz", + "ĺ èĽĽ", + "Ġ èĢĮ", + "ĠèĢ Į", + "ÙĪ ØŃ", + "Ñĭ п", + "Ġâĸ ¼", + "n ul", + "nu l", + "ĠS ev", + "ĠSe v", + "Ġ ruh", + "Ġr uh", + "Ġru h", + "Ġh ạ", + "Ġ Ñıн", + "ĠÑı н", + "Ġ기 본", + "Ġv elik", + "Ġve lik", + "Ġvel ik", + "ĠT ân", + "и лиÑģÑĮ", + "ил иÑģÑĮ", + "или ÑģÑĮ", + "ĠÑħ ÑĢа", + "åĤ ·", + "Ġà¤Ĩ à¤ı", + "Ġn ynÃŃ", + "Ġny nÃŃ", + "» ØĮ", + "ĠØ´ ع", + "æĿ Ĥ", + "Ġм ÑĭÑĪ", + "ĠмÑĭ ÑĪ", + "ãģĻ ãģIJ", + "Ġ ê³µì§Ģ", + "Ġê³µ ì§Ģ", + "Ġt á»Ļc", + "ãĥ¼ ãĥĩ", + "ãĥ¼ãĥ ĩ", + "ĠÑģ ело", + "ĠÑģел о", + "Ġا عÙĦاÙħ", + "Ġاع ÙĦاÙħ", + "ĠÅŁ imdi", + "ĠÅŁi mdi", + "ĠاÙĦÙħ ÙĬÙĦاد", + "ĠاÙĨÙĤÙĦ اب", + "Ġشخص ÙĬØ©", + "ĠK ür", + "ĠKü r", + "Ġ вÑĸÑĤ", + "Ġв ÑĸÑĤ", + "ĠвÑĸ ÑĤ", + "ĠاÙĨد ازÙĩ", + "Ġм оÑī", + "Ġмо Ñī", + "t ernet", + "ter net", + "tern et", + "ĠαÏħ ÏĦή", + "ĠÑĢоз ÑĤа", + "Ġв ив", + "Ġви в", + "l ej", + "le j", + "Ġ 表", + "Ġè¡ ¨", + "ÏĥÏĥ ÏĮÏĦε", + "ĠÙĬ ست", + "ĠÙĬس ت", + "Ġм аÑĪ", + "Ġма ÑĪ", + "åĿ ļ", + "Ġком наÑĤ", + "า หล", + "าห ล", + "Ġ çĻ¼", + "ĠçĻ ¼", + "ĠاÙĪÙĦ ÛĮÙĨ", + "è¿IJ åĬ¨", + "Ġп ÑĥнкÑĤ", + "ĠпÑĥнк ÑĤ", + "ĠоÑģоб енно", + "Ġм ам", + "Ġма м", + "ç» ©", + "￣￣￣￣ ￣￣￣￣", + "алÑĮ нÑĭм", + "ĠЦ енÑĤ", + "ĠЦе нÑĤ", + "- Ðľ", + "ç· Ĵ", + "Ġह à¤ľ", + "о ÑĤÑĭ", + "оÑĤ Ñĭ", + "ãĤ¤ ãĥī", + "د ارة", + "دار Ø©", + "دا رة", + "ãģ¨ ãģĹãģŁ", + "ั à¸ŀย", + "ัà¸ŀ ย", + "Ġot áz", + "Ġдопом огоÑİ", + "à¹ģละ à¸ģาร", + "ĠÑĤÑĢанÑģп оÑĢÑĤ", + "ĠÑĤÑĢанÑģпоÑĢ ÑĤ", + "ĠÙĤر Ø¢ÙĨ", + "Ġ 第ä¸Ģ", + "Ġ第 ä¸Ģ", + "Ġм ил", + "Ġми л", + "Ġng ôi", + "Ġl inh", + "Ġli nh", + "Ġlin h", + "ĠNh ân", + "ÑĮогод нÑĸ", + "æĢ Ģ", + "à¹ī าส", + "à¹īา ส", + ".:: .::", + "Ġbi rey", + "Ġbir ey", + "æĢĿ ãģĦ", + "à¹ĥ à¸Ķ", + "веÑĢ д", + "ве ÑĢд", + "Ġlistop adu", + "Ġ à¹ģม", + "Ġà¹ģ ม", + "г е", + "Ġк ÑĥÑħ", + "ĠкÑĥ Ñħ", + "Ġ íĻľëıĻ", + "ĠíĻľ ëıĻ", + "Ġ èİ", + "Ġè İ", + "ĠÐIJ лÑĮ", + "ĠÐIJл ÑĮ", + "íļĮ ìĿĺ", + "ĠÏĢ Ïģα", + "Ġv ui", + "Ġvu i", + "ว ร", + "à¤Ĥ व", + "Ġg ece", + "Ġge ce", + "Ġgec e", + "ç« ¶", + "Ġk uv", + "Ġku v", + "м еÑī", + "ме Ñī", + "ĠÑĤеп еÑĢÑĮ", + "ĠÑĤепеÑĢ ÑĮ", + "à¸Ń à¹Ģม", + "åĪ¶ 度", + "ĠÑĤ ÑĢеÑĤ", + "ĠÑĤÑĢ еÑĤ", + "ĠÙĨ تÛĮجÙĩ", + "ä»ĺ ãģį", + "Ġ ï¾ŀ", + "Ġï¾ ŀ", + "Ġ Ñĩого", + "ĠÑĩ ого", + "âĢIJ -", + "ĠÅĻÃŃ ká", + "à¸ĩ à¹ĥà¸Ļ", + "ĠnÄĽkol ika", + "ĠnÄĽkolik a", + "Ġb una", + "Ġbu na", + "Ġbun a", + "ï¼Į åŃĺäºİ", + "ล ำ", + "ãĢģ ãģ¨", + "Ġn á»Ļp", + "ĠاÙĦ جÙĨ", + "ĠاÙĦج ÙĨ", + "ĠÎł αν", + "Ġγα ν", + "Ðŀ Ðł", + "Ġدخ تر", + "Ġúda je", + "Ġúdaj e", + "Ġ å¼ł", + "Ġå¼ ł", + "r etim", + "re tim", + "ret im", + "s ınız", + "sın ız", + "ĠÙĩÙĨ اÙĥ", + "ĠÙĩÙĨا Ùĥ", + "ÐĽ Ь", + "æķ ¬", + "Îij Îľ", + "页éĿ¢ åŃĺæ¡£å¤ĩ份", + "ìĤ¬ ê°Ģ", + "Ġt rest", + "Ġtr est", + "Ġtre st", + "Ġtres t", + "v iÄį", + "vi Äį", + "ĠÙ¾ ÛĮدا", + "ĠÙ¾ÛĮ دا", + "ζ ε", + "ĠÐŁ ов", + "ĠÐŁÐ¾ в", + "ÙĦÙħ ات", + "o rex", + "or ex", + "ore x", + "è¬ Ľ", + "ĠвÑĸдк ÑĢиÑĤ", + "м аÑħ", + "ма Ñħ", + "ĠÑĩиÑģ ле", + "ت بار", + "تب ار", + "ĠÎŃ Îº", + "ìķĦ íĮĮíĬ¸", + "r avel", + "ra vel", + "rav el", + "α Ïĥία", + "αÏĥ ία", + "a Äį", + "Ġ à¤ıन", + "Ġà¤ı न", + "ละ à¹Ģà¸Ń", + "Ġз алеж", + "Ġза леж", + "Ġзал еж", + "Ġ æģ", + "Ġæ ģ", + "Ġмож еÑĤе", + "ĠможеÑĤ е", + "Ġможе ÑĤе", + "Ġпо вед", + "Ġпов ед", + "ĠبسÛĮ ارÛĮ", + "ĠبسÛĮار ÛĮ", + "Ġ poÄįet", + "Ġpo Äįet", + "ĠpoÄį et", + "ر بع", + "رب ع", + "e lez", + "el ez", + "ele z", + "ا ÙĪرÛĮ", + "اÙĪ رÛĮ", + "اÙĪر ÛĮ", + "Ġba ÅŁk", + "ĠbaÅŁ k", + "å° Ĥ", + "Ġhal de", + "æĭ Ł", + "S au", + "Sa u", + "о ÑĨи", + "ี à¸Ħ", + "Ġв лади", + "Ġвла ди", + "Ġвлад и", + "ÙIJ Ùħ", + "k ud", + "ku d", + "à¥Ĥ ब", + "å§Ķ åĵ¡", + "า รà¸ĵ", + "าร à¸ĵ", + "o rů", + "or ů", + "Ġ ÙħÙĪÙĦ", + "ĠÙħ ÙĪÙĦ", + "ĠÙħÙĪ ÙĦ", + "Ġ byt", + "Ġb yt", + "Ġby t", + "ĠpÅĻÃŃslu Å¡", + "èĭ± è¯Ń", + "éĢ IJ", + "Ġvel ké", + "Ġvelk é", + "Ġà¤Ĩ श", + "Ġph iếu", + "Ġphi ếu", + "à¹ĥ ส", + "Ġاس Ù¾", + "Ġzbo žÃŃ", + "ãģĵ ãĤĵãģª", + "ãģĵãĤĵ ãģª", + "ĠÙĪÙĩ ÙĬ", + "ĠÑĥÑĩа ÑģÑĤÑĮ", + "ĠÑĥÑĩаÑģÑĤ ÑĮ", + "ĠÑĥÑĩаÑģ ÑĤÑĮ", + "à¸Īำ à¸Ļวà¸Ļ", + "Ġتر Ú©", + "åįģ åĪĨ", + "ÎŁ Îł", + "ÎŁÎ ł", + "κ ολ", + "κο λ", + "Ġf akat", + "Ġfa kat", + "Ġfak at", + "Ġch á»Ĺ", + "éĢļ çŁ¥", + "Ġв одÑĥ", + "Ġво дÑĥ", + "Ġвод Ñĥ", + "ĠÎļα ÏĦηγοÏģία", + "aca ģını", + "acaÄŁ ını", + "л ого", + "ло го", + "ĠmÃ¼ÅŁ ter", + "Ġj ednou", + "Ġjed nou", + "Ġjedn ou", + "Ġjedno u", + "Ġб аÑĢ", + "Ġба ÑĢ", + "i dae", + "id ae", + "ida e", + "d ım", + "dı m", + "è¾ ²", + "åIJ ¹", + "ëIJ ©ëĭĪëĭ¤", + "ĠÅŁekl inde", + "e ným", + "en ým", + "ený m", + "ëĵ ¯", + "i tÄĽ", + "it ÄĽ", + "Ġк олÑĮ", + "Ġкол ÑĮ", + "Ġко лÑĮ", + "ëĮĢ íķĻ", + "ĠÃĸ r", + "Ġ ê½", + "Ġê ½", + "ĠUB ND", + "Ġh ik", + "Ġhi k", + "ãĤī ãģĹãģĦ", + "ãĤīãģĹ ãģĦ", + "åĩº åĵģ", + "C ó", + "Ġ Îŀ", + "ĠÎ ŀ", + "Ġ åħ¥", + "Ġåħ ¥", + "ĠNg uyên", + "ĠÙ¾ ÙĪØ´", + "лÑı ÑĶ", + "ĠØ¢ غاز", + "Ġnhiá»ħ m", + "d ivid", + "div id", + "di vid", + "ç ĺ", + "ا ÙģتÙĩ", + "اÙģ تÙĩ", + "اÙģت Ùĩ", + "а меÑĤ", + "ам еÑĤ", + "нÑĥ лÑģÑı", + "нÑĥл ÑģÑı", + "ä¼ģ æ¥Ń", + "ÑĢоб ÑĸÑĤ", + "dü ÄŁÃ¼", + "dÃ¼ÄŁ ü", + "Ġ کاÙĨ", + "ĠÚ© اÙĨ", + "à¸Ń à¸ĩà¸Ĺ", + "à¸Ńà¸ĩ à¸Ĺ", + "й н", + "Ġpoh yb", + "Ġpohy b", + "Ġb iá»ĩn", + "Ġbi á»ĩn", + "Ġ ï¼Ľ", + "Ġï¼ Ľ", + "Ùħ ÙĨد", + "ÙħÙĨ د", + "Ġà¤Ĩ à¤ķ", + "ĠÄįlov ÄĽk", + "ĠÄįlovÄĽ k", + "ãĤĴè¦ĭ ãĤĭ", + "ë· °", + "ĠÑĥв елиÑĩ", + "ĠÑĥвели Ñĩ", + "Ġ ê´", + "Ġê ´", + "Ġyan lÄ±ÅŁ", + "éº ¦", + "Ġå¤ĸ éĥ¨", + "ÏĦ οÏħÏģγ", + "ÏĦοÏħ Ïģγ", + "Ġп ÑĢоÑĩ", + "ĠпÑĢ оÑĩ", + "ĠпÑĢо Ñĩ", + "ĠÑĢÑĥ ковод", + "çĽ ¤", + "èľ ĺèĽĽ", + "å®ī è£ħ", + "ĠУ кÑĢа", + "Ġtart Ä±ÅŁ", + "ÑĤ аж", + "ÑĤа ж", + "ĠoluÅŁ an", + "ĠRus ya", + "Ġк лÑĥб", + "Ġкл Ñĥб", + "ĠклÑĥ б", + "ĠÎł Ρ", + "alı dır", + "k ın", + "kı n", + "ĠзмÑĸ ни", + "ĠзмÑĸн и", + "leÅŁ ik", + "еÑĢ п", + "об Ñīе", + "обÑī е", + "Ġqu áºŃn", + "Ġप श", + "ãĤĴ åıĹ", + "à¹Ģล à¸Ĥ", + "ا ضر", + "اض ر", + "Ġuž ivatel", + "λ ία", + "λί α", + "ĠÐĴ они", + "ĠÐĴо ни", + "ุà¸Ķ à¸Ĺ", + "ĠV Ãł", + "ãĥ³ ãĤ¿", + ") ëĬĶ", + "æ¸ Ľ", + "Ġ μÏĢ", + "Ġμ ÏĢ", + "å· §", + "ĠÑĪ кол", + "ĠÑĪк ол", + "Ġì²ĺ ìĿĮ", + "ัà¸ģ à¸Ķ", + "æ® Ĭ", + "Ġnh á»Ŀ", + "ĠοÏĢο ία", + "à¹ģ à¸Ļว", + "à¹ģà¸Ļ ว", + "меÑĢик ан", + "nÃŃ ka", + "nÃŃk a", + "Ġíĺ¸ íħĶ", + "سب ب", + "à¸ĩ ม", + "ìŀĪ ëĬĶ", + "غ Ø·", + "Ùı ÙĦ", + "¹ æŀľ", + "Ñĩ Ñĸв", + "ÑĩÑĸ в", + "ÑĪ аÑı", + "ÑĪа Ñı", + "ĠØ¥ ÙĦا", + "ĠØ¥ÙĦ ا", + "خص ÙĪص", + "ll ll", + "ĠÑį ÑĤим", + "ĠÑįÑĤ им", + "ĠÑįÑĤи м", + "Ġz vÃŃ", + "Ġzv ÃŃ", + "Ġqu án", + "Ġquá n", + "à¸Ļ à¸ģ", + "Ġп олов", + "Ġпо лов", + "Ġпол ов", + "Ġ æ·±", + "Ġæ· ±", + "Ġm iá»ģn", + "Ġmi á»ģn", + "人 éĸĵ", + "Ġз им", + "Ġmey dana", + "е ÑĦ", + "Ġb á»ģn", + "Ġbá»ģ n", + "ز ÙĬد", + "زÙĬ د", + "ĠÐł еÑģп", + "ĠÐłÐµ Ñģп", + "ÎĻ ΣΤ", + "ÎĻΣ Τ", + "Ġ æĶ¶", + "ĠæĶ ¶", + "r aya", + "ra ya", + "ray a", + "ĠتÙĪ اÙĨد", + "ĠتÙĪاÙĨ د", + "Ġ ister", + "Ġis ter", + "Ġi ster", + "Ġist er", + "Ġ ë°Ģ", + "Ġë° Ģ", + "ĠмеÑħ ани", + "Ġ à¸ķำ", + "Ġà¸ķ ำ", + "Ġд ека", + "Ġде ка", + "Ġдек а", + "à¤Ĥ à¤Ĺल", + "à¤Ĥà¤Ĺ ल", + "ãĥ¼ ãĤ«ãĥ¼", + "Ġnep ÅĻÃŃ", + "ĠnepÅĻ ÃŃ", + "ĠÑģ ÑĩиÑĤ", + "ĠÑģÑĩ иÑĤ", + "Ġο μά", + "Ġç ift", + "ب ÛĮÙĨÛĮ", + "بÛĮ ÙĨÛĮ", + "بÛĮÙĨ ÛĮ", + "m eleri", + "me leri", + "mel eri", + "meler i", + "Ġвоз дейÑģÑĤв", + "d ou", + "do u", + "ìĥģ ìĿĦ", + "ĠÐĴ олод", + "ĠÐĴо лод", + "ĠÐĴол од", + "ε β", + "ÐĿ Ðĺ", + "Ñı к", + "Ïį ÏĦε", + "з ано", + "за но", + "len ir", + "c elik", + "ce lik", + "cel ik", + "ĠÑģоÑģÑĤав лÑıеÑĤ", + "ι αÏĤ", + "ια ÏĤ", + "ĠÐĵ оÑĢ", + "ä¹ĭ ä¸Ģ", + "Ïĥμ ÏĮÏĤ", + "ÏĥμÏĮ ÏĤ", + "ãģ« éĸ¢", + "Ġв Ñĩ", + "Ġп оÑģк", + "Ġпо Ñģк", + "ĠпоÑģ к", + "è¼ ¯", + "à¥Ģ श", + "ĠØ¢ ثار", + "à¸Ħวาม ร", + "Ġ един", + "Ġе дин", + "Ġеди н", + "íħ IJ", + "å¹³ æĪIJ", + "ĠkiÅŁ inin", + "ĠkiÅŁi nin", + "ãĤ² ãĥ¼ãĥł", + "à¥įत व", + "Ġkapsam ında", + "Ġak tar", + "Ġakt ar", + "Ġtr ừ", + "Ġر شد", + "Ġна каз", + "Ġнак аз", + "ر ÙĬÙĦ", + "رÙĬ ÙĦ", + "à¸Ń à¸Ħ", + "Ġگذ شتÙĩ", + "Ġ æ°ij", + "Ġæ° ij", + "ĠÑĤеб Ñı", + "s por", + "sp or", + "spo r", + "Ñİ ÑīаÑı", + "ÑİÑī аÑı", + "окÑĢем а", + "в ад", + "ва д", + "ĠCh úng", + "ĠزÛĮ ادÛĮ", + "ĠزÛĮاد ÛĮ", + "е ного", + "ен ого", + "ено го", + "ĠÚ© سÛĮ", + "à ŀ", + "Ġad ına", + "Ġadı na", + "Ñĥ да", + "Ñĥд а", + "Ñĸ ÑĶ", + "аÑĤ ели", + "аÑĤе ли", + "Ġnáv Å¡tÄĽ", + "çĶ¨ äºİ", + "ĠپرÙĪ ÙĨدÙĩ", + "ĠÙĨ بÙĪد", + "ĠÙĨب ÙĪد", + "س ات", + "ìĹ ĺ", + "ãģ£ ãģ¦ãĤĤ", + "ãģ£ãģ¦ ãĤĤ", + "Ġ çī©", + "Ġçī ©", + "Ðĺ з", + "åĪ ·", + "Ġ íľ´", + "Ġí ľ´", + "ĠоÑģоб лив", + "ãģĹ ãģ¾ãģ£ãģŁ", + "ãģĹãģ¾ ãģ£ãģŁ", + "a ydı", + "ay dı", + "ayd ı", + "åĩº çļĦ", + "ĠìķĦëĭĪ ëĿ¼", + "ıs ını", + "à¸Ĺาà¸ĩ à¸ģาร", + "Ġzv uky", + "Ġ 管", + "Ġç® ¡", + "âĸĭ âĸĭ", + "ĠÑĤ елеÑĦ", + "ĠÑĤел еÑĦ", + "Ġн елÑĮзÑı", + "ãĥ« ãģ®", + "Ïĥ ÏĢ", + "Ġ ç³", + "Ġç ³", + "åł ¡", + "ÑĨ Ñĥз", + "ÑĨÑĥ з", + "رÙĬ ÙĤØ©", + "رÙĬÙĤ Ø©", + "à¤¿à¤Ľ ल", + "è² ©", + "ĠУ кÑĢаÑĹн", + "ĠУкÑĢаÑĹ Ð½", + "ĠÙħسئ ÙĪÙĦ", + "Ġо ÑĩÑĸ", + "ĠоÑĩ Ñĸ", + "æľĢ å¾Į", + "Ġзна Ñİ", + "Ġзн аÑİ", + "à¹ī à¸Ļà¸Ĺ", + "à¹īà¸Ļ à¸Ĺ", + "ĠÑĤ еÑĢап", + "ĠÑĤеÑĢ ап", + "ĠÑĤе ÑĢап", + "ĠÑģп ок", + "ĠØ®ÙĪد رÙĪ", + "éĺ »", + "Ġdüz ey", + "ä¸Ģ åĢĭ", + "ا ÙģÙĩ", + "اÙģ Ùĩ", + "à¤Ĥ य", + "èµĦ 产", + "继 ç»Ń", + "ĠÑģ лаб", + "ĠÑģл аб", + "æĦı æĢĿ", + "ĠíĻĺ ìĤ°", + "ĠÑı ÑĢ", + "Ġd ůvod", + "Ġdů vod", + "çĿ Ľ", + "تÛĮ ب", + "ĠÙĪ ÛĮر", + "ĠÙĪÛĮ ر", + "ĠÙĩ زÛĮÙĨÙĩ", + "Ġben zer", + "Ġbenz er", + "ĠÙħ ادÙĩ", + "ĠÙħا دÙĩ", + "ĠÙħاد Ùĩ", + "à¥Į à¤ķ", + "Ġ à¹Ģà¸ķ", + "Ġà¹Ģ à¸ķ", + "Ġà¹Ģภķ", + "ãĤĪ ãģı", + "ид енÑĤ", + "èĭ± èªŀ", + "е ÑĢÑĭ", + "еÑĢ Ñĭ", + "Ġê¸Ī ìķ¡", + "Ġ ãĥ¼", + "Ġãĥ ¼", + "Ġ ëį¤íĶĦ", + "Ġëį ¤íĶĦ", + "ÑĢ аÑĤÑĮ", + "ÑĢа ÑĤÑĮ", + "ÑĢаÑĤ ÑĮ", + "Ġ åįķ", + "Ġåį ķ", + "à¹Ģà¸ī à¸ŀาะ", + "Ġ æĶ¿", + "ĠæĶ ¿", + "Ġà¤Ĩ म", + "Ġз ни", + "Ġзн и", + "Ġ ëĿ¼ìĿ´", + "ĠëĿ¼ ìĿ´", + "æİ Į", + "çIJĨ çĶ±", + "Ġ اغ", + "Ġا غ", + "ĠØ§Ø º", + "ĠÑģ иг", + "ĠÑģи г", + "Ġе ÑĦекÑĤив", + "ĠÐŁ ÑĢед", + "ĠÐŁÑĢ ед", + "ãĥ´ ãĤ£", + "Ġви ко", + "Ġвик о", + "Ġt vrd", + "Ġtv rd", + "ëĤ´ 기", + "ãĥĭ ãĤ¢", + "ĠÙħشاÙĩ دÙĩ", + "Ġस à¤ļ", + "l Ã¼ÄŁ", + "lü ÄŁ", + "è¯ģ åĪ¸", + "Ġs iêu", + "Ġsi êu", + "Ġ оÑĤв", + "ĠоÑĤ в", + "Ġvyt voÅĻ", + "ĠØŃ ÙħÙĦ", + "ĠØŃÙħ ÙĦ", + "ĠÑĦ ÑĢан", + "à¹ī à¸Ķ", + "åĮ» éĻ¢", + "Ġв лад", + "Ġвла д", + "غ ÙĦ", + "建 ç«ĭ", + "os loven", + "osl oven", + "и лаÑģÑĮ", + "ила ÑģÑĮ", + "عÙĦ ÙĪÙħات", + "عÙĦÙĪÙħ ات", + "Ġ ترÛĮÙĨ", + "Ġتر ÛĮÙĨ", + "ÎŃ Ïģει", + "ÎŃÏģ ει", + "Ġb áºŃt", + "ĠÙħØ´ Ú©", + "Ġر ئÙĬس", + "Ġرئ ÙĬس", + "Ġìłľ ìŀij", + "γ η", + "Ġн Ñĸк", + "ĠнÑĸ к", + "Ġ구 ìĦ±", + "ĠÄij en", + "Ġà¤ļ र", + "Ġgeç miÅŁ", + "äºĨ 解", + "Ġл еÑģ", + "Ġqu anh", + "Ġqua nh", + "Ġquan h", + "ãĢĮ æĪij", + "ĠNÄĽkter á", + "ëŀ į", + "Ãħ Ÿ", + "à¤Ĥ दर", + "à¤Ĥद र", + "ìķĦ ìĿ´", + "å°ij ãģĹ", + "ĠØ´Ùĩر ÛĮ", + "ĠØ´Ùĩ رÛĮ", + "κ ÏĦη", + "ĠâĹ Ħ", + "Ġ Ùĥس", + "ĠÙĥ س", + "è· Į", + "à ı", + "å·¥ åħ·", + "åĬ ĥ", + "p om", + "po m", + "ĠнавÑĩ аннÑı", + "Ġ رج", + "Ġر ج", + "ÑĢ ÑĥеÑĤÑģÑı", + "ÑĢÑĥ еÑĤÑģÑı", + "ÑĢÑĥеÑĤ ÑģÑı", + "Ġν ÎŃ", + "ÛĮÙĨ Ú©", + "à¹Ĥ à¸ĭ", + "åĭ ¤", + "ãģĹãģ¾ ãģĨ", + "ĠÑģ оглаÑģ", + "ĠÑģог лаÑģ", + "éĩij èŀį", + "ç »¿", + "ç» ¿", + "ĠС ан", + "æķ µ", + "Ġпо вÑĸÑĤ", + "Ġпов ÑĸÑĤ", + "Ġпом оÑīи", + "ĠпомоÑī и", + "ãĥ¡ ãĥªãĤ«", + "ãĤ· ãĤ¢", + "ĠÏĢ ÏģοÏĤ", + "ĠÏĢÏģο ÏĤ", + "èĪª 空", + "ĠваÑĢи анÑĤ", + "ĠваÑĢиан ÑĤ", + "Ġyalnız ca", + "ç³» çµ±", + "ĠÙģ ÙĪر", + "ĠÙģÙĪ ر", + "оÑĩ ной", + "оÑĩно й", + "à¹Ģว à¸Ńร", + "ĠкÑĥлÑĮ ÑĤÑĥÑĢ", + "ĠкÑĥлÑĮÑĤÑĥ ÑĢ", + "Ïĩ ι", + "ÄįÃŃ ta", + " ĵ", + "人 ãģĮ", + "κ οÏį", + "κο Ïį", + "ĠÑĢе ÑĶ", + "Ġв ÑģÑİ", + "ĠвÑģ Ñİ", + "éº Ĺ", + "Ġز ÙĨاÙĨ", + "ĠزÙĨ اÙĨ", + "çĭ Ĥ", + "Ġ หม", + "Ġห ม", + "Ġx úc", + "åħ Ĵ", + "ÄŁ inin", + "ÄŁi nin", + "ÄŁini n", + "ÄŁin in", + "åĸľ 欢", + "ĠÑģÑĤ ад", + "ĠÑģÑĤа д", + "iy esi", + "iye si", + "ìļ ±", + "è Ŀ", + "Ġ kus", + "Ġk us", + "Ġku s", + "ÏĦ ολ", + "ÏĦο λ", + "г Ñĸв", + "Ñĸ ли", + "Ñĸл и", + "ãģĦ ãĤĦ", + "é© Ĺ", + "ont rol", + "ا ÙĦÙĥ", + "اÙĦ Ùĥ", + "к овиÑħ", + "ко виÑħ", + "ков иÑħ", + "ĠÑģÑĤ ало", + "ĠÑģÑĤал о", + "ĠÑģÑĤа ло", + "ĠÎł αÏģα", + "Ġγα Ïģα", + "ĠγαÏģ α", + "Ġ chy", + "Ġc hy", + "Ġch y", + "Ġcih az", + "ĩ ´", + "ìŀ¥ ìĿ´", + "a ceae", + "ace ae", + "acea e", + "Ø´ Ùĩر", + "Ø´Ùĩ ر", + "ил аннÑı", + "çļĦ å°ı", + "Ġth ụ", + "Ġthá» ¥", + "ÙĪ ÙĨت", + "ÙĪÙĨ ت", + "л оÑĢ", + "ло ÑĢ", + "ãĤĴ æĮģ", + "ĠÎĶ ι", + "Ġ 羣", + "Ġçľ Ł", + "ÐĽ Ðŀ", + "é½ IJ", + "çİ Ħ", + "ا ÙĪÙĩ", + "اÙĪ Ùĩ", + "Ġи нÑĤ", + "Ġин ÑĤ", + "à¥Ģ à¤Łà¤°", + "à¥Ģà¤Ł र", + "Ġ обÑīе", + "Ġоб Ñīе", + "ĠобÑī е", + "Ġдеп ÑĥÑĤ", + "μÎŃν εÏĤ", + "ĠÙĥ ÙĬÙģ", + "ع ÙħÙĦ", + "عÙħ ÙĦ", + "ï¼Į å¦Ĥæŀľ", + "ï¼Įå¦Ĥ æŀľ", + "Ġин ÑĦек", + "i tele", + "it ele", + "ite le", + "itel e", + "Ġ ãĢĢãĢĢĠãĢĢ", + "ĠãĢĢ ãĢĢĠãĢĢ", + "ĠãĢĢãĢĢ ĠãĢĢ", + "ãĤ¤ ãĥ³ãĥĪ", + "ãĤ¤ãĥ³ ãĥĪ", + "л ÑĸÑĤ", + "лÑĸ ÑĤ", + "Ġ ÑģÑİ", + "ĠÑģ Ñİ", + "Ġz ase", + "Ġza se", + "Ġzas e", + "d ech", + "de ch", + "dec h", + "е ко", + "ек о", + "è® ĵ", + "åı ¬", + "з ем", + "Îł Îij", + "Ġvz du", + "า à¸Īาà¸ģ", + "าà¸Ī าà¸ģ", + "ko liv", + "kol iv", + "koli v", + "zk um", + "èģ Ĭ", + "Ġì±Ħ ìļ©", + "๠į", + "Ġ asp", + "Ġa sp", + "Ġas p", + "Û² Û´", + "ìĿ¸ ëį°", + "ĠkarÅŁÄ± laÅŁ", + "ï¼Į åı¯ä»¥", + "ï¼Įåı¯ 以", + "Ġà¤ĩन à¤ķ", + "Ġ ìĬ¤íĥĢ", + "ĠìĬ¤ íĥĢ", + "éĥ¨ å±ĭ", + "åĪ¶ ä½ľ", + "ãĥ¼ ãĤ·ãĥ§ãĥ³", + "ον ÏĦαÏĤ", + "γ ο", + "Ġìŀij ìĦ±", + "èij £", + "oz ÅĻejmÄĽ", + "ĠÑĢезÑĥлÑĮÑĤаÑĤ е", + "ĠÑĢезÑĥлÑĮÑĤ аÑĤе", + "ĠIns ecta", + "Ġs kon", + "Ġsk on", + "o tu", + "ot u", + "Ġp ÄĽt", + "ĠpÄĽ t", + "Ñģ ÑĮого", + "ÑģÑĮ ого", + "ĠÄ°s lam", + "Ġl á»ħ", + "Ġlá» ħ", + "ä¸Ń åľĭ", + "ĠÐľÑĸ нÑĸÑģÑĤ", + "åIJĪ åIJĮ", + "asy onu", + "asyon u", + "ож еÑĤ", + "оже ÑĤ", + "èĩª åĬ¨", + "ÑģÑĮ коÑİ", + "ÑģÑĮк оÑİ", + "ÑģÑĮко Ñİ", + "ĠkiÅŁ isel", + "ĠkiÅŁi sel", + "ÏĦ ικοÏį", + "ÏĦικ οÏį", + "ÏĦι κοÏį", + "ÏĦικο Ïį", + "Ġ ÑĥÑĩаÑģ", + "ĠÑĥ ÑĩаÑģ", + "ĠÑĥÑĩ аÑģ", + "ĠÑĥÑĩа Ñģ", + "ıl mÄ±ÅŁtır", + "ılmÄ±ÅŁ tır", + "ĠÑı ке", + "ĠÑıк е", + "Ñī инÑĭ", + "Ñīи нÑĭ", + "Ñīин Ñĭ", + "м аÑĢ", + "ма ÑĢ", + "Ġso udu", + "Ġsou du", + "Ġsoud u", + "Âł Я", + "Ġд ÑĢÑĥ", + "ĠдÑĢ Ñĥ", + "ãģ¡ ãĤĩ", + "à¥ĭ à¥ľ", + "ï¾ ij", + "Ġ ÏĦÏĮ", + "ĠÏĦ ÏĮ", + "Ġ ضر", + "Ġض ر", + "l áš", + "lá Å¡", + "Ġд Ñĸв", + "ĠдÑĸ в", + "Ġج دÙĬد", + "Ġجد ÙĬد", + "Ġнеб олÑĮÑĪ", + "ĠнеболÑĮ ÑĪ", + "éģ Ń", + "ç» į", + "ĠKur ulu", + "ĠKurul u", + "ÑģÑĤÑĢ ÑĥменÑĤ", + "ÑģÑĤÑĢÑĥ менÑĤ", + "è¿Ļ æĺ¯", + "ìĻ Ķëĭ¤", + "ìĻĶ ëĭ¤", + "м елÑĮ", + "ме лÑĮ", + "Ġ ä¼Ĭ", + "ủ ng", + "ĠзавиÑģим оÑģÑĤи", + "ëį ¤íĶĦ", + "çĩ ĥ", + "è¿ĩ åİ»", + "ĠзаÑģÑĤоÑģ ÑĥваннÑı", + "Ġداخ ÙĦÛĮ", + "ĠداخÙĦ ÛĮ", + "Ñī Ñij", + "ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", + "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł", + "ïº ®", + "ĠاÙĦÙħ ÙħÙĦÙĥØ©", + "s ında", + "sı nda", + "sın da", + "è³ Ģ", + "å± ı", + "Ġ ê¿", + "Ġê ¿", + "Ġdo ktor", + "Ġdok tor", + "ĠÙĤ اب", + "ĠS ist", + "ĠSi st", + "ĠмеÑģÑĤ е", + "ĠÑģоÑħ ÑĢа", + "ا شتÙĩ", + "اش تÙĩ", + "اشت Ùĩ", + "Ġ æľŁ", + "ĠпоÑģк олÑĮкÑĥ", + "Ġp ev", + "Ġpe v", + "ا گر", + "اگ ر", + "Ùħ ز", + "Ġض ÙħÙĨ", + "ॠ©", + "g esi", + "ge si", + "ges i", + "a ÄŁa", + "aÄŁ a", + "解 åĨ³", + "ëħ¸ ì¶ľ", + "Ġl uyá»ĩn", + "Ġкон ÑĤак", + "ĠконÑĤ ак", + "ภº", + "Ġ NgÃły", + "ĠNg Ãły", + "Ġvý stav", + "Ġth uyết", + "اÛĮ ع", + "Ġ: /:", + "Ġ:/ :", + "Ġph ạt", + "ĠÎij ÏĢÏĮ", + "ĠÎijÏĢ ÏĮ", + "Ġ muz", + "Ġm uz", + "Ġmu z", + "Ġ ìĥī", + "Ġìĥ ī", + "ĠÃĩ in", + "Ġکار برد", + "Ġکاربر د", + "ائ د", + "ب اد", + "با د", + "à¥į तम", + "à¥įत म", + "Ġ ëijĺ", + "Ġëij ĺ", + "Ġм оз", + "Ġмо з", + "Å¡ ÃŃch", + "Å¡ÃŃ ch", + "Ġ มห", + "Ġม ห", + "ĠØ¢ س", + "ĠÑģ лиÑĪком", + "èĥ ¡", + "è£ ģ", + "æĪ »", + "ĠìĦ¤ ëªħ", + "Ġo tom", + "Ġot om", + "Ġoto m", + "Ġलà¤Ĺ à¤Ńà¤Ĺ", + "à¸ĩ à¸ģ", + "ا بد", + "اب د", + "à¸Ļ าม", + "à¸Ļา ม", + "èĤ ©", + "Ġشد ÙĨد", + "ĠشدÙĨ د", + "ãģĿãģ® ä»ĸ", + "ad lo", + "ÄĽ n", + "ĠÙĦ Ùĩا", + "ĠÙĦÙĩ ا", + "Ġмин им", + "Ġми ним", + "Ġd ÅĻev", + "ĠTh iên", + "ĠThi ên", + "ëŀ Ļ", + "en gin", + "eng in", + "à¥Ģ मत", + "à¥Ģम त", + "ĠÑĥп оÑĤÑĢеб", + "âĢĮ تر", + "Ġç¥ŀ 马", + "ov ánÃŃm", + "ová nÃŃm", + "ovánÃŃ m", + "ován ÃŃm", + "Ġд ело", + "Ġдел о", + "Ġде ло", + "Ġ ç¼ĸ", + "Ġç¼ ĸ", + "ĠاÙĦ ظ", + "Ġ вий", + "Ġв ий", + "Ġви й", + "а ÑĤом", + "аÑĤ ом", + "аÑĤо м", + "åħ¬ åijĬ", + "ĠÄij em", + "ãĤ· ãĥªãĥ¼ãĤº", + "ä¸ĭ çļĦ", + "l ası", + "la sı", + "las ı", + "ĠвÑĭ боÑĢ", + "ĠвÑĭб оÑĢ", + "ÑĤ оÑĤ", + "ÑĤо ÑĤ", + "ëıĦ ë³Ħ", + "ĠÑĥ ÑģÑĤан", + "ĠÑĥÑģÑĤ ан", + "Ġ íŀĪ", + "Ġíŀ Ī", + "лÑĥ аÑĤа", + "Ġth ác", + "а нием", + "ан ием", + "ани ем", + "ание м", + "ов аÑĤÑĮÑģÑı", + "ова ÑĤÑĮÑģÑı", + "оваÑĤÑĮ ÑģÑı", + "ÑĤ ÑĶ", + "ÐŃ ÑĤо", + "ï¼Į è¦ģ", + "ĠV z", + "ĠØŃ ÙĪزÙĩ", + "ĠØŃÙĪ زÙĩ", + "- к", + "V Ỽi", + "ent ů", + "Ġbulun duÄŁu", + "Ġbulundu ÄŁu", + "ر ÙĪØ·", + "رÙĪ Ø·", + "ĠÑĹ Ð¹", + "Ġçev r", + "Ġ ÅĻed", + "ĠÅĻ ed", + "ĠÅĻe d", + "Ġس اختÙĩ", + "Ġساخت Ùĩ", + "åĬŀ æ³ķ", + "Ġ ÙĤÙĦ", + "ĠÙĤ ÙĦ", + "i ÅŁi", + "iÅŁ i", + "ï¼Ŀ ï¼Ŀ", + "س اس", + "Ġúdaj ů", + "å ¬", + "æį Ł", + "á ct", + "ác t", + "ĠÎij ÏĢ", + "çĪ ·", + "Ġ ÅĻád", + "ĠÅĻ ád", + "Ġl á»Ĺi", + "Ġlá» Ĺi", + "Ġlá»Ĺ i", + "on tent", + "ont ent", + "onte nt", + "ĠÙħ Ø°", + "ol oji", + "olo ji", + "oloj i", + "Ġپرد اخت", + "à¹ī าà¸ŀ", + "à¹īา à¸ŀ", + "ĠдейÑģÑĤв иÑı", + "Ġmnož stvÃŃ", + "ìķĪ ë§Ī", + "åģ ¶", + "Ġ ÃĶng", + "ĠÃĶ ng", + "Ġdak ika", + "hen dis", + "hend is", + "Ġb ác", + "å¯ ¶", + "à¹ĩà¸ģ หà¸į", + "noc enÃŃ", + "ĠErd oÄŁan", + ": ::::::::::::", + ":: :::::::::::", + ":::: :::::::::", + ":::::: :::::::", + ":::::::: :::::", + "::: ::::::::::", + "::::: ::::::::", + "::::::: ::::::", + "::::::::: ::::", + ":::::::::: :::", + "::::::::::: ::", + ":::::::::::: :", + "аÑĤ ем", + "аÑĤе м", + "d ız", + "dı z", + "ĠØ£ÙĬ ضا", + "ĠØ£ÙĬض ا", + "ĠÑįÑĦ ÑĦек", + "ãĤĮ ãģ¦ãģĦãĤĭ", + "ãĤĮãģ¦ ãģĦãĤĭ", + "ĠbaÅŁv uru", + "ĠbaÅŁvur u", + "ά νει", + "άν ει", + "ĠÏĦε λεÏħÏĦα", + "Ġê²Ģ ìĥī", + "ĠÚ©ÙĨ ترÙĦ", + "Ġ शà¤ķ", + "Ġश à¤ķ", + "å¼ ¹", + "Ġol muÅŁtur", + "Ġolm uÅŁtur", + "ĠolmuÅŁ tur", + "Ġв ÑģÑĤÑĥп", + "ĠвÑģÑĤ Ñĥп", + "Ñĩ ила", + "Ñĩи ла", + "Ñĩил а", + "ย า", + "ĠØ£ØŃ Ùħد", + "os lav", + "osl av", + "ĠÑĩа Ñģов", + "ĠÑĩаÑģ ов", + "Ġzá kladnÃŃ", + "Ġzáklad nÃŃ", + "Ġस व", + "д он", + "до н", + "ĠÅĻÃŃj na", + "κ οÏħ", + "κο Ïħ", + "éĢģ æĸĻçĦ¡æĸĻ", + "éĢģæĸĻ çĦ¡æĸĻ", + "Ïĥ ίαÏĤ", + "Ïĥία ÏĤ", + "Ïĥί αÏĤ", + "ãĤ´ ãĥª", + "Ġв иб", + "Ġви б", + "å½ Ĵ", + "Ġназ ад", + "ĠçĻ¾åº¦ æĶ¶å½ķ", + "á» Ĩ", + "Ġkal dı", + "ì¼ ľ", + "Ġ íıŃ", + "Ġíı Ń", + "ĠÑĩи ном", + "ĠÑĩин ом", + "è ¹", + "Ñı л", + "ĠÑĢаз дел", + "ĠÑĢазд ел", + "d G", + "ĠT ento", + "ĠTen to", + "ĠTent o", + "Ñı ÑĤÑĮÑģÑı", + "ÑıÑĤÑĮ ÑģÑı", + "éĿ¢ çļĦ", + "ĠÎķ ÏĢι", + "ĠÎķÏĢ ι", + "ê° ij", + "Ġk èm", + "ни ÑĨÑı", + "ниÑĨ Ñı", + "çĸ «", + "éĽ Ļ", + "ĠÙħر Ùĥز", + "Ġна Ñĥк", + "å¢ Ĺ", + "ĠÑĤе пеÑĢ", + "ĠÑĤеп еÑĢ", + "ा à¤ł", + "ाठł", + "à¹ĩà¸ļ à¹Ħà¸ĭà¸ķ", + "μβ ÏģίοÏħ", + "ĠÑĦÑĸн анÑģов", + "ĠÑĦÑĸнанÑģ ов", + "Ñĸ ÑĶÑİ", + "ÑĸÑĶ Ñİ", + "Ïģ ίζ", + "Ïģί ζ", + "ì¤ Ħ", + "ĠباÙĨ Ú©", + "t ul", + "tu l", + "li ÄŁini", + "liÄŁi ni", + "liÄŁ ini", + "liÄŁin i", + "ĠпозволÑı еÑĤ", + "Ġпозвол ÑıеÑĤ", + "Ïĥ ί", + "Ġ ìĽĥ", + "ĠìĽ ĥ", + "à¹Į à¸Ħ", + "Ġpol ov", + "Ġpo lov", + "Ġpolo v", + "ìŀ¥ ìĿĦ", + "is té", + "ist é", + "ĠС Ð¡Ð¡Ðł", + "á hl", + "áh l", + "è ¥", + "Ġкомп лек", + "à¸Ĥ à¸Ļาà¸Ķ", + "ั ศ", + "ν αν", + "να ν", + "Ġç¥ŀ马 æĶ¶å½ķ", + "ìĭľ ìĺ¤", + "Ġé¦ĸ页 第", + "ĠçĻ¾åº¦ æµģéĩı", + "åij¨ æĶ¶å½ķ", + "Ġh atta", + "Ġhat ta", + "ÐĴ Ñĸд", + "ĠвÑĭ ÑģÑĤÑĥп", + "Ú© ارÛĮ", + "کار ÛĮ", + "کا رÛĮ", + "K hi", + "Kh i", + "Ġì°¾ ìķĦ", + "Ġn ặng", + "éĨ «", + "ĠV Å¡", + "ĠпеÑĢ ен", + "ĠпеÑĢе н", + "л ава", + "ла ва", + "лав а", + "ÙĬ ÙħÙĬ", + "ÙĬÙħ ÙĬ", + "Ġvat andaÅŁ", + "Ġ ιÏĥÏĦο", + "Ġι ÏĥÏĦο", + "Ġ à¸ĵ", + "Ġภĵ", + "स ल", + "г ен", + "ге н", + "Ġ بÙĪر", + "Ġب ÙĪر", + "ĠبÙĪ ر", + "âĢĮدÙĩ د", + "âĢĮد Ùĩد", + "l ıklı", + "lık lı", + "Ġ strate", + "Ġst rate", + "Ġstr ate", + "Ġstrat e", + "Ġstra te", + "ب ÙĪر", + "بÙĪ ر", + "ãĢģ ãĤ¢", + "Ġson uc", + "Ġsonu c", + "Ġна иболее", + "- в", + "Ġвод ой", + "oj enÃŃ", + "oje nÃŃ", + "Ġغ رب", + "Ġغر ب", + "Ġb eri", + "Ġbe ri", + "Ġber i", + "a dÄĽ", + "ad ÄĽ", + "Ġd ovol", + "Ġdo vol", + "Ġdov ol", + "âĢĮÚ©ÙĨ ÙĨدگاÙĨ", + "âĢĮÚ©ÙĨÙĨد گاÙĨ", + "ãģķ ãĤī", + "ãĥ³ ãĤº", + "ãĤ« ãĥ«", + "om etr", + "ome tr", + "omet r", + "åĩ Ģ", + "ĠÙģ ÙĪÙĦ", + "ĠÙģÙĪ ÙĦ", + "ĠÙħ ÙĪسÛĮ", + "ĠÙħÙĪ سÛĮ", + "ĠÙħÙĪس ÛĮ", + "ĠاÙĦÙħغ رب", + "e cko", + "ec ko", + "eck o", + "ÙĢÙĢÙĢÙĢ ÙĢÙĢÙĢÙĢ", + "ê°Ģ 격", + "ÑĢ ÑĥÑĤ", + "ÑĢÑĥ ÑĤ", + "Ġ ë¶Ģë¶Ħ", + "Ġë¶Ģ ë¶Ħ", + "ĠpÅĻed pis", + "Ġoprav du", + "еÑĤ иÑĩ", + "еÑĤи Ñĩ", + "à¹Ĥ à¸Ħรà¸ĩà¸ģาร", + "æ ħ§", + "æħ §", + "æĭ ľ", + "س Ùĥ", + "ìŀ¡ ëĭ´", + "à¸Ľà¸£à¸°à¸¡ าà¸ĵ", + "è´¨ éĩı", + "Ġголов Ñĥ", + "Ġгол овÑĥ", + "л ениÑİ", + "лен иÑİ", + "ле ниÑİ", + "Ġन à¤ı", + "Ġprojekt u", + "ا Ùģر", + "اÙģ ر", + "at ivnÃŃ", + "ati vnÃŃ", + "ativ nÃŃ", + "ÎŃ Î½ÏĦ", + "ÎŃν ÏĦ", + "ãĥī ãĥ©", + "Ġted av", + "ê ¼", + "à¸Ľà¸£à¸°à¸ģ าศ", + "Ġt uto", + "Ġtu to", + "Ġtut o", + "Ġch iếu", + "Ġchi ếu", + "Ġchiế u", + "Ġv yz", + "Ġvy z", + "ÑĢ оÑĪ", + "ÑĢо ÑĪ", + "åıĸ å¾Ĺ", + "Ġм иÑģÑĤ", + "Ġми ÑģÑĤ", + "ĠмиÑģ ÑĤ", + "ĠÑģлÑĥÑĩа ÑıÑħ", + "Ġغ Ø°", + "ĠÑĥ клад", + "ĠÑĥк лад", + "ĠÑĥÑģÑĤанов лен", + "Ġtes lim", + "Ġ ãĢį", + "ĠãĢ į", + "Ġ è£", + "Ġè £", + "æ¯ «", + "éĬĢ è¡Į", + "e cts", + "ect s", + "ec ts", + "k emiz", + "kem iz", + "ν ηÏĤ", + "νη ÏĤ", + "è¾ º", + "Ġп ÑĢем", + "ĠпÑĢ ем", + "ĠпÑĢе м", + "Ġson ucu", + "Ġsonuc u", + "Ġsonu cu", + "P okud", + "Po kud", + "Pok ud", + "ĠÐŀÑģ об", + "è¾ Ľ", + "è¼ ¸", + "ë³´ ê³ł", + "à¸ļ à¸Ħ", + "ãĢĤ ãĢį", + "ा ।ĊĊ", + "ा। ĊĊ", + "ा।Ċ Ċ", + "ĠÑģамоÑģÑĤ оÑıÑĤелÑĮ", + "ÙĦ ÛĮت", + "ÙĦÛĮ ت", + "λ εκ", + "λε κ", + "ĠÑĢай она", + "ĠÑĢайон а", + "ÑĮ и", + "à¹Ī าà¸Ĺ", + "à¹Īา à¸Ĺ", + "Ġ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", + "Ġà¸Ľà¸£à¸° à¹Ģà¸Ĺศ", + "ม à¸Ń", + "ا Ùĩر", + "اÙĩ ر", + "Ġви боÑĢ", + "Ġвиб оÑĢ", + "ÑİÑĩи ÑģÑĮ", + "Ġp ovol", + "Ġpo vol", + "Ġpov ol", + "a base", + "ab ase", + "aba se", + "âĢ³ N", + "Ú© ÙĪ", + "ĠУкÑĢаÑĹ Ð½Ð°", + "ĠУкÑĢа ÑĹна", + "ĠУкÑĢаÑĹн а", + "sta nov", + "stan ov", + "ĠÑĥÑĩ аÑģÑĤи", + "ĠÑĥÑĩа ÑģÑĤи", + "ĠÑĥÑĩаÑģÑĤ и", + "ĠÑĥÑĩаÑģ ÑĤи", + "Ġh lad", + "Ġhl ad", + "ĠÑĢаÑģÑģ каз", + "ãģ¿ ãģŁãģĦ", + "á½ °", + "Ġ åĽŀ", + "ĠåĽ ŀ", + "Ġ Æ°Æ¡ng", + "α Ïģά", + "αÏģ ά", + "Ø® ب", + "æį ķ", + "ÃŃ ÅĻ", + "Ġ سÛĮÙĨ", + "Ġس ÛĮÙĨ", + "ĠسÛĮ ÙĨ", + "Âł in", + "ĠM ÄĽst", + "æķĻ åѦ", + "ĠоÑģоб иÑģÑĤ", + "ĠоÑģоби ÑģÑĤ", + "u ji", + "uj i", + "çĶ» åĥı", + "ĠداÙĨ Ø´ÙĨاÙħÙĩ", + "ĠداÙĨØ´ ÙĨاÙħÙĩ", + "ìĿ´ ìķ¼", + "Ġзап иÑĤ", + "ĠÑģво ими", + "ĠÑģвои ми", + "ĠÑģвоим и", + "Û²Û° Û²", + "ï¼Į å°Ĩ", + "ãĥ¼ ãģ®", + "Ġth ÃŃ", + "ĠÙħت ÙĪسط", + "à¥ĩ Ċ", + "å¤ļ å°ij", + "ï¼Į çĦ¶åIJİ", + "íĹ Ī", + "Ġ à¤Ńà¤Ĺ", + "Ġà¤Ń à¤Ĺ", + "Ġ åı·", + "Ġåı ·", + "Ġt eor", + "Ġte or", + "å Ĥ¨", + "åĤ ¨", + "Ġ ÑĢÑĸÑĩ", + "ĠÑĢ ÑĸÑĩ", + "ĠÑģÑĤаÑĤ ÑĤÑĸ", + "Ġرابط Ùĩ", + "Ġ ï¼ľ", + "Ġï¼ ľ", + "ب اØŃ", + "با ØŃ", + "ิà¸Ļ à¸Ĺาà¸ĩ", + "ิà¸Ļà¸Ĺ าà¸ĩ", + "à¥ĩà¤Ĥ Ċ", + "ائ ÙĤ", + "ĠاÙĦج دÙĬد", + "l iÄį", + "li Äį", + "ا ØŃÙĦ", + "اØŃ ÙĦ", + "mé nÄĽ", + "Ġb ầu", + "ĠÐĴ ал", + "Ġб лагод", + "Ġбла год", + "Ġблаг од", + "еÑĤ елÑĮ", + "еÑĤе лÑĮ", + "å¹³ åĿĩ", + "м ин", + "ми н", + "Ġsü rec", + "Ġsür ec", + "Ġsüre c", + "Ġза вод", + "Ġзав од", + "èį IJ", + "ÑĤ ий", + "ÑĤи й", + "л об", + "ло б", + "Ġ вок", + "Ġв ок", + "Ġво к", + "l adıģı", + "la dıģı", + "lad ıģı", + "ladı ģı", + "ladıģ ı", + "اÙĬ ÙĬ", + "ê²ł ìĬµëĭĪëĭ¤", + "Ġamac ıyla", + "Ġamacı yla", + "ï¼Į åĽłä¸º", + "ï¼ĮåĽł 为", + "ãģ§ ãģĤãģ£ãģŁ", + "ãģ§ãģĤ ãģ£ãģŁ", + "ĠØ´ رÙĪع", + "Ġشر ÙĪع", + "æŁ Ķ", + "' nun", + "'n un", + "о кол", + "ок ол", + "око л", + "Ġc iddi", + "Ġcid di", + "Ġb ụ", + "Ġyap ılacak", + "Ġyapı lacak", + "Ġyapıl acak", + "ĠÑĩÑĥв ÑģÑĤв", + "ìĤ¬ ìĿĺ", + "à¸Ń à¸Ļà¸Ķ", + "à¸Ńà¸Ļ à¸Ķ", + "ΊΤ", + "Ġëĭ¤ ìĸij", + "ëĭ¤ ë©´", + "im izi", + "imi zi", + "imiz i", + "ä¹ Ĥ", + "ãģ² ãģ¨", + "Ġ éĿŀ", + "ĠéĿ ŀ", + "âĢĮپدÛĮ ا", + "ä¹ ĺ", + "ãĥĬ ãĥ«", + "ĠпÑĸдпÑĢиÑĶм ÑģÑĤва", + "ĠпÑĸдпÑĢиÑĶмÑģÑĤв а", + "๠ij", + "è¿ Ŀ", + "ĠÙħ ÙĨÙĩ", + "ĠÙħÙĨ Ùĩ", + "ÑĢ ик", + "ÑĢи к", + "а ÑĢÑĸв", + "аÑĢ Ñĸв", + "аÑĢÑĸ в", + "Ġ кого", + "Ġк ого", + "Ġко го", + "ĠÙĤ ص", + "Ġ æĿ¥", + "ĠæĿ ¥", + "ĠPh òng", + "Ġ ово", + "Ġо во", + "Ġов о", + "ĠпеÑĢ ева", + "ĠпеÑĢе ва", + "ĠпеÑĢев а", + "é£ ²", + "à¤Ĥ à¤Łà¤°", + "à¤Ĥà¤Ł र", + "ÙĬ را", + "ÙĬر ا", + "il diÄŁi", + "ildi ÄŁi", + "e tin", + "et in", + "eti n", + "Ïĩε ία", + "Ïĩεί α", + "Ġzah rani", + "ÙĪ جد", + "ÙĪج د", + "Ġ ç¯", + "Ġç ¯", + "าร ย", + "Ġза ко", + "Ġзак о", + "ĠتÙĤ س", + "ãĤ¹ ãĤ¿ãĥ¼", + "ãĤ¹ãĤ¿ ãĥ¼", + "æĿ °", + "Ġ ãĤ°", + "ĠãĤ °", + "Ġ é»Ħ", + "Ġé» Ħ", + "Ġ Ðļогда", + "ĠÐļ огда", + "ॠ«", + "Ġ 次", + "Ġæ¬ ¡", + "ĠвÑĭ ÑĢаж", + "Ġch Äĥm", + "лÑı ÑĶÑĤÑĮÑģÑı", + "лÑıÑĶ ÑĤÑĮÑģÑı", + "د ÙĩÙħ", + "دÙĩ Ùħ", + "Ġv rch", + "Ġvr ch", + "çº Į", + "п оÑĢ", + "по ÑĢ", + "Ġm aÄŁ", + "Ġma ÄŁ", + "å¾Ĵ æŃ©", + "po dob", + "pod ob", + "ะ à¹ģ", + "éģ¸ æīĭ", + "å¸ ¯", + "Ġse bou", + "Ġseb ou", + "in ize", + "ini ze", + "iniz e", + "ĠÐľ ак", + "ĠÐľÐ° к", + "Ġ æĻ®", + "ĠæĻ ®", + "ĠÏħÏĢ άÏģÏĩ", + "ĠÄIJ Ãł", + "ĠBr no", + "Ġ Å¡ÃŃ", + "ĠÅ¡ ÃŃ", + "اÙĦ ص", + "Ġngh iêm", + "Ġnghi êm", + "Ġon ları", + "Ġonlar ı", + "Ġu žÃŃ", + "Ġuž ÃŃ", + "èĩªåĪĨ ãģ®", + "ĠнаÑħод иÑĤÑģÑı", + "Ġj si", + "Ġjs i", + "Ġस मर", + "Ġसम र", + "ĠÏĨ Ïī", + "Û±Û¹ Û¸", + "Ġà¤ľà¤Ĺ ह", + "éŃ ļ", + "ìĿ¸ ê°Ģ", + "ÄIJ iá»ģu", + "ĠØ£ عÙĦاÙħ", + "Ġأع ÙĦاÙħ", + "à¥ĩà¤Ĥ ।Ċ", + "à¥ĩà¤Ĥ। Ċ", + "å½¢ æĪIJ", + "Ġ ikt", + "Ġi kt", + "Ġik t", + "Ġzd roj", + "ĠAmer ik", + "Ρ Îĵ", + "à¸ĩ ส", + "ĠíĴ Ģ", + "Ñģол ÑİÑĤ", + "ÙĪ ÙĬت", + "ÙĪÙĬ ت", + "Ġgörün tü", + "ан нÑĭÑħ", + "аннÑĭ Ñħ", + "ĠØ£ ÙĤ", + "Ġ миÑĢ", + "Ġм иÑĢ", + "Ġми ÑĢ", + "å« Į", + "Ġm á»iji", + "Ġd erin", + "Ġde rin", + "Ġder in", + "é ĴĪ", + "éĴ Ī", + "Ġма ÑĪи", + "ĠмаÑĪ и", + "ì¸ ¡", + "ĠجÙĨ ÙĪب", + "ĠÑģ ло", + "ĠÑģл о", + "ãĢĤ ä¸Ģ", + "ени ÑıÑħ", + "ениÑı Ñħ", + "ĠÑĩолов Ñĸк", + "Ġy ana", + "Ġya na", + "Ġyan a", + "Ġо кÑĤ", + "Ġок ÑĤ", + "Ġ неÑĢ", + "Ġн еÑĢ", + "Ġне ÑĢ", + "æĪ ¶", + "н ÑĮомÑĥ", + "нÑĮ омÑĥ", + "нÑĮо мÑĥ", + "ĠÑĸ мен", + "ĠÑĸм ен", + "ãĤı ãģŁãģĹ", + "ĠÎĵ ια", + "ĠÎĵι α", + "ãĢģ ç§ģ", + "Ġ kou", + "Ġk ou", + "Ġko u", + "ĠÑĨ еÑĢк", + "ĠÑĨеÑĢ к", + "lay arak", + "ãĢ ĩ", + "ا ÙĦس", + "اÙĦ س", + "Âł T", + "Ġд ÑĢÑĥж", + "ĠдÑĢÑĥ ж", + "ĠдÑĢ Ñĥж", + "Ġд воÑĢ", + "Ġдв оÑĢ", + "Ġдво ÑĢ", + "λ ί", + "ĠëĨ Ģ", + "Ġte plot", + "Ġtep lot", + "Ùģ ات", + "б Ñĸ", + "Ġgüven lik", + "Ġgüvenli k", + "n ÄĽn", + "nÄĽ n", + "è© ©", + "Ġinsan ların", + "Ġinsanlar ın", + "ĠìĦ¤ ì¹ĺ", + "èĵ Ŀ", + "a vatel", + "av atel", + "ava tel", + "j ev", + "je v", + "ĠÚĨ را", + "Ġgerek iyor", + "ãĥĥ ãĤ°", + "ĠÃĩ ok", + "Ġ ÙĪجÙĩ", + "ĠÙĪ جÙĩ", + "ĠÙĪج Ùĩ", + "Ġ Ñĥли", + "ĠÑĥ ли", + "ĠÑĥл и", + " ij", + "åij Ģ", + "ĠоÑĢгани заÑĨии", + "ĠоÑĢганиз аÑĨии", + "ĠоÑĢганиза ÑĨии", + "ĠÑĸÑģ нÑĥ", + "Ġneb ude", + "Ġnebu de", + "Ġë° ¤", + "ä¸Ĭ ãģĮ", + "Ġध न", + "ĠرÙĪ ابط", + "γγ ελ", + "Ġдо ÑģÑıг", + "ĠдоÑģ Ñıг", + "ĠاÙĦÙĤ دÙħ", + "ĠاÙĦÙĤد Ùħ", + "Ġзна Ñħод", + "ĠÄįÃŃs lo", + "ÅŁ k", + "ĠاÙĦد ÙĬÙĨ", + "Ġgün lük", + "ÙĥÙĬ ÙĬÙģ", + "ÎŃ Ïģα", + "ÎŃÏģ α", + "à¸ķ รว", + "à¸ķร ว", + "Ġнали Ñĩи", + "ا ÙħÛĮÙĨ", + "اÙħ ÛĮÙĨ", + "اÙħÛĮ ÙĨ", + "Ġμ ικ", + "Ġdönem de", + "à¹Ī à¸Ĺ", + "æĥ ij", + "à¥ĭà¤Ĥ ,", + "Ñĩ Ñı", + "ãģ¾ ãĤĭ", + "ĠاÙĦ تÙĨ", + "ĠاÙĦت ÙĨ", + "ÑĢ аг", + "ÑĢаР³", + "ÑĢа г", + "ëĵ¤ ê³¼", + "Ń Ķ", + "ĠÙħÙĨ Ùĩا", + "ĠÙħÙĨÙĩ ا", + "ĠTh ế", + "éIJ µ", + "Ġ ï¾Ħ", + "Ġï¾ Ħ", + "ĠاÙĦØ¥ سÙĦاÙħ", + "ãĤ¦ ãĤ¹", + "ÙĬ دÙĬ", + "ÙĬد ÙĬ", + "Ġ å¾Ĺ", + "Ġå¾ Ĺ", + "Ġза ÑĢаз", + "ãĤ¸ ãĥ¥", + "Ġت عد", + "Ġتع د", + "i ÃŃ", + "Ġç ocu", + "oz ici", + "Ġ ë²Ķ", + "Ġë² Ķ", + "ĠØ¢Ùħ دÙĩ", + "ĠØ¢Ùħد Ùĩ", + "ÑĦ ик", + "ÑĦи к", + "Ġпо ÑģÑĤанов", + "ĠпоÑģÑĤ анов", + "Ġkrál ov", + "¨ ¨", + "Ġì¤ij ìļĶ", + "ĠG Wei", + "ĠGW ei", + "Ġvý voj", + "Ġboy ut", + "Ġ nek", + "Ġn ek", + "Ġne k", + "ا ÙĩاÛĮ", + "اÙĩ اÛĮ", + "اÙĩا ÛĮ", + "Ġst ranÄĽ", + "Ġstran ÄĽ", + "Ġstra nÄĽ", + "и ем", + "ие м", + "Ġпо ÑĢаж", + "ĠпоÑĢ аж", + "à¥įर दर", + "à¥įरद र", + "é¡Ķ ãĤĴ", + "ĠY üz", + "Ġо знаÑĩа", + "Ġозна Ñĩа", + "à¹ģล à¸Ļà¸Ķ", + "Ġب ÙĩرÙĩ", + "ĠبÙĩ رÙĩ", + "ен ÑĤÑĥ", + "енÑĤ Ñĥ", + "ĠÐĿ ад", + "ĠÐĿа д", + "ĠÐŁ олÑĮ", + "ĠÐŁÐ¾ лÑĮ", + "ĠÐŁÐ¾Ð» ÑĮ", + "ãĥĹ ãĥª", + "á¿ ¶", + "âĢĮپدÛĮ اÛĮ", + "âĢĮپدÛĮا ÛĮ", + "ĠÙ¾ اÙĪرپÙĪÛĮÙĨت", + "ิà¸ģ า", + "Ġε νÏİ", + "Ġεν Ïİ", + "Ġس اÛĮر", + "éģ º", + "ãĢģ ä»Ĭ", + "ĠL ê", + "äºĭ æĥħ", + "ĠY er", + "ĠYe r", + "èħ °", + "ĠاÙĦر سÙħ", + "ĠاÙĦÙħ ÙĪÙĤع", + "ĠاÙĦÙħÙĪ ÙĤع", + "Ġh Ãłm", + "Ġд ÑĢев", + "ĠдÑĢ ев", + "á tel", + "át el", + "áte l", + "ĠвÑģ Ñij", + "ìĺ ¥", + "ĠM ec", + "ĠMe c", + "ãĤ Ľ", + "Ġص اد", + "ĠÚ¯ ردد", + "Ġگرد د", + "Ġگر دد", + "Ġkr ás", + "èĮĥ åĽ´", + "a larına", + "alar ına", + "aları na", + "aların a", + "èĻ ļ", + "ĠØ¢ ÙĪرد", + "ĠØ¢ÙĪر د", + "ç¼ ĵ", + "ิ à¸ŀ", + "Ġ ãĥĭ", + "Ġãĥ ĭ", + "Ġ æĢ§", + "ĠæĢ §", + "ĠÙħÙĨ Ø°", + "ç· ´", + "Ġ ê¶ģ", + "Ġê ¶ģ", + "в аем", + "ва ем", + "Ġζ Ïī", + "Ġn avr", + "Ġna vr", + "Ġnav r", + "Ïĥ ÏĦαÏĥη", + "ÏĥÏĦα Ïĥη", + "Ġر Ø£", + "Ġd opl", + "Ġdo pl", + "Ġdop l", + "＿ ＿＿", + "＿＿ ＿", + "çĶļ èĩ³", + "Äį el", + "Äįe l", + "æĦı åij³", + "ç¥ Ń", + "à ĺ", + "ÑģÑĤв еннÑĭе", + "ÑģÑĤвен нÑĭе", + "è£ ¡", + "Ġ ãĢī", + "ĠãĢ ī", + "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "Ġ вал", + "Ġв ал", + "Ġва л", + "Ġ ẩm", + "Ġd iyor", + "Ġdi yor", + "Ġdiy or", + "à¸Ńà¸ĩ à¸Īาà¸ģ", + "ĠPh ó", + "ĠÐĵ е", + "ĠвеÑĢ еÑģ", + "Ġве ÑĢеÑģ", + "Ġk onz", + "Ġko nz", + "Ġkon z", + "ر ز", + "ĠÑģоб оÑİ", + "Ġεκ εί", + "ìĺģ ìĸ´", + "i ag", + "ia g", + "ĠÑģ енÑĤ", + "Ġn ấu", + "Ġja ké", + "Ġjak é", + "Ġro zh", + "Ġroz h", + "Ġб ог", + "Ġбо г", + "ÙĨ اد", + "ÙĨا د", + "ĠاÙħ ÙĪر", + "à¹Į à¸ģาร", + "à¹Įà¸ģ าร", + "ĠY aÅŁ", + "ĠYa ÅŁ", + "é Ī", + "åķ ª", + "Ġon ay", + "Ġona y", + "ìĹ ĩ", + "o mu", + "om u", + "ÑĨ Ñĸйного", + "ÑĨÑĸй ного", + "ÑĨÑĸйно го", + "ĠÑģ ал", + "ĠΣ Ïħν", + "ĠΣÏħ ν", + "Ġsav un", + "å¦ Ļ", + "à¸Īะ ม", + "ãĤ¹ ãĤ¯", + "Ġd osy", + "Ġdo sy", + "Ġdos y", + "ľ ĺ", + "ë¨ ¹", + "Ġmin ul", + "Ġmi nul", + "ãĢĭ Ċ", + "åģ ı", + "ĠÐļ аÑĤ", + "ĠÐļа ÑĤ", + "Ġed ilmesi", + "Ġedilm esi", + "Ġedil mesi", + "ÑĨÑĸ ÑĶн", + "ìĦ± ìĿ´", + "åĸ Ķ", + "Ġв ÑĸÑĢ", + "ĠвÑĸ ÑĢ", + "è¯ ij", + "ाà¤ĩ ड", + "ĠÙĪÙĤ تÛĮ", + "ĠÙĪÙĤت ÛĮ", + "ÄIJ á»ĥ", + "Ġ vyÅ¡Å¡ÃŃ", + "Ġvy Å¡Å¡ÃŃ", + "Äį ila", + "Äįi la", + "Äįil a", + "а дÑĥ", + "ад Ñĥ", + "çī¹ åĪ¥", + "ĠìĿ¸ 기", + "u jÃŃcÃŃch", + "ujÃŃ cÃŃch", + "ujÃŃcÃŃ ch", + "ĠPo dle", + "ĠPod le", + "Ġy avaÅŁ", + "Ļ æ±Ł", + "Ġka yb", + "Ġkay b", + "åĬ ª", + "ç´ ¹", + "Ġоб ÑĢабоÑĤ", + "ĠобÑĢа боÑĤ", + "Ġм аÑı", + "Ġма Ñı", + "Ġ åıĬ", + "Ġåı Ĭ", + "æİ¥ åıĹ", + "ÙĨ تÛĮ", + "ÙĨت ÛĮ", + "Ġ ÏĩÏİ", + "ĠÏĩ Ïİ", + "ÑĤ ÑĢо", + "ÑĤÑĢ о", + "Ġu yar", + "Ġuy ar", + "ĠعÙħÙĦ کرد", + "Ġо ÑĨен", + "ĠмеÑģÑĤ а", + "à¸ķ ลาà¸Ķ", + "à¸ķล าà¸Ķ", + "Ùħ ÙĤ", + "ild ren", + "Ġзави ÑģиÑĤ", + "Âł ĠÂł", + "ÂłĠ Âł", + "Ġm ožná", + "Ġmož ná", + "æĺŃ åĴĮ", + "ır ken", + "к ин", + "ки н", + "åĿ Ĥ", + "ÏĦ Ïĥι", + "ÏĦÏĥ ι", + "ĠÑĩ Ñĥд", + "Ðļ он", + "is lav", + "isl av", + "ĠÐļ ÑĢаÑģ", + "ĠÐļÑĢа Ñģ", + "N ej", + "Ne j", + "Âł b", + "r of", + "ro f", + "Ġ ileri", + "Ġi leri", + "Ġil eri", + "Ġile ri", + "Ġiler i", + "ĠÐŀ ÑĢ", + "ĠCh á»ī", + "Ġn üfus", + "ĠÑĸ нÑĤ", + "ĠÑĸн ÑĤ", + "! âĢľ", + "Ġन र", + "主 ä¹ī", + "ĠتÙĨ ظ", + "ův odu", + "ůvod u", + "ĠгоÑĢ ода", + "ĠгоÑĢод а", + "Ġk ural", + "Ġkur al", + "Ġku ral", + "Ġj edin", + "Ġje din", + "Ġjed in", + "ÑĢ аÑĤег", + "ÑĢаÑĤ ег", + "åĢ º", + "Ġzpůsob em", + "ìĿ¸ ìĿĺ", + "Ġ ÙĨب", + "ĠÙĨ ب", + "ĠN ga", + "ĠNg a", + "ĠÐĿ ай", + "ĠÐĿа й", + "ĠاÙģ زار", + "ĠاÙģز ار", + "нÑĥ вÑģÑı", + "нÑĥв ÑģÑı", + "Ġдв оÑħ", + "Ġдво Ñħ", + "Ġro zp", + "Ġroz p", + "ε ίοÏħ", + "εί οÏħ", + "είο Ïħ", + "Ġο ικο", + "Ġοι κο", + "ĠG eç", + "ĠGe ç", + " Ĺ", + "Ġch iếm", + "Ġchiế m", + "ĠÑĢаÑģпÑĢоÑģÑĤ ÑĢан", + "Ġh Æ°Æ¡ng", + "èĩª åĭķ", + "ĠÙħÙĪ ÙģÙĤ", + "æĮ ¥", + "ï¼ģ âĢĿĊĊ", + "ï¼ģâĢĿ ĊĊ", + "Ïģο ÏĨοÏģ", + "èı Į", + "ãĥ´ ãĤ¡", + "欧 ç¾İ", + "ĠÑĤеп ло", + "ãģĤ ãģĤ", + "ãĤ¦ ãĥ³", + "ĠÅŁ eyi", + "ĠÅŁey i", + "Ġs üt", + "Ġsü t", + "ãģ¹ ãģ¦", + "ãĥ³ ãĥij", + "ãĥ³ãĥ ij", + "μÎŃν Ïīν", + "Ġgenel likle", + "Ġدر ÙħاÙĨ", + "Ù ª", + "Ġak ıl", + "ĠÐľ Ñĭ", + "Ġet miÅŁ", + "Ġetm iÅŁ", + "Å¡ la", + "Ġвозмож ноÑģÑĤÑĮ", + "Ġвозможно ÑģÑĤÑĮ", + "Ġgün cel", + "Ġná ro", + "å½¢ å¼ı", + "Ġα ÏĢοÏĦε", + "ĠαÏĢο ÏĦε", + "ĠмÑĸÑģ ÑĨÑı", + "Ġ رض", + "Ġر ض", + "ä¸į çŁ¥éģĵ", + "ä¸įçŁ¥ éģĵ", + "r ava", + "ra va", + "rav a", + "ĠÎļ ά", + "ิà¸Ļ à¸Ĺร", + "ิà¸Ļà¸Ĺ ร", + "Ġли ÑģÑĤÑĮ", + "ĠлиÑģÑĤ ÑĮ", + "èĨ ľ", + "ãģ«ãģª ãĤĬ", + "Ġ æĿ¾", + "ĠæĿ ¾", + "å® ı", + "Ġм иÑģ", + "Ġми Ñģ", + "át nÃŃ", + "Ġyıl lık", + "ĠMerk ezi", + "ĠMerkez i", + "Ġiç eri", + "Ġiçer i", + "ÅĻ ÃŃž", + "ÅĻÃŃ Å¾", + "Ġp ÅĻe", + "ĠpÅĻ e", + "Ïĩ Ïģι", + "Ġ åįĥ", + "Ġåį ĥ", + "Ġs rp", + "Ġsr p", + "à¹Ĥ à¸Ĺร", + "à¹Ĥà¸Ĺ ร", + "ĠK rál", + "ĠKr ál", + ". Σ", + "á val", + "áv al", + "l éd", + "lé d", + "Ġ λα", + "Ġλ α", + "ี ยวà¸ģ", + "ีย วà¸ģ", + "ียว à¸ģ", + "ãģı ãģª", + "ĠvÅ¡ ichni", + "ĠпÑĢед оÑģÑĤав", + "ì ¿", + "Ġ 구ê¸ĢìĥģìľĦ", + "Ġ구 ê¸ĢìĥģìľĦ", + "Ġ구ê¸Ģ ìĥģìľĦ", + "Ġà¤īप लब", + "в оз", + "во з", + "ĠëħĦ ëıĦë³Ħ", + "､ _", + "à¸ļ รร", + "à¸ļร ร", + "ĠÑģв ÑĸÑĤÑĥ", + "ĠÑģвÑĸÑĤ Ñĥ", + "ĠÑĢÑĥб лей", + "len me", + "lÃŃ Äį", + "ÏĦ ει", + "ÏĦε ι", + "Ġ åı¤", + "Ġåı ¤", + "ĠObr ázky", + "Ġìĺģ íĸ¥", + "ĠгÑĢаж дан", + "í Ĥ¹", + "íĤ ¹", + "Ġsahip tir", + "Ġп оÑĩаÑĤкÑĥ", + "ĠпоÑĩ аÑĤкÑĥ", + "ĠØ£ÙĬ ض", + "ĠÑĤоÑĢ гов", + "Ġgel ecek", + "Ġgele cek", + "Ġ 문íĻĶ", + "Ġ문 íĻĶ", + "ik leri", + "ikler i", + "ĠнеобÑħÑĸд но", + "Ġ äºij", + "o vol", + "ov ol", + "ovo l", + "Ġद ल", + "ĠìķĬ ê³ł", + "Ġм г", + "Ġz jist", + "an lı", + "ั à¸ĩà¸Ļ", + "ัà¸ĩ à¸Ļ", + "ÑĢа Ñħов", + "ÑĢаÑħ ов", + "ι νη", + "ιν η", + "Ġп лоÑĤ", + "Ġпл оÑĤ", + "Ġпло ÑĤ", + "Ġn itel", + "Ġni tel", + "Ġnit el", + "ìĬ¤ íģ¬", + "ĠSon ra", + "ĠÑģ боÑĢ", + "ĠÑģб оÑĢ", + "Ġ ÏĥοÏħ", + "ĠÏĥ οÏħ", + "Ġol mam", + "Ġolm am", + "Ġolma m", + "Ġan aliz", + "Ġanal iz", + "à¹Į ว", + "Ġm ỹ", + "Ġmá» ¹", + "ce ae", + "cea e", + "Ġ ден", + "Ġд ен", + "Ġде н", + "веÑĢ жд", + "веÑĢж д", + "Ạ¢", + "ãģĵ ãģ¨ãĤĤ", + "ãģĵãģ¨ ãĤĤ", + "ìĤ¬ íķŃ", + "è¨Ģ ãģ£ãģŁ", + "Ġ ì¹´ì§Ģëħ¸", + "Ġì¹´ ì§Ģëħ¸", + "ÑĢ иÑĤи", + "ÑĢи ÑĤи", + "ÑĢиÑĤ и", + "Ġch ce", + "Ġçev ir", + "ÛĮ ÛĮÙĨ", + "ä¼ļ è®®", + "ัม à¸ŀ", + "Ġ åĦ", + "Ġå Ħ", + "ĠÙ¾ در", + "å¼ı ä¼ļ社", + "Ġ ÑĨен", + "ĠÑĨ ен", + "ĠÑĨе н", + "ิ à¸ĸ", + "Ġji nak", + "Ġjin ak", + "Ġб лÑİ", + "Ġбл Ñİ", + "и ÑĨин", + "иÑĨ ин", + "ÙĴ Ùĩ", + "Ú© ÙĪر", + "Ú©ÙĪ ر", + "Ġ ìķħ", + "Ġìķ ħ", + "e ksiyon", + "ek siyon", + "eks iyon", + "ĠÑģ веÑĢ", + "ĠÑģв еÑĢ", + "ĠобÑĢаз ованиÑı", + "Ġ ãĥĻ", + "Ġãĥ Ļ", + "æľī 人", + "Ġbilg ileri", + "Ġbilgi leri", + "Ġbilgiler i", + "Ġh ầu", + "еÑĢ Ñĸг", + "еÑĢÑĸ г", + "Ġva Å¡e", + "Ġn edir", + "Ġne dir", + "Ġned ir", + "ä¸į å¾Ĺ", + "ĠbaÅŁar ılı", + "ĠbaÅŁarı lı", + "Ġkay bet", + "Ġkayb et", + "å© ·", + "ĠÐĿ ав", + "ĠÐĿа в", + "Ġê´Ģ íķľ", + "Ñģ ÑĤÑİ", + "ÑģÑĤ Ñİ", + "å®ŀ éĻħ", + "k lady", + "kl ady", + "klad y", + "kla dy", + "д аÑĤÑĮ", + "да ÑĤÑĮ", + "даÑĤ ÑĮ", + "r aç", + "ra ç", + "Ġkuv vet", + "à¸ģาร à¸Ĺ", + "å ļ", + "Ġ ÑĢеп", + "ĠÑĢ еп", + "ĠÑĢе п", + "Ġ à¸Ŀ", + "ĠภĿ", + "ĠDi ÄŁer", + "íĶĦ íĬ¸", + "Ġnej vÄĽtÅ¡ÃŃ", + "Ġìłģ ìļ©", + "Ġonemoc nÄĽnÃŃ", + "а ка", + "ак а", + "Ðł аз", + "ĠÙģ Ø¥ÙĨ", + "ãĤµ ãĤ¤ãĤº", + "ãĤµãĤ¤ ãĤº", + "Ġv lád", + "Ġvl ád", + "Ġvlá d", + "Ġr ady", + "Ġrad y", + "Ġra dy", + "ãĢģ ãģĵãĤĮ", + "ÑģÑĤв ие", + "lı ÄŁa", + "lıģ a", + "å ŃĶ", + "åŃ Ķ", + "Ġ áo", + "Ġá o", + "à¸Ń าà¸ģาศ", + "Ġ à¤ıम", + "Ġà¤ı म", + "δ αÏĤ", + "δα ÏĤ", + "Ġа пÑĢ", + "Ġап ÑĢ", + "æİ Ľ", + "Ġ ç«ĭ", + "Ġç« ĭ", + "âĸı âĸı", + "ĠС м", + "Ġne má", + "Ġnem á", + "Ġ è¢", + "Ġè ¢", + "νο μα", + "νομ α", + "ĠÙģ رÙĪد", + "ĠÙģر ÙĪد", + "ĠÙģرÙĪ د", + "Ġül ke", + "Ġülk e", + "Ġ æĺŁ", + "Ġæĺ Ł", + "ั à¸Ļà¸ģ", + "ัà¸Ļ à¸ģ", + "ãģķãĤĵ ãģ®", + "eÅŁ il", + "ÄŁ iz", + "ÄŁi z", + "ĠÐij оÑĢ", + "Ġt ầm", + "ει ÏĦοÏħÏģγ", + "Ġ γÏģα", + "Ġγ Ïģα", + "à¥įष à¤ķ", + "Ġv ẻ", + "Ġkend isine", + "Ġkendisi ne", + "ĠìķĮ ê³ł", + "Ġêµ Ńìłľ", + "ĠêµŃ ìłľ", + "ĠnÄĽk do", + "Ġ ÛĮÙĩ", + "ĠÛĮ Ùĩ", + "Ġکار بر", + "ãĥĻ ãĥ«", + "ï» ´", + "Ġt uyên", + "Ġtuy ên", + "Ġç at", + "Ġça t", + "âĢIJ âĢIJ", + " ı", + "Ġ ìĤ¬ìĹħ", + "ĠìĤ¬ ìĹħ", + "é ĨĴ", + "éĨ Ĵ", + "æıIJ é«ĺ", + "æ· ¡", + "Ġ ÄŁ", + "ĠÄ Ł", + "èĸ ¦", + "ãĢĭ ï¼Ī", + "æ¡ ĥ", + "ìĹ Ħ", + "Ġ æŀĹ", + "Ġæŀ Ĺ", + "Ä Ĥ", + "ĠÄĮ ech", + "α ιο", + "αι ο", + "ĠØ· رÙĬÙĤ", + "Ġطر ÙĬÙĤ", + "Ġзав еÑĢÑĪ", + "ĠзавеÑĢ ÑĪ", + "تÙĪ بر", + "ĠØŃ Ø¬", + "ĠÎŃÏĩ οÏħν", + "¿ ÃĤ", + "Ġd ÄĽtÃŃ", + "ĠdÄĽ tÃŃ", + "ĠdÄĽt ÃŃ", + "Ġiç ine", + "Ġiçin e", + "Ġiçi ne", + "ĠCh úa", + "ан нÑĭй", + "аннÑĭ й", + "ĠÙĪÛĮ Úĺ", + "Ġna stav", + "Ġnast av", + "ıs ına", + "ısı na", + "ĠÑĹ Ð¼", + "п он", + "по н", + "е нÑı", + "ен Ñı", + "ĠÙĪ ظ", + "Ú¯ ÙĦ", + "หล วà¸ĩ", + "Ġza stav", + "Ġzast av", + "а кон", + "ак он", + "³³³³³³³³³³³³³³³³ ³³³³³³³³³³³³³³³³", + "ĠK ır", + "ĠKı r", + "çµ ¶", + "ĠоÑĢганÑĸ заÑĨÑĸÑĹ", + "ĠоÑĢганÑĸз аÑĨÑĸÑĹ", + "ĠоÑĢганÑĸза ÑĨÑĸÑĹ", + "ãģŁ ãĤĬ", + "Ø° ÙĬ", + "Ġर à¤ķ", + "amp iyon", + "Ġ æ¸ħ", + "Ġæ¸ ħ", + "çľ¼ çĿĽ", + "Ġìķ ĬìĿĢ", + "ĠìķĬ ìĿĢ", + "é¹ ¿", + "Ġ å¿ĥ", + "Ġå¿ ĥ", + "ĠпÑĢек ÑĢаÑģ", + "ĠÑģ егоднÑı", + "Ġ सल", + "Ġस ल", + "ĠÏħ ÏĢÏĮ", + "ĠÏħÏĢ ÏĮ", + "ĠÐķ го", + "ĠÐĽ и", + "ãĤ¨ ãĥ«", + "Ġл ÑİÑĤ", + "ĠлÑİ ÑĤ", + "é¥ °", + "Ġvz dál", + "¯ ÃĤ", + "Ġна Ñıв", + "Ġتش Ú©ÛĮÙĦ", + "Ġس ÙĪÛĮ", + "ĠسÙĪ ÛĮ", + "Ġt ái", + "Ġtá i", + "Ġk apı", + "Ġkap ı", + "ĠsvÄĽt ÄĽ", + "ĠsvÄĽ tÄĽ", + "δ ÏĮν", + "δÏĮ ν", + "æ¼ ¢", + "ì į¨", + "ĠbaÅŁv ur", + "ÑĢ ина", + "ÑĢи на", + "ÑĢин а", + "Ġk elim", + "Ġke lim", + "Ġkel im", + "аÑĤ ок", + "аÑĤо к", + "Ġκά θε", + "ĠYük sek", + "à¹ĩà¸Ļ à¸ľ", + "éł Ĥ", + "åIJĮ æĻĤ", + "ÅŁ tır", + "ÅŁt ır", + "ว à¸ĩศ", + "วà¸ĩ ศ", + "o ty", + "ot y", + "Ġ ارد", + "Ġا رد", + "Ġار د", + "ĠìŀIJìĭł ìĿĺ", + "ĠÑıн ва", + "üyor du", + "æĿ ¨", + "ĠâĢĵ Ċ", + "ï¼Į å®ĥ", + "е йн", + "ей н", + "ĠпеÑĢ еÑĤ", + "ĠпеÑĢе ÑĤ", + "ĠdeÄŁiÅŁik lik", + "ĠогÑĢа ниÑĩ", + "ìĦľ ìļ¸", + "Ġgel iyor", + "ĠÙ¾ Ø°ÛĮر", + "åĵ ²", + "ey in", + "eyi n", + "Ġëı Ī", + "Ġun iverz", + "Ġh ned", + "Ġhn ed", + "Ġt áºŃn", + "vo ÅĻÃŃ", + "voÅĻ ÃŃ", + "Ġn iên", + "Ġni ên", + "dÄĽ podob", + "ìĤ¬ íļĮ", + "ãģĮ ãģĤãĤĬ", + "ĠÑģ ÑĸÑĩ", + "' '\"", + "'' \"", + "Ġtop lantı", + "ĠÑģ ÑĩеÑĤ", + "ĠÑģÑĩ еÑĤ", + "åĩĨ å¤ĩ", + "ан ÑĸÑı", + "анÑĸ Ñı", + "Ġ zel", + "Ġz el", + "Ġze l", + "v ala", + "val a", + "va la", + "Ġа пп", + "Ġап п", + "ĠاÙĦÙħ ÙĦÙĥ", + "ĠاÙĦÙħÙĦ Ùĥ", + "Ġho ÅŁ", + "ĠÐĵ ен", + "ĠÐĵе н", + "ÑĤ аб", + "ÑĤа б", + "ĠÄĮesk o", + "ĠÄĮes ko", + "Ġмай же", + "ĠmÄĽ sto", + "ĠmÄĽst o", + "yo nel", + "yon el", + "ê±° 리", + "Ġìĺ¨ ëĿ¼ìĿ¸", + "ç´ ¯", + "Ġde rec", + "Ġder ec", + "Ġdere c", + "Ġок ÑĢÑĥж", + "Ġy abancı", + "Ġ íĦ°", + "ĠíĦ °", + "Ġ èµĦ", + "Ġèµ Ħ", + "ÎĻÎļ ÎĹ", + "Ġп Ñĭ", + "Ġv ÄĽn", + "ĠvÄĽ n", + "и нки", + "ин ки", + "ụ p", + "æľº 械", + "ĠìķĮ 볤", + "ëħ ķ", + "Ġ λÏĮγ", + "Ġλ ÏĮγ", + "e yn", + "ey n", + "Ġ ëIJĺìĹĪëĭ¤", + "ĠëIJĺ ìĹĪëĭ¤", + "ĠëIJĺìĹĪ ëĭ¤", + "æ± ¡", + "Ġve dle", + "Ġved le", + "ĠÙĥ تب", + "ë§ ¨", + "ĠÙħÙĤ اÙĪ", + "å¹´ ãģ«", + "ाà¤ĩ à¤ķ", + "ĠÑģÑĤ оÑģ", + "ĠÑģÑĤо Ñģ", + "ĠÏĥ ÏĦοÏħÏĤ", + "м еÑĤÑĮ", + "ме ÑĤÑĮ", + "меÑĤ ÑĮ", + "Ġes as", + "Ġesa s", + "ëIJĺ ê³ł", + "ĠkvÄĽt na", + "Ġ éľ", + "Ġé ľ", + "d ük", + "dü k", + "åŁ ·", + "è ªĮ", + "èª Į", + "Ġm luv", + "Ġml uv", + "ĠпÑĢи нÑı", + "ĠпÑĢин Ñı", + "Ġpo té", + "Ġpot é", + "ĠÚ© ÙĨÙħ", + "ĠÚ©ÙĨ Ùħ", + "ĠпÑĢед лож", + "ĠÐľÐ¾Ñģк ва", + "ï¼Į å¦Ĥ", + "Ġsv ém", + "Ġsvé m", + "Ġا ÙħÙĨ", + "ĠاÙħ ÙĨ", + "ส าย", + "ĠÑĥм енÑĮ", + "Ġ ãģĵãģ®", + "åī Ĥ", + "ĠÑģ еÑĢÑĮ", + "ĠÑģеÑĢ ÑĮ", + "Ġm á»ĩ", + "Ġmá» ĩ", + "Ġ ä¹Ŀ", + "Ġä¹ Ŀ", + "Ġза кÑĸн", + "Ġзак Ñĸн", + "Ġв елиÑĩ", + "Ġвели Ñĩ", + "Ġвел иÑĩ", + "Ġве лиÑĩ", + "Ġкон ÑĤÑĢа", + "ĠконÑĤ ÑĢа", + "ĠконÑĤÑĢ а", + "ĠS osyal", + "Ġy ukarı", + "Ġد ÙĪب", + "ĠدÙĪ ب", + "ä¾ §", + "Ġза мен", + "Ġзам ен", + "ï» ®", + "Ġso bÄĽ", + "Ġsob ÄĽ", + "ĠТак же", + "Ð İ", + "ε δ", + "Ùħ ارÛĮ", + "Ùħا رÛĮ", + "Ùħار ÛĮ", + "ξ ι", + "ì¹ Ń", + "Ġп лаÑģÑĤи", + "Ġпл аÑģÑĤи", + "Ġпла ÑģÑĤи", + "Ïĥ οÏħν", + "Ïĥο Ïħν", + "ÏĥοÏħ ν", + "èľĺèĽĽ è¯į", + "ÙĪ ÛĮزÛĮ", + "ÙĪÛĮ زÛĮ", + "Ġnap ÅĻ", + "ĠÑĤип а", + "ĠÑĤи па", + "à¥Ĥ à¤Ľ", + "ĠÅŁ ah", + "л ÑıÑĤи", + "лÑı ÑĤи", + "ب ÛĮر", + "بÛĮ ر", + "ระ ยะ", + "ĠболÑĮ ÑĪин", + "ĠболÑĮÑĪ ин", + "ÏĦη ÏĦα", + "Ġíıī ê°Ģ", + "Ġpro jev", + "Ġproj ev", + "Ġproje v", + "ò i", + "Ġк нÑı", + "ÏĨ εÏģ", + "е ÑĢÑĥ", + "еÑĢ Ñĥ", + "Ñį н", + "ĠعÙħ ÙĦÛĮ", + "ĠعÙħÙĦ ÛĮ", + "à¤ł न", + "ãĥ³ ãĤ¯", + "ĠìķĦ ëŀĺ", + "Î Ī", + "Ġب است", + "Ġبا ست", + "Ġ تÙĥ", + "Ġت Ùĥ", + "a ÄįnÃŃ", + "aÄį nÃŃ", + "ĠлÑĸ кÑĥваннÑı", + "ĠлÑĸк ÑĥваннÑı", + "à¸Ħ à¹Ĥà¸Ļ", + "Ġ èĥ½", + "Ġè ĥ½", + "Ġèĥ ½", + "θ λη", + "len miÅŁ", + "Ġl á»Ļ", + "Ġlá» Ļ", + "Ġsi lah", + "Ġsil ah", + "ĠA ustr", + "ĠAust r", + "ĠAus tr", + "ĠAu str", + "ØŃ ÙĤ", + ".*** .***", + "ì ©", + "Ġg Ãł", + "Ġباز بÛĮÙĨÛĮ", + "ĠÄij Ãłn", + "ÃŃ ky", + "ÃŃk y", + "ĠÎķ ν", + "ض Ùħ", + "å§ ĵ", + "Ġ ÙĨÙĪÛĮس", + "ĠÙĨ ÙĪÛĮس", + "ĠÙĨÙĪ ÛĮس", + "Ġskup iny", + "Ġس ÛĮد", + "ĠسÛĮ د", + "Ġal dıģı", + "Ġald ıģı", + "Ġaldı ģı", + "m eli", + "me li", + "mel i", + "в иж", + "ви ж", + "ì¹ĺ ëĬĶ", + "ов аÑħ", + "ова Ñħ", + "Ġ æ©", + "Ġæ ©", + "Ø´ÙĨ اسÛĮ", + "Ø´ÙĨاس ÛĮ", + "Ġn imi", + "Ġni mi", + "Ġnim i", + "ĠÐĵ ÑĢи", + "íĹ Į", + "Ġк в", + "éŁ ĵ", + "Ġ íĽĦ기", + "ĠíĽĦ 기", + "Ġ stÅĻÃŃ", + "Ġst ÅĻÃŃ", + "ĠstÅĻ ÃŃ", + "ĠкÑĸлÑĮ кÑĸÑģÑĤÑĮ", + "ĠBakan lıģı", + "ĠменÑĮ ÑĪе", + "ا ÙĪÛĮ", + "اÙĪ ÛĮ", + "Ġار ÙĪÙ¾", + "Ġ èī²", + "Ġèī ²", + "ĠÚ©ÙĪÚĨ Ú©", + "ĠA ynı", + "Ġ äºĨ", + "Ġس Ùģر", + "ĠسÙģ ر", + "ĠÑĤе аÑĤ", + "Ġ vÄĽd", + "ĠvÄĽ d", + "а ÑĢов", + "аÑĢ ов", + "Ġоб меж", + "ĠìķĬ ìķĺ", + "追 åĬł", + "éł Ī", + "dÄĽ lenÃŃ", + "dÄĽl enÃŃ", + "dÄĽlen ÃŃ", + "Ġk ims", + "Ġki ms", + "Ġkim s", + "Ġ èı²", + "Ġèı ²", + "Ġг ÑĢÑĥн", + "ĠгÑĢÑĥ н", + "ĠгÑĢ Ñĥн", + "ĠØ¢ ÙĦÙħاÙĨ", + "ĠØ¢ÙĦ ÙħاÙĨ", + "Ġав г", + "ĠÑī оÑģÑĮ", + "ĠÑīо ÑģÑĮ", + "Ġ å¾·", + "Ġå¾ ·", + "ĠÐĿа ÑĨÑĸоналÑĮ", + "æĪIJ ç«ĭ", + "ูà¸Ļ ย", + "ãĥ¼ ãĥ«ãĥī", + "ãĥ¼ãĥ« ãĥī", + "éĽ ²", + "ĠT á»ķ", + "cı lık", + "ĠAlma nya", + "ĠAlman ya", + "Ġov Å¡em", + " ĭ", + "ĠÏĩÏģη ÏĥιμοÏĢοι", + "Ġörg üt", + "िस स", + "èĹ Ŀ", + "ĠGi ải", + "Ġsv ob", + "Ġsvo b", + "Ġrůzn ých", + "Ġrůz ných", + "Ġsmlou vy", + "ÑĢ еÑģÑģ", + "ÑĢеÑģ Ñģ", + "ี à¹Ģà¸Ķ", + "ĠاÙħ رÙĪز", + "ĠاÙħر ÙĪز", + "ãĤ ħ", + "åĿ ¦", + "à¹ī à¸Ħ", + "Ġ каж", + "Ġк аж", + "Ġка ж", + "å¼ Ĺ", + "Ñĩ ноÑĹ", + "Ñĩно ÑĹ", + "åľ Ī", + "ĠØ¢ ÙĩÙĨÚ¯", + "ëª °", + "Ġ æº", + "Ġæ º", + "Ġ èĦ", + "Ġè Ħ", + "ä¸Ģ æŃ¥", + "оÑĩ ка", + "Ġpro stor", + "Ġpros tor", + "Ġprost or", + "Ġng ắn", + "Ġ ç·", + "Ġç ·", + "н аÑĢ", + "на ÑĢ", + "Ġà¤ľ व", + "ĠнаÑĩ алÑĮ", + "Ġне дел", + "Ġнед ел", + "ĠÑģиÑģÑĤем Ñĥ", + "ج ÙĬ", + "اد ات", + "ادا ت", + "Ġ æ¢", + "Ġæ ¢", + "ĠجاÙħ عة", + "ĠجاÙħع Ø©", + "Ġ ä»İ", + "Ġà¤ħ फ", + "èĸ Ħ", + "Ġب اÙĤ", + "Ġبا ÙĤ", + "ب ÙĬع", + "بÙĬ ع", + "ãģķ ãĤĮãģ¦", + "ãģķãĤĮ ãģ¦", + "ĠÃĩ alÄ±ÅŁ", + "Ø®ÙĪ است", + "ãĥĥ ãĤ·ãĥ¥", + "ĠØŃ Ø³ÛĮÙĨ", + "ĠØŃس ÛĮÙĨ", + "Ġоб наÑĢÑĥж", + "в Ñĸдом", + "вÑĸ дом", + "вÑĸд ом", + "Ġh ôm", + "л анд", + "ла нд", + "лан д", + "Ġव à¤ľà¤¹", + "س ÙĬÙĨ", + "سÙĬ ÙĨ", + "æł ı", + "Ġna vÃŃc", + "Ġnav ÃŃc", + "ãĤµ ãĤ¤ãĥĪ", + "ãĤµãĤ¤ ãĥĪ", + "ĠÑı комÑĥ", + "ĠÑıк омÑĥ", + "Ġí Ľ", + "ĠY ani", + "ĠYan i", + "ĠYa ni", + "ãĤĵ ãģ§ãģĻ", + "ãĤĵãģ§ ãģĻ", + "Ġг ÑĢÑĥп", + "ĠгÑĢÑĥ п", + "ĠгÑĢ Ñĥп", + "Äį ný", + "ÑĨ ик", + "ÑĨи к", + "ÙĪ ÙĬر", + "ÙĪÙĬ ر", + "Ġ Xã", + "ĠX ã", + "Ġf yz", + "Ġfy z", + "Ġ ï½ī", + "Ġï½ ī", + "âĢĮ ترÛĮÙĨ", + "âĢĮتر ÛĮÙĨ", + "à¤Ł à¤ķ", + "ÑĦоÑĢм и", + "ÑĦоÑĢ ми", + "ĠO yun", + "ĠOy un", + "åł´ æīĢ", + "ØŃ Ø«", + "ĠìķĮ ìķĦ", + "ÑĢав илÑĮ", + "ÑĢави лÑĮ", + "ï¼Į âĢĿ", + "b oru", + "bo ru", + "bor u", + "ĠK ullan", + "ĠKul lan", + "ĠKay nak", + "Ġê° ĸ", + "ç´ Ķ", + "ï¼Į æ¯ı", + "ÎĹ Î¡", + "Ġp ůl", + "Ġpů l", + "Ġг оÑģÑĤ", + "ر ÙĪÙħ", + "رÙĪ Ùħ", + "ï¼Į åį³", + "Û² Û³", + "ĠÙĨØ® ست", + "ĠÚ© سب", + "Ġ à¹Ģà¸ļ", + "Ġà¹Ģ à¸ļ", + "Ġà¹Ģภļ", + "Ġy azar", + "Ġya zar", + "Ġyaz ar", + "j ekt", + "je kt", + "à¹Ĥล ย", + "Ġдоб ÑĢе", + "Ġپزش Ú©ÛĮ", + "ĠتÙĩ ÛĮÙĩ", + "ç¾İ åľĭ", + "но ÑģÑıÑĤ", + "ноÑģ ÑıÑĤ", + "ноÑģÑı ÑĤ", + "ëłĪ ìĬ¤", + "åĹ ¯", + "Ġr Ãłng", + "ĠÎķ ξ", + "а ÑĤаÑĢ", + "аÑĤ аÑĢ", + "аÑĤа ÑĢ", + "k ova", + "ko va", + "kov a", + "ĠÅŁey ler", + "Ø® اص", + "ĠìķĪ ìłĦ", + "Ñī ей", + "Ñīе й", + "Ġë° Ŀ", + "âĢĮتÙĪاÙĨ د", + "ãģĪ ãģ°", + "Ġv ữ", + "Ġvá» ¯", + "ĠÑģ ама", + "ĠÑģам а", + "ĠобоÑĢ Ñĥд", + "Ġобо ÑĢÑĥд", + "âĢĮ باشد", + "à¹Į à¸Ń", + "Ġdet ay", + "æĤ ²", + " Ī", + "ãĤ¦ ãĤ£", + "ĠпÑĢав ила", + "ĠпÑĢави ла", + "ĠпÑĢавил а", + "kr ét", + "à¹Į ร", + "åĮ ¹", + "Ġ åħį", + "Ġå ħį", + "Ġåħ į", + "ĠÑģилÑĮ но", + "ĠиÑģ ÑĤоÑĩ", + "ĠиÑģÑĤ оÑĩ", + "ĠsaÄŁ lar", + "Ġ æѦ", + "ĠæŃ ¦", + "íĸ ĪìĬµëĭĪëĭ¤", + "íĸĪ ìĬµëĭĪëĭ¤", + "Kh ông", + "à¹Īาà¸ĩ à¹Ĩ", + "Û° Û°Û°", + "Û°Û° Û°", + "Ġ رÙĤ", + "Ġر ÙĤ", + "âĢĻ ÑıÑĤ", + "âĢĻÑı ÑĤ", + "åĽ ²", + "à¹ģ à¸Ķà¸ĩ", + "Ġžád né", + "c ouz", + "co uz", + "cou z", + "à ĭ", + "ĠпÑĸд гоÑĤов", + "Ġ ëĮĢíķĻ", + "ĠëĮĢ íķĻ", + "Ġdüny anın", + "èĢģ å¸Ī", + "èģĮ ä¸ļ", + "Ġy eri", + "Ġye ri", + "Ġyer i", + "à¥ĭ à¤ķर", + "à¥ĭà¤ķ र", + "ĠبÙĩ تر", + "ëĭĪ ìķĦ", + "ìĿĮ ìĿĦ", + "Ġ æĮĩ", + "ĠæĮ ĩ", + "ãĢį ï¼Ī", + "ĠÑģооÑĤвеÑĤÑģÑĤв ии", + "æĬ ĵ", + "à¹Ĥ à¸Ĺ", + "Ġtr á»ĵng", + "ĠпÑĢа ÑĨÑĸ", + "Ġ ëĨĵ", + "ĠëĨ ĵ", + "à¤ĩ न", + "Ġìłķ ë§IJ", + "ãĢ ķ", + "Ġc áºŃn", + "åĸ Ŀ", + "Ġê³Ħ ìĨį", + "Ġ ä¸İ", + "Ġä¸ İ", + "å¥ ı", + "Ġع اÙĦÙħ", + "Ġvys vÄĽt", + "Ġдо ÑĢог", + "ĠдоÑĢ ог", + "Ġн еÑĢв", + "ĠнеÑĢ в", + "Ġб еÑĤ", + "Ġп ÑĢиÑĤ", + "ĠпÑĢ иÑĤ", + "ĠпÑĢи ÑĤ", + "ов Ñĭй", + "å· ¡", + "Ùģ اع", + "Ðļ Ðĺ", + "à¸ķ รวà¸Ī", + "à¸ķรว à¸Ī", + "ĠÐľ ай", + "ĠÐľÐ° й", + "ëıĦ ë¡ľ", + "Ġz lat", + "ĠsaÄŁ lam", + "Ïģ αν", + "Ïģα ν", + "à¸Ĭ ร", + "å¹´ ãģ®", + "à¸Ħ รà¸Ńà¸ĩ", + "à¸Ħร à¸Ńà¸ĩ", + " ħ", + "Ġho á", + "Ġдов олÑĮно", + "Ġol maz", + "Ġolm az", + "Ġolma z", + "ĠpodmÃŃn ky", + "ĠÑħозÑı й", + "æĻ ´", + "ÑĢ ова", + "ÑĢов а", + "ÑĢо ва", + "Ġl ược", + "ान न", + "Ġкап иÑĤ", + "ĠÚĺ اÙĨ", + "æľī äºĽ", + "ĠповеÑĢÑħ ноÑģÑĤи", + "ĠÑĨ Ñĸн", + "ĠÑĨÑĸ н", + "ü yle", + "üy le", + "Ġj azy", + "Ġja zy", + "Ġjaz y", + "ĠPh ú", + "Ġ सन", + "Ġस न", + "åĩº åĶ®", + "Âł д", + "Ġ ãĤ¯", + "ĠãĤ ¯", + "çĶ± äºİ", + "à¥į पत", + "à¥įप त", + "ĠاÙĦØ® اÙħ", + "Ġاص ÙĦاØŃ", + "ĠاصÙĦ اØŃ", + "Ġ تÛĮ", + "Ġت ÛĮ", + "Ġt ato", + "Ġta to", + "Ġtat o", + "å¹ ¹", + "æ³ ½", + "à¸Ńà¸ģ à¸Īาà¸ģ", + "Ñĥ лÑİ", + "Ñĥл Ñİ", + "Ġв Ñģп", + "ĠвÑģ п", + "m ekte", + "me kte", + "mek te", + "à¥Ģ फ", + "ĠÚĺ ÙĪئ", + "Ġl á»ĩnh", + "Ġlá»ĩ nh", + "âĢĮ کرد", + "âĢĮÚ© رد", + "íı¬ ì¸ł", + "an ki", + "ank i", + "Ġëĵ±ë¡Ŀ ëĮĢíĸī", + "Ġ ãĤĿ", + "ĠãĤ Ŀ", + "Ġار زش", + "Ġارز Ø´", + "Ġth ú", + "Ġ ấn", + "è¡Į 为", + "ĠÑģ нова", + "ê ¾¸", + "Ġsou hlas", + "Ġв озв", + "Ġвоз в", + "Ġво зв", + "ÏģÎŃ ÏĢει", + "ĠнÑĸ Ñĩого", + "ĠнÑĸÑĩ ого", + "н ож", + "но ж", + "ÑĤ ик", + "ÑĤи к", + "ãģ© ãģĵ", + "ĠоÑģнов е", + "ãĤ ¥", + "à¸Ľà¸£à¸° à¸Īำ", + "Ġ à¸Ĺà¸Ńà¸ĩ", + "Ġà¸Ĺ à¸Ńà¸ĩ", + "Ġek sik", + "Ġeks ik", + "ĠÙĦ Ø¥", + "ãģĭ ãģ®", + "Ġ ãģª", + "- प", + "Ïģ ει", + "Ïģε ι", + "ĠìłĦ 문", + "า à¸ģล", + "าà¸ģ ล", + "β ε", + "íĬ¹ ë³Ħ", + "íķĺ ë©´ìĦľ", + "íķĺë©´ ìĦľ", + "à¸Ħà¹Ĥà¸Ļ à¹Ĥลย", + "Ġ 好", + "Ġå¥ ½", + "Ġy ayım", + "Ġyay ım", + "ë§Į ëĤ¨", + "ĠкиÑģ лоÑĤ", + "ĠкиÑģл оÑĤ", + "ĠÑį неÑĢг", + "çĸ ¾", + "Ġد Ø´", + "Ġsor uml", + "Ġsoru ml", + "Ġза клад", + "Ġзак лад", + "à¸Ĭ à¸Ńà¸ļ", + "ĠÙģرÙĩÙĨÚ¯ ÛĮ", + "Ġà¤ı ल", + "Ġë¹Ħ êµIJ", + "l erce", + "ler ce", + "Ġ Ø·ÙĦب", + "ĠØ· ÙĦب", + "ĠØ·ÙĦ ب", + "ãģ« ãģĹãģ¦", + "ĠÑı коÑĹ", + "ĠÑıк оÑĹ", + "ĠاÙĦب تÙĩ", + "ĠÐľ аÑĤ", + "ĠÐľÐ° ÑĤ", + "åį ĵ", + "Ġ åħ¬åı¸", + "Ġåħ¬ åı¸", + "Ġsöy ley", + "Ġsöyl ey", + "ĠìĥĪ ë¡ľìļ´", + "ĠÑĦ аÑĢ", + "Ġalt ına", + "Ġaltın a", + "Ġaltı na", + "Ġsta vu", + "Ġstav u", + "âĢĻ ı", + "al izace", + "aliz ace", + "Ġви ÑģÑĤÑĥп", + "æķĻ å¸Ī", + "à¥Ģ à¤ıस", + "à¥Ģà¤ı स", + "o dÄĽ", + "od ÄĽ", + "ĠÑĨ Ñĸл", + "ĠÑĨÑĸ л", + "ĠëĮĢ ìĥģ", + "ĠкоÑĤоÑĢ ом", + "ĠкоÑĤ оÑĢом", + "Ġظ رÙģ", + "éİ ®", + "اÙģ ÙĬØ©", + "اÙģÙĬ Ø©", + "Ġ ìĹĨìĿ´", + "ĠìĹĨ ìĿ´", + "ĠμÏĮ νο", + "ĠC Æ¡", + "å¯ »", + "ÏĦ ιÏĥ", + "ÏĦι Ïĥ", + "Ġ ãĤĦ", + "ĠãĤ Ħ", + "Ġjed noho", + "Ġjedn oho", + "Ġjedno ho", + "ا ا", + "Ø§Ø §", + "et ler", + "Ġव स", + "ĠÑĢазлиÑĩ нÑĭÑħ", + "Ġج غراÙģ", + "Ġth ừa", + "Ġthá» «a", + "ĠгÑĢомад Ñıн", + "ॠ°", + "ĠاÙĦØ£ Ø®", + "Ġнаг ÑĢÑĥз", + "ç¸ ¾", + "à¥Ĥ ह", + "ĠпÑĢÑıм о", + "â Ĭ", + "ĠاÙĦØ£ÙĪÙĦ Ùī", + "æĸ° èģŀ", + "Ġìĥģ íĻ©", + "it esi", + "ite si", + "ites i", + "ëį° ìĿ´íĬ¸", + "æŃ ·", + "ï¼ĮèĢĮ ä¸Ķ", + "ãģ¯ ãģļ", + "产 çĶŁ", + "æ°Ĺ ãģĮ", + "y slu", + "ys lu", + "ysl u", + "ìĸ´ ëĤĺ", + "ا Ú©Ùħ", + "اک Ùħ", + "âĢ ĥ", + ") ìĿĢ", + "Ġجست ارÙĩاÛĮ", + "ÙĪ Ø«", + "ãħ İ", + "Ġkav ram", + "v ál", + "vá l", + "æľ Ń", + "æĤ ł", + "ìħ Ģ", + "h rad", + "hr ad", + "hra d", + "Ġت ÙĥÙĪÙĨ", + "ĠتÙĥ ÙĪÙĨ", + "ĠH òa", + "å¹´ çļĦ", + "Ġç arp", + "Ġça rp", + "Ġy olu", + "Ġyo lu", + "Ġyol u", + "Ġdub na", + "ĠÐĴ елик", + "ĠÐĴели к", + "Ġt ôn", + "Ġtô n", + "æ ķĮ", + "æķ Į", + "Ġc oi", + "Ġco i", + "Ġnak onec", + "ĠÑį ÑĤÑĥ", + "ĠÑįÑĤ Ñĥ", + "íĨµ ëł¹", + "ÑĪ ел", + "ÑĪе л", + "Ġneb yl", + "Ġneby l", + "in ç", + "ب اÙĦØ¥ÙĨجÙĦÙĬزÙĬØ©", + "باÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©", + "ï¼ ¡", + "о нÑĮ", + "он ÑĮ", + "Ġне маÑĶ", + "Ġнем аÑĶ", + "Ġê³ł ê°Ŀ", + "ĠÙĤ طع", + "ĠÙĤØ· ع", + "ĠÑĤеÑĢиÑĤоÑĢ ÑĸÑĹ", + "人 ãģ¯", + "ĠΣ α", + "éĤ£ äºĽ", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ios per", + "í Ĥ¨", + "íĤ ¨", + "r aki", + "ra ki", + "rak i", + "اÛĮ ج", + "Âł C", + "Ġан алÑĸз", + "ãĤı ãĤĬ", + "ĠìķĦëĭ Į", + "ĠاÙĦعÙħÙĦ ÙĬØ©", + "ĠاÙĦعÙħ ÙĦÙĬØ©", + "l ament", + "la ment", + "lam ent", + "é» ¨", + "u jÃŃcÃŃm", + "ujÃŃ cÃŃm", + "ujÃŃcÃŃ m", + "Ġr ẻ", + "ä¸į åĪ°", + "Ġrez erv", + "ĠاÙĦØ° ÙĬÙĨ", + "ĠاÙĦØ°ÙĬ ÙĨ", + "æĭ ¥", + "Ðĺ н", + "Ġतह त", + "r esi", + "re si", + "res i", + "Ġ ãĥ¢", + "Ġãĥ ¢", + "л ев", + "ле в", + "ãĢĢ r", + "Ġ ä»Ĭ", + "Ġö dem", + "Ġpot rav", + "ĠêµIJ ìĪĺ", + "ÑĢ еди", + "ÑĢед и", + "ÑĢе ди", + "ĠÎļ ÎijÎĻ", + "Ġ наÑĩала", + "ĠнаÑĩ ала", + "Ġиз б", + "ĠbÅĻez na", + "Ġle dna", + "Ġled na", + "ÑĢ ÑĥÑİÑĤ", + "ÑĢÑĥ ÑİÑĤ", + "Ġ моÑĤ", + "Ġм оÑĤ", + "Ġмо ÑĤ", + "åıĹ åĪ°", + "ĠÑĢÑĥ кÑĥ", + "ĠÑĢÑĥк Ñĥ", + "Ỽ m", + "ad ele", + "ade le", + "adel e", + "ĠÑĢоз глÑı", + "åħ IJ", + "Ġر ÙĪاÙĨ", + "ĠرÙĪ اÙĨ", + "а ков", + "ак ов", + "Ñĥ ÑĢÑĭ", + "ÑĥÑĢ Ñĭ", + "Ġaz al", + "ĠÑĥ кÑĢа", + "ĠÑĥк ÑĢа", + "пи он", + "ĠÄįlov ÄĽ", + "äºĮäºĮ äºĮäºĮ", + "ا بÙĬ", + "اب ÙĬ", + "Ġas lında", + "ë¹ Ī", + "Ġв ÑĢаÑĩ", + "ĠвÑĢа Ñĩ", + "ë£ ¹", + "Ġген еÑĢа", + "à¸ģาร ส", + "ĠÑģов Ñģем", + "ÙĪ ÙĦا", + "ÙĪÙĦ ا", + "Ġश ब", + "ाà¤ĸ ण", + "ست اÙĨÛĮ", + "ستاÙĨ ÛĮ", + "æĬ ½", + "Ġrů z", + "ĠíĮIJ 매", + "à¸ģาร à¸ķ", + "ائ ÛĮ", + "a sal", + "as al", + "asa l", + "ĠÑĢабоÑĤ Ñĥ", + "ĠÑĢаб оÑĤÑĥ", + "ĠÑĢабо ÑĤÑĥ", + "à¥ĭल न", + "Ġ 马", + "Ġé© ¬", + "Ġl ai", + "Ġla i", + "ó i", + "v ap", + "va p", + "ëħĦ ìĹIJëĬĶ", + "ëħĦìĹIJ ëĬĶ", + "ĠпеÑĢед баÑĩ", + "Ġп леÑĩ", + "Ġпл еÑĩ", + "id det", + "idd et", + "ĠÑĩ оÑĢ", + "i yan", + "iy an", + "iya n", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", + "ĠØŃر ÙģÙĩ", + "ĠØŃرÙģ Ùĩ", + "大 éĺª", + "Ñĩ ого", + "Ġ ки", + "Ġк и", + "ا ÙĪÙĬ", + "اÙĪ ÙĬ", + "ĠbaÅŁ lan", + "Ġmerk ezi", + "Ġmerkez i", + "© ©", + "Ġر است", + "Ġرا ست", + "Ġ ëĬĶ", + "ĠëĬ Ķ", + "ĠÑģ ÑĢав", + "ĠвнÑĥ ÑĤÑĢи", + "ĠвнÑĥÑĤÑĢ и", + "ĠвнÑĥÑĤ ÑĢи", + "ãĢĢ ãĥİ", + "åĿ Ľ", + "Ġв ÑĤ", + ": :/", + ":: /", + "Ġsöz leÅŁ", + "Ġver diÄŁi", + "Ġverdi ÄŁi", + "ิ ยม", + "ิย ม", + "ĠÐŁ ÑĢоÑĤ", + "ĠÐŁÑĢ оÑĤ", + "ĠÐŁÑĢо ÑĤ", + "Ùĥ ار", + "Ġب ÙĨدÛĮ", + "ĠبÙĨ دÛĮ", + "ĠبÙĨد ÛĮ", + "Ùı ÙĪ", + "缴 æĴŃ", + "ĠÙħ ÙĦÙĬ", + "ĠÙħÙĦ ÙĬ", + "Ġnut né", + "ะà¹ģ à¸Ļà¸Ļ", + "ĠM ã", + "Ġ ì´", + "Ġì ´", + "à¹Ī าม", + "à¹Īา ม", + "м оÑģ", + "мо Ñģ", + "Ġпо Ñıви", + "ĠпоÑıв и", + "Ġn ghi", + "Ġng hi", + "Ġngh i", + "Ġ ëIJĺëĬĶ", + "ĠëIJĺ ëĬĶ", + "Ñģ клад", + "Ñģк лад", + "à¤Ĺ ल", + "ĠC á»Ļng", + "çŁ¥ è¯Ĩ", + "Ġ taj", + "Ġt aj", + "Ġta j", + "Ġع بر", + "Ġعب ر", + "éĻĦ è¿ij", + "ü ÄŁ", + "Ġê³µ ê³ł", + "è£ ķ", + "âĢĮ Ø´ÙĨ", + "âĢĮØ´ ÙĨ", + "Ġgerç ekten", + "Ġgerçek ten", + "n un", + "nu n", + "Ùħ Ø´", + "ê°Ģ ëĬ¥", + "ãĥ© ãĥ³ãĥī", + "ãĥ©ãĥ³ ãĥī", + "ay acak", + "aya cak", + "åįģ ä¸Ģ", + "ĠB ảo", + "Ġyet erli", + "Ġyeter li", + "ž iv", + "ži v", + "ĠÙĬÙĨ اÙĬر", + "Ġb ýval", + "Ġbý val", + "ìĽĶ ê¹Įì§Ģ", + "Ġn ợ", + "Ġ ê´Ģê³Ħ", + "Ġê´Ģ ê³Ħ", + "Ġ íĿ¬", + "ĠíĿ ¬", + "а ÑİÑĤÑĮ", + "аÑİÑĤ ÑĮ", + "аÑİ ÑĤÑĮ", + "Ġgö tür", + "Ġваж но", + "Ġва жно", + "æµ ©", + "ĠìĿ¼ ë¶Ģ", + "ÑĨÑĸй ний", + "ëł¥ ìĿĦ", + "Ġл еÑĩение", + "ĠлеÑĩ ение", + "éĸ¢ ä¿Ĥ", + "ĠT üm", + "ìĻ Ķ", + "éģ Ĺ", + "ĠD ön", + "Ġ ÑģпÑĸлÑĮ", + "ĠÑģп ÑĸлÑĮ", + "ĠÑģпÑĸл ÑĮ", + "ãĥģ ãĤ§", + "н ÑıеÑĤÑģÑı", + "нÑı еÑĤÑģÑı", + "нÑıеÑĤ ÑģÑı", + "il tere", + "ilter e", + "ilt ere", + "Ġ íĮĢ", + "Ġí ĮĢ", + "ĠíĮ Ģ", + "è¨Ń å®ļ", + "Ġro din", + "Ġrod in", + "Ġrodi n", + "ĠاÙĤتص اد", + "алÑĮ не", + "à¥į à¤ķर", + "à¥įà¤ķ र", + "Ġvý bÄĽ", + "Ġteh lik", + "âĶ IJ", + "Ġ çĶ°", + "Ïģί ÏĤ", + "iy el", + "iye l", + "Ġth iá»ĩu", + "Ġthi á»ĩu", + "ÏĪ ηÏĤ", + "ÏĪη ÏĤ", + "Ġд ве", + "Ġдв е", + "ĠEl ekt", + "ĠEle kt", + "à¸ģ à¸İ", + "о ÑĢÑĥж", + "оÑĢ Ñĥж", + "оÑĢÑĥ ж", + "a ÅŁÄ±", + "aÅŁ ı", + "è© ³ç´°", + "Ġات ÙģاÙĤ", + "Ġg ắn", + "æ²Ĵ æľī", + "ĠÙħطاÙĦ عÙĩ", + "ÏĦ ιν", + "ÏĦι ν", + "Ġok res", + "Ñ ľ", + "ê° Ķëĭ¤", + "Ðł оз", + "å¾ĭ 宾", + "ï¼ī ï¼Ī", + "Ġìļ´ìĺģ ìŀIJ", + "ãĤ« ãĥĨ", + "l aÄį", + "la Äį", + "à¥ĩब स", + "Ġo Äįi", + "ĠoÄį i", + "- б", + "e lerden", + "eler den", + "elerde n", + "k ových", + "kov ých", + "kový ch", + "ĠÄ°z mir", + "สม าà¸Ĭ", + "lad atel", + "Ġ æ»", + "Ġæ »", + "éĶĢ åĶ®", + "ĠдоÑģлÑĸд женнÑı", + "ĠлÑĸ каÑĢ", + "ĠлÑĸка ÑĢ", + "ĠлÑĸк аÑĢ", + "Ġодна ко", + "ĠV ác", + "Ġ è«", + "Ġè «", + "éĢ² è¡Į", + "以 å¤ĸ", + "é³ ¥", + "Ġ ÙĨج", + "ĠÙĨ ج", + "ĠbaÅŁ kan", + "ĠbaÅŁka n", + "ĠbaÅŁk an", + "Ġopat ÅĻenÃŃ", + "ا رش", + "ار Ø´", + "ض اÙģØ©", + "ضا ÙģØ©", + "ãĤ¹ ãĥ¬", + "ή ν", + "ÄĽ tÃŃ", + "ÄĽt ÃŃ", + "ว ย", + "Ġرس ÙĪÙĦ", + "ÅĻ ich", + "ÅĻi ch", + "ĠpÅĻ ih", + "ĠpÅĻi h", + "ÑĮ ми", + "çĦ¶ èĢĮ", + "Ġth ẳng", + "l amaz", + "la maz", + "lam az", + "lama z", + "ÙĢ ÙĢÙĢ", + "ÙĢÙĢ ÙĢ", + "Ġì°¸ ìŬ", + "ĠÙĨÙĪ شتÙĩ", + "ĠÙĨÙĪØ´ تÙĩ", + "ĠÑģÑĤ ек", + "ãģ® ãģ¿", + "ĠÙĪ اÙĦع", + "ĠÙĪاÙĦ ع", + "ĠÙĪا ÙĦع", + "æķ ¢", + "à¥Ģà¤Ĥ ,", + "ÐŀÑģ нов", + "им оÑģÑĤи", + "имо ÑģÑĤи", + "ĠÄĮesk á", + "ĠÄĮes ká", + "Ñĸ Ñĩний", + "ÑĸÑĩ ний", + "าม ารà¸ĸ", + "ekk ür", + "Âł h", + "ι κη", + "ικ η", + "Ġتع ÛĮÛĮÙĨ", + "к оÑģÑĤÑĸ", + "ко ÑģÑĤÑĸ", + "ĠMust afa", + "Ġì¦ ī", + "ãģ§ ãģĤãĤĬ", + "ãģ§ãģĤ ãĤĬ", + "å·¥ ä¸ļ", + "ov ÃŃd", + "ovÃŃ d", + "ÐĿ о", + "Ġس پس", + "Ġسپ س", + "Ú¯ÛĮ رد", + "Ú¯ÛĮر د", + "Ġп едагог", + "Ġ کارÛĮ", + "ĠÚ© ارÛĮ", + "Ġکار ÛĮ", + "ĠÑĪ ÑĤÑĥ", + "ĠÑĪÑĤ Ñĥ", + "æĮ Ĥ", + "Ø¢ Ùħد", + "Ø¢Ùħ د", + "羣 æĺ¯", + "Ġ ابت", + "Ġا بت", + "Ġاب ت", + "Ġرئ ÛĮس", + "Ġد ÛĮÙĨ", + "ĠدÛĮ ÙĨ", + "ÏĪ ε", + "Ġse zon", + "Ġsez on", + "Ġ çĨ", + "Ġç Ĩ", + "स न", + "ãĥ» ãĤ¢", + "Ġ åħŃ", + "Ġåħ Ń", + "Ġ è±", + "Ġè ±", + "Ġìłľ 목", + "ĠÙħ عد", + "ĠÙħع د", + "ĠÙģ ÙĤد", + "ĠÙģÙĤ د", + "éĤ Ĭ", + "Ω Σ", + "Ġ å¡", + "Ġå ¡", + "Ġob vyk", + "ĠìĿ´ ëłĩê²Į", + "ĠбоÑĢ оÑĤÑĮ", + "Û² Û±", + "Ġ á»ijng", + "Ġá» ijng", + "è¯ Ĺ", + "Ġ ÄIJá»iji", + "ĠÄIJ á»iji", + "ĠбеÑĢез нÑı", + "Ġs oÄŁ", + "Ġso ÄŁ", + "Ġ ï¾į", + "Ġï¾ į", + "ãĤĴ ãģ¤", + "ãģĹ ãĤĥ", + "еÑĢ еÑĩ", + "еÑĢе Ñĩ", + "ãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", + "æĪ ª", + "ĠاÙĦسعÙĪد ÙĬØ©", + "ĠëĤ¨ ìŀIJ", + "ĠAng iosper", + "???????? ????????", + "Ġpr ům", + "Ġprů m", + "ĠплоÑī ад", + "Ġ ÏĦÏģα", + "ĠÏĦ Ïģα", + "д аÑİÑĤ", + "да ÑİÑĤ", + "Ġsı nav", + "Ġsın av", + "Ġm ặc", + "æ°´ å¹³", + "Ġви глÑı", + "Ġвиг лÑı", + "Ġn ást", + "Ġná st", + "Ġnás t", + "ĠобÑĭ Ñĩ", + "ĠìĿ´ìķ¼ 기", + "ë¹ Ľ", + "ĠB aÄŁ", + "ĠBa ÄŁ", + "ĠاÙĦØ« اÙĦØ«", + "Ġser vis", + "Ġserv is", + "Ġservi s", + "Ġ 룬", + "ĠëŁ ¬", + "ом ина", + "ί θ", + "Ġ Ấ", + "ĠẠ¤", + "ê²½ 기", + "Ġì¡ ¸", + "ี à¸ļ", + "Ġà¤ĺ à¤Łà¤¨", + "Ġ à¸Ļาà¸ĩ", + "Ġà¸Ļ าà¸ĩ", + ". Îł", + "ìķ ķ", + "r ün", + "Ġon ların", + "Ġonları n", + "Ġonlar ın", + "Ġзб ÑĸлÑĮÑĪ", + "à¹ģ à¸Ł", + "ĠìŬ 기", + "Ġ ëĮĢíijľ", + "ĠëĮĢ íijľ", + "ĠÑģи лÑĥ", + "ĠÑģил Ñĥ", + "à¹Ĥ à¸Ľ", + "Ġت ÙĤد", + "ĠتÙĤ د", + "ĠÐŁ ом", + "ĠÐŁÐ¾ м", + "ĠмаÑģ ла", + "Ġ ìĺģìĥģ", + "Ġìĺģ ìĥģ", + "н ение", + "не ние", + "нен ие", + "λα μβ", + "ĠB yl", + "ĠBy l", + "æĬ µ", + "æİ ª", + "Ġκαθ ÏİÏĤ", + "m ızı", + "æĸ° çļĦ", + "éĩį è¤ĩ", + "ั à¸Ľ", + "çŃ Ĩ", + "ĠÑĤ ка", + "ĠзнаÑĩ еннÑı", + "Ġзна ÑĩеннÑı", + "л аÑĤи", + "ла ÑĤи", + "лаÑĤ и", + "Ġv liv", + "Ġvl iv", + "ÐIJ н", + "ĠÚĨ اپ", + "ĠпиÑĤ анÑĮ", + ": ï½ī", + "æķĻ æİĪ", + "Ġì¹ľ 구", + "Ġtr ao", + "Ġtra o", + "à¥įयà¤ķ त", + "ุà¸Ħ à¸Ħล", + "ĠرÙĪ Ø´ÙĨ", + "ĠرÙĪØ´ ÙĨ", + "ĠعÙĦÙĬ Ùĩا", + "ĠعÙĦÙĬÙĩ ا", + "ãĢģ ãģĦ", + "ëħĦ ìĹIJ", + "éĢ Ĩ", + "Ġмаг аз", + "ï¾ŀ ï¾ŀ", + "Ġs ice", + "Ġsi ce", + "Ġsic e", + "âĢĻ te", + "âĢĻt e", + "ĠاÙĦÙĦ غة", + "á u", + "èĩª 身", + "Ġng Å©", + "ĠÑģк ладÑĥ", + "ĠÑģклад Ñĥ", + "Ġz ru", + "Ġtr uy", + "Ġ ilan", + "Ġi lan", + "Ġil an", + "ĠÙ¾ اÛĮÙĩ", + "ĠپاÛĮ Ùĩ", + "Ġپا ÛĮÙĩ", + ": :::::::::::::", + ":: ::::::::::::", + ":::: ::::::::::", + ":::::: ::::::::", + ":::::::: ::::::", + "::: :::::::::::", + "::::: :::::::::", + "::::::: :::::::", + "::::::::: :::::", + ":::::::::: ::::", + "::::::::::: :::", + ":::::::::::: ::", + "::::::::::::: :", + "f ak", + "fa k", + "ÑĤ еÑħ", + "ÑĤе Ñħ", + "Ġt aky", + "Ġta ky", + "Ġtak y", + "Ġìĸ¸ ìĸ´", + "ed enÃŃ", + "eden ÃŃ", + "ede nÃŃ", + "Ġà¤ļ लत", + "Ġà¤ļल त", + "Ġë°° ìļ°", + "Ġjmé no", + "ĠÙĦ Ø£ÙĨ", + "ĠÙĦØ£ ÙĨ", + "α νά", + "αν ά", + "к ÑĥлÑĮ", + "кÑĥ лÑĮ", + "кÑĥл ÑĮ", + "ĠØŃÙģ ظ", + "ĠآزÙħ ÙĪÙĨ", + "иÑĤелÑĮ нÑĭе", + "ĠÐŀ лекÑģанд", + "èį £", + "Ġà¤ľà¤¬ à¤ķ", + "Ġr odi", + "Ġro di", + "Ġrod i", + "Ġبرخ ÙĪرد", + "Ġhaf ta", + "Ġhaft a", + "λ ικά", + "λι κά", + "λικ ά", + "à¸ķ à¸Ļ", + "ĠбеÑĢ ег", + "αν δ", + "- С", + "Ġprav idel", + "ĠбÑĸ лÑı", + "ĠбÑĸл Ñı", + "íĴ į", + "ĠпÑĢед ÑĥÑģ", + "ĠмÑĥ ниÑĨип", + "åĮĸ åѦ", + "ĠتÙħ اس", + "Ġà¤ī ल", + "Ðĵ Ðŀ", + "غ ر", + "r adan", + "ra dan", + "rad an", + "rada n", + "ĠëĤĺ ìĺ¤", + "è¨ Ĥ", + "à¹Ģà¸ĺ à¸Ń", + "âĢĮ سÛĮ", + "âĢĮس ÛĮ", + "ĠобÑıз аÑĤелÑĮно", + "ĠобÑıзаÑĤелÑĮ но", + "о ÑĤе", + "оÑĤ е", + "à¹Į à¸Ĭ", + "çĶ¨ çļĦ", + "Ġalt ın", + "Ġaltı n", + "ĠÑģоÑĤ ÑĢÑĥд", + "Ñĸ нки", + "Ñĸн ки", + "озмож но", + "Î IJ", + "ë¹ Į", + " ķ", + "ĠÑĤ оÑĩно", + "ĠÑĤо Ñĩно", + "ĠÑĤоÑĩ но", + "Ġj men", + "Ġjm en", + "اÙĦ ÛĮا", + "اÙĦÛĮ ا", + "èĪ į", + "ch odu", + "cho du", + "chod u", + "ê³ ¤", + "ick ém", + "ické m", + "ĠÙħ ÙĪر", + "ĠÙħÙĪ ر", + "ãĥª ãĥ³ãĤ¯", + "ãĥªãĥ³ ãĤ¯", + "Ġa ÅŁam", + "ĠaÅŁ am", + "Ġ иÑĤ", + "Ġи ÑĤ", + "Ġन य", + "Ġ μο", + "Ġμ ο", + "éķ ľ", + "ĠبÙĨ ابر", + "ĠبÙĨا بر", + "Ġت خصص", + "Ġส à¸ŀ", + "ĠпÑĢоÑĦеÑģ Ñģи", + "Ġp uan", + "Ġpu an", + "ĠÙģر ÙħاÙĨ", + "ĠÙģرÙħ اÙĨ", + "ëĮĢ íļĮ", + "Ġп ÑıÑĤ", + "ĠÙħ ÙĪب", + "ĠÙħÙĪ ب", + "ĠvÄĽ ku", + "Ġ ëĥ", + "Ġë ĥ", + "ec ký", + "eck ý", + "ĠìĪĺ ëıĦ", + "Ġth ao", + "Ġtha o", + "Ġk apat", + "Ġka pat", + "Ġkap at", + "ĠзаÑħ воÑĢÑİ", + "Ġ åħī", + "Ġåħ ī", + "ر اÙĨÛĮ", + "راÙĨ ÛĮ", + "را ÙĨÛĮ", + "éĢł æĪIJ", + "ĠÑģв Ñĸй", + "ĠдоÑģ иÑĤÑĮ", + "Ġmil yar", + "Ġener ji", + "Ġenerj i", + "Ġк ип", + "Ġки п", + "Ġì¢ĭ ìķĦ", + "Ġب Ø¥", + "ê²Į ìĭľ", + "ĠL Æ°u", + "ĠÙħÙĨظ ÙĪر", + "Ïī μά", + "ζ ί", + "ım da", + "Ġ ìĿ´ë¥¼", + "ĠìĿ´ 를", + "๠Ĵ", + "Ġв важ", + "Ġвв аж", + "Ġga zet", + "Ġgaze t", + "Ġgaz et", + "à¥įत न", + "à¹īำ หà¸Ļ", + "åľŁ åľ°", + "Ġसद स", + "ت بة", + "تب Ø©", + "Ġpo ÄįÃŃta", + "Ġìĭľ ìĬ¤íħľ", + "ร à¸Ħ", + "Ġed ecek", + "ĠتØŃ ÙĦÛĮÙĦ", + "æĮī çħ§", + "åĿ ª", + "Ġê·¸ ê°Ģ", + "ت ÙĩÙħ", + "تÙĩ Ùħ", + "Ġб аж", + "Ġба ж", + "ا Ùģع", + "اÙģ ع", + "éĢļ 常", + "ĠТ и", + "γ νÏī", + "ì¹ Ļ", + "Ġznam ená", + "ï¼¼ ï¼¼", + "α ÏĢÏĮ", + "åĨĻ 羣", + "Ġ ï¼¼Ċ", + "Ġï¼¼ Ċ", + "åĬł å·¥", + "èĤ¡ä»½ æľīéĻIJåħ¬åı¸", + "Ñı ÑĤий", + "ÑıÑĤ ий", + "ÑıÑĤи й", + "Ġh âl", + "Ġç ab", + "Ġça b", + "ĠØŃ Ø§Ø¶Ø±", + "P ÅĻ", + "ĠاÙĦ تÙĤ", + "ĠاÙĦت ÙĤ", + "ξ ηÏĤ", + "ξη ÏĤ", + "б е", + "Ġkh ám", + "Ġkhá m", + "Ġ âĮĴ", + "Ġâ ĮĴ", + "Ġ éķ¿", + "Ġéķ ¿", + "Ġ âĢ¦Ċ", + "ĠâĢ¦ Ċ", + "द म", + "ĠSt udi", + "ĠStud i", + "Ġk odu", + "Ġko du", + "Ġkod u", + "Ġkom unik", + "Ġkomun ik", + "Ġkat kı", + "n ete", + "ne te", + "net e", + "Ġr apor", + "Ġrap or", + "Ġra por", + "éĨ ´", + "ãĤī ãģĽ", + "ĠнеÑģк олÑĮ", + "Ġhá»į p", + "ï¿£ ￣￣", + "￣￣ ï¿£", + "º ¼", + "è£ Ĥ", + "ед ÑĮ", + "Ġا ÙĦاØŃ", + "ĠاÙĦ اØŃ", + "l adık", + "la dık", + "lad ık", + "ladı k", + "Ġfot oÄŁraf", + "æĹ¥ ãģ®", + "ĠØŃ Ø§ÙĦت", + "ĠØŃاÙĦ ت", + "ĠØ« ÙĦاث", + "а ÑĤов", + "аÑĤ ов", + "аÑĤо в", + "ey se", + "Ġê°IJ ìĤ¬", + "á že", + "áž e", + "Ġн ада", + "Ġна да", + "Ġнад а", + "Ġà¤ķ हन", + "Ġà¤ķह न", + "Ġ ãĥĿ", + "Ġãĥ Ŀ", + "ãģ« ãģĤãĤĭ", + "ãģ«ãģª ãģ£ãģ¦", + "ÙĪ دÙĩ", + "ÙĪد Ùĩ", + "Ġpo Å¡k", + "太 éĺ³åŁİ", + "ç»ı éªĮ", + "æĴŃ æĶ¾", + "Ġma jet", + "Ġmaj et", + "Ñħ о", + "ĠÑĤ еÑģÑĤ", + "ĠÑĤе ÑģÑĤ", + "ï¼ı Ċ", + "Ïĥε ÏĦε", + "ĠТ омÑĥ", + "ĠТо мÑĥ", + "ĠТом Ñĥ", + "Ùİ ØŃ", + "ĠìŀĪ ìľ¼ë©°", + "Ġза знаÑĩ", + "éļ IJ", + "Ġд ÑĸÑĹ", + "ĠдÑĸ ÑĹ", + "к ÑĤив", + "кÑĤ ив", + "кÑĤи в", + "ÙĪ ÙģÙĬ", + "ÙĪÙģ ÙĬ", + "Ġt á»Ŀ", + "Ġtá» Ŀ", + "à¸¹à¸Ľ à¹ģà¸ļà¸ļ", + "ĠÑĢ едак", + "ĠÑĢед ак", + "Ġa teÅŁ", + "Ġat eÅŁ", + "Ġate ÅŁ", + "Ġkh iá»ĥn", + "Ġkhi á»ĥn", + "ü ny", + "ün y", + "ี ยà¸ģ", + "ีย à¸ģ", + "ĠÑĩа Ñīе", + "Ġt uy", + "Ġtu y", + "γ Ïīν", + "γÏī ν", + "ร à¸Ńà¸ļ", + "Ġtr ùng", + "à¹ģà¸Ĺ à¸Ļ", + "Ġα κÏĮ", + "Ġακ ÏĮ", + "ĠÐĴеÑĢ Ñħов", + "à¹ĥ à¸Ļส", + "à¹ĥà¸Ļ ส", + "ãĢģ ä½ķ", + "åĩ ¦", + "Ġ ç»ı", + "Ġç» ı", + "æ¨ ĵ", + "اÙĨÚ¯ ÙĦÛĮسÛĮ", + "Ġ lepÅ¡ÃŃ", + "Ġlep Å¡ÃŃ", + "Ġ å¼Ģå§ĭ", + "Ġå¼Ģ å§ĭ", + "éĻ º", + "ĠÑĩ еÑĤÑĭ", + "ĠÑĩеÑĤ Ñĭ", + "ĠС еÑĢ", + "оÑİ Ð·", + "Ġx ung", + "Ġxu ng", + "åĵģ çīĮ", + "Ġìĥģ íĥľ", + "ĠÙĨ صب", + "ĠÙĨص ب", + "ĠÑĩ омÑĥ", + "Ġتر Ú©ÛĮ", + "Ġترک ÛĮ", + "- ли", + "o vÃŃ", + "ov ÃŃ", + "Ġا ÙĨج", + "ĠاÙĨ ج", + "çµ ¡", + "Ġت ÙĪص", + "ĠتÙĪ ص", + "Ġ ì¿ł", + "Ġì ¿ł", + "Ġvar sa", + "Ġva rsa", + "Ġvars a", + "ĠÑĢаз ÑĢабоÑĤ", + "à¸Ĥ à¸Ńà¸ĩà¸Ħ", + "à¸Ĥà¸Ńà¸ĩ à¸Ħ", + "éŃ Ĥ", + "Ġà¤Ĭ पर", + "æĿ¥ 说", + "ĠÑĨенÑĤ ÑĢалÑĮ", + "ĠÑĨенÑĤÑĢ алÑĮ", + "ĠÑĨенÑĤÑĢа лÑĮ", + "ĠTak ım", + "Ġon lar", + "Ġسر عت", + "好 åĥı", + "Ġbu á»ķi", + "ĠÐij ел", + "Âł c", + "Ø£ ت", + "à¸Ĥ à¸ĵะ", + "ãģ« åĩº", + "Ġ+ **************", + "ÏĦη κε", + "ا جر", + "اج ر", + "Ġ âĢ²", + "ĠâĢ ²", + "ãĥ¼ ãĥ¬", + "ãĥ¼ãĥ ¬", + "é¥ Ń", + "Ġج ÙĦس", + "ĠجÙĦ س", + "Ġب ستÙĩ", + "Ġبس تÙĩ", + "ว าà¸ĩ", + "Ġ βά", + "Ġβ ά", + "Ġа меÑĢикан", + "ĠPr emi", + "ĠPre mi", + "ĠPrem i", + "m ae", + "ma e", + "ĠÑģ ÑĢеди", + "ĠÑģÑĢед и", + "Ạł", + "Ġв ÑĢед", + "ãĢĤ èĢĮ", + "åĴ ²", + "Ġê³µ ê°ľ", + "èĤ ¥", + "з виÑĩай", + "Ġpro cent", + "Ġproc ent", + "и лоÑģÑĮ", + "ил оÑģÑĮ", + "ило ÑģÑĮ", + "श न", + "é łģ", + "éł ģ", + "е кÑĤи", + "ек ÑĤи", + "екÑĤ и", + "د اشت", + "دا شت", + "íķĻ íļĮ", + "ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ", + "ĠÙħد ÙĬÙĨØ©", + "िल न", + "Ġ èĹ", + "Ġè Ĺ", + "м иÑĢ", + "ми ÑĢ", + "Ġн оÑĢ", + "Ġно ÑĢ", + "Ġ íķĺì§Ģ", + "Ġíķĺ ì§Ģ", + "в еÑī", + "ве Ñī", + "nÄĽ m", + "е ÑĢами", + "еÑĢ ами", + "еÑĢа ми", + "Ġpra cov", + "Ġprac ov", + "ĠبÙĬ اÙĨات", + "ĠÏĥ Ïįν", + "ĠÏĥÏį ν", + "Ġج Ø°", + "ãģĦ ãģ§", + "ĠB ÃŃ", + "è± Ĩ", + "Ġh mot", + "Ġhm ot", + "il eceÄŁi", + "ilece ÄŁi", + "Ġت اث", + "Ġتا Ø«", + "è´ ´", + "Ġ ê¸ī", + "Ġê¸ ī", + "Ġm ysl", + "Ġmy sl", + "Ġmys l", + "ĠìĿ´ íķ´", + "Ġ기 ëĬ¥", + "ĠТ ам", + "ĠТа м", + "ĠнаÑģ елениÑı", + "ĠM ez", + "ĠMe z", + "Ġ모 르", + "íĻĶ 를", + "ĠÙĨسخ Ùĩ", + "ĠتÙĦ ÙĪÛĮزÛĮ", + "ĠÄįerv na", + "Æ°á» ¡ng", + "ص ØŃ", + "ĠÑĤ ÑĢен", + "ĠÑĤÑĢ ен", + "Õ ¡", + "Ġce lou", + "Ġcel ou", + "Å© i", + "ìĹĨ ìĿ´", + "nÃŃ ku", + "nÃŃk u", + "Ġprogram u", + "à¥į पन", + "à¥įप न", + "Ġп ÑĢеж", + "ĠпÑĢ еж", + "ĠпÑĢе ж", + "ا رب", + "ار ب", + "æľŁ éĸĵ", + "Ġ μά", + "Ġμ ά", + "ëįĶ ëĭĪ", + "ụ n", + "ĠпеÑĢ еÑģÑĤ", + "ĠпеÑĢе ÑģÑĤ", + "ĠпеÑĢеÑģ ÑĤ", + "对 äºİ", + "è¿IJ è¡Į", + "ĠÑĤ ан", + "ĠÑĤа н", + "Ġ ìĤ¬ìĿ´íĬ¸", + "ĠìĤ¬ ìĿ´íĬ¸", + "ĠìĤ¬ìĿ´ íĬ¸", + "ĠQu ảng", + "ĠQuản g", + "Ġst ojÃŃ", + "Ġsto jÃŃ", + "ãĥŃ ãĥ¼", + "Ú¯ ار", + "Ġе неÑĢг", + "Ġkter ým", + "Ġkterý m", + "ĠпÑĢи мÑĸ", + "ĠпÑĢим Ñĸ", + "ĠкаÑĢÑĤ и", + "ĠкаÑĢ ÑĤи", + "Ġz engin", + "Ġzen gin", + "ï¼Į åĨį", + "Ġت رب", + "Ġتر ب", + "ĠÑĨенÑĤ ÑĢ", + "ĠÑĨен ÑĤÑĢ", + "ĠsaÄŁ lamak", + "ĠsaÄŁlam ak", + "ëĭ Ŀ", + "ãģ® åŃIJ", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "Ġs Æ¡n", + "ĠsÆ¡ n", + "z ı", + "ÑĤ аки", + "ÑĤа ки", + "ÑĤак и", + "ÄĽ stÃŃ", + "ÄĽst ÃŃ", + "Ġ à¥", + "Ġà ¥", + "é ®", + "åŁ¹ è®Ń", + "Ġ ìĶ¨", + "ĠìĶ ¨", + "Ġbel ki", + "ĠìĿ´ 벤íĬ¸", + "ëĶĶ ìĸ´", + "Ġs Ãłn", + "ни кам", + "ник ам", + "ника м", + "a lim", + "al im", + "ali m", + "対 å¿ľ", + "ĠS á»±", + "éģĵ è·¯", + "é«ĺ æ¸ħ", + "Ġd õi", + "ĠÙĦ ÙĢ", + "Ġ èĤ¡", + "ĠèĤ ¡", + "ν ι", + "âĢŀ J", + "' nde", + "'n de", + "Îij Îĵ", + "ãģ¨ ãģªãĤĭ", + "ãģ¨ãģª ãĤĭ", + "çĪ ¸", + "ع ÙĦÛĮ", + "عÙĦ ÛĮ", + "Ïģι ÏĥÏĦ", + "ÏģιÏĥ ÏĦ", + "Ġe ÄŁit", + "ĠeÄŁ it", + "Ġзов нÑĸÑĪ", + "Ġп ÑĢим", + "ĠпÑĢ им", + "ĠпÑĢи м", + "س Ùħبر", + "سÙħ بر", + "ĠmÄĽst ÄĽ", + "ĠÏĢεÏģι ÏĥÏĥÏĮÏĦε", + "ÐIJ Ðł", + "æĦŁ åĪ°", + "Ġ문 ìĦľ", + "ãģĭ ãĤĭ", + "ÙĤÙĬ ÙĤØ©", + "ÙĤÙĬÙĤ Ø©", + "Ġв ÑĤÑĢа", + "ĠвÑĤ ÑĢа", + "Ġ à¸Ńำ", + "Ġà¸Ń ำ", + "Ñģ кÑĥÑİ", + "Ñģк ÑĥÑİ", + "د ÙĩاÛĮ", + "دÙĩ اÛĮ", + "ĠÄ° st", + "ĠÄ°s t", + "ĠÐĹ Ð°Ð²", + "ĠÐĹа в", + "Ġ éĥ½", + "Ġé ĥ½", + "Ġéĥ ½", + "ÑĪ ем", + "ÑĪе м", + "Ġе ÑīÑij", + "ĠÐľÐ¸Ñħ ай", + "ĠÑĥпÑĢав лениÑı", + "л еннÑĭе", + "лен нÑĭе", + "ĠzaÄį al", + "æ¡ Į", + "Ġп Ñĸз", + "л ÑıÑĤÑĮÑģÑı", + "лÑı ÑĤÑĮÑģÑı", + "лÑıÑĤÑĮ ÑģÑı", + "Ġ ìŀIJë£Į", + "ĠìŀIJ ë£Į", + "ãĢĢ ãĢĢĠ", + "ãĢĢãĢĢ Ġ", + "ĠK ral", + "ĠKr al", + "ĠKra l", + "èĪ ī", + "Ġà¤Ń व", + "Ġ Ø®Ùħ", + "ĠØ® Ùħ", + "Ġа кадем", + "Ġ isten", + "Ġis ten", + "Ġi sten", + "Ġist en", + "ĠиÑģ кÑĥÑģ", + "ĠиÑģк ÑĥÑģ", + "ĠعÙĨد Ùħا", + "Ġا ÙĦاÙħ", + "ĠاÙĦ اÙħ", + "is mus", + "ism us", + "ismu s", + "Ġayr ıntı", + "Ġ Що", + "ĠЩ о", + "ĠÙĩ ÙĪØ´", + "ĠÙĩÙĪ Ø´", + "د ÙĪاج", + "دÙĪ اج", + "л аж", + "ла ж", + "ĠÚ©ÙĨ ار", + "Âł R", + "æĢ§ çļĦ", + "Ñģ Ñĸм", + "ÑģÑĸ м", + "ĠM üz", + "ĠMü z", + "ÑĢ овиÑĩ", + "ÑĢов иÑĩ", + "ÑĢо виÑĩ", + "Ġ Ω", + "ĠÎ ©", + "Ġìĸ´ ëĶĶ", + "س ÙħØ©", + "سÙħ Ø©", + "Ġ ÑĢÑı", + "ĠÑĢ Ñı", + "Ġt Æ°Æ¡i", + "ĠtÆ° Æ¡i", + "ĠÑĢаÑģ Ñħод", + "åı° çģ£", + "ĠاÙĦ ÙĪÙĤت", + "ĠاÙĦÙĪ ÙĤت", + "بر اÛĮ", + "ĠзÑĢоб иÑĤи", + "Ġб ÑĥÑĢ", + "ĠбÑĥ ÑĢ", + "ĠÄįin nosti", + "ĠÄįinnost i", + "Ġص اØŃ", + "ĠصÙĨ عت", + "ĠصÙĨع ت", + "Ġ Ø·ÙĦ", + "ĠØ· ÙĦ", + "ξ Ïį", + "ĠtisÃŃ c", + "ĠFr ansa", + "ĠFran sa", + "ì¦ ĺ", + "è» ½", + "Ñ ĺ", + "ÏĮÏĦη ÏĦαÏĤ", + "ÏĮÏĦηÏĦα ÏĤ", + "ĠM illet", + "ĠMill et", + "ĠMil let", + "ãĢĢ ãĥ¾", + "ĠпÑĢ ием", + "ĠпÑĢи ем", + "ĠترجÙħ Ùĩ", + "Ġس ÙĪد", + "ĠسÙĪ د", + "ĠsouÄįást ÃŃ", + "ÐĶ о", + "Ġtr ụ", + "足 çIJĥ", + "à¸Ľ à¸ģ", + "Ġu stanov", + "ÎŁ ÎĻ", + "ÎŁÎ Ļ", + "Ðŀ н", + "Ġн еж", + "Ġне ж", + "к ог", + "ко г", + "ä¸Ģ çĤ¹", + "Ġد ÙĪراÙĨ", + "ĠدÙĪ راÙĨ", + "ĠدÙĪر اÙĨ", + "å½± éŁ¿", + "el idir", + "eli dir", + "âĢŀ N", + "es iyle", + "esi yle", + "ÑĢем енно", + "ÑĢе менно", + "Ġilet iÅŁim", + "ม à¹Ģà¸ķ", + "以 åīį", + "ãĥĭ ãĥ¼", + "鼻 話", + "à¹Ĥ à¸ŀ", + "ov ky", + "Ġза мÑĸ", + "Ġзам Ñĸ", + "Ġव à¤ķ", + " Ļ", + "ĠвÑĸй ни", + "Ġol madıģı", + "Ġolm adıģı", + "Ġolmadı ģı", + "Ġolma dıģı", + "æ¢ ģ", + "ĠТ еп", + "ĠТе п", + "nÄĽ te", + "nÄĽt e", + "èħ ķ", + "ìĤ¬ ëĬĶ", + "m amak", + "ma mak", + "mam ak", + "Ġc iz", + "Ġci z", + "æ£ Ĵ", + "Ġ ï¼ı:", + "Ġï¼ı :", + "éģĭ åĭķ", + "ĠÙĩ ÙĨا", + "ĠÙĩÙĨ ا", + "Ġ ê°ij", + "Ġê° ij", + "ĠÙĩÙĨÚ¯ اÙħ", + "ĠuÄŁ ra", + "å½ ¦", + "Ġob jekt", + "Ġobj ekt", + "ãģ¨ ãģĻãĤĭ", + "åĽ½ åĨħ", + "ĠдеÑĢжав и", + "ĠдеÑĢж ави", + "Ġ èĮ", + "Ġè Į", + "Ġulus lararası", + "Ù £", + "Ġmut lak", + "Ġз обов", + "Ġ γεν", + "Ġγ εν", + "Ġγε ν", + "à¹Ħà¸Ł à¸Ł", + "Ġözg ür", + "íĦ ¸", + "Ġвипад кÑĥ", + "Ġà¤ķ ब", + "ĠاÙĦ خط", + "ĠاÙĦØ® Ø·", + "θη καν", + "ï¼Į æĬĬ", + "ÑıÑĤ ÑĤÑı", + "Ġolmadı ģını", + "Ġolma dıģını", + "Âłk W", + "ĠnÄĽkter ých", + "ãĥĩ ãĥ«", + "æ¤į çī©", + "μι λοÏĤ", + "ÐIJÑĢ ÑħÑĸв", + "ĠТ о", + "èĸ ¬", + "ÑģÑĤв иÑı", + "Ġ Ø®ÙĪاست", + "ĠØ®ÙĪ است", + "олог ÑĸÑĹ", + "ÙĪ اÙĩد", + "ÙĪاÙĩ د", + "ÙĪا Ùĩد", + "Ġ нак", + "Ġн ак", + "Ġна к", + "ĠкоÑĤоÑĢ ÑĥÑİ", + "Ġद à¤ķ", + "âĢŀ M", + "λ ια", + "λι α", + "æŃ ²", + "第 åĽĽ", + "à¤¾à¤ľ स", + "Ġ( «", + "Ġth ẻ", + "､ Ċ", + "ç£ ģ", + "Ġ ÙĦÙĤ", + "ĠÙĦ ÙĤ", + "Ġ ìķĶ", + "Ġìķ Ķ", + "Ġн ового", + "Ġнов ого", + "ĠìķĦ 주", + "Ġ ëIJĺìĸ´", + "ĠëIJĺ ìĸ´", + "Ġo lun", + "Ġol un", + "à ¾", + "Ġkar iy", + "Ġkari y", + "ĠØŃ Ø³Ø¨", + "ĠØŃس ب", + "ĠìĿĺ 미", + ". Ðľ", + "Ġoz naÄį", + "ÙĦ سÙĦ", + "ÙĦس ÙĦ", + "ĠÐĴ ид", + "ĠÐĴи д", + "ë¡ľ ëĤĺ", + "à¥įà¤Ł म", + "í ľ´", + "Ġbilg isayar", + "ìĿ¸ ì§Ģ", + "Ġв ов", + "Ġво в", + "nict vÃŃm", + "nictvÃŃ m", + "า à¸Ńย", + "าà¸Ń ย", + "Ġشخص ÛĮ", + "п Ñĸон", + "æľ¬ å½ĵ", + "Ġب ÙĢ", + "ĠмаÑģ ло", + "ĠPh át", + "Ġ ба", + "Ġб а", + "алÑĮ номÑĥ", + "алÑĮно мÑĥ", + "алÑĮном Ñĥ", + "社 åĮº", + "Ġ Ò", + ": ::|", + ":: :|", + "::: |", + "ê ´", + "Ġ ä¸ĥ", + "Ġä¸ ĥ", + "ĠÙĪ اÙĦد", + "ĠÙĪاÙĦ د", + "ни ке", + "ник е", + "à¸Ń ลล", + "à¸Ńล ล", + "Ġyer leÅŁ", + "Ġkom bin", + "Ġkomb in", + "u Å¡", + "Ġо ÑĤÑĢи", + "ĠоÑĤ ÑĢи", + "ä¹ Į", + "iÅŁ ti", + "Ġs óng", + "Ġsó ng", + "λ ηÏĤ", + "λη ÏĤ", + "Ġк ÑĥÑĢÑģ", + "ĠкÑĥÑĢ Ñģ", + "à¹Ī าà¸Ħ", + "à¹Īา à¸Ħ", + "Ġ ÙĬس", + "ĠÙĬ س", + "Ġ داÙħ", + "Ġد اÙħ", + "çĴ° å¢ĥ", + "Ñĩ енко", + "Ñĩен ко", + "ãĢį ãģ®", + "ĠmÃŃ sta", + "ĠmÃŃst a", + "ĠÑĦ оÑĤ", + "ĠpÅĻÃŃ zn", + "ĠÑĢ аза", + "ĠÑĢаз а", + "ĠÑĢа за", + "ç´ «", + "lá da", + "lád a", + "ĠÑģпеÑĨи алиÑģÑĤ", + "ĠبÛĮ ÙħارÛĮ", + "ĠبÛĮÙħ ارÛĮ", + "ĠبÛĮÙħار ÛĮ", + "Ġëĵ £", + "çĭ Ĺ", + "ÙĪ ÙĪ", + "ан ÑĸÑĤ", + "анÑĸ ÑĤ", + "ĠدÙĨ باÙĦ", + "ĠÙħجÙħÙĪع Ø©", + "ÃŃ na", + "ÃŃn a", + "ĠH alk", + "ĠHa lk", + "ĠHal k", + "á jem", + "áj em", + "enÃŃ ze", + "Ġim z", + "« ng", + "Ġ ÎķÎł", + "ĠÎķ Îł", + "ĠÙħ Ùĩد", + "ĠÙħÙĩ د", + "ìľĦìĽIJ íļĮ", + "Ġìľł íĺķ", + "ाप स", + "Ġje ž", + "ан Ñĸз", + "анÑĸ з", + "иÑĤ ай", + "иÑĤа й", + "á¿ ĸ", + "ir ler", + "irl er", + "기 ê°Ħ", + "Ġ воÑĢ", + "Ġв оÑĢ", + "Ġво ÑĢ", + "Ġ Ïİ", + "ĠÏ İ", + "Ġpo zn", + "Ġpoz n", + "Ġ ساÙĨ", + "Ġس اÙĨ", + "å ¯¿", + "å¯ ¿", + "æĸ¯ çī¹", + "Ġtu rist", + "Ġtur ist", + "ĠìŀIJ ìľł", + "à¥Ģ à¤ĸ", + "μ με", + "μμ ε", + "an sı", + "ans ı", + "ìĨĮ ëħĦ", + "Ġted avi", + "Ġtedav i", + "Ñĩ еÑģÑĤва", + "Ñĩе ÑģÑĤва", + "å£ ĵ", + "о ве", + "ов е", + "ï¼Į çľĭ", + "ĠпоÑģл Ñĥг", + "ĠпоÑģ лÑĥг", + "ĠÑĤÑĢ анÑģ", + "ĠÑĤÑĢан Ñģ", + "Ġz áz", + "Ġzá z", + "æĪ ´", + "Ġм она", + "Ġмон а", + "Ġмо на", + "ิ à¹Ģà¸Ħราะห", + "ĠÙĨ ÛĮÙħ", + "ĠÙĨÛĮ Ùħ", + "ĠìĤ¬ëŀĮ ìĿ´", + "a hat", + "ah at", + "aha t", + "Ïħ κ", + "ĠоÑĤ каз", + "ĠоÑĤк аз", + "ĠÐĴолод ими", + "ĠС к", + "िà¤ķ त", + "å¦ ĸ", + "Ġëĭ¤ìļ´ ë¡ľëĵľ", + "ìĺģ ìĥģ", + "Ġन à¤Ī", + "c ete", + "ce te", + "ĠгÑĢи б", + "ĠгÑĢ иб", + "ece ÄŁini", + "eceÄŁi ni", + "Ġç oÄŁu", + "ĠçoÄŁ u", + "ĠмаÑĤеÑĢи ала", + "ĠмаÑĤеÑĢиал а", + "ứ t", + "Ġz aten", + "Ġza ten", + "Ġzat en", + "ĠF RA", + "ĠFR A", + "ĠBir liÄŁi", + "Ġs itesi", + "Ġsit esi", + "Ġsite si", + "Ġsites i", + "Ġ åĶ", + "Ġå Ķ", + "ĠÐĴ ол", + "ĠÐĴо л", + "Âł PS", + "ÂłP S", + "ा लत", + "ाल त", + "Ġ баÑĩ", + "Ġб аÑĩ", + "Ġба Ñĩ", + "алÑĸ заÑĨÑĸÑĹ", + "алÑĸз аÑĨÑĸÑĹ", + "ĠS lov", + "ĠSl ov", + "ĠSlo v", + "ç³ ĸ", + "ĠговоÑĢ иÑĤ", + "Ġв вед", + "Ġвв ед", + "ุà¸ķ à¸ļà¸Ńล", + "ãģĨ ãģ¡", + "Ġyap tık", + "Ġyaptı k", + "Ġìłķ ì¹ĺ", + "ê°ľ 를", + "à¥Ī सल", + "à¥Īस ल", + "ج ÙĬÙĦ", + "جÙĬ ÙĦ", + "ĠзаÑģÑĤоÑģ ов", + "è¿ «", + "ĠKur ul", + "ĠNas ıl", + "ĠнапÑĢÑı м", + "Ġ ä½į", + "à¹Į à¸ļ", + "Ġ éģĵ", + "Ġéģ ĵ", + "Ġни же", + "Ġниж е", + "Ġк оÑģÑĤ", + "Ġко ÑģÑĤ", + "ĠкоÑģ ÑĤ", + "ظ Ùĩر", + "ظÙĩ ر", + "Т а", + "ì§ Ŀ", + "Ġön ünde", + "ж Ñĸ", + "Ġاجر اÛĮ", + "Ġاجرا ÛĮ", + "ĠоÑĢган Ñĸв", + "ĠоÑĢганÑĸ в", + "v ise", + "vis e", + "vi se", + "Ġ ìĿĦ", + "ĠìĿ Ħ", + "à¸ķ รà¸ĩ", + "à¸ķร à¸ĩ", + "Ú©ÙĨ ÙĪÙĨ", + "Ġdlou ho", + "Ġdlouh o", + "Ðŀ ÐĿ", + "Ġ ìľ¡", + "Ġìľ ¡", + "缮 æłĩ", + "ë¯ Ģë¡ľ", + "ï¼ıï¼ıï¼ıï¼ı ï¼ıï¼ıï¼ıï¼ı", + "ĠпоÑĩ емÑĥ", + "æķħ äºĭ", + "ÑĤ еÑģÑĮ", + "ÑĤе ÑģÑĮ", + "ĠÙĤ ÙĦب", + "ĠÙĤÙĦ ب", + "Ġت جÙĩ", + "Ġتج Ùĩ", + "i lendir", + "il endir", + "ilen dir", + "Ġи гÑĢа", + "Ġиг ÑĢа", + "ĠÐĶ он", + "ĠÐĶо н", + "ĠpÅĻÃŃ jem", + "è¦ Ĩ", + "С п", + "- ни", + "on se", + "ons e", + "и ной", + "ин ой", + "о Ñĩного", + "оÑĩ ного", + "оÑĩно го", + "ا ساÙĨ", + "اس اÙĨ", + "ĠполÑĥÑĩ иÑĤÑĮ", + "ĠполÑĥ ÑĩиÑĤÑĮ", + "ÑĤ ап", + "ÑĤа п", + "ĠL ý", + "ĠÃĤ u", + "Ġh üc", + "e bek", + "eb ek", + "ebe k", + "ĠY ayın", + "æĹ ĭ", + "ัà¸Ļ à¸Ĺร", + "ัà¸Ļà¸Ĺ ร", + "Ġвикон ав", + "Ġвико нав", + "Ġs ông", + "à¥ģ à¤ľ", + "à¥ģठľ", + "ĠÐĹ Ð°Ð³", + "ĠÐĹа г", + "¤ ëĭ¤", + "Ġc Å©", + "ĠÚ¯ رÙħ", + "Ġگر Ùħ", + "ä¼ ı", + "ãģ« ãģĻãĤĭ", + "- Ф", + "Ġ ÙĤÙħ", + "ĠÙĤ Ùħ", + "Ġo lacaÄŁ", + "Ġol acaÄŁ", + "æĿ¥ äºĨ", + "æĭĽ èģĺ", + "ĠÐĿаÑģ еленнÑı", + "Ġ ìĺģìĸ´", + "Ġìĺģ ìĸ´", + "Ġ æѤ", + "ĠæŃ ¤", + "Ġب دÙĨ", + "Ġبد ÙĨ", + "Û² Û¸", + "оÑĢ аÑĤив", + "оÑĢа ÑĤив", + "ï¼ ³", + "Ġneby lo", + "Ġnebyl o", + "ĠÑĥ ÑĩиÑĤ", + "ĠÑĥÑĩ иÑĤ", + "æĿ ľ", + "Ġд анÑĸ", + "Ġда нÑĸ", + "Ġдан Ñĸ", + "Ġsp otÅĻeb", + "Ġspot ÅĻeb", + "ãĥ¼ ãĥĨãĤ£", + "ãĥ¼ãĥĨ ãĤ£", + "ен нÑĥÑİ", + "ê¹Į ìļĶ", + "v em", + "ve m", + "P ÅĻÃŃ", + "PÅĻ ÃŃ", + "Ġy andan", + "Ġyan dan", + "é¼ ĵ", + "Ġدست ÙĪر", + "Ġدس تÙĪر", + "Ġhaf if", + "h ů", + "Ġv áž", + "Ġvá ž", + "ĠìķĦ ì§ģ", + "Ùı ر", + "Ġ ла", + "Ġл а", + "ëł ī", + "า à¸Ľà¸£à¸°", + "à¸²à¸Ľ ระ", + "lık lar", + "lıkla r", + "ĠÑģÑĤанд аÑĢÑĤ", + "à¸Ń à¹ĥห", + "å¥ ´", + "ĠоÑĤ п", + "âĪ ł", + "ãĥ¼ ãĥĢ", + "ãĥ¼ãĥ Ģ", + "ch áze", + "cház e", + "Ġê·¸ ëłĩê²Į", + "Ġê·¸ëłĩ ê²Į", + "os tel", + "ost el", + "oste l", + "Ġгал Ñĥз", + "â k", + "еÑĨ ÑĤ", + "ëŀij ìĬ¤", + "ĠÄį ist", + "ĠÄįi st", + "ÑĢ ана", + "ÑĢа на", + "ÑĢан а", + "Ġv ững", + "Ġvữ ng", + "Ġs eni", + "Ġse ni", + "Ġsen i", + "Ġg óc", + "Ġgó c", + "ÏĨ ÏĮ", + "á nu", + "án u", + "Ġ öt", + "Ġö t", + "Ġs óc", + "Ġsó c", + "ãģĦ ãģ®", + "ĠÑģк лада", + "ĠÑģклад а", + "ÐIJÑĢÑħÑĸв овано", + "ĠìĿ´ ë²Ī", + "ãĤ¹ ãģ®", + "il ebilir", + "ile bilir", + "ï½Ģ ãĥ½", + "ีย à¸į", + "Ġκα ὶ", + "Ġë ¯¿", + "æĽ´ å¤ļ", + "ıs ının", + "ısını n", + "ĠGi ám", + "ĠGiá m", + "æŃ£ å¼ı", + "Ïĥ μÏĮ", + "Ïĥμ ÏĮ", + "Ġarch it", + "Ġarc hit", + "Ġ ï½²", + "Ġï½ ²", + "Ñĩ аÑİÑĤÑģÑı", + "Ñĩа ÑİÑĤÑģÑı", + "ÑĩаÑİÑĤ ÑģÑı", + "ë²Ħ ì§Ģ", + "ãĤ¤ ãĥ¤", + "é«ĺ æł¡", + "è¨ ³", + "ĠÙħ ÛĮÚ©", + "ĠÙħÛĮ Ú©", + "Ġ æĥħ", + "Ġæĥ ħ", + "Ġ pha", + "Ġp ha", + "Ġph a", + "太 éĥİ", + "à¸ŀระ ราà¸Ĭ", + "ÙĤ ÙĬØ©", + "ÙĤÙĬ Ø©", + "ĠÑĥ лÑĥÑĩ", + "ĠÑĥл ÑĥÑĩ", + "ÑģÑĤв ÑĥеÑĤ", + "ÑģÑĤвÑĥ еÑĤ", + "Ġk eÅŁ", + "Ġke ÅŁ", + "é«ĺ çŃī", + "Ġs Ỽm", + "Ïģ κε", + "Ïģκ ε", + "μ οÏģ", + "μο Ïģ", + "Ġzá stup", + "o zÃŃ", + "oz ÃŃ", + "Ġm ili", + "Ġmil i", + "Ġmi li", + "Ġмог ли", + "Ġз ÑĢозÑĥм", + "Ġباش ÛĮد", + "Ġak ci", + "Ġд ÑĢа", + "ĠдÑĢ а", + "Ġα Ïģι", + "ĠαÏģ ι", + "ãģĭ ãĤīãģ®", + "ãģĭãĤī ãģ®", + "å¯ Ĵ", + "ĠZ aman", + "ĠZa man", + "ĠZam an", + "ĠÑĸ де", + "ĠÑĸд е", + "Ġ ãĢĢĠ", + "ĠãĢĢ Ġ", + "Ġk lu", + "Ġkl u", + "ak lı", + "à¥ĩ à¤ļ", + "à¥ĩठļ", + "ĠÑģвоб од", + "س اÙħ", + "Ġ ов", + "Ġо в", + "Ġu byt", + "Ġub yt", + "éĩĩ çĶ¨", + "Ġdavran Ä±ÅŁ", + "ĠnabÃŃ zÃŃ", + "ĠÐij Ñĥд", + "Ġ Ïī", + "ĠÏ ī", + "ĠاÙĦ رØŃ", + "ĠاÙĦر ØŃ", + "ั à¸ķà¸Ļ", + "ัà¸ķ à¸Ļ", + "и ме", + "им е", + "Ġت ÙĦÙĥ", + "ĠتÙĦ Ùĥ", + "ت Ùħع", + "تÙħ ع", + "Ġад миниÑģÑĤÑĢа", + "Ġzor unda", + "Ġzorun da", + "ĠÙĨ سبة", + "ĠÙĨسب Ø©", + "ĠÙĨس بة", + "ĠصÙĨع تÛĮ", + "ĠصÙĨعت ÛĮ", + "ĠÑĦÑĥн да", + "éı ¡", + "Ġpo tom", + "Ġpot om", + "Ġп ÑĢеÑģÑĤ", + "ĠпÑĢ еÑģÑĤ", + "ĠпÑĢе ÑģÑĤ", + "ĠпÑĢеÑģ ÑĤ", + "Ġsı rada", + "Ġsır ada", + "Ġsıra da", + "Ġ ayar", + "Ġa yar", + "Ġay ar", + "ا ÙĤÙĦ", + "اÙĤ ÙĦ", + "æº ª", + "ĠØ¢ÙĤ اÛĮ", + "ĠпеÑĢе Ñħод", + "ĠпÑĢакÑĤи ÑĩеÑģки", + "é» ĥ", + "ĠÑĥ Ñħод", + "ĠÑĥÑħ од", + "ĠÙħ تÙģ", + "ĠÙħت Ùģ", + "Ġsiyas i", + "Ġпо ÑĤен", + "ĠпоÑĤ ен", + "Ùİ Ùģ", + "ĠÐĽ Ñĥ", + "ĠконÑĤÑĢ олÑĮ", + "ĠÑģказ аÑĤÑĮ", + "à¥Ģ à¤ķरण", + "à¥Ģà¤ķ रण", + "åħ¨ çIJĥ", + "Û² Û¶", + "Ġt oto", + "Ġto to", + "Ġtot o", + "Ġ ÙĪد", + "ĠÙĪ د", + "ãĤ¿ãĤ¤ ãĥĹ", + "åľ į", + "å¼ķ çĶ¨", + "ï¼ £", + "èĬ ¸", + "ä»ĭ ç»į", + "ĠÑĤеÑĢÑĢиÑĤоÑĢ ии", + "æĹ¥ ãģ«", + "m ÃŃt", + "mÃŃ t", + "am ız", + "amı z", + "ìĿ´ ìĸ´", + "Ġyar Ä±ÅŁ", + "Ġyarı ÅŁ", + "Ġg üc", + "Ġgü c", + "Ġ Ïĩα", + "ĠÏĩ α", + "ัà¸Ļย ายà¸Ļ", + "ãĤĴ è¡Į", + "Ġm illi", + "Ġmill i", + "Ġmil li", + "Ġmi lli", + "Ġ çı¾", + "Ġçı ¾", + "K dyž", + "m azon", + "ma zon", + "maz on", + "ë³´ ëĤ´ê¸°", + "ĠÑĤÑĢÑĥ дов", + "ĠÑĤÑĢÑĥд ов", + "é£ ¾", + "Ġви ник", + "Ġвин ик", + "ĠÙĪز ارت", + "ĠÙĪزار ت", + "éĩĮ çļĦ", + "м аз", + "ма з", + "ĠR US", + "ĠRU S", + "е кÑĤÑĥ", + "ек ÑĤÑĥ", + "екÑĤ Ñĥ", + "Ġع اش", + "Ġk once", + "Ġko nce", + "Ġkon ce", + "ãĤĪãģĨ ãģ§ãģĻ", + "Ġмал ÑĭÑĪ", + "m eni", + "me ni", + "men i", + "е Ñģа", + "еÑģ а", + "ا ضÛĮ", + "اض ÛĮ", + "Ġb rat", + "Ġbr at", + "Ġbra t", + "ĠвÑĸд ноÑģ", + "θ εÏģ", + "θε Ïģ", + "ĠЧ ем", + "æij ĩ", + "ĠÙħ ادر", + "ĠÙħا در", + "ĠÙħاد ر", + "çĶ¨ åĵģ", + "ĠÙħØŃ Ø§Ùģظ", + "Ġm yÅ¡", + "Ġmy Å¡", + "ج ع", + "Ġis im", + "Ġi sim", + "Ġisi m", + "æ³ Ĭ", + "ıl maz", + "ĠÎĽ α", + "å¯ ©", + "Ġay ır", + "е ними", + "ен ими", + "ени ми", + "еним и", + "à¥ĩह तर", + "åľ Ĩ", + "ãģ¾ ãģ£ãģŁ", + "çĶ¢ åĵģ", + "ĠÑĸнÑĦоÑĢма ÑĨÑĸÑĹ", + "Ġt ủ", + "Ġtá» §", + "สม à¸ļ", + "Ġst ÅĻ", + "Ġë°ľ íijľ", + "а ÑĢÑĮ", + "аÑĢ ÑĮ", + "ĠC ao", + "ĠCa o", + "Ρ ÎĻ", + "à¸ģาร à¸Ī", + "Ġпод Ñĥм", + "ä»ķ äºĭ", + "ĠÐļ ÑĢоме", + "Ġ ìĹĶ", + "ĠìĹ Ķ", + "Ġ Ñĥда", + "ĠÑĥ да", + "ĠÑĥд а", + "ĠавÑĤом аÑĤи", + "Ġ à¸Ħà¸ĵะ", + "Ġà¸Ħ à¸ĵะ", + "ĠK iÅŁ", + "ĠKi ÅŁ", + "ĠÑģоÑģÑĤоÑı ние", + "l isi", + "li si", + "lis i", + "Ġëĸ ¨ìĸ´", + "oot ball", + "Ġ íį¼", + "Ġí į¼", + "Ġ лим", + "Ġл им", + "Ġли м", + "Ġç erç", + "ÙĪÙĦ ÙĬÙĪ", + "ÙĪÙĦÙĬ ÙĪ", + "Ġs lož", + "Ġsl ož", + "Ġslo ž", + "Ġ 먼", + "Ġë¨ ¼", + "ร à¸Ńà¸ĩ", + "ÑĪ ее", + "ÑĪе е", + "âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦", + "ãģĵ ãģ¡ãĤī", + "о ÑĢÑĭ", + "оÑĢ Ñĭ", + "çĥ Ł", + "Âł F", + "а ного", + "ан ого", + "ано го", + "Ø« ÛĮر", + "çı į", + "å¸Ĥ åł´", + "vÄĽ dom", + "vÄĽd om", + "ì² ¨ë¶Ģ", + "ĠìĤ¬ ê±´", + "ï¾ Į", + "à¹ĥ à¸Ļว", + "à¹ĥà¸Ļ ว", + "Ġzvlá Å¡t", + "ÏĦ εÏħ", + "ÏĦε Ïħ", + "Ġкак ие", + "Ġка кие", + "ÏĨοÏģ ά", + "ÏĨο Ïģά", + "åĦ Ħ", + "Ġzp ÄĽt", + "íķľ íħĮ", + "Ġz vol", + "Ġzv ol", + "Ġ çĹ", + "Ġç Ĺ", + "ÑĢа нениÑı", + "ÑĢан ениÑı", + "ĠسÛĮ است", + "ĠÐļ оли", + "ĠÐļол и", + "ĠÐļо ли", + "ĠоÑĢганиз ма", + "ĠоÑĢганизм а", + "ĠÑıнва ÑĢÑı", + "Ġد ادÙĨ", + "Ġداد ÙĨ", + "п ÑĢа", + "пÑĢ а", + "ï¼Į ä»ĸ们", + "ï¼Įä»ĸ 们", + "æijĺ è¦ģ", + "Ġqu ần", + "ÙĬ ÙĪÙĨ", + "ÙĬÙĪ ÙĨ", + "Ġви Ñħов", + "Âł à¹Ģà¸Ķ", + "Ġ елем", + "Ġе лем", + "eb ilecek", + "Ġд оÑĩ", + "Ġдо Ñĩ", + "Ġб лаг", + "Ġбл аг", + "Ġбла г", + "ĠÑı й", + "ad nÃŃ", + "Ġzá roveÅĪ", + "en stvÃŃ", + "âĢĮ اÙĨ", + "ãģķãĤĵ ãģ¯", + "/ |", + "ĠاÙĦع اÙħØ©", + "ĠاÙĦعاÙħ Ø©", + "éł ¼", + "Ġخدا ÙĪÙĨد", + "Ġخد اÙĪÙĨد", + "н ам", + "на м", + "ĠÑģ лиз", + "ĠÑģл из", + "æ¶ ī", + "ร ษ", + "e ÅŁtir", + "eÅŁ tir", + "ĠÙĨ دار", + "ĠÙĨد ار", + "ร าà¸Ħ", + "è¨Ģ ãĤı", + "Ġ èŃ", + "Ġè Ń", + "Ġк ÑĢиÑĤ", + "ĠкÑĢи ÑĤ", + "ĠвоздÑĥ Ñħа", + "ĠвоздÑĥÑħ а", + "Ġà¤Ĺ त", + "Ġprá vo", + "Ġpráv o", + "à¥ĭष ण", + "Ġs ắp", + "íı Ń", + "Ġص رÙģ", + "ĠراÛĮ گاÙĨ", + "ĠоÑĤ к", + "ëĨ ĵ", + "ĠÑģек ÑĢеÑĤ", + "Ä° n", + "on avir", + "ona vir", + "ĠV ys", + "ĠVy s", + "ĠbaÅŁ lat", + "ĠMu ham", + "ĠлиÑģÑĤ оп", + "ĠT icaret", + "ĠTi caret", + "ĠTic aret", + "Ġad landır", + "ĠÐĶ миÑĤ", + "Ïĥμ οÏį", + "Ïĥμο Ïį", + "ä¾ µ", + "ìĭľ ëĬĶ", + "à¹ģà¸Ľ ลà¸ĩ", + "ın ıza", + "ını za", + "ınız a", + "- г", + "и ÑĩноÑĹ", + "иÑĩ ноÑĹ", + "иÑĩно ÑĹ", + "Ñĥ ÑĢи", + "ÑĥÑĢ и", + "U Z", + "ìĽ ł", + "Ġتبد ÛĮÙĦ", + "æ º«", + "æº «", + "ĠÑĢам каÑħ", + "Ġn ét", + "Ġné t", + "æ² ¿", + "Ġroz Å¡ÃŃ", + "Ġस प", + "ĠÑĤак е", + "ĠÑĤа ке", + "ÑĢ аÑĩ", + "ÑĢа Ñĩ", + "ĠاÙĦ ÙĤد", + "ĠاÙĦÙĤ د", + "íķĻ ê³¼", + "Ñĥв аннÑıм", + "ÑĥваннÑı м", + "Ġm ám", + "Ġmá m", + "ë¡ ¯", + "á½ IJ", + "Ġet kili", + "Ġetk ili", + "Ġetkil i", + "Ġetki li", + "Ġار تÙģ", + "Ġtechn olog", + "Ġtechno log", + "Ġ ì½ĺ", + "Ġì½ ĺ", + "Ġت ÙĥÙĬÙĬÙģ", + "ĠpÅĻ ece", + "ĠpÅĻe ce", + "家 åºŃ", + "Ġ ãģı", + "âĶ ´", + "íģ ¼", + "ĠÎľ ά", + "à¹Ģ à¸ķร", + "à¹Ģà¸ķ ร", + "ĠÑģÑĤанов иÑĤÑģÑı", + "ç«ĭ ãģ¡", + "Ġ éĸĭ", + "Ġéĸ ĭ", + "ĠÄ° yi", + "ĠnÄĽkter é", + "ĠÑĢоб оÑĤ", + "ĠÄIJ Æ°á»Ŀng", + "ĠاÙĦ اج", + "Ġsp eci", + "Ġspec i", + "Ġspe ci", + "çī¹ åĪ«", + "åŃ Ŀ", + "âĢĮ گذ", + "âĢĮÚ¯ Ø°", + "a lıģı", + "al ıģı", + "alı ģı", + "Ġм иÑĢа", + "Ġми ÑĢа", + "ĠмиÑĢ а", + "í İĺìĿ´ì§Ģ", + "íİĺ ìĿ´ì§Ģ", + "Ø® Ùģ", + "ãĤª ãĥª", + "Ġس ÛĮÙħ", + "ĠسÛĮ Ùħ", + "Ġìĸ´ ëĬIJ", + "алÑĮ нÑĥ", + "Ñĩ ний", + "ümüz de", + "æĻº èĥ½", + "ý n", + "ĠتÙĤÙĪ ÛĮت", + "Ġп ÑĢиг", + "ĠпÑĢ иг", + "ĠпÑĢи г", + "ĠгÑĢÑĥпп Ñĭ", + "am ı", + "γ οÏį", + "γο Ïį", + "оÑĢ ÑĤÑĥ", + "оÑĢÑĤ Ñĥ", + "ĠG iang", + "ĠGi ang", + "ĠGian g", + "ĠGia ng", + "ÅĻ en", + "ÅĻe n", + "Ġokol ÃŃ", + "产 ä¸ļ", + "Ġ зм", + "Ġз м", + "Ġ é¾", + "Ġé ¾", + "ÙĬ ار", + "ÙĬا ر", + "ĠاÙĦØ´ÙĬ Ø®", + "иÑĤелÑĮ нÑĭй", + "Ġ اÙĩÙħ", + "Ġا ÙĩÙħ", + "ĠاÙĩ Ùħ", + "ĠباÙĦ رÙĬاض", + "ĠÙ¾ÛĮ اÙħ", + "Ġk redi", + "Ġkr edi", + "Ġkre di", + "Ġkred i", + "ĠA rap", + "ĠAr ap", + "ĠAra p", + "Ġ ÑĢаб", + "ĠÑĢ аб", + "ĠÑĢаР±", + "ĠÑĢа б", + "ĠнекоÑĤоÑĢ ÑĭÑħ", + "ĠØŃاÙģظ Ùĩ", + "иÑĤелÑĮ ного", + "иÑĤелÑĮно го", + "Ġgerek mektedir", + "ĠD eniz", + "ĠDen iz", + "ĠتÙĦ اش", + "st agram", + "sta gram", + "stag ram", + "áv ky", + "åĬł åħ¥", + "oz or", + "ozo r", + "Ġdurum unda", + "Ġdurumu nda", + "Ġíıī ëĭ¹", + "Ġ ë´ī", + "Ġë´ ī", + "Ġp enÄĽ", + "Ġpe nÄĽ", + "Ġpen ÄĽ", + "Ú¯ اÙĨÛĮ", + "گاÙĨ ÛĮ", + "ĠK up", + "ĠKu p", + "Ġ ÑĨеÑĢ", + "ĠÑĨ еÑĢ", + "ĠÑĨе ÑĢ", + "ul ması", + "âij ł", + "ĠÑģÑĸÑĩ нÑı", + "ım ıza", + "ımız a", + "ımı za", + "å®ļ çļĦ", + "Âł ÑĤ", + "åĬŀ åħ¬", + "ìľ¼ ëĭĪ", + "ĠاÙĦ Ø¥ÙĨ", + "ĠاÙĦØ¥ ÙĨ", + "Ġ çĥ", + "Ġç ĥ", + "ãĢį ï¼Į", + "ÑĹ Ð½Ð°", + "ĠпÑĢигоÑĤов лениÑı", + "Ð ħ", + "ĠÑģ олн", + "ĠÑģол н", + "Ġë¶Ģ ìĤ°", + "æħ ¶", + "ãĤ ¾", + "v oje", + "vo je", + "voj e", + "ÛĮ دÙĨ", + "ÛĮد ÙĨ", + "ìĥĿ ëĭĺ", + "ç¹ ģ", + "á du", + "ád u", + ": ::::::::::::::", + ":: :::::::::::::", + ":::: :::::::::::", + ":::::: :::::::::", + ":::::::: :::::::", + "::: ::::::::::::", + "::::: ::::::::::", + "::::::: ::::::::", + "::::::::: ::::::", + ":::::::::: :::::", + "::::::::::: ::::", + ":::::::::::: :::", + "::::::::::::: ::", + ":::::::::::::: :", + "س ÙĨÚ¯", + "سÙĨ Ú¯", + "éĶ ĭ", + "Ġ звиÑĩай", + "Ġз виÑĩай", + "å§Ķåijĺ ä¼ļ", + "ĠμÎŃ Ïĥα", + "ĠÑĢ ождениÑı", + "æĪIJ 人", + "Ġ dÃŃl", + "Ġd ÃŃl", + "ĠdÃŃ l", + "ĠÐĶ об", + "ĠÐĶо б", + "Ġ à¹ĥà¸Ĭ", + "ÏĢ ί", + "g amber", + "gam ber", + "ĠÙĪÛĮÚĺ Ú¯ÛĮ", + "Ġ èĬ±", + "ĠèĬ ±", + "Ġb Ãły", + "ĠbÃł y", + "ĠжовÑĤ нÑı", + "åħ¬ å¼Ģ", + "ĠÑĤ оÑĩки", + "ĠÑĤо Ñĩки", + "ĠÑĤоÑĩ ки", + "ãģĤ ãģ®", + "а лÑĸв", + "ал Ñĸв", + "алÑĸ в", + "Ġch arakter", + "Ġchar akter", + "ĠÎĴ α", + "Ġzku Å¡en", + "Ġà¤Ńà¤Ĺ व", + "Ñĩ ика", + "Ñĩи ка", + "Ñĩик а", + "à¥Ģà¤Ĥ ।", + "è£ ı", + "åijĬ è¯ī", + "iy atı", + "iya tı", + "iyat ı", + "ĠÑĨ елÑĮ", + "ĠÑĨе лÑĮ", + "ĠÑĨел ÑĮ", + "Ġ ìĬĪ", + "ĠìĬ Ī", + "а ÑĢд", + "аÑĢ д", + "ĠÃľl ke", + "Ġpro since", + "Ġpros ince", + "ĠÙĨ گاÙĩ", + "ĠÙĨÚ¯ اÙĩ", + "ãĢĮ ãģĬ", + "ÎŁ Τ", + "ÎŁÎ ¤", + "ìĦľ ëĬĶ", + "ÙĪ گر", + "ض اÙĨ", + "ضا ÙĨ", + "Ġdů sled", + "çIJ ´", + "à¸ķำ à¹ģหà¸Ļ", + "к ÑĤÑĸв", + "кÑĤ Ñĸв", + "lád á", + "á¿ Ĩ", + "ĠD oÄŁu", + "ĠDo ÄŁu", + "ĠDoÄŁ u", + "ãģij ãĤĮãģ°", + "缮 ãĤĴ", + "Ġ 缴", + "ĠçĽ ´", + "æ Ľ°", + "æĽ °", + "ĠвÑĤоÑĢ ой", + "Ġг лÑĥ", + "ĠìĿ ½", + "기 ì¤Ģ", + "Ġma dde", + "Ġmad de", + "Ġmadd e", + "Ġjed né", + "Ġjedn é", + "Ġо ÑĦÑĸ", + "ìĭĿ ìĿĦ", + "Ġch út", + "Ġchú t", + "åĩº ãģĹãģŁ", + "åĩºãģĹ ãģŁ", + "и ÑĩеÑģкаÑı", + "иÑĩеÑģ каÑı", + "Ġ лок", + "Ġл ок", + "Ġal tı", + "Ġalt ı", + "ëĵľ ëĬĶ", + "ey gamber", + "ĠÑģв ое", + "ĠÑģво е", + "ĠtaÅŁ ım", + "ĠÑĤо Ñīо", + "Ġgeç ti", + "Ġpr emi", + "Ġpre mi", + "Ġprem i", + "ĠMeh met", + "ï¼Į åĽłæѤ", + "ï¼ĮåĽł æѤ", + "ί κη", + "ίκ η", + "Ġönce ki", + "Ġönc eki", + "Ġ à¤ķन", + "Ġà¤ķ न", + "ĠÑĤемп еÑĢаÑĤÑĥÑĢа", + "ĠÑĤемпеÑĢаÑĤÑĥ ÑĢа", + "éĺ ´", + "Ġìĸ¼ ë§Ī", + "Ø´ ب", + "á ky", + "ák y", + "ãĢĢ V", + "воÑĢ еннÑı", + "l asyon", + "las yon", + "Ġд оказ", + "Ġдо каз", + "Ġдок аз", + "Ġëľ »", + "Ġоб лиÑĩ", + "Ġобл иÑĩ", + "ÎĻ ÎijÎļ", + "ÎĻÎij Îļ", + "Ġ ÑĢазд", + "ĠÑĢаз д", + "ĠÑĢа зд", + "ï¼Į 为", + "å® ½", + "Ġk orum", + "Ġko rum", + "Ġkor um", + "åķĬ åķĬ", + "ĠÅĻe kla", + "ĠÅĻekl a", + "ĠÅĻek la", + "ãĥĹ ãĥ¬", + "Ġв аÑĢÑĤ", + "ĠваÑĢ ÑĤ", + "ĠпÑĢоблем Ñĭ", + "Ġ ä½ł", + "Ġth Æ¡m", + "Ġta kové", + "Ġtak ové", + "Ġtako vé", + "л енÑĭ", + "лен Ñĭ", + "ле нÑĭ", + "Ġ åĪ¶", + "ĠåĪ ¶", + "Ġji ných", + "Ġjin ých", + "Ġjiný ch", + "ĠÙĨ ص", + "ĠгÑĢÑĥд нÑı", + "Ġ ãģĹ", + "иÑĤелÑĮ ной", + "иÑĤелÑĮно й", + "ĠاØŃ ØªÙħ", + "Ñİ ÑĢ", + "ÏĨ Ïħ", + "Ġ Ø´ÙħاÙĦÛĮ", + "ĠØ´Ùħ اÙĦÛĮ", + "ĠØ´Ùħا ÙĦÛĮ", + "ĠØ´ÙħاÙĦ ÛĮ", + "Ġ ì»´", + "Ġì» ´", + "acaÄŁ ız", + "acaģı z", + "ì§Ģ ë§ī", + "ĠÑĦин анÑģов", + "Ġ ê·¹", + "Ġê· ¹", + "ĠÚĨ ÛĮزÛĮ", + "ĠÚĨÛĮز ÛĮ", + "à¥Ģ à¤Ľ", + "ص ات", + "ान म", + "Ġв озможно", + "Ġвозмож но", + "è¨ İ", + "çĦ ¦", + "ĠاÙĦبÙĦ د", + "Ġ çĶ³åįļ", + "ç¥ ¥", + "Ġë°Ķ ëĿ¼", + "Ú¯ ÛĮر", + "Ú¯ÛĮ ر", + "Ûµ Û°", + "μι οÏħÏģγ", + "ĠpÅĻed sed", + "ç»ı èIJ¥", + "å§ ij", + "e mey", + "em ey", + "eme y", + "ĠÙĨ ÙĪÙģ", + "ĠÙĨÙĪ Ùģ", + "å¾ ½", + "Ġprá va", + "Ġpráv a", + "Ġво обÑīе", + "Ġ íĭ°", + "Ġíĭ °", + "Ġب Ø£ÙĨ", + "Ġبأ ÙĨ", + "ĠFr anti", + "ĠFran ti", + "ĠP aÅŁa", + "ĠPa ÅŁa", + "ĠÙ¾ ست", + "Ġپس ت", + "k ân", + "ĠÑģиг н", + "Ġd ần", + "æ IJľ", + "æIJ ľ", + "Ġr oky", + "Ġro ky", + "Ġrok y", + "Ùĥ ÙĪر", + "ÙĥÙĪ ر", + "ĠÎĶ ή", + "али заÑĨии", + "ализ аÑĨии", + "ализа ÑĨии", + "ä¼ł å¥ĩ", + "ı da", + "lÃŃ b", + "ĠÑĢÑĸв нÑı", + "Ġн оÑı", + "Ġно Ñı", + "bÄĽ hu", + "bÄĽh u", + "ิà¸ĩห าà¸Ħม", + "ï¼Į åį´", + "Ġ ÑĩеÑģ", + "ĠÑĩ еÑģ", + "lan mÄ±ÅŁtır", + "lanmÄ±ÅŁ tır", + "Ġ Æ°á»Ľc", + "áv acÃŃ", + "ีฬ า", + "δ ÎŃ", + "âĢĮØ´ ÙĪÙĨد", + "Ġ ÑĢобÑĸÑĤ", + "ĠÑĢоб ÑĸÑĤ", + "Ġ å·´", + "Ġå· ´", + "ĠM ev", + "ĠMe v", + "ĠÙħرØŃ ÙĦÙĩ", + "Ġвз ÑĢоÑģ", + "ç½ ļ", + "Ġب اÙĦÙħ", + "Ġبا ÙĦÙħ", + "ĠباÙĦ Ùħ", + "Ġиз гоÑĤов", + "ĠS por", + "ĠSp or", + "ĠSpo r", + "åĦ Ģ", + "ĠاÙĦ Ø£ÙĨ", + "ĠاÙĦØ£ ÙĨ", + "à¹Īา à¸ĩà¸ģ", + "à¹Īาà¸ĩ à¸ģ", + "л аÑģÑĤи", + "ла ÑģÑĤи", + "лаÑģ ÑĤи", + "ÎŁ Îļ", + "ÎŁÎ ļ", + "Ġ Ú©ÛĮ", + "ĠÚ© ÛĮ", + "åij½ 令", + "ØŃ Ø¯Ø«", + "ØŃد Ø«", + "ÙĬ ÙĥÙĬ", + "ÙĬÙĥ ÙĬ", + "ĠпеÑĢв Ñĭй", + "ãĤ¹ ãĤ³", + "ĠÅ¡ pat", + "ĠÅ¡p at", + "Ġnik do", + "ั à¸ĩม", + "ัà¸ĩ ม", + "èµ «", + "æĺ ¨", + "Ġв Ñĥли", + "ĠвÑĥл и", + "ĠÐļ а", + "à¹Ī ละ", + "Ġsa mot", + "Ġsam ot", + "Ġsamo t", + "ĠобеÑģп е", + "ĠÙħعرÙģ ÛĮ", + "ĠÙħØŃصÙĪÙĦ ات", + "в анов", + "ва нов", + "ван ов", + "вано в", + "ĠÙħستÙĤ ÛĮÙħ", + "å¢ Ļ", + "Âł Ðļ", + "Ġд оÑĤ", + "Ġдо ÑĤ", + "z im", + "zi m", + "ÙIJ ر", + "Ġ Ø´ÙĪ", + "ĠØ´ ÙĪ", + "åľ¨ åľ°", + "Ġ çİ°", + "Ġçİ °", + "Ġ åĮĸ", + "ĠåĮ ĸ", + "ز ÙĪ", + "Ġyay gın", + "Ġо ÑĢиг", + "ĠоÑĢ иг", + "Ùı ÙĨ", + "Ġev rop", + "Ġ ï½ľ", + "Ġï½ ľ", + "Ġëħ¸ì¶ľ ëĵ±ë¡Ŀ", + "åĩ Ŀ", + "л еннÑĭÑħ", + "лен нÑĭÑħ", + "Ġje nom", + "Ġjen om", + "Ġ ЧÑĤобÑĭ", + "ĠЧ ÑĤобÑĭ", + "ĠЧÑĤо бÑĭ", + "ĠìĹĨ ëĭ¤", + "ĠìŬ ìĦ±", + "Ġres mi", + "im álnÃŃ", + "缮 ãģ®", + "s ian", + "si an", + "-ни бÑĥдÑĮ", + "ο κ", + "çĭ¬ ç«ĭ", + "ÅŁ ehir", + "åIJ IJ", + "åĪ¶ éĢł", + "Ġ ÎĶεν", + "ĠÎĶ εν", + "ĠÎĶε ν", + "ãĥĭ ãĥ¥", + "иÑĤелÑĮ нÑĭÑħ", + "Ġ ÙĥاÙħ", + "ĠÙĥ اÙħ", + "Ïģ κ", + "Ġr au", + "Ġra u", + "ĠÑģм еÑĢÑĤи", + "ĠÑģмеÑĢ ÑĤи", + "ĠÏĮ ÏĦαν", + "Ġ Tại", + "ĠT ại", + "Ġ رب", + "Ġر ب", + "ε νο", + "εν ο", + "ر دد", + "رد د", + "Ġ à¸ģระ", + "Ġà¸ģ ระ", + "Ġà¸ģร ะ", + "Ïĥ μο", + "Ïĥμ ο", + "Ġ æ¼Ķ", + "Ġæ¼ Ķ", + "ิà¸Ī à¸ģรรม", + "ĠÑĢаз ви", + "ĠÑĢазв и", + "ãĤ¹ ãĥļ", + "Ñĸ ÑĩноÑĹ", + "ÑĸÑĩ ноÑĹ", + "lá Å¡enÃŃ", + "láš enÃŃ", + "اب عة", + "ابع Ø©", + "ov ými", + "ový mi", + "ovým i", + "а нг", + "ан г", + "Ġкап ÑĸÑĤ", + "ãĢģ âĢĭ", + "íĸĪ ëįĺ", + "ĠÑĥ ÑģÑĸ", + "ĠÑĥÑģ Ñĸ", + "ย าว", + "ยา ว", + "Ø£ Ùħ", + "ãĥ© ãĥĥãĤ¯", + "Ġë ķ", + "ĠسÙĨ ÙĪات", + "ĠÑģÑĤаÑĤ ÑĮи", + "ĠÑģÑĤаÑĤÑĮ и", + "ÑĹ Ñħ", + "Ïģο Ïĩή", + "ĠØ£Ùĥ تÙĪبر", + "lan ma", + "Ġmal zem", + "ç £¨", + "ç£ ¨", + "Ġб окÑĥ", + "Ġбо кÑĥ", + "Ġбок Ñĥ", + "åŃĹ å¹ķ", + "ĠоÑĢганÑĸ за", + "ĠоÑĢганÑĸз а", + "ãĥ© ãĤ¤ãĥ³", + "ãĥ©ãĤ¤ ãĥ³", + "ĠÙħع دÙĨ", + "ĠÙħعد ÙĨ", + "çĶ· åŃIJ", + "Ġ æĤ", + "Ġæ Ĥ", + "Ạ¾", + "Ġmez iná", + "Ġmezi ná", + "и ваÑİÑĤ", + "ив аÑİÑĤ", + "ива ÑİÑĤ", + "ĠطبÛĮ عÛĮ", + "èĻ ij", + "à¤Ł र", + "Ġпод Ñģ", + "ĠÅŁ aÅŁ", + "à¸Ļ à¹Ĩ", + "ĠÅ¡ p", + "v ÄĽÅĻ", + "vÄĽ ÅĻ", + "з ÑĮ", + "ëĿ¼ ë§Ī", + "ุ à¸ĺ", + "âĢ¦ Ø·", + "리 ì§Ģ", + "âĦĸâĦĸ âĦĸâĦĸ", + "Ġb ức", + "ĠSp oj", + "ĠSpo j", + "ĠиÑģполÑĮзов ани", + "ĠиÑģполÑĮз овани", + "å·¦ åı³", + "en ler", + "ĠоÑī ÑĥÑī", + "Ġоб лÑĸ", + "Ġобл Ñĸ", + "ظ ËĨ", + "ÙĦ ÛĮس", + "ÙĦÛĮ س", + "æıIJ åįĩ", + "ĠговоÑĢ иÑĤÑĮ", + "ĠговоÑĢиÑĤ ÑĮ", + "Ġk ür", + "Ġkü r", + "Ġλ ειÏĦοÏħÏģγ", + "ла га", + "лаг а", + "ĠÑģÑĥ дÑĥ", + "ĠÑģÑĥд Ñĥ", + "Ġ 측", + "Ġì¸ ¡", + "θε Ïĥη", + "Ġ нен", + "Ġн ен", + "Ġне н", + "Ġbiç imde", + "Ġbiçim de", + "ÑĨÑĸй ноÑĹ", + "ÑĨÑĸйно ÑĹ", + "à¹Ģà¸Ħ ย", + "ĠDal Å¡ÃŃ", + "Ġи меÑĤÑĮ", + "Ġим еÑĤÑĮ", + "Ġиме ÑĤÑĮ", + "èĭ Ĺ", + "ĠÙħع رÙĪÙģ", + "Ġt ạp", + "Ġm eÅŁ", + "Ġme ÅŁ", + "Âł N", + "оÑĢ они", + "оÑĢон и", + "оÑĢо ни", + "ع Ùģ", + "à¹Ĥ รà¸ĩà¹Ģร", + "à¹Ĥรà¸ĩ à¹Ģร", + "âĶ ¬", + "Ġ à¹Ģà¸ŀราะ", + "Ġà¹Ģà¸ŀ ราะ", + "Ġèı² å¾ĭ宾", + "ÑģÑĤв енное", + "ÑģÑĤвен ное", + "ÑģÑĤвенно е", + "Ġاز دÙĪاج", + "ĠÑĦ ев", + "éł »", + "Ġ สล", + "Ġส ล", + "à¸ķ à¸Ńà¸Ļ", + "Ġ 기ê°Ħ", + "Ġ기 ê°Ħ", + "ä½ ©", + "ÏĦ ην", + "ÏĦη ν", + "ëĤ¬ ëĭ¤", + "ĠQ uy", + "ĠQu y", + "Ġë¶ Ļ", + "ĠС Ñĥд", + "и ж", + "Ġ à¹Ģà¸ģม", + "Ġà¹Ģà¸ģ ม", + "ĠÑģв ÑıÑĤ", + "ĠÑģвÑı ÑĤ", + "et ooth", + "eto oth", + "ε Ïģο", + "εÏģ ο", + "ÙĦ ÙħØ©", + "ÙĦÙħ Ø©", + "Ø´ ÙĪر", + "Ø´ÙĪ ر", + "Ġd omu", + "Ġdo mu", + "Ġdom u", + "èį Ĵ", + "m î", + "ëıĦ 를", + "ĠÑĢекомендÑĥ еÑĤÑģÑı", + "Ġsonra sında", + "Ġsonrası nda", + "Ġд нÑĸв", + "Ġç al", + "Ġça l", + "ãĤ«ãĥĨ ãĤ´ãĥª", + "Ġ еж", + "Ġе ж", + "Ġìķ ī", + "èī² çļĦ", + "âĢĻ nde", + "âĢĻn de", + "Ġ ÏĢÏīÏĤ", + "ĠÏĢ ÏīÏĤ", + "ĠÑĩеÑĤ веÑĢ", + "k ili", + "ki li", + "kil i", + "æĢ§ èĥ½", + "اد ÙĬØ©", + "ادÙĬ Ø©", + "çº ¯", + "ĠاÙĦ تش", + "ĠاÙĦت Ø´", + "ĠÑĤ ела", + "ĠÑĤе ла", + "ĠÑĤел а", + "Ġоб ÑĬем", + "ĠобÑĬ ем", + "å²Ĺ ä½į", + "Ġkon krét", + "Ġa rada", + "Ġar ada", + "Ġara da", + "ìĭľ ìĹIJ", + "Ġor anı", + "Ġoran ı", + "ر Ùĥ", + "ÐĽ ÐIJ", + "Ġ ménÄĽ", + "Ġmé nÄĽ", + "ج ÙĪÛĮ", + "جÙĪ ÛĮ", + "Ġv ợ", + "Ġvá» £", + "ĠAngiosper mae", + "èĥ İ", + "Ġh ôn", + "äºĭ æ¥Ń", + "ĠоÑĤ веÑĢ", + "ĠоÑĤв еÑĢ", + "Ġs rd", + "Ġsr d", + "Å¡ li", + "ส à¸ģ", + "æ¼ ı", + "ĠØ´ رØŃ", + "Ġشر ØŃ", + "ÑĨ Ñıми", + "ÑĨÑı ми", + "Ġs lav", + "Ġsl av", + "Ġsla v", + "Ġc eny", + "Ġce ny", + "Ġcen y", + "à¸Ń à¹Ģร", + "Ġ ÙĪÙĦد", + "ĠÙĪÙĦ د", + "Ġк оÑĢа", + "ĠкоÑĢ а", + "Ġко ÑĢа", + "Ġб ÑĢон", + ": .:.:.:.:", + ":.:.: .:.:", + ":.: .:.:.:", + "Ġne mus", + "Ġnem us", + "è¿Ļ æł·çļĦ", + "è¿Ļæł· çļĦ", + "Ġبر ÙĨاÙħج", + "Ġú plnÄĽ", + "ีà¸Ļ าà¸Ħม", + "Ġë°Ľ ìķĦ", + "με Ïģα", + "μεÏģ α", + "ç¼ ©", + "Ġn ắm", + "ĠобÑĬ ÑıÑģ", + "ĠконÑĤÑĢ олÑİ", + "á vajÃŃcÃŃ", + "ávajÃŃ cÃŃ", + "Ġk um", + "Ġku m", + "çĶ· 人", + "Ġv nitÅĻ", + "Ġب دÙĩ", + "Ġبد Ùĩ", + "Ġأب رÙĬÙĦ", + "人æ°ij åħ±åĴĮåĽ½", + "Ġyap ılır", + "Ġyapıl ır", + "Ġna Å¡ÃŃ", + "ĠnaÅ¡ ÃŃ", + "ãĥ¼ ãĥŃ", + "ãĥ¼ãĥ Ń", + "Ġt ạm", + "Ġhen üz", + "Ġz emi", + "Ġze mi", + "Ġzem i", + "Ġkh áng", + "Ġkhá ng", + "åħ¬ åħ±", + "Ġ èĢģ", + "ĠèĢ ģ", + "ĠعÙĪ اÙħÙĦ", + "Âł V", + "à¹ī à¹ģà¸ģ", + "άν ÏĦα", + "ĠÑĤÑĢав нÑı", + "Ġη μÎŃ", + "è´ ¸", + "ส à¸Ķ", + "Ġس Ùħت", + "ĠسÙħ ت", + "ĠØ® اک", + "ĠÑĤак ий", + "ĠÑĤа кий", + "Ġet tik", + "Ġett ik", + "Ġetti k", + "ĠÏĮ λ", + "Ġп оли", + "Ġпо ли", + "Ġпол и", + "Ġ нож", + "Ġн ож", + "Ġно ж", + "غ اÙĨ", + "ÙĨ دÙĬ", + "ÙĨد ÙĬ", + "ĠÄįty ÅĻi", + "ĠÄįtyÅĻ i", + "ĠPh Æ°Æ¡ng", + "ĠÙĪر زش", + "ĠÙĪرز Ø´", + "ãģĦ ãģĭ", + "r vé", + "rv é", + "Ġतर फ", + "Ġन à¤Ĺर", + "m asında", + "ma sında", + "mas ında", + "ması nda", + "е виÑĩ", + "ев иÑĩ", + "еви Ñĩ", + "ve ÅĻej", + "ä¿Ŀ æĮģ", + "æĬĢ èĥ½", + "æݨ èįIJ", + "l âm", + "Ġ Ïį", + "ĠÏ į", + "å¢ŀ éķ¿", + "Ġاص ÙģÙĩ", + "ĠÐĹак онÑĥ", + "ĠÐŁ ÑĢез", + "ĠÐŁÑĢ ез", + "Ġpod por", + "Ġpodp or", + "기 íĥĢ", + "Ġ íıIJ", + "Ġíı IJ", + "Ġ ëĭĪ", + "Ġëĭ Ī", + "lar ınız", + "ların ız", + "larını z", + "ãĥĸ ãĥŃ", + "ĠÑĦÑĢан ÑĨÑĥз", + "ãĥĬ ãĥ¼", + "Ġb eled", + "Ġbe led", + "Ġbel ed", + "Ġbele d", + "ัà¸Ļว าà¸Ħม", + "ĠÙģ رÙĪ", + "ĠÙģر ÙĪ", + "ÑĦ ÑĢов", + "ĠìĿ´ 룬", + "ượ u", + "Ġê³µ ìĭĿ", + "Ġbird en", + "Ġbir den", + "Ġз елен", + "Ġзел ен", + "çĴ ĥ", + "Ġh á»ĵng", + "Ġhá»ĵ ng", + "ĠÅ¡ kola", + "ĠÅ¡kol a", + "ĠÅ¡k ola", + "ĠÑģам ом", + "ĠÑģамо м", + "an lık", + "anlı k", + "空 éĹ´", + "åįĹ çľģ", + "л еÑĢг", + "ле ÑĢг", + "леÑĢ г", + "Ñĸз неÑģ", + "Âł A", + "ãĢį ãĤĴ", + "Ġkend ine", + "Ġkendi ne", + "Ġ اÙĪÙĨ", + "Ġا ÙĪÙĨ", + "ĠاÙĪ ÙĨ", + "ãĢ Ķ", + "ĠΣ Ïį", + "à¹Ģ à¸Ħล", + "à¹Ģà¸Ħ ล", + "å¥ ¶", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "Ġú Äįet", + "ĠúÄį et", + "Ñĥ ла", + "Ñĥл а", + "éĢļ ä¿¡", + "Ġì¦ IJ", + ". čĊĊ", + ".čĊ Ċ", + "ĠÐľ ед", + "ا عÙĬ", + "اع ÙĬ", + "Ġjeho ž", + "ĠGü ney", + "ĠGün ey", + "ĠÎij ÏĢο", + "ĠÎijÏĢ ο", + "Ġп олÑĸ", + "Ġпо лÑĸ", + "Ġпол Ñĸ", + "ü me", + "üm e", + "ho dob", + "hod ob", + "ĠÎĿ α", + "ĠØ¢ ÙĦات", + "ĠØ¢ÙĦ ات", + "ĠpÅĻ iz", + "ĠpÅĻi z", + "Ġt avs", + "Ġta vs", + "Ġtav s", + "Ġتب ÙĦÛĮ", + "ãĥ³ ãĥĶ", + "ãĥ³ãĥ Ķ", + "Ø® رج", + "خر ج", + "Ġак кÑĥ", + "Ġú prav", + "ĠاØŃ Ø³Ø§Ø³", + "ì¹´ ëĿ¼", + "ı mızı", + "ım ızı", + "ımız ı", + "ımı zı", + "Ġд окÑĥменÑĤ", + "Ġдок ÑĥменÑĤ", + "ĠдокÑĥм енÑĤ", + "Ġ اصÙĦ", + "Ġا صÙĦ", + "Ġاص ÙĦ", + "ظ Ùĩ", + "ĠìĿ¸ ê°Ħ", + "Ġج رÛĮاÙĨ", + "Ġجر ÛĮاÙĨ", + "Î¥ ÎĿ", + "ÑĩеÑģ каÑı", + "Ñĩе ÑģкаÑı", + "ÙĬ ÙĨÙĬØ©", + "ÙĬÙĨ ÙĬØ©", + "ÙĬÙĨÙĬ Ø©", + "åĴ ¨", + "æĹħ 游", + "Ġ à¸Īำà¸Ļวà¸Ļ", + "Ġà¸Īำ à¸Ļวà¸Ļ", + "Ġ анг", + "Ġа нг", + "Ġан г", + "Ïħ Ïĩ", + "èĻ «", + "ĠÙħ ÙĤر", + "ĠÙħÙĤ ر", + "ĠÙħÙĪسÛĮ ÙĤÛĮ", + "ut ut", + "ĠÐĽ е", + "ĠÐŁ ÑĸÑģлÑı", + "ãĤŃ ãĥ¼", + "ุล าà¸Ħม", + "åĩ ¡", + "ÏĢ οÏį", + "ÏĢο Ïį", + "ĠÃĸ dül", + "Ïĥ κ", + "Ġ ÑĨÑİ", + "ĠÑĨ Ñİ", + "Ñĭ ваÑı", + "Ñĭв аÑı", + "Ñĭва Ñı", + "ï½ŀ ï½ŀ", + "ĠØ´ ÙħاÙĦ", + "ĠØ´Ùħ اÙĦ", + "ĠØ´Ùħا ÙĦ", + "è¿ ħ", + "ĠبÙĦ Ú©Ùĩ", + "çİ Ľ", + "Ġì§Ģ ëĤĺ", + "ĠÙģ کر", + "ĠÑģÑĤеп ени", + "Ġна Ñĥки", + "ĠнаÑĥк и", + "çī© çIJĨ", + "ÄĽ le", + "ÄĽl e", + "Ġо ÑģкÑĸлÑĮки", + "ĠкÑĥлÑĮÑĤÑĥ ÑĢи", + "ĠкÑĥлÑĮÑĤÑĥÑĢ и", + "èĢĥ è¯ķ", + "Ġmateri ál", + "ĠÑģÑĤ воÑĢеннÑı", + "ĠÑģÑĤвоÑĢ еннÑı", + "Ġà¤ħ द", + "æıIJ åĩº", + "Ġè©ķ 価", + "ÙĴ د", + "Ġë§Įëĵ¤ ìĸ´", + "duÄŁu nu", + "duÄŁ unu", + "ÙĬ ÙĨÙĩ", + "ÙĬÙĨ Ùĩ", + "åĵ ¦", + "оÑĩ нÑĭÑħ", + "ĠÙħ ض", + "is mu", + "ism u", + "Ġ Ñĩай", + "ĠÑĩ ай", + "ĠÑĩа й", + "ÙĪ رÙĪب", + "ÙĪر ÙĪب", + "Ġ англ", + "Ġан гл", + "Ġанг л", + "oÄŁun luk", + "ĠпÑĢед пол", + "ĠÎŃ ÏīÏĤ", + "ส à¸ĸ", + "ĠÎķλλά δα", + "ĠBi lg", + "ĠBil g", + "Ġ بات", + "Ġب ات", + "Ġبا ت", + "ĠÐĽÑĮв Ñĸв", + "Ġyap ılması", + "Ġyapıl ması", + "æ£Ģ æŁ¥", + "æķ° åѦ", + "Ġ :.:", + "Ġ: .:", + "Ġ:. :", + "Ġ çİ©", + "Ġçİ ©", + "Îļ α", + "à¹Ģà¸Ĺ à¸Ħà¹Ĥà¸Ļà¹Ĥลย", + "Ġساخت ÙħاÙĨ", + "ĠìĨĮ 리", + "é¼ »", + "Ġs mr", + "Ġsm r", + "Ġëĭ¤ìĸij íķľ", + "Ġjed nánÃŃ", + "Ġjedn ánÃŃ", + "Ġjedná nÃŃ", + "Ġserv isi", + "Ġservi si", + "Ġservis i", + "Ġey lem", + "Ġм али", + "Ġмал и", + "Ġма ли", + "Ġvý hod", + "éĻ¤ äºĨ", + "ĠпоÑĢÑıд кÑĥ", + "Ġn ový", + "Ġno vý", + "Ġnov ý", + "å¤ ķ", + "ĠнекоÑĤоÑĢ Ñĭе", + "Ġ^ {}", + "Ġ^{ }", + "γÏī γ", + "Ñĥ ÑĪки", + "ÑĥÑĪ ки", + "Ġp sik", + "Ġps ik", + "Ġpsi k", + "ĠíĶĦ 리", + "Ø´ اء", + "Ġ ван", + "Ġв ан", + "Ġва н", + "Ġس ÙĥاÙĨ", + "ç¢ ¼", + "ĠÎľ η", + "ĠÑĥÑĢов енÑĮ", + "ãĤµ ãĥ¼", + "ĠاÙĦب ØŃر", + "Ġd nÃŃ", + "Ġdn ÃŃ", + "à¸ģาร ศ", + "e diÄŁi", + "edi ÄŁi", + "Ġbelir li", + "Ùĭ ØĮ", + "ĠzamÄĽst nan", + "ĠzamÄĽstn an", + "æŁ ±", + "ا ÙģÙĬ", + "اÙģ ÙĬ", + "Ġh ải", + "æĢĿ æĥ³", + "Ġn eler", + "Ġne ler", + "Ġnel er", + "Ġرس ÙħÛĮ", + "ĠرسÙħ ÛĮ", + "Ñģ еÑĢ", + "Ñģе ÑĢ", + "ãģĵãģ¨ ãģ§", + "ĠZá kladnÃŃ", + "л ова", + "ло ва", + "лов а", + "к ÑĤÑĥ", + "кÑĤ Ñĥ", + "ÙĪس Ùģ", + "Ñĸб лÑĸ", + "Ì Ĥ", + "ÑĢ д", + "éĻ ³", + "æį ·", + "ĠyaÅŁ ayan", + "à¥ģ à¤ļ", + "à¥ģठļ", + "ÑĸÑĤ ÑĤÑı", + "Ġb á»ģ", + "ëĤĺ ëĿ¼", + "Ġм ÑıÑģ", + "Ġ{ [%", + "Ġ{[ %", + "θ α", + "Ġдоз волÑı", + "Ġдозвол Ñı", + "Ġ åIJĦ", + "ĠåIJ Ħ", + "ĠÐŁ еÑĢв", + "ĠÐŁÐµÑĢ в", + "ĠSaÄŁ lık", + "ÑģÑĤоÑĢ иÑı", + "Ġbun lar", + "Ġs á»ķ", + "़ à¥į", + "Ġ åĪ©", + "ĠåĪ ©", + "ĠÑģ поÑģ", + "ĠÑģп оÑģ", + "Ġyap tır", + "Ġyaptı r", + "Ġt Æ°á»Ŀng", + "ÙĪ ÙĨØ©", + "ÙĪÙĨ Ø©", + "Ġ еп", + "Ġе п", + "ãģ§ãģį ãģªãģĦ", + "Ùģ تÙħ", + "Ùģت Ùħ", + "ĠÐĵ ол", + "íķĺ ì§Ģë§Į", + "íķĺì§Ģ ë§Į", + "Ġì§Ħ ì§ľ", + "Ġob jedn", + "Ġobj edn", + "Ġизмен ениÑı", + "女 人", + "Ġпл ани", + "Ġпла ни", + "Ġплан и", + "ĠFak ült", + "Ġt zv", + "Ġtz v", + "ĠобÑıз аÑĤелÑĮ", + "Ġблиз ÑĮко", + "r ası", + "ra sı", + "ras ı", + "ĠεÏĢί ÏĥηÏĤ", + "ĠÑĦак ÑĤи", + "ĠÑĦакÑĤ и", + "ĠÄIJ ặc", + "ĠAlt ın", + "л иÑĤ", + "ли ÑĤ", + "Ġл ÑĸÑģ", + "ĠлÑĸ Ñģ", + "çī §", + "Ġп ÑĥÑģÑĤ", + "ĠпÑĥ ÑģÑĤ", + "Ġком ÑĸÑģ", + "ä¿Ŀ éļľ", + "åħ· ä½ĵ", + "- ÑĤ", + "Ġtr hu", + "Ġtrh u", + "Ġâī Ī", + "Ġдека бÑĢÑı", + "ĠÑĦоÑĢм Ñĭ", + "ĠÑĦоÑĢ мÑĭ", + "Ng oÃłi", + "Ġdo hod", + "رÙĬ ÙĥÙĬØ©", + "رÙĬÙĥ ÙĬØ©", + "رÙĬÙĥÙĬ Ø©", + "ĠØ¢ÙħÙĪز Ø´ÛĮ", + "ĠØ¢ÙħÙĪزش ÛĮ", + "ĠzajÃŃm av", + "Ġkat ılım", + "Ġkatıl ım", + "ä¸ ĺ", + "Ġko num", + "Ġkon um", + "Ġkonu m", + "Ġм оÑĩ", + "Ġмо Ñĩ", + "ãĥ³ ãĥķ", + "ãĥ³ãĥ ķ", + "диви дÑĥ", + "Ġ äºļ", + "Ġ æĴ", + "Ġæ Ĵ", + "γÏģά ÏĨ", + "ãĥIJ ãĤ¹", + "Ġп Ñĥнк", + "ĠBir leÅŁik", + "Ġqu en", + "Ġque n", + "Ġq uen", + "Ġв каз", + "Ġвк аз", + "à¥ĩ शà¤ķ", + "à¥ĩश à¤ķ", + "ĠY unan", + "ĠYu nan", + "ĠYun an", + "ãģł ãģ¨", + "Û±Û¹ Û·", + "á ty", + "át y", + "Ġ ÙĪص", + "ĠÙĪ ص", + "Ġнег аÑĤив", + "ãģ¤ ãģ®", + "Ġ åĬ¨", + "ĠåĬ ¨", + "ãĥį ãĥĥãĥĪ", + "Ġд Ñĸй", + "ĠдÑĸ й", + "ĠbaÅŁ ında", + "Ġtr Æ°ng", + "Ġm akin", + "Ġma kin", + "Ġmak in", + "Ġ æĦĽ", + "ĠæĦ Ľ", + "м еÑĩ", + "ме Ñĩ", + "Ġ è¿ij", + "Ġè¿ ij", + "ÙĤ در", + "ÙĤد ر", + "Ġاست اÙĨد", + "ĠاستاÙĨ د", + "Ġinform acÃŃ", + "ार à¤ķ", + "è¬ Ŀ", + "ÑĢаб аÑĤ", + "Ġ çŃĶ", + "Ġç ŃĶ", + "ĠçŃ Ķ", + "Ġ èĩ³", + "Ġèĩ ³", + "Ġп олÑĮ", + "Ġпо лÑĮ", + "Ġпол ÑĮ", + "ĠÙĩÙĨ ر", + "ëĮĢ ë¹Ħ", + "Ġخارج ÛĮ", + "r act", + "ra ct", + "rac t", + "ãĢĤ ãģĵãĤĮ", + "ĠØ´ÙĪر اÛĮ", + "л енно", + "лен но", + "Ġh isset", + "Ġhis set", + "Ġhiss et", + "Ġc Ãłi", + "ĠcÃł i", + "ĠÑĦ оÑĤо", + "ĠÑĦоÑĤ о", + "æģ Ĵ", + "Ġмед иÑĨин", + "Ġмеди ÑĨин", + "ÑģÑĤ вÑĸ", + "ÑģÑĤв Ñĸ", + "ĠاÙĦ عÙĦ", + "ĠاÙĦع ÙĦ", + "ĠпиÑģÑĮ мен", + "ãĢĤ ãģ¾ãģŁ", + "Ġvlast nÄĽ", + "Ġп ода", + "Ġпо да", + "Ġпод а", + "Ïģ οι", + "Ïģο ι", + "Ġ ìĦĿ", + "ĠìĦ Ŀ", + "ĠìĿ¼ ìĿ´", + "Ġ ìĽĮ", + "ĠìĽ Į", + "ок Ñģи", + "окÑģ и", + "Ġos oby", + "Ġosob y", + "ÐŁÐ¾Ñģ ле", + "ĠÑĸÑģÑĤоÑĢ ÑĸÑĹ", + "ع ÙĦÙī", + "عÙĦ Ùī", + "н ка", + "ت Ùħبر", + "تÙħ بر", + "à¥ĩ हर", + "à¥ĩह र", + "ĠJ ana", + "ĠJan a", + "ĠJa na", + "ÙĦ ÙĬات", + "ÙĦÙĬ ات", + "ĠмаÑĢ ÑĤа", + "ĠÐļи ÑĶ", + "ĠÑĢоб оÑĤÑĥ", + "ĠÑĢобоÑĤ Ñĥ", + "Ġnh ấn", + "и Ñģлов", + "иÑģ лов", + "ëŁ Ń", + "Ġo dv", + "Ġod v", + "ĠT á»īnh", + "âĢľ ê·¸", + "ãģ» ãģĨ", + "é² ľ", + "м еÑĨÑĮ", + "ме ÑĨÑĮ", + "า ศาสà¸ķร", + "าศ าสà¸ķร", + "à¥ģ à¤ĵ", + "à¥ģठĵ", + "ิ à¸Ļà¸Ĺ", + "ิà¸Ļ à¸Ĺ", + "m ada", + "ma da", + "mad a", + "ز اÙħ", + "زا Ùħ", + "ĠÙĥ بÙĬر", + "å®ŀ æĸ½", + "ze ÅĪ", + "Ġl ái", + "Ġlá i", + "Ïĥ μα", + "Ïĥμ α", + "ا سات", + "اس ات", + "ÑĦ ÑĤ", + "è° ±", + "çĮ ľ", + "Ġpro bÃŃ", + "Ġprob ÃŃ", + "æľĢ è¿ij", + "ÑĢ ад", + "ÑĢаР´", + "ÑĢа д", + "ãĤ½ ãĥ³", + "Ġ клад", + "Ġк лад", + "Ġкл ад", + "Ġкла д", + "à¥ľ à¤ķ", + "é v", + "ล าย", + "ลา ย", + "èİ İ", + "ĠμÎŃ ÏĩÏģι", + "Ġ кÑĥÑģ", + "Ġк ÑĥÑģ", + "ĠкÑĥ Ñģ", + "Ġ íĻĺê²½", + "ĠíĻĺ ê²½", + "Ñĩ оÑĹ", + "åıĺ åĮĸ", + "Ġب تÙĪاÙĨ", + "Ġبت ÙĪاÙĨ", + "Ġt ắt", + "Ġgöster en", + "а лÑİ", + "ал Ñİ", + "Ġкоман ди", + "Ġкоманд и", + "Ġ 컨", + "Ġì» ¨", + "Ñĥ нд", + "Ñĥн д", + "Ġج ÙĦÙĪ", + "ĠجÙĦ ÙĪ", + "åŃIJ çļĦ", + "ĠÑģ б", + "ĠÐł аÑģ", + "ĠÐłÐ° Ñģ", + "P CP", + "PC P", + "ĠCumhur baÅŁ", + "од аÑĤелÑĮ", + "ÃŃ sto", + "ÃŃs to", + "ÃŃst o", + "Ġo znám", + "Ġoz nám", + "ãĥ¼ ãĥĭ", + "ãĥ¼ãĥ ĭ", + "Ġok uy", + "Ġoku y", + "o phy", + "op hy", + "oph y", + "า à¸Ļà¸Ħร", + "าà¸Ļ à¸Ħร", + "ĠÎķ θν", + "ay ım", + "ayı m", + "Ùİ Ø£", + "æİ ¡", + "Ġfunk ce", + "Ġfunkc e", + "æļ ĸ", + "Ø· ار", + "ĠÐĿ аг", + "ĠÐĿа г", + "Ġ ä¸ĩåĨĨ", + "Ġä¸ĩ åĨĨ", + "Ġ íĴį", + "ĠíĴ į", + "Ġ ä½ı", + "Ġ ï¼İ", + "Ġï¼ İ", + "Ñĭ ваÑİÑĤÑģÑı", + "Ñĭв аÑİÑĤÑģÑı", + "Ñĭва ÑİÑĤÑģÑı", + "ÑĭваÑİÑĤ ÑģÑı", + "ĠP la", + "ĠPl a", + "ا ÙĬÙĦ", + "اÙĬ ÙĦ", + "Ġ무 ìĹĩ", + "Ġкон еÑĩно", + "к м", + "à¤Ĥ पर", + "à¤Ĥप र", + "Ġ ìłķë¶Ģ", + "Ġìłķ ë¶Ģ", + "ĠëĤ´ 볤", + "ãĤ° ãĥ«", + "çģ °", + "Ġc yk", + "Ġcy k", + "Ġжел Ñĥд", + "ĠëĨĴ ìĿĢ", + "çĶŁ åij½", + "æµ ´", + "Ġart Ä±ÅŁ", + "Ġ Ðĩ", + "ĠÐ ĩ", + "ï¼ ²", + "e kim", + "ek im", + "eki m", + "ĠÑĦ едеÑĢа", + "ĠвеÑĢеÑģ нÑı", + "н иÑĤе", + "ни ÑĤе", + "ниÑĤ е", + "ĠÄ°ÅŁ te", + "ĠÙĪضع ÛĮت", + "ãģķ ãģ¾", + "ĠtÅĻ etÃŃ", + "ĠtÅĻet ÃŃ", + "u luÄŁ", + "ulu ÄŁ", + "ĠCumhur iyet", + "ä¼ Ł", + "Ġ ë§Ŀ", + "Ġë§ Ŀ", + "Ġver mek", + "Ġverm ek", + "Ġn alez", + "Ġna lez", + "Ġnal ez", + "Ġnale z", + "çĵ ¶", + "Ġd iÅŁ", + "Ġdi ÅŁ", + "ĠH á»ĵng", + "ĠHá»ĵ ng", + "غ ÙĬرة", + "غÙĬر Ø©", + "å© Ĩ", + "н ив", + "ни в", + "Ġr út", + "' nda", + "'n da", + "Ġh roz", + "Ġhr oz", + "à¥ī प", + "Ġза коном", + "Ġзак оном", + "Ġзакон ом", + "Ġзако ном", + "Ġjed nu", + "Ġjedn u", + "ĠKa dın", + "ĠKad ın", + "in dir", + "ind ir", + "indi r", + "س ازÛĮ", + "åĮº åŁŁ", + "ĠkonuÅŁ tu", + "Ġز ÙĨد", + "ĠزÙĨ د", + "ा ĊĊ", + "ाĊ Ċ", + "ĠÐIJ з", + "à¸ĩ à¸Ĥà¸Ńà¸ĩ", + "à¸ĩà¸Ĥ à¸Ńà¸ĩ", + "ĠÑģвой ÑģÑĤва", + "Ġìŀij íĴĪ", + "пе ки", + "Ġ å°±", + "Ġå° ±", + "ев ого", + "ево го", + "ĠtaÅŁ ıy", + "ĠÙħÙĨ Ø·ÙĤØ©", + "ĠÙħÙĨØ· ÙĤØ©", + "ĠÃĩ ocuk", + "Û² Û·", + "ĠÏĥÏħ μÏĢ", + "é£Ł åĵģ", + "h á", + "ï¼ ¯", + "ÙĦ ÙħÙĩ", + "ÙĦÙħ Ùĩ", + "ãģ¨ãģª ãģ£ãģŁ", + "о ÑĢÑĸ", + "оÑĢ Ñĸ", + "° }", + "ĠtaÅŁ ın", + "çŁ ¿", + "ĠÑĩаÑģÑĤ ини", + "ĠÑĩаÑģÑĤи ни", + "ĠدÙĬ سÙħبر", + "Ġ èī¯", + "Ġèī ¯", + "st ÅĻÃŃ", + "Ġ ÑĨик", + "ĠÑĨ ик", + "ĠÑĨи к", + "âĢķâĢķ âĢķâĢķ", + "ĠÄ°ng iltere", + "ĠÑģÑĤ ÑĢаÑĤег", + "ĠÑģÑĤÑĢ аÑĤег", + "ÃĦ Ÿ", + "и Ñĩного", + "иÑĩ ного", + "иÑĩно го", + "ÃŃ rk", + "ÃŃr k", + "ĠÎij Ïģ", + "! âĢľĊĊ", + "!âĢľ ĊĊ", + "Ġ 깨", + "Ġê¹ ¨", + "à¥ģà¤Ĩ त", + "ĠدÙĨÛĮ ا", + "ĠدÙĨ ÛĮا", + "l ÃŃn", + "lÃŃ n", + "Ġà¤ķ ड", + "ĠÙħ بت", + "ĠÙħب ت", + "ем ÑĭÑħ", + "о би", + "об и", + "ย à¸Ļà¸ķ", + "ยà¸Ļ à¸ķ", + "à¤Ĥध न", + "ÚĨ ÛĮ", + "Ġ çŁ¥", + "Ġç Ł¥", + "ĠçŁ ¥", + "ĠXu ân", + "a daki", + "ad aki", + "ada ki", + "Ġ orta", + "Ġor ta", + "Ġort a", + "æł¹ æľ¬", + "åħ± åIJĮ", + "н ений", + "не ний", + "нен ий", + "ب ÙĬرة", + "بÙĬ رة", + "بÙĬر Ø©", + "çŃ ĭ", + "ïº Ķ", + "âĢĮ ÙĩاÙĬ", + "âĢĮÙĩا ÙĬ", + "Ġö deme", + "Ġödem e", + "ĠØ¢ÙĨ ÚĨÙĩ", + "Ġза Ñıви", + "ĠзаÑıв и", + "ĠÙĨÙĤ Ø´Ùĩ", + "ĠÙĨÙĤØ´ Ùĩ", + "Ġ ç³»", + "Ġç ³»", + "Ġç³ »", + "à¥ĭ ।", + "Ġì§Ģ ìłķ", + "Ġin sp", + "Ġins p", + "Ġ ÑĤен", + "ĠÑĤ ен", + "ĠÑĤе н", + "Ġت Ø·", + "Ġqu ảng", + "Ġquả ng", + "Ġquản g", + "åī £", + "ãģı ãģ®", + "ĠÑĨ им", + "ĠÑĨи м", + "k ovi", + "ko vi", + "kov i", + "i yah", + "iy ah", + "iya h", + "Ġ ëIJľëĭ¤", + "ĠëIJľ ëĭ¤", + "ص Ùĩ", + "ĠÄij u", + "Ġsu á»ijt", + "ı ma", + "ım a", + "ì§Ģ ê³ł", + "Ì ĥ", + "à¸ļ าย", + "ĠCert if", + "ĠCer tif", + "ĠÑĥÑģ ÑĸÑħ", + "ĠÑĥÑģÑĸ Ñħ", + "à¸ķะ ว", + "εί ÏĦε", + "Ġ č", + "Ġмож ливÑĸÑģÑĤÑĮ", + "Ġможлив ÑĸÑģÑĤÑĮ", + "Ġ -âĢIJ", + "Ġ- âĢIJ", + "Ġ íĺ¹", + "Ġíĺ ¹", + "ìĤ¬ ì§Ħ", + "Ġд аниÑħ", + "Ġда ниÑħ", + "Ġдан иÑħ", + "Ġzah áj", + "주 ëĬĶ", + "Ġг ид", + "n iž", + "ni ž", + "Ġ^{ °}", + "Ġk ro", + "Ġkr o", + "Äį en", + "Äįe n", + "ÏĨ ι", + "ımız da", + "Ġ æ¹ĸ", + "Ġæ¹ ĸ", + "Ġпов ÑĢежд", + "Ġì¡´ ìŀ¬", + "à¸Ļ าà¸Ļ", + "à¸Ļา à¸Ļ", + "μÎŃ Î½Î¿ÏĤ", + "μÎŃν οÏĤ", + "μÎŃνο ÏĤ", + "æ½ ľ", + "ï¼Į 使", + "Ġd osp", + "Ġdo sp", + "Ġdos p", + "Ġl iá»ģn", + "Ġli á»ģn", + "ัà¸ļ à¸Ħวาม", + "ัà¸ļà¸Ħ วาม", + "ĠÑĢабоÑĤ е", + "ĠÑĢаб оÑĤе", + "ĠÑĢабо ÑĤе", + "Ġмай бÑĥÑĤ", + "à¹Ģà¸ģ ษ", + "B aÅŁ", + "Ba ÅŁ", + "Ġ æĿ±äº¬", + "ĠæĿ± 京", + "наÑĩ ала", + "δ ει", + "δε ι", + "à¥Ī प", + "Ñĸ мÑĸ", + "Ñĸм Ñĸ", + "Ġf izik", + "Ġfi zik", + "Ġfiz ik", + "ว ล", + "ä¼ į", + "Ġ à¸Ĭà¸Ļะ", + "Ġà¸Ĭ à¸Ļะ", + "' ÑıÑĤ", + "'Ñı ÑĤ", + "н ил", + "ни л", + "и нов", + "ин ов", + "ĠÄijo án", + "รว à¸Ī", + "f et", + "fe t", + "à¹Į à¹Ĥ", + "Ġ маÑĤи", + "Ġм аÑĤи", + "ĠмаÑĤ и", + "Ġма ÑĤи", + "é¨ İ", + "Ðļ Т", + "à¹Ģส à¸Ļà¸Ń", + "à¹Ģสà¸Ļ à¸Ń", + "Ġм ав", + "Ġма в", + "lı ģına", + "lıģı na", + "lıģ ına", + "lıģın a", + "Ġпо Ñĩина", + "ĠпоÑĩ ина", + "ู à¸ķร", + "ูà¸ķ ร", + "ÑĨ еÑĢ", + "ÑĨе ÑĢ", + "uj ete", + "uje te", + "ujet e", + "Ġtah min", + "Ġвим ог", + "า à¸Ł", + "าภŁ", + "е дж", + "ед ж", + "ÏĦ εÏį", + "ÏĦε Ïį", + "ad la", + "ĠÄij Æ°Æ¡ng", + "Ġد استاÙĨ", + "Ġbas ın", + "Ġba sın", + "ĠÑħ в", + "Ġ reak", + "Ġre ak", + "ĠоÑĤ меÑĤ", + "æ³ ¥", + "Ġm áte", + "Ġmá te", + "Ġmát e", + "Ġzo run", + "Ġzor un", + "ãģ¨æĢĿ ãģĨ", + "Ġدر جة", + "ĠвÑĸд ÑģÑĥÑĤ", + "Ġع اÙħÙĦ", + "ĠعاÙħ ÙĦ", + "èĶ µ", + "Ġson raki", + "Ġsonra ki", + "Ġmoh li", + "Ġmohl i", + "и ваеÑĤ", + "ив аеÑĤ", + "ива еÑĤ", + "ĠпÑĸд ÑģÑĤав", + "Ġost rov", + "Ġostr ov", + "ान व", + "âĢŀ P", + "Ġви знаÑĩа", + "ĠвизнаÑĩ а", + "Ġprav dÄĽpodob", + "Ġz az", + "Ġza z", + "ìĿ´ 를", + "Ġдж еÑĢ", + "ĠÐł ад", + "ĠÐłÐ° д", + "ĠÑģеÑĢÑĮ ез", + "Ġ дем", + "Ġд ем", + "Ġде м", + "ÏĢ ή", + "ĠÐĦ вÑĢоп", + "ĠÐĦв ÑĢоп", + "ĠÄįesk é", + "ĠÄįe ské", + "ï¾ ı", + "Ġ ØŃÙĬ", + "ĠØŃ ÙĬ", + "ì¼ ĢìĿ´", + "ì¼Ģ ìĿ´", + "ĠØ® ÙĪÙĨ", + "ĠØ®ÙĪ ÙĨ", + "Âł L", + "ãģĦ ãģ«", + "из неÑģ", + "ĠÙħ ÙĤاÙħ", + "ĠÙħÙĤ اÙħ", + "ĠاÙĦ ØŃÙĦ", + "ĠاÙĦØŃ ÙĦ", + "ëĨ į", + "ĠØ¢ ÛĮا", + "ĠØ¢ÛĮ ا", + "ç¿ ¼", + "ï¼ ½", + "æ¸ IJ", + "ли вÑĸ", + "лив Ñĸ", + "ãģĦ ãģ¦ãģĦãĤĭ", + "ãģĦãģ¦ ãģĦãĤĭ", + "Ġ ÎijÎł", + "ĠÎij Îł", + "ĠиÑģполÑĮз ÑĥеÑĤÑģÑı", + "ĠиÑģполÑĮзÑĥ еÑĤÑģÑı", + "Ġm át", + "Ġmá t", + "Ġμε γά", + "Ġμεγ ά", + "ëħ ¼", + "æµ· éģĵ", + "ĠÙħØ´Ú© ÙĦات", + "ĠÙħØ´Ú©ÙĦ ات", + "Ñĩ на", + "'; ';", + "Ġ μία", + "Ġμ ία", + "Ïģ Ïİν", + "ÏģÏİ Î½", + "Ġby ste", + "ĠÑįлек ÑĤÑĢи", + "ĠÑįлекÑĤÑĢ и", + "ĠY ardım", + "ĠYard ım", + "ĠYar dım", + "Ġh át", + "Ġhá t", + "ĠÐĶ еÑĢжав", + ". С", + "Ġo rada", + "Ġor ada", + "Ġora da", + "Ġal anı", + "Ġalan ı", + "åľ° åŁŁ", + "ĠدÙĩ ÙĨد", + "мен ÑĪ", + "ĠоÑĢг анов", + "ĠоÑĢган ов", + "Ġع ص", + "ู à¸ĩส", + "ูà¸ĩ ส", + "ĠØ´ عر", + "Ġشع ر", + "Ġìĸ »", + "Ġά λλ", + "Ġάλ λ", + "Ġg ói", + "Ġgó i", + "ĠÙĨ اØŃ", + "å¼ ĺ", + "à¥įथ ल", + "i lim", + "il im", + "ili m", + "ëIJĺ ì§Ģ", + "Ġкон ÑĨе", + "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", + "Ġì¤Ģ ë¹Ħ", + "Ġostat nÃŃ", + "Ġvlá dy", + "Ġvlád y", + "ĠÑģо биÑĢа", + "ĠÑģоб иÑĢа", + "ĠìĹŃ ìĤ¬", + "à¹ģ à¸ģรม", + "à¹ģà¸ģ รม", + ". ï¼ı", + "Ùı ÙĪÙĨ", + "ÙıÙĪ ÙĨ", + "Ù¾ س", + "ĠW ikip", + "ĠWi kip", + "ĠWiki p", + "ĠWik ip", + "Ġ æ¾", + "Ġæ ¾", + "Ġж аÑĢ", + "容 æĺĵ", + "ĠprostÅĻed nictvÃŃm", + "Ġž eny", + "Ġže ny", + "Ġžen y", + "Ġèı²å¾ĭ宾 çĶ³åįļ", + "а ÑİÑĤÑģÑı", + "аÑİÑĤ ÑģÑı", + "Ġm iêu", + "Ġmi êu", + "Ġp enÃŃze", + "δ ιά", + "δι ά", + "ol dur", + "old ur", + "ĠпÑĢимеÑĢ но", + "ĠìŀĪ ê³ł", + "à¸ĩ à¸Ńย", + "к овий", + "ко вий", + "ков ий", + ". ÎŁ", + "à¹ĥ à¸Ħร", + "çĭ ł", + "ĠÐŁ Ñĸв", + "æĶ¹ éĿ©", + "ĠÐĿаÑģ еление", + "Å¡et ÅĻ", + "ÙĴ ب", + "Ġ âĶĢ", + "ĠâĶ Ģ", + "غ ÙĬÙĦ", + "ĠдÑĸÑıлÑĮ нÑĸÑģÑĤÑĮ", + "ĠÙĦ ÙĬس", + "ĠÙĦÙĬ س", + "Ġìĭľ ìŀ¥", + "ãĥŁ ãĥ¥", + "ĠÚ© ÙĪت", + "ĠÚ©ÙĪ ت", + "ĠÎĵ ι", + "ิ à¹Ģว", + "e ktor", + "ek tor", + "ekt or", + "ĠбÑĥд Ñĥ", + "ĠбÑĥ дÑĥ", + "но важ", + "нов аж", + "нова ж", + "Ñī аеÑĤÑģÑı", + "Ñīа еÑĤÑģÑı", + "Ġng ôn", + "ĠvÄĽ c", + "å¾ IJ", + "à¸Ńà¹Ģม ร", + "ัà¸į à¸Ĭ", + "ĠиÑģполÑĮз ÑĥÑİÑĤ", + "ĠиÑģполÑĮзÑĥ ÑİÑĤ", + "r ubu", + "ru bu", + "rub u", + "Ġnh á»±a", + "ãģĮ ãģĬ", + "ĠÐĵ аÑĢ", + "о ÑĢе", + "оÑĢ е", + "Ġз олоÑĤ", + "æ Ł³", + "æŁ ³", + "Ġ ÙĬØ´", + "ĠÙĬ Ø´", + "Ġповин нÑĸ", + "اÙĤ تص", + "ÙĦ ØŃ", + "ĠокÑĤ ÑıбÑĢÑı", + "ĠnÄĽk dy", + "Ġо бÑĢа", + "Ġоб ÑĢа", + "ست Ú¯ÛĮ", + "符 åIJĪ", + "Ġth iá»ĥu", + "æĺ¯ ä»Ģä¹Ī", + "Ġro zs", + "Ġroz s", + "ì½ľ 걸", + "Ġк аÑĦ", + "Ġка ÑĦ", + "åIJĮ æŃ¥", + "ì¼ ĵ", + "ÏĢ ÏĦÏħ", + "à¸ł ายà¹ĥà¸Ļ", + "ι ÏĥÏĦή", + "ιÏĥ ÏĦή", + "ĠدÙĪÙĦ ار", + "ĠÙħا ÙĬÙĪ", + "ĠÙħاÙĬ ÙĪ", + "Ġ peÄį", + "Ġp eÄį", + "Ġpe Äį", + "ัà¸ļ ม", + "ÎĻ ÎĶ", + "ı ydı", + "ıy dı", + "ัà¸ģ à¸Ĺ", + "à¸Ľà¸£à¸° à¸ĸม", + "κ αι", + "κα ι", + "Ġpro dej", + "Ġprod ej", + "ĠиÑİ Ð»Ñı", + "Ġv Å©", + "é© ±", + "Ġh vÄĽ", + "Ġhv ÄĽ", + "æĥ³ è¦ģ", + "ç¯ Ħ", + "ç ak", + "ça k", + "Ġм Ñıг", + "ım ın", + "ımı n", + "Ġdisp ozici", + "Ġu kaz", + "Ġuk az", + "r acak", + "ra cak", + "rac ak", + "Ġболез ни", + "ว à¹Ĥม", + "Ġз ел", + "ĠÐĴ ики", + "ĠÐĴи ки", + "ĠÐĴик и", + "ĠÐł од", + "ูà¸ģ à¸Ħ", + "í ij¸", + "Ġth ải", + "ĠbaÄŁ ımsız", + "ĠÑĢоÑģ Ñģий", + "ĠÐļ ам", + "ĠÐļа м", + "ĠиÑģполÑĮзов аниÑı", + "ĠиÑģполÑĮз ованиÑı", + "ĠиÑģполÑĮзовани Ñı", + "ĠØŃ Ø°", + "Âł ³³³³³³³³", + "³³ ³³³³³³³", + "³³³³ ³³³³³", + "³³³ ³³³³³³", + "³³³³³³³³ Âł", + "³³³³³³³ ³³", + "³³³³³ ³³³³", + "³³³³³³ ³³³", + "ĠاÙĨت ÙĤاÙĦ", + "Ġаб ÑģолÑİÑĤ", + "Ġ Ä±ÅŁÄ±k", + "ĠÄ±ÅŁÄ± k", + "ÏĦο γÏģαÏĨ", + "ĠболÑĮÑĪ ой", + "Ġعب ارت", + "Ġعبار ت", + "ÃŃ Å¾", + "Ġدر ست", + "Ġدرس ت", + "ĠÑģл ово", + "ĠÑģлов о", + "ĠÑģло во", + "à¥Ī Ċ", + "ب ÙĪب", + "بÙĪ ب", + "ĠÐĴ оÑĤ", + "ĠÐĴо ÑĤ", + "ว à¹Ħà¸Ľ", + "Ġbil inen", + "Ġbilin en", + "Ġ ÙĤÙĬ", + "ĠÙĤ ÙĬ", + "Ġbun ların", + "Ġbunlar ın", + "Ġbunları n", + "Ùij ت", + "Ġbas it", + "Ġba sit", + "ë¦ ¿", + "ائ رة", + "ائر Ø©", + "Ġp ů", + "Ġed ilmiÅŁ", + "Ġedilm iÅŁ", + "Ġedil miÅŁ", + "Ġ ä½IJ", + "ĠYön etim", + "ĠYönet im", + "Ùħ ÛĮر", + "ÙħÛĮ ر", + "Ġsp ou", + "Ġspo u", + "æ·± åľ³", + "Ġвза ÑĶм", + "ÎĻ ÎĽ", + "Ð ĥ", + "ĠдеÑĢжав ноÑĹ", + "Ġ mrt", + "Ġm rt", + "Ġmr t", + "ĠDe mir", + "ĠDem ir", + "é» İ", + "ĠÑĢег ÑĥлÑıÑĢ", + "Ġник огда", + "å¼ ¾", + "à¥ī ड", + "Ġг лаз", + "Ġгла з", + "ĠÙħÛĮ Ú©ÙĨ", + "ĠÙħÛĮÚ© ÙĨ", + "éĻIJ å®ļ", + "Ġнав к", + "Ġпод ÑĤ", + "ĠتصÙĪ ÛĮر", + "ĠاÙĦØŃ Ø¯ÙĬØ«", + "Ġdo Å¡lo", + "нÑİ Ñİ", + "ĠÑģ Ñħод", + "ĠÑģÑħ од", + "Ø· ÙĤØ©", + "ĠÑģенÑĤ ÑıбÑĢÑı", + "çī¹ æ®Ĭ", + "à¸ģาร à¹ģà¸Ĥ", + "á zd", + "áz d", + "ÑĶ ÑĤе", + "ĠΣ ε", + "ĠÙĦ ÙĥÙĦ", + "ĠÙĦÙĥ ÙĦ", + "åIJį åŃĹ", + "اÙĨ ÛĮا", + "اÙĨÛĮ ا", + "Ġc ins", + "Ġcin s", + "Ġci ns", + "기 ìĹħ", + "Ġ éŁ³", + "Ġé Ł³", + "ĠéŁ ³", + "éł ĥ", + "ย าย", + "ยา ย", + "ìļ ķ", + "ĠvÃŃ tÄĽz", + "à¥įर ब", + "Ġشر ÙĤÛĮ", + "ĠشرÙĤ ÛĮ", + "ĠbezpeÄį nost", + "Ġçerç ev", + "Ġ 맼", + "Ġë§ Ľ", + "c ky", + "ck y", + "ĵ ¨", + "ĠÑĥм оваÑħ", + "ĠÑĥмов аÑħ", + "л иÑħ", + "ли Ñħ", + "m eniz", + "men iz", + "meni z", + "Ġب Ú¯ÛĮر", + "Ġبگ ÛĮر", + "ÙĨ Ùī", + "Ġ à¸ģารà¹ģà¸Ĥ", + "Ġà¸ģาร à¹ģà¸Ĥ", + "ι Ïĥε", + "ιÏĥ ε", + "âĢ³ E", + "Ġdönem inde", + "리 ì¹´", + "Ġ åĪ°", + "ĠåĪ °", + "Ġhu kuk", + "Ġhuku k", + "а ÑĤоÑĢа", + "аÑĤ оÑĢа", + "аÑĤоÑĢ а", + "аÑĤо ÑĢа", + "ĠاÙĦ عÙĨ", + "ĠاÙĦع ÙĨ", + "ïº ĺ", + "ün üz", + "ünü z", + "Ñģ оÑĤ", + "Ñģо ÑĤ", + "ุ ษ", + "Ġd Æ°Æ¡ng", + "ov ny", + "Ġ ÑĦоÑĢма", + "ĠÑĦоÑĢм а", + "ĠÑĦоÑĢ ма", + "ãģĹ ãģ®", + "ãģĹãģ ®", + "ز ÙĬز", + "زÙĬ ز", + "ĠاÙĦÙĨ اس", + "Ġ Ñĩим", + "ĠÑĩ им", + "ĠÑĩи м", + "大 人", + "Ú¯ ÙĬ", + "ĠÐĵ оÑģп", + "é¢Ĩ 导", + "Ġn inh", + "Ġni nh", + "Ġnin h", + "Ġร าà¸Ħา", + "Ġราà¸Ħ า", + "ÙĤ اء", + "ìī ¬", + "ĠìĿ´ ìłĦ", + "ĠÃ¶ÄŁret men", + "ĠÑĨвеÑĤ а", + "ен ноÑģÑĤÑĮ", + "енно ÑģÑĤÑĮ", + "大 ãģį", + "ĠмиÑģÑĤ еÑĨÑĤ", + "ر ÙĪت", + "رÙĪ ت", + "p oÅĪ", + "po ÅĪ", + "ĠÅŀ irket", + "ĠкÑĢаÑģ ив", + "ĠÑĢеÑģ ÑĥÑĢÑģ", + "ĠÑĢеÑģÑĥÑĢ Ñģ", + "ä¹ ¾", + "Ġ ÙģÙĩ", + "ĠÙģ Ùĩ", + "ĠY Ãĸ", + "èĬ ³", + "μ ÏīÏĤ", + "ÄĽ ji", + "ÄĽj i", + "Ġв лаж", + "Ġвла ж", + "ĠÑĥв ели", + "ا ذا", + "اذ ا", + "ãĢĤ å¦Ĥæŀľ", + "ĠпÑĢи ÑģÑĥÑĤÑģÑĤв", + "ĠẤ n", + "æĢ ĸ", + "ĠÐľ еÑĤ", + "Ġje dna", + "Ġjed na", + "Ġjedn a", + "Ġc ục", + "Ġcụ c", + "ĠاÙĨت شار", + "Ġз окÑĢема", + "и ÑĩеÑģки", + "иÑĩеÑģ ки", + "ĠкÑĢаÑĹ Ð½Ð¸", + "ĠкÑĢаÑĹн и", + "и ÑĢÑĥ", + "иÑĢ Ñĥ", + "ĠÑĸн ÑĤеÑĢ", + "ĠÑĸнÑĤ еÑĢ", + "Ġан алог", + "Ñ Ľ", + "ี à¸ĭ", + "н Ñĥли", + "нÑĥ ли", + "нÑĥл и", + "ĠN inh", + "ĠNi nh", + "ĠNin h", + "еÑĢ аÑĤоÑĢ", + "еÑĢа ÑĤоÑĢ", + "Ġr uce", + "Ġru ce", + "ĠÑĪ кÑĸ", + "ĠÑĪк Ñĸ", + "تر ÙĨت", + "Ġson rası", + "Ġsonra sı", + "Ġ æį", + "Ġæ į", + "ÑĨен ÑĤÑĢа", + "ÑĨенÑĤ ÑĢа", + "Ġà¸Ńำ à¹Ģà¸ł", + "Ø· ÙĬ", + "ï¼Į å½ĵ", + "ĠÑĤ ÑĢеÑħ", + "ĠÑĤÑĢ еÑħ", + "Âł H", + "æ´ ª", + "ãĥ³ ãĥĦ", + "ãĥ³ãĥ Ħ", + "ĠвÑĸдповÑĸд алÑĮ", + "âĢĻ daki", + "âĢĻd aki", + "âĢĻda ki", + "á ÅĻi", + "áÅĻ i", + "ĠpÅĻ em", + "ĠpÅĻe m", + "t uk", + "tu k", + "ĠÙģر ÙħÙĪد", + "ĠÙģرÙħ ÙĪد", + "Ġ ìĿ¸ì¦Ŀ", + "ĠìĿ¸ ì¦Ŀ", + "สำ à¸Ļ", + "ìĥģ ìĿĺ", + "ÅĻ ÃŃm", + "ÅĻÃŃ m", + "æ¾ ¤", + "Ġ ÑĢей", + "ĠÑĢ ей", + "ĠÑĢе й", + "ĠлÑİб ой", + "u jte", + "uj te", + "ë³µ ì§Ģ", + "Ġ درس", + "Ġد رس", + "Ġدر س", + "ĠÐĴ лади", + "ĠÑģво им", + "ĠÑģвои м", + "ĠìĿ¸íĦ° ëĦ·", + "è± Ĭ", + "Ġн алог", + "Ġнал ог", + "ãĤĪ ãģ³", + "ĠØ® اطر", + "Ġ ìŀħëĭĪëĭ¤", + "Ġìŀħ ëĭĪëĭ¤", + "ãĢĤ ãģĹãģĭãģĹ", + "л аг", + "ла г", + "å° ĸ", + "ëĭ ¥", + "ìĬ¤ ëĬĶ", + "ìĭł ì²Ń", + "ãĥĩ ãĥ¼ãĤ¿", + "ĠÑĥÑĢов нÑı", + "Ġ무 ìĬ¨", + "ĠاÙĦØ£ رض", + "à¹ī à¸ķ", + "Ỽ t", + "ĠÙĨÛĮ رÙĪ", + "ĠÙĨÛĮر ÙĪ", + "å¢ ¨", + "ãĤ¶ ãĥ¼", + "r uba", + "ru ba", + "rub a", + "ĠÙĨØ´ دÙĩ", + "и лÑı", + "ил Ñı", + "a cÃŃm", + "ac ÃŃm", + "acÃŃ m", + "ãĥ© ãĤ¯", + "X H", + "Ġس رد", + "Ġسر د", + "Ġद स", + "t ember", + "tem ber", + "ĠDoÄŁ um", + "ĠDoÄŁu m", + "ĠпÑĢ оÑĢ", + "ĠпÑĢо ÑĢ", + "θ οÏĤ", + "θο ÏĤ", + "ĠiÅŁ e", + "à¸Ń à¸Ł", + "л аÑĪ", + "ла ÑĪ", + "اص ÙĦÙĩ", + "اصÙĦ Ùĩ", + "l ivÄĽ", + "li vÄĽ", + "liv ÄĽ", + "ë¶Ģ ë¶Ħ", + "н ак", + "на к", + "åįģ ä¸ī", + "ส าห", + "à¸Ľà¸£à¸°à¹Ģà¸Ĺศ à¹Ħà¸Ĺย", + "ãĤŃ ãĥ³ãĤ°", + "ĠмеÑĤ оÑİ", + "Ġkullan arak", + "âij ¡", + "ÛĮز ات", + "ĠÙħÙĪب اÛĮÙĦ", + "ĠзнаÑĩ иÑĤ", + "Ġзна ÑĩиÑĤ", + "Ġorgan izace", + "Ġorganiz ace", + "ÑĢ ии", + "ÑĢи и", + "ov na", + "Ġ ê²½ìłľ", + "Ġê²½ ìłľ", + "ãĢģ å½¼", + "Ġम स", + "Ġ à¹Ĥà¸Ľà¸£", + "Ġà¹Ĥ à¸Ľà¸£", + "L ARI", + "LA RI", + "LAR I", + "æĩ Ĥ", + "Ġ ва", + "Ġв а", + "ĠÙĥ ÙĨت", + "ĠÙĥÙĨ ت", + "ĠÑĢабоÑĤ а", + "ĠÑĢаб оÑĤа", + "ĠÑĢабо ÑĤа", + "Âł ĠÂłĠÂł", + "ÂłĠ ÂłĠÂł", + "ÂłĠÂł ĠÂł", + "好 äºĨ", + "ĠzamÄĽst n", + "ж енÑĮ", + "же нÑĮ", + "жен ÑĮ", + "Ġu kon", + "Ġuk on", + "nÄĽ né", + "nÄĽn é", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "ĠاÙĦخاص Ø©", + "ĠÄį asu", + "ĠÄįas u", + "å°ı 说", + "ĠØŃر کت", + "æij Ħ", + "Ïĩ Ïī", + "ĠÑģв еж", + "æĸ° éĹ»", + "Ġ ìĭ±", + "Ġìĭ ±", + "Ġe ÄŁer", + "ĠeÄŁ er", + "Ġsitu ace", + "Ġ ç·¨", + "Ġç ·¨", + "Ġç· ¨", + "f ik", + "fi k", + "ë§ Īëĭ¤", + "ë§Ī ëĭ¤", + "Îķ Îļ", + "Ġê°ľ ìµľ", + "Ġc Ãł", + "ا دث", + "اد Ø«", + "Ġsay ıda", + "Ġsayı da", + "ĠØ£ ÙģضÙĦ", + "ĠØ£Ùģ ضÙĦ", + "æ³ķ éĻ¢", + "Ġ .,", + "Ġ. ,", + "ĠTh Æ°Æ¡ng", + "Ïģ ÏĮÏĤ", + "ÏģÏĮ ÏĤ", + "ãģĹ ãĤĪãģĨ", + "Ç İ", + "æ ij¸", + "æij ¸", + "Ġ éĻ³", + "ĠéĻ ³", + "¥ IJ", + "ฤ à¸Ķ", + "Ġgi ảng", + "Ġgiả ng", + "Ġgiản g", + "ĠлÑİ Ð±Ð¾Ð²", + "ĠлÑİб ов", + "Ġek ran", + "о пиÑģ", + "оп иÑģ", + "еж дÑĥ", + "Ġназ ва", + "æĭ ĵ", + "ı f", + "à¹Ī à¸ģ", + "и ÑĩнÑĸ", + "иÑĩ нÑĸ", + "Ġ ê³Ħíļį", + "Ġê³Ħ íļį", + "à¸ł าà¸Ħม", + "à¸łà¸²à¸Ħ ม", + "Ġ اپ", + "Ġا Ù¾", + "리 ìĿĺ", + "ãģ§ãģĻ ãģĮ", + "Ġkon ci", + "Ġکار خاÙĨÙĩ", + "Ġ ä½ķ", + "ĠÑĤ ва", + "ĠÑĤв а", + "ĠÐŁ оÑģÑĤ", + "ĠÐŁÐ¾ ÑģÑĤ", + "ĠÐŁÐ¾Ñģ ÑĤ", + "ĠапÑĢ елÑı", + "ĠاÙĦع راÙĤ", + "ä¸Ń åįİ", + "à¹ĩ à¸Ńà¸ģ", + "à¥įत à¤ķ", + "Ġz ájem", + "Ġzá jem", + "Ġدر جÙĩ", + "Ġब à¥ľ", + "ĠÑģÑĤ ÑĢан", + "ĠÑģÑĤÑĢ ан", + "ĠÑģÑĤÑĢа н", + "èѦ å¯Ł", + "Ġyer leÅŁtir", + "ĠyerleÅŁ tir", + "ĠV Å©", + "ç¾İ åħĥ", + "Ġì¡° ê¸Ī", + "Ġ รà¸Ńà¸ĩ", + "Ġร à¸Ńà¸ĩ", + "Ġak adem", + "Ġaka dem", + "à¸Ħ à¸ĵะ", + "Ġpoz it", + "Ġkon eÄį", + "Ġkone Äį", + "è°ĥ æŁ¥", + "Ġ ãģĭ", + "ĠÄįerv ence", + "ĠOd kazy", + "ĠëıĦ ìĭľ", + "ั สà¸Ķ", + "ัส à¸Ķ", + "Ġg ái", + "ĠÐł об", + "Ġб оÑı", + "Ġбо Ñı", + "æī ©", + "å¼Ģ å±ķ", + "a nik", + "an ik", + "ani k", + "Ġvy ž", + "ĠbaÅŁ lay", + "Ġbak Ä±ÅŁ", + "ek ce", + "ÑģÑĤ ика", + "ÑģÑĤи ка", + "еÑĢа ÑĤÑĥÑĢа", + "еÑĢаÑĤÑĥ ÑĢа", + "Ġë¶Ħ ë¥ĺ", + "ĠPo Äįet", + "od áÅĻ", + "ëĭĺ ìĿĺ", + "Ġk lid", + "Ġkl id", + "Ġkli d", + "Û² Û¹", + "ĠÚĨ ÛĮز", + "m ür", + "Ġs ứ", + "ÙĬا ÙĨØ©", + "ÙĬاÙĨ Ø©", + "åĬ ±", + "Ġ oku", + "Ġo ku", + "Ġok u", + "Ġв оди", + "Ġво ди", + "Ġвод и", + "ĠزÛĮر ا", + "ĠزÛĮ را", + "大 åĪ©", + "ĠÙĦ ÛĮÙĨÚ©", + "ĠÙĬ جب", + "ĠÙĬج ب", + "Ùħ ÛĮÙĦ", + "ÙħÛĮ ÙĦ", + "ĠÏĥ ÏĦÏģα", + "æĻ ĵ", + "ิ สà¸ķ", + "ิส à¸ķ", + "ĠÅŁ iddet", + "ĠÑĢекомен да", + "Ġpožad av", + "Ġп ÑĸÑģ", + "åħ¬ å¼ı", + "Ġ Ú¯ÛĮرÛĮ", + "ĠÚ¯ ÛĮرÛĮ", + "ĠÚ¯ÛĮ رÛĮ", + "ĠÚ¯ÛĮر ÛĮ", + "к ÑĤа", + "кÑĤ а", + "ĠÙħÙĨ اطÙĤ", + "Ġfirm y", + "Ġfir my", + "Ġ à¹Ħà¸Ľ", + "Ġà¹Ħ à¸Ľ", + "Ġ ÎŃÏģγ", + "ĠÎŃ Ïģγ", + "å¿« éĢŁ", + "ãģĮ ãģªãģĦ", + "н еÑģÑĤи", + "не ÑģÑĤи", + "неÑģ ÑĤи", + "Ġ ç²¾", + "Ġç² ¾", + "ÑĢ ади", + "ÑĢа ди", + "ÑĢад и", + "ãĤĴ ãģĭ", + "ïº ª", + "ky nÄĽ", + "Ġह त", + "t ak", + "ta k", + "ĠÙĬÙĪÙĨ ÙĬÙĪ", + "ö ÄŁ", + "Ġ ÑĢÑĥк", + "ĠÑĢ Ñĥк", + "ĠÑĢÑĥ к", + "åľĭ éļĽ", + "Ñİ ÑģÑĮ", + "Ġдав но", + "Ġp opis", + "Ġpop is", + "Ġpo pis", + "ĠB Ä°L", + "ĠÙĨ ÙĤد", + "ĠÙĨÙĤ د", + "ĠÑģп ож", + "ÑĨион нÑĭÑħ", + "ĠÑĪ п", + "Ñĥ ÑİÑīиÑħ", + "ÑĥÑİ ÑīиÑħ", + "ÑĥÑİÑī иÑħ", + "ĠвоздÑĥ Ñħ", + "ÑĤ ие", + "ÑĤи е", + "ĠU ž", + "ÏĮ δ", + "à¸ģร าà¸Ħม", + "Ġalan ında", + "Ġalanı nda", + "Ġs ắt", + "ãĥIJ ãĤ¤", + "Ng Ãły", + "Ġ ë¹Į", + "Ġë¹ Į", + "ï¼ī ãģ¯", + "Ġ ä¿¡", + "Ðķ С", + "ĠT ato", + "ĠTa to", + "ĠTat o", + "Ġún ora", + "e rap", + "er ap", + "era p", + "Ä ł", + "ĠT áºŃp", + "Ġкомп ании", + "ãĥ© ãĤ¤ãĥĪ", + "ãĥ©ãĤ¤ ãĥĪ", + "éľĢ æ±Ĥ", + "Ġت ÙĪÙĤ", + "ĠتÙĪ ÙĤ", + "âĢĻ âĢĻ", + "ëŀį ëĭĪëĭ¤", + "ĠквÑĸÑĤ нÑı", + "Ġoyun cu", + "ÂĢÂĢÂĢÂĢ ÂĢÂĢÂĢÂĢ", + "åĨ Ĭ", + "Ġyap mÄ±ÅŁ", + "ัà¸ĩ à¹Ħม", + "Ġзап аÑħ", + "á la", + "ál a", + "ĠÑĤеÑħ ниÑĩеÑģ", + "Ġ ØŃص", + "ĠØŃ Øµ", + "ร à¸Ķ", + "å¼ Ħ", + "ĠÚ¯ÛĮ اÙĩ", + "اÙĩ رة", + "اÙĩر Ø©", + "Ġà¤ı ड", + "ним аеÑĤ", + "нима еÑĤ", + "ا دÙĨ", + "اد ÙĨ", + "Îľ Îij", + "Ġ 社", + "Ġç¤ ¾", + "аÑĢ Ñĩ", + "ت ز", + "æ¶ ¦", + "in izin", + "ini zin", + "iniz in", + "inizi n", + "Ġbey az", + "Ġ بÙĪÙĦ", + "Ġب ÙĪÙĦ", + "ĠبÙĪ ÙĦ", + "åĿ ¡", + "ãģ® ãĤĪãģĨãģ«", + "Ġyap tıģ", + "Ġyaptı ÄŁ", + "Ġd aģı", + "Ġda ģı", + "ĠdaÄŁ ı", + "ĠbaÅŁ arı", + "ĠbaÅŁar ı", + "Ġ ÏĢά", + "ĠÏĢ ά", + "ĠпÑĢод аж", + "B á»Ļ", + "Ġत त", + "Ġpod stat", + "Ġpods tat", + "Ġ æµģ", + "Ġæµ ģ", + "Ġzdrav ÃŃ", + "Ġ ç¡", + "Ġç ¡", + "Ġ opak", + "Ġo pak", + "Ġop ak", + "Ġhá»į a", + "æĭ Ķ", + "Ñĥ жд", + "Ñĥж д", + "Ġtr ứng", + "ÙĪر ÙĬØ©", + "ÙĪرÙĬ Ø©", + "Ñĭ л", + "um suz", + "ums uz", + "Ġ سبب", + "Ġسب ب", + "许 å¤ļ", + "å®ŀ éªĮ", + "Ġб оли", + "Ġбол и", + "Ġбо ли", + "Ġd uyá»ĩt", + "áºŃ c", + "ĠÐij ез", + "ĠبÙĦ ÙĨد", + "м м", + "ÑĢ ел", + "ÑĢе л", + "N Ä°", + "Ġ ãĥ¯", + "Ġãĥ ¯", + "éĭ ¼", + "ĠÑģв Ñı", + "Ġ åIJİ", + "ĠåIJ İ", + "Ġmu ht", + "Ġmuh t", + "ĠпÑĢоблем и", + "ĠÑĤÑıж ел", + "ĠС ем", + "ฤษ à¸łà¸²à¸Ħม", + "à¹Ī าà¸ķ", + "à¹Īา à¸ķ", + "ör ü", + "üy orum", + "üyor um", + "ĠاÙĦØ£ ØŃ", + "ĠÑģÑĤÑĢ аÑĪ", + "ĠÑģÑĤÑĢа ÑĪ", + "h oo", + "ho o", + "ध र", + "Ġt lak", + "Ġtl ak", + "Ġsrp na", + "ifik ace", + "Ġ reh", + "Ġre h", + "Ġr eh", + "Ġм инÑĥ", + "Ġмин Ñĥ", + "Ġми нÑĥ", + "ãĢĢ j", + "ĠгÑĢÑĥ пи", + "ĠгÑĢÑĥп и", + "Ġ άλ", + "Ġά λ", + "Ġolur sa", + "λογ ία", + "ĠÐĴ ик", + "ĠÐĴи к", + "Ġmüc adel", + "Ġz ávÄĽ", + "Ġzá vÄĽ", + "Ġzáv ÄĽ", + "ĠÑĦев ÑĢа", + "Äį ná", + "à¹Į à¹Ģà¸ĭ", + "ĠÙĦ ÙĦØŃ", + "ĠÙĦÙĦ ØŃ", + "ÑĢ ип", + "ÑĢи п", + "Ġб Ñĥк", + "ĠбÑĥ к", + "ãģĪ ãģªãģĦ", + "Ġpo rad", + "Ġpor ad", + "Ġsa mostat", + "Ġsam ostat", + "Ġsamo stat", + "Ġt esis", + "Ġte sis", + "Ġtes is", + "اب ÙĤÙĩ", + "ابÙĤ Ùĩ", + "Ġجد ÙĬدة", + "ĠجدÙĬد Ø©", + "éĢ Ĵ", + "âĶģ âĶ", + "س ÛĮÙĨ", + "سÛĮ ÙĨ", + "Ġgerek tiÄŁini", + "ียà¸Ļ à¸ļ", + "è¨Ģ ãģ£ãģ¦", + "ĠÑĸн ÑĤеÑĢеÑģ", + "ĠÑĸнÑĤеÑĢ еÑģ", + "ĠÑı ким", + "ĠÑıк им", + "Ġ æĢ»", + "ĠæĢ »", + "k ovou", + "ko vou", + "kov ou", + "Ġd emek", + "Ġde mek", + "Ġdem ek", + "اÙĨ ÙĬا", + "اÙĨÙĬ ا", + "Ġdom ů", + "Å¡ nÃŃ", + "ate ÅĻ", + "åĢ «", + "δο Ïĥη", + "Ġ 기ìĹħ", + "Ġ기 ìĹħ", + "åĶ ĩ", + "ì¹ ł", + "Ñĸ дÑĥ", + "Ñĸд Ñĥ", + "린 ìĿ´", + "æľĢ åĪĿ", + "è ¸ı", + "è¸ ı", + "æĥ³ åĪ°", + "à¥į बर", + "à¥įब र", + "Ġ ìŀĶ", + "Ġìŀ Ķ", + "ĠÑĢаз нÑĭÑħ", + "k rom", + "kr om", + "ι αν", + "ια ν", + "Ġд ÑĢÑĥз", + "ĠдÑĢÑĥ з", + "ĠдÑĢ Ñĥз", + "ä »¿", + "ä» ¿", + "Ġê·¸ ëłĩ", + "Ġд алÑĸ", + "Ġда лÑĸ", + "Ġдал Ñĸ", + "æķĪ æŀľ", + "Ġह व", + "è¼ Ŀ", + "Ġì°¸ ê³ł", + "Ġ ìĨĶ", + "ĠìĨ Ķ", + "Ġz nal", + "Ġzn al", + "ĠпеÑĢ Ñģ", + "ÙIJ Ùij", + "ĠÑĤ еж", + "ĠÑĤе ж", + "åĭ Ł", + "ι θ", + "Äį ů", + "Ġe kip", + "Ġek ip", + "Ġk hung", + "Ġkh ung", + "Ġkhu ng", + "éĹ ĺ", + "ĠتصÙħ ÛĮÙħ", + "о иÑĤ", + "ĠÑħ ол", + "æĬ ŀ", + "a mam", + "am am", + "ama m", + "Ġâĸ ³", + "ãģ ĩ", + "Ġع ÙĨÙĩ", + "ĠعÙĨ Ùĩ", + "Ġì°¸ ê°Ģ", + "ĠÎļ ÏĮ", + "åı¤ å±ĭ", + "к овоÑĹ", + "ков оÑĹ", + "ково ÑĹ", + "ศ à¸Ī", + "олог иÑı", + "ĠÙħØ« بت", + "ĠÐļÑĢа ÑĹна", + "ĠмеÑģÑı ÑĨев", + "Ġalın an", + "ĠÏĢÏģα γμα", + "Ġ ìŀ¡ëĭ´", + "Ġìŀ¡ ëĭ´", + "Ġп лод", + "Ġпл од", + "Ġпло д", + "ĠÑĤка ни", + "ÑģÑĭ лки", + "ÑģÑĭл ки", + "سط س", + "ra nÄĽ", + "ran ÄĽ", + "к аж", + "ка ж", + "е маÑĤи", + "ем аÑĤи", + "ема ÑĤи", + "Ġز ÛĮست", + "ĠزÛĮ ست", + "æ¿ Ł", + "Ġpop lat", + "Ġpo plat", + "γ ÎŃν", + "íĨł íĨł", + "Ġt ây", + "Ġìµľ ê·¼", + "ãĥ© ãĥ³ãĤ¹", + "ãĥ©ãĥ³ ãĤ¹", + "Ġgün eÅŁ", + "Ġ ÙģÙĤ", + "ĠÙģ ÙĤ", + "ĠsaÄŁ layan", + "ĠsaÄŁlay an", + "ĠØŃ Ø²Ø¨", + "à¥ģल न", + "ĠB ilim", + "ĠBi lim", + "ĠBil im", + "ĠB atı", + "ĠBa tı", + "ĠBat ı", + "æł· çļĦ", + "δ ικ", + "δι κ", + "α ÏģίοÏħ", + "αÏģ ίοÏħ", + "Ġ ìĽĢ", + "ĠìĽ Ģ", + "Ġl á»Ńa", + "ÙĨ ÙĪع", + "çİ ²", + "а ном", + "ан ом", + "ано м", + "Ġst átnÃŃ", + "Ġstát nÃŃ", + "Ġ äºİ", + "Ġm ùi", + "ĠÄij á»Ļt", + "ĠÄijá»Ļ t", + "æ² ĥ", + "åħ¬ åľĴ", + "ĠÑģ ÑĮогоднÑĸ", + "но Ñģи", + "ноÑģ и", + "Z a", + "Ġд ли", + "ĠÏĥÏħν ÎŃ", + "ĠV á»ĭ", + "m av", + "ma v", + "ĠM üslüman", + "/ ï¼ı", + "ĠзаÑī иÑĤ", + "é ĸī", + "éĸ ī", + "Ġ çģ«", + "Ġçģ «", + "Ġ å·Ŀ", + "Ġå· Ŀ", + "Ġ аж", + "Ġа ж", + "è¿ĩ æĿ¥", + "à¸Ĺ าà¸Ļ", + "ĠAr aÅŁtır", + "ĠAra ÅŁtır", + "Õ¡ Õ", + "Ġpo mÄĽr", + "Ġpom ÄĽr", + "Ġd ům", + "Ġdů m", + "å¦ ®", + "Ġhlav nÄĽ", + "Ġfin ans", + "Ġfinan s", + "Ġ γνÏī", + "Ġγ νÏī", + "ÏĥÏĦη μα", + "ï¼Į çĶ¨", + "ìĭŃ ìĭľìĺ¤", + "ĠÙħ ثاÙĦ", + "ĠÙħØ« اÙĦ", + "- Ðij", + "ÑĨÑĸй нÑĸ", + "Ġد ستÙĩ", + "Ġدست Ùĩ", + "Ġدس تÙĩ", + "à¥ī स", + "ÑĢ Ñĸп", + "ÑĢÑĸ п", + "ĠpÅĻi pom", + "ĠpÅĻip om", + "Ġ ÙĪÙĦÙĬ", + "ĠÙĪ ÙĦÙĬ", + "ĠÙĪÙĦ ÙĬ", + "ĠÙĪ زÙĨ", + "ĠÙĪز ÙĨ", + "Ġelekt rik", + "Ġelektr ik", + "ĠQu ân", + "i vé", + "iv é", + "Ġl ẽ", + "ç®Ģ åįķ", + "Ġon lara", + "Ġonlar a", + "оÑģ лав", + "ìĭľ íĤ¤", + "ëª ¬", + "ĠÙħÙĤ دار", + "ĠÙħÙĤد ار", + "ĠOr ta", + "ĠOrt a", + "ĠS eç", + "ĠSe ç", + "ĠÙĨÙĪÙģ Ùħبر", + "ุà¸Ļ ายà¸Ļ", + "ĠÑĥм ови", + "ĠÑĥмов и", + "Ġपर म", + "Ġ strom", + "Ġst rom", + "Ġstr om", + "Ġstro m", + "ĠкÑĢа Ñīе", + "ç§ ¦", + "缸 æīĭ", + "鼻 è¦ĸ", + "Ġuygu lama", + "Ġuygulam a", + "Ġ ÑĢиз", + "ĠÑĢ из", + "æĪ ²", + "य र", + "ĠH lav", + "Ġ ìĭ¸", + "Ġìĭ ¸", + "Ġли пнÑı", + "ÅĪ ujÃŃ", + "ÑĢ из", + "ÑĢи з", + "é«ĺ éĢŁ", + "缸 å½ĵ", + "k enin", + "ke nin", + "ken in", + "Ġо ÑģÑĤанов", + "ĠоÑģÑĤ анов", + "ĠоÑģÑĤан ов", + "Ġbit k", + "Ġbi tk", + "ova ného", + "ovan ého", + "ované ho", + "ĠÐľ аÑĢи", + "ĠÐľÐ°ÑĢ и", + "ĠÐľÐ° ÑĢи", + "èµ ¶", + "ì½ ©", + "Ġölç ü", + "ĠС еÑĢед", + "ĠСеÑĢ ед", + "ĠTh á»Ŀi", + "Ïī να", + "Ïīν α", + "ÙĪ بة", + "ÙĪب Ø©", + "Ġch ụp", + "âĢĮ د", + "Ġch áy", + "ĠÐĴ ели", + "Ġоб ÑģÑĤ", + "ĠобÑģ ÑĤ", + "Ġìĭľ ì¦Į", + "د ÙħØ©", + "دÙħ Ø©", + "п од", + "по д", + "l ue", + "lu e", + "ĠдÑĸ лÑıн", + "ĠÙ¾ ÙĪست", + "ĠاÙĦ ÙĨس", + "ĠاÙĦÙĨ س", + "èĤ Į", + "ìĪĺ 를", + "Ġú rov", + "ĠÙħØ´ Ú©ÙĦ", + "ĠÙħØ´Ú© ÙĦ", + "éĩįè¤ĩ éĩįè¤ĩ", + "н ез", + "не з", + "Ġdop oruÄį", + "Ġtas arım", + "Ġtasar ım", + "íģ¬ 기", + "ìĿ´ ìħĺ", + "Ġde set", + "Ġdes et", + "Ġdese t", + "ĠÙħرتب Ø·", + "ัà¸Ĵ à¸Ļา", + "ัà¸Ĵà¸Ļ า", + "' ı", + "Ñĩ ки", + "ĠìŀĪ ëįĺ", + "ÑĪ ка", + "n ám", + "ná m", + "ÑģÑĤ ÑĢов", + "ÑģÑĤÑĢ ов", + "ÑģÑĤÑĢо в", + "à¥į सर", + "à¥įस र", + "нÑĥ лаÑģÑĮ", + "нÑĥла ÑģÑĮ", + "ãģ¡ãĤĩ ãģ£ãģ¨", + "Ġ å¦", + "Ġå ¦", + "γ ÏĮ", + "Ġ é»ij", + "Ġé» ij", + "X em", + "Ġt á»ĩ", + "Ġtá» ĩ", + "ĠëĮĢ íĨµëł¹", + "기 ê´Ģ", + "æīį èĥ½", + "è¯Ń è¨Ģ", + "ed eyse", + "ĠТ Ñĭ", + "ĠÑģо един", + "ĠìĹĨ ìĬµëĭĪëĭ¤", + "Ñı ÑİÑĤ", + "à¹ģ หล", + "à¹ģห ล", + "Ġì§Ģ ë°©", + "Ġosob nÃŃ", + "ÛĮ ÙĦÛĮ", + "ÛĮÙĦ ÛĮ", + "Ġавг ÑĥÑģÑĤа", + "Ñī ик", + "Ñīи к", + "Ġvý Å¡e", + "g th", + "gt h", + "ĠÏĢ αν", + "ĠÏĢα ν", + "ج ار", + "جا ر", + "Ġвид ов", + "Ġви дов", + "ìĿ´ ìĬĪ", + "ĠÐij аÑĢ", + "ĠÏĮ ÏĢοÏħ", + "æ¤ ħ", + "Ġع اÙĦÛĮ", + "ĠQ uyết", + "ĠQuy ết", + "Ãľ M", + "ãĥĿ ãĤ¤ãĥ³ãĥĪ", + "Ġ ê¹Į", + "Ġê¹ Į", + "Ġкан ди", + "k ového", + "kov ého", + "kové ho", + "ĠMerk ez", + "Ġy iy", + "Ġyi y", + "ĠpÅĻÃŃ spÄĽ", + "ĠÑĤемпеÑĢаÑĤÑĥ ÑĢÑĭ", + "ĠÙ¾ ÙĬ", + "ฤ ศà¸Ī", + "è°ĥ çĶ¨", + "ĠÑģÑĤоÑĢ онÑĥ", + "ĠÑģÑĤоÑĢон Ñĥ", + "à¹ī à¸Ĭ", + "好 ãģį", + ". Åŀ", + "Ġп ÑĢоз", + "ĠпÑĢ оз", + "ĠпÑĢо з", + "ÙĨت اج", + "鼻 åŃIJ", + ".: .:.", + ".:.: .", + ".:. :.", + "è¨ ĵ", + "и ÑĩеÑģкое", + "иÑĩеÑģ кое", + "Ġн оги", + "Ġно ги", + "Ġног и", + "Ġ λÎŃ", + "Ġλ ÎŃ", + "Ġsık ıntı", + "Ġê°Ģ 족", + "ĠتÙĨ ظÙĬÙģ", + "ĠتÙĨظ ÙĬÙģ", + "Ġö dül", + "ĠaÅŁaģı daki", + "Ġž elez", + "Ġže lez", + "ĠاÙĦع دÙĬد", + "غ ÙĨ", + "Ġокон Ñĩ", + "ÑĢем Ñı", + "ÑĢе мÑı", + "L Ä°", + "Ġne jd", + "Ġnej d", + "Ġ ÏĢλα", + "ĠÏĢ λα", + "Ñģ ко", + "Ñģк о", + "Ġ ìĪĻ", + "ĠìĪ Ļ", + "ĠÙ¾ ÙĪÙĦ", + "θεν ήÏĤ", + "Ġ주 ìļĶ", + "Ġ æĬ¥", + "ĠæĬ ¥", + "ĠÙħ Ùħا", + "ĠÙħÙħ ا", + "Ðł Ð¡Ðł", + "ĠÑĢа дÑĸ", + "ĠÑĢад Ñĸ", + "ä¸Ģ ç§į", + "é¾ Ħ", + "Ġsö yl", + "Ġsöy l", + "Ïģκε ια", + "Ïģκ εια", + "Ġзем лÑĸ", + "Ġve Äįer", + "g eç", + "ge ç", + "س تÙħ", + "ست Ùħ", + "Ġse fer", + "ĠÑģ вÑĸд", + "ĠÑģв Ñĸд", + "ï»Ł ï»", + "а лов", + "ал ов", + "ало в", + "ìĬ¤ 를", + "âī ¥", + "ĠتÙĦ ÙģÙĨ", + "ĠتÙĦÙģ ÙĨ", + "åİ» äºĨ", + "़ à¥ĭà¤Ĥ", + "़à¥ĭ à¤Ĥ", + "ĠÑĦоÑĢм е", + "ĠÑĦоÑĢ ме", + "d üm", + "dü m", + "åħ ģ", + "ÑĢ ап", + "ÑĢаР¿", + "ÑĢа п", + "ĠV Æ°Æ¡ng", + "à¸Ńะ à¹Ħร", + "ัà¸ģษ à¸ĵ", + "Ġ åį³", + "Ġåį ³", + "ĠاÙĦ رÙħ", + "ĠاÙĦر Ùħ", + "ĠзаÑħиÑģÑĤ Ñĥ", + "° E", + "o dÃŃ", + "od ÃŃ", + "Ġव न", + "ĠÄij èn", + "Ġ åıĹ", + "Ġåı Ĺ", + "èIJ½ ãģ¡", + "Ġ zim", + "Ġz im", + "Ġzi m", + "리 ì¦Ī", + "èĪ Ĵ", + "Ġзб ÑĸÑĢ", + "Ġ ä»·æł¼", + "ĠлÑİ Ð´Ð¸Ð½Ð°", + "ĠлÑİд ина", + "ĠлÑİди на", + "ĠÐŁÐ¾Ñģ иланнÑı", + "и Ñī", + "ĠÎ ¨", + "ิà¸ģ ายà¸Ļ", + "ิà¸ģา ยà¸Ļ", + "Ġbu dete", + "Ġbud ete", + "Ġbude te", + "Ġз ÑĢоÑģÑĤ", + "Ġ vyk", + "Ġv yk", + "Ġvy k", + "ĠÐĹ ÐµÐ¼", + "ĠиÑİ Ð½Ñı", + "ĠmÄĽ lo", + "ĠmÄĽl o", + "ÙĦ اÙģ", + "ÙĦا Ùģ", + "Ġ ÙĪØ´", + "ĠÙĪ Ø´", + "ĠÑģп ÑĢави", + "ĠÑģпÑĢав и", + "ãģĻ ãģİ", + "ĠгÑĢа дÑĥ", + "ĠгÑĢ адÑĥ", + "R oz", + "Ro z", + "ι νή", + "ιν ή", + "Ġch á»ĵng", + "ä¸Ģ åį·", + "Ġ Xem", + "ĠX em", + "ĠÑģимв ол", + "ĠÑģим вол", + "Ġod mÃŃt", + "ĠÑĢÑıд ом", + "ĠÑĢÑı дом", + "ĠÑĩеÑĢв нÑı", + "à¸ģระ à¸Ĺ", + "人 人", + "æ°Ĺ æĮģãģ¡", + "un daki", + "und aki", + "unda ki", + "åľĭ 家", + "εÏģ μαν", + "Ġ лÑĮ", + "Ġл ÑĮ", + "ĠN üfus", + "Ġм еÑĢе", + "ĠмеÑĢ е", + "بر اÙĬر", + "н аннÑı", + "Ġ наÑĢ", + "Ġн аÑĢ", + "Ġна ÑĢ", + "Ġt ấm", + "æĸ½ å·¥", + "é¡ ¯", + "Ġh è", + "æĺİ çĻ½", + "Ġдо гов", + "Ġдог ов", + "ĠÙģ رÙħ", + "ĠÙģر Ùħ", + "èĢ Ĺ", + "ìĬ¤ ìĿĺ", + "ìĦ¸ ëĮĢ", + "è¯ ļ", + "Ġнеб олÑĮ", + "Ġ à¸Ľà¸£à¸°à¸ģ", + "Ġà¸Ľà¸£à¸° à¸ģ", + "Ġì¹ ¼", + "Ġov liv", + "Ġ NGC", + "ĠN GC", + "ĠNG C", + "ãĢĤ ä¸į", + "ا ÙĦÙī", + "اÙĦ Ùī", + "æī £", + ". ÐIJ", + "ÑĢа ÑģÑĤа", + "ÑĢаÑģ ÑĤа", + "ÑĢаÑģÑĤ а", + "ĠÃĩ ev", + "ãģ£ ãģ¡", + "ãģ£ãģ ¡", + "ï¼Į éĥ½", + "Ġrov nÄĽÅ¾", + "ĠÏĩÏģÏĮ νια", + "Ġì¡° ìĦł", + "ĠØ¢ باد", + "Ġآب اد", + "ĠÐľ аÑģ", + "ĠÐľÐ° Ñģ", + "çĻ¼ å±ķ", + "ä» Ķ", + "Ġkend isini", + "Ġkendisi ni", + "à¹Īà¸Ńà¸ĩ à¹Ģà¸Ĺ", + "ĠV ÄĽ", + "Ġr ượu", + "Ġm áme", + "Ġmá me", + "Ġmám e", + "ĠоÑĩеÑĢед ÑĮ", + "Ġسب تÙħبر", + "Ġб ок", + "Ġбо к", + "ì§Ģ ìĹŃ", + "Ġتا Ø«ÛĮر", + "Ġتاث ÛĮر", + "Ġl isans", + "Ġli sans", + "Ġlis ans", + "Ġger ektir", + "Ġgerek tir", + "Ġs izi", + "Ġsi zi", + "Ġsiz i", + "Ñĸ но", + "Ñĸн о", + "ĠM Ã¼ÅŁ", + "ĠMü ÅŁ", + "ãģı ãĤīãģĦ", + "ãģıãĤī ãģĦ", + "Ġза клÑİÑĩ", + "Ġзак лÑİÑĩ", + "ãģĵãģ¨ ãģ«", + "è¨Ģ ãģĦ", + "ãĢģ å°ı", + "Ġet mektedir", + "Ġetm ektedir", + "åł± åijĬ", + "Ġkar Ä±ÅŁ", + "Ġоб лад", + "Ġобла д", + "Ġобл ад", + "å¥ ij", + "ra cat", + "rac at", + "ĠارتÙģ اع", + "μ αι", + "μα ι", + "íĶ Ī", + "ĠÙĪ ÙĦÙħ", + "ĠÙĪÙĦ Ùħ", + "ëĬĶ ì§Ģ", + "lom ou", + "Ġли ÑĨа", + "ĠлиÑĨ а", + "ĠìĿĮ ìķħ", + "Ġhod nÄĽ", + "èĭ± æĸĩ", + " Ħ", + "à¹ī าà¸Ĥà¸Ńà¸ĩ", + "à¹īา à¸Ĥà¸Ńà¸ĩ", + "Ġê³Ħ ìķ½", + "åIJĦ ç§į", + "ĠÙħر Ú¯", + "éĶ ģ", + "Ġन द", + "ãĥĭ ãĥ¡", + "Ġ ем", + "Ġе м", + "Ġe leÅŁtir", + "Ġel eÅŁtir", + "Ġele ÅŁtir", + "Ġ íĬ¹ë³Ħ", + "ĠíĬ¹ ë³Ħ", + "ĠÎ¥ ÏĢο", + "Å¡ ker", + "Å¡k er", + "L ERÄ°", + "LER Ä°", + "æ² Ī", + "l ikleri", + "lik leri", + "likle ri", + "likler i", + "ĠÙħÙĩÙĨد سÛĮ", + "ĠbaÄŁ ır", + "dı ģını", + "dıģ ını", + "ĠاÙĦ تد", + "ĠاÙĦت د", + "à¸¸à¸Ľ à¸ģรà¸ĵ", + "ĠÑģлед ÑĥÑİÑīие", + "ĠÑģледÑĥÑİÑī ие", + "Ġì§ģ ìłij", + "å° ¤", + "ĠоÑģнов Ñĸ", + "Ġt ÄĽla", + "ĠtÄĽ la", + "ĠtÄĽl a", + "Ġп ак", + "Ġпа к", + "iz ace", + "iza ce", + "Ġná rod", + "Ġnáro d", + "a ný", + "an ý", + "ĠÑį п", + "Ġüç üncü", + "Î¥ Ρ", + "éĨ´ éĨ´", + "à¹Ģà¸ģ à¸Ńร", + "âĢĮاÙĨ بار", + "ç¶ Ļ", + "Îij Îł", + "ı lıģı", + "ılı ģı", + "ıl ıģı", + "ılıģ ı", + "ĠÃľ rün", + "Ġдоз вол", + "Ġ íĥĪ", + "Ġíĥ Ī", + "Ġà¤ĵ वर", + "è« ¸", + "èĺ ĩ", + "ĠпÑĢоÑģÑĤ ÑĢан", + "éĿĴ å¹´", + "ãģ® æĸ¹", + "ĠÚĨ Ú¯ÙĪÙĨÙĩ", + "ÙĦ Ø·", + "âĢľ æĪij", + "Ġëĭ¤ìļ´ ë°Ľ", + "ा .Ċ", + "ा. Ċ", + "Ġmüc adele", + "Ġmücadel e", + "Ġc ÃŃt", + "ĠcÃŃ t", + "à¹Īวม à¸ģ", + "ÄŁ ına", + "ģı na", + "ģın a", + "ê°ľ ë°ľ", + "ĠÏĢ αιδ", + "ĠÏĢα ιδ", + "ĠÏĢαι δ", + "ض اÛĮ", + "ضا ÛĮ", + "Ġbor ç", + "íĬ ľ", + "ĠخدÙħ ت", + "Ġخد Ùħت", + "Ġu dál", + "Ġud ál", + "Ġ виг", + "Ġв иг", + "Ġви г", + "Ġ ë°°ìĨ¡", + "Ġë°° ìĨ¡", + "å¹ ¾", + "Ùİ Ø¬", + "Ġ ìĹĺ", + "ĠìĹ ĺ", + "çĢ ¬", + "ï Ģ", + "ĠÎij θή", + "пÑĢи клад", + "ĠпÑĢи Ñĩина", + "ĠпÑĢиÑĩин а", + "ĠпÑĢиÑĩ ина", + "ĠÙģ شار", + "æ »¿", + "æ» ¿", + "Ġd ostat", + "Ġdo stat", + "Ġdos tat", + "Ġdost at", + "Ġ졸 ìĹħ", + "Ġا رز", + "Ġار ز", + "ÙĪÙĦ ÙĪج", + "ÙĪÙĦÙĪ ج", + "س ÙĪ", + "æĺł çĶ»", + "Ġth ôi", + "Ġ ³³³", + "ĠÂł ³³", + "Ġ³³ Âł", + "à¹ģ à¸Ļะ", + "à¹ģà¸Ļ ะ", + "è¨Ń åĤĻ", + "Ġмног ие", + "ÑĤ оÑĦ", + "ÑĤо ÑĦ", + "i Å¡tÄĽ", + "iÅ¡ tÄĽ", + "à¤Ĺ ढ", + "Ġин дивидÑĥ", + "Ġ ìĥĿíĻľ", + "ĠìĥĿ íĻľ", + "Ġзов ÑģÑĸм", + "íĥ ķ", + "çľ ł", + "ĠêµŃ ëĤ´", + "e ptal", + "ep tal", + "ept al", + "r aci", + "ra ci", + "rac i", + "è¡ ¡", + "ãĦ ·", + "ĠSt ÅĻed", + "اÙĦ ÙĬا", + "اÙĦÙĬ ا", + "Σ Τ", + "Ľ °", + "ãĥī ãĥ«", + "á zÃŃ", + "áz ÃŃ", + "Ġа Ñģп", + "ĠаÑģ п", + "ĠdÄ±ÅŁ arı", + "ĠвиÑĢоб ниÑĨÑĤва", + "e za", + "ez a", + "ï¼Į ä¸įè¿ĩ", + "ï¼Įä¸į è¿ĩ", + "çĥ ¦", + "ãĥ³ ãĤ°ãĥ«", + "ãĥ³ãĤ° ãĥ«", + "Ġroz voj", + "ĠÙħÙĨت شر", + "ĠÑĥÑĤ еп", + "Ġد ÙĬÙĨ", + "ĠدÙĬ ÙĨ", + "ĠзаÑģоб Ñĸв", + "Ng Æ°á»Ŀi", + "ãĤ· ãĥ¼", + "ĠFran sız", + "ÎĻ Τ", + "ائ Ùģ", + "ι Ïĩ", + "ี à¹Ģม", + "à¥į मन", + "à¥įम न", + "à¥įम à¤ļ", + "Ġس عر", + "Ġسع ر", + "ï¾ Ŀ", + "ë°© ë²ķ", + "ĠС о", + "Ġà¤ĸ बर", + "ìĨĮ ê°ľ", + "Ġsl ova", + "Ġslo va", + "Ġslov a", + "Q PCP", + "QP CP", + "ĠK ız", + "ĠKı z", + "Ø· ÙģاÙĦ", + "Ø·Ùģ اÙĦ", + "Ġк оÑĢм", + "ĠкоÑĢ м", + "ĠìĹħ ëį°ìĿ´íĬ¸", + "es poÅĪ", + "esp oÅĪ", + "à¸Ķ าว", + "à¸Ķา ว", + "о ÑĢом", + "оÑĢ ом", + "оÑĢо м", + "ĠгÑĢа ÑĦ", + "ĠгÑĢ аÑĦ", + "Ġп ÑĸÑĪ", + "Ġ ë¿IJ", + "Ġë ¿IJ", + "ý v", + "С ам", + "Ġk rev", + "Ġkr ev", + "Ġkre v", + "ĠB unu", + "ĠBu nu", + "ĠBun u", + "Ġz obraz", + "Ġسخ ÙĨ", + "Ġ æĶ¯", + "ĠæĶ ¯", + "лÑİ Ð±", + "Ùİ Ø§ÙĨ", + "Ùİا ÙĨ", + "маÑĤ ÑĢива", + "λ εÏį", + "λε Ïį", + "Ġпо Ñħод", + "ĠпоÑħ од", + "Ġг ÑĢе", + "ĠгÑĢ е", + "çľĭ çĿĢ", + "à¸Īำ à¸ģ", + "ัà¸ĩà¸Ħ ม", + "Ġseç enek", + "Ä° stanbul", + "ĠвÑĸд мов", + "m iyor", + "mi yor", + "Ġm ụn", + "ìĿ´ ìĹIJ", + "ĠNh Æ°", + "Âł tom", + "Âłt om", + "lık ları", + "lıkla rı", + "lıklar ı", + "Âł Äij", + "ãĥ» ãĥŀ", + "Ġ Ùģت", + "ĠÙģ ت", + "ĠFakült esi", + "ìłĦ íŀĪ", + "éª ij", + "Ġìŀij ìĿĢ", + "ç¼ ĺ", + "ìº IJ", + "Ġmü zik", + "Ġmüz ik", + "а лÑĭ", + "ал Ñĭ", + "Ġp ozem", + "Ġpo zem", + "Ġpoz em", + "çĥ §", + "Ġ 常", + "Ġå¸ ¸", + "Å¡ il", + "Å¡i l", + "à¤Ĩ प", + "à¸ģำ หà¸Ļà¸Ķ", + "Ġگرد Ø´", + "λ ιά", + "λι ά", + "Ġö den", + "åıª è¦ģ", + "ĠÄIJ o", + "Ġstrat ej", + "Ġstra tej", + "Ġstrate j", + "ĠÙĩ تÙĦ", + "ÙĤ Ùģ", + "Ġkullan ılır", + "Ġkullanıl ır", + "ĠÑģп оÑģÑĤ", + "ĠÑģпоÑģ ÑĤ", + "ĠnÄĽ ho", + "ĠÐŁ еÑĢед", + "ĠÐŁÐµÑĢ ед", + "Ġиз меÑĢ", + "] ]>", + "]] >", + "ĠнÑĸк оли", + "Ġha yal", + "Ġhay al", + "Ġhaya l", + "Ġдод аÑĤков", + "Ġन à¤ķ", + "Ġins anın", + "Ġinsan ın", + "ุม à¸łà¸²à¸ŀ", + "ograf ie", + "в об", + "во б", + "ĠاÙĨ ساÙĨÛĮ", + "ĠاÙĨساÙĨ ÛĮ", + "Ġm ük", + "Ġmü k", + "ĠÑĥ меÑĢ", + "ĠÑĥм еÑĢ", + "оÑĩ нÑĭе", + "ëıĦ ìĿĺ", + "Ġ ara", + "Ġa ra", + "Ġar a", + "Ġë¹ ¨", + "Ġκ Ïį", + "л ой", + "ло й", + "Ñģи он", + "Ġroz dÃŃl", + "ay ıf", + "ayı f", + "ĠÙĪاØŃ Ø¯Ø©", + "ĠÙĪاØŃد Ø©", + "ĠÙĪا ØŃدة", + "о ÑĢалÑĮ", + "оÑĢ алÑĮ", + "оÑĢа лÑĮ", + "Ġpo chop", + "Ġpoc hop", + "éļ ¨", + "à¹īà¸Ń à¸ĩà¸Ļ", + "à¹īà¸Ńà¸ĩ à¸Ļ", + "Ġ ÙĪاÙĨ", + "ĠÙĪ اÙĨ", + "ĠÙĪا ÙĨ", + "Îľ ε", + "Ġ μον", + "Ġμ ον", + "Ġμο ν", + "Ñĥ ÑĪка", + "ÑĥÑĪ ка", + "or dum", + "ord um", + "æ¸ħ æ¥ļ", + "ĠDe ÄŁ", + "ÏĢ Ïģο", + "ĠÙĪاÙĦ تÙĬ", + "ĠÙĪاÙĦت ÙĬ", + "Ġp okus", + "Ġpo kus", + "Ġpok us", + "íĽĦ 기", + "é¥ ®", + "æĹħ è¡Į", + "Ġжен Ñīин", + "ĠdoÄŁru dan", + "Ġ Ñıб", + "ĠÑı б", + "Ġza ÄįÃŃ", + "ĠzaÄį ÃŃ", + "Ġë³´ ìŬ", + "- CP", + "-C P", + "åIJ ¨", + "à¥ĭ à¤ĸ", + "ÑĢ огÑĢа", + "ÑĢо гÑĢа", + "ÑĢог ÑĢа", + "ler di", + "ìĬ ´", + "Ùı ÙĪا", + "ÙıÙĪ ا", + "Ġustanov enÃŃ", + "Ġд оÑģÑĤав", + "Ġдо ÑģÑĤав", + "ĠдоÑģÑĤ ав", + "Ġfır sat", + "ĠاÙĦÙħÙĩ ÙĨØ©", + "ĠвеÑī еÑģÑĤва", + "ĠвеÑīеÑģÑĤв а", + "Ġн еÑģп", + "Ġне Ñģп", + "ĠнеÑģ п", + "ĠاÙĦکتر ÙĪÙĨ", + "t aÅŁ", + "ta ÅŁ", + "æĪ Ĵ", + "Ġy urt", + "Ġyu rt", + "Ġgir di", + "ĠÐļ Ñĥб", + "Ġ 를", + "Ġë¥ ¼", + "ุ à¹Į", + "ãģĿãģĨ ãģª", + "à¹ī Ċ", + "ĠвÑĭ бÑĢа", + "ĠвÑĭб ÑĢа", + "k ovÄĽ", + "ko vÄĽ", + "kov ÄĽ", + "ĠS iz", + "ĠSi z", + "Ġ گاÙĩ", + "ĠÚ¯ اÙĩ", + "ĠЧ аÑģ", + "Ġзг Ñĸдно", + ". ÐŁ", + "å§ Ĭ", + "ĠÐļ ÑĥÑĢ", + "ĠìĿĺ íķ´", + "Ġet raf", + "Ġк аÑĪ", + "Ġка ÑĪ", + "ĠØ· ÛĮ", + "ξ ει", + "ξε ι", + "ç² Ĵ", + "ĠØ¢ Ø°", + "Ġböl ge", + "Ġbölg e", + "Ġम à¤ľà¤¬", + "Ġà¤®à¤ľ ब", + "ÙIJ Ùĥ", + "Ġvál ky", + "ãģł ãĤĪ", + "Ġmes aj", + "Ġmesa j", + "ĠpÅĻ ist", + "ĠpÅĻi st", + "Ġtyp u", + "Ġty pu", + "ĠкиÑĪ еÑĩ", + "ãĤī ãģ®", + "Ġkend isi", + "Ġkendi si", + "ĠвÑĸдб Ñĥва", + "ĠвÑĸдбÑĥ ва", + "ä¾ ¯", + "Ġди за", + "ãĢĢ Ċ", + "ĠпÑĢоÑĨеÑģ Ñĥ", + "ĠÑįлек ÑĤÑĢ", + "_P US", + "Ġмног иÑħ", + "Ġk ém", + "Ġké m", + "æŀ ª", + "çİ° 代", + "Ġ éħį", + "Ġé ħį", + "Ġéħ į", + "ë¡ Ń", + "ÑĤи ÑģÑı", + "Ġl ục", + "ĠÙĪ اÙĦØŃ", + "ĠÙĪاÙĦ ØŃ", + "ĠÙĪا ÙĦØŃ", + "p tal", + "pt al", + "pta l", + "ẵ ng", + "ẵn g", + "ÏĢ λ", + "Ġd olu", + "Ġdo lu", + "Ġdol u", + "Ġt òa", + "Ġин огда", + "ĠпоÑĢÑıд ок", + "Як Ñīо", + "âĶ ĺ", + "Ġغ ربÛĮ", + "Ġغرب ÛĮ", + "Ġغر بÛĮ", + "移 åĬ¨", + "ยà¸Ļ à¸ķร", + "ยà¸Ļà¸ķ ร", + "H DATA", + "HD ATA", + "_PUSH DATA", + "_PUS HDATA", + "ĠØ« ابت", + "åĮħ åIJ«", + "ĠÏĢ ÏģÎŃÏĢει", + "़ à¥ĭ", + "åIJį åīį", + "ÑĤ еÑĢи", + "ÑĤе ÑĢи", + "ÑĤеÑĢ и", + "ï½ ¯", + "Ġ åħĪ", + "Ġåħ Ī", + "н ед", + "не д", + "Ïģ οÏįν", + "Ïģο Ïįν", + "ÏģοÏį ν", + "в ей", + "ве й", + "èĤ ĸ", + "ĠÅĻed itel", + "Ġth ép", + "Ġthé p", + "ĠÙĩ ÙģتÙĩ", + "ĠÙĩÙģت Ùĩ", + "ĠдÑĢÑĥг а", + "ĠдÑĢÑĥ га", + "ER Ä°", + "Ġ Ả", + "ĠẠ¢", + "ĠпеÑĢ еÑĢ", + "ĠпеÑĢе ÑĢ", + "Ġж еÑģÑĤ", + "Ġже ÑģÑĤ", + "ĠÄij ẳng", + "ç¦ ®", + "алÑĮ ном", + "алÑĮно м", + "िष य", + "ид енÑĤа", + "иденÑĤ а", + "Ġآخر ÛĮÙĨ", + "Ġ æĵ", + "Ġæ ĵ", + "Ġ มหาว", + "Ġมห าว", + "ĠлÑİ ÑĤого", + "ĠлÑİÑĤ ого", + "Ġб ÑĸзнеÑģ", + "gı ç", + "Ġng á»ĵi", + "оÑĩ нÑĭй", + "Ġo Äįek", + "ĠoÄį ek", + "ĠÙħ رة", + "ĠÙħر Ø©", + "Ġt var", + "Ġtv ar", + "Ġsam ozÅĻejmÄĽ", + "ĠBeled iye", + "Ġв ода", + "Ġво да", + "Ġвод а", + "Ġ Ú¯ÛĮرد", + "ĠÚ¯ÛĮ رد", + "ĠÚ¯ÛĮر د", + "Ġг одÑĭ", + "Ġгод Ñĭ", + "ãģ« è¡Į", + "æĺ¯ æĪij", + "ÑĪ или", + "ÑĪи ли", + "Ġ åĽ½äº§", + "ĠåĽ½ 产", + "ủ i", + "ĠбÑĥд ÑĥÑĤÑĮ", + "ĠбÑĥдÑĥÑĤ ÑĮ", + "ĠбÑĥдÑĥ ÑĤÑĮ", + "ĠÑĢай онÑĥ", + "ĠÑĢайон Ñĥ", + "Ġì ĵ", + "ĠÙĪ اس", + "ĠÙĪا س", + "ĠاÛĮ شاÙĨ", + "ενο δο", + "Ġнез алеж", + "ĠÙ¾ شت", + "Ġپش ت", + "Ġgir iÅŁim", + "ĠgiriÅŁ im", + "Ġд еле", + "Ġдел е", + "Ġде ле", + "ĠاصÙģÙĩ اÙĨ", + "à¸Ķ วà¸ģ", + "ĠاÙĦ ÙĤÙĬ", + "ĠاÙĦÙĤ ÙĬ", + "à¹Į à¸Ī", + "ëª »", + "Ġd ru", + "Ġdr u", + "è¿ ¹", + "ад женнÑı", + "адж еннÑı", + "Ùģ ÙĨ", + "Ïĩ οÏĤ", + "Ïĩο ÏĤ", + "à¹Ĥ à¸Ī", + "e yle", + "ey le", + "å¡ ij", + "Ġu prav", + "Ġup rav", + "Ġз даÑĤ", + "Ġзд аÑĤ", + "Ġзда ÑĤ", + "Ġvid ÄĽt", + "Ġ à¸Ľà¸£", + "Ġà¸Ľ ร", + "Ġ ÑĦеÑĢ", + "ĠÑĦ еÑĢ", + "ÐĨ н", + "Ġ ìµľìĭł", + "Ġìµľ ìĭł", + "l oha", + "lo ha", + "loh a", + "ĠиÑģп ÑĭÑĤ", + "Ġ avan", + "Ġa van", + "Ġav an", + "Ġava n", + "γ οÏħ", + "γο Ïħ", + "ĠGi ấy", + "ãĤ»ãĥ³ ãĤ¿ãĥ¼", + "éģ į", + "е ÑĢаÑħ", + "еÑĢ аÑħ", + "еÑĢа Ñħ", + "Ġê°Ģ ì§Ģê³ł", + "Ġê°Ģì§Ģ ê³ł", + "Ġ ид", + "Ġи д", + "Ġmnoh em", + "æ£Ģ æµĭ", + "Ġet me", + "Ġetm e", + "Ġ تÙħر", + "Ġت Ùħر", + "ĠتÙħ ر", + "ĠbaÅŁ layan", + "ĠbaÅŁlay an", + "ãģı ãĤĮ", + "à¹ĩà¸Ļ à¸ģาร", + "ĠÑħаÑĢакÑĤеÑĢ из", + "Ġanlam ına", + "Ùı Ùĩ", + "ĠÑģеÑĢ пнÑı", + "çķª çµĦ", + "Ġ msgid", + "Ġmsg id", + "Ġms gid", + "Ġzv ÃŃÅĻ", + "ĠzvÃŃ ÅĻ", + "ĠíļĮ ìĽIJ", + "Ġya par", + "Ġyap ar", + "ä¼ĺ åĬ¿", + "ен нÑĭми", + "еннÑĭм и", + "ĠØ£ Ø«", + "ì² Ļ", + "Ġji ného", + "Ġjin ého", + "Ġjiné ho", + "Ġد Ùģاع", + "ĠدÙģ اع", + "ĠØŃÚ© ÙĪÙħ", + "Ġr izik", + "Ġri zik", + "ά λι", + "άλ ι", + "à¸ĩ à¸Ĥ", + "èµ ¢", + "Ġ ÎķÎĽ", + "ĠÎķ ÎĽ", + "Ġok um", + "Ġoku m", + "æĶ¶ åħ¥", + "ĠÚĨ ÛĮÙĨ", + "æľī çļĦ", + "ÑĨ ами", + "ÑĨа ми", + "d ÄĽnÃŃ", + "dÄĽ nÃŃ", + "ĠкоÑĢ аб", + "Ġко ÑĢаб", + "ĠкоÑĢа б", + "Ġa landa", + "Ġal anda", + "Ġalan da", + "ส à¸Ļาม", + "สà¸Ļ าม", + "ï¼ī ãģ®", + "ı sız", + "ıs ız", + "ısı z", + "ÙĬ ÙĬر", + "Ùĥ ÙĬØ©", + "ÙĥÙĬ Ø©", + "Ġnebo Å¥", + "Ġbit ir", + "Ġbi tir", + "Ġ ãĥľ", + "Ġãĥ ľ", + "Ùij ا", + "ï¼ Ĩ", + "ĠاÙĦت ارÙĬØ®", + "มห าà¸Ļà¸Ħร", + "at ürk", + "ãĤ¹ãĥĨ ãĥł", + "θή κη", + "Ġ καν", + "Ġκ αν", + "Ġκα ν", + "ĠS ür", + "ĠSü r", + "Ġd Ä±ÅŁÄ±", + "ĠdÄ±ÅŁ ı", + "Ġk ancel", + "Ġkan cel", + "ĠÙ¾ خش", + "h Pa", + "ĠÄį t", + "ĠпÑĢ оÑħ", + "ĠпÑĢо Ñħ", + "à¹ī à¸Ī", + "Ġê±° ìķ¼", + "ĠдеÑĢжав ного", + "èĤ¡ 举", + "ìĿ´ íģ¬", + "Ùĥ تÙĪر", + "Ùĥت ÙĪر", + "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "è¨ º", + "Ġب Ùħا", + "ĠبÙħ ا", + "ĠноÑĢм аÑĤив", + "ç iler", + "çi ler", + "à¸ĩ ศ", + "éĽĨ ä¸Ń", + "ÑĢ иÑģ", + "ÑĢи Ñģ", + "Ñĩ аÑĶ", + "Ñĩа ÑĶ", + "li ÄŁin", + "liÄŁi n", + "liÄŁ in", + "ãĥ¼ ãĤ¿ãĥ¼", + "ãĥ¼ãĤ¿ ãĥ¼", + "а ÑĢаÑĤ", + "аÑĢ аÑĤ", + "аÑĢа ÑĤ", + "åĬĽ éĩı", + "ĠÑģÑħ ем", + "åħ¥ åı£", + "离 å¼Ģ", + "ÏģοÏĨοÏģ ίεÏĤ", + "ĠÐĹ Ð°ÑĤем", + "ĠkarÅŁ ısında", + "ĠkarÅŁÄ± sında", + "ĠاÙĨت ظ", + "ï½ Ĭ", + "Ġ eÅŁit", + "ĠeÅŁ it", + "Ġyaz ılı", + "Ġyazı lı", + "Ðļ ом", + "ا زÙĬ", + "از ÙĬ", + "Ġki mse", + "Ġkim se", + "Ġkims e", + "ÑĢа Ñīи", + "ÑĢаÑī и", + "ัà¸ģ ส", + "Ġkan un", + "Ġka nun", + "Ġ ëIJĺìĹĪ", + "ĠëIJĺ ìĹĪ", + "Ġι ÏĥÏĩ", + "Ġм еди", + "Ġмед и", + "æ° §", + "ï¼Į åħ¶ä¸Ń", + "ï¼Įåħ¶ ä¸Ń", + "Ġyok tu", + "Ġ ãĤ½", + "ĠãĤ ½", + "ĠпÑĢи обÑĢеÑĤ", + "ÙĪ ÛĮØ´", + "ÙĪÛĮ Ø´", + "ãħł ãħł", + "Ġکرد Ùħ", + "Ġکر دÙħ", + "Ġdu var", + "Ġ ç¸", + "Ġç ¸", + "ıs ır", + "ısı r", + "Ġïº į", + "ĠÐłÐ¾Ñģ ÑģиÑı", + "à¹ī à¹ĥà¸Ļ", + "Ġ iÅŁi", + "Ġi ÅŁi", + "ĠiÅŁ i", + "d ol", + "do l", + "ĠÙħØŃ ÙħÙĪد", + "ĠÑģам ÑĭÑħ", + "ĠبÙĨابر اÛĮÙĨ", + "ãĤĮ ãģ©", + "ุà¸ķ สาห", + ". »", + "ู à¸Ĭ", + "ĠT ep", + "ĠTe p", + "ãģı ãĤĵ", + "Ġ å¸ĥ", + "Ġå¸ ĥ", + "Ġत ल", + "Ġs erm", + "Ġse rm", + "Ġser m", + "λ ÏĮγ", + "λÏĮ γ", + "ĠÅŀ imdi", + "Ġà¤ľà¤¨ त", + "- ÐĴ", + "è¨ ª", + "ĠвÑĸд пов", + "ิ à¸Ļà¸Ķ", + "ิà¸Ļ à¸Ķ", + "ι ÏĥμÏĮÏĤ", + "ιÏĥμ ÏĮÏĤ", + "Ω Τ", + "âĨĴ âĨĴ", + "ικο ί", + "ĠÑģп ÑĢава", + "ĠÑģпÑĢав а", + "æľº åħ³", + "Ġ ÃĿ", + "Ġà Ŀ", + "Ġм ова", + "Ġмо ва", + "Ġмов а", + "Ġмог ла", + "Ġд лиÑĤелÑĮ", + "ãģĹ ãģ¦ãĤĤ", + "ãģĹãģ¦ ãĤĤ", + "Ġβ Ïģί", + "Ġж од", + "éĹ ª", + "ĠмÑĸ ÑģÑĮкоÑĹ", + "η Ïģε", + "çł Ĥ", + "Ġkter ých", + "Ġkterý ch", + "ĠÐĵ олов", + "ĠÐĵол ов", + "Ġh á»Ļp", + "Ġhá»Ļ p", + "Ġpa nÃŃ", + "Ġpan ÃŃ", + "تÙħ اد", + " ľ", + "åįģ åħŃ", + "κ οÏĤ", + "κο ÏĤ", + "ев ÑĭÑħ", + "æĭ Ĵ", + "ĠÑģÑĤ оÑĢон", + "ĠÑģÑĤоÑĢ он", + "ĠÑģÑĤо ÑĢон", + "Ġph óng", + "ĠÑĥлÑĥÑĩ ÑĪ", + "m rt", + "mr t", + "m par", + "mp ar", + "ĠS lav", + "ĠSl av", + "Ġ kov", + "Ġk ov", + "Ġko v", + "ìĿ¸ ìĿĢ", + "Ġ åºĶ", + "Ġåº Ķ", + "ั à¸ļà¸Ħ", + "ัà¸ļ à¸Ħ", + "Ġk ì", + "Ġa Å¥", + "ÅĻ ÃŃt", + "ÅĻÃŃ t", + "ì° Į", + "Ùħ ÙĨت", + "ÙħÙĨ ت", + "ıyor lar", + "æŃ£ 常", + "н ÑıÑĤÑĤÑı", + "нÑıÑĤ ÑĤÑı", + "r acÃŃ", + "ra cÃŃ", + "rac ÃŃ", + "ĠпиÑĤ аниÑı", + "à¸Īะ à¹Ģà¸Ľ", + "ĠاÙĦÙĩ ÙĨد", + "ĠD ost", + "ĠDo st", + "ĠDos t", + "ĠÐĴаÑģ илÑĮ", + "Ġ íĥĦ", + "Ġíĥ Ħ", + "Ġn ạn", + "à¹Īà¸Ń à¹Ħà¸Ľ", + "رÙĪ ض", + "± ظ", + "Ġbych om", + "à¸Ļ วย", + "à¸Ļว ย", + "ãģł ãģ£ãģ¦", + "ĠÐĺ Ñģп", + "ĠÐĺÑģ п", + "à¸Ħร à¸ļ", + "Ġ สà¸ĸาà¸Ļ", + "Ġส à¸ĸาà¸Ļ", + "ĠëĤ ®", + "j iÅ¡tÄĽ", + "ji Å¡tÄĽ", + "ĠÙģ ÙĪت", + "ĠÙģÙĪ ت", + "ĠCh Æ°Æ¡ng", + "ĠìĿ´ 루", + "ĠpÅĻÃŃ tom", + "t ual", + "tu al", + "b ette", + "be tte", + "bet te", + "bett e", + "Ġsa bah", + "Ġsab ah", + "μ ί", + "Ġm á»ĩnh", + "Ġmá»ĩ nh", + "ãģ® ãģłãĤįãģĨ", + "ãģ®ãģł ãĤįãģĨ", + "Ġzam ÄĽÅĻ", + "åįģ äºĶ", + "ĠìķĬ ìĿĦ", + "اÙĨ ÙĪ", + "е нÑĥ", + "ен Ñĥ", + "ĠÑĥ год", + "ĠÑĥг од", + "ĠV Æ°á»Ŀn", + "Ġëĵ± ìĿĦ", + "Ġbelirt ilen", + "æŁ Ħ", + "Ġtek lif", + "¬ Ĥ", + "Ġпод аÑĤков", + "ĠاÙĦ ÙĨÙĩ", + "ĠاÙĦÙĨ Ùĩ", + "ï¼ ´", + "ìĽ ĥ", + "Ġ हल", + "Ġह ल", + "Ġ имÑĥ", + "Ġи мÑĥ", + "Ġим Ñĥ", + "ĠкоÑĤоÑĢ Ñĭм", + "ï¼Į 以åıĬ", + "ï¼Į以 åıĬ", + "ĠÑĤаб ли", + "ा :", + "Ġب رج", + "Ġبر ج", + "ĠÎŃ Î½Î±Î½", + "ĠÎŃνα ν", + "ĠÎŃν αν", + "ĠÙĬ ÙĪÙĦÙĬÙĪ", + "ý Å¡", + "Ġ ÙĬج", + "ĠÙĬ ج", + "ĠÑĤÑĢо Ñħи", + "æŀ Ŀ", + "Ġd Ãły", + "ĠBur ada", + "ĠBu rada", + "ĠÏĥÏħ μβ", + "ĠÎij ÏģÏĩ", + "ĠÎijÏģ Ïĩ", + "Ġsoci álnÃŃ", + "Ġ Ú¯ÙĪ", + "ĠÚ¯ ÙĪ", + "Ġyan ıt", + "Ġyanı t", + "ãģ¯ ãģªãģĦ", + "ãģ® ä¸Ĭ", + "Ġn úi", + "ĠرÙģت ار", + "ĠÙħ رات", + "ĠÙħر ات", + "ز ÙħاÙĨ", + "زÙħ اÙĨ", + "าà¸Ī ารย", + "ĠÑĩиÑģ лÑĸ", + "Ġس ÙĨت", + "ĠسÙĨ ت", + "ĠÃĸzel likle", + "ì ĩ¼", + "ìĩ ¼", + "ĠÄį ÃŃm", + "AD DE", + "ADD E", + "ãģ® ãĤĪãģĨãģª", + "ÙĪÙĦÙĪ ÚĺÛĮ", + "ĠíĻľ ìļ©", + "ãĢģ ãģ©ãģĨ", + "ĠÎł ÏģÏī", + "çĻ» åł´", + "Ġнад аннÑı", + "Ġм еÑĢеж", + "ĠмеÑĢ еж", + "ĠмеÑĢе ж", + "Ġ ìĿµ", + "ĠìĿ µ", + "jÃŃ cÃŃch", + "jÃŃcÃŃ ch", + "it ou", + "ito u", + "ÙĤ ÙĪÙĦ", + "Ùħ ج", + "Ġب ÙĨد", + "ĠبÙĨ د", + "Ġön üne", + "Ġ ï½°", + "Ġï½ °", + "з в", + "Ġе ÑģÑĤе", + "Ðł Ðĺ", + "ÑĢ ол", + "ÑĢо л", + "a yla", + "ay la", + "Ġк лÑĥ", + "Ġкл Ñĥ", + "æݨ èĸ¦", + "ĠÑĢоз ÑĢаÑħ", + "Ġ ìĥģëĭ´", + "Ġìĥģ ëĭ´", + "ĠÙĨ سÙħØ©", + "ĠÙĨس ÙħØ©", + "Ġви Ñħод", + "à¥Ģ à¤Ĩà¤Ī", + "ĠпÑĢи ÑģÑĤÑĥп", + "ÙĴ ع", + "ĠteÅŁ ekkür", + "дÑı ки", + "Ġfi kir", + "Ġfik ir", + "ัศ à¸Ļ", + "ĠآزÙħ اÛĮØ´", + "Ġb izi", + "Ġbi zi", + "Ġbiz i", + "ÏĨ αÏģ", + "ÏĨα Ïģ", + "æľª æĿ¥", + "æIJ º", + "Ġδ Ïħνα", + "ĠδÏħ να", + "Ġ رÙĪÙħ", + "Ġر ÙĪÙħ", + "ĠرÙĪ Ùħ", + "Ġb undan", + "Ġbund an", + "Ġbun dan", + "ĠÙĤ اÙĦب", + "ĠÙĤاÙĦ ب", + "Ġ haft", + "Ġh aft", + "Ġha ft", + "Ġhaf t", + "å¿ ½", + "ĠÐľ оÑĢ", + "Ġzá pas", + "Ġzáp as", + "Ġ ë¹Ľ", + "Ġë¹ Ľ", + "å» ·", + "äºĪ ç´Ħ", + "Ġkh uyến", + "Ġ ÎijÎĵ", + "ĠÎij Îĵ", + "Ġìŀij ìĹħ", + "ड र", + "Ġjednodu ch", + "à¥ī म", + "ĠdeÄŁ ildi", + "ĠdeÄŁil di", + "Ġk olo", + "Ġko lo", + "Ġkol o", + "Ġد ÙĤÛĮ", + "л ами", + "ла ми", + "лам и", + "ĠH á»įc", + "ĠHá»į c", + "Ġप स", + "ĠÎł ÏģÏĮ", + "ĠâĹ ij", + "Ġ наÑģлÑĸд", + "Ġна ÑģлÑĸд", + "ĠнаÑģ лÑĸд", + "Ġ диви", + "Ġди ви", + "Ġдив и", + "ĠpÅĻes nÄĽ", + "ĠТак им", + "ĠТа ким", + "Ġru kou", + "Ġruk ou", + "ä¸Ģ åĪĩ", + "ĠÑģ пÑĢи", + "ĠÑģп ÑĢи", + "en ské", + "ens ké", + "æĹ ¦", + "ĠÙĤ ÙĨ", + "Ġú stav", + "िश त", + "à¹Į )", + "ĠT rang", + "ĠTr ang", + "ĠTra ng", + "ĠTran g", + "Ġmoh la", + "Ġmohl a", + "ĠÎķ λλην", + "Ġп оки", + "Ġпо ки", + "Ġпок и", + "ĠØ¢ Ùħار", + "ĠØ¢Ùħ ار", + "åIJ ¾", + "ĠÑĢ еÑģп", + "ĠÑĢе Ñģп", + "ĠÑĢеÑģ п", + "Ġta kdir", + "Ġtak dir", + "Ġrahat sız", + "éŁ³ ä¹IJ", + "Ġ âĶĥ", + "ĠâĶ ĥ", + "i lis", + "il is", + "ili s", + "ĠÙĪ اÙĦØ¥", + "ĠÙĪاÙĦ Ø¥", + "å® Ļ", + "Ñĥ мов", + "Ñĥм ов", + "ĠÐĽ иÑĤ", + "ĠÐĽÐ¸ ÑĤ", + ": :::|", + ":: ::|", + ":::: |", + "::: :|", + "åħ ½", + "ĠÙĨزد ÛĮÚ©", + "е лÑĸв", + "ел Ñĸв", + "елÑĸ в", + "θ οÏįν", + "θο Ïįν", + "ìĹIJìĦľ ëıĦ", + "èµĦ æł¼", + "çIJĨ 论", + "ĠKe mal", + "ĠKem al", + "Ġк еÑĢ", + "ษ ายà¸Ļ", + "Ġ åįİ", + "Ġåį İ", + ") ìĹIJ", + "Ġ ëĬĺ", + "ĠëĬ ĺ", + "ãĥĿ ãĥ¼ãĥĪ", + "ĠÐĹ Ð´", + "اص ÙĬÙĦ", + "Ġk atı", + "Ġka tı", + "Ġkat ı", + "ãĤĤãģĹ ãĤĮãģªãģĦ", + "Ġкажд ого", + "Ġ дÑĢ", + "Ġд ÑĢ", + "Ġfut bol", + "ÙĦ ÙĬÙģ", + "ÙĦÙĬ Ùģ", + "Ġì§Ģ ëĤľ", + "ĠÙ¾ÛĮØ´ ÙĨÙĩ", + "ü lük", + "ül ük", + "ülü k", + "Ġ à¸ķำà¸ļล", + "Ġà¸ķำ à¸ļล", + "Ġb áºŃc", + "Ġ åĽł", + "ĠåĽ ł", + "ik ler", + "Ïģ ιά", + "Ïģι ά", + "Ġв важа", + "Ġвваж а", + "Ġvy pl", + "Ġvyp l", + "Ġв низ", + "í Ģ", + "çľ ¾", + "ĠÑģ ила", + "ĠÑģи ла", + "ĠÑģил а", + "ĠналиÑĩи и", + "Ġع راÙĤ", + "ĠاÙĦÙħ Ùĥ", + "å°± ä¼ļ", + "Ġм Ñĸг", + "ĠмÑĸ г", + "ĠÎĮ μιλοÏĤ", + "Ñī его", + "Ñīе го", + "Ġíĸī ìłķ", + "Âł mph", + "Âłm ph", + "Ġma lé", + "Ġmal é", + "ĠÛĮ اÙģتÙĩ", + "ĠÛĮا ÙģتÙĩ", + "ĠÛĮاÙģت Ùĩ", + "Ġmn oha", + "Ġmnoh a", + "γ ά", + "Ġпо ÑģÑĤÑĢо", + "ĠпоÑģ ÑĤÑĢо", + "ĠпоÑģÑĤ ÑĢо", + "ĠاÙĦÙħ ÙĪس", + "ĠاÙĦÙħÙĪ س", + "Ġol ma", + "Ġolm a", + "ëī´ ìĬ¤", + "Ġt utar", + "Ġtu tar", + "Ġtut ar", + "ãĥ¼ãĥĵ ãĤ¹", + "à¥įथ न", + "-ли бо", + "æ¥Ń åĭĻ", + "ĠоÑģоб ливо", + "ĠоÑģоблив о", + "è® Ģ", + "ÙģÙĩ ÙĪÙħ", + "Ġk ẻ", + "Ġ Å¡tÄĽ", + "ĠÅ¡ tÄĽ", + "ĠÅ¡t ÄĽ", + "Ġc ầm", + "ĠÄįlán ky", + "ĠÄIJ iá»ĩn", + "( =", + "OV Ãģ", + "ul du", + "uld u", + "a ft", + "af t", + "Ġl ãi", + "Ġlã i", + "Ġd oldur", + "Ġdol dur", + "³³ ³³³³³³³³³", + "³³³³ ³³³³³³³", + "³³³ ³³³³³³³³", + "³³³³³³³³ ³³³", + "³³³³³³³ ³³³³", + "³³³³³ ³³³³³³", + "³³³³³³ ³³³³³", + "³³³³³³³³³ ³³", + "β ι", + "ãģ£ãģ¦ ãģįãģŁ", + "ì¶ľìŀ¥ ìķĪë§Ī", + "å¯ Ŀ", + "Ġë¶Ģ íĥģ", + "ĠاÙĦ اخ", + "Ġγ Ïħνα", + "à¤ı म", + "à¥Į ल", + "ع ادة", + "عا دة", + "عاد Ø©", + "Ġ κοÏħ", + "Ġκ οÏħ", + "Ġκο Ïħ", + "ĠÙħØ· رØŃ", + "ĠÑĩелов еÑĩ", + "Ġn umar", + "Ġnum ar", + "Ġnu mar", + "Ġnuma r", + "Ġ дина", + "Ġд ина", + "Ġди на", + "ÏĦ ÏģÎŃ", + "ÏĦÏģ ÎŃ", + "λ ικ", + "λι κ", + "Ġдол го", + "Ġnh iêu", + "ĠвоÑģ ÑģÑĤанов", + "ap ı", + "Ġ kanı", + "Ġk anı", + "Ġkan ı", + "ĠK ế", + "ãĤī ãģļ", + "Ġhar ek", + "Ġha rek", + "Ġhare k", + "ãģłãģij ãģ§", + "æ» ħ", + "Ġo hled", + "Ġoh led", + "е ÑĢим", + "еÑĢ им", + "еÑĢи м", + "ĠØŃ ÙĬÙĨ", + "ĠØŃÙĬ ÙĨ", + "ĠÙĤ Ùĩر", + "Ġब à¥Ŀ", + "اپ ÛĮÙħ", + "è¶ħ è¿ĩ", + "Ġ æħ", + "Ġæ ħ", + "Ġت Ùģس", + "ĠتÙģ س", + "as ıyla", + "ası yla", + "б иÑĤ", + "би ÑĤ", + "ĠØŃ Ø§Ø¬", + "ĠÑĤÑĢеб ованиÑı", + "Ġ æݨ", + "Ġæİ ¨", + "Ġ ç±³", + "Ġç± ³", + "ãĤ³ ãĥ¼ãĥī", + "ĠÑĥ Ñģи", + "ĠÑĥÑģ и", + "Ġاخ ÙĦاÙĤ", + "Ġdo stup", + "Ġdost up", + "Ġع ÙĦاÙĤ", + "ĠعÙĦ اÙĤ", + "िव स", + "Ġ оди", + "Ġо ди", + "Ġод и", + "t ej", + "te j", + "Ġthá» ıa", + "ัà¸ģษ à¸ĵะ", + "ัà¸ģษà¸ĵ ะ", + "ĠÑĢаÑģ к", + "ĠÑĢа Ñģк", + "ĠÐĿ аÑĢод", + "ĠÐĿа ÑĢод", + "Ġза кÑĥп", + "Ġзак Ñĥп", + "o že", + "ož e", + "Ġاج را", + "Ġاجر ا", + "ê´ij ê³ł", + "аÑĢÑĤ ам", + "Ġп еÑĢеж", + "ĠпеÑĢ еж", + "ĠпеÑĢе ж", + "èij£ äºĭ", + "ĠÑı коÑģÑĤÑĸ", + "ĠÑıк оÑģÑĤÑĸ", + "Ġв Ñĥл", + "м он", + "мо н", + "Ġch lap", + "Ġ ÑįÑĤомÑĥ", + "ĠÑįÑĤ омÑĥ", + "ĠÑįÑĤо мÑĥ", + "ĠÑįÑĤом Ñĥ", + "а ÑĤÑĸ", + "аÑĤ Ñĸ", + "Ġ íĴĪ", + "Ġí ĴĪ", + "ĠíĴ Ī", + "è¡Ĺ éģĵ", + "س د", + "ÙĪ رÙĩ", + "ÙĪر Ùĩ", + "ĠزÛĮ اد", + "åľ¨çº¿ è§Ĩé¢ij", + "ا ÙĪÙĬØ©", + "اÙĪ ÙĬØ©", + "اÙĪÙĬ Ø©", + "ï¼Į å°±æĺ¯", + "ï¼Įå°± æĺ¯", + "e lerinden", + "eler inden", + "elerin den", + "elerinde n", + "ÑĢ ажд", + "ÑĢа жд", + "ÑĢаж д", + "Ġп озд", + "Ġпо зд", + "Ġпоз д", + "Ġзна ÑĤÑĮ", + "Ġзн аÑĤÑĮ", + "ัà¸ļ สà¸Ļ", + "ัà¸ļส à¸Ļ", + "à¥ĩà¤ĸ त", + "Ġ æĽ°", + "Ġæ Ľ°", + "ĠæĽ °", + "ê³¼ ìłķ", + "é® ®", + "ĠV iá»ĩn", + "ĠVi á»ĩn", + "Ġd voj", + "Ġdv oj", + "ίν εÏĦαι", + "Ġosob nÃŃch", + "ĠosobnÃŃ ch", + "Ġ âĢª", + "ĠâĢ ª", + "éĻ µ", + "ĠØ®ÙĪد Ø´", + "ĠاÙĨ ر", + "ĠпÑĢоÑĦеÑģÑģи оналÑĮ", + "k ám", + "ká m", + "ĠÙħ ÙĥاÙĨ", + "ĠÙħÙĥ اÙĨ", + "ĠاÙĦØ£ د", + "Ġ ê³µë¶Ģ", + "Ġê³µ ë¶Ģ", + "ĠÄij ức", + "ĠÄijứ c", + "ĠCumhur iyeti", + "ĠCumhuriyet i", + "åĩº ãģĹ", + "д ами", + "да ми", + "дам и", + "ĠìĪĺ ìĥģ", + "ĠÙģ براÙĬر", + "Ġsü resi", + "Ġsür esi", + "Ġsüre si", + "Ġب ج", + "Ġ æĶ¾", + "ĠæĶ ¾", + "ØŃ ÛĮ", + "çłĶ究 æīĢ", + "åĩºçīĪ 社", + "ĠÙħÙĪ تÙĪر", + "&& &&", + "ĠпеÑĢ ей", + "ĠпеÑĢе й", + "Ġ ìĦłê±°", + "ĠìĦł ê±°", + "ĠúspÄĽ Å¡", + "ار Ú©", + "Ġet tir", + "Ġett ir", + "Ġetti r", + "Ġ ì¶ľìŀ¥", + "Ġì¶ľ ìŀ¥", + "ĠKa nun", + "ĠKan un", + "ĠÑĥменÑĮ ÑĪ", + "ĠзаÑĤ веÑĢдж", + "ĠاÙĦد ÙĪÙĦÙĬ", + "ĠاÙĦدÙĪÙĦ ÙĬ", + "Ġ ãĥĵ", + "Ġãĥ ĵ", + "ĠB azı", + "ĠBa zı", + "ĠBaz ı", + "åŃIJ ãģ®", + "åĩ ¯", + "Ġse beb", + "Ġseb eb", + "Ġsebe b", + "Ġ åħ±", + "Ġåħ ±", + "Ġd nů", + "Ġdn ů", + "ä½į äºİ", + "ĠZ d", + "æī ±", + "Ġتج ربÙĩ", + "ÃĶ NG", + "Ġìĺ¬ ëĿ¼", + "Ïī ÏĦεÏģ", + "ĠÑģ вид", + "ĠÑģв ид", + "ĠÑģви д", + "æ¯Ķ èµĽ", + "ãģ« åIJij", + "ìľĦ 를", + "ãģĹ ãģ¾ãģĹãģŁ", + "ãģĹãģ¾ ãģĹãģŁ", + "Ġd á»ĭ", + "ĠÐł ÑĥÑģ", + "Ġv á»ı", + "Ġvá» ı", + "à¤Ĥड ल", + "Ġп иÑī", + "Ġпи Ñī", + "Ġsmr ti", + "Ġsmrt i", + "à¸Īาà¸ģ à¸ģาร", + "ĠÑģаÑħ аÑĢ", + "Ġtho át", + "ج ÙħØ©", + "جÙħ Ø©", + "Ġпоз вол", + "ĠاÙĦØ« اÙĨÙĬØ©", + "ĠاÙĦثاÙĨÙĬ Ø©", + "ز ادÙĩ", + "زا دÙĩ", + "ãĢģ ä¸Ń", + "ή μεÏģα", + "æ¦ ľ", + "l acaģı", + "lac aģı", + "lacaÄŁ ı", + "Ġна ÑĪиÑħ", + "ĠнаÑĪ иÑħ", + "ìĶ Ģ", + "ĠÐĺ ÑģÑĤоÑĢиÑı", + "ün deki", + "ünd eki", + "ünde ki", + "ĠпеÑĢ ел", + "ĠпеÑĢе л", + "Ġ목 ìĨĮ", + "ĠÑģÑĤаÑĤ ÑĥÑģ", + "о вали", + "ов али", + "ова ли", + "овал и", + "ÅĻ az", + "ĠдÑĢÑĥг ого", + "ĠдÑĢÑĥго го", + "ÙĥÙĪÙħ Ø©", + "ÙĥÙĪ ÙħØ©", + "Ñĩ иÑģÑĤ", + "Ñĩи ÑģÑĤ", + "ÑĩиÑģ ÑĤ", + "μ μ", + "åıį åºĶ", + "ic ari", + "ica ri", + "ĠÙ¾ اک", + "Ġپا Ú©", + "алÑĮ ним", + "ĠB una", + "ĠBu na", + "ĠBun a", + "и ÑĤив", + "иÑĤ ив", + "иÑĤи в", + "ÑĦ ÑĢа", + "ãĥ¼ ãĥĸãĥ«", + "ãĥ¼ãĥĸ ãĥ«", + "ĠÑĤоб ÑĤо", + "룬 ìĬ¤", + "ĠاÙĦ اع", + "åħ¬ éĸĭ", + "å¥ ī", + "ÙĪÙĦ د", + "åIJį çĦ¡ãģĹ", + "æ°ij 主", + "à¥ģ à¤ľà¤°", + "à¥ģà¤ľ र", + "ìĤ¬ 무", + "Ġön celik", + "Ġönce lik", + "Ġönc elik", + "Ġ å¨", + "Ġå ¨", + "Ñı б", + "çľ ī", + "à¥įव य", + "ĠH ình", + "çļĦ åľ°æĸ¹", + "çļĦåľ° æĸ¹", + "ĠاÙĦ تس", + "ĠاÙĦت س", + "ä¸Ī 夫", + "Ġп ÑĥблÑĸ", + "ĠnÄĽjak é", + "ÄIJ á»iji", + "ĠÑģоÑģÑĤоÑı ниÑı", + "à¥Ģ )", + "ĠÄij áºŃu", + "j ed", + "je d", + "ê ¶ģ", + "Ġs enin", + "Ġse nin", + "Ġsen in", + "Ġseni n", + "ĠH óa", + "âĻ ł", + "лÑı ÑİÑĤÑĮ", + "лÑıÑİÑĤ ÑĮ", + "éĹ ²", + "ìĿ¸ íĬ¸", + "ت بÙĩ", + "تب Ùĩ", + "Ġरà¤ĸ त", + "ĠÑģлов ами", + "ĠÑģлова ми", + "ĠÑģло вами", + "Ġطب ÙĤ", + "Ġuy du", + "ุà¸ĩà¹Ģà¸Ĺà¸ŀ มหาà¸Ļà¸Ħร", + "ĠSan at", + "ĠSa nat", + "à¹ī าà¸Ĭ", + "à¹īา à¸Ĭ", + "Ġкни ж", + "Ìģ c", + "ا Ùħج", + "اÙħ ج", + "δ Ïİ", + "Å ®", + "Ġb inh", + "Ġbi nh", + "Ġbin h", + "è¾ Ĩ", + "n eÄŁi", + "ne ÄŁi", + "Ø· ÙĨ", + "å¸ ķ", + "Ġ ìĩ¼", + "Ġì ĩ¼", + "оÑģ ÑĢед", + "ĠοÏĢο ίο", + "k ır", + "kı r", + "à¥Ī श", + "Ġ à¸ĩาà¸Ļ", + "Ġà¸ĩ าà¸Ļ", + "Ġd ruž", + "Ġdru ž", + "em atik", + "ema tik", + "emat ik", + "a dıģ", + "ad ıģ", + "adı ÄŁ", + "è¾ ŀ", + "ĠpoužÃŃ vá", + "Ġkur tar", + "ĠsaÄŁ lan", + "ãĢı ï¼Ī", + "Ġmůže me", + "Ġmůž eme", + "Ġ باد", + "Ġب اد", + "Ġبا د", + "æľŁ éĹ´", + "ا تÙģ", + "ات Ùģ", + "Ġyaz ılım", + "Ġyazılı m", + "ĠìĹ° ê²°", + "ÙĬ ÙģØ©", + "ÙĬÙģ Ø©", + "Ġ emin", + "Ġe min", + "Ġem in", + "ĠнеÑģколÑĮ киÑħ", + "Û´ Û°", + "å¯ §", + "ί ζει", + "ίζ ει", + "Ġd él", + "Ġdé l", + "ver iÅŁ", + "価 æł¼", + "Ġاست اد", + "Ġал ког", + ".H CM", + "ί οÏĤ", + "ίο ÏĤ", + "α κ", + "Ø· ع", + "ãģ£ ãģį", + "ãģ£ãģ į", + "Ñı еÑĤÑģÑı", + "ÑıеÑĤ ÑģÑı", + "л ика", + "ли ка", + "лик а", + "Ġ ÑĨÑı", + "ĠÑĨ Ñı", + "Ġë§Ī ì§Ģë§ī", + "ĠаÑĢ ми", + "Ġγ λÏİ", + "E NÃį", + "EN Ãį", + "ë ®¤", + "ŃIJ ï¸ı", + "Ġ æ¯ı", + "Ġæ¯ ı", + "Ġ æĸ¼", + "Ġæĸ ¼", + "Ġκα λÏį", + "ĠТ ом", + "ĠТо м", + "ul ur", + "ulu r", + "Ġak ce", + "ĠÙħÙĪ جب", + "ĠÙħÙĪج ب", + "e siz", + "es iz", + "esi z", + "н Ñıв", + "нÑı в", + "алÑĮ нÑĥÑİ", + "алÑĮнÑĥ Ñİ", + "ал ÑĸÑģÑĤ", + "алÑĸ ÑģÑĤ", + "Ġв аÑĢÑĸ", + "ĠваÑĢ Ñĸ", + "Ġва ÑĢÑĸ", + "ĠÙħؤ س", + "ĠÙħ اÛĮÙĦ", + "ĠÙħا ÛĮÙĦ", + "ĠμεÏĦα ξÏį", + "åĩº ãģĻ", + "Ġv á»Ŀi", + "Ġvá» Ŀi", + "ëŁ ´", + "ï¼ ĭ", + "æ¯ İ", + "Ġt abi", + "Ġtab i", + "Ġta bi", + "âĤ ĥ", + "æ£ĭ çīĮ", + "Ġ ÃIJ", + "Ġà IJ", + "ĠпÑĢоÑĦеÑģ Ñĸй", + "Ñĥв аннÑĸ", + "Îľ Îł", + "Ġж ил", + "Úĺ ÙĨ", + "л ÑĥÑĪ", + "лÑĥ ÑĪ", + "á½ ´", + "о веÑĢ", + "ов еÑĢ", + "ове ÑĢ", + "è¾¼ ãģ¿", + "ĠÐľ акÑģим", + "ĠÐľÐ°Ðº Ñģим", + "Ġвз глÑıд", + "Ġн аÑĤÑĥ", + "Ġна ÑĤÑĥ", + "ĠнаÑĤ Ñĥ", + "म à¤ķ", + "ĠÑħ ими", + "ĠÑĢозÑĤа ÑĪ", + "ÙĪ راÙĨ", + "ÙĪر اÙĨ", + "ÙĪرا ÙĨ", + "ĠØ´Ùĩر ÙĩاÛĮ", + "æ© Łèĥ½", + "æ©Ł èĥ½", + "Ø® Ø°", + "ĠÑģво ÑĶÑĹ", + "ĠÑģвоÑĶ ÑĹ", + "н ÑıеÑĤ", + "нÑı еÑĤ", + "Ġgh ế", + "ĠpÅĻed ch", + "ÑĶ ÑĪ", + "огÑĢаÑĦ ÑĸÑı", + "Ġ à¸Ĺำà¹ĥห", + "Ġà¸Ĺำ à¹ĥห", + "åĿ Ĭ", + "Ïģ Ïīν", + "ÏģÏī ν", + "า ระ", + "าร ะ", + "ĠK ết", + "ĠKế t", + "Ġch ặt", + "Ġ éĻĪ", + "ĠéĻ Ī", + "ĠdÄĽ lat", + "ĠdÄĽl at", + "ĠбÑĥд ÑĥÑī", + "ĠбÑĥдÑĥ Ñī", + "ĠAç ık", + "æłª å¼ıä¼ļ社", + "ĠÐŁ аÑĢ", + "ĠK hu", + "ĠKh u", + "ãĢģ æĸ°", + "Ġб ой", + "Ġбо й", + "ë§Ī íĬ¸", + "ĠÑģоп ÑĢов", + "س اب", + "н иÑģÑĤ", + "ни ÑģÑĤ", + "å¼ ĥ", + "Ġ Ø´ÙĨاس", + "ĠØ´ÙĨ اس", + "ен ном", + "енно м", + "Ġ 项", + "Ġé¡ ¹", + "èīº æľ¯", + "о зем", + "оз ем", + "ĠÑĢеÑĪ ениÑı", + "l ady", + "la dy", + "lad y", + "ĠвÑģ ей", + "ĠвÑģе й", + "æĶ» åĩ»", + "Ġê²° ìłķ", + "ãĢĢ ï¾ŀ", + "Ġê°IJ ëıħ", + "- ÐIJ", + "Ġm ÃŃr", + "ĠmÃŃ r", + "à¥ģप à¤ı", + "нÑĸ ÑĨип", + "б ом", + "бо м", + "Ġ Å¡t", + "ĠÅ¡ t", + "éľ į", + "ĠÑĢеÑĪ ение", + "Ġдиаг ноÑģÑĤи", + "i par", + "ip ar", + "ipa r", + "ا ÛĮز", + "اÛĮ ز", + "ã ng", + "ãn g", + "ั วร", + "ัว ร", + "ĠÑĨ аÑĢ", + "Ġs ly", + "Ġsl y", + "ν Ïİ", + "ĠK uzey", + "رÛĮ ب", + "Ġc enu", + "Ġce nu", + "Ġcen u", + "Ġcert if", + "Ġcer tif", + "ĠÑĤ ÑĢеÑĤÑĮ", + "ĠÑĤÑĢ еÑĤÑĮ", + "ĠÑĤÑĢеÑĤ ÑĮ", + "ิà¸Ķ à¸Ĥ", + "Ġпа ÑĨÑĸÑĶн", + "ÅĻ iv", + "ÅĻi v", + "èĦ Ĥ", + "¢ °", + "ĠPh ần", + "ĠмеÑĤод и", + "ĠмеÑĤ оди", + "Ạ¤", + "ìĨ Ķ", + "åIJĮ åѦ", + "Ġ åĢĭ", + "ĠåĢ ĭ", + "моÑĤ ÑĢÑı", + "моÑĤÑĢ Ñı", + "Ġuv ád", + "Û±Û¹ Û¶", + "éģ¸ æĬŀ", + "! »", + "ë ĺIJ", + "ĠÛĮ ÙĪتÛĮ", + "ĠاÙĦØŃ Ø±Ø¨", + "ĠاÙĦØŃر ب", + "олог ÑĸÑı", + "n ila", + "ni la", + "nil a", + "ĠÄij ảng", + "á zi", + "áz i", + "ÑĢ оÑī", + "ÑĢо Ñī", + "Ġort adan", + "Ġorta dan", + "Ġاخ بار", + "Ġà¤ħ à¤ľ", + "Ġ매 ìļ°", + "Ġп ой", + "Ġпо й", + "Ġ جÙĬ", + "Ġج ÙĬ", + "к ÑĥваÑĤи", + "кÑĥ ваÑĤи", + "Ġá» ŀ", + "Ġب شر", + "Ġبش ر", + "Ġ ÙĥÙĬÙĦ", + "ĠÙĥ ÙĬÙĦ", + "Ñī еÑģÑĤво", + "Ñīе ÑģÑĤво", + "ÑīеÑģÑĤв о", + "ĠìŬ íĸī", + "ا ÙħÙĬ", + "اÙħ ÙĬ", + "в ÑĸлÑĮ", + "вÑĸ лÑĮ", + "ĠPr vnÃŃ", + "Ġ ÙĪسÛĮ", + "ĠÙĪ سÛĮ", + "ĠÙĪس ÛĮ", + "ĠÄIJ á»", + "æĪ¿ éĹ´", + "åľ¨çº¿ éĺħ读", + "æķ ·", + "Ġt rai", + "Ġtr ai", + "Ġtra i", + "ä¿ Ĺ", + "ĠÑģамоÑģÑĤоÑıÑĤелÑĮ но", + "ĠÑĤÑĢеб ÑĥеÑĤÑģÑı", + "ĠÑĤÑĢебÑĥеÑĤ ÑģÑı", + "δ Ïģα", + "ĠÑĢеÑĩ ов", + "Ġв Ñĸк", + "ĠвÑĸ к", + "Ġ ÑĢÑĥÑĩ", + "ĠÑĢ ÑĥÑĩ", + "ĠÑĢÑĥ Ñĩ", + "å¥ §", + "ĠolduÄŁ una", + "ĠolduÄŁu na", + "ев Ñĭе", + "Ġ à¸Ħล", + "Ġà¸Ħ ล", + "ا ÙĦÙĤ", + "اÙĦ ÙĤ", + "ĠÑĸм енÑĸ", + "ĠÑĸмен Ñĸ", + "æĶ» æĴĥ", + "ĠÑĥнивеÑĢ ÑģиÑĤ", + "Ġth Äĥm", + "ĠлиÑģÑĤоп ада", + "२ ०", + "Ø® ÙĬ", + "Îķ Îł", + "Ġart tır", + "Ġس خت", + "Ġسخ ت", + "ï¼Ī æĺŃåĴĮ", + "ĠÎŁ Ïħ", + "и ваниÑı", + "ив аниÑı", + "ива ниÑı", + "Ġstav eb", + "âħ ¥", + "γÏī γή", + "γÏīγ ή", + "Ù ©", + "ĠиÑģÑģлед ованиÑı", + "åĢĭ 人", + "Ġëĭ¤ìļ´ë°Ľ 기", + "ĠÏĦ ελ", + "ĠÏĦε λ", + "° N", + "ĠباÙĦ ÙĨ", + "à¹Į à¸ŀ", + "Ġnem ůže", + "Ġголов а", + "Ġгол ова", + "à¹Į à¹ģ", + "æ¢ ¯", + " ĺ", + "δ ηÏĤ", + "δη ÏĤ", + "ìĿ¸ ì¦Ŀ", + "l ayın", + "lay ın", + "á½ ·", + "ĠÙĨت اÛĮج", + "ĠÑģоб лÑİд", + "Ġдви жениÑı", + "Ġдвиж ениÑı", + "ì Į", + "Ġ povÄĽ", + "Ġp ovÄĽ", + "Ġpo vÄĽ", + "Ġpov ÄĽ", + "Ġ ìłĦìĹIJ", + "ĠìłĦ ìĹIJ", + "å¦Ĥ ä¸ĭ", + "ĠاÙĦÙħ در", + "ĠاÙĦÙħد ر", + "ï¼Į æĪĸ", + "ا را", + "ار ا", + "æ°ij æĹı", + "Ġب رÙĤ", + "Ġبر ÙĤ", + "Ġзап аÑģ", + "à¸Ļ à¹ĥà¸Ī", + "é f", + "Ġ à¸Łà¸£", + "Ġà¸Ł ร", + "Ġë³´ ëĤ´", + "Ġ 欧ç¾İ", + "Ġ欧 ç¾İ", + "- ÑĤаки", + "-ÑĤ аки", + "é© ļ", + "ÑĢ ÑĸÑı", + "ÑĢÑĸ Ñı", + "æŁ ı", + "ĠповÑĸÑĤ ÑĢÑı", + "çµĦ ç¹Ķ", + "d aÅŁ", + "da ÅŁ", + "Ġहम ल", + "ĠÑĢеÑĶ ÑģÑĤÑĢа", + "ά β", + "ĠÎł ο", + "Ġê·¸ 림", + "Ñĩ аÑİÑĤ", + "Ñĩа ÑİÑĤ", + "à¸ĩ à¸ķ", + "íĥ ĢìĿ´", + "íĥĢ ìĿ´", + "æī ¬", + "Ġpo jist", + "Ġpoj ist", + "Ġ çłĶ", + "Ġçł Ķ", + "Ġ åıĸ", + "Ġåı ĸ", + "Ġüzer indeki", + "Ġüzerinde ki", + "j Å¡ÃŃch", + "jÅ¡ÃŃ ch", + "à¥Ģद व", + "æª ¢", + "ĠмаÑĤеÑĢи алов", + "ĠмаÑĤеÑĢиал ов", + "и ваннÑı", + "ив аннÑı", + "Ġ å°Ĩ", + "Ġå° Ĩ", + "л л", + "Ġнаб лÑİд", + "ĠнаблÑİ Ð´", + "ĠG öz", + "ĠGö z", + "Ġв зÑı", + "Ġвз Ñı", + "çĶµ è§Ĩ", + "Ġв ак", + "Ġва к", + "ç¿ Ķ", + "Ġвза им", + "Ġg itti", + "Ġgi tti", + "Ġgit ti", + "it eleri", + "ite leri", + "itel eri", + "itele ri", + "ä»· åĢ¼", + "ĠاÙĦ تص", + "ĠاÙĦت ص", + "िन à¤ķ", + "éĢļ ãĤĬ", + "ĠÑģ ÑĦеÑĢ", + "ĠÑģÑĦ еÑĢ", + "çĻº 売", + "âĿ ¤", + "ĠÚ¯ÙĪØ´ ÛĮ", + "ĠÚ¯ÙĪ Ø´ÛĮ", + "аг аÑĤо", + "ĠÏĥÏħ γκ", + "ав иÑģ", + "ави Ñģ", + "æĤ£ èĢħ", + "ĠØ® اÙħ", + "ÎĻÎļ ÎĹΣ", + "ÎĻÎļÎĹ Î£", + "ınız da", + "pan ÄĽl", + "pa nÄĽl", + "ĠÄIJ á»ĭa", + "à¹ģละ ส", + "Ġ ãĤĤ", + "ĠãĤ Ĥ", + "Ġsonuc unda", + "Ġsonucu nda", + "ìĿ į", + "e less", + "el ess", + "ele ss", + "ĠN ha", + "ĠNh a", + "Ġzak áz", + "Ġв оÑģÑĤ", + "Ġво ÑģÑĤ", + "ĠвоÑģ ÑĤ", + "ĠvzdÄĽl ávánÃŃ", + "- ม", + "Ġmet rů", + "ĠپاÛĮ ÛĮÙĨ", + "Ġپا ÛĮÛĮÙĨ", + "ĠÑĢаÑģÑĤ ение", + "Ġmu á»iji", + "èµĦ éĩij", + "ĠÅŁ üph", + "ÙĬ ÙĦÙħ", + "ÙĬÙĦ Ùħ", + "ĠdÃ¼ÅŁÃ¼n c", + "Ġк Ñĸм", + "ĠÏĩÏī ÏģίÏĤ", + "áz ev", + "áze v", + "ĠDe ÄŁer", + "ĠDeÄŁ er", + "å·¥ æ¥Ń", + "Ġر Ùħز", + "ĠرÙħ ز", + "Ġal espoÅĪ", + "ĠпÑĢе ÑģÑĤÑĥп", + "ĠпÑĢеÑģÑĤ Ñĥп", + "ĠعÙĦ اÙĪÙĩ", + "Ġme rak", + "Ġmer ak", + "à¹Į :", + "çİ° åľº", + "ÑĨ веÑĤ", + "Ġप à¥ľ", + "Ġëĭ¤ìĿĮ ê³¼", + "u dic", + "ud ic", + "udi c", + "ĠL ep", + "ĠLe p", + "Ġод нÑĸ", + "Ġa larak", + "Ġal arak", + "å®ī æİĴ", + "Ġ à¸Ĥà¸Ļาà¸Ķ", + "Ġà¸Ĥ à¸Ļาà¸Ķ", + "re zent", + "rez ent", + "is inden", + "isin den", + "isinde n", + "ر ÙĪÛĮ", + "رÙĪ ÛĮ", + "Ġp lu", + "Ġpl u", + "ç«ĭ ãģ¦", + "Ñĭ ваниÑı", + "Ñĭв аниÑı", + "Ñĭва ниÑı", + "Ġr ast", + "Ġra st", + "Ġras t", + "Ġdüzen lem", + "je zd", + "jez d", + "Ġве ÑīеÑģÑĤв", + "ĠвеÑī еÑģÑĤв", + "ĠдиÑĢ екÑĤоÑĢ", + "ÑĦ ÑĦ", + "t ainment", + "tain ment", + "ĠاÙĦ ÙĪز", + "ĠاÙĦÙĪ ز", + "l anda", + "land a", + "la nda", + "lan da", + "ĠÙĨÚ¯ Ùĩد", + "ĠпÑĢоÑĤив оп", + "ãģ£ ãģı", + "ãģ£ãģ ı", + "ãģ¨ãģª ãĤĬ", + "Ġë°ľ 견", + "i ctor", + "ic tor", + "ict or", + "ãĤ¸ ãĤª", + "ÎŁ Φ", + "ÎŁÎ ¦", + "ĠÑģклад Ñĸ", + "Ġob sahuje", + "Ġobsah uje", + "ĠUkr a", + "ĠUk ra", + "æķ ¦", + "ĠÏĩ αÏģα", + "ĠÏĩα Ïģα", + "ĠÑĢег Ñĥли", + "俺 ãģ¯", + "ัà¸ķ ว", + "éĦ ī", + "Ġب اÛĮ", + "Ġبا ÛĮ", + "éĬ ·", + "ĠN ẵng", + "л од", + "ло д", + "ا رÙģ", + "ار Ùģ", + "æ´ ģ", + "ĠëıĻ ìĿ¼", + "ÑĤив ного", + "âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ", + "Ġ- :-", + "Ġ-: -", + "ì» ¬", + "ĠÑĪ аг", + "ìłĦ ìŀIJ", + "çļĦ äºĭæĥħ", + "çļĦäºĭ æĥħ", + "ĠÑĢег Ñĸ", + "िय ल", + "ĠÐĿ аз", + "ĠÐĿа з", + "ĠÐĻ ого", + "ĠÐł ом", + "ĠÃĸr neÄŁin", + "Ġп ÑĢеÑģ", + "ĠпÑĢ еÑģ", + "ĠпÑĢе Ñģ", + "u luÄŁu", + "ulu ÄŁu", + "uluÄŁ u", + "Ġза дов", + "Ġзад ов", + "ÅĻ eh", + "ÅĻe h", + "æ¯ķ ä¸ļ", + "Ġth áºŃp", + "ëĤ ¸", + "Ġdlou hodob", + "Ġdlouho dob", + "дÑĸ лÑĥ", + "дÑĸл Ñĥ", + "a lat", + "al at", + "ala t", + "ä» °", + "о ком", + "ок ом", + "око м", + "ĠÑĦ ÑĸлÑĮ", + "ĠÑĦÑĸл ÑĮ", + "ĠNg ân", + "Ġ ترÙĥ", + "Ġت رÙĥ", + "Ġتر Ùĥ", + "ĠÑĤ Ñī", + "ر ÙĪد", + "رÙĪ د", + "ç uk", + "çu k", + "ra nÃŃ", + "ran ÃŃ", + "Ġdo laÅŁ", + "Ġdol aÅŁ", + "ĠQ uang", + "ĠQu ang", + "ĠpÅĻed pok", + "Ġnám ÄĽstÃŃ", + "ой Ñĩив", + "çĭ Ģ", + "Ġб изнеÑģ", + "ãģŁ ãģı", + "ĠìĿ¸ ì²ľ", + "о ÑĢо", + "оÑĢ о", + "ĠKü rt", + "ĠKür t", + "ê·¸ 룬", + "ÑĨ аÑĤÑĮ", + "ÑĨа ÑĤÑĮ", + "ĠB ên", + "Ġ acı", + "Ġa cı", + "Ġac ı", + "Ú© Ø´", + "ï¼Ī å¹³æĪIJ", + "Ġ èģĶ", + "Ġèģ Ķ", + ") ãĢģ", + "d iler", + "di ler", + "Ñĩ иÑĤÑĮ", + "ÑĩиÑĤ ÑĮ", + "Ñĩи ÑĤÑĮ", + "Ư á»", + "éĻ ¶", + "il eceÄŁini", + "ilece ÄŁini", + "ileceÄŁi ni", + "Ġv Å¡em", + "ĠvÅ¡ em", + "ĠvÅ¡e m", + "å¼Ģ å¥ĸ", + "è§Ħ 模", + "ul muÅŁ", + "Ġ åĪĺ", + "Ġå Īĺ", + "ĠåĪ ĺ", + "е о", + "еР¾", + "ĠпеÑĢев ÑĸÑĢ", + "åĪĨ åĪ«", + "Ġjed ná", + "Ġjedn á", + "li ÄŁe", + "liÄŁ e", + "ĠرÙħ ضاÙĨ", + "ık lı", + "ıkl ı", + "Ùĩ ÙĢ", + "éĩį çĤ¹", + "Ñĩ иваеÑĤÑģÑı", + "Ñĩи ваеÑĤÑģÑı", + "Ñĩив аеÑĤÑģÑı", + "Ñĩива еÑĤÑģÑı", + "ë¡ľ ìĦľ", + "ÏĦ εÏģο", + "ÏĦε Ïģο", + "ÏĦεÏģ ο", + "åľ° ä¸ĭ", + "д наннÑı", + "дн аннÑı", + "Ġng ược", + "ॠª", + "ĠÎij λ", + "Ġa lacak", + "Ġal acak", + "Ġ à¹Ģà¸ĩ", + "Ġà¹Ģ à¸ĩ", + "Ġà¹Ģภĩ", + "اÛĮ ÙĨد", + "اÛĮÙĨ د", + "Ġh Ãłi", + "ÑĢо из", + "ĠЧ и", + "Ġ ÑıÑģ", + "ĠÑı Ñģ", + "خر ÛĮد", + "Ġhu deb", + "Ġhud eb", + "åľ §", + "Ġ ìĦ¼", + "ĠìĦ ¼", + "åĶ¯ ä¸Ģ", + "Ġ вÑĸлÑĮ", + "Ġв ÑĸлÑĮ", + "ĠвÑĸ лÑĮ", + "ĠباÙĦ اتر", + "ĠباÙĦا تر", + "à¸Ńà¸ģ าส", + "Ġ Tôi", + "ĠT ôi", + "ม à¸Ĥ", + "o mor", + "om or", + "omo r", + "ĠO lomou", + "Ġx ong", + "Ġxo ng", + "Ġdomác ÃŃ", + "Ġ اختÛĮ", + "Ġاخ تÛĮ", + "Ġاخت ÛĮ", + "ĠÑĤеÑħ нÑĸÑĩ", + "ĠÑĤеÑħнÑĸ Ñĩ", + "ĠiÅŁ te", + "à¥Į द", + "Ġнад еж", + "Ø®ÛĮ ص", + "åĬª åĬĽ", + "ĠتجÙĩ ÛĮزات", + "Ġv ole", + "Ġvo le", + "Ġvol e", + "k inci", + "kin ci", + "Ġhes ab", + "ĠÑģ еÑģÑĤ", + "Ú© ا", + "ÑĤеÑĢ н", + "ร รà¸Ħ", + "รร à¸Ħ", + "åıĤ èĢĥ", + "ĠÐļ аб", + "ĠÐļа б", + "ĠÄ° mpar", + "Ġnáv rh", + "Ġnávr h", + "åĴ¨ 询", + "à¸ĸ าม", + "Ġye rel", + "Ġyer el", + "Ġyere l", + "ĠÃĸ l", + "çĮ Ľ", + "ĠاÙĦÙĪØ· ÙĨÙĬ", + "Ġ ìĿ´ìĸ´", + "ĠìĿ´ ìĸ´", + "ิà¸Ĺย าศาสà¸ķร", + "ิà¸Ĺยา ศาสà¸ķร", + "ĠA ÅŁ", + "Ġзем лÑİ", + "ĠдомаÑĪ ниÑħ", + "ĠÑĥ веÑĢ", + "ĠÑĥв еÑĢ", + "A LI", + "AL I", + "г ан", + "га н", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "Ġdo stan", + "Ġdos tan", + "Ġdost an", + "ez pe", + "ãģĭ ãģĦ", + "ر ÙģتÙĩ", + "رÙģ تÙĩ", + "رÙģت Ùĩ", + "Ġм ÑĥÑģ", + "ĠмÑĥ Ñģ", + "à¹Į à¸Ł", + "è¦ º", + "али за", + "ализ а", + "ĠÑĥÑĩ ÑĢежд", + "ĠÚ© اÙĦ", + "Ġetk isi", + "Ġetki si", + "ä½Ĩ æĺ¯", + "Ġsou vis", + "ĠSav aÅŁÄ±", + "ĠSavaÅŁ ı", + "Ġب سبب", + "ÎŁ ι", + "ÎŁÎ ¹", + "è ļ", + "Ġ æ®", + "Ġæ ®", + "Ġìĺģ êµŃ", + "ا سÛĮÙĪÙĨ", + "اسÛĮ ÙĪÙĨ", + "ĠاÙĦات ØŃاد", + "Ġ глÑı", + "Ġг лÑı", + "à¹ĩà¸ģ à¸ĭ", + "Ġج ÙĪÙĨ", + "ĠجÙĪ ÙĨ", + "ĠاÙĦرسÙħ ÙĬ", + "Âł G", + "ĠÑĤо бÑĸ", + "ĠÑĤоб Ñĸ", + " ĩ", + "Ġ ëĮĢíĸī", + "ĠëĮĢ íĸī", + "çĬ¶ æħĭ", + "Ġê·¸ ëĥ¥", + "Ġи мп", + "Ġим п", + "ĠتÙĨظ ÛĮÙħ", + "ÙĦ اÛĮÙĨ", + "ÙĦا ÛĮÙĨ", + "ÑģÑĤв еннÑĭм", + "ÑģÑĤвен нÑĭм", + "о пол", + "оп ол", + "ر ÙĪج", + "رÙĪ ج", + "Ġ à¸ĩ", + "Ġภĩ", + "Ġ çĤº", + "ĠçĤ º", + "ĠUlus lararası", + "à¥Į à¤Ĥ", + "ãĢģ ãģĿãģĨ", + "Ġس ادÙĩ", + "ÎŃ Î±ÏĤ", + "ÎŃα ÏĤ", + "Ġà¤Ĩ ल", + "- ÑĦ", + "ĠÎłÎ¿Î» ι", + "ĠÎłÎ¿ λι", + "Ġно ÑıбÑĢÑı", + "ĠноÑı бÑĢÑı", + "ÙĪ ÙĦÙĬ", + "ÙĪÙĦ ÙĬ", + "æĽľ æĹ¥", + "æĮģ ç»Ń", + "Ġê¼ Ń", + "ece ÄŁiz", + "eceÄŁi z", + "ĠÛĮ اÙģت", + "ĠÛĮا Ùģت", + "Ġ åı¸", + "Ġåı ¸", + "ाà¤Ĺ त", + "Ġ æķħ", + "Ġæķ ħ", + "Ġал леÑĢг", + "Ġt uz", + "Ġtu z", + "еÑĢ ÑĤи", + "еÑĢÑĤ и", + "Ġth ầu", + "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", + "- à¤ħ", + "Ġим мÑĥ", + "ÑĢ ай", + "ÑĢаР¹", + "ÑĢа й", + "主 義", + "ĠbaÅŁ lar", + "Ġä¸Ĭ 涨", + "ع ا", + "ĠÎĻ Ïī", + "ียà¸ĩ à¹ĥหม", + "ĠاÙĦÙħد ÙĬÙĨØ©", + "Ñģ ÑĮко", + "ÑģÑĮ ко", + "ÑģÑĮк о", + "ĠتارÛĮØ® ÛĮ", + "at ÃŃm", + "âĢļ Ø·", + "Ø¢ خر", + "ĠëĦ £", + "ĠÙĨÙħ اÛĮد", + "ĠÙĨÙħاÛĮ د", + "ãģķãĤĵ ãģĮ", + "Ġb ò", + "Ġ à¸ķาม", + "Ġà¸ķ าม", + "ë³´ ìķĺëĭ¤", + "а ÑĤÑĸв", + "аÑĤ Ñĸв", + "аÑĤÑĸ в", + "ĠÑĦ ил", + "Ġkısm ı", + "iá»ĩ ng", + "iá»ĩn g", + "Ġay dın", + "éģķ ãģĦ", + "е ви", + "ев и", + "Ġ å¾®", + "Ġå¾ ®", + "( íģ¬ê¸°", + "Ġ Ú¯ÛĮر", + "ĠÚ¯ ÛĮر", + "ĠÚ¯ÛĮ ر", + "ìķĦ ìĦľ", + "Ġδη μιοÏħÏģγ", + "ãģ«ãģĬ ãģĦãģ¦", + "ĠÃľ NÄ°", + "и ÑĤом", + "иÑĤ ом", + "ع ÙĦاÙħ", + "عÙĦ اÙħ", + "åIJİ çļĦ", + "Ġp lá", + "Ġpl á", + "à¸Ľà¸£à¸° à¹Ĥย", + "ç¢ İ", + "Ġ éĺ²", + "Ġéĺ ²", + "ëĬ Ķëĭ¤", + "ëĬĶ ëĭ¤", + "Ġ æĹ¥æľŁ", + "ĠæĹ¥ æľŁ", + "Ġgeç erli", + "л аÑĤÑĭ", + "ла ÑĤÑĭ", + "лаÑĤ Ñĭ", + "Ġmutlak a", + "ÙĪ غ", + "à¹Ģ ฮ", + "à¹Ģภ®", + "Ġï» £", + "e deki", + "ed eki", + "ede ki", + "à¹Į à¹Ģà¸Ļ", + "Ġнайб ÑĸлÑĮÑĪ", + "ĠнайбÑĸлÑĮ ÑĪ", + "ï¼ Ĭ", + "Ġ à¹Ĥรà¸ĩ", + "Ġà¹Ĥ รà¸ĩ", + "Ġfot bal", + "Ġ éĢģ", + "ĠéĢ ģ", + "âĢĮاÙĦ ÙħÙĦ", + "Ïīμά ÏĦιο", + "Ġú kol", + "åįļ 士", + "d ub", + "du b", + "ı lıģ", + "ılı ÄŁ", + "ıl ıģ", + "ëĵľ 를", + "çĭ IJ", + "α λλ", + "αλ λ", + "æŃ» 亡", + "ĠпÑĢед поÑĩ", + "çµ µ", + "Ġм ÑĥзÑĭ", + "ĠмÑĥ зÑĭ", + "ĠмÑĥз Ñĭ", + "еÑĢÑĤ в", + "ĠÙĥ ÙĨد", + "ĠÙĥÙĨ د", + "Ġu lož", + "Ġul ož", + "ÎŁÎ¥ ÎĽ", + "g ili", + "gi li", + "gil i", + "üs tü", + "üst ü", + "н ки", + "ĠÙĤ ÙĪاÙĨ", + "ι ακ", + "ια κ", + "ĠÅŁ er", + "ĠкиÑģ л", + "Ġки Ñģл", + "Ùģ ضÙĦ", + "ĠÐIJ ÑĦ", + "γ εν", + "γε ν", + "Ġdo stal", + "Ġdos tal", + "Ġdost al", + "ĠsaÄŁ lıklı", + "ĠsaÄŁlık lı", + "家 æĹı", + "ÄIJ T", + "е ÑĢин", + "еÑĢ ин", + "еÑĢи н", + "ĠìĿ´ëŁ¬ íķľ", + "Ġdüny ada", + "Ġdünya da", + "Ġnh ắc", + "Âł ÂłĊ", + "³³ Ċ", + "ν ηÏĥη", + "νη Ïĥη", + "γÏģα μμα", + "Ġtak son", + "ĠTürk çe", + "ĠÙģراÙĨ سÙĩ", + "天 åłĤ", + "æº ¶", + "Ġ oto", + "Ġo to", + "Ġot o", + "èµ µ", + "ch yb", + "chy b", + "Ġ å¾Ĵ", + "Ġå¾ Ĵ", + "ÏĦ Ïį", + "áh nout", + "à¥į पर", + "à¥įप र", + "Ġv las", + "Ġvl as", + "Ġíļ¨ ê³¼", + "Ġt hang", + "Ġth ang", + "Ġthan g", + "Ġtha ng", + "Ġol masına", + "Ġolm asına", + "Ġolması na", + "ĠпоÑĢÑĥÑĪ еннÑı", + "Ġqu ỹ", + "ĠíĿ IJ", + "ĠìĪ ¨", + "Ġ ë²Ī째", + "Ġë²Ī 째", + "ẹ n", + "Ġз год", + "Ġзг од", + "Ġ تز", + "Ġت ز", + "Ġاخ تص", + "Ġاخت ص", + "ĠзÑĥÑģÑĤ ÑĢÑĸ", + "Ġt ặng", + "ῶ ν", + "Ġ ì½ľ", + "Ġì½ ľ", + "ов аниÑħ", + "ова ниÑħ", + "овани Ñħ", + "ован иÑħ", + "âĢĮ شد", + "âĢĮØ´ د", + "Ġa raya", + "Ġar aya", + "Ġara ya", + "Ġaray a", + "r ové", + "ro vé", + "rov é", + "Ġاخ تÙĦ", + "Ġاخت ÙĦ", + "ли вий", + "лив ий", + "Ġات ØŃاد", + "Ġak ÅŁam", + "ĠÚ©ÙĦ اس", + "ãĤ¢ ãĥĥãĥĹ", + "Ġz ih", + "Ġzi h", + "å ĩĮ", + "åĩ Į", + "å±± å¸Ĥ", + "Ġçev res", + "Ġçevr es", + "Ġçevre s", + "Ġог ÑĢом", + "ĠØ¢ دÙħ", + "ĠtÄĽ lo", + "ĠtÄĽl o", + "ï¼Į æľ¬", + "ĠÚĺاÙĨ ÙĪÛĮÙĩ", + "Ġkr aje", + "Ġkra je", + "Ġkraj e", + "μ ία", + "μί α", + "èħ ¿", + "âĢŀ To", + "決 å®ļ", + "ì ĩ", + "Ġ éĴ", + "Ġé Ĵ", + "ĠΣ ÏĦα", + "ĠجÙħ ÙĩÙĪر", + "ĠGen ç", + "r ám", + "rá m", + "ĠÐł ез", + "ĠÐłÐµ з", + "Ġvyt vá", + "ĠпÑĢоизвод ÑģÑĤва", + "ĠÙħ Ø°Ùĩ", + "ĠÙħØ° Ùĩ", + "Ġihtiy ac", + "ãĤ¯ ãĤ»", + "Ġn êu", + "å¾ ³", + "Ġ ëĵĿ", + "Ġëĵ Ŀ", + "н аÑĩе", + "на Ñĩе", + "наÑĩ е", + "ĠÏĥÏħ μμε", + "ÏĨ Ïīν", + "в авÑģÑı", + "ва вÑģÑı", + "вав ÑģÑı", + "Ġви ÑĤами", + "ĠвиÑĤ ами", + "Ìģ t", + "Ġfinan ÄįnÃŃ", + "åıĬ åħ¶", + "âĢ ħ", + "çĭ ¼", + "ัà¸ļ à¸ķ", + "ãģĽ ãĤĭ", + "ÎĻÎļ ÎŁ", + "λ λι", + "λλ ι", + "ÑĤ оÑİ", + "ÑĤо Ñİ", + "ا عÙĬØ©", + "اع ÙĬØ©", + "اعÙĬ Ø©", + "vÃŃ ce", + "vÃŃc e", + "о нÑĸв", + "он Ñĸв", + "онÑĸ в", + "ì£ Ħ", + "å» ł", + "ĠØ´ÙĬ Ø¡", + "ĠТ ем", + "ĠТе м", + "Ġاب زار", + "ĠTH PT", + "γ γÏģαÏĨ", + "ĠëĮĢ íķ´ìĦľ", + "ĠëĮĢíķ´ ìĦľ", + "ĠPh ạm", + "ÑĨион ной", + "| /", + "Ġ ãĤ¸ãĥ£", + "ĠãĤ¸ ãĥ£", + "ÑĮ ÑİÑĤ", + "ÑĮÑİ ÑĤ", + "Ñĥ зÑĭ", + "Ñĥз Ñĭ", + "ĠÙħ اد", + "ĠÙħا د", + "ĠmÄĽ ly", + "ĠmÄĽl y", + "Ġ çĪ±", + "ĠçĪ ±", + "Ġr ád", + "Ġrá d", + "à¸Ħว à¸ļà¸Ħ", + "à¥Ī ?", + "Ġl idi", + "Ġli di", + "Ġlid i", + "m amız", + "mam ız", + "Ġ à¹ģà¸ģ", + "Ġà¹ģ à¸ģ", + "ãĤ¯ ãĤ·ãĥ§ãĥ³", + "à¸Ńำ à¸Ļวย", + "es át", + "Ġv iêm", + "Ġvi êm", + "è¡Į åĬ¨", + "มาà¸ģ à¸ģว", + "ĠØ®ÙĪ اب", + "Ġser best", + "ÅĻÃŃ z", + "ĠíĺĦ ëĮĢ", + "ãĢĮ ãģĿãģĨ", + "çĤ ¸", + "om ik", + "omi k", + "ĠÄ° ran", + "Ġer iÅŁ", + "ĠÑģ ела", + "ĠÑģел а", + "Ġار زÛĮ", + "Ġارز ÛĮ", + "ãĥĪ ãĥª", + "ĠB ÄĽ", + "е кÑĥ", + "ек Ñĥ", + "Ч ÑĤобÑĭ", + "ЧÑĤо бÑĭ", + "Ġanlam da", + "Îij Îĺ", + "ĠLINE AR", + "ĠLIN EAR", + "æľī çĤ¹", + "ÑĤ аÑĢ", + "ÑĤа ÑĢ", + "it ler", + "itle r", + "Ġn ÃŃž", + "ĠnÃŃ Å¾", + "ĠС ÑģÑĭлки", + "å ¶", + "Ġв пол", + "Ġвп ол", + "ĠدÙĤÛĮ ÙĤÙĩ", + "ĠدÙĤÛĮÙĤ Ùĩ", + "Ġ ä½ĵ", + "ر Ùī", + "ëĶ °", + "Ġà¤ķ व", + "Ġж иÑĢ", + "æij Ĩ", + "Ġì¤ij ìĭ¬", + "Ġк Ñĥб", + "ĠкÑĥ б", + "Ġz lep", + "ĠÑĢÑĭ б", + "é³ ´", + "à¹ģà¸ľ à¸Ļ", + "Ġ íĢ", + "Ġí Ģ", + "ĠÐĿ еÑĤ", + "ĠÐĿе ÑĤ", + "ž itÄĽ", + "žit ÄĽ", + "ži tÄĽ", + "Ġb Äĥng", + "ĠH ava", + "ĠHa va", + "ĠHav a", + "Ġ모 ëį¸", + "ĠH ãy", + "ĠìĿ´ ê²ĥ", + "Ġìĥģ ìĦ¸", + "me miÅŁ", + "mem iÅŁ", + "ĠθÎŃ Ïĥη", + "ण न", + "ĠskuteÄį nÄĽ", + "ĠTarih i", + "Ġtext u", + "Ġtex tu", + "ï¼Į éĢĻ", + "ĠاÛĮÙĨتر ÙĨتÛĮ", + "ĠÙ¾ اد", + "Ġپا د", + "ิà¸Ļ à¸ģาร", + "ĠNg á»įc", + "ĠÑĢоб иÑĤи", + "íĸĪ ê³ł", + "Ġम ण", + "ÐĽ Ðĺ", + "Ġпо ÑĤеÑĢ", + "ĠпоÑĤ еÑĢ", + "Ñģ ом", + "Ñģо м", + "ĠاÙĪ ÙĦÛĮÙĩ", + "ĠاÙĪÙĦ ÛĮÙĩ", + "éĽ ij", + "ĠGi á", + "Ġk anal", + "Ġkan al", + "Ġka nal", + "Ġavant aj", + "Ġavan taj", + "Ġr yb", + "Ġry b", + "Ø® تÙĩ", + "خت Ùĩ", + "ĠÙĪ رÙĪد", + "ĠÙĪر ÙĪد", + "ÐĴ ÑĤ", + "Ïī Ïĥε", + "기 ë¡ľ", + "ĠÐĽ Ñĸ", + "Ġt ảng", + "Ġtả ng", + "Ġص ÙĦÙī", + "ĠÑĥ лÑĭ", + "ĠÑĥл Ñĭ", + "Ġcu á»ijn", + "ĠÐIJ нг", + "ĠÐIJн г", + "Ġد اÙĪ", + "ĠÑĪлÑıÑħ ом", + "ĠÄįlovÄĽk a", + "ĠÄįlovÄĽ ka", + "d ete", + "de te", + "det e", + "ÑĬ ем", + "à¹Į à¹ĥà¸Ļ", + "à¤ķ न", + "åĪ ¤æĸŃ", + "åĪ¤ æĸŃ", + "ĸ ìĹIJ", + "ÏĦ ÏīÏĥη", + "ÏĦÏī Ïĥη", + "ĠÙģÙĨ اÙĪرÛĮ", + "ĠyaÅŁ ında", + "ĠÏĥÏĩ ÎŃ", + "Ġ yı", + "Ġy ı", + "Ġp ÅĻen", + "ĠpÅĻ en", + "ĠpÅĻe n", + "ĠÑĦоÑĢм ÑĥваннÑı", + "ĠÑĦоÑĢмÑĥ ваннÑı", + "ü mÃ¼ÅŁ", + "üm Ã¼ÅŁ", + "ümü ÅŁ", + "Ġ δο", + "Ġδ ο", + "ımız ın", + "ımızı n", + "Ġ é¢Ħ", + "Ġé¢ Ħ", + "оÑģÑĤ ÑĮÑİ", + "оÑģÑĤÑĮ Ñİ", + "ĠоÑĤкÑĢÑĭ ÑĤ", + "Ġأغ سطس", + "ĠA sp", + "ĠAs p", + "ĠÑĥ зн", + "ĠÑĥз н", + "ĠÙĪ است", + "ĠÙĪا ست", + "ĠÙĪاس ت", + "e lerle", + "eler le", + "èķ ī", + "Ġت Ú©ÙĨ", + "Ġتک ÙĨ", + "Ñĥ мÑĥ", + "Ñĥм Ñĥ", + "à¹Į à¸ĭ", + "ाद न", + "ĠâĢĭ âĢĭâĢĭ", + "ĠâĢĭâĢĭ âĢĭ", + "Ġa lıyor", + "Ġal ıyor", + "Ġî ¡", + "Ùħ دة", + "Ùħد Ø©", + "Ġ Ïĥει", + "ĠÏĥ ει", + "ĠÏĥε ι", + "Ġ è¿Ļ", + "Ġè¿ Ļ", + "ĠÅŀ ehir", + "ен ÑĤами", + "енÑĤ ами", + "енÑĤа ми", + "ãĤ¿ ãĥ«", + "ห าย", + "หา ย", + "ай ÑĤ", + "Ġh arc", + "Ġhar c", + "Ġha rc", + "ãĢĤ ãģĬ", + "Ġتأ Ø«ÛĮر", + "า à¸Ĭà¸Ļ", + "าà¸Ĭ à¸Ļ", + "Ġth áºŃm", + "Ġ æ¿", + "Ġæ ¿", + "Ġm Å©i", + "Ġprv nÃŃm", + "ĠprvnÃŃ m", + "Ġбаг аÑĤÑĮ", + "ĠбагаÑĤ ÑĮ", + "ãģķãĤī ãģ«", + "b iên", + "bi ên", + "åºĶ å½ĵ", + "ìĿ´ ë²Ħ", + "Ġpou žÃŃt", + "ĠpoužÃŃ t", + "Ġokam ž", + "e sin", + "es in", + "esi n", + "v ÄĽl", + "vÄĽ l", + "Ġ ضÙĪ", + "Ġض ÙĪ", + "è» Ł", + "- з", + "à¥Ī त", + "è¨Ī ç®Ĺ", + "r abilir", + "ra bilir", + "rab ilir", + "ĠÐłÐ¾Ñģ ÑĸÑĹ", + "Ġpla tÃŃ", + "Ġplat ÃŃ", + "Ġdosp ÄĽl", + "Ġر ضا", + "Ġرض ا", + "Ġn ového", + "Ġnov ého", + "Ġnové ho", + "Ġна ÑĨионалÑĮ", + "ĠÐIJ б", + "ãģĮ ãģĤãģ£ãģŁ", + "Ġ ë¹Ī", + "Ġë¹ Ī", + "âĢĮ Ùħ", + "å±ŀ äºİ", + "Ġt ane", + "Ġta ne", + "Ġtan e", + "ÙĬ اÙĩ", + "ÙĬا Ùĩ", + "Ġ βο", + "Ġβ ο", + "Ġ ëĬ¥", + "ĠëĬ ¥", + "ãĥĩãĤ£ ãĥ¼ãĤ¹", + "Ġ Ø°Ùĥر", + "ĠØ° Ùĥر", + "Ġobvyk le", + "Ġbir inci", + "ĠاÙĦ زر", + "ĠاÙĦز ر", + "ìĿ´ ë¹Ħ", + "ĠØ¥ د", + "ĠE kon", + "ĠEk on", + "ÐŁ ол", + "ÐŁÐ¾ л", + "ĠвеÑĢ оÑıÑĤ", + "Ġyarar lan", + "Ġа ÑĢом", + "ĠаÑĢ ом", + "Ġ éĦ", + "Ġé Ħ", + "Ġ iddi", + "Ġid di", + "i Äįka", + "iÄį ka", + "struk ce", + "mÃ¼ÅŁ tür", + "Ïħ ÏĦÏĮ", + "ë¡ ±", + "Ġal maktadır", + "Ġalmak tadır", + "ени Ñıми", + "ениÑı ми", + "ียà¸Ļ ร", + "à¹ĩ à¸Ļว", + "à¹ĩà¸Ļ ว", + "и кÑĥ", + "ик Ñĥ", + "е нка", + "ен ка", + "âĢĻ yi", + "âĢĻy i", + "Ġpo hod", + "Ġpoh od", + "Ġ زر", + "Ġز ر", + "Ġx ấu", + "Ġ à¸łà¸²à¸©", + "Ġà¸ł าษ", + "Âł Ðŀ", + "Ġ δικ", + "Ġδ ικ", + "Ġδι κ", + "Ġназ ива", + "åıª èĥ½", + "大 éĩı", + "ĠÄij ế", + "Ġ 第äºĮ", + "Ġ第 äºĮ", + "ĠkiÅŁ ilerin", + "ĠkiÅŁi lerin", + "ĠkiÅŁiler in", + "Ġdob ré", + "Ġdobr é", + "é© ¾", + "Ġdůležit é", + "ë¡ ¤", + "μÎŃ Î½Î¿Ïħ", + "μÎŃν οÏħ", + "μÎŃνο Ïħ", + "Ġtr ú", + "Ġbiç im", + "Ġ ÐĿÐIJ", + "ĠÐĿ ÐIJ", + "Ġ å¾Į", + "Ġå¾ Į", + "Ġdu yg", + "Ġduy g", + "åŀ Ĥ", + "ÐĨ ÐĨ", + "Ġet meye", + "Ġetm eye", + "Ġetme ye", + "ĠÙĦب اس", + "Ġд вÑĸ", + "Ġдв Ñĸ", + "Ġ 긴", + "Ġê¸ ´", + "ÑĨ Ñĸйно", + "ÑĨÑĸй но", + "κ ÏĦή", + "ï½ Ŀ", + "ĠÑĦевÑĢа лÑı", + "å¯ «", + "Ġ 겨", + "Ġê² ¨", + "Ġyıl larda", + "Ġyıllar da", + "Ġз Ñĥп", + "Ġobchod nÃŃ", + "Ġاض اÙģÙĩ", + "в еÑĢж", + "веÑĢ ж", + "Ġ æłĩ", + "Ġæł ĩ", + "ج اج", + "جا ج", + "Ġر ÙĪسÛĮ", + "ĠرÙĪ سÛĮ", + "Ġstand art", + "Ġstan dart", + "é ru", + "ér u", + ") ìĿĦ", + "д екÑģ", + "де кÑģ", + "Ġ âĪļ", + "ĠâĪ ļ", + "ĠÄ°ngiliz ce", + "èĬ Ŀ", + "身 ä¸Ĭ", + "ØŁ ØŁ", + "Ġm ẽ", + "Îij ÎĶ", + "енÑģ ив", + "âĢĻ ta", + "âĢĻt a", + "à¹ī าà¸ģ", + "à¹īา à¸ģ", + "ÎŁÎĽ ÎŁÎĵ", + "ä»ĺ ãģij", + "Ġs Ãłng", + "ĠsÃłn g", + "Ġह à¤Ł", + "ÑĭÑĪ лен", + "ĠØ® طر", + "Ġخط ر", + "Ġнай ÑĤи", + "缸 ä¿¡", + "Ïī δ", + "ठĶ", + "Ġdo pad", + "Ġdop ad", + "à¹Ħà¸Ł ล", + "æ ģµ", + "æģ µ", + "í Ĥ¬", + "íĤ ¬", + "Ä±ÅŁ ma", + "ãģı ãĤĮãģŁ", + "ãģıãĤĮ ãģŁ", + "Ġnap rost", + "ĠÑģоÑģÑĤав е", + "Ġ ÙĪسط", + "ĠÙĪ سط", + "ĠÙĪس Ø·", + "๠ķ", + "éĸĭ çĻº", + "ĠдеÑĢ ева", + "ĠдеÑĢев а", + "- ÐĶ", + "à¸ĩ à¸Ĭ", + "ิà¸ķ ย", + "ĠاÙĦÙĤ اÙĨÙĪÙĨ", + "ãĤ¹ ãĤ«", + "l ÃŃž", + "lÃŃ Å¾", + "Ġан ализ", + "Ġproblém y", + "æĸĩ åѦ", + "çĹħ éĻ¢", + "Ñģ ед", + "Ñģе д", + "ï¼Į å°ı", + "Ġعش ÙĤ", + "ãģ° ãģĭãĤĬ", + "Ġع ÙĤد", + "ĠعÙĤ د", + "ØŃ ÙĬØ©", + "ØŃÙĬ Ø©", + "Ġë°Ķ ëŀįëĭĪëĭ¤", + "inc lu", + "incl u", + "Ġ ëĵľë¦½ëĭĪëĭ¤", + "Ġëĵľ 립ëĭĪëĭ¤", + "åį« çĶŁ", + "Ġвид Ñĥ", + "Ġви дÑĥ", + "ุ à¸ļาล", + "ุà¸ļ าล", + "ÑĢ ÑĥкÑĤ", + "ÑĢÑĥ кÑĤ", + "ÑĢÑĥк ÑĤ", + "ĠоÑģ вÑĸÑĤ", + "ĠоÑģвÑĸ ÑĤ", + "Ġvel ký", + "Ġvelk ý", + "Ġch tÄĽl", + "ĠchtÄĽ l", + "æīĵ å¼Ģ", + "Ġзакон одаÑĤелÑĮ", + "ан Ñģи", + "анÑģ и", + "ì¶ ĺ", + "ĠÙħر اج", + "åģľ æŃ¢", + "Ġво но", + "ìłķ ìĿ´", + "Ġroz sah", + "Ġrozs ah", + "Ġ æĻ´", + "ĠæĻ ´", + "Ġza jist", + "Ġzaj ist", + "Âł м", + "tı ģını", + "tıģ ını", + "Ġhizmet i", + ". Îij", + "ĠÙħعÙħÙĪÙĦ ا", + "Ġ ži", + "Ġž i", + "Ġg á»įn", + "èĮ Ĥ", + "Ġh uz", + "Ġhu z", + "ζ ει", + "ζε ι", + "à¥ī à¤Ł", + "Ġиз дел", + "ìŀ ĸ", + "ĠëĶ° 른", + "Ġk ia", + "Ġki a", + "Ġz nÄĽnÃŃ", + "Ġzn ÄĽnÃŃ", + "ĠоÑĢгани за", + "ĠоÑĢганиз а", + "از ات", + "Ġrež im", + "Ġв енÑĤи", + "b ách", + "Ġод номÑĥ", + "Ġодно мÑĥ", + "Ġодном Ñĥ", + "Ġkit ab", + "Ġki tab", + "Ġkita b", + "Ġfran couz", + "Ġfranc ouz", + "ĠØ£ ÙĦ", + "Ġس رÙĪ", + "Ġسر ÙĪ", + "Ùij ÙĦ", + "Ġ ман", + "Ġм ан", + "Ġма н", + "ë° į", + "Ġк Ñĥда", + "ĠкÑĥ да", + "Ùı س", + "ãĢĤ æѤ", + "ا شة", + "اش Ø©", + "à¸Ĥà¸Ńà¸ĩ à¸ľ", + "主 ä»»", + "ив ÑĪи", + "Ġà¸ģ รà¸ģ", + "Ġà¸ģร à¸ģ", + "ек Ñģи", + "екÑģ и", + "иÑĤ еÑĤ", + "иÑĤе ÑĤ", + "ĠØ£ ÙĦÙģ", + "ĠØ£ÙĦ Ùģ", + "а ними", + "ан ими", + "ани ми", + "ãĥļ ãĥ¼ãĤ¸", + "ĠпÑĢав ил", + "ĠпÑĢави л", + "åªĴ ä½ĵ", + "Ñİ Ñīее", + "ÑİÑī ее", + "ä¸Ģ 人", + "β ο", + "ìĭ ¸", + "о зна", + "оз на", + "å¤ī æĽ´", + "ĠÙħØ´ Ùĩد", + "æ³ķ 人", + "ĠBa kanı", + "ĠBak anı", + "ĠBakan ı", + "ĠÑħоÑĩ а", + "Ġα ξ", + "Ġver ilm", + "Ġveri lm", + "Ġk onus", + "Ġkon us", + "Ġkonu s", + "με νη", + "μεν η", + "Ġ 馬", + "Ġé ¦¬", + "Ġé¦ ¬", + "Ġìĭ¤ ìłľ", + "Ġjed no", + "Ġjedn o", + "Ġб аб", + "Ġба б", + "åĥ į", + "æĺ¯ ä¸Ģ个", + "æĺ¯ä¸Ģ 个", + "- е", + "ĠpÅĻek vap", + "à¸Ń à¸ŀ", + "ĠY ol", + "ĠYo l", + "ĠÑĥÑģÑĤан авлива", + "ê² ¼", + "Ġ 件", + "اÙĦ Ø´", + "Ġоб ÑĥÑĩ", + "åĺ Ľ", + "ĠÑħоÑĩ Ñĥ", + "ĠÐķ в", + "ÑĦ оÑĢÑĤ", + "ÑĦоÑĢ ÑĤ", + "Ġर न", + "âĢŀ V", + "èľ ľ", + "Ġd oma", + "Ġdo ma", + "Ġdom a", + "æĶ¯ æı´", + "Ġ اخت", + "Ġا خت", + "Ġاخ ت", + "å¾ ª", + "à¥Ĥ à¤ļन", + "à¥Ĥà¤ļ न", + "ा हन", + "ाह न", + "Ġ å¤ı", + "Ġå¤ ı", + "ĠاÙĦØ£ Ùħر", + "ĠاÙĦØ£Ùħ ر", + "ĠбеÑĢем енноÑģÑĤи", + "ĠTh á»±c", + "é£İ éĻ©", + "Ġül kemiz", + "Ġülk emiz", + "çķª åı·", + "ÑģÑĤ ÑĢе", + "ÑģÑĤÑĢ е", + "ÑĪ ло", + "ĠصاØŃ Ø¨", + "ι νε", + "ιν ε", + "ĠK ıs", + "ĠKı s", + "ĠPr ahy", + "ĠPra hy", + "æ¹ ¿", + "Ġv ým", + "Ġvý m", + "çĽ Ĵ", + "ÎŁ ÎĶ", + "ÎŁÎ Ķ", + "ãģł ãģª", + "ĠpÅĻÃŃ ležit", + "Ġìĸ¸ ìłľ", + "ĠÑĪвид ко", + "Ġsitu aci", + "åħĥ ç´ł", + "Ä°T ESÄ°", + "ĠV ak", + "ĠVa k", + "Ġner edeyse", + "i iii", + "ii ii", + "iii i", + "ÑĢа зд", + "ÑĢаз д", + "Ġп олиÑĤ", + "Ġпо лиÑĤ", + "Ġпол иÑĤ", + "Ġполи ÑĤ", + "Ġп огод", + "Ġпо год", + "Ġпог од", + "ĠпÑĢоÑĨеÑģ Ñģе", + "ĠпÑĢоÑĨеÑģÑģ е", + "Ġмен ÑĪе", + "äºĮ 人", + "ĠÙħÙĪ اط", + "Ġp ÅĻik", + "ĠpÅĻ ik", + "ĠpÅĻi k", + "è· ¡", + "Ġs erg", + "Ġse rg", + "Ġser g", + "ĠÑĢаÑģ ÑģÑĤоÑı", + "и Ñĩно", + "иÑĩ но", + "ĠÎĶ ÎĹÎľ", + "¨ Ø·", + "ص بØŃ", + "صب ØŃ", + "สะ à¸Ķวà¸ģ", + "د رÛĮ", + "در ÛĮ", + "k ům", + "ků m", + "ç§ģ ãģ¯", + "Ġt vor", + "Ġtv or", + "à¥įव व", + "Ġp ÅĻiv", + "ĠpÅĻ iv", + "ĠpÅĻi v", + "Ġ íı´", + "Ġíı ´", + "Ġst átu", + "Ġstát u", + "Ġed ilmiÅŁtir", + "Ġedilm iÅŁtir", + "Ġedil miÅŁtir", + "ĠedilmiÅŁ tir", + "ØŃ Ùħ", + "Ġб ÑĥÑħ", + "ĠбÑĥ Ñħ", + "สำ à¹Ģร", + "ĠتÙĪ ضÛĮ", + "ãģĿãĤĮ ãģ¯", + "Ġà¤ħव ध", + "é ŀĭ", + "éŀ ĭ", + "âĤ¬ Ċ", + "Ġ éº", + "Ġé º", + "ĠÄĮ es", + "Ġpop rvé", + "ï¼Į åĽł", + "Ġal mÄ±ÅŁ", + "Ġalm Ä±ÅŁ", + "l al", + "la l", + "ĠØ® ÙĪبÛĮ", + "ĠØ®ÙĪ بÛĮ", + "ĠØ®ÙĪب ÛĮ", + "Ġκ οÏģ", + "Ġκο Ïģ", + "ìļ´ ëıĻ", + "m ayın", + "may ın", + "mayı n", + "Ġak tif", + "Ġakt if", + "ĠاÙĨج ÙħÙĨ", + "ĠÑģ ÑĤак", + "ĠÑģÑĤ ак", + "ĠÑģÑĤа к", + "ĠÑģÑĤ аÑĢа", + "ĠÑģÑĤаÑĢ а", + "ĠÑģÑĤа ÑĢа", + "ÙĦ ÙģØ©", + "ÙĦÙģ Ø©", + "Ġparç ası", + "ĠкоÑĢп ÑĥÑģ", + "ãĢģ é«ĺ", + "! ..", + "!. .", + "ĠÎł ÎijÎĿ", + "ĠÙĩÙĨ ÙĪز", + "ion álnÃŃ", + "Ġprá vnÃŃ", + "Ġpráv nÃŃ", + " Ŀ", + "Ġت ÛĮر", + "ĠتÛĮ ر", + "Ġ åŁİ", + "ĠåŁ İ", + "Ġзг ад", + "Ġsaldır ı", + "æŁ¥çľĭ æijĺè¦ģ", + "é« ª", + "Ùģ صÙĦ", + "ãģĻ ãģ¹ãģ¦", + "е во", + "ев о", + "ê´Ģ리 ìŀIJ", + "Ġìĺ Ĩ", + "udic ots", + "ÙĪر ÙĨ", + "Ġcel kem", + "ãĤ¤ ãĤº", + "ìĬ¤ ê°Ģ", + "販 売", + "ĠíĮĮìĿ¼ 첨ë¶Ģ", + "ë ¢°", + "Ġe nergie", + "Ġenerg ie", + "Ġener gie", + "es idir", + "esi dir", + "Ġm iá»ĩng", + "éĻ ·", + "Ġг аÑĢа", + "ĠгаÑĢ а", + "Ġb iliyor", + "Ġbil iyor", + "çį² å¾Ĺ", + "еÑĤ еÑĢб", + "à¹Īา à¹Ģà¸Ľ", + "Ġμα ζί", + "Ġzprac ovánÃŃ", + "Ñģ м", + "Ġh ala", + "Ġha la", + "Ġhal a", + "Ġز ÙĪج", + "ĠвÑĸд нов", + "à¹Ģหม าะ", + "ĠÐłÐµÑģп Ñĥбли", + "åĩºåĵģ èĢħ", + "Ñī ини", + "Ñīи ни", + "Ñīин и", + "ัà¸Ļ à¹Ģà¸Ľ", + "Ġtý den", + "Ġtýd en", + "Ġب ÙĬت", + "ĠبÙĬ ت", + "Ñģ комÑĥ", + "Ñģк омÑĥ", + "Ñģком Ñĥ", + "Ñģко мÑĥ", + "ĠÙĩÙĪ اپÛĮÙħ", + "оÑģ нов", + "é¸ Ł", + "Ġsou krom", + "Ġfa iz", + "Ġdem ok", + "Ġdemo k", + "Ġkter ém", + "Ġkteré m", + "Ġëħ ¹", + "л аÑĩ", + "ла Ñĩ", + "ĠоÑĤвеÑĤ ÑģÑĤвен", + "Ġ ï¼¼:", + "Ġï¼¼ :", + "Ġ λο", + "Ġλ ο", + "ÄĮ esk", + "ê°Ģ ìļĶ", + "Ġ ãĥĬ", + "Ġãĥ Ĭ", + "Ġnhu áºŃn", + "ĠÑģ или", + "ĠÑģи ли", + "ĠÑģил и", + "ĠÐľ он", + "Ġç ap", + "Ġça p", + "ĠRow Box", + "Ġм аÑģÑĤ", + "ĠмаÑģ ÑĤ", + "Ġма ÑģÑĤ", + "ĠÐľ а", + "ĠдÑĢÑĥг о", + "ĠдÑĢÑĥ го", + "ĠØ£ Ø´", + "ë°© ìĨ¡", + "ĠпÑĸд пиÑģ", + "èĩ ¨", + "åī ©", + "Ġh iá»ĥn", + "Ġhi á»ĥn", + "ĠÙĤر ارد", + "ĠÙĤرار د", + "ist rat", + "istr at", + "istra t", + "ÐŁ Ñĸд", + "ÏĦε Ïģα", + "ÏĦεÏģ α", + "Ġpoz dÄĽ", + "ĠbaÅŁ ta", + "夫 人", + "л ини", + "ли ни", + "лин и", + "Ġка ÑĩеÑģÑĤва", + "ĠкаÑĩе ÑģÑĤва", + "Ġkur tul", + "Ġ ì¢Į", + "Ġì¢ Į", + "ãģ«ãģĬ ãģijãĤĭ", + "åľ° åįĢ", + "ĠÑĩа Ñģом", + "ĠÑĩаÑģ ом", + "ìµľ ê³ł", + "Ġn gang", + "Ġng ang", + "Ġnga ng", + "ا Ùĩد", + "اÙĩ د", + "ĠШ ев", + "ĠpÅĻ itom", + "ĠpÅĻi tom", + "Ġch ấm", + "ĠÐľ еÑģÑĤо", + "ĠÑģовеÑĢÑĪ енно", + "ÃŃ cÃŃ", + "ÃŃc ÃŃ", + "Ń å·ŀ", + "åĪĽ æĸ°", + "äºĶ æľĪ", + "Ġا عÙħاÙĦ", + "Ġاع ÙħاÙĦ", + "Ġвозмож ноÑģÑĤи", + "Ġвозможно ÑģÑĤи", + "ĠпÑĢод овж", + "n ÄĽt", + "nÄĽ t", + "ĠÐĿа пÑĢимеÑĢ", + "ĠاÙĦ دÙħ", + "ĠاÙĦد Ùħ", + "Ġ à¹ģà¸ļà¸ļ", + "Ġà¹ģ à¸ļà¸ļ", + "çĶŁ çļĦ", + "ĠÑħ аÑĢÑĩ", + "ĠSon uç", + "Ġrůzn é", + "Ġrůz né", + "Ġ اذ", + "Ġا Ø°", + "ĠØ§Ø °", + "à¸ķ à¸Ńà¸ļ", + "P ÅĻed", + "PÅĻ ed", + "ĠдеÑĢев Ñıн", + "ë´ IJ", + "ĠëĬIJ ëĤ", + "جÙħ ÙĬع", + "ĠBöyle ce", + "èµ ı", + "Ġب سÙĬ", + "Ġبس ÙĬ", + "ĠÃĩ aÄŁ", + "Ġت اÛĮ", + "Ġتا ÛĮ", + "Ġnej vyÅ¡Å¡ÃŃ", + "èĸ ©", + "Ïĩε δÏĮν", + "Ġëĵ± ìĿĺ", + "e yh", + "ey h", + "æĸĻ çIJĨ", + "ا تÙĩ", + "ات Ùĩ", + "æī «", + "Ġ å©", + "Ġå ©", + "ĠпÑĢи вед", + "ĠпÑĢив ед", + "æī ¶", + "Ġ 견", + "Ġê² ¬", + "Ġا ÙħÛĮر", + "ĠاÙħ ÛĮر", + "ाय ल", + "æ¡ ij", + "à¸Ļ à¹Ģà¸ķ", + "ила кÑĤи", + "家 ä¼Ļ", + "Ġbulun uyor", + "y sa", + "ys a", + " Ĩ", + "ĠB Ä°R", + "íĨ ¤", + "à¤Ĥà¤Ĺ à¤łà¤¨", + "ÎĶ εν", + "à¥Į à¤ķर", + "à¥Įà¤ķ र", + "éĸĵ ãģ«", + "Ġм об", + "Ġмо б", + "ĠMo rav", + "ĠMor av", + "è§Ħ åĪĴ", + "ĠÑģвÑĸÑĤ Ñĸ", + "ul ts", + "ult s", + "Ġze mÃŃ", + "Ġzem ÃŃ", + "Âł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", + "ÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", + "ĠÐŁ оп", + "ĠÐŁÐ¾ п", + "ãģĤ ãģĴ", + "Ġpom oci", + "Ġpomoc i", + "ĠзмÑĸ ÑģÑĤ", + "Ġзм ÑĸÑģÑĤ", + "主 人", + "ĠS ı", + "ãĥĽ ãĥĨãĥ«", + "ĠÑĥва гÑĥ", + "å» ³", + "à¹Ģม à¸ķร", + "est li", + "Ġlo ạt", + "ãĤ¢ ãĥ¼", + "ĠÎĶ ε", + "Ġbun ları", + "Ġbunlar ı", + "Ġ çĤ¹åĩ»", + "ĠçĤ¹ åĩ»", + "Ġ BÃłi", + "ĠB Ãłi", + "ĠBÃł i", + "Ġ ä¸ĸ", + "Ġä¸ ĸ", + "Ġê³ł ê°ľë¥¼", + "ĠÐŃ ÑĤоÑĤ", + "ĠÐŃÑĤо ÑĤ", + "Ġmem nun", + "Ġ ।Ċ", + "Ġ। Ċ", + "ĠиÑģÑĤоÑĢ ии", + "Ġ ì°©", + "Ġì° ©", + "१ ९", + "१ॠ¯", + "ĠÐŀд нак", + "ĠÐŀдна к", + "Ġv ede", + "Ġve de", + "Ġved e", + "ÏĨ ÎŃÏģει", + "ÏĨÎŃ Ïģει", + "â b", + "çĬ¶ åĨµ", + "åįı è®®", + "Ġ ê°Ŀ", + "Ġê° Ŀ", + "е вид", + "ев ид", + "еви д", + "j mu", + "jm u", + "Ġколи ÑĩеÑģÑĤва", + "ĠколиÑĩ еÑģÑĤва", + "ĠколиÑĩе ÑģÑĤва", + "à Ľ", + "i Äįe", + "iÄį e", + "Ġfirm alar", + "Ġfir malar", + "Ġfirma lar", + "èĢ Ģ", + "к Ñĸн", + "кÑĸ н", + "ĠêµŃ 민", + "Ġ목 ë¡Ŀ", + "ĠÎļ αÏģ", + "ĠÎļα Ïģ", + "Ġhis sed", + "Ġhiss ed", + "ï¼ «", + "Ġ Tên", + "ĠT ên", + "ĠÑĤÑĭ ÑģÑıÑĩ", + "ØŃÙĬ ØŃ", + "Ġвпол не", + "ĠS ınıf", + "ĠSın ıf", + "Ġμ ην", + "Ġμη ν", + "Ġ íij¸", + "Ġí ij¸", + "ĠاÙĦ طبÙĬ", + "ĠاÙĦØ· بÙĬ", + "ĠاÙĦطب ÙĬ", + "ĠزÛĮ ب", + "Ġп Ñĥ", + "Ġp raž", + "Ġpr až", + "Ġpra ž", + "ìĹĨ ëĬĶ", + "θ ÏģÏī", + "Ġi çi", + "Ġiç i", + "Ġб Ñĸл", + "ĠбÑĸ л", + "Ðł Ñij", + "Ġì¶ķ 구", + "Ġl ạ", + "Ġ ãĥķãĤ¡", + "Ġãĥķ ãĤ¡", + "Ġ èĸ", + "Ġè ĸ", + "μα ÏĦο", + "éĩij å±ŀ", + "á li", + "ál i", + "ĠÙģ Ø£", + "ĠKar lov", + "ĠKarl ov", + "ĠZ áp", + "ĠZá p", + "ãĥª ãĥ³ãĤ°", + "ãĥªãĥ³ ãĤ°", + "ab ilmek", + "abil mek", + "ĠС и", + "Ġc ÃŃrk", + "ĠcÃŃ rk", + "Ġk á»ĭp", + "Ġà¤ij नल", + "ĠÙĪ ØŃدة", + "ĠÙĪØŃ Ø¯Ø©", + "ãĥĭ ãĥĥãĤ¯", + "Ġn Æ°á»Ľng", + "Ġа кÑĤÑĥ", + "Ġак ÑĤÑĥ", + "ĠакÑĤ Ñĥ", + "å¸Ŀ åĽ½", + "Ġn ázev", + "Ġnáz ev", + "ĠÑĢемон ÑĤ", + "ĠÑĢ инкÑĥ", + "ĠÏĢ άνÏī", + "ĠÏĢά νÏī", + "ÏĦ ικο", + "ÏĦικ ο", + "ÏĦι κο", + "ĠìĤ¼ ìĦ±", + "ĠÑģимпÑĤом Ñĭ", + "ĠÑĢа нÑĸÑĪе", + "ĠJ á", + "ĠÑģÑĩиÑĤа еÑĤÑģÑı", + "ĠÑģÑĩиÑĤ аеÑĤÑģÑı", + "Ġп оÑĢÑĸв", + "Ġпо ÑĢÑĸв", + "ĠпоÑĢ Ñĸв", + "ĠÐľ ал", + "ĠÐľÐ° л", + "éĿ¢ 积", + "ĠÙĦ غ", + "Ġج Ø´ÙĨ", + "Ġнед ели", + "Ġнедел и", + "Ġì¦Ŀ ê°Ģ", + "ãĨį ëıĻ", + "Ġl ượt", + "ĠÄIJ á»ĭnh", + "Ġ à¸Ńà¸Ńà¸Ļà¹Ħลà¸Ļ", + "Ġà¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ", + "Ġyap arak", + "Ġyapar ak", + "ĠÄij ai", + "ĠÄija i", + "Ġо ÑĦиÑĨи", + "Ġε μÏĢ", + "ξ ειÏĤ", + "ξει ÏĤ", + "Ġкон ÑĦеÑĢен", + "Ġ arası", + "Ġa rası", + "Ġar ası", + "Ġaras ı", + "Ġara sı", + "à¸ķ า", + "Ġ ë´IJ", + "Ġë´ IJ", + "о вана", + "ов ана", + "ова на", + "ован а", + "ì§Ģ ê°Ģ", + "ĠV ám", + "ि à¤ľà¤¨", + "à¤¿à¤ľ न", + "Ġ ç¼ĸè¾ij", + "Ġç¼ĸ è¾ij", + "ζ ÏĮ", + "ĠÏĦ ÏģÏĮ", + "Ġücret siz", + "ĠکاÙħ ÙĦا", + "ĠکاÙħÙĦ ا", + ": ::/", + ":: :/", + "::: /", + "à¹Į ĊĊ", + "à¹ĮĊ Ċ", + "Ġéĸ¢ éĢ£", + "Ġ kara", + "Ġk ara", + "Ġka ra", + "Ġkar a", + "Ġбез пеки", + "ĠzmÄĽ ny", + "ĠzmÄĽn y", + "Ġê¿ Ī", + "v rd", + "vr d", + "li ÄŁine", + "liÄŁi ne", + "liÄŁ ine", + "liÄŁin e", + "ĠاÙĨتخاب ات", + "ĠдоÑģ вÑĸд", + "Ġkter ého", + "Ġkteré ho", + "ен ÑĤом", + "енÑĤ ом", + "ê³µ ë¶Ģ", + "ìł Ŀ", + "Ġë§Į 족", + "Ġ æij", + "Ġæ ij", + "åĩº åı£", + "建 è®®", + "о ÑĤÑı", + "оÑĤ Ñı", + "Ġ Òij", + "ĠÒ ij", + "íĶĦ ë¡ľ", + "Ġg ió", + "Ġgi ó", + "ãĤ· ãĤ§", + "Ġλ εÏĢ", + "íķĺ 볤", + "Ġyok sa", + "Ġist ih", + "ï¼ ¶", + "ĠاÙĦ عÙħ", + "ĠاÙĦع Ùħ", + "Ġکار گرد", + "à¹Ģà¸ŀ ราะ", + "Ġn ových", + "Ġnov ých", + "Ġnový ch", + "ĠÑģ на", + "Ġs ana", + "Ġsa na", + "Ġsan a", + "व त", + "Ä±ÅŁ man", + "Ä±ÅŁma n", + "åı¦ å¤ĸ", + "ì¶ľìŀ¥ ìĥµ", + "å© ¦", + "ĠкоÑĪ ÑĤÑĸв", + "ĠÙĪاÙĦ ÙĨ", + "Ġب اÙĦØ¥", + "ĠباÙĦ Ø¥", + "Ġ æĬĢ", + "ĠæĬ Ģ", + "Ġмн оже", + "à¥Ĥ ड", + "ĠC ục", + "Ġe vet", + "Ġev et", + "Ġeve t", + "èģĶ åIJĪ", + "Ġ³³Ġ³³ Ġ³³Ġ³³", + "çļĦ å¿ĥ", + "Ġd áng", + "Ġdá ng", + "Ġdán g", + "اÛĮ سÙĩ", + "Ġ erken", + "Ġer ken", + "Ġerk en", + "æ³ ¡", + "ائ ب", + "Ġyap ıldı", + "Ġyapıl dı", + "ĠQu ản", + "æŶ 代", + "ìĽ ¨ìĸ´", + "ìĽ¨ ìĸ´", + "Ġг ÑĸÑĢ", + "ok oj", + "oko j", + "Ùĥ رة", + "Ùĥر Ø©", + "Ñİ Ðº", + "Ġvý j", + "Ġhod iny", + "Ġhodin y", + "Ġелек ÑĤÑĢон", + "m ıyor", + "ĠìŀĪ ëĭ¤ëĬĶ", + "ĠìŀĪëĭ¤ ëĬĶ", + "à¹ī à¹ī", + "иÑĤелÑĮ ное", + "иÑĤелÑĮно е", + "Ġyıl lar", + "Äı te", + "ĠÄįin nost", + "ุà¸ĵ à¸łà¸²à¸ŀ", + "í ĵ¨", + "н г", + "ู รà¸ĵ", + "ูร à¸ĵ", + "ĠпоÑĢÑıд ке", + "Ġëĭ¹ ìĭľ", + "ĠÐľ оÑģков", + "ĠÐľÐ¾Ñģк ов", + "Ġk red", + "Ġkr ed", + "Ġkre d", + "u rum", + "ur um", + "uru m", + "Ġ ÑĤÑı", + "ĠÑĤ Ñı", + "Ú© ÙĨاÙĨ", + "Ú©ÙĨ اÙĨ", + "д ии", + "ди и", + "ÑĢи мÑĸн", + "ÑĢим Ñĸн", + "ĠоÑĢгани зм", + "ĠоÑĢганиз м", + "Ġ éĽĨ", + "ĠéĽ Ĩ", + "ι ÏĥÏĦο", + "ιÏĥ ÏĦο", + "ä¿¡ çĶ¨", + "åįģ åĽĽ", + "à¹Ī à¹ĥà¸Ĭ", + "ĠÑĥ вид", + "ĠÑĥв ид", + "ัà¸ĩ à¸ģล", + "ัà¸ĩà¸ģ ล", + "åı¦ ä¸Ģ", + "ãĥ« ãĥķ", + "ัà¸ļ à¸Ľà¸£", + "ĠÃľ st", + "説 æĺİ", + "в ай", + "ва й", + "а Ñĩе", + "аÑĩ е", + "æ¬ £", + "Ġkat ıl", + "Ġkatı l", + "ĠC em", + "ĠCe m", + "ĠاÙĦ جÙĩ", + "ĠاÙĦج Ùĩ", + "Ġг ÑĢÑĥз", + "ĠгÑĢÑĥ з", + "ĠгÑĢ Ñĥз", + "Ġза ÑģÑĤав", + "ĠзаÑģÑĤ ав", + "cı lar", + "ĠÑħоÑĤ ел", + "Ġs nÃŃm", + "Ġsn ÃŃm", + "ĠsnÃŃ m", + "ï¼Į 被", + "Ġ виÑī", + "Ġв иÑī", + "Ġви Ñī", + "Ġdem okrat", + "Ġdemok rat", + "à¥ĩ à¤Łà¤°", + "à¥ĩà¤Ł र", + "åij¨ å¹´", + "Ġod pad", + "Ġodp ad", + "Ġda ÅĪ", + "Ġ 代", + "à¹ĩ à¸Ļà¸Ń", + "à¹ĩà¸Ļ à¸Ń", + "ĠÑģк олÑĮко", + "Ġα ÏĨ", + "ĠpÅĻes vÄĽd", + "Ġ åĵģ", + "Ġåĵ ģ", + "ĠинÑĦоÑĢм аÑĨии", + "ĠинÑĦоÑĢма ÑĨии", + "çĽ Ĺ", + "ãģ¾ ãģ¨", + "ĠÑģам ов", + "ĠÑģамо в", + "Ġpo cit", + "Ġpoc it", + "Ġíݸ ì§ij", + "ĠÑģм еÑģÑĮ", + "Ġpo jiÅ¡tÄĽnÃŃ", + "ãģ® ãĤĤ", + "à¹Ī าà¸ģาร", + "à¹Īา à¸ģาร", + "à¹Īาà¸ģ าร", + "ĠÛĮ ÙĪÙĨ", + "Ġ기 ìĸµ", + "ick ými", + "ický mi", + "ickým i", + "a lace", + "al ace", + "ala ce", + "鼻 å½±", + "Ñİ Ð²Ð°Ð½Ð½Ñı", + "缸 åIJĮ", + "Ġ ãĢĥ", + "ĠãĢ ĥ", + "ĠдокÑĥм енÑĤÑĸв", + "ĠдокÑĥменÑĤ Ñĸв", + "ï¼ ¹", + "åĪ° åºķ", + "ó z", + "ĠAh met", + "ĠÙħس اØŃت", + "Ġhl avou", + "Ġhlav ou", + "ül ebilir", + "üle bilir", + "ãĢĤ ä½ł", + "à¹ĩà¸ģ à¸Ĭาย", + "¤ ¤", + "Ġ æĦı", + "ĠæĦ ı", + "Ġch áºŃm", + ". д", + "Ġ cca", + "Ġc ca", + "Ġcc a", + "Ġol umsuz", + " ŀ", + "çĬ ¬", + "ĠпоÑģÑĤоÑıн но", + "Ġ.************** Ċ", + "Ġا ستر", + "Ġاست ر", + "Ġاس تر", + "ĠдалÑĮ ней", + "ů r", + "ä¿Ŀ èŃ·", + "боÑĢ аÑĤоÑĢ", + "боÑĢа ÑĤоÑĢ", + "à ·", + "Ïĥ ÏĦαν", + "ÏĥÏĦ αν", + "ÏĥÏĦα ν", + "ĠÙģ ÙĬÙĦÙħ", + "ĠÙģÙĬ ÙĦÙħ", + "ç ek", + "çe k", + "ìŀIJ 기", + "Ġ æ¥Ń", + "Ġæ¥ Ń", + "н Ñĸп", + "нÑĸ п", + "èī ĩ", + "Ġm oci", + "Ġmo ci", + "Ġmoc i", + "ìľ µ", + "리 ê·¸", + "ĠÐļ о", + "éĤ£ éĩĮ", + "ĠС ÑĤаÑĢ", + "ĠСÑĤ аÑĢ", + "ĠСÑĤа ÑĢ", + "ĠتÙĪاÙĨ ÛĮد", + "Ġng uyá»ĩn", + "Ġnguy á»ĩn", + "Ġ สามารà¸ĸ", + "Ġส ามารà¸ĸ", + "Ñĸ Ñĩна", + "ÑĸÑĩ на", + "Ġ 被", + "Ġè¢ «", + "ุà¸ķสาห à¸ģรรม", + "Ġع صر", + "Ġعص ر", + "ĠÃľNÄ° VERS", + "Ġteh dy", + "ĠÙĪصÙĦ ات", + "ĠÙĪص ÙĦات", + "ä¿Ŀ è¯ģ", + "ĠE udicots", + "ĠÎł ÎŃ", + "建 è¨Ń", + "ĠìłĦ êµŃ", + "Ġ ØŃÛĮ", + "ĠØŃ ÛĮ", + "ãĤ¤ ãĥĦ", + "ĠØŃ Ø§ØµÙĦ", + "ĠجÙĨ ÙĪبÛĮ", + "ĠجÙĨÙĪب ÛĮ", + "ãĢģ æĹ¥æľ¬", + "à Ļ", + "Ġ à¸Ĺาà¸ĩ", + "Ġà¸Ĺ าà¸ĩ", + "ĠÙĨØŃ ÙĪ", + "اÙĩ ÙĬÙħ", + "å¾Į ãģ«", + "à¸Īะ à¹Ħà¸Ķ", + "åĩł 个", + "à¥ģ à¤ģ", + "à¥ģठģ", + "ëĮĢ ìĿĺ", + "Ġl Ãłn", + "ĠlÃł n", + "ìĽĶ ë¶ĢíĦ°", + "Æ ł", + "Ġ еди", + "Ġе ди", + "Ġs pis", + "Ġsp is", + "Ġspi s", + "æľī ä»Ģä¹Ī", + "Ġneb yla", + "Ġneby la", + "Ġnebyl a", + "Ġíķ´ ìĻ¸", + "ë¡ľ ë¶ĢíĦ°", + "аÑĢ Ñħ", + "l ili", + "li li", + "lil i", + "Ġíķĺ 루", + "ma ması", + "mam ası", + "Ñĩ аеÑĤ", + "Ñĩа еÑĤ", + "ĠØŃ Ø§ÙĦØ©", + "ĠØŃاÙĦ Ø©", + "ĠBöl üm", + "缸 éĹľ", + "ĠдÑĢÑĥг ими", + "ĠдÑĢÑĥгим и", + "çĽ£ çĿ£", + "à¥Ī à¤ľ", + "Ġعبد اÙĦÙĦÙĩ", + "ĠعبداÙĦ ÙĦÙĩ", + "Ġ è¿ŀ", + "Ġè¿ ŀ", + "ĠÐľ ин", + "ĠÐľÐ¸ н", + "Ġê¸ °ëĭ¤", + "Ġ기 ëĭ¤", + "Ġê³µ 격", + "è¡Į åĭķ", + "ा मà¤ķ", + "ाम à¤ķ", + "æ±Ĥ è´Ń", + "模 åŀĭ", + "Ñģ оÑĢ", + "Ñģо ÑĢ", + "r ane", + "ra ne", + "ran e", + "à¹ĩà¸Ī à¸ŀระ", + "ĠÙħس ÛĮر", + "è£ħ ç½®", + "ìķ ¤", + "nÄĽ jÅ¡ÃŃch", + "nÄĽjÅ¡ÃŃ ch", + "αλ ÏįÏĦε", + "ĠH akk", + "ĠHa kk", + "ĠHak k", + "访 éĹ®", + "ĠÑĤ еÑĩ", + "ĠÑĤе Ñĩ", + "ĠL á»ĭch", + "Ġدش ÙħÙĨ", + "Î Į", + "Ġ ÏĢε", + "ĠÏĢ ε", + "Ġза мов", + "Ġзам ов", + "Ġb irim", + "Ġbi rim", + "Ġbir im", + "Ġbiri m", + "ãĤ· ãĤ¹ãĥĨãĥł", + "ĠÏĢÏģο ÏĬ", + "Ĭ ìĿĢ", + "в иг", + "ви г", + "Ġëıħ ìĿ¼", + "ĠÑĢев олÑİ", + "Ġ é¦Ļ港", + "Ġé¦Ļ 港", + "Ġ lez", + "Ġl ez", + "Ġle z", + "ĠبÛĮ Ùħار", + "ĠبÛĮÙħ ار", + "Ġduy gu", + "Ġduyg u", + "Ġë Ľ°", + "Ġa macı", + "Ġam acı", + "Ġama cı", + "Ġamac ı", + "à¥įय प", + "ĠìŀIJ ìĦ¸", + "اÙĪ ÛĮر", + "اÙĪÛĮ ر", + "Ġs pole", + "Ġsp ole", + "Ġspo le", + "Ġspol e", + "Ãĸ L", + "Ġ جع", + "Ġج ع", + "ÙĦ ÛĮÙħ", + "ÙĦÛĮ Ùħ", + "ãģªãģ© ãģ®", + "à¸Ľà¸£à¸°à¸ª à¸ļ", + "ĠnaÅ¡ ich", + "ĠпÑĢедÑģÑĤав лÑıеÑĤ", + "Ġзд об", + "Ġo bou", + "Ġob ou", + "Ø® ÙĪاÙĨ", + "Ø®ÙĪ اÙĨ", + "ãĥ¬ ãĥĥãĥĪ", + "о дейÑģÑĤв", + "од ейÑģÑĤв", + "Ú© رÛĮ", + "کر ÛĮ", + "Ġات اÙĤ", + "ĠÑįкÑģп лÑĥаÑĤа", + "ï½ ¢", + "ĠÙĦÙĦ Ø¥", + "ĠاÙĦÙĨ ظاÙħ", + "ĠíĶĦ ëŀijìĬ¤", + "ıs ıt", + "ısı t", + "åŃ Ļ", + "Ġžád ný", + "ÙĤ Ùī", + "ัà¸ģ à¹Ģร", + "Ġë²ł ìĬ¤íĬ¸", + "Ġ ãĥ«", + "Ġãĥ «", + "åı Ķ", + "n ické", + "nic ké", + "nick é", + "Ġε ιÏĥ", + "Ġει Ïĥ", + "ãĥ« ãĥī", + "Ġدار Ùħ", + "Ġг ем", + "Ġге м", + "Ġ åѸ", + "ĠåŃ ¸", + "ान सà¤Ń", + "ानस à¤Ń", + "али зи", + "ализ и", + "ов анÑĸ", + "ова нÑĸ", + "ован Ñĸ", + "Ġо бо", + "Ġоб о", + "ìłĦ ìĹIJ", + "ĠS inh", + "ĠSi nh", + "ĠSin h", + "Ġ ÙĨع", + "ĠÙĨ ع", + "Ġоб лаÑģ", + "Ġобла Ñģ", + "Ġобл аÑģ", + "Ïħ ÏĢ", + "èĥ ¶", + "Ġaz alt", + "Ġazal t", + "åħ¨ éĿ¢", + "ĠK romÄĽ", + "ĠKro mÄĽ", + "ĠC z", + "æĬ¥ åIJį", + "Ġnásled ujÃŃcÃŃ", + "Ġна пÑĢиклад", + "ĠнапÑĢи клад", + "ãģª ãģijãĤĮãģ°", + "à¸Ń าย", + "çľĭ çľĭ", + "Ġà¸ģร à¸ģà¸İ", + "Ġà¸ģรà¸ģ à¸İ", + "ed nou", + "edn ou", + "ا زÙĦ", + "از ÙĦ", + "ãĢģ æľ¬", + "е Ñģи", + "еÑģ и", + "Ġta rz", + "Ġtar z", + "ãĢĢ ï¾Ĭ", + "Ġroz um", + "ãĤ« ãĥ¼ãĥī", + "ãĤ«ãĥ¼ ãĥī", + "Ġà¤ĩ à¤ķ", + "Ġpros tÄĽ", + "Ġprost ÄĽ", + "ĠÎĵ κ", + "ç© ´", + "ĠH ük", + "la vÃŃ", + "lav ÃŃ", + "ê ¿", + "é¸ ¡", + "Ġвозник аеÑĤ", + "Ġвозника еÑĤ", + "ÑŁ ÑŁÑŁ", + "ÑŁÑŁ ÑŁ", + "Ġпо нима", + "Ġпон има", + "ÐŁ Ðŀ", + "ãģĶãģĸ ãģĦãģ¾ãģĻ", + "ãģ ħ", + "Ġtr val", + "Ġдал еко", + "ĠÙĨ ÙĬز", + "ĠÙĨÙĬ ز", + "ĠвÑĭ Ñıв", + "ิà¸Ĺย า", + "ิà¸Ĺ ยา", + "Ġl á»Ĺ", + "Ġlá» Ĺ", + "à¹Ģ สà¸Ļ", + "à¹Ģส à¸Ļ", + "ĠÑģÑĤ енÑĭ", + "ĠÑģÑĤен Ñĭ", + "à¥įड ल", + "Ġjednotliv ých", + "ĠпÑĢиб лиз", + "i kat", + "ik at", + "ika t", + "Ġп одав", + "Ġпо дав", + "Ġпод ав", + "Ġпода в", + "ر ÛĮز", + "رÛĮ ز", + "ĠØ¢ÙĨ جا", + "社 æľĥ", + "Ġà¤ľà¤¨ वर", + "Ġa ile", + "Ġai le", + "Ġail e", + "ี à¸Ľ", + "Ġ èħ", + "Ġè ħ", + "ãģ§ ãģĹãĤĩãģĨ", + "С Ðŀ", + "ãĢģ ãĢĬ", + "ìĿ¼ 본", + "ov anou", + "ova nou", + "ovan ou", + "ν ÏĮ", + "å± ¥", + "ع ÙĦÙĤ", + "عÙĦ ÙĤ", + "Ġìī ½", + "Ġгли б", + "Ġê²ĥ ìŀħëĭĪëĭ¤", + "ĠнеобÑħодим оÑģÑĤи", + "ĠнеобÑħодимо ÑģÑĤи", + "Ġتخصص ÛĮ", + "ا سر", + "اس ر", + "ï¼Į 说", + "ĠÐĿ Ñĸ", + "Ġvy rob", + "ÑĪ ÑĥÑİ", + "ÑĪÑĥ Ñİ", + "æĪ¿ å±ĭ", + "Âł ÐĹ", + "à¹Ģ à¸ŀล", + "à¹Ģà¸ŀ ล", + "åĨħ éĥ¨", + "ĠدÙĦ ار", + "Ġп ÑĤи", + "Å¡ ti", + "Å¡t i", + "ĠaraÅŁtır ma", + "Ġзна ком", + "Ġε λλην", + "Ġ ấm", + "ÑĢ ак", + "ÑĢаРº", + "ÑĢа к", + "ãĤŃ ãĥ¥", + "Ġth áºŃn", + "èŃ ľ", + "ëªħ ìĿĺ", + "Ġy eter", + "Ġyet er", + "Ġye ter", + "Ġна Ñģлед", + "ĠнаÑģ лед", + "ĠÐļ ан", + "ĠÐļа н", + "ĠвÑĭ биÑĢа", + "ĠвÑĭб иÑĢа", + "ĠΣ Ïĩ", + "ĠÑĤеÑĢ мÑĸн", + "Ġ æ´»", + "Ġæ´ »", + "ĠاÙĦ تÙģ", + "ĠاÙĦت Ùģ", + "ĠJ apon", + "ĠJa pon", + "ĠJap on", + "éĤ ª", + "ë¶Ħ ìĦĿ", + "Ġли ÑĨо", + "ĠлиÑĨ о", + "Ġm ê", + "à¸Ħ วร", + "à¸Ħว ร", + "Ġà¤ħ à¤Ĺल", + "Ġà¤ħà¤Ĺ ल", + "ĠÙĩ ج", + "룬 ìļ´", + "Ġвой нÑĭ", + "اÙĪر زÛĮ", + "ĠÑģп ÑĢÑı", + "çĦ ¼", + "è¢ ĸ", + "Ġiç eren", + "Ġiçer en", + "Ġëħ¸ ëŀĺ", + "ĠЧеÑĢ ез", + "ÙĪج ÙĪد", + "Ñı ÑĤие", + "ÑıÑĤ ие", + "ÑıÑĤи е", + "à¸Ńลล าร", + "è ·¨", + "è· ¨", + "ĠM illi", + "ĠMill i", + "ĠMil li", + "ĠMi lli", + "件 äºĭ", + "Ġ æľĿ", + "βολ ή", + "βο λή", + "Ġ ков", + "Ġк ов", + "Ġко в", + "ĠØ´Ùĩ ÛĮد", + "ä¸ĭ åİ»", + "Ġìłķ ìĭł", + "оÑĩ кÑĥ", + "ï¼Į 便", + "γ κε", + "γκ ε", + "ĠÙħ باش", + "ĠÙħب اش", + "Ġay ında", + "Ġ ä»»", + "ÑģÑĤоÑĢ ÑĸÑı", + "ä¸Ń åѦ", + "ç¸ ®", + "ĠÑĦ Ñĸл", + "ãĢģ ãĤĦ", + "Ġ æĺ¥", + "Ġæĺ ¥", + "Ġter ör", + "Ġповин ен", + "Ġmilion ů", + "ĠÙģ ارس", + "ĠÙģار س", + "Ġв вод", + "Ġвв од", + "Ø· اÙĦ", + "Ġê¶ģ ê¸Ī", + "Ġuk áz", + "çĶ ľ", + "æļ Ĥ", + "ص ت", + "Ðļ огда", + "Ġम ल", + "ά να", + "άν α", + "Ġдок ÑĤоÑĢ", + "Ġком мÑĥ", + "ĠпÑĸд Ñģ", + "Ġà¸ģรà¸ģà¸İ าà¸Ħม", + "Âł г", + "Ġö ne", + "Ġön e", + "ĠÄIJ á»ģ", + "ĠÄIJá» ģ", + "äºĭ åĭĻ", + "Ġs rov", + "Ġsr ov", + "Ġ άν", + "Ġά ν", + "ëıĦ ê°Ģ", + "acaÄŁ ım", + "acaģı m", + "к ол", + "ко л", + "Ġb á»ĵi", + "Ġپرد از", + "Ġ ä¸ļ", + "Ġä¸ ļ", + "ëĭ¤ ìļ´", + "Ġп ÑĢедел", + "ĠпÑĢед ел", + "ĠпÑĢе дел", + "ĠÑĦедеÑĢа лÑĮ", + "ĠاÙĦ Ø£Ùĥ", + "ĠاÙĦØ£ Ùĥ", + "ãĢĢ ãĢĢãĢĢãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ", + "Ġtr ấn", + "Ġд лин", + "Ġдли н", + "ĠÑĸ мп", + "ĠÑĸм п", + "ĠsmÄĽ rem", + "° ëĭ¤", + "Ġr ừng", + "ici álnÃŃ", + "è¡ Ĩ", + "μ ιο", + "μι ο", + "Ġاد ارÙĩ", + "ĠÑĤ ÑĢÑĮ", + "ĠÑĤÑĢ ÑĮ", + "ĠÄ° li", + "ĠÄ°l i", + "มà¸Ļ à¸ķร", + "à¥įव à¤ļ", + "е ÑĢо", + "еÑĢ о", + "ĠK UR", + "sk ými", + "ský mi", + "ským i", + "δ ί", + "u tin", + "ut in", + "uti n", + "Ġver iler", + "Ġveri ler", + "สà¸ĸาà¸Ļ à¸Ĺ", + "ĠзаÑħод Ñĸв", + "ĠÙģرÙĪد گاÙĩ", + "Ġ çĶ±", + "ู à¹ģล", + "éĥ ij", + "ĠJ ako", + "ĠJa ko", + "ĠJak o", + "ĠÑĢазвиÑĤи е", + "ĠÑĢазви ÑĤие", + "à¤ī न", + "ÙĬ دا", + "ÙĬد ا", + "Ġà¸ŀ à¸¤à¸©à¸łà¸²à¸Ħม", + "물 ìĿĦ", + "ë łĢ", + "ëł Ģ", + "- ÐĽ", + "ãĢĤ ãģĤ", + "Ġпод в", + "ï¼ī ï¼ļ", + "论 åĿĽ", + "ائ ع", + "ãĤĴ ãģĻãĤĭ", + "ĠØ£ ص", + "Ñĩ ики", + "Ñĩи ки", + "Ñĩик и", + "ĠÑģÑĤ ил", + "ley ici", + "Ñģ илÑĮ", + "Ñģи лÑĮ", + "Ġbulun du", + "ĠÑģеÑĢед ови", + "à¤Ĥ र", + "ĠاÛĮÙĨ جا", + "åľŃ åľŃ", + "ĠmyÅ¡ len", + "ĠÑĢозвиÑĤ ок", + "Ġiy ileÅŁ", + "Ġiyi leÅŁ", + "Ġв Ñĸз", + "ĠвÑĸ з", + "ëĤĺ 무", + "æĦı è§ģ", + "ι ÏĥÏĦη", + "ιÏĥ ÏĦη", + "ãĥĥ ãĥĦ", + "äºĭ æķħ", + "m adıģı", + "ma dıģı", + "mad ıģı", + "madı ģı", + "Ġà¤ħ पर", + "Ġà¤ħप र", + "ĠÚĨ رخ", + "Ġп лав", + "Ġпл ав", + "Ġпла в", + "以 æĿ¥", + "Ġë© Ģ", + "T uy", + "Tu y", + "ãĥ¼ ãĥį", + "ãĥ¼ãĥ į", + "Ġиз ÑĥÑĩ", + "ĠstÅĻed nÃŃ", + "课 ç¨ĭ", + "Ġê·¸ëħĢ ëĬĶ", + "ĠдоговоÑĢ Ñĥ", + "Ġдогов оÑĢÑĥ", + "ĠÄij á»ĭch", + "ĠÄijá»ĭ ch", + "Ġkar arı", + "Ġkarar ı", + "Ġkara rı", + "åIJ ´", + "Ùĥ اÙħ", + "ĠпоÑĤ ол", + "в ок", + "во к", + "ĠD üz", + "Τ α", + "å µ", + "âĢĻ na", + "âĢĻn a", + "а дж", + "ад ж", + "ĠdÅĻÃŃ ve", + "æ¢ ¨", + "ĠAv ust", + "åĬĽ ãĤĴ", + "à¹Ģ à¸ģล", + "à¹Ģà¸ģ ล", + "Ġпоб ед", + "Ġп ÑĢиÑĩ", + "ĠпÑĢ иÑĩ", + "ĠпÑĢи Ñĩ", + "ĠÐij Ñĸ", + "åŃ ¤", + "ĠÐł ег", + "ĠÐłÐµ г", + "Ġyet iÅŁ", + "Ġн еÑİ", + "Ġне Ñİ", + "Ġb ÃŃl", + "ĠbÃŃ l", + "ìĹĨ ìĿĮ", + "ĠÄ° talya", + "ÐĴ Ñģе", + "å¾Į ãģ®", + "Ġje jÃŃm", + "Ġjej ÃŃm", + "ĠjejÃŃ m", + "ĠвиглÑı дÑĸ", + "о гÑĢад", + "ог ÑĢад", + "огÑĢа д", + "Ġbo hat", + "Ġboh at", + "Ġ åħĭ", + "Ġåħ ĭ", + "ĠдиÑĤи ни", + "ĠдиÑĤ ини", + "лÑı ÑĤоÑĢ", + "ма га", + "маг а", + "ëĭĪ ìĬ¤", + "ĠÐł ади", + "ĠÐłÐ°Ð´ и", + "ĠÐłÐ° ди", + "ÏĢ οÏħÏģγ", + "ÏĢοÏħ Ïģγ", + "& ZeroWidthSpace", + "Ġ struk", + "Ġst ruk", + "Ġstr uk", + "Ġstru k", + "æIJ ŀ", + "Ġ ãģĿãģ®ä»ĸ", + "ìĿ¸ ìĿĦ", + "ĠпÑĢо веÑģÑĤи", + "ĠпÑĢов еÑģÑĤи", + "漫 çĶ»", + "Ġçİ© 家", + "ĠÙĪ رز", + "ĠÙĪر ز", + "ĠÑģвоÑĹ Ð¼", + "ĠL RV", + "ĠLR V", + "ิà¸ķ à¸ł", + "स त", + "ĠíĿ Ķ", + "âĹıâĹıâĹıâĹıâĹıâĹıâĹıâĹı âĹıâĹıâĹıâĹıâĹıâĹıâĹıâĹı", + "Ġt voÅĻÃŃ", + "ĠtvoÅĻ ÃŃ", + "Ġ ÐŁÐŀ", + "ĠÐŁ Ðŀ", + "é«ĺ 度", + ".h wp", + ".hw p", + "à¸ķำ à¸ļล", + "Ġد س", + "ìĪĺ ê°Ģ", + "ìĶ ©", + "ï¼ī ãĢĤĊ", + "ï¼īãĢĤ Ċ", + "æĭ ³", + "Ġl ô", + "ĠK ültür", + "اط عة", + "Ġku chy", + "Ġst roj", + "Ġstr oj", + "Ġstro j", + "μ ενο", + "με νο", + "μεν ο", + "ĠконÑģÑĤÑĢÑĥк ÑĨии", + "å°ı åѦ", + "Ġ åįļ", + "Ġåį ļ", + "Ġ èĢĥ", + "ĠèĢ ĥ", + "Ġas ıl", + "æĪij åĢij", + "خر اج", + "ĠO nun", + "ĠOn un", + "Ġ ç¾İåĽ½", + "Ġç¾İ åĽ½", + "à¥Ĥ बर", + "à¥Ĥब र", + "Ġmu ži", + "Ġmuž i", + "å§ «", + "Ġв б", + "Ġдо ме", + "Ġдом е", + "Ġ ам", + "Ġа м", + "Ġk uru", + "Ġkur u", + "Ġku ru", + "æ± Ĺ", + "l ediÄŁi", + "le diÄŁi", + "ledi ÄŁi", + "Ġv ẽ", + "å¾ ĵ", + "ĠгÑĥб еÑĢ", + "ĠÑģÑĤанов иÑĤÑĮ", + "ĠzemÄĽ dÄĽl", + "ÙĦ ÙĦ", + "Ġr amen", + "Ġra men", + "Ġram en", + "Ġprů bÄĽhu", + "Ġb lok", + "Ġbl ok", + "Ġblo k", + "ý val", + "ýv al", + "v ou", + "vo u", + "ν ά", + "ëĶĶ ìĭľ", + "ÑĨион нÑĭе", + "Ġê²Įìĭľ íĮIJ", + "ãĥ³ ãĥĩãĤ£", + "ãĥ³ãĥĩ ãĤ£", + "ä¸Ģ 级", + "и Ñĩа", + "иÑĩ а", + "ĠسرÛĮ اÙĦ", + "i lin", + "il in", + "ili n", + "ा यन", + "ाय न", + "ÙĨ ÙĪÛĮس", + "ĠÐĶ и", + "Ġاد بÛĮ", + "ĠÑĥ дов", + "ĠÑĥд ов", + "ĠÐĹ Ð°Ð¼", + "ĠÐĹа м", + "à¥ģà¤Ń व", + "Ñģ ок", + "Ñģо к", + "ĠÑĢай оне", + "ĠÑĢайон е", + "Ġ EK", + "ĠE K", + "æĤ ī", + "Ġsor umlu", + "Ġsoruml u", + "Ġzv yÅ¡", + "à¹Ģà¸ĭ à¸Ńร", + "in áÅĻ", + "iná ÅĻ", + "Ġu drž", + "Ġud rž", + "но вид", + "нов ид", + "ĠspoleÄį nÄĽ", + "Ġspole ÄįnÄĽ", + "æĪIJ äºĨ", + "ï¼ ¤", + "ัà¸ŀ à¸Ĺ", + "а ÑĪа", + "аÑĪ а", + "ĠÙĨ ادÙĬ", + "à¹ĥ à¸Ļà¸Ĺ", + "à¹ĥà¸Ļ à¸Ĺ", + "å¡ ļ", + "Ġس Ú©", + "ãĥģ ãĥ¥", + "ĠмаÑĢ ÑĪ", + "а леннÑı", + "ал еннÑı", + "ĠØŃÙħ اÛĮت", + "ãĥ³ ãĤ¸", + "รษ à¸IJ", + "Ġк ÑĢем", + "ĠK ažd", + "ê ½", + "Ġpar lament", + "Ġparl ament", + "ĠÅŁ un", + "ĠÅŁu n", + "Ġk ys", + "Ġky s", + "ÏĦ ÏĤ", + "ê°ľ ìĿĺ", + "Ġve lice", + "Ġvel ice", + "Ġce stu", + "Ġces tu", + "Ġcest u", + "ظ Ø©", + "è¯ Ĭ", + "Ġ út", + "Ġú t", + "ĠØ® ÙĪر", + "ĠØ®ÙĪ ر", + "ĠТ е", + "Ġобла ÑģÑĤ", + "ĠоблаÑģ ÑĤ", + "Ġобл аÑģÑĤ", + "à¹Ī à¸Ńà¸ķ", + "à¹Īà¸Ń à¸ķ", + "ĠAc adem", + "ĠAcad em", + "ãĢĤ æľ¬", + "Ġ 風", + "Ġé¢ ¨", + "Ñģ ен", + "Ñģе н", + "ãĥ¢ ãĥĩãĥ«", + "Ġзавд аннÑı", + "ãģ¾ ãĤĮ", + "моÑĤ ÑĢеÑĤÑĮ", + "моÑĤÑĢ еÑĤÑĮ", + "Ġkh á»ķ", + "à¹Ī ร", + "د رس", + "در س", + "ĠÄĮesk osloven", + "Ġ 计", + "Ġè® ¡", + "ĠÑĤак ом", + "ĠÑĤа ком", + "ĠÙĦ اعب", + "ĠÙĦا عب", + "ĠMuham med", + "ĠÙħ ÙĦÙģ", + "ĠÙħÙĦ Ùģ", + "ĠÙĪس ÙĦÙħ", + "ãĤ·ãĥ£ ãĥ«", + "Ġо кÑĢа", + "Ġок ÑĢа", + "à¥ģ मत", + "à¥ģम त", + "ĠëĪĦ 구", + "Ġned eni", + "Ġneden i", + "ĠëĤł ì§ľ", + "/ km", + "/k m", + "Ġд емон", + "Ġде мон", + "Ġдем он", + "ĠصÙĨ اÛĮع", + "m asından", + "mas ından", + "masında n", + "åīį ãģ®", + "æĪIJ 绩", + "ल à¤Ĺ", + "Ġ åĮħ", + "ĠåĮ ħ", + "à¸Ńà¸ģà¸Īาà¸ģ à¸Ļ", + "ا دا", + "اد ا", + "Ġay lık", + "ĠÙħ ÙĤد", + "ĠÙħÙĤ د", + "Ġönemli dir", + "ĠìĪľ ê°Ħ", + "Ġd inh", + "Ġdi nh", + "Ġdin h", + "Ġná kup", + "ist ické", + "istic ké", + "åº Ł", + "ìĬ¤ íĨł", + "Ġd ny", + "Ġdn y", + "ĠìŀĪ ëıĦë¡Ŀ", + "ìĽIJ ìĿĺ", + "ãĥķ ãĥ¬", + "p oz", + "po z", + "Ġ ев", + "Ġе в", + "ĠdÃ¼ÅŁ ür", + "à¥įर à¤ļ", + "Ġê²° íĺ¼", + "Ġ ÑĨенÑĤÑĢа", + "ĠÑĨенÑĤ ÑĢа", + "ĠÑĨен ÑĤÑĢа", + "ĠÑĨенÑĤÑĢ а", + "åŁ ĭ", + "ï¿£ ï½Ģ", + "æѦ åĻ¨", + "à¹Īาà¸Ļ มา", + "Ġर व", + "Ùij د", + "μÎŃ Î½Î¿Î¹", + "μÎŃν οι", + "μÎŃνο ι", + "Ġë§IJ ìĶĢ", + "Ġpo ÅĻad", + "Ġب غ", + "ĠÏĮ λα", + "ĠÏĮλ α", + "à¹ī à¹Ħà¸Ĥ", + "à¹Ģà¸ģ าะ", + "Ġb ạc", + "Ġd á", + "d ÄĽla", + "dÄĽ la", + "dÄĽl a", + "Ġt eb", + "Ġte b", + "Ġk èo", + "ãĤı ãĤĮ", + "Ġist iyorum", + "Ġistiyor um", + "λ ήÏĤ", + "λή ÏĤ", + "ÐIJ в", + "Ġa sla", + "Ġas la", + "Ġperform ans", + "Ġperfor mans", + "Ġperforman s", + "ĠVác lav", + "Ïģ ίαÏĤ", + "Ïģί αÏĤ", + "Ïģία ÏĤ", + "Ġ tÄĽl", + "Ġt ÄĽl", + "ĠtÄĽ l", + "æĮ Ļ", + "о ба", + "об а", + "ãģij ãĤĮãģ©", + "ĠëĶ ¸", + "ÙĪ اء", + "ÙĪا Ø¡", + "ĠÚ©ÙĪد کاÙĨ", + "ĠÚ©ÙĪدک اÙĨ", + "Ġп лиÑĤ", + "Ġпл иÑĤ", + "Ġ bilir", + "Ġb ilir", + "Ġbil ir", + "Ñĥ же", + "Ñĥж е", + "ÏĦÎŃ Î»Îµ", + "Ġà¤Ĩ à¤ķर", + "Ġà¤Ĩà¤ķ र", + "ĠÑĤÑĢ Ñĥда", + "ĠÑĤÑĢÑĥ да", + "ĠÑĤÑĢÑĥд а", + "Ġدر ÛĮا", + "ĠدرÛĮ ا", + "Ì §", + "Ġng á»įt", + "ÙĨس ا", + "а ÑģÑĤи", + "аÑģ ÑĤи", + "аÑģÑĤ и", + "ï½ £", + "Âł на", + "ем Ñĭе", + "Ġس عÙĪد", + "Ġسع ÙĪد", + "Ġ alım", + "Ġal ım", + "è´ «", + "åĪ° çļĦ", + "Ġkesin likle", + "Ġzá sad", + "Ġ ìĬ¤íĬ¸", + "ĠìĬ¤ íĬ¸", + "Ġd ahi", + "Ġda hi", + "Ġdah i", + "t é", + "åįģ åħ«", + "Ġz ayıf", + "Ø° ار", + "ذا ر", + "Ġا ÙĬراÙĨ", + "ĠاÙĬ راÙĨ", + "Ġhod nocenÃŃ", + "D ST", + "DS T", + "Ġìĸ ĺ", + "æĺ ĩ", + "éĻ £", + "Ġк ле", + "Ġкл е", + "Ġu plat", + "Ġup lat", + "ĠاÙĦتع ÙĦÙĬÙħ", + "ÏĢοί ηÏĥη", + "ек ÑĤоÑĢа", + "екÑĤ оÑĢа", + "екÑĤоÑĢ а", + "Ġë§IJ ìĿ´", + "ĠÙģ رÙĬÙĤ", + "ĠÙģر ÙĬÙĤ", + "帮 åĬ©", + "çĶŁ ãģį", + "åĨħ ãģ®", + "èģĶ 缣", + "г ÑĢад", + "гÑĢа д", + "Ġch uyến", + "ãĤĤ ãĤĬ", + "ĠÑĩаÑģÑĤ ина", + "ĠÑĩаÑģÑĤи на", + "ãģª ãģıãģª", + "ãģªãģı ãģª", + "ÑĶ в", + "ĠÑĦ аÑħ", + "k uk", + "ku k", + "çĶ· æĢ§", + "ĠÙħÛĮÙĦ ادÛĮ", + "Ġb eden", + "Ġbe den", + "Ġbed en", + "ê°Ģ 를", + "म र", + "Ġìĸ´ 머ëĭĪ", + "èģĶ ç½ij", + "Âł mi", + "Âłm i", + "Ġzah rn", + "æ² ĸ", + "Ġkhu ẩn", + "Ġo práv", + "Ġop ráv", + "Ġopr áv", + "ाह à¤ķ", + "ĠÚ©ÙĪت اÙĩ", + "Ġо бол", + "Ġоб ол", + "Ġобо л", + "Ġph úc", + "r ánÃŃ", + "rá nÃŃ", + "rán ÃŃ", + "à¥įर थ", + "æݪ æĸ½", + "Ġв олод", + "Ġво лод", + "Ġвол од", + "Ġsp ÃŃÅ¡e", + "Ġm Æ¡", + "ÑĬ ек", + "ng ör", + "à¤ī त", + "k siyon", + "ks iyon", + "ksi yon", + "а ÑĤе", + "аÑĤ е", + "Ġجز Ø¡", + "áv ka", + "ÐĴ С", + "laÅŁ ma", + "Ġ ç¿", + "Ġç ¿", + "à¸Ń าà¸Ĭ", + "ни ÑĨÑĥ", + "ниÑĨ Ñĥ", + "Ġ หาà¸ģ", + "Ġห าà¸ģ", + "ãģĭ ãģĹ", + "íı ´", + "Ġг аÑĢан", + "ĠгаÑĢ ан", + "ĠгаÑĢа н", + "Ġ Ïĥαν", + "ĠÏĥ αν", + "Ġдобав иÑĤÑĮ", + "ĠÑĢаз ÑĢеÑĪ", + "á ¾", + "æĺ¯ ä¸ª", + "μ ÎŃÏĤ", + "μÎŃ ÏĤ", + "ĠÄ°mpar ator", + "æ¨Ļ æºĸ", + "Ñģ ÑĤÑĭ", + "ÑģÑĤ Ñĭ", + "Ġg ücü", + "Ġgü cü", + "Ġgüc ü", + "Ġ íĥĢìĿ´", + "Ġíĥ ĢìĿ´", + "ĠíĥĢ ìĿ´", + "Ġ åħ¶ä»ĸ", + "Ġåħ¶ ä»ĸ", + "Ġt ông", + "Ġtô ng", + "Ġtôn g", + "Ġv edenÃŃ", + "Ġved enÃŃ", + "Ġvede nÃŃ", + "ëĵľ ë¡ľ", + "Ġm esel", + "Ġme sel", + "Ġmes el", + "Ġ Äįe", + "ĠÄį e", + "j de", + "jd e", + "Ïģ εια", + "Ïģε ια", + "Ïģει α", + "ãĤĪ ãģŃ", + "Ðł ÐĿ", + "è·Ŀ 离", + "ĠÙĤ ائÙħØ©", + "า à¸ļาล", + "าà¸ļ าล", + "ĠÑģай ÑĤÑĸ", + "Ġर स", + "ĠÙĤر ÙĨ", + "Ġná vr", + "Ġnáv r", + "Ú© Ùħ", + "çļĦ æīĭ", + "Ġsor unu", + "Ġsorun u", + "Ġsoru nu", + "/N ÄIJ", + "nut ÃŃm", + "nutÃŃ m", + "ĠØ® ÙĪرد", + "ĠØ®ÙĪ رد", + "ĠØ®ÙĪر د", + "Ġng á»Ŀ", + "Ġ: .|", + "Ġ:. |", + "Ġbudou c", + "i Äįky", + "iÄį ky", + "Ġد رد", + "Ġدر د", + "ÑĢо ниÑĩеÑģ", + "ÑĢон иÑĩеÑģ", + "ç¾ Ĭ", + "ĠìķĦ ë²Ħì§Ģ", + "ĠKan unu", + "ĠKanun u", + "ĠпÑĢивод иÑĤ", + "άλÏħ ÏĪηÏĤ", + "ĠVlad im", + "Ġal ıp", + "Ġе ÑĤап", + "Ġà¤Ĺ लत", + "ĠراÙĩ ÙĨÙħ", + "Ġpoz isyon", + "Ġgö ç", + "èµ ŀ", + "Ġм ой", + "Ġмо й", + "ĠÎł ά", + "Ġ ìĪł", + "ĠìĪ ł", + "ĠØ¢ÛĮ ÙĨدÙĩ", + "a ná", + "an á", + "举 çľģ", + "ĠÙħت عدد", + "Ġ åįĬ", + "Ġåį Ĭ", + "ãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "Ġth á»Ŀ", + "Ġthá» Ŀ", + "Ġвд ÑĢÑĥг", + "п аÑĤ", + "па ÑĤ", + "ĠпÑĢовед ениÑı", + "ÙĨ ز", + "ĠاÙĦب ØŃØ«", + "æģ ¢", + "Ġbak tı", + "Ġ è·¯", + "Ġè· ¯", + "Ġзаболева ний", + "ĠÐķ вÑĢоп", + "ĠÐķв ÑĢоп", + "Ġtarih li", + "ê¹ ¨", + "ĠÚ© ÙĪÙĩ", + "ĠÚ©ÙĪ Ùĩ", + "Ġìĸ´ 볤", + "Ġtit ul", + "Ġti tul", + "Ġvyd ánÃŃ", + "éĺ¶ æ®µ", + "à¸Īะ à¸ķ", + "Ġм оÑı", + "Ġмо Ñı", + "ĠкоÑĢ ол", + "Ġко ÑĢол", + "Ġб анк", + "Ġбан к", + "วรร à¸ĵ", + "วร รà¸ĵ", + "ĠÙĥس ارة", + "ĠK hoa", + "ĠKh oa", + "ĠKho a", + "ĠÑĥнÑĸвеÑĢÑģиÑĤ еÑĤ", + "ãģ«éĸ¢ ãģĻãĤĭ", + "r uary", + "ru ary", + "Ġ à¸Ĥาย", + "Ġà¸Ĥ าย", + "Ġsv az", + "ĠØ´ رÙĤ", + "Ġشر ÙĤ", + "Ġд ÑĭÑħ", + "Ġиз бав", + "Ġизб ав", + "ĠÑı кÑĸй", + "ĠÑıк Ñĸй", + "ĠÑıкÑĸ й", + "ĠÎľ ον", + "Ġg ön", + "Ġgö n", + "ĠUkr aj", + "ĠUk raj", + "ĠUkra j", + "ัà¸Ļ à¸Ńà¸Ńà¸ģ", + "ัà¸Ļà¸Ń à¸Ńà¸ģ", + "Ġม à¸ģราà¸Ħม", + "и ÑĤов", + "иÑĤ ов", + "Ġanal ý", + "Ġana lý", + "ĠоÑĤ меÑĩ", + "Ġبر اÙī", + "âĪ ı", + "ัà¸ģ à¸ģ", + "æĭ¥ æľī", + "ĠÑĸнÑĪ ого", + "Ġкомп анÑĸÑĹ", + "Ġk ÅĻes", + "ĠÑĢаб оÑĩ", + "ĠÑĢабо Ñĩ", + "a dÃŃ", + "ad ÃŃ", + "ìł ł", + "à¹Ħ หà¸Ļ", + "à¥ģब ह", + "âĢĻ deki", + "âĢĻd eki", + "âĢĻde ki", + "çħ ¤", + "ĠпаÑĢ Ñĥ", + "Ġпа ÑĢÑĥ", + "ìĦ Ń", + "Ġнеп оÑģÑĢед", + "ĠÄ° b", + "Ġà¸ŀ ฤศà¸Ī", + "íĭ ´", + "Ġ ëłĪìĿ´", + "ĠëłĪ ìĿ´", + "ĠTh á»ķ", + "Ñı еÑĤ", + "ائ ج", + "» çĴĥ", + "ÐĴ Ðŀ", + "åĸ Ĭ", + "Ġ 第ä¸ī", + "Ġ第 ä¸ī", + "Ġвок ÑĢÑĥг", + "Ñĩ енÑĮ", + "Ñĩе нÑĮ", + "Ñĩен ÑĮ", + "Ġolan ak", + "Ġola nak", + "t ura", + "tu ra", + "tur a", + "Ġ ÙħÙĬÙĦ", + "ĠÙħ ÙĬÙĦ", + "ĠÙħÙĬ ÙĦ", + "ey di", + "ĠÙħد ÙĬر", + "Ġnel ze", + "ัว à¸Ńย", + "ìħ ľ", + "Ġhlav u", + "Ġkor uy", + "ÑĨ ин", + "ÑĨи н", + "ĠдиÑģ ÑĨип", + "ĠÙħ اÙĨد", + "ĠÙħا ÙĨد", + "ĠÙħاÙĨ د", + "Ġпод ÑĢоб", + "Т Ðŀ", + "ÙĤر ار", + "à¹ģà¸Ļะ à¸Ļำ", + "문 ìĿĦ", + "æĮ¯ ãĤĬ", + "P ÅĻi", + "PÅĻ i", + "Ġy ên", + "श à¤ķ", + "Âł je", + "ĠÐļон ÑģÑĤиÑĤÑĥ", + "à¥ģ ह", + "à¥ģठ¹", + "ĠÙ¾ ا", + "ìĨĮ 를", + "Ġд ела", + "Ġдел а", + "Ġде ла", + "к ид", + "ки д", + "à¹Ĥ à¸Ĭ", + "커 ìĬ¤", + "dÄĽ len", + "dÄĽl en", + "à¤Ķ र", + "äºİ æĺ¯", + "ĠÙĩÙħ ÛĮØ´Ùĩ", + "ĠbaÅŁ lam", + "Ġ ìĽ¨", + "ĠìĽ ¨", + "Ġden eyim", + "Ġdeney im", + "Ġü ye", + "Ġüy e", + "Ġ νÏĮ", + "Ġν ÏĮ", + "Ġà¤ĸ ड", + "n ÄĽl", + "nÄĽ l", + "ĠÑģÑĦ еÑĢÑĸ", + "ĠÑģÑĦеÑĢ Ñĸ", + "à¸Ńà¸Ķ à¸ł", + "ä¸Ģ å¹´", + "Ġvur gu", + "Äŀ Ä°", + "âĢĻ Ċ", + "ĠÑĸн ÑĪими", + "ĠÑĸнÑĪ ими", + "Ġз менÑĪ", + "Ġठĭ", + "Ġв ека", + "Ġве ка", + "ĠØŃÚ©ÙĪÙħ ت", + "ĠتÙħ اÙħÛĮ", + "ĠتÙħاÙħ ÛĮ", + "Ġs mrt", + "Ġsm rt", + "Ġsmr t", + "Ġh ủy", + "Ġyap ılmÄ±ÅŁ", + "Ġyapıl mÄ±ÅŁ", + "à¹ī à¸ľ", + "ĠY en", + "ĠYe n", + "Ġ Ñĥл", + "ĠÑĥ л", + "ĠS vÄĽt", + "ĠSv ÄĽt", + "ั à¸Ħ", + "ĠmÄĽsÃŃ ců", + "д енÑĤи", + "ден ÑĤи", + "Ġ ï¾ĺ", + "Ġï¾ ĺ", + "Ġпол иÑĤи", + "Ġполи ÑĤи", + "ĠполиÑĤ и", + "s kyt", + "sk yt", + "sky t", + "ä¹Ł æľī", + "Ġê°Ļ ìĬµëĭĪëĭ¤", + "Ġê·¸ëŀĺ ìĦľ", + "ÏĦε Ïģη", + "ÏĦεÏģ η", + "Ñĩ еÑĢ", + "Ñĩе ÑĢ", + "ĠÃľNÄ°VERS Ä°TESÄ°", + "ส à¸ł", + "Ġ สร", + "Ġส ร", + "ान द", + "ĠaÅŁ ırı", + "λ ίοÏħ", + "λί οÏħ", + "Ġ ÙĦÙģ", + "ĠÙĦ Ùģ", + "ÃŃ nu", + "ÃŃn u", + "à¸Ń าร", + "ÑĤ ÑĥÑĢа", + "ÑĤÑĥ ÑĢа", + "ÑĤÑĥÑĢ а", + "ĠÄįesk ých", + "ĠÄįe ských", + "ĠÄįeský ch", + "Ġph ức", + "以 为", + "ÏģÏī ÏĢα", + "ĠاÙĨر ÚĺÛĮ", + "» )", + "a lardan", + "alar dan", + "alarda n", + "ĠÑģÑĤ воÑĢÑİ", + "ĠÑģÑĤвоÑĢ Ñİ", + "Ġt ráv", + "Ġtr áv", + "ॠ¬", + "ãģĬ ãĤĪãģ³", + "ïľ ĭ", + "ad il", + "adi l", + "ĠΤ ι", + "Ġ ëIJ©ëĭĪëĭ¤", + "ĠëIJ ©ëĭĪëĭ¤", + "Ġε μÏĨ", + "Ġ구 ì¡°", + "ìĹŃ ìĭľ", + "ĠاÙĦ جاÙħ", + "ĠاÙĦج اÙħ", + "主 é¢ĺ", + "ãĤ¹ ãĥĿ", + "Ġ ìĹŃìĭľ", + "ĠìĹŃ ìĭľ", + "ĠÚ©Ùħ تر", + "ĠSp oleÄį", + "ол оÑĪ", + "оло ÑĪ", + "ĠSur iye", + "Ч еÑĢ", + "æĪĺ æĸĹ", + "Ġz ávis", + "Ġzá vis", + "Ġzáv is", + "æĽ¸ 館", + "Ġmus el", + "Ġmu sel", + "Ġmuse l", + "Ġ çĿ", + "Ġç Ŀ", + "Ùħ Ùħ", + "ĠاÙĦØ® ارج", + "Ġ ÐĵÐŀ", + "ĠÐĵ Ðŀ", + "ĠваÑĢ ÑĤо", + "ĠваÑĢÑĤ о", + "Ïģα β", + "Ġपह à¤ļ", + "ub lice", + "ublic e", + "ubl ice", + "ÑĨион ного", + "è Į¨", + "èĮ ¨", + "ĠدÙģ تر", + "Ġ Ùģس", + "ĠÙģ س", + "Ġन à¤ľà¤°", + "t arı", + "ta rı", + "tar ı", + "Ġоб ÑĢоб", + "ĠÐł а", + "ĠاÙĦ صÙĨ", + "ĠاÙĦص ÙĨ", + "Ø´ Ø©", + "ĠìĹĨ ìĹĪ", + "ož ná", + "æľĢ çµĤ", + "Ù ¥", + "r ech", + "re ch", + "rec h", + "ĠاÙĦØ£ سر", + "ĠاÙĦأس ر", + "Ġм ови", + "Ġмо ви", + "Ġмов и", + "Ġì¡° êµIJ", + "Ñĸ меÑĩ", + "Ñĸм еÑĩ", + "ãĥ¯ ãĥ¼", + "б ÑĥÑĢг", + "бÑĥ ÑĢг", + "Ġس ÙĦس", + "ĠسÙĦ س", + "åѦ ä¼ļ", + "Ġ ë¦", + "Ġë ¦", + "åħĭ æĸ¯", + "æĸĩ çĮ®", + "Ġx Æ°Æ¡ng", + "Ġyo lc", + "Ġyol c", + "Ġ ìĤ¬ë¬´", + "ĠìĤ¬ 무", + "ãĤı ãģļ", + "ĠÑĢаÑģÑĤ ений", + "ĠÙģ ضاÛĮ", + "ĠÙģض اÛĮ", + "Ġna opak", + "Ġnao pak", + "ĠпÑĢи вÑĭ", + "ĠпÑĢив Ñĭ", + "Ġد ÛĮدÙĩ", + "ĠدÛĮ دÙĩ", + "ĠدÛĮد Ùĩ", + "à¸ģาร à¹ĥà¸Ĭ", + "Ġ åŀ", + "Ġå ŀ", + "çij Ł", + "以 åIJİ", + "ĠpÅĻib liž", + "ĠdÃ¼ÅŁ man", + "Ġt emin", + "Ġte min", + "Ġtem in", + "ĠÑĥÑģл Ñĥг", + "ĠÑĥÑģ лÑĥг", + "Ġद ब", + "ĠìĥĪ ê¸Ģ", + "ĠÑĥÑģÑĤÑĢой ÑģÑĤва", + "ĠТ ÑĥÑĤ", + "ÏĦ ίοÏħ", + "ÏĦί οÏħ", + "ĠÄ°s lâm", + "Ù ¤", + "åıĤ ä¸İ", + "Ġк ÑĥÑģÑĤ", + "ĠкÑĥ ÑģÑĤ", + "ĠкÑĥÑģ ÑĤ", + "éĻIJ åĪ¶", + "ت ÙĬÙĨ", + "تÙĬ ÙĨ", + "ĠоÑģÑĤ аннÑĸ", + "ĠоÑģÑĤан нÑĸ", + "ic ations", + "ication s", + "ا Ú©ÛĮ", + "اک ÛĮ", + "но ÑģÑı", + "ноÑģ Ñı", + "ÄŁ an", + "ÄŁa n", + "ãģı ãĤĮãĤĭ", + "ãģıãĤĮ ãĤĭ", + "Ġyap ıyor", + "Ġyapı yor", + "Ġê°ķ ëĤ¨", + "Ùħ ÙĬÙħ", + "ÙħÙĬ Ùħ", + "æ ŃIJ", + "æŃ IJ", + "Ġر ع", + "Ġb oÄŁ", + "Ġbo ÄŁ", + "ĠиÑģ Ñħод", + "èª ł", + "æł· åŃIJ", + "Ġbu deme", + "Ġbud eme", + "Ġbude me", + "ĠÑģ еÑĤ", + "ι ÏĥμοÏį", + "ιÏĥμ οÏį", + "Ġ å¾ĴæŃ©", + "Ġå¾Ĵ æŃ©", + "u álnÃŃ", + "ĠاÙĦع ÙĤ", + "Ġسب Ú©", + "ĠاÙĦØ£ خرÙī", + "ĠاÙĦأخ رÙī", + "E FA", + "EF A", + "åĽº å®ļ", + "Ġ ãĤ¬", + "ĠãĤ ¬", + "ĠìŀIJ ìĹ°", + "ยว à¸Ĥ", + "ب س", + "un ma", + "Ġза ним", + "Ġзан им", + "à¹ĥà¸Ļ ร", + "èĢĥ èĻij", + "æ·· åIJĪ", + "å° ĭ", + "Ġçık Ä±ÅŁ", + "Ġm aliyet", + "Ġmal iyet", + "éľ Ĭ", + "ãģŁãĤģ ãģ®", + "ĠÙ¾ Ø´", + "Ġз лоÑĩ", + "Ġvý Å¡i", + "Ġsch vál", + "ĠÙĨÙħ ÙĪدÙĩ", + "ĠÙĨÙħÙĪد Ùĩ", + "Î Ĩ", + "Ġz ách", + "Ġzá ch", + "Ġ Ïĥκ", + "ĠÏĥ κ", + "ãĤ¹ ãĥŀ", + "ĠÙħس ائÙĦ", + "ĠاÙĦاج تÙħاع", + "åľ° çĤ¹", + "ا ÛĮاÙĨ", + "اÛĮ اÙĨ", + "ĠÐŀ к", + "ê¸ Ķ", + "e lease", + "el ease", + "ele ase", + "Ġطب ÙĤÙĩ", + "ĠطبÙĤ Ùĩ", + "éij ij", + "Ġì½Ķ ë¡ľëĤĺ", + "é¼ ł", + "大 åħ¨", + "ĠпÑĢи веÑģÑĤи", + "ĠпÑĢив еÑģÑĤи", + "Ġاب تد", + "Ġابت د", + "리 ë¡ľ", + "ĠÑģÑĤÑĢ анÑĭ", + "ĠÑģÑĤÑĢа нÑĭ", + "ĠÑģÑĤÑĢан Ñĭ", + "ĠzatÃŃm co", + "Ġh uyết", + "Ġhuy ết", + "سÛĮ ÙĪÙĨ", + "Ġsor du", + "âĢĮ رس", + "ĠÑĦ ÑĢон", + "Ġed ip", + "Ġedi p", + "ÙĨ Ú¯ÛĮ", + "ÙĨÚ¯ ÛĮ", + "Ġк иÑĢ", + "Ġки ÑĢ", + "Ġ íķ´ìķ¼", + "Ġíķ´ ìķ¼", + "ì» ´", + "ÑĨик лоп", + "ĠпÑĢимен ениÑı", + "Ġоб л", + "éļ ª", + "Ġk romÄĽ", + "Ġkro mÄĽ", + "æł¸ å¿ĥ", + "ra him", + "rah im", + "о ÑĢд", + "оÑĢ д", + "Ġl Ãłnh", + "ĠlÃł nh", + "ĠlÃłn h", + "Ġо ÑģÑĤÑĢов", + "ĠоÑģÑĤ ÑĢов", + "; |", + "b uz", + "bu z", + "Ġ ÏĦÏģο", + "ĠÏĦ Ïģο", + "ĠÐĴ аÑĢ", + "æī İ", + "ı lÄ±ÅŁ", + "ılı ÅŁ", + "ıl Ä±ÅŁ", + "éĿ¢ ç©į", + "身 份", + "é¢Ĩ åŁŁ", + "ĠاÙĦÙĤر ÙĨ", + "Ġ пÑĢиклад", + "ĠпÑĢи клад", + "ĠпÑĢик лад", + "ãĥģ ãĥ¼ãĥł", + "Ġสà¸ŀ à¸Ľ", + "Ġо ÑĩиÑģÑĤ", + "ĠоÑĩ иÑģÑĤ", + "ĠоÑĩи ÑģÑĤ", + "Ġмил ли", + "а ÑĨÑĸÑĹ", + "ี à¹Ģà¸Ń", + "Ġt anın", + "Ġtan ın", + "Ġtanı n", + "çĪ¶ 亲", + "Ġmsg str", + "Ġmsgs tr", + "ĠØ´ ÛĮÙħÛĮ", + "ĠØ´ÛĮ ÙħÛĮ", + "ĠÙģر اÙĩÙħ", + "Ġ 맥", + "Ġë§ ¥", + "ãĢĤ å½ĵ", + "Ġкон ÑĨенÑĤÑĢа", + "êµIJ íļĮ", + "ãĤī ãĤĮãģ¦", + "ãĤīãĤĮ ãģ¦", + "Ġya sak", + "Ġyas ak", + "ĠÐij ол", + "Ġ æ¾³", + "Ġæ¾ ³", + "çĩ ķ", + "Ġ جا", + "Ġج ا", + "ëij ĺ", + "Ġدر Ø®ÙĪاست", + "ĠmÃŃst nÃŃ", + "ÂĤ ÃĮ", + "Ġbas kı", + "Ġu çak", + "Ġuç ak", + "ä» ĵ", + "Ġìľł ì§Ģ", + "Ġп оба", + "Ġпо ба", + "Ġпоб а", + "Ġz eptal", + "Ġze ptal", + "ç»Ļ æĪij", + "ĠAt atürk", + "ĠÙħÙĨ اس", + "Ñ Ĵ", + "Ġar acı", + "Ġarac ı", + "Ġara cı", + "лÑİ ÑĶ", + "Ġnit elik", + "Ġnitel ik", + "ĠM ezi", + "ĠMe zi", + "ĠMez i", + "ĠÎŃ Î½Î±ÏĤ", + "ĠÎŃνα ÏĤ", + "ĠÎŃν αÏĤ", + "Ïİν ÏĦαÏĤ", + "v až", + "va ž", + "Ġk uzey", + "Ġ ÏİÏģα", + "ĠÏİ Ïģα", + "ĠÑĢоз пов", + "ĠÑĢозп ов", + "à¹Ī าà¸ģ", + "à¹Īา à¸ģ", + "ãĢģ ä¸ī", + "ĠÑģÑĤ аÑĢи", + "ĠÑģÑĤаÑĢ и", + "ĠÑģÑĤа ÑĢи", + "Ġhak kı", + "Ġhakk ı", + "ĠØ¢Ùħ ادÙĩ", + "íĮ Ķ", + "о мÑĸ", + "ом Ñĸ", + "Ġ âĢł", + "ĠâĢ ł", + "ãģĭ ãĤı", + "ãĢĮ ä½ł", + "æ³ķ åĽ½", + "ÙIJ ÙĬÙĨ", + "ÙIJÙĬ ÙĨ", + "æī ķ", + "н или", + "ни ли", + "нил и", + "ĠÑĥÑģÑĤанов ки", + "Ġl ông", + "Ġlô ng", + "त म", + "ÙĪ ÙĨÙĬØ©", + "ÙĪÙĨ ÙĬØ©", + "ÙĪÙĨÙĬ Ø©", + "ÙĬ تÙĬ", + "ÙĬت ÙĬ", + "Ġê²Įìĭľ 물", + "Ġve Å¡ker", + "ÎŃ Ïģγ", + "ÎŃÏģ γ", + "ĠÑĥ Ñģе", + "ĠÑĥÑģ е", + "Ġk ıl", + "Ġkı l", + "Ġil gi", + "Ġilg i", + "μ Ïīν", + "Ġз вÑĸлÑĮ", + "Ġзв ÑĸлÑĮ", + "Ġön lem", + "à¸ģà¸İ หมาย", + "ĠH iá»ĩp", + "Ġг оÑĢм", + "ĠгоÑĢ м", + "лÑı ÑİÑĤÑĮÑģÑı", + "лÑıÑİÑĤÑĮ ÑģÑı", + "la maya", + "lam aya", + "lama ya", + "ĠÑģпоÑģоб ом", + "ãģ¸ ãģ¨", + "ç¦ģ æŃ¢", + "ĠÑĢаÑħ Ñĥнок", + "ĠоÑĤвеÑĢ ÑģÑĤи", + ".: .:.:.", + ".:.: .:.", + ".:.:.: .", + ".:.:. :.", + "Ġmü da", + "о наÑħ", + "он аÑħ", + "она Ñħ", + "Ì£ c", + "Ġyap acak", + "Ġн азвание", + "Ġназ вание", + "Ġназва ние", + "对 æĸ¹", + "ëĮĢ íijľ", + "çĪ Ń", + "в ана", + "ва на", + "ван а", + "ह न", + "ĠпÑĢоблем а", + "Ġжен ÑīинÑĭ", + "ĠженÑīин Ñĭ", + "èŀ º", + "Ġhosp odáÅĻ", + "ĠСÑĤ еп", + "ĠodpovÄĽ d", + "ĠS á»Ń", + "e view", + "ev iew", + "evi ew", + "åĩł ä¹İ", + "çŁ ¢", + "æĿ¥ ãģŁ", + "Ġп олоÑģ", + "Ġпол оÑģ", + "ĠÑģ ел", + "å± Ĩ", + "ĠпеÑĢв ой", + "ĠпÑĢоÑĨеÑģ Ñģа", + "ĠпÑĢоÑĨеÑģÑģ а", + "ãĢĢ ãĤĿ", + "ت اÙħبر", + "تا Ùħبر", + "и лаÑģÑı", + "ила ÑģÑı", + "ï¼Į æĹł", + "ĠвлаÑģ ноÑģÑĤÑĸ", + "íķĺ ìŀIJ", + "аÑĤ ки", + "ĠB Ãł", + "ĠK arel", + "ĠKar el", + "ĠKa rel", + "ĠKare l", + "è· µ", + "ر ÛĮÙĩ", + "رÛĮ Ùĩ", + "ĠëĤĺ 를", + "ĠобеÑģпеÑĩ ива", + "ĠобеÑģпе Ñĩива", + "à¥įर पत", + "ãģĹ ãĤĩ", + "åį Ĵ", + "Ġ 奥", + "Ġå¥ ¥", + "ĠпÑĢ оÑĤе", + "ĠпÑĢо ÑĤе", + "ĠпÑĢоÑĤ е", + "Ġ æĭĽ", + "Ġæĭ Ľ", + "ĠСÑĤ ÑĢана", + "ĠÑĢабоÑĤ аÑĤÑĮ", + "ĠÑĢабоÑĤа ÑĤÑĮ", + "Ġتش Ø®ÛĮص", + "ек ÑģÑĥ", + "екÑģ Ñĥ", + "Ġ 리그", + "Ġ리 ê·¸", + "Ġص اÙĦØŃ", + "ĠbaÅŁ lamÄ±ÅŁ", + "ĠbaÅŁlam Ä±ÅŁ", + "ĠÙ¾ÛĮ اÙħبر", + "ĠÙ¾ÛĮاÙħ بر", + "ز ا", + "Ġм аÑģÑģ", + "ĠмаÑģ Ñģ", + "ĠÎł αÏģ", + "Ġγα Ïģ", + "ëĿ¼ íĶ¼", + "Ġy arı", + "Ġya rı", + "Ġyar ı", + "ĠÑĤип Ñĥ", + "Ðŀ п", + "ãģij ãģªãģĦ", + "e mem", + "em em", + "eme m", + "ĠnÄĽ mu", + "ĠnÄĽm u", + "ĠÙĨ شر", + "ĠÙĨØ´ ر", + "ĠÎijθή να", + "Ùģ راÙĨ", + "Ùģر اÙĨ", + "Ġ 網", + "Ġç¶ ²", + "ĠпÑĢом иÑģлов", + "ĠBu gün", + "ĠBug ün", + "ìŀ Ķ", + "ĠжÑĸн ок", + "Ġ à¸Ľà¸£à¸°à¹Ģà¸łà¸Ĺ", + "Ġà¸Ľà¸£à¸° à¹Ģà¸łà¸Ĺ", + "ĠвикоÑĢиÑģÑĤов ÑĥваÑĤи", + "ĠТ им", + "ĠТи м", + ") 를", + "еж аÑĤÑĮ", + "Ġs ona", + "Ġso na", + "Ġson a", + "Ø´ÙĨ بÙĩ", + "Ġnich ž", + "åī Ľ", + "ĠÙģ تØŃ", + "ĠÙģت ØŃ", + "ĠÙħÙĤ دÙħ", + "ĠÙħÙĤد Ùħ", + "ĠGüven lik", + "e um", + "eu m", + "ç»ı è¿ĩ", + "è·Ŀ éĽ¢", + "Âł не", + "Ġا صÙĪÙĦ", + "Ġاص ÙĪÙĦ", + "ĠzaÄį átku", + "ิà¹Ģว à¸ĵ", + "Ġà¤ķ à¤Ł", + "Ġk riz", + "Ġkr iz", + "Ġp án", + "Ġpá n", + "ĠбоÑĢ ÑĮ", + "Ġбо ÑĢÑĮ", + "ظ ÙħØ©", + "ظÙħ Ø©", + "Ġê²½ ë¶ģ", + "ĠاÙĦÙĬ ÙħÙĨ", + "ĠاÙĦعرب ÙĬ", + "Ġh lub", + "Ġhl ub", + "Ġch á»Ŀ", + "è¥ ²", + "ëĵľ 리", + "ãĥĸ ãĥª", + "ĠÑģÑĤол ÑĸÑĤÑĤÑı", + "ر بÙĬØ©", + "رب ÙĬØ©", + "Ġ æ°¸", + "Ġæ° ¸", + "Ġê±° ìĿĺ", + "Ġβ αÏĥ", + "Ġβα Ïĥ", + "Ġa rz", + "Ġar z", + "ãĥ¢ ãĥ³", + "ĠÑĢÑĸв енÑĮ", + "ä¸į çŁ¥", + "导 èĩ´", + "ا ÙĬØ´", + "اÙĬ Ø´", + "ĠпÑĢев ÑĭÑĪ", + "Ġп н", + "ĠÎĴ ÏģοÏĩή", + "Ġ 身", + "Ġè º«", + "ĠÄIJ ầu", + "ĠÏĮ μÏīÏĤ", + "j ÃŃž", + "jÃŃ Å¾", + "Ġλ ίγ", + "ĠÑĪк оли", + "ĠÑĪкол и", + "ãģ£ãģ± ãģĦ", + "z dy", + "zd y", + "Ġê³ §", + "t eÅŁ", + "te ÅŁ", + "ÑĢ еÑī", + "ÑĢе Ñī", + "κ ει", + "κε ι", + "sah uje", + "Ġà¤īस स", + "ĠTan rı", + "ä¸į 好", + "éĥ Ń", + "ĠвÑĭ глÑıд", + "Ġç oÄŁ", + "Ġин ÑģÑĤÑĢÑĥменÑĤ", + "r ej", + "re j", + "èĪ Į", + "ãģĭ ãĤīãģªãģĦ", + "ãģĭãĤī ãģªãģĦ", + "ĠнепÑĢи ÑıÑĤ", + "Ġк ÑĢоме", + "ζ η", + "Ġл ог", + "ा वर", + "ाव र", + "ëħķ íķĺìĦ¸ìļĶ", + "ाह रण", + "ाहर ण", + "Ġgüven ilir", + "T ại", + "ĠØ´Ùĩر د", + "ĠØ´Ùĩ رد", + "ĠΤ ε", + "о ÑĢаз", + "оÑĢ аз", + "оÑĢа з", + "Ġl Ãłng", + "ĠlÃł ng", + "ĠlÃłn g", + "ï¼ ©", + "æĬķ 注", + "Ġsiyas et", + "ÐĽ Ñİ", + "Ġt ÅĻet", + "ĠtÅĻ et", + "ĠÏĢÏģÏİ ÏĦη", + "ĠÑĥлÑĭ б", + "ĠL âm", + "ÑĥлÑĮÑĤ а", + "ÑĥлÑĮ ÑĤа", + "åŁº åľ°", + "Ġskup ina", + "æ°¸ ä¹ħ", + "лÑĥ гов", + "лÑĥг ов", + "Ġ ÑĨÑĸй", + "ĠÑĨ Ñĸй", + "ĠÑĨÑĸ й", + "ĠP oh", + "ĠPo h", + "i д", + "ĠTr uy", + "ĠTru y", + "çļĦ ä¸Ģ个", + "çļĦä¸Ģ 个", + "ë²Ħ ìłĦ", + "Ġx ứ", + "à¸ĩ à¹ģรà¸ģ", + "à¸Ħ à¸Ńม", + "Ġelektron ik", + "ĠaÄŁ aç", + "Ġà¤ľ य", + "ĠповеÑĢÑħ ноÑģÑĤÑĮ", + "ĠاÙĩÙħ ÛĮت", + "ли виÑħ", + "лив иÑħ", + "ĠolduÄŁ undan", + "ï¼ī :", + "ÑĨи ÑıÑħ", + "ÑĨиÑı Ñħ", + "製 ä½ľ", + "à¸Ĺ รà¸ĩ", + "à¸Ĺร à¸ĩ", + "ey im", + "eyi m", + "Ġná klad", + "c ilik", + "ci lik", + "cil ik", + "ĠÐĵ лав", + "ĠUy gu", + "ĠÑĢег ÑĥлÑİ", + "à¤Ĥ à¤ľà¤¨", + "à¤Ĥà¤ľ न", + "Ġkayn aģı", + "à¹ī าà¸Ń", + "à¹īา à¸Ń", + "Ġgör mek", + "ĠíĮ ¬", + "Ġ å®Į", + "Ġå® Į", + "Ø« ÙħاÙĨ", + "ĠÑĤак аÑı", + "ĠÑĤа каÑı", + "ĠÑĤака Ñı", + "Ġне из", + "Ġzpráv y", + "ĠاÙĦØ´ خص", + "Ġìĺ¤ íĽĦ", + "ĠاÙĦ طب", + "ĠاÙĦØ· ب", + "atır ım", + "ر ÙĬر", + "رÙĬ ر", + "ĠÙħع ÙħارÛĮ", + "Ãľ RK", + "ÃľR K", + "ĠÒ IJ", + "ĠìĦ ¬", + "æīĭ ãģ«", + "Ġë³Ģ íĻĶ", + "u lace", + "ul ace", + "ula ce", + "Ġs ợ", + "ÑĢ иÑĩ", + "ÑĢи Ñĩ", + "มห าว", + "Ġk â", + "ĠÑģп ÑĢоб", + "Ùĩ رÙĩ", + "Ùĩر Ùĩ", + "ाध न", + "ĠÏĢ αι", + "ĠÏĢα ι", + "ب عد", + "بع د", + "ĠاÙĦ تÙĪ", + "ĠاÙĦت ÙĪ", + "ç»ı çIJĨ", + "p ůsob", + "æ¬ ł", + "ĠзаÑħвоÑĢÑİ Ð²Ð°Ð½Ð½Ñı", + "Ø® Ø©", + "ÚĨ ار", + "Ġboz uk", + "] âĢı", + "ĠSoc orro", + "Ġ hrad", + "Ġh rad", + "Ġhr ad", + "Ġhra d", + "над леж", + "ĠÑĥÑĩаÑģÑĤ ие", + "ĠÑĥÑĩаÑģ ÑĤие", + "ĠÑĥÑĩаÑģÑĤи е", + "å¤ī ãĤı", + "Ġy ans", + "Ġya ns", + "Ġyan s", + "ĠØ¥ ÙĦ", + "Ø® بر", + "خب ر", + "ÑĨиклоп ед", + "ι Ïİν", + "ιÏİ Î½", + "Ïĥ ÏĦÏģο", + "ÏĥÏĦ Ïģο", + "Ġb anka", + "Ġbank a", + "Ġban ka", + "ĠsoÄŁ uk", + "Ġün lü", + "é¢ ľ", + "Ġر Ùģع", + "ĠرÙģ ع", + "çIJ ³", + "ĠÑģоÑģÑĤоÑı нии", + "ν ονÏĦαÏĤ", + "νον ÏĦαÏĤ", + "Ġа кÑĤи", + "Ġак ÑĤи", + "ĠакÑĤ и", + "ĠÏĢολ Ïħ", + "ĠÏĢο λÏħ", + "Ġм оÑĹ", + "Ġмо ÑĹ", + "Ġ æł¼", + "Ġæł ¼", + "ç² Ĺ", + "ĠÑģлÑĥÑĩ ай", + "ĠÑģлÑĥ Ñĩай", + "ĠÑģлÑĥÑĩа й", + "ìĿ¼ ìĹIJ", + "ĠÑĤÑĢеб ÑĥеÑĤ", + "Ġ åıĤèĢĥ", + "ĠåıĤ èĢĥ", + "an gl", + "ang l", + "am ik", + "ami k", + "Ġ Ä°Åŀ", + "ĠÄ° Åŀ", + "æ¹ ¯", + "ĠÄij áo", + "ĠÄijá o", + "ละ à¸Ħร", + "Ñģ о", + "Âł ob", + "Ġk lim", + "Ġkl im", + "Ġkli m", + "èĥ Ĩ", + "ìĥĿ íĻľ", + "ãĥij ãĥ³", + "- ब", + "Ġк ад", + "Ġка д", + "à¹Ī สามารà¸ĸ", + "ĠÙħس ÙĦÙħاÙĨ", + "ç¿ °", + "ĠB ütün", + "ĠK raj", + "ĠKr aj", + "ĠKra j", + "ĠпеÑĢ Ñģп", + "ĠпеÑĢÑģ п", + "Ġener j", + "ãģķ ãģĽãĤĭ", + "ãģķãģĽ ãĤĭ", + "è¾¾ åĪ°", + "ा à¤Ĭ", + "ाठĬ", + "ĠگرÙģ تÙĨ", + "ĠگرÙģت ÙĨ", + "ÑĪ кÑĥ", + "ĠÐŁ ло", + "ÃŃ ny", + "ÃŃn y", + "ĠH ra", + "ĠÚĨ ÙĨاÙĨ", + "Ġ à¹Ħà¸Ĺย", + "Ġà¹Ħ à¸Ĺย", + "vise jÃŃcÃŃ", + "Û³ Û³", + "ĠÐľÑĸнÑĸÑģÑĤ еÑĢ", + "à¹Ĥ à¸Ń", + "ĠدÙĩ ÛĮد", + "æ¯Ķ ä¾ĭ", + "Ïĥι εÏį", + "Ç IJ", + "ãĢģ ãģª", + "Ġत स", + "ĠÄ° t", + "ĠìłĦ ìŁģ", + "à¹Ģ à¸Īร", + "à¹Ģà¸Ī ร", + "Ġelek tr", + "Ġelekt r", + "Ġd Æ°", + "â ĶĶ", + "âĶ Ķ", + "Ġ ìĥ¤", + "Ġìĥ ¤", + "ä» ®", + "à¸ģาร à¹Ģล", + "Ġм ÑĥлÑĮ", + "ĠмÑĥ лÑĮ", + "Ġ 度", + "Ġåº ¦", + "ĠH uyá»ĩn", + "в ен", + "ве н", + "Ġl Æ°á»Ľi", + "Ġprovoz u", + "Ñĥ ÑĢÑĥ", + "ÑĥÑĢ Ñĥ", + "ÑĢ ÑĸÑĹ", + "ÑĢÑĸ ÑĹ", + "Ġçocu ÄŁ", + "ัà¸IJ à¸ļาล", + "ÙĦ ÙĬÙĩ", + "ÙĦÙĬ Ùĩ", + "Ġ[âĢ¦] ...Ċ", + "åİŁ å§ĭ", + "Ġs klad", + "Ġsk lad", + "Ġskl ad", + "Ġسپ تاÙħبر", + "ĠTom áš", + "Ġس ÙĪاÙĦ", + "ĠسÙĪ اÙĦ", + "çģ Ń", + "ãĤĵ ãģ©", + "на знаÑĩ", + "ĠÄij Ä©a", + "ĠudÄĽl at", + "Ġà¤Ĩ दम", + "Ġà¤Ĩद म", + "ï¼ ¬", + "ι νÏĮ", + "ιν ÏĮ", + "iÅŁ leri", + "ÄIJ ây", + "Ġرس اÙĨÙĩ", + "ع اÙħ", + "عا Ùħ", + "ãĥ¼ãĥij ãĥ¼", + "Ġdo prov", + "Ġdop rov", + "ĠмÑĸÑģ ÑĤо", + "ĠмÑĸÑģÑĤ о", + "ï¼ ¥", + "ел Ñĸг", + "елÑĸ г", + "ائ ز", + "ä¸į äºĨ", + "ĠÐIJлекÑģанд ÑĢ", + "ĠвÑĢем ен", + "Ġdve ÅĻe", + "Ġch ảy", + "Ġ otel", + "Ġo tel", + "Ġot el", + "èĤ¯ å®ļ", + "ĠÑĥÑĤ веÑĢжд", + "ĠÐļом п", + "ĠÐļо мп", + "Ġ ëĤĺëĿ¼", + "ĠëĤĺ ëĿ¼", + "ĠвÑĸдбÑĥва ÑĶÑĤÑĮÑģÑı", + "ãĢģ ãĢİ", + "ĠkarÅŁÄ± lık", + "Ġl ẫn", + "çħ Ļ", + "ع کس", + "å¼ ¥", + "Ġte cr", + "Ġtec r", + "Ġne od", + "Ġneo d", + "æĪIJ çĤº", + "åħ¥ ãĤĬ", + "ĠÐŁ ÑĢод", + "ĠÐŁÑĢ од", + "ĠÐŁÑĢо д", + "ĠÏĢ Ïģά", + "ื à¸Ńà¸Ķ", + "ืà¸Ń à¸Ķ", + "ÑģÑĤ аÑĤи", + "ÑģÑĤа ÑĤи", + "е ноÑĹ", + "ен оÑĹ", + "ено ÑĹ", + "Ñĩи Ñģл", + "ÑĩиÑģ л", + "羣 æŃ£", + "Ġ ราà¸Ħ", + "Ġร าà¸Ħ", + "Ñĥ ÑĢе", + "ÑĥÑĢ е", + "ĠØ´ اÙĩد", + "ĠشاÙĩ د", + "ا عر", + "اع ر", + "Ġê²½ íĹĺ", + "à¸Ļ à¸Ħ", + "ãĥį ãĥ«", + "ÏĢοÏħ λοÏĤ", + "Ġम à¤Ī", + "ìĬ¤ ì½Ķ", + "itel né", + "å¼Ģ æĶ¾", + "ç į¨", + "çį ¨", + "ĠpÅĻ ech", + "ĠpÅĻe ch", + "ú Äįast", + "å¢ ĵ", + "Ġ å½±", + "Ġå½ ±", + "ÙĨ ساÙĨ", + "ÙĨس اÙĨ", + "ÙĨسا ÙĨ", + "Ġд вад", + "Ġдв ад", + "Ġдва д", + "Ġи деÑĤ", + "Ġиде ÑĤ", + "Ġид еÑĤ", + "Ġпод клÑİÑĩ", + "Ġподк лÑİÑĩ", + "íĬ¹ë³Ħ ìĭľ", + "B Ãłi", + "Å¡ ku", + "Å¡k u", + "i lerden", + "iler den", + "åıĺ å¾Ĺ", + "ëıĻ ìķĪ", + "Ġpostup nÄĽ", + "ĠиÑĤ ог", + "Ġd ůvodu", + "Ġdůvod u", + "siz lik", + "ÙĦ اÙĨ", + "ÙĦا ÙĨ", + "éĤ£ ç§į", + "ĠÑĩа Ñģа", + "ĠÑĩаÑģ а", + "ä¸į æĸŃ", + "ĠØ®ÛĮ اباÙĨ", + "ĠاÙĦد اخ", + "ĠÑģÑĤоÑĢ Ñĸн", + "Ġì¶ľ ìĹ°", + "æ² Ł", + "Ġh ry", + "Ġhr y", + "ĠG Ãľ", + "ĠìĿ¸ 구", + "l ied", + "li ed", + "lie d", + "Ġع اÙĦÙĬØ©", + "ĠпÑĢед ваÑĢ", + "ан ной", + "åı¥ è¯Ŀ", + "éł ĵ", + "ë°Ķ ìĿ¼", + "ï¼ı /", + "ĠÙħخت صات", + "ëŀ «", + "ĠçalÄ±ÅŁ maları", + "ĠçalÄ±ÅŁmalar ı", + "ĠçalÄ±ÅŁma ları", + "Ġrepublik a", + "Ġ ì³", + "Ġì ³", + "ा )", + "Ġê±´ ê°ķ", + "Ġê³µ ëıĻ", + "èħ ¦", + "ĠìĦľ ë¡ľ", + "ĠпÑĢовод иÑĤÑĮ", + "ĠдейÑģÑĤв иÑĤелÑĮно", + "v eç", + "ve ç", + "Ø« اÙĦ", + "Ġgöster ir", + "ır lar", + "ĠÑģам Ñĭм", + "á lo", + "ál o", + "é¢ij 次", + "à¥Ī à¤Ĺ", + "ا دÙħ", + "اد Ùħ", + "çĮ ª", + "ĠS ản", + "Ġ çı", + "Ġç ı", + "Ġl ety", + "Ġle ty", + "Ġlet y", + "Ġrep ublice", + "Ġrepublic e", + "æĿ¥ èĩª", + "Ġv ết", + "Ġbi rik", + "Ġbir ik", + "Ġbiri k", + "Ġm ekt", + "Ġme kt", + "Ġmek t", + "ĠاÙĦ ÙĪÙģ", + "ĠاÙĦÙĪ Ùģ", + "Ġj ich", + "Ġji ch", + "ä¸Ģ 覧", + "éľ² åĩº", + "ĠH iá»ĩn", + "ĠHi á»ĩn", + "Ġd iá»ĩt", + "ĠÑħ ÑĢиÑģÑĤи", + "åĪļ æīį", + "k ate", + "ka te", + "kat e", + "Ġb azen", + "Ġba zen", + "Ġbaz en", + "ĠurÄįit ÄĽ", + "ĠurÄį itÄĽ", + "Ġumož ÅĪuje", + "é¡ĺ ãģĦ", + "/Q ÄIJ", + "Ġmen Å¡ÃŃ", + "ÏĥκεÏħ ή", + "ĠÑĨеÑĢк ов", + "ĠÑĨеÑĢ ков", + "Ġ è´Ń", + "Ġè´ Ń", + "ок ÑĢаÑĤи", + "ĠÑĢоз к", + "α νοÏħ", + "αν οÏħ", + "Ġyön etic", + "Ġyönet ic", + "Ġol madan", + "Ġolm adan", + "Ġolma dan", + "åĨľ ä¸ļ", + "Ġë°Ķ ëŀĮ", + "çĵ ľ", + "ÑĪ аеÑĤÑģÑı", + "ÑĪа еÑĤÑģÑı", + "ĠÐļ оÑģÑĤ", + "ĠÐļо ÑģÑĤ", + "ĠÙħ عت", + "ĠÙħع ت", + "Ġ à¸ŀล", + "Ġà¸ŀ ล", + "ĠÙħتÙģ اÙĪت", + "ãĤī ãģı", + "èĪ Ĺ", + "Ġتع رÛĮÙģ", + "éīĦ éģĵ", + "Ġpé Äįe", + "ì» µ", + "Ġпод ÑĢаз", + "Ġбан кÑĥ", + "Ġбанк Ñĥ", + "Ä°S Ä°", + "æ¡ IJ", + "à¹Ĥ รà¸Ħ", + "à¹Ĥร à¸Ħ", + "ĠØŃØ° Ùģ", + "Ġ ë£", + "Ġë £", + "л иж", + "ли ж", + "Ġ ìĤ°ìĹħ", + "ĠìĤ° ìĹħ", + "ĠпÑĢи ÑĩинÑĭ", + "ĠпÑĢиÑĩин Ñĭ", + "ĠпÑĢиÑĩ инÑĭ", + "Ġна зна", + "Ġназ на", + "ãĥª ãĤ¹ãĥĪ", + "ãĥªãĤ¹ ãĥĪ", + "ìłķ ë¶Ģ", + "Ïĥ ÏĨα", + "ÏĥÏĨ α", + "å¦ ĥ", + "Ġголов и", + "Ġгол ови", + "ëIJĺìĹĪ ìĬµëĭĪëĭ¤", + "Ġεν ÏĮÏĤ", + "ãĤ¤ ãĥ³ãĤ¿", + "ãĤ¤ãĥ³ ãĤ¿", + "Ġs lun", + "Ġsl un", + "Ġslu n", + "ëł ´", + "ĠÑģÑĥÑīеÑģÑĤв ÑĥеÑĤ", + "ĠÑģÑĥÑīе ÑģÑĤвÑĥеÑĤ", + "з аб", + "за б", + "æĽ´ åĬł", + "Ġблагод аÑĢÑı", + "ĠëĮĢ 구", + "è¾ ħ", + "ห าà¸ģ", + "หา à¸ģ", + "Ġ æİ¥", + "Ġæİ ¥", + "ëĮĢ 를", + "人 ç±»", + "j eme", + "je me", + "jem e", + "åĪĨ å¸ĥ", + "ìŀ¥ ìĿĢ", + "Ġдопом оги", + "ìĻĦ ë£Į", + "o sy", + "os y", + "èĭ± éĽĦ", + "Ġ ÙĦس", + "ĠÙĦ س", + "म ह", + "Ġ à¸ģำ", + "Ġà¸ģ ำ", + "Ġداش تÙĨ", + "Ġداشت ÙĨ", + "Ń ìłľ", + "Ä° ng", + "Ä°n g", + "ĠTh Æ°á»Ŀng", + "íĻ Ģ", + "Ñį ÑĦ", + "íķ´ ìļĶ", + "ĠÐľ Ñĸж", + "ĠÐľÑĸ ж", + "еÑĢÑĸ га", + "еÑĢÑĸг а", + "Ġε á¼", + "à¹ģ สà¸ĩ", + "à¹ģส à¸ĩ", + "ãĥĢ ãĤ¤", + "Ġc esty", + "Ġce sty", + "Ġces ty", + "Ġcest y", + "Ġpr ázd", + "Ġprá zd", + "第 ä¸Ģ次", + "第ä¸Ģ 次", + "ĠÙĩÙħ سر", + "Ġz ev", + "Ġze v", + "Âł E", + "ĠBeled iyesi", + "ĠBelediye si", + "ĠпÑĢоп ози", + "Ġanlay Ä±ÅŁ", + "Âł Ùħ", + "ĠÑĢаÑģÑģ ÑĩиÑĤ", + "ĠاÙĦØ£Ùħ رÙĬÙĥÙĬØ©", + "ĠاÙĦØ£ÙħرÙĬÙĥÙĬ Ø©", + "Ġž ena", + "Ġže na", + "Ġžen a", + "d eniz", + "den iz", + "Ġn oci", + "Ġno ci", + "Ġnoc i", + "Ġst ál", + "ุ ย", + "주 ìĨĮ", + "Ġз еÑĢ", + "Ġ ìĨĮê°ľ", + "ĠìĨĮ ê°ľ", + "Ġkh ẳng", + "at ıcı", + "atı cı", + "ÄĽ ž", + "ĠÑĩ ÑĥÑĤÑĮ", + "Ġc áºŃu", + "ĠاطÙĦ اع", + "æµ ħ", + "Ġst rav", + "Ġstr av", + "Ġstra v", + "ĠSan ayi", + "Ġ طبÙĬ", + "ĠØ· بÙĬ", + "Ġطب ÙĬ", + "Ġhız la", + "Ïİ Î½Î±", + "Ïİν α", + "à¤¿à¤ľ ल", + "ÙħØŃ Ùħد", + "à¸ļ à¸ģ", + "Ġvzdál en", + "ĠÑĤак ими", + "ĠÑĤа кими", + "ĠÑĤаким и", + "ãĢĤ ãģĿãģĹãģ¦", + "Ġka lp", + "Ġkal p", + "Ġкож ного", + "Ðł µ", + "ÙĦع اب", + "ĠÙħ ÙĪÙĨ", + "ĠÙħÙĪ ÙĨ", + "ĠìĿ¼ ìĿĦ", + "Ġ ë°ĶìĿ´", + "Ġë°Ķ ìĿ´", + "Ġme kan", + "Ġmek an", + "ĠجاÙħ ع", + "Ġجا Ùħع", + "ĠÙĨ Ùģت", + "ĠÙĨÙģ ت", + "ĠاÙĦ سÙħ", + "ĠاÙĦس Ùħ", + "л ÑĭÑħ", + "лÑĭ Ñħ", + "èĥĮ æĻ¯", + "Ġê²ĥ ëıĦ", + "ĠìĤ´ ìķĦ", + "y dı", + "yd ı", + "Ġна веÑĢ", + "Ġнав еÑĢ", + "åŃIJ ãģ¯", + "l uluk", + "lu luk", + "Ġhá»Ĺ n", + "Ġ Ø´Ùģ", + "ĠØ´ Ùģ", + "Ġع ÙĦت", + "ĠعÙĦ ت", + "à¸Ħร าม", + "ĠÎļ ÏįÏĢ", + "Ġà¹Ģม ษายà¸Ļ", + "ÙĨد ÙĤ", + "ĠÑĥ ÑģÑĤÑĢа", + "ĠÑĥÑģÑĤ ÑĢа", + "ĠÑĥÑģ ÑĤÑĢа", + "ĠÎĵ εν", + "ĠÐĨ ван", + "ĠP hong", + "ĠPh ong", + "ĠPhon g", + "家 çļĦ", + "ĠÐIJ лекÑģ", + "ĠÐIJле кÑģ", + "ĠÐIJл екÑģ", + "Ġзб еÑĢÑĸг", + "ĠÅŁark ı", + "ĠÅŁar kı", + "ĠظرÙģ ÛĮت", + "ĠÙħ عÙĨÛĮ", + "ĠÙħع ÙĨÛĮ", + "ĠÙħعÙĨ ÛĮ", + "Ġ лов", + "Ġл ов", + "ĠìĤ ¶", + "èħ IJ", + "Ġ å¯Į", + "Ġå¯ Į", + "E RG", + "ER G", + "ĠÑģÑĤо имоÑģÑĤÑĮ", + "ÅĻ et", + "ÅĻe t", + "à¥ī य", + "à¹Ī าร", + "à¹Īา ร", + "ĠارÙĪÙ¾ ا", + "Ġб ÑĢоÑģ", + "ĠоÑĤ ноÑģÑıÑĤ", + "ĠоÑĤноÑģ ÑıÑĤ", + "ĠÎŁ κ", + "ÑĨÑĮ кий", + "ÏĬ κ", + "ãģĤãĤĬ ãģ¾ãģĽãĤĵ", + "ĠÑĥ ник", + "ĠÄij iá»ĥn", + "ĠÄiji á»ĥn", + "Ġvý zkum", + "Ġh ứ", + "Ġhá» ©", + "Ġ ÙĪات", + "ĠÙĪ ات", + "ĠÙĪا ت", + "Ġ å¹³æĸ¹", + "Ġå¹³ æĸ¹", + "Ïħ μ", + "ãĤĴ 使", + "εί ÏĦαι", + "两 人", + "Ġ åĮ»", + "ĠåĮ »", + "ÑĢаÑĤ иÑĤÑĮ", + "ÑĢаÑĤи ÑĤÑĮ", + "ĠاÙĦ اÙĨت", + "ĠاÙĦاÙĨ ت", + "ãģ® 人", + "ر Ø´", + "ĠТ ÑĥÑĢ", + "r nÄĽ", + "rn ÄĽ", + "天 天", + "ม าร", + "มา ร", + "Ġort alama", + "Ġorta lama", + "ĠпеÑĢе пиÑģ", + "ĠпеÑĢеп иÑģ", + "ĠìĥĿ ìĤ°", + "å¿ Ĩ", + "í ĩ´", + "ï¼Į 该", + "éĮ ¢", + "ÏĢα ίδ", + "ĠмеÑĢ опÑĢи", + "Ġг ÑĢав", + "ĠгÑĢа в", + "ĠгÑĢ ав", + "ÃĶ ng", + "Ġ æ¤", + "Ġæ ¤", + "ĠاÙĦد ÙĪÙĦØ©", + "ĠاÙĦدÙĪÙĦ Ø©", + "Ġ оÑģÑĮ", + "Ġо ÑģÑĮ", + "ĠоÑģ ÑĮ", + "å¥ Ķ", + "Ġgüven li", + "íķĺ ìĭł", + "Ġ éĬ", + "Ġé Ĭ", + "éŁ³ æ¨Ĥ", + "Ġmed ya", + "Ġب ÙĨا", + "ĠبÙĨ ا", + "а ма", + "ам а", + "Ġ ãĤŃãĥ£", + "ĠãĤŃ ãĥ£", + "èĹ ¥", + "l arım", + "lar ım", + "ları m", + "ĠT iếng", + "iyor lar", + "ï¼ ¢", + "æĶ Ŀ", + "Ñĸй ÑģÑĮкоÑĹ", + "Ġyet iÅŁtir", + "ĠyetiÅŁ tir", + "ĠÙ¾ سر", + "Ġپس ر", + "ãĤī ãģĹ", + " ļ", + "ìĥ ¤", + "à¸Ķ าห", + "à¸Ķา ห", + "ĠتØŃص ÛĮÙĦ", + "Ġб енз", + "éģ £", + "Ġнаб лÑİ", + "ä½ĵ ç³»", + "ãĥ¯ ãĤ¤ãĥĪ", + "Âł ÂłĠ", + "³³ Ġ", + "书 è®°", + "ĠMü hendis", + "p lor", + "pl or", + "l az", + "la z", + "лÑı ли", + "Ġpom áh", + "Ġб лиж", + "Ġбл иж", + "Ġбли ж", + "ĠÑĩиÑģ ла", + "Ġubyt ovánÃŃ", + "ÑĢаÑĤ но", + "Ġtr Äĥm", + "Ġاب راÙĩ", + "át ka", + "Ġiç indeki", + "Ġiçin deki", + "Ġiçinde ki", + "ั à¸ļà¸Ļ", + "ัà¸ļ à¸Ļ", + "ĠاÙħ ÛĮد", + "n ave", + "na ve", + "nav e", + "e cut", + "ec ut", + "å°± åľ¨", + "Ġt radi", + "Ġtr adi", + "Ġtrad i", + "Ġtra di", + "Ø· ÙĦÙĤ", + "Ø·ÙĦ ÙĤ", + "ãĤ¦ ãĤ©", + "Ġkhu ôn", + "ìĬ¤ ë¡ľ", + "ÏĦ ÎŃÏģα", + "ÏĦÎŃ Ïģα", + "ĠÏĥ κο", + "ĠÏĥκ ο", + "ë§ Ľ", + "ĠÙģ ÙĨÛĮ", + "ĠÙģÙĨ ÛĮ", + "à¹Į à¹Ģà¸ŀ", + "ĠاÙĦع ظ", + "Ġth ôn", + "기 ìĿĺ", + "Ġภ¿", + "Ñĥ ÑİÑĤÑģÑı", + "ÑĥÑİÑĤ ÑģÑı", + "ĠÙħ کاÙĨ", + "ĠÙħÚ© اÙĨ", + "Ġ âĹİ", + "ĠâĹ İ", + "Ġ çľģ", + "Ġçľ ģ", + "Ġ åį¡", + "Ġåį ¡", + "ĠпеÑĢ ÑĪий", + "ĠпеÑĢÑĪ ий", + "ĠíĽĦ ë³´", + "Ġآر اÙħ", + "ãģĮ ãģĦ", + "ย าà¸Ļ", + "ยา à¸Ļ", + "μ ει", + "με ι", + "ĠM áy", + "Ġz ů", + "Ġpodp oru", + "Ġpodpor u", + "ì» ¨", + "Ñģ ÑĤÑĢи", + "ÑģÑĤ ÑĢи", + "ÑģÑĤÑĢ и", + "ÏĢ ÏĦÏīÏĥη", + "Ф ÐĽ", + "åĵª éĩĮ", + "ĠпеÑĢв ÑĥÑİ", + "Ġyer inde", + "Ġyeri nde", + "ĠزÛĮ با", + "ĠزÛĮب ا", + "Ġodst ran", + "à¥Ģ à¤Ĺ", + "ĠÑĢÑĸз нÑĸ", + "Ïģ ηÏĥη", + "Ïģη Ïĥη", + "âĢĮاÙĦÙħÙĦ ÙĦÛĮ", + "ع اد", + "عا د", + "à¥įप ष", + "ÑŁ N", + "ï½ Ľ", + "ãĥ¼ ãĥľ", + "ãĥ¼ãĥ ľ", + "è´Ń ä¹°", + "ĠìĿ¸ê¸° ê¸Ģ", + "ĠÙħÛĮ Ø´ÙĪد", + "ĠбезопаÑģ ноÑģÑĤи", + "ĠνεÏĨ οκ", + "ãģ« ãģ¨", + "ĠÑĨеÑĢк ви", + "ت Ùĥ", + "ĠH Ãłng", + "ĠHÃł ng", + "ĠHÃłn g", + "ĠÙĦ ÙĦس", + "ĠÙĦÙĦ س", + "ĠνεÏĨοκ άλÏħÏĪηÏĤ", + "r aman", + "ra man", + "ram an", + "rama n", + "Ġvy vol", + "n iÄį", + "ni Äį", + "ر اÙĨÙĩ", + "راÙĨ Ùĩ", + "را ÙĨÙĩ", + "Ġp eÅŁ", + "Ġpe ÅŁ", + "ãĥ« ãĤ¯", + "å´ ĩ", + "Ġim kân", + "åĮ» çĸĹ", + "Ġप à¥Ŀ", + "άν νηÏĤ", + "Ġ جÛĮ", + "Ġج ÛĮ", + "Ġp roje", + "Ġpro je", + "Ġpr oje", + "Ġproj e", + "Ġül kenin", + "Ġülk enin", + "Ġülke nin", + "ĠK ew", + "ĠKe w", + "ĠاÙĦÙħ Ùģ", + "Ø£ Ùĥ", + "çĻº 表", + "Ġ δÏħ", + "Ġδ Ïħ", + "Ġ åĽ½å®¶", + "ĠåĽ½ 家", + "ĠKiÅŁ isel", + "ãĥ³ ãĤ¬", + "Ġzpráv a", + "V iá»ĩc", + "e rif", + "er if", + "eri f", + "Ġstrán ky", + "éļ ł", + "è¼ ķ", + "к оз", + "ко з", + "Ġस à¤ľ", + "Ùĩد اÙģ", + "l oub", + "lo ub", + "lou b", + "à¸łà¸²à¸ŀ ยà¸Ļà¸ķร", + "Ġíķł ìĿ¸", + "ĠÄIJ Ãło", + "ĠÄIJÃł o", + "ĠÙĨاØŃ ÛĮÙĩ", + "(= )", + "ĠÅŀ ampiyon", + "Ġp iÅŁ", + "Ġpi ÅŁ", + "Ġ Ø°Ùĩ", + "ĠØ° Ùĩ", + "ॠ¯", + "ĠÑģÑĢед ÑģÑĤво", + "ĠÑģÑĢедÑģÑĤв о", + "Ġ à¹Ģวลา", + "Ġà¹Ģว ลา", + "ĠÑĩ Ñĥж", + "Ġver ileri", + "Ġveri leri", + "Ġveriler i", + "ĠÚ© ارت", + "Ġکار ت", + "а ви", + "ав и", + "Ġà¤ķर व", + "Ġres tau", + "Ġrest au", + "Ġresta u", + "ê°ľ ìĽĶ", + "Ġм иÑĢов", + "Ġми ÑĢов", + "ĠмиÑĢ ов", + "ì° ®", + "ĠnÄĽjak ý", + "Ġses siz", + "Ġsess iz", + "اء ات", + "ĠÐĹ Ð°Ñħ", + "ĠÐĹа Ñħ", + "Ñı ÑīиÑħ", + "ÑıÑī иÑħ", + "п ÑĢ", + "Ġпод алÑĮ", + "Ġпода лÑĮ", + "ĠопÑĢедел иÑĤÑĮ", + "ॠŃ", + "Ġ رÙģ", + "Ġر Ùģ", + "幸 ç¦ı", + "à »", + "Ġ vÄĽdom", + "ĠvÄĽ dom", + "ĠvÄĽd om", + "ĠÑģвид еÑĤелÑĮ", + "ĠÎĵ οÏħ", + "ılıģı yla", + "ılıģ ıyla", + "çĻ» éĮ²", + "Ġä¸ĭ è·Į", + "Ġп лÑİ", + "Ġпл Ñİ", + "н од", + "но д", + "ĠØ£ جÙĦ", + "Ġأج ÙĦ", + "Ġà¤ķ थ", + "éĥ½ ä¸į", + "Ġs ene", + "Ġse ne", + "Ġsen e", + "Ġp ÄĽ", + "è¨Ī åĬĥ", + "Ġа Ñĥд", + "Ġод ном", + "Ġодно м", + "Ġ ä¸ĩåħĥ", + "Ġä¸ĩ åħĥ", + "ĠÙĪ Ùħا", + "ĠÙĪÙħ ا", + "ĠÐĶ ÑĢÑĥг", + "èµ· ãģĵ", + "в аÑİÑĤÑģÑı", + "ва ÑİÑĤÑģÑı", + "ваÑİÑĤ ÑģÑı", + "л аÑĤÑĥ", + "ла ÑĤÑĥ", + "лаÑĤ Ñĥ", + "Ġ تÙĪÙĨ", + "Ġت ÙĪÙĨ", + "ĠتÙĪ ÙĨ", + "Ñī аÑı", + "Ñīа Ñı", + "ή λ", + "ĠÐŁ ÑĢа", + "ĠÐŁÑĢ а", + "Ġاست رات", + "Ġاستر ات", + "ิà¸Ļ à¹Ģà¸Ķ", + "à¥įà¤Ĺ त", + "Âł з", + "Ġп олоÑĤ", + "Ġпо лоÑĤ", + "Ġпол оÑĤ", + "æ® ĸ", + "æ¡ Ĩ", + "ĠS istem", + "ĠSi stem", + "ĠSist em", + "Ġr uku", + "Ġru ku", + "Ġruk u", + "ãĥĥ ãĤ«ãĥ¼", + "ĠобÑıз ан", + "Ġkö ÅŁ", + "Ġad ını", + "Ø´Ùħ اÙĦÛĮ", + "na ÄįenÃŃ", + "naÄį enÃŃ", + "Ġ .ï¼ı", + "Ġ. ï¼ı", + "Ġ å®ĺ", + "Ġå® ĺ", + "Ġtoplum sal", + "èª ¤", + "ĠبÙĩ بÙĪد", + "ÑģÑĤв еннаÑı", + "ÑģÑĤвен наÑı", + "ĠØ¢ Ù¾", + "ĠجÙĦ سÙĩ", + "ĠجÙĦس Ùĩ", + "ãĢĢ ï½", + "åĵ Ń", + "æīĢ å±ŀ", + "æĴ ®", + "ì¢ Ģ", + "Ġ ει", + "Ġε ι", + "ì¹ĺ 를", + "Ġ ê³¼ìłķ", + "Ġê³¼ ìłķ", + "u uml", + "uum l", + "uu ml", + "δ ά", + "Ġ زد", + "Ġز د", + "ìĽIJ ìĿĦ", + "ĠvÄĽ cÃŃ", + "ĠvÄĽc ÃŃ", + "د Ø«", + "Ġs anki", + "Ġsan ki", + "Ġsank i", + "åĥı æĺ¯", + "л аÑĢа", + "ла ÑĢа", + "ìĤ¬ ìĿ´", + "ãĤı ãĤĮãģŁ", + "ãĤıãĤĮ ãģŁ", + "ĠÄij ón", + "ĠÄijó n", + "åIJ¯ åĬ¨", + "Ġgi Ãłnh", + "ĠgiÃł nh", + "Ġkır mızı", + "Ø® Ùħ", + "æIJ į", + "åĪĩ ãĤĬ", + "ãĤµ ãĥ¼ãĥĵãĤ¹", + "Ùĩ ار", + "Ùĩا ر", + "Ø° Ùĥر", + "о ÑĢоз", + "оÑĢ оз", + "оÑĢо з", + "à¥Īà¤Ĥ ।ĊĊ", + "à¥Īà¤Ĥ। ĊĊ", + "à¥Īà¤Ĥ।Ċ Ċ", + "ĠíĻĪ íİĺìĿ´ì§Ģ", + "ĠÙĥ بÙĬرة", + "ĠÙĥبÙĬر Ø©", + "н ина", + "ни на", + "нин а", + "íķĺ ìļ°", + "å¼ķçĶ¨ é¢ij次", + "ॠ®", + "ĠбаÑĤÑĮ кÑĸв", + "à¸Ł à¸Ńร", + "ี .", + "ìłĿ íĬ¸", + "éĺħ读 次æķ°", + "Ġit ir", + "Ġi tir", + "ÑĪ ин", + "ÑĪи н", + "ĠV áºŃy", + "çĤ ®", + "ла год", + "лаг од", + "Ø´ÙĨ اس", + "á» IJ", + "ĠÑı год", + "Ġì¤ij ìķĻ", + "ر ÙĬØ·", + "رÙĬ Ø·", + "ĠìĪĺ íĸī", + "Ġ ä¸ĢèĪ¬", + "Ġä¸Ģ èĪ¬", + "ĠÑħви лин", + "ĠÐľÐ¾Ð¶ но", + "ĠнаÑĩ але", + "Ġод нов", + "Ġодно в", + "ĠÃľ ç", + "ÑĨион нÑĭй", + "Ġ ìļķ", + "Ġìļ ķ", + "æ¼ Ĥ", + "å² ³", + "ت دÙī", + "تد Ùī", + "κ ηÏĤ", + "κη ÏĤ", + "âĢĻ nda", + "âĢĻn da", + "ï¼IJ ï¼IJ", + "èª ī", + "é§ħ å¾ĴæŃ©", + "ĠÙģرز ÙĨد", + "åħ¬ è·¯", + "α ÏĥίαÏĤ", + "αÏĥ ίαÏĤ", + "αÏĥία ÏĤ", + "าà¸ĵ าà¸Ī", + "ëij ¥", + "Ġ ÏĢοι", + "ĠÏĢ οι", + "ĠÏĢο ι", + "Ġب داÙĨ", + "Ġبد اÙĨ", + "к ап", + "ка п", + "ĠìŀĪ ëĬĶëį°", + "ĠìŀĪëĬĶ ëį°", + "ï¼Į æѤ", + "à¸Ľà¸£à¸°à¹Ĥย à¸Ĭà¸Ļ", + "ĠÚ©Ø´ÙĪر ÙĩاÛĮ", + "ุ ส", + "ãģ¹ ãģį", + "ĠÑģам Ñĭй", + "Ġп лÑı", + "Ġпл Ñı", + "Ġб ед", + "人 æīį", + "ส หร", + "ู à¸ķ", + "Ġkullan ımı", + "Ġkullanım ı", + "íķĻ ëħĦ", + "æ²» çĸĹ", + "ãĢĤ ä¸įè¿ĩ", + "ãĢĤä¸į è¿ĩ", + "æ£ ļ", + "ëĤ¨ ëıĦ", + "ĠØ¢ تش", + "Ïĩ ÎŃÏĤ", + "Ġfunk ci", + "Ġfunkc i", + "н ообÑĢаз", + "но обÑĢаз", + "à¥ĭ फ", + "Ġk aps", + "Ġka ps", + "Ġkap s", + "าษ à¸İ", + "( ع", + "ï¼Į åĬł", + "à¹Ĭ à¸ģ", + "ĠÙĩ Ø´", + "Ġدر ÙĪÙĨ", + "Ġ меÑĩ", + "Ġм еÑĩ", + "ĠпÑĢеж де", + "à¹Ī ย", + "Ġار شد", + "า à¹Ģล", + "æ¯Ķ è¼ĥ", + "ĠØ° کر", + "Ġ æĿ¡", + "ĠæĿ ¡", + "Ð Ĭ", + "Ñĥ кÑĢаÑĹн", + "ÙĬÙĨ ات", + "ÙĬÙĨا ت", + "ì¢ ĭ", + "д иÑı", + "ди Ñı", + "ÏĦ Ïģι", + "ÏĦÏģ ι", + "ĠÐļ аз", + "ĠÐļа з", + "ÙĤ ÙĦاÙĦ", + "ÙĤÙĦ اÙĦ", + "_ ,,", + "_, ,", + "ĠÚĨ ت", + "ĠìĿ¼ ìłķ", + "ĠÐŁ ÑĢоÑĦ", + "ĠÐŁÑĢ оÑĦ", + "ĠÐŁÑĢо ÑĦ", + "æ³ Ľ", + "Ġdruh ý", + "Ñĩ Ñĥк", + "ÑĩÑĥ к", + "l edik", + "le dik", + "led ik", + "ledi k", + "Ġhey ec", + "Ñĭ вал", + "Ñĭв ал", + "Ñĭва л", + "ĠD üny", + "ĠDün y", + "Ġ çĻº", + "ĠçĻ º", + "ĠpÅĻ átel", + "β άλ", + "βά λ", + "Ġ غر", + "Ġغ ر", + "ëĭ¨ ì²´", + "ìĽ¨ ëĶĶìĭľ", + "ÑĢаÑī ениÑı", + "н ÑĨиклопед", + "Ġpodnik atel", + "Ġìĭł ìŀħ", + "ĠÙģر Ø¢", + "и лиÑģÑı", + "или ÑģÑı", + "Ġol umlu", + "à¥įष मत", + "ĠÙħت خصص", + "й ом", + "ؤ اÙĦ", + "ĠÐĿ аÑĤ", + "ĠÐĿа ÑĤ", + "ìĺ¤ ëĬĶ", + "ĠMüdür lÃ¼ÄŁÃ¼", + "ĠH Ãłnh", + "ĠHÃł nh", + "ĠHÃłn h", + "Ġس ابÙĤ", + "ï¼ī çļĦ", + "ĠQu ý", + "lád ánÃŃ", + "ládá nÃŃ", + "Ġ ìļ´ëıĻ", + "Ġìļ´ ëıĻ", + "ĠÐĺ Ñħ", + "è« ¾", + "lıģ ının", + "lıģını n", + "lıģın ın", + "l il", + "li l", + "u Äį", + "ĠÑĩем пÑĸон", + "ÑĤ ож", + "ÑĤо ж", + "Ġ ä½Ľ", + "ни ÑĨе", + "ниÑĨ е", + "ĠпеÑĢв ого", + "Ġ Ñģом", + "ĠÑģ ом", + "ĠÑģо м", + "Ïĩ Ïİ", + "ÅĻ ik", + "ÅĻi k", + "иÑĤелÑĮ ÑģÑĤва", + "ĠÄ° ki", + "Ġask eri", + "Ġasker i", + "c isi", + "ci si", + "cis i", + "Ġjed nÃŃm", + "Ġjedn ÃŃm", + "Ġsta nice", + "Ġstan ice", + "èĤ¡ 票", + "à¸ľ ม", + "T ừ", + "Å¡ ak", + "ÏĦ ία", + "ÏĦί α", + "м ами", + "ма ми", + "ãģĮ åĩº", + "μο ί", + "м аÑĶ", + "ма ÑĶ", + "ëł¥ ìĿ´", + "ãĤĦ ãģ£ãģ¦", + "Ġ å¼µ", + "Ġå¼ µ", + "ØĮ Ċ", + "Ġ »Ċ", + "Ġ» Ċ", + "ا جات", + "اج ات", + "á½ ³", + "æĻĤ ãģ®", + "Ġп окол", + "Ġпо кол", + "Ġпок ол", + "ÑĸÑĤ еÑĤ", + "Ġíķ´ ê²°", + "Ġde dim", + "Ġded im", + "Ġdedi m", + "ĠÑĤ веÑĢд", + "Ġжен Ñīина", + "ĠженÑīин а", + "ед ини", + "еди ни", + "един и", + "ĠÙ¾ ÛĮÚ©", + "ĠÙ¾ÛĮ Ú©", + "iver site", + "ivers ite", + "iversit e", + "ĠآسÛĮ اب", + "ĠÑħаÑĢакÑĤеÑĢиÑģÑĤи ки", + "ĠØ£ÙĨ Ùĩا", + "ĠØ£ÙĨÙĩ ا", + "ĠÑĥкÑĢаÑĹн ÑģÑĮкоÑĹ", + "ĠاختÙĦ اÙģ", + "Ġاخت ÙĦاÙģ", + "Ġt ez", + "Ġte z", + "Ïģ εÏħ", + "Ïģε Ïħ", + "Ġkon umu", + "Ġkonu mu", + "Ġkonum u", + "ĠÑĤеÑħ нÑĸ", + "м Ñĸв", + "мÑĸ в", + "èĬ ¯", + "ĠÏĥ ελ", + "ĠÏĥε λ", + "Ä ¢", + "μ ιÏĥ", + "μι Ïĥ", + "ี à¹īĊ", + "ีà¹ī Ċ", + "Ġm ne", + "Ġmn e", + "ĠоÑĤв еÑĩ", + "ĠÎ ī", + "Ġ éĩİ", + "Ġéĩ İ", + "Ġg ấp", + "ĠпÑĢодÑĥк ÑĤÑĭ", + "ĠпÑĢодÑĥкÑĤ Ñĭ", + "ĠС ÑĢед", + "Ñĸл лÑı", + "à¸ļ à¸Ńà¸ģ", + "ĠtÅĻÃŃ dy", + "Ġth á»ķ", + "Ġthá» ķ", + "ãĥĩãĤ£ ãĤ¢", + "ÏĢοι η", + "ν ει", + "νε ι", + "æĪij们 çļĦ", + "Ġprofes yonel", + "ĠRa kou", + "ĠRak ou", + "Ġвид но", + "Ġz by", + "Ġzb y", + "ĠØŃ Ø§ÙĦÛĮ", + "ĠØŃاÙĦ ÛĮ", + "Ġ é£Ł", + "Ġé£ Ł", + "ĠL Ãłm", + "ĠÚ¯ ست", + "ĠТ ип", + "ĠТи п", + "θ ι", + "á vis", + "áv is", + "ÙIJ ب", + "åı¯èĥ½ æĢ§", + "ĠÑģем ей", + "ãĤī ãĤĮãģ¦ãģĦãĤĭ", + "ãĤīãĤĮ ãģ¦ãģĦãĤĭ", + "ãĤīãĤĮãģ¦ ãģĦãĤĭ", + "ìĥģ íĴĪ", + "Ġ οÏħ", + "Ġο Ïħ", + "Ġà¤ħà¤Ĺ स", + "о лом", + "ол ом", + "оло м", + "γ ον", + "γο ν", + "ĠÑģв ÑıÑī", + "ĠÑģвÑı Ñī", + "æĵ ¦", + "Ïĥ ÏĦηκε", + "ÏĥÏĦη κε", + "èĢħ çļĦ", + "- à¤ķ", + "ÑĤ ии", + "ÑĤи и", + "ĠвизнаÑĩ еннÑı", + "åıij åĩº", + "д аÑħ", + "да Ñħ", + "ĠмоÑĢ Ñı", + "Ġмо ÑĢÑı", + "æī¾ åĪ°", + "ÙĦ ÙĪب", + "ÙĦÙĪ ب", + "èĬ Ļ", + "ĠÑĦак ÑĤ", + "æ¯į 亲", + "id lo", + "idl o", + "ĠSt ad", + "ĠSta d", + "Ñį й", + "ìĽIJ ìĿ´", + "à¤ı न", + "æķ´ 个", + "Ġf ık", + "ĠÙħ ات", + "ĠÙħا ت", + "ÏĢ ον", + "ÏĢο ν", + "Ġê²½ 기ëıĦ", + "Ġ경기 ëıĦ", + "Ġα δ", + "Ġvz pom", + "Ġn á»ĵi", + "ĠÙĨÙĤ اط", + "ожд ение", + "Ġз алÑĸз", + "Ġзал Ñĸз", + "Ġr ủi", + "è¾ °", + ".:.:.:.:.:.:.:.: .:.:.:.:.:.:.:.:", + "ĠM Ãľ", + "Ġk ari", + "Ġka ri", + "Ġkar i", + "ĠÑģ обÑĭ", + "ĠÑģо бÑĭ", + "ĠÑģоб Ñĭ", + "ìĸ´ ì§Ħ", + "ر ÙĬس", + "رÙĬ س", + "u bu", + "ub u", + "ĠØ® ÙĦÙģ", + "ĠØ®ÙĦ Ùģ", + "ظٹ Ø·", + "æĿ ī", + "Ġ æĻ®éĢļ", + "ĠæĻ® éĢļ", + "ĠÙħÙĪاط ÙĨØ©", + "ĠÑģÑĤ анÑĥ", + "ĠÑģÑĤан Ñĥ", + "ĠÑģÑĤа нÑĥ", + "Ġê·¸ëħĢ ìĿĺ", + "ĠÙĦ Ùĥرة", + "ĠÙĦÙĥ رة", + "Ġo sm", + "Ġos m", + "ĠÑĥ ÑĢож", + "е га", + "ег а", + "Ġf else", + "Ġfe lse", + "Ġfel se", + "æĢĿ èĢĥ", + "ãĢĮ ãģĪ", + "Ġн овиÑħ", + "Ġнов иÑħ", + "Ġно виÑħ", + "๠IJ", + "ü ml", + "üm l", + "ĠíĶ¼ íķ´", + "ìĿ¼ ë°ĺ", + "Ġtür ü", + "ĠмÑĸ ÑģÑĤÑĸ", + "ĠмÑĸÑģ ÑĤÑĸ", + "ĠмÑĸÑģÑĤ Ñĸ", + "Ġkažd é", + "ĠÙħس جد", + "ấ c", + "ĠÙģ Ú©ÛĮ", + "Ġ yasal", + "Ġy asal", + "Ġya sal", + "Ġyas al", + "å°± ç®Ĺ", + "ĠоблиÑĩ ÑĩÑı", + "ĠÙĦ دÙĬ", + "ĠÙĦد ÙĬ", + "ا بات", + "اب ات", + "ĠÑģп аÑģ", + "êµ° ìļĶ", + "Ġп ад", + "Ġпа д", + "Ġб ÑĢаÑĤ", + "ĠбÑĢа ÑĤ", + "éĩį 大", + "Ġdüzen lenen", + "G ün", + "Ġaplik ace", + "à¸Ń ห", + "Ġ çħ", + "Ġç ħ", + "ĠÑģоÑģÑĤ оиÑĤ", + "è¯Ħ ä»·", + "ĠD uy", + "ĠDu y", + "Ø· اÙĤ", + "ĠпÑĢид еÑĤÑģÑı", + "Ġt olik", + "Ġto lik", + "Ġtol ik", + "Ġob rov", + "Ġobr ov", + "ĠpÅĻip oj", + "Ġ Ä±ÅŁÄ±", + "Ġı ÅŁÄ±", + "Ú¯ ÙĪÛĮ", + "Ú¯ÙĪ ÛĮ", + "æľŁ å¾ħ", + "ип лом", + "Ġ ince", + "Ġin ce", + "Ġi nce", + "Ġinc e", + "ĠС об", + "ĠСо б", + "ен ÑĮÑİ", + "енÑĮ Ñİ", + "è§Ĵ èī²", + "Ġ à¸ķร", + "Ġà¸ķ ร", + "Ġb ại", + "Ġê°ĢëĬ¥ íķľ", + "ĠblÃŃ zk", + "Ġt ách", + "Ġtá ch", + "Ġtác h", + "Ġвид Ñĭ", + "Ġви дÑĭ", + "и Ñĩна", + "иÑĩ на", + "Ġvyž ad", + "ĠìĨIJ ìĿĦ", + "ĠÐĿ ÑĸмеÑĩ", + "ĠÐĿÑĸ меÑĩ", + "åŁº äºİ", + "ĠÐļ ÑĢи", + "Ġعز ÛĮز", + "t iler", + "til er", + "ti ler", + "tile r", + "е вÑĸ", + "ев Ñĸ", + "Ġmož nosti", + "Ġmožnost i", + "ب از", + "با ز", + "ĠìĤ¬ ë§Ŀ", + "Ġz ÅĻejmÄĽ", + "íĹ ¤", + "Ġürün leri", + "ĠÎł λη", + "а ки", + "ак и", + "ãĤĴ éĸĭ", + "a nou", + "an ou", + "ano u", + "åĽ½ ãģ®", + "ĠyaÅŁ anan", + "ĠyaÅŁan an", + "ĠÑģ евеÑĢ", + "Ġ æ©Ł", + "Ġæ© Ł", + "มาà¸ģ มาย", + "Ġíijľ íĺĦ", + "ร ส", + "Ġض ربÙĩ", + "Ġضر بÙĩ", + "ĠE vet", + "ĠEv et", + "ĠEve t", + "æĨ ¶", + "Ġد ÙĤÛĮÙĤ", + "ĠدÙĤÛĮ ÙĤ", + "Ġвозник нов", + "ìľł 머", + "Ġíijľ ìĭľ", + "ÛĮ Ø´ÙĨ", + "ÛĮØ´ ÙĨ", + "ãĥĹ ãĥ©", + "ÑĤ Ñİ", + "ÙĪ سÛĮ", + "ÙĪس ÛĮ", + ") ìĿ´", + "è¯ģ æĺİ", + "ãģ§ãģį ãģ¾ãģĻ", + "ìĪĺ ìĿĺ", + "çĸ Ĩ", + "ĠÙħ ÙģÙĩÙĪÙħ", + "оÑĩ аÑĤкÑĥ", + "ाल à¤ķ", + "æ¡ Ĥ", + "ĠоÑħ оÑĢони", + "ĠارزÛĮ ابÛĮ", + "Ġìµľ ëĮĢ", + "Ġtho ải", + "ĠЦенÑĤ ÑĢалÑĮ", + "Ġ çķĻ", + "Ġçķ Ļ", + "à¸Ľà¸£à¸° à¹Ģà¸łà¸Ĺ", + "æµ· å¤ĸ", + "ĠÅŀ u", + "íĻľ ëıĻ", + "ĠdvÄĽ ma", + "istrov stvÃŃ", + "Ġarac ılıģıyla", + "Ġtr á»Ļn", + "» :", + "íĭ ±", + "ĠÙĦÛĮ Ú¯", + ". Ðļ", + "ĠÙħÙĤ اÛĮسÙĩ", + "Ġв мÑĸ", + "ر ÙĪب", + "رÙĪ ب", + "ĠاÙĦ Ø´Ùħ", + "ĠاÙĦØ´ Ùħ", + "Ġden nÄĽ", + "Ġdenn ÄĽ", + "Ñĥ Ñĩа", + "ÑĥÑĩ а", + "åħ ¹", + "Ñī им", + "Ñīи м", + "ĠíĬ¹ íŀĪ", + "ĠاستاÙĨد ارد", + "à¥Ģ ध", + "ãĤ¸ ãĤ¢", + "à¹ĩ à¹ĩ", + "иÑģ Ñģ", + "Ġkazan ç", + "ĠzÃŃsk al", + "åĽŀ æĿ¥", + "Ġп ÑıÑĤÑĮ", + "ĠпÑıÑĤ ÑĮ", + "ĠÄij ãi", + "ĠÄijã i", + "Ġ ÙĪرد", + "ĠÙĪ رد", + "ĠÙĪر د", + "Ġ ìķķ", + "Ġìķ ķ", + "ุ à¸Ĺร", + "ุà¸Ĺ ร", + "åĬ¨ çī©", + "Ġp ublik", + "Ġpub lik", + "Ġpubli k", + "æĪIJ æľ¬", + "æĪIJ åijĺ", + "ãĤ¤ ãĤ¯", + "شر ÙĥØ©", + "á¿Ĩ ÏĤ", + "Ġy ola", + "Ġyo la", + "Ġyol a", + "üyor uz", + "Ġк ÑĥÑĢи", + "ĠкÑĥÑĢ и", + "ĠкÑĥ ÑĢи", + "ĠпоÑħ ож", + "Ġìłľ ê°Ģ", + "िय त", + "ائ ÙĦØ©", + "ائÙĦ Ø©", + "Ġ ãģ¾", + "़ à¥ĩà¤Ĥ", + "़à¥ĩ à¤Ĥ", + "ÑģÑĮ кими", + "ÑģÑĮк ими", + "ÑģÑĮким и", + "âĢľ ä½ł", + "imiz de", + "ìµľ ìĭł", + "Ạ¬", + "è Ł", + "à¸Ħ รà¸Ńà¸ļ", + "à¸Ħร à¸Ńà¸ļ", + "ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "ت غ", + "ĠVÅ¡ ech", + "à¸±à¸Ľ à¸Ķาห", + "Ġa td", + "Ġat d", + "в оÑİ", + "во Ñİ", + "Ġyap ım", + "Ġyapı m", + "olog ické", + "ologic ké", + "Ġп лен", + "Ġпл ен", + "Ġlaz ım", + "r ung", + "ru ng", + "run g", + "ìĦľ ê´Ģ", + "Ġji ný", + "Ġjin ý", + "Ġtr òn", + "Ġtrò n", + "ĠполÑĸÑĤи ки", + "ا ÙĥÙħ", + "اÙĥ Ùħ", + "دÛĮ گر", + "à¥Īà¤Ĥ .Ċ", + "à¥Īà¤Ĥ. Ċ", + "Ġ اÙĩد", + "Ġا Ùĩد", + "ĠاÙĩ د", + "Ġ ãĥį", + "Ġãĥ į", + "ĠпÑĢодÑĥк ÑĤов", + "ĠпÑĢодÑĥкÑĤ ов", + "æĤ Ł", + "ĠpÅĻÃŃpad ech", + "ĠzaÄį ala", + "ĠzaÄįal a", + "åħ¥ ãĤĮ", + "ĠÑĢÑĸв нÑĸ", + "æĦŁ æĥħ", + "ĠΧ α", + "ì £½", + "ì£ ½", + "ิà¸Ī ารà¸ĵ", + "Âł б", + "Ñĸ ÑĹв", + "ÑĸÑĹ Ð²", + "ب Ø´", + "çļĦ éĹ®é¢ĺ", + "Ġza stup", + "Ġzast up", + "볤 ìļĶ", + "ãģ§ãģĻ ãģŃ", + "âĢĮ داÙĨ", + "âĢĮد اÙĨ", + "ï¼Į æĤ¨", + "Ġu vÄĽdom", + "ãģ¦ ãĤĭ", + "ìĤ¬ ëŀĮ", + "l un", + "lu n", + "éĽĨ åIJĪ", + "ë§ ¹", + "Ġž id", + "Ġži d", + "ठĬ", + "Ġt rp", + "Ġtr p", + "л ениÑħ", + "лен иÑħ", + "ле ниÑħ", + "＿ __", + "＿_ _", + "Ðľ Ðŀ", + "å¼ ĭ", + "λÎŃ Î¿Î½", + "ĠÄij òi", + "Ġк ÑĢок", + "lay ıcı", + "ì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ", + "åij Ī", + "éľ ŀ", + "Ġпо глÑıд", + "Ġпог лÑıд", + "ت رÙĥ", + "تر Ùĥ", + "ĠتÙģ اÙĪت", + "Ġ å®®", + "Ġå® ®", + "ĠدÙĪر بÛĮÙĨ", + "æĶ¾ åľ¨", + "ĠÑģлÑĥÑĩа ев", + "ĠÑģлÑĥÑĩае в", + "ĠÏħÏĢ ηÏģε", + "ë§ ŀ", + "ãģĻ ãģĻ", + "ê²ł ëĭ¤", + "ราย à¸ģาร", + "ĠÏĢ Ïģιν", + "ĠÑģ меÑĪ", + "ĠÑģм еÑĪ", + "å§ ī", + "Ġvýsled ky", + "Ġpot vr", + "åıij è¡Į", + "Ġt úi", + "Ġtú i", + "ĠìĤ¬ ëĿ¼", + "ç«Ļ åľ¨", + "Ġja ký", + "Ġjak ý", + "Ġ à¸ļาà¸ĩ", + "Ġà¸ļ าà¸ĩ", + "Ġdik kate", + "Ġdikkat e", + "Ġدر Ø¢Ùħد", + "æİĴ åIJį", + "r álnÃŃ", + "rál nÃŃ", + "ê³¼ ìĿĺ", + "ä½ µ", + "о лаг", + "ол аг", + "ола г", + "is iyle", + "isi yle", + "Ġ æ½", + "Ġæ ½", + "Ġ तम", + "Ġत म", + "Ġd ij", + "Ġdi j", + "Ġnh ánh", + "ĠR ek", + "ĠRe k", + "设 æĸ½", + "ĠpodmÃŃn ek", + "并 ä¸į", + "к ÑĥÑĤ", + "кÑĥ ÑĤ", + "Ġê³ł 볤", + "çļĦ å£°éŁ³", + "æĪĺ äºī", + "д аÑı", + "да Ñı", + "Ġê´Ģ ìĭ¬", + "ĠÑĦÑĸн анÑģ", + "ĠK öy", + "ĠKö y", + "Ġж ал", + "ĠÑģлÑĥж би", + "м ена", + "мен а", + "ме на", + "ت ÙĬار", + "تÙĬ ار", + "ĠÑĩем пион", + "ÏĢ ιÏĥ", + "ÏĢι Ïĥ", + "landır ma", + "mak tan", + "makta n", + "makt an", + "Ġ 丶", + "Ġä¸ ¶", + "à¹Ī à¸Ńส", + "à¹Īà¸Ń ส", + "ĠmÃ¼ÅŁ teri", + "ĠmÃ¼ÅŁter i", + "Ġص ÙĨد", + "ĠصÙĨ د", + "Ġet mesi", + "Ġetm esi", + "Ġetme si", + "Ġп оÑĢÑĤ", + "ĠпоÑĢ ÑĤ", + "ν ονÏĦαι", + "νον ÏĦαι", + "Ġ ãħĭãħĭ", + "ĠK AR", + "ĠKA R", + "Ġ uch", + "Ġu ch", + "Ġuc h", + "ĠØ® ÙĦÙĤ", + "ĠØ®ÙĦ ÙĤ", + "าษà¸İ ร", + "æŃ ¡", + "Ġи мени", + "Ġим ени", + "Ġиме ни", + "ãģł ãģijãģ©", + "ãģłãģij ãģ©", + "Ġìĭ¤ ìĭľ", + "ÏĥÏī ÏĢ", + "Ġ ì£", + "Ġì £", + "t ÄĽÅ¾", + "tÄĽ ž", + "Ġözel likleri", + "Ġözellik leri", + "Ġözellikle ri", + "Ġب Ù¾", + "Ġиз обÑĢаж", + "ÙĬÙħ ÙĥÙĨ", + "Ġ ãĥĶ", + "Ġãĥ Ķ", + "ĠÐĶ ив", + "ĠÐĶи в", + "ĠØ¥ ÙĬ", + "Ùĥ ÙĬÙĦ", + "ÙĥÙĬ ÙĦ", + "ĠÅŁ ik", + "ĠÅŁi k", + "Ġà¤Ĩ à¤ĸ", + "lar ınızı", + "ların ızı", + "larını zı", + "larınız ı", + "ĠвÑĸд ÑĢÑĸз", + "ĠÑĢоб оÑĤа", + "ĠÑĢобоÑĤ а", + "Ġta rif", + "Ġtar if", + "Ġ اÙĪت", + "Ġا ÙĪت", + "ĠاÙĪ ت", + "ın ma", + "é£Ł ãģ¹", + "Ġuzav ÅĻ", + "ë£ ¸", + "çĽij çĿ£", + "Ġ: ï¼¼", + "θ Ïħν", + "θÏħ ν", + "à¸Ķ ร", + "a larından", + "alar ından", + "aların dan", + "alarında n", + "èĩª æĭį", + "Ġro ÄįnÃŃ", + "ाà¤ĩ व", + "Ġ ÙĥÙĪر", + "ĠÙĥ ÙĪر", + "ĠÏĦ αιν", + "ĠÏĦα ιν", + "ĠÑĸн див", + "r ve", + "rv e", + "ĠνεÏĨ ÏİÏĥειÏĤ", + "Ġb á»ijn", + "Ġbá»ij n", + "Ġ å¿«", + "Ġå¿ «", + "ĠÑģ олÑĮ", + "ĠÑģо лÑĮ", + "ĠÑģол ÑĮ", + "li ÄŁinde", + "liÄŁi nde", + "liÄŁ inde", + "liÄŁin de", + "िन à¤Ł", + "a htar", + "ah tar", + "Ġneb ezpeÄį", + "æĹ¢ çĦ¶", + "ĠëĮĢ ìłĦ", + "ĠÙĨÚ¯Ùĩد ارÛĮ", + "ĠzÃŃsk at", + "Ġнали Ñĩие", + "ĠналиÑĩи е", + "Ġ aks", + "Ġa ks", + "Ġak s", + "ï¼ī ãĢĤĊĊ", + "ï¼īãĢĤ ĊĊ", + "ï¼īãĢĤĊ Ċ", + "Ġrod iny", + "Ġrodin y", + "Ġrodi ny", + "Ġза ÑħÑĸд", + "ĠзаÑħ Ñĸд", + "å¾® ç¬ij", + "ÂłÐĶ а", + "r adu", + "ra du", + "rad u", + "ī nh", + "p les", + "pl es", + "ple s", + "ĠK ons", + "ĠKon s", + "ĠKo ns", + "ิ à¹Ĥล", + "ิà¹Ĥ ล", + "ĠاÙĦ ÙĪص", + "ĠاÙĦÙĪ ص", + "åIJ¬ åĪ°", + "ĠÑģпоÑĢ ÑĤив", + "ĠÑģ айÑĤе", + "ĠÑģай ÑĤе", + "Ġ اظ", + "Ġا ظ", + "ĠØ§Ø ¸", + "lar ındaki", + "ların daki", + "larında ki", + "Ġtá»ķ n", + "ÐĿ ÐĨ", + "Ġned ost", + "ĠÑĤоÑĢ гÑĸв", + "Ġ اÛĮت", + "Ġا ÛĮت", + "ĠاÛĮ ت", + "Ġاختص اص", + "ĠÃľ y", + "ĠS adece", + "ĠSad ece", + "ĠÙħØ® رÙĪØ·", + "Ä ģn", + "Äģ n", + "ç esi", + "çe si", + "Ġ çĬ", + "Ġç Ĭ", + "ãĤĤ ãģ£ãģ¨", + "Ġ éŁĵ", + "ĠéŁ ĵ", + "èµ ĸ", + "ĠполÑĥÑĩ ениÑı", + "Ġë ĺ", + "âĢĻ ÑĹ", + "b ÃŃr", + "bÃŃ r", + "Ġб ÑĸблÑĸ", + "ĠD á»±", + "же неÑĢ", + "жен еÑĢ", + "ç½ij åĪĬ", + "Ġल à¥ľà¤ķ", + "ĠÑĥÑĩ нÑĸв", + "èĪ °", + "ĠÃĸÄŁ ren", + "Ġ ola", + "Ġo la", + "Ġol a", + "Ġ। âĢĿĊĊ", + "ระ à¹Ģà¸ļ", + "á½ ²", + "Ġ رز", + "Ġر ز", + "е и", + "еР¸", + "Ñı Ñĩи", + "ÑıÑĩ и", + "ØŃ Ø¨", + "æĴ ¤", + "ãģ¾ãģŁ ãģ¯", + "б ина", + "би на", + "бин а", + "ĠÎł εÏģ", + "ĠоÑĤноÑģ иÑĤÑģÑı", + "åīį çļĦ", + "Ġ Å¡Å¥", + "ĠÅ¡ Å¥", + "Ġyıl da", + "Ġyı lda", + ": ::::|", + ":: :::|", + ":::: :|", + "::: ::|", + "::::: |", + "us til", + "ust il", + "اÙĦ Ø¥", + "ĠsouÄįas né", + "ĠÙĨÛĮ رÙĪÛĮ", + "ĠÙĨÛĮر ÙĪÛĮ", + "ĠÙĨÛĮرÙĪ ÛĮ", + "ÑĩеÑģ кое", + "Ñĩе Ñģкое", + "ظ Ùģ", + "ĠÙ¾ÛĮØ´ ÛĮÙĨÙĩ", + "Ġع ÙģØ´", + "Ġrost lin", + "ç½ijåĪĬ ä¸ĭ载次æķ°", + "ĠпÑĢигоÑĤов иÑĤÑĮ", + "ãĥ Į", + "ĠÙĪ Ùħع", + "ĠÙĪÙħ ع", + "Ġb ecer", + "Ġbe cer", + "Ġbec er", + "Ġ ãĤ±", + "ĠãĤ ±", + "Ïĩ ήÏĤ", + "Ïĩή ÏĤ", + "о ÑģÑĤÑĥп", + "оÑģÑĤ Ñĥп", + "Ġë°ľ 매", + "Ñĸй ного", + "Ñĸйно го", + "Ġh rd", + "Ġhr d", + "ĠпÑĢепаÑĢа ÑĤÑĭ", + "ĠÙģ رض", + "ĠÙģر ض", + "ĠTy to", + "Ġ кÑĢаÑĹн", + "ĠкÑĢаÑĹ Ð½", + "Ġز اد", + "Ġikt idar", + "ì§ ĵ", + "Ùij ر", + "ÑĢÑı дÑĥ", + "ÑĢÑıд Ñĥ", + "к Ñĸй", + "кÑĸ й", + "âĶ £", + "Ġко жи", + "Ġкож и", + "Ġت ازÙĩ", + "Ġتا زÙĩ", + "o bec", + "ob ec", + "obe c", + "in ae", + "ina e", + "Ġvyj ád", + "Ġ رÙģتÙĩ", + "Ġر ÙģتÙĩ", + "ĠرÙģت Ùĩ", + "ĠرÙģ تÙĩ", + "Щ о", + "ĠBy lo", + "ĠByl o", + "оÑĤ в", + "ĠденÑĮ ги", + "é§ Ĩ", + "Ġма ÑĪин", + "ĠмаÑĪ ин", + "ĠмаÑĪи н", + "ĠØ£ ج", + "ì´Ī ëĵ±íķĻêµIJ", + "dı ģında", + "dıģı nda", + "dıģ ında", + "б аÑģ", + "ба Ñģ", + "Ġ æł¹", + "Ġæł ¹", + "ÎijÎĿ Τ", + "ÙĴ ØŃ", + "Ġjejich ž", + "ìĹIJìĦľ ìĿĺ", + "Ġад же", + "Ġì ı", + "Ïĥ οÏħ", + "Ïĥο Ïħ", + "et leri", + "etler i", + "Ġبعد ÛĮ", + "Ġبع دÛĮ", + "ĠìŀIJëıĻ ì°¨", + "ิà¸į à¸į", + "Ġt isk", + "Ġti sk", + "ãĥ¼ ãĤ¹ãĥĪ", + "ãĥ¼ãĤ¹ ãĥĪ", + "Ġमत लब", + "ê³Ħ íļį", + "ãĤ¦ ãĥĪ", + "Ġ à¹Ģมà¸ķร", + "Ġà¹Ģม à¸ķร", + "Ġop siyon", + "Ġops iyon", + "ĠÑĢав но", + "ĠبÛĮ ÙħÙĩ", + "ĠبÛĮÙħ Ùĩ", + "Ġ먼 ìłĢ", + "иÑĤелÑĮ нÑĭм", + "ĠнÑĸ би", + "Ġде ÑģÑıÑĤ", + "ĠдеÑģÑı ÑĤ", + "ĠÑģиÑĤÑĥа ÑĨии", + "еÑĢ ÑĪе", + "еÑĢÑĪ е", + "Ä ¾", + "ุ à¸ķร", + "ุà¸ķ ร", + "Ġyönet imi", + "Ġyönetim i", + "éIJ ĺ", + "ĠÙħÛĮ تÙĪاÙĨ", + "Ġز ÙĨدÙĩ", + "ĠزÙĨ دÙĩ", + "ĠزÙĨد Ùĩ", + "ãĥŃ ãĥ³", + "ĠK BS", + "ĠKB S", + "ìĦľ ë¹ĦìĬ¤", + "ï» ł", + "eck ého", + "ecké ho", + "ĠÙĤابÙĦ ÛĮت", + "ĠÙĤاب ÙĦÛĮت", + "ãĢĤ ä»Ĭ", + "ÃŃ nÄĽ", + "ÃŃn ÄĽ", + "ĠÑģм ог", + "ĠÑģл ÑĭÑĪ", + "ÙĴ Ùģ", + "po ÅĻád", + "елÑĮ но", + "Ġεί Ïĩαν", + "-ÐŁ еÑĤеÑĢб", + "ĠCh iến", + "ĠChi ến", + "é ry", + "ér y", + "ĠÑĸн ÑģÑĤиÑĤÑĥÑĤ", + "ç»Ĩ èĥŀ", + "Ñĭ ÑŁN", + "Ġv ua", + "Ġvu a", + "Ġà¤ħ श", + "ÑĢоÑģ ÑĤо", + "ÑĢоÑģÑĤ о", + "Ġvů Äįi", + "ë ¿IJ", + "Ġl iá»ĩt", + "Ġíķ µ", + "Ġ اÙģر", + "Ġا Ùģر", + "ĠاÙģ ر", + "ĠTek nik", + "Ġr oli", + "Ġro li", + "Ġrol i", + "Ġпоп ÑĭÑĤ", + "аÑĤ кÑĸв", + "Ġün iversit", + "аÑĤ оÑĢÑĭ", + "аÑĤоÑĢ Ñĭ", + "аÑĤо ÑĢÑĭ", + "ÑİÑīиÑħ ÑģÑı", + "Ġت ض", + "лÑİ ÑĩаеÑĤÑģÑı", + "лÑİÑĩ аеÑĤÑģÑı", + "лÑİÑĩа еÑĤÑģÑı", + "Ġíĸī ë³µ", + "Ġayrıntı lı", + "ĠкиÑĢ п", + "æĭ ¼", + "ëģ Ķ", + "л аÑĤа", + "ла ÑĤа", + "лаÑĤ а", + "Ġkho án", + "Ġhâl â", + "Ïĥ Ïħ", + "ог лаÑģ", + "æİ¥ çĿĢ", + "éĿ© åij½", + "Ġp ÅĻeb", + "ĠpÅĻ eb", + "ĠpÅĻe b", + "à¹Ģà¸ī ล", + "ĠاÙĦÙħÙĦ ÙĦÛĮ", + "åł Ĩ", + "íı IJ", + "à¸ķล à¸Ńà¸Ķ", + "° С", + "ìĤ¬ ëŀij", + "Ġг иб", + "ë²Ī 째", + "æĶ¹ åıĺ", + "表 çİ°", + "и ÑĩеÑģким", + "иÑĩеÑģ ким", + "иÑĩеÑģки м", + "สม à¹Ģà¸Ķ", + "å±ħ æ°ij", + " Ľ", + "ĠìķĦìĿ´ ëĶĶ", + "ĠмеждÑĥ наÑĢод", + "Ġy em", + "Ġye m", + "Ġm ül", + "Ġmü l", + "Ġا ÛĮست", + "ĠاÛĮ ست", + "Ġ ãĥ´", + "Ġãĥ ´", + "ัà¸Ļ à¹Ħà¸Ķ", + "à¥Ģ ण", + "åħ¶ å®ŀ", + "Ġgel enek", + "Ġgele nek", + "Ġgelen ek", + "ë¶ģ ëıĦ", + "à¹ī าà¸ķ", + "à¹īา à¸ķ", + "Ġ ìī¬", + "Ġìī ¬", + "Ġ ÏĢÎŃ", + "ĠÏĢ ÎŃ", + "ĠÙĥ اÙħÙĦ", + "ĠÙĥاÙħ ÙĦ", + "Ġتع ÙħÛĮر", + "è¨ ´", + "ë¹ Ļ", + "iy im", + "iyi m", + "å° ¿", + "éĤ£ æł·", + "êµŃ ìĿĺ", + "ãģĹãģ¦ ãģĬãĤĬ", + "Ġ niž", + "Ġn iž", + "Ġni ž", + "Ġκ ον", + "Ġκο ν", + "à¹Ī าà¸Ń", + "à¹Īา à¸Ń", + "Ġ γε", + "Ġγ ε", + "ĠС евеÑĢ", + "edi álnÃŃ", + "ãģŁãģ¡ ãģ®", + "m ayacak", + "may acak", + "maya cak", + "Ñ Ļ", + "ĠÑĥ гл", + "ĠÑĥг л", + "Ġk apas", + "Ġka pas", + "Ġkap as", + "Ñĥв алиÑģÑı", + "Ñĥва лиÑģÑı", + "Ñĥвали ÑģÑı", + "ĠмеÑģÑı ÑĨа", + "á» ¯u", + "ữ u", + "ิ ลล", + "ิล ล", + "ãĤĪãĤĬ ãĤĤ", + "à¥ĩ ण", + "à¥ĩठ£", + "Ġ 客", + "Ġå® ¢", + "ĠdeÄŁ erli", + "ĠdeÄŁer li", + "ÙĪ از", + "ÙĪا ز", + "ี à¸Ńย", + "ีà¸Ń ย", + "Ġ åıĪ", + "Ġåı Ī", + "Ġ à¸Ķร", + "Ġà¸Ķ ร", + "ĠÙĨ اب", + "ĠتÙĦÙĪÛĮزÛĮ ÙĪÙĨ", + "Ġol anlar", + "Ġolan lar", + "ä¼ĺ ç§Ģ", + "Ùĥ اÙĦ", + "ĠдеÑģÑı ÑĤи", + "ĠдеÑģÑıÑĤ и", + "m án", + "má n", + "ĠÑĢ анÑĮ", + "ĠÑĢа нÑĮ", + "ĠÑĢан ÑĮ", + "Ġìłľ ì¶ľ", + "è³ ¢", + "а бо", + "аб о", + "Ġtechn ik", + "Ġtech nik", + "ĠK iá»ĥm", + "ĠKi á»ĥm", + "t eki", + "te ki", + "tek i", + "á ¹", + "Ġm nÄĽ", + "Ġmn ÄĽ", + "Ġê³µ ê°Ħ", + "ĠM ek", + "ĠMe k", + "Ġاع تÙħاد", + "à¹Į à¹Ħà¸Ķ", + "ε ÏģÏĮ", + "εÏģ ÏĮ", + "ĠÑĥд аÑĢ", + "ĠÑĥда ÑĢ", + "оÑĩ ÑĮ", + "æ¦Ĥ 念", + "ÑĢ ал", + "ÑĢаР»", + "ÑĢа л", + "алÑĮ нÑĭми", + "алÑĮнÑĭм и", + "à¥ģर स", + "r áci", + "rá ci", + "Ġ ÙĤÙĪÙĦ", + "ĠÙĤ ÙĪÙĦ", + "Ġद व", + "ĠпÑĢав да", + "Ġ å¿ħ", + "Ġå¿ ħ", + "Ġdos ud", + "нÑĥ ÑĤÑĮÑģÑı", + "нÑĥÑĤÑĮ ÑģÑı", + "N Äĥm", + "à¸ĺ à¸Ļ", + "Ġdok un", + "Ġ åľ¨çº¿", + "Ġåľ¨ 线", + "ู à¹Ħ", + "ụ y", + "Ġн овÑĭÑħ", + "Ġнов ÑĭÑħ", + "Ġmez un", + "ĠC ần", + "à¸ģาร à¸ŀ", + "ĠìĺĪ ìłķ", + "Ïĥ ή", + "à¹Īà¸Ļ à¹Ģà¸ģม", + "ĠÙĪ اÙĦس", + "ĠÙĪاÙĦ س", + "ĠÙĪا ÙĦس", + "ãĥ³ ãĥĨãĤ£", + "ãĥ³ãĥĨ ãĤ£", + "çľĭ è§ģ", + "Ġس اÙĦÙħ", + "ĠساÙĦ Ùħ", + "ĠбагаÑĤÑĮ оÑħ", + "ĠÄij Ãłi", + "Ġد ستÛĮ", + "Ġدست ÛĮ", + "Ġدس تÛĮ", + "à¸ŀ à¸Ń", + "еп ÑĤи", + "ĠìłĦ íĻĶ", + "æĻĤ ãģ«", + "ĠSe znam", + "ĠSez nam", + "мÑĸ нÑĥ", + "мÑĸн Ñĥ", + "; ?#", + "à¥Ģ सर", + "à¥Ģस र", + "ĠÚĨ ÛĮست", + "νο ια", + "νοι α", + "ั à¸Ļà¸Ń", + "ัà¸Ļ à¸Ń", + "Ġ à¸Ħำ", + "Ġà¸Ħ ำ", + "Ġë³´ íĺ¸", + "Ġid dia", + "Ġiddi a", + "Ġβ ιβ", + "é«ĺ ä¸Ń", + "Ù ¨", + "ÐĴ аж", + "ĠиÑģп олн", + "ÑĪ ÑĤов", + "ÑĪÑĤ ов", + "ĠT aÅŁ", + "ĠTa ÅŁ", + "ìĽ ħ", + "åĬ ¹", + "Ġ åıĥ", + "Ġåı ĥ", + "Ġprost oru", + "Ġprostor u", + "ĠÑģп ад", + "е ÑĢина", + "еÑĢ ина", + "еÑĢи на", + "еÑĢин а", + "ĠpÅĻek lad", + "ĠpÅĻe klad", + "Å¡ ov", + "ĠÙģ ÙĩÙħ", + "ĠÙģÙĩ Ùħ", + "æĬ ij", + "Ġابت دا", + "Ġابتد ا", + "ãĤĴ ãģĬ", + "l ikler", + "lik ler", + "likle r", + "ĠÙħ اÙĥ", + "ĠÙħا Ùĥ", + "Ġko nut", + "Ġkon ut", + "Ġkonu t", + "ĠداÙĨØ´ جÙĪÛĮ", + "Ġоп ÑĤим", + "Ġб Ñĥма", + "ĠбÑĥ ма", + "ĠлÑİд Ñıм", + "Ġл Ñĸка", + "ĠлÑĸ ка", + "ĠлÑĸк а", + "ĠÑĢоз повÑĸд", + "ĠÑĢозп овÑĸд", + "ĠÑĢозпов Ñĸд", + "nes enÃŃ", + "Ġ à¸łà¸²à¸ŀ", + "Ġà¸ł าà¸ŀ", + "и Ñĩний", + "иÑĩ ний", + "ا Ø·ÙĦ", + "اط ÙĦ", + "Ñİ Ñīими", + "ÑİÑī ими", + "ÑİÑīим и", + "ãģı ãģ¨", + "éŃ ¯", + "ĠجÙĨ سÛĮ", + "Ðĺ Т", + "र ल", + "ĠÚ©ÙĪد Ú©", + "о лиÑĤ", + "ол иÑĤ", + "оли ÑĤ", + "ĠÑģÑĤÑĢÑĥкÑĤÑĥ ÑĢ", + "ve kili", + "vek ili", + "Ġब य", + "Ġgel miÅŁ", + "िर फ", + "Ġнай кÑĢа", + "ĠÐĶж он", + "Ġ ãĥĹãĥŃ", + "ĠãĥĹ ãĥŃ", + "ĠyaÅŁ lı", + "Ġkar Ä±ÅŁtır", + "ĠkarÄ±ÅŁ tır", + "ĠvÄĽtÅ¡ inou", + "Ġvaz geç", + "à¹ī าà¸Ħ", + "à¹īา à¸Ħ", + "lendir me", + "Ġ ç¨ĭ", + "Ġç¨ ĭ", + "说 è¯Ŀ", + "ĠíķĦìļĶ íķľ", + "aÅĻ ilo", + "Ġle žÃŃ", + "ĠAmer ikan", + "ĠAmerika n", + "ĠAmerik an", + "ãĤĦ ãģĻ", + "va jÃŃcÃŃ", + "vajÃŃ cÃŃ", + "ÐĿ Я", + "ĠìĹĦ ë§Ī", + "Ġ åĥ", + "Ġå ĥ", + "r ál", + "rá l", + "Ġç ay", + "Ġça y", + "tu ÄŁ", + "ุà¸į าà¸ķ", + "ĠÑģ лив", + "ĠÑģл ив", + "ν οÏħ", + "νο Ïħ", + "ĠO v", + "ĠC HP", + "ĠCH P", + "ĠZe mÄĽ", + "ĠZem ÄĽ", + "ĠÄįesk ý", + "ĠÄįe ský", + "ĠTh ánh", + "иÑĤелÑĮ ноÑģÑĤÑĮ", + "иÑĤелÑĮно ÑģÑĤÑĮ", + "æĦı ä¹ī", + "à¥įरम ण", + "Ġди амеÑĤ", + "Ġk lin", + "Ġkl in", + "Ġkli n", + "Ġ کرÛĮ", + "ĠÚ© رÛĮ", + "Ġکر ÛĮ", + "ãģ§ãģ¯ ãģªãģı", + "飯 åºĹ", + "Ġk ênh", + "Ġkê nh", + "ĠÑĢанÑĮ ÑĪе", + "ãĤĴ ãģĹãģŁ", + "ĠпÑĢи боÑĢ", + "ĠпÑĢиб оÑĢ", + "Ġà¤ĸ तर", + "Ġ yu", + "Ġy u", + "é§ IJ", + "ĠÑĢ або", + "ĠÑĢа бо", + "ĠÑĢаб о", + "ĠС ÐłÐ¡Ðł", + "èĬ ¬", + "ž ila", + "ži la", + "žil a", + "еÑĢ ÑĤа", + "еÑĢÑĤ а", + "и ÑģÑĤÑĢа", + "иÑģ ÑĤÑĢа", + "иÑģÑĤ ÑĢа", + "Ġкни ги", + "ĠFranc ie", + "ĠFran cie", + "ĠÚĺ اپ", + "ĠÎļ οÏħ", + "ĠÎļο Ïħ", + "ัว à¹Ģà¸Ńà¸ĩ", + "Ġl ắng", + "Ġ нами", + "Ġн ами", + "Ġна ми", + "Ġнам и", + "Ġпод ой", + "д ÑĢом", + "дÑĢ ом", + "o bus", + "ob us", + "ÐĴ Ñĸн", + "Ġst alo", + "Ġsta lo", + "Ġstal o", + "Ġà¤ı à¤ľ", + "ĠL inh", + "ĠLin h", + "ĠLi nh", + "ebilir iz", + "Ġзав ÑĤÑĢа", + "μ εÏģο", + "με Ïģο", + "μεÏģ ο", + "Ġ ÎŃν", + "ĠÎŃ Î½", + "ÑıÑĤ но", + "Ġд оÑĢож", + "Ġдо ÑĢож", + "ĠдоÑĢ ож", + "åıĤ çħ§", + "Ïĥ ιο", + "Ïĥι ο", + "à¹ī à¹Ģà¸ģ", + "a ných", + "an ých", + "aný ch", + "ç· ł", + "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", + "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", + "åĬĽ çļĦ", + "ĠS ır", + "ĠSı r", + "Ġ ì§ĢëıĦ", + "Ġì§Ģ ëıĦ", + "ç· Ĭ", + "ĠpoÄį tu", + "ï¼Į ä¸İ", + "ä¸ĸ ç´Ģ", + "ем ого", + "емо го", + "Ġhus us", + "Ġhu sus", + "Ġölçü de", + "Ġtr ục", + "Ġtrụ c", + "à¸Ľà¸¥ à¸Ńà¸Ķà¸ł", + "Âłp ÅĻÃŃ", + "ĠBöl gesi", + "м ом", + "мо м", + "ãģ« ãģ¦", + "Ġ쪽 ì§Ģ", + "ÄĽt Å¡", + "ĠìĦ± ê³µ", + "र त", + "ur du", + "urd u", + "ĠìĽĢ ì§ģ", + "ÑŁ ÐŃ", + "nÃŃ kem", + "nÃŃk em", + "ĠskuteÄį nosti", + "Ġ даÑĤ", + "Ġд аÑĤ", + "Ġда ÑĤ", + "n eum", + "ne um", + "ĠÑĤаб леÑĤ", + "j vu", + "Ġs edm", + "Ġse dm", + "Ġsed m", + "س ÙĬØ©", + "سÙĬ Ø©", + "ĠкоÑĢ об", + "Ġко ÑĢоб", + "em mel", + "emm el", + "emme l", + "ãģ¤ ãģij", + "é¦Ļ èķī", + "Ġشخص ÛĮت", + "ĠشخصÛĮ ت", + "ä¸Ĭ äºĨ", + "ÙĪ را", + "ÙĪر ا", + "ĠаÑĤ моÑģ", + "Ġ лей", + "Ġл ей", + "Ġz prav", + "Ġzp rav", + "Ġëķ ħ", + "ู à¸Ĺ", + "Ġ اسر", + "Ġا سر", + "Ġاس ر", + "ĠAy dın", + "ĠعÙħ ÙĦÙĬØ©", + "ĠعÙħÙĦ ÙĬØ©", + "Ġд ÑĸÑĶ", + "ĠдÑĸ ÑĶ", + "Ġd ök", + "Ġdö k", + "Ġफ ल", + "ĠìĤ¬ëŀĮ ëĵ¤ìĿ´", + "ĠнаÑĤÑĥ ÑĢалÑĮ", + "æŁ ľ", + "温 度", + "Ġk les", + "Ġkl es", + "Ġkle s", + "Ġин веÑģÑĤи", + "s üz", + "æĴ °", + "Ġ ãĤ¢ãĥ«", + "ĠãĤ¢ ãĥ«", + "Ġ èĴ", + "Ġè Ĵ", + "ад ки", + "Ġk lÃŃÄį", + "Ïĩ εί", + "Ïĩε ί", + "ĠTh iết", + "ĠThi ết", + "ĠسرÛĮ ع", + "ĠÏĢεÏģιο Ïĩή", + "ÙĪ ÙĤÙģ", + "ÙĪÙĤ Ùģ", + "Ïģ ÏīÏĥη", + "ÏģÏī Ïĥη", + "ĠسÙĦ اÙħت", + "ĠسÙĦاÙħ ت", + "ëĵ¤ ëıĦ", + "ĠveÅĻej né", + "Ġvi tam", + "Ġvit am", + "Ġvita m", + "ĠبازÛĮ گر", + "ĠÑĢеÑĨеп ÑĤ", + "ĠìľĦ íķ´ìĦľ", + "ĠìľĦíķ´ ìĦľ", + "ĠØ£Ùĥ بر", + "Ġk üt", + "Ġkü t", + "민 주", + "Ġté ž", + "Ġ å¼ķ", + "Ġå¼ ķ", + "ÑĩаÑģ но", + "çļĦ åľ°", + "Ġarchit ekt", + "Ġбак ÑĤеÑĢ", + "Ġ ãģį", + "Ġ одеÑĢж", + "Ġо деÑĢж", + "Ġод еÑĢж", + "Ġتج ارÛĮ", + "éĿ Ī", + "Ġre cep", + "Ġrec ep", + "Ġrece p", + "é© ¶", + "Ġد ÙĩÙĩ", + "ĠدÙĩ Ùĩ", + "è² Į", + "çµIJ å©ļ", + "ılı ç", + "ãģĭãĤī ãģ¯", + "å¿ĥ éĩĮ", + "æĬķ è³ĩ", + "è² Ŀ", + "ĠкÑĥлÑĮÑĤÑĥ ÑĢÑĭ", + "ĠкÑĥлÑĮÑĤÑĥÑĢ Ñĭ", + "Ġ å°ij", + "Ġå° ij", + "à¹ģ à¸ŀร", + "à¹ģà¸ŀ ร", + "γ κÏĮ", + "γκ ÏĮ", + "ar ım", + "arı m", + "Ġاس اسÛĮ", + "Ġاساس ÛĮ", + "Ġposled nÃŃch", + "ĠposlednÃŃ ch", + "ĠÙħ ÙħÙĨ", + "ĠÙħÙħ ÙĨ", + "Ġпоз иÑĤив", + "Ġпози ÑĤив", + "ìł ¤", + "Ñĥ вавÑģÑı", + "Ñĥв авÑģÑı", + "Ñĥва вÑģÑı", + "Ñĥвав ÑģÑı", + "Ġجز ئ", + "ìĿ´ ìŀIJ", + "Ġин ÑģÑĤÑĢÑĥк", + "Ġη λεκ", + "Ġde mir", + "Ġdem ir", + "Ġdemi r", + "ä¸Ńæĸĩ åŃĹå¹ķ", + "Ġعاش ÙĤ", + "Ġب اÙĦÙĤ", + "Ġبا ÙĦÙĤ", + "ĠباÙĦ ÙĤ", + "Ġ maz", + "Ġm az", + "Ġma z", + "ά νι", + "άν ι", + "Ġ dÃ¼ÄŁ", + "Ġd Ã¼ÄŁ", + "Ġdü ÄŁ", + "Ġ κÏģα", + "Ġκ Ïģα", + "ĠбÑĥд ÑĤо", + "ç¦ı åĪ©", + "ĠпÑĢед назнаÑĩ", + "Ùħ ÙĦØ©", + "ÙħÙĦ Ø©", + "ĠбÑĥд инкÑĥ", + "Å¥ an", + "íķ Ģ", + "ç´¹ ä»ĭ", + "Ú© ز", + "ĠкаÑĦ ед", + "ãģ« è¦ĭ", + "าà¸ķร à¸IJาà¸Ļ", + "ë¡ľ ëĬĶ", + "i vÄĽ", + "iv ÄĽ", + "èĥ½ æºIJ", + "ï¼Į åħ¨", + "ĠÑĥ див", + "ĠÑĥд ив", + "Ġë§Į ëĤĺ", + "ÐĴ ÐIJ", + "ĠG ür", + "ĠGü r", + "ĠдÑĢÑĥг им", + "Ïĥ Ïį", + "Ġ oÄŁlu", + "ĠoÄŁ lu", + "Ġê°Ģ ê¹Į", + "ĠзнаÑĩ иÑĤелÑĮно", + "ĠзнаÑĩиÑĤ елÑĮно", + "о зÑĸ", + "оз Ñĸ", + "Ġm á»±c", + "ĠB eÅŁ", + "ĠBe ÅŁ", + "Ġ jezd", + "Ġje zd", + "á vÄĽ", + "áv ÄĽ", + "ÏĦη Ïĥε", + "ãģ¦ãģĦ ãģªãģĦ", + "ĠСв ÑıÑĤ", + "Ġम श", + "ĠΤ οÏħ", + "ĠΤο Ïħ", + "声 ãĤĴ", + "ĠÑģам ое", + "ĠÑģамо е", + "Ġ åĮº", + "ĠåĮ º", + "ĠìĤ¬ëŀĮ ìĿĢ", + "ĠÙħ ÙĦت", + "ĠÙħÙĦ ت", + "Ġj oker", + "Ġjo ker", + "Ġjoke r", + "Ġne ob", + "Ġneo b", + "ĠÑĤ ака", + "ĠÑĤак а", + "ĠÑĤа ка", + "ĠÙĩ Ùģت", + "Ġδε δο", + "ĠзаÑħ оп", + "ĠاÙĦÙħ خت", + "ез да", + "езд а", + "Ġíķľ ë²Ī", + "Ġع اÙħØ©", + "ĠعاÙħ Ø©", + "Ġdo state", + "Ġdost ate", + "Ġdostat e", + "Ġp lav", + "Ġpl av", + "Ġpla v", + "楽 ãģĹ", + ".;.; .;.;", + "в аÑĶ", + "ва ÑĶ", + "Ġbụ i", + "ĠÄij ỡ", + "ĠÄijá» ¡", + "Ġmys lÃŃ", + "Ġmysl ÃŃ", + "ĠÙĨ ار", + "Ġn út", + "Ġм ала", + "Ġмал а", + "Ġма ла", + "Τ Ρ", + "ĠاÙĦرÙħ زÙĬØ©", + "la dım", + "lad ım", + "ladı m", + "ä¸Ģ ç·Ĵ", + "ĠiÅŁ ç", + "l ivé", + "li vé", + "liv é", + "르 ê²Į", + "ан наÑı", + "ظËĨ Ø·", + "Ġd ừng", + "ÙĦÙĥ تر", + "çŃĶ æ¡Ī", + "ĠÙħÙĪÙĤع ÛĮت", + "ĠÑĸн озем", + "ĠиÑģ Ñĩ", + "Ġнеп ÑĢавилÑĮ", + "b akan", + "ba kan", + "bak an", + "Ġ çīĪ", + "Ġçī Ī", + "ен нÑİ", + "à¸ĩ à¹Ģศ", + "à¸Ħวาม à¸Ħ", + "% .Ċ", + "%. Ċ", + "à¹Ī à¹Ģà¸Ľ", + "ĠØ¢ بÛĮ", + "Ġآب ÛĮ", + "Ġst áty", + "Ġstát y", + "Ġتر تÛĮب", + "Äįem ž", + "Ġ é¹", + "Ġé ¹", + "Ġ ÙģاÙĦ", + "ĠÙģ اÙĦ", + "Ġbelir len", + "ĠâĨ ĺ", + "èĩ³ å°ij", + "ĠBun lar", + "Ġ ä¸ĵ", + "Ġä¸ ĵ", + "ĠÙħØŃ Ø§Ø³", + "ĠìĦľ ë²Ħ", + "Ġc anh", + "Ġcan h", + "Ġca nh", + "ĠпÑĢоÑĤ Ñıж", + "ĠнÑĸ меÑĨÑĮ", + "à¥Īà¤ł à¤ķ", + "ëĭ ī", + "Ġна неÑģ", + "Ġвоз ÑĢаÑģÑĤа", + "ĠвозÑĢаÑģÑĤ а", + "Ġ[âĢ¦ ]Ċ", + "Ġ[âĢ¦] Ċ", + ". à¸ŀ", + "ิ ศาสà¸ķร", + "ิศ าสà¸ķร", + "çģ ½", + "ê°Ļ ìĿĢ", + "ล à¸ĩà¸Ĺ", + "ลà¸ĩ à¸Ĺ", + "ãĤ± ãĥ¼ãĤ¹", + "Ġ ãĤ¢ãĤ¤", + "ĠãĤ¢ ãĤ¤", + "Ñģ Ñİ", + "ĠÙĦ ر", + "ãģĭ ãģ£ãģ¦", + "Ġ기 ë°ĺ", + "Ġ !:", + "Ġ! :", + "ĠÑģ ÑĬ", + "Ġ Ø´ÙĨاسÛĮ", + "ĠØ´ÙĨ اسÛĮ", + "ĠØ´ÙĨاس ÛĮ", + "ĠìķĦ 침", + "Ġعب اس", + "Ġ à¸ķà¸Ńà¸Ļ", + "Ġà¸ķ à¸Ńà¸Ļ", + "ĠмеÑĤал ли", + "ÑĪ ила", + "ÑĪи ла", + "Ġpod rob", + "Ġpodr ob", + "ÑĸÑģ но", + "Ġ 赤", + "Ġèµ ¤", + "c iler", + "ci ler", + "cil er", + "o zem", + "oz em", + "oze m", + "ĠоÑģнов нÑĭÑħ", + "Âł à¤ķ", + "à¸ĸ à¸Ļà¸Ļ", + "ан ÑĤаж", + "анÑĤ аж", + "анÑĤа ж", + "ĠD ÃŃky", + "Ġگذ ارÛĮ", + "æľº ä¼ļ", + "οÏħ λίοÏħ", + "οÏħλ ίοÏħ", + "оÑĩ ек", + "Ġнап иÑĤ", + "ĠبÛĮØ´ ترÛĮ", + "ĠبÛĮشتر ÛĮ", + "ä¾ į", + "ĠاÙĦ ÙħÙħ", + "ĠاÙĦÙħ Ùħ", + "ÙĪز ÙĬع", + "Ġgöz lem", + "è°ĥ æķ´", + "Âłm iles", + "Âłmi les", + "Ġk oc", + "Ġko c", + "ัà¸į ห", + "æ³ ³", + "ĠÎij γγ", + "ĠÎijγ γ", + "ĠÙĨÙħ از", + "ุ à¸Ĺ", + "ãĥı ãĤ¤", + "Ġth ù", + "к ÑĥлÑı", + "кÑĥ лÑı", + "кÑĥл Ñı", + "ĠпÑĥÑĤ ем", + "èĩº çģ£", + "Ġver gi", + "Ġverg i", + "åł´åIJĪ ãģ¯", + "ĠÑĤÑĢÑĮ оÑħ", + "Ġë³´ ë©´", + "âĸ ²", + "Ïħ γ", + "ĠдоÑĤ ÑĢим", + "æľ µ", + "Ġum ÄĽnÃŃ", + "èī¯ ãģĦ", + "Âł à¸Ļาà¸ĩ", + "Ðİ ÑĭÑŁN", + "ä¸ī 个", + "ียร à¸ķ", + "ï¼Į åIJĮæŶ", + "ĠÑĢозÑĢаÑħ Ñĥн", + "ĠD ers", + "ĠDe rs", + "ĠDer s", + "ãģª ãģ®", + "Ġê·¸ 를", + "d ikleri", + "dik leri", + "Ġhay ata", + "Ġhaya ta", + "Ġhayat a", + "è§Ħ èĮĥ", + "ç»ĵ åIJĪ", + "Ġs cé", + "Ġsc é", + "Ġc Æ¡m", + "ĠcÆ¡ m", + "åѸ éĻ¢", + "ĠÐĦ в", + "ĠÄįlán ek", + "ĠдоÑģÑĤ иг", + "ĠдоÑģÑĤи г", + "ा à¤ĩस", + "ाà¤ĩ स", + "εÏħ Ïĥη", + "éģ© çĶ¨", + "Ïĥ ον", + "Ïĥο ν", + "ıl maktadır", + "ëªħ ìĿĦ", + "ı b", + "Ġstar Å¡ÃŃ", + "Ġch ÃŃn", + "ĠchÃŃ n", + "ä¸Ģ 个人", + "ä¸Ģ个 人", + "ĠFranti Å¡ek", + "n ÄĽji", + "nÄĽ ji", + "ï» ¨", + "ĠÙĦÙĦ د", + "Ġp okoj", + "Ġpok oj", + "Ġj ih", + "Ġji h", + "ãĢį ãĢĤ", + "Ġعبد اÙĦ", + "ãĤĵãģ§ ãģĦãĤĭ", + "Ġмод елÑĮ", + "ĠteÅŁ kil", + "ĠÄĮ er", + "à¹Ģà¸Ķ à¸Ńร", + "' na", + "'n a", + "λο γή", + "λογ ή", + "Ġ kola", + "Ġk ola", + "Ġko la", + "Ġkol a", + "ãĥĢ ãĥ¼", + "иÑĤ елем", + "иÑĤе лем", + "ĠÏĥÏħ νο", + "ĠÏĥÏħν ο", + "ĠK urum", + "ĠKur um", + "ĠKu rum", + "Ġsnad no", + "ĠاÙĦÙĤر Ø¢ÙĨ", + "ĠV á»ģ", + "é«ĺ ãģĦ", + "Ġyıl dız", + "Ġbir isi", + "Ġbiri si", + "Ġkh úc", + "ÙĪ ÛĮÙĦ", + "ÙĪÛĮ ÙĦ", + "æľĢ ä½³", + "Ġส าà¸Ĥ", + "ĠÐŁ ок", + "ĠÐŁÐ¾ к", + "âī ł", + "à¹Ĥà¸Ľà¸£ à¹ģà¸ģรม", + "à¥įय यन", + "èij ¡", + "Ġn ovÄĽ", + "Ġno vÄĽ", + "Ġnov ÄĽ", + "ay ıp", + "ayı p", + "ĠSing ap", + "ĠSin gap", + "è° ĵ", + "ãĤ¶ ãĤ¤ãĥ³", + "Ġн овÑĭе", + "Ġнов Ñĭе", + "Ġh ảo", + "Ġ èŤ", + "ĠèĹ ¤", + "ãĥ³ ãĥĸ", + "ãĥ³ãĥ ĸ", + "Âł ĊĊ", + "ÂłĊ Ċ", + "θ εια", + "θε ια", + "Ġпоп ада", + "ĠëĶĶ ìŀIJìĿ¸", + "Ġداشت ÙĨد", + "ĠداشتÙĨ د", + "ĠØ´ÙĨ اختÙĩ", + "Ïĥ μαÏĦα", + "Ïĥμα ÏĦα", + "å¹³æĸ¹ åħ¬éĩĮ", + "Ġg öl", + "Ġgö l", + "ек оÑĤоÑĢ", + "еко ÑĤоÑĢ", + "Ġm álo", + "Ġmá lo", + "Ġاج ازÙĩ", + "Ú© اراÙĨ", + "کار اÙĨ", + "کا راÙĨ", + "ĠпÑĸдпÑĢиÑĶм ÑģÑĤв", + "ä¸ī å¹´", + "ĠسÙģ ÛĮد", + "ĠμÎŃ ÏģοÏĤ", + "ÐĻ ÐĻ", + "Ġh Æ°", + "س ÙĪب", + "سÙĪ ب", + "ĠÙĦ ذا", + "Ġnem ovit", + "Ġd ÃŃv", + "ĠdÃŃ v", + "Ä° s", + "¶ ¶", + "Ġph Æ°á»Ŀng", + "ĠÙĨØŃ ÙĪÙĩ", + "ĠÙĨØŃÙĪ Ùĩ", + "Ð ĭ", + "Ġz byt", + "Ġzb yt", + "Ġzby t", + "ed ii", + "edi i", + "n ech", + "ne ch", + "ĠадмÑĸнÑĸÑģÑĤÑĢа ÑĤив", + "Ġne vÄĽ", + "Ġnev ÄĽ", + "Ġ ож", + "Ġо ж", + "ĠÄIJ ó", + "à¸Ľà¸£à¸° ว", + "Ġvhod né", + "Ġum ÄĽl", + "ĠÑĢазлиÑĩ нÑĭе", + "ĠpÅĻi roz", + "Ġبخ Ø´ÛĮ", + "Ġبخش ÛĮ", + "ãģ® 大", + "ĠاÙĦ ÙĥÙĩ", + "ĠاÙĦÙĥ Ùĩ", + "ec ká", + "eck á", + "Ġzorun lu", + "ĠÐľÐ¸Ðº ола", + "Ġ amel", + "Ġa mel", + "Ġam el", + "к овÑĭе", + "ков Ñĭе", + ": :::/", + ":: ::/", + ":::: /", + "::: :/", + "ä¸įåIJĮ çļĦ", + "ĠÙĪÙĥ اÙĨت", + "ĠÙĪÙĥاÙĨ ت", + "à¸Ń à¸Ń", + "lá sil", + "ĠпÑĢедпол аг", + "ï½ ±", + "Ġ νε", + "Ġν ε", + "Ġн овÑĭй", + "Ġнов Ñĭй", + "Ġìĺģíĸ¥ ìĿĦ", + "Ġê°Ģ ì§Ħ", + "åĥ ħ", + "Y D", + "Ġب اغ", + "Ġبا غ", + "ĠØ´Ú© ست", + "Ġgü ney", + "Ġgün ey", + "и ÑģÑĮ", + "иÑģ ÑĮ", + "ãģĭ ãģªãģĦ", + "ãģĭãģª ãģĦ", + "ĠT òa", + "Ġگرد ÛĮد", + "Ġگر دÛĮد", + "ØŃ ÙĦ", + "lu vÃŃ", + "luv ÃŃ", + "v éd", + "vé d", + "Ġìĺ ·", + "Ġε ÏĢα", + "ĠεÏĢ α", + "ĠÑĤи ÑģÑıÑĩ", + "ĠÑĤиÑģ ÑıÑĩ", + "Ġê½ ĥ", + "ĠP US", + "ĠPU S", + "ĠдÑĥм кÑĥ", + "Ġ âĢĿĊ", + "ĠâĢĿ Ċ", + "ĠìĬ¤ íı¬ì¸ł", + "Ùĩ Ùĩ", + "Ġg ắng", + "Ġgắn g", + "ิ à¸łà¸²à¸ŀ", + "éĩĮ éĿ¢", + "br ıs", + "Ġz áb", + "Ġzá b", + "κ αÏĤ", + "κα ÏĤ", + "ĠåıĮ 线", + "ล ล", + "ĠÄIJ Ãłi", + "ĠÄIJÃł i", + "åѸ æł¡", + "ĠÑĢаÑģп ÑĢед", + "ĠÑģÑĤан еÑĤ", + "ĠÑģÑĤа неÑĤ", + "Ġл ак", + "Ġла к", + "Ġпод к", + "Ġg ören", + "Ġgö ren", + "Ġgör en", + "Ġgöre n", + "르 ê³ł", + "ĠÑĦ ÑĢÑĥкÑĤ", + "íĵ¨ íĦ°", + "ãģĻ ãĤĮãģ°", + "ãĤĴ ä½ľ", + "à¸Ńà¸Ńà¸ģ à¹ģà¸ļà¸ļ", + "Ġku lak", + "Ġkul ak", + "ĠíĶĮ ëłĪìĿ´", + "ĠØŃ Ø¯ÙĬØ«", + "ĠØŃد ÙĬØ«", + "ãģĨ ãĤĵ", + "Ġм Ñĸк", + "ĠмÑĸ к", + "à¤ĩस à¤ķ", + "ĠÑĥ ÑĤоÑĩ", + "ĠÑĥÑĤ оÑĩ", + "ĠÙĥ Ø«ÙĬر", + "ĠY ine", + "ĠYi ne", + "ĠYin e", + "ัว หà¸Ļ", + "н ÑĸÑĹ", + "нÑĸ ÑĹ", + "åį ¢", + "Ñĥ Ñģлов", + "ÑĥÑģ лов", + "ìĽĮ íģ¬", + "Ġà¤ħ à¤ĸ", + "ĠÑĨ Ñĸка", + "ĠÑĨÑĸ ка", + "ìĦł ìĿĦ", + "ĠØ£ ر", + "гал ÑĤеÑĢ", + "angl icky", + "ĠÑģ оÑģÑĥд", + "ĠÑģоÑģ Ñĥд", + "ĠÑĥ Ñıв", + "ĠпÑĢодÑĥк ÑĨÑĸÑĹ", + "Ġc hua", + "Ġch ua", + "Ġchu a", + "Ġd án", + "Ġdá n", + "ाम à¤Ĺ", + "ئ ت", + "ĠФ ед", + "Ġh rom", + "Ġhr om", + "íķ´ ë³´", + "ĠØ¢ÙĨ ÙĦاÛĮÙĨ", + "- пÑĢав", + "-п ÑĢав", + "Ġì¤ijìļĶ íķľ", + "Ġв кÑĥ", + "Ġвк Ñĥ", + "Ġ 大éĺª", + "Ġ大 éĺª", + "Ġt erk", + "Ġte rk", + "Ġter k", + "Ġпод Ñĸб", + "ĠвÑĸд вÑĸд", + "à¥Į à¤Ł", + "è³ £", + "Ġب تÙĨ", + "Ġبت ÙĨ", + "Ġبع ضÛĮ", + "Ġبعض ÛĮ", + "ãģª ãģĬ", + "ä»ĸ åĢij", + "Ġtavs iye", + "ĠM ısır", + "ĠØ¥ Ø°", + "Ġ æIJ", + "Ġæ IJ", + "íķĺ ëĤĺ", + "ĠÙĪ Ø®", + "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", + "ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", + "Ġta kový", + "Ġtak ový", + "Ġtako vý", + "Ġबन न", + "Ġз ÑĢениÑı", + "ĠÙĪ ÙģÙĤ", + "ĠÙĪÙģ ÙĤ", + "ë¹Ħ ìķĦ", + "Ġпом ожеÑĤ", + "åĮĹ å¸Ĥ", + "dık ları", + "Ġ éĵģ", + "Ġéĵ ģ", + "Ġakt uálnÃŃ", + "Ġaktu álnÃŃ", + "Ġв в", + "ãĤĤ ãģªãģĦ", + "íĨµ ìĭł", + "ÏĦα Ïĥη", + "Ġìĥģ ëĮĢ", + "Ġ æł¡", + "Ġæł ¡", + "ãĢĤ éĤ£", + "ĠرÙĪسÛĮ Ùĩ", + "Ġtelev izyon", + "å¹´ é¾Ħ", + "ĠÐijоÑĢ иÑģ", + "리 ìĸ´", + "Ġz veÅĻej", + "ж но", + "ĠÐŀ ÑģÑĤ", + "ĠÐŀÑģ ÑĤ", + "ĠмÑĥж Ñĩин", + "Ġy eÅŁil", + "ĠСов еÑĤ", + "ĠСо веÑĤ", + "ĠB ÃĸL", + "ĠТак ож", + "Ġob nov", + "ĠпÑĢи надлеж", + "ĠвиÑģ нов", + "Ø· Ùħ", + "ĠìĹĨ ìĸ´", + "ĠM ùa", + "ä½ı å®ħ", + "åĮ» åѦ", + "Ġна ÑĢез", + "ĠнаÑĢ ез", + "ãĥĭ ï¾Ĩ", + "ĠM ặt", + "Ġvu ông", + "ä¸Ģ åĮº", + "ĠẢ nh", + "ÑĢ иÑĦ", + "ÑĢи ÑĦ", + "ä¿Ŀ éĻ©", + "ĠÏĩÏģή Ïĥη", + "åIJĮ æĦı", + "Ġ æīĵ", + "Ġæī ĵ", + "e tÄĽ", + "et ÄĽ", + "ĠÙĪ Ø°ÙĦÙĥ", + "ĠÑĤ иж", + "ĠÑĤи ж", + "ĠÎŁ ικο", + "ĠÎŁÎ¹ κο", + "ĠмÑĸÑģ ÑĨÑĸ", + "ĠÑĢебен ок", + "ĠÅŀ ah", + "عÙĦ ÙĪÙħ", + "l adıģ", + "la dıģ", + "lad ıģ", + "ladı ÄŁ", + "Ġg iden", + "Ġgi den", + "Ġgid en", + "лив оÑģÑĤÑĸ", + "ливо ÑģÑĤÑĸ", + "ÙĴ س", + "ĠT HB", + "ĠTH B", + "Ġmes lek", + "Âł ÐĿе", + "ÂłÐĿ е", + "μÏĨ Ïīνα", + "Ġ ÙĪاج", + "ĠÙĪ اج", + "ĠÙĪا ج", + "на ÑģлÑĸд", + "æĺŁ æľŁ", + "ÐĶ ж", + "ĠÑĢабоÑĤ аеÑĤ", + "ĠÑĢабоÑĤа еÑĤ", + "Ġs ánh", + "ìļ° 리", + "Ġا بÙĪ", + "Ġاب ÙĪ", + "çļĦ æĥħ", + "ĠìĻ¸ êµŃ", + "Ġk abil", + "Ġka bil", + "Ġkab il", + "еÑĢв Ñĭе", + "Ġgi Ãłu", + "ĠgiÃł u", + "Ġt á»ı", + "Ġtá» ı", + "Âł Ðij", + "å®Į æķ´", + "Ġmuž ů", + "ĠpomÄĽr nÄĽ", + "ĠÙħ خصÙĪص", + "ĠÐĶ ем", + "ãĤı ãĤĮãĤĭ", + "ãĤıãĤĮ ãĤĭ", + "ĠпÑĢи бÑĭ", + "ĠпÑĢиб Ñĭ", + "ĠکاÙħ Ù¾ÛĮ", + "ï¼ Ń", + "Ġt rh", + "Ġtr h", + "ĠÐij олÑĮÑĪ", + "´ :", + "и ваеÑĤÑģÑı", + "ив аеÑĤÑģÑı", + "ива еÑĤÑģÑı", + "иваеÑĤ ÑģÑı", + "Ġ ìĤ¬íķŃ", + "ĠìĤ¬ íķŃ", + "è¿Ľ ä¸ĢæŃ¥", + "ÑĨ ей", + "ÑĨе й", + "ãģ¾ ãģļ", + "аÑĤ елем", + "аÑĤе лем", + "éĮ ¯", + "Ġžal ob", + "ÑĨ ез", + "ÑĨе з", + "и нÑĥв", + "ин Ñĥв", + "инÑĥ в", + "Ġver ze", + "Ġve rze", + "Ġverz e", + "åĽŀ åĪ°", + "Ġd ược", + "ائ ÙĬÙĦ", + "ائÙĬ ÙĦ", + "sto upil", + "stoup il", + "论 æĸĩ", + "ĠÐŁ аÑĢи", + "ĠÐŁÐ°ÑĢ и", + "Ġдек оÑĢаÑĤив", + "اخ تÛĮ", + "اخت ÛĮ", + "ĠÑģÑĤ ÑĢем", + "ĠÑģÑĤÑĢ ем", + "ãĥ»âĶģãĥ»âĶģ ãĥ»âĶģãĥ»âĶģ", + "ĠÑģам ой", + "ĠÑģамо й", + "Ñĩ ÑĤо", + "ìĥģ ëĭ´", + "âī ¤", + "ÑĤ ого", + "ÑĤо го", + "ëIJ ¨", + "ı lacak", + "ıl acak", + "ä¸Ń ãģ«", + "ĠÏħÏĢάÏģÏĩ οÏħν", + "ĠвÑĸд бÑĥ", + "ĠвÑĸдб Ñĥ", + "çİ »çĴĥ", + "Ġвп еÑĢед", + "ĠPl zeÅĪ", + "Ú¯ اب", + "à¹Ģศ รษà¸IJ", + "ï¼Į æľĢ", + "Ùħ ÙĨÛĮ", + "ÙħÙĨ ÛĮ", + "çħ§ çīĩ", + "缮 å½ķ", + "ÑĢиÑĤ ÑĤÑı", + "âĢĮ اش", + "Ġ ëĮĢíļĮ", + "ĠëĮĢ íļĮ", + "ĠÅĻ adu", + "- ÑĤеÑħ", + "-ÑĤ еÑħ", + "Ġ ÙĬÙĪ", + "ĠÙĬ ÙĪ", + "Ġ à¹ģà¸ŀ", + "Ġà¹ģ à¸ŀ", + "ا ÙĥÙĨ", + "اÙĥ ÙĨ", + "Ġ기 ìŀIJ", + "Ġг Ñĸд", + "Ġìļ° 리ëĬĶ", + "Ġìļ°ë¦¬ ëĬĶ", + "Ø´ ÙħارÛĮ", + "Ø´Ùħ ارÛĮ", + "Ġt icari", + "Ġtic ari", + "âij ¢", + "ĠاÙĦ بد", + "ĠاÙĦب د", + "ĠÑĢаÑģ Ñĩ", + "Ġ اÙĦÛĮ", + "Ġا ÙĦÛĮ", + "ĠاÙĦ ÛĮ", + "Ġsü rede", + "Ġsür ede", + "Ġsüre de", + "Ġاع تر", + "Ġпо нÑıÑĤÑĮ", + "Ġпон ÑıÑĤÑĮ", + "γ κο", + "γκ ο", + "ï¼Į æ¯Ķ", + "ĠS eb", + "ĠSe b", + "Ġìĭł ê·ľ", + "æĶ¶ çĽĬ", + "ĠÙ¾ÛĮØ´ÙĨÙĩ اد", + "Îľ ÎijΤ", + "ÎľÎij Τ", + "ë°Ķ ìĿ´", + "ä¾Ľ åºĶ", + "б ин", + "би н", + "人 æ°Ĺ", + "ãģı ãĤī", + "Ġsk vÄĽl", + "Ġëĵ± ìŀ¥", + "æĭħ å½ĵ", + "Ġim kan", + "æ Ļ¨", + "æĻ ¨", + "ï¼Į çİ°åľ¨", + "Ġsrd ce", + "ìĤ° ìĹħ", + "Ġмод ели", + "æľ¬å½ĵ ãģ«", + "а нка", + "ан ка", + "анк а", + "Ġyür üy", + "ĠоÑĩ евид", + "ĠØŃ Ø³ÙĬÙĨ", + "ĠØŃس ÙĬÙĨ", + "Ñī аÑİÑĤ", + "Ñīа ÑİÑĤ", + "lé dl", + "léd l", + "ÑĨ о", + "ĠcÃŃ sa", + "ãģĭ ãģij", + "èĹ į", + "ĠØ®ÙĪاÙĩ ÙĨد", + "Ġmu že", + "Ġmuž e", + "Ġна коп", + "Ġнак оп", + "di ÄŁini", + "diÄŁi ni", + "er seniz", + "ers eniz", + "ersen iz", + "ĠпÑĢаÑĨÑĸв никÑĸв", + "д лÑı", + "Ġα ÏĥÏĦ", + "ĠαÏĥ ÏĦ", + "æ¶Ī è´¹", + "Ġ è¨Ģ", + "Ġè¨ Ģ", + "Ġb át", + "ĠØ´ ÙĥÙĦ", + "ĠÑģп иÑĢ", + "ÏĢο ÏĦε", + "Ġس اÙĦÙĩ", + "ĠساÙĦ Ùĩ", + "e kil", + "ek il", + "eki l", + "à¹ģ à¸Ĭม", + "ĠÏĥ ÏĦι", + "ĠÙħ Ø·ÙĦب", + "ĠÙħØ· ÙĦب", + "Ġìłķ ì±ħ", + "ê´Ģ ê³Ħ", + "å¹¹ ç·ļ", + "Ġ 京", + "éĢļ éģİ", + "ĠدÛĮ گراÙĨ", + "ĠدÛĮگر اÙĨ", + "ĠØ£ Ùħا", + "ĠØ£Ùħ ا", + "æĺ¯ ä¸į", + "ĠëĮĢ ëĭµ", + "ĠE rk", + "ĠEr k", + "p erty", + "per ty", + "pert y", + "ĠнаÑĩина еÑĤ", + "Ġê·¸ 리", + "ë£ ¡", + "ĠìĽ¹ ìĤ¬ìĿ´íĬ¸", + "ार न", + "æĦı è¯Ĩ", + "ĠС ÐŁ", + "Ġب اÙĬد", + "Ġبا ÙĬد", + "Ġbakım ından", + "/ TT", + "/T T", + "ĠÙģ اصÙĦÙĩ", + "ĠÙħØ« ÙĦا", + "ĠÙħØ«ÙĦ ا", + "Ġк вад", + "Ġкв ад", + "ĠØ´ اÛĮد", + "ĠuÄį itel", + "çĪ ½", + "Ġعرض Ùĩ", + "Ġ 交", + "ĠÑĩ еÑģÑĤÑĮ", + "ĠÑĩеÑģ ÑĤÑĮ", + "à¥Ī ?Ċ", + "à¥Ī? Ċ", + "ĠخاÙĨ Ùħ", + "et iyle", + "eti yle", + "Ġε γκα", + "ĠÑģÑĥ Ñīе", + "ĠìĿ¼ ìĸ´", + "ĠÐĽ ени", + "ĠÐĽÐµ ни", + "Ġ 声", + "Ġå£ °", + "á lie", + "ál ie", + "áli e", + "ãĥ¡ ãĥ¼ãĤ¸", + "à¥Ģ तर", + "à¥Ģत र", + "г алÑĸ", + "га лÑĸ", + "гал Ñĸ", + "ĠмÑĸ нÑĸм", + "ĠE ÅŁ", + "ĠпÑĢоиз оÑĪ", + "ÐĿ аÑģ", + "ÐĿа Ñģ", + "Ġب ÙĨÛĮ", + "ĠبÙĨ ÛĮ", + "让 æĪij", + "ĠпоÑģÑĤ еп", + "ĠìļĶ 구", + "ılı p", + "ıl ıp", + "Ġج ÙĪر", + "ĠجÙĪ ر", + "ĠëĮĢ ë¶Ģë¶Ħ", + "à¹ĩ à¸ķาม", + "à¹ĩà¸ķ าม", + "ĠÑĦ аÑģ", + "Ġìłķ ê·ľ", + "ла менÑĤ", + "лам енÑĤ", + "ÄŁ en", + "ÄŁe n", + "à¥ĩà¤Ĥ ĊĊ", + "à¥ĩà¤ĤĊ Ċ", + "ĠÐĺ ванов", + "ĠØŃ Ú©Ùħ", + "ĠØŃÚ© Ùħ", + "Ġ ï¾ļ", + "Ġï¾ ļ", + "ï¼ »", + "Ġne vid", + "Ġnev id", + "Ġла боÑĢаÑĤоÑĢ", + "à¸ŀย าà¸ļาล", + "Ġed iyorum", + "Ġediyor um", + "Ġhl avy", + "Ġhlav y", + "ĠEvrop ské", + "Ġph ái", + "Ġphá i", + "ãĥĵ ãĥ¼", + "ê´ij ìĹŃìĭľ", + "äº ľ", + "ØŃد اث", + "ĠпÑĢоÑĦ илакÑĤи", + "ro stÅĻed", + "rost ÅĻed", + "Ġм алÑĮ", + "Ġмал ÑĮ", + "Ġма лÑĮ", + "Ġmü dür", + "ا ساس", + "اس اس", + "ĠгалÑĥз Ñĸ", + "ี à¸Ł", + "Ġغذ اÛĮÛĮ", + "åŃIJ ä¾Ľ", + "Ġbah sed", + "ĠKrál ové", + "åį »", + "Ġ %,", + "Ġ% ,", + "ç½Ĺ æĸ¯", + "ë ļ", + "Ġ çij", + "Ġç ij", + "Ġξε ÏĦα", + "ĠÐŃ ÑĤи", + "ĠíĨµ íķ©", + "Ġاک تبر", + "ĠmÄĽsÃŃ ce", + "ìĪĺ ë¡ľ", + "ÑĦ Ñĸк", + "ÑĦÑĸ к", + "ĠÐĴ оз", + "ĠÐĴо з", + "ÑĩеÑģ ким", + "Ñĩе Ñģким", + "ÑĩеÑģки м", + "ìļ´ ëĵľ", + "Ġná klady", + "Ġnáklad y", + "ĠпоÑĤ ÑĢап", + "ĠÑĢÑĥ каÑħ", + "ĠÑĢÑĥк аÑħ", + "ι λο", + "ιλ ο", + "ĠG ül", + "ĠGü l", + "ë© ĺ", + "à¹ī ย", + "m akt", + "ma kt", + "mak t", + "ãĥ³ ãĥIJãĥ¼", + "ãĥ³ãĥIJ ãĥ¼", + "Ġн ÑĸÑı", + "ĠнÑĸ Ñı", + "ĠоÑĤ ÑĤен", + "m esinin", + "mes inin", + "mesi nin", + "mesini n", + "ĠвÑģп ом", + "Ġ ìĿ´ëĬĶ", + "ĠìĿ´ ëĬĶ", + "dy by", + "ãĤ¿ ãĥ³", + "âĹ İ", + "à¹īา หà¸Ļ", + "à¹īาห à¸Ļ", + "اد Ú¯ÛĮ", + "Ïĩ ία", + "Ġsna žÃŃ", + "Ġà¤ļ à¤ķ", + "μή μα", + "Ġ Ùĥر", + "ĠÙĥ ر", + "Ġκ οι", + "Ġκο ι", + "éĢ ¸", + "Ġne ust", + "Ġneu st", + "ĠÙĨظ اÙħÛĮ", + "ĠÙĨظاÙħ ÛĮ", + "åįļ çī©", + "Ġ ë²½", + "Ġë² ½", + "á½ ±", + "Ġì¶ľ ìĭľ", + "Ġar má", + "Ġarm á", + "ĠÙĩÙħ کارÛĮ", + "çļĦ æĥħåĨµ", + "çļĦæĥħ åĨµ", + "ÙĤ اÙħ", + "ÙĤ ب", + "Ġ éĤ£", + "ĠéĤ £", + "Ġë§ ¡", + "Ġo lası", + "Ġol ası", + "Ġola sı", + "β ÎŃÏģ", + "ä½ķ ãģĭ", + "ĠÑĥÑĩ еб", + "Ġв Ñĥз", + "Ġبر گز", + "Ġبرگ ز", + "' yi", + "'y i", + "Ġп ÑĢазд", + "ĠпÑĢа зд", + "ĠÐŀ ÑĢг", + "ĠÐŀÑĢ г", + "Ġ 并", + "Ġå¹ ¶", + "ĠÑģ ви", + "ĠÑģв и", + "ĠÙħÛĮ داÙĨ", + "ĠnaÅ¡e ho", + "ĠBA Åŀ", + "å» Ĭ", + "Ì Ī", + "ãģĵ ãģĿ", + "à¹ĩà¸Ļ à¸ŀ", + "οÏģ ειο", + "Ġбаг аÑĤ", + "γ ει", + "γε ι", + "μ είο", + "με ίο", + "à¹Īà¸ĩ à¸Ĭาà¸ķ", + "ĠHizmet leri", + "ĠAfr ika", + "ĠAf rika", + "Ġted bir", + ", ､", + "ä¸ī 级", + "ÐİÑĭÑŁN ÐİÑĭÑŁN", + "ĠÐļ ÑĢÑĸм", + "Ġa ray", + "Ġar ay", + "Ġara y", + "Ġböyle ce", + "к оÑĤ", + "ко ÑĤ", + "éĻ °", + "åĽ½ éļĽ", + "t ÄĽl", + "tÄĽ l", + "Ġp olis", + "Ġpol is", + "Ġpo lis", + "Ġu vol", + "Ġuv ol", + "ĠìĪĺ ê°ķ", + "çĶµ èĦij", + "Ġs ami", + "Ġsa mi", + "Ġsam i", + "Ġشاخ Ùĩ", + "Ġв ÑģÑĮого", + "ĠвÑģ ÑĮого", + "ĠØŃد اÙĤÙĦ", + "Ġ iken", + "Ġi ken", + "Ġik en", + "ãĤ¯ãĥ© ãĥĸ", + "Ġzá vod", + "Ġzáv od", + "ब ल", + "ë°° ìĨ¡", + "éĩĩ è´Ń", + "ëł ¬", + "Ġ ।ĊĊ", + "Ġ। ĊĊ", + "Ġ।Ċ Ċ", + "Ġê°ģ ê°ģ", + "Ġм ак", + "Ġма к", + "Ïģα Ïĥη", + "ĠiÅŁlem i", + "ãģĹ ãģ¦ãģĦãģ¾ãģĻ", + "ãģĹãģ¦ ãģĦãģ¾ãģĻ", + "ãģĹãģ¦ãģĦ ãģ¾ãģĻ", + "ĠP ek", + "ĠPe k", + "Ñİ Ð½", + "Ġvel kou", + "Ġvelk ou", + "åĬŀ çIJĨ", + "å®ĥ 们", + "Ġ èIJ¬", + "ĠèIJ ¬", + "ĠнаÑĢод Ñĥ", + "ĠнаÑĢ одÑĥ", + "Ġch ó", + "ĠH iç", + "ĠHi ç", + "Û³ Ûµ", + "Ġ รà¸Ńà¸ļ", + "Ġร à¸Ńà¸ļ", + "Û³ Û¶", + "à¸Ĥ ว", + "ä½į æĸ¼", + "ĠС ÑĤа", + "ĠСÑĤ а", + "ั à¸Ļม", + "ัà¸Ļ ม", + "ाप à¤ķ", + "ĠÑĥ ÑĢок", + "ãĤ¢ ãĥ¡ãĥªãĤ«", + "Ġз мож", + "Ġзм ож", + "sk ému", + "ské mu", + "ském u", + "Ġ è»Ĭ", + "Ġè» Ĭ", + "ĠاختÛĮ ار", + "ĠP Åĺ", + "л Ñıв", + "лÑı в", + "Ġ маз", + "Ġм аз", + "Ġма з", + "Ġözel liÄŁi", + "åij¼ ãģ°", + "Ġbir inin", + "Ġbiri nin", + "Ġод не", + "Ì Ĩ", + "ä»ĸ ãģ®", + "建 ç¯ī", + "поÑģ еÑĢед", + "ห ลà¸Ķ", + "หล à¸Ķ", + "å¤ļ ãģĦ", + "ÏĦή ÏĥειÏĤ", + "Ġر ÙĪÙĨد", + "ĠرÙĪ ÙĨد", + "èģ ½", + "ì¤ij ìĹIJ", + "ìĬ¤ íĭ°", + "Ġз вÑĸÑĤ", + "Ġзв ÑĸÑĤ", + "ĠаÑĢ ÑĤи", + "Ġc Æ°á»Ŀi", + "ĠcÆ° á»Ŀi", + "ın dır", + "ınd ır", + "Ġг олод", + "Ġгол од", + "ا زد", + "از د", + "à¹Īาว ว", + "ãĥ¡ ãĥ©", + "عÙĨ ÙĪاÙĨ", + "% )Ċ", + "%) Ċ", + "ĠÑħолод илÑĮ", + "人 们", + "C Ä°", + "ÐĹ Ð°Ð¿", + "ÐĹа п", + "ĠpÅĻ isp", + "ĠpÅĻi sp", + "Ġdurum larda", + "ÑĢ Ñĸд", + "ÑĢÑĸ д", + "Âł У", + "Ġε ÏĨαÏģ", + "Ġs prav", + "Ġsp rav", + "Ġspr av", + "ĠоÑĤÑĢим аннÑı", + "ï¼Į 没æľī", + "о вала", + "ов ала", + "ова ла", + "овал а", + "Ġng ại", + "ãĢĤ 大", + "Ġ даеÑĤ", + "Ġд аеÑĤ", + "Ġда еÑĤ", + "ĠpÃŃs em", + "ÑĨ ÑıÑĤÑĮ", + "ÑĨÑı ÑĤÑĮ", + "ov nÄĽ", + "ë¦ ī", + "Ġê² ģ", + "Ñģ ÑĤин", + "ÑģÑĤ ин", + "ÑģÑĤи н", + "ĠS ayı", + "ĠSa yı", + "ĠSay ı", + "ãĢĭ çļĦ", + "Ġyol uyla", + "Ġyolu yla", + "елеÑĦ он", + "Ġr áno", + "Ġrá no", + "Ġíĸī ëıĻ", + "ĠاÙĦخاÙħ سة", + "Ġповин на", + "ÅĻ ila", + "ÅĻi la", + "ÅĻil a", + "Ġà¤ļ रण", + "Ġà¤ļर ण", + "Ġبرگ زار", + "Ġبرگز ار", + "ìļ´ ëį°", + "à¹Ģà¸Ľ à¸Ńร", + "Ġdal eko", + "led nÃŃ", + "åIJį 稱", + "лив ÑĸÑģÑĤÑĮ", + "ливÑĸ ÑģÑĤÑĮ", + "Ġ몸 ìĿĦ", + "о ÑĢÑĸв", + "оÑĢ Ñĸв", + "оÑĢÑĸ в", + "Ц е", + "بد Ø£", + "ë°ĺ 기", + "k rát", + "kr át", + "ä¸į 足", + "Ġolduk ları", + "len iyor", + "Ġìĭľ íĸī", + "ĠпÑĢи нимаÑĤÑĮ", + "à¸Ĥà¸Ńà¸ĩ ร", + "ÏĪ ει", + "ÏĪε ι", + "Ġ ẩn", + "ت س", + "ĠÑĤ ай", + "ĠÑĤа й", + "Ġнев озможно", + "åıĬ ãģ³", + "r oti", + "ro ti", + "rot i", + "ï½ Ń", + "д ом", + "до м", + "ой но", + "å£ Ĭ", + "说 çļĦ", + "Ġsk oro", + "ni ÄįnÃŃ", + "niÄį nÃŃ", + "ĠProf es", + "ĠÑħ ÑĢониÑĩеÑģ", + "Ġ주 문", + "ĠZ n", + "ĠÑģ лой", + "ĠÑģл ой", + "ĠÑģло й", + "Îł Ïģο", + "æĮĩ æķ°", + "ĠпеÑĢ еÑĪ", + "ĠпеÑĢе ÑĪ", + "à¥ģà¤ķ स", + "Ġê°Ģ ìłķ", + "Ġ íķĺë©´", + "Ġíķĺ ë©´", + "Û±Û¹ Û´", + "к Ñĥл", + "кÑĥ л", + "ÙĬ ÙĦا", + "ÙĬÙĦ ا", + "ĠدÙĪب ارÙĩ", + "| l", + "ĠÐľ Ñĥ", + "н ила", + "ни ла", + "нил а", + "ãģ¦ ãģĦãģ¾ãģĻ", + "ãģ¦ãģĦ ãģ¾ãģĻ", + "m acı", + "ma cı", + "mac ı", + "ãģŁ ãģ¡ãģ¯", + "ãģŁãģ¡ ãģ¯", + "ĠاÙĦÙĥ تاب", + "移 åĭķ", + "λ μ", + "_ ï¼ı", + "Ġê°Ģ ìŀħ", + "èħ ¾", + "ĠпÑĢез иденÑĤ", + "Ġë¶Ħ ìķ¼", + "a hy", + "ah y", + "Å¡et ÅĻenÃŃ", + "Å¡etÅĻ enÃŃ", + "éĵ º", + "ĠpÅĻÃŃ ro", + "Ðķ Т", + "ĠìļĶ ì²Ń", + "Ġmoh lo", + "Ġmohl o", + "å¿ĥ çIJĨ", + "Ġvysok é", + "& uuml", + "ÏĦ ικα", + "ÏĦικ α", + "ÏĦι κα", + "ìĹħ ì²´", + "ãģ§ ãģĤ", + "ราย à¸ĩาà¸Ļ", + "ĠpÅĻÃŃspÄĽ v", + "Ġet miÅŁtir", + "Ġetm iÅŁtir", + "ĠetmiÅŁ tir", + "她 们", + "ÏĢ λα", + "ÏĢλ α", + "ứ a", + "Ġ 说", + "Ġè¯ ´", + "ĠÑģо Ñģед", + "ĠÑģоÑģ ед", + "åĩ ī", + "ĠÐł е", + "åİŁ æĿ¥", + "ĠÐIJÑĢ Ñħ", + "ب ÙĬÙĨ", + "بÙĬ ÙĨ", + "åľ° 说", + "Ġ ört", + "Ġö rt", + "Ġör t", + "ĠΣ εÏĢ", + "ĠΣε ÏĢ", + "ÂŃ ÙĩاÛĮ", + "ĠاÙĦ اÙĤتص", + "å°½ 管", + "ÑĤ Ñĭй", + "ÑĤÑĭ й", + "t ains", + "ta ins", + "tain s", + "ÙĢ ÙĦ", + "ç§ijæĬĢ æľīéĻIJåħ¬åı¸", + "æı ®", + "ัà¸ķ à¸ĸ", + "á»Ĺ ng", + "ล าà¸Ķ", + "ลา à¸Ķ", + "æļ ®", + "ĠÙĨÙģ سÙĩ", + "ĠÙĨÙģس Ùĩ", + "Ġ çľĭ", + "Ġçľ ĭ", + "Ġ ãģ¿", + "Ġt arım", + "Ġtar ım", + "Û±Û¹ Ûµ", + "ĠÎ Ĭ", + "Ġkom plex", + "Ġkomple x", + "Ġkomp lex", + "ĠNh Ä©", + "è´¹ çĶ¨", + "Ġکاربر اÙĨ", + "ÅĪ ovánÃŃ", + "Ġ ků", + "Ġk ů", + "д ап", + "да п", + "Îķ Χ", + "ê·¸ ëŀĺ", + "Ġdön dü", + "人 åĵ¡", + "ĠT iá»ĥu", + "ĠÙĪÛĮر اÛĮØ´", + "Ġö ngör", + "ĠÙĪ غÙĬر", + "ĠÑģ кÑĢÑĭ", + "ĠÑģк ÑĢÑĭ", + "âĢIJ '", + "Ġ немÑĥ", + "Ġн емÑĥ", + "Ġне мÑĥ", + "Ġнем Ñĥ", + "ĠH á»ĩ", + "ĠHá» ĩ", + "Ġdüzen li", + "ĠsoutÄĽ že", + "ãĢģ ãĥŀ", + "ÏĦο μα", + "ÄĽ lÃŃ", + "ÄĽl ÃŃ", + "ĠØ£ ÙĦÙħاÙĨ", + "ĠØ£ÙĦ ÙħاÙĨ", + "çł ²", + "Ġtr Ãł", + "Ġ ä¸ĸçķĮ", + "Ġä¸ĸ çķĮ", + "ay ız", + "ayı z", + "ım lı", + "ĠاÙĦØ£ Ùģ", + "íķĺ ëĬĶëį°", + "íķĺëĬĶ ëį°", + "в ано", + "ва но", + "ван о", + "ĠpÅĻi Äįemž", + "Ùĥ ÙĬب", + "ÙĥÙĬ ب", + "ĠмаÑĤ емаÑĤи", + "м ени", + "мен и", + "ме ни", + "ĠпÑĢо екÑĤÑĥ", + "ĠпÑĢоек ÑĤÑĥ", + "ĠпÑĢоекÑĤ Ñĥ", + "ี à¹Ĥà¸Ń", + "ีà¹Ĥ à¸Ń", + "о Ñĥ", + "ĠاÙĦ شرÙĥØ©", + "ĠاÙĦشر ÙĥØ©", + "æ³ £", + "ÙĪÙĤ ÙĬت", + "ÑĪ ив", + "ÑĪи в", + "Ġperson el", + "Ġpersone l", + "Ġperso nel", + "Ø´ تر", + "شت ر", + "à¸Ķ า", + "Ġë ª½", + "Ġëª ½", + "åĿIJ åľ¨", + "о ке", + "ок е", + "Ġë§Ī ë²ķ", + "ĠØ£ ÙĨا", + "ĠØ£ÙĨ ا", + "ëł µ", + "ĠÙħب اÙĨÛĮ", + "èĭ ¹æŀľ", + "Ġ ศร", + "Ġศ ร", + "ĠÐĽ ÑĥÑĩ", + "ĠÐĽÑĥ Ñĩ", + "ÎŁ ΥΣ", + "ÎŁÎ¥ Σ", + "ĠÄį á", + "ãģĽ ãģ¦", + "Ġk Ä±ÅŁ", + "Ġkı ÅŁ", + "ÑĪ ев", + "ÑĪе в", + "æĮĩ 导", + "à¹ģละ ม", + "Ġvol eb", + "Ġvole b", + "ĠÑģи лÑĭ", + "ĠÑģил Ñĭ", + "Ġdruh ou", + "Ġdru hou", + "Ġ ì°¬", + "Ġì° ¬", + "ĠìŀĪ ìĿĮ", + "Î¥ Σ", + "ä¸į å®ī", + "Ġ ìĹĨìĿĮ", + "ĠìĹĨ ìĿĮ", + "Ġde term", + "Ġdet erm", + "Ġdeter m", + "ĠاÙĦÙħ عÙĦÙĪÙħات", + "íĺ ¹", + "âĻ ¡", + "à¥įब न", + "Ġخش Ú©", + "ĠN ová", + "ĠNo vá", + "ĠNov á", + "ĠÑĦÑĥнда менÑĤ", + "ĠпÑĢогÑĢам и", + "ĠпÑĢогÑĢа ми", + "ĠпÑĢог ÑĢами", + "ĠعÙĦ ÙĬÙĥ", + "ĠعÙĦÙĬ Ùĥ", + "। ĊĊ", + "।Ċ Ċ", + "Ġver iyor", + "Ġveri yor", + "Ġ ÑĶв", + "ĠÑĶ в", + "ĠìŀĪ ëĭ¤ê³ł", + "ĠìŀĪëĭ¤ ê³ł", + "ĠاÙĦØ£Ùħ رÙĬÙĥÙĬ", + "ĠاÙĦØ£Ùħر ÙĬÙĥÙĬ", + "Ġå¤ĸéĥ¨ ãĥªãĥ³ãĤ¯", + "Ġ ä¿®", + "Ġп ÑĥÑĤи", + "ĠпÑĥÑĤ и", + "ĠпÑĥ ÑĤи", + "Ġο Ïģγ", + "ĠоÑģнов ном", + "Ġ наÑĢÑĥж", + "Ġна ÑĢÑĥж", + "ĠнаÑĢ Ñĥж", + "Ġми ÑĢе", + "ĠмиÑĢ е", + "o vÄĽt", + "ov ÄĽt", + "ovÄĽ t", + "Ġíĥ IJ", + "Ġsok ak", + "Ġspolup ráci", + "ÐĶ Ðļ", + "Ġ åĺ", + "Ġå ĺ", + "âĸįâĸįâĸįâĸįâĸįâĸįâĸįâĸį âĸįâĸįâĸįâĸįâĸįâĸįâĸįâĸį", + "Ġ ³³³³", + "ĠÂł ³³³", + "Ġ³³ ³³", + "Ġ³³³ Âł", + "Ġhay ır", + "Ġ ìĻĶ", + "ĠìĻ Ķ", + "æĤ¨ çļĦ", + "æĮ º", + "Ġ 민주", + "Ġ민 주", + "Ġhot elu", + "Ġhotel u", + "ี à¸ľ", + "ìŀIJ ëıĻ", + "ä¼¼ çļĦ", + "ÎŃν ÏĦÏģο", + "ÎŃνÏĦ Ïģο", + "Ø´ ÙĪ", + "Ġ é¤", + "Ġé ¤", + "Ġ λι", + "Ġλ ι", + "Ġol maktadır", + "Ġolmak tadır", + "ĠоÑģ веÑī", + "Ġв ина", + "Ġви на", + "Ġвин а", + "Ġخاص Ø©", + "r ana", + "ra na", + "ran a", + "γÏģα ÏĨή", + "γÏģαÏĨ ή", + "ÑĨ еÑģ", + "ÑĨе Ñģ", + "ĠdoÄŁru lt", + "ĠdoÄŁr ult", + "ĠÙĤرار داد", + "ĠÙĤرارد اد", + "ĠÐļ ал", + "ĠÐļа л", + "ê²½ ìłľ", + "Ïĩ ÏĮ", + "Ñĥ ÑİÑīий", + "ÑĥÑİ Ñīий", + "ÑĥÑİÑī ий", + "ëĭ ĺìĿ´", + "ëĭĺ ìĿ´", + "ë Į", + "л аз", + "ла з", + "Ġng ừng", + "i sku", + "is ku", + "isk u", + "ìĦł ê±°", + "ĠÑįлек ÑĤÑĢон", + "ĠÑįлекÑĤÑĢ он", + "ĠV oj", + "ĠVo j", + "н Ñıми", + "нÑı ми", + "ĠÙĪ Ø£ÙĨ", + "ĠÙĪØ£ ÙĨ", + "äº Ń", + "绣 计", + "ĠÅŁ iÅŁ", + "ĠÅŁi ÅŁ", + "ãĢį çļĦ", + "æŃ ¯", + "Ġкол лек", + "Ġд виж", + "Ġдв иж", + "Ġдви ж", + "Ġn á»Ńa", + "Äįas ÃŃ", + "Ġs onu", + "Ġso nu", + "Ġson u", + "ĠмеÑħ анÑĸз", + "ž ený", + "že ný", + "žen ý", + "Ġза ÑģÑĤÑĥп", + "ĠзаÑģÑĤ Ñĥп", + "ê´Ģ 볨", + "ĠÑĤоваÑĢ Ñĸв", + "ĠÑĤов аÑĢÑĸв", + "Ġ ì¼ĢìĿ´", + "Ġì¼ ĢìĿ´", + "à¥ģà¤Ĺ त", + "Ġzá sob", + "мов ÑĸÑĢ", + "u fac", + "uf ac", + "ů ležit", + "Ġви гоÑĤов", + "Ġвиг оÑĤов", + "ĠاÙĦÙĨ ÙĪ", + "Ġع اÙħا", + "ĠعاÙħ ا", + "æģ ¨", + "ĠìĿ´ë¯¸ ì§Ģ", + "Ġt voÅĻ", + "Ġtv oÅĻ", + "Ġvyu žitÃŃ", + "Ġgel iÅŁim", + "ĠgeliÅŁ im", + "ì³ ¤ëĭ¤", + "หà¸Ļ à¸Ńà¸ĩ", + "ĠìĿ¸ ìłķ", + "à¥į दर", + "à¥įद र", + "ĠпеÑĢед а", + "ĠпеÑĢе да", + "ĠздÑĸйÑģ неннÑı", + "ÙĨ ع", + "è¡£ æľį", + "Ġl oa", + "Ġlo a", + "íĻ Ī", + "èĭ± åĽ½", + "ĠD ruh", + "ĠDr uh", + "Ø® اÙĨ", + "д ам", + "да м", + "аÑĤелÑĮ нÑĭÑħ", + "θ ÏģÏİ", + "ĠØ£ Ùħر", + "ĠØ£Ùħ ر", + "ĠÅĻ ada", + "Ġbul uÅŁ", + "ĠÑĤÑĢанÑģп оÑĢ", + "ĠÑĤÑĢанÑģ поÑĢ", + "ĠÙĤ تÙĦ", + "ĠTa rif", + "ĠTar if", + "R us", + "Ru s", + "ĠзаÑģ Ñĸд", + "ĠÄ° h", + "l eyin", + "ley in", + "Ġvy rá", + "ĠD ÄĽ", + "иб ли", + "a vou", + "av ou", + "avo u", + "ĠÐĵеÑĢ м", + "н емÑĥ", + "не мÑĥ", + "нем Ñĥ", + "Ġкон ÑĨеп", + "ĠконÑĨе п", + "ĠÙĤ ادر", + "Ġsou bor", + "Ġl á»iji", + "Ġ çµIJ", + "Ġçµ IJ", + "л еннÑĭй", + "лен нÑĭй", + "κ Ïħ", + "Ġдопом аг", + "à¸ŀวà¸ģ à¹Ģà¸Ĥ", + "Ġqu ang", + "Ġq uang", + "Ġqua ng", + "Ġquan g", + "ĠØ· ÙĦا", + "ĠØ·ÙĦ ا", + "Ġ éĩĮ", + "Ġé ĩĮ", + "Ġéĩ Į", + "ĠÙĨÙħÙĪد ار", + "ĠÅŁ ar", + "ĠÑģп Ñĸл", + "ÂŃ n", + "ì§Ģ ìļĶ", + "åīį å¾Ģ", + "åħ³ éĶ®", + "å®ŀ åľ¨", + "éŁ³ 楽", + "ĠÙħسئ ÙĦÙĩ", + "Ġy eme", + "Ġye me", + "Ġyem e", + "ĠÑĪ аÑħ", + "기 ìĪł", + "Ġ สำà¸Ļ", + "Ġสำ à¸Ļ", + "ĠÙĪرزش ÛĮ", + "ĠÙĪرز Ø´ÛĮ", + "ãģĹ ãģŁãĤī", + "ãģĹãģŁ ãĤī", + "ί ÏĥÏī", + "ίÏĥ Ïī", + "о кон", + "ок он", + "око н", + "ãģŁ ãĤī", + "ĠØ¥ ÙĦÙĬÙĩ", + "ĠØ¥ÙĦÙĬ Ùĩ", + "ĠØ¥ÙĦ ÙĬÙĩ", + "Ġآذ رب", + "Ġr á»Ŀi", + "Ġod ak", + "Ġм огÑĥ", + "Ġмог Ñĥ", + "Ġмо гÑĥ", + "ĠÚ¯ ÙĨ", + "è² ¼", + "ed la", + "edl a", + "Ġоп ÑĭÑĤ", + "la maktadır", + "lamak tadır", + "å°¼ äºļ", + "éĥ½ ä¼ļ", + "ĠÎĺε ÏĥÏĥα", + "Ġв ог", + "Ġво г", + "ç»Ī äºİ", + "ĠÑĥÑĢов не", + "Ġv lak", + "Ġvl ak", + "ĠØ¢ ÙĦØ©", + "ĠØ¢ÙĦ Ø©", + "Ġε ιδ", + "Ġει δ", + "â ĩ", + "д ÑĥÑĤ", + "дÑĥ ÑĤ", + "Ñĸ нг", + "Ñĸн г", + "ĠØ£Ùħ رÙĬÙĥÙĬ", + "ĠØ£Ùħر ÙĬÙĥÙĬ", + "از ÙĨد", + "Ġب اÙĦØ£", + "ĠباÙĦ Ø£", + "Ġत न", + "Ġkay det", + "룬 리", + "Ġ drž", + "Ġd rž", + "Ġdr ž", + "Ġп енÑģ", + "Ġпен Ñģ", + "ĠpÅĻÃŃ Äį", + "ĠТ олÑĮко", + "Ġб аÑĤаÑĢ", + "Ġба ÑĤаÑĢ", + "éĵģ è·¯", + "ĠÙ¾ÛĮ ÚĨ", + "ĠÎĵ εÏī", + "ĠαÏħ ÏĦά", + "Äŀ I", + "ĠакÑĤив но", + "ÎĹ ÎľÎij", + "ÎĹÎľ Îij", + "Ġvar lık", + "Ġ åıª", + "Ġåı ª", + "ĠзаÑī иÑĤÑĭ", + "ĠзаÑīиÑĤ Ñĭ", + "л им", + "ли м", + "ĠÙħشاÙĩ دة", + "и ком", + "ик ом", + "Ġì¡° ìĤ¬", + "о ген", + "ог ен", + "Ġm ấy", + "g ii", + "gi i", + "èĽ ĩ", + "ĠØ® ÙĪÛĮØ´", + "ĠØ®ÙĪ ÛĮØ´", + "Ġn ová", + "Ġno vá", + "Ġnov á", + "к овой", + "ков ой", + "ково й", + "Ġkan ıt", + "Ġkanı t", + "éĿ¢ è®®", + "ĠرÙĪست ا", + "ìĸ´ ê°Ģ", + "ĠоÑĤноÑĪ ениÑı", + "Ġhodnot y", + "ÙĪ رات", + "ÙĪر ات", + "ÙĪرا ت", + "ĠpÅĻ ÃŃst", + "ĠpÅĻÃŃ st", + "Ġth á»į", + "Ġthá» į", + "Ġçık art", + "Ġçıkar t", + "Ġçı kart", + "о обÑĢаз", + "Ġnem ÄĽl", + "Âł ro", + "ĠدÙĪÙĦ تÛĮ", + "ĠدÙĪÙĦت ÛĮ", + "ี ,", + "ä¸Ģ 度", + "ia omi", + "iao mi", + "åĹ İ", + "Ùı ع", + "ĠваÑĢи ан", + "Ġpod aÅĻilo", + "ĠëĤĺ ê°Ģ", + "èIJ¥ ä¸ļ", + "ĠабÑģолÑİÑĤ но", + "Ġë¸Į ëĿ¼", + "ĠгоÑĢ из", + "a ģın", + "aÄŁ ın", + "aģı n", + "Ġyer ini", + "Ġyeri ni", + "à¹īา à¸Ļà¸Ķ", + "à¹īาà¸Ļ à¸Ķ", + "æIJ ¬", + "Ġb alık", + "Ġbal ık", + "Ġba lık", + "ĠÅŁ ans", + "认 è¯Ĩ", + "Ġistedi ÄŁiniz", + "Ġjist ÄĽ", + "Ġ ìĪĺê°Ģ", + "ĠìĪĺ ê°Ģ", + "ï¼Į ä¸Ĭ", + "à¤ľ ब", + "Ġви Ñıви", + "ĠвиÑıв и", + "ë§ ¥", + "ãģĹ ãģ¦ãĤĭ", + "ãģĹãģ¦ ãĤĭ", + "ÙĬÙĥ ا", + "ĠH üs", + "c ının", + "Ġश त", + "ĠÑĢаÑģп олаг", + "ĠÑģпÑĢав ж", + "ืà¸Ń à¸ĸ", + "ĠвеÑĢ ÑĤик", + "Ġvy stav", + "ĠÑĢе алÑĸзаÑĨÑĸÑĹ", + "в ами", + "ва ми", + "ãĤ¹ ãĥĨãĤ£", + "ãĤ¹ãĥĨ ãĤ£", + "ëħ ģ", + "ĠÑĢе ÑĩÑĸ", + "ĠÑĢеÑĩ Ñĸ", + "Ùģ اÙĦ", + "िà¤ķ à¤Ł", + "ĠвозÑĢаÑģÑĤ е", + "к аÑģ", + "ка Ñģ", + "ĠÐĺ Ñģ", + "Ġл Ñĸк", + "ĠлÑĸ к", + "ĠÏĥη μαν", + "м енÑĤÑĥ", + "мен ÑĤÑĥ", + "менÑĤ Ñĥ", + "н ÑıÑİÑĤ", + "нÑı ÑİÑĤ", + "æŁ ´", + "Ġθ εÏī", + "Ġθε Ïī", + "çĬ¯ 罪", + "ĠÙĤ طر", + "ĠÙĤØ· ر", + "ÐĶ ÐIJ", + "- |", + "Ġ ÑģÑĤÑĸ", + "ĠÑģ ÑĤÑĸ", + "ĠÑģÑĤ Ñĸ", + "Ġu yum", + "Ġuy um", + "Ġpot ÅĻeba", + "ĠpotÅĻeb a", + "ĠعÙħÙĦ ÛĮات", + "ĠعÙħÙĦÛĮ ات", + "å¥ ª", + "ا خر", + "اخ ر", + "ĠÚ© ساÙĨÛĮ", + "ت Ùħر", + "تÙħ ر", + "ÑĮ еÑĢ", + "ÑĮе ÑĢ", + "ĠN ez", + "ĠNe z", + "íļĮ ìĤ¬", + "ĠBank ası", + "е гÑĢа", + "ег ÑĢа", + "à¸Ĥà¸ĵะ à¸Ĺ", + "åIJĪ æł¼", + "ĠìŬ룬 ë¶Ħ", + "y asal", + "ya sal", + "yas al", + "Ġ è¡ĮæĶ¿", + "Ġè¡Į æĶ¿", + "åĬ ī", + "dık tan", + "ãĤ¢ãĥ« ãĥIJ", + "ĠاÛĮÙĨ ÚĨ", + "Ġdij ital", + "å° ĺ", + "ĠÑĢаз меÑī", + "ĠÑĢазм еÑī", + "ĠкÑĸлÑĮ коÑģÑĤÑĸ", + "ĠEv ropy", + "ĠEvrop y", + "ĠÑĢоз ви", + "ÑİÑī ÑĥÑİ", + "Ġ ong", + "Ġo ng", + "Ġon g", + "Ġhe psi", + "Ġhep si", + "v ailability", + "vail ability", + "Ġتص ÙħÙĬÙħ", + "ĠتصÙħ ÙĬÙħ", + "Ñĥ йÑĤе", + "Ñĥй ÑĤе", + "ह ल", + "ĠÅ¡ iro", + "Ġp ás", + "Ġpá s", + ";; ;;;;", + ";;;; ;;", + ";;; ;;;", + "éħį åIJĪ", + "ĠاÙĦعاÙĦÙħ ÙĬØ©", + "ÐĴ о", + "h af", + "ha f", + "l áv", + "lá v", + "Ġb ì", + "Ġm ůj", + "Ġmů j", + "ê»ĺ ìĦľ", + "ÂłB f", + "ĠÑģпÑĢоÑģ ил", + "âĢĮÚ©ÙĨ ÙĨدÙĩ", + "âĢĮÚ©ÙĨÙĨد Ùĩ", + "ÙĨد ÙĬØ©", + "ÙĨدÙĬ Ø©", + "çī¹ èī²", + "Ġìķ ¨", + "ุษ ย", + "ĠФ оÑĢ", + "пиÑģ ок", + "пи Ñģок", + "u žel", + "ım lar", + "çĬ¶ æ³ģ", + "Ġãĥ¬ ãĥĩãĤ£ãĥ¼ãĤ¹", + "Ñħ ови", + "Ñħов и", + "Ñħо ви", + "ÂłK Äį", + "Ñĩ им", + "Ñĩи м", + "Ġت ÙĪÙħ", + "ĠتÙĪ Ùħ", + "à¹Ģà¸ģษ à¸ķร", + "Ġìĭ± ê¸Ģ", + "Ùħ ارات", + "Ùħا رات", + "Ùħار ات", + "ê nh", + "ên h", + "ĠÅĻ id", + "æĬ ¬", + "Ñģ иÑİ", + "Ñģи Ñİ", + "æħ İ", + "Ġçev re", + "Ġçevr e", + "ãĥĪ ãĥ«", + "Ġyıl dır", + "Ġzá znam", + "Ġzáz nam", + "æľº åľº", + "Ġпо ÑĶ", + "ĠвÑĭ ÑĢаÑīи", + "Ġ Ùģع", + "ĠÙģ ع", + "ë »", + "Ġدار ÛĮÙħ", + "ï¼Į æĽ´", + "Ġзем ли", + "اب ÙĤات", + "ابÙĤ ات", + "Ġm á»Ŀi", + "Ġmá» Ŀi", + "k ých", + "ký ch", + "ÙĦ اة", + "ÙĦا Ø©", + "å¸ ½", + "بر اÙĩÙĬÙħ", + "Ġпо баÑĩ", + "Ġпоб аÑĩ", + "Ġпоба Ñĩ", + "ाà¤ĩ म", + "à¹Īาà¸ĩ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", + "ĠìĦ¸ ìĥģ", + "Ġпомог аеÑĤ", + "ĠÏĦÏĮ Ïĥο", + "æĸ ·", + "ĠÙģر اÙĪ", + "à¹Ħà¸Ľ ย", + "erg isi", + "Ġ éĻIJ", + "ĠéĻ IJ", + ". xz", + ".x z", + "ĠÑģл ÑĥÑħ", + "ĠÑģлÑĥ Ñħ", + "е коном", + "ек оном", + "еко ном", + "ĠNh ất", + "± Ø·", + "ĠëĪĪ ìĿĦ", + "Ġ íļĮìĤ¬", + "ĠíļĮ ìĤ¬", + "Ñ ĵ", + "Ġ åIJįçĦ¡ãģĹ", + "ĠåIJį çĦ¡ãģĹ", + "Ġομά δα", + "ĩ Į", + "li ÄŁinin", + "liÄŁi nin", + "liÄŁ inin", + "liÄŁini n", + "liÄŁin in", + "ع اÙĨ", + "عا ÙĨ", + "Ġز ÙĨÛĮ", + "ĠزÙĨ ÛĮ", + "T ôi", + "Ġet ki", + "Ġetk i", + "ĠìĹ° ëĿ½", + "Ġкон ÑĨа", + "è° ĭ", + "Ġзем лÑı", + "íĻĺ ê²½", + "ĠÙħÚ© اÙĨÛĮ", + "ĠÙħکاÙĨ ÛĮ", + "çĸ ²", + "Ġ ç¢", + "Ġç ¢", + "Ġkur ulan", + "Ġkurul an", + "Ġkuru lan", + "ؤ ÙĪÙĦ", + "د Ùī", + "ĠاÙĦÙħÙĨ Ø·ÙĤØ©", + "Ġn ắng", + "ÐŁ Ðļ", + "ол ай", + "ола й", + "Y K", + "åij Ĩ", + "λ αν", + "λα ν", + "西 çľģ", + "ĠÎĴ αÏĥ", + "ĠÎĴα Ïĥ", + "ĠíĻķ ìĭ¤", + "Z D", + "п Ñĸд", + "Ġ наÑĩе", + "Ġн аÑĩе", + "Ġна Ñĩе", + "ĠнаÑĩ е", + "Ġ ÏĦά", + "ĠÏĦ ά", + "å½ »", + "âĢŀ D", + "Ġ èĩº", + "Ġèĩ º", + "Ġна ÑĪей", + "ĠнаÑĪ ей", + "ĠtÃŃm to", + "Ġت سÙħ", + "Ġتس Ùħ", + "Ïģθ Ïģο", + "令 人", + "ĠP azar", + "ĠPa zar", + "ĠPaz ar", + "ãĤĵ ãģ¨", + "ç«ĭ åĪ»", + "Âģ @", + "Ġb ắc", + "ìĬ¤ íħĮ", + "Ġkadın lar", + "fig ur", + "ãģ¤ ãģ¶", + "Ġæµ Ļæ±Ł", + "Ġдек ÑĸлÑĮ", + "è¡ Ŀ", + "ยà¸Ļ à¹ģà¸Ľà¸¥à¸ĩ", + "o let", + "ol et", + "ole t", + "Ġned ok", + "n amen", + "name n", + "na men", + "nam en", + "åħĦ å¼Ł", + "ืà¸Ń à¸Ĥ", + "èĤ ĥ", + "Ġb üny", + "Ġbü ny", + "ĠÑĢад Ñıн", + "ãĢģ äºĮ", + "ан нÑİ", + "Ġ æīĭæľº", + "Ġæīĭ æľº", + "ĠоÑģ лож", + "Ġо глÑı", + "Ġог лÑı", + "Ġسب ز", + "Ġaktiv it", + "Ġà¤ı प", + "ç« ľ", + "Ġd iren", + "Ġdi ren", + "Ġdir en", + "Ġdire n", + "i в", + "ĠY atırım", + "ÑĨÑĸй на", + "Ġдо мов", + "Ġдом ов", + "ẳ n", + "ĠC oÄŁraf", + "Ùģ ÙĪ", + "æ°Ĺ ãģ«åħ¥", + "ç§ģ ãģ®", + "ï½ į", + "à¥Į ड", + "ĠÐĵÑĢи гоÑĢ", + "ĠP eygamber", + "ĠPey gamber", + "Ġα γα", + "Ġαγ α", + "Ġef ekt", + "ĠìŀĪ ìĸ´ìĦľ", + "ĠìŀĪìĸ´ ìĦľ", + "ĠплаÑĤ еж", + "ĠT rab", + "ĠTr ab", + "ĠTra b", + "o very", + "ov ery", + "ove ry", + "over y", + "âĢ¦âĢ¦ ãĢĤ", + "Ġyap maya", + "Ġнайб ÑĸлÑĮ", + "ĠÙħÙĨ زÙĦ", + "ÙĪ ÙĬÙĥ", + "ÙĪÙĬ Ùĥ", + "ıl dıģında", + "ıldı ģında", + "ıldıģı nda", + "ĠpÅĻÃŃpad nÄĽ", + "ĠμÏĢο ÏģοÏį", + "Ġëĵľ ëĿ¼ë§Ī", + "Ġë°© 문", + "ĠС им", + "ĠСи м", + "Ú© ات", + "کا ت", + "е ком", + "ек ом", + "еко м", + "ر ÙĬع", + "رÙĬ ع", + "Ùĩد Ùģ", + "æĹı èĩªæ²»", + "Ġzm ÄĽn", + "ĠzmÄĽ n", + "Ġв клад", + "Ġвк лад", + "Ġ بÙĦغ", + "ĠبÙĦ غ", + "Ġ ç§ĭ", + "Ġç§ ĭ", + "N gh", + "Ng h", + "Ġend iÅŁ", + "ĠCumhurbaÅŁ kanı", + "ĠK af", + "ĠKa f", + "Ġ à¹ģหล", + "Ġà¹ģ หล", + "Ġmut lu", + "ĠÑģ иÑĢ", + "ĠÑģи ÑĢ", + "Ġг Ñĥм", + "æ¿ ĥ", + "çĤ ī", + "ĠB áo", + "à¥Ĥ ष", + "Ġìłķ íĻķ", + "ान स", + "ï» ¤", + "наÑģлÑĸд ок", + "po Äįet", + "poÄį et", + "ë§ĮìĽIJ ìŀħëĭĪëĭ¤", + "ĠìĦľìļ¸ íĬ¹ë³Ħìĭľ", + "Îķ ÎĻΣ", + "ÎķÎĻ Σ", + "ุม à¸Ĭà¸Ļ", + "Ġм ÑĸлÑĮ", + "ĠмÑĸ лÑĮ", + "æ ħĮ", + "æħ Į", + "Ïĥκε ÏĦαι", + "Ïĥκ εÏĦαι", + "Ġ ãĢľ", + "ĠãĢ ľ", + "Ġkal iteli", + "ĠÑģмеÑĢ ÑĤÑĮ", + "è¼ Ķ", + "Ġ биÑĤ", + "Ġб иÑĤ", + "Ġби ÑĤ", + "ĠΣ ÏĦο", + "à¸ĩà¹Ģศ ส", + "åİŁ æľ¬", + "Ġk nÃŃ", + "Ġkn ÃŃ", + "äºĴ èģĶç½ij", + "ĠÑĩеловеÑĩ еÑģ", + "çŃ Ĵ", + "à¸Īำ หà¸Ļ", + "åĩº åİ»", + "ãĤ¢ ãĥĭãĥ¡", + "å±ķ 示", + "r ych", + "ry ch", + "à¤ħ ब", + "o ÅĪ", + "jÃŃ cÃŃm", + "jÃŃcÃŃ m", + "ا ØŃØ«", + "اØŃ Ø«", + "ĠÙĪاÙĤع ÛĮ", + "ĠФедеÑĢа лÑĮ", + "ĠФед еÑĢалÑĮ", + "Ñģ ам", + "Ñģа м", + "Ġ ìĺ¥", + "Ġìĺ ¥", + "åľ° çIJĥ", + "Ġs uyu", + "Ġsu yu", + "Ġsuy u", + "s eniz", + "sen iz", + "à¥ī फ", + "Ġê°Ļ ëĭ¤", + "ĠпÑĢизна ÑĩеннÑı", + "ĠпÑĢизнаÑĩ еннÑı", + "ĠS ın", + "ĠSı n", + "ĠاÙħÙĨ ÛĮت", + "Ġl átky", + "ĠÐij и", + "Ġsür eci", + "Ġsüre ci", + "Ġsürec i", + "·· ··", + "Ġê²½ ì°°", + "Ġк алÑĮ", + "Ġка лÑĮ", + "Ġкал ÑĮ", + "Ġник ÑĤо", + "Ùij Ùħ", + "ĠدÙĬ گر", + "Ġalın ması", + "л еннÑĸ", + "лен нÑĸ", + "ิว à¹Ģà¸ķà¸Ńร", + "à¸Ľà¸ģ à¸Ħรà¸Ńà¸ĩ", + "Ġзаконодав ÑģÑĤва", + "ãĢĢ ãĤ¤", + "Ġëħ¸ íķĺìļ°", + "ĠD Ã¼ÅŁ", + "Ġг ÑĥÑģÑĤ", + "ĠÐĴ аÑĪ", + "ĠاÙħ تÛĮ", + "Ġpar amet", + "Ġparam et", + "Ġpara met", + "ĠÎłÎ±Î½ εÏĢ", + "à¹Į à¸ģร", + "à¹Įà¸ģ ร", + "ζ α", + "ĠëįĶ ìļ±", + "ÙĪ ÙĦات", + "ÙĪÙĦ ات", + "ÙĪÙĦا ت", + "в 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"pad_token": "", + "padding_side": "right", + "processor_class": "LlavaProcessor", + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/comfy/text_encoders/long_clipl.py b/comfy/text_encoders/long_clipl.py new file mode 100644 index 0000000000000000000000000000000000000000..8d4c7619d4b2007462cf14721bcdae093a5b4a7b --- /dev/null +++ b/comfy/text_encoders/long_clipl.py @@ -0,0 +1,27 @@ + + +def model_options_long_clip(sd, tokenizer_data, model_options): + w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) + if w is None: + w = sd.get("clip_g.text_model.embeddings.position_embedding.weight", None) + else: + model_name = "clip_g" + + if w is None: + w = sd.get("text_model.embeddings.position_embedding.weight", None) + if w is not None: + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + model_name = "clip_g" + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + model_name = "clip_l" + else: + model_name = "clip_l" + + if w is not None: + tokenizer_data = tokenizer_data.copy() + model_options = model_options.copy() + model_config = model_options.get("model_config", {}) + model_config["max_position_embeddings"] = w.shape[0] + model_options["{}_model_config".format(model_name)] = model_config + tokenizer_data["{}_max_length".format(model_name)] = w.shape[0] + return tokenizer_data, model_options diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py new file mode 100644 index 0000000000000000000000000000000000000000..48ea67e6782388637dbe55b3e6366cda051ec519 --- /dev/null +++ b/comfy/text_encoders/lt.py @@ -0,0 +1,18 @@ +from comfy import sd1_clip +import os +from transformers import T5TokenizerFast +import comfy.text_encoders.genmo + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) #pad to 128? + + +class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + + +def ltxv_te(*args, **kwargs): + return comfy.text_encoders.genmo.mochi_te(*args, **kwargs) diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py new file mode 100644 index 0000000000000000000000000000000000000000..674461b75077ac21d92667d96a6787b1277367fe --- /dev/null +++ b/comfy/text_encoders/lumina2.py @@ -0,0 +1,39 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.llama + + +class Gemma2BTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + + +class LuminaTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer) + + +class Gemma2_2BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class LuminaModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options) + + +def te(dtype_llama=None, llama_scaled_fp8=None): + class LuminaTEModel_(LuminaModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(device=device, dtype=dtype, model_options=model_options) + return LuminaTEModel_ diff --git a/comfy/text_encoders/mt5_config_xl.json b/comfy/text_encoders/mt5_config_xl.json new file mode 100644 index 0000000000000000000000000000000000000000..092fefd6e32dac566e443fc03eae53f6a8b57400 --- /dev/null +++ b/comfy/text_encoders/mt5_config_xl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 5120, + "d_kv": 64, + "d_model": 2048, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "mt5", + "num_decoder_layers": 24, + "num_heads": 32, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 250112 +} diff --git a/comfy/text_encoders/omnigen2.py b/comfy/text_encoders/omnigen2.py new file mode 100644 index 0000000000000000000000000000000000000000..1a01b2dd430461a20af92b75e9a1930b8d153fea --- /dev/null +++ b/comfy/text_encoders/omnigen2.py @@ -0,0 +1,44 @@ +from transformers import Qwen2Tokenizer +from comfy import sd1_clip +import comfy.text_encoders.llama +import os + + +class Qwen25_3BTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer) + self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n' + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs): + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs) + +class Qwen25_3BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class Omnigen2Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options) + + +def te(dtype_llama=None, llama_scaled_fp8=None): + class Omnigen2TEModel_(Omnigen2Model): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(device=device, dtype=dtype, model_options=model_options) + return Omnigen2TEModel_ diff --git a/comfy/text_encoders/pixart_t5.py b/comfy/text_encoders/pixart_t5.py new file mode 100644 index 0000000000000000000000000000000000000000..b8de6bc4e346db3a099955504ec6cd91a6b8e05a --- /dev/null +++ b/comfy/text_encoders/pixart_t5.py @@ -0,0 +1,42 @@ +import os + +from comfy import sd1_clip +import comfy.text_encoders.t5 +import comfy.text_encoders.sd3_clip +from comfy.sd1_clip import gen_empty_tokens + +from transformers import T5TokenizerFast + +class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def gen_empty_tokens(self, special_tokens, *args, **kwargs): + # PixArt expects the negative to be all pad tokens + special_tokens = special_tokens.copy() + special_tokens.pop("end") + return gen_empty_tokens(special_tokens, *args, **kwargs) + +class PixArtT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding + +class PixArtTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + +def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None): + class PixArtTEModel_(PixArtT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return PixArtTEModel_ diff --git a/comfy/text_encoders/qwen25_tokenizer/merges.txt b/comfy/text_encoders/qwen25_tokenizer/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..31349551d90c7606f325fe0f11bbb8bd5fa0d7c7 --- /dev/null +++ b/comfy/text_encoders/qwen25_tokenizer/merges.txt @@ -0,0 +1,151388 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ 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+ÙĬ ج +íĴ Ī +à¸ŀ à¸ļ +ห ัว +з на +ר ק +à¹Ĥ ร +Ġ×ij ס +ĠBaÅŁ kan +ĠëĶ ° +à¸Ń ัà¸Ļ +ีà¹Īย ว +н еÑģ +à¹Ģà¸Ķ ิà¸Ļ +ÙĬ اÙĨ +×ķ׾ ×Ļ +ا خت +צ ×ķת +ãģĵ ãģĵ +ĠاÙĦ اÙĨ +ĠпÑĢо ÑĨ +ãģ¾ ãģł +׼ ס +ĠاÙĦ Ø¢ +ÙĬ ز +ĠاÙĦد ÙĪÙĦ +Ġíķĺ ëĤĺ +ض ع +ê» ĺ +ÅĽ wi +ย ิ +ãģ¡ãĤĥ ãĤĵ +ĠÙħ Ø´ +à¸ĺ ี +ãģ¨ ãģį +׳×Ļ ×ķת +Ġë ¯ +Ġë¯ ¸ +Ġs ı +ëĭĪ ê¹Į +Ġп л +غ ÙĦ +à¹ģ รà¸ĩ +ب ÙĬر +ãģĤãĤĬ ãģ¾ãģĽãĤĵ +ê· ¼ +Ġy üz +ĠdeÄŁ er +åł´ åIJĪ +á» ¡ +м аÑĤ +รา à¸Ĭ +ÙĪر ÙĬ +ж ен +ãģ¾ ãĤĬ +ãģ® ä¸Ń +×Ļ×ĵ ×¢ +à¸Ń ุ +à¸ļ à¸Ńล +à¸Ľà¸±à¸į หา +ز Ùħ +ÄŁ a +à¸Ń ืà¹Ī +à¸Ńืà¹Ī à¸Ļ +п л +Ġне обÑħодим +׼ ×ij +à¹Ģ ศ +קר ×Ķ +ì² ĺ +ëł ¨ +×ŀק ×ķ×Ŀ +jÄħ c +Ùĩ ÙĦ +Ġ×¢ ×ij×ķ×ĵ +à¹Ħม à¹ī +à¸ģล ัà¸ļ +×ķ׼ ׾ +ק ×ĵ +اÙĦ ÙĬØ© +ر Ùĩ +ãģij ãĤĮãģ° +ĠÙĨ Ùģس +ãĤ¢ ãĥ« +ìĹ Īëĭ¤ +ק ×ķר +н еÑĢ +ب اب +ãĤ ¶ +سب ب +ÙĦ ÙĬÙĦ +ص ÙĨ +ص در +ế m +à¸Ĭà¹Īว à¸ĩ +ØŃ ÙĨ +Ġ×ij ×Ĵ +×ŀ ×ķ×¢ +׾ ×Ĺ +大 ãģį +ت ب +н еÑĤ +×Ļ×ij ×Ķ +б л +ãĥĹ ãĥª +اص Ø© +ãģ¤ ãģij +×Ļ×ŀ ×ķש +ãģĮ ãģĤ +ëĭ ´ +ãģĭãĤĤ ãģĹ +ãģĭãĤĤãģĹ ãĤĮ +ãģ¡ ãĤī +×ij ×ĺ +Ġba ÄŁ +×Ļ×Ĺ ×¡ +×ij ×ķ×¢ +ล ี +פע ×Ļ׾ +им и +g ÅĤ +Ġим е +خد اÙħ +×IJ ×Ļר +Ġy apt +ãģ¨ ãģĦ +à¸ĩ à¹Īาย +׾×Ļ ×ķ +ØŃد Ø« +را ÙĤ +ĠÄIJ i +اد ر +ãģĵãģ¨ ãĤĤ +×ij ×Ļר +Ġв з +ض اÙģ +ת ×ķ׼ +ÑĢ ом +ر ات +à¹Ģà¸Ĺ à¹Īา +ãģĺ ãĤĥ +ãģĿ ãģĵ +اج تÙħاع +à¹īà¸Ń à¸Ļ +ÙĤ Ùħ +ë³ ¸ +Ä ŀ +ש ×Ļ×ķ +×ij ׳×Ļ +ìľĦ ìĽIJ +à¹ģ à¸Ī +×Ĺ ×ķר +دÙĬ ÙĨØ© +ت Ø· +ằ m +ò a +ย à¸Ńà¸Ķ +Ġëĭ ¹ +สุ à¸Ĥ +×ĵר ×ļ +د ÙĨ +س ÙĬÙĨ +ÙĪÙĤ Ùģ +ÑĨ Ñĭ +г оÑĤов +еж дÑĥ +à¸ŀ วà¸ģ +اÙĤ تص +اÙĤتص اد +cz ÄĻ +ni ÄĻ +ÑĢ еб +ØŃ ÙĪ +à¸Ĺ à¹Į +ãĤĪ ãģŃ +д ж +à¸ģล à¹Īาว +دÙĬ Ø« +ãĤ³ ãĥŁ +ÙĤ ÙĪÙħ +Ġت ØŃ +à¹Ģ à¸ķิ +اÙģ ظ +à¸Ī ุ +رÙĬ اض +×ŀש ×ļ +à¹Ĥ ย +еÑĢ е +ãģ¿ ãģŁãģĦ +ìĿ´ ëĿ¼ +ĠاÙĦÙħ ÙĪ +ĠÑģÑĤ о +à¹Ģรà¹ĩ ว +Ġд еÑĤ +ĠÑģ дел +à¹Ģà¸Ĭ ืà¹Īà¸Ń +פ ׳×Ļ +ÙĪض ÙĪع +×ij ס +à¹ģ à¸Ķ +ó c +ริ ม +ÑĢаР´ +ìĪ ł +ãĥ¼ãĤ º +ãģ« ãģĬ +и но +פ ×Ļ׾ +à¸Ĭั à¹Īà¸Ļ +×Ĺ×ĵ ש +à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ +׳ ×Ļס +غ رب +ãĤ¸ ãĥ£ +ส ัà¸ĩ +à¹Ģ à¸Ĺีà¹Ī +à¹Ģà¸Ĺีà¹Ī ยว +ëŁ ¼ +à¹ģ à¸Ł +ãĥ¼ãĤ · +ãĥ¼ãĤ· ãĥ§ãĥ³ +Ġвоз мож +جÙħ ÙĪع +×ijר ×Ļ×Ŀ +ãĥĪ ãĥ© +ĠкаÑĩ еÑģÑĤв +Ø· ÙĬ +ÑĤ Ñı +צ ×ķ×¢ +ÄŁ ını +ع ÙĦÙī +ا Ø° +ÙĪاÙĤ ع +Ùħ ÙĪا +ائ ÙĬÙĦ +к ол +á»ģ m +à¸ľà¸¥ ิà¸ķ +×Ļ׳ ×ĺר +س Ùĥ +ש ×Ļר +ศึà¸ģ ษา +à¸ļ ั +Ñĩ аÑģ +×ķפ ×Ķ +×Ļפ ×ķ׾ +ĠاÙĦس اب +رÙĬ ب +ĠاÙĦ بÙĬ +ãĤ¹ ãĥĨ +Ñĩ ен +à¹ģ à¸ľ +Ġ׳ ש +ز ÙĬد +ØŃ Ø§Ø¯ +ëį Ķ +رÙĪ ع +à¸Ĺุ à¸Ļ +ส มา +c zeÅĦ +×Ļ×ĵ ×Ķ +ãģ§ ãģĤ +Ġçoc uk +Ø® ب +à¸ļ าย +à¸Ľà¸£à¸° à¸Ĭา +×ŀש ׾ +ãģª ãģĭ +à¸ģ าย +ãĥģ ãĥ£ +аÑĢ и +ĠÑĩ а +à¸Ķ ำ +à¸Ĺั à¹Īว +Ñĥ Ñħ +Ġö z +Ġì¢ ĭ +ج رÙĬ +ائ ÙĤ +à¸ł ัย +Ø· ار +د ارة +Ä© nh +Ø« ÙĨ +zell ik +اÙĦ ت +Ġg eli +ãĥķãĤ © +ол од +رب ع +שת ×ŀש +à¸ļร ร +íĿ ¬ +Ġü rün +Ġê·¸ ëłĩ +ศาส à¸ķรà¹Į +ãģ ľ +×Ļ×ij ׾ +ĠпÑĢед ÑģÑĤав +سط ÙĬÙĨ +ãĤĴ 使 +Ġпом оÑī +×ķק ר +ãĥ¯ ãĥ¼ +Ġyö net +×Ļק ר +à¸Ĥ า +еÑĢи ал +ØŃ Ùģ +Ġ×Ļ צ +à¸Ĺ ิ +å£ ² +à¸Ļ à¸Ńà¸ģ +×ķ׼ ר +íĻ ľ +ủ y +ĠاÙĦÙĤ ر +×Ļ×ij ×ķת +ÅĽ ni +Ùħ شار +ượ t +ĠÙĦ دÙĬ +ÑĤ ел +ĠØ¥ ÙĦÙĬ +عÙĦ ÙĪÙħ +ìķ ĺ +в иÑĤ +à¸Ħ ะ +yr ı +ãģ¨ ãģ£ãģ¦ +à¹Ģ à¸ī +à¸ĸ าม +ÙĤ ار +عÙĦ اÙħ +ặ ng +Ùħ ÙĴ +×Ļ×ŀ ת +سب Ø© +ãĤ¯ ãĥ© +×ķס ×£ +ĠпÑĢ ин +ãģĦ ãĤį +س اس +عت بر +วิ à¸Ĺย +วิà¸Ĺย า +س Ùĥر +ãĤ· ãĥ§ +ãģ ģ +ัà¸ģ ษ +×ij ×ķ×Ķ +ห ย +ãģ¾ ãĤĮ +ĠоÑĢг аниз +каз ал +ĠÑģв Ñıз +uy ết +ĠпÑĢо из +Ġק ×ĺ +à¹ģà¸ģ à¹ī +п ÑĥÑģ +Ġê·¸ ê²ĥ +ëĬ IJ +л екÑģ +ãĥ¼ãĥ Ĺ +à¸ķ ำ +ת×Ĺ ×Ļ׾ +à¸Ńà¸ĩ à¸Ħà¹Į +Ạµ +׳ צ +Ø£ Ø´ +Ø´ Ùĩ +ย ะ +à¸ģ à¸İ +ĠاÙĦØ¥ سÙĦاÙħ +ед ÑĮ +ãģ² ãģ¨ +ëıĦ ë¡Ŀ +ãģ© ãģ® +Ñĥ в +еÑĩ ение +ĠاÙĦت ج +ãģ« è¡Į +Ġп озв +ãĤı ãĤĬ +ÙĦ اث +íķĺ ìĺĢ +Ġм аÑĢ +Ġkon uÅŁ +ãĥ¬ ãĤ¹ +ãĤĴ æĮģ +ĠоÑģ нов +×Ĺ ×ij +ÙĪج ÙĪد +פ ×ķף +в оÑĢ +Ġн ик +ãģĭ ãĤĭ +ÅŁtır ma +×Ļס ×ĺ +Ø£ ÙĦ +ห à¹Į +и она +лÑĮ н +Ġг оÑģ +ĠÐľÐ¾Ñģ к +ÑĢ об +×ķ×IJ ×Ļ +ãģĬãĤĬ ãģ¾ãģĻ +ãģ£ãģ ± +к л +à¸Ļ à¸Ķà¹Į +رÙĬ Ùģ +اس ب +ĠÑĢ еÑĪ +Ġд ол +ãģ¹ ãģį +×Ļ×ij ×ķר +м еÑī +Ġна ÑĪ +à¹ģ à¸Ľà¸¥ +ÑĢ иÑĤ +кÑĥ Ñģ +и ÑĢа +аÑĤ ÑĥÑĢ +ÙĪا صÙĦ +à¹Ģà¸ľ ย +à¸Ń ำ +à¹Ģà¸ģ ิà¸Ļ +غ Ùħ +ãģĻ ãģİ +lı kl +ÅĦ sk +ê² ¬ +×Ļ׼ ×Ķ +׊ש×ij +ÙĪر ÙĬØ© +Ġд ейÑģÑĤв +×Ĺ׾ ×ĺ +Ġ׾ ×ŀ×¢ +צ׾ ×Ļ×Ĺ +еÑĩ а +Ùģ اع +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ еб +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ е +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ е +ק ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ãģ¨æĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ ا +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ اÙĨ +Ġ׾×Ķ ת +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ од +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +ק ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ פ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +Ø° Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ Ø¨ +ан Ñģ +س ÙĪ +ĠпÑĢ е +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +ק ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ùģض ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ Øª +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ Ø§ +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ãģ¨æĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +ק ×ķ׾ +ÑĢ ез +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ ан +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ سط +Ġв оп +Ġвоп ÑĢоÑģ +Ùħ ÙĬÙĨ +à¸Ħ à¸ĩ +×Ļר ×Ļ×Ŀ +c ów +ê² © +Ġê·¸ 룰 +Ġì§ Ħ +Ġש ׾×Ķ +à¹Ģร ิà¹Īม +à¸Ĭ à¸Ńà¸ļ +д еÑĤ +ÑİÑī иÑħ +à¸ļ à¸Ńà¸ģ +æĢĿ ãģĦ +ع ÙĬد +ס ×ŀ +×Ĵ ×Ļ×¢ +צ ×ĵ +ب ات +ĠëĶ° ëĿ¼ +à¸Ī ัà¸ĩ +ãģłãģij ãģ§ +×¢ ×Ļר +ĠÑĩ ел +ĠÑĩел ов +ĠÑĩелов ек +ãĥĥ ãĥģ +à¹Ģà¸ģ ีà¹Īยว +à¸Ķ ิ +Ġפ ×¢ +×Ļ×ŀ ×Ļ +ë° ĺ +Ø® ار +×ij ×Ļת +×¢ ×Ļ×Ŀ +ü yor +ãĤģ ãģ¦ +к лад +Ġ à¸Īาà¸ģ +à¹Ģà¸Ħ ย +ส à¸Ńà¸ĩ +à¹ģ à¸Ħà¹Ī +ẫ u +หà¸Ļ ัà¸ĩ +ש׾ ×ķ×Ŀ +اÙĨ ÙĬØ© +åĩº ä¼ļ +åĩºä¼ļ ãģĦ +à¸ł าย +à¸ļา à¸Ĺ +à¸Ĭา ว +mu ÅŁ +Ġ׾ק ×ij׾ +ãĤ· ãĥ£ +ĠÄ° ÅŁ +×Ĵ×ĵ ×ķ׾ +ج عÙĦ +ë³ Ģ +ยิ à¹Īà¸ĩ +à¸Ļ าย +à¸Ļ ีà¹Ī +วิ à¸ĺี +ãĤī ãģªãģĦ +ëł Ī +Ġ문 ìłľ +Ġ à¸ģ +à¸Ĺำ à¸ĩาà¸Ļ +à¹Ģว à¹ĩà¸ļ +ÑĦ е +楽 ãģĹ +สำ à¸Ħ +สำà¸Ħ ัà¸į +ر Ùħ +ãģķãĤĮ ãģ¦ +Ġоб ла +ר×IJ ×Ļ +หม à¸Ķ +ÙĨ ÙĬØ© +ли н +Ġe ÄŁ +it im +ëł ¹ +ص اÙĦ +ÅĽ l +à¸ľ ิà¸Ķ +ãĥŀ ãĥ³ +åħ¥ ãĤĮ +à¹Ģà¸ķ à¸Ńรà¹Į +ار ÙĬ +ĠÐ ¦ +d ür +ส วย +ë¦ ½ +رÙĥ Ø© +Ġh ã +×Ļת ×Ķ +à¸Ĥ à¸Ļา +à¸Ĥà¸Ļา à¸Ķ +à¸Īำ à¸Ļ +à¸Īำà¸Ļ วà¸Ļ +ש ×ķק +Ġд ом +ì± ħ +ãģĭ ãģij +פ ×ķ׾ +à¸Ĭ าย +Ñģ моÑĤÑĢ +Ñģл Ñĥж +ש ×IJ׾ +кÑĢÑĭ ÑĤ +Ġìŀ ĺ +é«ĺ ãģĦ +ĠÑĢ Ñĥк +ÙĨ ص +д ав +Æ°á» ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪا ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪد +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +ĠÄ° l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ ز +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ׾ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ од +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ اء +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +ק ×ķת +ÙĪج د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° à¸Īำ +ов ание +ãģĦ ãģ¤ +ãĥĥãĤ¯ ãĤ¹ +åIJĪ ãĤı +åIJĪãĤı ãģĽ +×Ļ׳ ×ķ×Ļ +ạ y +Ø« ÙĤ +ĠпÑĢ об +ĠпÑĢоб лем +ÅŁ eh +ÅŁeh ir +ع ادة +اÙĨ ÙĪÙĨ +à¸ķัว à¹Ģà¸Ńà¸ĩ +ì¶ ķ +ı lan +б ан +ãĥ³ ãĥī +à¸Ī ี +Ġ×Ķש ׳×Ļ +п оÑĤ +×ķ׾ ×Ļ×Ŀ +ล ัà¸ļ +ĠÑį ÑĤи +×ijק ש +ë¹Ħ ìĬ¤ +à¸Ńยà¹Īาà¸ĩ à¹Ħร +×Ļ׾ ×Ļ +à¹ĥà¸Ĭ à¹Ī +ĠاÙĦ ÙĥÙĦ +ãĥļ ãĥ¼ãĤ¸ +ص Ø© +ÑĤи ÑĢ +ãĤĵ ãģ© +зÑĭ к +wy ż +Ùĩ ÙĬ +ĠÙħ ÙĦÙĬ +Ġвид е +ظ اÙħ +دا ÙĪÙĦ +×ŀ ת×Ļ +Ġs ık +à¹Ģà¸ķิ ม +ãĤ¢ ãĤ¤ +ка Ñħ +צ ×Ļ׾ +à¹Ģà¸Ĭ à¹Īà¸Ļ +м аг +маг аз +магаз ин +à¸Ľ ั +à¸Ľà¸± à¸Ī +Ġש ×Ļר×ķת +ีย ม +ãĥĸ ãĥ« +Ġد ÙĪÙĦ +קר ×Ļ×Ŀ +Ùĩ Ùı +ов о +Ġü ret +د ÙĪÙĨ +à¹ģà¸Ļ ว +à¹Ģà¸Ļ ืà¹īà¸Ń +ĠÑĦ оÑĤ +ãĥ ĺ +ãģ¤ ãģĭ +Ñı Ñģ +ĠíķĺëĤĺ ëĭĺ +ائ ع +Ġп лаÑĤ +ìĺ Ī +Ġdost ÄĻp +ÙĪج Ùĩ +Ġ×Ķ ×Ĺ×Ļ +׳ ×Ļק +д ей +í ĽĦ +ı y +بØŃ Ø± +à¹Ģส ริม +Ġ׾ ×Ĵ +Ø°Ùĩ ب +ج ÙĬÙĦ +رÙĥ ز +Ġë ħ +Ġëħ ¸ +פ×Ļ׾ ×ķ +ãģ¾ ãģļ +iri ÅŁ +ĠÙĥ ÙĬÙģ +Ġ×ij צ +Ġêµ IJ +ÑĢоÑģ Ñģ +ĠØ´ ÙĬ +Ġiç er +×Ĵ ×ķ×ij×Ķ +мен но +×¢ ×ij×Ļר +×ķ×ŀ ×Ķ +ãĤī ãģĹãģĦ +ãģ ¼ +Ñī ин +è²· ãģĦ +جÙħÙĪع Ø© +Ġdön em +Ġ×ij ×IJר +в еÑģÑĤ +×ķר ×ķת +س Ùģ +à¹ģà¸Ĺ à¸Ļ +Ġд окÑĥменÑĤ +Ġا ÙĬ +ج اÙĨ +צ×ķ×¢ ×Ļ +ĠоÑģ об +ĠاÙĦÙħ س +ÑĢаР± +à¸ł ู +à¸Ķ าว +л екÑĤ +ع ÙĤ +×ķ×ĵ ×ķת +Ġol u +Ġolu ÅŁtur +ãģ¾ ãģ¾ +ед ин +à¹Ģ à¸Ńà¸ģ +ãĤµ ãĤ¤ +ëĦ Ī +Ø· ÙĨÙĬ +Ø· ÙĤØ© +ĠÐł аз +ÙĦ Ùij +Ñĩ ем +Ġ׾ ×ĺ +สั à¹Īà¸ĩ +سر ائÙĬÙĦ +Ġפר ×ĺ×Ļ +д еÑģÑĮ +Ġ׳ ׼ +اÙĨ ب +ÙĬا Ø© +Ùħ بر +Ġk ı +à¸Ľ à¸ı +à¸Ľà¸ı ิ +à¸ļั à¸ķิ +׳ ת×Ļ +ìĨ ¡ +ر اب +à¹ĥ à¸ķ +à¹ĥà¸ķ à¹ī +×Ļ׳ ת +ÙĪ ÙĬر +Ġ×Ķ×ŀ 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+èIJ½ ãģ¡ +åĩº ãģ¦ +ล ัà¸ģษ +Ġ×Ĵ ×ij×ķ×Ķ +ãĥĻ ãĥ« +ê±° ëĤĺ +ë§ IJ +×Ļ׾ ×ĵ×Ļ×Ŀ +ĠëĦ Ī +×ŀר ×Ļ +ร ส +ãĥŃ ãĥ³ +и ло +ноÑģÑĤÑĮ Ñİ +×ĸר ×Ĺ +п он +Ġ×Ķש ׾ +ê²ł ìĬµëĭĪëĭ¤ +Ġki ÅŁ +ĠÐļ и +ว ร +د اع +ÅŁ im +ÙĨ Ùij +в аÑĤ +را Ùĥ +ب اÙĦ +ид е +Ġ×Ķ×ŀ ×Ĺ +ìĸ µ +تÙģ اع +Ø£ ت +ëĬ ĺ +ש ×Ļת +ست Ùħر +ĠÑĦ ак +ĠاÙĦØ£Ùħ رÙĬ +ëŀ ¨ +اس Ùħ +Ġa ÄŁ +Ġç ev +Ùĥ ÙĪر +ãģķ ãģ¾ +Ġç öz +Ġر س +Äħ da +สà¸Ļ ุ +ãģĹãģ¦ ãģıãĤĮ +н Ñİ +leÅŁ me +ãĤª ãĥ³ +ãģ¨ ãģªãĤĬ +ava ÅŁ +×ĺ ×Ļ×ij +ØŃ Ø¶ +×ķצ ×IJ×ķת +ÙĨ ÙħÙĪ +ı t +ĠÑħ а +ĠÑħа ÑĢак +ĠÑħаÑĢак ÑĤеÑĢ +Ġd ÅĤ +ãĥĹ ãĥ© +à¸Ĭ ุม +à¹Ī à¸Ńà¸Ļ +×ķ×ij ׾ +Ñģ ол +×ĵ ×Ĵ +аÑĢ аÑĤ +n ivers +Ġgerçek leÅŁtir +ĠاÙĦ ÙĦÙĬ +ระ ยะ +ĠÙħ ختÙĦÙģ +Ġgö nder +Ùģ ار +do ÄŁ +doÄŁ an +ص ÙĦاØŃ +Ġyay ın +ãĥĨ ãĥ³ +รว à¸Ī +×Ļ×Ĺ ×Ļ×ĵ +ünk ü +ÑĨи алÑĮн +à¸ļ ู +ม ุ +h ä +Ø® Ùģ +å¢ Ĺ +å¢Ĺ ãģĪ +еÑĩ но +ĠاÙĦس ÙĨ +à¸Ĥ าว +im di +Ð « +à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ +à¸ļา ล +ת ש +Ġdüzen le +мÑĭ Ñģл +ãģı ãģª +ż u +Ġwsp óÅĤ +Ġн аз +ınd aki +تر Ø© +ÅŁ ek +Ġö d +ĠÙĪ Ùĥ +Ġпозв олÑı +Ġת ×ķ׼ +ÙħÙĨ تج +ë§ ī +ĠاÙĦØ« ÙĦاث +аÑĨи Ñİ +ÙĪر ÙĪ +Ñĭв 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ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh Æ°ng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +ĠØ° ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt Æ° +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ק +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪاÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch ủ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij Æ°á»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth Æ°á»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ Øª +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ ت +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth Æ°Æ¡ng +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ ت +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪر +Ġtr ÃŃ +ĠÐŁÑĢ о +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch Æ°a +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph Æ°Æ¡ng +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ о +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth ủ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ а +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд 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+ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +ק ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ Ø¯Ø© +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij ủ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġק ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ׼×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth Æ° +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ פר +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦØ° ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ Ø° +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk ý +Ġch ân +Ġ à¹Ħมà¹Ī +اØŃ Ø© +r án +׳×Ļ ×Ļ×Ŀ +Ġ×ij ף +ĠÐ ĸ +à¸ķร à¸ĩ +д Ñĭ +Ġs ắc +ÙĦ ت +ãĥŃ ãĥ¼ +ĠÙĦ ÙĨ +Ġר ×ķ +Ġd Æ°á»Ľi +à¹Ģ à¸ĺ +à¹Ģà¸ĺ à¸Ń +e ÄŁi +Ġ×ķ ש +ĠÙĦ Ø£ +Ġg ặp +Ġc á»ij +ãģ¨ ãģ¦ãĤĤ +رÙĪ س +Ġ׾×Ķ ×Ļ +Ġë³ ¸ +ä¸Ĭ ãģĴ +Ġm ức +Ñħ а +Ġìŀ ¬ +à¸ī ัà¸Ļ +ÑĢÑĥ ж +Ġaç ık +ÙĪ اÙĦ +Ġ×ĸ ×ŀף +人 ãģ¯ +ع ÙĬÙĨ +Ñı Ñħ +Ġ×Ĵ×ĵ ×ķ׾ +ר ×ķ×ij +g ó +ëĿ¼ ê³ł +Ġark adaÅŁ +ÙĨ شر +Ġгод Ñĥ +ĠболÑĮ ÑĪе +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġcâ u 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Ñĭе +ëĤ ł +Ġв ам +بÙĦ غ +Ġê°Ļ ìĿĢ +íĮ IJ +ÙĦ ب +Ġnas ıl +Ġод ин +м ан +ĠعÙĦÙĬ Ùĩا +б и +Ġפ ש×ķ×ĺ +×ijר ×Ļ +Ġש ׳×Ķ +Ġëı Ħ +ĠÄIJ ại +Ġ×IJ×ķת ×Ŀ +ĠاÙĦØŃ Ø± +Ġб о +à¸Ī ุà¸Ķ +Ġr õ +ĠdeÄŁi ÅŁ +Ġëĭ ¨ +ĠÑģлÑĥÑĩ а +ĠÑģлÑĥÑĩа е +Ġ×IJ׳ ש×Ļ×Ŀ +×ĵ ×£ +ש×ij ת +Ġש׾ ׼×Ŀ +Ġch ú +nik ów +Ġtan ı +Ġcá o +ĠÄij á +Ġ×IJ ×ĵ×Ŀ +Ġê° ķ +Ġnhi á»ĩm +Ġ׾ ס +Ġ×Ľ×ª ×ij +Ġ×Ķס פר +ĠÄij Äĥng +Ġë ijIJ +à¸ľ ิ +à¸ľà¸´ ว +ج ا +Ġê° IJ +ر Ø£ +ست خدÙħ +ãģ«ãģªãĤĬ ãģ¾ãģĻ +Ġtá» · +×ĺ ×ķר +г овоÑĢ +Ġв оÑģ +ĠÙħÙĨ Ùĩا +иÑĢов аÑĤÑĮ +ĠÄij ầy +׳ ×Ĵ +ĠÙħ ÙĪ +ĠÙħ ÙĪÙĤع +ר׼ ×Ļ +ت Ùı +ëª ¨ +Ġת ×ķ +ÙĬا Ùĭ +à¹ĥ à¸Ķ +ãĤĬ ãģ¾ãģĻ +à¸Ńยูà¹Ī à¹ĥà¸Ļ +ĠØ£ ÙĪÙĦ +ĠØ£ خرÙī +Ġc Æ° +ص ار +×ŀ׊ש×ij +б ÑĢа +ÅĦ ski +б ÑĢ +ĠÙĬ Ùı +à¸ģ ิà¸Ļ +Ġch á»ijng +Ùħ Ùı +Ġ à¸Ħืà¸Ń +Ġت ÙĨ +t ÃŃ +y Äĩ +Ġm ạng +Ùģ ÙĪ +Ġdü nya +ק ר×IJ +Ġק ׾ +ĠØŃ Ø§ÙĦ +c ÃŃa +Ġà¹Ģ รา +Ġר ×ķצ×Ķ +Ġá p +ë° ķ +ا ÙĤØ© +ни Ñİ +Ġ×IJ ׾×ķ +Ġ×ŀס ×ķ +ãģ§ãģ¯ ãģªãģı +Ġtr ả +Ġק שר +mi ÅŁtir +Ġl Æ°u +Ġh á»Ĺ +ĠбÑĭ ли +Ġl ấy +عÙĦ Ùħ +Ġö zel +æ°Ĺ ãģĮ +Ġ×ĵ ר×ļ +Ùħ د +s ını +׳ ×ķש×IJ +r ów +Ñĩ еÑĢ +êµIJ ìľ¡ +ĠÐľ о +л ег +ĠV Ỽi +วัà¸Ļ à¸Ļีà¹ī +ÑİÑī ие +ãģĬ ãģĻ +ãģĬãģĻ ãģĻ +ãģĬãģĻãģĻ ãĤģ +ëı ħ +Ġ×Ļ×Ķ ×Ļ×Ķ +×ŀ ×ĺר +Ñı ми +Ġl á»±a +ĠÄij ấu +à¹Ģส ียà¸ĩ +Ġt Æ°Æ¡ng +ëĵ ± +ĠÑģÑĤ аÑĢ +à¹ĥ à¸ļ +ว ัà¸Ķ +ĠÄ° stanbul +Ġ à¸Īะ +à¸ķ ลาà¸Ķ +Ġب ÙĬ +à¹ģà¸Ļ ะ +à¹ģà¸Ļะ à¸Ļำ +س اعد +Ġب Ø£ +Ġki á»ĥm +ØŃ Ø³Ø¨ +à¸Ĭั à¹īà¸Ļ +Ġ×ķ ×¢×ķ×ĵ +ов ÑĭÑħ +оÑģ нов +Ġtr Æ°á»Łng +צ ×ij×¢ +ĠÃŃ t +Ġk ỹ +cr é +Ñı м +êµ ° +ãģĮ ãģªãģĦ +ÙĬÙĦ Ø© +ãĥķ ãĤ£ +ر Ùī +ĠÙĬ جب +Ġ×IJ ×£ +Ġc á»±c +ãĤīãĤĮ ãģŁ +Ġ à¸ľà¸¹à¹ī +Ġ à¸Ń +lar ımız +Ġkad ın +Ġê·¸ ëŀĺ +Ġê·¸ëŀĺ ìĦľ +ĠëĺIJ ëĬĶ +ĠÄij ả +ĠÄijả m +Ġ×IJ ×ķ×ŀר +Ġy ếu +ci Äħ +ciÄħ g +Ġt á»ij +Ġש×IJ ׳×Ļ +Ġdz iaÅĤa +Ñī а +ĠÄij Ãłn +s ına +ãģĵãĤĮ ãģ¯ +Ġ×ij ׾×Ļ +Ġ×ij ×Ļשר×IJ׾ +л оÑģÑĮ +Ġgi ữ +ê° IJ +ÑĢ он +تج ار +г лав +в ин +Ġh ạn +Ġyapı lan +ب س +Ġ à¸ŀรà¹īà¸Ńม +ê´Ģ 리 +mÄ±ÅŁ tır +b ü +r ück +ĠBaÅŁkan ı +ĠÙĦ ÙĬس +Ġs Æ¡ +à¸Īัà¸ĩ หว +à¸Īัà¸ĩหว ัà¸Ķ +د اء +Ġ×Ķ ׼ +v ÃŃ +ש ×IJר +Ġh Æ°á»Łng +Ġb óng +ĠCh ÃŃnh +Äħ c +à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ +Ġtá» © +Ġtứ c +ĠÑĨ веÑĤ +Ġt á»iji +ĠnghÄ© a +ÙĦا عب +د ÙĦ +Ġפע ×Ŀ +h ör +à¸Ĭ 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×ķ×Ļ×ķת +ei ÃŁ +ãĥ ¨ +ìĥ Ī +ĠÃĩ a +Æ ¯ +ש ×Ĵ +ÙĬÙĨ Ø© +ร à¹īà¸Ńà¸ĩ +ãĤµ ãĥ³ +ÑĢоÑģÑģ ий +ÑĢоÑģÑģий Ñģк +a ÄŁa +ĠнаÑĩ ина +Ġص ÙĦÙī +à¸Ĺุà¸ģ à¸Ħà¸Ļ +íļĮ ìĤ¬ +Ġли ÑĨ +Ø´ ÙĬر +ĠØ´ÙĬ Ø¡ +ÙĬÙĨ ا +Ġפ ×Ĺ×ķת +Ġiçer is +Ġiçeris inde +ĠØ£ ØŃÙħد +Ġże by +ì´ Ŀ +Ġп оказ +Ġи менно +หà¸Ļัà¸ĩ ส +หà¸Ļัà¸ĩส ืà¸Ń +ĠÑĤÑĢ е +สัà¸ĩ à¸Ħม +Ø¥ ÙIJ +ãģĮ å¿ħè¦ģ +ÙĬÙij Ø© +פ צ +íĭ ° +ĠÙħ جاÙĦ +׳ פש +к ан +×Ĺ ×ķפ +×Ĺ×ķפ ש +ì²ĺ ëŁ¼ +ов аÑı +з ов +Ġh ạ +Ġdzi ÄĻki +×Ļר ×ķ +Ġ׾ ×ŀצ +Ġ׾×ŀצ ×ķ×IJ +×Ļ×ĵ ×ķ +Ġs ợ +Ġ׾×Ķ ×Ĵ×Ļ×¢ +ק ×ij×¢ +Ġchi á»ģu +ãĥŀ ãĤ¤ +Ġd Ãłng +à¹ģà¸Ł à¸Ļ +Ġü ye +×Ļ׳ ×Ĵ +à¹Ģรีย à¸ģ +ç§ģ ãģĮ +th é +ĠÑĦ илÑĮ +ĠÑĦилÑĮ м +ĠNg Ãły +Ġж ен +Ġжен Ñīин +ج ÙĬد +n ç +à¸Ľ รา +×Ļ×ŀ ×ķ +Ġn á»ģn +×IJ ×ķ׾×Ŀ +Ġвозмож ноÑģÑĤÑĮ +Ġëĭ¤ ìĭľ +è¦ĭ ãģŁ +à¸ĸ à¸Ļ +à¸ĸà¸Ļ à¸Ļ +mız ı +ĠÙħ جÙħÙĪعة +c jÄħ +ĠÐł Ф +à¸ģำ หà¸Ļ +à¸ģำหà¸Ļ à¸Ķ +ĠìŬ 기 +land ı +ни ÑĨ +ÑģÑĤв е +Ġ×ĵ ×ijר×Ļ×Ŀ +Ġsk ÅĤad +ãĤĬ ãģ¾ãģĹãģŁ +ĠоÑĤ кÑĢÑĭÑĤ +нÑı ÑĤ +ĠÑģво ей +à¸Ī ิà¸ķ +ĠкаÑĩеÑģÑĤв е +Ġet tiÄŁi +ìĤ¬ íķŃ +ĠاÙĦÙĬ ÙħÙĨ +иÑĩеÑģки й +ë¸ Į +Ġ×ij×IJר ×¥ +Ġا سÙħ 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+تط ÙĪÙĬر +Ġsay ı +ĠÑģ ейÑĩаÑģ +Ġ×IJ×Ĺר ת +ק ×ķפ×Ķ +ק×ķר ס +Ġس Ùħ +Ġ×ĺ ×Ļפ×ķ׾ +ìĿ´ëĿ¼ ëĬĶ +دراس Ø© +èµ· ãģĵ +×Ĺ ×Ļ׳ +×Ĺ×Ļ׳ ×ķ×ļ +×ĵ ק +Ġë§ ŀ +Ġком анд +ĠÐij о +Ġиг ÑĢÑĭ +à¸ļ ี +ĠØ£ Ùİ +в ен +ĠاÙĦج دÙĬد +ĠÙĦ Ø¥ +Ġ×ķ×IJ ׳×Ļ +Ġ×Ķס ×Ļ +иÑĩеÑģк ого +رÙĪ ØŃ +à¸ģาร ศึà¸ģษา +ĠTr Æ°á»Ŀng +иг ÑĢа +ıl ması +Ġм аÑģÑģ +ãģ¨ãģį ãģ« +à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ +à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา +ĠاÙĦساب ÙĤ +Ġ×ŀ×¢ ×ĺ +в аÑĤÑĮ +m Ã¼ÅŁ +Ġ׾ ׼×ļ +Ġt á»ĭch +Ùģ ÙĩÙħ +تد رÙĬب +Ø´ Ùĥ +Ġ×ij ×ŀ×Ļ +Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ +ÙĤØ· اع +ãģª ãģĹ +×ķצ ×Ļ×IJ +ĠÙĪ سÙĬ +з Ñĥ +Ġy at +Ġyat ırım +ë§ İ +Ġth ắng +ãģĬ 客 +ãģĬ客 æ§ĺ +ĠThi ên +ãģ«å¯¾ ãģĹãģ¦ +ÑĢ иÑģ +ÙĨت ائ +ÙĨتائ ج +Ġ×ŀ שר +Ġ×ŀשר ×ĵ +Ġتع اÙĦ +ĠتعاÙĦ Ùī +ש ׳×Ļ +Ùĩ اÙħ +×IJ׳ ש×Ļ×Ŀ +Ġżyc ia +ĠÑĢÑĥб лей +ÙĬ ض +Ġkat ıl +ĠÙħ ÙĪضÙĪع +Ġvard ır +ĠÙħÙĨ Ø·ÙĤØ© +ĠTr ần +Ġв еÑģ +ü p +Ùħ ÙĪÙĨ +ÑĪ ли +Ġn óng +Ø® ÙĦÙģ +ĠС ÑĤа +Ġд оÑĢ +ĠдоÑĢ ог +ĠwÅĤa ÅĽnie +eÄŁ in +Ġhi á»ĥm +ĠС ам +ê»ĺ ìĦľ +ĠÑĦ а +ãģ» ãģĨ +ãģ»ãģĨ ãģĮ +×ķפ ×Ļ×¢ +ê° Ī +د ÙĪÙĦ +Ġthu ê +Ġch á»Ĺ +Ġëĭ¹ ìĭł +ãģij ãĤĮ +ãģijãĤĮ ãģ© +ë³´ íĺ¸ +ãģķãĤĮ 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½ +ðĿĴ ° +ðĿĴ · +ðĿĴ ¿ +ðĿĵ ģ +ðĿĵ ĭ +ðĿĵ İ +ðĿĵ Ĵ +ðĿ ĵĺ +ðĿĵ ¢ +ðĿĵ ¦ +ðĿĵ « +ðĿĵ ¿ +ðĿĶ İ +ðĿĶ ± +ðĿĶ ´ +ðĿĶ · +ðĿĶ ¸ +ðĿĶ ½ +ðĿķ Ĥ +ðĿķ ĥ +ðĿķ ĭ +ðĿķ ı +ðĿķ IJ +ðĿķ ¥ +ðĿķ ´ +ðĿķ º +ðĿĸ IJ +ðĿĸ Ľ +ðĿĸ Ŀ +ðĿĸ ŀ +ðĿĹ © +ðĿĹ ³ +ðĿĹ ½ +ðĿĺ Ĭ +ðĿĺ ĭ +ðĿĺ Ķ +ðĿĺ ± +ðĿĺ ´ +ðĿĺ ¿ +ðĿĻ Ĵ +ðĿĻ Ŀ +ðĿĻ Ł +ðĿĻ ¬ +ðĿĻ Ń +ðĿĻ » +ðĿĻ ¾ +ðĿļ Ī +ðĿļ ĭ +ðĿļ ij +ðĿļ Ł +ðĿļ ł +ðĿļ £ +ðĿĽ ½ +ðĿľ Ĥ +ðĿľ Ķ +ðĿľ Ļ +ðŁ Ģ +ðŁĢ Ħ +ðŁĦ ² +ðŁĦ ¶ +ðŁħ IJ +ðŁħ ĸ +ðŁħ ļ +ðŁħ Ľ +ðŁħ ¦ +ðŁħ ¶ +ðŁħ » +ðŁħ ¼ +ðŁĨ ĥ +ðŁĨ Ĩ +ðŁĨ İ +ðŁĪ ¯ +ðŁĪ ² +ðŁĪ ¹ +ðŁĮ ĩ +ðŁĮ ĵ +ðŁį ĺ +ðŁİ ij +ðŁİ ¿ +ðŁı ı +ðŁı Ĵ +ðŁı © +ðŁı ¯ +ðŁIJ Ģ +ðŁij Ŀ +ðŁĴ ¹ +ðŁĴ º +ðŁĵ Ł +ðŁĵ ª +ðŁĵ ¼ +ðŁĶ Ģ +ðŁĶ Ĥ +ðŁĶ ĥ +ðŁĶ ĩ +ðŁĶ ĵ +ðŁĶ ¢ +ðŁĶ ¤ +ðŁĶ © +ðŁķ ĸ +ðŁķ ļ +ðŁķ ľ +ðŁķ Ŀ +ðŁķ ŀ +ðŁķ ł +ðŁķ ¢ +ðŁķ ³ +ðŁĸ ĩ +ðŁĸ ij +ðŁĸ ¶ +ðŁĹ ģ +Ñ ¨ +Ú İ +á¡ Į +Ḡ° +ẠĢ +á¼ ® +á½ Ŀ +âĦ ¬ +âļ § +⼠¤ +ã³ ¬ +êĻ ĭ +ê¸ ij +ëĶ ī +ëĹ į +ë¡ ij +ë¯ ij +ë» ħ +ë¼ Ŀ +ìĦ IJ +ìī ¡ +ìĭ ² +ìı ± +ìĹ ¤ +ìĿ © +ìĿ ¿ +ìŁ Ļ +ìł ° +ì¥ ī +íĬ Ń +íķ ® +ï® ı +ðŁħ ± +ðŁĨ Ĵ +ðŁķ ĭ +É ĺ +Ê ĵ +Õ ĥ +à´ ´ +འħ +áĨ º +áĪ Ĭ +áĪ ¨ +áĪ ¾ +áī IJ +áĮ ĥ +áĮ ½ +áĶ Ń +áł Ĥ +áł ¬ +ᨠ¸ +á© ĭ +ᶠı +á¾ Ķ +á¿ IJ +á¿ ļ +âĻ Ļ +âļ Ĥ +âļ Ĺ +â¡ ¢ +⤠¦ +ëĸ ° +ë¤ Ĥ +ë§ ł +ë± ĭ +ë± IJ +ìĽ ¢ +ìľ ¾ +ì³ ħ +ì» ģ +íģ » +íĥ Ļ +íĵ ĸ +íĵ Ń +íķ ± +íĽ ľ +ï¤ ħ +ï¤ Ĩ +ï¦ ĥ +ï§ © +ï¨ Ĥ +ðIJ¤ Ķ +ðIJŃ ĵ +ðIJ° ¼ +ðĿĵ ŀ +ðĿĵ ° +ðĿĻ ľ +ðĿļ ģ +ðŁħ ¢ +ðŁı ĩ +È ² +Ê ¶ +Ô Ī +Ô ij +Ý ĵ +Ý ¥ +ठij +ॠ± +ଠī +à° ³ +à° µ +ಠŁ +áĢ ı +áģ ¼ +áī ¨ +áĬ Ĵ +áĭ © +áĮ Ħ +áĮ Ķ +áIJ § +á ĴĮ +áĶ ħ +áĶ Ĭ +áł Ħ +ᨠģ +Ḡĥ +Ḡ» +âĶ ŀ +âĺ µ +âļ £ +â² ¢ +ãĪ ª +ä¶ µ +ê² Ļ +ê² ´ +ê³ Ĥ +ë¡ ¼ +ìĨ Ĭ +ì¼ ĩ +íĭ į +íĵ ¬ +íĵ ® +íĵ ¶ +íĵ » +ï¤ ¦ +ï¥ ł +ï¥ ± +ïŃ ² +ðIJŃ Ĭ +ðIJ ±ħ +ðĸ ¥ +ðĸ¥ ¨ +ðĿij ³ +ðĿĵ ķ +ðĿĵ ¬ +ðĿĵ ¹ +ðĿĵ ¾ +ðĿĶ ĵ +ðĿķ į +ðĿķ ¡ +ðĿķ ± +ðĿĸ ĸ +ðĿĺ ı +ðĿĺ IJ +ðĿĺ ļ +ðĿĻ ® +ðĿĻ ° +ðĿĻ ¸ +ðĿĻ º +ðĿĻ ¼ +ðĿĻ ½ +ðĿĻ ¿ +ðĿļ Ħ +ðĿļ ı +ðŁħ ħ +ðŁħ ĵ +Æ Ī +àł Į +áĻ ³ +á ļĮ +ἠħ +ἠIJ +ᤠĬ +ḠĬ +âĶ ½ +âķ Ĭ +⼠ĩ +⼠ı +âĿ ª +âĿ « +⣠° +ãĦ į +ãĦ ĵ +ãĦ § +ãħ ĸ +ãī « +ê¦ Ķ +ï± Ĭ +ຠĤ +áħ £ +ᥠĶ +ᥠ¤ +âĨ ¤ +âĨ · +âĩ ŀ +âĸ ¤ +âŀ ¶ +ãĪ ¼ +ï¨ · +ðĵı § +âĶ ² +âĢ ´ +âĴ Ł +âĴ ¡ +â° Ĥ +â° į +â° İ +â° IJ +â° ij +â° Ł +â° ł +â° ¡ +â¼ Ń +ãĬ ¥ +âĴ ł +â½ º +ãĩ º +ãĩ ½ +ï¨ Ĭ +áķ · +âį ¨ +⺠Ł +â½ Ĺ diff --git a/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..67688e82ccf6bf5b0fa2d95130fb8564acce6398 --- /dev/null +++ b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json @@ -0,0 +1,241 @@ +{ + "add_bos_token": false, + "add_prefix_space": false, + "added_tokens_decoder": { + "151643": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151644": { + "content": "<|im_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151645": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151646": { + "content": "<|object_ref_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151647": { + "content": "<|object_ref_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151648": { + "content": "<|box_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151649": { + "content": "<|box_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151650": { + "content": "<|quad_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151651": { + "content": "<|quad_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151652": { + "content": "<|vision_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151653": { + "content": "<|vision_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151654": { + "content": "<|vision_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151655": { + "content": "<|image_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151656": { + "content": "<|video_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151657": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151658": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151659": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151660": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151661": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151662": { + "content": "<|fim_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151663": { + "content": "<|repo_name|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151664": { + "content": "<|file_sep|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151665": { + "content": "<|img|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151666": { + "content": "<|endofimg|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151667": { + "content": "<|meta|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151668": { + "content": "<|endofmeta|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|im_start|>", + "<|im_end|>", + "<|object_ref_start|>", + "<|object_ref_end|>", + "<|box_start|>", + "<|box_end|>", + "<|quad_start|>", + "<|quad_end|>", + "<|vision_start|>", + "<|vision_end|>", + "<|vision_pad|>", + "<|image_pad|>", + "<|video_pad|>" + ], + "bos_token": null, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role 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\ No newline at end of file diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py new file mode 100644 index 0000000000000000000000000000000000000000..2803926ac01867edc686c4ca6625a5c5aba32e0b --- /dev/null +++ b/comfy/text_encoders/sa_t5.py @@ -0,0 +1,22 @@ +from comfy import sd1_clip +from transformers import T5TokenizerFast +import comfy.text_encoders.t5 +import os + +class T5BaseModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True) + +class T5BaseTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) + +class SAT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer) + +class SAT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, name="t5base", clip_model=T5BaseModel, **kwargs) diff --git a/comfy/text_encoders/sd2_clip.py b/comfy/text_encoders/sd2_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..700a23bf09f03f09290f677092bd25f29d6f065a --- /dev/null +++ b/comfy/text_encoders/sd2_clip.py @@ -0,0 +1,23 @@ +from comfy import sd1_clip +import os + +class SD2ClipHModel(sd1_clip.SDClipModel): + def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options) + +class SD2ClipHTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data) + +class SD2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer) + +class SD2ClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs) diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json new file mode 100644 index 0000000000000000000000000000000000000000..00893cfdc9b00f8eb7cf5aaa9c343e7fcd298d82 --- /dev/null +++ b/comfy/text_encoders/sd2_clip_config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 49407, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "pad_token_id": 1, + "projection_dim": 1024, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..ff5d412db1481fee09cf7742b9e32e90f6ed74d9 --- /dev/null +++ b/comfy/text_encoders/sd3_clip.py @@ -0,0 +1,166 @@ +from comfy import sd1_clip +from comfy import sdxl_clip +from transformers import T5TokenizerFast +import comfy.text_encoders.t5 +import torch +import os +import comfy.model_management +import logging + +class T5XXLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") + t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) + if t5xxl_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + + model_options = {**model_options, "model_name": "t5xxl"} + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +def t5_xxl_detect(state_dict, prefix=""): + out = {} + t5_key = "{}encoder.final_layer_norm.weight".format(prefix) + if t5_key in state_dict: + out["dtype_t5"] = state_dict[t5_key].dtype + + scaled_fp8_key = "{}scaled_fp8".format(prefix) + if scaled_fp8_key in state_dict: + out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype + + return out + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data) + + +class SD3Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + + def state_dict(self): + return {} + +class SD3ClipModel(torch.nn.Module): + def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}): + super().__init__() + self.dtypes = set() + if clip_l: + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_l = None + + if clip_g: + self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_g = None + + if t5: + dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) + self.t5_attention_mask = t5_attention_mask + self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask) + self.dtypes.add(dtype_t5) + else: + self.t5xxl = None + + logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) + + def set_clip_options(self, options): + if self.clip_l is not None: + self.clip_l.set_clip_options(options) + if self.clip_g is not None: + self.clip_g.set_clip_options(options) + if self.t5xxl is not None: + self.t5xxl.set_clip_options(options) + + def reset_clip_options(self): + if self.clip_l is not None: + self.clip_l.reset_clip_options() + if self.clip_g is not None: + self.clip_g.reset_clip_options() + if self.t5xxl is not None: + self.t5xxl.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + lg_out = None + pooled = None + out = None + extra = {} + + if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: + if self.clip_l is not None: + lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + else: + l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) + + if self.clip_g is not None: + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + if lg_out is not None: + cut_to = min(lg_out.shape[1], g_out.shape[1]) + lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1) + else: + lg_out = torch.nn.functional.pad(g_out, (768, 0)) + else: + g_out = None + g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) + + if lg_out is not None: + lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) + out = lg_out + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + + if self.t5xxl is not None: + t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + t5_out, t5_pooled = t5_output[:2] + if self.t5_attention_mask: + extra["attention_mask"] = t5_output[2]["attention_mask"] + + if lg_out is not None: + out = torch.cat([lg_out, t5_out], dim=-2) + else: + out = t5_out + + if out is None: + out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) + + if pooled is None: + pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) + + return out, pooled, extra + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + else: + return self.t5xxl.load_sd(sd) + +def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False): + class SD3ClipModel_(SD3ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options) + return SD3ClipModel_ diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..caccb3ca283b3f129f942ebfc9e5c274a71d9f3d --- /dev/null +++ b/comfy/text_encoders/spiece_tokenizer.py @@ -0,0 +1,34 @@ +import torch +import os + +class SPieceTokenizer: + @staticmethod + def from_pretrained(path, **kwargs): + return SPieceTokenizer(path, **kwargs) + + def __init__(self, tokenizer_path, add_bos=False, add_eos=True): + self.add_bos = add_bos + self.add_eos = add_eos + import sentencepiece + if torch.is_tensor(tokenizer_path): + tokenizer_path = tokenizer_path.numpy().tobytes() + + if isinstance(tokenizer_path, bytes): + self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) + else: + if not os.path.isfile(tokenizer_path): + raise ValueError("invalid tokenizer") + self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) + + def get_vocab(self): + out = {} + for i in range(self.tokenizer.get_piece_size()): + out[self.tokenizer.id_to_piece(i)] = i + return out + + def __call__(self, string): + out = self.tokenizer.encode(string) + return {"input_ids": out} + + def serialize_model(self): + return torch.ByteTensor(list(self.tokenizer.serialized_model_proto())) diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py new file mode 100644 index 0000000000000000000000000000000000000000..36bf353095b6d9756f694cb6dd2172e5e5dbfcbd --- /dev/null +++ b/comfy/text_encoders/t5.py @@ -0,0 +1,249 @@ +import torch +import math +from comfy.ldm.modules.attention import optimized_attention_for_device +import comfy.ops + +class T5LayerNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) + self.variance_epsilon = eps + + def forward(self, x): + variance = x.pow(2).mean(-1, keepdim=True) + x = x * torch.rsqrt(variance + self.variance_epsilon) + return comfy.ops.cast_to_input(self.weight, x) * x + +activations = { + "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), + "relu": torch.nn.functional.relu, +} + +class T5DenseActDense(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): + super().__init__() + self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) + # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] + + def forward(self, x): + x = self.act(self.wi(x)) + # x = self.dropout(x) + x = self.wo(x) + return x + +class T5DenseGatedActDense(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): + super().__init__() + self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) + self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) + # self.dropout = nn.Dropout(config.dropout_rate) + self.act = activations[ff_activation] + + def forward(self, x): + hidden_gelu = self.act(self.wi_0(x)) + hidden_linear = self.wi_1(x) + x = hidden_gelu * hidden_linear + # x = self.dropout(x) + x = self.wo(x) + return x + +class T5LayerFF(torch.nn.Module): + def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations): + super().__init__() + if gated_act: + self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) + else: + self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) + + self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x): + forwarded_states = self.layer_norm(x) + forwarded_states = self.DenseReluDense(forwarded_states) + # x = x + self.dropout(forwarded_states) + x += forwarded_states + return x + +class T5Attention(torch.nn.Module): + def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + + # Mesh TensorFlow initialization to avoid scaling before softmax + self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device) + self.num_heads = num_heads + + self.relative_attention_bias = None + if relative_attention_bias: + self.relative_attention_num_buckets = 32 + self.relative_attention_max_distance = 128 + self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype) + + @staticmethod + def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. The relative position is defined as + memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for + small absolute relative_position and larger buckets for larger absolute relative_positions. All relative + positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. + This should allow for more graceful generalization to longer sequences than the model has been trained on + + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + + Returns: + a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) + """ + relative_buckets = 0 + if bidirectional: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) + return relative_buckets + + def compute_bias(self, query_length, key_length, device, dtype): + """Compute binned relative position bias""" + context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] + memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] + relative_position = memory_position - context_position # shape (query_length, key_length) + relative_position_bucket = self._relative_position_bucket( + relative_position, # shape (query_length, key_length) + bidirectional=True, + num_buckets=self.relative_attention_num_buckets, + max_distance=self.relative_attention_max_distance, + ) + values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) + return values.contiguous() + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + q = self.q(x) + k = self.k(x) + v = self.v(x) + if self.relative_attention_bias is not None: + past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype) + + if past_bias is not None: + if mask is not None: + mask = mask + past_bias + else: + mask = past_bias + + out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask) + return self.o(out), past_bias + +class T5LayerSelfAttention(torch.nn.Module): + def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations) + self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention) + # x = x + self.dropout(attention_output) + x += output + return x, past_bias + +class T5Block(torch.nn.Module): + def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations): + super().__init__() + self.layer = torch.nn.ModuleList() + self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) + self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations)) + + def forward(self, x, mask=None, past_bias=None, optimized_attention=None): + x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) + x = self.layer[-1](x) + return x, past_bias + +class T5Stack(torch.nn.Module): + def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations): + super().__init__() + + self.block = torch.nn.ModuleList( + [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] + ) + self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) + # self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) + + intermediate = None + optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) + past_bias = None + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.block) + intermediate_output + + for i, l in enumerate(self.block): + x, past_bias = l(x, mask, past_bias, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + x = self.final_layer_norm(x) + if intermediate is not None and final_layer_norm_intermediate: + intermediate = self.final_layer_norm(intermediate) + return x, intermediate + +class T5(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_layers"] + model_dim = config_dict["d_model"] + inner_dim = config_dict["d_kv"] * config_dict["num_heads"] + + self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations) + self.dtype = dtype + self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype) + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, embeddings): + self.shared = embeddings + + def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs): + if input_ids is None: + x = embeds + else: + x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) + if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: + x = torch.nan_to_num(x) #Fix for fp8 T5 base + return self.encoder(x, attention_mask=attention_mask, **kwargs) diff --git a/comfy/text_encoders/t5_config_base.json b/comfy/text_encoders/t5_config_base.json new file mode 100644 index 0000000000000000000000000000000000000000..71f68327c27280ce150d0c8e92fd61eca0b52a63 --- /dev/null +++ b/comfy/text_encoders/t5_config_base.json @@ -0,0 +1,22 @@ +{ + "d_ff": 3072, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "relu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": false, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/text_encoders/t5_config_xxl.json b/comfy/text_encoders/t5_config_xxl.json new file mode 100644 index 0000000000000000000000000000000000000000..28283b51a11bed6a874499f82d411c16cc646eb1 --- /dev/null +++ b/comfy/text_encoders/t5_config_xxl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/text_encoders/t5_old_config_xxl.json b/comfy/text_encoders/t5_old_config_xxl.json new file mode 100644 index 0000000000000000000000000000000000000000..c9fdd7782197ca0523ee82133e9b417fa947a5c5 --- /dev/null +++ b/comfy/text_encoders/t5_old_config_xxl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 65536, + "d_kv": 128, + "d_model": 1024, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "relu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": false, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 128, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json new file mode 100644 index 0000000000000000000000000000000000000000..ee4e03f97a5b3a9927fc676816f210a364ee234b --- /dev/null +++ b/comfy/text_encoders/t5_pile_config_xl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 5120, + "d_kv": 64, + "d_model": 2048, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 2, + "dense_act_fn": 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"", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "clean_up_tokenization_spaces": true, + "eos_token": "", + "extra_ids": 100, + "legacy": false, + "model_max_length": 512, + "pad_token": "", + "sp_model_kwargs": {}, + "tokenizer_class": "T5Tokenizer", + "unk_token": "" +} diff --git a/comfy/text_encoders/umt5_config_base.json b/comfy/text_encoders/umt5_config_base.json new file mode 100644 index 0000000000000000000000000000000000000000..6b3618f075eb4503d0666bd50f2259cc34b653c4 --- /dev/null +++ b/comfy/text_encoders/umt5_config_base.json @@ -0,0 +1,22 @@ +{ + "d_ff": 2048, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "umt5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 256384 +} diff --git a/comfy/text_encoders/umt5_config_xxl.json b/comfy/text_encoders/umt5_config_xxl.json new file mode 100644 index 0000000000000000000000000000000000000000..dfcb4b54bc95469e7b519aed847bb1efb034bdc1 --- /dev/null +++ b/comfy/text_encoders/umt5_config_xxl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "umt5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 256384 +} diff --git a/comfy/text_encoders/wan.py b/comfy/text_encoders/wan.py new file mode 100644 index 0000000000000000000000000000000000000000..d50fa4b28df39ab358ae19bd9745b702985ba74d --- /dev/null +++ b/comfy/text_encoders/wan.py @@ -0,0 +1,37 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os + +class UMT5XXlModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_xxl.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) + +class UMT5XXlTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + + +class WanT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer) + +class WanT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs) + +def te(dtype_t5=None, t5xxl_scaled_fp8=None): + class WanTEModel(WanT5Model): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + if dtype_t5 is not None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return WanTEModel diff --git a/comfy/utils.py b/comfy/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1f8d71292511a2eaede3ad56c49f71022eb87505 --- /dev/null +++ b/comfy/utils.py @@ -0,0 +1,1078 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Comfy + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + + +import torch +import math +import struct +import comfy.checkpoint_pickle +import safetensors.torch +import numpy as np +from PIL import Image +import logging +import itertools +from torch.nn.functional import interpolate +from einops import rearrange +from comfy.cli_args import args + +MMAP_TORCH_FILES = args.mmap_torch_files + +ALWAYS_SAFE_LOAD = False +if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated + class ModelCheckpoint: + pass + ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint" + + from numpy.core.multiarray import scalar + from numpy import dtype + from numpy.dtypes import Float64DType + from _codecs import encode + + torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode]) + ALWAYS_SAFE_LOAD = True + logging.info("Checkpoint files will always be loaded safely.") +else: + logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.") + +def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False): + if device is None: + device = torch.device("cpu") + metadata = None + if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): + try: + with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f: + sd = {} + for k in f.keys(): + sd[k] = f.get_tensor(k) + if return_metadata: + metadata = f.metadata() + except Exception as e: + if len(e.args) > 0: + message = e.args[0] + if "HeaderTooLarge" in message: + raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt)) + if "MetadataIncompleteBuffer" in message: + raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt)) + raise e + else: + torch_args = {} + if MMAP_TORCH_FILES: + torch_args["mmap"] = True + + if safe_load or ALWAYS_SAFE_LOAD: + pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args) + else: + pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) + if "state_dict" in pl_sd: + sd = pl_sd["state_dict"] + else: + if len(pl_sd) == 1: + key = list(pl_sd.keys())[0] + sd = pl_sd[key] + if not isinstance(sd, dict): + sd = pl_sd + else: + sd = pl_sd + return (sd, metadata) if return_metadata else sd + +def save_torch_file(sd, ckpt, metadata=None): + if metadata is not None: + safetensors.torch.save_file(sd, ckpt, metadata=metadata) + else: + safetensors.torch.save_file(sd, ckpt) + +def calculate_parameters(sd, prefix=""): + params = 0 + for k in sd.keys(): + if k.startswith(prefix): + w = sd[k] + params += w.nelement() + return params + +def weight_dtype(sd, prefix=""): + dtypes = {} + for k in sd.keys(): + if k.startswith(prefix): + w = sd[k] + dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel() + + if len(dtypes) == 0: + return None + + return max(dtypes, key=dtypes.get) + +def state_dict_key_replace(state_dict, keys_to_replace): + for x in keys_to_replace: + if x in state_dict: + state_dict[keys_to_replace[x]] = state_dict.pop(x) + return state_dict + +def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): + if filter_keys: + out = {} + else: + out = state_dict + for rp in replace_prefix: + replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) + for x in replace: + w = state_dict.pop(x[0]) + out[x[1]] = w + return out + + +def transformers_convert(sd, prefix_from, prefix_to, number): + keys_to_replace = { + "{}positional_embedding": "{}embeddings.position_embedding.weight", + "{}token_embedding.weight": "{}embeddings.token_embedding.weight", + "{}ln_final.weight": "{}final_layer_norm.weight", + "{}ln_final.bias": "{}final_layer_norm.bias", + } + + for k in keys_to_replace: + x = k.format(prefix_from) + if x in sd: + sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) + + resblock_to_replace = { + "ln_1": "layer_norm1", + "ln_2": "layer_norm2", + "mlp.c_fc": "mlp.fc1", + "mlp.c_proj": "mlp.fc2", + "attn.out_proj": "self_attn.out_proj", + } + + for resblock in range(number): + for x in resblock_to_replace: + for y in ["weight", "bias"]: + k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) + if k in sd: + sd[k_to] = sd.pop(k) + + for y in ["weight", "bias"]: + k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) + if k_from in sd: + weights = sd.pop(k_from) + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) + sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] + + return sd + +def clip_text_transformers_convert(sd, prefix_from, prefix_to): + sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32) + + tp = "{}text_projection.weight".format(prefix_from) + if tp in sd: + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp) + + tp = "{}text_projection".format(prefix_from) + if tp in sd: + sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous() + return sd + + +UNET_MAP_ATTENTIONS = { + "proj_in.weight", + "proj_in.bias", + "proj_out.weight", + "proj_out.bias", + "norm.weight", + "norm.bias", +} + +TRANSFORMER_BLOCKS = { + "norm1.weight", + "norm1.bias", + "norm2.weight", + "norm2.bias", + "norm3.weight", + "norm3.bias", + "attn1.to_q.weight", + "attn1.to_k.weight", + "attn1.to_v.weight", + "attn1.to_out.0.weight", + "attn1.to_out.0.bias", + "attn2.to_q.weight", + "attn2.to_k.weight", + "attn2.to_v.weight", + "attn2.to_out.0.weight", + "attn2.to_out.0.bias", + "ff.net.0.proj.weight", + "ff.net.0.proj.bias", + "ff.net.2.weight", + "ff.net.2.bias", +} + +UNET_MAP_RESNET = { + "in_layers.2.weight": "conv1.weight", + "in_layers.2.bias": "conv1.bias", + "emb_layers.1.weight": "time_emb_proj.weight", + "emb_layers.1.bias": "time_emb_proj.bias", + "out_layers.3.weight": "conv2.weight", + "out_layers.3.bias": "conv2.bias", + "skip_connection.weight": "conv_shortcut.weight", + "skip_connection.bias": "conv_shortcut.bias", + "in_layers.0.weight": "norm1.weight", + "in_layers.0.bias": "norm1.bias", + "out_layers.0.weight": "norm2.weight", + "out_layers.0.bias": "norm2.bias", +} + +UNET_MAP_BASIC = { + ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias") +} + +def unet_to_diffusers(unet_config): + if "num_res_blocks" not in unet_config: + return {} + num_res_blocks = unet_config["num_res_blocks"] + channel_mult = unet_config["channel_mult"] + transformer_depth = unet_config["transformer_depth"][:] + transformer_depth_output = unet_config["transformer_depth_output"][:] + num_blocks = len(channel_mult) + + transformers_mid = unet_config.get("transformer_depth_middle", None) + + diffusers_unet_map = {} + for x in range(num_blocks): + n = 1 + (num_res_blocks[x] + 1) * x + for i in range(num_res_blocks[x]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + n += 1 + for k in ["weight", "bias"]: + diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) + + i = 0 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) + for t in range(transformers_mid): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) + + for i, n in enumerate([0, 2]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) + + num_res_blocks = list(reversed(num_res_blocks)) + for x in range(num_blocks): + n = (num_res_blocks[x] + 1) * x + l = num_res_blocks[x] + 1 + for i in range(l): + c = 0 + for b in UNET_MAP_RESNET: + diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) + c += 1 + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + c += 1 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + if i == l - 1: + for k in ["weight", "bias"]: + diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) + n += 1 + + for k in UNET_MAP_BASIC: + diffusers_unet_map[k[1]] = k[0] + + return diffusers_unet_map + +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + +MMDIT_MAP_BASIC = { + ("context_embedder.bias", "context_embedder.bias"), + ("context_embedder.weight", "context_embedder.weight"), + ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"), + ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"), + ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"), + ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"), + ("x_embedder.proj.bias", "pos_embed.proj.bias"), + ("x_embedder.proj.weight", "pos_embed.proj.weight"), + ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"), + ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"), + ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), + ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), + ("pos_embed", "pos_embed.pos_embed"), + ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), + ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.linear.weight", "proj_out.weight"), +} + +MMDIT_MAP_BLOCK = { + ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"), + ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"), + ("context_block.attn.proj.bias", "attn.to_add_out.bias"), + ("context_block.attn.proj.weight", "attn.to_add_out.weight"), + ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"), + ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"), + ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"), + ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"), + ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"), + ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"), + ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"), + ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"), + ("x_block.attn.proj.bias", "attn.to_out.0.bias"), + ("x_block.attn.proj.weight", "attn.to_out.0.weight"), + ("x_block.attn.ln_q.weight", "attn.norm_q.weight"), + ("x_block.attn.ln_k.weight", "attn.norm_k.weight"), + ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"), + ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"), + ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"), + ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"), + ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"), + ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), + ("x_block.mlp.fc2.bias", "ff.net.2.bias"), + ("x_block.mlp.fc2.weight", "ff.net.2.weight"), +} + +def mmdit_to_diffusers(mmdit_config, output_prefix=""): + key_map = {} + + depth = mmdit_config.get("depth", 0) + num_blocks = mmdit_config.get("num_blocks", depth) + for i in range(num_blocks): + block_from = "transformer_blocks.{}".format(i) + block_to = "{}joint_blocks.{}".format(output_prefix, i) + + offset = depth * 64 + + for end in ("weight", "bias"): + k = "{}.attn.".format(block_from) + qkv = "{}.x_block.attn.qkv.{}".format(block_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + qkv = "{}.context_block.attn.qkv.{}".format(block_to, end) + key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + k = "{}.attn2.".format(block_from) + qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) + + for k in MMDIT_MAP_BLOCK: + key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) + + map_basic = MMDIT_MAP_BASIC.copy() + map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift)) + map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift)) + + for k in map_basic: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + +PIXART_MAP_BASIC = { + ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"), + ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"), + ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"), + ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"), + ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"), + ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"), + ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"), + ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"), + ("x_embedder.proj.weight", "pos_embed.proj.weight"), + ("x_embedder.proj.bias", "pos_embed.proj.bias"), + ("y_embedder.y_embedding", "caption_projection.y_embedding"), + ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"), + ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"), + ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"), + ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"), + ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"), + ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"), + ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"), + ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"), + ("t_block.1.weight", "adaln_single.linear.weight"), + ("t_block.1.bias", "adaln_single.linear.bias"), + ("final_layer.linear.weight", "proj_out.weight"), + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.scale_shift_table", "scale_shift_table"), +} + +PIXART_MAP_BLOCK = { + ("scale_shift_table", "scale_shift_table"), + ("attn.proj.weight", "attn1.to_out.0.weight"), + ("attn.proj.bias", "attn1.to_out.0.bias"), + ("mlp.fc1.weight", "ff.net.0.proj.weight"), + ("mlp.fc1.bias", "ff.net.0.proj.bias"), + ("mlp.fc2.weight", "ff.net.2.weight"), + ("mlp.fc2.bias", "ff.net.2.bias"), + ("cross_attn.proj.weight" ,"attn2.to_out.0.weight"), + ("cross_attn.proj.bias" ,"attn2.to_out.0.bias"), +} + +def pixart_to_diffusers(mmdit_config, output_prefix=""): + key_map = {} + + depth = mmdit_config.get("depth", 0) + offset = mmdit_config.get("hidden_size", 1152) + + for i in range(depth): + block_from = "transformer_blocks.{}".format(i) + block_to = "{}blocks.{}".format(output_prefix, i) + + for end in ("weight", "bias"): + s = "{}.attn1.".format(block_from) + qkv = "{}.attn.qkv.{}".format(block_to, end) + key_map["{}to_q.{}".format(s, end)] = (qkv, (0, 0, offset)) + key_map["{}to_k.{}".format(s, end)] = (qkv, (0, offset, offset)) + key_map["{}to_v.{}".format(s, end)] = (qkv, (0, offset * 2, offset)) + + s = "{}.attn2.".format(block_from) + q = "{}.cross_attn.q_linear.{}".format(block_to, end) + kv = "{}.cross_attn.kv_linear.{}".format(block_to, end) + + key_map["{}to_q.{}".format(s, end)] = q + key_map["{}to_k.{}".format(s, end)] = (kv, (0, 0, offset)) + key_map["{}to_v.{}".format(s, end)] = (kv, (0, offset, offset)) + + for k in PIXART_MAP_BLOCK: + key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) + + for k in PIXART_MAP_BASIC: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + +def auraflow_to_diffusers(mmdit_config, output_prefix=""): + n_double_layers = mmdit_config.get("n_double_layers", 0) + n_layers = mmdit_config.get("n_layers", 0) + + key_map = {} + for i in range(n_layers): + if i < n_double_layers: + index = i + prefix_from = "joint_transformer_blocks" + prefix_to = "{}double_layers".format(output_prefix) + block_map = { + "attn.to_q.weight": "attn.w2q.weight", + "attn.to_k.weight": "attn.w2k.weight", + "attn.to_v.weight": "attn.w2v.weight", + "attn.to_out.0.weight": "attn.w2o.weight", + "attn.add_q_proj.weight": "attn.w1q.weight", + "attn.add_k_proj.weight": "attn.w1k.weight", + "attn.add_v_proj.weight": "attn.w1v.weight", + "attn.to_add_out.weight": "attn.w1o.weight", + "ff.linear_1.weight": "mlpX.c_fc1.weight", + "ff.linear_2.weight": "mlpX.c_fc2.weight", + "ff.out_projection.weight": "mlpX.c_proj.weight", + "ff_context.linear_1.weight": "mlpC.c_fc1.weight", + "ff_context.linear_2.weight": "mlpC.c_fc2.weight", + "ff_context.out_projection.weight": "mlpC.c_proj.weight", + "norm1.linear.weight": "modX.1.weight", + "norm1_context.linear.weight": "modC.1.weight", + } + else: + index = i - n_double_layers + prefix_from = "single_transformer_blocks" + prefix_to = "{}single_layers".format(output_prefix) + + block_map = { + "attn.to_q.weight": "attn.w1q.weight", + "attn.to_k.weight": "attn.w1k.weight", + "attn.to_v.weight": "attn.w1v.weight", + "attn.to_out.0.weight": "attn.w1o.weight", + "norm1.linear.weight": "modCX.1.weight", + "ff.linear_1.weight": "mlp.c_fc1.weight", + "ff.linear_2.weight": "mlp.c_fc2.weight", + "ff.out_projection.weight": "mlp.c_proj.weight" + } + + for k in block_map: + key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k]) + + MAP_BASIC = { + ("positional_encoding", "pos_embed.pos_embed"), + ("register_tokens", "register_tokens"), + ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"), + ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"), + ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"), + ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"), + ("cond_seq_linear.weight", "context_embedder.weight"), + ("init_x_linear.weight", "pos_embed.proj.weight"), + ("init_x_linear.bias", "pos_embed.proj.bias"), + ("final_linear.weight", "proj_out.weight"), + ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift), + } + + for k in MAP_BASIC: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + +def flux_to_diffusers(mmdit_config, output_prefix=""): + n_double_layers = mmdit_config.get("depth", 0) + n_single_layers = mmdit_config.get("depth_single_blocks", 0) + hidden_size = mmdit_config.get("hidden_size", 0) + + key_map = {} + for index in range(n_double_layers): + prefix_from = "transformer_blocks.{}".format(index) + prefix_to = "{}double_blocks.{}".format(output_prefix, index) + + for end in ("weight", "bias"): + k = "{}.attn.".format(prefix_from) + qkv = "{}.img_attn.qkv.{}".format(prefix_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) + + k = "{}.attn.".format(prefix_from) + qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end) + key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) + key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) + key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) + + block_map = { + "attn.to_out.0.weight": "img_attn.proj.weight", + "attn.to_out.0.bias": "img_attn.proj.bias", + "norm1.linear.weight": "img_mod.lin.weight", + "norm1.linear.bias": "img_mod.lin.bias", + "norm1_context.linear.weight": "txt_mod.lin.weight", + "norm1_context.linear.bias": "txt_mod.lin.bias", + "attn.to_add_out.weight": "txt_attn.proj.weight", + "attn.to_add_out.bias": "txt_attn.proj.bias", + "ff.net.0.proj.weight": "img_mlp.0.weight", + "ff.net.0.proj.bias": "img_mlp.0.bias", + "ff.net.2.weight": "img_mlp.2.weight", + "ff.net.2.bias": "img_mlp.2.bias", + "ff_context.net.0.proj.weight": "txt_mlp.0.weight", + "ff_context.net.0.proj.bias": "txt_mlp.0.bias", + "ff_context.net.2.weight": "txt_mlp.2.weight", + "ff_context.net.2.bias": "txt_mlp.2.bias", + "attn.norm_q.weight": "img_attn.norm.query_norm.scale", + "attn.norm_k.weight": "img_attn.norm.key_norm.scale", + "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale", + "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale", + } + + for k in block_map: + key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) + + for index in range(n_single_layers): + prefix_from = "single_transformer_blocks.{}".format(index) + prefix_to = "{}single_blocks.{}".format(output_prefix, index) + + for end in ("weight", "bias"): + k = "{}.attn.".format(prefix_from) + qkv = "{}.linear1.{}".format(prefix_to, end) + key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) + key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) + key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) + key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4)) + + block_map = { + "norm.linear.weight": "modulation.lin.weight", + "norm.linear.bias": "modulation.lin.bias", + "proj_out.weight": "linear2.weight", + "proj_out.bias": "linear2.bias", + "attn.norm_q.weight": "norm.query_norm.scale", + "attn.norm_k.weight": "norm.key_norm.scale", + } + + for k in block_map: + key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) + + MAP_BASIC = { + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.linear.weight", "proj_out.weight"), + ("img_in.bias", "x_embedder.bias"), + ("img_in.weight", "x_embedder.weight"), + ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"), + ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"), + ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"), + ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"), + ("txt_in.bias", "context_embedder.bias"), + ("txt_in.weight", "context_embedder.weight"), + ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"), + ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"), + ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"), + ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"), + ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"), + ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"), + ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"), + ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"), + ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), + ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), + ("pos_embed_input.bias", "controlnet_x_embedder.bias"), + ("pos_embed_input.weight", "controlnet_x_embedder.weight"), + } + + for k in MAP_BASIC: + if len(k) > 2: + key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) + else: + key_map[k[1]] = "{}{}".format(output_prefix, k[0]) + + return key_map + +def repeat_to_batch_size(tensor, batch_size, dim=0): + if tensor.shape[dim] > batch_size: + return tensor.narrow(dim, 0, batch_size) + elif tensor.shape[dim] < batch_size: + return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size) + return tensor + +def resize_to_batch_size(tensor, batch_size): + in_batch_size = tensor.shape[0] + if in_batch_size == batch_size: + return tensor + + if batch_size <= 1: + return tensor[:batch_size] + + output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) + if batch_size < in_batch_size: + scale = (in_batch_size - 1) / (batch_size - 1) + for i in range(batch_size): + output[i] = tensor[min(round(i * scale), in_batch_size - 1)] + else: + scale = in_batch_size / batch_size + for i in range(batch_size): + output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] + + return output + +def convert_sd_to(state_dict, dtype): + keys = list(state_dict.keys()) + for k in keys: + state_dict[k] = state_dict[k].to(dtype) + return state_dict + +def safetensors_header(safetensors_path, max_size=100*1024*1024): + with open(safetensors_path, "rb") as f: + header = f.read(8) + length_of_header = struct.unpack(' max_size: + return None + return f.read(length_of_header) + +def set_attr(obj, attr, value): + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], value) + return prev + +def set_attr_param(obj, attr, value): + return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) + +def copy_to_param(obj, attr, value): + # inplace update tensor instead of replacing it + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + prev.data.copy_(value) + +def get_attr(obj, attr: str): + """Retrieves a nested attribute from an object using dot notation. + + Args: + obj: The object to get the attribute from + attr (str): The attribute path using dot notation (e.g. "model.layer.weight") + + Returns: + The value of the requested attribute + + Example: + model = MyModel() + weight = get_attr(model, "layer1.conv.weight") + # Equivalent to: model.layer1.conv.weight + + Important: + Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when + accessing nested model objects under `ModelPatcher.model`. + """ + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + +def bislerp(samples, width, height): + def slerp(b1, b2, r): + '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' + + c = b1.shape[-1] + + #norms + b1_norms = torch.norm(b1, dim=-1, keepdim=True) + b2_norms = torch.norm(b2, dim=-1, keepdim=True) + + #normalize + b1_normalized = b1 / b1_norms + b2_normalized = b2 / b2_norms + + #zero when norms are zero + b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 + b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 + + #slerp + dot = (b1_normalized*b2_normalized).sum(1) + omega = torch.acos(dot) + so = torch.sin(omega) + + #technically not mathematically correct, but more pleasing? + res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized + res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) + + #edge cases for same or polar opposites + res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] + res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] + return res + + def generate_bilinear_data(length_old, length_new, device): + coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") + ratios = coords_1 - coords_1.floor() + coords_1 = coords_1.to(torch.int64) + + coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 + coords_2[:,:,:,-1] -= 1 + coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") + coords_2 = coords_2.to(torch.int64) + return ratios, coords_1, coords_2 + + orig_dtype = samples.dtype + samples = samples.float() + n,c,h,w = samples.shape + h_new, w_new = (height, width) + + #linear w + ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) + coords_1 = coords_1.expand((n, c, h, -1)) + coords_2 = coords_2.expand((n, c, h, -1)) + ratios = ratios.expand((n, 1, h, -1)) + + pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h, w_new, c).movedim(-1, 1) + + #linear h + ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) + coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) + + pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) + return result.to(orig_dtype) + +def lanczos(samples, width, height): + images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] + images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] + images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] + result = torch.stack(images) + return result.to(samples.device, samples.dtype) + +def common_upscale(samples, width, height, upscale_method, crop): + orig_shape = tuple(samples.shape) + if len(orig_shape) > 4: + samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1]) + samples = samples.movedim(2, 1) + samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1]) + if crop == "center": + old_width = samples.shape[-1] + old_height = samples.shape[-2] + old_aspect = old_width / old_height + new_aspect = width / height + x = 0 + y = 0 + if old_aspect > new_aspect: + x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) + elif old_aspect < new_aspect: + y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) + s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2) + else: + s = samples + + if upscale_method == "bislerp": + out = bislerp(s, width, height) + elif upscale_method == "lanczos": + out = lanczos(s, width, height) + else: + out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) + + if len(orig_shape) == 4: + return out + + out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width)) + return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width)) + +def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): + rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap)) + cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap)) + return rows * cols + +@torch.inference_mode() +def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None): + dims = len(tile) + + if not (isinstance(upscale_amount, (tuple, list))): + upscale_amount = [upscale_amount] * dims + + if not (isinstance(overlap, (tuple, list))): + overlap = [overlap] * dims + + if index_formulas is None: + index_formulas = upscale_amount + + if not (isinstance(index_formulas, (tuple, list))): + index_formulas = [index_formulas] * dims + + def get_upscale(dim, val): + up = upscale_amount[dim] + if callable(up): + return up(val) + else: + return up * val + + def get_downscale(dim, val): + up = upscale_amount[dim] + if callable(up): + return up(val) + else: + return val / up + + def get_upscale_pos(dim, val): + up = index_formulas[dim] + if callable(up): + return up(val) + else: + return up * val + + def get_downscale_pos(dim, val): + up = index_formulas[dim] + if callable(up): + return up(val) + else: + return val / up + + if downscale: + get_scale = get_downscale + get_pos = get_downscale_pos + else: + get_scale = get_upscale + get_pos = get_upscale_pos + + def mult_list_upscale(a): + out = [] + for i in range(len(a)): + out.append(round(get_scale(i, a[i]))) + return out + + output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device) + + for b in range(samples.shape[0]): + s = samples[b:b+1] + + # handle entire input fitting in a single tile + if all(s.shape[d+2] <= tile[d] for d in range(dims)): + output[b:b+1] = function(s).to(output_device) + if pbar is not None: + pbar.update(1) + continue + + out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) + out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) + + positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)] + + for it in itertools.product(*positions): + s_in = s + upscaled = [] + + for d in range(dims): + pos = max(0, min(s.shape[d + 2] - overlap[d], it[d])) + l = min(tile[d], s.shape[d + 2] - pos) + s_in = s_in.narrow(d + 2, pos, l) + upscaled.append(round(get_pos(d, pos))) + + ps = function(s_in).to(output_device) + mask = torch.ones_like(ps) + + for d in range(2, dims + 2): + feather = round(get_scale(d - 2, overlap[d - 2])) + if feather >= mask.shape[d]: + continue + for t in range(feather): + a = (t + 1) / feather + mask.narrow(d, t, 1).mul_(a) + mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a) + + o = out + o_d = out_div + for d in range(dims): + o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) + + o.add_(ps * mask) + o_d.add_(mask) + + if pbar is not None: + pbar.update(1) + + output[b:b+1] = out/out_div + return output + +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar) + +PROGRESS_BAR_ENABLED = True +def set_progress_bar_enabled(enabled): + global PROGRESS_BAR_ENABLED + PROGRESS_BAR_ENABLED = enabled + +PROGRESS_BAR_HOOK = None +def set_progress_bar_global_hook(function): + global PROGRESS_BAR_HOOK + PROGRESS_BAR_HOOK = function + +class ProgressBar: + def __init__(self, total): + global PROGRESS_BAR_HOOK + self.total = total + self.current = 0 + self.hook = PROGRESS_BAR_HOOK + + def update_absolute(self, value, total=None, preview=None): + if total is not None: + self.total = total + if value > self.total: + value = self.total + self.current = value + if self.hook is not None: + self.hook(self.current, self.total, preview) + + def update(self, value): + self.update_absolute(self.current + value) + +def reshape_mask(input_mask, output_shape): + dims = len(output_shape) - 2 + + if dims == 1: + scale_mode = "linear" + + if dims == 2: + input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1])) + scale_mode = "bilinear" + + if dims == 3: + if len(input_mask.shape) < 5: + input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1])) + scale_mode = "trilinear" + + mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode) + if mask.shape[1] < output_shape[1]: + mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]] + mask = repeat_to_batch_size(mask, output_shape[0]) + return mask + +def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out): + hi, wi = img_size_in + ho, wo = img_size_out + # if it's already the correct size, no need to do anything + if (hi, wi) == (ho, wo): + return mask + if mask.ndim == 2: + mask = mask.unsqueeze(0) + if mask.ndim != 3: + raise ValueError(f"Got a mask of shape {list(mask.shape)}, expected [b, q, k] or [q, k]") + txt_tokens = mask.shape[1] - (hi * wi) + # quadrants of the mask + txt_to_txt = mask[:, :txt_tokens, :txt_tokens] + txt_to_img = mask[:, :txt_tokens, txt_tokens:] + img_to_img = mask[:, txt_tokens:, txt_tokens:] + img_to_txt = mask[:, txt_tokens:, :txt_tokens] + + # convert to 1d x 2d, interpolate, then back to 1d x 1d + txt_to_img = rearrange (txt_to_img, "b t (h w) -> b t h w", h=hi, w=wi) + txt_to_img = interpolate(txt_to_img, size=img_size_out, mode="bilinear") + txt_to_img = rearrange (txt_to_img, "b t h w -> b t (h w)") + # this one is hard because we have to do it twice + # convert to 1d x 2d, interpolate, then to 2d x 1d, interpolate, then 1d x 1d + img_to_img = rearrange (img_to_img, "b hw (h w) -> b hw h w", h=hi, w=wi) + img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear") + img_to_img = rearrange (img_to_img, "b (hk wk) hq wq -> b (hq wq) hk wk", hk=hi, wk=wi) + img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear") + img_to_img = rearrange (img_to_img, "b (hq wq) hk wk -> b (hk wk) (hq wq)", hq=ho, wq=wo) + # convert to 2d x 1d, interpolate, then back to 1d x 1d + img_to_txt = rearrange (img_to_txt, "b (h w) t -> b t h w", h=hi, w=wi) + img_to_txt = interpolate(img_to_txt, size=img_size_out, mode="bilinear") + img_to_txt = rearrange (img_to_txt, "b t h w -> b (h w) t") + + # reassemble the mask from blocks + out = torch.cat([ + torch.cat([txt_to_txt, txt_to_img], dim=2), + torch.cat([img_to_txt, img_to_img], dim=2)], + dim=1 + ) + return out diff --git a/comfy/weight_adapter/__init__.py b/comfy/weight_adapter/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..560b82be3e67e5464e1b6bd5441209f8cbf9f320 --- /dev/null +++ b/comfy/weight_adapter/__init__.py @@ -0,0 +1,23 @@ +from .base import WeightAdapterBase, WeightAdapterTrainBase +from .lora import LoRAAdapter +from .loha import LoHaAdapter +from .lokr import LoKrAdapter +from .glora import GLoRAAdapter +from .oft import OFTAdapter +from .boft import BOFTAdapter + + +adapters: list[type[WeightAdapterBase]] = [ + LoRAAdapter, + LoHaAdapter, + LoKrAdapter, + GLoRAAdapter, + OFTAdapter, + BOFTAdapter, +] + +__all__ = [ + "WeightAdapterBase", + "WeightAdapterTrainBase", + "adapters" +] + [a.__name__ for a in adapters] diff --git a/comfy/weight_adapter/base.py b/comfy/weight_adapter/base.py new file mode 100644 index 0000000000000000000000000000000000000000..b5c7db423fa6e1ff09557506b5a680399372eb0c --- /dev/null +++ b/comfy/weight_adapter/base.py @@ -0,0 +1,135 @@ +from typing import Optional + +import torch +import torch.nn as nn + +import comfy.model_management + + +class WeightAdapterBase: + name: str + loaded_keys: set[str] + weights: list[torch.Tensor] + + @classmethod + def load(cls, x: str, lora: dict[str, torch.Tensor], alpha: float, dora_scale: torch.Tensor) -> Optional["WeightAdapterBase"]: + raise NotImplementedError + + def to_train(self) -> "WeightAdapterTrainBase": + raise NotImplementedError + + @classmethod + def create_train(cls, weight, *args) -> "WeightAdapterTrainBase": + """ + weight: The original weight tensor to be modified. + *args: Additional arguments for configuration, such as rank, alpha etc. + """ + raise NotImplementedError + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + raise NotImplementedError + + +class WeightAdapterTrainBase(nn.Module): + # We follow the scheme of PR #7032 + def __init__(self): + super().__init__() + + def __call__(self, w): + """ + w: The original weight tensor to be modified. + """ + raise NotImplementedError + + def passive_memory_usage(self): + raise NotImplementedError("passive_memory_usage is not implemented") + + def move_to(self, device): + self.to(device) + return self.passive_memory_usage() + + +def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): + dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) + lora_diff *= alpha + weight_calc = weight + function(lora_diff).type(weight.dtype) + + wd_on_output_axis = dora_scale.shape[0] == weight_calc.shape[0] + if wd_on_output_axis: + weight_norm = ( + weight.reshape(weight.shape[0], -1) + .norm(dim=1, keepdim=True) + .reshape(weight.shape[0], *[1] * (weight.dim() - 1)) + ) + else: + weight_norm = ( + weight_calc.transpose(0, 1) + .reshape(weight_calc.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) + .transpose(0, 1) + ) + weight_norm = weight_norm + torch.finfo(weight.dtype).eps + + weight_calc *= (dora_scale / weight_norm).type(weight.dtype) + if strength != 1.0: + weight_calc -= weight + weight += strength * (weight_calc) + else: + weight[:] = weight_calc + return weight + + +def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: + """ + Pad a tensor to a new shape with zeros. + + Args: + tensor (torch.Tensor): The original tensor to be padded. + new_shape (List[int]): The desired shape of the padded tensor. + + Returns: + torch.Tensor: A new tensor padded with zeros to the specified shape. + + Note: + If the new shape is smaller than the original tensor in any dimension, + the original tensor will be truncated in that dimension. + """ + if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): + raise ValueError("The new shape must be larger than the original tensor in all dimensions") + + if len(new_shape) != len(tensor.shape): + raise ValueError("The new shape must have the same number of dimensions as the original tensor") + + # Create a new tensor filled with zeros + padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) + + # Create slicing tuples for both tensors + orig_slices = tuple(slice(0, dim) for dim in tensor.shape) + new_slices = tuple(slice(0, dim) for dim in tensor.shape) + + # Copy the original tensor into the new tensor + padded_tensor[new_slices] = tensor[orig_slices] + + return padded_tensor + + +def tucker_weight_from_conv(up, down, mid): + up = up.reshape(up.size(0), up.size(1)) + down = down.reshape(down.size(0), down.size(1)) + return torch.einsum("m n ..., i m, n j -> i j ...", mid, up, down) + + +def tucker_weight(wa, wb, t): + temp = torch.einsum("i j ..., j r -> i r ...", t, wb) + return torch.einsum("i j ..., i r -> r j ...", temp, wa) diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py new file mode 100644 index 0000000000000000000000000000000000000000..b2a2f1bd46bed5b177f9c583e8d329dc167af1c3 --- /dev/null +++ b/comfy/weight_adapter/boft.py @@ -0,0 +1,115 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class BOFTAdapter(WeightAdapterBase): + name = "boft" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["BOFTAdapter"]: + if loaded_keys is None: + loaded_keys = set() + blocks_name = "{}.oft_blocks".format(x) + rescale_name = "{}.rescale".format(x) + + blocks = None + if blocks_name in lora.keys(): + blocks = lora[blocks_name] + if blocks.ndim == 4: + loaded_keys.add(blocks_name) + else: + blocks = None + if blocks is None: + return None + + rescale = None + if rescale_name in lora.keys(): + rescale = lora[rescale_name] + loaded_keys.add(rescale_name) + + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + blocks = v[0] + rescale = v[1] + alpha = v[2] + dora_scale = v[3] + + blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) + if rescale is not None: + rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) + + boft_m, block_num, boft_b, *_ = blocks.shape + + try: + # Get r + I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype) + # for Q = -Q^T + q = blocks - blocks.transpose(-1, -2) + normed_q = q + if alpha > 0: # alpha in boft/bboft is for constraint + q_norm = torch.norm(q) + 1e-8 + if q_norm > alpha: + normed_q = q * alpha / q_norm + # use float() to prevent unsupported type in .inverse() + r = (I + normed_q) @ (I - normed_q).float().inverse() + r = r.to(weight) + inp = org = weight + + r_b = boft_b//2 + for i in range(boft_m): + bi = r[i] + g = 2 + k = 2**i * r_b + if strength != 1: + bi = bi * strength + (1-strength) * I + inp = ( + inp.unflatten(0, (-1, g, k)) + .transpose(1, 2) + .flatten(0, 2) + .unflatten(0, (-1, boft_b)) + ) + inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp) + inp = ( + inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2) + ) + + if rescale is not None: + inp = inp * rescale + + lora_diff = inp - org + lora_diff = comfy.model_management.cast_to_device(lora_diff, weight.device, intermediate_dtype) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function((strength * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/glora.py b/comfy/weight_adapter/glora.py new file mode 100644 index 0000000000000000000000000000000000000000..939abbba584549b049da0073b4336efab05e1b65 --- /dev/null +++ b/comfy/weight_adapter/glora.py @@ -0,0 +1,93 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class GLoRAAdapter(WeightAdapterBase): + name = "glora" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["GLoRAAdapter"]: + if loaded_keys is None: + loaded_keys = set() + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + weights = (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + dora_scale = v[5] + + old_glora = False + if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: + rank = v[0].shape[0] + old_glora = True + + if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: + if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: + pass + else: + old_glora = False + rank = v[1].shape[0] + + a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) + a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) + b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) + b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) + + if v[4] is not None: + alpha = v[4] / rank + else: + alpha = 1.0 + + try: + if old_glora: + lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora + else: + if weight.dim() > 2: + lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + else: + lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + lora_diff += torch.mm(b1, b2).reshape(weight.shape) + + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/loha.py b/comfy/weight_adapter/loha.py new file mode 100644 index 0000000000000000000000000000000000000000..ce79abad5ae65443daad25a7f06c464e86bb995b --- /dev/null +++ b/comfy/weight_adapter/loha.py @@ -0,0 +1,100 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class LoHaAdapter(WeightAdapterBase): + name = "loha" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoHaAdapter"]: + if loaded_keys is None: + loaded_keys = set() + + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + weights = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha = v[2] / w1b.shape[0] + else: + alpha = 1.0 + + w2a = v[3] + w2b = v[4] + dora_scale = v[7] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) + else: + m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) + m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) + + try: + lora_diff = (m1 * m2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/lokr.py b/comfy/weight_adapter/lokr.py new file mode 100644 index 0000000000000000000000000000000000000000..51233db2db3f7459370bcfc581479d533cc000cf --- /dev/null +++ b/comfy/weight_adapter/lokr.py @@ -0,0 +1,133 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class LoKrAdapter(WeightAdapterBase): + name = "lokr" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoKrAdapter"]: + if loaded_keys is None: + loaded_keys = set() + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + weights = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dora_scale = v[8] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) + else: + w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) + else: + w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha = v[2] / dim + else: + alpha = 1.0 + + try: + lora_diff = torch.kron(w1, w2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/lora.py b/comfy/weight_adapter/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..729dbd9e6f538af3991a222f7bdc2b11cf7f1644 --- /dev/null +++ b/comfy/weight_adapter/lora.py @@ -0,0 +1,206 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import ( + WeightAdapterBase, + WeightAdapterTrainBase, + weight_decompose, + pad_tensor_to_shape, + tucker_weight_from_conv, +) + + +class LoraDiff(WeightAdapterTrainBase): + def __init__(self, weights): + super().__init__() + mat1, mat2, alpha, mid, dora_scale, reshape = weights + out_dim, rank = mat1.shape[0], mat1.shape[1] + rank, in_dim = mat2.shape[0], mat2.shape[1] + if mid is not None: + convdim = mid.ndim - 2 + layer = ( + torch.nn.Conv1d, + torch.nn.Conv2d, + torch.nn.Conv3d + )[convdim] + else: + layer = torch.nn.Linear + self.lora_up = layer(rank, out_dim, bias=False) + self.lora_down = layer(in_dim, rank, bias=False) + self.lora_up.weight.data.copy_(mat1) + self.lora_down.weight.data.copy_(mat2) + if mid is not None: + self.lora_mid = layer(mid, rank, bias=False) + self.lora_mid.weight.data.copy_(mid) + else: + self.lora_mid = None + self.rank = rank + self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) + + def __call__(self, w): + org_dtype = w.dtype + if self.lora_mid is None: + diff = self.lora_up.weight @ self.lora_down.weight + else: + diff = tucker_weight_from_conv( + self.lora_up.weight, self.lora_down.weight, self.lora_mid.weight + ) + scale = self.alpha / self.rank + weight = w + scale * diff.reshape(w.shape) + return weight.to(org_dtype) + + def passive_memory_usage(self): + return sum(param.numel() * param.element_size() for param in self.parameters()) + + +class LoRAAdapter(WeightAdapterBase): + name = "lora" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def create_train(cls, weight, rank=1, alpha=1.0): + out_dim = weight.shape[0] + in_dim = weight.shape[1:].numel() + mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype) + mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype) + torch.nn.init.kaiming_uniform_(mat1, a=5**0.5) + torch.nn.init.constant_(mat2, 0.0) + return LoraDiff( + (mat1, mat2, alpha, None, None, None) + ) + + def to_train(self): + return LoraDiff(self.weights) + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoRAAdapter"]: + if loaded_keys is None: + loaded_keys = set() + + reshape_name = "{}.reshape_weight".format(x) + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + diffusers2_lora = "{}.lora_B.weight".format(x) + diffusers3_lora = "{}.lora.up.weight".format(x) + mochi_lora = "{}.lora_B".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif diffusers2_lora in lora.keys(): + A_name = diffusers2_lora + B_name = "{}.lora_A.weight".format(x) + mid_name = None + elif diffusers3_lora in lora.keys(): + A_name = diffusers3_lora + B_name = "{}.lora.down.weight".format(x) + mid_name = None + elif mochi_lora in lora.keys(): + A_name = mochi_lora + B_name = "{}.lora_A".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name = "{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + reshape = None + if reshape_name in lora.keys(): + try: + reshape = lora[reshape_name].tolist() + loaded_keys.add(reshape_name) + except: + pass + weights = (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + mat1 = comfy.model_management.cast_to_device( + v[0], weight.device, intermediate_dtype + ) + mat2 = comfy.model_management.cast_to_device( + v[1], weight.device, intermediate_dtype + ) + dora_scale = v[4] + reshape = v[5] + + if reshape is not None: + weight = pad_tensor_to_shape(weight, reshape) + + if v[2] is not None: + alpha = v[2] / mat2.shape[0] + else: + alpha = 1.0 + + if v[3] is not None: + # locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = comfy.model_management.cast_to_device( + v[3], weight.device, intermediate_dtype + ) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = ( + torch.mm( + mat2.transpose(0, 1).flatten(start_dim=1), + mat3.transpose(0, 1).flatten(start_dim=1), + ) + .reshape(final_shape) + .transpose(0, 1) + ) + try: + lora_diff = torch.mm( + mat1.flatten(start_dim=1), mat2.flatten(start_dim=1) + ).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose( + dora_scale, + weight, + lora_diff, + alpha, + strength, + intermediate_dtype, + function, + ) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py new file mode 100644 index 0000000000000000000000000000000000000000..25009eca3a9e7f1ba1c8a0c3af1d8d18b1181520 --- /dev/null +++ b/comfy/weight_adapter/oft.py @@ -0,0 +1,96 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class OFTAdapter(WeightAdapterBase): + name = "oft" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["OFTAdapter"]: + if loaded_keys is None: + loaded_keys = set() + blocks_name = "{}.oft_blocks".format(x) + rescale_name = "{}.rescale".format(x) + + blocks = None + if blocks_name in lora.keys(): + blocks = lora[blocks_name] + if blocks.ndim == 3: + loaded_keys.add(blocks_name) + else: + blocks = None + if blocks is None: + return None + + rescale = None + if rescale_name in lora.keys(): + rescale = lora[rescale_name] + loaded_keys.add(rescale_name) + + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + blocks = v[0] + rescale = v[1] + alpha = v[2] + dora_scale = v[3] + + blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) + if rescale is not None: + rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) + + block_num, block_size, *_ = blocks.shape + + try: + # Get r + I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype) + # for Q = -Q^T + q = blocks - blocks.transpose(1, 2) + normed_q = q + if alpha > 0: # alpha in oft/boft is for constraint + q_norm = torch.norm(q) + 1e-8 + if q_norm > alpha: + normed_q = q * alpha / q_norm + # use float() to prevent unsupported type in .inverse() + r = (I + normed_q) @ (I - normed_q).float().inverse() + r = r.to(weight) + _, *shape = weight.shape + lora_diff = torch.einsum( + "k n m, k n ... -> k m ...", + (r * strength) - strength * I, + weight.view(block_num, block_size, *shape), + ).view(-1, *shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function((strength * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..66667946f117bcafaa5081ec4c0ade05b9888511 --- /dev/null +++ b/comfy_api/input/__init__.py @@ -0,0 +1,8 @@ +from .basic_types import ImageInput, AudioInput +from .video_types import VideoInput + +__all__ = [ + "ImageInput", + "AudioInput", + "VideoInput", +] diff --git a/comfy_api/input/basic_types.py b/comfy_api/input/basic_types.py new file mode 100644 index 0000000000000000000000000000000000000000..033fb7e272bc20b84ec4c2078ffa4dc5013fc65e --- /dev/null +++ b/comfy_api/input/basic_types.py @@ -0,0 +1,20 @@ +import torch +from typing import TypedDict + +ImageInput = torch.Tensor +""" +An image in format [B, H, W, C] where B is the batch size, C is the number of channels, +""" + +class AudioInput(TypedDict): + """ + TypedDict representing audio input. + """ + + waveform: torch.Tensor + """ + Tensor in the format [B, C, T] where B is the batch size, C is the number of channels, + """ + + sample_rate: int + diff --git a/comfy_api/input/video_types.py b/comfy_api/input/video_types.py new file mode 100644 index 0000000000000000000000000000000000000000..dc22d34fffdce366fe8843949a3209a7536b89a8 --- /dev/null +++ b/comfy_api/input/video_types.py @@ -0,0 +1,55 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Optional +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents + +class VideoInput(ABC): + """ + Abstract base class for video input types. + """ + + @abstractmethod + def get_components(self) -> VideoComponents: + """ + Abstract method to get the video components (images, audio, and frame rate). + + Returns: + VideoComponents containing images, audio, and frame rate + """ + pass + + @abstractmethod + def save_to( + self, + path: str, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + """ + Abstract method to save the video input to a file. + """ + pass + + # Provide a default implementation, but subclasses can provide optimized versions + # if possible. + def get_dimensions(self) -> tuple[int, int]: + """ + Returns the dimensions of the video input. + + Returns: + Tuple of (width, height) + """ + components = self.get_components() + return components.images.shape[2], components.images.shape[1] + + def get_duration(self) -> float: + """ + Returns the duration of the video in seconds. + + Returns: + Duration in seconds + """ + components = self.get_components() + frame_count = components.images.shape[0] + return float(frame_count / components.frame_rate) diff --git a/comfy_api/input_impl/__init__.py b/comfy_api/input_impl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..02901b8b933caa569cd9dd45eb8e39e7c66e43a1 --- /dev/null +++ b/comfy_api/input_impl/__init__.py @@ -0,0 +1,7 @@ +from .video_types import VideoFromFile, VideoFromComponents + +__all__ = [ + # Implementations + "VideoFromFile", + "VideoFromComponents", +] diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py new file mode 100644 index 0000000000000000000000000000000000000000..197f6558c1c7f67f5a3f7f697c57be2a82e6596d --- /dev/null +++ b/comfy_api/input_impl/video_types.py @@ -0,0 +1,303 @@ +from __future__ import annotations +from av.container import InputContainer +from av.subtitles.stream import SubtitleStream +from fractions import Fraction +from typing import Optional +from comfy_api.input import AudioInput +import av +import io +import json +import numpy as np +import torch +from comfy_api.input import VideoInput +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents + + +def container_to_output_format(container_format: str | None) -> str | None: + """ + A container's `format` may be a comma-separated list of formats. + E.g., iso container's `format` may be `mov,mp4,m4a,3gp,3g2,mj2`. + However, writing to a file/stream with `av.open` requires a single format, + or `None` to auto-detect. + """ + if not container_format: + return None # Auto-detect + + if "," not in container_format: + return container_format + + formats = container_format.split(",") + return formats[0] + + +def get_open_write_kwargs( + dest: str | io.BytesIO, container_format: str, to_format: str | None +) -> dict: + """Get kwargs for writing a `VideoFromFile` to a file/stream with `av.open`""" + open_kwargs = { + "mode": "w", + # If isobmff, preserve custom metadata tags (workflow, prompt, extra_pnginfo) + "options": {"movflags": "use_metadata_tags"}, + } + + is_write_to_buffer = isinstance(dest, io.BytesIO) + if is_write_to_buffer: + # Set output format explicitly, since it cannot be inferred from file extension + if to_format == VideoContainer.AUTO: + to_format = container_format.lower() + elif isinstance(to_format, str): + to_format = to_format.lower() + open_kwargs["format"] = container_to_output_format(to_format) + + return open_kwargs + + +class VideoFromFile(VideoInput): + """ + Class representing video input from a file. + """ + + def __init__(self, file: str | io.BytesIO): + """ + Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object + containing the file contents. + """ + self.__file = file + + def get_dimensions(self) -> tuple[int, int]: + """ + Returns the dimensions of the video input. + + Returns: + Tuple of (width, height) + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + for stream in container.streams: + if stream.type == 'video': + assert isinstance(stream, av.VideoStream) + return stream.width, stream.height + raise ValueError(f"No video stream found in file '{self.__file}'") + + def get_duration(self) -> float: + """ + Returns the duration of the video in seconds. + + Returns: + Duration in seconds + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + with av.open(self.__file, mode="r") as container: + if container.duration is not None: + return float(container.duration / av.time_base) + + # Fallback: calculate from frame count and frame rate + video_stream = next( + (s for s in container.streams if s.type == "video"), None + ) + if video_stream and video_stream.frames and video_stream.average_rate: + return float(video_stream.frames / video_stream.average_rate) + + # Last resort: decode frames to count them + if video_stream and video_stream.average_rate: + frame_count = 0 + container.seek(0) + for packet in container.demux(video_stream): + for _ in packet.decode(): + frame_count += 1 + if frame_count > 0: + return float(frame_count / video_stream.average_rate) + + raise ValueError(f"Could not determine duration for file '{self.__file}'") + + def get_components_internal(self, container: InputContainer) -> VideoComponents: + # Get video frames + frames = [] + for frame in container.decode(video=0): + img = frame.to_ndarray(format='rgb24') # shape: (H, W, 3) + img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3) + frames.append(img) + + images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0) + + # Get frame rate + video_stream = next(s for s in container.streams if s.type == 'video') + frame_rate = Fraction(video_stream.average_rate) if video_stream and video_stream.average_rate else Fraction(1) + + # Get audio if available + audio = None + try: + container.seek(0) # Reset the container to the beginning + for stream in container.streams: + if stream.type != 'audio': + continue + assert isinstance(stream, av.AudioStream) + audio_frames = [] + for packet in container.demux(stream): + for frame in packet.decode(): + assert isinstance(frame, av.AudioFrame) + audio_frames.append(frame.to_ndarray()) # shape: (channels, samples) + if len(audio_frames) > 0: + audio_data = np.concatenate(audio_frames, axis=1) # shape: (channels, total_samples) + audio_tensor = torch.from_numpy(audio_data).unsqueeze(0) # shape: (1, channels, total_samples) + audio = AudioInput({ + "waveform": audio_tensor, + "sample_rate": int(stream.sample_rate) if stream.sample_rate else 1, + }) + except StopIteration: + pass # No audio stream + + metadata = container.metadata + return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata) + + def get_components(self) -> VideoComponents: + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + return self.get_components_internal(container) + raise ValueError(f"No video stream found in file '{self.__file}'") + + def save_to( + self, + path: str | io.BytesIO, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + container_format = container.format.name + video_encoding = container.streams.video[0].codec.name if len(container.streams.video) > 0 else None + reuse_streams = True + if format != VideoContainer.AUTO and format not in container_format.split(","): + reuse_streams = False + if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None: + reuse_streams = False + + if not reuse_streams: + components = self.get_components_internal(container) + video = VideoFromComponents(components) + return video.save_to( + path, + format=format, + codec=codec, + metadata=metadata + ) + + streams = container.streams + + open_kwargs = get_open_write_kwargs(path, container_format, format) + with av.open(path, **open_kwargs) as output_container: + # Copy over the original metadata + for key, value in container.metadata.items(): + if metadata is None or key not in metadata: + output_container.metadata[key] = value + + # Add our new metadata + if metadata is not None: + for key, value in metadata.items(): + if isinstance(value, str): + output_container.metadata[key] = value + else: + output_container.metadata[key] = json.dumps(value) + + # Add streams to the new container + stream_map = {} + for stream in streams: + if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)): + out_stream = output_container.add_stream_from_template(template=stream, opaque=True) + stream_map[stream] = out_stream + + # Write packets to the new container + for packet in container.demux(): + if packet.stream in stream_map and packet.dts is not None: + packet.stream = stream_map[packet.stream] + output_container.mux(packet) + +class VideoFromComponents(VideoInput): + """ + Class representing video input from tensors. + """ + + def __init__(self, components: VideoComponents): + self.__components = components + + def get_components(self) -> VideoComponents: + return VideoComponents( + images=self.__components.images, + audio=self.__components.audio, + frame_rate=self.__components.frame_rate + ) + + def save_to( + self, + path: str, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + if format != VideoContainer.AUTO and format != VideoContainer.MP4: + raise ValueError("Only MP4 format is supported for now") + if codec != VideoCodec.AUTO and codec != VideoCodec.H264: + raise ValueError("Only H264 codec is supported for now") + with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output: + # Add metadata before writing any streams + if metadata is not None: + for key, value in metadata.items(): + output.metadata[key] = json.dumps(value) + + frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000) + # Create a video stream + video_stream = output.add_stream('h264', rate=frame_rate) + video_stream.width = self.__components.images.shape[2] + video_stream.height = self.__components.images.shape[1] + video_stream.pix_fmt = 'yuv420p' + + # Create an audio stream + audio_sample_rate = 1 + audio_stream: Optional[av.AudioStream] = None + if self.__components.audio: + audio_sample_rate = int(self.__components.audio['sample_rate']) + audio_stream = output.add_stream('aac', rate=audio_sample_rate) + audio_stream.sample_rate = audio_sample_rate + audio_stream.format = 'fltp' + + # Encode video + for i, frame in enumerate(self.__components.images): + img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3) + frame = av.VideoFrame.from_ndarray(img, format='rgb24') + frame = frame.reformat(format='yuv420p') # Convert to YUV420P as required by h264 + packet = video_stream.encode(frame) + output.mux(packet) + + # Flush video + packet = video_stream.encode(None) + output.mux(packet) + + if audio_stream and self.__components.audio: + # Encode audio + samples_per_frame = int(audio_sample_rate / frame_rate) + num_frames = self.__components.audio['waveform'].shape[2] // samples_per_frame + for i in range(num_frames): + start = i * samples_per_frame + end = start + samples_per_frame + # TODO(Feature) - Add support for stereo audio + chunk = ( + self.__components.audio["waveform"][0, 0, start:end] + .unsqueeze(0) + .contiguous() + .numpy() + ) + audio_frame = av.AudioFrame.from_ndarray(chunk, format='fltp', layout='mono') + audio_frame.sample_rate = audio_sample_rate + audio_frame.pts = i * samples_per_frame + for packet in audio_stream.encode(audio_frame): + output.mux(packet) + + # Flush audio + for packet in audio_stream.encode(None): + output.mux(packet) + diff --git a/comfy_api/torch_helpers/__init__.py b/comfy_api/torch_helpers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..be7ae7a6165bdeed5013806a7adde1a02c0ac93f --- /dev/null +++ b/comfy_api/torch_helpers/__init__.py @@ -0,0 +1,5 @@ +from .torch_compile import set_torch_compile_wrapper + +__all__ = [ + "set_torch_compile_wrapper", +] diff --git a/comfy_api/torch_helpers/torch_compile.py b/comfy_api/torch_helpers/torch_compile.py new file mode 100644 index 0000000000000000000000000000000000000000..9223f58db22415897cbf3d6239511d289f5e1091 --- /dev/null +++ b/comfy_api/torch_helpers/torch_compile.py @@ -0,0 +1,69 @@ +from __future__ import annotations +import torch + +import comfy.utils +from comfy.patcher_extension import WrappersMP +from typing import TYPE_CHECKING, Callable, Optional +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher + from comfy.patcher_extension import WrapperExecutor + + +COMPILE_KEY = "torch.compile" +TORCH_COMPILE_KWARGS = "torch_compile_kwargs" + + +def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable: + ''' + Create a wrapper that will refer to the compiled_diffusion_model. + ''' + def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs): + try: + orig_modules = {} + for key, value in compiled_module_dict.items(): + orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key) + comfy.utils.set_attr(executor.class_obj, key, value) + return executor(*args, **kwargs) + finally: + for key, value in orig_modules.items(): + comfy.utils.set_attr(executor.class_obj, key, value) + return apply_torch_compile_wrapper + + +def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None, + mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None, + keys: list[str]=["diffusion_model"], *args, **kwargs): + ''' + Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance. + + When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model. + When a list of keys is provided, it will perform torch.compile on only the selected modules. + ''' + # clear out any other torch.compile wrappers + model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY) + # if no keys, default to 'diffusion_model' + if not keys: + keys = ["diffusion_model"] + # create kwargs dict that can be referenced later + compile_kwargs = { + "backend": backend, + "options": options, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + # get a dict of compiled keys + compiled_modules = {} + for key in keys: + compiled_modules[key] = torch.compile( + model=model.get_model_object(key), + **compile_kwargs, + ) + # add torch.compile wrapper + wrapper_func = apply_torch_compile_factory( + compiled_module_dict=compiled_modules, + ) + # store wrapper to run on BaseModel's apply_model function + model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func) + # keep compile kwargs for reference + model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs diff --git a/comfy_api/util/__init__.py b/comfy_api/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9019c46dbbcd58a7024301d3bc1bd53570a83bc0 --- /dev/null +++ b/comfy_api/util/__init__.py @@ -0,0 +1,8 @@ +from .video_types import VideoContainer, VideoCodec, VideoComponents + +__all__ = [ + # Utility Types + "VideoContainer", + "VideoCodec", + "VideoComponents", +] diff --git a/comfy_api/util/video_types.py b/comfy_api/util/video_types.py new file mode 100644 index 0000000000000000000000000000000000000000..d09663db92eec5f278a61bc6475bba28d13a8b44 --- /dev/null +++ b/comfy_api/util/video_types.py @@ -0,0 +1,51 @@ +from __future__ import annotations +from dataclasses import dataclass +from enum import Enum +from fractions import Fraction +from typing import Optional +from comfy_api.input import ImageInput, AudioInput + +class VideoCodec(str, Enum): + AUTO = "auto" + H264 = "h264" + + @classmethod + def as_input(cls) -> list[str]: + """ + Returns a list of codec names that can be used as node input. + """ + return [member.value for member in cls] + +class VideoContainer(str, Enum): + AUTO = "auto" + MP4 = "mp4" + + @classmethod + def as_input(cls) -> list[str]: + """ + Returns a list of container names that can be used as node input. + """ + return [member.value for member in cls] + + @classmethod + def get_extension(cls, value) -> str: + """ + Returns the file extension for the container. + """ + if isinstance(value, str): + value = cls(value) + if value == VideoContainer.MP4 or value == VideoContainer.AUTO: + return "mp4" + return "" + +@dataclass +class VideoComponents: + """ + Dataclass representing the components of a video. + """ + + images: ImageInput + frame_rate: Fraction + audio: Optional[AudioInput] = None + metadata: Optional[dict] = None + diff --git a/comfy_api_nodes/README.md b/comfy_api_nodes/README.md new file mode 100644 index 0000000000000000000000000000000000000000..64a389cc1e101f5546ba76cacaf195aa4b549bcf --- /dev/null +++ b/comfy_api_nodes/README.md @@ -0,0 +1,65 @@ +# ComfyUI API Nodes + +## Introduction + +Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview#api-nodes). + +## Development + +While developing, you should be testing against the Staging environment. To test against staging: + +**Install ComfyUI_frontend** + +Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to start the frontend server. By default, it will connect to Staging authentication. + +> **Hint:** If you use --front-end-version argument for ComfyUI, it will use production authentication. + +```bash +python run main.py --comfy-api-base https://stagingapi.comfy.org +``` + +To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging. + +API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes. + +### Redocly Instructions + +**Tip** +When developing locally, use the `redocly-dev.yaml` file to generate pydantic models. This lets you use stubs for APIs that are not marked `Released` yet. + +Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging. + +```bash +# Download the OpenAPI file from staging server. +curl -o openapi.yaml https://stagingapi.comfy.org/openapi + +# Filter out unneeded API definitions. +npm install -g @redocly/cli +redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly-dev.yaml --remove-unused-components + +# Generate the pydantic datamodels for validation. +datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel + +``` + + +# Merging to Master + +Before merging to comfyanonymous/ComfyUI master, follow these steps: + +1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. +1. Make sure the ComfyUI API is deployed to prod with your changes. +1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file. + +```bash +# Download the OpenAPI file from prod server. +curl -o openapi.yaml https://api.comfy.org/openapi + +# Filter out unneeded API definitions. +npm install -g @redocly/cli +redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components + +# Generate the pydantic datamodels for validation. +datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel + +``` diff --git a/comfy_api_nodes/__init__.py b/comfy_api_nodes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..788e2803f5d645ad03b8285349a11d4d160a3903 --- /dev/null +++ b/comfy_api_nodes/apinode_utils.py @@ -0,0 +1,678 @@ +from __future__ import annotations +import io +import logging +import mimetypes +from typing import Optional, Union +from comfy.utils import common_upscale +from comfy_api.input_impl import VideoFromFile +from comfy_api.util import VideoContainer, VideoCodec +from comfy_api.input.video_types import VideoInput +from comfy_api.input.basic_types import AudioInput +from comfy_api_nodes.apis.client import ( + ApiClient, + ApiEndpoint, + HttpMethod, + SynchronousOperation, + UploadRequest, + UploadResponse, +) +from server import PromptServer + + +import numpy as np +from PIL import Image +import requests +import torch +import math +import base64 +import uuid +from io import BytesIO +import av + + +def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile: + """Downloads a video from a URL and returns a `VIDEO` output. + + Args: + video_url: The URL of the video to download. + + Returns: + A Comfy node `VIDEO` output. + """ + video_io = download_url_to_bytesio(video_url, timeout) + if video_io is None: + error_msg = f"Failed to download video from {video_url}" + logging.error(error_msg) + raise ValueError(error_msg) + return VideoFromFile(video_io) + + +def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor: + """Downscale input image tensor to roughly the specified total pixels.""" + samples = image.movedim(-1, 1) + total = int(total_pixels) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + if scale_by >= 1: + return image + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = common_upscale(samples, width, height, "lanczos", "disabled") + s = s.movedim(1, -1) + return s + + +def validate_and_cast_response( + response, timeout: int = None, node_id: Union[str, None] = None +) -> torch.Tensor: + """Validates and casts a response to a torch.Tensor. + + Args: + response: The response to validate and cast. + timeout: Request timeout in seconds. Defaults to None (no timeout). + + Returns: + A torch.Tensor representing the image (1, H, W, C). + + Raises: + ValueError: If the response is not valid. + """ + # validate raw JSON response + data = response.data + if not data or len(data) == 0: + raise ValueError("No images returned from API endpoint") + + # Initialize list to store image tensors + image_tensors: list[torch.Tensor] = [] + + # Process each image in the data array + for image_data in data: + image_url = image_data.url + b64_data = image_data.b64_json + + if not image_url and not b64_data: + raise ValueError("No image was generated in the response") + + if b64_data: + img_data = base64.b64decode(b64_data) + img = Image.open(io.BytesIO(img_data)) + + elif image_url: + if node_id: + PromptServer.instance.send_progress_text( + f"Result URL: {image_url}", node_id + ) + img_response = requests.get(image_url, timeout=timeout) + if img_response.status_code != 200: + raise ValueError("Failed to download the image") + img = Image.open(io.BytesIO(img_response.content)) + + img = img.convert("RGBA") + + # Convert to numpy array, normalize to float32 between 0 and 1 + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array) + + # Add to list of tensors + image_tensors.append(img_tensor) + + return torch.stack(image_tensors, dim=0) + + +def validate_aspect_ratio( + aspect_ratio: str, + minimum_ratio: float, + maximum_ratio: float, + minimum_ratio_str: str, + maximum_ratio_str: str, +) -> float: + """Validates and casts an aspect ratio string to a float. + + Args: + aspect_ratio: The aspect ratio string to validate. + minimum_ratio: The minimum aspect ratio. + maximum_ratio: The maximum aspect ratio. + minimum_ratio_str: The minimum aspect ratio string. + maximum_ratio_str: The maximum aspect ratio string. + + Returns: + The validated and cast aspect ratio. + + Raises: + Exception: If the aspect ratio is not valid. + """ + # get ratio values + numbers = aspect_ratio.split(":") + if len(numbers) != 2: + raise TypeError( + f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}." + ) + try: + numerator = int(numbers[0]) + denominator = int(numbers[1]) + except ValueError as exc: + raise TypeError( + f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}." + ) from exc + calculated_ratio = numerator / denominator + # if not close to minimum and maximum, check bounds + if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose( + calculated_ratio, maximum_ratio + ): + if calculated_ratio < minimum_ratio: + raise TypeError( + f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})." + ) + elif calculated_ratio > maximum_ratio: + raise TypeError( + f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})." + ) + return aspect_ratio + + +def mimetype_to_extension(mime_type: str) -> str: + """Converts a MIME type to a file extension.""" + return mime_type.split("/")[-1].lower() + + +def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO: + """Downloads content from a URL using requests and returns it as BytesIO. + + Args: + url: The URL to download. + timeout: Request timeout in seconds. Defaults to None (no timeout). + + Returns: + BytesIO object containing the downloaded content. + """ + response = requests.get(url, stream=True, timeout=timeout) + response.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX) + return BytesIO(response.content) + + +def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch.Tensor: + """Converts image data from BytesIO to a torch.Tensor. + + Args: + image_bytesio: BytesIO object containing the image data. + mode: The PIL mode to convert the image to (e.g., "RGB", "RGBA"). + + Returns: + A torch.Tensor representing the image (1, H, W, C). + + Raises: + PIL.UnidentifiedImageError: If the image data cannot be identified. + ValueError: If the specified mode is invalid. + """ + image = Image.open(image_bytesio) + image = image.convert(mode) + image_array = np.array(image).astype(np.float32) / 255.0 + return torch.from_numpy(image_array).unsqueeze(0) + + +def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor: + """Downloads an image from a URL and returns a [B, H, W, C] tensor.""" + image_bytesio = download_url_to_bytesio(url, timeout) + return bytesio_to_image_tensor(image_bytesio) + + +def process_image_response(response: requests.Response) -> torch.Tensor: + """Uses content from a Response object and converts it to a torch.Tensor""" + return bytesio_to_image_tensor(BytesIO(response.content)) + + +def _tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image: + """Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling.""" + if len(image.shape) > 3: + image = image[0] + # TODO: remove alpha if not allowed and present + input_tensor = image.cpu() + input_tensor = downscale_image_tensor( + input_tensor.unsqueeze(0), total_pixels=total_pixels + ).squeeze() + image_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + return img + + +def _pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO: + """Converts a PIL Image to a BytesIO object.""" + if not mime_type: + mime_type = "image/png" + + img_byte_arr = io.BytesIO() + # Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG') + pil_format = mime_type.split("/")[-1].upper() + if pil_format == "JPG": + pil_format = "JPEG" + img.save(img_byte_arr, format=pil_format) + img_byte_arr.seek(0) + return img_byte_arr + + +def tensor_to_bytesio( + image: torch.Tensor, + name: Optional[str] = None, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> BytesIO: + """Converts a torch.Tensor image to a named BytesIO object. + + Args: + image: Input torch.Tensor image. + name: Optional filename for the BytesIO object. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). + + Returns: + Named BytesIO object containing the image data. + """ + if not mime_type: + mime_type = "image/png" + + pil_image = _tensor_to_pil(image, total_pixels=total_pixels) + img_binary = _pil_to_bytesio(pil_image, mime_type=mime_type) + img_binary.name = ( + f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}" + ) + return img_binary + + +def tensor_to_base64_string( + image_tensor: torch.Tensor, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> str: + """Convert [B, H, W, C] or [H, W, C] tensor to a base64 string. + + Args: + image_tensor: Input torch.Tensor image. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). + + Returns: + Base64 encoded string of the image. + """ + pil_image = _tensor_to_pil(image_tensor, total_pixels=total_pixels) + img_byte_arr = _pil_to_bytesio(pil_image, mime_type=mime_type) + img_bytes = img_byte_arr.getvalue() + # Encode bytes to base64 string + base64_encoded_string = base64.b64encode(img_bytes).decode("utf-8") + return base64_encoded_string + + +def tensor_to_data_uri( + image_tensor: torch.Tensor, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> str: + """Converts a tensor image to a Data URI string. + + Args: + image_tensor: Input torch.Tensor image. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp'). + + Returns: + Data URI string (e.g., 'data:image/png;base64,...'). + """ + base64_string = tensor_to_base64_string(image_tensor, total_pixels, mime_type) + return f"data:{mime_type};base64,{base64_string}" + + +def text_filepath_to_base64_string(filepath: str) -> str: + """Converts a text file to a base64 string.""" + with open(filepath, "rb") as f: + file_content = f.read() + return base64.b64encode(file_content).decode("utf-8") + + +def text_filepath_to_data_uri(filepath: str) -> str: + """Converts a text file to a data URI.""" + base64_string = text_filepath_to_base64_string(filepath) + mime_type, _ = mimetypes.guess_type(filepath) + if mime_type is None: + mime_type = "application/octet-stream" + return f"data:{mime_type};base64,{base64_string}" + + +def upload_file_to_comfyapi( + file_bytes_io: BytesIO, + filename: str, + upload_mime_type: str, + auth_kwargs: Optional[dict[str, str]] = None, +) -> str: + """ + Uploads a single file to ComfyUI API and returns its download URL. + + Args: + file_bytes_io: BytesIO object containing the file data. + filename: The filename of the file. + upload_mime_type: MIME type of the file. + auth_kwargs: Optional authentication token(s). + + Returns: + The download URL for the uploaded file. + """ + request_object = UploadRequest(file_name=filename, content_type=upload_mime_type) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/customers/storage", + method=HttpMethod.POST, + request_model=UploadRequest, + response_model=UploadResponse, + ), + request=request_object, + auth_kwargs=auth_kwargs, + ) + + response: UploadResponse = operation.execute() + upload_response = ApiClient.upload_file( + response.upload_url, file_bytes_io, content_type=upload_mime_type + ) + upload_response.raise_for_status() + + return response.download_url + + +def video_to_base64_string( + video: VideoInput, + container_format: VideoContainer = None, + codec: VideoCodec = None +) -> str: + """ + Converts a video input to a base64 string. + + Args: + video: The video input to convert + container_format: Optional container format to use (defaults to video.container if available) + codec: Optional codec to use (defaults to video.codec if available) + """ + video_bytes_io = io.BytesIO() + + # Use provided format/codec if specified, otherwise use video's own if available + format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4) + codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264) + + video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use) + video_bytes_io.seek(0) + return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8") + + +def upload_video_to_comfyapi( + video: VideoInput, + auth_kwargs: Optional[dict[str, str]] = None, + container: VideoContainer = VideoContainer.MP4, + codec: VideoCodec = VideoCodec.H264, + max_duration: Optional[int] = None, +) -> str: + """ + Uploads a single video to ComfyUI API and returns its download URL. + Uses the specified container and codec for saving the video before upload. + + Args: + video: VideoInput object (Comfy VIDEO type). + auth_kwargs: Optional authentication token(s). + container: The video container format to use (default: MP4). + codec: The video codec to use (default: H264). + max_duration: Optional maximum duration of the video in seconds. If the video is longer than this, an error will be raised. + + Returns: + The download URL for the uploaded video file. + """ + if max_duration is not None: + try: + actual_duration = video.duration_seconds + if actual_duration is not None and actual_duration > max_duration: + raise ValueError( + f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)." + ) + except Exception as e: + logging.error(f"Error getting video duration: {e}") + raise ValueError(f"Could not verify video duration from source: {e}") from e + + upload_mime_type = f"video/{container.value.lower()}" + filename = f"uploaded_video.{container.value.lower()}" + + # Convert VideoInput to BytesIO using specified container/codec + video_bytes_io = io.BytesIO() + video.save_to(video_bytes_io, format=container, codec=codec) + video_bytes_io.seek(0) + + return upload_file_to_comfyapi( + video_bytes_io, filename, upload_mime_type, auth_kwargs + ) + + +def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray: + """ + Prepares audio waveform for av library by converting to a contiguous numpy array. + + Args: + waveform: a tensor of shape (1, channels, samples) derived from a Comfy `AUDIO` type. + + Returns: + Contiguous numpy array of the audio waveform. If the audio was batched, + the first item is taken. + """ + if waveform.ndim != 3 or waveform.shape[0] != 1: + raise ValueError("Expected waveform tensor shape (1, channels, samples)") + + # If batch is > 1, take first item + if waveform.shape[0] > 1: + waveform = waveform[0] + + # Prepare for av: remove batch dim, move to CPU, make contiguous, convert to numpy array + audio_data_np = waveform.squeeze(0).cpu().contiguous().numpy() + if audio_data_np.dtype != np.float32: + audio_data_np = audio_data_np.astype(np.float32) + + return audio_data_np + + +def audio_ndarray_to_bytesio( + audio_data_np: np.ndarray, + sample_rate: int, + container_format: str = "mp4", + codec_name: str = "aac", +) -> BytesIO: + """ + Encodes a numpy array of audio data into a BytesIO object. + """ + audio_bytes_io = io.BytesIO() + with av.open(audio_bytes_io, mode="w", format=container_format) as output_container: + audio_stream = output_container.add_stream(codec_name, rate=sample_rate) + frame = av.AudioFrame.from_ndarray( + audio_data_np, + format="fltp", + layout="stereo" if audio_data_np.shape[0] > 1 else "mono", + ) + frame.sample_rate = sample_rate + frame.pts = 0 + + for packet in audio_stream.encode(frame): + output_container.mux(packet) + + # Flush stream + for packet in audio_stream.encode(None): + output_container.mux(packet) + + audio_bytes_io.seek(0) + return audio_bytes_io + + +def upload_audio_to_comfyapi( + audio: AudioInput, + auth_kwargs: Optional[dict[str, str]] = None, + container_format: str = "mp4", + codec_name: str = "aac", + mime_type: str = "audio/mp4", + filename: str = "uploaded_audio.mp4", +) -> str: + """ + Uploads a single audio input to ComfyUI API and returns its download URL. + Encodes the raw waveform into the specified format before uploading. + + Args: + audio: a Comfy `AUDIO` type (contains waveform tensor and sample_rate) + auth_kwargs: Optional authentication token(s). + + Returns: + The download URL for the uploaded audio file. + """ + sample_rate: int = audio["sample_rate"] + waveform: torch.Tensor = audio["waveform"] + audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) + audio_bytes_io = audio_ndarray_to_bytesio( + audio_data_np, sample_rate, container_format, codec_name + ) + + return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs) + + +def audio_to_base64_string( + audio: AudioInput, container_format: str = "mp4", codec_name: str = "aac" +) -> str: + """Converts an audio input to a base64 string.""" + sample_rate: int = audio["sample_rate"] + waveform: torch.Tensor = audio["waveform"] + audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) + audio_bytes_io = audio_ndarray_to_bytesio( + audio_data_np, sample_rate, container_format, codec_name + ) + audio_bytes = audio_bytes_io.getvalue() + return base64.b64encode(audio_bytes).decode("utf-8") + + +def upload_images_to_comfyapi( + image: torch.Tensor, + max_images=8, + auth_kwargs: Optional[dict[str, str]] = None, + mime_type: Optional[str] = None, +) -> list[str]: + """ + Uploads images to ComfyUI API and returns download URLs. + To upload multiple images, stack them in the batch dimension first. + + Args: + image: Input torch.Tensor image. + max_images: Maximum number of images to upload. + auth_kwargs: Optional authentication token(s). + mime_type: Optional MIME type for the image. + """ + # if batch, try to upload each file if max_images is greater than 0 + idx_image = 0 + download_urls: list[str] = [] + is_batch = len(image.shape) > 3 + batch_length = 1 + if is_batch: + batch_length = image.shape[0] + while True: + curr_image = image + if len(image.shape) > 3: + curr_image = image[idx_image] + # get BytesIO version of image + img_binary = tensor_to_bytesio(curr_image, mime_type=mime_type) + # first, request upload/download urls from comfy API + if not mime_type: + request_object = UploadRequest(file_name=img_binary.name) + else: + request_object = UploadRequest( + file_name=img_binary.name, content_type=mime_type + ) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/customers/storage", + method=HttpMethod.POST, + request_model=UploadRequest, + response_model=UploadResponse, + ), + request=request_object, + auth_kwargs=auth_kwargs, + ) + response = operation.execute() + + upload_response = ApiClient.upload_file( + response.upload_url, img_binary, content_type=mime_type + ) + # verify success + try: + upload_response.raise_for_status() + except requests.exceptions.HTTPError as e: + raise ValueError(f"Could not upload one or more images: {e}") from e + # add download_url to list + download_urls.append(response.download_url) + + idx_image += 1 + # stop uploading additional files if done + if is_batch and max_images > 0: + if idx_image >= max_images: + break + if idx_image >= batch_length: + break + return download_urls + + +def resize_mask_to_image( + mask: torch.Tensor, + image: torch.Tensor, + upscale_method="nearest-exact", + crop="disabled", + allow_gradient=True, + add_channel_dim=False, +): + """ + Resize mask to be the same dimensions as an image, while maintaining proper format for API calls. + """ + _, H, W, _ = image.shape + mask = mask.unsqueeze(-1) + mask = mask.movedim(-1, 1) + mask = common_upscale( + mask, width=W, height=H, upscale_method=upscale_method, crop=crop + ) + mask = mask.movedim(1, -1) + if not add_channel_dim: + mask = mask.squeeze(-1) + if not allow_gradient: + mask = (mask > 0.5).float() + return mask + + +def validate_string( + string: str, + strip_whitespace=True, + field_name="prompt", + min_length=None, + max_length=None, +): + if string is None: + raise Exception(f"Field '{field_name}' cannot be empty.") + if strip_whitespace: + string = string.strip() + if min_length and len(string) < min_length: + raise Exception( + f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long." + ) + if max_length and len(string) > max_length: + raise Exception( + f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long." + ) + + +def image_tensor_pair_to_batch( + image1: torch.Tensor, image2: torch.Tensor +) -> torch.Tensor: + """ + Converts a pair of image tensors to a batch tensor. + If the images are not the same size, the smaller image is resized to + match the larger image. + """ + if image1.shape[1:] != image2.shape[1:]: + image2 = common_upscale( + image2.movedim(-1, 1), + image1.shape[2], + image1.shape[1], + "bilinear", + "center", + ).movedim(1, -1) + return torch.cat((image1, image2), dim=0) diff --git a/comfy_api_nodes/apis/PixverseController.py b/comfy_api_nodes/apis/PixverseController.py new file mode 100644 index 0000000000000000000000000000000000000000..310c0f54655d72a76eb2abe2d843cc47e28bc37a --- /dev/null +++ b/comfy_api_nodes/apis/PixverseController.py @@ -0,0 +1,17 @@ +# generated by datamodel-codegen: +# filename: filtered-openapi.yaml +# timestamp: 2025-04-29T23:44:54+00:00 + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel + +from . import PixverseDto + + +class ResponseData(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None diff --git a/comfy_api_nodes/apis/PixverseDto.py b/comfy_api_nodes/apis/PixverseDto.py new file mode 100644 index 0000000000000000000000000000000000000000..323c38e963903e523431fac19f067b88d051e396 --- /dev/null +++ b/comfy_api_nodes/apis/PixverseDto.py @@ -0,0 +1,57 @@ +# generated by datamodel-codegen: +# filename: filtered-openapi.yaml +# timestamp: 2025-04-29T23:44:54+00:00 + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel, Field + + +class V2OpenAPII2VResp(BaseModel): + video_id: Optional[int] = Field(None, description='Video_id') + + +class V2OpenAPIT2VReq(BaseModel): + aspect_ratio: str = Field( + ..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9'] + ) + duration: int = Field( + ..., + description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)', + examples=[5], + ) + model: str = Field( + ..., description='Model version (only supports v3.5)', examples=['v3.5'] + ) + motion_mode: Optional[str] = Field( + 'normal', + description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)', + examples=['normal'], + ) + negative_prompt: Optional[str] = Field( + None, description='Negative prompt\n', max_length=2048 + ) + prompt: str = Field(..., description='Prompt', max_length=2048) + quality: str = Field( + ..., + description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")', + examples=['540p'], + ) + seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647') + style: Optional[str] = Field( + None, + description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed', + examples=['anime'], + ) + template_id: Optional[int] = Field( + None, + description='Template ID (template_id must be activated before use)', + examples=[302325299692608], + ) + water_mark: Optional[bool] = Field( + False, + description='Watermark (true: add watermark, false: no watermark)', + examples=[False], + ) diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e38d38cc908879d66b440b9f97c753288dcfde9b --- /dev/null +++ b/comfy_api_nodes/apis/__init__.py @@ -0,0 +1,3453 @@ +# generated by datamodel-codegen: +# filename: filtered-openapi.yaml +# timestamp: 2025-05-19T21:38:55+00:00 + +from __future__ import annotations + +from datetime import date, datetime +from enum import Enum +from typing import Any, Dict, List, Literal, Optional, Union +from uuid import UUID + +from pydantic import AnyUrl, BaseModel, ConfigDict, Field, RootModel, StrictBytes + + +class APIKey(BaseModel): + created_at: Optional[datetime] = None + description: Optional[str] = None + id: Optional[str] = None + key_prefix: Optional[str] = None + name: Optional[str] = None + + +class APIKeyWithPlaintext(APIKey): + plaintext_key: Optional[str] = Field( + None, description='The full API key (only returned at creation)' + ) + + +class AuditLog(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the event was created' + ) + event_id: Optional[str] = Field(None, description='the id of the event') + event_type: Optional[str] = Field(None, description='the type of the event') + params: Optional[Dict[str, Any]] = Field( + None, description='data related to the event' + ) + + +class OutputFormat(str, Enum): + jpeg = 'jpeg' + png = 'png' + + +class BFLFluxPro11GenerateRequest(BaseModel): + height: int = Field(..., description='Height of the generated image') + image_prompt: Optional[str] = Field(None, description='Optional image prompt') + output_format: Optional[OutputFormat] = Field( + None, description='Output image format' + ) + prompt: str = Field(..., description='The main text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to use prompt upsampling' + ) + safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level') + seed: Optional[int] = Field(None, description='Random seed for reproducibility') + webhook_secret: Optional[str] = Field( + None, description='Optional webhook secret for async processing' + ) + webhook_url: Optional[str] = Field( + None, description='Optional webhook URL for async processing' + ) + width: int = Field(..., description='Width of the generated image') + + +class BFLFluxPro11GenerateResponse(BaseModel): + id: str = Field(..., description='Job ID for tracking') + polling_url: str = Field(..., description='URL to poll for results') + + +class BFLFluxProGenerateRequest(BaseModel): + guidance_scale: Optional[float] = Field( + None, description='The guidance scale for generation.', ge=1.0, le=20.0 + ) + height: int = Field( + ..., description='The height of the image to generate.', ge=64, le=2048 + ) + negative_prompt: Optional[str] = Field( + None, description='The negative prompt for image generation.' + ) + num_images: Optional[int] = Field( + None, description='The number of images to generate.', ge=1, le=4 + ) + num_inference_steps: Optional[int] = Field( + None, description='The number of inference steps.', ge=1, le=100 + ) + prompt: str = Field(..., description='The text prompt for image generation.') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + width: int = Field( + ..., description='The width of the image to generate.', ge=64, le=2048 + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class Status(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type(str, Enum): + computer_call = 'computer_call' + + +class ComputerToolCall(BaseModel): + action: Dict[str, Any] + call_id: str = Field( + ..., + description='An identifier used when responding to the tool call with output.\n', + ) + id: str = Field(..., description='The unique ID of the computer call.') + status: Status = Field( + ..., + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + type: Type = Field( + ..., description='The type of the computer call. Always `computer_call`.' + ) + + +class Environment(str, Enum): + windows = 'windows' + mac = 'mac' + linux = 'linux' + ubuntu = 'ubuntu' + browser = 'browser' + + +class Type1(str, Enum): + computer_use_preview = 'computer_use_preview' + + +class ComputerUsePreviewTool(BaseModel): + display_height: int = Field(..., description='The height of the computer display.') + display_width: int = Field(..., description='The width of the computer display.') + environment: Environment = Field( + ..., description='The type of computer environment to control.' + ) + type: Literal['ComputerUsePreviewTool'] = Field( + ..., + description='The type of the computer use tool. Always `computer_use_preview`.', + ) + + +class CreateAPIKeyRequest(BaseModel): + description: Optional[str] = None + name: str + + +class Customer(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the user was created' + ) + email: Optional[str] = Field(None, description='The email address for this user') + id: str = Field(..., description='The firebase UID of the user') + is_admin: Optional[bool] = Field(None, description='Whether the user is an admin') + metronome_id: Optional[str] = Field(None, description='The Metronome customer ID') + name: Optional[str] = Field(None, description='The name for this user') + stripe_id: Optional[str] = Field(None, description='The Stripe customer ID') + updatedAt: Optional[datetime] = Field( + None, description='The date and time the user was last updated' + ) + + +class CustomerStorageResourceResponse(BaseModel): + download_url: Optional[str] = Field( + None, + description='The signed URL to use for downloading the file from the specified path', + ) + existing_file: Optional[bool] = Field( + None, description='Whether an existing file with the same hash was found' + ) + expires_at: Optional[datetime] = Field( + None, description='When the signed URL will expire' + ) + upload_url: Optional[str] = Field( + None, + description='The signed URL to use for uploading the file to the specified path', + ) + + +class Role(str, Enum): + user = 'user' + assistant = 'assistant' + system = 'system' + developer = 'developer' + + +class Type2(str, Enum): + message = 'message' + + +class ErrorResponse(BaseModel): + error: str + message: str + + +class Type3(str, Enum): + file_search = 'file_search' + + +class FileSearchTool(BaseModel): + type: Literal['FileSearchTool'] = Field(..., description='The type of tool') + vector_store_ids: List[str] = Field( + ..., description='IDs of vector stores to search in' + ) + + +class Result(BaseModel): + file_id: Optional[str] = Field(None, description='The unique ID of the file.\n') + filename: Optional[str] = Field(None, description='The name of the file.\n') + score: Optional[float] = Field( + None, description='The relevance score of the file - a value between 0 and 1.\n' + ) + text: Optional[str] = Field( + None, description='The text that was retrieved from the file.\n' + ) + + +class Status1(str, Enum): + in_progress = 'in_progress' + searching = 'searching' + completed = 'completed' + incomplete = 'incomplete' + failed = 'failed' + + +class Type4(str, Enum): + file_search_call = 'file_search_call' + + +class FileSearchToolCall(BaseModel): + id: str = Field(..., description='The unique ID of the file search tool call.\n') + queries: List[str] = Field( + ..., description='The queries used to search for files.\n' + ) + results: Optional[List[Result]] = Field( + None, description='The results of the file search tool call.\n' + ) + status: Status1 = Field( + ..., + description='The status of the file search tool call. One of `in_progress`, \n`searching`, `incomplete` or `failed`,\n', + ) + type: Type4 = Field( + ..., + description='The type of the file search tool call. Always `file_search_call`.\n', + ) + + +class Type5(str, Enum): + function = 'function' + + +class FunctionTool(BaseModel): + description: Optional[str] = Field( + None, description='Description of what the function does' + ) + name: str = Field(..., description='Name of the function') + parameters: Dict[str, Any] = Field( + ..., description='JSON Schema object describing the function parameters' + ) + type: Literal['FunctionTool'] = Field(..., description='The type of tool') + + +class Status2(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type6(str, Enum): + function_call = 'function_call' + + +class FunctionToolCall(BaseModel): + arguments: str = Field( + ..., description='A JSON string of the arguments to pass to the function.\n' + ) + call_id: str = Field( + ..., + description='The unique ID of the function tool call generated by the model.\n', + ) + id: Optional[str] = Field( + None, description='The unique ID of the function tool call.\n' + ) + name: str = Field(..., description='The name of the function to run.\n') + status: Optional[Status2] = Field( + None, + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + type: Type6 = Field( + ..., description='The type of the function tool call. Always `function_call`.\n' + ) + + +class GeminiCitation(BaseModel): + authors: Optional[List[str]] = None + endIndex: Optional[int] = None + license: Optional[str] = None + publicationDate: Optional[date] = None + startIndex: Optional[int] = None + title: Optional[str] = None + uri: Optional[str] = None + + +class GeminiCitationMetadata(BaseModel): + citations: Optional[List[GeminiCitation]] = None + + +class Role1(str, Enum): + user = 'user' + model = 'model' + + +class GeminiFunctionDeclaration(BaseModel): + description: Optional[str] = None + name: str + parameters: Dict[str, Any] = Field( + ..., description='JSON schema for the function parameters' + ) + + +class GeminiGenerationConfig(BaseModel): + maxOutputTokens: Optional[int] = Field( + None, + description='Maximum number of tokens that can be generated in the response. A token is approximately 4 characters. 100 tokens correspond to roughly 60-80 words.\n', + examples=[2048], + ge=16, + le=8192, + ) + seed: Optional[int] = Field( + None, + description="When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used. Available for the following models:, gemini-2.5-flash-preview-04-1, gemini-2.5-pro-preview-05-0, gemini-2.0-flash-lite-00, gemini-2.0-flash-001\n", + examples=[343940597], + ) + stopSequences: Optional[List[str]] = None + temperature: Optional[float] = Field( + 1, + description="The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible. If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature\n", + ge=0.0, + le=2.0, + ) + topK: Optional[int] = Field( + 40, + description="Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary. A top-K of 3 means that the next token is selected from among the 3 most probable tokens by using temperature.\n", + examples=[40], + ge=1, + ) + topP: Optional[float] = Field( + 0.95, + description='If specified, nucleus sampling is used.\nTop-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.\nSpecify a lower value for less random responses and a higher value for more random responses.\n', + ge=0.0, + le=1.0, + ) + + +class GeminiMimeType(str, Enum): + application_pdf = 'application/pdf' + audio_mpeg = 'audio/mpeg' + audio_mp3 = 'audio/mp3' + audio_wav = 'audio/wav' + image_png = 'image/png' + image_jpeg = 'image/jpeg' + image_webp = 'image/webp' + text_plain = 'text/plain' + video_mov = 'video/mov' + video_mpeg = 'video/mpeg' + video_mp4 = 'video/mp4' + video_mpg = 'video/mpg' + video_avi = 'video/avi' + video_wmv = 'video/wmv' + video_mpegps = 'video/mpegps' + video_flv = 'video/flv' + + +class GeminiOffset(BaseModel): + nanos: Optional[int] = Field( + None, + description='Signed fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values.\n', + examples=[0], + ge=0, + le=999999999, + ) + seconds: Optional[int] = Field( + None, + description='Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive.\n', + examples=[60], + ge=-315576000000, + le=315576000000, + ) + + +class GeminiSafetyCategory(str, Enum): + HARM_CATEGORY_SEXUALLY_EXPLICIT = 'HARM_CATEGORY_SEXUALLY_EXPLICIT' + HARM_CATEGORY_HATE_SPEECH = 'HARM_CATEGORY_HATE_SPEECH' + HARM_CATEGORY_HARASSMENT = 'HARM_CATEGORY_HARASSMENT' + HARM_CATEGORY_DANGEROUS_CONTENT = 'HARM_CATEGORY_DANGEROUS_CONTENT' + + +class Probability(str, Enum): + NEGLIGIBLE = 'NEGLIGIBLE' + LOW = 'LOW' + MEDIUM = 'MEDIUM' + HIGH = 'HIGH' + UNKNOWN = 'UNKNOWN' + + +class GeminiSafetyRating(BaseModel): + category: Optional[GeminiSafetyCategory] = None + probability: Optional[Probability] = Field( + None, + description='The probability that the content violates the specified safety category', + ) + + +class GeminiSafetyThreshold(str, Enum): + OFF = 'OFF' + BLOCK_NONE = 'BLOCK_NONE' + BLOCK_LOW_AND_ABOVE = 'BLOCK_LOW_AND_ABOVE' + BLOCK_MEDIUM_AND_ABOVE = 'BLOCK_MEDIUM_AND_ABOVE' + BLOCK_ONLY_HIGH = 'BLOCK_ONLY_HIGH' + + +class GeminiTextPart(BaseModel): + text: Optional[str] = Field( + None, + description='A text prompt or code snippet.', + examples=['Answer as concisely as possible'], + ) + + +class GeminiTool(BaseModel): + functionDeclarations: Optional[List[GeminiFunctionDeclaration]] = None + + +class GeminiVideoMetadata(BaseModel): + endOffset: Optional[GeminiOffset] = None + startOffset: Optional[GeminiOffset] = None + + +class IdeogramColorPalette1(BaseModel): + name: str = Field(..., description='Name of the preset color palette') + + +class Member(BaseModel): + color: Optional[str] = Field( + None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$' + ) + weight: Optional[float] = Field( + None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0 + ) + + +class IdeogramColorPalette2(BaseModel): + members: List[Member] = Field( + ..., description='Array of color definitions with optional weights' + ) + + +class IdeogramColorPalette( + RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]] +): + root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field( + ..., + description='A color palette specification that can either use a preset name or explicit color definitions with weights', + ) + + +class ImageRequest(BaseModel): + aspect_ratio: Optional[str] = Field( + None, + description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.", + ) + color_palette: Optional[Dict[str, Any]] = Field( + None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' + ) + magic_prompt_option: Optional[str] = Field( + None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')." + ) + model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") + negative_prompt: Optional[str] = Field( + None, + description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', + ) + num_images: Optional[int] = Field( + 1, + description='Optional. Number of images to generate (1-8). Defaults to 1.', + ge=1, + le=8, + ) + prompt: str = Field( + ..., description='Required. The prompt to use to generate the image.' + ) + resolution: Optional[str] = Field( + None, + description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", + ) + seed: Optional[int] = Field( + None, + description='Optional. A number between 0 and 2147483647.', + ge=0, + le=2147483647, + ) + style_type: Optional[str] = Field( + None, + description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.", + ) + + +class IdeogramGenerateRequest(BaseModel): + image_request: ImageRequest = Field( + ..., description='The image generation request parameters.' + ) + + +class Datum(BaseModel): + is_image_safe: Optional[bool] = Field( + None, description='Indicates whether the image is considered safe.' + ) + prompt: Optional[str] = Field( + None, description='The prompt used to generate this image.' + ) + resolution: Optional[str] = Field( + None, description="The resolution of the generated image (e.g., '1024x1024')." + ) + seed: Optional[int] = Field( + None, description='The seed value used for this generation.' + ) + style_type: Optional[str] = Field( + None, + description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').", + ) + url: Optional[str] = Field(None, description='URL to the generated image.') + + +class IdeogramGenerateResponse(BaseModel): + created: Optional[datetime] = Field( + None, description='Timestamp when the generation was created.' + ) + data: Optional[List[Datum]] = Field( + None, description='Array of generated image information.' + ) + + +class StyleCode(RootModel[str]): + root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$') + + +class Datum1(BaseModel): + is_image_safe: Optional[bool] = None + prompt: Optional[str] = None + resolution: Optional[str] = None + seed: Optional[int] = None + style_type: Optional[str] = None + url: Optional[str] = None + + +class IdeogramV3IdeogramResponse(BaseModel): + created: Optional[datetime] = None + data: Optional[List[Datum1]] = None + + +class RenderingSpeed1(str, Enum): + TURBO = 'TURBO' + DEFAULT = 'DEFAULT' + QUALITY = 'QUALITY' + + +class IdeogramV3ReframeRequest(BaseModel): + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + rendering_speed: Optional[RenderingSpeed1] = None + resolution: str + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class MagicPrompt(str, Enum): + AUTO = 'AUTO' + ON = 'ON' + OFF = 'OFF' + + +class StyleType(str, Enum): + AUTO = 'AUTO' + GENERAL = 'GENERAL' + REALISTIC = 'REALISTIC' + DESIGN = 'DESIGN' + + +class IdeogramV3RemixRequest(BaseModel): + aspect_ratio: Optional[str] = None + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + image_weight: Optional[int] = Field(50, ge=1, le=100) + magic_prompt: Optional[MagicPrompt] = None + negative_prompt: Optional[str] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + prompt: str + rendering_speed: Optional[RenderingSpeed1] = None + resolution: Optional[str] = None + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + style_type: Optional[StyleType] = None + + +class IdeogramV3ReplaceBackgroundRequest(BaseModel): + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + magic_prompt: Optional[MagicPrompt] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + prompt: str + rendering_speed: Optional[RenderingSpeed1] = None + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class ColorPalette(BaseModel): + name: str = Field(..., description='Name of the color palette', examples=['PASTEL']) + + +class MagicPrompt2(str, Enum): + ON = 'ON' + OFF = 'OFF' + + +class StyleType1(str, Enum): + GENERAL = 'GENERAL' + + +class ImagenImageGenerationInstance(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + + +class AspectRatio(str, Enum): + field_1_1 = '1:1' + field_9_16 = '9:16' + field_16_9 = '16:9' + field_3_4 = '3:4' + field_4_3 = '4:3' + + +class PersonGeneration(str, Enum): + dont_allow = 'dont_allow' + allow_adult = 'allow_adult' + allow_all = 'allow_all' + + +class SafetySetting(str, Enum): + block_most = 'block_most' + block_some = 'block_some' + block_few = 'block_few' + block_fewest = 'block_fewest' + + +class ImagenImagePrediction(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded image content' + ) + mimeType: Optional[str] = Field( + None, description='MIME type of the generated image' + ) + prompt: Optional[str] = Field( + None, description='Enhanced or rewritten prompt used to generate this image' + ) + + +class MimeType(str, Enum): + image_png = 'image/png' + image_jpeg = 'image/jpeg' + + +class ImagenOutputOptions(BaseModel): + compressionQuality: Optional[int] = Field(None, ge=0, le=100) + mimeType: Optional[MimeType] = None + + +class Includable(str, Enum): + file_search_call_results = 'file_search_call.results' + message_input_image_image_url = 'message.input_image.image_url' + computer_call_output_output_image_url = 'computer_call_output.output.image_url' + + +class Type7(str, Enum): + input_file = 'input_file' + + +class InputFileContent(BaseModel): + file_data: Optional[str] = Field( + None, description='The content of the file to be sent to the model.\n' + ) + file_id: Optional[str] = Field( + None, description='The ID of the file to be sent to the model.' + ) + filename: Optional[str] = Field( + None, description='The name of the file to be sent to the model.' + ) + type: Type7 = Field( + ..., description='The type of the input item. Always `input_file`.' + ) + + +class Detail(str, Enum): + low = 'low' + high = 'high' + auto = 'auto' + + +class Type8(str, Enum): + input_image = 'input_image' + + +class InputImageContent(BaseModel): + detail: Detail = Field( + ..., + description='The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.', + ) + file_id: Optional[str] = Field( + None, description='The ID of the file to be sent to the model.' + ) + image_url: Optional[str] = Field( + None, + description='The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.', + ) + type: Type8 = Field( + ..., description='The type of the input item. Always `input_image`.' + ) + + +class Role3(str, Enum): + user = 'user' + system = 'system' + developer = 'developer' + + +class Type9(str, Enum): + message = 'message' + + +class Type10(str, Enum): + input_text = 'input_text' + + +class InputTextContent(BaseModel): + text: str = Field(..., description='The text input to the model.') + type: Type10 = Field( + ..., description='The type of the input item. Always `input_text`.' + ) + + +class KlingAudioUploadType(str, Enum): + file = 'file' + url = 'url' + + +class KlingCameraConfig(BaseModel): + horizontal: Optional[float] = Field( + None, + description="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right.", + ge=-10.0, + le=10.0, + ) + pan: Optional[float] = Field( + None, + description="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", + ge=-10.0, + le=10.0, + ) + roll: Optional[float] = Field( + None, + description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ge=-10.0, + le=10.0, + ) + tilt: Optional[float] = Field( + None, + description="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", + ge=-10.0, + le=10.0, + ) + vertical: Optional[float] = Field( + None, + description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", + ge=-10.0, + le=10.0, + ) + zoom: Optional[float] = Field( + None, + description="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", + ge=-10.0, + le=10.0, + ) + + +class KlingCameraControlType(str, Enum): + simple = 'simple' + down_back = 'down_back' + forward_up = 'forward_up' + right_turn_forward = 'right_turn_forward' + left_turn_forward = 'left_turn_forward' + + +class KlingCharacterEffectModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + + +class KlingDualCharacterEffectsScene(str, Enum): + hug = 'hug' + kiss = 'kiss' + heart_gesture = 'heart_gesture' + + +class KlingDualCharacterImages(RootModel[List[str]]): + root: List[str] = Field(..., max_length=2, min_length=2) + + +class KlingErrorResponse(BaseModel): + code: int = Field( + ..., + description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n', + ) + message: str = Field(..., description='Human-readable error message') + request_id: str = Field( + ..., description='Request ID for tracking and troubleshooting' + ) + + +class Trajectory(BaseModel): + x: Optional[int] = Field( + None, + description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + y: Optional[int] = Field( + None, + description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + + +class DynamicMask(BaseModel): + mask: Optional[AnyUrl] = Field( + None, + description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + trajectories: Optional[List[Trajectory]] = None + + +class TaskInfo(BaseModel): + external_task_id: Optional[str] = None + + +class KlingImageGenAspectRatio(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_3_2 = '3:2' + field_2_3 = '2:3' + field_21_9 = '21:9' + + +class KlingImageGenImageReferenceType(str, Enum): + subject = 'subject' + face = 'face' + + +class KlingImageGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v2 = 'kling-v2' + + +class KlingImageGenerationsRequest(BaseModel): + aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + human_fidelity: Optional[float] = Field( + 0.45, description='Subject reference similarity', ge=0.0, le=1.0 + ) + image: Optional[str] = Field( + None, description='Reference Image - Base64 encoded string or image URL' + ) + image_fidelity: Optional[float] = Field( + 0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0 + ) + image_reference: Optional[KlingImageGenImageReferenceType] = None + model_name: Optional[KlingImageGenModelName] = 'kling-v1' + n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9) + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=200 + ) + prompt: str = Field(..., description='Positive text prompt', max_length=500) + + +class KlingImageResult(BaseModel): + index: Optional[int] = Field(None, description='Image Number (0-9)') + url: Optional[AnyUrl] = Field(None, description='URL for generated image') + + +class KlingLipSyncMode(str, Enum): + text2video = 'text2video' + audio2video = 'audio2video' + + +class KlingLipSyncVoiceLanguage(str, Enum): + zh = 'zh' + en = 'en' + + +class ResourcePackType(str, Enum): + decreasing_total = 'decreasing_total' + constant_period = 'constant_period' + + +class Status4(str, Enum): + toBeOnline = 'toBeOnline' + online = 'online' + expired = 'expired' + runOut = 'runOut' + + +class ResourcePackSubscribeInfo(BaseModel): + effective_time: Optional[int] = Field( + None, description='Effective time, Unix timestamp in ms' + ) + invalid_time: Optional[int] = Field( + None, description='Expiration time, Unix timestamp in ms' + ) + purchase_time: Optional[int] = Field( + None, description='Purchase time, Unix timestamp in ms' + ) + remaining_quantity: Optional[float] = Field( + None, description='Remaining quantity (updated with a 12-hour delay)' + ) + resource_pack_id: Optional[str] = Field(None, description='Resource package ID') + resource_pack_name: Optional[str] = Field(None, description='Resource package name') + resource_pack_type: Optional[ResourcePackType] = Field( + None, + description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', + ) + status: Optional[Status4] = Field(None, description='Resource Package Status') + total_quantity: Optional[float] = Field(None, description='Total quantity') + + +class Data3(BaseModel): + code: Optional[int] = Field(None, description='Error code; 0 indicates success') + msg: Optional[str] = Field(None, description='Error information') + resource_pack_subscribe_infos: Optional[List[ResourcePackSubscribeInfo]] = Field( + None, description='Resource package list' + ) + + +class KlingResourcePackageResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code; 0 indicates success') + data: Optional[Data3] = None + message: Optional[str] = Field(None, description='Error information') + request_id: Optional[str] = Field( + None, + description='Request ID, generated by the system, used to track requests and troubleshoot problems', + ) + + +class KlingSingleImageEffectDuration(str, Enum): + field_5 = '5' + + +class KlingSingleImageEffectModelName(str, Enum): + kling_v1_6 = 'kling-v1-6' + + +class KlingSingleImageEffectsScene(str, Enum): + bloombloom = 'bloombloom' + dizzydizzy = 'dizzydizzy' + fuzzyfuzzy = 'fuzzyfuzzy' + squish = 'squish' + expansion = 'expansion' + + +class KlingTaskStatus(str, Enum): + submitted = 'submitted' + processing = 'processing' + succeed = 'succeed' + failed = 'failed' + + +class KlingTextToVideoModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_6 = 'kling-v1-6' + + +class KlingVideoGenAspectRatio(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + + +class KlingVideoGenCfgScale(RootModel[float]): + root: float = Field( + ..., + description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.", + ge=0.0, + le=1.0, + ) + + +class KlingVideoGenDuration(str, Enum): + field_5 = '5' + field_10 = '10' + + +class KlingVideoGenMode(str, Enum): + std = 'std' + pro = 'pro' + + +class KlingVideoGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + kling_v2_master = 'kling-v2-master' + + +class KlingVideoResult(BaseModel): + duration: Optional[str] = Field(None, description='Total video duration') + id: Optional[str] = Field(None, description='Generated video ID') + url: Optional[AnyUrl] = Field(None, description='URL for generated video') + + +class KlingVirtualTryOnModelName(str, Enum): + kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1' + kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5' + + +class KlingVirtualTryOnRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + cloth_image: Optional[str] = Field( + None, + description='Reference clothing image - Base64 encoded string or image URL', + ) + human_image: str = Field( + ..., description='Reference human image - Base64 encoded string or image URL' + ) + model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1' + + +class TaskResult6(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data7(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult6] = None + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVirtualTryOnResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data7] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class LumaAspectRatio(str, Enum): + field_1_1 = '1:1' + field_16_9 = '16:9' + field_9_16 = '9:16' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_21_9 = '21:9' + field_9_21 = '9:21' + + +class LumaAssets(BaseModel): + image: Optional[AnyUrl] = Field(None, description='The URL of the image') + progress_video: Optional[AnyUrl] = Field( + None, description='The URL of the progress video' + ) + video: Optional[AnyUrl] = Field(None, description='The URL of the video') + + +class GenerationType(str, Enum): + add_audio = 'add_audio' + + +class LumaAudioGenerationRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the audio' + ) + generation_type: Optional[GenerationType] = 'add_audio' + negative_prompt: Optional[str] = Field( + None, description='The negative prompt of the audio' + ) + prompt: Optional[str] = Field(None, description='The prompt of the audio') + + +class LumaError(BaseModel): + detail: Optional[str] = Field(None, description='The error message') + + +class Type11(str, Enum): + generation = 'generation' + + +class LumaGenerationReference(BaseModel): + id: UUID = Field(..., description='The ID of the generation') + type: Literal['generation'] + + +class GenerationType1(str, Enum): + video = 'video' + + +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' + + +class GenerationType2(str, Enum): + image = 'image' + + +class LumaImageIdentity(BaseModel): + images: Optional[List[AnyUrl]] = Field( + None, description='The URLs of the image identity' + ) + + +class LumaImageModel(str, Enum): + photon_1 = 'photon-1' + photon_flash_1 = 'photon-flash-1' + + +class LumaImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the image reference' + ) + + +class Type12(str, Enum): + image = 'image' + + +class LumaImageReference(BaseModel): + type: Literal['image'] + url: AnyUrl = Field(..., description='The URL of the image') + + +class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]): + root: Union[LumaGenerationReference, LumaImageReference] = Field( + ..., + description='A keyframe can be either a Generation reference, an Image, or a Video', + discriminator='type', + ) + + +class LumaKeyframes(BaseModel): + frame0: Optional[LumaKeyframe] = None + frame1: Optional[LumaKeyframe] = None + + +class LumaModifyImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the modify image reference' + ) + + +class LumaState(str, Enum): + queued = 'queued' + dreaming = 'dreaming' + completed = 'completed' + failed = 'failed' + + +class GenerationType3(str, Enum): + upscale_video = 'upscale_video' + + +class LumaVideoModel(str, Enum): + ray_2 = 'ray-2' + ray_flash_2 = 'ray-flash-2' + ray_1_6 = 'ray-1-6' + + +class LumaVideoModelOutputDuration1(str, Enum): + field_5s = '5s' + field_9s = '9s' + + +class LumaVideoModelOutputDuration( + RootModel[Union[LumaVideoModelOutputDuration1, str]] +): + root: Union[LumaVideoModelOutputDuration1, str] + + +class LumaVideoModelOutputResolution1(str, Enum): + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + field_4k = '4k' + + +class LumaVideoModelOutputResolution( + RootModel[Union[LumaVideoModelOutputResolution1, str]] +): + root: Union[LumaVideoModelOutputResolution1, str] + + +class MinimaxBaseResponse(BaseModel): + status_code: int = Field( + ..., + description='Status code. 0 indicates success, other values indicate errors.', + ) + status_msg: str = Field( + ..., description='Specific error details or success message.' + ) + + +class File(BaseModel): + bytes: Optional[int] = Field(None, description='File size in bytes') + created_at: Optional[int] = Field( + None, description='Unix timestamp when the file was created, in seconds' + ) + download_url: Optional[str] = Field( + None, description='The URL to download the video' + ) + file_id: Optional[int] = Field(None, description='Unique identifier for the file') + filename: Optional[str] = Field(None, description='The name of the file') + purpose: Optional[str] = Field(None, description='The purpose of using the file') + + +class MinimaxFileRetrieveResponse(BaseModel): + base_resp: MinimaxBaseResponse + file: File + + +class Status5(str, Enum): + Queueing = 'Queueing' + Preparing = 'Preparing' + Processing = 'Processing' + Success = 'Success' + Fail = 'Fail' + + +class MinimaxTaskResultResponse(BaseModel): + base_resp: MinimaxBaseResponse + file_id: Optional[str] = Field( + None, + description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.', + ) + status: Status5 = Field( + ..., + description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).", + ) + task_id: str = Field(..., description='The task ID being queried.') + + +class Model(str, Enum): + T2V_01_Director = 'T2V-01-Director' + I2V_01_Director = 'I2V-01-Director' + S2V_01 = 'S2V-01' + I2V_01 = 'I2V-01' + I2V_01_live = 'I2V-01-live' + T2V_01 = 'T2V-01' + + +class SubjectReferenceItem(BaseModel): + image: Optional[str] = Field( + None, description='URL or base64 encoding of the subject reference image.' + ) + mask: Optional[str] = Field( + None, + description='URL or base64 encoding of the mask for the subject reference image.', + ) + + +class MinimaxVideoGenerationRequest(BaseModel): + callback_url: Optional[str] = Field( + None, + description='Optional. URL to receive real-time status updates about the video generation task.', + ) + first_frame_image: Optional[str] = Field( + None, + description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.', + ) + model: Model = Field( + ..., + description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01', + ) + prompt: Optional[str] = Field( + None, + description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].', + max_length=2000, + ) + prompt_optimizer: Optional[bool] = Field( + True, + description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.', + ) + subject_reference: Optional[List[SubjectReferenceItem]] = Field( + None, + description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.', + ) + + +class MinimaxVideoGenerationResponse(BaseModel): + base_resp: MinimaxBaseResponse + task_id: str = Field( + ..., description='The task ID for the asynchronous video generation task.' + ) + + +class Truncation(str, Enum): + disabled = 'disabled' + auto = 'auto' + + +class ModelResponseProperties(BaseModel): + instructions: Optional[str] = Field( + None, description='Instructions for the model on how to generate the response' + ) + max_output_tokens: Optional[int] = Field( + None, description='Maximum number of tokens to generate' + ) + model: Optional[str] = Field( + None, description='The model used to generate the response' + ) + temperature: Optional[float] = Field( + 1, description='Controls randomness in the response', ge=0.0, le=2.0 + ) + top_p: Optional[float] = Field( + 1, + description='Controls diversity of the response via nucleus sampling', + ge=0.0, + le=1.0, + ) + truncation: Optional[Truncation] = Field( + 'disabled', description='How to handle truncation of the response' + ) + + +class Moderation(str, Enum): + low = 'low' + auto = 'auto' + + +class OutputFormat1(str, Enum): + png = 'png' + webp = 'webp' + jpeg = 'jpeg' + + +class OpenAIImageEditRequest(BaseModel): + background: Optional[str] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: str = Field( + ..., description='The model to use for image editing', examples=['gpt-image-1'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, description='The number of images to generate', examples=[1] + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired edit', + examples=['Give the rocketship rainbow coloring'], + ) + quality: Optional[str] = Field( + None, description='The quality of the edited image', examples=['low'] + ) + size: Optional[str] = Field( + None, description='Size of the output image', examples=['1024x1024'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Background(str, Enum): + transparent = 'transparent' + opaque = 'opaque' + + +class Quality(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + standard = 'standard' + hd = 'hd' + + +class ResponseFormat(str, Enum): + url = 'url' + b64_json = 'b64_json' + + +class Style(str, Enum): + vivid = 'vivid' + natural = 'natural' + + +class OpenAIImageGenerationRequest(BaseModel): + background: Optional[Background] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: Optional[str] = Field( + None, description='The model to use for image generation', examples=['dall-e-3'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, + description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', + examples=[1], + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired image', + examples=['Draw a rocket in front of a blackhole in deep space'], + ) + quality: Optional[Quality] = Field( + None, description='The quality of the generated image', examples=['high'] + ) + response_format: Optional[ResponseFormat] = Field( + None, description='Response format of image data', examples=['b64_json'] + ) + size: Optional[str] = Field( + None, + description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', + examples=['1024x1536'], + ) + style: Optional[Style] = Field( + None, description='Style of the image (only for dall-e-3)', examples=['vivid'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Datum2(BaseModel): + b64_json: Optional[str] = Field(None, description='Base64 encoded image data') + revised_prompt: Optional[str] = Field(None, description='Revised prompt') + url: Optional[str] = Field(None, description='URL of the image') + + +class InputTokensDetails(BaseModel): + image_tokens: Optional[int] = None + text_tokens: Optional[int] = None + + +class Usage(BaseModel): + input_tokens: Optional[int] = None + input_tokens_details: Optional[InputTokensDetails] = None + output_tokens: Optional[int] = None + total_tokens: Optional[int] = None + + +class OpenAIImageGenerationResponse(BaseModel): + data: Optional[List[Datum2]] = None + usage: Optional[Usage] = None + + +class OpenAIModels(str, Enum): + gpt_4 = 'gpt-4' + gpt_4_0314 = 'gpt-4-0314' + gpt_4_0613 = 'gpt-4-0613' + gpt_4_32k = 'gpt-4-32k' + gpt_4_32k_0314 = 'gpt-4-32k-0314' + gpt_4_32k_0613 = 'gpt-4-32k-0613' + gpt_4_0125_preview = 'gpt-4-0125-preview' + gpt_4_turbo = 'gpt-4-turbo' + gpt_4_turbo_2024_04_09 = 'gpt-4-turbo-2024-04-09' + gpt_4_turbo_preview = 'gpt-4-turbo-preview' + gpt_4_1106_preview = 'gpt-4-1106-preview' + gpt_4_vision_preview = 'gpt-4-vision-preview' + gpt_3_5_turbo = 'gpt-3.5-turbo' + gpt_3_5_turbo_16k = 'gpt-3.5-turbo-16k' + gpt_3_5_turbo_0301 = 'gpt-3.5-turbo-0301' + gpt_3_5_turbo_0613 = 'gpt-3.5-turbo-0613' + gpt_3_5_turbo_1106 = 'gpt-3.5-turbo-1106' + gpt_3_5_turbo_0125 = 'gpt-3.5-turbo-0125' + gpt_3_5_turbo_16k_0613 = 'gpt-3.5-turbo-16k-0613' + gpt_4_1 = 'gpt-4.1' + gpt_4_1_mini = 'gpt-4.1-mini' + gpt_4_1_nano = 'gpt-4.1-nano' + gpt_4_1_2025_04_14 = 'gpt-4.1-2025-04-14' + gpt_4_1_mini_2025_04_14 = 'gpt-4.1-mini-2025-04-14' + gpt_4_1_nano_2025_04_14 = 'gpt-4.1-nano-2025-04-14' + o1 = 'o1' + o1_mini = 'o1-mini' + o1_preview = 'o1-preview' + o1_pro = 'o1-pro' + o1_2024_12_17 = 'o1-2024-12-17' + o1_preview_2024_09_12 = 'o1-preview-2024-09-12' + o1_mini_2024_09_12 = 'o1-mini-2024-09-12' + o1_pro_2025_03_19 = 'o1-pro-2025-03-19' + o3 = 'o3' + o3_mini = 'o3-mini' + o3_2025_04_16 = 'o3-2025-04-16' + o3_mini_2025_01_31 = 'o3-mini-2025-01-31' + o4_mini = 'o4-mini' + o4_mini_2025_04_16 = 'o4-mini-2025-04-16' + gpt_4o = 'gpt-4o' + gpt_4o_mini = 'gpt-4o-mini' + gpt_4o_2024_11_20 = 'gpt-4o-2024-11-20' + gpt_4o_2024_08_06 = 'gpt-4o-2024-08-06' + gpt_4o_2024_05_13 = 'gpt-4o-2024-05-13' + gpt_4o_mini_2024_07_18 = 'gpt-4o-mini-2024-07-18' + gpt_4o_audio_preview = 'gpt-4o-audio-preview' + gpt_4o_audio_preview_2024_10_01 = 'gpt-4o-audio-preview-2024-10-01' + gpt_4o_audio_preview_2024_12_17 = 'gpt-4o-audio-preview-2024-12-17' + gpt_4o_mini_audio_preview = 'gpt-4o-mini-audio-preview' + gpt_4o_mini_audio_preview_2024_12_17 = 'gpt-4o-mini-audio-preview-2024-12-17' + gpt_4o_search_preview = 'gpt-4o-search-preview' + gpt_4o_mini_search_preview = 'gpt-4o-mini-search-preview' + gpt_4o_search_preview_2025_03_11 = 'gpt-4o-search-preview-2025-03-11' + gpt_4o_mini_search_preview_2025_03_11 = 'gpt-4o-mini-search-preview-2025-03-11' + computer_use_preview = 'computer-use-preview' + computer_use_preview_2025_03_11 = 'computer-use-preview-2025-03-11' + chatgpt_4o_latest = 'chatgpt-4o-latest' + + +class Reason(str, Enum): + max_output_tokens = 'max_output_tokens' + content_filter = 'content_filter' + + +class IncompleteDetails(BaseModel): + reason: Optional[Reason] = Field( + None, description='The reason why the response is incomplete.' + ) + + +class Object(str, Enum): + response = 'response' + + +class Status6(str, Enum): + completed = 'completed' + failed = 'failed' + in_progress = 'in_progress' + incomplete = 'incomplete' + + +class Type13(str, Enum): + output_audio = 'output_audio' + + +class OutputAudioContent(BaseModel): + data: str = Field(..., description='Base64-encoded audio data') + transcript: str = Field(..., description='Transcript of the audio') + type: Type13 = Field(..., description='The type of output content') + + +class Role4(str, Enum): + assistant = 'assistant' + + +class Type14(str, Enum): + message = 'message' + + +class Type15(str, Enum): + output_text = 'output_text' + + +class OutputTextContent(BaseModel): + text: str = Field(..., description='The text content') + type: Type15 = Field(..., description='The type of output content') + + +class AspectRatio1(RootModel[float]): + root: float = Field( + ..., + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + + +class IngredientsMode(str, Enum): + creative = 'creative' + precise = 'precise' + + +class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): + aspectRatio: Optional[AspectRatio1] = Field( + None, description='Aspect ratio (width / height)', title='Aspectratio' + ) + duration: Optional[int] = Field(5, title='Duration') + images: Optional[List[StrictBytes]] = Field(None, title='Images') + ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + resolution: Optional[str] = Field('1080p', title='Resolution') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + video: Optional[StrictBytes] = Field(None, title='Video') + + +class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + modifyRegionMask: Optional[StrictBytes] = Field( + None, + description='A mask image that specifies the region to modify, where the mask is white and the background is black', + title='Modifyregionmask', + ) + modifyRegionRoi: Optional[str] = Field( + None, + description='Plaintext description of the object / region to modify', + title='Modifyregionroi', + ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + video: Optional[StrictBytes] = Field(None, title='Video') + + +class PikaDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class PikaGenerateResponse(BaseModel): + video_id: str = Field(..., title='Video Id') + + +class PikaResolutionEnum(str, Enum): + field_1080p = '1080p' + field_720p = '720p' + + +class PikaStatusEnum(str, Enum): + queued = 'queued' + started = 'started' + finished = 'finished' + + +class PikaValidationError(BaseModel): + loc: List[Union[str, int]] = Field(..., title='Location') + msg: str = Field(..., title='Message') + type: str = Field(..., title='Error Type') + + +class PikaVideoResponse(BaseModel): + id: str = Field(..., title='Id') + progress: Optional[int] = Field(None, title='Progress') + status: PikaStatusEnum + url: Optional[str] = Field(None, title='Url') + + +class Pikaffect(str, Enum): + Cake_ify = 'Cake-ify' + Crumble = 'Crumble' + Crush = 'Crush' + Decapitate = 'Decapitate' + Deflate = 'Deflate' + Dissolve = 'Dissolve' + Explode = 'Explode' + Eye_pop = 'Eye-pop' + Inflate = 'Inflate' + Levitate = 'Levitate' + Melt = 'Melt' + Peel = 'Peel' + Poke = 'Poke' + Squish = 'Squish' + Ta_da = 'Ta-da' + Tear = 'Tear' + + +class Resp(BaseModel): + img_id: Optional[int] = None + + +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp_1: Optional[Resp] = Field(None, alias='Resp') + + +class Duration(int, Enum): + integer_5 = 5 + integer_8 = 8 + + +class Model1(str, Enum): + v3_5 = 'v3.5' + + +class MotionMode(str, Enum): + normal = 'normal' + fast = 'fast' + + +class Quality1(str, Enum): + field_360p = '360p' + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + + +class Style1(str, Enum): + anime = 'anime' + field_3d_animation = '3d_animation' + clay = 'clay' + comic = 'comic' + cyberpunk = 'cyberpunk' + + +class PixverseImageVideoRequest(BaseModel): + duration: Duration + img_id: int + model: Model1 + motion_mode: Optional[MotionMode] = None + prompt: str + quality: Quality1 + seed: Optional[int] = None + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class AspectRatio2(str, Enum): + field_16_9 = '16:9' + field_4_3 = '4:3' + field_1_1 = '1:1' + field_3_4 = '3:4' + field_9_16 = '9:16' + + +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: AspectRatio2 + duration: Duration + model: Model1 + motion_mode: Optional[MotionMode] = None + negative_prompt: Optional[str] = None + prompt: str + quality: Quality1 + seed: Optional[int] = None + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class PixverseTransitionVideoRequest(BaseModel): + duration: Duration + first_frame_img: int + last_frame_img: int + model: Model1 + motion_mode: MotionMode + prompt: str + quality: Quality1 + seed: int + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class Resp1(BaseModel): + video_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp1] = None + + +class Status7(int, Enum): + integer_1 = 1 + integer_5 = 5 + integer_6 = 6 + integer_7 = 7 + integer_8 = 8 + + +class Resp2(BaseModel): + create_time: Optional[str] = None + id: Optional[int] = None + modify_time: Optional[str] = None + negative_prompt: Optional[str] = None + outputHeight: Optional[int] = None + outputWidth: Optional[int] = None + prompt: Optional[str] = None + resolution_ratio: Optional[int] = None + seed: Optional[int] = None + size: Optional[int] = None + status: Optional[Status7] = Field( + None, + description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n', + ) + style: Optional[str] = None + url: Optional[str] = None + + +class PixverseVideoResultResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp2] = None + + +class RgbItem(RootModel[int]): + root: int = Field(..., ge=0, le=255) + + +class RGBColor(BaseModel): + rgb: List[RgbItem] = Field(..., max_length=3, min_length=3) + + +class GenerateSummary(str, Enum): + auto = 'auto' + concise = 'concise' + detailed = 'detailed' + + +class Summary(str, Enum): + auto = 'auto' + concise = 'concise' + detailed = 'detailed' + + +class ReasoningEffort(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Status8(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type16(str, Enum): + summary_text = 'summary_text' + + +class SummaryItem(BaseModel): + text: str = Field( + ..., + description='A short summary of the reasoning used by the model when generating\nthe response.\n', + ) + type: Type16 = Field( + ..., description='The type of the object. Always `summary_text`.\n' + ) + + +class Type17(str, Enum): + reasoning = 'reasoning' + + +class ReasoningItem(BaseModel): + id: str = Field( + ..., description='The unique identifier of the reasoning content.\n' + ) + status: Optional[Status8] = Field( + None, + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + summary: List[SummaryItem] = Field(..., description='Reasoning text contents.\n') + type: Type17 = Field( + ..., description='The type of the object. Always `reasoning`.\n' + ) + + +class Controls(BaseModel): + artistic_level: Optional[int] = Field( + None, + description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.', + ge=0, + le=5, + ) + background_color: Optional[RGBColor] = None + colors: Optional[List[RGBColor]] = Field( + None, description='An array of preferable colors' + ) + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + + +class RecraftImageGenerationRequest(BaseModel): + controls: Optional[Controls] = Field( + None, description='The controls for the generated image' + ) + model: str = Field( + ..., description='The model to use for generation (e.g., "recraftv3")' + ) + n: int = Field(..., description='The number of images to generate', ge=1, le=4) + prompt: str = Field( + ..., description='The text prompt describing the image to generate' + ) + size: str = Field( + ..., description='The size of the generated image (e.g., "1024x1024")' + ) + style: Optional[str] = Field( + None, + description='The style to apply to the generated image (e.g., "digital_illustration")', + ) + style_id: Optional[str] = Field( + None, + description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.', + ) + + +class Datum3(BaseModel): + image_id: Optional[str] = Field( + None, description='Unique identifier for the generated image' + ) + url: Optional[str] = Field(None, description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field( + ..., description='Unix timestamp when the generation was created' + ) + credits: int = Field(..., description='Number of credits used for the generation') + data: List[Datum3] = Field(..., description='Array of generated image information') + + +class RenderingSpeed(str, Enum): + BALANCED = 'BALANCED' + TURBO = 'TURBO' + QUALITY = 'QUALITY' + + +class ResponseErrorCode(str, Enum): + server_error = 'server_error' + rate_limit_exceeded = 'rate_limit_exceeded' + invalid_prompt = 'invalid_prompt' + vector_store_timeout = 'vector_store_timeout' + invalid_image = 'invalid_image' + invalid_image_format = 'invalid_image_format' + invalid_base64_image = 'invalid_base64_image' + invalid_image_url = 'invalid_image_url' + image_too_large = 'image_too_large' + image_too_small = 'image_too_small' + image_parse_error = 'image_parse_error' + image_content_policy_violation = 'image_content_policy_violation' + invalid_image_mode = 'invalid_image_mode' + image_file_too_large = 'image_file_too_large' + unsupported_image_media_type = 'unsupported_image_media_type' + empty_image_file = 'empty_image_file' + failed_to_download_image = 'failed_to_download_image' + image_file_not_found = 'image_file_not_found' + + +class Type18(str, Enum): + json_object = 'json_object' + + +class ResponseFormatJsonObject(BaseModel): + type: Type18 = Field( + ..., + description='The type of response format being defined. Always `json_object`.', + ) + + +class ResponseFormatJsonSchemaSchema(BaseModel): + pass + model_config = ConfigDict( + extra='allow', + ) + + +class Type19(str, Enum): + text = 'text' + + +class ResponseFormatText(BaseModel): + type: Type19 = Field( + ..., description='The type of response format being defined. Always `text`.' + ) + + +class Truncation1(str, Enum): + auto = 'auto' + disabled = 'disabled' + + +class InputTokensDetails1(BaseModel): + cached_tokens: int = Field( + ..., + description='The number of tokens that were retrieved from the cache. \n[More on prompt caching](/docs/guides/prompt-caching).\n', + ) + + +class OutputTokensDetails(BaseModel): + reasoning_tokens: int = Field(..., description='The number of reasoning tokens.') + + +class ResponseUsage(BaseModel): + input_tokens: int = Field(..., description='The number of input tokens.') + input_tokens_details: InputTokensDetails1 = Field( + ..., description='A detailed breakdown of the input tokens.' + ) + output_tokens: int = Field(..., description='The number of output tokens.') + output_tokens_details: OutputTokensDetails = Field( + ..., description='A detailed breakdown of the output tokens.' + ) + total_tokens: int = Field(..., description='The total number of tokens used.') + + +class Rodin3DCheckStatusRequest(BaseModel): + subscription_key: str = Field( + ..., description='subscription from generate endpoint' + ) + + +class Rodin3DCheckStatusResponse(BaseModel): + pass + + +class Rodin3DDownloadRequest(BaseModel): + task_uuid: str = Field(..., description='Task UUID') + + +class RodinGenerateJobsData(BaseModel): + subscription_key: Optional[str] = Field(None, description='Subscription Key.') + uuids: Optional[List[str]] = Field(None, description='subjobs uuid.') + + +class RodinMaterialType(str, Enum): + PBR = 'PBR' + Shaded = 'Shaded' + + +class RodinMeshModeType(str, Enum): + Quad = 'Quad' + Raw = 'Raw' + + +class RodinQualityType(str, Enum): + extra_low = 'extra-low' + low = 'low' + medium = 'medium' + high = 'high' + + +class RodinResourceItem(BaseModel): + name: Optional[str] = Field(None, description='File name') + url: Optional[str] = Field(None, description='Download url') + + +class RodinTierType(str, Enum): + Regular = 'Regular' + Sketch = 'Sketch' + Detail = 'Detail' + Smooth = 'Smooth' + + +class RunwayAspectRatioEnum(str, Enum): + field_1280_720 = '1280:720' + field_720_1280 = '720:1280' + field_1104_832 = '1104:832' + field_832_1104 = '832:1104' + field_960_960 = '960:960' + field_1584_672 = '1584:672' + field_1280_768 = '1280:768' + field_768_1280 = '768:1280' + + +class RunwayDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class RunwayImageToVideoResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class RunwayModelEnum(str, Enum): + gen4_turbo = 'gen4_turbo' + gen3a_turbo = 'gen3a_turbo' + + +class Position(str, Enum): + first = 'first' + last = 'last' + + +class RunwayPromptImageDetailedObject(BaseModel): + position: Position = Field( + ..., + description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.", + ) + uri: str = Field( + ..., description='A HTTPS URL or data URI containing an encoded image.' + ) + + +class RunwayPromptImageObject( + RootModel[Union[str, List[RunwayPromptImageDetailedObject]]] +): + root: Union[str, List[RunwayPromptImageDetailedObject]] = Field( + ..., + description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.', + ) + + +class RunwayTaskStatusEnum(str, Enum): + SUCCEEDED = 'SUCCEEDED' + RUNNING = 'RUNNING' + FAILED = 'FAILED' + PENDING = 'PENDING' + CANCELLED = 'CANCELLED' + THROTTLED = 'THROTTLED' + + +class RunwayTaskStatusResponse(BaseModel): + createdAt: datetime = Field(..., description='Task creation timestamp') + id: str = Field(..., description='Task ID') + output: Optional[List[str]] = Field(None, description='Array of output video URLs') + progress: Optional[float] = Field( + None, + description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.', + ge=0.0, + le=1.0, + ) + status: RunwayTaskStatusEnum + + +class RunwayTextToImageAspectRatioEnum(str, Enum): + field_1920_1080 = '1920:1080' + field_1080_1920 = '1080:1920' + field_1024_1024 = '1024:1024' + field_1360_768 = '1360:768' + field_1080_1080 = '1080:1080' + field_1168_880 = '1168:880' + field_1440_1080 = '1440:1080' + field_1080_1440 = '1080:1440' + field_1808_768 = '1808:768' + field_2112_912 = '2112:912' + +class Model4(str, Enum): + gen4_image = 'gen4_image' + + +class ReferenceImage(BaseModel): + uri: Optional[str] = Field( + None, description='A HTTPS URL or data URI containing an encoded image' + ) + + +class RunwayTextToImageRequest(BaseModel): + model: Model4 = Field(..., description='Model to use for generation') + promptText: str = Field( + ..., description='Text prompt for the image generation', max_length=1000 + ) + ratio: RunwayTextToImageAspectRatioEnum + referenceImages: Optional[List[ReferenceImage]] = Field( + None, description='Array of reference images to guide the generation' + ) + + +class RunwayTextToImageResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class StabilityError(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[[{'some-field': 'is required'}]], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.\n', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class Status9(str, Enum): + in_progress = 'in-progress' + + +class StabilityGetResultResponse202(BaseModel): + id: Optional[str] = Field( + None, description='The ID of the generation result.', examples=[1234567890] + ) + status: Optional[Status9] = None + + +class Type20(str, Enum): + json_schema = 'json_schema' + + +class TextResponseFormatJsonSchema(BaseModel): + description: Optional[str] = Field( + None, + description='A description of what the response format is for, used by the model to\ndetermine how to respond in the format.\n', + ) + name: str = Field( + ..., + description='The name of the response format. Must be a-z, A-Z, 0-9, or contain\nunderscores and dashes, with a maximum length of 64.\n', + ) + schema_: ResponseFormatJsonSchemaSchema = Field(..., alias='schema') + strict: Optional[bool] = Field( + False, + description='Whether to enable strict schema adherence when generating the output.\nIf set to true, the model will always follow the exact schema defined\nin the `schema` field. Only a subset of JSON Schema is supported when\n`strict` is `true`. To learn more, read the [Structured Outputs\nguide](/docs/guides/structured-outputs).\n', + ) + type: Type20 = Field( + ..., + description='The type of response format being defined. Always `json_schema`.', + ) + + +class Type21(str, Enum): + function = 'function' + + +class ToolChoiceFunction(BaseModel): + name: str = Field(..., description='The name of the function to call.') + type: Type21 = Field( + ..., description='For function calling, the type is always `function`.' + ) + + +class ToolChoiceOptions(str, Enum): + none = 'none' + auto = 'auto' + required = 'required' + + +class Type22(str, Enum): + file_search = 'file_search' + web_search_preview = 'web_search_preview' + computer_use_preview = 'computer_use_preview' + web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' + + +class ToolChoiceTypes(BaseModel): + type: Type22 = Field( + ..., + description='The type of hosted tool the model should to use. Learn more about\n[built-in tools](/docs/guides/tools).\n\nAllowed values are:\n- `file_search`\n- `web_search_preview`\n- `computer_use_preview`\n', + ) + + +class TripoAnimation(str, Enum): + preset_idle = 'preset:idle' + preset_walk = 'preset:walk' + preset_climb = 'preset:climb' + preset_jump = 'preset:jump' + preset_run = 'preset:run' + preset_slash = 'preset:slash' + preset_shoot = 'preset:shoot' + preset_hurt = 'preset:hurt' + preset_fall = 'preset:fall' + preset_turn = 'preset:turn' + + +class TripoBalance(BaseModel): + balance: float + frozen: float + + +class TripoConvertFormat(str, Enum): + GLTF = 'GLTF' + USDZ = 'USDZ' + FBX = 'FBX' + OBJ = 'OBJ' + STL = 'STL' + field_3MF = '3MF' + + +class Code(int, Enum): + integer_1001 = 1001 + integer_2000 = 2000 + integer_2001 = 2001 + integer_2002 = 2002 + integer_2003 = 2003 + integer_2004 = 2004 + integer_2006 = 2006 + integer_2007 = 2007 + integer_2008 = 2008 + integer_2010 = 2010 + + +class TripoErrorResponse(BaseModel): + code: Code + message: str + suggestion: str + + +class TripoImageToModel(str, Enum): + image_to_model = 'image_to_model' + + +class TripoModelStyle(str, Enum): + person_person2cartoon = 'person:person2cartoon' + animal_venom = 'animal:venom' + object_clay = 'object:clay' + object_steampunk = 'object:steampunk' + object_christmas = 'object:christmas' + object_barbie = 'object:barbie' + gold = 'gold' + ancient_bronze = 'ancient_bronze' + + +class TripoModelVersion(str, Enum): + V2_5 = 'v2.5-20250123' + V2_0 = 'v2.0-20240919' + V1_4 = 'v1.4-20240625' + + +class TripoMultiviewMode(str, Enum): + LEFT = 'LEFT' + RIGHT = 'RIGHT' + + +class TripoMultiviewToModel(str, Enum): + multiview_to_model = 'multiview_to_model' + + +class TripoOrientation(str, Enum): + align_image = 'align_image' + default = 'default' + + +class TripoResponseSuccessCode(RootModel[int]): + root: int = Field( + ..., + description='Standard success code for Tripo API responses. Typically 0 for success.', + examples=[0], + ) + + +class TripoSpec(str, Enum): + mixamo = 'mixamo' + tripo = 'tripo' + + +class TripoStandardFormat(str, Enum): + glb = 'glb' + fbx = 'fbx' + + +class TripoStylizeOptions(str, Enum): + lego = 'lego' + voxel = 'voxel' + voronoi = 'voronoi' + minecraft = 'minecraft' + + +class Code1(int, Enum): + integer_0 = 0 + + +class Data8(BaseModel): + task_id: str = Field(..., description='used for getTask') + + +class TripoSuccessTask(BaseModel): + code: Code1 + data: Data8 + + +class Topology(str, Enum): + bip = 'bip' + quad = 'quad' + + +class Output(BaseModel): + base_model: Optional[str] = None + model: Optional[str] = None + pbr_model: Optional[str] = None + rendered_image: Optional[str] = None + riggable: Optional[bool] = None + topology: Optional[Topology] = None + + +class Status10(str, Enum): + queued = 'queued' + running = 'running' + success = 'success' + failed = 'failed' + cancelled = 'cancelled' + unknown = 'unknown' + banned = 'banned' + expired = 'expired' + + +class TripoTask(BaseModel): + create_time: int + input: Dict[str, Any] + output: Output + progress: int = Field(..., ge=0, le=100) + status: Status10 + task_id: str + type: str + + +class TripoTextToModel(str, Enum): + text_to_model = 'text_to_model' + + +class TripoTextureAlignment(str, Enum): + original_image = 'original_image' + geometry = 'geometry' + + +class TripoTextureFormat(str, Enum): + BMP = 'BMP' + DPX = 'DPX' + HDR = 'HDR' + JPEG = 'JPEG' + OPEN_EXR = 'OPEN_EXR' + PNG = 'PNG' + TARGA = 'TARGA' + TIFF = 'TIFF' + WEBP = 'WEBP' + + +class TripoTextureQuality(str, Enum): + standard = 'standard' + detailed = 'detailed' + + +class TripoTopology(str, Enum): + bip = 'bip' + quad = 'quad' + + +class TripoTypeAnimatePrerigcheck(str, Enum): + animate_prerigcheck = 'animate_prerigcheck' + + +class TripoTypeAnimateRetarget(str, Enum): + animate_retarget = 'animate_retarget' + + +class TripoTypeAnimateRig(str, Enum): + animate_rig = 'animate_rig' + + +class TripoTypeConvertModel(str, Enum): + convert_model = 'convert_model' + + +class TripoTypeRefineModel(str, Enum): + refine_model = 'refine_model' + + +class TripoTypeStylizeModel(str, Enum): + stylize_model = 'stylize_model' + + +class TripoTypeTextureModel(str, Enum): + texture_model = 'texture_model' + + +class Veo2GenVidPollRequest(BaseModel): + operationName: str = Field( + ..., + description='Full operation name (from predict response)', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' + ], + ) + + +class Error(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + + +class Video(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded video content' + ) + gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') + mimeType: Optional[str] = Field(None, description='Video MIME type') + + +class Response(BaseModel): + field_type: Optional[str] = Field( + None, + alias='@type', + examples=[ + 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' + ], + ) + raiMediaFilteredCount: Optional[int] = Field( + None, description='Count of media filtered by responsible AI policies' + ) + raiMediaFilteredReasons: Optional[List[str]] = Field( + None, description='Reasons why media was filtered by responsible AI policies' + ) + videos: Optional[List[Video]] = None + + +class Veo2GenVidPollResponse(BaseModel): + done: Optional[bool] = None + error: Optional[Error] = Field( + None, description='Error details if operation failed' + ) + name: Optional[str] = None + response: Optional[Response] = Field( + None, description='The actual prediction response if done is true' + ) + + +class Image(BaseModel): + bytesBase64Encoded: str + gcsUri: Optional[str] = None + mimeType: Optional[str] = None + + +class Image1(BaseModel): + bytesBase64Encoded: Optional[str] = None + gcsUri: str + mimeType: Optional[str] = None + + +class Instance(BaseModel): + image: Optional[Union[Image, Image1]] = Field( + None, description='Optional image to guide video generation' + ) + prompt: str = Field(..., description='Text description of the video') + + +class PersonGeneration1(str, Enum): + ALLOW = 'ALLOW' + BLOCK = 'BLOCK' + + +class Parameters(BaseModel): + aspectRatio: Optional[str] = Field(None, examples=['16:9']) + durationSeconds: Optional[int] = None + enhancePrompt: Optional[bool] = None + negativePrompt: Optional[str] = None + personGeneration: Optional[PersonGeneration1] = None + sampleCount: Optional[int] = None + seed: Optional[int] = None + storageUri: Optional[str] = Field( + None, description='Optional Cloud Storage URI to upload the video' + ) + + +class Veo2GenVidRequest(BaseModel): + instances: Optional[List[Instance]] = None + parameters: Optional[Parameters] = None + + +class Veo2GenVidResponse(BaseModel): + name: str = Field( + ..., + description='Operation resource name', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' + ], + ) + + +class SearchContextSize(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Type23(str, Enum): + web_search_preview = 'web_search_preview' + web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' + + +class WebSearchPreviewTool(BaseModel): + search_context_size: Optional[SearchContextSize] = Field( + None, + description='High level guidance for the amount of context window space to use for the search. One of `low`, `medium`, or `high`. `medium` is the default.', + ) + type: Literal['WebSearchPreviewTool'] = Field( + ..., + description='The type of the web search tool. One of `web_search_preview` or `web_search_preview_2025_03_11`.', + ) + + +class Status11(str, Enum): + in_progress = 'in_progress' + searching = 'searching' + completed = 'completed' + failed = 'failed' + + +class Type24(str, Enum): + web_search_call = 'web_search_call' + + +class WebSearchToolCall(BaseModel): + id: str = Field(..., description='The unique ID of the web search tool call.\n') + status: Status11 = Field( + ..., description='The status of the web search tool call.\n' + ) + type: Type24 = Field( + ..., + description='The type of the web search tool call. Always `web_search_call`.\n', + ) + + +class CreateModelResponseProperties(ModelResponseProperties): + pass + + +class GeminiInlineData(BaseModel): + data: Optional[str] = Field( + None, + description='The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB\n', + ) + mimeType: Optional[GeminiMimeType] = None + + +class GeminiPart(BaseModel): + inlineData: Optional[GeminiInlineData] = None + text: Optional[str] = Field( + None, + description='A text prompt or code snippet.', + examples=['Write a story about a robot learning to paint'], + ) + + +class GeminiPromptFeedback(BaseModel): + blockReason: Optional[str] = None + blockReasonMessage: Optional[str] = None + safetyRatings: Optional[List[GeminiSafetyRating]] = None + + +class GeminiSafetySetting(BaseModel): + category: GeminiSafetyCategory + threshold: GeminiSafetyThreshold + + +class GeminiSystemInstructionContent(BaseModel): + parts: List[GeminiTextPart] = Field( + ..., + description='A list of ordered parts that make up a single message. Different parts may have different IANA MIME types. For limits on the inputs, such as the maximum number of tokens or the number of images, see the model specifications on the Google models page.\n', + ) + role: Role1 = Field( + ..., + description='The identity of the entity that creates the message. The following values are supported: user: This indicates that the message is sent by a real person, typically a user-generated message. model: This indicates that the message is generated by the model. The model value is used to insert messages from the model into the conversation during multi-turn conversations. For non-multi-turn conversations, this field can be left blank or unset.\n', + examples=['user'], + ) + + +class IdeogramV3EditRequest(BaseModel): + color_palette: Optional[IdeogramColorPalette] = None + image: Optional[StrictBytes] = Field( + None, + description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.', + ) + magic_prompt: Optional[str] = Field( + None, + description='Determine if MagicPrompt should be used in generating the request or not.', + ) + mask: Optional[StrictBytes] = Field( + None, + description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.', + ) + num_images: Optional[int] = Field( + None, description='The number of images to generate.' + ) + prompt: str = Field( + ..., description='The prompt used to describe the edited result.' + ) + rendering_speed: RenderingSpeed + seed: Optional[int] = Field( + None, description='Random seed. Set for reproducible generation.' + ) + style_codes: Optional[List[StyleCode]] = Field( + None, + description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.', + ) + style_reference_images: Optional[List[StrictBytes]] = Field( + None, + description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.', + ) + + +class IdeogramV3Request(BaseModel): + aspect_ratio: Optional[str] = Field( + None, description='Aspect ratio in format WxH', examples=['1x3'] + ) + color_palette: Optional[ColorPalette] = None + magic_prompt: Optional[MagicPrompt2] = Field( + None, description='Whether to enable magic prompt enhancement' + ) + negative_prompt: Optional[str] = Field( + None, description='Text prompt specifying what to avoid in the generation' + ) + num_images: Optional[int] = Field( + None, description='Number of images to generate', ge=1 + ) + prompt: str = Field(..., description='The text prompt for image generation') + rendering_speed: RenderingSpeed + resolution: Optional[str] = Field( + None, description='Image resolution in format WxH', examples=['1280x800'] + ) + seed: Optional[int] = Field( + None, description='Seed value for reproducible generation' + ) + style_codes: Optional[List[StyleCode]] = Field( + None, description='Array of style codes in hexadecimal format' + ) + style_reference_images: Optional[List[str]] = Field( + None, description='Array of reference image URLs or identifiers' + ) + style_type: Optional[StyleType1] = Field( + None, description='The type of style to apply' + ) + + +class ImagenGenerateImageResponse(BaseModel): + predictions: Optional[List[ImagenImagePrediction]] = None + + +class ImagenImageGenerationParameters(BaseModel): + addWatermark: Optional[bool] = None + aspectRatio: Optional[AspectRatio] = None + enhancePrompt: Optional[bool] = None + includeRaiReason: Optional[bool] = None + includeSafetyAttributes: Optional[bool] = None + outputOptions: Optional[ImagenOutputOptions] = None + personGeneration: Optional[PersonGeneration] = None + safetySetting: Optional[SafetySetting] = None + sampleCount: Optional[int] = Field(None, ge=1, le=4) + seed: Optional[int] = None + storageUri: Optional[AnyUrl] = None + + +class InputContent( + RootModel[Union[InputTextContent, InputImageContent, InputFileContent]] +): + root: Union[InputTextContent, InputImageContent, InputFileContent] + + +class InputMessageContentList(RootModel[List[InputContent]]): + root: List[InputContent] = Field( + ..., + description='A list of one or many input items to the model, containing different content \ntypes.\n', + title='Input item content list', + ) + + +class KlingCameraControl(BaseModel): + config: Optional[KlingCameraConfig] = None + type: Optional[KlingCameraControlType] = None + + +class KlingDualCharacterEffectInput(BaseModel): + duration: KlingVideoGenDuration + images: KlingDualCharacterImages + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1' + + +class KlingImage2VideoRequest(BaseModel): + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + camera_control: Optional[KlingCameraControl] = None + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + duration: Optional[KlingVideoGenDuration] = '5' + dynamic_masks: Optional[List[DynamicMask]] = Field( + None, + description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.', + ) + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + image: Optional[str] = Field( + None, + description='Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.', + ) + image_tail: Optional[str] = Field( + None, + description='Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.', + ) + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + static_mask: Optional[str] = Field( + None, + description='Static Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + + +class TaskResult(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingImage2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class TaskResult1(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data1(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult1] = None + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingImageGenerationsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data1] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingLipSyncInputObject(BaseModel): + audio_file: Optional[str] = Field( + None, + description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.', + ) + audio_type: Optional[KlingAudioUploadType] = None + audio_url: Optional[str] = Field( + None, + description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.', + ) + mode: KlingLipSyncMode + text: Optional[str] = Field( + None, + description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.', + ) + video_id: Optional[str] = Field( + None, + description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.', + ) + video_url: Optional[str] = Field( + None, + description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.', + ) + voice_id: Optional[str] = Field( + None, + description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.', + ) + voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en' + voice_speed: Optional[float] = Field( + 1, + description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.', + ge=0.8, + le=2.0, + ) + + +class KlingLipSyncRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + input: KlingLipSyncInputObject + + +class TaskResult2(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data2(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingLipSyncResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data2] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingSingleImageEffectInput(BaseModel): + duration: KlingSingleImageEffectDuration + image: str = Field( + ..., + description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.', + ) + model_name: KlingSingleImageEffectModelName + + +class KlingText2VideoRequest(BaseModel): + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + camera_control: Optional[KlingCameraControl] = None + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + duration: Optional[KlingVideoGenDuration] = '5' + external_task_id: Optional[str] = Field(None, description='Customized Task ID') + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingTextToVideoModelName] = 'kling-v1' + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + + +class Data4(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingText2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data4] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingVideoEffectsInput( + RootModel[Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput]] +): + root: Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput] + + +class KlingVideoEffectsRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address for the result of this task.', + ) + effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene] + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + input: KlingVideoEffectsInput + + +class Data5(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVideoEffectsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data5] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingVideoExtendRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + negative_prompt: Optional[str] = Field( + None, + description='Negative text prompt for elements to avoid in the extended video', + max_length=2500, + ) + prompt: Optional[str] = Field( + None, + description='Positive text prompt for guiding the video extension', + max_length=2500, + ) + video_id: Optional[str] = Field( + None, + description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.', + ) + + +class Data6(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVideoExtendResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data6] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class LumaGenerationRequest(BaseModel): + aspect_ratio: LumaAspectRatio + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback URL of the generation, a POST request with Generation object will be sent to the callback URL when the generation is dreaming, completed, or failed', + ) + duration: LumaVideoModelOutputDuration + generation_type: Optional[GenerationType1] = 'video' + keyframes: Optional[LumaKeyframes] = None + loop: Optional[bool] = Field(None, description='Whether to loop the video') + model: LumaVideoModel + prompt: str = Field(..., description='The prompt of the generation') + resolution: LumaVideoModelOutputResolution + + +class CharacterRef(BaseModel): + identity0: Optional[LumaImageIdentity] = None + + +class LumaImageGenerationRequest(BaseModel): + aspect_ratio: Optional[LumaAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the generation' + ) + character_ref: Optional[CharacterRef] = None + generation_type: Optional[GenerationType2] = 'image' + image_ref: Optional[List[LumaImageRef]] = None + model: Optional[LumaImageModel] = 'photon-1' + modify_image_ref: Optional[LumaModifyImageRef] = None + prompt: Optional[str] = Field(None, description='The prompt of the generation') + style_ref: Optional[List[LumaImageRef]] = None + + +class LumaUpscaleVideoGenerationRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the upscale' + ) + generation_type: Optional[GenerationType3] = 'upscale_video' + resolution: Optional[LumaVideoModelOutputResolution] = None + + +class OutputContent(RootModel[Union[OutputTextContent, OutputAudioContent]]): + root: Union[OutputTextContent, OutputAudioContent] + + +class OutputMessage(BaseModel): + content: List[OutputContent] = Field(..., description='The content of the message') + role: Role4 = Field(..., description='The role of the message') + type: Type14 = Field(..., description='The type of output item') + + +class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): + duration: Optional[PikaDurationEnum] = 5 + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): + duration: Optional[int] = Field(None, ge=5, le=10, title='Duration') + keyFrames: Optional[List[StrictBytes]] = Field( + None, description='Array of keyframe images', title='Keyframes' + ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: str = Field(..., title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): + aspectRatio: Optional[float] = Field( + 1.7777777777777777, + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + duration: Optional[PikaDurationEnum] = 5 + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: str = Field(..., title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + pikaffect: Optional[Pikaffect] = None + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaHTTPValidationError(BaseModel): + detail: Optional[List[PikaValidationError]] = Field(None, title='Detail') + + +class Reasoning(BaseModel): + effort: Optional[ReasoningEffort] = 'medium' + generate_summary: Optional[GenerateSummary] = Field( + None, + description="**Deprecated:** use `summary` instead.\n\nA summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", + ) + summary: Optional[Summary] = Field( + None, + description="A summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", + ) + + +class ResponseError(BaseModel): + code: ResponseErrorCode + message: str = Field(..., description='A human-readable description of the error.') + + +class Rodin3DDownloadResponse(BaseModel): + list: Optional[RodinResourceItem] = None + + +class Rodin3DGenerateRequest(BaseModel): + images: str = Field(..., description='The reference images to generate 3D Assets.') + material: Optional[RodinMaterialType] = None + mesh_mode: Optional[RodinMeshModeType] = None + quality: Optional[RodinQualityType] = None + seed: Optional[int] = Field(None, description='Seed.') + tier: Optional[RodinTierType] = None + + +class Rodin3DGenerateResponse(BaseModel): + jobs: Optional[RodinGenerateJobsData] = None + message: Optional[str] = Field(None, description='message') + prompt: Optional[str] = Field(None, description='prompt') + submit_time: Optional[str] = Field(None, description='Time') + uuid: Optional[str] = Field(None, description='Task UUID') + + +class RunwayImageToVideoRequest(BaseModel): + duration: RunwayDurationEnum + model: RunwayModelEnum + promptImage: RunwayPromptImageObject + promptText: Optional[str] = Field( + None, description='Text prompt for the generation', max_length=1000 + ) + ratio: RunwayAspectRatioEnum + seed: int = Field( + ..., description='Random seed for generation', ge=0, le=4294967295 + ) + + +class TextResponseFormatConfiguration( + RootModel[ + Union[ + ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject + ] + ] +): + root: Union[ + ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject + ] = Field( + ..., + description='An object specifying the format that the model must output.\n\nConfiguring `{ "type": "json_schema" }` enables Structured Outputs, \nwhich ensures the model will match your supplied JSON schema. Learn more in the \n[Structured Outputs guide](/docs/guides/structured-outputs).\n\nThe default format is `{ "type": "text" }` with no additional options.\n\n**Not recommended for gpt-4o and newer models:**\n\nSetting to `{ "type": "json_object" }` enables the older JSON mode, which\nensures the message the model generates is valid JSON. Using `json_schema`\nis preferred for models that support it.\n', + ) + + +class Tool( + RootModel[ + Union[ + FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool + ] + ] +): + root: Union[ + FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool + ] = Field(..., discriminator='type') + + +class EasyInputMessage(BaseModel): + content: Union[str, InputMessageContentList] = Field( + ..., + description='Text, image, or audio input to the model, used to generate a response.\nCan also contain previous assistant responses.\n', + ) + role: Role = Field( + ..., + description='The role of the message input. One of `user`, `assistant`, `system`, or\n`developer`.\n', + ) + type: Optional[Type2] = Field( + None, description='The type of the message input. Always `message`.\n' + ) + + +class GeminiContent(BaseModel): + parts: List[GeminiPart] + role: Role1 = Field(..., examples=['user']) + + +class GeminiGenerateContentRequest(BaseModel): + contents: List[GeminiContent] + generationConfig: Optional[GeminiGenerationConfig] = None + safetySettings: Optional[List[GeminiSafetySetting]] = None + systemInstruction: Optional[GeminiSystemInstructionContent] = None + tools: Optional[List[GeminiTool]] = None + videoMetadata: Optional[GeminiVideoMetadata] = None + + +class ImagenGenerateImageRequest(BaseModel): + instances: List[ImagenImageGenerationInstance] + parameters: ImagenImageGenerationParameters + + +class InputMessage(BaseModel): + content: Optional[InputMessageContentList] = None + role: Optional[Role3] = None + status: Optional[Status2] = None + type: Optional[Type9] = None + + +class Item( + RootModel[ + Union[ + InputMessage, + OutputMessage, + FileSearchToolCall, + ComputerToolCall, + WebSearchToolCall, + FunctionToolCall, + ReasoningItem, + ] + ] +): + root: Union[ + InputMessage, + OutputMessage, + FileSearchToolCall, + ComputerToolCall, + WebSearchToolCall, + FunctionToolCall, + ReasoningItem, + ] = Field(..., description='Content item used to generate a response.\n') + + +class LumaGeneration(BaseModel): + assets: Optional[LumaAssets] = None + created_at: Optional[datetime] = Field( + None, description='The date and time when the generation was created' + ) + failure_reason: Optional[str] = Field( + None, description='The reason for the state of the generation' + ) + generation_type: Optional[LumaGenerationType] = None + id: Optional[UUID] = Field(None, description='The ID of the generation') + model: Optional[str] = Field(None, description='The model used for the generation') + request: Optional[ + Union[ + LumaGenerationRequest, + LumaImageGenerationRequest, + LumaUpscaleVideoGenerationRequest, + LumaAudioGenerationRequest, + ] + ] = Field(None, description='The request of the generation') + state: Optional[LumaState] = None + + +class OutputItem( + RootModel[ + Union[ + OutputMessage, + FileSearchToolCall, + FunctionToolCall, + WebSearchToolCall, + ComputerToolCall, + ReasoningItem, + ] + ] +): + root: Union[ + OutputMessage, + FileSearchToolCall, + FunctionToolCall, + WebSearchToolCall, + ComputerToolCall, + ReasoningItem, + ] + + +class Text(BaseModel): + format: Optional[TextResponseFormatConfiguration] = None + + +class ResponseProperties(BaseModel): + instructions: Optional[str] = Field( + None, + description="Inserts a system (or developer) message as the first item in the model's context.\n\nWhen using along with `previous_response_id`, the instructions from a previous\nresponse will not be carried over to the next response. This makes it simple\nto swap out system (or developer) messages in new responses.\n", + ) + max_output_tokens: Optional[int] = Field( + None, + description='An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](/docs/guides/reasoning).\n', + ) + model: Optional[OpenAIModels] = None + previous_response_id: Optional[str] = Field( + None, + description='The unique ID of the previous response to the model. Use this to\ncreate multi-turn conversations. Learn more about \n[conversation state](/docs/guides/conversation-state).\n', + ) + reasoning: Optional[Reasoning] = None + text: Optional[Text] = None + tool_choice: Optional[ + Union[ToolChoiceOptions, ToolChoiceTypes, ToolChoiceFunction] + ] = Field( + None, + description='How the model should select which tool (or tools) to use when generating\na response. See the `tools` parameter to see how to specify which tools\nthe model can call.\n', + ) + tools: Optional[List[Tool]] = None + truncation: Optional[Truncation1] = Field( + 'disabled', + description="The truncation strategy to use for the model response.\n- `auto`: If the context of this response and previous ones exceeds\n the model's context window size, the model will truncate the \n response to fit the context window by dropping input items in the\n middle of the conversation. \n- `disabled` (default): If a model response will exceed the context window \n size for a model, the request will fail with a 400 error.\n", + ) + + +class GeminiCandidate(BaseModel): + citationMetadata: Optional[GeminiCitationMetadata] = None + content: Optional[GeminiContent] = None + finishReason: Optional[str] = None + safetyRatings: Optional[List[GeminiSafetyRating]] = None + + +class GeminiGenerateContentResponse(BaseModel): + candidates: Optional[List[GeminiCandidate]] = None + promptFeedback: Optional[GeminiPromptFeedback] = None + + +class InputItem(RootModel[Union[EasyInputMessage, Item]]): + root: Union[EasyInputMessage, Item] + + +class OpenAICreateResponse(CreateModelResponseProperties, ResponseProperties): + include: Optional[List[Includable]] = Field( + None, + description='Specify additional output data to include in the model response. Currently\nsupported values are:\n- `file_search_call.results`: Include the search results of\n the file search tool call.\n- `message.input_image.image_url`: Include image urls from the input message.\n- `computer_call_output.output.image_url`: Include image urls from the computer call output.\n', + ) + input: Union[str, List[InputItem]] = Field( + ..., + description='Text, image, or file inputs to the model, used to generate a response.\n\nLearn more:\n- [Text inputs and outputs](/docs/guides/text)\n- [Image inputs](/docs/guides/images)\n- [File inputs](/docs/guides/pdf-files)\n- [Conversation state](/docs/guides/conversation-state)\n- [Function calling](/docs/guides/function-calling)\n', + ) + parallel_tool_calls: Optional[bool] = Field( + True, description='Whether to allow the model to run tool calls in parallel.\n' + ) + store: Optional[bool] = Field( + True, + description='Whether to store the generated model response for later retrieval via\nAPI.\n', + ) + stream: Optional[bool] = Field( + False, + description='If set to true, the model response data will be streamed to the client\nas it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).\nSee the [Streaming section below](/docs/api-reference/responses-streaming)\nfor more information.\n', + ) + usage: Optional[ResponseUsage] = None + + +class OpenAIResponse(ModelResponseProperties, ResponseProperties): + created_at: Optional[float] = Field( + None, + description='Unix timestamp (in seconds) of when this Response was created.', + ) + error: Optional[ResponseError] = None + id: Optional[str] = Field(None, description='Unique identifier for this Response.') + incomplete_details: Optional[IncompleteDetails] = Field( + None, description='Details about why the response is incomplete.\n' + ) + object: Optional[Object] = Field( + None, description='The object type of this resource - always set to `response`.' + ) + output: Optional[List[OutputItem]] = Field( + None, + description="An array of content items generated by the model.\n\n- The length and order of items in the `output` array is dependent\n on the model's response.\n- Rather than accessing the first item in the `output` array and \n assuming it's an `assistant` message with the content generated by\n the model, you might consider using the `output_text` property where\n supported in SDKs.\n", + ) + output_text: Optional[str] = Field( + None, + description='SDK-only convenience property that contains the aggregated text output \nfrom all `output_text` items in the `output` array, if any are present. \nSupported in the Python and JavaScript SDKs.\n', + ) + parallel_tool_calls: Optional[bool] = Field( + True, description='Whether to allow the model to run tool calls in parallel.\n' + ) + status: Optional[Status6] = Field( + None, + description='The status of the response generation. One of `completed`, `failed`, `in_progress`, or `incomplete`.', + ) + usage: Optional[ResponseUsage] = None diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py new file mode 100644 index 0000000000000000000000000000000000000000..0e90aef7c6811e2df28a0675563750d35669bf39 --- /dev/null +++ b/comfy_api_nodes/apis/bfl_api.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +from enum import Enum +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field, confloat, conint + + +class BFLOutputFormat(str, Enum): + png = 'png' + jpeg = 'jpeg' + + +class BFLFluxExpandImageRequest(BaseModel): + prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image') + bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image') + left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image') + right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand') + + +class BFLFluxFillImageRequest(BaseModel): + prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.') + mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.') + + +class BFLFluxCannyImageRequest(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + canny_low_threshold: Optional[int] = Field(None, description='Low threshold for Canny edge detection') + canny_high_threshold: Optional[int] = Field(None, description='High threshold for Canny edge detection') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided') + preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step') + + +class BFLFluxDepthImageRequest(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided') + preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step') + + +class BFLFluxProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.') + height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') + # image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( + # None, description='Blend between the prompt and the image prompt.' + # ) + + +class BFLFluxKontextProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for what you wannt to edit.') + input_image: Optional[str] = Field(None, description='Image to edit in base64 format') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') + steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=2)] = Field( + 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + + +class BFLFluxProUltraGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.') + image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') + image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( + None, description='Blend between the prompt and the image prompt.' + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class BFLStatus(str, Enum): + task_not_found = "Task not found" + pending = "Pending" + request_moderated = "Request Moderated" + content_moderated = "Content Moderated" + ready = "Ready" + error = "Error" + + +class BFLFluxProStatusResponse(BaseModel): + id: str = Field(..., description="The unique identifier for the generation task.") + status: BFLStatus = Field(..., description="The status of the task.") + result: Optional[Dict[str, Any]] = Field( + None, description="The result of the task (null if not completed)." + ) + progress: confloat(ge=0.0, le=1.0) = Field( + ..., description="The progress of the task (0.0 to 1.0)." + ) + details: Optional[Dict[str, Any]] = Field( + None, description="Additional details about the task (null if not available)." + ) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py new file mode 100644 index 0000000000000000000000000000000000000000..2a4bac88b246860eadc3a687389391306f803232 --- /dev/null +++ b/comfy_api_nodes/apis/client.py @@ -0,0 +1,1126 @@ +""" +API Client Framework for api.comfy.org. + +This module provides a flexible framework for making API requests from ComfyUI nodes. +It supports both synchronous and asynchronous API operations with proper type validation. + +Key Components: +-------------- +1. ApiClient - Handles HTTP requests with authentication and error handling +2. ApiEndpoint - Defines a single HTTP endpoint with its request/response models +3. ApiOperation - Executes a single synchronous API operation + +Usage Examples: +-------------- + +# Example 1: Synchronous API Operation +# ------------------------------------ +# For a simple API call that returns the result immediately: + +# 1. Create the API client +api_client = ApiClient( + base_url="https://api.example.com", + auth_token="your_auth_token_here", + comfy_api_key="your_comfy_api_key_here", + timeout=30.0, + verify_ssl=True +) + +# 2. Define the endpoint +user_info_endpoint = ApiEndpoint( + path="/v1/users/me", + method=HttpMethod.GET, + request_model=EmptyRequest, # No request body needed + response_model=UserProfile, # Pydantic model for the response + query_params=None +) + +# 3. Create the request object +request = EmptyRequest() + +# 4. Create and execute the operation +operation = ApiOperation( + endpoint=user_info_endpoint, + request=request +) +user_profile = operation.execute(client=api_client) # Returns immediately with the result + + +# Example 2: Asynchronous API Operation with Polling +# ------------------------------------------------- +# For an API that starts a task and requires polling for completion: + +# 1. Define the endpoints (initial request and polling) +generate_image_endpoint = ApiEndpoint( + path="/v1/images/generate", + method=HttpMethod.POST, + request_model=ImageGenerationRequest, + response_model=TaskCreatedResponse, + query_params=None +) + +check_task_endpoint = ApiEndpoint( + path="/v1/tasks/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=ImageGenerationResult, + query_params=None +) + +# 2. Create the request object +request = ImageGenerationRequest( + prompt="a beautiful sunset over mountains", + width=1024, + height=1024, + num_images=1 +) + +# 3. Create and execute the polling operation +operation = PollingOperation( + initial_endpoint=generate_image_endpoint, + initial_request=request, + poll_endpoint=check_task_endpoint, + task_id_field="task_id", + status_field="status", + completed_statuses=["completed"], + failed_statuses=["failed", "error"] +) + +# This will make the initial request and then poll until completion +result = operation.execute(client=api_client) # Returns the final ImageGenerationResult when done +""" + +from __future__ import annotations +import logging +import time +import io +import socket +from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple +from enum import Enum +import json +import requests +from urllib.parse import urljoin, urlparse +from pydantic import BaseModel, Field +import uuid # For generating unique operation IDs + +from server import PromptServer +from comfy.cli_args import args +from comfy import utils +from . import request_logger + +T = TypeVar("T", bound=BaseModel) +R = TypeVar("R", bound=BaseModel) +P = TypeVar("P", bound=BaseModel) # For poll response + +PROGRESS_BAR_MAX = 100 + + +class NetworkError(Exception): + """Base exception for network-related errors with diagnostic information.""" + pass + + +class LocalNetworkError(NetworkError): + """Exception raised when local network connectivity issues are detected.""" + pass + + +class ApiServerError(NetworkError): + """Exception raised when the API server is unreachable but internet is working.""" + pass + + +class EmptyRequest(BaseModel): + """Base class for empty request bodies. + For GET requests, fields will be sent as query parameters.""" + + pass + + +class UploadRequest(BaseModel): + file_name: str = Field(..., description="Filename to upload") + content_type: Optional[str] = Field( + None, + description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.", + ) + + +class UploadResponse(BaseModel): + download_url: str = Field(..., description="URL to GET uploaded file") + upload_url: str = Field(..., description="URL to PUT file to upload") + + +class HttpMethod(str, Enum): + GET = "GET" + POST = "POST" + PUT = "PUT" + DELETE = "DELETE" + PATCH = "PATCH" + + +class ApiClient: + """ + Client for making HTTP requests to an API with authentication, error handling, and retry logic. + """ + + def __init__( + self, + base_url: str, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + timeout: float = 3600.0, + verify_ssl: bool = True, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + retry_status_codes: Optional[Tuple[int, ...]] = None, + ): + self.base_url = base_url + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + self.timeout = timeout + self.verify_ssl = verify_ssl + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests), + # 500, 502, 503, 504 (Server Errors) + self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504) + + def _generate_operation_id(self, path: str) -> str: + """Generates a unique operation ID for logging.""" + return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}" + + def _create_json_payload_args( + self, + data: Optional[Dict[str, Any]] = None, + headers: Optional[Dict[str, str]] = None, + ) -> Dict[str, Any]: + return { + "json": data, + "headers": headers, + } + + def _create_form_data_args( + self, + data: Dict[str, Any], + files: Dict[str, Any], + headers: Optional[Dict[str, str]] = None, + multipart_parser = None, + ) -> Dict[str, Any]: + if headers and "Content-Type" in headers: + del headers["Content-Type"] + + if multipart_parser: + data = multipart_parser(data) + + return { + "data": data, + "files": files, + "headers": headers, + } + + def _create_urlencoded_form_data_args( + self, + data: Dict[str, Any], + headers: Optional[Dict[str, str]] = None, + ) -> Dict[str, Any]: + headers = headers or {} + headers["Content-Type"] = "application/x-www-form-urlencoded" + + return { + "data": data, + "headers": headers, + } + + def get_headers(self) -> Dict[str, str]: + """Get headers for API requests, including authentication if available""" + headers = {"Content-Type": "application/json", "Accept": "application/json"} + + if self.auth_token: + headers["Authorization"] = f"Bearer {self.auth_token}" + elif self.comfy_api_key: + headers["X-API-KEY"] = self.comfy_api_key + + return headers + + def _check_connectivity(self, target_url: str) -> Dict[str, bool]: + """ + Check connectivity to determine if network issues are local or server-related. + + Args: + target_url: URL to check connectivity to + + Returns: + Dictionary with connectivity status details + """ + results = { + "internet_accessible": False, + "api_accessible": False, + "is_local_issue": False, + "is_api_issue": False + } + + # First check basic internet connectivity using a reliable external site + try: + # Use a reliable external domain for checking basic connectivity + check_response = requests.get("https://www.google.com", + timeout=5.0, + verify=self.verify_ssl) + if check_response.status_code < 500: + results["internet_accessible"] = True + except (requests.RequestException, socket.error): + results["internet_accessible"] = False + results["is_local_issue"] = True + return results + + # Now check API server connectivity + try: + # Extract domain from the target URL to do a simpler health check + parsed_url = urlparse(target_url) + api_base = f"{parsed_url.scheme}://{parsed_url.netloc}" + + # Try to reach the API domain + api_response = requests.get(f"{api_base}/health", timeout=5.0, verify=self.verify_ssl) + if api_response.status_code < 500: + results["api_accessible"] = True + else: + results["api_accessible"] = False + results["is_api_issue"] = True + except requests.RequestException: + results["api_accessible"] = False + # If we can reach the internet but not the API, it's an API issue + results["is_api_issue"] = True + + return results + + def request( + self, + method: str, + path: str, + params: Optional[Dict[str, Any]] = None, + data: Optional[Dict[str, Any]] = None, + files: Optional[Dict[str, Any]] = None, + headers: Optional[Dict[str, str]] = None, + content_type: str = "application/json", + multipart_parser: Callable = None, + retry_count: int = 0, # Used internally for tracking retries + ) -> Dict[str, Any]: + """ + Make an HTTP request to the API with automatic retries for transient errors. + + Args: + method: HTTP method (GET, POST, etc.) + path: API endpoint path (will be joined with base_url) + params: Query parameters + data: body data + files: Files to upload + headers: Additional headers + content_type: Content type of the request. Defaults to application/json. + retry_count: Internal parameter for tracking retries, do not set manually + + Returns: + Parsed JSON response + + Raises: + LocalNetworkError: If local network connectivity issues are detected + ApiServerError: If the API server is unreachable but internet is working + Exception: For other request failures + """ + # Use urljoin but ensure path is relative to avoid absolute path behavior + relative_path = path.lstrip('/') + url = urljoin(self.base_url, relative_path) + self.check_auth(self.auth_token, self.comfy_api_key) + # Combine default headers with any provided headers + request_headers = self.get_headers() + if headers: + request_headers.update(headers) + + # Let requests handle the content type when files are present. + if files: + del request_headers["Content-Type"] + + logging.debug(f"[DEBUG] Request Headers: {request_headers}") + logging.debug(f"[DEBUG] Files: {files}") + logging.debug(f"[DEBUG] Params: {params}") + logging.debug(f"[DEBUG] Data: {data}") + + if content_type == "application/x-www-form-urlencoded": + payload_args = self._create_urlencoded_form_data_args(data, request_headers) + elif content_type == "multipart/form-data": + payload_args = self._create_form_data_args( + data, files, request_headers, multipart_parser + ) + else: + payload_args = self._create_json_payload_args(data, request_headers) + + operation_id = self._generate_operation_id(path) + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=request_headers, + request_params=params, + request_data=data if content_type == "application/json" else "[form-data or other]" + ) + + try: + response = requests.request( + method=method, + url=url, + params=params, + timeout=self.timeout, + verify=self.verify_ssl, + **payload_args, + ) + + # Check if we should retry based on status code + if (response.status_code in self.retry_status_codes and + retry_count < self.max_retries): + + # Calculate delay with exponential backoff + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + + logging.warning( + f"Request failed with status {response.status_code}. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + + # Raise exception for error status codes + response.raise_for_status() + + # Log successful response + response_content_to_log = response.content + try: + # Attempt to parse JSON for prettier logging, fallback to raw content + response_content_to_log = response.json() + except json.JSONDecodeError: + pass # Keep as bytes/str if not JSON + + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, # Pass request details again for context in log + request_url=url, + response_status_code=response.status_code, + response_headers=dict(response.headers), + response_content=response_content_to_log + ) + + except requests.ConnectionError as e: + error_message = f"ConnectionError: {str(e)}" + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + error_message=error_message + ) + # Only perform connectivity check if we've exhausted all retries + if retry_count >= self.max_retries: + # Check connectivity to determine if it's a local or API issue + connectivity = self._check_connectivity(self.base_url) + + if connectivity["is_local_issue"]: + raise LocalNetworkError( + "Unable to connect to the API server due to local network issues. " + "Please check your internet connection and try again." + ) from e + elif connectivity["is_api_issue"]: + raise ApiServerError( + f"The API server at {self.base_url} is currently unreachable. " + f"The service may be experiencing issues. Please try again later." + ) from e + + # If we haven't exhausted retries yet, retry the request + if retry_count < self.max_retries: + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + f"Connection error: {str(e)}. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + + # If we've exhausted retries and didn't identify the specific issue, + # raise a generic exception + final_error_message = ( + f"Unable to connect to the API server after {self.max_retries} attempts. " + f"Please check your internet connection or try again later." + ) + request_logger.log_request_response( # Log final failure + operation_id=operation_id, + request_method=method, request_url=url, + error_message=final_error_message + ) + raise Exception(final_error_message) from e + + except requests.Timeout as e: + error_message = f"Timeout: {str(e)}" + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, request_url=url, + error_message=error_message + ) + # Retry timeouts if we haven't exhausted retries + if retry_count < self.max_retries: + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + f"Request timed out. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + final_error_message = ( + f"Request timed out after {self.timeout} seconds and {self.max_retries} retry attempts. " + f"The server might be experiencing high load or the operation is taking longer than expected." + ) + request_logger.log_request_response( # Log final failure + operation_id=operation_id, + request_method=method, request_url=url, + error_message=final_error_message + ) + raise Exception(final_error_message) from e + + except requests.HTTPError as e: + status_code = e.response.status_code if hasattr(e, "response") else None + original_error_message = f"HTTP Error: {str(e)}" + error_content_for_log = None + if hasattr(e, "response") and e.response is not None: + error_content_for_log = e.response.content + try: + error_content_for_log = e.response.json() + except json.JSONDecodeError: + pass + + + # Try to extract detailed error message from JSON response for user display + # but log the full error content. + user_display_error_message = original_error_message + + try: + if hasattr(e, "response") and e.response is not None and e.response.content: + error_json = e.response.json() + if "error" in error_json and "message" in error_json["error"]: + user_display_error_message = f"API Error: {error_json['error']['message']}" + if "type" in error_json["error"]: + user_display_error_message += f" (Type: {error_json['error']['type']})" + elif isinstance(error_json, dict): # Handle cases where error is just a JSON dict + user_display_error_message = f"API Error: {json.dumps(error_json)}" + else: # Non-dict JSON error + user_display_error_message = f"API Error: {str(error_json)}" + except json.JSONDecodeError: + # If not JSON, use the raw content if it's not too long, or a summary + if hasattr(e, "response") and e.response is not None and e.response.content: + raw_content = e.response.content.decode(errors='ignore') + if len(raw_content) < 200: # Arbitrary limit for display + user_display_error_message = f"API Error (raw): {raw_content}" + else: + user_display_error_message = f"API Error (raw, status {status_code})" + + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, request_url=url, + response_status_code=status_code, + response_headers=dict(e.response.headers) if hasattr(e, "response") and e.response is not None else None, + response_content=error_content_for_log, + error_message=original_error_message # Log the original exception string as error + ) + + logging.debug(f"[DEBUG] API Error: {user_display_error_message} (Status: {status_code})") + if hasattr(e, "response") and e.response is not None and e.response.content: + logging.debug(f"[DEBUG] Response content: {e.response.content}") + + # Retry if the status code is in our retry list and we haven't exhausted retries + if (status_code in self.retry_status_codes and + retry_count < self.max_retries): + + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + f"HTTP error {status_code}. " + f"Retrying in {delay:.2f}s ({retry_count + 1}/{self.max_retries})" + ) + time.sleep(delay) + return self.request( + method=method, + path=path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + + # Specific error messages for common status codes for user display + if status_code == 401: + user_display_error_message = "Unauthorized: Please login first to use this node." + elif status_code == 402: + user_display_error_message = "Payment Required: Please add credits to your account to use this node." + elif status_code == 409: + user_display_error_message = "There is a problem with your account. Please contact support@comfy.org." + elif status_code == 429: + user_display_error_message = "Rate Limit Exceeded: Please try again later." + # else, user_display_error_message remains as parsed from response or original HTTPError string + + raise Exception(user_display_error_message) # Raise with the user-friendly message + + # Parse and return JSON response + if response.content: + return response.json() + return {} + + def check_auth(self, auth_token, comfy_api_key): + """Verify that an auth token is present or comfy_api_key is present""" + if auth_token is None and comfy_api_key is None: + raise Exception("Unauthorized: Please login first to use this node.") + return auth_token or comfy_api_key + + @staticmethod + def upload_file( + upload_url: str, + file: io.BytesIO | str, + content_type: str | None = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + ): + """Upload a file to the API with retry logic. + + Args: + upload_url: The URL to upload to + file: Either a file path string, BytesIO object, or tuple of (file_path, filename) + content_type: Optional mime type to set for the upload + max_retries: Maximum number of retry attempts + retry_delay: Initial delay between retries in seconds + retry_backoff_factor: Multiplier for the delay after each retry + """ + headers = {} + if content_type: + headers["Content-Type"] = content_type + + # Prepare the file data + if isinstance(file, io.BytesIO): + file.seek(0) # Ensure we're at the start of the file + data = file.read() + elif isinstance(file, str): + with open(file, "rb") as f: + data = f.read() + else: + raise ValueError("File must be either a BytesIO object or a file path string") + + # Try the upload with retries + last_exception = None + operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}" # Simplified ID for uploads + + # Log initial attempt (without full file data for brevity) + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + request_headers=headers, + request_data=f"[File data of type {content_type or 'unknown'}, size {len(data)} bytes]" + ) + + for retry_attempt in range(max_retries + 1): + try: + response = requests.put(upload_url, data=data, headers=headers) + response.raise_for_status() + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", request_url=upload_url, # For context + response_status_code=response.status_code, + response_headers=dict(response.headers), + response_content="File uploaded successfully." # Or response.text if available + ) + return response + + except (requests.ConnectionError, requests.Timeout, requests.HTTPError) as e: + last_exception = e + error_message_for_log = f"{type(e).__name__}: {str(e)}" + response_content_for_log = None + status_code_for_log = None + headers_for_log = None + + if hasattr(e, 'response') and e.response is not None: + status_code_for_log = e.response.status_code + headers_for_log = dict(e.response.headers) + try: + response_content_for_log = e.response.json() + except json.JSONDecodeError: + response_content_for_log = e.response.content + + + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", request_url=upload_url, + response_status_code=status_code_for_log, + response_headers=headers_for_log, + response_content=response_content_for_log, + error_message=error_message_for_log + ) + + if retry_attempt < max_retries: + delay = retry_delay * (retry_backoff_factor ** retry_attempt) + logging.warning( + f"File upload failed: {str(e)}. " + f"Retrying in {delay:.2f}s ({retry_attempt + 1}/{max_retries})" + ) + time.sleep(delay) + else: + break # Max retries reached + + # If we've exhausted all retries, determine the final error type and raise + final_error_message = f"Failed to upload file after {max_retries + 1} attempts. Error: {str(last_exception)}" + try: + # Check basic internet connectivity + check_response = requests.get("https://www.google.com", timeout=5.0, verify=True) # Assuming verify=True is desired + if check_response.status_code >= 500: # Google itself has an issue (rare) + final_error_message = (f"Failed to upload file. Internet connectivity check to Google failed " + f"(status {check_response.status_code}). Original error: {str(last_exception)}") + # Not raising LocalNetworkError here as Google itself might be down. + # If Google is reachable, the issue is likely with the upload server or a more specific local problem + # not caught by a simple Google ping (e.g., DNS for the specific upload URL, firewall). + # The original last_exception is probably most relevant. + + except (requests.RequestException, socket.error) as conn_check_exc: + # Could not reach Google, likely a local network issue + final_error_message = (f"Failed to upload file due to network connectivity issues " + f"(cannot reach Google: {str(conn_check_exc)}). " + f"Original upload error: {str(last_exception)}") + request_logger.log_request_response( # Log final failure reason + operation_id=operation_id, + request_method="PUT", request_url=upload_url, + error_message=final_error_message + ) + raise LocalNetworkError(final_error_message) from last_exception + + request_logger.log_request_response( # Log final failure reason if not LocalNetworkError + operation_id=operation_id, + request_method="PUT", request_url=upload_url, + error_message=final_error_message + ) + raise Exception(final_error_message) from last_exception + + +class ApiEndpoint(Generic[T, R]): + """Defines an API endpoint with its request and response types""" + + def __init__( + self, + path: str, + method: HttpMethod, + request_model: Type[T], + response_model: Type[R], + query_params: Optional[Dict[str, Any]] = None, + ): + """Initialize an API endpoint definition. + + Args: + path: The URL path for this endpoint, can include placeholders like {id} + method: The HTTP method to use (GET, POST, etc.) + request_model: Pydantic model class that defines the structure and validation rules for API requests to this endpoint + response_model: Pydantic model class that defines the structure and validation rules for API responses from this endpoint + query_params: Optional dictionary of query parameters to include in the request + """ + self.path = path + self.method = method + self.request_model = request_model + self.response_model = response_model + self.query_params = query_params or {} + + +class SynchronousOperation(Generic[T, R]): + """ + Represents a single synchronous API operation. + """ + + def __init__( + self, + endpoint: ApiEndpoint[T, R], + request: T, + files: Optional[Dict[str, Any]] = None, + api_base: str | None = None, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + auth_kwargs: Optional[Dict[str,str]] = None, + timeout: float = 604800.0, + verify_ssl: bool = True, + content_type: str = "application/json", + multipart_parser: Callable = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + ): + self.endpoint = endpoint + self.request = request + self.response = None + self.error = None + self.api_base: str = api_base or args.comfy_api_base + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + if auth_kwargs is not None: + self.auth_token = auth_kwargs.get("auth_token", self.auth_token) + self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) + self.timeout = timeout + self.verify_ssl = verify_ssl + self.files = files + self.content_type = content_type + self.multipart_parser = multipart_parser + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + + def execute(self, client: Optional[ApiClient] = None) -> R: + """Execute the API operation using the provided client or create one with retry support""" + try: + # Create client if not provided + if client is None: + client = ApiClient( + base_url=self.api_base, + auth_token=self.auth_token, + comfy_api_key=self.comfy_api_key, + timeout=self.timeout, + verify_ssl=self.verify_ssl, + max_retries=self.max_retries, + retry_delay=self.retry_delay, + retry_backoff_factor=self.retry_backoff_factor, + ) + + # Convert request model to dict, but use None for EmptyRequest + request_dict = ( + None + if isinstance(self.request, EmptyRequest) + else self.request.model_dump(exclude_none=True) + ) + if request_dict: + for key, value in request_dict.items(): + if isinstance(value, Enum): + request_dict[key] = value.value + + # Debug log for request + logging.debug( + f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}" + ) + logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}") + logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}") + + # Make the request with built-in retry + resp = client.request( + method=self.endpoint.method.value, + path=self.endpoint.path, + data=request_dict, + params=self.endpoint.query_params, + files=self.files, + content_type=self.content_type, + multipart_parser=self.multipart_parser + ) + + # Debug log for response + logging.debug("=" * 50) + logging.debug("[DEBUG] RESPONSE DETAILS:") + logging.debug("[DEBUG] Status Code: 200 (Success)") + logging.debug(f"[DEBUG] Response Body: {json.dumps(resp, indent=2)}") + logging.debug("=" * 50) + + # Parse and return the response + return self._parse_response(resp) + + except LocalNetworkError as e: + # Propagate specific network error types + logging.error(f"[ERROR] Local network error: {str(e)}") + raise + + except ApiServerError as e: + # Propagate API server errors + logging.error(f"[ERROR] API server error: {str(e)}") + raise + + except Exception as e: + logging.error(f"[ERROR] API Exception: {str(e)}") + raise Exception(str(e)) + + def _parse_response(self, resp): + """Parse response data - can be overridden by subclasses""" + # The response is already the complete object, don't extract just the "data" field + # as that would lose the outer structure (created timestamp, etc.) + + # Parse response using the provided model + self.response = self.endpoint.response_model.model_validate(resp) + logging.debug(f"[DEBUG] Parsed Response: {self.response}") + return self.response + + +class TaskStatus(str, Enum): + """Enum for task status values""" + + COMPLETED = "completed" + FAILED = "failed" + PENDING = "pending" + + +class PollingOperation(Generic[T, R]): + """ + Represents an asynchronous API operation that requires polling for completion. + """ + + def __init__( + self, + poll_endpoint: ApiEndpoint[EmptyRequest, R], + completed_statuses: list, + failed_statuses: list, + status_extractor: Callable[[R], str], + progress_extractor: Callable[[R], float] = None, + result_url_extractor: Callable[[R], str] = None, + request: Optional[T] = None, + api_base: str | None = None, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + auth_kwargs: Optional[Dict[str,str]] = None, + poll_interval: float = 5.0, + max_poll_attempts: int = 120, # Default max polling attempts (10 minutes with 5s interval) + max_retries: int = 3, # Max retries per individual API call + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + estimated_duration: Optional[float] = None, + node_id: Optional[str] = None, + ): + self.poll_endpoint = poll_endpoint + self.request = request + self.api_base: str = api_base or args.comfy_api_base + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + if auth_kwargs is not None: + self.auth_token = auth_kwargs.get("auth_token", self.auth_token) + self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) + self.poll_interval = poll_interval + self.max_poll_attempts = max_poll_attempts + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + self.estimated_duration = estimated_duration + + # Polling configuration + self.status_extractor = status_extractor or ( + lambda x: getattr(x, "status", None) + ) + self.progress_extractor = progress_extractor + self.result_url_extractor = result_url_extractor + self.node_id = node_id + self.completed_statuses = completed_statuses + self.failed_statuses = failed_statuses + + # For storing response data + self.final_response = None + self.error = None + + def execute(self, client: Optional[ApiClient] = None) -> R: + """Execute the polling operation using the provided client. If failed, raise an exception.""" + try: + if client is None: + client = ApiClient( + base_url=self.api_base, + auth_token=self.auth_token, + comfy_api_key=self.comfy_api_key, + max_retries=self.max_retries, + retry_delay=self.retry_delay, + retry_backoff_factor=self.retry_backoff_factor, + ) + return self._poll_until_complete(client) + except LocalNetworkError as e: + # Provide clear message for local network issues + raise Exception( + f"Polling failed due to local network issues. Please check your internet connection. " + f"Details: {str(e)}" + ) from e + except ApiServerError as e: + # Provide clear message for API server issues + raise Exception( + f"Polling failed due to API server issues. The service may be experiencing problems. " + f"Please try again later. Details: {str(e)}" + ) from e + except Exception as e: + raise Exception(f"Error during polling: {str(e)}") + + def _display_text_on_node(self, text: str): + """Sends text to the client which will be displayed on the node in the UI""" + if not self.node_id: + return + + PromptServer.instance.send_progress_text(text, self.node_id) + + def _display_time_progress_on_node(self, time_completed: int): + if not self.node_id: + return + + if self.estimated_duration is not None: + estimated_time_remaining = max( + 0, int(self.estimated_duration) - int(time_completed) + ) + message = f"Task in progress: {time_completed:.0f}s (~{estimated_time_remaining:.0f}s remaining)" + else: + message = f"Task in progress: {time_completed:.0f}s" + self._display_text_on_node(message) + + def _check_task_status(self, response: R) -> TaskStatus: + """Check task status using the status extractor function""" + try: + status = self.status_extractor(response) + if status in self.completed_statuses: + return TaskStatus.COMPLETED + elif status in self.failed_statuses: + return TaskStatus.FAILED + return TaskStatus.PENDING + except Exception as e: + logging.error(f"Error extracting status: {e}") + return TaskStatus.PENDING + + def _poll_until_complete(self, client: ApiClient) -> R: + """Poll until the task is complete""" + poll_count = 0 + consecutive_errors = 0 + max_consecutive_errors = min(5, self.max_retries * 2) # Limit consecutive errors + + if self.progress_extractor: + progress = utils.ProgressBar(PROGRESS_BAR_MAX) + + while poll_count < self.max_poll_attempts: + try: + poll_count += 1 + logging.debug(f"[DEBUG] Polling attempt #{poll_count}") + + request_dict = ( + self.request.model_dump(exclude_none=True) + if self.request is not None + else None + ) + + if poll_count == 1: + logging.debug( + f"[DEBUG] Poll Request: {self.poll_endpoint.method.value} {self.poll_endpoint.path}" + ) + logging.debug( + f"[DEBUG] Poll Request Data: {json.dumps(request_dict, indent=2) if request_dict else 'None'}" + ) + + # Query task status + resp = client.request( + method=self.poll_endpoint.method.value, + path=self.poll_endpoint.path, + params=self.poll_endpoint.query_params, + data=request_dict, + ) + + # Successfully got a response, reset consecutive error count + consecutive_errors = 0 + + # Parse response + response_obj = self.poll_endpoint.response_model.model_validate(resp) + + # Check if task is complete + status = self._check_task_status(response_obj) + logging.debug(f"[DEBUG] Task Status: {status}") + + # If progress extractor is provided, extract progress + if self.progress_extractor: + new_progress = self.progress_extractor(response_obj) + if new_progress is not None: + progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX) + + if status == TaskStatus.COMPLETED: + message = "Task completed successfully" + if self.result_url_extractor: + result_url = self.result_url_extractor(response_obj) + if result_url: + message = f"Result URL: {result_url}" + else: + message = "Task completed successfully!" + logging.debug(f"[DEBUG] {message}") + self._display_text_on_node(message) + self.final_response = response_obj + if self.progress_extractor: + progress.update(100) + return self.final_response + elif status == TaskStatus.FAILED: + message = f"Task failed: {json.dumps(resp)}" + logging.error(f"[DEBUG] {message}") + raise Exception(message) + else: + logging.debug("[DEBUG] Task still pending, continuing to poll...") + + # Wait before polling again + logging.debug( + f"[DEBUG] Waiting {self.poll_interval} seconds before next poll" + ) + for i in range(int(self.poll_interval)): + time_completed = (poll_count * self.poll_interval) + i + self._display_time_progress_on_node(time_completed) + time.sleep(1) + + except (LocalNetworkError, ApiServerError) as e: + # For network-related errors, increment error count and potentially abort + consecutive_errors += 1 + if consecutive_errors >= max_consecutive_errors: + raise Exception( + f"Polling aborted after {consecutive_errors} consecutive network errors: {str(e)}" + ) from e + + # Log the error but continue polling + logging.warning( + f"Network error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. " + f"Will retry in {self.poll_interval} seconds." + ) + time.sleep(self.poll_interval) + + except Exception as e: + # For other errors, increment count and potentially abort + consecutive_errors += 1 + if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED: + raise Exception( + f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}" + ) from e + + logging.error(f"[DEBUG] Polling error: {str(e)}") + logging.warning( + f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. " + f"Will retry in {self.poll_interval} seconds." + ) + time.sleep(self.poll_interval) + + # If we've exhausted all polling attempts + raise Exception( + f"Polling timed out after {poll_count} attempts ({poll_count * self.poll_interval} seconds). " + f"The operation may still be running on the server but is taking longer than expected." + ) diff --git a/comfy_api_nodes/apis/luma_api.py b/comfy_api_nodes/apis/luma_api.py new file mode 100644 index 0000000000000000000000000000000000000000..632c4ab9697a8ab2659dc0171890e739788b286d --- /dev/null +++ b/comfy_api_nodes/apis/luma_api.py @@ -0,0 +1,253 @@ +from __future__ import annotations + + +import torch + +from enum import Enum +from typing import Optional, Union + +from pydantic import BaseModel, Field, confloat + + + +class LumaIO: + LUMA_REF = "LUMA_REF" + LUMA_CONCEPTS = "LUMA_CONCEPTS" + + +class LumaReference: + def __init__(self, image: torch.Tensor, weight: float): + self.image = image + self.weight = weight + + def create_api_model(self, download_url: str): + return LumaImageRef(url=download_url, weight=self.weight) + +class LumaReferenceChain: + def __init__(self, first_ref: LumaReference=None): + self.refs: list[LumaReference] = [] + if first_ref: + self.refs.append(first_ref) + + def add(self, luma_ref: LumaReference=None): + self.refs.append(luma_ref) + + def create_api_model(self, download_urls: list[str], max_refs=4): + if len(self.refs) == 0: + return None + api_refs: list[LumaImageRef] = [] + for ref, url in zip(self.refs, download_urls): + api_ref = LumaImageRef(url=url, weight=ref.weight) + api_refs.append(api_ref) + return api_refs + + def clone(self): + c = LumaReferenceChain() + for ref in self.refs: + c.add(ref) + return c + + +class LumaConcept: + def __init__(self, key: str): + self.key = key + + +class LumaConceptChain: + def __init__(self, str_list: list[str] = None): + self.concepts: list[LumaConcept] = [] + if str_list is not None: + for c in str_list: + if c != "None": + self.add(LumaConcept(key=c)) + + def add(self, concept: LumaConcept): + self.concepts.append(concept) + + def create_api_model(self): + if len(self.concepts) == 0: + return None + api_concepts: list[LumaConceptObject] = [] + for concept in self.concepts: + if concept.key == "None": + continue + api_concepts.append(LumaConceptObject(key=concept.key)) + if len(api_concepts) == 0: + return None + return api_concepts + + def clone(self): + c = LumaConceptChain() + for concept in self.concepts: + c.add(concept) + return c + + def clone_and_merge(self, other: LumaConceptChain): + c = self.clone() + for concept in other.concepts: + c.add(concept) + return c + + +def get_luma_concepts(include_none=False): + concepts = [] + if include_none: + concepts.append("None") + return concepts + [ + "truck_left", + "pan_right", + "pedestal_down", + "low_angle", + "pedestal_up", + "selfie", + "pan_left", + "roll_right", + "zoom_in", + "over_the_shoulder", + "orbit_right", + "orbit_left", + "static", + "tiny_planet", + "high_angle", + "bolt_cam", + "dolly_zoom", + "overhead", + "zoom_out", + "handheld", + "roll_left", + "pov", + "aerial_drone", + "push_in", + "crane_down", + "truck_right", + "tilt_down", + "elevator_doors", + "tilt_up", + "ground_level", + "pull_out", + "aerial", + "crane_up", + "eye_level" + ] + + +class LumaImageModel(str, Enum): + photon_1 = "photon-1" + photon_flash_1 = "photon-flash-1" + + +class LumaVideoModel(str, Enum): + ray_2 = "ray-2" + ray_flash_2 = "ray-flash-2" + ray_1_6 = "ray-1-6" + + +class LumaAspectRatio(str, Enum): + ratio_1_1 = "1:1" + ratio_16_9 = "16:9" + ratio_9_16 = "9:16" + ratio_4_3 = "4:3" + ratio_3_4 = "3:4" + ratio_21_9 = "21:9" + ratio_9_21 = "9:21" + + +class LumaVideoOutputResolution(str, Enum): + res_540p = "540p" + res_720p = "720p" + res_1080p = "1080p" + res_4k = "4k" + + +class LumaVideoModelOutputDuration(str, Enum): + dur_5s = "5s" + dur_9s = "9s" + + +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' + + +class LumaState(str, Enum): + queued = "queued" + dreaming = "dreaming" + completed = "completed" + failed = "failed" + + +class LumaAssets(BaseModel): + video: Optional[str] = Field(None, description='The URL of the video') + image: Optional[str] = Field(None, description='The URL of the image') + progress_video: Optional[str] = Field(None, description='The URL of the progress video') + + +class LumaImageRef(BaseModel): + '''Used for image gen''' + url: str = Field(..., description='The URL of the image reference') + weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + +class LumaImageReference(BaseModel): + '''Used for video gen''' + type: Optional[str] = Field('image', description='Input type, defaults to image') + url: str = Field(..., description='The URL of the image') + + +class LumaModifyImageRef(BaseModel): + url: str = Field(..., description='The URL of the image reference') + weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + +class LumaCharacterRef(BaseModel): + identity0: LumaImageIdentity = Field(..., description='The image identity object') + + +class LumaImageIdentity(BaseModel): + images: list[str] = Field(..., description='The URLs of the image identity') + + +class LumaGenerationReference(BaseModel): + type: str = Field('generation', description='Input type, defaults to generation') + id: str = Field(..., description='The ID of the generation') + + +class LumaKeyframes(BaseModel): + frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + + +class LumaConceptObject(BaseModel): + key: str = Field(..., description='Camera Concept name') + + +class LumaImageGenerationRequest(BaseModel): + prompt: str = Field(..., description='The prompt of the generation') + model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation') + aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation') + image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects') + style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects') + character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object') + modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object') + + +class LumaGenerationRequest(BaseModel): + prompt: str = Field(..., description='The prompt of the generation') + model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation') + duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation') + aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation') + resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation') + loop: Optional[bool] = Field(None, description='Whether to loop the video') + keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation') + concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation') + + +class LumaGeneration(BaseModel): + id: str = Field(..., description='The ID of the generation') + generation_type: LumaGenerationType = Field(..., description='Generation type, image or video') + state: LumaState = Field(..., description='The state of the generation') + failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation') + created_at: str = Field(..., description='The date and time when the generation was created') + assets: Optional[LumaAssets] = Field(None, description='The assets of the generation') + model: str = Field(..., description='The model used for the generation') + request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation") diff --git a/comfy_api_nodes/apis/pixverse_api.py b/comfy_api_nodes/apis/pixverse_api.py new file mode 100644 index 0000000000000000000000000000000000000000..9bb29c3835104784087d99821c85f9ddf6ba218c --- /dev/null +++ b/comfy_api_nodes/apis/pixverse_api.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field + + +pixverse_templates = { + "Microwave": 324641385496960, + "Suit Swagger": 328545151283968, + "Anything, Robot": 313358700761536, + "Subject 3 Fever": 327828816843648, + "kiss kiss": 315446315336768, +} + + +class PixverseIO: + TEMPLATE = "PIXVERSE_TEMPLATE" + + +class PixverseStatus(int, Enum): + successful = 1 + generating = 5 + deleted = 6 + contents_moderation = 7 + failed = 8 + + +class PixverseAspectRatio(str, Enum): + ratio_16_9 = "16:9" + ratio_4_3 = "4:3" + ratio_1_1 = "1:1" + ratio_3_4 = "3:4" + ratio_9_16 = "9:16" + + +class PixverseQuality(str, Enum): + res_360p = "360p" + res_540p = "540p" + res_720p = "720p" + res_1080p = "1080p" + + +class PixverseDuration(int, Enum): + dur_5 = 5 + dur_8 = 8 + + +class PixverseMotionMode(str, Enum): + normal = "normal" + fast = "fast" + + +class PixverseStyle(str, Enum): + anime = "anime" + animation_3d = "3d_animation" + clay = "clay" + comic = "comic" + cyberpunk = "cyberpunk" + + +# NOTE: forgoing descriptions for now in return for dev speed +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: PixverseAspectRatio = Field(...) + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + style: Optional[str] = Field(None) + template_id: Optional[int] = Field(None) + water_mark: Optional[bool] = Field(None) + + +class PixverseImageVideoRequest(BaseModel): + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + img_id: int = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + style: Optional[str] = Field(None) + template_id: Optional[int] = Field(None) + water_mark: Optional[bool] = Field(None) + + +class PixverseTransitionVideoRequest(BaseModel): + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + first_frame_img: int = Field(...) + last_frame_img: int = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + # negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + # style: Optional[str] = Field(None) + # template_id: Optional[int] = Field(None) + # water_mark: Optional[bool] = Field(None) + + +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[PixverseImgIdResponseObject] = Field(None, alias='Resp') + + +class PixverseImgIdResponseObject(BaseModel): + img_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = Field(None) + ErrMsg: Optional[str] = Field(None) + Resp: Optional[PixverseVideoIdResponseObject] = Field(None) + + +class PixverseVideoIdResponseObject(BaseModel): + video_id: int = Field(..., description='Video_id') + + +class PixverseGenerationStatusResponse(BaseModel): + ErrCode: Optional[int] = Field(None) + ErrMsg: Optional[str] = Field(None) + Resp: Optional[PixverseGenerationStatusResponseObject] = Field(None) + + +class PixverseGenerationStatusResponseObject(BaseModel): + create_time: Optional[str] = Field(None) + id: Optional[int] = Field(None) + modify_time: Optional[str] = Field(None) + negative_prompt: Optional[str] = Field(None) + outputHeight: Optional[int] = Field(None) + outputWidth: Optional[int] = Field(None) + prompt: Optional[str] = Field(None) + resolution_ratio: Optional[int] = Field(None) + seed: Optional[int] = Field(None) + size: Optional[int] = Field(None) + status: Optional[int] = Field(None) + style: Optional[str] = Field(None) + url: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/recraft_api.py b/comfy_api_nodes/apis/recraft_api.py new file mode 100644 index 0000000000000000000000000000000000000000..c36d95f24e6ab53f5790b0ac97f71a26087a410f --- /dev/null +++ b/comfy_api_nodes/apis/recraft_api.py @@ -0,0 +1,262 @@ +from __future__ import annotations + + + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field, conint, confloat + + +class RecraftColor: + def __init__(self, r: int, g: int, b: int): + self.color = [r, g, b] + + def create_api_model(self): + return RecraftColorObject(rgb=self.color) + + +class RecraftColorChain: + def __init__(self): + self.colors: list[RecraftColor] = [] + + def get_first(self): + if len(self.colors) > 0: + return self.colors[0] + return None + + def add(self, color: RecraftColor): + self.colors.append(color) + + def create_api_model(self): + if not self.colors: + return None + colors_api = [x.create_api_model() for x in self.colors] + return colors_api + + def clone(self): + c = RecraftColorChain() + for color in self.colors: + c.add(color) + return c + + def clone_and_merge(self, other: RecraftColorChain): + c = self.clone() + for color in other.colors: + c.add(color) + return c + + +class RecraftControls: + def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None, + artistic_level: int=None, no_text: bool=None): + self.colors = colors + self.background_color = background_color + self.artistic_level = artistic_level + self.no_text = no_text + + def create_api_model(self): + if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None: + return None + colors_api = None + background_color_api = None + if self.colors: + colors_api = self.colors.create_api_model() + if self.background_color: + first_background = self.background_color.get_first() + background_color_api = first_background.create_api_model() if first_background else None + + return RecraftControlsObject(colors=colors_api, background_color=background_color_api, + artistic_level=self.artistic_level, no_text=self.no_text) + + +class RecraftStyle: + def __init__(self, style: str=None, substyle: str=None, style_id: str=None): + self.style = style + if substyle == "None": + substyle = None + self.substyle = substyle + self.style_id = style_id + + +class RecraftIO: + STYLEV3 = "RECRAFT_V3_STYLE" + COLOR = "RECRAFT_COLOR" + CONTROLS = "RECRAFT_CONTROLS" + + +class RecraftStyleV3(str, Enum): + #any = 'any' NOTE: this does not work for some reason... why? + realistic_image = 'realistic_image' + digital_illustration = 'digital_illustration' + vector_illustration = 'vector_illustration' + logo_raster = 'logo_raster' + + +def get_v3_substyles(style_v3: str, include_none=True) -> list[str]: + substyles: list[str] = [] + if include_none: + substyles.append("None") + return substyles + dict_recraft_substyles_v3.get(style_v3, []) + + +dict_recraft_substyles_v3 = { + RecraftStyleV3.realistic_image: [ + "b_and_w", + "enterprise", + "evening_light", + "faded_nostalgia", + "forest_life", + "hard_flash", + "hdr", + "motion_blur", + "mystic_naturalism", + "natural_light", + "natural_tones", + "organic_calm", + "real_life_glow", + "retro_realism", + "retro_snapshot", + "studio_portrait", + "urban_drama", + "village_realism", + "warm_folk" + ], + RecraftStyleV3.digital_illustration: [ + "2d_art_poster", + "2d_art_poster_2", + "antiquarian", + "bold_fantasy", + "child_book", + "child_books", + "cover", + "crosshatch", + "digital_engraving", + "engraving_color", + "expressionism", + "freehand_details", + "grain", + "grain_20", + "graphic_intensity", + "hand_drawn", + "hand_drawn_outline", + "handmade_3d", + "hard_comics", + "infantile_sketch", + "long_shadow", + "modern_folk", + "multicolor", + "neon_calm", + "noir", + "nostalgic_pastel", + "outline_details", + "pastel_gradient", + "pastel_sketch", + "pixel_art", + "plastic", + "pop_art", + "pop_renaissance", + "seamless", + "street_art", + "tablet_sketch", + "urban_glow", + "urban_sketching", + "vanilla_dreams", + "young_adult_book", + "young_adult_book_2" + ], + RecraftStyleV3.vector_illustration: [ + "bold_stroke", + "chemistry", + "colored_stencil", + "contour_pop_art", + "cosmics", + "cutout", + "depressive", + "editorial", + "emotional_flat", + "engraving", + "infographical", + "line_art", + "line_circuit", + "linocut", + "marker_outline", + "mosaic", + "naivector", + "roundish_flat", + "seamless", + "segmented_colors", + "sharp_contrast", + "thin", + "vector_photo", + "vivid_shapes" + ], + RecraftStyleV3.logo_raster: [ + "emblem_graffiti", + "emblem_pop_art", + "emblem_punk", + "emblem_stamp", + "emblem_vintage" + ], +} + + +class RecraftModel(str, Enum): + recraftv3 = 'recraftv3' + recraftv2 = 'recraftv2' + + +class RecraftImageSize(str, Enum): + res_1024x1024 = '1024x1024' + res_1365x1024 = '1365x1024' + res_1024x1365 = '1024x1365' + res_1536x1024 = '1536x1024' + res_1024x1536 = '1024x1536' + res_1820x1024 = '1820x1024' + res_1024x1820 = '1024x1820' + res_1024x2048 = '1024x2048' + res_2048x1024 = '2048x1024' + res_1434x1024 = '1434x1024' + res_1024x1434 = '1024x1434' + res_1024x1280 = '1024x1280' + res_1280x1024 = '1280x1024' + res_1024x1707 = '1024x1707' + res_1707x1024 = '1707x1024' + + +class RecraftColorObject(BaseModel): + rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model') + + +class RecraftControlsObject(BaseModel): + colors: Optional[list[RecraftColorObject]] = Field(None, description='An array of preferable colors') + background_color: Optional[RecraftColorObject] = Field(None, description='Use given color as a desired background color') + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + artistic_level: Optional[conint(ge=0, le=5)] = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].') + + +class RecraftImageGenerationRequest(BaseModel): + prompt: str = Field(..., description='The text prompt describing the image to generate') + size: Optional[RecraftImageSize] = Field(None, description='The size of the generated image (e.g., "1024x1024")') + n: conint(ge=1, le=6) = Field(..., description='The number of images to generate') + negative_prompt: Optional[str] = Field(None, description='A text description of undesired elements on an image') + model: Optional[RecraftModel] = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")') + style: Optional[str] = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")') + substyle: Optional[str] = Field(None, description='The substyle to apply to the generated image, depending on the style input') + controls: Optional[RecraftControlsObject] = Field(None, description='A set of custom parameters to tweak generation process') + style_id: Optional[str] = Field(None, description='Use a previously uploaded style as a reference; UUID') + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity') + random_seed: Optional[int] = Field(None, description="Seed for video generation") + # text_layout + + +class RecraftReturnedObject(BaseModel): + image_id: str = Field(..., description='Unique identifier for the generated image') + url: str = Field(..., description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field(..., description='Unix timestamp when the generation was created') + credits: int = Field(..., description='Number of credits used for the generation') + data: Optional[list[RecraftReturnedObject]] = Field(None, description='Array of generated image information') + image: Optional[RecraftReturnedObject] = Field(None, description='Single generated image') diff --git a/comfy_api_nodes/apis/request_logger.py b/comfy_api_nodes/apis/request_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..93517ede939884ad0a6e9dcd3da9b650fcd69633 --- /dev/null +++ b/comfy_api_nodes/apis/request_logger.py @@ -0,0 +1,125 @@ +import os +import datetime +import json +import logging +import folder_paths + +# Get the logger instance +logger = logging.getLogger(__name__) + +def get_log_directory(): + """ + Ensures the API log directory exists within ComfyUI's temp directory + and returns its path. + """ + base_temp_dir = folder_paths.get_temp_directory() + log_dir = os.path.join(base_temp_dir, "api_logs") + try: + os.makedirs(log_dir, exist_ok=True) + except Exception as e: + logger.error(f"Error creating API log directory {log_dir}: {e}") + # Fallback to base temp directory if sub-directory creation fails + return base_temp_dir + return log_dir + +def _format_data_for_logging(data): + """Helper to format data (dict, str, bytes) for logging.""" + if isinstance(data, bytes): + try: + return data.decode('utf-8') # Try to decode as text + except UnicodeDecodeError: + return f"[Binary data of length {len(data)} bytes]" + elif isinstance(data, (dict, list)): + try: + return json.dumps(data, indent=2, ensure_ascii=False) + except TypeError: + return str(data) # Fallback for non-serializable objects + return str(data) + +def log_request_response( + operation_id: str, + request_method: str, + request_url: str, + request_headers: dict | None = None, + request_params: dict | None = None, + request_data: any = None, + response_status_code: int | None = None, + response_headers: dict | None = None, + response_content: any = None, + error_message: str | None = None +): + """ + Logs API request and response details to a file in the temp/api_logs directory. + """ + log_dir = get_log_directory() + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f") + filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log" + filepath = os.path.join(log_dir, filename) + + log_content = [] + + log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}") + log_content.append(f"Operation ID: {operation_id}") + log_content.append("-" * 30 + " REQUEST " + "-" * 30) + log_content.append(f"Method: {request_method}") + log_content.append(f"URL: {request_url}") + if request_headers: + log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}") + if request_params: + log_content.append(f"Params:\n{_format_data_for_logging(request_params)}") + if request_data: + log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}") + + log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30) + if response_status_code is not None: + log_content.append(f"Status Code: {response_status_code}") + if response_headers: + log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}") + if response_content: + log_content.append(f"Content:\n{_format_data_for_logging(response_content)}") + if error_message: + log_content.append(f"Error:\n{error_message}") + + try: + with open(filepath, "w", encoding="utf-8") as f: + f.write("\n".join(log_content)) + logger.debug(f"API log saved to: {filepath}") + except Exception as e: + logger.error(f"Error writing API log to {filepath}: {e}") + +if __name__ == '__main__': + # Example usage (for testing the logger directly) + logger.setLevel(logging.DEBUG) + # Mock folder_paths for direct execution if not running within ComfyUI full context + if not hasattr(folder_paths, 'get_temp_directory'): + class MockFolderPaths: + def get_temp_directory(self): + # Create a local temp dir for testing if needed + p = os.path.join(os.path.dirname(__file__), 'temp_test_logs') + os.makedirs(p, exist_ok=True) + return p + folder_paths = MockFolderPaths() + + log_request_response( + operation_id="test_operation_get", + request_method="GET", + request_url="https://api.example.com/test", + request_headers={"Authorization": "Bearer testtoken"}, + request_params={"param1": "value1"}, + response_status_code=200, + response_content={"message": "Success!"} + ) + log_request_response( + operation_id="test_operation_post_error", + request_method="POST", + request_url="https://api.example.com/submit", + request_data={"key": "value", "nested": {"num": 123}}, + error_message="Connection timed out" + ) + log_request_response( + operation_id="test_binary_response", + request_method="GET", + request_url="https://api.example.com/image.png", + response_status_code=200, + response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data + ) diff --git a/comfy_api_nodes/apis/rodin_api.py b/comfy_api_nodes/apis/rodin_api.py new file mode 100644 index 0000000000000000000000000000000000000000..b0cf171fa636a0571f2e771732c0cc7f7536835b --- /dev/null +++ b/comfy_api_nodes/apis/rodin_api.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional, List +from pydantic import BaseModel, Field + + +class Rodin3DGenerateRequest(BaseModel): + seed: int = Field(..., description="seed_") + tier: str = Field(..., description="Tier of generation.") + material: str = Field(..., description="The material type.") + quality: str = Field(..., description="The generation quality of the mesh.") + mesh_mode: str = Field(..., description="It controls the type of faces of generated models.") + +class GenerateJobsData(BaseModel): + uuids: List[str] = Field(..., description="str LIST") + subscription_key: str = Field(..., description="subscription key") + +class Rodin3DGenerateResponse(BaseModel): + message: Optional[str] = Field(None, description="Return message.") + prompt: Optional[str] = Field(None, description="Generated Prompt from image.") + submit_time: Optional[str] = Field(None, description="Submit Time") + uuid: Optional[str] = Field(None, description="Task str") + jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs") + +class JobStatus(str, Enum): + """ + Status for jobs + """ + Done = "Done" + Failed = "Failed" + Generating = "Generating" + Waiting = "Waiting" + +class Rodin3DCheckStatusRequest(BaseModel): + subscription_key: str = Field(..., description="subscription from generate endpoint") + +class JobItem(BaseModel): + uuid: str = Field(..., description="uuid") + status: JobStatus = Field(...,description="Status Currently") + +class Rodin3DCheckStatusResponse(BaseModel): + jobs: List[JobItem] = Field(..., description="Job status List") + +class Rodin3DDownloadRequest(BaseModel): + task_uuid: str = Field(..., description="Task str") + +class RodinResourceItem(BaseModel): + url: str = Field(..., description="Download Url") + name: str = Field(..., description="File name with ext") + +class Rodin3DDownloadResponse(BaseModel): + list: List[RodinResourceItem] = Field(..., description="Source List") + + + + diff --git a/comfy_api_nodes/apis/stability_api.py b/comfy_api_nodes/apis/stability_api.py new file mode 100644 index 0000000000000000000000000000000000000000..47c87daec1bc3d3f5384855c93ad2c4b929227ff --- /dev/null +++ b/comfy_api_nodes/apis/stability_api.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field, confloat + + +class StabilityFormat(str, Enum): + png = 'png' + jpeg = 'jpeg' + webp = 'webp' + + +class StabilityAspectRatio(str, Enum): + ratio_1_1 = "1:1" + ratio_16_9 = "16:9" + ratio_9_16 = "9:16" + ratio_3_2 = "3:2" + ratio_2_3 = "2:3" + ratio_5_4 = "5:4" + ratio_4_5 = "4:5" + ratio_21_9 = "21:9" + ratio_9_21 = "9:21" + + +def get_stability_style_presets(include_none=True): + presets = [] + if include_none: + presets.append("None") + return presets + [x.value for x in StabilityStylePreset] + + +class StabilityStylePreset(str, Enum): + _3d_model = "3d-model" + analog_film = "analog-film" + anime = "anime" + cinematic = "cinematic" + comic_book = "comic-book" + digital_art = "digital-art" + enhance = "enhance" + fantasy_art = "fantasy-art" + isometric = "isometric" + line_art = "line-art" + low_poly = "low-poly" + modeling_compound = "modeling-compound" + neon_punk = "neon-punk" + origami = "origami" + photographic = "photographic" + pixel_art = "pixel-art" + tile_texture = "tile-texture" + + +class Stability_SD3_5_Model(str, Enum): + sd3_5_large = "sd3.5-large" + # sd3_5_large_turbo = "sd3.5-large-turbo" + sd3_5_medium = "sd3.5-medium" + + +class Stability_SD3_5_GenerationMode(str, Enum): + text_to_image = "text-to-image" + image_to_image = "image-to-image" + + +class StabilityStable3_5Request(BaseModel): + model: str = Field(...) + mode: str = Field(...) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + aspect_ratio: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + style_preset: Optional[str] = Field(None) + cfg_scale: float = Field(...) + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) + + +class StabilityUpscaleConservativeRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None) + + +class StabilityUpscaleCreativeRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None) + style_preset: Optional[str] = Field(None) + + +class StabilityStableUltraRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + aspect_ratio: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + style_preset: Optional[str] = Field(None) + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) + + +class StabilityStableUltraResponse(BaseModel): + image: Optional[str] = Field(None) + finish_reason: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + + +class StabilityResultsGetResponse(BaseModel): + image: Optional[str] = Field(None) + finish_reason: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + id: Optional[str] = Field(None) + name: Optional[str] = Field(None) + errors: Optional[list[str]] = Field(None) + status: Optional[str] = Field(None) + result: Optional[str] = Field(None) + + +class StabilityAsyncResponse(BaseModel): + id: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/tripo_api.py b/comfy_api_nodes/apis/tripo_api.py new file mode 100644 index 0000000000000000000000000000000000000000..626e8d277da3c35049d0b6a6d47eaaaf1316a64f --- /dev/null +++ b/comfy_api_nodes/apis/tripo_api.py @@ -0,0 +1,275 @@ +from __future__ import annotations +from comfy_api_nodes.apis import ( + TripoModelVersion, + TripoTextureQuality, +) +from enum import Enum +from typing import Optional, List, Dict, Any, Union + +from pydantic import BaseModel, Field, RootModel + +class TripoStyle(str, Enum): + PERSON_TO_CARTOON = "person:person2cartoon" + ANIMAL_VENOM = "animal:venom" + OBJECT_CLAY = "object:clay" + OBJECT_STEAMPUNK = "object:steampunk" + OBJECT_CHRISTMAS = "object:christmas" + OBJECT_BARBIE = "object:barbie" + GOLD = "gold" + ANCIENT_BRONZE = "ancient_bronze" + NONE = "None" + +class TripoTaskType(str, Enum): + TEXT_TO_MODEL = "text_to_model" + IMAGE_TO_MODEL = "image_to_model" + MULTIVIEW_TO_MODEL = "multiview_to_model" + TEXTURE_MODEL = "texture_model" + REFINE_MODEL = "refine_model" + ANIMATE_PRERIGCHECK = "animate_prerigcheck" + ANIMATE_RIG = "animate_rig" + ANIMATE_RETARGET = "animate_retarget" + STYLIZE_MODEL = "stylize_model" + CONVERT_MODEL = "convert_model" + +class TripoTextureAlignment(str, Enum): + ORIGINAL_IMAGE = "original_image" + GEOMETRY = "geometry" + +class TripoOrientation(str, Enum): + ALIGN_IMAGE = "align_image" + DEFAULT = "default" + +class TripoOutFormat(str, Enum): + GLB = "glb" + FBX = "fbx" + +class TripoTopology(str, Enum): + BIP = "bip" + QUAD = "quad" + +class TripoSpec(str, Enum): + MIXAMO = "mixamo" + TRIPO = "tripo" + +class TripoAnimation(str, Enum): + IDLE = "preset:idle" + WALK = "preset:walk" + CLIMB = "preset:climb" + JUMP = "preset:jump" + RUN = "preset:run" + SLASH = "preset:slash" + SHOOT = "preset:shoot" + HURT = "preset:hurt" + FALL = "preset:fall" + TURN = "preset:turn" + +class TripoStylizeStyle(str, Enum): + LEGO = "lego" + VOXEL = "voxel" + VORONOI = "voronoi" + MINECRAFT = "minecraft" + +class TripoConvertFormat(str, Enum): + GLTF = "GLTF" + USDZ = "USDZ" + FBX = "FBX" + OBJ = "OBJ" + STL = "STL" + _3MF = "3MF" + +class TripoTextureFormat(str, Enum): + BMP = "BMP" + DPX = "DPX" + HDR = "HDR" + JPEG = "JPEG" + OPEN_EXR = "OPEN_EXR" + PNG = "PNG" + TARGA = "TARGA" + TIFF = "TIFF" + WEBP = "WEBP" + +class TripoTaskStatus(str, Enum): + QUEUED = "queued" + RUNNING = "running" + SUCCESS = "success" + FAILED = "failed" + CANCELLED = "cancelled" + UNKNOWN = "unknown" + BANNED = "banned" + EXPIRED = "expired" + +class TripoFileTokenReference(BaseModel): + type: Optional[str] = Field(None, description='The type of the reference') + file_token: str + +class TripoUrlReference(BaseModel): + type: Optional[str] = Field(None, description='The type of the reference') + url: str + +class TripoObjectStorage(BaseModel): + bucket: str + key: str + +class TripoObjectReference(BaseModel): + type: str + object: TripoObjectStorage + +class TripoFileEmptyReference(BaseModel): + pass + +class TripoFileReference(RootModel): + root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference] + +class TripoGetStsTokenRequest(BaseModel): + format: str = Field(..., description='The format of the image') + +class TripoTextToModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description='Type of task') + prompt: str = Field(..., description='The text prompt describing the model to generate', max_length=1024) + negative_prompt: Optional[str] = Field(None, description='The negative text prompt', max_length=1024) + model_version: Optional[TripoModelVersion] = TripoModelVersion.V2_5 + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + image_seed: Optional[int] = Field(None, description='The seed for the text') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + style: Optional[TripoStyle] = None + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoImageToModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description='Type of task') + file: TripoFileReference = Field(..., description='The file reference to convert to a model') + model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') + style: Optional[TripoStyle] = Field(None, description='The style to apply to the generated model') + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + orientation: Optional[TripoOrientation] = TripoOrientation.DEFAULT + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoMultiviewToModelRequest(BaseModel): + type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL + files: List[TripoFileReference] = Field(..., description='The file references to convert to a model') + model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') + orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection') + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + texture_alignment: Optional[TripoTextureAlignment] = TripoTextureAlignment.ORIGINAL_IMAGE + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + orientation: Optional[TripoOrientation] = Field(TripoOrientation.DEFAULT, description='The orientation for the model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoTextureModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = Field(None, description='The quality of the texture') + texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') + +class TripoRefineModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.REFINE_MODEL, description='Type of task') + draft_model_task_id: str = Field(..., description='The task ID of the draft model') + +class TripoAnimatePrerigcheckRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_PRERIGCHECK, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + +class TripoAnimateRigRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') + spec: Optional[TripoSpec] = Field(TripoSpec.TRIPO, description='The specification for rigging') + +class TripoAnimateRetargetRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + animation: TripoAnimation = Field(..., description='The animation to apply') + out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') + bake_animation: Optional[bool] = Field(True, description='Whether to bake the animation') + +class TripoStylizeModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.STYLIZE_MODEL, description='Type of task') + style: TripoStylizeStyle = Field(..., description='The style to apply to the model') + original_model_task_id: str = Field(..., description='The task ID of the original model') + block_size: Optional[int] = Field(80, description='The block size for stylization') + +class TripoConvertModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description='Type of task') + format: TripoConvertFormat = Field(..., description='The format to convert to') + original_model_task_id: str = Field(..., description='The task ID of the original model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the model') + force_symmetry: Optional[bool] = Field(False, description='Whether to force symmetry') + face_limit: Optional[int] = Field(10000, description='The number of faces to limit the conversion to') + flatten_bottom: Optional[bool] = Field(False, description='Whether to flatten the bottom of the model') + flatten_bottom_threshold: Optional[float] = Field(0.01, description='The threshold for flattening the bottom') + texture_size: Optional[int] = Field(4096, description='The size of the texture') + texture_format: Optional[TripoTextureFormat] = Field(TripoTextureFormat.JPEG, description='The format of the texture') + pivot_to_center_bottom: Optional[bool] = Field(False, description='Whether to pivot to the center bottom') + +class TripoTaskRequest(RootModel): + root: Union[ + TripoTextToModelRequest, + TripoImageToModelRequest, + TripoMultiviewToModelRequest, + TripoTextureModelRequest, + TripoRefineModelRequest, + TripoAnimatePrerigcheckRequest, + TripoAnimateRigRequest, + TripoAnimateRetargetRequest, + TripoStylizeModelRequest, + TripoConvertModelRequest + ] + +class TripoTaskOutput(BaseModel): + model: Optional[str] = Field(None, description='URL to the model') + base_model: Optional[str] = Field(None, description='URL to the base model') + pbr_model: Optional[str] = Field(None, description='URL to the PBR model') + rendered_image: Optional[str] = Field(None, description='URL to the rendered image') + riggable: Optional[bool] = Field(None, description='Whether the model is riggable') + +class TripoTask(BaseModel): + task_id: str = Field(..., description='The task ID') + type: Optional[str] = Field(None, description='The type of task') + status: Optional[TripoTaskStatus] = Field(None, description='The status of the task') + input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task') + output: Optional[TripoTaskOutput] = Field(None, description='The output of the task') + progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100) + create_time: Optional[int] = Field(None, description='The creation time of the task') + running_left_time: Optional[int] = Field(None, description='The estimated time left for the task') + queue_position: Optional[int] = Field(None, description='The position in the queue') + +class TripoTaskResponse(BaseModel): + code: int = Field(0, description='The response code') + data: TripoTask = Field(..., description='The task data') + +class TripoGeneralResponse(BaseModel): + code: int = Field(0, description='The response code') + data: Dict[str, str] = Field(..., description='The task ID data') + +class TripoBalanceData(BaseModel): + balance: float = Field(..., description='The account balance') + frozen: float = Field(..., description='The frozen balance') + +class TripoBalanceResponse(BaseModel): + code: int = Field(0, description='The response code') + data: TripoBalanceData = Field(..., description='The balance data') + +class TripoErrorResponse(BaseModel): + code: int = Field(..., description='The error code') + message: str = Field(..., description='The error message') + suggestion: str = Field(..., description='The suggestion for fixing the error') diff --git a/comfy_api_nodes/canary.py b/comfy_api_nodes/canary.py new file mode 100644 index 0000000000000000000000000000000000000000..4df7590b64fea8f6f3f77fde912cb2f64fdad357 --- /dev/null +++ b/comfy_api_nodes/canary.py @@ -0,0 +1,10 @@ +import av + +ver = av.__version__.split(".") +if int(ver[0]) < 14: + raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") + +if int(ver[0]) == 14 and int(ver[1]) < 2: + raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") + +NODE_CLASS_MAPPINGS = {} diff --git a/comfy_api_nodes/mapper_utils.py b/comfy_api_nodes/mapper_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6fab8f4bbc6ce4495ce41588f2fba8103abeffeb --- /dev/null +++ b/comfy_api_nodes/mapper_utils.py @@ -0,0 +1,116 @@ +from enum import Enum + +from pydantic.fields import FieldInfo +from pydantic import BaseModel +from pydantic_core import PydanticUndefined + +from comfy.comfy_types.node_typing import IO, InputTypeOptions + +NodeInput = tuple[IO, InputTypeOptions] + + +def _create_base_config(field_info: FieldInfo) -> InputTypeOptions: + config = {} + if hasattr(field_info, "default") and field_info.default is not PydanticUndefined: + config["default"] = field_info.default + if hasattr(field_info, "description") and field_info.description is not None: + config["tooltip"] = field_info.description + return config + + +def _get_number_constraints_config(field_info: FieldInfo) -> dict: + config = {} + if hasattr(field_info, "metadata"): + metadata = field_info.metadata + for constraint in metadata: + if hasattr(constraint, "ge"): + config["min"] = constraint.ge + if hasattr(constraint, "le"): + config["max"] = constraint.le + if hasattr(constraint, "multiple_of"): + config["step"] = constraint.multiple_of + return config + + +def _model_field_to_image_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.IMAGE, { + **_create_base_config(field_info), + **kwargs, + } + + +def _model_field_to_string_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.STRING, { + **_create_base_config(field_info), + **kwargs, + } + + +def _model_field_to_float_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.FLOAT, { + **_create_base_config(field_info), + **_get_number_constraints_config(field_info), + **kwargs, + } + + +def _model_field_to_int_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.INT, { + **_create_base_config(field_info), + **_get_number_constraints_config(field_info), + **kwargs, + } + + +def _model_field_to_combo_input( + field_info: FieldInfo, enum_type: type[Enum] = None, **kwargs +) -> NodeInput: + combo_config = {} + if enum_type is not None: + combo_config["options"] = [option.value for option in enum_type] + combo_config = { + **combo_config, + **_create_base_config(field_info), + **kwargs, + } + return IO.COMBO, combo_config + + +def model_field_to_node_input( + input_type: IO, base_model: type[BaseModel], field_name: str, **kwargs +) -> NodeInput: + """ + Maps a field from a Pydantic model to a Comfy node input. + + Args: + input_type: The type of the input. + base_model: The Pydantic model to map the field from. + field_name: The name of the field to map. + **kwargs: Additional key/values to include in the input options. + + Note: + For combo inputs, pass an `Enum` to the `enum_type` keyword argument to populate the options automatically. + + Example: + >>> model_field_to_node_input(IO.STRING, MyModel, "my_field", multiline=True) + >>> model_field_to_node_input(IO.COMBO, MyModel, "my_field", enum_type=MyEnum) + >>> model_field_to_node_input(IO.FLOAT, MyModel, "my_field", slider=True) + """ + field_info: FieldInfo = base_model.model_fields[field_name] + result: NodeInput + + if input_type == IO.IMAGE: + result = _model_field_to_image_input(field_info, **kwargs) + elif input_type == IO.STRING: + result = _model_field_to_string_input(field_info, **kwargs) + elif input_type == IO.FLOAT: + result = _model_field_to_float_input(field_info, **kwargs) + elif input_type == IO.INT: + result = _model_field_to_int_input(field_info, **kwargs) + elif input_type == IO.COMBO: + result = _model_field_to_combo_input(field_info, **kwargs) + else: + message = f"Invalid input type: {input_type}" + raise ValueError(message) + + return result diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py new file mode 100644 index 0000000000000000000000000000000000000000..d93fbd77822beddc596830dccfde1de51e8dbded --- /dev/null +++ b/comfy_api_nodes/nodes_bfl.py @@ -0,0 +1,1075 @@ +import io +from inspect import cleandoc +from typing import Union, Optional +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api_nodes.apis.bfl_api import ( + BFLStatus, + BFLFluxExpandImageRequest, + BFLFluxFillImageRequest, + BFLFluxCannyImageRequest, + BFLFluxDepthImageRequest, + BFLFluxProGenerateRequest, + BFLFluxKontextProGenerateRequest, + BFLFluxProUltraGenerateRequest, + BFLFluxProGenerateResponse, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) +from comfy_api_nodes.apinode_utils import ( + downscale_image_tensor, + validate_aspect_ratio, + process_image_response, + resize_mask_to_image, + validate_string, +) + +import numpy as np +from PIL import Image +import requests +import torch +import base64 +import time +from server import PromptServer + + +def convert_mask_to_image(mask: torch.Tensor): + """ + Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image. + """ + mask = mask.unsqueeze(-1) + mask = torch.cat([mask]*3, dim=-1) + return mask + + +def handle_bfl_synchronous_operation( + operation: SynchronousOperation, + timeout_bfl_calls=360, + node_id: Union[str, None] = None, +): + response_api: BFLFluxProGenerateResponse = operation.execute() + return _poll_until_generated( + response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id + ) + + +def _poll_until_generated( + polling_url: str, timeout=360, node_id: Union[str, None] = None +): + # used bfl-comfy-nodes to verify code implementation: + # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main + start_time = time.time() + retries_404 = 0 + max_retries_404 = 5 + retry_404_seconds = 2 + retry_202_seconds = 2 + retry_pending_seconds = 1 + request = requests.Request(method=HttpMethod.GET, url=polling_url) + # NOTE: should True loop be replaced with checking if workflow has been interrupted? + while True: + if node_id: + time_elapsed = time.time() - start_time + PromptServer.instance.send_progress_text( + f"Generating ({time_elapsed:.0f}s)", node_id + ) + + response = requests.Session().send(request.prepare()) + if response.status_code == 200: + result = response.json() + if result["status"] == BFLStatus.ready: + img_url = result["result"]["sample"] + if node_id: + PromptServer.instance.send_progress_text( + f"Result URL: {img_url}", node_id + ) + img_response = requests.get(img_url) + return process_image_response(img_response) + elif result["status"] in [ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + ]: + status = result["status"] + raise Exception( + f"BFL API did not return an image due to: {status}." + ) + elif result["status"] == BFLStatus.error: + raise Exception(f"BFL API encountered an error: {result}.") + elif result["status"] == BFLStatus.pending: + time.sleep(retry_pending_seconds) + continue + elif response.status_code == 404: + if retries_404 < max_retries_404: + retries_404 += 1 + time.sleep(retry_404_seconds) + continue + raise Exception( + f"BFL API could not find task after {max_retries_404} tries." + ) + elif response.status_code == 202: + time.sleep(retry_202_seconds) + elif time.time() - start_time > timeout: + raise Exception( + f"BFL API experienced a timeout; could not return request under {timeout} seconds." + ) + else: + raise Exception(f"BFL API encountered an error: {response.json()}") + +def convert_image_to_base64(image: torch.Tensor): + scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048) + # remove batch dimension if present + if len(scaled_image.shape) > 3: + scaled_image = scaled_image[0] + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + return base64.b64encode(img_byte_arr.getvalue()).decode() + + +class FluxProUltraImageNode(ComfyNodeABC): + """ + Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + "aspect_ratio": ( + IO.STRING, + { + "default": "16:9", + "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", + }, + ), + "raw": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "When True, generate less processed, more natural-looking images.", + }, + ), + }, + "optional": { + "image_prompt": (IO.IMAGE,), + "image_prompt_strength": ( + IO.FLOAT, + { + "default": 0.1, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Blend between the prompt and the image prompt.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + @classmethod + def VALIDATE_INPUTS(cls, aspect_ratio: str): + try: + validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ) + except Exception as e: + return str(e) + return True + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + prompt: str, + aspect_ratio: str, + prompt_upsampling=False, + raw=False, + seed=0, + image_prompt=None, + image_prompt_strength=0.1, + unique_id: Union[str, None] = None, + **kwargs, + ): + if image_prompt is None: + validate_string(prompt, strip_whitespace=False) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.1-ultra/generate", + method=HttpMethod.POST, + request_model=BFLFluxProUltraGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxProUltraGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + seed=seed, + aspect_ratio=validate_aspect_ratio( + aspect_ratio, + minimum_ratio=self.MINIMUM_RATIO, + maximum_ratio=self.MAXIMUM_RATIO, + minimum_ratio_str=self.MINIMUM_RATIO_STR, + maximum_ratio_str=self.MAXIMUM_RATIO_STR, + ), + raw=raw, + image_prompt=( + image_prompt + if image_prompt is None + else convert_image_to_base64(image_prompt) + ), + image_prompt_strength=( + None if image_prompt is None else round(image_prompt_strength, 2) + ), + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + +class FluxKontextProImageNode(ComfyNodeABC): + """ + Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation - specify what and how to edit.", + }, + ), + "aspect_ratio": ( + IO.STRING, + { + "default": "16:9", + "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 3.0, + "min": 0.1, + "max": 99.0, + "step": 0.1, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 1, + "max": 150, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 1234, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + }, + "optional": { + "input_image": (IO.IMAGE,), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate" + + def api_call( + self, + prompt: str, + aspect_ratio: str, + guidance: float, + steps: int, + input_image: Optional[torch.Tensor]=None, + seed=0, + prompt_upsampling=False, + unique_id: Union[str, None] = None, + **kwargs, + ): + aspect_ratio = validate_aspect_ratio( + aspect_ratio, + minimum_ratio=self.MINIMUM_RATIO, + maximum_ratio=self.MAXIMUM_RATIO, + minimum_ratio_str=self.MINIMUM_RATIO_STR, + maximum_ratio_str=self.MAXIMUM_RATIO_STR, + ) + if input_image is None: + validate_string(prompt, strip_whitespace=False) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=self.BFL_PATH, + method=HttpMethod.POST, + request_model=BFLFluxKontextProGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxKontextProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + guidance=round(guidance, 1), + steps=steps, + seed=seed, + aspect_ratio=aspect_ratio, + input_image=( + input_image + if input_image is None + else convert_image_to_base64(input_image) + ) + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + +class FluxKontextMaxImageNode(FluxKontextProImageNode): + """ + Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio. + """ + + DESCRIPTION = cleandoc(__doc__ or "") + BFL_PATH = "/proxy/bfl/flux-kontext-max/generate" + + +class FluxProImageNode(ComfyNodeABC): + """ + Generates images synchronously based on prompt and resolution. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "width": ( + IO.INT, + { + "default": 1024, + "min": 256, + "max": 1440, + "step": 32, + }, + ), + "height": ( + IO.INT, + { + "default": 768, + "min": 256, + "max": 1440, + "step": 32, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "image_prompt": (IO.IMAGE,), + # "image_prompt_strength": ( + # IO.FLOAT, + # { + # "default": 0.1, + # "min": 0.0, + # "max": 1.0, + # "step": 0.01, + # "tooltip": "Blend between the prompt and the image prompt.", + # }, + # ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + prompt: str, + prompt_upsampling, + width: int, + height: int, + seed=0, + image_prompt=None, + # image_prompt_strength=0.1, + unique_id: Union[str, None] = None, + **kwargs, + ): + image_prompt = ( + image_prompt + if image_prompt is None + else convert_image_to_base64(image_prompt) + ) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.1/generate", + method=HttpMethod.POST, + request_model=BFLFluxProGenerateRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + width=width, + height=height, + seed=seed, + image_prompt=image_prompt, + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + +class FluxProExpandNode(ComfyNodeABC): + """ + Outpaints image based on prompt. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "top": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the top of the image" + }, + ), + "bottom": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the bottom of the image" + }, + ), + "left": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the left side of the image" + }, + ), + "right": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2048, + "tooltip": "Number of pixels to expand at the right side of the image" + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 60, + "min": 1.5, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": {}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + top: int, + bottom: int, + left: int, + right: int, + steps: int, + guidance: float, + seed=0, + unique_id: Union[str, None] = None, + **kwargs, + ): + image = convert_image_to_base64(image) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-expand/generate", + method=HttpMethod.POST, + request_model=BFLFluxExpandImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxExpandImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + top=top, + bottom=bottom, + left=left, + right=right, + steps=steps, + guidance=guidance, + seed=seed, + image=image, + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + + +class FluxProFillNode(ComfyNodeABC): + """ + Inpaints image based on mask and prompt. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "mask": (IO.MASK,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 60, + "min": 1.5, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": {}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + image: torch.Tensor, + mask: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + steps: int, + guidance: float, + seed=0, + unique_id: Union[str, None] = None, + **kwargs, + ): + # prepare mask + mask = resize_mask_to_image(mask, image) + mask = convert_image_to_base64(convert_mask_to_image(mask)) + # make sure image will have alpha channel removed + image = convert_image_to_base64(image[:, :, :, :3]) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-fill/generate", + method=HttpMethod.POST, + request_model=BFLFluxFillImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxFillImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + image=image, + mask=mask, + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + +class FluxProCannyNode(ComfyNodeABC): + """ + Generate image using a control image (canny). + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "control_image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "canny_low_threshold": ( + IO.FLOAT, + { + "default": 0.1, + "min": 0.01, + "max": 0.99, + "step": 0.01, + "tooltip": "Low threshold for Canny edge detection; ignored if skip_processing is True" + }, + ), + "canny_high_threshold": ( + IO.FLOAT, + { + "default": 0.4, + "min": 0.01, + "max": 0.99, + "step": 0.01, + "tooltip": "High threshold for Canny edge detection; ignored if skip_processing is True" + }, + ), + "skip_preprocessing": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to skip preprocessing; set to True if control_image already is canny-fied, False if it is a raw image.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 30, + "min": 1, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": {}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + control_image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + canny_low_threshold: float, + canny_high_threshold: float, + skip_preprocessing: bool, + steps: int, + guidance: float, + seed=0, + unique_id: Union[str, None] = None, + **kwargs, + ): + control_image = convert_image_to_base64(control_image[:, :, :, :3]) + preprocessed_image = None + + # scale canny threshold between 0-500, to match BFL's API + def scale_value(value: float, min_val=0, max_val=500): + return min_val + value * (max_val - min_val) + canny_low_threshold = int(round(scale_value(canny_low_threshold))) + canny_high_threshold = int(round(scale_value(canny_high_threshold))) + + + if skip_preprocessing: + preprocessed_image = control_image + control_image = None + canny_low_threshold = None + canny_high_threshold = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-canny/generate", + method=HttpMethod.POST, + request_model=BFLFluxCannyImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxCannyImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + control_image=control_image, + canny_low_threshold=canny_low_threshold, + canny_high_threshold=canny_high_threshold, + preprocessed_image=preprocessed_image, + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + +class FluxProDepthNode(ComfyNodeABC): + """ + Generate image using a control image (depth). + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "control_image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "prompt_upsampling": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + }, + ), + "skip_preprocessing": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to skip preprocessing; set to True if control_image already is depth-ified, False if it is a raw image.", + }, + ), + "guidance": ( + IO.FLOAT, + { + "default": 15, + "min": 1, + "max": 100, + "tooltip": "Guidance strength for the image generation process" + }, + ), + "steps": ( + IO.INT, + { + "default": 50, + "min": 15, + "max": 50, + "tooltip": "Number of steps for the image generation process" + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": {}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/BFL" + + def api_call( + self, + control_image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + skip_preprocessing: bool, + steps: int, + guidance: float, + seed=0, + unique_id: Union[str, None] = None, + **kwargs, + ): + control_image = convert_image_to_base64(control_image[:,:,:,:3]) + preprocessed_image = None + + if skip_preprocessing: + preprocessed_image = control_image + control_image = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/bfl/flux-pro-1.0-depth/generate", + method=HttpMethod.POST, + request_model=BFLFluxDepthImageRequest, + response_model=BFLFluxProGenerateResponse, + ), + request=BFLFluxDepthImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + control_image=control_image, + preprocessed_image=preprocessed_image, + ), + auth_kwargs=kwargs, + ) + output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id) + return (output_image,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "FluxProUltraImageNode": FluxProUltraImageNode, + # "FluxProImageNode": FluxProImageNode, + "FluxKontextProImageNode": FluxKontextProImageNode, + "FluxKontextMaxImageNode": FluxKontextMaxImageNode, + "FluxProExpandNode": FluxProExpandNode, + "FluxProFillNode": FluxProFillNode, + "FluxProCannyNode": FluxProCannyNode, + "FluxProDepthNode": FluxProDepthNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", + # "FluxProImageNode": "Flux 1.1 [pro] Image", + "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image", + "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image", + "FluxProExpandNode": "Flux.1 Expand Image", + "FluxProFillNode": "Flux.1 Fill Image", + "FluxProCannyNode": "Flux.1 Canny Control Image", + "FluxProDepthNode": "Flux.1 Depth Control Image", +} diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py new file mode 100644 index 0000000000000000000000000000000000000000..ae7b048462a236132018277bafce01f57943077e --- /dev/null +++ b/comfy_api_nodes/nodes_gemini.py @@ -0,0 +1,446 @@ +""" +API Nodes for Gemini Multimodal LLM Usage via Remote API +See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference +""" + +import os +from enum import Enum +from typing import Optional, Literal + +import torch + +import folder_paths +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from server import PromptServer +from comfy_api_nodes.apis import ( + GeminiContent, + GeminiGenerateContentRequest, + GeminiGenerateContentResponse, + GeminiInlineData, + GeminiPart, + GeminiMimeType, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) +from comfy_api_nodes.apinode_utils import ( + validate_string, + audio_to_base64_string, + video_to_base64_string, + tensor_to_base64_string, +) + + +GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini" +GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB + + +class GeminiModel(str, Enum): + """ + Gemini Model Names allowed by comfy-api + """ + + gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06" + gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17" + + +def get_gemini_endpoint( + model: GeminiModel, +) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]: + """ + Get the API endpoint for a given Gemini model. + + Args: + model: The Gemini model to use, either as enum or string value. + + Returns: + ApiEndpoint configured for the specific Gemini model. + """ + if isinstance(model, str): + model = GeminiModel(model) + return ApiEndpoint( + path=f"{GEMINI_BASE_ENDPOINT}/{model.value}", + method=HttpMethod.POST, + request_model=GeminiGenerateContentRequest, + response_model=GeminiGenerateContentResponse, + ) + + +class GeminiNode(ComfyNodeABC): + """ + Node to generate text responses from a Gemini model. + + This node allows users to interact with Google's Gemini AI models, providing + multimodal inputs (text, images, audio, video, files) to generate coherent + text responses. The node works with the latest Gemini models, handling the + API communication and response parsing. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text inputs to the model, used to generate a response. You can include detailed instructions, questions, or context for the model.", + }, + ), + "model": ( + IO.COMBO, + { + "tooltip": "The Gemini model to use for generating responses.", + "options": [model.value for model in GeminiModel], + "default": GeminiModel.gemini_2_5_pro_preview_05_06.value, + }, + ), + "seed": ( + IO.INT, + { + "default": 42, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.", + }, + ), + }, + "optional": { + "images": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", + }, + ), + "audio": ( + IO.AUDIO, + { + "tooltip": "Optional audio to use as context for the model.", + "default": None, + }, + ), + "video": ( + IO.VIDEO, + { + "tooltip": "Optional video to use as context for the model.", + "default": None, + }, + ), + "files": ( + "GEMINI_INPUT_FILES", + { + "default": None, + "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate text responses with Google's Gemini AI model. You can provide multiple types of inputs (text, images, audio, video) as context for generating more relevant and meaningful responses." + RETURN_TYPES = ("STRING",) + FUNCTION = "api_call" + CATEGORY = "api node/text/Gemini" + API_NODE = True + + def get_parts_from_response( + self, response: GeminiGenerateContentResponse + ) -> list[GeminiPart]: + """ + Extract all parts from the Gemini API response. + + Args: + response: The API response from Gemini. + + Returns: + List of response parts from the first candidate. + """ + return response.candidates[0].content.parts + + def get_parts_by_type( + self, response: GeminiGenerateContentResponse, part_type: Literal["text"] | str + ) -> list[GeminiPart]: + """ + Filter response parts by their type. + + Args: + response: The API response from Gemini. + part_type: Type of parts to extract ("text" or a MIME type). + + Returns: + List of response parts matching the requested type. + """ + parts = [] + for part in self.get_parts_from_response(response): + if part_type == "text" and hasattr(part, "text") and part.text: + parts.append(part) + elif ( + hasattr(part, "inlineData") + and part.inlineData + and part.inlineData.mimeType == part_type + ): + parts.append(part) + # Skip parts that don't match the requested type + return parts + + def get_text_from_response(self, response: GeminiGenerateContentResponse) -> str: + """ + Extract and concatenate all text parts from the response. + + Args: + response: The API response from Gemini. + + Returns: + Combined text from all text parts in the response. + """ + parts = self.get_parts_by_type(response, "text") + return "\n".join([part.text for part in parts]) + + def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]: + """ + Convert video input to Gemini API compatible parts. + + Args: + video_input: Video tensor from ComfyUI. + **kwargs: Additional arguments to pass to the conversion function. + + Returns: + List of GeminiPart objects containing the encoded video. + """ + from comfy_api.util import VideoContainer, VideoCodec + base_64_string = video_to_base64_string( + video_input, + container_format=VideoContainer.MP4, + codec=VideoCodec.H264 + ) + return [ + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.video_mp4, + data=base_64_string, + ) + ) + ] + + def create_audio_parts(self, audio_input: IO.AUDIO) -> list[GeminiPart]: + """ + Convert audio input to Gemini API compatible parts. + + Args: + audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate. + + Returns: + List of GeminiPart objects containing the encoded audio. + """ + audio_parts: list[GeminiPart] = [] + for batch_index in range(audio_input["waveform"].shape[0]): + # Recreate an IO.AUDIO object for the given batch dimension index + audio_at_index = { + "waveform": audio_input["waveform"][batch_index].unsqueeze(0), + "sample_rate": audio_input["sample_rate"], + } + # Convert to MP3 format for compatibility with Gemini API + audio_bytes = audio_to_base64_string( + audio_at_index, + container_format="mp3", + codec_name="libmp3lame", + ) + audio_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.audio_mp3, + data=audio_bytes, + ) + ) + ) + return audio_parts + + def create_image_parts(self, image_input: torch.Tensor) -> list[GeminiPart]: + """ + Convert image tensor input to Gemini API compatible parts. + + Args: + image_input: Batch of image tensors from ComfyUI. + + Returns: + List of GeminiPart objects containing the encoded images. + """ + image_parts: list[GeminiPart] = [] + for image_index in range(image_input.shape[0]): + image_as_b64 = tensor_to_base64_string( + image_input[image_index].unsqueeze(0) + ) + image_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.image_png, + data=image_as_b64, + ) + ) + ) + return image_parts + + def create_text_part(self, text: str) -> GeminiPart: + """ + Create a text part for the Gemini API request. + + Args: + text: The text content to include in the request. + + Returns: + A GeminiPart object with the text content. + """ + return GeminiPart(text=text) + + def api_call( + self, + prompt: str, + model: GeminiModel, + images: Optional[IO.IMAGE] = None, + audio: Optional[IO.AUDIO] = None, + video: Optional[IO.VIDEO] = None, + files: Optional[list[GeminiPart]] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[str]: + # Validate inputs + validate_string(prompt, strip_whitespace=False) + + # Create parts list with text prompt as the first part + parts: list[GeminiPart] = [self.create_text_part(prompt)] + + # Add other modal parts + if images is not None: + image_parts = self.create_image_parts(images) + parts.extend(image_parts) + if audio is not None: + parts.extend(self.create_audio_parts(audio)) + if video is not None: + parts.extend(self.create_video_parts(video)) + if files is not None: + parts.extend(files) + + # Create response + response = SynchronousOperation( + endpoint=get_gemini_endpoint(model), + request=GeminiGenerateContentRequest( + contents=[ + GeminiContent( + role="user", + parts=parts, + ) + ] + ), + auth_kwargs=kwargs, + ).execute() + + # Get result output + output_text = self.get_text_from_response(response) + if unique_id and output_text: + PromptServer.instance.send_progress_text(output_text, node_id=unique_id) + + return (output_text or "Empty response from Gemini model...",) + + +class GeminiInputFiles(ComfyNodeABC): + """ + Loads and formats input files for use with the Gemini API. + + This node allows users to include text (.txt) and PDF (.pdf) files as input + context for the Gemini model. Files are converted to the appropriate format + required by the API and can be chained together to include multiple files + in a single request. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + """ + For details about the supported file input types, see: + https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference + """ + input_dir = folder_paths.get_input_directory() + input_files = [ + f + for f in os.scandir(input_dir) + if f.is_file() + and (f.name.endswith(".txt") or f.name.endswith(".pdf")) + and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE + ] + input_files = sorted(input_files, key=lambda x: x.name) + input_files = [f.name for f in input_files] + return { + "required": { + "file": ( + IO.COMBO, + { + "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", + "options": input_files, + "default": input_files[0] if input_files else None, + }, + ), + }, + "optional": { + "GEMINI_INPUT_FILES": ( + "GEMINI_INPUT_FILES", + { + "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", + "default": None, + }, + ), + }, + } + + DESCRIPTION = "Loads and prepares input files to include as inputs for Gemini LLM nodes. The files will be read by the Gemini model when generating a response. The contents of the text file count toward the token limit. 🛈 TIP: Can be chained together with other Gemini Input File nodes." + RETURN_TYPES = ("GEMINI_INPUT_FILES",) + FUNCTION = "prepare_files" + CATEGORY = "api node/text/Gemini" + + def create_file_part(self, file_path: str) -> GeminiPart: + mime_type = ( + GeminiMimeType.pdf + if file_path.endswith(".pdf") + else GeminiMimeType.text_plain + ) + # Use base64 string directly, not the data URI + with open(file_path, "rb") as f: + file_content = f.read() + import base64 + base64_str = base64.b64encode(file_content).decode("utf-8") + + return GeminiPart( + inlineData=GeminiInlineData( + mimeType=mime_type, + data=base64_str, + ) + ) + + def prepare_files( + self, file: str, GEMINI_INPUT_FILES: list[GeminiPart] = [] + ) -> tuple[list[GeminiPart]]: + """ + Loads and formats input files for Gemini API. + """ + file_path = folder_paths.get_annotated_filepath(file) + input_file_content = self.create_file_part(file_path) + files = [input_file_content] + GEMINI_INPUT_FILES + return (files,) + + +NODE_CLASS_MAPPINGS = { + "GeminiNode": GeminiNode, + "GeminiInputFiles": GeminiInputFiles, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "GeminiNode": "Google Gemini", + "GeminiInputFiles": "Gemini Input Files", +} diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py new file mode 100644 index 0000000000000000000000000000000000000000..b8487355fadb6a7d56226df5a6d692bf1b94532a --- /dev/null +++ b/comfy_api_nodes/nodes_ideogram.py @@ -0,0 +1,801 @@ +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from inspect import cleandoc +from PIL import Image +import numpy as np +import io +import torch +from comfy_api_nodes.apis import ( + IdeogramGenerateRequest, + IdeogramGenerateResponse, + ImageRequest, + IdeogramV3Request, + IdeogramV3EditRequest, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) + +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + bytesio_to_image_tensor, + resize_mask_to_image, +) +from server import PromptServer + +V1_V1_RES_MAP = { + "Auto":"AUTO", + "512 x 1536":"RESOLUTION_512_1536", + "576 x 1408":"RESOLUTION_576_1408", + "576 x 1472":"RESOLUTION_576_1472", + "576 x 1536":"RESOLUTION_576_1536", + "640 x 1024":"RESOLUTION_640_1024", + "640 x 1344":"RESOLUTION_640_1344", + "640 x 1408":"RESOLUTION_640_1408", + "640 x 1472":"RESOLUTION_640_1472", + "640 x 1536":"RESOLUTION_640_1536", + "704 x 1152":"RESOLUTION_704_1152", + "704 x 1216":"RESOLUTION_704_1216", + "704 x 1280":"RESOLUTION_704_1280", + "704 x 1344":"RESOLUTION_704_1344", + "704 x 1408":"RESOLUTION_704_1408", + "704 x 1472":"RESOLUTION_704_1472", + "720 x 1280":"RESOLUTION_720_1280", + "736 x 1312":"RESOLUTION_736_1312", + "768 x 1024":"RESOLUTION_768_1024", + "768 x 1088":"RESOLUTION_768_1088", + "768 x 1152":"RESOLUTION_768_1152", + "768 x 1216":"RESOLUTION_768_1216", + "768 x 1232":"RESOLUTION_768_1232", + "768 x 1280":"RESOLUTION_768_1280", + "768 x 1344":"RESOLUTION_768_1344", + "832 x 960":"RESOLUTION_832_960", + "832 x 1024":"RESOLUTION_832_1024", + "832 x 1088":"RESOLUTION_832_1088", + "832 x 1152":"RESOLUTION_832_1152", + "832 x 1216":"RESOLUTION_832_1216", + "832 x 1248":"RESOLUTION_832_1248", + "864 x 1152":"RESOLUTION_864_1152", + "896 x 960":"RESOLUTION_896_960", + "896 x 1024":"RESOLUTION_896_1024", + "896 x 1088":"RESOLUTION_896_1088", + "896 x 1120":"RESOLUTION_896_1120", + "896 x 1152":"RESOLUTION_896_1152", + "960 x 832":"RESOLUTION_960_832", + "960 x 896":"RESOLUTION_960_896", + "960 x 1024":"RESOLUTION_960_1024", + "960 x 1088":"RESOLUTION_960_1088", + "1024 x 640":"RESOLUTION_1024_640", + "1024 x 768":"RESOLUTION_1024_768", + "1024 x 832":"RESOLUTION_1024_832", + "1024 x 896":"RESOLUTION_1024_896", + "1024 x 960":"RESOLUTION_1024_960", + "1024 x 1024":"RESOLUTION_1024_1024", + "1088 x 768":"RESOLUTION_1088_768", + "1088 x 832":"RESOLUTION_1088_832", + "1088 x 896":"RESOLUTION_1088_896", + "1088 x 960":"RESOLUTION_1088_960", + "1120 x 896":"RESOLUTION_1120_896", + "1152 x 704":"RESOLUTION_1152_704", + "1152 x 768":"RESOLUTION_1152_768", + "1152 x 832":"RESOLUTION_1152_832", + "1152 x 864":"RESOLUTION_1152_864", + "1152 x 896":"RESOLUTION_1152_896", + "1216 x 704":"RESOLUTION_1216_704", + "1216 x 768":"RESOLUTION_1216_768", + "1216 x 832":"RESOLUTION_1216_832", + "1232 x 768":"RESOLUTION_1232_768", + "1248 x 832":"RESOLUTION_1248_832", + "1280 x 704":"RESOLUTION_1280_704", + "1280 x 720":"RESOLUTION_1280_720", + "1280 x 768":"RESOLUTION_1280_768", + "1280 x 800":"RESOLUTION_1280_800", + "1312 x 736":"RESOLUTION_1312_736", + "1344 x 640":"RESOLUTION_1344_640", + "1344 x 704":"RESOLUTION_1344_704", + "1344 x 768":"RESOLUTION_1344_768", + "1408 x 576":"RESOLUTION_1408_576", + "1408 x 640":"RESOLUTION_1408_640", + "1408 x 704":"RESOLUTION_1408_704", + "1472 x 576":"RESOLUTION_1472_576", + "1472 x 640":"RESOLUTION_1472_640", + "1472 x 704":"RESOLUTION_1472_704", + "1536 x 512":"RESOLUTION_1536_512", + "1536 x 576":"RESOLUTION_1536_576", + "1536 x 640":"RESOLUTION_1536_640", +} + +V1_V2_RATIO_MAP = { + "1:1":"ASPECT_1_1", + "4:3":"ASPECT_4_3", + "3:4":"ASPECT_3_4", + "16:9":"ASPECT_16_9", + "9:16":"ASPECT_9_16", + "2:1":"ASPECT_2_1", + "1:2":"ASPECT_1_2", + "3:2":"ASPECT_3_2", + "2:3":"ASPECT_2_3", + "4:5":"ASPECT_4_5", + "5:4":"ASPECT_5_4", +} + +V3_RATIO_MAP = { + "1:3":"1x3", + "3:1":"3x1", + "1:2":"1x2", + "2:1":"2x1", + "9:16":"9x16", + "16:9":"16x9", + "10:16":"10x16", + "16:10":"16x10", + "2:3":"2x3", + "3:2":"3x2", + "3:4":"3x4", + "4:3":"4x3", + "4:5":"4x5", + "5:4":"5x4", + "1:1":"1x1", +} + +V3_RESOLUTIONS= [ + "Auto", + "512x1536", + "576x1408", + "576x1472", + "576x1536", + "640x1344", + "640x1408", + "640x1472", + "640x1536", + "704x1152", + "704x1216", + "704x1280", + "704x1344", + "704x1408", + "704x1472", + "736x1312", + "768x1088", + "768x1216", + "768x1280", + "768x1344", + "800x1280", + "832x960", + "832x1024", + "832x1088", + "832x1152", + "832x1216", + "832x1248", + "864x1152", + "896x960", + "896x1024", + "896x1088", + "896x1120", + "896x1152", + "960x832", + "960x896", + "960x1024", + "960x1088", + "1024x832", + "1024x896", + "1024x960", + "1024x1024", + "1088x768", + "1088x832", + "1088x896", + "1088x960", + "1120x896", + "1152x704", + "1152x832", + "1152x864", + "1152x896", + "1216x704", + "1216x768", + "1216x832", + "1248x832", + "1280x704", + "1280x768", + "1280x800", + "1312x736", + "1344x640", + "1344x704", + "1344x768", + "1408x576", + "1408x640", + "1408x704", + "1472x576", + "1472x640", + "1472x704", + "1536x512", + "1536x576", + "1536x640" +] + +def download_and_process_images(image_urls): + """Helper function to download and process multiple images from URLs""" + + # Initialize list to store image tensors + image_tensors = [] + + for image_url in image_urls: + # Using functions from apinode_utils.py to handle downloading and processing + image_bytesio = download_url_to_bytesio(image_url) # Download image content to BytesIO + img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB") # Convert to torch.Tensor with RGB mode + image_tensors.append(img_tensor) + + # Stack tensors to match (N, width, height, channels) + if image_tensors: + stacked_tensors = torch.cat(image_tensors, dim=0) + else: + raise Exception("No valid images were processed") + + return stacked_tensors + + +def display_image_urls_on_node(image_urls, node_id): + if node_id and image_urls: + if len(image_urls) == 1: + PromptServer.instance.send_progress_text( + f"Generated Image URL:\n{image_urls[0]}", node_id + ) + else: + urls_text = "Generated Image URLs:\n" + "\n".join( + f"{i+1}. {url}" for i, url in enumerate(image_urls) + ) + PromptServer.instance.send_progress_text(urls_text, node_id) + + +class IdeogramV1(ComfyNodeABC): + """ + Generates images using the Ideogram V1 model. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "turbo": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)", + } + ), + }, + "optional": { + "aspect_ratio": ( + IO.COMBO, + { + "options": list(V1_V2_RATIO_MAP.keys()), + "default": "1:1", + "tooltip": "The aspect ratio for image generation.", + }, + ), + "magic_prompt_option": ( + IO.COMBO, + { + "options": ["AUTO", "ON", "OFF"], + "default": "AUTO", + "tooltip": "Determine if MagicPrompt should be used in generation", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "step": 1, + "control_after_generate": True, + "display": "number", + }, + ), + "negative_prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Description of what to exclude from the image", + }, + ), + "num_images": ( + IO.INT, + {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/Ideogram" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + turbo=False, + aspect_ratio="1:1", + magic_prompt_option="AUTO", + seed=0, + negative_prompt="", + num_images=1, + unique_id=None, + **kwargs, + ): + # Determine the model based on turbo setting + aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) + model = "V_1_TURBO" if turbo else "V_1" + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/ideogram/generate", + method=HttpMethod.POST, + request_model=IdeogramGenerateRequest, + response_model=IdeogramGenerateResponse, + ), + request=IdeogramGenerateRequest( + image_request=ImageRequest( + prompt=prompt, + model=model, + num_images=num_images, + seed=seed, + aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None, + magic_prompt_option=( + magic_prompt_option if magic_prompt_option != "AUTO" else None + ), + negative_prompt=negative_prompt if negative_prompt else None, + ) + ), + auth_kwargs=kwargs, + ) + + response = operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + display_image_urls_on_node(image_urls, unique_id) + return (download_and_process_images(image_urls),) + + +class IdeogramV2(ComfyNodeABC): + """ + Generates images using the Ideogram V2 model. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "turbo": ( + IO.BOOLEAN, + { + "default": False, + "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)", + } + ), + }, + "optional": { + "aspect_ratio": ( + IO.COMBO, + { + "options": list(V1_V2_RATIO_MAP.keys()), + "default": "1:1", + "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to AUTO.", + }, + ), + "resolution": ( + IO.COMBO, + { + "options": list(V1_V1_RES_MAP.keys()), + "default": "Auto", + "tooltip": "The resolution for image generation. If not set to AUTO, this overrides the aspect_ratio setting.", + }, + ), + "magic_prompt_option": ( + IO.COMBO, + { + "options": ["AUTO", "ON", "OFF"], + "default": "AUTO", + "tooltip": "Determine if MagicPrompt should be used in generation", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "step": 1, + "control_after_generate": True, + "display": "number", + }, + ), + "style_type": ( + IO.COMBO, + { + "options": ["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"], + "default": "NONE", + "tooltip": "Style type for generation (V2 only)", + }, + ), + "negative_prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Description of what to exclude from the image", + }, + ), + "num_images": ( + IO.INT, + {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, + ), + #"color_palette": ( + # IO.STRING, + # { + # "multiline": False, + # "default": "", + # "tooltip": "Color palette preset name or hex colors with weights", + # }, + #), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/Ideogram" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + turbo=False, + aspect_ratio="1:1", + resolution="Auto", + magic_prompt_option="AUTO", + seed=0, + style_type="NONE", + negative_prompt="", + num_images=1, + color_palette="", + unique_id=None, + **kwargs, + ): + aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) + resolution = V1_V1_RES_MAP.get(resolution, None) + # Determine the model based on turbo setting + model = "V_2_TURBO" if turbo else "V_2" + + # Handle resolution vs aspect_ratio logic + # If resolution is not AUTO, it overrides aspect_ratio + final_resolution = None + final_aspect_ratio = None + + if resolution != "AUTO": + final_resolution = resolution + else: + final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/ideogram/generate", + method=HttpMethod.POST, + request_model=IdeogramGenerateRequest, + response_model=IdeogramGenerateResponse, + ), + request=IdeogramGenerateRequest( + image_request=ImageRequest( + prompt=prompt, + model=model, + num_images=num_images, + seed=seed, + aspect_ratio=final_aspect_ratio, + resolution=final_resolution, + magic_prompt_option=( + magic_prompt_option if magic_prompt_option != "AUTO" else None + ), + style_type=style_type if style_type != "NONE" else None, + negative_prompt=negative_prompt if negative_prompt else None, + color_palette=color_palette if color_palette else None, + ) + ), + auth_kwargs=kwargs, + ) + + response = operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + display_image_urls_on_node(image_urls, unique_id) + return (download_and_process_images(image_urls),) + +class IdeogramV3(ComfyNodeABC): + """ + Generates images using the Ideogram V3 model. Supports both regular image generation from text prompts and image editing with mask. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation or editing", + }, + ), + }, + "optional": { + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + "aspect_ratio": ( + IO.COMBO, + { + "options": list(V3_RATIO_MAP.keys()), + "default": "1:1", + "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to Auto.", + }, + ), + "resolution": ( + IO.COMBO, + { + "options": V3_RESOLUTIONS, + "default": "Auto", + "tooltip": "The resolution for image generation. If not set to Auto, this overrides the aspect_ratio setting.", + }, + ), + "magic_prompt_option": ( + IO.COMBO, + { + "options": ["AUTO", "ON", "OFF"], + "default": "AUTO", + "tooltip": "Determine if MagicPrompt should be used in generation", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "step": 1, + "control_after_generate": True, + "display": "number", + }, + ), + "num_images": ( + IO.INT, + {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, + ), + "rendering_speed": ( + IO.COMBO, + { + "options": ["BALANCED", "TURBO", "QUALITY"], + "default": "BALANCED", + "tooltip": "Controls the trade-off between generation speed and quality", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/Ideogram" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + image=None, + mask=None, + resolution="Auto", + aspect_ratio="1:1", + magic_prompt_option="AUTO", + seed=0, + num_images=1, + rendering_speed="BALANCED", + unique_id=None, + **kwargs, + ): + # Check if both image and mask are provided for editing mode + if image is not None and mask is not None: + # Edit mode + path = "/proxy/ideogram/ideogram-v3/edit" + + # Process image and mask + input_tensor = image.squeeze().cpu() + # Resize mask to match image dimension + mask = resize_mask_to_image(mask, image, allow_gradient=False) + # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention). + mask = 1.0 - mask + + # Validate mask dimensions match image + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + + # Process image + img_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(img_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr + img_binary.name = "image.png" + + # Process mask - white areas will be replaced + mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_byte_arr = io.BytesIO() + mask_img.save(mask_byte_arr, format="PNG") + mask_byte_arr.seek(0) + mask_binary = mask_byte_arr + mask_binary.name = "mask.png" + + # Create edit request + edit_request = IdeogramV3EditRequest( + prompt=prompt, + rendering_speed=rendering_speed, + ) + + # Add optional parameters + if magic_prompt_option != "AUTO": + edit_request.magic_prompt = magic_prompt_option + if seed != 0: + edit_request.seed = seed + if num_images > 1: + edit_request.num_images = num_images + + # Execute the operation for edit mode + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=IdeogramV3EditRequest, + response_model=IdeogramGenerateResponse, + ), + request=edit_request, + files={ + "image": img_binary, + "mask": mask_binary, + }, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + elif image is not None or mask is not None: + # If only one of image or mask is provided, raise an error + raise Exception("Ideogram V3 image editing requires both an image AND a mask") + else: + # Generation mode + path = "/proxy/ideogram/ideogram-v3/generate" + + # Create generation request + gen_request = IdeogramV3Request( + prompt=prompt, + rendering_speed=rendering_speed, + ) + + # Handle resolution vs aspect ratio + if resolution != "Auto": + gen_request.resolution = resolution + elif aspect_ratio != "1:1": + v3_aspect = V3_RATIO_MAP.get(aspect_ratio) + if v3_aspect: + gen_request.aspect_ratio = v3_aspect + + # Add optional parameters + if magic_prompt_option != "AUTO": + gen_request.magic_prompt = magic_prompt_option + if seed != 0: + gen_request.seed = seed + if num_images > 1: + gen_request.num_images = num_images + + # Execute the operation for generation mode + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=IdeogramV3Request, + response_model=IdeogramGenerateResponse, + ), + request=gen_request, + auth_kwargs=kwargs, + ) + + # Execute the operation and process response + response = operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + display_image_urls_on_node(image_urls, unique_id) + return (download_and_process_images(image_urls),) + + +NODE_CLASS_MAPPINGS = { + "IdeogramV1": IdeogramV1, + "IdeogramV2": IdeogramV2, + "IdeogramV3": IdeogramV3, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "IdeogramV1": "Ideogram V1", + "IdeogramV2": "Ideogram V2", + "IdeogramV3": "Ideogram V3", +} diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py new file mode 100644 index 0000000000000000000000000000000000000000..641cd6353c0dc06d06d1a2bfed6de2f6b9854875 --- /dev/null +++ b/comfy_api_nodes/nodes_kling.py @@ -0,0 +1,1758 @@ +"""Kling API Nodes + +For source of truth on the allowed permutations of request fields, please reference: +- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) +""" + +from __future__ import annotations +from typing import Optional, TypeVar, Any +from collections.abc import Callable +import math +import logging + +import torch + +from comfy_api_nodes.apis import ( + KlingTaskStatus, + KlingCameraControl, + KlingCameraConfig, + KlingCameraControlType, + KlingVideoGenDuration, + KlingVideoGenMode, + KlingVideoGenAspectRatio, + KlingVideoGenModelName, + KlingText2VideoRequest, + KlingText2VideoResponse, + KlingImage2VideoRequest, + KlingImage2VideoResponse, + KlingVideoExtendRequest, + KlingVideoExtendResponse, + KlingLipSyncVoiceLanguage, + KlingLipSyncInputObject, + KlingLipSyncRequest, + KlingLipSyncResponse, + KlingVirtualTryOnModelName, + KlingVirtualTryOnRequest, + KlingVirtualTryOnResponse, + KlingVideoResult, + KlingImageResult, + KlingImageGenerationsRequest, + KlingImageGenerationsResponse, + KlingImageGenImageReferenceType, + KlingImageGenModelName, + KlingImageGenAspectRatio, + KlingVideoEffectsRequest, + KlingVideoEffectsResponse, + KlingDualCharacterEffectsScene, + KlingSingleImageEffectsScene, + KlingDualCharacterEffectInput, + KlingSingleImageEffectInput, + KlingCharacterEffectModelName, + KlingSingleImageEffectModelName, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + tensor_to_base64_string, + download_url_to_video_output, + upload_video_to_comfyapi, + upload_audio_to_comfyapi, + download_url_to_image_tensor, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api_nodes.util.validation_utils import ( + validate_image_dimensions, + validate_image_aspect_ratio, + validate_video_dimensions, + validate_video_duration, +) +from comfy_api.input.basic_types import AudioInput +from comfy_api.input.video_types import VideoInput +from comfy_api.input_impl import VideoFromFile +from comfy.comfy_types.node_typing import IO, InputTypeOptions, ComfyNodeABC + +KLING_API_VERSION = "v1" +PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video" +PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video" +PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend" +PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync" +PATH_VIDEO_EFFECTS = f"/proxy/kling/{KLING_API_VERSION}/videos/effects" +PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations" +PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on" +PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations" + +MAX_PROMPT_LENGTH_T2V = 2500 +MAX_PROMPT_LENGTH_I2V = 500 +MAX_PROMPT_LENGTH_IMAGE_GEN = 500 +MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200 +MAX_PROMPT_LENGTH_LIP_SYNC = 120 + +AVERAGE_DURATION_T2V = 319 +AVERAGE_DURATION_I2V = 164 +AVERAGE_DURATION_LIP_SYNC = 455 +AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 +AVERAGE_DURATION_IMAGE_GEN = 32 +AVERAGE_DURATION_VIDEO_EFFECTS = 320 +AVERAGE_DURATION_VIDEO_EXTEND = 320 + +R = TypeVar("R") + + +class KlingApiError(Exception): + """Base exception for Kling API errors.""" + + pass + + +def poll_until_finished( + auth_kwargs: dict[str, str], + api_endpoint: ApiEndpoint[Any, R], + result_url_extractor: Optional[Callable[[R], str]] = None, + estimated_duration: Optional[int] = None, + node_id: Optional[str] = None, +) -> R: + """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response.""" + return PollingOperation( + poll_endpoint=api_endpoint, + completed_statuses=[ + KlingTaskStatus.succeed.value, + ], + failed_statuses=[KlingTaskStatus.failed.value], + status_extractor=lambda response: ( + response.data.task_status.value + if response.data and response.data.task_status + else None + ), + auth_kwargs=auth_kwargs, + result_url_extractor=result_url_extractor, + estimated_duration=estimated_duration, + node_id=node_id, + ).execute() + + +def is_valid_camera_control_configs(configs: list[float]) -> bool: + """Verifies that at least one camera control configuration is non-zero.""" + return any(not math.isclose(value, 0.0) for value in configs) + + +def is_valid_prompt(prompt: str) -> bool: + """Verifies that the prompt is not empty.""" + return bool(prompt) + + +def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool: + """Verifies that the initial response contains a task ID.""" + return bool(response.data.task_id) + + +def is_valid_video_response(response: KlingText2VideoResponse) -> bool: + """Verifies that the response contains a task result with at least one video.""" + return ( + response.data is not None + and response.data.task_result is not None + and response.data.task_result.videos is not None + and len(response.data.task_result.videos) > 0 + ) + + +def is_valid_image_response(response: KlingVirtualTryOnResponse) -> bool: + """Verifies that the response contains a task result with at least one image.""" + return ( + response.data is not None + and response.data.task_result is not None + and response.data.task_result.images is not None + and len(response.data.task_result.images) > 0 + ) + + +def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool: + """Verifies that the positive prompt is not empty and that neither promt is too long.""" + if not prompt: + raise ValueError("Positive prompt is empty") + if len(prompt) > max_length: + raise ValueError(f"Positive prompt is too long: {len(prompt)} characters") + if negative_prompt and len(negative_prompt) > max_length: + raise ValueError( + f"Negative prompt is too long: {len(negative_prompt)} characters" + ) + return True + + +def validate_task_creation_response(response) -> None: + """Validates that the Kling task creation request was successful.""" + if not is_valid_task_creation_response(response): + error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}" + logging.error(error_msg) + raise KlingApiError(error_msg) + + +def validate_video_result_response(response) -> None: + """Validates that the Kling task result contains a video.""" + if not is_valid_video_response(response): + error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response." + logging.error(f"Error: {error_msg}.\nResponse: {response}") + raise KlingApiError(error_msg) + + +def validate_image_result_response(response) -> None: + """Validates that the Kling task result contains an image.""" + if not is_valid_image_response(response): + error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response." + logging.error(f"Error: {error_msg}.\nResponse: {response}") + raise KlingApiError(error_msg) + + +def validate_input_image(image: torch.Tensor) -> None: + """ + Validates the input image adheres to the expectations of the Kling API: + - The image resolution should not be less than 300*300px + - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1 + + See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo + """ + validate_image_dimensions(image, min_width=300, min_height=300) + validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5) + + +def get_camera_control_input_config( + tooltip: str, default: float = 0.0 +) -> tuple[IO, InputTypeOptions]: + """Returns common InputTypeOptions for Kling camera control configurations.""" + input_config = { + "default": default, + "min": -10.0, + "max": 10.0, + "step": 0.25, + "display": "slider", + "tooltip": tooltip, + } + return IO.FLOAT, input_config + + +def get_video_from_response(response) -> KlingVideoResult: + """Returns the first video object from the Kling video generation task result. + Will raise an error if the response is not valid. + """ + video = response.data.task_result.videos[0] + logging.info( + "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url + ) + return video + + +def get_video_url_from_response(response) -> Optional[str]: + """Returns the first video url from the Kling video generation task result. + Will not raise an error if the response is not valid. + """ + if response and is_valid_video_response(response): + return str(get_video_from_response(response).url) + else: + return None + + +def get_images_from_response(response) -> list[KlingImageResult]: + """Returns the list of image objects from the Kling image generation task result. + Will raise an error if the response is not valid. + """ + images = response.data.task_result.images + logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images) + return images + + +def get_images_urls_from_response(response) -> Optional[str]: + """Returns the list of image urls from the Kling image generation task result. + Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls. + """ + if response and is_valid_image_response(response): + images = get_images_from_response(response) + image_urls = [str(image.url) for image in images] + return "\n".join(image_urls) + else: + return None + + +def video_result_to_node_output( + video: KlingVideoResult, +) -> tuple[VideoFromFile, str, str]: + """Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output.""" + return ( + download_url_to_video_output(video.url), + str(video.id), + str(video.duration), + ) + + +def image_result_to_node_output( + images: list[KlingImageResult], +) -> torch.Tensor: + """ + Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor. + If multiple images are returned, they will be stacked along the batch dimension. + """ + if len(images) == 1: + return download_url_to_image_tensor(images[0].url) + else: + return torch.cat([download_url_to_image_tensor(image.url) for image in images]) + + +class KlingNodeBase(ComfyNodeABC): + """Base class for Kling nodes.""" + + FUNCTION = "api_call" + CATEGORY = "api node/video/Kling" + API_NODE = True + + +class KlingCameraControls(KlingNodeBase): + """Kling Camera Controls Node""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "camera_control_type": model_field_to_node_input( + IO.COMBO, + KlingCameraControl, + "type", + enum_type=KlingCameraControlType, + ), + "horizontal_movement": get_camera_control_input_config( + "Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right" + ), + "vertical_movement": get_camera_control_input_config( + "Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward." + ), + "pan": get_camera_control_input_config( + "Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", + default=0.5, + ), + "tilt": get_camera_control_input_config( + "Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", + ), + "roll": get_camera_control_input_config( + "Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ), + "zoom": get_camera_control_input_config( + "Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", + ), + } + } + + DESCRIPTION = "Allows specifying configuration options for Kling Camera Controls and motion control effects." + RETURN_TYPES = ("CAMERA_CONTROL",) + RETURN_NAMES = ("camera_control",) + FUNCTION = "main" + API_NODE = False # This is just a helper node, it doesn't make an API call + + @classmethod + def VALIDATE_INPUTS( + cls, + horizontal_movement: float, + vertical_movement: float, + pan: float, + tilt: float, + roll: float, + zoom: float, + ) -> bool | str: + if not is_valid_camera_control_configs( + [ + horizontal_movement, + vertical_movement, + pan, + tilt, + roll, + zoom, + ] + ): + return "Invalid camera control configs: at least one of the values must be non-zero" + return True + + def main( + self, + camera_control_type: str, + horizontal_movement: float, + vertical_movement: float, + pan: float, + tilt: float, + roll: float, + zoom: float, + ) -> tuple[KlingCameraControl]: + return ( + KlingCameraControl( + type=KlingCameraControlType(camera_control_type), + config=KlingCameraConfig( + horizontal=horizontal_movement, + vertical=vertical_movement, + pan=pan, + roll=roll, + tilt=tilt, + zoom=zoom, + ), + ), + ) + + +class KlingTextToVideoNode(KlingNodeBase): + """Kling Text to Video Node""" + + @staticmethod + def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]: + """ + Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. + Only includes config combos that support the `image_tail` request field. + + See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) + """ + return { + "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), + "standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"), + "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), + "pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"), + "standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"), + "standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"), + "pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"), + "pro mode / 10s duration / kling-v2-master": ("pro", "10", "kling-v2-master"), + "standard mode / 5s duration / kling-v2-master": ("std", "5", "kling-v2-master"), + "standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"), + } + + @classmethod + def INPUT_TYPES(s): + modes = list(KlingTextToVideoNode.get_mode_string_mapping().keys()) + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, KlingText2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, KlingText2VideoRequest, "negative_prompt", multiline=True + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingText2VideoRequest, + "cfg_scale", + default=1.0, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingText2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "mode": ( + modes, + { + "default": modes[4], + "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + DESCRIPTION = "Kling Text to Video Node" + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingText2VideoResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_TEXT_TO_VIDEO}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingText2VideoResponse, + ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, + node_id=node_id, + ) + + def api_call( + self, + prompt: str, + negative_prompt: str, + cfg_scale: float, + mode: str, + aspect_ratio: str, + camera_control: Optional[KlingCameraControl] = None, + model_name: Optional[str] = None, + duration: Optional[str] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile, str, str]: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) + if model_name is None: + mode, duration, model_name = self.get_mode_string_mapping()[mode] + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_VIDEO, + method=HttpMethod.POST, + request_model=KlingText2VideoRequest, + response_model=KlingText2VideoResponse, + ), + request=KlingText2VideoRequest( + prompt=prompt if prompt else None, + negative_prompt=negative_prompt if negative_prompt else None, + duration=KlingVideoGenDuration(duration), + mode=KlingVideoGenMode(mode), + model_name=KlingVideoGenModelName(model_name), + cfg_scale=cfg_scale, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + camera_control=camera_control, + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + + task_id = task_creation_response.data.task_id + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingCameraControlT2VNode(KlingTextToVideoNode): + """ + Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera. + Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, KlingText2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingText2VideoRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingText2VideoRequest, + "cfg_scale", + default=0.75, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingText2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "camera_control": ( + "CAMERA_CONTROL", + { + "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text." + + def api_call( + self, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + camera_control: Optional[KlingCameraControl] = None, + unique_id: Optional[str] = None, + **kwargs, + ): + return super().api_call( + model_name=KlingVideoGenModelName.kling_v1, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode.std, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration.field_5, + prompt=prompt, + negative_prompt=negative_prompt, + camera_control=camera_control, + **kwargs, + ) + + +class KlingImage2VideoNode(KlingNodeBase): + """Kling Image to Video Node""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_frame": model_field_to_node_input( + IO.IMAGE, + KlingImage2VideoRequest, + "image", + tooltip="The reference image used to generate the video.", + ), + "prompt": model_field_to_node_input( + IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImage2VideoRequest, + "negative_prompt", + multiline=True, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "model_name", + enum_type=KlingVideoGenModelName, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingImage2VideoRequest, + "cfg_scale", + default=0.8, + min=0.0, + max=1.0, + ), + "mode": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "mode", + enum_type=KlingVideoGenMode, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "duration": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "duration", + enum_type=KlingVideoGenDuration, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + DESCRIPTION = "Kling Image to Video Node" + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingImage2VideoResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}", + method=HttpMethod.GET, + request_model=KlingImage2VideoRequest, + response_model=KlingImage2VideoResponse, + ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_I2V, + node_id=node_id, + ) + + def api_call( + self, + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + model_name: str, + cfg_scale: float, + mode: str, + aspect_ratio: str, + duration: str, + camera_control: Optional[KlingCameraControl] = None, + end_frame: Optional[torch.Tensor] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) + validate_input_image(start_frame) + + if camera_control is not None: + # Camera control type for image 2 video is always `simple` + camera_control.type = KlingCameraControlType.simple + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=KlingImage2VideoRequest, + response_model=KlingImage2VideoResponse, + ), + request=KlingImage2VideoRequest( + model_name=KlingVideoGenModelName(model_name), + image=tensor_to_base64_string(start_frame), + image_tail=( + tensor_to_base64_string(end_frame) + if end_frame is not None + else None + ), + prompt=prompt, + negative_prompt=negative_prompt if negative_prompt else None, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode(mode), + duration=KlingVideoGenDuration(duration), + camera_control=camera_control, + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingCameraControlI2VNode(KlingImage2VideoNode): + """ + Kling Image to Video Camera Control Node. This node is a image to video node, but it supports controlling the camera. + Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image" + ), + "prompt": model_field_to_node_input( + IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImage2VideoRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingImage2VideoRequest, + "cfg_scale", + default=0.75, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "camera_control": ( + "CAMERA_CONTROL", + { + "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image." + + def api_call( + self, + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + camera_control: KlingCameraControl, + unique_id: Optional[str] = None, + **kwargs, + ): + return super().api_call( + model_name=KlingVideoGenModelName.kling_v1_5, + start_frame=start_frame, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode.pro, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration.field_5, + prompt=prompt, + negative_prompt=negative_prompt, + camera_control=camera_control, + unique_id=unique_id, + **kwargs, + ) + + +class KlingStartEndFrameNode(KlingImage2VideoNode): + """ + Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field. + """ + + @staticmethod + def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]: + """ + Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. + Only includes config combos that support the `image_tail` request field. + + See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) + """ + return { + "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), + "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), + "pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"), + "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"), + "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"), + "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"), + } + + @classmethod + def INPUT_TYPES(s): + modes = list(KlingStartEndFrameNode.get_mode_string_mapping().keys()) + return { + "required": { + "start_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image" + ), + "end_frame": model_field_to_node_input( + IO.IMAGE, KlingImage2VideoRequest, "image_tail" + ), + "prompt": model_field_to_node_input( + IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImage2VideoRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingImage2VideoRequest, + "cfg_scale", + default=0.5, + min=0.0, + max=1.0, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImage2VideoRequest, + "aspect_ratio", + enum_type=KlingVideoGenAspectRatio, + ), + "mode": ( + modes, + { + "default": modes[2], + "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last." + + def api_call( + self, + start_frame: torch.Tensor, + end_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + mode: str, + unique_id: Optional[str] = None, + **kwargs, + ): + mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[ + mode + ] + return super().api_call( + prompt=prompt, + negative_prompt=negative_prompt, + model_name=model_name, + start_frame=start_frame, + cfg_scale=cfg_scale, + mode=mode, + aspect_ratio=aspect_ratio, + duration=duration, + end_frame=end_frame, + unique_id=unique_id, + **kwargs, + ) + + +class KlingVideoExtendNode(KlingNodeBase): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, KlingVideoExtendRequest, "prompt", multiline=True + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingVideoExtendRequest, + "negative_prompt", + multiline=True, + ), + "cfg_scale": model_field_to_node_input( + IO.FLOAT, + KlingVideoExtendRequest, + "cfg_scale", + default=0.5, + min=0.0, + max=1.0, + ), + "video_id": model_field_to_node_input( + IO.STRING, KlingVideoExtendRequest, "video_id", forceInput=True + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes." + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingVideoExtendResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_VIDEO_EXTEND}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingVideoExtendResponse, + ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND, + node_id=node_id, + ) + + def api_call( + self, + prompt: str, + negative_prompt: str, + cfg_scale: float, + video_id: str, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile, str, str]: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_VIDEO_EXTEND, + method=HttpMethod.POST, + request_model=KlingVideoExtendRequest, + response_model=KlingVideoExtendResponse, + ), + request=KlingVideoExtendRequest( + prompt=prompt if prompt else None, + negative_prompt=negative_prompt if negative_prompt else None, + cfg_scale=cfg_scale, + video_id=video_id, + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingVideoEffectsBase(KlingNodeBase): + """Kling Video Effects Base""" + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingVideoEffectsResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_VIDEO_EFFECTS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingVideoEffectsResponse, + ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS, + node_id=node_id, + ) + + def api_call( + self, + dual_character: bool, + effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene, + model_name: str, + duration: KlingVideoGenDuration, + image_1: torch.Tensor, + image_2: Optional[torch.Tensor] = None, + mode: Optional[KlingVideoGenMode] = None, + unique_id: Optional[str] = None, + **kwargs, + ): + if dual_character: + request_input_field = KlingDualCharacterEffectInput( + model_name=model_name, + mode=mode, + images=[ + tensor_to_base64_string(image_1), + tensor_to_base64_string(image_2), + ], + duration=duration, + ) + else: + request_input_field = KlingSingleImageEffectInput( + model_name=model_name, + image=tensor_to_base64_string(image_1), + duration=duration, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_VIDEO_EFFECTS, + method=HttpMethod.POST, + request_model=KlingVideoEffectsRequest, + response_model=KlingVideoEffectsResponse, + ), + request=KlingVideoEffectsRequest( + effect_scene=effect_scene, + input=request_input_field, + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): + """Kling Dual Character Video Effect Node""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image_left": (IO.IMAGE, {"tooltip": "Left side image"}), + "image_right": (IO.IMAGE, {"tooltip": "Right side image"}), + "effect_scene": model_field_to_node_input( + IO.COMBO, + KlingVideoEffectsRequest, + "effect_scene", + enum_type=KlingDualCharacterEffectsScene, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingDualCharacterEffectInput, + "model_name", + enum_type=KlingCharacterEffectModelName, + ), + "mode": model_field_to_node_input( + IO.COMBO, + KlingDualCharacterEffectInput, + "mode", + enum_type=KlingVideoGenMode, + ), + "duration": model_field_to_node_input( + IO.COMBO, + KlingDualCharacterEffectInput, + "duration", + enum_type=KlingVideoGenDuration, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite." + RETURN_TYPES = ("VIDEO", "STRING") + RETURN_NAMES = ("VIDEO", "duration") + + def api_call( + self, + image_left: torch.Tensor, + image_right: torch.Tensor, + effect_scene: KlingDualCharacterEffectsScene, + model_name: KlingCharacterEffectModelName, + mode: KlingVideoGenMode, + duration: KlingVideoGenDuration, + unique_id: Optional[str] = None, + **kwargs, + ): + video, _, duration = super().api_call( + dual_character=True, + effect_scene=effect_scene, + model_name=model_name, + mode=mode, + duration=duration, + image_1=image_left, + image_2=image_right, + unique_id=unique_id, + **kwargs, + ) + return video, duration + + +class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): + """Kling Single Image Video Effect Node""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + { + "tooltip": " Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1" + }, + ), + "effect_scene": model_field_to_node_input( + IO.COMBO, + KlingVideoEffectsRequest, + "effect_scene", + enum_type=KlingSingleImageEffectsScene, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingSingleImageEffectInput, + "model_name", + enum_type=KlingSingleImageEffectModelName, + ), + "duration": model_field_to_node_input( + IO.COMBO, + KlingSingleImageEffectInput, + "duration", + enum_type=KlingVideoGenDuration, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene." + + def api_call( + self, + image: torch.Tensor, + effect_scene: KlingSingleImageEffectsScene, + model_name: KlingSingleImageEffectModelName, + duration: KlingVideoGenDuration, + unique_id: Optional[str] = None, + **kwargs, + ): + return super().api_call( + dual_character=False, + effect_scene=effect_scene, + model_name=model_name, + duration=duration, + image_1=image, + unique_id=unique_id, + **kwargs, + ) + + +class KlingLipSyncBase(KlingNodeBase): + """Kling Lip Sync Base""" + + RETURN_TYPES = ("VIDEO", "STRING", "STRING") + RETURN_NAMES = ("VIDEO", "video_id", "duration") + + def validate_lip_sync_video(self, video: VideoInput): + """ + Validates the input video adheres to the expectations of the Kling Lip Sync API: + - Video length does not exceed 10s and is not shorter than 2s + - Length and width dimensions should both be between 720px and 1920px + + See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip + """ + validate_video_dimensions(video, 720, 1920) + validate_video_duration(video, 2, 10) + + def validate_text(self, text: str): + if not text: + raise ValueError("Text is required") + if len(text) > MAX_PROMPT_LENGTH_LIP_SYNC: + raise ValueError( + f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters." + ) + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingLipSyncResponse: + """Polls the Kling API endpoint until the task reaches a terminal state.""" + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_LIP_SYNC}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingLipSyncResponse, + ), + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_LIP_SYNC, + node_id=node_id, + ) + + def api_call( + self, + video: VideoInput, + audio: Optional[AudioInput] = None, + voice_language: Optional[str] = None, + mode: Optional[str] = None, + text: Optional[str] = None, + voice_speed: Optional[float] = None, + voice_id: Optional[str] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile, str, str]: + if text: + self.validate_text(text) + self.validate_lip_sync_video(video) + + # Upload video to Comfy API and get download URL + video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs) + logging.info("Uploaded video to Comfy API. URL: %s", video_url) + + # Upload the audio file to Comfy API and get download URL + if audio: + audio_url = upload_audio_to_comfyapi(audio, auth_kwargs=kwargs) + logging.info("Uploaded audio to Comfy API. URL: %s", audio_url) + else: + audio_url = None + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_LIP_SYNC, + method=HttpMethod.POST, + request_model=KlingLipSyncRequest, + response_model=KlingLipSyncResponse, + ), + request=KlingLipSyncRequest( + input=KlingLipSyncInputObject( + video_url=video_url, + mode=mode, + text=text, + voice_language=voice_language, + voice_speed=voice_speed, + audio_type="url", + audio_url=audio_url, + voice_id=voice_id, + ), + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return video_result_to_node_output(video) + + +class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): + """Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "video": (IO.VIDEO, {}), + "audio": (IO.AUDIO, {}), + "voice_language": model_field_to_node_input( + IO.COMBO, + KlingLipSyncInputObject, + "voice_language", + enum_type=KlingLipSyncVoiceLanguage, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length." + + def api_call( + self, + video: VideoInput, + audio: AudioInput, + voice_language: str, + unique_id: Optional[str] = None, + **kwargs, + ): + return super().api_call( + video=video, + audio=audio, + voice_language=voice_language, + mode="audio2video", + unique_id=unique_id, + **kwargs, + ) + + +class KlingLipSyncTextToVideoNode(KlingLipSyncBase): + """Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt.""" + + @staticmethod + def get_voice_config() -> dict[str, tuple[str, str]]: + return { + # English voices + "Melody": ("girlfriend_4_speech02", "en"), + "Sunny": ("genshin_vindi2", "en"), + "Sage": ("zhinen_xuesheng", "en"), + "Ace": ("AOT", "en"), + "Blossom": ("ai_shatang", "en"), + "Peppy": ("genshin_klee2", "en"), + "Dove": ("genshin_kirara", "en"), + "Shine": ("ai_kaiya", "en"), + "Anchor": ("oversea_male1", "en"), + "Lyric": ("ai_chenjiahao_712", "en"), + "Tender": ("chat1_female_new-3", "en"), + "Siren": ("chat_0407_5-1", "en"), + "Zippy": ("cartoon-boy-07", "en"), + "Bud": ("uk_boy1", "en"), + "Sprite": ("cartoon-girl-01", "en"), + "Candy": ("PeppaPig_platform", "en"), + "Beacon": ("ai_huangzhong_712", "en"), + "Rock": ("ai_huangyaoshi_712", "en"), + "Titan": ("ai_laoguowang_712", "en"), + "Grace": ("chengshu_jiejie", "en"), + "Helen": ("you_pingjing", "en"), + "Lore": ("calm_story1", "en"), + "Crag": ("uk_man2", "en"), + "Prattle": ("laopopo_speech02", "en"), + "Hearth": ("heainainai_speech02", "en"), + "The Reader": ("reader_en_m-v1", "en"), + "Commercial Lady": ("commercial_lady_en_f-v1", "en"), + # Chinese voices + "阳光少年": ("genshin_vindi2", "zh"), + "懂事小弟": ("zhinen_xuesheng", "zh"), + "运动少年": ("tiyuxi_xuedi", "zh"), + "青春少女": ("ai_shatang", "zh"), + "温柔小妹": ("genshin_klee2", "zh"), + "元气少女": ("genshin_kirara", "zh"), + "阳光男生": ("ai_kaiya", "zh"), + "幽默小哥": ("tiexin_nanyou", "zh"), + "文艺小哥": ("ai_chenjiahao_712", "zh"), + "甜美邻家": ("girlfriend_1_speech02", "zh"), + "温柔姐姐": ("chat1_female_new-3", "zh"), + "职场女青": ("girlfriend_2_speech02", "zh"), + "活泼男童": ("cartoon-boy-07", "zh"), + "俏皮女童": ("cartoon-girl-01", "zh"), + "稳重老爸": ("ai_huangyaoshi_712", "zh"), + "温柔妈妈": ("you_pingjing", "zh"), + "严肃上司": ("ai_laoguowang_712", "zh"), + "优雅贵妇": ("chengshu_jiejie", "zh"), + "慈祥爷爷": ("zhuxi_speech02", "zh"), + "唠叨爷爷": ("uk_oldman3", "zh"), + "唠叨奶奶": ("laopopo_speech02", "zh"), + "和蔼奶奶": ("heainainai_speech02", "zh"), + "东北老铁": ("dongbeilaotie_speech02", "zh"), + "重庆小伙": ("chongqingxiaohuo_speech02", "zh"), + "四川妹子": ("chuanmeizi_speech02", "zh"), + "潮汕大叔": ("chaoshandashu_speech02", "zh"), + "台湾男生": ("ai_taiwan_man2_speech02", "zh"), + "西安掌柜": ("xianzhanggui_speech02", "zh"), + "天津姐姐": ("tianjinjiejie_speech02", "zh"), + "新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"), + "译制片男": ("yizhipiannan-v1", "zh"), + "撒娇女友": ("tianmeixuemei-v1", "zh"), + "刀片烟嗓": ("daopianyansang-v1", "zh"), + "乖巧正太": ("mengwa-v1", "zh"), + } + + @classmethod + def INPUT_TYPES(s): + voice_options = list(s.get_voice_config().keys()) + return { + "required": { + "video": (IO.VIDEO, {}), + "text": model_field_to_node_input( + IO.STRING, KlingLipSyncInputObject, "text", multiline=True + ), + "voice": (voice_options, {"default": voice_options[0]}), + "voice_speed": model_field_to_node_input( + IO.FLOAT, KlingLipSyncInputObject, "voice_speed", slider=True + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length." + + def api_call( + self, + video: VideoInput, + text: str, + voice: str, + voice_speed: float, + unique_id: Optional[str] = None, + **kwargs, + ): + voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice] + return super().api_call( + video=video, + text=text, + voice_language=voice_language, + voice_id=voice_id, + voice_speed=voice_speed, + mode="text2video", + unique_id=unique_id, + **kwargs, + ) + + +class KlingImageGenerationBase(KlingNodeBase): + """Kling Image Generation Base Node.""" + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "api node/image/Kling" + + def validate_prompt(self, prompt: str, negative_prompt: Optional[str] = None): + if not prompt or len(prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN: + raise ValueError( + f"Prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters" + ) + if negative_prompt and len(negative_prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN: + raise ValueError( + f"Negative prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters" + ) + + +class KlingVirtualTryOnNode(KlingImageGenerationBase): + """Kling Virtual Try On Node.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "human_image": (IO.IMAGE, {}), + "cloth_image": (IO.IMAGE, {}), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingVirtualTryOnRequest, + "model_name", + enum_type=KlingVirtualTryOnModelName, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background." + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> KlingVirtualTryOnResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingVirtualTryOnResponse, + ), + result_url_extractor=get_images_urls_from_response, + estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON, + node_id=node_id, + ) + + def api_call( + self, + human_image: torch.Tensor, + cloth_image: torch.Tensor, + model_name: KlingVirtualTryOnModelName, + unique_id: Optional[str] = None, + **kwargs, + ): + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_VIRTUAL_TRY_ON, + method=HttpMethod.POST, + request_model=KlingVirtualTryOnRequest, + response_model=KlingVirtualTryOnResponse, + ), + request=KlingVirtualTryOnRequest( + human_image=tensor_to_base64_string(human_image), + cloth_image=tensor_to_base64_string(cloth_image), + model_name=model_name, + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_image_result_response(final_response) + + images = get_images_from_response(final_response) + return (image_result_to_node_output(images),) + + +class KlingImageGenerationNode(KlingImageGenerationBase): + """Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, + KlingImageGenerationsRequest, + "prompt", + multiline=True, + max_length=MAX_PROMPT_LENGTH_IMAGE_GEN, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + KlingImageGenerationsRequest, + "negative_prompt", + multiline=True, + ), + "image_type": model_field_to_node_input( + IO.COMBO, + KlingImageGenerationsRequest, + "image_reference", + enum_type=KlingImageGenImageReferenceType, + ), + "image_fidelity": model_field_to_node_input( + IO.FLOAT, + KlingImageGenerationsRequest, + "image_fidelity", + slider=True, + step=0.01, + ), + "human_fidelity": model_field_to_node_input( + IO.FLOAT, + KlingImageGenerationsRequest, + "human_fidelity", + slider=True, + step=0.01, + ), + "model_name": model_field_to_node_input( + IO.COMBO, + KlingImageGenerationsRequest, + "model_name", + enum_type=KlingImageGenModelName, + ), + "aspect_ratio": model_field_to_node_input( + IO.COMBO, + KlingImageGenerationsRequest, + "aspect_ratio", + enum_type=KlingImageGenAspectRatio, + ), + "n": model_field_to_node_input( + IO.INT, + KlingImageGenerationsRequest, + "n", + ), + }, + "optional": { + "image": (IO.IMAGE, {}), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image." + + def get_response( + self, + task_id: str, + auth_kwargs: Optional[dict[str, str]], + node_id: Optional[str] = None, + ) -> KlingImageGenerationsResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_IMAGE_GENERATIONS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=KlingImageGenerationsResponse, + ), + result_url_extractor=get_images_urls_from_response, + estimated_duration=AVERAGE_DURATION_IMAGE_GEN, + node_id=node_id, + ) + + def api_call( + self, + model_name: KlingImageGenModelName, + prompt: str, + negative_prompt: str, + image_type: KlingImageGenImageReferenceType, + image_fidelity: float, + human_fidelity: float, + n: int, + aspect_ratio: KlingImageGenAspectRatio, + image: Optional[torch.Tensor] = None, + unique_id: Optional[str] = None, + **kwargs, + ): + self.validate_prompt(prompt, negative_prompt) + + if image is not None: + image = tensor_to_base64_string(image) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_GENERATIONS, + method=HttpMethod.POST, + request_model=KlingImageGenerationsRequest, + response_model=KlingImageGenerationsResponse, + ), + request=KlingImageGenerationsRequest( + model_name=model_name, + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + image_reference=image_type, + image_fidelity=image_fidelity, + human_fidelity=human_fidelity, + n=n, + aspect_ratio=aspect_ratio, + ), + auth_kwargs=kwargs, + ) + + task_creation_response = initial_operation.execute() + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + validate_image_result_response(final_response) + + images = get_images_from_response(final_response) + return (image_result_to_node_output(images),) + + +NODE_CLASS_MAPPINGS = { + "KlingCameraControls": KlingCameraControls, + "KlingTextToVideoNode": KlingTextToVideoNode, + "KlingImage2VideoNode": KlingImage2VideoNode, + "KlingCameraControlI2VNode": KlingCameraControlI2VNode, + "KlingCameraControlT2VNode": KlingCameraControlT2VNode, + "KlingStartEndFrameNode": KlingStartEndFrameNode, + "KlingVideoExtendNode": KlingVideoExtendNode, + "KlingLipSyncAudioToVideoNode": KlingLipSyncAudioToVideoNode, + "KlingLipSyncTextToVideoNode": KlingLipSyncTextToVideoNode, + "KlingVirtualTryOnNode": KlingVirtualTryOnNode, + "KlingImageGenerationNode": KlingImageGenerationNode, + "KlingSingleImageVideoEffectNode": KlingSingleImageVideoEffectNode, + "KlingDualCharacterVideoEffectNode": KlingDualCharacterVideoEffectNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "KlingCameraControls": "Kling Camera Controls", + "KlingTextToVideoNode": "Kling Text to Video", + "KlingImage2VideoNode": "Kling Image to Video", + "KlingCameraControlI2VNode": "Kling Image to Video (Camera Control)", + "KlingCameraControlT2VNode": "Kling Text to Video (Camera Control)", + "KlingStartEndFrameNode": "Kling Start-End Frame to Video", + "KlingVideoExtendNode": "Kling Video Extend", + "KlingLipSyncAudioToVideoNode": "Kling Lip Sync Video with Audio", + "KlingLipSyncTextToVideoNode": "Kling Lip Sync Video with Text", + "KlingVirtualTryOnNode": "Kling Virtual Try On", + "KlingImageGenerationNode": "Kling Image Generation", + "KlingSingleImageVideoEffectNode": "Kling Video Effects", + "KlingDualCharacterVideoEffectNode": "Kling Dual Character Video Effects", +} diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py new file mode 100644 index 0000000000000000000000000000000000000000..525dc38e628b932911ac86177e1970b426a5c8a6 --- /dev/null +++ b/comfy_api_nodes/nodes_luma.py @@ -0,0 +1,737 @@ +from __future__ import annotations +from inspect import cleandoc +from typing import Optional +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis.luma_api import ( + LumaImageModel, + LumaVideoModel, + LumaVideoOutputResolution, + LumaVideoModelOutputDuration, + LumaAspectRatio, + LumaState, + LumaImageGenerationRequest, + LumaGenerationRequest, + LumaGeneration, + LumaCharacterRef, + LumaModifyImageRef, + LumaImageIdentity, + LumaReference, + LumaReferenceChain, + LumaImageReference, + LumaKeyframes, + LumaConceptChain, + LumaIO, + get_luma_concepts, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + process_image_response, + validate_string, +) +from server import PromptServer + +import requests +import torch +from io import BytesIO + +LUMA_T2V_AVERAGE_DURATION = 105 +LUMA_I2V_AVERAGE_DURATION = 100 + +def image_result_url_extractor(response: LumaGeneration): + return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None + +def video_result_url_extractor(response: LumaGeneration): + return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None + +class LumaReferenceNode(ComfyNodeABC): + """ + Holds an image and weight for use with Luma Generate Image node. + """ + + RETURN_TYPES = (LumaIO.LUMA_REF,) + RETURN_NAMES = ("luma_ref",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_luma_reference" + CATEGORY = "api node/image/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + { + "tooltip": "Image to use as reference.", + }, + ), + "weight": ( + IO.FLOAT, + { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Weight of image reference.", + }, + ), + }, + "optional": {"luma_ref": (LumaIO.LUMA_REF,)}, + } + + def create_luma_reference( + self, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None + ): + if luma_ref is not None: + luma_ref = luma_ref.clone() + else: + luma_ref = LumaReferenceChain() + luma_ref.add(LumaReference(image=image, weight=round(weight, 2))) + return (luma_ref,) + + +class LumaConceptsNode(ComfyNodeABC): + """ + Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes. + """ + + RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,) + RETURN_NAMES = ("luma_concepts",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_concepts" + CATEGORY = "api node/video/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "concept1": (get_luma_concepts(include_none=True),), + "concept2": (get_luma_concepts(include_none=True),), + "concept3": (get_luma_concepts(include_none=True),), + "concept4": (get_luma_concepts(include_none=True),), + }, + "optional": { + "luma_concepts": ( + LumaIO.LUMA_CONCEPTS, + { + "tooltip": "Optional Camera Concepts to add to the ones chosen here." + }, + ), + }, + } + + def create_concepts( + self, + concept1: str, + concept2: str, + concept3: str, + concept4: str, + luma_concepts: LumaConceptChain = None, + ): + chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4]) + if luma_concepts is not None: + chain = luma_concepts.clone_and_merge(chain) + return (chain,) + + +class LumaImageGenerationNode(ComfyNodeABC): + """ + Generates images synchronously based on prompt and aspect ratio. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "model": ([model.value for model in LumaImageModel],), + "aspect_ratio": ( + [ratio.value for ratio in LumaAspectRatio], + { + "default": LumaAspectRatio.ratio_16_9, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + "style_image_weight": ( + IO.FLOAT, + { + "default": 1.0, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Weight of style image. Ignored if no style_image provided.", + }, + ), + }, + "optional": { + "image_luma_ref": ( + LumaIO.LUMA_REF, + { + "tooltip": "Luma Reference node connection to influence generation with input images; up to 4 images can be considered." + }, + ), + "style_image": ( + IO.IMAGE, + {"tooltip": "Style reference image; only 1 image will be used."}, + ), + "character_image": ( + IO.IMAGE, + { + "tooltip": "Character reference images; can be a batch of multiple, up to 4 images can be considered." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + model: str, + aspect_ratio: str, + seed, + style_image_weight: float, + image_luma_ref: LumaReferenceChain = None, + style_image: torch.Tensor = None, + character_image: torch.Tensor = None, + unique_id: str = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=True, min_length=3) + # handle image_luma_ref + api_image_ref = None + if image_luma_ref is not None: + api_image_ref = self._convert_luma_refs( + image_luma_ref, max_refs=4, auth_kwargs=kwargs, + ) + # handle style_luma_ref + api_style_ref = None + if style_image is not None: + api_style_ref = self._convert_style_image( + style_image, weight=style_image_weight, auth_kwargs=kwargs, + ) + # handle character_ref images + character_ref = None + if character_image is not None: + download_urls = upload_images_to_comfyapi( + character_image, max_images=4, auth_kwargs=kwargs, + ) + character_ref = LumaCharacterRef( + identity0=LumaImageIdentity(images=download_urls) + ) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations/image", + method=HttpMethod.POST, + request_model=LumaImageGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaImageGenerationRequest( + prompt=prompt, + model=model, + aspect_ratio=aspect_ratio, + image_ref=api_image_ref, + style_ref=api_style_ref, + character_ref=character_ref, + ), + auth_kwargs=kwargs, + ) + response_api: LumaGeneration = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=image_result_url_extractor, + node_id=unique_id, + auth_kwargs=kwargs, + ) + response_poll = operation.execute() + + img_response = requests.get(response_poll.assets.image) + img = process_image_response(img_response) + return (img,) + + def _convert_luma_refs( + self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None + ): + luma_urls = [] + ref_count = 0 + for ref in luma_ref.refs: + download_urls = upload_images_to_comfyapi( + ref.image, max_images=1, auth_kwargs=auth_kwargs + ) + luma_urls.append(download_urls[0]) + ref_count += 1 + if ref_count >= max_refs: + break + return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs) + + def _convert_style_image( + self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None + ): + chain = LumaReferenceChain( + first_ref=LumaReference(image=style_image, weight=weight) + ) + return self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs) + + +class LumaImageModifyNode(ComfyNodeABC): + """ + Modifies images synchronously based on prompt and aspect ratio. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation", + }, + ), + "image_weight": ( + IO.FLOAT, + { + "default": 0.1, + "min": 0.0, + "max": 0.98, + "step": 0.01, + "tooltip": "Weight of the image; the closer to 1.0, the less the image will be modified.", + }, + ), + "model": ([model.value for model in LumaImageModel],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": {}, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + model: str, + image: torch.Tensor, + image_weight: float, + seed, + unique_id: str = None, + **kwargs, + ): + # first, upload image + download_urls = upload_images_to_comfyapi( + image, max_images=1, auth_kwargs=kwargs, + ) + image_url = download_urls[0] + # next, make Luma call with download url provided + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations/image", + method=HttpMethod.POST, + request_model=LumaImageGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaImageGenerationRequest( + prompt=prompt, + model=model, + modify_image_ref=LumaModifyImageRef( + url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2) + ), + ), + auth_kwargs=kwargs, + ) + response_api: LumaGeneration = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=image_result_url_extractor, + node_id=unique_id, + auth_kwargs=kwargs, + ) + response_poll = operation.execute() + + img_response = requests.get(response_poll.assets.image) + img = process_image_response(img_response) + return (img,) + + +class LumaTextToVideoGenerationNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "model": ([model.value for model in LumaVideoModel],), + "aspect_ratio": ( + [ratio.value for ratio in LumaAspectRatio], + { + "default": LumaAspectRatio.ratio_16_9, + }, + ), + "resolution": ( + [resolution.value for resolution in LumaVideoOutputResolution], + { + "default": LumaVideoOutputResolution.res_540p, + }, + ), + "duration": ([dur.value for dur in LumaVideoModelOutputDuration],), + "loop": ( + IO.BOOLEAN, + { + "default": False, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "luma_concepts": ( + LumaIO.LUMA_CONCEPTS, + { + "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + model: str, + aspect_ratio: str, + resolution: str, + duration: str, + loop: bool, + seed, + luma_concepts: LumaConceptChain = None, + unique_id: str = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, min_length=3) + duration = duration if model != LumaVideoModel.ray_1_6 else None + resolution = resolution if model != LumaVideoModel.ray_1_6 else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations", + method=HttpMethod.POST, + request_model=LumaGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaGenerationRequest( + prompt=prompt, + model=model, + resolution=resolution, + aspect_ratio=aspect_ratio, + duration=duration, + loop=loop, + concepts=luma_concepts.create_api_model() if luma_concepts else None, + ), + auth_kwargs=kwargs, + ) + response_api: LumaGeneration = operation.execute() + + if unique_id: + PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id) + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=video_result_url_extractor, + node_id=unique_id, + estimated_duration=LUMA_T2V_AVERAGE_DURATION, + auth_kwargs=kwargs, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.assets.video) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +class LumaImageToVideoGenerationNode(ComfyNodeABC): + """ + Generates videos synchronously based on prompt, input images, and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/Luma" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "model": ([model.value for model in LumaVideoModel],), + # "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], { + # "default": LumaAspectRatio.ratio_16_9, + # }), + "resolution": ( + [resolution.value for resolution in LumaVideoOutputResolution], + { + "default": LumaVideoOutputResolution.res_540p, + }, + ), + "duration": ([dur.value for dur in LumaVideoModelOutputDuration],), + "loop": ( + IO.BOOLEAN, + { + "default": False, + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "first_image": ( + IO.IMAGE, + {"tooltip": "First frame of generated video."}, + ), + "last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}), + "luma_concepts": ( + LumaIO.LUMA_CONCEPTS, + { + "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + model: str, + resolution: str, + duration: str, + loop: bool, + seed, + first_image: torch.Tensor = None, + last_image: torch.Tensor = None, + luma_concepts: LumaConceptChain = None, + unique_id: str = None, + **kwargs, + ): + if first_image is None and last_image is None: + raise Exception( + "At least one of first_image and last_image requires an input." + ) + keyframes = self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs) + duration = duration if model != LumaVideoModel.ray_1_6 else None + resolution = resolution if model != LumaVideoModel.ray_1_6 else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations", + method=HttpMethod.POST, + request_model=LumaGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaGenerationRequest( + prompt=prompt, + model=model, + aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason + resolution=resolution, + duration=duration, + loop=loop, + keyframes=keyframes, + concepts=luma_concepts.create_api_model() if luma_concepts else None, + ), + auth_kwargs=kwargs, + ) + response_api: LumaGeneration = operation.execute() + + if unique_id: + PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id) + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=video_result_url_extractor, + node_id=unique_id, + estimated_duration=LUMA_I2V_AVERAGE_DURATION, + auth_kwargs=kwargs, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.assets.video) + return (VideoFromFile(BytesIO(vid_response.content)),) + + def _convert_to_keyframes( + self, + first_image: torch.Tensor = None, + last_image: torch.Tensor = None, + auth_kwargs: Optional[dict[str,str]] = None, + ): + if first_image is None and last_image is None: + return None + frame0 = None + frame1 = None + if first_image is not None: + download_urls = upload_images_to_comfyapi( + first_image, max_images=1, auth_kwargs=auth_kwargs, + ) + frame0 = LumaImageReference(type="image", url=download_urls[0]) + if last_image is not None: + download_urls = upload_images_to_comfyapi( + last_image, max_images=1, auth_kwargs=auth_kwargs, + ) + frame1 = LumaImageReference(type="image", url=download_urls[0]) + return LumaKeyframes(frame0=frame0, frame1=frame1) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "LumaImageNode": LumaImageGenerationNode, + "LumaImageModifyNode": LumaImageModifyNode, + "LumaVideoNode": LumaTextToVideoGenerationNode, + "LumaImageToVideoNode": LumaImageToVideoGenerationNode, + "LumaReferenceNode": LumaReferenceNode, + "LumaConceptsNode": LumaConceptsNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "LumaImageNode": "Luma Text to Image", + "LumaImageModifyNode": "Luma Image to Image", + "LumaVideoNode": "Luma Text to Video", + "LumaImageToVideoNode": "Luma Image to Video", + "LumaReferenceNode": "Luma Reference", + "LumaConceptsNode": "Luma Concepts", +} diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py new file mode 100644 index 0000000000000000000000000000000000000000..9b46636dbbd1d96fc2505e7f73cfbacb43b1f5f5 --- /dev/null +++ b/comfy_api_nodes/nodes_minimax.py @@ -0,0 +1,332 @@ +from typing import Union +import logging +import torch + +from comfy.comfy_types.node_typing import IO +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis import ( + MinimaxVideoGenerationRequest, + MinimaxVideoGenerationResponse, + MinimaxFileRetrieveResponse, + MinimaxTaskResultResponse, + SubjectReferenceItem, + Model +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + upload_images_to_comfyapi, + validate_string, +) +from server import PromptServer + + +I2V_AVERAGE_DURATION = 114 +T2V_AVERAGE_DURATION = 234 + +class MinimaxTextToVideoNode: + """ + Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API. + """ + + AVERAGE_DURATION = T2V_AVERAGE_DURATION + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt_text": ( + "STRING", + { + "multiline": True, + "default": "", + "tooltip": "Text prompt to guide the video generation", + }, + ), + "model": ( + [ + "T2V-01", + "T2V-01-Director", + ], + { + "default": "T2V-01", + "tooltip": "Model to use for video generation", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("VIDEO",) + DESCRIPTION = "Generates videos from prompts using MiniMax's API" + FUNCTION = "generate_video" + CATEGORY = "api node/video/MiniMax" + API_NODE = True + OUTPUT_NODE = True + + def generate_video( + self, + prompt_text, + seed=0, + model="T2V-01", + image: torch.Tensor=None, # used for ImageToVideo + subject: torch.Tensor=None, # used for SubjectToVideo + unique_id: Union[str, None]=None, + **kwargs, + ): + ''' + Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments. + ''' + if image is None: + validate_string(prompt_text, field_name="prompt_text") + # upload image, if passed in + image_url = None + if image is not None: + image_url = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs)[0] + + # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model + subject_reference = None + if subject is not None: + subject_url = upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=kwargs)[0] + subject_reference = [SubjectReferenceItem(image=subject_url)] + + + video_generate_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/video_generation", + method=HttpMethod.POST, + request_model=MinimaxVideoGenerationRequest, + response_model=MinimaxVideoGenerationResponse, + ), + request=MinimaxVideoGenerationRequest( + model=Model(model), + prompt=prompt_text, + callback_url=None, + first_frame_image=image_url, + subject_reference=subject_reference, + prompt_optimizer=None, + ), + auth_kwargs=kwargs, + ) + response = video_generate_operation.execute() + + task_id = response.task_id + if not task_id: + raise Exception(f"MiniMax generation failed: {response.base_resp}") + + video_generate_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/minimax/query/video_generation", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxTaskResultResponse, + query_params={"task_id": task_id}, + ), + completed_statuses=["Success"], + failed_statuses=["Fail"], + status_extractor=lambda x: x.status.value, + estimated_duration=self.AVERAGE_DURATION, + node_id=unique_id, + auth_kwargs=kwargs, + ) + task_result = video_generate_operation.execute() + + file_id = task_result.file_id + if file_id is None: + raise Exception("Request was not successful. Missing file ID.") + file_retrieve_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/files/retrieve", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxFileRetrieveResponse, + query_params={"file_id": int(file_id)}, + ), + request=EmptyRequest(), + auth_kwargs=kwargs, + ) + file_result = file_retrieve_operation.execute() + + file_url = file_result.file.download_url + if file_url is None: + raise Exception( + f"No video was found in the response. Full response: {file_result.model_dump()}" + ) + logging.info(f"Generated video URL: {file_url}") + if unique_id: + if hasattr(file_result.file, "backup_download_url"): + message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" + else: + message = f"Result URL: {file_url}" + PromptServer.instance.send_progress_text(message, unique_id) + + video_io = download_url_to_bytesio(file_url) + if video_io is None: + error_msg = f"Failed to download video from {file_url}" + logging.error(error_msg) + raise Exception(error_msg) + return (VideoFromFile(video_io),) + + +class MinimaxImageToVideoNode(MinimaxTextToVideoNode): + """ + Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. + """ + + AVERAGE_DURATION = I2V_AVERAGE_DURATION + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ( + IO.IMAGE, + { + "tooltip": "Image to use as first frame of video generation" + }, + ), + "prompt_text": ( + "STRING", + { + "multiline": True, + "default": "", + "tooltip": "Text prompt to guide the video generation", + }, + ), + "model": ( + [ + "I2V-01-Director", + "I2V-01", + "I2V-01-live", + ], + { + "default": "I2V-01", + "tooltip": "Model to use for video generation", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("VIDEO",) + DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" + FUNCTION = "generate_video" + CATEGORY = "api node/video/MiniMax" + API_NODE = True + OUTPUT_NODE = True + + +class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): + """ + Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. + """ + + AVERAGE_DURATION = T2V_AVERAGE_DURATION + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "subject": ( + IO.IMAGE, + { + "tooltip": "Image of subject to reference video generation" + }, + ), + "prompt_text": ( + "STRING", + { + "multiline": True, + "default": "", + "tooltip": "Text prompt to guide the video generation", + }, + ), + "model": ( + [ + "S2V-01", + ], + { + "default": "S2V-01", + "tooltip": "Model to use for video generation", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("VIDEO",) + DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" + FUNCTION = "generate_video" + CATEGORY = "api node/video/MiniMax" + API_NODE = True + OUTPUT_NODE = True + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "MinimaxTextToVideoNode": MinimaxTextToVideoNode, + "MinimaxImageToVideoNode": MinimaxImageToVideoNode, + # "MinimaxSubjectToVideoNode": MinimaxSubjectToVideoNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "MinimaxTextToVideoNode": "MiniMax Text to Video", + "MinimaxImageToVideoNode": "MiniMax Image to Video", + "MinimaxSubjectToVideoNode": "MiniMax Subject to Video", +} diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..be1d2de4aeb8ae64ba1bb29cbb492efb1b53dddf --- /dev/null +++ b/comfy_api_nodes/nodes_openai.py @@ -0,0 +1,1008 @@ +import io +from typing import TypedDict, Optional +import json +import os +import time +import re +import uuid +from enum import Enum +from inspect import cleandoc +import numpy as np +import torch +from PIL import Image +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from server import PromptServer +import folder_paths + + +from comfy_api_nodes.apis import ( + OpenAIImageGenerationRequest, + OpenAIImageEditRequest, + OpenAIImageGenerationResponse, + OpenAICreateResponse, + OpenAIResponse, + CreateModelResponseProperties, + Item, + Includable, + OutputContent, + InputImageContent, + Detail, + InputTextContent, + InputMessage, + InputMessageContentList, + InputContent, + InputFileContent, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) + +from comfy_api_nodes.apinode_utils import ( + downscale_image_tensor, + validate_and_cast_response, + validate_string, + tensor_to_base64_string, + text_filepath_to_data_uri, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input + + +RESPONSES_ENDPOINT = "/proxy/openai/v1/responses" +STARTING_POINT_ID_PATTERN = r"" + + +class HistoryEntry(TypedDict): + """Type definition for a single history entry in the chat.""" + + prompt: str + response: str + response_id: str + timestamp: float + + +class ChatHistory(TypedDict): + """Type definition for the chat history dictionary.""" + + __annotations__: dict[str, list[HistoryEntry]] + + +class SupportedOpenAIModel(str, Enum): + o4_mini = "o4-mini" + o1 = "o1" + o3 = "o3" + o1_pro = "o1-pro" + gpt_4o = "gpt-4o" + gpt_4_1 = "gpt-4.1" + gpt_4_1_mini = "gpt-4.1-mini" + gpt_4_1_nano = "gpt-4.1-nano" + + +class OpenAIDalle2(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 2 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["256x256", "512x512", "1024x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }, + ), + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + seed=0, + image=None, + mask=None, + n=1, + size="1024x1024", + unique_id=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + model = "dall-e-2" + path = "/proxy/openai/images/generations" + content_type = "application/json" + request_class = OpenAIImageGenerationRequest + img_binary = None + + if image is not None and mask is not None: + path = "/proxy/openai/images/edits" + content_type = "multipart/form-data" + request_class = OpenAIImageEditRequest + + input_tensor = image.squeeze().cpu() + height, width, channels = input_tensor.shape + rgba_tensor = torch.ones(height, width, 4, device="cpu") + rgba_tensor[:, :, :channels] = input_tensor + + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu() + + rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze() + + image_np = (rgba_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr # .getvalue() + img_binary.name = "image.png" + elif image is not None or mask is not None: + raise Exception("Dall-E 2 image editing requires an image AND a mask") + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse, + ), + request=request_class( + model=model, + prompt=prompt, + n=n, + size=size, + seed=seed, + ), + files=( + { + "image": img_binary, + } + if img_binary + else None + ), + content_type=content_type, + auth_kwargs=kwargs, + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response, node_id=unique_id) + return (img_tensor,) + + +class OpenAIDalle3(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 3 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "quality": ( + IO.COMBO, + { + "options": ["standard", "hd"], + "default": "standard", + "tooltip": "Image quality", + }, + ), + "style": ( + IO.COMBO, + { + "options": ["natural", "vivid"], + "default": "natural", + "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["1024x1024", "1024x1792", "1792x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + seed=0, + style="natural", + quality="standard", + size="1024x1024", + unique_id=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + model = "dall-e-3" + + # build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/openai/images/generations", + method=HttpMethod.POST, + request_model=OpenAIImageGenerationRequest, + response_model=OpenAIImageGenerationResponse, + ), + request=OpenAIImageGenerationRequest( + model=model, + prompt=prompt, + quality=quality, + size=size, + style=style, + seed=seed, + ), + auth_kwargs=kwargs, + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response, node_id=unique_id) + return (img_tensor,) + + +class OpenAIGPTImage1(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's GPT Image 1 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for GPT Image 1", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "quality": ( + IO.COMBO, + { + "options": ["low", "medium", "high"], + "default": "low", + "tooltip": "Image quality, affects cost and generation time.", + }, + ), + "background": ( + IO.COMBO, + { + "options": ["opaque", "transparent"], + "default": "opaque", + "tooltip": "Return image with or without background", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["auto", "1024x1024", "1024x1536", "1536x1024"], + "default": "auto", + "tooltip": "Image size", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }, + ), + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + def api_call( + self, + prompt, + seed=0, + quality="low", + background="opaque", + image=None, + mask=None, + n=1, + size="1024x1024", + unique_id=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + model = "gpt-image-1" + path = "/proxy/openai/images/generations" + content_type = "application/json" + request_class = OpenAIImageGenerationRequest + img_binaries = [] + mask_binary = None + files = [] + + if image is not None: + path = "/proxy/openai/images/edits" + request_class = OpenAIImageEditRequest + content_type = "multipart/form-data" + + batch_size = image.shape[0] + + for i in range(batch_size): + single_image = image[i : i + 1] + scaled_image = downscale_image_tensor(single_image).squeeze() + + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr + img_binary.name = f"image_{i}.png" + + img_binaries.append(img_binary) + if batch_size == 1: + files.append(("image", img_binary)) + else: + files.append(("image[]", img_binary)) + + if mask is not None: + if image is None: + raise Exception("Cannot use a mask without an input image") + if image.shape[0] != 1: + raise Exception("Cannot use a mask with multiple image") + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + batch, height, width = mask.shape + rgba_mask = torch.zeros(height, width, 4, device="cpu") + rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu() + + scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0)).squeeze() + + mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_img_byte_arr = io.BytesIO() + mask_img.save(mask_img_byte_arr, format="PNG") + mask_img_byte_arr.seek(0) + mask_binary = mask_img_byte_arr + mask_binary.name = "mask.png" + files.append(("mask", mask_binary)) + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse, + ), + request=request_class( + model=model, + prompt=prompt, + quality=quality, + background=background, + n=n, + seed=seed, + size=size, + ), + files=files if files else None, + content_type=content_type, + auth_kwargs=kwargs, + ) + + response = operation.execute() + + img_tensor = validate_and_cast_response(response, node_id=unique_id) + return (img_tensor,) + + +class OpenAITextNode(ComfyNodeABC): + """ + Base class for OpenAI text generation nodes. + """ + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "api_call" + CATEGORY = "api node/text/OpenAI" + API_NODE = True + + +class OpenAIChatNode(OpenAITextNode): + """ + Node to generate text responses from an OpenAI model. + """ + + def __init__(self) -> None: + """Initialize the chat node with a new session ID and empty history.""" + self.current_session_id: str = str(uuid.uuid4()) + self.history: dict[str, list[HistoryEntry]] = {} + self.previous_response_id: Optional[str] = None + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text inputs to the model, used to generate a response.", + }, + ), + "persist_context": ( + IO.BOOLEAN, + { + "default": True, + "tooltip": "Persist chat context between calls (multi-turn conversation)", + }, + ), + "model": model_field_to_node_input( + IO.COMBO, + OpenAICreateResponse, + "model", + enum_type=SupportedOpenAIModel, + ), + }, + "optional": { + "images": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", + }, + ), + "files": ( + "OPENAI_INPUT_FILES", + { + "default": None, + "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.", + }, + ), + "advanced_options": ( + "OPENAI_CHAT_CONFIG", + { + "default": None, + "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate text responses from an OpenAI model." + + def get_result_response( + self, + response_id: str, + include: Optional[list[Includable]] = None, + auth_kwargs: Optional[dict[str, str]] = None, + ) -> OpenAIResponse: + """ + Retrieve a model response with the given ID from the OpenAI API. + + Args: + response_id (str): The ID of the response to retrieve. + include (Optional[List[Includable]]): Additional fields to include + in the response. See the `include` parameter for Response + creation above for more information. + + """ + return PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"{RESPONSES_ENDPOINT}/{response_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=OpenAIResponse, + query_params={"include": include}, + ), + completed_statuses=["completed"], + failed_statuses=["failed"], + status_extractor=lambda response: response.status, + auth_kwargs=auth_kwargs, + ).execute() + + def get_message_content_from_response( + self, response: OpenAIResponse + ) -> list[OutputContent]: + """Extract message content from the API response.""" + for output in response.output: + if output.root.type == "message": + return output.root.content + raise TypeError("No output message found in response") + + def get_text_from_message_content( + self, message_content: list[OutputContent] + ) -> str: + """Extract text content from message content.""" + for content_item in message_content: + if content_item.root.type == "output_text": + return str(content_item.root.text) + return "No text output found in response" + + def get_history_text(self, session_id: str) -> str: + """Convert the entire history for a given session to JSON string.""" + return json.dumps(self.history[session_id]) + + def display_history_on_node(self, session_id: str, node_id: str) -> None: + """Display formatted chat history on the node UI.""" + render_spec = { + "node_id": node_id, + "component": "ChatHistoryWidget", + "props": { + "history": self.get_history_text(session_id), + }, + } + PromptServer.instance.send_sync( + "display_component", + render_spec, + ) + + def add_to_history( + self, session_id: str, prompt: str, output_text: str, response_id: str + ) -> None: + """Add a new entry to the chat history.""" + if session_id not in self.history: + self.history[session_id] = [] + self.history[session_id].append( + { + "prompt": prompt, + "response": output_text, + "response_id": response_id, + "timestamp": time.time(), + } + ) + + def parse_output_text_from_response(self, response: OpenAIResponse) -> str: + """Extract text output from the API response.""" + message_contents = self.get_message_content_from_response(response) + return self.get_text_from_message_content(message_contents) + + def generate_new_session_id(self) -> str: + """Generate a new unique session ID.""" + return str(uuid.uuid4()) + + def get_session_id(self, persist_context: bool) -> str: + """Get the current or generate a new session ID based on context persistence.""" + return ( + self.current_session_id + if persist_context + else self.generate_new_session_id() + ) + + def tensor_to_input_image_content( + self, image: torch.Tensor, detail_level: Detail = "auto" + ) -> InputImageContent: + """Convert a tensor to an input image content object.""" + return InputImageContent( + detail=detail_level, + image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}", + type="input_image", + ) + + def create_input_message_contents( + self, + prompt: str, + image: Optional[torch.Tensor] = None, + files: Optional[list[InputFileContent]] = None, + ) -> InputMessageContentList: + """Create a list of input message contents from prompt and optional image.""" + content_list: list[InputContent] = [ + InputTextContent(text=prompt, type="input_text"), + ] + if image is not None: + for i in range(image.shape[0]): + content_list.append( + self.tensor_to_input_image_content(image[i].unsqueeze(0)) + ) + if files is not None: + content_list.extend(files) + + return InputMessageContentList( + root=content_list, + ) + + def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]: + """Extract response ID from prompt if it exists.""" + parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt) + return parsed_id.group(1) if parsed_id else None + + def strip_response_tag_from_prompt(self, prompt: str) -> str: + """Remove the response ID tag from the prompt.""" + return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip()) + + def delete_history_after_response_id( + self, new_start_id: str, session_id: str + ) -> None: + """Delete history entries after a specific response ID.""" + if session_id not in self.history: + return + + new_history = [] + i = 0 + while ( + i < len(self.history[session_id]) + and self.history[session_id][i]["response_id"] != new_start_id + ): + new_history.append(self.history[session_id][i]) + i += 1 + + # Since it's the new starting point (not the response being edited), we include it as well + if i < len(self.history[session_id]): + new_history.append(self.history[session_id][i]) + + self.history[session_id] = new_history + + def api_call( + self, + prompt: str, + persist_context: bool, + model: SupportedOpenAIModel, + unique_id: Optional[str] = None, + images: Optional[torch.Tensor] = None, + files: Optional[list[InputFileContent]] = None, + advanced_options: Optional[CreateModelResponseProperties] = None, + **kwargs, + ) -> tuple[str]: + # Validate inputs + validate_string(prompt, strip_whitespace=False) + + session_id = self.get_session_id(persist_context) + response_id_override = self.parse_response_id_from_prompt(prompt) + if response_id_override: + is_starting_from_beginning = response_id_override == "start" + if is_starting_from_beginning: + self.history[session_id] = [] + previous_response_id = None + else: + previous_response_id = response_id_override + self.delete_history_after_response_id(response_id_override, session_id) + prompt = self.strip_response_tag_from_prompt(prompt) + elif persist_context: + previous_response_id = self.previous_response_id + else: + previous_response_id = None + + # Create response + create_response = SynchronousOperation( + endpoint=ApiEndpoint( + path=RESPONSES_ENDPOINT, + method=HttpMethod.POST, + request_model=OpenAICreateResponse, + response_model=OpenAIResponse, + ), + request=OpenAICreateResponse( + input=[ + Item( + root=InputMessage( + content=self.create_input_message_contents( + prompt, images, files + ), + role="user", + ) + ), + ], + store=True, + stream=False, + model=model, + previous_response_id=previous_response_id, + **( + advanced_options.model_dump(exclude_none=True) + if advanced_options + else {} + ), + ), + auth_kwargs=kwargs, + ).execute() + response_id = create_response.id + + # Get result output + result_response = self.get_result_response(response_id, auth_kwargs=kwargs) + output_text = self.parse_output_text_from_response(result_response) + + # Update history + self.add_to_history(session_id, prompt, output_text, response_id) + self.display_history_on_node(session_id, unique_id) + self.previous_response_id = response_id + + return (output_text,) + + +class OpenAIInputFiles(ComfyNodeABC): + """ + Loads and formats input files for OpenAI API. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + """ + For details about the supported file input types, see: + https://platform.openai.com/docs/guides/pdf-files?api-mode=responses + """ + input_dir = folder_paths.get_input_directory() + input_files = [ + f + for f in os.scandir(input_dir) + if f.is_file() + and (f.name.endswith(".txt") or f.name.endswith(".pdf")) + and f.stat().st_size < 32 * 1024 * 1024 + ] + input_files = sorted(input_files, key=lambda x: x.name) + input_files = [f.name for f in input_files] + return { + "required": { + "file": ( + IO.COMBO, + { + "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", + "options": input_files, + "default": input_files[0] if input_files else None, + }, + ), + }, + "optional": { + "OPENAI_INPUT_FILES": ( + "OPENAI_INPUT_FILES", + { + "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", + "default": None, + }, + ), + }, + } + + DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes." + RETURN_TYPES = ("OPENAI_INPUT_FILES",) + FUNCTION = "prepare_files" + CATEGORY = "api node/text/OpenAI" + + def create_input_file_content(self, file_path: str) -> InputFileContent: + return InputFileContent( + file_data=text_filepath_to_data_uri(file_path), + filename=os.path.basename(file_path), + type="input_file", + ) + + def prepare_files( + self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = [] + ) -> tuple[list[InputFileContent]]: + """ + Loads and formats input files for OpenAI API. + """ + file_path = folder_paths.get_annotated_filepath(file) + input_file_content = self.create_input_file_content(file_path) + files = [input_file_content] + OPENAI_INPUT_FILES + return (files,) + + +class OpenAIChatConfig(ComfyNodeABC): + """Allows setting additional configuration for the OpenAI Chat Node.""" + + RETURN_TYPES = ("OPENAI_CHAT_CONFIG",) + FUNCTION = "configure" + DESCRIPTION = ( + "Allows specifying advanced configuration options for the OpenAI Chat Nodes." + ) + CATEGORY = "api node/text/OpenAI" + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "truncation": ( + IO.COMBO, + { + "options": ["auto", "disabled"], + "default": "auto", + "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error", + }, + ), + }, + "optional": { + "max_output_tokens": model_field_to_node_input( + IO.INT, + OpenAICreateResponse, + "max_output_tokens", + min=16, + default=4096, + max=16384, + tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens", + ), + "instructions": model_field_to_node_input( + IO.STRING, OpenAICreateResponse, "instructions", multiline=True + ), + }, + } + + def configure( + self, + truncation: bool, + instructions: Optional[str] = None, + max_output_tokens: Optional[int] = None, + ) -> tuple[CreateModelResponseProperties]: + """ + Configure advanced options for the OpenAI Chat Node. + + Note: + While `top_p` and `temperature` are listed as properties in the + spec, they are not supported for all models (e.g., o4-mini). + They are not exposed as inputs at all to avoid having to manually + remove depending on model choice. + """ + return ( + CreateModelResponseProperties( + instructions=instructions, + truncation=truncation, + max_output_tokens=max_output_tokens, + ), + ) + + +NODE_CLASS_MAPPINGS = { + "OpenAIDalle2": OpenAIDalle2, + "OpenAIDalle3": OpenAIDalle3, + "OpenAIGPTImage1": OpenAIGPTImage1, + "OpenAIChatNode": OpenAIChatNode, + "OpenAIInputFiles": OpenAIInputFiles, + "OpenAIChatConfig": OpenAIChatConfig, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "OpenAIDalle2": "OpenAI DALL·E 2", + "OpenAIDalle3": "OpenAI DALL·E 3", + "OpenAIGPTImage1": "OpenAI GPT Image 1", + "OpenAIChatNode": "OpenAI Chat", + "OpenAIInputFiles": "OpenAI Chat Input Files", + "OpenAIChatConfig": "OpenAI Chat Advanced Options", +} diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py new file mode 100644 index 0000000000000000000000000000000000000000..1cc7085645ebfbdd8e0415736a03c5b8b26f4412 --- /dev/null +++ b/comfy_api_nodes/nodes_pika.py @@ -0,0 +1,782 @@ +""" +Pika x ComfyUI API Nodes + +Pika API docs: https://pika-827374fb.mintlify.app/api-reference +""" +from __future__ import annotations + +import io +import logging +from typing import Optional, TypeVar + +import numpy as np +import torch + +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions +from comfy_api.input_impl import VideoFromFile +from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput +from comfy_api_nodes.apinode_utils import ( + download_url_to_video_output, + tensor_to_bytesio, +) +from comfy_api_nodes.apis import ( + IngredientsMode, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + PikaBodyGenerate22I2vGenerate22I2vPost, + PikaBodyGenerate22KeyframeGenerate22PikaframesPost, + PikaBodyGenerate22T2vGenerate22T2vPost, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + PikaDurationEnum, + Pikaffect, + PikaGenerateResponse, + PikaResolutionEnum, + PikaVideoResponse, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + EmptyRequest, + HttpMethod, + PollingOperation, + SynchronousOperation, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input + +R = TypeVar("R") + +PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions" +PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps" +PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects" + +PIKA_API_VERSION = "2.2" +PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v" +PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v" +PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes" +PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes" + +PATH_VIDEO_GET = "/proxy/pika/videos" + + +class PikaApiError(Exception): + """Exception for Pika API errors.""" + + pass + + +def is_valid_video_response(response: PikaVideoResponse) -> bool: + """Check if the video response is valid.""" + return hasattr(response, "url") and response.url is not None + + +def is_valid_initial_response(response: PikaGenerateResponse) -> bool: + """Check if the initial response is valid.""" + return hasattr(response, "video_id") and response.video_id is not None + + +class PikaNodeBase(ComfyNodeABC): + """Base class for Pika nodes.""" + + @classmethod + def get_base_inputs_types( + cls, request_model + ) -> dict[str, tuple[IO, InputTypeOptions]]: + """Get the base required inputs types common to all Pika nodes.""" + return { + "prompt_text": model_field_to_node_input( + IO.STRING, + request_model, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + request_model, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + request_model, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + "resolution": model_field_to_node_input( + IO.COMBO, + request_model, + "resolution", + enum_type=PikaResolutionEnum, + ), + "duration": model_field_to_node_input( + IO.COMBO, + request_model, + "duration", + enum_type=PikaDurationEnum, + ), + } + + CATEGORY = "api node/video/Pika" + API_NODE = True + FUNCTION = "api_call" + RETURN_TYPES = ("VIDEO",) + + def poll_for_task_status( + self, + task_id: str, + auth_kwargs: Optional[dict[str, str]] = None, + node_id: Optional[str] = None, + ) -> PikaGenerateResponse: + polling_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"{PATH_VIDEO_GET}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PikaVideoResponse, + ), + completed_statuses=[ + "finished", + ], + failed_statuses=["failed", "cancelled"], + status_extractor=lambda response: ( + response.status.value if response.status else None + ), + progress_extractor=lambda response: ( + response.progress if hasattr(response, "progress") else None + ), + auth_kwargs=auth_kwargs, + result_url_extractor=lambda response: ( + response.url if hasattr(response, "url") else None + ), + node_id=node_id, + estimated_duration=60 + ) + return polling_operation.execute() + + def execute_task( + self, + initial_operation: SynchronousOperation[R, PikaGenerateResponse], + auth_kwargs: Optional[dict[str, str]] = None, + node_id: Optional[str] = None, + ) -> tuple[VideoFromFile]: + """Executes the initial operation then polls for the task status until it is completed. + + Args: + initial_operation: The initial operation to execute. + auth_kwargs: The authentication token(s) to use for the API call. + + Returns: + A tuple containing the video file as a VIDEO output. + """ + initial_response = initial_operation.execute() + if not is_valid_initial_response(initial_response): + error_msg = f"Pika initial request failed. Code: {initial_response.code}, Message: {initial_response.message}, Data: {initial_response.data}" + logging.error(error_msg) + raise PikaApiError(error_msg) + + task_id = initial_response.video_id + final_response = self.poll_for_task_status(task_id, auth_kwargs) + if not is_valid_video_response(final_response): + error_msg = ( + f"Pika task {task_id} succeeded but no video data found in response." + ) + logging.error(error_msg) + raise PikaApiError(error_msg) + + video_url = str(final_response.url) + logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url) + + return (download_url_to_video_output(video_url),) + + +class PikaImageToVideoV2_2(PikaNodeBase): + """Pika 2.2 Image to Video Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ( + IO.IMAGE, + {"tooltip": "The image to convert to video"}, + ), + **cls.get_base_inputs_types(PikaBodyGenerate22I2vGenerate22I2vPost), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Sends an image and prompt to the Pika API v2.2 to generate a video." + + def api_call( + self, + image: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + unique_id: str, + **kwargs, + ) -> tuple[VideoFromFile]: + # Convert image to BytesIO + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + + pika_files = {"image": ("image.png", image_bytes_io, "image/png")} + + # Prepare non-file data + pika_request_data = PikaBodyGenerate22I2vGenerate22I2vPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22I2vGenerate22I2vPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +class PikaTextToVideoNodeV2_2(PikaNodeBase): + """Pika Text2Video v2.2 Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + **cls.get_base_inputs_types(PikaBodyGenerate22T2vGenerate22T2vPost), + "aspect_ratio": model_field_to_node_input( + IO.FLOAT, + PikaBodyGenerate22T2vGenerate22T2vPost, + "aspectRatio", + step=0.001, + min=0.4, + max=2.5, + default=1.7777777777777777, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Sends a text prompt to the Pika API v2.2 to generate a video." + + def api_call( + self, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + aspect_ratio: float, + unique_id: str, + **kwargs, + ) -> tuple[VideoFromFile]: + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_VIDEO, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22T2vGenerate22T2vPost, + response_model=PikaGenerateResponse, + ), + request=PikaBodyGenerate22T2vGenerate22T2vPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + aspectRatio=aspect_ratio, + ), + auth_kwargs=kwargs, + content_type="application/x-www-form-urlencoded", + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +class PikaScenesV2_2(PikaNodeBase): + """PikaScenes v2.2 Node.""" + + @classmethod + def INPUT_TYPES(cls): + image_ingredient_input = ( + IO.IMAGE, + {"tooltip": "Image that will be used as ingredient to create a video."}, + ) + return { + "required": { + **cls.get_base_inputs_types( + PikaBodyGenerate22C2vGenerate22PikascenesPost, + ), + "ingredients_mode": model_field_to_node_input( + IO.COMBO, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + "ingredientsMode", + enum_type=IngredientsMode, + default="creative", + ), + "aspect_ratio": model_field_to_node_input( + IO.FLOAT, + PikaBodyGenerate22C2vGenerate22PikascenesPost, + "aspectRatio", + step=0.001, + min=0.4, + max=2.5, + default=1.7777777777777777, + ), + }, + "optional": { + "image_ingredient_1": image_ingredient_input, + "image_ingredient_2": image_ingredient_input, + "image_ingredient_3": image_ingredient_input, + "image_ingredient_4": image_ingredient_input, + "image_ingredient_5": image_ingredient_input, + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them." + + def api_call( + self, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + ingredients_mode: str, + aspect_ratio: float, + unique_id: str, + image_ingredient_1: Optional[torch.Tensor] = None, + image_ingredient_2: Optional[torch.Tensor] = None, + image_ingredient_3: Optional[torch.Tensor] = None, + image_ingredient_4: Optional[torch.Tensor] = None, + image_ingredient_5: Optional[torch.Tensor] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Convert all passed images to BytesIO + all_image_bytes_io = [] + for image in [ + image_ingredient_1, + image_ingredient_2, + image_ingredient_3, + image_ingredient_4, + image_ingredient_5, + ]: + if image is not None: + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + all_image_bytes_io.append(image_bytes_io) + + pika_files = [ + ("images", (f"image_{i}.png", image_bytes_io, "image/png")) + for i, image_bytes_io in enumerate(all_image_bytes_io) + ] + + pika_request_data = PikaBodyGenerate22C2vGenerate22PikascenesPost( + ingredientsMode=ingredients_mode, + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + aspectRatio=aspect_ratio, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKASCENES, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22C2vGenerate22PikascenesPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +class PikAdditionsNode(PikaNodeBase): + """Pika Pikadditions Node. Add an image into a video.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to add an image to."}), + "image": (IO.IMAGE, {"tooltip": "The image to add to the video."}), + "prompt_text": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result." + + def api_call( + self, + video: VideoInput, + image: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + unique_id: str, + **kwargs, + ) -> tuple[VideoFromFile]: + # Convert video to BytesIO + video_bytes_io = io.BytesIO() + video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) + video_bytes_io.seek(0) + + # Convert image to BytesIO + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + + pika_files = [ + ("video", ("video.mp4", video_bytes_io, "video/mp4")), + ("image", ("image.png", image_bytes_io, "image/png")), + ] + + # Prepare non-file data + pika_request_data = PikaBodyGeneratePikadditionsGeneratePikadditionsPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKADDITIONS, + method=HttpMethod.POST, + request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +class PikaSwapsNode(PikaNodeBase): + """Pika Pikaswaps Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to swap an object in."}), + "image": ( + IO.IMAGE, + { + "tooltip": "The image used to replace the masked object in the video." + }, + ), + "mask": ( + IO.MASK, + {"tooltip": "Use the mask to define areas in the video to replace"}, + ), + "prompt_text": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates." + RETURN_TYPES = ("VIDEO",) + + def api_call( + self, + video: VideoInput, + image: torch.Tensor, + mask: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + unique_id: str, + **kwargs, + ) -> tuple[VideoFromFile]: + # Convert video to BytesIO + video_bytes_io = io.BytesIO() + video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) + video_bytes_io.seek(0) + + # Convert mask to binary mask with three channels + mask = torch.round(mask) + mask = mask.repeat(1, 3, 1, 1) + + # Convert 3-channel binary mask to BytesIO + mask_bytes_io = io.BytesIO() + mask_bytes_io.write(mask.numpy().astype(np.uint8)) + mask_bytes_io.seek(0) + + # Convert image to BytesIO + image_bytes_io = tensor_to_bytesio(image) + image_bytes_io.seek(0) + + pika_files = [ + ("video", ("video.mp4", video_bytes_io, "video/mp4")), + ("image", ("image.png", image_bytes_io, "image/png")), + ("modifyRegionMask", ("mask.png", mask_bytes_io, "image/png")), + ] + + # Prepare non-file data + pika_request_data = PikaBodyGeneratePikaswapsGeneratePikaswapsPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ) + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKADDITIONS, + method=HttpMethod.POST, + request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + response_model=PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +class PikaffectsNode(PikaNodeBase): + """Pika Pikaffects Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ( + IO.IMAGE, + {"tooltip": "The reference image to apply the Pikaffect to."}, + ), + "pikaffect": model_field_to_node_input( + IO.COMBO, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "pikaffect", + enum_type=Pikaffect, + default="Cake-ify", + ), + "prompt_text": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "promptText", + multiline=True, + ), + "negative_prompt": model_field_to_node_input( + IO.STRING, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "negativePrompt", + multiline=True, + ), + "seed": model_field_to_node_input( + IO.INT, + PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear" + + def api_call( + self, + image: torch.Tensor, + pikaffect: str, + prompt_text: str, + negative_prompt: str, + seed: int, + unique_id: str, + **kwargs, + ) -> tuple[VideoFromFile]: + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKAFFECTS, + method=HttpMethod.POST, + request_model=PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + response_model=PikaGenerateResponse, + ), + request=PikaBodyGeneratePikaffectsGeneratePikaffectsPost( + pikaffect=pikaffect, + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ), + files={"image": ("image.png", tensor_to_bytesio(image), "image/png")}, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +class PikaStartEndFrameNode2_2(PikaNodeBase): + """PikaFrames v2.2 Node.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_start": (IO.IMAGE, {"tooltip": "The first image to combine."}), + "image_end": (IO.IMAGE, {"tooltip": "The last image to combine."}), + **cls.get_base_inputs_types( + PikaBodyGenerate22KeyframeGenerate22PikaframesPost + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them." + + def api_call( + self, + image_start: torch.Tensor, + image_end: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + unique_id: str, + **kwargs, + ) -> tuple[VideoFromFile]: + + pika_files = [ + ( + "keyFrames", + ("image_start.png", tensor_to_bytesio(image_start), "image/png"), + ), + ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")), + ] + + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKAFRAMES, + method=HttpMethod.POST, + request_model=PikaBodyGenerate22KeyframeGenerate22PikaframesPost, + response_model=PikaGenerateResponse, + ), + request=PikaBodyGenerate22KeyframeGenerate22PikaframesPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + ), + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + + return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) + + +NODE_CLASS_MAPPINGS = { + "PikaImageToVideoNode2_2": PikaImageToVideoV2_2, + "PikaTextToVideoNode2_2": PikaTextToVideoNodeV2_2, + "PikaScenesV2_2": PikaScenesV2_2, + "Pikadditions": PikAdditionsNode, + "Pikaswaps": PikaSwapsNode, + "Pikaffects": PikaffectsNode, + "PikaStartEndFrameNode2_2": PikaStartEndFrameNode2_2, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PikaImageToVideoNode2_2": "Pika Image to Video", + "PikaTextToVideoNode2_2": "Pika Text to Video", + "PikaScenesV2_2": "Pika Scenes (Video Image Composition)", + "Pikadditions": "Pikadditions (Video Object Insertion)", + "Pikaswaps": "Pika Swaps (Video Object Replacement)", + "Pikaffects": "Pikaffects (Video Effects)", + "PikaStartEndFrameNode2_2": "Pika Start and End Frame to Video", +} diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py new file mode 100644 index 0000000000000000000000000000000000000000..ef4a9a8023f29475d3608ecaebc239e53532e1a1 --- /dev/null +++ b/comfy_api_nodes/nodes_pixverse.py @@ -0,0 +1,525 @@ +from inspect import cleandoc +from typing import Optional +from comfy_api_nodes.apis.pixverse_api import ( + PixverseTextVideoRequest, + PixverseImageVideoRequest, + PixverseTransitionVideoRequest, + PixverseImageUploadResponse, + PixverseVideoResponse, + PixverseGenerationStatusResponse, + PixverseAspectRatio, + PixverseQuality, + PixverseDuration, + PixverseMotionMode, + PixverseStatus, + PixverseIO, + pixverse_templates, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + tensor_to_bytesio, + validate_string, +) +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api.input_impl import VideoFromFile + +import torch +import requests +from io import BytesIO + + +AVERAGE_DURATION_T2V = 32 +AVERAGE_DURATION_I2V = 30 +AVERAGE_DURATION_T2T = 52 + + +def get_video_url_from_response( + response: PixverseGenerationStatusResponse, +) -> Optional[str]: + if response.Resp is None or response.Resp.url is None: + return None + return str(response.Resp.url) + + +def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None): + # first, upload image to Pixverse and get image id to use in actual generation call + files = {"image": tensor_to_bytesio(image)} + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/image/upload", + method=HttpMethod.POST, + request_model=EmptyRequest, + response_model=PixverseImageUploadResponse, + ), + request=EmptyRequest(), + files=files, + content_type="multipart/form-data", + auth_kwargs=auth_kwargs, + ) + response_upload: PixverseImageUploadResponse = operation.execute() + + if response_upload.Resp is None: + raise Exception( + f"PixVerse image upload request failed: '{response_upload.ErrMsg}'" + ) + + return response_upload.Resp.img_id + + +class PixverseTemplateNode: + """ + Select template for PixVerse Video generation. + """ + + RETURN_TYPES = (PixverseIO.TEMPLATE,) + RETURN_NAMES = ("pixverse_template",) + FUNCTION = "create_template" + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "template": (list(pixverse_templates.keys()),), + } + } + + def create_template(self, template: str): + template_id = pixverse_templates.get(template, None) + if template_id is None: + raise Exception(f"Template '{template}' is not recognized.") + # just return the integer + return (template_id,) + + +class PixverseTextToVideoNode(ComfyNodeABC): + """ + Generates videos based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],), + "quality": ( + [resolution.value for resolution in PixverseQuality], + { + "default": PixverseQuality.res_540p, + }, + ), + "duration_seconds": ([dur.value for dur in PixverseDuration],), + "motion_mode": ([mode.value for mode in PixverseMotionMode],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "control_after_generate": True, + "tooltip": "Seed for video generation.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "pixverse_template": ( + PixverseIO.TEMPLATE, + { + "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + aspect_ratio: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str = None, + pixverse_template: int = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/text/generate", + method=HttpMethod.POST, + request_model=PixverseTextVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseTextVideoRequest( + prompt=prompt, + aspect_ratio=aspect_ratio, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + template_id=pixverse_template, + seed=seed, + ), + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], + status_extractor=lambda x: x.Resp.status, + auth_kwargs=kwargs, + node_id=unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.Resp.url) + + return (VideoFromFile(BytesIO(vid_response.content)),) + + +class PixverseImageToVideoNode(ComfyNodeABC): + """ + Generates videos based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "quality": ( + [resolution.value for resolution in PixverseQuality], + { + "default": PixverseQuality.res_540p, + }, + ), + "duration_seconds": ([dur.value for dur in PixverseDuration],), + "motion_mode": ([mode.value for mode in PixverseMotionMode],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "control_after_generate": True, + "tooltip": "Seed for video generation.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "pixverse_template": ( + PixverseIO.TEMPLATE, + { + "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + image: torch.Tensor, + prompt: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str = None, + pixverse_template: int = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + img_id = upload_image_to_pixverse(image, auth_kwargs=kwargs) + + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/img/generate", + method=HttpMethod.POST, + request_model=PixverseImageVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseImageVideoRequest( + img_id=img_id, + prompt=prompt, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + template_id=pixverse_template, + seed=seed, + ), + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], + status_extractor=lambda x: x.Resp.status, + auth_kwargs=kwargs, + node_id=unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_I2V, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.Resp.url) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +class PixverseTransitionVideoNode(ComfyNodeABC): + """ + Generates videos based on prompt and output_size. + """ + + RETURN_TYPES = (IO.VIDEO,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/video/PixVerse" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "first_frame": (IO.IMAGE,), + "last_frame": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the video generation", + }, + ), + "quality": ( + [resolution.value for resolution in PixverseQuality], + { + "default": PixverseQuality.res_540p, + }, + ), + "duration_seconds": ([dur.value for dur in PixverseDuration],), + "motion_mode": ([mode.value for mode in PixverseMotionMode],), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2147483647, + "control_after_generate": True, + "tooltip": "Seed for video generation.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + first_frame: torch.Tensor, + last_frame: torch.Tensor, + prompt: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + first_frame_id = upload_image_to_pixverse(first_frame, auth_kwargs=kwargs) + last_frame_id = upload_image_to_pixverse(last_frame, auth_kwargs=kwargs) + + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/transition/generate", + method=HttpMethod.POST, + request_model=PixverseTransitionVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseTransitionVideoRequest( + first_frame_img=first_frame_id, + last_frame_img=last_frame_id, + prompt=prompt, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + seed=seed, + ), + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], + status_extractor=lambda x: x.Resp.status, + auth_kwargs=kwargs, + node_id=unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, + ) + response_poll = operation.execute() + + vid_response = requests.get(response_poll.Resp.url) + return (VideoFromFile(BytesIO(vid_response.content)),) + + +NODE_CLASS_MAPPINGS = { + "PixverseTextToVideoNode": PixverseTextToVideoNode, + "PixverseImageToVideoNode": PixverseImageToVideoNode, + "PixverseTransitionVideoNode": PixverseTransitionVideoNode, + "PixverseTemplateNode": PixverseTemplateNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PixverseTextToVideoNode": "PixVerse Text to Video", + "PixverseImageToVideoNode": "PixVerse Image to Video", + "PixverseTransitionVideoNode": "PixVerse Transition Video", + "PixverseTemplateNode": "PixVerse Template", +} diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py new file mode 100644 index 0000000000000000000000000000000000000000..e369c4b7e2830ff780b5a9f60d439951ff21b615 --- /dev/null +++ b/comfy_api_nodes/nodes_recraft.py @@ -0,0 +1,1138 @@ +from __future__ import annotations +from inspect import cleandoc +from typing import Optional +from comfy.utils import ProgressBar +from comfy_extras.nodes_images import SVG # Added +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis.recraft_api import ( + RecraftImageGenerationRequest, + RecraftImageGenerationResponse, + RecraftImageSize, + RecraftModel, + RecraftStyle, + RecraftStyleV3, + RecraftColor, + RecraftColorChain, + RecraftControls, + RecraftIO, + get_v3_substyles, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + bytesio_to_image_tensor, + download_url_to_bytesio, + tensor_to_bytesio, + resize_mask_to_image, + validate_string, +) +from server import PromptServer + +import torch +from io import BytesIO +from PIL import UnidentifiedImageError + + +def handle_recraft_file_request( + image: torch.Tensor, + path: str, + mask: torch.Tensor=None, + total_pixels=4096*4096, + timeout=1024, + request=None, + auth_kwargs: dict[str,str] = None, + ) -> list[BytesIO]: + """ + Handle sending common Recraft file-only request to get back file bytes. + """ + if request is None: + request = EmptyRequest() + + files = { + 'image': tensor_to_bytesio(image, total_pixels=total_pixels).read() + } + if mask is not None: + files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read() + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=type(request), + response_model=RecraftImageGenerationResponse, + ), + request=request, + files=files, + content_type="multipart/form-data", + auth_kwargs=auth_kwargs, + multipart_parser=recraft_multipart_parser, + ) + response: RecraftImageGenerationResponse = operation.execute() + all_bytesio = [] + if response.image is not None: + all_bytesio.append(download_url_to_bytesio(response.image.url, timeout=timeout)) + else: + for data in response.data: + all_bytesio.append(download_url_to_bytesio(data.url, timeout=timeout)) + + return all_bytesio + + +def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, converted_to_check: list[list]=None, is_list=False) -> dict: + """ + Formats data such that multipart/form-data will work with requests library + when both files and data are present. + + The OpenAI client that Recraft uses has a bizarre way of serializing lists: + + It does NOT keep track of indeces of each list, so for background_color, that must be serialized as: + 'background_color[rgb][]' = [0, 0, 255] + where the array is assigned to a key that has '[]' at the end, to signal it's an array. + + This has the consequence of nested lists having the exact same key, forcing arrays to merge; all colors inputs fall under the same key: + if 1 color -> 'controls[colors][][rgb][]' = [0, 0, 255] + if 2 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0] + if 3 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0, 0, 255, 0] + etc. + Whoever made this serialization up at OpenAI added the constraint that lists must be of uniform length on objects of same 'type'. + """ + # Modification of a function that handled a different type of multipart parsing, big ups: + # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b + + def handle_converted_lists(data, parent_key, lists_to_check=tuple[list]): + # if list already exists exists, just extend list with data + for check_list in lists_to_check: + for conv_tuple in check_list: + if conv_tuple[0] == parent_key and type(conv_tuple[1]) is list: + conv_tuple[1].append(formatter(data)) + return True + return False + + if converted_to_check is None: + converted_to_check = [] + + + if formatter is None: + formatter = lambda v: v # Multipart representation of value + + if type(data) is not dict: + # if list already exists exists, just extend list with data + added = handle_converted_lists(data, parent_key, converted_to_check) + if added: + return {} + # otherwise if is_list, create new list with data + if is_list: + return {parent_key: [formatter(data)]} + # return new key with data + return {parent_key: formatter(data)} + + converted = [] + next_check = [converted] + next_check.extend(converted_to_check) + + for key, value in data.items(): + current_key = key if parent_key is None else f"{parent_key}[{key}]" + if type(value) is dict: + converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items()) + elif type(value) is list: + for ind, list_value in enumerate(value): + iter_key = f"{current_key}[]" + converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items()) + else: + converted.append((current_key, formatter(value))) + + return dict(converted) + + +class handle_recraft_image_output: + """ + Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use. + """ + def __init__(self): + pass + + def __enter__(self): + pass + + def __exit__(self, exc_type, exc_val, exc_tb): + if exc_type is not None and exc_type is UnidentifiedImageError: + raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.") + + +class RecraftColorRGBNode: + """ + Create Recraft Color by choosing specific RGB values. + """ + + RETURN_TYPES = (RecraftIO.COLOR,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + RETURN_NAMES = ("recraft_color",) + FUNCTION = "create_color" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "r": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Red value of color." + }), + "g": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Green value of color." + }), + "b": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Blue value of color." + }), + }, + "optional": { + "recraft_color": (RecraftIO.COLOR,), + } + } + + def create_color(self, r: int, g: int, b: int, recraft_color: RecraftColorChain=None): + recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain() + recraft_color.add(RecraftColor(r, g, b)) + return (recraft_color, ) + + +class RecraftControlsNode: + """ + Create Recraft Controls for customizing Recraft generation. + """ + + RETURN_TYPES = (RecraftIO.CONTROLS,) + RETURN_NAMES = ("recraft_controls",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_controls" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "colors": (RecraftIO.COLOR,), + "background_color": (RecraftIO.COLOR,), + } + } + + def create_controls(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None): + return (RecraftControls(colors=colors, background_color=background_color), ) + + +class RecraftStyleV3RealisticImageNode: + """ + Select realistic_image style and optional substyle. + """ + + RETURN_TYPES = (RecraftIO.STYLEV3,) + RETURN_NAMES = ("recraft_style",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_style" + CATEGORY = "api node/image/Recraft" + + RECRAFT_STYLE = RecraftStyleV3.realistic_image + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "substyle": (get_v3_substyles(s.RECRAFT_STYLE),), + } + } + + def create_style(self, substyle: str): + if substyle == "None": + substyle = None + return (RecraftStyle(self.RECRAFT_STYLE, substyle),) + + +class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode): + """ + Select digital_illustration style and optional substyle. + """ + + RECRAFT_STYLE = RecraftStyleV3.digital_illustration + + +class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode): + """ + Select vector_illustration style and optional substyle. + """ + + RECRAFT_STYLE = RecraftStyleV3.vector_illustration + + +class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode): + """ + Select vector_illustration style and optional substyle. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "substyle": (get_v3_substyles(s.RECRAFT_STYLE, include_none=False),), + } + } + + RECRAFT_STYLE = RecraftStyleV3.logo_raster + + +class RecraftStyleInfiniteStyleLibrary: + """ + Select style based on preexisting UUID from Recraft's Infinite Style Library. + """ + + RETURN_TYPES = (RecraftIO.STYLEV3,) + RETURN_NAMES = ("recraft_style",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_style" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "style_id": (IO.STRING, { + "default": "", + "tooltip": "UUID of style from Infinite Style Library.", + }) + } + } + + def create_style(self, style_id: str): + if not style_id: + raise Exception("The style_id input cannot be empty.") + return (RecraftStyle(style_id=style_id),) + + +class RecraftTextToImageNode: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "size": ( + [res.value for res in RecraftImageSize], + { + "default": RecraftImageSize.res_1024x1024, + "tooltip": "The size of the generated image.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + size: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/recraft/image_generation", + method=HttpMethod.POST, + request_model=RecraftImageGenerationRequest, + response_model=RecraftImageGenerationResponse, + ), + request=RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + size=size, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + controls=controls_api, + ), + auth_kwargs=kwargs, + ) + response: RecraftImageGenerationResponse = operation.execute() + images = [] + urls = [] + for data in response.data: + with handle_recraft_image_output(): + if unique_id and data.url: + urls.append(data.url) + urls_string = '\n'.join(urls) + PromptServer.instance.send_progress_text( + f"Result URL: {urls_string}", unique_id + ) + image = bytesio_to_image_tensor( + download_url_to_bytesio(data.url, timeout=1024) + ) + if len(image.shape) < 4: + image = image.unsqueeze(0) + images.append(image) + output_image = torch.cat(images, dim=0) + + return (output_image,) + + +class RecraftImageToImageNode: + """ + Modify image based on prompt and strength. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "strength": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity." + } + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + prompt: str, + n: int, + strength: float, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + strength=round(strength, 2), + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + controls=controls_api, + ) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/imageToImage", + request=request, + auth_kwargs=kwargs, + ) + with handle_recraft_image_output(): + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftImageInpaintingNode: + """ + Modify image based on prompt and mask. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "mask": (IO.MASK, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + mask: torch.Tensor, + prompt: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + ) + + # prepare mask tensor + mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + mask=mask[i:i+1], + path="/proxy/recraft/images/inpaint", + request=request, + auth_kwargs=kwargs, + ) + with handle_recraft_image_output(): + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftTextToVectorNode: + """ + Generates SVG synchronously based on prompt and resolution. + """ + + RETURN_TYPES = ("SVG",) # Changed + DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "substyle": (get_v3_substyles(RecraftStyleV3.vector_illustration),), + "size": ( + [res.value for res in RecraftImageSize], + { + "default": RecraftImageSize.res_1024x1024, + "tooltip": "The size of the generated image.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + substyle: str, + size: str, + n: int, + seed, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None) + recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle) + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/recraft/image_generation", + method=HttpMethod.POST, + request_model=RecraftImageGenerationRequest, + response_model=RecraftImageGenerationResponse, + ), + request=RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + size=size, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + controls=controls_api, + ), + auth_kwargs=kwargs, + ) + response: RecraftImageGenerationResponse = operation.execute() + svg_data = [] + urls = [] + for data in response.data: + if unique_id and data.url: + urls.append(data.url) + # Print result on each iteration in case of error + PromptServer.instance.send_progress_text( + f"Result URL: {' '.join(urls)}", unique_id + ) + svg_data.append(download_url_to_bytesio(data.url, timeout=1024)) + + return (SVG(svg_data),) + + +class RecraftVectorizeImageNode: + """ + Generates SVG synchronously from an input image. + """ + + RETURN_TYPES = ("SVG",) # Changed + DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + **kwargs, + ): + svgs = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/vectorize", + auth_kwargs=kwargs, + ) + svgs.append(SVG(sub_bytes)) + pbar.update(1) + + return (SVG.combine_all(svgs), ) + + +class RecraftReplaceBackgroundNode: + """ + Replace background on image, based on provided prompt. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + prompt: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + **kwargs, + ): + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + ) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/replaceBackground", + request=request, + auth_kwargs=kwargs, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftRemoveBackgroundNode: + """ + Remove background from image, and return processed image and mask. + """ + + RETURN_TYPES = (IO.IMAGE, IO.MASK) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + **kwargs, + ): + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/removeBackground", + auth_kwargs=kwargs, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + # use alpha channel as masks, in B,H,W format + masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1) + return (images_tensor, masks_tensor) + + +class RecraftCrispUpscaleNode: + """ + Upscale image synchronously. + Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + RECRAFT_PATH = "/proxy/recraft/images/crispUpscale" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + image: torch.Tensor, + **kwargs, + ): + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = handle_recraft_file_request( + image=image[i], + path=self.RECRAFT_PATH, + auth_kwargs=kwargs, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor,) + + +class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode): + """ + Upscale image synchronously. + Enhances a given raster image using ‘creative upscale’ tool, boosting resolution with a focus on refining small details and faces. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale" + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "RecraftTextToImageNode": RecraftTextToImageNode, + "RecraftImageToImageNode": RecraftImageToImageNode, + "RecraftImageInpaintingNode": RecraftImageInpaintingNode, + "RecraftTextToVectorNode": RecraftTextToVectorNode, + "RecraftVectorizeImageNode": RecraftVectorizeImageNode, + "RecraftRemoveBackgroundNode": RecraftRemoveBackgroundNode, + "RecraftReplaceBackgroundNode": RecraftReplaceBackgroundNode, + "RecraftCrispUpscaleNode": RecraftCrispUpscaleNode, + "RecraftCreativeUpscaleNode": RecraftCreativeUpscaleNode, + "RecraftStyleV3RealisticImage": RecraftStyleV3RealisticImageNode, + "RecraftStyleV3DigitalIllustration": RecraftStyleV3DigitalIllustrationNode, + "RecraftStyleV3LogoRaster": RecraftStyleV3LogoRasterNode, + "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary, + "RecraftColorRGB": RecraftColorRGBNode, + "RecraftControls": RecraftControlsNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "RecraftTextToImageNode": "Recraft Text to Image", + "RecraftImageToImageNode": "Recraft Image to Image", + "RecraftImageInpaintingNode": "Recraft Image Inpainting", + "RecraftTextToVectorNode": "Recraft Text to Vector", + "RecraftVectorizeImageNode": "Recraft Vectorize Image", + "RecraftRemoveBackgroundNode": "Recraft Remove Background", + "RecraftReplaceBackgroundNode": "Recraft Replace Background", + "RecraftCrispUpscaleNode": "Recraft Crisp Upscale Image", + "RecraftCreativeUpscaleNode": "Recraft Creative Upscale Image", + "RecraftStyleV3RealisticImage": "Recraft Style - Realistic Image", + "RecraftStyleV3DigitalIllustration": "Recraft Style - Digital Illustration", + "RecraftStyleV3LogoRaster": "Recraft Style - Logo Raster", + "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library", + "RecraftColorRGB": "Recraft Color RGB", + "RecraftControls": "Recraft Controls", +} diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py new file mode 100644 index 0000000000000000000000000000000000000000..67f90478c1bc6ee6f035db8eb4338fec4fcb623e --- /dev/null +++ b/comfy_api_nodes/nodes_rodin.py @@ -0,0 +1,462 @@ +""" +ComfyUI X Rodin3D(Deemos) API Nodes + +Rodin API docs: https://developer.hyper3d.ai/ + +""" + +from __future__ import annotations +from inspect import cleandoc +from comfy.comfy_types.node_typing import IO +import folder_paths as comfy_paths +import requests +import os +import datetime +import shutil +import time +import io +import logging +import math +from PIL import Image +from comfy_api_nodes.apis.rodin_api import ( + Rodin3DGenerateRequest, + Rodin3DGenerateResponse, + Rodin3DCheckStatusRequest, + Rodin3DCheckStatusResponse, + Rodin3DDownloadRequest, + Rodin3DDownloadResponse, + JobStatus, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, +) + + +COMMON_PARAMETERS = { + "Seed": ( + IO.INT, + { + "default":0, + "min":0, + "max":65535, + "display":"number" + } + ), + "Material_Type": ( + IO.COMBO, + { + "options": ["PBR", "Shaded"], + "default": "PBR" + } + ), + "Polygon_count": ( + IO.COMBO, + { + "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"], + "default": "18K-Quad" + } + ) +} + +def create_task_error(response: Rodin3DGenerateResponse): + """Check if the response has error""" + return hasattr(response, "error") + + + +class Rodin3DAPI: + """ + Generate 3D Assets using Rodin API + """ + RETURN_TYPES = (IO.STRING,) + RETURN_NAMES = ("3D Model Path",) + CATEGORY = "api node/3d/Rodin" + DESCRIPTION = cleandoc(__doc__ or "") + FUNCTION = "api_call" + API_NODE = True + + def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048): + """ + Converts a PyTorch tensor to a file-like object. + + Args: + - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C) + where C is the number of channels (3 for RGB), H is height, and W is width. + + Returns: + - io.BytesIO: A file-like object containing the image data. + """ + array = tensor.cpu().numpy() + array = (array * 255).astype('uint8') + image = Image.fromarray(array, 'RGB') + + original_width, original_height = image.size + original_pixels = original_width * original_height + if original_pixels > max_pixels: + scale = math.sqrt(max_pixels / original_pixels) + new_width = int(original_width * scale) + new_height = int(original_height * scale) + else: + new_width, new_height = original_width, original_height + + if new_width != original_width or new_height != original_height: + image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) + + img_byte_arr = io.BytesIO() + image.save(img_byte_arr, format='PNG') # PNG is used for lossless compression + img_byte_arr.seek(0) + return img_byte_arr + + def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str: + has_failed = any(job.status == JobStatus.Failed for job in response.jobs) + all_done = all(job.status == JobStatus.Done for job in response.jobs) + status_list = [str(job.status) for job in response.jobs] + logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}") + if has_failed: + logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.") + raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.") + elif all_done: + return "DONE" + else: + return "Generating" + + def CreateGenerateTask(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs): + if images == None: + raise Exception("Rodin 3D generate requires at least 1 image.") + if len(images) >= 5: + raise Exception("Rodin 3D generate requires up to 5 image.") + + path = "/proxy/rodin/api/v2/rodin" + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=Rodin3DGenerateRequest, + response_model=Rodin3DGenerateResponse, + ), + request=Rodin3DGenerateRequest( + seed=seed, + tier=tier, + material=material, + quality=quality, + mesh_mode=mesh_mode + ), + files=[ + ( + "images", + open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image) + ) + for image in images if image is not None + ], + content_type = "multipart/form-data", + auth_kwargs=kwargs, + ) + + response = operation.execute() + + if create_task_error(response): + error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}" + logging.error(error_message) + raise Exception(error_message) + + logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!") + subscription_key = response.jobs.subscription_key + task_uuid = response.uuid + logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}") + return task_uuid, subscription_key + + def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse: + + path = "/proxy/rodin/api/v2/status" + + poll_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path = path, + method=HttpMethod.POST, + request_model=Rodin3DCheckStatusRequest, + response_model=Rodin3DCheckStatusResponse, + ), + request=Rodin3DCheckStatusRequest( + subscription_key = subscription_key + ), + completed_statuses=["DONE"], + failed_statuses=["FAILED"], + status_extractor=self.check_rodin_status, + poll_interval=3.0, + auth_kwargs=kwargs, + ) + + logging.info("[ Rodin3D API - CheckStatus ] Generate Start!") + + return poll_operation.execute() + + + + def GetRodinDownloadList(self, uuid, **kwargs) -> Rodin3DDownloadResponse: + logging.info("[ Rodin3D API - Downloading ] Generate Successfully!") + + path = "/proxy/rodin/api/v2/download" + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=Rodin3DDownloadRequest, + response_model=Rodin3DDownloadResponse, + ), + request=Rodin3DDownloadRequest( + task_uuid=uuid + ), + auth_kwargs=kwargs + ) + + return operation.execute() + + def GetQualityAndMode(self, PolyCount): + if PolyCount == "200K-Triangle": + mesh_mode = "Raw" + quality = "medium" + else: + mesh_mode = "Quad" + if PolyCount == "4K-Quad": + quality = "extra-low" + elif PolyCount == "8K-Quad": + quality = "low" + elif PolyCount == "18K-Quad": + quality = "medium" + elif PolyCount == "50K-Quad": + quality = "high" + else: + quality = "medium" + + return mesh_mode, quality + + def DownLoadFiles(self, Url_List): + Save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) + os.makedirs(Save_path, exist_ok=True) + model_file_path = None + for Item in Url_List.list: + url = Item.url + file_name = Item.name + file_path = os.path.join(Save_path, file_name) + if file_path.endswith(".glb"): + model_file_path = file_path + logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}") + max_retries = 5 + for attempt in range(max_retries): + try: + with requests.get(url, stream=True) as r: + r.raise_for_status() + with open(file_path, "wb") as f: + shutil.copyfileobj(r.raw, f) + break + except Exception as e: + logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}") + if attempt < max_retries - 1: + logging.info("Retrying...") + time.sleep(2) + else: + logging.info(f"[ Rodin3D API - download_files ] Failed to download {file_path} after {max_retries} attempts.") + + return model_file_path + + +class Rodin3D_Regular(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + **COMMON_PARAMETERS + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + Material_Type, + Polygon_count, + **kwargs + ): + tier = "Regular" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality = self.GetQualityAndMode(Polygon_count) + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +class Rodin3D_Detail(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + **COMMON_PARAMETERS + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + Material_Type, + Polygon_count, + **kwargs + ): + tier = "Detail" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality = self.GetQualityAndMode(Polygon_count) + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +class Rodin3D_Smooth(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + **COMMON_PARAMETERS + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + Material_Type, + Polygon_count, + **kwargs + ): + tier = "Smooth" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality = self.GetQualityAndMode(Polygon_count) + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +class Rodin3D_Sketch(Rodin3DAPI): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "Images": + ( + IO.IMAGE, + { + "forceInput":True, + } + ) + }, + "optional": { + "Seed": + ( + IO.INT, + { + "default":0, + "min":0, + "max":65535, + "display":"number" + } + ) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + Images, + Seed, + **kwargs + ): + tier = "Sketch" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + material_type = "PBR" + quality = "medium" + mesh_mode = "Quad" + task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs) + self.poll_for_task_status(subscription_key, **kwargs) + Download_List = self.GetRodinDownloadList(task_uuid, **kwargs) + model = self.DownLoadFiles(Download_List) + + return (model,) + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "Rodin3D_Regular": Rodin3D_Regular, + "Rodin3D_Detail": Rodin3D_Detail, + "Rodin3D_Smooth": Rodin3D_Smooth, + "Rodin3D_Sketch": Rodin3D_Sketch, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate", + "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate", + "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate", + "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate", +} diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py new file mode 100644 index 0000000000000000000000000000000000000000..af4b321f96d08cb2a4bb8f9c041f81d35e407b32 --- /dev/null +++ b/comfy_api_nodes/nodes_runway.py @@ -0,0 +1,635 @@ +"""Runway API Nodes + +API Docs: + - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete + +User Guides: + - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha + - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video + - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo + - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3 + +""" + +from typing import Union, Optional, Any +from enum import Enum + +import torch + +from comfy_api_nodes.apis import ( + RunwayImageToVideoRequest, + RunwayImageToVideoResponse, + RunwayTaskStatusResponse as TaskStatusResponse, + RunwayTaskStatusEnum as TaskStatus, + RunwayModelEnum as Model, + RunwayDurationEnum as Duration, + RunwayAspectRatioEnum as AspectRatio, + RunwayPromptImageObject, + RunwayPromptImageDetailedObject, + RunwayTextToImageRequest, + RunwayTextToImageResponse, + Model4, + ReferenceImage, + RunwayTextToImageAspectRatioEnum, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + download_url_to_video_output, + image_tensor_pair_to_batch, + validate_string, + download_url_to_image_tensor, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api.input_impl import VideoFromFile +from comfy.comfy_types.node_typing import IO, ComfyNodeABC + +PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video" +PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image" +PATH_GET_TASK_STATUS = "/proxy/runway/tasks" + +AVERAGE_DURATION_I2V_SECONDS = 64 +AVERAGE_DURATION_FLF_SECONDS = 256 +AVERAGE_DURATION_T2I_SECONDS = 41 + + +class RunwayApiError(Exception): + """Base exception for Runway API errors.""" + + pass + + +class RunwayGen4TurboAspectRatio(str, Enum): + """Aspect ratios supported for Image to Video API when using gen4_turbo model.""" + + field_1280_720 = "1280:720" + field_720_1280 = "720:1280" + field_1104_832 = "1104:832" + field_832_1104 = "832:1104" + field_960_960 = "960:960" + field_1584_672 = "1584:672" + + +class RunwayGen3aAspectRatio(str, Enum): + """Aspect ratios supported for Image to Video API when using gen3a_turbo model.""" + + field_768_1280 = "768:1280" + field_1280_768 = "1280:768" + + +def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the video URL from the task status response if it exists.""" + if response.output and len(response.output) > 0: + return response.output[0] + return None + + +# TODO: replace with updated image validation utils (upstream) +def validate_input_image(image: torch.Tensor) -> bool: + """ + Validate the input image is within the size limits for the Runway API. + See: https://docs.dev.runwayml.com/assets/inputs/#common-error-reasons + """ + return image.shape[2] < 8000 and image.shape[1] < 8000 + + +def poll_until_finished( + auth_kwargs: dict[str, str], + api_endpoint: ApiEndpoint[Any, TaskStatusResponse], + estimated_duration: Optional[int] = None, + node_id: Optional[str] = None, +) -> TaskStatusResponse: + """Polls the Runway API endpoint until the task reaches a terminal state, then returns the response.""" + return PollingOperation( + poll_endpoint=api_endpoint, + completed_statuses=[ + TaskStatus.SUCCEEDED.value, + ], + failed_statuses=[ + TaskStatus.FAILED.value, + TaskStatus.CANCELLED.value, + ], + status_extractor=lambda response: (response.status.value), + auth_kwargs=auth_kwargs, + result_url_extractor=get_video_url_from_task_status, + estimated_duration=estimated_duration, + node_id=node_id, + progress_extractor=extract_progress_from_task_status, + ).execute() + + +def extract_progress_from_task_status( + response: TaskStatusResponse, +) -> Union[float, None]: + if hasattr(response, "progress") and response.progress is not None: + return response.progress * 100 + return None + + +def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the image URL from the task status response if it exists.""" + if response.output and len(response.output) > 0: + return response.output[0] + return None + + +class RunwayVideoGenNode(ComfyNodeABC): + """Runway Video Node Base.""" + + RETURN_TYPES = ("VIDEO",) + FUNCTION = "api_call" + CATEGORY = "api node/video/Runway" + API_NODE = True + + def validate_task_created(self, response: RunwayImageToVideoResponse) -> bool: + """ + Validate the task creation response from the Runway API matches + expected format. + """ + if not bool(response.id): + raise RunwayApiError("Invalid initial response from Runway API.") + return True + + def validate_response(self, response: RunwayImageToVideoResponse) -> bool: + """ + Validate the successful task status response from the Runway API + matches expected format. + """ + if not response.output or len(response.output) == 0: + raise RunwayApiError( + "Runway task succeeded but no video data found in response." + ) + return True + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> RunwayImageToVideoResponse: + """Poll the task status until it is finished then get the response.""" + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_GET_TASK_STATUS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TaskStatusResponse, + ), + estimated_duration=AVERAGE_DURATION_FLF_SECONDS, + node_id=node_id, + ) + + def generate_video( + self, + request: RunwayImageToVideoRequest, + auth_kwargs: dict[str, str], + node_id: Optional[str] = None, + ) -> tuple[VideoFromFile]: + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=RunwayImageToVideoRequest, + response_model=RunwayImageToVideoResponse, + ), + request=request, + auth_kwargs=auth_kwargs, + ) + + initial_response = initial_operation.execute() + self.validate_task_created(initial_response) + task_id = initial_response.id + + final_response = self.get_response(task_id, auth_kwargs, node_id) + self.validate_response(final_response) + + video_url = get_video_url_from_task_status(final_response) + return (download_url_to_video_output(video_url),) + + +class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode): + """Runway Image to Video Node using Gen3a Turbo model.""" + + DESCRIPTION = "Generate a video from a single starting frame using Gen3a Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo." + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True + ), + "start_frame": ( + IO.IMAGE, + {"tooltip": "Start frame to be used for the video"}, + ), + "duration": model_field_to_node_input( + IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayImageToVideoRequest, + "ratio", + enum_type=RunwayGen3aAspectRatio, + ), + "seed": model_field_to_node_input( + IO.INT, + RunwayImageToVideoRequest, + "seed", + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + start_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Validate inputs + validate_string(prompt, min_length=1) + validate_input_image(start_frame) + + # Upload image + download_urls = upload_images_to_comfyapi( + start_frame, + max_images=1, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 1: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return self.generate_video( + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen3a_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject( + uri=str(download_urls[0]), position="first" + ) + ] + ), + ), + auth_kwargs=kwargs, + node_id=unique_id, + ) + + +class RunwayImageToVideoNodeGen4(RunwayVideoGenNode): + """Runway Image to Video Node using Gen4 Turbo model.""" + + DESCRIPTION = "Generate a video from a single starting frame using Gen4 Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video." + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True + ), + "start_frame": ( + IO.IMAGE, + {"tooltip": "Start frame to be used for the video"}, + ), + "duration": model_field_to_node_input( + IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayImageToVideoRequest, + "ratio", + enum_type=RunwayGen4TurboAspectRatio, + ), + "seed": model_field_to_node_input( + IO.INT, + RunwayImageToVideoRequest, + "seed", + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def api_call( + self, + prompt: str, + start_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Validate inputs + validate_string(prompt, min_length=1) + validate_input_image(start_frame) + + # Upload image + download_urls = upload_images_to_comfyapi( + start_frame, + max_images=1, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 1: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return self.generate_video( + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen4_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject( + uri=str(download_urls[0]), position="first" + ) + ] + ), + ), + auth_kwargs=kwargs, + node_id=unique_id, + ) + + +class RunwayFirstLastFrameNode(RunwayVideoGenNode): + """Runway First-Last Frame Node.""" + + DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3." + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> RunwayImageToVideoResponse: + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_GET_TASK_STATUS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TaskStatusResponse, + ), + estimated_duration=AVERAGE_DURATION_FLF_SECONDS, + node_id=node_id, + ) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True + ), + "start_frame": ( + IO.IMAGE, + {"tooltip": "Start frame to be used for the video"}, + ), + "end_frame": ( + IO.IMAGE, + { + "tooltip": "End frame to be used for the video. Supported for gen3a_turbo only." + }, + ), + "duration": model_field_to_node_input( + IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayImageToVideoRequest, + "ratio", + enum_type=RunwayGen3aAspectRatio, + ), + "seed": model_field_to_node_input( + IO.INT, + RunwayImageToVideoRequest, + "seed", + control_after_generate=True, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "unique_id": "UNIQUE_ID", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call( + self, + prompt: str, + start_frame: torch.Tensor, + end_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[VideoFromFile]: + # Validate inputs + validate_string(prompt, min_length=1) + validate_input_image(start_frame) + validate_input_image(end_frame) + + # Upload images + stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame) + download_urls = upload_images_to_comfyapi( + stacked_input_images, + max_images=2, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 2: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return self.generate_video( + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen3a_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject( + uri=str(download_urls[0]), position="first" + ), + RunwayPromptImageDetailedObject( + uri=str(download_urls[1]), position="last" + ), + ] + ), + ), + auth_kwargs=kwargs, + node_id=unique_id, + ) + + +class RunwayTextToImageNode(ComfyNodeABC): + """Runway Text to Image Node.""" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "api_call" + CATEGORY = "api node/image/Runway" + API_NODE = True + DESCRIPTION = "Generate an image from a text prompt using Runway's Gen 4 model. You can also include reference images to guide the generation." + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": model_field_to_node_input( + IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True + ), + "ratio": model_field_to_node_input( + IO.COMBO, + RunwayTextToImageRequest, + "ratio", + enum_type=RunwayTextToImageAspectRatioEnum, + ), + }, + "optional": { + "reference_image": ( + IO.IMAGE, + {"tooltip": "Optional reference image to guide the generation"}, + ) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + def validate_task_created(self, response: RunwayTextToImageResponse) -> bool: + """ + Validate the task creation response from the Runway API matches + expected format. + """ + if not bool(response.id): + raise RunwayApiError("Invalid initial response from Runway API.") + return True + + def validate_response(self, response: TaskStatusResponse) -> bool: + """ + Validate the successful task status response from the Runway API + matches expected format. + """ + if not response.output or len(response.output) == 0: + raise RunwayApiError( + "Runway task succeeded but no image data found in response." + ) + return True + + def get_response( + self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None + ) -> TaskStatusResponse: + """Poll the task status until it is finished then get the response.""" + return poll_until_finished( + auth_kwargs, + ApiEndpoint( + path=f"{PATH_GET_TASK_STATUS}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TaskStatusResponse, + ), + estimated_duration=AVERAGE_DURATION_T2I_SECONDS, + node_id=node_id, + ) + + def api_call( + self, + prompt: str, + ratio: str, + reference_image: Optional[torch.Tensor] = None, + unique_id: Optional[str] = None, + **kwargs, + ) -> tuple[torch.Tensor]: + # Validate inputs + validate_string(prompt, min_length=1) + + # Prepare reference images if provided + reference_images = None + if reference_image is not None: + validate_input_image(reference_image) + download_urls = upload_images_to_comfyapi( + reference_image, + max_images=1, + mime_type="image/png", + auth_kwargs=kwargs, + ) + if len(download_urls) != 1: + raise RunwayApiError("Failed to upload reference image to comfy api.") + + reference_images = [ReferenceImage(uri=str(download_urls[0]))] + + # Create request + request = RunwayTextToImageRequest( + promptText=prompt, + model=Model4.gen4_image, + ratio=ratio, + referenceImages=reference_images, + ) + + # Execute initial request + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_IMAGE, + method=HttpMethod.POST, + request_model=RunwayTextToImageRequest, + response_model=RunwayTextToImageResponse, + ), + request=request, + auth_kwargs=kwargs, + ) + + initial_response = initial_operation.execute() + self.validate_task_created(initial_response) + task_id = initial_response.id + + # Poll for completion + final_response = self.get_response( + task_id, auth_kwargs=kwargs, node_id=unique_id + ) + self.validate_response(final_response) + + # Download and return image + image_url = get_image_url_from_task_status(final_response) + return (download_url_to_image_tensor(image_url),) + + +NODE_CLASS_MAPPINGS = { + "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode, + "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a, + "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4, + "RunwayTextToImageNode": RunwayTextToImageNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RunwayFirstLastFrameNode": "Runway First-Last-Frame to Video", + "RunwayImageToVideoNodeGen3a": "Runway Image to Video (Gen3a Turbo)", + "RunwayImageToVideoNodeGen4": "Runway Image to Video (Gen4 Turbo)", + "RunwayTextToImageNode": "Runway Text to Image", +} diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py new file mode 100644 index 0000000000000000000000000000000000000000..02e4216780b937e7a01c75c775b5f1a18b8cabc3 --- /dev/null +++ b/comfy_api_nodes/nodes_stability.py @@ -0,0 +1,614 @@ +from inspect import cleandoc +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis.stability_api import ( + StabilityUpscaleConservativeRequest, + StabilityUpscaleCreativeRequest, + StabilityAsyncResponse, + StabilityResultsGetResponse, + StabilityStable3_5Request, + StabilityStableUltraRequest, + StabilityStableUltraResponse, + StabilityAspectRatio, + Stability_SD3_5_Model, + Stability_SD3_5_GenerationMode, + get_stability_style_presets, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + bytesio_to_image_tensor, + tensor_to_bytesio, + validate_string, +) + +import torch +import base64 +from io import BytesIO +from enum import Enum + + +class StabilityPollStatus(str, Enum): + finished = "finished" + in_progress = "in_progress" + failed = "failed" + + +def get_async_dummy_status(x: StabilityResultsGetResponse): + if x.name is not None or x.errors is not None: + return StabilityPollStatus.failed + elif x.finish_reason is not None: + return StabilityPollStatus.finished + return StabilityPollStatus.in_progress + + +class StabilityStableImageUltraNode: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + + "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + + "elements, colors, and subjects will lead to better results. " + + "To control the weight of a given word use the format `(word:weight)`," + + "where `word` is the word you'd like to control the weight of and `weight`" + + "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" + + "would convey a sky that was blue and green, but more green than blue." + }, + ), + "aspect_ratio": ([x.value for x in StabilityAspectRatio], + { + "default": StabilityAspectRatio.ratio_1_1, + "tooltip": "Aspect ratio of generated image.", + }, + ), + "style_preset": (get_stability_style_presets(), + { + "tooltip": "Optional desired style of generated image.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "image": (IO.IMAGE,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "A blurb of text describing what you do not wish to see in the output image. This is an advanced feature." + }, + ), + "image_denoise": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int, + negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, + **kwargs): + validate_string(prompt, strip_whitespace=False) + # prepare image binary if image present + image_binary = None + if image is not None: + image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() + else: + image_denoise = None + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/generate/ultra", + method=HttpMethod.POST, + request_model=StabilityStableUltraRequest, + response_model=StabilityStableUltraResponse, + ), + request=StabilityStableUltraRequest( + prompt=prompt, + negative_prompt=negative_prompt, + aspect_ratio=aspect_ratio, + seed=seed, + strength=image_denoise, + style_preset=style_preset, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityStableImageSD_3_5Node: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." + }, + ), + "model": ([x.value for x in Stability_SD3_5_Model],), + "aspect_ratio": ([x.value for x in StabilityAspectRatio], + { + "default": StabilityAspectRatio.ratio_1_1, + "tooltip": "Aspect ratio of generated image.", + }, + ), + "style_preset": (get_stability_style_presets(), + { + "tooltip": "Optional desired style of generated image.", + }, + ), + "cfg_scale": ( + IO.FLOAT, + { + "default": 4.0, + "min": 1.0, + "max": 10.0, + "step": 0.1, + "tooltip": "How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "image": (IO.IMAGE,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." + }, + ), + "image_denoise": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float, + negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, + **kwargs): + validate_string(prompt, strip_whitespace=False) + # prepare image binary if image present + image_binary = None + mode = Stability_SD3_5_GenerationMode.text_to_image + if image is not None: + image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() + mode = Stability_SD3_5_GenerationMode.image_to_image + aspect_ratio = None + else: + image_denoise = None + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/generate/sd3", + method=HttpMethod.POST, + request_model=StabilityStable3_5Request, + response_model=StabilityStableUltraResponse, + ), + request=StabilityStable3_5Request( + prompt=prompt, + negative_prompt=negative_prompt, + aspect_ratio=aspect_ratio, + seed=seed, + strength=image_denoise, + style_preset=style_preset, + cfg_scale=cfg_scale, + model=model, + mode=mode, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityUpscaleConservativeNode: + """ + Upscale image with minimal alterations to 4K resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." + }, + ), + "creativity": ( + IO.FLOAT, + { + "default": 0.35, + "min": 0.2, + "max": 0.5, + "step": 0.01, + "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None, + **kwargs): + validate_string(prompt, strip_whitespace=False) + image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() + + if not negative_prompt: + negative_prompt = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/conservative", + method=HttpMethod.POST, + request_model=StabilityUpscaleConservativeRequest, + response_model=StabilityStableUltraResponse, + ), + request=StabilityUpscaleConservativeRequest( + prompt=prompt, + negative_prompt=negative_prompt, + creativity=round(creativity,2), + seed=seed, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityUpscaleCreativeNode: + """ + Upscale image with minimal alterations to 4K resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." + }, + ), + "creativity": ( + IO.FLOAT, + { + "default": 0.3, + "min": 0.1, + "max": 0.5, + "step": 0.01, + "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.", + }, + ), + "style_preset": (get_stability_style_presets(), + { + "tooltip": "Optional desired style of generated image.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 4294967294, + "control_after_generate": True, + "tooltip": "The random seed used for creating the noise.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None, + **kwargs): + validate_string(prompt, strip_whitespace=False) + image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/creative", + method=HttpMethod.POST, + request_model=StabilityUpscaleCreativeRequest, + response_model=StabilityAsyncResponse, + ), + request=StabilityUpscaleCreativeRequest( + prompt=prompt, + negative_prompt=negative_prompt, + creativity=round(creativity,2), + style_preset=style_preset, + seed=seed, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/stability/v2beta/results/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=StabilityResultsGetResponse, + ), + poll_interval=3, + completed_statuses=[StabilityPollStatus.finished], + failed_statuses=[StabilityPollStatus.failed], + status_extractor=lambda x: get_async_dummy_status(x), + auth_kwargs=kwargs, + ) + response_poll: StabilityResultsGetResponse = operation.execute() + + if response_poll.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.") + + image_data = base64.b64decode(response_poll.result) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +class StabilityUpscaleFastNode: + """ + Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Stability AI" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + def api_call(self, image: torch.Tensor, + **kwargs): + image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read() + + files = { + "image": image_binary + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/fast", + method=HttpMethod.POST, + request_model=EmptyRequest, + response_model=StabilityStableUltraResponse, + ), + request=EmptyRequest(), + files=files, + content_type="multipart/form-data", + auth_kwargs=kwargs, + ) + response_api = operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return (returned_image,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "StabilityStableImageUltraNode": StabilityStableImageUltraNode, + "StabilityStableImageSD_3_5Node": StabilityStableImageSD_3_5Node, + "StabilityUpscaleConservativeNode": StabilityUpscaleConservativeNode, + "StabilityUpscaleCreativeNode": StabilityUpscaleCreativeNode, + "StabilityUpscaleFastNode": StabilityUpscaleFastNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "StabilityStableImageUltraNode": "Stability AI Stable Image Ultra", + "StabilityStableImageSD_3_5Node": "Stability AI Stable Diffusion 3.5 Image", + "StabilityUpscaleConservativeNode": "Stability AI Upscale Conservative", + "StabilityUpscaleCreativeNode": "Stability AI Upscale Creative", + "StabilityUpscaleFastNode": "Stability AI Upscale Fast", +} diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py new file mode 100644 index 0000000000000000000000000000000000000000..65f3b21f5cc1e7b11edb90f7b72cf5bbbc29bd29 --- /dev/null +++ b/comfy_api_nodes/nodes_tripo.py @@ -0,0 +1,574 @@ +import os +from folder_paths import get_output_directory +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis import ( + TripoOrientation, + TripoModelVersion, +) +from comfy_api_nodes.apis.tripo_api import ( + TripoTaskType, + TripoStyle, + TripoFileReference, + TripoFileEmptyReference, + TripoUrlReference, + TripoTaskResponse, + TripoTaskStatus, + TripoTextToModelRequest, + TripoImageToModelRequest, + TripoMultiviewToModelRequest, + TripoTextureModelRequest, + TripoRefineModelRequest, + TripoAnimateRigRequest, + TripoAnimateRetargetRequest, + TripoConvertModelRequest, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + download_url_to_bytesio, +) + + +def upload_image_to_tripo(image, **kwargs): + urls = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs) + return TripoFileReference(TripoUrlReference(url=urls[0], type="jpeg")) + +def get_model_url_from_response(response: TripoTaskResponse) -> str: + if response.data is not None: + for key in ["pbr_model", "model", "base_model"]: + if getattr(response.data.output, key, None) is not None: + return getattr(response.data.output, key) + raise RuntimeError(f"Failed to get model url from response: {response}") + + +def poll_until_finished( + kwargs: dict[str, str], + response: TripoTaskResponse, +) -> tuple[str, str]: + """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response.""" + if response.code != 0: + raise RuntimeError(f"Failed to generate mesh: {response.error}") + task_id = response.data.task_id + response_poll = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/tripo/v2/openapi/task/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=TripoTaskResponse, + ), + completed_statuses=[TripoTaskStatus.SUCCESS], + failed_statuses=[ + TripoTaskStatus.FAILED, + TripoTaskStatus.CANCELLED, + TripoTaskStatus.UNKNOWN, + TripoTaskStatus.BANNED, + TripoTaskStatus.EXPIRED, + ], + status_extractor=lambda x: x.data.status, + auth_kwargs=kwargs, + node_id=kwargs["unique_id"], + result_url_extractor=get_model_url_from_response, + progress_extractor=lambda x: x.data.progress, + ).execute() + if response_poll.data.status == TripoTaskStatus.SUCCESS: + url = get_model_url_from_response(response_poll) + bytesio = download_url_to_bytesio(url) + # Save the downloaded model file + model_file = f"tripo_model_{task_id}.glb" + with open(os.path.join(get_output_directory(), model_file), "wb") as f: + f.write(bytesio.getvalue()) + return model_file, task_id + raise RuntimeError(f"Failed to generate mesh: {response_poll}") + +class TripoTextToModelNode: + """ + Generates 3D models synchronously based on a text prompt using Tripo's API. + """ + AVERAGE_DURATION = 80 + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ("STRING", {"multiline": True}), + }, + "optional": { + "negative_prompt": ("STRING", {"multiline": True}), + "model_version": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "model_version", enum_type=TripoModelVersion), + "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"), + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "image_seed": ("INT", {"default": 42}), + "model_seed": ("INT", {"default": 42}), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "quad": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + + def generate_mesh(self, prompt, negative_prompt=None, model_version=None, style=None, texture=None, pbr=None, image_seed=None, model_seed=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs): + style_enum = None if style == "None" else style + if not prompt: + raise RuntimeError("Prompt is required") + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoTextToModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoTextToModelRequest( + type=TripoTaskType.TEXT_TO_MODEL, + prompt=prompt, + negative_prompt=negative_prompt if negative_prompt else None, + model_version=model_version, + style=style_enum, + texture=texture, + pbr=pbr, + image_seed=image_seed, + model_seed=model_seed, + texture_seed=texture_seed, + texture_quality=texture_quality, + face_limit=face_limit, + auto_size=True, + quad=quad + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoImageToModelNode: + """ + Generates 3D models synchronously based on a single image using Tripo's API. + """ + AVERAGE_DURATION = 80 + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + }, + "optional": { + "model_version": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "model_version", enum_type=TripoModelVersion), + "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"), + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "model_seed": ("INT", {"default": 42}), + "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "quad": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + + def generate_mesh(self, image, model_version=None, style=None, texture=None, pbr=None, model_seed=None, orientation=None, texture_alignment=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs): + style_enum = None if style == "None" else style + if image is None: + raise RuntimeError("Image is required") + tripo_file = upload_image_to_tripo(image, **kwargs) + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoImageToModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoImageToModelRequest( + type=TripoTaskType.IMAGE_TO_MODEL, + file=tripo_file, + model_version=model_version, + style=style_enum, + texture=texture, + pbr=pbr, + model_seed=model_seed, + orientation=orientation, + texture_alignment=texture_alignment, + texture_seed=texture_seed, + texture_quality=texture_quality, + face_limit=face_limit, + auto_size=True, + quad=quad + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoMultiviewToModelNode: + """ + Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API. + """ + AVERAGE_DURATION = 80 + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + }, + "optional": { + "image_left": ("IMAGE",), + "image_back": ("IMAGE",), + "image_right": ("IMAGE",), + "model_version": model_field_to_node_input(IO.COMBO, TripoMultiviewToModelRequest, "model_version", enum_type=TripoModelVersion), + "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation), + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "model_seed": ("INT", {"default": 42}), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "quad": ("BOOLEAN", {"default": False}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + + def generate_mesh(self, image, image_left=None, image_back=None, image_right=None, model_version=None, orientation=None, texture=None, pbr=None, model_seed=None, texture_seed=None, texture_quality=None, texture_alignment=None, face_limit=None, quad=None, **kwargs): + if image is None: + raise RuntimeError("front image for multiview is required") + images = [] + image_dict = { + "image": image, + "image_left": image_left, + "image_back": image_back, + "image_right": image_right + } + if image_left is None and image_back is None and image_right is None: + raise RuntimeError("At least one of left, back, or right image must be provided for multiview") + for image_name in ["image", "image_left", "image_back", "image_right"]: + image_ = image_dict[image_name] + if image_ is not None: + tripo_file = upload_image_to_tripo(image_, **kwargs) + images.append(tripo_file) + else: + images.append(TripoFileEmptyReference()) + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoMultiviewToModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoMultiviewToModelRequest( + type=TripoTaskType.MULTIVIEW_TO_MODEL, + files=images, + model_version=model_version, + orientation=orientation, + texture=texture, + pbr=pbr, + model_seed=model_seed, + texture_seed=texture_seed, + texture_quality=texture_quality, + texture_alignment=texture_alignment, + face_limit=face_limit, + quad=quad, + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoTextureNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_task_id": ("MODEL_TASK_ID",), + }, + "optional": { + "texture": ("BOOLEAN", {"default": True}), + "pbr": ("BOOLEAN", {"default": True}), + "texture_seed": ("INT", {"default": 42}), + "texture_quality": (["standard", "detailed"], {"default": "standard"}), + "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 80 + + def generate_mesh(self, model_task_id, texture=None, pbr=None, texture_seed=None, texture_quality=None, texture_alignment=None, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoTextureModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoTextureModelRequest( + original_model_task_id=model_task_id, + texture=texture, + pbr=pbr, + texture_seed=texture_seed, + texture_quality=texture_quality, + texture_alignment=texture_alignment + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + + +class TripoRefineNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_task_id": ("MODEL_TASK_ID", { + "tooltip": "Must be a v1.4 Tripo model" + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Refine a draft model created by v1.4 Tripo models only." + + RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) + RETURN_NAMES = ("model_file", "model task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 240 + + def generate_mesh(self, model_task_id, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoRefineModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoRefineModelRequest( + draft_model_task_id=model_task_id + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + + +class TripoRigNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_model_task_id": ("MODEL_TASK_ID",), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "RIG_TASK_ID") + RETURN_NAMES = ("model_file", "rig task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 180 + + def generate_mesh(self, original_model_task_id, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoAnimateRigRequest, + response_model=TripoTaskResponse, + ), + request=TripoAnimateRigRequest( + original_model_task_id=original_model_task_id, + out_format="glb", + spec="tripo" + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoRetargetNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_model_task_id": ("RIG_TASK_ID",), + "animation": ([ + "preset:idle", + "preset:walk", + "preset:climb", + "preset:jump", + "preset:slash", + "preset:shoot", + "preset:hurt", + "preset:fall", + "preset:turn", + ],), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("STRING", "RETARGET_TASK_ID") + RETURN_NAMES = ("model_file", "retarget task_id") + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 30 + + def generate_mesh(self, animation, original_model_task_id, **kwargs): + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoAnimateRetargetRequest, + response_model=TripoTaskResponse, + ), + request=TripoAnimateRetargetRequest( + original_model_task_id=original_model_task_id, + animation=animation, + out_format="glb", + bake_animation=True + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +class TripoConversionNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_model_task_id": ("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID",), + "format": (["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"],), + }, + "optional": { + "quad": ("BOOLEAN", {"default": False}), + "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), + "texture_size": ("INT", {"min": 128, "max": 4096, "default": 4096}), + "texture_format": (["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], {"default": "JPEG"}) + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + @classmethod + def VALIDATE_INPUTS(cls, input_types): + # The min and max of input1 and input2 are still validated because + # we didn't take `input1` or `input2` as arguments + if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"): + return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type" + return True + + RETURN_TYPES = () + FUNCTION = "generate_mesh" + CATEGORY = "api node/3d/Tripo" + API_NODE = True + OUTPUT_NODE = True + AVERAGE_DURATION = 30 + + def generate_mesh(self, original_model_task_id, format, quad, face_limit, texture_size, texture_format, **kwargs): + if not original_model_task_id: + raise RuntimeError("original_model_task_id is required") + response = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/tripo/v2/openapi/task", + method=HttpMethod.POST, + request_model=TripoConvertModelRequest, + response_model=TripoTaskResponse, + ), + request=TripoConvertModelRequest( + original_model_task_id=original_model_task_id, + format=format, + quad=quad if quad else None, + face_limit=face_limit if face_limit != -1 else None, + texture_size=texture_size if texture_size != 4096 else None, + texture_format=texture_format if texture_format != "JPEG" else None + ), + auth_kwargs=kwargs, + ).execute() + return poll_until_finished(kwargs, response) + +NODE_CLASS_MAPPINGS = { + "TripoTextToModelNode": TripoTextToModelNode, + "TripoImageToModelNode": TripoImageToModelNode, + "TripoMultiviewToModelNode": TripoMultiviewToModelNode, + "TripoTextureNode": TripoTextureNode, + "TripoRefineNode": TripoRefineNode, + "TripoRigNode": TripoRigNode, + "TripoRetargetNode": TripoRetargetNode, + "TripoConversionNode": TripoConversionNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TripoTextToModelNode": "Tripo: Text to Model", + "TripoImageToModelNode": "Tripo: Image to Model", + "TripoMultiviewToModelNode": "Tripo: Multiview to Model", + "TripoTextureNode": "Tripo: Texture model", + "TripoRefineNode": "Tripo: Refine Draft model", + "TripoRigNode": "Tripo: Rig model", + "TripoRetargetNode": "Tripo: Retarget rigged model", + "TripoConversionNode": "Tripo: Convert model", +} diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py new file mode 100644 index 0000000000000000000000000000000000000000..df846d5dd1d105f78659b0bd9b74d3c1e3803cd6 --- /dev/null +++ b/comfy_api_nodes/nodes_veo2.py @@ -0,0 +1,308 @@ +import io +import logging +import base64 +import requests +import torch +from typing import Optional + +from comfy.comfy_types.node_typing import IO, ComfyNodeABC +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis import ( + Veo2GenVidRequest, + Veo2GenVidResponse, + Veo2GenVidPollRequest, + Veo2GenVidPollResponse +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, +) + +from comfy_api_nodes.apinode_utils import ( + downscale_image_tensor, + tensor_to_base64_string +) + +AVERAGE_DURATION_VIDEO_GEN = 32 + +def convert_image_to_base64(image: torch.Tensor): + if image is None: + return None + + scaled_image = downscale_image_tensor(image, total_pixels=2048*2048) + return tensor_to_base64_string(scaled_image) + + +def get_video_url_from_response(poll_response: Veo2GenVidPollResponse) -> Optional[str]: + if ( + poll_response.response + and hasattr(poll_response.response, "videos") + and poll_response.response.videos + and len(poll_response.response.videos) > 0 + ): + video = poll_response.response.videos[0] + else: + return None + if hasattr(video, "gcsUri") and video.gcsUri: + return str(video.gcsUri) + return None + + +class VeoVideoGenerationNode(ComfyNodeABC): + """ + Generates videos from text prompts using Google's Veo API. + + This node can create videos from text descriptions and optional image inputs, + with control over parameters like aspect ratio, duration, and more. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text description of the video", + }, + ), + "aspect_ratio": ( + IO.COMBO, + { + "options": ["16:9", "9:16"], + "default": "16:9", + "tooltip": "Aspect ratio of the output video", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Negative text prompt to guide what to avoid in the video", + }, + ), + "duration_seconds": ( + IO.INT, + { + "default": 5, + "min": 5, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "Duration of the output video in seconds", + }, + ), + "enhance_prompt": ( + IO.BOOLEAN, + { + "default": True, + "tooltip": "Whether to enhance the prompt with AI assistance", + } + ), + "person_generation": ( + IO.COMBO, + { + "options": ["ALLOW", "BLOCK"], + "default": "ALLOW", + "tooltip": "Whether to allow generating people in the video", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFF, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "Seed for video generation (0 for random)", + }, + ), + "image": (IO.IMAGE, { + "default": None, + "tooltip": "Optional reference image to guide video generation", + }), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.VIDEO,) + FUNCTION = "generate_video" + CATEGORY = "api node/video/Veo" + DESCRIPTION = "Generates videos from text prompts using Google's Veo API" + API_NODE = True + + def generate_video( + self, + prompt, + aspect_ratio="16:9", + negative_prompt="", + duration_seconds=5, + enhance_prompt=True, + person_generation="ALLOW", + seed=0, + image=None, + unique_id: Optional[str] = None, + **kwargs, + ): + # Prepare the instances for the request + instances = [] + + instance = { + "prompt": prompt + } + + # Add image if provided + if image is not None: + image_base64 = convert_image_to_base64(image) + if image_base64: + instance["image"] = { + "bytesBase64Encoded": image_base64, + "mimeType": "image/png" + } + + instances.append(instance) + + # Create parameters dictionary + parameters = { + "aspectRatio": aspect_ratio, + "personGeneration": person_generation, + "durationSeconds": duration_seconds, + "enhancePrompt": enhance_prompt, + } + + # Add optional parameters if provided + if negative_prompt: + parameters["negativePrompt"] = negative_prompt + if seed > 0: + parameters["seed"] = seed + + # Initial request to start video generation + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/veo/generate", + method=HttpMethod.POST, + request_model=Veo2GenVidRequest, + response_model=Veo2GenVidResponse + ), + request=Veo2GenVidRequest( + instances=instances, + parameters=parameters + ), + auth_kwargs=kwargs, + ) + + initial_response = initial_operation.execute() + operation_name = initial_response.name + + logging.info(f"Veo generation started with operation name: {operation_name}") + + # Define status extractor function + def status_extractor(response): + # Only return "completed" if the operation is done, regardless of success or failure + # We'll check for errors after polling completes + return "completed" if response.done else "pending" + + # Define progress extractor function + def progress_extractor(response): + # Could be enhanced if the API provides progress information + return None + + # Define the polling operation + poll_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/veo/poll", + method=HttpMethod.POST, + request_model=Veo2GenVidPollRequest, + response_model=Veo2GenVidPollResponse + ), + completed_statuses=["completed"], + failed_statuses=[], # No failed statuses, we'll handle errors after polling + status_extractor=status_extractor, + progress_extractor=progress_extractor, + request=Veo2GenVidPollRequest( + operationName=operation_name + ), + auth_kwargs=kwargs, + poll_interval=5.0, + result_url_extractor=get_video_url_from_response, + node_id=unique_id, + estimated_duration=AVERAGE_DURATION_VIDEO_GEN, + ) + + # Execute the polling operation + poll_response = poll_operation.execute() + + # Now check for errors in the final response + # Check for error in poll response + if hasattr(poll_response, 'error') and poll_response.error: + error_message = f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})" + logging.error(error_message) + raise Exception(error_message) + + # Check for RAI filtered content + if (hasattr(poll_response.response, 'raiMediaFilteredCount') and + poll_response.response.raiMediaFilteredCount > 0): + + # Extract reason message if available + if (hasattr(poll_response.response, 'raiMediaFilteredReasons') and + poll_response.response.raiMediaFilteredReasons): + reason = poll_response.response.raiMediaFilteredReasons[0] + error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)" + else: + error_message = f"Content filtered by Google's Responsible AI practices ({poll_response.response.raiMediaFilteredCount} videos filtered.)" + + logging.error(error_message) + raise Exception(error_message) + + # Extract video data + video_data = None + if poll_response.response and hasattr(poll_response.response, 'videos') and poll_response.response.videos and len(poll_response.response.videos) > 0: + video = poll_response.response.videos[0] + + # Check if video is provided as base64 or URL + if hasattr(video, 'bytesBase64Encoded') and video.bytesBase64Encoded: + # Decode base64 string to bytes + video_data = base64.b64decode(video.bytesBase64Encoded) + elif hasattr(video, 'gcsUri') and video.gcsUri: + # Download from URL + video_url = video.gcsUri + video_response = requests.get(video_url) + video_data = video_response.content + else: + raise Exception("Video returned but no data or URL was provided") + else: + raise Exception("Video generation completed but no video was returned") + + if not video_data: + raise Exception("No video data was returned") + + logging.info("Video generation completed successfully") + + # Convert video data to BytesIO object + video_io = io.BytesIO(video_data) + + # Return VideoFromFile object + return (VideoFromFile(video_io),) + + +# Register the node +NODE_CLASS_MAPPINGS = { + "VeoVideoGenerationNode": VeoVideoGenerationNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "VeoVideoGenerationNode": "Google Veo2 Video Generation", +} diff --git a/comfy_api_nodes/redocly-dev.yaml b/comfy_api_nodes/redocly-dev.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9e3cab70ff18a924faf3f793a71710846beeae5 --- /dev/null +++ b/comfy_api_nodes/redocly-dev.yaml @@ -0,0 +1,10 @@ +# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. +# This is used for development purposes to generate stubs for unreleased API endpoints. +apis: + filter: + root: openapi.yaml + decorators: + filter-in: + property: tags + value: ['API Nodes'] + matchStrategy: all diff --git a/comfy_api_nodes/redocly.yaml b/comfy_api_nodes/redocly.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d102345b1ec932577e310f2e07c4e32017c189d5 --- /dev/null +++ b/comfy_api_nodes/redocly.yaml @@ -0,0 +1,10 @@ +# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. + +apis: + filter: + root: openapi.yaml + decorators: + filter-in: + property: tags + value: ['API Nodes', 'Released'] + matchStrategy: all diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..031b9fbd31b40bb8ebf58243970e529d4e78db95 --- /dev/null +++ b/comfy_api_nodes/util/validation_utils.py @@ -0,0 +1,100 @@ +import logging +from typing import Optional + +import torch +from comfy_api.input.video_types import VideoInput + + +def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]: + if len(image.shape) == 4: + return image.shape[1], image.shape[2] + elif len(image.shape) == 3: + return image.shape[0], image.shape[1] + else: + raise ValueError("Invalid image tensor shape.") + + +def validate_image_dimensions( + image: torch.Tensor, + min_width: Optional[int] = None, + max_width: Optional[int] = None, + min_height: Optional[int] = None, + max_height: Optional[int] = None, +): + height, width = get_image_dimensions(image) + + if min_width is not None and width < min_width: + raise ValueError(f"Image width must be at least {min_width}px, got {width}px") + if max_width is not None and width > max_width: + raise ValueError(f"Image width must be at most {max_width}px, got {width}px") + if min_height is not None and height < min_height: + raise ValueError( + f"Image height must be at least {min_height}px, got {height}px" + ) + if max_height is not None and height > max_height: + raise ValueError(f"Image height must be at most {max_height}px, got {height}px") + + +def validate_image_aspect_ratio( + image: torch.Tensor, + min_aspect_ratio: Optional[float] = None, + max_aspect_ratio: Optional[float] = None, +): + width, height = get_image_dimensions(image) + aspect_ratio = width / height + + if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio: + raise ValueError( + f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}" + ) + if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio: + raise ValueError( + f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}" + ) + + +def validate_video_dimensions( + video: VideoInput, + min_width: Optional[int] = None, + max_width: Optional[int] = None, + min_height: Optional[int] = None, + max_height: Optional[int] = None, +): + try: + width, height = video.get_dimensions() + except Exception as e: + logging.error("Error getting dimensions of video: %s", e) + return + + if min_width is not None and width < min_width: + raise ValueError(f"Video width must be at least {min_width}px, got {width}px") + if max_width is not None and width > max_width: + raise ValueError(f"Video width must be at most {max_width}px, got {width}px") + if min_height is not None and height < min_height: + raise ValueError( + f"Video height must be at least {min_height}px, got {height}px" + ) + if max_height is not None and height > max_height: + raise ValueError(f"Video height must be at most {max_height}px, got {height}px") + + +def validate_video_duration( + video: VideoInput, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, +): + try: + duration = video.get_duration() + except Exception as e: + logging.error("Error getting duration of video: %s", e) + return + + epsilon = 0.0001 + if min_duration is not None and min_duration - epsilon > duration: + raise ValueError( + f"Video duration must be at least {min_duration}s, got {duration}s" + ) + if max_duration is not None and duration > max_duration + epsilon: + raise ValueError( + f"Video duration must be at most {max_duration}s, got {duration}s" + ) diff --git a/comfy_config/config_parser.py b/comfy_config/config_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..8da7bd901f421f3aaa5b620c9595313c30585ae1 --- /dev/null +++ b/comfy_config/config_parser.py @@ -0,0 +1,152 @@ +import os +from pathlib import Path +from typing import Optional + +from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource + +from comfy_config.types import ( + ComfyConfig, + ProjectConfig, + PyProjectConfig, + PyProjectSettings +) + +def validate_and_extract_os_classifiers(classifiers: list) -> list: + os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")] + if not os_classifiers: + return [] + + os_values = [c[len("Operating System :: ") :] for c in os_classifiers] + valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"} + + for os_value in os_values: + if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes): + return [] + + return os_values + + +def validate_and_extract_accelerator_classifiers(classifiers: list) -> list: + accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")] + if not accelerator_classifiers: + return [] + + accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers] + + valid_accelerators = { + "GPU :: NVIDIA CUDA", + "GPU :: AMD ROCm", + "GPU :: Intel Arc", + "NPU :: Huawei Ascend", + "GPU :: Apple Metal", + } + + for accelerator_value in accelerator_values: + if accelerator_value not in valid_accelerators: + return [] + + return accelerator_values + + +""" +Extract configuration from a custom node directory's pyproject.toml file or a Python file. + +This function reads and parses the pyproject.toml file in the specified directory +to extract project and ComfyUI-specific configuration information. If no +pyproject.toml file is found, it creates a minimal configuration using the +folder name as the project name. If a Python file is provided, it uses the +file name (without extension) as the project name. + +Args: + path (str): Path to the directory containing the pyproject.toml file, or + path to a .py file. If pyproject.toml doesn't exist in a directory, + the folder name will be used as the default project name. If a .py + file is provided, the filename (without .py extension) will be used + as the project name. + +Returns: + Optional[PyProjectConfig]: A PyProjectConfig object containing: + - project: Basic project information (name, version, dependencies, etc.) + - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.) + Returns None if configuration extraction fails or if the provided file + is not a Python file. + +Notes: + - If pyproject.toml is missing in a directory, creates a default config with folder name + - If a .py file is provided, creates a default config with filename (without extension) + - Returns None for non-Python files + +Example: + >>> from comfy_config import config_parser + >>> # For directory + >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__)) + >>> project_config = config_parser.extract_node_configuration(custom_node_dir) + >>> print(project_config.project.name) # "my_custom_node" or name from pyproject.toml + >>> + >>> # For single-file Python node file + >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py" + >>> project_config = config_parser.extract_node_configuration(py_file_path) + >>> print(project_config.project.name) # "my_node" +""" +def extract_node_configuration(path) -> Optional[PyProjectConfig]: + if os.path.isfile(path): + file_path = Path(path) + + if file_path.suffix.lower() != '.py': + return None + + project_name = file_path.stem + project = ProjectConfig(name=project_name) + comfy = ComfyConfig() + return PyProjectConfig(project=project, tool_comfy=comfy) + + folder_name = os.path.basename(path) + toml_path = Path(path) / "pyproject.toml" + + if not toml_path.exists(): + project = ProjectConfig(name=folder_name) + comfy = ComfyConfig() + return PyProjectConfig(project=project, tool_comfy=comfy) + + raw_settings = load_pyproject_settings(toml_path) + + project_data = raw_settings.project + + tool_data = raw_settings.tool + comfy_data = tool_data.get("comfy", {}) if tool_data else {} + + dependencies = project_data.get("dependencies", []) + supported_comfyui_frontend_version = "" + for dep in dependencies: + if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"): + supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package") + break + + supported_comfyui_version = comfy_data.get("requires-comfyui", "") + + classifiers = project_data.get('classifiers', []) + supported_os = validate_and_extract_os_classifiers(classifiers) + supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers) + + project_data['supported_os'] = supported_os + project_data['supported_accelerators'] = supported_accelerators + project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version + project_data['supported_comfyui_version'] = supported_comfyui_version + + return PyProjectConfig(project=project_data, tool_comfy=comfy_data) + + +def load_pyproject_settings(toml_path: Path) -> PyProjectSettings: + class PyProjectLoader(PyProjectSettings): + @classmethod + def settings_customise_sources( + cls, + settings_cls, + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ): + return (TomlConfigSettingsSource(settings_cls, toml_path),) + + return PyProjectLoader() diff --git a/comfy_config/types.py b/comfy_config/types.py new file mode 100644 index 0000000000000000000000000000000000000000..59448466b2cb6725c622fea1151f51e521d850fc --- /dev/null +++ b/comfy_config/types.py @@ -0,0 +1,97 @@ +from pydantic import BaseModel, Field, field_validator +from pydantic_settings import BaseSettings, SettingsConfigDict +from typing import List, Optional + +# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes +# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py. +# Any changes to one must be reflected in the other to maintain consistency. + +class NodeVersion(BaseModel): + changelog: str + dependencies: List[str] + deprecated: bool + id: str + version: str + download_url: str + + +class Node(BaseModel): + id: str + name: str + description: str + author: Optional[str] = None + license: Optional[str] = None + icon: Optional[str] = None + repository: Optional[str] = None + tags: List[str] = Field(default_factory=list) + latest_version: Optional[NodeVersion] = None + + +class PublishNodeVersionResponse(BaseModel): + node_version: NodeVersion + signedUrl: str + + +class URLs(BaseModel): + homepage: str = Field(default="", alias="Homepage") + documentation: str = Field(default="", alias="Documentation") + repository: str = Field(default="", alias="Repository") + issues: str = Field(default="", alias="Issues") + + +class Model(BaseModel): + location: str + model_url: str + + +class ComfyConfig(BaseModel): + publisher_id: str = Field(default="", alias="PublisherId") + display_name: str = Field(default="", alias="DisplayName") + icon: str = Field(default="", alias="Icon") + models: List[Model] = Field(default_factory=list, alias="Models") + includes: List[str] = Field(default_factory=list) + web: Optional[str] = None + banner_url: str = "" + +class License(BaseModel): + file: str = "" + text: str = "" + + +class ProjectConfig(BaseModel): + name: str = "" + description: str = "" + version: str = "1.0.0" + requires_python: str = Field(default=">= 3.9", alias="requires-python") + dependencies: List[str] = Field(default_factory=list) + license: License = Field(default_factory=License) + urls: URLs = Field(default_factory=URLs) + supported_os: List[str] = Field(default_factory=list) + supported_accelerators: List[str] = Field(default_factory=list) + supported_comfyui_version: str = "" + supported_comfyui_frontend_version: str = "" + + @field_validator('license', mode='before') + @classmethod + def validate_license(cls, v): + if isinstance(v, str): + return License(text=v) + elif isinstance(v, dict): + return License(**v) + elif isinstance(v, License): + return v + else: + return License() + + +class PyProjectConfig(BaseModel): + project: ProjectConfig = Field(default_factory=ProjectConfig) + tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig) + + +class PyProjectSettings(BaseSettings): + project: dict = Field(default_factory=dict) + + tool: dict = Field(default_factory=dict) + + model_config = SettingsConfigDict(extra='allow') diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb37b89fafc0ac21916498c3f7da671f6eafd96 --- /dev/null +++ b/comfy_execution/caching.py @@ -0,0 +1,471 @@ +import itertools +from typing import Sequence, Mapping, Dict +from comfy_execution.graph import DynamicPrompt + +import nodes + +from comfy_execution.graph_utils import is_link + +NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {} + + +def include_unique_id_in_input(class_type: str) -> bool: + if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID: + return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values() + return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] + +class CacheKeySet: + def __init__(self, dynprompt, node_ids, is_changed_cache): + self.keys = {} + self.subcache_keys = {} + + def add_keys(self, node_ids): + raise NotImplementedError() + + def all_node_ids(self): + return set(self.keys.keys()) + + def get_used_keys(self): + return self.keys.values() + + def get_used_subcache_keys(self): + return self.subcache_keys.values() + + def get_data_key(self, node_id): + return self.keys.get(node_id, None) + + def get_subcache_key(self, node_id): + return self.subcache_keys.get(node_id, None) + +class Unhashable: + def __init__(self): + self.value = float("NaN") + +def to_hashable(obj): + # So that we don't infinitely recurse since frozenset and tuples + # are Sequences. + if isinstance(obj, (int, float, str, bool, type(None))): + return obj + elif isinstance(obj, Mapping): + return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())]) + elif isinstance(obj, Sequence): + return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj])) + else: + # TODO - Support other objects like tensors? + return Unhashable() + +class CacheKeySetID(CacheKeySet): + def __init__(self, dynprompt, node_ids, is_changed_cache): + super().__init__(dynprompt, node_ids, is_changed_cache) + self.dynprompt = dynprompt + self.add_keys(node_ids) + + def add_keys(self, node_ids): + for node_id in node_ids: + if node_id in self.keys: + continue + if not self.dynprompt.has_node(node_id): + continue + node = self.dynprompt.get_node(node_id) + self.keys[node_id] = (node_id, node["class_type"]) + self.subcache_keys[node_id] = (node_id, node["class_type"]) + +class CacheKeySetInputSignature(CacheKeySet): + def __init__(self, dynprompt, node_ids, is_changed_cache): + super().__init__(dynprompt, node_ids, is_changed_cache) + self.dynprompt = dynprompt + self.is_changed_cache = is_changed_cache + self.add_keys(node_ids) + + def include_node_id_in_input(self) -> bool: + return False + + def add_keys(self, node_ids): + for node_id in node_ids: + if node_id in self.keys: + continue + if not self.dynprompt.has_node(node_id): + continue + node = self.dynprompt.get_node(node_id) + self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id) + self.subcache_keys[node_id] = (node_id, node["class_type"]) + + def get_node_signature(self, dynprompt, node_id): + signature = [] + ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id) + signature.append(self.get_immediate_node_signature(dynprompt, node_id, order_mapping)) + for ancestor_id in ancestors: + signature.append(self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping)) + return to_hashable(signature) + + def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping): + if not dynprompt.has_node(node_id): + # This node doesn't exist -- we can't cache it. + return [float("NaN")] + node = dynprompt.get_node(node_id) + class_type = node["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + signature = [class_type, self.is_changed_cache.get(node_id)] + if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type): + signature.append(node_id) + inputs = node["inputs"] + for key in sorted(inputs.keys()): + if is_link(inputs[key]): + (ancestor_id, ancestor_socket) = inputs[key] + ancestor_index = ancestor_order_mapping[ancestor_id] + signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket))) + else: + signature.append((key, inputs[key])) + return signature + + # This function returns a list of all ancestors of the given node. The order of the list is + # deterministic based on which specific inputs the ancestor is connected by. + def get_ordered_ancestry(self, dynprompt, node_id): + ancestors = [] + order_mapping = {} + self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping) + return ancestors, order_mapping + + def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping): + if not dynprompt.has_node(node_id): + return + inputs = dynprompt.get_node(node_id)["inputs"] + input_keys = sorted(inputs.keys()) + for key in input_keys: + if is_link(inputs[key]): + ancestor_id = inputs[key][0] + if ancestor_id not in order_mapping: + ancestors.append(ancestor_id) + order_mapping[ancestor_id] = len(ancestors) - 1 + self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping) + +class BasicCache: + def __init__(self, key_class): + self.key_class = key_class + self.initialized = False + self.dynprompt: DynamicPrompt + self.cache_key_set: CacheKeySet + self.cache = {} + self.subcaches = {} + + def set_prompt(self, dynprompt, node_ids, is_changed_cache): + self.dynprompt = dynprompt + self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache) + self.is_changed_cache = is_changed_cache + self.initialized = True + + def all_node_ids(self): + assert self.initialized + node_ids = self.cache_key_set.all_node_ids() + for subcache in self.subcaches.values(): + node_ids = node_ids.union(subcache.all_node_ids()) + return node_ids + + def _clean_cache(self): + preserve_keys = set(self.cache_key_set.get_used_keys()) + to_remove = [] + for key in self.cache: + if key not in preserve_keys: + to_remove.append(key) + for key in to_remove: + del self.cache[key] + + def _clean_subcaches(self): + preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys()) + + to_remove = [] + for key in self.subcaches: + if key not in preserve_subcaches: + to_remove.append(key) + for key in to_remove: + del self.subcaches[key] + + def clean_unused(self): + assert self.initialized + self._clean_cache() + self._clean_subcaches() + + def _set_immediate(self, node_id, value): + assert self.initialized + cache_key = self.cache_key_set.get_data_key(node_id) + self.cache[cache_key] = value + + def _get_immediate(self, node_id): + if not self.initialized: + return None + cache_key = self.cache_key_set.get_data_key(node_id) + if cache_key in self.cache: + return self.cache[cache_key] + else: + return None + + def _ensure_subcache(self, node_id, children_ids): + subcache_key = self.cache_key_set.get_subcache_key(node_id) + subcache = self.subcaches.get(subcache_key, None) + if subcache is None: + subcache = BasicCache(self.key_class) + self.subcaches[subcache_key] = subcache + subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache) + return subcache + + def _get_subcache(self, node_id): + assert self.initialized + subcache_key = self.cache_key_set.get_subcache_key(node_id) + if subcache_key in self.subcaches: + return self.subcaches[subcache_key] + else: + return None + + def recursive_debug_dump(self): + result = [] + for key in self.cache: + result.append({"key": key, "value": self.cache[key]}) + for key in self.subcaches: + result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()}) + return result + +class HierarchicalCache(BasicCache): + def __init__(self, key_class): + super().__init__(key_class) + + def _get_cache_for(self, node_id): + assert self.dynprompt is not None + parent_id = self.dynprompt.get_parent_node_id(node_id) + if parent_id is None: + return self + + hierarchy = [] + while parent_id is not None: + hierarchy.append(parent_id) + parent_id = self.dynprompt.get_parent_node_id(parent_id) + + cache = self + for parent_id in reversed(hierarchy): + cache = cache._get_subcache(parent_id) + if cache is None: + return None + return cache + + def get(self, node_id): + cache = self._get_cache_for(node_id) + if cache is None: + return None + return cache._get_immediate(node_id) + + def set(self, node_id, value): + cache = self._get_cache_for(node_id) + assert cache is not None + cache._set_immediate(node_id, value) + + def ensure_subcache_for(self, node_id, children_ids): + cache = self._get_cache_for(node_id) + assert cache is not None + return cache._ensure_subcache(node_id, children_ids) + +class LRUCache(BasicCache): + def __init__(self, key_class, max_size=100): + super().__init__(key_class) + self.max_size = max_size + self.min_generation = 0 + self.generation = 0 + self.used_generation = {} + self.children = {} + + def set_prompt(self, dynprompt, node_ids, is_changed_cache): + super().set_prompt(dynprompt, node_ids, is_changed_cache) + self.generation += 1 + for node_id in node_ids: + self._mark_used(node_id) + + def clean_unused(self): + while len(self.cache) > self.max_size and self.min_generation < self.generation: + self.min_generation += 1 + to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation] + for key in to_remove: + del self.cache[key] + del self.used_generation[key] + if key in self.children: + del self.children[key] + self._clean_subcaches() + + def get(self, node_id): + self._mark_used(node_id) + return self._get_immediate(node_id) + + def _mark_used(self, node_id): + cache_key = self.cache_key_set.get_data_key(node_id) + if cache_key is not None: + self.used_generation[cache_key] = self.generation + + def set(self, node_id, value): + self._mark_used(node_id) + return self._set_immediate(node_id, value) + + def ensure_subcache_for(self, node_id, children_ids): + # Just uses subcaches for tracking 'live' nodes + super()._ensure_subcache(node_id, children_ids) + + self.cache_key_set.add_keys(children_ids) + self._mark_used(node_id) + cache_key = self.cache_key_set.get_data_key(node_id) + self.children[cache_key] = [] + for child_id in children_ids: + self._mark_used(child_id) + self.children[cache_key].append(self.cache_key_set.get_data_key(child_id)) + return self + + +class DependencyAwareCache(BasicCache): + """ + A cache implementation that tracks dependencies between nodes and manages + their execution and caching accordingly. It extends the BasicCache class. + Nodes are removed from this cache once all of their descendants have been + executed. + """ + + def __init__(self, key_class): + """ + Initialize the DependencyAwareCache. + + Args: + key_class: The class used for generating cache keys. + """ + super().__init__(key_class) + self.descendants = {} # Maps node_id -> set of descendant node_ids + self.ancestors = {} # Maps node_id -> set of ancestor node_ids + self.executed_nodes = set() # Tracks nodes that have been executed + + def set_prompt(self, dynprompt, node_ids, is_changed_cache): + """ + Clear the entire cache and rebuild the dependency graph. + + Args: + dynprompt: The dynamic prompt object containing node information. + node_ids: List of node IDs to initialize the cache for. + is_changed_cache: Flag indicating if the cache has changed. + """ + # Clear all existing cache data + self.cache.clear() + self.subcaches.clear() + self.descendants.clear() + self.ancestors.clear() + self.executed_nodes.clear() + + # Call the parent method to initialize the cache with the new prompt + super().set_prompt(dynprompt, node_ids, is_changed_cache) + + # Rebuild the dependency graph + self._build_dependency_graph(dynprompt, node_ids) + + def _build_dependency_graph(self, dynprompt, node_ids): + """ + Build the dependency graph for all nodes. + + Args: + dynprompt: The dynamic prompt object containing node information. + node_ids: List of node IDs to build the graph for. + """ + self.descendants.clear() + self.ancestors.clear() + for node_id in node_ids: + self.descendants[node_id] = set() + self.ancestors[node_id] = set() + + for node_id in node_ids: + inputs = dynprompt.get_node(node_id)["inputs"] + for input_data in inputs.values(): + if is_link(input_data): # Check if the input is a link to another node + ancestor_id = input_data[0] + self.descendants[ancestor_id].add(node_id) + self.ancestors[node_id].add(ancestor_id) + + def set(self, node_id, value): + """ + Mark a node as executed and store its value in the cache. + + Args: + node_id: The ID of the node to store. + value: The value to store for the node. + """ + self._set_immediate(node_id, value) + self.executed_nodes.add(node_id) + self._cleanup_ancestors(node_id) + + def get(self, node_id): + """ + Retrieve the cached value for a node. + + Args: + node_id: The ID of the node to retrieve. + + Returns: + The cached value for the node. + """ + return self._get_immediate(node_id) + + def ensure_subcache_for(self, node_id, children_ids): + """ + Ensure a subcache exists for a node and update dependencies. + + Args: + node_id: The ID of the parent node. + children_ids: List of child node IDs to associate with the parent node. + + Returns: + The subcache object for the node. + """ + subcache = super()._ensure_subcache(node_id, children_ids) + for child_id in children_ids: + self.descendants[node_id].add(child_id) + self.ancestors[child_id].add(node_id) + return subcache + + def _cleanup_ancestors(self, node_id): + """ + Check if ancestors of a node can be removed from the cache. + + Args: + node_id: The ID of the node whose ancestors are to be checked. + """ + for ancestor_id in self.ancestors.get(node_id, []): + if ancestor_id in self.executed_nodes: + # Remove ancestor if all its descendants have been executed + if all(descendant in self.executed_nodes for descendant in self.descendants[ancestor_id]): + self._remove_node(ancestor_id) + + def _remove_node(self, node_id): + """ + Remove a node from the cache. + + Args: + node_id: The ID of the node to remove. + """ + cache_key = self.cache_key_set.get_data_key(node_id) + if cache_key in self.cache: + del self.cache[cache_key] + subcache_key = self.cache_key_set.get_subcache_key(node_id) + if subcache_key in self.subcaches: + del self.subcaches[subcache_key] + + def clean_unused(self): + """ + Clean up unused nodes. This is a no-op for this cache implementation. + """ + pass + + def recursive_debug_dump(self): + """ + Dump the cache and dependency graph for debugging. + + Returns: + A list containing the cache state and dependency graph. + """ + result = super().recursive_debug_dump() + result.append({ + "descendants": self.descendants, + "ancestors": self.ancestors, + "executed_nodes": list(self.executed_nodes), + }) + return result diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..a2799b52e1020c1dde743134c3d2eb3bf953b4a8 --- /dev/null +++ b/comfy_execution/graph.py @@ -0,0 +1,288 @@ +from __future__ import annotations +from typing import Type, Literal + +import nodes +from comfy_execution.graph_utils import is_link +from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions + +class DependencyCycleError(Exception): + pass + +class NodeInputError(Exception): + pass + +class NodeNotFoundError(Exception): + pass + +class DynamicPrompt: + def __init__(self, original_prompt): + # The original prompt provided by the user + self.original_prompt = original_prompt + # Any extra pieces of the graph created during execution + self.ephemeral_prompt = {} + self.ephemeral_parents = {} + self.ephemeral_display = {} + + def get_node(self, node_id): + if node_id in self.ephemeral_prompt: + return self.ephemeral_prompt[node_id] + if node_id in self.original_prompt: + return self.original_prompt[node_id] + raise NodeNotFoundError(f"Node {node_id} not found") + + def has_node(self, node_id): + return node_id in self.original_prompt or node_id in self.ephemeral_prompt + + def add_ephemeral_node(self, node_id, node_info, parent_id, display_id): + self.ephemeral_prompt[node_id] = node_info + self.ephemeral_parents[node_id] = parent_id + self.ephemeral_display[node_id] = display_id + + def get_real_node_id(self, node_id): + while node_id in self.ephemeral_parents: + node_id = self.ephemeral_parents[node_id] + return node_id + + def get_parent_node_id(self, node_id): + return self.ephemeral_parents.get(node_id, None) + + def get_display_node_id(self, node_id): + while node_id in self.ephemeral_display: + node_id = self.ephemeral_display[node_id] + return node_id + + def all_node_ids(self): + return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys())) + + def get_original_prompt(self): + return self.original_prompt + +def get_input_info( + class_def: Type[ComfyNodeABC], + input_name: str, + valid_inputs: InputTypeDict | None = None +) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]: + """Get the input type, category, and extra info for a given input name. + + Arguments: + class_def: The class definition of the node. + input_name: The name of the input to get info for. + valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES(). + + Returns: + tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name. + """ + + valid_inputs = valid_inputs or class_def.INPUT_TYPES() + input_info = None + input_category = None + if "required" in valid_inputs and input_name in valid_inputs["required"]: + input_category = "required" + input_info = valid_inputs["required"][input_name] + elif "optional" in valid_inputs and input_name in valid_inputs["optional"]: + input_category = "optional" + input_info = valid_inputs["optional"][input_name] + elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]: + input_category = "hidden" + input_info = valid_inputs["hidden"][input_name] + if input_info is None: + return None, None, None + input_type = input_info[0] + if len(input_info) > 1: + extra_info = input_info[1] + else: + extra_info = {} + return input_type, input_category, extra_info + +class TopologicalSort: + def __init__(self, dynprompt): + self.dynprompt = dynprompt + self.pendingNodes = {} + self.blockCount = {} # Number of nodes this node is directly blocked by + self.blocking = {} # Which nodes are blocked by this node + + def get_input_info(self, unique_id, input_name): + class_type = self.dynprompt.get_node(unique_id)["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + return get_input_info(class_def, input_name) + + def make_input_strong_link(self, to_node_id, to_input): + inputs = self.dynprompt.get_node(to_node_id)["inputs"] + if to_input not in inputs: + raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all") + value = inputs[to_input] + if not is_link(value): + raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant") + from_node_id, from_socket = value + self.add_strong_link(from_node_id, from_socket, to_node_id) + + def add_strong_link(self, from_node_id, from_socket, to_node_id): + if not self.is_cached(from_node_id): + self.add_node(from_node_id) + if to_node_id not in self.blocking[from_node_id]: + self.blocking[from_node_id][to_node_id] = {} + self.blockCount[to_node_id] += 1 + self.blocking[from_node_id][to_node_id][from_socket] = True + + def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None): + node_ids = [node_unique_id] + links = [] + + while len(node_ids) > 0: + unique_id = node_ids.pop() + if unique_id in self.pendingNodes: + continue + + self.pendingNodes[unique_id] = True + self.blockCount[unique_id] = 0 + self.blocking[unique_id] = {} + + inputs = self.dynprompt.get_node(unique_id)["inputs"] + for input_name in inputs: + value = inputs[input_name] + if is_link(value): + from_node_id, from_socket = value + if subgraph_nodes is not None and from_node_id not in subgraph_nodes: + continue + _, _, input_info = self.get_input_info(unique_id, input_name) + is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"] + if (include_lazy or not is_lazy) and not self.is_cached(from_node_id): + node_ids.append(from_node_id) + links.append((from_node_id, from_socket, unique_id)) + + for link in links: + self.add_strong_link(*link) + + def is_cached(self, node_id): + return False + + def get_ready_nodes(self): + return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0] + + def pop_node(self, unique_id): + del self.pendingNodes[unique_id] + for blocked_node_id in self.blocking[unique_id]: + self.blockCount[blocked_node_id] -= 1 + del self.blocking[unique_id] + + def is_empty(self): + return len(self.pendingNodes) == 0 + +class ExecutionList(TopologicalSort): + """ + ExecutionList implements a topological dissolve of the graph. After a node is staged for execution, + it can still be returned to the graph after having further dependencies added. + """ + def __init__(self, dynprompt, output_cache): + super().__init__(dynprompt) + self.output_cache = output_cache + self.staged_node_id = None + + def is_cached(self, node_id): + return self.output_cache.get(node_id) is not None + + def stage_node_execution(self): + assert self.staged_node_id is None + if self.is_empty(): + return None, None, None + available = self.get_ready_nodes() + if len(available) == 0: + cycled_nodes = self.get_nodes_in_cycle() + # Because cycles composed entirely of static nodes are caught during initial validation, + # we will 'blame' the first node in the cycle that is not a static node. + blamed_node = cycled_nodes[0] + for node_id in cycled_nodes: + display_node_id = self.dynprompt.get_display_node_id(node_id) + if display_node_id != node_id: + blamed_node = display_node_id + break + ex = DependencyCycleError("Dependency cycle detected") + error_details = { + "node_id": blamed_node, + "exception_message": str(ex), + "exception_type": "graph.DependencyCycleError", + "traceback": [], + "current_inputs": [] + } + return None, error_details, ex + + self.staged_node_id = self.ux_friendly_pick_node(available) + return self.staged_node_id, None, None + + def ux_friendly_pick_node(self, node_list): + # If an output node is available, do that first. + # Technically this has no effect on the overall length of execution, but it feels better as a user + # for a PreviewImage to display a result as soon as it can + # Some other heuristics could probably be used here to improve the UX further. + def is_output(node_id): + class_type = self.dynprompt.get_node(node_id)["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: + return True + return False + + for node_id in node_list: + if is_output(node_id): + return node_id + + #This should handle the VAEDecode -> preview case + for node_id in node_list: + for blocked_node_id in self.blocking[node_id]: + if is_output(blocked_node_id): + return node_id + + #This should handle the VAELoader -> VAEDecode -> preview case + for node_id in node_list: + for blocked_node_id in self.blocking[node_id]: + for blocked_node_id1 in self.blocking[blocked_node_id]: + if is_output(blocked_node_id1): + return node_id + + #TODO: this function should be improved + return node_list[0] + + def unstage_node_execution(self): + assert self.staged_node_id is not None + self.staged_node_id = None + + def complete_node_execution(self): + node_id = self.staged_node_id + self.pop_node(node_id) + self.staged_node_id = None + + def get_nodes_in_cycle(self): + # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle. + # We're skipping some of the performance optimizations from the original TopologicalSort to keep + # the code simple (and because having a cycle in the first place is a catastrophic error) + blocked_by = { node_id: {} for node_id in self.pendingNodes } + for from_node_id in self.blocking: + for to_node_id in self.blocking[from_node_id]: + if True in self.blocking[from_node_id][to_node_id].values(): + blocked_by[to_node_id][from_node_id] = True + to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] + while len(to_remove) > 0: + for node_id in to_remove: + for to_node_id in blocked_by: + if node_id in blocked_by[to_node_id]: + del blocked_by[to_node_id][node_id] + del blocked_by[node_id] + to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] + return list(blocked_by.keys()) + +class ExecutionBlocker: + """ + Return this from a node and any users will be blocked with the given error message. + If the message is None, execution will be blocked silently instead. + Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's + possible, a lazy input will be more efficient and have a better user experience. + This functionality is useful in two cases: + 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node + like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using + lazy evaluation to let it conditionally disable itself.) + 2. You have a node with multiple possible outputs, some of which are invalid and should not be used. + (I would recommend not making nodes like this in the future -- instead, make multiple nodes with + different outputs. Unfortunately, there are several popular existing nodes using this pattern.) + """ + def __init__(self, message): + self.message = message + diff --git a/comfy_execution/graph_utils.py b/comfy_execution/graph_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8595e942d32160152c3c0163c85ad9bcd2e68d45 --- /dev/null +++ b/comfy_execution/graph_utils.py @@ -0,0 +1,139 @@ +def is_link(obj): + if not isinstance(obj, list): + return False + if len(obj) != 2: + return False + if not isinstance(obj[0], str): + return False + if not isinstance(obj[1], int) and not isinstance(obj[1], float): + return False + return True + +# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end +class GraphBuilder: + _default_prefix_root = "" + _default_prefix_call_index = 0 + _default_prefix_graph_index = 0 + + def __init__(self, prefix = None): + if prefix is None: + self.prefix = GraphBuilder.alloc_prefix() + else: + self.prefix = prefix + self.nodes = {} + self.id_gen = 1 + + @classmethod + def set_default_prefix(cls, prefix_root, call_index, graph_index = 0): + cls._default_prefix_root = prefix_root + cls._default_prefix_call_index = call_index + cls._default_prefix_graph_index = graph_index + + @classmethod + def alloc_prefix(cls, root=None, call_index=None, graph_index=None): + if root is None: + root = GraphBuilder._default_prefix_root + if call_index is None: + call_index = GraphBuilder._default_prefix_call_index + if graph_index is None: + graph_index = GraphBuilder._default_prefix_graph_index + result = f"{root}.{call_index}.{graph_index}." + GraphBuilder._default_prefix_graph_index += 1 + return result + + def node(self, class_type, id=None, **kwargs): + if id is None: + id = str(self.id_gen) + self.id_gen += 1 + id = self.prefix + id + if id in self.nodes: + return self.nodes[id] + + node = Node(id, class_type, kwargs) + self.nodes[id] = node + return node + + def lookup_node(self, id): + id = self.prefix + id + return self.nodes.get(id) + + def finalize(self): + output = {} + for node_id, node in self.nodes.items(): + output[node_id] = node.serialize() + return output + + def replace_node_output(self, node_id, index, new_value): + node_id = self.prefix + node_id + to_remove = [] + for node in self.nodes.values(): + for key, value in node.inputs.items(): + if is_link(value) and value[0] == node_id and value[1] == index: + if new_value is None: + to_remove.append((node, key)) + else: + node.inputs[key] = new_value + for node, key in to_remove: + del node.inputs[key] + + def remove_node(self, id): + id = self.prefix + id + del self.nodes[id] + +class Node: + def __init__(self, id, class_type, inputs): + self.id = id + self.class_type = class_type + self.inputs = inputs + self.override_display_id = None + + def out(self, index): + return [self.id, index] + + def set_input(self, key, value): + if value is None: + if key in self.inputs: + del self.inputs[key] + else: + self.inputs[key] = value + + def get_input(self, key): + return self.inputs.get(key) + + def set_override_display_id(self, override_display_id): + self.override_display_id = override_display_id + + def serialize(self): + serialized = { + "class_type": self.class_type, + "inputs": self.inputs + } + if self.override_display_id is not None: + serialized["override_display_id"] = self.override_display_id + return serialized + +def add_graph_prefix(graph, outputs, prefix): + # Change the node IDs and any internal links + new_graph = {} + for node_id, node_info in graph.items(): + # Make sure the added nodes have unique IDs + new_node_id = prefix + node_id + new_node = { "class_type": node_info["class_type"], "inputs": {} } + for input_name, input_value in node_info.get("inputs", {}).items(): + if is_link(input_value): + new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]] + else: + new_node["inputs"][input_name] = input_value + new_graph[new_node_id] = new_node + + # Change the node IDs in the outputs + new_outputs = [] + for n in range(len(outputs)): + output = outputs[n] + if is_link(output): + new_outputs.append([prefix + output[0], output[1]]) + else: + new_outputs.append(output) + + return new_graph, tuple(new_outputs) + diff --git a/comfy_execution/validation.py b/comfy_execution/validation.py new file mode 100644 index 0000000000000000000000000000000000000000..cec105fc9f1bfbecdc17c37cfec9ecf60f188814 --- /dev/null +++ b/comfy_execution/validation.py @@ -0,0 +1,39 @@ +from __future__ import annotations + + +def validate_node_input( + received_type: str, input_type: str, strict: bool = False +) -> bool: + """ + received_type and input_type are both strings of the form "T1,T2,...". + + If strict is True, the input_type must contain the received_type. + For example, if received_type is "STRING" and input_type is "STRING,INT", + this will return True. But if received_type is "STRING,INT" and input_type is + "INT", this will return False. + + If strict is False, the input_type must have overlap with the received_type. + For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT", + this will return True. + + Supports pre-union type extension behaviour of ``__ne__`` overrides. + """ + # If the types are exactly the same, we can return immediately + # Use pre-union behaviour: inverse of `__ne__` + if not received_type != input_type: + return True + + # Not equal, and not strings + if not isinstance(received_type, str) or not isinstance(input_type, str): + return False + + # Split the type strings into sets for comparison + received_types = set(t.strip() for t in received_type.split(",")) + input_types = set(t.strip() for t in input_type.split(",")) + + if strict: + # In strict mode, all received types must be in the input types + return received_types.issubset(input_types) + else: + # In non-strict mode, there must be at least one type in common + return len(received_types.intersection(input_types)) > 0 diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..1bec4476f6171e9f1b2a4a3b967fc63bbf9e9c3c --- /dev/null +++ b/comfy_extras/chainner_models/model_loading.py @@ -0,0 +1,6 @@ +import logging +from spandrel import ModelLoader + +def load_state_dict(state_dict): + logging.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.") + return ModelLoader().load_from_state_dict(state_dict).eval() diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py new file mode 100644 index 0000000000000000000000000000000000000000..cbfec15a2198e542171508704e931021d6df569c --- /dev/null +++ b/comfy_extras/nodes_ace.py @@ -0,0 +1,49 @@ +import torch +import comfy.model_management +import node_helpers + +class TextEncodeAceStepAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "tags": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "lyrics": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "lyrics_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip, tags, lyrics, lyrics_strength): + tokens = clip.tokenize(tags, lyrics=lyrics) + conditioning = clip.encode_from_tokens_scheduled(tokens) + conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength}) + return (conditioning, ) + + +class EmptyAceStepLatentAudio: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": {"seconds": ("FLOAT", {"default": 120.0, "min": 1.0, "max": 1000.0, "step": 0.1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/audio" + + def generate(self, seconds, batch_size): + length = int(seconds * 44100 / 512 / 8) + latent = torch.zeros([batch_size, 8, 16, length], device=self.device) + return ({"samples": latent, "type": "audio"}, ) + + +NODE_CLASS_MAPPINGS = { + "TextEncodeAceStepAudio": TextEncodeAceStepAudio, + "EmptyAceStepLatentAudio": EmptyAceStepLatentAudio, +} diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..5fbb096fbf808e0c6d96159bee024873fb1f4c3e --- /dev/null +++ b/comfy_extras/nodes_advanced_samplers.py @@ -0,0 +1,111 @@ +import comfy.samplers +import comfy.utils +import torch +import numpy as np +from tqdm.auto import trange + + +@torch.no_grad() +def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None): + extra_args = {} if extra_args is None else extra_args + + if upscale_steps is None: + upscale_steps = max(len(sigmas) // 2 + 1, 2) + else: + upscale_steps += 1 + upscale_steps = min(upscale_steps, len(sigmas) + 1) + + upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:] + + orig_shape = x.size() + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if i < len(upscales): + x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled") + + if sigmas[i + 1] > 0: + x += sigmas[i + 1] * torch.randn_like(x) + return x + + +class SamplerLCMUpscale: + upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}), + "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}), + "upscale_method": (s.upscale_methods,), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, scale_ratio, scale_steps, upscale_method): + if scale_steps < 0: + scale_steps = None + sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) + return (sampler, ) + +from comfy.k_diffusion.sampling import to_d +import comfy.model_patcher + +@torch.no_grad() +def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + extra_args = {} if extra_args is None else extra_args + + temp = [0] + def post_cfg_function(args): + temp[0] = args["uncond_denoised"] + return args["denoised"] + + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + sigma_hat = sigmas[i] + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x - denoised + temp[0], sigmas[i], denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + return x + + +class SamplerEulerCFGpp: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"version": (["regular", "alternative"],),} + } + RETURN_TYPES = ("SAMPLER",) + # CATEGORY = "sampling/custom_sampling/samplers" + CATEGORY = "_for_testing" + + FUNCTION = "get_sampler" + + def get_sampler(self, version): + if version == "alternative": + sampler = comfy.samplers.KSAMPLER(sample_euler_pp) + else: + sampler = comfy.samplers.ksampler("euler_cfg_pp") + return (sampler, ) + +NODE_CLASS_MAPPINGS = { + "SamplerLCMUpscale": SamplerLCMUpscale, + "SamplerEulerCFGpp": SamplerEulerCFGpp, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SamplerEulerCFGpp": "SamplerEulerCFG++", +} diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py new file mode 100644 index 0000000000000000000000000000000000000000..8d856d0e8592414df823af27d53d421af7753f27 --- /dev/null +++ b/comfy_extras/nodes_align_your_steps.py @@ -0,0 +1,53 @@ +#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], + "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], + "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} + +class AlignYourStepsScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model_type": (["SD1", "SDXL", "SVD"], ), + "steps": ("INT", {"default": 10, "min": 1, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model_type, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + sigmas = NOISE_LEVELS[model_type][:] + if (steps + 1) != len(sigmas): + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "AlignYourStepsScheduler": AlignYourStepsScheduler, +} diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py new file mode 100644 index 0000000000000000000000000000000000000000..25b21b1b8b2b6ecc61911e7c594be59deeab60dc --- /dev/null +++ b/comfy_extras/nodes_apg.py @@ -0,0 +1,76 @@ +import torch + +def project(v0, v1): + v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3]) + v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1 + v0_orthogonal = v0 - v0_parallel + return v0_parallel, v0_orthogonal + +class APG: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}), + "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}), + "momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "sampling/custom_sampling" + + def patch(self, model, eta, norm_threshold, momentum): + running_avg = 0 + prev_sigma = None + + def pre_cfg_function(args): + nonlocal running_avg, prev_sigma + + if len(args["conds_out"]) == 1: return args["conds_out"] + + cond = args["conds_out"][0] + uncond = args["conds_out"][1] + sigma = args["sigma"][0] + cond_scale = args["cond_scale"] + + if prev_sigma is not None and sigma > prev_sigma: + running_avg = 0 + prev_sigma = sigma + + guidance = cond - uncond + + if momentum != 0: + if not torch.is_tensor(running_avg): + running_avg = guidance + else: + running_avg = momentum * running_avg + guidance + guidance = running_avg + + if norm_threshold > 0: + guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True) + scale = torch.minimum( + torch.ones_like(guidance_norm), + norm_threshold / guidance_norm + ) + guidance = guidance * scale + + guidance_parallel, guidance_orthogonal = project(guidance, cond) + modified_guidance = guidance_orthogonal + eta * guidance_parallel + + modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale + + return [modified_cond, uncond] + args["conds_out"][2:] + + m = model.clone() + m.set_model_sampler_pre_cfg_function(pre_cfg_function) + return (m,) + +NODE_CLASS_MAPPINGS = { + "APG": APG, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "APG": "Adaptive Projected Guidance", +} diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py new file mode 100644 index 0000000000000000000000000000000000000000..4747eb39568afe9e19d34fd583531fcab1acba69 --- /dev/null +++ b/comfy_extras/nodes_attention_multiply.py @@ -0,0 +1,120 @@ + +def attention_multiply(attn, model, q, k, v, out): + m = model.clone() + sd = model.model_state_dict() + + for key in sd: + if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, q) + if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, k) + if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, v) + if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)): + m.add_patches({key: (None,)}, 0.0, out) + + return m + + +class UNetSelfAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, q, k, v, out): + m = attention_multiply("attn1", model, q, k, v, out) + return (m, ) + +class UNetCrossAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, q, k, v, out): + m = attention_multiply("attn2", model, q, k, v, out) + return (m, ) + +class CLIPAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP",), + "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, clip, q, k, v, out): + m = clip.clone() + sd = m.patcher.model_state_dict() + + for key in sd: + if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"): + m.add_patches({key: (None,)}, 0.0, q) + if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"): + m.add_patches({key: (None,)}, 0.0, k) + if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"): + m.add_patches({key: (None,)}, 0.0, v) + if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"): + m.add_patches({key: (None,)}, 0.0, out) + return (m, ) + +class UNetTemporalAttentionMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing/attention_experiments" + + def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal): + m = model.clone() + sd = model.model_state_dict() + + for k in sd: + if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")): + if '.time_stack.' in k: + m.add_patches({k: (None,)}, 0.0, self_temporal) + else: + m.add_patches({k: (None,)}, 0.0, self_structural) + elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")): + if '.time_stack.' in k: + m.add_patches({k: (None,)}, 0.0, cross_temporal) + else: + m.add_patches({k: (None,)}, 0.0, cross_structural) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "UNetSelfAttentionMultiply": UNetSelfAttentionMultiply, + "UNetCrossAttentionMultiply": UNetCrossAttentionMultiply, + "CLIPAttentionMultiply": CLIPAttentionMultiply, + "UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply, +} diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..49af1eae43aa59b6edd2500adce650da01cbc629 --- /dev/null +++ b/comfy_extras/nodes_audio.py @@ -0,0 +1,345 @@ +from __future__ import annotations + +import av +import torchaudio +import torch +import comfy.model_management +import folder_paths +import os +import io +import json +import random +import hashlib +import node_helpers +from comfy.cli_args import args +from comfy.comfy_types import FileLocator + +class EmptyLatentAudio: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/audio" + + def generate(self, seconds, batch_size): + length = round((seconds * 44100 / 2048) / 2) * 2 + latent = torch.zeros([batch_size, 64, length], device=self.device) + return ({"samples":latent, "type": "audio"}, ) + +class ConditioningStableAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "seconds_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.1}), + "seconds_total": ("FLOAT", {"default": 47.0, "min": 0.0, "max": 1000.0, "step": 0.1}), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING") + RETURN_NAMES = ("positive", "negative") + + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, positive, negative, seconds_start, seconds_total): + positive = node_helpers.conditioning_set_values(positive, {"seconds_start": seconds_start, "seconds_total": seconds_total}) + negative = node_helpers.conditioning_set_values(negative, {"seconds_start": seconds_start, "seconds_total": seconds_total}) + return (positive, negative) + +class VAEEncodeAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/audio" + + def encode(self, vae, audio): + sample_rate = audio["sample_rate"] + if 44100 != sample_rate: + waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100) + else: + waveform = audio["waveform"] + + t = vae.encode(waveform.movedim(1, -1)) + return ({"samples":t}, ) + +class VAEDecodeAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + RETURN_TYPES = ("AUDIO",) + FUNCTION = "decode" + + CATEGORY = "latent/audio" + + def decode(self, vae, samples): + audio = vae.decode(samples["samples"]).movedim(-1, 1) + std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0 + std[std < 1.0] = 1.0 + audio /= std + return ({"waveform": audio, "sample_rate": 44100}, ) + + +def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"): + + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results: list[FileLocator] = [] + + # Prepare metadata dictionary + metadata = {} + if not args.disable_metadata: + if prompt is not None: + metadata["prompt"] = json.dumps(prompt) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + # Opus supported sample rates + OPUS_RATES = [8000, 12000, 16000, 24000, 48000] + + for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.{format}" + output_path = os.path.join(full_output_folder, file) + + # Use original sample rate initially + sample_rate = audio["sample_rate"] + + # Handle Opus sample rate requirements + if format == "opus": + if sample_rate > 48000: + sample_rate = 48000 + elif sample_rate not in OPUS_RATES: + # Find the next highest supported rate + for rate in sorted(OPUS_RATES): + if rate > sample_rate: + sample_rate = rate + break + if sample_rate not in OPUS_RATES: # Fallback if still not supported + sample_rate = 48000 + + # Resample if necessary + if sample_rate != audio["sample_rate"]: + waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate) + + # Create in-memory WAV buffer + wav_buffer = io.BytesIO() + torchaudio.save(wav_buffer, waveform, sample_rate, format="WAV") + wav_buffer.seek(0) # Rewind for reading + + # Use PyAV to convert and add metadata + input_container = av.open(wav_buffer) + + # Create output with specified format + output_buffer = io.BytesIO() + output_container = av.open(output_buffer, mode='w', format=format) + + # Set metadata on the container + for key, value in metadata.items(): + output_container.metadata[key] = value + + # Set up the output stream with appropriate properties + input_container.streams.audio[0] + if format == "opus": + out_stream = output_container.add_stream("libopus", rate=sample_rate) + if quality == "64k": + out_stream.bit_rate = 64000 + elif quality == "96k": + out_stream.bit_rate = 96000 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "192k": + out_stream.bit_rate = 192000 + elif quality == "320k": + out_stream.bit_rate = 320000 + elif format == "mp3": + out_stream = output_container.add_stream("libmp3lame", rate=sample_rate) + if quality == "V0": + #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool + out_stream.codec_context.qscale = 1 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "320k": + out_stream.bit_rate = 320000 + else: #format == "flac": + out_stream = output_container.add_stream("flac", rate=sample_rate) + + + # Copy frames from input to output + for frame in input_container.decode(audio=0): + frame.pts = None # Let PyAV handle timestamps + output_container.mux(out_stream.encode(frame)) + + # Flush encoder + output_container.mux(out_stream.encode(None)) + + # Close containers + output_container.close() + input_container.close() + + # Write the output to file + output_buffer.seek(0) + with open(output_path, 'wb') as f: + f.write(output_buffer.getbuffer()) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "audio": results } } + +class SaveAudio: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_flac" + + OUTPUT_NODE = True + + CATEGORY = "audio" + + def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo) + +class SaveAudioMP3: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + "quality": (["V0", "128k", "320k"], {"default": "V0"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_mp3" + + OUTPUT_NODE = True + + CATEGORY = "audio" + + def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) + +class SaveAudioOpus: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_opus" + + OUTPUT_NODE = True + + CATEGORY = "audio" + + def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) + +class PreviewAudio(SaveAudio): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"audio": ("AUDIO", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadAudio: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"]) + return {"required": {"audio": (sorted(files), {"audio_upload": True})}} + + CATEGORY = "audio" + + RETURN_TYPES = ("AUDIO", ) + FUNCTION = "load" + + def load(self, audio): + audio_path = folder_paths.get_annotated_filepath(audio) + waveform, sample_rate = torchaudio.load(audio_path) + audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} + return (audio, ) + + @classmethod + def IS_CHANGED(s, audio): + image_path = folder_paths.get_annotated_filepath(audio) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, audio): + if not folder_paths.exists_annotated_filepath(audio): + return "Invalid audio file: {}".format(audio) + return True + +NODE_CLASS_MAPPINGS = { + "EmptyLatentAudio": EmptyLatentAudio, + "VAEEncodeAudio": VAEEncodeAudio, + "VAEDecodeAudio": VAEDecodeAudio, + "SaveAudio": SaveAudio, + "SaveAudioMP3": SaveAudioMP3, + "SaveAudioOpus": SaveAudioOpus, + "LoadAudio": LoadAudio, + "PreviewAudio": PreviewAudio, + "ConditioningStableAudio": ConditioningStableAudio, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "EmptyLatentAudio": "Empty Latent Audio", + "VAEEncodeAudio": "VAE Encode Audio", + "VAEDecodeAudio": "VAE Decode Audio", + "PreviewAudio": "Preview Audio", + "LoadAudio": "Load Audio", + "SaveAudio": "Save Audio (FLAC)", + "SaveAudioMP3": "Save Audio (MP3)", + "SaveAudioOpus": "Save Audio (Opus)", +} diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py new file mode 100644 index 0000000000000000000000000000000000000000..5e0e39f914d8ad23eca3b04a433c35decf527546 --- /dev/null +++ b/comfy_extras/nodes_camera_trajectory.py @@ -0,0 +1,218 @@ +import nodes +import torch +import numpy as np +from einops import rearrange +import comfy.model_management + + + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + +CAMERA_DICT = { + "base_T_norm": 1.5, + "base_angle": np.pi/3, + "Static": { "angle":[0., 0., 0.], "T":[0., 0., 0.]}, + "Pan Up": { "angle":[0., 0., 0.], "T":[0., -1., 0.]}, + "Pan Down": { "angle":[0., 0., 0.], "T":[0.,1.,0.]}, + "Pan Left": { "angle":[0., 0., 0.], "T":[-1.,0.,0.]}, + "Pan Right": { "angle":[0., 0., 0.], "T": [1.,0.,0.]}, + "Zoom In": { "angle":[0., 0., 0.], "T": [0.,0.,2.]}, + "Zoom Out": { "angle":[0., 0., 0.], "T": [0.,0.,-2.]}, + "Anti Clockwise (ACW)": { "angle": [0., 0., -1.], "T":[0., 0., 0.]}, + "ClockWise (CW)": { "angle": [0., 0., 1.], "T":[0., 0., 0.]}, +} + + +def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'): + + def get_relative_pose(cam_params): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] + abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] + cam_to_origin = 0 + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, -cam_to_origin], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ abs_w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] + ret_poses = np.array(ret_poses, dtype=np.float32) + return ret_poses + + """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + cam_params = [Camera(cam_param) for cam_param in cam_params] + + sample_wh_ratio = width / height + pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed + + if pose_wh_ratio > sample_wh_ratio: + resized_ori_w = height * pose_wh_ratio + for cam_param in cam_params: + cam_param.fx = resized_ori_w * cam_param.fx / width + else: + resized_ori_h = width / pose_wh_ratio + for cam_param in cam_params: + cam_param.fy = resized_ori_h * cam_param.fy / height + + intrinsic = np.asarray([[cam_param.fx * width, + cam_param.fy * height, + cam_param.cx * width, + cam_param.cy * height] + for cam_param in cam_params], dtype=np.float32) + + K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] + c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere + c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] + plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W + plucker_embedding = plucker_embedding[None] + plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] + return plucker_embedding + +class Camera(object): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + def __init__(self, entry): + fx, fy, cx, cy = entry[1:5] + self.fx = fx + self.fy = fy + self.cx = cx + self.cy = cy + c2w_mat = np.array(entry[7:]).reshape(4, 4) + self.c2w_mat = c2w_mat + self.w2c_mat = np.linalg.inv(c2w_mat) + +def ray_condition(K, c2w, H, W, device): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B = K.shape[0] + + j, i = torch.meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + indexing='ij' + ) + i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW + # c2w @ dirctions + rays_dxo = torch.cross(rays_o, rays_d) + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + +def get_camera_motion(angle, T, speed, n=81): + def compute_R_form_rad_angle(angles): + theta_x, theta_y, theta_z = angles + Rx = np.array([[1, 0, 0], + [0, np.cos(theta_x), -np.sin(theta_x)], + [0, np.sin(theta_x), np.cos(theta_x)]]) + + Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], + [0, 1, 0], + [-np.sin(theta_y), 0, np.cos(theta_y)]]) + + Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], + [np.sin(theta_z), np.cos(theta_z), 0], + [0, 0, 1]]) + + R = np.dot(Rz, np.dot(Ry, Rx)) + return R + RT = [] + for i in range(n): + _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle + R = compute_R_form_rad_angle(_angle) + _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1)) + _RT = np.concatenate([R,_T], axis=1) + RT.append(_RT) + RT = np.stack(RT) + return RT + +class WanCameraEmbedding: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}), + "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}), + }, + "optional":{ + "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}), + "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), + "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), + "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), + "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), + } + + } + + RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT") + RETURN_NAMES = ("camera_embedding","width","height","length") + FUNCTION = "run" + CATEGORY = "camera" + + def run(self, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5): + """ + Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021) + Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py + """ + motion_list = [camera_pose] + speed = speed + angle = np.array(CAMERA_DICT[motion_list[0]]["angle"]) + T = np.array(CAMERA_DICT[motion_list[0]]["T"]) + RT = get_camera_motion(angle, T, speed, length) + + trajs=[] + for cp in RT.tolist(): + traj=[fx,fy,cx,cy,0,0] + traj.extend(cp[0]) + traj.extend(cp[1]) + traj.extend(cp[2]) + traj.extend([0,0,0,1]) + trajs.append(traj) + + cam_params = np.array([[float(x) for x in pose] for pose in trajs]) + cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1) + control_camera_video = process_pose_params(cam_params, width=width, height=height) + control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device()) + + control_camera_video = torch.concat( + [ + torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), + control_camera_video[:, :, 1:] + ], dim=2 + ).transpose(1, 2) + + # Reshape, transpose, and view into desired shape + b, f, c, h, w = control_camera_video.shape + control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) + control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) + + return (control_camera_video, width, height, length) + + +NODE_CLASS_MAPPINGS = { + "WanCameraEmbedding": WanCameraEmbedding, +} diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py new file mode 100644 index 0000000000000000000000000000000000000000..d85e6b85691dcdcc55e31705039934846d466fc1 --- /dev/null +++ b/comfy_extras/nodes_canny.py @@ -0,0 +1,25 @@ +from kornia.filters import canny +import comfy.model_management + + +class Canny: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), + "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "detect_edge" + + CATEGORY = "image/preprocessors" + + def detect_edge(self, image, low_threshold, high_threshold): + output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) + return (img_out,) + +NODE_CLASS_MAPPINGS = { + "Canny": Canny, +} diff --git a/comfy_extras/nodes_cfg.py b/comfy_extras/nodes_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..1fb686644169120739bbdeaedbffb0e1f67293b9 --- /dev/null +++ b/comfy_extras/nodes_cfg.py @@ -0,0 +1,45 @@ +import torch + +# https://github.com/WeichenFan/CFG-Zero-star +def optimized_scale(positive, negative): + positive_flat = positive.reshape(positive.shape[0], -1) + negative_flat = negative.reshape(negative.shape[0], -1) + + # Calculate dot production + dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) + + # Squared norm of uncondition + squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8 + + # st_star = v_cond^T * v_uncond / ||v_uncond||^2 + st_star = dot_product / squared_norm + + return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1)) + +class CFGZeroStar: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL",), + }} + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("patched_model",) + FUNCTION = "patch" + CATEGORY = "advanced/guidance" + + def patch(self, model): + m = model.clone() + def cfg_zero_star(args): + guidance_scale = args['cond_scale'] + x = args['input'] + cond_p = args['cond_denoised'] + uncond_p = args['uncond_denoised'] + out = args["denoised"] + alpha = optimized_scale(x - cond_p, x - uncond_p) + + return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha) + m.set_model_sampler_post_cfg_function(cfg_zero_star) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "CFGZeroStar": CFGZeroStar +} diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py new file mode 100644 index 0000000000000000000000000000000000000000..14269caf352edee45d9f1ca49db3effd5b60b7fb --- /dev/null +++ b/comfy_extras/nodes_clip_sdxl.py @@ -0,0 +1,54 @@ +from nodes import MAX_RESOLUTION + +class CLIPTextEncodeSDXLRefiner: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, ascore, width, height, text): + tokens = clip.tokenize(text) + return (clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}), ) + +class CLIPTextEncodeSDXL: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): + tokens = clip.tokenize(text_g) + tokens["l"] = clip.tokenize(text_l)["l"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}), ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, + "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, +} diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py new file mode 100644 index 0000000000000000000000000000000000000000..2f994fa11d370d31dc5412da90d0582fe6bd6159 --- /dev/null +++ b/comfy_extras/nodes_compositing.py @@ -0,0 +1,214 @@ +import torch +import comfy.utils +from enum import Enum + +def resize_mask(mask, shape): + return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) + +class PorterDuffMode(Enum): + ADD = 0 + CLEAR = 1 + DARKEN = 2 + DST = 3 + DST_ATOP = 4 + DST_IN = 5 + DST_OUT = 6 + DST_OVER = 7 + LIGHTEN = 8 + MULTIPLY = 9 + OVERLAY = 10 + SCREEN = 11 + SRC = 12 + SRC_ATOP = 13 + SRC_IN = 14 + SRC_OUT = 15 + SRC_OVER = 16 + XOR = 17 + + +def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): + # convert mask to alpha + src_alpha = 1 - src_alpha + dst_alpha = 1 - dst_alpha + # premultiply alpha + src_image = src_image * src_alpha + dst_image = dst_image * dst_alpha + + # composite ops below assume alpha-premultiplied images + if mode == PorterDuffMode.ADD: + out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) + out_image = torch.clamp(src_image + dst_image, 0, 1) + elif mode == PorterDuffMode.CLEAR: + out_alpha = torch.zeros_like(dst_alpha) + out_image = torch.zeros_like(dst_image) + elif mode == PorterDuffMode.DARKEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) + elif mode == PorterDuffMode.DST: + out_alpha = dst_alpha + out_image = dst_image + elif mode == PorterDuffMode.DST_ATOP: + out_alpha = src_alpha + out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.DST_IN: + out_alpha = src_alpha * dst_alpha + out_image = dst_image * src_alpha + elif mode == PorterDuffMode.DST_OUT: + out_alpha = (1 - src_alpha) * dst_alpha + out_image = (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.DST_OVER: + out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha + out_image = dst_image + (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.LIGHTEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image) + elif mode == PorterDuffMode.MULTIPLY: + out_alpha = src_alpha * dst_alpha + out_image = src_image * dst_image + elif mode == PorterDuffMode.OVERLAY: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image, + src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image)) + elif mode == PorterDuffMode.SCREEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = src_image + dst_image - src_image * dst_image + elif mode == PorterDuffMode.SRC: + out_alpha = src_alpha + out_image = src_image + elif mode == PorterDuffMode.SRC_ATOP: + out_alpha = dst_alpha + out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.SRC_IN: + out_alpha = src_alpha * dst_alpha + out_image = src_image * dst_alpha + elif mode == PorterDuffMode.SRC_OUT: + out_alpha = (1 - dst_alpha) * src_alpha + out_image = (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.SRC_OVER: + out_alpha = src_alpha + (1 - src_alpha) * dst_alpha + out_image = src_image + (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.XOR: + out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + else: + return None, None + + # back to non-premultiplied alpha + out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image)) + out_image = torch.clamp(out_image, 0, 1) + # convert alpha to mask + out_alpha = 1 - out_alpha + return out_image, out_alpha + + +class PorterDuffImageComposite: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "source": ("IMAGE",), + "source_alpha": ("MASK",), + "destination": ("IMAGE",), + "destination_alpha": ("MASK",), + "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "composite" + CATEGORY = "mask/compositing" + + def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): + batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) + out_images = [] + out_alphas = [] + + for i in range(batch_size): + src_image = source[i] + dst_image = destination[i] + + assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels + + src_alpha = source_alpha[i].unsqueeze(2) + dst_alpha = destination_alpha[i].unsqueeze(2) + + if dst_alpha.shape[:2] != dst_image.shape[:2]: + upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) + if src_image.shape != dst_image.shape: + upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) + if src_alpha.shape != dst_alpha.shape: + upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') + src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) + + out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) + + out_images.append(out_image) + out_alphas.append(out_alpha.squeeze(2)) + + result = (torch.stack(out_images), torch.stack(out_alphas)) + return result + + +class SplitImageWithAlpha: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + } + } + + CATEGORY = "mask/compositing" + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "split_image_with_alpha" + + def split_image_with_alpha(self, image: torch.Tensor): + out_images = [i[:,:,:3] for i in image] + out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] + result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) + return result + + +class JoinImageWithAlpha: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "alpha": ("MASK",), + } + } + + CATEGORY = "mask/compositing" + RETURN_TYPES = ("IMAGE",) + FUNCTION = "join_image_with_alpha" + + def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): + batch_size = min(len(image), len(alpha)) + out_images = [] + + alpha = 1.0 - resize_mask(alpha, image.shape[1:]) + for i in range(batch_size): + out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) + + result = (torch.stack(out_images),) + return result + + +NODE_CLASS_MAPPINGS = { + "PorterDuffImageComposite": PorterDuffImageComposite, + "SplitImageWithAlpha": SplitImageWithAlpha, + "JoinImageWithAlpha": JoinImageWithAlpha, +} + + +NODE_DISPLAY_NAME_MAPPINGS = { + "PorterDuffImageComposite": "Porter-Duff Image Composite", + "SplitImageWithAlpha": "Split Image with Alpha", + "JoinImageWithAlpha": "Join Image with Alpha", +} diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py new file mode 100644 index 0000000000000000000000000000000000000000..58c16f621cd1c5f5abd66dba639ebf5740862789 --- /dev/null +++ b/comfy_extras/nodes_cond.py @@ -0,0 +1,49 @@ + + +class CLIPTextEncodeControlnet: + @classmethod + def INPUT_TYPES(s): + return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "_for_testing/conditioning" + + def encode(self, clip, conditioning, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['cross_attn_controlnet'] = cond + n[1]['pooled_output_controlnet'] = pooled + c.append(n) + return (c, ) + +class T5TokenizerOptions: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip": ("CLIP", ), + "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), + "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), + } + } + + CATEGORY = "_for_testing/conditioning" + RETURN_TYPES = ("CLIP",) + FUNCTION = "set_options" + + def set_options(self, clip, min_padding, min_length): + clip = clip.clone() + for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]: + clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding) + clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length) + + return (clip, ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet, + "T5TokenizerOptions": T5TokenizerOptions, +} diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..2d20e1fed7c26c8115f4b9878b0b54911e7a2e7f --- /dev/null +++ b/comfy_extras/nodes_controlnet.py @@ -0,0 +1,60 @@ +from comfy.cldm.control_types import UNION_CONTROLNET_TYPES +import nodes +import comfy.utils + +class SetUnionControlNetType: + @classmethod + def INPUT_TYPES(s): + return {"required": {"control_net": ("CONTROL_NET", ), + "type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),) + }} + + CATEGORY = "conditioning/controlnet" + RETURN_TYPES = ("CONTROL_NET",) + + FUNCTION = "set_controlnet_type" + + def set_controlnet_type(self, control_net, type): + control_net = control_net.copy() + type_number = UNION_CONTROLNET_TYPES.get(type, -1) + if type_number >= 0: + control_net.set_extra_arg("control_type", [type_number]) + else: + control_net.set_extra_arg("control_type", []) + + return (control_net,) + +class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced): + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "vae": ("VAE", ), + "image": ("IMAGE", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + + FUNCTION = "apply_inpaint_controlnet" + + CATEGORY = "conditioning/controlnet" + + def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent): + extra_concat = [] + if control_net.concat_mask: + mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round() + image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3]) + extra_concat = [mask] + + return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat) + + + +NODE_CLASS_MAPPINGS = { + "SetUnionControlNetType": SetUnionControlNetType, + "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply, +} diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py new file mode 100644 index 0000000000000000000000000000000000000000..4f49605510e4986f5d030d7bbde14fbf2f7a9c2d --- /dev/null +++ b/comfy_extras/nodes_cosmos.py @@ -0,0 +1,128 @@ +import nodes +import torch +import comfy.model_management +import comfy.utils +import comfy.latent_formats + + +class EmptyCosmosLatentVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/video" + + def generate(self, width, height, length, batch_size=1): + latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + return ({"samples": latent}, ) + + +def vae_encode_with_padding(vae, image, width, height, length, padding=0): + pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + pixel_len = min(pixels.shape[0], length) + padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7) + padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5 + padded_pixels[:pixel_len] = pixels[:pixel_len] + latent_len = ((pixel_len - 1) // 8) + 1 + latent_temp = vae.encode(padded_pixels) + return latent_temp[:, :, :latent_len] + + +class CosmosImageToVideoLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": {"vae": ("VAE", ), + "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"start_image": ("IMAGE", ), + "end_image": ("IMAGE", ), + }} + + + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "conditioning/inpaint" + + def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None): + latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is None and end_image is None: + out_latent = {} + out_latent["samples"] = latent + return (out_latent,) + + mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + + if start_image is not None: + latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1) + latent[:, :, :latent_temp.shape[-3]] = latent_temp + mask[:, :, :latent_temp.shape[-3]] *= 0.0 + + if end_image is not None: + latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0) + latent[:, :, -latent_temp.shape[-3]:] = latent_temp + mask[:, :, -latent_temp.shape[-3]:] *= 0.0 + + out_latent = {} + out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) + out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) + return (out_latent,) + +class CosmosPredict2ImageToVideoLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": {"vae": ("VAE", ), + "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 93, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"start_image": ("IMAGE", ), + "end_image": ("IMAGE", ), + }} + + + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "conditioning/inpaint" + + def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None): + latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is None and end_image is None: + out_latent = {} + out_latent["samples"] = latent + return (out_latent,) + + mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + + if start_image is not None: + latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1) + latent[:, :, :latent_temp.shape[-3]] = latent_temp + mask[:, :, :latent_temp.shape[-3]] *= 0.0 + + if end_image is not None: + latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0) + latent[:, :, -latent_temp.shape[-3]:] = latent_temp + mask[:, :, -latent_temp.shape[-3]:] *= 0.0 + + out_latent = {} + latent_format = comfy.latent_formats.Wan21() + latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) + out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) + out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) + return (out_latent,) + +NODE_CLASS_MAPPINGS = { + "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo, + "CosmosImageToVideoLatent": CosmosImageToVideoLatent, + "CosmosPredict2ImageToVideoLatent": CosmosPredict2ImageToVideoLatent, +} diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..3e5be3d3c7b31d0b3284b6cc6c925705ededb0d9 --- /dev/null +++ b/comfy_extras/nodes_custom_sampler.py @@ -0,0 +1,801 @@ +import math +import comfy.samplers +import comfy.sample +from comfy.k_diffusion import sampling as k_diffusion_sampling +import latent_preview +import torch +import comfy.utils +import node_helpers + + +class BasicScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "scheduler": (comfy.samplers.SCHEDULER_NAMES, ), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, scheduler, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = int(steps/denoise) + + sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu() + sigmas = sigmas[-(steps + 1):] + return (sigmas, ) + + +class KarrasScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class ExponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min): + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) + return (sigmas, ) + +class PolyexponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class LaplaceScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}), + "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta): + sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta) + return (sigmas, ) + + +class SDTurboScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 1, "min": 1, "max": 10}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, steps, denoise): + start_step = 10 - int(10 * denoise) + timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] + sigmas = model.get_model_object("model_sampling").sigma(timesteps) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + return (sigmas, ) + +class BetaSamplingScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), + "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, steps, alpha, beta): + sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta) + return (sigmas, ) + +class VPScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values + "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), + "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, beta_d, beta_min, eps_s): + sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) + return (sigmas, ) + +class SplitSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "step": ("INT", {"default": 0, "min": 0, "max": 10000}), + } + } + RETURN_TYPES = ("SIGMAS","SIGMAS") + RETURN_NAMES = ("high_sigmas", "low_sigmas") + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas, step): + sigmas1 = sigmas[:step + 1] + sigmas2 = sigmas[step:] + return (sigmas1, sigmas2) + +class SplitSigmasDenoise: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS","SIGMAS") + RETURN_NAMES = ("high_sigmas", "low_sigmas") + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas, denoise): + steps = max(sigmas.shape[-1] - 1, 0) + total_steps = round(steps * denoise) + sigmas1 = sigmas[:-(total_steps)] + sigmas2 = sigmas[-(total_steps + 1):] + return (sigmas1, sigmas2) + +class FlipSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas): + if len(sigmas) == 0: + return (sigmas,) + + sigmas = sigmas.flip(0) + if sigmas[0] == 0: + sigmas[0] = 0.0001 + return (sigmas,) + +class SetFirstSigma: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "set_first_sigma" + + def set_first_sigma(self, sigmas, sigma): + sigmas = sigmas.clone() + sigmas[0] = sigma + return (sigmas, ) + +class ExtendIntermediateSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "steps": ("INT", {"default": 2, "min": 1, "max": 100}), + "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}), + "end_at_sigma": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 20000.0, "step": 0.01, "round": False}), + "spacing": (['linear', 'cosine', 'sine'],), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "extend" + + def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str): + if start_at_sigma < 0: + start_at_sigma = float("inf") + + interpolator = { + 'linear': lambda x: x, + 'cosine': lambda x: torch.sin(x*math.pi/2), + 'sine': lambda x: 1 - torch.cos(x*math.pi/2) + }[spacing] + + # linear space for our interpolation function + x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1] + computed_spacing = interpolator(x) + + extended_sigmas = [] + for i in range(len(sigmas) - 1): + sigma_current = sigmas[i] + sigma_next = sigmas[i+1] + + extended_sigmas.append(sigma_current) + + if end_at_sigma <= sigma_current <= start_at_sigma: + interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current + extended_sigmas.extend(interpolated_steps.tolist()) + + # Add the last sigma value + if len(sigmas) > 0: + extended_sigmas.append(sigmas[-1]) + + extended_sigmas = torch.FloatTensor(extended_sigmas) + + return (extended_sigmas,) + +class KSamplerSelect: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, sampler_name): + sampler = comfy.samplers.sampler_object(sampler_name) + return (sampler, ) + +class SamplerDPMPP_3M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_3m_sde" + else: + sampler_name = "dpmpp_3m_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerDPMPP_2M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"solver_type": (['midpoint', 'heun'], ), + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, solver_type, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_2m_sde" + else: + sampler_name = "dpmpp_2m_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) + return (sampler, ) + + +class SamplerDPMPP_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, r, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_sde" + else: + sampler_name = "dpmpp_sde_gpu" + sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) + return (sampler, ) + +class SamplerDPMPP_2S_Ancestral: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerEulerAncestral: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerEulerAncestralCFGPP: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}), + }} + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise): + sampler = comfy.samplers.ksampler( + "euler_ancestral_cfg_pp", + {"eta": eta, "s_noise": s_noise}) + return (sampler, ) + +class SamplerLMS: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"order": ("INT", {"default": 4, "min": 1, "max": 100}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, order): + sampler = comfy.samplers.ksampler("lms", {"order": order}) + return (sampler, ) + +class SamplerDPMAdaptative: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"order": ("INT", {"default": 3, "min": 2, "max": 3}), + "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise): + sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff, + "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta, + "s_noise":s_noise }) + return (sampler, ) + +class Noise_EmptyNoise: + def __init__(self): + self.seed = 0 + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + + +class Noise_RandomNoise: + def __init__(self, seed): + self.seed = seed + + def generate_noise(self, input_latent): + latent_image = input_latent["samples"] + batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None + return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds) + +class SamplerCustom: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": ("BOOLEAN", {"default": True}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT","LATENT") + RETURN_NAMES = ("output", "denoised_output") + + FUNCTION = "sample" + + CATEGORY = "sampling/custom_sampling" + + def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): + latent = latent_image + latent_image = latent["samples"] + latent = latent.copy() + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + latent["samples"] = latent_image + + if not add_noise: + noise = Noise_EmptyNoise().generate_noise(latent) + else: + noise = Noise_RandomNoise(noise_seed).generate_noise(latent) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + +class Guider_Basic(comfy.samplers.CFGGuider): + def set_conds(self, positive): + self.inner_set_conds({"positive": positive}) + +class BasicGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "conditioning": ("CONDITIONING", ), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, conditioning): + guider = Guider_Basic(model) + guider.set_conds(conditioning) + return (guider,) + +class CFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, positive, negative, cfg): + guider = comfy.samplers.CFGGuider(model) + guider.set_conds(positive, negative) + guider.set_cfg(cfg) + return (guider,) + +class Guider_DualCFG(comfy.samplers.CFGGuider): + def set_cfg(self, cfg1, cfg2): + self.cfg1 = cfg1 + self.cfg2 = cfg2 + + def set_conds(self, positive, middle, negative): + middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"}) + self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + negative_cond = self.conds.get("negative", None) + middle_cond = self.conds.get("middle", None) + + out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options) + return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 + +class DualCFGGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "cond1": ("CONDITIONING", ), + "cond2": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "sampling/custom_sampling/guiders" + + def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative): + guider = Guider_DualCFG(model) + guider.set_conds(cond1, cond2, negative) + guider.set_cfg(cfg_conds, cfg_cond2_negative) + return (guider,) + +class DisableNoise: + @classmethod + def INPUT_TYPES(s): + return {"required":{ + } + } + + RETURN_TYPES = ("NOISE",) + FUNCTION = "get_noise" + CATEGORY = "sampling/custom_sampling/noise" + + def get_noise(self): + return (Noise_EmptyNoise(),) + + +class RandomNoise(DisableNoise): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "noise_seed": ("INT", { + "default": 0, + "min": 0, + "max": 0xffffffffffffffff, + "control_after_generate": True, + }), + } + } + + def get_noise(self, noise_seed): + return (Noise_RandomNoise(noise_seed),) + + +class SamplerCustomAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"noise": ("NOISE", ), + "guider": ("GUIDER", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT","LATENT") + RETURN_NAMES = ("output", "denoised_output") + + FUNCTION = "sample" + + CATEGORY = "sampling/custom_sampling" + + def sample(self, noise, guider, sampler, sigmas, latent_image): + latent = latent_image + latent_image = latent["samples"] + latent = latent.copy() + latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) + latent["samples"] = latent_image + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) + + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed) + samples = samples.to(comfy.model_management.intermediate_device()) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + +class AddNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "noise": ("NOISE", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT",) + + FUNCTION = "add_noise" + + CATEGORY = "_for_testing/custom_sampling/noise" + + def add_noise(self, model, noise, sigmas, latent_image): + if len(sigmas) == 0: + return latent_image + + latent = latent_image + latent_image = latent["samples"] + + noisy = noise.generate_noise(latent) + + model_sampling = model.get_model_object("model_sampling") + process_latent_out = model.get_model_object("process_latent_out") + process_latent_in = model.get_model_object("process_latent_in") + + if len(sigmas) > 1: + scale = torch.abs(sigmas[0] - sigmas[-1]) + else: + scale = sigmas[0] + + if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. + latent_image = process_latent_in(latent_image) + noisy = model_sampling.noise_scaling(scale, noisy, latent_image) + noisy = process_latent_out(noisy) + noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0) + + out = latent.copy() + out["samples"] = noisy + return (out,) + + +NODE_CLASS_MAPPINGS = { + "SamplerCustom": SamplerCustom, + "BasicScheduler": BasicScheduler, + "KarrasScheduler": KarrasScheduler, + "ExponentialScheduler": ExponentialScheduler, + "PolyexponentialScheduler": PolyexponentialScheduler, + "LaplaceScheduler": LaplaceScheduler, + "VPScheduler": VPScheduler, + "BetaSamplingScheduler": BetaSamplingScheduler, + "SDTurboScheduler": SDTurboScheduler, + "KSamplerSelect": KSamplerSelect, + "SamplerEulerAncestral": SamplerEulerAncestral, + "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP, + "SamplerLMS": SamplerLMS, + "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, + "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, + "SamplerDPMPP_SDE": SamplerDPMPP_SDE, + "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral, + "SamplerDPMAdaptative": SamplerDPMAdaptative, + "SplitSigmas": SplitSigmas, + "SplitSigmasDenoise": SplitSigmasDenoise, + "FlipSigmas": FlipSigmas, + "SetFirstSigma": SetFirstSigma, + "ExtendIntermediateSigmas": ExtendIntermediateSigmas, + + "CFGGuider": CFGGuider, + "DualCFGGuider": DualCFGGuider, + "BasicGuider": BasicGuider, + "RandomNoise": RandomNoise, + "DisableNoise": DisableNoise, + "AddNoise": AddNoise, + "SamplerCustomAdvanced": SamplerCustomAdvanced, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++", +} diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..98dbbf102dac861cfb65ed19ad1af499abf7465d --- /dev/null +++ b/comfy_extras/nodes_differential_diffusion.py @@ -0,0 +1,42 @@ +# code adapted from https://github.com/exx8/differential-diffusion + +import torch + +class DifferentialDiffusion(): + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply" + CATEGORY = "_for_testing" + INIT = False + + def apply(self, model): + model = model.clone() + model.set_model_denoise_mask_function(self.forward) + return (model,) + + def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): + model = extra_options["model"] + step_sigmas = extra_options["sigmas"] + sigma_to = model.inner_model.model_sampling.sigma_min + if step_sigmas[-1] > sigma_to: + sigma_to = step_sigmas[-1] + sigma_from = step_sigmas[0] + + ts_from = model.inner_model.model_sampling.timestep(sigma_from) + ts_to = model.inner_model.model_sampling.timestep(sigma_to) + current_ts = model.inner_model.model_sampling.timestep(sigma[0]) + + threshold = (current_ts - ts_to) / (ts_from - ts_to) + + return (denoise_mask >= threshold).to(denoise_mask.dtype) + + +NODE_CLASS_MAPPINGS = { + "DifferentialDiffusion": DifferentialDiffusion, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DifferentialDiffusion": "Differential Diffusion", +} diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b69f7971591b383774d322b022e3b3b39ec0d704 --- /dev/null +++ b/comfy_extras/nodes_edit_model.py @@ -0,0 +1,26 @@ +import node_helpers + + +class ReferenceLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + }, + "optional": {"latent": ("LATENT", ),} + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "advanced/conditioning/edit_models" + DESCRIPTION = "This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images." + + def append(self, conditioning, latent=None): + if latent is not None: + conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True) + return (conditioning, ) + + +NODE_CLASS_MAPPINGS = { + "ReferenceLatent": ReferenceLatent, +} diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py new file mode 100644 index 0000000000000000000000000000000000000000..8a8a1769801c046c8001d1f3f7ad913794997147 --- /dev/null +++ b/comfy_extras/nodes_flux.py @@ -0,0 +1,108 @@ +import node_helpers +import comfy.utils + +class CLIPTextEncodeFlux: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning/flux" + + def encode(self, clip, clip_l, t5xxl, guidance): + tokens = clip.tokenize(clip_l) + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + + return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}), ) + +class FluxGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning": ("CONDITIONING", ), + "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "advanced/conditioning/flux" + + def append(self, conditioning, guidance): + c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance}) + return (c, ) + + +class FluxDisableGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning": ("CONDITIONING", ), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "advanced/conditioning/flux" + DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models" + + def append(self, conditioning): + c = node_helpers.conditioning_set_values(conditioning, {"guidance": None}) + return (c, ) + + +PREFERED_KONTEXT_RESOLUTIONS = [ + (672, 1568), + (688, 1504), + (720, 1456), + (752, 1392), + (800, 1328), + (832, 1248), + (880, 1184), + (944, 1104), + (1024, 1024), + (1104, 944), + (1184, 880), + (1248, 832), + (1328, 800), + (1392, 752), + (1456, 720), + (1504, 688), + (1568, 672), +] + + +class FluxKontextImageScale: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE", ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "scale" + + CATEGORY = "advanced/conditioning/flux" + DESCRIPTION = "This node resizes the image to one that is more optimal for flux kontext." + + def scale(self, image): + width = image.shape[2] + height = image.shape[1] + aspect_ratio = width / height + _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS) + image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1) + return (image, ) + + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeFlux": CLIPTextEncodeFlux, + "FluxGuidance": FluxGuidance, + "FluxDisableGuidance": FluxDisableGuidance, + "FluxKontextImageScale": FluxKontextImageScale, +} diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py new file mode 100644 index 0000000000000000000000000000000000000000..e3ac58447b29f604debb5bfc0aed3a5f100a4ae9 --- /dev/null +++ b/comfy_extras/nodes_freelunch.py @@ -0,0 +1,113 @@ +#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) + +import torch +import logging + +def Fourier_filter(x, threshold, scale): + # FFT + x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) + x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) + + B, C, H, W = x_freq.shape + mask = torch.ones((B, C, H, W), device=x.device) + + crow, ccol = H // 2, W //2 + mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale + x_freq = x_freq * mask + + # IFFT + x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real + + return x_filtered.to(x.dtype) + + +class FreeU: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(int(h.shape[1]), None) + if scale is not None: + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except: + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +class FreeU_V2: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(int(h.shape[1]), None) + if scale is not None: + hidden_mean = h.mean(1).unsqueeze(1) + B = hidden_mean.shape[0] + hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) + + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) + + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except: + logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "FreeU": FreeU, + "FreeU_V2": FreeU_V2, +} diff --git a/comfy_extras/nodes_fresca.py b/comfy_extras/nodes_fresca.py new file mode 100644 index 0000000000000000000000000000000000000000..ee310c874eeae0fb021b8377b4cf12f2174ad822 --- /dev/null +++ b/comfy_extras/nodes_fresca.py @@ -0,0 +1,100 @@ +# Code based on https://github.com/WikiChao/FreSca (MIT License) +import torch +import torch.fft as fft + + +def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20): + """ + Apply frequency-dependent scaling to an image tensor using Fourier transforms. + + Parameters: + x: Input tensor of shape (B, C, H, W) + scale_low: Scaling factor for low-frequency components (default: 1.0) + scale_high: Scaling factor for high-frequency components (default: 1.5) + freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20) + + Returns: + x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied. + """ + # Preserve input dtype and device + dtype, device = x.dtype, x.device + + # Convert to float32 for FFT computations + x = x.to(torch.float32) + + # 1) Apply FFT and shift low frequencies to center + x_freq = fft.fftn(x, dim=(-2, -1)) + x_freq = fft.fftshift(x_freq, dim=(-2, -1)) + + # Initialize mask with high-frequency scaling factor + mask = torch.ones(x_freq.shape, device=device) * scale_high + m = mask + for d in range(len(x_freq.shape) - 2): + dim = d + 2 + cc = x_freq.shape[dim] // 2 + f_c = min(freq_cutoff, cc) + m = m.narrow(dim, cc - f_c, f_c * 2) + + # Apply low-frequency scaling factor to center region + m[:] = scale_low + + # 3) Apply frequency-specific scaling + x_freq = x_freq * mask + + # 4) Convert back to spatial domain + x_freq = fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real + + # 5) Restore original dtype + x_filtered = x_filtered.to(dtype) + + return x_filtered + + +class FreSca: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01, + "tooltip": "Scaling factor for low-frequency components"}), + "scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01, + "tooltip": "Scaling factor for high-frequency components"}), + "freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1, + "tooltip": "Number of frequency indices around center to consider as low-frequency"}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "_for_testing" + DESCRIPTION = "Applies frequency-dependent scaling to the guidance" + def patch(self, model, scale_low, scale_high, freq_cutoff): + def custom_cfg_function(args): + cond = args["conds_out"][0] + uncond = args["conds_out"][1] + + guidance = cond - uncond + filtered_guidance = Fourier_filter( + guidance, + scale_low=scale_low, + scale_high=scale_high, + freq_cutoff=freq_cutoff, + ) + filtered_cond = filtered_guidance + uncond + + return [filtered_cond, uncond] + + m = model.clone() + m.set_model_sampler_pre_cfg_function(custom_cfg_function) + + return (m,) + + +NODE_CLASS_MAPPINGS = { + "FreSca": FreSca, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "FreSca": "FreSca", +} diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py new file mode 100644 index 0000000000000000000000000000000000000000..47b1dd049702cc481550dd04d2c4edebfdcf7a0e --- /dev/null +++ b/comfy_extras/nodes_gits.py @@ -0,0 +1,369 @@ +# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + +NOISE_LEVELS = { + 0.80: [ + [14.61464119, 7.49001646, 0.02916753], + [14.61464119, 11.54541874, 6.77309084, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], + [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], + [14.61464119, 12.2308979, 8.75849152, 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2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], + ], +} + +class GITSScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), + "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, coeff, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + if steps <= 20: + sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] + else: + sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "GITSScheduler": GITSScheduler, +} diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py new file mode 100644 index 0000000000000000000000000000000000000000..dfb98597b8427622360588994c5fc8f75c3e6a1e --- /dev/null +++ b/comfy_extras/nodes_hidream.py @@ -0,0 +1,55 @@ +import folder_paths +import comfy.sd +import comfy.model_management + + +class QuadrupleCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), + "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), + "clip_name3": (folder_paths.get_filename_list("text_encoders"), ), + "clip_name4": (folder_paths.get_filename_list("text_encoders"), ) + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + DESCRIPTION = "[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct" + + def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4): + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) + clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) + clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) + clip_path4 = folder_paths.get_full_path_or_raise("text_encoders", clip_name4) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3, clip_path4], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + +class CLIPTextEncodeHiDream: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "llama": ("STRING", {"multiline": True, "dynamicPrompts": True}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, clip_l, clip_g, t5xxl, llama): + + tokens = clip.tokenize(clip_g) + tokens["l"] = clip.tokenize(clip_l)["l"] + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + tokens["llama"] = clip.tokenize(llama)["llama"] + return (clip.encode_from_tokens_scheduled(tokens), ) + +NODE_CLASS_MAPPINGS = { + "QuadrupleCLIPLoader": QuadrupleCLIPLoader, + "CLIPTextEncodeHiDream": CLIPTextEncodeHiDream, +} diff --git a/comfy_extras/nodes_hooks.py b/comfy_extras/nodes_hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..1edc06f3d7ae6b0682b03afe666ef936b16f2f28 --- /dev/null +++ b/comfy_extras/nodes_hooks.py @@ -0,0 +1,745 @@ +from __future__ import annotations +from typing import TYPE_CHECKING, Union +import logging +import torch +from collections.abc import Iterable + +if TYPE_CHECKING: + from comfy.sd import CLIP + +import comfy.hooks +import comfy.sd +import comfy.utils +import folder_paths + +########################################### +# Mask, Combine, and Hook Conditioning +#------------------------------------------ +class PairConditioningSetProperties: + NodeId = 'PairConditioningSetProperties' + NodeName = 'Cond Pair Set Props' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "positive_NEW": ("CONDITIONING", ), + "negative_NEW": ("CONDITIONING", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }, + "optional": { + "mask": ("MASK", ), + "hooks": ("HOOKS",), + "timesteps": ("TIMESTEPS_RANGE",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + CATEGORY = "advanced/hooks/cond pair" + FUNCTION = "set_properties" + + def set_properties(self, positive_NEW, negative_NEW, + strength: float, set_cond_area: str, + mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): + final_positive, final_negative = comfy.hooks.set_conds_props(conds=[positive_NEW, negative_NEW], + strength=strength, set_cond_area=set_cond_area, + mask=mask, hooks=hooks, timesteps_range=timesteps) + return (final_positive, final_negative) + +class PairConditioningSetPropertiesAndCombine: + NodeId = 'PairConditioningSetPropertiesAndCombine' + NodeName = 'Cond Pair Set Props Combine' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "positive_NEW": ("CONDITIONING", ), + "negative_NEW": ("CONDITIONING", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }, + "optional": { + "mask": ("MASK", ), + "hooks": ("HOOKS",), + "timesteps": ("TIMESTEPS_RANGE",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + CATEGORY = "advanced/hooks/cond pair" + FUNCTION = "set_properties" + + def set_properties(self, positive, negative, positive_NEW, negative_NEW, + strength: float, set_cond_area: str, + mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): + final_positive, final_negative = comfy.hooks.set_conds_props_and_combine(conds=[positive, negative], new_conds=[positive_NEW, negative_NEW], + strength=strength, set_cond_area=set_cond_area, + mask=mask, hooks=hooks, timesteps_range=timesteps) + return (final_positive, final_negative) + +class ConditioningSetProperties: + NodeId = 'ConditioningSetProperties' + NodeName = 'Cond Set Props' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cond_NEW": ("CONDITIONING", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }, + "optional": { + "mask": ("MASK", ), + "hooks": ("HOOKS",), + "timesteps": ("TIMESTEPS_RANGE",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "advanced/hooks/cond single" + FUNCTION = "set_properties" + + def set_properties(self, cond_NEW, + strength: float, set_cond_area: str, + mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): + (final_cond,) = comfy.hooks.set_conds_props(conds=[cond_NEW], + strength=strength, set_cond_area=set_cond_area, + mask=mask, hooks=hooks, timesteps_range=timesteps) + return (final_cond,) + +class ConditioningSetPropertiesAndCombine: + NodeId = 'ConditioningSetPropertiesAndCombine' + NodeName = 'Cond Set Props Combine' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cond": ("CONDITIONING", ), + "cond_NEW": ("CONDITIONING", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }, + "optional": { + "mask": ("MASK", ), + "hooks": ("HOOKS",), + "timesteps": ("TIMESTEPS_RANGE",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "advanced/hooks/cond single" + FUNCTION = "set_properties" + + def set_properties(self, cond, cond_NEW, + strength: float, set_cond_area: str, + mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): + (final_cond,) = comfy.hooks.set_conds_props_and_combine(conds=[cond], new_conds=[cond_NEW], + strength=strength, set_cond_area=set_cond_area, + mask=mask, hooks=hooks, timesteps_range=timesteps) + return (final_cond,) + +class PairConditioningCombine: + NodeId = 'PairConditioningCombine' + NodeName = 'Cond Pair Combine' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "positive_A": ("CONDITIONING",), + "negative_A": ("CONDITIONING",), + "positive_B": ("CONDITIONING",), + "negative_B": ("CONDITIONING",), + }, + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + CATEGORY = "advanced/hooks/cond pair" + FUNCTION = "combine" + + def combine(self, positive_A, negative_A, positive_B, negative_B): + final_positive, final_negative = comfy.hooks.set_conds_props_and_combine(conds=[positive_A, negative_A], new_conds=[positive_B, negative_B],) + return (final_positive, final_negative,) + +class PairConditioningSetDefaultAndCombine: + NodeId = 'PairConditioningSetDefaultCombine' + NodeName = 'Cond Pair Set Default Combine' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "positive_DEFAULT": ("CONDITIONING",), + "negative_DEFAULT": ("CONDITIONING",), + }, + "optional": { + "hooks": ("HOOKS",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + CATEGORY = "advanced/hooks/cond pair" + FUNCTION = "set_default_and_combine" + + def set_default_and_combine(self, positive, negative, positive_DEFAULT, negative_DEFAULT, + hooks: comfy.hooks.HookGroup=None): + final_positive, final_negative = comfy.hooks.set_default_conds_and_combine(conds=[positive, negative], new_conds=[positive_DEFAULT, negative_DEFAULT], + hooks=hooks) + return (final_positive, final_negative) + +class ConditioningSetDefaultAndCombine: + NodeId = 'ConditioningSetDefaultCombine' + NodeName = 'Cond Set Default Combine' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cond": ("CONDITIONING",), + "cond_DEFAULT": ("CONDITIONING",), + }, + "optional": { + "hooks": ("HOOKS",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "advanced/hooks/cond single" + FUNCTION = "set_default_and_combine" + + def set_default_and_combine(self, cond, cond_DEFAULT, + hooks: comfy.hooks.HookGroup=None): + (final_conditioning,) = comfy.hooks.set_default_conds_and_combine(conds=[cond], new_conds=[cond_DEFAULT], + hooks=hooks) + return (final_conditioning,) + +class SetClipHooks: + NodeId = 'SetClipHooks' + NodeName = 'Set CLIP Hooks' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip": ("CLIP",), + "apply_to_conds": ("BOOLEAN", {"default": True}), + "schedule_clip": ("BOOLEAN", {"default": False}) + }, + "optional": { + "hooks": ("HOOKS",) + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CLIP",) + CATEGORY = "advanced/hooks/clip" + FUNCTION = "apply_hooks" + + def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None): + if hooks is not None: + clip = clip.clone() + if apply_to_conds: + clip.apply_hooks_to_conds = hooks + clip.patcher.forced_hooks = hooks.clone() + clip.use_clip_schedule = schedule_clip + if not clip.use_clip_schedule: + clip.patcher.forced_hooks.set_keyframes_on_hooks(None) + clip.patcher.register_all_hook_patches(hooks, comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Clip)) + return (clip,) + +class ConditioningTimestepsRange: + NodeId = 'ConditioningTimestepsRange' + NodeName = 'Timesteps Range' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }, + } + + EXPERIMENTAL = True + RETURN_TYPES = ("TIMESTEPS_RANGE", "TIMESTEPS_RANGE", "TIMESTEPS_RANGE") + RETURN_NAMES = ("TIMESTEPS_RANGE", "BEFORE_RANGE", "AFTER_RANGE") + CATEGORY = "advanced/hooks" + FUNCTION = "create_range" + + def create_range(self, start_percent: float, end_percent: float): + return ((start_percent, end_percent), (0.0, start_percent), (end_percent, 1.0)) +#------------------------------------------ +########################################### + + +########################################### +# Create Hooks +#------------------------------------------ +class CreateHookLora: + NodeId = 'CreateHookLora' + NodeName = 'Create Hook LoRA' + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }, + "optional": { + "prev_hooks": ("HOOKS",) + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/create" + FUNCTION = "create_hook" + + def create_hook(self, lora_name: str, strength_model: float, strength_clip: float, prev_hooks: comfy.hooks.HookGroup=None): + if prev_hooks is None: + prev_hooks = comfy.hooks.HookGroup() + prev_hooks.clone() + + if strength_model == 0 and strength_clip == 0: + return (prev_hooks,) + + lora_path = folder_paths.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + hooks = comfy.hooks.create_hook_lora(lora=lora, strength_model=strength_model, strength_clip=strength_clip) + return (prev_hooks.clone_and_combine(hooks),) + +class CreateHookLoraModelOnly(CreateHookLora): + NodeId = 'CreateHookLoraModelOnly' + NodeName = 'Create Hook LoRA (MO)' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }, + "optional": { + "prev_hooks": ("HOOKS",) + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/create" + FUNCTION = "create_hook_model_only" + + def create_hook_model_only(self, lora_name: str, strength_model: float, prev_hooks: comfy.hooks.HookGroup=None): + return self.create_hook(lora_name=lora_name, strength_model=strength_model, strength_clip=0, prev_hooks=prev_hooks) + +class CreateHookModelAsLora: + NodeId = 'CreateHookModelAsLora' + NodeName = 'Create Hook Model as LoRA' + + def __init__(self): + # when not None, will be in following format: + # (ckpt_path: str, weights_model: dict, weights_clip: dict) + self.loaded_weights = None + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }, + "optional": { + "prev_hooks": ("HOOKS",) + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/create" + FUNCTION = "create_hook" + + def create_hook(self, ckpt_name: str, strength_model: float, strength_clip: float, + prev_hooks: comfy.hooks.HookGroup=None): + if prev_hooks is None: + prev_hooks = comfy.hooks.HookGroup() + prev_hooks.clone() + + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + weights_model = None + weights_clip = None + if self.loaded_weights is not None: + if self.loaded_weights[0] == ckpt_path: + weights_model = self.loaded_weights[1] + weights_clip = self.loaded_weights[2] + else: + temp = self.loaded_weights + self.loaded_weights = None + del temp + + if weights_model is None: + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + weights_model = comfy.hooks.get_patch_weights_from_model(out[0]) + weights_clip = comfy.hooks.get_patch_weights_from_model(out[1].patcher if out[1] else out[1]) + self.loaded_weights = (ckpt_path, weights_model, weights_clip) + + hooks = comfy.hooks.create_hook_model_as_lora(weights_model=weights_model, weights_clip=weights_clip, + strength_model=strength_model, strength_clip=strength_clip) + return (prev_hooks.clone_and_combine(hooks),) + +class CreateHookModelAsLoraModelOnly(CreateHookModelAsLora): + NodeId = 'CreateHookModelAsLoraModelOnly' + NodeName = 'Create Hook Model as LoRA (MO)' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }, + "optional": { + "prev_hooks": ("HOOKS",) + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/create" + FUNCTION = "create_hook_model_only" + + def create_hook_model_only(self, ckpt_name: str, strength_model: float, + prev_hooks: comfy.hooks.HookGroup=None): + return self.create_hook(ckpt_name=ckpt_name, strength_model=strength_model, strength_clip=0.0, prev_hooks=prev_hooks) +#------------------------------------------ +########################################### + + +########################################### +# Schedule Hooks +#------------------------------------------ +class SetHookKeyframes: + NodeId = 'SetHookKeyframes' + NodeName = 'Set Hook Keyframes' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "hooks": ("HOOKS",), + }, + "optional": { + "hook_kf": ("HOOK_KEYFRAMES",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/scheduling" + FUNCTION = "set_hook_keyframes" + + def set_hook_keyframes(self, hooks: comfy.hooks.HookGroup, hook_kf: comfy.hooks.HookKeyframeGroup=None): + if hook_kf is not None: + hooks = hooks.clone() + hooks.set_keyframes_on_hooks(hook_kf=hook_kf) + return (hooks,) + +class CreateHookKeyframe: + NodeId = 'CreateHookKeyframe' + NodeName = 'Create Hook Keyframe' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "strength_mult": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }, + "optional": { + "prev_hook_kf": ("HOOK_KEYFRAMES",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOK_KEYFRAMES",) + RETURN_NAMES = ("HOOK_KF",) + CATEGORY = "advanced/hooks/scheduling" + FUNCTION = "create_hook_keyframe" + + def create_hook_keyframe(self, strength_mult: float, start_percent: float, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None): + if prev_hook_kf is None: + prev_hook_kf = comfy.hooks.HookKeyframeGroup() + prev_hook_kf = prev_hook_kf.clone() + keyframe = comfy.hooks.HookKeyframe(strength=strength_mult, start_percent=start_percent) + prev_hook_kf.add(keyframe) + return (prev_hook_kf,) + +class CreateHookKeyframesInterpolated: + NodeId = 'CreateHookKeyframesInterpolated' + NodeName = 'Create Hook Keyframes Interp.' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), + "strength_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), + "interpolation": (comfy.hooks.InterpolationMethod._LIST, ), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}), + "print_keyframes": ("BOOLEAN", {"default": False}), + }, + "optional": { + "prev_hook_kf": ("HOOK_KEYFRAMES",), + }, + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOK_KEYFRAMES",) + RETURN_NAMES = ("HOOK_KF",) + CATEGORY = "advanced/hooks/scheduling" + FUNCTION = "create_hook_keyframes" + + def create_hook_keyframes(self, strength_start: float, strength_end: float, interpolation: str, + start_percent: float, end_percent: float, keyframes_count: int, + print_keyframes=False, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None): + if prev_hook_kf is None: + prev_hook_kf = comfy.hooks.HookKeyframeGroup() + prev_hook_kf = prev_hook_kf.clone() + percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=keyframes_count, + method=comfy.hooks.InterpolationMethod.LINEAR) + strengths = comfy.hooks.InterpolationMethod.get_weights(num_from=strength_start, num_to=strength_end, length=keyframes_count, method=interpolation) + + is_first = True + for percent, strength in zip(percents, strengths): + guarantee_steps = 0 + if is_first: + guarantee_steps = 1 + is_first = False + prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps)) + if print_keyframes: + logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}") + return (prev_hook_kf,) + +class CreateHookKeyframesFromFloats: + NodeId = 'CreateHookKeyframesFromFloats' + NodeName = 'Create Hook Keyframes From Floats' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "print_keyframes": ("BOOLEAN", {"default": False}), + }, + "optional": { + "prev_hook_kf": ("HOOK_KEYFRAMES",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOK_KEYFRAMES",) + RETURN_NAMES = ("HOOK_KF",) + CATEGORY = "advanced/hooks/scheduling" + FUNCTION = "create_hook_keyframes" + + def create_hook_keyframes(self, floats_strength: Union[float, list[float]], + start_percent: float, end_percent: float, + prev_hook_kf: comfy.hooks.HookKeyframeGroup=None, print_keyframes=False): + if prev_hook_kf is None: + prev_hook_kf = comfy.hooks.HookKeyframeGroup() + prev_hook_kf = prev_hook_kf.clone() + if type(floats_strength) in (float, int): + floats_strength = [float(floats_strength)] + elif isinstance(floats_strength, Iterable): + pass + else: + raise Exception(f"floats_strength must be either an iterable input or a float, but was{type(floats_strength).__repr__}.") + percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=len(floats_strength), + method=comfy.hooks.InterpolationMethod.LINEAR) + + is_first = True + for percent, strength in zip(percents, floats_strength): + guarantee_steps = 0 + if is_first: + guarantee_steps = 1 + is_first = False + prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps)) + if print_keyframes: + logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}") + return (prev_hook_kf,) +#------------------------------------------ +########################################### + + +class SetModelHooksOnCond: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "conditioning": ("CONDITIONING",), + "hooks": ("HOOKS",), + }, + } + + EXPERIMENTAL = True + RETURN_TYPES = ("CONDITIONING",) + CATEGORY = "advanced/hooks/manual" + FUNCTION = "attach_hook" + + def attach_hook(self, conditioning, hooks: comfy.hooks.HookGroup): + return (comfy.hooks.set_hooks_for_conditioning(conditioning, hooks),) + + +########################################### +# Combine Hooks +#------------------------------------------ +class CombineHooks: + NodeId = 'CombineHooks2' + NodeName = 'Combine Hooks [2]' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "hooks_A": ("HOOKS",), + "hooks_B": ("HOOKS",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/combine" + FUNCTION = "combine_hooks" + + def combine_hooks(self, + hooks_A: comfy.hooks.HookGroup=None, + hooks_B: comfy.hooks.HookGroup=None): + candidates = [hooks_A, hooks_B] + return (comfy.hooks.HookGroup.combine_all_hooks(candidates),) + +class CombineHooksFour: + NodeId = 'CombineHooks4' + NodeName = 'Combine Hooks [4]' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "hooks_A": ("HOOKS",), + "hooks_B": ("HOOKS",), + "hooks_C": ("HOOKS",), + "hooks_D": ("HOOKS",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/combine" + FUNCTION = "combine_hooks" + + def combine_hooks(self, + hooks_A: comfy.hooks.HookGroup=None, + hooks_B: comfy.hooks.HookGroup=None, + hooks_C: comfy.hooks.HookGroup=None, + hooks_D: comfy.hooks.HookGroup=None): + candidates = [hooks_A, hooks_B, hooks_C, hooks_D] + return (comfy.hooks.HookGroup.combine_all_hooks(candidates),) + +class CombineHooksEight: + NodeId = 'CombineHooks8' + NodeName = 'Combine Hooks [8]' + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "hooks_A": ("HOOKS",), + "hooks_B": ("HOOKS",), + "hooks_C": ("HOOKS",), + "hooks_D": ("HOOKS",), + "hooks_E": ("HOOKS",), + "hooks_F": ("HOOKS",), + "hooks_G": ("HOOKS",), + "hooks_H": ("HOOKS",), + } + } + + EXPERIMENTAL = True + RETURN_TYPES = ("HOOKS",) + CATEGORY = "advanced/hooks/combine" + FUNCTION = "combine_hooks" + + def combine_hooks(self, + hooks_A: comfy.hooks.HookGroup=None, + hooks_B: comfy.hooks.HookGroup=None, + hooks_C: comfy.hooks.HookGroup=None, + hooks_D: comfy.hooks.HookGroup=None, + hooks_E: comfy.hooks.HookGroup=None, + hooks_F: comfy.hooks.HookGroup=None, + hooks_G: comfy.hooks.HookGroup=None, + hooks_H: comfy.hooks.HookGroup=None): + candidates = [hooks_A, hooks_B, hooks_C, hooks_D, hooks_E, hooks_F, hooks_G, hooks_H] + return (comfy.hooks.HookGroup.combine_all_hooks(candidates),) +#------------------------------------------ +########################################### + +node_list = [ + # Create + CreateHookLora, + CreateHookLoraModelOnly, + CreateHookModelAsLora, + CreateHookModelAsLoraModelOnly, + # Scheduling + SetHookKeyframes, + CreateHookKeyframe, + CreateHookKeyframesInterpolated, + CreateHookKeyframesFromFloats, + # Combine + CombineHooks, + CombineHooksFour, + CombineHooksEight, + # Attach + ConditioningSetProperties, + ConditioningSetPropertiesAndCombine, + PairConditioningSetProperties, + PairConditioningSetPropertiesAndCombine, + ConditioningSetDefaultAndCombine, + PairConditioningSetDefaultAndCombine, + PairConditioningCombine, + SetClipHooks, + # Other + ConditioningTimestepsRange, +] +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +for node in node_list: + NODE_CLASS_MAPPINGS[node.NodeId] = node + NODE_DISPLAY_NAME_MAPPINGS[node.NodeId] = node.NodeName diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py new file mode 100644 index 0000000000000000000000000000000000000000..d7278e7a7d866dcc0519c66f1f5894d8b7344e1c --- /dev/null +++ b/comfy_extras/nodes_hunyuan.py @@ -0,0 +1,123 @@ +import nodes +import node_helpers +import torch +import comfy.model_management + + +class CLIPTextEncodeHunyuanDiT: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, bert, mt5xl): + tokens = clip.tokenize(bert) + tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"] + + return (clip.encode_from_tokens_scheduled(tokens), ) + +class EmptyHunyuanLatentVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 25, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/video" + + def generate(self, width, height, length, batch_size=1): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + return ({"samples":latent}, ) + +PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +class TextEncodeHunyuanVideo_ImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "image_interleave": ("INT", {"default": 2, "min": 1, "max": 512, "tooltip": "How much the image influences things vs the text prompt. Higher number means more influence from the text prompt."}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, clip_vision_output, prompt, image_interleave): + tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave) + return (clip.encode_from_tokens_scheduled(tokens), ) + +class HunyuanImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], ) + }, + "optional": {"start_image": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, vae, width, height, length, batch_size, guidance_type, start_image=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + out_latent = {} + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + concat_latent_image = vae.encode(start_image) + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + if guidance_type == "v1 (concat)": + cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask} + elif guidance_type == "v2 (replace)": + cond = {'guiding_frame_index': 0} + latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image + out_latent["noise_mask"] = mask + elif guidance_type == "custom": + cond = {"ref_latent": concat_latent_image} + + positive = node_helpers.conditioning_set_values(positive, cond) + + out_latent["samples"] = latent + return (positive, out_latent) + + + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT, + "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo, + "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo, + "HunyuanImageToVideo": HunyuanImageToVideo, +} diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py new file mode 100644 index 0000000000000000000000000000000000000000..51e45336ad4a450b8f83be1e22f0033ad3cd4433 --- /dev/null +++ b/comfy_extras/nodes_hunyuan3d.py @@ -0,0 +1,634 @@ +import torch +import os +import json +import struct +import numpy as np +from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_from_grid_torch +import folder_paths +import comfy.model_management +from comfy.cli_args import args + + +class EmptyLatentHunyuan3Dv2: + @classmethod + def INPUT_TYPES(s): + return {"required": {"resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/3d" + + def generate(self, resolution, batch_size): + latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device()) + return ({"samples": latent, "type": "hunyuan3dv2"}, ) + + +class Hunyuan3Dv2Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"clip_vision_output": ("CLIP_VISION_OUTPUT",), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, clip_vision_output): + embeds = clip_vision_output.last_hidden_state + positive = [[embeds, {}]] + negative = [[torch.zeros_like(embeds), {}]] + return (positive, negative) + + +class Hunyuan3Dv2ConditioningMultiView: + @classmethod + def INPUT_TYPES(s): + return {"required": {}, + "optional": {"front": ("CLIP_VISION_OUTPUT",), + "left": ("CLIP_VISION_OUTPUT",), + "back": ("CLIP_VISION_OUTPUT",), + "right": ("CLIP_VISION_OUTPUT",), }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, front=None, left=None, back=None, right=None): + all_embeds = [front, left, back, right] + out = [] + pos_embeds = None + for i, e in enumerate(all_embeds): + if e is not None: + if pos_embeds is None: + pos_embeds = get_1d_sincos_pos_embed_from_grid_torch(e.last_hidden_state.shape[-1], torch.arange(4)) + out.append(e.last_hidden_state + pos_embeds[i].reshape(1, 1, -1)) + + embeds = torch.cat(out, dim=1) + positive = [[embeds, {}]] + negative = [[torch.zeros_like(embeds), {}]] + return (positive, negative) + + +class VOXEL: + def __init__(self, data): + self.data = data + + +class VAEDecodeHunyuan3D: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), + "vae": ("VAE", ), + "num_chunks": ("INT", {"default": 8000, "min": 1000, "max": 500000}), + "octree_resolution": ("INT", {"default": 256, "min": 16, "max": 512}), + }} + RETURN_TYPES = ("VOXEL",) + FUNCTION = "decode" + + CATEGORY = "latent/3d" + + def decode(self, vae, samples, num_chunks, octree_resolution): + voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution})) + return (voxels, ) + + +def voxel_to_mesh(voxels, threshold=0.5, device=None): + if device is None: + device = torch.device("cpu") + voxels = voxels.to(device) + + binary = (voxels > threshold).float() + padded = torch.nn.functional.pad(binary, (1, 1, 1, 1, 1, 1), 'constant', 0) + + D, H, W = binary.shape + + neighbors = torch.tensor([ + [0, 0, 1], + [0, 0, -1], + [0, 1, 0], + [0, -1, 0], + [1, 0, 0], + [-1, 0, 0] + ], device=device) + + z, y, x = torch.meshgrid( + torch.arange(D, device=device), + torch.arange(H, device=device), + torch.arange(W, device=device), + indexing='ij' + ) + voxel_indices = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1) + + solid_mask = binary.flatten() > 0 + solid_indices = voxel_indices[solid_mask] + + corner_offsets = [ + torch.tensor([ + [0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1] + ], device=device), + torch.tensor([ + [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0] + ], device=device), + torch.tensor([ + [0, 1, 0], [1, 1, 0], [1, 1, 1], [0, 1, 1] + ], device=device), + torch.tensor([ + [0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0] + ], device=device), + torch.tensor([ + [1, 0, 1], [1, 1, 1], [1, 1, 0], [1, 0, 0] + ], device=device), + torch.tensor([ + [0, 1, 0], [0, 1, 1], [0, 0, 1], [0, 0, 0] + ], device=device) + ] + + all_vertices = [] + all_indices = [] + + vertex_count = 0 + + for face_idx, offset in enumerate(neighbors): + neighbor_indices = solid_indices + offset + + padded_indices = neighbor_indices + 1 + + is_exposed = padded[ + padded_indices[:, 0], + padded_indices[:, 1], + padded_indices[:, 2] + ] == 0 + + if not is_exposed.any(): + continue + + exposed_indices = solid_indices[is_exposed] + + corners = corner_offsets[face_idx].unsqueeze(0) + + face_vertices = exposed_indices.unsqueeze(1) + corners + + all_vertices.append(face_vertices.reshape(-1, 3)) + + num_faces = exposed_indices.shape[0] + face_indices = torch.arange( + vertex_count, + vertex_count + 4 * num_faces, + device=device + ).reshape(-1, 4) + + all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 1], face_indices[:, 2]], dim=1)) + all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 2], face_indices[:, 3]], dim=1)) + + vertex_count += 4 * num_faces + + if len(all_vertices) > 0: + vertices = torch.cat(all_vertices, dim=0) + faces = torch.cat(all_indices, dim=0) + else: + vertices = torch.zeros((1, 3)) + faces = torch.zeros((1, 3)) + + v_min = 0 + v_max = max(voxels.shape) + + vertices = vertices - (v_min + v_max) / 2 + + scale = (v_max - v_min) / 2 + if scale > 0: + vertices = vertices / scale + + vertices = torch.fliplr(vertices) + return vertices, faces + +def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None): + if device is None: + device = torch.device("cpu") + voxels = voxels.to(device) + + D, H, W = voxels.shape + + padded = torch.nn.functional.pad(voxels, (1, 1, 1, 1, 1, 1), 'constant', 0) + z, y, x = torch.meshgrid( + torch.arange(D, device=device), + torch.arange(H, device=device), + torch.arange(W, device=device), + indexing='ij' + ) + cell_positions = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1) + + corner_offsets = torch.tensor([ + [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], + [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1] + ], device=device) + + corner_values = torch.zeros((cell_positions.shape[0], 8), device=device) + for c, (dz, dy, dx) in enumerate(corner_offsets): + corner_values[:, c] = padded[ + cell_positions[:, 0] + dz, + cell_positions[:, 1] + dy, + cell_positions[:, 2] + dx + ] + + corner_signs = corner_values > threshold + has_inside = torch.any(corner_signs, dim=1) + has_outside = torch.any(~corner_signs, dim=1) + contains_surface = has_inside & has_outside + + active_cells = cell_positions[contains_surface] + active_signs = corner_signs[contains_surface] + active_values = corner_values[contains_surface] + + if active_cells.shape[0] == 0: + return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device) + + edges = torch.tensor([ + [0, 1], [0, 2], [0, 4], [1, 3], + [1, 5], [2, 3], [2, 6], [3, 7], + [4, 5], [4, 6], [5, 7], [6, 7] + ], device=device) + + cell_vertices = {} + progress = comfy.utils.ProgressBar(100) + + for edge_idx, (e1, e2) in enumerate(edges): + progress.update(1) + crossing = active_signs[:, e1] != active_signs[:, e2] + if not crossing.any(): + continue + + cell_indices = torch.nonzero(crossing, as_tuple=True)[0] + + v1 = active_values[cell_indices, e1] + v2 = active_values[cell_indices, e2] + + t = torch.zeros_like(v1, device=device) + denom = v2 - v1 + valid = denom != 0 + t[valid] = (threshold - v1[valid]) / denom[valid] + t[~valid] = 0.5 + + p1 = corner_offsets[e1].float() + p2 = corner_offsets[e2].float() + + intersection = p1.unsqueeze(0) + t.unsqueeze(1) * (p2.unsqueeze(0) - p1.unsqueeze(0)) + + for i, point in zip(cell_indices.tolist(), intersection): + if i not in cell_vertices: + cell_vertices[i] = [] + cell_vertices[i].append(point) + + # Calculate the final vertices as the average of intersection points for each cell + vertices = [] + vertex_lookup = {} + + vert_progress_mod = round(len(cell_vertices)/50) + + for i, points in cell_vertices.items(): + if not i % vert_progress_mod: + progress.update(1) + + if points: + vertex = torch.stack(points).mean(dim=0) + vertex = vertex + active_cells[i].float() + vertex_lookup[tuple(active_cells[i].tolist())] = len(vertices) + vertices.append(vertex) + + if not vertices: + return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device) + + final_vertices = torch.stack(vertices) + + inside_corners_mask = active_signs + outside_corners_mask = ~active_signs + + inside_counts = inside_corners_mask.sum(dim=1, keepdim=True).float() + outside_counts = outside_corners_mask.sum(dim=1, keepdim=True).float() + + inside_pos = torch.zeros((active_cells.shape[0], 3), device=device) + outside_pos = torch.zeros((active_cells.shape[0], 3), device=device) + + for i in range(8): + mask_inside = inside_corners_mask[:, i].unsqueeze(1) + mask_outside = outside_corners_mask[:, i].unsqueeze(1) + inside_pos += corner_offsets[i].float().unsqueeze(0) * mask_inside + outside_pos += corner_offsets[i].float().unsqueeze(0) * mask_outside + + inside_pos /= inside_counts + outside_pos /= outside_counts + gradients = inside_pos - outside_pos + + pos_dirs = torch.tensor([ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1] + ], device=device) + + cross_products = [ + torch.linalg.cross(pos_dirs[i].float(), pos_dirs[j].float()) + for i in range(3) for j in range(i+1, 3) + ] + + faces = [] + all_keys = set(vertex_lookup.keys()) + + face_progress_mod = round(len(active_cells)/38*3) + + for pair_idx, (i, j) in enumerate([(0,1), (0,2), (1,2)]): + dir_i = pos_dirs[i] + dir_j = pos_dirs[j] + cross_product = cross_products[pair_idx] + + ni_positions = active_cells + dir_i + nj_positions = active_cells + dir_j + diag_positions = active_cells + dir_i + dir_j + + alignments = torch.matmul(gradients, cross_product) + + valid_quads = [] + quad_indices = [] + + for idx, active_cell in enumerate(active_cells): + if not idx % face_progress_mod: + progress.update(1) + cell_key = tuple(active_cell.tolist()) + ni_key = tuple(ni_positions[idx].tolist()) + nj_key = tuple(nj_positions[idx].tolist()) + diag_key = tuple(diag_positions[idx].tolist()) + + if cell_key in all_keys and ni_key in all_keys and nj_key in all_keys and diag_key in all_keys: + v0 = vertex_lookup[cell_key] + v1 = vertex_lookup[ni_key] + v2 = vertex_lookup[nj_key] + v3 = vertex_lookup[diag_key] + + valid_quads.append((v0, v1, v2, v3)) + quad_indices.append(idx) + + for q_idx, (v0, v1, v2, v3) in enumerate(valid_quads): + cell_idx = quad_indices[q_idx] + if alignments[cell_idx] > 0: + faces.append(torch.tensor([v0, v1, v3], device=device, dtype=torch.long)) + faces.append(torch.tensor([v0, v3, v2], device=device, dtype=torch.long)) + else: + faces.append(torch.tensor([v0, v3, v1], device=device, dtype=torch.long)) + faces.append(torch.tensor([v0, v2, v3], device=device, dtype=torch.long)) + + if faces: + faces = torch.stack(faces) + else: + faces = torch.zeros((0, 3), dtype=torch.long, device=device) + + v_min = 0 + v_max = max(D, H, W) + + final_vertices = final_vertices - (v_min + v_max) / 2 + + scale = (v_max - v_min) / 2 + if scale > 0: + final_vertices = final_vertices / scale + + final_vertices = torch.fliplr(final_vertices) + + return final_vertices, faces + +class MESH: + def __init__(self, vertices, faces): + self.vertices = vertices + self.faces = faces + + +class VoxelToMeshBasic: + @classmethod + def INPUT_TYPES(s): + return {"required": {"voxel": ("VOXEL", ), + "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MESH",) + FUNCTION = "decode" + + CATEGORY = "3d" + + def decode(self, voxel, threshold): + vertices = [] + faces = [] + for x in voxel.data: + v, f = voxel_to_mesh(x, threshold=threshold, device=None) + vertices.append(v) + faces.append(f) + + return (MESH(torch.stack(vertices), torch.stack(faces)), ) + +class VoxelToMesh: + @classmethod + def INPUT_TYPES(s): + return {"required": {"voxel": ("VOXEL", ), + "algorithm": (["surface net", "basic"], ), + "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MESH",) + FUNCTION = "decode" + + CATEGORY = "3d" + + def decode(self, voxel, algorithm, threshold): + vertices = [] + faces = [] + + if algorithm == "basic": + mesh_function = voxel_to_mesh + elif algorithm == "surface net": + mesh_function = voxel_to_mesh_surfnet + + for x in voxel.data: + v, f = mesh_function(x, threshold=threshold, device=None) + vertices.append(v) + faces.append(f) + + return (MESH(torch.stack(vertices), torch.stack(faces)), ) + + +def save_glb(vertices, faces, filepath, metadata=None): + """ + Save PyTorch tensor vertices and faces as a GLB file without external dependencies. + + Parameters: + vertices: torch.Tensor of shape (N, 3) - The vertex coordinates + faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces) + filepath: str - Output filepath (should end with .glb) + """ + + # Convert tensors to numpy arrays + vertices_np = vertices.cpu().numpy().astype(np.float32) + faces_np = faces.cpu().numpy().astype(np.uint32) + + vertices_buffer = vertices_np.tobytes() + indices_buffer = faces_np.tobytes() + + def pad_to_4_bytes(buffer): + padding_length = (4 - (len(buffer) % 4)) % 4 + return buffer + b'\x00' * padding_length + + vertices_buffer_padded = pad_to_4_bytes(vertices_buffer) + indices_buffer_padded = pad_to_4_bytes(indices_buffer) + + buffer_data = vertices_buffer_padded + indices_buffer_padded + + vertices_byte_length = len(vertices_buffer) + vertices_byte_offset = 0 + indices_byte_length = len(indices_buffer) + indices_byte_offset = len(vertices_buffer_padded) + + gltf = { + "asset": {"version": "2.0", "generator": "ComfyUI"}, + "buffers": [ + { + "byteLength": len(buffer_data) + } + ], + "bufferViews": [ + { + "buffer": 0, + "byteOffset": vertices_byte_offset, + "byteLength": vertices_byte_length, + "target": 34962 # ARRAY_BUFFER + }, + { + "buffer": 0, + "byteOffset": indices_byte_offset, + "byteLength": indices_byte_length, + "target": 34963 # ELEMENT_ARRAY_BUFFER + } + ], + "accessors": [ + { + "bufferView": 0, + "byteOffset": 0, + "componentType": 5126, # FLOAT + "count": len(vertices_np), + "type": "VEC3", + "max": vertices_np.max(axis=0).tolist(), + "min": vertices_np.min(axis=0).tolist() + }, + { + "bufferView": 1, + "byteOffset": 0, + "componentType": 5125, # UNSIGNED_INT + "count": faces_np.size, + "type": "SCALAR" + } + ], + "meshes": [ + { + "primitives": [ + { + "attributes": { + "POSITION": 0 + }, + "indices": 1, + "mode": 4 # TRIANGLES + } + ] + } + ], + "nodes": [ + { + "mesh": 0 + } + ], + "scenes": [ + { + "nodes": [0] + } + ], + "scene": 0 + } + + if metadata is not None: + gltf["asset"]["extras"] = metadata + + # Convert the JSON to bytes + gltf_json = json.dumps(gltf).encode('utf8') + + def pad_json_to_4_bytes(buffer): + padding_length = (4 - (len(buffer) % 4)) % 4 + return buffer + b' ' * padding_length + + gltf_json_padded = pad_json_to_4_bytes(gltf_json) + + # Create the GLB header + # Magic glTF + glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data)) + + # Create JSON chunk header (chunk type 0) + json_chunk_header = struct.pack(' int: + min_value = min(min_value, value) + + # All big divisors of value (inclusive) + divisors = [i for i in range(min_value, value + 1) if value % i == 0] + + ns = [value // i for i in divisors[:max_options]] # has at least 1 element + + if len(ns) - 1 > 0: + idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() + else: + idx = 0 + + return ns[idx] + +class HyperTile: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), + "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), + "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), + "scale_depth": ("BOOLEAN", {"default": False}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, tile_size, swap_size, max_depth, scale_depth): + latent_tile_size = max(32, tile_size) // 8 + self.temp = None + + def hypertile_in(q, k, v, extra_options): + model_chans = q.shape[-2] + orig_shape = extra_options['original_shape'] + apply_to = [] + for i in range(max_depth + 1): + apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) + + if model_chans in apply_to: + shape = extra_options["original_shape"] + aspect_ratio = shape[-1] / shape[-2] + + hw = q.size(1) + h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) + + factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 + nh = random_divisor(h, latent_tile_size * factor, swap_size) + nw = random_divisor(w, latent_tile_size * factor, swap_size) + + if nh * nw > 1: + q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) + self.temp = (nh, nw, h, w) + return q, k, v + + return q, k, v + def hypertile_out(out, extra_options): + if self.temp is not None: + nh, nw, h, w = self.temp + self.temp = None + out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) + out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) + return out + + + m = model.clone() + m.set_model_attn1_patch(hypertile_in) + m.set_model_attn1_output_patch(hypertile_out) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "HyperTile": HyperTile, +} diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py new file mode 100644 index 0000000000000000000000000000000000000000..ed54ccc57a58cc707af3e1d0b83fb3c959e4d0d7 --- /dev/null +++ b/comfy_extras/nodes_images.py @@ -0,0 +1,597 @@ +from __future__ import annotations + +import nodes +import folder_paths +from comfy.cli_args import args + +from PIL import Image +from PIL.PngImagePlugin import PngInfo + +import numpy as np +import json +import os +import re +from io import BytesIO +from inspect import cleandoc +import torch +import comfy.utils + +from comfy.comfy_types import FileLocator, IO +from server import PromptServer + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + +class ImageCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crop" + + CATEGORY = "image/transform" + + def crop(self, image, width, height, x, y): + x = min(x, image.shape[2] - 1) + y = min(y, image.shape[1] - 1) + to_x = width + x + to_y = height + y + img = image[:,y:to_y, x:to_x, :] + return (img,) + +class RepeatImageBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "amount": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "repeat" + + CATEGORY = "image/batch" + + def repeat(self, image, amount): + s = image.repeat((amount, 1,1,1)) + return (s,) + +class ImageFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}), + "length": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "frombatch" + + CATEGORY = "image/batch" + + def frombatch(self, image, batch_index, length): + s_in = image + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s = s_in[batch_index:batch_index + length].clone() + return (s,) + + +class ImageAddNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), + "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "repeat" + + CATEGORY = "image" + + def repeat(self, image, seed, strength): + generator = torch.manual_seed(seed) + s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0) + return (s,) + +class SaveAnimatedWEBP: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + methods = {"default": 4, "fastest": 0, "slowest": 6} + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "lossless": ("BOOLEAN", {"default": True}), + "quality": ("INT", {"default": 80, "min": 0, "max": 100}), + "method": (list(s.methods.keys()),), + # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/animation" + + def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): + method = self.methods.get(method) + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results: list[FileLocator] = [] + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = pil_images[0].getexif() + if not args.disable_metadata: + if prompt is not None: + metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) + if extra_pnginfo is not None: + inital_exif = 0x010f + for x in extra_pnginfo: + metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) + inital_exif -= 1 + + if num_frames == 0: + num_frames = len(pil_images) + + c = len(pil_images) + for i in range(0, c, num_frames): + file = f"{filename}_{counter:05}_.webp" + pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + animated = num_frames != 1 + return { "ui": { "images": results, "animated": (animated,) } } + +class SaveAnimatedPNG: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/animation" + + def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) + + file = f"{filename}_{counter:05}_.png" + pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + return { "ui": { "images": results, "animated": (True,)} } + +class SVG: + """ + Stores SVG representations via a list of BytesIO objects. + """ + def __init__(self, data: list[BytesIO]): + self.data = data + + def combine(self, other: 'SVG') -> 'SVG': + return SVG(self.data + other.data) + + @staticmethod + def combine_all(svgs: list['SVG']) -> 'SVG': + all_svgs_list: list[BytesIO] = [] + for svg_item in svgs: + all_svgs_list.extend(svg_item.data) + return SVG(all_svgs_list) + + +class ImageStitch: + """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "direction": (["right", "down", "left", "up"], {"default": "right"}), + "match_image_size": ("BOOLEAN", {"default": True}), + "spacing_width": ( + "INT", + {"default": 0, "min": 0, "max": 1024, "step": 2}, + ), + "spacing_color": ( + ["white", "black", "red", "green", "blue"], + {"default": "white"}, + ), + }, + "optional": { + "image2": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "stitch" + CATEGORY = "image/transform" + DESCRIPTION = """ +Stitches image2 to image1 in the specified direction. +If image2 is not provided, returns image1 unchanged. +Optional spacing can be added between images. +""" + + def stitch( + self, + image1, + direction, + match_image_size, + spacing_width, + spacing_color, + image2=None, + ): + if image2 is None: + return (image1,) + + # Handle batch size differences + if image1.shape[0] != image2.shape[0]: + max_batch = max(image1.shape[0], image2.shape[0]) + if image1.shape[0] < max_batch: + image1 = torch.cat( + [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)] + ) + if image2.shape[0] < max_batch: + image2 = torch.cat( + [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)] + ) + + # Match image sizes if requested + if match_image_size: + h1, w1 = image1.shape[1:3] + h2, w2 = image2.shape[1:3] + aspect_ratio = w2 / h2 + + if direction in ["left", "right"]: + target_h, target_w = h1, int(h1 * aspect_ratio) + else: # up, down + target_w, target_h = w1, int(w1 / aspect_ratio) + + image2 = comfy.utils.common_upscale( + image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled" + ).movedim(1, -1) + + color_map = { + "white": 1.0, + "black": 0.0, + "red": (1.0, 0.0, 0.0), + "green": (0.0, 1.0, 0.0), + "blue": (0.0, 0.0, 1.0), + } + + color_val = color_map[spacing_color] + + # When not matching sizes, pad to align non-concat dimensions + if not match_image_size: + h1, w1 = image1.shape[1:3] + h2, w2 = image2.shape[1:3] + pad_value = 0.0 + if not isinstance(color_val, tuple): + pad_value = color_val + + if direction in ["left", "right"]: + # For horizontal concat, pad heights to match + if h1 != h2: + target_h = max(h1, h2) + if h1 < target_h: + pad_h = target_h - h1 + pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 + image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value) + if h2 < target_h: + pad_h = target_h - h2 + pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 + image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value) + else: # up, down + # For vertical concat, pad widths to match + if w1 != w2: + target_w = max(w1, w2) + if w1 < target_w: + pad_w = target_w - w1 + pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 + image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value) + if w2 < target_w: + pad_w = target_w - w2 + pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 + image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value) + + # Ensure same number of channels + if image1.shape[-1] != image2.shape[-1]: + max_channels = max(image1.shape[-1], image2.shape[-1]) + if image1.shape[-1] < max_channels: + image1 = torch.cat( + [ + image1, + torch.ones( + *image1.shape[:-1], + max_channels - image1.shape[-1], + device=image1.device, + ), + ], + dim=-1, + ) + if image2.shape[-1] < max_channels: + image2 = torch.cat( + [ + image2, + torch.ones( + *image2.shape[:-1], + max_channels - image2.shape[-1], + device=image2.device, + ), + ], + dim=-1, + ) + + # Add spacing if specified + if spacing_width > 0: + spacing_width = spacing_width + (spacing_width % 2) # Ensure even + + if direction in ["left", "right"]: + spacing_shape = ( + image1.shape[0], + max(image1.shape[1], image2.shape[1]), + spacing_width, + image1.shape[-1], + ) + else: + spacing_shape = ( + image1.shape[0], + spacing_width, + max(image1.shape[2], image2.shape[2]), + image1.shape[-1], + ) + + spacing = torch.full(spacing_shape, 0.0, device=image1.device) + if isinstance(color_val, tuple): + for i, c in enumerate(color_val): + if i < spacing.shape[-1]: + spacing[..., i] = c + if spacing.shape[-1] == 4: # Add alpha + spacing[..., 3] = 1.0 + else: + spacing[..., : min(3, spacing.shape[-1])] = color_val + if spacing.shape[-1] == 4: + spacing[..., 3] = 1.0 + + # Concatenate images + images = [image2, image1] if direction in ["left", "up"] else [image1, image2] + if spacing_width > 0: + images.insert(1, spacing) + + concat_dim = 2 if direction in ["left", "right"] else 1 + return (torch.cat(images, dim=concat_dim),) + +class ResizeAndPadImage: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "target_width": ("INT", { + "default": 512, + "min": 1, + "max": MAX_RESOLUTION, + "step": 1 + }), + "target_height": ("INT", { + "default": 512, + "min": 1, + "max": MAX_RESOLUTION, + "step": 1 + }), + "padding_color": (["white", "black"],), + "interpolation": (["area", "bicubic", "nearest-exact", "bilinear", "lanczos"],), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "resize_and_pad" + CATEGORY = "image/transform" + + def resize_and_pad(self, image, target_width, target_height, padding_color, interpolation): + batch_size, orig_height, orig_width, channels = image.shape + + scale_w = target_width / orig_width + scale_h = target_height / orig_height + scale = min(scale_w, scale_h) + + new_width = int(orig_width * scale) + new_height = int(orig_height * scale) + + image_permuted = image.permute(0, 3, 1, 2) + + resized = comfy.utils.common_upscale(image_permuted, new_width, new_height, interpolation, "disabled") + + pad_value = 0.0 if padding_color == "black" else 1.0 + padded = torch.full( + (batch_size, channels, target_height, target_width), + pad_value, + dtype=image.dtype, + device=image.device + ) + + y_offset = (target_height - new_height) // 2 + x_offset = (target_width - new_width) // 2 + + padded[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized + + output = padded.permute(0, 2, 3, 1) + return (output,) + +class SaveSVGNode: + """ + Save SVG files on disk. + """ + + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + RETURN_TYPES = () + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "save_svg" + CATEGORY = "image/save" # Changed + OUTPUT_NODE = True + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "svg": ("SVG",), # Changed + "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + } + } + + def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + + # Prepare metadata JSON + metadata_dict = {} + if prompt is not None: + metadata_dict["prompt"] = prompt + if extra_pnginfo is not None: + metadata_dict.update(extra_pnginfo) + + # Convert metadata to JSON string + metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None + + for batch_number, svg_bytes in enumerate(svg.data): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.svg" + + # Read SVG content + svg_bytes.seek(0) + svg_content = svg_bytes.read().decode('utf-8') + + # Inject metadata if available + if metadata_json: + # Create metadata element with CDATA section + metadata_element = f""" + + + """ + # Insert metadata after opening svg tag using regex with a replacement function + def replacement(match): + # match.group(1) contains the captured tag + return match.group(1) + '\n' + metadata_element + + # Apply the substitution + svg_content = re.sub(r'(]*>)', replacement, svg_content, flags=re.UNICODE) + + # Write the modified SVG to file + with open(os.path.join(full_output_folder, file), 'wb') as svg_file: + svg_file.write(svg_content.encode('utf-8')) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + return { "ui": { "images": results } } + +class GetImageSize: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + } + } + + RETURN_TYPES = (IO.INT, IO.INT, IO.INT) + RETURN_NAMES = ("width", "height", "batch_size") + FUNCTION = "get_size" + + CATEGORY = "image" + DESCRIPTION = """Returns width and height of the image, and passes it through unchanged.""" + + def get_size(self, image, unique_id=None) -> tuple[int, int]: + height = image.shape[1] + width = image.shape[2] + batch_size = image.shape[0] + + # Send progress text to display size on the node + if unique_id: + PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id) + + return width, height, batch_size + +NODE_CLASS_MAPPINGS = { + "ImageCrop": ImageCrop, + "RepeatImageBatch": RepeatImageBatch, + "ImageFromBatch": ImageFromBatch, + "ImageAddNoise": ImageAddNoise, + "SaveAnimatedWEBP": SaveAnimatedWEBP, + "SaveAnimatedPNG": SaveAnimatedPNG, + "SaveSVGNode": SaveSVGNode, + "ImageStitch": ImageStitch, + "ResizeAndPadImage": ResizeAndPadImage, + "GetImageSize": GetImageSize, +} diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py new file mode 100644 index 0000000000000000000000000000000000000000..c2e70a84c10ca5cc1b3ca853a97adc3c64fbb315 --- /dev/null +++ b/comfy_extras/nodes_ip2p.py @@ -0,0 +1,45 @@ +import torch + +class InstructPixToPixConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/instructpix2pix" + + def encode(self, positive, negative, pixels, vae): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + + concat_latent = vae.encode(pixels) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1], out_latent) + +NODE_CLASS_MAPPINGS = { + "InstructPixToPixConditioning": InstructPixToPixConditioning, +} diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py new file mode 100644 index 0000000000000000000000000000000000000000..f33ed1beea538ee28871e6fcb04da73ae8d4b91c --- /dev/null +++ b/comfy_extras/nodes_latent.py @@ -0,0 +1,288 @@ +import comfy.utils +import comfy_extras.nodes_post_processing +import torch + + +def reshape_latent_to(target_shape, latent, repeat_batch=True): + if latent.shape[1:] != target_shape[1:]: + latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center") + if repeat_batch: + return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) + else: + return latent + + +class LatentAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 + s2 + return (samples_out,) + +class LatentSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 - s2 + return (samples_out,) + +class LatentMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, multiplier): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1 * multiplier + return (samples_out,) + +class LatentInterpolate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), + "samples2": ("LATENT",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2, ratio): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + + m1 = torch.linalg.vector_norm(s1, dim=(1)) + m2 = torch.linalg.vector_norm(s2, dim=(1)) + + s1 = torch.nan_to_num(s1 / m1) + s2 = torch.nan_to_num(s2 / m2) + + t = (s1 * ratio + s2 * (1.0 - ratio)) + mt = torch.linalg.vector_norm(t, dim=(1)) + st = torch.nan_to_num(t / mt) + + samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) + return (samples_out,) + +class LatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "batch" + + CATEGORY = "latent/batch" + + def batch(self, samples1, samples2): + samples_out = samples1.copy() + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False) + s = torch.cat((s1, s2), dim=0) + samples_out["samples"] = s + samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) + return (samples_out,) + +class LatentBatchSeedBehavior: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, seed_behavior): + samples_out = samples.copy() + latent = samples["samples"] + if seed_behavior == "random": + if 'batch_index' in samples_out: + samples_out.pop('batch_index') + elif seed_behavior == "fixed": + batch_number = samples_out.get("batch_index", [0])[0] + samples_out["batch_index"] = [batch_number] * latent.shape[0] + + return (samples_out,) + +class LatentApplyOperation: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "operation": ("LATENT_OPERATION",), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def op(self, samples, operation): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = operation(latent=s1) + return (samples_out,) + +class LatentApplyOperationCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "operation": ("LATENT_OPERATION",), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def patch(self, model, operation): + m = model.clone() + + def pre_cfg_function(args): + conds_out = args["conds_out"] + if len(conds_out) == 2: + conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1] + else: + conds_out[0] = operation(latent=conds_out[0]) + return conds_out + + m.set_model_sampler_pre_cfg_function(pre_cfg_function) + return (m, ) + +class LatentOperationTonemapReinhard: + @classmethod + def INPUT_TYPES(s): + return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT_OPERATION",) + FUNCTION = "op" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def op(self, multiplier): + def tonemap_reinhard(latent, **kwargs): + latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None] + normalized_latent = latent / latent_vector_magnitude + + mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True) + std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True) + + top = (std * 5 + mean) * multiplier + + #reinhard + latent_vector_magnitude *= (1.0 / top) + new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0) + new_magnitude *= top + + return normalized_latent * new_magnitude + return (tonemap_reinhard,) + +class LatentOperationSharpen: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sharpen_radius": ("INT", { + "default": 9, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + "alpha": ("FLOAT", { + "default": 0.1, + "min": 0.0, + "max": 5.0, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT_OPERATION",) + FUNCTION = "op" + + CATEGORY = "latent/advanced/operations" + EXPERIMENTAL = True + + def op(self, sharpen_radius, sigma, alpha): + def sharpen(latent, **kwargs): + luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None] + normalized_latent = latent / luminance + channels = latent.shape[1] + + kernel_size = sharpen_radius * 2 + 1 + kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device) + center = kernel_size // 2 + + kernel *= alpha * -10 + kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 + + padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') + sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] + + return luminance * sharpened + return (sharpen,) + +NODE_CLASS_MAPPINGS = { + "LatentAdd": LatentAdd, + "LatentSubtract": LatentSubtract, + "LatentMultiply": LatentMultiply, + "LatentInterpolate": LatentInterpolate, + "LatentBatch": LatentBatch, + "LatentBatchSeedBehavior": LatentBatchSeedBehavior, + "LatentApplyOperation": LatentApplyOperation, + "LatentApplyOperationCFG": LatentApplyOperationCFG, + "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard, + "LatentOperationSharpen": LatentOperationSharpen, +} diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..40d03e18abcc45d16ad0b500a9d202cf8fe19943 --- /dev/null +++ b/comfy_extras/nodes_load_3d.py @@ -0,0 +1,166 @@ +import nodes +import folder_paths +import os + +from comfy.comfy_types import IO +from comfy_api.input_impl import VideoFromFile + + +def normalize_path(path): + return path.replace('\\', '/') + +class Load3D(): + @classmethod + def INPUT_TYPES(s): + input_dir = os.path.join(folder_paths.get_input_directory(), "3d") + + os.makedirs(input_dir, exist_ok=True) + + files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.fbx', '.stl'))] + + return {"required": { + "model_file": (sorted(files), {"file_upload": True}), + "image": ("LOAD_3D", {}), + "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), + "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), + }} + + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) + RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video") + + FUNCTION = "process" + EXPERIMENTAL = True + + CATEGORY = "3d" + + def process(self, model_file, image, **kwargs): + image_path = folder_paths.get_annotated_filepath(image['image']) + mask_path = folder_paths.get_annotated_filepath(image['mask']) + normal_path = folder_paths.get_annotated_filepath(image['normal']) + lineart_path = folder_paths.get_annotated_filepath(image['lineart']) + + load_image_node = nodes.LoadImage() + output_image, ignore_mask = load_image_node.load_image(image=image_path) + ignore_image, output_mask = load_image_node.load_image(image=mask_path) + normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) + lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path) + + video = None + + if image['recording'] != "": + recording_video_path = folder_paths.get_annotated_filepath(image['recording']) + + video = VideoFromFile(recording_video_path) + + return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video + +class Load3DAnimation(): + @classmethod + def INPUT_TYPES(s): + input_dir = os.path.join(folder_paths.get_input_directory(), "3d") + + os.makedirs(input_dir, exist_ok=True) + + files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.fbx'))] + + return {"required": { + "model_file": (sorted(files), {"file_upload": True}), + "image": ("LOAD_3D_ANIMATION", {}), + "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), + "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), + }} + + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) + RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video") + + FUNCTION = "process" + EXPERIMENTAL = True + + CATEGORY = "3d" + + def process(self, model_file, image, **kwargs): + image_path = folder_paths.get_annotated_filepath(image['image']) + mask_path = folder_paths.get_annotated_filepath(image['mask']) + normal_path = folder_paths.get_annotated_filepath(image['normal']) + + load_image_node = nodes.LoadImage() + output_image, ignore_mask = load_image_node.load_image(image=image_path) + ignore_image, output_mask = load_image_node.load_image(image=mask_path) + normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) + + video = None + + if image['recording'] != "": + recording_video_path = folder_paths.get_annotated_filepath(image['recording']) + + video = VideoFromFile(recording_video_path) + + return output_image, output_mask, model_file, normal_image, image['camera_info'], video + +class Preview3D(): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model_file": ("STRING", {"default": "", "multiline": False}), + }, + "optional": { + "camera_info": ("LOAD3D_CAMERA", {}) + }} + + OUTPUT_NODE = True + RETURN_TYPES = () + + CATEGORY = "3d" + + FUNCTION = "process" + EXPERIMENTAL = True + + def process(self, model_file, **kwargs): + camera_info = kwargs.get("camera_info", None) + + return { + "ui": { + "result": [model_file, camera_info] + } + } + +class Preview3DAnimation(): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model_file": ("STRING", {"default": "", "multiline": False}), + }, + "optional": { + "camera_info": ("LOAD3D_CAMERA", {}) + }} + + OUTPUT_NODE = True + RETURN_TYPES = () + + CATEGORY = "3d" + + FUNCTION = "process" + EXPERIMENTAL = True + + def process(self, model_file, **kwargs): + camera_info = kwargs.get("camera_info", None) + + return { + "ui": { + "result": [model_file, camera_info] + } + } + +NODE_CLASS_MAPPINGS = { + "Load3D": Load3D, + "Load3DAnimation": Load3DAnimation, + "Preview3D": Preview3D, + "Preview3DAnimation": Preview3DAnimation +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Load3D": "Load 3D", + "Load3DAnimation": "Load 3D - Animation", + "Preview3D": "Preview 3D", + "Preview3DAnimation": "Preview 3D - Animation" +} diff --git a/comfy_extras/nodes_lora_extract.py b/comfy_extras/nodes_lora_extract.py new file mode 100644 index 0000000000000000000000000000000000000000..dfd4fe9f4a5c4b7aff37d244fc25033e9a286119 --- /dev/null +++ b/comfy_extras/nodes_lora_extract.py @@ -0,0 +1,119 @@ +import torch +import comfy.model_management +import comfy.utils +import folder_paths +import os +import logging +from enum import Enum + +CLAMP_QUANTILE = 0.99 + +def extract_lora(diff, rank): + conv2d = (len(diff.shape) == 4) + kernel_size = None if not conv2d else diff.size()[2:4] + conv2d_3x3 = conv2d and kernel_size != (1, 1) + out_dim, in_dim = diff.size()[0:2] + rank = min(rank, in_dim, out_dim) + + if conv2d: + if conv2d_3x3: + diff = diff.flatten(start_dim=1) + else: + diff = diff.squeeze() + + + U, S, Vh = torch.linalg.svd(diff.float()) + U = U[:, :rank] + S = S[:rank] + U = U @ torch.diag(S) + Vh = Vh[:rank, :] + + dist = torch.cat([U.flatten(), Vh.flatten()]) + hi_val = torch.quantile(dist, CLAMP_QUANTILE) + low_val = -hi_val + + U = U.clamp(low_val, hi_val) + Vh = Vh.clamp(low_val, hi_val) + if conv2d: + U = U.reshape(out_dim, rank, 1, 1) + Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) + return (U, Vh) + +class LORAType(Enum): + STANDARD = 0 + FULL_DIFF = 1 + +LORA_TYPES = {"standard": LORAType.STANDARD, + "full_diff": LORAType.FULL_DIFF} + +def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False): + comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True) + sd = model_diff.model_state_dict(filter_prefix=prefix_model) + + for k in sd: + if k.endswith(".weight"): + weight_diff = sd[k] + if lora_type == LORAType.STANDARD: + if weight_diff.ndim < 2: + if bias_diff: + output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu() + continue + try: + out = extract_lora(weight_diff, rank) + output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu() + output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu() + except: + logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k)) + elif lora_type == LORAType.FULL_DIFF: + output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu() + + elif bias_diff and k.endswith(".bias"): + output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu() + return output_sd + +class LoraSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}), + "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}), + "lora_type": (tuple(LORA_TYPES.keys()),), + "bias_diff": ("BOOLEAN", {"default": True}), + }, + "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}), + "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})}, + } + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None): + if model_diff is None and text_encoder_diff is None: + return {} + + lora_type = LORA_TYPES.get(lora_type) + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + output_sd = {} + if model_diff is not None: + output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff) + if text_encoder_diff is not None: + output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None) + return {} + +NODE_CLASS_MAPPINGS = { + "LoraSave": LoraSave +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoraSave": "Extract and Save Lora" +} diff --git a/comfy_extras/nodes_lotus.py b/comfy_extras/nodes_lotus.py new file mode 100644 index 0000000000000000000000000000000000000000..739dbdd3dd49d1fd007c511e0f6d0da5bb619550 --- /dev/null +++ b/comfy_extras/nodes_lotus.py @@ -0,0 +1,29 @@ +import torch +import comfy.model_management as mm + +class LotusConditioning: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + } + + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("conditioning",) + FUNCTION = "conditioning" + CATEGORY = "conditioning/lotus" + + def conditioning(self): + device = mm.get_torch_device() + #lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change + #and getting parity with the reference implementation would otherwise require inference and 800mb of tensors + prompt_embeds = torch.tensor([[[-0.3134765625, -0.447509765625, -0.00823974609375, 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[[prompt_embeds, {}]] + + return (cond,) + +NODE_CLASS_MAPPINGS = { + "LotusConditioning" : LotusConditioning, +} diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py new file mode 100644 index 0000000000000000000000000000000000000000..e6dc122ca7ff9b78a0dd47b97d58a10611abc740 --- /dev/null +++ b/comfy_extras/nodes_lt.py @@ -0,0 +1,474 @@ +import io +import nodes +import node_helpers +import torch +import comfy.model_management +import comfy.model_sampling +import comfy.utils +import math +import numpy as np +import av +from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords + +class EmptyLTXVLatentVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), + "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), + "length": ("INT", {"default": 97, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/video/ltxv" + + def generate(self, width, height, length, batch_size=1): + latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) + return ({"samples": latent}, ) + + +class LTXVImgToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE",), + "image": ("IMAGE",), + "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), + "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), + "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + CATEGORY = "conditioning/video_models" + FUNCTION = "generate" + + def generate(self, positive, negative, image, vae, width, height, length, batch_size, strength): + pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + encode_pixels = pixels[:, :, :, :3] + t = vae.encode(encode_pixels) + + latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) + latent[:, :, :t.shape[2]] = t + + conditioning_latent_frames_mask = torch.ones( + (batch_size, 1, latent.shape[2], 1, 1), + dtype=torch.float32, + device=latent.device, + ) + conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength + + return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, ) + + +def conditioning_get_any_value(conditioning, key, default=None): + for t in conditioning: + if key in t[1]: + return t[1][key] + return default + + +def get_noise_mask(latent): + noise_mask = latent.get("noise_mask", None) + latent_image = latent["samples"] + if noise_mask is None: + batch_size, _, latent_length, _, _ = latent_image.shape + noise_mask = torch.ones( + (batch_size, 1, latent_length, 1, 1), + dtype=torch.float32, + device=latent_image.device, + ) + else: + noise_mask = noise_mask.clone() + return noise_mask + +def get_keyframe_idxs(cond): + keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None) + if keyframe_idxs is None: + return None, 0 + num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0] + return keyframe_idxs, num_keyframes + +class LTXVAddGuide: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE",), + "latent": ("LATENT",), + "image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames." + "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames."}), + "frame_idx": ("INT", {"default": 0, "min": -9999, "max": 9999, + "tooltip": "Frame index to start the conditioning at. For single-frame images or " + "videos with 1-8 frames, any frame_idx value is acceptable. For videos with 9+ " + "frames, frame_idx must be divisible by 8, otherwise it will be rounded down to " + "the nearest multiple of 8. Negative values are counted from the end of the video."}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + CATEGORY = "conditioning/video_models" + FUNCTION = "generate" + + def __init__(self): + self._num_prefix_frames = 2 + self._patchifier = SymmetricPatchifier(1) + + def encode(self, vae, latent_width, latent_height, images, scale_factors): + time_scale_factor, width_scale_factor, height_scale_factor = scale_factors + images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1] + pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1) + encode_pixels = pixels[:, :, :, :3] + t = vae.encode(encode_pixels) + return encode_pixels, t + + def get_latent_index(self, cond, latent_length, guide_length, frame_idx, scale_factors): + time_scale_factor, _, _ = scale_factors + _, num_keyframes = get_keyframe_idxs(cond) + latent_count = latent_length - num_keyframes + frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0) + if guide_length > 1: + frame_idx = frame_idx // time_scale_factor * time_scale_factor # frame index must be divisible by 8 + + latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor + + return frame_idx, latent_idx + + def add_keyframe_index(self, cond, frame_idx, guiding_latent, scale_factors): + keyframe_idxs, _ = get_keyframe_idxs(cond) + _, latent_coords = self._patchifier.patchify(guiding_latent) + pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, True) + pixel_coords[:, 0] += frame_idx + if keyframe_idxs is None: + keyframe_idxs = pixel_coords + else: + keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2) + return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs}) + + def append_keyframe(self, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors): + _, latent_idx = self.get_latent_index( + cond=positive, + latent_length=latent_image.shape[2], + guide_length=guiding_latent.shape[2], + frame_idx=frame_idx, + scale_factors=scale_factors, + ) + noise_mask[:, :, latent_idx:latent_idx + guiding_latent.shape[2]] = 1.0 + + positive = self.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors) + negative = self.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors) + + mask = torch.full( + (noise_mask.shape[0], 1, guiding_latent.shape[2], 1, 1), + 1.0 - strength, + dtype=noise_mask.dtype, + device=noise_mask.device, + ) + + latent_image = torch.cat([latent_image, guiding_latent], dim=2) + noise_mask = torch.cat([noise_mask, mask], dim=2) + return positive, negative, latent_image, noise_mask + + def replace_latent_frames(self, latent_image, noise_mask, guiding_latent, latent_idx, strength): + cond_length = guiding_latent.shape[2] + assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence." + + mask = torch.full( + (noise_mask.shape[0], 1, cond_length, 1, 1), + 1.0 - strength, + dtype=noise_mask.dtype, + device=noise_mask.device, + ) + + latent_image = latent_image.clone() + noise_mask = noise_mask.clone() + + latent_image[:, :, latent_idx : latent_idx + cond_length] = guiding_latent + noise_mask[:, :, latent_idx : latent_idx + cond_length] = mask + + return latent_image, noise_mask + + def generate(self, positive, negative, vae, latent, image, frame_idx, strength): + scale_factors = vae.downscale_index_formula + latent_image = latent["samples"] + noise_mask = get_noise_mask(latent) + + _, _, latent_length, latent_height, latent_width = latent_image.shape + image, t = self.encode(vae, latent_width, latent_height, image, scale_factors) + + frame_idx, latent_idx = self.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors) + assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence." + + num_prefix_frames = min(self._num_prefix_frames, t.shape[2]) + + positive, negative, latent_image, noise_mask = self.append_keyframe( + positive, + negative, + frame_idx, + latent_image, + noise_mask, + t[:, :, :num_prefix_frames], + strength, + scale_factors, + ) + + latent_idx += num_prefix_frames + + t = t[:, :, num_prefix_frames:] + if t.shape[2] == 0: + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + latent_image, noise_mask = self.replace_latent_frames( + latent_image, + noise_mask, + t, + latent_idx, + strength, + ) + + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + +class LTXVCropGuides: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent": ("LATENT",), + } + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + CATEGORY = "conditioning/video_models" + FUNCTION = "crop" + + def __init__(self): + self._patchifier = SymmetricPatchifier(1) + + def crop(self, positive, negative, latent): + latent_image = latent["samples"].clone() + noise_mask = get_noise_mask(latent) + + _, num_keyframes = get_keyframe_idxs(positive) + if num_keyframes == 0: + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + latent_image = latent_image[:, :, :-num_keyframes] + noise_mask = noise_mask[:, :, :-num_keyframes] + + positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None}) + negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None}) + + return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + +class LTXVConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "append" + + CATEGORY = "conditioning/video_models" + + def append(self, positive, negative, frame_rate): + positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate}) + negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate}) + return (positive, negative) + + +class ModelSamplingLTXV: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), + "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), + }, + "optional": {"latent": ("LATENT",), } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, max_shift, base_shift, latent=None): + m = model.clone() + + if latent is None: + tokens = 4096 + else: + tokens = math.prod(latent["samples"].shape[2:]) + + x1 = 1024 + x2 = 4096 + mm = (max_shift - base_shift) / (x2 - x1) + b = base_shift - mm * x1 + shift = (tokens) * mm + b + + sampling_base = comfy.model_sampling.ModelSamplingFlux + sampling_type = comfy.model_sampling.CONST + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift) + m.add_object_patch("model_sampling", model_sampling) + + return (m, ) + + +class LTXVScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), + "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), + "stretch": ("BOOLEAN", { + "default": True, + "tooltip": "Stretch the sigmas to be in the range [terminal, 1]." + }), + "terminal": ( + "FLOAT", + { + "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01, + "tooltip": "The terminal value of the sigmas after stretching." + }, + ), + }, + "optional": {"latent": ("LATENT",), } + } + + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, max_shift, base_shift, stretch, terminal, latent=None): + if latent is None: + tokens = 4096 + else: + tokens = math.prod(latent["samples"].shape[2:]) + + sigmas = torch.linspace(1.0, 0.0, steps + 1) + + x1 = 1024 + x2 = 4096 + mm = (max_shift - base_shift) / (x2 - x1) + b = base_shift - mm * x1 + sigma_shift = (tokens) * mm + b + + power = 1 + sigmas = torch.where( + sigmas != 0, + math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power), + 0, + ) + + # Stretch sigmas so that its final value matches the given terminal value. + if stretch: + non_zero_mask = sigmas != 0 + non_zero_sigmas = sigmas[non_zero_mask] + one_minus_z = 1.0 - non_zero_sigmas + scale_factor = one_minus_z[-1] / (1.0 - terminal) + stretched = 1.0 - (one_minus_z / scale_factor) + sigmas[non_zero_mask] = stretched + + return (sigmas,) + +def encode_single_frame(output_file, image_array: np.ndarray, crf): + container = av.open(output_file, "w", format="mp4") + try: + stream = container.add_stream( + "libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"} + ) + stream.height = image_array.shape[0] + stream.width = image_array.shape[1] + av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat( + format="yuv420p" + ) + container.mux(stream.encode(av_frame)) + container.mux(stream.encode()) + finally: + container.close() + + +def decode_single_frame(video_file): + container = av.open(video_file) + try: + stream = next(s for s in container.streams if s.type == "video") + frame = next(container.decode(stream)) + finally: + container.close() + return frame.to_ndarray(format="rgb24") + + +def preprocess(image: torch.Tensor, crf=29): + if crf == 0: + return image + + image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy() + with io.BytesIO() as output_file: + encode_single_frame(output_file, image_array, crf) + video_bytes = output_file.getvalue() + with io.BytesIO(video_bytes) as video_file: + image_array = decode_single_frame(video_file) + tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0 + return tensor + + +class LTXVPreprocess: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "img_compression": ( + "INT", + { + "default": 35, + "min": 0, + "max": 100, + "tooltip": "Amount of compression to apply on image.", + }, + ), + } + } + + FUNCTION = "preprocess" + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("output_image",) + CATEGORY = "image" + + def preprocess(self, image, img_compression): + output_images = [] + for i in range(image.shape[0]): + output_images.append(preprocess(image[i], img_compression)) + return (torch.stack(output_images),) + + +NODE_CLASS_MAPPINGS = { + "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo, + "LTXVImgToVideo": LTXVImgToVideo, + "ModelSamplingLTXV": ModelSamplingLTXV, + "LTXVConditioning": LTXVConditioning, + "LTXVScheduler": LTXVScheduler, + "LTXVAddGuide": LTXVAddGuide, + "LTXVPreprocess": LTXVPreprocess, + "LTXVCropGuides": LTXVCropGuides, +} diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py new file mode 100644 index 0000000000000000000000000000000000000000..275189785dca4dd6a9c56b58e87a0ab93342d9ec --- /dev/null +++ b/comfy_extras/nodes_lumina2.py @@ -0,0 +1,104 @@ +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict +import torch + + +class RenormCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}), + "renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, cfg_trunc, renorm_cfg): + def renorm_cfg_func(args): + cond_denoised = args["cond_denoised"] + uncond_denoised = args["uncond_denoised"] + cond_scale = args["cond_scale"] + timestep = args["timestep"] + x_orig = args["input"] + in_channels = model.model.diffusion_model.in_channels + + if timestep[0] < cfg_trunc: + cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels] + cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:] + half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps) + half_rest = cond_rest + + if float(renorm_cfg) > 0.0: + ori_pos_norm = torch.linalg.vector_norm(cond_eps + , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True + ) + max_new_norm = ori_pos_norm * float(renorm_cfg) + new_pos_norm = torch.linalg.vector_norm( + half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True + ) + if new_pos_norm >= max_new_norm: + half_eps = half_eps * (max_new_norm / new_pos_norm) + else: + cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels] + cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:] + half_eps = cond_eps + half_rest = cond_rest + + cfg_result = torch.cat([half_eps, half_rest], dim=1) + + # cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale + + return x_orig - cfg_result + + m = model.clone() + m.set_model_sampler_cfg_function(renorm_cfg_func) + return (m, ) + + +class CLIPTextEncodeLumina2(ComfyNodeABC): + SYSTEM_PROMPT = { + "superior": "You are an assistant designed to generate superior images with the superior "\ + "degree of image-text alignment based on textual prompts or user prompts.", + "alignment": "You are an assistant designed to generate high-quality images with the "\ + "highest degree of image-text alignment based on textual prompts." + } + SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \ + "Superior: You are an assistant designed to generate superior images with the superior "\ + "degree of image-text alignment based on textual prompts or user prompts. "\ + "Alignment: You are an assistant designed to generate high-quality images with the highest "\ + "degree of image-text alignment based on textual prompts." + @classmethod + def INPUT_TYPES(s) -> InputTypeDict: + return { + "required": { + "system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}), + "user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}), + "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}) + } + } + RETURN_TYPES = (IO.CONDITIONING,) + OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + DESCRIPTION = "Encodes a system prompt and a user prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." + + def encode(self, clip, user_prompt, system_prompt): + if clip is None: + raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") + system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt] + prompt = f'{system_prompt} {user_prompt}' + tokens = clip.tokenize(prompt) + return (clip.encode_from_tokens_scheduled(tokens), ) + + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeLumina2": CLIPTextEncodeLumina2, + "RenormCFG": RenormCFG +} + + +NODE_DISPLAY_NAME_MAPPINGS = { + "CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2", +} diff --git a/comfy_extras/nodes_mahiro.py b/comfy_extras/nodes_mahiro.py new file mode 100644 index 0000000000000000000000000000000000000000..8fcdfba759f97515e5f64d3a130b0d892005cff2 --- /dev/null +++ b/comfy_extras/nodes_mahiro.py @@ -0,0 +1,41 @@ +import torch +import torch.nn.functional as F + +class Mahiro: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL",), + }} + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("patched_model",) + FUNCTION = "patch" + CATEGORY = "_for_testing" + DESCRIPTION = "Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt." + def patch(self, model): + m = model.clone() + def mahiro_normd(args): + scale: float = args['cond_scale'] + cond_p: torch.Tensor = args['cond_denoised'] + uncond_p: torch.Tensor = args['uncond_denoised'] + #naive leap + leap = cond_p * scale + #sim with uncond leap + u_leap = uncond_p * scale + cfg = args["denoised"] + merge = (leap + cfg) / 2 + normu = torch.sqrt(u_leap.abs()) * u_leap.sign() + normm = torch.sqrt(merge.abs()) * merge.sign() + sim = F.cosine_similarity(normu, normm).mean() + simsc = 2 * (sim+1) + wm = (simsc*cfg + (4-simsc)*leap) / 4 + return wm + m.set_model_sampler_post_cfg_function(mahiro_normd) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "Mahiro": Mahiro +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Mahiro": "Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)", +} diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..99b264a3264af793fe038d0a0da82b48ec256860 --- /dev/null +++ b/comfy_extras/nodes_mask.py @@ -0,0 +1,412 @@ +import numpy as np +import scipy.ndimage +import torch +import comfy.utils +import node_helpers +import folder_paths +import random + +import nodes +from nodes import MAX_RESOLUTION + +def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): + source = source.to(destination.device) + if resize_source: + source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") + + source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) + + x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) + y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) + + left, top = (x // multiplier, y // multiplier) + right, bottom = (left + source.shape[3], top + source.shape[2],) + + if mask is None: + mask = torch.ones_like(source) + else: + mask = mask.to(destination.device, copy=True) + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") + mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) + + # calculate the bounds of the source that will be overlapping the destination + # this prevents the source trying to overwrite latent pixels that are out of bounds + # of the destination + visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) + + mask = mask[:, :, :visible_height, :visible_width] + inverse_mask = torch.ones_like(mask) - mask + + source_portion = mask * source[:, :, :visible_height, :visible_width] + destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] + + destination[:, :, top:bottom, left:right] = source_portion + destination_portion + return destination + +class LatentCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("LATENT",), + "source": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, destination, source, x, y, resize_source, mask = None): + output = destination.copy() + destination = destination["samples"].clone() + source = source["samples"] + output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) + return (output,) + +class ImageCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("IMAGE",), + "source": ("IMAGE",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "composite" + + CATEGORY = "image" + + def composite(self, destination, source, x, y, resize_source, mask = None): + destination, source = node_helpers.image_alpha_fix(destination, source) + destination = destination.clone().movedim(-1, 1) + output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) + return (output,) + +class MaskToImage: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "mask_to_image" + + def mask_to_image(self, mask): + result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return (result,) + +class ImageToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "channel": (["red", "green", "blue", "alpha"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, channel): + channels = ["red", "green", "blue", "alpha"] + mask = image[:, :, :, channels.index(channel)] + return (mask,) + +class ImageColorToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, color): + temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) + temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] + mask = torch.where(temp == color, 255, 0).float() + return (mask,) + +class SolidMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "solid" + + def solid(self, value, width, height): + out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") + return (out,) + +class InvertMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "invert" + + def invert(self, mask): + out = 1.0 - mask + return (out,) + +class CropMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "crop" + + def crop(self, mask, x, y, width, height): + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = mask[:, y:y + height, x:x + width] + return (out,) + +class MaskComposite: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "destination": ("MASK",), + "source": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "combine" + + def combine(self, destination, source, x, y, operation): + output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() + source = source.reshape((-1, source.shape[-2], source.shape[-1])) + + left, top = (x, y,) + right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) + visible_width, visible_height = (right - left, bottom - top,) + + source_portion = source[:, :visible_height, :visible_width] + destination_portion = destination[:, top:bottom, left:right] + + if operation == "multiply": + output[:, top:bottom, left:right] = destination_portion * source_portion + elif operation == "add": + output[:, top:bottom, left:right] = destination_portion + source_portion + elif operation == "subtract": + output[:, top:bottom, left:right] = destination_portion - source_portion + elif operation == "and": + output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "or": + output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "xor": + output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() + + output = torch.clamp(output, 0.0, 1.0) + + return (output,) + +class FeatherMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "feather" + + def feather(self, mask, left, top, right, bottom): + output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() + + left = min(left, output.shape[-1]) + right = min(right, output.shape[-1]) + top = min(top, output.shape[-2]) + bottom = min(bottom, output.shape[-2]) + + for x in range(left): + feather_rate = (x + 1.0) / left + output[:, :, x] *= feather_rate + + for x in range(right): + feather_rate = (x + 1) / right + output[:, :, -x] *= feather_rate + + for y in range(top): + feather_rate = (y + 1) / top + output[:, y, :] *= feather_rate + + for y in range(bottom): + feather_rate = (y + 1) / bottom + output[:, -y, :] *= feather_rate + + return (output,) + +class GrowMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), + "tapered_corners": ("BOOLEAN", {"default": True}), + }, + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "expand_mask" + + def expand_mask(self, mask, expand, tapered_corners): + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = [] + for m in mask: + output = m.numpy() + for _ in range(abs(expand)): + if expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) + output = torch.from_numpy(output) + out.append(output) + return (torch.stack(out, dim=0),) + +class ThresholdMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, mask, value): + mask = (mask > value).float() + return (mask,) + +# Mask Preview - original implement from +# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81 +# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes +class MaskPreview(nodes.SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": {"mask": ("MASK",), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + FUNCTION = "execute" + CATEGORY = "mask" + + def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return self.save_images(preview, filename_prefix, prompt, extra_pnginfo) + + +NODE_CLASS_MAPPINGS = { + "LatentCompositeMasked": LatentCompositeMasked, + "ImageCompositeMasked": ImageCompositeMasked, + "MaskToImage": MaskToImage, + "ImageToMask": ImageToMask, + "ImageColorToMask": ImageColorToMask, + "SolidMask": SolidMask, + "InvertMask": InvertMask, + "CropMask": CropMask, + "MaskComposite": MaskComposite, + "FeatherMask": FeatherMask, + "GrowMask": GrowMask, + "ThresholdMask": ThresholdMask, + "MaskPreview": MaskPreview +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageToMask": "Convert Image to Mask", + "MaskToImage": "Convert Mask to Image", +} diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py new file mode 100644 index 0000000000000000000000000000000000000000..1c474faa94eac8dc48f778460c0d833bee94dcf9 --- /dev/null +++ b/comfy_extras/nodes_mochi.py @@ -0,0 +1,23 @@ +import nodes +import torch +import comfy.model_management + +class EmptyMochiLatentVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/video" + + def generate(self, width, height, length, batch_size=1): + latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + return ({"samples":latent}, ) + +NODE_CLASS_MAPPINGS = { + "EmptyMochiLatentVideo": EmptyMochiLatentVideo, +} diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py new file mode 100644 index 0000000000000000000000000000000000000000..ae5d2c563183262456fd6a9668445b6b798108ac --- /dev/null +++ b/comfy_extras/nodes_model_advanced.py @@ -0,0 +1,329 @@ +import comfy.sd +import comfy.model_sampling +import comfy.latent_formats +import nodes +import torch +import node_helpers + + +class LCM(comfy.model_sampling.EPS): + def calculate_denoised(self, sigma, model_output, model_input): + timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + x0 = model_input - model_output * sigma + + sigma_data = 0.5 + scaled_timestep = timestep * 10.0 #timestep_scaling + + c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) + c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 + + return c_out * x0 + c_skip * model_input + +class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): + original_timesteps = 50 + + def __init__(self, model_config=None, zsnr=None): + super().__init__(model_config, zsnr=zsnr) + + self.skip_steps = self.num_timesteps // self.original_timesteps + + sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) + for x in range(self.original_timesteps): + sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] + + self.set_sigmas(sigmas_valid) + + def timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) + + def sigma(self, timestep): + t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + +class ModelSamplingDiscrete: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],), + "zsnr": ("BOOLEAN", {"default": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, zsnr): + m = model.clone() + + sampling_base = comfy.model_sampling.ModelSamplingDiscrete + if sampling == "eps": + sampling_type = comfy.model_sampling.EPS + elif sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + elif sampling == "lcm": + sampling_type = LCM + sampling_base = ModelSamplingDiscreteDistilled + elif sampling == "x0": + sampling_type = comfy.model_sampling.X0 + elif sampling == "img_to_img": + sampling_type = comfy.model_sampling.IMG_TO_IMG + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config, zsnr=zsnr) + + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingStableCascade: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, shift): + m = model.clone() + + sampling_base = comfy.model_sampling.StableCascadeSampling + sampling_type = comfy.model_sampling.EPS + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingSD3: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, shift, multiplier=1000): + m = model.clone() + + sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow + sampling_type = comfy.model_sampling.CONST + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift, multiplier=multiplier) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingAuraFlow(ModelSamplingSD3): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}), + }} + + FUNCTION = "patch_aura" + + def patch_aura(self, model, shift): + return self.patch(model, shift, multiplier=1.0) + +class ModelSamplingFlux: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}), + "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}), + "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, max_shift, base_shift, width, height): + m = model.clone() + + x1 = 256 + x2 = 4096 + mm = (max_shift - base_shift) / (x2 - x1) + b = base_shift - mm * x1 + shift = (width * height / (8 * 8 * 2 * 2)) * mm + b + + sampling_base = comfy.model_sampling.ModelSamplingFlux + sampling_type = comfy.model_sampling.CONST + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(shift=shift) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + + +class ModelSamplingContinuousEDM: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],), + "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + sampling_base = comfy.model_sampling.ModelSamplingContinuousEDM + latent_format = None + sigma_data = 1.0 + if sampling == "eps": + sampling_type = comfy.model_sampling.EPS + elif sampling == "edm": + sampling_type = comfy.model_sampling.EDM + sigma_data = 0.5 + elif sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + elif sampling == "edm_playground_v2.5": + sampling_type = comfy.model_sampling.EDM + sigma_data = 0.5 + latent_format = comfy.latent_formats.SDXL_Playground_2_5() + elif sampling == "cosmos_rflow": + sampling_type = comfy.model_sampling.COSMOS_RFLOW + sampling_base = comfy.model_sampling.ModelSamplingCosmosRFlow + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) + m.add_object_patch("model_sampling", model_sampling) + if latent_format is not None: + m.add_object_patch("latent_format", latent_format) + return (m, ) + +class ModelSamplingContinuousV: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction"],), + "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + sigma_data = 1.0 + if sampling == "v_prediction": + sampling_type = comfy.model_sampling.V_PREDICTION + + class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class RescaleCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, multiplier): + def rescale_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + sigma = args["sigma"] + sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) + x_orig = args["input"] + + #rescale cfg has to be done on v-pred model output + x = x_orig / (sigma * sigma + 1.0) + cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) + uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) + + #rescalecfg + x_cfg = uncond + cond_scale * (cond - uncond) + ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) + ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) + + x_rescaled = x_cfg * (ro_pos / ro_cfg) + x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg + + return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) + + m = model.clone() + m.set_model_sampler_cfg_function(rescale_cfg) + return (m, ) + +class ModelComputeDtype: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "dtype": (["default", "fp32", "fp16", "bf16"],), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/debug/model" + + def patch(self, model, dtype): + m = model.clone() + m.set_model_compute_dtype(node_helpers.string_to_torch_dtype(dtype)) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "ModelSamplingDiscrete": ModelSamplingDiscrete, + "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, + "ModelSamplingContinuousV": ModelSamplingContinuousV, + "ModelSamplingStableCascade": ModelSamplingStableCascade, + "ModelSamplingSD3": ModelSamplingSD3, + "ModelSamplingAuraFlow": ModelSamplingAuraFlow, + "ModelSamplingFlux": ModelSamplingFlux, + "RescaleCFG": RescaleCFG, + "ModelComputeDtype": ModelComputeDtype, +} diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py new file mode 100644 index 0000000000000000000000000000000000000000..49420dee9260ced4e0d08c196be937354c5e1d4e --- /dev/null +++ b/comfy_extras/nodes_model_downscale.py @@ -0,0 +1,53 @@ +import comfy.utils + +class PatchModelAddDownscale: + upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), + "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), + "downscale_after_skip": ("BOOLEAN", {"default": True}), + "downscale_method": (s.upscale_methods,), + "upscale_method": (s.upscale_methods,), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): + model_sampling = model.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) + + def input_block_patch(h, transformer_options): + if transformer_options["block"][1] == block_number: + sigma = transformer_options["sigmas"][0].item() + if sigma <= sigma_start and sigma >= sigma_end: + h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") + return h + + def output_block_patch(h, hsp, transformer_options): + if h.shape[2] != hsp.shape[2]: + h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") + return h, hsp + + m = model.clone() + if downscale_after_skip: + m.set_model_input_block_patch_after_skip(input_block_patch) + else: + m.set_model_input_block_patch(input_block_patch) + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "PatchModelAddDownscale": PatchModelAddDownscale, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", +} diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py new file mode 100644 index 0000000000000000000000000000000000000000..f20beab7d480391cbd4deb2f855a43c18d19ae33 --- /dev/null +++ b/comfy_extras/nodes_model_merging.py @@ -0,0 +1,374 @@ +import comfy.sd +import comfy.utils +import comfy.model_base +import comfy.model_management +import comfy.model_sampling + +import torch +import folder_paths +import json +import os + +from comfy.cli_args import args + +class ModelMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, ratio): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class ModelSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, multiplier): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + +class ModelAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + +class CLIPMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, ratio): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + + +class CLIPSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, multiplier): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + + +class CLIPAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + +class ModelMergeBlocks: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, **kwargs): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + default_ratio = next(iter(kwargs.values())) + + for k in kp: + ratio = default_ratio + k_unet = k[len("diffusion_model."):] + + last_arg_size = 0 + for arg in kwargs: + if k_unet.startswith(arg) and last_arg_size < len(arg): + ratio = kwargs[arg] + last_arg_size = len(arg) + + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + + enable_modelspec = True + if isinstance(model.model, comfy.model_base.SDXL): + if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit" + else: + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" + elif isinstance(model.model, comfy.model_base.SDXLRefiner): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" + elif isinstance(model.model, comfy.model_base.SVD_img2vid): + metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" + elif isinstance(model.model, comfy.model_base.SD3): + metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants + else: + enable_modelspec = False + + if enable_modelspec: + metadata["modelspec.sai_model_spec"] = "1.0.0" + metadata["modelspec.implementation"] = "sgm" + metadata["modelspec.title"] = "{} {}".format(filename, counter) + + #TODO: + # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512", + # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", + # "v2-inpainting" + + extra_keys = {} + model_sampling = model.get_model_object("model_sampling") + if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM): + if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION): + extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float() + extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float() + + if model.model.model_type == comfy.model_base.ModelType.EPS: + metadata["modelspec.predict_key"] = "epsilon" + elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: + metadata["modelspec.predict_key"] = "v" + extra_keys["v_pred"] = torch.tensor([]) + if getattr(model_sampling, "zsnr", False): + extra_keys["ztsnr"] = torch.tensor([]) + + if not args.disable_metadata: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys) + +class CheckpointSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): + save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +class CLIPSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP",), + "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_metadata: + metadata["format"] = "pt" + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) + clip_sd = clip.get_sd() + + for prefix in ["clip_l.", "clip_g.", "clip_h.", "t5xxl.", "pile_t5xl.", "mt5xl.", "umt5xxl.", "t5base.", "gemma2_2b.", "llama.", "hydit_clip.", ""]: + k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) + current_clip_sd = {} + for x in k: + current_clip_sd[x] = clip_sd.pop(x) + if len(current_clip_sd) == 0: + continue + + p = prefix[:-1] + replace_prefix = {} + filename_prefix_ = filename_prefix + if len(p) > 0: + filename_prefix_ = "{}_{}".format(filename_prefix_, p) + replace_prefix[prefix] = "" + replace_prefix["transformer."] = "" + + full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) + + comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) + return {} + +class VAESave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_metadata: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) + return {} + +class ModelSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None): + save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +NODE_CLASS_MAPPINGS = { + "ModelMergeSimple": ModelMergeSimple, + "ModelMergeBlocks": ModelMergeBlocks, + "ModelMergeSubtract": ModelSubtract, + "ModelMergeAdd": ModelAdd, + "CheckpointSave": CheckpointSave, + "CLIPMergeSimple": CLIPMergeSimple, + "CLIPMergeSubtract": CLIPSubtract, + "CLIPMergeAdd": CLIPAdd, + "CLIPSave": CLIPSave, + "VAESave": VAESave, + "ModelSave": ModelSave, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "CheckpointSave": "Save Checkpoint", +} diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py new file mode 100644 index 0000000000000000000000000000000000000000..2c93cd84f8ac459234b8491b1892677a4464a855 --- /dev/null +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -0,0 +1,332 @@ +import comfy_extras.nodes_model_merging + +class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["time_embed."] = argument + arg_dict["label_emb."] = argument + + for i in range(12): + arg_dict["input_blocks.{}.".format(i)] = argument + + for i in range(3): + arg_dict["middle_block.{}.".format(i)] = argument + + for i in range(12): + arg_dict["output_blocks.{}.".format(i)] = argument + + arg_dict["out."] = argument + + return {"required": arg_dict} + + +class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["time_embed."] = argument + arg_dict["label_emb."] = argument + + for i in range(9): + arg_dict["input_blocks.{}".format(i)] = argument + + for i in range(3): + arg_dict["middle_block.{}".format(i)] = argument + + for i in range(9): + arg_dict["output_blocks.{}".format(i)] = argument + + arg_dict["out."] = argument + + return {"required": arg_dict} + +class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embed."] = argument + arg_dict["x_embedder."] = argument + arg_dict["context_embedder."] = argument + arg_dict["y_embedder."] = argument + arg_dict["t_embedder."] = argument + + for i in range(24): + arg_dict["joint_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + + +class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["init_x_linear."] = argument + arg_dict["positional_encoding"] = argument + arg_dict["cond_seq_linear."] = argument + arg_dict["register_tokens"] = argument + arg_dict["t_embedder."] = argument + + for i in range(4): + arg_dict["double_layers.{}.".format(i)] = argument + + for i in range(32): + arg_dict["single_layers.{}.".format(i)] = argument + + arg_dict["modF."] = argument + arg_dict["final_linear."] = argument + + return {"required": arg_dict} + +class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["img_in."] = argument + arg_dict["time_in."] = argument + arg_dict["guidance_in"] = argument + arg_dict["vector_in."] = argument + arg_dict["txt_in."] = argument + + for i in range(19): + arg_dict["double_blocks.{}.".format(i)] = argument + + for i in range(38): + arg_dict["single_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embed."] = argument + arg_dict["x_embedder."] = argument + arg_dict["context_embedder."] = argument + arg_dict["y_embedder."] = argument + arg_dict["t_embedder."] = argument + + for i in range(38): + arg_dict["joint_blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_frequencies."] = argument + arg_dict["t_embedder."] = argument + arg_dict["t5_y_embedder."] = argument + arg_dict["t5_yproj."] = argument + + for i in range(48): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["patchify_proj."] = argument + arg_dict["adaln_single."] = argument + arg_dict["caption_projection."] = argument + + for i in range(28): + arg_dict["transformer_blocks.{}.".format(i)] = argument + + arg_dict["scale_shift_table"] = argument + arg_dict["proj_out."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["extra_pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["affline_norm."] = argument + + + for i in range(28): + arg_dict["blocks.block{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["extra_pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["affline_norm."] = argument + + + for i in range(36): + arg_dict["blocks.block{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb." + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["patch_embedding."] = argument + arg_dict["time_embedding."] = argument + arg_dict["time_projection."] = argument + arg_dict["text_embedding."] = argument + arg_dict["img_emb."] = argument + + for i in range(40): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["head."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["t_embedding_norm."] = argument + + + for i in range(28): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["t_embedding_norm."] = argument + + + for i in range(36): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +NODE_CLASS_MAPPINGS = { + "ModelMergeSD1": ModelMergeSD1, + "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks + "ModelMergeSDXL": ModelMergeSDXL, + "ModelMergeSD3_2B": ModelMergeSD3_2B, + "ModelMergeAuraflow": ModelMergeAuraflow, + "ModelMergeFlux1": ModelMergeFlux1, + "ModelMergeSD35_Large": ModelMergeSD35_Large, + "ModelMergeMochiPreview": ModelMergeMochiPreview, + "ModelMergeLTXV": ModelMergeLTXV, + "ModelMergeCosmos7B": ModelMergeCosmos7B, + "ModelMergeCosmos14B": ModelMergeCosmos14B, + "ModelMergeWAN2_1": ModelMergeWAN2_1, + "ModelMergeCosmosPredict2_2B": ModelMergeCosmosPredict2_2B, + "ModelMergeCosmosPredict2_14B": ModelMergeCosmosPredict2_14B, +} diff --git a/comfy_extras/nodes_morphology.py b/comfy_extras/nodes_morphology.py new file mode 100644 index 0000000000000000000000000000000000000000..075b26c4024bccb510b9052f40f51eb88382ab30 --- /dev/null +++ b/comfy_extras/nodes_morphology.py @@ -0,0 +1,87 @@ +import torch +import comfy.model_management + +from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat +import kornia.color + + +class Morphology: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), + "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + + CATEGORY = "image/postprocessing" + + def process(self, image, operation, kernel_size): + device = comfy.model_management.get_torch_device() + kernel = torch.ones(kernel_size, kernel_size, device=device) + image_k = image.to(device).movedim(-1, 1) + if operation == "erode": + output = erosion(image_k, kernel) + elif operation == "dilate": + output = dilation(image_k, kernel) + elif operation == "open": + output = opening(image_k, kernel) + elif operation == "close": + output = closing(image_k, kernel) + elif operation == "gradient": + output = gradient(image_k, kernel) + elif operation == "top_hat": + output = top_hat(image_k, kernel) + elif operation == "bottom_hat": + output = bottom_hat(image_k, kernel) + else: + raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") + img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) + return (img_out,) + + +class ImageRGBToYUV: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE") + RETURN_NAMES = ("Y", "U", "V") + FUNCTION = "execute" + + CATEGORY = "image/batch" + + def execute(self, image): + out = kornia.color.rgb_to_ycbcr(image.movedim(-1, 1)).movedim(1, -1) + return (out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image)) + +class ImageYUVToRGB: + @classmethod + def INPUT_TYPES(s): + return {"required": {"Y": ("IMAGE",), + "U": ("IMAGE",), + "V": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "execute" + + CATEGORY = "image/batch" + + def execute(self, Y, U, V): + image = torch.cat([torch.mean(Y, dim=-1, keepdim=True), torch.mean(U, dim=-1, keepdim=True), torch.mean(V, dim=-1, keepdim=True)], dim=-1) + out = kornia.color.ycbcr_to_rgb(image.movedim(-1, 1)).movedim(1, -1) + return (out,) + +NODE_CLASS_MAPPINGS = { + "Morphology": Morphology, + "ImageRGBToYUV": ImageRGBToYUV, + "ImageYUVToRGB": ImageYUVToRGB, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Morphology": "ImageMorphology", +} diff --git a/comfy_extras/nodes_optimalsteps.py b/comfy_extras/nodes_optimalsteps.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c851ca211c923f48506fd01f5a643f60dfaf62 --- /dev/null +++ b/comfy_extras/nodes_optimalsteps.py @@ -0,0 +1,57 @@ +# from https://github.com/bebebe666/OptimalSteps + + +import numpy as np +import torch + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + + +NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001], +"Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001], +"Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001], +} + +class OptimalStepsScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model_type": (["FLUX", "Wan", "Chroma"], ), + "steps": ("INT", {"default": 20, "min": 3, "max": 1000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model_type, steps, denoise): + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return (torch.FloatTensor([]),) + total_steps = round(steps * denoise) + + sigmas = NOISE_LEVELS[model_type][:] + if (steps + 1) != len(sigmas): + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return (torch.FloatTensor(sigmas), ) + +NODE_CLASS_MAPPINGS = { + "OptimalStepsScheduler": OptimalStepsScheduler, +} diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py new file mode 100644 index 0000000000000000000000000000000000000000..eb28196f41c56fd45fda051a42d0814b96558fb8 --- /dev/null +++ b/comfy_extras/nodes_pag.py @@ -0,0 +1,56 @@ +#Modified/simplified version of the node from: https://github.com/pamparamm/sd-perturbed-attention +#If you want the one with more options see the above repo. + +#My modified one here is more basic but has less chances of breaking with ComfyUI updates. + +import comfy.model_patcher +import comfy.samplers + +class PerturbedAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, scale): + unet_block = "middle" + unet_block_id = 0 + m = model.clone() + + def perturbed_attention(q, k, v, extra_options, mask=None): + return v + + def post_cfg_function(args): + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"].copy() + x = args["input"] + + if scale == 0: + return cfg_result + + # Replace Self-attention with PAG + model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id) + (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) + + return cfg_result + (cond_pred - pag) * scale + + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return (m,) + +NODE_CLASS_MAPPINGS = { + "PerturbedAttentionGuidance": PerturbedAttentionGuidance, +} diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py new file mode 100644 index 0000000000000000000000000000000000000000..f051cbf9a2be2b008bda01e5c35f270502befbdf --- /dev/null +++ b/comfy_extras/nodes_perpneg.py @@ -0,0 +1,139 @@ +import torch +import comfy.model_management +import comfy.sampler_helpers +import comfy.samplers +import comfy.utils +import node_helpers + +def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): + pos = noise_pred_pos - noise_pred_nocond + neg = noise_pred_neg - noise_pred_nocond + + perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos + perp_neg = perp * neg_scale + cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) + return cfg_result + +#TODO: This node should be removed, it has been replaced with PerpNegGuider +class PerpNeg: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "empty_conditioning": ("CONDITIONING", ), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + DEPRECATED = True + + def patch(self, model, empty_conditioning, neg_scale): + m = model.clone() + nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) + + def cfg_function(args): + model = args["model"] + noise_pred_pos = args["cond_denoised"] + noise_pred_neg = args["uncond_denoised"] + cond_scale = args["cond_scale"] + x = args["input"] + sigma = args["sigma"] + model_options = args["model_options"] + nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") + + (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) + + cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale) + return cfg_result + + m.set_model_sampler_cfg_function(cfg_function) + + return (m, ) + + +class Guider_PerpNeg(comfy.samplers.CFGGuider): + def set_conds(self, positive, negative, empty_negative_prompt): + empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"}) + self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative}) + + def set_cfg(self, cfg, neg_scale): + self.cfg = cfg + self.neg_scale = neg_scale + + def predict_noise(self, x, timestep, model_options={}, seed=None): + # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg + # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly + + positive_cond = self.conds.get("positive", None) + negative_cond = self.conds.get("negative", None) + empty_cond = self.conds.get("empty_negative_prompt", None) + + conds = [positive_cond, negative_cond, empty_cond] + + out = comfy.samplers.calc_cond_batch(self.inner_model, conds, x, timestep, model_options) + + # Apply pre_cfg_functions since sampling_function() is skipped + for fn in model_options.get("sampler_pre_cfg_function", []): + args = {"conds":conds, "conds_out": out, "cond_scale": self.cfg, "timestep": timestep, + "input": x, "sigma": timestep, "model": self.inner_model, "model_options": model_options} + out = fn(args) + + noise_pred_pos, noise_pred_neg, noise_pred_empty = out + cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) + + # normally this would be done in cfg_function, but we skipped + # that for efficiency: we can compute the noise predictions in + # a single call to calc_cond_batch() (rather than two) + # so we replicate the hook here + for fn in model_options.get("sampler_post_cfg_function", []): + args = { + "denoised": cfg_result, + "cond": positive_cond, + "uncond": negative_cond, + "cond_scale": self.cfg, + "model": self.inner_model, + "uncond_denoised": noise_pred_neg, + "cond_denoised": noise_pred_pos, + "sigma": timestep, + "model_options": model_options, + "input": x, + # not in the original call in samplers.py:cfg_function, but made available for future hooks + "empty_cond": empty_cond, + "empty_cond_denoised": noise_pred_empty,} + cfg_result = fn(args) + + return cfg_result + +class PerpNegGuider: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "empty_conditioning": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("GUIDER",) + + FUNCTION = "get_guider" + CATEGORY = "_for_testing" + + def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): + guider = Guider_PerpNeg(model) + guider.set_conds(positive, negative, empty_conditioning) + guider.set_cfg(cfg, neg_scale) + return (guider,) + +NODE_CLASS_MAPPINGS = { + "PerpNeg": PerpNeg, + "PerpNegGuider": PerpNegGuider, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", +} diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py new file mode 100644 index 0000000000000000000000000000000000000000..d358ed6d5b75a37f9195e1f0c663a188eb8aedc3 --- /dev/null +++ b/comfy_extras/nodes_photomaker.py @@ -0,0 +1,188 @@ +import torch +import torch.nn as nn +import folder_paths +import comfy.clip_model +import comfy.clip_vision +import comfy.ops + +# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0 +VISION_CONFIG_DICT = { + "hidden_size": 1024, + "image_size": 224, + "intermediate_size": 4096, + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "hidden_act": "quick_gelu", + "model_type": "clip_vision_model", +} + +class MLP(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops): + super().__init__() + if use_residual: + assert in_dim == out_dim + self.layernorm = operations.LayerNorm(in_dim) + self.fc1 = operations.Linear(in_dim, hidden_dim) + self.fc2 = operations.Linear(hidden_dim, out_dim) + self.use_residual = use_residual + self.act_fn = nn.GELU() + + def forward(self, x): + residual = x + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + if self.use_residual: + x = x + residual + return x + + +class FuseModule(nn.Module): + def __init__(self, embed_dim, operations): + super().__init__() + self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) + self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) + self.layer_norm = operations.LayerNorm(embed_dim) + + def fuse_fn(self, prompt_embeds, id_embeds): + stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) + stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds + stacked_id_embeds = self.mlp2(stacked_id_embeds) + stacked_id_embeds = self.layer_norm(stacked_id_embeds) + return stacked_id_embeds + + def forward( + self, + prompt_embeds, + id_embeds, + class_tokens_mask, + ) -> torch.Tensor: + # id_embeds shape: [b, max_num_inputs, 1, 2048] + id_embeds = id_embeds.to(prompt_embeds.dtype) + num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case + batch_size, max_num_inputs = id_embeds.shape[:2] + # seq_length: 77 + seq_length = prompt_embeds.shape[1] + # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] + flat_id_embeds = id_embeds.view( + -1, id_embeds.shape[-2], id_embeds.shape[-1] + ) + # valid_id_mask [b*max_num_inputs] + valid_id_mask = ( + torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] + < num_inputs[:, None] + ) + valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] + + prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) + class_tokens_mask = class_tokens_mask.view(-1) + valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) + # slice out the image token embeddings + image_token_embeds = prompt_embeds[class_tokens_mask] + stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) + assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" + prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) + updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) + return updated_prompt_embeds + +class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection): + def __init__(self): + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + dtype = comfy.model_management.text_encoder_dtype(self.load_device) + + super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast) + self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False) + self.fuse_module = FuseModule(2048, comfy.ops.manual_cast) + + def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): + b, num_inputs, c, h, w = id_pixel_values.shape + id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) + + shared_id_embeds = self.vision_model(id_pixel_values)[2] + id_embeds = self.visual_projection(shared_id_embeds) + id_embeds_2 = self.visual_projection_2(shared_id_embeds) + + id_embeds = id_embeds.view(b, num_inputs, 1, -1) + id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) + + id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) + updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) + + return updated_prompt_embeds + + +class PhotoMakerLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}} + + RETURN_TYPES = ("PHOTOMAKER",) + FUNCTION = "load_photomaker_model" + + CATEGORY = "_for_testing/photomaker" + + def load_photomaker_model(self, photomaker_model_name): + photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name) + photomaker_model = PhotoMakerIDEncoder() + data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True) + if "id_encoder" in data: + data = data["id_encoder"] + photomaker_model.load_state_dict(data) + return (photomaker_model,) + + +class PhotoMakerEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "photomaker": ("PHOTOMAKER",), + "image": ("IMAGE",), + "clip": ("CLIP", ), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_photomaker" + + CATEGORY = "_for_testing/photomaker" + + def apply_photomaker(self, photomaker, image, clip, text): + special_token = "photomaker" + pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() + try: + index = text.split(" ").index(special_token) + 1 + except ValueError: + index = -1 + tokens = clip.tokenize(text, return_word_ids=True) + out_tokens = {} + for k in tokens: + out_tokens[k] = [] + for t in tokens[k]: + f = list(filter(lambda x: x[2] != index, t)) + while len(f) < len(t): + f.append(t[-1]) + out_tokens[k].append(f) + + cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) + + if index > 0: + token_index = index - 1 + num_id_images = 1 + class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] + out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), + class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) + else: + out = cond + + return ([[out, {"pooled_output": pooled}]], ) + + +NODE_CLASS_MAPPINGS = { + "PhotoMakerLoader": PhotoMakerLoader, + "PhotoMakerEncode": PhotoMakerEncode, +} + diff --git a/comfy_extras/nodes_pixart.py b/comfy_extras/nodes_pixart.py new file mode 100644 index 0000000000000000000000000000000000000000..c7209c46850d42c134f830ef5634de58652dee5f --- /dev/null +++ b/comfy_extras/nodes_pixart.py @@ -0,0 +1,24 @@ +from nodes import MAX_RESOLUTION + +class CLIPTextEncodePixArtAlpha: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + # "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + CATEGORY = "advanced/conditioning" + DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma." + + def encode(self, clip, width, height, text): + tokens = clip.tokenize(text) + return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha, +} diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..cb1a0d88303eef19fff34ce2b19611cfcb162e91 --- /dev/null +++ b/comfy_extras/nodes_post_processing.py @@ -0,0 +1,281 @@ +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +import math + +import comfy.utils +import comfy.model_management +import node_helpers + +class Blend: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01 + }), + "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blend_images" + + CATEGORY = "image/postprocessing" + + def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): + image1, image2 = node_helpers.image_alpha_fix(image1, image2) + image2 = image2.to(image1.device) + if image1.shape != image2.shape: + image2 = image2.permute(0, 3, 1, 2) + image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') + image2 = image2.permute(0, 2, 3, 1) + + blended_image = self.blend_mode(image1, image2, blend_mode) + blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor + blended_image = torch.clamp(blended_image, 0, 1) + return (blended_image,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + elif mode == "multiply": + return img1 * img2 + elif mode == "screen": + return 1 - (1 - img1) * (1 - img2) + elif mode == "overlay": + return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) + elif mode == "soft_light": + return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) + elif mode == "difference": + return img1 - img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + + def g(self, x): + return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) + +def gaussian_kernel(kernel_size: int, sigma: float, device=None): + x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") + d = torch.sqrt(x * x + y * y) + g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) + return g / g.sum() + +class Blur: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "blur_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blur" + + CATEGORY = "image/postprocessing" + + def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): + if blur_radius == 0: + return (image,) + + image = image.to(comfy.model_management.get_torch_device()) + batch_size, height, width, channels = image.shape + + kernel_size = blur_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) + + image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') + blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] + blurred = blurred.permute(0, 2, 3, 1) + + return (blurred.to(comfy.model_management.intermediate_device()),) + +class Quantize: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "colors": ("INT", { + "default": 256, + "min": 1, + "max": 256, + "step": 1 + }), + "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "quantize" + + CATEGORY = "image/postprocessing" + + @staticmethod + def bayer(im, pal_im, order): + def normalized_bayer_matrix(n): + if n == 0: + return np.zeros((1,1), "float32") + else: + q = 4 ** n + m = q * normalized_bayer_matrix(n - 1) + return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q + + num_colors = len(pal_im.getpalette()) // 3 + spread = 2 * 256 / num_colors + bayer_n = int(math.log2(order)) + bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) + + result = torch.from_numpy(np.array(im).astype(np.float32)) + tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) + th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) + tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) + result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) + result = result.to(dtype=torch.uint8) + + im = Image.fromarray(result.cpu().numpy()) + im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) + return im + + def quantize(self, image: torch.Tensor, colors: int, dither: str): + batch_size, height, width, _ = image.shape + result = torch.zeros_like(image) + + for b in range(batch_size): + im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') + + pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 + + if dither == "none": + quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) + elif dither == "floyd-steinberg": + quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) + elif dither.startswith("bayer"): + order = int(dither.split('-')[-1]) + quantized_image = Quantize.bayer(im, pal_im, order) + + quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 + result[b] = quantized_array + + return (result,) + +class Sharpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "sharpen_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.01 + }), + "alpha": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 5.0, + "step": 0.01 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "sharpen" + + CATEGORY = "image/postprocessing" + + def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): + if sharpen_radius == 0: + return (image,) + + batch_size, height, width, channels = image.shape + image = image.to(comfy.model_management.get_torch_device()) + + kernel_size = sharpen_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) + center = kernel_size // 2 + kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 + kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) + + tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') + sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] + sharpened = sharpened.permute(0, 2, 3, 1) + + result = torch.clamp(sharpened, 0, 1) + + return (result.to(comfy.model_management.intermediate_device()),) + +class ImageScaleToTotalPixels: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, megapixels): + samples = image.movedim(-1,1) + total = int(megapixels * 1024 * 1024) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +NODE_CLASS_MAPPINGS = { + "ImageBlend": Blend, + "ImageBlur": Blur, + "ImageQuantize": Quantize, + "ImageSharpen": Sharpen, + "ImageScaleToTotalPixels": ImageScaleToTotalPixels, +} diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py new file mode 100644 index 0000000000000000000000000000000000000000..e6805696f302e785f0ea4afe4ca82aa2eeca0495 --- /dev/null +++ b/comfy_extras/nodes_preview_any.py @@ -0,0 +1,43 @@ +import json +from comfy.comfy_types.node_typing import IO + +# Preview Any - original implement from +# https://github.com/rgthree/rgthree-comfy/blob/main/py/display_any.py +# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes +class PreviewAny(): + @classmethod + def INPUT_TYPES(cls): + return { + "required": {"source": (IO.ANY, {})}, + } + + RETURN_TYPES = () + FUNCTION = "main" + OUTPUT_NODE = True + + CATEGORY = "utils" + + def main(self, source=None): + value = 'None' + if isinstance(source, str): + value = source + elif isinstance(source, (int, float, bool)): + value = str(source) + elif source is not None: + try: + value = json.dumps(source) + except Exception: + try: + value = str(source) + except Exception: + value = 'source exists, but could not be serialized.' + + return {"ui": {"text": (value,)}} + +NODE_CLASS_MAPPINGS = { + "PreviewAny": PreviewAny, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PreviewAny": "Preview Any", +} diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py new file mode 100644 index 0000000000000000000000000000000000000000..1f93f87a79531b3650981a651d355466db518ede --- /dev/null +++ b/comfy_extras/nodes_primitive.py @@ -0,0 +1,98 @@ +# Primitive nodes that are evaluated at backend. +from __future__ import annotations + +import sys + +from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO + + +class String(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": {"value": (IO.STRING, {})}, + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/primitive" + + def execute(self, value: str) -> tuple[str]: + return (value,) + + +class StringMultiline(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": {"value": (IO.STRING, {"multiline": True,},)}, + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/primitive" + + def execute(self, value: str) -> tuple[str]: + return (value,) + + +class Int(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": {"value": (IO.INT, {"min": -sys.maxsize, "max": sys.maxsize, "control_after_generate": True})}, + } + + RETURN_TYPES = (IO.INT,) + FUNCTION = "execute" + CATEGORY = "utils/primitive" + + def execute(self, value: int) -> tuple[int]: + return (value,) + + +class Float(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": {"value": (IO.FLOAT, {"min": -sys.maxsize, "max": sys.maxsize})}, + } + + RETURN_TYPES = (IO.FLOAT,) + FUNCTION = "execute" + CATEGORY = "utils/primitive" + + def execute(self, value: float) -> tuple[float]: + return (value,) + + +class Boolean(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": {"value": (IO.BOOLEAN, {})}, + } + + RETURN_TYPES = (IO.BOOLEAN,) + FUNCTION = "execute" + CATEGORY = "utils/primitive" + + def execute(self, value: bool) -> tuple[bool]: + return (value,) + + +NODE_CLASS_MAPPINGS = { + "PrimitiveString": String, + "PrimitiveStringMultiline": StringMultiline, + "PrimitiveInt": Int, + "PrimitiveFloat": Float, + "PrimitiveBoolean": Boolean, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PrimitiveString": "String", + "PrimitiveStringMultiline": "String (Multiline)", + "PrimitiveInt": "Int", + "PrimitiveFloat": "Float", + "PrimitiveBoolean": "Boolean", +} diff --git a/comfy_extras/nodes_rebatch.py b/comfy_extras/nodes_rebatch.py new file mode 100644 index 0000000000000000000000000000000000000000..e29cb9ed10dae329ac2bea313df72f2387bd7ade --- /dev/null +++ b/comfy_extras/nodes_rebatch.py @@ -0,0 +1,138 @@ +import torch + +class LatentRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "latents": ("LATENT",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("LATENT",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "latent/batch" + + @staticmethod + def get_batch(latents, list_ind, offset): + '''prepare a batch out of the list of latents''' + samples = latents[list_ind]['samples'] + shape = samples.shape + mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') + if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: + torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") + if mask.shape[0] < samples.shape[0]: + mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] + if 'batch_index' in latents[list_ind]: + batch_inds = latents[list_ind]['batch_index'] + else: + batch_inds = [x+offset for x in range(shape[0])] + return samples, mask, batch_inds + + @staticmethod + def get_slices(indexable, num, batch_size): + '''divides an indexable object into num slices of length batch_size, and a remainder''' + slices = [] + for i in range(num): + slices.append(indexable[i*batch_size:(i+1)*batch_size]) + if num * batch_size < len(indexable): + return slices, indexable[num * batch_size:] + else: + return slices, None + + @staticmethod + def slice_batch(batch, num, batch_size): + result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] + return list(zip(*result)) + + @staticmethod + def cat_batch(batch1, batch2): + if batch1[0] is None: + return batch2 + result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] + return result + + def rebatch(self, latents, batch_size): + batch_size = batch_size[0] + + output_list = [] + current_batch = (None, None, None) + processed = 0 + + for i in range(len(latents)): + # fetch new entry of list + #samples, masks, indices = self.get_batch(latents, i) + next_batch = self.get_batch(latents, i, processed) + processed += len(next_batch[2]) + # set to current if current is None + if current_batch[0] is None: + current_batch = next_batch + # add previous to list if dimensions do not match + elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + current_batch = next_batch + # cat if everything checks out + else: + current_batch = self.cat_batch(current_batch, next_batch) + + # add to list if dimensions gone above target batch size + if current_batch[0].shape[0] > batch_size: + num = current_batch[0].shape[0] // batch_size + sliced, remainder = self.slice_batch(current_batch, num, batch_size) + + for i in range(num): + output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) + + current_batch = remainder + + #add remainder + if current_batch[0] is not None: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + + #get rid of empty masks + for s in output_list: + if s['noise_mask'].mean() == 1.0: + del s['noise_mask'] + + return (output_list,) + +class ImageRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "image/batch" + + def rebatch(self, images, batch_size): + batch_size = batch_size[0] + + output_list = [] + all_images = [] + for img in images: + for i in range(img.shape[0]): + all_images.append(img[i:i+1]) + + for i in range(0, len(all_images), batch_size): + output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) + + return (output_list,) + +NODE_CLASS_MAPPINGS = { + "RebatchLatents": LatentRebatch, + "RebatchImages": ImageRebatch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RebatchLatents": "Rebatch Latents", + "RebatchImages": "Rebatch Images", +} diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py new file mode 100644 index 0000000000000000000000000000000000000000..1bd8d7364fdde4bd8ce9b11f342dfce71d3a4214 --- /dev/null +++ b/comfy_extras/nodes_sag.py @@ -0,0 +1,181 @@ +import torch +from torch import einsum +import torch.nn.functional as F +import math + +from einops import rearrange, repeat +from comfy.ldm.modules.attention import optimized_attention +import comfy.samplers + +# from comfy/ldm/modules/attention.py +# but modified to return attention scores as well as output +def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None): + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 + + h = heads + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if attn_precision == torch.float32: + sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return (out, sim) + +def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): + # reshape and GAP the attention map + _, hw1, hw2 = attn.shape + b, _, lh, lw = x0.shape + attn = attn.reshape(b, -1, hw1, hw2) + # Global Average Pool + mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold + + total = mask.shape[-1] + x = round(math.sqrt((lh / lw) * total)) + xx = None + for i in range(0, math.floor(math.sqrt(total) / 2)): + for j in [(x + i), max(1, x - i)]: + if total % j == 0: + xx = j + break + if xx is not None: + break + + x = xx + y = total // x + + # Reshape + mask = ( + mask.reshape(b, x, y) + .unsqueeze(1) + .type(attn.dtype) + ) + # Upsample + mask = F.interpolate(mask, (lh, lw)) + + blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) + blurred = blurred * mask + x0 * (1 - mask) + return blurred + +def gaussian_blur_2d(img, kernel_size, sigma): + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = F.pad(img, padding, mode="reflect") + img = F.conv2d(img, kernel2d, groups=img.shape[-3]) + return img + +class SelfAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}), + "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, scale, blur_sigma): + m = model.clone() + + attn_scores = None + + # TODO: make this work properly with chunked batches + # currently, we can only save the attn from one UNet call + def attn_and_record(q, k, v, extra_options): + nonlocal attn_scores + # if uncond, save the attention scores + heads = extra_options["n_heads"] + cond_or_uncond = extra_options["cond_or_uncond"] + b = q.shape[0] // len(cond_or_uncond) + if 1 in cond_or_uncond: + uncond_index = cond_or_uncond.index(1) + # do the entire attention operation, but save the attention scores to attn_scores + (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) + # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] + n_slices = heads * b + attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] + return out + else: + return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) + + def post_cfg_function(args): + nonlocal attn_scores + uncond_attn = attn_scores + + sag_scale = scale + sag_sigma = blur_sigma + sag_threshold = 1.0 + model = args["model"] + uncond_pred = args["uncond_denoised"] + uncond = args["uncond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"] + x = args["input"] + if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding + return cfg_result + + # create the adversarially blurred image + degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) + degraded_noised = degraded + x - uncond_pred + # call into the UNet + (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options) + return cfg_result + (degraded - sag) * sag_scale + + m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) + + # from diffusers: + # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch + m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) + + return (m, ) + +NODE_CLASS_MAPPINGS = { + "SelfAttentionGuidance": SelfAttentionGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SelfAttentionGuidance": "Self-Attention Guidance", +} diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py new file mode 100644 index 0000000000000000000000000000000000000000..d75b29e606feaf1e0eb125bd63f4abe2bc03b2f5 --- /dev/null +++ b/comfy_extras/nodes_sd3.py @@ -0,0 +1,138 @@ +import folder_paths +import comfy.sd +import comfy.model_management +import nodes +import torch +import comfy_extras.nodes_slg + + +class TripleCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), ) + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + DESCRIPTION = "[Recipes]\n\nsd3: clip-l, clip-g, t5" + + def load_clip(self, clip_name1, clip_name2, clip_name3): + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) + clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) + clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (clip,) + + +class EmptySD3LatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/sd3" + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device) + return ({"samples":latent}, ) + + +class CLIPTextEncodeSD3: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip": ("CLIP", ), + "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "empty_padding": (["none", "empty_prompt"], ) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): + no_padding = empty_padding == "none" + + tokens = clip.tokenize(clip_g) + if len(clip_g) == 0 and no_padding: + tokens["g"] = [] + + if len(clip_l) == 0 and no_padding: + tokens["l"] = [] + else: + tokens["l"] = clip.tokenize(clip_l)["l"] + + if len(t5xxl) == 0 and no_padding: + tokens["t5xxl"] = [] + else: + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + return (clip.encode_from_tokens_scheduled(tokens), ) + + +class ControlNetApplySD3(nodes.ControlNetApplyAdvanced): + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "vae": ("VAE", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + CATEGORY = "conditioning/controlnet" + DEPRECATED = True + + +class SkipLayerGuidanceSD3(comfy_extras.nodes_slg.SkipLayerGuidanceDiT): + ''' + Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. + Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) + Experimental implementation by Dango233@StabilityAI. + ''' + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), + "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "skip_guidance_sd3" + + CATEGORY = "advanced/guidance" + + def skip_guidance_sd3(self, model, layers, scale, start_percent, end_percent): + return self.skip_guidance(model=model, scale=scale, start_percent=start_percent, end_percent=end_percent, double_layers=layers) + + +NODE_CLASS_MAPPINGS = { + "TripleCLIPLoader": TripleCLIPLoader, + "EmptySD3LatentImage": EmptySD3LatentImage, + "CLIPTextEncodeSD3": CLIPTextEncodeSD3, + "ControlNetApplySD3": ControlNetApplySD3, + "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "ControlNetApplySD3": "Apply Controlnet with VAE", +} diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py new file mode 100644 index 0000000000000000000000000000000000000000..bba67e8ddff8064a90ec0f8e71e953ca4e56c4c6 --- /dev/null +++ b/comfy_extras/nodes_sdupscale.py @@ -0,0 +1,46 @@ +import torch +import comfy.utils + +class SD_4XUpscale_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/upscale_diffusion" + + def encode(self, images, positive, negative, scale_ratio, noise_augmentation): + width = max(1, round(images.shape[-2] * scale_ratio)) + height = max(1, round(images.shape[-3] * scale_ratio)) + + pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center") + + out_cp = [] + out_cn = [] + + for t in positive: + n = [t[0], t[1].copy()] + n[1]['concat_image'] = pixels + n[1]['noise_augmentation'] = noise_augmentation + out_cp.append(n) + + for t in negative: + n = [t[0], t[1].copy()] + n[1]['concat_image'] = pixels + n[1]['noise_augmentation'] = noise_augmentation + out_cn.append(n) + + latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) + return (out_cp, out_cn, {"samples":latent}) + +NODE_CLASS_MAPPINGS = { + "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, +} diff --git a/comfy_extras/nodes_slg.py b/comfy_extras/nodes_slg.py new file mode 100644 index 0000000000000000000000000000000000000000..2fa09e2505ec8e075ab6100ae4a6f6e5b0634830 --- /dev/null +++ b/comfy_extras/nodes_slg.py @@ -0,0 +1,84 @@ +import comfy.model_patcher +import comfy.samplers +import re + + +class SkipLayerGuidanceDiT: + ''' + Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. + Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) + Original experimental implementation for SD3 by Dango233@StabilityAI. + ''' + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), + "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), + "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), + "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}), + "rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "skip_guidance" + EXPERIMENTAL = True + + DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model." + + CATEGORY = "advanced/guidance" + + def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0): + # check if layer is comma separated integers + def skip(args, extra_args): + return args + + model_sampling = model.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) + + double_layers = re.findall(r'\d+', double_layers) + double_layers = [int(i) for i in double_layers] + + single_layers = re.findall(r'\d+', single_layers) + single_layers = [int(i) for i in single_layers] + + if len(double_layers) == 0 and len(single_layers) == 0: + return (model, ) + + def post_cfg_function(args): + model = args["model"] + cond_pred = args["cond_denoised"] + cond = args["cond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + x = args["input"] + model_options = args["model_options"].copy() + + for layer in double_layers: + model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer) + + for layer in single_layers: + model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "single_block", layer) + + model_sampling.percent_to_sigma(start_percent) + + sigma_ = sigma[0].item() + if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start: + (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) + cfg_result = cfg_result + (cond_pred - slg) * scale + if rescaling_scale != 0: + factor = cond_pred.std() / cfg_result.std() + factor = rescaling_scale * factor + (1 - rescaling_scale) + cfg_result *= factor + + return cfg_result + + m = model.clone() + m.set_model_sampler_post_cfg_function(post_cfg_function) + + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "SkipLayerGuidanceDiT": SkipLayerGuidanceDiT, +} diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py new file mode 100644 index 0000000000000000000000000000000000000000..be2e34c28f49f160a21703c313305193ed00546f --- /dev/null +++ b/comfy_extras/nodes_stable3d.py @@ -0,0 +1,143 @@ +import torch +import nodes +import comfy.utils + +def camera_embeddings(elevation, azimuth): + elevation = torch.as_tensor([elevation]) + azimuth = torch.as_tensor([azimuth]) + embeddings = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + torch.deg2rad( + 90 - torch.full_like(elevation, 0) + ), + ], dim=-1).unsqueeze(1) + + return embeddings + + +class StableZero123_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + cam_embeds = camera_embeddings(elevation, azimuth) + cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class StableZero123_Conditioning_Batched: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + cam_embeds = [] + for i in range(batch_size): + cam_embeds.append(camera_embeddings(elevation, azimuth)) + elevation += elevation_batch_increment + azimuth += azimuth_batch_increment + + cam_embeds = torch.cat(cam_embeds, dim=0) + cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) + +class SV3D_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + azimuth = 0 + azimuth_increment = 360 / (max(video_frames, 2) - 1) + + elevations = [] + azimuths = [] + for i in range(video_frames): + elevations.append(elevation) + azimuths.append(azimuth) + azimuth += azimuth_increment + + positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + + +NODE_CLASS_MAPPINGS = { + "StableZero123_Conditioning": StableZero123_Conditioning, + "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, + "SV3D_Conditioning": SV3D_Conditioning, +} diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py new file mode 100644 index 0000000000000000000000000000000000000000..0034032150e6eac48d18bb6bd35819114a01fe8d --- /dev/null +++ b/comfy_extras/nodes_stable_cascade.py @@ -0,0 +1,141 @@ +""" + This file is part of ComfyUI. + Copyright (C) 2024 Stability AI + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import torch +import nodes +import comfy.utils + + +class StableCascade_EmptyLatentImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), + "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) + }} + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "latent/stable_cascade" + + def generate(self, width, height, compression, batch_size=1): + c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) + b_latent = torch.zeros([batch_size, 4, height // 4, width // 4]) + return ({ + "samples": c_latent, + }, { + "samples": b_latent, + }) + +class StableCascade_StageC_VAEEncode: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "vae": ("VAE", ), + "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), + }} + RETURN_TYPES = ("LATENT", "LATENT") + RETURN_NAMES = ("stage_c", "stage_b") + FUNCTION = "generate" + + CATEGORY = "latent/stable_cascade" + + def generate(self, image, vae, compression): + width = image.shape[-2] + height = image.shape[-3] + out_width = (width // compression) * vae.downscale_ratio + out_height = (height // compression) * vae.downscale_ratio + + s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) + + c_latent = vae.encode(s[:,:,:,:3]) + b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2]) + return ({ + "samples": c_latent, + }, { + "samples": b_latent, + }) + +class StableCascade_StageB_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "conditioning": ("CONDITIONING",), + "stage_c": ("LATENT",), + }} + RETURN_TYPES = ("CONDITIONING",) + + FUNCTION = "set_prior" + + CATEGORY = "conditioning/stable_cascade" + + def set_prior(self, conditioning, stage_c): + c = [] + for t in conditioning: + d = t[1].copy() + d['stable_cascade_prior'] = stage_c['samples'] + n = [t[0], d] + c.append(n) + return (c, ) + +class StableCascade_SuperResolutionControlnet: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "vae": ("VAE", ), + }} + RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") + RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") + FUNCTION = "generate" + + EXPERIMENTAL = True + CATEGORY = "_for_testing/stable_cascade" + + def generate(self, image, vae): + width = image.shape[-2] + height = image.shape[-3] + batch_size = image.shape[0] + controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1) + + c_latent = torch.zeros([batch_size, 16, height // 16, width // 16]) + b_latent = torch.zeros([batch_size, 4, height // 2, width // 2]) + return (controlnet_input, { + "samples": c_latent, + }, { + "samples": b_latent, + }) + +NODE_CLASS_MAPPINGS = { + "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, + "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, + "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, + "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, +} diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a8ceef0fd25c47b9238749e59e06f20f6d346d --- /dev/null +++ b/comfy_extras/nodes_string.py @@ -0,0 +1,360 @@ +import re + +from comfy.comfy_types.node_typing import IO + +class StringConcatenate(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string_a": (IO.STRING, {"multiline": True}), + "string_b": (IO.STRING, {"multiline": True}), + "delimiter": (IO.STRING, {"multiline": False, "default": ""}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string_a, string_b, delimiter, **kwargs): + return delimiter.join((string_a, string_b)), + +class StringSubstring(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "start": (IO.INT, {}), + "end": (IO.INT, {}), + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, start, end, **kwargs): + return string[start:end], + +class StringLength(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}) + } + } + + RETURN_TYPES = (IO.INT,) + RETURN_NAMES = ("length",) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, **kwargs): + length = len(string) + + return length, + +class CaseConverter(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["UPPERCASE", "lowercase", "Capitalize", "Title Case"]}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, mode, **kwargs): + if mode == "UPPERCASE": + result = string.upper() + elif mode == "lowercase": + result = string.lower() + elif mode == "Capitalize": + result = string.capitalize() + elif mode == "Title Case": + result = string.title() + else: + result = string + + return result, + + +class StringTrim(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["Both", "Left", "Right"]}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, mode, **kwargs): + if mode == "Both": + result = string.strip() + elif mode == "Left": + result = string.lstrip() + elif mode == "Right": + result = string.rstrip() + else: + result = string + + return result, + +class StringReplace(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "find": (IO.STRING, {"multiline": True}), + "replace": (IO.STRING, {"multiline": True}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, find, replace, **kwargs): + result = string.replace(find, replace) + return result, + + +class StringContains(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "substring": (IO.STRING, {"multiline": True}), + "case_sensitive": (IO.BOOLEAN, {"default": True}) + } + } + + RETURN_TYPES = (IO.BOOLEAN,) + RETURN_NAMES = ("contains",) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, substring, case_sensitive, **kwargs): + if case_sensitive: + contains = substring in string + else: + contains = substring.lower() in string.lower() + + return contains, + + +class StringCompare(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string_a": (IO.STRING, {"multiline": True}), + "string_b": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["Starts With", "Ends With", "Equal"]}), + "case_sensitive": (IO.BOOLEAN, {"default": True}) + } + } + + RETURN_TYPES = (IO.BOOLEAN,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string_a, string_b, mode, case_sensitive, **kwargs): + if case_sensitive: + a = string_a + b = string_b + else: + a = string_a.lower() + b = string_b.lower() + + if mode == "Equal": + return a == b, + elif mode == "Starts With": + return a.startswith(b), + elif mode == "Ends With": + return a.endswith(b), + +class RegexMatch(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "regex_pattern": (IO.STRING, {"multiline": True}), + "case_insensitive": (IO.BOOLEAN, {"default": True}), + "multiline": (IO.BOOLEAN, {"default": False}), + "dotall": (IO.BOOLEAN, {"default": False}) + } + } + + RETURN_TYPES = (IO.BOOLEAN,) + RETURN_NAMES = ("matches",) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, regex_pattern, case_insensitive, multiline, dotall, **kwargs): + flags = 0 + + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + + try: + match = re.search(regex_pattern, string, flags) + result = match is not None + + except re.error: + result = False + + return result, + + +class RegexExtract(): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "regex_pattern": (IO.STRING, {"multiline": True}), + "mode": (IO.COMBO, {"options": ["First Match", "All Matches", "First Group", "All Groups"]}), + "case_insensitive": (IO.BOOLEAN, {"default": True}), + "multiline": (IO.BOOLEAN, {"default": False}), + "dotall": (IO.BOOLEAN, {"default": False}), + "group_index": (IO.INT, {"default": 1, "min": 0, "max": 100}) + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index, **kwargs): + join_delimiter = "\n" + + flags = 0 + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + + try: + if mode == "First Match": + match = re.search(regex_pattern, string, flags) + if match: + result = match.group(0) + else: + result = "" + + elif mode == "All Matches": + matches = re.findall(regex_pattern, string, flags) + if matches: + if isinstance(matches[0], tuple): + result = join_delimiter.join([m[0] for m in matches]) + else: + result = join_delimiter.join(matches) + else: + result = "" + + elif mode == "First Group": + match = re.search(regex_pattern, string, flags) + if match and len(match.groups()) >= group_index: + result = match.group(group_index) + else: + result = "" + + elif mode == "All Groups": + matches = re.finditer(regex_pattern, string, flags) + results = [] + for match in matches: + if match.groups() and len(match.groups()) >= group_index: + results.append(match.group(group_index)) + result = join_delimiter.join(results) + else: + result = "" + + except re.error: + result = "" + + return result, + + +class RegexReplace(): + DESCRIPTION = "Find and replace text using regex patterns." + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": (IO.STRING, {"multiline": True}), + "regex_pattern": (IO.STRING, {"multiline": True}), + "replace": (IO.STRING, {"multiline": True}), + }, + "optional": { + "case_insensitive": (IO.BOOLEAN, {"default": True}), + "multiline": (IO.BOOLEAN, {"default": False}), + "dotall": (IO.BOOLEAN, {"default": False, "tooltip": "When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."}), + "count": (IO.INT, {"default": 0, "min": 0, "max": 100, "tooltip": "Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."}), + } + } + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "execute" + CATEGORY = "utils/string" + + def execute(self, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0, **kwargs): + flags = 0 + + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + result = re.sub(regex_pattern, replace, string, count=count, flags=flags) + return result, + +NODE_CLASS_MAPPINGS = { + "StringConcatenate": StringConcatenate, + "StringSubstring": StringSubstring, + "StringLength": StringLength, + "CaseConverter": CaseConverter, + "StringTrim": StringTrim, + "StringReplace": StringReplace, + "StringContains": StringContains, + "StringCompare": StringCompare, + "RegexMatch": RegexMatch, + "RegexExtract": RegexExtract, + "RegexReplace": RegexReplace, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "StringConcatenate": "Concatenate", + "StringSubstring": "Substring", + "StringLength": "Length", + "CaseConverter": "Case Converter", + "StringTrim": "Trim", + "StringReplace": "Replace", + "StringContains": "Contains", + "StringCompare": "Compare", + "RegexMatch": "Regex Match", + "RegexExtract": "Regex Extract", + "RegexReplace": "Regex Replace", +} diff --git a/comfy_extras/nodes_tomesd.py b/comfy_extras/nodes_tomesd.py new file mode 100644 index 0000000000000000000000000000000000000000..9f77c06fcb12a2dafbb891cbceb50ba8addaa81a --- /dev/null +++ b/comfy_extras/nodes_tomesd.py @@ -0,0 +1,176 @@ +#Taken from: https://github.com/dbolya/tomesd + +import torch +from typing import Tuple, Callable +import math + +def do_nothing(x: torch.Tensor, mode:str=None): + return x + + +def mps_gather_workaround(input, dim, index): + if input.shape[-1] == 1: + return torch.gather( + input.unsqueeze(-1), + dim - 1 if dim < 0 else dim, + index.unsqueeze(-1) + ).squeeze(-1) + else: + return torch.gather(input, dim, index) + + +def bipartite_soft_matching_random2d(metric: torch.Tensor, + w: int, h: int, sx: int, sy: int, r: int, + no_rand: bool = False) -> Tuple[Callable, Callable]: + """ + Partitions the tokens into src and dst and merges r tokens from src to dst. + Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. + Args: + - metric [B, N, C]: metric to use for similarity + - w: image width in tokens + - h: image height in tokens + - sx: stride in the x dimension for dst, must divide w + - sy: stride in the y dimension for dst, must divide h + - r: number of tokens to remove (by merging) + - no_rand: if true, disable randomness (use top left corner only) + """ + B, N, _ = metric.shape + + if r <= 0 or w == 1 or h == 1: + return do_nothing, do_nothing + + gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather + + with torch.no_grad(): + hsy, wsx = h // sy, w // sx + + # For each sy by sx kernel, randomly assign one token to be dst and the rest src + if no_rand: + rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) + else: + rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) + + # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead + idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) + idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) + idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) + + # Image is not divisible by sx or sy so we need to move it into a new buffer + if (hsy * sy) < h or (wsx * sx) < w: + idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) + idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view + else: + idx_buffer = idx_buffer_view + + # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices + rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) + + # We're finished with these + del idx_buffer, idx_buffer_view + + # rand_idx is currently dst|src, so split them + num_dst = hsy * wsx + a_idx = rand_idx[:, num_dst:, :] # src + b_idx = rand_idx[:, :num_dst, :] # dst + + def split(x): + C = x.shape[-1] + src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) + dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) + return src, dst + + # Cosine similarity between A and B + metric = metric / metric.norm(dim=-1, keepdim=True) + a, b = split(metric) + scores = a @ b.transpose(-1, -2) + + # Can't reduce more than the # tokens in src + r = min(a.shape[1], r) + + # Find the most similar greedily + node_max, node_idx = scores.max(dim=-1) + edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] + + unm_idx = edge_idx[..., r:, :] # Unmerged Tokens + src_idx = edge_idx[..., :r, :] # Merged Tokens + dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) + + def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: + src, dst = split(x) + n, t1, c = src.shape + + unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) + src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) + dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) + + return torch.cat([unm, dst], dim=1) + + def unmerge(x: torch.Tensor) -> torch.Tensor: + unm_len = unm_idx.shape[1] + unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] + _, _, c = unm.shape + + src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) + + # Combine back to the original shape + out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) + out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) + + return out + + return merge, unmerge + + +def get_functions(x, ratio, original_shape): + b, c, original_h, original_w = original_shape + original_tokens = original_h * original_w + downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) + stride_x = 2 + stride_y = 2 + max_downsample = 1 + + if downsample <= max_downsample: + w = int(math.ceil(original_w / downsample)) + h = int(math.ceil(original_h / downsample)) + r = int(x.shape[1] * ratio) + no_rand = False + m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) + return m, u + + nothing = lambda y: y + return nothing, nothing + + + +class TomePatchModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches/unet" + + def patch(self, model, ratio): + self.u = None + def tomesd_m(q, k, v, extra_options): + #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q + #however from my basic testing it seems that using q instead gives better results + m, self.u = get_functions(q, ratio, extra_options["original_shape"]) + return m(q), k, v + def tomesd_u(n, extra_options): + return self.u(n) + + m = model.clone() + m.set_model_attn1_patch(tomesd_m) + m.set_model_attn1_output_patch(tomesd_u) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "TomePatchModel": TomePatchModel, +} diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py new file mode 100644 index 0000000000000000000000000000000000000000..6055366784d0d46de863d8278b0bb2979e904d2a --- /dev/null +++ b/comfy_extras/nodes_torch_compile.py @@ -0,0 +1,23 @@ +from comfy_api.torch_helpers import set_torch_compile_wrapper + + +class TorchCompileModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "backend": (["inductor", "cudagraphs"],), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + EXPERIMENTAL = True + + def patch(self, model, backend): + m = model.clone() + set_torch_compile_wrapper(model=m, backend=backend) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "TorchCompileModel": TorchCompileModel, +} diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fbff01010c95775a863b97e78b3f61d395c2df02 --- /dev/null +++ b/comfy_extras/nodes_train.py @@ -0,0 +1,709 @@ +import datetime +import json +import logging +import os + +import numpy as np +import safetensors +import torch +from PIL import Image, ImageDraw, ImageFont +from PIL.PngImagePlugin import PngInfo +import torch.utils.checkpoint +import tqdm + +import comfy.samplers +import comfy.sd +import comfy.utils +import comfy.model_management +import comfy_extras.nodes_custom_sampler +import folder_paths +import node_helpers +from comfy.cli_args import args +from comfy.comfy_types.node_typing import IO +from comfy.weight_adapter import adapters + + +class TrainSampler(comfy.samplers.Sampler): + + def __init__(self, loss_fn, optimizer, loss_callback=None): + self.loss_fn = loss_fn + self.optimizer = optimizer + self.loss_callback = loss_callback + + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + self.optimizer.zero_grad() + noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas, noise, latent_image, False) + latent = model_wrap.inner_model.model_sampling.noise_scaling( + torch.zeros_like(sigmas), + torch.zeros_like(noise, requires_grad=True), + latent_image, + False + ) + + # Ensure model is in training mode and computing gradients + # x0 pred + denoised = model_wrap(noise, sigmas, **extra_args) + try: + loss = self.loss_fn(denoised, latent.clone()) + except RuntimeError as e: + if "does not require grad and does not have a grad_fn" in str(e): + logging.info("WARNING: This is likely due to the model is loaded in inference mode.") + loss.backward() + if self.loss_callback: + self.loss_callback(loss.item()) + + self.optimizer.step() + # torch.cuda.memory._dump_snapshot("trainn.pickle") + # torch.cuda.memory._record_memory_history(enabled=None) + return torch.zeros_like(latent_image) + + +class BiasDiff(torch.nn.Module): + def __init__(self, bias): + super().__init__() + self.bias = bias + + def __call__(self, b): + org_dtype = b.dtype + return (b.to(self.bias) + self.bias).to(org_dtype) + + def passive_memory_usage(self): + return self.bias.nelement() * self.bias.element_size() + + def move_to(self, device): + self.to(device=device) + return self.passive_memory_usage() + + +def load_and_process_images(image_files, input_dir, resize_method="None"): + """Utility function to load and process a list of images. + + Args: + image_files: List of image filenames + input_dir: Base directory containing the images + resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad") + + Returns: + torch.Tensor: Batch of processed images + """ + if not image_files: + raise ValueError("No valid images found in input") + + output_images = [] + w, h = None, None + + for file in image_files: + image_path = os.path.join(input_dir, file) + img = node_helpers.pillow(Image.open, image_path) + + if img.mode == "I": + img = img.point(lambda i: i * (1 / 255)) + img = img.convert("RGB") + + if w is None and h is None: + w, h = img.size[0], img.size[1] + + # Resize image to first image + if img.size[0] != w or img.size[1] != h: + if resize_method == "Stretch": + img = img.resize((w, h), Image.Resampling.LANCZOS) + elif resize_method == "Crop": + img = img.crop((0, 0, w, h)) + elif resize_method == "Pad": + img = img.resize((w, h), Image.Resampling.LANCZOS) + elif resize_method == "None": + raise ValueError( + "Your input image size does not match the first image in the dataset. Either select a valid resize method or use the same size for all images." + ) + + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array)[None,] + output_images.append(img_tensor) + + return torch.cat(output_images, dim=0) + + +class LoadImageSetNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ( + [ + f + for f in os.listdir(folder_paths.get_input_directory()) + if f.endswith((".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff")) + ], + {"image_upload": True, "allow_batch": True}, + ) + }, + "optional": { + "resize_method": ( + ["None", "Stretch", "Crop", "Pad"], + {"default": "None"}, + ), + }, + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_images" + CATEGORY = "loaders" + EXPERIMENTAL = True + DESCRIPTION = "Loads a batch of images from a directory for training." + + @classmethod + def VALIDATE_INPUTS(s, images, resize_method): + filenames = images[0] if isinstance(images[0], list) else images + + for image in filenames: + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + return True + + def load_images(self, input_files, resize_method): + input_dir = folder_paths.get_input_directory() + valid_extensions = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff"] + image_files = [ + f + for f in input_files + if any(f.lower().endswith(ext) for ext in valid_extensions) + ] + output_tensor = load_and_process_images(image_files, input_dir, resize_method) + return (output_tensor,) + + +class LoadImageSetFromFolderNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}) + }, + "optional": { + "resize_method": ( + ["None", "Stretch", "Crop", "Pad"], + {"default": "None"}, + ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_images" + CATEGORY = "loaders" + EXPERIMENTAL = True + DESCRIPTION = "Loads a batch of images from a directory for training." + + def load_images(self, folder, resize_method): + sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) + valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] + image_files = [ + f + for f in os.listdir(sub_input_dir) + if any(f.lower().endswith(ext) for ext in valid_extensions) + ] + output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method) + return (output_tensor,) + + +def draw_loss_graph(loss_map, steps): + width, height = 500, 300 + img = Image.new("RGB", (width, height), "white") + draw = ImageDraw.Draw(img) + + min_loss, max_loss = min(loss_map.values()), max(loss_map.values()) + scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_map.values()] + + prev_point = (0, height - int(scaled_loss[0] * height)) + for i, l in enumerate(scaled_loss[1:], start=1): + x = int(i / (steps - 1) * width) + y = height - int(l * height) + draw.line([prev_point, (x, y)], fill="blue", width=2) + prev_point = (x, y) + + return img + + +def find_all_highest_child_module_with_forward(model: torch.nn.Module, result = None, name = None): + if result is None: + result = [] + elif hasattr(model, "forward") and not isinstance(model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)): + result.append(model) + logging.debug(f"Found module with forward: {name} ({model.__class__.__name__})") + return result + name = name or "root" + for next_name, child in model.named_children(): + find_all_highest_child_module_with_forward(child, result, f"{name}.{next_name}") + return result + + +def patch(m): + if not hasattr(m, "forward"): + return + org_forward = m.forward + def fwd(args, kwargs): + return org_forward(*args, **kwargs) + def checkpointing_fwd(*args, **kwargs): + return torch.utils.checkpoint.checkpoint( + fwd, args, kwargs, use_reentrant=False + ) + m.org_forward = org_forward + m.forward = checkpointing_fwd + + +def unpatch(m): + if hasattr(m, "org_forward"): + m.forward = m.org_forward + del m.org_forward + + +class TrainLoraNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (IO.MODEL, {"tooltip": "The model to train the LoRA on."}), + "latents": ( + "LATENT", + { + "tooltip": "The Latents to use for training, serve as dataset/input of the model." + }, + ), + "positive": ( + IO.CONDITIONING, + {"tooltip": "The positive conditioning to use for training."}, + ), + "batch_size": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 10000, + "step": 1, + "tooltip": "The batch size to use for training.", + }, + ), + "steps": ( + IO.INT, + { + "default": 16, + "min": 1, + "max": 100000, + "tooltip": "The number of steps to train the LoRA for.", + }, + ), + "learning_rate": ( + IO.FLOAT, + { + "default": 0.0005, + "min": 0.0000001, + "max": 1.0, + "step": 0.000001, + "tooltip": "The learning rate to use for training.", + }, + ), + "rank": ( + IO.INT, + { + "default": 8, + "min": 1, + "max": 128, + "tooltip": "The rank of the LoRA layers.", + }, + ), + "optimizer": ( + ["AdamW", "Adam", "SGD", "RMSprop"], + { + "default": "AdamW", + "tooltip": "The optimizer to use for training.", + }, + ), + "loss_function": ( + ["MSE", "L1", "Huber", "SmoothL1"], + { + "default": "MSE", + "tooltip": "The loss function to use for training.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "tooltip": "The seed to use for training (used in generator for LoRA weight initialization and noise sampling)", + }, + ), + "training_dtype": ( + ["bf16", "fp32"], + {"default": "bf16", "tooltip": "The dtype to use for training."}, + ), + "lora_dtype": ( + ["bf16", "fp32"], + {"default": "bf16", "tooltip": "The dtype to use for lora."}, + ), + "existing_lora": ( + folder_paths.get_filename_list("loras") + ["[None]"], + { + "default": "[None]", + "tooltip": "The existing LoRA to append to. Set to None for new LoRA.", + }, + ), + }, + } + + RETURN_TYPES = (IO.MODEL, IO.LORA_MODEL, IO.LOSS_MAP, IO.INT) + RETURN_NAMES = ("model_with_lora", "lora", "loss", "steps") + FUNCTION = "train" + CATEGORY = "training" + EXPERIMENTAL = True + + def train( + self, + model, + latents, + positive, + batch_size, + steps, + learning_rate, + rank, + optimizer, + loss_function, + seed, + training_dtype, + lora_dtype, + existing_lora, + ): + mp = model.clone() + dtype = node_helpers.string_to_torch_dtype(training_dtype) + lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) + mp.set_model_compute_dtype(dtype) + + latents = latents["samples"].to(dtype) + num_images = latents.shape[0] + + with torch.inference_mode(False): + lora_sd = {} + generator = torch.Generator() + generator.manual_seed(seed) + + # Load existing LoRA weights if provided + existing_weights = {} + existing_steps = 0 + if existing_lora != "[None]": + lora_path = folder_paths.get_full_path_or_raise("loras", existing_lora) + # Extract steps from filename like "trained_lora_10_steps_20250225_203716" + existing_steps = int(existing_lora.split("_steps_")[0].split("_")[-1]) + if lora_path: + existing_weights = comfy.utils.load_torch_file(lora_path) + + all_weight_adapters = [] + for n, m in mp.model.named_modules(): + if hasattr(m, "weight_function"): + if m.weight is not None: + key = "{}.weight".format(n) + shape = m.weight.shape + if len(shape) >= 2: + alpha = float(existing_weights.get(f"{key}.alpha", 1.0)) + dora_scale = existing_weights.get( + f"{key}.dora_scale", None + ) + for adapter_cls in adapters: + existing_adapter = adapter_cls.load( + n, existing_weights, alpha, dora_scale + ) + if existing_adapter is not None: + break + else: + # If no existing adapter found, use LoRA + # We will add algo option in the future + existing_adapter = None + adapter_cls = adapters[0] + + if existing_adapter is not None: + train_adapter = existing_adapter.to_train().to(lora_dtype) + else: + # Use LoRA with alpha=1.0 by default + train_adapter = adapter_cls.create_train( + m.weight, rank=rank, alpha=1.0 + ).to(lora_dtype) + for name, parameter in train_adapter.named_parameters(): + lora_sd[f"{n}.{name}"] = parameter + + mp.add_weight_wrapper(key, train_adapter) + all_weight_adapters.append(train_adapter) + else: + diff = torch.nn.Parameter( + torch.zeros( + m.weight.shape, dtype=lora_dtype, requires_grad=True + ) + ) + diff_module = BiasDiff(diff) + mp.add_weight_wrapper(key, BiasDiff(diff)) + all_weight_adapters.append(diff_module) + lora_sd["{}.diff".format(n)] = diff + if hasattr(m, "bias") and m.bias is not None: + key = "{}.bias".format(n) + bias = torch.nn.Parameter( + torch.zeros(m.bias.shape, dtype=lora_dtype, requires_grad=True) + ) + bias_module = BiasDiff(bias) + lora_sd["{}.diff_b".format(n)] = bias + mp.add_weight_wrapper(key, BiasDiff(bias)) + all_weight_adapters.append(bias_module) + + if optimizer == "Adam": + optimizer = torch.optim.Adam(lora_sd.values(), lr=learning_rate) + elif optimizer == "AdamW": + optimizer = torch.optim.AdamW(lora_sd.values(), lr=learning_rate) + elif optimizer == "SGD": + optimizer = torch.optim.SGD(lora_sd.values(), lr=learning_rate) + elif optimizer == "RMSprop": + optimizer = torch.optim.RMSprop(lora_sd.values(), lr=learning_rate) + + # Setup loss function based on selection + if loss_function == "MSE": + criterion = torch.nn.MSELoss() + elif loss_function == "L1": + criterion = torch.nn.L1Loss() + elif loss_function == "Huber": + criterion = torch.nn.HuberLoss() + elif loss_function == "SmoothL1": + criterion = torch.nn.SmoothL1Loss() + + # setup models + for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model): + patch(m) + comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True) + + # Setup sampler and guider like in test script + loss_map = {"loss": []} + def loss_callback(loss): + loss_map["loss"].append(loss) + pbar.set_postfix({"loss": f"{loss:.4f}"}) + train_sampler = TrainSampler( + criterion, optimizer, loss_callback=loss_callback + ) + guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp) + guider.set_conds(positive) # Set conditioning from input + ss = comfy_extras.nodes_custom_sampler.SamplerCustomAdvanced() + + # yoland: this currently resize to the first image in the dataset + + # Training loop + torch.cuda.empty_cache() + try: + for step in (pbar:=tqdm.trange(steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)): + # Generate random sigma + sigma = mp.model.model_sampling.percent_to_sigma( + torch.rand((1,)).item() + ) + sigma = torch.tensor([sigma]) + + noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(step * 1000 + seed) + + indices = torch.randperm(num_images)[:batch_size] + ss.sample( + noise, guider, train_sampler, sigma, {"samples": latents[indices].clone()} + ) + finally: + for m in mp.model.modules(): + unpatch(m) + del ss, train_sampler, optimizer + torch.cuda.empty_cache() + + for adapter in all_weight_adapters: + adapter.requires_grad_(False) + + for param in lora_sd: + lora_sd[param] = lora_sd[param].to(lora_dtype) + + return (mp, lora_sd, loss_map, steps + existing_steps) + + +class LoraModelLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), + "lora": (IO.LORA_MODEL, {"tooltip": "The LoRA model to apply to the diffusion model."}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), + } + } + + RETURN_TYPES = ("MODEL",) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora_model" + + CATEGORY = "loaders" + DESCRIPTION = "Load Trained LoRA weights from Train LoRA node." + EXPERIMENTAL = True + + def load_lora_model(self, model, lora, strength_model): + if strength_model == 0: + return (model, ) + + model_lora, _ = comfy.sd.load_lora_for_models(model, None, lora, strength_model, 0) + return (model_lora, ) + + +class SaveLoRA: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora": ( + IO.LORA_MODEL, + { + "tooltip": "The LoRA model to save. Do not use the model with LoRA layers." + }, + ), + "prefix": ( + "STRING", + { + "default": "loras/ComfyUI_trained_lora", + "tooltip": "The prefix to use for the saved LoRA file.", + }, + ), + }, + "optional": { + "steps": ( + IO.INT, + { + "forceInput": True, + "tooltip": "Optional: The number of steps to LoRA has been trained for, used to name the saved file.", + }, + ), + }, + } + + RETURN_TYPES = () + FUNCTION = "save" + CATEGORY = "loaders" + EXPERIMENTAL = True + OUTPUT_NODE = True + + def save(self, lora, prefix, steps=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(prefix, self.output_dir) + if steps is None: + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + else: + output_checkpoint = f"{filename}_{steps}_steps_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + safetensors.torch.save_file(lora, output_checkpoint) + return {} + + +class LossGraphNode: + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "loss": (IO.LOSS_MAP, {"default": {}}), + "filename_prefix": (IO.STRING, {"default": "loss_graph"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "plot_loss" + OUTPUT_NODE = True + CATEGORY = "training" + EXPERIMENTAL = True + DESCRIPTION = "Plots the loss graph and saves it to the output directory." + + def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None): + loss_values = loss["loss"] + width, height = 800, 480 + margin = 40 + + img = Image.new( + "RGB", (width + margin, height + margin), "white" + ) # Extend canvas + draw = ImageDraw.Draw(img) + + min_loss, max_loss = min(loss_values), max(loss_values) + scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_values] + + steps = len(loss_values) + + prev_point = (margin, height - int(scaled_loss[0] * height)) + for i, l in enumerate(scaled_loss[1:], start=1): + x = margin + int(i / steps * width) # Scale X properly + y = height - int(l * height) + draw.line([prev_point, (x, y)], fill="blue", width=2) + prev_point = (x, y) + + draw.line([(margin, 0), (margin, height)], fill="black", width=2) # Y-axis + draw.line( + [(margin, height), (width + margin, height)], fill="black", width=2 + ) # X-axis + + font = None + try: + font = ImageFont.truetype("arial.ttf", 12) + except IOError: + font = ImageFont.load_default() + + # Add axis labels + draw.text((5, height // 2), "Loss", font=font, fill="black") + draw.text((width // 2, height + 10), "Steps", font=font, fill="black") + + # Add min/max loss values + draw.text((margin - 30, 0), f"{max_loss:.2f}", font=font, fill="black") + draw.text( + (margin - 30, height - 10), f"{min_loss:.2f}", font=font, fill="black" + ) + + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + img.save( + os.path.join(self.output_dir, f"{filename_prefix}_{date}.png"), + pnginfo=metadata, + ) + return { + "ui": { + "images": [ + { + "filename": f"{filename_prefix}_{date}.png", + "subfolder": "", + "type": "temp", + } + ] + } + } + + +NODE_CLASS_MAPPINGS = { + "TrainLoraNode": TrainLoraNode, + "SaveLoRANode": SaveLoRA, + "LoraModelLoader": LoraModelLoader, + "LoadImageSetFromFolderNode": LoadImageSetFromFolderNode, + "LossGraphNode": LossGraphNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TrainLoraNode": "Train LoRA", + "SaveLoRANode": "Save LoRA Weights", + "LoraModelLoader": "Load LoRA Model", + "LoadImageSetFromFolderNode": "Load Image Dataset from Folder", + "LossGraphNode": "Plot Loss Graph", +} diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py new file mode 100644 index 0000000000000000000000000000000000000000..04c948341296dfa7e385141498973977c17c906a --- /dev/null +++ b/comfy_extras/nodes_upscale_model.py @@ -0,0 +1,83 @@ +import logging +from spandrel import ModelLoader, ImageModelDescriptor +from comfy import model_management +import torch +import comfy.utils +import folder_paths + +try: + from spandrel_extra_arches import EXTRA_REGISTRY + from spandrel import MAIN_REGISTRY + MAIN_REGISTRY.add(*EXTRA_REGISTRY) + logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.") +except: + pass + +class UpscaleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), + }} + RETURN_TYPES = ("UPSCALE_MODEL",) + FUNCTION = "load_model" + + CATEGORY = "loaders" + + def load_model(self, model_name): + model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name) + sd = comfy.utils.load_torch_file(model_path, safe_load=True) + if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) + out = ModelLoader().load_from_state_dict(sd).eval() + + if not isinstance(out, ImageModelDescriptor): + raise Exception("Upscale model must be a single-image model.") + + return (out, ) + + +class ImageUpscaleWithModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "upscale_model": ("UPSCALE_MODEL",), + "image": ("IMAGE",), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, upscale_model, image): + device = model_management.get_torch_device() + + memory_required = model_management.module_size(upscale_model.model) + memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate + memory_required += image.nelement() * image.element_size() + model_management.free_memory(memory_required, device) + + upscale_model.to(device) + in_img = image.movedim(-1,-3).to(device) + + tile = 512 + overlap = 32 + + oom = True + while oom: + try: + steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) + pbar = comfy.utils.ProgressBar(steps) + s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + oom = False + except model_management.OOM_EXCEPTION as e: + tile //= 2 + if tile < 128: + raise e + + upscale_model.to("cpu") + s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) + return (s,) + +NODE_CLASS_MAPPINGS = { + "UpscaleModelLoader": UpscaleModelLoader, + "ImageUpscaleWithModel": ImageUpscaleWithModel +} diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py new file mode 100644 index 0000000000000000000000000000000000000000..61f7171b210ea441c93ca36dfa0d8181b081b14d --- /dev/null +++ b/comfy_extras/nodes_video.py @@ -0,0 +1,241 @@ +from __future__ import annotations + +import os +import av +import torch +import folder_paths +import json +from typing import Optional, Literal +from fractions import Fraction +from comfy.comfy_types import IO, FileLocator, ComfyNodeABC +from comfy_api.input import ImageInput, AudioInput, VideoInput +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents +from comfy_api.input_impl import VideoFromFile, VideoFromComponents +from comfy.cli_args import args + +class SaveWEBM: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"}), + "codec": (["vp9", "av1"],), + "fps": ("FLOAT", {"default": 24.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "crf": ("FLOAT", {"default": 32.0, "min": 0, "max": 63.0, "step": 1, "tooltip": "Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/video" + + EXPERIMENTAL = True + + def save_images(self, images, codec, fps, filename_prefix, crf, prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + + file = f"{filename}_{counter:05}_.webm" + container = av.open(os.path.join(full_output_folder, file), mode="w") + + if prompt is not None: + container.metadata["prompt"] = json.dumps(prompt) + + if extra_pnginfo is not None: + for x in extra_pnginfo: + container.metadata[x] = json.dumps(extra_pnginfo[x]) + + codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"} + stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000)) + stream.width = images.shape[-2] + stream.height = images.shape[-3] + stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p" + stream.bit_rate = 0 + stream.options = {'crf': str(crf)} + if codec == "av1": + stream.options["preset"] = "6" + + for frame in images: + frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24") + for packet in stream.encode(frame): + container.mux(packet) + container.mux(stream.encode()) + container.close() + + results: list[FileLocator] = [{ + "filename": file, + "subfolder": subfolder, + "type": self.type + }] + + return {"ui": {"images": results, "animated": (True,)}} # TODO: frontend side + +class SaveVideo(ComfyNodeABC): + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type: Literal["output"] = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to save."}), + "filename_prefix": ("STRING", {"default": "video/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), + "format": (VideoContainer.as_input(), {"default": "auto", "tooltip": "The format to save the video as."}), + "codec": (VideoCodec.as_input(), {"default": "auto", "tooltip": "The codec to use for the video."}), + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = () + FUNCTION = "save_video" + + OUTPUT_NODE = True + + CATEGORY = "image/video" + DESCRIPTION = "Saves the input images to your ComfyUI output directory." + + def save_video(self, video: VideoInput, filename_prefix, format, codec, prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + width, height = video.get_dimensions() + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, + self.output_dir, + width, + height + ) + results: list[FileLocator] = list() + saved_metadata = None + if not args.disable_metadata: + metadata = {} + if extra_pnginfo is not None: + metadata.update(extra_pnginfo) + if prompt is not None: + metadata["prompt"] = prompt + if len(metadata) > 0: + saved_metadata = metadata + file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}" + video.save_to( + os.path.join(full_output_folder, file), + format=format, + codec=codec, + metadata=saved_metadata + ) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results, "animated": (True,) } } + +class CreateVideo(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": (IO.IMAGE, {"tooltip": "The images to create a video from."}), + "fps": ("FLOAT", {"default": 30.0, "min": 1.0, "max": 120.0, "step": 1.0}), + }, + "optional": { + "audio": (IO.AUDIO, {"tooltip": "The audio to add to the video."}), + } + } + + RETURN_TYPES = (IO.VIDEO,) + FUNCTION = "create_video" + + CATEGORY = "image/video" + DESCRIPTION = "Create a video from images." + + def create_video(self, images: ImageInput, fps: float, audio: Optional[AudioInput] = None): + return (VideoFromComponents( + VideoComponents( + images=images, + audio=audio, + frame_rate=Fraction(fps), + ) + ),) + +class GetVideoComponents(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, {"tooltip": "The video to extract components from."}), + } + } + RETURN_TYPES = (IO.IMAGE, IO.AUDIO, IO.FLOAT) + RETURN_NAMES = ("images", "audio", "fps") + FUNCTION = "get_components" + + CATEGORY = "image/video" + DESCRIPTION = "Extracts all components from a video: frames, audio, and framerate." + + def get_components(self, video: VideoInput): + components = video.get_components() + + return (components.images, components.audio, float(components.frame_rate)) + +class LoadVideo(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + files = folder_paths.filter_files_content_types(files, ["video"]) + return {"required": + {"file": (sorted(files), {"video_upload": True})}, + } + + CATEGORY = "image/video" + + RETURN_TYPES = (IO.VIDEO,) + FUNCTION = "load_video" + def load_video(self, file): + video_path = folder_paths.get_annotated_filepath(file) + return (VideoFromFile(video_path),) + + @classmethod + def IS_CHANGED(cls, file): + video_path = folder_paths.get_annotated_filepath(file) + mod_time = os.path.getmtime(video_path) + # Instead of hashing the file, we can just use the modification time to avoid + # rehashing large files. + return mod_time + + @classmethod + def VALIDATE_INPUTS(cls, file): + if not folder_paths.exists_annotated_filepath(file): + return "Invalid video file: {}".format(file) + + return True + +NODE_CLASS_MAPPINGS = { + "SaveWEBM": SaveWEBM, + "SaveVideo": SaveVideo, + "CreateVideo": CreateVideo, + "GetVideoComponents": GetVideoComponents, + "LoadVideo": LoadVideo, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SaveVideo": "Save Video", + "CreateVideo": "Create Video", + "GetVideoComponents": "Get Video Components", + "LoadVideo": "Load Video", +} diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py new file mode 100644 index 0000000000000000000000000000000000000000..0f760aa26627f7dcc9e0d4483e05dae1326a4c66 --- /dev/null +++ b/comfy_extras/nodes_video_model.py @@ -0,0 +1,161 @@ +import nodes +import torch +import comfy.utils +import comfy.sd +import folder_paths +import comfy_extras.nodes_model_merging +import node_helpers + + +class ImageOnlyCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders/video_models" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return (out[0], out[3], out[2]) + + +class SVD_img2vid_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), + "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), + "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), + "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + if augmentation_level > 0: + encode_pixels += torch.randn_like(pixels) * augmentation_level + t = vae.encode(encode_pixels) + positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class VideoLinearCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + + scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + +class VideoTriangleCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + period = 1.0 + values = torch.linspace(0, 1, cond.shape[0], device=cond.device) + values = 2 * (values / period - torch.floor(values / period + 0.5)).abs() + scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1)) + + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + +class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): + CATEGORY = "advanced/model_merging" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip_vision": ("CLIP_VISION",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + + def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): + comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + + +class ConditioningSetAreaPercentageVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "temporal": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "z": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, temporal, x, y, z, strength): + c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + + +NODE_CLASS_MAPPINGS = { + "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, + "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, + "VideoLinearCFGGuidance": VideoLinearCFGGuidance, + "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, + "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, + "ConditioningSetAreaPercentageVideo": ConditioningSetAreaPercentageVideo, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", +} diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py new file mode 100644 index 0000000000000000000000000000000000000000..d6097a10448f3adaab4449ac2bfc7e5096347b60 --- /dev/null +++ b/comfy_extras/nodes_wan.py @@ -0,0 +1,395 @@ +import nodes +import node_helpers +import torch +import comfy.model_management +import comfy.utils +import comfy.latent_formats +import comfy.clip_vision + + +class WanImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "start_image": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5 + image[:start_image.shape[0]] = start_image + + concat_latent_image = vae.encode(image[:, :, :, :3]) + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, negative, out_latent) + + +class WanFunControlToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "start_image": ("IMAGE", ), + "control_video": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, control_video=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) + concat_latent = concat_latent.repeat(1, 2, 1, 1, 1) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(start_image[:, :, :, :3]) + concat_latent[:,16:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + if control_video is not None: + control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(control_video[:, :, :, :3]) + concat_latent[:,:16,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, negative, out_latent) + +class WanFirstLastFrameToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"clip_vision_start_image": ("CLIP_VISION_OUTPUT", ), + "clip_vision_end_image": ("CLIP_VISION_OUTPUT", ), + "start_image": ("IMAGE", ), + "end_image": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_start_image=None, clip_vision_end_image=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + if end_image is not None: + end_image = comfy.utils.common_upscale(end_image[-length:].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + image = torch.ones((length, height, width, 3)) * 0.5 + mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) + + if start_image is not None: + image[:start_image.shape[0]] = start_image + mask[:, :, :start_image.shape[0] + 3] = 0.0 + + if end_image is not None: + image[-end_image.shape[0]:] = end_image + mask[:, :, -end_image.shape[0]:] = 0.0 + + concat_latent_image = vae.encode(image[:, :, :, :3]) + mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + if clip_vision_start_image is not None: + clip_vision_output = clip_vision_start_image + + if clip_vision_end_image is not None: + if clip_vision_output is not None: + states = torch.cat([clip_vision_output.penultimate_hidden_states, clip_vision_end_image.penultimate_hidden_states], dim=-2) + clip_vision_output = comfy.clip_vision.Output() + clip_vision_output.penultimate_hidden_states = states + else: + clip_vision_output = clip_vision_end_image + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, negative, out_latent) + + +class WanFunInpaintToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "start_image": ("IMAGE", ), + "end_image": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_output=None): + flfv = WanFirstLastFrameToVideo() + return flfv.encode(positive, negative, vae, width, height, length, batch_size, start_image=start_image, end_image=end_image, clip_vision_start_image=clip_vision_output) + + +class WanVaceToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + }, + "optional": {"control_video": ("IMAGE", ), + "control_masks": ("MASK", ), + "reference_image": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT", "INT") + RETURN_NAMES = ("positive", "negative", "latent", "trim_latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + EXPERIMENTAL = True + + def encode(self, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None): + latent_length = ((length - 1) // 4) + 1 + if control_video is not None: + control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + if control_video.shape[0] < length: + control_video = torch.nn.functional.pad(control_video, (0, 0, 0, 0, 0, 0, 0, length - control_video.shape[0]), value=0.5) + else: + control_video = torch.ones((length, height, width, 3)) * 0.5 + + if reference_image is not None: + reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + reference_image = vae.encode(reference_image[:, :, :, :3]) + reference_image = torch.cat([reference_image, comfy.latent_formats.Wan21().process_out(torch.zeros_like(reference_image))], dim=1) + + if control_masks is None: + mask = torch.ones((length, height, width, 1)) + else: + mask = control_masks + if mask.ndim == 3: + mask = mask.unsqueeze(1) + mask = comfy.utils.common_upscale(mask[:length], width, height, "bilinear", "center").movedim(1, -1) + if mask.shape[0] < length: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, 0, 0, length - mask.shape[0]), value=1.0) + + control_video = control_video - 0.5 + inactive = (control_video * (1 - mask)) + 0.5 + reactive = (control_video * mask) + 0.5 + + inactive = vae.encode(inactive[:, :, :, :3]) + reactive = vae.encode(reactive[:, :, :, :3]) + control_video_latent = torch.cat((inactive, reactive), dim=1) + if reference_image is not None: + control_video_latent = torch.cat((reference_image, control_video_latent), dim=2) + + vae_stride = 8 + height_mask = height // vae_stride + width_mask = width // vae_stride + mask = mask.view(length, height_mask, vae_stride, width_mask, vae_stride) + mask = mask.permute(2, 4, 0, 1, 3) + mask = mask.reshape(vae_stride * vae_stride, length, height_mask, width_mask) + mask = torch.nn.functional.interpolate(mask.unsqueeze(0), size=(latent_length, height_mask, width_mask), mode='nearest-exact').squeeze(0) + + trim_latent = 0 + if reference_image is not None: + mask_pad = torch.zeros_like(mask[:, :reference_image.shape[2], :, :]) + mask = torch.cat((mask_pad, mask), dim=1) + latent_length += reference_image.shape[2] + trim_latent = reference_image.shape[2] + + mask = mask.unsqueeze(0) + + positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) + + latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + out_latent = {} + out_latent["samples"] = latent + return (positive, negative, out_latent, trim_latent) + +class TrimVideoLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "trim_amount": ("INT", {"default": 0, "min": 0, "max": 99999}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/video" + + EXPERIMENTAL = True + + def op(self, samples, trim_amount): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1[:, :, trim_amount:] + return (samples_out,) + +class WanCameraImageToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "start_image": ("IMAGE", ), + "camera_conditions": ("WAN_CAMERA_EMBEDDING", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(start_image[:, :, :, :3]) + concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent}) + + if camera_conditions is not None: + positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions}) + negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, negative, out_latent) + +class WanPhantomSubjectToVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), + "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": {"images": ("IMAGE", ), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, positive, negative, vae, width, height, length, batch_size, images): + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + cond2 = negative + if images is not None: + images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + latent_images = [] + for i in images: + latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])] + concat_latent_image = torch.cat(latent_images, dim=2) + + positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image}) + cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image}) + negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))}) + + out_latent = {} + out_latent["samples"] = latent + return (positive, cond2, negative, out_latent) + +NODE_CLASS_MAPPINGS = { + "WanImageToVideo": WanImageToVideo, + "WanFunControlToVideo": WanFunControlToVideo, + "WanFunInpaintToVideo": WanFunInpaintToVideo, + "WanFirstLastFrameToVideo": WanFirstLastFrameToVideo, + "WanVaceToVideo": WanVaceToVideo, + "TrimVideoLatent": TrimVideoLatent, + "WanCameraImageToVideo": WanCameraImageToVideo, + "WanPhantomSubjectToVideo": WanPhantomSubjectToVideo, +} diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py new file mode 100644 index 0000000000000000000000000000000000000000..5bf80b4c6e055f70ed277ec0a9bbbeb180151a3d --- /dev/null +++ b/comfy_extras/nodes_webcam.py @@ -0,0 +1,37 @@ +import nodes +import folder_paths + +MAX_RESOLUTION = nodes.MAX_RESOLUTION + + +class WebcamCapture(nodes.LoadImage): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("WEBCAM", {}), + "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "capture_on_queue": ("BOOLEAN", {"default": True}), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_capture" + + CATEGORY = "image" + + def load_capture(self, image, **kwargs): + return super().load_image(folder_paths.get_annotated_filepath(image)) + + @classmethod + def IS_CHANGED(cls, image, width, height, capture_on_queue): + return super().IS_CHANGED(image) + + +NODE_CLASS_MAPPINGS = { + "WebcamCapture": WebcamCapture, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "WebcamCapture": "Webcam Capture", +} diff --git a/comfyui_version.py b/comfyui_version.py new file mode 100644 index 0000000000000000000000000000000000000000..c98c90499a618c43e10d8b6ee1c4ed3a1ceb03ea --- /dev/null +++ b/comfyui_version.py @@ -0,0 +1,3 @@ +# This file is automatically generated by the build process when version is +# updated in pyproject.toml. +__version__ = "0.3.43" diff --git a/cuda_malloc.py b/cuda_malloc.py new file mode 100644 index 0000000000000000000000000000000000000000..eb2857c5fe2db47c8e86fa64d3c53dcd7574408f --- /dev/null +++ b/cuda_malloc.py @@ -0,0 +1,90 @@ +import os +import importlib.util +from comfy.cli_args import args +import subprocess + +#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import. +def get_gpu_names(): + if os.name == 'nt': + import ctypes + + # Define necessary C structures and types + class DISPLAY_DEVICEA(ctypes.Structure): + _fields_ = [ + ('cb', ctypes.c_ulong), + ('DeviceName', ctypes.c_char * 32), + ('DeviceString', ctypes.c_char * 128), + ('StateFlags', ctypes.c_ulong), + ('DeviceID', ctypes.c_char * 128), + ('DeviceKey', ctypes.c_char * 128) + ] + + # Load user32.dll + user32 = ctypes.windll.user32 + + # Call EnumDisplayDevicesA + def enum_display_devices(): + device_info = DISPLAY_DEVICEA() + device_info.cb = ctypes.sizeof(device_info) + device_index = 0 + gpu_names = set() + + while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0): + device_index += 1 + gpu_names.add(device_info.DeviceString.decode('utf-8')) + return gpu_names + return enum_display_devices() + else: + gpu_names = set() + out = subprocess.check_output(['nvidia-smi', '-L']) + for l in out.split(b'\n'): + if len(l) > 0: + gpu_names.add(l.decode('utf-8').split(' (UUID')[0]) + return gpu_names + +blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M", + "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620", + "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000", + "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000", + "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M", + "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60" + } + +def cuda_malloc_supported(): + try: + names = get_gpu_names() + except: + names = set() + for x in names: + if "NVIDIA" in x: + for b in blacklist: + if b in x: + return False + return True + + +if not args.cuda_malloc: + try: + version = "" + torch_spec = importlib.util.find_spec("torch") + for folder in torch_spec.submodule_search_locations: + ver_file = os.path.join(folder, "version.py") + if os.path.isfile(ver_file): + spec = importlib.util.spec_from_file_location("torch_version_import", ver_file) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + version = module.__version__ + if int(version[0]) >= 2: #enable by default for torch version 2.0 and up + args.cuda_malloc = cuda_malloc_supported() + except: + pass + + +if args.cuda_malloc and not args.disable_cuda_malloc: + env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None) + if env_var is None: + env_var = "backend:cudaMallocAsync" + else: + env_var += ",backend:cudaMallocAsync" + + os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var diff --git a/custom_nodes/ComfyUI-Custom-Scripts/.gitignore b/custom_nodes/ComfyUI-Custom-Scripts/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..8d303a4effdf5bf2a332042b2cb31a77589ab333 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/.gitignore @@ -0,0 +1,5 @@ +__pycache__ +pysssss.json +user/autocomplete.txt +web/js/assets/favicon.user.ico +web/js/assets/favicon-active.user.ico \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Custom-Scripts/LICENSE b/custom_nodes/ComfyUI-Custom-Scripts/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..acf31806a2507192169eca2137ea9ceb7685f51c --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pythongosssss + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Custom-Scripts/README.md b/custom_nodes/ComfyUI-Custom-Scripts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..690bfe1c46018bdd63385abf77a58d226388623f --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/README.md @@ -0,0 +1,160 @@ +# ComfyUI-Custom-Scripts + +### ⚠️ While these extensions work for the most part, i'm very busy at the moment and so unable to keep on top of everything here, thanks for your patience! + +# Installation + +1. Clone the repository: +`git clone https://github.com/pythongosssss/ComfyUI-Custom-Scripts.git` +to your ComfyUI `custom_nodes` directory + + The script will then automatically install all custom scripts and nodes. + It will attempt to use symlinks and junctions to prevent having to copy files and keep them up to date. + +- For uninstallation: + - Delete the cloned repo in `custom_nodes` + - Ensure `web/extensions/pysssss/CustomScripts` has also been removed + +# Update +1. Navigate to the cloned repo e.g. `custom_nodes/ComfyUI-Custom-Scripts` +2. `git pull` + +# Features + +## Autocomplete +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b5971135-414f-4f4e-a6cf-2650dc01085f) +Provides embedding and custom word autocomplete. You can view embedding details by clicking on the info icon on the list. +Define your list of custom words via the settings. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/160ef61c-7d7e-49d0-b60f-5a1501b74c9d) +You can quickly default to danbooru tags using the Load button, or load/manage other custom word lists. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/cc180b35-5f45-442f-9285-3ddf3fa320d0) + +## Auto Arrange Graph +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/04b06081-ca6f-4c0f-8584-d0a157c36747) +Adds a menu option to auto arrange the graph in order of execution, this makes very wide graphs! + +## Always Snap to Grid +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/66f36d1f-e579-4959-9880-9a9624922e3a) +Adds a setting to make moving nodes always snap to grid. + +## [Testing] "Better" Loader Lists +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/664caa71-f25f-4a96-a04a-1466d6b2b8b4) +Adds custom Lora and Checkpoint loader nodes, these have the ability to show preview images, just place a png or jpg next to the file and it'll display in the list on hover (e.g. sdxl.safetensors and sdxl.png). +Optionally enable subfolders via the settings: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/e15b5e83-4f9d-4d57-8324-742bedf75439) +Adds an "examples" widget to load sample prompts, triggerwords, etc: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ad1751e4-4c85-42e7-9490-e94fb1cbc8e7) +These should be stored in a folder matching the name of the model, e.g. if it is `loras/add_detail.safetensors` put your files in as `loras/add_detail/*.txt` +To quickly save a generated image as the preview to use for the model, you can right click on an image on a node, and select Save as Preview and choose the model to save the preview for: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9fa8e9db-27b3-45cb-85c2-0860a238fd3a) + +## Checkpoint/LoRA/Embedding Info +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/6b67bf40-ee17-4fa6-a0c1-7947066bafc2) +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/32405df6-b367-404f-a5df-2d4347089a9e) +Adds "View Info" menu option to view details about the selected LoRA or Checkpoint. To view embedding details, click the info button when using embedding autocomplete. + +## Constrain Image +Adds a node for resizing an image to a max & min size optionally cropping if required. + +## Custom Colors +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/fa7883f3-f81c-49f6-9ab6-9526e4debab6) +Adds a custom color picker to nodes & groups + +## Favicon Status +![image](https://user-images.githubusercontent.com/125205205/230171227-31f061a6-6324-4976-bed9-723a87500cf3.png) +![image](https://user-images.githubusercontent.com/125205205/230171445-c7202a45-b511-4d69-87fa-945ad44c063f.png) +Adds a favicon and title to the window, favicon changes color while generating and the window title includes the number of prompts in the queue + +## Image Feed +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/caea0d48-85b9-4ca9-9771-5c795db35fbc) +Adds a panel showing images that have been generated in the current session, you can control the direction that images are added and the position of the panel via the ComfyUI settings screen and the size of the panel and the images via the sliders at the top of the panel. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ca093d38-41a3-4647-9223-5bd0b9ee4f1e) + +## KSampler (Advanced) denoise helper +Provides a simple method to set custom denoise on the advanced sampler +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/42946bd8-0078-4c7a-bfe9-7adb1382b5e2) +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7cfccb22-f155-4848-934b-a2b2a6efe16f) + +## Math Expression +Allows for evaluating complex expressions using values from the graph. You can input `INT`, `FLOAT`, `IMAGE` and `LATENT` values. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/1593edde-67b8-45d8-88cb-e75f52dba039) +Other nodes values can be referenced via the `Node name for S&R` via the `Properties` menu item on a node, or the node title. +Supported operators: `+ - * /` (basic ops) `//` (floor division) `**` (power) `^` (xor) `%` (mod) +Supported functions `floor(num, dp?)` `floor(num)` `ceil(num)` `randomint(min,max)` +If using a `LATENT` or `IMAGE` you can get the dimensions using `a.width` or `a.height` where `a` is the input name. + +## Node Finder +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/177d2b67-acbc-4ec3-ab31-7c295a98c194) +Adds a menu item for following/jumping to the executing node, and a menu to quickly go to a node of a specific type. + +## Preset Text +![image](https://user-images.githubusercontent.com/125205205/230173939-08459efc-785b-46da-93d1-b02f0300c6f4.png) +Adds a node that lets you save and use text presets (e.g. for your 'normal' negatives) + +## Quick Nodes +![image](https://user-images.githubusercontent.com/125205205/230174266-5232831a-a03b-4bf7-bc8b-c45466a0bc64.png) +Adds various menu items to some nodes for quickly setting up common parts of graphs + +## Play Sound +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9bcf9fb3-5898-4432-a974-fb1e17d3b7e8) +Plays a sound when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts. +You can customize the sound by replacing the mp3 file `ComfyUI/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/notify.mp3` + +## System Notification +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/30354775/993fd783-5cd6-4779-aa97-173bc06cc405) +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/30354775/e45227fb-5714-4f45-b96b-6601902ef6e2) + +Sends a system notification via the browser when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts. + +## [WIP] Repeater +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ec0dac25-14e4-4d44-b975-52193656709d) +Node allows you to either create a list of N repeats of the input node, or create N outputs from the input node. +You can optionally decide if you want to reuse the input node, or create a new instance each time (e.g. a Checkpoint Loader would want to be re-used, but a random number would want to be unique) +TODO: Type safety on the wildcard outputs to require match with input + +## Show Text +![image](https://user-images.githubusercontent.com/125205205/230174888-c004fd48-da78-4de9-81c2-93a866fcfcd1.png) +Takes input from a node that produces a string and displays it, useful for things like interrogator, prompt generators, etc. + +## Show Image on Menu +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b6ab58f2-583b-448c-bcfc-f93f5cdab0fc) +Shows the current generating image on the menu at the bottom, you can disable this via the settings menu. + +## String Function +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/01107137-8a93-4765-bae0-fcc110a09091) +Supports appending and replacing text +`tidy_tags` will add commas between parts when in `append` mode. +`replace` mode supports regex replace by using `/your regex here/` and you can reference capturing groups using `\number` e.g. `\1` + +## Touch Support +Provides basic support for touch screen devices, its not perfect but better than nothing + +## Widget Defaults +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/3d675032-2b19-4da8-a7d7-fa2d7c555daa) +Allows you to specify default values for widgets when adding new nodes, the values are configured via the settings menu +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b57a3d8-98d3-46e9-9b33-6645c0da41e7) + +## Workflows +Adds options to the menu for saving + loading workflows: +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b5a3012-4c59-47c6-8eea-85cf534403ea) + +## Workflow Images +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/06453fd2-c020-46ee-a7db-2b8bf5bcba7e) +Adds menu options for importing/exporting the graph as SVG and PNG showing a view of the nodes + +## (Testing) Reroute Primitive +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/8b870eef-d572-43f9-b394-cfa7abbd2f98) Provides a node that allows rerouting primitives. +The node can also be collapsed to a single point that you can drag around. +![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/a9bd0112-cf8f-44f3-af6d-f9a8fed152a7) +Warning: Don't use normal reroutes or primitives with these nodes, it isn't tested and this node replaces their functionality. + +
+
+ + +## WD14 Tagger +Moved to: https://github.com/pythongosssss/ComfyUI-WD14-Tagger + +## ~~Lock Nodes & Groups~~ +This is now a standard feature of ComfyUI +~~Adds a lock option to nodes & groups that prevents you from moving them until unlocked~~ diff --git a/custom_nodes/ComfyUI-Custom-Scripts/__init__.py b/custom_nodes/ComfyUI-Custom-Scripts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae43dd2090ce782074bf72f2b7f49ad28dff1a5c --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/__init__.py @@ -0,0 +1,25 @@ +import importlib.util +import glob +import os +import sys +from .pysssss import init, get_ext_dir + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +if init(): + py = get_ext_dir("py") + files = glob.glob(os.path.join(py, "*.py"), recursive=False) + for file in files: + name = os.path.splitext(file)[0] + spec = importlib.util.spec_from_file_location(name, file) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + spec.loader.exec_module(module) + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS) + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + +WEB_DIRECTORY = "./web" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/autocomplete.py b/custom_nodes/ComfyUI-Custom-Scripts/py/autocomplete.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac6a0524823d13849b6cbe02efedc7bffea8f38 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/autocomplete.py @@ -0,0 +1,29 @@ +from server import PromptServer +from aiohttp import web +import os +import folder_paths + +dir = os.path.abspath(os.path.join(__file__, "../../user")) +if not os.path.exists(dir): + os.mkdir(dir) +file = os.path.join(dir, "autocomplete.txt") + + +@PromptServer.instance.routes.get("/pysssss/autocomplete") +async def get_autocomplete(request): + if os.path.isfile(file): + return web.FileResponse(file) + return web.Response(status=404) + + +@PromptServer.instance.routes.post("/pysssss/autocomplete") +async def update_autocomplete(request): + with open(file, "w", encoding="utf-8") as f: + f.write(await request.text()) + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/pysssss/loras") +async def get_loras(request): + loras = folder_paths.get_filename_list("loras") + return web.json_response(list(map(lambda a: os.path.splitext(a)[0], loras))) diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/better_combos.py b/custom_nodes/ComfyUI-Custom-Scripts/py/better_combos.py new file mode 100644 index 0000000000000000000000000000000000000000..d31043b0afdb8943409f3d97adbdde2db9dc38ee --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/better_combos.py @@ -0,0 +1,172 @@ +import glob +import os +from nodes import LoraLoader, CheckpointLoaderSimple +import folder_paths +from server import PromptServer +from folder_paths import get_directory_by_type +from aiohttp import web +import shutil + + +@PromptServer.instance.routes.get("/pysssss/view/{name}") +async def view(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + image_path = folder_paths.get_full_path( + type, name) + if not image_path: + return web.Response(status=404) + + filename = os.path.basename(image_path) + return web.FileResponse(image_path, headers={"Content-Disposition": f"filename=\"{filename}\""}) + + +@PromptServer.instance.routes.post("/pysssss/save/{name}") +async def save_preview(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + body = await request.json() + + dir = get_directory_by_type(body.get("type", "output")) + subfolder = body.get("subfolder", "") + full_output_folder = os.path.join(dir, os.path.normpath(subfolder)) + + filepath = os.path.join(full_output_folder, body.get("filename", "")) + + if os.path.commonpath((dir, os.path.abspath(filepath))) != dir: + return web.Response(status=400) + + image_path = folder_paths.get_full_path(type, name) + image_path = os.path.splitext( + image_path)[0] + os.path.splitext(filepath)[1] + + shutil.copyfile(filepath, image_path) + + return web.json_response({ + "image": type + "/" + os.path.basename(image_path) + }) + + +@PromptServer.instance.routes.get("/pysssss/examples/{name}") +async def get_examples(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + file_path_no_ext = os.path.splitext(file_path)[0] + examples = [] + + if os.path.isdir(file_path_no_ext): + examples += sorted(map(lambda t: os.path.relpath(t, file_path_no_ext), + glob.glob(file_path_no_ext + "/*.txt"))) + + if os.path.isfile(file_path_no_ext + ".txt"): + examples += ["notes"] + + return web.json_response(examples) + + +@PromptServer.instance.routes.post("/pysssss/examples/{name}") +async def save_example(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + body = await request.json() + example_name = body["name"] + example = body["example"] + + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + if not example_name.endswith(".txt"): + example_name += ".txt" + + file_path_no_ext = os.path.splitext(file_path)[0] + example_file = os.path.join(file_path_no_ext, example_name) + if not os.path.exists(file_path_no_ext): + os.mkdir(file_path_no_ext) + with open(example_file, 'w', encoding='utf8') as f: + f.write(example) + + return web.Response(status=201) + + +@PromptServer.instance.routes.get("/pysssss/images/{type}") +async def get_images(request): + type = request.match_info["type"] + names = folder_paths.get_filename_list(type) + + images = {} + for item_name in names: + file_name = os.path.splitext(item_name)[0] + file_path = folder_paths.get_full_path(type, item_name) + + if file_path is None: + continue + + file_path_no_ext = os.path.splitext(file_path)[0] + + for ext in ["png", "jpg", "jpeg", "preview.png", "preview.jpeg"]: + if os.path.isfile(file_path_no_ext + "." + ext): + images[item_name] = f"{type}/{file_name}.{ext}" + break + + return web.json_response(images) + + +class LoraLoaderWithImages(LoraLoader): + RETURN_TYPES = (*LoraLoader.RETURN_TYPES, "STRING",) + RETURN_NAMES = (*getattr(LoraLoader, "RETURN_NAMES", + LoraLoader.RETURN_TYPES), "example") + + @classmethod + def INPUT_TYPES(s): + types = super().INPUT_TYPES() + types["optional"] = {"prompt": ("STRING", {"hidden": True})} + return types + + def load_lora(self, **kwargs): + prompt = kwargs.pop("prompt", "") + return (*super().load_lora(**kwargs), prompt) + + +class CheckpointLoaderSimpleWithImages(CheckpointLoaderSimple): + RETURN_TYPES = (*CheckpointLoaderSimple.RETURN_TYPES, "STRING",) + RETURN_NAMES = (*getattr(CheckpointLoaderSimple, "RETURN_NAMES", + CheckpointLoaderSimple.RETURN_TYPES), "example") + + @classmethod + def INPUT_TYPES(s): + types = super().INPUT_TYPES() + types["optional"] = {"prompt": ("STRING", {"hidden": True})} + return types + + def load_checkpoint(self, **kwargs): + prompt = kwargs.pop("prompt", "") + return (*super().load_checkpoint(**kwargs), prompt) + + +NODE_CLASS_MAPPINGS = { + "LoraLoader|pysssss": LoraLoaderWithImages, + "CheckpointLoader|pysssss": CheckpointLoaderSimpleWithImages, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoraLoader|pysssss": "Lora Loader 🐍", + "CheckpointLoader|pysssss": "Checkpoint Loader 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/constrain_image.py b/custom_nodes/ComfyUI-Custom-Scripts/py/constrain_image.py new file mode 100644 index 0000000000000000000000000000000000000000..1c768893c5ed5c1cb1247bca77414dc8762d3fde --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/constrain_image.py @@ -0,0 +1,71 @@ +import torch +import numpy as np +from PIL import Image + +class ConstrainImage: + """ + A node that constrains an image to a maximum and minimum size while maintaining aspect ratio. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "max_width": ("INT", {"default": 1024, "min": 0}), + "max_height": ("INT", {"default": 1024, "min": 0}), + "min_width": ("INT", {"default": 0, "min": 0}), + "min_height": ("INT", {"default": 0, "min": 0}), + "crop_if_required": (["yes", "no"], {"default": "no"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "constrain_image" + CATEGORY = "image" + OUTPUT_IS_LIST = (True,) + + def constrain_image(self, images, max_width, max_height, min_width, min_height, crop_if_required): + crop_if_required = crop_if_required == "yes" + results = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB") + + current_width, current_height = img.size + aspect_ratio = current_width / current_height + + constrained_width = min(max(current_width, min_width), max_width) + constrained_height = min(max(current_height, min_height), max_height) + + if constrained_width / constrained_height > aspect_ratio: + constrained_width = max(int(constrained_height * aspect_ratio), min_width) + if crop_if_required: + constrained_height = int(current_height / (current_width / constrained_width)) + else: + constrained_height = max(int(constrained_width / aspect_ratio), min_height) + if crop_if_required: + constrained_width = int(current_width / (current_height / constrained_height)) + + resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS) + + if crop_if_required and (constrained_width > max_width or constrained_height > max_height): + left = max((constrained_width - max_width) // 2, 0) + top = max((constrained_height - max_height) // 2, 0) + right = min(constrained_width, max_width) + left + bottom = min(constrained_height, max_height) + top + resized_image = resized_image.crop((left, top, right, bottom)) + + resized_image = np.array(resized_image).astype(np.float32) / 255.0 + resized_image = torch.from_numpy(resized_image)[None,] + results.append(resized_image) + + return (results,) + +NODE_CLASS_MAPPINGS = { + "ConstrainImage|pysssss": ConstrainImage, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ConstrainImage|pysssss": "Constrain Image 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/constrain_image_for_video.py b/custom_nodes/ComfyUI-Custom-Scripts/py/constrain_image_for_video.py new file mode 100644 index 0000000000000000000000000000000000000000..91c0c502b8d8f6202d96a26b045e393b4552fc8e --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/constrain_image_for_video.py @@ -0,0 +1,72 @@ +import torch +import numpy as np +from PIL import Image + +class ConstrainImageforVideo: + """ + A node that constrains an image to a maximum and minimum size while maintaining aspect ratio. + """ + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "max_width": ("INT", {"default": 1024, "min": 0}), + "max_height": ("INT", {"default": 1024, "min": 0}), + "min_width": ("INT", {"default": 0, "min": 0}), + "min_height": ("INT", {"default": 0, "min": 0}), + "crop_if_required": (["yes", "no"], {"default": "no"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("IMAGE",) + FUNCTION = "constrain_image_for_video" + CATEGORY = "image" + + def constrain_image_for_video(self, images, max_width, max_height, min_width, min_height, crop_if_required): + crop_if_required = crop_if_required == "yes" + results = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)).convert("RGB") + + current_width, current_height = img.size + aspect_ratio = current_width / current_height + + constrained_width = max(min(current_width, min_width), max_width) + constrained_height = max(min(current_height, min_height), max_height) + + if constrained_width / constrained_height > aspect_ratio: + constrained_width = max(int(constrained_height * aspect_ratio), min_width) + if crop_if_required: + constrained_height = int(current_height / (current_width / constrained_width)) + else: + constrained_height = max(int(constrained_width / aspect_ratio), min_height) + if crop_if_required: + constrained_width = int(current_width / (current_height / constrained_height)) + + resized_image = img.resize((constrained_width, constrained_height), Image.LANCZOS) + + if crop_if_required and (constrained_width > max_width or constrained_height > max_height): + left = max((constrained_width - max_width) // 2, 0) + top = max((constrained_height - max_height) // 2, 0) + right = min(constrained_width, max_width) + left + bottom = min(constrained_height, max_height) + top + resized_image = resized_image.crop((left, top, right, bottom)) + + resized_image = np.array(resized_image).astype(np.float32) / 255.0 + resized_image = torch.from_numpy(resized_image)[None,] + results.append(resized_image) + all_images = torch.cat(results, dim=0) + + return (all_images, all_images.size(0),) + +NODE_CLASS_MAPPINGS = { + "ConstrainImageforVideo|pysssss": ConstrainImageforVideo, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ConstrainImageforVideo|pysssss": "Constrain Image for Video 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/math_expression.py b/custom_nodes/ComfyUI-Custom-Scripts/py/math_expression.py new file mode 100644 index 0000000000000000000000000000000000000000..4a41655b6876a077338040b0099c4d4e8c9dbcd2 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/math_expression.py @@ -0,0 +1,252 @@ +import ast +import math +import random +import operator as op + +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + +operators = { + ast.Add: op.add, + ast.Sub: op.sub, + ast.Mult: op.mul, + ast.Div: op.truediv, + ast.FloorDiv: op.floordiv, + ast.Pow: op.pow, + ast.BitXor: op.xor, + ast.USub: op.neg, + ast.Mod: op.mod, + ast.BitAnd: op.and_, + ast.BitOr: op.or_, + ast.Invert: op.invert, + ast.And: lambda a, b: 1 if a and b else 0, + ast.Or: lambda a, b: 1 if a or b else 0, + ast.Not: lambda a: 0 if a else 1, + ast.RShift: op.rshift, + ast.LShift: op.lshift +} + +# TODO: restructure args to provide more info, generate hint based on args to save duplication +functions = { + "round": { + "args": (1, 2), + "call": lambda a, b = None: round(a, b), + "hint": "number, dp? = 0" + }, + "ceil": { + "args": (1, 1), + "call": lambda a: math.ceil(a), + "hint": "number" + }, + "floor": { + "args": (1, 1), + "call": lambda a: math.floor(a), + "hint": "number" + }, + "min": { + "args": (2, None), + "call": lambda *args: min(*args), + "hint": "...numbers" + }, + "max": { + "args": (2, None), + "call": lambda *args: max(*args), + "hint": "...numbers" + }, + "randomint": { + "args": (2, 2), + "call": lambda a, b: random.randint(a, b), + "hint": "min, max" + }, + "randomchoice": { + "args": (2, None), + "call": lambda *args: random.choice(args), + "hint": "...numbers" + }, + "sqrt": { + "args": (1, 1), + "call": lambda a: math.sqrt(a), + "hint": "number" + }, + "int": { + "args": (1, 1), + "call": lambda a = None: int(a), + "hint": "number" + }, + "iif": { + "args": (3, 3), + "call": lambda a, b, c = None: b if a else c, + "hint": "value, truepart, falsepart" + }, +} + +autocompleteWords = list({ + "text": x, + "value": f"{x}()", + "showValue": False, + "hint": f"{functions[x]['hint']}", + "caretOffset": -1 +} for x in functions.keys()) + + +class MathExpression: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "expression": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": { + "words": autocompleteWords, + "separator": "" + }}), + }, + "optional": { + "a": (any, ), + "b": (any,), + "c": (any, ), + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT"}, + } + + RETURN_TYPES = ("INT", "FLOAT", ) + FUNCTION = "evaluate" + CATEGORY = "utils" + OUTPUT_NODE = True + + @classmethod + def IS_CHANGED(s, expression, **kwargs): + if "random" in expression: + return float("nan") + return expression + + def get_widget_value(self, extra_pnginfo, prompt, node_name, widget_name): + workflow = extra_pnginfo["workflow"] if "workflow" in extra_pnginfo else { "nodes": [] } + node_id = None + for node in workflow["nodes"]: + name = node["type"] + if "properties" in node: + if "Node name for S&R" in node["properties"]: + name = node["properties"]["Node name for S&R"] + if name == node_name: + node_id = node["id"] + break + if "title" in node: + name = node["title"] + if name == node_name: + node_id = node["id"] + break + if node_id is not None: + values = prompt[str(node_id)] + if "inputs" in values: + if widget_name in values["inputs"]: + value = values["inputs"][widget_name] + if isinstance(value, list): + raise ValueError("Converted widgets are not supported via named reference, use the inputs instead.") + return value + raise NameError(f"Widget not found: {node_name}.{widget_name}") + raise NameError(f"Node not found: {node_name}.{widget_name}") + + def get_size(self, target, property): + if isinstance(target, dict) and "samples" in target: + # Latent + if property == "width": + return target["samples"].shape[3] * 8 + return target["samples"].shape[2] * 8 + else: + # Image + if property == "width": + return target.shape[2] + return target.shape[1] + + def evaluate(self, expression, prompt, extra_pnginfo={}, a=None, b=None, c=None): + expression = expression.replace('\n', ' ').replace('\r', '') + node = ast.parse(expression, mode='eval').body + + lookup = {"a": a, "b": b, "c": c} + + def eval_op(node, l, r): + l = eval_expr(l) + r = eval_expr(r) + l = l if isinstance(l, int) else float(l) + r = r if isinstance(r, int) else float(r) + return operators[type(node.op)](l, r) + + def eval_expr(node): + if isinstance(node, ast.Constant) or isinstance(node, ast.Num): + return node.n + elif isinstance(node, ast.BinOp): + return eval_op(node, node.left, node.right) + elif isinstance(node, ast.BoolOp): + return eval_op(node, node.values[0], node.values[1]) + elif isinstance(node, ast.UnaryOp): + return operators[type(node.op)](eval_expr(node.operand)) + elif isinstance(node, ast.Attribute): + if node.value.id in lookup: + if node.attr == "width" or node.attr == "height": + return self.get_size(lookup[node.value.id], node.attr) + + return self.get_widget_value(extra_pnginfo, prompt, node.value.id, node.attr) + elif isinstance(node, ast.Name): + if node.id in lookup: + val = lookup[node.id] + if isinstance(val, (int, float, complex)): + return val + else: + raise TypeError( + f"Compex types (LATENT/IMAGE) need to reference their width/height, e.g. {node.id}.width") + raise NameError(f"Name not found: {node.id}") + elif isinstance(node, ast.Call): + if node.func.id in functions: + fn = functions[node.func.id] + l = len(node.args) + if l < fn["args"][0] or (fn["args"][1] is not None and l > fn["args"][1]): + if fn["args"][1] is None: + toErr = " or more" + else: + toErr = f" to {fn['args'][1]}" + raise SyntaxError( + f"Invalid function call: {node.func.id} requires {fn['args'][0]}{toErr} arguments") + args = [] + for arg in node.args: + args.append(eval_expr(arg)) + return fn["call"](*args) + raise NameError(f"Invalid function call: {node.func.id}") + elif isinstance(node, ast.Compare): + l = eval_expr(node.left) + r = eval_expr(node.comparators[0]) + if isinstance(node.ops[0], ast.Eq): + return 1 if l == r else 0 + if isinstance(node.ops[0], ast.NotEq): + return 1 if l != r else 0 + if isinstance(node.ops[0], ast.Gt): + return 1 if l > r else 0 + if isinstance(node.ops[0], ast.GtE): + return 1 if l >= r else 0 + if isinstance(node.ops[0], ast.Lt): + return 1 if l < r else 0 + if isinstance(node.ops[0], ast.LtE): + return 1 if l <= r else 0 + raise NotImplementedError( + "Operator " + node.ops[0].__class__.__name__ + " not supported.") + else: + raise TypeError(node) + + r = eval_expr(node) + return {"ui": {"value": [r]}, "result": (int(r), float(r),)} + + +NODE_CLASS_MAPPINGS = { + "MathExpression|pysssss": MathExpression, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "MathExpression|pysssss": "Math Expression 🐍", +} + diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/model_info.py b/custom_nodes/ComfyUI-Custom-Scripts/py/model_info.py new file mode 100644 index 0000000000000000000000000000000000000000..b360bafb35fdfc25dd94902076fab6baa39ee23c --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/model_info.py @@ -0,0 +1,115 @@ +import hashlib +import json +from aiohttp import web +from server import PromptServer +import folder_paths +import os + + +def get_metadata(filepath): + with open(filepath, "rb") as file: + # https://github.com/huggingface/safetensors#format + # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header + header_size = int.from_bytes(file.read(8), "little", signed=False) + + if header_size <= 0: + raise BufferError("Invalid header size") + + header = file.read(header_size) + if header_size <= 0: + raise BufferError("Invalid header") + + header_json = json.loads(header) + return header_json["__metadata__"] if "__metadata__" in header_json else None + + +@PromptServer.instance.routes.post("/pysssss/metadata/notes/{name}") +async def save_notes(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = None + if type == "embeddings" or type == "loras": + name = name.lower() + files = folder_paths.get_filename_list(type) + for f in files: + lower_f = f.lower() + if lower_f == name: + file_path = folder_paths.get_full_path(type, f) + else: + n = os.path.splitext(f)[0].lower() + if n == name: + file_path = folder_paths.get_full_path(type, f) + + if file_path is not None: + break + else: + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + file_no_ext = os.path.splitext(file_path)[0] + info_file = file_no_ext + ".txt" + with open(info_file, "w") as f: + f.write(await request.text()) + + return web.Response(status=200) + + +@PromptServer.instance.routes.get("/pysssss/metadata/{name}") +async def load_metadata(request): + name = request.match_info["name"] + pos = name.index("/") + type = name[0:pos] + name = name[pos+1:] + + file_path = None + if type == "embeddings" or type == "loras": + name = name.lower() + files = folder_paths.get_filename_list(type) + for f in files: + lower_f = f.lower() + if lower_f == name: + file_path = folder_paths.get_full_path(type, f) + else: + n = os.path.splitext(f)[0].lower() + if n == name: + file_path = folder_paths.get_full_path(type, f) + + if file_path is not None: + break + else: + file_path = folder_paths.get_full_path( + type, name) + if not file_path: + return web.Response(status=404) + + try: + meta = get_metadata(file_path) + except: + meta = None + + if meta is None: + meta = {} + + file_no_ext = os.path.splitext(file_path)[0] + + info_file = file_no_ext + ".txt" + if os.path.isfile(info_file): + with open(info_file, "r") as f: + meta["pysssss.notes"] = f.read() + + hash_file = file_no_ext + ".sha256" + if os.path.isfile(hash_file): + with open(hash_file, "rt") as f: + meta["pysssss.sha256"] = f.read() + else: + with open(file_path, "rb") as f: + meta["pysssss.sha256"] = hashlib.sha256(f.read()).hexdigest() + with open(hash_file, "wt") as f: + f.write(meta["pysssss.sha256"]) + + return web.json_response(meta) diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/play_sound.py b/custom_nodes/ComfyUI-Custom-Scripts/py/play_sound.py new file mode 100644 index 0000000000000000000000000000000000000000..65f599d2680553a20031d81d1fdf161d73edefef --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/play_sound.py @@ -0,0 +1,42 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class PlaySound: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "any": (any, {}), + "mode": (["always", "on empty queue"], {}), + "volume": ("FLOAT", {"min": 0, "max": 1, "step": 0.1, "default": 0.5}), + "file": ("STRING", { "default": "notify.mp3" }) + }} + + FUNCTION = "nop" + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True,) + OUTPUT_NODE = True + RETURN_TYPES = (any,) + + CATEGORY = "utils" + + def IS_CHANGED(self, **kwargs): + return float("NaN") + + def nop(self, any, mode, volume, file): + return {"ui": {"a": []}, "result": (any,)} + + +NODE_CLASS_MAPPINGS = { + "PlaySound|pysssss": PlaySound, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PlaySound|pysssss": "PlaySound 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/repeater.py b/custom_nodes/ComfyUI-Custom-Scripts/py/repeater.py new file mode 100644 index 0000000000000000000000000000000000000000..1d83c67e2132b9db708ef087ad788d22c58c80c2 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/repeater.py @@ -0,0 +1,46 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class Repeater: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "source": (any, {}), + "repeats": ("INT", {"min": 0, "max": 5000, "default": 2}), + "output": (["single", "multi"], {}), + "node_mode": (["reuse", "create"], {}), + }} + + RETURN_TYPES = (any,) + FUNCTION = "repeat" + OUTPUT_NODE = False + OUTPUT_IS_LIST = (True,) + + CATEGORY = "utils" + + def repeat(self, repeats, output, node_mode, **kwargs): + if output == "multi": + # Multi outputs are split to indiviual nodes on the frontend when serializing + return ([kwargs["source"]],) + elif node_mode == "reuse": + # When reusing we have a single input node, repeat that N times + return ([kwargs["source"]] * repeats,) + else: + # When creating new nodes, they'll be added dynamically when the graph is serialized + return ((list(kwargs.values())),) + + +NODE_CLASS_MAPPINGS = { + "Repeater|pysssss": Repeater, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "Repeater|pysssss": "Repeater 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/reroute_primitive.py b/custom_nodes/ComfyUI-Custom-Scripts/py/reroute_primitive.py new file mode 100644 index 0000000000000000000000000000000000000000..c22a7d1ef00eddfbde6185e702b6c3462521ccda --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/reroute_primitive.py @@ -0,0 +1,59 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class ReroutePrimitive: + @classmethod + def INPUT_TYPES(cls): + return { + "required": {"value": (any, )}, + } + + @classmethod + def VALIDATE_INPUTS(s, **kwargs): + return True + + RETURN_TYPES = (any,) + FUNCTION = "route" + CATEGORY = "__hidden__" + + def route(self, value): + return (value,) + + +class MultiPrimitive: + @classmethod + def INPUT_TYPES(cls): + return { + "required": {}, + "optional": {"value": (any, )}, + } + + @classmethod + def VALIDATE_INPUTS(s, **kwargs): + return True + + RETURN_TYPES = (any,) + FUNCTION = "listify" + CATEGORY = "utils" + OUTPUT_IS_LIST = (True,) + + def listify(self, **kwargs): + return (list(kwargs.values()),) + + +NODE_CLASS_MAPPINGS = { + "ReroutePrimitive|pysssss": ReroutePrimitive, + # "MultiPrimitive|pysssss": MultiPrimitive, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ReroutePrimitive|pysssss": "Reroute Primitive 🐍", + # "MultiPrimitive|pysssss": "Multi Primitive 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/show_text.py b/custom_nodes/ComfyUI-Custom-Scripts/py/show_text.py new file mode 100644 index 0000000000000000000000000000000000000000..5a013a5bc7a82e29f38f35ff85dab827ba42a254 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/show_text.py @@ -0,0 +1,49 @@ +class ShowText: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "text": ("STRING", {"forceInput": True}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + "extra_pnginfo": "EXTRA_PNGINFO", + }, + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("STRING",) + FUNCTION = "notify" + OUTPUT_NODE = True + OUTPUT_IS_LIST = (True,) + + CATEGORY = "utils" + + def notify(self, text, unique_id=None, extra_pnginfo=None): + if unique_id is not None and extra_pnginfo is not None: + if not isinstance(extra_pnginfo, list): + print("Error: extra_pnginfo is not a list") + elif ( + not isinstance(extra_pnginfo[0], dict) + or "workflow" not in extra_pnginfo[0] + ): + print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key") + else: + workflow = extra_pnginfo[0]["workflow"] + node = next( + (x for x in workflow["nodes"] if str(x["id"]) == str(unique_id[0])), + None, + ) + if node: + node["widgets_values"] = [text] + + return {"ui": {"text": text}, "result": (text,)} + + +NODE_CLASS_MAPPINGS = { + "ShowText|pysssss": ShowText, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ShowText|pysssss": "Show Text 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/string_function.py b/custom_nodes/ComfyUI-Custom-Scripts/py/string_function.py new file mode 100644 index 0000000000000000000000000000000000000000..f502ba31f52720f019c9f1d155d17a9cb9e335f9 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/string_function.py @@ -0,0 +1,49 @@ +import re + +class StringFunction: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "action": (["append", "replace"], {}), + "tidy_tags": (["yes", "no"], {}), + }, + "optional": { + "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}), + "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}) + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "exec" + CATEGORY = "utils" + OUTPUT_NODE = True + + def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""): + tidy_tags = tidy_tags == "yes" + out = "" + if action == "append": + out = (", " if tidy_tags else "").join(filter(None, [text_a, text_b, text_c])) + else: + if text_c is None: + text_c = "" + if text_b.startswith("/") and text_b.endswith("/"): + regex = text_b[1:-1] + out = re.sub(regex, text_c, text_a) + else: + out = text_a.replace(text_b, text_c) + if tidy_tags: + out = re.sub(r"\s{2,}", " ", out) + out = out.replace(" ,", ",") + out = re.sub(r",{2,}", ",", out) + out = out.strip() + return {"ui": {"text": (out,)}, "result": (out,)} + +NODE_CLASS_MAPPINGS = { + "StringFunction|pysssss": StringFunction, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "StringFunction|pysssss": "String Function 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/system_notification.py b/custom_nodes/ComfyUI-Custom-Scripts/py/system_notification.py new file mode 100644 index 0000000000000000000000000000000000000000..d5a52c14773ff86bbb3b29c6405a05238d8b46eb --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/system_notification.py @@ -0,0 +1,41 @@ +# Hack: string type that is always equal in not equal comparisons +class AnyType(str): + def __ne__(self, __value: object) -> bool: + return False + + +# Our any instance wants to be a wildcard string +any = AnyType("*") + + +class SystemNotification: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "message": ("STRING", {"default": "Your notification has triggered."}), + "any": (any, {}), + "mode": (["always", "on empty queue"], {}), + }} + + FUNCTION = "nop" + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True,) + OUTPUT_NODE = True + RETURN_TYPES = (any,) + + CATEGORY = "utils" + + def IS_CHANGED(self, **kwargs): + return float("NaN") + + def nop(self, any, message, mode): + return {"ui": {"message": message, "mode": mode}, "result": (any,)} + + +NODE_CLASS_MAPPINGS = { + "SystemNotification|pysssss": SystemNotification, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SystemNotification|pysssss": "SystemNotification 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/text_files.py b/custom_nodes/ComfyUI-Custom-Scripts/py/text_files.py new file mode 100644 index 0000000000000000000000000000000000000000..253de9b6bde24b8daa15df627516801faf68e9a0 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/text_files.py @@ -0,0 +1,200 @@ +import os +import folder_paths +import json +from server import PromptServer +import glob +from aiohttp import web + + +def get_allowed_dirs(): + dir = os.path.abspath(os.path.join(__file__, "../../user")) + file = os.path.join(dir, "text_file_dirs.json") + with open(file, "r") as f: + return json.loads(f.read()) + + +def get_valid_dirs(): + return get_allowed_dirs().keys() + + +def get_dir_from_name(name): + dirs = get_allowed_dirs() + if name not in dirs: + raise KeyError(name + " dir not found") + + path = dirs[name] + path = path.replace("$input", folder_paths.get_input_directory()) + path = path.replace("$output", folder_paths.get_output_directory()) + path = path.replace("$temp", folder_paths.get_temp_directory()) + return path + + +def is_child_dir(parent_path, child_path): + parent_path = os.path.abspath(parent_path) + child_path = os.path.abspath(child_path) + return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path]) + + +def get_real_path(dir): + dir = dir.replace("/**/", "/") + dir = os.path.abspath(dir) + dir = os.path.split(dir)[0] + return dir + + +@PromptServer.instance.routes.get("/pysssss/text-file/{name}") +async def get_files(request): + name = request.match_info["name"] + dir = get_dir_from_name(name) + recursive = "/**/" in dir + # Ugh cant use root_path on glob... lazy hack.. + pre = get_real_path(dir) + + files = list(map(lambda t: os.path.relpath(t, pre), + glob.glob(dir, recursive=recursive))) + + if len(files) == 0: + files = ["[none]"] + return web.json_response(files) + + +def get_file(root_dir, file): + if file == "[none]" or not file or not file.strip(): + raise ValueError("No file") + + root_dir = get_dir_from_name(root_dir) + root_dir = get_real_path(root_dir) + if not os.path.exists(root_dir): + os.mkdir(root_dir) + full_path = os.path.join(root_dir, file) + + if not is_child_dir(root_dir, full_path): + raise ReferenceError() + + return full_path + + +class TextFileNode: + RETURN_TYPES = ("STRING",) + CATEGORY = "utils" + + @classmethod + def VALIDATE_INPUTS(self, root_dir, file, **kwargs): + if file == "[none]" or not file or not file.strip(): + return True + get_file(root_dir, file) + return True + + def load_text(self, **kwargs): + self.file = get_file(kwargs["root_dir"], kwargs["file"]) + with open(self.file, "r") as f: + return (f.read(), ) + + +class LoadText(TextFileNode): + @classmethod + def IS_CHANGED(self, **kwargs): + return os.path.getmtime(self.file) + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "root_dir": (list(get_valid_dirs()), {}), + "file": (["[none]"], { + "pysssss.binding": [{ + "source": "root_dir", + "callback": [{ + "type": "set", + "target": "$this.disabled", + "value": True + }, { + "type": "fetch", + "url": "/pysssss/text-file/{$source.value}", + "then": [{ + "type": "set", + "target": "$this.options.values", + "value": "$result" + }, { + "type": "validate-combo" + }, { + "type": "set", + "target": "$this.disabled", + "value": False + }] + }], + }] + }) + }, + } + + FUNCTION = "load_text" + + +class SaveText(TextFileNode): + OUTPUT_NODE = True + + @classmethod + def IS_CHANGED(self, **kwargs): + return float("nan") + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "root_dir": (list(get_valid_dirs()), {}), + "file": ("STRING", {"default": "file.txt"}), + "append": (["append", "overwrite", "new only"], {}), + "insert": ("BOOLEAN", { + "default": True, "label_on": "new line", "label_off": "none", + "pysssss.binding": [{ + "source": "append", + "callback": [{ + "type": "if", + "condition": [{ + "left": "$source.value", + "op": "eq", + "right": '"append"' + }], + "true": [{ + "type": "set", + "target": "$this.disabled", + "value": False + }], + "false": [{ + "type": "set", + "target": "$this.disabled", + "value": True + }], + }] + }] + }), + "text": ("STRING", {"forceInput": True, "multiline": True}) + }, + } + + FUNCTION = "write_text" + + def write_text(self, **kwargs): + self.file = get_file(kwargs["root_dir"], kwargs["file"]) + if kwargs["append"] == "new only" and os.path.exists(self.file): + raise FileExistsError( + self.file + " already exists and 'new only' is selected.") + with open(self.file, "a+" if kwargs["append"] == "append" else "w") as f: + is_append = f.tell() != 0 + if is_append and kwargs["insert"]: + f.write("\n") + f.write(kwargs["text"]) + + return super().load_text(**kwargs) + + +NODE_CLASS_MAPPINGS = { + "LoadText|pysssss": LoadText, + "SaveText|pysssss": SaveText, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LoadText|pysssss": "Load Text 🐍", + "SaveText|pysssss": "Save Text 🐍", +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/py/workflows.py b/custom_nodes/ComfyUI-Custom-Scripts/py/workflows.py new file mode 100644 index 0000000000000000000000000000000000000000..233e8c84ce490a1fd2a51e603b9836d869dc0262 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/py/workflows.py @@ -0,0 +1,61 @@ +from server import PromptServer +from aiohttp import web +import os +import inspect +import json +import importlib +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) +import pysssss + +root_directory = os.path.dirname(inspect.getfile(PromptServer)) +workflows_directory = os.path.join(root_directory, "pysssss-workflows") +workflows_directory = pysssss.get_config_value( + "workflows.directory", workflows_directory) +if not os.path.isabs(workflows_directory): + workflows_directory = os.path.abspath(os.path.join(root_directory, workflows_directory)) + +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + + +@PromptServer.instance.routes.get("/pysssss/workflows") +async def get_workflows(request): + files = [] + for dirpath, directories, file in os.walk(workflows_directory): + for file in file: + if (file.endswith(".json")): + files.append(os.path.relpath(os.path.join( + dirpath, file), workflows_directory)) + return web.json_response(list(map(lambda f: os.path.splitext(f)[0].replace("\\", "/"), files))) + + +@PromptServer.instance.routes.get("/pysssss/workflows/{name:.+}") +async def get_workflow(request): + file = os.path.abspath(os.path.join( + workflows_directory, request.match_info["name"] + ".json")) + if os.path.commonpath([file, workflows_directory]) != workflows_directory: + return web.Response(status=403) + + return web.FileResponse(file) + + +@PromptServer.instance.routes.post("/pysssss/workflows") +async def save_workflow(request): + json_data = await request.json() + file = os.path.abspath(os.path.join( + workflows_directory, json_data["name"] + ".json")) + if os.path.commonpath([file, workflows_directory]) != workflows_directory: + return web.Response(status=403) + + if os.path.exists(file) and ("overwrite" not in json_data or json_data["overwrite"] == False): + return web.Response(status=409) + + sub_path = os.path.dirname(file) + if not os.path.exists(sub_path): + os.makedirs(sub_path) + + with open(file, "w") as f: + f.write(json.dumps(json_data["workflow"])) + + return web.Response(status=201) diff --git a/custom_nodes/ComfyUI-Custom-Scripts/pyproject.toml b/custom_nodes/ComfyUI-Custom-Scripts/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b34f8cfc4a4af33575586c2c11313bdd34d1dfee --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/pyproject.toml @@ -0,0 +1,13 @@ +[project] +name = "comfyui-custom-scripts" +description = "Enhancements & experiments for ComfyUI, mostly focusing on UI features" +version = "1.2.5" +license = { file = "LICENSE" } + +[project.urls] +Repository = "https://github.com/pythongosssss/ComfyUI-Custom-Scripts" + +[tool.comfy] +PublisherId = "pythongosssss" +DisplayName = "ComfyUI-Custom-Scripts" +Icon = "" diff --git a/custom_nodes/ComfyUI-Custom-Scripts/pysssss.default.json b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.default.json new file mode 100644 index 0000000000000000000000000000000000000000..6ca06420135d024f925a0d70d5dd0328c69b271e --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.default.json @@ -0,0 +1,4 @@ +{ + "name": "CustomScripts", + "logging": false +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/pysssss.example.json b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.example.json new file mode 100644 index 0000000000000000000000000000000000000000..251e3c720067fc06b4458db51cb8c12182c05dee --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.example.json @@ -0,0 +1,7 @@ +{ + "name": "CustomScripts", + "logging": false, + "workflows": { + "directory": "C:\\ComfyUI-Workflows" + } +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/pysssss.json b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.json new file mode 100644 index 0000000000000000000000000000000000000000..6ca06420135d024f925a0d70d5dd0328c69b271e --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.json @@ -0,0 +1,4 @@ +{ + "name": "CustomScripts", + "logging": false +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/pysssss.py b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.py new file mode 100644 index 0000000000000000000000000000000000000000..b1024f5a220779239bdadc56aaf7b9431cfa77cc --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/pysssss.py @@ -0,0 +1,300 @@ +import asyncio +import os +import json +import shutil +import inspect +import aiohttp +from server import PromptServer +from tqdm import tqdm + +config = None + + +def is_logging_enabled(): + config = get_extension_config() + if "logging" not in config: + return False + return config["logging"] + + +def log(message, type=None, always=False, name=None): + if not always and not is_logging_enabled(): + return + + if type is not None: + message = f"[{type}] {message}" + + if name is None: + name = get_extension_config()["name"] + + print(f"(pysssss:{name}) {message}") + + +def get_ext_dir(subpath=None, mkdir=False): + dir = os.path.dirname(__file__) + if subpath is not None: + dir = os.path.join(dir, subpath) + + dir = os.path.abspath(dir) + + if mkdir and not os.path.exists(dir): + os.makedirs(dir) + return dir + + +def get_comfy_dir(subpath=None, mkdir=False): + dir = os.path.dirname(inspect.getfile(PromptServer)) + if subpath is not None: + dir = os.path.join(dir, subpath) + + dir = os.path.abspath(dir) + + if mkdir and not os.path.exists(dir): + os.makedirs(dir) + return dir + + +def get_web_ext_dir(): + config = get_extension_config() + name = config["name"] + dir = get_comfy_dir("web/extensions/pysssss") + if not os.path.exists(dir): + os.makedirs(dir) + dir = os.path.join(dir, name) + return dir + + +def get_extension_config(reload=False): + global config + if reload == False and config is not None: + return config + + config_path = get_ext_dir("pysssss.json") + default_config_path = get_ext_dir("pysssss.default.json") + if not os.path.exists(config_path): + if os.path.exists(default_config_path): + shutil.copy(default_config_path, config_path) + if not os.path.exists(config_path): + log(f"Failed to create config at {config_path}", type="ERROR", always=True, name="???") + print(f"Extension path: {get_ext_dir()}") + return {"name": "Unknown", "version": -1} + + else: + log("Missing pysssss.default.json, this extension may not work correctly. Please reinstall the extension.", + type="ERROR", always=True, name="???") + print(f"Extension path: {get_ext_dir()}") + return {"name": "Unknown", "version": -1} + + with open(config_path, "r") as f: + config = json.loads(f.read()) + return config + + +def link_js(src, dst): + src = os.path.abspath(src) + dst = os.path.abspath(dst) + if os.name == "nt": + try: + import _winapi + _winapi.CreateJunction(src, dst) + return True + except: + pass + try: + os.symlink(src, dst) + return True + except: + import logging + logging.exception('') + return False + + +def is_junction(path): + if os.name != "nt": + return False + try: + return bool(os.readlink(path)) + except OSError: + return False + + +def install_js(): + src_dir = get_ext_dir("web/js") + if not os.path.exists(src_dir): + log("No JS") + return + + should_install = should_install_js() + if should_install: + log("it looks like you're running an old version of ComfyUI that requires manual setup of web files, it is recommended you update your installation.", "warning", True) + dst_dir = get_web_ext_dir() + linked = os.path.islink(dst_dir) or is_junction(dst_dir) + if linked or os.path.exists(dst_dir): + if linked: + if should_install: + log("JS already linked") + else: + os.unlink(dst_dir) + log("JS unlinked, PromptServer will serve extension") + elif not should_install: + shutil.rmtree(dst_dir) + log("JS deleted, PromptServer will serve extension") + return + + if not should_install: + log("JS skipped, PromptServer will serve extension") + return + + if link_js(src_dir, dst_dir): + log("JS linked") + return + + log("Copying JS files") + shutil.copytree(src_dir, dst_dir, dirs_exist_ok=True) + + +def should_install_js(): + return not hasattr(PromptServer.instance, "supports") or "custom_nodes_from_web" not in PromptServer.instance.supports + + +def init(check_imports=None): + log("Init") + + if check_imports is not None: + import importlib.util + for imp in check_imports: + spec = importlib.util.find_spec(imp) + if spec is None: + log(f"{imp} is required, please check requirements are installed.", + type="ERROR", always=True) + return False + + install_js() + return True + + +def get_async_loop(): + loop = None + try: + loop = asyncio.get_event_loop() + except: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + return loop + + +def get_http_session(): + loop = get_async_loop() + return aiohttp.ClientSession(loop=loop) + + +async def download(url, stream, update_callback=None, session=None): + close_session = False + if session is None: + close_session = True + session = get_http_session() + try: + async with session.get(url) as response: + size = int(response.headers.get('content-length', 0)) or None + + with tqdm( + unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1], total=size, + ) as progressbar: + perc = 0 + async for chunk in response.content.iter_chunked(2048): + stream.write(chunk) + progressbar.update(len(chunk)) + if update_callback is not None and progressbar.total is not None and progressbar.total != 0: + last = perc + perc = round(progressbar.n / progressbar.total, 2) + if perc != last: + last = perc + await update_callback(perc) + finally: + if close_session and session is not None: + await session.close() + + +async def download_to_file(url, destination, update_callback=None, is_ext_subpath=True, session=None): + if is_ext_subpath: + destination = get_ext_dir(destination) + with open(destination, mode='wb') as f: + download(url, f, update_callback, session) + + +def wait_for_async(async_fn, loop=None): + res = [] + + async def run_async(): + r = await async_fn() + res.append(r) + + if loop is None: + try: + loop = asyncio.get_event_loop() + except: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + loop.run_until_complete(run_async()) + + return res[0] + + +def update_node_status(client_id, node, text, progress=None): + if client_id is None: + client_id = PromptServer.instance.client_id + + if client_id is None: + return + + PromptServer.instance.send_sync("pysssss/update_status", { + "node": node, + "progress": progress, + "text": text + }, client_id) + + +async def update_node_status_async(client_id, node, text, progress=None): + if client_id is None: + client_id = PromptServer.instance.client_id + + if client_id is None: + return + + await PromptServer.instance.send("pysssss/update_status", { + "node": node, + "progress": progress, + "text": text + }, client_id) + + +def get_config_value(key, default=None, throw=False): + split = key.split(".") + obj = get_extension_config() + for s in split: + if s in obj: + obj = obj[s] + else: + if throw: + raise KeyError("Configuration key missing: " + key) + else: + return default + return obj + + +def is_inside_dir(root_dir, check_path): + root_dir = os.path.abspath(root_dir) + if not os.path.isabs(check_path): + check_path = os.path.abspath(os.path.join(root_dir, check_path)) + return os.path.commonpath([check_path, root_dir]) == root_dir + + +def get_child_dir(root_dir, child_path, throw_if_outside=True): + child_path = os.path.abspath(os.path.join(root_dir, child_path)) + if is_inside_dir(root_dir, child_path): + return child_path + if throw_if_outside: + raise NotADirectoryError( + "Saving outside the target folder is not allowed.") + return None diff --git a/custom_nodes/ComfyUI-Custom-Scripts/user/text_file_dirs.json b/custom_nodes/ComfyUI-Custom-Scripts/user/text_file_dirs.json new file mode 100644 index 0000000000000000000000000000000000000000..15adc2255e0109be7b860d591f54187520c673aa --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/user/text_file_dirs.json @@ -0,0 +1,5 @@ +{ + "input": "$input/**/*.txt", + "output": "$output/**/*.txt", + "temp": "$temp/**/*.txt" +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/canvas2svg.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/canvas2svg.js new file mode 100644 index 0000000000000000000000000000000000000000..03dc3923759ccbe8cf70eaab387b792cc0ba32ca --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/canvas2svg.js @@ -0,0 +1,1192 @@ +/*!! + * Canvas 2 Svg v1.0.19 + * A low level canvas to SVG converter. Uses a mock canvas context to build an SVG document. + * + * Licensed under the MIT license: + * http://www.opensource.org/licenses/mit-license.php + * + * Author: + * Kerry Liu + * + * Copyright (c) 2014 Gliffy Inc. + */ + +;(function() { + "use strict"; + + var STYLES, ctx, CanvasGradient, CanvasPattern, namedEntities; + + //helper function to format a string + function format(str, args) { + var keys = Object.keys(args), i; + for (i=0; i 1) { + options = defaultOptions; + options.width = arguments[0]; + options.height = arguments[1]; + } else if( !o ) { + options = defaultOptions; + } else { + options = o; + } + + if(!(this instanceof ctx)) { + //did someone call this without new? + return new ctx(options); + } + + //setup options + this.width = options.width || defaultOptions.width; + this.height = options.height || defaultOptions.height; + this.enableMirroring = options.enableMirroring !== undefined ? options.enableMirroring : defaultOptions.enableMirroring; + + this.canvas = this; ///point back to this instance! + this.__document = options.document || document; + this.__canvas = this.__document.createElement("canvas"); + this.__ctx = this.__canvas.getContext("2d"); + + this.__setDefaultStyles(); + this.__stack = [this.__getStyleState()]; + this.__groupStack = []; + + //the root svg element + this.__root = this.__document.createElementNS("http://www.w3.org/2000/svg", "svg"); + this.__root.setAttribute("version", 1.1); + this.__root.setAttribute("xmlns", "http://www.w3.org/2000/svg"); + this.__root.setAttributeNS("http://www.w3.org/2000/xmlns/", "xmlns:xlink", "http://www.w3.org/1999/xlink"); + this.__root.setAttribute("width", this.width); + this.__root.setAttribute("height", this.height); + + //make sure we don't generate the same ids in defs + this.__ids = {}; + + //defs tag + this.__defs = this.__document.createElementNS("http://www.w3.org/2000/svg", "defs"); + this.__root.appendChild(this.__defs); + + //also add a group child. the svg element can't use the transform attribute + this.__currentElement = this.__document.createElementNS("http://www.w3.org/2000/svg", "g"); + this.__root.appendChild(this.__currentElement); + }; + + + /** + * Creates the specified svg element + * @private + */ + ctx.prototype.__createElement = function (elementName, properties, resetFill) { + if (typeof properties === "undefined") { + properties = {}; + } + + var element = this.__document.createElementNS("http://www.w3.org/2000/svg", elementName), + keys = Object.keys(properties), i, key; + if(resetFill) { + //if fill or stroke is not specified, the svg element should not display. By default SVG's fill is black. + element.setAttribute("fill", "none"); + element.setAttribute("stroke", "none"); + } + for(i=0; i 0) { + var group = this.__createElement("g"); + parent.appendChild(group); + this.__currentElement = group; + } + + var transform = this.__currentElement.getAttribute("transform"); + if(transform) { + transform += " "; + } else { + transform = ""; + } + transform += t; + this.__currentElement.setAttribute("transform", transform); + }; + + /** + * scales the current element + */ + ctx.prototype.scale = function(x, y) { + if(y === undefined) { + y = x; + } + this.__addTransform(format("scale({x},{y})", {x:x, y:y})); + }; + + /** + * rotates the current element + */ + ctx.prototype.rotate = function(angle){ + var degrees = (angle * 180 / Math.PI); + this.__addTransform(format("rotate({angle},{cx},{cy})", {angle:degrees, cx:0, cy:0})); + }; + + /** + * translates the current element + */ + ctx.prototype.translate = function(x, y){ + this.__addTransform(format("translate({x},{y})", {x:x,y:y})); + }; + + /** + * applies a transform to the current element + */ + ctx.prototype.transform = function(a, b, c, d, e, f){ + this.__addTransform(format("matrix({a},{b},{c},{d},{e},{f})", {a:a, b:b, c:c, d:d, e:e, f:f})); + }; + + /** + * Create a new Path Element + */ + ctx.prototype.beginPath = function(){ + var path, parent; + + // Note that there is only one current default path, it is not part of the drawing state. + // See also: https://html.spec.whatwg.org/multipage/scripting.html#current-default-path + this.__currentDefaultPath = ""; + this.__currentPosition = {}; + + path = this.__createElement("path", {}, true); + parent = this.__closestGroupOrSvg(); + parent.appendChild(path); + this.__currentElement = path; + }; + + /** + * Helper function to apply currentDefaultPath to current path element + * @private + */ + ctx.prototype.__applyCurrentDefaultPath = function() { + if(this.__currentElement.nodeName === "path") { + var d = this.__currentDefaultPath; + this.__currentElement.setAttribute("d", d); + } else { + throw new Error("Attempted to apply path command to node " + this.__currentElement.nodeName); + } + }; + + /** + * Helper function to add path command + * @private + */ + ctx.prototype.__addPathCommand = function(command){ + this.__currentDefaultPath += " "; + this.__currentDefaultPath += command; + }; + + /** + * Adds the move command to the current path element, + * if the currentPathElement is not empty create a new path element + */ + ctx.prototype.moveTo = function(x,y){ + if(this.__currentElement.nodeName !== "path") { + this.beginPath(); + } + + // creates a new subpath with the given point + this.__currentPosition = {x: x, y: y}; + this.__addPathCommand(format("M {x} {y}", {x:x, y:y})); + }; + + /** + * Closes the current path + */ + ctx.prototype.closePath = function(){ + this.__addPathCommand("Z"); + }; + + /** + * Adds a line to command + */ + ctx.prototype.lineTo = function(x, y){ + this.__currentPosition = {x: x, y: y}; + if (this.__currentDefaultPath.indexOf('M') > -1) { + this.__addPathCommand(format("L {x} {y}", {x:x, y:y})); + } else { + this.__addPathCommand(format("M {x} {y}", {x:x, y:y})); + } + }; + + /** + * Add a bezier command + */ + ctx.prototype.bezierCurveTo = function(cp1x, cp1y, cp2x, cp2y, x, y) { + this.__currentPosition = {x: x, y: y}; + this.__addPathCommand(format("C {cp1x} {cp1y} {cp2x} {cp2y} {x} {y}", + {cp1x:cp1x, cp1y:cp1y, cp2x:cp2x, cp2y:cp2y, x:x, y:y})); + }; + + /** + * Adds a quadratic curve to command + */ + ctx.prototype.quadraticCurveTo = function(cpx, cpy, x, y){ + this.__currentPosition = {x: x, y: y}; + this.__addPathCommand(format("Q {cpx} {cpy} {x} {y}", {cpx:cpx, cpy:cpy, x:x, y:y})); + }; + + + /** + * Return a new normalized vector of given vector + */ + var normalize = function(vector) { + var len = Math.sqrt(vector[0] * vector[0] + vector[1] * vector[1]); + return [vector[0] / len, vector[1] / len]; + }; + + /** + * Adds the arcTo to the current path + * + * @see http://www.w3.org/TR/2015/WD-2dcontext-20150514/#dom-context-2d-arcto + */ + ctx.prototype.arcTo = function(x1, y1, x2, y2, radius) { + // Let the point (x0, y0) be the last point in the subpath. + var x0 = this.__currentPosition && this.__currentPosition.x; + var y0 = this.__currentPosition && this.__currentPosition.y; + + // First ensure there is a subpath for (x1, y1). + if (typeof x0 == "undefined" || typeof y0 == "undefined") { + return; + } + + // Negative values for radius must cause the implementation to throw an IndexSizeError exception. + if (radius < 0) { + throw new Error("IndexSizeError: The radius provided (" + radius + ") is negative."); + } + + // If the point (x0, y0) is equal to the point (x1, y1), + // or if the point (x1, y1) is equal to the point (x2, y2), + // or if the radius radius is zero, + // then the method must add the point (x1, y1) to the subpath, + // and connect that point to the previous point (x0, y0) by a straight line. + if (((x0 === x1) && (y0 === y1)) + || ((x1 === x2) && (y1 === y2)) + || (radius === 0)) { + this.lineTo(x1, y1); + return; + } + + // Otherwise, if the points (x0, y0), (x1, y1), and (x2, y2) all lie on a single straight line, + // then the method must add the point (x1, y1) to the subpath, + // and connect that point to the previous point (x0, y0) by a straight line. + var unit_vec_p1_p0 = normalize([x0 - x1, y0 - y1]); + var unit_vec_p1_p2 = normalize([x2 - x1, y2 - y1]); + if (unit_vec_p1_p0[0] * unit_vec_p1_p2[1] === unit_vec_p1_p0[1] * unit_vec_p1_p2[0]) { + this.lineTo(x1, y1); + return; + } + + // Otherwise, let The Arc be the shortest arc given by circumference of the circle that has radius radius, + // and that has one point tangent to the half-infinite line that crosses the point (x0, y0) and ends at the point (x1, y1), + // and that has a different point tangent to the half-infinite line that ends at the point (x1, y1), and crosses the point (x2, y2). + // The points at which this circle touches these two lines are called the start and end tangent points respectively. + + // note that both vectors are unit vectors, so the length is 1 + var cos = (unit_vec_p1_p0[0] * unit_vec_p1_p2[0] + unit_vec_p1_p0[1] * unit_vec_p1_p2[1]); + var theta = Math.acos(Math.abs(cos)); + + // Calculate origin + var unit_vec_p1_origin = normalize([ + unit_vec_p1_p0[0] + unit_vec_p1_p2[0], + unit_vec_p1_p0[1] + unit_vec_p1_p2[1] + ]); + var len_p1_origin = radius / Math.sin(theta / 2); + var x = x1 + len_p1_origin * unit_vec_p1_origin[0]; + var y = y1 + len_p1_origin * unit_vec_p1_origin[1]; + + // Calculate start angle and end angle + // rotate 90deg clockwise (note that y axis points to its down) + var unit_vec_origin_start_tangent = [ + -unit_vec_p1_p0[1], + unit_vec_p1_p0[0] + ]; + // rotate 90deg counter clockwise (note that y axis points to its down) + var unit_vec_origin_end_tangent = [ + unit_vec_p1_p2[1], + -unit_vec_p1_p2[0] + ]; + var getAngle = function(vector) { + // get angle (clockwise) between vector and (1, 0) + var x = vector[0]; + var y = vector[1]; + if (y >= 0) { // note that y axis points to its down + return Math.acos(x); + } else { + return -Math.acos(x); + } + }; + var startAngle = getAngle(unit_vec_origin_start_tangent); + var endAngle = getAngle(unit_vec_origin_end_tangent); + + // Connect the point (x0, y0) to the start tangent point by a straight line + this.lineTo(x + unit_vec_origin_start_tangent[0] * radius, + y + unit_vec_origin_start_tangent[1] * radius); + + // Connect the start tangent point to the end tangent point by arc + // and adding the end tangent point to the subpath. + this.arc(x, y, radius, startAngle, endAngle); + }; + + /** + * Sets the stroke property on the current element + */ + ctx.prototype.stroke = function(){ + if(this.__currentElement.nodeName === "path") { + this.__currentElement.setAttribute("paint-order", "fill stroke markers"); + } + this.__applyCurrentDefaultPath(); + this.__applyStyleToCurrentElement("stroke"); + }; + + /** + * Sets fill properties on the current element + */ + ctx.prototype.fill = function(){ + if(this.__currentElement.nodeName === "path") { + this.__currentElement.setAttribute("paint-order", "stroke fill markers"); + } + this.__applyCurrentDefaultPath(); + this.__applyStyleToCurrentElement("fill"); + }; + + /** + * Adds a rectangle to the path. + */ + ctx.prototype.rect = function(x, y, width, height){ + if(this.__currentElement.nodeName !== "path") { + this.beginPath(); + } + this.moveTo(x, y); + this.lineTo(x+width, y); + this.lineTo(x+width, y+height); + this.lineTo(x, y+height); + this.lineTo(x, y); + this.closePath(); + }; + + + /** + * adds a rectangle element + */ + ctx.prototype.fillRect = function(x, y, width, height){ + var rect, parent; + rect = this.__createElement("rect", { + x : x, + y : y, + width : width, + height : height + }, true); + parent = this.__closestGroupOrSvg(); + parent.appendChild(rect); + this.__currentElement = rect; + this.__applyStyleToCurrentElement("fill"); + }; + + /** + * Draws a rectangle with no fill + * @param x + * @param y + * @param width + * @param height + */ + ctx.prototype.strokeRect = function(x, y, width, height){ + var rect, parent; + rect = this.__createElement("rect", { + x : x, + y : y, + width : width, + height : height + }, true); + parent = this.__closestGroupOrSvg(); + parent.appendChild(rect); + this.__currentElement = rect; + this.__applyStyleToCurrentElement("stroke"); + }; + + + /** + * Clear entire canvas: + * 1. save current transforms + * 2. remove all the childNodes of the root g element + */ + ctx.prototype.__clearCanvas = function() { + var current = this.__closestGroupOrSvg(), + transform = current.getAttribute("transform"); + var rootGroup = this.__root.childNodes[1]; + var childNodes = rootGroup.childNodes; + for (var i = childNodes.length - 1; i >= 0; i--) { + if (childNodes[i]) { + rootGroup.removeChild(childNodes[i]); + } + } + this.__currentElement = rootGroup; + //reset __groupStack as all the child group nodes are all removed. + this.__groupStack = []; + if (transform) { + this.__addTransform(transform); + } + }; + + /** + * "Clears" a canvas by just drawing a white rectangle in the current group. + */ + ctx.prototype.clearRect = function(x, y, width, height) { + //clear entire canvas + if (x === 0 && y === 0 && width === this.width && height === this.height) { + this.__clearCanvas(); + return; + } + var rect, parent = this.__closestGroupOrSvg(); + rect = this.__createElement("rect", { + x : x, + y : y, + width : width, + height : height, + fill : "#FFFFFF" + }, true); + parent.appendChild(rect); + }; + + /** + * Adds a linear gradient to a defs tag. + * Returns a canvas gradient object that has a reference to it's parent def + */ + ctx.prototype.createLinearGradient = function(x1, y1, x2, y2){ + var grad = this.__createElement("linearGradient", { + id : randomString(this.__ids), + x1 : x1+"px", + x2 : x2+"px", + y1 : y1+"px", + y2 : y2+"px", + "gradientUnits" : "userSpaceOnUse" + }, false); + this.__defs.appendChild(grad); + return new CanvasGradient(grad, this); + }; + + /** + * Adds a radial gradient to a defs tag. + * Returns a canvas gradient object that has a reference to it's parent def + */ + ctx.prototype.createRadialGradient = function(x0, y0, r0, x1, y1, r1){ + var grad = this.__createElement("radialGradient", { + id : randomString(this.__ids), + cx : x1+"px", + cy : y1+"px", + r : r1+"px", + fx : x0+"px", + fy : y0+"px", + "gradientUnits" : "userSpaceOnUse" + }, false); + this.__defs.appendChild(grad); + return new CanvasGradient(grad, this); + + }; + + /** + * Parses the font string and returns svg mapping + * @private + */ + ctx.prototype.__parseFont = function() { + var regex = /^\s*(?=(?:(?:[-a-z]+\s*){0,2}(italic|oblique))?)(?=(?:(?:[-a-z]+\s*){0,2}(small-caps))?)(?=(?:(?:[-a-z]+\s*){0,2}(bold(?:er)?|lighter|[1-9]00))?)(?:(?:normal|\1|\2|\3)\s*){0,3}((?:xx?-)?(?:small|large)|medium|smaller|larger|[.\d]+(?:\%|in|[cem]m|ex|p[ctx]))(?:\s*\/\s*(normal|[.\d]+(?:\%|in|[cem]m|ex|p[ctx])))?\s*([-,\'\"\sa-z]+?)\s*$/i; + var fontPart = regex.exec( this.font ); + var data = { + style : fontPart[1] || 'normal', + size : fontPart[4] || '10px', + family : fontPart[6] || 'sans-serif', + weight: fontPart[3] || 'normal', + decoration : fontPart[2] || 'normal', + href : null + }; + + //canvas doesn't support underline natively, but we can pass this attribute + if(this.__fontUnderline === "underline") { + data.decoration = "underline"; + } + + //canvas also doesn't support linking, but we can pass this as well + if(this.__fontHref) { + data.href = this.__fontHref; + } + + return data; + }; + + /** + * Helper to link text fragments + * @param font + * @param element + * @return {*} + * @private + */ + ctx.prototype.__wrapTextLink = function(font, element) { + if(font.href) { + var a = this.__createElement("a"); + a.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", font.href); + a.appendChild(element); + return a; + } + return element; + }; + + /** + * Fills or strokes text + * @param text + * @param x + * @param y + * @param action - stroke or fill + * @private + */ + ctx.prototype.__applyText = function(text, x, y, action) { + var font = this.__parseFont(), + parent = this.__closestGroupOrSvg(), + textElement = this.__createElement("text", { + "font-family" : font.family, + "font-size" : font.size, + "font-style" : font.style, + "font-weight" : font.weight, + "text-decoration" : font.decoration, + "x" : x, + "y" : y, + "text-anchor": getTextAnchor(this.textAlign), + "dominant-baseline": getDominantBaseline(this.textBaseline) + }, true); + + textElement.appendChild(this.__document.createTextNode(text)); + this.__currentElement = textElement; + this.__applyStyleToCurrentElement(action); + parent.appendChild(this.__wrapTextLink(font,textElement)); + }; + + /** + * Creates a text element + * @param text + * @param x + * @param y + */ + ctx.prototype.fillText = function(text, x, y){ + this.__applyText(text, x, y, "fill"); + }; + + /** + * Strokes text + * @param text + * @param x + * @param y + */ + ctx.prototype.strokeText = function(text, x, y){ + this.__applyText(text, x, y, "stroke"); + }; + + /** + * No need to implement this for svg. + * @param text + * @return {TextMetrics} + */ + ctx.prototype.measureText = function(text){ + this.__ctx.font = this.font; + return this.__ctx.measureText(text); + }; + + /** + * Arc command! + */ + ctx.prototype.arc = function(x, y, radius, startAngle, endAngle, counterClockwise) { + // in canvas no circle is drawn if no angle is provided. + if (startAngle === endAngle) { + return; + } + startAngle = startAngle % (2*Math.PI); + endAngle = endAngle % (2*Math.PI); + if(startAngle === endAngle) { + //circle time! subtract some of the angle so svg is happy (svg elliptical arc can't draw a full circle) + endAngle = ((endAngle + (2*Math.PI)) - 0.001 * (counterClockwise ? -1 : 1)) % (2*Math.PI); + } + var endX = x+radius*Math.cos(endAngle), + endY = y+radius*Math.sin(endAngle), + startX = x+radius*Math.cos(startAngle), + startY = y+radius*Math.sin(startAngle), + sweepFlag = counterClockwise ? 0 : 1, + largeArcFlag = 0, + diff = endAngle - startAngle; + + // https://github.com/gliffy/canvas2svg/issues/4 + if(diff < 0) { + diff += 2*Math.PI; + } + + if(counterClockwise) { + largeArcFlag = diff > Math.PI ? 0 : 1; + } else { + largeArcFlag = diff > Math.PI ? 1 : 0; + } + + this.lineTo(startX, startY); + this.__addPathCommand(format("A {rx} {ry} {xAxisRotation} {largeArcFlag} {sweepFlag} {endX} {endY}", + {rx:radius, ry:radius, xAxisRotation:0, largeArcFlag:largeArcFlag, sweepFlag:sweepFlag, endX:endX, endY:endY})); + + this.__currentPosition = {x: endX, y: endY}; + }; + + /** + * Generates a ClipPath from the clip command. + */ + ctx.prototype.clip = function(){ + var group = this.__closestGroupOrSvg(), + clipPath = this.__createElement("clipPath"), + id = randomString(this.__ids), + newGroup = this.__createElement("g"); + + this.__applyCurrentDefaultPath(); + group.removeChild(this.__currentElement); + clipPath.setAttribute("id", id); + clipPath.appendChild(this.__currentElement); + + this.__defs.appendChild(clipPath); + + //set the clip path to this group + group.setAttribute("clip-path", format("url(#{id})", {id:id})); + + //clip paths can be scaled and transformed, we need to add another wrapper group to avoid later transformations + // to this path + group.appendChild(newGroup); + + this.__currentElement = newGroup; + + }; + + /** + * Draws a canvas, image or mock context to this canvas. + * Note that all svg dom manipulation uses node.childNodes rather than node.children for IE support. + * http://www.whatwg.org/specs/web-apps/current-work/multipage/the-canvas-element.html#dom-context-2d-drawimage + */ + ctx.prototype.drawImage = function(){ + //convert arguments to a real array + var args = Array.prototype.slice.call(arguments), + image=args[0], + dx, dy, dw, dh, sx=0, sy=0, sw, sh, parent, svg, defs, group, + currentElement, svgImage, canvas, context, id; + + if(args.length === 3) { + dx = args[1]; + dy = args[2]; + sw = image.width; + sh = image.height; + dw = sw; + dh = sh; + } else if(args.length === 5) { + dx = args[1]; + dy = args[2]; + dw = args[3]; + dh = args[4]; + sw = image.width; + sh = image.height; + } else if(args.length === 9) { + sx = args[1]; + sy = args[2]; + sw = args[3]; + sh = args[4]; + dx = args[5]; + dy = args[6]; + dw = args[7]; + dh = args[8]; + } else { + throw new Error("Inavlid number of arguments passed to drawImage: " + arguments.length); + } + + parent = this.__closestGroupOrSvg(); + currentElement = this.__currentElement; + var translateDirective = "translate(" + dx + ", " + dy + ")"; + if(image instanceof ctx) { + //canvas2svg mock canvas context. In the future we may want to clone nodes instead. + //also I'm currently ignoring dw, dh, sw, sh, sx, sy for a mock context. + svg = image.getSvg().cloneNode(true); + if (svg.childNodes && svg.childNodes.length > 1) { + defs = svg.childNodes[0]; + while(defs.childNodes.length) { + id = defs.childNodes[0].getAttribute("id"); + this.__ids[id] = id; + this.__defs.appendChild(defs.childNodes[0]); + } + group = svg.childNodes[1]; + if (group) { + //save original transform + var originTransform = group.getAttribute("transform"); + var transformDirective; + if (originTransform) { + transformDirective = originTransform+" "+translateDirective; + } else { + transformDirective = translateDirective; + } + group.setAttribute("transform", transformDirective); + parent.appendChild(group); + } + } + } else if(image.nodeName === "CANVAS" || image.nodeName === "IMG") { + //canvas or image + svgImage = this.__createElement("image"); + svgImage.setAttribute("width", dw); + svgImage.setAttribute("height", dh); + svgImage.setAttribute("preserveAspectRatio", "none"); + + if(sx || sy || sw !== image.width || sh !== image.height) { + //crop the image using a temporary canvas + canvas = this.__document.createElement("canvas"); + canvas.width = dw; + canvas.height = dh; + context = canvas.getContext("2d"); + context.drawImage(image, sx, sy, sw, sh, 0, 0, dw, dh); + image = canvas; + } + svgImage.setAttribute("transform", translateDirective); + svgImage.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", + image.nodeName === "CANVAS" ? image.toDataURL() : image.getAttribute("src")); + parent.appendChild(svgImage); + } + }; + + /** + * Generates a pattern tag + */ + ctx.prototype.createPattern = function(image, repetition){ + var pattern = this.__document.createElementNS("http://www.w3.org/2000/svg", "pattern"), id = randomString(this.__ids), + img; + pattern.setAttribute("id", id); + pattern.setAttribute("width", image.width); + pattern.setAttribute("height", image.height); + if(image.nodeName === "CANVAS" || image.nodeName === "IMG") { + img = this.__document.createElementNS("http://www.w3.org/2000/svg", "image"); + img.setAttribute("width", image.width); + img.setAttribute("height", image.height); + img.setAttributeNS("http://www.w3.org/1999/xlink", "xlink:href", + image.nodeName === "CANVAS" ? image.toDataURL() : image.getAttribute("src")); + pattern.appendChild(img); + this.__defs.appendChild(pattern); + } else if(image instanceof ctx) { + pattern.appendChild(image.__root.childNodes[1]); + this.__defs.appendChild(pattern); + } + return new CanvasPattern(pattern, this); + }; + + ctx.prototype.setLineDash = function(dashArray) { + if (dashArray && dashArray.length > 0) { + this.lineDash = dashArray.join(","); + } else { + this.lineDash = null; + } + }; + + /** + * Not yet implemented + */ + ctx.prototype.drawFocusRing = function(){}; + ctx.prototype.createImageData = function(){}; + ctx.prototype.getImageData = function(){}; + ctx.prototype.putImageData = function(){}; + ctx.prototype.globalCompositeOperation = function(){}; + ctx.prototype.setTransform = function(){}; + + //add options for alternative namespace + if (typeof window === "object") { + window.C2S = ctx; + } + + // CommonJS/Browserify + if (typeof module === "object" && typeof module.exports === "object") { + module.exports = ctx; + } + +}()); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/favicon-active.ico b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/favicon-active.ico new file mode 100644 index 0000000000000000000000000000000000000000..64045ab56e87879adb039b1fdb0bbbe0462143e7 Binary files /dev/null and b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/favicon-active.ico differ diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/favicon.ico b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..08df2481551bb6903735fa69d658b5abfb0a5ae1 Binary files /dev/null and b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/favicon.ico differ diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/no-image.png b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/no-image.png new file mode 100644 index 0000000000000000000000000000000000000000..d0b58b2c9d3ad82a2a949933648f8691ff36ba84 Binary files /dev/null and b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/no-image.png differ diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/notify.mp3 b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/notify.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..5e3fdabbb12142dfa75702a0b0e0ca5e5425a7dd Binary files /dev/null and b/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/notify.mp3 differ diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/autocompleter.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/autocompleter.js new file mode 100644 index 0000000000000000000000000000000000000000..d0fb2ecae710d291d0d42845a6e0395f61c74e4d --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/autocompleter.js @@ -0,0 +1,602 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; +import { api } from "../../../scripts/api.js"; +import { $el, ComfyDialog } from "../../../scripts/ui.js"; +import { TextAreaAutoComplete } from "./common/autocomplete.js"; +import { ModelInfoDialog } from "./common/modelInfoDialog.js"; +import { LoraInfoDialog } from "./modelInfo.js"; + +function parseCSV(csvText) { + const rows = []; + const delimiter = ","; + const quote = '"'; + let currentField = ""; + let inQuotedField = false; + + function pushField() { + rows[rows.length - 1].push(currentField); + currentField = ""; + inQuotedField = false; + } + + rows.push([]); // Initialize the first row + + for (let i = 0; i < csvText.length; i++) { + const char = csvText[i]; + const nextChar = csvText[i + 1]; + + // Special handling for backslash escaped quotes + if (char === "\\" && nextChar === quote) { + currentField += quote; + i++; + } + + if (!inQuotedField) { + if (char === quote) { + inQuotedField = true; + } else if (char === delimiter) { + pushField(); + } else if (char === "\r" || char === "\n" || i === csvText.length - 1) { + pushField(); + if (nextChar === "\n") { + i++; // Handle Windows line endings (\r\n) + } + rows.push([]); // Start a new row + } else { + currentField += char; + } + } else { + if (char === quote && nextChar === quote) { + currentField += quote; + i++; // Skip the next quote + } else if (char === quote) { + inQuotedField = false; + } else if (char === "\r" || char === "\n" || i === csvText.length - 1) { + // Dont allow new lines in quoted text, assume its wrong + const parsed = parseCSV(currentField); + rows.pop(); + rows.push(...parsed); + inQuotedField = false; + currentField = ""; + rows.push([]); + } else { + currentField += char; + } + } + } + + if (currentField || csvText[csvText.length - 1] === ",") { + pushField(); + } + + // Remove the last row if it's empty + if (rows[rows.length - 1].length === 0) { + rows.pop(); + } + + return rows; +} + +async function getCustomWords() { + const resp = await api.fetchApi("/pysssss/autocomplete", { cache: "no-store" }); + if (resp.status === 200) { + return await resp.text(); + } + return undefined; +} + +async function addCustomWords(text) { + if (!text) { + text = await getCustomWords(); + } + if (text) { + TextAreaAutoComplete.updateWords( + "pysssss.customwords", + parseCSV(text).reduce((p, n) => { + let text; + let priority; + let value; + let num; + switch (n.length) { + case 0: + return; + case 1: + // Single word + text = n[0]; + break; + case 2: + // Word,[priority|alias] + num = +n[1]; + if (isNaN(num)) { + text = n[0] + "🔄️" + n[1]; + value = n[0]; + } else { + text = n[0]; + priority = num; + } + break; + case 4: + // a1111 csv format? + value = n[0]; + priority = +n[2]; + const aliases = n[3]?.trim(); + if (aliases && aliases !== "null") { // Weird null in an example csv, maybe they are JSON.parsing the last column? + const split = aliases.split(","); + for (const text of split) { + p[text] = { text, priority, value }; + } + } + text = value; + break; + default: + // Word,alias,priority + text = n[1]; + value = n[0]; + priority = +n[2]; + break; + } + p[text] = { text, priority, value }; + return p; + }, {}) + ); + } +} + +function toggleLoras() { + [TextAreaAutoComplete.globalWords, TextAreaAutoComplete.globalWordsExclLoras] = [ + TextAreaAutoComplete.globalWordsExclLoras, + TextAreaAutoComplete.globalWords, + ]; +} + +class EmbeddingInfoDialog extends ModelInfoDialog { + async addInfo() { + super.addInfo(); + const info = await this.addCivitaiInfo(); + if (info) { + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } +} + +class CustomWordsDialog extends ComfyDialog { + async show() { + const text = await getCustomWords(); + this.words = $el("textarea", { + textContent: text, + style: { + width: "70vw", + height: "70vh", + }, + }); + + const input = $el("input", { + style: { + flex: "auto", + }, + value: + "https://gist.githubusercontent.com/pythongosssss/1d3efa6050356a08cea975183088159a/raw/a18fb2f94f9156cf4476b0c24a09544d6c0baec6/danbooru-tags.txt", + }); + + super.show( + $el( + "div", + { + style: { + display: "flex", + flexDirection: "column", + overflow: "hidden", + maxHeight: "100%", + }, + }, + [ + $el("h2", { + textContent: "Custom Autocomplete Words", + style: { + color: "#fff", + marginTop: 0, + textAlign: "center", + fontFamily: "sans-serif", + }, + }), + $el( + "div", + { + style: { + color: "#fff", + fontFamily: "sans-serif", + display: "flex", + alignItems: "center", + gap: "5px", + }, + }, + [ + $el("label", { textContent: "Load Custom List: " }), + input, + $el("button", { + textContent: "Load", + onclick: async () => { + try { + const res = await fetch(input.value); + if (res.status !== 200) { + throw new Error("Error loading: " + res.status + " " + res.statusText); + } + this.words.value = await res.text(); + } catch (error) { + alert("Error loading custom list, try manually copy + pasting the list"); + } + }, + }), + ] + ), + this.words, + ] + ) + ); + } + + createButtons() { + const btns = super.createButtons(); + const save = $el("button", { + type: "button", + textContent: "Save", + onclick: async (e) => { + try { + const res = await api.fetchApi("/pysssss/autocomplete", { method: "POST", body: this.words.value }); + if (res.status !== 200) { + throw new Error("Error saving: " + res.status + " " + res.statusText); + } + save.textContent = "Saved!"; + addCustomWords(this.words.value); + setTimeout(() => { + save.textContent = "Save"; + }, 500); + } catch (error) { + alert("Error saving word list!"); + console.error(error); + } + }, + }); + + btns.unshift(save); + return btns; + } +} + +const id = "pysssss.AutoCompleter"; + +app.registerExtension({ + name: id, + init() { + const STRING = ComfyWidgets.STRING; + const SKIP_WIDGETS = new Set(["ttN xyPlot.x_values", "ttN xyPlot.y_values"]); + ComfyWidgets.STRING = function (node, inputName, inputData) { + const r = STRING.apply(this, arguments); + + if (inputData[1]?.multiline) { + // Disabled on this input + const config = inputData[1]?.["pysssss.autocomplete"]; + if (config === false) return r; + + // In list of widgets to skip + const id = `${node.comfyClass}.${inputName}`; + if (SKIP_WIDGETS.has(id)) return r; + + let words; + let separator; + if (typeof config === "object") { + separator = config.separator; + words = {}; + if (config.words) { + // Custom wordlist, this will have been registered on setup + Object.assign(words, TextAreaAutoComplete.groups[node.comfyClass + "." + inputName] ?? {}); + } + + for (const item of config.groups ?? []) { + if (item === "*") { + // This widget wants all global words included + Object.assign(words, TextAreaAutoComplete.globalWords); + } else { + // This widget wants a specific group included + Object.assign(words, TextAreaAutoComplete.groups[item] ?? {}); + } + } + } + + new TextAreaAutoComplete(r.widget.inputEl, words, separator); + } + + return r; + }; + + TextAreaAutoComplete.globalSeparator = localStorage.getItem(id + ".AutoSeparate") ?? ", "; + const enabledSetting = app.ui.settings.addSetting({ + id, + name: "🐍 Text Autocomplete", + defaultValue: true, + type: (name, setter, value) => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: name, + }), + ]), + $el("td", [ + $el( + "label", + { + textContent: "Enabled ", + style: { + display: "block", + }, + }, + [ + $el("input", { + id: id.replaceAll(".", "-"), + type: "checkbox", + checked: value, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.enabled = checked; + setter(checked); + }, + }), + ] + ), + $el( + "label.comfy-tooltip-indicator", + { + title: "This requires other ComfyUI nodes/extensions that support using LoRAs in the prompt.", + textContent: "Loras enabled ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.lorasEnabled, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.lorasEnabled = checked; + toggleLoras(); + localStorage.setItem(id + ".ShowLoras", TextAreaAutoComplete.lorasEnabled); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Auto-insert comma ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.globalSeparator, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.globalSeparator = checked ? ", " : ""; + localStorage.setItem(id + ".AutoSeparate", TextAreaAutoComplete.globalSeparator); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Replace _ with space ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.replacer, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.replacer = checked ? (v) => v.replaceAll("_", " ") : undefined; + localStorage.setItem(id + ".ReplaceUnderscore", checked); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Insert suggestion on: ", + style: { + display: "block", + }, + }, + [ + $el( + "label", + { + textContent: "Tab", + style: { + display: "block", + marginLeft: "20px", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.insertOnTab, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.insertOnTab = checked; + localStorage.setItem(id + ".InsertOnTab", checked); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Enter", + style: { + display: "block", + marginLeft: "20px", + }, + }, + [ + $el("input", { + type: "checkbox", + checked: !!TextAreaAutoComplete.insertOnEnter, + onchange: (event) => { + const checked = !!event.target.checked; + TextAreaAutoComplete.insertOnEnter = checked; + localStorage.setItem(id + ".InsertOnEnter", checked); + }, + }), + ] + ), + ] + ), + $el( + "label", + { + textContent: "Max suggestions: ", + style: { + display: "block", + }, + }, + [ + $el("input", { + type: "number", + value: +TextAreaAutoComplete.suggestionCount, + style: { + width: "80px" + }, + onchange: (event) => { + const value = +event.target.value; + TextAreaAutoComplete.suggestionCount = value;; + localStorage.setItem(id + ".SuggestionCount", TextAreaAutoComplete.suggestionCount); + }, + }), + ] + ), + $el("button", { + textContent: "Manage Custom Words", + onclick: () => { + try { + // Try closing old settings window + if (typeof app.ui.settings.element?.close === "function") { + app.ui.settings.element.close(); + } + } catch (error) { + } + try { + // Try closing new vue dialog + document.querySelector(".p-dialog-close-button").click(); + } catch (error) { + // Fallback to just hiding the element + app.ui.settings.element.style.display = "none"; + } + + new CustomWordsDialog().show(); + }, + style: { + fontSize: "14px", + display: "block", + marginTop: "5px", + }, + }), + ]), + ]); + }, + }); + + TextAreaAutoComplete.enabled = enabledSetting.value; + TextAreaAutoComplete.replacer = localStorage.getItem(id + ".ReplaceUnderscore") === "true" ? (v) => v.replaceAll("_", " ") : undefined; + TextAreaAutoComplete.insertOnTab = localStorage.getItem(id + ".InsertOnTab") !== "false"; + TextAreaAutoComplete.insertOnEnter = localStorage.getItem(id + ".InsertOnEnter") !== "false"; + TextAreaAutoComplete.lorasEnabled = localStorage.getItem(id + ".ShowLoras") === "true"; + TextAreaAutoComplete.suggestionCount = +localStorage.getItem(id + ".SuggestionCount") || 20; + }, + setup() { + async function addEmbeddings() { + const embeddings = await api.getEmbeddings(); + const words = {}; + words["embedding:"] = { text: "embedding:" }; + + for (const emb of embeddings) { + const v = `embedding:${emb}`; + words[v] = { + text: v, + info: () => new EmbeddingInfoDialog(emb).show("embeddings", emb), + use_replacer: false, + }; + } + + TextAreaAutoComplete.updateWords("pysssss.embeddings", words); + } + + async function addLoras() { + let loras; + try { + loras = LiteGraph.registered_node_types["LoraLoader"]?.nodeData.input.required.lora_name[0]; + } catch (error) {} + + if (!loras?.length) { + loras = await api.fetchApi("/pysssss/loras", { cache: "no-store" }).then((res) => res.json()); + } + + const words = {}; + words["lora:"] = { text: "lora:" }; + + for (const lora of loras) { + const v = ``; + words[v] = { + text: v, + info: () => new LoraInfoDialog(lora).show("loras", lora), + use_replacer: false, + }; + } + + TextAreaAutoComplete.updateWords("pysssss.loras", words); + } + + // store global words with/without loras + Promise.all([addEmbeddings(), addCustomWords()]) + .then(() => { + TextAreaAutoComplete.globalWordsExclLoras = Object.assign({}, TextAreaAutoComplete.globalWords); + }) + .then(addLoras) + .then(() => { + if (!TextAreaAutoComplete.lorasEnabled) { + toggleLoras(); // off by default + } + }); + }, + beforeRegisterNodeDef(_, def) { + // Process each input to see if there is a custom word list for + // { input: { required: { something: ["STRING", { "pysssss.autocomplete": ["groupid", ["custom", "words"] ] }] } } } + const inputs = { ...def.input?.required, ...def.input?.optional }; + for (const input in inputs) { + const config = inputs[input][1]?.["pysssss.autocomplete"]; + if (!config) continue; + if (typeof config === "object" && config.words) { + const words = {}; + for (const text of config.words || []) { + const obj = typeof text === "string" ? { text } : text; + words[obj.text] = obj; + } + TextAreaAutoComplete.updateWords(def.name + "." + input, words, false); + } + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/betterCombos.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/betterCombos.js new file mode 100644 index 0000000000000000000000000000000000000000..cbf0a0740471035dc1d1d4f14101c4888dd91c2a --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/betterCombos.js @@ -0,0 +1,534 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; +import { $el } from "../../../scripts/ui.js"; +import { api } from "../../../scripts/api.js"; + +const CHECKPOINT_LOADER = "CheckpointLoader|pysssss"; +const LORA_LOADER = "LoraLoader|pysssss"; +const IMAGE_WIDTH = 384; +const IMAGE_HEIGHT = 384; + +function getType(node) { + if (node.comfyClass === CHECKPOINT_LOADER) { + return "checkpoints"; + } + return "loras"; +} + +function getWidgetName(type) { + return type === "checkpoints" ? "ckpt_name" : "lora_name"; +} + +function encodeRFC3986URIComponent(str) { + return encodeURIComponent(str).replace(/[!'()*]/g, (c) => `%${c.charCodeAt(0).toString(16).toUpperCase()}`); +} + +const calculateImagePosition = (el, bodyRect) => { + let { top, left, right } = el.getBoundingClientRect(); + const { width: bodyWidth, height: bodyHeight } = bodyRect; + + const isSpaceRight = right + IMAGE_WIDTH <= bodyWidth; + if (isSpaceRight) { + left = right; + } else { + left -= IMAGE_WIDTH; + } + + top = top - IMAGE_HEIGHT / 2; + if (top + IMAGE_HEIGHT > bodyHeight) { + top = bodyHeight - IMAGE_HEIGHT; + } + if (top < 0) { + top = 0; + } + + return { left: Math.round(left), top: Math.round(top), isLeft: !isSpaceRight }; +}; + +function showImage(relativeToEl, imageEl) { + const bodyRect = document.body.getBoundingClientRect(); + if (!bodyRect) return; + + const { left, top, isLeft } = calculateImagePosition(relativeToEl, bodyRect); + + imageEl.style.left = `${left}px`; + imageEl.style.top = `${top}px`; + + if (isLeft) { + imageEl.classList.add("left"); + } else { + imageEl.classList.remove("left"); + } + + document.body.appendChild(imageEl); +} + +let imagesByType = {}; +const loadImageList = async (type) => { + imagesByType[type] = await (await api.fetchApi(`/pysssss/images/${type}`)).json(); +}; + +app.registerExtension({ + name: "pysssss.Combo++", + init() { + const displayOptions = { "List (normal)": 0, "Tree (subfolders)": 1, "Thumbnails (grid)": 2 }; + const displaySetting = app.ui.settings.addSetting({ + id: "pysssss.Combo++.Submenu", + name: "🐍 Lora & Checkpoint loader display mode", + defaultValue: 1, + type: "combo", + options: (value) => { + value = +value; + + return Object.entries(displayOptions).map(([k, v]) => ({ + value: v, + text: k, + selected: k === value, + })); + }, + }); + + $el("style", { + textContent: ` + .pysssss-combo-image { + position: absolute; + left: 0; + top: 0; + width: ${IMAGE_WIDTH}px; + height: ${IMAGE_HEIGHT}px; + object-fit: contain; + object-position: top left; + z-index: 9999; + } + .pysssss-combo-image.left { + object-position: top right; + } + .pysssss-combo-folder { opacity: 0.7 } + .pysssss-combo-folder-arrow { display: inline-block; width: 15px; } + .pysssss-combo-folder:hover { background-color: rgba(255, 255, 255, 0.1); } + .pysssss-combo-prefix { display: none } + + /* Special handling for when the filter input is populated to revert to normal */ + .litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder-contents { + display: block !important; + } + .litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder { + display: none; + } + .litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-prefix { + display: inline; + } + .litecontextmenu:has(input:not(:placeholder-shown)) .litemenu-entry { + padding-left: 2px !important; + } + + /* Grid mode */ + .pysssss-combo-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); + gap: 10px; + overflow-x: hidden; + max-width: 60vw; + } + .pysssss-combo-grid .comfy-context-menu-filter { + grid-column: 1 / -1; + position: sticky; + top: 0; + } + .pysssss-combo-grid .litemenu-entry { + word-break: break-word; + display: flex; + flex-direction: column; + justify-content: space-between; + align-items: center; + } + .pysssss-combo-grid .litemenu-entry:before { + content: ""; + display: block; + width: 100%; + height: 250px; + background-size: contain; + background-position: center; + background-repeat: no-repeat; + /* No-image image attribution: Picture icons created by Pixel perfect - Flaticon */ + background-image: var(--background-image, url(extensions/ComfyUI-Custom-Scripts/js/assets/no-image.png)); + } + + `, + parent: document.body, + }); + const p1 = loadImageList("checkpoints"); + const p2 = loadImageList("loras"); + + const refreshComboInNodes = app.refreshComboInNodes; + app.refreshComboInNodes = async function () { + const r = await Promise.all([ + refreshComboInNodes.apply(this, arguments), + loadImageList("checkpoints").catch(() => {}), + loadImageList("loras").catch(() => {}), + ]); + return r[0]; + }; + + const imageHost = $el("img.pysssss-combo-image"); + + const positionMenu = (menu, fillWidth) => { + // compute best position + let left = app.canvas.last_mouse[0] - 10; + let top = app.canvas.last_mouse[1] - 10; + + const body_rect = document.body.getBoundingClientRect(); + const root_rect = menu.getBoundingClientRect(); + + if (body_rect.width && left > body_rect.width - root_rect.width - 10) left = body_rect.width - root_rect.width - 10; + if (body_rect.height && top > body_rect.height - root_rect.height - 10) top = body_rect.height - root_rect.height - 10; + + menu.style.left = `${left}px`; + menu.style.top = `${top}px`; + if (fillWidth) { + menu.style.right = "10px"; + } + }; + + const updateMenu = async (menu, type) => { + try { + await p1; + await p2; + } catch (error) { + console.error(error); + console.error("Error loading pysssss.betterCombos data"); + } + + // Clamp max height so it doesn't overflow the screen + const position = menu.getBoundingClientRect(); + const maxHeight = window.innerHeight - position.top - 20; + menu.style.maxHeight = `${maxHeight}px`; + + const images = imagesByType[type]; + const items = menu.querySelectorAll(".litemenu-entry"); + + // Add image handler to items + const addImageHandler = (item) => { + const text = item.getAttribute("data-value").trim(); + if (images[text]) { + const textNode = document.createTextNode("*"); + item.appendChild(textNode); + + item.addEventListener( + "mouseover", + () => { + imageHost.src = `/pysssss/view/${encodeRFC3986URIComponent(images[text])}?${+new Date()}`; + document.body.appendChild(imageHost); + showImage(item, imageHost); + }, + { passive: true } + ); + item.addEventListener( + "mouseout", + () => { + imageHost.remove(); + }, + { passive: true } + ); + item.addEventListener( + "click", + () => { + imageHost.remove(); + }, + { passive: true } + ); + } + }; + + const createTree = () => { + // Create a map to store folder structures + const folderMap = new Map(); + const rootItems = []; + const splitBy = (navigator.platform || navigator.userAgent).includes("Win") ? /\/|\\/ : /\//; + const itemsSymbol = Symbol("items"); + + // First pass - organize items into folder structure + for (const item of items) { + const path = item.getAttribute("data-value").split(splitBy); + + // Remove path from visible text + item.textContent = path[path.length - 1]; + if (path.length > 1) { + // Add the prefix path back in so it can be filtered on + const prefix = $el("span.pysssss-combo-prefix", { + textContent: path.slice(0, -1).join("/") + "/", + }); + item.prepend(prefix); + } + + addImageHandler(item); + + if (path.length === 1) { + rootItems.push(item); + continue; + } + + // Temporarily remove the item from current position + item.remove(); + + // Create folder hierarchy + let currentLevel = folderMap; + for (let i = 0; i < path.length - 1; i++) { + const folder = path[i]; + if (!currentLevel.has(folder)) { + currentLevel.set(folder, new Map()); + } + currentLevel = currentLevel.get(folder); + } + + // Store the actual item in the deepest folder + if (!currentLevel.has(itemsSymbol)) { + currentLevel.set(itemsSymbol, []); + } + currentLevel.get(itemsSymbol).push(item); + } + + const createFolderElement = (name) => { + const folder = $el("div.litemenu-entry.pysssss-combo-folder", { + innerHTML: `▶ ${name}`, + style: { paddingLeft: "5px" }, + }); + return folder; + }; + + const insertFolderStructure = (parentElement, map, level = 0) => { + for (const [folderName, content] of map.entries()) { + if (folderName === itemsSymbol) continue; + + const folderElement = createFolderElement(folderName); + folderElement.style.paddingLeft = `${level * 10 + 5}px`; + parentElement.appendChild(folderElement); + + const childContainer = $el("div.pysssss-combo-folder-contents", { + style: { display: "none" }, + }); + + // Add items in this folder + const items = content.get(itemsSymbol) || []; + for (const item of items) { + item.style.paddingLeft = `${(level + 1) * 10 + 14}px`; + childContainer.appendChild(item); + } + + // Recursively add subfolders + insertFolderStructure(childContainer, content, level + 1); + parentElement.appendChild(childContainer); + + // Add click handler for folder + folderElement.addEventListener("click", (e) => { + e.stopPropagation(); + const arrow = folderElement.querySelector(".pysssss-combo-folder-arrow"); + const contents = folderElement.nextElementSibling; + if (contents.style.display === "none") { + contents.style.display = "block"; + arrow.textContent = "▼"; + } else { + contents.style.display = "none"; + arrow.textContent = "▶"; + } + }); + } + }; + + insertFolderStructure(items[0]?.parentElement || menu, folderMap); + positionMenu(menu); + }; + + const addImageData = (item) => { + const text = item.getAttribute("data-value").trim(); + if (images[text]) { + item.style.setProperty("--background-image", `url(/pysssss/view/${encodeRFC3986URIComponent(images[text])})`); + } + }; + + if (displaySetting.value === 1 || displaySetting.value === true) { + createTree(); + } else if (displaySetting.value === 2) { + menu.classList.add("pysssss-combo-grid"); + + for (const item of items) { + addImageData(item); + } + positionMenu(menu, true); + } else { + for (const item of items) { + addImageHandler(item); + } + } + }; + + const mutationObserver = new MutationObserver((mutations) => { + const node = app.canvas.current_node; + + if (!node || (node.comfyClass !== LORA_LOADER && node.comfyClass !== CHECKPOINT_LOADER)) { + return; + } + + for (const mutation of mutations) { + for (const removed of mutation.removedNodes) { + if (removed.classList?.contains("litecontextmenu")) { + imageHost.remove(); + } + } + + for (const added of mutation.addedNodes) { + if (added.classList?.contains("litecontextmenu")) { + const overWidget = app.canvas.getWidgetAtCursor(); + const type = getType(node); + if (overWidget?.name === getWidgetName(type)) { + requestAnimationFrame(() => { + // Bad hack to prevent showing on right click menu by checking for the filter input + if (!added.querySelector(".comfy-context-menu-filter")) return; + updateMenu(added, type); + }); + } + return; + } + } + } + }); + mutationObserver.observe(document.body, { childList: true, subtree: false }); + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + const isCkpt = nodeData.name === CHECKPOINT_LOADER; + const isLora = nodeData.name === LORA_LOADER; + if (isCkpt || isLora) { + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = function () { + onAdded?.apply(this, arguments); + const { widget: exampleList } = ComfyWidgets["COMBO"](this, "example", [[""], {}], app); + this.widgets.find((w) => w.name === "prompt").computeSize = () => [0, -4]; + let exampleWidget; + + const get = async (route, suffix) => { + const url = encodeRFC3986URIComponent(`${getType(nodeType)}${suffix || ""}`); + return await api.fetchApi(`/pysssss/${route}/${url}`); + }; + + const getExample = async () => { + if (exampleList.value === "[none]") { + if (exampleWidget) { + exampleWidget.inputEl.remove(); + exampleWidget = null; + this.widgets.length -= 1; + } + return; + } + + const v = this.widgets[0].value; + const pos = v.lastIndexOf("."); + const name = v.substr(0, pos); + let exampleName = exampleList.value; + let viewPath = `/${name}`; + if (exampleName === "notes") { + viewPath += ".txt"; + } else { + viewPath += `/${exampleName}`; + } + const example = await (await get("view", viewPath)).text(); + if (!exampleWidget) { + exampleWidget = ComfyWidgets["STRING"](this, "prompt", ["STRING", { multiline: true }], app).widget; + exampleWidget.inputEl.readOnly = true; + exampleWidget.inputEl.style.opacity = 0.6; + } + exampleWidget.value = example; + }; + + const exampleCb = exampleList.callback; + exampleList.callback = function () { + getExample(); + return exampleCb?.apply(this, arguments) ?? exampleList.value; + }; + + const listExamples = async () => { + exampleList.disabled = true; + exampleList.options.values = ["[none]"]; + exampleList.value = "[none]"; + let examples = []; + if (this.widgets[0].value) { + try { + examples = await (await get("examples", `/${this.widgets[0].value}`)).json(); + } catch (error) {} + } + exampleList.options.values = ["[none]", ...examples]; + exampleList.value = exampleList.options.values[+!!examples.length]; + exampleList.callback(); + exampleList.disabled = !examples.length; + app.graph.setDirtyCanvas(true, true); + }; + + // Expose function to update examples + nodeType.prototype["pysssss.updateExamples"] = listExamples; + + const modelWidget = this.widgets[0]; + const modelCb = modelWidget.callback; + let prev = undefined; + modelWidget.callback = function () { + let ret = modelCb?.apply(this, arguments) ?? modelWidget.value; + if (typeof ret === "object" && "content" in ret) { + ret = ret.content; + modelWidget.value = ret; + } + let v = ret; + if (prev !== v) { + listExamples(); + prev = v; + } + return ret; + }; + setTimeout(() => { + modelWidget.callback(); + }, 30); + }; + } + + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + if (this.imgs) { + // If this node has images then we add an open in new tab item + let img; + if (this.imageIndex != null) { + // An image is selected so select that + img = this.imgs[this.imageIndex]; + } else if (this.overIndex != null) { + // No image is selected but one is hovered + img = this.imgs[this.overIndex]; + } + if (img) { + const nodes = app.graph._nodes.filter((n) => n.comfyClass === LORA_LOADER || n.comfyClass === CHECKPOINT_LOADER); + if (nodes.length) { + options.unshift({ + content: "Save as Preview", + submenu: { + options: nodes.map((n) => ({ + content: n.widgets[0].value, + callback: async () => { + const url = new URL(img.src); + await api.fetchApi("/pysssss/save/" + encodeRFC3986URIComponent(`${getType(n)}/${n.widgets[0].value}`), { + method: "POST", + body: JSON.stringify({ + filename: url.searchParams.get("filename"), + subfolder: url.searchParams.get("subfolder"), + type: url.searchParams.get("type"), + }), + headers: { + "content-type": "application/json", + }, + }); + loadImageList(getType(n)); + }, + })), + }, + }); + } + } + } + return getExtraMenuOptions?.apply(this, arguments); + }; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/autocomplete.css b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/autocomplete.css new file mode 100644 index 0000000000000000000000000000000000000000..1f371d6203a94202c48c851a3321c7e423de477b --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/autocomplete.css @@ -0,0 +1,62 @@ +.pysssss-autocomplete { + color: var(--descrip-text); + background-color: var(--comfy-menu-bg); + position: absolute; + font-family: sans-serif; + box-shadow: 3px 3px 8px rgba(0, 0, 0, 0.4); + z-index: 9999; + overflow: auto; +} + +.pysssss-autocomplete-item { + cursor: pointer; + padding: 3px 7px; + display: flex; + border-left: 3px solid transparent; + align-items: center; +} + +.pysssss-autocomplete-item--selected { + border-left-color: dodgerblue; +} + +.pysssss-autocomplete-highlight { + font-weight: bold; + text-decoration: underline; + text-decoration-color: dodgerblue; +} + +.pysssss-autocomplete-pill { + margin-left: auto; + font-size: 10px; + color: #fff; + padding: 2px 4px 2px 14px; + position: relative; +} + +.pysssss-autocomplete-pill::after { + content: ""; + display: block; + background: rgba(255, 255, 255, 0.25); + width: calc(100% - 10px); + height: 100%; + position: absolute; + left: 10px; + top: 0; + border-radius: 5px; +} + +.pysssss-autocomplete-pill + .pysssss-autocomplete-pill { + margin-left: 0; +} + +.pysssss-autocomplete-item-info { + margin-left: auto; + transition: filter 0.2s; + will-change: filter; + text-decoration: none; + padding-left: 10px; +} +.pysssss-autocomplete-item-info:hover { + filter: invert(1); +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/autocomplete.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/autocomplete.js new file mode 100644 index 0000000000000000000000000000000000000000..ac5fb1d006bad80c67870655ccfc82a120ddc4a7 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/autocomplete.js @@ -0,0 +1,692 @@ +import { $el } from "../../../../scripts/ui.js"; +import { addStylesheet } from "./utils.js"; + +addStylesheet(import.meta.url); + +/* + https://github.com/component/textarea-caret-position + The MIT License (MIT) + + Copyright (c) 2015 Jonathan Ong me@jongleberry.com + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +*/ +const getCaretCoordinates = (function () { + // We'll copy the properties below into the mirror div. + // Note that some browsers, such as Firefox, do not concatenate properties + // into their shorthand (e.g. padding-top, padding-bottom etc. -> padding), + // so we have to list every single property explicitly. + var properties = [ + "direction", // RTL support + "boxSizing", + "width", // on Chrome and IE, exclude the scrollbar, so the mirror div wraps exactly as the textarea does + "height", + "overflowX", + "overflowY", // copy the scrollbar for IE + + "borderTopWidth", + "borderRightWidth", + "borderBottomWidth", + "borderLeftWidth", + "borderStyle", + + "paddingTop", + "paddingRight", + "paddingBottom", + "paddingLeft", + + // https://developer.mozilla.org/en-US/docs/Web/CSS/font + "fontStyle", + "fontVariant", + "fontWeight", + "fontStretch", + "fontSize", + "fontSizeAdjust", + "lineHeight", + "fontFamily", + + "textAlign", + "textTransform", + "textIndent", + "textDecoration", // might not make a difference, but better be safe + + "letterSpacing", + "wordSpacing", + + "tabSize", + "MozTabSize", + ]; + + var isBrowser = typeof window !== "undefined"; + var isFirefox = isBrowser && window.mozInnerScreenX != null; + + return function getCaretCoordinates(element, position, options) { + if (!isBrowser) { + throw new Error("textarea-caret-position#getCaretCoordinates should only be called in a browser"); + } + + var debug = (options && options.debug) || false; + if (debug) { + var el = document.querySelector("#input-textarea-caret-position-mirror-div"); + if (el) el.parentNode.removeChild(el); + } + + // The mirror div will replicate the textarea's style + var div = document.createElement("div"); + div.id = "input-textarea-caret-position-mirror-div"; + document.body.appendChild(div); + + var style = div.style; + var computed = window.getComputedStyle ? window.getComputedStyle(element) : element.currentStyle; // currentStyle for IE < 9 + var isInput = element.nodeName === "INPUT"; + + // Default textarea styles + style.whiteSpace = "pre-wrap"; + if (!isInput) style.wordWrap = "break-word"; // only for textarea-s + + // Position off-screen + style.position = "absolute"; // required to return coordinates properly + if (!debug) style.visibility = "hidden"; // not 'display: none' because we want rendering + + // Transfer the element's properties to the div + properties.forEach(function (prop) { + if (isInput && prop === "lineHeight") { + // Special case for s because text is rendered centered and line height may be != height + if (computed.boxSizing === "border-box") { + var height = parseInt(computed.height); + var outerHeight = + parseInt(computed.paddingTop) + + parseInt(computed.paddingBottom) + + parseInt(computed.borderTopWidth) + + parseInt(computed.borderBottomWidth); + var targetHeight = outerHeight + parseInt(computed.lineHeight); + if (height > targetHeight) { + style.lineHeight = height - outerHeight + "px"; + } else if (height === targetHeight) { + style.lineHeight = computed.lineHeight; + } else { + style.lineHeight = 0; + } + } else { + style.lineHeight = computed.height; + } + } else { + style[prop] = computed[prop]; + } + }); + + if (isFirefox) { + // Firefox lies about the overflow property for textareas: https://bugzilla.mozilla.org/show_bug.cgi?id=984275 + if (element.scrollHeight > parseInt(computed.height)) style.overflowY = "scroll"; + } else { + style.overflow = "hidden"; // for Chrome to not render a scrollbar; IE keeps overflowY = 'scroll' + } + + div.textContent = element.value.substring(0, position); + // The second special handling for input type="text" vs textarea: + // spaces need to be replaced with non-breaking spaces - http://stackoverflow.com/a/13402035/1269037 + if (isInput) div.textContent = div.textContent.replace(/\s/g, "\u00a0"); + + var span = document.createElement("span"); + // Wrapping must be replicated *exactly*, including when a long word gets + // onto the next line, with whitespace at the end of the line before (#7). + // The *only* reliable way to do that is to copy the *entire* rest of the + // textarea's content into the created at the caret position. + // For inputs, just '.' would be enough, but no need to bother. + span.textContent = element.value.substring(position) || "."; // || because a completely empty faux span doesn't render at all + div.appendChild(span); + + var coordinates = { + top: span.offsetTop + parseInt(computed["borderTopWidth"]), + left: span.offsetLeft + parseInt(computed["borderLeftWidth"]), + height: parseInt(computed["lineHeight"]), + }; + + if (debug) { + span.style.backgroundColor = "#aaa"; + } else { + document.body.removeChild(div); + } + + return coordinates; + }; +})(); + +/* + Key functions from: + https://github.com/yuku/textcomplete + © Yuku Takahashi - This software is licensed under the MIT license. + + The MIT License (MIT) + + Copyright (c) 2015 Jonathan Ong me@jongleberry.com + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +*/ +const CHAR_CODE_ZERO = "0".charCodeAt(0); +const CHAR_CODE_NINE = "9".charCodeAt(0); + +class TextAreaCaretHelper { + constructor(el, getScale) { + this.el = el; + this.getScale = getScale; + } + + #calculateElementOffset() { + const rect = this.el.getBoundingClientRect(); + const owner = this.el.ownerDocument; + if (owner == null) { + throw new Error("Given element does not belong to document"); + } + const { defaultView, documentElement } = owner; + if (defaultView == null) { + throw new Error("Given element does not belong to window"); + } + const offset = { + top: rect.top + defaultView.pageYOffset, + left: rect.left + defaultView.pageXOffset, + }; + if (documentElement) { + offset.top -= documentElement.clientTop; + offset.left -= documentElement.clientLeft; + } + return offset; + } + + #isDigit(charCode) { + return CHAR_CODE_ZERO <= charCode && charCode <= CHAR_CODE_NINE; + } + + #getLineHeightPx() { + const computedStyle = getComputedStyle(this.el); + const lineHeight = computedStyle.lineHeight; + // If the char code starts with a digit, it is either a value in pixels, + // or unitless, as per: + // https://drafts.csswg.org/css2/visudet.html#propdef-line-height + // https://drafts.csswg.org/css2/cascade.html#computed-value + if (this.#isDigit(lineHeight.charCodeAt(0))) { + const floatLineHeight = parseFloat(lineHeight); + // In real browsers the value is *always* in pixels, even for unit-less + // line-heights. However, we still check as per the spec. + return this.#isDigit(lineHeight.charCodeAt(lineHeight.length - 1)) + ? floatLineHeight * parseFloat(computedStyle.fontSize) + : floatLineHeight; + } + // Otherwise, the value is "normal". + // If the line-height is "normal", calculate by font-size + return this.#calculateLineHeightPx(this.el.nodeName, computedStyle); + } + + /** + * Returns calculated line-height of the given node in pixels. + */ + #calculateLineHeightPx(nodeName, computedStyle) { + const body = document.body; + if (!body) return 0; + + const tempNode = document.createElement(nodeName); + tempNode.innerHTML = " "; + Object.assign(tempNode.style, { + fontSize: computedStyle.fontSize, + fontFamily: computedStyle.fontFamily, + padding: "0", + position: "absolute", + }); + body.appendChild(tempNode); + + // Make sure textarea has only 1 row + if (tempNode instanceof HTMLTextAreaElement) { + tempNode.rows = 1; + } + + // Assume the height of the element is the line-height + const height = tempNode.offsetHeight; + body.removeChild(tempNode); + + return height; + } + + getCursorOffset() { + const scale = this.getScale(); + const elOffset = this.#calculateElementOffset(); + const elScroll = this.#getElScroll(); + const cursorPosition = this.#getCursorPosition(); + const lineHeight = this.#getLineHeightPx(); + const top = elOffset.top - (elScroll.top * scale) + (cursorPosition.top + lineHeight) * scale; + const left = elOffset.left - elScroll.left + cursorPosition.left; + const clientTop = this.el.getBoundingClientRect().top; + if (this.el.dir !== "rtl") { + return { top, left, lineHeight, clientTop }; + } else { + const right = document.documentElement ? document.documentElement.clientWidth - left : 0; + return { top, right, lineHeight, clientTop }; + } + } + + #getElScroll() { + return { top: this.el.scrollTop, left: this.el.scrollLeft }; + } + + #getCursorPosition() { + return getCaretCoordinates(this.el, this.el.selectionEnd); + } + + getBeforeCursor() { + return this.el.selectionStart !== this.el.selectionEnd ? null : this.el.value.substring(0, this.el.selectionEnd); + } + + getAfterCursor() { + return this.el.value.substring(this.el.selectionEnd); + } + + insertAtCursor(value, offset, finalOffset) { + if (this.el.selectionStart != null) { + const startPos = this.el.selectionStart; + const endPos = this.el.selectionEnd; + + // Move selection to beginning of offset + this.el.selectionStart = this.el.selectionStart + offset; + + // Using execCommand to support undo, but since it's officially + // 'deprecated' we need a backup solution, but it won't support undo :( + let pasted = true; + try { + if (!document.execCommand("insertText", false, value)) { + pasted = false; + } + } catch (e) { + console.error("Error caught during execCommand:", e); + pasted = false; + } + + if (!pasted) { + console.error( + "execCommand unsuccessful; not supported. Adding text manually, no undo support."); + textarea.setRangeText(modifiedText, this.el.selectionStart, this.el.selectionEnd, 'end'); + } + + this.el.selectionEnd = this.el.selectionStart = startPos + value.length + offset + (finalOffset ?? 0); + } else { + // Using execCommand to support undo, but since it's officially + // 'deprecated' we need a backup solution, but it won't support undo :( + let pasted = true; + try { + if (!document.execCommand("insertText", false, value)) { + pasted = false; + } + } catch (e) { + console.error("Error caught during execCommand:", e); + pasted = false; + } + + if (!pasted) { + console.error( + "execCommand unsuccessful; not supported. Adding text manually, no undo support."); + this.el.value += value; + } + } + } +} + +/*********************/ + +/** + * @typedef {{ + * text: string, + * priority?: number, + * info?: Function, + * hint?: string, + * showValue?: boolean, + * caretOffset?: number + * }} AutoCompleteEntry + */ +export class TextAreaAutoComplete { + static globalSeparator = ""; + static enabled = true; + static insertOnTab = true; + static insertOnEnter = true; + static replacer = undefined; + static lorasEnabled = false; + static suggestionCount = 20; + + /** @type {Record>} */ + static groups = {}; + /** @type {Set} */ + static globalGroups = new Set(); + /** @type {Record} */ + static globalWords = {}; + /** @type {Record} */ + static globalWordsExclLoras = {}; + + /** @type {HTMLTextAreaElement} */ + el; + + /** @type {Record} */ + overrideWords; + overrideSeparator = ""; + + get words() { + return this.overrideWords ?? TextAreaAutoComplete.globalWords; + } + + get separator() { + return this.overrideSeparator ?? TextAreaAutoComplete.globalSeparator; + } + + /** + * @param {HTMLTextAreaElement} el + */ + constructor(el, words = null, separator = null) { + this.el = el; + this.helper = new TextAreaCaretHelper(el, () => app.canvas.ds.scale); + this.dropdown = $el("div.pysssss-autocomplete"); + this.overrideWords = words; + this.overrideSeparator = separator; + + this.#setup(); + } + + #setup() { + this.el.addEventListener("keydown", this.#keyDown.bind(this)); + this.el.addEventListener("keypress", this.#keyPress.bind(this)); + this.el.addEventListener("keyup", this.#keyUp.bind(this)); + this.el.addEventListener("click", this.#hide.bind(this)); + this.el.addEventListener("blur", () => setTimeout(() => this.#hide(), 150)); + } + + /** + * @param {KeyboardEvent} e + */ + #keyDown(e) { + if (!TextAreaAutoComplete.enabled) return; + + if (this.dropdown.parentElement) { + // We are visible + switch (e.key) { + case "ArrowUp": + e.preventDefault(); + if (this.selected.index) { + this.#setSelected(this.currentWords[this.selected.index - 1].wordInfo); + } else { + this.#setSelected(this.currentWords[this.currentWords.length - 1].wordInfo); + } + break; + case "ArrowDown": + e.preventDefault(); + if (this.selected.index === this.currentWords.length - 1) { + this.#setSelected(this.currentWords[0].wordInfo); + } else { + this.#setSelected(this.currentWords[this.selected.index + 1].wordInfo); + } + break; + case "Tab": + if (TextAreaAutoComplete.insertOnTab) { + this.#insertItem(); + e.preventDefault(); + } + break; + } + } + } + + /** + * @param {KeyboardEvent} e + */ + #keyPress(e) { + if (!TextAreaAutoComplete.enabled) return; + if (this.dropdown.parentElement) { + // We are visible + switch (e.key) { + case "Enter": + if (!e.ctrlKey) { + if (TextAreaAutoComplete.insertOnEnter) { + this.#insertItem(); + e.preventDefault(); + } + } + break; + } + } + + if (!e.defaultPrevented) { + this.#update(); + } + } + + #keyUp(e) { + if (!TextAreaAutoComplete.enabled) return; + if (this.dropdown.parentElement) { + // We are visible + switch (e.key) { + case "Escape": + e.preventDefault(); + this.#hide(); + break; + } + } else if (e.key.length > 1 && e.key != "Delete" && e.key != "Backspace") { + return; + } + if (!e.defaultPrevented) { + this.#update(); + } + } + + #setSelected(item) { + if (this.selected) { + this.selected.el.classList.remove("pysssss-autocomplete-item--selected"); + } + + this.selected = item; + this.selected.el.classList.add("pysssss-autocomplete-item--selected"); + } + + #insertItem() { + if (!this.selected) return; + this.selected.el.click(); + } + + #getFilteredWords(term) { + term = term.toLocaleLowerCase(); + + const priorityMatches = []; + const prefixMatches = []; + const includesMatches = []; + for (const word of Object.keys(this.words)) { + const lowerWord = word.toLocaleLowerCase(); + if (lowerWord === term) { + // Dont include exact matches + continue; + } + + const pos = lowerWord.indexOf(term); + if (pos === -1) { + // No match + continue; + } + + const wordInfo = this.words[word]; + if (wordInfo.priority) { + priorityMatches.push({ pos, wordInfo }); + } else if (pos) { + includesMatches.push({ pos, wordInfo }); + } else { + prefixMatches.push({ pos, wordInfo }); + } + } + + priorityMatches.sort( + (a, b) => + b.wordInfo.priority - a.wordInfo.priority || + a.wordInfo.text.length - b.wordInfo.text.length || + a.wordInfo.text.localeCompare(b.wordInfo.text) + ); + + const top = priorityMatches.length * 0.2; + return priorityMatches.slice(0, top).concat(prefixMatches, priorityMatches.slice(top), includesMatches).slice(0, TextAreaAutoComplete.suggestionCount); + } + + #update() { + let before = this.helper.getBeforeCursor(); + if (before?.length) { + const m = before.match(/([^,;"|{}()\n]+)$/); + if (m) { + before = m[0] + .replace(/^\s+/, "") + .replace(/\s/g, "_") || null; + } else { + before = null; + } + } + + if (!before) { + this.#hide(); + return; + } + + this.currentWords = this.#getFilteredWords(before); + if (!this.currentWords.length) { + this.#hide(); + return; + } + + this.dropdown.style.display = ""; + + let hasSelected = false; + const items = this.currentWords.map(({ wordInfo, pos }, i) => { + const parts = [ + $el("span", { + textContent: wordInfo.text.substr(0, pos), + }), + $el("span.pysssss-autocomplete-highlight", { + textContent: wordInfo.text.substr(pos, before.length), + }), + $el("span", { + textContent: wordInfo.text.substr(pos + before.length), + }), + ]; + + if (wordInfo.hint) { + parts.push( + $el("span.pysssss-autocomplete-pill", { + textContent: wordInfo.hint, + }) + ); + } + + if (wordInfo.priority) { + parts.push( + $el("span.pysssss-autocomplete-pill", { + textContent: wordInfo.priority, + }) + ); + } + + if (wordInfo.value && wordInfo.text !== wordInfo.value && wordInfo.showValue !== false) { + parts.push( + $el("span.pysssss-autocomplete-pill", { + textContent: wordInfo.value, + }) + ); + } + + if (wordInfo.info) { + parts.push( + $el("a.pysssss-autocomplete-item-info", { + textContent: "ℹ️", + title: "View info...", + onclick: (e) => { + e.stopPropagation(); + wordInfo.info(); + e.preventDefault(); + }, + }) + ); + } + const item = $el( + "div.pysssss-autocomplete-item", + { + onclick: () => { + this.el.focus(); + let value = wordInfo.value ?? wordInfo.text; + const use_replacer = wordInfo.use_replacer ?? true; + if (TextAreaAutoComplete.replacer && use_replacer) { + value = TextAreaAutoComplete.replacer(value); + } + value = this.#escapeParentheses(value); + + const afterCursor = this.helper.getAfterCursor(); + const shouldAddSeparator = !afterCursor.trim().startsWith(this.separator.trim()); + this.helper.insertAtCursor( + value + (shouldAddSeparator ? this.separator : ''), + -before.length, + wordInfo.caretOffset + ); + setTimeout(() => { + this.#update(); + }, 150); + }, + }, + parts + ); + + if (wordInfo === this.selected) { + hasSelected = true; + } + + wordInfo.index = i; + wordInfo.el = item; + + return item; + }); + + this.#setSelected(hasSelected ? this.selected : this.currentWords[0].wordInfo); + this.dropdown.replaceChildren(...items); + + if (!this.dropdown.parentElement) { + document.body.append(this.dropdown); + } + + const position = this.helper.getCursorOffset(); + this.dropdown.style.left = (position.left ?? 0) + "px"; + this.dropdown.style.top = (position.top ?? 0) + "px"; + this.dropdown.style.maxHeight = (window.innerHeight - position.top) + "px"; + } + + #escapeParentheses(text) { + return text.replace(/\(/g, '\\(').replace(/\)/g, '\\)'); + } + + #hide() { + this.selected = null; + this.dropdown.remove(); + } + + static updateWords(id, words, addGlobal = true) { + const isUpdate = id in TextAreaAutoComplete.groups; + TextAreaAutoComplete.groups[id] = words; + if (addGlobal) { + TextAreaAutoComplete.globalGroups.add(id); + } + + if (isUpdate) { + // Remerge all words + TextAreaAutoComplete.globalWords = Object.assign( + {}, + ...Object.keys(TextAreaAutoComplete.groups) + .filter((k) => TextAreaAutoComplete.globalGroups.has(k)) + .map((k) => TextAreaAutoComplete.groups[k]) + ); + } else if (addGlobal) { + // Just insert the new words + Object.assign(TextAreaAutoComplete.globalWords, words); + } + } +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/binding.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/binding.js new file mode 100644 index 0000000000000000000000000000000000000000..7533e7e5e93beee798123a8ed84d36a50d2a4702 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/binding.js @@ -0,0 +1,244 @@ +// @ts-check +// @ts-ignore +import { ComfyWidgets } from "../../../../scripts/widgets.js"; +// @ts-ignore +import { api } from "../../../../scripts/api.js"; +// @ts-ignore +import { app } from "../../../../scripts/app.js"; + +const PathHelper = { + get(obj, path) { + if (typeof path !== "string") { + // Hardcoded value + return path; + } + + if (path[0] === '"' && path[path.length - 1] === '"') { + // Hardcoded string + return JSON.parse(path); + } + + // Evaluate the path + path = path.split(".").filter(Boolean); + for (const p of path) { + const k = isNaN(+p) ? p : +p; + obj = obj[k]; + } + + return obj; + }, + set(obj, path, value) { + // https://stackoverflow.com/a/54733755 + if (Object(obj) !== obj) return obj; // When obj is not an object + // If not yet an array, get the keys from the string-path + if (!Array.isArray(path)) path = path.toString().match(/[^.[\]]+/g) || []; + path.slice(0, -1).reduce( + ( + a, + c, + i // Iterate all of them except the last one + ) => + Object(a[c]) === a[c] // Does the key exist and is its value an object? + ? // Yes: then follow that path + a[c] + : // No: create the key. Is the next key a potential array-index? + (a[c] = + Math.abs(path[i + 1]) >> 0 === +path[i + 1] + ? [] // Yes: assign a new array object + : {}), // No: assign a new plain object + obj + )[path[path.length - 1]] = value; // Finally assign the value to the last key + return obj; // Return the top-level object to allow chaining + }, +}; + +/*** + @typedef { { + left: string; + op: "eq" | "ne", + right: string + } } IfCondition + + @typedef { { + type: "if", + condition: Array, + true?: Array, + false?: Array + } } IfCallback + + @typedef { { + type: "fetch", + url: string, + then: Array + } } FetchCallback + + @typedef { { + type: "set", + target: string, + value: string + } } SetCallback + + @typedef { { + type: "validate-combo", + } } ValidateComboCallback + + @typedef { IfCallback | FetchCallback | SetCallback | ValidateComboCallback } BindingCallback + + @typedef { { + source: string, + callback: Array + } } Binding +***/ + +/** + * @param {IfCondition} condition + */ +function evaluateCondition(condition, state) { + const left = PathHelper.get(state, condition.left); + const right = PathHelper.get(state, condition.right); + + let r; + if (condition.op === "eq") { + r = left === right; + } else { + r = left !== right; + } + + return r; +} + +/** + * @type { Record) => Promise> } + */ +const callbacks = { + /** + * @param {IfCallback} cb + */ + async if(cb, state) { + // For now only support ANDs + let success = true; + for (const condition of cb.condition) { + const r = evaluateCondition(condition, state); + if (!r) { + success = false; + break; + } + } + + for (const m of cb[success + ""] ?? []) { + await invokeCallback(m, state); + } + }, + /** + * @param {FetchCallback} cb + */ + async fetch(cb, state) { + const url = cb.url.replace(/\{([^\}]+)\}/g, (m, v) => { + return PathHelper.get(state, v); + }); + const res = await (await api.fetchApi(url)).json(); + state["$result"] = res; + for (const m of cb.then) { + await invokeCallback(m, state); + } + }, + /** + * @param {SetCallback} cb + */ + async set(cb, state) { + const value = PathHelper.get(state, cb.value); + PathHelper.set(state, cb.target, value); + }, + async "validate-combo"(cb, state) { + const w = state["$this"]; + const valid = w.options.values.includes(w.value); + if (!valid) { + w.value = w.options.values[0]; + } + }, +}; + +async function invokeCallback(callback, state) { + if (callback.type in callbacks) { + // @ts-ignore + await callbacks[callback.type](callback, state); + } else { + console.warn( + "%c[🐍 pysssss]", + "color: limegreen", + `[binding ${state.$node.comfyClass}.${state.$this.name}]`, + "unsupported binding callback type:", + callback.type + ); + } +} + +app.registerExtension({ + name: "pysssss.Binding", + beforeRegisterNodeDef(node, nodeData) { + const hasBinding = (v) => { + if (!v) return false; + return Object.values(v).find((c) => c[1]?.["pysssss.binding"]); + }; + const inputs = { ...nodeData.input?.required, ...nodeData.input?.optional }; + if (hasBinding(inputs)) { + const onAdded = node.prototype.onAdded; + node.prototype.onAdded = function () { + const r = onAdded?.apply(this, arguments); + + for (const widget of this.widgets || []) { + const bindings = inputs[widget.name][1]?.["pysssss.binding"]; + if (!bindings) continue; + + for (const binding of bindings) { + /** + * @type {import("../../../../../web/types/litegraph.d.ts").IWidget} + */ + const source = this.widgets.find((w) => w.name === binding.source); + if (!source) { + console.warn( + "%c[🐍 pysssss]", + "color: limegreen", + `[binding ${node.comfyClass}.${widget.name}]`, + "unable to find source binding widget:", + binding.source, + binding + ); + continue; + } + + let lastValue; + async function valueChanged() { + const state = { + $this: widget, + $source: source, + $node: node, + }; + + for (const callback of binding.callback) { + await invokeCallback(callback, state); + } + + app.graph.setDirtyCanvas(true, false); + } + + const cb = source.callback; + source.callback = function () { + const v = cb?.apply(this, arguments) ?? source.value; + if (v !== lastValue) { + lastValue = v; + valueChanged(); + } + return v; + }; + + lastValue = source.value; + valueChanged(); + } + } + + return r; + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/lightbox.css b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/lightbox.css new file mode 100644 index 0000000000000000000000000000000000000000..c4307fd0a201f6ae396e0cb9e2b5e00dba8fc285 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/lightbox.css @@ -0,0 +1,102 @@ +.pysssss-lightbox { + width: 100vw; + height: 100vh; + position: fixed; + top: 0; + left: 0; + z-index: 1001; + background: rgba(0, 0, 0, 0.6); + display: flex; + align-items: center; + transition: opacity 0.2s; +} + +.pysssss-lightbox-prev, +.pysssss-lightbox-next { + height: 60px; + display: flex; + align-items: center; +} + +.pysssss-lightbox-prev:after, +.pysssss-lightbox-next:after { + border-style: solid; + border-width: 0.25em 0.25em 0 0; + display: inline-block; + height: 0.45em; + left: 0.15em; + position: relative; + top: 0.15em; + transform: rotate(-135deg) scale(0.75); + vertical-align: top; + width: 0.45em; + padding: 10px; + font-size: 20px; + margin: 0 10px 0 20px; + transition: color 0.2s; + flex-shrink: 0; + content: ""; +} + +.pysssss-lightbox-next:after { + transform: rotate(45deg) scale(0.75); + margin: 0 20px 0 0px; +} + +.pysssss-lightbox-main { + display: grid; + flex: auto; + place-content: center; + text-align: center; +} + +.pysssss-lightbox-link { + display: flex; + justify-content: center; + align-items: center; + position: relative; +} + +.pysssss-lightbox .lds-ring { + position: absolute; + left: 50%; + top: 50%; + transform: translate(-50%, -50%); +} + +.pysssss-lightbox-img { + max-height: 90vh; + max-width: calc(100vw - 130px); + height: auto; + object-fit: contain; + border: 3px solid white; + border-radius: 4px; + transition: opacity 0.2s; + user-select: none; +} + +.pysssss-lightbox-img:hover { + border-color: dodgerblue; +} + +.pysssss-lightbox-close { + font-size: 80px; + line-height: 1ch; + height: 1ch; + width: 1ch; + position: absolute; + right: 10px; + top: 10px; + padding: 5px; +} + +.pysssss-lightbox-close:after { + content: "\00d7"; +} + +.pysssss-lightbox-close:hover, +.pysssss-lightbox-prev:hover, +.pysssss-lightbox-next:hover { + color: dodgerblue; + cursor: pointer; +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/lightbox.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/lightbox.js new file mode 100644 index 0000000000000000000000000000000000000000..5c9079067f6fd26ab8e392e55add76d52a885858 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/lightbox.js @@ -0,0 +1,149 @@ +import { $el } from "../../../../scripts/ui.js"; +import { addStylesheet, getUrl, loadImage } from "./utils.js"; +import { createSpinner } from "./spinner.js"; + +addStylesheet(getUrl("lightbox.css", import.meta.url)); + +const $$el = (tag, name, ...args) => { + if (name) name = "-" + name; + return $el(tag + ".pysssss-lightbox" + name, ...args); +}; + +const ani = async (a, t, b) => { + a(); + await new Promise((r) => setTimeout(r, t)); + b(); +}; + +export class Lightbox { + constructor() { + this.el = $$el("div", "", { + parent: document.body, + onclick: (e) => { + e.stopImmediatePropagation(); + this.close(); + }, + style: { + display: "none", + opacity: 0, + }, + }); + this.closeBtn = $$el("div", "close", { + parent: this.el, + }); + this.prev = $$el("div", "prev", { + parent: this.el, + onclick: (e) => { + this.update(-1); + e.stopImmediatePropagation(); + }, + }); + this.main = $$el("div", "main", { + parent: this.el, + }); + this.next = $$el("div", "next", { + parent: this.el, + onclick: (e) => { + this.update(1); + e.stopImmediatePropagation(); + }, + }); + this.link = $$el("a", "link", { + parent: this.main, + target: "_blank", + }); + this.spinner = createSpinner(); + this.link.appendChild(this.spinner); + this.img = $$el("img", "img", { + style: { + opacity: 0, + }, + parent: this.link, + onclick: (e) => { + e.stopImmediatePropagation(); + }, + onwheel: (e) => { + if (!(e instanceof WheelEvent) || e.ctrlKey) { + return; + } + const direction = Math.sign(e.deltaY); + this.update(direction); + }, + }); + } + + close() { + ani( + () => (this.el.style.opacity = 0), + 200, + () => (this.el.style.display = "none") + ); + } + + async show(images, index) { + this.images = images; + this.index = index || 0; + await this.update(0); + } + + async update(shift) { + if (shift < 0 && this.index <= 0) { + return; + } + if (shift > 0 && this.index >= this.images.length - 1) { + return; + } + this.index += shift; + + this.prev.style.visibility = this.index ? "unset" : "hidden"; + this.next.style.visibility = this.index === this.images.length - 1 ? "hidden" : "unset"; + + const img = this.images[this.index]; + this.el.style.display = "flex"; + this.el.clientWidth; // Force a reflow + this.el.style.opacity = 1; + this.img.style.opacity = 0; + this.spinner.style.display = "inline-block"; + try { + await loadImage(img); + } catch (err) { + console.error('failed to load image', img, err); + } + this.spinner.style.display = "none"; + this.link.href = img; + this.img.src = img; + this.img.style.opacity = 1; + } + + async updateWithNewImage(img, feedDirection) { + // No-op if lightbox is not open + if (this.el.style.display === "none" || this.el.style.opacity === "0") return; + + // Ensure currently shown image does not change + const [method, shift] = feedDirection === "newest first" ? ["unshift", 1] : ["push", 0]; + this.images[method](img); + await this.update(shift); + } +} + +export const lightbox = new Lightbox(); + +addEventListener('keydown', (event) => { + if (lightbox.el.style.display === 'none') { + return; + } + const { key } = event; + switch (key) { + case 'ArrowLeft': + case 'a': + lightbox.update(-1); + break; + case 'ArrowRight': + case 'd': + lightbox.update(1); + break; + case 'Escape': + lightbox.close(); + break; + } +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/modelInfoDialog.css b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/modelInfoDialog.css new file mode 100644 index 0000000000000000000000000000000000000000..a5b9f627fc348ca17db78e5b28bc43a86e46560a --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/modelInfoDialog.css @@ -0,0 +1,119 @@ +.pysssss-model-info { + color: white; + font-family: sans-serif; + max-width: 90vw; +} +.pysssss-model-content { + display: flex; + flex-direction: column; + overflow: hidden; +} +.pysssss-model-info h2 { + text-align: center; + margin: 0 0 10px 0; +} +.pysssss-model-info p { + margin: 5px 0; +} +.pysssss-model-info a { + color: dodgerblue; +} +.pysssss-model-info a:hover { + text-decoration: underline; +} +.pysssss-model-tags-list { + display: flex; + flex-wrap: wrap; + list-style: none; + gap: 10px; + max-height: 200px; + overflow: auto; + margin: 10px 0; + padding: 0; +} +.pysssss-model-tag { + background-color: rgb(128, 213, 247); + color: #000; + display: flex; + align-items: center; + gap: 5px; + border-radius: 5px; + padding: 2px 5px; + cursor: pointer; +} +.pysssss-model-tag--selected span::before { + content: "✅"; + position: absolute; + background-color: dodgerblue; + left: 0; + top: 0; + right: 0; + bottom: 0; + text-align: center; +} +.pysssss-model-tag:hover { + outline: 2px solid dodgerblue; +} +.pysssss-model-tag p { + margin: 0; +} +.pysssss-model-tag span { + text-align: center; + border-radius: 5px; + background-color: dodgerblue; + color: #fff; + padding: 2px; + position: relative; + min-width: 20px; + overflow: hidden; +} + +.pysssss-model-metadata .comfy-modal-content { + max-width: 100%; +} +.pysssss-model-metadata label { + margin-right: 1ch; + color: #ccc; +} + +.pysssss-model-metadata span { + color: dodgerblue; +} + +.pysssss-preview { + max-width: 50%; + margin-left: 10px; + position: relative; +} +.pysssss-preview img { + max-height: 300px; +} +.pysssss-preview button { + position: absolute; + font-size: 12px; + bottom: 10px; + right: 10px; +} +.pysssss-preview button+button { + bottom: 34px; +} + +.pysssss-preview button.pysssss-preview-nav { + bottom: unset; + right: 30px; + top: 10px; + font-size: 14px; + line-height: 14px; +} + +.pysssss-preview button.pysssss-preview-nav+.pysssss-preview-nav { + right: 10px; +} +.pysssss-model-notes { + background-color: rgba(0, 0, 0, 0.25); + padding: 5px; + margin-top: 5px; +} +.pysssss-model-notes:empty { + display: none; +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/modelInfoDialog.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/modelInfoDialog.js new file mode 100644 index 0000000000000000000000000000000000000000..032767da17bfc12f60ac8c6f926d8366a3b436e6 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/modelInfoDialog.js @@ -0,0 +1,358 @@ +import { $el, ComfyDialog } from "../../../../scripts/ui.js"; +import { api } from "../../../../scripts/api.js"; +import { addStylesheet } from "./utils.js"; + +addStylesheet(import.meta.url); + +class MetadataDialog extends ComfyDialog { + constructor() { + super(); + + this.element.classList.add("pysssss-model-metadata"); + } + show(metadata) { + super.show( + $el( + "div", + Object.keys(metadata).map((k) => + $el("div", [ + $el("label", { textContent: k }), + $el("span", { textContent: typeof metadata[k] === "object" ? JSON.stringify(metadata[k]) : metadata[k] }), + ]) + ) + ) + ); + } +} + +export class ModelInfoDialog extends ComfyDialog { + constructor(name, node) { + super(); + this.name = name; + this.node = node; + this.element.classList.add("pysssss-model-info"); + } + + get customNotes() { + return this.metadata["pysssss.notes"]; + } + + set customNotes(v) { + this.metadata["pysssss.notes"] = v; + } + + get hash() { + return this.metadata["pysssss.sha256"]; + } + + async show(type, value) { + this.type = type; + + const req = api.fetchApi("/pysssss/metadata/" + encodeURIComponent(`${type}/${value}`)); + this.info = $el("div", { style: { flex: "auto" } }); + this.img = $el("img", { style: { display: "none" } }); + this.imgWrapper = $el("div.pysssss-preview", [this.img]); + this.main = $el("main", { style: { display: "flex" } }, [this.info, this.imgWrapper]); + this.content = $el("div.pysssss-model-content", [$el("h2", { textContent: this.name }), this.main]); + + const loading = $el("div", { textContent: "ℹ️ Loading...", parent: this.content }); + + super.show(this.content); + + this.metadata = await (await req).json(); + this.viewMetadata.style.cursor = this.viewMetadata.style.opacity = ""; + this.viewMetadata.removeAttribute("disabled"); + + loading.remove(); + this.addInfo(); + } + + createButtons() { + const btns = super.createButtons(); + this.viewMetadata = $el("button", { + type: "button", + textContent: "View raw metadata", + disabled: "disabled", + style: { + opacity: 0.5, + cursor: "not-allowed", + }, + onclick: (e) => { + if (this.metadata) { + new MetadataDialog().show(this.metadata); + } + }, + }); + + btns.unshift(this.viewMetadata); + return btns; + } + + getNoteInfo() { + function parseNote() { + if (!this.customNotes) return []; + + let notes = []; + // Extract links from notes + const r = new RegExp("(\\bhttps?:\\/\\/[^\\s]+)", "g"); + let end = 0; + let m; + do { + m = r.exec(this.customNotes); + let pos; + let fin = 0; + if (m) { + pos = m.index; + fin = m.index + m[0].length; + } else { + pos = this.customNotes.length; + } + + let pre = this.customNotes.substring(end, pos); + if (pre) { + pre = pre.replaceAll("\n", "
"); + notes.push( + $el("span", { + innerHTML: pre, + }) + ); + } + if (m) { + notes.push( + $el("a", { + href: m[0], + textContent: m[0], + target: "_blank", + }) + ); + } + + end = fin; + } while (m); + return notes; + } + + let textarea; + let notesContainer; + const editText = "✏️ Edit"; + const edit = $el("a", { + textContent: editText, + href: "#", + style: { + float: "right", + color: "greenyellow", + textDecoration: "none", + }, + onclick: async (e) => { + e.preventDefault(); + + if (textarea) { + this.customNotes = textarea.value; + + const resp = await api.fetchApi("/pysssss/metadata/notes/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: this.customNotes, + }); + + if (resp.status !== 200) { + console.error(resp); + alert(`Error saving notes (${req.status}) ${req.statusText}`); + return; + } + + e.target.textContent = editText; + textarea.remove(); + textarea = null; + + notesContainer.replaceChildren(...parseNote.call(this)); + this.node?.["pysssss.updateExamples"]?.(); + } else { + e.target.textContent = "💾 Save"; + textarea = $el("textarea", { + style: { + width: "100%", + minWidth: "200px", + minHeight: "50px", + }, + textContent: this.customNotes, + }); + e.target.after(textarea); + notesContainer.replaceChildren(); + textarea.style.height = Math.min(textarea.scrollHeight, 300) + "px"; + } + }, + }); + + notesContainer = $el("div.pysssss-model-notes", parseNote.call(this)); + return $el( + "div", + { + style: { display: "contents" }, + }, + [edit, notesContainer] + ); + } + + addInfo() { + const usageHint = this.metadata["modelspec.usage_hint"]; + if (usageHint) { + this.addInfoEntry("Usage Hint", usageHint); + } + this.addInfoEntry("Notes", this.getNoteInfo()); + } + + addInfoEntry(name, value) { + return $el( + "p", + { + parent: this.info, + }, + [ + typeof name === "string" ? $el("label", { textContent: name + ": " }) : name, + typeof value === "string" ? $el("span", { textContent: value }) : value, + ] + ); + } + + async getCivitaiDetails() { + const req = await fetch("https://civitai.com/api/v1/model-versions/by-hash/" + this.hash); + if (req.status === 200) { + return await req.json(); + } else if (req.status === 404) { + throw new Error("Model not found"); + } else { + throw new Error(`Error loading info (${req.status}) ${req.statusText}`); + } + } + + addCivitaiInfo() { + const promise = this.getCivitaiDetails(); + const content = $el("span", { textContent: "ℹ️ Loading..." }); + + this.addInfoEntry( + $el("label", [ + $el("img", { + style: { + width: "18px", + position: "relative", + top: "3px", + margin: "0 5px 0 0", + }, + src: "https://civitai.com/favicon.ico", + }), + $el("span", { textContent: "Civitai: " }), + ]), + content + ); + + return promise + .then((info) => { + content.replaceChildren( + $el("a", { + href: "https://civitai.com/models/" + info.modelId, + textContent: "View " + info.model.name, + target: "_blank", + }) + ); + + const allPreviews = info.images?.filter((i) => i.type === "image"); + const previews = allPreviews?.filter((i) => i.nsfwLevel <= ModelInfoDialog.nsfwLevel); + if (previews?.length) { + let previewIndex = 0; + let preview; + const updatePreview = () => { + preview = previews[previewIndex]; + this.img.src = preview.url; + }; + + updatePreview(); + this.img.style.display = ""; + + this.img.title = `${previews.length} previews.`; + if (allPreviews.length !== previews.length) { + this.img.title += ` ${allPreviews.length - previews.length} images hidden due to NSFW level.`; + } + + this.imgSave = $el("button", { + textContent: "Use as preview", + parent: this.imgWrapper, + onclick: async () => { + // Convert the preview to a blob + const blob = await (await fetch(this.img.src)).blob(); + + // Store it in temp + const name = "temp_preview." + new URL(this.img.src).pathname.split(".")[1]; + const body = new FormData(); + body.append("image", new File([blob], name)); + body.append("overwrite", "true"); + body.append("type", "temp"); + + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status !== 200) { + console.error(resp); + alert(`Error saving preview (${req.status}) ${req.statusText}`); + return; + } + + // Use as preview + await api.fetchApi("/pysssss/save/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: JSON.stringify({ + filename: name, + type: "temp", + }), + headers: { + "content-type": "application/json", + }, + }); + app.refreshComboInNodes(); + }, + }); + + $el("button", { + textContent: "Show metadata", + parent: this.imgWrapper, + onclick: async () => { + if (preview.meta && Object.keys(preview.meta).length) { + new MetadataDialog().show(preview.meta); + } else { + alert("No image metadata found"); + } + }, + }); + + const addNavButton = (icon, direction) => { + $el("button.pysssss-preview-nav", { + textContent: icon, + parent: this.imgWrapper, + onclick: async () => { + previewIndex += direction; + if (previewIndex < 0) { + previewIndex = previews.length - 1; + } else if (previewIndex >= previews.length) { + previewIndex = 0; + } + updatePreview(); + }, + }); + }; + + if (previews.length > 1) { + addNavButton("‹", -1); + addNavButton("›", 1); + } + } else if (info.images?.length) { + $el("span", { style: { opacity: 0.6 }, textContent: "⚠️ All images hidden due to NSFW level setting.", parent: this.imgWrapper }); + } + + return info; + }) + .catch((err) => { + content.textContent = "⚠️ " + err.message; + }); + } +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/spinner.css b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/spinner.css new file mode 100644 index 0000000000000000000000000000000000000000..cfb7a0d961de791e721f5351476a0d014285736e --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/spinner.css @@ -0,0 +1,35 @@ +.pysssss-lds-ring { + display: inline-block; + position: absolute; + width: 80px; + height: 80px; +} +.pysssss-lds-ring div { + box-sizing: border-box; + display: block; + position: absolute; + width: 64px; + height: 64px; + margin: 8px; + border: 5px solid #fff; + border-radius: 50%; + animation: lds-ring 1.2s cubic-bezier(0.5, 0, 0.5, 1) infinite; + border-color: #fff transparent transparent transparent; +} +.pysssss-lds-ring div:nth-child(1) { + animation-delay: -0.45s; +} +.pysssss-lds-ring div:nth-child(2) { + animation-delay: -0.3s; +} +.pysssss-lds-ring div:nth-child(3) { + animation-delay: -0.15s; +} +@keyframes lds-ring { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(360deg); + } +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/spinner.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/spinner.js new file mode 100644 index 0000000000000000000000000000000000000000..407599017aab85cddf6edab18afe91c3013c3cf0 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/spinner.js @@ -0,0 +1,9 @@ +import { addStylesheet } from "./utils.js"; + +addStylesheet(import.meta.url); + +export function createSpinner() { + const div = document.createElement("div"); + div.innerHTML = `

`; + return div.firstElementChild; +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/utils.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/utils.js new file mode 100644 index 0000000000000000000000000000000000000000..cd7539d449a2ac85672fb36da4be725f352b86f8 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/common/utils.js @@ -0,0 +1,30 @@ +import { $el } from "../../../../scripts/ui.js"; + +export function addStylesheet(url) { + if (url.endsWith(".js")) { + url = url.substr(0, url.length - 2) + "css"; + } + $el("link", { + parent: document.head, + rel: "stylesheet", + type: "text/css", + href: url.startsWith("http") ? url : getUrl(url), + }); +} + +export function getUrl(path, baseUrl) { + if (baseUrl) { + return new URL(path, baseUrl).toString(); + } else { + return new URL("../" + path, import.meta.url).toString(); + } +} + +export async function loadImage(url) { + return new Promise((res, rej) => { + const img = new Image(); + img.onload = res; + img.onerror = rej; + img.src = url; + }); +} diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/contextMenuHook.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/contextMenuHook.js new file mode 100644 index 0000000000000000000000000000000000000000..0fd35b41a4a4f6b7110c417d76235e07b22e7ce4 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/contextMenuHook.js @@ -0,0 +1,90 @@ +import { app } from "../../../scripts/app.js"; +app.registerExtension({ + name: "pysssss.ContextMenuHook", + init() { + const getOrSet = (target, name, create) => { + if (name in target) return target[name]; + return (target[name] = create()); + }; + const symbol = getOrSet(window, "__pysssss__", () => Symbol("__pysssss__")); + const store = getOrSet(window, symbol, () => ({})); + const contextMenuHook = getOrSet(store, "contextMenuHook", () => ({})); + for (const e of ["ctor", "preAddItem", "addItem"]) { + if (!contextMenuHook[e]) { + contextMenuHook[e] = []; + } + } + + // Big ol' hack to get allow customizing the context menu + // Replace the addItem function with our own that wraps the context of "this" with a proxy + // That proxy then replaces the constructor with another proxy + // That proxy then calls the custom ContextMenu that supports filters + const ctorProxy = new Proxy(LiteGraph.ContextMenu, { + construct(target, args) { + return new LiteGraph.ContextMenu(...args); + }, + }); + + function triggerCallbacks(name, getArgs, handler) { + const callbacks = contextMenuHook[name]; + if (callbacks && callbacks instanceof Array) { + for (const cb of callbacks) { + const r = cb(...getArgs()); + handler?.call(this, r); + } + } else { + console.warn("[pysssss 🐍]", `invalid ${name} callbacks`, callbacks, name in contextMenuHook); + } + } + + const addItem = LiteGraph.ContextMenu.prototype.addItem; + LiteGraph.ContextMenu.prototype.addItem = function () { + const proxy = new Proxy(this, { + get(target, prop) { + if (prop === "constructor") { + return ctorProxy; + } + return target[prop]; + }, + }); + proxy.__target__ = this; + + let el; + let args = arguments; + triggerCallbacks( + "preAddItem", + () => [el, this, args], + (r) => { + if (r !== undefined) el = r; + } + ); + + if (el === undefined) { + el = addItem.apply(proxy, arguments); + } + + triggerCallbacks( + "addItem", + () => [el, this, args], + (r) => { + if (r !== undefined) el = r; + } + ); + return el; + }; + + // We also need to patch the ContextMenu constructor to unwrap the parent else it fails a LiteGraph type check + const ctxMenu = LiteGraph.ContextMenu; + LiteGraph.ContextMenu = function (values, options) { + if (options?.parentMenu) { + if (options.parentMenu.__target__) { + options.parentMenu = options.parentMenu.__target__; + } + } + + triggerCallbacks("ctor", () => [values, options]); + return ctxMenu.call(this, values, options); + }; + LiteGraph.ContextMenu.prototype = ctxMenu.prototype; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/customColors.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/customColors.js new file mode 100644 index 0000000000000000000000000000000000000000..2b0fefca7aa86b48d4e81126a6549e4b5fe16505 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/customColors.js @@ -0,0 +1,98 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; + +const colorShade = (col, amt) => { + col = col.replace(/^#/, ""); + if (col.length === 3) col = col[0] + col[0] + col[1] + col[1] + col[2] + col[2]; + + let [r, g, b] = col.match(/.{2}/g); + [r, g, b] = [parseInt(r, 16) + amt, parseInt(g, 16) + amt, parseInt(b, 16) + amt]; + + r = Math.max(Math.min(255, r), 0).toString(16); + g = Math.max(Math.min(255, g), 0).toString(16); + b = Math.max(Math.min(255, b), 0).toString(16); + + const rr = (r.length < 2 ? "0" : "") + r; + const gg = (g.length < 2 ? "0" : "") + g; + const bb = (b.length < 2 ? "0" : "") + b; + + return `#${rr}${gg}${bb}`; +}; + +app.registerExtension({ + name: "pysssss.CustomColors", + setup() { + let picker; + let activeNode; + const onMenuNodeColors = LGraphCanvas.onMenuNodeColors; + LGraphCanvas.onMenuNodeColors = function (value, options, e, menu, node) { + const r = onMenuNodeColors.apply(this, arguments); + requestAnimationFrame(() => { + const menus = document.querySelectorAll(".litecontextmenu"); + for (let i = menus.length - 1; i >= 0; i--) { + if (menus[i].firstElementChild.textContent.includes("No color") || menus[i].firstElementChild.value?.content?.includes("No color")) { + $el( + "div.litemenu-entry.submenu", + { + parent: menus[i], + $: (el) => { + el.onclick = () => { + LiteGraph.closeAllContextMenus(); + if (!picker) { + picker = $el("input", { + type: "color", + parent: document.body, + style: { + display: "none", + }, + }); + picker.onchange = () => { + if (activeNode) { + const fApplyColor = function(node){ + if (picker.value) { + if (node.constructor === LiteGraph.LGraphGroup) { + node.color = picker.value; + } else { + node.color = colorShade(picker.value, 20); + node.bgcolor = picker.value; + } + } + } + const graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyColor(activeNode); + } else { + for (let i in graphcanvas.selected_nodes) { + fApplyColor(graphcanvas.selected_nodes[i]); + } + } + + activeNode.setDirtyCanvas(true, true); + } + }; + } + activeNode = null; + picker.value = node.bgcolor; + activeNode = node; + picker.click(); + }; + }, + }, + [ + $el("span", { + style: { + paddingLeft: "4px", + display: "block", + }, + textContent: "🎨 Custom", + }), + ] + ); + break; + } + } + }); + return r; + }; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/faviconStatus.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/faviconStatus.js new file mode 100644 index 0000000000000000000000000000000000000000..3dcd196bcbc83082144faef0d247ef5eac3d668c --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/faviconStatus.js @@ -0,0 +1,58 @@ +import { api } from "../../../scripts/api.js"; +import { app } from "../../../scripts/app.js"; + +// Simple script that adds the current queue size to the window title +// Adds a favicon that changes color while active + +app.registerExtension({ + name: "pysssss.FaviconStatus", + async setup() { + let link = document.querySelector("link[rel~='icon']"); + if (!link) { + link = document.createElement("link"); + link.rel = "icon"; + document.head.appendChild(link); + } + + const getUrl = (active, user) => new URL(`assets/favicon${active ? "-active" : ""}${user ? ".user" : ""}.ico`, import.meta.url); + const testUrl = async (active) => { + const url = getUrl(active, true); + const r = await fetch(url, { + method: "HEAD", + }); + if (r.status === 200) { + return url; + } + return getUrl(active, false); + }; + const activeUrl = await testUrl(true); + const idleUrl = await testUrl(false); + + let executing = false; + const update = () => (link.href = executing ? activeUrl : idleUrl); + + for (const e of ["execution_start", "progress"]) { + api.addEventListener(e, () => { + executing = true; + update(); + }); + } + + api.addEventListener("executing", ({ detail }) => { + // null will be sent when it's finished + executing = !!detail; + update(); + }); + + api.addEventListener("status", ({ detail }) => { + let title = "ComfyUI"; + if (detail && detail.exec_info.queue_remaining) { + title = `(${detail.exec_info.queue_remaining}) ${title}`; + } + document.title = title; + update(); + executing = false; + }); + update(); + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js new file mode 100644 index 0000000000000000000000000000000000000000..8f25e241a2b91a01ca95a376385bdbe572994752 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js @@ -0,0 +1,91 @@ +import { app } from "../../../scripts/app.js"; + +app.registerExtension({ + name: "pysssss.GraphArrange", + setup(app) { + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + options.push({ content: "Arrange (float left)", callback: () => graph.arrange() }); + options.push({ + content: "Arrange (float right)", + callback: () => { + (function () { + var margin = 50; + var layout; + + const nodes = this.computeExecutionOrder(false, true); + const columns = []; + + // Find node first use + for (let i = nodes.length - 1; i >= 0; i--) { + const node = nodes[i]; + let max = null; + for (const out of node.outputs || []) { + if (out.links) { + for (const link of out.links) { + const outNode = app.graph.getNodeById(app.graph.links[link].target_id); + if (!outNode) continue; + var l = outNode._level - 1; + if (max === null) max = l; + else if (l < max) max = l; + } + } + } + if (max != null) node._level = max; + } + + for (let i = 0; i < nodes.length; ++i) { + const node = nodes[i]; + const col = node._level || 1; + if (!columns[col]) { + columns[col] = []; + } + columns[col].push(node); + } + + let x = margin; + + for (let i = 0; i < columns.length; ++i) { + const column = columns[i]; + if (!column) { + continue; + } + column.sort((a, b) => { + var as = !(a.type === "SaveImage" || a.type === "PreviewImage"); + var bs = !(b.type === "SaveImage" || b.type === "PreviewImage"); + var r = as - bs; + if (r === 0) r = (a.inputs?.length || 0) - (b.inputs?.length || 0); + if (r === 0) r = (a.outputs?.length || 0) - (b.outputs?.length || 0); + return r; + }); + let max_size = 100; + let y = margin + LiteGraph.NODE_TITLE_HEIGHT; + for (let j = 0; j < column.length; ++j) { + const node = column[j]; + node.pos[0] = layout == LiteGraph.VERTICAL_LAYOUT ? y : x; + node.pos[1] = layout == LiteGraph.VERTICAL_LAYOUT ? x : y; + const max_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 1 : 0; + if (node.size[max_size_index] > max_size) { + max_size = node.size[max_size_index]; + } + const node_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 0 : 1; + y += node.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT + j; + } + + // Right align in column + for (let j = 0; j < column.length; ++j) { + const node = column[j]; + node.pos[0] += max_size - node.size[0]; + } + x += max_size + margin; + } + + this.setDirtyCanvas(true, true); + }).apply(app.graph); + }, + }); + return options; + }; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/imageFeed.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/imageFeed.js new file mode 100644 index 0000000000000000000000000000000000000000..4afdc066b023a43dccaf91884c8910b1fc470961 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/imageFeed.js @@ -0,0 +1,604 @@ +import { api } from "../../../scripts/api.js"; +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; +import { lightbox } from "./common/lightbox.js"; + +$el("style", { + textContent: ` + .pysssss-image-feed { + position: absolute; + background: var(--comfy-menu-bg); + color: var(--fg-color); + z-index: 99; + font-family: sans-serif; + font-size: 12px; + display: flex; + flex-direction: column; + } + div > .pysssss-image-feed { + position: static; + } + .pysssss-image-feed--top, .pysssss-image-feed--bottom { + width: 100vw; + min-height: 30px; + max-height: calc(var(--max-size, 20) * 1vh); + } + .pysssss-image-feed--top { + top: 0; + } + .pysssss-image-feed--bottom { + bottom: 0; + flex-direction: column-reverse; + padding-top: 5px; + } + .pysssss-image-feed--left, .pysssss-image-feed--right { + top: 0; + height: 100vh; + min-width: 200px; + max-width: calc(var(--max-size, 10) * 1vw); + } + .comfyui-body-left .pysssss-image-feed--left, .comfyui-body-right .pysssss-image-feed--right { + height: 100%; + } + .pysssss-image-feed--left { + left: 0; + } + .pysssss-image-feed--right { + right: 0; + } + + .pysssss-image-feed--left .pysssss-image-feed-menu, .pysssss-image-feed--right .pysssss-image-feed-menu { + flex-direction: column; + } + + .pysssss-image-feed-menu { + position: relative; + flex: 0 1 min-content; + display: flex; + gap: 5px; + padding: 5px; + justify-content: space-between; + } + .pysssss-image-feed-btn-group { + align-items: stretch; + display: flex; + gap: .5rem; + flex: 0 1 fit-content; + justify-content: flex-end; + } + .pysssss-image-feed-btn { + background-color:var(--comfy-input-bg); + border-radius:5px; + border:2px solid var(--border-color); + color: var(--fg-color); + cursor:pointer; + display:inline-block; + flex: 0 1 fit-content; + text-decoration:none; + } + .pysssss-image-feed-btn.sizing-btn:checked { + filter: invert(); + } + .pysssss-image-feed-btn.clear-btn { + padding: 5px 20px; + } + .pysssss-image-feed-btn.hide-btn { + padding: 5px; + aspect-ratio: 1 / 1; + } + .pysssss-image-feed-btn:hover { + filter: brightness(1.2); + } + .pysssss-image-feed-btn:active { + position:relative; + top:1px; + } + + .pysssss-image-feed-menu section { + border-radius: 5px; + background: rgba(0,0,0,0.6); + padding: 0 5px; + display: flex; + gap: 5px; + align-items: center; + position: relative; + } + .pysssss-image-feed-menu section span { + white-space: nowrap; + } + .pysssss-image-feed-menu section input { + flex: 1 1 100%; + background: rgba(0,0,0,0.6); + border-radius: 5px; + overflow: hidden; + z-index: 100; + } + + .sizing-menu { + position: relative; + } + + .size-controls-flyout { + position: absolute; + transform: scaleX(0%); + transition: 200ms ease-out; + transition-delay: 500ms; + z-index: 101; + width: 300px; + } + + .sizing-menu:hover .size-controls-flyout { + transform: scale(1, 1); + transition: 200ms linear; + transition-delay: 0; + } + .pysssss-image-feed--bottom .size-controls-flyout { + transform: scale(1,0); + transform-origin: bottom; + bottom: 0; + left: 0; + } + .pysssss-image-feed--top .size-controls-flyout { + transform: scale(1,0); + transform-origin: top; + top: 0; + left: 0; + } + .pysssss-image-feed--left .size-controls-flyout { + transform: scale(0, 1); + transform-origin: left; + top: 0; + left: 0; + } + .pysssss-image-feed--right .size-controls-flyout { + transform: scale(0, 1); + transform-origin: right; + top: 0; + right: 0; + } + + .pysssss-image-feed-menu > * { + min-height: 24px; + } + .pysssss-image-feed-list { + flex: 1 1 auto; + overflow-y: auto; + display: grid; + align-items: center; + justify-content: center; + gap: 4px; + grid-auto-rows: min-content; + grid-template-columns: repeat(var(--img-sz, 3), 1fr); + transition: 100ms linear; + scrollbar-gutter: stable both-edges; + padding: 5px; + background: var(--comfy-input-bg); + border-radius: 5px; + margin: 5px; + margin-top: 0px; + } + .pysssss-image-feed-list:empty { + display: none; + } + .pysssss-image-feed-list div { + height: 100%; + text-align: center; + } + .pysssss-image-feed-list::-webkit-scrollbar { + background: var(--comfy-input-bg); + border-radius: 5px; + } + .pysssss-image-feed-list::-webkit-scrollbar-thumb { + background:var(--comfy-menu-bg); + border: 5px solid transparent; + border-radius: 8px; + background-clip: content-box; + } + .pysssss-image-feed-list::-webkit-scrollbar-thumb:hover { + background: var(--border-color); + background-clip: content-box; + } + .pysssss-image-feed-list img { + object-fit: var(--img-fit, contain); + max-width: 100%; + max-height: calc(var(--max-size) * 1vh); + border-radius: 4px; + } + .pysssss-image-feed-list img:hover { + filter: brightness(1.2); + }`, + parent: document.body, +}); + +app.registerExtension({ + name: "pysssss.ImageFeed", + async setup() { + let visible = true; + const seenImages = new Map(); + const showButton = $el("button.comfy-settings-btn", { + textContent: "🖼️", + style: { + right: "16px", + cursor: "pointer", + display: "none", + }, + }); + let showMenuButton; + if (!app.menu?.element.style.display && app.menu?.settingsGroup) { + showMenuButton = new (await import("../../../scripts/ui/components/button.js")).ComfyButton({ + icon: "image-multiple", + action: () => showButton.click(), + tooltip: "Show Image Feed 🐍", + content: "Show Image Feed 🐍", + }); + showMenuButton.enabled = false; + showMenuButton.element.style.display = "none"; + app.menu.settingsGroup.append(showMenuButton); + } + + const getVal = (n, d) => { + const v = localStorage.getItem("pysssss.ImageFeed." + n); + if (v && !isNaN(+v)) { + return v; + } + return d; + }; + + const saveVal = (n, v) => { + localStorage.setItem("pysssss.ImageFeed." + n, v); + }; + + const imageFeed = $el("div.pysssss-image-feed"); + const imageList = $el("div.pysssss-image-feed-list"); + + function updateMenuParent(location) { + if (showMenuButton) { + const el = document.querySelector(".comfyui-body-" + location); + if (!el) return; + el.append(imageFeed); + } else { + if (!imageFeed.parent) { + document.body.append(imageFeed); + } + } + } + + const feedLocation = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.Location", + name: "🐍 Image Feed Location", + defaultValue: "bottom", + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + textContent: "🐍 Image Feed Location:", + }), + ]), + $el("td", [ + $el( + "select", + { + style: { + fontSize: "14px", + }, + oninput: (e) => { + feedLocation.value = e.target.value; + imageFeed.className = `pysssss-image-feed pysssss-image-feed--${feedLocation.value}`; + updateMenuParent(feedLocation.value); + saveVal("Location", feedLocation.value); + window.dispatchEvent(new Event("resize")); + }, + }, + ["left", "top", "right", "bottom", "hidden"].map((m) => + $el("option", { + value: m, + textContent: m, + selected: feedLocation.value === m, + }) + ) + ), + ]), + ]); + }, + onChange(value) { + if (value === "hidden") { + imageFeed.remove(); + if (showMenuButton) { + requestAnimationFrame(() => { + showMenuButton.element.style.display = "none"; + }); + } + showButton.style.display = "none"; + } else { + showMenuButton.element.style.display = "unset"; + showButton.style.display = visible ? "none" : "unset"; + imageFeed.className = `pysssss-image-feed pysssss-image-feed--${value}`; + updateMenuParent(value); + } + }, + }); + + const feedDirection = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.Direction", + name: "🐍 Image Feed Direction", + defaultValue: "newest first", + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + textContent: "🐍 Image Feed Direction:", + }), + ]), + $el("td", [ + $el( + "select", + { + style: { + fontSize: "14px", + }, + oninput: (e) => { + feedDirection.value = e.target.value; + imageList.replaceChildren(...[...imageList.childNodes].reverse()); + }, + }, + ["newest first", "oldest first"].map((m) => + $el("option", { + value: m, + textContent: m, + selected: feedDirection.value === m, + }) + ) + ), + ]), + ]); + }, + }); + + const deduplicateFeed = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.Deduplication", + name: "🐍 Image Feed Deduplication", + tooltip: `Ensures unique images in the image feed but at the cost of CPU-bound performance impact \ +(from hundreds of milliseconds to seconds per image, depending on byte size). For workflows that produce duplicate images, turning this setting on may yield overall client-side performance improvements \ +by reducing the number of images in the feed. + +Recommended: "enabled (max performance)" uness images are erroneously deduplicated.`, + defaultValue: 0, + type: "combo", + options: (value) => { + let dedupeOptions = { disabled: 0, "enabled (slow)": 1, "enabled (performance)": 0.5, "enabled (max performance)": 0.25 }; + return Object.entries(dedupeOptions).map(([k, v]) => ({ + value: v, + text: k, + selected: k === value, + })); + }, + }); + + const maxImages = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.MaxImages", + name: "🐍 Image Feed Max Images", + tooltip: `Limits the number of images in the feed to a maximum, removing the oldest images as new ones are added.`, + defaultValue: 0, + type: "number", + }); + + const saveNodeOnly = app.ui.settings.addSetting({ + id: "pysssss.ImageFeed.SaveNodeOnly", + name: "🐍 Image Feed Display 'SaveImage' Only", + tooltip: `Only show images from 'SaveImage' nodes. This prevents 'PreviewImage' node outputs from appearing in the feed.`, + defaultValue: false, + type: "boolean", + }); + + const clearButton = $el("button.pysssss-image-feed-btn.clear-btn", { + textContent: "Clear", + onclick: () => { + imageList.replaceChildren(); + window.dispatchEvent(new Event("resize")); + }, + }); + + const hideButton = $el("button.pysssss-image-feed-btn.hide-btn", { + textContent: "❌", + onclick: () => { + imageFeed.style.display = "none"; + showButton.style.display = feedLocation.value === "hidden" ? "none" : "unset"; + if (showMenuButton) { + showMenuButton.enabled = true; + showMenuButton.element.style.display = ""; + } + saveVal("Visible", 0); + visible = false; + window.dispatchEvent(new Event("resize")); + }, + }); + + let columnInput; + function updateColumnCount(v) { + columnInput.parentElement.title = `Controls the number of columns in the feed (${v} columns).\nClick label to set custom value.`; + imageFeed.style.setProperty("--img-sz", v); + saveVal("ImageSize", v); + columnInput.max = Math.max(10, v, columnInput.max); + columnInput.value = v; + window.dispatchEvent(new Event("resize")); + } + + function addImageToFeed(href) { + const method = feedDirection.value === "newest first" ? "prepend" : "append"; + + if (maxImages.value > 0 && imageList.children.length >= maxImages.value) { + imageList.children[method === "prepend" ? imageList.children.length - 1 : 0].remove(); + } + + imageList[method]( + $el("div", [ + $el( + "a", + { + target: "_blank", + href, + onclick: (e) => { + const imgs = [...imageList.querySelectorAll("img")].map((img) => img.getAttribute("src")); + lightbox.show(imgs, imgs.indexOf(href)); + e.preventDefault(); + }, + }, + [$el("img", { src: href })] + ), + ]) + ); + // If lightbox is open, update it with new image + lightbox.updateWithNewImage(href, feedDirection.value); + } + + imageFeed.append( + $el("div.pysssss-image-feed-menu", [ + $el("section.sizing-menu", {}, [ + $el("label.size-control-handle", { textContent: "↹ Resize Feed" }), + $el("div.size-controls-flyout", {}, [ + $el("section.size-control.feed-size-control", {}, [ + $el("span", { + textContent: "Feed Size...", + }), + $el("input", { + type: "range", + min: 10, + max: 80, + oninput: (e) => { + e.target.parentElement.title = `Controls the maximum size of the image feed panel (${e.target.value}vh)`; + imageFeed.style.setProperty("--max-size", e.target.value); + saveVal("FeedSize", e.target.value); + window.dispatchEvent(new Event("resize")); + }, + $: (el) => { + requestAnimationFrame(() => { + el.value = getVal("FeedSize", 25); + el.oninput({ target: el }); + }); + }, + }), + ]), + $el("section.size-control.image-size-control", {}, [ + $el("a", { + textContent: "Column count...", + style: { + cursor: "pointer", + textDecoration: "underline", + }, + onclick: () => { + const v = +prompt("Enter custom column count", 20); + if (!isNaN(v)) { + updateColumnCount(v); + } + }, + }), + $el("input", { + type: "range", + min: 1, + max: 10, + step: 1, + oninput: (e) => { + updateColumnCount(e.target.value); + }, + $: (el) => { + columnInput = el; + requestAnimationFrame(() => { + updateColumnCount(getVal("ImageSize", 4)); + }); + }, + }), + ]), + ]), + ]), + $el("div.pysssss-image-feed-btn-group", {}, [clearButton, hideButton]), + ]), + imageList + ); + showButton.onclick = () => { + imageFeed.style.display = "flex"; + showButton.style.display = "none"; + if (showMenuButton) { + showMenuButton.enabled = false; + showMenuButton.element.style.display = "none"; + } + + saveVal("Visible", 1); + visible = true; + window.dispatchEvent(new Event("resize")); + }; + document.querySelector(".comfy-settings-btn").after(showButton); + window.dispatchEvent(new Event("resize")); + + if (!+getVal("Visible", 1)) { + hideButton.onclick(); + } + + api.addEventListener("executed", ({ detail }) => { + if (visible && detail?.output?.images) { + if (detail.node?.includes?.(":")) { + // Ignore group nodes + const n = app.graph.getNodeById(detail.node.split(":")[0]); + if (n?.getInnerNodes) return; + } + + // Apply "Display Save Image Node Only" filter if setting is enabled + const nodeName = detail.node?.split(":")?.[0]; + if (nodeName) { + const node = app.graph.getNodeById(nodeName); + + if (saveNodeOnly.value && node?.type !== "SaveImage") return; + } + + for (const src of detail.output.images) { + const href = `./view?filename=${encodeURIComponent(src.filename)}&type=${src.type}& + subfolder=${encodeURIComponent(src.subfolder)}&t=${+new Date()}`; + + // deduplicateFeed.value is essentially the scaling factor used for image hashing + // but when deduplication is disabled, this value is "0" + if (deduplicateFeed.value > 0) { + // deduplicate by ignoring images with the same filename/type/subfolder + const fingerprint = JSON.stringify({ filename: src.filename, type: src.type, subfolder: src.subfolder }); + if (seenImages.has(fingerprint)) { + // NOOP: image is a duplicate + } else { + seenImages.set(fingerprint, true); + let img = $el("img", { src: href }); + img.onerror = () => { + // fall back to default behavior + addImageToFeed(href); + }; + img.onload = () => { + // redraw the image onto a canvas to strip metadata (resize if performance mode) + let imgCanvas = document.createElement("canvas"); + let imgScalar = deduplicateFeed.value; + imgCanvas.width = imgScalar * img.width; + imgCanvas.height = imgScalar * img.height; + + let imgContext = imgCanvas.getContext("2d"); + imgContext.drawImage(img, 0, 0, imgCanvas.width, imgCanvas.height); + const data = imgContext.getImageData(0, 0, imgCanvas.width, imgCanvas.height); + + // calculate fast hash of the image data + let hash = 0; + for (const b of data.data) { + hash = (hash << 5) - hash + b; + } + + // add image to feed if we've never seen the hash before + if (seenImages.has(hash)) { + // NOOP: image is a duplicate + } else { + // if we got to here, then the image is unique--so add to feed + seenImages.set(hash, true); + addImageToFeed(href); + } + }; + } + } else { + addImageToFeed(href); + } + } + } + }); + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/kSamplerAdvDenoise.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/kSamplerAdvDenoise.js new file mode 100644 index 0000000000000000000000000000000000000000..7b92d4f1d6596a6d9679d3ef4c7dffe9ea297327 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/kSamplerAdvDenoise.js @@ -0,0 +1,54 @@ +import { app } from "../../../scripts/app.js"; +app.registerExtension({ + name: "pysssss.KSamplerAdvDenoise", + async beforeRegisterNodeDef(nodeType) { + // Add menu options to conver to/from widgets + const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = origGetExtraMenuOptions?.apply?.(this, arguments); + + let stepsWidget = null; + let startAtWidget = null; + let endAtWidget = null; + for (const w of this.widgets || []) { + if (w.name === "steps") { + stepsWidget = w; + } else if (w.name === "start_at_step") { + startAtWidget = w; + } else if (w.name === "end_at_step") { + endAtWidget = w; + } + } + + if (stepsWidget && startAtWidget && endAtWidget) { + options.push( + { + content: "Set Denoise", + callback: () => { + const steps = +prompt("How many steps do you want?", 15); + if (isNaN(steps)) { + return; + } + const denoise = +prompt("How much denoise? (0-1)", 0.5); + if (isNaN(denoise)) { + return; + } + + stepsWidget.value = Math.floor(steps / Math.max(0, Math.min(1, denoise))); + stepsWidget.callback?.(stepsWidget.value); + + startAtWidget.value = stepsWidget.value - steps; + startAtWidget.callback?.(startAtWidget.value); + + endAtWidget.value = stepsWidget.value; + endAtWidget.callback?.(endAtWidget.value); + }, + }, + null + ); + } + + return r; + }; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/linkRenderMode.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/linkRenderMode.js new file mode 100644 index 0000000000000000000000000000000000000000..010de6ee1085e3fbb4e5b3d3a381e38e261b53c7 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/linkRenderMode.js @@ -0,0 +1,57 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; + +const id = "pysssss.LinkRenderMode"; +const ext = { + name: id, + async setup(app) { + if (app.extensions.find((ext) => ext.name === "Comfy.LinkRenderMode")) { + console.log("%c[🐍 pysssss]", "color: limegreen", "Skipping LinkRenderMode as core extension found"); + return; + } + const setting = app.ui.settings.addSetting({ + id, + name: "🐍 Link Render Mode", + defaultValue: 2, + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: "🐍 Link Render Mode:", + }), + ]), + $el("td", [ + $el( + "select", + { + textContent: "Manage", + style: { + fontSize: "14px", + }, + oninput: (e) => { + setting.value = e.target.value; + app.canvas.links_render_mode = +e.target.value; + app.graph.setDirtyCanvas(true, true); + }, + }, + LiteGraph.LINK_RENDER_MODES.map((m, i) => + $el("option", { + value: i, + textContent: m, + selected: i == app.canvas.links_render_mode, + }) + ) + ), + ]), + ]); + }, + onChange(value) { + app.canvas.links_render_mode = +value; + app.graph.setDirtyCanvas(true); + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/mathExpression.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/mathExpression.js new file mode 100644 index 0000000000000000000000000000000000000000..d0dde50ec2fa00a556b56baa17ec2be340035ef3 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/mathExpression.js @@ -0,0 +1,44 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +app.registerExtension({ + name: "pysssss.MathExpression", + init() { + const STRING = ComfyWidgets.STRING; + ComfyWidgets.STRING = function (node, inputName, inputData) { + const r = STRING.apply(this, arguments); + r.widget.dynamicPrompts = inputData?.[1].dynamicPrompts; + return r; + }; + }, + beforeRegisterNodeDef(nodeType) { + if (nodeType.comfyClass === "MathExpression|pysssss") { + const onDrawForeground = nodeType.prototype.onDrawForeground; + + nodeType.prototype.onNodeCreated = function() { + // These are typed as any to bypass backend validation + // update frontend to restrict types + for(const input of this.inputs) { + input.type = "INT,FLOAT,IMAGE,LATENT"; + } + } + + nodeType.prototype.onDrawForeground = function (ctx) { + const r = onDrawForeground?.apply?.(this, arguments); + + const v = app.nodeOutputs?.[this.id + ""]; + if (!this.flags.collapsed && v) { + const text = v.value[0] + ""; + ctx.save(); + ctx.font = "bold 12px sans-serif"; + ctx.fillStyle = "dodgerblue"; + const sz = ctx.measureText(text); + ctx.fillText(text, this.size[0] - sz.width - 5, LiteGraph.NODE_SLOT_HEIGHT * 3); + ctx.restore(); + } + + return r; + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/middleClickAddDefaultNode.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/middleClickAddDefaultNode.js new file mode 100644 index 0000000000000000000000000000000000000000..43f944f2835f56357b7701c2ef987261ceff9a8b --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/middleClickAddDefaultNode.js @@ -0,0 +1,49 @@ +import { app } from "../../../scripts/app.js"; + +const id = "pysssss.MiddleClickAddDefaultNode"; +const ext = { + name: id, + async setup(app) { + app.ui.settings.addSetting({ + id, + name: "🐍 Middle click slot to add", + defaultValue: "Reroute", + type: "combo", + options: (value) => + [ + ...Object.keys(LiteGraph.registered_node_types) + .filter((k) => k.includes("Reroute")) + .sort((a, b) => { + if (a === "Reroute") return -1; + if (b === "Reroute") return 1; + return a.localeCompare(b); + }), + "[None]", + ].map((m) => ({ + value: m, + text: m, + selected: !value ? m === "[None]" : m === value, + })), + onChange(value) { + const enable = value && value !== "[None]"; + if (value === true) { + value = "Reroute"; + } + LiteGraph.middle_click_slot_add_default_node = enable; + if (enable) { + for (const arr of Object.values(LiteGraph.slot_types_default_in).concat( + Object.values(LiteGraph.slot_types_default_out) + )) { + const idx = arr.indexOf(value); + if (idx !== 0) { + arr.splice(idx, 1); + } + arr.unshift(value); + } + } + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/modelInfo.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/modelInfo.js new file mode 100644 index 0000000000000000000000000000000000000000..b5db20a113fdb8f75b820306985fbf743e5f6bcc --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/modelInfo.js @@ -0,0 +1,412 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; +import { $el } from "../../../scripts/ui.js"; +import { ModelInfoDialog } from "./common/modelInfoDialog.js"; + +const MAX_TAGS = 500; +const NsfwLevel = { + PG: 1, + PG13: 2, + R: 4, + X: 8, + XXX: 16, + Blocked: 32, +}; + +export class LoraInfoDialog extends ModelInfoDialog { + getTagFrequency() { + if (!this.metadata.ss_tag_frequency) return []; + + const datasets = JSON.parse(this.metadata.ss_tag_frequency); + const tags = {}; + for (const setName in datasets) { + const set = datasets[setName]; + for (const t in set) { + if (t in tags) { + tags[t] += set[t]; + } else { + tags[t] = set[t]; + } + } + } + + return Object.entries(tags).sort((a, b) => b[1] - a[1]); + } + + getResolutions() { + let res = []; + if (this.metadata.ss_bucket_info) { + const parsed = JSON.parse(this.metadata.ss_bucket_info); + if (parsed?.buckets) { + for (const { resolution, count } of Object.values(parsed.buckets)) { + res.push([count, `${resolution.join("x")} * ${count}`]); + } + } + } + res = res.sort((a, b) => b[0] - a[0]).map((a) => a[1]); + let r = this.metadata.ss_resolution; + if (r) { + const s = r.split(","); + const w = s[0].replace("(", ""); + const h = s[1].replace(")", ""); + res.push(`${w.trim()}x${h.trim()} (Base res)`); + } else if ((r = this.metadata["modelspec.resolution"])) { + res.push(r + " (Base res"); + } + if (!res.length) { + res.push("⚠️ Unknown"); + } + return res; + } + + getTagList(tags) { + return tags.map((t) => + $el( + "li.pysssss-model-tag", + { + dataset: { + tag: t[0], + }, + $: (el) => { + el.onclick = () => { + el.classList.toggle("pysssss-model-tag--selected"); + }; + }, + }, + [ + $el("p", { + textContent: t[0], + }), + $el("span", { + textContent: t[1], + }), + ] + ) + ); + } + + addTags() { + let tags = this.getTagFrequency(); + if (!tags?.length) { + tags = this.metadata["modelspec.tags"]?.split(",").map((t) => [t.trim(), 1]); + } + let hasMore; + if (tags?.length) { + const c = tags.length; + let list; + if (c > MAX_TAGS) { + tags = tags.slice(0, MAX_TAGS); + hasMore = $el("p", [ + $el("span", { textContent: `⚠️ Only showing first ${MAX_TAGS} tags ` }), + $el("a", { + href: "#", + textContent: `Show all ${c}`, + onclick: () => { + list.replaceChildren(...this.getTagList(this.getTagFrequency())); + hasMore.remove(); + }, + }), + ]); + } + list = $el("ol.pysssss-model-tags-list", this.getTagList(tags)); + this.tags = $el("div", [list]); + } else { + this.tags = $el("p", { textContent: "⚠️ No tag frequency metadata found" }); + } + + this.content.append(this.tags); + + if (hasMore) { + this.content.append(hasMore); + } + } + + addExample(title, value, name) { + const textArea = $el("textarea", { + textContent: value, + style: { + whiteSpace: "pre-wrap", + margin: "10px 0", + color: "#fff", + background: "#222", + padding: "5px", + borderRadius: "5px", + maxHeight: "250px", + overflow: "auto", + display: "block", + border: "none", + width: "calc(100% - 10px)", + }, + }); + $el( + "p", + { + parent: this.content, + textContent: `${title}: `, + }, + [ + textArea, + $el("button", { + onclick: async () => { + await this.saveAsExample(textArea.value, `${name}.txt`); + }, + textContent: "Save as Example", + style: { + fontSize: "14px", + }, + }), + $el("hr"), + ] + ); + } + + async addInfo() { + this.addInfoEntry("Name", this.metadata.ss_output_name || "⚠️ Unknown"); + this.addInfoEntry("Base Model", this.metadata.ss_sd_model_name || "⚠️ Unknown"); + this.addInfoEntry("Clip Skip", this.metadata.ss_clip_skip || "⚠️ Unknown"); + + this.addInfoEntry( + "Resolution", + $el( + "select", + this.getResolutions().map((r) => $el("option", { textContent: r })) + ) + ); + + super.addInfo(); + const p = this.addCivitaiInfo(); + this.addTags(); + + const info = await p; + this.addExample("Trained Words", info?.trainedWords?.join(", ") ?? "", "trainedwords"); + + const triggerPhrase = this.metadata["modelspec.trigger_phrase"]; + if (triggerPhrase) { + this.addExample("Trigger Phrase", triggerPhrase, "triggerphrase"); + } + + $el("div", { + parent: this.content, + innerHTML: info?.description ?? this.metadata["modelspec.description"] ?? "[No description provided]", + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + + async saveAsExample(example, name = "example.txt") { + if (!example.length) { + return; + } + try { + name = prompt("Enter example name", name); + if (!name) return; + + await api.fetchApi("/pysssss/examples/" + encodeURIComponent(`${this.type}/${this.name}`), { + method: "POST", + body: JSON.stringify({ + name, + example, + }), + headers: { + "content-type": "application/json", + }, + }); + this.node?.["pysssss.updateExamples"]?.(); + alert("Saved!"); + } catch (error) { + console.error(error); + alert("Error saving: " + error); + } + } + + createButtons() { + const btns = super.createButtons(); + function tagsToCsv(tags) { + return tags.map((el) => el.dataset.tag).join(", "); + } + function copyTags(e, tags) { + const textarea = $el("textarea", { + parent: document.body, + style: { + position: "fixed", + }, + textContent: tagsToCsv(tags), + }); + textarea.select(); + try { + document.execCommand("copy"); + if (!e.target.dataset.text) { + e.target.dataset.text = e.target.textContent; + } + e.target.textContent = "Copied " + tags.length + " tags"; + setTimeout(() => { + e.target.textContent = e.target.dataset.text; + }, 1000); + } catch (ex) { + prompt("Copy to clipboard: Ctrl+C, Enter", text); + } finally { + document.body.removeChild(textarea); + } + } + + btns.unshift( + $el("button", { + type: "button", + textContent: "Save Selected as Example", + onclick: async (e) => { + const tags = tagsToCsv([...this.tags.querySelectorAll(".pysssss-model-tag--selected")]); + await this.saveAsExample(tags); + }, + }), + $el("button", { + type: "button", + textContent: "Copy Selected", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag--selected")]); + }, + }), + $el("button", { + type: "button", + textContent: "Copy All", + onclick: (e) => { + copyTags(e, [...this.tags.querySelectorAll(".pysssss-model-tag")]); + }, + }) + ); + + return btns; + } +} + +class CheckpointInfoDialog extends ModelInfoDialog { + async addInfo() { + super.addInfo(); + const info = await this.addCivitaiInfo(); + if (info) { + this.addInfoEntry("Base Model", info.baseModel || "⚠️ Unknown"); + + $el("div", { + parent: this.content, + innerHTML: info.description, + style: { + maxHeight: "250px", + overflow: "auto", + }, + }); + } + } +} + +const lookups = {}; + +function addInfoOption(node, type, infoClass, widgetNamePattern, opts) { + const widgets = widgetNamePattern + ? node.widgets.filter((w) => w.name === widgetNamePattern || w.name.match(`^${widgetNamePattern}$`)) + : [node.widgets[0]]; + for (const widget of widgets) { + let value = widget.value; + if (value?.content) { + value = value.content; + } + if (!value || value === "None") { + return; + } + let optName; + const split = value.split(/[.\\/]/); + optName = split[split.length - 2]; + opts.push({ + content: optName, + callback: async () => { + new infoClass(value, node).show(type, value); + }, + }); + } +} + +function addTypeOptions(node, typeName, options) { + const type = typeName.toLowerCase() + "s"; + const values = lookups[typeName][node.type]; + if (!values) return; + + const widgets = Object.keys(values); + const cls = type === "loras" ? LoraInfoDialog : CheckpointInfoDialog; + + const opts = []; + for (const w of widgets) { + addInfoOption(node, type, cls, w, opts); + } + + if (!opts.length) return; + + if (opts.length === 1) { + opts[0].content = `View ${typeName} info...`; + options.unshift(opts[0]); + } else { + options.unshift({ + title: `View ${typeName} info...`, + has_submenu: true, + submenu: { + options: opts, + }, + }); + } +} + +app.registerExtension({ + name: "pysssss.ModelInfo", + setup() { + const addSetting = (type, defaultValue) => { + app.ui.settings.addSetting({ + id: `pysssss.ModelInfo.${type}Nodes`, + name: `🐍 Model Info - ${type} Nodes/Widgets`, + type: "text", + defaultValue, + tooltip: `Comma separated list of NodeTypeName or NodeTypeName.WidgetName that contain ${type} node names that should have the View Info option available.\nIf no widget name is specifed the first widget will be used. Regex matches (e.g. NodeName..*lora_\\d+) are supported in the widget name.`, + onChange(value) { + lookups[type] = value.split(",").reduce((p, n) => { + n = n.trim(); + const pos = n.indexOf("."); + const split = pos === -1 ? [n] : [n.substring(0, pos), n.substring(pos + 1)]; + p[split[0]] ??= {}; + p[split[0]][split[1] ?? ""] = true; + return p; + }, {}); + }, + }); + }; + addSetting( + "Lora", + ["LoraLoader.lora_name", "LoraLoader|pysssss", "LoraLoaderModelOnly.lora_name", "LoRA Stacker.lora_name.*"].join(",") + ); + addSetting( + "Checkpoint", + ["CheckpointLoader.ckpt_name", "CheckpointLoaderSimple", "CheckpointLoader|pysssss", "Efficient Loader", "Eff. Loader SDXL"].join(",") + ); + + app.ui.settings.addSetting({ + id: `pysssss.ModelInfo.NsfwLevel`, + name: `🐍 Model Info - Image Preview Max NSFW Level`, + type: "combo", + defaultValue: "PG13", + options: Object.keys(NsfwLevel), + tooltip: `Hides preview images that are tagged as a higher NSFW level`, + onChange(value) { + ModelInfoDialog.nsfwLevel = NsfwLevel[value] ?? NsfwLevel.PG; + }, + }); + }, + beforeRegisterNodeDef(nodeType) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + if (this.widgets) { + for (const type in lookups) { + addTypeOptions(this, type, options); + } + } + + return getExtraMenuOptions?.apply(this, arguments); + }; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/nodeFinder.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/nodeFinder.js new file mode 100644 index 0000000000000000000000000000000000000000..d3727724b4ad09454aa77736115d81d6426be9ef --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/nodeFinder.js @@ -0,0 +1,82 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; + +// Adds a menu option to toggle follow the executing node +// Adds a menu option to go to the currently executing node +// Adds a menu option to go to a node by type + +app.registerExtension({ + name: "pysssss.NodeFinder", + setup() { + let followExecution = false; + + const centerNode = (id) => { + if (!followExecution || !id) return; + const node = app.graph.getNodeById(id); + if (!node) return; + app.canvas.centerOnNode(node); + }; + + api.addEventListener("executing", ({ detail }) => centerNode(detail)); + + // Add canvas menu options + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + options.push(null, { + content: followExecution ? "Stop following execution" : "Follow execution", + callback: () => { + if ((followExecution = !followExecution)) { + centerNode(app.runningNodeId); + } + }, + }); + if (app.runningNodeId) { + options.push({ + content: "Show executing node", + callback: () => { + const node = app.graph.getNodeById(app.runningNodeId); + if (!node) return; + app.canvas.centerOnNode(node); + }, + }); + } + + const nodes = app.graph._nodes; + const types = nodes.reduce((p, n) => { + if (n.type in p) { + p[n.type].push(n); + } else { + p[n.type] = [n]; + } + return p; + }, {}); + options.push({ + content: "Go to node", + has_submenu: true, + submenu: { + options: Object.keys(types) + .sort() + .map((t) => ({ + content: t, + has_submenu: true, + submenu: { + options: types[t] + .sort((a, b) => { + return a.pos[0] - b.pos[0]; + }) + .map((n) => ({ + content: `${n.getTitle()} - #${n.id} (${n.pos[0]}, ${n.pos[1]})`, + callback: () => { + app.canvas.centerOnNode(n); + }, + })), + }, + })), + }, + }); + + return options; + }; + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/playSound.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/playSound.js new file mode 100644 index 0000000000000000000000000000000000000000..de71058989079786807ad4b66ca6853c4b0b0ac7 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/playSound.js @@ -0,0 +1,36 @@ +import { app } from "../../../scripts/app.js"; + +app.registerExtension({ + name: "pysssss.PlaySound", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "PlaySound|pysssss") { + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = async function () { + onExecuted?.apply(this, arguments); + if (this.widgets[0].value === "on empty queue") { + if (app.ui.lastQueueSize !== 0) { + await new Promise((r) => setTimeout(r, 500)); + } + if (app.ui.lastQueueSize !== 0) { + return; + } + } + let file = this.widgets[2].value; + if (!file) { + file = "notify.mp3"; + } + if (!file.startsWith("http")) { + if (!file.includes("/")) { + file = "assets/" + file; + } + file = new URL(file, import.meta.url) + } + + const url = new URL(file); + const audio = new Audio(url); + audio.volume = this.widgets[1].value; + audio.play(); + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/presetText.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/presetText.js new file mode 100644 index 0000000000000000000000000000000000000000..472f11bef9caa382703f6144c32d48c647b2443d --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/presetText.js @@ -0,0 +1,257 @@ +import { app } from "../../../scripts/app.js"; + +// Allows you to manage preset tags for e.g. common negative prompt +// Also performs replacements on any text field e.g. allowing you to use preset text in CLIP Text encode fields + +let replaceRegex; +const id = "pysssss.PresetText.Presets"; +const MISSING = Symbol(); + +const getPresets = () => { + let items; + try { + items = JSON.parse(localStorage.getItem(id)); + } catch (error) {} + if (!items || !items.length) { + items = [{ name: "default negative", value: "worst quality" }]; + } + return items; +}; + +let presets = getPresets(); + +app.registerExtension({ + name: "pysssss.PresetText", + setup() { + app.ui.settings.addSetting({ + id: "pysssss.PresetText.ReplacementRegex", + name: "🐍 Preset Text Replacement Regex", + type: "text", + defaultValue: "(?:^|[^\\w])(?@(?[\\w-]+))", + tooltip: + "The regex should return two named capture groups: id (the name of the preset text to use), replace (the matched text to replace)", + attrs: { + style: { + fontFamily: "monospace", + }, + }, + onChange(value) { + if (!value) { + replaceRegex = null; + return; + } + try { + replaceRegex = new RegExp(value, "g"); + } catch (error) { + alert("Error creating regex for preset text replacement, no replacements will be performed."); + replaceRegex = null; + } + }, + }); + + const drawNodeWidgets = LGraphCanvas.prototype.drawNodeWidgets + LGraphCanvas.prototype.drawNodeWidgets = function(node) { + const c = LiteGraph.WIDGET_BGCOLOR; + try { + if(node[MISSING]) { + LiteGraph.WIDGET_BGCOLOR = "red" + } + return drawNodeWidgets.apply(this, arguments); + } finally { + LiteGraph.WIDGET_BGCOLOR = c; + } + } + }, + registerCustomNodes() { + class PresetTextNode extends LiteGraph.LGraphNode { + constructor() { + super(); + this.title = "Preset Text 🐍"; + this.isVirtualNode = true; + this.serialize_widgets = true; + this.addOutput("text", "STRING"); + + const widget = this.addWidget("combo", "value", presets[0].name, () => {}, { + values: presets.map((p) => p.name), + }); + this.addWidget("button", "Manage", "Manage", () => { + const container = document.createElement("div"); + Object.assign(container.style, { + display: "grid", + gridTemplateColumns: "1fr 1fr", + gap: "10px", + }); + + const addNew = document.createElement("button"); + addNew.textContent = "Add New"; + addNew.classList.add("pysssss-presettext-addnew"); + Object.assign(addNew.style, { + fontSize: "13px", + gridColumn: "1 / 3", + color: "dodgerblue", + width: "auto", + textAlign: "center", + }); + addNew.onclick = () => { + addRow({ name: "", value: "" }); + }; + container.append(addNew); + + function addRow(p) { + const name = document.createElement("input"); + const nameLbl = document.createElement("label"); + name.value = p.name; + nameLbl.textContent = "Name:"; + nameLbl.append(name); + + const value = document.createElement("input"); + const valueLbl = document.createElement("label"); + value.value = p.value; + valueLbl.textContent = "Value:"; + valueLbl.append(value); + + addNew.before(nameLbl, valueLbl); + } + for (const p of presets) { + addRow(p); + } + + const help = document.createElement("span"); + help.textContent = "To remove a preset set the name or value to blank"; + help.style.gridColumn = "1 / 3"; + container.append(help); + + dialog.show(""); + dialog.textElement.append(container); + }); + + const dialog = new app.ui.dialog.constructor(); + dialog.element.classList.add("comfy-settings"); + + const closeButton = dialog.element.querySelector("button"); + closeButton.textContent = "CANCEL"; + const saveButton = document.createElement("button"); + saveButton.textContent = "SAVE"; + saveButton.onclick = function () { + const inputs = dialog.element.querySelectorAll("input"); + const p = []; + for (let i = 0; i < inputs.length; i += 2) { + const n = inputs[i]; + const v = inputs[i + 1]; + if (!n.value.trim() || !v.value.trim()) { + continue; + } + p.push({ name: n.value, value: v.value }); + } + + widget.options.values = p.map((p) => p.name); + if (!widget.options.values.includes(widget.value)) { + widget.value = widget.options.values[0]; + } + + presets = p; + localStorage.setItem(id, JSON.stringify(presets)); + + dialog.close(); + }; + + closeButton.before(saveButton); + + this.applyToGraph = function (workflow) { + // For each output link copy our value over the original widget value + if (this.outputs[0].links && this.outputs[0].links.length) { + for (const l of this.outputs[0].links) { + const link_info = app.graph.links[l]; + const outNode = app.graph.getNodeById(link_info.target_id); + const outIn = outNode && outNode.inputs && outNode.inputs[link_info.target_slot]; + if (outIn.widget) { + const w = outNode.widgets.find((w) => w.name === outIn.widget.name); + if (!w) continue; + const preset = presets.find((p) => p.name === widget.value); + if (!preset) { + this[MISSING] = true; + app.graph.setDirtyCanvas(true, true); + const msg = `Preset text '${widget.value}' not found. Please fix this and queue again.`; + throw new Error(msg); + } + delete this[MISSING]; + w.value = preset.value; + } + } + } + }; + } + } + + LiteGraph.registerNodeType( + "PresetText|pysssss", + Object.assign(PresetTextNode, { + title: "Preset Text 🐍", + }) + ); + + PresetTextNode.category = "utils"; + }, + nodeCreated(node) { + if (node.widgets) { + // Locate dynamic prompt text widgets + const widgets = node.widgets.filter((n) => n.type === "customtext" || n.type === "text"); + for (const widget of widgets) { + const callbacks = [ + () => { + let prompt = widget.value; + if (replaceRegex && typeof prompt.replace !== 'undefined') { + prompt = prompt.replace(replaceRegex, (match, p1, p2, index, text, groups) => { + if (!groups.replace || !groups.id) return match; // No match, bad regex? + + const preset = presets.find((p) => p.name.replaceAll(/\s/g, "-") === groups.id); + if (!preset) return match; // Invalid name + + const pos = match.indexOf(groups.replace); + return match.substring(0, pos) + preset.value; + }); + } + return prompt; + }, + ]; + let inheritedSerializeValue = widget.serializeValue || null; + + let called = false; + const serializeValue = async (workflowNode, widgetIndex) => { + const origWidgetValue = widget.value; + if (called) return origWidgetValue; + called = true; + + let allCallbacks = [...callbacks]; + if (inheritedSerializeValue) { + allCallbacks.push(inheritedSerializeValue) + } + let valueIsUndefined = false; + + for (const cb of allCallbacks) { + let value = await cb(workflowNode, widgetIndex); + // Need to check the callback return value before it is set on widget.value as it coerces it to a string (even for undefined) + if (value === undefined) valueIsUndefined = true; + widget.value = value; + } + + const prompt = valueIsUndefined ? undefined : widget.value; + widget.value = origWidgetValue; + + called = false; + + return prompt; + }; + + Object.defineProperty(widget, "serializeValue", { + get() { + return serializeValue; + }, + set(cb) { + inheritedSerializeValue = cb; + }, + }); + } + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/quickNodes.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/quickNodes.js new file mode 100644 index 0000000000000000000000000000000000000000..a52581b32ed9cc6af01f016774518d17693b2132 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/quickNodes.js @@ -0,0 +1,196 @@ +import { app } from "../../../scripts/app.js"; + +// Adds a bunch of context menu entries for quickly adding common steps + +function addMenuHandler(nodeType, cb) { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} + +function getOrAddVAELoader(node) { + let vaeNode = app.graph._nodes.find((n) => n.type === "VAELoader"); + if (!vaeNode) { + vaeNode = addNode("VAELoader", node); + } + return vaeNode; +} + +function addNode(name, nextTo, options) { + options = { select: true, shiftY: 0, before: false, ...(options || {}) }; + const node = LiteGraph.createNode(name); + app.graph.add(node); + node.pos = [ + options.before ? nextTo.pos[0] - node.size[0] - 30 : nextTo.pos[0] + nextTo.size[0] + 30, + nextTo.pos[1] + options.shiftY, + ]; + if (options.select) { + app.canvas.selectNode(node, false); + } + return node; +} + +app.registerExtension({ + name: "pysssss.QuickNodes", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.input && nodeData.input.required) { + const keys = Object.keys(nodeData.input.required); + for (let i = 0; i < keys.length; i++) { + if (nodeData.input.required[keys[i]][0] === "VAE") { + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: "Use VAE", + callback: () => { + getOrAddVAELoader(this).connect(0, this, i); + }, + }); + }); + break; + } + } + } + + if (nodeData.name === "KSampler") { + addMenuHandler(nodeType, function (_, options) { + options.unshift( + { + content: "Add Blank Input", + callback: () => { + const imageNode = addNode("EmptyLatentImage", this, { before: true }); + imageNode.connect(0, this, 3); + }, + }, + { + content: "Add Hi-res Fix", + callback: () => { + const upscaleNode = addNode("LatentUpscale", this); + this.connect(0, upscaleNode, 0); + + const sampleNode = addNode("KSampler", upscaleNode); + + for (let i = 0; i < 3; i++) { + const l = this.getInputLink(i); + if (l) { + app.graph.getNodeById(l.origin_id).connect(l.origin_slot, sampleNode, i); + } + } + + upscaleNode.connect(0, sampleNode, 3); + }, + }, + { + content: "Add 2nd Pass", + callback: () => { + const upscaleNode = addNode("LatentUpscale", this); + this.connect(0, upscaleNode, 0); + + const ckptNode = addNode("CheckpointLoaderSimple", this); + const sampleNode = addNode("KSampler", ckptNode); + + const positiveLink = this.getInputLink(1); + const negativeLink = this.getInputLink(2); + const positiveNode = positiveLink + ? app.graph.add(app.graph.getNodeById(positiveLink.origin_id).clone()) + : addNode("CLIPTextEncode"); + const negativeNode = negativeLink + ? app.graph.add(app.graph.getNodeById(negativeLink.origin_id).clone()) + : addNode("CLIPTextEncode"); + + ckptNode.connect(0, sampleNode, 0); + ckptNode.connect(1, positiveNode, 0); + ckptNode.connect(1, negativeNode, 0); + positiveNode.connect(0, sampleNode, 1); + negativeNode.connect(0, sampleNode, 2); + upscaleNode.connect(0, sampleNode, 3); + }, + }, + { + content: "Add Save Image", + callback: () => { + const decodeNode = addNode("VAEDecode", this); + this.connect(0, decodeNode, 0); + + getOrAddVAELoader(decodeNode).connect(0, decodeNode, 1); + + const saveNode = addNode("SaveImage", decodeNode); + decodeNode.connect(0, saveNode, 0); + }, + } + ); + }); + } + + if (nodeData.name === "CheckpointLoaderSimple") { + addMenuHandler(nodeType, function (_, options) { + options.unshift({ + content: "Add Clip Skip", + callback: () => { + const clipSkipNode = addNode("CLIPSetLastLayer", this); + const clipLinks = this.outputs[1].links ? this.outputs[1].links.map((l) => ({ ...graph.links[l] })) : []; + + this.disconnectOutput(1); + this.connect(1, clipSkipNode, 0); + + for (const clipLink of clipLinks) { + clipSkipNode.connect(0, clipLink.target_id, clipLink.target_slot); + } + }, + }); + }); + } + + if ( + nodeData.name === "CheckpointLoaderSimple" || + nodeData.name === "CheckpointLoader" || + nodeData.name === "CheckpointLoader|pysssss" || + nodeData.name === "LoraLoader" || + nodeData.name === "LoraLoader|pysssss" + ) { + addMenuHandler(nodeType, function (_, options) { + function addLora(type) { + const loraNode = addNode(type, this); + + const modelLinks = this.outputs[0].links ? this.outputs[0].links.map((l) => ({ ...graph.links[l] })) : []; + const clipLinks = this.outputs[1].links ? this.outputs[1].links.map((l) => ({ ...graph.links[l] })) : []; + + this.disconnectOutput(0); + this.disconnectOutput(1); + + this.connect(0, loraNode, 0); + this.connect(1, loraNode, 1); + + for (const modelLink of modelLinks) { + loraNode.connect(0, modelLink.target_id, modelLink.target_slot); + } + + for (const clipLink of clipLinks) { + loraNode.connect(1, clipLink.target_id, clipLink.target_slot); + } + } + options.unshift( + { + content: "Add LoRA", + callback: () => addLora.call(this, "LoraLoader"), + }, + { + content: "Add 🐍 LoRA", + callback: () => addLora.call(this, "LoraLoader|pysssss"), + }, + { + content: "Add Prompts", + callback: () => { + const positiveNode = addNode("CLIPTextEncode", this); + const negativeNode = addNode("CLIPTextEncode", this, { shiftY: positiveNode.size[1] + 30 }); + + this.connect(1, positiveNode, 0); + this.connect(1, negativeNode, 0); + }, + } + ); + }); + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/repeater.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/repeater.js new file mode 100644 index 0000000000000000000000000000000000000000..23432c4d4fa89f899312a7fa4bd83a04c635a6b5 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/repeater.js @@ -0,0 +1,123 @@ +import { app } from "../../../scripts/app.js"; + +const REPEATER = "Repeater|pysssss"; + +app.registerExtension({ + name: "pysssss.Repeater", + init() { + const graphToPrompt = app.graphToPrompt; + app.graphToPrompt = async function () { + const res = await graphToPrompt.apply(this, arguments); + + const id = Date.now() + "_"; + let u = 0; + + let newNodes = {}; + const newRepeaters = {}; + for (const nodeId in res.output) { + let output = res.output[nodeId]; + if (output.class_type === REPEATER) { + const isMulti = output.inputs.output === "multi"; + if (output.inputs.node_mode === "create") { + // We need to clone the input for every repeat + const orig = res.output[output.inputs.source[0]]; + if (isMulti) { + if (!newRepeaters[nodeId]) { + newRepeaters[nodeId] = []; + newRepeaters[nodeId][output.inputs.repeats - 1] = nodeId; + } + } + for (let i = 0; i < output.inputs.repeats - 1; i++) { + const clonedInputId = id + ++u; + + if (isMulti) { + // If multi create we need to clone the repeater too + newNodes[clonedInputId] = structuredClone(orig); + + output = structuredClone(output); + + const clonedRepeaterId = id + ++u; + newNodes[clonedRepeaterId] = output; + output.inputs["source"][0] = clonedInputId; + + newRepeaters[nodeId][i] = clonedRepeaterId; + } else { + newNodes[clonedInputId] = orig; + } + output.inputs[clonedInputId] = [clonedInputId, output.inputs.source[1]]; + } + } else if (isMulti) { + newRepeaters[nodeId] = Array(output.inputs.repeats).fill(nodeId); + } + } + } + + Object.assign(res.output, newNodes); + newNodes = {}; + + for (const nodeId in res.output) { + const output = res.output[nodeId]; + for (const k in output.inputs) { + const v = output.inputs[k]; + if (v instanceof Array) { + const repeaterId = v[0]; + const source = newRepeaters[repeaterId]; + if (source) { + v[0] = source.pop(); + v[1] = 0; + } + } + } + } + + // Object.assign(res.output, newNodes); + + return res; + }; + }, + beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === REPEATER) { + const SETUP_OUTPUTS = Symbol(); + nodeType.prototype[SETUP_OUTPUTS] = function (repeats) { + if (repeats == null) { + repeats = this.widgets[0].value; + } + while (this.outputs.length > repeats) { + this.removeOutput(repeats); + } + const id = Date.now() + "_"; + let u = 0; + while (this.outputs.length < repeats) { + this.addOutput(id + ++u, "*", { label: "*" }); + } + }; + + const onAdded = nodeType.prototype.onAdded; + nodeType.prototype.onAdded = function () { + const self = this; + const repeatsCb = this.widgets[0].callback; + this.widgets[0].callback = async function () { + const v = (await repeatsCb?.apply(this, arguments)) ?? this.value; + if (self.widgets[1].value === "multi") { + self[SETUP_OUTPUTS](v); + } + return v; + }; + + const outputCb = this.widgets[1].callback; + this.widgets[1].callback = async function () { + const v = (await outputCb?.apply(this, arguments)) ?? this.value; + if (v === "single") { + self.outputs[0].shape = 6; + self[SETUP_OUTPUTS](1); + } else { + delete self.outputs[0].shape; + self[SETUP_OUTPUTS](); + } + return v; + }; + return onAdded?.apply(this, arguments); + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/reroutePrimitive.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/reroutePrimitive.js new file mode 100644 index 0000000000000000000000000000000000000000..7b934d717073b3b4dc210a48356580c615802375 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/reroutePrimitive.js @@ -0,0 +1,348 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +const REROUTE_PRIMITIVE = "ReroutePrimitive|pysssss"; +const MULTI_PRIMITIVE = "MultiPrimitive|pysssss"; +const LAST_TYPE = Symbol("LastType"); + +app.registerExtension({ + name: "pysssss.ReroutePrimitive", + init() { + // On graph configure, fire onGraphConfigured to create widgets + const graphConfigure = LGraph.prototype.configure; + LGraph.prototype.configure = function () { + const r = graphConfigure.apply(this, arguments); + for (const n of app.graph._nodes) { + if (n.type === REROUTE_PRIMITIVE) { + n.onGraphConfigured(); + } + } + + return r; + }; + + // Hide this node as it is no longer supported + const getNodeTypesCategories = LiteGraph.getNodeTypesCategories; + LiteGraph.getNodeTypesCategories = function() { + return getNodeTypesCategories.apply(this, arguments).filter(c => !c.startsWith("__hidden__")); + } + + const graphToPrompt = app.graphToPrompt; + app.graphToPrompt = async function () { + const res = await graphToPrompt.apply(this, arguments); + + const multiOutputs = []; + for (const nodeId in res.output) { + const output = res.output[nodeId]; + if (output.class_type === MULTI_PRIMITIVE) { + multiOutputs.push({ id: nodeId, inputs: output.inputs }); + } + } + + function permute(outputs) { + function generatePermutations(inputs, currentIndex, currentPermutation, result) { + if (currentIndex === inputs.length) { + result.push({ ...currentPermutation }); + return; + } + + const input = inputs[currentIndex]; + + for (const k in input) { + currentPermutation[currentIndex] = input[k]; + generatePermutations(inputs, currentIndex + 1, currentPermutation, result); + } + } + + const inputs = outputs.map((output) => output.inputs); + const result = []; + const current = new Array(inputs.length); + + generatePermutations(inputs, 0, current, result); + + return outputs.map((output, index) => ({ + ...output, + inputs: result.reduce((p, permutation) => { + const count = Object.keys(p).length; + p["value" + (count || "")] = permutation[index]; + return p; + }, {}), + })); + } + + const permutations = permute(multiOutputs); + for (let i = 0; i < permutations.length; i++) { + res.output[multiOutputs[i].id].inputs = permutations[i].inputs; + } + + return res; + }; + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + function addOutputHandler() { + // Finds the first non reroute output node down the chain + nodeType.prototype.getFirstReroutedOutput = function (slot) { + if (nodeData.name === MULTI_PRIMITIVE) { + slot = 0; + } + const links = this.outputs[slot].links; + if (!links) return null; + + const search = []; + for (const l of links) { + const link = app.graph.links[l]; + if (!link) continue; + + const node = app.graph.getNodeById(link.target_id); + if (node.type !== REROUTE_PRIMITIVE && node.type !== MULTI_PRIMITIVE) { + return { node, link }; + } + search.push({ node, link }); + } + + for (const { link, node } of search) { + const r = node.getFirstReroutedOutput(link.target_slot); + if (r) { + return r; + } + } + }; + } + + if (nodeData.name === REROUTE_PRIMITIVE) { + const configure = nodeType.prototype.configure || LGraphNode.prototype.configure; + const onConnectionsChange = nodeType.prototype.onConnectionsChange; + const onAdded = nodeType.prototype.onAdded; + + nodeType.title_mode = LiteGraph.NO_TITLE; + + function hasAnyInput(node) { + for (const input of node.inputs) { + if (input.link) { + return true; + } + } + return false; + } + + // Remove input text + nodeType.prototype.onAdded = function () { + onAdded?.apply(this, arguments); + this.inputs[0].label = ""; + this.outputs[0].label = "value"; + this.setSize(this.computeSize()); + }; + + // Restore any widgets + nodeType.prototype.onGraphConfigured = function () { + if (hasAnyInput(this)) return; + + const outputNode = this.getFirstReroutedOutput(0); + if (outputNode) { + this.checkPrimitiveWidget(outputNode); + } + }; + + // Check if we need to create (or remove) a widget on the node + nodeType.prototype.checkPrimitiveWidget = function ({ node, link }) { + let widgetType = link.type; + let targetLabel = widgetType; + const input = node.inputs[link.target_slot]; + if (input.widget?.config?.[0] instanceof Array) { + targetLabel = input.widget.name; + widgetType = "COMBO"; + } + + if (widgetType in ComfyWidgets) { + if (!this.widgets?.length) { + let v; + if (this.widgets_values?.length) { + v = this.widgets_values[0]; + } + let config = [link.type, {}]; + if (input.widget?.config) { + config = input.widget.config; + } + const { widget } = ComfyWidgets[widgetType](this, "value", config, app); + if (v !== undefined && (!this[LAST_TYPE] || this[LAST_TYPE] === widgetType)) { + widget.value = v; + } + this[LAST_TYPE] = widgetType; + } + } else if (this.widgets) { + this.widgets.length = 0; + } + + return targetLabel; + }; + + // Finds all input nodes from the current reroute + nodeType.prototype.getReroutedInputs = function (slot) { + let nodes = [{ node: this }]; + let node = this; + while (node?.type === REROUTE_PRIMITIVE) { + const input = node.inputs[slot]; + if (input.link) { + const link = app.graph.links[input.link]; + node = app.graph.getNodeById(link.origin_id); + slot = link.origin_slot; + nodes.push({ + node, + link, + }); + } else { + node = null; + } + } + + return nodes; + }; + + addOutputHandler(); + + // Update the type of all reroutes in a chain + nodeType.prototype.changeRerouteType = function (slot, type, label) { + const color = LGraphCanvas.link_type_colors[type]; + const output = this.outputs[slot]; + this.inputs[slot].label = " "; + output.label = label || (type === "*" ? "value" : type); + output.type = type; + + // Process all linked outputs + for (const linkId of output.links || []) { + const link = app.graph.links[linkId]; + if (!link) continue; + link.color = color; + const node = app.graph.getNodeById(link.target_id); + if (node.changeRerouteType) { + // Recursively update reroutes + node.changeRerouteType(link.target_slot, type, label); + } else { + // Validate links to 'real' nodes + const theirType = node.inputs[link.target_slot].type; + if (theirType !== type && theirType !== "*") { + node.disconnectInput(link.target_slot); + } + } + } + + if (this.inputs[slot].link) { + const link = app.graph.links[this.inputs[slot].link]; + if (link) link.color = color; + } + }; + + // Override configure so we can flag that we are configuring to avoid link validation breaking + let configuring = false; + nodeType.prototype.configure = function () { + configuring = true; + const r = configure?.apply(this, arguments); + configuring = false; + + return r; + }; + + Object.defineProperty(nodeType, "title_mode", { + get() { + return app.canvas.current_node?.widgets?.length ? LiteGraph.NORMAL_TITLE : LiteGraph.NO_TITLE; + }, + }); + + nodeType.prototype.onConnectionsChange = function (type, _, connected, link_info) { + // If configuring treat everything as OK as links may not be set by litegraph yet + if (configuring) return; + + const isInput = type === LiteGraph.INPUT; + const slot = isInput ? link_info.target_slot : link_info.origin_slot; + + let targetLabel = null; + let targetNode = null; + let targetType = "*"; + let targetSlot = slot; + + const inputPath = this.getReroutedInputs(slot); + const rootInput = inputPath[inputPath.length - 1]; + const outputNode = this.getFirstReroutedOutput(slot); + if (rootInput.node.type === REROUTE_PRIMITIVE) { + // Our input node is a reroute, so see if we have an output + if (outputNode) { + targetType = outputNode.link.type; + } else if (rootInput.node.widgets) { + rootInput.node.widgets.length = 0; + } + targetNode = rootInput; + targetSlot = rootInput.link?.target_slot ?? slot; + } else { + // We have a real input, so we want to use that type + targetNode = inputPath[inputPath.length - 2]; + targetType = rootInput.node.outputs[rootInput.link.origin_slot].type; + targetSlot = rootInput.link.target_slot; + } + + if (this.widgets && inputPath.length > 1) { + // We have an input node so remove our widget + this.widgets.length = 0; + } + + if (outputNode && rootInput.node.checkPrimitiveWidget) { + // We have an output, check if we need to create a widget + targetLabel = rootInput.node.checkPrimitiveWidget(outputNode); + } + + // Trigger an update of the type to all child nodes + targetNode.node.changeRerouteType(targetSlot, targetType, targetLabel); + + return onConnectionsChange?.apply(this, arguments); + }; + + // When collapsed fix the size to just the dot + const computeSize = nodeType.prototype.computeSize || LGraphNode.prototype.computeSize; + nodeType.prototype.computeSize = function () { + const r = computeSize.apply(this, arguments); + if (this.flags?.collapsed) { + return [1, 25]; + } else if (this.widgets?.length) { + return r; + } else { + let w = 75; + if (this.outputs?.[0]?.label) { + const t = LiteGraph.NODE_TEXT_SIZE * this.outputs[0].label.length * 0.6 + 30; + if (t > w) { + w = t; + } + } + return [w, r[1]]; + } + }; + + // On collapse shrink the node to just a dot + const collapse = nodeType.prototype.collapse || LGraphNode.prototype.collapse; + nodeType.prototype.collapse = function () { + collapse.apply(this, arguments); + this.setSize(this.computeSize()); + requestAnimationFrame(() => { + this.setDirtyCanvas(true, true); + }); + }; + + // Shift the bounding area up slightly as LiteGraph miscalculates it for collapsed nodes + nodeType.prototype.onBounding = function (area) { + if (this.flags?.collapsed) { + area[1] -= 15; + } + }; + } else if (nodeData.name === MULTI_PRIMITIVE) { + addOutputHandler(); + nodeType.prototype.onConnectionsChange = function (type, _, connected, link_info) { + for (let i = 0; i < this.inputs.length - 1; i++) { + if (!this.inputs[i].link) { + this.removeInput(i--); + } + } + if (this.inputs[this.inputs.length - 1].link) { + this.addInput("v" + +new Date(), this.inputs[0].type).label = "value"; + } + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/showImageOnMenu.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/showImageOnMenu.js new file mode 100644 index 0000000000000000000000000000000000000000..4fed9de92e333270f1c2066e7667e34fd40e539b --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/showImageOnMenu.js @@ -0,0 +1,81 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; +import { $el } from "../../../scripts/ui.js"; + +const id = "pysssss.ShowImageOnMenu"; +const ext = { + name: id, + async setup(app) { + let enabled = true; + let nodeId = null; + const img = $el("img", { + style: { + width: "100%", + height: "150px", + objectFit: "contain", + }, + }); + const link = $el( + "a", + { + style: { + width: "100%", + height: "150px", + marginTop: "10px", + order: 100, // Place this item last (until someone else has a higher order) + display: "none", + }, + href: "#", + onclick: (e) => { + e.stopPropagation(); + e.preventDefault(); + const node = app.graph.getNodeById(nodeId); + if (!node) return; + app.canvas.centerOnNode(node); + app.canvas.setZoom(1); + }, + }, + [img] + ); + + app.ui.menuContainer.append(link); + + const show = (src, node) => { + img.src = src; + nodeId = Number(node); + link.style.display = "unset"; + }; + + api.addEventListener("executed", ({ detail }) => { + if (!enabled) return; + const images = detail?.output?.images; + if (!images || !images.length) return; + const format = app.getPreviewFormatParam(); + const src = [ + `./view?filename=${encodeURIComponent(images[0].filename)}`, + `type=${images[0].type}`, + `subfolder=${encodeURIComponent(images[0].subfolder)}`, + `t=${+new Date()}${format}`,].join('&'); + show(src, detail.node); + }); + + api.addEventListener("b_preview", ({ detail }) => { + if (!enabled) return; + show(URL.createObjectURL(detail), app.runningNodeId); + }); + + app.ui.settings.addSetting({ + id, + name: "🐍 Show Image On Menu", + defaultValue: true, + type: "boolean", + onChange(value) { + enabled = value; + + if (!enabled) link.style.display = "none"; + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/showText.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/showText.js new file mode 100644 index 0000000000000000000000000000000000000000..464fd404632397e26c2af1d2297970da6c726920 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/showText.js @@ -0,0 +1,78 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +// Displays input text on a node + +// TODO: This should need to be so complicated. Refactor at some point. + +app.registerExtension({ + name: "pysssss.ShowText", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "ShowText|pysssss") { + function populate(text) { + if (this.widgets) { + // On older frontend versions there is a hidden converted-widget + const isConvertedWidget = +!!this.inputs?.[0].widget; + for (let i = isConvertedWidget; i < this.widgets.length; i++) { + this.widgets[i].onRemove?.(); + } + this.widgets.length = isConvertedWidget; + } + + const v = [...text]; + if (!v[0]) { + v.shift(); + } + for (let list of v) { + // Force list to be an array, not sure why sometimes it is/isn't + if (!(list instanceof Array)) list = [list]; + for (const l of list) { + const w = ComfyWidgets["STRING"](this, "text_" + this.widgets?.length ?? 0, ["STRING", { multiline: true }], app).widget; + w.inputEl.readOnly = true; + w.inputEl.style.opacity = 0.6; + w.value = l; + } + } + + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + }); + } + + // When the node is executed we will be sent the input text, display this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + populate.call(this, message.text); + }; + + const VALUES = Symbol(); + const configure = nodeType.prototype.configure; + nodeType.prototype.configure = function () { + // Store unmodified widget values as they get removed on configure by new frontend + this[VALUES] = arguments[0]?.widgets_values; + return configure?.apply(this, arguments); + }; + + const onConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + onConfigure?.apply(this, arguments); + const widgets_values = this[VALUES]; + if (widgets_values?.length) { + // In newer frontend there seems to be a delay in creating the initial widget + requestAnimationFrame(() => { + populate.call(this, widgets_values.slice(+(widgets_values.length > 1 && this.inputs?.[0].widget))); + }); + } + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/snapToGrid.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/snapToGrid.js new file mode 100644 index 0000000000000000000000000000000000000000..07d5aa8c7bec97e2e14a8470fc8df2e82c14c39d --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/snapToGrid.js @@ -0,0 +1,73 @@ +import { app } from "../../../scripts/app.js"; + +let setting; +const id = "pysssss.SnapToGrid"; + +/** Wraps the provided function call to set/reset shiftDown when setting is enabled. */ +function wrapCallInSettingCheck(fn) { + if (setting?.value) { + const shift = app.shiftDown; + app.shiftDown = true; + const r = fn(); + app.shiftDown = shift; + return r; + } + return fn(); +} + +const ext = { + name: id, + init() { + setting = app.ui.settings.addSetting({ + id, + name: "🐍 Always snap to grid", + defaultValue: false, + type: "boolean", + onChange(value) { + app.canvas.align_to_grid = value; + }, + }); + + // We need to register our hooks after the core snap to grid extension runs + // Do this from the graph configure function so we still get onNodeAdded calls + const configure = LGraph.prototype.configure; + LGraph.prototype.configure = function () { + // Override drawNode to draw the drop position + const drawNode = LGraphCanvas.prototype.drawNode; + LGraphCanvas.prototype.drawNode = function () { + wrapCallInSettingCheck(() => drawNode.apply(this, arguments)); + }; + + // Override node added to add a resize handler to force grid alignment + const onNodeAdded = app.graph.onNodeAdded; + app.graph.onNodeAdded = function (node) { + const r = onNodeAdded?.apply(this, arguments); + const onResize = node.onResize; + node.onResize = function () { + wrapCallInSettingCheck(() => onResize?.apply(this, arguments)); + }; + return r; + }; + + + const groupMove = LGraphGroup.prototype.move; + LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) { + wrapCallInSettingCheck(() => groupMove.apply(this, arguments)); + } + + const canvasDrawGroups = LGraphCanvas.prototype.drawGroups; + LGraphCanvas.prototype.drawGroups = function (canvas, ctx) { + wrapCallInSettingCheck(() => canvasDrawGroups.apply(this, arguments)); + } + + const canvasOnGroupAdd = LGraphCanvas.onGroupAdd; + LGraphCanvas.onGroupAdd = function() { + wrapCallInSettingCheck(() => canvasOnGroupAdd.apply(this, arguments)); + } + + return configure.apply(this, arguments); + }; + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/snapToGridGuide.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/snapToGridGuide.js new file mode 100644 index 0000000000000000000000000000000000000000..54a8a0c70b2705620be0c63c2cecdb3ea032420f --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/snapToGridGuide.js @@ -0,0 +1,163 @@ +import { app } from "../../../scripts/app.js"; +import { $el } from "../../../scripts/ui.js"; + +let guide_config; +const id = "pysssss.SnapToGrid.Guide"; +const guide_config_default = { + lines: { + enabled: false, + fillStyle: "rgba(255, 0, 0, 0.5)", + }, + block: { + enabled: false, + fillStyle: "rgba(0, 0, 255, 0.5)", + }, +} + +const ext = { + name: id, + init() { + if (localStorage.getItem(id) === null) { + localStorage.setItem(id, JSON.stringify(guide_config_default)); + } + guide_config = JSON.parse(localStorage.getItem(id)); + + app.ui.settings.addSetting({ + id, + name: "🐍 Display drag-and-drop guides", + type: (name, setter, value) => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: name, + }), + ]), + $el("td", [ + $el( + "label", + { + textContent: "Lines: ", + style: { + display: "inline-block", + }, + }, + [ + $el("input", { + id: id.replaceAll(".", "-") + "-line-text", + type: "text", + value: guide_config.lines.fillStyle, + onchange: (event) => { + guide_config.lines.fillStyle = event.target.value; + localStorage.setItem(id, JSON.stringify(guide_config)); + } + }), + $el("input", { + id: id.replaceAll(".", "-") + "-line-checkbox", + type: "checkbox", + checked: guide_config.lines.enabled, + onchange: (event) => { + guide_config.lines.enabled = !!event.target.checked; + localStorage.setItem(id, JSON.stringify(guide_config)); + }, + }), + ] + ), + $el( + "label", + { + textContent: "Block: ", + style: { + display: "inline-block", + }, + }, + [ + $el("input", { + id: id.replaceAll(".", "-") + "-block-text", + type: "text", + value: guide_config.block.fillStyle, + onchange: (event) => { + guide_config.block.fillStyle = event.target.value; + localStorage.setItem(id, JSON.stringify(guide_config)); + } + }), + $el("input", { + id: id.replaceAll(".", "-") + '-block-checkbox', + type: "checkbox", + checked: guide_config.block.enabled, + onchange: (event) => { + guide_config.block.enabled = !!event.target.checked; + localStorage.setItem(id, JSON.stringify(guide_config)); + }, + }), + ] + ), + ]), + ]); + } + }); + + const alwaysSnapToGrid = () => + app.ui.settings.getSettingValue("pysssss.SnapToGrid", /* default=*/ false); + const snapToGridEnabled = () => + app.shiftDown || alwaysSnapToGrid(); + + // Override drag-and-drop behavior to show orthogonal guide lines around selected node(s) and preview of where the node(s) will be placed + const origDrawNode = LGraphCanvas.prototype.drawNode; + LGraphCanvas.prototype.drawNode = function (node, ctx) { + const enabled = guide_config.lines.enabled || guide_config.block.enabled; + if (enabled && this.node_dragged && node.id in this.selected_nodes && snapToGridEnabled()) { + // discretize the canvas into grid + let x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE); + let y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE); + + // calculate the width and height of the node + // (also need to shift the y position of the node, depending on whether the title is visible) + x -= node.pos[0]; + y -= node.pos[1]; + let w, h; + if (node.flags.collapsed) { + w = node._collapsed_width; + h = LiteGraph.NODE_TITLE_HEIGHT; + y -= LiteGraph.NODE_TITLE_HEIGHT; + } else { + w = node.size[0]; + h = node.size[1]; + let titleMode = node.constructor.title_mode; + if (titleMode !== LiteGraph.TRANSPARENT_TITLE && titleMode !== LiteGraph.NO_TITLE) { + h += LiteGraph.NODE_TITLE_HEIGHT; + y -= LiteGraph.NODE_TITLE_HEIGHT; + } + } + + // save the original fill style + const f = ctx.fillStyle; + + // draw preview for drag-and-drop (rectangle to show where the node will be placed) + if (guide_config.block.enabled) { + ctx.fillStyle = guide_config.block.fillStyle; + ctx.fillRect(x, y, w, h); + } + + // add guide lines around node (arbitrarily long enough to span most workflows) + if (guide_config.lines.enabled) { + const xd = 10000; + const yd = 10000; + const thickness = 3; + ctx.fillStyle = guide_config.lines.fillStyle; + ctx.fillRect(x - xd, y, 2*xd, thickness); + ctx.fillRect(x, y - yd, thickness, 2*yd); + ctx.fillRect(x - xd, y + h, 2*xd, thickness); + ctx.fillRect(x + w, y - yd, thickness, 2*yd); + } + + // restore the original fill style + ctx.fillStyle = f; + } + + return origDrawNode.apply(this, arguments); + }; + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/stringFunction.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/stringFunction.js new file mode 100644 index 0000000000000000000000000000000000000000..cc150dff04a2e94b9bc768f6331964c13a96ddbe --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/stringFunction.js @@ -0,0 +1,33 @@ +import { app } from "../../../scripts/app.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +// Displays input text on a node + +app.registerExtension({ + name: "pysssss.StringFunction", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "StringFunction|pysssss") { + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "result"); + if (pos !== -1) { + for (let i = pos; i < this.widgets.length; i++) { + this.widgets[i].onRemove?.(); + } + this.widgets.length = pos; + } + } + + const w = ComfyWidgets["STRING"](this, "result", ["STRING", { multiline: true }], app).widget; + w.inputEl.readOnly = true; + w.inputEl.style.opacity = 0.6; + w.value = message.text; + + this.onResize?.(this.size); + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/swapResolution.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/swapResolution.js new file mode 100644 index 0000000000000000000000000000000000000000..40f6dcc851780077c79e7e142fd0f97104e85a09 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/swapResolution.js @@ -0,0 +1,30 @@ +import { app } from "../../../scripts/app.js"; +app.registerExtension({ + name: "pysssss.SwapResolution", + async beforeRegisterNodeDef(nodeType, nodeData) { + const inputs = { ...nodeData.input?.required, ...nodeData.input?.optional }; + if (inputs.width && inputs.height) { + const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = origGetExtraMenuOptions?.apply?.(this, arguments); + + options.push( + { + content: "Swap width/height", + callback: () => { + const w = this.widgets.find((w) => w.name === "width"); + const h = this.widgets.find((w) => w.name === "height"); + const a = w.value; + w.value = h.value; + h.value = a; + app.graph.setDirtyCanvas(true); + }, + }, + null + ); + + return r; + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/systemNotification.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/systemNotification.js new file mode 100644 index 0000000000000000000000000000000000000000..803b8c90629f6722efd090e0bf5839e395c4402b --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/systemNotification.js @@ -0,0 +1,47 @@ +import { app } from "../../../scripts/app.js"; + +const notificationSetup = () => { + if (!("Notification" in window)) { + console.log("This browser does not support notifications."); + alert("This browser does not support notifications."); + return; + } + if (Notification.permission === "denied") { + console.log("Notifications are blocked. Please enable them in your browser settings."); + alert("Notifications are blocked. Please enable them in your browser settings."); + return; + } + if (Notification.permission !== "granted") { + Notification.requestPermission(); + } + return true; +}; + +app.registerExtension({ + name: "pysssss.SystemNotification", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "SystemNotification|pysssss") { + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = async function ({ message, mode }) { + onExecuted?.apply(this, arguments); + + if (mode === "on empty queue") { + if (app.ui.lastQueueSize !== 0) { + await new Promise((r) => setTimeout(r, 500)); + } + if (app.ui.lastQueueSize !== 0) { + return; + } + } + if (!notificationSetup()) return; + const notification = new Notification("ComfyUI", { body: message ?? "Your notification has triggered." }); + }; + + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + onNodeCreated?.apply(this, arguments); + notificationSetup(); + }; + } + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/useNumberInputPrompt.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/useNumberInputPrompt.js new file mode 100644 index 0000000000000000000000000000000000000000..a92aa51095bc474b3cf80697aa238ad8814b4c3d --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/useNumberInputPrompt.js @@ -0,0 +1,36 @@ +import { app } from "../../../scripts/app.js"; + +const id = "pysssss.UseNumberInputPrompt"; +const ext = { + name: id, + async setup(app) { + const prompt = LGraphCanvas.prototype.prompt; + + const setting = app.ui.settings.addSetting({ + id, + name: "🐍 Use number input on value entry", + defaultValue: false, + type: "boolean", + }); + + LGraphCanvas.prototype.prompt = function () { + const dialog = prompt.apply(this, arguments); + if (setting.value && typeof arguments[1] === "number") { + // If this should be a number then update the imput + const input = dialog.querySelector("input"); + input.type = "number"; + + // Add constraints + const widget = app.canvas.node_widget?.[1]; + if (widget?.options) { + for (const prop of ["min", "max", "step"]) { + if (widget.options[prop]) input[prop] = widget.options[prop]; + } + } + } + return dialog; + }; + }, +}; + +app.registerExtension(ext); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/widgetDefaults.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/widgetDefaults.js new file mode 100644 index 0000000000000000000000000000000000000000..859749d16fda455bcdfb4a6fba89a7340c839183 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/widgetDefaults.js @@ -0,0 +1,297 @@ +import { app } from "../../../scripts/app.js"; +import { $el, ComfyDialog } from "../../../scripts/ui.js"; + +// Allows you to specify custom default values for any widget on any node + +const id = "pysssss.WidgetDefaults"; +const nodeDataKey = Symbol(); + +app.registerExtension({ + name: id, + beforeRegisterNodeDef(nodeType, nodeData) { + nodeType[nodeDataKey] = nodeData; + }, + setup() { + let defaults; + let regexDefaults; + let setting; + + const getNodeDefaults = (node, defaults) => { + const nodeDefaults = defaults[node.type] ?? {}; + const propSetBy = {}; + + Object.keys(regexDefaults) + .filter((r) => new RegExp(r).test(node.type)) + .reduce((p, n) => { + const props = regexDefaults[n]; + for (const k in props) { + // Use the longest matching key as its probably the most specific + if (!(k in nodeDefaults) || (k in propSetBy && n.length > propSetBy[k].length)) { + propSetBy[k] = n; + nodeDefaults[k] = props[k]; + } + } + return p; + }, nodeDefaults); + + return nodeDefaults; + }; + + const applyDefaults = (defaults) => { + for (const node of Object.values(LiteGraph.registered_node_types)) { + const nodeData = node[nodeDataKey]; + if (!nodeData) continue; + const nodeDefaults = getNodeDefaults(node, defaults); + if (!nodeDefaults) continue; + const inputs = { ...(nodeData.input?.required || {}), ...(nodeData.input?.optional || {}) }; + + for (const w in nodeDefaults) { + const widgetDef = inputs[w]; + if (widgetDef) { + let v = nodeDefaults[w]; + if (widgetDef[0] === "INT" || widgetDef[0] === "FLOAT") { + v = +v; + } + if (widgetDef[1]) { + widgetDef[1].default = v; + } else { + widgetDef[1] = { default: v }; + } + } + } + } + }; + + const getDefaults = () => { + let items; + regexDefaults = {}; + try { + items = JSON.parse(setting.value); + items = items.reduce((p, n) => { + if (n.node.startsWith("/") && n.node.endsWith("/")) { + const name = n.node.substring(1, n.node.length - 1); + try { + // Validate regex + new RegExp(name); + + if (!regexDefaults[name]) regexDefaults[name] = {}; + regexDefaults[name][n.widget] = n.value; + } catch (error) {} + } + + if (!p[n.node]) p[n.node] = {}; + p[n.node][n.widget] = n.value; + return p; + }, {}); + } catch (error) {} + if (!items) { + items = {}; + } + applyDefaults(items); + return items; + }; + + const onNodeAdded = app.graph.onNodeAdded; + app.graph.onNodeAdded = function (node) { + onNodeAdded?.apply?.(this, arguments); + + // See if we have any defaults for this type of node + const nodeDefaults = getNodeDefaults(node.constructor, defaults); + if (!nodeDefaults) return; + + // Dont run if they are pre-configured nodes from load/pastes + const stack = new Error().stack; + if (stack.includes("pasteFromClipboard") || stack.includes("loadGraphData")) { + return; + } + + for (const k in nodeDefaults) { + if (k.startsWith("property.")) { + const name = k.substring(9); + let v = nodeDefaults[k]; + // Special handling for some built in values + if (name in node || ["color", "bgcolor", "title"].includes(name)) { + node[name] = v; + } else { + // Try using the correct type + if (!node.properties) node.properties = {}; + if (typeof node.properties[name] === "number") v = +v; + else if (typeof node.properties[name] === "boolean") v = v === "true"; + else if (v === "true") v = true; + + node.properties[name] = v; + } + } + } + }; + + class WidgetDefaultsDialog extends ComfyDialog { + constructor() { + super(); + this.element.classList.add("comfy-manage-templates"); + this.grid = $el( + "div", + { + style: { + display: "grid", + gridTemplateColumns: "1fr auto auto auto", + gap: "5px", + }, + className: "pysssss-widget-defaults", + }, + [ + $el("label", { + textContent: "Node Class", + }), + $el("label", { + textContent: "Widget Name", + }), + $el("label", { + textContent: "Default Value", + }), + $el("label"), + (this.rows = $el("div", { + style: { + display: "contents", + }, + })), + ] + ); + } + + createButtons() { + const btns = super.createButtons(); + btns[0].textContent = "Cancel"; + btns.unshift( + $el("button", { + type: "button", + textContent: "Add New", + onclick: () => this.addRow(), + }), + $el("button", { + type: "button", + textContent: "Save", + onclick: () => this.save(), + }) + ); + return btns; + } + + addRow(node = "", widget = "", value = "") { + let nameInput; + this.rows.append( + $el( + "div", + { + style: { + display: "contents", + }, + className: "pysssss-widget-defaults-row", + }, + [ + $el("input", { + placeholder: "e.g. CheckpointLoaderSimple", + value: node, + }), + $el("input", { + placeholder: "e.g. ckpt_name", + value: widget, + $: (el) => (nameInput = el), + }), + $el("input", { + placeholder: "e.g. myBestModel.safetensors", + value, + }), + $el("button", { + textContent: "Delete", + style: { + fontSize: "12px", + color: "red", + fontWeight: "normal", + }, + onclick: (e) => { + nameInput.value = ""; + e.target.parentElement.style.display = "none"; + }, + }), + ] + ) + ); + } + + save() { + const rows = this.rows.children; + const items = []; + + for (const row of rows) { + const inputs = row.querySelectorAll("input"); + const node = inputs[0].value.trim(); + const widget = inputs[1].value.trim(); + const value = inputs[2].value; + if (node && widget) { + items.push({ node, widget, value }); + } + } + + setting.value = JSON.stringify(items); + defaults = getDefaults(); + + this.close(); + } + + show() { + this.rows.replaceChildren(); + for (const nodeName in defaults) { + const node = defaults[nodeName]; + for (const widgetName in node) { + this.addRow(nodeName, widgetName, node[widgetName]); + } + } + + this.addRow(); + super.show(this.grid); + } + } + + setting = app.ui.settings.addSetting({ + id, + name: "🐍 Widget Defaults", + type: () => { + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: "🐍 Widget & Property Defaults:", + }), + ]), + $el("td", [ + $el("button", { + textContent: "Manage", + onclick: () => { + try { + // Try closing old settings window + if (typeof app.ui.settings.element?.close === "function") { + app.ui.settings.element.close(); + } + } catch (error) {} + try { + // Try closing new vue dialog + document.querySelector(".p-dialog-close-button").click(); + } catch (error) { + // Fallback to just hiding the element + app.ui.settings.element.style.display = "none"; + } + const dialog = new WidgetDefaultsDialog(); + dialog.show(); + }, + style: { + fontSize: "14px", + }, + }), + ]), + ]); + }, + }); + defaults = getDefaults(); + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/workflowImage.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/workflowImage.js new file mode 100644 index 0000000000000000000000000000000000000000..37c3ad06c6272c1a8b1cab264515285412cec0d2 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/workflowImage.js @@ -0,0 +1,640 @@ +import { app } from "../../../scripts/app.js"; +import { importA1111 } from "../../../scripts/pnginfo.js"; +import { ComfyWidgets } from "../../../scripts/widgets.js"; + +let getDrawTextConfig = null; +let fileInput; + +class WorkflowImage { + static accept = ""; + + getBounds() { + // Calculate the min max bounds for the nodes on the graph + const bounds = app.graph._nodes.reduce( + (p, n) => { + if (n.pos[0] < p[0]) p[0] = n.pos[0]; + if (n.pos[1] < p[1]) p[1] = n.pos[1]; + const bounds = n.getBounding(); + const r = n.pos[0] + bounds[2]; + const b = n.pos[1] + bounds[3]; + if (r > p[2]) p[2] = r; + if (b > p[3]) p[3] = b; + return p; + }, + [99999, 99999, -99999, -99999] + ); + + bounds[0] -= 100; + bounds[1] -= 100; + bounds[2] += 100; + bounds[3] += 100; + return bounds; + } + + saveState() { + this.state = { + scale: app.canvas.ds.scale, + width: app.canvas.canvas.width, + height: app.canvas.canvas.height, + offset: app.canvas.ds.offset, + transform: app.canvas.canvas.getContext("2d").getTransform(), // Save the original transformation matrix + }; + } + + restoreState() { + app.canvas.ds.scale = this.state.scale; + app.canvas.canvas.width = this.state.width; + app.canvas.canvas.height = this.state.height; + app.canvas.ds.offset = this.state.offset; + app.canvas.canvas.getContext("2d").setTransform(this.state.transform); // Reapply the original transformation matrix + } + + updateView(bounds) { + const scale = window.devicePixelRatio || 1; + app.canvas.ds.scale = 1; + app.canvas.canvas.width = (bounds[2] - bounds[0]) * scale; + app.canvas.canvas.height = (bounds[3] - bounds[1]) * scale; + app.canvas.ds.offset = [-bounds[0], -bounds[1]]; + app.canvas.canvas.getContext("2d").setTransform(scale, 0, 0, scale, 0, 0); + } + + getDrawTextConfig(_, widget) { + return { + x: 10, + y: widget.last_y + 10, + resetTransform: false, + }; + } + + async export(includeWorkflow) { + // Save the current state of the canvas + this.saveState(); + // Update to render the whole workflow + this.updateView(this.getBounds()); + + // Flag that we are saving and render the canvas + getDrawTextConfig = this.getDrawTextConfig; + app.canvas.draw(true, true); + getDrawTextConfig = null; + + // Generate a blob of the image containing the workflow + const blob = await this.getBlob(includeWorkflow ? JSON.stringify(app.graph.serialize()) : undefined); + + // Restore initial state and redraw + this.restoreState(); + app.canvas.draw(true, true); + + // Download the generated image + this.download(blob); + } + + download(blob) { + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + Object.assign(a, { + href: url, + download: "workflow." + this.extension, + style: "display: none", + }); + document.body.append(a); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + } + + static import() { + if (!fileInput) { + fileInput = document.createElement("input"); + Object.assign(fileInput, { + type: "file", + style: "display: none", + onchange: () => { + app.handleFile(fileInput.files[0]); + }, + }); + document.body.append(fileInput); + } + fileInput.accept = WorkflowImage.accept; + fileInput.click(); + } +} + +class PngWorkflowImage extends WorkflowImage { + static accept = ".png,image/png"; + extension = "png"; + + n2b(n) { + return new Uint8Array([(n >> 24) & 0xff, (n >> 16) & 0xff, (n >> 8) & 0xff, n & 0xff]); + } + + joinArrayBuffer(...bufs) { + const result = new Uint8Array(bufs.reduce((totalSize, buf) => totalSize + buf.byteLength, 0)); + bufs.reduce((offset, buf) => { + result.set(buf, offset); + return offset + buf.byteLength; + }, 0); + return result; + } + + crc32(data) { + const crcTable = + PngWorkflowImage.crcTable || + (PngWorkflowImage.crcTable = (() => { + let c; + const crcTable = []; + for (let n = 0; n < 256; n++) { + c = n; + for (let k = 0; k < 8; k++) { + c = c & 1 ? 0xedb88320 ^ (c >>> 1) : c >>> 1; + } + crcTable[n] = c; + } + return crcTable; + })()); + let crc = 0 ^ -1; + for (let i = 0; i < data.byteLength; i++) { + crc = (crc >>> 8) ^ crcTable[(crc ^ data[i]) & 0xff]; + } + return (crc ^ -1) >>> 0; + } + + async getBlob(workflow) { + return new Promise((r) => { + app.canvasEl.toBlob(async (blob) => { + if (workflow) { + // If we have a workflow embed it in the PNG + const buffer = await blob.arrayBuffer(); + const typedArr = new Uint8Array(buffer); + const view = new DataView(buffer); + + const data = new TextEncoder().encode(`tEXtworkflow\0${workflow}`); + const chunk = this.joinArrayBuffer(this.n2b(data.byteLength - 4), data, this.n2b(this.crc32(data))); + + const sz = view.getUint32(8) + 20; + const result = this.joinArrayBuffer(typedArr.subarray(0, sz), chunk, typedArr.subarray(sz)); + + blob = new Blob([result], { type: "image/png" }); + } + + r(blob); + }); + }); + } +} + +class DataReader { + /** @type {DataView} */ + view; + /** @type {boolean | undefined} */ + littleEndian; + offset = 0; + + /** + * @param {DataView} view + */ + constructor(view) { + this.view = view; + } + + /** + * Reads N bytes and increments the offset + * @param {1 | 2 | 4 | 8} size + */ + read(size, signed = false, littleEndian = undefined) { + const v = this.peek(size, signed, littleEndian); + this.offset += size; + return v; + } + + /** + * Reads N bytes + * @param {1 | 2 | 4 | 8} size + */ + peek(size, signed = false, littleEndian = undefined) { + this.view.getBigInt64; + let m = ""; + if (size === 8) m += "Big"; + m += signed ? "Int" : "Uint"; + m += size * 8; + m = "get" + m; + if (!this.view[m]) { + throw new Error("Method not found: " + m); + } + + return this.view[m](this.offset, littleEndian == null ? this.littleEndian : littleEndian); + } + + /** + * Seeks to the specified position or by the number of bytes specified relative to the current offset + * @param {number} pos + * @param {boolean} relative + */ + seek(pos, relative = true) { + if (relative) { + this.offset += pos; + } else { + this.offset = pos; + } + } +} + +class Tiff { + /** @type {DataReader} */ + #reader; + #start; + + readExif(reader) { + const TIFF_MARKER = 0x2a; + const EXIF_IFD = 0x8769; + + this.#reader = reader; + this.#start = this.#reader.offset; + this.#readEndianness(); + + if (!this.#reader.read(2) === TIFF_MARKER) { + throw new Error("Invalid TIFF: Marker not found."); + } + + const dirOffset = this.#reader.read(4); + this.#reader.seek(this.#start + dirOffset, false); + + for (const t of this.#readTags()) { + if (t.id === EXIF_IFD) { + return this.#readExifTag(t); + } + } + throw new Error("No EXIF: TIFF Exif IFD tag not found"); + } + + #readUserComment(tag) { + this.#reader.seek(this.#start + tag.offset, false); + const encoding = this.#reader.read(8); + if (encoding !== 0x45444f43494e55n) { + throw new Error("Unable to read non-Unicode data"); + } + const decoder = new TextDecoder("utf-16be"); + return decoder.decode(new DataView(this.#reader.view.buffer, this.#reader.offset, tag.count - 8)); + } + + #readExifTag(exifTag) { + const EXIF_USER_COMMENT = 0x9286; + + this.#reader.seek(this.#start + exifTag.offset, false); + for (const t of this.#readTags()) { + if (t.id === EXIF_USER_COMMENT) { + return this.#readUserComment(t); + } + } + throw new Error("No embedded data: UserComment Exif tag not found"); + } + + *#readTags() { + const count = this.#reader.read(2); + for (let i = 0; i < count; i++) { + yield { + id: this.#reader.read(2), + type: this.#reader.read(2), + count: this.#reader.read(4), + offset: this.#reader.read(4), + }; + } + } + + #readEndianness() { + const II = 0x4949; + const MM = 0x4d4d; + const endianness = this.#reader.read(2); + if (endianness === II) { + this.#reader.littleEndian = true; + } else if (endianness === MM) { + this.#reader.littleEndian = false; + } else { + throw new Error("Invalid JPEG: Endianness marker not found."); + } + } +} + +class Jpeg { + /** @type {DataReader} */ + #reader; + + /** + * @param {ArrayBuffer} buffer + */ + readExif(buffer) { + const JPEG_MARKER = 0xffd8; + const EXIF_SIG = 0x45786966; + + this.#reader = new DataReader(new DataView(buffer)); + if (!this.#reader.read(2) === JPEG_MARKER) { + throw new Error("Invalid JPEG: SOI not found."); + } + + const app0 = this.#readAppMarkerId(); + if (app0 !== 0) { + throw new Error(`Invalid JPEG: APP0 not found [found: ${app0}].`); + } + + this.#consumeAppSegment(); + const app1 = this.#readAppMarkerId(); + if (app1 !== 1) { + throw new Error(`No EXIF: APP1 not found [found: ${app0}].`); + } + + // Skip size + this.#reader.seek(2); + + if (this.#reader.read(4) !== EXIF_SIG) { + throw new Error(`No EXIF: Invalid EXIF header signature.`); + } + if (this.#reader.read(2) !== 0) { + throw new Error(`No EXIF: Invalid EXIF header.`); + } + + return new Tiff().readExif(this.#reader); + } + + #readAppMarkerId() { + const APP0_MARKER = 0xffe0; + return this.#reader.read(2) - APP0_MARKER; + } + + #consumeAppSegment() { + this.#reader.seek(this.#reader.read(2) - 2); + } +} + +class SvgWorkflowImage extends WorkflowImage { + static accept = ".svg,image/svg+xml"; + extension = "svg"; + + static init() { + // Override file handling to allow drag & drop of SVG + const handleFile = app.handleFile; + app.handleFile = async function (file) { + if (file && (file.type === "image/svg+xml" || file.name?.endsWith(".svg"))) { + const reader = new FileReader(); + reader.onload = () => { + // Extract embedded workflow from desc tags + const descEnd = reader.result.lastIndexOf(""); + if (descEnd !== -1) { + const descStart = reader.result.lastIndexOf("", descEnd); + if (descStart !== -1) { + const json = reader.result.substring(descStart + 6, descEnd); + this.loadGraphData(JSON.parse(SvgWorkflowImage.unescapeXml(json))); + } + } + }; + reader.readAsText(file); + return; + } else if (file && (file.type === "image/jpeg" || file.name?.endsWith(".jpg") || file.name?.endsWith(".jpeg"))) { + if ( + await new Promise((resolve) => { + try { + // This shouldnt go in here but it's easier than refactoring handleFile + const reader = new FileReader(); + reader.onload = async () => { + try { + const value = new Jpeg().readExif(reader.result); + importA1111(app.graph, value); + resolve(true); + } catch (error) { + resolve(false); + } + }; + reader.onerror = () => resolve(false); + reader.readAsArrayBuffer(file); + } catch (error) { + resolve(false); + } + }) + ) { + return; + } + } + return handleFile.apply(this, arguments); + }; + } + + static escapeXml(unsafe) { + return unsafe.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">"); + } + + static unescapeXml(safe) { + return safe.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">"); + } + + getDrawTextConfig(_, widget) { + const domWrapper = widget.inputEl.closest(".dom-widget") ?? widget.inputEl; + return { + x: parseInt(domWrapper.style.left), + y: parseInt(domWrapper.style.top), + resetTransform: true, + }; + } + + saveState() { + super.saveState(); + this.state.ctx = app.canvas.ctx; + } + + restoreState() { + super.restoreState(); + app.canvas.ctx = this.state.ctx; + } + + updateView(bounds) { + super.updateView(bounds); + this.createSvgCtx(bounds); + } + + createSvgCtx(bounds) { + const ctx = this.state.ctx; + const svgCtx = (this.svgCtx = new C2S(bounds[2] - bounds[0], bounds[3] - bounds[1])); + svgCtx.canvas.getBoundingClientRect = function () { + return { width: svgCtx.width, height: svgCtx.height }; + }; + + // Override the c2s handling of images to draw images as canvases + const drawImage = svgCtx.drawImage; + svgCtx.drawImage = function (...args) { + const image = args[0]; + // If we are an image node and not a datauri then we need to replace with a canvas + // we cant convert to data uri here as it is an async process + if (image.nodeName === "IMG" && !image.src.startsWith("data:image/")) { + const canvas = document.createElement("canvas"); + canvas.width = image.width; + canvas.height = image.height; + const imgCtx = canvas.getContext("2d"); + imgCtx.drawImage(image, 0, 0); + args[0] = canvas; + } + + return drawImage.apply(this, args); + }; + + // Implement missing required functions + svgCtx.getTransform = function () { + return ctx.getTransform(); + }; + svgCtx.resetTransform = function () { + return ctx.resetTransform(); + }; + svgCtx.roundRect = svgCtx.rect; + app.canvas.ctx = svgCtx; + } + + getBlob(workflow) { + let svg = this.svgCtx.getSerializedSvg(true).replace("", `${SvgWorkflowImage.escapeXml(workflow)}`); + } + + return new Blob([svg], { type: "image/svg+xml" }); + } +} + +app.registerExtension({ + name: "pysssss.WorkflowImage", + init() { + // https://codepen.io/peterhry/pen/nbMaYg + function wrapText(context, text, x, y, maxWidth, lineHeight) { + var words = text.split(" "), + line = "", + i, + test, + metrics; + + for (i = 0; i < words.length; i++) { + test = words[i]; + metrics = context.measureText(test); + while (metrics.width > maxWidth) { + // Determine how much of the word will fit + test = test.substring(0, test.length - 1); + metrics = context.measureText(test); + } + if (words[i] != test) { + words.splice(i + 1, 0, words[i].substr(test.length)); + words[i] = test; + } + + test = line + words[i] + " "; + metrics = context.measureText(test); + + if (metrics.width > maxWidth && i > 0) { + context.fillText(line, x, y); + line = words[i] + " "; + y += lineHeight; + } else { + line = test; + } + } + + context.fillText(line, x, y); + } + + const stringWidget = ComfyWidgets.STRING; + // Override multiline string widgets to draw text using canvas while saving as svg + ComfyWidgets.STRING = function () { + const w = stringWidget.apply(this, arguments); + if (w.widget && w.widget.type === "customtext") { + const draw = w.widget.draw; + w.widget.draw = function (ctx) { + draw.apply(this, arguments); + if (this.inputEl.hidden) return; + + if (getDrawTextConfig) { + const config = getDrawTextConfig(ctx, this); + const t = ctx.getTransform(); + ctx.save(); + if (config.resetTransform) { + ctx.resetTransform(); + } + + const style = document.defaultView.getComputedStyle(this.inputEl, null); + const x = config.x; + const y = config.y; + const domWrapper = this.inputEl.closest(".dom-widget") ?? widget.inputEl; + let w = parseInt(domWrapper.style.width); + if (w === 0) { + w = this.node.size[0] - 20; + } + const h = parseInt(domWrapper.style.height); + ctx.fillStyle = style.getPropertyValue("background-color"); + ctx.fillRect(x, y, w, h); + + ctx.fillStyle = style.getPropertyValue("color"); + ctx.font = style.getPropertyValue("font"); + + const line = t.d * 12; + const split = this.inputEl.value.split("\n"); + let start = y; + for (const l of split) { + start += line; + wrapText(ctx, l, x + 4, start, w, line); + } + + ctx.restore(); + } + }; + } + return w; + }; + }, + setup() { + const script = document.createElement("script"); + script.onload = function () { + const formats = [SvgWorkflowImage, PngWorkflowImage]; + for (const f of formats) { + f.init?.call(); + WorkflowImage.accept += (WorkflowImage.accept ? "," : "") + f.accept; + } + + // Add canvas menu options + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + + options.push(null, { + content: "Workflow Image", + submenu: { + options: [ + { + content: "Import", + callback: () => { + WorkflowImage.import(); + }, + }, + { + content: "Export", + submenu: { + options: formats.flatMap((f) => [ + { + content: f.name.replace("WorkflowImage", "").toLocaleLowerCase(), + callback: () => { + new f().export(true); + }, + }, + { + content: f.name.replace("WorkflowImage", "").toLocaleLowerCase() + " (no embedded workflow)", + callback: () => { + new f().export(); + }, + }, + ]), + }, + }, + ], + }, + }); + return options; + }; + }; + + script.src = new URL(`assets/canvas2svg.js`, import.meta.url); + document.body.append(script); + }, +}); diff --git a/custom_nodes/ComfyUI-Custom-Scripts/web/js/workflows.js b/custom_nodes/ComfyUI-Custom-Scripts/web/js/workflows.js new file mode 100644 index 0000000000000000000000000000000000000000..0225260ae7cc6b420c272ab3f54e389910a93815 --- /dev/null +++ b/custom_nodes/ComfyUI-Custom-Scripts/web/js/workflows.js @@ -0,0 +1,343 @@ +import { app } from "../../../scripts/app.js"; +import { api } from "../../../scripts/api.js"; +import { $el } from "../../../scripts/ui.js"; + +// Adds workflow management +// Original implementation by https://github.com/i-h4x +// Thanks for permission to reimplement as an extension + +const style = ` +#comfy-save-button, #comfy-load-button { + position: relative; + overflow: hidden; +} +.pysssss-workflow-arrow { + position: absolute; + top: 0; + bottom: 0; + right: 0; + font-size: 12px; + display: flex; + align-items: center; + width: 24px; + justify-content: center; + background: rgba(255,255,255,0.1); +} +.pysssss-workflow-arrow:after { + content: "▼"; +} +.pysssss-workflow-arrow:hover { + filter: brightness(1.6); + background-color: var(--comfy-menu-bg); +} +.pysssss-workflow-load .litemenu-entry:not(.has_submenu):before, +.pysssss-workflow-load ~ .litecontextmenu .litemenu-entry:not(.has_submenu):before { + content: "🎛️"; + padding-right: 5px; +} +.pysssss-workflow-load .litemenu-entry.has_submenu:before, +.pysssss-workflow-load ~ .litecontextmenu .litemenu-entry.has_submenu:before { + content: "📂"; + padding-right: 5px; + position: relative; + top: -1px; +} +.pysssss-workflow-popup ~ .litecontextmenu { + transform: scale(1.3); +} +`; + +async function getWorkflows() { + const response = await api.fetchApi("/pysssss/workflows", { cache: "no-store" }); + return await response.json(); +} + +async function getWorkflow(name) { + const response = await api.fetchApi(`/pysssss/workflows/${encodeURIComponent(name)}`, { cache: "no-store" }); + return await response.json(); +} + +async function saveWorkflow(name, workflow, overwrite) { + try { + const response = await api.fetchApi("/pysssss/workflows", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify({ name, workflow, overwrite }), + }); + if (response.status === 201) { + return true; + } + if (response.status === 409) { + return false; + } + throw new Error(response.statusText); + } catch (error) { + console.error(error); + } +} + +class PysssssWorkflows { + async load() { + this.workflows = await getWorkflows(); + if(this.workflows.length) { + this.workflows.sort(); + } + this.loadMenu.style.display = this.workflows.length ? "flex" : "none"; + } + + getMenuOptions(callback) { + const menu = []; + const directories = new Map(); + for (const workflow of this.workflows || []) { + const path = workflow.split("/"); + let parent = menu; + let currentPath = ""; + for (let i = 0; i < path.length - 1; i++) { + currentPath += "/" + path[i]; + let newParent = directories.get(currentPath); + if (!newParent) { + newParent = { + title: path[i], + has_submenu: true, + submenu: { + options: [], + }, + }; + parent.push(newParent); + newParent = newParent.submenu.options; + directories.set(currentPath, newParent); + } + parent = newParent; + } + parent.push({ + title: path[path.length - 1], + callback: () => callback(workflow), + }); + } + return menu; + } + + constructor() { + function addWorkflowMenu(type, getOptions) { + return $el("div.pysssss-workflow-arrow", { + parent: document.getElementById(`comfy-${type}-button`), + onclick: (e) => { + e.preventDefault(); + e.stopPropagation(); + + LiteGraph.closeAllContextMenus(); + const menu = new LiteGraph.ContextMenu( + getOptions(), + { + event: e, + scale: 1.3, + }, + window + ); + menu.root.classList.add("pysssss-workflow-popup"); + menu.root.classList.add(`pysssss-workflow-${type}`); + }, + }); + } + + this.loadMenu = addWorkflowMenu("load", () => + this.getMenuOptions(async (workflow) => { + const json = await getWorkflow(workflow); + app.loadGraphData(json); + }) + ); + addWorkflowMenu("save", () => { + return [ + { + title: "Save as", + callback: () => { + let filename = prompt("Enter filename", this.workflowName || "workflow"); + if (filename) { + if (!filename.toLowerCase().endsWith(".json")) { + filename += ".json"; + } + + this.workflowName = filename; + + const json = JSON.stringify(app.graph.serialize(), null, 2); // convert the data to a JSON string + const blob = new Blob([json], { type: "application/json" }); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: filename, + style: { display: "none" }, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + } + }, + }, + { + title: "Save to workflows", + callback: async () => { + const name = prompt("Enter filename", this.workflowName || "workflow"); + if (name) { + this.workflowName = name; + + const data = app.graph.serialize(); + if (!(await saveWorkflow(name, data))) { + if (confirm("A workspace with this name already exists, do you want to overwrite it?")) { + await saveWorkflow(name, app.graph.serialize(), true); + } else { + return; + } + } + await this.load(); + } + }, + }, + ]; + }); + this.load(); + + const handleFile = app.handleFile; + const self = this; + app.handleFile = function (file) { + if (file?.name?.endsWith(".json")) { + self.workflowName = file.name; + } else { + self.workflowName = null; + } + return handleFile.apply(this, arguments); + }; + } +} + +const refreshComboInNodes = app.refreshComboInNodes; +let workflows; + +async function sendToWorkflow(img, workflow) { + const graph = !workflow ? app.graph.serialize() : await getWorkflow(workflow); + const nodes = graph.nodes.filter((n) => n.type === "LoadImage"); + let targetNode; + if (nodes.length === 0) { + alert("To send the image to another workflow, that workflow must have a LoadImage node."); + return; + } else if (nodes.length > 1) { + targetNode = nodes.find((n) => n.title?.toLowerCase().includes("input")); + if (!targetNode) { + targetNode = nodes[0]; + alert( + "The target workflow has multiple LoadImage nodes, include 'input' in the name of the one you want to use. The first one will be used here." + ); + } + } else { + targetNode = nodes[0]; + } + + const blob = await (await fetch(img.src)).blob(); + const name = + (workflow || "sendtoworkflow").replace(/\//g, "_") + + "-" + + +new Date() + + new URLSearchParams(img.src.split("?")[1]).get("filename"); + const body = new FormData(); + body.append("image", new File([blob], name)); + + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status === 200) { + await refreshComboInNodes.call(app); + targetNode.widgets_values[0] = name; + app.loadGraphData(graph); + app.graph.getNodeById(targetNode.id); + } else { + alert(resp.status + " - " + resp.statusText); + } +} + +app.registerExtension({ + name: "pysssss.Workflows", + init() { + $el("style", { + textContent: style, + parent: document.head, + }); + }, + + async refreshComboInNodes() { + workflows.load() + }, + + async setup() { + workflows = new PysssssWorkflows(); + + const comfyDefault = "[ComfyUI Default]"; + const defaultWorkflow = app.ui.settings.addSetting({ + id: "pysssss.Workflows.Default", + name: "🐍 Default Workflow", + defaultValue: comfyDefault, + type: "combo", + options: (value) => + [comfyDefault, ...workflows.workflows].map((m) => ({ + value: m, + text: m, + selected: m === value, + })), + }); + + document.getElementById("comfy-load-default-button").onclick = async function () { + if ( + localStorage["Comfy.Settings.Comfy.ConfirmClear"] === "false" || + confirm(`Load default workflow (${defaultWorkflow.value})?`) + ) { + if (defaultWorkflow.value === comfyDefault) { + app.loadGraphData(); + } else { + const json = await getWorkflow(defaultWorkflow.value); + app.loadGraphData(json); + } + } + }; + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = getExtraMenuOptions?.apply?.(this, arguments); + let img; + if (this.imageIndex != null) { + // An image is selected so select that + img = this.imgs[this.imageIndex]; + } else if (this.overIndex != null) { + // No image is selected but one is hovered + img = this.imgs[this.overIndex]; + } + + if (img) { + let pos = options.findIndex((o) => o.content === "Save Image"); + if (pos === -1) { + pos = 0; + } else { + pos++; + } + + options.splice(pos, 0, { + content: "Send to workflow", + has_submenu: true, + submenu: { + options: [ + { callback: () => sendToWorkflow(img), title: "[Current workflow]" }, + ...workflows.getMenuOptions(sendToWorkflow.bind(null, img)), + ], + }, + }); + } + + return r; + }; + }, +}); diff --git a/custom_nodes/ComfyUI-KJNodes/.gitignore b/custom_nodes/ComfyUI-KJNodes/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d462a62fdd67858a783934170db4091ea95eb18f --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/.gitignore @@ -0,0 +1,11 @@ +__pycache__ +/venv +*.code-workspace +.history +.vscode +*.ckpt +*.pth +types +models +jsconfig.json +custom_dimensions.json diff --git a/custom_nodes/ComfyUI-KJNodes/LICENSE b/custom_nodes/ComfyUI-KJNodes/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/ComfyUI-KJNodes/README.md b/custom_nodes/ComfyUI-KJNodes/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6371f5014823bc66ffd1f378fea8715a7dd590ff --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/README.md @@ -0,0 +1,65 @@ +# KJNodes for ComfyUI + +Various quality of life and masking related -nodes and scripts made by combining functionality of existing nodes for ComfyUI. + +I know I'm bad at documentation, especially this project that has grown from random practice nodes to... too many lines in one file. +I have however started to add descriptions to the nodes themselves, there's a small ? you can click for info what the node does. +This is still work in progress, like everything else. + +# Installation +1. Clone this repo into `custom_nodes` folder. +2. Install dependencies: `pip install -r requirements.txt` + or if you use the portable install, run this in ComfyUI_windows_portable -folder: + + `python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-KJNodes\requirements.txt` + + +## Javascript + +### browserstatus.js +Sets the favicon to green circle when not processing anything, sets it to red when processing and shows progress percentage and the length of your queue. +Default off, needs to be enabled from options, overrides Custom-Scripts favicon when enabled. + +## Nodes: + +### Set/Get + +Javascript nodes to set and get constants to reduce unnecessary lines. Takes in and returns anything, purely visual nodes. +On the right click menu of these nodes there's now an options to visualize the paths, as well as option to jump to the corresponding node on the other end. + +**Known limitations**: + - Will not work with any node that dynamically sets it's outpute, such as reroute or other Set/Get node + - Will not work when directly connected to a bypassed node + - Other possible conflicts with javascript based nodes. + +### ColorToMask + +RBG color value to mask, works with batches and AnimateDiff. + +### ConditioningMultiCombine + +Combine any number of conditions, saves space. + +### ConditioningSetMaskAndCombine + +Mask and combine two sets of conditions, saves space. + +### GrowMaskWithBlur + +Grows or shrinks (with negative values) mask, option to invert input, returns mask and inverted mask. Additionally Blurs the mask, this is a slow operation especially with big batches. + +### RoundMask + +![image](https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40) + +### WidgetToString +Outputs the value of a widget on any node as a string +![example of use](docs/images/2024-04-03_20_49_29-ComfyUI.png) + +Enable node id display from Manager menu, to get the ID of the node you want to read a widget from: +![enable node id display](docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png) + +Use the node id of the target node, and add the name of the widget to read from +![use node id and widget name](docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png) + +Recreating or reloading the target node will change its id, and the WidgetToString node will no longer be able to find it until you update the node id value with the new id. diff --git a/custom_nodes/ComfyUI-KJNodes/__init__.py b/custom_nodes/ComfyUI-KJNodes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fce7a50fb20726cefb84215911b01500acfbab35 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/__init__.py @@ -0,0 +1,235 @@ +from .nodes.nodes import * +from .nodes.curve_nodes import * +from .nodes.batchcrop_nodes import * +from .nodes.audioscheduler_nodes import * +from .nodes.image_nodes import * +from .nodes.intrinsic_lora_nodes import * +from .nodes.mask_nodes import * +from .nodes.model_optimization_nodes import * +NODE_CONFIG = { + #constants + "BOOLConstant": {"class": BOOLConstant, "name": "BOOL Constant"}, + "INTConstant": {"class": INTConstant, "name": "INT Constant"}, + "FloatConstant": {"class": FloatConstant, "name": "Float Constant"}, + "StringConstant": {"class": StringConstant, "name": "String Constant"}, + "StringConstantMultiline": {"class": StringConstantMultiline, "name": "String Constant Multiline"}, + #conditioning + "ConditioningMultiCombine": {"class": ConditioningMultiCombine, "name": "Conditioning Multi Combine"}, + "ConditioningSetMaskAndCombine": {"class": ConditioningSetMaskAndCombine, "name": "ConditioningSetMaskAndCombine"}, + "ConditioningSetMaskAndCombine3": {"class": ConditioningSetMaskAndCombine3, "name": "ConditioningSetMaskAndCombine3"}, + "ConditioningSetMaskAndCombine4": {"class": ConditioningSetMaskAndCombine4, "name": "ConditioningSetMaskAndCombine4"}, + "ConditioningSetMaskAndCombine5": {"class": ConditioningSetMaskAndCombine5, "name": "ConditioningSetMaskAndCombine5"}, + "CondPassThrough": {"class": CondPassThrough}, + #masking + "DownloadAndLoadCLIPSeg": {"class": DownloadAndLoadCLIPSeg, "name": "(Down)load CLIPSeg"}, + "BatchCLIPSeg": {"class": BatchCLIPSeg, "name": "Batch CLIPSeg"}, + "ColorToMask": {"class": ColorToMask, "name": "Color To Mask"}, + "CreateGradientMask": {"class": CreateGradientMask, "name": "Create Gradient Mask"}, + "CreateTextMask": {"class": CreateTextMask, "name": "Create Text Mask"}, + "CreateAudioMask": {"class": CreateAudioMask, "name": "Create Audio Mask"}, + "CreateFadeMask": {"class": CreateFadeMask, "name": "Create Fade Mask"}, + "CreateFadeMaskAdvanced": {"class": CreateFadeMaskAdvanced, "name": "Create Fade Mask Advanced"}, + "CreateFluidMask": {"class": CreateFluidMask, "name": "Create Fluid Mask"}, + "CreateShapeMask": {"class": CreateShapeMask, "name": "Create Shape Mask"}, + "CreateVoronoiMask": {"class": CreateVoronoiMask, "name": "Create Voronoi Mask"}, + "CreateMagicMask": {"class": CreateMagicMask, "name": "Create Magic Mask"}, + "GetMaskSizeAndCount": {"class": GetMaskSizeAndCount, "name": "Get Mask Size & Count"}, + "GrowMaskWithBlur": {"class": GrowMaskWithBlur, "name": "Grow Mask With Blur"}, + "MaskBatchMulti": {"class": MaskBatchMulti, "name": "Mask Batch Multi"}, + "OffsetMask": {"class": OffsetMask, "name": "Offset Mask"}, + "RemapMaskRange": {"class": RemapMaskRange, "name": "Remap Mask Range"}, + "ResizeMask": {"class": ResizeMask, "name": "Resize Mask"}, + "RoundMask": {"class": RoundMask, "name": "Round Mask"}, + "SeparateMasks": {"class": SeparateMasks, "name": "Separate Masks"}, + #images + "AddLabel": {"class": AddLabel, "name": "Add Label"}, + "ColorMatch": {"class": ColorMatch, "name": "Color Match"}, + "ImageTensorList": {"class": ImageTensorList, "name": "Image Tensor List"}, + "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"}, + "CrossFadeImagesMulti": {"class": CrossFadeImagesMulti, "name": "Cross Fade Images Multi"}, + "GetImagesFromBatchIndexed": {"class": GetImagesFromBatchIndexed, "name": "Get Images From Batch Indexed"}, + "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image or Mask Range From Batch"}, + "GetLatentRangeFromBatch": {"class": GetLatentRangeFromBatch, "name": "Get Latent Range From Batch"}, + "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"}, + "FastPreview": {"class": FastPreview, "name": "Fast Preview"}, + "ImageBatchFilter": {"class": ImageBatchFilter, "name": "Image Batch Filter"}, + "ImageAndMaskPreview": {"class": ImageAndMaskPreview}, + "ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"}, + "ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"}, + "ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving}, + "ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"}, + "ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"}, + "ImageConcatFromBatch": {"class": ImageConcatFromBatch, "name": "Image Concatenate From Batch"}, + "ImageConcatMulti": {"class": ImageConcatMulti, "name": "Image Concatenate Multi"}, + "ImageCropByMask": {"class": ImageCropByMask, "name": "Image Crop By Mask"}, + "ImageCropByMaskAndResize": {"class": ImageCropByMaskAndResize, "name": "Image Crop By Mask And Resize"}, + "ImageCropByMaskBatch": {"class": ImageCropByMaskBatch, "name": "Image Crop By Mask Batch"}, + "ImageUncropByMask": {"class": ImageUncropByMask, "name": "Image Uncrop By Mask"}, + "ImageGrabPIL": {"class": ImageGrabPIL, "name": "Image Grab PIL"}, + "ImageGridComposite2x2": {"class": ImageGridComposite2x2, "name": "Image Grid Composite 2x2"}, + "ImageGridComposite3x3": {"class": ImageGridComposite3x3, "name": "Image Grid Composite 3x3"}, + "ImageGridtoBatch": {"class": ImageGridtoBatch, "name": "Image Grid To Batch"}, + "ImageNoiseAugmentation": {"class": ImageNoiseAugmentation, "name": "Image Noise Augmentation"}, + "ImageNormalize_Neg1_To_1": {"class": ImageNormalize_Neg1_To_1, "name": "Image Normalize -1 to 1"}, + "ImagePass": {"class": ImagePass}, + "ImagePadKJ": {"class": ImagePadKJ, "name": "ImagePad KJ"}, + "ImagePadForOutpaintMasked": {"class": ImagePadForOutpaintMasked, "name": "Image Pad For Outpaint Masked"}, + "ImagePadForOutpaintTargetSize": {"class": ImagePadForOutpaintTargetSize, "name": "Image Pad For Outpaint Target Size"}, + "ImagePrepForICLora": {"class": ImagePrepForICLora, "name": "Image Prep For ICLora"}, + "ImageResizeKJ": {"class": ImageResizeKJ, "name": "Resize Image (deprecated)"}, + "ImageResizeKJv2": {"class": ImageResizeKJv2, "name": "Resize Image v2"}, + "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"}, + "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"}, + "InsertLatentToIndexed": {"class": InsertLatentToIndex, "name": "Insert Latent To Index"}, + "LoadAndResizeImage": {"class": LoadAndResizeImage, "name": "Load & Resize Image"}, + "LoadImagesFromFolderKJ": {"class": LoadImagesFromFolderKJ, "name": "Load Images From Folder (KJ)"}, + "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"}, + "PadImageBatchInterleaved": {"class": PadImageBatchInterleaved, "name": "Pad Image Batch Interleaved"}, + "PreviewAnimation": {"class": PreviewAnimation, "name": "Preview Animation"}, + "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"}, + "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"}, + "ReplaceImagesInBatch": {"class": ReplaceImagesInBatch, "name": "Replace Images In Batch"}, + "SaveImageWithAlpha": {"class": SaveImageWithAlpha, "name": "Save Image With Alpha"}, + "SaveImageKJ": {"class": SaveImageKJ, "name": "Save Image KJ"}, + "ShuffleImageBatch": {"class": ShuffleImageBatch, "name": "Shuffle Image Batch"}, + "SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"}, + "TransitionImagesMulti": {"class": TransitionImagesMulti, "name": "Transition Images Multi"}, + "TransitionImagesInBatch": {"class": TransitionImagesInBatch, "name": "Transition Images In Batch"}, + #batch cropping + "BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"}, + "BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"}, + "FilterZeroMasksAndCorrespondingImages": {"class": FilterZeroMasksAndCorrespondingImages}, + "InsertImageBatchByIndexes": {"class": InsertImageBatchByIndexes, "name": "Insert Image Batch By Indexes"}, + "BatchUncrop": {"class": BatchUncrop, "name": "Batch Uncrop"}, + "BatchUncropAdvanced": {"class": BatchUncropAdvanced, "name": "Batch Uncrop Advanced"}, + "SplitBboxes": {"class": SplitBboxes, "name": "Split Bboxes"}, + "BboxToInt": {"class": BboxToInt, "name": "Bbox To Int"}, + "BboxVisualize": {"class": BboxVisualize, "name": "Bbox Visualize"}, + #noise + "GenerateNoise": {"class": GenerateNoise, "name": "Generate Noise"}, + "FlipSigmasAdjusted": {"class": FlipSigmasAdjusted, "name": "Flip Sigmas Adjusted"}, + "InjectNoiseToLatent": {"class": InjectNoiseToLatent, "name": "Inject Noise To Latent"}, + "CustomSigmas": {"class": CustomSigmas, "name": "Custom Sigmas"}, + #utility + "StringToFloatList": {"class": StringToFloatList, "name": "String to Float List"}, + "WidgetToString": {"class": WidgetToString, "name": "Widget To String"}, + "SaveStringKJ": {"class": SaveStringKJ, "name": "Save String KJ"}, + "DummyOut": {"class": DummyOut, "name": "Dummy Out"}, + "GetLatentsFromBatchIndexed": {"class": GetLatentsFromBatchIndexed, "name": "Get Latents From Batch Indexed"}, + "ScaleBatchPromptSchedule": {"class": ScaleBatchPromptSchedule, "name": "Scale Batch Prompt Schedule"}, + "CameraPoseVisualizer": {"class": CameraPoseVisualizer, "name": "Camera Pose Visualizer"}, + "AppendStringsToList": {"class": AppendStringsToList, "name": "Append Strings To List"}, + "JoinStrings": {"class": JoinStrings, "name": "Join Strings"}, + "JoinStringMulti": {"class": JoinStringMulti, "name": "Join String Multi"}, + "SomethingToString": {"class": SomethingToString, "name": "Something To String"}, + "Sleep": {"class": Sleep, "name": "Sleep"}, + "VRAM_Debug": {"class": VRAM_Debug, "name": "VRAM Debug"}, + "SomethingToString": {"class": SomethingToString, "name": "Something To String"}, + "EmptyLatentImagePresets": {"class": EmptyLatentImagePresets, "name": "Empty Latent Image Presets"}, + "EmptyLatentImageCustomPresets": {"class": EmptyLatentImageCustomPresets, "name": "Empty Latent Image Custom Presets"}, + "ModelPassThrough": {"class": ModelPassThrough, "name": "ModelPass"}, + "ModelSaveKJ": {"class": ModelSaveKJ, "name": "Model Save KJ"}, + "SetShakkerLabsUnionControlNetType": {"class": SetShakkerLabsUnionControlNetType, "name": "Set Shakker Labs Union ControlNet Type"}, + "StyleModelApplyAdvanced": {"class": StyleModelApplyAdvanced, "name": "Style Model Apply Advanced"}, + #audioscheduler stuff + "NormalizedAmplitudeToMask": {"class": NormalizedAmplitudeToMask}, + "NormalizedAmplitudeToFloatList": {"class": NormalizedAmplitudeToFloatList}, + "OffsetMaskByNormalizedAmplitude": {"class": OffsetMaskByNormalizedAmplitude}, + "ImageTransformByNormalizedAmplitude": {"class": ImageTransformByNormalizedAmplitude}, + "AudioConcatenate": {"class": AudioConcatenate}, + #curve nodes + "SplineEditor": {"class": SplineEditor, "name": "Spline Editor"}, + "CreateShapeImageOnPath": {"class": CreateShapeImageOnPath, "name": "Create Shape Image On Path"}, + "CreateShapeMaskOnPath": {"class": CreateShapeMaskOnPath, "name": "Create Shape Mask On Path"}, + "CreateTextOnPath": {"class": CreateTextOnPath, "name": "Create Text On Path"}, + "CreateGradientFromCoords": {"class": CreateGradientFromCoords, "name": "Create Gradient From Coords"}, + "CutAndDragOnPath": {"class": CutAndDragOnPath, "name": "Cut And Drag On Path"}, + "GradientToFloat": {"class": GradientToFloat, "name": "Gradient To Float"}, + "WeightScheduleExtend": {"class": WeightScheduleExtend, "name": "Weight Schedule Extend"}, + "MaskOrImageToWeight": {"class": MaskOrImageToWeight, "name": "Mask Or Image To Weight"}, + "WeightScheduleConvert": {"class": WeightScheduleConvert, "name": "Weight Schedule Convert"}, + "FloatToMask": {"class": FloatToMask, "name": "Float To Mask"}, + "FloatToSigmas": {"class": FloatToSigmas, "name": "Float To Sigmas"}, + "SigmasToFloat": {"class": SigmasToFloat, "name": "Sigmas To Float"}, + "PlotCoordinates": {"class": PlotCoordinates, "name": "Plot Coordinates"}, + "InterpolateCoords": {"class": InterpolateCoords, "name": "Interpolate Coords"}, + "PointsEditor": {"class": PointsEditor, "name": "Points Editor"}, + #experimental + "SoundReactive": {"class": SoundReactive, "name": "Sound Reactive"}, + "StableZero123_BatchSchedule": {"class": StableZero123_BatchSchedule, "name": "Stable Zero123 Batch Schedule"}, + "SV3D_BatchSchedule": {"class": SV3D_BatchSchedule, "name": "SV3D Batch Schedule"}, + "LoadResAdapterNormalization": {"class": LoadResAdapterNormalization}, + "Superprompt": {"class": Superprompt, "name": "Superprompt"}, + "GLIGENTextBoxApplyBatchCoords": {"class": GLIGENTextBoxApplyBatchCoords}, + "Intrinsic_lora_sampling": {"class": Intrinsic_lora_sampling, "name": "Intrinsic Lora Sampling"}, + "CheckpointPerturbWeights": {"class": CheckpointPerturbWeights, "name": "CheckpointPerturbWeights"}, + "Screencap_mss": {"class": Screencap_mss, "name": "Screencap mss"}, + "WebcamCaptureCV2": {"class": WebcamCaptureCV2, "name": "Webcam Capture CV2"}, + "DifferentialDiffusionAdvanced": {"class": DifferentialDiffusionAdvanced, "name": "Differential Diffusion Advanced"}, + "DiTBlockLoraLoader": {"class": DiTBlockLoraLoader, "name": "DiT Block Lora Loader"}, + "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"}, + "HunyuanVideoBlockLoraSelect": {"class": HunyuanVideoBlockLoraSelect, "name": "Hunyuan Video Block Lora Select"}, + "Wan21BlockLoraSelect": {"class": Wan21BlockLoraSelect, "name": "Wan21 Block Lora Select"}, + "CustomControlNetWeightsFluxFromList": {"class": CustomControlNetWeightsFluxFromList, "name": "Custom ControlNet Weights Flux From List"}, + "CheckpointLoaderKJ": {"class": CheckpointLoaderKJ, "name": "CheckpointLoaderKJ"}, + "DiffusionModelLoaderKJ": {"class": DiffusionModelLoaderKJ, "name": "Diffusion Model Loader KJ"}, + "TorchCompileModelFluxAdvanced": {"class": TorchCompileModelFluxAdvanced, "name": "TorchCompileModelFluxAdvanced"}, + "TorchCompileModelFluxAdvancedV2": {"class": TorchCompileModelFluxAdvancedV2, "name": "TorchCompileModelFluxAdvancedV2"}, + "TorchCompileModelHyVideo": {"class": TorchCompileModelHyVideo, "name": "TorchCompileModelHyVideo"}, + "TorchCompileVAE": {"class": TorchCompileVAE, "name": "TorchCompileVAE"}, + "TorchCompileControlNet": {"class": TorchCompileControlNet, "name": "TorchCompileControlNet"}, + "PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"}, + "TorchCompileLTXModel": {"class": TorchCompileLTXModel, "name": "TorchCompileLTXModel"}, + "TorchCompileCosmosModel": {"class": TorchCompileCosmosModel, "name": "TorchCompileCosmosModel"}, + "TorchCompileModelWanVideo": {"class": TorchCompileModelWanVideo, "name": "TorchCompileModelWanVideo"}, + "TorchCompileModelWanVideoV2": {"class": TorchCompileModelWanVideoV2, "name": "TorchCompileModelWanVideoV2"}, + "PathchSageAttentionKJ": {"class": PathchSageAttentionKJ, "name": "Patch Sage Attention KJ"}, + "LeapfusionHunyuanI2VPatcher": {"class": LeapfusionHunyuanI2V, "name": "Leapfusion Hunyuan I2V Patcher"}, + "VAELoaderKJ": {"class": VAELoaderKJ, "name": "VAELoader KJ"}, + "ScheduledCFGGuidance": {"class": ScheduledCFGGuidance, "name": "Scheduled CFG Guidance"}, + "ApplyRifleXRoPE_HunuyanVideo": {"class": ApplyRifleXRoPE_HunuyanVideo, "name": "Apply RifleXRoPE HunuyanVideo"}, + "ApplyRifleXRoPE_WanVideo": {"class": ApplyRifleXRoPE_WanVideo, "name": "Apply RifleXRoPE WanVideo"}, + "WanVideoTeaCacheKJ": {"class": WanVideoTeaCacheKJ, "name": "WanVideo Tea Cache (native)"}, + "WanVideoEnhanceAVideoKJ": {"class": WanVideoEnhanceAVideoKJ, "name": "WanVideo Enhance A Video (native)"}, + "SkipLayerGuidanceWanVideo": {"class": SkipLayerGuidanceWanVideo, "name": "Skip Layer Guidance WanVideo"}, + "TimerNodeKJ": {"class": TimerNodeKJ, "name": "Timer Node KJ"}, + "HunyuanVideoEncodeKeyframesToCond": {"class": HunyuanVideoEncodeKeyframesToCond, "name": "HunyuanVideo Encode Keyframes To Cond"}, + "CFGZeroStarAndInit": {"class": CFGZeroStarAndInit, "name": "CFG Zero Star/Init"}, + "ModelPatchTorchSettings": {"class": ModelPatchTorchSettings, "name": "Model Patch Torch Settings"}, + "WanVideoNAG": {"class": WanVideoNAG, "name": "WanVideoNAG"}, + + #instance diffusion + "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking}, + "AppendInstanceDiffusionTracking": {"class": AppendInstanceDiffusionTracking}, + "DrawInstanceDiffusionTracking": {"class": DrawInstanceDiffusionTracking}, +} + +def generate_node_mappings(node_config): + node_class_mappings = {} + node_display_name_mappings = {} + + for node_name, node_info in node_config.items(): + node_class_mappings[node_name] = node_info["class"] + node_display_name_mappings[node_name] = node_info.get("name", node_info["class"].__name__) + + return node_class_mappings, node_display_name_mappings + +NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS = generate_node_mappings(NODE_CONFIG) + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] + +WEB_DIRECTORY = "./web" + +from aiohttp import web +from server import PromptServer +from pathlib import Path + +if hasattr(PromptServer, "instance"): + try: + # NOTE: we add an extra static path to avoid comfy mechanism + # that loads every script in web. + PromptServer.instance.app.add_routes( + [web.static("/kjweb_async", (Path(__file__).parent.absolute() / "kjweb_async").as_posix())] + ) + except: + pass \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/custom_dimensions_example.json b/custom_nodes/ComfyUI-KJNodes/custom_dimensions_example.json new file mode 100644 index 0000000000000000000000000000000000000000..5c4814d377d44916a14b4d4a83b7cba72ae2958b --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/custom_dimensions_example.json @@ -0,0 +1,22 @@ +[ + { + "label": "SD", + "value": "512x512" + }, + { + "label": "HD", + "value": "768x768" + }, + { + "label": "Full HD", + "value": "1024x1024" + }, + { + "label": "4k", + "value": "2048x2048" + }, + { + "label": "SVD", + "value": "1024x576" + } +] diff --git a/custom_nodes/ComfyUI-KJNodes/docs/images/2024-04-03_20_49_29-ComfyUI.png b/custom_nodes/ComfyUI-KJNodes/docs/images/2024-04-03_20_49_29-ComfyUI.png new file mode 100644 index 0000000000000000000000000000000000000000..b42cfe56374c6db142326761a2a3d96211519664 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/docs/images/2024-04-03_20_49_29-ComfyUI.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85805d3c7ca8f5d281886ea0ad61f9a78edad755ef8014b3870f91b871807ac9 +size 176158 diff --git a/custom_nodes/ComfyUI-KJNodes/docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png 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index 0000000000000000000000000000000000000000..134a83788815d04b5574a807db67eb6e45bf9263 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json @@ -0,0 +1,1188 @@ +{ + "last_node_id": 86, + "last_link_id": 144, + "nodes": [ + { + "id": 62, + "type": "FluxGuidance", + "pos": [ + -630, + -170 + ], + "size": [ + 317.4000244140625, + 58 + ], + "flags": {}, + "order": 13, + "mode": 0, + "inputs": [ + { + "name": "conditioning", + "type": "CONDITIONING", + "link": 82 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "links": [ + 83 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "FluxGuidance" + }, + "widgets_values": [ + 6 + ] + }, + { + "id": 51, + "type": "KSamplerSelect", + "pos": [ + -610, + -480 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "SAMPLER", + "type": "SAMPLER", + "links": [ + 61 + ] + } + ], + "properties": { + "Node name for S&R": "KSamplerSelect" + }, + "widgets_values": [ + "euler" + ] + }, + { + "id": 57, + "type": "VAEDecodeTiled", + "pos": [ + -200, + 90 + ], + "size": [ + 315, + 150 + ], + "flags": {}, + "order": 20, + "mode": 0, + "inputs": [ + { + "name": "samples", + "type": "LATENT", + "link": 142 + }, + { + "name": "vae", + "type": "VAE", + "link": 74 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 105 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "VAEDecodeTiled" + }, + "widgets_values": [ + 128, + 64, + 64, + 8 + ] + }, + { + "id": 65, + "type": "LoadImage", + "pos": [ + -2212.498779296875, + -632.4085083007812 + ], + "size": [ + 315, + 314 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 86 + ], + "slot_index": 0 + }, + { + "name": "MASK", + "type": "MASK", + "links": null + } + ], + "properties": { + "Node name for S&R": "LoadImage" + }, + "widgets_values": [ + "Mona-Lisa-oil-wood-panel-Leonardo-da.webp", + "image" + ] + }, + { + "id": 64, + "type": "VAEEncode", + "pos": [ + -1336.7884521484375, + -492.5806884765625 + ], + "size": [ + 210, + 46 + ], + "flags": {}, + "order": 14, + "mode": 0, + "inputs": [ + { + "name": "pixels", + "type": "IMAGE", + "link": 144 + }, + { + "name": "vae", + "type": "VAE", + "link": 88 + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 137 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "VAEEncode" + }, + "widgets_values": [] + }, + { + "id": 44, + "type": "UNETLoader", + "pos": [ + -2373.55029296875, + -193.91510009765625 + ], + "size": [ + 459.56060791015625, + 82 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "MODEL", + "type": "MODEL", + "links": [ + 135 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "UNETLoader" + }, + 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"llava_llama3_fp16.safetensors", + "hunyuan_video", + "default" + ] + }, + { + "id": 45, + "type": "CLIPTextEncode", + "pos": [ + -1839.1649169921875, + 143.5203094482422 + ], + "size": [ + 400, + 200 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 56 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "links": [ + 69, + 82 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "woman puts on sunglasses" + ] + }, + { + "id": 53, + "type": "EmptyHunyuanLatentVideo", + "pos": [ + -1120, + 90 + ], + "size": [ + 315, + 130 + ], + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [ + { + "name": "width", + "type": "INT", + "link": 89, + "widget": { + "name": "width" + } + }, + { + "name": "height", + "type": "INT", + "link": 90, + "widget": { + "name": "height" + } + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + 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+ } + ], + "properties": { + "Node name for S&R": "BasicScheduler" + }, + "widgets_values": [ + "simple", + 20, + 1 + ] + }, + { + "id": 42, + "type": "SamplerCustom", + "pos": [ + -640, + 10 + ], + "size": [ + 355.20001220703125, + 467.4666748046875 + ], + "flags": {}, + "order": 18, + "mode": 0, + "inputs": [ + { + "name": "model", + "type": "MODEL", + "link": 77 + }, + { + "name": "positive", + "type": "CONDITIONING", + "link": 83 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 70 + }, + { + "name": "sampler", + "type": "SAMPLER", + "link": 61 + }, + { + "name": "sigmas", + "type": "SIGMAS", + "link": 62 + }, + { + "name": "latent_image", + "type": "LATENT", + "link": 119 + } + ], + "outputs": [ + { + "name": "output", + "type": "LATENT", + "links": null + }, + { + "name": "denoised_output", + "type": "LATENT", + "links": [ + 141 + ], + "slot_index": 1 + } + ], + "properties": { + "Node name for S&R": "SamplerCustom" + }, + "widgets_values": [ + true, + 6, + "fixed", + 1, + null + ] + }, + { + "id": 84, + "type": "GetLatentRangeFromBatch", + "pos": [ + -240, + -100 + ], + "size": [ + 340.20001220703125, + 82 + ], + "flags": {}, + "order": 19, + "mode": 0, + "inputs": [ + { + "name": "latents", + "type": "LATENT", + "link": 141 + } + ], + "outputs": [ + { + "name": "LATENT", + "type": "LATENT", + "links": [ + 142 + ], + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "GetLatentRangeFromBatch" + }, + "widgets_values": [ + 1, + -1 + ] + }, + { + "id": 50, + "type": "VHS_VideoCombine", + "pos": [ + 165.77645874023438, + -619.0606079101562 + ], + "size": [ + 1112.6898193359375, + 1076.4598388671875 + ], + "flags": {}, + "order": 21, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 105 + }, + { + "name": "audio", + "type": "AUDIO", + "link": null, + "shape": 7 + }, + { + "name": "meta_batch", + "type": "VHS_BatchManager", + "link": null, + "shape": 7 + }, + { + "name": "vae", + "type": "VAE", + "link": null, + "shape": 7 + } + ], + "outputs": [ + { + "name": "Filenames", + "type": "VHS_FILENAMES", + "links": null + } + ], + "properties": { + "Node name for S&R": "VHS_VideoCombine" + }, + "widgets_values": { + "frame_rate": 24, + "loop_count": 0, + "filename_prefix": "hyvidcomfy", + "format": "video/h264-mp4", + "pix_fmt": "yuv420p", + "crf": 19, + "save_metadata": true, + "trim_to_audio": false, + "pingpong": false, + "save_output": false, + "videopreview": { + "hidden": false, + "paused": false, + "params": { + "filename": "hyvidcomfy_00001.mp4", + "subfolder": "", + "type": "temp", + "format": "video/h264-mp4", + "frame_rate": 24, + "workflow": "hyvidcomfy_00001.png", + "fullpath": "N:\\AI\\ComfyUI\\temp\\hyvidcomfy_00001.mp4" + }, + "muted": false + } + } + }, + { + "id": 54, + "type": "ModelSamplingSD3", + "pos": [ + -1079.9112548828125, + -146.69448852539062 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 16, + "mode": 0, + "inputs": [ + { + "name": "model", 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{2,}\n)|[\s\S]*?(?:(?=[\\t+" ".repeat(n.length)));e;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.space(e))e=e.substring(n.raw.length),1===n.raw.length&&t.length>0?t[t.length-1].raw+="\n":t.push(n);else if(n=this.tokenizer.code(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?t.push(n):(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.fences(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.heading(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.hr(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.blockquote(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.list(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.html(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.def(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?this.tokens.links[n.tag]||(this.tokens.links[n.tag]={href:n.href,title:n.title}):(s.raw+="\n"+n.raw,s.text+="\n"+n.raw,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.table(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.lheading(e))e=e.substring(n.raw.length),t.push(n);else{if(r=e,this.options.extensions&&this.options.extensions.startBlock){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startBlock.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof 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+moccasin:"#ffe4b5",navajowhite:"#ffdead",navy:"#000080",oldlace:"#fdf5e6",olive:"#808000",olivedrab:"#6b8e23",orange:"#ffa500",orangered:"#ff4500",orchid:"#da70d6",palegoldenrod:"#eee8aa",palegreen:"#98fb98",paleturquoise:"#afeeee",palevioletred:"#db7093",papayawhip:"#ffefd5",peachpuff:"#ffdab9",peru:"#cd853f",pink:"#ffc0cb",plum:"#dda0dd",powderblue:"#b0e0e6",purple:"#800080",red:"#ff0000",rosybrown:"#bc8f8f",royalblue:"#4169e1",saddlebrown:"#8b4513",salmon:"#fa8072",sandybrown:"#f4a460",seagreen:"#2e8b57", +seashell:"#fff5ee",sienna:"#a0522d",silver:"#c0c0c0",skyblue:"#87ceeb",slateblue:"#6a5acd",slategray:"#708090",slategrey:"#708090",snow:"#fffafa",springgreen:"#00ff7f",steelblue:"#4682b4",tan:"#d2b48c",teal:"#008080",thistle:"#d8bfd8",tomato:"#ff6347",turquoise:"#40e0d0",violet:"#ee82ee",wheat:"#f5deb3",white:"#ffffff",whitesmoke:"#f5f5f5",yellow:"#ffff00",yellowgreen:"#9acd32",transparent:pv.Color.transparent=pv.rgb(0,0,0,0)};(function(){var b=pv.Color.names;for(var c in b)b[c]=pv.color(b[c])})(); +pv.colors=function(){var b=pv.Scale.ordinal();b.range.apply(b,arguments);return b};pv.Colors={};pv.Colors.category10=function(){var b=pv.colors("#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd","#8c564b","#e377c2","#7f7f7f","#bcbd22","#17becf");b.domain.apply(b,arguments);return b}; +pv.Colors.category20=function(){var b=pv.colors("#1f77b4","#aec7e8","#ff7f0e","#ffbb78","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5","#8c564b","#c49c94","#e377c2","#f7b6d2","#7f7f7f","#c7c7c7","#bcbd22","#dbdb8d","#17becf","#9edae5");b.domain.apply(b,arguments);return b}; +pv.Colors.category19=function(){var b=pv.colors("#9c9ede","#7375b5","#4a5584","#cedb9c","#b5cf6b","#8ca252","#637939","#e7cb94","#e7ba52","#bd9e39","#8c6d31","#e7969c","#d6616b","#ad494a","#843c39","#de9ed6","#ce6dbd","#a55194","#7b4173");b.domain.apply(b,arguments);return b};pv.ramp=function(){var b=pv.Scale.linear();b.range.apply(b,arguments);return b}; 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b=this.create(c);for(var g in d){c=d[g];if(c==this.implicit.svg[g])c=null;c==null?b.removeAttribute(g):b.setAttribute(g,c)}for(g in f){c=f[g];if(c==this.implicit.css[g])c=null;if(c==null)b.style.removeProperty(g);else b.style[g]=c}return b}; +pv.SvgScene.append=function(b,c,d){b.$scene={scenes:c,index:d};b=this.title(b,c[d]);b.parentNode||c.$g.appendChild(b);return b.nextSibling};pv.SvgScene.title=function(b,c){var d=b.parentNode;if(d&&d.tagName!="a")d=null;if(c.title){if(!d){d=this.create("a");b.parentNode&&b.parentNode.replaceChild(d,b);d.appendChild(b)}d.setAttributeNS(this.xlink,"title",c.title);return d}d&&d.parentNode.replaceChild(b,d);return b}; +pv.SvgScene.dispatch=pv.listener(function(b){var c=b.target.$scene;if(c){var d=b.type;switch(d){case "DOMMouseScroll":d="mousewheel";b.wheel=-480*b.detail;break;case "mousewheel":b.wheel=(window.opera?12:1)*b.wheelDelta;break}pv.Mark.dispatch(d,c.scenes,c.index)&&b.preventDefault()}});pv.SvgScene.removeSiblings=function(b){for(;b;){var c=b.nextSibling;b.parentNode.removeChild(b);b=c}};pv.SvgScene.undefined=function(){}; +pv.SvgScene.pathBasis=function(){function b(f,g,h,i,j){return{x:f[0]*g.left+f[1]*h.left+f[2]*i.left+f[3]*j.left,y:f[0]*g.top+f[1]*h.top+f[2]*i.top+f[3]*j.top}}var c=[[1/6,2/3,1/6,0],[0,2/3,1/3,0],[0,1/3,2/3,0],[0,1/6,2/3,1/6]],d=function(f,g,h,i){var j=b(c[1],f,g,h,i),k=b(c[2],f,g,h,i);f=b(c[3],f,g,h,i);return"C"+j.x+","+j.y+","+k.x+","+k.y+","+f.x+","+f.y};d.segment=function(f,g,h,i){var j=b(c[0],f,g,h,i),k=b(c[1],f,g,h,i),l=b(c[2],f,g,h,i);f=b(c[3],f,g,h,i);return"M"+j.x+","+j.y+"C"+k.x+","+k.y+ +","+l.x+","+l.y+","+f.x+","+f.y};return d}();pv.SvgScene.curveBasis=function(b){if(b.length<=2)return"";var c="",d=b[0],f=d,g=d,h=b[1];c+=this.pathBasis(d,f,g,h);for(var i=2;i1){j=c[1];h=b[k];k++;f+="C"+(g.left+i.x)+","+(g.top+i.y)+","+(h.left-j.x)+","+(h.top-j.y)+","+h.left+","+h.top;for(g=2;g9){k=3/Math.sqrt(k);f[h]= +k*i*d[h];f[h+1]=k*j*d[h]}}for(h=0;h2&&(g.interpolate=="basis"||g.interpolate=="cardinal"||g.interpolate=="monotone")?d:c)(l,q-1));l=q-1}}if(!j.length)return f;f=this.expect(f,"path",{"shape-rendering":g.antialias?null:"crispEdges","pointer-events":g.events,cursor:g.cursor,d:"M"+j.join("ZM")+"Z",fill:h.color,"fill-opacity":h.opacity|| +null,stroke:i.color,"stroke-opacity":i.opacity||null,"stroke-width":i.opacity?g.lineWidth/this.scale:null});return this.append(f,b,0)}; +pv.SvgScene.areaSegment=function(b){var c=b.$g.firstChild,d=b[0],f,g;if(d.interpolate=="basis"||d.interpolate=="cardinal"||d.interpolate=="monotone"){f=[];g=[];for(var h=0,i=b.length;h2&&(d.interpolate=="basis"||d.interpolate=="cardinal"||d.interpolate=="monotone"))switch(d.interpolate){case "basis":h+=this.curveBasis(b);break;case "cardinal":h+=this.curveCardinal(b,d.tension);break;case "monotone":h+=this.curveMonotone(b); +break}else for(var i=1;i1)break;return"A"+f+","+f+" 0 0,"+d+" "+c.left+","+c.top;case "step-before":return"V"+c.top+"H"+c.left;case "step-after":return"H"+c.left+"V"+c.top}return"L"+c.left+","+c.top};pv.SvgScene.lineIntersect=function(b,c,d,f){return b.plus(c.times(d.minus(b).dot(f.perp())/c.dot(f.perp())))}; +pv.SvgScene.pathJoin=function(b,c,d,f){var g=pv.vector(c.left,c.top);d=pv.vector(d.left,d.top);var h=d.minus(g),i=h.perp().norm(),j=i.times(c.lineWidth/(2*this.scale));c=g.plus(j);var k=d.plus(j),l=d.minus(j);j=g.minus(j);if(b&&b.visible){b=g.minus(b.left,b.top).perp().norm().plus(i);j=this.lineIntersect(g,b,j,h);c=this.lineIntersect(g,b,c,h)}if(f&&f.visible){f=pv.vector(f.left,f.top).minus(d).perp().norm().plus(i);l=this.lineIntersect(d,f,l,h);k=this.lineIntersect(d,f,k,h)}return"M"+c.x+","+c.y+ +"L"+k.x+","+k.y+" "+l.x+","+l.y+" "+j.x+","+j.y}; +pv.SvgScene.panel=function(b){for(var c=b.$g,d=c&&c.firstChild,f=0;f=2*Math.PI)i=i?"M0,"+j+"A"+j+","+j+" 0 1,1 0,"+-j+"A"+j+","+j+" 0 1,1 0,"+j+"M0,"+i+"A"+i+","+i+" 0 1,1 0,"+-i+"A"+i+","+i+" 0 1,1 0,"+i+"Z":"M0,"+j+"A"+j+","+j+" 0 1,1 0,"+-j+"A"+j+","+j+" 0 1,1 0,"+j+"Z";else{var l=Math.min(f.startAngle,f.endAngle),q=Math.max(f.startAngle,f.endAngle), +n=Math.cos(l),p=Math.cos(q);l=Math.sin(l);q=Math.sin(q);i=i?"M"+j*n+","+j*l+"A"+j+","+j+" 0 "+(k1?c:null)}; +a.anchor=function(b){b||(b="center");return(new pv.Anchor(this)).name(b).data(function(){return this.scene.target.map(function(c){return c.data})}).visible(function(){return this.scene.target[this.index].visible}).left(function(){var c=this.scene.target[this.index],d=c.width||0;switch(this.name()){case "bottom":case "top":case "center":return c.left+d/2;case "left":return null}return c.left+d}).top(function(){var c=this.scene.target[this.index],d=c.height||0;switch(this.name()){case "left":case "right":case "center":return c.top+ +d/2;case "top":return null}return c.top+d}).right(function(){var c=this.scene.target[this.index];return this.name()=="left"?c.right+(c.width||0):null}).bottom(function(){var c=this.scene.target[this.index];return this.name()=="top"?c.bottom+(c.height||0):null}).textAlign(function(){switch(this.name()){case "bottom":case "top":case "center":return"center";case "right":return"right"}return"left"}).textBaseline(function(){switch(this.name()){case "right":case "left":case "center":return"middle";case "top":return"top"}return"bottom"})}; +a.anchorTarget=function(){return this.target};a.margin=function(b){return this.left(b).right(b).top(b).bottom(b)};a.instance=function(b){var c=this.scene||this.parent.instance(-1).children[this.childIndex],d=!arguments.length||this.hasOwnProperty("index")?this.index:b;return c[d<0?c.length-1:d]}; +a.instances=function(b){for(var c=this,d=[],f;!(f=c.scene);){b=b.parent;d.push({index:b.index,childIndex:c.childIndex});c=c.parent}for(;d.length;){b=d.pop();f=f[b.index].children[b.childIndex]}if(this.hasOwnProperty("index")){d=pv.extend(f[this.index]);d.right=d.top=d.left=d.bottom=0;return[d]}return f};a.first=function(){return this.scene[0]};a.last=function(){return this.scene[this.scene.length-1]};a.sibling=function(){return this.index==0?null:this.scene[this.index-1]}; +a.cousin=function(){var b=this.parent;return(b=b&&b.sibling())&&b.children?b.children[this.childIndex][this.index]:null}; +a.render=function(){function b(i,j,k){i.scale=k;if(j=0;l--){var q=k[l];if(!(q.name in c)){c[q.name]=q;switch(q.name){case "data":f=q;break;case "visible":g=q;break;default:d[q.type].push(q);break}}}while(j=j.proto)}var c={},d=[[],[],[],[]],f,g;b(this);b(this.defaults);d[1].reverse();d[3].reverse();var h=this;do for(var i in h.properties)i in c||d[2].push(c[i]={name:i,type:2,value:null});while(h=h.proto);h=d[0].concat(d[1]);for(i=0;ih.id)d[g.name]={id:0,value:g.type&1?g.value.apply(this,c):g.value}}}d=this.binds.data;d=d.type& +1?d.value.apply(this,c):d.value;c.unshift(null);b.length=d.length;for(f=0;f0;l--){p=m[l];p.scale=q;q*=p.scene[p.index].transform.k}if(n.children){l=0;for(m=n.children.length;l=3*Math.PI/2};pv.Wedge.prototype.buildImplied=function(b){if(b.angle==null)b.angle=b.endAngle-b.startAngle;else if(b.endAngle==null)b.endAngle=b.startAngle+b.angle;pv.Mark.prototype.buildImplied.call(this,b)};pv.simulation=function(b){return new pv.Simulation(b)};pv.Simulation=function(b){for(var c=0;c=s,u=q.y>=t;l.leaf=false;switch((u<<1)+x){case 0:l=l.c1||(l.c1=new pv.Quadtree.Node);break;case 1:l=l.c2||(l.c2=new pv.Quadtree.Node);break;case 2:l=l.c3||(l.c3=new pv.Quadtree.Node);break;case 3:l=l.c4||(l.c4=new pv.Quadtree.Node); +break}if(x)n=s;else m=s;if(u)p=t;else r=t;c(l,q,n,p,m,r)}var f,g=Number.POSITIVE_INFINITY,h=g,i=Number.NEGATIVE_INFINITY,j=i;for(f=b;f;f=f.next){if(f.xi)i=f.x;if(f.y>j)j=f.y}f=i-g;var k=j-h;if(f>k)j=h+f;else i=g+k;this.xMin=g;this.yMin=h;this.xMax=i;this.yMax=j;this.root=new pv.Quadtree.Node;for(f=b;f;f=f.next)c(this.root,f,g,h,i,j)};pv.Quadtree.Node=function(){this.leaf=true;this.p=this.c4=this.c3=this.c2=this.c1=null};pv.Force={}; +pv.Force.charge=function(b){function c(l){function q(m){c(m);l.cn+=m.cn;n+=m.cn*m.cx;p+=m.cn*m.cy}var n=0,p=0;l.cn=0;if(!l.leaf){l.c1&&q(l.c1);l.c2&&q(l.c2);l.c3&&q(l.c3);l.c4&&q(l.c4)}if(l.p){l.cn+=b;n+=b*l.p.x;p+=b*l.p.y}l.cx=n/l.cn;l.cy=p/l.cn}function d(l,q,n,p,m,r){var s=l.cx-q.x,t=l.cy-q.y,x=1/Math.sqrt(s*s+t*t);if(l.leaf&&l.p!=q||(m-n)*xg)x=g;l=l.cn*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}else if(!l.leaf){var u=(n+m)*0.5,o=(p+r)*0.5;l.c1&&d(l.c1,q,n,p,u,o);l.c2&&d(l.c2,q,u,p, +m,o);l.c3&&d(l.c3,q,n,o,u,r);l.c4&&d(l.c4,q,u,o,m,r);if(!(xg)x=g;if(l.p&&l.p!=q){l=b*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}}}var f=2,g=1/f,h=500,i=1/h,j=0.9,k={};arguments.length||(b=-40);k.constant=function(l){if(arguments.length){b=Number(l);return k}return b};k.domain=function(l,q){if(arguments.length){f=Number(l);g=1/f;h=Number(q);i=1/h;return k}return[f,h]};k.theta=function(l){if(arguments.length){j=Number(l);return k}return j};k.apply=function(l,q){c(q.root);for(l=l;l;l=l.next)d(q.root, +l,q.xMin,q.yMin,q.xMax,q.yMax)};return k};pv.Force.drag=function(b){var c={};arguments.length||(b=0.1);c.constant=function(d){if(arguments.length){b=d;return c}return b};c.apply=function(d){if(b)for(d=d;d;d=d.next){d.fx-=b*d.vx;d.fy-=b*d.vy}};return c}; +pv.Force.spring=function(b){var c=0.1,d=20,f,g,h={};arguments.length||(b=0.1);h.links=function(i){if(arguments.length){f=i;g=i.map(function(j){return 1/Math.sqrt(Math.max(j.sourceNode.linkDegree,j.targetNode.linkDegree))});return h}return f};h.constant=function(i){if(arguments.length){b=Number(i);return h}return b};h.damping=function(i){if(arguments.length){c=Number(i);return h}return c};h.length=function(i){if(arguments.length){d=Number(i);return h}return d};h.apply=function(){for(var i=0;ig,o=sh){l.c1&&u&&c(l.c1,q,n,p,s,t);l.c2&&o&&c(l.c2,q,s,p,m,t)}if(x){l.c3&&u&&c(l.c3,q,n,t,s,r);l.c4&&o&&c(l.c4,q,s,t,m,r)}}if(l.p&&l.p!=q){n=q.x-l.p.x;p=q.y-l.p.y;m=Math.sqrt(n*n+p*p);r=f+b(l.p);if(mm)m=p}for(var r=0;rc.max?c.max:g.x;if(d)for(g=f;g;g=g.next)g.y=g.yd.max?d.max:g.y};return b};pv.Layout=function(){pv.Panel.call(this)};pv.Layout.prototype=pv.extend(pv.Panel); +pv.Layout.prototype.property=function(b,c){if(!this.hasOwnProperty("properties"))this.properties=pv.extend(this.properties);this.properties[b]=true;this.propertyMethod(b,false,pv.Mark.cast[b]=c);return this}; +pv.Layout.Network=function(){pv.Layout.call(this);var b=this;this.$id=pv.id();(this.node=(new pv.Mark).data(function(){return b.nodes()}).strokeStyle("#1f77b4").fillStyle("#fff").left(function(c){return c.x}).top(function(c){return c.y})).parent=this;this.link=(new pv.Mark).extend(this.node).data(function(c){return[c.sourceNode,c.targetNode]}).fillStyle(null).lineWidth(function(c,d){return d.linkValue*1.5}).strokeStyle("rgba(0,0,0,.2)");this.link.add=function(c){return b.add(pv.Panel).data(function(){return b.links()}).add(c).extend(this)}; +(this.label=(new pv.Mark).extend(this.node).textMargin(7).textBaseline("middle").text(function(c){return c.nodeName||c.nodeValue}).textAngle(function(c){c=c.midAngle;return pv.Wedge.upright(c)?c:c+Math.PI}).textAlign(function(c){return pv.Wedge.upright(c.midAngle)?"left":"right"})).parent=this}; +pv.Layout.Network.prototype=pv.extend(pv.Layout).property("nodes",function(b){return b.map(function(c,d){if(typeof c!="object")c={nodeValue:c};c.index=d;return c})}).property("links",function(b){return b.map(function(c){if(isNaN(c.linkValue))c.linkValue=isNaN(c.value)?1:c.value;return c})});pv.Layout.Network.prototype.reset=function(){this.$id=pv.id();return this};pv.Layout.Network.prototype.buildProperties=function(b,c){if((b.$id||0)=this.$id)return true;b.$id=this.$id;b.nodes.forEach(function(c){c.linkDegree=0});b.links.forEach(function(c){var d=c.linkValue;(c.sourceNode||(c.sourceNode=b.nodes[c.source])).linkDegree+=d;(c.targetNode||(c.targetNode=b.nodes[c.target])).linkDegree+=d})};pv.Layout.Hierarchy=function(){pv.Layout.Network.call(this);this.link.strokeStyle("#ccc")};pv.Layout.Hierarchy.prototype=pv.extend(pv.Layout.Network); +pv.Layout.Hierarchy.prototype.buildImplied=function(b){if(!b.links)b.links=pv.Layout.Hierarchy.links.call(this);pv.Layout.Network.prototype.buildImplied.call(this,b)};pv.Layout.Hierarchy.links=function(){return this.nodes().filter(function(b){return b.parentNode}).map(function(b){return{sourceNode:b,targetNode:b.parentNode,linkValue:1}})}; +pv.Layout.Hierarchy.NodeLink={buildImplied:function(b){function c(m){return m.parentNode?m.depth*(n-q)+q:0}function d(m){return m.parentNode?(m.breadth-0.25)*2*Math.PI:0}function f(m){switch(i){case "left":return m.depth*k;case "right":return k-m.depth*k;case "top":return m.breadth*k;case "bottom":return k-m.breadth*k;case "radial":return k/2+c(m)*Math.cos(m.midAngle)}}function g(m){switch(i){case "left":return m.breadth*l;case "right":return l-m.breadth*l;case "top":return m.depth*l;case "bottom":return l- +m.depth*l;case "radial":return l/2+c(m)*Math.sin(m.midAngle)}}var h=b.nodes,i=b.orient,j=/^(top|bottom)$/.test(i),k=b.width,l=b.height;if(i=="radial"){var q=b.innerRadius,n=b.outerRadius;if(q==null)q=0;if(n==null)n=Math.min(k,l)/2}for(b=0;bb.dy?0:-Math.PI/2});(this.leaf=(new pv.Mark).extend(this.node).fillStyle(null).strokeStyle(null).visible(function(b){return!b.firstChild})).parent= +this;delete this.link};pv.Layout.Treemap.prototype=pv.extend(pv.Layout.Hierarchy).property("round",Boolean).property("paddingLeft",Number).property("paddingRight",Number).property("paddingTop",Number).property("paddingBottom",Number).property("mode",String).property("order",String);a=pv.Layout.Treemap.prototype;a.defaults=(new pv.Layout.Treemap).extend(pv.Layout.Hierarchy.prototype.defaults).mode("squarify").order("ascending");a.padding=function(b){return this.paddingLeft(b).paddingRight(b).paddingTop(b).paddingBottom(b)}; +a.$size=function(b){return Number(b.nodeValue)};a.size=function(b){this.$size=pv.functor(b);return this}; +a.buildImplied=function(b){function c(r,s,t,x,u,o,v){for(var w=0,y=0;wt)t=v;u+=v}u*=u;s*=s;return Math.max(s*t/u,u/(s*x))}function f(r,s){function t(A){var D=o==y,G=pv.sum(A,n),E=y?p(G/y):0;c(A,G,D,x,u,D?o:E,D?E:v);if(D){u+=E;v-=E}else{x+= +E;o-=E}y=Math.min(o,v);return D}var x=r.x+j,u=r.y+l,o=r.dx-j-k,v=r.dy-l-q;if(m!="squarify")c(r.childNodes,r.size,m=="slice"?true:m=="dice"?false:s&1,x,u,o,v);else{var w=[];s=Infinity;var y=Math.min(o,v),z=o*v/r.size;if(!(r.size<=0)){r.visitBefore(function(A){A.size*=z});for(r=r.childNodes.slice();r.length;){var C=r[r.length-1];if(C.size){w.push(C);z=d(w,y);if(z<=s){r.pop();s=z}else{w.pop();t(w);w.length=0;s=Infinity}}else r.pop()}if(t(w))for(s=0;s0){i(k(C,o,v),o,B);A+=B;D+=B}G+=C.mod;A+=y.mod;E+=w.mod;D+=z.mod;C=h(C);y=g(y)}if(C&&!h(z)){z.thread=C;z.mod+=G-D}if(y&&!g(w)){w.thread=y;w.mod+=A-E;v=o}}return v}function g(o){return o.firstChild||o.thread}function h(o){return o.lastChild||o.thread}function i(o,v,w){var y=v.number-o.number;v.change-=w/y;v.shift+=w;o.change+= +w/y;v.prelim+=w;v.mod+=w}function j(o){var v=0,w=0;for(o=o.lastChild;o;o=o.previousSibling){o.prelim+=v;o.mod+=v;w+=o.change;v+=o.shift+w}}function k(o,v,w){return o.ancestor.parentNode==v.parentNode?o.ancestor:w}function l(o,v){return(v?1:t+1)/(m=="radial"?o:1)}function q(o){return m=="radial"?o.breadth/r:0}function n(o){switch(m){case "left":return o.depth;case "right":return x-o.depth;case "top":case "bottom":return o.breadth+x/2;case "radial":return x/2+o.depth*Math.cos(q(o))}}function p(o){switch(m){case "left":case "right":return o.breadth+ +u/2;case "top":return o.depth;case "bottom":return u-o.depth;case "radial":return u/2+o.depth*Math.sin(q(o))}}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var m=b.orient,r=b.depth,s=b.breadth,t=b.group,x=b.width,u=b.height;b=b.nodes[0];b.visitAfter(function(o,v){o.ancestor=o;o.prelim=0;o.mod=0;o.change=0;o.shift=0;o.number=o.previousSibling?o.previousSibling.number+1:0;o.depth=v});c(b);d(b,-b.prelim,0);b.visitAfter(function(o){o.breadth*=s;o.depth*=r;o.midAngle=q(o);o.x=n(o);o.y=p(o); +if(o.firstChild)o.midAngle+=Math.PI;delete o.breadth;delete o.depth;delete o.ancestor;delete o.prelim;delete o.mod;delete o.change;delete o.shift;delete o.number;delete o.thread})}};pv.Layout.Indent=function(){pv.Layout.Hierarchy.call(this);this.link.interpolate("step-after")};pv.Layout.Indent.prototype=pv.extend(pv.Layout.Hierarchy).property("depth",Number).property("breadth",Number);pv.Layout.Indent.prototype.defaults=(new pv.Layout.Indent).extend(pv.Layout.Hierarchy.prototype.defaults).depth(15).breadth(15); +pv.Layout.Indent.prototype.buildImplied=function(b){function c(i,j,k){i.x=g+k++*f;i.y=h+j++*d;i.midAngle=0;for(i=i.firstChild;i;i=i.nextSibling)j=c(i,j,k);return j}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var d=b.breadth,f=b.depth,g=0,h=0;c(b.nodes[0],1,1)}};pv.Layout.Pack=function(){pv.Layout.Hierarchy.call(this);this.node.radius(function(b){return b.radius}).strokeStyle("rgb(31, 119, 180)").fillStyle("rgba(31, 119, 180, .25)");this.label.textAlign("center");delete this.link}; +pv.Layout.Pack.prototype=pv.extend(pv.Layout.Hierarchy).property("spacing",Number).property("order",String);pv.Layout.Pack.prototype.defaults=(new pv.Layout.Pack).extend(pv.Layout.Hierarchy.prototype.defaults).spacing(1).order("ascending");pv.Layout.Pack.prototype.$radius=function(){return 1};pv.Layout.Pack.prototype.size=function(b){this.$radius=typeof b=="function"?function(){return Math.sqrt(b.apply(this,arguments))}:(b=Math.sqrt(b),function(){return b});return this}; +pv.Layout.Pack.prototype.buildImplied=function(b){function c(n){var p=pv.Mark.stack;p.unshift(null);for(var m=0,r=n.length;m0.0010}var t=Infinity,x=-Infinity,u=Infinity,o=-Infinity,v,w,y,z,C;v=n[0];v.x=-v.radius;v.y=0;p(v);if(n.length>1){w=n[1];w.x=w.radius;w.y=0;p(w);if(n.length>2){y=n[2];g(v,w,y);p(y);m(v,y);v.p= +y;m(y,w);w=v.n;for(var A=3;A0){r(v,z);w=z;A--}else if(D<0){r(z,w);v=z;A--}}}}v=(t+x)/2;w=(u+o)/2;for(A=y=0;An.min){n.sim.step(); +q=true}q&&d.render()},42)}else for(k=0;kg)g=j;i.size=i.firstChild?pv.sum(i.childNodes,function(k){return k.size}):c.$size.apply(c,(f[0]=i,f))});f.shift();switch(b.order){case "ascending":d.sort(function(i,j){return i.size-j.size});break;case "descending":d.sort(function(i,j){return j.size-i.size});break}var h=1/g;d.minBreadth=0;d.breadth= +0.5;d.maxBreadth=1;d.visitBefore(function(i){for(var j=i.minBreadth,k=i.maxBreadth-j,l=i.firstChild;l;l=l.nextSibling){l.minBreadth=j;l.maxBreadth=j+=l.size/i.size*k;l.breadth=(j+l.minBreadth)/2}});d.visitAfter(function(i,j){i.minDepth=(j-1)*h;i.maxDepth=i.depth=j*h});pv.Layout.Hierarchy.NodeLink.buildImplied.call(this,b)}};pv.Layout.Partition.Fill=function(){pv.Layout.Partition.call(this);pv.Layout.Hierarchy.Fill.constructor.call(this)};pv.Layout.Partition.Fill.prototype=pv.extend(pv.Layout.Partition); +pv.Layout.Partition.Fill.prototype.buildImplied=function(b){pv.Layout.Partition.prototype.buildImplied.call(this,b)||pv.Layout.Hierarchy.Fill.buildImplied.call(this,b)};pv.Layout.Arc=function(){pv.Layout.Network.call(this);var b,c,d,f=this.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.directed;b=g.orient=="radial"?"linear":"polar";d=g.orient=="right"||g.orient=="top"};this.link.data(function(g){var h=g.sourceNode;g=g.targetNode;return d!=(c||h.breadth>1)*f:null}).bottom(function(k,l){return d=="mirror"?l&1?null:(l+1>>1)*-f:(l&1||-1)*(l+1>>1)*f}).fillStyle(function(k,l){return(l&1?h:i)((l>>1)+1)});this.band.add=function(k){return b.add(pv.Panel).extend(c).add(k).extend(this)}};pv.Layout.Horizon.prototype=pv.extend(pv.Layout).property("bands",Number).property("mode",String).property("backgroundStyle",pv.color).property("positiveStyle",pv.color).property("negativeStyle",pv.color); +pv.Layout.Horizon.prototype.defaults=(new pv.Layout.Horizon).extend(pv.Layout.prototype.defaults).bands(2).mode("offset").backgroundStyle("white").positiveStyle("#1f77b4").negativeStyle("#d62728"); +pv.Layout.Rollup=function(){pv.Layout.Network.call(this);var b=this,c,d,f=b.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.$rollup.nodes;d=g.$rollup.links};this.node.data(function(){return c}).size(function(g){return g.nodes.length*20});this.link.interpolate("polar").eccentricity(0.8);this.link.add=function(g){return b.add(pv.Panel).data(function(){return d}).add(g).extend(this)}};pv.Layout.Rollup.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean); +pv.Layout.Rollup.prototype.x=function(b){this.$x=pv.functor(b);return this};pv.Layout.Rollup.prototype.y=function(b){this.$y=pv.functor(b);return this}; +pv.Layout.Rollup.prototype.buildImplied=function(b){function c(r){return i[r]+","+j[r]}if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var d=b.nodes,f=b.links,g=b.directed,h=d.length,i=[],j=[],k=0,l={},q={},n=pv.Mark.stack,p={parent:this};n.unshift(null);for(var m=0;mk.index?k.index+","+d.index:d.index+","+k.index;(n=q[h])||(n=q[h]={sourceNode:d,targetNode:k,linkValue:0,links:[]});n.links.push(f[m]);n.linkValue+=f[m].linkValue}b.$rollup={nodes:pv.values(l),links:pv.values(q)}}}; +pv.Layout.Matrix=function(){pv.Layout.Network.call(this);var b,c,d,f,g,h=this.buildImplied;this.buildImplied=function(i){h.call(this,i);b=i.nodes.length;c=i.width/b;d=i.height/b;f=i.$matrix.labels;g=i.$matrix.pairs};this.link.data(function(){return g}).left(function(){return c*(this.index%b)}).top(function(){return d*Math.floor(this.index/b)}).width(function(){return c}).height(function(){return d}).lineWidth(1.5).strokeStyle("#fff").fillStyle(function(i){return i.linkValue?"#555":"#eee"}).parent= +this;delete this.link.add;this.label.data(function(){return f}).left(function(){return this.index&1?c*((this.index>>1)+0.5):0}).top(function(){return this.index&1?0:d*((this.index>>1)+0.5)}).textMargin(4).textAlign(function(){return this.index&1?"left":"right"}).textAngle(function(){return this.index&1?-Math.PI/2:0});delete this.node};pv.Layout.Matrix.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean);pv.Layout.Matrix.prototype.sort=function(b){this.$sort=b;return this}; +pv.Layout.Matrix.prototype.buildImplied=function(b){if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var c=b.nodes,d=b.links,f=this.$sort,g=c.length,h=pv.range(g),i=[],j=[],k={};b.$matrix={labels:i,pairs:j};f&&h.sort(function(m,r){return f(c[m],c[r])});for(var l=0;lk)l=null;if(g){if(l&&g.scene==l.scene&&g.index==l.index)return;pv.Mark.dispatch("unpoint",g.scene,g.index)}if(g=l){pv.Mark.dispatch("point",l.scene,l.index);pv.listen(this.root.canvas(),"mouseout",f)}}function f(l){if(g&&!pv.ancestor(this,l.relatedTarget)){pv.Mark.dispatch("unpoint",g.scene,g.index);g=null}}var g,h=null,i=1,j=1,k=arguments.length?b*b:900;d.collapse=function(l){if(arguments.length){h=String(l);switch(h){case "y":i= +1;j=0;break;case "x":i=0;j=1;break;default:j=i=1;break}return d}return h};return d}; +pv.Behavior.select=function(){function b(j){g=this.index;f=this.scene;i=this.mouse();h=j;h.x=i.x;h.y=i.y;h.dx=h.dy=0;pv.Mark.dispatch("selectstart",f,g)}function c(){if(f){f.mark.context(f,g,function(){var j=this.mouse();h.x=Math.max(0,Math.min(i.x,j.x));h.y=Math.max(0,Math.min(i.y,j.y));h.dx=Math.min(this.width(),Math.max(j.x,i.x))-h.x;h.dy=Math.min(this.height(),Math.max(j.y,i.y))-h.y;this.render()});pv.Mark.dispatch("select",f,g)}}function d(){if(f){pv.Mark.dispatch("selectend",f,g);f=null}}var f, +g,h,i;pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b}; +pv.Behavior.resize=function(b){function c(k){h=this.index;g=this.scene;j=this.mouse();i=k;switch(b){case "left":j.x=i.x+i.dx;break;case "right":j.x=i.x;break;case "top":j.y=i.y+i.dy;break;case "bottom":j.y=i.y;break}pv.Mark.dispatch("resizestart",g,h)}function d(){if(g){g.mark.context(g,h,function(){var k=this.mouse();i.x=Math.max(0,Math.min(j.x,k.x));i.y=Math.max(0,Math.min(j.y,k.y));i.dx=Math.min(this.parent.width(),Math.max(k.x,j.x))-i.x;i.dy=Math.min(this.parent.height(),Math.max(k.y,j.y))-i.y; +this.render()});pv.Mark.dispatch("resize",g,h)}}function f(){if(g){pv.Mark.dispatch("resizeend",g,h);g=null}}var g,h,i,j;pv.listen(window,"mousemove",d);pv.listen(window,"mouseup",f);return c}; +pv.Behavior.pan=function(){function b(){g=this.index;f=this.scene;i=pv.vector(pv.event.pageX,pv.event.pageY);h=this.transform();j=1/(h.k*this.scale);if(k)k={x:(1-h.k)*this.width(),y:(1-h.k)*this.height()}}function c(){if(f){f.mark.context(f,g,function(){var l=h.translate((pv.event.pageX-i.x)*j,(pv.event.pageY-i.y)*j);if(k){l.x=Math.max(k.x,Math.min(0,l.x));l.y=Math.max(k.y,Math.min(0,l.y))}this.transform(l).render()});pv.Mark.dispatch("pan",f,g)}}function d(){f=null}var f,g,h,i,j,k;b.bound=function(l){if(arguments.length){k= +Boolean(l);return this}return Boolean(k)};pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b}; +pv.Behavior.zoom=function(b){function c(){var f=this.mouse(),g=pv.event.wheel*b;f=this.transform().translate(f.x,f.y).scale(g<0?1E3/(1E3-g):(1E3+g)/1E3).translate(-f.x,-f.y);if(d){f.k=Math.max(1,f.k);f.x=Math.max((1-f.k)*this.width(),Math.min(0,f.x));f.y=Math.max((1-f.k)*this.height(),Math.min(0,f.y))}this.transform(f).render();pv.Mark.dispatch("zoom",this.scene,this.index)}var d;arguments.length||(b=1/48);c.bound=function(f){if(arguments.length){d=Boolean(f);return this}return Boolean(d)};return c}; +pv.Geo=function(){}; +pv.Geo.projections={mercator:{project:function(b){return{x:b.lng/180,y:b.lat>85?1:b.lat<-85?-1:Math.log(Math.tan(Math.PI/4+pv.radians(b.lat)/2))/Math.PI}},invert:function(b){return{lng:b.x*180,lat:pv.degrees(2*Math.atan(Math.exp(b.y*Math.PI))-Math.PI/2)}}},"gall-peters":{project:function(b){return{x:b.lng/180,y:Math.sin(pv.radians(b.lat))}},invert:function(b){return{lng:b.x*180,lat:pv.degrees(Math.asin(b.y))}}},sinusoidal:{project:function(b){return{x:pv.radians(b.lng)*Math.cos(pv.radians(b.lat))/Math.PI, +y:b.lat/90}},invert:function(b){return{lng:pv.degrees(b.x*Math.PI/Math.cos(b.y*Math.PI/2)),lat:b.y*90}}},aitoff:{project:function(b){var c=pv.radians(b.lng);b=pv.radians(b.lat);var d=Math.acos(Math.cos(b)*Math.cos(c/2));return{x:2*(d?Math.cos(b)*Math.sin(c/2)*d/Math.sin(d):0)/Math.PI,y:2*(d?Math.sin(b)*d/Math.sin(d):0)/Math.PI}},invert:function(b){var c=b.y*Math.PI/2;return{lng:pv.degrees(b.x*Math.PI/2/Math.cos(c)),lat:pv.degrees(c)}}},hammer:{project:function(b){var c=pv.radians(b.lng);b=pv.radians(b.lat); +var d=Math.sqrt(1+Math.cos(b)*Math.cos(c/2));return{x:2*Math.SQRT2*Math.cos(b)*Math.sin(c/2)/d/3,y:Math.SQRT2*Math.sin(b)/d/1.5}},invert:function(b){var c=b.x*3;b=b.y*1.5;var d=Math.sqrt(1-c*c/16-b*b/4);return{lng:pv.degrees(2*Math.atan2(d*c,2*(2*d*d-1))),lat:pv.degrees(Math.asin(d*b))}}},identity:{project:function(b){return{x:b.lng/180,y:b.lat/90}},invert:function(b){return{lng:b.x*180,lat:b.y*90}}}}; +pv.Geo.scale=function(b){function c(m){if(!n||m.lng!=n.lng||m.lat!=n.lat){n=m;m=d(m);p={x:k(m.x),y:l(m.y)}}return p}function d(m){return j.project({lng:m.lng-q.lng,lat:m.lat})}function f(m){m=j.invert(m);m.lng+=q.lng;return m}var g={x:0,y:0},h={x:1,y:1},i=[],j=pv.Geo.projections.identity,k=pv.Scale.linear(-1,1).range(0,1),l=pv.Scale.linear(-1,1).range(1,0),q={lng:0,lat:0},n,p;c.x=function(m){return c(m).x};c.y=function(m){return c(m).y};c.ticks={lng:function(m){var r;if(i.length>1){var s=pv.Scale.linear(); +if(m==undefined)m=10;r=s.domain(i,function(t){return t.lat}).ticks(m);m=s.domain(i,function(t){return t.lng}).ticks(m)}else{r=pv.range(-80,81,10);m=pv.range(-180,181,10)}return m.map(function(t){return r.map(function(x){return{lat:x,lng:t}})})},lat:function(m){return pv.transpose(c.ticks.lng(m))}};c.invert=function(m){return f({x:k.invert(m.x),y:l.invert(m.y)})};c.domain=function(m,r){if(arguments.length){i=m instanceof Array?arguments.length>1?pv.map(m,r):m:Array.prototype.slice.call(arguments); +if(i.length>1){var 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b/custom_nodes/ComfyUI-KJNodes/nodes/audioscheduler_nodes.py @@ -0,0 +1,251 @@ +# to be used with https://github.com/a1lazydog/ComfyUI-AudioScheduler +import torch +from torchvision.transforms import functional as TF +from PIL import Image, ImageDraw +import numpy as np +from ..utility.utility import pil2tensor +from nodes import MAX_RESOLUTION + +class NormalizedAmplitudeToMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "normalized_amp": ("NORMALIZED_AMPLITUDE",), + "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_offset": ("INT", {"default": 0,"min": -255, "max": 255, "step": 1}), + "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), + "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), + "size": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), + "shape": ( + [ + 'none', + 'circle', + 'square', + 'triangle', + ], + { + "default": 'none' + }), + "color": ( + [ + 'white', + 'amplitude', + ], + { + "default": 'amplitude' + }), + },} + + CATEGORY = "KJNodes/audio" + RETURN_TYPES = ("MASK",) + FUNCTION = "convert" + DESCRIPTION = """ +Works as a bridge to the AudioScheduler -nodes: +https://github.com/a1lazydog/ComfyUI-AudioScheduler +Creates masks based on the normalized amplitude. +""" + + def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color): + # Ensure normalized_amp is an array and within the range [0, 1] + normalized_amp = np.clip(normalized_amp, 0.0, 1.0) + + # Offset the amplitude values by rolling the array + normalized_amp = np.roll(normalized_amp, frame_offset) + + # Initialize an empty list to hold the image tensors + out = [] + # Iterate over each amplitude value to create an image + for amp in normalized_amp: + # Scale the amplitude value to cover the full range of grayscale values + if color == 'amplitude': + grayscale_value = int(amp * 255) + elif color == 'white': + grayscale_value = 255 + # Convert the grayscale value to an RGB format + gray_color = (grayscale_value, grayscale_value, grayscale_value) + finalsize = size * amp + + if shape == 'none': + shapeimage = Image.new("RGB", (width, height), gray_color) + else: + shapeimage = Image.new("RGB", (width, height), "black") + + draw = ImageDraw.Draw(shapeimage) + if shape == 'circle' or shape == 'square': + # Define the bounding box for the shape + left_up_point = (location_x - finalsize, location_y - finalsize) + right_down_point = (location_x + finalsize,location_y + finalsize) + two_points = [left_up_point, right_down_point] + + if shape == 'circle': + draw.ellipse(two_points, fill=gray_color) + elif shape == 'square': + draw.rectangle(two_points, fill=gray_color) + + elif shape == 'triangle': + # Define the points for the triangle + left_up_point = (location_x - finalsize, location_y + finalsize) # bottom left + right_down_point = (location_x + finalsize, location_y + finalsize) # bottom right + top_point = (location_x, location_y) # top point + draw.polygon([top_point, left_up_point, right_down_point], fill=gray_color) + + shapeimage = pil2tensor(shapeimage) + mask = shapeimage[:, :, :, 0] + out.append(mask) + + return (torch.cat(out, dim=0),) + +class NormalizedAmplitudeToFloatList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "normalized_amp": ("NORMALIZED_AMPLITUDE",), + },} + + CATEGORY = "KJNodes/audio" + RETURN_TYPES = ("FLOAT",) + FUNCTION = "convert" + DESCRIPTION = """ +Works as a bridge to the AudioScheduler -nodes: +https://github.com/a1lazydog/ComfyUI-AudioScheduler +Creates a list of floats from the normalized amplitude. +""" + + def convert(self, normalized_amp): + # Ensure normalized_amp is an array and within the range [0, 1] + normalized_amp = np.clip(normalized_amp, 0.0, 1.0) + return (normalized_amp.tolist(),) + +class OffsetMaskByNormalizedAmplitude: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "normalized_amp": ("NORMALIZED_AMPLITUDE",), + "mask": ("MASK",), + "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "rotate": ("BOOLEAN", { "default": False }), + "angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }), + } + } + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("mask",) + FUNCTION = "offset" + CATEGORY = "KJNodes/audio" + DESCRIPTION = """ +Works as a bridge to the AudioScheduler -nodes: +https://github.com/a1lazydog/ComfyUI-AudioScheduler +Offsets masks based on the normalized amplitude. +""" + + def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp): + + # Ensure normalized_amp is an array and within the range [0, 1] + offsetmask = mask.clone() + normalized_amp = np.clip(normalized_amp, 0.0, 1.0) + + batch_size, height, width = mask.shape + + if rotate: + for i in range(batch_size): + rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier)) + rotation_angle = rotation_amp + offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0) + if x != 0 or y != 0: + for i in range(batch_size): + offset_amp = normalized_amp[i] * 10 + shift_x = min(x*offset_amp, width-1) + shift_y = min(y*offset_amp, height-1) + if shift_x != 0: + offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1) + if shift_y != 0: + offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0) + + return offsetmask, + +class ImageTransformByNormalizedAmplitude: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "normalized_amp": ("NORMALIZED_AMPLITUDE",), + "zoom_scale": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }), + "x_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "y_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "cumulative": ("BOOLEAN", { "default": False }), + "image": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "amptransform" + CATEGORY = "KJNodes/audio" + DESCRIPTION = """ +Works as a bridge to the AudioScheduler -nodes: +https://github.com/a1lazydog/ComfyUI-AudioScheduler +Transforms image based on the normalized amplitude. +""" + + def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset): + # Ensure normalized_amp is an array and within the range [0, 1] + normalized_amp = np.clip(normalized_amp, 0.0, 1.0) + transformed_images = [] + + # Initialize the cumulative zoom factor + prev_amp = 0.0 + + for i in range(image.shape[0]): + img = image[i] # Get the i-th image in the batch + amp = normalized_amp[i] # Get the corresponding amplitude value + + # Incrementally increase the cumulative zoom factor + if cumulative: + prev_amp += amp + amp += prev_amp + + # Convert the image tensor from BxHxWxC to CxHxW format expected by torchvision + img = img.permute(2, 0, 1) + + # Convert PyTorch tensor to PIL Image for processing + pil_img = TF.to_pil_image(img) + + # Calculate the crop size based on the amplitude + width, height = pil_img.size + crop_size = int(min(width, height) * (1 - amp * zoom_scale)) + crop_size = max(crop_size, 1) + + # Calculate the crop box coordinates (centered crop) + left = (width - crop_size) // 2 + top = (height - crop_size) // 2 + right = (width + crop_size) // 2 + bottom = (height + crop_size) // 2 + + # Crop and resize back to original size + cropped_img = TF.crop(pil_img, top, left, crop_size, crop_size) + resized_img = TF.resize(cropped_img, (height, width)) + + # Convert back to tensor in CxHxW format + tensor_img = TF.to_tensor(resized_img) + + # Convert the tensor back to BxHxWxC format + tensor_img = tensor_img.permute(1, 2, 0) + + # Offset the image based on the amplitude + offset_amp = amp * 10 # Calculate the offset magnitude based on the amplitude + shift_x = min(x_offset * offset_amp, img.shape[1] - 1) # Calculate the shift in x direction + shift_y = min(y_offset * offset_amp, img.shape[0] - 1) # Calculate the shift in y direction + + # Apply the offset to the image tensor + if shift_x != 0: + tensor_img = torch.roll(tensor_img, shifts=int(shift_x), dims=1) + if shift_y != 0: + tensor_img = torch.roll(tensor_img, shifts=int(shift_y), dims=0) + + # Add to the list + transformed_images.append(tensor_img) + + # Stack all transformed images into a batch + transformed_batch = torch.stack(transformed_images) + + return (transformed_batch,) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..61e7446f7567f74421d4b05742cc3a340fec73c9 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py @@ -0,0 +1,757 @@ +from ..utility.utility import tensor2pil, pil2tensor +from PIL import Image, ImageDraw, ImageFilter +import numpy as np +import torch +from torchvision.transforms import Resize, CenterCrop, InterpolationMode +import math + +#based on nodes from mtb https://github.com/melMass/comfy_mtb + +def bbox_to_region(bbox, target_size=None): + bbox = bbox_check(bbox, target_size) + return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]) + +def bbox_check(bbox, target_size=None): + if not target_size: + return bbox + + new_bbox = ( + bbox[0], + bbox[1], + min(target_size[0] - bbox[0], bbox[2]), + min(target_size[1] - bbox[1], bbox[3]), + ) + return new_bbox + +class BatchCropFromMask: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_images": ("IMAGE",), + "masks": ("MASK",), + "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), + "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ( + "IMAGE", + "IMAGE", + "BBOX", + "INT", + "INT", + ) + RETURN_NAMES = ( + "original_images", + "cropped_images", + "bboxes", + "width", + "height", + ) + FUNCTION = "crop" + CATEGORY = "KJNodes/masking" + + def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): + if alpha == 0: + return prev_bbox_size + return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) + + def smooth_center(self, prev_center, curr_center, alpha=0.5): + if alpha == 0: + return prev_center + return ( + round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), + round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]) + ) + + def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha): + + bounding_boxes = [] + cropped_images = [] + + self.max_bbox_width = 0 + self.max_bbox_height = 0 + + # First, calculate the maximum bounding box size across all masks + curr_max_bbox_width = 0 + curr_max_bbox_height = 0 + for mask in masks: + _mask = tensor2pil(mask)[0] + non_zero_indices = np.nonzero(np.array(_mask)) + min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) + min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) + width = max_x - min_x + height = max_y - min_y + curr_max_bbox_width = max(curr_max_bbox_width, width) + curr_max_bbox_height = max(curr_max_bbox_height, height) + + # Smooth the changes in the bounding box size + self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha) + self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha) + + # Apply the crop size multiplier + self.max_bbox_width = round(self.max_bbox_width * crop_size_mult) + self.max_bbox_height = round(self.max_bbox_height * crop_size_mult) + bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height + + # Then, for each mask and corresponding image... + for i, (mask, img) in enumerate(zip(masks, original_images)): + _mask = tensor2pil(mask)[0] + non_zero_indices = np.nonzero(np.array(_mask)) + min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) + min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) + + # Calculate center of bounding box + center_x = np.mean(non_zero_indices[1]) + center_y = np.mean(non_zero_indices[0]) + curr_center = (round(center_x), round(center_y)) + + # If this is the first frame, initialize prev_center with curr_center + if not hasattr(self, 'prev_center'): + self.prev_center = curr_center + + # Smooth the changes in the center coordinates from the second frame onwards + if i > 0: + center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha) + else: + center = curr_center + + # Update prev_center for the next frame + self.prev_center = center + + # Create bounding box using max_bbox_width and max_bbox_height + half_box_width = round(self.max_bbox_width / 2) + half_box_height = round(self.max_bbox_height / 2) + min_x = max(0, center[0] - half_box_width) + max_x = min(img.shape[1], center[0] + half_box_width) + min_y = max(0, center[1] - half_box_height) + max_y = min(img.shape[0], center[1] + half_box_height) + + # Append bounding box coordinates + bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y)) + + # Crop the image from the bounding box + cropped_img = img[min_y:max_y, min_x:max_x, :] + + # Calculate the new dimensions while maintaining the aspect ratio + new_height = min(cropped_img.shape[0], self.max_bbox_height) + new_width = round(new_height * bbox_aspect_ratio) + + # Resize the image + resize_transform = Resize((new_height, new_width)) + resized_img = resize_transform(cropped_img.permute(2, 0, 1)) + + # Perform the center crop to the desired size + crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary + cropped_resized_img = crop_transform(resized_img) + + cropped_images.append(cropped_resized_img.permute(1, 2, 0)) + + cropped_out = torch.stack(cropped_images, dim=0) + + return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, ) + +class BatchUncrop: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_images": ("IMAGE",), + "cropped_images": ("IMAGE",), + "bboxes": ("BBOX",), + "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ), + "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "border_top": ("BOOLEAN", {"default": True}), + "border_bottom": ("BOOLEAN", {"default": True}), + "border_left": ("BOOLEAN", {"default": True}), + "border_right": ("BOOLEAN", {"default": True}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "uncrop" + + CATEGORY = "KJNodes/masking" + + def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale, border_top, border_bottom, border_left, border_right): + def inset_border(image, border_width, border_color, border_top, border_bottom, border_left, border_right): + draw = ImageDraw.Draw(image) + width, height = image.size + if border_top: + draw.rectangle((0, 0, width, border_width), fill=border_color) + if border_bottom: + draw.rectangle((0, height - border_width, width, height), fill=border_color) + if border_left: + draw.rectangle((0, 0, border_width, height), fill=border_color) + if border_right: + draw.rectangle((width - border_width, 0, width, height), fill=border_color) + return image + + if len(original_images) != len(cropped_images): + raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same") + + # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images + if len(bboxes) > len(original_images): + print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}") + bboxes = bboxes[:len(original_images)] + elif len(bboxes) < len(original_images): + raise ValueError("There should be at least as many bboxes as there are original and cropped images") + + input_images = tensor2pil(original_images) + crop_imgs = tensor2pil(cropped_images) + + out_images = [] + for i in range(len(input_images)): + img = input_images[i] + crop = crop_imgs[i] + bbox = bboxes[i] + + # uncrop the image based on the bounding box + bb_x, bb_y, bb_width, bb_height = bbox + + paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) + + # scale factors + scale_x = crop_rescale + scale_y = crop_rescale + + # scaled paste_region + paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y)) + + # rescale the crop image to fit the paste_region + crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1]))) + crop_img = crop.convert("RGB") + + if border_blending > 1.0: + border_blending = 1.0 + elif border_blending < 0.0: + border_blending = 0.0 + + blend_ratio = (max(crop_img.size) / 2) * float(border_blending) + + blend = img.convert("RGBA") + mask = Image.new("L", img.size, 0) + + mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255) + mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right) + + mask.paste(mask_block, paste_region) + blend.paste(crop_img, paste_region) + + mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4)) + mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4)) + + blend.putalpha(mask) + img = Image.alpha_composite(img.convert("RGBA"), blend) + out_images.append(img.convert("RGB")) + + return (pil2tensor(out_images),) + +class BatchCropFromMaskAdvanced: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_images": ("IMAGE",), + "masks": ("MASK",), + "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ( + "IMAGE", + "IMAGE", + "MASK", + "IMAGE", + "MASK", + "BBOX", + "BBOX", + "INT", + "INT", + ) + RETURN_NAMES = ( + "original_images", + "cropped_images", + "cropped_masks", + "combined_crop_image", + "combined_crop_masks", + "bboxes", + "combined_bounding_box", + "bbox_width", + "bbox_height", + ) + FUNCTION = "crop" + CATEGORY = "KJNodes/masking" + + def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): + return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) + + def smooth_center(self, prev_center, curr_center, alpha=0.5): + return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), + round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])) + + def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha): + bounding_boxes = [] + combined_bounding_box = [] + cropped_images = [] + cropped_masks = [] + cropped_masks_out = [] + combined_crop_out = [] + combined_cropped_images = [] + combined_cropped_masks = [] + + def calculate_bbox(mask): + non_zero_indices = np.nonzero(np.array(mask)) + + # handle empty masks + min_x, max_x, min_y, max_y = 0, 0, 0, 0 + if len(non_zero_indices[1]) > 0 and len(non_zero_indices[0]) > 0: + min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) + min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) + + width = max_x - min_x + height = max_y - min_y + bbox_size = max(width, height) + return min_x, max_x, min_y, max_y, bbox_size + + combined_mask = torch.max(masks, dim=0)[0] + _mask = tensor2pil(combined_mask)[0] + new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask) + center_x = (new_min_x + new_max_x) / 2 + center_y = (new_min_y + new_max_y) / 2 + half_box_size = round(combined_bbox_size // 2) + new_min_x = max(0, round(center_x - half_box_size)) + new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size)) + new_min_y = max(0, round(center_y - half_box_size)) + new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size)) + + combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y)) + + self.max_bbox_size = 0 + + # First, calculate the maximum bounding box size across all masks + curr_max_bbox_size = max(calculate_bbox(tensor2pil(mask)[0])[-1] for mask in masks) + # Smooth the changes in the bounding box size + self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha) + # Apply the crop size multiplier + self.max_bbox_size = round(self.max_bbox_size * crop_size_mult) + # Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is + self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16 + + if self.max_bbox_size > original_images[0].shape[0] or self.max_bbox_size > original_images[0].shape[1]: + # max_bbox_size can only be as big as our input's width or height, and it has to be even + self.max_bbox_size = math.floor(min(original_images[0].shape[0], original_images[0].shape[1]) / 2) * 2 + + # Then, for each mask and corresponding image... + for i, (mask, img) in enumerate(zip(masks, original_images)): + _mask = tensor2pil(mask)[0] + non_zero_indices = np.nonzero(np.array(_mask)) + + # check for empty masks + if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0: + min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) + min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) + + # Calculate center of bounding box + center_x = np.mean(non_zero_indices[1]) + center_y = np.mean(non_zero_indices[0]) + curr_center = (round(center_x), round(center_y)) + + # If this is the first frame, initialize prev_center with curr_center + if not hasattr(self, 'prev_center'): + self.prev_center = curr_center + + # Smooth the changes in the center coordinates from the second frame onwards + if i > 0: + center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha) + else: + center = curr_center + + # Update prev_center for the next frame + self.prev_center = center + + # Create bounding box using max_bbox_size + half_box_size = self.max_bbox_size // 2 + min_x = max(0, center[0] - half_box_size) + max_x = min(img.shape[1], center[0] + half_box_size) + min_y = max(0, center[1] - half_box_size) + max_y = min(img.shape[0], center[1] + half_box_size) + + # Append bounding box coordinates + bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y)) + + # Crop the image from the bounding box + cropped_img = img[min_y:max_y, min_x:max_x, :] + cropped_mask = mask[min_y:max_y, min_x:max_x] + + # Resize the cropped image to a fixed size + new_size = max(cropped_img.shape[0], cropped_img.shape[1]) + resize_transform = Resize(new_size, interpolation=InterpolationMode.NEAREST, max_size=max(img.shape[0], img.shape[1])) + resized_mask = resize_transform(cropped_mask.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0) + resized_img = resize_transform(cropped_img.permute(2, 0, 1)) + # Perform the center crop to the desired size + # Constrain the crop to the smaller of our bbox or our image so we don't expand past the image dimensions. + crop_transform = CenterCrop((min(self.max_bbox_size, resized_img.shape[1]), min(self.max_bbox_size, resized_img.shape[2]))) + + cropped_resized_img = crop_transform(resized_img) + cropped_images.append(cropped_resized_img.permute(1, 2, 0)) + + cropped_resized_mask = crop_transform(resized_mask) + cropped_masks.append(cropped_resized_mask) + + combined_cropped_img = original_images[i][new_min_y:new_max_y, new_min_x:new_max_x, :] + combined_cropped_images.append(combined_cropped_img) + + combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x] + combined_cropped_masks.append(combined_cropped_mask) + else: + bounding_boxes.append((0, 0, img.shape[1], img.shape[0])) + cropped_images.append(img) + cropped_masks.append(mask) + combined_cropped_images.append(img) + combined_cropped_masks.append(mask) + + cropped_out = torch.stack(cropped_images, dim=0) + combined_crop_out = torch.stack(combined_cropped_images, dim=0) + cropped_masks_out = torch.stack(cropped_masks, dim=0) + combined_crop_mask_out = torch.stack(combined_cropped_masks, dim=0) + + return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size) + +class FilterZeroMasksAndCorrespondingImages: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "masks": ("MASK",), + }, + "optional": { + "original_images": ("IMAGE",), + }, + } + + RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",) + RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",) + FUNCTION = "filter" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Filter out all the empty (i.e. all zero) mask in masks +Also filter out all the corresponding images in original_images by indexes if provide + +original_images (optional): If provided, need have same length as masks. +""" + + def filter(self, masks, original_images=None): + non_zero_masks = [] + non_zero_mask_images = [] + zero_mask_images = [] + zero_mask_images_indexes = [] + + masks_num = len(masks) + also_process_images = False + if original_images is not None: + imgs_num = len(original_images) + if len(original_images) == masks_num: + also_process_images = True + else: + print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})") + + for i in range(masks_num): + non_zero_num = np.count_nonzero(np.array(masks[i])) + if non_zero_num > 0: + non_zero_masks.append(masks[i]) + if also_process_images: + non_zero_mask_images.append(original_images[i]) + else: + zero_mask_images.append(original_images[i]) + zero_mask_images_indexes.append(i) + + non_zero_masks_out = torch.stack(non_zero_masks, dim=0) + non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None + + if also_process_images: + non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0) + if len(zero_mask_images) > 0: + zero_mask_images_out = torch.stack(zero_mask_images, dim=0) + zero_mask_images_out_indexes = zero_mask_images_indexes + + return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes) + +class InsertImageBatchByIndexes: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "images_to_insert": ("IMAGE",), + "insert_indexes": ("INDEXES",), + }, + } + + RETURN_TYPES = ("IMAGE", ) + RETURN_NAMES = ("images_after_insert", ) + FUNCTION = "insert" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +This node is designed to be use with node FilterZeroMasksAndCorrespondingImages +It inserts the images_to_insert into images according to insert_indexes + +Returns: + images_after_insert: updated original images with origonal sequence order +""" + + def insert(self, images, images_to_insert, insert_indexes): + images_after_insert = images + + if images_to_insert is not None and insert_indexes is not None: + images_to_insert_num = len(images_to_insert) + insert_indexes_num = len(insert_indexes) + if images_to_insert_num == insert_indexes_num: + images_after_insert = [] + + i_images = 0 + for i in range(len(images) + images_to_insert_num): + if i in insert_indexes: + images_after_insert.append(images_to_insert[insert_indexes.index(i)]) + else: + images_after_insert.append(images[i_images]) + i_images += 1 + + images_after_insert = torch.stack(images_after_insert, dim=0) + + else: + print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})") + + + return (images_after_insert, ) + +class BatchUncropAdvanced: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_images": ("IMAGE",), + "cropped_images": ("IMAGE",), + "cropped_masks": ("MASK",), + "combined_crop_mask": ("MASK",), + "bboxes": ("BBOX",), + "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ), + "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "use_combined_mask": ("BOOLEAN", {"default": False}), + "use_square_mask": ("BOOLEAN", {"default": True}), + }, + "optional": { + "combined_bounding_box": ("BBOX", {"default": None}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "uncrop" + CATEGORY = "KJNodes/masking" + + + def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None): + + def inset_border(image, border_width=20, border_color=(0)): + width, height = image.size + bordered_image = Image.new(image.mode, (width, height), border_color) + bordered_image.paste(image, (0, 0)) + draw = ImageDraw.Draw(bordered_image) + draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width) + return bordered_image + + if len(original_images) != len(cropped_images): + raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same") + + # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images + if len(bboxes) > len(original_images): + print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}") + bboxes = bboxes[:len(original_images)] + elif len(bboxes) < len(original_images): + raise ValueError("There should be at least as many bboxes as there are original and cropped images") + + crop_imgs = tensor2pil(cropped_images) + input_images = tensor2pil(original_images) + out_images = [] + + for i in range(len(input_images)): + img = input_images[i] + crop = crop_imgs[i] + bbox = bboxes[i] + + if use_combined_mask: + bb_x, bb_y, bb_width, bb_height = combined_bounding_box[0] + paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) + mask = combined_crop_mask[i] + else: + bb_x, bb_y, bb_width, bb_height = bbox + paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) + mask = cropped_masks[i] + + # scale paste_region + scale_x = scale_y = crop_rescale + paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y)) + + # rescale the crop image to fit the paste_region + crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1]))) + crop_img = crop.convert("RGB") + + #border blending + if border_blending > 1.0: + border_blending = 1.0 + elif border_blending < 0.0: + border_blending = 0.0 + + blend_ratio = (max(crop_img.size) / 2) * float(border_blending) + blend = img.convert("RGBA") + + if use_square_mask: + mask = Image.new("L", img.size, 0) + mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255) + mask_block = inset_border(mask_block, round(blend_ratio / 2), (0)) + mask.paste(mask_block, paste_region) + else: + original_mask = tensor2pil(mask)[0] + original_mask = original_mask.resize((paste_region[2]-paste_region[0], paste_region[3]-paste_region[1])) + mask = Image.new("L", img.size, 0) + mask.paste(original_mask, paste_region) + + mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4)) + mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4)) + + blend.paste(crop_img, paste_region) + blend.putalpha(mask) + + img = Image.alpha_composite(img.convert("RGBA"), blend) + out_images.append(img.convert("RGB")) + + return (pil2tensor(out_images),) + +class SplitBboxes: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "bboxes": ("BBOX",), + "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}), + }, + } + + RETURN_TYPES = ("BBOX","BBOX",) + RETURN_NAMES = ("bboxes_a","bboxes_b",) + FUNCTION = "splitbbox" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Splits the specified bbox list at the given index into two lists. +""" + + def splitbbox(self, bboxes, index): + bboxes_a = bboxes[:index] # Sub-list from the start of bboxes up to (but not including) the index + bboxes_b = bboxes[index:] # Sub-list from the index to the end of bboxes + + return (bboxes_a, bboxes_b,) + +class BboxToInt: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "bboxes": ("BBOX",), + "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}), + }, + } + + RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",) + RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",) + FUNCTION = "bboxtoint" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Returns selected index from bounding box list as integers. +""" + def bboxtoint(self, bboxes, index): + x_min, y_min, width, height = bboxes[index] + center_x = int(x_min + width / 2) + center_y = int(y_min + height / 2) + + return (x_min, y_min, width, height, center_x, center_y,) + +class BboxVisualize: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "bboxes": ("BBOX",), + "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "visualizebbox" + DESCRIPTION = """ +Visualizes the specified bbox on the image. +""" + + CATEGORY = "KJNodes/masking" + + def visualizebbox(self, bboxes, images, line_width): + image_list = [] + for image, bbox in zip(images, bboxes): + x_min, y_min, width, height = bbox + + # Ensure bbox coordinates are integers + x_min = int(x_min) + y_min = int(y_min) + width = int(width) + height = int(height) + + # Permute the image dimensions + image = image.permute(2, 0, 1) + + # Clone the image to draw bounding boxes + img_with_bbox = image.clone() + + # Define the color for the bbox, e.g., red + color = torch.tensor([1, 0, 0], dtype=torch.float32) + + # Ensure color tensor matches the image channels + if color.shape[0] != img_with_bbox.shape[0]: + color = color.unsqueeze(1).expand(-1, line_width) + + # Draw lines for each side of the bbox with the specified line width + for lw in range(line_width): + # Top horizontal line + if y_min + lw < img_with_bbox.shape[1]: + img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None] + + # Bottom horizontal line + if y_min + height - lw < img_with_bbox.shape[1]: + img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None] + + # Left vertical line + if x_min + lw < img_with_bbox.shape[2]: + img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None] + + # Right vertical line + if x_min + width - lw < img_with_bbox.shape[2]: + img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None] + + # Permute the image dimensions back + img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0) + image_list.append(img_with_bbox) + + return (torch.cat(image_list, dim=0),) + + return (torch.cat(image_list, dim=0),) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/curve_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/curve_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..bfa446abef42db5363d4908bd2aef096dc096d2e --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/curve_nodes.py @@ -0,0 +1,1628 @@ +import torch +from torchvision import transforms +import json +from PIL import Image, ImageDraw, ImageFont, ImageColor, ImageFilter, ImageChops +import numpy as np +from ..utility.utility import pil2tensor, tensor2pil +import folder_paths +import io +import base64 + +from comfy.utils import common_upscale + +def parse_json_tracks(tracks): + tracks_data = [] + try: + # If tracks is a string, try to parse it as JSON + if isinstance(tracks, str): + parsed = json.loads(tracks.replace("'", '"')) + tracks_data.extend(parsed) + else: + # If tracks is a list of strings, parse each one + for track_str in tracks: + parsed = json.loads(track_str.replace("'", '"')) + tracks_data.append(parsed) + + # Check if we have a single track (dict with x,y) or a list of tracks + if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]: + # Single track detected, wrap it in a list + tracks_data = [tracks_data] + elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]: + # Already a list of tracks, nothing to do + pass + else: + # Unexpected format + print(f"Warning: Unexpected track format: {type(tracks_data[0])}") + + except json.JSONDecodeError as e: + print(f"Error parsing tracks JSON: {e}") + tracks_data = [] + + return tracks_data + +def plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, prompt): + import matplotlib + matplotlib.use('Agg') + from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas + text_color = '#999999' + bg_color = '#353535' + matplotlib.pyplot.rcParams['text.color'] = text_color + fig, ax = matplotlib.pyplot.subplots(figsize=(width/100, height/100), dpi=100) + fig.patch.set_facecolor(bg_color) + ax.set_facecolor(bg_color) + ax.grid(color=text_color, linestyle='-', linewidth=0.5) + ax.set_xlabel('x', color=text_color) + ax.set_ylabel('y', color=text_color) + for text in ax.get_xticklabels() + ax.get_yticklabels(): + text.set_color(text_color) + ax.set_title('position for: ' + prompt) + ax.set_xlabel('X Coordinate') + ax.set_ylabel('Y Coordinate') + #ax.legend().remove() + ax.set_xlim(0, width) # Set the x-axis to match the input latent width + ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left + # Adjust the margins of the subplot + matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2) + + cmap = matplotlib.pyplot.get_cmap('rainbow') + image_batch = [] + canvas = FigureCanvas(fig) + width, height = fig.get_size_inches() * fig.get_dpi() + # Draw a box at each coordinate + for i, ((x, y), size) in enumerate(zip(coordinates, size_multiplier)): + color_index = i / (len(coordinates) - 1) + color = cmap(color_index) + draw_height = bbox_height * size + draw_width = bbox_width * size + rect = matplotlib.patches.Rectangle((x - draw_width/2, y - draw_height/2), draw_width, draw_height, + linewidth=1, edgecolor=color, facecolor='none', alpha=0.5) + ax.add_patch(rect) + + # Check if there is a next coordinate to draw an arrow to + if i < len(coordinates) - 1: + x1, y1 = coordinates[i] + x2, y2 = coordinates[i + 1] + ax.annotate("", xy=(x2, y2), xytext=(x1, y1), + arrowprops=dict(arrowstyle="->", + linestyle="-", + lw=1, + color=color, + mutation_scale=20)) + canvas.draw() + image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3).copy() + image_tensor = torch.from_numpy(image_np).float() / 255.0 + image_tensor = image_tensor.unsqueeze(0) + image_batch.append(image_tensor) + + matplotlib.pyplot.close(fig) + image_batch_tensor = torch.cat(image_batch, dim=0) + + return image_batch_tensor + +class PlotCoordinates: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "coordinates": ("STRING", {"forceInput": True}), + "text": ("STRING", {"default": 'title', "multiline": False}), + "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), + "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), + "bbox_width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), + "bbox_height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), + }, + "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})}, + } + RETURN_TYPES = ("IMAGE", "INT", "INT", "INT", "INT",) + RETURN_NAMES = ("images", "width", "height", "bbox_width", "bbox_height",) + FUNCTION = "append" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = """ +Plots coordinates to sequence of images using Matplotlib. + +""" + + def append(self, coordinates, text, width, height, bbox_width, bbox_height, size_multiplier=[1.0]): + coordinates = json.loads(coordinates.replace("'", '"')) + coordinates = [(coord['x'], coord['y']) for coord in coordinates] + batch_size = len(coordinates) + if not size_multiplier or len(size_multiplier) != batch_size: + size_multiplier = [0] * batch_size + else: + size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] + + plot_image_tensor = plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, text) + + return (plot_image_tensor, width, height, bbox_width, bbox_height) + +class SplineEditor: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "points_store": ("STRING", {"multiline": False}), + "coordinates": ("STRING", {"multiline": False}), + "mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), + "mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), + "points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}), + "sampling_method": ( + [ + 'path', + 'time', + 'controlpoints', + 'speed' + ], + { + "default": 'time' + }), + "interpolation": ( + [ + 'cardinal', + 'monotone', + 'basis', + 'linear', + 'step-before', + 'step-after', + 'polar', + 'polar-reverse', + ], + { + "default": 'cardinal' + }), + "tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}), + "float_output_type": ( + [ + 'list', + 'pandas series', + 'tensor', + ], + { + "default": 'list' + }), + }, + "optional": { + "min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}), + "max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}), + "bg_image": ("IMAGE", ), + } + } + + RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT", "STRING",) + RETURN_NAMES = ("mask", "coord_str", "float", "count", "normalized_str",) + FUNCTION = "splinedata" + CATEGORY = "KJNodes/weights" + DESCRIPTION = """ +# WORK IN PROGRESS +Do not count on this as part of your workflow yet, +probably contains lots of bugs and stability is not +guaranteed!! + +## Graphical editor to create values for various +## schedules and/or mask batches. + +**Shift + click** to add control point at end. +**Ctrl + click** to add control point (subdivide) between two points. +**Right click on a point** to delete it. +Note that you can't delete from start/end. + +Right click on canvas for context menu: +NEW!: +- Add new spline + - Creates a new spline on same canvas, currently these paths are only outputed + as coordinates. +- Add single point + - Creates a single point that only returns it's current position coords +- Delete spline + - Deletes the currently selected spline, you can select a spline by clicking on + it's path, or cycle through them with the 'Next spline' -option. + +These are purely visual options, doesn't affect the output: + - Toggle handles visibility + - Display sample points: display the points to be returned. + +**points_to_sample** value sets the number of samples +returned from the **drawn spline itself**, this is independent from the +actual control points, so the interpolation type matters. +sampling_method: + - time: samples along the time axis, used for schedules + - path: samples along the path itself, useful for coordinates + - controlpoints: samples only the control points themselves + +output types: + - mask batch + example compatible nodes: anything that takes masks + - list of floats + example compatible nodes: IPAdapter weights + - pandas series + example compatible nodes: anything that takes Fizz' + nodes Batch Value Schedule + - torch tensor + example compatible nodes: unknown +""" + + def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation, + points_to_sample, sampling_method, points_store, tension, repeat_output, + min_value=0.0, max_value=1.0, bg_image=None): + + coordinates = json.loads(coordinates) + + # Handle nested list structure if present + all_normalized = [] + all_normalized_y_values = [] + + # Check if we have a nested list structure + if isinstance(coordinates, list) and len(coordinates) > 0 and isinstance(coordinates[0], list): + # Process each list of coordinates in the nested structure + coordinate_sets = coordinates + else: + # If not nested, treat as a single list of coordinates + coordinate_sets = [coordinates] + + # Process each set of coordinates + for coord_set in coordinate_sets: + normalized = [] + normalized_y_values = [] + + for coord in coord_set: + coord['x'] = int(round(coord['x'])) + coord['y'] = int(round(coord['y'])) + norm_x = (1.0 - (coord['x'] / mask_height) - 0.0) * (max_value - min_value) + min_value + norm_y = (1.0 - (coord['y'] / mask_height) - 0.0) * (max_value - min_value) + min_value + normalized_y_values.append(norm_y) + normalized.append({'x':norm_x, 'y':norm_y}) + + all_normalized.extend(normalized) + all_normalized_y_values.extend(normalized_y_values) + + # Use the combined normalized values for output + if float_output_type == 'list': + out_floats = all_normalized_y_values * repeat_output + elif float_output_type == 'pandas series': + try: + import pandas as pd + except: + raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type") + out_floats = pd.Series(all_normalized_y_values * repeat_output), + elif float_output_type == 'tensor': + out_floats = torch.tensor(all_normalized_y_values * repeat_output, dtype=torch.float32) + + # Create a color map for grayscale intensities + color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32) + + # Create image tensors for each normalized y value + mask_tensors = [color_map(y) for y in all_normalized_y_values] + masks_out = torch.stack(mask_tensors) + masks_out = masks_out.repeat(repeat_output, 1, 1, 1) + masks_out = masks_out.mean(dim=-1) + + if bg_image is None: + return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized)) + else: + transform = transforms.ToPILImage() + image = transform(bg_image[0].permute(2, 0, 1)) + buffered = io.BytesIO() + image.save(buffered, format="JPEG", quality=75) + + # Encode the image bytes to a Base64 string + img_bytes = buffered.getvalue() + img_base64 = base64.b64encode(img_bytes).decode('utf-8') + + return { + "ui": {"bg_image": [img_base64]}, + "result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized)) + } + + +class CreateShapeMaskOnPath: + + RETURN_TYPES = ("MASK", "MASK",) + RETURN_NAMES = ("mask", "mask_inverted",) + FUNCTION = "createshapemask" + CATEGORY = "KJNodes/masking/generate" + DESCRIPTION = """ +Creates a mask or batch of masks with the specified shape. +Locations are center locations. +""" + DEPRECATED = True + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "shape": ( + [ 'circle', + 'square', + 'triangle', + ], + { + "default": 'circle' + }), + "coordinates": ("STRING", {"forceInput": True}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), + "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), + }, + "optional": { + "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), + } + } + + def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape, size_multiplier=[1.0]): + # Define the number of images in the batch + coordinates = coordinates.replace("'", '"') + coordinates = json.loads(coordinates) + + batch_size = len(coordinates) + out = [] + color = "white" + if not size_multiplier or len(size_multiplier) != batch_size: + size_multiplier = [0] * batch_size + else: + size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] + for i, coord in enumerate(coordinates): + image = Image.new("RGB", (frame_width, frame_height), "black") + draw = ImageDraw.Draw(image) + + # Calculate the size for this frame and ensure it's not less than 0 + current_width = max(0, shape_width + i * size_multiplier[i]) + current_height = max(0, shape_height + i * size_multiplier[i]) + + location_x = coord['x'] + location_y = coord['y'] + + if shape == 'circle' or shape == 'square': + # Define the bounding box for the shape + left_up_point = (location_x - current_width // 2, location_y - current_height // 2) + right_down_point = (location_x + current_width // 2, location_y + current_height // 2) + two_points = [left_up_point, right_down_point] + + if shape == 'circle': + draw.ellipse(two_points, fill=color) + elif shape == 'square': + draw.rectangle(two_points, fill=color) + + elif shape == 'triangle': + # Define the points for the triangle + left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left + right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right + top_point = (location_x, location_y - current_height // 2) # top point + draw.polygon([top_point, left_up_point, right_down_point], fill=color) + + image = pil2tensor(image) + mask = image[:, :, :, 0] + out.append(mask) + outstack = torch.cat(out, dim=0) + return (outstack, 1.0 - outstack,) + + + +class CreateShapeImageOnPath: + + RETURN_TYPES = ("IMAGE", "MASK",) + RETURN_NAMES = ("image","mask", ) + FUNCTION = "createshapemask" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates an image or batch of images with the specified shape. +Locations are center locations. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "shape": ( + [ 'circle', + 'square', + 'triangle', + ], + { + "default": 'circle' + }), + "coordinates": ("STRING", {"forceInput": True}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "shape_width": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}), + "shape_height": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}), + "shape_color": ("STRING", {"default": 'white'}), + "bg_color": ("STRING", {"default": 'black'}), + "blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}), + "intensity": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}), + }, + "optional": { + "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), + "trailing": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "border_width": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), + "border_color": ("STRING", {"default": 'black'}), + } + } + + def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape_color, + bg_color, blur_radius, shape, intensity, size_multiplier=[1.0], trailing=1.0, border_width=0, border_color='black'): + + coords_list = parse_json_tracks(coordinates) + + batch_size = len(coords_list[0]) + images_list = [] + masks_list = [] + + if not size_multiplier or len(size_multiplier) != batch_size: + size_multiplier = [1] * batch_size + else: + size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] + + previous_output = None + + for i in range(batch_size): + image = Image.new("RGB", (frame_width, frame_height), bg_color) + draw = ImageDraw.Draw(image) + + # Calculate the size for this frame and ensure it's not less than 0 + current_width = shape_width * size_multiplier[i] + current_height = shape_height * size_multiplier[i] + + for coords in coords_list: + location_x = coords[i]['x'] + location_y = coords[i]['y'] + + if shape == 'circle' or shape == 'square': + # Define the bounding box for the shape + left_up_point = (location_x - current_width // 2, location_y - current_height // 2) + right_down_point = (location_x + current_width // 2, location_y + current_height // 2) + two_points = [left_up_point, right_down_point] + + if shape == 'circle': + if border_width > 0: + draw.ellipse(two_points, fill=shape_color, outline=border_color, width=border_width) + else: + draw.ellipse(two_points, fill=shape_color) + elif shape == 'square': + if border_width > 0: + draw.rectangle(two_points, fill=shape_color, outline=border_color, width=border_width) + else: + draw.rectangle(two_points, fill=shape_color) + + elif shape == 'triangle': + # Define the points for the triangle + left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left + right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right + top_point = (location_x, location_y - current_height // 2) # top point + + if border_width > 0: + draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color, outline=border_color, width=border_width) + else: + draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color) + + if blur_radius != 0: + image = image.filter(ImageFilter.GaussianBlur(blur_radius)) + # Blend the current image with the accumulated image + + image = pil2tensor(image) + if trailing != 1.0 and previous_output is not None: + # Add the decayed previous output to the current frame + image += trailing * previous_output + image = image / image.max() + previous_output = image + image = image * intensity + mask = image[:, :, :, 0] + masks_list.append(mask) + images_list.append(image) + out_images = torch.cat(images_list, dim=0).cpu().float() + out_masks = torch.cat(masks_list, dim=0) + return (out_images, out_masks) + +class CreateTextOnPath: + + RETURN_TYPES = ("IMAGE", "MASK", "MASK",) + RETURN_NAMES = ("image", "mask", "mask_inverted",) + FUNCTION = "createtextmask" + CATEGORY = "KJNodes/masking/generate" + DESCRIPTION = """ +Creates a mask or batch of masks with the specified text. +Locations are center locations. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "coordinates": ("STRING", {"forceInput": True}), + "text": ("STRING", {"default": 'text', "multiline": True}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), + "font_size": ("INT", {"default": 42}), + "alignment": ( + [ 'left', + 'center', + 'right' + ], + {"default": 'center'} + ), + "text_color": ("STRING", {"default": 'white'}), + }, + "optional": { + "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), + } + } + + def createtextmask(self, coordinates, frame_width, frame_height, font, font_size, text, text_color, alignment, size_multiplier=[1.0]): + coordinates = coordinates.replace("'", '"') + coordinates = json.loads(coordinates) + + batch_size = len(coordinates) + mask_list = [] + image_list = [] + color = text_color + font_path = folder_paths.get_full_path("kjnodes_fonts", font) + + if len(size_multiplier) != batch_size: + size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] + + for i, coord in enumerate(coordinates): + image = Image.new("RGB", (frame_width, frame_height), "black") + draw = ImageDraw.Draw(image) + lines = text.split('\n') # Split the text into lines + # Apply the size multiplier to the font size for this iteration + current_font_size = int(font_size * size_multiplier[i]) + current_font = ImageFont.truetype(font_path, current_font_size) + line_heights = [current_font.getbbox(line)[3] for line in lines] # List of line heights + total_text_height = sum(line_heights) # Total height of text block + + # Calculate the starting Y position to center the block of text + start_y = coord['y'] - total_text_height // 2 + for j, line in enumerate(lines): + text_width, text_height = current_font.getbbox(line)[2], line_heights[j] + if alignment == 'left': + location_x = coord['x'] + elif alignment == 'center': + location_x = int(coord['x'] - text_width // 2) + elif alignment == 'right': + location_x = int(coord['x'] - text_width) + + location_y = int(start_y + sum(line_heights[:j])) + text_position = (location_x, location_y) + # Draw the text + try: + draw.text(text_position, line, fill=color, font=current_font, features=['-liga']) + except: + draw.text(text_position, line, fill=color, font=current_font) + + image = pil2tensor(image) + non_black_pixels = (image > 0).any(dim=-1) + mask = non_black_pixels.to(image.dtype) + mask_list.append(mask) + image_list.append(image) + + out_images = torch.cat(image_list, dim=0).cpu().float() + out_masks = torch.cat(mask_list, dim=0) + return (out_images, out_masks, 1.0 - out_masks,) + +class CreateGradientFromCoords: + + RETURN_TYPES = ("IMAGE", ) + RETURN_NAMES = ("image", ) + FUNCTION = "generate" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates a gradient image from coordinates. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "coordinates": ("STRING", {"forceInput": True}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "start_color": ("STRING", {"default": 'white'}), + "end_color": ("STRING", {"default": 'black'}), + "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}), + }, + } + + def generate(self, coordinates, frame_width, frame_height, start_color, end_color, multiplier): + # Parse the coordinates + coordinates = json.loads(coordinates.replace("'", '"')) + + # Create an image + image = Image.new("RGB", (frame_width, frame_height)) + draw = ImageDraw.Draw(image) + + # Extract start and end points for the gradient + start_coord = coordinates[0] + end_coord = coordinates[1] + + start_color = ImageColor.getrgb(start_color) + end_color = ImageColor.getrgb(end_color) + + # Calculate the gradient direction (vector) + gradient_direction = (end_coord['x'] - start_coord['x'], end_coord['y'] - start_coord['y']) + gradient_length = (gradient_direction[0] ** 2 + gradient_direction[1] ** 2) ** 0.5 + + # Iterate over each pixel in the image + for y in range(frame_height): + for x in range(frame_width): + # Calculate the projection of the point on the gradient line + point_vector = (x - start_coord['x'], y - start_coord['y']) + projection = (point_vector[0] * gradient_direction[0] + point_vector[1] * gradient_direction[1]) / gradient_length + projection = max(min(projection, gradient_length), 0) # Clamp the projection value + + # Calculate the blend factor for the current pixel + blend = projection * multiplier / gradient_length + + # Determine the color of the current pixel + color = ( + int(start_color[0] + (end_color[0] - start_color[0]) * blend), + int(start_color[1] + (end_color[1] - start_color[1]) * blend), + int(start_color[2] + (end_color[2] - start_color[2]) * blend) + ) + + # Set the pixel color + draw.point((x, y), fill=color) + + # Convert the PIL image to a tensor (assuming such a function exists in your context) + image_tensor = pil2tensor(image) + + return (image_tensor,) + +class GradientToFloat: + + RETURN_TYPES = ("FLOAT", "FLOAT",) + RETURN_NAMES = ("float_x", "float_y", ) + FUNCTION = "sample" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Calculates list of floats from image. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE", ), + "steps": ("INT", {"default": 10, "min": 2, "max": 10000, "step": 1}), + }, + } + + def sample(self, image, steps): + # Assuming image is a tensor with shape [B, H, W, C] + B, H, W, C = image.shape + + # Sample along the width axis (W) + w_intervals = torch.linspace(0, W - 1, steps=steps, dtype=torch.int64) + # Assuming we're sampling from the first batch and the first channel + w_sampled = image[0, :, w_intervals, 0] + + # Sample along the height axis (H) + h_intervals = torch.linspace(0, H - 1, steps=steps, dtype=torch.int64) + # Assuming we're sampling from the first batch and the first channel + h_sampled = image[0, h_intervals, :, 0] + + # Taking the mean across the height for width sampling, and across the width for height sampling + w_values = w_sampled.mean(dim=0).tolist() + h_values = h_sampled.mean(dim=1).tolist() + + return (w_values, h_values) + +class MaskOrImageToWeight: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "output_type": ( + [ + 'list', + 'pandas series', + 'tensor', + 'string' + ], + { + "default": 'list' + }), + }, + "optional": { + "images": ("IMAGE",), + "masks": ("MASK",), + }, + + } + RETURN_TYPES = ("FLOAT", "STRING",) + FUNCTION = "execute" + CATEGORY = "KJNodes/weights" + DESCRIPTION = """ +Gets the mean values from mask or image batch +and returns that as the selected output type. +""" + + def execute(self, output_type, images=None, masks=None): + mean_values = [] + if masks is not None and images is None: + for mask in masks: + mean_values.append(mask.mean().item()) + elif masks is None and images is not None: + for image in images: + mean_values.append(image.mean().item()) + elif masks is not None and images is not None: + raise Exception("MaskOrImageToWeight: Use either mask or image input only.") + + # Convert mean_values to the specified output_type + if output_type == 'list': + out = mean_values + elif output_type == 'pandas series': + try: + import pandas as pd + except: + raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type") + out = pd.Series(mean_values), + elif output_type == 'tensor': + out = torch.tensor(mean_values, dtype=torch.float32), + return (out, [str(value) for value in mean_values],) + +class WeightScheduleConvert: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input_values": ("FLOAT", {"default": 0.0, "forceInput": True}), + "output_type": ( + [ + 'match_input', + 'list', + 'pandas series', + 'tensor', + ], + { + "default": 'list' + }), + "invert": ("BOOLEAN", {"default": False}), + "repeat": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), + }, + "optional": { + "remap_to_frames": ("INT", {"default": 0}), + "interpolation_curve": ("FLOAT", {"forceInput": True}), + "remap_values": ("BOOLEAN", {"default": False}), + "remap_min": ("FLOAT", {"default": 0.0, "min": -100000, "max": 100000.0, "step": 0.01}), + "remap_max": ("FLOAT", {"default": 1.0, "min": -100000, "max": 100000.0, "step": 0.01}), + }, + + } + RETURN_TYPES = ("FLOAT", "STRING", "INT",) + FUNCTION = "execute" + CATEGORY = "KJNodes/weights" + DESCRIPTION = """ +Converts different value lists/series to another type. +""" + + def detect_input_type(self, input_values): + import pandas as pd + if isinstance(input_values, list): + return 'list' + elif isinstance(input_values, pd.Series): + return 'pandas series' + elif isinstance(input_values, torch.Tensor): + return 'tensor' + else: + raise ValueError("Unsupported input type") + + def execute(self, input_values, output_type, invert, repeat, remap_to_frames=0, interpolation_curve=None, remap_min=0.0, remap_max=1.0, remap_values=False): + import pandas as pd + input_type = self.detect_input_type(input_values) + + if input_type == 'pandas series': + float_values = input_values.tolist() + elif input_type == 'tensor': + float_values = input_values + else: + float_values = input_values + + if invert: + float_values = [1 - value for value in float_values] + + if interpolation_curve is not None: + interpolated_pattern = [] + orig_float_values = float_values + for value in interpolation_curve: + min_val = min(orig_float_values) + max_val = max(orig_float_values) + # Normalize the values to [0, 1] + normalized_values = [(value - min_val) / (max_val - min_val) for value in orig_float_values] + # Interpolate the normalized values to the new frame count + remapped_float_values = np.interp(np.linspace(0, 1, int(remap_to_frames * value)), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist() + interpolated_pattern.extend(remapped_float_values) + float_values = interpolated_pattern + else: + # Remap float_values to match target_frame_amount + if remap_to_frames > 0 and remap_to_frames != len(float_values): + min_val = min(float_values) + max_val = max(float_values) + # Normalize the values to [0, 1] + normalized_values = [(value - min_val) / (max_val - min_val) for value in float_values] + # Interpolate the normalized values to the new frame count + float_values = np.interp(np.linspace(0, 1, remap_to_frames), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist() + + float_values = float_values * repeat + if remap_values: + float_values = self.remap_values(float_values, remap_min, remap_max) + + if output_type == 'list': + out = float_values, + elif output_type == 'pandas series': + out = pd.Series(float_values), + elif output_type == 'tensor': + if input_type == 'pandas series': + out = torch.tensor(float_values.values, dtype=torch.float32), + else: + out = torch.tensor(float_values, dtype=torch.float32), + elif output_type == 'match_input': + out = float_values, + return (out, [str(value) for value in float_values], [int(value) for value in float_values]) + + def remap_values(self, values, target_min, target_max): + # Determine the current range + current_min = min(values) + current_max = max(values) + current_range = current_max - current_min + + # Determine the target range + target_range = target_max - target_min + + # Perform the linear interpolation for each value + remapped_values = [(value - current_min) / current_range * target_range + target_min for value in values] + + return remapped_values + + +class FloatToMask: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input_values": ("FLOAT", {"forceInput": True, "default": 0}), + "width": ("INT", {"default": 100, "min": 1}), + "height": ("INT", {"default": 100, "min": 1}), + }, + } + RETURN_TYPES = ("MASK",) + FUNCTION = "execute" + CATEGORY = "KJNodes/masking/generate" + DESCRIPTION = """ +Generates a batch of masks based on the input float values. +The batch size is determined by the length of the input float values. +Each mask is generated with the specified width and height. +""" + + def execute(self, input_values, width, height): + import pandas as pd + # Ensure input_values is a list + if isinstance(input_values, (float, int)): + input_values = [input_values] + elif isinstance(input_values, pd.Series): + input_values = input_values.tolist() + elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values): + input_values = [item for sublist in input_values for item in sublist] + + # Generate a batch of masks based on the input_values + masks = [] + for value in input_values: + # Assuming value is a float between 0 and 1 representing the mask's intensity + mask = torch.ones((height, width), dtype=torch.float32) * value + masks.append(mask) + masks_out = torch.stack(masks, dim=0) + + return(masks_out,) +class WeightScheduleExtend: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input_values_1": ("FLOAT", {"default": 0.0, "forceInput": True}), + "input_values_2": ("FLOAT", {"default": 0.0, "forceInput": True}), + "output_type": ( + [ + 'match_input', + 'list', + 'pandas series', + 'tensor', + ], + { + "default": 'match_input' + }), + }, + + } + RETURN_TYPES = ("FLOAT",) + FUNCTION = "execute" + CATEGORY = "KJNodes/weights" + DESCRIPTION = """ +Extends, and converts if needed, different value lists/series +""" + + def detect_input_type(self, input_values): + import pandas as pd + if isinstance(input_values, list): + return 'list' + elif isinstance(input_values, pd.Series): + return 'pandas series' + elif isinstance(input_values, torch.Tensor): + return 'tensor' + else: + raise ValueError("Unsupported input type") + + def execute(self, input_values_1, input_values_2, output_type): + import pandas as pd + input_type_1 = self.detect_input_type(input_values_1) + input_type_2 = self.detect_input_type(input_values_2) + # Convert input_values_2 to the same format as input_values_1 if they do not match + if not input_type_1 == input_type_2: + print("Converting input_values_2 to the same format as input_values_1") + if input_type_1 == 'pandas series': + # Convert input_values_2 to a pandas Series + float_values_2 = pd.Series(input_values_2) + elif input_type_1 == 'tensor': + # Convert input_values_2 to a tensor + float_values_2 = torch.tensor(input_values_2, dtype=torch.float32) + else: + print("Input types match, no conversion needed") + # If the types match, no conversion is needed + float_values_2 = input_values_2 + + float_values = input_values_1 + float_values_2 + + if output_type == 'list': + return float_values, + elif output_type == 'pandas series': + return pd.Series(float_values), + elif output_type == 'tensor': + if input_type_1 == 'pandas series': + return torch.tensor(float_values.values, dtype=torch.float32), + else: + return torch.tensor(float_values, dtype=torch.float32), + elif output_type == 'match_input': + return float_values, + else: + raise ValueError(f"Unsupported output_type: {output_type}") + +class FloatToSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "float_list": ("FLOAT", {"default": 0.0, "forceInput": True}), + } + } + RETURN_TYPES = ("SIGMAS",) + RETURN_NAMES = ("SIGMAS",) + CATEGORY = "KJNodes/noise" + FUNCTION = "customsigmas" + DESCRIPTION = """ +Creates a sigmas tensor from list of float values. + +""" + def customsigmas(self, float_list): + return torch.tensor(float_list, dtype=torch.float32), + +class SigmasToFloat: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "sigmas": ("SIGMAS",), + } + } + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("float",) + CATEGORY = "KJNodes/noise" + FUNCTION = "customsigmas" + DESCRIPTION = """ +Creates a float list from sigmas tensors. + +""" + def customsigmas(self, sigmas): + return sigmas.tolist(), + +class GLIGENTextBoxApplyBatchCoords: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), + "latents": ("LATENT", ), + "clip": ("CLIP", ), + "gligen_textbox_model": ("GLIGEN", ), + "coordinates": ("STRING", {"forceInput": True}), + "text": ("STRING", {"multiline": True}), + "width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), + "height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), + }, + "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})}, + } + RETURN_TYPES = ("CONDITIONING", "IMAGE", ) + RETURN_NAMES = ("conditioning", "coord_preview", ) + FUNCTION = "append" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = """ +This node allows scheduling GLIGEN text box positions in a batch, +to be used with AnimateDiff-Evolved. Intended to pair with the +Spline Editor -node. + +GLIGEN model can be downloaded through the Manage's "Install Models" menu. +Or directly from here: +https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/tree/main + +Inputs: +- **latents** input is used to calculate batch size +- **clip** is your standard text encoder, use same as for the main prompt +- **gligen_textbox_model** connects to GLIGEN Loader +- **coordinates** takes a json string of points, directly compatible +with the spline editor node. +- **text** is the part of the prompt to set position for +- **width** and **height** are the size of the GLIGEN bounding box + +Outputs: +- **conditioning** goes between to clip text encode and the sampler +- **coord_preview** is an optional preview of the coordinates and +bounding boxes. + +""" + + def append(self, latents, coordinates, conditioning_to, clip, gligen_textbox_model, text, width, height, size_multiplier=[1.0]): + coordinates = json.loads(coordinates.replace("'", '"')) + coordinates = [(coord['x'], coord['y']) for coord in coordinates] + + batch_size = sum(tensor.size(0) for tensor in latents.values()) + if len(coordinates) != batch_size: + print("GLIGENTextBoxApplyBatchCoords WARNING: The number of coordinates does not match the number of latents") + + c = [] + _, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True) + + for t in conditioning_to: + n = [t[0], t[1].copy()] + + position_params_batch = [[] for _ in range(batch_size)] # Initialize a list of empty lists for each batch item + if len(size_multiplier) != batch_size: + size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] + + for i in range(batch_size): + x_position, y_position = coordinates[i] + position_param = (cond_pooled, int((height // 8) * size_multiplier[i]), int((width // 8) * size_multiplier[i]), (y_position - height // 2) // 8, (x_position - width // 2) // 8) + position_params_batch[i].append(position_param) # Append position_param to the correct sublist + + prev = [] + if "gligen" in n[1]: + prev = n[1]['gligen'][2] + else: + prev = [[] for _ in range(batch_size)] + # Concatenate prev and position_params_batch, ensuring both are lists of lists + # and each sublist corresponds to a batch item + combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)] + n[1]['gligen'] = ("position_batched", gligen_textbox_model, combined_position_params) + c.append(n) + + image_height = latents['samples'].shape[-2] * 8 + image_width = latents['samples'].shape[-1] * 8 + plot_image_tensor = plot_coordinates_to_tensor(coordinates, image_height, image_width, height, width, size_multiplier, text) + + return (c, plot_image_tensor,) + +class CreateInstanceDiffusionTracking: + + RETURN_TYPES = ("TRACKING", "STRING", "INT", "INT", "INT", "INT",) + RETURN_NAMES = ("tracking", "prompt", "width", "height", "bbox_width", "bbox_height",) + FUNCTION = "tracking" + CATEGORY = "KJNodes/InstanceDiffusion" + DESCRIPTION = """ +Creates tracking data to be used with InstanceDiffusion: +https://github.com/logtd/ComfyUI-InstanceDiffusion + +InstanceDiffusion prompt format: +"class_id.class_name": "prompt", +for example: +"1.head": "((head))", +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "coordinates": ("STRING", {"forceInput": True}), + "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "bbox_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "bbox_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "class_name": ("STRING", {"default": "class_name"}), + "class_id": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "prompt": ("STRING", {"default": "prompt", "multiline": True}), + }, + "optional": { + "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), + "fit_in_frame": ("BOOLEAN", {"default": True}), + } + } + + def tracking(self, coordinates, class_name, class_id, width, height, bbox_width, bbox_height, prompt, size_multiplier=[1.0], fit_in_frame=True): + # Define the number of images in the batch + coordinates = coordinates.replace("'", '"') + coordinates = json.loads(coordinates) + + tracked = {} + tracked[class_name] = {} + batch_size = len(coordinates) + # Initialize a list to hold the coordinates for the current ID + id_coordinates = [] + if not size_multiplier or len(size_multiplier) != batch_size: + size_multiplier = [0] * batch_size + else: + size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] + for i, coord in enumerate(coordinates): + x = coord['x'] + y = coord['y'] + adjusted_bbox_width = bbox_width * size_multiplier[i] + adjusted_bbox_height = bbox_height * size_multiplier[i] + # Calculate the top left and bottom right coordinates + top_left_x = x - adjusted_bbox_width // 2 + top_left_y = y - adjusted_bbox_height // 2 + bottom_right_x = x + adjusted_bbox_width // 2 + bottom_right_y = y + adjusted_bbox_height // 2 + + if fit_in_frame: + # Clip the coordinates to the frame boundaries + top_left_x = max(0, top_left_x) + top_left_y = max(0, top_left_y) + bottom_right_x = min(width, bottom_right_x) + bottom_right_y = min(height, bottom_right_y) + # Ensure width and height are positive + adjusted_bbox_width = max(1, bottom_right_x - top_left_x) + adjusted_bbox_height = max(1, bottom_right_y - top_left_y) + + # Update the coordinates with the new width and height + bottom_right_x = top_left_x + adjusted_bbox_width + bottom_right_y = top_left_y + adjusted_bbox_height + + # Append the top left and bottom right coordinates to the list for the current ID + id_coordinates.append([top_left_x, top_left_y, bottom_right_x, bottom_right_y, width, height]) + + class_id = int(class_id) + # Assign the list of coordinates to the specified ID within the class_id dictionary + tracked[class_name][class_id] = id_coordinates + + prompt_string = "" + for class_name, class_data in tracked.items(): + for class_id in class_data.keys(): + class_id_str = str(class_id) + # Use the incoming prompt for each class name and ID + prompt_string += f'"{class_id_str}.{class_name}": "({prompt})",\n' + + # Remove the last comma and newline + prompt_string = prompt_string.rstrip(",\n") + + return (tracked, prompt_string, width, height, bbox_width, bbox_height) + +class AppendInstanceDiffusionTracking: + + RETURN_TYPES = ("TRACKING", "STRING",) + RETURN_NAMES = ("tracking", "prompt",) + FUNCTION = "append" + CATEGORY = "KJNodes/InstanceDiffusion" + DESCRIPTION = """ +Appends tracking data to be used with InstanceDiffusion: +https://github.com/logtd/ComfyUI-InstanceDiffusion + +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "tracking_1": ("TRACKING", {"forceInput": True}), + "tracking_2": ("TRACKING", {"forceInput": True}), + }, + "optional": { + "prompt_1": ("STRING", {"default": "", "forceInput": True}), + "prompt_2": ("STRING", {"default": "", "forceInput": True}), + } + } + + def append(self, tracking_1, tracking_2, prompt_1="", prompt_2=""): + tracking_copy = tracking_1.copy() + # Check for existing class names and class IDs, and raise an error if they exist + for class_name, class_data in tracking_2.items(): + if class_name not in tracking_copy: + tracking_copy[class_name] = class_data + else: + # If the class name exists, merge the class data from tracking_2 into tracking_copy + # This will add new class IDs under the same class name without raising an error + tracking_copy[class_name].update(class_data) + prompt_string = prompt_1 + "," + prompt_2 + return (tracking_copy, prompt_string) + +class InterpolateCoords: + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("coordinates",) + FUNCTION = "interpolate" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = """ +Interpolates coordinates based on a curve. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "coordinates": ("STRING", {"forceInput": True}), + "interpolation_curve": ("FLOAT", {"forceInput": True}), + + }, + } + + def interpolate(self, coordinates, interpolation_curve): + # Parse the JSON string to get the list of coordinates + coordinates = json.loads(coordinates.replace("'", '"')) + + # Convert the list of dictionaries to a list of (x, y) tuples for easier processing + coordinates = [(coord['x'], coord['y']) for coord in coordinates] + + # Calculate the total length of the original path + path_length = sum(np.linalg.norm(np.array(coordinates[i]) - np.array(coordinates[i-1])) + for i in range(1, len(coordinates))) + + # Initialize variables for interpolation + interpolated_coords = [] + current_length = 0 + current_index = 0 + + # Iterate over the normalized curve + for normalized_length in interpolation_curve: + target_length = normalized_length * path_length # Convert to the original scale + while current_index < len(coordinates) - 1: + segment_start, segment_end = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1]) + segment_length = np.linalg.norm(segment_end - segment_start) + if current_length + segment_length >= target_length: + break + current_length += segment_length + current_index += 1 + + # Interpolate between the last two points + if current_index < len(coordinates) - 1: + p1, p2 = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1]) + segment_length = np.linalg.norm(p2 - p1) + if segment_length > 0: + t = (target_length - current_length) / segment_length + interpolated_point = p1 + t * (p2 - p1) + interpolated_coords.append(interpolated_point.tolist()) + else: + interpolated_coords.append(p1.tolist()) + else: + # If the target_length is at or beyond the end of the path, add the last coordinate + interpolated_coords.append(coordinates[-1]) + + # Convert back to string format if necessary + interpolated_coords_str = "[" + ", ".join([f"{{'x': {round(coord[0])}, 'y': {round(coord[1])}}}" for coord in interpolated_coords]) + "]" + print(interpolated_coords_str) + + return (interpolated_coords_str,) + +class DrawInstanceDiffusionTracking: + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image", ) + FUNCTION = "draw" + CATEGORY = "KJNodes/InstanceDiffusion" + DESCRIPTION = """ +Draws the tracking data from +CreateInstanceDiffusionTracking -node. + +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE", ), + "tracking": ("TRACKING", {"forceInput": True}), + "box_line_width": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}), + "draw_text": ("BOOLEAN", {"default": True}), + "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), + "font_size": ("INT", {"default": 20}), + }, + } + + def draw(self, image, tracking, box_line_width, draw_text, font, font_size): + import matplotlib.cm as cm + + modified_images = [] + + colormap = cm.get_cmap('rainbow', len(tracking)) + if draw_text: + font_path = folder_paths.get_full_path("kjnodes_fonts", font) + font = ImageFont.truetype(font_path, font_size) + + # Iterate over each image in the batch + for i in range(image.shape[0]): + # Extract the current image and convert it to a PIL image + current_image = image[i, :, :, :].permute(2, 0, 1) + pil_image = transforms.ToPILImage()(current_image) + + draw = ImageDraw.Draw(pil_image) + + # Iterate over the bounding boxes for the current image + for j, (class_name, class_data) in enumerate(tracking.items()): + for class_id, bbox_list in class_data.items(): + # Check if the current index is within the bounds of the bbox_list + if i < len(bbox_list): + bbox = bbox_list[i] + # Ensure bbox is a list or tuple before unpacking + if isinstance(bbox, (list, tuple)): + x1, y1, x2, y2, _, _ = bbox + # Convert coordinates to integers + x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) + # Generate a color from the rainbow colormap + color = tuple(int(255 * x) for x in colormap(j / len(tracking)))[:3] + # Draw the bounding box on the image with the generated color + draw.rectangle([x1, y1, x2, y2], outline=color, width=box_line_width) + if draw_text: + # Draw the class name and ID as text above the box with the generated color + text = f"{class_id}.{class_name}" + # Calculate the width and height of the text + _, _, text_width, text_height = draw.textbbox((0, 0), text=text, font=font) + # Position the text above the top-left corner of the box + text_position = (x1, y1 - text_height) + draw.text(text_position, text, fill=color, font=font) + else: + print(f"Unexpected data type for bbox: {type(bbox)}") + + # Convert the drawn image back to a torch tensor and adjust back to (H, W, C) + modified_image_tensor = transforms.ToTensor()(pil_image).permute(1, 2, 0) + modified_images.append(modified_image_tensor) + + # Stack the modified images back into a batch + image_tensor_batch = torch.stack(modified_images).cpu().float() + + return image_tensor_batch, + +class PointsEditor: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "points_store": ("STRING", {"multiline": False}), + "coordinates": ("STRING", {"multiline": False}), + "neg_coordinates": ("STRING", {"multiline": False}), + "bbox_store": ("STRING", {"multiline": False}), + "bboxes": ("STRING", {"multiline": False}), + "bbox_format": ( + [ + 'xyxy', + 'xywh', + ], + ), + "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), + "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), + "normalize": ("BOOLEAN", {"default": False}), + }, + "optional": { + "bg_image": ("IMAGE", ), + }, + } + + RETURN_TYPES = ("STRING", "STRING", "BBOX", "MASK", "IMAGE") + RETURN_NAMES = ("positive_coords", "negative_coords", "bbox", "bbox_mask", "cropped_image") + FUNCTION = "pointdata" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = """ +# WORK IN PROGRESS +Do not count on this as part of your workflow yet, +probably contains lots of bugs and stability is not +guaranteed!! + +## Graphical editor to create coordinates + +**Shift + click** to add a positive (green) point. +**Shift + right click** to add a negative (red) point. +**Ctrl + click** to draw a box. +**Right click on a point** to delete it. +Note that you can't delete from start/end of the points array. + +To add an image select the node and copy/paste or drag in the image. +Or from the bg_image input on queue (first frame of the batch). + +**THE IMAGE IS SAVED TO THE NODE AND WORKFLOW METADATA** +you can clear the image from the context menu by right clicking on the canvas + +""" + + def pointdata(self, points_store, bbox_store, width, height, coordinates, neg_coordinates, normalize, bboxes, bbox_format="xyxy", bg_image=None): + coordinates = json.loads(coordinates) + pos_coordinates = [] + for coord in coordinates: + coord['x'] = int(round(coord['x'])) + coord['y'] = int(round(coord['y'])) + if normalize: + norm_x = coord['x'] / width + norm_y = coord['y'] / height + pos_coordinates.append({'x': norm_x, 'y': norm_y}) + else: + pos_coordinates.append({'x': coord['x'], 'y': coord['y']}) + + if neg_coordinates: + coordinates = json.loads(neg_coordinates) + neg_coordinates = [] + for coord in coordinates: + coord['x'] = int(round(coord['x'])) + coord['y'] = int(round(coord['y'])) + if normalize: + norm_x = coord['x'] / width + norm_y = coord['y'] / height + neg_coordinates.append({'x': norm_x, 'y': norm_y}) + else: + neg_coordinates.append({'x': coord['x'], 'y': coord['y']}) + + # Create a blank mask + mask = np.zeros((height, width), dtype=np.uint8) + bboxes = json.loads(bboxes) + print(bboxes) + valid_bboxes = [] + for bbox in bboxes: + if (bbox.get("startX") is None or + bbox.get("startY") is None or + bbox.get("endX") is None or + bbox.get("endY") is None): + continue # Skip this bounding box if any value is None + else: + # Ensure that endX and endY are greater than startX and startY + x_min = min(int(bbox["startX"]), int(bbox["endX"])) + y_min = min(int(bbox["startY"]), int(bbox["endY"])) + x_max = max(int(bbox["startX"]), int(bbox["endX"])) + y_max = max(int(bbox["startY"]), int(bbox["endY"])) + + valid_bboxes.append((x_min, y_min, x_max, y_max)) + + bboxes_xyxy = [] + for bbox in valid_bboxes: + x_min, y_min, x_max, y_max = bbox + bboxes_xyxy.append((x_min, y_min, x_max, y_max)) + mask[y_min:y_max, x_min:x_max] = 1 # Fill the bounding box area with 1s + + if bbox_format == "xywh": + bboxes_xywh = [] + for bbox in valid_bboxes: + x_min, y_min, x_max, y_max = bbox + width = x_max - x_min + height = y_max - y_min + bboxes_xywh.append((x_min, y_min, width, height)) + bboxes = bboxes_xywh + else: + bboxes = bboxes_xyxy + + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.unsqueeze(0).float().cpu() + + if bg_image is not None and len(valid_bboxes) > 0: + x_min, y_min, x_max, y_max = bboxes[0] + cropped_image = bg_image[:, y_min:y_max, x_min:x_max, :] + + elif bg_image is not None: + cropped_image = bg_image + + if bg_image is None: + return (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor) + else: + transform = transforms.ToPILImage() + image = transform(bg_image[0].permute(2, 0, 1)) + buffered = io.BytesIO() + image.save(buffered, format="JPEG", quality=75) + + # Step 3: Encode the image bytes to a Base64 string + img_bytes = buffered.getvalue() + img_base64 = base64.b64encode(img_bytes).decode('utf-8') + + return { + "ui": {"bg_image": [img_base64]}, + "result": (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor, cropped_image) + } + +class CutAndDragOnPath: + RETURN_TYPES = ("IMAGE", "MASK",) + RETURN_NAMES = ("image","mask", ) + FUNCTION = "cutanddrag" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Cuts the masked area from the image, and drags it along the path. If inpaint is enabled, and no bg_image is provided, the cut area is filled using cv2 TELEA algorithm. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "coordinates": ("STRING", {"forceInput": True}), + "mask": ("MASK",), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "inpaint": ("BOOLEAN", {"default": True}), + }, + "optional": { + "bg_image": ("IMAGE",), + } + } + + def cutanddrag(self, image, coordinates, mask, frame_width, frame_height, inpaint, bg_image=None): + # Parse coordinates + coords_list = parse_json_tracks(coordinates) + + batch_size = len(coords_list[0]) + images_list = [] + masks_list = [] + + # Convert input image and mask to PIL + input_image = tensor2pil(image)[0] + input_mask = tensor2pil(mask)[0] + + # Find masked region bounds + mask_array = np.array(input_mask) + y_indices, x_indices = np.where(mask_array > 0) + if len(x_indices) == 0 or len(y_indices) == 0: + return (image, mask) + + x_min, x_max = x_indices.min(), x_indices.max() + y_min, y_max = y_indices.min(), y_indices.max() + + # Cut out the masked region + cut_width = x_max - x_min + cut_height = y_max - y_min + cut_image = input_image.crop((x_min, y_min, x_max, y_max)) + cut_mask = input_mask.crop((x_min, y_min, x_max, y_max)) + + # Create inpainted background + if bg_image is None: + background = input_image.copy() + # Inpaint the cut area + if inpaint: + import cv2 + border = 5 # Create small border around cut area for better inpainting + fill_mask = Image.new("L", background.size, 0) + draw = ImageDraw.Draw(fill_mask) + draw.rectangle([x_min-border, y_min-border, x_max+border, y_max+border], fill=255) + background = cv2.inpaint( + np.array(background), + np.array(fill_mask), + inpaintRadius=3, + flags=cv2.INPAINT_TELEA + ) + background = Image.fromarray(background) + else: + background = tensor2pil(bg_image)[0] + + # Create batch of images with cut region at different positions + for i in range(batch_size): + # Create new image + new_image = background.copy() + new_mask = Image.new("L", (frame_width, frame_height), 0) + + # Get target position from coordinates + for coords in coords_list: + target_x = int(coords[i]['x'] - cut_width/2) + target_y = int(coords[i]['y'] - cut_height/2) + + # Paste cut region at new position + new_image.paste(cut_image, (target_x, target_y), cut_mask) + new_mask.paste(cut_mask, (target_x, target_y)) + + # Convert to tensor and append + image_tensor = pil2tensor(new_image) + mask_tensor = pil2tensor(new_mask) + + images_list.append(image_tensor) + masks_list.append(mask_tensor) + + # Stack tensors into batches + out_images = torch.cat(images_list, dim=0).cpu().float() + out_masks = torch.cat(masks_list, dim=0) + + return (out_images, out_masks) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/image_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/image_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..5d0a553a5b385206225c20a153cdf01c17e30b57 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/image_nodes.py @@ -0,0 +1,3540 @@ +import numpy as np +import time +import torch +import torch.nn.functional as F +import torchvision.transforms as T +import io +import base64 +import random +import math +import os +import re +import json +from PIL.PngImagePlugin import PngInfo +try: + import cv2 +except: + print("OpenCV not installed") + pass +from PIL import ImageGrab, ImageDraw, ImageFont, Image, ImageSequence, ImageOps + +from nodes import MAX_RESOLUTION, SaveImage +from comfy_extras.nodes_mask import ImageCompositeMasked +from comfy.cli_args import args +from comfy.utils import ProgressBar, common_upscale +import folder_paths +from comfy import model_management + +script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + +class ImagePass: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "image": ("IMAGE",), + }, + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "passthrough" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Passes the image through without modifying it. +""" + + def passthrough(self, image=None): + return image, + +class ColorMatch: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image_ref": ("IMAGE",), + "image_target": ("IMAGE",), + "method": ( + [ + 'mkl', + 'hm', + 'reinhard', + 'mvgd', + 'hm-mvgd-hm', + 'hm-mkl-hm', + ], { + "default": 'mkl' + }), + }, + "optional": { + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + } + } + + CATEGORY = "KJNodes/image" + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "colormatch" + DESCRIPTION = """ +color-matcher enables color transfer across images which comes in handy for automatic +color-grading of photographs, paintings and film sequences as well as light-field +and stopmotion corrections. + +The methods behind the mappings are based on the approach from Reinhard et al., +the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution +to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram +matching. As shown below our HM-MVGD-HM compound outperforms existing methods. +https://github.com/hahnec/color-matcher/ + +""" + + def colormatch(self, image_ref, image_target, method, strength=1.0): + try: + from color_matcher import ColorMatcher + except: + raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher") + cm = ColorMatcher() + image_ref = image_ref.cpu() + image_target = image_target.cpu() + batch_size = image_target.size(0) + out = [] + images_target = image_target.squeeze() + images_ref = image_ref.squeeze() + + image_ref_np = images_ref.numpy() + images_target_np = images_target.numpy() + + if image_ref.size(0) > 1 and image_ref.size(0) != batch_size: + raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.") + + for i in range(batch_size): + image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy() + image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy() + try: + image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method) + except BaseException as e: + print(f"Error occurred during transfer: {e}") + break + # Apply the strength multiplier + image_result = image_target_np + strength * (image_result - image_target_np) + out.append(torch.from_numpy(image_result)) + + out = torch.stack(out, dim=0).to(torch.float32) + out.clamp_(0, 1) + return (out,) + +class SaveImageWithAlpha: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "mask": ("MASK", ), + "filename_prefix": ("STRING", {"default": "ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images_alpha" + OUTPUT_NODE = True + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Saves an image and mask as .PNG with the mask as the alpha channel. +""" + + def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None): + from PIL.PngImagePlugin import PngInfo + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + if mask.dtype == torch.float16: + mask = mask.to(torch.float32) + def file_counter(): + max_counter = 0 + # Loop through the existing files + for existing_file in os.listdir(full_output_folder): + # Check if the file matches the expected format + match = re.fullmatch(fr"{filename}_(\d+)_?\.[a-zA-Z0-9]+", existing_file) + if match: + # Extract the numeric portion of the filename + file_counter = int(match.group(1)) + # Update the maximum counter value if necessary + if file_counter > max_counter: + max_counter = file_counter + return max_counter + + for image, alpha in zip(images, mask): + i = 255. * image.cpu().numpy() + a = 255. * alpha.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + + # Resize the mask to match the image size + a_resized = Image.fromarray(a).resize(img.size, Image.LANCZOS) + a_resized = np.clip(a_resized, 0, 255).astype(np.uint8) + img.putalpha(Image.fromarray(a_resized, mode='L')) + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + # Increment the counter by 1 to get the next available value + counter = file_counter() + 1 + file = f"{filename}_{counter:05}.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + return { "ui": { "images": results } } + +class ImageConcanate: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "direction": ( + [ 'right', + 'down', + 'left', + 'up', + ], + { + "default": 'right' + }), + "match_image_size": ("BOOLEAN", {"default": True}), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "concatenate" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Concatenates the image2 to image1 in the specified direction. +""" + + def concatenate(self, image1, image2, direction, match_image_size, first_image_shape=None): + # Check if the batch sizes are different + batch_size1 = image1.shape[0] + batch_size2 = image2.shape[0] + + if batch_size1 != batch_size2: + # Calculate the number of repetitions needed + max_batch_size = max(batch_size1, batch_size2) + repeats1 = max_batch_size - batch_size1 + repeats2 = max_batch_size - batch_size2 + + # Repeat the last image to match the largest batch size + if repeats1 > 0: + last_image1 = image1[-1].unsqueeze(0).repeat(repeats1, 1, 1, 1) + image1 = torch.cat([image1.clone(), last_image1], dim=0) + if repeats2 > 0: + last_image2 = image2[-1].unsqueeze(0).repeat(repeats2, 1, 1, 1) + image2 = torch.cat([image2.clone(), last_image2], dim=0) + + if match_image_size: + # Use first_image_shape if provided; otherwise, default to image1's shape + target_shape = first_image_shape if first_image_shape is not None else image1.shape + + original_height = image2.shape[1] + original_width = image2.shape[2] + original_aspect_ratio = original_width / original_height + + if direction in ['left', 'right']: + # Match the height and adjust the width to preserve aspect ratio + target_height = target_shape[1] # B, H, W, C format + target_width = int(target_height * original_aspect_ratio) + elif direction in ['up', 'down']: + # Match the width and adjust the height to preserve aspect ratio + target_width = target_shape[2] # B, H, W, C format + target_height = int(target_width / original_aspect_ratio) + + # Adjust image2 to the expected format for common_upscale + image2_for_upscale = image2.movedim(-1, 1) # Move C to the second position (B, C, H, W) + + # Resize image2 to match the target size while preserving aspect ratio + image2_resized = common_upscale(image2_for_upscale, target_width, target_height, "lanczos", "disabled") + + # Adjust image2 back to the original format (B, H, W, C) after resizing + image2_resized = image2_resized.movedim(1, -1) + else: + image2_resized = image2 + + # Ensure both images have the same number of channels + channels_image1 = image1.shape[-1] + channels_image2 = image2_resized.shape[-1] + + if channels_image1 != channels_image2: + if channels_image1 < channels_image2: + # Add alpha channel to image1 if image2 has it + alpha_channel = torch.ones((*image1.shape[:-1], channels_image2 - channels_image1), device=image1.device) + image1 = torch.cat((image1, alpha_channel), dim=-1) + else: + # Add alpha channel to image2 if image1 has it + alpha_channel = torch.ones((*image2_resized.shape[:-1], channels_image1 - channels_image2), device=image2_resized.device) + image2_resized = torch.cat((image2_resized, alpha_channel), dim=-1) + + + # Concatenate based on the specified direction + if direction == 'right': + concatenated_image = torch.cat((image1, image2_resized), dim=2) # Concatenate along width + elif direction == 'down': + concatenated_image = torch.cat((image1, image2_resized), dim=1) # Concatenate along height + elif direction == 'left': + concatenated_image = torch.cat((image2_resized, image1), dim=2) # Concatenate along width + elif direction == 'up': + concatenated_image = torch.cat((image2_resized, image1), dim=1) # Concatenate along height + return concatenated_image, + +import torch # Make sure you have PyTorch installed + +class ImageConcatFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE",), + "num_columns": ("INT", {"default": 3, "min": 1, "max": 255, "step": 1}), + "match_image_size": ("BOOLEAN", {"default": False}), + "max_resolution": ("INT", {"default": 4096}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "concat" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ + Concatenates images from a batch into a grid with a specified number of columns. + """ + + def concat(self, images, num_columns, match_image_size, max_resolution): + # Assuming images is a batch of images (B, H, W, C) + batch_size, height, width, channels = images.shape + num_rows = (batch_size + num_columns - 1) // num_columns # Calculate number of rows + + print(f"Initial dimensions: batch_size={batch_size}, height={height}, width={width}, channels={channels}") + print(f"num_rows={num_rows}, num_columns={num_columns}") + + if match_image_size: + target_shape = images[0].shape + + resized_images = [] + for image in images: + original_height = image.shape[0] + original_width = image.shape[1] + original_aspect_ratio = original_width / original_height + + if original_aspect_ratio > 1: + target_height = target_shape[0] + target_width = int(target_height * original_aspect_ratio) + else: + target_width = target_shape[1] + target_height = int(target_width / original_aspect_ratio) + + print(f"Resizing image from ({original_height}, {original_width}) to ({target_height}, {target_width})") + + # Resize the image to match the target size while preserving aspect ratio + resized_image = common_upscale(image.movedim(-1, 0), target_width, target_height, "lanczos", "disabled") + resized_image = resized_image.movedim(0, -1) # Move channels back to the last dimension + resized_images.append(resized_image) + + # Convert the list of resized images back to a tensor + images = torch.stack(resized_images) + + height, width = target_shape[:2] # Update height and width + + # Initialize an empty grid + grid_height = num_rows * height + grid_width = num_columns * width + + print(f"Grid dimensions before scaling: grid_height={grid_height}, grid_width={grid_width}") + + # Original scale factor calculation remains unchanged + scale_factor = min(max_resolution / grid_height, max_resolution / grid_width, 1.0) + + # Apply scale factor to height and width + scaled_height = height * scale_factor + scaled_width = width * scale_factor + + # Round scaled dimensions to the nearest number divisible by 8 + height = max(1, int(round(scaled_height / 8) * 8)) + width = max(1, int(round(scaled_width / 8) * 8)) + + if abs(scaled_height - height) > 4: + height = max(1, int(round((scaled_height + 4) / 8) * 8)) + if abs(scaled_width - width) > 4: + width = max(1, int(round((scaled_width + 4) / 8) * 8)) + + # Recalculate grid dimensions with adjusted height and width + grid_height = num_rows * height + grid_width = num_columns * width + print(f"Grid dimensions after scaling: grid_height={grid_height}, grid_width={grid_width}") + print(f"Final image dimensions: height={height}, width={width}") + + grid = torch.zeros((grid_height, grid_width, channels), dtype=images.dtype) + + for idx, image in enumerate(images): + resized_image = torch.nn.functional.interpolate(image.unsqueeze(0).permute(0, 3, 1, 2), size=(height, width), mode="bilinear").squeeze().permute(1, 2, 0) + row = idx // num_columns + col = idx % num_columns + grid[row*height:(row+1)*height, col*width:(col+1)*width, :] = resized_image + + return grid.unsqueeze(0), + + +class ImageGridComposite2x2: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "image3": ("IMAGE",), + "image4": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "compositegrid" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Concatenates the 4 input images into a 2x2 grid. +""" + + def compositegrid(self, image1, image2, image3, image4): + top_row = torch.cat((image1, image2), dim=2) + bottom_row = torch.cat((image3, image4), dim=2) + grid = torch.cat((top_row, bottom_row), dim=1) + return (grid,) + +class ImageGridComposite3x3: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "image3": ("IMAGE",), + "image4": ("IMAGE",), + "image5": ("IMAGE",), + "image6": ("IMAGE",), + "image7": ("IMAGE",), + "image8": ("IMAGE",), + "image9": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "compositegrid" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Concatenates the 9 input images into a 3x3 grid. +""" + + def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9): + top_row = torch.cat((image1, image2, image3), dim=2) + mid_row = torch.cat((image4, image5, image6), dim=2) + bottom_row = torch.cat((image7, image8, image9), dim=2) + grid = torch.cat((top_row, mid_row, bottom_row), dim=1) + return (grid,) + +class ImageBatchTestPattern: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "batch_size": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), + "start_from": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "text_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), + "text_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), + "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), + "font_size": ("INT", {"default": 255,"min": 8, "max": 4096, "step": 1}), + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generatetestpattern" + CATEGORY = "KJNodes/text" + + def generatetestpattern(self, batch_size, font, font_size, start_from, width, height, text_x, text_y): + out = [] + # Generate the sequential numbers for each image + numbers = np.arange(start_from, start_from + batch_size) + font_path = folder_paths.get_full_path("kjnodes_fonts", font) + + for number in numbers: + # Create a black image with the number as a random color text + image = Image.new("RGB", (width, height), color='black') + draw = ImageDraw.Draw(image) + + # Generate a random color for the text + font_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) + + font = ImageFont.truetype(font_path, font_size) + + # Get the size of the text and position it in the center + text = str(number) + + try: + draw.text((text_x, text_y), text, font=font, fill=font_color, features=['-liga']) + except: + draw.text((text_x, text_y), text, font=font, fill=font_color,) + + # Convert the image to a numpy array and normalize the pixel values + image_np = np.array(image).astype(np.float32) / 255.0 + image_tensor = torch.from_numpy(image_np).unsqueeze(0) + out.append(image_tensor) + out_tensor = torch.cat(out, dim=0) + + return (out_tensor,) + +class ImageGrabPIL: + + @classmethod + def IS_CHANGED(cls): + + return + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "screencap" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Captures an area specified by screen coordinates. +Can be used for realtime diffusion with autoqueue. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), + "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), + "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}), + }, + } + + def screencap(self, x, y, width, height, num_frames, delay): + start_time = time.time() + captures = [] + bbox = (x, y, x + width, y + height) + + for _ in range(num_frames): + # Capture screen + screen_capture = ImageGrab.grab(bbox=bbox) + screen_capture_torch = torch.from_numpy(np.array(screen_capture, dtype=np.float32) / 255.0).unsqueeze(0) + captures.append(screen_capture_torch) + + # Wait for a short delay if more than one frame is to be captured + if num_frames > 1: + time.sleep(delay) + + elapsed_time = time.time() - start_time + print(f"screengrab took {elapsed_time} seconds.") + + return (torch.cat(captures, dim=0),) + +class Screencap_mss: + + @classmethod + def IS_CHANGED(s, **kwargs): + return float("NaN") + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "screencap" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Captures an area specified by screen coordinates. +Can be used for realtime diffusion with autoqueue. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "x": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}), + "y": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}), + "width": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}), + "height": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}), + "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), + "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}), + }, + } + + def screencap(self, x, y, width, height, num_frames, delay): + from mss import mss + captures = [] + with mss() as sct: + bbox = {'top': y, 'left': x, 'width': width, 'height': height} + + for _ in range(num_frames): + sct_img = sct.grab(bbox) + img_np = np.array(sct_img) + img_torch = torch.from_numpy(img_np[..., [2, 1, 0]]).float() / 255.0 + captures.append(img_torch) + + if num_frames > 1: + time.sleep(delay) + + return (torch.stack(captures, 0),) + +class WebcamCaptureCV2: + + @classmethod + def IS_CHANGED(cls): + return + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "capture" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = """ +Captures a frame from a webcam using CV2. +Can be used for realtime diffusion with autoqueue. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), + "cam_index": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "release": ("BOOLEAN", {"default": False}), + }, + } + + def capture(self, x, y, cam_index, width, height, release): + # Check if the camera index has changed or the capture object doesn't exist + if not hasattr(self, "cap") or self.cap is None or self.current_cam_index != cam_index: + if hasattr(self, "cap") and self.cap is not None: + self.cap.release() + self.current_cam_index = cam_index + self.cap = cv2.VideoCapture(cam_index) + try: + self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width) + self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height) + except: + pass + if not self.cap.isOpened(): + raise Exception("Could not open webcam") + + ret, frame = self.cap.read() + if not ret: + raise Exception("Failed to capture image from webcam") + + # Crop the frame to the specified bbox + frame = frame[y:y+height, x:x+width] + img_torch = torch.from_numpy(frame[..., [2, 1, 0]]).float() / 255.0 + + if release: + self.cap.release() + self.cap = None + + return (img_torch.unsqueeze(0),) + +class AddLabel: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image":("IMAGE",), + "text_x": ("INT", {"default": 10, "min": 0, "max": 4096, "step": 1}), + "text_y": ("INT", {"default": 2, "min": 0, "max": 4096, "step": 1}), + "height": ("INT", {"default": 48, "min": -1, "max": 4096, "step": 1}), + "font_size": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 1}), + "font_color": ("STRING", {"default": "white"}), + "label_color": ("STRING", {"default": "black"}), + "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), + "text": ("STRING", {"default": "Text"}), + "direction": ( + [ 'up', + 'down', + 'left', + 'right', + 'overlay' + ], + { + "default": 'up' + }), + }, + "optional":{ + "caption": ("STRING", {"default": "", "forceInput": True}), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "addlabel" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +Creates a new with the given text, and concatenates it to +either above or below the input image. +Note that this changes the input image's height! +Fonts are loaded from this folder: +ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts +""" + + def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction, caption=""): + batch_size = image.shape[0] + width = image.shape[2] + + font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf") if font == "TTNorms-Black.otf" else folder_paths.get_full_path("kjnodes_fonts", font) + + def process_image(input_image, caption_text): + font = ImageFont.truetype(font_path, font_size) + words = caption_text.split() + lines = [] + current_line = [] + current_line_width = 0 + + for word in words: + word_width = font.getbbox(word)[2] + if current_line_width + word_width <= width - 2 * text_x: + current_line.append(word) + current_line_width += word_width + font.getbbox(" ")[2] # Add space width + else: + lines.append(" ".join(current_line)) + current_line = [word] + current_line_width = word_width + + if current_line: + lines.append(" ".join(current_line)) + + if direction == 'overlay': + pil_image = Image.fromarray((input_image.cpu().numpy() * 255).astype(np.uint8)) + else: + if height == -1: + # Adjust the image height automatically + margin = 8 + required_height = (text_y + len(lines) * font_size) + margin # Calculate required height + pil_image = Image.new("RGB", (width, required_height), label_color) + else: + # Initialize with a minimal height + label_image = Image.new("RGB", (width, height), label_color) + pil_image = label_image + + draw = ImageDraw.Draw(pil_image) + + + y_offset = text_y + for line in lines: + try: + draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga']) + except: + draw.text((text_x, y_offset), line, font=font, fill=font_color) + y_offset += font_size + + processed_image = torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0) + return processed_image + + if caption == "": + processed_images = [process_image(img, text) for img in image] + else: + assert len(caption) == batch_size, f"Number of captions {(len(caption))} does not match number of images" + processed_images = [process_image(img, cap) for img, cap in zip(image, caption)] + processed_batch = torch.cat(processed_images, dim=0) + + # Combine images based on direction + if direction == 'down': + combined_images = torch.cat((image, processed_batch), dim=1) + elif direction == 'up': + combined_images = torch.cat((processed_batch, image), dim=1) + elif direction == 'left': + processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2) + combined_images = torch.cat((processed_batch, image), dim=2) + elif direction == 'right': + processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2) + combined_images = torch.cat((image, processed_batch), dim=2) + else: + combined_images = processed_batch + + return (combined_images,) + +class GetImageSizeAndCount: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE","INT", "INT", "INT",) + RETURN_NAMES = ("image", "width", "height", "count",) + FUNCTION = "getsize" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Returns width, height and batch size of the image, +and passes it through unchanged. + +""" + + def getsize(self, image): + width = image.shape[2] + height = image.shape[1] + count = image.shape[0] + return {"ui": { + "text": [f"{count}x{width}x{height}"]}, + "result": (image, width, height, count) + } + +class ImageBatchRepeatInterleaving: + + RETURN_TYPES = ("IMAGE", "MASK",) + FUNCTION = "repeat" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Repeats each image in a batch by the specified number of times. +Example batch of 5 images: 0, 1 ,2, 3, 4 +with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4 +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "repeats": ("INT", {"default": 1, "min": 1, "max": 4096}), + }, + "optional": { + "mask": ("MASK",), + } + } + + def repeat(self, images, repeats, mask=None): + original_count = images.shape[0] + total_count = original_count * repeats + + repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0) + if mask is not None: + mask = torch.repeat_interleave(mask, repeats=repeats, dim=0) + else: + mask = torch.zeros((total_count, images.shape[1], images.shape[2]), + device=images.device, dtype=images.dtype) + for i in range(original_count): + mask[i * repeats] = 1.0 + + print("mask shape", mask.shape) + return (repeated_images, mask) + +class ImageUpscaleWithModelBatched: + @classmethod + def INPUT_TYPES(s): + return {"required": { "upscale_model": ("UPSCALE_MODEL",), + "images": ("IMAGE",), + "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Same as ComfyUI native model upscaling node, +but allows setting sub-batches for reduced VRAM usage. +""" + def upscale(self, upscale_model, images, per_batch): + + device = model_management.get_torch_device() + upscale_model.to(device) + in_img = images.movedim(-1,-3) + + steps = in_img.shape[0] + pbar = ProgressBar(steps) + t = [] + + for start_idx in range(0, in_img.shape[0], per_batch): + sub_images = upscale_model(in_img[start_idx:start_idx+per_batch].to(device)) + t.append(sub_images.cpu()) + # Calculate the number of images processed in this batch + batch_count = sub_images.shape[0] + # Update the progress bar by the number of images processed in this batch + pbar.update(batch_count) + upscale_model.cpu() + + t = torch.cat(t, dim=0).permute(0, 2, 3, 1).cpu() + + return (t,) + +class ImageNormalize_Neg1_To_1: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE",), + + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "normalize" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Normalize the images to be in the range [-1, 1] +""" + + def normalize(self,images): + images = images * 2.0 - 1.0 + return (images,) + +class RemapImageRange: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}), + "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}), + "clamp": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "remap" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Remaps the image values to the specified range. +""" + + def remap(self, image, min, max, clamp): + if image.dtype == torch.float16: + image = image.to(torch.float32) + image = min + image * (max - min) + if clamp: + image = torch.clamp(image, min=0.0, max=1.0) + return (image, ) + +class SplitImageChannels: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK") + RETURN_NAMES = ("red", "green", "blue", "mask") + FUNCTION = "split" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Splits image channels into images where the selected channel +is repeated for all channels, and the alpha as a mask. +""" + + def split(self, image): + red = image[:, :, :, 0:1] # Red channel + green = image[:, :, :, 1:2] # Green channel + blue = image[:, :, :, 2:3] # Blue channel + alpha = image[:, :, :, 3:4] # Alpha channel + alpha = alpha.squeeze(-1) + + # Repeat the selected channel for all channels + red = torch.cat([red, red, red], dim=3) + green = torch.cat([green, green, green], dim=3) + blue = torch.cat([blue, blue, blue], dim=3) + return (red, green, blue, alpha) + +class MergeImageChannels: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "red": ("IMAGE",), + "green": ("IMAGE",), + "blue": ("IMAGE",), + + }, + "optional": { + "alpha": ("MASK", {"default": None}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "merge" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Merges channel data into an image. +""" + + def merge(self, red, green, blue, alpha=None): + image = torch.stack([ + red[..., 0, None], # Red channel + green[..., 1, None], # Green channel + blue[..., 2, None] # Blue channel + ], dim=-1) + image = image.squeeze(-2) + if alpha is not None: + image = torch.cat([image, alpha.unsqueeze(-1)], dim=-1) + return (image,) + +class ImagePadForOutpaintMasked: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + }, + "optional": { + "mask": ("MASK",), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, left, top, right, bottom, feathering, mask=None): + if mask is not None: + if torch.allclose(mask, torch.zeros_like(mask)): + print("Warning: The incoming mask is fully black. Handling it as None.") + mask = None + B, H, W, C = image.size() + + new_image = torch.ones( + (B, H + top + bottom, W + left + right, C), + dtype=torch.float32, + ) * 0.5 + + new_image[:, top:top + H, left:left + W, :] = image + + if mask is None: + new_mask = torch.ones( + (B, H + top + bottom, W + left + right), + dtype=torch.float32, + ) + + t = torch.zeros( + (B, H, W), + dtype=torch.float32 + ) + else: + # If a mask is provided, pad it to fit the new image size + mask = F.pad(mask, (left, right, top, bottom), mode='constant', value=0) + mask = 1 - mask + t = torch.zeros_like(mask) + + if feathering > 0 and feathering * 2 < H and feathering * 2 < W: + + for i in range(H): + for j in range(W): + dt = i if top != 0 else H + db = H - i if bottom != 0 else H + + dl = j if left != 0 else W + dr = W - j if right != 0 else W + + d = min(dt, db, dl, dr) + + if d >= feathering: + continue + + v = (feathering - d) / feathering + + if mask is None: + t[:, i, j] = v * v + else: + t[:, top + i, left + j] = v * v + + if mask is None: + new_mask[:, top:top + H, left:left + W] = t + return (new_image, new_mask,) + else: + return (new_image, mask,) + +class ImagePadForOutpaintTargetSize: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "target_width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "target_height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "upscale_method": (s.upscale_methods,), + }, + "optional": { + "mask": ("MASK",), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, target_width, target_height, feathering, upscale_method, mask=None): + B, H, W, C = image.size() + new_height = H + new_width = W + # Calculate the scaling factor while maintaining aspect ratio + scaling_factor = min(target_width / W, target_height / H) + + # Check if the image needs to be downscaled + if scaling_factor < 1: + image = image.movedim(-1,1) + # Calculate the new width and height after downscaling + new_width = int(W * scaling_factor) + new_height = int(H * scaling_factor) + + # Downscale the image + image_scaled = common_upscale(image, new_width, new_height, upscale_method, "disabled").movedim(1,-1) + if mask is not None: + mask_scaled = mask.unsqueeze(0) # Add an extra dimension for batch size + mask_scaled = F.interpolate(mask_scaled, size=(new_height, new_width), mode="nearest") + mask_scaled = mask_scaled.squeeze(0) # Remove the extra dimension after interpolation + else: + mask_scaled = mask + else: + # If downscaling is not needed, use the original image dimensions + image_scaled = image + mask_scaled = mask + + # Calculate how much padding is needed to reach the target dimensions + pad_top = max(0, (target_height - new_height) // 2) + pad_bottom = max(0, target_height - new_height - pad_top) + pad_left = max(0, (target_width - new_width) // 2) + pad_right = max(0, target_width - new_width - pad_left) + + # Now call the original expand_image with the calculated padding + return ImagePadForOutpaintMasked.expand_image(self, image_scaled, pad_left, pad_top, pad_right, pad_bottom, feathering, mask_scaled) + +class ImagePrepForICLora: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "reference_image": ("IMAGE",), + "output_width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), + "output_height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), + "border_width": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}), + }, + "optional": { + "latent_image": ("IMAGE",), + "latent_mask": ("MASK",), + "reference_mask": ("MASK",), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, reference_image, output_width, output_height, border_width, latent_image=None, reference_mask=None, latent_mask=None): + + if reference_mask is not None: + if torch.allclose(reference_mask, torch.zeros_like(reference_mask)): + print("Warning: The incoming mask is fully black. Handling it as None.") + reference_mask = None + image = reference_image + if latent_image is not None: + if image.shape[0] != latent_image.shape[0]: + image = image.repeat(latent_image.shape[0], 1, 1, 1) + B, H, W, C = image.size() + + # Handle mask + if reference_mask is not None: + resized_mask = torch.nn.functional.interpolate( + reference_mask.unsqueeze(1), + size=(H, W), + mode='nearest' + ).squeeze(1) + print(resized_mask.shape) + image = image * resized_mask.unsqueeze(-1) + + # Calculate new width maintaining aspect ratio + new_width = int((W / H) * output_height) + + # Resize image to new height while maintaining aspect ratio + resized_image = common_upscale(image.movedim(-1,1), new_width, output_height, "lanczos", "disabled").movedim(1,-1) + + # Create padded image + if latent_image is None: + pad_image = torch.zeros((B, output_height, output_width, C), device=image.device) + else: + resized_latent_image = common_upscale(latent_image.movedim(-1,1), output_width, output_height, "lanczos", "disabled").movedim(1,-1) + pad_image = resized_latent_image + if latent_mask is not None: + resized_latent_mask = torch.nn.functional.interpolate( + latent_mask.unsqueeze(1), + size=(pad_image.shape[1], pad_image.shape[2]), + mode='nearest' + ).squeeze(1) + + if border_width > 0: + border = torch.zeros((B, output_height, border_width, C), device=image.device) + padded_image = torch.cat((resized_image, border, pad_image), dim=2) + if latent_mask is not None: + padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) + padded_mask[:, :, (new_width + border_width):] = resized_latent_mask + else: + padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) + padded_mask[:, :, :new_width + border_width] = 0 + else: + padded_image = torch.cat((resized_image, pad_image), dim=2) + if latent_mask is not None: + padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) + padded_mask[:, :, new_width:] = resized_latent_mask + else: + padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) + padded_mask[:, :, :new_width] = 0 + + return (padded_image, padded_mask) + + +class ImageAndMaskPreview(SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask_opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "mask_color": ("STRING", {"default": "255, 255, 255"}), + "pass_through": ("BOOLEAN", {"default": False}), + }, + "optional": { + "image": ("IMAGE",), + "mask": ("MASK",), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("composite",) + FUNCTION = "execute" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Preview an image or a mask, when both inputs are used +composites the mask on top of the image. +with pass_through on the preview is disabled and the +composite is returned from the composite slot instead, +this allows for the preview to be passed for video combine +nodes for example. +""" + + def execute(self, mask_opacity, mask_color, pass_through, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None): + if mask is not None and image is None: + preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + elif mask is None and image is not None: + preview = image + elif mask is not None and image is not None: + mask_adjusted = mask * mask_opacity + mask_image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3).clone() + + if ',' in mask_color: + color_list = np.clip([int(channel) for channel in mask_color.split(',')], 0, 255) # RGB format + else: + mask_color = mask_color.lstrip('#') + color_list = [int(mask_color[i:i+2], 16) for i in (0, 2, 4)] # Hex format + mask_image[:, :, :, 0] = color_list[0] / 255 # Red channel + mask_image[:, :, :, 1] = color_list[1] / 255 # Green channel + mask_image[:, :, :, 2] = color_list[2] / 255 # Blue channel + + preview, = ImageCompositeMasked.composite(self, image, mask_image, 0, 0, True, mask_adjusted) + if pass_through: + return (preview, ) + return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo)) + +class CrossFadeImages: + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crossfadeimages" + CATEGORY = "KJNodes/image" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images_1": ("IMAGE",), + "images_2": ("IMAGE",), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), + "transition_start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + "start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), + "end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level): + + def crossfade(images_1, images_2, alpha): + crossfade = (1 - alpha) * images_1 + alpha * images_2 + return crossfade + def ease_in(t): + return t * t + def ease_out(t): + return 1 - (1 - t) * (1 - t) + def ease_in_out(t): + return 3 * t * t - 2 * t * t * t + def bounce(t): + if t < 0.5: + return self.ease_out(t * 2) * 0.5 + else: + return self.ease_in((t - 0.5) * 2) * 0.5 + 0.5 + def elastic(t): + return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1)) + def glitchy(t): + return t + 0.1 * math.sin(40 * t) + def exponential_ease_out(t): + return 1 - (1 - t) ** 4 + + easing_functions = { + "linear": lambda t: t, + "ease_in": ease_in, + "ease_out": ease_out, + "ease_in_out": ease_in_out, + "bounce": bounce, + "elastic": elastic, + "glitchy": glitchy, + "exponential_ease_out": exponential_ease_out, + } + + crossfade_images = [] + + alphas = torch.linspace(start_level, end_level, transitioning_frames) + for i in range(transitioning_frames): + alpha = alphas[i] + image1 = images_1[i + transition_start_index] + image2 = images_2[i + transition_start_index] + easing_function = easing_functions.get(interpolation) + alpha = easing_function(alpha) # Apply the easing function to the alpha value + + crossfade_image = crossfade(image1, image2, alpha) + crossfade_images.append(crossfade_image) + + # Convert crossfade_images to tensor + crossfade_images = torch.stack(crossfade_images, dim=0) + # Get the last frame result of the interpolation + last_frame = crossfade_images[-1] + # Calculate the number of remaining frames from images_2 + remaining_frames = len(images_2) - (transition_start_index + transitioning_frames) + # Crossfade the remaining frames with the last used alpha value + for i in range(remaining_frames): + alpha = alphas[-1] + image1 = images_1[i + transition_start_index + transitioning_frames] + image2 = images_2[i + transition_start_index + transitioning_frames] + easing_function = easing_functions.get(interpolation) + alpha = easing_function(alpha) # Apply the easing function to the alpha value + + crossfade_image = crossfade(image1, image2, alpha) + crossfade_images = torch.cat([crossfade_images, crossfade_image.unsqueeze(0)], dim=0) + # Append the beginning of images_1 + beginning_images_1 = images_1[:transition_start_index] + crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0) + return (crossfade_images, ) + +class CrossFadeImagesMulti: + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crossfadeimages" + CATEGORY = "KJNodes/image" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "image_1": ("IMAGE",), + "image_2": ("IMAGE",), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), + "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + }, + } + + def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs): + + def crossfade(images_1, images_2, alpha): + crossfade = (1 - alpha) * images_1 + alpha * images_2 + return crossfade + def ease_in(t): + return t * t + def ease_out(t): + return 1 - (1 - t) * (1 - t) + def ease_in_out(t): + return 3 * t * t - 2 * t * t * t + def bounce(t): + if t < 0.5: + return self.ease_out(t * 2) * 0.5 + else: + return self.ease_in((t - 0.5) * 2) * 0.5 + 0.5 + def elastic(t): + return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1)) + def glitchy(t): + return t + 0.1 * math.sin(40 * t) + def exponential_ease_out(t): + return 1 - (1 - t) ** 4 + + easing_functions = { + "linear": lambda t: t, + "ease_in": ease_in, + "ease_out": ease_out, + "ease_in_out": ease_in_out, + "bounce": bounce, + "elastic": elastic, + "glitchy": glitchy, + "exponential_ease_out": exponential_ease_out, + } + + image_1 = kwargs["image_1"] + height = image_1.shape[1] + width = image_1.shape[2] + + easing_function = easing_functions[interpolation] + + for c in range(1, inputcount): + frames = [] + new_image = kwargs[f"image_{c + 1}"] + new_image_height = new_image.shape[1] + new_image_width = new_image.shape[2] + + if new_image_height != height or new_image_width != width: + new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled") + new_image = new_image.movedim(1, -1) # Move channels back to the last dimension + + last_frame_image_1 = image_1[-1] + first_frame_image_2 = new_image[0] + + for frame in range(transitioning_frames): + t = frame / (transitioning_frames - 1) + alpha = easing_function(t) + alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device) + frame_image = crossfade(last_frame_image_1, first_frame_image_2, alpha_tensor) + frames.append(frame_image) + + frames = torch.stack(frames) + image_1 = torch.cat((image_1, frames, new_image), dim=0) + + return image_1, + +def transition_images(images_1, images_2, alpha, transition_type, blur_radius, reverse): + width = images_1.shape[1] + height = images_1.shape[0] + + mask = torch.zeros_like(images_1, device=images_1.device) + + alpha = alpha.item() + if reverse: + alpha = 1 - alpha + + #transitions from matteo's essential nodes + if "horizontal slide" in transition_type: + pos = round(width * alpha) + mask[:, :pos, :] = 1.0 + elif "vertical slide" in transition_type: + pos = round(height * alpha) + mask[:pos, :, :] = 1.0 + elif "box" in transition_type: + box_w = round(width * alpha) + box_h = round(height * alpha) + x1 = (width - box_w) // 2 + y1 = (height - box_h) // 2 + x2 = x1 + box_w + y2 = y1 + box_h + mask[y1:y2, x1:x2, :] = 1.0 + elif "circle" in transition_type: + radius = math.ceil(math.sqrt(pow(width, 2) + pow(height, 2)) * alpha / 2) + c_x = width // 2 + c_y = height // 2 + x = torch.arange(0, width, dtype=torch.float32, device="cpu") + y = torch.arange(0, height, dtype=torch.float32, device="cpu") + y, x = torch.meshgrid((y, x), indexing="ij") + circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2) + mask[circle] = 1.0 + elif "horizontal door" in transition_type: + bar = math.ceil(height * alpha / 2) + if bar > 0: + mask[:bar, :, :] = 1.0 + mask[-bar:,:, :] = 1.0 + elif "vertical door" in transition_type: + bar = math.ceil(width * alpha / 2) + if bar > 0: + mask[:, :bar,:] = 1.0 + mask[:, -bar:,:] = 1.0 + elif "fade" in transition_type: + mask[:, :, :] = alpha + + mask = gaussian_blur(mask, blur_radius) + + return images_1 * (1 - mask) + images_2 * mask + +def ease_in(t): + return t * t +def ease_out(t): + return 1 - (1 - t) * (1 - t) +def ease_in_out(t): + return 3 * t * t - 2 * t * t * t +def bounce(t): + if t < 0.5: + return ease_out(t * 2) * 0.5 + else: + return ease_in((t - 0.5) * 2) * 0.5 + 0.5 +def elastic(t): + return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1)) +def glitchy(t): + return t + 0.1 * math.sin(40 * t) +def exponential_ease_out(t): + return 1 - (1 - t) ** 4 + +def gaussian_blur(mask, blur_radius): + if blur_radius > 0: + kernel_size = int(blur_radius * 2) + 1 + if kernel_size % 2 == 0: + kernel_size += 1 # Ensure kernel size is odd + sigma = blur_radius / 3 + x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32) + x = torch.exp(-0.5 * (x / sigma) ** 2) + kernel1d = x / x.sum() + kernel2d = kernel1d[:, None] * kernel1d[None, :] + kernel2d = kernel2d.to(mask.device) + kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1]) + mask = mask.permute(2, 0, 1).unsqueeze(0) # Change to [C, H, W] and add batch dimension + mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1]) + mask = mask.squeeze(0).permute(1, 2, 0) # Change back to [H, W, C] + return mask + +easing_functions = { + "linear": lambda t: t, + "ease_in": ease_in, + "ease_out": ease_out, + "ease_in_out": ease_in_out, + "bounce": bounce, + "elastic": elastic, + "glitchy": glitchy, + "exponential_ease_out": exponential_ease_out, +} + +class TransitionImagesMulti: + RETURN_TYPES = ("IMAGE",) + FUNCTION = "transition" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates transitions between images. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "image_1": ("IMAGE",), + "image_2": ("IMAGE",), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), + "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],), + "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + "blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}), + "reverse": ("BOOLEAN", {"default": False}), + "device": (["CPU", "GPU"], {"default": "CPU"}), + }, + } + + def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs): + + gpu = model_management.get_torch_device() + + image_1 = kwargs["image_1"] + height = image_1.shape[1] + width = image_1.shape[2] + + easing_function = easing_functions[interpolation] + + for c in range(1, inputcount): + frames = [] + new_image = kwargs[f"image_{c + 1}"] + new_image_height = new_image.shape[1] + new_image_width = new_image.shape[2] + + if new_image_height != height or new_image_width != width: + new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled") + new_image = new_image.movedim(1, -1) # Move channels back to the last dimension + + last_frame_image_1 = image_1[-1] + first_frame_image_2 = new_image[0] + if device == "GPU": + last_frame_image_1 = last_frame_image_1.to(gpu) + first_frame_image_2 = first_frame_image_2.to(gpu) + + if reverse: + last_frame_image_1, first_frame_image_2 = first_frame_image_2, last_frame_image_1 + + for frame in range(transitioning_frames): + t = frame / (transitioning_frames - 1) + alpha = easing_function(t) + alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device) + frame_image = transition_images(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse) + frames.append(frame_image) + + frames = torch.stack(frames).cpu() + image_1 = torch.cat((image_1, frames, new_image), dim=0) + + return image_1.cpu(), + +class TransitionImagesInBatch: + RETURN_TYPES = ("IMAGE",) + FUNCTION = "transition" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates transitions between images in a batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), + "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],), + "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + "blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}), + "reverse": ("BOOLEAN", {"default": False}), + "device": (["CPU", "GPU"], {"default": "CPU"}), + }, + } + + #transitions from matteo's essential nodes + def transition(self, images, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse): + if images.shape[0] == 1: + return images, + + gpu = model_management.get_torch_device() + + easing_function = easing_functions[interpolation] + + images_list = [] + pbar = ProgressBar(images.shape[0] - 1) + for i in range(images.shape[0] - 1): + frames = [] + image_1 = images[i] + image_2 = images[i + 1] + + if device == "GPU": + image_1 = image_1.to(gpu) + image_2 = image_2.to(gpu) + + if reverse: + image_1, image_2 = image_2, image_1 + + for frame in range(transitioning_frames): + t = frame / (transitioning_frames - 1) + alpha = easing_function(t) + alpha_tensor = torch.tensor(alpha, dtype=image_1.dtype, device=image_1.device) + frame_image = transition_images(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse) + frames.append(frame_image) + pbar.update(1) + + frames = torch.stack(frames).cpu() + images_list.append(frames) + images = torch.cat(images_list, dim=0) + + return images.cpu(), + +class ShuffleImageBatch: + RETURN_TYPES = ("IMAGE",) + FUNCTION = "shuffle" + CATEGORY = "KJNodes/image" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + }, + } + + def shuffle(self, images, seed): + torch.manual_seed(seed) + B, H, W, C = images.shape + indices = torch.randperm(B) + shuffled_images = images[indices] + + return shuffled_images, + +class GetImageRangeFromBatch: + + RETURN_TYPES = ("IMAGE", "MASK", ) + FUNCTION = "imagesfrombatch" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Returns a range of images from a batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}), + "num_frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), + }, + "optional": { + "images": ("IMAGE",), + "masks": ("MASK",), + } + } + + def imagesfrombatch(self, start_index, num_frames, images=None, masks=None): + chosen_images = None + chosen_masks = None + + # Process images if provided + if images is not None: + if start_index == -1: + start_index = max(0, len(images) - num_frames) + if start_index < 0 or start_index >= len(images): + raise ValueError("Start index is out of range") + end_index = min(start_index + num_frames, len(images)) + chosen_images = images[start_index:end_index] + + # Process masks if provided + if masks is not None: + if start_index == -1: + start_index = max(0, len(masks) - num_frames) + if start_index < 0 or start_index >= len(masks): + raise ValueError("Start index is out of range for masks") + end_index = min(start_index + num_frames, len(masks)) + chosen_masks = masks[start_index:end_index] + + return (chosen_images, chosen_masks,) + +class GetLatentRangeFromBatch: + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "latentsfrombatch" + CATEGORY = "KJNodes/latents" + DESCRIPTION = """ +Returns a range of latents from a batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "latents": ("LATENT",), + "start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}), + "num_frames": ("INT", {"default": 1,"min": -1, "max": 4096, "step": 1}), + }, + } + + def latentsfrombatch(self, latents, start_index, num_frames): + chosen_latents = None + samples = latents["samples"] + if len(samples.shape) == 4: + B, C, H, W = samples.shape + num_latents = B + elif len(samples.shape) == 5: + B, C, T, H, W = samples.shape + num_latents = T + + if start_index == -1: + start_index = max(0, num_latents - num_frames) + if start_index < 0 or start_index >= num_latents: + raise ValueError("Start index is out of range") + + end_index = num_latents if num_frames == -1 else min(start_index + num_frames, num_latents) + + if len(samples.shape) == 4: + chosen_latents = samples[start_index:end_index] + elif len(samples.shape) == 5: + chosen_latents = samples[:, :, start_index:end_index] + + return ({"samples": chosen_latents.contiguous(),},) + +class InsertLatentToIndex: + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "insert" + CATEGORY = "KJNodes/latents" + DESCRIPTION = """ +Inserts a latent at the specified index into the original latent batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "source": ("LATENT",), + "destination": ("LATENT",), + "index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}), + }, + } + + def insert(self, source, destination, index): + samples_destination = destination["samples"] + samples_source = source["samples"].to(samples_destination) + + if len(samples_source.shape) == 4: + B, C, H, W = samples_source.shape + num_latents = B + elif len(samples_source.shape) == 5: + B, C, T, H, W = samples_source.shape + num_latents = T + + if index >= num_latents or index < 0: + raise ValueError(f"Index {index} out of bounds for tensor with {num_latents} latents") + + if len(samples_source.shape) == 4: + joined_latents = torch.cat([ + samples_destination[:index], + samples_source, + samples_destination[index+1:] + ], dim=0) + else: + joined_latents = torch.cat([ + samples_destination[:, :, :index], + samples_source, + samples_destination[:, :, index+1:] + ], dim=2) + + return ({"samples": joined_latents,},) + +class ImageBatchFilter: + + RETURN_TYPES = ("IMAGE", "STRING",) + RETURN_NAMES = ("images", "removed_indices",) + FUNCTION = "filter" + CATEGORY = "KJNodes/image" + DESCRIPTION = "Removes empty images from a batch" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "empty_color": ("STRING", {"default": "0, 0, 0"}), + "empty_threshold": ("FLOAT", {"default": 0.01,"min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "replacement_image": ("IMAGE",), + } + } + + def filter(self, images, empty_color, empty_threshold, replacement_image=None): + B, H, W, C = images.shape + + input_images = images.clone() + + empty_color_list = [int(color.strip()) for color in empty_color.split(',')] + empty_color_tensor = torch.tensor(empty_color_list, dtype=torch.float32).to(input_images.device) + + color_diff = torch.abs(input_images - empty_color_tensor) + mean_diff = color_diff.mean(dim=(1, 2, 3)) + + empty_indices = mean_diff <= empty_threshold + empty_indices_string = ', '.join([str(i) for i in range(B) if empty_indices[i]]) + + if replacement_image is not None: + B_rep, H_rep, W_rep, C_rep = replacement_image.shape + replacement = replacement_image.clone() + if (H_rep != images.shape[1]) or (W_rep != images.shape[2]) or (C_rep != images.shape[3]): + replacement = common_upscale(replacement.movedim(-1, 1), W, H, "lanczos", "center").movedim(1, -1) + input_images[empty_indices] = replacement[0] + + return (input_images, empty_indices_string,) + else: + non_empty_images = input_images[~empty_indices] + return (non_empty_images, empty_indices_string,) + +class GetImagesFromBatchIndexed: + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "indexedimagesfrombatch" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Selects and returns the images at the specified indices as an image batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), + }, + } + + def indexedimagesfrombatch(self, images, indexes): + + # Parse the indexes string into a list of integers + index_list = [int(index.strip()) for index in indexes.split(',')] + + # Convert list of indices to a PyTorch tensor + indices_tensor = torch.tensor(index_list, dtype=torch.long) + + # Select the images at the specified indices + chosen_images = images[indices_tensor] + + return (chosen_images,) + +class InsertImagesToBatchIndexed: + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "insertimagesfrombatch" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Inserts images at the specified indices into the original image batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_images": ("IMAGE",), + "images_to_insert": ("IMAGE",), + "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), + }, + "optional": { + "mode": (["replace", "insert"],), + } + } + + def insertimagesfrombatch(self, original_images, images_to_insert, indexes, mode="replace"): + if indexes == "": + return (original_images,) + + input_images = original_images.clone() + + # Parse the indexes string into a list of integers + index_list = [int(index.strip()) for index in indexes.split(',')] + + # Convert list of indices to a PyTorch tensor + indices_tensor = torch.tensor(index_list, dtype=torch.long) + + # Ensure the images_to_insert is a tensor + if not isinstance(images_to_insert, torch.Tensor): + images_to_insert = torch.tensor(images_to_insert) + + if mode == "replace": + # Replace the images at the specified indices + for index, image in zip(indices_tensor, images_to_insert): + input_images[index] = image + else: + # Create a list to hold the new image sequence + new_images = [] + insert_offset = 0 + + for i in range(len(input_images) + len(indices_tensor)): + if insert_offset < len(indices_tensor) and i == indices_tensor[insert_offset]: + # Use modulo to cycle through images_to_insert + new_images.append(images_to_insert[insert_offset % len(images_to_insert)]) + insert_offset += 1 + else: + new_images.append(input_images[i - insert_offset]) + + # Convert the list back to a tensor + input_images = torch.stack(new_images, dim=0) + + return (input_images,) + +class PadImageBatchInterleaved: + + RETURN_TYPES = ("IMAGE", "MASK",) + RETURN_NAMES = ("images", "masks",) + FUNCTION = "pad" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Inserts empty frames between the images in a batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "empty_frames_per_image": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + "pad_frame_value": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), + "add_after_last": ("BOOLEAN", {"default": False}), + }, + } + + def pad(self, images, empty_frames_per_image, pad_frame_value, add_after_last): + B, H, W, C = images.shape + + # Handle single frame case specifically + if B == 1: + total_frames = 1 + empty_frames_per_image if add_after_last else 1 + else: + # Original B images + (B-1) sets of empty frames between them + total_frames = B + (B-1) * empty_frames_per_image + # Add additional empty frames after the last image if requested + if add_after_last: + total_frames += empty_frames_per_image + + # Create new tensor with zeros (empty frames) + padded_batch = torch.ones((total_frames, H, W, C), + dtype=images.dtype, + device=images.device) * pad_frame_value + # Create mask tensor (1 for original frames, 0 for empty frames) + mask = torch.zeros((total_frames, H, W), + dtype=images.dtype, + device=images.device) + + # Fill in original images at their new positions + for i in range(B): + if B == 1: + # For single frame, just place it at the beginning + new_pos = 0 + else: + # Each image is separated by empty_frames_per_image blank frames + new_pos = i * (empty_frames_per_image + 1) + + padded_batch[new_pos] = images[i] + mask[new_pos] = 1.0 # Mark this as an original frame + + return (padded_batch, mask) + +class ReplaceImagesInBatch: + + RETURN_TYPES = ("IMAGE", "MASK",) + FUNCTION = "replace" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Replaces the images in a batch, starting from the specified start index, +with the replacement images. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "original_images": ("IMAGE",), + "replacement_images": ("IMAGE",), + "start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), + }, + "optional": { + "original_masks": ("MASK",), + "replacement_masks": ("MASK",), + } + } + + def replace(self, original_images, replacement_images, start_index, original_masks=None, replacement_masks=None): + images = None + if start_index >= len(original_images): + raise ValueError("GetImageRangeFromBatch: Start index is out of range") + end_index = start_index + len(replacement_images) + if end_index > len(original_images): + raise ValueError("GetImageRangeFromBatch: End index is out of range") + + if original_masks is not None and replacement_masks is not None: + original_masks_copy = original_masks.clone() + if original_masks_copy.shape[1] != replacement_masks.shape[1] or original_masks_copy.shape[2] != replacement_masks.shape[2]: + replacement_masks = common_upscale(replacement_masks.unsqueeze(1), original_masks_copy.shape[1], original_masks_copy.shape[2], "nearest-exact", "center").squeeze(0) + + original_masks_copy[start_index:end_index] = replacement_masks + masks = original_masks_copy + else: + masks = torch.zeros(1,64,64, device=original_images.device, dtype=original_images.dtype) + + original_images_copy = original_images.clone() + + if original_images_copy.shape[2] != replacement_images.shape[2] or original_images_copy.shape[3] != replacement_images.shape[3]: + replacement_images = common_upscale(replacement_images.movedim(-1, 1), original_images_copy.shape[1], original_images_copy.shape[2], "lanczos", "center").movedim(1, -1) + + original_images_copy[start_index:end_index] = replacement_images + images = original_images_copy + return (images, masks) + + +class ReverseImageBatch: + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "reverseimagebatch" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Reverses the order of the images in a batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + }, + } + + def reverseimagebatch(self, images): + reversed_images = torch.flip(images, [0]) + return (reversed_images, ) + +class ImageBatchMulti: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "image_1": ("IMAGE", ), + "image_2": ("IMAGE", ), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "combine" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates an image batch from multiple images. +You can set how many inputs the node has, +with the **inputcount** and clicking update. +""" + + def combine(self, inputcount, **kwargs): + from nodes import ImageBatch + image_batch_node = ImageBatch() + image = kwargs["image_1"] + for c in range(1, inputcount): + new_image = kwargs[f"image_{c + 1}"] + image, = image_batch_node.batch(image, new_image) + return (image,) + + +class ImageTensorList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + }} + + RETURN_TYPES = ("IMAGE",) + #OUTPUT_IS_LIST = (True,) + FUNCTION = "append" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates an image list from the input images. +""" + + def append(self, image1, image2): + image_list = [] + if isinstance(image1, torch.Tensor) and isinstance(image2, torch.Tensor): + image_list = [image1, image2] + elif isinstance(image1, list) and isinstance(image2, torch.Tensor): + image_list = image1 + [image2] + elif isinstance(image1, torch.Tensor) and isinstance(image2, list): + image_list = [image1] + image2 + elif isinstance(image1, list) and isinstance(image2, list): + image_list = image1 + image2 + return image_list, + +class ImageAddMulti: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "image_1": ("IMAGE", ), + "image_2": ("IMAGE", ), + "blending": ( + [ 'add', + 'subtract', + 'multiply', + 'difference', + ], + { + "default": 'add' + }), + "blend_amount": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "add" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Add blends multiple images together. +You can set how many inputs the node has, +with the **inputcount** and clicking update. +""" + + def add(self, inputcount, blending, blend_amount, **kwargs): + image = kwargs["image_1"] + for c in range(1, inputcount): + new_image = kwargs[f"image_{c + 1}"] + if blending == "add": + image = torch.add(image * blend_amount, new_image * blend_amount) + elif blending == "subtract": + image = torch.sub(image * blend_amount, new_image * blend_amount) + elif blending == "multiply": + image = torch.mul(image * blend_amount, new_image * blend_amount) + elif blending == "difference": + image = torch.sub(image, new_image) + return (image,) + +class ImageConcatMulti: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "image_1": ("IMAGE", ), + "image_2": ("IMAGE", ), + "direction": ( + [ 'right', + 'down', + 'left', + 'up', + ], + { + "default": 'right' + }), + "match_image_size": ("BOOLEAN", {"default": False}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "combine" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Creates an image from multiple images. +You can set how many inputs the node has, +with the **inputcount** and clicking update. +""" + + def combine(self, inputcount, direction, match_image_size, **kwargs): + image = kwargs["image_1"] + first_image_shape = None + if first_image_shape is None: + first_image_shape = image.shape + for c in range(1, inputcount): + new_image = kwargs[f"image_{c + 1}"] + image, = ImageConcanate.concatenate(self, image, new_image, direction, match_image_size, first_image_shape=first_image_shape) + first_image_shape = None + return (image,) + +class PreviewAnimation: + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + + methods = {"default": 4, "fastest": 0, "slowest": 6} + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "fps": ("FLOAT", {"default": 8.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + }, + "optional": { + "images": ("IMAGE", ), + "masks": ("MASK", ), + }, + } + + RETURN_TYPES = () + FUNCTION = "preview" + OUTPUT_NODE = True + CATEGORY = "KJNodes/image" + + def preview(self, fps, images=None, masks=None): + filename_prefix = "AnimPreview" + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + + pil_images = [] + + if images is not None and masks is not None: + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + for mask in masks: + if pil_images: + mask_np = mask.cpu().numpy() + mask_np = np.clip(mask_np * 255, 0, 255).astype(np.uint8) # Convert to values between 0 and 255 + mask_img = Image.fromarray(mask_np, mode='L') + img = pil_images.pop(0) # Remove and get the first image + img = img.convert("RGBA") # Convert base image to RGBA + + # Create a new RGBA image based on the grayscale mask + rgba_mask_img = Image.new("RGBA", img.size, (255, 255, 255, 255)) + rgba_mask_img.putalpha(mask_img) # Use the mask image as the alpha channel + + # Composite the RGBA mask onto the base image + composited_img = Image.alpha_composite(img, rgba_mask_img) + pil_images.append(composited_img) # Add the composited image back + + elif images is not None and masks is None: + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + elif masks is not None and images is None: + for mask in masks: + mask_np = 255. * mask.cpu().numpy() + mask_img = Image.fromarray(np.clip(mask_np, 0, 255).astype(np.uint8)) + pil_images.append(mask_img) + else: + print("PreviewAnimation: No images or masks provided") + return { "ui": { "images": results, "animated": (None,), "text": "empty" }} + + num_frames = len(pil_images) + + c = len(pil_images) + for i in range(0, c, num_frames): + file = f"{filename}_{counter:05}_.webp" + pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], lossless=False, quality=50, method=0) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + animated = num_frames != 1 + return { "ui": { "images": results, "animated": (animated,), "text": [f"{num_frames}x{pil_images[0].size[0]}x{pil_images[0].size[1]}"] } } + +class ImageResizeKJ: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "upscale_method": (s.upscale_methods,), + "keep_proportion": ("BOOLEAN", { "default": False }), + "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }), + }, + "optional" : { + #"width_input": ("INT", { "forceInput": True}), + #"height_input": ("INT", { "forceInput": True}), + "get_image_size": ("IMAGE",), + "crop": (["disabled","center", 0], { "tooltip": "0 will do the default center crop, this is a workaround for the widget order changing with the new frontend, as in old workflows the value of this widget becomes 0 automatically" }), + } + } + + RETURN_TYPES = ("IMAGE", "INT", "INT",) + RETURN_NAMES = ("IMAGE", "width", "height",) + FUNCTION = "resize" + CATEGORY = "KJNodes/image" + DEPRECATED = True + DESCRIPTION = """ +DEPRECATED! + +Due to ComfyUI frontend changes, this node should no longer be used, please check the +v2 of the node. This node is only kept to not completely break older workflows. + +""" + + def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, + width_input=None, height_input=None, get_image_size=None, crop="disabled"): + B, H, W, C = image.shape + + if width_input: + width = width_input + if height_input: + height = height_input + if get_image_size is not None: + _, height, width, _ = get_image_size.shape + + if keep_proportion and get_image_size is None: + # If one of the dimensions is zero, calculate it to maintain the aspect ratio + if width == 0 and height != 0: + ratio = height / H + width = round(W * ratio) + elif height == 0 and width != 0: + ratio = width / W + height = round(H * ratio) + elif width != 0 and height != 0: + # Scale based on which dimension is smaller in proportion to the desired dimensions + ratio = min(width / W, height / H) + width = round(W * ratio) + height = round(H * ratio) + else: + if width == 0: + width = W + if height == 0: + height = H + + if divisible_by > 1 and get_image_size is None: + width = width - (width % divisible_by) + height = height - (height % divisible_by) + + if crop == 0: #workaround for old workflows + crop = "center" + + image = image.movedim(-1,1) + image = common_upscale(image, width, height, upscale_method, crop) + image = image.movedim(1,-1) + + return(image, image.shape[2], image.shape[1],) + +class ImageResizeKJv2: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "upscale_method": (s.upscale_methods,), + "keep_proportion": (["stretch", "resize", "pad", "pad_edge", "crop"], { "default": False }), + "pad_color": ("STRING", { "default": "0, 0, 0", "tooltip": "Color to use for padding."}), + "crop_position": (["center", "top", "bottom", "left", "right"], { "default": "center" }), + "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }), + }, + "optional" : { + "mask": ("MASK",), + "device": (["cpu", "gpu"],), + } + } + + RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK",) + RETURN_NAMES = ("IMAGE", "width", "height", "mask",) + FUNCTION = "resize" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Resizes the image to the specified width and height. +Size can be retrieved from the input. + +Keep proportions keeps the aspect ratio of the image, by +highest dimension. +""" + + def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position, device="cpu", mask=None): + B, H, W, C = image.shape + + if device == "gpu": + if upscale_method == "lanczos": + raise Exception("Lanczos is not supported on the GPU") + device = model_management.get_torch_device() + else: + device = torch.device("cpu") + + if width == 0: + width = W + if height == 0: + height = H + + if keep_proportion == "resize" or keep_proportion.startswith("pad"): + # If one of the dimensions is zero, calculate it to maintain the aspect ratio + if width == 0 and height != 0: + ratio = height / H + new_width = round(W * ratio) + elif height == 0 and width != 0: + ratio = width / W + new_height = round(H * ratio) + elif width != 0 and height != 0: + # Scale based on which dimension is smaller in proportion to the desired dimensions + ratio = min(width / W, height / H) + new_width = round(W * ratio) + new_height = round(H * ratio) + + if keep_proportion.startswith("pad"): + pad_left = (width - new_width) // 2 + pad_right = width - new_width - pad_left + pad_top = (height - new_height) // 2 + pad_bottom = height - new_height - pad_top + + width = new_width + height = new_height + + if divisible_by > 1: + width = width - (width % divisible_by) + height = height - (height % divisible_by) + + out_image = image.clone().to(device) + + if mask is not None: + out_mask = mask.clone().to(device) + + if keep_proportion == "crop": + old_width = W + old_height = H + old_aspect = old_width / old_height + new_aspect = width / height + + # Calculate dimensions to keep + if old_aspect > new_aspect: # Image is wider than target + crop_w = round(old_height * new_aspect) + crop_h = old_height + else: # Image is taller than target + crop_w = old_width + crop_h = round(old_width / new_aspect) + + # Calculate crop position + if crop_position == "center": + x = (old_width - crop_w) // 2 + y = (old_height - crop_h) // 2 + elif crop_position == "top": + x = (old_width - crop_w) // 2 + y = 0 + elif crop_position == "bottom": + x = (old_width - crop_w) // 2 + y = old_height - crop_h + elif crop_position == "left": + x = 0 + y = (old_height - crop_h) // 2 + elif crop_position == "right": + x = old_width - crop_w + y = (old_height - crop_h) // 2 + + # Apply crop + out_image = out_image.narrow(-2, x, crop_w).narrow(-3, y, crop_h) + if mask is not None: + out_mask = out_mask.narrow(-1, x, crop_w).narrow(-2, y, crop_h) + + out_image = common_upscale(out_image.movedim(-1,1), width, height, upscale_method, crop="disabled").movedim(1,-1) + + if mask is not None: + if upscale_method == "lanczos": + out_mask = common_upscale(out_mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop="disabled").movedim(1,-1)[:, :, :, 0] + else: + out_mask = common_upscale(out_mask.unsqueeze(1), width, height, upscale_method, crop="disabled").squeeze(1) + + if keep_proportion.startswith("pad"): + if pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0: + padded_width = width + pad_left + pad_right + padded_height = height + pad_top + pad_bottom + if divisible_by > 1: + width_remainder = padded_width % divisible_by + height_remainder = padded_height % divisible_by + if width_remainder > 0: + extra_width = divisible_by - width_remainder + pad_right += extra_width + if height_remainder > 0: + extra_height = divisible_by - height_remainder + pad_bottom += extra_height + out_image, _ = ImagePadKJ.pad(self, out_image, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, "edge" if keep_proportion == "pad_edge" else "color") + if mask is not None: + out_mask = out_mask.unsqueeze(1).repeat(1, 3, 1, 1).movedim(1,-1) + out_mask, _ = ImagePadKJ.pad(self, out_mask, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, "edge" if keep_proportion == "pad_edge" else "color") + out_mask = out_mask[:, :, :, 0] + + + return(out_image.cpu(), out_image.shape[2], out_image.shape[1], out_mask.cpu() if mask is not None else torch.zeros(64,64, device=torch.device("cpu"), dtype=torch.float32)) + +import pathlib +class LoadAndResizeImage: + _color_channels = ["alpha", "red", "green", "blue"] + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f.name for f in pathlib.Path(input_dir).iterdir() if f.is_file()] + return {"required": + { + "image": (sorted(files), {"image_upload": True}), + "resize": ("BOOLEAN", { "default": False }), + "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + "repeat": ("INT", { "default": 1, "min": 1, "max": 4096, "step": 1, }), + "keep_proportion": ("BOOLEAN", { "default": False }), + "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }), + "mask_channel": (s._color_channels, {"tooltip": "Channel to use for the mask output"}), + "background_color": ("STRING", { "default": "", "tooltip": "Fills the alpha channel with the specified color."}), + }, + } + + CATEGORY = "KJNodes/image" + RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING",) + RETURN_NAMES = ("image", "mask", "width", "height","image_path",) + FUNCTION = "load_image" + + def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel, background_color): + from PIL import ImageColor, Image, ImageOps, ImageSequence + import numpy as np + import torch + image_path = folder_paths.get_annotated_filepath(image) + + import node_helpers + img = node_helpers.pillow(Image.open, image_path) + + # Process the background_color + if background_color: + try: + # Try to parse as RGB tuple + bg_color_rgba = tuple(int(x.strip()) for x in background_color.split(',')) + except ValueError: + # If parsing fails, it might be a hex color or named color + if background_color.startswith('#') or background_color.lower() in ImageColor.colormap: + bg_color_rgba = ImageColor.getrgb(background_color) + else: + raise ValueError(f"Invalid background color: {background_color}") + + bg_color_rgba += (255,) # Add alpha channel + else: + bg_color_rgba = None # No background color specified + + output_images = [] + output_masks = [] + w, h = None, None + + excluded_formats = ['MPO'] + + W, H = img.size + if resize: + if keep_proportion: + ratio = min(width / W, height / H) + width = round(W * ratio) + height = round(H * ratio) + else: + if width == 0: + width = W + if height == 0: + height = H + + if divisible_by > 1: + width = width - (width % divisible_by) + height = height - (height % divisible_by) + else: + width, height = W, H + + for frame in ImageSequence.Iterator(img): + frame = node_helpers.pillow(ImageOps.exif_transpose, frame) + + if frame.mode == 'I': + frame = frame.point(lambda i: i * (1 / 255)) + + if frame.mode == 'P': + frame = frame.convert("RGBA") + elif 'A' in frame.getbands(): + frame = frame.convert("RGBA") + + # Extract alpha channel if it exists + if 'A' in frame.getbands() and bg_color_rgba: + alpha_mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0 + alpha_mask = 1. - torch.from_numpy(alpha_mask) + bg_image = Image.new("RGBA", frame.size, bg_color_rgba) + # Composite the frame onto the background + frame = Image.alpha_composite(bg_image, frame) + else: + alpha_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + image = frame.convert("RGB") + + if len(output_images) == 0: + w = image.size[0] + h = image.size[1] + + if image.size[0] != w or image.size[1] != h: + continue + if resize: + image = image.resize((width, height), Image.Resampling.BILINEAR) + + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + c = mask_channel[0].upper() + if c in frame.getbands(): + if resize: + frame = frame.resize((width, height), Image.Resampling.BILINEAR) + mask = np.array(frame.getchannel(c)).astype(np.float32) / 255.0 + mask = torch.from_numpy(mask) + if c == 'A' and bg_color_rgba: + mask = alpha_mask + elif c == 'A': + mask = 1. - mask + else: + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") + + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1 and img.format not in excluded_formats: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + if repeat > 1: + output_image = output_image.repeat(repeat, 1, 1, 1) + output_mask = output_mask.repeat(repeat, 1, 1) + + return (output_image, output_mask, width, height, image_path) + + + # @classmethod + # def IS_CHANGED(s, image, **kwargs): + # image_path = folder_paths.get_annotated_filepath(image) + # m = hashlib.sha256() + # with open(image_path, 'rb') as f: + # m.update(f.read()) + # return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +import hashlib +class LoadImagesFromFolderKJ: + # Dictionary to store folder hashes + folder_hashes = {} + + @classmethod + def IS_CHANGED(cls, folder, **kwargs): + if not os.path.isdir(folder): + return float("NaN") + + valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga'] + include_subfolders = kwargs.get('include_subfolders', False) + + file_data = [] + if include_subfolders: + for root, _, files in os.walk(folder): + for file in files: + if any(file.lower().endswith(ext) for ext in valid_extensions): + path = os.path.join(root, file) + try: + mtime = os.path.getmtime(path) + file_data.append((path, mtime)) + except OSError: + pass + else: + for file in os.listdir(folder): + if any(file.lower().endswith(ext) for ext in valid_extensions): + path = os.path.join(folder, file) + try: + mtime = os.path.getmtime(path) + file_data.append((path, mtime)) + except OSError: + pass + + file_data.sort() + + combined_hash = hashlib.md5() + combined_hash.update(folder.encode('utf-8')) + combined_hash.update(str(len(file_data)).encode('utf-8')) + + for path, mtime in file_data: + combined_hash.update(f"{path}:{mtime}".encode('utf-8')) + + current_hash = combined_hash.hexdigest() + + old_hash = cls.folder_hashes.get(folder) + cls.folder_hashes[folder] = current_hash + + if old_hash == current_hash: + return old_hash + + return current_hash + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "folder": ("STRING", {"default": ""}), + "width": ("INT", {"default": 1024, "min": -1, "step": 1}), + "height": ("INT", {"default": 1024, "min": -1, "step": 1}), + "keep_aspect_ratio": (["crop", "pad", "stretch",],), + }, + "optional": { + "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}), + "start_index": ("INT", {"default": 0, "min": 0, "step": 1}), + "include_subfolders": ("BOOLEAN", {"default": False}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",) + RETURN_NAMES = ("image", "mask", "count", "image_path",) + FUNCTION = "load_images" + CATEGORY = "KJNodes/image" + DESCRIPTION = """Loads images from a folder into a batch, images are resized and loaded into a batch.""" + + def load_images(self, folder, width, height, image_load_cap, start_index, keep_aspect_ratio, include_subfolders=False): + if not os.path.isdir(folder): + raise FileNotFoundError(f"Folder '{folder} cannot be found.'") + + valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga'] + image_paths = [] + if include_subfolders: + for root, _, files in os.walk(folder): + for file in files: + if any(file.lower().endswith(ext) for ext in valid_extensions): + image_paths.append(os.path.join(root, file)) + else: + for file in os.listdir(folder): + if any(file.lower().endswith(ext) for ext in valid_extensions): + image_paths.append(os.path.join(folder, file)) + + dir_files = sorted(image_paths) + + if len(dir_files) == 0: + raise FileNotFoundError(f"No files in directory '{folder}'.") + + # start at start_index + dir_files = dir_files[start_index:] + + images = [] + masks = [] + image_path_list = [] + + limit_images = False + if image_load_cap > 0: + limit_images = True + image_count = 0 + + for image_path in dir_files: + if os.path.isdir(image_path): + continue + if limit_images and image_count >= image_load_cap: + break + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + + # Resize image to maximum dimensions + if width == -1 and height == -1: + width = i.size[0] + height = i.size[1] + if i.size != (width, height): + i = self.resize_with_aspect_ratio(i, width, height, keep_aspect_ratio) + + + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + if mask.shape != (height, width): + mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), + size=(height, width), + mode='bilinear', + align_corners=False).squeeze() + else: + mask = torch.zeros((height, width), dtype=torch.float32, device="cpu") + + images.append(image) + masks.append(mask) + image_path_list.append(image_path) + image_count += 1 + + if len(images) == 1: + return (images[0], masks[0], 1, image_path_list) + + elif len(images) > 1: + image1 = images[0] + mask1 = masks[0].unsqueeze(0) + + for image2 in images[1:]: + image1 = torch.cat((image1, image2), dim=0) + + for mask2 in masks[1:]: + mask1 = torch.cat((mask1, mask2.unsqueeze(0)), dim=0) + + return (image1, mask1, len(images), image_path_list) + def resize_with_aspect_ratio(self, img, width, height, mode): + if mode == "stretch": + return img.resize((width, height), Image.Resampling.LANCZOS) + + img_width, img_height = img.size + aspect_ratio = img_width / img_height + target_ratio = width / height + + if mode == "crop": + # Calculate dimensions for center crop + if aspect_ratio > target_ratio: + # Image is wider - crop width + new_width = int(height * aspect_ratio) + img = img.resize((new_width, height), Image.Resampling.LANCZOS) + left = (new_width - width) // 2 + return img.crop((left, 0, left + width, height)) + else: + # Image is taller - crop height + new_height = int(width / aspect_ratio) + img = img.resize((width, new_height), Image.Resampling.LANCZOS) + top = (new_height - height) // 2 + return img.crop((0, top, width, top + height)) + + elif mode == "pad": + pad_color = self.get_edge_color(img) + # Calculate dimensions for padding + if aspect_ratio > target_ratio: + # Image is wider - pad height + new_height = int(width / aspect_ratio) + img = img.resize((width, new_height), Image.Resampling.LANCZOS) + padding = (height - new_height) // 2 + padded = Image.new('RGBA', (width, height), pad_color) + padded.paste(img, (0, padding)) + return padded + else: + # Image is taller - pad width + new_width = int(height * aspect_ratio) + img = img.resize((new_width, height), Image.Resampling.LANCZOS) + padding = (width - new_width) // 2 + padded = Image.new('RGBA', (width, height), pad_color) + padded.paste(img, (padding, 0)) + return padded + def get_edge_color(self, img): + from PIL import ImageStat + """Sample edges and return dominant color""" + width, height = img.size + img = img.convert('RGBA') + + # Create 1-pixel high/wide images from edges + top = img.crop((0, 0, width, 1)) + bottom = img.crop((0, height-1, width, height)) + left = img.crop((0, 0, 1, height)) + right = img.crop((width-1, 0, width, height)) + + # Combine edges into single image + edges = Image.new('RGBA', (width*2 + height*2, 1)) + edges.paste(top, (0, 0)) + edges.paste(bottom, (width, 0)) + edges.paste(left.resize((height, 1)), (width*2, 0)) + edges.paste(right.resize((height, 1)), (width*2 + height, 0)) + + # Get median color + stat = ImageStat.Stat(edges) + median = tuple(map(int, stat.median)) + return median + + +class ImageGridtoBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "columns": ("INT", {"default": 3, "min": 1, "max": 8, "tooltip": "The number of columns in the grid."}), + "rows": ("INT", {"default": 0, "min": 1, "max": 8, "tooltip": "The number of rows in the grid. Set to 0 for automatic calculation."}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decompose" + CATEGORY = "KJNodes/image" + DESCRIPTION = "Converts a grid of images to a batch of images." + + def decompose(self, image, columns, rows): + B, H, W, C = image.shape + print("input size: ", image.shape) + + # Calculate cell width, rounding down + cell_width = W // columns + + if rows == 0: + # If rows is 0, calculate number of full rows + rows = H // cell_height + else: + # If rows is specified, adjust cell_height + cell_height = H // rows + + # Crop the image to fit full cells + image = image[:, :rows*cell_height, :columns*cell_width, :] + + # Reshape and permute the image to get the grid + image = image.view(B, rows, cell_height, columns, cell_width, C) + image = image.permute(0, 1, 3, 2, 4, 5).contiguous() + image = image.view(B, rows * columns, cell_height, cell_width, C) + + # Reshape to the final batch tensor + img_tensor = image.view(-1, cell_height, cell_width, C) + + return (img_tensor,) + +class SaveImageKJ: + def __init__(self): + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE", {"tooltip": "The images to save."}), + "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), + "output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}), + }, + "optional": { + "caption_file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file."}), + "caption": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}), + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("filename",) + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "KJNodes/image" + DESCRIPTION = "Saves the input images to your ComfyUI output directory." + + def save_images(self, images, output_folder, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, caption=None, caption_file_extension=".txt"): + filename_prefix += self.prefix_append + + if os.path.isabs(output_folder): + if not os.path.exists(output_folder): + os.makedirs(output_folder, exist_ok=True) + full_output_folder = output_folder + _, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_folder, images[0].shape[1], images[0].shape[0]) + else: + self.output_dir = folder_paths.get_output_directory() + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + + results = list() + for (batch_number, image) in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + base_file_name = f"{filename_with_batch_num}_{counter:05}_" + file = f"{base_file_name}.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + if caption is not None: + txt_file = base_file_name + caption_file_extension + file_path = os.path.join(full_output_folder, txt_file) + with open(file_path, 'w') as f: + f.write(caption) + + counter += 1 + + return file, + +class SaveStringKJ: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "string": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}), + "filename_prefix": ("STRING", {"default": "text", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), + "output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}), + }, + "optional": { + "file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file."}), + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("filename",) + FUNCTION = "save_string" + + OUTPUT_NODE = True + + CATEGORY = "KJNodes/misc" + DESCRIPTION = "Saves the input string to your ComfyUI output directory." + + def save_string(self, string, output_folder, filename_prefix="text", file_extension=".txt"): + filename_prefix += self.prefix_append + + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + if output_folder != "output": + if not os.path.exists(output_folder): + os.makedirs(output_folder, exist_ok=True) + full_output_folder = output_folder + + base_file_name = f"{filename_prefix}_{counter:05}_" + results = list() + + txt_file = base_file_name + file_extension + file_path = os.path.join(full_output_folder, txt_file) + with open(file_path, 'w') as f: + f.write(string) + + return results, + +to_pil_image = T.ToPILImage() + +class FastPreview: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE", ), + "format": (["JPEG", "PNG", "WEBP"], {"default": "JPEG"}), + "quality" : ("INT", {"default": 75, "min": 1, "max": 100, "step": 1}), + }, + } + + RETURN_TYPES = () + FUNCTION = "preview" + CATEGORY = "KJNodes/experimental" + OUTPUT_NODE = True + DESCRIPTION = "Experimental node for faster image previews by displaying through base64 it without saving to disk." + + def preview(self, image, format, quality): + pil_image = to_pil_image(image[0].permute(2, 0, 1)) + + with io.BytesIO() as buffered: + pil_image.save(buffered, format=format, quality=quality) + img_bytes = buffered.getvalue() + + img_base64 = base64.b64encode(img_bytes).decode('utf-8') + + return { + "ui": {"bg_image": [img_base64]}, + "result": () + } + +class ImageCropByMaskAndResize: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE", ), + "mask": ("MASK", ), + "base_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + "padding": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "min_crop_resolution": ("INT", { "default": 128, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + "max_crop_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + + }, + } + + RETURN_TYPES = ("IMAGE", "MASK", "BBOX", ) + RETURN_NAMES = ("images", "masks", "bbox",) + FUNCTION = "crop" + CATEGORY = "KJNodes/image" + + def crop_by_mask(self, mask, padding=0, min_crop_resolution=None, max_crop_resolution=None): + iy, ix = (mask == 1).nonzero(as_tuple=True) + h0, w0 = mask.shape + + if iy.numel() == 0: + x_c = w0 / 2.0 + y_c = h0 / 2.0 + width = 0 + height = 0 + else: + x_min = ix.min().item() + x_max = ix.max().item() + y_min = iy.min().item() + y_max = iy.max().item() + + width = x_max - x_min + height = y_max - y_min + + if width > w0 or height > h0: + raise Exception("Masked area out of bounds") + + x_c = (x_min + x_max) / 2.0 + y_c = (y_min + y_max) / 2.0 + + if min_crop_resolution: + width = max(width, min_crop_resolution) + height = max(height, min_crop_resolution) + + if max_crop_resolution: + width = min(width, max_crop_resolution) + height = min(height, max_crop_resolution) + + if w0 <= width: + x0 = 0 + w = w0 + else: + x0 = max(0, x_c - width / 2 - padding) + w = width + 2 * padding + if x0 + w > w0: + x0 = w0 - w + + if h0 <= height: + y0 = 0 + h = h0 + else: + y0 = max(0, y_c - height / 2 - padding) + h = height + 2 * padding + if y0 + h > h0: + y0 = h0 - h + + return (int(x0), int(y0), int(w), int(h)) + + def crop(self, image, mask, base_resolution, padding=0, min_crop_resolution=128, max_crop_resolution=512): + mask = mask.round() + image_list = [] + mask_list = [] + bbox_list = [] + + # First, collect all bounding boxes + bbox_params = [] + aspect_ratios = [] + for i in range(image.shape[0]): + x0, y0, w, h = self.crop_by_mask(mask[i], padding, min_crop_resolution, max_crop_resolution) + bbox_params.append((x0, y0, w, h)) + aspect_ratios.append(w / h) + + # Find maximum width and height + max_w = max([w for x0, y0, w, h in bbox_params]) + max_h = max([h for x0, y0, w, h in bbox_params]) + max_aspect_ratio = max(aspect_ratios) + + # Ensure dimensions are divisible by 16 + max_w = (max_w + 15) // 16 * 16 + max_h = (max_h + 15) // 16 * 16 + # Calculate common target dimensions + if max_aspect_ratio > 1: + target_width = base_resolution + target_height = int(base_resolution / max_aspect_ratio) + else: + target_height = base_resolution + target_width = int(base_resolution * max_aspect_ratio) + + for i in range(image.shape[0]): + x0, y0, w, h = bbox_params[i] + + # Adjust cropping to use maximum width and height + x_center = x0 + w / 2 + y_center = y0 + h / 2 + + x0_new = int(max(0, x_center - max_w / 2)) + y0_new = int(max(0, y_center - max_h / 2)) + x1_new = int(min(x0_new + max_w, image.shape[2])) + y1_new = int(min(y0_new + max_h, image.shape[1])) + x0_new = x1_new - max_w + y0_new = y1_new - max_h + + cropped_image = image[i][y0_new:y1_new, x0_new:x1_new, :] + cropped_mask = mask[i][y0_new:y1_new, x0_new:x1_new] + + # Ensure dimensions are divisible by 16 + target_width = (target_width + 15) // 16 * 16 + target_height = (target_height + 15) // 16 * 16 + + cropped_image = cropped_image.unsqueeze(0).movedim(-1, 1) # Move C to the second position (B, C, H, W) + cropped_image = common_upscale(cropped_image, target_width, target_height, "lanczos", "disabled") + cropped_image = cropped_image.movedim(1, -1).squeeze(0) + + cropped_mask = cropped_mask.unsqueeze(0).unsqueeze(0) + cropped_mask = common_upscale(cropped_mask, target_width, target_height, 'bilinear', "disabled") + cropped_mask = cropped_mask.squeeze(0).squeeze(0) + + image_list.append(cropped_image) + mask_list.append(cropped_mask) + bbox_list.append((x0_new, y0_new, x1_new, y1_new)) + + + return (torch.stack(image_list), torch.stack(mask_list), bbox_list) + +class ImageCropByMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE", ), + "mask": ("MASK", ), + }, + } + + RETURN_TYPES = ("IMAGE", ) + RETURN_NAMES = ("image", ) + FUNCTION = "crop" + CATEGORY = "KJNodes/image" + DESCRIPTION = "Crops the input images based on the provided mask." + + def crop(self, image, mask): + B, H, W, C = image.shape + mask = mask.round() + + # Find bounding box for each batch + crops = [] + + for b in range(B): + # Get coordinates of non-zero elements + rows = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=1) + cols = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=0) + + # Find boundaries + y_min, y_max = torch.where(rows)[0][[0, -1]] + x_min, x_max = torch.where(cols)[0][[0, -1]] + + # Crop image and mask + crop = image[b:b+1, y_min:y_max+1, x_min:x_max+1, :] + crops.append(crop) + + # Stack results back together + cropped_images = torch.cat(crops, dim=0) + + return (cropped_images, ) + + + +class ImageUncropByMask: + + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "destination": ("IMAGE",), + "source": ("IMAGE",), + "mask": ("MASK",), + "bbox": ("BBOX",), + }, + } + + CATEGORY = "KJNodes/image" + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("image",) + FUNCTION = "uncrop" + + def uncrop(self, destination, source, mask, bbox=None): + + output_list = [] + + B, H, W, C = destination.shape + + for i in range(source.shape[0]): + x0, y0, x1, y1 = bbox[i] + bbox_height = y1 - y0 + bbox_width = x1 - x0 + + # Resize source image to match the bounding box dimensions + #resized_source = F.interpolate(source[i].unsqueeze(0).movedim(-1, 1), size=(bbox_height, bbox_width), mode='bilinear', align_corners=False) + resized_source = common_upscale(source[i].unsqueeze(0).movedim(-1, 1), bbox_width, bbox_height, "lanczos", "disabled") + resized_source = resized_source.movedim(1, -1).squeeze(0) + + # Resize mask to match the bounding box dimensions + resized_mask = common_upscale(mask[i].unsqueeze(0).unsqueeze(0), bbox_width, bbox_height, "bilinear", "disabled") + resized_mask = resized_mask.squeeze(0).squeeze(0) + + # Calculate padding values + pad_left = x0 + pad_right = W - x1 + pad_top = y0 + pad_bottom = H - y1 + + # Pad the resized source image and mask to fit the destination dimensions + padded_source = F.pad(resized_source, pad=(0, 0, pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0) + padded_mask = F.pad(resized_mask, pad=(pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0) + + # Ensure the padded mask has the correct shape + padded_mask = padded_mask.unsqueeze(2).expand(-1, -1, destination[i].shape[2]) + # Ensure the padded source has the correct shape + padded_source = padded_source.unsqueeze(2).expand(-1, -1, -1, destination[i].shape[2]).squeeze(2) + + # Combine the destination and padded source images using the mask + result = destination[i] * (1.0 - padded_mask) + padded_source * padded_mask + + output_list.append(result) + + + return (torch.stack(output_list),) + +class ImageCropByMaskBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "masks": ("MASK", ), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), + "padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1, }), + "preserve_size": ("BOOLEAN", {"default": False}), + "bg_color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", ) + RETURN_NAMES = ("images", "masks",) + FUNCTION = "crop" + CATEGORY = "KJNodes/image" + DESCRIPTION = "Crops the input images based on the provided masks." + + def crop(self, image, masks, width, height, bg_color, padding, preserve_size): + B, H, W, C = image.shape + BM, HM, WM = masks.shape + mask_count = BM + if HM != H or WM != W: + masks = F.interpolate(masks.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1) + print(masks.shape) + output_images = [] + output_masks = [] + + bg_color = [int(x.strip())/255.0 for x in bg_color.split(",")] + + # For each mask + for i in range(mask_count): + curr_mask = masks[i] + + # Find bounds + y_indices, x_indices = torch.nonzero(curr_mask, as_tuple=True) + if len(y_indices) == 0 or len(x_indices) == 0: + continue + + # Get exact bounds with padding + min_y = max(0, y_indices.min().item() - padding) + max_y = min(H, y_indices.max().item() + 1 + padding) + min_x = max(0, x_indices.min().item() - padding) + max_x = min(W, x_indices.max().item() + 1 + padding) + + # Ensure mask has correct shape for multiplication + curr_mask = curr_mask.unsqueeze(-1).expand(-1, -1, C) + + # Crop image and mask together + cropped_img = image[0, min_y:max_y, min_x:max_x, :] + cropped_mask = curr_mask[min_y:max_y, min_x:max_x, :] + + crop_h, crop_w = cropped_img.shape[0:2] + new_w = crop_w + new_h = crop_h + + if not preserve_size or crop_w > width or crop_h > height: + scale = min(width/crop_w, height/crop_h) + new_w = int(crop_w * scale) + new_h = int(crop_h * scale) + + # Resize RGB + resized_img = common_upscale(cropped_img.permute(2,0,1).unsqueeze(0), new_w, new_h, "lanczos", "disabled").squeeze(0).permute(1,2,0) + resized_mask = torch.nn.functional.interpolate( + cropped_mask.permute(2,0,1).unsqueeze(0), + size=(new_h, new_w), + mode='nearest' + ).squeeze(0).permute(1,2,0) + else: + resized_img = cropped_img + resized_mask = cropped_mask + + # Create empty tensors + new_img = torch.zeros((height, width, 3), dtype=image.dtype) + new_mask = torch.zeros((height, width), dtype=image.dtype) + + # Pad both + pad_x = (width - new_w) // 2 + pad_y = (height - new_h) // 2 + new_img[pad_y:pad_y+new_h, pad_x:pad_x+new_w, :] = resized_img + if len(resized_mask.shape) == 3: + resized_mask = resized_mask[:,:,0] # Take first channel if 3D + new_mask[pad_y:pad_y+new_h, pad_x:pad_x+new_w] = resized_mask + + output_images.append(new_img) + output_masks.append(new_mask) + + if not output_images: + return (torch.zeros((0, height, width, 3), dtype=image.dtype),) + + out_rgb = torch.stack(output_images, dim=0) + out_masks = torch.stack(output_masks, dim=0) + + # Apply mask to RGB + mask_expanded = out_masks.unsqueeze(-1).expand(-1, -1, -1, 3) + background_color = torch.tensor(bg_color, dtype=torch.float32, device=image.device) + out_rgb = out_rgb * mask_expanded + background_color * (1 - mask_expanded) + + return (out_rgb, out_masks) + +class ImagePadKJ: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", ), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "extra_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), + "pad_mode": (["edge", "color"],), + "color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}), + }, + "optional": { + "mask": ("MASK", ), + "target_width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}), + "target_height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", ) + RETURN_NAMES = ("images", "masks",) + FUNCTION = "pad" + CATEGORY = "KJNodes/image" + DESCRIPTION = "Pad the input image and optionally mask with the specified padding." + + def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None, target_height=None): + B, H, W, C = image.shape + + # Resize masks to image dimensions if necessary + if mask is not None: + BM, HM, WM = mask.shape + if HM != H or WM != W: + mask = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1) + + # Parse background color + bg_color = [int(x.strip())/255.0 for x in color.split(",")] + if len(bg_color) == 1: + bg_color = bg_color * 3 # Grayscale to RGB + bg_color = torch.tensor(bg_color, dtype=image.dtype, device=image.device) + + # Calculate padding sizes with extra padding + if target_width is not None and target_height is not None: + if extra_padding > 0: + image = common_upscale(image.movedim(-1, 1), W - extra_padding, H - extra_padding, "lanczos", "disabled").movedim(1, -1) + B, H, W, C = image.shape + + padded_width = target_width + padded_height = target_height + pad_left = (padded_width - W) // 2 + pad_right = padded_width - W - pad_left + pad_top = (padded_height - H) // 2 + pad_bottom = padded_height - H - pad_top + else: + pad_left = left + extra_padding + pad_right = right + extra_padding + pad_top = top + extra_padding + pad_bottom = bottom + extra_padding + + padded_width = W + pad_left + pad_right + padded_height = H + pad_top + pad_bottom + out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device) + + # Fill padded areas + for b in range(B): + if pad_mode == "edge": + # Pad with edge color + # Define edge pixels + top_edge = image[b, 0, :, :] + bottom_edge = image[b, H-1, :, :] + left_edge = image[b, :, 0, :] + right_edge = image[b, :, W-1, :] + + # Fill borders with edge colors + out_image[b, :pad_top, :, :] = top_edge.mean(dim=0) + out_image[b, pad_top+H:, :, :] = bottom_edge.mean(dim=0) + out_image[b, :, :pad_left, :] = left_edge.mean(dim=0) + out_image[b, :, pad_left+W:, :] = right_edge.mean(dim=0) + out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b] + else: + # Pad with specified background color + out_image[b, :, :, :] = bg_color.unsqueeze(0).unsqueeze(0) # Expand for H and W dimensions + out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b] + + + if mask is not None: + out_masks = torch.nn.functional.pad( + mask, + (pad_left, pad_right, pad_top, pad_bottom), + mode='replicate' + ) + else: + out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device) + for m in range(B): + out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0 + + return (out_image, out_masks) diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/intrinsic_lora_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/intrinsic_lora_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..c8f125363836cc7721b4b61d100702594522d389 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/intrinsic_lora_nodes.py @@ -0,0 +1,115 @@ +import folder_paths +import os +import torch +import torch.nn.functional as F +from comfy.utils import ProgressBar, load_torch_file +import comfy.sample +from nodes import CLIPTextEncode + +script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +folder_paths.add_model_folder_path("intrinsic_loras", os.path.join(script_directory, "intrinsic_loras")) + +class Intrinsic_lora_sampling: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (folder_paths.get_filename_list("intrinsic_loras"), ), + "task": ( + [ + 'depth map', + 'surface normals', + 'albedo', + 'shading', + ], + { + "default": 'depth map' + }), + "text": ("STRING", {"multiline": True, "default": ""}), + "clip": ("CLIP", ), + "vae": ("VAE", ), + "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), + }, + "optional": { + "image": ("IMAGE",), + "optional_latent": ("LATENT",), + }, + } + + RETURN_TYPES = ("IMAGE", "LATENT",) + FUNCTION = "onestepsample" + CATEGORY = "KJNodes" + DESCRIPTION = """ +Sampler to use the intrinsic loras: +https://github.com/duxiaodan/intrinsic-lora +These LoRAs are tiny and thus included +with this node pack. +""" + + def onestepsample(self, model, lora_name, clip, vae, text, task, per_batch, image=None, optional_latent=None): + pbar = ProgressBar(3) + + if optional_latent is None: + image_list = [] + for start_idx in range(0, image.shape[0], per_batch): + sub_pixels = vae.vae_encode_crop_pixels(image[start_idx:start_idx+per_batch]) + image_list.append(vae.encode(sub_pixels[:,:,:,:3])) + sample = torch.cat(image_list, dim=0) + else: + sample = optional_latent["samples"] + noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu") + prompt = task + "," + text + positive, = CLIPTextEncode.encode(self, clip, prompt) + negative = positive #negative shouldn't do anything in this scenario + + pbar.update(1) + + #custom model sampling to pass latent through as it is + class X0_PassThrough(comfy.model_sampling.EPS): + def calculate_denoised(self, sigma, model_output, model_input): + return model_output + def calculate_input(self, sigma, noise): + return noise + sampling_base = comfy.model_sampling.ModelSamplingDiscrete + sampling_type = X0_PassThrough + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + model_sampling = ModelSamplingAdvanced(model.model.model_config) + + #load lora + model_clone = model.clone() + lora_path = folder_paths.get_full_path("intrinsic_loras", lora_name) + lora = load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_clone_with_lora = comfy.sd.load_lora_for_models(model_clone, None, lora, 1.0, 0)[0] + + model_clone_with_lora.add_object_patch("model_sampling", model_sampling) + + samples = {"samples": comfy.sample.sample(model_clone_with_lora, noise, 1, 1.0, "euler", "simple", positive, negative, sample, + denoise=1.0, disable_noise=True, start_step=0, last_step=1, + force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)} + pbar.update(1) + + decoded = [] + for start_idx in range(0, samples["samples"].shape[0], per_batch): + decoded.append(vae.decode(samples["samples"][start_idx:start_idx+per_batch])) + image_out = torch.cat(decoded, dim=0) + + pbar.update(1) + + if task == 'depth map': + imax = image_out.max() + imin = image_out.min() + image_out = (image_out-imin)/(imax-imin) + image_out = torch.max(image_out, dim=3, keepdim=True)[0].repeat(1, 1, 1, 3) + elif task == 'surface normals': + image_out = F.normalize(image_out * 2 - 1, dim=3) / 2 + 0.5 + image_out = 1.0 - image_out + else: + image_out = image_out.clamp(-1.,1.) + + return (image_out, samples,) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/mask_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/mask_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..ba374bf891979289ae17ca308d0125db62d14212 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/mask_nodes.py @@ -0,0 +1,1421 @@ +import torch +import torch.nn.functional as F +from torchvision.transforms import functional as TF +from PIL import Image, ImageDraw, ImageFilter, ImageFont +import scipy.ndimage +import numpy as np +from contextlib import nullcontext +import os + +from comfy import model_management +from comfy.utils import ProgressBar +from comfy.utils import common_upscale +from nodes import MAX_RESOLUTION + +import folder_paths + +from ..utility.utility import tensor2pil, pil2tensor + +script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + +class BatchCLIPSeg: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + + return {"required": + { + "images": ("IMAGE",), + "text": ("STRING", {"multiline": False}), + "threshold": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 10.0, "step": 0.001}), + "binary_mask": ("BOOLEAN", {"default": True}), + "combine_mask": ("BOOLEAN", {"default": False}), + "use_cuda": ("BOOLEAN", {"default": True}), + }, + "optional": + { + "blur_sigma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "opt_model": ("CLIPSEGMODEL", ), + "prev_mask": ("MASK", {"default": None}), + "image_bg_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "invert": ("BOOLEAN", {"default": False}), + } + } + + CATEGORY = "KJNodes/masking" + RETURN_TYPES = ("MASK", "IMAGE", ) + RETURN_NAMES = ("Mask", "Image", ) + FUNCTION = "segment_image" + DESCRIPTION = """ +Segments an image or batch of images using CLIPSeg. +""" + + def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda, blur_sigma=0.0, opt_model=None, prev_mask=None, invert= False, image_bg_level=0.5): + from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation + import torchvision.transforms as transforms + offload_device = model_management.unet_offload_device() + device = model_management.get_torch_device() + if not use_cuda: + device = torch.device("cpu") + dtype = model_management.unet_dtype() + + if opt_model is None: + checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', 'clipseg-rd64-refined-fp16') + if not hasattr(self, "model"): + try: + if not os.path.exists(checkpoint_path): + from huggingface_hub import snapshot_download + snapshot_download(repo_id="Kijai/clipseg-rd64-refined-fp16", local_dir=checkpoint_path, local_dir_use_symlinks=False) + self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) + except: + checkpoint_path = "CIDAS/clipseg-rd64-refined" + self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) + processor = CLIPSegProcessor.from_pretrained(checkpoint_path) + + else: + self.model = opt_model['model'] + processor = opt_model['processor'] + + self.model.to(dtype).to(device) + + B, H, W, C = images.shape + images = images.to(device) + + autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device) + with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + + PIL_images = [Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) for image in images ] + prompt = [text] * len(images) + input_prc = processor(text=prompt, images=PIL_images, return_tensors="pt") + + for key in input_prc: + input_prc[key] = input_prc[key].to(device) + outputs = self.model(**input_prc) + + mask_tensor = torch.sigmoid(outputs.logits) + mask_tensor = (mask_tensor - mask_tensor.min()) / (mask_tensor.max() - mask_tensor.min()) + mask_tensor = torch.where(mask_tensor > (threshold), mask_tensor, torch.tensor(0, dtype=torch.float)) + print(mask_tensor.shape) + if len(mask_tensor.shape) == 2: + mask_tensor = mask_tensor.unsqueeze(0) + mask_tensor = F.interpolate(mask_tensor.unsqueeze(1), size=(H, W), mode='nearest') + mask_tensor = mask_tensor.squeeze(1) + + self.model.to(offload_device) + + if binary_mask: + mask_tensor = (mask_tensor > 0).float() + if blur_sigma > 0: + kernel_size = int(6 * int(blur_sigma) + 1) + blur = transforms.GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma)) + mask_tensor = blur(mask_tensor) + + if combine_mask: + mask_tensor = torch.max(mask_tensor, dim=0)[0] + mask_tensor = mask_tensor.unsqueeze(0).repeat(len(images),1,1) + + del outputs + model_management.soft_empty_cache() + + if prev_mask is not None: + if prev_mask.shape != mask_tensor.shape: + prev_mask = F.interpolate(prev_mask.unsqueeze(1), size=(H, W), mode='nearest') + mask_tensor = mask_tensor + prev_mask.to(device) + torch.clamp(mask_tensor, min=0.0, max=1.0) + + if invert: + mask_tensor = 1 - mask_tensor + + image_tensor = images * mask_tensor.unsqueeze(-1) + (1 - mask_tensor.unsqueeze(-1)) * image_bg_level + image_tensor = torch.clamp(image_tensor, min=0.0, max=1.0).cpu().float() + + mask_tensor = mask_tensor.cpu().float() + + return mask_tensor, image_tensor, + +class DownloadAndLoadCLIPSeg: + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + + return {"required": + { + "model": ( + [ 'Kijai/clipseg-rd64-refined-fp16', + 'CIDAS/clipseg-rd64-refined', + ], + ), + }, + } + + CATEGORY = "KJNodes/masking" + RETURN_TYPES = ("CLIPSEGMODEL",) + RETURN_NAMES = ("clipseg_model",) + FUNCTION = "segment_image" + DESCRIPTION = """ +Downloads and loads CLIPSeg model with huggingface_hub, +to ComfyUI/models/clip_seg +""" + + def segment_image(self, model): + from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation + checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', os.path.basename(model)) + if not hasattr(self, "model"): + if not os.path.exists(checkpoint_path): + from huggingface_hub import snapshot_download + snapshot_download(repo_id=model, local_dir=checkpoint_path, local_dir_use_symlinks=False) + self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) + + processor = CLIPSegProcessor.from_pretrained(checkpoint_path) + + clipseg_model = {} + clipseg_model['model'] = self.model + clipseg_model['processor'] = processor + + return clipseg_model, + +class CreateTextMask: + + RETURN_TYPES = ("IMAGE", "MASK",) + FUNCTION = "createtextmask" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +Creates a text image and mask. +Looks for fonts from this folder: +ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts + +If start_rotation and/or end_rotation are different values, +creates animation between them. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "invert": ("BOOLEAN", {"default": False}), + "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), + "text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + "text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + "font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}), + "font_color": ("STRING", {"default": "white"}), + "text": ("STRING", {"default": "HELLO!", "multiline": True}), + "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), + "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}), + "end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}), + }, + } + + def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation): + # Define the number of images in the batch + batch_size = frames + out = [] + masks = [] + rotation = start_rotation + if start_rotation != end_rotation: + rotation_increment = (end_rotation - start_rotation) / (batch_size - 1) + + font_path = folder_paths.get_full_path("kjnodes_fonts", font) + # Generate the text + for i in range(batch_size): + image = Image.new("RGB", (width, height), "black") + draw = ImageDraw.Draw(image) + font = ImageFont.truetype(font_path, font_size) + + # Split the text into words + words = text.split() + + # Initialize variables for line creation + lines = [] + current_line = [] + current_line_width = 0 + try: #new pillow + # Iterate through words to create lines + for word in words: + word_width = font.getbbox(word)[2] + if current_line_width + word_width <= width - 2 * text_x: + current_line.append(word) + current_line_width += word_width + font.getbbox(" ")[2] # Add space width + else: + lines.append(" ".join(current_line)) + current_line = [word] + current_line_width = word_width + except: #old pillow + for word in words: + word_width = font.getsize(word)[0] + if current_line_width + word_width <= width - 2 * text_x: + current_line.append(word) + current_line_width += word_width + font.getsize(" ")[0] # Add space width + else: + lines.append(" ".join(current_line)) + current_line = [word] + current_line_width = word_width + + # Add the last line if it's not empty + if current_line: + lines.append(" ".join(current_line)) + + # Draw each line of text separately + y_offset = text_y + for line in lines: + text_width = font.getlength(line) + text_height = font_size + text_center_x = text_x + text_width / 2 + text_center_y = y_offset + text_height / 2 + try: + draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga']) + except: + draw.text((text_x, y_offset), line, font=font, fill=font_color) + y_offset += text_height # Move to the next line + + if start_rotation != end_rotation: + image = image.rotate(rotation, center=(text_center_x, text_center_y)) + rotation += rotation_increment + + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + mask = image[:, :, :, 0] + masks.append(mask) + out.append(image) + + if invert: + return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),) + return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) + +class ColorToMask: + + RETURN_TYPES = ("MASK",) + FUNCTION = "clip" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Converts chosen RGB value to a mask. +With batch inputs, the **per_batch** +controls the number of images processed at once. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE",), + "invert": ("BOOLEAN", {"default": False}), + "red": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}), + "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), + }, + } + + def clip(self, images, red, green, blue, threshold, invert, per_batch): + + color = torch.tensor([red, green, blue], dtype=torch.uint8) + black = torch.tensor([0, 0, 0], dtype=torch.uint8) + white = torch.tensor([255, 255, 255], dtype=torch.uint8) + + if invert: + black, white = white, black + + steps = images.shape[0] + pbar = ProgressBar(steps) + tensors_out = [] + + for start_idx in range(0, images.shape[0], per_batch): + + # Calculate color distances + color_distances = torch.norm(images[start_idx:start_idx+per_batch] * 255 - color, dim=-1) + + # Create a mask based on the threshold + mask = color_distances <= threshold + + # Apply the mask to create new images + mask_out = torch.where(mask.unsqueeze(-1), white, black).float() + mask_out = mask_out.mean(dim=-1) + + tensors_out.append(mask_out.cpu()) + batch_count = mask_out.shape[0] + pbar.update(batch_count) + + tensors_out = torch.cat(tensors_out, dim=0) + tensors_out = torch.clamp(tensors_out, min=0.0, max=1.0) + return tensors_out, + +class CreateFluidMask: + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "createfluidmask" + CATEGORY = "KJNodes/masking/generate" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "invert": ("BOOLEAN", {"default": False}), + "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), + "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + "inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}), + "inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}), + "inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}), + "inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}), + "inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}), + }, + } + #using code from https://github.com/GregTJ/stable-fluids + def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration): + from ..utility.fluid import Fluid + try: + from scipy.special import erf + except: + from scipy.spatial import erf + out = [] + masks = [] + RESOLUTION = width, height + DURATION = frames + + INFLOW_PADDING = inflow_padding + INFLOW_DURATION = inflow_duration + INFLOW_RADIUS = inflow_radius + INFLOW_VELOCITY = inflow_velocity + INFLOW_COUNT = inflow_count + + print('Generating fluid solver, this may take some time.') + fluid = Fluid(RESOLUTION, 'dye') + + center = np.floor_divide(RESOLUTION, 2) + r = np.min(center) - INFLOW_PADDING + + points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False) + points = tuple(np.array((np.cos(p), np.sin(p))) for p in points) + normals = tuple(-p for p in points) + points = tuple(r * p + center for p in points) + + inflow_velocity = np.zeros_like(fluid.velocity) + inflow_dye = np.zeros(fluid.shape) + for p, n in zip(points, normals): + mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS + inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY + inflow_dye[mask] = 1 + + + for f in range(DURATION): + print(f'Computing frame {f + 1} of {DURATION}.') + if f <= INFLOW_DURATION: + fluid.velocity += inflow_velocity + fluid.dye += inflow_dye + + curl = fluid.step()[1] + # Using the error function to make the contrast a bit higher. + # Any other sigmoid function e.g. smoothstep would work. + curl = (erf(curl * 2) + 1) / 4 + + color = np.dstack((curl, np.ones(fluid.shape), fluid.dye)) + color = (np.clip(color, 0, 1) * 255).astype('uint8') + image = np.array(color).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + mask = image[:, :, :, 0] + masks.append(mask) + out.append(image) + + if invert: + return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),) + return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) + +class CreateAudioMask: + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "createaudiomask" + CATEGORY = "KJNodes/deprecated" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "invert": ("BOOLEAN", {"default": False}), + "frames": ("INT", {"default": 16,"min": 1, "max": 255, "step": 1}), + "scale": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 2.0, "step": 0.01}), + "audio_path": ("STRING", {"default": "audio.wav"}), + "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + }, + } + + def createaudiomask(self, frames, width, height, invert, audio_path, scale): + try: + import librosa + except ImportError: + raise Exception("Can not import librosa. Install it with 'pip install librosa'") + batch_size = frames + out = [] + masks = [] + if audio_path == "audio.wav": #I don't know why relative path won't work otherwise... + audio_path = os.path.join(script_directory, audio_path) + audio, sr = librosa.load(audio_path) + spectrogram = np.abs(librosa.stft(audio)) + + for i in range(batch_size): + image = Image.new("RGB", (width, height), "black") + draw = ImageDraw.Draw(image) + frame = spectrogram[:, i] + circle_radius = int(height * np.mean(frame)) + circle_radius *= scale + circle_center = (width // 2, height // 2) # Calculate the center of the image + + draw.ellipse([(circle_center[0] - circle_radius, circle_center[1] - circle_radius), + (circle_center[0] + circle_radius, circle_center[1] + circle_radius)], + fill='white') + + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + mask = image[:, :, :, 0] + masks.append(mask) + out.append(image) + + if invert: + return (1.0 - torch.cat(out, dim=0),) + return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) + +class CreateGradientMask: + + RETURN_TYPES = ("MASK",) + FUNCTION = "createmask" + CATEGORY = "KJNodes/masking/generate" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "invert": ("BOOLEAN", {"default": False}), + "frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), + "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + }, + } + def createmask(self, frames, width, height, invert): + # Define the number of images in the batch + batch_size = frames + out = [] + # Create an empty array to store the image batch + image_batch = np.zeros((batch_size, height, width), dtype=np.float32) + # Generate the black to white gradient for each image + for i in range(batch_size): + gradient = np.linspace(1.0, 0.0, width, dtype=np.float32) + time = i / frames # Calculate the time variable + offset_gradient = gradient - time # Offset the gradient values based on time + image_batch[i] = offset_gradient.reshape(1, -1) + output = torch.from_numpy(image_batch) + mask = output + out.append(mask) + if invert: + return (1.0 - torch.cat(out, dim=0),) + return (torch.cat(out, dim=0),) + +class CreateFadeMask: + + RETURN_TYPES = ("MASK",) + FUNCTION = "createfademask" + CATEGORY = "KJNodes/deprecated" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "invert": ("BOOLEAN", {"default": False}), + "frames": ("INT", {"default": 2,"min": 2, "max": 10000, "step": 1}), + "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), + "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), + "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), + "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), + "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), + }, + } + + def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame): + def ease_in(t): + return t * t + + def ease_out(t): + return 1 - (1 - t) * (1 - t) + + def ease_in_out(t): + return 3 * t * t - 2 * t * t * t + + batch_size = frames + out = [] + image_batch = np.zeros((batch_size, height, width), dtype=np.float32) + + if midpoint_frame == 0: + midpoint_frame = batch_size // 2 + + for i in range(batch_size): + if i <= midpoint_frame: + t = i / midpoint_frame + if interpolation == "ease_in": + t = ease_in(t) + elif interpolation == "ease_out": + t = ease_out(t) + elif interpolation == "ease_in_out": + t = ease_in_out(t) + color = start_level - t * (start_level - midpoint_level) + else: + t = (i - midpoint_frame) / (batch_size - midpoint_frame) + if interpolation == "ease_in": + t = ease_in(t) + elif interpolation == "ease_out": + t = ease_out(t) + elif interpolation == "ease_in_out": + t = ease_in_out(t) + color = midpoint_level - t * (midpoint_level - end_level) + + color = np.clip(color, 0, 255) + image = np.full((height, width), color, dtype=np.float32) + image_batch[i] = image + + output = torch.from_numpy(image_batch) + mask = output + out.append(mask) + + if invert: + return (1.0 - torch.cat(out, dim=0),) + return (torch.cat(out, dim=0),) + +class CreateFadeMaskAdvanced: + + RETURN_TYPES = ("MASK",) + FUNCTION = "createfademask" + CATEGORY = "KJNodes/masking/generate" + DESCRIPTION = """ +Create a batch of masks interpolated between given frames and values. +Uses same syntax as Fizz' BatchValueSchedule. +First value is the frame index (not that this starts from 0, not 1) +and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0 + +For example the default values: +0:(0.0) +7:(1.0) +15:(0.0) + +Would create a mask batch fo 16 frames, starting from black, +interpolating with the chosen curve to fully white at the 8th frame, +and interpolating from that to fully black at the 16th frame. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), + "invert": ("BOOLEAN", {"default": False}), + "frames": ("INT", {"default": 16,"min": 2, "max": 10000, "step": 1}), + "width": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "none", "default_to_black"],), + }, + } + + def createfademask(self, frames, width, height, invert, points_string, interpolation): + def ease_in(t): + return t * t + + def ease_out(t): + return 1 - (1 - t) * (1 - t) + + def ease_in_out(t): + return 3 * t * t - 2 * t * t * t + + # Parse the input string into a list of tuples + points = [] + points_string = points_string.rstrip(',\n') + for point_str in points_string.split(','): + frame_str, color_str = point_str.split(':') + frame = int(frame_str.strip()) + color = float(color_str.strip()[1:-1]) # Remove parentheses around color + points.append((frame, color)) + + # Check if the last frame is already in the points + if (interpolation != "default_to_black") and (len(points) == 0 or points[-1][0] != frames - 1): + # If not, add it with the color of the last specified frame + points.append((frames - 1, points[-1][1] if points else 0)) + + # Sort the points by frame number + points.sort(key=lambda x: x[0]) + + batch_size = frames + out = [] + image_batch = np.zeros((batch_size, height, width), dtype=np.float32) + + # Index of the next point to interpolate towards + next_point = 1 + + for i in range(batch_size): + while next_point < len(points) and i > points[next_point][0]: + next_point += 1 + + # Interpolate between the previous point and the next point + prev_point = next_point - 1 + + if interpolation == "none": + exact_match = False + for p in points: + if p[0] == i: # Exact frame match + color = p[1] + exact_match = True + break + if not exact_match: + color = points[prev_point][1] + + elif interpolation == "default_to_black": + exact_match = False + for p in points: + if p[0] == i: # Exact frame match + color = p[1] + exact_match = True + break + if not exact_match: + color = 0 + else: + t = (i - points[prev_point][0]) / (points[next_point][0] - points[prev_point][0]) + if interpolation == "ease_in": + t = ease_in(t) + elif interpolation == "ease_out": + t = ease_out(t) + elif interpolation == "ease_in_out": + t = ease_in_out(t) + elif interpolation == "linear": + pass # No need to modify `t` for linear interpolation + + color = points[prev_point][1] - t * (points[prev_point][1] - points[next_point][1]) + + color = np.clip(color, 0, 255) + image = np.full((height, width), color, dtype=np.float32) + image_batch[i] = image + + output = torch.from_numpy(image_batch) + mask = output + out.append(mask) + + if invert: + return (1.0 - torch.cat(out, dim=0),) + return (torch.cat(out, dim=0),) + +class CreateMagicMask: + + RETURN_TYPES = ("MASK", "MASK",) + RETURN_NAMES = ("mask", "mask_inverted",) + FUNCTION = "createmagicmask" + CATEGORY = "KJNodes/masking/generate" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}), + "depth": ("INT", {"default": 12,"min": 1, "max": 500, "step": 1}), + "distortion": ("FLOAT", {"default": 1.5,"min": 0.0, "max": 100.0, "step": 0.01}), + "seed": ("INT", {"default": 123,"min": 0, "max": 99999999, "step": 1}), + "transitions": ("INT", {"default": 1,"min": 1, "max": 20, "step": 1}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + }, + } + + def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height): + from ..utility.magictex import coordinate_grid, random_transform, magic + import matplotlib.pyplot as plt + rng = np.random.default_rng(seed) + out = [] + coords = coordinate_grid((frame_width, frame_height)) + + # Calculate the number of frames for each transition + frames_per_transition = frames // transitions + + # Generate a base set of parameters + base_params = { + "coords": random_transform(coords, rng), + "depth": depth, + "distortion": distortion, + } + for t in range(transitions): + # Generate a second set of parameters that is at most max_diff away from the base parameters + params1 = base_params.copy() + params2 = base_params.copy() + + params1['coords'] = random_transform(coords, rng) + params2['coords'] = random_transform(coords, rng) + + for i in range(frames_per_transition): + # Compute the interpolation factor + alpha = i / frames_per_transition + + # Interpolate between the two sets of parameters + params = params1.copy() + params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords'] + + tex = magic(**params) + + dpi = frame_width / 10 + fig = plt.figure(figsize=(10, 10), dpi=dpi) + + ax = fig.add_subplot(111) + plt.subplots_adjust(left=0, right=1, bottom=0, top=1) + + ax.get_yaxis().set_ticks([]) + ax.get_xaxis().set_ticks([]) + ax.imshow(tex, aspect='auto') + + fig.canvas.draw() + img = np.array(fig.canvas.renderer._renderer) + + plt.close(fig) + + pil_img = Image.fromarray(img).convert("L") + mask = torch.tensor(np.array(pil_img)) / 255.0 + + out.append(mask) + + return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) + +class CreateShapeMask: + + RETURN_TYPES = ("MASK", "MASK",) + RETURN_NAMES = ("mask", "mask_inverted",) + FUNCTION = "createshapemask" + CATEGORY = "KJNodes/masking/generate" + DESCRIPTION = """ +Creates a mask or batch of masks with the specified shape. +Locations are center locations. +Grow value is the amount to grow the shape on each frame, creating animated masks. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "shape": ( + [ 'circle', + 'square', + 'triangle', + ], + { + "default": 'circle' + }), + "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), + "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), + "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), + "grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), + "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), + }, + } + + def createshapemask(self, frames, frame_width, frame_height, location_x, location_y, shape_width, shape_height, grow, shape): + # Define the number of images in the batch + batch_size = frames + out = [] + color = "white" + for i in range(batch_size): + image = Image.new("RGB", (frame_width, frame_height), "black") + draw = ImageDraw.Draw(image) + + # Calculate the size for this frame and ensure it's not less than 0 + current_width = max(0, shape_width + i*grow) + current_height = max(0, shape_height + i*grow) + + if shape == 'circle' or shape == 'square': + # Define the bounding box for the shape + left_up_point = (location_x - current_width // 2, location_y - current_height // 2) + right_down_point = (location_x + current_width // 2, location_y + current_height // 2) + two_points = [left_up_point, right_down_point] + + if shape == 'circle': + draw.ellipse(two_points, fill=color) + elif shape == 'square': + draw.rectangle(two_points, fill=color) + + elif shape == 'triangle': + # Define the points for the triangle + left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left + right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right + top_point = (location_x, location_y - current_height // 2) # top point + draw.polygon([top_point, left_up_point, right_down_point], fill=color) + + image = pil2tensor(image) + mask = image[:, :, :, 0] + out.append(mask) + outstack = torch.cat(out, dim=0) + return (outstack, 1.0 - outstack,) + +class CreateVoronoiMask: + + RETURN_TYPES = ("MASK", "MASK",) + RETURN_NAMES = ("mask", "mask_inverted",) + FUNCTION = "createvoronoi" + CATEGORY = "KJNodes/masking/generate" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}), + "num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}), + "line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}), + "speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), + "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + }, + } + + def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height): + from scipy.spatial import Voronoi + # Define the number of images in the batch + batch_size = frames + out = [] + + # Calculate aspect ratio + aspect_ratio = frame_width / frame_height + + # Create start and end points for each point, considering the aspect ratio + start_points = np.random.rand(num_points, 2) + start_points[:, 0] *= aspect_ratio + + end_points = np.random.rand(num_points, 2) + end_points[:, 0] *= aspect_ratio + + for i in range(batch_size): + # Interpolate the points' positions based on the current frame + t = (i * speed) / (batch_size - 1) # normalize to [0, 1] over the frames + t = np.clip(t, 0, 1) # ensure t is in [0, 1] + points = (1 - t) * start_points + t * end_points # lerp + + # Adjust points for aspect ratio + points[:, 0] *= aspect_ratio + + vor = Voronoi(points) + + # Create a blank image with a white background + fig, ax = plt.subplots() + plt.subplots_adjust(left=0, right=1, bottom=0, top=1) + ax.set_xlim([0, aspect_ratio]); ax.set_ylim([0, 1]) # adjust x limits + ax.axis('off') + ax.margins(0, 0) + fig.set_size_inches(aspect_ratio * frame_height/100, frame_height/100) # adjust figure size + ax.fill_between([0, 1], [0, 1], color='white') + + # Plot each Voronoi ridge + for simplex in vor.ridge_vertices: + simplex = np.asarray(simplex) + if np.all(simplex >= 0): + plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width) + + fig.canvas.draw() + img = np.array(fig.canvas.renderer._renderer) + + plt.close(fig) + + pil_img = Image.fromarray(img).convert("L") + mask = torch.tensor(np.array(pil_img)) / 255.0 + + out.append(mask) + + return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) + +class GetMaskSizeAndCount: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK",), + }} + + RETURN_TYPES = ("MASK","INT", "INT", "INT",) + RETURN_NAMES = ("mask", "width", "height", "count",) + FUNCTION = "getsize" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Returns the width, height and batch size of the mask, +and passes it through unchanged. + +""" + + def getsize(self, mask): + width = mask.shape[2] + height = mask.shape[1] + count = mask.shape[0] + return {"ui": { + "text": [f"{count}x{width}x{height}"]}, + "result": (mask, width, height, count) + } + +class GrowMaskWithBlur: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), + "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "tapered_corners": ("BOOLEAN", {"default": True}), + "flip_input": ("BOOLEAN", {"default": False}), + "blur_radius": ("FLOAT", { + "default": 0.0, + "min": 0.0, + "max": 100, + "step": 0.1 + }), + "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + "optional": { + "fill_holes": ("BOOLEAN", {"default": False}), + }, + } + + CATEGORY = "KJNodes/masking" + RETURN_TYPES = ("MASK", "MASK",) + RETURN_NAMES = ("mask", "mask_inverted",) + FUNCTION = "expand_mask" + DESCRIPTION = """ +# GrowMaskWithBlur +- mask: Input mask or mask batch +- expand: Expand or contract mask or mask batch by a given amount +- incremental_expandrate: increase expand rate by a given amount per frame +- tapered_corners: use tapered corners +- flip_input: flip input mask +- blur_radius: value higher than 0 will blur the mask +- lerp_alpha: alpha value for interpolation between frames +- decay_factor: decay value for interpolation between frames +- fill_holes: fill holes in the mask (slow)""" + + def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False): + alpha = lerp_alpha + decay = decay_factor + if flip_input: + mask = 1.0 - mask + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).cpu() + out = [] + previous_output = None + current_expand = expand + for m in growmask: + output = m.numpy().astype(np.float32) + for _ in range(abs(round(current_expand))): + if current_expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) + if current_expand < 0: + current_expand -= abs(incremental_expandrate) + else: + current_expand += abs(incremental_expandrate) + if fill_holes: + binary_mask = output > 0 + output = scipy.ndimage.binary_fill_holes(binary_mask) + output = output.astype(np.float32) * 255 + output = torch.from_numpy(output) + if alpha < 1.0 and previous_output is not None: + # Interpolate between the previous and current frame + output = alpha * output + (1 - alpha) * previous_output + if decay < 1.0 and previous_output is not None: + # Add the decayed previous output to the current frame + output += decay * previous_output + output = output / output.max() + previous_output = output + out.append(output) + + if blur_radius != 0: + # Convert the tensor list to PIL images, apply blur, and convert back + for idx, tensor in enumerate(out): + # Convert tensor to PIL image + pil_image = tensor2pil(tensor.cpu().detach())[0] + # Apply Gaussian blur + pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius)) + # Convert back to tensor + out[idx] = pil2tensor(pil_image) + blurred = torch.cat(out, dim=0) + return (blurred, 1.0 - blurred) + else: + return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) + +class MaskBatchMulti: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "mask_1": ("MASK", ), + "mask_2": ("MASK", ), + }, + } + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("masks",) + FUNCTION = "combine" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Creates an image batch from multiple masks. +You can set how many inputs the node has, +with the **inputcount** and clicking update. +""" + + def combine(self, inputcount, **kwargs): + mask = kwargs["mask_1"] + for c in range(1, inputcount): + new_mask = kwargs[f"mask_{c + 1}"] + if mask.shape[1:] != new_mask.shape[1:]: + new_mask = F.interpolate(new_mask.unsqueeze(1), size=(mask.shape[1], mask.shape[2]), mode="bicubic").squeeze(1) + mask = torch.cat((mask, new_mask), dim=0) + return (mask,) + +class OffsetMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }), + "duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }), + "roll": ("BOOLEAN", { "default": False }), + "incremental": ("BOOLEAN", { "default": False }), + "padding_mode": ( + [ + 'empty', + 'border', + 'reflection', + + ], { + "default": 'empty' + }), + } + } + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("mask",) + FUNCTION = "offset" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Offsets the mask by the specified amount. + - mask: Input mask or mask batch + - x: Horizontal offset + - y: Vertical offset + - angle: Angle in degrees + - roll: roll edge wrapping + - duplication_factor: Number of times to duplicate the mask to form a batch + - border padding_mode: Padding mode for the mask +""" + + def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"): + # Create duplicates of the mask batch + mask = mask.repeat(duplication_factor, 1, 1).clone() + + batch_size, height, width = mask.shape + + if angle != 0 and incremental: + for i in range(batch_size): + rotation_angle = angle * (i+1) + mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0) + elif angle > 0: + for i in range(batch_size): + mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0) + + if roll: + if incremental: + for i in range(batch_size): + shift_x = min(x*(i+1), width-1) + shift_y = min(y*(i+1), height-1) + if shift_x != 0: + mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1) + if shift_y != 0: + mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0) + else: + shift_x = min(x, width-1) + shift_y = min(y, height-1) + if shift_x != 0: + mask = torch.roll(mask, shifts=shift_x, dims=2) + if shift_y != 0: + mask = torch.roll(mask, shifts=shift_y, dims=1) + else: + + for i in range(batch_size): + if incremental: + temp_x = min(x * (i+1), width-1) + temp_y = min(y * (i+1), height-1) + else: + temp_x = min(x, width-1) + temp_y = min(y, height-1) + if temp_x > 0: + if padding_mode == 'empty': + mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1) + elif padding_mode in ['replicate', 'reflect']: + mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode) + elif temp_x < 0: + if padding_mode == 'empty': + mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1) + elif padding_mode in ['replicate', 'reflect']: + mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode) + + if temp_y > 0: + if padding_mode == 'empty': + mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0) + elif padding_mode in ['replicate', 'reflect']: + mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode) + elif temp_y < 0: + if padding_mode == 'empty': + mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0) + elif padding_mode in ['replicate', 'reflect']: + mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode) + + return mask, + +class RoundMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "mask": ("MASK",), + }} + + RETURN_TYPES = ("MASK",) + FUNCTION = "round" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Rounds the mask or batch of masks to a binary mask. +RoundMask example + +""" + + def round(self, mask): + mask = mask.round() + return (mask,) + +class ResizeMask: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), + "keep_proportions": ("BOOLEAN", { "default": False }), + "upscale_method": (s.upscale_methods,), + "crop": (["disabled","center"],), + } + } + + RETURN_TYPES = ("MASK", "INT", "INT",) + RETURN_NAMES = ("mask", "width", "height",) + FUNCTION = "resize" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Resizes the mask or batch of masks to the specified width and height. +""" + + def resize(self, mask, width, height, keep_proportions, upscale_method,crop): + if keep_proportions: + _, oh, ow = mask.shape + width = ow if width == 0 else width + height = oh if height == 0 else height + ratio = min(width / ow, height / oh) + width = round(ow*ratio) + height = round(oh*ratio) + + if upscale_method == "lanczos": + out_mask = common_upscale(mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop=crop).movedim(1,-1)[:, :, :, 0] + else: + out_mask = common_upscale(mask.unsqueeze(1), width, height, upscale_method, crop=crop).squeeze(1) + + return(out_mask, out_mask.shape[2], out_mask.shape[1],) + +class RemapMaskRange: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}), + "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("mask",) + FUNCTION = "remap" + CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Sets new min and max values for the mask. +""" + + def remap(self, mask, min, max): + + # Find the maximum value in the mask + mask_max = torch.max(mask) + + # If the maximum mask value is zero, avoid division by zero by setting it to 1 + mask_max = mask_max if mask_max > 0 else 1 + + # Scale the mask values to the new range defined by min and max + # The highest pixel value in the mask will be scaled to max + scaled_mask = (mask / mask_max) * (max - min) + min + + # Clamp the values to ensure they are within [0.0, 1.0] + scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0) + + return (scaled_mask, ) + + +def get_mask_polygon(self, mask_np): + import cv2 + """Helper function to get polygon points from mask""" + # Find contours + contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if not contours: + return None + + # Get the largest contour + largest_contour = max(contours, key=cv2.contourArea) + + # Approximate polygon + epsilon = 0.02 * cv2.arcLength(largest_contour, True) + polygon = cv2.approxPolyDP(largest_contour, epsilon, True) + + return polygon.squeeze() + +import cv2 +class SeparateMasks: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK", ), + "size_threshold_width" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}), + "size_threshold_height" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}), + "mode": (["convex_polygons", "area"],), + "max_poly_points": ("INT", {"default": 8, "min": 3, "max": 32, "step": 1}), + + }, + } + + RETURN_TYPES = ("MASK",) + RETURN_NAMES = ("mask",) + FUNCTION = "separate" + CATEGORY = "KJNodes/masking" + OUTPUT_NODE = True + DESCRIPTION = "Separates a mask into multiple masks based on the size of the connected components." + + def polygon_to_mask(self, polygon, shape): + mask = np.zeros((shape[0], shape[1]), dtype=np.uint8) # Fixed shape handling + + if len(polygon.shape) == 2: # Check if polygon points are valid + polygon = polygon.astype(np.int32) + cv2.fillPoly(mask, [polygon], 1) + return mask + + def get_mask_polygon(self, mask_np, max_points): + contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return None + + largest_contour = max(contours, key=cv2.contourArea) + hull = cv2.convexHull(largest_contour) + + # Initialize with smaller epsilon for more points + perimeter = cv2.arcLength(hull, True) + epsilon = perimeter * 0.01 # Start smaller + + min_eps = perimeter * 0.001 # Much smaller minimum + max_eps = perimeter * 0.2 # Smaller maximum + + best_approx = None + best_diff = float('inf') + max_iterations = 20 + + #print(f"Target points: {max_points}, Perimeter: {perimeter}") + + for i in range(max_iterations): + curr_eps = (min_eps + max_eps) / 2 + approx = cv2.approxPolyDP(hull, curr_eps, True) + points_diff = len(approx) - max_points + + #print(f"Iteration {i}: points={len(approx)}, eps={curr_eps:.4f}") + + if abs(points_diff) < best_diff: + best_approx = approx + best_diff = abs(points_diff) + + if len(approx) > max_points: + min_eps = curr_eps * 1.1 # More gradual adjustment + elif len(approx) < max_points: + max_eps = curr_eps * 0.9 # More gradual adjustment + else: + return approx.squeeze() + + if abs(max_eps - min_eps) < perimeter * 0.0001: # Relative tolerance + break + + # If we didn't find exact match, return best approximation + return best_approx.squeeze() if best_approx is not None else hull.squeeze() + + def separate(self, mask: torch.Tensor, size_threshold_width: int, size_threshold_height: int, max_poly_points: int, mode: str): + from scipy.ndimage import label, center_of_mass + import numpy as np + + B, H, W = mask.shape + separated = [] + + mask = mask.round() + + for b in range(B): + mask_np = mask[b].cpu().numpy().astype(np.uint8) + structure = np.ones((3, 3), dtype=np.int8) + labeled, ncomponents = label(mask_np, structure=structure) + pbar = ProgressBar(ncomponents) + + for component in range(1, ncomponents + 1): + component_mask_np = (labeled == component).astype(np.uint8) + + rows = np.any(component_mask_np, axis=1) + cols = np.any(component_mask_np, axis=0) + y_min, y_max = np.where(rows)[0][[0, -1]] + x_min, x_max = np.where(cols)[0][[0, -1]] + + width = x_max - x_min + 1 + height = y_max - y_min + 1 + centroid_x = (x_min + x_max) / 2 # Calculate x centroid + print(f"Component {component}: width={width}, height={height}, x_pos={centroid_x}") + + if width >= size_threshold_width and height >= size_threshold_height: + if mode != "area": + polygon = self.get_mask_polygon(component_mask_np, max_poly_points) + if polygon is not None: + poly_mask = self.polygon_to_mask(polygon, (H, W)) + poly_mask = torch.tensor(poly_mask, device=mask.device) + separated.append((centroid_x, poly_mask)) + else: + area_mask = torch.tensor(component_mask_np, device=mask.device) + separated.append((centroid_x, area_mask)) + pbar.update(1) + + if len(separated) > 0: + # Sort by x position and extract only the masks + separated.sort(key=lambda x: x[0]) + separated = [x[1] for x in separated] + out_masks = torch.stack(separated, dim=0) + return out_masks, + else: + return torch.empty((1, 64, 64), device=mask.device), + \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/model_optimization_nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/model_optimization_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..e0b55fe7d31c4ccfe6d9eb4dc0604e1612e0bd4b --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/model_optimization_nodes.py @@ -0,0 +1,1747 @@ +from comfy.ldm.modules import attention as comfy_attention +import logging +import comfy.model_patcher +import comfy.utils +import comfy.sd +import torch +import folder_paths +import comfy.model_management as mm +from comfy.cli_args import args + +sageattn_modes = ["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda", "sageattn_qk_int8_pv_fp8_cuda++"] + +_initialized = False +_original_functions = {} + +if not _initialized: + _original_functions["orig_attention"] = comfy_attention.optimized_attention + _original_functions["original_patch_model"] = comfy.model_patcher.ModelPatcher.patch_model + _original_functions["original_load_lora_for_models"] = comfy.sd.load_lora_for_models + _initialized = True + +class BaseLoaderKJ: + original_linear = None + cublas_patched = False + + @torch.compiler.disable() + def _patch_modules(self, patch_cublaslinear, sage_attention): + from comfy.ops import disable_weight_init, CastWeightBiasOp, cast_bias_weight + + if sage_attention != "disabled": + print("Patching comfy attention to use sageattn") + from sageattention import sageattn + def set_sage_func(sage_attention): + if sage_attention == "auto": + def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): + return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout) + return func + elif sage_attention == "sageattn_qk_int8_pv_fp16_cuda": + from sageattention import sageattn_qk_int8_pv_fp16_cuda + def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): + return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32", tensor_layout=tensor_layout) + return func + elif sage_attention == "sageattn_qk_int8_pv_fp16_triton": + from sageattention import sageattn_qk_int8_pv_fp16_triton + def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): + return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout) + return func + elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda": + from sageattention import sageattn_qk_int8_pv_fp8_cuda + def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): + return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32", tensor_layout=tensor_layout) + return func + elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda++": + from sageattention import sageattn_qk_int8_pv_fp8_cuda + def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): + return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp16", tensor_layout=tensor_layout) + return func + + sage_func = set_sage_func(sage_attention) + + @torch.compiler.disable() + def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): + if skip_reshape: + b, _, _, dim_head = q.shape + tensor_layout="HND" + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head), + (q, k, v), + ) + tensor_layout="NHD" + if mask is not None: + # add a batch dimension if there isn't already one + if mask.ndim == 2: + mask = mask.unsqueeze(0) + # add a heads dimension if there isn't already one + if mask.ndim == 3: + mask = mask.unsqueeze(1) + out = sage_func(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) + if tensor_layout == "HND": + if not skip_output_reshape: + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + else: + if skip_output_reshape: + out = out.transpose(1, 2) + else: + out = out.reshape(b, -1, heads * dim_head) + return out + + comfy_attention.optimized_attention = attention_sage + comfy.ldm.hunyuan_video.model.optimized_attention = attention_sage + comfy.ldm.flux.math.optimized_attention = attention_sage + comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = attention_sage + comfy.ldm.cosmos.blocks.optimized_attention = attention_sage + comfy.ldm.wan.model.optimized_attention = attention_sage + + else: + print("Restoring initial comfy attention") + comfy_attention.optimized_attention = _original_functions.get("orig_attention") + comfy.ldm.hunyuan_video.model.optimized_attention = _original_functions.get("orig_attention") + comfy.ldm.flux.math.optimized_attention = _original_functions.get("orig_attention") + comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = _original_functions.get("orig_attention") + comfy.ldm.cosmos.blocks.optimized_attention = _original_functions.get("orig_attention") + comfy.ldm.wan.model.optimized_attention = _original_functions.get("orig_attention") + + if patch_cublaslinear: + if not BaseLoaderKJ.cublas_patched: + BaseLoaderKJ.original_linear = disable_weight_init.Linear + try: + from cublas_ops import CublasLinear + except ImportError: + raise Exception("Can't import 'torch-cublas-hgemm', install it from here https://github.com/aredden/torch-cublas-hgemm") + + class PatchedLinear(CublasLinear, CastWeightBiasOp): + def reset_parameters(self): + pass + + def forward_comfy_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.comfy_cast_weights: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + disable_weight_init.Linear = PatchedLinear + BaseLoaderKJ.cublas_patched = True + else: + if BaseLoaderKJ.cublas_patched: + disable_weight_init.Linear = BaseLoaderKJ.original_linear + BaseLoaderKJ.cublas_patched = False + +from comfy.patcher_extension import CallbacksMP +class PathchSageAttentionKJ(BaseLoaderKJ): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Global patch comfy attention to use sageattn, once patched to revert back to normal you would need to run this node again with disabled option."}), + }} + + RETURN_TYPES = ("MODEL", ) + FUNCTION = "patch" + DESCRIPTION = "Experimental node for patching attention mode. This doesn't use the model patching system and thus can't be disabled without running the node again with 'disabled' option." + EXPERIMENTAL = True + CATEGORY = "KJNodes/experimental" + + def patch(self, model, sage_attention): + model_clone = model.clone() + @torch.compiler.disable() + def patch_attention_enable(model): + self._patch_modules(False, sage_attention) + @torch.compiler.disable() + def patch_attention_disable(model): + self._patch_modules(False, "disabled") + + model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_attention_enable) + model_clone.add_callback(CallbacksMP.ON_CLEANUP, patch_attention_disable) + + return model_clone, + +class CheckpointLoaderKJ(BaseLoaderKJ): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],), + "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}), + "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}), + "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}), + "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}), + }} + + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "patch" + OUTPUT_NODE = True + DESCRIPTION = "Experimental node for patching torch.nn.Linear with CublasLinear." + EXPERIMENTAL = True + CATEGORY = "KJNodes/experimental" + + def patch(self, ckpt_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation): + DTYPE_MAP = { + "fp8_e4m3fn": torch.float8_e4m3fn, + "fp8_e5m2": torch.float8_e5m2, + "fp16": torch.float16, + "bf16": torch.bfloat16, + "fp32": torch.float32 + } + model_options = {} + if dtype := DTYPE_MAP.get(weight_dtype): + model_options["dtype"] = dtype + print(f"Setting {ckpt_name} weight dtype to {dtype}") + + if weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) + + model, clip, vae = self.load_state_dict_guess_config( + sd, + output_vae=True, + output_clip=True, + embedding_directory=folder_paths.get_folder_paths("embeddings"), + metadata=metadata, + model_options=model_options) + + if dtype := DTYPE_MAP.get(compute_dtype): + model.set_model_compute_dtype(dtype) + model.force_cast_weights = False + print(f"Setting {ckpt_name} compute dtype to {dtype}") + + if enable_fp16_accumulation: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + torch.backends.cuda.matmul.allow_fp16_accumulation = True + else: + raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") + else: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + torch.backends.cuda.matmul.allow_fp16_accumulation = False + + def patch_attention(model): + self._patch_modules(patch_cublaslinear, sage_attention) + model.add_callback(CallbacksMP.ON_PRE_RUN,patch_attention) + return model, clip, vae + + def load_state_dict_guess_config(self, sd, output_vae=True, output_clip=True, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None): + from comfy.sd import load_diffusion_model_state_dict, model_detection, VAE, CLIP + clip = None + vae = None + model = None + model_patcher = None + + diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) + parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) + weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) + load_device = mm.get_torch_device() + + model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) + if model_config is None: + logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.") + diffusion_model = load_diffusion_model_state_dict(sd, model_options={}) + if diffusion_model is None: + return None + return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used' + + + unet_weight_dtype = list(model_config.supported_inference_dtypes) + if model_config.scaled_fp8 is not None: + weight_dtype = None + + model_config.custom_operations = model_options.get("custom_operations", None) + unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) + + if unet_dtype is None: + unet_dtype = mm.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) + + manual_cast_dtype = mm.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + + if output_model: + inital_load_device = mm.unet_inital_load_device(parameters, unet_dtype) + model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) + model.load_model_weights(sd, diffusion_model_prefix) + + if output_vae: + vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) + vae_sd = model_config.process_vae_state_dict(vae_sd) + vae = VAE(sd=vae_sd, metadata=metadata) + + if output_clip: + clip_target = model_config.clip_target(state_dict=sd) + if clip_target is not None: + clip_sd = model_config.process_clip_state_dict(sd) + if len(clip_sd) > 0: + parameters = comfy.utils.calculate_parameters(clip_sd) + clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options) + m, u = clip.load_sd(clip_sd, full_model=True) + if len(m) > 0: + m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) + if len(m_filter) > 0: + logging.warning("clip missing: {}".format(m)) + else: + logging.debug("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected {}:".format(u)) + else: + logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") + + left_over = sd.keys() + if len(left_over) > 0: + logging.debug("left over keys: {}".format(left_over)) + + if output_model: + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=mm.unet_offload_device()) + if inital_load_device != torch.device("cpu"): + logging.info("loaded diffusion model directly to GPU") + mm.load_models_gpu([model_patcher], force_full_load=True) + + return (model_patcher, clip, vae) + +class DiffusionModelLoaderKJ(BaseLoaderKJ): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load."}), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],), + "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}), + "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}), + "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}), + "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch_and_load" + OUTPUT_NODE = True + DESCRIPTION = "Node for patching torch.nn.Linear with CublasLinear." + EXPERIMENTAL = True + CATEGORY = "KJNodes/experimental" + + def patch_and_load(self, model_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation): + DTYPE_MAP = { + "fp8_e4m3fn": torch.float8_e4m3fn, + "fp8_e5m2": torch.float8_e5m2, + "fp16": torch.float16, + "bf16": torch.bfloat16, + "fp32": torch.float32 + } + model_options = {} + if dtype := DTYPE_MAP.get(weight_dtype): + model_options["dtype"] = dtype + print(f"Setting {model_name} weight dtype to {dtype}") + + if weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + + if enable_fp16_accumulation: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + torch.backends.cuda.matmul.allow_fp16_accumulation = True + else: + raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") + else: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + torch.backends.cuda.matmul.allow_fp16_accumulation = False + + unet_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name) + model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options) + if dtype := DTYPE_MAP.get(compute_dtype): + model.set_model_compute_dtype(dtype) + model.force_cast_weights = False + print(f"Setting {model_name} compute dtype to {dtype}") + + def patch_attention(model): + self._patch_modules(patch_cublaslinear, sage_attention) + model.add_callback(CallbacksMP.ON_PRE_RUN,patch_attention) + + return (model,) + +class ModelPatchTorchSettings: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + OUTPUT_NODE = True + DESCRIPTION = "Adds callbacks to model to set torch settings before and after running the model." + EXPERIMENTAL = True + CATEGORY = "KJNodes/experimental" + + def patch(self, model, enable_fp16_accumulation): + model_clone = model.clone() + + def patch_enable_fp16_accum(model): + print("Patching torch settings: torch.backends.cuda.matmul.allow_fp16_accumulation = True") + torch.backends.cuda.matmul.allow_fp16_accumulation = True + def patch_disable_fp16_accum(model): + print("Patching torch settings: torch.backends.cuda.matmul.allow_fp16_accumulation = False") + torch.backends.cuda.matmul.allow_fp16_accumulation = False + + if enable_fp16_accumulation: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_enable_fp16_accum) + model_clone.add_callback(CallbacksMP.ON_CLEANUP, patch_disable_fp16_accum) + else: + raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") + else: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_disable_fp16_accum) + else: + raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") + + return (model_clone,) + +def patched_patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): + with self.use_ejected(): + + device_to = mm.get_torch_device() + + full_load_override = getattr(self.model, "full_load_override", "auto") + if full_load_override in ["enabled", "disabled"]: + full_load = full_load_override == "enabled" + else: + full_load = lowvram_model_memory == 0 + + self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load) + + for k in self.object_patches: + old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) + if k not in self.object_patches_backup: + self.object_patches_backup[k] = old + + self.inject_model() + return self.model + +def patched_load_lora_for_models(model, clip, lora, strength_model, strength_clip): + + patch_keys = list(model.object_patches_backup.keys()) + for k in patch_keys: + #print("backing up object patch: ", k) + comfy.utils.set_attr(model.model, k, model.object_patches_backup[k]) + + key_map = {} + if model is not None: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + if clip is not None: + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + + lora = comfy.lora_convert.convert_lora(lora) + loaded = comfy.lora.load_lora(lora, key_map) + #print(temp_object_patches_backup) + + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + else: + k = () + new_modelpatcher = None + + if clip is not None: + new_clip = clip.clone() + k1 = new_clip.add_patches(loaded, strength_clip) + else: + k1 = () + new_clip = None + k = set(k) + k1 = set(k1) + for x in loaded: + if (x not in k) and (x not in k1): + print("NOT LOADED {}".format(x)) + + if patch_keys: + if hasattr(model.model, "compile_settings"): + compile_settings = getattr(model.model, "compile_settings") + print("compile_settings: ", compile_settings) + for k in patch_keys: + if "diffusion_model." in k: + # Remove the prefix to get the attribute path + key = k.replace('diffusion_model.', '') + attributes = key.split('.') + # Start with the diffusion_model object + block = model.get_model_object("diffusion_model") + # Navigate through the attributes to get to the block + for attr in attributes: + if attr.isdigit(): + block = block[int(attr)] + else: + block = getattr(block, attr) + # Compile the block + compiled_block = torch.compile(block, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"]) + # Add the compiled block back as an object patch + model.add_object_patch(k, compiled_block) + return (new_modelpatcher, new_clip) + +class PatchModelPatcherOrder: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "patch_order": (["object_patch_first", "weight_patch_first"], {"default": "weight_patch_first", "tooltip": "Patch the comfy patch_model function to load weight patches (LoRAs) before compiling the model"}), + "full_load": (["enabled", "disabled", "auto"], {"default": "auto", "tooltip": "Disabling may help with memory issues when loading large models, when changing this you should probably force model reload to avoid issues!"}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Patch the comfy patch_model function patching order, useful for torch.compile (used as object_patch) as it should come last if you want to use LoRAs with compile" + EXPERIMENTAL = True + + def patch(self, model, patch_order, full_load): + comfy.model_patcher.ModelPatcher.temp_object_patches_backup = {} + setattr(model.model, "full_load_override", full_load) + if patch_order == "weight_patch_first": + comfy.model_patcher.ModelPatcher.patch_model = patched_patch_model + comfy.sd.load_lora_for_models = patched_load_lora_for_models + else: + comfy.model_patcher.ModelPatcher.patch_model = _original_functions.get("original_patch_model") + comfy.sd.load_lora_for_models = _original_functions.get("original_load_lora_for_models") + + return model, + +class TorchCompileModelFluxAdvanced: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "backend": (["inductor", "cudagraphs"],), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "double_blocks": ("STRING", {"default": "0-18", "multiline": True}), + "single_blocks": ("STRING", {"default": "0-37", "multiline": True}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + }, + "optional": { + "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def parse_blocks(self, blocks_str): + blocks = [] + for part in blocks_str.split(','): + part = part.strip() + if '-' in part: + start, end = map(int, part.split('-')) + blocks.extend(range(start, end + 1)) + else: + blocks.append(int(part)) + return blocks + + def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit): + single_block_list = self.parse_blocks(single_blocks) + double_block_list = self.parse_blocks(double_blocks) + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit + + if not self._compiled: + try: + for i, block in enumerate(diffusion_model.double_blocks): + if i in double_block_list: + #print("Compiling double_block", i) + m.add_object_patch(f"diffusion_model.double_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)) + for i, block in enumerate(diffusion_model.single_blocks): + if i in single_block_list: + #print("Compiling single block", i) + m.add_object_patch(f"diffusion_model.single_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)) + self._compiled = True + compile_settings = { + "backend": backend, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + setattr(m.model, "compile_settings", compile_settings) + except: + raise RuntimeError("Failed to compile model") + + return (m, ) + # rest of the layers that are not patched + # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend) + +class TorchCompileModelFluxAdvancedV2: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "backend": (["inductor", "cudagraphs"],), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}), + "single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + }, + "optional": { + "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit): + from comfy_api.torch_helpers import set_torch_compile_wrapper + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit + + compile_key_list = [] + + try: + if double_blocks: + for i, block in enumerate(diffusion_model.double_blocks): + compile_key_list.append(f"diffusion_model.double_blocks.{i}") + if single_blocks: + for i, block in enumerate(diffusion_model.single_blocks): + compile_key_list.append(f"diffusion_model.single_blocks.{i}") + + set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph) + except: + raise RuntimeError("Failed to compile model") + + return (m, ) + # rest of the layers that are not patched + # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend) + # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend) + + +class TorchCompileModelHyVideo: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "backend": (["inductor","cudagraphs"], {"default": "inductor"}), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), + "compile_single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}), + "compile_double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}), + "compile_txt_in": ("BOOLEAN", {"default": False, "tooltip": "Compile txt_in layers"}), + "compile_vector_in": ("BOOLEAN", {"default": False, "tooltip": "Compile vector_in layers"}), + "compile_final_layer": ("BOOLEAN", {"default": False, "tooltip": "Compile final layer"}), + + }, + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_single_blocks, compile_double_blocks, compile_txt_in, compile_vector_in, compile_final_layer): + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit + if not self._compiled: + try: + if compile_single_blocks: + for i, block in enumerate(diffusion_model.single_blocks): + compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch(f"diffusion_model.single_blocks.{i}", compiled_block) + if compile_double_blocks: + for i, block in enumerate(diffusion_model.double_blocks): + compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch(f"diffusion_model.double_blocks.{i}", compiled_block) + if compile_txt_in: + compiled_block = torch.compile(diffusion_model.txt_in, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch("diffusion_model.txt_in", compiled_block) + if compile_vector_in: + compiled_block = torch.compile(diffusion_model.vector_in, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch("diffusion_model.vector_in", compiled_block) + if compile_final_layer: + compiled_block = torch.compile(diffusion_model.final_layer, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch("diffusion_model.final_layer", compiled_block) + self._compiled = True + compile_settings = { + "backend": backend, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + setattr(m.model, "compile_settings", compile_settings) + except: + raise RuntimeError("Failed to compile model") + return (m, ) + +class TorchCompileModelWanVideo: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "backend": (["inductor","cudagraphs"], {"default": "inductor"}), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), + "compile_transformer_blocks_only": ("BOOLEAN", {"default": False, "tooltip": "Compile only transformer blocks"}), + }, + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only): + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit + try: + if compile_transformer_blocks_only: + for i, block in enumerate(diffusion_model.blocks): + if hasattr(block, "_orig_mod"): + block = block._orig_mod + compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch(f"diffusion_model.blocks.{i}", compiled_block) + else: + compiled_model = torch.compile(diffusion_model, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) + m.add_object_patch("diffusion_model", compiled_model) + + compile_settings = { + "backend": backend, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + setattr(m.model, "compile_settings", compile_settings) + except: + raise RuntimeError("Failed to compile model") + return (m, ) + +class TorchCompileModelWanVideoV2: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "backend": (["inductor","cudagraphs"], {"default": "inductor"}), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}), + "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), + }, + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only): + from comfy_api.torch_helpers import set_torch_compile_wrapper + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit + try: + if compile_transformer_blocks_only: + compile_key_list = [] + for i, block in enumerate(diffusion_model.blocks): + compile_key_list.append(f"diffusion_model.blocks.{i}") + else: + compile_key_list =["diffusion_model"] + + set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph) + except: + raise RuntimeError("Failed to compile model") + + return (m, ) + +class TorchCompileVAE: + def __init__(self): + self._compiled_encoder = False + self._compiled_decoder = False + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "vae": ("VAE",), + "backend": (["inductor", "cudagraphs"],), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "compile_encoder": ("BOOLEAN", {"default": True, "tooltip": "Compile encoder"}), + "compile_decoder": ("BOOLEAN", {"default": True, "tooltip": "Compile decoder"}), + }} + RETURN_TYPES = ("VAE",) + FUNCTION = "compile" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def compile(self, vae, backend, mode, fullgraph, compile_encoder, compile_decoder): + if compile_encoder: + if not self._compiled_encoder: + encoder_name = "encoder" + if hasattr(vae.first_stage_model, "taesd_encoder"): + encoder_name = "taesd_encoder" + + try: + setattr( + vae.first_stage_model, + encoder_name, + torch.compile( + getattr(vae.first_stage_model, encoder_name), + mode=mode, + fullgraph=fullgraph, + backend=backend, + ), + ) + self._compiled_encoder = True + except: + raise RuntimeError("Failed to compile model") + if compile_decoder: + if not self._compiled_decoder: + decoder_name = "decoder" + if hasattr(vae.first_stage_model, "taesd_decoder"): + decoder_name = "taesd_decoder" + + try: + setattr( + vae.first_stage_model, + decoder_name, + torch.compile( + getattr(vae.first_stage_model, decoder_name), + mode=mode, + fullgraph=fullgraph, + backend=backend, + ), + ) + self._compiled_decoder = True + except: + raise RuntimeError("Failed to compile model") + return (vae, ) + +class TorchCompileControlNet: + def __init__(self): + self._compiled= False + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "controlnet": ("CONTROL_NET",), + "backend": (["inductor", "cudagraphs"],), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + }} + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "compile" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def compile(self, controlnet, backend, mode, fullgraph): + if not self._compiled: + try: + # for i, block in enumerate(controlnet.control_model.double_blocks): + # print("Compiling controlnet double_block", i) + # controlnet.control_model.double_blocks[i] = torch.compile(block, mode=mode, fullgraph=fullgraph, backend=backend) + controlnet.control_model = torch.compile(controlnet.control_model, mode=mode, fullgraph=fullgraph, backend=backend) + self._compiled = True + except: + self._compiled = False + raise RuntimeError("Failed to compile model") + + return (controlnet, ) + +class TorchCompileLTXModel: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "backend": (["inductor", "cudagraphs"],), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def patch(self, model, backend, mode, fullgraph, dynamic): + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + + if not self._compiled: + try: + for i, block in enumerate(diffusion_model.transformer_blocks): + compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend) + m.add_object_patch(f"diffusion_model.transformer_blocks.{i}", compiled_block) + self._compiled = True + compile_settings = { + "backend": backend, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + setattr(m.model, "compile_settings", compile_settings) + + except: + raise RuntimeError("Failed to compile model") + + return (m, ) + +class TorchCompileCosmosModel: + def __init__(self): + self._compiled = False + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "backend": (["inductor", "cudagraphs"],), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + "dynamo_cache_size_limit": ("INT", {"default": 64, "tooltip": "Set the dynamo cache size limit"}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/torchcompile" + EXPERIMENTAL = True + + def patch(self, model, backend, mode, fullgraph, dynamic, dynamo_cache_size_limit): + + m = model.clone() + diffusion_model = m.get_model_object("diffusion_model") + torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit + + if not self._compiled: + try: + for name, block in diffusion_model.blocks.items(): + #print(f"Compiling block {name}") + compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend) + m.add_object_patch(f"diffusion_model.blocks.{name}", compiled_block) + #diffusion_model.blocks[name] = compiled_block + + self._compiled = True + compile_settings = { + "backend": backend, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + setattr(m.model, "compile_settings", compile_settings) + + except: + raise RuntimeError("Failed to compile model") + + return (m, ) + + +#teacache + +try: + from comfy.ldm.wan.model import sinusoidal_embedding_1d +except: + pass +from einops import repeat +from unittest.mock import patch +from contextlib import nullcontext +import numpy as np + +def relative_l1_distance(last_tensor, current_tensor): + l1_distance = torch.abs(last_tensor - current_tensor).mean() + norm = torch.abs(last_tensor).mean() + relative_l1_distance = l1_distance / norm + return relative_l1_distance.to(torch.float32) + +@torch.compiler.disable() +def tea_cache(self, x, e0, e, transformer_options): + #teacache for cond and uncond separately + rel_l1_thresh = transformer_options["rel_l1_thresh"] + + is_cond = True if transformer_options["cond_or_uncond"] == [0] else False + + should_calc = True + suffix = "cond" if is_cond else "uncond" + + # Init cache dict if not exists + if not hasattr(self, 'teacache_state'): + self.teacache_state = { + 'cond': {'accumulated_rel_l1_distance': 0, 'prev_input': None, + 'teacache_skipped_steps': 0, 'previous_residual': None}, + 'uncond': {'accumulated_rel_l1_distance': 0, 'prev_input': None, + 'teacache_skipped_steps': 0, 'previous_residual': None} + } + logging.info("\nTeaCache: Initialized") + + cache = self.teacache_state[suffix] + + if cache['prev_input'] is not None: + if transformer_options["coefficients"] == []: + temb_relative_l1 = relative_l1_distance(cache['prev_input'], e0) + curr_acc_dist = cache['accumulated_rel_l1_distance'] + temb_relative_l1 + else: + rescale_func = np.poly1d(transformer_options["coefficients"]) + curr_acc_dist = cache['accumulated_rel_l1_distance'] + rescale_func(((e-cache['prev_input']).abs().mean() / cache['prev_input'].abs().mean()).cpu().item()) + try: + if curr_acc_dist < rel_l1_thresh: + should_calc = False + cache['accumulated_rel_l1_distance'] = curr_acc_dist + else: + should_calc = True + cache['accumulated_rel_l1_distance'] = 0 + except: + should_calc = True + cache['accumulated_rel_l1_distance'] = 0 + + if transformer_options["coefficients"] == []: + cache['prev_input'] = e0.clone().detach() + else: + cache['prev_input'] = e.clone().detach() + + if not should_calc: + x += cache['previous_residual'].to(x.device) + cache['teacache_skipped_steps'] += 1 + #print(f"TeaCache: Skipping {suffix} step") + return should_calc, cache + +def teacache_wanvideo_vace_forward_orig(self, x, t, context, vace_context, vace_strength, clip_fea=None, freqs=None, transformer_options={}, **kwargs): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + orig_shape = list(vace_context.shape) + vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) + c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) + c = c.flatten(2).transpose(1, 2) + c = list(c.split(orig_shape[0], dim=0)) + + if not transformer_options: + raise RuntimeError("Can't access transformer_options, this requires ComfyUI nightly version from Mar 14, 2025 or later") + + teacache_enabled = transformer_options.get("teacache_enabled", False) + if not teacache_enabled: + should_calc = True + else: + should_calc, cache = tea_cache(self, x, e0, e, transformer_options) + + if should_calc: + original_x = x.clone().detach() + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap, "transformer_options": transformer_options}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + + ii = self.vace_layers_mapping.get(i, None) + if ii is not None: + for iii in range(len(c)): + c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=original_x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + x += c_skip * vace_strength[iii] + del c_skip + + if teacache_enabled: + cache['previous_residual'] = (x - original_x).to(transformer_options["teacache_device"]) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + +def teacache_wanvideo_forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, **kwargs): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + + teacache_enabled = transformer_options.get("teacache_enabled", False) + if not teacache_enabled: + should_calc = True + else: + should_calc, cache = tea_cache(self, x, e0, e, transformer_options) + + if should_calc: + original_x = x.clone().detach() + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap, "transformer_options": transformer_options}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) + + if teacache_enabled: + cache['previous_residual'] = (x - original_x).to(transformer_options["teacache_device"]) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + +class WanVideoTeaCacheKJ: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4 for all but 1.3B model, which should be about 10 times smaller, same as when not using coefficients."}), + "start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of the steps to use with TeaCache."}), + "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}), + "coefficients": (["disabled", "1.3B", "14B", "i2v_480", "i2v_720"], {"default": "i2v_480", "tooltip": "Coefficients for rescaling the relative l1 distance, if disabled the threshold value should be about 10 times smaller than the value used with coefficients."}), + } + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model",) + FUNCTION = "patch_teacache" + CATEGORY = "KJNodes/teacache" + DESCRIPTION = """ +Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and +applying it instead of doing the step. Best results are achieved by choosing the +appropriate coefficients for the model. Early steps should never be skipped, with too +aggressive values this can happen and the motion suffers. Starting later can help with that too. +When NOT using coefficients, the threshold value should be +about 10 times smaller than the value used with coefficients. + +Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1: + + +
    ++-------------------+--------+---------+--------+
+|       Model       |  Low   | Medium  |  High  |
++-------------------+--------+---------+--------+
+| Wan2.1 t2v 1.3B  |  0.05  |  0.07   |  0.08  |
+| Wan2.1 t2v 14B   |  0.14  |  0.15   |  0.20  |
+| Wan2.1 i2v 480P  |  0.13  |  0.19   |  0.26  |
+| Wan2.1 i2v 720P  |  0.18  |  0.20   |  0.30  |
++-------------------+--------+---------+--------+
+
    +""" + EXPERIMENTAL = True + + def patch_teacache(self, model, rel_l1_thresh, start_percent, end_percent, cache_device, coefficients): + if rel_l1_thresh == 0: + return (model,) + + if coefficients == "disabled" and rel_l1_thresh > 0.1: + logging.warning("Threshold value is too high for TeaCache without coefficients, consider using coefficients for better results.") + if coefficients != "disabled" and rel_l1_thresh < 0.1 and "1.3B" not in coefficients: + logging.warning("Threshold value is too low for TeaCache with coefficients, consider using higher threshold value for better results.") + + # type_str = str(type(model.model.model_config).__name__) + #if model.model.diffusion_model.dim == 1536: + # model_type ="1.3B" + # else: + # if "WAN21_T2V" in type_str: + # model_type = "14B" + # elif "WAN21_I2V" in type_str: + # model_type = "i2v_480" + # else: + # model_type = "i2v_720" #how to detect this? + + + teacache_coefficients_map = { + "disabled": [], + "1.3B": [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01], + "14B": [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404], + "i2v_480": [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01], + "i2v_720": [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683], + } + coefficients = teacache_coefficients_map[coefficients] + + teacache_device = mm.get_torch_device() if cache_device == "main_device" else mm.unet_offload_device() + + model_clone = model.clone() + if 'transformer_options' not in model_clone.model_options: + model_clone.model_options['transformer_options'] = {} + model_clone.model_options["transformer_options"]["rel_l1_thresh"] = rel_l1_thresh + model_clone.model_options["transformer_options"]["teacache_device"] = teacache_device + model_clone.model_options["transformer_options"]["coefficients"] = coefficients + diffusion_model = model_clone.get_model_object("diffusion_model") + + def outer_wrapper(start_percent, end_percent): + def unet_wrapper_function(model_function, kwargs): + input = kwargs["input"] + timestep = kwargs["timestep"] + c = kwargs["c"] + sigmas = c["transformer_options"]["sample_sigmas"] + cond_or_uncond = kwargs["cond_or_uncond"] + last_step = (len(sigmas) - 1) + + matched_step_index = (sigmas == timestep[0] ).nonzero() + if len(matched_step_index) > 0: + current_step_index = matched_step_index.item() + else: + for i in range(len(sigmas) - 1): + # walk from beginning of steps until crossing the timestep + if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0: + current_step_index = i + break + else: + current_step_index = 0 + + if current_step_index == 0: + if (len(cond_or_uncond) == 1 and cond_or_uncond[0] == 1) or len(cond_or_uncond) == 2: + if hasattr(diffusion_model, "teacache_state"): + delattr(diffusion_model, "teacache_state") + logging.info("\nResetting TeaCache state") + + current_percent = current_step_index / (len(sigmas) - 1) + c["transformer_options"]["current_percent"] = current_percent + if start_percent <= current_percent <= end_percent: + c["transformer_options"]["teacache_enabled"] = True + + forward_function = teacache_wanvideo_vace_forward_orig if hasattr(diffusion_model, "vace_layers") else teacache_wanvideo_forward_orig + context = patch.multiple( + diffusion_model, + forward_orig=forward_function.__get__(diffusion_model, diffusion_model.__class__) + ) + + with context: + out = model_function(input, timestep, **c) + if current_step_index+1 == last_step and hasattr(diffusion_model, "teacache_state"): + if len(cond_or_uncond) == 1 and cond_or_uncond[0] == 0: + skipped_steps_cond = diffusion_model.teacache_state["cond"]["teacache_skipped_steps"] + skipped_steps_uncond = diffusion_model.teacache_state["uncond"]["teacache_skipped_steps"] + logging.info("-----------------------------------") + logging.info(f"TeaCache skipped:") + logging.info(f"{skipped_steps_cond} cond steps") + logging.info(f"{skipped_steps_uncond} uncond step") + logging.info(f"out of {last_step} steps") + logging.info("-----------------------------------") + elif len(cond_or_uncond) == 2: + skipped_steps_cond = diffusion_model.teacache_state["uncond"]["teacache_skipped_steps"] + logging.info("-----------------------------------") + logging.info(f"TeaCache skipped:") + logging.info(f"{skipped_steps_cond} cond steps") + logging.info(f"out of {last_step} steps") + logging.info("-----------------------------------") + + return out + return unet_wrapper_function + + model_clone.set_model_unet_function_wrapper(outer_wrapper(start_percent=start_percent, end_percent=end_percent)) + + return (model_clone,) + + + + +from comfy.ldm.flux.math import apply_rope + +def modified_wan_self_attention_forward(self, x, freqs): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n * d) + return q, k, v + + q, k, v = qkv_fn(x) + + q, k = apply_rope(q, k, freqs) + + feta_scores = get_feta_scores(q, k, self.num_frames, self.enhance_weight) + + x = comfy.ldm.modules.attention.optimized_attention( + q.view(b, s, n * d), + k.view(b, s, n * d), + v, + heads=self.num_heads, + ) + + x = self.o(x) + + x *= feta_scores + + return x + +from einops import rearrange +def get_feta_scores(query, key, num_frames, enhance_weight): + img_q, img_k = query, key #torch.Size([2, 9216, 12, 128]) + + _, ST, num_heads, head_dim = img_q.shape + spatial_dim = ST / num_frames + spatial_dim = int(spatial_dim) + + query_image = rearrange( + img_q, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim + ) + key_image = rearrange( + img_k, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim + ) + + return feta_score(query_image, key_image, head_dim, num_frames, enhance_weight) + +def feta_score(query_image, key_image, head_dim, num_frames, enhance_weight): + scale = head_dim**-0.5 + query_image = query_image * scale + attn_temp = query_image @ key_image.transpose(-2, -1) # translate attn to float32 + attn_temp = attn_temp.to(torch.float32) + attn_temp = attn_temp.softmax(dim=-1) + + # Reshape to [batch_size * num_tokens, num_frames, num_frames] + attn_temp = attn_temp.reshape(-1, num_frames, num_frames) + + # Create a mask for diagonal elements + diag_mask = torch.eye(num_frames, device=attn_temp.device).bool() + diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1) + + # Zero out diagonal elements + attn_wo_diag = attn_temp.masked_fill(diag_mask, 0) + + # Calculate mean for each token's attention matrix + # Number of off-diagonal elements per matrix is n*n - n + num_off_diag = num_frames * num_frames - num_frames + mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag + + enhance_scores = mean_scores.mean() * (num_frames + enhance_weight) + enhance_scores = enhance_scores.clamp(min=1) + return enhance_scores + +import types +class WanAttentionPatch: + def __init__(self, num_frames, weight): + self.num_frames = num_frames + self.enhance_weight = weight + + def __get__(self, obj, objtype=None): + # Create bound method with stored parameters + def wrapped_attention(self_module, *args, **kwargs): + self_module.num_frames = self.num_frames + self_module.enhance_weight = self.enhance_weight + return modified_wan_self_attention_forward(self_module, *args, **kwargs) + return types.MethodType(wrapped_attention, obj) + +class WanVideoEnhanceAVideoKJ: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), + "weight": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of the enhance effect"}), + } + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model",) + FUNCTION = "enhance" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video" + EXPERIMENTAL = True + + def enhance(self, model, weight, latent): + if weight == 0: + return (model,) + + num_frames = latent["samples"].shape[2] + + model_clone = model.clone() + if 'transformer_options' not in model_clone.model_options: + model_clone.model_options['transformer_options'] = {} + model_clone.model_options["transformer_options"]["enhance_weight"] = weight + diffusion_model = model_clone.get_model_object("diffusion_model") + + compile_settings = getattr(model.model, "compile_settings", None) + for idx, block in enumerate(diffusion_model.blocks): + patched_attn = WanAttentionPatch(num_frames, weight).__get__(block.self_attn, block.__class__) + if compile_settings is not None: + patched_attn = torch.compile(patched_attn, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"]) + + model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.self_attn.forward", patched_attn) + + return (model_clone,) + +def normalized_attention_guidance(self, query, context_positive, context_negative): + nag_scale = self.nag_scale + nag_alpha = self.nag_alpha + nag_tau = self.nag_tau + + k_positive = self.norm_k(self.k(context_positive)) + v_positive = self.v(context_positive) + k_negative = self.norm_k(self.k(context_negative)) + v_negative = self.v(context_negative) + + x_positive = comfy.ldm.modules.attention.optimized_attention(query, k_positive, v_positive, heads=self.num_heads) + x_positive = x_positive.flatten(2) + + x_negative = comfy.ldm.modules.attention.optimized_attention(query, k_negative, v_negative, heads=self.num_heads) + x_negative = x_negative.flatten(2) + + nag_guidance = x_positive * nag_scale - x_negative * (nag_scale - 1) + + norm_positive = torch.norm(x_positive, p=1, dim=-1, keepdim=True).expand_as(x_positive) + norm_guidance = torch.norm(nag_guidance, p=1, dim=-1, keepdim=True).expand_as(nag_guidance) + + scale = norm_guidance / norm_positive + scale = torch.nan_to_num(scale, nan=10.0) + + mask = scale > nag_tau + adjustment = (norm_positive * nag_tau) / (norm_guidance + 1e-7) + nag_guidance = torch.where(mask, nag_guidance * adjustment, nag_guidance) + + x = nag_guidance * nag_alpha + x_positive * (1 - nag_alpha) + del nag_guidance + + return x + +#region NAG +def wan_crossattn_forward_nag(self, x, context, **kwargs): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + """ + + if context.shape[0] == 2: + x, x_real_negative = torch.chunk(x, 2, dim=0) + context_positive, context_negative = torch.chunk(context, 2, dim=0) + else: + context_positive = context + context_negative = None + + q = self.norm_q(self.q(x)) + + x = normalized_attention_guidance(self, q, context_positive, self.nag_context) + + if context_negative is not None: + q_real_negative = self.norm_q(self.q(x_real_negative)) + k_real_negative = self.norm_k(self.k(context_negative)) + v_real_negative = self.v(context_negative) + x_real_negative = comfy.ldm.modules.attention.optimized_attention(q_real_negative, k_real_negative, v_real_negative, heads=self.num_heads) + x = torch.cat([x, x_real_negative], dim=0) + + x = self.o(x) + return x + + +def wan_i2v_crossattn_forward_nag(self, x, context, context_img_len): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + """ + context_img = context[:, :context_img_len] + context = context[:, context_img_len:] + + q_img = self.norm_q(self.q(x)) + k_img = self.norm_k_img(self.k_img(context_img)) + v_img = self.v_img(context_img) + img_x = comfy.ldm.modules.attention.optimized_attention(q_img, k_img, v_img, heads=self.num_heads) + + if context.shape[0] == 2: + x, x_real_negative = torch.chunk(x, 2, dim=0) + context_positive, context_negative = torch.chunk(context, 2, dim=0) + else: + context_positive = context + context_negative = None + + q = self.norm_q(self.q(x)) + + x = normalized_attention_guidance(self, q, context_positive, self.nag_context) + + if context_negative is not None: + q_real_negative = self.norm_q(self.q(x_real_negative)) + k_real_negative = self.norm_k(self.k(context_negative)) + v_real_negative = self.v(context_negative) + x_real_negative = comfy.ldm.modules.attention.optimized_attention(q_real_negative, k_real_negative, v_real_negative, heads=self.num_heads) + x = torch.cat([x, x_real_negative], dim=0) + + # output + x = x + img_x + x = self.o(x) + return x + +class WanCrossAttentionPatch: + def __init__(self, context, nag_scale, nag_alpha, nag_tau, i2v=False): + self.nag_context = context + self.nag_scale = nag_scale + self.nag_alpha = nag_alpha + self.nag_tau = nag_tau + self.i2v = i2v + def __get__(self, obj, objtype=None): + # Create bound method with stored parameters + def wrapped_attention(self_module, *args, **kwargs): + self_module.nag_context = self.nag_context + self_module.nag_scale = self.nag_scale + self_module.nag_alpha = self.nag_alpha + self_module.nag_tau = self.nag_tau + if self.i2v: + return wan_i2v_crossattn_forward_nag(self_module, *args, **kwargs) + else: + return wan_crossattn_forward_nag(self_module, *args, **kwargs) + return types.MethodType(wrapped_attention, obj) + +class WanVideoNAG: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "conditioning": ("CONDITIONING",), + "nag_scale": ("FLOAT", {"default": 11.0, "min": 0.0, "max": 100.0, "step": 0.001, "tooltip": "Strength of negative guidance effect"}), + "nag_alpha": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Mixing coefficient in that controls the balance between the normalized guided representation and the original positive representation."}), + "nag_tau": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Clipping threshold that controls how much the guided attention can deviate from the positive attention."}), + } + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model",) + FUNCTION = "patch" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "https://github.com/ChenDarYen/Normalized-Attention-Guidance" + EXPERIMENTAL = True + + def patch(self, model, conditioning, nag_scale, nag_alpha, nag_tau): + if nag_scale == 0: + return (model,) + + device = mm.get_torch_device() + dtype = mm.unet_dtype() + + model_clone = model.clone() + + diffusion_model = model_clone.get_model_object("diffusion_model") + + diffusion_model.text_embedding.to(device) + context = diffusion_model.text_embedding(conditioning[0][0].to(device, dtype)) + + type_str = str(type(model.model.model_config).__name__) + i2v = True if "WAN21_I2V" in type_str else False + + for idx, block in enumerate(diffusion_model.blocks): + patched_attn = WanCrossAttentionPatch(context, nag_scale, nag_alpha, nag_tau, i2v).__get__(block.cross_attn, block.__class__) + + model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.cross_attn.forward", patched_attn) + + return (model_clone,) + +class SkipLayerGuidanceWanVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "blocks": ("STRING", {"default": "10", "multiline": False}), + "start_percent": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "slg" + EXPERIMENTAL = True + DESCRIPTION = "Simplified skip layer guidance that only skips the uncond on selected blocks" + + CATEGORY = "advanced/guidance" + + def slg(self, model, start_percent, end_percent, blocks): + def skip(args, extra_args): + transformer_options = extra_args.get("transformer_options", {}) + original_block = extra_args["original_block"] + + if not transformer_options: + raise ValueError("transformer_options not found in extra_args, currently SkipLayerGuidanceWanVideo only works with TeaCacheKJ") + if start_percent <= transformer_options["current_percent"] <= end_percent: + if args["img"].shape[0] == 2: + prev_img_uncond = args["img"][0].unsqueeze(0) + + new_args = { + "img": args["img"][1].unsqueeze(0), + "txt": args["txt"][1].unsqueeze(0), + "vec": args["vec"][1].unsqueeze(0), + "pe": args["pe"][1].unsqueeze(0) + } + + block_out = original_block(new_args) + + out = { + "img": torch.cat([prev_img_uncond, block_out["img"]], dim=0), + "txt": args["txt"], + "vec": args["vec"], + "pe": args["pe"] + } + else: + if transformer_options.get("cond_or_uncond") == [0]: + out = original_block(args) + else: + out = args + else: + out = original_block(args) + return out + + block_list = [int(x.strip()) for x in blocks.split(",")] + blocks = [int(i) for i in block_list] + logging.info(f"Selected blocks to skip uncond on: {blocks}") + + m = model.clone() + + for b in blocks: + #m.set_model_patch_replace(skip, "dit", "double_block", b) + model_options = m.model_options["transformer_options"].copy() + if "patches_replace" not in model_options: + model_options["patches_replace"] = {} + else: + model_options["patches_replace"] = model_options["patches_replace"].copy() + + if "dit" not in model_options["patches_replace"]: + model_options["patches_replace"]["dit"] = {} + else: + model_options["patches_replace"]["dit"] = model_options["patches_replace"]["dit"].copy() + + block = ("double_block", b) + + model_options["patches_replace"]["dit"][block] = skip + m.model_options["transformer_options"] = model_options + + + return (m, ) + +class CFGZeroStarAndInit: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "use_zero_init": ("BOOLEAN", {"default": True}), + "zero_init_steps": ("INT", {"default": 0, "min": 0, "tooltip": "for zero init, starts from 0 so first step is always zeroed out if use_zero_init enabled"}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + DESCRIPTION = "https://github.com/WeichenFan/CFG-Zero-star" + CATEGORY = "KJNodes/experimental" + EXPERIMENTAL = True + + def patch(self, model, use_zero_init, zero_init_steps): + def cfg_zerostar(args): + #zero init + cond = args["cond"] + timestep = args["timestep"] + sigmas = args["model_options"]["transformer_options"]["sample_sigmas"] + matched_step_index = (sigmas == timestep[0]).nonzero() + if len(matched_step_index) > 0: + current_step_index = matched_step_index.item() + else: + for i in range(len(sigmas) - 1): + if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0: + current_step_index = i + break + else: + current_step_index = 0 + + if (current_step_index <= zero_init_steps) and use_zero_init: + return cond * 0 + + uncond = args["uncond"] + cond_scale = args["cond_scale"] + + batch_size = cond.shape[0] + + positive_flat = cond.view(batch_size, -1) + negative_flat = uncond.view(batch_size, -1) + + dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) + squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8 + alpha = dot_product / squared_norm + alpha = alpha.view(batch_size, *([1] * (len(cond.shape) - 1))) + + noise_pred = uncond * alpha + cond_scale * (cond - uncond * alpha) + return noise_pred + + m = model.clone() + m.set_model_sampler_cfg_function(cfg_zerostar) + return (m, ) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/nodes/nodes.py b/custom_nodes/ComfyUI-KJNodes/nodes/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..4cdcd41dd4089492a0ff433afa8f79e8d9f1ab84 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/nodes/nodes.py @@ -0,0 +1,2589 @@ +import torch +import torch.nn as nn +import numpy as np +from PIL import Image +from typing import Union +import json, re, os, io, time, platform +import re +import importlib + +from comfy import model_management +import folder_paths +from nodes import MAX_RESOLUTION +from comfy.utils import common_upscale, ProgressBar, load_torch_file + +script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts")) + +class AnyType(str): + """A special class that is always equal in not equal comparisons. Credit to pythongosssss""" + + def __ne__(self, __value: object) -> bool: + return False +any = AnyType("*") + +class BOOLConstant: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "value": ("BOOLEAN", {"default": True}), + }, + } + RETURN_TYPES = ("BOOLEAN",) + RETURN_NAMES = ("value",) + FUNCTION = "get_value" + CATEGORY = "KJNodes/constants" + + def get_value(self, value): + return (value,) + +class INTConstant: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "value": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}), + }, + } + RETURN_TYPES = ("INT",) + RETURN_NAMES = ("value",) + FUNCTION = "get_value" + CATEGORY = "KJNodes/constants" + + def get_value(self, value): + return (value,) + +class FloatConstant: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.00001}), + }, + } + + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("value",) + FUNCTION = "get_value" + CATEGORY = "KJNodes/constants" + + def get_value(self, value): + return (round(value, 6),) + +class StringConstant: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string": ("STRING", {"default": '', "multiline": False}), + } + } + RETURN_TYPES = ("STRING",) + FUNCTION = "passtring" + CATEGORY = "KJNodes/constants" + + def passtring(self, string): + return (string, ) + +class StringConstantMultiline: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string": ("STRING", {"default": "", "multiline": True}), + "strip_newlines": ("BOOLEAN", {"default": True}), + } + } + RETURN_TYPES = ("STRING",) + FUNCTION = "stringify" + CATEGORY = "KJNodes/constants" + + def stringify(self, string, strip_newlines): + new_string = [] + for line in io.StringIO(string): + if not line.strip().startswith("\n") and strip_newlines: + line = line.replace("\n", '') + new_string.append(line) + new_string = "\n".join(new_string) + + return (new_string, ) + + + +class ScaleBatchPromptSchedule: + + RETURN_TYPES = ("STRING",) + FUNCTION = "scaleschedule" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ +Scales a batch schedule from Fizz' nodes BatchPromptSchedule +to a different frame count. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input_str": ("STRING", {"forceInput": True,"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n"}), + "old_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}), + "new_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}), + + }, + } + + def scaleschedule(self, old_frame_count, input_str, new_frame_count): + pattern = r'"(\d+)"\s*:\s*"(.*?)"(?:,|\Z)' + frame_strings = dict(re.findall(pattern, input_str)) + + # Calculate the scaling factor + scaling_factor = (new_frame_count - 1) / (old_frame_count - 1) + + # Initialize a dictionary to store the new frame numbers and strings + new_frame_strings = {} + + # Iterate over the frame numbers and strings + for old_frame, string in frame_strings.items(): + # Calculate the new frame number + new_frame = int(round(int(old_frame) * scaling_factor)) + + # Store the new frame number and corresponding string + new_frame_strings[new_frame] = string + + # Format the output string + output_str = ', '.join([f'"{k}":"{v}"' for k, v in sorted(new_frame_strings.items())]) + return (output_str,) + + +class GetLatentsFromBatchIndexed: + + RETURN_TYPES = ("LATENT",) + FUNCTION = "indexedlatentsfrombatch" + CATEGORY = "KJNodes/latents" + DESCRIPTION = """ +Selects and returns the latents at the specified indices as an latent batch. +""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "latents": ("LATENT",), + "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), + "latent_format": (["BCHW", "BTCHW", "BCTHW"], {"default": "BCHW"}), + }, + } + + def indexedlatentsfrombatch(self, latents, indexes, latent_format): + + samples = latents.copy() + latent_samples = samples["samples"] + + # Parse the indexes string into a list of integers + index_list = [int(index.strip()) for index in indexes.split(',')] + + # Convert list of indices to a PyTorch tensor + indices_tensor = torch.tensor(index_list, dtype=torch.long) + + # Select the latents at the specified indices + if latent_format == "BCHW": + chosen_latents = latent_samples[indices_tensor] + elif latent_format == "BTCHW": + chosen_latents = latent_samples[:, indices_tensor] + elif latent_format == "BCTHW": + chosen_latents = latent_samples[:, :, indices_tensor] + + samples["samples"] = chosen_latents + return (samples,) + + +class ConditioningMultiCombine: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 20, "step": 1}), + "operation": (["combine", "concat"], {"default": "combine"}), + "conditioning_1": ("CONDITIONING", ), + "conditioning_2": ("CONDITIONING", ), + }, + } + + RETURN_TYPES = ("CONDITIONING", "INT") + RETURN_NAMES = ("combined", "inputcount") + FUNCTION = "combine" + CATEGORY = "KJNodes/masking/conditioning" + DESCRIPTION = """ +Combines multiple conditioning nodes into one +""" + + def combine(self, inputcount, operation, **kwargs): + from nodes import ConditioningCombine + from nodes import ConditioningConcat + cond_combine_node = ConditioningCombine() + cond_concat_node = ConditioningConcat() + cond = kwargs["conditioning_1"] + for c in range(1, inputcount): + new_cond = kwargs[f"conditioning_{c + 1}"] + if operation == "combine": + cond = cond_combine_node.combine(new_cond, cond)[0] + elif operation == "concat": + cond = cond_concat_node.concat(cond, new_cond)[0] + return (cond, inputcount,) + +class AppendStringsToList: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string1": ("STRING", {"default": '', "forceInput": True}), + "string2": ("STRING", {"default": '', "forceInput": True}), + } + } + RETURN_TYPES = ("STRING",) + FUNCTION = "joinstring" + CATEGORY = "KJNodes/text" + + def joinstring(self, string1, string2): + if not isinstance(string1, list): + string1 = [string1] + if not isinstance(string2, list): + string2 = [string2] + + joined_string = string1 + string2 + return (joined_string, ) + +class JoinStrings: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "string1": ("STRING", {"default": '', "forceInput": True}), + "string2": ("STRING", {"default": '', "forceInput": True}), + "delimiter": ("STRING", {"default": ' ', "multiline": False}), + } + } + RETURN_TYPES = ("STRING",) + FUNCTION = "joinstring" + CATEGORY = "KJNodes/text" + + def joinstring(self, string1, string2, delimiter): + joined_string = string1 + delimiter + string2 + return (joined_string, ) + +class JoinStringMulti: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), + "string_1": ("STRING", {"default": '', "forceInput": True}), + "string_2": ("STRING", {"default": '', "forceInput": True}), + "delimiter": ("STRING", {"default": ' ', "multiline": False}), + "return_list": ("BOOLEAN", {"default": False}), + }, + } + + RETURN_TYPES = ("STRING",) + RETURN_NAMES = ("string",) + FUNCTION = "combine" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +Creates single string, or a list of strings, from +multiple input strings. +You can set how many inputs the node has, +with the **inputcount** and clicking update. +""" + + def combine(self, inputcount, delimiter, **kwargs): + string = kwargs["string_1"] + return_list = kwargs["return_list"] + strings = [string] # Initialize a list with the first string + for c in range(1, inputcount): + new_string = kwargs[f"string_{c + 1}"] + if return_list: + strings.append(new_string) # Add new string to the list + else: + string = string + delimiter + new_string + if return_list: + return (strings,) # Return the list of strings + else: + return (string,) # Return the combined string + +class CondPassThrough: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + }, + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING",) + RETURN_NAMES = ("positive", "negative") + FUNCTION = "passthrough" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ + Simply passes through the positive and negative conditioning, + workaround for Set node not allowing bypassed inputs. +""" + + def passthrough(self, positive=None, negative=None): + return (positive, negative,) + +class ModelPassThrough: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "model": ("MODEL", ), + }, + } + + RETURN_TYPES = ("MODEL", ) + RETURN_NAMES = ("model",) + FUNCTION = "passthrough" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ + Simply passes through the model, + workaround for Set node not allowing bypassed inputs. +""" + + def passthrough(self, model=None): + return (model,) + +def append_helper(t, mask, c, set_area_to_bounds, strength): + n = [t[0], t[1].copy()] + _, h, w = mask.shape + n[1]['mask'] = mask + n[1]['set_area_to_bounds'] = set_area_to_bounds + n[1]['mask_strength'] = strength + c.append(n) + +class ConditioningSetMaskAndCombine: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive_1": ("CONDITIONING", ), + "negative_1": ("CONDITIONING", ), + "positive_2": ("CONDITIONING", ), + "negative_2": ("CONDITIONING", ), + "mask_1": ("MASK", ), + "mask_2": ("MASK", ), + "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + } + } + + RETURN_TYPES = ("CONDITIONING","CONDITIONING",) + RETURN_NAMES = ("combined_positive", "combined_negative",) + FUNCTION = "append" + CATEGORY = "KJNodes/masking/conditioning" + DESCRIPTION = """ +Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes +""" + + def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength): + c = [] + c2 = [] + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask_1.shape) < 3: + mask_1 = mask_1.unsqueeze(0) + if len(mask_2.shape) < 3: + mask_2 = mask_2.unsqueeze(0) + for t in positive_1: + append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) + for t in positive_2: + append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) + for t in negative_1: + append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) + for t in negative_2: + append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) + return (c, c2) + +class ConditioningSetMaskAndCombine3: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive_1": ("CONDITIONING", ), + "negative_1": ("CONDITIONING", ), + "positive_2": ("CONDITIONING", ), + "negative_2": ("CONDITIONING", ), + "positive_3": ("CONDITIONING", ), + "negative_3": ("CONDITIONING", ), + "mask_1": ("MASK", ), + "mask_2": ("MASK", ), + "mask_3": ("MASK", ), + "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + } + } + + RETURN_TYPES = ("CONDITIONING","CONDITIONING",) + RETURN_NAMES = ("combined_positive", "combined_negative",) + FUNCTION = "append" + CATEGORY = "KJNodes/masking/conditioning" + DESCRIPTION = """ +Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes +""" + + def append(self, positive_1, negative_1, positive_2, positive_3, negative_2, negative_3, mask_1, mask_2, mask_3, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength): + c = [] + c2 = [] + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask_1.shape) < 3: + mask_1 = mask_1.unsqueeze(0) + if len(mask_2.shape) < 3: + mask_2 = mask_2.unsqueeze(0) + if len(mask_3.shape) < 3: + mask_3 = mask_3.unsqueeze(0) + for t in positive_1: + append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) + for t in positive_2: + append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) + for t in positive_3: + append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) + for t in negative_1: + append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) + for t in negative_2: + append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) + for t in negative_3: + append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) + return (c, c2) + +class ConditioningSetMaskAndCombine4: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive_1": ("CONDITIONING", ), + "negative_1": ("CONDITIONING", ), + "positive_2": ("CONDITIONING", ), + "negative_2": ("CONDITIONING", ), + "positive_3": ("CONDITIONING", ), + "negative_3": ("CONDITIONING", ), + "positive_4": ("CONDITIONING", ), + "negative_4": ("CONDITIONING", ), + "mask_1": ("MASK", ), + "mask_2": ("MASK", ), + "mask_3": ("MASK", ), + "mask_4": ("MASK", ), + "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + } + } + + RETURN_TYPES = ("CONDITIONING","CONDITIONING",) + RETURN_NAMES = ("combined_positive", "combined_negative",) + FUNCTION = "append" + CATEGORY = "KJNodes/masking/conditioning" + DESCRIPTION = """ +Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes +""" + + def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, negative_2, negative_3, negative_4, mask_1, mask_2, mask_3, mask_4, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength): + c = [] + c2 = [] + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask_1.shape) < 3: + mask_1 = mask_1.unsqueeze(0) + if len(mask_2.shape) < 3: + mask_2 = mask_2.unsqueeze(0) + if len(mask_3.shape) < 3: + mask_3 = mask_3.unsqueeze(0) + if len(mask_4.shape) < 3: + mask_4 = mask_4.unsqueeze(0) + for t in positive_1: + append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) + for t in positive_2: + append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) + for t in positive_3: + append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) + for t in positive_4: + append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength) + for t in negative_1: + append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) + for t in negative_2: + append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) + for t in negative_3: + append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) + for t in negative_4: + append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength) + return (c, c2) + +class ConditioningSetMaskAndCombine5: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "positive_1": ("CONDITIONING", ), + "negative_1": ("CONDITIONING", ), + "positive_2": ("CONDITIONING", ), + "negative_2": ("CONDITIONING", ), + "positive_3": ("CONDITIONING", ), + "negative_3": ("CONDITIONING", ), + "positive_4": ("CONDITIONING", ), + "negative_4": ("CONDITIONING", ), + "positive_5": ("CONDITIONING", ), + "negative_5": ("CONDITIONING", ), + "mask_1": ("MASK", ), + "mask_2": ("MASK", ), + "mask_3": ("MASK", ), + "mask_4": ("MASK", ), + "mask_5": ("MASK", ), + "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "mask_5_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + } + } + + RETURN_TYPES = ("CONDITIONING","CONDITIONING",) + RETURN_NAMES = ("combined_positive", "combined_negative",) + FUNCTION = "append" + CATEGORY = "KJNodes/masking/conditioning" + DESCRIPTION = """ +Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes +""" + + def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength): + c = [] + c2 = [] + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask_1.shape) < 3: + mask_1 = mask_1.unsqueeze(0) + if len(mask_2.shape) < 3: + mask_2 = mask_2.unsqueeze(0) + if len(mask_3.shape) < 3: + mask_3 = mask_3.unsqueeze(0) + if len(mask_4.shape) < 3: + mask_4 = mask_4.unsqueeze(0) + if len(mask_5.shape) < 3: + mask_5 = mask_5.unsqueeze(0) + for t in positive_1: + append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) + for t in positive_2: + append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) + for t in positive_3: + append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) + for t in positive_4: + append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength) + for t in positive_5: + append_helper(t, mask_5, c, set_area_to_bounds, mask_5_strength) + for t in negative_1: + append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) + for t in negative_2: + append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) + for t in negative_3: + append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) + for t in negative_4: + append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength) + for t in negative_5: + append_helper(t, mask_5, c2, set_area_to_bounds, mask_5_strength) + return (c, c2) + +class VRAM_Debug: + + @classmethod + + def INPUT_TYPES(s): + return { + "required": { + + "empty_cache": ("BOOLEAN", {"default": True}), + "gc_collect": ("BOOLEAN", {"default": True}), + "unload_all_models": ("BOOLEAN", {"default": False}), + }, + "optional": { + "any_input": (any, {}), + "image_pass": ("IMAGE",), + "model_pass": ("MODEL",), + } + } + + RETURN_TYPES = (any, "IMAGE","MODEL","INT", "INT",) + RETURN_NAMES = ("any_output", "image_pass", "model_pass", "freemem_before", "freemem_after") + FUNCTION = "VRAMdebug" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ +Returns the inputs unchanged, they are only used as triggers, +and performs comfy model management functions and garbage collection, +reports free VRAM before and after the operations. +""" + + def VRAMdebug(self, gc_collect, empty_cache, unload_all_models, image_pass=None, model_pass=None, any_input=None): + freemem_before = model_management.get_free_memory() + print("VRAMdebug: free memory before: ", f"{freemem_before:,.0f}") + if empty_cache: + model_management.soft_empty_cache() + if unload_all_models: + model_management.unload_all_models() + if gc_collect: + import gc + gc.collect() + freemem_after = model_management.get_free_memory() + print("VRAMdebug: free memory after: ", f"{freemem_after:,.0f}") + print("VRAMdebug: freed memory: ", f"{freemem_after - freemem_before:,.0f}") + return {"ui": { + "text": [f"{freemem_before:,.0f}x{freemem_after:,.0f}"]}, + "result": (any_input, image_pass, model_pass, freemem_before, freemem_after) + } + +class SomethingToString: + @classmethod + + def INPUT_TYPES(s): + return { + "required": { + "input": (any, {}), + }, + "optional": { + "prefix": ("STRING", {"default": ""}), + "suffix": ("STRING", {"default": ""}), + } + } + RETURN_TYPES = ("STRING",) + FUNCTION = "stringify" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +Converts any type to a string. +""" + + def stringify(self, input, prefix="", suffix=""): + if isinstance(input, (int, float, bool)): + stringified = str(input) + elif isinstance(input, list): + stringified = ', '.join(str(item) for item in input) + else: + return + if prefix: # Check if prefix is not empty + stringified = prefix + stringified # Add the prefix + if suffix: # Check if suffix is not empty + stringified = stringified + suffix # Add the suffix + + return (stringified,) + +class Sleep: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "input": (any, {}), + "minutes": ("INT", {"default": 0, "min": 0, "max": 1439}), + "seconds": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 59.99, "step": 0.01}), + }, + } + RETURN_TYPES = (any,) + FUNCTION = "sleepdelay" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ +Delays the execution for the input amount of time. +""" + + def sleepdelay(self, input, minutes, seconds): + total_seconds = minutes * 60 + seconds + time.sleep(total_seconds) + return input, + +class EmptyLatentImagePresets: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "dimensions": ( + [ + '512 x 512 (1:1)', + '768 x 512 (1.5:1)', + '960 x 512 (1.875:1)', + '1024 x 512 (2:1)', + '1024 x 576 (1.778:1)', + '1536 x 640 (2.4:1)', + '1344 x 768 (1.75:1)', + '1216 x 832 (1.46:1)', + '1152 x 896 (1.286:1)', + '1024 x 1024 (1:1)', + ], + { + "default": '512 x 512 (1:1)' + }), + + "invert": ("BOOLEAN", {"default": False}), + "batch_size": ("INT", { + "default": 1, + "min": 1, + "max": 4096 + }), + }, + } + + RETURN_TYPES = ("LATENT", "INT", "INT") + RETURN_NAMES = ("Latent", "Width", "Height") + FUNCTION = "generate" + CATEGORY = "KJNodes/latents" + + def generate(self, dimensions, invert, batch_size): + from nodes import EmptyLatentImage + result = [x.strip() for x in dimensions.split('x')] + + # Remove the aspect ratio part + result[0] = result[0].split('(')[0].strip() + result[1] = result[1].split('(')[0].strip() + + if invert: + width = int(result[1].split(' ')[0]) + height = int(result[0]) + else: + width = int(result[0]) + height = int(result[1].split(' ')[0]) + latent = EmptyLatentImage().generate(width, height, batch_size)[0] + + return (latent, int(width), int(height),) + +class EmptyLatentImageCustomPresets: + @classmethod + def INPUT_TYPES(cls): + try: + with open(os.path.join(script_directory, 'custom_dimensions.json')) as f: + dimensions_dict = json.load(f) + except FileNotFoundError: + dimensions_dict = [] + return { + "required": { + "dimensions": ( + [f"{d['label']} - {d['value']}" for d in dimensions_dict], + ), + + "invert": ("BOOLEAN", {"default": False}), + "batch_size": ("INT", { + "default": 1, + "min": 1, + "max": 4096 + }), + }, + } + + RETURN_TYPES = ("LATENT", "INT", "INT") + RETURN_NAMES = ("Latent", "Width", "Height") + FUNCTION = "generate" + CATEGORY = "KJNodes/latents" + DESCRIPTION = """ +Generates an empty latent image with the specified dimensions. +The choices are loaded from 'custom_dimensions.json' in the nodes folder. +""" + + def generate(self, dimensions, invert, batch_size): + from nodes import EmptyLatentImage + # Split the string into label and value + label, value = dimensions.split(' - ') + # Split the value into width and height + width, height = [x.strip() for x in value.split('x')] + + if invert: + width, height = height, width + + latent = EmptyLatentImage().generate(int(width), int(height), batch_size)[0] + + return (latent, int(width), int(height),) + +class WidgetToString: + @classmethod + def IS_CHANGED(cls, id, node_title, any_input, **kwargs): + if any_input is not None and (id != 0 or node_title != ""): + return float("NaN") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "id": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}), + "widget_name": ("STRING", {"multiline": False}), + "return_all": ("BOOLEAN", {"default": False}), + }, + "optional": { + "any_input": (any, {}), + "node_title": ("STRING", {"multiline": False}), + "allowed_float_decimals": ("INT", {"default": 2, "min": 0, "max": 10, "tooltip": "Number of decimal places to display for float values"}), + + }, + "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", + "prompt": "PROMPT", + "unique_id": "UNIQUE_ID",}, + } + + RETURN_TYPES = ("STRING", ) + FUNCTION = "get_widget_value" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +Selects a node and it's specified widget and outputs the value as a string. +If no node id or title is provided it will use the 'any_input' link and use that node. +To see node id's, enable node id display from Manager badge menu. +Alternatively you can search with the node title. Node titles ONLY exist if they +are manually edited! +The 'any_input' is required for making sure the node you want the value from exists in the workflow. +""" + + def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, unique_id, return_all=False, any_input=None, node_title="", allowed_float_decimals=2): + workflow = extra_pnginfo["workflow"] + #print(json.dumps(workflow, indent=4)) + results = [] + node_id = None # Initialize node_id to handle cases where no match is found + link_id = None + link_to_node_map = {} + + for node in workflow["nodes"]: + if node_title: + if "title" in node: + if node["title"] == node_title: + node_id = node["id"] + break + else: + print("Node title not found.") + elif id != 0: + if node["id"] == id: + node_id = id + break + elif any_input is not None: + if node["type"] == "WidgetToString" and node["id"] == int(unique_id) and not link_id: + for node_input in node["inputs"]: + if node_input["name"] == "any_input": + link_id = node_input["link"] + + # Construct a map of links to node IDs for future reference + node_outputs = node.get("outputs", None) + if not node_outputs: + continue + for output in node_outputs: + node_links = output.get("links", None) + if not node_links: + continue + for link in node_links: + link_to_node_map[link] = node["id"] + if link_id and link == link_id: + break + + if link_id: + node_id = link_to_node_map.get(link_id, None) + + if node_id is None: + raise ValueError("No matching node found for the given title or id") + + values = prompt[str(node_id)] + if "inputs" in values: + if return_all: + # Format items based on type + formatted_items = [] + for k, v in values["inputs"].items(): + if isinstance(v, float): + item = f"{k}: {v:.{allowed_float_decimals}f}" + else: + item = f"{k}: {str(v)}" + formatted_items.append(item) + results.append(', '.join(formatted_items)) + elif widget_name in values["inputs"]: + v = values["inputs"][widget_name] + if isinstance(v, float): + v = f"{v:.{allowed_float_decimals}f}" + else: + v = str(v) + return (v, ) + else: + raise NameError(f"Widget not found: {node_id}.{widget_name}") + return (', '.join(results).strip(', '), ) + +class DummyOut: + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "any_input": (any, {}), + } + } + + RETURN_TYPES = (any,) + FUNCTION = "dummy" + CATEGORY = "KJNodes/misc" + OUTPUT_NODE = True + DESCRIPTION = """ +Does nothing, used to trigger generic workflow output. +A way to get previews in the UI without saving anything to disk. +""" + + def dummy(self, any_input): + return (any_input,) + +class FlipSigmasAdjusted: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "divide_by_last_sigma": ("BOOLEAN", {"default": False}), + "divide_by": ("FLOAT", {"default": 1,"min": 1, "max": 255, "step": 0.01}), + "offset_by": ("INT", {"default": 1,"min": -100, "max": 100, "step": 1}), + } + } + RETURN_TYPES = ("SIGMAS", "STRING",) + RETURN_NAMES = ("SIGMAS", "sigmas_string",) + CATEGORY = "KJNodes/noise" + FUNCTION = "get_sigmas_adjusted" + + def get_sigmas_adjusted(self, sigmas, divide_by_last_sigma, divide_by, offset_by): + + sigmas = sigmas.flip(0) + if sigmas[0] == 0: + sigmas[0] = 0.0001 + adjusted_sigmas = sigmas.clone() + #offset sigma + for i in range(1, len(sigmas)): + offset_index = i - offset_by + if 0 <= offset_index < len(sigmas): + adjusted_sigmas[i] = sigmas[offset_index] + else: + adjusted_sigmas[i] = 0.0001 + if adjusted_sigmas[0] == 0: + adjusted_sigmas[0] = 0.0001 + if divide_by_last_sigma: + adjusted_sigmas = adjusted_sigmas / adjusted_sigmas[-1] + + sigma_np_array = adjusted_sigmas.numpy() + array_string = np.array2string(sigma_np_array, precision=2, separator=', ', threshold=np.inf) + adjusted_sigmas = adjusted_sigmas / divide_by + return (adjusted_sigmas, array_string,) + +class CustomSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "sigmas_string" :("STRING", {"default": "14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029","multiline": True}), + "interpolate_to_steps": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}), + } + } + RETURN_TYPES = ("SIGMAS",) + RETURN_NAMES = ("SIGMAS",) + CATEGORY = "KJNodes/noise" + FUNCTION = "customsigmas" + DESCRIPTION = """ +Creates a sigmas tensor from a string of comma separated values. +Examples: + +Nvidia's optimized AYS 10 step schedule for SD 1.5: +14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029 +SDXL: +14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029 +SVD: +700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002 +""" + def customsigmas(self, sigmas_string, interpolate_to_steps): + sigmas_list = sigmas_string.split(', ') + sigmas_float_list = [float(sigma) for sigma in sigmas_list] + sigmas_tensor = torch.FloatTensor(sigmas_float_list) + if len(sigmas_tensor) != interpolate_to_steps + 1: + sigmas_tensor = self.loglinear_interp(sigmas_tensor, interpolate_to_steps + 1) + sigmas_tensor[-1] = 0 + return (sigmas_tensor.float(),) + + def loglinear_interp(self, t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + t_steps_np = t_steps.numpy() + + xs = np.linspace(0, 1, len(t_steps_np)) + ys = np.log(t_steps_np[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + interped_ys_tensor = torch.tensor(interped_ys) + return interped_ys_tensor + +class StringToFloatList: + @classmethod + def INPUT_TYPES(s): + return {"required": + { + "string" :("STRING", {"default": "1, 2, 3", "multiline": True}), + } + } + RETURN_TYPES = ("FLOAT",) + RETURN_NAMES = ("FLOAT",) + CATEGORY = "KJNodes/misc" + FUNCTION = "createlist" + + def createlist(self, string): + float_list = [float(x.strip()) for x in string.split(',')] + return (float_list,) + + +class InjectNoiseToLatent: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "latents":("LATENT",), + "strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.0001}), + "noise": ("LATENT",), + "normalize": ("BOOLEAN", {"default": False}), + "average": ("BOOLEAN", {"default": False}), + }, + "optional":{ + "mask": ("MASK", ), + "mix_randn_amount": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.001}), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "injectnoise" + CATEGORY = "KJNodes/noise" + + def injectnoise(self, latents, strength, noise, normalize, average, mix_randn_amount=0, seed=None, mask=None): + samples = latents["samples"].clone().cpu() + noise = noise["samples"].clone().cpu() + if samples.shape != samples.shape: + raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape") + if average: + noised = (samples + noise) / 2 + else: + noised = samples + noise * strength + if normalize: + noised = noised / noised.std() + if mask is not None: + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noised.shape[2], noised.shape[3]), mode="bilinear") + mask = mask.expand((-1,noised.shape[1],-1,-1)) + if mask.shape[0] < noised.shape[0]: + mask = mask.repeat((noised.shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:noised.shape[0]] + noised = mask * noised + (1-mask) * samples + if mix_randn_amount > 0: + if seed is not None: + generator = torch.manual_seed(seed) + rand_noise = torch.randn(noised.size(), dtype=noised.dtype, layout=noised.layout, generator=generator, device="cpu") + noised = noised + (mix_randn_amount * rand_noise) + + return ({"samples":noised},) + +class SoundReactive: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "sound_level": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 99999, "step": 0.01}), + "start_range_hz": ("INT", {"default": 150, "min": 0, "max": 9999, "step": 1}), + "end_range_hz": ("INT", {"default": 2000, "min": 0, "max": 9999, "step": 1}), + "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 99999, "step": 0.01}), + "smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "normalize": ("BOOLEAN", {"default": False}), + }, + } + + RETURN_TYPES = ("FLOAT","INT",) + RETURN_NAMES =("sound_level", "sound_level_int",) + FUNCTION = "react" + CATEGORY = "KJNodes/audio" + DESCRIPTION = """ +Reacts to the sound level of the input. +Uses your browsers sound input options and requires. +Meant to be used with realtime diffusion with autoqueue. +""" + + def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize): + + sound_level *= multiplier + + if normalize: + sound_level /= 255 + + sound_level_int = int(sound_level) + return (sound_level, sound_level_int, ) + +class GenerateNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + "multiplier": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 4096, "step": 0.01}), + "constant_batch_noise": ("BOOLEAN", {"default": False}), + "normalize": ("BOOLEAN", {"default": False}), + }, + "optional": { + "model": ("MODEL", ), + "sigmas": ("SIGMAS", ), + "latent_channels": (['4', '16', ],), + "shape": (["BCHW", "BCTHW","BTCHW",],), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "generatenoise" + CATEGORY = "KJNodes/noise" + DESCRIPTION = """ +Generates noise for injection or to be used as empty latents on samplers with add_noise off. +""" + + def generatenoise(self, batch_size, width, height, seed, multiplier, constant_batch_noise, normalize, sigmas=None, model=None, latent_channels=4, shape="BCHW"): + + generator = torch.manual_seed(seed) + if shape == "BCHW": + noise = torch.randn([batch_size, int(latent_channels), height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") + elif shape == "BCTHW": + noise = torch.randn([1, int(latent_channels), batch_size,height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") + elif shape == "BTCHW": + noise = torch.randn([1, batch_size, int(latent_channels), height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") + if sigmas is not None: + sigma = sigmas[0] - sigmas[-1] + sigma /= model.model.latent_format.scale_factor + noise *= sigma + + noise *=multiplier + + if normalize: + noise = noise / noise.std() + if constant_batch_noise: + noise = noise[0].repeat(batch_size, 1, 1, 1) + + + return ({"samples":noise}, ) + +def camera_embeddings(elevation, azimuth): + elevation = torch.as_tensor([elevation]) + azimuth = torch.as_tensor([azimuth]) + embeddings = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + torch.deg2rad( + 90 - torch.full_like(elevation, 0) + ), + ], dim=-1).unsqueeze(1) + + return embeddings + +def interpolate_angle(start, end, fraction): + # Calculate the difference in angles and adjust for wraparound if necessary + diff = (end - start + 540) % 360 - 180 + # Apply fraction to the difference + interpolated = start + fraction * diff + # Normalize the result to be within the range of -180 to 180 + return (interpolated + 180) % 360 - 180 + + +class StableZero123_BatchSchedule: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), + "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), + "elevation_points_string": ("STRING", {"default": "0:(0.0),\n7:(0.0),\n15:(0.0)\n", "multiline": True}), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + CATEGORY = "KJNodes/experimental" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + def ease_in(t): + return t * t + def ease_out(t): + return 1 - (1 - t) * (1 - t) + def ease_in_out(t): + return 3 * t * t - 2 * t * t * t + + # Parse the azimuth input string into a list of tuples + azimuth_points = [] + azimuth_points_string = azimuth_points_string.rstrip(',\n') + for point_str in azimuth_points_string.split(','): + frame_str, azimuth_str = point_str.split(':') + frame = int(frame_str.strip()) + azimuth = float(azimuth_str.strip()[1:-1]) + azimuth_points.append((frame, azimuth)) + # Sort the points by frame number + azimuth_points.sort(key=lambda x: x[0]) + + # Parse the elevation input string into a list of tuples + elevation_points = [] + elevation_points_string = elevation_points_string.rstrip(',\n') + for point_str in elevation_points_string.split(','): + frame_str, elevation_str = point_str.split(':') + frame = int(frame_str.strip()) + elevation_val = float(elevation_str.strip()[1:-1]) + elevation_points.append((frame, elevation_val)) + # Sort the points by frame number + elevation_points.sort(key=lambda x: x[0]) + + # Index of the next point to interpolate towards + next_point = 1 + next_elevation_point = 1 + + positive_cond_out = [] + positive_pooled_out = [] + negative_cond_out = [] + negative_pooled_out = [] + + #azimuth interpolation + for i in range(batch_size): + # Find the interpolated azimuth for the current frame + while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: + next_point += 1 + # If next_point is equal to the length of points, we've gone past the last point + if next_point == len(azimuth_points): + next_point -= 1 # Set next_point to the last index of points + prev_point = max(next_point - 1, 0) # Ensure prev_point is not less than 0 + + # Calculate fraction + if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: # Prevent division by zero + fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0]) + if interpolation == "ease_in": + fraction = ease_in(fraction) + elif interpolation == "ease_out": + fraction = ease_out(fraction) + elif interpolation == "ease_in_out": + fraction = ease_in_out(fraction) + + # Use the new interpolate_angle function + interpolated_azimuth = interpolate_angle(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction) + else: + interpolated_azimuth = azimuth_points[prev_point][1] + # Interpolate the elevation + next_elevation_point = 1 + while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: + next_elevation_point += 1 + if next_elevation_point == len(elevation_points): + next_elevation_point -= 1 + prev_elevation_point = max(next_elevation_point - 1, 0) + + if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: + fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) + if interpolation == "ease_in": + fraction = ease_in(fraction) + elif interpolation == "ease_out": + fraction = ease_out(fraction) + elif interpolation == "ease_in_out": + fraction = ease_in_out(fraction) + + interpolated_elevation = interpolate_angle(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction) + else: + interpolated_elevation = elevation_points[prev_elevation_point][1] + + cam_embeds = camera_embeddings(interpolated_elevation, interpolated_azimuth) + cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1) + + positive_pooled_out.append(t) + positive_cond_out.append(cond) + negative_pooled_out.append(torch.zeros_like(t)) + negative_cond_out.append(torch.zeros_like(pooled)) + + # Concatenate the conditions and pooled outputs + final_positive_cond = torch.cat(positive_cond_out, dim=0) + final_positive_pooled = torch.cat(positive_pooled_out, dim=0) + final_negative_cond = torch.cat(negative_cond_out, dim=0) + final_negative_pooled = torch.cat(negative_pooled_out, dim=0) + + # Structure the final output + final_positive = [[final_positive_cond, {"concat_latent_image": final_positive_pooled}]] + final_negative = [[final_negative_cond, {"concat_latent_image": final_negative_pooled}]] + + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (final_positive, final_negative, {"samples": latent}) + +def linear_interpolate(start, end, fraction): + return start + (end - start) * fraction + +class SV3D_BatchSchedule: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}), + "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), + "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}), + "elevation_points_string": ("STRING", {"default": "0:(0.0),\n9:(0.0),\n20:(0.0)\n", "multiline": True}), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = """ +Allow scheduling of the azimuth and elevation conditions for SV3D. +Note that SV3D is still a video model and the schedule needs to always go forward +https://huggingface.co/stabilityai/sv3d +""" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + def ease_in(t): + return t * t + def ease_out(t): + return 1 - (1 - t) * (1 - t) + def ease_in_out(t): + return 3 * t * t - 2 * t * t * t + + # Parse the azimuth input string into a list of tuples + azimuth_points = [] + azimuth_points_string = azimuth_points_string.rstrip(',\n') + for point_str in azimuth_points_string.split(','): + frame_str, azimuth_str = point_str.split(':') + frame = int(frame_str.strip()) + azimuth = float(azimuth_str.strip()[1:-1]) + azimuth_points.append((frame, azimuth)) + # Sort the points by frame number + azimuth_points.sort(key=lambda x: x[0]) + + # Parse the elevation input string into a list of tuples + elevation_points = [] + elevation_points_string = elevation_points_string.rstrip(',\n') + for point_str in elevation_points_string.split(','): + frame_str, elevation_str = point_str.split(':') + frame = int(frame_str.strip()) + elevation_val = float(elevation_str.strip()[1:-1]) + elevation_points.append((frame, elevation_val)) + # Sort the points by frame number + elevation_points.sort(key=lambda x: x[0]) + + # Index of the next point to interpolate towards + next_point = 1 + next_elevation_point = 1 + elevations = [] + azimuths = [] + # For azimuth interpolation + for i in range(batch_size): + # Find the interpolated azimuth for the current frame + while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: + next_point += 1 + if next_point == len(azimuth_points): + next_point -= 1 + prev_point = max(next_point - 1, 0) + + if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: + fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0]) + # Apply the ease function to the fraction + if interpolation == "ease_in": + fraction = ease_in(fraction) + elif interpolation == "ease_out": + fraction = ease_out(fraction) + elif interpolation == "ease_in_out": + fraction = ease_in_out(fraction) + + interpolated_azimuth = linear_interpolate(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction) + else: + interpolated_azimuth = azimuth_points[prev_point][1] + + # Interpolate the elevation + next_elevation_point = 1 + while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: + next_elevation_point += 1 + if next_elevation_point == len(elevation_points): + next_elevation_point -= 1 + prev_elevation_point = max(next_elevation_point - 1, 0) + + if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: + fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) + # Apply the ease function to the fraction + if interpolation == "ease_in": + fraction = ease_in(fraction) + elif interpolation == "ease_out": + fraction = ease_out(fraction) + elif interpolation == "ease_in_out": + fraction = ease_in_out(fraction) + + interpolated_elevation = linear_interpolate(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction) + else: + interpolated_elevation = elevation_points[prev_elevation_point][1] + + azimuths.append(interpolated_azimuth) + elevations.append(interpolated_elevation) + + #print("azimuths", azimuths) + #print("elevations", elevations) + + # Structure the final output + final_positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] + final_negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t),"elevation": elevations, "azimuth": azimuths}]] + + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (final_positive, final_negative, {"samples": latent}) + +class LoadResAdapterNormalization: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "resadapter_path": (folder_paths.get_filename_list("checkpoints"), ) + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_res_adapter" + CATEGORY = "KJNodes/experimental" + + def load_res_adapter(self, model, resadapter_path): + print("ResAdapter: Checking ResAdapter path") + resadapter_full_path = folder_paths.get_full_path("checkpoints", resadapter_path) + if not os.path.exists(resadapter_full_path): + raise Exception("Invalid model path") + else: + print("ResAdapter: Loading ResAdapter normalization weights") + from comfy.utils import load_torch_file + prefix_to_remove = 'diffusion_model.' + model_clone = model.clone() + norm_state_dict = load_torch_file(resadapter_full_path) + new_values = {key[len(prefix_to_remove):]: value for key, value in norm_state_dict.items() if key.startswith(prefix_to_remove)} + print("ResAdapter: Attempting to add patches with ResAdapter weights") + try: + for key in model.model.diffusion_model.state_dict().keys(): + if key in new_values: + original_tensor = model.model.diffusion_model.state_dict()[key] + new_tensor = new_values[key].to(model.model.diffusion_model.dtype) + if original_tensor.shape == new_tensor.shape: + model_clone.add_object_patch(f"diffusion_model.{key}.data", new_tensor) + else: + print("ResAdapter: No match for key: ",key) + except: + raise Exception("Could not patch model, this way of patching was added to ComfyUI on March 3rd 2024, is your ComfyUI up to date?") + print("ResAdapter: Added resnet normalization patches") + return (model_clone, ) + +class Superprompt: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "instruction_prompt": ("STRING", {"default": 'Expand the following prompt to add more detail', "multiline": True}), + "prompt": ("STRING", {"default": '', "multiline": True, "forceInput": True}), + "max_new_tokens": ("INT", {"default": 128, "min": 1, "max": 4096, "step": 1}), + } + } + + RETURN_TYPES = ("STRING",) + FUNCTION = "process" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +# SuperPrompt +A T5 model fine-tuned on the SuperPrompt dataset for +upsampling text prompts to more detailed descriptions. +Meant to be used as a pre-generation step for text-to-image +models that benefit from more detailed prompts. +https://huggingface.co/roborovski/superprompt-v1 +""" + + def process(self, instruction_prompt, prompt, max_new_tokens): + device = model_management.get_torch_device() + from transformers import T5Tokenizer, T5ForConditionalGeneration + + checkpoint_path = os.path.join(script_directory, "models","superprompt-v1") + if not os.path.exists(checkpoint_path): + print(f"Downloading model to: {checkpoint_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id="roborovski/superprompt-v1", + local_dir=checkpoint_path, + local_dir_use_symlinks=False) + tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False) + + model = T5ForConditionalGeneration.from_pretrained(checkpoint_path, device_map=device) + model.to(device) + input_text = instruction_prompt + ": " + prompt + + input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device) + outputs = model.generate(input_ids, max_new_tokens=max_new_tokens) + out = (tokenizer.decode(outputs[0])) + out = out.replace('', '') + out = out.replace('    ', '') + + return (out, ) + + +class CameraPoseVisualizer: + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pose_file_path": ("STRING", {"default": '', "multiline": False}), + "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}), + "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}), + "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}), + "use_exact_fx": ("BOOLEAN", {"default": False}), + "relative_c2w": ("BOOLEAN", {"default": True}), + "use_viewer": ("BOOLEAN", {"default": False}), + }, + "optional": { + "cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "plot" + CATEGORY = "KJNodes/misc" + DESCRIPTION = """ +Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose +or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. +""" + + def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None): + import matplotlib as mpl + import matplotlib.pyplot as plt + from torchvision.transforms import ToTensor + + x_min = -2.0 * scale + x_max = 2.0 * scale + y_min = -2.0 * scale + y_max = 2.0 * scale + z_min = -2.0 * scale + z_max = 2.0 * scale + plt.rcParams['text.color'] = '#999999' + self.fig = plt.figure(figsize=(18, 7)) + self.fig.patch.set_facecolor('#353535') + self.ax = self.fig.add_subplot(projection='3d') + self.ax.set_facecolor('#353535') # Set the background color here + self.ax.grid(color='#999999', linestyle='-', linewidth=0.5) + self.plotly_data = None # plotly data traces + self.ax.set_aspect("auto") + self.ax.set_xlim(x_min, x_max) + self.ax.set_ylim(y_min, y_max) + self.ax.set_zlim(z_min, z_max) + self.ax.set_xlabel('x', color='#999999') + self.ax.set_ylabel('y', color='#999999') + self.ax.set_zlabel('z', color='#999999') + for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels(): + text.set_color('#999999') + print('initialize camera pose visualizer') + + if pose_file_path != "": + with open(pose_file_path, 'r') as f: + poses = f.readlines() + w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]] + fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]] + #print(poses) + elif cameractrl_poses is not None: + poses = cameractrl_poses + w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses] + fxs = [pose[1] for pose in cameractrl_poses] + else: + raise ValueError("Please provide either pose_file_path or cameractrl_poses") + + total_frames = len(w2cs) + transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4) + last_row = np.zeros((1, 4)) + last_row[0, -1] = 1.0 + + w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs] + c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w) + + for frame_idx, c2w in enumerate(c2ws): + self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval, + zval=(fxs[frame_idx] if use_exact_fx else zval)) + + # Create the colorbar + cmap = mpl.cm.rainbow + norm = mpl.colors.Normalize(vmin=0, vmax=total_frames) + colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical') + + # Change the colorbar label + colorbar.set_label('Frame', color='#999999') # Change the label and its color + + # Change the tick colors + colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color + + # Change the tick frequency + # Assuming you want to set the ticks at every 10th frame + ticks = np.arange(0, total_frames, 10) + colorbar.ax.yaxis.set_ticks(ticks) + + plt.title('') + plt.draw() + buf = io.BytesIO() + plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) + buf.seek(0) + img = Image.open(buf) + tensor_img = ToTensor()(img) + buf.close() + tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0) + if use_viewer: + time.sleep(1) + plt.show() + return (tensor_img,) + + def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3): + import matplotlib.pyplot as plt + from mpl_toolkits.mplot3d.art3d import Poly3DCollection + vertex_std = np.array([[0, 0, 0, 1], + [base_xval, -base_xval * hw_ratio, zval, 1], + [base_xval, base_xval * hw_ratio, zval, 1], + [-base_xval, base_xval * hw_ratio, zval, 1], + [-base_xval, -base_xval * hw_ratio, zval, 1]]) + vertex_transformed = vertex_std @ extrinsic.T + meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], + [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] + + color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) + + self.ax.add_collection3d( + Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25)) + + def customize_legend(self, list_label): + from matplotlib.patches import Patch + import matplotlib.pyplot as plt + list_handle = [] + for idx, label in enumerate(list_label): + color = plt.cm.rainbow(idx / len(list_label)) + patch = Patch(color=color, label=label) + list_handle.append(patch) + plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) + + def get_c2w(self, w2cs, transform_matrix, relative_c2w): + if relative_c2w: + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] + else: + ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] + ret_poses = [transform_matrix @ x for x in ret_poses] + return np.array(ret_poses, dtype=np.float32) + + + +class CheckpointPerturbWeights: + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "joint_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), + "final_layer": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), + "rest_of_the_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + } + } + RETURN_TYPES = ("MODEL",) + FUNCTION = "mod" + OUTPUT_NODE = True + + CATEGORY = "KJNodes/experimental" + + def mod(self, seed, model, joint_blocks, final_layer, rest_of_the_blocks): + import copy + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + device = model_management.get_torch_device() + model_copy = copy.deepcopy(model) + model_copy.model.to(device) + keys = model_copy.model.diffusion_model.state_dict().keys() + + dict = {} + for key in keys: + dict[key] = model_copy.model.diffusion_model.state_dict()[key] + + pbar = ProgressBar(len(keys)) + for k in keys: + v = dict[k] + print(f'{k}: {v.std()}') + if k.startswith('joint_blocks'): + multiplier = joint_blocks + elif k.startswith('final_layer'): + multiplier = final_layer + else: + multiplier = rest_of_the_blocks + dict[k] += torch.normal(torch.zeros_like(v) * v.mean(), torch.ones_like(v) * v.std() * multiplier).to(device) + pbar.update(1) + model_copy.model.diffusion_model.load_state_dict(dict) + return model_copy, + +class DifferentialDiffusionAdvanced(): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "samples": ("LATENT",), + "mask": ("MASK",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), + }} + RETURN_TYPES = ("MODEL", "LATENT") + FUNCTION = "apply" + CATEGORY = "_for_testing" + INIT = False + + def apply(self, model, samples, mask, multiplier): + self.multiplier = multiplier + model = model.clone() + model.set_model_denoise_mask_function(self.forward) + s = samples.copy() + s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + return (model, s) + + def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): + model = extra_options["model"] + step_sigmas = extra_options["sigmas"] + sigma_to = model.inner_model.model_sampling.sigma_min + if step_sigmas[-1] > sigma_to: + sigma_to = step_sigmas[-1] + sigma_from = step_sigmas[0] + + ts_from = model.inner_model.model_sampling.timestep(sigma_from) + ts_to = model.inner_model.model_sampling.timestep(sigma_to) + current_ts = model.inner_model.model_sampling.timestep(sigma[0]) + + threshold = (current_ts - ts_to) / (ts_from - ts_to) / self.multiplier + + return (denoise_mask >= threshold).to(denoise_mask.dtype) + +class FluxBlockLoraSelect: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(19): + arg_dict["double_blocks.{}.".format(i)] = argument + + for i in range(38): + arg_dict["single_blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + +class HunyuanVideoBlockLoraSelect: + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(20): + arg_dict["double_blocks.{}.".format(i)] = argument + + for i in range(40): + arg_dict["single_blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + +class Wan21BlockLoraSelect: + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) + + for i in range(40): + arg_dict["blocks.{}.".format(i)] = argument + + return {"required": arg_dict} + + RETURN_TYPES = ("SELECTEDDITBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora" + + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" + + def load_lora(self, **kwargs): + return (kwargs,) + +class DiTBlockLoraLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), + + }, + "optional": { + "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}), + "opt_lora_path": ("STRING", {"forceInput": True, "tooltip": "Absolute path of the LoRA."}), + "blocks": ("SELECTEDDITBLOCKS",), + } + } + + RETURN_TYPES = ("MODEL", "STRING", ) + RETURN_NAMES = ("model", "rank", ) + OUTPUT_TOOLTIPS = ("The modified diffusion model.", "possible rank of the LoRA.") + FUNCTION = "load_lora" + CATEGORY = "KJNodes/experimental" + + def load_lora(self, model, strength_model, lora_name=None, opt_lora_path=None, blocks=None): + + import comfy.lora + + if opt_lora_path: + lora_path = opt_lora_path + else: + lora_path = folder_paths.get_full_path("loras", lora_name) + + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + self.loaded_lora = None + + if lora is None: + lora = load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + # Find the first key that ends with "weight" + rank = "unknown" + weight_key = next((key for key in lora.keys() if key.endswith('weight')), None) + # Print the shape of the value corresponding to the key + if weight_key: + print(f"Shape of the first 'weight' key ({weight_key}): {lora[weight_key].shape}") + rank = str(lora[weight_key].shape[0]) + else: + print("No key ending with 'weight' found.") + rank = "Couldn't find rank" + self.loaded_lora = (lora_path, lora) + + key_map = {} + if model is not None: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) + + loaded = comfy.lora.load_lora(lora, key_map) + + if blocks is not None: + keys_to_delete = [] + + for block in blocks: + for key in list(loaded.keys()): + match = False + if isinstance(key, str) and block in key: + match = True + elif isinstance(key, tuple): + for k in key: + if block in k: + match = True + break + + if match: + ratio = blocks[block] + if ratio == 0: + keys_to_delete.append(key) + else: + value = loaded[key].weights + weights_list = list(loaded[key].weights) + weights_list[2] = ratio + loaded[key].weights = tuple(weights_list) + + for key in keys_to_delete: + del loaded[key] + + print("loading lora keys:") + for key, value in loaded.items(): + print(f"Key: {key}, Alpha: {value.weights[2]}") + + + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + + k = set(k) + for x in loaded: + if (x not in k): + print("NOT LOADED {}".format(x)) + + return (new_modelpatcher, rank) + +class CustomControlNetWeightsFluxFromList: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "list_of_floats": ("FLOAT", {"forceInput": True}, ), + }, + "optional": { + "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), + "cn_extras": ("CN_WEIGHTS_EXTRAS",), + "autosize": ("ACNAUTOSIZE", {"padding": 0}), + } + } + + RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) + RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") + FUNCTION = "load_weights" + DESCRIPTION = "Creates controlnet weights from a list of floats for Advanced-ControlNet" + + CATEGORY = "KJNodes/controlnet" + + def load_weights(self, list_of_floats: list[float], + uncond_multiplier: float=1.0, cn_extras: dict[str]={}): + + adv_control = importlib.import_module("ComfyUI-Advanced-ControlNet.adv_control") + ControlWeights = adv_control.utils.ControlWeights + TimestepKeyframeGroup = adv_control.utils.TimestepKeyframeGroup + TimestepKeyframe = adv_control.utils.TimestepKeyframe + + weights = ControlWeights.controlnet(weights_input=list_of_floats, uncond_multiplier=uncond_multiplier, extras=cn_extras) + print(weights.weights_input) + return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) + +SHAKKERLABS_UNION_CONTROLNET_TYPES = { + "canny": 0, + "tile": 1, + "depth": 2, + "blur": 3, + "pose": 4, + "gray": 5, + "low quality": 6, +} + +class SetShakkerLabsUnionControlNetType: + @classmethod + def INPUT_TYPES(s): + return {"required": {"control_net": ("CONTROL_NET", ), + "type": (["auto"] + list(SHAKKERLABS_UNION_CONTROLNET_TYPES.keys()),) + }} + + CATEGORY = "conditioning/controlnet" + RETURN_TYPES = ("CONTROL_NET",) + + FUNCTION = "set_controlnet_type" + + def set_controlnet_type(self, control_net, type): + control_net = control_net.copy() + type_number = SHAKKERLABS_UNION_CONTROLNET_TYPES.get(type, -1) + if type_number >= 0: + control_net.set_extra_arg("control_type", [type_number]) + else: + control_net.set_extra_arg("control_type", []) + + return (control_net,) + +class ModelSaveKJ: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}), + "model_key_prefix": ("STRING", {"default": "model.diffusion_model."}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, filename_prefix, model_key_prefix, prompt=None, extra_pnginfo=None): + from comfy.utils import save_torch_file + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + load_models = [model] + + model_management.load_models_gpu(load_models, force_patch_weights=True) + default_prefix = "model.diffusion_model." + + sd = model.model.state_dict_for_saving(None, None, None) + + new_sd = {} + for k in sd: + if k.startswith(default_prefix): + new_key = model_key_prefix + k[len(default_prefix):] + else: + new_key = k # In case the key doesn't start with the default prefix, keep it unchanged + t = sd[k] + if not t.is_contiguous(): + t = t.contiguous() + new_sd[new_key] = t + print(full_output_folder) + if not os.path.exists(full_output_folder): + os.makedirs(full_output_folder) + save_torch_file(new_sd, os.path.join(full_output_folder, output_checkpoint)) + return {} + +class StyleModelApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "style_model": ("STYLE_MODEL", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_stylemodel" + CATEGORY = "KJNodes/experimental" + DESCRIPTION = "StyleModelApply but with strength parameter" + + def apply_stylemodel(self, clip_vision_output, style_model, conditioning, strength=1.0): + cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + cond = strength * cond + c = [] + for t in conditioning: + n = [torch.cat((t[0], cond), dim=1), t[1].copy()] + c.append(n) + return (c, ) + +class AudioConcatenate: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "audio1": ("AUDIO",), + "audio2": ("AUDIO",), + "direction": ( + [ 'right', + 'left', + ], + { + "default": 'right' + }), + }} + + RETURN_TYPES = ("AUDIO",) + FUNCTION = "concanate" + CATEGORY = "KJNodes/audio" + DESCRIPTION = """ +Concatenates the audio1 to audio2 in the specified direction. +""" + + def concanate(self, audio1, audio2, direction): + sample_rate_1 = audio1["sample_rate"] + sample_rate_2 = audio2["sample_rate"] + if sample_rate_1 != sample_rate_2: + raise Exception("Sample rates of the two audios do not match") + + waveform_1 = audio1["waveform"] + print(waveform_1.shape) + waveform_2 = audio2["waveform"] + + # Concatenate based on the specified direction + if direction == 'right': + concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2) # Concatenate along width + elif direction == 'left': + concatenated_audio= torch.cat((waveform_2, waveform_1), dim=2) # Concatenate along width + return ({"waveform": concatenated_audio, "sample_rate": sample_rate_1},) + +class LeapfusionHunyuanI2V: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT",), + "index": ("INT", {"default": 0, "min": -1, "max": 1000, "step": 1,"tooltip": "The index of the latent to be replaced. 0 for first frame and -1 for last"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of steps to apply"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of steps to apply"}), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "KJNodes/experimental" + + def patch(self, model, latent, index, strength, start_percent, end_percent): + + def outer_wrapper(samples, index, start_percent, end_percent): + def unet_wrapper(apply_model, args): + steps = args["c"]["transformer_options"]["sample_sigmas"] + inp, timestep, c = args["input"], args["timestep"], args["c"] + matched_step_index = (steps == timestep).nonzero() + if len(matched_step_index) > 0: + current_step_index = matched_step_index.item() + else: + for i in range(len(steps) - 1): + # walk from beginning of steps until crossing the timestep + if (steps[i] - timestep[0]) * (steps[i + 1] - timestep[0]) <= 0: + current_step_index = i + break + else: + current_step_index = 0 + current_percent = current_step_index / (len(steps) - 1) + if samples is not None: + if start_percent <= current_percent <= end_percent: + inp[:, :, [index], :, :] = samples[:, :, [0], :, :].to(inp) + else: + inp[:, :, [index], :, :] = torch.zeros(1) + return apply_model(inp, timestep, **c) + return unet_wrapper + + samples = latent["samples"] * 0.476986 * strength + m = model.clone() + m.set_model_unet_function_wrapper(outer_wrapper(samples, index, start_percent, end_percent)) + + return (m,) + +class ImageNoiseAugmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "noise_aug_strength": ("FLOAT", {"default": None, "min": 0.0, "max": 100.0, "step": 0.001}), + "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "add_noise" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ + Add noise to an image. + """ + + def add_noise(self, image, noise_aug_strength, seed): + torch.manual_seed(seed) + sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * noise_aug_strength + image_noise = torch.randn_like(image) * sigma[:, None, None, None] + image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise) + image_out = image + image_noise + return image_out, + +class VAELoaderKJ: + @staticmethod + def vae_list(): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + sd3_taesd_enc = False + sd3_taesd_dec = False + f1_taesd_enc = False + f1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + elif v.startswith("taesd3_decoder."): + sd3_taesd_dec = True + elif v.startswith("taesd3_encoder."): + sd3_taesd_enc = True + elif v.startswith("taef1_encoder."): + f1_taesd_dec = True + elif v.startswith("taef1_decoder."): + f1_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + if sd3_taesd_dec and sd3_taesd_enc: + vaes.append("taesd3") + if f1_taesd_dec and f1_taesd_enc: + vaes.append("taef1") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesd3": + sd["vae_scale"] = torch.tensor(1.5305) + sd["vae_shift"] = torch.tensor(0.0609) + elif name == "taef1": + sd["vae_scale"] = torch.tensor(0.3611) + sd["vae_shift"] = torch.tensor(0.1159) + return sd + + @classmethod + def INPUT_TYPES(s): + return { + "required": { "vae_name": (s.vae_list(), ), + "device": (["main_device", "cpu"],), + "weight_dtype": (["bf16", "fp16", "fp32" ],), + } + } + + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + CATEGORY = "KJNodes/vae" + + def load_vae(self, vae_name, device, weight_dtype): + from comfy.sd import VAE + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[weight_dtype] + if device == "main_device": + device = model_management.get_torch_device() + elif device == "cpu": + device = torch.device("cpu") + if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: + sd = self.load_taesd(vae_name) + else: + vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) + sd = load_torch_file(vae_path) + vae = VAE(sd=sd, device=device, dtype=dtype) + return (vae,) + +from comfy.samplers import sampling_function, CFGGuider +class Guider_ScheduledCFG(CFGGuider): + + def set_cfg(self, cfg, start_percent, end_percent): + self.cfg = cfg + self.start_percent = start_percent + self.end_percent = end_percent + + def predict_noise(self, x, timestep, model_options={}, seed=None): + steps = model_options["transformer_options"]["sample_sigmas"] + matched_step_index = (steps == timestep).nonzero() + assert not (isinstance(self.cfg, list) and len(self.cfg) != (len(steps) - 1)), "cfg list length must match step count" + if len(matched_step_index) > 0: + current_step_index = matched_step_index.item() + else: + for i in range(len(steps) - 1): + # walk from beginning of steps until crossing the timestep + if (steps[i] - timestep[0]) * (steps[i + 1] - timestep[0]) <= 0: + current_step_index = i + break + else: + current_step_index = 0 + current_percent = current_step_index / (len(steps) - 1) + + if self.start_percent <= current_percent <= self.end_percent: + if isinstance(self.cfg, list): + cfg = self.cfg[current_step_index] + else: + cfg = self.cfg + uncond = self.conds.get("negative", None) + else: + uncond = None + cfg = 1.0 + + return sampling_function(self.inner_model, x, timestep, uncond, self.conds.get("positive", None), cfg, model_options=model_options, seed=seed) + +class ScheduledCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step":0.01}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01}), + }, + } + RETURN_TYPES = ("GUIDER",) + FUNCTION = "get_guider" + CATEGORY = "KJNodes/experimental" + DESCRiPTION = """ +CFG Guider that allows for scheduled CFG changes over steps, the steps outside the range will use CFG 1.0 thus being processed faster. +cfg input can be a list of floats matching step count, or a single float for all steps. +""" + + def get_guider(self, model, cfg, positive, negative, start_percent, end_percent): + guider = Guider_ScheduledCFG(model) + guider.set_conds(positive, negative) + guider.set_cfg(cfg, start_percent, end_percent) + return (guider, ) + + +class ApplyRifleXRoPE_WanVideo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), + "k": ("INT", {"default": 6, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "KJNodes/experimental" + EXPERIMENTAL = True + DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx" + + def patch(self, model, latent, k): + model_class = model.model.diffusion_model + + model_clone = model.clone() + num_frames = latent["samples"].shape[2] + d = model_class.dim // model_class.num_heads + + rope_embedder = EmbedND_RifleX( + d, + 10000.0, + [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)], + num_frames, + k + ) + + model_clone.add_object_patch(f"diffusion_model.rope_embedder", rope_embedder) + + return (model_clone, ) + +class ApplyRifleXRoPE_HunuyanVideo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), + "k": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + CATEGORY = "KJNodes/experimental" + EXPERIMENTAL = True + DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx" + + def patch(self, model, latent, k): + model_class = model.model.diffusion_model + + model_clone = model.clone() + num_frames = latent["samples"].shape[2] + + pe_embedder = EmbedND_RifleX( + model_class.params.hidden_size // model_class.params.num_heads, + model_class.params.theta, + model_class.params.axes_dim, + num_frames, + k + ) + + model_clone.add_object_patch(f"diffusion_model.pe_embedder", pe_embedder) + + return (model_clone, ) + +def rope_riflex(pos, dim, theta, L_test, k): + from einops import rearrange + assert dim % 2 == 0 + if model_management.is_device_mps(pos.device) or model_management.is_intel_xpu() or model_management.is_directml_enabled(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + + # RIFLEX modification - adjust last frequency component if L_test and k are provided + if k and L_test: + omega[k-1] = 0.9 * 2 * torch.pi / L_test + + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + +class EmbedND_RifleX(nn.Module): + def __init__(self, dim, theta, axes_dim, num_frames, k): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + self.num_frames = num_frames + self.k = k + + def forward(self, ids): + n_axes = ids.shape[-1] + emb = torch.cat( + [rope_riflex(ids[..., i], self.axes_dim[i], self.theta, self.num_frames, self.k if i == 0 else 0) for i in range(n_axes)], + dim=-3, + ) + return emb.unsqueeze(1) + + +class Timer: + def __init__(self, name): + self.name = name + self.start_time = None + self.elapsed = 0 + +class TimerNodeKJ: + @classmethod + + def INPUT_TYPES(s): + return { + "required": { + "any_input": (any, {}), + "mode": (["start", "stop"],), + "name": ("STRING", {"default": "Timer"}), + }, + "optional": { + "timer": ("TIMER",), + }, + } + + RETURN_TYPES = (any, "TIMER", "INT", ) + RETURN_NAMES = ("any_output", "timer", "time") + FUNCTION = "timer" + CATEGORY = "KJNodes/misc" + + def timer(self, mode, name, any_input=None, timer=None): + if timer is None: + if mode == "start": + timer = Timer(name=name) + timer.start_time = time.time() + return {"ui": { + "text": [f"{timer.start_time}"]}, + "result": (any_input, timer, 0) + } + elif mode == "stop" and timer is not None: + end_time = time.time() + timer.elapsed = int((end_time - timer.start_time) * 1000) + timer.start_time = None + return (any_input, timer, timer.elapsed) + +class HunyuanVideoEncodeKeyframesToCond: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL",), + "positive": ("CONDITIONING", ), + "vae": ("VAE", ), + "start_frame": ("IMAGE", ), + "end_frame": ("IMAGE", ), + "num_frames": ("INT", {"default": 33, "min": 2, "max": 4096, "step": 1}), + "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), + "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}), + "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), + }, + "optional": { + "negative": ("CONDITIONING", ), + } + } + + RETURN_TYPES = ("MODEL", "CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("model", "positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "KJNodes/videomodels" + + def encode(self, model, positive, start_frame, end_frame, num_frames, vae, tile_size, overlap, temporal_size, temporal_overlap, negative=None): + + model_clone = model.clone() + + model_clone.add_object_patch("concat_keys", ("concat_image",)) + + + x = (start_frame.shape[1] // 8) * 8 + y = (start_frame.shape[2] // 8) * 8 + + if start_frame.shape[1] != x or start_frame.shape[2] != y: + x_offset = (start_frame.shape[1] % 8) // 2 + y_offset = (start_frame.shape[2] % 8) // 2 + start_frame = start_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + if end_frame.shape[1] != x or end_frame.shape[2] != y: + x_offset = (start_frame.shape[1] % 8) // 2 + y_offset = (start_frame.shape[2] % 8) // 2 + end_frame = end_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + + video_frames = torch.zeros(num_frames-2, start_frame.shape[1], start_frame.shape[2], start_frame.shape[3], device=start_frame.device, dtype=start_frame.dtype) + video_frames = torch.cat([start_frame, video_frames, end_frame], dim=0) + + concat_latent = vae.encode_tiled(video_frames[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap) + + out_latent = {} + out_latent["samples"] = torch.zeros_like(concat_latent) + + out = [] + for conditioning in [positive, negative if negative is not None else []]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + n = [t[0], d] + c.append(n) + out.append(c) + if len(out) == 1: + out.append(out[0]) + return (model_clone, out[0], out[1], out_latent) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/pyproject.toml b/custom_nodes/ComfyUI-KJNodes/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..166946f91c7a48c113bd437bee55a49fbe11eb85 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-kjnodes" +description = "Various quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability." +version = "1.1.2" +license = {file = "LICENSE"} +dependencies = ["librosa", "numpy", "pillow>=10.3.0", "scipy", "color-matcher", "matplotlib", "huggingface_hub"] + +[project.urls] +Repository = "https://github.com/kijai/ComfyUI-KJNodes" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "kijai" +DisplayName = "ComfyUI-KJNodes" +Icon = "https://avatars.githubusercontent.com/u/40791699" diff --git a/custom_nodes/ComfyUI-KJNodes/requirements.txt b/custom_nodes/ComfyUI-KJNodes/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5bc18ca95b226298cbb88bd4de3307c157e0a88b --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/requirements.txt @@ -0,0 +1,7 @@ +pillow>=10.3.0 +scipy +color-matcher +matplotlib +huggingface_hub +mss +opencv-python \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/fluid.py b/custom_nodes/ComfyUI-KJNodes/utility/fluid.py new file mode 100644 index 0000000000000000000000000000000000000000..c0691987f5249a031ecbb74329ba513d5788b691 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/fluid.py @@ -0,0 +1,67 @@ +import numpy as np +from scipy.ndimage import map_coordinates, spline_filter +from scipy.sparse.linalg import factorized + +from .numerical import difference, operator + + +class Fluid: + def __init__(self, shape, *quantities, pressure_order=1, advect_order=3): + self.shape = shape + self.dimensions = len(shape) + + # Prototyping is simplified by dynamically + # creating advected quantities as needed. + self.quantities = quantities + for q in quantities: + setattr(self, q, np.zeros(shape)) + + self.indices = np.indices(shape) + self.velocity = np.zeros((self.dimensions, *shape)) + + laplacian = operator(shape, difference(2, pressure_order)) + self.pressure_solver = factorized(laplacian) + + self.advect_order = advect_order + + def step(self): + # Advection is computed backwards in time as described in Stable Fluids. + advection_map = self.indices - self.velocity + + # SciPy's spline filter introduces checkerboard divergence. + # A linear blend of the filtered and unfiltered fields based + # on some value epsilon eliminates this error. + def advect(field, filter_epsilon=10e-2, mode='constant'): + filtered = spline_filter(field, order=self.advect_order, mode=mode) + field = filtered * (1 - filter_epsilon) + field * filter_epsilon + return map_coordinates(field, advection_map, prefilter=False, order=self.advect_order, mode=mode) + + # Apply advection to each axis of the + # velocity field and each user-defined quantity. + for d in range(self.dimensions): + self.velocity[d] = advect(self.velocity[d]) + + for q in self.quantities: + setattr(self, q, advect(getattr(self, q))) + + # Compute the jacobian at each point in the + # velocity field to extract curl and divergence. + jacobian_shape = (self.dimensions,) * 2 + partials = tuple(np.gradient(d) for d in self.velocity) + jacobian = np.stack(partials).reshape(*jacobian_shape, *self.shape) + + divergence = jacobian.trace() + + # If this curl calculation is extended to 3D, the y-axis value must be negated. + # This corresponds to the coefficients of the levi-civita symbol in that dimension. + # Higher dimensions do not have a vector -> scalar, or vector -> vector, + # correspondence between velocity and curl due to differing isomorphisms + # between exterior powers in dimensions != 2 or 3 respectively. + curl_mask = np.triu(np.ones(jacobian_shape, dtype=bool), k=1) + curl = (jacobian[curl_mask] - jacobian[curl_mask.T]).squeeze() + + # Apply the pressure correction to the fluid's velocity field. + pressure = self.pressure_solver(divergence.flatten()).reshape(self.shape) + self.velocity -= np.gradient(pressure) + + return divergence, curl, pressure \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/magictex.py b/custom_nodes/ComfyUI-KJNodes/utility/magictex.py new file mode 100644 index 0000000000000000000000000000000000000000..e6d426f7deb3deb977604dd37581eb4e9fe9e6a9 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/magictex.py @@ -0,0 +1,95 @@ +"""Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy + +https://github.com/cheind/magic-texture +""" +from typing import Tuple, Optional +import numpy as np + + +def coordinate_grid(shape: Tuple[int, int], dtype=np.float32): + """Returns a three-dimensional coordinate grid of given shape for use in `magic`.""" + x = np.linspace(-1, 1, shape[1], endpoint=True, dtype=dtype) + y = np.linspace(-1, 1, shape[0], endpoint=True, dtype=dtype) + X, Y = np.meshgrid(x, y) + XYZ = np.stack((X, Y, np.ones_like(X)), -1) + return XYZ + + +def random_transform(coords: np.ndarray, rng: np.random.Generator = None): + """Returns randomly transformed coordinates""" + H, W = coords.shape[:2] + rng = rng or np.random.default_rng() + m = rng.uniform(-1.0, 1.0, size=(3, 3)).astype(coords.dtype) + return (coords.reshape(-1, 3) @ m.T).reshape(H, W, 3) + + +def magic( + coords: np.ndarray, + depth: Optional[int] = None, + distortion: Optional[int] = None, + rng: np.random.Generator = None, +): + """Returns color magic color texture. + + The implementation is based on Blender's (https://www.blender.org/) magic + texture shader. The following adaptions have been made: + - we exchange the nested if-cascade by a probabilistic iterative approach + + Kwargs + ------ + coords: HxWx3 array + Coordinates transformed into colors by this method. See + `magictex.coordinate_grid` to generate the default. + depth: int (optional) + Number of transformations applied. Higher numbers lead to more + nested patterns. If not specified, randomly sampled. + distortion: float (optional) + Distortion of patterns. Larger values indicate more distortion, + lower values tend to generate smoother patterns. If not specified, + randomly sampled. + rng: np.random.Generator + Optional random generator to draw samples from. + + Returns + ------- + colors: HxWx3 array + Three channel color image in range [0,1] + """ + rng = rng or np.random.default_rng() + if distortion is None: + distortion = rng.uniform(1, 4) + if depth is None: + depth = rng.integers(1, 5) + + H, W = coords.shape[:2] + XYZ = coords + x = np.sin((XYZ[..., 0] + XYZ[..., 1] + XYZ[..., 2]) * distortion) + y = np.cos((-XYZ[..., 0] + XYZ[..., 1] - XYZ[..., 2]) * distortion) + z = -np.cos((-XYZ[..., 0] - XYZ[..., 1] + XYZ[..., 2]) * distortion) + + if depth > 0: + x *= distortion + y *= distortion + z *= distortion + y = -np.cos(x - y + z) + y *= distortion + + xyz = [x, y, z] + fns = [np.cos, np.sin] + for _ in range(1, depth): + axis = rng.choice(3) + fn = fns[rng.choice(2)] + signs = rng.binomial(n=1, p=0.5, size=4) * 2 - 1 + + xyz[axis] = signs[-1] * fn( + signs[0] * xyz[0] + signs[1] * xyz[1] + signs[2] * xyz[2] + ) + xyz[axis] *= distortion + + x, y, z = xyz + x /= 2 * distortion + y /= 2 * distortion + z /= 2 * distortion + c = 0.5 - np.stack((x, y, z), -1) + np.clip(c, 0, 1.0) + return c \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/numerical.py b/custom_nodes/ComfyUI-KJNodes/utility/numerical.py new file mode 100644 index 0000000000000000000000000000000000000000..b5b88bc63c45d63d8913e56cbd06eb7ab413fe4f --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/numerical.py @@ -0,0 +1,25 @@ +from functools import reduce +from itertools import cycle +from math import factorial + +import numpy as np +import scipy.sparse as sp + + +def difference(derivative, accuracy=1): + # Central differences implemented based on the article here: + # http://web.media.mit.edu/~crtaylor/calculator.html + derivative += 1 + radius = accuracy + derivative // 2 - 1 + points = range(-radius, radius + 1) + coefficients = np.linalg.inv(np.vander(points)) + return coefficients[-derivative] * factorial(derivative - 1), points + + +def operator(shape, *differences): + # Credit to Philip Zucker for figuring out + # that kronsum's argument order is reversed. + # Without that bit of wisdom I'd have lost it. + differences = zip(shape, cycle(differences)) + factors = (sp.diags(*diff, shape=(dim,) * 2) for dim, diff in differences) + return reduce(lambda a, f: sp.kronsum(f, a, format='csc'), factors) \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/utility/utility.py b/custom_nodes/ComfyUI-KJNodes/utility/utility.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b5c425922784522791e33c225c29be1e8249e0 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/utility/utility.py @@ -0,0 +1,39 @@ +import torch +import numpy as np +from PIL import Image +from typing import Union, List + +# Utility functions from mtb nodes: https://github.com/melMass/comfy_mtb +def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor: + if isinstance(image, list): + return torch.cat([pil2tensor(img) for img in image], dim=0) + + return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) + + +def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor: + if isinstance(img_np, list): + return torch.cat([np2tensor(img) for img in img_np], dim=0) + + return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0) + + +def tensor2np(tensor: torch.Tensor): + if len(tensor.shape) == 3: # Single image + return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8) + else: # Batch of images + return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor] + +def tensor2pil(image: torch.Tensor) -> List[Image.Image]: + batch_count = image.size(0) if len(image.shape) > 3 else 1 + if batch_count > 1: + out = [] + for i in range(batch_count): + out.extend(tensor2pil(image[i])) + return out + + return [ + Image.fromarray( + np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) + ) + ] \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/green.png b/custom_nodes/ComfyUI-KJNodes/web/green.png new file mode 100644 index 0000000000000000000000000000000000000000..900964e4b3907145fe1e75a5b58473567450e16d Binary files /dev/null and b/custom_nodes/ComfyUI-KJNodes/web/green.png differ diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js b/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js new file mode 100644 index 0000000000000000000000000000000000000000..d90b4aa34d4c52b22a4411194100972c83eed88d --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/appearance.js @@ -0,0 +1,23 @@ +import { app } from "../../../scripts/app.js"; + +app.registerExtension({ + name: "KJNodes.appearance", + nodeCreated(node) { + switch (node.comfyClass) { + case "INTConstant": + node.setSize([200, 58]); + node.color = "#1b4669"; + node.bgcolor = "#29699c"; + break; + case "FloatConstant": + node.setSize([200, 58]); + node.color = LGraphCanvas.node_colors.green.color; + node.bgcolor = LGraphCanvas.node_colors.green.bgcolor; + break; + case "ConditioningMultiCombine": + node.color = LGraphCanvas.node_colors.brown.color; + node.bgcolor = LGraphCanvas.node_colors.brown.bgcolor; + break; + } + } +}); diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/browserstatus.js b/custom_nodes/ComfyUI-KJNodes/web/js/browserstatus.js new file mode 100644 index 0000000000000000000000000000000000000000..45abafb163481d9d760c8b273aad4d2a00db1e92 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/browserstatus.js @@ -0,0 +1,55 @@ +import { api } from "../../../scripts/api.js"; +import { app } from "../../../scripts/app.js"; + +app.registerExtension({ + name: "KJNodes.browserstatus", + setup() { + if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) { + return; + } + api.addEventListener("status", ({ detail }) => { + let title = "ComfyUI"; + let favicon = "green"; + let queueRemaining = detail && detail.exec_info.queue_remaining; + + if (queueRemaining) { + favicon = "red"; + title = `00% - ${queueRemaining} | ${title}`; + } + let link = document.querySelector("link[rel~='icon']"); + if (!link) { + link = document.createElement("link"); + link.rel = "icon"; + document.head.appendChild(link); + } + link.href = new URL(`../${favicon}.png`, import.meta.url); + document.title = title; + }); + //add progress to the title + api.addEventListener("progress", ({ detail }) => { + const { value, max } = detail; + const progress = Math.floor((value / max) * 100); + let title = document.title; + + if (!isNaN(progress) && progress >= 0 && progress <= 100) { + const paddedProgress = String(progress).padStart(2, '0'); + title = `${paddedProgress}% ${title.replace(/^\d+%\s/, '')}`; + } + document.title = title; + }); + }, + init() { + if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) { + return; + } + const pythongossFeed = app.extensions.find( + (e) => e.name === 'pysssss.FaviconStatus', + ) + if (pythongossFeed) { + console.warn("KJNodes - Overriding pysssss.FaviconStatus") + pythongossFeed.setup = function() { + console.warn("Disabled by KJNodes") + }; + } + }, +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js b/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js new file mode 100644 index 0000000000000000000000000000000000000000..8485658ef819722280160b124a2acf39353bb96d --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/contextmenu.js @@ -0,0 +1,147 @@ +import { app } from "../../../scripts/app.js"; + +// Adds context menu entries, code partly from pyssssscustom-scripts + +function addMenuHandler(nodeType, cb) { + const getOpts = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function () { + const r = getOpts.apply(this, arguments); + cb.apply(this, arguments); + return r; + }; +} + +function addNode(name, nextTo, options) { + console.log("name:", name); + console.log("nextTo:", nextTo); + options = { side: "left", select: true, shiftY: 0, shiftX: 0, ...(options || {}) }; + const node = LiteGraph.createNode(name); + app.graph.add(node); + + node.pos = [ + options.side === "left" ? nextTo.pos[0] - (node.size[0] + options.offset): nextTo.pos[0] + nextTo.size[0] + options.offset, + + nextTo.pos[1] + options.shiftY, + ]; + if (options.select) { + app.canvas.selectNode(node, false); + } + return node; +} + +app.registerExtension({ + name: "KJNodesContextmenu", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.input && nodeData.input.required) { + addMenuHandler(nodeType, function (_, options) { + options.unshift( + { + content: "Add GetNode", + callback: () => {addNode("GetNode", this, { side:"left", offset: 30});} + }, + { + content: "Add SetNode", + callback: () => {addNode("SetNode", this, { side:"right", offset: 30 }); + }, + }); + }); + } + }, + async setup(app) { + const updateSlots = (value) => { + const valuesToAddToIn = ["GetNode"]; + const valuesToAddToOut = ["SetNode"]; + // Remove entries if they exist + for (const arr of Object.values(LiteGraph.slot_types_default_in)) { + for (const valueToAdd of valuesToAddToIn) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + } + } + + for (const arr of Object.values(LiteGraph.slot_types_default_out)) { + for (const valueToAdd of valuesToAddToOut) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + } + } + if (value!="disabled") { + for (const arr of Object.values(LiteGraph.slot_types_default_in)) { + for (const valueToAdd of valuesToAddToIn) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + if (value === "top") { + arr.unshift(valueToAdd); + } else { + arr.push(valueToAdd); + } + } + } + + for (const arr of Object.values(LiteGraph.slot_types_default_out)) { + for (const valueToAdd of valuesToAddToOut) { + const idx = arr.indexOf(valueToAdd); + if (idx !== -1) { + arr.splice(idx, 1); + } + if (value === "top") { + arr.unshift(valueToAdd); + } else { + arr.push(valueToAdd); + } + } + } + } + }; + + app.ui.settings.addSetting({ + id: "KJNodes.SetGetMenu", + name: "KJNodes: Make Set/Get -nodes defaults", + tooltip: 'Adds Set/Get nodes to the top or bottom of the list of available node suggestions.', + options: ['disabled', 'top', 'bottom'], + defaultValue: 'disabled', + type: "combo", + onChange: updateSlots, + + }); + app.ui.settings.addSetting({ + id: "KJNodes.MiddleClickDefault", + name: "KJNodes: Middle click default node adding", + defaultValue: false, + type: "boolean", + onChange: (value) => { + LiteGraph.middle_click_slot_add_default_node = value; + }, + }); + app.ui.settings.addSetting({ + id: "KJNodes.nodeAutoColor", + name: "KJNodes: Automatically set node colors", + type: "boolean", + defaultValue: true, + }); + app.ui.settings.addSetting({ + id: "KJNodes.helpPopup", + name: "KJNodes: Help popups", + defaultValue: true, + type: "boolean", + }); + app.ui.settings.addSetting({ + id: "KJNodes.disablePrefix", + name: "KJNodes: Disable automatic Set_ and Get_ prefix", + defaultValue: true, + type: "boolean", + }); + app.ui.settings.addSetting({ + id: "KJNodes.browserStatus", + name: "KJNodes: 🟢 Stoplight browser status icon 🔴", + defaultValue: false, + type: "boolean", + }); +} +}); diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js new file mode 100644 index 0000000000000000000000000000000000000000..822c1f745ba4e895364664f7dbed3d225f0430b2 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/fast_preview.js @@ -0,0 +1,95 @@ +import { app } from '../../../scripts/app.js' + +//from melmass +export function makeUUID() { + let dt = new Date().getTime() + const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { + const r = ((dt + Math.random() * 16) % 16) | 0 + dt = Math.floor(dt / 16) + return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) + }) + return uuid +} + +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} +app.registerExtension({ + name: 'KJNodes.FastPreview', + + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === 'FastPreview') { + chainCallback(nodeType.prototype, "onNodeCreated", function () { + + var element = document.createElement("div"); + this.uuid = makeUUID() + element.id = `fast-preview-${this.uuid}` + + this.previewWidget = this.addDOMWidget(nodeData.name, "FastPreviewWidget", element, { + serialize: false, + hideOnZoom: false, + }); + + this.previewer = new Previewer(this); + + this.setSize([550, 550]); + this.resizable = false; + this.previewWidget.parentEl = document.createElement("div"); + this.previewWidget.parentEl.className = "fast-preview"; + this.previewWidget.parentEl.id = `fast-preview-${this.uuid}` + element.appendChild(this.previewWidget.parentEl); + + chainCallback(this, "onExecuted", function (message) { + let bg_image = message["bg_image"]; + this.properties.imgData = { + name: "bg_image", + base64: bg_image + }; + this.previewer.refreshBackgroundImage(this); + }); + + + }); // onAfterGraphConfigured + }//node created + } //before register +})//register + +class Previewer { + constructor(context) { + this.node = context; + this.previousWidth = null; + this.previousHeight = null; + } + refreshBackgroundImage = () => { + const imgData = this.node?.properties?.imgData; + if (imgData?.base64) { + const base64String = imgData.base64; + const imageUrl = `data:${imgData.type};base64,${base64String}`; + const img = new Image(); + img.src = imageUrl; + img.onload = () => { + const { width, height } = img; + if (width !== this.previousWidth || height !== this.previousHeight) { + this.node.setSize([width, height]); + this.previousWidth = width; + this.previousHeight = height; + } + this.node.previewWidget.element.style.backgroundImage = `url(${imageUrl})`; + }; + } + }; + } \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js b/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js new file mode 100644 index 0000000000000000000000000000000000000000..c4734befd7726d6190b246e66140e99dfb9f7e65 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/help_popup.js @@ -0,0 +1,326 @@ +import { app } from "../../../scripts/app.js"; + +// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/ +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', +) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) +} + +loadScript('kjweb_async/marked.min.js').catch((e) => { + console.log(e) +}) +loadScript('kjweb_async/purify.min.js').catch((e) => { + console.log(e) +}) + +const categories = ["KJNodes", "SUPIR", "VoiceCraft", "Marigold", "IC-Light", "WanVideoWrapper"]; +app.registerExtension({ + name: "KJNodes.HelpPopup", + async beforeRegisterNodeDef(nodeType, nodeData) { + + if (app.ui.settings.getSettingValue("KJNodes.helpPopup") === false) { + return; + } + try { + categories.forEach(category => { + if (nodeData?.category?.startsWith(category)) { + addDocumentation(nodeData, nodeType); + } + else return + }); + } catch (error) { + console.error("Error in registering KJNodes.HelpPopup", error); + } + }, +}); + +const create_documentation_stylesheet = () => { + const tag = 'kj-documentation-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .kj-documentation-popup { + background: var(--comfy-menu-bg); + position: absolute; + color: var(--fg-color); + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + border-radius: 10px; + border-style: solid; + border-width: medium; + border-color: var(--border-color); + z-index: 5; + overflow: hidden; + } + .content-wrapper { + overflow: auto; + max-height: 100%; + /* Scrollbar styling for Chrome */ + &::-webkit-scrollbar { + width: 6px; + } + &::-webkit-scrollbar-track { + background: var(--bg-color); + } + &::-webkit-scrollbar-thumb { + background-color: var(--fg-color); + border-radius: 6px; + border: 3px solid var(--bg-color); + } + + /* Scrollbar styling for Firefox */ + scrollbar-width: thin; + scrollbar-color: var(--fg-color) var(--bg-color); + a { + color: yellow; + } + a:visited { + color: orange; + } + a:hover { + color: red; + } + } + ` + document.head.appendChild(styleTag) + } + } + + /** Add documentation widget to the selected node */ + export const addDocumentation = ( + nodeData, + nodeType, + opts = { icon_size: 14, icon_margin: 4 },) => { + + opts = opts || {} + const iconSize = opts.icon_size ? opts.icon_size : 14 + const iconMargin = opts.icon_margin ? opts.icon_margin : 4 + let docElement = null + let contentWrapper = null + //if no description in the node python code, don't do anything + if (!nodeData.description) { + return + } + + const drawFg = nodeType.prototype.onDrawForeground + nodeType.prototype.onDrawForeground = function (ctx) { + const r = drawFg ? drawFg.apply(this, arguments) : undefined + if (this.flags.collapsed) return r + + // icon position + const x = this.size[0] - iconSize - iconMargin + + // create the popup + if (this.show_doc && docElement === null) { + docElement = document.createElement('div') + contentWrapper = document.createElement('div'); + docElement.appendChild(contentWrapper); + + create_documentation_stylesheet() + contentWrapper.classList.add('content-wrapper'); + docElement.classList.add('kj-documentation-popup') + + //parse the string from the python node code to html with marked, and sanitize the html with DOMPurify + contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(nodeData.description,)) + + // resize handle + const resizeHandle = document.createElement('div'); + resizeHandle.style.width = '0'; + resizeHandle.style.height = '0'; + resizeHandle.style.position = 'absolute'; + resizeHandle.style.bottom = '0'; + resizeHandle.style.right = '0'; + resizeHandle.style.cursor = 'se-resize'; + + // Add pseudo-elements to create a triangle shape + const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim(); + resizeHandle.style.borderTop = '10px solid transparent'; + resizeHandle.style.borderLeft = '10px solid transparent'; + resizeHandle.style.borderBottom = `10px solid ${borderColor}`; + resizeHandle.style.borderRight = `10px solid ${borderColor}`; + + docElement.appendChild(resizeHandle) + let isResizing = false + let startX, startY, startWidth, startHeight + + resizeHandle.addEventListener('mousedown', function (e) { + e.preventDefault(); + e.stopPropagation(); + isResizing = true; + startX = e.clientX; + startY = e.clientY; + startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10); + startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10); + }, + { signal: this.docCtrl.signal }, + ); + + // close button + const closeButton = document.createElement('div'); + closeButton.textContent = '❌'; + closeButton.style.position = 'absolute'; + closeButton.style.top = '0'; + closeButton.style.right = '0'; + closeButton.style.cursor = 'pointer'; + closeButton.style.padding = '5px'; + closeButton.style.color = 'red'; + closeButton.style.fontSize = '12px'; + + docElement.appendChild(closeButton) + + closeButton.addEventListener('mousedown', (e) => { + e.stopPropagation(); + this.show_doc = !this.show_doc + docElement.parentNode.removeChild(docElement) + docElement = null + if (contentWrapper) { + contentWrapper.remove() + contentWrapper = null + } + }, + { signal: this.docCtrl.signal }, + ); + + document.addEventListener('mousemove', function (e) { + if (!isResizing) return; + const scale = app.canvas.ds.scale; + const newWidth = startWidth + (e.clientX - startX) / scale; + const newHeight = startHeight + (e.clientY - startY) / scale;; + docElement.style.width = `${newWidth}px`; + docElement.style.height = `${newHeight}px`; + }, + { signal: this.docCtrl.signal }, + ); + + document.addEventListener('mouseup', function () { + isResizing = false + }, + { signal: this.docCtrl.signal }, + ) + + document.body.appendChild(docElement) + } + // close the popup + else if (!this.show_doc && docElement !== null) { + docElement.parentNode.removeChild(docElement) + docElement = null + } + // update position of the popup + if (this.show_doc && docElement !== null) { + const rect = ctx.canvas.getBoundingClientRect() + const scaleX = rect.width / ctx.canvas.width + const scaleY = rect.height / ctx.canvas.height + + const transform = new DOMMatrix() + .scaleSelf(scaleX, scaleY) + .multiplySelf(ctx.getTransform()) + .translateSelf(this.size[0] * scaleX * Math.max(1.0,window.devicePixelRatio) , 0) + .translateSelf(10, -32) + + const scale = new DOMMatrix() + .scaleSelf(transform.a, transform.d); + const bcr = app.canvas.canvas.getBoundingClientRect() + + const styleObject = { + transformOrigin: '0 0', + transform: scale, + left: `${transform.a + bcr.x + transform.e}px`, + top: `${transform.d + bcr.y + transform.f}px`, + }; + Object.assign(docElement.style, styleObject); + } + + ctx.save() + ctx.translate(x - 2, iconSize - 34) + ctx.scale(iconSize / 32, iconSize / 32) + ctx.strokeStyle = 'rgba(255,255,255,0.3)' + ctx.lineCap = 'round' + ctx.lineJoin = 'round' + ctx.lineWidth = 2.4 + ctx.font = 'bold 36px monospace' + ctx.fillStyle = 'orange'; + ctx.fillText('?', 0, 24) + ctx.restore() + return r + } + // handle clicking of the icon + const mouseDown = nodeType.prototype.onMouseDown + nodeType.prototype.onMouseDown = function (e, localPos, canvas) { + const r = mouseDown ? mouseDown.apply(this, arguments) : undefined + const iconX = this.size[0] - iconSize - iconMargin + const iconY = iconSize - 34 + if ( + localPos[0] > iconX && + localPos[0] < iconX + iconSize && + localPos[1] > iconY && + localPos[1] < iconY + iconSize + ) { + if (this.show_doc === undefined) { + this.show_doc = true + } else { + this.show_doc = !this.show_doc + } + if (this.show_doc) { + this.docCtrl = new AbortController() + } else { + this.docCtrl.abort() + } + return true; + } + return r; + } + const onRem = nodeType.prototype.onRemoved + + nodeType.prototype.onRemoved = function () { + const r = onRem ? onRem.apply(this, []) : undefined + + if (docElement) { + docElement.remove() + docElement = null + } + + if (contentWrapper) { + contentWrapper.remove() + contentWrapper = null + } + return r + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js b/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js new file mode 100644 index 0000000000000000000000000000000000000000..d4359846d1c9f4b44267f17207547fd909e3e50b --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/jsnodes.js @@ -0,0 +1,386 @@ +import { app } from "../../../scripts/app.js"; +import { applyTextReplacements } from "../../../scripts/utils.js"; + +app.registerExtension({ + name: "KJNodes.jsnodes", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if(!nodeData?.category?.startsWith("KJNodes")) { + return; + } + switch (nodeData.name) { + case "ConditioningMultiCombine": + nodeType.prototype.onNodeCreated = function () { + this._type = "CONDITIONING" + this.inputs_offset = nodeData.name.includes("selective")?1:0 + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.type === this._type).length + if(target_number_of_inputs===num_inputs)return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`conditioning_${i}`, this._type) + } + }); + } + break; + case "ImageBatchMulti": + case "ImageAddMulti": + case "ImageConcatMulti": + case "CrossFadeImagesMulti": + case "TransitionImagesMulti": + nodeType.prototype.onNodeCreated = function () { + this._type = "IMAGE" + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.type === this._type).length + if(target_number_of_inputs===num_inputs)return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`image_${i}`, this._type) + } + + }); + } + break; + case "MaskBatchMulti": + nodeType.prototype.onNodeCreated = function () { + this._type = "MASK" + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.type === this._type).length + if(target_number_of_inputs===num_inputs)return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`mask_${i}`, this._type) + } + }); + } + break; + + case "FluxBlockLoraSelect": + case "HunyuanVideoBlockLoraSelect": + case "Wan21BlockLoraSelect": + nodeType.prototype.onNodeCreated = function () { + this.addWidget("button", "Set all", null, () => { + const userInput = prompt("Enter the values to set for widgets (e.g., s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0):", ""); + if (userInput) { + const regex = /([sd])?(\d+(?:,\d+|-?\d+)*?)?=(\d+(\.\d+)?)/; + const match = userInput.match(regex); + if (match) { + const type = match[1]; + const indicesPart = match[2]; + const value = parseFloat(match[3]); + + let targetWidgets = []; + if (type === 's') { + targetWidgets = this.widgets.filter(widget => widget.name.includes("single")); + } else if (type === 'd') { + targetWidgets = this.widgets.filter(widget => widget.name.includes("double")); + } else { + targetWidgets = this.widgets; // No type specified, all widgets + } + + if (indicesPart) { + const indices = indicesPart.split(',').flatMap(part => { + if (part.includes('-')) { + const [start, end] = part.split('-').map(Number); + return Array.from({ length: end - start + 1 }, (_, i) => start + i); + } + return Number(part); + }); + + for (const index of indices) { + if (index < targetWidgets.length) { + targetWidgets[index].value = value; + } + } + } else { + // No indices provided, set value for all target widgets + for (const widget of targetWidgets) { + widget.value = value; + } + } + } else if (!isNaN(parseFloat(userInput))) { + // Single value provided, set it for all widgets + const value = parseFloat(userInput); + for (const widget of this.widgets) { + widget.value = value; + } + } else { + alert("Invalid input format. Please use the format s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0"); + } + } else { + alert("Invalid input. Please enter a value."); + } + }); + }; + break; + + case "GetMaskSizeAndCount": + const onGetMaskSizeConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + const v = onGetMaskSizeConnectInput? onGetMaskSizeConnectInput.apply(this, arguments): undefined + this.outputs[1]["label"] = "width" + this.outputs[2]["label"] = "height" + this.outputs[3]["label"] = "count" + return v; + } + const onGetMaskSizeExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + const r = onGetMaskSizeExecuted? onGetMaskSizeExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x').map(Number); + this.outputs[1]["label"] = values[1] + " width" + this.outputs[2]["label"] = values[2] + " height" + this.outputs[3]["label"] = values[0] + " count" + return r + } + break; + + case "GetImageSizeAndCount": + const onGetImageSizeConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + console.log(this) + const v = onGetImageSizeConnectInput? onGetImageSizeConnectInput.apply(this, arguments): undefined + //console.log(this) + this.outputs[1]["label"] = "width" + this.outputs[2]["label"] = "height" + this.outputs[3]["label"] = "count" + return v; + } + //const onGetImageSizeExecuted = nodeType.prototype.onExecuted; + const onGetImageSizeExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + console.log(this) + const r = onGetImageSizeExecuted? onGetImageSizeExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x').map(Number); + console.log(values) + this.outputs[1]["label"] = values[1] + " width" + this.outputs[2]["label"] = values[2] + " height" + this.outputs[3]["label"] = values[0] + " count" + return r + } + break; + + case "PreviewAnimation": + const onPreviewAnimationConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + const v = onPreviewAnimationConnectInput? onPreviewAnimationConnectInput.apply(this, arguments): undefined + this.title = "Preview Animation" + return v; + } + const onPreviewAnimationExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + const r = onPreviewAnimationExecuted? onPreviewAnimationExecuted.apply(this,arguments): undefined + let values = message["text"].toString(); + this.title = "Preview Animation " + values + return r + } + break; + + case "VRAM_Debug": + const onVRAM_DebugConnectInput = nodeType.prototype.onConnectInput; + nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { + const v = onVRAM_DebugConnectInput? onVRAM_DebugConnectInput.apply(this, arguments): undefined + this.outputs[3]["label"] = "freemem_before" + this.outputs[4]["label"] = "freemem_after" + return v; + } + const onVRAM_DebugExecuted = nodeType.prototype.onAfterExecuteNode; + nodeType.prototype.onExecuted = function(message) { + const r = onVRAM_DebugExecuted? onVRAM_DebugExecuted.apply(this,arguments): undefined + let values = message["text"].toString().split('x'); + this.outputs[3]["label"] = values[0] + " freemem_before" + this.outputs[4]["label"] = values[1] + " freemem_after" + return r + } + break; + + case "JoinStringMulti": + const originalOnNodeCreated = nodeType.prototype.onNodeCreated || function() {}; + nodeType.prototype.onNodeCreated = function () { + originalOnNodeCreated.apply(this, arguments); + + this._type = "STRING"; + this.addWidget("button", "Update inputs", null, () => { + if (!this.inputs) { + this.inputs = []; + } + const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; + const num_inputs = this.inputs.filter(input => input.name && input.name.toLowerCase().includes("string_")).length + if (target_number_of_inputs === num_inputs) return; // already set, do nothing + + if(target_number_of_inputs < num_inputs){ + const inputs_to_remove = num_inputs - target_number_of_inputs; + for(let i = 0; i < inputs_to_remove; i++) { + this.removeInput(this.inputs.length - 1); + } + } + else{ + for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) + this.addInput(`string_${i}`, this._type) + } + }); + } + break; + case "SoundReactive": + nodeType.prototype.onNodeCreated = function () { + let audioContext; + let microphoneStream; + let animationFrameId; + let analyser; + let dataArray; + let startRangeHz; + let endRangeHz; + let smoothingFactor = 0.5; + let smoothedSoundLevel = 0; + + // Function to update the widget value in real-time + const updateWidgetValueInRealTime = () => { + // Ensure analyser and dataArray are defined before using them + if (analyser && dataArray) { + analyser.getByteFrequencyData(dataArray); + + const startRangeHzWidget = this.widgets.find(w => w.name === "start_range_hz"); + if (startRangeHzWidget) startRangeHz = startRangeHzWidget.value; + const endRangeHzWidget = this.widgets.find(w => w.name === "end_range_hz"); + if (endRangeHzWidget) endRangeHz = endRangeHzWidget.value; + const smoothingFactorWidget = this.widgets.find(w => w.name === "smoothing_factor"); + if (smoothingFactorWidget) smoothingFactor = smoothingFactorWidget.value; + + // Calculate frequency bin width (frequency resolution) + const frequencyBinWidth = audioContext.sampleRate / analyser.fftSize; + // Convert the widget values from Hz to indices + const startRangeIndex = Math.floor(startRangeHz / frequencyBinWidth); + const endRangeIndex = Math.floor(endRangeHz / frequencyBinWidth); + + // Function to calculate the average value for a frequency range + const calculateAverage = (start, end) => { + const sum = dataArray.slice(start, end).reduce((acc, val) => acc + val, 0); + const average = sum / (end - start); + + // Apply exponential moving average smoothing + smoothedSoundLevel = (average * (1 - smoothingFactor)) + (smoothedSoundLevel * smoothingFactor); + return smoothedSoundLevel; + }; + // Calculate the average levels for each frequency range + const soundLevel = calculateAverage(startRangeIndex, endRangeIndex); + + // Update the widget values + + const lowLevelWidget = this.widgets.find(w => w.name === "sound_level"); + if (lowLevelWidget) lowLevelWidget.value = soundLevel; + + animationFrameId = requestAnimationFrame(updateWidgetValueInRealTime); + } + }; + + // Function to start capturing audio from the microphone + const startMicrophoneCapture = () => { + // Only create the audio context and analyser once + if (!audioContext) { + audioContext = new (window.AudioContext || window.webkitAudioContext)(); + // Access the sample rate of the audio context + console.log(`Sample rate: ${audioContext.sampleRate}Hz`); + analyser = audioContext.createAnalyser(); + analyser.fftSize = 2048; + dataArray = new Uint8Array(analyser.frequencyBinCount); + // Get the range values from widgets (assumed to be in Hz) + const lowRangeWidget = this.widgets.find(w => w.name === "low_range_hz"); + if (lowRangeWidget) startRangeHz = lowRangeWidget.value; + + const midRangeWidget = this.widgets.find(w => w.name === "mid_range_hz"); + if (midRangeWidget) endRangeHz = midRangeWidget.value; + } + + navigator.mediaDevices.getUserMedia({ audio: true }).then(stream => { + microphoneStream = stream; + const microphone = audioContext.createMediaStreamSource(stream); + microphone.connect(analyser); + updateWidgetValueInRealTime(); + }).catch(error => { + console.error('Access to microphone was denied or an error occurred:', error); + }); + }; + + // Function to stop capturing audio from the microphone + const stopMicrophoneCapture = () => { + if (animationFrameId) { + cancelAnimationFrame(animationFrameId); + } + if (microphoneStream) { + microphoneStream.getTracks().forEach(track => track.stop()); + } + if (audioContext) { + audioContext.close(); + // Reset audioContext to ensure it can be created again when starting + audioContext = null; + } + }; + + // Add start button + this.addWidget("button", "Start mic capture", null, startMicrophoneCapture); + + // Add stop button + this.addWidget("button", "Stop mic capture", null, stopMicrophoneCapture); + }; + break; + case "SaveImageKJ": + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function() { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0; + const widget = this.widgets.find((w) => w.name === "filename_prefix"); + widget.serializeValue = () => { + return applyTextReplacements(app, widget.value); + }; + return r; + }; + break; + + } + + }, + async setup() { + // to keep Set/Get node virtual connections visible when offscreen + const originalComputeVisibleNodes = LGraphCanvas.prototype.computeVisibleNodes; + LGraphCanvas.prototype.computeVisibleNodes = function () { + const visibleNodesSet = new Set(originalComputeVisibleNodes.apply(this, arguments)); + for (const node of this.graph._nodes) { + if ((node.type === "SetNode" || node.type === "GetNode") && node.drawConnection) { + visibleNodesSet.add(node); + } + } + return Array.from(visibleNodesSet); + }; + + } +}); \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/point_editor.js b/custom_nodes/ComfyUI-KJNodes/web/js/point_editor.js new file mode 100644 index 0000000000000000000000000000000000000000..6baa10830d5cba754eaa440685fdabc0c9d9f8e8 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/point_editor.js @@ -0,0 +1,734 @@ +import { app } from '../../../scripts/app.js' + +//from melmass +export function makeUUID() { + let dt = new Date().getTime() + const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { + const r = ((dt + Math.random() * 16) % 16) | 0 + dt = Math.floor(dt / 16) + return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) + }) + return uuid +} + +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', +) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) +} +const create_documentation_stylesheet = () => { + const tag = 'kj-pointseditor-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .points-editor { + + position: absolute; + + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + z-index: 0; + overflow: hidden; + } + ` + document.head.appendChild(styleTag) + } +} + +loadScript('kjweb_async/svg-path-properties.min.js').catch((e) => { + console.log(e) +}) +loadScript('kjweb_async/protovis.min.js').catch((e) => { + console.log(e) +}) +create_documentation_stylesheet() + +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} +app.registerExtension({ + name: 'KJNodes.PointEditor', + + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === 'PointsEditor') { + chainCallback(nodeType.prototype, "onNodeCreated", function () { + + hideWidgetForGood(this, this.widgets.find(w => w.name === "coordinates")) + hideWidgetForGood(this, this.widgets.find(w => w.name === "neg_coordinates")) + hideWidgetForGood(this, this.widgets.find(w => w.name === "bboxes")) + + var element = document.createElement("div"); + this.uuid = makeUUID() + element.id = `points-editor-${this.uuid}` + + this.previewMediaType = 'image' + + this.pointsEditor = this.addDOMWidget(nodeData.name, "PointsEditorWidget", element, { + serialize: false, + hideOnZoom: false, + }); + + // context menu + this.contextMenu = document.createElement("div"); + this.contextMenu.id = "context-menu"; + this.contextMenu.style.display = "none"; + this.contextMenu.style.position = "absolute"; + this.contextMenu.style.backgroundColor = "#202020"; + this.contextMenu.style.minWidth = "100px"; + this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)"; + this.contextMenu.style.zIndex = "100"; + this.contextMenu.style.padding = "5px"; + + function styleMenuItem(menuItem) { + menuItem.style.display = "block"; + menuItem.style.padding = "5px"; + menuItem.style.color = "#FFF"; + menuItem.style.fontFamily = "Arial, sans-serif"; + menuItem.style.fontSize = "16px"; + menuItem.style.textDecoration = "none"; + menuItem.style.marginBottom = "5px"; + } + function createMenuItem(id, textContent) { + let menuItem = document.createElement("a"); + menuItem.href = "#"; + menuItem.id = `menu-item-${id}`; + menuItem.textContent = textContent; + styleMenuItem(menuItem); + return menuItem; + } + + // Create an array of menu items using the createMenuItem function + this.menuItems = [ + createMenuItem(0, "Load Image"), + createMenuItem(1, "Clear Image"), + ]; + + // Add mouseover and mouseout event listeners to each menu item for styling + this.menuItems.forEach(menuItem => { + menuItem.addEventListener('mouseover', function () { + this.style.backgroundColor = "gray"; + }); + + menuItem.addEventListener('mouseout', function () { + this.style.backgroundColor = "#202020"; + }); + }); + + // Append each menu item to the context menu + this.menuItems.forEach(menuItem => { + this.contextMenu.appendChild(menuItem); + }); + + document.body.appendChild(this.contextMenu); + + this.addWidget("button", "New canvas", null, () => { + if (!this.properties || !("points" in this.properties)) { + this.editor = new PointsEditor(this); + this.addProperty("points", this.constructor.type, "string"); + this.addProperty("neg_points", this.constructor.type, "string"); + + } + else { + this.editor = new PointsEditor(this, true); + } + }); + + this.setSize([550, 550]); + this.resizable = false; + this.pointsEditor.parentEl = document.createElement("div"); + this.pointsEditor.parentEl.className = "points-editor"; + this.pointsEditor.parentEl.id = `points-editor-${this.uuid}` + element.appendChild(this.pointsEditor.parentEl); + + chainCallback(this, "onConfigure", function () { + try { + this.editor = new PointsEditor(this); + } catch (error) { + console.error("An error occurred while configuring the editor:", error); + } + }); + chainCallback(this, "onExecuted", function (message) { + let bg_image = message["bg_image"]; + this.properties.imgData = { + name: "bg_image", + base64: bg_image + }; + this.editor.refreshBackgroundImage(this); + }); + + }); // onAfterGraphConfigured + }//node created + } //before register +})//register + +class PointsEditor { + constructor(context, reset = false) { + this.node = context; + this.reset = reset; + const self = this; // Keep a reference to the main class context + + console.log("creatingPointEditor") + + this.node.pasteFile = (file) => { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + return true; + } + return false; + }; + + this.node.onDragOver = function (e) { + if (e.dataTransfer && e.dataTransfer.items) { + return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/")); + } + return false; + }; + + // On drop upload files + this.node.onDragDrop = (e) => { + console.log("onDragDrop called"); + let handled = false; + for (const file of e.dataTransfer.files) { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + handled = true; + } + } + return handled; + }; + + // context menu + this.createContextMenu(); + + if (reset && context.pointsEditor.element) { + context.pointsEditor.element.innerHTML = ''; // Clear the container + } + this.pos_coordWidget = context.widgets.find(w => w.name === "coordinates"); + this.neg_coordWidget = context.widgets.find(w => w.name === "neg_coordinates"); + this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store"); + this.widthWidget = context.widgets.find(w => w.name === "width"); + this.heightWidget = context.widgets.find(w => w.name === "height"); + this.bboxStoreWidget = context.widgets.find(w => w.name === "bbox_store"); + this.bboxWidget = context.widgets.find(w => w.name === "bboxes"); + + //widget callbacks + this.widthWidget.callback = () => { + this.width = this.widthWidget.value; + if (this.width > 256) { + context.setSize([this.width + 45, context.size[1]]); + } + this.vis.width(this.width); + this.updateData(); + } + this.heightWidget.callback = () => { + this.height = this.heightWidget.value + this.vis.height(this.height) + context.setSize([context.size[0], this.height + 300]); + this.updateData(); + } + this.pointsStoreWidget.callback = () => { + this.points = JSON.parse(pointsStoreWidget.value).positive; + this.neg_points = JSON.parse(pointsStoreWidget.value).negative; + this.updateData(); + } + this.bboxStoreWidget.callback = () => { + this.bbox = JSON.parse(bboxStoreWidget.value) + this.updateData(); + } + + this.width = this.widthWidget.value; + this.height = this.heightWidget.value; + var i = 3; + this.points = []; + this.neg_points = []; + this.bbox = [{}]; + var drawing = false; + + // Initialize or reset points array + if (!reset && this.pointsStoreWidget.value != "") { + this.points = JSON.parse(this.pointsStoreWidget.value).positive; + this.neg_points = JSON.parse(this.pointsStoreWidget.value).negative; + this.bbox = JSON.parse(this.bboxStoreWidget.value); + console.log(this.bbox) + } else { + this.points = [ + { + x: this.width / 2, // Middle point horizontally centered + y: this.height / 2 // Middle point vertically centered + } + ]; + this.neg_points = [ + { + x: 0, // Middle point horizontally centered + y: 0 // Middle point vertically centered + } + ]; + const combinedPoints = { + positive: this.points, + negative: this.neg_points, + }; + this.pointsStoreWidget.value = JSON.stringify(combinedPoints); + this.bboxStoreWidget.value = JSON.stringify(this.bbox); + } + + //create main canvas panel + this.vis = new pv.Panel() + .width(this.width) + .height(this.height) + .fillStyle("#222") + .strokeStyle("gray") + .lineWidth(2) + .antialias(false) + .margin(10) + .event("mousedown", function () { + if (pv.event.shiftKey && pv.event.button === 2) { // Use pv.event to access the event object + let scaledMouse = { + x: this.mouse().x / app.canvas.ds.scale, + y: this.mouse().y / app.canvas.ds.scale + }; + i = self.neg_points.push(scaledMouse) - 1; + self.updateData(); + return this; + } + else if (pv.event.shiftKey) { + let scaledMouse = { + x: this.mouse().x / app.canvas.ds.scale, + y: this.mouse().y / app.canvas.ds.scale + }; + i = self.points.push(scaledMouse) - 1; + self.updateData(); + return this; + } + else if (pv.event.ctrlKey) { + console.log("start drawing at " + this.mouse().x / app.canvas.ds.scale + ", " + this.mouse().y / app.canvas.ds.scale); + drawing = true; + self.bbox[0].startX = this.mouse().x / app.canvas.ds.scale; + self.bbox[0].startY = this.mouse().y / app.canvas.ds.scale; + } + else if (pv.event.button === 2) { + self.node.contextMenu.style.display = 'block'; + self.node.contextMenu.style.left = `${pv.event.clientX}px`; + self.node.contextMenu.style.top = `${pv.event.clientY}px`; + } + }) + .event("mousemove", function () { + if (drawing) { + self.bbox[0].endX = this.mouse().x / app.canvas.ds.scale; + self.bbox[0].endY = this.mouse().y / app.canvas.ds.scale; + self.vis.render(); + } + }) + .event("mouseup", function () { + console.log("end drawing at " + this.mouse().x / app.canvas.ds.scale + ", " + this.mouse().y / app.canvas.ds.scale); + drawing = false; + self.updateData(); + }); + + this.backgroundImage = this.vis.add(pv.Image).visible(false) + + //create bounding box + this.bounding_box = this.vis.add(pv.Area) + .data(function () { + if (drawing || (self.bbox && self.bbox[0] && Object.keys(self.bbox[0]).length > 0)) { + return [self.bbox[0].startX, self.bbox[0].endX]; + } else { + return []; + } + }) + .bottom(function () {return self.height - Math.max(self.bbox[0].startY, self.bbox[0].endY); }) + .left(function (d) {return d; }) + .height(function () {return Math.abs(self.bbox[0].startY - self.bbox[0].endY);}) + .fillStyle("rgba(70, 130, 180, 0.5)") + .strokeStyle("steelblue") + .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; }) + .add(pv.Dot) + .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; }) + .data(() => { + if (self.bbox && Object.keys(self.bbox[0]).length > 0) { + return [{ + x: self.bbox[0].endX, + y: self.bbox[0].endY + }]; + } else { + return []; + } + }) + .left(d => d.x) + .top(d => d.y) + .radius(Math.log(Math.min(self.width, self.height)) * 1) + .shape("square") + .cursor("move") + .strokeStyle("steelblue") + .lineWidth(2) + .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("drag", function () { + let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new position by the inverse of the scale factor + let adjustedY = this.mouse().y / app.canvas.ds.scale; + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(self.vis.width(), adjustedX)); + adjustedY = Math.max(0, Math.min(self.vis.height(), adjustedY)); + self.bbox[0].endX = this.mouse().x / app.canvas.ds.scale; + self.bbox[0].endY = this.mouse().y / app.canvas.ds.scale; + self.vis.render(); + }) + .event("dragend", function () { + self.updateData(); + }); + + //create positive points + this.vis.add(pv.Dot) + .data(() => this.points) + .left(d => d.x) + .top(d => d.y) + .radius(Math.log(Math.min(self.width, self.height)) * 4) + .shape("circle") + .cursor("move") + .strokeStyle(function () { return i == this.index ? "#07f907" : "#139613"; }) + .lineWidth(4) + .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function () { + i = this.index; + }) + .event("dragend", function () { + if (pv.event.button === 2 && i !== 0 && i !== self.points.length - 1) { + this.index = i; + self.points.splice(i--, 1); + } + self.updateData(); + + }) + .event("drag", function () { + let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor + let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + self.points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position + self.vis.render(); // Re-render the visualization to reflect the new position + }) + + .anchor("center") + .add(pv.Label) + .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left + .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25) // Shift label down if on top half, otherwise shift up + .font(25 + "px sans-serif") + .text(d => {return this.points.indexOf(d); }) + .textStyle("#139613") + .textShadow("2px 2px 2px black") + .add(pv.Dot) // Add smaller point in the center + .data(() => this.points) + .left(d => d.x) + .top(d => d.y) + .radius(2) // Smaller radius for the center point + .shape("circle") + .fillStyle("red") // Color for the center point + .lineWidth(1); // Stroke thickness for the center point + + //create negative points + this.vis.add(pv.Dot) + .data(() => this.neg_points) + .left(d => d.x) + .top(d => d.y) + .radius(Math.log(Math.min(self.width, self.height)) * 4) + .shape("circle") + .cursor("move") + .strokeStyle(function () { return i == this.index ? "#f91111" : "#891616"; }) + .lineWidth(4) + .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function () { + i = this.index; + }) + .event("dragend", function () { + if (pv.event.button === 2 && i !== 0 && i !== self.neg_points.length - 1) { + this.index = i; + self.neg_points.splice(i--, 1); + } + self.updateData(); + + }) + .event("drag", function () { + let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor + let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + self.neg_points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position + self.vis.render(); // Re-render the visualization to reflect the new position + }) + .anchor("center") + .add(pv.Label) + .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left + .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25) // Shift label down if on top half, otherwise shift up + .font(25 + "px sans-serif") + .text(d => {return this.neg_points.indexOf(d); }) + .textStyle("red") + .textShadow("2px 2px 2px black") + .add(pv.Dot) // Add smaller point in the center + .data(() => this.neg_points) + .left(d => d.x) + .top(d => d.y) + .radius(2) // Smaller radius for the center point + .shape("circle") + .fillStyle("red") // Color for the center point + .lineWidth(1); // Stroke thickness for the center point + + if (this.points.length != 0) { + this.vis.render(); + } + + var svgElement = this.vis.canvas(); + svgElement.style['zIndex'] = "2" + svgElement.style['position'] = "relative" + this.node.pointsEditor.element.appendChild(svgElement); + + if (this.width > 256) { + this.node.setSize([this.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], this.height + 300]); + this.updateData(); + this.refreshBackgroundImage(); + + }//end constructor + + updateData = () => { + if (!this.points || this.points.length === 0) { + console.log("no points"); + return; + } + const combinedPoints = { + positive: this.points, + negative: this.neg_points, + }; + this.pointsStoreWidget.value = JSON.stringify(combinedPoints); + this.pos_coordWidget.value = JSON.stringify(this.points); + this.neg_coordWidget.value = JSON.stringify(this.neg_points); + + if (this.bbox.length != 0) { + let bboxString = JSON.stringify(this.bbox); + this.bboxStoreWidget.value = bboxString; + this.bboxWidget.value = bboxString; + } + + this.vis.render(); + }; + + handleImageLoad = (img, file, base64String) => { + console.log(img.width, img.height); // Access width and height here + this.widthWidget.value = img.width; + this.heightWidget.value = img.height; + + if (img.width != this.vis.width() || img.height != this.vis.height()) { + if (img.width > 256) { + this.node.setSize([img.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], img.height + 300]); + this.vis.width(img.width); + this.vis.height(img.height); + this.height = img.height; + this.width = img.width; + this.updateData(); + } + this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render(); + }; + + processImage = (img, file) => { + const canvas = document.createElement('canvas'); + const ctx = canvas.getContext('2d'); + + const maxWidth = 800; // maximum width + const maxHeight = 600; // maximum height + let width = img.width; + let height = img.height; + + // Calculate the new dimensions while preserving the aspect ratio + if (width > height) { + if (width > maxWidth) { + height *= maxWidth / width; + width = maxWidth; + } + } else { + if (height > maxHeight) { + width *= maxHeight / height; + height = maxHeight; + } + } + + canvas.width = width; + canvas.height = height; + ctx.drawImage(img, 0, 0, width, height); + + // Get the compressed image data as a Base64 string + const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1 + + this.node.properties.imgData = { + name: file.name, + lastModified: file.lastModified, + size: file.size, + type: file.type, + base64: base64String + }; + handleImageLoad(img, file, base64String); +}; + + handleImageFile = (file) => { + const reader = new FileReader(); + reader.onloadend = () => { + const img = new Image(); + img.src = reader.result; + img.onload = () => processImage(img, file); + }; + reader.readAsDataURL(file); + + const imageUrl = URL.createObjectURL(file); + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, file, null); + }; + + refreshBackgroundImage = () => { + if (this.node.properties.imgData && this.node.properties.imgData.base64) { + const base64String = this.node.properties.imgData.base64; + const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`; + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, null, base64String); + } + }; + + createContextMenu = () => { + self = this; + document.addEventListener('contextmenu', function (e) { + e.preventDefault(); + }); + + document.addEventListener('click', function (e) { + if (!self.node.contextMenu.contains(e.target)) { + self.node.contextMenu.style.display = 'none'; + } + }); + + this.node.menuItems.forEach((menuItem, index) => { + self = this; + menuItem.addEventListener('click', function (e) { + e.preventDefault(); + switch (index) { + case 0: + // Create file input element + const fileInput = document.createElement('input'); + fileInput.type = 'file'; + fileInput.accept = 'image/*'; // Accept only image files + + // Listen for file selection + fileInput.addEventListener('change', function (event) { + const file = event.target.files[0]; // Get the selected file + + if (file) { + const imageUrl = URL.createObjectURL(file); + let img = new Image(); + img.src = imageUrl; + img.onload = () => self.handleImageLoad(img, file, null); + } + }); + + fileInput.click(); + + self.node.contextMenu.style.display = 'none'; + break; + case 1: + self.backgroundImage.visible(false).root.render(); + self.node.properties.imgData = null; + self.node.contextMenu.style.display = 'none'; + break; + } + }); + }); + }//end createContextMenu +}//end class + + +//from melmass +export function hideWidgetForGood(node, widget, suffix = '') { + widget.origType = widget.type + widget.origComputeSize = widget.computeSize + widget.origSerializeValue = widget.serializeValue + widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically + widget.type = "converted-widget" + suffix + // widget.serializeValue = () => { + // // Prevent serializing the widget if we have no input linked + // const w = node.inputs?.find((i) => i.widget?.name === widget.name); + // if (w?.link == null) { + // return undefined; + // } + // return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; + // }; + + // Hide any linked widgets, e.g. seed+seedControl + if (widget.linkedWidgets) { + for (const w of widget.linkedWidgets) { + hideWidgetForGood(node, w, ':' + widget.name) + } + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js b/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js new file mode 100644 index 0000000000000000000000000000000000000000..c4531881378c51128b1885b212aabc70d2a8c602 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/setgetnodes.js @@ -0,0 +1,565 @@ +import { app } from "../../../scripts/app.js"; + +//based on diffus3's SetGet: https://github.com/diffus3/ComfyUI-extensions + +// Nodes that allow you to tunnel connections for cleaner graphs +function setColorAndBgColor(type) { + const colorMap = { + "MODEL": LGraphCanvas.node_colors.blue, + "LATENT": LGraphCanvas.node_colors.purple, + "VAE": LGraphCanvas.node_colors.red, + "CONDITIONING": LGraphCanvas.node_colors.brown, + "IMAGE": LGraphCanvas.node_colors.pale_blue, + "CLIP": LGraphCanvas.node_colors.yellow, + "FLOAT": LGraphCanvas.node_colors.green, + "MASK": { color: "#1c5715", bgcolor: "#1f401b"}, + "INT": { color: "#1b4669", bgcolor: "#29699c"}, + "CONTROL_NET": { color: "#156653", bgcolor: "#1c453b"}, + "NOISE": { color: "#2e2e2e", bgcolor: "#242121"}, + "GUIDER": { color: "#3c7878", bgcolor: "#1c453b"}, + "SAMPLER": { color: "#614a4a", bgcolor: "#3b2c2c"}, + "SIGMAS": { color: "#485248", bgcolor: "#272e27"}, + + }; + + const colors = colorMap[type]; + if (colors) { + this.color = colors.color; + this.bgcolor = colors.bgcolor; + } +} +let disablePrefix = app.ui.settings.getSettingValue("KJNodes.disablePrefix") +const LGraphNode = LiteGraph.LGraphNode + +function showAlert(message) { + app.extensionManager.toast.add({ + severity: 'warn', + summary: "KJ Get/Set", + detail: `${message}. Most likely you're missing custom nodes`, + life: 5000, + }) +} +app.registerExtension({ + name: "SetNode", + registerCustomNodes() { + class SetNode extends LGraphNode { + defaultVisibility = true; + serialize_widgets = true; + drawConnection = false; + currentGetters = null; + slotColor = "#FFF"; + canvas = app.canvas; + menuEntry = "Show connections"; + + constructor(title) { + super(title) + if (!this.properties) { + this.properties = { + "previousName": "" + }; + } + this.properties.showOutputText = SetNode.defaultVisibility; + + const node = this; + + this.addWidget( + "text", + "Constant", + '', + (s, t, u, v, x) => { + node.validateName(node.graph); + if(this.widgets[0].value !== ''){ + this.title = (!disablePrefix ? "Set_" : "") + this.widgets[0].value; + } + this.update(); + this.properties.previousName = this.widgets[0].value; + }, + {} + ) + + this.addInput("*", "*"); + this.addOutput("*", '*'); + + this.onConnectionsChange = function( + slotType, //1 = input, 2 = output + slot, + isChangeConnect, + link_info, + output + ) { + //On Disconnect + if (slotType == 1 && !isChangeConnect) { + if(this.inputs[slot].name === ''){ + this.inputs[slot].type = '*'; + this.inputs[slot].name = '*'; + this.title = "Set" + } + } + if (slotType == 2 && !isChangeConnect) { + if (this.outputs && this.outputs[slot]) { + this.outputs[slot].type = '*'; + this.outputs[slot].name = '*'; + } + } + //On Connect + if (link_info && node.graph && slotType == 1 && isChangeConnect) { + const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id); + + if (fromNode && fromNode.outputs && fromNode.outputs[link_info.origin_slot]) { + const type = fromNode.outputs[link_info.origin_slot].type; + + if (this.title === "Set"){ + this.title = (!disablePrefix ? "Set_" : "") + type; + } + if (this.widgets[0].value === '*'){ + this.widgets[0].value = type + } + + this.validateName(node.graph); + this.inputs[0].type = type; + this.inputs[0].name = type; + + if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){ + setColorAndBgColor.call(this, type); + } + } else { + showAlert("node input undefined.") + } + } + if (link_info && node.graph && slotType == 2 && isChangeConnect) { + const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id); + + if (fromNode && fromNode.inputs && fromNode.inputs[link_info.origin_slot]) { + const type = fromNode.inputs[link_info.origin_slot].type; + + this.outputs[0].type = type; + this.outputs[0].name = type; + } else { + showAlert('node output undefined'); + } + } + + + //Update either way + this.update(); + } + + this.validateName = function(graph) { + let widgetValue = node.widgets[0].value; + + if (widgetValue !== '') { + let tries = 0; + const existingValues = new Set(); + + graph._nodes.forEach(otherNode => { + if (otherNode !== this && otherNode.type === 'SetNode') { + existingValues.add(otherNode.widgets[0].value); + } + }); + + while (existingValues.has(widgetValue)) { + widgetValue = node.widgets[0].value + "_" + tries; + tries++; + } + + node.widgets[0].value = widgetValue; + this.update(); + } + } + + this.clone = function () { + const cloned = SetNode.prototype.clone.apply(this); + cloned.inputs[0].name = '*'; + cloned.inputs[0].type = '*'; + cloned.value = ''; + cloned.properties.previousName = ''; + cloned.size = cloned.computeSize(); + return cloned; + }; + + this.onAdded = function(graph) { + this.validateName(graph); + } + + + this.update = function() { + if (!node.graph) { + return; + } + + const getters = this.findGetters(node.graph); + getters.forEach(getter => { + getter.setType(this.inputs[0].type); + }); + + if (this.widgets[0].value) { + const gettersWithPreviousName = this.findGetters(node.graph, true); + gettersWithPreviousName.forEach(getter => { + getter.setName(this.widgets[0].value); + }); + } + + const allGetters = node.graph._nodes.filter(otherNode => otherNode.type === "GetNode"); + allGetters.forEach(otherNode => { + if (otherNode.setComboValues) { + otherNode.setComboValues(); + } + }); + } + + + this.findGetters = function(graph, checkForPreviousName) { + const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value; + return graph._nodes.filter(otherNode => otherNode.type === 'GetNode' && otherNode.widgets[0].value === name && name !== ''); + } + + + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + + onRemoved() { + const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "GetNode"); + allGetters.forEach((otherNode) => { + if (otherNode.setComboValues) { + otherNode.setComboValues([this]); + } + }) + } + getExtraMenuOptions(_, options) { + this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + options.unshift( + { + content: this.menuEntry, + callback: () => { + this.currentGetters = this.findGetters(this.graph); + if (this.currentGetters.length == 0) return; + let linkType = (this.currentGetters[0].outputs[0].type); + this.slotColor = this.canvas.default_connection_color_byType[linkType] + this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + this.drawConnection = !this.drawConnection; + this.canvas.setDirty(true, true); + + }, + has_submenu: true, + submenu: { + title: "Color", + options: [ + { + content: "Highlight", + callback: () => { + this.slotColor = "orange" + this.canvas.setDirty(true, true); + } + } + ], + }, + }, + { + content: "Hide all connections", + callback: () => { + const allGetters = this.graph._nodes.filter(otherNode => otherNode.type === "GetNode" || otherNode.type === "SetNode"); + allGetters.forEach(otherNode => { + otherNode.drawConnection = false; + console.log(otherNode); + }); + + this.menuEntry = "Show connections"; + this.drawConnection = false + this.canvas.setDirty(true, true); + + }, + + }, + ); + // Dynamically add a submenu for all getters + this.currentGetters = this.findGetters(this.graph); + if (this.currentGetters) { + + let gettersSubmenu = this.currentGetters.map(getter => ({ + + content: `${getter.title} id: ${getter.id}`, + callback: () => { + this.canvas.centerOnNode(getter); + this.canvas.selectNode(getter, false); + this.canvas.setDirty(true, true); + + }, + })); + + options.unshift({ + content: "Getters", + has_submenu: true, + submenu: { + title: "GetNodes", + options: gettersSubmenu, + } + }); + } + } + + + onDrawForeground(ctx, lGraphCanvas) { + if (this.drawConnection) { + this._drawVirtualLinks(lGraphCanvas, ctx); + } + } + // onDrawCollapsed(ctx, lGraphCanvas) { + // if (this.drawConnection) { + // this._drawVirtualLinks(lGraphCanvas, ctx); + // } + // } + _drawVirtualLinks(lGraphCanvas, ctx) { + if (!this.currentGetters?.length) return; + var title = this.getTitle ? this.getTitle() : this.title; + var title_width = ctx.measureText(title).width; + if (!this.flags.collapsed) { + var start_node_slotpos = [ + this.size[0], + LiteGraph.NODE_TITLE_HEIGHT * 0.5, + ]; + } + else { + + var start_node_slotpos = [ + title_width + 55, + -15, + + ]; + } + // Provide a default link object with necessary properties, to avoid errors as link can't be null anymore + const defaultLink = { type: 'default', color: this.slotColor }; + + for (const getter of this.currentGetters) { + if (!this.flags.collapsed) { + var end_node_slotpos = this.getConnectionPos(false, 0); + end_node_slotpos = [ + getter.pos[0] - end_node_slotpos[0] + this.size[0], + getter.pos[1] - end_node_slotpos[1] + ]; + } + else { + var end_node_slotpos = this.getConnectionPos(false, 0); + end_node_slotpos = [ + getter.pos[0] - end_node_slotpos[0] + title_width + 50, + getter.pos[1] - end_node_slotpos[1] - 30 + ]; + } + lGraphCanvas.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + defaultLink, + false, + null, + this.slotColor, + LiteGraph.RIGHT, + LiteGraph.LEFT + ); + } + } + } + + LiteGraph.registerNodeType( + "SetNode", + Object.assign(SetNode, { + title: "Set", + }) + ); + + SetNode.category = "KJNodes"; + }, +}); + +app.registerExtension({ + name: "GetNode", + registerCustomNodes() { + class GetNode extends LGraphNode { + + defaultVisibility = true; + serialize_widgets = true; + drawConnection = false; + slotColor = "#FFF"; + currentSetter = null; + canvas = app.canvas; + + constructor(title) { + super(title) + if (!this.properties) { + this.properties = {}; + } + this.properties.showOutputText = GetNode.defaultVisibility; + const node = this; + this.addWidget( + "combo", + "Constant", + "", + (e) => { + this.onRename(); + }, + { + values: () => { + const setterNodes = node.graph._nodes.filter((otherNode) => otherNode.type == 'SetNode'); + return setterNodes.map((otherNode) => otherNode.widgets[0].value).sort(); + } + } + ) + + this.addOutput("*", '*'); + this.onConnectionsChange = function( + slotType, //0 = output, 1 = input + slot, //self-explanatory + isChangeConnect, + link_info, + output + ) { + this.validateLinks(); + } + + this.setName = function(name) { + node.widgets[0].value = name; + node.onRename(); + node.serialize(); + } + + this.onRename = function() { + const setter = this.findSetter(node.graph); + if (setter) { + let linkType = (setter.inputs[0].type); + + this.setType(linkType); + this.title = (!disablePrefix ? "Get_" : "") + setter.widgets[0].value; + + if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){ + setColorAndBgColor.call(this, linkType); + } + + } else { + this.setType('*'); + } + } + + this.clone = function () { + const cloned = GetNode.prototype.clone.apply(this); + cloned.size = cloned.computeSize(); + return cloned; + }; + + this.validateLinks = function() { + if (this.outputs[0].type !== '*' && this.outputs[0].links) { + this.outputs[0].links.filter(linkId => { + const link = node.graph.links[linkId]; + return link && (!link.type.split(",").includes(this.outputs[0].type) && link.type !== '*'); + }).forEach(linkId => { + node.graph.removeLink(linkId); + }); + } + }; + + this.setType = function(type) { + this.outputs[0].name = type; + this.outputs[0].type = type; + this.validateLinks(); + } + + this.findSetter = function(graph) { + const name = this.widgets[0].value; + const foundNode = graph._nodes.find(otherNode => otherNode.type === 'SetNode' && otherNode.widgets[0].value === name && name !== ''); + return foundNode; + }; + + this.goToSetter = function() { + const setter = this.findSetter(this.graph); + this.canvas.centerOnNode(setter); + this.canvas.selectNode(setter, false); + }; + + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + getInputLink(slot) { + const setter = this.findSetter(this.graph); + + if (setter) { + const slotInfo = setter.inputs[slot]; + const link = this.graph.links[slotInfo.link]; + return link; + } else { + const errorMessage = "No SetNode found for " + this.widgets[0].value + "(" + this.type + ")"; + showAlert(errorMessage); + //throw new Error(errorMessage); + } + } + onAdded(graph) { + } + getExtraMenuOptions(_, options) { + let menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + + options.unshift( + { + content: "Go to setter", + callback: () => { + this.goToSetter(); + }, + }, + { + content: menuEntry, + callback: () => { + this.currentSetter = this.findSetter(this.graph); + if (this.currentSetter.length == 0) return; + let linkType = (this.currentSetter.inputs[0].type); + this.drawConnection = !this.drawConnection; + this.slotColor = this.canvas.default_connection_color_byType[linkType] + menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; + this.canvas.setDirty(true, true); + }, + }, + ); + } + + onDrawForeground(ctx, lGraphCanvas) { + if (this.drawConnection) { + this._drawVirtualLink(lGraphCanvas, ctx); + } + } + // onDrawCollapsed(ctx, lGraphCanvas) { + // if (this.drawConnection) { + // this._drawVirtualLink(lGraphCanvas, ctx); + // } + // } + _drawVirtualLink(lGraphCanvas, ctx) { + if (!this.currentSetter) return; + + // Provide a default link object with necessary properties, to avoid errors as link can't be null anymore + const defaultLink = { type: 'default', color: this.slotColor }; + + let start_node_slotpos = this.currentSetter.getConnectionPos(false, 0); + start_node_slotpos = [ + start_node_slotpos[0] - this.pos[0], + start_node_slotpos[1] - this.pos[1], + ]; + let end_node_slotpos = [0, -LiteGraph.NODE_TITLE_HEIGHT * 0.5]; + lGraphCanvas.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + defaultLink, + false, + null, + this.slotColor + ); + } + } + + LiteGraph.registerNodeType( + "GetNode", + Object.assign(GetNode, { + title: "Get", + }) + ); + + GetNode.category = "KJNodes"; + }, +}); diff --git a/custom_nodes/ComfyUI-KJNodes/web/js/spline_editor.js b/custom_nodes/ComfyUI-KJNodes/web/js/spline_editor.js new file mode 100644 index 0000000000000000000000000000000000000000..cc095990a7ab48b4ea930563e0755982ddc39cf5 --- /dev/null +++ b/custom_nodes/ComfyUI-KJNodes/web/js/spline_editor.js @@ -0,0 +1,1379 @@ +import { app } from '../../../scripts/app.js' + +//from melmass +export function makeUUID() { + let dt = new Date().getTime() + const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { + const r = ((dt + Math.random() * 16) % 16) | 0 + dt = Math.floor(dt / 16) + return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) + }) + return uuid +} + +export const loadScript = ( + FILE_URL, + async = true, + type = 'text/javascript', + ) => { + return new Promise((resolve, reject) => { + try { + // Check if the script already exists + const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) + if (existingScript) { + resolve({ status: true, message: 'Script already loaded' }) + return + } + + const scriptEle = document.createElement('script') + scriptEle.type = type + scriptEle.async = async + scriptEle.src = FILE_URL + + scriptEle.addEventListener('load', (ev) => { + resolve({ status: true }) + }) + + scriptEle.addEventListener('error', (ev) => { + reject({ + status: false, + message: `Failed to load the script ${FILE_URL}`, + }) + }) + + document.body.appendChild(scriptEle) + } catch (error) { + reject(error) + } + }) + } + const create_documentation_stylesheet = () => { + const tag = 'kj-splineditor-stylesheet' + + let styleTag = document.head.querySelector(tag) + + if (!styleTag) { + styleTag = document.createElement('style') + styleTag.type = 'text/css' + styleTag.id = tag + styleTag.innerHTML = ` + .spline-editor { + + position: absolute; + + font: 12px monospace; + line-height: 1.5em; + padding: 10px; + z-index: 0; + overflow: hidden; + } + ` + document.head.appendChild(styleTag) + } + } + +loadScript('kjweb_async/svg-path-properties.min.js').catch((e) => { + console.log(e) +}) +loadScript('kjweb_async/protovis.min.js').catch((e) => { + console.log(e) +}) +create_documentation_stylesheet() + +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} +app.registerExtension({ + name: 'KJNodes.SplineEditor', + + async beforeRegisterNodeDef(nodeType, nodeData) { + if (nodeData?.name === 'SplineEditor') { + chainCallback(nodeType.prototype, "onNodeCreated", function () { + + this.widgets.find(w => w.name === "coordinates").hidden = true + + var element = document.createElement("div"); + this.uuid = makeUUID() + element.id = `spline-editor-${this.uuid}` + + this.previewMediaType = 'image' + + this.splineEditor = this.addDOMWidget(nodeData.name, "SplineEditorWidget", element, { + serialize: false, + hideOnZoom: false, + }); + + // context menu + this.contextMenu = document.createElement("div"); + this.contextMenu.className = 'spline-editor-context-menu'; + this.contextMenu.id = "context-menu"; + this.contextMenu.style.display = "none"; + this.contextMenu.style.position = "absolute"; + this.contextMenu.style.backgroundColor = "#202020"; + this.contextMenu.style.minWidth = "100px"; + this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)"; + this.contextMenu.style.zIndex = "100"; + this.contextMenu.style.padding = "5px"; + + function styleMenuItem(menuItem) { + menuItem.style.display = "block"; + menuItem.style.padding = "5px"; + menuItem.style.color = "#FFF"; + menuItem.style.fontFamily = "Arial, sans-serif"; + menuItem.style.fontSize = "16px"; + menuItem.style.textDecoration = "none"; + menuItem.style.marginBottom = "5px"; + } + function createMenuItem(id, textContent) { + let menuItem = document.createElement("a"); + menuItem.href = "#"; + menuItem.id = `menu-item-${id}`; + menuItem.textContent = textContent; + styleMenuItem(menuItem); + return menuItem; + } + + // Create an array of menu items using the createMenuItem function + this.menuItems = [ + createMenuItem(0, "Toggle handles"), + createMenuItem(1, "Display sample points"), + createMenuItem(2, "Switch point shape"), + createMenuItem(3, "Background image"), + createMenuItem(4, "Invert point order"), + createMenuItem(5, "Clear Image"), + createMenuItem(6, "Add new spline"), + createMenuItem(7, "Add new single point"), + createMenuItem(8, "Delete current spline"), + createMenuItem(9, "Next spline"), + ]; + + // Add mouseover and mouseout event listeners to each menu item for styling + this.menuItems.forEach(menuItem => { + menuItem.addEventListener('mouseover', function() { + this.style.backgroundColor = "gray"; + }); + + menuItem.addEventListener('mouseout', function() { + this.style.backgroundColor = "#202020"; + }); + }); + + // Append each menu item to the context menu + this.menuItems.forEach(menuItem => { + this.contextMenu.appendChild(menuItem); + }); + + document.body.appendChild(this.contextMenu); + + this.addWidget("button", "New canvas", null, () => { + if (!this.properties || !("points" in this.properties)) { + this.editor = new SplineEditor(this); + this.addProperty("points", this.constructor.type, "string"); + } + else { + this.editor = new SplineEditor(this, true); + } + }); + + this.setSize([550, 1000]); + this.resizable = false; + this.splineEditor.parentEl = document.createElement("div"); + this.splineEditor.parentEl.className = "spline-editor"; + this.splineEditor.parentEl.id = `spline-editor-${this.uuid}` + element.appendChild(this.splineEditor.parentEl); + + chainCallback(this, "onConfigure", function () { + try { + this.editor = new SplineEditor(this); + } catch (error) { + console.error("An error occurred while configuring the editor:", error); + } + }); + chainCallback(this, "onExecuted", function (message) { + let bg_image = message["bg_image"]; + this.properties.imgData = { + name: "bg_image", + base64: bg_image + }; + this.editor.refreshBackgroundImage(this); + }); + + }); // onAfterGraphConfigured + }//node created + } //before register +})//register + + +class SplineEditor{ + constructor(context, reset = false) { + this.node = context; + this.reset=reset; + const self = this; + console.log("creatingSplineEditor") + + this.node.pasteFile = (file) => { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + return true; + } + return false; + }; + + this.node.onDragOver = function (e) { + if (e.dataTransfer && e.dataTransfer.items) { + return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/")); + } + return false; + }; + + // On drop upload files + this.node.onDragDrop = (e) => { + console.log("onDragDrop called"); + let handled = false; + for (const file of e.dataTransfer.files) { + if (file.type.startsWith("image/")) { + this.handleImageFile(file); + handled = true; + } + } + return handled; + }; + + // context menu + this.createContextMenu(); + + + this.dotShape = "circle"; + this.drawSamplePoints = false; + + if (reset && context.splineEditor.element) { + context.splineEditor.element.innerHTML = ''; // Clear the container + } + this.coordWidget = context.widgets.find(w => w.name === "coordinates"); + this.interpolationWidget = context.widgets.find(w => w.name === "interpolation"); + this.pointsWidget = context.widgets.find(w => w.name === "points_to_sample"); + this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store"); + this.tensionWidget = context.widgets.find(w => w.name === "tension"); + this.minValueWidget = context.widgets.find(w => w.name === "min_value"); + this.maxValueWidget = context.widgets.find(w => w.name === "max_value"); + this.samplingMethodWidget = context.widgets.find(w => w.name === "sampling_method"); + this.widthWidget = context.widgets.find(w => w.name === "mask_width"); + this.heightWidget = context.widgets.find(w => w.name === "mask_height"); + + this.interpolation = this.interpolationWidget.value + this.tension = this.tensionWidget.value + this.points_to_sample = this.pointsWidget.value + this.rangeMin = this.minValueWidget.value + this.rangeMax = this.maxValueWidget.value + this.pointsLayer = null; + this.samplingMethod = this.samplingMethodWidget.value + + if (this.samplingMethod == "path"||this.samplingMethod == "speed") { + this.dotShape = "triangle" + } + + + this.interpolationWidget.callback = () => { + this.interpolation = this.interpolationWidget.value + this.updatePath(); + } + this.samplingMethodWidget.callback = () => { + this.samplingMethod = this.samplingMethodWidget.value + if (this.samplingMethod == "path") { + this.dotShape = "triangle" + } + else if (this.samplingMethod == "controlpoints") { + this.dotShape = "circle" + this.drawSamplePoints = true; + } + this.updatePath(); + } + this.tensionWidget.callback = () => { + this.tension = this.tensionWidget.value + this.updatePath(); + } + this.pointsWidget.callback = () => { + this.points_to_sample = this.pointsWidget.value + this.updatePath(); + } + this.minValueWidget.callback = () => { + this.rangeMin = this.minValueWidget.value + this.updatePath(); + } + this.maxValueWidget.callback = () => { + this.rangeMax = this.maxValueWidget.value + this.updatePath(); + } + this.widthWidget.callback = () => { + this.width = this.widthWidget.value; + if (this.width > 256) { + context.setSize([this.width + 45, context.size[1]]); + } + this.vis.width(this.width); + this.updatePath(); +} +this.heightWidget.callback = () => { + this.height = this.heightWidget.value + this.vis.height(this.height) + context.setSize([context.size[0], this.height + 450]); + this.updatePath(); + } + this.pointsStoreWidget.callback = () => { + points = JSON.parse(this.pointsStoreWidget.value); + this.updatePath(); + } + + // Initialize or reset points array + this.drawHandles = false; + this.drawRuler = true; + var hoverIndex = -1; + var isDragging = false; + this.width = this.widthWidget.value; + this.height = this.heightWidget.value; + var i = 3; + this.splines = []; + this.activeSplineIndex = 0; // Track which spline is being edited + // init mouse position +this.lastMousePosition = { x: this.width/2, y: this.height/2 }; + + if (!reset && this.pointsStoreWidget.value != "") { + try { + const parsedData = JSON.parse(this.pointsStoreWidget.value); + // Check if it's already in the new format (array of splines) + if (Array.isArray(parsedData) && parsedData.length > 0 && parsedData[0].hasOwnProperty('points')) { + this.splines = parsedData; + } else { + // Convert old format (single array of points) to new format + this.splines = [{ + points: parsedData, + color: "#1f77b4", + name: "Spline 1" + }]; + } + } catch (e) { + console.error("Error parsing spline data:", e); + this.initializeDefaultSplines(); + } +} else { + this.initializeDefaultSplines(); + this.pointsStoreWidget.value = JSON.stringify(this.splines); +} + + this.vis = new pv.Panel() + .width(this.width) + .height(this.height) + .fillStyle("#222") + .strokeStyle("gray") + .lineWidth(2) + .antialias(false) + .margin(10) + .event("mousedown", function () { + if (pv.event.shiftKey) { // Use pv.event to access the event object + let scaledMouse = { + x: this.mouse().x / app.canvas.ds.scale, + y: this.mouse().y / app.canvas.ds.scale + }; + i = self.splines[self.activeSplineIndex].points.push(scaledMouse) - 1; + self.updatePath(); + return this; + } + else if (pv.event.ctrlKey) { + // Capture the clicked location + let clickedPoint = { + x: this.mouse().x / app.canvas.ds.scale, + y: this.mouse().y / app.canvas.ds.scale + }; + + // Find the two closest points to the clicked location + const activePoints = self.splines[self.activeSplineIndex].points; + let { point1Index, point2Index } = self.findClosestPoints(self.splines[self.activeSplineIndex].points, clickedPoint); + + // Calculate the midpoint between the two closest points + let midpoint = { + x: (activePoints[point1Index].x + activePoints[point2Index].x) / 2, + y: (activePoints[point1Index].y + activePoints[point2Index].y) / 2 + }; + + // Insert the midpoint into the array + activePoints.splice(point2Index, 0, midpoint); + i = point2Index; + self.updatePath(); + } + else if (pv.event.button === 2) { + // Store the current mouse position adjusted for scale + self.lastMousePosition = { + x: this.mouse().x / app.canvas.ds.scale, + y: this.mouse().y / app.canvas.ds.scale + }; + + self.node.contextMenu.style.display = 'block'; + self.node.contextMenu.style.left = `${pv.event.clientX}px`; + self.node.contextMenu.style.top = `${pv.event.clientY}px`; + } + }) + this.backgroundImage = this.vis.add(pv.Image).visible(false) + + this.vis.add(pv.Rule) + .data(pv.range(0, this.height, 64)) + .bottom(d => d) + .strokeStyle("gray") + .lineWidth(3) + .visible(() => self.drawRuler) + + this.hoverSplineIndex = -1; + + this.splines.forEach((spline, splineIndex) => { + const strokeObj = this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle("black") // Stroke color + .lineWidth(() => { + // Make stroke slightly wider than the main line + if (splineIndex === this.activeSplineIndex) return 5; + if (splineIndex === this.hoverSplineIndex) return 4; + return 3.5; + }); + + this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle(spline.color) + .lineWidth(() => { + // Change line width based on active or hover state + if (splineIndex === this.activeSplineIndex) return 3; + if (splineIndex === this.hoverSplineIndex) return 2; + return 1.5; + }) + .event("mouseover", () => { + this.hoverSplineIndex = splineIndex; + this.vis.render(); + }) + .event("mouseout", () => { + this.hoverSplineIndex = -1; + this.vis.render(); + }) + .event("mousedown", () => { + if (this.activeSplineIndex !== splineIndex) { + this.activeSplineIndex = splineIndex; + this.refreshSplineElements(); + } + }); + }); + + this.vis.add(pv.Dot) + .data(() => { + const activeSpline = this.splines[this.activeSplineIndex]; + // If this is a single point, don't show it in the main visualization + if (activeSpline.isSinglePoint || (activeSpline.points && activeSpline.points.length === 1)) { + return []; // Return empty array to hide in main visualization + } + return activeSpline.points; + }) + .left(d => d.x) + .top(d => d.y) + .radius(12) + .shape(function() { + return self.dotShape; + }) + .angle(function() { + const index = this.index; + let angle = 0; + + if (self.dotShape === "triangle") { + const activePoints = self.splines[self.activeSplineIndex].points; + let dxNext = 0, dyNext = 0; + if (index < activePoints.length - 1) { + dxNext = activePoints[index + 1].x - activePoints[index].x; + dyNext = activePoints[index + 1].y - activePoints[index].y; + } + + let dxPrev = 0, dyPrev = 0; + if (index > 0) { + dxPrev = activePoints[index].x - activePoints[index - 1].x; + dyPrev = activePoints[index].y - activePoints[index - 1].y; + } + + const dx = (dxNext + dxPrev) / 2; + const dy = (dyNext + dyPrev) / 2; + + angle = Math.atan2(dy, dx); + angle -= Math.PI / 2; + angle = (angle + 2 * Math.PI) % (2 * Math.PI); + } + + return angle; + }) + .cursor("move") + .strokeStyle(function () { return i == this.index ? "#ff7f0e" : "#1f77b4"; }) + .fillStyle(function () { return "rgba(100, 100, 100, 0.3)"; }) + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function () { + i = this.index; + hoverIndex = this.index; + isDragging = true; + const activePoints = self.splines[self.activeSplineIndex].points; + if (pv.event.button === 2 && i !== 0 && i !== activePoints.length - 1) { + activePoints.splice(i--, 1); + self.vis.render(); + } + return this; + }) + .event("dragend", function() { + if (this.pathElements !== null) { + self.updatePath(); + } + isDragging = false; + }) + .event("drag", function () { + let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor + let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds of the vis.Panel + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + self.splines[self.activeSplineIndex].points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position + self.vis.render(); // Re-render the visualization to reflect the new position + }) + .event("mouseover", function() { + hoverIndex = this.index; // Set the hover index to the index of the hovered dot + self.vis.render(); // Re-render the visualization + }) + .event("mouseout", function() { + !isDragging && (hoverIndex = -1); // Reset the hover index when the mouse leaves the dot + self.vis.render(); // Re-render the visualization + }) + .anchor("center") + .add(pv.Label) + .visible(function() { + return hoverIndex === this.index; // Only show the label for the hovered dot + }) + .left(d => d.x < this.width / 2 ? d.x + 80 : d.x - 70) // Shift label to right if on left half, otherwise shift to left + .top(d => d.y < this.height / 2 ? d.y + 20 : d.y - 20) // Shift label down if on top half, otherwise shift up + .font(12 + "px sans-serif") + .text(d => { + if (this.samplingMethod == "path") { + return `X: ${Math.round(d.x)}, Y: ${Math.round(d.y)}`; + } else { + let frame = Math.round((d.x / self.width) * self.points_to_sample); + let normalizedY = (1.0 - (d.y / self.height) - 0.0) * (self.rangeMax - self.rangeMin) + self.rangeMin; + let normalizedX = (d.x / self.width); + return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`; + } + }) + .textStyle("orange") + + // single points + this.vis.add(pv.Dot) + .data(() => { + // Collect all single points from all splines + const singlePoints = []; + this.splines.forEach((spline, splineIndex) => { + if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) { + singlePoints.push({ + x: spline.points[0].x, + y: spline.points[0].y, + splineIndex: splineIndex, + color: spline.color + }); + } + }); + return singlePoints; + }) + .left(d => d.x) + .top(d => d.y) + .radius(6) + .shape("square") + .strokeStyle(d => d.splineIndex === this.activeSplineIndex ? "#ff7f0e" : d.color) + .fillStyle(d => "rgba(100, 100, 100, 0.9)") + .lineWidth(d => d.splineIndex === this.activeSplineIndex ? 3 : 1.5) + .cursor("move") + .event("mousedown", pv.Behavior.drag()) + .event("dragstart", function(d) { + self.activeSplineIndex = d.splineIndex; + self.refreshSplineElements(); + return this; + }) + .event("drag", function(d) { + let adjustedX = this.mouse().x / app.canvas.ds.scale; + let adjustedY = this.mouse().y / app.canvas.ds.scale; + + // Determine the bounds of the vis.Panel + const panelWidth = self.vis.width(); + const panelHeight = self.vis.height(); + + // Adjust the new position if it would place the dot outside the bounds + adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); + adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); + + // Update the point position + const spline = self.splines[d.splineIndex]; + spline.points[0] = { x: adjustedX, y: adjustedY }; + + // For single points, we need to refresh the entire spline element + // to prevent the line-drawing effect + + }) + .event("dragend", function(d) { + self.refreshSplineElements(); + self.updatePath(); + }) + .visible(d => true); // Make always visible + + if (this.splines.length != 0) { + this.vis.render(); + } + var svgElement = this.vis.canvas(); + svgElement.style['zIndex'] = "2" + svgElement.style['position'] = "relative" + this.node.splineEditor.element.appendChild(svgElement); + this.pathElements = svgElement.getElementsByTagName('path'); // Get all path elements + + if (this.width > 256) { + this.node.setSize([this.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], this.height + 450]); + this.updatePath(); + this.refreshBackgroundImage(); + } + + updatePath = () => { + if (!this.splines || this.splines.length === 0) { + console.log("no splines"); + return; + } + // Get active spline points + console.log("this.activeSplineIndex", this.activeSplineIndex); + const activeSpline = this.splines[this.activeSplineIndex]; + const activePoints = activeSpline.points; + + if (!activePoints || activePoints.length === 0) { + console.log("no points in active spline"); + return; + } + + + let coords; + if (this.samplingMethod != "controlpoints") { + coords = this.samplePoints(this.pathElements[this.activeSplineIndex], this.points_to_sample, this.samplingMethod, this.width, this.activeSplineIndex); + } else { + coords = activePoints; + } + + let allSplineCoords = []; + for (let i = 0; i < this.splines.length; i++) { + // Use the same sampling method for all splines + let splineCoords; + const pathElement = this.pathElements[i]; + + if (this.samplingMethod != "controlpoints" && pathElement) { + splineCoords = this.samplePoints(pathElement, this.points_to_sample, this.samplingMethod, this.width, i); + } else { + // Fall back to control points if no path element or sampling method is "controlpoints" + splineCoords = this.splines[i].points; + } + + allSplineCoords.push(splineCoords); + } + + if (this.drawSamplePoints) { + if (this.pointsLayer) { + // Update the data of the existing points layer + this.pointsLayer.data(coords); + } else { + // Create the points layer if it doesn't exist + this.pointsLayer = this.vis.add(pv.Dot) + .data(coords) + .left(function(d) { return d.x; }) + .top(function(d) { return d.y; }) + .radius(5) // Adjust the radius as needed + .fillStyle("red") // Change the color as needed + .strokeStyle("black") // Change the stroke color as needed + .lineWidth(1); // Adjust the line width as needed + } + } else { + if (this.pointsLayer) { + // Remove the points layer + this.pointsLayer.data([]); + this.vis.render(); + } + } + this.pointsStoreWidget.value = JSON.stringify(this.splines); + if (this.coordWidget) { + this.coordWidget.value = JSON.stringify(allSplineCoords); + } + this.vis.render(); + }; + + handleImageLoad = (img, file, base64String) => { + //console.log(img.width, img.height); // Access width and height here + this.widthWidget.value = img.width; + this.heightWidget.value = img.height; + this.drawRuler = false; + + if (img.width != this.vis.width() || img.height != this.vis.height()) { + if (img.width > 256) { + this.node.setSize([img.width + 45, this.node.size[1]]); + } + this.node.setSize([this.node.size[0], img.height + 520]); + this.vis.width(img.width); + this.vis.height(img.height); + this.height = img.height; + this.width = img.width; + + this.updatePath(); + } + this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render(); + }; + + processImage = (img, file) => { + const canvas = document.createElement('canvas'); + const ctx = canvas.getContext('2d'); + + const maxWidth = 800; // maximum width + const maxHeight = 600; // maximum height + let width = img.width; + let height = img.height; + + // Calculate the new dimensions while preserving the aspect ratio + if (width > height) { + if (width > maxWidth) { + height *= maxWidth / width; + width = maxWidth; + } + } else { + if (height > maxHeight) { + width *= maxHeight / height; + height = maxHeight; + } + } + + canvas.width = width; + canvas.height = height; + ctx.drawImage(img, 0, 0, width, height); + + // Get the compressed image data as a Base64 string + const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1 + + this.node.properties.imgData = { + name: file.name, + lastModified: file.lastModified, + size: file.size, + type: file.type, + base64: base64String + }; + handleImageLoad(img, file, base64String); + }; + + handleImageFile = (file) => { + const reader = new FileReader(); + reader.onloadend = () => { + const img = new Image(); + img.src = reader.result; + img.onload = () => processImage(img, file); + }; + reader.readAsDataURL(file); + + const imageUrl = URL.createObjectURL(file); + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, file, null); + }; + + refreshBackgroundImage = () => { + if (this.node.properties.imgData && this.node.properties.imgData.base64) { + const base64String = this.node.properties.imgData.base64; + const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`; + const img = new Image(); + img.src = imageUrl; + img.onload = () => this.handleImageLoad(img, null, base64String); + } + }; + + refreshSplineElements = () => { + // Clear existing line elements and recreate them + const svgElement = this.vis.canvas(); + + // Remove all existing line elements + const oldLines = svgElement.querySelectorAll('path'); + oldLines.forEach(line => line.remove()); + + this.pathElements = []; + this.lineObjects = []; + + const originalChildren = [...this.vis.children]; + + // Find line objects to remove (those that represent splines) + const linesToRemove = originalChildren.filter(child => + child instanceof pv.Line + ); + linesToRemove.forEach(line => line.visible(false)); + + // Re-add all spline lines and store references to them + this.splines.forEach((spline, splineIndex) => { + // For single points, we need a special handling + if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) { + const point = spline.points[0]; + // For single points, create a tiny line at the same point + // This ensures we have a path element for the point + const lineObj = this.vis.add(pv.Line) + .data([point, {x: point.x + 0.001, y: point.y + 0.001}]) + .left(d => d.x) + .top(d => d.y) + .strokeStyle(spline.color) + .lineWidth(() => { + if (splineIndex === this.activeSplineIndex) return 3; + if (splineIndex === this.hoverSplineIndex) return 2; + return 1.5; + }) + .event("mouseover", () => { + this.hoverSplineIndex = splineIndex; + this.vis.render(); + }) + .event("mouseout", () => { + this.hoverSplineIndex = -1; + this.vis.render(); + }) + .event("mousedown", () => { + if (this.activeSplineIndex !== splineIndex) { + this.activeSplineIndex = splineIndex; + this.refreshSplineElements(); + } + }); + this.lineObjects.push(lineObj); + } else { + // For normal multi-point splines + const strokeObj = this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle("black") // Stroke color + .lineWidth(() => { + // Make stroke slightly wider than the main line + if (splineIndex === this.activeSplineIndex) return 5; + if (splineIndex === this.hoverSplineIndex) return 4; + return 3.5; + }); + const lineObj = this.vis.add(pv.Line) + .data(() => spline.points) + .left(d => d.x) + .top(d => d.y) + .interpolate(() => this.interpolation) + .tension(() => this.tension) + .segmented(() => false) + .strokeStyle(spline.color) + .lineWidth(() => { + if (splineIndex === this.activeSplineIndex) return 3; + if (splineIndex === this.hoverSplineIndex) return 2; + return 1.5; + }) + .event("mouseover", () => { + this.hoverSplineIndex = splineIndex; + this.vis.render(); + }) + .event("mouseout", () => { + this.hoverSplineIndex = -1; + this.vis.render(); + }) + .event("mousedown", () => { + if (this.activeSplineIndex !== splineIndex) { + this.activeSplineIndex = splineIndex; + this.refreshSplineElements(); + } + }); + + // // Add invisible wider hit area for easier selection + // this.vis.add(pv.Line) + // .data(() => spline.points) + // .left(d => d.x) + // .top(d => d.y) + // .interpolate(() => this.interpolation) + // .tension(() => this.tension) + // .segmented(() => false) + // .strokeStyle("rgba(0,0,0,0.01)") // Nearly invisible + // .lineWidth(15) // Much wider hit area + // .event("mouseover", () => { + // this.hoverSplineIndex = splineIndex; + // this.vis.render(); + // }) + // .event("mouseout", () => { + // this.hoverSplineIndex = -1; + // this.vis.render(); + // }) + // .event("mousedown", () => { + // if (pv.event.shiftKey) { + // if (this.activeSplineIndex !== splineIndex) { + // this.activeSplineIndex = splineIndex; + // this.refreshSplineElements(); + // } + // }} + // ); + + this.lineObjects.push(lineObj); + } + }); + + this.vis.render(); + + requestAnimationFrame(() => { + const allPaths = Array.from(svgElement.querySelectorAll('path')); + this.pathElements = []; + + // First try: look at paths with specific childIndex values + this.lineObjects.forEach((lineObj, i) => { + // Find paths that correspond to our line objects + const childIndex = lineObj.childIndex; + const matchingPath = allPaths.find(path => + path.$scene && path.$scene.scenes && + path.$scene.scenes.childIndex === childIndex + ); + + if (matchingPath) { + //console.log("matchingPath:", matchingPath); + this.pathElements[i] = matchingPath; + } + }); + + // Check if we found all paths + if (this.pathElements.filter(p => p).length !== this.splines.length) { + // Fallback to color matching + this.pathElements = []; + for (let i = 0; i < this.splines.length; i++) { + const color = this.splines[i].color; + const matchingPath = allPaths.find(p => + p.getAttribute('style')?.includes(color) && + !this.pathElements.includes(p) + ); + + if (matchingPath) { + this.pathElements[i] = matchingPath; + } + } + } + + // If we still don't have the right number of paths, use the first N paths + if (this.pathElements.filter(p => p).length !== this.splines.length) { + this.pathElements = allPaths.slice(0, this.splines.length); + } + + this.updatePath(); + }); + }; + + + initializeDefaultSplines() { + this.splines = [{ + points: pv.range(1, 4).map((i, index) => { + if (index === 0) { + return { x: 0, y: this.height }; + } else if (index === 2) { + return { x: this.width, y: 0 }; + } else { + return { + x: i * this.width / 5, + y: 50 + Math.random() * (this.height - 100) + }; + } + }), + color: this.getSplineColor(0), + name: "Spline 1" + }]; + } + + getSplineColor(index) { + const colors = [ + "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", + "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", + "#bcbd22", "#17becf" + ]; + return colors[index % colors.length]; + } + + createContextMenu = () => { + const self = this; + const oldMenu = this.node.contextMenu; + const newMenu = oldMenu.cloneNode(true); + oldMenu.parentNode.replaceChild(newMenu, oldMenu); + this.node.contextMenu = newMenu; + + document.addEventListener('contextmenu', function (e) { + e.preventDefault(); + }); + + document.addEventListener('click', function (e) { + document.querySelectorAll('.spline-editor-context-menu').forEach(menu => { + menu.style.display = 'none'; + }); + }); + + this.node.contextMenu.addEventListener('click', function(e) { + e.preventDefault(); + if (e.target.tagName === 'A') { + const id = parseInt(e.target.id.split('-')[2]); + + switch(id) { + case 0: + e.preventDefault(); + if (!self.drawHandles) { + self.drawHandles = true + self.vis.add(pv.Line) + .data(() => self.splines[self.activeSplineIndex].points.map((point, index) => ({ + start: point, + end: [index] + }))) + .left(d => d.start.x) + .top(d => d.start.y) + .interpolate("linear") + .tension(0) // Straight lines + .strokeStyle("#ff7f0e") // Same color as control points + .lineWidth(1) + .visible(() => self.drawHandles); + self.vis.render(); + } else { + self.drawHandles = false + self.vis.render(); + } + self.node.contextMenu.style.display = 'none'; + break; + case 1: + + self.drawSamplePoints = !self.drawSamplePoints; + self.updatePath(); + break; + case 2: + if (self.dotShape == "circle"){ + self.dotShape = "triangle" + } + else { + self.dotShape = "circle" + } + self.updatePath(); + break; + case 3: + // Create file input element + const fileInput = document.createElement('input'); + fileInput.type = 'file'; + fileInput.accept = 'image/*'; // Accept only image files + + // Listen for file selection + fileInput.addEventListener('change', function (event) { + const file = event.target.files[0]; // Get the selected file + + if (file) { + const imageUrl = URL.createObjectURL(file); + let img = new Image(); + img.src = imageUrl; + img.onload = () => self.handleImageLoad(img, file, null); + } + }); + + fileInput.click(); + + self.node.contextMenu.style.display = 'none'; + break; + case 4: + self.splines[self.activeSplineIndex].points.reverse(); + self.updatePath(); + break; + case 5: + self.backgroundImage.visible(false).root.render(); + self.node.properties.imgData = null; + self.node.contextMenu.style.display = 'none'; + break; + case 6: // Add new spline + const newSplineIndex = self.splines.length; + self.splines.push({ + points: [ + // Create default points for the new spline + { x: 0, y: self.height }, + { x: self.width/2, y: self.height/2 }, + { x: self.width, y: 0 } + ], + color: self.getSplineColor(newSplineIndex), + name: `Spline ${newSplineIndex + 1}` + }); + self.activeSplineIndex = newSplineIndex; + self.refreshSplineElements(); + self.node.contextMenu.style.display = 'none'; + break; + case 7: // Add new single point + const newSingleSplineIndex = self.splines.length; + self.splines.push({ + points: [ + { x: self.lastMousePosition.x, y: self.lastMousePosition.y }, + ], + color: self.getSplineColor(newSingleSplineIndex), + name: `Spline ${newSingleSplineIndex + 1}`, + isSinglePoint: true + }); + self.activeSplineIndex = newSingleSplineIndex; + self.refreshSplineElements(); + self.node.contextMenu.style.display = 'none'; + break; + case 8: // Delete current spline + if (self.splines.length > 1) { + self.splines.splice(self.activeSplineIndex, 1); + self.activeSplineIndex = Math.min(self.activeSplineIndex, self.splines.length - 1); + self.refreshSplineElements(); + } + self.node.contextMenu.style.display = 'none'; + break; + case 9: // Next spline + self.activeSplineIndex = (self.activeSplineIndex + 1) % self.splines.length; + self.refreshSplineElements(); + self.node.contextMenu.style.display = 'none'; + break; + } + } + }); + } + + samplePoints(svgPathElement, numSamples, samplingMethod, width, splineIndex) { + const spline = this.splines[splineIndex]; + + // Check if this is a single point spline + if (spline && (spline.isSinglePoint || (spline.points && spline.points.length === 1))) { + // For a single point, return an array with the same coordinates repeated + const point = spline.points[0]; + return Array(numSamples).fill().map(() => ({ x: point.x, y: point.y })); + } + + if (!svgPathElement) { + console.warn(`Path element not found for spline index: ${splineIndex}. Available paths: ${this.pathElements.length}`); + + + const splinePoints = this.splines[splineIndex].points; + + // If we have no points, return an empty array + if (!splinePoints || splinePoints.length === 0) { + return []; + } + + // Create a simple interpolation between control points + const result = []; + for (let i = 0; i < numSamples; i++) { + const t = i / (numSamples - 1); + const idx = Math.min( + Math.floor(t * (splinePoints.length - 1)), + splinePoints.length - 2 + ); + const fraction = (t * (splinePoints.length - 1)) - idx; + + const x = splinePoints[idx].x + fraction * (splinePoints[idx + 1].x - splinePoints[idx].x); + const y = splinePoints[idx].y + fraction * (splinePoints[idx + 1].y - splinePoints[idx].y); + + result.push({ x, y }); + } + return result; + } + + var svgWidth = width; // Fixed width of the SVG element + var pathLength = svgPathElement.getTotalLength(); + var points = []; + + if (samplingMethod === "speed") { + // Calculate control point distances along the path + const controlPoints = this.splines[splineIndex].points; + const pathPositions = []; + + // Find approximate path positions for each control point + for (const cp of controlPoints) { + let bestDist = Infinity; + let bestPos = 0; + + // Sample the path to find closest point to each control point + for (let pos = 0; pos <= pathLength; pos += pathLength / 100) { + const pt = svgPathElement.getPointAtLength(pos); + const dist = Math.sqrt(Math.pow(pt.x - cp.x, 2) + Math.pow(pt.y - cp.y, 2)); + + if (dist < bestDist) { + bestDist = dist; + bestPos = pos; + } + } + pathPositions.push(bestPos); + } + + // Sort positions along path + pathPositions.sort((a, b) => a - b); + + // Create a smooth speed mapping function with synchronization + const createSynchronizedMapping = () => { + // Calculate segment lengths and densities + const segments = []; + let totalLength = pathPositions[pathPositions.length - 1] - pathPositions[0]; + + for (let i = 0; i < pathPositions.length - 1; i++) { + const segLength = pathPositions[i+1] - pathPositions[i]; + // Inverse relationship - shorter segments = higher density = slower speed + const density = 1 / Math.max(segLength, 0.0001); + segments.push({ + position: pathPositions[i], + length: segLength, + density: density + }); + } + + // Create mapping function with forced synchronization at endpoints + return t => { + // Force synchronization at t=0 and t=1 + if (t === 0) return 0; + if (t === 1) return pathLength; + + // For intermediate points, use the speed control + // Scale t to fit between first and last control points + const firstPos = pathPositions[0]; + const lastPos = pathPositions[pathPositions.length - 1]; + + // Create a density-weighted position mapping + let totalWeight = 0; + let weights = []; + + for (let i = 0; i < segments.length; i++) { + totalWeight += segments[i].density; + weights.push(segments[i].density); + } + + // Normalize weights + const normalizedWeights = weights.map(w => w / totalWeight); + + // Calculate cumulative weights + let cumulativeWeight = 0; + const cumulativeWeights = normalizedWeights.map(w => { + cumulativeWeight += w; + return cumulativeWeight; + }); + + // Find the segment for this t value + let segmentIndex = 0; + for (let i = 0; i < cumulativeWeights.length; i++) { + if (t <= cumulativeWeights[i]) { + segmentIndex = i; + break; + } + } + + // Calculate position within segment + const segmentStart = segmentIndex > 0 ? cumulativeWeights[segmentIndex - 1] : 0; + const segmentEnd = cumulativeWeights[segmentIndex]; + const segmentT = (t - segmentStart) / (segmentEnd - segmentStart); + + // Map to path position + const pathStart = pathPositions[segmentIndex]; + const pathEnd = pathPositions[segmentIndex + 1]; + const pos = pathStart + segmentT * (pathEnd - pathStart); + + // Scale to fill entire path + return pos; + }; + }; + + const mapToPath = createSynchronizedMapping(); + + // Sample using the synchronized mapping function + for (let i = 0; i < numSamples; i++) { + const t = i / (numSamples - 1); + const pathPos = mapToPath(t); + const point = svgPathElement.getPointAtLength(pathPos); + points.push({ x: point.x, y: point.y }); + } + + return points; + + } + else{ + for (var i = 0; i < numSamples; i++) { + if (samplingMethod === "time") { + // Calculate the x-coordinate for the current sample based on the SVG's width + var x = (svgWidth / (numSamples - 1)) * i; + // Find the point on the path that intersects the vertical line at the calculated x-coordinate + var point = this.findPointAtX(svgPathElement, x, pathLength); + } + else if (samplingMethod === "path") { + // Calculate the distance along the path for the current sample + var distance = (pathLength / (numSamples - 1)) * i; + // Get the point at the current distance + var point = svgPathElement.getPointAtLength(distance); + } + + // Add the point to the array of points + points.push({ x: point.x, y: point.y }); + } + return points; + } + } + + findClosestPoints(points, clickedPoint) { + // Calculate distances from clickedPoint to each point in the array + let distances = points.map(point => { + let dx = clickedPoint.x - point.x; + let dy = clickedPoint.y - point.y; + return { index: points.indexOf(point), distance: Math.sqrt(dx * dx + dy * dy) }; + }); + // Sort distances and get the indices of the two closest points + let sortedDistances = distances.sort((a, b) => a.distance - b.distance); + let closestPoint1Index = sortedDistances[0].index; + let closestPoint2Index = sortedDistances[1].index; + // Ensure point1Index is always the smaller index + if (closestPoint1Index > closestPoint2Index) { + [closestPoint1Index, closestPoint2Index] = [closestPoint2Index, closestPoint1Index]; + } + return { point1Index: closestPoint1Index, point2Index: closestPoint2Index }; + } + + findPointAtX(svgPathElement, targetX, pathLength) { + let low = 0; + let high = pathLength; + let bestPoint = svgPathElement.getPointAtLength(0); + + while (low <= high) { + let mid = low + (high - low) / 2; + let point = svgPathElement.getPointAtLength(mid); + + if (Math.abs(point.x - targetX) < 1) { + return point; // The point is close enough to the target + } + + if (point.x < targetX) { + low = mid + 1; + } else { + high = mid - 1; + } + + // Keep track of the closest point found so far + if (Math.abs(point.x - targetX) < Math.abs(bestPoint.x - targetX)) { + bestPoint = point; + } + } + + // Return the closest point found + return bestPoint; + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI-KJNodes/web/red.png b/custom_nodes/ComfyUI-KJNodes/web/red.png new file mode 100644 index 0000000000000000000000000000000000000000..4352c118b2c5fa6f33edc4d99a5e4d22649ff827 Binary files /dev/null and b/custom_nodes/ComfyUI-KJNodes/web/red.png differ diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/.gitignore b/custom_nodes/ComfyUI-VideoHelperSuite/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..9b5379e8f93da3909706b0a7d37f5c8ab5e2a2a5 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/README.md b/custom_nodes/ComfyUI-VideoHelperSuite/README.md new file mode 100644 index 0000000000000000000000000000000000000000..753fcecf9979b40b2427869c6ad5bed1b61df482 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/README.md @@ -0,0 +1,110 @@ +# ComfyUI-VideoHelperSuite +Nodes related to video workflows + +## I/O Nodes +### Load Video +Converts a video file into a series of images +- video: The video file to be loaded +- force_rate: Discards or duplicates frames as needed to hit a target frame rate. Disabled by setting to 0. This can be used to quickly match a suggested frame rate like the 8 fps of AnimateDiff. +- force_size: Allows for quick resizing to a number of suggested sizes. Several options allow you to set only width or height and determine the other from aspect ratio. +- frame_load_cap: The maximum number of frames which will be returned. This could also be thought of as the maximum batch size. +- skip_first_frames: How many frames to skip from the start of the video after adjusting for a forced frame rate. By incrementing this number by the frame_load_cap, you can easily process a longer input video in parts. +- select_every_nth: Allows for skipping a number of frames without considering the base frame rate or risking frame duplication. Often useful when working with animated gifs +A path variant of the Load Video node exists that allows loading videos from external paths +![step](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/assets/4284322/b5fc993c-5c9b-4608-afa4-48ae2e1380ef) +![resize](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/assets/4284322/98d2e78e-1c44-443c-a8fe-0dab0b5947f3) +If [Advanced Previews](#advanced-previews) is enabled in the options menu of the web ui, the preview will reflect the current settings on the node. +### Load Image Sequence +Loads all image files from a subfolder. Options are similar to Load Video. +- image_load_cap: The maximum number of images which will be returned. This could also be thought of as the maximum batch size. +- skip_first_images: How many images to skip. By incrementing this number by image_load_cap, you can easily divide a long sequence of images into multiple batches. +- select_every_nth: Allows for skipping a number of images between every returned frame. + +A path variant of Load Image sequence also exists. +### Video Combine +Combines a series of images into an output video +If the optional audio input is provided, it will also be combined into the output video +- frame_rate: How many of the input frames are displayed per second. A higher frame rate means that the output video plays faster and has less duration. This should usually be kept to 8 for AnimateDiff, or matched to the force_rate of a Load Video node. +- loop_count: How many additional times the video should repeat +- filename_prefix: The base file name used for output. + - You can save output to a subfolder: `subfolder/video` + - Like the builtin Save Image node, you can add timestamps. `%date:yyyy-MM-ddThh:mm:ss%` might become 2023-10-31T6:45:25 +- format: The file format to use. Advanced information on configuring or adding additional video formats can be found in the [Video Formats](#video-formats) section. +- pingpong: Causes the input to be played back in the reverse to create a clean loop. +- save_output: Whether the image should be put into the output directory or the temp directory. +Returns: a `VHS_FILENAMES` which consists of a boolean indicating if save_output is enabled and a list of the full filepaths of all generated outputs in the order created. Accordingly `output[1][-1]` will be the most complete output. + +Depending on the format chosen, additional options may become available, including +- crf: Describes the quality of the output video. A lower number gives a higher quality video and a larger file size, while a higher number gives a lower quality video with a smaller size. Scaling varies by codec, but visually lossless output generally occurs around 20. +- save_metadata: Includes a copy of the workflow in the output video which can be loaded by dragging and dropping the video, just like with images. +- pix_fmt: Changes how the pixel data is stored. `yuv420p10le` has higher color quality, but won't work on all devices +### Load Audio +Provides a way to load standalone audio files. +- seek_seconds: An optional start time for the audio file in seconds. + +## Latent/Image Nodes +A number of utility nodes exist for managing latents. For each, there is an equivalent node which works on images. +### Split Batch +Divides the latents into two sets. The first `split_index` latents go to output A and the remainder to output B. If less then `split_index` latents are provided as input, all are passed to output A and output B is empty. +### Merge Batch +Combines two groups of latents into a single output. The order of the output is the latents in A followed by the latents in B. +If the input groups are not the same size, the node provides options for rescaling the latents before merging. +### Select Every Nth +The first of every `select_every_nth` input is passed and the remainder are discarded +### Get Count +### Duplicate Batch + +## Video Previews +Load Video (Upload), Load Video (Path), Load Images (Upload), Load Images (Path) and Video Combine provide animated previews. +Nodes with previews provide additional functionality when right clicked +- Open preview +- Save preview +- Pause preview: Can improve performance with very large videos +- Hide preview: Can improve performance, save space +- Sync preview: Restarts all previews for side-by-side comparisons + +### Advanced Previews +Advanced Previews must be manually enabled by clicking the settings gear next to Queue Prompt and checking the box for VHS Advanced Previews. +If enabled, videos which are displayed in the ui will be converted with ffmpeg on request. This has several benefits +- Previews for Load Video nodes will reflect the settings on the node such as skip_first_frames and frame_load_cap + - This makes it easy to select an exact portion of an input video and sync it with outputs +- It can use substantially less bandwidth if running the server remotely +- It can greatly improve the browser performance by downsizing videos to the in ui resolution, particularly useful with animated gifs +- It allows for previews of videos that would not normally be playable in browser. +- Can be limited to subdirectories of ComyUI if `VHS_STRICT_PATHS` is set as an environment variable. + +This fucntionality is disabled since it comes with several downsides +- There is a delay before videos show in the browser. This delay can become quite large if the input video is long +- The preview videos are lower quality (The original can always be viewed with Right Click -> Open preview) + +## Video Formats +Those familiar with ffmpeg are able to add json files to the video_formats folders to add new output types to Video Combine. +Consider the following example for av1-webm +```json +{ + "main_pass": + [ + "-n", "-c:v", "libsvtav1", + "-pix_fmt", "yuv420p10le", + "-crf", ["crf","INT", {"default": 23, "min": 0, "max": 100, "step": 1}] + ], + "audio_pass": ["-c:a", "libopus"], + "extension": "webm", + "environment": {"SVT_LOG": "1"} +} +``` +Most configuration takes place in `main_pass`, which is a list of arguments that are passed to ffmpeg. +- `"-n"` designates that the command should fail if a file of the same name already exists. This should never happen, but if some bug were to occur, it would ensure other files aren't overwritten. +- `"-c:v", "libsvtav1"` designates that the video should be encoded with an av1 codec using the new SVT-AV1 encoder. SVT-AV1 is much faster than libaom-av1, but may not exist in older versions of ffmpeg. Alternatively, av1_nvenc could be used for gpu encoding with newer nvidia cards. +- `"-pix_fmt", "yuv420p10le"` designates the standard pixel format with 10-bit color. It's important that some pixel format be specified to ensure a nonconfigurable input pix_fmt isn't used. + +`audio pass` contains a list of arguments which are passed to ffmpeg when audio is passed into Video Combine + +`extension` designates both the file extension and the container format that is used. If some of the above options are omitted from `main_pass` it can affect what default options are chosen. +`environment` can optionally be provided to set environment variables during execution. For av1 it's used to reduce the verbosity of logging so that only major errors are displayed. +`input_color_depth` effects the format in which pixels are passed to the ffmpeg subprocess. Current valid options are `8bit` and `16bit`. The later will produce higher quality output, but is experimental. + +Fields can be exposed in the webui as a widget using a format similar to what is used in the creation of custom nodes. In the above example, the argument for `-crf` will be exposed as a format widget in the webui. Format widgets are a list of up to 3 terms +- The name of the widget that will be displayed in the web ui +- Either a primitive such as "INT" or "BOOLEAN", or a list of string options +- A dictionary of options diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/__init__.py b/custom_nodes/ComfyUI-VideoHelperSuite/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9fa995f0ac7e296a62bcba9df68b5250a0f7d041 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/__init__.py @@ -0,0 +1,9 @@ +from .videohelpersuite.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS +import folder_paths +from .videohelpersuite.server import server +from .videohelpersuite import documentation +from .videohelpersuite import latent_preview + +WEB_DIRECTORY = "./web" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] +documentation.format_descriptions(NODE_CLASS_MAPPINGS) diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/pyproject.toml b/custom_nodes/ComfyUI-VideoHelperSuite/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..33959752e7cd73d6177ae7c30a1aaaa7fd7364f0 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-videohelpersuite" +description = "Nodes related to video workflows" +version = "1.5.10" +license = { file = "LICENSE" } +dependencies = ["opencv-python", "imageio-ffmpeg"] + +[project.urls] +Repository = "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite" + +# Used by Comfy Registry https://comfyregistry.org +[tool.comfy] +PublisherId = "kosinkadink" +DisplayName = "ComfyUI-VideoHelperSuite" +Icon = "" diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/requirements.txt b/custom_nodes/ComfyUI-VideoHelperSuite/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4fa34aa21b85c4b974e2a2b6891eae5fd6dd4164 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/requirements.txt @@ -0,0 +1,2 @@ +opencv-python +imageio-ffmpeg diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/README.md b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/README.md new file mode 100644 index 0000000000000000000000000000000000000000..93cd503563080c9c7d785d419bef7dddc4c11cd8 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/README.md @@ -0,0 +1,5 @@ +Code to automate execution of the tests and evaluate the results. +Distributed as a `custom node`, and can be installed by copying or simlinking to the `custom_nodes` directory. +Requires that ffprobe be available and added to the path. Note that imageio-ffmpeg does not bundle ffprobe. + +When installed, it adds a new sidebar tab to automate running one, or a folder of tests. This requires that the `Use new menu and workflow management` setting not be disabled diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/__init__.py b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c75b11c164596ef873898dd843700c7164cc0932 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/__init__.py @@ -0,0 +1,6 @@ +from . import server +NODE_CLASS_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS = {} + +WEB_DIRECTORY = "./web" +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/server.py b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/server.py new file mode 100644 index 0000000000000000000000000000000000000000..a9c20ea3eab31bab288d6089a89410ff409d43ad --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/server.py @@ -0,0 +1,60 @@ +import subprocess +import json +import os +import torch +import shutil + +import server +import folder_paths + +web = server.web + +@server.PromptServer.instance.routes.post("/VHS_test") +async def test(request): + try: + req_data = await request.json() + output = req_data['output']['gifs'][0] + filename = output['filename'] + typ = output['type'] + base_args = ["ffprobe", "-v", "error", '-count_packets', "-show_entries", "stream", "-of", "json"] + video = folder_paths.get_annotated_filepath(f'{filename} [{typ}]') + vprobe = json.loads(subprocess.run(base_args + ['-select_streams', 'v:0', video], + capture_output=True, check=True).stdout)['streams'][0] + aprobe = json.loads(subprocess.run(base_args + ['-select_streams', 'a:0', video], + capture_output=True, check=True).stdout)['streams'] + probe = {'video': vprobe} + if len(aprobe) > 0: + probe['audio'] = aprobe[0] + errors = [] + compare = None + for test in req_data['tests']: + if test['type'] == 'compare': + compare = test + continue + key = test['key'] + expected = test['value'] + actual = probe[test['type']][key] + if expected != actual: + #Consider always dumping type? + errors.append(f'{key}: {expected} != {actual}') + if len(errors) == 0 and compare is not None: + if not os.path.exists(compare['filename']): + os.makedirs(os.path.split(compare['filename'])[0], exist_ok=True) + shutil.copy(video, compare['filename']) + print("Missing comparison file has been initialized from output:", os.path.abspath(compare['filename'])) + else: + #NOTE: This does not include the full memory optimizations of VHS + #Tests should be small + #TODO: Figure out way to do opacity comparison. May need to do blending in python + #(easy, but slower and more memory intensive) + diff = subprocess.run(['ffmpeg', '-v', 'error', '-i', video, '-i', compare['filename'], '-filter_complex', 'blend=all_mode=grainextract', '-pix_fmt', 'rgb24', '-f', 'rawvideo', '-'], stdout=subprocess.PIPE, check=True).stdout + diff = torch.frombuffer(diff, dtype=torch.uint8).to(dtype=torch.float32).div_(255) + #diff = diff.reshape((-1,4)) + d = (diff-0.5).abs().sum()/diff.size(0) + if d > compare['tolerance']: + errors.append(f'Similarity is outside specified tolerance: {d}') + else: + print('d:', d) + return web.json_response(errors) + except Exception as e: + return web.json_response(str(e)) diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/testframework/web/js/testRunner.js b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/web/js/testRunner.js new file mode 100644 index 0000000000000000000000000000000000000000..fd5b9da9c82ef1c3024a4c54f316b6b69e14fc8b --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/testframework/web/js/testRunner.js @@ -0,0 +1,93 @@ + +import {app} from "../../../scripts/app.js"; +import {api} from "../../../scripts/api.js"; + +let watched_nodes = {} +let resolve = undefined +let testURL = api.apiURL("/VHS_test") +let errors = [] +api.addEventListener("executed", async function ({detail}) { + if (watched_nodes && watched_nodes[detail?.node]) { + if (detail?.output?.unfinished_batch) { + return + } + let requestBody = {tests: watched_nodes[detail.node], output: detail.output} + try { + let req = await fetch(api.apiURL("/VHS_test"), + {method: "POST", body: JSON.stringify(requestBody)}); + let testResult = await req.json() + if (testResult.length != 0) { + errors.push(testResult) + } + } catch(e) { + errors.push(e) + } + if (!(watched_nodes.length -= 1)) { + resolve() + } + } +}); + +async function runTest(file) { + if (!file?.name?.endsWith(".json")) { + return false + } + let workflow = JSON.parse(await file.text()) + await app.loadGraphData(workflow) + //NOTE: API is not used so workflow data is actually processed + watched_nodes = workflow.tests + errors = [] + let p = new Promise((r) => resolve = r) + await app.queuePrompt() + //block until execution completes + await p + watched_nodes = {} + if (errors.length > 0) { + app.ui.dialog.show("Failed " + errors.length + " tests:\n" + errors) + return true + } + return false +} +let iconOverride = document.createElement("style") +iconOverride.innerHTML = `.VHSTestIcon:before {content: '🧪';}` +document.body.append(iconOverride) + +let testSidebar = {id: 'VHStest', title: 'VHS Test', icon: 'VHSTestIcon', type: 'custom', + render: (e) => { + e.innerHTML = `Select a folder containing tests + + Or select a single test + + ` + + const folderInput = e.children[0] + const fileInput = e.children[1] + Object.assign(folderInput, { + type: "file", + webkitdirectory: true, + onchange: async function() { + const startTime = Date.now() + let failedTests = false + for(const file of this.files) { + failedTests ||= await runTest(file) + } + this.value="" + if (!failedTests) { + console.log("All tests passed in " + ((Date.now() - startTime)/1000) + "s") + } + }, + }); + Object.assign(fileInput, { + type: "file", + accept: ".json", + onchange: async function() { + if (this.files.length) { + if(!(await runTest(this.files[0]))) { + console.log("Test complete") + } + this.value="" + } + }, + }); + }} +app.extensionManager.registerSidebarTab(testSidebar) diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/README.md b/custom_nodes/ComfyUI-VideoHelperSuite/tests/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ec025a7d169c7783f34523c63a0a6ae9a2e00fdb --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/README.md @@ -0,0 +1 @@ +Workflows for automated testing of VHS. Most include an additional tests key to check the properties or perform comparisons on node outputs diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/batch4x4.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/batch4x4.json new file mode 100644 index 0000000000000000000000000000000000000000..34b9647329642a9e5ca233a60b8bbf2ca8f72aca --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/batch4x4.json @@ -0,0 +1,535 @@ +{ + "last_node_id": 18, + "last_link_id": 28, + "nodes": [ + { + "id": 16, + "type": "SolidMask", + "pos": [ + 38, + 1066 + ], + "size": { + "0": 315, + "1": 106 + }, + "flags": {}, + "order": 0, + "mode": 0, + "outputs": [ + { + "name": "MASK", + "type": "MASK", + "links": [ + 18 + ], + "slot_index": 0, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "SolidMask" + }, + "widgets_values": [ + 1, + 512, + 512 + ] + }, + { + "id": 15, + "type": "ImageCompositeMasked", + "pos": [ + 412.0800030517579, + 591.4099975585939 + ], + "size": { + "0": 315, + "1": 146 + }, + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "name": "destination", + "type": "IMAGE", + "link": 16 + }, + { + "name": "source", + "type": "IMAGE", + "link": 17 + }, + { + "name": "mask", + "type": "MASK", + "link": 18 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 19 + ], + "slot_index": 0, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "ImageCompositeMasked" + }, + "widgets_values": [ + 0, + 0, + false + ] + }, + { + "id": 18, + "type": "VHS_BatchManager", + "pos": [ + 329.8000881958008, + -87.40008056640622 + ], + "size": { + "0": 315, + "1": 58 + }, + "flags": {}, + "order": 1, + "mode": 0, + "outputs": [ + { + "name": "VHS_BatchManager", + "type": "VHS_BatchManager", + "links": [ + 24, + 25, + 26, + 27 + ], + "slot_index": 0, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "VHS_BatchManager" + }, + "widgets_values": { + "frames_per_batch": 4, + "count": 2 + } + }, + { + "id": 10, 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"select_every_nth": 1, + "videopreview": { + "hidden": false, + "paused": false, + "params": { + "frame_load_cap": 16, + "skip_first_frames": 64, + "force_rate": 8, + "filename": "input/bigbuckbunny.mp4", + "type": "path", + "format": "video/mp4", + "select_every_nth": 1, + "force_size": "512x?" + } + } + } + }, + { + "id": 14, + "type": "VHS_LoadVideoPath", + "pos": [ + 26, + 598 + ], + "size": [ + 320, + 444.7188019966722 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "name": "meta_batch", + "type": "VHS_BatchManager", + "link": 26, + "slot_index": 0 + }, + { + "name": "vae", + "type": "VAE", + "link": null + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 17 + ], + "slot_index": 0, + "shape": 3 + }, + { + "name": "frame_count", + "type": "INT", + "links": null, + "shape": 3 + }, + { + "name": "audio", + "type": "VHS_AUDIO", + "links": [], + "slot_index": 2, + "shape": 3 + }, + { + "name": "video_info", + "type": "VHS_VIDEOINFO", + 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+ 1, + "VAE" + ], + [ + 8, + 10, + 0, + 7, + 3, + "VAE" + ], + [ + 9, + 6, + 0, + 7, + 0, + "LATENT" + ] + ], + "groups": [], + "config": {}, + "extra": {}, + "version": 0.4, + "tests": { + "7": [{"type": "video", "key": "width", "value": 1920}, + {"type": "video", "key": "height", "value": 1080}, + {"type": "video", "key": "nb_read_packets", "value": "64"} + ], + "length": 1 + } +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/tests/simple.json b/custom_nodes/ComfyUI-VideoHelperSuite/tests/simple.json new file mode 100644 index 0000000000000000000000000000000000000000..4d7f8ddd68a24cc90519ecd127daae9d8ab81b85 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/tests/simple.json @@ -0,0 +1,134 @@ +{ + "last_node_id": 3, + "last_link_id": 1, + "nodes": [ + { + "id": 1, + "type": "VHS_LoadVideo", + "pos": [ + 54, + 89 + ], + "size": [ + 235.1999969482422, + 384.56999829610186 + ], + "flags": {}, + "order": 0, + "mode": 0, + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 1 + ], + "shape": 3, + "slot_index": 0 + }, + { + "name": "frame_count", + "type": "INT", + "links": null, + "shape": 3 + }, + { + "name": "audio", + "type": "VHS_AUDIO", + "links": null, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "VHS_LoadVideo" + }, + "widgets_values": { + "video": "leader.webm", + "force_rate": 8, + "force_size": "Custom Width", + "custom_width": 304, + "custom_height": 312, + "frame_load_cap": 16, + "skip_first_frames": 1, + "select_every_nth": 1, + "choose video to upload": "image", + "videopreview": { + "hidden": false, + "paused": false, + "params": { + "frame_load_cap": 0, + "skip_first_frames": 0, + "force_rate": 0, + "filename": "leader.webm", + "type": "input", + "format": "video/mp4", + "force_size": "410.4x?", + "select_every_nth": 1 + } + } + } + }, + { + "id": 3, + "type": "VHS_VideoCombine", + "pos": [ + 629, + 222 + ], + "size": { + "0": 315, + "1": 250 + }, + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 1 + } + ], + "outputs": [], + "properties": { + "Node name for S&R": "VHS_VideoCombine" + }, + "widgets_values": { + "frame_rate": 8, + "loop_count": 0, + "filename_prefix": "AnimateDiff", + "format": "video/webm", + "pingpong": false, + "save_image": false, + "crf": 20, + "save_metadata": false, + "audio_file": "", + "videopreview": { + "hidden": false, + "paused": false + } + } + } + ], + "links": [ + [ + 1, + 1, + 0, + 3, + 0, + "IMAGE" + ] + ], + "groups": [], + "config": {}, + "extra": {}, + "version": 0.4, + "tests": { + "3": [{"type": "video", "key": "width", "value": 304}, + {"type": "video", "key": "height", "value": 232}, + {"type": "compare", "filename": "custom_nodes/ComfyUI-VideoHelperSuite/tests/outputs/simple.webm", "tolerance": 0.02} + ], + "length": 1 + } +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/16bit-png.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/16bit-png.json new file mode 100644 index 0000000000000000000000000000000000000000..b768bdbcfe8950cb7bde37d02444f0191ad29f51 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/16bit-png.json @@ -0,0 +1,9 @@ +{ + "main_pass": + [ + "-n", + "-pix_fmt", "rgba64" + ], + "input_color_depth": "16bit", + "extension": "%03d.png" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/8bit-png.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/8bit-png.json new file mode 100644 index 0000000000000000000000000000000000000000..88b27a75f9c979f18979ec46da8c8843a82e0759 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/8bit-png.json @@ -0,0 +1,7 @@ +{ + "main_pass": + [ + "-n" + ], + "extension": "%03d.png" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ProRes.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ProRes.json new file mode 100644 index 0000000000000000000000000000000000000000..1cc7a33d4f8d50cea0702d83734cf24ef6680f4b --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ProRes.json @@ -0,0 +1,11 @@ +{ + "main_pass": + [ + "-n", "-c:v", "prores_ks", + "-profile:v", ["profile",["1","2","3","4"], {"default": "3"}], + "-colorspace", "1", "-color_primaries", "1", "-color_trc", "1" + + ], + "audio_pass": ["-c:a", "pcm_s16le"], + "extension": "mov" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/av1-webm.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/av1-webm.json new file mode 100644 index 0000000000000000000000000000000000000000..adb90cc142c9ab19b98db8e57a9a2bc4759d5368 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/av1-webm.json @@ -0,0 +1,15 @@ +{ + "main_pass": + [ + "-n", "-c:v", "libsvtav1", + "-pix_fmt", ["pix_fmt", ["yuv420p10le", "yuv420p"]], + "-crf", ["crf","INT", {"default": 23, "min": 0, "max": 100, "step": 1}], + "-vf", "colorspace=all=bt709:iall=bt601-6-625:fast=1", + "-colorspace", "1", "-color_primaries", "1", "-color_trc", "1" + ], + "audio_pass": ["-c:a", "libopus"], + "input_color_depth": ["input_color_depth", ["8bit", "16bit"]], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "webm", + "environment": {"SVT_LOG": "1"} +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffmpeg-gif.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffmpeg-gif.json new file mode 100644 index 0000000000000000000000000000000000000000..3ca0865033c7dabc06176c737652f375e7badb27 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/ffmpeg-gif.json @@ -0,0 +1,8 @@ +{ + "main_pass": + [ + "-n", + "-filter_complex", ["dither", ["bayer", "heckbert", "floyd_steinberg", "sierra2", "sierra2_4a", "sierra3", "burkes", "atkinson", "none"], {"default": "sierra2_4a"}, "[0:v] split [a][b]; [a] palettegen=reserve_transparent=on:transparency_color=ffffff [p]; [b][p] paletteuse=dither=$val"] + ], + "extension": "gif" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/gifski.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/gifski.json new file mode 100644 index 0000000000000000000000000000000000000000..2f89447568b659678efafb3b2c7919e762600ede --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/gifski.json @@ -0,0 +1,10 @@ +{ + "main_pass": + [ + "-pix_fmt", "yuv420p" + ], + "extension": "gif", + "gifski_pass": [ + "-Q", ["quality","INT", {"default": 90, "min": 1, "max": 100, "step": 1}] + ] +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h264-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h264-mp4.json new file mode 100644 index 0000000000000000000000000000000000000000..c591b5b6c4604276d86a6b6bd453cf44fb74566d --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h264-mp4.json @@ -0,0 +1,14 @@ +{ + "main_pass": + [ + "-n", "-c:v", "libx264", + "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le"]], + "-crf", ["crf","INT", {"default": 19, "min": 0, "max": 100, "step": 1}], + "-vf", "colorspace=all=bt709:iall=bt601-6-625:fast=1", + "-colorspace", "1", "-color_primaries", "1", "-color_trc", "1" + ], + "audio_pass": ["-c:a", "aac"], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "trim_to_audio": ["trim_to_audio", "BOOLEAN", {"default": false}], + "extension": "mp4" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h265-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h265-mp4.json new file mode 100644 index 0000000000000000000000000000000000000000..02f4475bac1c8081637d9f026a34581939ade004 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/h265-mp4.json @@ -0,0 +1,16 @@ +{ + "main_pass": + [ + "-n", "-c:v", "libx265", + "-vtag", "hvc1", + "-pix_fmt", ["pix_fmt", ["yuv420p10le", "yuv420p"]], + "-crf", ["crf","INT", {"default": 22, "min": 0, "max": 100, "step": 1}], + "-preset", "medium", + "-vf", "colorspace=all=bt709:iall=bt601-6-625:fast=1", + "-colorspace", "1", "-color_primaries", "1", "-color_trc", "1", + "-x265-params", "log-level=quiet" + ], + "audio_pass": ["-c:a", "aac"], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_h264-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_h264-mp4.json new file mode 100644 index 0000000000000000000000000000000000000000..85c9fb8ec13bd7be95b271a7b9a38f63dcee88b8 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_h264-mp4.json @@ -0,0 +1,12 @@ +{ + "main_pass": + [ + "-n", "-c:v", "h264_nvenc", + "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le", "rgba"]] + ], + "audio_pass": ["-c:a", "aac"], + "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }], + "megabit": ["megabit","BOOLEAN", {"default": true}], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_hevc-mp4.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_hevc-mp4.json new file mode 100644 index 0000000000000000000000000000000000000000..269d51ad3529a3bb8378d04d8660e52e7268e6d4 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/nvenc_hevc-mp4.json @@ -0,0 +1,13 @@ +{ + "main_pass": + [ + "-n", "-c:v", "hevc_nvenc", + "-vtag", "hvc1", + "-pix_fmt", ["pix_fmt", ["yuv420p", "yuv420p10le", "rgba"]] + ], + "audio_pass": ["-c:a", "aac"], + "bitrate": ["bitrate","INT", {"default": 10, "min": 1, "max": 999, "step": 1 }], + "megabit": ["megabit","BOOLEAN", {"default": true}], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "extension": "mp4" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/webm.json b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/webm.json new file mode 100644 index 0000000000000000000000000000000000000000..fcf0c47e07059409a8a891b564c8e7177d015a6a --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/video_formats/webm.json @@ -0,0 +1,14 @@ +{ + "main_pass": + [ + "-n", + "-pix_fmt", ["pix_fmt",["yuv420p","yuva420p"]], + "-crf", ["crf","INT", {"default": 20, "min": 0, "max": 100, "step": 1}], + "-b:v", "0", + "-colorspace", "1", "-color_primaries", "1", "-color_trc", "1" + ], + "audio_pass": ["-c:a", "libvorbis"], + "save_metadata": ["save_metadata", "BOOLEAN", {"default": true}], + "trim_to_audio": ["trim_to_audio", "BOOLEAN", {"default": false}], + "extension": "webm" +} diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/batched_nodes.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/batched_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..624bb84f73cce1d8e51abf4fef0b80367ad4b9a3 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/batched_nodes.py @@ -0,0 +1,56 @@ +import torch +from nodes import VAEEncode +from comfy.utils import ProgressBar + + +class VAEDecodeBatched: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "samples": ("LATENT", ), + "vae": ("VAE", ), + "per_batch": ("INT", {"default": 16, "min": 1}) + } + } + + CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/batched nodes" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + def decode(self, vae, samples, per_batch): + decoded = [] + pbar = ProgressBar(samples["samples"].shape[0]) + for start_idx in range(0, samples["samples"].shape[0], per_batch): + decoded.append(vae.decode(samples["samples"][start_idx:start_idx+per_batch])) + pbar.update(per_batch) + return (torch.cat(decoded, dim=0), ) + + +class VAEEncodeBatched: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "pixels": ("IMAGE", ), "vae": ("VAE", ), + "per_batch": ("INT", {"default": 16, "min": 1}) + } + } + + CATEGORY = "Video Helper Suite 🎥🅥🅗🅢/batched nodes" + + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + def encode(self, vae, pixels, per_batch): + t = [] + pbar = ProgressBar(pixels.shape[0]) + for start_idx in range(0, pixels.shape[0], per_batch): + try: + sub_pixels = vae.vae_encode_crop_pixels(pixels[start_idx:start_idx+per_batch]) + except: + sub_pixels = VAEEncode.vae_encode_crop_pixels(pixels[start_idx:start_idx+per_batch]) + t.append(vae.encode(sub_pixels[:,:,:,:3])) + pbar.update(per_batch) + return ({"samples": torch.cat(t, dim=0)}, ) diff --git a/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/documentation.py b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/documentation.py new file mode 100644 index 0000000000000000000000000000000000000000..857de6c6a5b4a3917f49d0b1c5c142f274203f22 --- /dev/null +++ b/custom_nodes/ComfyUI-VideoHelperSuite/videohelpersuite/documentation.py @@ -0,0 +1,616 @@ +from .logger import logger + +def image(src): + return f'' +def video(src): + return f'
    + +
    + ` + + saveButtonText.onclick = () => savePythonScript(); + + dropdownMenu.append(saveButtonText); + + + + console.log("SaveAsScript loaded"); + } +}); diff --git a/custom_nodes/ComfyUI-to-Python-Extension/pyproject.toml b/custom_nodes/ComfyUI-to-Python-Extension/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..98535d19b3dce4d4cc191f2da84c256e4d0ff5c0 --- /dev/null +++ b/custom_nodes/ComfyUI-to-Python-Extension/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-to-python-extension" +description = "This custom node allows you to generate pure python code from your ComfyUI workflow with the click of a button. Great for rapid experimentation or production deployment." +version = "1.3.1" +license = { text = "MIT License" } +dependencies = ["black"] + +[project.urls] +Repository = "https://github.com/pydn/ComfyUI-to-Python-Extension" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "pydn" +DisplayName = "ComfyUI-to-Python-Extension" +Icon = "" diff --git a/custom_nodes/ComfyUI-to-Python-Extension/requirements.txt b/custom_nodes/ComfyUI-to-Python-Extension/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3ca90996f43470a3c2e4fb802ca8c41ce6583d7 --- /dev/null +++ b/custom_nodes/ComfyUI-to-Python-Extension/requirements.txt @@ -0,0 +1 @@ +black \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/.gitattributes b/custom_nodes/ComfyUI_WanVideoWrapper/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..dfe0770424b2a19faf507a501ebfc23be8f54e7b --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/.gitignore b/custom_nodes/ComfyUI_WanVideoWrapper/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d32eaccb39749ed6d9e0b7bd75ec02149d6b59bb --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/.gitignore @@ -0,0 +1,13 @@ +output/ +*__pycache__/ +samples*/ +runs/ +checkpoints/ +master_ip +logs/ +*.DS_Store +.idea +tools/ +.vscode/ +convert_* +*.pt \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/ATI/motion.py b/custom_nodes/ComfyUI_WanVideoWrapper/ATI/motion.py new file mode 100644 index 0000000000000000000000000000000000000000..3d615e65c63c28c2a11696148e0fd53e3fb5067d --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/ATI/motion.py @@ -0,0 +1,42 @@ +# Copyright (c) 2024-2025 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Dict, List, Optional, Tuple, Union +import numpy as np +import torch + +def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], quant_multi: int = 8, **kwargs): + # tracks: shape [t, h, w, 3] => samples align with 24 fps, model trained with 16 fps. + # frame_size: tuple (W, H) + + tracks = torch.from_numpy(tracks_np).float() + + if tracks.shape[1] == 121: + tracks = torch.permute(tracks, (1, 0, 2, 3)) + + tracks, visibles = tracks[..., :2], tracks[..., 2:3] + short_edge = min(*frame_size) + + tracks = tracks - torch.tensor([*frame_size]).type_as(tracks) / 2 + tracks = tracks / short_edge * 2 + + visibles = visibles * 2 - 1 + + trange = torch.linspace(-1, 1, tracks.shape[0]).view(-1, 1, 1, 1).expand(*visibles.shape) + + out_ = torch.cat([trange, tracks, visibles], dim=-1).view(121, -1, 4) + out_0 = out_[:1] + out_l = out_[1:] # 121 => 120 | 1 + out_l = torch.repeat_interleave(out_l, 2, dim=0)[1::3] # 120 => 240 => 80 + return torch.cat([out_0, out_l], dim=0) diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/ATI/motion_patch.py b/custom_nodes/ComfyUI_WanVideoWrapper/ATI/motion_patch.py new file mode 100644 index 0000000000000000000000000000000000000000..1adf1e5eae11805d1f86e388e53c31b3c1e0973a --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/ATI/motion_patch.py @@ -0,0 +1,142 @@ +# Copyright (c) 2024-2025 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional, Tuple, Union +import torch + + +# Refer to https://github.com/Angtian/VoGE/blob/main/VoGE/Utils.py +def ind_sel(target: torch.Tensor, ind: torch.Tensor, dim: int = 1): + """ + :param target: [... (can be k or 1), n > M, ...] + :param ind: [... (k), M] + :param dim: dim to apply index on + :return: sel_target [... (k), M, ...] + """ + assert ( + len(ind.shape) > dim + ), "Index must have the target dim, but get dim: %d, ind shape: %s" % (dim, str(ind.shape)) + + target = target.expand( + *tuple( + [ind.shape[k] if target.shape[k] == 1 else -1 for k in range(dim)] + + [ + -1, + ] + * (len(target.shape) - dim) + ) + ) + + ind_pad = ind + + if len(target.shape) > dim + 1: + for _ in range(len(target.shape) - (dim + 1)): + ind_pad = ind_pad.unsqueeze(-1) + ind_pad = ind_pad.expand(*(-1,) * (dim + 1), *target.shape[(dim + 1) : :]) + + return torch.gather(target, dim=dim, index=ind_pad) + + +def merge_final(vert_attr: torch.Tensor, weight: torch.Tensor, vert_assign: torch.Tensor): + """ + + :param vert_attr: [n, d] or [b, n, d] color or feature of each vertex + :param weight: [b(optional), w, h, M] weight of selected vertices + :param vert_assign: [b(optional), w, h, M] selective index + :return: + """ + target_dim = len(vert_assign.shape) - 1 + if len(vert_attr.shape) == 2: + assert vert_attr.shape[0] > vert_assign.max() + # [n, d] ind: [b(optional), w, h, M]-> [b(optional), w, h, M, d] + # sel_attr = ind_sel( + # vert_attr[(None,) * target_dim], vert_assign.type(torch.long), dim=target_dim + # ) + new_shape = [1] * target_dim + list(vert_attr.shape) + tensor = vert_attr.reshape(new_shape) + sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim) + else: + assert vert_attr.shape[1] > vert_assign.max() + #sel_attr = ind_sel( + # vert_attr[:, *(None,) * (target_dim - 1)], vert_assign.type(torch.long), dim=target_dim + #) + new_shape = [vert_attr.shape[0]] + [1] * (target_dim - 1) + list(vert_attr.shape[1:]) + tensor = vert_attr.reshape(new_shape) + sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim) + + # [b(optional), w, h, M] + final_attr = torch.sum(sel_attr * weight.unsqueeze(-1), dim=-2) + return final_attr + + +def patch_motion( + tracks: torch.FloatTensor, # (B, T, N, 4) + vid: torch.FloatTensor, # (C, T, H, W) + temperature: float = 220.0, + vae_divide: tuple = (4, 16), + topk: int = 2, +): + with torch.no_grad(): + _, T, H, W = vid.shape + N = tracks.shape[2] + _, tracks, visible = torch.split( + tracks, [1, 2, 1], dim=-1 + ) # (B, T, N, 2) | (B, T, N, 1) + tracks_n = tracks / torch.tensor([W / min(H, W), H / min(H, W)], device=tracks.device) + tracks_n = tracks_n.clamp(-1, 1) + visible = visible.clamp(0, 1) + + xx = torch.linspace(-W / min(H, W), W / min(H, W), W) + yy = torch.linspace(-H / min(H, W), H / min(H, W), H) + + grid = torch.stack(torch.meshgrid(yy, xx, indexing="ij")[::-1], dim=-1).to( + tracks.device + ) + + tracks_pad = tracks[:, 1:] + visible_pad = visible[:, 1:] + + visible_align = visible_pad.view(T - 1, 4, *visible_pad.shape[2:]).sum(1) + tracks_align = (tracks_pad * visible_pad).view(T - 1, 4, *tracks_pad.shape[2:]).sum( + 1 + ) / (visible_align + 1e-5) + dist_ = ( + (tracks_align[:, None, None] - grid[None, :, :, None]).pow(2).sum(-1) + ) # T, H, W, N + weight = torch.exp(-dist_ * temperature) * visible_align.clamp(0, 1).view( + T - 1, 1, 1, N + ) + vert_weight, vert_index = torch.topk( + weight, k=min(topk, weight.shape[-1]), dim=-1 + ) + + grid_mode = "bilinear" + point_feature = torch.nn.functional.grid_sample( + vid[vae_divide[0]:].permute(1, 0, 2, 3)[:1], + tracks_n[:, :1].type(vid.dtype), + mode=grid_mode, + padding_mode="zeros", + align_corners=False, + ) + point_feature = point_feature.squeeze(0).squeeze(1).permute(1, 0) # N, C=16 + + out_feature = merge_final(point_feature, vert_weight, vert_index).permute(3, 0, 1, 2) # T - 1, H, W, C => C, T - 1, H, W + out_weight = vert_weight.sum(-1) # T - 1, H, W + + # out feature -> already soft weighted + mix_feature = out_feature + vid[vae_divide[0]:, 1:] * (1 - out_weight.clamp(0, 1)) + + out_feature_full = torch.cat([vid[vae_divide[0]:, :1], mix_feature], dim=1) # C, T, H, W + out_mask_full = torch.cat([torch.ones_like(out_weight[:1]), out_weight], dim=0) # T, H, W + return torch.cat([out_mask_full[None].expand(vae_divide[0], -1, -1, -1), out_feature_full], dim=0) diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/ATI/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/ATI/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..cf25eff760c11e2e5a94e49af0b9050da5faba20 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/ATI/nodes.py @@ -0,0 +1,329 @@ +import json +from .motion import process_tracks +import numpy as np +from typing import List, Tuple +import torch +FIXED_LENGTH = 121 +def pad_pts(tr): + """Convert list of {x,y} to (FIXED_LENGTH,1,3) array, padding/truncating.""" + pts = np.array([[p['x'], p['y'], 1] for p in tr], dtype=np.float32) + n = pts.shape[0] + if n < FIXED_LENGTH: + pad = np.zeros((FIXED_LENGTH - n, 3), dtype=np.float32) + pts = np.vstack((pts, pad)) + else: + pts = pts[:FIXED_LENGTH] + return pts.reshape(FIXED_LENGTH, 1, 3) + +def age_to_bgr(ratio: float) -> Tuple[int,int,int]: + """ + Map ratio∈[0,1] through: 0→blue, 1/3→green, 2/3→yellow, 1→red. + Returns (B,G,R) for OpenCV. + """ + if ratio <= 1/3: + # blue→green + t = ratio / (1/3) + b = int(255 * (1 - t)) + g = int(255 * t) + r = 0 + elif ratio <= 2/3: + # green→yellow + t = (ratio - 1/3) / (1/3) + b = 0 + g = 255 + r = int(255 * t) + else: + # yellow→red + t = (ratio - 2/3) / (1/3) + b = 0 + g = int(255 * (1 - t)) + r = 255 + return (r, g, b) + +def paint_point_track( + frames: np.ndarray, + point_tracks: np.ndarray, + visibles: np.ndarray, + min_radius: int = 1, + max_radius: int = 6, + max_retain: int = 50 +) -> np.ndarray: + """ + Draws every past point of each track on each frame, with radius and color + interpolated by the point's age (old→small to new→large). + + Args: + frames: [F, H, W, 3] uint8 RGB + point_tracks:[N, F, 2] float32 – (x,y) in pixel coords + visibles: [N, F] bool – visibility mask + min_radius: radius for the very first point (oldest) + max_radius: radius for the current point (newest) + + Returns: + video: [F, H, W, 3] uint8 RGB + """ + import cv2 + num_points, num_frames = point_tracks.shape[:2] + H, W = frames.shape[1:3] + + video = frames.copy() + + for t in range(num_frames): + # start from the original frame + frame = video[t].copy() + + for i in range(num_points): + # draw every past step τ = 0..t + for τ in range(t + 1): + if not visibles[i, τ]: + continue + + if t - τ > max_retain: + continue + + # sub-pixel offset + clamp + x, y = point_tracks[i, τ] + 0.5 + xi = int(np.clip(x, 0, W - 1)) + yi = int(np.clip(y, 0, H - 1)) + + # age‐ratio in [0,1] + if num_frames > 1: + ratio = 1 - float(t - τ) / max_retain + else: + ratio = 1.0 + + # interpolated radius + radius = int(round(min_radius + (max_radius - min_radius) * ratio)) + + # OpenCV draws in BGR order: + color_rgb = age_to_bgr(ratio) + + # filled circle + cv2.circle(frame, (xi, yi), radius, color_rgb, thickness=-1) + + video[t] = frame + + return video + +def parse_json_tracks(tracks): + tracks_data = [] + try: + # If tracks is a string, try to parse it as JSON + if isinstance(tracks, str): + parsed = json.loads(tracks.replace("'", '"')) + tracks_data.extend(parsed) + else: + # If tracks is a list of strings, parse each one + for track_str in tracks: + parsed = json.loads(track_str.replace("'", '"')) + tracks_data.append(parsed) + + # Check if we have a single track (dict with x,y) or a list of tracks + if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]: + # Single track detected, wrap it in a list + tracks_data = [tracks_data] + elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]: + # Already a list of tracks, nothing to do + pass + else: + # Unexpected format + print(f"Warning: Unexpected track format: {type(tracks_data[0])}") + + except json.JSONDecodeError as e: + print(f"Error parsing tracks JSON: {e}") + tracks_data = [] + + return tracks_data + +class WanVideoATITracks: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("WANVIDEOMODEL", ), + "tracks": ("STRING",), + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "temperature": ("FLOAT", {"default": 220.0, "min": 0.0, "max": 1000.0, "step": 0.1}), + "topk": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply ATI"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply ATI"}), + }, + } + + RETURN_TYPES = ("WANVIDEOMODEL",) + RETURN_NAMES = ("model",) + FUNCTION = "patchmodel" + CATEGORY = "WanVideoWrapper" + + def patchmodel(self, model, tracks, width, height, temperature, topk, start_percent, end_percent): + tracks_data = parse_json_tracks(tracks) + arrs = [] + for track in tracks_data: + pts = pad_pts(track) + arrs.append(pts) + + tracks_np = np.stack(arrs, axis=0) + + processed_tracks = process_tracks(tracks_np, (width, height)) + + patcher = model.clone() + patcher.model_options["transformer_options"]["ati_tracks"] = processed_tracks.unsqueeze(0) + patcher.model_options["transformer_options"]["ati_temperature"] = temperature + patcher.model_options["transformer_options"]["ati_topk"] = topk + patcher.model_options["transformer_options"]["ati_start_percent"] = start_percent + patcher.model_options["transformer_options"]["ati_end_percent"] = end_percent + + return (patcher,) + +class WanVideoATITracksVisualize: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "images": ("IMAGE",), + "tracks": ("STRING",), + "min_radius": ("INT", {"default": 1, "min": 0, "max": 100, "step": 1, "tooltip": "radius for the very first point (oldest)"}), + "max_radius": ("INT", {"default": 6, "min": 0, "max": 100, "step": 1, "tooltip": "radius for the current point (newest)"}), + "max_retain": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1, "tooltip": "Maximum number of points to retain"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "patchmodel" + CATEGORY = "WanVideoWrapper" + + def patchmodel(self, images, tracks, min_radius, max_radius, max_retain): + tracks_data = parse_json_tracks(tracks) + arrs = [] + for track in tracks_data: + pts = pad_pts(track) + arrs.append(pts) + + tracks_np = np.stack(arrs, axis=0) + track = np.repeat(tracks_np, 2, axis=1)[:, ::3] + points = track[:, :, 0, :2].astype(np.float32) + visibles = track[:, :, 0, 2].astype(np.float32) + + if images.shape[0] < points.shape[1]: + repeat_count = (points.shape[1] + images.shape[0] - 1) // images.shape[0] + images = images.repeat(repeat_count, 1, 1, 1) + images = images[:points.shape[1]] + elif images.shape[0] > points.shape[1]: + images = images[:points.shape[1]] + + video_viz = paint_point_track(images.cpu().numpy(), points, visibles, min_radius, max_radius, max_retain) + video_viz = torch.from_numpy(video_viz).float() + + return (video_viz,) + +from comfy import utils +import types +from .motion_patch import patch_motion + +class WanConcatCondPatch: + def __init__(self, tracks, temperature, topk): + self.tracks = tracks + self.temperature = temperature + self.topk = topk + + def __get__(self, obj, objtype=None): + # Create bound method with stored parameters + def wrapped_concat_cond(self_module, *args, **kwargs): + return modified_concat_cond(self_module, self.tracks, self.temperature, self.topk, *args, **kwargs) + return types.MethodType(wrapped_concat_cond, obj) + +def modified_concat_cond(self, tracks, temperature, topk, **kwargs): + noise = kwargs.get("noise", None) + extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1] + if extra_channels == 0: + return None + + image = kwargs.get("concat_latent_image", None) + device = kwargs["device"] + + if image is None: + shape_image = list(noise.shape) + shape_image[1] = extra_channels + image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) + else: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + for i in range(0, image.shape[1], 16): + image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16]) + image = utils.resize_to_batch_size(image, noise.shape[0]) + + if not self.image_to_video or extra_channels == image.shape[1]: + return image + + if image.shape[1] > (extra_channels - 4): + image = image[:, :(extra_channels - 4)] + + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + mask = torch.zeros_like(noise)[:, :4] + else: + if mask.shape[1] != 4: + mask = torch.mean(mask, dim=1, keepdim=True) + mask = 1.0 - mask + mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + if mask.shape[-3] < noise.shape[-3]: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) + if mask.shape[1] == 1: + mask = mask.repeat(1, 4, 1, 1, 1) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + + image_cond = torch.cat((mask, image), dim=1) + image_cond_ati = patch_motion(tracks.to(image_cond.device, image_cond.dtype), image_cond[0], + temperature=temperature, topk=topk) + + return image_cond_ati.unsqueeze(0) + +class WanVideoATI_comfy: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ("MODEL", ), + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "tracks": ("STRING",), + "temperature": ("FLOAT", {"default": 220.0, "min": 0.0, "max": 1000.0, "step": 0.1}), + "topk": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}), + }, + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model", ) + FUNCTION = "patchcond" + CATEGORY = "WanVideoWrapper" + + def patchcond(self, model, tracks, width, height, temperature, topk): + + tracks_data = parse_json_tracks(tracks) + arrs = [] + for track in tracks_data: + pts = pad_pts(track) + arrs.append(pts) + + tracks_np = np.stack(arrs, axis=0) + + processed_tracks = process_tracks(tracks_np, (width, height)) + + model_clone = model.clone() + model_clone.add_object_patch( + "concat_cond", + WanConcatCondPatch( + processed_tracks.unsqueeze(0), temperature, topk + ).__get__(model.model, model.model.__class__) + ) + + return (model_clone,) + +NODE_CLASS_MAPPINGS = { + "WanVideoATITracks": WanVideoATITracks, + "WanVideoATITracksVisualize": WanVideoATITracksVisualize, + "WanVideoATI_comfy": WanVideoATI_comfy, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoATITracks": "WanVideo ATI Tracks", + "WanVideoATITracksVisualize": "WanVideo ATI Tracks Visualize", + "WanVideoATI_comfy": "WanVideo ATI Comfy", + } diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/LICENSE b/custom_nodes/ComfyUI_WanVideoWrapper/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8035acd9ba8b340fb39c6315a8c096eb5af1024e --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/__init__.py @@ -0,0 +1,35 @@ +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS +from .recammaster.nodes import NODE_CLASS_MAPPINGS as RECAM_MASTER_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as RECAM_MASTER_NODE_DISPLAY_NAME_MAPPINGS +from .unianimate.nodes import NODE_CLASS_MAPPINGS as UNIANIMATE_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as UNIANIMATE_NODE_DISPLAY_NAME_MAPPINGS +from .skyreels.nodes import NODE_CLASS_MAPPINGS as SKYREELS_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as SKYREELS_NODE_DISPLAY_NAME_MAPPINGS +from .fantasytalking.nodes import NODE_CLASS_MAPPINGS as FANTASYTALKING_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as FANTASYTALKING_NODE_DISPLAY_NAME_MAPPINGS +from .fun_camera.nodes import NODE_CLASS_MAPPINGS as FUN_CAMERA_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as FUN_CAMERA_NODE_DISPLAY_NAME_MAPPINGS +from .uni3c.nodes import NODE_CLASS_MAPPINGS as UNI3C_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as UNI3C_NODE_DISPLAY_NAME_MAPPINGS +from .controlnet.nodes import NODE_CLASS_MAPPINGS as CONTROLNET_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as CONTROLNET_NODE_DISPLAY_NAME_MAPPINGS +from .ATI.nodes import NODE_CLASS_MAPPINGS as ATI_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as ATI_NODE_DISPLAY_NAME_MAPPINGS + +#from .causvid.nodes import NODE_CLASS_MAPPINGS as CAUSVID_NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as CAUSVID_NODE_DISPLAY_NAME_MAPPINGS + +NODE_CLASS_MAPPINGS.update(RECAM_MASTER_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(UNIANIMATE_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(SKYREELS_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(FANTASYTALKING_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(FUN_CAMERA_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(UNI3C_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(CONTROLNET_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(ATI_NODE_CLASS_MAPPINGS) + +#NODE_CLASS_MAPPINGS.update(CAUSVID_NODE_CLASS_MAPPINGS) + +NODE_DISPLAY_NAME_MAPPINGS.update(RECAM_MASTER_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(UNIANIMATE_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(SKYREELS_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(FANTASYTALKING_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(FUN_CAMERA_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(UNI3C_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(CONTROLNET_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(ATI_NODE_DISPLAY_NAME_MAPPINGS) + +#NODE_DISPLAY_NAME_MAPPINGS.update(CAUSVID_NODE_DISPLAY_NAME_MAPPINGS) + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/special_tokens_map.json b/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..14855e7052ffbb595057dfd791d293c1c940db2c --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/special_tokens_map.json @@ -0,0 +1,308 @@ +{ + "additional_special_tokens": [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "bos_token": "", + "eos_token": "", + "pad_token": "", + "unk_token": "" +} diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/spiece.model b/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/spiece.model new file mode 100644 index 0000000000000000000000000000000000000000..2fe5f347e9f9367585589ae89e997dfbd5cf802c --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/spiece.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458 +size 4548313 diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/tokenizer.json b/custom_nodes/ComfyUI_WanVideoWrapper/configs/T5_tokenizer/tokenizer.json new file mode 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+ "bos_token": "", + "clean_up_tokenization_spaces": true, + "eos_token": "", + "extra_ids": 300, + "model_max_length": 1000000000000000019884624838656, + "pad_token": "", + "sp_model_kwargs": {}, + "spaces_between_special_tokens": false, + "tokenizer_class": "T5Tokenizer", + "unk_token": "" +} diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/configs/transformer_config_i2v.json b/custom_nodes/ComfyUI_WanVideoWrapper/configs/transformer_config_i2v.json new file mode 100644 index 0000000000000000000000000000000000000000..85b524cd9a820e2dde4ee9a4ed921e708e5919f9 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/configs/transformer_config_i2v.json @@ -0,0 +1,14 @@ +{ + "_class_name": "WanModel", + "_diffusers_version": "0.30.0", + "dim": 5120, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_dim": 36, + "model_type": "i2v", + "num_heads": 40, + "num_layers": 40, + "out_dim": 16, + "text_len": 512 + } \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/context.py b/custom_nodes/ComfyUI_WanVideoWrapper/context.py new file mode 100644 index 0000000000000000000000000000000000000000..6a30fed309b289f802055767d65afac6e5063e4f --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/context.py @@ -0,0 +1,184 @@ +import numpy as np +from typing import Callable, Optional, List + + +def ordered_halving(val): + bin_str = f"{val:064b}" + bin_flip = bin_str[::-1] + as_int = int(bin_flip, 2) + + return as_int / (1 << 64) + +def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]: + prev_val = -1 + for i, val in enumerate(window): + val = val % num_frames + if val < prev_val: + return True, i + prev_val = val + return False, -1 + +def shift_window_to_start(window: list[int], num_frames: int): + start_val = window[0] + for i in range(len(window)): + # 1) subtract each element by start_val to move vals relative to the start of all frames + # 2) add num_frames and take modulus to get adjusted vals + window[i] = ((window[i] - start_val) + num_frames) % num_frames + +def shift_window_to_end(window: list[int], num_frames: int): + # 1) shift window to start + shift_window_to_start(window, num_frames) + end_val = window[-1] + end_delta = num_frames - end_val - 1 + for i in range(len(window)): + # 2) add end_delta to each val to slide windows to end + window[i] = window[i] + end_delta + +def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]: + all_indexes = list(range(num_frames)) + for w in windows: + for val in w: + try: + all_indexes.remove(val) + except ValueError: + pass + return all_indexes + +def uniform_looped( + step: int = ..., + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = True, +): + if num_frames <= context_size: + yield list(range(num_frames)) + return + + context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1) + + for context_step in 1 << np.arange(context_stride): + pad = int(round(num_frames * ordered_halving(step))) + for j in range( + int(ordered_halving(step) * context_step) + pad, + num_frames + pad + (0 if closed_loop else -context_overlap), + (context_size * context_step - context_overlap), + ): + yield [e % num_frames for e in range(j, j + context_size * context_step, context_step)] + +#from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) +def uniform_standard( + step: int = ..., + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = True, +): + windows = [] + if num_frames <= context_size: + windows.append(list(range(num_frames))) + return windows + + context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1) + + for context_step in 1 << np.arange(context_stride): + pad = int(round(num_frames * ordered_halving(step))) + for j in range( + int(ordered_halving(step) * context_step) + pad, + num_frames + pad + (0 if closed_loop else -context_overlap), + (context_size * context_step - context_overlap), + ): + windows.append([e % num_frames for e in range(j, j + context_size * context_step, context_step)]) + + # now that windows are created, shift any windows that loop, and delete duplicate windows + delete_idxs = [] + win_i = 0 + while win_i < len(windows): + # if window is rolls over itself, need to shift it + is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames) + if is_roll: + roll_val = windows[win_i][roll_idx] # roll_val might not be 0 for windows of higher strides + shift_window_to_end(windows[win_i], num_frames=num_frames) + # check if next window (cyclical) is missing roll_val + if roll_val not in windows[(win_i+1) % len(windows)]: + # need to insert new window here - just insert window starting at roll_val + windows.insert(win_i+1, list(range(roll_val, roll_val + context_size))) + # delete window if it's not unique + for pre_i in range(0, win_i): + if windows[win_i] == windows[pre_i]: + delete_idxs.append(win_i) + break + win_i += 1 + + # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation + delete_idxs.reverse() + for i in delete_idxs: + windows.pop(i) + return windows + +def static_standard( + step: int = ..., + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = True, +): + windows = [] + if num_frames <= context_size: + windows.append(list(range(num_frames))) + return windows + # always return the same set of windows + delta = context_size - context_overlap + for start_idx in range(0, num_frames, delta): + # if past the end of frames, move start_idx back to allow same context_length + ending = start_idx + context_size + if ending >= num_frames: + final_delta = ending - num_frames + final_start_idx = start_idx - final_delta + windows.append(list(range(final_start_idx, final_start_idx + context_size))) + break + windows.append(list(range(start_idx, start_idx + context_size))) + return windows + +def get_context_scheduler(name: str) -> Callable: + if name == "uniform_looped": + return uniform_looped + elif name == "uniform_standard": + return uniform_standard + elif name == "static_standard": + return static_standard + else: + raise ValueError(f"Unknown context_overlap policy {name}") + + +def get_total_steps( + scheduler, + timesteps: List[int], + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = True, +): + return sum( + len( + list( + scheduler( + i, + num_steps, + num_frames, + context_size, + context_stride, + context_overlap, + ) + ) + ) + for i in range(len(timesteps)) + ) diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/controlnet/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/controlnet/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..14975d1b6eed18fcd43903f83d917e6d69bed594 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/controlnet/nodes.py @@ -0,0 +1,170 @@ + +import torch +from ..utils import log +import comfy.model_management as mm +from comfy.utils import load_torch_file +from tqdm import tqdm +import gc + +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device +import folder_paths + +class WanVideoControlnetLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (folder_paths.get_filename_list("controlnet"), {"tooltip": "These models are loaded from the 'ComfyUI/models/controlnet' -folder",}), + + "base_precision": (["fp32", "bf16", "fp16"], {"default": "bf16"}), + "quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2', 'fp8_e4m3fn_fast_no_ffn'], {"default": 'disabled', "tooltip": "optional quantization method"}), + "load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}), + }, + } + + RETURN_TYPES = ("WANVIDEOCONTROLNET",) + RETURN_NAMES = ("controlnet", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Loads ControlNet model from 'https://huggingface.co/collections/TheDenk/wan21-controlnets-68302b430411dafc0d74d2fc'" + + def loadmodel(self, model, base_precision, load_device, quantization): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + transformer_load_device = device if load_device == "main_device" else offload_device + + base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision] + + model_path = folder_paths.get_full_path_or_raise("controlnet", model) + + sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True) + + num_layers = 8 if "blocks.7.scale_shift_table" in sd else 6 + out_proj_dim = 5120 if num_layers == 6 else 1536 + + if not "control_encoder.0.0.weight" in sd: + raise ValueError("Invalid ControlNet model") + + controlnet_cfg = { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": None, + "downscale_coef": 8, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "image_dim": None, + "in_channels": 3, + "num_attention_heads": 12, + "num_layers": num_layers, + "out_proj_dim": out_proj_dim, + "patch_size": [ + 1, + 2, + 2 + ], + "qk_norm": "rms_norm_across_heads", + "rope_max_seq_len": 1024, + "text_dim": 4096, + "vae_channels": 16 + } + + from .wan_controlnet import WanControlnet + + with init_empty_weights(): + controlnet = WanControlnet(**controlnet_cfg) + controlnet.eval() + + if quantization == "disabled": + for k, v in sd.items(): + if isinstance(v, torch.Tensor): + if v.dtype == torch.float8_e4m3fn: + quantization = "fp8_e4m3fn" + break + elif v.dtype == torch.float8_e5m2: + quantization = "fp8_e5m2" + break + + if "fp8_e4m3fn" in quantization: + dtype = torch.float8_e4m3fn + elif quantization == "fp8_e5m2": + dtype = torch.float8_e5m2 + else: + dtype = base_dtype + params_to_keep = {"norm", "head", "time_in", "vector_in", "controlnet_patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter"} + + log.info("Using accelerate to load and assign controlnet model weights to device...") + param_count = sum(1 for _ in controlnet.named_parameters()) + for name, param in tqdm(controlnet.named_parameters(), + desc=f"Loading transformer parameters to {transformer_load_device}", + total=param_count, + leave=True): + dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype + if "controlnet_patch_embedding" in name: + dtype_to_use = torch.float32 + set_module_tensor_to_device(controlnet, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name]) + + del sd + + if load_device == "offload_device" and controlnet.device != offload_device: + log.info(f"Moving controlnet model from {controlnet.device} to {offload_device}") + controlnet.to(offload_device) + gc.collect() + mm.soft_empty_cache() + + return (controlnet,) + +class WanVideoControlnetApply: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("WANVIDEOMODEL", ), + "controlnet": ("WANVIDEOCONTROLNET", ), + "control_images": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001, "tooltip": "controlnet strength"}), + "control_stride": ("INT", {"default": 3, "min": 1, "max": 8, "step": 1, "tooltip": "controlnet stride"}), + "control_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply controlnet"}), + "control_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply controlnet"}), + } + } + + RETURN_TYPES = ("WANVIDEOMODEL",) + RETURN_NAMES = ("model", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + + def loadmodel(self, model, controlnet, control_images, strength, control_stride, control_start_percent, control_end_percent): + + patcher = model.clone() + if 'transformer_options' not in patcher.model_options: + patcher.model_options['transformer_options'] = {} + + control_input = control_images.permute(3, 0, 1, 2).unsqueeze(0).contiguous() + control_input = control_input * 2.0 - 1.0 + + controlnet = { + "controlnet": controlnet, + "control_latents": control_input, + "controlnet_strength": strength, + "control_stride": control_stride, + "controlnet_start": control_start_percent, + "controlnet_end": control_end_percent + } + patcher.model_options["transformer_options"]["controlnet"] = controlnet + + return (patcher,) + +NODE_CLASS_MAPPINGS = { + "WanVideoControlnetLoader": WanVideoControlnetLoader, + "WanVideoControlnet": WanVideoControlnetApply, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoControlnetLoader": "WanVideo Controlnet Loader", + "WanVideoControlnet": "WanVideo Controlnet Apply", + } + + \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/controlnet/wan_controlnet.py b/custom_nodes/ComfyUI_WanVideoWrapper/controlnet/wan_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..af4c71e17e530be061bcb7b2b0139bf69e32c454 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/controlnet/wan_controlnet.py @@ -0,0 +1,267 @@ +# source https://github.com/TheDenk/wan2.1-dilated-controlnet/blob/main/wan_controlnet.py +from typing import Any, Dict, Optional, Tuple, Union + +import torch +import torch.nn as nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin +from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.transformers.transformer_wan import ( + WanTimeTextImageEmbedding, + WanRotaryPosEmbed, + WanTransformerBlock +) + + +def zero_module(module): + for p in module.parameters(): + nn.init.zeros_(p) + return module + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +class WanControlnet(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin): + r""" + A Controlnet Transformer model for video-like data used in the Wan model. + + Args: + patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch). + num_attention_heads (`int`, defaults to `40`): + Fixed length for text embeddings. + attention_head_dim (`int`, defaults to `128`): + The number of channels in each head. + vae_channels (`int`, defaults to `16`): + The number of channels in the vae input. + in_channels (`int`, defaults to `16`): + The number of channels in the controlnet input. + text_dim (`int`, defaults to `512`): + Input dimension for text embeddings. + freq_dim (`int`, defaults to `256`): + Dimension for sinusoidal time embeddings. + ffn_dim (`int`, defaults to `13824`): + Intermediate dimension in feed-forward network. + num_layers (`int`, defaults to `40`): + The number of layers of transformer blocks to use. + window_size (`Tuple[int]`, defaults to `(-1, -1)`): + Window size for local attention (-1 indicates global attention). + cross_attn_norm (`bool`, defaults to `True`): + Enable cross-attention normalization. + qk_norm (`bool`, defaults to `True`): + Enable query/key normalization. + eps (`float`, defaults to `1e-6`): + Epsilon value for normalization layers. + add_img_emb (`bool`, defaults to `False`): + Whether to use img_emb. + added_kv_proj_dim (`int`, *optional*, defaults to `None`): + The number of channels to use for the added key and value projections. If `None`, no projection is used. + downscale_coef (`int`, *optional*, defaults to `8`): + Coeficient for downscale controlnet input video. + out_proj_dim (`int`, *optional*, defaults to `128 * 12`): + Output projection dimention for last linear layers. + """ + + _supports_gradient_checkpointing = True + _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"] + _no_split_modules = ["WanTransformerBlock"] + _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"] + _keys_to_ignore_on_load_unexpected = ["norm_added_q"] + + @register_to_config + def __init__( + self, + patch_size: Tuple[int] = (1, 2, 2), + num_attention_heads: int = 40, + attention_head_dim: int = 128, + in_channels: int = 3, + vae_channels: int = 16, + text_dim: int = 4096, + freq_dim: int = 256, + ffn_dim: int = 13824, + num_layers: int = 20, + cross_attn_norm: bool = True, + qk_norm: Optional[str] = "rms_norm_across_heads", + eps: float = 1e-6, + image_dim: Optional[int] = None, + added_kv_proj_dim: Optional[int] = None, + rope_max_seq_len: int = 1024, + downscale_coef: int = 8, + out_proj_dim: int = 128 * 12, + ) -> None: + super().__init__() + + start_channels = in_channels * (downscale_coef ** 2) + input_channels = [start_channels, start_channels // 2, start_channels // 4] + + self.control_encoder = nn.ModuleList([ + ## Spatial compression with time awareness + nn.Sequential( + nn.Conv3d( + in_channels, + input_channels[0], + kernel_size=(3, downscale_coef + 1, downscale_coef + 1), + stride=(1, downscale_coef, downscale_coef), + padding=(1, downscale_coef // 2, downscale_coef // 2) + ), + nn.GELU(approximate="tanh"), + nn.GroupNorm(2, input_channels[0]), + ), + ## Temporal compression with spatial awareness + nn.Sequential( + nn.Conv3d(input_channels[0], input_channels[1], kernel_size=3, stride=(2, 1, 1), padding=1), + nn.GELU(approximate="tanh"), + nn.GroupNorm(2, input_channels[1]), + ), + ## Temporal compression with spatial awareness + nn.Sequential( + nn.Conv3d(input_channels[1], input_channels[2], kernel_size=3, stride=(2, 1, 1), padding=1), + nn.GELU(approximate="tanh"), + nn.GroupNorm(2, input_channels[2]), + ) + ]) + + inner_dim = num_attention_heads * attention_head_dim + + # 1. Patch & position embedding + self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len) + self.patch_embedding = nn.Conv3d(vae_channels + input_channels[2], inner_dim, kernel_size=patch_size, stride=patch_size) + + # 2. Condition embeddings + # image_embedding_dim=1280 for I2V model + self.condition_embedder = WanTimeTextImageEmbedding( + dim=inner_dim, + time_freq_dim=freq_dim, + time_proj_dim=inner_dim * 6, + text_embed_dim=text_dim, + image_embed_dim=image_dim, + ) + # 3. Transformer blocks + self.blocks = nn.ModuleList( + [ + WanTransformerBlock( + inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim + ) + for _ in range(num_layers) + ] + ) + + # 4 Controlnet modules + self.controlnet_blocks = nn.ModuleList([]) + + for _ in range(len(self.blocks)): + controlnet_block = nn.Linear(inner_dim, out_proj_dim) + controlnet_block = zero_module(controlnet_block) + self.controlnet_blocks.append(controlnet_block) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + timestep: torch.LongTensor, + encoder_hidden_states: torch.Tensor, + controlnet_states: torch.Tensor, + encoder_hidden_states_image: Optional[torch.Tensor] = None, + return_dict: bool = True, + attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: + if attention_kwargs is not None: + attention_kwargs = attention_kwargs.copy() + lora_scale = attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + else: + if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None: + logger.warning( + "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." + ) + + rotary_emb = self.rope(hidden_states) + + # 0. Controlnet encoder + for control_encoder_block in self.control_encoder: + controlnet_states = control_encoder_block(controlnet_states) + + hidden_states = torch.cat([hidden_states, controlnet_states], dim=1) + + ## 1. Patch embedding and stack + hidden_states = self.patch_embedding(hidden_states) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + + temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder( + timestep, encoder_hidden_states, encoder_hidden_states_image + ) + timestep_proj = timestep_proj.unflatten(1, (6, -1)) + + if encoder_hidden_states_image is not None: + encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1) + + # 2. Transformer blocks + controlnet_hidden_states = () + if torch.is_grad_enabled() and self.gradient_checkpointing: + for block, controlnet_block in zip(self.blocks, self.controlnet_blocks): + hidden_states = self._gradient_checkpointing_func( + block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb + ) + controlnet_hidden_states += (controlnet_block(hidden_states),) + else: + for block, controlnet_block in zip(self.blocks, self.controlnet_blocks): + hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb) + controlnet_hidden_states += (controlnet_block(hidden_states),) + + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (controlnet_hidden_states,) + + return Transformer2DModelOutput(sample=controlnet_hidden_states) + + +if __name__ == "__main__": + parameters = { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "image_dim": None, + "in_channels": 3, + "num_attention_heads": 12, + "num_layers": 2, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "rope_max_seq_len": 1024, + "text_dim": 4096, + "downscale_coef": 8, + "out_proj_dim": 12 * 128, + } + controlnet = WanControlnet(**parameters) + + hidden_states = torch.rand(1, 16, 21, 60, 90) + timestep = torch.randint(low=0, high=1000, size=(1,), dtype=torch.long) + encoder_hidden_states = torch.rand(1, 512, 4096) + controlnet_states = torch.rand(1, 3, 81, 480, 720) + + controlnet_hidden_states = controlnet( + hidden_states=hidden_states, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + controlnet_states=controlnet_states, + return_dict=False + ) + print("Output states count", len(controlnet_hidden_states[0])) + for out_hidden_states in controlnet_hidden_states[0]: + print(out_hidden_states.shape) + \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/LICENSE b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0e5a49f5f70af9e9d37278f72315d1b1afd34895 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2023] [Zhongjie Duan] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..09ad170540f5d7901698ae6c9c59608d5f71112c --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/__init__.py @@ -0,0 +1 @@ +from .layers import * \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/layers.py b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..9c97ff73706334358d3328c489cce95840418630 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/layers.py @@ -0,0 +1,103 @@ +import torch, copy +from .utils import init_weights_on_device + + +def cast_to(weight, dtype, device): + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + +class AutoWrappedModule(torch.nn.Module): + def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device): + super().__init__() + self.module = module.to(dtype=offload_dtype, device=offload_device) + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.state = 0 + + def offload(self): + if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): + self.module.to(dtype=self.offload_dtype, device=self.offload_device) + self.state = 0 + + def onload(self): + if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): + self.module.to(dtype=self.onload_dtype, device=self.onload_device) + self.state = 1 + + def forward(self, *args, **kwargs): + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + module = self.module + else: + module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device) + return module(*args, **kwargs) + + +class AutoWrappedLinear(torch.nn.Linear): + def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.state = 0 + + def offload(self): + if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): + self.to(dtype=self.offload_dtype, device=self.offload_device) + self.state = 0 + + def onload(self): + if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device): + self.to(dtype=self.onload_dtype, device=self.onload_device) + self.state = 1 + + def forward(self, x, *args, **kwargs): + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + else: + weight = cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + return torch.nn.functional.linear(x, weight, bias) + + +def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0, compile_args=None): + for name, module in model.named_children(): + for source_module, target_module in module_map.items(): + if isinstance(module, source_module): + if "rope_embedder" in name or "patch_embedding" in name or "emb_pos" in name: + continue + + num_param = sum(p.numel() for p in module.parameters()) + if max_num_param is not None and total_num_param + num_param > max_num_param: + module_config_ = overflow_module_config + else: + module_config_ = module_config + if compile_args is not None: + print("Compiling", name) + torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"] + torch._dynamo.config.recompile_limit = compile_args["dynamo_cache_size_limit"] + module_ = torch.compile(target_module(module, **module_config_), fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"]) + else: + module_ = target_module(module, **module_config_) + setattr(model, name, module_) + total_num_param += num_param + break + else: + total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param) + return total_num_param + + +def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, compile_args=None): + enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0, compile_args=compile_args) + model.vram_management_enabled = True \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/utils.py b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..72ed1f780b516d4323eb56addde1b0a41ba63977 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/diffsynth/vram_management/utils.py @@ -0,0 +1,51 @@ +import torch +from contextlib import contextmanager + +@contextmanager +def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False): + + old_register_parameter = torch.nn.Module.register_parameter + if include_buffers: + old_register_buffer = torch.nn.Module.register_buffer + + def register_empty_parameter(module, name, param): + old_register_parameter(module, name, param) + if param is not None: + param_cls = type(module._parameters[name]) + kwargs = module._parameters[name].__dict__ + kwargs["requires_grad"] = param.requires_grad + module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) + + def register_empty_buffer(module, name, buffer, persistent=True): + old_register_buffer(module, name, buffer, persistent=persistent) + if buffer is not None: + module._buffers[name] = module._buffers[name].to(device) + + def patch_tensor_constructor(fn): + def wrapper(*args, **kwargs): + kwargs["device"] = device + return fn(*args, **kwargs) + + return wrapper + + if include_buffers: + tensor_constructors_to_patch = { + torch_function_name: getattr(torch, torch_function_name) + for torch_function_name in ["empty", "zeros", "ones", "full"] + } + else: + tensor_constructors_to_patch = {} + + try: + torch.nn.Module.register_parameter = register_empty_parameter + if include_buffers: + torch.nn.Module.register_buffer = register_empty_buffer + for torch_function_name in tensor_constructors_to_patch.keys(): + setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) + yield + finally: + torch.nn.Module.register_parameter = old_register_parameter + if include_buffers: + torch.nn.Module.register_buffer = old_register_buffer + for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(): + setattr(torch, torch_function_name, old_torch_function) \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/LICENSE b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..971f205c7bf8531345cca0fec4b722fb733cda17 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/LICENSE @@ -0,0 +1,562 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright VideoSys + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + ## Some of Enhance-A-Video's model is derived from others projects, which is subject to the following copyright notice: + + ================================= Diffusers ================================= + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/enhance.py b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/enhance.py new file mode 100644 index 0000000000000000000000000000000000000000..1c14a84d138a8d0d5c2b00aaf854f45e0e82d94c --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/enhance.py @@ -0,0 +1,55 @@ +import torch +from einops import rearrange +from .globals import get_enhance_weight, get_num_frames + +@torch.compiler.disable() +def get_feta_scores(query, key): + img_q, img_k = query, key + + num_frames = get_num_frames() + + B, S, N, C = img_q.shape + + # Calculate spatial dimension + spatial_dim = S // num_frames + + # Add time dimension between spatial and head dims + query_image = img_q.reshape(B, spatial_dim, num_frames, N, C) + key_image = img_k.reshape(B, spatial_dim, num_frames, N, C) + + # Expand time dimension + query_image = query_image.expand(-1, -1, num_frames, -1, -1) # [B, S, T, N, C] + key_image = key_image.expand(-1, -1, num_frames, -1, -1) # [B, S, T, N, C] + + # Reshape to match feta_score input format: [(B S) N T C] + query_image = rearrange(query_image, "b s t n c -> (b s) n t c") #torch.Size([3200, 24, 5, 128]) + key_image = rearrange(key_image, "b s t n c -> (b s) n t c") + + return feta_score(query_image, key_image, C, num_frames) + +@torch.compiler.disable() +def feta_score(query_image, key_image, head_dim, num_frames): + scale = head_dim**-0.5 + query_image = query_image * scale + attn_temp = query_image @ key_image.transpose(-2, -1) # translate attn to float32 + attn_temp = attn_temp.to(torch.float32) + attn_temp = attn_temp.softmax(dim=-1) + + # Reshape to [batch_size * num_tokens, num_frames, num_frames] + attn_temp = attn_temp.reshape(-1, num_frames, num_frames) + + # Create a mask for diagonal elements + diag_mask = torch.eye(num_frames, device=attn_temp.device).bool() + diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1) + + # Zero out diagonal elements + attn_wo_diag = attn_temp.masked_fill(diag_mask, 0) + + # Calculate mean for each token's attention matrix + # Number of off-diagonal elements per matrix is n*n - n + num_off_diag = num_frames * num_frames - num_frames + mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag + + enhance_scores = mean_scores.mean() * (num_frames + get_enhance_weight()) + enhance_scores = enhance_scores.clamp(min=1) + return enhance_scores diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/globals.py b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/globals.py new file mode 100644 index 0000000000000000000000000000000000000000..d2bcddd22e6950ff5bbd05ec7b70a09d20873b23 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/enhance_a_video/globals.py @@ -0,0 +1,36 @@ +import torch + +NUM_FRAMES = None +FETA_WEIGHT = None +ENABLE_FETA= False + +@torch.compiler.disable() +def set_num_frames(num_frames: int): + global NUM_FRAMES + NUM_FRAMES = num_frames + +@torch.compiler.disable() +def get_num_frames() -> int: + return NUM_FRAMES + + +def enable_enhance(): + global ENABLE_FETA + ENABLE_FETA = True + +def disable_enhance(): + global ENABLE_FETA + ENABLE_FETA = False + +@torch.compiler.disable() +def is_enhance_enabled() -> bool: + return ENABLE_FETA + +@torch.compiler.disable() +def set_enhance_weight(feta_weight: float): + global FETA_WEIGHT + FETA_WEIGHT = feta_weight + +@torch.compiler.disable() +def get_enhance_weight() -> float: + return FETA_WEIGHT diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_1_3B_ReCamMaster_example_01.json b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_1_3B_ReCamMaster_example_01.json new file mode 100644 index 0000000000000000000000000000000000000000..3baa3f938299c11a542a13ad4071c878e7802835 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_1_3B_ReCamMaster_example_01.json @@ -0,0 +1,2435 @@ +{ + "id": "c6e410bc-5e2c-460b-ae81-c91b6094fbb1", + "revision": 0, + "last_node_id": 206, + "last_link_id": 341, + "nodes": [ + { + "id": 42, + "type": "Note", + "pos": [ + -1446.69775390625, + -855.9483032226562 + ], + "size": [ + 314.96246337890625, + 152.77333068847656 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Adjust the blocks to swap based on your VRAM, this is a tradeoff between speed and memory usage.\n\nAlternatively there's option to use VRAM management introduced in DiffSynt-Studios. This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 48, + "type": "CLIPLoader", + "pos": [ + 219.2454376220703, + -1894.9801025390625 + ], + "size": [ + 315, + 106 + ], + "flags": {}, + "order": 1, + "mode": 2, + "inputs": [], + "outputs": [ + { + "name": "CLIP", + "type": "CLIP", + "slot_index": 0, + "links": [ + 52, + 53 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.27", + "Node name for S&R": "CLIPLoader" + }, + "widgets_values": [ + "umt5_xxl_fp16.safetensors", + "wan", + "default" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 51, + "type": "Note", + "pos": [ + 249.245361328125, + -2064.9814453125 + ], + "size": [ + 253.16725158691406, + 88 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "You can also use native ComfyUI text encoding with these nodes instead of the original, the models are node specific and can't otherwise be mixed." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 49, + "type": "CLIPTextEncode", + "pos": [ + 579.2454833984375, + -1894.9801025390625 + ], + "size": [ + 400, + 200 + ], + "flags": {}, + "order": 22, + "mode": 2, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 52 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "slot_index": 0, + "links": [ + 54 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.27", + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "high quality nature video featuring a red panda balancing on a bamboo stem while a bird lands on it's head, on the background there is a waterfall" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 45, + "type": "WanVideoVRAMManagement", + "pos": [ + -1436.69775390625, + -645.9483032226562 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + 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"#653" + }, + { + "id": 42, + "type": "Note", + "pos": [ + -1569.8389892578125, + -901.3384399414062 + ], + "size": [ + 325.3857421875, + 177.342529296875 + ], + "flags": {}, + "order": 31, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Adjust the blocks to swap based on your VRAM, this is a tradeoff between speed and memory usage.\n\n\nIMPORTANT!\nWhen at least 1 VACE block is swapped, the intermediate outputs are also offloaded reducing VRAM usage considerably" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 182, + "type": "Note", + "pos": [ + -606.8529663085938, + -673.1156005859375 + ], + "size": [ + 344.17034912109375, + 107.31356811523438 + ], + "flags": {}, + "order": 32, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "While the WanVideo 1.3B model is in FP32, the VACE module in it is in BF16, so using fp16_fast is technically lossy, but seems to be fine if you want the speed boost." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 120, + "type": "MarkdownNote", + "pos": [ + -750.4815063476562, + -1047.1776123046875 + ], + "size": [ + 368.480712890625, + 147.49659729003906 + ], + "flags": {}, + "order": 33, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Model download link", + "properties": {}, + "widgets_values": [ + "Original model link, you can rename to whatever you like:\n\n[https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview/blob/main/diffusion_pytorch_model.safetensors](https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview/blob/main/diffusion_pytorch_model.safetensors)" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 22, + "type": "WanVideoModelLoader", + "pos": [ + -1144.8314208984375, + -807.3967895507812 + ], + "size": [ + 477.4410095214844, + 254 + ], + "flags": {}, + "order": 57, + "mode": 0, + "inputs": [ + { + "name": "compile_args", + "shape": 7, + "type": "WANCOMPILEARGS", + "link": null + }, + { 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-507.6167297363281 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 34, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn if you have it installed can be used for almost double inference speed" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 225, + "type": "MarkdownNote", + "pos": [ + -1619.8641357421875, + -327.2918395996094 + ], + "size": [ + 536.0852661132812, + 204.4370574951172 + ], + "flags": {}, + "order": 35, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "You can also use the VACE as separate module with any normal matching size T2V model (not Fun models):\n\n1.3B:\n\n[https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VACE_1_3B_preview_bf16.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VACE_1_3B_preview_bf16.safetensors)\n\n14B:\n\nfp8:\n\n[https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1-VACE_module_14B_fp8_e4m3fn.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1-VACE_module_14B_fp8_e4m3fn.safetensors)\n\nbf16:\n\n[https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1-VACE_module_14B_bf16.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1-VACE_module_14B_bf16.safetensors)" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 224, + "type": "WanVideoVACEModelSelect", + "pos": [ + -1585.6165771484375, + -456.09454345703125 + ], + "size": [ + 486.3644714355469, + 75.51844787597656 + ], + "flags": {}, + "order": 36, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "vace_model", + "type": "VACEPATH", + "links": [ 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nature video featuring a red panda balancing on a bamboo stem while a bird lands on it's head, on the background there is a waterfall" + ] + }, + { + "id": 42, + "type": "Note", + "pos": [ + -580, + -760 + ], + "size": [ + 314.96246337890625, + 152.77333068847656 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Adjust the blocks to swap based on your VRAM, this is a tradeoff between speed and memory usage.\n\nAlternatively there's option to use VRAM management introduced in DiffSynt-Studios. This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 45, + "type": "WanVideoVRAMManagement", + "pos": [ + -210, + -580 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "vram_management_args", + "type": "VRAM_MANAGEMENTARGS", + "links": [] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node name for S&R": "WanVideoVRAMManagement" + }, + "widgets_values": [ + 1 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 36, + "type": "Note", + "pos": [ + 160, + -1010 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause huge quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn if you have it installed can be used for almost double inference speed" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 33, + "type": "Note", + "pos": [ + 170, + -1150 + ], + "size": [ + 359.0753479003906, + 88 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Models:\nhttps://huggingface.co/Kijai/WanVideo_comfy/tree/main" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 51, + "type": "Note", + "pos": [ + -294.4300231933594, + 495.39044189453125 + ], + "size": [ + 253.16725158691406, + 88 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "You can also use native ComfyUI text encoding with these nodes instead of the original, the models are node specific and can't otherwise be mixed." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 11, + "type": "LoadWanVideoT5TextEncoder", + "pos": [ + 161.7229461669922, + -501.2225036621094 + ], + "size": [ + 377.1661376953125, + 130 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "wan_t5_model", + "type": "WANTEXTENCODER", + "slot_index": 0, + "links": [ + 15 + ] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node name for S&R": "LoadWanVideoT5TextEncoder" + }, + "widgets_values": [ + "umt5-xxl-enc-bf16.safetensors", + "bf16", + "offload_device", + "disabled" + ], + "color": "#332922", + "bgcolor": "#593930" + }, + { + "id": 54, + "type": "Note", + "pos": [ + 961.6879272460938, + -580.803466796875 + ], + "size": [ + 327.61932373046875, + 88 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Enhance-a-video can increase the fidelity of the results, too high values lead to noisy results." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 53, + "type": "Note", + "pos": [ + 960.3718872070312, + -810.77099609375 + ], + "size": [ + 324.64129638671875, + 159.47401428222656 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "TeaCache could be considered to be sort of an automated step skipper \n\nThe relative l1 threshold -value determines how aggressive this is, higher values are faster but quality suffers more. Very first steps should NEVER be skipped with this model or it kills the motion. When using the pre-calculated coefficients, the treshold value should be much higher than with the default coefficients." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 44, + "type": "Note", + "pos": [ + -620.9041137695312, + -1049.732421875 + ], + "size": [ + 303.0501403808594, + 88 + ], + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "If you have Triton installed, connect this for ~30% speed increase" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 35, + "type": "WanVideoTorchCompileSettings", + "pos": [ + -276.8500671386719, + -1050.6326904296875 + ], + "size": [ + 390.5999755859375, + 178 + ], + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "torch_compile_args", + "type": "WANCOMPILEARGS", + "slot_index": 0, + "links": [] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node 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This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 45, + "type": "WanVideoVRAMManagement", + "pos": [ + -210, + -580 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "vram_management_args", + "type": "VRAM_MANAGEMENTARGS", + "links": [] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node name for S&R": "WanVideoVRAMManagement" + }, + "widgets_values": [ + 1 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 36, + "type": "Note", + "pos": [ + 160, + -1010 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause huge quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn if you have it installed can be used for almost double inference speed" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 33, + "type": "Note", + "pos": [ + 170, + -1150 + ], + "size": [ + 359.0753479003906, + 88 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Models:\nhttps://huggingface.co/Kijai/WanVideo_comfy/tree/main" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 51, + "type": "Note", + "pos": [ + 424.153076171875, + 547.1480712890625 + ], + "size": [ + 253.16725158691406, + 88 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "You can also use native ComfyUI text encoding with these nodes instead of the original, the models are node specific and can't otherwise be mixed." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 62, + "type": "Note", + "pos": [ + 315.68389892578125, + 215.8892364501953 + ], + "size": [ + 268.73455810546875, + 90.03050994873047 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "The original code had automatic resolution adjustment based on input image total pixels and aspect ratio. If you want to set it manually, disable the adjust_resolution" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 11, + "type": "LoadWanVideoT5TextEncoder", + "pos": [ + 161.7229461669922, + -501.2225036621094 + ], + "size": [ + 377.1661376953125, + 130 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "wan_t5_model", + "type": "WANTEXTENCODER", + "slot_index": 0, + "links": [ + 15 + ] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node name for S&R": "LoadWanVideoT5TextEncoder" + }, + "widgets_values": [ + "umt5-xxl-enc-bf16.safetensors", + "bf16", + "offload_device", + "disabled" + ], + "color": "#332922", + "bgcolor": "#593930" + }, + { + "id": 52, + "type": "WanVideoTeaCache", + "pos": [ + 1307.6705322265625, + -787.4303588867188 + ], + "size": [ + 315, + 178 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "cache_args", + "type": "CACHEARGS", + "links": [ + 56 + ] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node name for S&R": "WanVideoTeaCache" + }, + "widgets_values": [ + 0.25, + 1, + -1, + "offload_device", + "true", + "e" + ] + }, + { + "id": 54, + "type": "Note", + "pos": [ + 961.6879272460938, + -580.803466796875 + ], + "size": [ + 327.61932373046875, + 88 + ], + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Enhance-a-video can increase the fidelity of the results, too high values lead to noisy results." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 53, + "type": "Note", + "pos": [ + 960.3718872070312, + -810.77099609375 + ], + "size": [ + 324.64129638671875, + 159.47401428222656 + ], + "flags": {}, + "order": 10, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "TeaCache could be considered to be sort of an automated step skipper \n\nThe relative l1 threshold -value determines how aggressive this is, higher values are faster but quality suffers more. Very first steps should NEVER be skipped with this model or it kills the motion. When using the pre-calculated coefficients, the treshold value should be much higher than with the default coefficients." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 44, + "type": "Note", + "pos": [ + -620.9041137695312, + -1049.732421875 + ], + "size": [ + 303.0501403808594, + 88 + ], + "flags": {}, + "order": 11, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "If you have Triton installed, connect this for ~30% speed increase" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 39, + "type": "WanVideoBlockSwap", + "pos": [ + -210, + -760 + ], + "size": [ + 315, + 130 + ], + "flags": {}, + "order": 12, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "block_swap_args", + "type": "BLOCKSWAPARGS", + "slot_index": 0, + "links": [ + 50 + ] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d9b1f4d1a5aea91d101ae97a54714a5861af3f50", + "Node name for S&R": "WanVideoBlockSwap" + }, + 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name for S&R": "CLIPLoader" + }, + "widgets_values": [ + "umt5_xxl_fp16.safetensors", + "wan", + "default" + ] + }, + { + "id": 49, + "type": "CLIPTextEncode", + "pos": [ + -2010.6832275390625, + 1706.2386474609375 + ], + "size": [ + 400, + 200 + ], + "flags": {}, + "order": 17, + "mode": 2, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 52 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "slot_index": 0, + "links": [ + 54 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.26", + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "high quality nature video featuring a red panda balancing on a bamboo stem while a bird lands on it's head, on the background there is a waterfall" + ] + }, + { + "id": 42, + "type": "Note", + "pos": [ + -580, + -760 + ], + "size": [ + 314.96246337890625, + 152.77333068847656 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Adjust the blocks to swap based on your VRAM, this is a tradeoff between speed and memory usage.\n\nAlternatively there's option to use VRAM management introduced in DiffSynt-Studios. This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 36, + "type": "Note", + "pos": [ + 160, + -1010 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause huge quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn if you have it installed can be used for almost double inference speed" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 33, + "type": "Note", + "pos": [ + 170, + -1150 + ], + "size": [ + 359.0753479003906, + 88 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Models:\nhttps://huggingface.co/Kijai/WanVideo_comfy/tree/main" + ], + 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a/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_Fun_control_example_01.json b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_Fun_control_example_01.json new file mode 100644 index 0000000000000000000000000000000000000000..ad8215ab558bfa13be1de5cb249038e03bfb573e --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_Fun_control_example_01.json @@ -0,0 +1,2635 @@ +{ + "id": "206247b6-9fec-4ed2-8927-e4f388c674d4", + "revision": 0, + "last_node_id": 95, + "last_link_id": 125, + "nodes": [ + { + "id": 46, + "type": "WanVideoTextEmbedBridge", + "pos": [ + 1303.4276123046875, + 1086.20947265625 + ], + "size": [ + 315, + 46 + ], + "flags": {}, + "order": 31, + "mode": 2, + "inputs": [ + { + "name": "positive", + "type": "CONDITIONING", + "link": 54 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 55 + } + ], + "outputs": [ + { + "name": "text_embeds", + "type": "WANVIDEOTEXTEMBEDS", + "links": null + } + ], + 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This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 45, + "type": "WanVideoVRAMManagement", + "pos": [ + -620, + -850 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "vram_management_args", + "type": "VRAM_MANAGEMENTARGS", + "links": [] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d20baf00247fd06553fdc9253e18732244e54172", + "Node name for S&R": "WanVideoVRAMManagement" + }, + "widgets_values": [ + 1 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 33, + "type": "Note", + "pos": [ + -240, + -1420 + ], + "size": [ + 359.0753479003906, + 88 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "Models:\nhttps://huggingface.co/Kijai/WanVideo_comfy/tree/main" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 51, + "type": "Note", + "pos": [ + 523.4271850585938, + 926.2090454101562 + ], + "size": [ + 253.16725158691406, + 88 + ], + "flags": {}, + "order": 4, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "You can also use native ComfyUI text encoding with these nodes instead of the original, the models are node specific and can't otherwise be mixed." + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 44, + "type": "Note", + "pos": [ + -1030, + -1320 + ], + "size": [ + 303.0501403808594, + 88 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "If you have Triton installed, connect this for ~30% speed increase" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 39, + "type": "WanVideoBlockSwap", + "pos": [ + -620, + -1030 + ], + "size": [ + 315, + 154 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "block_swap_args", + "type": "BLOCKSWAPARGS", + "slot_index": 0, + "links": [] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d20baf00247fd06553fdc9253e18732244e54172", + "Node name for S&R": "WanVideoBlockSwap" + }, + "widgets_values": [ + 10, + false, + false, + true, + 0 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 35, + "type": "WanVideoTorchCompileSettings", + "pos": [ + -690, + -1320 + ], + "size": [ + 390.5999755859375, + 202 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "torch_compile_args", + "type": "WANCOMPILEARGS", + "slot_index": 0, + "links": [] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "d20baf00247fd06553fdc9253e18732244e54172", + "Node name for S&R": "WanVideoTorchCompileSettings" + }, + "widgets_values": [ + "inductor", + false, + "default", + false, + 64, + true, + 128 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 53, + "type": "Note", + "pos": [ + 1301.80224609375, + -1006.259765625 + ], + "size": [ + 324.64129638671875, + 159.47401428222656 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "The relative l1 threshold -value determines how aggressive this is, higher values are faster but quality suffers more. 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true, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true + }, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_I2V_FantasyTalking_example_01.json b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_I2V_FantasyTalking_example_01.json new file mode 100644 index 0000000000000000000000000000000000000000..eedf2de9cafb9390a7d0dd2446a23d7810eebb88 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_I2V_FantasyTalking_example_01.json @@ -0,0 +1,2134 @@ +{ + "id": "206247b6-9fec-4ed2-8927-e4f388c674d4", + "revision": 0, + "last_node_id": 86, + "last_link_id": 122, + "nodes": [ + { + "id": 46, + "type": "WanVideoTextEmbedBridge", + "pos": [ + 1015.870849609375, + 941.0142822265625 + ], + "size": [ + 315, + 46 + ], + "flags": {}, + "order": 32, + "mode": 2, + "inputs": [ + { + "name": "positive", + "type": "CONDITIONING", + "link": 54 + 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b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_flowedit_I2V_example_01.json new file mode 100644 index 0000000000000000000000000000000000000000..18f16afb08cd54b9e3def6244aa419d650501f35 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_flowedit_I2V_example_01.json @@ -0,0 +1,1965 @@ +{ + "last_node_id": 73, + "last_link_id": 92, + "nodes": [ + { + "id": 46, + "type": "WanVideoTextEmbedBridge", + "pos": [ + -947.5358276367188, + -63.66567611694336 + ], + "size": [ + 315, + 46 + ], + "flags": {}, + "order": 25, + "mode": 2, + "inputs": [ + { + "name": "positive", + "type": "CONDITIONING", + "link": 54 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 55 + } + ], + "outputs": [ + { + "name": "text_embeds", + "type": "WANVIDEOTEXTEMBEDS", + "links": null + } + ], + "properties": { + "Node name for S&R": "WanVideoTextEmbedBridge" + }, + "widgets_values": [] + }, + { + "id": 50, + "type": "CLIPTextEncode", + "pos": [ + 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use VRAM management introduced in DiffSynt-Studios. This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 45, + "type": "WanVideoVRAMManagement", + "pos": [ + -210, + -580 + ], + "size": [ + 315, + 58 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "vram_management_args", + "type": "VRAM_MANAGEMENTARGS", + "links": [] + } + ], + "properties": { + "Node name for S&R": "WanVideoVRAMManagement" + }, + "widgets_values": [ + 1 + ], + "color": "#223", + "bgcolor": "#335" + }, + { + "id": 36, + "type": "Note", + "pos": [ + 160, + -1010 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause huge quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn 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b/custom_nodes/ComfyUI_WanVideoWrapper/example_workflows/wanvideo_phantom_subject2vid_example_01.json @@ -0,0 +1,1992 @@ +{ + "id": "c6e410bc-5e2c-460b-ae81-c91b6094fbb1", + "revision": 0, + "last_node_id": 77, + "last_link_id": 120, + "nodes": [ + { + "id": 11, + "type": "LoadWanVideoT5TextEncoder", + "pos": [ + 224.15325927734375, + -34.481563568115234 + ], + "size": [ + 377.1661376953125, + 130 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "wan_t5_model", + "type": "WANTEXTENCODER", + "slot_index": 0, + "links": [ + 15 + ] + } + ], + "properties": { + "cnr_id": "ComfyUI-WanVideoWrapper", + "ver": "6099ad393b071728032fd481e96d77d2900eee2c", + "Node name for S&R": "LoadWanVideoT5TextEncoder" + }, + "widgets_values": [ + "umt5-xxl-enc-bf16.safetensors", + "bf16", + "offload_device", + "disabled" + ], + "color": "#332922", + "bgcolor": "#593930" + }, + { + "id": 42, + "type": "Note", + "pos": [ + -165.44613647460938, + -344.9282531738281 + 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This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 50, + "type": "CLIPTextEncode", + "pos": [ + -78.64810180664062, + 1769.301513671875 + ], + "size": [ + 400, + 200 + ], + "flags": {}, + "order": 22, + "mode": 2, + "inputs": [ + { + "name": "clip", + "type": "CLIP", + "link": 53 + } + ], + "outputs": [ + { + "name": "CONDITIONING", + "type": "CONDITIONING", + "slot_index": 0, + "links": [ + 55 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.29", + "Node name for S&R": "CLIPTextEncode" + }, + "widgets_values": [ + "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 48, + "type": "CLIPLoader", + "pos": [ + -438.6482238769531, + 1519.30126953125 + ], + "size": [ + 315, + 106 + ], + "flags": {}, + "order": 2, + "mode": 2, + "inputs": [], + 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This is usually slower, but saves even more VRAM compared to BlockSwap" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 36, + "type": "Note", + "pos": [ + -183.5550079345703, + -1437.346923828125 + ], + "size": [ + 374.3061828613281, + 171.9547576904297 + ], + "flags": {}, + "order": 2, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + "fp8_fast seems to cause huge quality degradation\n\nfp_16_fast enables \"Full FP16 Accmumulation in FP16 GEMMs\" feature available in the very latest pytorch nightly, this is around 20% speed boost. \n\nSageattn if you have it installed can be used for almost double inference speed" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 33, + "type": "Note", + "pos": [ + -173.5550079345703, + -1577.346923828125 + ], + "size": [ + 359.0753479003906, + 88 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [], + "outputs": [], + "properties": {}, + "widgets_values": [ + 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"5a2383621a05825d0d0437781afcb8552d9590fd", + "ComfyUI-KJNodes": "a5bd3c86c8ed6b83c55c2d0e7a59515b15a0137f", + "ComfyUI-VideoHelperSuite": "0a75c7958fe320efcb052f1d9f8451fd20c730a8" + }, + "VHS_latentpreview": true, + "VHS_latentpreviewrate": 0, + "VHS_MetadataImage": true, + "VHS_KeepIntermediate": true + }, + "version": 0.4 +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/fantasytalking/model.py b/custom_nodes/ComfyUI_WanVideoWrapper/fantasytalking/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bd24553c6cd11b4d567356d4dd4178238c55d1 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/fantasytalking/model.py @@ -0,0 +1,130 @@ +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F +from safetensors import safe_open + +class AudioProjModel(nn.Module): + def __init__(self, audio_in_dim=1024, cross_attention_dim=1024): + super().__init__() + self.cross_attention_dim = cross_attention_dim + self.proj = torch.nn.Linear(audio_in_dim, cross_attention_dim, bias=False) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, audio_embeds): + context_tokens = self.proj(audio_embeds) + context_tokens = self.norm(context_tokens) + return context_tokens # [B,L,C] + +class FantasyTalkingAudioConditionModel(nn.Module): + def __init__(self, audio_in_dim: int, audio_proj_dim: int): + super().__init__() + + self.audio_in_dim = audio_in_dim + self.audio_proj_dim = audio_proj_dim + + # audio proj model + self.proj_model = self.init_proj(self.audio_proj_dim) + + def init_proj(self, cross_attention_dim=5120): + proj_model = AudioProjModel( + audio_in_dim=self.audio_in_dim, cross_attention_dim=cross_attention_dim + ) + return proj_model + + def get_proj_fea(self, audio_fea=None): + return self.proj_model(audio_fea) if audio_fea is not None else None + + def split_audio_sequence(self, audio_proj_length, num_frames=81): + """ + Map the audio feature sequence to corresponding latent frame slices. + + Args: + audio_proj_length (int): The total length of the audio feature sequence + (e.g., 173 in audio_proj[1, 173, 768]). + num_frames (int): The number of video frames in the training data (default: 81). + + Returns: + list: A list of [start_idx, end_idx] pairs. Each pair represents the index range + (within the audio feature sequence) corresponding to a latent frame. + """ + # Average number of tokens per original video frame + tokens_per_frame = audio_proj_length / num_frames + + # Each latent frame covers 4 video frames, and we want the center + tokens_per_latent_frame = tokens_per_frame * 4 + half_tokens = int(tokens_per_latent_frame / 2) + + pos_indices = [] + for i in range(int((num_frames - 1) / 4) + 1): + if i == 0: + pos_indices.append(0) + else: + start_token = tokens_per_frame * ((i - 1) * 4 + 1) + end_token = tokens_per_frame * (i * 4 + 1) + center_token = int((start_token + end_token) / 2) - 1 + pos_indices.append(center_token) + + # Build index ranges centered around each position + pos_idx_ranges = [[idx - half_tokens, idx + half_tokens] for idx in pos_indices] + + # Adjust the first range to avoid negative start index + pos_idx_ranges[0] = [ + -(half_tokens * 2 - pos_idx_ranges[1][0]), + pos_idx_ranges[1][0], + ] + + return pos_idx_ranges + + def split_tensor_with_padding(self, input_tensor, pos_idx_ranges, expand_length=0): + """ + Split the input tensor into subsequences based on index ranges, and apply right-side zero-padding + if the range exceeds the input boundaries. + + Args: + input_tensor (Tensor): Input audio tensor of shape [1, L, 768]. + pos_idx_ranges (list): A list of index ranges, e.g. [[-7, 1], [1, 9], ..., [165, 173]]. + expand_length (int): Number of tokens to expand on both sides of each subsequence. + + Returns: + sub_sequences (Tensor): A tensor of shape [1, F, L, 768], where L is the length after padding. + Each element is a padded subsequence. + k_lens (Tensor): A tensor of shape [F], representing the actual (unpadded) length of each subsequence. + Useful for ignoring padding tokens in attention masks. + """ + pos_idx_ranges = [ + [idx[0] - expand_length, idx[1] + expand_length] for idx in pos_idx_ranges + ] + sub_sequences = [] + seq_len = input_tensor.size(1) # 173 + max_valid_idx = seq_len - 1 # 172 + k_lens_list = [] + for start, end in pos_idx_ranges: + # Calculate the fill amount + pad_front = max(-start, 0) + pad_back = max(end - max_valid_idx, 0) + + # Calculate the start and end indices of the valid part + valid_start = max(start, 0) + valid_end = min(end, max_valid_idx) + + # Extract the valid part + if valid_start <= valid_end: + valid_part = input_tensor[:, valid_start : valid_end + 1, :] + else: + valid_part = input_tensor.new_zeros((1, 0, input_tensor.size(2))) + + # In the sequence dimension (the 1st dimension) perform padding + padded_subseq = F.pad( + valid_part, + (0, 0, 0, pad_back + pad_front, 0, 0), + mode="constant", + value=0, + ) + k_lens_list.append(padded_subseq.size(-2) - pad_back - pad_front) + + sub_sequences.append(padded_subseq) + return torch.stack(sub_sequences, dim=1), torch.tensor( + k_lens_list, dtype=torch.long + ) diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/fantasytalking/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/fantasytalking/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..b35374111cbf8bb2a4caf4bd145615d54ab9e7af --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/fantasytalking/nodes.py @@ -0,0 +1,192 @@ +import os +import torch +import gc +from ..utils import log + +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device + +import comfy.model_management as mm +from comfy.utils import load_torch_file +import folder_paths + +script_directory = os.path.dirname(os.path.abspath(__file__)) + + +class DownloadAndLoadWav2VecModel: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (["facebook/wav2vec2-base-960h"],), + + "base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}), + "load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}), + }, + } + + RETURN_TYPES = ("WAV2VECMODEL",) + RETURN_NAMES = ("wav2vec_model", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + + def loadmodel(self, model, base_precision, load_device): + from transformers import Wav2Vec2Model, Wav2Vec2Processor + + base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision] + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + if load_device == "offload_device": + transfomer_load_device = offload_device + else: + transfomer_load_device = device + + model_path = os.path.join(folder_paths.models_dir, "transformers", model) + if not os.path.exists(model_path): + log.info(f"Downloading Qwen model to: {model_path}") + from huggingface_hub import snapshot_download + snapshot_download( + repo_id=model, + ignore_patterns=["*.bin", "*.h5"], + local_dir=model_path, + local_dir_use_symlinks=False, + ) + + wav2vec_processor = Wav2Vec2Processor.from_pretrained(model_path) + wav2vec = Wav2Vec2Model.from_pretrained(model_path).to(base_dtype).to(transfomer_load_device).eval() + + wav2vec_processor_model = { + "processor": wav2vec_processor, + "model": wav2vec, + "dtype": base_dtype,} + + return (wav2vec_processor_model,) + +class FantasyTalkingModelLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}), + + "base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}), + }, + } + + RETURN_TYPES = ("FANTASYTALKINGMODEL",) + RETURN_NAMES = ("model", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + + def loadmodel(self, model, base_precision): + from .model import FantasyTalkingAudioConditionModel + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision] + + model_path = folder_paths.get_full_path_or_raise("diffusion_models", model) + sd = load_torch_file(model_path, device=offload_device, safe_load=True) + + with init_empty_weights(): + fantasytalking_proj_model = FantasyTalkingAudioConditionModel(audio_in_dim=768, audio_proj_dim=2048) + #fantasytalking_proj_model.load_state_dict(sd, strict=False) + + for name, param in fantasytalking_proj_model.named_parameters(): + set_module_tensor_to_device(fantasytalking_proj_model, name, device=offload_device, dtype=base_dtype, value=sd[name]) + + fantasytalking = { + "proj_model": fantasytalking_proj_model, + "sd": sd, + } + + return (fantasytalking,) + +class FantasyTalkingWav2VecEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "wav2vec_model": ("WAV2VECMODEL",), + "fantasytalking_model": ("FANTASYTALKINGMODEL",), + "audio": ("AUDIO",), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 1000, "step": 1}), + "fps": ("FLOAT", {"default": 23.0, "min": 1.0, "max": 60.0, "step": 0.1}), + "audio_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "Strength of the audio conditioning"}), + "audio_cfg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "When not 1.0, an extra model pass without audio conditioning is done: slower inference but more motion is allowed"}), + }, + } + + RETURN_TYPES = ("FANTASYTALKING_EMBEDS", ) + RETURN_NAMES = ("fantasytalking_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, wav2vec_model, fantasytalking_model, fps, num_frames, audio_scale, audio_cfg_scale, audio): + import torchaudio + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + dtype = wav2vec_model["dtype"] + wav2vec = wav2vec_model["model"] + wav2vec_processor = wav2vec_model["processor"] + audio_proj_model = fantasytalking_model["proj_model"] + + sr = 16000 + + audio_input = audio["waveform"] + sample_rate = audio["sample_rate"] + if sample_rate != sr: + audio_input = torchaudio.functional.resample(audio_input, sample_rate, sr) + audio_input = audio_input[0][0] + + start_time = 0 + end_time = num_frames / fps + + start_sample = int(start_time * sr) + end_sample = int(end_time * sr) + + try: + audio_segment = audio_input[start_sample:end_sample] + except: + audio_segment = audio_input + + print("audio_segment.shape", audio_segment.shape) + + input_values = wav2vec_processor( + audio_segment.numpy(), sampling_rate=sr, return_tensors="pt" + ).input_values.to(dtype).to(device) + + audio_features = wav2vec(input_values).last_hidden_state + + audio_proj_model.proj_model.to(device) + audio_proj_fea = audio_proj_model.get_proj_fea(audio_features) + pos_idx_ranges = audio_proj_model.split_audio_sequence( + audio_proj_fea.size(1), num_frames=num_frames + ) + audio_proj_split, audio_context_lens = audio_proj_model.split_tensor_with_padding( + audio_proj_fea, pos_idx_ranges, expand_length=4 + ) # [b,21,9+8,768] + audio_proj_model.proj_model.to(offload_device) + mm.soft_empty_cache() + + out = { + "audio_proj": audio_proj_split, + "audio_context_lens": audio_context_lens, + "audio_scale": audio_scale, + "audio_cfg_scale": audio_cfg_scale + } + + return (out,) + + +NODE_CLASS_MAPPINGS = { + "DownloadAndLoadWav2VecModel": DownloadAndLoadWav2VecModel, + "FantasyTalkingModelLoader": FantasyTalkingModelLoader, + "FantasyTalkingWav2VecEmbeds": FantasyTalkingWav2VecEmbeds, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "DownloadAndLoadWav2VecModel": "(Down)load Wav2Vec Model", + "FantasyTalkingModelLoader": "FantasyTalking Model Loader", + "FantasyTalkingWav2VecEmbeds": "FantasyTalking Wav2Vec Embeds", + } diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/fp8_optimization.py b/custom_nodes/ComfyUI_WanVideoWrapper/fp8_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..f32eae3ab1fe04d988ed616acc1de5958a3632a6 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/fp8_optimization.py @@ -0,0 +1,39 @@ +#based on ComfyUI's and MinusZoneAI's fp8_linear optimization + +import torch +import torch.nn as nn + +def fp8_linear_forward(cls, original_dtype, input): + weight_dtype = cls.weight.dtype + if weight_dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: + if len(input.shape) == 3: + #target_dtype = torch.float8_e5m2 if weight_dtype == torch.float8_e4m3fn else torch.float8_e4m3fn + inn = input.reshape(-1, input.shape[2]).to(weight_dtype) + w = cls.weight.t() + + scale = torch.ones((1), device=input.device, dtype=torch.float32) + bias = cls.bias.to(original_dtype) if cls.bias is not None else None + + if bias is not None: + o = torch._scaled_mm(inn, w, out_dtype=original_dtype, bias=bias, scale_a=scale, scale_b=scale) + else: + o = torch._scaled_mm(inn, w, out_dtype=original_dtype, scale_a=scale, scale_b=scale) + + if isinstance(o, tuple): + o = o[0] + + return o.reshape((-1, input.shape[1], cls.weight.shape[0])) + else: + return cls.original_forward(input.to(original_dtype)) + else: + return cls.original_forward(input) + +def convert_fp8_linear(module, original_dtype, params_to_keep={}): + setattr(module, "fp8_matmul_enabled", True) + + for name, module in module.named_modules(): + if not any(keyword in name for keyword in params_to_keep): + if isinstance(module, nn.Linear): + original_forward = module.forward + setattr(module, "original_forward", original_forward) + setattr(module, "forward", lambda input, m=module: fp8_linear_forward(m, original_dtype, input)) diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/fun_camera/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/fun_camera/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..d7e94797c45a30f4945506c3dbf2bab35bb2b341 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/fun_camera/nodes.py @@ -0,0 +1,186 @@ +import numpy as np +import os +import torch +from einops import rearrange + +script_directory = os.path.dirname(os.path.abspath(__file__)) + +class Camera(object): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + def __init__(self, entry): + fx, fy, cx, cy = entry[1:5] + self.fx = fx + self.fy = fy + self.cx = cx + self.cy = cy + w2c_mat = np.array(entry[7:]).reshape(3, 4) + w2c_mat_4x4 = np.eye(4) + w2c_mat_4x4[:3, :] = w2c_mat + self.w2c_mat = w2c_mat_4x4 + self.c2w_mat = np.linalg.inv(w2c_mat_4x4) + +def custom_meshgrid(*args): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid + return torch.meshgrid(*args) + + +def get_relative_pose(cam_params): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] + abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] + cam_to_origin = 0 + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, -cam_to_origin], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ abs_w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] + ret_poses = np.array(ret_poses, dtype=np.float32) + return ret_poses + +def ray_condition(K, c2w, H, W, device): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B = K.shape[0] + + j, i = custom_meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + ) + i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW + # c2w @ dirctions + rays_dxo = torch.cross(rays_o, rays_d) + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + +def process_poses(poses, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False): + """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + + cam_params = [[float(x) for x in pose] for pose in poses] + if return_poses: + return cam_params + else: + cam_params = [Camera(cam_param) for cam_param in cam_params] + + sample_wh_ratio = width / height + pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed + + if pose_wh_ratio > sample_wh_ratio: + resized_ori_w = height * pose_wh_ratio + for cam_param in cam_params: + cam_param.fx = resized_ori_w * cam_param.fx / width + else: + resized_ori_h = width / pose_wh_ratio + for cam_param in cam_params: + cam_param.fy = resized_ori_h * cam_param.fy / height + + intrinsic = np.asarray([[cam_param.fx * width, + cam_param.fy * height, + cam_param.cx * width, + cam_param.cy * height] + for cam_param in cam_params], dtype=np.float32) + + K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] + c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere + c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] + plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W + plucker_embedding = plucker_embedding[None] + plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] + return plucker_embedding + +class WanVideoFunCameraEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "poses": ("CAMERACTRL_POSES", ), + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Strength of the camera motion"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply camera motion"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply camera motion"}), + }, + # "optional": { + # "fun_ref_image": ("LATENT", {"tooltip": "Reference latent for the Fun 1.1 -model"}), + # } + } + + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, poses, width, height, strength, start_percent, end_percent, fun_ref_image=None): + num_frames = len(poses) + + control_camera_video = process_poses(poses, width, height) + control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0) + print("control_camera_video.shape", control_camera_video.shape) + + # Rearrange dimensions + # Concatenate and transpose dimensions + control_camera_latents = torch.concat( + [ + torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), + control_camera_video[:, :, 1:] + ], dim=2 + ).transpose(1, 2) + + # Reshape, transpose, and view into desired shape + b, f, c, h, w = control_camera_latents.shape + control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) + control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) + print("control_camera_latents.shape", control_camera_latents.shape) + + vae_stride = (4, 8, 8) + + target_shape = (16, (num_frames - 1) // vae_stride[0] + 1, + height // vae_stride[1], + width // vae_stride[2]) + + embeds = { + "target_shape": target_shape, + "num_frames": num_frames, + "control_embeds": { + "control_camera_latents": control_camera_latents * strength, + "control_camera_start_percent": start_percent, + "control_camera_end_percent": end_percent, + "fun_ref_image": fun_ref_image["samples"][:,:, 0] if fun_ref_image is not None else None, + } + } + + return (embeds,) + +NODE_CLASS_MAPPINGS = { + "WanVideoFunCameraEmbeds": WanVideoFunCameraEmbeds, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoFunCameraEmbeds": "WanVideo FunCamera Embeds", + } diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/latent_preview.py b/custom_nodes/ComfyUI_WanVideoWrapper/latent_preview.py new file mode 100644 index 0000000000000000000000000000000000000000..5d36d2ca8d986928f1895c8b0be60ddddb6ca6c7 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/latent_preview.py @@ -0,0 +1,229 @@ +import torch +from PIL import Image +from comfy.cli_args import args, LatentPreviewMethod +from comfy.taesd.taesd import TAESD +import comfy.model_management +import folder_paths +import comfy.utils +import logging +import os + +from .taehv import TAEHV + +MAX_PREVIEW_RESOLUTION = args.preview_size + +def preview_to_image(latent_image): + print("latent_image shape: ", latent_image.shape)#torch.Size([60, 104, 3]) + latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1 + .mul(0xFF) # to 0..255 + ) + if comfy.model_management.directml_enabled: + latents_ubyte = latents_ubyte.to(dtype=torch.uint8) + latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) + + return Image.fromarray(latents_ubyte.numpy()) + +class LatentPreviewer: + def decode_latent_to_preview(self, x0): + pass + + def decode_latent_to_preview_image(self, preview_format, x0): + preview_image = self.decode_latent_to_preview(x0) + return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) + +class TAESDPreviewerImpl(LatentPreviewer): + def __init__(self, taesd): + self.taesd = taesd + + # def decode_latent_to_preview(self, x0): + # #x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2) + # print("x0 shape: ", x0.shape) #torch.Size([5, 16, 60, 104]) + # x0 = x0.unsqueeze(0) + # print("x0 shape: ", x0.shape) #torch.Size([5, 16, 60, 104]) + # x_sample = self.taesd.decode_video(x0, parallel=False)[0].permute(0, 2, 3, 1)[0] + # print("x_sample shape: ", x_sample.shape) + # return preview_to_image(x_sample) + + +class Latent2RGBPreviewer(LatentPreviewer): + def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None): + self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1) + self.latent_rgb_factors_bias = None + if latent_rgb_factors_bias is not None: + self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu") + + def decode_latent_to_preview(self, x0): + self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) + if self.latent_rgb_factors_bias is not None: + self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device) + + if x0.ndim == 5: + x0 = x0[0, :, 0] + else: + x0 = x0[0] + + latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias) + # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors + + return preview_to_image(latent_image) + + +def get_previewer(device, latent_format): + previewer = None + method = args.preview_method + if method != LatentPreviewMethod.NoPreviews: + # TODO previewer methods + + if method == LatentPreviewMethod.Auto: + method = LatentPreviewMethod.Latent2RGB + + if method == LatentPreviewMethod.TAESD: + taehv_path = os.path.join(folder_paths.models_dir, "vae_approx", "taew2_1.safetensors") + if not os.path.exists(taehv_path): + raise RuntimeError(f"Could not find {taehv_path}") + taew_sd = comfy.utils.load_torch_file(taehv_path) + taesd = TAEHV(taew_sd).to(device) + previewer = TAESDPreviewerImpl(taesd) + previewer = WrappedPreviewer(previewer, rate=16) + + if previewer is None: + if latent_format.latent_rgb_factors is not None: + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias) + previewer = WrappedPreviewer(previewer, rate=4) + return previewer + +def prepare_callback(model, steps, x0_output_dict=None): + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = get_previewer(model.load_device, model.model.latent_format) + print("previewer: ", previewer) + + pbar = comfy.utils.ProgressBar(steps) + def callback(step, x0, x, total_steps): + if x0_output_dict is not None: + x0_output_dict["x0"] = x0 + + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + return callback + +#borrowed VideoHelperSuite https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/blob/main/videohelpersuite/latent_preview.py +import server +from threading import Thread +import torch.nn.functional as F +import io +import time +import struct +from importlib.util import find_spec +serv = server.PromptServer.instance + +class WrappedPreviewer(LatentPreviewer): + def __init__(self, previewer, rate=16): + self.first_preview = True + self.last_time = 0 + self.c_index = 0 + self.rate = rate + self.swarmui_env = find_spec("SwarmComfyCommon") is not None + if self.swarmui_env: + print("previewer: SwarmUI output enabled") + if hasattr(previewer, 'taesd'): + self.taesd = previewer.taesd + elif hasattr(previewer, 'latent_rgb_factors'): + self.latent_rgb_factors = previewer.latent_rgb_factors + self.latent_rgb_factors_bias = previewer.latent_rgb_factors_bias + else: + raise Exception('Unsupported preview type for VHS animated previews') + + def decode_latent_to_preview_image(self, preview_format, x0): + if x0.ndim == 5: + #Keep batch major + x0 = x0.movedim(2,1) + x0 = x0.reshape((-1,)+x0.shape[-3:]) + num_images = x0.size(0) + new_time = time.time() + num_previews = int((new_time - self.last_time) * self.rate) + self.last_time = self.last_time + num_previews/self.rate + if num_previews > num_images: + num_previews = num_images + elif num_previews <= 0: + return None + if self.first_preview: + self.first_preview = False + serv.send_sync('VHS_latentpreview', {'length':num_images, 'rate': self.rate}) + self.last_time = new_time + 1/self.rate + if self.c_index + num_previews > num_images: + x0 = x0.roll(-self.c_index, 0)[:num_previews] + else: + x0 = x0[self.c_index:self.c_index + num_previews] + Thread(target=self.process_previews, args=(x0, self.c_index, + num_images)).run() + self.c_index = (self.c_index + num_previews) % num_images + return None + def process_previews(self, image_tensor, ind, leng): + max_size = 256 + image_tensor = self.decode_latent_to_preview(image_tensor) + if image_tensor.size(1) > max_size or image_tensor.size(2) > max_size: + image_tensor = image_tensor.movedim(-1,0) + if image_tensor.size(2) < image_tensor.size(3): + height = (max_size * image_tensor.size(2)) // image_tensor.size(3) + image_tensor = F.interpolate(image_tensor, (height,max_size), mode='bilinear') + else: + width = (max_size * image_tensor.size(3)) // image_tensor.size(2) + image_tensor = F.interpolate(image_tensor, (max_size, width), mode='bilinear') + image_tensor = image_tensor.movedim(0,-1) + previews_ubyte = (image_tensor.clamp(0, 1) + .mul(0xFF) # to 0..255 + ).to(device="cpu", dtype=torch.uint8) + + # Send VHS preview + for preview in previews_ubyte: + i = Image.fromarray(preview.numpy()) + message = io.BytesIO() + message.write((1).to_bytes(length=4, byteorder='big')*2) + message.write(ind.to_bytes(length=4, byteorder='big')) + i.save(message, format="JPEG", quality=95, compress_level=1) + #NOTE: send sync already uses call_soon_threadsafe + serv.send_sync(server.BinaryEventTypes.PREVIEW_IMAGE, + message.getvalue(), serv.client_id) + if self.rate == 16: + ind = (ind + 1) % ((leng-1) * 4 - 1) + else: + ind = (ind + 1) % leng + + # Send SwarmUI preview if detected + if self.swarmui_env: + images = [Image.fromarray(preview.numpy()) for preview in previews_ubyte] + message = io.BytesIO() + header = struct.pack(">I", 3) + message.write(header) + images[0].save( + message, + save_all=True, + duration=int(1000.0/self.rate), + append_images=images[1 : len(images)], + lossless=False, + quality=80, + method=0, + format="WEBP", + ) + message.seek(0) + preview_bytes = message.getvalue() + serv.send_sync(1, preview_bytes, sid=serv.client_id) + def decode_latent_to_preview(self, x0): + if hasattr(self, 'taesd'): + x0 = x0.unsqueeze(0) + x_sample = self.taesd.decode_video(x0, parallel=False, show_progress_bar=False)[0].permute(0, 2, 3, 1) + return x_sample + else: + self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) + if self.latent_rgb_factors_bias is not None: + self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device) + latent_image = F.linear(x0.movedim(1, -1), self.latent_rgb_factors, + bias=self.latent_rgb_factors_bias) + latent_image = (latent_image + 1.0) / 2.0 + return latent_image + diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..5ccc5b129ddcf650e16c3779edf1e3f13b7bd996 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/nodes.py @@ -0,0 +1,4082 @@ +import os +import torch +import torch.nn.functional as F +import gc +from .utils import log, print_memory, apply_lora, clip_encode_image_tiled, fourier_filter +import numpy as np +import math +from tqdm import tqdm + +from .wanvideo.modules.clip import CLIPModel +from .wanvideo.modules.model import WanModel, rope_params +from .wanvideo.modules.t5 import T5EncoderModel +from .wanvideo.utils.fm_solvers import (FlowDPMSolverMultistepScheduler, + get_sampling_sigmas, retrieve_timesteps) +from .wanvideo.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler +from .wanvideo.utils.basic_flowmatch import FlowMatchScheduler +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, DEISMultistepScheduler +from .wanvideo.utils.scheduling_flow_match_lcm import FlowMatchLCMScheduler + +from .enhance_a_video.globals import enable_enhance, disable_enhance, set_enhance_weight, set_num_frames +from .taehv import TAEHV + +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device +from einops import rearrange + +import folder_paths +import comfy.model_management as mm +from comfy.utils import load_torch_file, ProgressBar, common_upscale +import comfy.model_base +import comfy.latent_formats +from comfy.clip_vision import clip_preprocess, ClipVisionModel +from comfy.sd import load_lora_for_models +from comfy.cli_args import args, LatentPreviewMethod + +script_directory = os.path.dirname(os.path.abspath(__file__)) + +def add_noise_to_reference_video(image, ratio=None): + sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio + image_noise = torch.randn_like(image) * sigma[:, None, None, None] + image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise) + image = image + image_noise + return image + +def optimized_scale(positive_flat, negative_flat): + + # Calculate dot production + dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) + + # Squared norm of uncondition + squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8 + + # st_star = v_cond^T * v_uncond / ||v_uncond||^2 + st_star = dot_product / squared_norm + + return st_star + +class WanVideoBlockSwap: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "blocks_to_swap": ("INT", {"default": 20, "min": 0, "max": 40, "step": 1, "tooltip": "Number of transformer blocks to swap, the 14B model has 40, while the 1.3B model has 30 blocks"}), + "offload_img_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload img_emb to offload_device"}), + "offload_txt_emb": ("BOOLEAN", {"default": False, "tooltip": "Offload time_emb to offload_device"}), + }, + "optional": { + "use_non_blocking": ("BOOLEAN", {"default": True, "tooltip": "Use non-blocking memory transfer for offloading, reserves more RAM but is faster"}), + "vace_blocks_to_swap": ("INT", {"default": 0, "min": 0, "max": 15, "step": 1, "tooltip": "Number of VACE blocks to swap, the VACE model has 15 blocks"}), + }, + } + RETURN_TYPES = ("BLOCKSWAPARGS",) + RETURN_NAMES = ("block_swap_args",) + FUNCTION = "setargs" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Settings for block swapping, reduces VRAM use by swapping blocks to CPU memory" + + def setargs(self, **kwargs): + return (kwargs, ) + +class WanVideoVRAMManagement: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "offload_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Percentage of parameters to offload"}), + }, + } + RETURN_TYPES = ("VRAM_MANAGEMENTARGS",) + RETURN_NAMES = ("vram_management_args",) + FUNCTION = "setargs" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Alternative offloading method from DiffSynth-Studio, more aggressive in reducing memory use than block swapping, but can be slower" + + def setargs(self, **kwargs): + return (kwargs, ) + +class WanVideoTeaCache: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "rel_l1_thresh": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.001, + "tooltip": "Higher values will make TeaCache more aggressive, faster, but may cause artifacts. Good value range for 1.3B: 0.05 - 0.08, for other models 0.15-0.30"}), + "start_step": ("INT", {"default": 1, "min": 0, "max": 9999, "step": 1, "tooltip": "Start percentage of the steps to apply TeaCache"}), + "end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "End steps to apply TeaCache"}), + "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}), + "use_coefficients": ("BOOLEAN", {"default": True, "tooltip": "Use calculated coefficients for more accuracy. When enabled therel_l1_thresh should be about 10 times higher than without"}), + }, + "optional": { + "mode": (["e", "e0"], {"default": "e", "tooltip": "Choice between using e (time embeds, default) or e0 (modulated time embeds)"}), + }, + } + RETURN_TYPES = ("CACHEARGS",) + RETURN_NAMES = ("cache_args",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = """ +Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and +applying it instead of doing the step. Best results are achieved by choosing the +appropriate coefficients for the model. Early steps should never be skipped, with too +aggressive values this can happen and the motion suffers. Starting later can help with that too. +When NOT using coefficients, the threshold value should be +about 10 times smaller than the value used with coefficients. + +Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1: + + +
    ++-------------------+--------+---------+--------+
+|       Model       |  Low   | Medium  |  High  |
++-------------------+--------+---------+--------+
+| Wan2.1 t2v 1.3B  |  0.05  |  0.07   |  0.08  |
+| Wan2.1 t2v 14B   |  0.14  |  0.15   |  0.20  |
+| Wan2.1 i2v 480P  |  0.13  |  0.19   |  0.26  |
+| Wan2.1 i2v 720P  |  0.18  |  0.20   |  0.30  |
++-------------------+--------+---------+--------+
+
    +""" + EXPERIMENTAL = True + + def process(self, rel_l1_thresh, start_step, end_step, cache_device, use_coefficients, mode="e"): + if cache_device == "main_device": + cache_device = mm.get_torch_device() + else: + cache_device = mm.unet_offload_device() + cache_args = { + "cache_type": "TeaCache", + "rel_l1_thresh": rel_l1_thresh, + "start_step": start_step, + "end_step": end_step, + "cache_device": cache_device, + "use_coefficients": use_coefficients, + "mode": mode, + } + return (cache_args,) + +class WanVideoMagCache: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "magcache_thresh": ("FLOAT", {"default": 0.02, "min": 0.0, "max": 0.3, "step": 0.001, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."}), + "magcache_K": ("INT", {"default": 4, "min": 0, "max": 6, "step": 1, "tooltip": "The maxium skip steps of MagCache."}), + "start_step": ("INT", {"default": 1, "min": 0, "max": 9999, "step": 1, "tooltip": "Step to start applying MagCache"}), + "end_step": ("INT", {"default": -1, "min": -1, "max": 9999, "step": 1, "tooltip": "Step to end applying MagCache"}), + "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}), + }, + } + RETURN_TYPES = ("CACHEARGS",) + RETURN_NAMES = ("cache_args",) + FUNCTION = "setargs" + CATEGORY = "WanVideoWrapper" + EXPERIMENTAL = True + DESCRIPTION = "MagCache for WanVideoWrapper, source https://github.com/Zehong-Ma/MagCache" + + def setargs(self, magcache_thresh, magcache_K, start_step, end_step, cache_device): + if cache_device == "main_device": + cache_device = mm.get_torch_device() + else: + cache_device = mm.unet_offload_device() + + cache_args = { + "cache_type": "MagCache", + "magcache_thresh": magcache_thresh, + "magcache_K": magcache_K, + "start_step": start_step, + "end_step": end_step, + "cache_device": cache_device, + } + return (cache_args,) + + +class WanVideoModel(comfy.model_base.BaseModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.pipeline = {} + + def __getitem__(self, k): + return self.pipeline[k] + + def __setitem__(self, k, v): + self.pipeline[k] = v + +try: + from comfy.latent_formats import Wan21 + latent_format = Wan21 +except: #for backwards compatibility + log.warning("Wan21 latent format not found, update ComfyUI for better livepreview") + from comfy.latent_formats import HunyuanVideo + latent_format = HunyuanVideo + +class WanVideoModelConfig: + def __init__(self, dtype): + self.unet_config = {} + self.unet_extra_config = {} + self.latent_format = latent_format + self.latent_format.latent_channels = 16 + self.manual_cast_dtype = dtype + self.sampling_settings = {"multiplier": 1.0} + self.memory_usage_factor = 2.0 + self.unet_config["disable_unet_model_creation"] = True + +def filter_state_dict_by_blocks(state_dict, blocks_mapping, layer_filter=[]): + filtered_dict = {} + + if isinstance(layer_filter, str): + layer_filters = [layer_filter] if layer_filter else [] + else: + # Filter out empty strings + layer_filters = [f for f in layer_filter if f] if layer_filter else [] + + #print("layer_filter: ", layer_filters) + + for key in state_dict: + if not any(filter_str in key for filter_str in layer_filters): + if 'blocks.' in key: + + block_pattern = key.split('diffusion_model.')[1].split('.', 2)[0:2] + block_key = f'{block_pattern[0]}.{block_pattern[1]}.' + + if block_key in blocks_mapping: + filtered_dict[key] = state_dict[key] + else: + filtered_dict[key] = state_dict[key] + + for key in filtered_dict: + print(key) + + #from safetensors.torch import save_file + #save_file(filtered_dict, "filtered_state_dict_2.safetensors") + + return filtered_dict + +def standardize_lora_key_format(lora_sd): + new_sd = {} + for k, v in lora_sd.items(): + # Diffusers format + if k.startswith('transformer.'): + k = k.replace('transformer.', 'diffusion_model.') + if k.startswith('pipe.dit.'): #unianimate-dit/diffsynth + k = k.replace('pipe.dit.', 'diffusion_model.') + + # Fun LoRA format + if k.startswith('lora_unet__'): + # Split into main path and weight type parts + parts = k.split('.') + main_part = parts[0] # e.g. lora_unet__blocks_0_cross_attn_k + weight_type = '.'.join(parts[1:]) if len(parts) > 1 else None # e.g. lora_down.weight + + # Process the main part - convert from underscore to dot format + if 'blocks_' in main_part: + # Extract components + components = main_part[len('lora_unet__'):].split('_') + + # Start with diffusion_model + new_key = "diffusion_model" + + # Add blocks.N + if components[0] == 'blocks': + new_key += f".blocks.{components[1]}" + + # Handle different module types + idx = 2 + if idx < len(components): + if components[idx] == 'self' and idx+1 < len(components) and components[idx+1] == 'attn': + new_key += ".self_attn" + idx += 2 + elif components[idx] == 'cross' and idx+1 < len(components) and components[idx+1] == 'attn': + new_key += ".cross_attn" + idx += 2 + elif components[idx] == 'ffn': + new_key += ".ffn" + idx += 1 + + # Add the component (k, q, v, o) and handle img suffix + if idx < len(components): + component = components[idx] + idx += 1 + + # Check for img suffix + if idx < len(components) and components[idx] == 'img': + component += '_img' + idx += 1 + + new_key += f".{component}" + + # Handle weight type - this is the critical fix + if weight_type: + if weight_type == 'alpha': + new_key += '.alpha' + elif weight_type == 'lora_down.weight' or weight_type == 'lora_down': + new_key += '.lora_A.weight' + elif weight_type == 'lora_up.weight' or weight_type == 'lora_up': + new_key += '.lora_B.weight' + else: + # Keep original weight type if not matching our patterns + new_key += f'.{weight_type}' + # Add .weight suffix if missing + if not new_key.endswith('.weight'): + new_key += '.weight' + + k = new_key + else: + # For other lora_unet__ formats (head, embeddings, etc.) + new_key = main_part.replace('lora_unet__', 'diffusion_model.') + + # Fix specific component naming patterns + new_key = new_key.replace('_self_attn', '.self_attn') + new_key = new_key.replace('_cross_attn', '.cross_attn') + new_key = new_key.replace('_ffn', '.ffn') + new_key = new_key.replace('blocks_', 'blocks.') + new_key = new_key.replace('head_head', 'head.head') + new_key = new_key.replace('img_emb', 'img_emb') + new_key = new_key.replace('text_embedding', 'text.embedding') + new_key = new_key.replace('time_embedding', 'time.embedding') + new_key = new_key.replace('time_projection', 'time.projection') + + # Replace remaining underscores with dots, carefully + parts = new_key.split('.') + final_parts = [] + for part in parts: + if part in ['img_emb', 'self_attn', 'cross_attn']: + final_parts.append(part) # Keep these intact + else: + final_parts.append(part.replace('_', '.')) + new_key = '.'.join(final_parts) + + # Handle weight type + if weight_type: + if weight_type == 'alpha': + new_key += '.alpha' + elif weight_type == 'lora_down.weight' or weight_type == 'lora_down': + new_key += '.lora_A.weight' + elif weight_type == 'lora_up.weight' or weight_type == 'lora_up': + new_key += '.lora_B.weight' + else: + new_key += f'.{weight_type}' + if not new_key.endswith('.weight'): + new_key += '.weight' + + k = new_key + + # Handle special embedded components + special_components = { + 'time.projection': 'time_projection', + 'img.emb': 'img_emb', + 'text.emb': 'text_emb', + 'time.emb': 'time_emb', + } + for old, new in special_components.items(): + if old in k: + k = k.replace(old, new) + + # Fix diffusion.model -> diffusion_model + if k.startswith('diffusion.model.'): + k = k.replace('diffusion.model.', 'diffusion_model.') + + # Finetrainer format + if '.attn1.' in k: + k = k.replace('.attn1.', '.cross_attn.') + k = k.replace('.to_k.', '.k.') + k = k.replace('.to_q.', '.q.') + k = k.replace('.to_v.', '.v.') + k = k.replace('.to_out.0.', '.o.') + elif '.attn2.' in k: + k = k.replace('.attn2.', '.cross_attn.') + k = k.replace('.to_k.', '.k.') + k = k.replace('.to_q.', '.q.') + k = k.replace('.to_v.', '.v.') + k = k.replace('.to_out.0.', '.o.') + + if "img_attn.proj" in k: + k = k.replace("img_attn.proj", "img_attn_proj") + if "img_attn.qkv" in k: + k = k.replace("img_attn.qkv", "img_attn_qkv") + if "txt_attn.proj" in k: + k = k.replace("txt_attn.proj", "txt_attn_proj") + if "txt_attn.qkv" in k: + k = k.replace("txt_attn.qkv", "txt_attn_qkv") + new_sd[k] = v + return new_sd + +class WanVideoEnhanceAVideo: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "weight": ("FLOAT", {"default": 2.0, "min": 0, "max": 100, "step": 0.01, "tooltip": "The feta Weight of the Enhance-A-Video"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the steps to apply Enhance-A-Video"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the steps to apply Enhance-A-Video"}), + }, + } + RETURN_TYPES = ("FETAARGS",) + RETURN_NAMES = ("feta_args",) + FUNCTION = "setargs" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video" + + def setargs(self, **kwargs): + return (kwargs, ) + +class WanVideoLoraSelect: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora": (folder_paths.get_filename_list("loras"), + {"tooltip": "LORA models are expected to be in ComfyUI/models/loras with .safetensors extension"}), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}), + }, + "optional": { + "prev_lora":("WANVIDLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}), + "blocks":("SELECTEDBLOCKS", ), + "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}), + } + } + + RETURN_TYPES = ("WANVIDLORA",) + RETURN_NAMES = ("lora", ) + FUNCTION = "getlorapath" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Select a LoRA model from ComfyUI/models/loras" + + def getlorapath(self, lora, strength, blocks={}, prev_lora=None, low_mem_load=False): + loras_list = [] + + lora = { + "path": folder_paths.get_full_path("loras", lora), + "strength": strength, + "name": lora.split(".")[0], + "blocks": blocks.get("selected_blocks", {}), + "layer_filter": blocks.get("layer_filter", ""), + "low_mem_load": low_mem_load, + } + if prev_lora is not None: + loras_list.extend(prev_lora) + + loras_list.append(lora) + return (loras_list,) + +class WanVideoLoraSelectMulti: + @classmethod + def INPUT_TYPES(s): + lora_files = folder_paths.get_filename_list("loras") + lora_files = ["none"] + lora_files # Add "none" as the first option + return { + "required": { + "lora_0": (lora_files, {"default": "none"}), + "strength_0": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}), + "lora_1": (lora_files, {"default": "none"}), + "strength_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}), + "lora_2": (lora_files, {"default": "none"}), + "strength_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}), + "lora_3": (lora_files, {"default": "none"}), + "strength_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}), + "lora_4": (lora_files, {"default": "none"}), + "strength_4": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}), + }, + "optional": { + "prev_lora":("WANVIDLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}), + "blocks":("SELECTEDBLOCKS", ), + "low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load the LORA model with less VRAM usage, slower loading"}), + } + } + + RETURN_TYPES = ("WANVIDLORA",) + RETURN_NAMES = ("lora", ) + FUNCTION = "getlorapath" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Select a LoRA model from ComfyUI/models/loras" + + def getlorapath(self, lora_0, strength_0, lora_1, strength_1, lora_2, strength_2, + lora_3, strength_3, lora_4, strength_4, blocks={}, prev_lora=None, + low_mem_load=False): + loras_list = [] + + if prev_lora is not None: + loras_list.extend(prev_lora) + + # Process each LoRA + lora_inputs = [ + (lora_0, strength_0), + (lora_1, strength_1), + (lora_2, strength_2), + (lora_3, strength_3), + (lora_4, strength_4) + ] + + for lora_name, strength in lora_inputs: + # Skip if the LoRA is empty + if not lora_name or lora_name == "none": + continue + + lora = { + "path": folder_paths.get_full_path("loras", lora_name), + "strength": strength, + "name": lora_name.split(".")[0], + "blocks": blocks.get("selected_blocks", {}), + "layer_filter": blocks.get("layer_filter", ""), + "low_mem_load": low_mem_load, + } + + loras_list.append(lora) + + return (loras_list,) + +class WanVideoVACEModelSelect: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "vace_model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' VACE model to use when not using model that has it included"}), + }, + } + + RETURN_TYPES = ("VACEPATH",) + RETURN_NAMES = ("vace_model", ) + FUNCTION = "getvacepath" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "VACE model to use when not using model that has it included, loaded from 'ComfyUI/models/diffusion_models'" + + def getvacepath(self, vace_model): + vace_model = { + "path": folder_paths.get_full_path("diffusion_models", vace_model), + } + return (vace_model,) + +class WanVideoLoraBlockEdit: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + arg_dict = {} + argument = ("BOOLEAN", {"default": True}) + + for i in range(40): + arg_dict["blocks.{}.".format(i)] = argument + + return {"required": arg_dict, "optional": {"layer_filter": ("STRING", {"default": "", "multiline": True})}} + + RETURN_TYPES = ("SELECTEDBLOCKS", ) + RETURN_NAMES = ("blocks", ) + OUTPUT_TOOLTIPS = ("The modified lora model",) + FUNCTION = "select" + + CATEGORY = "WanVideoWrapper" + + def select(self, layer_filter=[], **kwargs): + selected_blocks = {k: v for k, v in kwargs.items() if v is True and isinstance(v, bool)} + print("Selected blocks LoRA: ", selected_blocks) + selected = { + "selected_blocks": selected_blocks, + "layer_filter": [x.strip() for x in layer_filter.split(",")] + } + return (selected,) + +#region Model loading +class WanVideoModelLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}), + + "base_precision": (["fp32", "bf16", "fp16", "fp16_fast"], {"default": "bf16"}), + "quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2', 'fp8_e4m3fn_fast_no_ffn'], {"default": 'disabled', "tooltip": "optional quantization method"}), + "load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}), + }, + "optional": { + "attention_mode": ([ + "sdpa", + "flash_attn_2", + "flash_attn_3", + "sageattn", + "flex_attention", + #"spargeattn", needs tuning + #"spargeattn_tune", + ], {"default": "sdpa"}), + "compile_args": ("WANCOMPILEARGS", ), + "block_swap_args": ("BLOCKSWAPARGS", ), + "lora": ("WANVIDLORA", {"default": None}), + "vram_management_args": ("VRAM_MANAGEMENTARGS", {"default": None, "tooltip": "Alternative offloading method from DiffSynth-Studio, more aggressive in reducing memory use than block swapping, but can be slower"}), + "vace_model": ("VACEPATH", {"default": None, "tooltip": "VACE model to use when not using model that has it included"}), + "fantasytalking_model": ("FANTASYTALKINGMODEL", {"default": None, "tooltip": "FantasyTalking model https://github.com/Fantasy-AMAP"}), + } + } + + RETURN_TYPES = ("WANVIDEOMODEL",) + RETURN_NAMES = ("model", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + + def loadmodel(self, model, base_precision, load_device, quantization, + compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None, vram_management_args=None, vace_model=None, fantasytalking_model=None): + assert not (vram_management_args is not None and block_swap_args is not None), "Can't use both block_swap_args and vram_management_args at the same time" + lora_low_mem_load = False + if lora is not None: + for l in lora: + lora_low_mem_load = l.get("low_mem_load") if lora is not None else False + + transformer = None + mm.unload_all_models() + mm.cleanup_models() + mm.soft_empty_cache() + manual_offloading = True + if "sage" in attention_mode: + try: + from sageattention import sageattn + except Exception as e: + raise ValueError(f"Can't import SageAttention: {str(e)}") + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + + manual_offloading = True + transformer_load_device = device if load_device == "main_device" else offload_device + + base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision] + + if base_precision == "fp16_fast": + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + torch.backends.cuda.matmul.allow_fp16_accumulation = True + else: + raise ValueError("torch.backends.cuda.matmul.allow_fp16_accumulation is not available in this version of torch, requires torch 2.7.0.dev2025 02 26 nightly minimum currently") + else: + try: + if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): + torch.backends.cuda.matmul.allow_fp16_accumulation = False + except: + pass + + model_path = folder_paths.get_full_path_or_raise("diffusion_models", model) + + sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True) + + + if "vace_blocks.0.after_proj.weight" in sd and not "patch_embedding.weight" in sd: + raise ValueError("You are attempting to load a VACE module as a WanVideo model, instead you should use the vace_model input and matching T2V base model") + + if vace_model is not None: + vace_sd = load_torch_file(vace_model["path"], device=transformer_load_device, safe_load=True) + sd.update(vace_sd) + + first_key = next(iter(sd)) + if first_key.startswith("model.diffusion_model."): + new_sd = {} + for key, value in sd.items(): + new_key = key.replace("model.diffusion_model.", "", 1) + new_sd[new_key] = value + sd = new_sd + elif first_key.startswith("model."): + new_sd = {} + for key, value in sd.items(): + new_key = key.replace("model.", "", 1) + new_sd[new_key] = value + sd = new_sd + if not "patch_embedding.weight" in sd: + raise ValueError("Invalid WanVideo model selected") + dim = sd["patch_embedding.weight"].shape[0] + in_channels = sd["patch_embedding.weight"].shape[1] + log.info(f"Detected model in_channels: {in_channels}") + ffn_dim = sd["blocks.0.ffn.0.bias"].shape[0] + + if not "text_embedding.0.weight" in sd: + model_type = "no_cross_attn" #minimaxremover + elif "model_type.Wan2_1-FLF2V-14B-720P" in sd or "img_emb.emb_pos" in sd or "flf2v" in model.lower(): + model_type = "fl2v" + elif in_channels in [36, 48]: + model_type = "i2v" + elif in_channels == 16: + model_type = "t2v" + elif "control_adapter.conv.weight" in sd: + model_type = "t2v" + + num_heads = 40 if dim == 5120 else 12 + num_layers = 40 if dim == 5120 else 30 + + vace_layers, vace_in_dim = None, None + if "vace_blocks.0.after_proj.weight" in sd: + if in_channels != 16: + raise ValueError("VACE only works properly with T2V models.") + model_type = "t2v" + if dim == 5120: + vace_layers = [0, 5, 10, 15, 20, 25, 30, 35] + else: + vace_layers = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28] + vace_in_dim = 96 + + log.info(f"Model type: {model_type}, num_heads: {num_heads}, num_layers: {num_layers}") + + teacache_coefficients_map = { + "1_3B": { + "e": [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01], + "e0": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], + }, + "14B": { + "e": [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404], + "e0": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], + }, + "i2v_480": { + "e": [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01], + "e0": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], + }, + "i2v_720":{ + "e": [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683], + "e0": [8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], + }, + } + + magcache_ratios_map = { + "1_3B": np.array([1.0]*2+[1.0124, 1.02213, 1.00166, 1.0041, 0.99791, 1.00061, 0.99682, 0.99762, 0.99634, 0.99685, 0.99567, 0.99586, 0.99416, 0.99422, 0.99578, 0.99575, 0.9957, 0.99563, 0.99511, 0.99506, 0.99535, 0.99531, 0.99552, 0.99549, 0.99541, 0.99539, 0.9954, 0.99536, 0.99489, 0.99485, 0.99518, 0.99514, 0.99484, 0.99478, 0.99481, 0.99479, 0.99415, 0.99413, 0.99419, 0.99416, 0.99396, 0.99393, 0.99388, 0.99386, 0.99349, 0.99349, 0.99309, 0.99304, 0.9927, 0.9927, 0.99228, 0.99226, 0.99171, 0.9917, 0.99137, 0.99135, 0.99068, 0.99063, 0.99005, 0.99003, 0.98944, 0.98942, 0.98849, 0.98849, 0.98758, 0.98757, 0.98644, 0.98643, 0.98504, 0.98503, 0.9836, 0.98359, 0.98202, 0.98201, 0.97977, 0.97978, 0.97717, 0.97718, 0.9741, 0.97411, 0.97003, 0.97002, 0.96538, 0.96541, 0.9593, 0.95933, 0.95086, 0.95089, 0.94013, 0.94019, 0.92402, 0.92414, 0.90241, 0.9026, 0.86821, 0.86868, 0.81838, 0.81939]), + "14B": np.array([1.0]*2+[1.02504, 1.03017, 1.00025, 1.00251, 0.9985, 0.99962, 0.99779, 0.99771, 0.9966, 0.99658, 0.99482, 0.99476, 0.99467, 0.99451, 0.99664, 0.99656, 0.99434, 0.99431, 0.99533, 0.99545, 0.99468, 0.99465, 0.99438, 0.99434, 0.99516, 0.99517, 0.99384, 0.9938, 0.99404, 0.99401, 0.99517, 0.99516, 0.99409, 0.99408, 0.99428, 0.99426, 0.99347, 0.99343, 0.99418, 0.99416, 0.99271, 0.99269, 0.99313, 0.99311, 0.99215, 0.99215, 0.99218, 0.99215, 0.99216, 0.99217, 0.99163, 0.99161, 0.99138, 0.99135, 0.98982, 0.9898, 0.98996, 0.98995, 0.9887, 0.98866, 0.98772, 0.9877, 0.98767, 0.98765, 0.98573, 0.9857, 0.98501, 0.98498, 0.9838, 0.98376, 0.98177, 0.98173, 0.98037, 0.98035, 0.97678, 0.97677, 0.97546, 0.97543, 0.97184, 0.97183, 0.96711, 0.96708, 0.96349, 0.96345, 0.95629, 0.95625, 0.94926, 0.94929, 0.93964, 0.93961, 0.92511, 0.92504, 0.90693, 0.90678, 0.8796, 0.87945, 0.86111, 0.86189]), + "i2v_480": np.array([1.0]*2+[0.98783, 0.98993, 0.97559, 0.97593, 0.98311, 0.98319, 0.98202, 0.98225, 0.9888, 0.98878, 0.98762, 0.98759, 0.98957, 0.98971, 0.99052, 0.99043, 0.99383, 0.99384, 0.98857, 0.9886, 0.99065, 0.99068, 0.98845, 0.98847, 0.99057, 0.99057, 0.98957, 0.98961, 0.98601, 0.9861, 0.98823, 0.98823, 0.98756, 0.98759, 0.98808, 0.98814, 0.98721, 0.98724, 0.98571, 0.98572, 0.98543, 0.98544, 0.98157, 0.98165, 0.98411, 0.98413, 0.97952, 0.97953, 0.98149, 0.9815, 0.9774, 0.97742, 0.97825, 0.97826, 0.97355, 0.97361, 0.97085, 0.97087, 0.97056, 0.97055, 0.96588, 0.96587, 0.96113, 0.96124, 0.9567, 0.95681, 0.94961, 0.94969, 0.93973, 0.93988, 0.93217, 0.93224, 0.91878, 0.91896, 0.90955, 0.90954, 0.92617, 0.92616]), + "i2v_720": np.array([1.0]*2+[0.99428, 0.99498, 0.98588, 0.98621, 0.98273, 0.98281, 0.99018, 0.99023, 0.98911, 0.98917, 0.98646, 0.98652, 0.99454, 0.99456, 0.9891, 0.98909, 0.99124, 0.99127, 0.99102, 0.99103, 0.99215, 0.99212, 0.99515, 0.99515, 0.99576, 0.99572, 0.99068, 0.99072, 0.99097, 0.99097, 0.99166, 0.99169, 0.99041, 0.99042, 0.99201, 0.99198, 0.99101, 0.99101, 0.98599, 0.98603, 0.98845, 0.98844, 0.98848, 0.98851, 0.98862, 0.98857, 0.98718, 0.98719, 0.98497, 0.98497, 0.98264, 0.98263, 0.98389, 0.98393, 0.97938, 0.9794, 0.97535, 0.97536, 0.97498, 0.97499, 0.973, 0.97301, 0.96827, 0.96828, 0.96261, 0.96263, 0.95335, 0.9534, 0.94649, 0.94655, 0.93397, 0.93414, 0.91636, 0.9165, 0.89088, 0.89109, 0.8679, 0.86768]), + } + + model_variant = "14B" #default to this + if model_type == "i2v" or model_type == "fl2v": + if "480" in model or "fun" in model.lower() or "a2" in model.lower() or "540" in model: #just a guess for the Fun model for now... + model_variant = "i2v_480" + elif "720" in model: + model_variant = "i2v_720" + elif model_type == "t2v": + model_variant = "14B" + + if dim == 1536: + model_variant = "1_3B" + log.info(f"Model variant detected: {model_variant}") + + TRANSFORMER_CONFIG= { + "dim": dim, + "ffn_dim": ffn_dim, + "eps": 1e-06, + "freq_dim": 256, + "in_dim": in_channels, + "model_type": model_type, + "out_dim": 16, + "text_len": 512, + "num_heads": num_heads, + "num_layers": num_layers, + "attention_mode": attention_mode, + "main_device": device, + "offload_device": offload_device, + "teacache_coefficients": teacache_coefficients_map[model_variant], + "magcache_ratios": magcache_ratios_map[model_variant], + "vace_layers": vace_layers, + "vace_in_dim": vace_in_dim, + "inject_sample_info": True if "fps_embedding.weight" in sd else False, + "add_ref_conv": True if "ref_conv.weight" in sd else False, + "in_dim_ref_conv": sd["ref_conv.weight"].shape[1] if "ref_conv.weight" in sd else None, + "add_control_adapter": True if "control_adapter.conv.weight" in sd else False, + } + + with init_empty_weights(): + transformer = WanModel(**TRANSFORMER_CONFIG) + transformer.eval() + + #ReCamMaster + if "blocks.0.cam_encoder.weight" in sd: + log.info("ReCamMaster model detected, patching model...") + import torch.nn as nn + for block in transformer.blocks: + block.cam_encoder = nn.Linear(12, dim) + block.projector = nn.Linear(dim, dim) + block.cam_encoder.weight.data.zero_() + block.cam_encoder.bias.data.zero_() + block.projector.weight = nn.Parameter(torch.eye(dim)) + block.projector.bias = nn.Parameter(torch.zeros(dim)) + + # FantasyTalking https://github.com/Fantasy-AMAP + if fantasytalking_model is not None: + log.info("FantasyTalking model detected, patching model...") + context_dim = fantasytalking_model["sd"]["proj_model.proj.weight"].shape[0] + import torch.nn as nn + for block in transformer.blocks: + block.cross_attn.k_proj = nn.Linear(context_dim, dim, bias=False) + block.cross_attn.v_proj = nn.Linear(context_dim, dim, bias=False) + sd.update(fantasytalking_model["sd"]) + + # RealisDance-DiT + if "add_conv_in.weight" in sd: + def zero_module(module): + for p in module.parameters(): + torch.nn.init.zeros_(p) + return module + inner_dim = sd["add_conv_in.weight"].shape[0] + add_cond_in_dim = sd["add_conv_in.weight"].shape[1] + attn_cond_in_dim = sd["attn_conv_in.weight"].shape[1] + transformer.add_conv_in = torch.nn.Conv3d(add_cond_in_dim, inner_dim, kernel_size=transformer.patch_size, stride=transformer.patch_size) + transformer.add_proj = zero_module(torch.nn.Linear(inner_dim, inner_dim)) + transformer.attn_conv_in = torch.nn.Conv3d(attn_cond_in_dim, inner_dim, kernel_size=transformer.patch_size, stride=transformer.patch_size) + + comfy_model = WanVideoModel( + WanVideoModelConfig(base_dtype), + model_type=comfy.model_base.ModelType.FLOW, + device=device, + ) + + if quantization == "disabled": + for k, v in sd.items(): + if isinstance(v, torch.Tensor): + if v.dtype == torch.float8_e4m3fn: + quantization = "fp8_e4m3fn" + break + elif v.dtype == torch.float8_e5m2: + quantization = "fp8_e5m2" + break + + if "fp8_e4m3fn" in quantization: + dtype = torch.float8_e4m3fn + elif quantization == "fp8_e5m2": + dtype = torch.float8_e5m2 + else: + dtype = base_dtype + params_to_keep = {"norm", "head", "bias", "time_in", "vector_in", "patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter", "add"} + #if lora is not None: + # transformer_load_device = device + if not lora_low_mem_load: + log.info("Using accelerate to load and assign model weights to device...") + param_count = sum(1 for _ in transformer.named_parameters()) + for name, param in tqdm(transformer.named_parameters(), + desc=f"Loading transformer parameters to {transformer_load_device}", + total=param_count, + leave=True): + dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype + if "patch_embedding" in name: + dtype_to_use = torch.float32 + set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name]) + comfy_model.diffusion_model = transformer + comfy_model.load_device = transformer_load_device + + patcher = comfy.model_patcher.ModelPatcher(comfy_model, device, offload_device) + patcher.model.is_patched = False + + control_lora = False + + if lora is not None: + for l in lora: + log.info(f"Loading LoRA: {l['name']} with strength: {l['strength']}") + lora_path = l["path"] + lora_strength = l["strength"] + lora_sd = load_torch_file(lora_path, safe_load=True) + if "dwpose_embedding.0.weight" in lora_sd: #unianimate + from .unianimate.nodes import update_transformer + log.info("Unianimate LoRA detected, patching model...") + transformer = update_transformer(transformer, lora_sd) + + lora_sd = standardize_lora_key_format(lora_sd) + if l["blocks"]: + lora_sd = filter_state_dict_by_blocks(lora_sd, l["blocks"], l.get("layer_filter", [])) + + #spacepxl's control LoRA patch + # for key in lora_sd.keys(): + # print(key) + + if "diffusion_model.patch_embedding.lora_A.weight" in lora_sd: + log.info("Control-LoRA detected, patching model...") + control_lora = True + + in_cls = transformer.patch_embedding.__class__ # nn.Conv3d + old_in_dim = transformer.in_dim # 16 + new_in_dim = lora_sd["diffusion_model.patch_embedding.lora_A.weight"].shape[1] + assert new_in_dim == 32 + + new_in = in_cls( + new_in_dim, + transformer.patch_embedding.out_channels, + transformer.patch_embedding.kernel_size, + transformer.patch_embedding.stride, + transformer.patch_embedding.padding, + ).to(device=device, dtype=torch.float32) + + new_in.weight.zero_() + new_in.bias.zero_() + + new_in.weight[:, :old_in_dim].copy_(transformer.patch_embedding.weight) + new_in.bias.copy_(transformer.patch_embedding.bias) + + transformer.patch_embedding = new_in + transformer.expanded_patch_embedding = new_in + transformer.register_to_config(in_dim=new_in_dim) + + patcher, _ = load_lora_for_models(patcher, None, lora_sd, lora_strength, 0) + + del lora_sd + + patcher = apply_lora(patcher, device, transformer_load_device, params_to_keep=params_to_keep, dtype=dtype, base_dtype=base_dtype, state_dict=sd, low_mem_load=lora_low_mem_load) + #patcher.load(device, full_load=True) + patcher.model.is_patched = True + + + + if "fast" in quantization: + from .fp8_optimization import convert_fp8_linear + if quantization == "fp8_e4m3fn_fast_no_ffn": + params_to_keep.update({"ffn"}) + print(params_to_keep) + convert_fp8_linear(patcher.model.diffusion_model, base_dtype, params_to_keep=params_to_keep) + + del sd + + if vram_management_args is not None: + from .diffsynth.vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear + from .wanvideo.modules.model import WanLayerNorm, WanRMSNorm + + total_params_in_model = sum(p.numel() for p in patcher.model.diffusion_model.parameters()) + log.info(f"Total number of parameters in the loaded model: {total_params_in_model}") + + offload_percent = vram_management_args["offload_percent"] + offload_params = int(total_params_in_model * offload_percent) + params_to_keep = total_params_in_model - offload_params + log.info(f"Selected params to offload: {offload_params}") + + enable_vram_management( + patcher.model.diffusion_model, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + WanLayerNorm: AutoWrappedModule, + WanRMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device=offload_device, + onload_dtype=dtype, + onload_device=device, + computation_dtype=base_dtype, + computation_device=device, + ), + max_num_param=params_to_keep, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device=offload_device, + onload_dtype=dtype, + onload_device=offload_device, + computation_dtype=base_dtype, + computation_device=device, + ), + compile_args = compile_args, + ) + + #compile + if compile_args is not None and vram_management_args is None: + torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"] + try: + if hasattr(torch, '_dynamo') and hasattr(torch._dynamo, 'config'): + torch._dynamo.config.recompile_limit = compile_args["dynamo_recompile_limit"] + except Exception as e: + log.warning(f"Could not set recompile_limit: {e}") + if compile_args["compile_transformer_blocks_only"]: + for i, block in enumerate(patcher.model.diffusion_model.blocks): + patcher.model.diffusion_model.blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"]) + if vace_layers is not None: + for i, block in enumerate(patcher.model.diffusion_model.vace_blocks): + patcher.model.diffusion_model.vace_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"]) + else: + patcher.model.diffusion_model = torch.compile(patcher.model.diffusion_model, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"]) + + if load_device == "offload_device" and patcher.model.diffusion_model.device != offload_device: + log.info(f"Moving diffusion model from {patcher.model.diffusion_model.device} to {offload_device}") + patcher.model.diffusion_model.to(offload_device) + gc.collect() + mm.soft_empty_cache() + + patcher.model["dtype"] = base_dtype + patcher.model["base_path"] = model_path + patcher.model["model_name"] = model + patcher.model["manual_offloading"] = manual_offloading + patcher.model["quantization"] = quantization + patcher.model["auto_cpu_offload"] = True if vram_management_args is not None else False + patcher.model["control_lora"] = control_lora + + if 'transformer_options' not in patcher.model_options: + patcher.model_options['transformer_options'] = {} + patcher.model_options["transformer_options"]["block_swap_args"] = block_swap_args + + for model in mm.current_loaded_models: + if model._model() == patcher: + mm.current_loaded_models.remove(model) + + return (patcher,) + +class WanVideoSetBlockSwap: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("WANVIDEOMODEL", ), + "block_swap_args": ("BLOCKSWAPARGS", ), + } + } + + RETURN_TYPES = ("WANVIDEOMODEL",) + RETURN_NAMES = ("model", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + + def loadmodel(self, model, block_swap_args): + + patcher = model.clone() + if 'transformer_options' not in patcher.model_options: + patcher.model_options['transformer_options'] = {} + patcher.model_options["transformer_options"]["block_swap_args"] = block_swap_args + + return (patcher,) + +#region load VAE + +class WanVideoVAELoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}), + }, + "optional": { + "precision": (["fp16", "fp32", "bf16"], + {"default": "bf16"} + ), + } + } + + RETURN_TYPES = ("WANVAE",) + RETURN_NAMES = ("vae", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae'" + + def loadmodel(self, model_name, precision): + from .wanvideo.wan_video_vae import WanVideoVAE + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + #with open(os.path.join(script_directory, 'configs', 'hy_vae_config.json')) as f: + # vae_config = json.load(f) + model_path = folder_paths.get_full_path("vae", model_name) + vae_sd = load_torch_file(model_path, safe_load=True) + + has_model_prefix = any(k.startswith("model.") for k in vae_sd.keys()) + if not has_model_prefix: + vae_sd = {f"model.{k}": v for k, v in vae_sd.items()} + + vae = WanVideoVAE(dtype=dtype) + vae.load_state_dict(vae_sd) + vae.eval() + vae.to(device = offload_device, dtype = dtype) + + + return (vae,) + +class WanVideoTinyVAELoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_name": (folder_paths.get_filename_list("vae_approx"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae_approx'"}), + }, + "optional": { + "precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}), + "parallel": ("BOOLEAN", {"default": False, "tooltip": "uses more memory but is faster"}), + } + } + + RETURN_TYPES = ("WANVAE",) + RETURN_NAMES = ("vae", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Loads Wan VAE model from 'ComfyUI/models/vae'" + + def loadmodel(self, model_name, precision, parallel=False): + from .taehv import TAEHV + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + model_path = folder_paths.get_full_path("vae_approx", model_name) + vae_sd = load_torch_file(model_path, safe_load=True) + + vae = TAEHV(vae_sd, parallel=parallel) + + vae.to(device = offload_device, dtype = dtype) + + return (vae,) + + + +class WanVideoTorchCompileSettings: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "backend": (["inductor","cudagraphs"], {"default": "inductor"}), + "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), + "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), + "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), + "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), + "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only the transformer blocks, usually enough and can make compilation faster and less error prone"}), + }, + "optional": { + "dynamo_recompile_limit": ("INT", {"default": 128, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.recompile_limit"}), + }, + } + RETURN_TYPES = ("WANCOMPILEARGS",) + RETURN_NAMES = ("torch_compile_args",) + FUNCTION = "set_args" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "torch.compile settings, when connected to the model loader, torch.compile of the selected layers is attempted. Requires Triton and torch 2.5.0 is recommended" + + def set_args(self, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only, dynamo_recompile_limit=128): + + compile_args = { + "backend": backend, + "fullgraph": fullgraph, + "mode": mode, + "dynamic": dynamic, + "dynamo_cache_size_limit": dynamo_cache_size_limit, + "dynamo_recompile_limit": dynamo_recompile_limit, + "compile_transformer_blocks_only": compile_transformer_blocks_only, + } + + return (compile_args, ) + +#region TextEncode + +class LoadWanVideoT5TextEncoder: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_name": (folder_paths.get_filename_list("text_encoders"), {"tooltip": "These models are loaded from 'ComfyUI/models/text_encoders'"}), + "precision": (["fp32", "bf16"], + {"default": "bf16"} + ), + }, + "optional": { + "load_device": (["main_device", "offload_device"], {"default": "offload_device"}), + "quantization": (['disabled', 'fp8_e4m3fn'], {"default": 'disabled', "tooltip": "optional quantization method"}), + } + } + + RETURN_TYPES = ("WANTEXTENCODER",) + RETURN_NAMES = ("wan_t5_model", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Loads Wan text_encoder model from 'ComfyUI/models/LLM'" + + def loadmodel(self, model_name, precision, load_device="offload_device", quantization="disabled"): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + text_encoder_load_device = device if load_device == "main_device" else offload_device + + tokenizer_path = os.path.join(script_directory, "configs", "T5_tokenizer") + + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + + model_path = folder_paths.get_full_path("text_encoders", model_name) + sd = load_torch_file(model_path, safe_load=True) + + if "token_embedding.weight" not in sd and "shared.weight" not in sd: + raise ValueError("Invalid T5 text encoder model, this node expects the 'umt5-xxl' model") + if "scaled_fp8" in sd: + raise ValueError("Invalid T5 text encoder model, fp8 scaled is not supported by this node") + + # Convert state dict keys from T5 format to the expected format + if "shared.weight" in sd: + log.info("Converting T5 text encoder model to the expected format...") + converted_sd = {} + + for key, value in sd.items(): + # Handle encoder block patterns + if key.startswith('encoder.block.'): + parts = key.split('.') + block_num = parts[2] + + # Self-attention components + if 'layer.0.SelfAttention' in key: + if key.endswith('.k.weight'): + new_key = f"blocks.{block_num}.attn.k.weight" + elif key.endswith('.o.weight'): + new_key = f"blocks.{block_num}.attn.o.weight" + elif key.endswith('.q.weight'): + new_key = f"blocks.{block_num}.attn.q.weight" + elif key.endswith('.v.weight'): + new_key = f"blocks.{block_num}.attn.v.weight" + elif 'relative_attention_bias' in key: + new_key = f"blocks.{block_num}.pos_embedding.embedding.weight" + else: + new_key = key + + # Layer norms + elif 'layer.0.layer_norm' in key: + new_key = f"blocks.{block_num}.norm1.weight" + elif 'layer.1.layer_norm' in key: + new_key = f"blocks.{block_num}.norm2.weight" + + # Feed-forward components + elif 'layer.1.DenseReluDense' in key: + if 'wi_0' in key: + new_key = f"blocks.{block_num}.ffn.gate.0.weight" + elif 'wi_1' in key: + new_key = f"blocks.{block_num}.ffn.fc1.weight" + elif 'wo' in key: + new_key = f"blocks.{block_num}.ffn.fc2.weight" + else: + new_key = key + else: + new_key = key + elif key == "shared.weight": + new_key = "token_embedding.weight" + elif key == "encoder.final_layer_norm.weight": + new_key = "norm.weight" + else: + new_key = key + converted_sd[new_key] = value + sd = converted_sd + + T5_text_encoder = T5EncoderModel( + text_len=512, + dtype=dtype, + device=text_encoder_load_device, + state_dict=sd, + tokenizer_path=tokenizer_path, + quantization=quantization + ) + text_encoder = { + "model": T5_text_encoder, + "dtype": dtype, + } + + return (text_encoder,) + +class LoadWanVideoClipTextEncoder: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model_name": (folder_paths.get_filename_list("clip_vision") + folder_paths.get_filename_list("text_encoders"), {"tooltip": "These models are loaded from 'ComfyUI/models/clip_vision'"}), + "precision": (["fp16", "fp32", "bf16"], + {"default": "fp16"} + ), + }, + "optional": { + "load_device": (["main_device", "offload_device"], {"default": "offload_device"}), + } + } + + RETURN_TYPES = ("CLIP_VISION",) + RETURN_NAMES = ("wan_clip_vision", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Loads Wan clip_vision model from 'ComfyUI/models/clip_vision'" + + def loadmodel(self, model_name, precision, load_device="offload_device"): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + text_encoder_load_device = device if load_device == "main_device" else offload_device + + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + + model_path = folder_paths.get_full_path("clip_vision", model_name) + # We also support legacy setups where the model is in the text_encoders folder + if model_path is None: + model_path = folder_paths.get_full_path("text_encoders", model_name) + sd = load_torch_file(model_path, safe_load=True) + if "log_scale" not in sd: + raise ValueError("Invalid CLIP model, this node expectes the 'open-clip-xlm-roberta-large-vit-huge-14' model") + + clip_model = CLIPModel(dtype=dtype, device=device, state_dict=sd) + clip_model.model.to(text_encoder_load_device) + del sd + + return (clip_model,) + + +class WanVideoTextEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "t5": ("WANTEXTENCODER",), + "positive_prompt": ("STRING", {"default": "", "multiline": True} ), + "negative_prompt": ("STRING", {"default": "", "multiline": True} ), + }, + "optional": { + "force_offload": ("BOOLEAN", {"default": True}), + "model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}), + } + } + + RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", ) + RETURN_NAMES = ("text_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Encodes text prompts into text embeddings. For rudimentary prompt travel you can input multiple prompts separated by '|', they will be equally spread over the video length" + + def process(self, t5, positive_prompt, negative_prompt,force_offload=True, model_to_offload=None): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + if model_to_offload is not None: + log.info(f"Moving video model to {offload_device}") + model_to_offload.model.to(offload_device) + mm.soft_empty_cache() + + encoder = t5["model"] + dtype = t5["dtype"] + + # Split positive prompts and process each with weights + positive_prompts_raw = [p.strip() for p in positive_prompt.split('|')] + positive_prompts = [] + all_weights = [] + + for p in positive_prompts_raw: + cleaned_prompt, weights = self.parse_prompt_weights(p) + positive_prompts.append(cleaned_prompt) + all_weights.append(weights) + + encoder.model.to(device) + + with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=True): + context = encoder(positive_prompts, device) + context_null = encoder([negative_prompt], device) + + # Apply weights to embeddings if any were extracted + for i, weights in enumerate(all_weights): + for text, weight in weights.items(): + log.info(f"Applying weight {weight} to prompt: {text}") + if len(weights) > 0: + context[i] = context[i] * weight + + if force_offload: + encoder.model.to(offload_device) + mm.soft_empty_cache() + + prompt_embeds_dict = { + "prompt_embeds": context, + "negative_prompt_embeds": context_null, + } + return (prompt_embeds_dict,) + + def parse_prompt_weights(self, prompt): + """Extract text and weights from prompts with (text:weight) format""" + import re + + # Parse all instances of (text:weight) in the prompt + pattern = r'\((.*?):([\d\.]+)\)' + matches = re.findall(pattern, prompt) + + # Replace each match with just the text part + cleaned_prompt = prompt + weights = {} + + for match in matches: + text, weight = match + orig_text = f"({text}:{weight})" + cleaned_prompt = cleaned_prompt.replace(orig_text, text) + weights[text] = float(weight) + + return cleaned_prompt, weights + +class WanVideoTextEncodeSingle: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "t5": ("WANTEXTENCODER",), + "prompt": ("STRING", {"default": "", "multiline": True} ), + }, + "optional": { + "force_offload": ("BOOLEAN", {"default": True}), + "model_to_offload": ("WANVIDEOMODEL", {"tooltip": "Model to move to offload_device before encoding"}), + } + } + + RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", ) + RETURN_NAMES = ("text_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Encodes text prompt into text embedding." + + def process(self, t5, prompt, force_offload=True, model_to_offload=None): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + if model_to_offload is not None: + log.info(f"Moving video model to {offload_device}") + model_to_offload.model.to(offload_device) + mm.soft_empty_cache() + + encoder = t5["model"] + dtype = t5["dtype"] + + encoder.model.to(device) + + with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=True): + encoded = encoder([prompt], device) + + if force_offload: + encoder.model.to(offload_device) + mm.soft_empty_cache() + + prompt_embeds_dict = { + "prompt_embeds": encoded, + "negative_prompt_embeds": None, + } + return (prompt_embeds_dict,) + +class WanVideoApplyNAG: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "original_text_embeds": ("WANVIDEOTEXTEMBEDS",), + "nag_text_embeds": ("WANVIDEOTEXTEMBEDS",), + "nag_scale": ("FLOAT", {"default": 11.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "nag_tau": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.1}), + "nag_alpha": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", ) + RETURN_NAMES = ("text_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Adds NAG prompt embeds to original prompt embeds: 'https://github.com/ChenDarYen/Normalized-Attention-Guidance'" + + def process(self, original_text_embeds, nag_text_embeds, nag_scale, nag_tau, nag_alpha): + prompt_embeds_dict_copy = original_text_embeds.copy() + prompt_embeds_dict_copy.update({ + "nag_prompt_embeds": nag_text_embeds["prompt_embeds"], + "nag_params": { + "nag_scale": nag_scale, + "nag_tau": nag_tau, + "nag_alpha": nag_alpha, + } + }) + return (prompt_embeds_dict_copy,) + +class WanVideoTextEmbedBridge: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "positive": ("CONDITIONING",), + }, + "optional": { + "negative": ("CONDITIONING",), + } + } + + RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", ) + RETURN_NAMES = ("text_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Bridge between ComfyUI native text embedding and WanVideoWrapper text embedding" + + def process(self, positive, negative=None): + device=mm.get_torch_device() + prompt_embeds_dict = { + "prompt_embeds": positive[0][0].to(device), + "negative_prompt_embeds": negative[0][0].to(device) if negative is not None else None, + } + return (prompt_embeds_dict,) + +#region clip image encode +class WanVideoImageClipEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip_vision": ("CLIP_VISION",), + "image": ("IMAGE", {"tooltip": "Image to encode"}), + "vae": ("WANVAE",), + "generation_width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "generation_height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + }, + "optional": { + "force_offload": ("BOOLEAN", {"default": True}), + "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for I2V where some noise can add motion and give sharper results"}), + "latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}), + "clip_embed_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional clip embed multiplier"}), + "adjust_resolution": ("BOOLEAN", {"default": True, "tooltip": "Performs the same resolution adjustment as in the original code"}), + + } + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DEPRECATED = True + + def process(self, clip_vision, vae, image, num_frames, generation_width, generation_height, force_offload=True, noise_aug_strength=0.0, + latent_strength=1.0, clip_embed_strength=1.0, adjust_resolution=True): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + self.image_mean = [0.48145466, 0.4578275, 0.40821073] + self.image_std = [0.26862954, 0.26130258, 0.27577711] + patch_size = (1, 2, 2) + vae_stride = (4, 8, 8) + + H, W = image.shape[1], image.shape[2] + max_area = generation_width * generation_height + + + print(clip_vision) + clip_vision.model.to(device) + if isinstance(clip_vision, ClipVisionModel): + clip_context = clip_vision.encode_image(image).last_hidden_state.to(device) + else: + pixel_values = clip_preprocess(image.to(device), size=224, mean=self.image_mean, std=self.image_std, crop=True).float() + clip_context = clip_vision.visual(pixel_values) + if clip_embed_strength != 1.0: + clip_context *= clip_embed_strength + + if force_offload: + clip_vision.model.to(offload_device) + mm.soft_empty_cache() + + if adjust_resolution: + aspect_ratio = H / W + lat_h = round( + np.sqrt(max_area * aspect_ratio) // vae_stride[1] // + patch_size[1] * patch_size[1]) + lat_w = round( + np.sqrt(max_area / aspect_ratio) // vae_stride[2] // + patch_size[2] * patch_size[2]) + h = lat_h * vae_stride[1] + w = lat_w * vae_stride[2] + else: + h = generation_height + w = generation_width + lat_h = h // 8 + lat_w = w // 8 + + # Step 1: Create initial mask with ones for first frame, zeros for others + mask = torch.ones(1, num_frames, lat_h, lat_w, device=device) + mask[:, 1:] = 0 + + # Step 2: Repeat first frame 4 times and concatenate with remaining frames + first_frame_repeated = torch.repeat_interleave(mask[:, 0:1], repeats=4, dim=1) + mask = torch.concat([first_frame_repeated, mask[:, 1:]], dim=1) + + # Step 3: Reshape mask into groups of 4 frames + mask = mask.view(1, mask.shape[1] // 4, 4, lat_h, lat_w) + + # Step 4: Transpose dimensions and select first batch + mask = mask.transpose(1, 2)[0] + + # Calculate maximum sequence length + frames_per_stride = (num_frames - 1) // vae_stride[0] + 1 + patches_per_frame = lat_h * lat_w // (patch_size[1] * patch_size[2]) + max_seq_len = frames_per_stride * patches_per_frame + + vae.to(device) + + # Step 1: Resize and rearrange the input image dimensions + #resized_image = image.permute(0, 3, 1, 2) # Rearrange dimensions to (B, C, H, W) + #resized_image = torch.nn.functional.interpolate(resized_image, size=(h, w), mode='bicubic') + resized_image = common_upscale(image.movedim(-1, 1), w, h, "lanczos", "disabled") + resized_image = resized_image.transpose(0, 1) # Transpose to match required format + resized_image = resized_image * 2 - 1 + + if noise_aug_strength > 0.0: + resized_image = add_noise_to_reference_video(resized_image, ratio=noise_aug_strength) + + # Step 2: Create zero padding frames + zero_frames = torch.zeros(3, num_frames-1, h, w, device=device) + + # Step 3: Concatenate image with zero frames + concatenated = torch.concat([resized_image.to(device), zero_frames, resized_image.to(device)], dim=1).to(device = device, dtype = vae.dtype) + concatenated *= latent_strength + y = vae.encode([concatenated], device)[0] + + y = torch.concat([mask, y]) + + vae.model.clear_cache() + vae.to(offload_device) + + image_embeds = { + "image_embeds": y, + "clip_context": clip_context, + "max_seq_len": max_seq_len, + "num_frames": num_frames, + "lat_h": lat_h, + "lat_w": lat_w, + } + + return (image_embeds,) + +class WanVideoImageResizeToClosest: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "image": ("IMAGE", {"tooltip": "Image to resize"}), + "generation_width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "generation_height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "aspect_ratio_preservation": (["keep_input", "stretch_to_new", "crop_to_new"],), + }, + } + + RETURN_TYPES = ("IMAGE", "INT", "INT", ) + RETURN_NAMES = ("image","width","height",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Resizes image to the closest supported resolution based on aspect ratio and max pixels, according to the original code" + + def process(self, image, generation_width, generation_height, aspect_ratio_preservation ): + + patch_size = (1, 2, 2) + vae_stride = (4, 8, 8) + + H, W = image.shape[1], image.shape[2] + max_area = generation_width * generation_height + + crop = "disabled" + + if aspect_ratio_preservation == "keep_input": + aspect_ratio = H / W + elif aspect_ratio_preservation == "stretch_to_new" or aspect_ratio_preservation == "crop_to_new": + aspect_ratio = generation_height / generation_width + if aspect_ratio_preservation == "crop_to_new": + crop = "center" + + lat_h = round( + np.sqrt(max_area * aspect_ratio) // vae_stride[1] // + patch_size[1] * patch_size[1]) + lat_w = round( + np.sqrt(max_area / aspect_ratio) // vae_stride[2] // + patch_size[2] * patch_size[2]) + h = lat_h * vae_stride[1] + w = lat_w * vae_stride[2] + + resized_image = common_upscale(image.movedim(-1, 1), w, h, "lanczos", crop).movedim(1, -1) + + return (resized_image, w, h) + +#region clip vision +class WanVideoClipVisionEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "clip_vision": ("CLIP_VISION",), + "image_1": ("IMAGE", {"tooltip": "Image to encode"}), + "strength_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional clip embed multiplier"}), + "strength_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional clip embed multiplier"}), + "crop": (["center", "disabled"], {"default": "center", "tooltip": "Crop image to 224x224 before encoding"}), + "combine_embeds": (["average", "sum", "concat", "batch"], {"default": "average", "tooltip": "Method to combine multiple clip embeds"}), + "force_offload": ("BOOLEAN", {"default": True}), + }, + "optional": { + "image_2": ("IMAGE", ), + "negative_image": ("IMAGE", {"tooltip": "image to use for uncond"}), + "tiles": ("INT", {"default": 0, "min": 0, "max": 16, "step": 2, "tooltip": "Use matteo's tiled image encoding for improved accuracy"}), + "ratio": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Ratio of the tile average"}), + } + } + + RETURN_TYPES = ("WANVIDIMAGE_CLIPEMBEDS",) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, clip_vision, image_1, strength_1, strength_2, force_offload, crop, combine_embeds, image_2=None, negative_image=None, tiles=0, ratio=1.0): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + + if image_2 is not None: + image = torch.cat([image_1, image_2], dim=0) + else: + image = image_1 + + clip_vision.model.to(device) + + negative_clip_embeds = None + + if tiles > 0: + log.info("Using tiled image encoding") + clip_embeds = clip_encode_image_tiled(clip_vision, image.to(device), tiles=tiles, ratio=ratio) + if negative_image is not None: + negative_clip_embeds = clip_encode_image_tiled(clip_vision, negative_image.to(device), tiles=tiles, ratio=ratio) + else: + if isinstance(clip_vision, ClipVisionModel): + clip_embeds = clip_vision.encode_image(image).penultimate_hidden_states.to(device) + if negative_image is not None: + negative_clip_embeds = clip_vision.encode_image(negative_image).penultimate_hidden_states.to(device) + else: + pixel_values = clip_preprocess(image.to(device), size=224, mean=image_mean, std=image_std, crop=(not crop == "disabled")).float() + clip_embeds = clip_vision.visual(pixel_values) + if negative_image is not None: + pixel_values = clip_preprocess(negative_image.to(device), size=224, mean=image_mean, std=image_std, crop=(not crop == "disabled")).float() + negative_clip_embeds = clip_vision.visual(pixel_values) + + log.info(f"Clip embeds shape: {clip_embeds.shape}, dtype: {clip_embeds.dtype}") + + weighted_embeds = [] + weighted_embeds.append(clip_embeds[0:1] * strength_1) + + # Handle all additional embeddings + if clip_embeds.shape[0] > 1: + weighted_embeds.append(clip_embeds[1:2] * strength_2) + + if clip_embeds.shape[0] > 2: + for i in range(2, clip_embeds.shape[0]): + weighted_embeds.append(clip_embeds[i:i+1]) # Add as-is without strength modifier + + # Combine all weighted embeddings + if combine_embeds == "average": + clip_embeds = torch.mean(torch.stack(weighted_embeds), dim=0) + elif combine_embeds == "sum": + clip_embeds = torch.sum(torch.stack(weighted_embeds), dim=0) + elif combine_embeds == "concat": + clip_embeds = torch.cat(weighted_embeds, dim=1) + elif combine_embeds == "batch": + clip_embeds = torch.cat(weighted_embeds, dim=0) + else: + clip_embeds = weighted_embeds[0] + + + log.info(f"Combined clip embeds shape: {clip_embeds.shape}") + + if force_offload: + clip_vision.model.to(offload_device) + mm.soft_empty_cache() + + clip_embeds_dict = { + "clip_embeds": clip_embeds, + "negative_clip_embeds": negative_clip_embeds + } + + return (clip_embeds_dict,) + +class WanVideoRealisDanceLatents: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ref_latent": ("LATENT", {"tooltip": "Reference image to encode"}), + "smpl_latent": ("LATENT", {"tooltip": "SMPL pose image to encode"}), + "pose_cond_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the SMPL model"}), + "pose_cond_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the SMPL model"}), + }, + "optional": { + "hamer_latent": ("LATENT", {"tooltip": "Hamer hand pose image to encode"}), + }, + } + + RETURN_TYPES = ("REALISDANCELATENTS",) + RETURN_NAMES = ("realisdance_latents",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, ref_latent, smpl_latent, pose_cond_start_percent, pose_cond_end_percent, hamer_latent=None): + if hamer_latent is None: + hamer = torch.zeros_like(smpl_latent["samples"]) + else: + hamer = hamer_latent["samples"] + + pose_latent = torch.cat((smpl_latent["samples"], hamer), dim=1) + + realisdance_latents = { + "ref_latent": ref_latent["samples"], + "pose_latent": pose_latent, + "pose_cond_start_percent": pose_cond_start_percent, + "pose_cond_end_percent": pose_cond_end_percent, + } + + return (realisdance_latents,) + +class WanVideoImageToVideoEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "vae": ("WANVAE",), + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for I2V where some noise can add motion and give sharper results"}), + "start_latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}), + "end_latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for I2V where lower values allow for more motion"}), + "force_offload": ("BOOLEAN", {"default": True}), + }, + "optional": { + "clip_embeds": ("WANVIDIMAGE_CLIPEMBEDS", {"tooltip": "Clip vision encoded image"}), + "start_image": ("IMAGE", {"tooltip": "Image to encode"}), + "end_image": ("IMAGE", {"tooltip": "end frame"}), + "control_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "Control signal for the Fun -model"}), + "fun_or_fl2v_model": ("BOOLEAN", {"default": True, "tooltip": "Enable when using official FLF2V or Fun model"}), + "temporal_mask": ("MASK", {"tooltip": "mask"}), + "extra_latents": ("LATENT", {"tooltip": "Extra latents to add to the input front, used for Skyreels A2 reference images"}), + "tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}), + "realisdance_latents": ("REALISDANCELATENTS", {"tooltip": "RealisDance latents"}), + } + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, vae, width, height, num_frames, force_offload, noise_aug_strength, + start_latent_strength, end_latent_strength, start_image=None, end_image=None, control_embeds=None, fun_or_fl2v_model=False, + temporal_mask=None, extra_latents=None, clip_embeds=None, tiled_vae=False, realisdance_latents=None): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + patch_size = (1, 2, 2) + + H = height + W = width + + lat_h = H // 8 + lat_w = W // 8 + + num_frames = ((num_frames - 1) // 4) * 4 + 1 + two_ref_images = start_image is not None and end_image is not None + + base_frames = num_frames + (1 if two_ref_images and not fun_or_fl2v_model else 0) + if temporal_mask is None: + mask = torch.zeros(1, base_frames, lat_h, lat_w, device=device) + if start_image is not None: + mask[:, 0:start_image.shape[0]] = 1 # First frame + if end_image is not None: + mask[:, -end_image.shape[0]:] = 1 # End frame if exists + else: + mask = common_upscale(temporal_mask.unsqueeze(1).to(device), lat_w, lat_h, "nearest", "disabled").squeeze(1) + if mask.shape[0] > base_frames: + mask = mask[:base_frames] + elif mask.shape[0] < base_frames: + mask = torch.cat([mask, torch.zeros(base_frames - mask.shape[0], lat_h, lat_w, device=device)]) + mask = mask.unsqueeze(0).to(device) + + # Repeat first frame and optionally end frame + start_mask_repeated = torch.repeat_interleave(mask[:, 0:1], repeats=4, dim=1) # T, C, H, W + if end_image is not None and not fun_or_fl2v_model: + end_mask_repeated = torch.repeat_interleave(mask[:, -1:], repeats=4, dim=1) # T, C, H, W + mask = torch.cat([start_mask_repeated, mask[:, 1:-1], end_mask_repeated], dim=1) + else: + mask = torch.cat([start_mask_repeated, mask[:, 1:]], dim=1) + + # Reshape mask into groups of 4 frames + mask = mask.view(1, mask.shape[1] // 4, 4, lat_h, lat_w) # 1, T, C, H, W + mask = mask.movedim(1, 2)[0]# C, T, H, W + + # Resize and rearrange the input image dimensions + if start_image is not None: + resized_start_image = common_upscale(start_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1) + resized_start_image = resized_start_image * 2 - 1 + if noise_aug_strength > 0.0: + resized_start_image = add_noise_to_reference_video(resized_start_image, ratio=noise_aug_strength) + + if end_image is not None: + resized_end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1) + resized_end_image = resized_end_image * 2 - 1 + if noise_aug_strength > 0.0: + resized_end_image = add_noise_to_reference_video(resized_end_image, ratio=noise_aug_strength) + + # Concatenate image with zero frames and encode + vae.to(device) + + if temporal_mask is None: + if start_image is not None and end_image is None: + zero_frames = torch.zeros(3, num_frames-start_image.shape[0], H, W, device=device) + concatenated = torch.cat([resized_start_image.to(device), zero_frames], dim=1) + elif start_image is None and end_image is not None: + zero_frames = torch.zeros(3, num_frames-end_image.shape[0], H, W, device=device) + concatenated = torch.cat([zero_frames, resized_end_image.to(device)], dim=1) + elif start_image is None and end_image is None: + concatenated = torch.zeros(3, num_frames, H, W, device=device) + else: + if fun_or_fl2v_model: + zero_frames = torch.zeros(3, num_frames-(start_image.shape[0]+end_image.shape[0]), H, W, device=device) + else: + zero_frames = torch.zeros(3, num_frames-1, H, W, device=device) + concatenated = torch.cat([resized_start_image.to(device), zero_frames, resized_end_image.to(device)], dim=1) + else: + temporal_mask = common_upscale(temporal_mask.unsqueeze(1), W, H, "nearest", "disabled").squeeze(1) + concatenated = resized_start_image[:,:num_frames] * temporal_mask[:num_frames].unsqueeze(0) + + y = vae.encode([concatenated.to(device=device, dtype=vae.dtype)], device, end_=(end_image is not None and not fun_or_fl2v_model),tiled=tiled_vae)[0] + has_ref = False + if extra_latents is not None: + samples = extra_latents["samples"].squeeze(0) + y = torch.cat([samples, y], dim=1) + mask = torch.cat([torch.ones_like(mask[:, 0:samples.shape[1]]), mask], dim=1) + num_frames += samples.shape[1] * 4 + has_ref = True + y[:, :1] *= start_latent_strength + y[:, -1:] *= end_latent_strength + if control_embeds is None: + y = torch.cat([mask, y]) + else: + if end_image is None: + y[:, 1:] = 0 + elif start_image is None: + y[:, -1:] = 0 + else: + y[:, 1:-1] = 0 # doesn't seem to work anyway though... + + # Calculate maximum sequence length + patches_per_frame = lat_h * lat_w // (patch_size[1] * patch_size[2]) + frames_per_stride = (num_frames - 1) // 4 + (2 if end_image is not None and not fun_or_fl2v_model else 1) + max_seq_len = frames_per_stride * patches_per_frame + + if realisdance_latents is not None: + realisdance_latents["ref_latent_neg"] = vae.encode(torch.zeros(1, 3, 1, H, W, device=device, dtype=vae.dtype), device) + + vae.model.clear_cache() + if force_offload: + vae.model.to(offload_device) + mm.soft_empty_cache() + gc.collect() + + image_embeds = { + "image_embeds": y, + "clip_context": clip_embeds.get("clip_embeds", None) if clip_embeds is not None else None, + "negative_clip_context": clip_embeds.get("negative_clip_embeds", None) if clip_embeds is not None else None, + "max_seq_len": max_seq_len, + "num_frames": num_frames, + "lat_h": lat_h, + "lat_w": lat_w, + "control_embeds": control_embeds["control_embeds"] if control_embeds is not None else None, + "end_image": resized_end_image if end_image is not None else None, + "fun_or_fl2v_model": fun_or_fl2v_model, + "has_ref": has_ref, + "realisdance_latents": realisdance_latents + } + + return (image_embeds,) + +class WanVideoEmptyEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + }, + "optional": { + "control_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "control signal for the Fun -model"}), + } + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, num_frames, width, height, control_embeds=None): + vae_stride = (4, 8, 8) + + target_shape = (16, (num_frames - 1) // vae_stride[0] + 1, + height // vae_stride[1], + width // vae_stride[2]) + + embeds = { + "target_shape": target_shape, + "num_frames": num_frames, + "control_embeds": control_embeds["control_embeds"] if control_embeds is not None else None, + } + + return (embeds,) + +class WanVideoMiniMaxRemoverEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + "latents": ("LATENT", {"tooltip": "Encoded latents to use as control signals"}), + "mask_latents": ("LATENT", {"tooltip": "Encoded latents to use as mask"}), + }, + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, num_frames, width, height, latents, mask_latents): + vae_stride = (4, 8, 8) + + target_shape = (16, (num_frames - 1) // vae_stride[0] + 1, + height // vae_stride[1], + width // vae_stride[2]) + + embeds = { + "target_shape": target_shape, + "num_frames": num_frames, + "minimax_latents": latents["samples"].squeeze(0), + "minimax_mask_latents": mask_latents["samples"].squeeze(0), + } + + return (embeds,) + +# region phantom +class WanVideoPhantomEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + "phantom_latent_1": ("LATENT", {"tooltip": "reference latents for the phantom model"}), + + "phantom_cfg_scale": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "CFG scale for the extra phantom cond pass"}), + "phantom_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the phantom model"}), + "phantom_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the phantom model"}), + }, + "optional": { + "phantom_latent_2": ("LATENT", {"tooltip": "reference latents for the phantom model"}), + "phantom_latent_3": ("LATENT", {"tooltip": "reference latents for the phantom model"}), + "phantom_latent_4": ("LATENT", {"tooltip": "reference latents for the phantom model"}), + "vace_embeds": ("WANVIDIMAGE_EMBEDS", {"tooltip": "VACE embeds"}), + } + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, num_frames, phantom_cfg_scale, phantom_start_percent, phantom_end_percent, phantom_latent_1, phantom_latent_2=None, phantom_latent_3=None, phantom_latent_4=None, vace_embeds=None): + vae_stride = (4, 8, 8) + samples = phantom_latent_1["samples"].squeeze(0) + if phantom_latent_2 is not None: + samples = torch.cat([samples, phantom_latent_2["samples"].squeeze(0)], dim=1) + if phantom_latent_3 is not None: + samples = torch.cat([samples, phantom_latent_3["samples"].squeeze(0)], dim=1) + if phantom_latent_4 is not None: + samples = torch.cat([samples, phantom_latent_4["samples"].squeeze(0)], dim=1) + C, T, H, W = samples.shape + + log.info(f"Phantom latents shape: {samples.shape}") + + target_shape = (16, (num_frames - 1) // vae_stride[0] + 1 + T, + H * 8 // vae_stride[1], + W * 8 // vae_stride[2]) + + embeds = { + "target_shape": target_shape, + "num_frames": num_frames, + "phantom_latents": samples, + "phantom_cfg_scale": phantom_cfg_scale, + "phantom_start_percent": phantom_start_percent, + "phantom_end_percent": phantom_end_percent, + } + if vace_embeds is not None: + vace_input = { + "vace_context": vace_embeds["vace_context"], + "vace_scale": vace_embeds["vace_scale"], + "has_ref": vace_embeds["has_ref"], + "vace_start_percent": vace_embeds["vace_start_percent"], + "vace_end_percent": vace_embeds["vace_end_percent"], + "vace_seq_len": vace_embeds["vace_seq_len"], + "additional_vace_inputs": vace_embeds["additional_vace_inputs"], + } + embeds.update(vace_input) + + return (embeds,) + +class WanVideoControlEmbeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "latents": ("LATENT", {"tooltip": "Encoded latents to use as control signals"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the control signal"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the control signal"}), + }, + "optional": { + "fun_ref_image": ("LATENT", {"tooltip": "Reference latent for the Fun 1.1 -model"}), + } + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("image_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, latents, start_percent, end_percent, fun_ref_image=None): + + samples = latents["samples"].squeeze(0) + C, T, H, W = samples.shape + + num_frames = (T - 1) * 4 + 1 + seq_len = math.ceil((H * W) / 4 * ((num_frames - 1) // 4 + 1)) + + embeds = { + "max_seq_len": seq_len, + "target_shape": samples.shape, + "num_frames": num_frames, + "control_embeds": { + "control_images": samples, + "start_percent": start_percent, + "end_percent": end_percent, + "fun_ref_image": fun_ref_image["samples"][:,:, 0] if fun_ref_image is not None else None, + } + } + + return (embeds,) + +class WanVideoSLG: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "blocks": ("STRING", {"default": "10", "tooltip": "Blocks to skip uncond on, separated by comma, index starts from 0"}), + "start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the control signal"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the control signal"}), + }, + } + + RETURN_TYPES = ("SLGARGS", ) + RETURN_NAMES = ("slg_args",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Skips uncond on the selected blocks" + + def process(self, blocks, start_percent, end_percent): + slg_block_list = [int(x.strip()) for x in blocks.split(",")] + + slg_args = { + "blocks": slg_block_list, + "start_percent": start_percent, + "end_percent": end_percent, + } + return (slg_args,) + +#region VACE +class WanVideoVACEEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "vae": ("WANVAE",), + "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the image to encode"}), + "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the image to encode"}), + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), + "vace_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply VACE"}), + "vace_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply VACE"}), + }, + "optional": { + "input_frames": ("IMAGE",), + "ref_images": ("IMAGE",), + "input_masks": ("MASK",), + "prev_vace_embeds": ("WANVIDIMAGE_EMBEDS",), + "tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}), + }, + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", ) + RETURN_NAMES = ("vace_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, vae, width, height, num_frames, strength, vace_start_percent, vace_end_percent, input_frames=None, ref_images=None, input_masks=None, prev_vace_embeds=None, tiled_vae=False): + + self.device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + self.vae = vae.to(self.device) + self.vae_stride = (4, 8, 8) + + width = (width // 16) * 16 + height = (height // 16) * 16 + + target_shape = (16, (num_frames - 1) // self.vae_stride[0] + 1, + height // self.vae_stride[1], + width // self.vae_stride[2]) + # vace context encode + if input_frames is None: + input_frames = torch.zeros((1, 3, num_frames, height, width), device=self.device, dtype=self.vae.dtype) + else: + input_frames = input_frames[:num_frames] + input_frames = common_upscale(input_frames.clone().movedim(-1, 1), width, height, "lanczos", "disabled").movedim(1, -1) + input_frames = input_frames.to(self.vae.dtype).to(self.device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W + input_frames = input_frames * 2 - 1 + if input_masks is None: + input_masks = torch.ones_like(input_frames, device=self.device) + else: + print("input_masks shape", input_masks.shape) + input_masks = input_masks[:num_frames] + input_masks = common_upscale(input_masks.clone().unsqueeze(1), width, height, "nearest-exact", "disabled").squeeze(1) + input_masks = input_masks.to(self.vae.dtype).to(self.device) + input_masks = input_masks.unsqueeze(-1).unsqueeze(0).permute(0, 4, 1, 2, 3).repeat(1, 3, 1, 1, 1) # B, C, T, H, W + + if ref_images is not None: + # Create padded image + if ref_images.shape[0] > 1: + ref_images = torch.cat([ref_images[i] for i in range(ref_images.shape[0])], dim=1).unsqueeze(0) + + B, H, W, C = ref_images.shape + current_aspect = W / H + target_aspect = width / height + if current_aspect > target_aspect: + # Image is wider than target, pad height + new_h = int(W / target_aspect) + pad_h = (new_h - H) // 2 + padded = torch.ones(ref_images.shape[0], new_h, W, ref_images.shape[3], device=ref_images.device, dtype=ref_images.dtype) + padded[:, pad_h:pad_h+H, :, :] = ref_images + ref_images = padded + elif current_aspect < target_aspect: + # Image is taller than target, pad width + new_w = int(H * target_aspect) + pad_w = (new_w - W) // 2 + padded = torch.ones(ref_images.shape[0], H, new_w, ref_images.shape[3], device=ref_images.device, dtype=ref_images.dtype) + padded[:, :, pad_w:pad_w+W, :] = ref_images + ref_images = padded + ref_images = common_upscale(ref_images.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1) + + ref_images = ref_images.to(self.vae.dtype).to(self.device).unsqueeze(0).permute(0, 4, 1, 2, 3).unsqueeze(0) + ref_images = ref_images * 2 - 1 + + z0 = self.vace_encode_frames(input_frames, ref_images, masks=input_masks, tiled_vae=tiled_vae) + self.vae.model.clear_cache() + m0 = self.vace_encode_masks(input_masks, ref_images) + z = self.vace_latent(z0, m0) + + self.vae.to(offload_device) + + vace_input = { + "vace_context": z, + "vace_scale": strength, + "has_ref": ref_images is not None, + "num_frames": num_frames, + "target_shape": target_shape, + "vace_start_percent": vace_start_percent, + "vace_end_percent": vace_end_percent, + "vace_seq_len": math.ceil((z[0].shape[2] * z[0].shape[3]) / 4 * z[0].shape[1]), + "additional_vace_inputs": [], + } + + if prev_vace_embeds is not None: + if "additional_vace_inputs" in prev_vace_embeds and prev_vace_embeds["additional_vace_inputs"]: + vace_input["additional_vace_inputs"] = prev_vace_embeds["additional_vace_inputs"].copy() + vace_input["additional_vace_inputs"].append(prev_vace_embeds) + + return (vace_input,) + def vace_encode_frames(self, frames, ref_images, masks=None, tiled_vae=False): + if ref_images is None: + ref_images = [None] * len(frames) + else: + assert len(frames) == len(ref_images) + + if masks is None: + latents = self.vae.encode(frames, device=self.device, tiled=tiled_vae) + else: + inactive = [i * (1 - m) + 0 * m for i, m in zip(frames, masks)] + reactive = [i * m + 0 * (1 - m) for i, m in zip(frames, masks)] + inactive = self.vae.encode(inactive, device=self.device, tiled=tiled_vae) + reactive = self.vae.encode(reactive, device=self.device, tiled=tiled_vae) + latents = [torch.cat((u, c), dim=0) for u, c in zip(inactive, reactive)] + self.vae.model.clear_cache() + cat_latents = [] + for latent, refs in zip(latents, ref_images): + if refs is not None: + if masks is None: + ref_latent = self.vae.encode(refs, device=self.device, tiled=tiled_vae) + else: + print("refs shape", refs.shape)#torch.Size([3, 1, 512, 512]) + ref_latent = self.vae.encode(refs, device=self.device, tiled=tiled_vae) + ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent] + assert all([x.shape[1] == 1 for x in ref_latent]) + latent = torch.cat([*ref_latent, latent], dim=1) + cat_latents.append(latent) + return cat_latents + + def vace_encode_masks(self, masks, ref_images=None): + if ref_images is None: + ref_images = [None] * len(masks) + else: + assert len(masks) == len(ref_images) + + result_masks = [] + for mask, refs in zip(masks, ref_images): + c, depth, height, width = mask.shape + new_depth = int((depth + 3) // self.vae_stride[0]) + height = 2 * (int(height) // (self.vae_stride[1] * 2)) + width = 2 * (int(width) // (self.vae_stride[2] * 2)) + + # reshape + mask = mask[0, :, :, :] + mask = mask.view( + depth, height, self.vae_stride[1], width, self.vae_stride[1] + ) # depth, height, 8, width, 8 + mask = mask.permute(2, 4, 0, 1, 3) # 8, 8, depth, height, width + mask = mask.reshape( + self.vae_stride[1] * self.vae_stride[2], depth, height, width + ) # 8*8, depth, height, width + + # interpolation + mask = F.interpolate(mask.unsqueeze(0), size=(new_depth, height, width), mode='nearest-exact').squeeze(0) + + if refs is not None: + length = len(refs) + mask_pad = torch.zeros_like(mask[:, :length, :, :]) + mask = torch.cat((mask_pad, mask), dim=1) + result_masks.append(mask) + return result_masks + + def vace_latent(self, z, m): + return [torch.cat([zz, mm], dim=0) for zz, mm in zip(z, m)] + +class WanVideoVACEStartToEndFrame: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "num_frames": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "Number of frames to encode"}), + "empty_frame_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "White level of empty frame to use"}), + }, + "optional": { + "start_image": ("IMAGE",), + "end_image": ("IMAGE",), + "control_images": ("IMAGE",), + "inpaint_mask": ("MASK", {"tooltip": "Inpaint mask to use for the empty frames"}), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK", ) + RETURN_NAMES = ("images", "masks",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Helper node to create start/end frame batch and masks for VACE" + + def process(self, num_frames, empty_frame_level, start_image=None, end_image=None, control_images=None, inpaint_mask=None): + + B, H, W, C = start_image.shape if start_image is not None else end_image.shape + device = start_image.device if start_image is not None else end_image.device + + masks = torch.ones((num_frames, H, W), device=device) + + if control_images is not None: + control_images = common_upscale(control_images.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(1, -1) + + if start_image is not None and end_image is not None: + if start_image.shape != end_image.shape: + end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(1, -1) + if control_images is None: + empty_frames = torch.ones((num_frames - start_image.shape[0] - end_image.shape[0], H, W, 3), device=device) * empty_frame_level + else: + empty_frames = control_images[start_image.shape[0]:num_frames - end_image.shape[0]] + out_batch = torch.cat([start_image, empty_frames, end_image], dim=0) + masks[0:start_image.shape[0]] = 0 + masks[-end_image.shape[0]:] = 0 + elif start_image is not None: + if control_images is None: + empty_frames = torch.ones((num_frames - start_image.shape[0], H, W, 3), device=device) * empty_frame_level + else: + empty_frames = control_images[start_image.shape[0]:num_frames] + out_batch = torch.cat([start_image, empty_frames], dim=0) + masks[0:start_image.shape[0]] = 0 + elif end_image is not None: + if control_images is None: + empty_frames = torch.ones((num_frames - end_image.shape[0], H, W, 3), device=device) * empty_frame_level + else: + empty_frames = control_images[:num_frames - end_image.shape[0]] + out_batch = torch.cat([empty_frames, end_image], dim=0) + masks[-end_image.shape[0]:] = 0 + + if inpaint_mask is not None: + inpaint_mask = common_upscale(inpaint_mask.unsqueeze(1), W, H, "nearest-exact", "disabled").squeeze(1).to(device) + if inpaint_mask.shape[0] > num_frames: + inpaint_mask = inpaint_mask[:num_frames] + elif inpaint_mask.shape[0] < num_frames: + inpaint_mask = inpaint_mask.repeat(num_frames // inpaint_mask.shape[0] + 1, 1, 1)[:num_frames] + + empty_mask = torch.ones_like(masks, device=device) + masks = inpaint_mask * empty_mask + + return (out_batch.cpu().float(), masks.cpu().float()) + + +#region context options +class WanVideoContextOptions: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "context_schedule": (["uniform_standard", "uniform_looped", "static_standard"],), + "context_frames": ("INT", {"default": 81, "min": 2, "max": 1000, "step": 1, "tooltip": "Number of pixel frames in the context, NOTE: the latent space has 4 frames in 1"} ), + "context_stride": ("INT", {"default": 4, "min": 4, "max": 100, "step": 1, "tooltip": "Context stride as pixel frames, NOTE: the latent space has 4 frames in 1"} ), + "context_overlap": ("INT", {"default": 16, "min": 4, "max": 100, "step": 1, "tooltip": "Context overlap as pixel frames, NOTE: the latent space has 4 frames in 1"} ), + "freenoise": ("BOOLEAN", {"default": True, "tooltip": "Shuffle the noise"}), + "verbose": ("BOOLEAN", {"default": False, "tooltip": "Print debug output"}), + }, + "optional": { + "vae": ("WANVAE",), + } + } + + RETURN_TYPES = ("WANVIDCONTEXT", ) + RETURN_NAMES = ("context_options",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Context options for WanVideo, allows splitting the video into context windows and attemps blending them for longer generations than the model and memory otherwise would allow." + + def process(self, context_schedule, context_frames, context_stride, context_overlap, freenoise, verbose, image_cond_start_step=6, image_cond_window_count=2, vae=None): + context_options = { + "context_schedule":context_schedule, + "context_frames":context_frames, + "context_stride":context_stride, + "context_overlap":context_overlap, + "freenoise":freenoise, + "verbose":verbose, + "vae": vae, + } + + return (context_options,) + +class CreateCFGScheduleFloatList: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "steps": ("INT", {"default": 30, "min": 2, "max": 1000, "step": 1, "tooltip": "Number of steps to schedule cfg for"} ), + "cfg_scale_start": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.01, "round": 0.01, "tooltip": "CFG scale to use for the steps"}), + "cfg_scale_end": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.01, "round": 0.01, "tooltip": "CFG scale to use for the steps"}), + "interpolation": (["linear", "ease_in", "ease_out"], {"default": "linear", "tooltip": "Interpolation method to use for the cfg scale"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.01,"tooltip": "Start percent of the steps to apply cfg"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.01,"tooltip": "End percent of the steps to apply cfg"}), + } + } + + RETURN_TYPES = ("FLOAT", ) + RETURN_NAMES = ("float_list",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Helper node to generate a list of floats that can be used to schedule cfg scale for the steps, outside the set range cfg is set to 1.0" + + def process(self, steps, cfg_scale_start, cfg_scale_end, interpolation, start_percent, end_percent): + + # Create a list of floats for the cfg schedule + cfg_list = [1.0] * steps + start_idx = min(int(steps * start_percent), steps - 1) + end_idx = min(int(steps * end_percent), steps - 1) + + for i in range(start_idx, end_idx + 1): + if i >= steps: + break + + if end_idx == start_idx: + t = 0 + else: + t = (i - start_idx) / (end_idx - start_idx) + + if interpolation == "linear": + factor = t + elif interpolation == "ease_in": + factor = t * t + elif interpolation == "ease_out": + factor = t * (2 - t) + + cfg_list[i] = round(cfg_scale_start + factor * (cfg_scale_end - cfg_scale_start), 2) + + # If start_percent > 0, always include the first step + if start_percent > 0: + cfg_list[0] = 1.0 + + return (cfg_list,) + +class WanVideoFlowEdit: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "source_embeds": ("WANVIDEOTEXTEMBEDS", ), + "skip_steps": ("INT", {"default": 4, "min": 0}), + "drift_steps": ("INT", {"default": 0, "min": 0}), + "drift_flow_shift": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 30.0, "step": 0.01}), + "source_cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}), + "drift_cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}), + }, + "optional": { + "source_image_embeds": ("WANVIDIMAGE_EMBEDS", ), + } + } + + RETURN_TYPES = ("FLOWEDITARGS", ) + RETURN_NAMES = ("flowedit_args",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Flowedit options for WanVideo" + + def process(self, **kwargs): + return (kwargs,) + +class WanVideoLoopArgs: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "shift_skip": ("INT", {"default": 6, "min": 0, "tooltip": "Skip step of latent shift"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the looping effect"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the looping effect"}), + }, + } + + RETURN_TYPES = ("LOOPARGS", ) + RETURN_NAMES = ("loop_args",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Looping through latent shift as shown in https://github.com/YisuiTT/Mobius/" + + def process(self, **kwargs): + return (kwargs,) + +class WanVideoExperimentalArgs: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "video_attention_split_steps": ("STRING", {"default": "", "tooltip": "Steps to split self attention when using multiple prompts"}), + "cfg_zero_star": ("BOOLEAN", {"default": False, "tooltip": "https://github.com/WeichenFan/CFG-Zero-star"}), + "use_zero_init": ("BOOLEAN", {"default": False}), + "zero_star_steps": ("INT", {"default": 0, "min": 0, "tooltip": "Steps to split self attention when using multiple prompts"}), + "use_fresca": ("BOOLEAN", {"default": False, "tooltip": "https://github.com/WikiChao/FreSca"}), + "fresca_scale_low": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "fresca_scale_high": ("FLOAT", {"default": 1.25, "min": 0.0, "max": 10.0, "step": 0.01}), + "fresca_freq_cutoff": ("INT", {"default": 20, "min": 0, "max": 10000, "step": 1}), + }, + } + + RETURN_TYPES = ("EXPERIMENTALARGS", ) + RETURN_NAMES = ("exp_args",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "Experimental stuff" + EXPERIMENTAL = True + + def process(self, **kwargs): + return (kwargs,) + +#region Sampler +class WanVideoSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("WANVIDEOMODEL",), + + "image_embeds": ("WANVIDIMAGE_EMBEDS", ), + "steps": ("INT", {"default": 30, "min": 1}), + "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}), + "shift": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "force_offload": ("BOOLEAN", {"default": True, "tooltip": "Moves the model to the offload device after sampling"}), + "scheduler": (["unipc", "unipc/beta", "dpm++", "dpm++/beta","dpm++_sde", "dpm++_sde/beta", "euler", "euler/beta", "euler/accvideo", "deis", "lcm", "lcm/beta", "flowmatch_causvid", "flowmatch_distill"], + { + "default": 'unipc' + }), + "riflex_freq_index": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping"}), + + + }, + "optional": { + "text_embeds": ("WANVIDEOTEXTEMBEDS", ), + "samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ), + "denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "feta_args": ("FETAARGS", ), + "context_options": ("WANVIDCONTEXT", ), + "cache_args": ("CACHEARGS", ), + "flowedit_args": ("FLOWEDITARGS", ), + "batched_cfg": ("BOOLEAN", {"default": False, "tooltip": "Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory"}), + "slg_args": ("SLGARGS", ), + "rope_function": (["default", "comfy"], {"default": "comfy", "tooltip": "Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile"}), + "loop_args": ("LOOPARGS", ), + "experimental_args": ("EXPERIMENTALARGS", ), + "sigmas": ("SIGMAS", ), + "unianimate_poses": ("UNIANIMATE_POSE", ), + "fantasytalking_embeds": ("FANTASYTALKING_EMBEDS", ), + "uni3c_embeds": ("UNI3C_EMBEDS", ), + } + } + + RETURN_TYPES = ("LATENT", ) + RETURN_NAMES = ("samples",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, model, image_embeds, shift, steps, cfg, seed, scheduler, riflex_freq_index, text_embeds=None, + force_offload=True, samples=None, feta_args=None, denoise_strength=1.0, context_options=None, + cache_args=None, teacache_args=None, flowedit_args=None, batched_cfg=False, slg_args=None, rope_function="default", loop_args=None, + experimental_args=None, sigmas=None, unianimate_poses=None, fantasytalking_embeds=None, uni3c_embeds=None): + + patcher = model + model = model.model + transformer = model.diffusion_model + dtype = model["dtype"] + control_lora = model["control_lora"] + + transformer_options = patcher.model_options.get("transformer_options", None) + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + steps = int(steps/denoise_strength) + + if text_embeds == None: + text_embeds = { + "prompt_embeds": [], + "negative_prompt_embeds": [], + } + + if isinstance(cfg, list): + if steps != len(cfg): + log.info(f"Received {len(cfg)} cfg values, but only {steps} steps. Setting step count to match.") + steps = len(cfg) + + timesteps = None + if 'unipc' in scheduler: + sample_scheduler = FlowUniPCMultistepScheduler(shift=shift) + if sigmas is None: + sample_scheduler.set_timesteps(steps, device=device, shift=shift, use_beta_sigmas=('beta' in scheduler)) + else: + sample_scheduler.sigmas = sigmas.to(device) + sample_scheduler.timesteps = (sample_scheduler.sigmas[:-1] * 1000).to(torch.int64).to(device) + sample_scheduler.num_inference_steps = len(sample_scheduler.timesteps) + + elif scheduler in ['euler/beta', 'euler']: + sample_scheduler = FlowMatchEulerDiscreteScheduler(shift=shift, use_beta_sigmas=(scheduler == 'euler/beta')) + if flowedit_args: #seems to work better + timesteps, _ = retrieve_timesteps(sample_scheduler, device=device, sigmas=get_sampling_sigmas(steps, shift)) + else: + sample_scheduler.set_timesteps(steps, device=device, sigmas=sigmas.tolist() if sigmas is not None else None) + elif scheduler in ['euler/accvideo']: + if steps != 50: + raise Exception("Steps must be set to 50 for accvideo scheduler, 10 actual steps are used") + sample_scheduler = FlowMatchEulerDiscreteScheduler(shift=shift, use_beta_sigmas=(scheduler == 'euler/beta')) + sample_scheduler.set_timesteps(steps, device=device, sigmas=sigmas.tolist() if sigmas is not None else None) + start_latent_list = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50] + sample_scheduler.sigmas = sample_scheduler.sigmas[start_latent_list] + steps = len(start_latent_list) - 1 + sample_scheduler.timesteps = timesteps = sample_scheduler.timesteps[start_latent_list[:steps]] + elif 'dpm++' in scheduler: + if 'sde' in scheduler: + algorithm_type = "sde-dpmsolver++" + else: + algorithm_type = "dpmsolver++" + sample_scheduler = FlowDPMSolverMultistepScheduler(shift=shift, algorithm_type=algorithm_type) + if sigmas is None: + sample_scheduler.set_timesteps(steps, device=device, use_beta_sigmas=('beta' in scheduler)) + else: + sample_scheduler.sigmas = sigmas.to(device) + sample_scheduler.timesteps = (sample_scheduler.sigmas[:-1] * 1000).to(torch.int64).to(device) + sample_scheduler.num_inference_steps = len(sample_scheduler.timesteps) + elif scheduler == 'deis': + sample_scheduler = DEISMultistepScheduler(use_flow_sigmas=True, prediction_type="flow_prediction", flow_shift=shift) + sample_scheduler.set_timesteps(steps, device=device) + sample_scheduler.sigmas[-1] = 1e-6 + elif 'lcm' in scheduler: + sample_scheduler = FlowMatchLCMScheduler(shift=shift, use_beta_sigmas=(scheduler == 'lcm/beta')) + sample_scheduler.set_timesteps(steps, device=device, sigmas=sigmas.tolist() if sigmas is not None else None) + elif 'flowmatch_causvid' in scheduler: + if transformer.dim == 5120: + denoising_list = [999, 934, 862, 756, 603, 410, 250, 140, 74] + else: + if steps != 4: + raise ValueError("CausVid 1.3B schedule is only for 4 steps") + denoising_list = [1000, 750, 500, 250] + sample_scheduler = FlowMatchScheduler(num_inference_steps=steps, shift=shift, sigma_min=0, extra_one_step=True) + sample_scheduler.timesteps = torch.tensor(denoising_list)[:steps].to(device) + sample_scheduler.sigmas = torch.cat([sample_scheduler.timesteps / 1000, torch.tensor([0.0], device=device)]) + elif 'flowmatch_distill' in scheduler: + sample_scheduler = FlowMatchScheduler( + shift=shift, sigma_min=0.0, extra_one_step=True + ) + sample_scheduler.set_timesteps(1000, training=True) + + denoising_step_list = torch.tensor([999, 750, 500, 250] , dtype=torch.long) + temp_timesteps = torch.cat((sample_scheduler.timesteps.cpu(), torch.tensor([0], dtype=torch.float32))) + denoising_step_list = temp_timesteps[1000 - denoising_step_list] + print("denoising_step_list: ", denoising_step_list) + + + #denoising_step_list = [999, 750, 500, 250] + if steps != 4: + raise ValueError("This scheduler is only for 4 steps") + #sample_scheduler = FlowMatchScheduler(num_inference_steps=steps, shift=shift, sigma_min=0, extra_one_step=True) + sample_scheduler.timesteps = torch.tensor(denoising_step_list)[:steps].to(device) + sample_scheduler.sigmas = torch.cat([sample_scheduler.timesteps / 1000, torch.tensor([0.0], device=device)]) + + if timesteps is None: + timesteps = sample_scheduler.timesteps + log.info(f"timesteps: {timesteps}") + + if denoise_strength < 1.0: + steps = int(steps * denoise_strength) + timesteps = timesteps[-(steps + 1):] + + seed_g = torch.Generator(device=torch.device("cpu")) + seed_g.manual_seed(seed) + + control_latents = control_camera_latents = clip_fea = clip_fea_neg = end_image = recammaster = camera_embed = unianim_data = None + vace_data = vace_context = vace_scale = None + fun_or_fl2v_model = has_ref = drop_last = False + phantom_latents = None + fun_ref_image = None + + image_cond = image_embeds.get("image_embeds", None) + ATI_tracks = None + add_cond = attn_cond = attn_cond_neg = None + + if image_cond is not None: + log.info(f"image_cond shape: {image_cond.shape}") + #ATI tracks + if transformer_options is not None: + ATI_tracks = transformer_options.get("ati_tracks", None) + if ATI_tracks is not None: + from .ATI.motion_patch import patch_motion + topk = transformer_options.get("ati_topk", 2) + temperature = transformer_options.get("ati_temperature", 220.0) + ati_start_percent = transformer_options.get("ati_start_percent", 0.0) + ati_end_percent = transformer_options.get("ati_end_percent", 1.0) + image_cond_ati = patch_motion(ATI_tracks.to(image_cond.device, image_cond.dtype), image_cond, topk=topk, temperature=temperature) + log.info(f"ATI tracks shape: {ATI_tracks.shape}") + + realisdance_latents = image_embeds.get("realisdance_latents", None) + if realisdance_latents is not None: + add_cond = realisdance_latents["pose_latent"] + attn_cond = realisdance_latents["ref_latent"] + attn_cond_neg = realisdance_latents["ref_latent_neg"] + add_cond_start_percent = realisdance_latents["pose_cond_start_percent"] + add_cond_end_percent = realisdance_latents["pose_cond_end_percent"] + + end_image = image_embeds.get("end_image", None) + lat_h = image_embeds.get("lat_h", None) + lat_w = image_embeds.get("lat_w", None) + if lat_h is None or lat_w is None: + raise ValueError("Clip encoded image embeds must be provided for I2V (Image to Video) model") + fun_or_fl2v_model = image_embeds.get("fun_or_fl2v_model", False) + noise = torch.randn( + 16, + (image_embeds["num_frames"] - 1) // 4 + (2 if end_image is not None and not fun_or_fl2v_model else 1), + lat_h, + lat_w, + dtype=torch.float32, + generator=seed_g, + device=torch.device("cpu")) + seq_len = image_embeds["max_seq_len"] + + clip_fea = image_embeds.get("clip_context", None) + if clip_fea is not None: + clip_fea = clip_fea.to(dtype) + clip_fea_neg = image_embeds.get("negative_clip_context", None) + if clip_fea_neg is not None: + clip_fea_neg = clip_fea_neg.to(dtype) + + control_embeds = image_embeds.get("control_embeds", None) + if control_embeds is not None: + if transformer.in_dim not in [48, 32]: + raise ValueError("Control signal only works with Fun-Control model") + control_latents = control_embeds.get("control_images", None) + control_camera_latents = control_embeds.get("control_camera_latents", None) + control_camera_start_percent = control_embeds.get("control_camera_start_percent", 0.0) + control_camera_end_percent = control_embeds.get("control_camera_end_percent", 1.0) + control_start_percent = control_embeds.get("start_percent", 0.0) + control_end_percent = control_embeds.get("end_percent", 1.0) + drop_last = image_embeds.get("drop_last", False) + has_ref = image_embeds.get("has_ref", False) + else: #t2v + target_shape = image_embeds.get("target_shape", None) + if target_shape is None: + raise ValueError("Empty image embeds must be provided for T2V (Text to Video") + + has_ref = image_embeds.get("has_ref", False) + vace_context = image_embeds.get("vace_context", None) + vace_scale = image_embeds.get("vace_scale", None) + if not isinstance(vace_scale, list): + vace_scale = [vace_scale] * (steps+1) + vace_start_percent = image_embeds.get("vace_start_percent", 0.0) + vace_end_percent = image_embeds.get("vace_end_percent", 1.0) + vace_seqlen = image_embeds.get("vace_seq_len", None) + + vace_additional_embeds = image_embeds.get("additional_vace_inputs", []) + if vace_context is not None: + vace_data = [ + {"context": vace_context, + "scale": vace_scale, + "start": vace_start_percent, + "end": vace_end_percent, + "seq_len": vace_seqlen + } + ] + if len(vace_additional_embeds) > 0: + for i in range(len(vace_additional_embeds)): + if vace_additional_embeds[i].get("has_ref", False): + has_ref = True + vace_scale = vace_additional_embeds[i]["vace_scale"] + if not isinstance(vace_scale, list): + vace_scale = [vace_scale] * (steps+1) + vace_data.append({ + "context": vace_additional_embeds[i]["vace_context"], + "scale": vace_scale, + "start": vace_additional_embeds[i]["vace_start_percent"], + "end": vace_additional_embeds[i]["vace_end_percent"], + "seq_len": vace_additional_embeds[i]["vace_seq_len"] + }) + + noise = torch.randn( + target_shape[0], + target_shape[1] + 1 if has_ref else target_shape[1], + target_shape[2], + target_shape[3], + dtype=torch.float32, + device=torch.device("cpu"), + generator=seed_g) + + seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1]) + + recammaster = image_embeds.get("recammaster", None) + if recammaster is not None: + camera_embed = recammaster.get("camera_embed", None) + recam_latents = recammaster.get("source_latents", None) + orig_noise_len = noise.shape[1] + log.info(f"RecamMaster camera embed shape: {camera_embed.shape}") + log.info(f"RecamMaster source video shape: {recam_latents.shape}") + seq_len *= 2 + + control_embeds = image_embeds.get("control_embeds", None) + if control_embeds is not None: + control_latents = control_embeds.get("control_images", None) + if control_latents is not None: + control_latents = control_latents.to(device) + control_camera_latents = control_embeds.get("control_camera_latents", None) + control_camera_start_percent = control_embeds.get("control_camera_start_percent", 0.0) + control_camera_end_percent = control_embeds.get("control_camera_end_percent", 1.0) + if control_camera_latents is not None: + control_camera_latents = control_camera_latents.to(device) + + if control_lora: + image_cond = control_latents.to(device) + if not patcher.model.is_patched: + log.info("Re-loading control LoRA...") + patcher = apply_lora(patcher, device, device, low_mem_load=False) + patcher.model.is_patched = True + else: + if transformer.in_dim not in [48, 32]: + raise ValueError("Control signal only works with Fun-Control model") + image_cond = torch.zeros_like(noise).to(device) #fun control + clip_fea = None + fun_ref_image = control_embeds.get("fun_ref_image", None) + control_start_percent = control_embeds.get("start_percent", 0.0) + control_end_percent = control_embeds.get("end_percent", 1.0) + else: + if transformer.in_dim == 36: #fun inp + mask_latents = torch.tile( + torch.zeros_like(noise[:1]), [4, 1, 1, 1] + ) + masked_video_latents_input = torch.zeros_like(noise) + image_cond = torch.cat([mask_latents, masked_video_latents_input], dim=0).to(device) + + phantom_latents = image_embeds.get("phantom_latents", None) + phantom_cfg_scale = image_embeds.get("phantom_cfg_scale", None) + if not isinstance(phantom_cfg_scale, list): + phantom_cfg_scale = [phantom_cfg_scale] * (steps +1) + phantom_start_percent = image_embeds.get("phantom_start_percent", 0.0) + phantom_end_percent = image_embeds.get("phantom_end_percent", 1.0) + if phantom_latents is not None: + phantom_latents = phantom_latents.to(device) + + latent_video_length = noise.shape[1] + + if unianimate_poses is not None: + transformer.dwpose_embedding.to(device, model["dtype"]) + dwpose_data = unianimate_poses["pose"].to(device, model["dtype"]) + dwpose_data = torch.cat([dwpose_data[:,:,:1].repeat(1,1,3,1,1), dwpose_data], dim=2) + dwpose_data = transformer.dwpose_embedding(dwpose_data) + log.info(f"UniAnimate pose embed shape: {dwpose_data.shape}") + if dwpose_data.shape[2] > latent_video_length: + log.warning(f"UniAnimate pose embed length {dwpose_data.shape[2]} is longer than the video length {latent_video_length}, truncating") + dwpose_data = dwpose_data[:,:, :latent_video_length] + elif dwpose_data.shape[2] < latent_video_length: + log.warning(f"UniAnimate pose embed length {dwpose_data.shape[2]} is shorter than the video length {latent_video_length}, padding with last pose") + pad_len = latent_video_length - dwpose_data.shape[2] + pad = dwpose_data[:,:,:1].repeat(1,1,pad_len,1,1) + dwpose_data = torch.cat([dwpose_data, pad], dim=2) + dwpose_data_flat = rearrange(dwpose_data, 'b c f h w -> b (f h w) c').contiguous() + + random_ref_dwpose_data = None + if image_cond is not None: + transformer.randomref_embedding_pose.to(device) + random_ref_dwpose = unianimate_poses.get("ref", None) + if random_ref_dwpose is not None: + random_ref_dwpose_data = transformer.randomref_embedding_pose( + random_ref_dwpose.to(device) + ).unsqueeze(2).to(model["dtype"]) # [1, 20, 104, 60] + + unianim_data = { + "dwpose": dwpose_data_flat, + "random_ref": random_ref_dwpose_data.squeeze(0) if random_ref_dwpose_data is not None else None, + "strength": unianimate_poses["strength"], + "start_percent": unianimate_poses["start_percent"], + "end_percent": unianimate_poses["end_percent"] + } + + audio_proj = None + if fantasytalking_embeds is not None: + audio_proj = fantasytalking_embeds["audio_proj"].to(device) + audio_context_lens = fantasytalking_embeds["audio_context_lens"] + audio_scale = fantasytalking_embeds["audio_scale"] + audio_cfg_scale = fantasytalking_embeds["audio_cfg_scale"] + if not isinstance(audio_cfg_scale, list): + audio_cfg_scale = [audio_cfg_scale] * (steps +1) + log.info(f"Audio proj shape: {audio_proj.shape}, audio context lens: {audio_context_lens}") + + minimax_latents = minimax_mask_latents = None + minimax_latents = image_embeds.get("minimax_latents", None) + minimax_mask_latents = image_embeds.get("minimax_mask_latents", None) + if minimax_latents is not None: + log.info(f"minimax_latents: {minimax_latents.shape}") + log.info(f"minimax_mask_latents: {minimax_mask_latents.shape}") + minimax_latents = minimax_latents.to(device, dtype) + minimax_mask_latents = minimax_mask_latents.to(device, dtype) + + is_looped = False + if context_options is not None: + def create_window_mask(noise_pred_context, c, latent_video_length, context_overlap, looped=False): + window_mask = torch.ones_like(noise_pred_context) + + # Apply left-side blending for all except first chunk (or always in loop mode) + if min(c) > 0 or (looped and max(c) == latent_video_length - 1): + ramp_up = torch.linspace(0, 1, context_overlap, device=noise_pred_context.device) + ramp_up = ramp_up.view(1, -1, 1, 1) + window_mask[:, :context_overlap] = ramp_up + + # Apply right-side blending for all except last chunk (or always in loop mode) + if max(c) < latent_video_length - 1 or (looped and min(c) == 0): + ramp_down = torch.linspace(1, 0, context_overlap, device=noise_pred_context.device) + ramp_down = ramp_down.view(1, -1, 1, 1) + window_mask[:, -context_overlap:] = ramp_down + + return window_mask + + context_schedule = context_options["context_schedule"] + context_frames = (context_options["context_frames"] - 1) // 4 + 1 + context_stride = context_options["context_stride"] // 4 + context_overlap = context_options["context_overlap"] // 4 + context_vae = context_options.get("vae", None) + if context_vae is not None: + context_vae.to(device) + + self.window_tracker = WindowTracker(verbose=context_options["verbose"]) + + # Get total number of prompts + num_prompts = len(text_embeds["prompt_embeds"]) + log.info(f"Number of prompts: {num_prompts}") + # Calculate which section this context window belongs to + section_size = latent_video_length / num_prompts + log.info(f"Section size: {section_size}") + is_looped = context_schedule == "uniform_looped" + + seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * context_frames) + + if context_options["freenoise"]: + log.info("Applying FreeNoise") + # code from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) + delta = context_frames - context_overlap + for start_idx in range(0, latent_video_length-context_frames, delta): + place_idx = start_idx + context_frames + if place_idx >= latent_video_length: + break + end_idx = place_idx - 1 + + if end_idx + delta >= latent_video_length: + final_delta = latent_video_length - place_idx + list_idx = torch.tensor(list(range(start_idx,start_idx+final_delta)), device=torch.device("cpu"), dtype=torch.long) + list_idx = list_idx[torch.randperm(final_delta, generator=seed_g)] + noise[:, place_idx:place_idx + final_delta, :, :] = noise[:, list_idx, :, :] + break + list_idx = torch.tensor(list(range(start_idx,start_idx+delta)), device=torch.device("cpu"), dtype=torch.long) + list_idx = list_idx[torch.randperm(delta, generator=seed_g)] + noise[:, place_idx:place_idx + delta, :, :] = noise[:, list_idx, :, :] + + log.info(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap") + from .context import get_context_scheduler + context = get_context_scheduler(context_schedule) + + if samples is not None: + input_samples = samples["samples"].squeeze(0).to(noise) + if input_samples.shape[1] != noise.shape[1]: + input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1) + original_image = input_samples.to(device) + if denoise_strength < 1.0: + latent_timestep = timesteps[:1].to(noise) + noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples + + mask = samples.get("mask", None) + if mask is not None: + if mask.shape[2] != noise.shape[1]: + mask = torch.cat([torch.zeros(1, noise.shape[0], noise.shape[1] - mask.shape[2], noise.shape[2], noise.shape[3]), mask], dim=2) + + latent = noise.to(device) + + freqs = None + transformer.rope_embedder.k = None + transformer.rope_embedder.num_frames = None + if rope_function=="comfy": + transformer.rope_embedder.k = riflex_freq_index + transformer.rope_embedder.num_frames = latent_video_length + else: + d = transformer.dim // transformer.num_heads + freqs = torch.cat([ + rope_params(1024, d - 4 * (d // 6), L_test=latent_video_length, k=riflex_freq_index), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)) + ], + dim=1) + + if not isinstance(cfg, list): + cfg = [cfg] * (steps +1) + + log.info(f"Seq len: {seq_len}") + + pbar = ProgressBar(steps) + + if args.preview_method in [LatentPreviewMethod.Auto, LatentPreviewMethod.Latent2RGB]: #default for latent2rgb + from latent_preview import prepare_callback + else: + from .latent_preview import prepare_callback #custom for tiny VAE previews + callback = prepare_callback(patcher, steps) + + #blockswap init + if transformer_options is not None: + block_swap_args = transformer_options.get("block_swap_args", None) + + if block_swap_args is not None: + transformer.use_non_blocking = block_swap_args.get("use_non_blocking", True) + for name, param in transformer.named_parameters(): + if "block" not in name: + param.data = param.data.to(device) + if "control_adapter" in name: + param.data = param.data.to(device) + elif block_swap_args["offload_txt_emb"] and "txt_emb" in name: + param.data = param.data.to(offload_device, non_blocking=transformer.use_non_blocking) + elif block_swap_args["offload_img_emb"] and "img_emb" in name: + param.data = param.data.to(offload_device, non_blocking=transformer.use_non_blocking) + + transformer.block_swap( + block_swap_args["blocks_to_swap"] - 1 , + block_swap_args["offload_txt_emb"], + block_swap_args["offload_img_emb"], + vace_blocks_to_swap = block_swap_args.get("vace_blocks_to_swap", None), + ) + + elif model["auto_cpu_offload"]: + for module in transformer.modules(): + if hasattr(module, "offload"): + module.offload() + if hasattr(module, "onload"): + module.onload() + elif model["manual_offloading"]: + transformer.to(device) + + #controlnet + controlnet_latents = controlnet = None + if transformer_options is not None: + controlnet = transformer_options.get("controlnet", None) + if controlnet is not None: + self.controlnet = controlnet["controlnet"] + controlnet_start = controlnet["controlnet_start"] + controlnet_end = controlnet["controlnet_end"] + controlnet_latents = controlnet["control_latents"] + controlnet["controlnet_weight"] = controlnet["controlnet_strength"] + controlnet["controlnet_stride"] = controlnet["control_stride"] + + #uni3c + pcd_data = None + if uni3c_embeds is not None: + transformer.controlnet = uni3c_embeds["controlnet"] + pcd_data = { + "render_latent": uni3c_embeds["render_latent"], + "render_mask": uni3c_embeds["render_mask"], + "camera_embedding": uni3c_embeds["camera_embedding"], + "controlnet_weight": uni3c_embeds["controlnet_weight"], + "start": uni3c_embeds["start"], + "end": uni3c_embeds["end"], + } + + #feta + if feta_args is not None and latent_video_length > 1: + set_enhance_weight(feta_args["weight"]) + feta_start_percent = feta_args["start_percent"] + feta_end_percent = feta_args["end_percent"] + if context_options is not None: + set_num_frames(context_frames) + else: + set_num_frames(latent_video_length) + enable_enhance() + else: + feta_args = None + disable_enhance() + + # Initialize Cache if enabled + transformer.enable_teacache = transformer.enable_magcache = False + if teacache_args is not None: #for backward compatibility on old workflows + cache_args = teacache_args + if cache_args is not None: + transformer.cache_device = cache_args["cache_device"] + if cache_args["cache_type"] == "TeaCache": + log.info(f"TeaCache: Using cache device: {transformer.cache_device}") + transformer.teacache_state.clear_all() + transformer.enable_teacache = True + transformer.rel_l1_thresh = cache_args["rel_l1_thresh"] + transformer.teacache_start_step = cache_args["start_step"] + transformer.teacache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"] + transformer.teacache_use_coefficients = cache_args["use_coefficients"] + transformer.teacache_mode = cache_args["mode"] + elif cache_args["cache_type"] == "MagCache": + log.info(f"MagCache: Using cache device: {transformer.cache_device}") + transformer.magcache_state.clear_all() + transformer.enable_magcache = True + transformer.magcache_start_step = cache_args["start_step"] + transformer.magcache_end_step = len(timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"] + transformer.magcache_thresh = cache_args["magcache_thresh"] + transformer.magcache_K = cache_args["magcache_K"] + + if slg_args is not None: + assert batched_cfg is not None, "Batched cfg is not supported with SLG" + transformer.slg_blocks = slg_args["blocks"] + transformer.slg_start_percent = slg_args["start_percent"] + transformer.slg_end_percent = slg_args["end_percent"] + else: + transformer.slg_blocks = None + + self.cache_state = [None, None] + if phantom_latents is not None: + log.info(f"Phantom latents shape: {phantom_latents.shape}") + self.cache_state = [None, None, None] + self.cache_state_source = [None, None] + self.cache_states_context = [] + + if flowedit_args is not None: + source_embeds = flowedit_args["source_embeds"] + source_image_embeds = flowedit_args.get("source_image_embeds", image_embeds) + source_image_cond = source_image_embeds.get("image_embeds", None) + source_clip_fea = source_image_embeds.get("clip_fea", clip_fea) + if source_image_cond is not None: + source_image_cond = source_image_cond.to(dtype) + skip_steps = flowedit_args["skip_steps"] + drift_steps = flowedit_args["drift_steps"] + source_cfg = flowedit_args["source_cfg"] + if not isinstance(source_cfg, list): + source_cfg = [source_cfg] * (steps +1) + drift_cfg = flowedit_args["drift_cfg"] + if not isinstance(drift_cfg, list): + drift_cfg = [drift_cfg] * (steps +1) + + x_init = samples["samples"].clone().squeeze(0).to(device) + x_tgt = samples["samples"].squeeze(0).to(device) + + sample_scheduler = FlowMatchEulerDiscreteScheduler( + num_train_timesteps=1000, + shift=flowedit_args["drift_flow_shift"], + use_dynamic_shifting=False) + + sampling_sigmas = get_sampling_sigmas(steps, flowedit_args["drift_flow_shift"]) + + drift_timesteps, _ = retrieve_timesteps( + sample_scheduler, + device=device, + sigmas=sampling_sigmas) + + if drift_steps > 0: + drift_timesteps = torch.cat([drift_timesteps, torch.tensor([0]).to(drift_timesteps.device)]).to(drift_timesteps.device) + timesteps[-drift_steps:] = drift_timesteps[-drift_steps:] + + use_cfg_zero_star = use_fresca = False + if experimental_args is not None: + video_attention_split_steps = experimental_args.get("video_attention_split_steps", []) + if video_attention_split_steps: + transformer.video_attention_split_steps = [int(x.strip()) for x in video_attention_split_steps.split(",")] + else: + transformer.video_attention_split_steps = [] + + use_zero_init = experimental_args.get("use_zero_init", True) + use_cfg_zero_star = experimental_args.get("cfg_zero_star", False) + zero_star_steps = experimental_args.get("zero_star_steps", 0) + + use_fresca = experimental_args.get("use_fresca", False) + if use_fresca: + fresca_scale_low = experimental_args.get("fresca_scale_low", 1.0) + fresca_scale_high = experimental_args.get("fresca_scale_high", 1.25) + fresca_freq_cutoff = experimental_args.get("fresca_freq_cutoff", 20) + + #region model pred + def predict_with_cfg(z, cfg_scale, positive_embeds, negative_embeds, timestep, idx, image_cond=None, clip_fea=None, + control_latents=None, vace_data=None, unianim_data=None, audio_proj=None, control_camera_latents=None, add_cond=None, cache_state=None): + z = z.to(dtype) + with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=("fp8" in model["quantization"])): + + if use_cfg_zero_star and (idx <= zero_star_steps) and use_zero_init: + return z*0, None + + nonlocal patcher + current_step_percentage = idx / len(timesteps) + control_lora_enabled = False + image_cond_input = None + if control_latents is not None: + if control_lora: + control_lora_enabled = True + else: + if (control_start_percent <= current_step_percentage <= control_end_percent) or \ + (control_end_percent > 0 and idx == 0 and current_step_percentage >= control_start_percent): + image_cond_input = torch.cat([control_latents.to(z), image_cond.to(z)]) + else: + image_cond_input = torch.cat([torch.zeros_like(image_cond, dtype=dtype), image_cond.to(z)]) + if fun_ref_image is not None: + fun_ref_input = fun_ref_image.to(z) + else: + fun_ref_input = torch.zeros_like(z, dtype=z.dtype)[:, 0].unsqueeze(1) + #fun_ref_input = None + + if control_lora: + if not control_start_percent <= current_step_percentage <= control_end_percent: + control_lora_enabled = False + if patcher.model.is_patched: + log.info("Unloading LoRA...") + patcher.unpatch_model(device) + patcher.model.is_patched = False + else: + image_cond_input = control_latents.to(z) + if not patcher.model.is_patched: + log.info("Loading LoRA...") + patcher = apply_lora(patcher, device, device, low_mem_load=False) + patcher.model.is_patched = True + + elif ATI_tracks is not None and ((ati_start_percent <= current_step_percentage <= ati_end_percent) or + (ati_end_percent > 0 and idx == 0 and current_step_percentage >= ati_start_percent)): + image_cond_input = image_cond_ati.to(z) + else: + image_cond_input = image_cond.to(z) if image_cond is not None else None + + if control_camera_latents is not None: + if (control_camera_start_percent <= current_step_percentage <= control_camera_end_percent) or \ + (control_end_percent > 0 and idx == 0 and current_step_percentage >= control_camera_start_percent): + control_camera_input = control_camera_latents.to(z) + else: + control_camera_input = None + + if recammaster is not None: + z = torch.cat([z, recam_latents.to(z)], dim=1) + + use_phantom = False + if phantom_latents is not None: + if (phantom_start_percent <= current_step_percentage <= phantom_end_percent) or \ + (phantom_end_percent > 0 and idx == 0 and current_step_percentage >= phantom_start_percent): + + z_pos = torch.cat([z[:,:-phantom_latents.shape[1]], phantom_latents.to(z)], dim=1) + z_phantom_img = torch.cat([z[:,:-phantom_latents.shape[1]], phantom_latents.to(z)], dim=1) + z_neg = torch.cat([z[:,:-phantom_latents.shape[1]], torch.zeros_like(phantom_latents).to(z)], dim=1) + use_phantom = True + if cache_state is not None and len(cache_state) != 3: + cache_state.append(None) + if not use_phantom: + z_pos = z_neg = z + + if controlnet_latents is not None: + if (controlnet_start <= current_step_percentage < controlnet_end): + self.controlnet.to(device) + controlnet_states = self.controlnet( + hidden_states=z.unsqueeze(0).to(device, self.controlnet.dtype), + timestep=timestep, + encoder_hidden_states=positive_embeds[0].unsqueeze(0).to(device, self.controlnet.dtype), + attention_kwargs=None, + controlnet_states=controlnet_latents.to(device, self.controlnet.dtype), + return_dict=False, + )[0] + if isinstance(controlnet_states, (tuple, list)): + controlnet["controlnet_states"] = [x.to(z) for x in controlnet_states] + else: + controlnet["controlnet_states"] = controlnet_states.to(z) + + add_cond_input = None + if add_cond is not None: + if (add_cond_start_percent <= current_step_percentage <= add_cond_end_percent) or \ + (add_cond_end_percent > 0 and idx == 0 and current_step_percentage >= add_cond_start_percent): + add_cond_input = add_cond + + if minimax_latents is not None: + z_pos = z_neg = torch.cat([z, minimax_latents, minimax_mask_latents], dim=0) + + base_params = { + 'seq_len': seq_len, + 'device': device, + 'freqs': freqs, + 't': timestep, + 'current_step': idx, + 'control_lora_enabled': control_lora_enabled, + 'camera_embed': camera_embed, + 'unianim_data': unianim_data, + 'fun_ref': fun_ref_input if fun_ref_image is not None else None, + 'fun_camera': control_camera_input if control_camera_latents is not None else None, + 'audio_proj': audio_proj if fantasytalking_embeds is not None else None, + 'audio_context_lens': audio_context_lens if fantasytalking_embeds is not None else None, + 'audio_scale': audio_scale if fantasytalking_embeds is not None else None, + "pcd_data": pcd_data, + "controlnet": controlnet, + "add_cond": add_cond_input, + "nag_params": text_embeds.get("nag_params", {}), + "nag_context": text_embeds.get("nag_prompt_embeds", None), + } + + batch_size = 1 + + if not math.isclose(cfg_scale, 1.0) and len(positive_embeds) > 1: + negative_embeds = negative_embeds * len(positive_embeds) + + if not batched_cfg: + #cond + noise_pred_cond, cache_state_cond = transformer( + [z_pos], context=positive_embeds, y=[image_cond_input] if image_cond_input is not None else None, + clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage, + pred_id=cache_state[0] if cache_state else None, + vace_data=vace_data, attn_cond=attn_cond, + **base_params + ) + noise_pred_cond = noise_pred_cond[0].to(intermediate_device) + if math.isclose(cfg_scale, 1.0): + if use_fresca: + noise_pred_cond = fourier_filter( + noise_pred_cond, + scale_low=fresca_scale_low, + scale_high=fresca_scale_high, + freq_cutoff=fresca_freq_cutoff, + ) + return noise_pred_cond, [cache_state_cond] + #uncond + if fantasytalking_embeds is not None: + if not math.isclose(audio_cfg_scale[idx], 1.0): + base_params['audio_proj'] = None + noise_pred_uncond, cache_state_uncond = transformer( + [z_neg], context=negative_embeds, clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea, + y=[image_cond_input] if image_cond_input is not None else None, + is_uncond=True, current_step_percentage=current_step_percentage, + pred_id=cache_state[1] if cache_state else None, + vace_data=vace_data, attn_cond=attn_cond_neg, + **base_params + ) + noise_pred_uncond = noise_pred_uncond[0].to(intermediate_device) + #phantom + if use_phantom and not math.isclose(phantom_cfg_scale[idx], 1.0): + noise_pred_phantom, cache_state_phantom = transformer( + [z_phantom_img], context=negative_embeds, clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea, + y=[image_cond_input] if image_cond_input is not None else None, + is_uncond=True, current_step_percentage=current_step_percentage, + pred_id=cache_state[2] if cache_state else None, + vace_data=None, + **base_params + ) + noise_pred_phantom = noise_pred_phantom[0].to(intermediate_device) + + noise_pred = noise_pred_uncond + phantom_cfg_scale[idx] * (noise_pred_phantom - noise_pred_uncond) + cfg_scale * (noise_pred_cond - noise_pred_phantom) + return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_phantom] + #fantasytalking + if fantasytalking_embeds is not None: + if not math.isclose(audio_cfg_scale[idx], 1.0): + if cache_state is not None and len(cache_state) != 3: + cache_state.append(None) + base_params['audio_proj'] = None + noise_pred_no_audio, cache_state_audio = transformer( + [z_pos], context=positive_embeds, y=[image_cond_input] if image_cond_input is not None else None, + clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage, + pred_id=cache_state[2] if cache_state else None, + vace_data=vace_data, + **base_params + ) + noise_pred_no_audio = noise_pred_no_audio[0].to(intermediate_device) + noise_pred = ( + noise_pred_uncond + + cfg_scale * (noise_pred_no_audio - noise_pred_uncond) + + audio_cfg_scale[idx] * (noise_pred_cond - noise_pred_no_audio) + ) + return noise_pred, [cache_state_cond, cache_state_uncond, cache_state_audio] + + #batched + else: + cache_state_uncond = None + [noise_pred_cond, noise_pred_uncond], cache_state_cond = transformer( + [z] + [z], context=positive_embeds + negative_embeds, + y=[image_cond_input] + [image_cond_input] if image_cond_input is not None else None, + clip_fea=clip_fea.repeat(2,1,1), is_uncond=False, current_step_percentage=current_step_percentage, + pred_id=cache_state[0] if cache_state else None, + **base_params + ) + #cfg + + #https://github.com/WeichenFan/CFG-Zero-star/ + if use_cfg_zero_star: + alpha = optimized_scale( + noise_pred_cond.view(batch_size, -1), + noise_pred_uncond.view(batch_size, -1) + ).view(batch_size, 1, 1, 1) + else: + alpha = 1.0 + + #https://github.com/WikiChao/FreSca + if use_fresca: + filtered_cond = fourier_filter( + noise_pred_cond - noise_pred_uncond, + scale_low=fresca_scale_low, + scale_high=fresca_scale_high, + freq_cutoff=fresca_freq_cutoff, + ) + noise_pred = noise_pred_uncond * alpha + cfg_scale * filtered_cond * alpha + else: + noise_pred = noise_pred_uncond * alpha + cfg_scale * (noise_pred_cond - noise_pred_uncond * alpha) + + + return noise_pred, [cache_state_cond, cache_state_uncond] + + log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latent.shape[3]*8}x{latent.shape[2]*8} with {steps} steps") + + intermediate_device = device + + # diff diff prep + masks = None + if samples is not None and mask is not None: + mask = 1 - mask + thresholds = torch.arange(len(timesteps), dtype=original_image.dtype) / len(timesteps) + thresholds = thresholds.unsqueeze(1).unsqueeze(1).unsqueeze(1).unsqueeze(1).to(device) + masks = mask.repeat(len(timesteps), 1, 1, 1, 1).to(device) + masks = masks > thresholds + + latent_shift_loop = False + if loop_args is not None: + latent_shift_loop = True + is_looped = True + latent_skip = loop_args["shift_skip"] + latent_shift_start_percent = loop_args["start_percent"] + latent_shift_end_percent = loop_args["end_percent"] + shift_idx = 0 + + #clear memory before sampling + mm.unload_all_models() + mm.soft_empty_cache() + gc.collect() + try: + torch.cuda.reset_peak_memory_stats(device) + except: + pass + + #region main loop start + for idx, t in enumerate(tqdm(timesteps)): + if flowedit_args is not None: + if idx < skip_steps: + continue + + # diff diff + if masks is not None: + if idx < len(timesteps) - 1: + noise_timestep = timesteps[idx+1] + image_latent = sample_scheduler.scale_noise( + original_image, torch.tensor([noise_timestep]), noise.to(device) + ) + mask = masks[idx] + mask = mask.to(latent) + latent = image_latent * mask + latent * (1-mask) + # end diff diff + + latent_model_input = latent.to(device) + + timestep = torch.tensor([t]).to(device) + current_step_percentage = idx / len(timesteps) + + ### latent shift + if latent_shift_loop: + if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent: + latent_model_input = torch.cat([latent_model_input[:, shift_idx:]] + [latent_model_input[:, :shift_idx]], dim=1) + + #enhance-a-video + if feta_args is not None and feta_start_percent <= current_step_percentage <= feta_end_percent: + enable_enhance() + else: + disable_enhance() + + #flow-edit + if flowedit_args is not None: + sigma = t / 1000.0 + sigma_prev = (timesteps[idx + 1] if idx < len(timesteps) - 1 else timesteps[-1]) / 1000.0 + noise = torch.randn(x_init.shape, generator=seed_g, device=torch.device("cpu")) + if idx < len(timesteps) - drift_steps: + cfg = drift_cfg + + zt_src = (1-sigma) * x_init + sigma * noise.to(t) + zt_tgt = x_tgt + zt_src - x_init + + #source + if idx < len(timesteps) - drift_steps: + if context_options is not None: + counter = torch.zeros_like(zt_src, device=intermediate_device) + vt_src = torch.zeros_like(zt_src, device=intermediate_device) + context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap)) + for c in context_queue: + window_id = self.window_tracker.get_window_id(c) + + if cache_args is not None: + current_teacache = self.window_tracker.get_teacache(window_id, self.cache_state) + else: + current_teacache = None + + prompt_index = min(int(max(c) / section_size), num_prompts - 1) + if context_options["verbose"]: + log.info(f"Prompt index: {prompt_index}") + + if len(source_embeds["prompt_embeds"]) > 1: + positive = source_embeds["prompt_embeds"][prompt_index] + else: + positive = source_embeds["prompt_embeds"] + + partial_img_emb = None + if source_image_cond is not None: + partial_img_emb = source_image_cond[:, c, :, :] + partial_img_emb[:, 0, :, :] = source_image_cond[:, 0, :, :].to(intermediate_device) + + partial_zt_src = zt_src[:, c, :, :] + vt_src_context, new_teacache = predict_with_cfg( + partial_zt_src, cfg[idx], + positive, source_embeds["negative_prompt_embeds"], + timestep, idx, partial_img_emb, control_latents, + source_clip_fea, current_teacache) + + if cache_args is not None: + self.window_tracker.cache_states[window_id] = new_teacache + + window_mask = create_window_mask(vt_src_context, c, latent_video_length, context_overlap) + vt_src[:, c, :, :] += vt_src_context * window_mask + counter[:, c, :, :] += window_mask + vt_src /= counter + else: + vt_src, self.cache_state_source = predict_with_cfg( + zt_src, cfg[idx], + source_embeds["prompt_embeds"], + source_embeds["negative_prompt_embeds"], + timestep, idx, source_image_cond, + source_clip_fea, control_latents, + cache_state=self.cache_state_source) + else: + if idx == len(timesteps) - drift_steps: + x_tgt = zt_tgt + zt_tgt = x_tgt + vt_src = 0 + #target + if context_options is not None: + counter = torch.zeros_like(zt_tgt, device=intermediate_device) + vt_tgt = torch.zeros_like(zt_tgt, device=intermediate_device) + context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap)) + for c in context_queue: + window_id = self.window_tracker.get_window_id(c) + + if cache_args is not None: + current_teacache = self.window_tracker.get_teacache(window_id, self.cache_state) + else: + current_teacache = None + + prompt_index = min(int(max(c) / section_size), num_prompts - 1) + if context_options["verbose"]: + log.info(f"Prompt index: {prompt_index}") + + if len(text_embeds["prompt_embeds"]) > 1: + positive = text_embeds["prompt_embeds"][prompt_index] + else: + positive = text_embeds["prompt_embeds"] + + partial_img_emb = None + partial_control_latents = None + if image_cond is not None: + partial_img_emb = image_cond[:, c, :, :] + partial_img_emb[:, 0, :, :] = image_cond[:, 0, :, :].to(intermediate_device) + if control_latents is not None: + partial_control_latents = control_latents[:, c, :, :] + + partial_zt_tgt = zt_tgt[:, c, :, :] + vt_tgt_context, new_teacache = predict_with_cfg( + partial_zt_tgt, cfg[idx], + positive, text_embeds["negative_prompt_embeds"], + timestep, idx, partial_img_emb, partial_control_latents, + clip_fea, current_teacache) + + if cache_args is not None: + self.window_tracker.cache_states[window_id] = new_teacache + + window_mask = create_window_mask(vt_tgt_context, c, latent_video_length, context_overlap) + vt_tgt[:, c, :, :] += vt_tgt_context * window_mask + counter[:, c, :, :] += window_mask + vt_tgt /= counter + else: + vt_tgt, self.cache_state = predict_with_cfg( + zt_tgt, cfg[idx], + text_embeds["prompt_embeds"], + text_embeds["negative_prompt_embeds"], + timestep, idx, image_cond, clip_fea, control_latents, + cache_state=self.cache_state) + v_delta = vt_tgt - vt_src + x_tgt = x_tgt.to(torch.float32) + v_delta = v_delta.to(torch.float32) + x_tgt = x_tgt + (sigma_prev - sigma) * v_delta + x0 = x_tgt + #context windowing + elif context_options is not None: + counter = torch.zeros_like(latent_model_input, device=intermediate_device) + noise_pred = torch.zeros_like(latent_model_input, device=intermediate_device) + context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap)) + + for c in context_queue: + window_id = self.window_tracker.get_window_id(c) + + if cache_args is not None: + current_teacache = self.window_tracker.get_teacache(window_id, self.cache_state) + else: + current_teacache = None + + prompt_index = min(int(max(c) / section_size), num_prompts - 1) + if context_options["verbose"]: + log.info(f"Prompt index: {prompt_index}") + + # Use the appropriate prompt for this section + if len(text_embeds["prompt_embeds"]) > 1: + positive = text_embeds["prompt_embeds"][prompt_index] + else: + positive = text_embeds["prompt_embeds"] + + partial_img_emb = None + partial_control_latents = None + if image_cond is not None: + partial_img_emb = image_cond[:, c] + partial_img_emb[:, 0] = image_cond[:, 0].to(intermediate_device) + + if control_latents is not None: + partial_control_latents = control_latents[:, c] + + partial_control_camera_latents = None + if control_camera_latents is not None: + partial_control_camera_latents = control_camera_latents[:, :, c] + + partial_vace_context = None + if vace_data is not None: + window_vace_data = [] + for vace_entry in vace_data: + partial_context = vace_entry["context"][0][:, c] + if has_ref: + partial_context[:, 0] = vace_entry["context"][0][:, 0] + + window_vace_data.append({ + "context": [partial_context], + "scale": vace_entry["scale"], + "start": vace_entry["start"], + "end": vace_entry["end"], + "seq_len": vace_entry["seq_len"] + }) + + partial_vace_context = window_vace_data + + partial_audio_proj = None + if fantasytalking_embeds is not None: + partial_audio_proj = audio_proj[:, c] + + partial_latent_model_input = latent_model_input[:, c] + + partial_unianim_data = None + if unianim_data is not None: + partial_dwpose = dwpose_data[:, :, c] + partial_dwpose_flat=rearrange(partial_dwpose, 'b c f h w -> b (f h w) c') + partial_unianim_data = { + "dwpose": partial_dwpose_flat, + "random_ref": unianim_data["random_ref"], + "strength": unianimate_poses["strength"], + "start_percent": unianimate_poses["start_percent"], + "end_percent": unianimate_poses["end_percent"] + } + + partial_add_cond = None + if add_cond is not None: + partial_add_cond = add_cond[:, :, c].to(device, dtype) + + noise_pred_context, new_teacache = predict_with_cfg( + partial_latent_model_input, + cfg[idx], positive, + text_embeds["negative_prompt_embeds"], + timestep, idx, partial_img_emb, clip_fea, partial_control_latents, partial_vace_context, partial_unianim_data,partial_audio_proj, + partial_control_camera_latents, partial_add_cond, + current_teacache) + + if cache_args is not None: + self.window_tracker.cache_states[window_id] = new_teacache + + window_mask = create_window_mask(noise_pred_context, c, latent_video_length, context_overlap, looped=is_looped) + noise_pred[:, c] += noise_pred_context * window_mask + counter[:, c] += window_mask + noise_pred /= counter + #region normal inference + else: + noise_pred, self.cache_state = predict_with_cfg( + latent_model_input, + cfg[idx], + text_embeds["prompt_embeds"], + text_embeds["negative_prompt_embeds"], + timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond, + cache_state=self.cache_state) + + if latent_shift_loop: + #reverse latent shift + if latent_shift_start_percent <= current_step_percentage <= latent_shift_end_percent: + noise_pred = torch.cat([noise_pred[:, latent_video_length - shift_idx:]] + [noise_pred[:, :latent_video_length - shift_idx]], dim=1) + shift_idx = (shift_idx + latent_skip) % latent_video_length + + + if flowedit_args is None: + latent = latent.to(intermediate_device) + step_args = { + "generator": seed_g, + } + if isinstance(sample_scheduler, DEISMultistepScheduler) or isinstance(sample_scheduler, FlowMatchScheduler): + step_args.pop("generator", None) + temp_x0 = sample_scheduler.step( + noise_pred[:, :orig_noise_len].unsqueeze(0) if recammaster is not None else noise_pred.unsqueeze(0), + t, + latent[:, :orig_noise_len].unsqueeze(0) if recammaster is not None else latent.unsqueeze(0), + #return_dict=False, + **step_args)[0] + latent = temp_x0.squeeze(0) + + x0 = latent.to(device) + if callback is not None: + if recammaster is not None: + callback_latent = (latent_model_input[:, :orig_noise_len].to(device) - noise_pred[:, :orig_noise_len].to(device) * t.to(device) / 1000).detach().permute(1,0,2,3) + elif phantom_latents is not None: + callback_latent = (latent_model_input[:,:-phantom_latents.shape[1]].to(device) - noise_pred[:,:-phantom_latents.shape[1]].to(device) * t.to(device) / 1000).detach().permute(1,0,2,3) + else: + callback_latent = (latent_model_input.to(device) - noise_pred.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3) + callback(idx, callback_latent, None, steps) + else: + pbar.update(1) + del latent_model_input, timestep + else: + if callback is not None: + callback_latent = (zt_tgt.to(device) - vt_tgt.to(device) * t.to(device) / 1000).detach().permute(1,0,2,3) + callback(idx, callback_latent, None, steps) + else: + pbar.update(1) + + if phantom_latents is not None: + x0 = x0[:,:-phantom_latents.shape[1]] + + if cache_args is not None: + cache_type = cache_args["cache_type"] + states = transformer.teacache_state.states if cache_type == "TeaCache" else transformer.magcache_state.states + state_names = { + 0: "conditional", + 1: "unconditional" + } + for pred_id, state in states.items(): + name = state_names.get(pred_id, f"prediction_{pred_id}") + if 'skipped_steps' in state: + log.info(f"{cache_type} skipped: {len(state['skipped_steps'])} {name} steps: {state['skipped_steps']}") + transformer.teacache_state.clear_all() + transformer.magcache_state.clear_all() + del states + + if force_offload: + if model["manual_offloading"]: + transformer.to(offload_device) + mm.soft_empty_cache() + gc.collect() + + try: + print_memory(device) + torch.cuda.reset_peak_memory_stats(device) + except: + pass + + return ({ + "samples": x0.unsqueeze(0).cpu(), "looped": is_looped, "end_image": end_image if not fun_or_fl2v_model else None, "has_ref": has_ref, "drop_last": drop_last, + }, ) + +class WindowTracker: + def __init__(self, verbose=False): + self.window_map = {} # Maps frame sequence to persistent ID + self.next_id = 0 + self.cache_states = {} # Maps persistent ID to teacache state + self.verbose = verbose + + def get_window_id(self, frames): + key = tuple(sorted(frames)) # Order-independent frame sequence + if key not in self.window_map: + self.window_map[key] = self.next_id + if self.verbose: + log.info(f"New window pattern {key} -> ID {self.next_id}") + self.next_id += 1 + return self.window_map[key] + + def get_teacache(self, window_id, base_state): + if window_id not in self.cache_states: + if self.verbose: + log.info(f"Initializing persistent teacache for window {window_id}") + self.cache_states[window_id] = base_state.copy() + return self.cache_states[window_id] + +#region VideoDecode +class WanVideoDecode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "vae": ("WANVAE",), + "samples": ("LATENT",), + "enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": ( + "Drastically reduces memory use but will introduce seams at tile stride boundaries. " + "The location and number of seams is dictated by the tile stride size. " + "The visibility of seams can be controlled by increasing the tile size. " + "Seams become less obvious at 1.5x stride and are barely noticeable at 2x stride size. " + "Which is to say if you use a stride width of 160, the seams are barely noticeable with a tile width of 320." + )}), + "tile_x": ("INT", {"default": 272, "min": 40, "max": 2048, "step": 8, "tooltip": "Tile width in pixels. Smaller values use less VRAM but will make seams more obvious."}), + "tile_y": ("INT", {"default": 272, "min": 40, "max": 2048, "step": 8, "tooltip": "Tile height in pixels. Smaller values use less VRAM but will make seams more obvious."}), + "tile_stride_x": ("INT", {"default": 144, "min": 32, "max": 2040, "step": 8, "tooltip": "Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams."}), + "tile_stride_y": ("INT", {"default": 128, "min": 32, "max": 2040, "step": 8, "tooltip": "Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams."}), + }, + } + + @classmethod + def VALIDATE_INPUTS(s, tile_x, tile_y, tile_stride_x, tile_stride_y): + if tile_x <= tile_stride_x: + return "Tile width must be larger than the tile stride width." + if tile_y <= tile_stride_y: + return "Tile height must be larger than the tile stride height." + return True + + RETURN_TYPES = ("IMAGE",) + RETURN_NAMES = ("images",) + FUNCTION = "decode" + CATEGORY = "WanVideoWrapper" + + def decode(self, vae, samples, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y): + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + mm.soft_empty_cache() + latents = samples["samples"] + end_image = samples.get("end_image", None) + has_ref = samples.get("has_ref", False) + drop_last = samples.get("drop_last", False) + is_looped = samples.get("looped", False) + + vae.to(device) + + latents = latents.to(device = device, dtype = vae.dtype) + + mm.soft_empty_cache() + + if has_ref: + latents = latents[:, :, 1:] + if drop_last: + latents = latents[:, :, :-1] + + #if is_looped: + # latents = torch.cat([latents[:, :, :warmup_latent_count],latents], dim=2) + if type(vae).__name__ == "TAEHV": + images = vae.decode_video(latents.permute(0, 2, 1, 3, 4))[0].permute(1, 0, 2, 3) + images = torch.clamp(images, 0.0, 1.0) + images = images.permute(1, 2, 3, 0).cpu().float() + return (images,) + else: + if end_image is not None: + enable_vae_tiling = False + images = vae.decode(latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0] + vae.model.clear_cache() + + #images = (images - images.min()) / (images.max() - images.min()) + images = torch.clamp(images, -1.0, 1.0) + images = (images + 1.0) / 2.0 + + if is_looped: + #images = images[:, warmup_latent_count * 4:] + temp_latents = torch.cat([latents[:, :, -3:]] + [latents[:, :, :2]], dim=2) + temp_images = vae.decode(temp_latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0] + temp_images = (temp_images - temp_images.min()) / (temp_images.max() - temp_images.min()) + out = temp_images[:, 9:] + out = torch.cat([out, images[:, 5:]], dim=1) + images = out + + if end_image is not None: + #end_image = (end_image - end_image.min()) / (end_image.max() - end_image.min()) + #image[:, -1] = end_image[:, 0].to(image) #not sure about this + images = images[:, 0:-1] + + vae.model.clear_cache() + vae.to(offload_device) + mm.soft_empty_cache() + + images = torch.clamp(images, 0.0, 1.0) + images = images.permute(1, 2, 3, 0).cpu().float() + + return (images,) + +#region VideoEncode +class WanVideoEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "vae": ("WANVAE",), + "image": ("IMAGE",), + "enable_vae_tiling": ("BOOLEAN", {"default": False, "tooltip": "Drastically reduces memory use but may introduce seams"}), + "tile_x": ("INT", {"default": 272, "min": 64, "max": 2048, "step": 1, "tooltip": "Tile size in pixels, smaller values use less VRAM, may introduce more seams"}), + "tile_y": ("INT", {"default": 272, "min": 64, "max": 2048, "step": 1, "tooltip": "Tile size in pixels, smaller values use less VRAM, may introduce more seams"}), + "tile_stride_x": ("INT", {"default": 144, "min": 32, "max": 2048, "step": 32, "tooltip": "Tile stride in pixels, smaller values use less VRAM, may introduce more seams"}), + "tile_stride_y": ("INT", {"default": 128, "min": 32, "max": 2048, "step": 32, "tooltip": "Tile stride in pixels, smaller values use less VRAM, may introduce more seams"}), + }, + "optional": { + "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for leapfusion I2V where some noise can add motion and give sharper results"}), + "latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for leapfusion I2V where lower values allow for more motion"}), + "mask": ("MASK", ), + } + } + + RETURN_TYPES = ("LATENT",) + RETURN_NAMES = ("samples",) + FUNCTION = "encode" + CATEGORY = "WanVideoWrapper" + + def encode(self, vae, image, enable_vae_tiling, tile_x, tile_y, tile_stride_x, tile_stride_y, noise_aug_strength=0.0, latent_strength=1.0, mask=None): + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + vae.to(device) + + image = image.clone() + + B, H, W, C = image.shape + if W % 16 != 0 or H % 16 != 0: + new_height = (H // 16) * 16 + new_width = (W // 16) * 16 + log.warning(f"Image size {W}x{H} is not divisible by 16, resizing to {new_width}x{new_height}") + image = common_upscale(image.movedim(-1, 1), new_width, new_height, "lanczos", "disabled").movedim(1, -1) + + image = image.to(vae.dtype).to(device).unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W + if noise_aug_strength > 0.0: + image = add_noise_to_reference_video(image, ratio=noise_aug_strength) + + if isinstance(vae, TAEHV): + latents = vae.encode_video(image.permute(0, 2, 1, 3, 4), parallel=False)# B, T, C, H, W + latents = latents.permute(0, 2, 1, 3, 4) + else: + latents = vae.encode(image * 2.0 - 1.0, device=device, tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8)) + vae.model.clear_cache() + if latent_strength != 1.0: + latents *= latent_strength + + log.info(f"encoded latents shape {latents.shape}") + latent_mask = None + if mask is None: + vae.to(offload_device) + else: + #latent_mask = mask.clone().to(vae.dtype).to(device) * 2.0 - 1.0 + #latent_mask = latent_mask.unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1, 1) + #latent_mask = vae.encode(latent_mask, device=device, tiled=enable_vae_tiling, tile_size=(tile_x, tile_y), tile_stride=(tile_stride_x, tile_stride_y)) + target_h, target_w = latents.shape[3:] + + mask = torch.nn.functional.interpolate( + mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W] + size=(latents.shape[2], target_h, target_w), + mode='trilinear', + align_corners=False + ).squeeze(0) # Remove batch dim, keep channel dim + + # Add batch & channel dims for final output + latent_mask = mask.unsqueeze(0).repeat(1, latents.shape[1], 1, 1, 1) + log.info(f"latent mask shape {latent_mask.shape}") + vae.to(offload_device) + mm.soft_empty_cache() + + return ({"samples": latents, "mask": latent_mask},) + +NODE_CLASS_MAPPINGS = { + "WanVideoSampler": WanVideoSampler, + "WanVideoDecode": WanVideoDecode, + "WanVideoTextEncode": WanVideoTextEncode, + "WanVideoTextEncodeSingle": WanVideoTextEncodeSingle, + "WanVideoModelLoader": WanVideoModelLoader, + "WanVideoVAELoader": WanVideoVAELoader, + "LoadWanVideoT5TextEncoder": LoadWanVideoT5TextEncoder, + "WanVideoImageClipEncode": WanVideoImageClipEncode,#deprecated + "WanVideoClipVisionEncode": WanVideoClipVisionEncode, + "WanVideoImageToVideoEncode": WanVideoImageToVideoEncode, + "LoadWanVideoClipTextEncoder": LoadWanVideoClipTextEncoder, + "WanVideoEncode": WanVideoEncode, + "WanVideoBlockSwap": WanVideoBlockSwap, + "WanVideoTorchCompileSettings": WanVideoTorchCompileSettings, + "WanVideoEmptyEmbeds": WanVideoEmptyEmbeds, + "WanVideoLoraSelect": WanVideoLoraSelect, + "WanVideoLoraBlockEdit": WanVideoLoraBlockEdit, + "WanVideoEnhanceAVideo": WanVideoEnhanceAVideo, + "WanVideoContextOptions": WanVideoContextOptions, + "WanVideoTeaCache": WanVideoTeaCache, + "WanVideoMagCache": WanVideoMagCache, + "WanVideoVRAMManagement": WanVideoVRAMManagement, + "WanVideoTextEmbedBridge": WanVideoTextEmbedBridge, + "WanVideoFlowEdit": WanVideoFlowEdit, + "WanVideoControlEmbeds": WanVideoControlEmbeds, + "WanVideoSLG": WanVideoSLG, + "WanVideoTinyVAELoader": WanVideoTinyVAELoader, + "WanVideoLoopArgs": WanVideoLoopArgs, + "WanVideoImageResizeToClosest": WanVideoImageResizeToClosest, + "WanVideoSetBlockSwap": WanVideoSetBlockSwap, + "WanVideoExperimentalArgs": WanVideoExperimentalArgs, + "WanVideoVACEEncode": WanVideoVACEEncode, + "WanVideoVACEStartToEndFrame": WanVideoVACEStartToEndFrame, + "WanVideoVACEModelSelect": WanVideoVACEModelSelect, + "WanVideoPhantomEmbeds": WanVideoPhantomEmbeds, + "CreateCFGScheduleFloatList": CreateCFGScheduleFloatList, + "WanVideoRealisDanceLatents": WanVideoRealisDanceLatents, + "WanVideoApplyNAG": WanVideoApplyNAG, + "WanVideoMiniMaxRemoverEmbeds": WanVideoMiniMaxRemoverEmbeds, + "WanVideoLoraSelectMulti": WanVideoLoraSelectMulti + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoSampler": "WanVideo Sampler", + "WanVideoDecode": "WanVideo Decode", + "WanVideoTextEncode": "WanVideo TextEncode", + "WanVideoTextEncodeSingle": "WanVideo TextEncodeSingle", + "WanVideoTextImageEncode": "WanVideo TextImageEncode (IP2V)", + "WanVideoModelLoader": "WanVideo Model Loader", + "WanVideoVAELoader": "WanVideo VAE Loader", + "LoadWanVideoT5TextEncoder": "Load WanVideo T5 TextEncoder", + "WanVideoImageClipEncode": "WanVideo ImageClip Encode (Deprecated)", + "WanVideoClipVisionEncode": "WanVideo ClipVision Encode", + "WanVideoImageToVideoEncode": "WanVideo ImageToVideo Encode", + "LoadWanVideoClipTextEncoder": "Load WanVideo Clip Encoder", + "WanVideoEncode": "WanVideo Encode", + "WanVideoBlockSwap": "WanVideo BlockSwap", + "WanVideoTorchCompileSettings": "WanVideo Torch Compile Settings", + "WanVideoEmptyEmbeds": "WanVideo Empty Embeds", + "WanVideoLoraSelect": "WanVideo Lora Select", + "WanVideoLoraBlockEdit": "WanVideo Lora Block Edit", + "WanVideoEnhanceAVideo": "WanVideo Enhance-A-Video", + "WanVideoContextOptions": "WanVideo Context Options", + "WanVideoTeaCache": "WanVideo TeaCache", + "WanVideoMagCache": "WanVideo MagCache", + "WanVideoVRAMManagement": "WanVideo VRAM Management", + "WanVideoTextEmbedBridge": "WanVideo TextEmbed Bridge", + "WanVideoFlowEdit": "WanVideo FlowEdit", + "WanVideoControlEmbeds": "WanVideo Control Embeds", + "WanVideoSLG": "WanVideo SLG", + "WanVideoTinyVAELoader": "WanVideo Tiny VAE Loader", + "WanVideoLoopArgs": "WanVideo Loop Args", + "WanVideoImageResizeToClosest": "WanVideo Image Resize To Closest", + "WanVideoSetBlockSwap": "WanVideo Set BlockSwap", + "WanVideoExperimentalArgs": "WanVideo Experimental Args", + "WanVideoVACEEncode": "WanVideo VACE Encode", + "WanVideoVACEStartToEndFrame": "WanVideo VACE Start To End Frame", + "WanVideoVACEModelSelect": "WanVideo VACE Model Select", + "WanVideoPhantomEmbeds": "WanVideo Phantom Embeds", + "CreateCFGScheduleFloatList": "WanVideo CFG Schedule Float List", + "WanVideoRealisDanceLatents": "WanVideo RealisDance Latents", + "WanVideoApplyNAG": "WanVideo Apply NAG", + "WanVideoMiniMaxRemoverEmbeds": "WanVideo MiniMax Remover Embeds", + "WanVideoLoraSelectMulti": "WanVideo Lora Select Multi" + } diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/pyproject.toml b/custom_nodes/ComfyUI_WanVideoWrapper/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b6e50e073a4070847cd7ee11ec7736fae18b362f --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "ComfyUI-WanVideoWrapper" +description = "ComfyUI wrapper nodes for WanVideo" +version = "1.2.0" +license = {file = "LICENSE"} +dependencies = ["accelerate >= 1.2.1", "diffusers >= 0.32.0", "ftfy"] + +[project.urls] +Repository = "https://github.com/kijai/ComfyUI-WanVideoWrapper" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "kijai" +DisplayName = "ComfyUI-WanVideoWrapper" +Icon = "" diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/readme.md b/custom_nodes/ComfyUI_WanVideoWrapper/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..dafb43aa209e6bc9bec5908b6bd34b8ad853a6fa --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/readme.md @@ -0,0 +1,74 @@ +# ComfyUI wrapper nodes for [WanVideo](https://github.com/Wan-Video/Wan2.1) + +# WORK IN PROGRESS + +# Installation +1. Clone this repo into `custom_nodes` folder. +2. Install dependencies: `pip install -r requirements.txt` + or if you use the portable install, run this in ComfyUI_windows_portable -folder: + + `python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-WanVideoWrapper\requirements.txt` + +## Models + +https://huggingface.co/Kijai/WanVideo_comfy/tree/main + +Text encoders to `ComfyUI/models/text_encoders` + +Clip vision to `ComfyUI/models/clip_vision` + +Transformer to `ComfyUI/models/diffusion_models` + +Vae to `ComfyUI/models/vae` + +You can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models: + +![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4) + +--- + +Examples: +--- + +[ReCamMaster](https://github.com/KwaiVGI/ReCamMaster): + +https://github.com/user-attachments/assets/c58a12c2-13ba-4af8-8041-e283dbef197e + + +TeaCache (with the old temporary WIP naive version, I2V): + +**Note that with the new version the threshold values should be 10x higher** + +Range of 0.25-0.30 seems good when using the coefficients, start step can be 0, with more aggressive threshold values it may make sense to start later to avoid any potential step skips early on, that generally ruin the motion. + +https://github.com/user-attachments/assets/504a9a50-3337-43d2-97b8-8e1661f29f46 + + +Context window test: + +1025 frames using window size of 81 frames, with 16 overlap. With the 1.3B T2V model this used under 5GB VRAM and took 10 minutes to gen on a 5090: + +https://github.com/user-attachments/assets/89b393af-cf1b-49ae-aa29-23e57f65911e + +--- + + +This very first test was 512x512x81 + +~16GB used with 20/40 blocks offloaded + +https://github.com/user-attachments/assets/fa6d0a4f-4a4d-4de5-84a4-877cc37b715f + +Vid2vid example: + + +with 14B T2V model: + +https://github.com/user-attachments/assets/ef228b8a-a13a-4327-8a1b-1eb343cf00d8 + +with 1.3B T2V model + +https://github.com/user-attachments/assets/4f35ba84-da7a-4d5b-97ee-9641296f391e + + + diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/recammaster/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/recammaster/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..c41f254675d27a5dc7701b6dd6009728d8c737eb --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/recammaster/nodes.py @@ -0,0 +1,368 @@ +import numpy as np +import os, io +import torch +from PIL import Image +import math, time +script_directory = os.path.dirname(os.path.abspath(__file__)) + +class Camera(object): + def __init__(self, c2w): + c2w_mat = np.array(c2w).reshape(4, 4) + self.c2w_mat = c2w_mat + self.w2c_mat = np.linalg.inv(c2w_mat) + +def parse_matrix(matrix_str): + rows = matrix_str.strip().split('] [') + matrix = [] + for row in rows: + row = row.replace('[', '').replace(']', '') + matrix.append(list(map(float, row.split()))) + return np.array(matrix) + +class WanVideoReCamMasterDefaultCamera: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "camera_type": ([ + "pan_right", + "pan_left", + "tilt_up", + "tilt_down", + "zoom_in", + "zoom_out", + "translate_up", + "translate_down", + "arc_left", + "arc_right", + ], {"default": "pan_right", "tooltip": "Camera type to use"}), + "latents": ("LATENT", {"tooltip": "source video"}), + }, + } + + RETURN_TYPES = ("CAMERAPOSES",) + RETURN_NAMES = ("camera_poses",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "https://github.com/KwaiVGI/ReCamMaster" + + def process(self, camera_type, latents): + import json + + camera_data_path = os.path.join(script_directory, "recam_extrinsics.json") + with open(camera_data_path, 'r') as file: + cam_data = json.load(file) + + samples = latents["samples"].squeeze(0) + C, T, H, W = samples.shape + num_frames = (T - 1) * 4 + 1 + + camera_type_map = { + "pan_right": 1, + "pan_left": 2, + "tilt_up": 3, + "tilt_down": 4, + "zoom_in": 5, + "zoom_out": 6, + "translate_up": 7, + "translate_down": 8, + "arc_left": 9, + "arc_right": 10, + } + + cam_idx = list(range(num_frames))[::4] + traj = [parse_matrix(cam_data[f"frame{idx}"][f"cam{int(camera_type_map[camera_type]):02d}"]) for idx in cam_idx] + traj = np.stack(traj).transpose(0, 2, 1) + + return (traj,) + +class WanVideoReCamMasterGenerateOrbitCamera: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "num_frames": ("INT", {"default": 81, "min": 1, "max": 1000, "step": 1, "tooltip": "Number of frames to generate"}), + "degrees": ("INT", {"default": 90, "min": -180, "max": 180, "step": 1, "tooltip": "Degrees to orbit"}), + }, + } + + RETURN_TYPES = ("CAMERAPOSES",) + RETURN_NAMES = ("camera_poses",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "https://github.com/KwaiVGI/ReCamMaster" + + def process(self, degrees, num_frames): + + def generate_orbit(num_frames=num_frames, degrees=degrees): + camera_data = [] + center = np.array([3390, 1380, 240]) # Center point of orbit + + for i in range(num_frames): + # Calculate angle from 0 to specified degrees + angle = i * degrees / (num_frames - 1) + angle_rad = np.radians(angle) + + # Calculate position - circular path around center + x = center[0] - np.cos(angle_rad) + y = center[1] - np.sin(angle_rad) + z = center[2] + + # Calculate direction from camera to center point + camera_pos = np.array([x, y, z]) + dir_to_center = center - camera_pos + + # Calculate the angle needed to face the center + look_angle = np.arctan2(dir_to_center[1], dir_to_center[0]) + + # Rotation matrix for facing the center (corrected) + cos_look = np.cos(look_angle) + sin_look = np.sin(look_angle) + + # Create transformation matrix directly + transform = np.array([ + [cos_look, -sin_look, 0, x], + [sin_look, cos_look, 0, y], + [0, 0, 1, z], + [0, 0, 0, 1] + ]) + + camera_data.append(transform) + + return camera_data + + # Generate orbit data + camera_transforms = generate_orbit(num_frames=num_frames, degrees=degrees) + + traj = camera_transforms[::4] + traj = np.stack(traj) + + return (traj,) +class WanVideoReCamMasterCameraEmbed: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "camera_poses": ("CAMERAPOSES",), + "latents": ("LATENT", {"tooltip": "source video"}), + }, + } + + RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", "CAMERAPOSES",) + RETURN_NAMES = ("camera_embeds", "camera_poses",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = "https://github.com/KwaiVGI/ReCamMaster" + + def process(self, camera_poses, latents): + from einops import rearrange + samples = latents["samples"].squeeze(0) + C, T, H, W = samples.shape + num_frames = (T - 1) * 4 + 1 + + + c2ws = [] + for c2w in camera_poses: + c2w = c2w[:, [1, 2, 0, 3]] + c2w[:3, 1] *= -1. + c2w[:3, 3] /= 100 + c2ws.append(c2w) + tgt_cam_params = [Camera(cam_param) for cam_param in c2ws] + relative_poses = [] + for i in range(len(tgt_cam_params)): + relative_pose = self.get_relative_pose([tgt_cam_params[0], tgt_cam_params[i]]) + relative_poses.append(torch.as_tensor(relative_pose)[:,:3,:][1]) + pose_embedding = torch.stack(relative_poses, dim=0) # 21x3x4 + pose_embedding = rearrange(pose_embedding, 'b c d -> b (c d)') + + seq_len = math.ceil((H * W) / 4 * ((num_frames - 1) // 4 + 1)) + + embeds = { + "max_seq_len": seq_len, + "target_shape": samples.shape, + "num_frames": num_frames, + "recammaster": { + "camera_embed": pose_embedding, + "source_latents": samples + } + } + + return (embeds, camera_poses,) + + def get_relative_pose(self, cam_params): + abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] + abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] + + cam_to_origin = 0 + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, -cam_to_origin], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ abs_w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] + ret_poses = np.array(ret_poses, dtype=np.float32) + return ret_poses + +def get_c2w(w2cs, transform_matrix, relative_c2w=True): + if relative_c2w: + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] + else: + ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] + ret_poses = [transform_matrix @ x for x in ret_poses] + return np.array(ret_poses, dtype=np.float32) + +class ReCamMasterPoseVisualizer: + + @classmethod + def INPUT_TYPES(s): + return {"required": { + "camera_poses": ("CAMERAPOSES",), + "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}), + "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}), + "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}), + "arrow_length": ("FLOAT", {"default": 1,"min": 0, "max": 100, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "plot" + CATEGORY = "WanVideoWrapper" + DESCRIPTION = """ +Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose +or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. +""" + + def plot(self, camera_poses, scale, base_xval, zval, arrow_length): + import matplotlib as mpl + mpl.use('Agg') + + import matplotlib.pyplot as plt + from torchvision.transforms import ToTensor + + x_min = -2.0 * scale + x_max = 2.0 * scale + y_min = -2.0 * scale + y_max = 2.0 * scale + z_min = -2.0 * scale + z_max = 2.0 * scale + plt.rcParams['text.color'] = '#999999' + self.fig = plt.figure(figsize=(18, 7)) + self.fig.patch.set_facecolor('#353535') + self.ax = self.fig.add_subplot(projection='3d') + self.ax.set_facecolor('#353535') # Set the background color here + self.ax.grid(color='#999999', linestyle='-', linewidth=0.5) + self.plotly_data = None # plotly data traces + self.ax.set_aspect("auto") + self.ax.set_xlim(x_min, x_max) + self.ax.set_ylim(y_min, y_max) + self.ax.set_zlim(z_min, z_max) + self.ax.set_xlabel('x', color='#999999') + self.ax.set_ylabel('y', color='#999999') + self.ax.set_zlabel('z', color='#999999') + for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels(): + text.set_color('#999999') + print('initialize camera pose visualizer') + + total_frames = len(camera_poses) + + w2cs = [] + for cam in camera_poses: + if cam.shape[0] == 3: + cam = np.vstack((cam, np.array([[0, 0, 0, 1]]))) + cam = cam[:, [1, 2, 0, 3]] + cam[:3, 1] *= -1. + w2cs.append(np.linalg.inv(cam)) + transform_matrix = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]) + c2ws = get_c2w(w2cs, transform_matrix, True) + scale = max(max(abs(c2w[:3, 3])) for c2w in c2ws) + if scale > 1e-3: # otherwise, pan or tilt + for c2w in c2ws: + c2w[:3, 3] /= scale + + for frame_idx, c2w in enumerate(c2ws): + self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1, base_xval=base_xval, zval=(zval)) + + if arrow_length > 0: + pos = c2w[:3, 3] + forward = c2w[:3, 2] + arrow_start = pos + forward * base_xval + arrow_length = arrow_length + self.ax.quiver(arrow_start[0], arrow_start[1], arrow_start[2], + forward[0], forward[1], forward[2], + color='black', length=arrow_length, arrow_length_ratio=0.1) + + # Create the colorbar + cmap = mpl.cm.rainbow + norm = mpl.colors.Normalize(vmin=0, vmax=total_frames) + colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical') + + # Change the colorbar label + colorbar.set_label('Frame', color='#999999') # Change the label and its color + + # Change the tick colors + colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color + + # Change the tick frequency + # Assuming you want to set the ticks at every 10th frame + ticks = np.arange(0, total_frames, 10) + colorbar.ax.yaxis.set_ticks(ticks) + + plt.title('') + plt.draw() + buf = io.BytesIO() + plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) + buf.seek(0) + img = Image.open(buf) + tensor_img = ToTensor()(img) + buf.close() + tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0) + return (tensor_img,) + + def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=9/16, base_xval=1, zval=3): + from mpl_toolkits.mplot3d.art3d import Poly3DCollection + import matplotlib.pyplot as plt + vertex_std = np.array([[0, 0, 0, 1], + [base_xval, -base_xval * hw_ratio, zval, 1], + [base_xval, base_xval * hw_ratio, zval, 1], + [-base_xval, base_xval * hw_ratio, zval, 1], + [-base_xval, -base_xval * hw_ratio, zval, 1]]) + vertex_transformed = vertex_std @ extrinsic.T + meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], + [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], + [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] + + color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) + + self.ax.add_collection3d( + Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.35)) + + def customize_legend(self, list_label): + from matplotlib.patches import Patch + import matplotlib.pyplot as plt + list_handle = [] + for idx, label in enumerate(list_label): + color = plt.cm.rainbow(idx / len(list_label)) + patch = Patch(color=color, label=label) + list_handle.append(patch) + plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) + +NODE_CLASS_MAPPINGS = { + "WanVideoReCamMasterCameraEmbed": WanVideoReCamMasterCameraEmbed, + "ReCamMasterPoseVisualizer": ReCamMasterPoseVisualizer, + "WanVideoReCamMasterGenerateOrbitCamera": WanVideoReCamMasterGenerateOrbitCamera, + "WanVideoReCamMasterDefaultCamera": WanVideoReCamMasterDefaultCamera, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoReCamMasterCameraEmbed": "WanVideo ReCamMaster Camera Embed", + "ReCamMasterPoseVisualizer": "ReCamMaster Pose Visualizer", + "WanVideoReCamMasterGenerateOrbitCamera": "WanVideo ReCamMaster Generate Orbit Camera", + "WanVideoReCamMasterDefaultCamera": "WanVideo ReCamMaster Default Camera", + } diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/recammaster/recam_extrinsics.json b/custom_nodes/ComfyUI_WanVideoWrapper/recammaster/recam_extrinsics.json new file 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[3390 1380 339.379 1] ", + "cam08": "[0.970496 0 0.241117 0] [-0 1 0 0] [-0.241117 -0 0.970496 0] [3390 1380 140.621 1] ", + "cam09": "[0.867575 0.497306 0 0] [-0.497306 0.867575 0 0] [0 -0 1 0] [3443.26 1181.24 240 1] ", + "cam10": "[0.867575 -0.497306 0 0] [0.497306 0.867575 -0 0] [0 0 1 0] [3443.26 1578.76 240 1] " + } +} \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/requirements.txt b/custom_nodes/ComfyUI_WanVideoWrapper/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a66d5eb1443295daebdbeeaa9bd0aa7f54047ea --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/requirements.txt @@ -0,0 +1,6 @@ +ftfy +accelerate>=1.2.1 +einops +diffusers>=0.32.0 +sentencepiece>=0.2.0 +protobuf \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/skyreels/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/skyreels/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..663d37a527c1ab7941890d96078a661b999c38cf --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/skyreels/nodes.py @@ -0,0 +1,607 @@ +import os +import torch +import gc +from ..utils import log, print_memory, fourier_filter +import math +from tqdm import tqdm + +from ..wanvideo.modules.model import rope_params +from ..wanvideo.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler +from diffusers.schedulers import FlowMatchEulerDiscreteScheduler +from ..wanvideo.utils.scheduling_flow_match_lcm import FlowMatchLCMScheduler +from ..nodes import optimized_scale +from einops import rearrange + +from ..enhance_a_video.globals import disable_enhance + +import comfy.model_management as mm +from comfy.utils import load_torch_file, ProgressBar, common_upscale +from comfy.clip_vision import clip_preprocess, ClipVisionModel +from comfy.cli_args import args, LatentPreviewMethod + +script_directory = os.path.dirname(os.path.abspath(__file__)) + +def generate_timestep_matrix( + num_frames, + step_template, + base_num_frames, + ar_step=5, + num_pre_ready=0, + casual_block_size=1, + shrink_interval_with_mask=False, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[tuple]]: + step_matrix, step_index = [], [] + update_mask, valid_interval = [], [] + num_iterations = len(step_template) + 1 + num_frames_block = num_frames // casual_block_size + base_num_frames_block = base_num_frames // casual_block_size + if base_num_frames_block < num_frames_block: + infer_step_num = len(step_template) + gen_block = base_num_frames_block + min_ar_step = infer_step_num / gen_block + assert ar_step >= min_ar_step, f"ar_step should be at least {math.ceil(min_ar_step)} in your setting" + # print(num_frames, step_template, base_num_frames, ar_step, num_pre_ready, casual_block_size, num_frames_block, base_num_frames_block) + step_template = torch.cat( + [ + torch.tensor([999], dtype=torch.int64, device=step_template.device), + step_template.long(), + torch.tensor([0], dtype=torch.int64, device=step_template.device), + ] + ) # to handle the counter in row works starting from 1 + pre_row = torch.zeros(num_frames_block, dtype=torch.long) + if num_pre_ready > 0: + pre_row[: num_pre_ready // casual_block_size] = num_iterations + + while torch.all(pre_row >= (num_iterations - 1)) == False: + new_row = torch.zeros(num_frames_block, dtype=torch.long) + for i in range(num_frames_block): + if i == 0 or pre_row[i - 1] >= ( + num_iterations - 1 + ): # the first frame or the last frame is completely denoised + new_row[i] = pre_row[i] + 1 + else: + new_row[i] = new_row[i - 1] - ar_step + new_row = new_row.clamp(0, num_iterations) + + update_mask.append( + (new_row != pre_row) & (new_row != num_iterations) + ) # False: no need to update， True: need to update + step_index.append(new_row) + step_matrix.append(step_template[new_row]) + pre_row = new_row + + # for long video we split into several sequences, base_num_frames is set to the model max length (for training) + terminal_flag = base_num_frames_block + if shrink_interval_with_mask: + idx_sequence = torch.arange(num_frames_block, dtype=torch.int64) + update_mask = update_mask[0] + update_mask_idx = idx_sequence[update_mask] + last_update_idx = update_mask_idx[-1].item() + terminal_flag = last_update_idx + 1 + # for i in range(0, len(update_mask)): + for curr_mask in update_mask: + if terminal_flag < num_frames_block and curr_mask[terminal_flag]: + terminal_flag += 1 + valid_interval.append((max(terminal_flag - base_num_frames_block, 0), terminal_flag)) + + step_update_mask = torch.stack(update_mask, dim=0) + step_index = torch.stack(step_index, dim=0) + step_matrix = torch.stack(step_matrix, dim=0) + + if casual_block_size > 1: + step_update_mask = step_update_mask.unsqueeze(-1).repeat(1, 1, casual_block_size).flatten(1).contiguous() + step_index = step_index.unsqueeze(-1).repeat(1, 1, casual_block_size).flatten(1).contiguous() + step_matrix = step_matrix.unsqueeze(-1).repeat(1, 1, casual_block_size).flatten(1).contiguous() + valid_interval = [(s * casual_block_size, e * casual_block_size) for s, e in valid_interval] + + return step_matrix, step_index, step_update_mask, valid_interval + +#region Sampler +class WanVideoDiffusionForcingSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("WANVIDEOMODEL",), + "text_embeds": ("WANVIDEOTEXTEMBEDS", ), + "image_embeds": ("WANVIDIMAGE_EMBEDS", ), + "addnoise_condition": ("INT", {"default": 10, "min": 0, "max": 1000, "tooltip": "Improves consistency in long video generation"}), + "fps": ("FLOAT", {"default": 24.0, "min": 1.0, "max": 120.0, "step": 0.01}), + "steps": ("INT", {"default": 30, "min": 1}), + "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}), + "shift": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "force_offload": ("BOOLEAN", {"default": True, "tooltip": "Moves the model to the offload device after sampling"}), + "scheduler": (["unipc", "unipc/beta", "euler", "euler/beta", "lcm", "lcm/beta"], + { + "default": 'unipc' + }), + }, + "optional": { + "samples": ("LATENT", {"tooltip": "init Latents to use for video2video process"} ), + "prefix_samples": ("LATENT", {"tooltip": "prefix latents"} ), + "denoise_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "cache_args": ("CACHEARGS", ), + "slg_args": ("SLGARGS", ), + "rope_function": (["default", "comfy"], {"default": "comfy", "tooltip": "Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile"}), + "experimental_args": ("EXPERIMENTALARGS", ), + "unianimate_poses": ("UNIANIMATE_POSE", ), + } + } + + RETURN_TYPES = ("LATENT", ) + RETURN_NAMES = ("samples",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, model, text_embeds, image_embeds, shift, fps, steps, addnoise_condition, cfg, seed, scheduler, + force_offload=True, samples=None, prefix_samples=None, denoise_strength=1.0, slg_args=None, rope_function="default", cache_args=None, teacache_args=None, + experimental_args=None, unianimate_poses=None): + #assert not (context_options and teacache_args), "Context options cannot currently be used together with teacache." + patcher = model + model = model.model + transformer = model.diffusion_model + dtype = model["dtype"] + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + steps = int(steps/denoise_strength) + + timesteps = None + if 'unipc' in scheduler: + sample_scheduler = FlowUniPCMultistepScheduler(shift=shift) + sample_scheduler.set_timesteps(steps, device=device, shift=shift, use_beta_sigmas=('beta' in scheduler)) + elif 'euler' in scheduler: + sample_scheduler = FlowMatchEulerDiscreteScheduler(shift=shift, use_beta_sigmas=(scheduler == 'euler/beta')) + sample_scheduler.set_timesteps(steps, device=device) + elif 'lcm' in scheduler: + sample_scheduler = FlowMatchLCMScheduler(shift=shift, use_beta_sigmas=(scheduler == 'lcm/beta')) + sample_scheduler.set_timesteps(steps, device=device) + + + init_timesteps = sample_scheduler.timesteps + + if denoise_strength < 1.0: + steps = int(steps * denoise_strength) + timesteps = timesteps[-(steps + 1):] + + seed_g = torch.Generator(device=torch.device("cpu")) + seed_g.manual_seed(seed) + + clip_fea, clip_fea_neg = None, None + vace_data, vace_context, vace_scale = None, None, None + + image_cond = image_embeds.get("image_embeds", None) + + target_shape = image_embeds.get("target_shape", None) + if target_shape is None: + raise ValueError("Empty image embeds must be provided for T2V (Text to Video") + + has_ref = image_embeds.get("has_ref", False) + vace_context = image_embeds.get("vace_context", None) + vace_scale = image_embeds.get("vace_scale", None) + if not isinstance(vace_scale, list): + vace_scale = [vace_scale] * (steps+1) + vace_start_percent = image_embeds.get("vace_start_percent", 0.0) + vace_end_percent = image_embeds.get("vace_end_percent", 1.0) + vace_seqlen = image_embeds.get("vace_seq_len", None) + + vace_additional_embeds = image_embeds.get("additional_vace_inputs", []) + if vace_context is not None: + vace_data = [ + {"context": vace_context, + "scale": vace_scale, + "start": vace_start_percent, + "end": vace_end_percent, + "seq_len": vace_seqlen + } + ] + if len(vace_additional_embeds) > 0: + for i in range(len(vace_additional_embeds)): + if vace_additional_embeds[i].get("has_ref", False): + has_ref = True + vace_scale = vace_additional_embeds[i]["vace_scale"] + if not isinstance(vace_scale, list): + vace_scale = [vace_scale] * (steps+1) + vace_data.append({ + "context": vace_additional_embeds[i]["vace_context"], + "scale": vace_scale, + "start": vace_additional_embeds[i]["vace_start_percent"], + "end": vace_additional_embeds[i]["vace_end_percent"], + "seq_len": vace_additional_embeds[i]["vace_seq_len"] + }) + + noise = torch.randn( + target_shape[0], + target_shape[1] + 1 if has_ref else target_shape[1], + target_shape[2], + target_shape[3], + dtype=torch.float32, + device=torch.device("cpu"), + generator=seed_g) + + latent_video_length = noise.shape[1] + seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1]) + + + + if samples is not None: + input_samples = samples["samples"].squeeze(0).to(noise) + if input_samples.shape[1] != noise.shape[1]: + input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1) + original_image = input_samples.to(device) + if denoise_strength < 1.0: + latent_timestep = timesteps[:1].to(noise) + noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples + + mask = samples.get("mask", None) + if mask is not None: + if mask.shape[2] != noise.shape[1]: + mask = torch.cat([torch.zeros(1, noise.shape[0], noise.shape[1] - mask.shape[2], noise.shape[2], noise.shape[3]), mask], dim=2) + + latents = noise.to(device) + + fps_embeds = None + if hasattr(transformer, "fps_embedding"): + fps = round(fps, 2) + log.info(f"Model has fps embedding, using {fps} fps") + fps_embeds = [fps] + fps_embeds = [0 if i == 16 else 1 for i in fps_embeds] + + prefix_video = prefix_samples["samples"].to(noise) if prefix_samples is not None else None + prefix_video_latent_length = prefix_video.shape[2] if prefix_video is not None else 0 + if prefix_video is not None: + log.info(f"Prefix video of length: {prefix_video_latent_length}") + latents[:, :prefix_video_latent_length] = prefix_video[0] + #base_num_frames = (base_num_frames - 1) // 4 + 1 if base_num_frames is not None else latent_video_length + base_num_frames=latent_video_length + + ar_step = 0 + causal_block_size = 1 + step_matrix, _, step_update_mask, valid_interval = generate_timestep_matrix( + latent_video_length, init_timesteps, base_num_frames, ar_step, prefix_video_latent_length, causal_block_size + ) + + sample_schedulers = [] + for _ in range(latent_video_length): + if 'unipc' in scheduler: + sample_scheduler = FlowUniPCMultistepScheduler(shift=shift) + sample_scheduler.set_timesteps(steps, device=device, shift=shift, use_beta_sigmas=('beta' in scheduler)) + elif 'euler' in scheduler: + sample_scheduler = FlowMatchEulerDiscreteScheduler(shift=shift) + sample_scheduler.set_timesteps(steps, device=device) + elif 'lcm' in scheduler: + sample_scheduler = FlowMatchLCMScheduler(shift=shift, use_beta_sigmas=(scheduler == 'lcm/beta')) + sample_scheduler.set_timesteps(steps, device=device) + + sample_schedulers.append(sample_scheduler) + sample_schedulers_counter = [0] * latent_video_length + + unianim_data = None + if unianimate_poses is not None: + transformer.dwpose_embedding.to(device) + transformer.randomref_embedding_pose.to(device) + dwpose_data = unianimate_poses["pose"] + dwpose_data = transformer.dwpose_embedding( + (torch.cat([dwpose_data[:,:,:1].repeat(1,1,3,1,1), dwpose_data], dim=2) + ).to(device)).to(model["dtype"]) + log.info(f"UniAnimate pose embed shape: {dwpose_data.shape}") + if dwpose_data.shape[2] > latent_video_length: + log.warning(f"UniAnimate pose embed length {dwpose_data.shape[2]} is longer than the video length {latent_video_length}, truncating") + dwpose_data = dwpose_data[:,:, :latent_video_length] + elif dwpose_data.shape[2] < latent_video_length: + log.warning(f"UniAnimate pose embed length {dwpose_data.shape[2]} is shorter than the video length {latent_video_length}, padding with last pose") + pad_len = latent_video_length - dwpose_data.shape[2] + pad = dwpose_data[:,:,:1].repeat(1,1,pad_len,1,1) + dwpose_data = torch.cat([dwpose_data, pad], dim=2) + dwpose_data_flat = rearrange(dwpose_data, 'b c f h w -> b (f h w) c').contiguous() + + random_ref_dwpose_data = None + if image_cond is not None: + random_ref_dwpose = unianimate_poses.get("ref", None) + if random_ref_dwpose is not None: + random_ref_dwpose_data = transformer.randomref_embedding_pose( + random_ref_dwpose.to(device) + ).unsqueeze(2).to(model["dtype"]) # [1, 20, 104, 60] + + unianim_data = { + "dwpose": dwpose_data_flat, + "random_ref": random_ref_dwpose_data.squeeze(0) if random_ref_dwpose_data is not None else None, + "strength": unianimate_poses["strength"], + "start_percent": unianimate_poses["start_percent"], + "end_percent": unianimate_poses["end_percent"] + } + + disable_enhance() #not sure if this can work, disabling for now to avoid errors if it's enabled by another sampler + + freqs = None + transformer.rope_embedder.k = None + transformer.rope_embedder.num_frames = None + if rope_function=="comfy": + transformer.rope_embedder.k = 0 + transformer.rope_embedder.num_frames = latent_video_length + else: + d = transformer.dim // transformer.num_heads + freqs = torch.cat([ + rope_params(1024, d - 4 * (d // 6), L_test=latent_video_length, k=0), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)) + ], + dim=1) + + if not isinstance(cfg, list): + cfg = [cfg] * (steps +1) + + log.info(f"Seq len: {seq_len}") + + pbar = ProgressBar(steps) + + if args.preview_method in [LatentPreviewMethod.Auto, LatentPreviewMethod.Latent2RGB]: #default for latent2rgb + from latent_preview import prepare_callback + else: + from ..latent_preview import prepare_callback #custom for tiny VAE previews + callback = prepare_callback(patcher, steps) + + #blockswap init + transformer_options = patcher.model_options.get("transformer_options", None) + if transformer_options is not None: + block_swap_args = transformer_options.get("block_swap_args", None) + + if block_swap_args is not None: + transformer.use_non_blocking = block_swap_args.get("use_non_blocking", True) + for name, param in transformer.named_parameters(): + if "block" not in name: + param.data = param.data.to(device) + elif block_swap_args["offload_txt_emb"] and "txt_emb" in name: + param.data = param.data.to(offload_device, non_blocking=transformer.use_non_blocking) + elif block_swap_args["offload_img_emb"] and "img_emb" in name: + param.data = param.data.to(offload_device, non_blocking=transformer.use_non_blocking) + + transformer.block_swap( + block_swap_args["blocks_to_swap"] - 1 , + block_swap_args["offload_txt_emb"], + block_swap_args["offload_img_emb"], + vace_blocks_to_swap = block_swap_args.get("vace_blocks_to_swap", None), + ) + + elif model["auto_cpu_offload"]: + for module in transformer.modules(): + if hasattr(module, "offload"): + module.offload() + if hasattr(module, "onload"): + module.onload() + elif model["manual_offloading"]: + transformer.to(device) + + # Initialize Cache if enabled + transformer.enable_teacache = transformer.enable_magcache = False + if teacache_args is not None: #for backward compatibility on old workflows + cache_args = teacache_args + if cache_args is not None: + transformer.cache_device = cache_args["cache_device"] + if cache_args["cache_type"] == "TeaCache": + log.info(f"TeaCache: Using cache device: {transformer.cache_device}") + transformer.teacache_state.clear_all() + transformer.enable_teacache = True + transformer.rel_l1_thresh = cache_args["rel_l1_thresh"] + transformer.teacache_start_step = cache_args["start_step"] + transformer.teacache_end_step = len(init_timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"] + transformer.teacache_use_coefficients = cache_args["use_coefficients"] + transformer.teacache_mode = cache_args["mode"] + elif cache_args["cache_type"] == "MagCache": + log.info(f"MagCache: Using cache device: {transformer.cache_device}") + transformer.magcache_state.clear_all() + transformer.enable_magcache = True + transformer.magcache_start_step = cache_args["start_step"] + transformer.magcache_end_step = len(init_timesteps)-1 if cache_args["end_step"] == -1 else cache_args["end_step"] + transformer.magcache_thresh = cache_args["magcache_thresh"] + transformer.magcache_K = cache_args["magcache_K"] + + if slg_args is not None: + transformer.slg_blocks = slg_args["blocks"] + transformer.slg_start_percent = slg_args["start_percent"] + transformer.slg_end_percent = slg_args["end_percent"] + else: + transformer.slg_blocks = None + + self.teacache_state = [None, None] + self.teacache_state_source = [None, None] + self.teacache_states_context = [] + + + use_cfg_zero_star, use_fresca = False, False + if experimental_args is not None: + video_attention_split_steps = experimental_args.get("video_attention_split_steps", []) + if video_attention_split_steps: + transformer.video_attention_split_steps = [int(x.strip()) for x in video_attention_split_steps.split(",")] + else: + transformer.video_attention_split_steps = [] + use_zero_init = experimental_args.get("use_zero_init", True) + use_cfg_zero_star = experimental_args.get("cfg_zero_star", False) + zero_star_steps = experimental_args.get("zero_star_steps", 0) + + use_fresca = experimental_args.get("use_fresca", False) + if use_fresca: + fresca_scale_low = experimental_args.get("fresca_scale_low", 1.0) + fresca_scale_high = experimental_args.get("fresca_scale_high", 1.25) + fresca_freq_cutoff = experimental_args.get("fresca_freq_cutoff", 20) + + #region model pred + def predict_with_cfg(z, cfg_scale, positive_embeds, negative_embeds, timestep, idx, image_cond=None, clip_fea=None, + vace_data=None, unianim_data=None, teacache_state=None): + with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=("fp8" in model["quantization"])): + + if use_cfg_zero_star and (idx <= zero_star_steps) and use_zero_init: + return latent_model_input*0, None + + nonlocal patcher + current_step_percentage = idx / len(init_timesteps) + control_lora_enabled = False + + image_cond_input = image_cond + + base_params = { + 'seq_len': seq_len, + 'device': device, + 'freqs': freqs, + 't': timestep, + 'current_step': idx, + 'control_lora_enabled': control_lora_enabled, + 'vace_data': vace_data, + 'unianim_data': unianim_data, + 'fps_embeds': fps_embeds, + "nag_params": text_embeds.get("nag_params", {}), + "nag_context": text_embeds.get("nag_prompt_embeds", None), + } + + batch_size = 1 + + if not math.isclose(cfg_scale, 1.0) and len(positive_embeds) > 1: + negative_embeds = negative_embeds * len(positive_embeds) + + + #cond + noise_pred_cond, teacache_state_cond = transformer( + [z], context=positive_embeds, y=[image_cond_input] if image_cond_input is not None else None, + clip_fea=clip_fea, is_uncond=False, current_step_percentage=current_step_percentage, + pred_id=teacache_state[0] if teacache_state else None, + **base_params + ) + noise_pred_cond = noise_pred_cond[0].to(intermediate_device) + if math.isclose(cfg_scale, 1.0): + if use_fresca: + noise_pred_cond = fourier_filter( + noise_pred_cond, + scale_low=fresca_scale_low, + scale_high=fresca_scale_high, + freq_cutoff=fresca_freq_cutoff, + ) + return noise_pred_cond, [teacache_state_cond] + #uncond + noise_pred_uncond, teacache_state_uncond = transformer( + [z], context=negative_embeds, clip_fea=clip_fea_neg if clip_fea_neg is not None else clip_fea, + y=[image_cond_input] if image_cond_input is not None else None, + is_uncond=True, current_step_percentage=current_step_percentage, + pred_id=teacache_state[1] if teacache_state else None, + **base_params + ) + noise_pred_uncond = noise_pred_uncond[0].to(intermediate_device) + + #cfg + + #https://github.com/WeichenFan/CFG-Zero-star/ + if use_cfg_zero_star: + alpha = optimized_scale( + noise_pred_cond.view(batch_size, -1), + noise_pred_uncond.view(batch_size, -1) + ).view(batch_size, 1, 1, 1) + else: + alpha = 1.0 + + #https://github.com/WikiChao/FreSca + if use_fresca: + filtered_cond = fourier_filter( + noise_pred_cond - noise_pred_uncond, + scale_low=fresca_scale_low, + scale_high=fresca_scale_high, + freq_cutoff=fresca_freq_cutoff, + ) + noise_pred = noise_pred_uncond * alpha + cfg_scale * filtered_cond * alpha + else: + noise_pred = noise_pred_uncond * alpha + cfg_scale * (noise_pred_cond - noise_pred_uncond * alpha) + + + return noise_pred, [teacache_state_cond, teacache_state_uncond] + + log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latents.shape[3]*8}x{latents.shape[2]*8} with {steps} steps") + + intermediate_device = device + + #clear memory before sampling + mm.unload_all_models() + mm.soft_empty_cache() + gc.collect() + try: + torch.cuda.reset_peak_memory_stats(device) + except: + pass + + #region main loop start + for i, timestep_i in enumerate(tqdm(step_matrix)): + update_mask_i = step_update_mask[i] + valid_interval_i = valid_interval[i] + valid_interval_start, valid_interval_end = valid_interval_i + timestep = timestep_i[None, valid_interval_start:valid_interval_end].clone() + latent_model_input = latents[:, valid_interval_start:valid_interval_end, :, :].clone() + if addnoise_condition > 0 and valid_interval_start < prefix_video_latent_length: + noise_factor = 0.001 * addnoise_condition + timestep_for_noised_condition = addnoise_condition + latent_model_input[:, valid_interval_start:prefix_video_latent_length] = ( + latent_model_input[:, valid_interval_start:prefix_video_latent_length] * (1.0 - noise_factor) + + torch.randn_like(latent_model_input[:, valid_interval_start:prefix_video_latent_length]) + * noise_factor + ) + timestep[:, valid_interval_start:prefix_video_latent_length] = timestep_for_noised_condition + + + #print("timestep", timestep) + noise_pred, self.teacache_state = predict_with_cfg( + latent_model_input.to(dtype), + cfg[i], + text_embeds["prompt_embeds"], + text_embeds["negative_prompt_embeds"], + timestep, i, image_cond, clip_fea, unianim_data=unianim_data, vace_data=vace_data, + teacache_state=self.teacache_state) + + for idx in range(valid_interval_start, valid_interval_end): + if update_mask_i[idx].item(): + latents[:, idx] = sample_schedulers[idx].step( + noise_pred[:, idx - valid_interval_start], + timestep_i[idx], + latents[:, idx], + return_dict=False, + generator=seed_g, + )[0] + sample_schedulers_counter[idx] += 1 + + x0 = latents.unsqueeze(0) + if callback is not None: + callback_latent = (latent_model_input - noise_pred.to(timestep_i[idx].device) * timestep_i[idx] / 1000).detach().permute(1,0,2,3) + callback(i, callback_latent, None, steps) + else: + pbar.update(1) + + if teacache_args is not None: + states = transformer.teacache_state.states + state_names = { + 0: "conditional", + 1: "unconditional" + } + for pred_id, state in states.items(): + name = state_names.get(pred_id, f"prediction_{pred_id}") + if 'skipped_steps' in state: + log.info(f"TeaCache skipped: {len(state['skipped_steps'])} {name} steps: {state['skipped_steps']}") + transformer.teacache_state.clear_all() + + if force_offload: + if model["manual_offloading"]: + transformer.to(offload_device) + mm.soft_empty_cache() + gc.collect() + + try: + print_memory(device) + torch.cuda.reset_peak_memory_stats(device) + except: + pass + + return ({ + "samples": x0.cpu(), + }, ) + +NODE_CLASS_MAPPINGS = { + "WanVideoDiffusionForcingSampler": WanVideoDiffusionForcingSampler, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoDiffusionForcingSampler": "WanVideo Diffusion Forcing Sampler", + } diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/taehv/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/taehv/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c6e076ba2151824cff2432743ff5514574ea1d12 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/taehv/__init__.py @@ -0,0 +1 @@ +from .taehv import TAEHV \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/taehv/taehv.py b/custom_nodes/ComfyUI_WanVideoWrapper/taehv/taehv.py new file mode 100644 index 0000000000000000000000000000000000000000..b9324ec4060fb7155561b4b5c85a07781eb78c6e --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/taehv/taehv.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +""" +Tiny AutoEncoder for Hunyuan Video https://github.com/madebyollin/taehv +(DNN for encoding / decoding videos to Hunyuan Video's latent space) +""" +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm.auto import tqdm +from collections import namedtuple + +DecoderResult = namedtuple("DecoderResult", ("frame", "memory")) +TWorkItem = namedtuple("TWorkItem", ("input_tensor", "block_index")) + +def conv(n_in, n_out, **kwargs): + return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + +class Clamp(nn.Module): + def forward(self, x): + return torch.tanh(x / 3) * 3 + +class MemBlock(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in * 2, n_out), nn.ReLU(inplace=True), conv(n_out, n_out), nn.ReLU(inplace=True), conv(n_out, n_out)) + self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.act = nn.ReLU(inplace=True) + def forward(self, x, past): + return self.act(self.conv(torch.cat([x, past], 1)) + self.skip(x)) + +class TPool(nn.Module): + def __init__(self, n_f, stride): + super().__init__() + self.stride = stride + self.conv = nn.Conv2d(n_f*stride,n_f, 1, bias=False) + def forward(self, x): + _NT, C, H, W = x.shape + return self.conv(x.reshape(-1, self.stride * C, H, W)) + +class TGrow(nn.Module): + def __init__(self, n_f, stride): + super().__init__() + self.stride = stride + self.conv = nn.Conv2d(n_f, n_f*stride, 1, bias=False) + def forward(self, x): + _NT, C, H, W = x.shape + x = self.conv(x) + return x.reshape(-1, C, H, W) + +def apply_model_with_memblocks(model, x, parallel, show_progress_bar): + """ + Apply a sequential model with memblocks to the given input. + Args: + - model: nn.Sequential of blocks to apply + - x: input data, of dimensions NTCHW + - parallel: if True, parallelize over timesteps (fast but uses O(T) memory) + if False, each timestep will be processed sequentially (slow but uses O(1) memory) + - show_progress_bar: if True, enables tqdm progressbar display + + Returns NTCHW tensor of output data. + """ + assert x.ndim == 5, f"TAEHV operates on NTCHW tensors, but got {x.ndim}-dim tensor" + N, T, C, H, W = x.shape + if parallel: + x = x.reshape(N*T, C, H, W) + # parallel over input timesteps, iterate over blocks + for b in tqdm(model, disable=not show_progress_bar): + if isinstance(b, MemBlock): + NT, C, H, W = x.shape + T = NT // N + _x = x.reshape(N, T, C, H, W) + mem = F.pad(_x, (0,0,0,0,0,0,1,0), value=0)[:,:T].reshape(x.shape) + x = b(x, mem) + else: + x = b(x) + NT, C, H, W = x.shape + T = NT // N + x = x.view(N, T, C, H, W) + else: + # TODO(oboerbohan): at least on macos this still gradually uses more memory during decode... + # need to fix :( + out = [] + # iterate over input timesteps and also iterate over blocks. + # because of the cursed TPool/TGrow blocks, this is not a nested loop, + # it's actually a ***graph traversal*** problem! so let's make a queue + work_queue = [TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(N, T * C, H, W).chunk(T, dim=1))] + # in addition to manually managing our queue, we also need to manually manage our progressbar. + # we'll update it for every source node that we consume. + progress_bar = tqdm(range(T), disable=not show_progress_bar) + # we'll also need a separate addressable memory per node as well + mem = [None] * len(model) + while work_queue: + xt, i = work_queue.pop(0) + if i == 0: + # new source node consumed + progress_bar.update(1) + if i == len(model): + # reached end of the graph, append result to output list + out.append(xt) + else: + # fetch the block to process + b = model[i] + if isinstance(b, MemBlock): + # mem blocks are simple since we're visiting the graph in causal order + if mem[i] is None: + xt_new = b(xt, xt * 0) + mem[i] = xt + else: + xt_new = b(xt, mem[i]) + mem[i].copy_(xt) # inplace might reduce mysterious pytorch memory allocations? doesn't help though + # add successor to work queue + work_queue.insert(0, TWorkItem(xt_new, i+1)) + elif isinstance(b, TPool): + # pool blocks are miserable + if mem[i] is None: + mem[i] = [] # pool memory is itself a queue of inputs to pool + mem[i].append(xt) + if len(mem[i]) > b.stride: + # pool mem is in invalid state, we should have pooled before this + raise ValueError("???") + elif len(mem[i]) < b.stride: + # pool mem is not yet full, go back to processing the work queue + pass + else: + # pool mem is ready, run the pool block + N, C, H, W = xt.shape + xt = b(torch.cat(mem[i], 1).view(N*b.stride, C, H, W)) + # reset the pool mem + mem[i] = [] + # add successor to work queue + work_queue.insert(0, TWorkItem(xt, i+1)) + elif isinstance(b, TGrow): + xt = b(xt) + NT, C, H, W = xt.shape + # each tgrow has multiple successor nodes + for xt_next in reversed(xt.view(N, b.stride*C, H, W).chunk(b.stride, 1)): + # add successor to work queue + work_queue.insert(0, TWorkItem(xt_next, i+1)) + else: + # normal block with no funny business + xt = b(xt) + # add successor to work queue + work_queue.insert(0, TWorkItem(xt, i+1)) + progress_bar.close() + x = torch.stack(out, 1) + return x + +class TAEHV(nn.Module): + latent_channels = 16 + image_channels = 3 + def __init__(self, state_dict, parallel=False, decoder_time_upscale=(True, True), decoder_space_upscale=(True, True, True)): + """Initialize pretrained TAEHV from the given checkpoint. + + Arg: + checkpoint_path: path to weight file to load. taehv.pth for Hunyuan, taew2_1.pth for Wan 2.1. + decoder_time_upscale: whether temporal upsampling is enabled for each block. upsampling can be disabled for a cheaper preview. + decoder_space_upscale: whether spatial upsampling is enabled for each block. upsampling can be disabled for a cheaper preview. + """ + super().__init__() + self.encoder = nn.Sequential( + conv(TAEHV.image_channels, 64), nn.ReLU(inplace=True), + TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64), + TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64), + TPool(64, 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64), MemBlock(64, 64), MemBlock(64, 64), + conv(64, TAEHV.latent_channels), + ) + n_f = [256, 128, 64, 64] + self.frames_to_trim = 2**sum(decoder_time_upscale) - 1 + self.decoder = nn.Sequential( + Clamp(), conv(TAEHV.latent_channels, n_f[0]), nn.ReLU(inplace=True), + MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), MemBlock(n_f[0], n_f[0]), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False), + MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), MemBlock(n_f[1], n_f[1]), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False), + MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), MemBlock(n_f[2], n_f[2]), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False), + nn.ReLU(inplace=True), conv(n_f[3], TAEHV.image_channels), + ) + if state_dict is not None: + self.load_state_dict(self.patch_tgrow_layers(state_dict)) + self.dtype = torch.float16 + self.parallel = parallel + + def patch_tgrow_layers(self, sd): + """Patch TGrow layers to use a smaller kernel if needed. + + Args: + sd: state dict to patch + """ + new_sd = self.state_dict() + for i, layer in enumerate(self.decoder): + if isinstance(layer, TGrow): + key = f"decoder.{i}.conv.weight" + if sd[key].shape[0] > new_sd[key].shape[0]: + # take the last-timestep output channels + sd[key] = sd[key][-new_sd[key].shape[0]:] + return sd + + def encode_video(self, x, parallel=False, show_progress_bar=True): + """Encode a sequence of frames. + + Args: + x: input NTCHW RGB (C=3) tensor with values in [0, 1]. + parallel: if True, all frames will be processed at once. + (this is faster but may require more memory). + if False, frames will be processed sequentially. + Returns NTCHW latent tensor with ~Gaussian values. + """ + return apply_model_with_memblocks(self.encoder, x, self.parallel, show_progress_bar) + + def decode_video(self, x, parallel=False, show_progress_bar=True): + """Decode a sequence of frames. + + Args: + x: input NTCHW latent (C=12) tensor with ~Gaussian values. + parallel: if True, all frames will be processed at once. + (this is faster but may require more memory). + if False, frames will be processed sequentially. + Returns NTCHW RGB tensor with ~[0, 1] values. + """ + x = apply_model_with_memblocks(self.decoder, x, self.parallel, show_progress_bar) + return x[:, self.frames_to_trim:] + + def forward(self, x): + return self.c(x) diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/camera.py b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/camera.py new file mode 100644 index 0000000000000000000000000000000000000000..77d55bb8ec20fe5f4930420fab81c81c8f4fcdd1 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/camera.py @@ -0,0 +1,89 @@ +import einops +import torch +import torch.nn.functional as F + + +@torch.amp.autocast("cuda", enabled=False) +def batch_sample_rays(intrinsic, extrinsic, image_h=None, image_w=None): + ''' get rays + Args: + intrinsic: [BF, 3, 3], + extrinsic: [BF, 4, 4], + h, w: int + # normalize: let the first camera R=I + Returns: + rays_o, rays_d: [BF, N, 3] + ''' + + # FIXME: PPU does not support inverse in GPU + device = intrinsic.device + B = intrinsic.shape[0] + + c2w = torch.inverse(extrinsic)[:, :3, :4].to(device) # [BF,3,4] + x = torch.arange(image_w, device=device).float() - 0.5 + y = torch.arange(image_h, device=device).float() + 0.5 + points = torch.stack(torch.meshgrid(x, y, indexing='ij'), -1) + points = einops.repeat(points, 'w h c -> b (h w) c', b=B) + points = torch.cat([points, torch.ones_like(points)[:, :, 0:1]], dim=-1) + directions = points @ intrinsic.inverse().to(device).transpose(-1, -2) * 1 # depth is 1 + + rays_d = F.normalize(directions @ c2w[:, :3, :3].transpose(-1, -2), dim=-1) # [BF,N,3] + rays_o = c2w[..., :3, 3] # [BF, 3] + + rays_o = rays_o[:, None, :].expand_as(rays_d) # [BF, N, 3] + + return rays_o, rays_d + + +@torch.amp.autocast("cuda", enabled=False) +def embed_rays(rays_o, rays_d, nframe): + if len(rays_o.shape) == 4: # [b,f,n,3] + rays_o = einops.rearrange(rays_o, "b f n c -> (b f) n c") + rays_d = einops.rearrange(rays_d, "b f n c -> (b f) n c") + cross_od = torch.cross(rays_o, rays_d, dim=-1) + cam_emb = torch.cat([rays_d, cross_od], dim=-1) + cam_emb = einops.rearrange(cam_emb, "(b f) n c -> b f n c", f=nframe) + return cam_emb + + +@torch.amp.autocast("cuda", enabled=False) +def camera_center_normalization(w2c, nframe, camera_scale=2.0): + # copy from SEVA, w2c: [BF, 4, 4] + # ensure the first view is eye matrix + c2w_view0 = w2c[::nframe].inverse() # [B,4,4] + c2w_view0 = c2w_view0.repeat_interleave(nframe, dim=0) # [BF,4,4] + w2c = c2w_view0 @ w2c + + # camera centering + c2w = torch.linalg.inv(w2c) + camera_dist_2med = torch.norm(c2w[:, :3, 3] - c2w[:, :3, 3].median(0, keepdim=True).values, dim=-1) + valid_mask = camera_dist_2med <= torch.clamp(torch.quantile(camera_dist_2med, 0.97) * 10, max=1e6) + c2w[:, :3, 3] -= c2w[valid_mask, :3, 3].mean(0, keepdim=True) + w2c = torch.linalg.inv(c2w) + + # camera normalization + camera_dists = c2w[:, :3, 3].clone() + translation_scaling_factor = ( + camera_scale + if torch.isclose( + torch.norm(camera_dists[0]), + torch.zeros(1, dtype=camera_dists.dtype, device=camera_dists.device), + atol=1e-5, + ).any() + else (camera_scale / torch.norm(camera_dists[0])) + ) + w2c[:, :3, 3] *= translation_scaling_factor + c2w[:, :3, 3] *= translation_scaling_factor + + return w2c + + +def get_camera_embedding(intrinsic, extrinsic, f, h, w, normalize=True): + if normalize: + extrinsic = camera_center_normalization(extrinsic, nframe=f) + + rays_o, rays_d = batch_sample_rays(intrinsic, extrinsic, image_h=h, image_w=w) + camera_embedding = embed_rays(rays_o, rays_d, nframe=f) + camera_embedding = einops.rearrange(camera_embedding, "b f (h w) c -> b c f h w", h=h, w=w) + + return camera_embedding diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/controlnet.py b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..0517e74757324e2469475727f6c849c71de2fa78 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/controlnet.py @@ -0,0 +1,263 @@ +import torch +import torch.nn as nn +from diffusers.models import ModelMixin +from typing import Optional +import torch.nn.functional as F +from diffusers.models.attention_processor import Attention +from diffusers.models.transformers.transformer_wan import WanRotaryPosEmbed +from einops import rearrange + +from ..wanvideo.modules.attention import sageattn_func + +def zero_module(module): + # Zero out the parameters of a module and return it. + for p in module.parameters(): + p.detach().zero_() + return module + + +class SimpleAttnProcessor2_0: + def __init__(self, attention_mode): + self.attention_mode = attention_mode + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + rotary_emb: Optional[torch.Tensor] = None, + **kwargs + ) -> torch.Tensor: + + query = attn.to_q(hidden_states) + key = attn.to_k(hidden_states) + value = attn.to_v(hidden_states) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2) + key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2) + value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2) # [b,head,l,c] + + if rotary_emb is not None: + def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor): + x_rotated = torch.view_as_complex(hidden_states.to(torch.float64).unflatten(3, (-1, 2))) + x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4) + return x_out.type_as(hidden_states) + + query = apply_rotary_emb(query, rotary_emb) + key = apply_rotary_emb(key, rotary_emb) + + if self.attention_mode == 'sdpa': + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + elif self.attention_mode == 'sageattn': + hidden_states = sageattn_func( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + hidden_states = hidden_states.transpose(1, 2).flatten(2, 3) + hidden_states = hidden_states.type_as(query) + + hidden_states = attn.to_out[0](hidden_states) + hidden_states = attn.to_out[1](hidden_states) + return hidden_states + + +class SimpleCogVideoXLayerNormZero(nn.Module): + def __init__( + self, + conditioning_dim: int, + embedding_dim: int, + elementwise_affine: bool = True, + eps: float = 1e-5, + bias: bool = True, + ) -> None: + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(conditioning_dim, 3 * embedding_dim, bias=bias) + self.norm = nn.LayerNorm(embedding_dim, eps=eps, elementwise_affine=elementwise_affine) + + def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor): + shift, scale, gate = self.linear(self.silu(temb)).chunk(3, dim=1) + hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :] + return hidden_states, gate[:, None, :] + + +class SingleAttentionBlock(nn.Module): + + def __init__( + self, + dim, + ffn_dim, + num_heads, + time_embed_dim=512, + qk_norm="rms_norm_across_heads", + eps=1e-6, + attention_mode="sdpa", + ): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.qk_norm = qk_norm + self.eps = eps + + # layers + self.norm1 = SimpleCogVideoXLayerNormZero( + time_embed_dim, dim, elementwise_affine=True, eps=1e-5, bias=True + ) + self.self_attn = Attention( + query_dim=dim, + heads=num_heads, + kv_heads=num_heads, + dim_head=dim // num_heads, + qk_norm=qk_norm, + eps=eps, + bias=True, + cross_attention_dim=None, + out_bias=True, + processor=SimpleAttnProcessor2_0(attention_mode), + ) + self.norm2 = SimpleCogVideoXLayerNormZero( + time_embed_dim, dim, elementwise_affine=True, eps=1e-5, bias=True + ) + self.ffn = nn.Sequential( + nn.Linear(dim, ffn_dim), + nn.GELU(approximate='tanh'), + nn.Linear(ffn_dim, dim) + ) + + def forward( + self, + hidden_states, + temb, + rotary_emb, + ): + # norm & modulate + norm_hidden_states, gate_msa = self.norm1(hidden_states, temb) + + # attention + attn_hidden_states = self.self_attn(hidden_states=norm_hidden_states, + rotary_emb=rotary_emb) + + hidden_states = hidden_states + gate_msa * attn_hidden_states + + # norm & modulate + norm_hidden_states, gate_ff = self.norm2(hidden_states, temb) + + # feed-forward + ff_output = self.ffn(norm_hidden_states) + + hidden_states = hidden_states + gate_ff * ff_output + + return hidden_states + +class MaskCamEmbed(nn.Module): + def __init__(self, controlnet_cfg) -> None: + super().__init__() + + # padding bug fixed + if controlnet_cfg.get("interp", False): + self.mask_padding = [0, 0, 0, 0, 3, 3] # 左右上下前后, I2V-interp，首尾帧 + else: + self.mask_padding = [0, 0, 0, 0, 3, 0] # 左右上下前后, I2V + add_channels = controlnet_cfg.get("add_channels", 1) + mid_channels = controlnet_cfg.get("mid_channels", 64) + self.mask_proj = nn.Sequential(nn.Conv3d(add_channels, mid_channels, kernel_size=(4, 8, 8), stride=(4, 8, 8)), + nn.GroupNorm(mid_channels // 8, mid_channels), nn.SiLU()) + self.mask_zero_proj = zero_module(nn.Conv3d(mid_channels, controlnet_cfg["conv_out_dim"], kernel_size=(1, 2, 2), stride=(1, 2, 2))) + + def forward(self, add_inputs: torch.Tensor): + # render_mask.shape [b,c,f,h,w] + warp_add_pad = F.pad(add_inputs, self.mask_padding, mode="constant", value=0) + add_embeds = self.mask_proj(warp_add_pad) # [B,C,F,H,W] + add_embeds = self.mask_zero_proj(add_embeds) + add_embeds = rearrange(add_embeds, "b c f h w -> b (f h w) c") + + return add_embeds + +class WanControlNet(ModelMixin): + def __init__(self, controlnet_cfg): + super().__init__() + + self.rope_max_seq_len = 1024 + self.patch_size = (1, 2, 2) + self.in_channels = controlnet_cfg["in_channels"] + self.dim = controlnet_cfg["dim"] + self.num_heads = controlnet_cfg["num_heads"] + + if controlnet_cfg["conv_out_dim"] != controlnet_cfg["dim"]: + self.proj_in = nn.Linear(controlnet_cfg["conv_out_dim"], controlnet_cfg["dim"]) + else: + self.proj_in = nn.Identity() + + self.controlnet_blocks = nn.ModuleList( + [ + SingleAttentionBlock( + dim=self.dim, + ffn_dim=controlnet_cfg["ffn_dim"], + num_heads=self.num_heads, + time_embed_dim=controlnet_cfg["time_embed_dim"], + qk_norm="rms_norm_across_heads", + attention_mode=controlnet_cfg["attention_mode"], + ) + for _ in range(controlnet_cfg["num_layers"]) + ] + ) + self.proj_out = nn.ModuleList( + [ + zero_module(nn.Linear(self.dim, 5120)) + for _ in range(controlnet_cfg["num_layers"]) + ] + ) + + self.gradient_checkpointing = False + + self.controlnet_rope = WanRotaryPosEmbed(self.dim // self.num_heads, + self.patch_size, self.rope_max_seq_len) + + self.controlnet_patch_embedding = nn.Conv3d( + self.in_channels, + controlnet_cfg["conv_out_dim"], + kernel_size=self.patch_size, + stride=self.patch_size, + dtype=torch.float32 + ) + + self.controlnet_mask_embedding = MaskCamEmbed(controlnet_cfg) + + def forward(self, render_latent, render_mask, camera_embedding, temb, device): + controlnet_rotary_emb = self.controlnet_rope(render_latent) + + controlnet_inputs = self.controlnet_patch_embedding(render_latent.to(torch.float32)).to(render_latent.dtype) + controlnet_inputs = controlnet_inputs.to(render_latent.dtype) + + controlnet_inputs = controlnet_inputs.flatten(2).transpose(1, 2) + + # additional inputs (mask, camera embedding) + add_inputs = None + if camera_embedding is not None and render_mask is not None: + add_inputs = torch.cat([render_mask, camera_embedding], dim=1) + elif render_mask is not None: + add_inputs = render_mask + + if add_inputs is not None: + add_inputs = self.controlnet_mask_embedding(add_inputs) + controlnet_inputs = controlnet_inputs + add_inputs + + hidden_states = self.proj_in(controlnet_inputs) + + controlnet_states = [] + for i, block in enumerate(self.controlnet_blocks): + hidden_states = block( + hidden_states=hidden_states, + temb=temb, + rotary_emb=controlnet_rotary_emb + ) + controlnet_states.append(self.proj_out[i](hidden_states).to(device)) + + return controlnet_states diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..efc2b29819846c390ec6e50a8f0ef38ca7350872 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/nodes.py @@ -0,0 +1,241 @@ + +import torch +from ..utils import log +import comfy.model_management as mm +from comfy.utils import ProgressBar, load_torch_file +from tqdm import tqdm +import gc + +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device +import folder_paths + +import json +import numpy as np + +class WanVideoUni3C_ControlnetLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (folder_paths.get_filename_list("controlnet"), {"tooltip": "These models are loaded from the 'ComfyUI/models/controlnet' -folder",}), + + "base_precision": (["fp32", "bf16", "fp16"], {"default": "fp16"}), + "quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2', 'fp8_e4m3fn_fast_no_ffn'], {"default": 'disabled', "tooltip": "optional quantization method"}), + "load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}), + "attention_mode": ([ + "sdpa", + "sageattn", + ], {"default": "sdpa"}), + }, + "optional": { + "compile_args": ("WANCOMPILEARGS", ), + #"block_swap_args": ("BLOCKSWAPARGS", ), + } + } + + RETURN_TYPES = ("WANVIDEOCONTROLNET",) + RETURN_NAMES = ("controlnet", ) + FUNCTION = "loadmodel" + CATEGORY = "WanVideoWrapper" + + def loadmodel(self, model, base_precision, load_device, quantization, attention_mode, compile_args=None): + + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + + transformer_load_device = device if load_device == "main_device" else offload_device + + base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[base_precision] + + + model_path = folder_paths.get_full_path_or_raise("controlnet", model) + + sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True) + + if not "controlnet_patch_embedding.weight" in sd: + raise ValueError("Invalid ControlNet model") + + in_channels = sd["controlnet_patch_embedding.weight"].shape[1] + ffn_dim = sd["controlnet_blocks.0.ffn.0.bias"].shape[0] + + controlnet_cfg = { + "in_channels": in_channels, + "conv_out_dim": 5120, + "time_embed_dim": 5120, + "dim": 1024, + "ffn_dim": ffn_dim, + "num_heads": 16, + "num_layers": 20, + "add_channels": 7, + "mid_channels": 256, + "attention_mode": attention_mode + } + + from .controlnet import WanControlNet + + with init_empty_weights(): + controlnet = WanControlNet(controlnet_cfg) + controlnet.eval() + + if quantization == "disabled": + for k, v in sd.items(): + if isinstance(v, torch.Tensor): + if v.dtype == torch.float8_e4m3fn: + quantization = "fp8_e4m3fn" + break + elif v.dtype == torch.float8_e5m2: + quantization = "fp8_e5m2" + break + + if "fp8_e4m3fn" in quantization: + dtype = torch.float8_e4m3fn + elif quantization == "fp8_e5m2": + dtype = torch.float8_e5m2 + else: + dtype = base_dtype + params_to_keep = {"norm", "head", "time_in", "vector_in", "controlnet_patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter"} + + log.info("Using accelerate to load and assign controlnet model weights to device...") + param_count = sum(1 for _ in controlnet.named_parameters()) + for name, param in tqdm(controlnet.named_parameters(), + desc=f"Loading transformer parameters to {transformer_load_device}", + total=param_count, + leave=True): + dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype + if "controlnet_patch_embedding" in name: + dtype_to_use = torch.float32 + set_module_tensor_to_device(controlnet, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name]) + + del sd + + if compile_args is not None: + torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"] + try: + if hasattr(torch, '_dynamo') and hasattr(torch._dynamo, 'config'): + torch._dynamo.config.recompile_limit = compile_args["dynamo_recompile_limit"] + except Exception as e: + log.warning(f"Could not set recompile_limit: {e}") + if compile_args["compile_transformer_blocks_only"]: + for i, block in enumerate(controlnet.controlnet_blocks): + controlnet.controlnet_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"]) + else: + controlnet = torch.compile(controlnet, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"]) + + + if load_device == "offload_device" and controlnet.device != offload_device: + log.info(f"Moving controlnet model from {controlnet.device} to {offload_device}") + controlnet.to(offload_device) + gc.collect() + mm.soft_empty_cache() + + return (controlnet,) + +class WanVideoUni3C_embeds: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "controlnet": ("WANVIDEOCONTROLNET",), + "render_latent": ("LATENT",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percent of the steps to apply the controlnet"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percent of the steps to apply the controlnet"}), + }, + "optional": { + "render_mask": ("MASK", {"tooltip": "NOT IMPLEMENTED!"}), + }, + } + + RETURN_TYPES = ("UNI3C_EMBEDS", ) + RETURN_NAMES = ("uni3c_embeds",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, controlnet, render_latent, strength, start_percent, end_percent, render_mask=None): + + device = mm.get_torch_device() + + latent_mask = None + + latents = render_latent["samples"] + nframe = latents.shape[2] * 4 + height = latents.shape[3] * 8 + width = latents.shape[4] * 8 + + if render_mask is not None: + raise NotImplementedError("render_mask is not implemented at this time") + mask = torch.nn.functional.interpolate( + render_mask.unsqueeze(0).unsqueeze(0), # Add batch and channel dims [1,1,T,H,W] + size=(nframe, height, width), + mode='trilinear', + align_corners=False + ).squeeze(0) + latent_mask = mask.unsqueeze(0).to(device) + log.info(f"latent mask shape {latent_mask.shape}") + + # # load camera + # cam_info = json.load(open(f"{render_path}/cam_info.json")) + # w2cs = torch.tensor(np.array(cam_info["extrinsic"]), dtype=torch.float32, device=device) + # intrinsic = torch.tensor(np.array(cam_info["intrinsic"]), dtype=torch.float32, device=device) + # intrinsic[0, :] = intrinsic[0, :] / cam_info["width"] * width + # intrinsic[1, :] = intrinsic[1, :] / cam_info["height"] * height + # intrinsic = intrinsic[None].repeat(nframe, 1, 1) + + # from .utils import build_cameras, set_initial_camera, traj_map + + # focal_length = 1.0 + # start_elevation = 5.0 + # depth_avg = 0.5 + # traj_type = "orbit" + # cam_traj, x_offset, y_offset, z_offset, d_theta, d_phi, d_r = traj_map(traj_type) + # focallength_px = focal_length * width + + # K = torch.tensor([[focallength_px, 0, width / 2], + # [0, focallength_px, height / 2], + # [0, 0, 1]], dtype=torch.float32) + # K_inv = K.inverse() + # intrinsic = K[None].repeat(nframe, 1, 1) + + + # w2c_0, c2w_0 = set_initial_camera(start_elevation, depth_avg) + # w2cs, c2ws, intrinsic = build_cameras(cam_traj=cam_traj, + # w2c_0=w2c_0, + # c2w_0=c2w_0, + # intrinsic=intrinsic, + # nframe=nframe, + # focal_length=focal_length, + # d_theta=d_theta, + # d_phi=d_phi, + # d_r=d_r, + # radius=depth_avg, + # x_offset=x_offset, + # y_offset=y_offset, + # z_offset=z_offset) + + + # from .camera import get_camera_embedding + # camera_embedding = get_camera_embedding(intrinsic, w2cs, nframe, height, width, normalize=True) + #print("camera embedding shape", camera_embedding.shape) + + uni3c_embeds = { + "controlnet": controlnet, + "controlnet_weight": strength, + "start": start_percent, + "end": end_percent, + "render_latent": latents.to(device), + "render_mask": latent_mask, + "camera_embedding": None + } + + return (uni3c_embeds,) + +NODE_CLASS_MAPPINGS = { + "WanVideoUni3C_ControlnetLoader": WanVideoUni3C_ControlnetLoader, + "WanVideoUni3C_embeds": WanVideoUni3C_embeds, + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoUni3C_ControlnetLoader": "WanVideo Uni3C Controlnet Loader", + "WanVideoUni3C_embeds": "WanVideo Uni3C Embeds", + } + + \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/utils.py b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7ba7f2c228d69f6ab691e3b08a9b2de614b11eaa --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/uni3c/utils.py @@ -0,0 +1,206 @@ +import imageio +import numpy as np +import torch +from PIL import Image +from scipy.interpolate import UnivariateSpline +from scipy.interpolate import interp1d + + +def load_video(video_path): + reader = imageio.get_reader(video_path) + total_frames = reader.count_frames() + frames = [] + for i in range(total_frames): + frame = reader.get_data(i) + frames.append(Image.fromarray(frame)) + + reader.close() + + return frames + + +def points_padding(points): + padding = torch.ones_like(points)[..., 0:1] + points = torch.cat([points, padding], dim=-1) + return points + + +def np_points_padding(points): + padding = np.ones_like(points)[..., 0:1] + points = np.concatenate([points, padding], axis=-1) + return points + + +def txt_interpolation(input_list, n, mode='smooth'): + x = np.linspace(0, 1, len(input_list)) + if mode == 'smooth': + f = UnivariateSpline(x, input_list, k=3) + elif mode == 'linear': + f = interp1d(x, input_list) + else: + raise KeyError(f"Invalid txt interpolation mode: {mode}") + xnew = np.linspace(0, 1, n) + ynew = f(xnew) + return ynew + + +def traj_map(traj_type): + # pre-defined trajectories + if traj_type == "free1": # Zoom out and rotate to the upper left + cam_traj = "free" + x_offset = 0.0 + y_offset = 0.0 + z_offset = 0.0 + d_theta = -15.0 + d_phi = 45.0 + d_r = 1.6 + elif traj_type == "free2": # Rotate to the right horizontally + cam_traj = "free" + x_offset = -0.05 + y_offset = 0.0 + z_offset = 0.0 + d_theta = 0.0 + d_phi = -60.0 + d_r = 1.0 + elif traj_type == "free3": # Move back to the left + cam_traj = "free" + x_offset = -0.25 + y_offset = 0.0 + z_offset = 0.0 + d_theta = 0.0 + d_phi = 0.0 + d_r = 1.7 + elif traj_type == "free4": # Rotate and approach to the upper right + cam_traj = "free" + x_offset = 0.0 + y_offset = 0.0 + z_offset = 0.0 + d_theta = -15.0 + d_phi = -60.0 + d_r = 0.75 + elif traj_type == "free5": # Large-angle camera movement to the upper right + cam_traj = "free" + x_offset = 0.0 + y_offset = 0.0 + z_offset = 0.0 + d_theta = -15.0 + d_phi = -120.0 + d_r = 1.6 + elif traj_type == "swing1": # Swing shot 1 + cam_traj = "swing1" + x_offset = 0.0 + y_offset = 0.0 + z_offset = 0.0 + d_theta = 0.0 + d_phi = 0.0 + d_r = 1.0 + elif traj_type == "swing2": # Swing shot 2 + cam_traj = "swing2" + x_offset = 0.0 + y_offset = 0.0 + z_offset = 0.0 + d_theta = 0.0 + d_phi = 0.0 + d_r = 1.0 + elif traj_type == "orbit": # 360-degree counterclockwise rotation + cam_traj = "free" + x_offset = 0.0 + y_offset = 0.0 + z_offset = 0.0 + d_theta = 0.0 + d_phi = -360.0 + d_r = 1.0 + else: + raise NotImplementedError + return cam_traj, x_offset, y_offset, z_offset, d_theta, d_phi, d_r + + +def set_initial_camera(start_elevation, radius): + c2w_0 = torch.tensor([[1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, -radius], + [0, 0, 0, 1]], dtype=torch.float32) + elevation_rad = np.deg2rad(start_elevation) + R_elevation = torch.tensor([[1, 0, 0, 0], + [0, np.cos(-elevation_rad), -np.sin(-elevation_rad), 0], + [0, np.sin(-elevation_rad), np.cos(-elevation_rad), 0], + [0, 0, 0, 1]], dtype=torch.float32) + c2w_0 = R_elevation @ c2w_0 + w2c_0 = c2w_0.inverse() + + return w2c_0, c2w_0 + + +def build_cameras(cam_traj, w2c_0, c2w_0, intrinsic, nframe, focal_length, + d_theta, d_phi, d_r, radius, x_offset, y_offset, z_offset): + # build camera viewpoints according to d_theta，d_phi, d_r + # return: w2cs:[V,4,4], c2ws:[V,4,4], intrinsic:[V,3,3] + if intrinsic.ndim == 2: + intrinsic = intrinsic[None].repeat(nframe, 1, 1) + + c2ws = [c2w_0] + w2cs = [w2c_0] + d_thetas, d_phis, d_rs = [], [], [] + x_offsets, y_offsets, z_offsets = [], [], [] + if cam_traj == "free": + for i in range(nframe - 1): + coef = (i + 1) / (nframe - 1) + d_thetas.append(d_theta * coef) + d_phis.append(d_phi * coef) + d_rs.append(coef * d_r + (1 - coef) * 1.0) + x_offsets.append(radius * x_offset * ((i + 1) / nframe)) + y_offsets.append(radius * y_offset * ((i + 1) / nframe)) + z_offsets.append(radius * z_offset * ((i + 1) / nframe)) + elif cam_traj == "swing1": + phis__ = [0, -5, -25, -30, -20, -8, 0] + thetas__ = [0, -8, -12, -20, -17, -12, -5, -2, 1, 5, 3, 1, 0] + rs__ = [0, 0.2] + d_phis = txt_interpolation(phis__, nframe, mode='smooth') + d_phis[0] = phis__[0] + d_phis[-1] = phis__[-1] + d_thetas = txt_interpolation(thetas__, nframe, mode='smooth') + d_thetas[0] = thetas__[0] + d_thetas[-1] = thetas__[-1] + d_rs = txt_interpolation(rs__, nframe, mode='linear') + d_rs = 1.0 + d_rs + elif cam_traj == "swing2": + phis__ = [0, 5, 25, 30, 20, 10, 0] + thetas__ = [0, -5, -14, -11, 0, 1, 5, 3, 0] + rs__ = [0, -0.03, -0.1, -0.2, -0.17, -0.1, 0] + d_phis = txt_interpolation(phis__, nframe, mode='smooth') + d_phis[0] = phis__[0] + d_phis[-1] = phis__[-1] + d_thetas = txt_interpolation(thetas__, nframe, mode='smooth') + d_thetas[0] = thetas__[0] + d_thetas[-1] = thetas__[-1] + d_rs = txt_interpolation(rs__, nframe, mode='smooth') + d_rs = 1.0 + d_rs + else: + raise NotImplementedError("Unknown trajectory type...") + + for i in range(nframe - 1): + d_theta_rad = np.deg2rad(d_thetas[i]) + R_theta = torch.tensor([[1, 0, 0, 0], + [0, np.cos(d_theta_rad), -np.sin(d_theta_rad), 0], + [0, np.sin(d_theta_rad), np.cos(d_theta_rad), 0], + [0, 0, 0, 1]], dtype=torch.float32) + d_phi_rad = np.deg2rad(d_phis[i]) + R_phi = torch.tensor([[np.cos(d_phi_rad), 0, np.sin(d_phi_rad), 0], + [0, 1, 0, 0], + [-np.sin(d_phi_rad), 0, np.cos(d_phi_rad), 0], + [0, 0, 0, 1]], dtype=torch.float32) + c2w_1 = R_phi @ R_theta @ c2w_0 + if i < len(x_offsets) and i < len(y_offsets) and i < len(z_offsets): + c2w_1[:3, -1] += torch.tensor([x_offsets[i], y_offsets[i], z_offsets[i]]) + c2w_1[:3, -1] *= d_rs[i] + w2c_1 = c2w_1.inverse() + c2ws.append(c2w_1) + w2cs.append(w2c_1) + + intrinsic[i + 1, :2, :2] = intrinsic[i + 1, :2, :2] * focal_length * ((i + 1) / nframe) + \ + intrinsic[i + 1, :2, :2] * ((nframe - (i + 1)) / nframe) + + w2cs = torch.stack(w2cs, dim=0) + c2ws = torch.stack(c2ws, dim=0) + + return w2cs, c2ws, intrinsic diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/__init__.py new file mode 100644 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-0,0 +1,125 @@ +import cv2 +import numpy as np +import torch + +def nms(boxes, scores, nms_thr): + """Single class NMS implemented in Numpy.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= nms_thr)[0] + order = order[inds + 1] + + return keep + +def multiclass_nms(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-aware version.""" + final_dets = [] + num_classes = scores.shape[1] + for cls_ind in range(num_classes): + cls_scores = scores[:, cls_ind] + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + continue + else: + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if len(keep) > 0: + cls_inds = np.ones((len(keep), 1)) * cls_ind + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 + ) + final_dets.append(dets) + if len(final_dets) == 0: + return None + return np.concatenate(final_dets, 0) + +def demo_postprocess(outputs, img_size, p6=False): + grids = [] + expanded_strides = [] + strides = [8, 16, 32] if not p6 else [8, 16, 32, 64] + + hsizes = [img_size[0] // stride for stride in strides] + wsizes = [img_size[1] // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + grids = np.concatenate(grids, 1) + expanded_strides = np.concatenate(expanded_strides, 1) + outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides + outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides + + return outputs + +def preprocess(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + +def inference_detector(model, oriImg, detect_classes=[0]): + input_shape = (640,640) + img, ratio = preprocess(oriImg, input_shape) + + device, dtype = next(model.parameters()).device, next(model.parameters()).dtype + input = img[None, :, :, :] + input = torch.from_numpy(input).to(device, dtype) + + output = model(input).float().cpu().detach().numpy() + predictions = demo_postprocess(output[0], input_shape) + + boxes = predictions[:, :4] + scores = predictions[:, 4:5] * predictions[:, 5:] + + boxes_xyxy = np.ones_like(boxes) + boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2. + boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2. + boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2. + boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2. + boxes_xyxy /= ratio + dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1) + if dets is None: + return None + final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] + isscore = final_scores>0.3 + iscat = np.isin(final_cls_inds, detect_classes) + isbbox = [ i and j for (i, j) in zip(isscore, iscat)] + final_boxes = final_boxes[isbbox] + return final_boxes \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/jit_pose.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/jit_pose.py new file mode 100644 index 0000000000000000000000000000000000000000..7a7b6acee1cd39657cec719aef1aaa09648da4e2 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/jit_pose.py @@ -0,0 +1,363 @@ +from typing import List, Tuple + +import cv2 +import numpy as np +import torch + +def preprocess( + img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256) +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Do preprocessing for DWPose model inference. + + Args: + img (np.ndarray): Input image in shape. + input_size (tuple): Input image size in shape (w, h). + + Returns: + tuple: + - resized_img (np.ndarray): Preprocessed image. + - center (np.ndarray): Center of image. + - scale (np.ndarray): Scale of image. + """ + # get shape of image + img_shape = img.shape[:2] + out_img, out_center, out_scale = [], [], [] + if len(out_bbox) == 0: + out_bbox = [[0, 0, img_shape[1], img_shape[0]]] + for i in range(len(out_bbox)): + x0 = out_bbox[i][0] + y0 = out_bbox[i][1] + x1 = out_bbox[i][2] + y1 = out_bbox[i][3] + bbox = np.array([x0, y0, x1, y1]) + + # get center and scale + center, scale = bbox_xyxy2cs(bbox, padding=1.25) + + # do affine transformation + resized_img, scale = top_down_affine(input_size, scale, center, img) + + # normalize image + mean = np.array([123.675, 116.28, 103.53]) + std = np.array([58.395, 57.12, 57.375]) + resized_img = (resized_img - mean) / std + + out_img.append(resized_img) + out_center.append(center) + out_scale.append(scale) + + return out_img, out_center, out_scale + +def inference(model, img, bs=5): + """Inference DWPose model implemented in TorchScript. + + Args: + model : TorchScript Model. + img : Input image in shape. + + Returns: + outputs : Output of DWPose model. + """ + all_out = [] + # build input + orig_img_count = len(img) + #Pad zeros to fit batch size + for _ in range(bs - (orig_img_count % bs)): + img.append(np.zeros_like(img[0])) + input = np.stack(img, axis=0).transpose(0, 3, 1, 2) + device, dtype = next(model.parameters()).device, next(model.parameters()).dtype + input = torch.from_numpy(input).to(device, dtype) + + out1, out2 = [], [] + for i in range(input.shape[0] // bs): + curr_batch_output = model(input[i*bs:(i+1)*bs]) + out1.append(curr_batch_output[0].float()) + out2.append(curr_batch_output[1].float()) + out1, out2 = torch.cat(out1, dim=0)[:orig_img_count], torch.cat(out2, dim=0)[:orig_img_count] + out1, out2 = out1.float().cpu().detach().numpy(), out2.float().cpu().detach().numpy() + all_outputs = out1, out2 + + for batch_idx in range(len(all_outputs[0])): + outputs = [all_outputs[i][batch_idx:batch_idx+1,...] for i in range(len(all_outputs))] + all_out.append(outputs) + return all_out +def postprocess(outputs: List[np.ndarray], + model_input_size: Tuple[int, int], + center: Tuple[int, int], + scale: Tuple[int, int], + simcc_split_ratio: float = 2.0 + ) -> Tuple[np.ndarray, np.ndarray]: + """Postprocess for DWPose model output. + + Args: + outputs (np.ndarray): Output of RTMPose model. + model_input_size (tuple): RTMPose model Input image size. + center (tuple): Center of bbox in shape (x, y). + scale (tuple): Scale of bbox in shape (w, h). + simcc_split_ratio (float): Split ratio of simcc. + + Returns: + tuple: + - keypoints (np.ndarray): Rescaled keypoints. + - scores (np.ndarray): Model predict scores. + """ + all_key = [] + all_score = [] + for i in range(len(outputs)): + # use simcc to decode + simcc_x, simcc_y = outputs[i] + keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio) + + # rescale keypoints + keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2 + all_key.append(keypoints[0]) + all_score.append(scores[0]) + + return np.array(all_key), np.array(all_score) + + +def bbox_xyxy2cs(bbox: np.ndarray, + padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]: + """Transform the bbox format from (x,y,w,h) into (center, scale) + + Args: + bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted + as (left, top, right, bottom) + padding (float): BBox padding factor that will be multilied to scale. + Default: 1.0 + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or + (n, 2) + - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or + (n, 2) + """ + # convert single bbox from (4, ) to (1, 4) + dim = bbox.ndim + if dim == 1: + bbox = bbox[None, :] + + # get bbox center and scale + x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3]) + center = np.hstack([x1 + x2, y1 + y2]) * 0.5 + scale = np.hstack([x2 - x1, y2 - y1]) * padding + + if dim == 1: + center = center[0] + scale = scale[0] + + return center, scale + + +def _fix_aspect_ratio(bbox_scale: np.ndarray, + aspect_ratio: float) -> np.ndarray: + """Extend the scale to match the given aspect ratio. + + Args: + scale (np.ndarray): The image scale (w, h) in shape (2, ) + aspect_ratio (float): The ratio of ``w/h`` + + Returns: + np.ndarray: The reshaped image scale in (2, ) + """ + w, h = np.hsplit(bbox_scale, [1]) + bbox_scale = np.where(w > h * aspect_ratio, + np.hstack([w, w / aspect_ratio]), + np.hstack([h * aspect_ratio, h])) + return bbox_scale + + +def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray: + """Rotate a point by an angle. + + Args: + pt (np.ndarray): 2D point coordinates (x, y) in shape (2, ) + angle_rad (float): rotation angle in radian + + Returns: + np.ndarray: Rotated point in shape (2, ) + """ + sn, cs = np.sin(angle_rad), np.cos(angle_rad) + rot_mat = np.array([[cs, -sn], [sn, cs]]) + return rot_mat @ pt + + +def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """To calculate the affine matrix, three pairs of points are required. This + function is used to get the 3rd point, given 2D points a & b. + + The 3rd point is defined by rotating vector `a - b` by 90 degrees + anticlockwise, using b as the rotation center. + + Args: + a (np.ndarray): The 1st point (x,y) in shape (2, ) + b (np.ndarray): The 2nd point (x,y) in shape (2, ) + + Returns: + np.ndarray: The 3rd point. + """ + direction = a - b + c = b + np.r_[-direction[1], direction[0]] + return c + + +def get_warp_matrix(center: np.ndarray, + scale: np.ndarray, + rot: float, + output_size: Tuple[int, int], + shift: Tuple[float, float] = (0., 0.), + inv: bool = False) -> np.ndarray: + """Calculate the affine transformation matrix that can warp the bbox area + in the input image to the output size. + + Args: + center (np.ndarray[2, ]): Center of the bounding box (x, y). + scale (np.ndarray[2, ]): Scale of the bounding box + wrt [width, height]. + rot (float): Rotation angle (degree). + output_size (np.ndarray[2, ] | list(2,)): Size of the + destination heatmaps. + shift (0-100%): Shift translation ratio wrt the width/height. + Default (0., 0.). + inv (bool): Option to inverse the affine transform direction. + (inv=False: src->dst or inv=True: dst->src) + + Returns: + np.ndarray: A 2x3 transformation matrix + """ + shift = np.array(shift) + src_w = scale[0] + dst_w = output_size[0] + dst_h = output_size[1] + + # compute transformation matrix + rot_rad = np.deg2rad(rot) + src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad) + dst_dir = np.array([0., dst_w * -0.5]) + + # get four corners of the src rectangle in the original image + src = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale * shift + src[1, :] = center + src_dir + scale * shift + src[2, :] = _get_3rd_point(src[0, :], src[1, :]) + + # get four corners of the dst rectangle in the input image + dst = np.zeros((3, 2), dtype=np.float32) + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return warp_mat + + +def top_down_affine(input_size: dict, bbox_scale: dict, bbox_center: dict, + img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get the bbox image as the model input by affine transform. + + Args: + input_size (dict): The input size of the model. + bbox_scale (dict): The bbox scale of the img. + bbox_center (dict): The bbox center of the img. + img (np.ndarray): The original image. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: img after affine transform. + - np.ndarray[float32]: bbox scale after affine transform. + """ + w, h = input_size + warp_size = (int(w), int(h)) + + # reshape bbox to fixed aspect ratio + bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h) + + # get the affine matrix + center = bbox_center + scale = bbox_scale + rot = 0 + warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h)) + + # do affine transform + img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR) + + return img, bbox_scale + + +def get_simcc_maximum(simcc_x: np.ndarray, + simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get maximum response location and value from simcc representations. + + Note: + instance number: N + num_keypoints: K + heatmap height: H + heatmap width: W + + Args: + simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx) + simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy) + + Returns: + tuple: + - locs (np.ndarray): locations of maximum heatmap responses in shape + (K, 2) or (N, K, 2) + - vals (np.ndarray): values of maximum heatmap responses in shape + (K,) or (N, K) + """ + N, K, Wx = simcc_x.shape + simcc_x = simcc_x.reshape(N * K, -1) + simcc_y = simcc_y.reshape(N * K, -1) + + # get maximum value locations + x_locs = np.argmax(simcc_x, axis=1) + y_locs = np.argmax(simcc_y, axis=1) + locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32) + max_val_x = np.amax(simcc_x, axis=1) + max_val_y = np.amax(simcc_y, axis=1) + + # get maximum value across x and y axis + mask = max_val_x > max_val_y + max_val_x[mask] = max_val_y[mask] + vals = max_val_x + locs[vals <= 0.] = -1 + + # reshape + locs = locs.reshape(N, K, 2) + vals = vals.reshape(N, K) + + return locs, vals + + +def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, + simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]: + """Modulate simcc distribution with Gaussian. + + Args: + simcc_x (np.ndarray[K, Wx]): model predicted simcc in x. + simcc_y (np.ndarray[K, Wy]): model predicted simcc in y. + simcc_split_ratio (int): The split ratio of simcc. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2) + - np.ndarray[float32]: scores in shape (K,) or (n, K) + """ + keypoints, scores = get_simcc_maximum(simcc_x, simcc_y) + keypoints /= simcc_split_ratio + + return keypoints, scores + +def inference_pose(model, out_bbox, oriImg, model_input_size=(288, 384)): + resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size) + #outputs = inference(session, resized_img, dtype) + outputs = inference(model, resized_img) + + keypoints, scores = postprocess(outputs, model_input_size, center, scale) + + return keypoints, scores \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/onnxdet.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/onnxdet.py new file mode 100644 index 0000000000000000000000000000000000000000..15ae797a5125082f0de30dfbe42bd9ffc8937352 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/onnxdet.py @@ -0,0 +1,127 @@ +import cv2 +import numpy as np + +import onnxruntime + +def nms(boxes, scores, nms_thr): + """Single class NMS implemented in Numpy.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= nms_thr)[0] + order = order[inds + 1] + + return keep + +def multiclass_nms(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-aware version.""" + final_dets = [] + num_classes = scores.shape[1] + for cls_ind in range(num_classes): + cls_scores = scores[:, cls_ind] + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + continue + else: + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if len(keep) > 0: + cls_inds = np.ones((len(keep), 1)) * cls_ind + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 + ) + final_dets.append(dets) + if len(final_dets) == 0: + return None + return np.concatenate(final_dets, 0) + +def demo_postprocess(outputs, img_size, p6=False): + grids = [] + expanded_strides = [] + strides = [8, 16, 32] if not p6 else [8, 16, 32, 64] + + hsizes = [img_size[0] // stride for stride in strides] + wsizes = [img_size[1] // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + grids = np.concatenate(grids, 1) + expanded_strides = np.concatenate(expanded_strides, 1) + outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides + outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides + + return outputs + +def preprocess(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + +def inference_detector(session, oriImg): + input_shape = (640,640) + img, ratio = preprocess(oriImg, input_shape) + + ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]} + + output = session.run(None, ort_inputs) + + predictions = demo_postprocess(output[0], input_shape)[0] + + boxes = predictions[:, :4] + scores = predictions[:, 4:5] * predictions[:, 5:] + + boxes_xyxy = np.ones_like(boxes) + boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2. + boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2. + boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2. + boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2. + boxes_xyxy /= ratio + dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1) + if dets is not None: + final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] + isscore = final_scores>0.3 + iscat = final_cls_inds == 0 + isbbox = [ i and j for (i, j) in zip(isscore, iscat)] + final_boxes = final_boxes[isbbox] + else: + final_boxes = np.array([]) + + return final_boxes diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/onnxpose.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/onnxpose.py new file mode 100644 index 0000000000000000000000000000000000000000..79cd4a06241123af81ea22446a4ca8816716443f --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/onnxpose.py @@ -0,0 +1,360 @@ +from typing import List, Tuple + +import cv2 +import numpy as np +import onnxruntime as ort + +def preprocess( + img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256) +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Do preprocessing for RTMPose model inference. + + Args: + img (np.ndarray): Input image in shape. + input_size (tuple): Input image size in shape (w, h). + + Returns: + tuple: + - resized_img (np.ndarray): Preprocessed image. + - center (np.ndarray): Center of image. + - scale (np.ndarray): Scale of image. + """ + # get shape of image + img_shape = img.shape[:2] + out_img, out_center, out_scale = [], [], [] + if len(out_bbox) == 0: + out_bbox = [[0, 0, img_shape[1], img_shape[0]]] + for i in range(len(out_bbox)): + x0 = out_bbox[i][0] + y0 = out_bbox[i][1] + x1 = out_bbox[i][2] + y1 = out_bbox[i][3] + bbox = np.array([x0, y0, x1, y1]) + + # get center and scale + center, scale = bbox_xyxy2cs(bbox, padding=1.25) + + # do affine transformation + resized_img, scale = top_down_affine(input_size, scale, center, img) + + # normalize image + mean = np.array([123.675, 116.28, 103.53]) + std = np.array([58.395, 57.12, 57.375]) + resized_img = (resized_img - mean) / std + + out_img.append(resized_img) + out_center.append(center) + out_scale.append(scale) + + return out_img, out_center, out_scale + + +def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray: + """Inference RTMPose model. + + Args: + sess (ort.InferenceSession): ONNXRuntime session. + img (np.ndarray): Input image in shape. + + Returns: + outputs (np.ndarray): Output of RTMPose model. + """ + all_out = [] + # build input + for i in range(len(img)): + input = [img[i].transpose(2, 0, 1)] + + # build output + sess_input = {sess.get_inputs()[0].name: input} + sess_output = [] + for out in sess.get_outputs(): + sess_output.append(out.name) + + # run model + outputs = sess.run(sess_output, sess_input) + all_out.append(outputs) + + return all_out + + +def postprocess(outputs: List[np.ndarray], + model_input_size: Tuple[int, int], + center: Tuple[int, int], + scale: Tuple[int, int], + simcc_split_ratio: float = 2.0 + ) -> Tuple[np.ndarray, np.ndarray]: + """Postprocess for RTMPose model output. + + Args: + outputs (np.ndarray): Output of RTMPose model. + model_input_size (tuple): RTMPose model Input image size. + center (tuple): Center of bbox in shape (x, y). + scale (tuple): Scale of bbox in shape (w, h). + simcc_split_ratio (float): Split ratio of simcc. + + Returns: + tuple: + - keypoints (np.ndarray): Rescaled keypoints. + - scores (np.ndarray): Model predict scores. + """ + all_key = [] + all_score = [] + for i in range(len(outputs)): + # use simcc to decode + simcc_x, simcc_y = outputs[i] + keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio) + + # rescale keypoints + keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2 + all_key.append(keypoints[0]) + all_score.append(scores[0]) + + return np.array(all_key), np.array(all_score) + + +def bbox_xyxy2cs(bbox: np.ndarray, + padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]: + """Transform the bbox format from (x,y,w,h) into (center, scale) + + Args: + bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted + as (left, top, right, bottom) + padding (float): BBox padding factor that will be multilied to scale. + Default: 1.0 + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or + (n, 2) + - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or + (n, 2) + """ + # convert single bbox from (4, ) to (1, 4) + dim = bbox.ndim + if dim == 1: + bbox = bbox[None, :] + + # get bbox center and scale + x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3]) + center = np.hstack([x1 + x2, y1 + y2]) * 0.5 + scale = np.hstack([x2 - x1, y2 - y1]) * padding + + if dim == 1: + center = center[0] + scale = scale[0] + + return center, scale + + +def _fix_aspect_ratio(bbox_scale: np.ndarray, + aspect_ratio: float) -> np.ndarray: + """Extend the scale to match the given aspect ratio. + + Args: + scale (np.ndarray): The image scale (w, h) in shape (2, ) + aspect_ratio (float): The ratio of ``w/h`` + + Returns: + np.ndarray: The reshaped image scale in (2, ) + """ + w, h = np.hsplit(bbox_scale, [1]) + bbox_scale = np.where(w > h * aspect_ratio, + np.hstack([w, w / aspect_ratio]), + np.hstack([h * aspect_ratio, h])) + return bbox_scale + + +def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray: + """Rotate a point by an angle. + + Args: + pt (np.ndarray): 2D point coordinates (x, y) in shape (2, ) + angle_rad (float): rotation angle in radian + + Returns: + np.ndarray: Rotated point in shape (2, ) + """ + sn, cs = np.sin(angle_rad), np.cos(angle_rad) + rot_mat = np.array([[cs, -sn], [sn, cs]]) + return rot_mat @ pt + + +def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """To calculate the affine matrix, three pairs of points are required. This + function is used to get the 3rd point, given 2D points a & b. + + The 3rd point is defined by rotating vector `a - b` by 90 degrees + anticlockwise, using b as the rotation center. + + Args: + a (np.ndarray): The 1st point (x,y) in shape (2, ) + b (np.ndarray): The 2nd point (x,y) in shape (2, ) + + Returns: + np.ndarray: The 3rd point. + """ + direction = a - b + c = b + np.r_[-direction[1], direction[0]] + return c + + +def get_warp_matrix(center: np.ndarray, + scale: np.ndarray, + rot: float, + output_size: Tuple[int, int], + shift: Tuple[float, float] = (0., 0.), + inv: bool = False) -> np.ndarray: + """Calculate the affine transformation matrix that can warp the bbox area + in the input image to the output size. + + Args: + center (np.ndarray[2, ]): Center of the bounding box (x, y). + scale (np.ndarray[2, ]): Scale of the bounding box + wrt [width, height]. + rot (float): Rotation angle (degree). + output_size (np.ndarray[2, ] | list(2,)): Size of the + destination heatmaps. + shift (0-100%): Shift translation ratio wrt the width/height. + Default (0., 0.). + inv (bool): Option to inverse the affine transform direction. + (inv=False: src->dst or inv=True: dst->src) + + Returns: + np.ndarray: A 2x3 transformation matrix + """ + shift = np.array(shift) + src_w = scale[0] + dst_w = output_size[0] + dst_h = output_size[1] + + # compute transformation matrix + rot_rad = np.deg2rad(rot) + src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad) + dst_dir = np.array([0., dst_w * -0.5]) + + # get four corners of the src rectangle in the original image + src = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale * shift + src[1, :] = center + src_dir + scale * shift + src[2, :] = _get_3rd_point(src[0, :], src[1, :]) + + # get four corners of the dst rectangle in the input image + dst = np.zeros((3, 2), dtype=np.float32) + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return warp_mat + + +def top_down_affine(input_size: dict, bbox_scale: dict, bbox_center: dict, + img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get the bbox image as the model input by affine transform. + + Args: + input_size (dict): The input size of the model. + bbox_scale (dict): The bbox scale of the img. + bbox_center (dict): The bbox center of the img. + img (np.ndarray): The original image. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: img after affine transform. + - np.ndarray[float32]: bbox scale after affine transform. + """ + w, h = input_size + warp_size = (int(w), int(h)) + + # reshape bbox to fixed aspect ratio + bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h) + + # get the affine matrix + center = bbox_center + scale = bbox_scale + rot = 0 + warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h)) + + # do affine transform + img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR) + + return img, bbox_scale + + +def get_simcc_maximum(simcc_x: np.ndarray, + simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get maximum response location and value from simcc representations. + + Note: + instance number: N + num_keypoints: K + heatmap height: H + heatmap width: W + + Args: + simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx) + simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy) + + Returns: + tuple: + - locs (np.ndarray): locations of maximum heatmap responses in shape + (K, 2) or (N, K, 2) + - vals (np.ndarray): values of maximum heatmap responses in shape + (K,) or (N, K) + """ + N, K, Wx = simcc_x.shape + simcc_x = simcc_x.reshape(N * K, -1) + simcc_y = simcc_y.reshape(N * K, -1) + + # get maximum value locations + x_locs = np.argmax(simcc_x, axis=1) + y_locs = np.argmax(simcc_y, axis=1) + locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32) + max_val_x = np.amax(simcc_x, axis=1) + max_val_y = np.amax(simcc_y, axis=1) + + # get maximum value across x and y axis + mask = max_val_x > max_val_y + max_val_x[mask] = max_val_y[mask] + vals = max_val_x + locs[vals <= 0.] = -1 + + # reshape + locs = locs.reshape(N, K, 2) + vals = vals.reshape(N, K) + + return locs, vals + + +def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, + simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]: + """Modulate simcc distribution with Gaussian. + + Args: + simcc_x (np.ndarray[K, Wx]): model predicted simcc in x. + simcc_y (np.ndarray[K, Wy]): model predicted simcc in y. + simcc_split_ratio (int): The split ratio of simcc. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2) + - np.ndarray[float32]: scores in shape (K,) or (n, K) + """ + keypoints, scores = get_simcc_maximum(simcc_x, simcc_y) + keypoints /= simcc_split_ratio + + return keypoints, scores + + +def inference_pose(session, out_bbox, oriImg): + h, w = session.get_inputs()[0].shape[2:] + model_input_size = (w, h) + resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size) + outputs = inference(session, resized_img) + keypoints, scores = postprocess(outputs, model_input_size, center, scale) + + return keypoints, scores \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/util.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/util.py new file mode 100644 index 0000000000000000000000000000000000000000..15cbc71f83b06d616ae44274d2770e01a80fa15f --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/util.py @@ -0,0 +1,402 @@ +import math +import numpy as np +import colorsys +import cv2 + + +eps = 0.01 + + +def smart_resize(x, s): + Ht, Wt = s + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2) + + +def smart_resize_k(x, fx, fy): + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + Ht, Wt = Ho * fy, Wo * fx + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2) + + +def padRightDownCorner(img, stride, padValue): + h = img.shape[0] + w = img.shape[1] + + pad = 4 * [None] + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + + img_padded = img + pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) + img_padded = np.concatenate((pad_up, img_padded), axis=0) + pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) + img_padded = np.concatenate((pad_left, img_padded), axis=1) + pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) + img_padded = np.concatenate((img_padded, pad_down), axis=0) + pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) + img_padded = np.concatenate((img_padded, pad_right), axis=1) + + return img_padded, pad + + +def transfer(model, model_weights): + transfered_model_weights = {} + for weights_name in model.state_dict().keys(): + transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + return transfered_model_weights + + +def draw_bodypose(canvas, candidate, subset): + H, W, C = canvas.shape + candidate = np.array(candidate) + subset = np.array(subset) + + stickwidth = 4 + + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] + + for i in range(17): + for n in range(len(subset)): + index = subset[n][np.array(limbSeq[i]) - 1] + if -1 in index: + continue + Y = candidate[index.astype(int), 0] * float(W) + X = candidate[index.astype(int), 1] * float(H) + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) + cv2.fillConvexPoly(canvas, polygon, colors[i]) + + canvas = (canvas * 0.6).astype(np.uint8) + + for i in range(18): + for n in range(len(subset)): + index = int(subset[n][i]) + if index == -1: + continue + x, y = candidate[index][0:2] + x = int(x * W) + y = int(y * H) + cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) + + return canvas + +def alpha_blend_color(color, alpha): + """blend color according to point conf + """ + return [int(c * alpha) for c in color] + +def draw_body_and_foot(canvas, candidate, subset, score, stick_width=4, draw_body=True, draw_feet=True, body_keypoint_size=4, draw_head=True): + H, W, C = canvas.shape + candidate = np.array(candidate) + subset = np.array(subset) + limbSeq_and_colors = [] + + if draw_body: + limbSeq_and_colors = [ + ([2, 3], [255, 0, 0]), # Neck to Right Shoulder + ([2, 6], [255, 85, 0]), # Neck to Left Shoulder + ([3, 4], [255, 170, 0]), # Right Shoulder to Right Elbow + ([4, 5], [255, 255, 0]), # Right Elbow to Right Wrist + ([6, 7], [170, 255, 0]), # Left Shoulder to Left Elbow + ([7, 8], [85, 255, 0]), # Left Elbow to Left Wrist + ([2, 9], [0, 255, 0]), # Neck to Right Hip + ([9, 10], [0, 255, 85]), # Right Hip to Right Knee + ([10, 11], [0, 255, 170]), # Right Knee to Right Ankle + ([2, 12], [0, 255, 255]), # Neck to Left Hip + ([12, 13], [0, 170, 255]), # Left Hip to Left Knee + ([13, 14], [0, 85, 255]), # Left Knee to Left Ankle + ] + else: + limbSeq_and_colors = [ + ([2, 3], [0, 0, 0]), # Neck to Right Shoulder + ([2, 6], [0, 0, 0]), # Neck to Left Shoulder + ([3, 4], [0, 0, 0]), # Right Shoulder to Right Elbow + ([4, 5], [0, 0, 0]), # Right Elbow to Right Wrist + ([6, 7], [0, 0, 0]), # Left Shoulder to Left Elbow + ([7, 8], [0, 0, 0]), # Left Elbow to Left Wrist + ([2, 9], [0, 0, 0]), # Neck to Right Hip + ([9, 10], [0, 0, 0]), # Right Hip to Right Knee + ([10, 11], [0, 0, 0]), # Right Knee to Right Ankle + ([2, 12], [0, 0, 0]), # Neck to Left Hip + ([12, 13], [0, 0, 0]), # Left Hip to Left Knee + ([13, 14], [0, 0, 0]), # Left Knee to Left Ankle + ] + + # Conditionally add head-related elements + if draw_head: + head_elements = [ + ([2, 1], [0, 0, 255]), # Neck to Nose + ([1, 15], [0, 0, 255]), # Nose to Right Eye + ([15, 17], [85, 0, 255]), # Right Eye to Right Ear + ([1, 16], [170, 0, 255]), # Nose to Left Eye + ([16, 18], [255, 0, 255]), # Left Eye to Left Ear + ([3, 17], [255, 0, 170]), # Right Shoulder to Right Ear + ([6, 18], [255, 0, 85]) # Left Shoulder to Left Ear + ] + else: + head_elements = [ + ([2, 1], [0, 0, 0]), # Neck to Nose + ([1, 15], [0, 0, 0]), # Nose to Right Eye + ([15, 17], [0, 0, 0]), # Right Eye to Right Ear + ([1, 16], [0, 0, 0]), # Nose to Left Eye + ([16, 18], [0, 0, 0]), # Left Eye to Left Ear + ([3, 17], [0, 0, 0]), # Right Shoulder to Right Ear + ([6, 18], [0, 0, 0]) # Left Shoulder to Left Ear + ] + if draw_feet: + limbSeq_and_colors += [ + ([14, 19], [170, 255, 255]), # Left Ankle to Right Foot + ([11, 20], [255, 255, 0]), # Right Ankle to Left Foot + ] + + # Append head elements based on the condition + limbSeq_and_colors += head_elements + + for limb_info in limbSeq_and_colors[:17]: + limbSeq, color = limb_info + for n in range(len(subset)): + index = subset[n][np.array(limbSeq) - 1] + conf = score[n][np.array(limbSeq) - 1] + if conf[0] < 0.3 or conf[1] < 0.3: + continue + Y = candidate[index.astype(int), 0] * float(W) + X = candidate[index.astype(int), 1] * float(H) + mX = np.mean(X) + mY = np.mean(Y) + length = np.sqrt((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stick_width), int(angle), 0, 360, 1) + cv2.fillConvexPoly(canvas, polygon, alpha_blend_color(color, conf[0] * conf[1])) + + canvas = (canvas * 0.6).astype(np.uint8) + + for limb_info in limbSeq_and_colors[:18]: + limbSeq, color = limb_info + for i in limbSeq: + for n in range(len(subset)): + index = int(subset[n][i - 1]) + if index == -1: + continue + x, y = candidate[index][0:2] + conf = score[n][i - 1] + x = int(x * W) + y = int(y * H) + cv2.circle(canvas, (x, y), 4, alpha_blend_color(color, conf), thickness=-1) + + return canvas + + +def draw_handpose(canvas, all_hand_peaks, draw_hands=True, hand_keypoint_size=4): + H, W, C = canvas.shape + + edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ + [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] + + for peaks in all_hand_peaks: + peaks = np.array(peaks) + + if draw_hands: + for ie, e in enumerate(edges): + x1, y1 = peaks[e[0]] + x2, y2 = peaks[e[1]] + x1 = int(x1 * W) + y1 = int(y1 * H) + x2 = int(x2 * W) + y2 = int(y2 * H) + if x1 > eps and y1 > eps and x2 > eps and y2 > eps: + h = (ie / float(len(edges))) % 1.0 + s, v = 1.0, 1.0 + r, g, b = colorsys.hsv_to_rgb(h, s, v) + color = (int(255 * r), int(255 * g), int(255 * b)) + cv2.line(canvas, (x1, y1), (x2, y2), color, thickness=2) + if hand_keypoint_size > 0: + for i, keypoint in enumerate(peaks): + x, y = keypoint + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), hand_keypoint_size, (0, 0, 255), thickness=-1) + return canvas + + +def draw_facepose(canvas, all_lmks): + H, W, C = canvas.shape + for lmks in all_lmks: + lmks = np.array(lmks) + for lmk in lmks: + x, y = lmk + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1) + return canvas + + +# detect hand according to body pose keypoints +# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp +def handDetect(candidate, subset, oriImg): + # right hand: wrist 4, elbow 3, shoulder 2 + # left hand: wrist 7, elbow 6, shoulder 5 + ratioWristElbow = 0.33 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + for person in subset.astype(int): + # if any of three not detected + has_left = np.sum(person[[5, 6, 7]] == -1) == 0 + has_right = np.sum(person[[2, 3, 4]] == -1) == 0 + if not (has_left or has_right): + continue + hands = [] + #left hand + if has_left: + left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]] + x1, y1 = candidate[left_shoulder_index][:2] + x2, y2 = candidate[left_elbow_index][:2] + x3, y3 = candidate[left_wrist_index][:2] + hands.append([x1, y1, x2, y2, x3, y3, True]) + # right hand + if has_right: + right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]] + x1, y1 = candidate[right_shoulder_index][:2] + x2, y2 = candidate[right_elbow_index][:2] + x3, y3 = candidate[right_wrist_index][:2] + hands.append([x1, y1, x2, y2, x3, y3, False]) + + for x1, y1, x2, y2, x3, y3, is_left in hands: + + x = x3 + ratioWristElbow * (x3 - x2) + y = y3 + ratioWristElbow * (y3 - y2) + distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) + distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder) + # x-y refers to the center --> offset to topLeft point + # handRectangle.x -= handRectangle.width / 2.f; + # handRectangle.y -= handRectangle.height / 2.f; + x -= width / 2 + y -= width / 2 # width = height + # overflow the image + if x < 0: x = 0 + if y < 0: y = 0 + width1 = width + width2 = width + if x + width > image_width: width1 = image_width - x + if y + width > image_height: width2 = image_height - y + width = min(width1, width2) + # the max hand box value is 20 pixels + if width >= 20: + detect_result.append([int(x), int(y), int(width), is_left]) + + ''' + return value: [[x, y, w, True if left hand else False]]. + width=height since the network require squared input. + x, y is the coordinate of top left + ''' + return detect_result + + +# Written by Lvmin +def faceDetect(candidate, subset, oriImg): + # left right eye ear 14 15 16 17 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + for person in subset.astype(int): + has_head = person[0] > -1 + if not has_head: + continue + + has_left_eye = person[14] > -1 + has_right_eye = person[15] > -1 + has_left_ear = person[16] > -1 + has_right_ear = person[17] > -1 + + if not (has_left_eye or has_right_eye or has_left_ear or has_right_ear): + continue + + head, left_eye, right_eye, left_ear, right_ear = person[[0, 14, 15, 16, 17]] + + width = 0.0 + x0, y0 = candidate[head][:2] + + if has_left_eye: + x1, y1 = candidate[left_eye][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if has_right_eye: + x1, y1 = candidate[right_eye][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if has_left_ear: + x1, y1 = candidate[left_ear][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + if has_right_ear: + x1, y1 = candidate[right_ear][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + x, y = x0, y0 + + x -= width + y -= width + + if x < 0: + x = 0 + + if y < 0: + y = 0 + + width1 = width * 2 + width2 = width * 2 + + if x + width > image_width: + width1 = image_width - x + + if y + width > image_height: + width2 = image_height - y + + width = min(width1, width2) + + if width >= 20: + detect_result.append([int(x), int(y), int(width)]) + + return detect_result + + +# get max index of 2d array +def npmax(array): + arrayindex = array.argmax(1) + arrayvalue = array.max(1) + i = arrayvalue.argmax() + j = arrayindex[i] + return i, j diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/wholebody.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/wholebody.py new file mode 100644 index 0000000000000000000000000000000000000000..b838661e9aa6d4c6bf4b9e4c03e90d426d9b1563 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/dwpose/wholebody.py @@ -0,0 +1,42 @@ +import numpy as np +from .jit_det import inference_detector as inference_jit_yolox +from .jit_pose import inference_pose as inference_jit_pose +import os + + +class Wholebody: + def __init__(self, model_det, model_pose): + self.model_det = model_det + self.model_pose = model_pose + + + def __call__(self, oriImg): + det_result = inference_jit_yolox(self.model_det, oriImg, detect_classes=[0]) + keypoints, scores = inference_jit_pose(self.model_pose, det_result, oriImg) + + keypoints_info = np.concatenate( + (keypoints, scores[..., None]), axis=-1) + # compute neck joint + neck = np.mean(keypoints_info[:, [5, 6]], axis=1) + # neck score when visualizing pred + neck[:, 2:4] = np.logical_and( + keypoints_info[:, 5, 2:4] > 0.3, + keypoints_info[:, 6, 2:4] > 0.3).astype(int) + new_keypoints_info = np.insert( + keypoints_info, 17, neck, axis=1) + mmpose_idx = [ + 17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3 + ] + openpose_idx = [ + 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17 + ] + new_keypoints_info[:, openpose_idx] = \ + new_keypoints_info[:, mmpose_idx] + keypoints_info = new_keypoints_info + + keypoints, scores = keypoints_info[ + ..., :2], keypoints_info[..., 2] + + return keypoints, scores + + diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/nodes.py b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..c533a2a3771f6889892acf88cf3e6d44b263ecaa --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/unianimate/nodes.py @@ -0,0 +1,836 @@ + +import torch.nn as nn +from ..utils import log +import comfy.model_management as mm +from comfy.utils import ProgressBar +from tqdm import tqdm + +def update_transformer(transformer, state_dict): + + concat_dim = 4 + transformer.dwpose_embedding = nn.Sequential( + nn.Conv3d(3, concat_dim * 4, (3,3,3), stride=(1,1,1), padding=(1,1,1)), + nn.SiLU(), + nn.Conv3d(concat_dim * 4, concat_dim * 4, (3,3,3), stride=(1,1,1), padding=(1,1,1)), + nn.SiLU(), + nn.Conv3d(concat_dim * 4, concat_dim * 4, (3,3,3), stride=(1,1,1), padding=(1,1,1)), + nn.SiLU(), + nn.Conv3d(concat_dim * 4, concat_dim * 4, (3,3,3), stride=(1,2,2), padding=(1,1,1)), + nn.SiLU(), + nn.Conv3d(concat_dim * 4, concat_dim * 4, 3, stride=(2,2,2), padding=1), + nn.SiLU(), + nn.Conv3d(concat_dim * 4, concat_dim * 4, 3, stride=(2,2,2), padding=1), + nn.SiLU(), + nn.Conv3d(concat_dim * 4, 5120, (1,2,2), stride=(1,2,2), padding=0)) + + randomref_dim = 20 + transformer.randomref_embedding_pose = nn.Sequential( + nn.Conv2d(3, concat_dim * 4, 3, stride=1, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=1, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=1, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, randomref_dim, 3, stride=2, padding=1), + ) + state_dict_new = {} + for key in list(state_dict.keys()): + if "dwpose_embedding" in key: + state_dict_new[key.split("dwpose_embedding.")[1]] = state_dict.pop(key) + transformer.dwpose_embedding.load_state_dict(state_dict_new, strict=True) + state_dict_new = {} + for key in list(state_dict.keys()): + if "randomref_embedding_pose" in key: + state_dict_new[key.split("randomref_embedding_pose.")[1]] = state_dict.pop(key) + transformer.randomref_embedding_pose.load_state_dict(state_dict_new,strict=True) + return transformer + +# Openpose +# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose +# 2nd Edited by https://github.com/Hzzone/pytorch-openpose +# 3rd Edited by ControlNet +# 4th Edited by ControlNet (added face and correct hands) + +import os +import torch +import numpy as np +import copy +import torch +import numpy as np +import math + +from .dwpose.wholebody import Wholebody + +def smoothing_factor(t_e, cutoff): + r = 2 * math.pi * cutoff * t_e + return r / (r + 1) + + +def exponential_smoothing(a, x, x_prev): + return a * x + (1 - a) * x_prev + + +class OneEuroFilter: + def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, + d_cutoff=1.0): + """Initialize the one euro filter.""" + # The parameters. + self.min_cutoff = float(min_cutoff) + self.beta = float(beta) + self.d_cutoff = float(d_cutoff) + # Previous values. + self.x_prev = x0 + self.dx_prev = float(dx0) + self.t_prev = float(t0) + + def __call__(self, t, x): + """Compute the filtered signal.""" + t_e = t - self.t_prev + + # The filtered derivative of the signal. + a_d = smoothing_factor(t_e, self.d_cutoff) + dx = (x - self.x_prev) / t_e + dx_hat = exponential_smoothing(a_d, dx, self.dx_prev) + + # The filtered signal. + cutoff = self.min_cutoff + self.beta * abs(dx_hat) + a = smoothing_factor(t_e, cutoff) + x_hat = exponential_smoothing(a, x, self.x_prev) + + # Memorize the previous values. + self.x_prev = x_hat + self.dx_prev = dx_hat + self.t_prev = t + + return x_hat + +class DWposeDetector: + def __init__(self, model_det, model_pose): + self.pose_estimation = Wholebody(model_det, model_pose) + + def __call__(self, oriImg, score_threshold=0.3): + oriImg = oriImg.copy() + H, W, C = oriImg.shape + with torch.no_grad(): + candidate, subset = self.pose_estimation(oriImg) + candidate = candidate[0][np.newaxis, :, :] + subset = subset[0][np.newaxis, :] + nums, keys, locs = candidate.shape + candidate[..., 0] /= float(W) + candidate[..., 1] /= float(H) + body = candidate[:,:18].copy() + body = body.reshape(nums*18, locs) + score = subset[:,:18].copy() + + for i in range(len(score)): + for j in range(len(score[i])): + if score[i][j] > score_threshold: + score[i][j] = int(18*i+j) + else: + score[i][j] = -1 + + un_visible = subset score_threshold: + bodyfoot_score[i][j] = int(18*i+j) + else: + bodyfoot_score[i][j] = -1 + if -1 not in bodyfoot_score[:,18] and -1 not in bodyfoot_score[:,19]: + bodyfoot_score[:,18] = np.array([18.]) + else: + bodyfoot_score[:,18] = np.array([-1.]) + if -1 not in bodyfoot_score[:,21] and -1 not in bodyfoot_score[:,22]: + bodyfoot_score[:,19] = np.array([19.]) + else: + bodyfoot_score[:,19] = np.array([-1.]) + bodyfoot_score = bodyfoot_score[:, :20] + + bodyfoot = candidate[:,:24].copy() + + for i in range(nums): + if -1 not in bodyfoot[i][18] and -1 not in bodyfoot[i][19]: + bodyfoot[i][18] = (bodyfoot[i][18]+bodyfoot[i][19])/2 + else: + bodyfoot[i][18] = np.array([-1., -1.]) + if -1 not in bodyfoot[i][21] and -1 not in bodyfoot[i][22]: + bodyfoot[i][19] = (bodyfoot[i][21]+bodyfoot[i][22])/2 + else: + bodyfoot[i][19] = np.array([-1., -1.]) + + bodyfoot = bodyfoot[:,:20,:] + bodyfoot = bodyfoot.reshape(nums*20, locs) + + foot = candidate[:,18:24] + + faces = candidate[:,24:92] + + hands = candidate[:,92:113] + hands = np.vstack([hands, candidate[:,113:]]) + + # bodies = dict(candidate=body, subset=score) + bodies = dict(candidate=bodyfoot, subset=bodyfoot_score, score=bodyfoot_score) + pose = dict(bodies=bodies, hands=hands, faces=faces) + + # return draw_pose(pose, H, W) + return pose + +def draw_pose(pose, H, W, stick_width=4,draw_body=True, draw_hands=True, draw_feet=True, + body_keypoint_size=4, hand_keypoint_size=4, draw_head=True): + from .dwpose.util import draw_body_and_foot, draw_handpose, draw_facepose + bodies = pose['bodies'] + faces = pose['faces'] + hands = pose['hands'] + candidate = bodies['candidate'] + subset = bodies['subset'] + score=bodies['score'] + + canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) + canvas = draw_body_and_foot(canvas, candidate, subset, score, draw_body=draw_body, stick_width=stick_width, draw_feet=draw_feet, draw_head=draw_head, body_keypoint_size=body_keypoint_size) + canvas = draw_handpose(canvas, hands, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size) + canvas_without_face = copy.deepcopy(canvas) + canvas = draw_facepose(canvas, faces) + + return canvas_without_face, canvas + + +def pose_extract(pose_images, ref_image, dwpose_model, height, width, score_threshold, stick_width, + draw_body=True, draw_hands=True, hand_keypoint_size=4, draw_feet=True, + body_keypoint_size=4, handle_not_detected="repeat", draw_head=True): + + results_vis = [] + comfy_pbar = ProgressBar(len(pose_images)) + + if ref_image is not None: + try: + pose_ref = dwpose_model(ref_image.squeeze(0), score_threshold=score_threshold) + except: + raise ValueError("No pose detected in reference image") + prev_pose = None + for img in tqdm(pose_images, desc="Pose Extraction", unit="image", total=len(pose_images)): + try: + pose = dwpose_model(img, score_threshold=score_threshold) + if handle_not_detected == "repeat": + prev_pose = pose + except: + if prev_pose is not None: + pose = prev_pose + else: + pose = np.zeros_like(img) + results_vis.append(pose) + comfy_pbar.update(1) + + bodies = results_vis[0]['bodies'] + faces = results_vis[0]['faces'] + hands = results_vis[0]['hands'] + candidate = bodies['candidate'] + + if ref_image is not None: + ref_bodies = pose_ref['bodies'] + ref_faces = pose_ref['faces'] + ref_hands = pose_ref['hands'] + ref_candidate = ref_bodies['candidate'] + + + ref_2_x = ref_candidate[2][0] + ref_2_y = ref_candidate[2][1] + ref_5_x = ref_candidate[5][0] + ref_5_y = ref_candidate[5][1] + ref_8_x = ref_candidate[8][0] + ref_8_y = ref_candidate[8][1] + ref_11_x = ref_candidate[11][0] + ref_11_y = ref_candidate[11][1] + ref_center1 = 0.5*(ref_candidate[2]+ref_candidate[5]) + ref_center2 = 0.5*(ref_candidate[8]+ref_candidate[11]) + + zero_2_x = candidate[2][0] + zero_2_y = candidate[2][1] + zero_5_x = candidate[5][0] + zero_5_y = candidate[5][1] + zero_8_x = candidate[8][0] + zero_8_y = candidate[8][1] + zero_11_x = candidate[11][0] + zero_11_y = candidate[11][1] + zero_center1 = 0.5*(candidate[2]+candidate[5]) + zero_center2 = 0.5*(candidate[8]+candidate[11]) + + x_ratio = (ref_5_x-ref_2_x)/(zero_5_x-zero_2_x) + y_ratio = (ref_center2[1]-ref_center1[1])/(zero_center2[1]-zero_center1[1]) + + results_vis[0]['bodies']['candidate'][:,0] *= x_ratio + results_vis[0]['bodies']['candidate'][:,1] *= y_ratio + results_vis[0]['faces'][:,:,0] *= x_ratio + results_vis[0]['faces'][:,:,1] *= y_ratio + results_vis[0]['hands'][:,:,0] *= x_ratio + results_vis[0]['hands'][:,:,1] *= y_ratio + + ########neck######## + l_neck_ref = ((ref_candidate[0][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[0][1] - ref_candidate[1][1]) ** 2) ** 0.5 + l_neck_0 = ((candidate[0][0] - candidate[1][0]) ** 2 + (candidate[0][1] - candidate[1][1]) ** 2) ** 0.5 + neck_ratio = l_neck_ref / l_neck_0 + + x_offset_neck = (candidate[1][0]-candidate[0][0])*(1.-neck_ratio) + y_offset_neck = (candidate[1][1]-candidate[0][1])*(1.-neck_ratio) + + results_vis[0]['bodies']['candidate'][0,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][0,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][14,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][14,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][15,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][15,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][16,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][16,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][17,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][17,1] += y_offset_neck + + ########shoulder2######## + l_shoulder2_ref = ((ref_candidate[2][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[2][1] - ref_candidate[1][1]) ** 2) ** 0.5 + l_shoulder2_0 = ((candidate[2][0] - candidate[1][0]) ** 2 + (candidate[2][1] - candidate[1][1]) ** 2) ** 0.5 + + shoulder2_ratio = l_shoulder2_ref / l_shoulder2_0 + + x_offset_shoulder2 = (candidate[1][0]-candidate[2][0])*(1.-shoulder2_ratio) + y_offset_shoulder2 = (candidate[1][1]-candidate[2][1])*(1.-shoulder2_ratio) + + results_vis[0]['bodies']['candidate'][2,0] += x_offset_shoulder2 + results_vis[0]['bodies']['candidate'][2,1] += y_offset_shoulder2 + results_vis[0]['bodies']['candidate'][3,0] += x_offset_shoulder2 + results_vis[0]['bodies']['candidate'][3,1] += y_offset_shoulder2 + results_vis[0]['bodies']['candidate'][4,0] += x_offset_shoulder2 + results_vis[0]['bodies']['candidate'][4,1] += y_offset_shoulder2 + results_vis[0]['hands'][1,:,0] += x_offset_shoulder2 + results_vis[0]['hands'][1,:,1] += y_offset_shoulder2 + + ########shoulder5######## + l_shoulder5_ref = ((ref_candidate[5][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[5][1] - ref_candidate[1][1]) ** 2) ** 0.5 + l_shoulder5_0 = ((candidate[5][0] - candidate[1][0]) ** 2 + (candidate[5][1] - candidate[1][1]) ** 2) ** 0.5 + + shoulder5_ratio = l_shoulder5_ref / l_shoulder5_0 + + x_offset_shoulder5 = (candidate[1][0]-candidate[5][0])*(1.-shoulder5_ratio) + y_offset_shoulder5 = (candidate[1][1]-candidate[5][1])*(1.-shoulder5_ratio) + + results_vis[0]['bodies']['candidate'][5,0] += x_offset_shoulder5 + results_vis[0]['bodies']['candidate'][5,1] += y_offset_shoulder5 + results_vis[0]['bodies']['candidate'][6,0] += x_offset_shoulder5 + results_vis[0]['bodies']['candidate'][6,1] += y_offset_shoulder5 + results_vis[0]['bodies']['candidate'][7,0] += x_offset_shoulder5 + results_vis[0]['bodies']['candidate'][7,1] += y_offset_shoulder5 + results_vis[0]['hands'][0,:,0] += x_offset_shoulder5 + results_vis[0]['hands'][0,:,1] += y_offset_shoulder5 + + ########arm3######## + l_arm3_ref = ((ref_candidate[3][0] - ref_candidate[2][0]) ** 2 + (ref_candidate[3][1] - ref_candidate[2][1]) ** 2) ** 0.5 + l_arm3_0 = ((candidate[3][0] - candidate[2][0]) ** 2 + (candidate[3][1] - candidate[2][1]) ** 2) ** 0.5 + + arm3_ratio = l_arm3_ref / l_arm3_0 + + x_offset_arm3 = (candidate[2][0]-candidate[3][0])*(1.-arm3_ratio) + y_offset_arm3 = (candidate[2][1]-candidate[3][1])*(1.-arm3_ratio) + + results_vis[0]['bodies']['candidate'][3,0] += x_offset_arm3 + results_vis[0]['bodies']['candidate'][3,1] += y_offset_arm3 + results_vis[0]['bodies']['candidate'][4,0] += x_offset_arm3 + results_vis[0]['bodies']['candidate'][4,1] += y_offset_arm3 + results_vis[0]['hands'][1,:,0] += x_offset_arm3 + results_vis[0]['hands'][1,:,1] += y_offset_arm3 + + ########arm4######## + l_arm4_ref = ((ref_candidate[4][0] - ref_candidate[3][0]) ** 2 + (ref_candidate[4][1] - ref_candidate[3][1]) ** 2) ** 0.5 + l_arm4_0 = ((candidate[4][0] - candidate[3][0]) ** 2 + (candidate[4][1] - candidate[3][1]) ** 2) ** 0.5 + + arm4_ratio = l_arm4_ref / l_arm4_0 + + x_offset_arm4 = (candidate[3][0]-candidate[4][0])*(1.-arm4_ratio) + y_offset_arm4 = (candidate[3][1]-candidate[4][1])*(1.-arm4_ratio) + + results_vis[0]['bodies']['candidate'][4,0] += x_offset_arm4 + results_vis[0]['bodies']['candidate'][4,1] += y_offset_arm4 + results_vis[0]['hands'][1,:,0] += x_offset_arm4 + results_vis[0]['hands'][1,:,1] += y_offset_arm4 + + ########arm6######## + l_arm6_ref = ((ref_candidate[6][0] - ref_candidate[5][0]) ** 2 + (ref_candidate[6][1] - ref_candidate[5][1]) ** 2) ** 0.5 + l_arm6_0 = ((candidate[6][0] - candidate[5][0]) ** 2 + (candidate[6][1] - candidate[5][1]) ** 2) ** 0.5 + + arm6_ratio = l_arm6_ref / l_arm6_0 + + x_offset_arm6 = (candidate[5][0]-candidate[6][0])*(1.-arm6_ratio) + y_offset_arm6 = (candidate[5][1]-candidate[6][1])*(1.-arm6_ratio) + + results_vis[0]['bodies']['candidate'][6,0] += x_offset_arm6 + results_vis[0]['bodies']['candidate'][6,1] += y_offset_arm6 + results_vis[0]['bodies']['candidate'][7,0] += x_offset_arm6 + results_vis[0]['bodies']['candidate'][7,1] += y_offset_arm6 + results_vis[0]['hands'][0,:,0] += x_offset_arm6 + results_vis[0]['hands'][0,:,1] += y_offset_arm6 + + ########arm7######## + l_arm7_ref = ((ref_candidate[7][0] - ref_candidate[6][0]) ** 2 + (ref_candidate[7][1] - ref_candidate[6][1]) ** 2) ** 0.5 + l_arm7_0 = ((candidate[7][0] - candidate[6][0]) ** 2 + (candidate[7][1] - candidate[6][1]) ** 2) ** 0.5 + + arm7_ratio = l_arm7_ref / l_arm7_0 + + x_offset_arm7 = (candidate[6][0]-candidate[7][0])*(1.-arm7_ratio) + y_offset_arm7 = (candidate[6][1]-candidate[7][1])*(1.-arm7_ratio) + + results_vis[0]['bodies']['candidate'][7,0] += x_offset_arm7 + results_vis[0]['bodies']['candidate'][7,1] += y_offset_arm7 + results_vis[0]['hands'][0,:,0] += x_offset_arm7 + results_vis[0]['hands'][0,:,1] += y_offset_arm7 + + ########head14######## + l_head14_ref = ((ref_candidate[14][0] - ref_candidate[0][0]) ** 2 + (ref_candidate[14][1] - ref_candidate[0][1]) ** 2) ** 0.5 + l_head14_0 = ((candidate[14][0] - candidate[0][0]) ** 2 + (candidate[14][1] - candidate[0][1]) ** 2) ** 0.5 + + head14_ratio = l_head14_ref / l_head14_0 + + x_offset_head14 = (candidate[0][0]-candidate[14][0])*(1.-head14_ratio) + y_offset_head14 = (candidate[0][1]-candidate[14][1])*(1.-head14_ratio) + + results_vis[0]['bodies']['candidate'][14,0] += x_offset_head14 + results_vis[0]['bodies']['candidate'][14,1] += y_offset_head14 + results_vis[0]['bodies']['candidate'][16,0] += x_offset_head14 + results_vis[0]['bodies']['candidate'][16,1] += y_offset_head14 + + ########head15######## + l_head15_ref = ((ref_candidate[15][0] - ref_candidate[0][0]) ** 2 + (ref_candidate[15][1] - ref_candidate[0][1]) ** 2) ** 0.5 + l_head15_0 = ((candidate[15][0] - candidate[0][0]) ** 2 + (candidate[15][1] - candidate[0][1]) ** 2) ** 0.5 + + head15_ratio = l_head15_ref / l_head15_0 + + x_offset_head15 = (candidate[0][0]-candidate[15][0])*(1.-head15_ratio) + y_offset_head15 = (candidate[0][1]-candidate[15][1])*(1.-head15_ratio) + + results_vis[0]['bodies']['candidate'][15,0] += x_offset_head15 + results_vis[0]['bodies']['candidate'][15,1] += y_offset_head15 + results_vis[0]['bodies']['candidate'][17,0] += x_offset_head15 + results_vis[0]['bodies']['candidate'][17,1] += y_offset_head15 + + ########head16######## + l_head16_ref = ((ref_candidate[16][0] - ref_candidate[14][0]) ** 2 + (ref_candidate[16][1] - ref_candidate[14][1]) ** 2) ** 0.5 + l_head16_0 = ((candidate[16][0] - candidate[14][0]) ** 2 + (candidate[16][1] - candidate[14][1]) ** 2) ** 0.5 + + head16_ratio = l_head16_ref / l_head16_0 + + x_offset_head16 = (candidate[14][0]-candidate[16][0])*(1.-head16_ratio) + y_offset_head16 = (candidate[14][1]-candidate[16][1])*(1.-head16_ratio) + + results_vis[0]['bodies']['candidate'][16,0] += x_offset_head16 + results_vis[0]['bodies']['candidate'][16,1] += y_offset_head16 + + ########head17######## + l_head17_ref = ((ref_candidate[17][0] - ref_candidate[15][0]) ** 2 + (ref_candidate[17][1] - ref_candidate[15][1]) ** 2) ** 0.5 + l_head17_0 = ((candidate[17][0] - candidate[15][0]) ** 2 + (candidate[17][1] - candidate[15][1]) ** 2) ** 0.5 + + head17_ratio = l_head17_ref / l_head17_0 + + x_offset_head17 = (candidate[15][0]-candidate[17][0])*(1.-head17_ratio) + y_offset_head17 = (candidate[15][1]-candidate[17][1])*(1.-head17_ratio) + + results_vis[0]['bodies']['candidate'][17,0] += x_offset_head17 + results_vis[0]['bodies']['candidate'][17,1] += y_offset_head17 + + ########MovingAverage######## + + ########left leg######## + l_ll1_ref = ((ref_candidate[8][0] - ref_candidate[9][0]) ** 2 + (ref_candidate[8][1] - ref_candidate[9][1]) ** 2) ** 0.5 + l_ll1_0 = ((candidate[8][0] - candidate[9][0]) ** 2 + (candidate[8][1] - candidate[9][1]) ** 2) ** 0.5 + ll1_ratio = l_ll1_ref / l_ll1_0 + + x_offset_ll1 = (candidate[9][0]-candidate[8][0])*(ll1_ratio-1.) + y_offset_ll1 = (candidate[9][1]-candidate[8][1])*(ll1_ratio-1.) + + results_vis[0]['bodies']['candidate'][9,0] += x_offset_ll1 + results_vis[0]['bodies']['candidate'][9,1] += y_offset_ll1 + results_vis[0]['bodies']['candidate'][10,0] += x_offset_ll1 + results_vis[0]['bodies']['candidate'][10,1] += y_offset_ll1 + results_vis[0]['bodies']['candidate'][19,0] += x_offset_ll1 + results_vis[0]['bodies']['candidate'][19,1] += y_offset_ll1 + + l_ll2_ref = ((ref_candidate[9][0] - ref_candidate[10][0]) ** 2 + (ref_candidate[9][1] - ref_candidate[10][1]) ** 2) ** 0.5 + l_ll2_0 = ((candidate[9][0] - candidate[10][0]) ** 2 + (candidate[9][1] - candidate[10][1]) ** 2) ** 0.5 + ll2_ratio = l_ll2_ref / l_ll2_0 + + x_offset_ll2 = (candidate[10][0]-candidate[9][0])*(ll2_ratio-1.) + y_offset_ll2 = (candidate[10][1]-candidate[9][1])*(ll2_ratio-1.) + + results_vis[0]['bodies']['candidate'][10,0] += x_offset_ll2 + results_vis[0]['bodies']['candidate'][10,1] += y_offset_ll2 + results_vis[0]['bodies']['candidate'][19,0] += x_offset_ll2 + results_vis[0]['bodies']['candidate'][19,1] += y_offset_ll2 + + ########right leg######## + l_rl1_ref = ((ref_candidate[11][0] - ref_candidate[12][0]) ** 2 + (ref_candidate[11][1] - ref_candidate[12][1]) ** 2) ** 0.5 + l_rl1_0 = ((candidate[11][0] - candidate[12][0]) ** 2 + (candidate[11][1] - candidate[12][1]) ** 2) ** 0.5 + rl1_ratio = l_rl1_ref / l_rl1_0 + + x_offset_rl1 = (candidate[12][0]-candidate[11][0])*(rl1_ratio-1.) + y_offset_rl1 = (candidate[12][1]-candidate[11][1])*(rl1_ratio-1.) + + results_vis[0]['bodies']['candidate'][12,0] += x_offset_rl1 + results_vis[0]['bodies']['candidate'][12,1] += y_offset_rl1 + results_vis[0]['bodies']['candidate'][13,0] += x_offset_rl1 + results_vis[0]['bodies']['candidate'][13,1] += y_offset_rl1 + results_vis[0]['bodies']['candidate'][18,0] += x_offset_rl1 + results_vis[0]['bodies']['candidate'][18,1] += y_offset_rl1 + + l_rl2_ref = ((ref_candidate[12][0] - ref_candidate[13][0]) ** 2 + (ref_candidate[12][1] - ref_candidate[13][1]) ** 2) ** 0.5 + l_rl2_0 = ((candidate[12][0] - candidate[13][0]) ** 2 + (candidate[12][1] - candidate[13][1]) ** 2) ** 0.5 + rl2_ratio = l_rl2_ref / l_rl2_0 + + x_offset_rl2 = (candidate[13][0]-candidate[12][0])*(rl2_ratio-1.) + y_offset_rl2 = (candidate[13][1]-candidate[12][1])*(rl2_ratio-1.) + + results_vis[0]['bodies']['candidate'][13,0] += x_offset_rl2 + results_vis[0]['bodies']['candidate'][13,1] += y_offset_rl2 + results_vis[0]['bodies']['candidate'][18,0] += x_offset_rl2 + results_vis[0]['bodies']['candidate'][18,1] += y_offset_rl2 + + offset = ref_candidate[1] - results_vis[0]['bodies']['candidate'][1] + + results_vis[0]['bodies']['candidate'] += offset[np.newaxis, :] + results_vis[0]['faces'] += offset[np.newaxis, np.newaxis, :] + results_vis[0]['hands'] += offset[np.newaxis, np.newaxis, :] + + for i in range(1, len(results_vis)): + results_vis[i]['bodies']['candidate'][:,0] *= x_ratio + results_vis[i]['bodies']['candidate'][:,1] *= y_ratio + results_vis[i]['faces'][:,:,0] *= x_ratio + results_vis[i]['faces'][:,:,1] *= y_ratio + results_vis[i]['hands'][:,:,0] *= x_ratio + results_vis[i]['hands'][:,:,1] *= y_ratio + + ########neck######## + x_offset_neck = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][0][0])*(1.-neck_ratio) + y_offset_neck = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][0][1])*(1.-neck_ratio) + + results_vis[i]['bodies']['candidate'][0,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][0,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][14,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][14,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][15,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][15,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][16,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][16,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][17,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][17,1] += y_offset_neck + + ########shoulder2######## + + + x_offset_shoulder2 = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][2][0])*(1.-shoulder2_ratio) + y_offset_shoulder2 = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][2][1])*(1.-shoulder2_ratio) + + results_vis[i]['bodies']['candidate'][2,0] += x_offset_shoulder2 + results_vis[i]['bodies']['candidate'][2,1] += y_offset_shoulder2 + results_vis[i]['bodies']['candidate'][3,0] += x_offset_shoulder2 + results_vis[i]['bodies']['candidate'][3,1] += y_offset_shoulder2 + results_vis[i]['bodies']['candidate'][4,0] += x_offset_shoulder2 + results_vis[i]['bodies']['candidate'][4,1] += y_offset_shoulder2 + results_vis[i]['hands'][1,:,0] += x_offset_shoulder2 + results_vis[i]['hands'][1,:,1] += y_offset_shoulder2 + + ########shoulder5######## + + x_offset_shoulder5 = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][5][0])*(1.-shoulder5_ratio) + y_offset_shoulder5 = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][5][1])*(1.-shoulder5_ratio) + + results_vis[i]['bodies']['candidate'][5,0] += x_offset_shoulder5 + results_vis[i]['bodies']['candidate'][5,1] += y_offset_shoulder5 + results_vis[i]['bodies']['candidate'][6,0] += x_offset_shoulder5 + results_vis[i]['bodies']['candidate'][6,1] += y_offset_shoulder5 + results_vis[i]['bodies']['candidate'][7,0] += x_offset_shoulder5 + results_vis[i]['bodies']['candidate'][7,1] += y_offset_shoulder5 + results_vis[i]['hands'][0,:,0] += x_offset_shoulder5 + results_vis[i]['hands'][0,:,1] += y_offset_shoulder5 + + ########arm3######## + + x_offset_arm3 = (results_vis[i]['bodies']['candidate'][2][0]-results_vis[i]['bodies']['candidate'][3][0])*(1.-arm3_ratio) + y_offset_arm3 = (results_vis[i]['bodies']['candidate'][2][1]-results_vis[i]['bodies']['candidate'][3][1])*(1.-arm3_ratio) + + results_vis[i]['bodies']['candidate'][3,0] += x_offset_arm3 + results_vis[i]['bodies']['candidate'][3,1] += y_offset_arm3 + results_vis[i]['bodies']['candidate'][4,0] += x_offset_arm3 + results_vis[i]['bodies']['candidate'][4,1] += y_offset_arm3 + results_vis[i]['hands'][1,:,0] += x_offset_arm3 + results_vis[i]['hands'][1,:,1] += y_offset_arm3 + + ########arm4######## + + x_offset_arm4 = (results_vis[i]['bodies']['candidate'][3][0]-results_vis[i]['bodies']['candidate'][4][0])*(1.-arm4_ratio) + y_offset_arm4 = (results_vis[i]['bodies']['candidate'][3][1]-results_vis[i]['bodies']['candidate'][4][1])*(1.-arm4_ratio) + + results_vis[i]['bodies']['candidate'][4,0] += x_offset_arm4 + results_vis[i]['bodies']['candidate'][4,1] += y_offset_arm4 + results_vis[i]['hands'][1,:,0] += x_offset_arm4 + results_vis[i]['hands'][1,:,1] += y_offset_arm4 + + ########arm6######## + + x_offset_arm6 = (results_vis[i]['bodies']['candidate'][5][0]-results_vis[i]['bodies']['candidate'][6][0])*(1.-arm6_ratio) + y_offset_arm6 = (results_vis[i]['bodies']['candidate'][5][1]-results_vis[i]['bodies']['candidate'][6][1])*(1.-arm6_ratio) + + results_vis[i]['bodies']['candidate'][6,0] += x_offset_arm6 + results_vis[i]['bodies']['candidate'][6,1] += y_offset_arm6 + results_vis[i]['bodies']['candidate'][7,0] += x_offset_arm6 + results_vis[i]['bodies']['candidate'][7,1] += y_offset_arm6 + results_vis[i]['hands'][0,:,0] += x_offset_arm6 + results_vis[i]['hands'][0,:,1] += y_offset_arm6 + + ########arm7######## + + x_offset_arm7 = (results_vis[i]['bodies']['candidate'][6][0]-results_vis[i]['bodies']['candidate'][7][0])*(1.-arm7_ratio) + y_offset_arm7 = (results_vis[i]['bodies']['candidate'][6][1]-results_vis[i]['bodies']['candidate'][7][1])*(1.-arm7_ratio) + + results_vis[i]['bodies']['candidate'][7,0] += x_offset_arm7 + results_vis[i]['bodies']['candidate'][7,1] += y_offset_arm7 + results_vis[i]['hands'][0,:,0] += x_offset_arm7 + results_vis[i]['hands'][0,:,1] += y_offset_arm7 + + ########head14######## + + x_offset_head14 = (results_vis[i]['bodies']['candidate'][0][0]-results_vis[i]['bodies']['candidate'][14][0])*(1.-head14_ratio) + y_offset_head14 = (results_vis[i]['bodies']['candidate'][0][1]-results_vis[i]['bodies']['candidate'][14][1])*(1.-head14_ratio) + + results_vis[i]['bodies']['candidate'][14,0] += x_offset_head14 + results_vis[i]['bodies']['candidate'][14,1] += y_offset_head14 + results_vis[i]['bodies']['candidate'][16,0] += x_offset_head14 + results_vis[i]['bodies']['candidate'][16,1] += y_offset_head14 + + ########head15######## + + x_offset_head15 = (results_vis[i]['bodies']['candidate'][0][0]-results_vis[i]['bodies']['candidate'][15][0])*(1.-head15_ratio) + y_offset_head15 = (results_vis[i]['bodies']['candidate'][0][1]-results_vis[i]['bodies']['candidate'][15][1])*(1.-head15_ratio) + + results_vis[i]['bodies']['candidate'][15,0] += x_offset_head15 + results_vis[i]['bodies']['candidate'][15,1] += y_offset_head15 + results_vis[i]['bodies']['candidate'][17,0] += x_offset_head15 + results_vis[i]['bodies']['candidate'][17,1] += y_offset_head15 + + ########head16######## + + x_offset_head16 = (results_vis[i]['bodies']['candidate'][14][0]-results_vis[i]['bodies']['candidate'][16][0])*(1.-head16_ratio) + y_offset_head16 = (results_vis[i]['bodies']['candidate'][14][1]-results_vis[i]['bodies']['candidate'][16][1])*(1.-head16_ratio) + + results_vis[i]['bodies']['candidate'][16,0] += x_offset_head16 + results_vis[i]['bodies']['candidate'][16,1] += y_offset_head16 + + ########head17######## + x_offset_head17 = (results_vis[i]['bodies']['candidate'][15][0]-results_vis[i]['bodies']['candidate'][17][0])*(1.-head17_ratio) + y_offset_head17 = (results_vis[i]['bodies']['candidate'][15][1]-results_vis[i]['bodies']['candidate'][17][1])*(1.-head17_ratio) + + results_vis[i]['bodies']['candidate'][17,0] += x_offset_head17 + results_vis[i]['bodies']['candidate'][17,1] += y_offset_head17 + + # ########MovingAverage######## + + ########left leg######## + x_offset_ll1 = (results_vis[i]['bodies']['candidate'][9][0]-results_vis[i]['bodies']['candidate'][8][0])*(ll1_ratio-1.) + y_offset_ll1 = (results_vis[i]['bodies']['candidate'][9][1]-results_vis[i]['bodies']['candidate'][8][1])*(ll1_ratio-1.) + + results_vis[i]['bodies']['candidate'][9,0] += x_offset_ll1 + results_vis[i]['bodies']['candidate'][9,1] += y_offset_ll1 + results_vis[i]['bodies']['candidate'][10,0] += x_offset_ll1 + results_vis[i]['bodies']['candidate'][10,1] += y_offset_ll1 + results_vis[i]['bodies']['candidate'][19,0] += x_offset_ll1 + results_vis[i]['bodies']['candidate'][19,1] += y_offset_ll1 + + + + x_offset_ll2 = (results_vis[i]['bodies']['candidate'][10][0]-results_vis[i]['bodies']['candidate'][9][0])*(ll2_ratio-1.) + y_offset_ll2 = (results_vis[i]['bodies']['candidate'][10][1]-results_vis[i]['bodies']['candidate'][9][1])*(ll2_ratio-1.) + + results_vis[i]['bodies']['candidate'][10,0] += x_offset_ll2 + results_vis[i]['bodies']['candidate'][10,1] += y_offset_ll2 + results_vis[i]['bodies']['candidate'][19,0] += x_offset_ll2 + results_vis[i]['bodies']['candidate'][19,1] += y_offset_ll2 + + ########right leg######## + + x_offset_rl1 = (results_vis[i]['bodies']['candidate'][12][0]-results_vis[i]['bodies']['candidate'][11][0])*(rl1_ratio-1.) + y_offset_rl1 = (results_vis[i]['bodies']['candidate'][12][1]-results_vis[i]['bodies']['candidate'][11][1])*(rl1_ratio-1.) + + results_vis[i]['bodies']['candidate'][12,0] += x_offset_rl1 + results_vis[i]['bodies']['candidate'][12,1] += y_offset_rl1 + results_vis[i]['bodies']['candidate'][13,0] += x_offset_rl1 + results_vis[i]['bodies']['candidate'][13,1] += y_offset_rl1 + results_vis[i]['bodies']['candidate'][18,0] += x_offset_rl1 + results_vis[i]['bodies']['candidate'][18,1] += y_offset_rl1 + + + x_offset_rl2 = (results_vis[i]['bodies']['candidate'][13][0]-results_vis[i]['bodies']['candidate'][12][0])*(rl2_ratio-1.) + y_offset_rl2 = (results_vis[i]['bodies']['candidate'][13][1]-results_vis[i]['bodies']['candidate'][12][1])*(rl2_ratio-1.) + + results_vis[i]['bodies']['candidate'][13,0] += x_offset_rl2 + results_vis[i]['bodies']['candidate'][13,1] += y_offset_rl2 + results_vis[i]['bodies']['candidate'][18,0] += x_offset_rl2 + results_vis[i]['bodies']['candidate'][18,1] += y_offset_rl2 + + results_vis[i]['bodies']['candidate'] += offset[np.newaxis, :] + results_vis[i]['faces'] += offset[np.newaxis, np.newaxis, :] + results_vis[i]['hands'] += offset[np.newaxis, np.newaxis, :] + + dwpose_woface_list = [] + for i in range(len(results_vis)): + #try: + dwpose_woface, dwpose_wface = draw_pose(results_vis[i], H=height, W=width, stick_width=stick_width, + draw_body=draw_body, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size, + draw_feet=draw_feet, body_keypoint_size=body_keypoint_size, draw_head=draw_head) + result = torch.from_numpy(dwpose_woface) + #except: + # result = torch.zeros((height, width, 3), dtype=torch.uint8) + dwpose_woface_list.append(result) + dwpose_woface_tensor = torch.stack(dwpose_woface_list, dim=0) + + dwpose_woface_ref_tensor = None + if ref_image is not None: + dwpose_woface_ref, dwpose_wface_ref = draw_pose(pose_ref, H=height, W=width, stick_width=stick_width, + draw_body=draw_body, draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size, + draw_feet=draw_feet, body_keypoint_size=body_keypoint_size, draw_head=draw_head) + dwpose_woface_ref_tensor = torch.from_numpy(dwpose_woface_ref) + + return dwpose_woface_tensor, dwpose_woface_ref_tensor + +class WanVideoUniAnimateDWPoseDetector: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pose_images": ("IMAGE", {"tooltip": "Pose images"}), + "score_threshold": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Score threshold for pose detection"}), + "stick_width": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Stick width for drawing keypoints"}), + "draw_body": ("BOOLEAN", {"default": True, "tooltip": "Draw body keypoints"}), + "body_keypoint_size": ("INT", {"default": 4, "min": 0, "max": 100, "step": 1, "tooltip": "Body keypoint size"}), + "draw_feet": ("BOOLEAN", {"default": True, "tooltip": "Draw feet keypoints"}), + "draw_hands": ("BOOLEAN", {"default": True, "tooltip": "Draw hand keypoints"}), + "hand_keypoint_size": ("INT", {"default": 4, "min": 0, "max": 100, "step": 1, "tooltip": "Hand keypoint size"}), + "colorspace": (["RGB", "BGR"], {"tooltip": "Color space for the output image"}), + "handle_not_detected": (["empty", "repeat"], {"default": "empty", "tooltip": "How to handle undetected poses, empty inserts black and repeat inserts previous detection"}), + "draw_head": ("BOOLEAN", {"default": True, "tooltip": "Draw head keypoints"}), + }, + "optional": { + "reference_pose_image": ("IMAGE", {"tooltip": "Reference pose image"}), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE", ) + RETURN_NAMES = ("poses", "reference_pose",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, pose_images, score_threshold, stick_width, reference_pose_image=None, draw_body=True, body_keypoint_size=4, + draw_feet=True, draw_hands=True, hand_keypoint_size=4, colorspace="RGB", handle_not_detected="empty", draw_head=True): + + device = mm.get_torch_device() + + #model loading + dw_pose_model = "dw-ll_ucoco_384_bs5.torchscript.pt" + yolo_model = "yolox_l.torchscript.pt" + + script_directory = os.path.dirname(os.path.abspath(__file__)) + model_base_path = os.path.join(script_directory, "models", "DWPose") + + model_det=os.path.join(model_base_path, yolo_model) + model_pose=os.path.join(model_base_path, dw_pose_model) + + if not os.path.exists(model_det): + log.info(f"Downloading yolo model to: {model_base_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id="hr16/yolox-onnx", + allow_patterns=[f"*{yolo_model}*"], + local_dir=model_base_path, + local_dir_use_symlinks=False) + + if not os.path.exists(model_pose): + log.info(f"Downloading dwpose model to: {model_base_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id="hr16/DWPose-TorchScript-BatchSize5", + allow_patterns=[f"*{dw_pose_model}*"], + local_dir=model_base_path, + local_dir_use_symlinks=False) + + if not hasattr(self, "det") or not hasattr(self, "pose"): + self.det = torch.jit.load(model_det, map_location=device) + self.pose = torch.jit.load(model_pose, map_location=device) + self.dwpose_detector = DWposeDetector(self.det, self.pose) + + #model inference + height, width = pose_images.shape[1:3] + + pose_np = pose_images.cpu().numpy() * 255 + ref_np = None + if reference_pose_image is not None: + ref = reference_pose_image + ref_np = ref.cpu().numpy() * 255 + + poses, reference_pose = pose_extract(pose_np, ref_np, self.dwpose_detector, height, width, score_threshold, stick_width=stick_width, + draw_body=draw_body, body_keypoint_size=body_keypoint_size, draw_feet=draw_feet, + draw_hands=draw_hands, hand_keypoint_size=hand_keypoint_size, handle_not_detected=handle_not_detected, draw_head=draw_head) + poses = poses / 255.0 + if reference_pose_image is not None: + reference_pose = reference_pose.unsqueeze(0) / 255.0 + else: + reference_pose = torch.zeros(1, 64, 64, 3, device=torch.device("cpu")) + + if colorspace == "BGR": + poses=torch.flip(poses, dims=[-1]) + + return (poses, reference_pose, ) + +class WanVideoUniAnimatePoseInput: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "pose_images": ("IMAGE", {"tooltip": "Pose images"}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Strength of the pose control"}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage for the pose control"}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage for the pose control"}), + }, + "optional": { + "reference_pose_image": ("IMAGE", {"tooltip": "Reference pose image"}), + }, + } + + RETURN_TYPES = ("UNIANIMATE_POSE", ) + RETURN_NAMES = ("unianimate_poses",) + FUNCTION = "process" + CATEGORY = "WanVideoWrapper" + + def process(self, pose_images, strength, start_percent, end_percent, reference_pose_image=None): + + pose = pose_images.permute(3, 0, 1, 2).unsqueeze(0).contiguous() + + ref = None + if reference_pose_image is not None: + ref = reference_pose_image.permute(0, 3, 1, 2).contiguous() + + unianim_poses = { + "pose": pose, + "ref": ref, + "strength": strength, + "start_percent": start_percent, + "end_percent": end_percent + } + + return (unianim_poses,) + + +NODE_CLASS_MAPPINGS = { + "WanVideoUniAnimatePoseInput": WanVideoUniAnimatePoseInput, + "WanVideoUniAnimateDWPoseDetector": WanVideoUniAnimateDWPoseDetector, + + } +NODE_DISPLAY_NAME_MAPPINGS = { + "WanVideoUniAnimatePoseInput": "WanVideo UniAnimate Pose Input", + "WanVideoUniAnimateDWPoseDetector": "WanVideo UniAnimate DWPose Detector", + } + + \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/utils.py b/custom_nodes/ComfyUI_WanVideoWrapper/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0a43c26d035fc0562c006f570c8259d623cdd2e6 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/utils.py @@ -0,0 +1,228 @@ +import importlib.metadata +import torch +import logging +from tqdm import tqdm +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +log = logging.getLogger(__name__) + +from accelerate.utils import set_module_tensor_to_device +def check_diffusers_version(): + try: + version = importlib.metadata.version('diffusers') + required_version = '0.31.0' + if version < required_version: + raise AssertionError(f"diffusers version {version} is installed, but version {required_version} or higher is required.") + except importlib.metadata.PackageNotFoundError: + raise AssertionError("diffusers is not installed.") + +def print_memory(device): + memory = torch.cuda.memory_allocated(device) / 1024**3 + max_memory = torch.cuda.max_memory_allocated(device) / 1024**3 + max_reserved = torch.cuda.max_memory_reserved(device) / 1024**3 + log.info(f"Allocated memory: {memory=:.3f} GB") + log.info(f"Max allocated memory: {max_memory=:.3f} GB") + log.info(f"Max reserved memory: {max_reserved=:.3f} GB") + #memory_summary = torch.cuda.memory_summary(device=device, abbreviated=False) + #log.info(f"Memory Summary:\n{memory_summary}") + +def get_module_memory_mb(module): + memory = 0 + for param in module.parameters(): + if param.data is not None: + memory += param.nelement() * param.element_size() + return memory / (1024 * 1024) # Convert to MB + +def apply_lora(model, device_to, transformer_load_device, params_to_keep=None, dtype=None, base_dtype=None, state_dict=None, low_mem_load=False): + to_load = [] + for n, m in model.model.named_modules(): + params = [] + skip = False + for name, param in m.named_parameters(recurse=False): + params.append(name) + for name, param in m.named_parameters(recurse=True): + if name not in params: + skip = True # skip random weights in non leaf modules + break + if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0): + to_load.append((n, m, params)) + + to_load.sort(reverse=True) + for x in tqdm(to_load, desc="Loading model and applying LoRA weights:", leave=True): + name = x[0] + m = x[1] + params = x[2] + if hasattr(m, "comfy_patched_weights"): + if m.comfy_patched_weights == True: + continue + for param in params: + name = name.replace("._orig_mod.", ".") # torch compiled modules have this prefix + if low_mem_load: + dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype + if "patch_embedding" in name: + dtype_to_use = torch.float32 + if name.startswith("diffusion_model."): + name_no_prefix = name[len("diffusion_model."):] + key = "{}.{}".format(name_no_prefix, param) + try: + set_module_tensor_to_device(model.model.diffusion_model, key, device=transformer_load_device, dtype=dtype_to_use, value=state_dict[key]) + except: + continue + model.patch_weight_to_device("{}.{}".format(name, param), device_to=device_to) + if low_mem_load: + try: + set_module_tensor_to_device(model.model.diffusion_model, key, device=transformer_load_device, dtype=dtype_to_use, value=model.model.diffusion_model.state_dict()[key]) + except: + continue + m.comfy_patched_weights = True + + model.current_weight_patches_uuid = model.patches_uuid + if low_mem_load: + for name, param in model.model.diffusion_model.named_parameters(): + if param.device != transformer_load_device: + dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype + if "patch_embedding" in name: + dtype_to_use = torch.float32 + try: + set_module_tensor_to_device(model.model.diffusion_model, name, device=transformer_load_device, dtype=dtype_to_use, value=state_dict[name]) + except: + continue + return model + + +# from https://github.com/cubiq/ComfyUI_IPAdapter_plus/blob/9d076a3df0d2763cef5510ec5ab807f6632c39f5/utils.py#L181 +def split_tiles(embeds, num_split): + _, H, W, _ = embeds.shape + out = [] + for x in embeds: + x = x.unsqueeze(0) + h, w = H // num_split, W // num_split + x_split = torch.cat([x[:, i*h:(i+1)*h, j*w:(j+1)*w, :] for i in range(num_split) for j in range(num_split)], dim=0) + out.append(x_split) + + x_split = torch.stack(out, dim=0) + + return x_split + +def merge_hiddenstates(x, tiles): + chunk_size = tiles*tiles + x = x.split(chunk_size) + + out = [] + for embeds in x: + num_tiles = embeds.shape[0] + tile_size = int((embeds.shape[1]-1) ** 0.5) + grid_size = int(num_tiles ** 0.5) + + # Extract class tokens + class_tokens = embeds[:, 0, :] # Save class tokens: [num_tiles, embeds[-1]] + avg_class_token = class_tokens.mean(dim=0, keepdim=True).unsqueeze(0) # Average token, shape: [1, 1, embeds[-1]] + + patch_embeds = embeds[:, 1:, :] # Shape: [num_tiles, tile_size^2, embeds[-1]] + reshaped = patch_embeds.reshape(grid_size, grid_size, tile_size, tile_size, embeds.shape[-1]) + + merged = torch.cat([torch.cat([reshaped[i, j] for j in range(grid_size)], dim=1) + for i in range(grid_size)], dim=0) + + merged = merged.unsqueeze(0) # Shape: [1, grid_size*tile_size, grid_size*tile_size, embeds[-1]] + + # Pool to original size + pooled = torch.nn.functional.adaptive_avg_pool2d(merged.permute(0, 3, 1, 2), (tile_size, tile_size)).permute(0, 2, 3, 1) + flattened = pooled.reshape(1, tile_size*tile_size, embeds.shape[-1]) + + # Add back the class token + with_class = torch.cat([avg_class_token, flattened], dim=1) # Shape: original shape + out.append(with_class) + + out = torch.cat(out, dim=0) + + return out + +from comfy.clip_vision import clip_preprocess, ClipVisionModel + +def clip_encode_image_tiled(clip_vision, image, tiles=1, ratio=1.0): + embeds = encode_image_(clip_vision, image) + tiles = min(tiles, 16) + + if tiles > 1: + # split in tiles + image_split = split_tiles(image, tiles) + + # get the embeds for each tile + embeds_split = {} + for i in image_split: + encoded = encode_image_(clip_vision, i) + if not hasattr(embeds_split, "last_hidden_state"): + embeds_split["last_hidden_state"] = encoded + else: + embeds_split["last_hidden_state"] = torch.cat(embeds_split["last_hidden_state"], encoded, dim=0) + + embeds_split['last_hidden_state'] = merge_hiddenstates(embeds_split['last_hidden_state'], tiles) + + if embeds.shape[0] > 1: # if we have more than one image we need to average the embeddings for consistency + embeds = embeds * ratio + embeds_split['last_hidden_state']*(1-ratio) + else: # otherwise we can concatenate them, they can be averaged later + embeds = torch.cat([embeds * ratio, embeds_split['last_hidden_state']]) + + return embeds + +def encode_image_(clip_vision, image): + if isinstance(clip_vision, ClipVisionModel): + out = clip_vision.encode_image(image).last_hidden_state + else: + pixel_values = clip_preprocess(image, size=224, crop=True).float() + out = clip_vision.visual(pixel_values) + + return out + +# Code based on https://github.com/WikiChao/FreSca (MIT License) +import torch +import torch.fft as fft + +def fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20): + """ + Apply frequency-dependent scaling to an image tensor using Fourier transforms. + + Parameters: + x: Input tensor of shape (B, C, H, W) + scale_low: Scaling factor for low-frequency components (default: 1.0) + scale_high: Scaling factor for high-frequency components (default: 1.5) + freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20) + + Returns: + x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied. + """ + # Preserve input dtype and device + dtype, device = x.dtype, x.device + + # Convert to float32 for FFT computations + x = x.to(torch.float32) + + # 1) Apply FFT and shift low frequencies to center + x_freq = fft.fftn(x, dim=(-2, -1)) + x_freq = fft.fftshift(x_freq, dim=(-2, -1)) + + # 2) Create a mask to scale frequencies differently + C, B, H, W = x_freq.shape + crow, ccol = H // 2, W // 2 + + # Initialize mask with high-frequency scaling factor + mask = torch.ones((C, B, H, W), device=device) * scale_high + + # Apply low-frequency scaling factor to center region + mask[ + ..., + crow - freq_cutoff : crow + freq_cutoff, + ccol - freq_cutoff : ccol + freq_cutoff, + ] = scale_low + + # 3) Apply frequency-specific scaling + x_freq = x_freq * mask + + # 4) Convert back to spatial domain + x_freq = fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real + + # 5) Restore original dtype + x_filtered = x_filtered.to(dtype) + + return x_filtered \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/LICENSE b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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All rights reserved. +import copy +import os + +os.environ['TOKENIZERS_PARALLELISM'] = 'false' + +from .wan_i2v_14B import i2v_14B +from .wan_t2v_1_3B import t2v_1_3B +from .wan_t2v_14B import t2v_14B + +# the config of t2i_14B is the same as t2v_14B +t2i_14B = copy.deepcopy(t2v_14B) +t2i_14B.__name__ = 'Config: Wan T2I 14B' + +WAN_CONFIGS = { + 't2v-14B': t2v_14B, + 't2v-1.3B': t2v_1_3B, + 'i2v-14B': i2v_14B, + 't2i-14B': t2i_14B, +} + +SIZE_CONFIGS = { + '720*1280': (720, 1280), + '1280*720': (1280, 720), + '480*832': (480, 832), + '832*480': (832, 480), + '1024*1024': (1024, 1024), +} + +MAX_AREA_CONFIGS = { + '720*1280': 720 * 1280, + '1280*720': 1280 * 720, + '480*832': 480 * 832, + '832*480': 832 * 480, +} + +SUPPORTED_SIZES = { + 't2v-14B': ('720*1280', '1280*720', '480*832', '832*480'), + 't2v-1.3B': ('480*832', '832*480'), + 'i2v-14B': ('720*1280', '1280*720', '480*832', '832*480'), + 't2i-14B': tuple(SIZE_CONFIGS.keys()), +} diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/shared_config.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/shared_config.py new file mode 100644 index 0000000000000000000000000000000000000000..04a9f454218fc1ce958b628e71ad5738222e2aa4 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/shared_config.py @@ -0,0 +1,19 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +from easydict import EasyDict + +#------------------------ Wan shared config ------------------------# +wan_shared_cfg = EasyDict() + +# t5 +wan_shared_cfg.t5_model = 'umt5_xxl' +wan_shared_cfg.t5_dtype = torch.bfloat16 +wan_shared_cfg.text_len = 512 + +# transformer +wan_shared_cfg.param_dtype = torch.bfloat16 + +# inference +wan_shared_cfg.num_train_timesteps = 1000 +wan_shared_cfg.sample_fps = 16 +wan_shared_cfg.sample_neg_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走' diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_i2v_14B.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_i2v_14B.py new file mode 100644 index 0000000000000000000000000000000000000000..12e8e205bffb343a6e27d2828fb573db1d6349f8 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_i2v_14B.py @@ -0,0 +1,35 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch +from easydict import EasyDict + +from .shared_config import wan_shared_cfg + +#------------------------ Wan I2V 14B ------------------------# + +i2v_14B = EasyDict(__name__='Config: Wan I2V 14B') +i2v_14B.update(wan_shared_cfg) + +i2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' +i2v_14B.t5_tokenizer = 'google/umt5-xxl' + +# clip +i2v_14B.clip_model = 'clip_xlm_roberta_vit_h_14' +i2v_14B.clip_dtype = torch.float16 +i2v_14B.clip_checkpoint = 'models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth' +i2v_14B.clip_tokenizer = 'xlm-roberta-large' + +# vae +i2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth' +i2v_14B.vae_stride = (4, 8, 8) + +# transformer +i2v_14B.patch_size = (1, 2, 2) +i2v_14B.dim = 5120 +i2v_14B.ffn_dim = 13824 +i2v_14B.freq_dim = 256 +i2v_14B.num_heads = 40 +i2v_14B.num_layers = 40 +i2v_14B.window_size = (-1, -1) +i2v_14B.qk_norm = True +i2v_14B.cross_attn_norm = True +i2v_14B.eps = 1e-6 diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_t2v_14B.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_t2v_14B.py new file mode 100644 index 0000000000000000000000000000000000000000..9d0ee69dea796bfd6eccdedf4ec04835086227a6 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_t2v_14B.py @@ -0,0 +1,29 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +from easydict import EasyDict + +from .shared_config import wan_shared_cfg + +#------------------------ Wan T2V 14B ------------------------# + +t2v_14B = EasyDict(__name__='Config: Wan T2V 14B') +t2v_14B.update(wan_shared_cfg) + +# t5 +t2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' +t2v_14B.t5_tokenizer = 'google/umt5-xxl' + +# vae +t2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth' +t2v_14B.vae_stride = (4, 8, 8) + +# transformer +t2v_14B.patch_size = (1, 2, 2) +t2v_14B.dim = 5120 +t2v_14B.ffn_dim = 13824 +t2v_14B.freq_dim = 256 +t2v_14B.num_heads = 40 +t2v_14B.num_layers = 40 +t2v_14B.window_size = (-1, -1) +t2v_14B.qk_norm = True +t2v_14B.cross_attn_norm = True +t2v_14B.eps = 1e-6 diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_t2v_1_3B.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_t2v_1_3B.py new file mode 100644 index 0000000000000000000000000000000000000000..ea9502b0df685b5d22f9091cc8cdf5c6a7880c4b --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/configs/wan_t2v_1_3B.py @@ -0,0 +1,29 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +from easydict import EasyDict + +from .shared_config import wan_shared_cfg + +#------------------------ Wan T2V 1.3B ------------------------# + +t2v_1_3B = EasyDict(__name__='Config: Wan T2V 1.3B') +t2v_1_3B.update(wan_shared_cfg) + +# t5 +t2v_1_3B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth' +t2v_1_3B.t5_tokenizer = 'google/umt5-xxl' + +# vae +t2v_1_3B.vae_checkpoint = 'Wan2.1_VAE.pth' +t2v_1_3B.vae_stride = (4, 8, 8) + +# transformer +t2v_1_3B.patch_size = (1, 2, 2) +t2v_1_3B.dim = 1536 +t2v_1_3B.ffn_dim = 8960 +t2v_1_3B.freq_dim = 256 +t2v_1_3B.num_heads = 12 +t2v_1_3B.num_layers = 30 +t2v_1_3B.window_size = (-1, -1) +t2v_1_3B.qk_norm = True +t2v_1_3B.cross_attn_norm = True +t2v_1_3B.eps = 1e-6 diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2d9adae5fcbf486bba919e1716aaaa179f345128 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__init__.py @@ -0,0 +1,12 @@ +from .model import WanModel +from .t5 import T5Decoder, T5Encoder, T5EncoderModel, T5Model +from .tokenizers import HuggingfaceTokenizer + +__all__ = [ + 'WanModel', + 'T5Model', + 'T5Encoder', + 'T5Decoder', + 'T5EncoderModel', + 'HuggingfaceTokenizer', +] diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__pycache__/__init__.cpython-310.pyc b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7b77706e46a6bae174f6f7bcdda8ac7cb71910c0 Binary files /dev/null and b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__pycache__/__init__.cpython-310.pyc differ diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__pycache__/__init__.cpython-312.pyc b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/__pycache__/__init__.cpython-312.pyc new file 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-0,0 +1,210 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import torch + +try: + import flash_attn_interface + FLASH_ATTN_3_AVAILABLE = True +except ModuleNotFoundError: + FLASH_ATTN_3_AVAILABLE = False + +try: + import flash_attn + FLASH_ATTN_2_AVAILABLE = True +except ModuleNotFoundError: + FLASH_ATTN_2_AVAILABLE = False + +try: + from sageattention import sageattn + @torch.compiler.disable() + def sageattn_func(q, k, v, attn_mask=None, dropout_p=0, is_causal=False): + if q.dtype == torch.float32: + return sageattn(q.to(torch.float16), k.to(torch.float16), v.to(torch.float16), attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal).to(torch.float32) + else: + return sageattn(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal) +except Exception as e: + print(f"Warning: Could not load sageattention: {str(e)}") + if isinstance(e, ModuleNotFoundError): + print("sageattention package is not installed") + elif isinstance(e, ImportError) and "DLL" in str(e): + print("sageattention DLL loading error") + sageattn_func = None +import warnings + +__all__ = [ + 'flash_attention', + 'attention', +] + + +def flash_attention( + q, + k, + v, + q_lens=None, + k_lens=None, + dropout_p=0., + softmax_scale=None, + q_scale=None, + causal=False, + window_size=(-1, -1), + deterministic=False, + dtype=torch.bfloat16, + version=None, +): + """ + q: [B, Lq, Nq, C1]. + k: [B, Lk, Nk, C1]. + v: [B, Lk, Nk, C2]. Nq must be divisible by Nk. + q_lens: [B]. + k_lens: [B]. + dropout_p: float. Dropout probability. + softmax_scale: float. The scaling of QK^T before applying softmax. + causal: bool. Whether to apply causal attention mask. + window_size: (left right). If not (-1, -1), apply sliding window local attention. + deterministic: bool. If True, slightly slower and uses more memory. + dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16. + """ + half_dtypes = (torch.float16, torch.bfloat16) + #assert dtype in half_dtypes + #assert q.device.type == 'cuda' and q.size(-1) <= 256 + + # params + b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype + + def half(x): + return x if x.dtype in half_dtypes else x.to(dtype) + + # preprocess query + if q_lens is None: + q = half(q.flatten(0, 1)) + q_lens = torch.tensor( + [lq] * b, dtype=torch.int32).to( + device=q.device, non_blocking=True) + else: + q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)])) + + # preprocess key, value + if k_lens is None: + k = half(k.flatten(0, 1)) + v = half(v.flatten(0, 1)) + k_lens = torch.tensor( + [lk] * b, dtype=torch.int32).to( + device=k.device, non_blocking=True) + else: + k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)])) + v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)])) + + q = q.to(v.dtype) + k = k.to(v.dtype) + + if q_scale is not None: + q = q * q_scale + + if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE: + warnings.warn( + 'Flash attention 3 is not available, use flash attention 2 instead.' + ) + + # apply attention + if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE: + # Note: dropout_p, window_size are not supported in FA3 now. + x = flash_attn_interface.flash_attn_varlen_func( + q=q, + k=k, + v=v, + cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + seqused_q=None, + seqused_k=None, + max_seqlen_q=lq, + max_seqlen_k=lk, + softmax_scale=softmax_scale, + causal=causal, + deterministic=deterministic)[0].unflatten(0, (b, lq)) + else: + assert FLASH_ATTN_2_AVAILABLE + x = flash_attn.flash_attn_varlen_func( + q=q, + k=k, + v=v, + cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum( + 0, dtype=torch.int32).to(q.device, non_blocking=True), + max_seqlen_q=lq, + max_seqlen_k=lk, + dropout_p=dropout_p, + softmax_scale=softmax_scale, + causal=causal, + window_size=window_size, + deterministic=deterministic).unflatten(0, (b, lq)) + + # output + return x.type(out_dtype) + + +def attention( + q, + k, + v, + q_lens=None, + k_lens=None, + dropout_p=0., + softmax_scale=None, + q_scale=None, + causal=False, + window_size=(-1, -1), + deterministic=False, + dtype=torch.bfloat16, + attention_mode='sdpa', +): + if "flash" in attention_mode: + if attention_mode == 'flash_attn_2': + fa_version = 2 + elif attention_mode == 'flash_attn_3': + fa_version = 3 + return flash_attention( + q=q, + k=k, + v=v, + q_lens=q_lens, + k_lens=k_lens, + dropout_p=dropout_p, + softmax_scale=softmax_scale, + q_scale=q_scale, + causal=causal, + window_size=window_size, + deterministic=deterministic, + dtype=dtype, + version=fa_version, + ) + elif attention_mode == 'sdpa': + # if q_lens is not None or k_lens is not None: + # warnings.warn( + # 'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.' + # ) + attn_mask = None + + q = q.transpose(1, 2)#.to(dtype) + k = k.transpose(1, 2)#.to(dtype) + v = v.transpose(1, 2)#.to(dtype) + + out = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p) + + out = out.transpose(1, 2).contiguous() + return out + elif attention_mode == 'sageattn': + attn_mask = None + + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + + out = sageattn_func( + q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p) + + out = out.transpose(1, 2).contiguous() + return out diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/clip.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/clip.py new file mode 100644 index 0000000000000000000000000000000000000000..eb39f36a52a23a1f974fc51caee766966e6cc5b3 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/clip.py @@ -0,0 +1,462 @@ +# Modified from ``https://github.com/openai/CLIP'' and ``https://github.com/mlfoundations/open_clip'' +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import logging +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as T + +from .attention import attention + +__all__ = [ + 'XLMRobertaCLIP', + 'clip_xlm_roberta_vit_h_14', + 'CLIPModel', +] +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device + +import comfy.model_management as mm + +def pos_interpolate(pos, seq_len): + if pos.size(1) == seq_len: + return pos + else: + src_grid = int(math.sqrt(pos.size(1))) + tar_grid = int(math.sqrt(seq_len)) + n = pos.size(1) - src_grid * src_grid + return torch.cat([ + pos[:, :n], + F.interpolate( + pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute( + 0, 3, 1, 2), + size=(tar_grid, tar_grid), + mode='bicubic', + align_corners=False).flatten(2).transpose(1, 2) + ], + dim=1) + + +class QuickGELU(nn.Module): + + def forward(self, x): + return x * torch.sigmoid(1.702 * x) + + +class LayerNorm(nn.LayerNorm): + + def forward(self, x): + return super().forward(x.float()).type_as(x) + + +class SelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + causal=False, + attn_dropout=0.0, + proj_dropout=0.0): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.causal = causal + self.attn_dropout = attn_dropout + self.proj_dropout = proj_dropout + + # layers + self.to_qkv = nn.Linear(dim, dim * 3) + self.proj = nn.Linear(dim, dim) + + def forward(self, x): + """ + x: [B, L, C]. + """ + b, s, c, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + q, k, v = self.to_qkv(x).view(b, s, 3, n, d).unbind(2) + + # compute attention + p = self.attn_dropout if self.training else 0.0 + x = attention(q, k, v, dropout_p=p, causal=self.causal, attention_mode="sdpa") + x = x.reshape(b, s, c) + + # output + x = self.proj(x) + x = F.dropout(x, self.proj_dropout, self.training) + return x + + +class SwiGLU(nn.Module): + + def __init__(self, dim, mid_dim): + super().__init__() + self.dim = dim + self.mid_dim = mid_dim + + # layers + self.fc1 = nn.Linear(dim, mid_dim) + self.fc2 = nn.Linear(dim, mid_dim) + self.fc3 = nn.Linear(mid_dim, dim) + + def forward(self, x): + x = F.silu(self.fc1(x)) * self.fc2(x) + x = self.fc3(x) + return x + + +class AttentionBlock(nn.Module): + + def __init__(self, + dim, + mlp_ratio, + num_heads, + post_norm=False, + causal=False, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + norm_eps=1e-5): + assert activation in ['quick_gelu', 'gelu', 'swi_glu'] + super().__init__() + self.dim = dim + self.mlp_ratio = mlp_ratio + self.num_heads = num_heads + self.post_norm = post_norm + self.causal = causal + self.norm_eps = norm_eps + + # layers + self.norm1 = LayerNorm(dim, eps=norm_eps) + self.attn = SelfAttention(dim, num_heads, causal, attn_dropout, + proj_dropout) + self.norm2 = LayerNorm(dim, eps=norm_eps) + if activation == 'swi_glu': + self.mlp = SwiGLU(dim, int(dim * mlp_ratio)) + else: + self.mlp = nn.Sequential( + nn.Linear(dim, int(dim * mlp_ratio)), + QuickGELU() if activation == 'quick_gelu' else nn.GELU(), + nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout)) + + def forward(self, x): + if self.post_norm: + x = x + self.norm1(self.attn(x)) + x = x + self.norm2(self.mlp(x)) + else: + x = x + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x + +class VisionTransformer(nn.Module): + + def __init__(self, + image_size=224, + patch_size=16, + dim=768, + mlp_ratio=4, + out_dim=512, + num_heads=12, + num_layers=12, + pool_type='token', + pre_norm=True, + post_norm=False, + activation='quick_gelu', + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + if image_size % patch_size != 0: + print( + '[WARNING] image_size is not divisible by patch_size', + flush=True) + assert pool_type in ('token', 'token_fc', 'attn_pool') + out_dim = out_dim or dim + super().__init__() + self.image_size = image_size + self.patch_size = patch_size + self.num_patches = (image_size // patch_size)**2 + self.dim = dim + self.mlp_ratio = mlp_ratio + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.pool_type = pool_type + self.post_norm = post_norm + self.norm_eps = norm_eps + + # embeddings + gain = 1.0 / math.sqrt(dim) + self.patch_embedding = nn.Conv2d( + 3, + dim, + kernel_size=patch_size, + stride=patch_size, + bias=not pre_norm) + + if pool_type in ('token', 'token_fc'): + self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim)) + self.pos_embedding = nn.Parameter(gain * torch.randn( + 1, self.num_patches + + (1 if pool_type in ('token', 'token_fc') else 0), dim)) #torch.Size([1, 257, 1280]) + self.dropout = nn.Dropout(embedding_dropout) + + # transformer + self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None + self.transformer = nn.Sequential(*[ + AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False, + activation, attn_dropout, proj_dropout, norm_eps) + for _ in range(num_layers) + ]) + self.post_norm = LayerNorm(dim, eps=norm_eps) + + # head + if pool_type == 'token': + self.head = nn.Parameter(gain * torch.randn(dim, out_dim)) + elif pool_type == 'token_fc': + self.head = nn.Linear(dim, out_dim) + + def forward(self, x, interpolation=False, use_31_block=False): + b = x.size(0) + height, width = x.size(2), x.size(3) + + # embeddings + x_patch = self.patch_embedding(x) + B, C, H, W = x_patch.shape + x = x_patch.flatten(2).permute(0, 2, 1) + if self.pool_type in ('token', 'token_fc'): + x = torch.cat([self.cls_embedding.expand(b, -1, -1), x], dim=1) + + if interpolation: + embeddings = x + num_patches = embeddings.shape[1] - 1 + position_embedding = self.pos_embedding + num_positions = position_embedding.shape[1] - 1 + + class_pos_embed = position_embedding[:, :1] + patch_pos_embed = position_embedding[:, 1:] + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ) + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + e = torch.cat((class_pos_embed, patch_pos_embed), dim=1) + else: + e = self.pos_embedding + x = self.dropout(x + e) + if self.pre_norm is not None: + x = self.pre_norm(x) + + # transformer + if use_31_block: + x = self.transformer[:-1](x) + return x + else: + x = self.transformer(x) + return x + + +class XLMRobertaCLIP(nn.Module): + + def __init__(self, + embed_dim=1024, + image_size=224, + patch_size=14, + vision_dim=1280, + vision_mlp_ratio=4, + vision_heads=16, + vision_layers=32, + vision_pool='token', + vision_pre_norm=True, + vision_post_norm=False, + activation='gelu', + vocab_size=250002, + max_text_len=514, + type_size=1, + pad_id=1, + text_dim=1024, + text_heads=16, + text_layers=24, + text_post_norm=True, + text_dropout=0.1, + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0, + norm_eps=1e-5): + super().__init__() + self.embed_dim = embed_dim + self.image_size = image_size + self.patch_size = patch_size + self.vision_dim = vision_dim + self.vision_mlp_ratio = vision_mlp_ratio + self.vision_heads = vision_heads + self.vision_layers = vision_layers + self.vision_pre_norm = vision_pre_norm + self.vision_post_norm = vision_post_norm + self.activation = activation + self.vocab_size = vocab_size + self.max_text_len = max_text_len + self.type_size = type_size + self.pad_id = pad_id + self.text_dim = text_dim + self.text_heads = text_heads + self.text_layers = text_layers + self.text_post_norm = text_post_norm + self.norm_eps = norm_eps + + # models + self.visual = VisionTransformer( + image_size=image_size, + patch_size=patch_size, + dim=vision_dim, + mlp_ratio=vision_mlp_ratio, + out_dim=embed_dim, + num_heads=vision_heads, + num_layers=vision_layers, + pool_type=vision_pool, + pre_norm=vision_pre_norm, + post_norm=vision_post_norm, + activation=activation, + attn_dropout=attn_dropout, + proj_dropout=proj_dropout, + embedding_dropout=embedding_dropout, + norm_eps=norm_eps) + self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([])) + + def forward(self, imgs, txt_ids): + """ + imgs: [B, 3, H, W] of torch.float32. + - mean: [0.48145466, 0.4578275, 0.40821073] + - std: [0.26862954, 0.26130258, 0.27577711] + txt_ids: [B, L] of torch.long. + Encoded by data.CLIPTokenizer. + """ + xi = self.visual(imgs) + xt = self.textual(txt_ids) + return xi, xt + + def param_groups(self): + groups = [{ + 'params': [ + p for n, p in self.named_parameters() + if 'norm' in n or n.endswith('bias') + ], + 'weight_decay': 0.0 + }, { + 'params': [ + p for n, p in self.named_parameters() + if not ('norm' in n or n.endswith('bias')) + ] + }] + return groups + + +def _clip(pretrained=False, + pretrained_name=None, + model_cls=XLMRobertaCLIP, + return_transforms=False, + return_tokenizer=False, + tokenizer_padding='eos', + dtype=torch.float32, + device='cpu', + **kwargs): + # init a model on device + with torch.device(device): + model = model_cls(**kwargs) + + # set device + #model = model.to(dtype=dtype, device=device) + output = (model,) + + # init transforms + if return_transforms: + # mean and std + if 'siglip' in pretrained_name.lower(): + mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] + else: + mean = [0.48145466, 0.4578275, 0.40821073] + std = [0.26862954, 0.26130258, 0.27577711] + + # transforms + transforms = T.Compose([ + T.Resize((model.image_size, model.image_size), + interpolation=T.InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=mean, std=std) + ]) + output += (transforms,) + return output[0] if len(output) == 1 else output + + +def clip_xlm_roberta_vit_h_14( + pretrained=False, + pretrained_name='open-clip-xlm-roberta-large-vit-huge-14', + **kwargs): + cfg = dict( + embed_dim=1024, + image_size=224, + patch_size=14, + vision_dim=1280, + vision_mlp_ratio=4, + vision_heads=16, + vision_layers=32, + vision_pool='token', + activation='gelu', + vocab_size=250002, + max_text_len=514, + type_size=1, + pad_id=1, + text_dim=1024, + text_heads=16, + text_layers=24, + text_post_norm=True, + text_dropout=0.1, + attn_dropout=0.0, + proj_dropout=0.0, + embedding_dropout=0.0) + cfg.update(**kwargs) + return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg) + + +class CLIPModel: + + def __init__(self, dtype, device, state_dict): + self.dtype = dtype + self.device = device + + # init model + with init_empty_weights(): + self.model, self.transforms = clip_xlm_roberta_vit_h_14( + pretrained=False, + return_transforms=True, + return_tokenizer=False, + dtype=dtype, + device=device + ) + self.model = self.model.eval().requires_grad_(False) + + for name, param in self.model.named_parameters(): + set_module_tensor_to_device(self.model, name, device=device, dtype=dtype, value=state_dict[name]) + + def visual(self, image, interpolation=False): + # forward + with torch.autocast(device_type=mm.get_autocast_device(self.device), dtype=self.dtype): + out = self.model.visual(image, interpolation=interpolation, use_31_block=True) + return out diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/model.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d7d2a498724ed143bd8aa7a0dcbf52bf73bf32e0 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/model.py @@ -0,0 +1,1726 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import math + +import torch +import torch.nn as nn +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.modeling_utils import ModelMixin +from einops import repeat, rearrange +from ...enhance_a_video.enhance import get_feta_scores +from ...enhance_a_video.globals import is_enhance_enabled + +try: + from torch.nn.attention.flex_attention import create_block_mask, flex_attention, BlockMask + create_block_mask = torch.compile(create_block_mask) + flex_attention = torch.compile(flex_attention) +except: + BlockMask = create_block_mask = flex_attention = None + pass + +from .attention import attention +import numpy as np +__all__ = ['WanModel'] + +from tqdm import tqdm +import gc +import comfy.model_management as mm +from ...utils import log, get_module_memory_mb + +from comfy.ldm.flux.math import apply_rope as apply_rope_comfy + +def rope_riflex(pos, dim, theta, L_test, k, temporal): + assert dim % 2 == 0 + if mm.is_device_mps(pos.device) or mm.is_intel_xpu() or mm.is_directml_enabled(): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) + omega = 1.0 / (theta**scale) + + # RIFLEX modification - adjust last frequency component if L_test and k are provided + if temporal and k > 0 and L_test: + omega[k-1] = 0.9 * 2 * torch.pi / L_test + + out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.to(dtype=torch.float32, device=pos.device) + +class EmbedND_RifleX(nn.Module): + def __init__(self, dim, theta, axes_dim, num_frames, k): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + self.num_frames = num_frames + self.k = k + + def forward(self, ids): + n_axes = ids.shape[-1] + emb = torch.cat( + [rope_riflex(ids[..., i], self.axes_dim[i], self.theta, self.num_frames, self.k, temporal=True if i == 0 else False) for i in range(n_axes)], + dim=-3, + ) + return emb.unsqueeze(1) + +def poly1d(coefficients, x): + result = torch.zeros_like(x) + for i, coeff in enumerate(coefficients): + result += coeff * (x ** (len(coefficients) - 1 - i)) + return result.abs() + +def sinusoidal_embedding_1d(dim, position): + # preprocess + assert dim % 2 == 0 + half = dim // 2 + position = position.type(torch.float64) + + # calculation + sinusoid = torch.outer( + position, torch.pow(10000, -torch.arange(half).to(position).div(half))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x + + +def rope_params(max_seq_len, dim, theta=10000, L_test=25, k=0): + assert dim % 2 == 0 + exponents = torch.arange(0, dim, 2, dtype=torch.float64).div(dim) + inv_theta_pow = 1.0 / torch.pow(theta, exponents) + + if k > 0: + print(f"RifleX: Using {k}th freq") + inv_theta_pow[k-1] = 0.9 * 2 * torch.pi / L_test + + freqs = torch.outer(torch.arange(max_seq_len), inv_theta_pow) + freqs = torch.polar(torch.ones_like(freqs), freqs) + return freqs + +from comfy.model_management import get_torch_device, get_autocast_device +@torch.autocast(device_type=get_autocast_device(get_torch_device()), enabled=False) +@torch.compiler.disable() +def rope_apply(x, grid_sizes, freqs): + n, c = x.size(2), x.size(3) // 2 + + # split freqs + freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) + + # loop over samples + output = [] + for i, (f, h, w) in enumerate(grid_sizes.tolist()): + seq_len = f * h * w + + # precompute multipliers + x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( + seq_len, n, -1, 2)) + freqs_i = torch.cat([ + freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], + dim=-1).reshape(seq_len, 1, -1) + + # apply rotary embedding + x_i = torch.view_as_real(x_i * freqs_i).flatten(2) + x_i = torch.cat([x_i, x[i, seq_len:]]) + + # append to collection + output.append(x_i) + return torch.stack(output).to(x.dtype) + + +class WanRMSNorm(nn.Module): + + def __init__(self, dim, eps=1e-5): + super().__init__() + self.dim = dim + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + r""" + Args: + x(Tensor): Shape [B, L, C] + """ + return self._norm(x)* self.weight + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps).to(x.dtype) + + +class WanLayerNorm(nn.LayerNorm): + + def __init__(self, dim, eps=1e-6, elementwise_affine=False): + super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps) + + def forward(self, x): + r""" + Args: + x(Tensor): Shape [B, L, C] + """ + return super().forward(x) + + +class WanSelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6, + attention_mode='sdpa'): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.eps = eps + self.attention_mode = attention_mode + + # layers + self.q = nn.Linear(dim, dim) + self.k = nn.Linear(dim, dim) + self.v = nn.Linear(dim, dim) + self.o = nn.Linear(dim, dim) + self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + + def forward(self, x, seq_lens, grid_sizes, freqs, rope_func = "default", block_mask=None): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + seq_lens(Tensor): Shape [B] + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n, d) + return q, k, v + + q, k, v = qkv_fn(x) + + + if is_enhance_enabled(): + feta_scores = get_feta_scores(q, k) + + if self.attention_mode == 'flex_attention': + if rope_func == "comfy": + roped_query, roped_key = apply_rope_comfy(q, k, freqs) + else: + roped_query = rope_apply(q, grid_sizes, freqs).type_as(v) + roped_key = rope_apply(k, grid_sizes, freqs).type_as(v) + + padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1] + padded_roped_query = torch.cat( + [roped_query, + torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], + device=q.device, dtype=v.dtype)], + dim=1 + ) + + padded_roped_key = torch.cat( + [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], + device=k.device, dtype=v.dtype)], + dim=1 + ) + + padded_v = torch.cat( + [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], + device=v.device, dtype=v.dtype)], + dim=1 + ) + + x = flex_attention( + query=padded_roped_query.transpose(2, 1), + key=padded_roped_key.transpose(2, 1), + value=padded_v.transpose(2, 1), + block_mask=block_mask + )[:, :, :-padded_length].transpose(2, 1) + + else: + if rope_func == "comfy": + q, k = apply_rope_comfy(q, k, freqs) + else: + q=rope_apply(q, grid_sizes, freqs) + k=rope_apply(k, grid_sizes, freqs) + + x = attention( + q=q, + k=k, + v=v, + k_lens=seq_lens, + window_size=self.window_size, + attention_mode=self.attention_mode) + + # output + x = x.flatten(2) + x = self.o(x) + + if is_enhance_enabled(): + x *= feta_scores + + return x + + def forward_split(self, x, seq_lens, grid_sizes, freqs, seq_chunks=1,current_step=0, video_attention_split_steps = [], rope_func = "default"): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + seq_lens(Tensor): Shape [B] + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + # query, key, value function + def qkv_fn(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + k = self.norm_k(self.k(x)).view(b, s, n, d) + v = self.v(x).view(b, s, n, d) + return q, k, v + + q, k, v = qkv_fn(x) + + if rope_func == "comfy": + q, k = apply_rope_comfy(q, k, freqs) + else: + q=rope_apply(q, grid_sizes, freqs) + k=rope_apply(k, grid_sizes, freqs) + + if is_enhance_enabled(): + feta_scores = get_feta_scores(q, k) + + # Split by frames if multiple prompts are provided + if seq_chunks > 1 and current_step in video_attention_split_steps: + outputs = [] + # Extract frame, height, width from grid_sizes - force to CPU scalars + frames = grid_sizes[0][0].item() + height = grid_sizes[0][1].item() + width = grid_sizes[0][2].item() + tokens_per_frame = height * width + + actual_chunks = min(seq_chunks, frames) + if isinstance(actual_chunks, torch.Tensor): + actual_chunks = actual_chunks.item() + + frame_chunks = [] # Pre-calculate all chunk boundaries + start_frame = 0 + base_frames_per_chunk = frames // actual_chunks + extra_frames = frames % actual_chunks + + # Pre-calculate all chunks + for i in range(actual_chunks): + chunk_size = base_frames_per_chunk + (1 if i < extra_frames else 0) + end_frame = start_frame + chunk_size + frame_chunks.append((start_frame, end_frame)) + start_frame = end_frame + + # Process each chunk using the pre-calculated boundaries + for start_frame, end_frame in frame_chunks: + # Convert to token indices + start_idx = int(start_frame * tokens_per_frame) + end_idx = int(end_frame * tokens_per_frame) + + chunk_q = q[:, start_idx:end_idx, :, :] + chunk_k = k[:, start_idx:end_idx, :, :] + chunk_v = v[:, start_idx:end_idx, :, :] + + chunk_out = attention( + q=chunk_q, + k=chunk_k, + v=chunk_v, + k_lens=seq_lens, + window_size=self.window_size, + attention_mode=self.attention_mode) + + outputs.append(chunk_out) + + # Concatenate outputs along the sequence dimension + x = torch.cat(outputs, dim=1) + else: + # Original attention computation + x = attention( + q=q, + k=k, + v=v, + k_lens=seq_lens, + window_size=self.window_size, + attention_mode=self.attention_mode) + + # output + x = x.flatten(2) + x = self.o(x) + + if is_enhance_enabled(): + x *= feta_scores + + return x + + def normalized_attention_guidance(self, b, n, d, q, context, nag_context=None, nag_params={}): + # NAG text attention + context_positive = context + context_negative = nag_context + nag_scale = nag_params['nag_scale'] + nag_alpha = nag_params['nag_alpha'] + nag_tau = nag_params['nag_tau'] + + k_positive = self.norm_k(self.k(context_positive)).view(b, -1, n, d) + v_positive = self.v(context_positive).view(b, -1, n, d) + k_negative = self.norm_k(self.k(context_negative)).view(b, -1, n, d) + v_negative = self.v(context_negative).view(b, -1, n, d) + + x_positive = attention(q, k_positive, v_positive, k_lens=None, attention_mode=self.attention_mode) + x_positive = x_positive.flatten(2) + + x_negative = attention(q, k_negative, v_negative, k_lens=None, attention_mode=self.attention_mode) + x_negative = x_negative.flatten(2) + + nag_guidance = x_positive * nag_scale - x_negative * (nag_scale - 1) + + norm_positive = torch.norm(x_positive, p=1, dim=-1, keepdim=True) + norm_guidance = torch.norm(nag_guidance, p=1, dim=-1, keepdim=True) + + scale = norm_guidance / norm_positive + scale = torch.nan_to_num(scale, nan=10.0) + + mask = scale > nag_tau + adjustment = (norm_positive * nag_tau) / (norm_guidance + 1e-7) + nag_guidance = torch.where(mask, nag_guidance * adjustment, nag_guidance) + del mask, adjustment + + return nag_guidance * nag_alpha + x_positive * (1 - nag_alpha) + +#region T2V crossattn +class WanT2VCrossAttention(WanSelfAttention): + + def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, attention_mode='sdpa'): + super().__init__(dim, num_heads, window_size, qk_norm, eps) + self.attention_mode = attention_mode + + def forward(self, x, context, context_lens, clip_embed=None, audio_proj=None, audio_context_lens=None, audio_scale=1.0, + num_latent_frames=21, nag_params={}, nag_context=None, is_uncond=False): + b, n, d = x.size(0), self.num_heads, self.head_dim + # compute query + q = self.norm_q(self.q(x)).view(b, -1, n, d) + + if nag_context is not None and not is_uncond: + x_text = self.normalized_attention_guidance(b, n, d, q, context, nag_context, nag_params) + else: + k = self.norm_k(self.k(context)).view(b, -1, n, d) + v = self.v(context).view(b, -1, n, d) + x_text = attention(q, k, v, k_lens=None, attention_mode=self.attention_mode) + x_text = x_text.flatten(2) + + x = x_text + + # FantasyTalking audio attention + if audio_proj is not None: + if len(audio_proj.shape) == 4: + audio_q = q.view(b * num_latent_frames, -1, n, d) + ip_key = self.k_proj(audio_proj).view(b * num_latent_frames, -1, n, d) + ip_value = self.v_proj(audio_proj).view(b * num_latent_frames, -1, n, d) + audio_x = attention( + audio_q, ip_key, ip_value, k_lens=audio_context_lens, attention_mode=self.attention_mode + ) + audio_x = audio_x.view(b, q.size(1), n, d).flatten(2) + elif len(audio_proj.shape) == 3: + ip_key = self.k_proj(audio_proj).view(b, -1, n, d) + ip_value = self.v_proj(audio_proj).view(b, -1, n, d) + audio_x = attention(q, ip_key, ip_value, k_lens=audio_context_lens, attention_mode=self.attention_mode).flatten(2) + + x = x + audio_x * audio_scale + + x = self.o(x) + return x + + +class WanI2VCrossAttention(WanSelfAttention): + + def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6, attention_mode='sdpa'): + super().__init__(dim, num_heads, window_size, qk_norm, eps) + self.k_img = nn.Linear(dim, dim) + self.v_img = nn.Linear(dim, dim) + self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() + self.attention_mode = attention_mode + + def forward(self, x, context, context_lens, clip_embed, audio_proj=None, audio_context_lens=None, + audio_scale=1.0, num_latent_frames=21, nag_params={}, nag_context=None, is_uncond=False): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + context_lens(Tensor): Shape [B] + """ + b, n, d = x.size(0), self.num_heads, self.head_dim + # compute query + q = self.norm_q(self.q(x)).view(b, -1, n, d) + + if nag_context is not None and not is_uncond: + x_text = self.normalized_attention_guidance(b, n, d, q, context, nag_context, nag_params) + else: + # text attention + k = self.norm_k(self.k(context)).view(b, -1, n, d) + v = self.v(context).view(b, -1, n, d) + x_text = attention(q, k, v, k_lens=context_lens, attention_mode=self.attention_mode).flatten(2) + + #img attention + if clip_embed is not None: + k_img = self.norm_k_img(self.k_img(clip_embed)).view(b, -1, n, d) + v_img = self.v_img(clip_embed).view(b, -1, n, d) + img_x = attention(q, k_img, v_img, k_lens=None, attention_mode=self.attention_mode).flatten(2) + x = x_text + img_x + else: + x = x_text + + # FantasyTalking audio attention + if audio_proj is not None: + if len(audio_proj.shape) == 4: + audio_q = q.view(b * num_latent_frames, -1, n, d) + ip_key = self.k_proj(audio_proj).view(b * num_latent_frames, -1, n, d) + ip_value = self.v_proj(audio_proj).view(b * num_latent_frames, -1, n, d) + audio_x = attention( + audio_q, ip_key, ip_value, k_lens=audio_context_lens, attention_mode=self.attention_mode + ) + audio_x = audio_x.view(b, q.size(1), n, d).flatten(2) + elif len(audio_proj.shape) == 3: + ip_key = self.k_proj(audio_proj).view(b, -1, n, d) + ip_value = self.v_proj(audio_proj).view(b, -1, n, d) + audio_x = attention(q, ip_key, ip_value, k_lens=audio_context_lens, attention_mode=self.attention_mode).flatten(2) + + x = x + audio_x * audio_scale + + x = self.o(x) + return x + + +WAN_CROSSATTENTION_CLASSES = { + 't2v_cross_attn': WanT2VCrossAttention, + 'i2v_cross_attn': WanI2VCrossAttention, +} + + +class WanAttentionBlock(nn.Module): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, + attention_mode='sdpa'): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + self.attention_mode = attention_mode + + # layers + self.norm1 = WanLayerNorm(dim, eps) + self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, + eps, self.attention_mode) + if cross_attn_type != "no_cross_attn": + self.norm3 = WanLayerNorm( + dim, eps, + elementwise_affine=True) if cross_attn_norm else nn.Identity() + self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, + num_heads, + (-1, -1), + qk_norm, + eps,#attention_mode=attention_mode sageattn doesn't seem faster here + ) + self.norm2 = WanLayerNorm(dim, eps) + self.ffn = nn.Sequential( + nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), + nn.Linear(ffn_dim, dim)) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) + + @torch.compiler.disable() + def get_mod(self, e): + if e.dim() == 3: + modulation = self.modulation # 1, 6, dim + e = (modulation.to(e.device) + e).chunk(6, dim=1) + elif e.dim() == 4: + modulation = self.modulation.unsqueeze(2) # 1, 6, 1, dim + e = (modulation.to(e.device) + e).chunk(6, dim=1) + e = [ei.squeeze(1) for ei in e] + return e + + def modulate(self, x, e): + return x * (1 + e[1]) + e[0] + + #region attention forward + def forward( + self, + x, + e, + seq_lens, + grid_sizes, + freqs, + context, + context_lens, + current_step, + video_attention_split_steps=[], + rope_func = "default", + clip_embed=None, + camera_embed=None, + audio_proj=None, + audio_context_lens=None, + audio_scale=1.0, + num_latent_frames=21, + block_mask=None, + nag_params={}, + nag_context=None, + is_uncond=False + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, 6, C] + seq_lens(Tensor): Shape [B], length of each sequence in batch + grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + #e = (self.modulation.to(e.device) + e).chunk(6, dim=1) + e = self.get_mod(e) + + input_x = self.modulate(self.norm1(x), e) + + if camera_embed is not None: + # encode ReCamMaster camera + camera_embed = self.cam_encoder(camera_embed.to(x)) + camera_embed = camera_embed.repeat(1, 2, 1) + camera_embed = camera_embed.unsqueeze(2).unsqueeze(3).repeat(1, 1, grid_sizes[0][1], grid_sizes[0][2], 1) + camera_embed = rearrange(camera_embed, 'b f h w d -> b (f h w) d') + input_x += camera_embed + + # self-attention + if context is not None and (context.shape[0] > 1 or (clip_embed is not None and clip_embed.shape[0] > 1)) and x.shape[0] == 1: + y = self.self_attn.forward_split( + input_x, + seq_lens, grid_sizes, + freqs, rope_func=rope_func, + seq_chunks=max(context.shape[0], clip_embed.shape[0] if clip_embed is not None else 0), + current_step=current_step, + video_attention_split_steps=video_attention_split_steps + ) + else: + y = self.self_attn.forward( + input_x, + seq_lens, grid_sizes, + freqs, rope_func=rope_func, + block_mask=block_mask, + ) + #ReCamMaster + if camera_embed is not None: + y = self.projector(y) + + del input_x + + x = x + (y * e[2]) + del y + + # cross-attention & ffn function + if context is not None: + if (context.shape[0] > 1 or (clip_embed is not None and clip_embed.shape[0] > 1)) and x.shape[0] == 1: + if nag_context is not None: + raise NotImplementedError("nag_context is not supported in split_cross_attn_ffn") + x = self.split_cross_attn_ffn(x, context, context_lens, e, clip_embed=clip_embed, grid_sizes=grid_sizes) + else: + x = self.cross_attn_ffn(x, context, context_lens, e, clip_embed=clip_embed, grid_sizes=grid_sizes, + audio_proj=audio_proj, audio_context_lens=audio_context_lens, audio_scale=audio_scale, + num_latent_frames=num_latent_frames, nag_params=nag_params, nag_context=nag_context, is_uncond=is_uncond) + else: + y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3]) + x = x + (y * e[5]) + del e + return x + #@torch.compiler.disable() + def cross_attn_ffn(self, x, context, context_lens, e, clip_embed=None, grid_sizes=None, + audio_proj=None, audio_context_lens=None, audio_scale=1.0, num_latent_frames=21, nag_params={}, + nag_context=None, is_uncond=False): + x = x + self.cross_attn(self.norm3(x), context, context_lens, clip_embed=clip_embed, + audio_proj=audio_proj, audio_context_lens=audio_context_lens, audio_scale=audio_scale, + num_latent_frames=num_latent_frames, nag_params=nag_params, nag_context=nag_context, is_uncond=is_uncond) + y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3]) + x = x + (y * e[5]) + return x + + @torch.compiler.disable() + def split_cross_attn_ffn(self, x, context, context_lens, e, clip_embed=None, grid_sizes=None): + # Get number of prompts + num_prompts = context.shape[0] + num_clip_embeds = 0 if clip_embed is None else clip_embed.shape[0] + num_segments = max(num_prompts, num_clip_embeds) + + # Extract spatial dimensions + frames, height, width = grid_sizes[0] # Assuming batch size 1 + tokens_per_frame = height * width + + # Distribute frames across prompts + frames_per_segment = max(1, frames // num_segments) + + # Process each prompt segment + x_combined = torch.zeros_like(x) + + for i in range(num_segments): + # Calculate frame boundaries for this segment + start_frame = i * frames_per_segment + end_frame = min((i+1) * frames_per_segment, frames) if i < num_segments-1 else frames + + # Convert frame indices to token indices + start_idx = start_frame * tokens_per_frame + end_idx = end_frame * tokens_per_frame + segment_indices = torch.arange(start_idx, end_idx, device=x.device, dtype=torch.long) + + # Get prompt segment (cycle through available prompts if needed) + prompt_idx = i % num_prompts + segment_context = context[prompt_idx:prompt_idx+1] + segment_context_lens = None + if context_lens is not None: + segment_context_lens = context_lens[prompt_idx:prompt_idx+1] + + # Handle clip_embed for this segment (cycle through available embeddings) + segment_clip_embed = None + if clip_embed is not None: + clip_idx = i % num_clip_embeds + segment_clip_embed = clip_embed[clip_idx:clip_idx+1] + + # Get tensor segment + x_segment = x[:, segment_indices, :] + + # Process segment with its prompt and clip embedding + processed_segment = self.cross_attn(self.norm3(x_segment), segment_context, segment_context_lens, clip_embed=segment_clip_embed) + processed_segment = processed_segment.to(x.dtype) + + # Add to combined result + x_combined[:, segment_indices, :] = processed_segment + + # Continue with FFN + x = x + x_combined + y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3]) + x = x + (y * e[5]) + return x + +class VaceWanAttentionBlock(WanAttentionBlock): + def __init__( + self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, + block_id=0 + ): + super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps) + self.block_id = block_id + if block_id == 0: + self.before_proj = nn.Linear(self.dim, self.dim) + nn.init.zeros_(self.before_proj.weight) + nn.init.zeros_(self.before_proj.bias) + self.after_proj = nn.Linear(self.dim, self.dim) + nn.init.zeros_(self.after_proj.weight) + nn.init.zeros_(self.after_proj.bias) + + def forward(self, c_list, x, intermediate_device=None, nonblocking=True, **kwargs): + if self.block_id == 0: + c = self.before_proj(c_list[0]) + x + all_c = [] + else: + all_c = c_list + c = all_c.pop(-1) + c = super().forward(c, **kwargs) + c_skip = self.after_proj(c) + + all_c += [c_skip.to(intermediate_device, non_blocking=nonblocking), c] + + return all_c + +class BaseWanAttentionBlock(WanAttentionBlock): + def __init__( + self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, + block_id=None, + attention_mode='sdpa' + ): + super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, attention_mode) + self.block_id = block_id + + def forward(self, x, vace_hints=None, vace_context_scale=[1.0], **kwargs): + x = super().forward(x, **kwargs) + if vace_hints is None: + return x + + if self.block_id is not None: + for i in range(len(vace_hints)): + x = x + vace_hints[i][self.block_id].to(x.device) * vace_context_scale[i] + return x + +class Head(nn.Module): + + def __init__(self, dim, out_dim, patch_size, eps=1e-6): + super().__init__() + self.dim = dim + self.out_dim = out_dim + self.patch_size = patch_size + self.eps = eps + + # layers + out_dim = math.prod(patch_size) * out_dim + self.norm = WanLayerNorm(dim, eps) + self.head = nn.Linear(dim, out_dim) + + # modulation + self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5) + + def get_mod(self, e): + if e.dim() == 2: + modulation = self.modulation.to(e.device) # 1, 2, dim + e = (modulation + e.unsqueeze(1)).chunk(2, dim=1) + elif e.dim() == 3: + modulation = self.modulation.to(e.device).unsqueeze(2) # 1, 2, seq, dim + e = (modulation + e.unsqueeze(1)).chunk(2, dim=1) + e = [ei.squeeze(1) for ei in e] + return e + + def forward(self, x, e): + r""" + Args: + x(Tensor): Shape [B, L1, C] + e(Tensor): Shape [B, C] + """ + + # e = (self.modulation.to(e.device) + e.unsqueeze(1)).chunk(2, dim=1) + # normed = self.norm(x) + # x = self.head(normed * (1 + e[1]) + e[0]) + + e = self.get_mod(e) + x = self.head(self.norm(x) * (1 + e[1]) + e[0]) + return x + + +class MLPProj(torch.nn.Module): + + def __init__(self, in_dim, out_dim, fl_pos_emb=False): + super().__init__() + + self.proj = torch.nn.Sequential( + torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim), + torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim), + torch.nn.LayerNorm(out_dim)) + if fl_pos_emb: # NOTE: we only use this for `fl2v` + self.emb_pos = nn.Parameter(torch.zeros(1, 257 * 2, 1280)) + + def forward(self, image_embeds): + if hasattr(self, 'emb_pos'): + image_embeds = image_embeds + self.emb_pos.to(image_embeds.device) + clip_extra_context_tokens = self.proj(image_embeds) + return clip_extra_context_tokens + + +class WanModel(ModelMixin, ConfigMixin): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + ignore_for_config = [ + 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size' + ] + _no_split_modules = ['WanAttentionBlock'] + + @register_to_config + def __init__(self, + model_type='t2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + attention_mode='sdpa', + main_device=torch.device('cuda'), + offload_device=torch.device('cpu'), + teacache_coefficients=[], + magcache_ratios=[], + vace_layers=None, + vace_in_dim=None, + inject_sample_info=False, + add_ref_conv=False, + in_dim_ref_conv=16, + add_control_adapter=False, + in_dim_control_adapter=24, + ): + r""" + Initialize the diffusion model backbone. + + Args: + model_type (`str`, *optional*, defaults to 't2v'): + Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) + patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) + text_len (`int`, *optional*, defaults to 512): + Fixed length for text embeddings + in_dim (`int`, *optional*, defaults to 16): + Input video channels (C_in) + dim (`int`, *optional*, defaults to 2048): + Hidden dimension of the transformer + ffn_dim (`int`, *optional*, defaults to 8192): + Intermediate dimension in feed-forward network + freq_dim (`int`, *optional*, defaults to 256): + Dimension for sinusoidal time embeddings + text_dim (`int`, *optional*, defaults to 4096): + Input dimension for text embeddings + out_dim (`int`, *optional*, defaults to 16): + Output video channels (C_out) + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads + num_layers (`int`, *optional*, defaults to 32): + Number of transformer blocks + window_size (`tuple`, *optional*, defaults to (-1, -1)): + Window size for local attention (-1 indicates global attention) + qk_norm (`bool`, *optional*, defaults to True): + Enable query/key normalization + cross_attn_norm (`bool`, *optional*, defaults to False): + Enable cross-attention normalization + eps (`float`, *optional*, defaults to 1e-6): + Epsilon value for normalization layers + """ + + super().__init__() + + self.model_type = model_type + + self.patch_size = patch_size + self.text_len = text_len + self.in_dim = in_dim + self.dim = dim + self.ffn_dim = ffn_dim + self.freq_dim = freq_dim + self.text_dim = text_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + self.attention_mode = attention_mode + self.main_device = main_device + self.offload_device = offload_device + + self.blocks_to_swap = -1 + self.offload_txt_emb = False + self.offload_img_emb = False + self.vace_blocks_to_swap = -1 + + self.cache_device = offload_device + + #init TeaCache variables + self.enable_teacache = False + self.rel_l1_thresh = 0.15 + self.teacache_start_step= 0 + self.teacache_end_step = -1 + self.teacache_state = TeaCacheState(cache_device=self.cache_device) + self.teacache_coefficients = teacache_coefficients + self.teacache_use_coefficients = False + self.teacache_mode = 'e' + + #init MagCache variables + self.enable_magcache = False + self.magcache_state = MagCacheState(cache_device=self.cache_device) + self.magcache_thresh = 0.24 + self.magcache_K = 4 + self.magcache_start_step = 0 + self.magcache_end_step = -1 + self.magcache_ratios = magcache_ratios + + self.slg_blocks = None + self.slg_start_percent = 0.0 + self.slg_end_percent = 1.0 + + self.use_non_blocking = True + + self.video_attention_split_steps = [] + + # embeddings + self.patch_embedding = nn.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size) + + self.original_patch_embedding = self.patch_embedding + self.expanded_patch_embedding = self.patch_embedding + + if model_type != 'no_cross_attn': + self.text_embedding = nn.Sequential( + nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), + nn.Linear(dim, dim)) + + self.time_embedding = nn.Sequential( + nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6)) + + if vace_layers is not None: + self.vace_layers = [i for i in range(0, self.num_layers, 2)] if vace_layers is None else vace_layers + self.vace_in_dim = self.in_dim if vace_in_dim is None else vace_in_dim + + self.vace_layers_mapping = {i: n for n, i in enumerate(self.vace_layers)} + + # vace blocks + self.vace_blocks = nn.ModuleList([ + VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, + self.cross_attn_norm, self.eps, block_id=i) + for i in self.vace_layers + ]) + + # vace patch embeddings + self.vace_patch_embedding = nn.Conv3d( + self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size + ) + self.blocks = nn.ModuleList([ + BaseWanAttentionBlock('t2v_cross_attn', dim, ffn_dim, num_heads, + window_size, qk_norm, cross_attn_norm, eps, + attention_mode=self.attention_mode, + block_id=self.vace_layers_mapping[i] if i in self.vace_layers else None) + for i in range(num_layers) + ]) + else: + # blocks + if model_type == 't2v': + cross_attn_type = 't2v_cross_attn' + elif model_type == 'i2v' or model_type == 'fl2v': + cross_attn_type = 'i2v_cross_attn' + else: + cross_attn_type = 'no_cross_attn' + + self.blocks = nn.ModuleList([ + WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, + window_size, qk_norm, cross_attn_norm, eps, + attention_mode=self.attention_mode) + for _ in range(num_layers) + ]) + + # head + self.head = Head(dim, out_dim, patch_size, eps) + + + d = self.dim // self.num_heads + self.rope_embedder = EmbedND_RifleX( + d, + 10000.0, + [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)], + num_frames=None, + k=None, + ) + + # buffers (don't use register_buffer otherwise dtype will be changed in to()) + assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0 + + if model_type == 'i2v' or model_type == 'fl2v': + self.img_emb = MLPProj(1280, dim, fl_pos_emb=model_type == 'fl2v') + + #skyreels v2 + if inject_sample_info: + self.fps_embedding = nn.Embedding(2, dim) + self.fps_projection = nn.Sequential(nn.Linear(dim, dim), nn.SiLU(), nn.Linear(dim, dim * 6)) + #fun 1.1 + if add_ref_conv: + self.ref_conv = nn.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:]) + else: + self.ref_conv = None + + if add_control_adapter: + from .wan_camera_adapter import SimpleAdapter + self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:]) + else: + self.control_adapter = None + + self.block_mask=None + + @staticmethod + def _prepare_blockwise_causal_attn_mask( + device: torch.device | str, num_frames: int = 21, + frame_seqlen: int = 1560, num_frame_per_block=1 + ): + """ + we will divide the token sequence into the following format + [1 latent frame] [1 latent frame] ... [1 latent frame] + We use flexattention to construct the attention mask + """ + print("num_frames", num_frames) + print("frame_seqlen", frame_seqlen) + total_length = num_frames * frame_seqlen + + # we do right padding to get to a multiple of 128 + padded_length = math.ceil(total_length / 128) * 128 - total_length + + ends = torch.zeros(total_length + padded_length, + device=device, dtype=torch.long) + + # Block-wise causal mask will attend to all elements that are before the end of the current chunk + frame_indices = torch.arange( + start=0, + end=total_length, + step=frame_seqlen * num_frame_per_block, + device=device + ) + + for tmp in frame_indices: + ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \ + frame_seqlen * num_frame_per_block + + def attention_mask(b, h, q_idx, kv_idx): + return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) + # return ((kv_idx < total_length) & (q_idx < total_length)) | (q_idx == kv_idx) # bidirectional mask + + + block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length, + KV_LEN=total_length + padded_length, _compile=False, device=device) + + return block_mask + + def block_swap(self, blocks_to_swap, offload_txt_emb=False, offload_img_emb=False, vace_blocks_to_swap=None): + log.info(f"Swapping {blocks_to_swap + 1} transformer blocks") + self.blocks_to_swap = blocks_to_swap + + self.offload_img_emb = offload_img_emb + self.offload_txt_emb = offload_txt_emb + + total_offload_memory = 0 + total_main_memory = 0 + + for b, block in tqdm(enumerate(self.blocks), total=len(self.blocks), desc="Initializing block swap"): + block_memory = get_module_memory_mb(block) + + if b > self.blocks_to_swap: + block.to(self.main_device) + total_main_memory += block_memory + else: + block.to(self.offload_device, non_blocking=self.use_non_blocking) + total_offload_memory += block_memory + + if blocks_to_swap != -1 and vace_blocks_to_swap == 0: + vace_blocks_to_swap = 1 + + if vace_blocks_to_swap > 0 and self.vace_layers is not None: + self.vace_blocks_to_swap = vace_blocks_to_swap + + for b, block in tqdm(enumerate(self.vace_blocks), total=len(self.vace_blocks), desc="Initializing vace block swap"): + block_memory = get_module_memory_mb(block) + + if b > self.vace_blocks_to_swap: + block.to(self.main_device) + total_main_memory += block_memory + else: + block.to(self.offload_device, non_blocking=self.use_non_blocking) + total_offload_memory += block_memory + + mm.soft_empty_cache() + gc.collect() + + log.info("----------------------") + log.info(f"Block swap memory summary:") + log.info(f"Transformer blocks on {self.offload_device}: {total_offload_memory:.2f}MB") + log.info(f"Transformer blocks on {self.main_device}: {total_main_memory:.2f}MB") + log.info(f"Total memory used by transformer blocks: {(total_offload_memory + total_main_memory):.2f}MB") + log.info(f"Non-blocking memory transfer: {self.use_non_blocking}") + log.info("----------------------") + + def forward_vace( + self, + x, + vace_context, + seq_len, + kwargs + ): + # embeddings + c = [self.vace_patch_embedding(u.unsqueeze(0).float()).to(x.dtype) for u in vace_context] + c = [u.flatten(2).transpose(1, 2) for u in c] + c = torch.cat([ + torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], + dim=1) for u in c + ]) + + if x.shape[1] > c.shape[1]: + c = torch.cat([c.new_zeros(x.shape[0], x.shape[1] - c.shape[1], c.shape[2]), c], dim=1) + if c.shape[1] > x.shape[1]: + c = c[:, :x.shape[1]] + + c_list = [c] + for b, block in enumerate(self.vace_blocks): + if b <= self.vace_blocks_to_swap and self.vace_blocks_to_swap >= 0: + block.to(self.main_device) + c_list = block( + c_list, x, + intermediate_device=self.offload_device if self.vace_blocks_to_swap != -1 else self.main_device, + nonblocking=self.use_non_blocking, + **kwargs) + if b <= self.vace_blocks_to_swap and self.vace_blocks_to_swap >= 0: + block.to(self.offload_device, non_blocking=self.use_non_blocking) + + hints = c_list[:-1] + + return hints + + def forward( + self, + x, + t, + context, + seq_len, + is_uncond=False, + current_step_percentage=0.0, + current_step=0, + total_steps=50, + clip_fea=None, + y=None, + device=torch.device('cuda'), + freqs=None, + pred_id=None, + control_lora_enabled=False, + vace_data=None, + camera_embed=None, + unianim_data=None, + fps_embeds=None, + fun_ref=None, + fun_camera=None, + audio_proj=None, + audio_context_lens=None, + audio_scale=1.0, + pcd_data=None, + controlnet=None, + add_cond=None, + attn_cond=None, + nag_params={}, + nag_context=None + ): + r""" + Forward pass through the diffusion model + + Args: + x (List[Tensor]): + List of input video tensors, each with shape [C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + # params + device = self.patch_embedding.weight.device + if freqs is not None and freqs.device != device: + freqs = freqs.to(device) + + _, F, H, W = x[0].shape + + # Construct blockwise causal attn mask + if self.attention_mode == 'flex_attention' and current_step == 0: + self.block_mask = self._prepare_blockwise_causal_attn_mask( + device, num_frames=F, + frame_seqlen=H * W // (self.patch_size[1] * self.patch_size[2]), + num_frame_per_block=3 + ) + + if y is not None: + if hasattr(self, "randomref_embedding_pose") and unianim_data is not None: + if unianim_data['start_percent'] <= current_step_percentage <= unianim_data['end_percent']: + random_ref_emb = unianim_data["random_ref"] + if random_ref_emb is not None: + y[0] = y[0] + random_ref_emb * unianim_data["strength"] + x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)] + + #uni3c controlnet + if pcd_data is not None: + hidden_states = x[0].unsqueeze(0).clone().float() + render_latent = torch.cat([hidden_states[:, :20], pcd_data["render_latent"]], dim=1) + + # embeddings + if control_lora_enabled: + self.expanded_patch_embedding.to(device) + x = [ + self.expanded_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) + for u in x + ] + else: + self.original_patch_embedding.to(self.main_device) + x = [ + self.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) + for u in x + ] + + if self.control_adapter is not None and fun_camera is not None: + fun_camera = self.control_adapter(fun_camera) + x = [u + v for u, v in zip(x, fun_camera)] + + grid_sizes = torch.stack( + [torch.tensor(u.shape[2:], dtype=torch.long) for u in x]) + + x = [u.flatten(2).transpose(1, 2) for u in x] + + x_len = x[0].shape[1] + + if add_cond is not None: + add_cond = self.add_conv_in(add_cond.to(self.add_conv_in.weight.dtype)).to(x[0].dtype) + add_cond = add_cond.flatten(2).transpose(1, 2) + x[0] = x[0] + self.add_proj(add_cond) + if attn_cond is not None: + F_cond, H_cond, W_cond = attn_cond.shape[2], attn_cond.shape[3], attn_cond.shape[4] + grid_sizes = torch.stack([torch.tensor([u[0] + 1, u[1], u[2]]) for u in grid_sizes]).to(grid_sizes.device) + attn_cond = self.attn_conv_in(attn_cond.to(self.attn_conv_in.weight.dtype)).to(x[0].dtype) + attn_cond = attn_cond.flatten(2).transpose(1, 2) + x[0] = torch.cat([x[0], attn_cond], dim=1) + seq_len += attn_cond.size(1) + + if self.ref_conv is not None and fun_ref is not None: + fun_ref = self.ref_conv(fun_ref).flatten(2).transpose(1, 2) + grid_sizes = torch.stack([torch.tensor([u[0] + 1, u[1], u[2]]) for u in grid_sizes]).to(grid_sizes.device) + seq_len += fun_ref.size(1) + F += 1 + x = [torch.concat([_fun_ref.unsqueeze(0), u], dim=1) for _fun_ref, u in zip(fun_ref, x)] + + seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long) + assert seq_lens.max() <= seq_len + x = torch.cat([ + torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], + dim=1) for u in x + ]) + + if freqs is None: #comfy rope + rope_func = "comfy" + f_len = ((F + (self.patch_size[0] // 2)) // self.patch_size[0]) + h_len = ((H + (self.patch_size[1] // 2)) // self.patch_size[1]) + w_len = ((W + (self.patch_size[2] // 2)) // self.patch_size[2]) + img_ids = torch.zeros((f_len, h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, f_len - 1, steps=f_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) + img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) + img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) + + if attn_cond is not None: + cond_f_len = ((F_cond + (self.patch_size[0] // 2)) // self.patch_size[0]) + cond_h_len = ((H_cond + (self.patch_size[1] // 2)) // self.patch_size[1]) + cond_w_len = ((W_cond + (self.patch_size[2] // 2)) // self.patch_size[2]) + cond_img_ids = torch.zeros((cond_f_len, cond_h_len, cond_w_len, 3), device=x.device, dtype=x.dtype) + + #shift + shift_f_size = 81 # Default value + shift_f = False + if shift_f: + cond_img_ids[:, :, :, 0] = cond_img_ids[:, :, :, 0] + torch.linspace(shift_f_size, shift_f_size + cond_f_len - 1,steps=cond_f_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) + else: + cond_img_ids[:, :, :, 0] = cond_img_ids[:, :, :, 0] + torch.linspace(0, cond_f_len - 1, steps=cond_f_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) + cond_img_ids[:, :, :, 1] = cond_img_ids[:, :, :, 1] + torch.linspace(h_len, h_len + cond_h_len - 1, steps=cond_h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) + cond_img_ids[:, :, :, 2] = cond_img_ids[:, :, :, 2] + torch.linspace(w_len, w_len + cond_w_len - 1, steps=cond_w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) + + # Combine original and conditional position ids + img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=1) + cond_img_ids = repeat(cond_img_ids, "t h w c -> b (t h w) c", b=1) + combined_img_ids = torch.cat([img_ids, cond_img_ids], dim=1) + + # Generate RoPE frequencies for the combined positions + freqs = self.rope_embedder(combined_img_ids).movedim(1, 2) + else: + img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=1) + freqs = self.rope_embedder(img_ids).movedim(1, 2) + else: + rope_func = "default" + + # time embeddings + if t.dim() == 2: + b, f = t.shape + _flag_df = True + else: + _flag_df = False + + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(x.dtype) + ) # b, dim + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) # b, 6, dim + + if fps_embeds is not None: + fps_embeds = torch.tensor(fps_embeds, dtype=torch.long, device=device) + + fps_emb = self.fps_embedding(fps_embeds).to(e0.dtype) + if _flag_df: + e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)).repeat(t.shape[1], 1, 1) + else: + e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)) + + if _flag_df: + e = e.view(b, f, 1, 1, self.dim).expand(b, f, grid_sizes[0][1], grid_sizes[0][2], self.dim) + e0 = e0.view(b, f, 1, 1, 6, self.dim).expand(b, f, grid_sizes[0][1], grid_sizes[0][2], 6, self.dim) + + e = e.flatten(1, 3) + e0 = e0.flatten(1, 3) + + e0 = e0.transpose(1, 2) + if not e0.is_contiguous(): + e0 = e0.contiguous() + + e = e.to(self.offload_device, non_blocking=self.use_non_blocking) + + # context (test embedding) + context_lens = None + if hasattr(self, "text_embedding"): + if self.offload_txt_emb: + self.text_embedding.to(self.main_device) + context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in context + ]).to(x.dtype)) + # NAG + if nag_context is not None: + nag_context = self.text_embedding( + torch.stack([ + torch.cat( + [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) + for u in nag_context + ]).to(x.dtype)) + + if self.offload_txt_emb: + self.text_embedding.to(self.offload_device, non_blocking=self.use_non_blocking) + else: + context = None + + clip_embed = None + if clip_fea is not None and hasattr(self, "img_emb"): + clip_fea = clip_fea.to(self.main_device) + if self.offload_img_emb: + self.img_emb.to(self.main_device) + clip_embed = self.img_emb(clip_fea) # bs x 257 x dim + #context = torch.concat([context_clip, context], dim=1) + if self.offload_img_emb: + self.img_emb.to(self.offload_device, non_blocking=self.use_non_blocking) + + should_calc = True + accumulated_rel_l1_distance = torch.tensor(0.0, dtype=torch.float32, device=device) + if self.enable_teacache and self.teacache_start_step <= current_step <= self.teacache_end_step: + if pred_id is None: + pred_id = self.teacache_state.new_prediction(cache_device=self.cache_device) + should_calc = True + else: + previous_modulated_input = self.teacache_state.get(pred_id)['previous_modulated_input'] + previous_modulated_input = previous_modulated_input.to(device) + previous_residual = self.teacache_state.get(pred_id)['previous_residual'] + accumulated_rel_l1_distance = self.teacache_state.get(pred_id)['accumulated_rel_l1_distance'] + + if self.teacache_use_coefficients: + rescale_func = np.poly1d(self.teacache_coefficients[self.teacache_mode]) + temb = e if self.teacache_mode == 'e' else e0 + accumulated_rel_l1_distance += rescale_func(( + (temb.to(device) - previous_modulated_input).abs().mean() / previous_modulated_input.abs().mean() + ).cpu().item()) + del temb + else: + temb_relative_l1 = relative_l1_distance(previous_modulated_input, e0) + accumulated_rel_l1_distance = accumulated_rel_l1_distance.to(e0.device) + temb_relative_l1 + del temb_relative_l1 + + + if accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + accumulated_rel_l1_distance = torch.tensor(0.0, dtype=torch.float32, device=device) + accumulated_rel_l1_distance = accumulated_rel_l1_distance.to(self.cache_device) + + previous_modulated_input = e.to(self.cache_device).clone() if (self.teacache_use_coefficients and self.teacache_mode == 'e') else e0.to(self.cache_device).clone() + + if not should_calc: + x = x.to(previous_residual.dtype) + previous_residual.to(x.device) + self.teacache_state.update( + pred_id, + accumulated_rel_l1_distance=accumulated_rel_l1_distance, + ) + self.teacache_state.get(pred_id)['skipped_steps'].append(current_step) + + # enable magcache + if self.enable_magcache and self.magcache_start_step <= current_step <= self.magcache_end_step: + if pred_id is None: + pred_id = self.magcache_state.new_prediction(cache_device=self.cache_device) + should_calc = True + else: + accumulated_ratio = self.magcache_state.get(pred_id)['accumulated_ratio'] + accumulated_err = self.magcache_state.get(pred_id)['accumulated_err'] + accumulated_steps = self.magcache_state.get(pred_id)['accumulated_steps'] + + calibration_len = len(self.magcache_ratios) // 2 + cur_mag_ratio = self.magcache_ratios[int((current_step*(calibration_len/total_steps)))] + + accumulated_ratio *= cur_mag_ratio + accumulated_err += np.abs(1-accumulated_ratio) + accumulated_steps += 1 + + self.magcache_state.update( + pred_id, + accumulated_ratio=accumulated_ratio, + accumulated_steps=accumulated_steps, + accumulated_err=accumulated_err + ) + + if accumulated_err<=self.magcache_thresh and accumulated_steps<=self.magcache_K: + should_calc = False + x += self.magcache_state.get(pred_id)['residual_cache'].to(x.device) + self.magcache_state.get(pred_id)['skipped_steps'].append(current_step) + else: + should_calc = True + self.magcache_state.update( + pred_id, + accumulated_ratio=1.0, + accumulated_steps=0, + accumulated_err=0 + ) + + if should_calc: + if self.enable_teacache or self.enable_magcache: + original_x = x.to(self.cache_device).clone() + + if hasattr(self, "dwpose_embedding") and unianim_data is not None: + if unianim_data['start_percent'] <= current_step_percentage <= unianim_data['end_percent']: + dwpose_emb = unianim_data['dwpose'] + x += dwpose_emb * unianim_data['strength'] + # arguments + kwargs = dict( + e=e0, + seq_lens=seq_lens, + grid_sizes=grid_sizes, + freqs=freqs, + context=context, + context_lens=context_lens, + clip_embed=clip_embed, + rope_func=rope_func, + current_step=current_step, + video_attention_split_steps=self.video_attention_split_steps, + camera_embed=camera_embed, + audio_proj=audio_proj, + audio_context_lens=audio_context_lens, + num_latent_frames = F, + audio_scale=audio_scale, + block_mask=self.block_mask, + nag_params=nag_params, + nag_context=nag_context, + is_uncond = is_uncond + ) + + if vace_data is not None: + vace_hint_list = [] + vace_scale_list = [] + if isinstance(vace_data[0], dict): + for data in vace_data: + if (data["start"] <= current_step_percentage <= data["end"]) or \ + (data["end"] > 0 and current_step == 0 and current_step_percentage >= data["start"]): + + vace_hints = self.forward_vace(x, data["context"], data["seq_len"], kwargs) + vace_hint_list.append(vace_hints) + vace_scale_list.append(data["scale"][current_step]) + else: + vace_hints = self.forward_vace(x, vace_data, seq_len, kwargs) + vace_hint_list.append(vace_hints) + vace_scale_list.append(1.0) + + kwargs['vace_hints'] = vace_hint_list + kwargs['vace_context_scale'] = vace_scale_list + + #uni3c controlnet + pdc_controlnet_states = None + if pcd_data is not None: + if (pcd_data["start"] <= current_step_percentage <= pcd_data["end"]) or \ + (pcd_data["end"] > 0 and current_step == 0 and current_step_percentage >= pcd_data["start"]): + self.controlnet.to(self.main_device) + pdc_controlnet_states = self.controlnet( + render_latent=render_latent.to(self.main_device, self.controlnet.dtype), + render_mask=pcd_data["render_mask"], + camera_embedding=pcd_data["camera_embedding"], + temb=e.to(self.main_device), + device=self.offload_device) + self.controlnet.to(self.offload_device) + + for b, block in enumerate(self.blocks): + #skip layer guidance + if self.slg_blocks is not None: + if b in self.slg_blocks and is_uncond: + if self.slg_start_percent <= current_step_percentage <= self.slg_end_percent: + continue + if b <= self.blocks_to_swap and self.blocks_to_swap >= 0: + block.to(self.main_device) + x = block(x, **kwargs) + + #uni3c controlnet + if pdc_controlnet_states is not None and b < len(pdc_controlnet_states): + x[:, :x_len] += pdc_controlnet_states[b].to(x) * pcd_data["controlnet_weight"] + #controlnet + if (controlnet is not None) and (b % controlnet["controlnet_stride"] == 0) and (b // controlnet["controlnet_stride"] < len(controlnet["controlnet_states"])): + x[:, :x_len] += controlnet["controlnet_states"][b // controlnet["controlnet_stride"]].to(x) * controlnet["controlnet_weight"] + + if b <= self.blocks_to_swap and self.blocks_to_swap >= 0: + block.to(self.offload_device, non_blocking=self.use_non_blocking) + + if self.enable_teacache and (self.teacache_start_step <= current_step <= self.teacache_end_step) and pred_id is not None: + self.teacache_state.update( + pred_id, + previous_residual=(x.to(original_x.device) - original_x), + accumulated_rel_l1_distance=accumulated_rel_l1_distance, + previous_modulated_input=previous_modulated_input + ) + elif self.enable_magcache and (self.magcache_start_step <= current_step <= self.magcache_end_step) and pred_id is not None: + self.magcache_state.update( + pred_id, + residual_cache=(x.to(original_x.device) - original_x) + ) + + if self.ref_conv is not None and fun_ref is not None: + full_ref_length = fun_ref.size(1) + x = x[:, full_ref_length:] + grid_sizes = torch.stack([torch.tensor([u[0] - 1, u[1], u[2]]) for u in grid_sizes]).to(grid_sizes.device) + + if attn_cond is not None: + x = x[:, :x_len] + grid_sizes = torch.stack([torch.tensor([u[0] - 1, u[1], u[2]]) for u in grid_sizes]).to(grid_sizes.device) + + x = self.head(x, e.to(x.device)) + x = self.unpatchify(x, grid_sizes) # type: ignore[arg-type] + x = [u.float() for u in x] + return (x, pred_id) if pred_id is not None else (x, None) + + def unpatchify(self, x, grid_sizes): + r""" + Reconstruct video tensors from patch embeddings. + + Args: + x (List[Tensor]): + List of patchified features, each with shape [L, C_out * prod(patch_size)] + grid_sizes (Tensor): + Original spatial-temporal grid dimensions before patching, + shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) + + Returns: + List[Tensor]: + Reconstructed video tensors with shape [C_out, F, H / 8, W / 8] + """ + + c = self.out_dim + out = [] + for u, v in zip(x, grid_sizes.tolist()): + u = u[: math.prod(v)].view(*v, *self.patch_size, c) + u = torch.einsum("fhwpqrc->cfphqwr", u) + u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)]) + out.append(u) + return out + +class TeaCacheState: + def __init__(self, cache_device='cpu'): + self.cache_device = cache_device + self.states = {} + self._next_pred_id = 0 + + def new_prediction(self, cache_device='cpu'): + """Create new prediction state and return its ID""" + self.cache_device = cache_device + pred_id = self._next_pred_id + self._next_pred_id += 1 + self.states[pred_id] = { + 'previous_residual': None, + 'accumulated_rel_l1_distance': 0, + 'previous_modulated_input': None, + 'skipped_steps': [], + } + return pred_id + + def update(self, pred_id, **kwargs): + """Update state for specific prediction""" + if pred_id not in self.states: + return None + for key, value in kwargs.items(): + self.states[pred_id][key] = value + + def get(self, pred_id): + return self.states.get(pred_id, {}) + + def clear_all(self): + self.states = {} + self._next_pred_id = 0 + +class MagCacheState: + def __init__(self, cache_device='cpu'): + self.cache_device = cache_device + self.states = {} + self._next_pred_id = 0 + + def new_prediction(self, cache_device='cpu'): + """Create new prediction state and return its ID""" + self.cache_device = cache_device + pred_id = self._next_pred_id + self._next_pred_id += 1 + self.states[pred_id] = { + 'residual_cache': None, + 'accumulated_ratio': 1.0, + 'accumulated_steps': 0, + 'accumulated_err': 0, + 'skipped_steps': [], + } + return pred_id + + def update(self, pred_id, **kwargs): + """Update state for specific prediction""" + if pred_id not in self.states: + return None + for key, value in kwargs.items(): + self.states[pred_id][key] = value + + def get(self, pred_id): + return self.states.get(pred_id, {}) + + def clear_all(self): + self.states = {} + self._next_pred_id = 0 + +def relative_l1_distance(last_tensor, current_tensor): + l1_distance = torch.abs(last_tensor.to(current_tensor.device) - current_tensor).mean() + norm = torch.abs(last_tensor).mean() + relative_l1_distance = l1_distance / norm + return relative_l1_distance.to(torch.float32).to(current_tensor.device) + +def get_tensor_memory(tensor): + memory_bytes = tensor.element_size() * tensor.nelement() + return f"{memory_bytes / (1024 * 1024):.2f} MB" \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/t5.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/t5.py new file mode 100644 index 0000000000000000000000000000000000000000..9fc7f6736af45aace3d5ac00568381af169cb880 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/t5.py @@ -0,0 +1,519 @@ +# Modified from transformers.models.t5.modeling_t5 +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import logging +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .tokenizers import HuggingfaceTokenizer + +__all__ = [ + 'T5Model', + 'T5Encoder', + 'T5Decoder', + 'T5EncoderModel', +] + +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device + +def fp16_clamp(x): + if x.dtype == torch.float16 and torch.isinf(x).any(): + clamp = torch.finfo(x.dtype).max - 1000 + x = torch.clamp(x, min=-clamp, max=clamp) + return x + + +def init_weights(m): + if isinstance(m, T5LayerNorm): + nn.init.ones_(m.weight) + elif isinstance(m, T5Model): + nn.init.normal_(m.token_embedding.weight, std=1.0) + elif isinstance(m, T5FeedForward): + nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5) + nn.init.normal_(m.fc1.weight, std=m.dim**-0.5) + nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5) + elif isinstance(m, T5Attention): + nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5) + nn.init.normal_(m.k.weight, std=m.dim**-0.5) + nn.init.normal_(m.v.weight, std=m.dim**-0.5) + nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5) + elif isinstance(m, T5RelativeEmbedding): + nn.init.normal_( + m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5) + + +class GELU(nn.Module): + + def forward(self, x): + return 0.5 * x * (1.0 + torch.tanh( + math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0)))) + + +class T5LayerNorm(nn.Module): + + def __init__(self, dim, eps=1e-6): + super(T5LayerNorm, self).__init__() + self.dim = dim + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + + self.eps) + if self.weight.dtype in [torch.float16, torch.bfloat16]: + x = x.type_as(self.weight) + return self.weight * x + + +class T5Attention(nn.Module): + + def __init__(self, dim, dim_attn, num_heads, dropout=0.1): + assert dim_attn % num_heads == 0 + super(T5Attention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.num_heads = num_heads + self.head_dim = dim_attn // num_heads + + # layers + self.q = nn.Linear(dim, dim_attn, bias=False) + self.k = nn.Linear(dim, dim_attn, bias=False) + self.v = nn.Linear(dim, dim_attn, bias=False) + self.o = nn.Linear(dim_attn, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, context=None, mask=None, pos_bias=None): + """ + x: [B, L1, C]. + context: [B, L2, C] or None. + mask: [B, L2] or [B, L1, L2] or None. + """ + # check inputs + context = x if context is None else context + b, n, c = x.size(0), self.num_heads, self.head_dim + + # compute query, key, value + q = self.q(x).view(b, -1, n, c) + k = self.k(context).view(b, -1, n, c) + v = self.v(context).view(b, -1, n, c) + + # attention bias + attn_bias = x.new_zeros(b, n, q.size(1), k.size(1)) + if pos_bias is not None: + attn_bias += pos_bias + if mask is not None: + assert mask.ndim in [2, 3] + mask = mask.view(b, 1, 1, + -1) if mask.ndim == 2 else mask.unsqueeze(1) + attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min) + + # compute attention (T5 does not use scaling) + attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias + attn = F.softmax(attn.float(), dim=-1).type_as(attn) + x = torch.einsum('bnij,bjnc->binc', attn, v) + + # output + x = x.reshape(b, -1, n * c) + x = self.o(x) + x = self.dropout(x) + return x + + +class T5FeedForward(nn.Module): + + def __init__(self, dim, dim_ffn, dropout=0.1): + super(T5FeedForward, self).__init__() + self.dim = dim + self.dim_ffn = dim_ffn + + # layers + self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU()) + self.fc1 = nn.Linear(dim, dim_ffn, bias=False) + self.fc2 = nn.Linear(dim_ffn, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.fc1(x) * self.gate(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + + +class T5SelfAttention(nn.Module): + + def __init__(self, + dim, + dim_attn, + dim_ffn, + num_heads, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5SelfAttention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.norm1 = T5LayerNorm(dim) + self.attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm2 = T5LayerNorm(dim) + self.ffn = T5FeedForward(dim, dim_ffn, dropout) + self.pos_embedding = None if shared_pos else T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=True) + + def forward(self, x, mask=None, pos_bias=None): + e = pos_bias if self.shared_pos else self.pos_embedding( + x.size(1), x.size(1)) + x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e)) + x = fp16_clamp(x + self.ffn(self.norm2(x))) + return x + + +class T5CrossAttention(nn.Module): + + def __init__(self, + dim, + dim_attn, + dim_ffn, + num_heads, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5CrossAttention, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.norm1 = T5LayerNorm(dim) + self.self_attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm2 = T5LayerNorm(dim) + self.cross_attn = T5Attention(dim, dim_attn, num_heads, dropout) + self.norm3 = T5LayerNorm(dim) + self.ffn = T5FeedForward(dim, dim_ffn, dropout) + self.pos_embedding = None if shared_pos else T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=False) + + def forward(self, + x, + mask=None, + encoder_states=None, + encoder_mask=None, + pos_bias=None): + e = pos_bias if self.shared_pos else self.pos_embedding( + x.size(1), x.size(1)) + x = fp16_clamp(x + self.self_attn(self.norm1(x), mask=mask, pos_bias=e)) + x = fp16_clamp(x + self.cross_attn( + self.norm2(x), context=encoder_states, mask=encoder_mask)) + x = fp16_clamp(x + self.ffn(self.norm3(x))) + return x + + +class T5RelativeEmbedding(nn.Module): + + def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128): + super(T5RelativeEmbedding, self).__init__() + self.num_buckets = num_buckets + self.num_heads = num_heads + self.bidirectional = bidirectional + self.max_dist = max_dist + + # layers + self.embedding = nn.Embedding(num_buckets, num_heads) + + def forward(self, lq, lk): + device = self.embedding.weight.device + # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \ + # torch.arange(lq).unsqueeze(1).to(device) + rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \ + torch.arange(lq, device=device).unsqueeze(1) + rel_pos = self._relative_position_bucket(rel_pos) + rel_pos_embeds = self.embedding(rel_pos) + rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze( + 0) # [1, N, Lq, Lk] + return rel_pos_embeds.contiguous() + + def _relative_position_bucket(self, rel_pos): + # preprocess + if self.bidirectional: + num_buckets = self.num_buckets // 2 + rel_buckets = (rel_pos > 0).long() * num_buckets + rel_pos = torch.abs(rel_pos) + else: + num_buckets = self.num_buckets + rel_buckets = 0 + rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos)) + + # embeddings for small and large positions + max_exact = num_buckets // 2 + rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) / + math.log(self.max_dist / max_exact) * + (num_buckets - max_exact)).long() + rel_pos_large = torch.min( + rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1)) + rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large) + return rel_buckets + + +class T5Encoder(nn.Module): + + def __init__(self, + vocab, + dim, + dim_attn, + dim_ffn, + num_heads, + num_layers, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5Encoder, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_layers = num_layers + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ + else nn.Embedding(vocab, dim) + self.pos_embedding = T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=True) if shared_pos else None + self.dropout = nn.Dropout(dropout) + self.blocks = nn.ModuleList([ + T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, + shared_pos, dropout) for _ in range(num_layers) + ]) + self.norm = T5LayerNorm(dim) + + # initialize weights + self.apply(init_weights) + + def forward(self, ids, mask=None): + x = self.token_embedding(ids) + x = self.dropout(x) + e = self.pos_embedding(x.size(1), + x.size(1)) if self.shared_pos else None + for block in self.blocks: + x = block(x, mask, pos_bias=e) + x = self.norm(x) + x = self.dropout(x) + return x + + +class T5Decoder(nn.Module): + + def __init__(self, + vocab, + dim, + dim_attn, + dim_ffn, + num_heads, + num_layers, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5Decoder, self).__init__() + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.num_layers = num_layers + self.num_buckets = num_buckets + self.shared_pos = shared_pos + + # layers + self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \ + else nn.Embedding(vocab, dim) + self.pos_embedding = T5RelativeEmbedding( + num_buckets, num_heads, bidirectional=False) if shared_pos else None + self.dropout = nn.Dropout(dropout) + self.blocks = nn.ModuleList([ + T5CrossAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, + shared_pos, dropout) for _ in range(num_layers) + ]) + self.norm = T5LayerNorm(dim) + + # initialize weights + self.apply(init_weights) + + def forward(self, ids, mask=None, encoder_states=None, encoder_mask=None): + b, s = ids.size() + + # causal mask + if mask is None: + mask = torch.tril(torch.ones(1, s, s).to(ids.device)) + elif mask.ndim == 2: + mask = torch.tril(mask.unsqueeze(1).expand(-1, s, -1)) + + # layers + x = self.token_embedding(ids) + x = self.dropout(x) + e = self.pos_embedding(x.size(1), + x.size(1)) if self.shared_pos else None + for block in self.blocks: + x = block(x, mask, encoder_states, encoder_mask, pos_bias=e) + x = self.norm(x) + x = self.dropout(x) + return x + + +class T5Model(nn.Module): + + def __init__(self, + vocab_size, + dim, + dim_attn, + dim_ffn, + num_heads, + encoder_layers, + decoder_layers, + num_buckets, + shared_pos=True, + dropout=0.1): + super(T5Model, self).__init__() + self.vocab_size = vocab_size + self.dim = dim + self.dim_attn = dim_attn + self.dim_ffn = dim_ffn + self.num_heads = num_heads + self.encoder_layers = encoder_layers + self.decoder_layers = decoder_layers + self.num_buckets = num_buckets + + # layers + self.token_embedding = nn.Embedding(vocab_size, dim) + self.encoder = T5Encoder(self.token_embedding, dim, dim_attn, dim_ffn, + num_heads, encoder_layers, num_buckets, + shared_pos, dropout) + self.decoder = T5Decoder(self.token_embedding, dim, dim_attn, dim_ffn, + num_heads, decoder_layers, num_buckets, + shared_pos, dropout) + self.head = nn.Linear(dim, vocab_size, bias=False) + + # initialize weights + self.apply(init_weights) + + def forward(self, encoder_ids, encoder_mask, decoder_ids, decoder_mask): + x = self.encoder(encoder_ids, encoder_mask) + x = self.decoder(decoder_ids, decoder_mask, x, encoder_mask) + x = self.head(x) + return x + + +def _t5(name, + encoder_only=False, + decoder_only=False, + return_tokenizer=False, + tokenizer_kwargs={}, + dtype=torch.float32, + device='cpu', + **kwargs): + # sanity check + assert not (encoder_only and decoder_only) + + # params + if encoder_only: + model_cls = T5Encoder + kwargs['vocab'] = kwargs.pop('vocab_size') + kwargs['num_layers'] = kwargs.pop('encoder_layers') + _ = kwargs.pop('decoder_layers') + elif decoder_only: + model_cls = T5Decoder + kwargs['vocab'] = kwargs.pop('vocab_size') + kwargs['num_layers'] = kwargs.pop('decoder_layers') + _ = kwargs.pop('encoder_layers') + else: + model_cls = T5Model + + # init model + with torch.device(device): + model = model_cls(**kwargs) + + # set device + #model = model.to(dtype=dtype, device=device) + + # init tokenizer + if return_tokenizer: + from .tokenizers import HuggingfaceTokenizer + tokenizer = HuggingfaceTokenizer(f'google/{name}', **tokenizer_kwargs) + return model, tokenizer + else: + return model + + +def umt5_xxl(**kwargs): + cfg = dict( + vocab_size=256384, + dim=4096, + dim_attn=4096, + dim_ffn=10240, + num_heads=64, + encoder_layers=24, + decoder_layers=24, + num_buckets=32, + shared_pos=False, + dropout=0.1) + cfg.update(**kwargs) + return _t5('umt5-xxl', **cfg) + + +class T5EncoderModel: + + def __init__( + self, + text_len, + dtype=torch.bfloat16, + device=torch.device('cuda'), + state_dict=None, + tokenizer_path=None, + quantization="disabled", + ): + self.text_len = text_len + self.dtype = dtype + self.device = device + self.tokenizer_path = tokenizer_path + + # init model + with init_empty_weights(): + model = umt5_xxl( + encoder_only=True, + return_tokenizer=False, + dtype=dtype, + device=device).eval().requires_grad_(False) + + if quantization == "fp8_e4m3fn": + cast_dtype = torch.float8_e4m3fn + else: + cast_dtype = dtype + + params_to_keep = {'norm', 'pos_embedding', 'token_embedding'} + for name, param in model.named_parameters(): + dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype + set_module_tensor_to_device(model, name, device=device, dtype=dtype_to_use, value=state_dict[name]) + del state_dict + self.model = model + self.tokenizer = HuggingfaceTokenizer( + name=tokenizer_path, seq_len=text_len, clean='whitespace') + + def __call__(self, texts, device): + ids, mask = self.tokenizer( + texts, return_mask=True, add_special_tokens=True) + ids = ids.to(device) + mask = mask.to(device) + seq_lens = mask.gt(0).sum(dim=1).long() + context = self.model(ids, mask) + return [u[:v] for u, v in zip(context, seq_lens)] diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/tokenizers.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/tokenizers.py new file mode 100644 index 0000000000000000000000000000000000000000..b363d5ecfca8ffd8482eba8df523c50ae0a40e0f --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/tokenizers.py @@ -0,0 +1,102 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import html +import string + +import ftfy +import regex as re +from transformers import AutoTokenizer + +__all__ = ['HuggingfaceTokenizer'] + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +def canonicalize(text, keep_punctuation_exact_string=None): + text = text.replace('_', ' ') + if keep_punctuation_exact_string: + text = keep_punctuation_exact_string.join( + part.translate(str.maketrans('', '', string.punctuation)) + for part in text.split(keep_punctuation_exact_string)) + else: + text = text.translate(str.maketrans('', '', string.punctuation)) + text = text.lower() + text = re.sub(r'\s+', ' ', text) + return text.strip() + + +class HuggingfaceTokenizer: + + def __init__(self, name, seq_len=None, clean=None, **kwargs): + assert clean in (None, 'whitespace', 'lower', 'canonicalize') + self.name = name + self.seq_len = seq_len + self.clean = clean + + # init tokenizer + # Prefer slow tokenizer to avoid issues with missing/corrupted tokenizer.json (e.g., Git LFS pointers) + kwargs.setdefault('use_fast', False) + kwargs.setdefault('trust_remote_code', False) + + tokenizer = None + try: + # First try the provided path (local folder) + tokenizer = AutoTokenizer.from_pretrained(name, **kwargs) + if not hasattr(tokenizer, 'vocab_size'): + raise ValueError(f"Loaded tokenizer from {name} is invalid (no vocab_size)") + except Exception as e: + # Fallback to online model if local fails + print(f"Failed to load tokenizer from {name}: {e}") + print("Attempting to load from google/umt5-xxl online...") + fallback_kwargs = kwargs.copy() + fallback_kwargs['local_files_only'] = False + tokenizer = AutoTokenizer.from_pretrained('google/umt5-xxl', **fallback_kwargs) + if not hasattr(tokenizer, 'vocab_size'): + raise RuntimeError("Failed to load tokenizer from both local and online sources") + + self.tokenizer = tokenizer + self.vocab_size = self.tokenizer.vocab_size + + def __call__(self, sequence, **kwargs): + return_mask = kwargs.pop('return_mask', False) + + # arguments + _kwargs = {'return_tensors': 'pt'} + if self.seq_len is not None: + _kwargs.update({ + 'padding': 'max_length', + 'truncation': True, + 'max_length': self.seq_len + }) + _kwargs.update(**kwargs) + + # tokenization + if isinstance(sequence, str): + sequence = [sequence] + if self.clean: + sequence = [self._clean(u) for u in sequence] + ids = self.tokenizer(sequence, **_kwargs) + + # output + if return_mask: + return ids.input_ids, ids.attention_mask + else: + return ids.input_ids + + def _clean(self, text): + if self.clean == 'whitespace': + text = whitespace_clean(basic_clean(text)) + elif self.clean == 'lower': + text = whitespace_clean(basic_clean(text)).lower() + elif self.clean == 'canonicalize': + text = canonicalize(basic_clean(text)) + return text diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/vae.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/vae.py new file mode 100644 index 0000000000000000000000000000000000000000..69f9bad537788e82fc42d7135040570992eb6a62 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/vae.py @@ -0,0 +1,662 @@ +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from comfy.model_management import get_torch_device, get_autocast_device +__all__ = [ + 'WanVAE', +] + +CACHE_T = 2 + + +class CausalConv3d(nn.Conv3d): + """ + Causal 3d convolusion. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._padding = (self.padding[2], self.padding[2], self.padding[1], + self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + + def forward(self, x, cache_x=None): + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + x = F.pad(x, padding) + + return super().forward(x) + + +class RMS_norm(nn.Module): + + def __init__(self, dim, channel_first=True, images=True, bias=False): + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. + + def forward(self, x): + return F.normalize( + x, dim=(1 if self.channel_first else + -1)) * self.scale * self.gamma + self.bias + + +class Upsample(nn.Upsample): + + def forward(self, x): + """ + Fix bfloat16 support for nearest neighbor interpolation. + """ + return super().forward(x.float()).type_as(x) + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', + 'downsample3d') + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == 'upsample2d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + elif mode == 'upsample3d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + self.time_conv = CausalConv3d( + dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + + elif mode == 'downsample2d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == 'downsample3d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d( + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == 'upsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = 'Rep' + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] != 'Rep': + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] == 'Rep': + cache_x = torch.cat([ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2) + if feat_cache[idx] == 'Rep': + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.resample(x) + x = rearrange(x, '(b t) c h w -> b c t h w', t=t) + + if self.mode == 'downsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -1:, :, :].clone() + # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': + # # cache last frame of last two chunk + # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + def init_weight(self, conv): + conv_weight = conv.weight + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + one_matrix = torch.eye(c1, c2) + init_matrix = one_matrix + nn.init.zeros_(conv_weight) + #conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5 + conv_weight.data[:, :, 1, 0, 0] = init_matrix #* 0.5 + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + def init_weight2(self, conv): + conv_weight = conv.weight.data + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + init_matrix = torch.eye(c1 // 2, c2) + #init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2) + conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix + conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1)) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ + if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + h = self.shortcut(x) + for layer in self.residual: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + h + + +class AttentionBlock(nn.Module): + """ + Causal self-attention with a single head. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = RMS_norm(dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + self.proj = nn.Conv2d(dim, dim, 1) + + # zero out the last layer params + nn.init.zeros_(self.proj.weight) + + def forward(self, x): + identity = x + b, c, t, h, w = x.size() + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.norm(x) + # compute query, key, value + q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, + -1).permute(0, 1, 3, + 2).contiguous().chunk( + 3, dim=-1) + + # apply attention + x = F.scaled_dot_product_attention( + q, + k, + v, + ) + x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) + + # output + x = self.proj(x) + x = rearrange(x, '(b t) c h w-> b c t h w', t=t) + return x + identity + + +class Encoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + downsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = 'downsample3d' if temperal_downsample[ + i] else 'downsample2d' + downsamples.append(Resample(out_dim, mode=mode)) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout)) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +class Decoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2**(len(dim_mult) - 2) + + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout)) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i == 1 or i == 2 or i == 3: + in_dim = in_dim // 2 + for _ in range(num_res_blocks + 1): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + upsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # upsample block + if i != len(dim_mult) - 1: + mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' + upsamples.append(Resample(out_dim, mode=mode)) + scale *= 2.0 + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, 3, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if isinstance(m, CausalConv3d): + count += 1 + return count + + +class WanVAE_(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, + attn_scales, self.temperal_downsample, dropout) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, + attn_scales, self.temperal_upsample, dropout) + + def forward(self, x): + mu, log_var = self.encode(x) + z = self.reparameterize(mu, log_var) + x_recon = self.decode(z) + return x_recon, mu, log_var + + def encode(self, x, scale): + self.clear_cache() + ## cache + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + ## 对encode输入的x，按时间拆分为1、4、4、4.... + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder( + x[:, :, :1, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + else: + out_ = self.encoder( + x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + if isinstance(scale[0], torch.Tensor): + mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( + 1, self.z_dim, 1, 1, 1) + else: + mu = (mu - scale[0]) * scale[1] + self.clear_cache() + return mu + + def decode(self, z, scale): + self.clear_cache() + # z: [b,c,t,h,w] + if isinstance(scale[0], torch.Tensor): + z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( + 1, self.z_dim, 1, 1, 1) + else: + z = z / scale[1] + scale[0] + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + self._conv_idx = [0] + if i == 0: + out = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + else: + out_ = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) + self.clear_cache() + return out + + def reparameterize(self, mu, log_var): + std = torch.exp(0.5 * log_var) + eps = torch.randn_like(std) + return eps * std + mu + + def sample(self, imgs, deterministic=False): + mu, log_var = self.encode(imgs) + if deterministic: + return mu + std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) + return mu + std * torch.randn_like(std) + + def clear_cache(self): + self._conv_num = count_conv3d(self.decoder) + self._conv_idx = [0] + self._feat_map = [None] * self._conv_num + #cache encode + self._enc_conv_num = count_conv3d(self.encoder) + self._enc_conv_idx = [0] + self._enc_feat_map = [None] * self._enc_conv_num + + +def _video_vae(pretrained_path=None, z_dim=None, device='cpu', **kwargs): + """ + Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL. + """ + # params + cfg = dict( + dim=96, + z_dim=z_dim, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[False, True, True], + dropout=0.0) + cfg.update(**kwargs) + + # init model + with torch.device('meta'): + model = WanVAE_(**cfg) + + # load checkpoint + logging.info(f'loading {pretrained_path}') + model.load_state_dict( + torch.load(pretrained_path, map_location=device), assign=True) + + return model + + +class WanVAE: + + def __init__(self, + z_dim=16, + vae_pth='cache/vae_step_411000.pth', + dtype=torch.float, + device="cuda"): + self.dtype = dtype + self.device = device + + mean = [ + -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, + 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 + ] + std = [ + 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, + 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 + ] + self.mean = torch.tensor(mean, dtype=dtype, device=device) + self.std = torch.tensor(std, dtype=dtype, device=device) + self.scale = [self.mean, 1.0 / self.std] + + # init model + self.model = _video_vae( + pretrained_path=vae_pth, + z_dim=z_dim, + ).eval().requires_grad_(False).to(device) + + def encode(self, videos): + """ + videos: A list of videos each with shape [C, T, H, W]. + """ + with torch.autocast(device_type=get_autocast_device(get_torch_device()), enabled=False): + return [ + self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0) + for u in videos + ] + + def decode(self, zs): + with torch.autocast(device_type=get_autocast_device(get_torch_device()), enabled=False): + return [ + self.model.decode(u.unsqueeze(0), + self.scale).float().clamp_(-1, 1).squeeze(0) + for u in zs + ] diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/wan_camera_adapter.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/wan_camera_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..0b9199075f41692d08b1de0d620946722624df4b --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/modules/wan_camera_adapter.py @@ -0,0 +1,63 @@ +#https://github.com/aigc-apps/VideoX-Fun/blob/wan_fun_v1.1/videox_fun/models/wan_camera_adapter.py +import torch +import torch.nn as nn + +class SimpleAdapter(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1): + super(SimpleAdapter, self).__init__() + + # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 + self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) + + # Convolution: reduce spatial dimensions by a factor + # of 2 (without overlap) + self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0) + + # Residual blocks for feature extraction + self.residual_blocks = nn.Sequential( + *[ResidualBlock(out_dim) for _ in range(num_residual_blocks)] + ) + + def forward(self, x): + # Reshape to merge the frame dimension into batch + bs, c, f, h, w = x.size() + x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) + + # Pixel Unshuffle operation + x_unshuffled = self.pixel_unshuffle(x) + + # Convolution operation + x_conv = self.conv(x_unshuffled) + + # Feature extraction with residual blocks + out = self.residual_blocks(x_conv) + + # Reshape to restore original bf dimension + out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) + + # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames + out = out.permute(0, 2, 1, 3, 4) + + return out + +class ResidualBlock(nn.Module): + def __init__(self, dim): + super(ResidualBlock, self).__init__() + self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1) + + def forward(self, x): + residual = x + out = self.relu(self.conv1(x)) + out = self.conv2(out) + out += residual + return out + +# Example usage +# in_dim = 3 +# out_dim = 64 +# adapter = SimpleAdapterWithReshape(in_dim, out_dim) +# x = torch.randn(1, in_dim, 4, 64, 64) # e.g., batch size = 1, channels = 3, frames/features = 4 +# output = adapter(x) +# print(output.shape) # Should reflect transformed dimensions diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/__init__.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6e9a339e69fd55dd226d3ce242613c19bd690522 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/__init__.py @@ -0,0 +1,8 @@ +from .fm_solvers import (FlowDPMSolverMultistepScheduler, get_sampling_sigmas, + retrieve_timesteps) +from .fm_solvers_unipc import FlowUniPCMultistepScheduler + +__all__ = [ + 'HuggingfaceTokenizer', 'get_sampling_sigmas', 'retrieve_timesteps', + 'FlowDPMSolverMultistepScheduler', 'FlowUniPCMultistepScheduler' +] diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/__pycache__/__init__.cpython-310.pyc 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+class FlowMatchScheduler(): + + def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003 / 1.002, inverse_timesteps=False, extra_one_step=False, reverse_sigmas=False): + self.num_train_timesteps = num_train_timesteps + self.shift = shift + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.inverse_timesteps = inverse_timesteps + self.extra_one_step = extra_one_step + self.reverse_sigmas = reverse_sigmas + self.set_timesteps(num_inference_steps) + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False): + sigma_start = self.sigma_min + \ + (self.sigma_max - self.sigma_min) * denoising_strength + if self.extra_one_step: + self.sigmas = torch.linspace( + sigma_start, self.sigma_min, num_inference_steps + 1)[:-1] + else: + self.sigmas = torch.linspace( + sigma_start, self.sigma_min, num_inference_steps) + if self.inverse_timesteps: + self.sigmas = torch.flip(self.sigmas, dims=[0]) + self.sigmas = self.shift * self.sigmas / \ + (1 + (self.shift - 1) * self.sigmas) + if self.reverse_sigmas: + self.sigmas = 1 - self.sigmas + self.timesteps = self.sigmas * self.num_train_timesteps + if training: + x = self.timesteps + y = torch.exp(-2 * ((x - num_inference_steps / 2) / + num_inference_steps) ** 2) + y_shifted = y - y.min() + bsmntw_weighing = y_shifted * \ + (num_inference_steps / y_shifted.sum()) + self.linear_timesteps_weights = bsmntw_weighing + + def step(self, model_output, timestep, sample, to_final=False): + if timestep.ndim == 2: + timestep = timestep.flatten(0, 1) + self.sigmas = self.sigmas.to(model_output.device) + self.timesteps = self.timesteps.to(model_output.device) + if timestep.ndim == 0: + timestep_id = torch.argmin((self.timesteps - timestep).abs(), dim=0) + else: + timestep_id = torch.argmin((self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1) + + sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1) + if to_final or (timestep_id + 1 >= len(self.timesteps)).any(): + sigma_ = 1 if ( + self.inverse_timesteps or self.reverse_sigmas) else 0 + else: + sigma_ = self.sigmas[timestep_id + 1].reshape(-1, 1, 1, 1) + prev_sample = sample + model_output * (sigma_ - sigma) + return prev_sample + + def add_noise(self, original_samples, noise, timestep): + """ + Diffusion forward corruption process. + Input: + - clean_latent: the clean latent with shape [B*T, C, H, W] + - noise: the noise with shape [B*T, C, H, W] + - timestep: the timestep with shape [B*T] + Output: the corrupted latent with shape [B*T, C, H, W] + """ + if timestep.ndim == 2: + timestep = timestep.flatten(0, 1) + self.sigmas = self.sigmas.to(noise.device) + self.timesteps = self.timesteps.to(noise.device) + timestep_id = torch.argmin( + (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1) + sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1) + sample = (1 - sigma) * original_samples + sigma * noise + return sample.type_as(noise) + + def training_target(self, sample, noise, timestep): + target = noise - sample + return target + + def training_weight(self, timestep): + timestep_id = torch.argmin( + (self.timesteps - timestep.to(self.timesteps.device)).abs()) + weights = self.linear_timesteps_weights[timestep_id] + return weights \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/fm_solvers.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/fm_solvers.py new file mode 100644 index 0000000000000000000000000000000000000000..e63bdeda2af283f6ab35fadf95d73b58272728f4 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/fm_solvers.py @@ -0,0 +1,931 @@ +# Copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py +# Convert dpm solver for flow matching +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import inspect +import math +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import (KarrasDiffusionSchedulers, + SchedulerMixin, + SchedulerOutput) +from diffusers.utils import deprecate, is_scipy_available +from diffusers.utils.torch_utils import randn_tensor + +if is_scipy_available(): + pass + + +def get_sampling_sigmas(sampling_steps, shift): + sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps] + sigma = (shift * sigma / (1 + (shift - 1) * sigma)) + + return sigma + + +def retrieve_timesteps( + scheduler, + num_inference_steps=None, + device=None, + timesteps=None, + sigmas=None, + **kwargs, +): + if timesteps is not None and sigmas is not None: + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) + if timesteps is not None: + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class FlowDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin): + """ + `FlowDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs. + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. This determines the resolution of the diffusion process. + solver_order (`int`, defaults to 2): + The DPMSolver order which can be `1`, `2`, or `3`. It is recommended to use `solver_order=2` for guided + sampling, and `solver_order=3` for unconditional sampling. This affects the number of model outputs stored + and used in multistep updates. + prediction_type (`str`, defaults to "flow_prediction"): + Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts + the flow of the diffusion process. + shift (`float`, *optional*, defaults to 1.0): + A factor used to adjust the sigmas in the noise schedule. It modifies the step sizes during the sampling + process. + use_dynamic_shifting (`bool`, defaults to `False`): + Whether to apply dynamic shifting to the timesteps based on image resolution. If `True`, the shifting is + applied on the fly. + thresholding (`bool`, defaults to `False`): + Whether to use the "dynamic thresholding" method. This method adjusts the predicted sample to prevent + saturation and improve photorealism. + dynamic_thresholding_ratio (`float`, defaults to 0.995): + The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. + sample_max_value (`float`, defaults to 1.0): + The threshold value for dynamic thresholding. Valid only when `thresholding=True` and + `algorithm_type="dpmsolver++"`. + algorithm_type (`str`, defaults to `dpmsolver++`): + Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The + `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) + paper, and the `dpmsolver++` type implements the algorithms in the + [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or + `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion. + solver_type (`str`, defaults to `midpoint`): + Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the + sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers. + lower_order_final (`bool`, defaults to `True`): + Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can + stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. + euler_at_final (`bool`, defaults to `False`): + Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail + richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference + steps, but sometimes may result in blurring. + final_sigmas_type (`str`, *optional*, defaults to "zero"): + The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final + sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. + lambda_min_clipped (`float`, defaults to `-inf`): + Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the + cosine (`squaredcos_cap_v2`) noise schedule. + variance_type (`str`, *optional*): + Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output + contains the predicted Gaussian variance. + """ + + _compatibles = [e.name for e in KarrasDiffusionSchedulers] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + solver_order: int = 2, + prediction_type: str = "flow_prediction", + shift: Optional[float] = 1.0, + use_dynamic_shifting=False, + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + sample_max_value: float = 1.0, + algorithm_type: str = "dpmsolver++", + solver_type: str = "midpoint", + lower_order_final: bool = True, + euler_at_final: bool = False, + final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" + lambda_min_clipped: float = -float("inf"), + variance_type: Optional[str] = None, + invert_sigmas: bool = False, + ): + if algorithm_type in ["dpmsolver", "sde-dpmsolver"]: + deprecation_message = f"algorithm_type {algorithm_type} is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead" + deprecate("algorithm_types dpmsolver and sde-dpmsolver", "1.0.0", + deprecation_message) + + # settings for DPM-Solver + if algorithm_type not in [ + "dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++" + ]: + if algorithm_type == "deis": + self.register_to_config(algorithm_type="dpmsolver++") + else: + raise NotImplementedError( + f"{algorithm_type} is not implemented for {self.__class__}") + + if solver_type not in ["midpoint", "heun"]: + if solver_type in ["logrho", "bh1", "bh2"]: + self.register_to_config(solver_type="midpoint") + else: + raise NotImplementedError( + f"{solver_type} is not implemented for {self.__class__}") + + if algorithm_type not in ["dpmsolver++", "sde-dpmsolver++" + ] and final_sigmas_type == "zero": + raise ValueError( + f"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please choose `sigma_min` instead." + ) + + # setable values + self.num_inference_steps = None + alphas = np.linspace(1, 1 / num_train_timesteps, + num_train_timesteps)[::-1].copy() + sigmas = 1.0 - alphas + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32) + + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + self.sigmas = sigmas + self.timesteps = sigmas * num_train_timesteps + + self.model_outputs = [None] * solver_order + self.lower_order_nums = 0 + self._step_index = None + self._begin_index = None + + # self.sigmas = self.sigmas.to( + # "cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + # Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps + def set_timesteps( + self, + num_inference_steps: Union[int, None] = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[Union[float, None]] = None, + shift: Optional[Union[float, None]] = None, + use_beta_sigmas: Optional[bool] = False, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + Args: + num_inference_steps (`int`): + Total number of the spacing of the time steps. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + + if self.config.use_dynamic_shifting and mu is None: + raise ValueError( + " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`" + ) + + if sigmas is None: + sigmas = np.linspace(self.sigma_max, self.sigma_min, + num_inference_steps +1).copy()[:-1] # pyright: ignore + + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore + else: + if shift is None: + shift = self.config.shift + sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) # pyright: ignore + + if use_beta_sigmas: + sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + + if self.config.final_sigmas_type == "zero": + sigma_last = 0 + + timesteps = sigmas * self.config.num_train_timesteps + sigmas = np.concatenate([sigmas, [sigma_last] + ]).astype(np.float32) # pyright: ignore + + self.sigmas = torch.from_numpy(sigmas) + self.timesteps = torch.from_numpy(timesteps).to( + device=device, dtype=torch.int64) + + self.num_inference_steps = len(timesteps) + + self.model_outputs = [ + None, + ] * self.config.solver_order + self.lower_order_nums = 0 + + self._step_index = None + self._begin_index = None + # self.sigmas = self.sigmas.to( + # "cpu") # to avoid too much CPU/GPU communication + + def _convert_to_beta( + self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6 + ) -> torch.Tensor: + import scipy + """From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + sigmas = np.array( + [ + sigma_min + (ppf * (sigma_max - sigma_min)) + for ppf in [ + scipy.stats.beta.ppf(timestep, alpha, beta) + for timestep in 1 - np.linspace(0, 1, num_inference_steps) + ] + ] + ) + return sigmas + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample + def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, *remaining_dims = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = sample.float( + ) # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile( + abs_sample, self.config.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.config.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + s = s.unsqueeze( + 1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = torch.clamp( + sample, -s, s + ) / s # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, *remaining_dims) + sample = sample.to(dtype) + + return sample + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def _sigma_to_alpha_sigma_t(self, sigma): + return 1 - sigma, sigma + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma) + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.convert_model_output + def convert_model_output( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + **kwargs, + ) -> torch.Tensor: + """ + Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is + designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an + integral of the data prediction model. + + The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise + prediction and data prediction models. + + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + Returns: + `torch.Tensor`: + The converted model output. + """ + timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) + if sample is None: + if len(args) > 1: + sample = args[1] + else: + raise ValueError( + "missing `sample` as a required keyward argument") + if timestep is not None: + deprecate( + "timesteps", + "1.0.0", + "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + # DPM-Solver++ needs to solve an integral of the data prediction model. + if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction`, or `flow_prediction` for the FlowDPMSolverMultistepScheduler." + ) + + if self.config.thresholding: + x0_pred = self._threshold_sample(x0_pred) + + return x0_pred + + # DPM-Solver needs to solve an integral of the noise prediction model. + elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + epsilon = sample - (1 - sigma_t) * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction` or `flow_prediction` for the FlowDPMSolverMultistepScheduler." + ) + + if self.config.thresholding: + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + x0_pred = self._threshold_sample(x0_pred) + epsilon = model_output + x0_pred + + return epsilon + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.dpm_solver_first_order_update + def dpm_solver_first_order_update( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + noise: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + One step for the first-order DPMSolver (equivalent to DDIM). + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) + prev_timestep = args[1] if len(args) > 1 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 2: + sample = args[2] + else: + raise ValueError( + " missing `sample` as a required keyward argument") + if timestep is not None: + deprecate( + "timesteps", + "1.0.0", + "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[ + self.step_index] # pyright: ignore + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s) + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s = torch.log(alpha_s) - torch.log(sigma_s) + + h = lambda_t - lambda_s + if self.config.algorithm_type == "dpmsolver++": + x_t = (sigma_t / + sigma_s) * sample - (alpha_t * + (torch.exp(-h) - 1.0)) * model_output + elif self.config.algorithm_type == "dpmsolver": + x_t = (alpha_t / + alpha_s) * sample - (sigma_t * + (torch.exp(h) - 1.0)) * model_output + elif self.config.algorithm_type == "sde-dpmsolver++": + assert noise is not None + x_t = ((sigma_t / sigma_s * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) + elif self.config.algorithm_type == "sde-dpmsolver": + assert noise is not None + x_t = ((alpha_t / alpha_s) * sample - 2.0 * + (sigma_t * (torch.exp(h) - 1.0)) * model_output + + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) + return x_t # pyright: ignore + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update + def multistep_dpm_solver_second_order_update( + self, + model_output_list: List[torch.Tensor], + *args, + sample: torch.Tensor = None, + noise: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + One step for the second-order multistep DPMSolver. + Args: + model_output_list (`List[torch.Tensor]`): + The direct outputs from learned diffusion model at current and latter timesteps. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + timestep_list = args[0] if len(args) > 0 else kwargs.pop( + "timestep_list", None) + prev_timestep = args[1] if len(args) > 1 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 2: + sample = args[2] + else: + raise ValueError( + " missing `sample` as a required keyward argument") + if timestep_list is not None: + deprecate( + "timestep_list", + "1.0.0", + "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma_t, sigma_s0, sigma_s1 = ( + self.sigmas[self.step_index + 1], # pyright: ignore + self.sigmas[self.step_index], + self.sigmas[self.step_index - 1], # pyright: ignore + ) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) + + m0, m1 = model_output_list[-1], model_output_list[-2] + + h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 + r0 = h_0 / h + D0, D1 = m0, (1.0 / r0) * (m0 - m1) + if self.config.algorithm_type == "dpmsolver++": + # See https://arxiv.org/abs/2211.01095 for detailed derivations + if self.config.solver_type == "midpoint": + x_t = ((sigma_t / sigma_s0) * sample - + (alpha_t * (torch.exp(-h) - 1.0)) * D0 - 0.5 * + (alpha_t * (torch.exp(-h) - 1.0)) * D1) + elif self.config.solver_type == "heun": + x_t = ((sigma_t / sigma_s0) * sample - + (alpha_t * (torch.exp(-h) - 1.0)) * D0 + + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1) + elif self.config.algorithm_type == "dpmsolver": + # See https://arxiv.org/abs/2206.00927 for detailed derivations + if self.config.solver_type == "midpoint": + x_t = ((alpha_t / alpha_s0) * sample - + (sigma_t * (torch.exp(h) - 1.0)) * D0 - 0.5 * + (sigma_t * (torch.exp(h) - 1.0)) * D1) + elif self.config.solver_type == "heun": + x_t = ((alpha_t / alpha_s0) * sample - + (sigma_t * (torch.exp(h) - 1.0)) * D0 - + (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1) + elif self.config.algorithm_type == "sde-dpmsolver++": + assert noise is not None + if self.config.solver_type == "midpoint": + x_t = ((sigma_t / sigma_s0 * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + 0.5 * + (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) + elif self.config.solver_type == "heun": + x_t = ((sigma_t / sigma_s0 * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / + (-2.0 * h) + 1.0)) * D1 + + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise) + elif self.config.algorithm_type == "sde-dpmsolver": + assert noise is not None + if self.config.solver_type == "midpoint": + x_t = ((alpha_t / alpha_s0) * sample - 2.0 * + (sigma_t * (torch.exp(h) - 1.0)) * D0 - + (sigma_t * (torch.exp(h) - 1.0)) * D1 + + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) + elif self.config.solver_type == "heun": + x_t = ((alpha_t / alpha_s0) * sample - 2.0 * + (sigma_t * (torch.exp(h) - 1.0)) * D0 - 2.0 * + (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 + + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise) + return x_t # pyright: ignore + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update + def multistep_dpm_solver_third_order_update( + self, + model_output_list: List[torch.Tensor], + *args, + sample: torch.Tensor = None, + **kwargs, + ) -> torch.Tensor: + """ + One step for the third-order multistep DPMSolver. + Args: + model_output_list (`List[torch.Tensor]`): + The direct outputs from learned diffusion model at current and latter timesteps. + sample (`torch.Tensor`): + A current instance of a sample created by diffusion process. + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + + timestep_list = args[0] if len(args) > 0 else kwargs.pop( + "timestep_list", None) + prev_timestep = args[1] if len(args) > 1 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 2: + sample = args[2] + else: + raise ValueError( + " missing`sample` as a required keyward argument") + if timestep_list is not None: + deprecate( + "timestep_list", + "1.0.0", + "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma_t, sigma_s0, sigma_s1, sigma_s2 = ( + self.sigmas[self.step_index + 1], # pyright: ignore + self.sigmas[self.step_index], + self.sigmas[self.step_index - 1], # pyright: ignore + self.sigmas[self.step_index - 2], # pyright: ignore + ) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) + alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) + lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2) + + m0, m1, m2 = model_output_list[-1], model_output_list[ + -2], model_output_list[-3] + + h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 + r0, r1 = h_0 / h, h_1 / h + D0 = m0 + D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) + D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) + D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) + if self.config.algorithm_type == "dpmsolver++": + # See https://arxiv.org/abs/2206.00927 for detailed derivations + x_t = ((sigma_t / sigma_s0) * sample - + (alpha_t * (torch.exp(-h) - 1.0)) * D0 + + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 - + (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2) + elif self.config.algorithm_type == "dpmsolver": + # See https://arxiv.org/abs/2206.00927 for detailed derivations + x_t = ((alpha_t / alpha_s0) * sample - (sigma_t * + (torch.exp(h) - 1.0)) * D0 - + (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 - + (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2) + return x_t # pyright: ignore + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + """ + Initialize the step_index counter for the scheduler. + """ + + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + # Modified from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.step + def step( + self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.Tensor, + generator=None, + variance_noise: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[SchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with + the multistep DPMSolver. + Args: + model_output (`torch.Tensor`): + The direct output from learned diffusion model. + timestep (`int`): + The current discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + generator (`torch.Generator`, *optional*): + A random number generator. + variance_noise (`torch.Tensor`): + Alternative to generating noise with `generator` by directly providing the noise for the variance + itself. Useful for methods such as [`LEdits++`]. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. + Returns: + [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a + tuple is returned where the first element is the sample tensor. + """ + if self.num_inference_steps is None: + raise ValueError( + "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Improve numerical stability for small number of steps + lower_order_final = (self.step_index == len(self.timesteps) - 1) and ( + self.config.euler_at_final or + (self.config.lower_order_final and len(self.timesteps) < 15) or + self.config.final_sigmas_type == "zero") + lower_order_second = ((self.step_index == len(self.timesteps) - 2) and + self.config.lower_order_final and + len(self.timesteps) < 15) + + model_output = self.convert_model_output(model_output, sample=sample) + for i in range(self.config.solver_order - 1): + self.model_outputs[i] = self.model_outputs[i + 1] + self.model_outputs[-1] = model_output + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++" + ] and variance_noise is None: + noise = randn_tensor( + model_output.shape, + generator=generator, + device=model_output.device, + dtype=torch.float32) + elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + noise = variance_noise.to( + device=model_output.device, + dtype=torch.float32) # pyright: ignore + else: + noise = None + + if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: + prev_sample = self.dpm_solver_first_order_update( + model_output, sample=sample, noise=noise) + elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: + prev_sample = self.multistep_dpm_solver_second_order_update( + self.model_outputs, sample=sample, noise=noise) + else: + prev_sample = self.multistep_dpm_solver_third_order_update( + self.model_outputs, sample=sample) + + if self.lower_order_nums < self.config.solver_order: + self.lower_order_nums += 1 + + # Cast sample back to expected dtype + prev_sample = prev_sample.to(model_output.dtype) + + # upon completion increase step index by one + self._step_index += 1 # pyright: ignore + + if not return_dict: + return (prev_sample,) + + return SchedulerOutput(prev_sample=prev_sample) + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input + def scale_model_input(self, sample: torch.Tensor, *args, + **kwargs) -> torch.Tensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + Args: + sample (`torch.Tensor`): + The input sample. + Returns: + `torch.Tensor`: + A scaled input sample. + """ + return sample + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input + def add_noise( + self, + original_samples: torch.Tensor, + noise: torch.Tensor, + timesteps: torch.IntTensor, + ) -> torch.Tensor: + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to( + device=original_samples.device, dtype=original_samples.dtype) + if original_samples.device.type == "mps" and torch.is_floating_point( + timesteps): + # mps does not support float64 + schedule_timesteps = self.timesteps.to( + original_samples.device, dtype=torch.float32) + timesteps = timesteps.to( + original_samples.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(original_samples.device) + timesteps = timesteps.to(original_samples.device) + + # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [ + self.index_for_timestep(t, schedule_timesteps) + for t in timesteps + ] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timesteps.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timesteps.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(original_samples.shape): + sigma = sigma.unsqueeze(-1) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) + noisy_samples = alpha_t * original_samples + sigma_t * noise + return noisy_samples + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype) + + if sample.device.type == "mps" and torch.is_floating_point(timestep): + # mps does not support float64 + schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32) + timestep = timestep.to(sample.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(sample.device) + timestep = timestep.to(sample.device) + + # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timestep.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timestep.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(sample.shape): + sigma = sigma.unsqueeze(-1) + + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def __len__(self): + return self.config.num_train_timesteps diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/fm_solvers_unipc.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/fm_solvers_unipc.py new file mode 100644 index 0000000000000000000000000000000000000000..14e854fe20cde1ad567e7398436283ae7b24add5 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/fm_solvers_unipc.py @@ -0,0 +1,884 @@ +# Copied from https://github.com/huggingface/diffusers/blob/v0.31.0/src/diffusers/schedulers/scheduling_unipc_multistep.py +# Convert unipc for flow matching +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import math +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import (KarrasDiffusionSchedulers, + SchedulerMixin, + SchedulerOutput) +from diffusers.utils import deprecate, is_scipy_available + +if is_scipy_available(): + import scipy.stats + + +class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin): + """ + `UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + solver_order (`int`, default `2`): + The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1` + due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for + unconditional sampling. + prediction_type (`str`, defaults to "flow_prediction"): + Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts + the flow of the diffusion process. + thresholding (`bool`, defaults to `False`): + Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such + as Stable Diffusion. + dynamic_thresholding_ratio (`float`, defaults to 0.995): + The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. + sample_max_value (`float`, defaults to 1.0): + The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`. + predict_x0 (`bool`, defaults to `True`): + Whether to use the updating algorithm on the predicted x0. + solver_type (`str`, default `bh2`): + Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2` + otherwise. + lower_order_final (`bool`, default `True`): + Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can + stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. + disable_corrector (`list`, default `[]`): + Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)` + and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is + usually disabled during the first few steps. + solver_p (`SchedulerMixin`, default `None`): + Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`. + use_karras_sigmas (`bool`, *optional*, defaults to `False`): + Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, + the sigmas are determined according to a sequence of noise levels {σi}. + use_exponential_sigmas (`bool`, *optional*, defaults to `False`): + Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + steps_offset (`int`, defaults to 0): + An offset added to the inference steps, as required by some model families. + final_sigmas_type (`str`, defaults to `"zero"`): + The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final + sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. + """ + + _compatibles = [e.name for e in KarrasDiffusionSchedulers] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + solver_order: int = 2, + prediction_type: str = "flow_prediction", + shift: Optional[float] = 1.0, + use_dynamic_shifting=False, + thresholding: bool = False, + dynamic_thresholding_ratio: float = 0.995, + sample_max_value: float = 1.0, + predict_x0: bool = True, + solver_type: str = "bh2", + lower_order_final: bool = True, + disable_corrector: List[int] = [], + solver_p: SchedulerMixin = None, + timestep_spacing: str = "linspace", + steps_offset: int = 0, + final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" + ): + + if solver_type not in ["bh1", "bh2"]: + if solver_type in ["midpoint", "heun", "logrho"]: + self.register_to_config(solver_type="bh2") + else: + raise NotImplementedError( + f"{solver_type} is not implemented for {self.__class__}") + + self.predict_x0 = predict_x0 + # setable values + self.num_inference_steps = None + alphas = np.linspace(1, 1 / num_train_timesteps, + num_train_timesteps)[::-1].copy() + sigmas = 1.0 - alphas + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32) + + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + self.sigmas = sigmas + self.timesteps = sigmas * num_train_timesteps + + self.model_outputs = [None] * solver_order + self.timestep_list = [None] * solver_order + self.lower_order_nums = 0 + self.disable_corrector = disable_corrector + self.solver_p = solver_p + self.last_sample = None + self._step_index = None + self._begin_index = None + + self.sigmas = self.sigmas.to( + "cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + # Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps + def set_timesteps( + self, + num_inference_steps: Union[int, None] = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[Union[float, None]] = None, + shift: Optional[Union[float, None]] = None, + use_beta_sigmas: Optional[bool] = False, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + Args: + num_inference_steps (`int`): + Total number of the spacing of the time steps. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + + if self.config.use_dynamic_shifting and mu is None: + raise ValueError( + " you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`" + ) + + if sigmas is None: + sigmas = np.linspace(self.sigma_max, self.sigma_min, + num_inference_steps + + 1).copy()[:-1] # pyright: ignore + + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore + else: + if shift is None: + shift = self.config.shift + sigmas = shift * sigmas / (1 + + (shift - 1) * sigmas) # pyright: ignore + + if use_beta_sigmas: + sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + + if self.config.final_sigmas_type == "sigma_min": + sigma_last = ((1 - self.alphas_cumprod[0]) / + self.alphas_cumprod[0])**0.5 + elif self.config.final_sigmas_type == "zero": + sigma_last = 0 + else: + raise ValueError( + f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}" + ) + + timesteps = sigmas * self.config.num_train_timesteps + sigmas = np.concatenate([sigmas, [sigma_last] + ]).astype(np.float32) # pyright: ignore + + self.sigmas = torch.from_numpy(sigmas) + self.timesteps = torch.from_numpy(timesteps).to( + device=device, dtype=torch.int64) + + self.num_inference_steps = len(timesteps) + + self.model_outputs = [ + None, + ] * self.config.solver_order + self.lower_order_nums = 0 + self.last_sample = None + if self.solver_p: + self.solver_p.set_timesteps(self.num_inference_steps, device=device) + + # add an index counter for schedulers that allow duplicated timesteps + self._step_index = None + self._begin_index = None + self.sigmas = self.sigmas.to( + "cpu") # to avoid too much CPU/GPU communication + + def _convert_to_beta( + self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6 + ) -> torch.Tensor: + import scipy + """From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + sigmas = np.array( + [ + sigma_min + (ppf * (sigma_max - sigma_min)) + for ppf in [ + scipy.stats.beta.ppf(timestep, alpha, beta) + for timestep in 1 - np.linspace(0, 1, num_inference_steps) + ] + ] + ) + return sigmas + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample + def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor: + """ + "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the + prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by + s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing + pixels from saturation at each step. We find that dynamic thresholding results in significantly better + photorealism as well as better image-text alignment, especially when using very large guidance weights." + + https://arxiv.org/abs/2205.11487 + """ + dtype = sample.dtype + batch_size, channels, *remaining_dims = sample.shape + + if dtype not in (torch.float32, torch.float64): + sample = sample.float( + ) # upcast for quantile calculation, and clamp not implemented for cpu half + + # Flatten sample for doing quantile calculation along each image + sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) + + abs_sample = sample.abs() # "a certain percentile absolute pixel value" + + s = torch.quantile( + abs_sample, self.config.dynamic_thresholding_ratio, dim=1) + s = torch.clamp( + s, min=1, max=self.config.sample_max_value + ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] + s = s.unsqueeze( + 1) # (batch_size, 1) because clamp will broadcast along dim=0 + sample = torch.clamp( + sample, -s, s + ) / s # "we threshold xt0 to the range [-s, s] and then divide by s" + + sample = sample.reshape(batch_size, channels, *remaining_dims) + sample = sample.to(dtype) + + return sample + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def _sigma_to_alpha_sigma_t(self, sigma): + return 1 - sigma, sigma + + # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma) + + def convert_model_output( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + **kwargs, + ) -> torch.Tensor: + r""" + Convert the model output to the corresponding type the UniPC algorithm needs. + + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model. + timestep (`int`): + The current discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + + Returns: + `torch.Tensor`: + The converted model output. + """ + timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) + if sample is None: + if len(args) > 1: + sample = args[1] + else: + raise ValueError( + "missing `sample` as a required keyward argument") + if timestep is not None: + deprecate( + "timesteps", + "1.0.0", + "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + sigma = self.sigmas[self.step_index] + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) + + if self.predict_x0: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler." + ) + + if self.config.thresholding: + x0_pred = self._threshold_sample(x0_pred) + + return x0_pred + else: + if self.config.prediction_type == "flow_prediction": + sigma_t = self.sigmas[self.step_index] + epsilon = sample - (1 - sigma_t) * model_output + else: + raise ValueError( + f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`," + " `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler." + ) + + if self.config.thresholding: + sigma_t = self.sigmas[self.step_index] + x0_pred = sample - sigma_t * model_output + x0_pred = self._threshold_sample(x0_pred) + epsilon = model_output + x0_pred + + return epsilon + + def multistep_uni_p_bh_update( + self, + model_output: torch.Tensor, + *args, + sample: torch.Tensor = None, + order: int = None, # pyright: ignore + **kwargs, + ) -> torch.Tensor: + """ + One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. + + Args: + model_output (`torch.Tensor`): + The direct output from the learned diffusion model at the current timestep. + prev_timestep (`int`): + The previous discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + order (`int`): + The order of UniP at this timestep (corresponds to the *p* in UniPC-p). + + Returns: + `torch.Tensor`: + The sample tensor at the previous timestep. + """ + prev_timestep = args[0] if len(args) > 0 else kwargs.pop( + "prev_timestep", None) + if sample is None: + if len(args) > 1: + sample = args[1] + else: + raise ValueError( + " missing `sample` as a required keyward argument") + if order is None: + if len(args) > 2: + order = args[2] + else: + raise ValueError( + " missing `order` as a required keyward argument") + if prev_timestep is not None: + deprecate( + "prev_timestep", + "1.0.0", + "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + model_output_list = self.model_outputs + + s0 = self.timestep_list[-1] + m0 = model_output_list[-1] + x = sample + + if self.solver_p: + x_t = self.solver_p.step(model_output, s0, x).prev_sample + return x_t + + sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[ + self.step_index] # pyright: ignore + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + + h = lambda_t - lambda_s0 + device = sample.device + + rks = [] + D1s = [] + for i in range(1, order): + si = self.step_index - i # pyright: ignore + mi = model_output_list[-(i + 1)] + alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si]) + lambda_si = torch.log(alpha_si) - torch.log(sigma_si) + rk = (lambda_si - lambda_s0) / h + rks.append(rk) + D1s.append((mi - m0) / rk) # pyright: ignore + + rks.append(1.0) + rks = torch.tensor(rks, device=device) + + R = [] + b = [] + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.config.solver_type == "bh1": + B_h = hh + elif self.config.solver_type == "bh2": + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= i + 1 + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=device) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], + b[:-1]).to(device).to(x.dtype) + else: + D1s = None + + if self.predict_x0: + x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 + if D1s is not None: + pred_res = torch.einsum("k,bkc...->bc...", rhos_p, + D1s) # pyright: ignore + else: + pred_res = 0 + x_t = x_t_ - alpha_t * B_h * pred_res + else: + x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 + if D1s is not None: + pred_res = torch.einsum("k,bkc...->bc...", rhos_p, + D1s) # pyright: ignore + else: + pred_res = 0 + x_t = x_t_ - sigma_t * B_h * pred_res + + x_t = x_t.to(x.dtype) + return x_t + + def multistep_uni_c_bh_update( + self, + this_model_output: torch.Tensor, + *args, + last_sample: torch.Tensor = None, + this_sample: torch.Tensor = None, + order: int = None, # pyright: ignore + **kwargs, + ) -> torch.Tensor: + """ + One step for the UniC (B(h) version). + + Args: + this_model_output (`torch.Tensor`): + The model outputs at `x_t`. + this_timestep (`int`): + The current timestep `t`. + last_sample (`torch.Tensor`): + The generated sample before the last predictor `x_{t-1}`. + this_sample (`torch.Tensor`): + The generated sample after the last predictor `x_{t}`. + order (`int`): + The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`. + + Returns: + `torch.Tensor`: + The corrected sample tensor at the current timestep. + """ + this_timestep = args[0] if len(args) > 0 else kwargs.pop( + "this_timestep", None) + if last_sample is None: + if len(args) > 1: + last_sample = args[1] + else: + raise ValueError( + " missing`last_sample` as a required keyward argument") + if this_sample is None: + if len(args) > 2: + this_sample = args[2] + else: + raise ValueError( + " missing`this_sample` as a required keyward argument") + if order is None: + if len(args) > 3: + order = args[3] + else: + raise ValueError( + " missing`order` as a required keyward argument") + if this_timestep is not None: + deprecate( + "this_timestep", + "1.0.0", + "Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", + ) + + model_output_list = self.model_outputs + + m0 = model_output_list[-1] + x = last_sample + x_t = this_sample + model_t = this_model_output + + sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[ + self.step_index - 1] # pyright: ignore + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + + h = lambda_t - lambda_s0 + device = this_sample.device + + rks = [] + D1s = [] + for i in range(1, order): + si = self.step_index - (i + 1) # pyright: ignore + mi = model_output_list[-(i + 1)] + alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si]) + lambda_si = torch.log(alpha_si) - torch.log(sigma_si) + rk = (lambda_si - lambda_s0) / h + rks.append(rk) + D1s.append((mi - m0) / rk) # pyright: ignore + + rks.append(1.0) + rks = torch.tensor(rks, device=device) + + R = [] + b = [] + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.config.solver_type == "bh1": + B_h = hh + elif self.config.solver_type == "bh2": + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= i + 1 + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=device) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) + else: + D1s = None + + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device) + else: + rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype) + + if self.predict_x0: + x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0 + if D1s is not None: + corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = model_t - m0 + x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0 + if D1s is not None: + corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = model_t - m0 + x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t) + x_t = x_t.to(x.dtype) + return x_t + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index + def _init_step_index(self, timestep): + """ + Initialize the step_index counter for the scheduler. + """ + + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step(self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.Tensor, + return_dict: bool = True, + generator=None) -> Union[SchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with + the multistep UniPC. + + Args: + model_output (`torch.Tensor`): + The direct output from learned diffusion model. + timestep (`int`): + The current discrete timestep in the diffusion chain. + sample (`torch.Tensor`): + A current instance of a sample created by the diffusion process. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. + + Returns: + [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a + tuple is returned where the first element is the sample tensor. + + """ + if self.num_inference_steps is None: + raise ValueError( + "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" + ) + + if self.step_index is None: + self._init_step_index(timestep) + + use_corrector = ( + self.step_index > 0 and + self.step_index - 1 not in self.disable_corrector and + self.last_sample is not None # pyright: ignore + ) + + model_output_convert = self.convert_model_output( + model_output, sample=sample) + if use_corrector: + sample = self.multistep_uni_c_bh_update( + this_model_output=model_output_convert, + last_sample=self.last_sample, + this_sample=sample, + order=self.this_order, + ) + + for i in range(self.config.solver_order - 1): + self.model_outputs[i] = self.model_outputs[i + 1] + self.timestep_list[i] = self.timestep_list[i + 1] + + self.model_outputs[-1] = model_output_convert + self.timestep_list[-1] = timestep # pyright: ignore + + if self.config.lower_order_final: + this_order = max(1, min(self.config.solver_order, + len(self.timesteps) - + self.step_index)) # pyright: ignore + else: + this_order = self.config.solver_order + + self.this_order = min(this_order, + self.lower_order_nums + 1) # warmup for multistep + assert self.this_order > 0 + + self.last_sample = sample + prev_sample = self.multistep_uni_p_bh_update( + model_output=model_output, # pass the original non-converted model output, in case solver-p is used + sample=sample, + order=self.this_order, + ) + + if self.lower_order_nums < self.config.solver_order: + self.lower_order_nums += 1 + + # upon completion increase step index by one + self._step_index += 1 # pyright: ignore + + if not return_dict: + return (prev_sample,) + + return SchedulerOutput(prev_sample=prev_sample) + + def scale_model_input(self, sample: torch.Tensor, *args, + **kwargs) -> torch.Tensor: + """ + Ensures interchangeability with schedulers that need to scale the denoising model input depending on the + current timestep. + + Args: + sample (`torch.Tensor`): + The input sample. + + Returns: + `torch.Tensor`: + A scaled input sample. + """ + return sample + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise + def add_noise( + self, + original_samples: torch.Tensor, + noise: torch.Tensor, + timesteps: torch.IntTensor, + ) -> torch.Tensor: + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to( + device=original_samples.device, dtype=original_samples.dtype) + if original_samples.device.type == "mps" and torch.is_floating_point( + timesteps): + # mps does not support float64 + schedule_timesteps = self.timesteps.to( + original_samples.device, dtype=torch.float32) + timesteps = timesteps.to( + original_samples.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(original_samples.device) + timesteps = timesteps.to(original_samples.device) + + # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [ + self.index_for_timestep(t, schedule_timesteps) + for t in timesteps + ] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timesteps.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timesteps.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(original_samples.shape): + sigma = sigma.unsqueeze(-1) + + alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) + noisy_samples = alpha_t * original_samples + sigma_t * noise + return noisy_samples + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype) + + if sample.device.type == "mps" and torch.is_floating_point(timestep): + # mps does not support float64 + schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32) + timestep = timestep.to(sample.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(sample.device) + timestep = timestep.to(sample.device) + + # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timestep.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timestep.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(sample.shape): + sigma = sigma.unsqueeze(-1) + + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def __len__(self): + return self.config.num_train_timesteps diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/scheduling_flow_match_lcm.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/scheduling_flow_match_lcm.py new file mode 100644 index 0000000000000000000000000000000000000000..b3f33867f13bd2999a0987c9aa41ed3ae97a2b3f --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/utils/scheduling_flow_match_lcm.py @@ -0,0 +1,587 @@ +# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.utils import BaseOutput, is_scipy_available, logging +from diffusers.utils.torch_utils import randn_tensor +from diffusers.schedulers.scheduling_utils import SchedulerMixin + +if is_scipy_available(): + import scipy.stats + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class FlowMatchLCMSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchLCMScheduler(SchedulerMixin, ConfigMixin): + """ + LCM scheduler for Flow Matching. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + use_dynamic_shifting (`bool`, defaults to False): + Whether to apply timestep shifting on-the-fly based on the image resolution. + base_shift (`float`, defaults to 0.5): + Value to stabilize image generation. Increasing `base_shift` reduces variation and image is more consistent + with desired output. + max_shift (`float`, defaults to 1.15): + Value change allowed to latent vectors. Increasing `max_shift` encourages more variation and image may be + more exaggerated or stylized. + base_image_seq_len (`int`, defaults to 256): + The base image sequence length. + max_image_seq_len (`int`, defaults to 4096): + The maximum image sequence length. + invert_sigmas (`bool`, defaults to False): + Whether to invert the sigmas. + shift_terminal (`float`, defaults to None): + The end value of the shifted timestep schedule. + use_karras_sigmas (`bool`, defaults to False): + Whether to use Karras sigmas for step sizes in the noise schedule during sampling. + use_exponential_sigmas (`bool`, defaults to False): + Whether to use exponential sigmas for step sizes in the noise schedule during sampling. + use_beta_sigmas (`bool`, defaults to False): + Whether to use beta sigmas for step sizes in the noise schedule during sampling. + time_shift_type (`str`, defaults to "exponential"): + The type of dynamic resolution-dependent timestep shifting to apply. Either "exponential" or "linear". + scale_factors ('list', defaults to None) + It defines how to scale the latents at which predictions are made. + upscale_mode ('str', defaults to 'bicubic') + Upscaling method, applied if scale-wise generation is considered + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + use_dynamic_shifting: bool = False, + base_shift: Optional[float] = 0.5, + max_shift: Optional[float] = 1.15, + base_image_seq_len: Optional[int] = 256, + max_image_seq_len: Optional[int] = 4096, + invert_sigmas: bool = False, + shift_terminal: Optional[float] = None, + use_karras_sigmas: Optional[bool] = False, + use_exponential_sigmas: Optional[bool] = False, + use_beta_sigmas: Optional[bool] = False, + time_shift_type: str = "exponential", + scale_factors: Optional[List[float]] = None, + upscale_mode: Optional[str] = 'bicubic' + ): + if self.config.use_beta_sigmas and not is_scipy_available(): + raise ImportError("Make sure to install scipy if you want to use beta sigmas.") + if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1: + raise ValueError( + "Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used." + ) + if time_shift_type not in {"exponential", "linear"}: + raise ValueError("`time_shift_type` must either be 'exponential' or 'linear'.") + + timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy() + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) + + sigmas = timesteps / num_train_timesteps + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) + + self.timesteps = sigmas * num_train_timesteps + + self._step_index = None + self._begin_index = None + + self._shift = shift + + self._init_size = None + self._scale_factors = scale_factors + self._upscale_mode = upscale_mode + + self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def shift(self): + """ + The value used for shifting. + """ + return self._shift + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def set_shift(self, shift: float): + self._shift = shift + + def set_scale_factors(self, scale_factors: list, upscale_mode): + """ + Sets scale factors for a scale-wise generation regime. + + Args: + scale_factors (`list`): + The scale factors for each step + upscale_mode (`str`): + Upscaling method + """ + self._scale_factors = scale_factors + self._upscale_mode = upscale_mode + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype) + + if sample.device.type == "mps" and torch.is_floating_point(timestep): + # mps does not support float64 + schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32) + timestep = timestep.to(sample.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(sample.device) + timestep = timestep.to(sample.device) + + # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timestep.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timestep.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(sample.shape): + sigma = sigma.unsqueeze(-1) + + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + if self.config.time_shift_type == "exponential": + return self._time_shift_exponential(mu, sigma, t) + elif self.config.time_shift_type == "linear": + return self._time_shift_linear(mu, sigma, t) + + def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor: + r""" + Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_terminal` config + value. + + Reference: + https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51 + + Args: + t (`torch.Tensor`): + A tensor of timesteps to be stretched and shifted. + + Returns: + `torch.Tensor`: + A tensor of adjusted timesteps such that the final value equals `self.config.shift_terminal`. + """ + one_minus_z = 1 - t + scale_factor = one_minus_z[-1] / (1 - self.config.shift_terminal) + stretched_t = 1 - (one_minus_z / scale_factor) + return stretched_t + + def set_timesteps( + self, + num_inference_steps: Optional[int] = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[float] = None, + timesteps: Optional[List[float]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`, *optional*): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + sigmas (`List[float]`, *optional*): + Custom values for sigmas to be used for each diffusion step. If `None`, the sigmas are computed + automatically. + mu (`float`, *optional*): + Determines the amount of shifting applied to sigmas when performing resolution-dependent timestep + shifting. + timesteps (`List[float]`, *optional*): + Custom values for timesteps to be used for each diffusion step. If `None`, the timesteps are computed + automatically. + """ + if self.config.use_dynamic_shifting and mu is None: + raise ValueError("`mu` must be passed when `use_dynamic_shifting` is set to be `True`") + + if sigmas is not None and timesteps is not None: + if len(sigmas) != len(timesteps): + raise ValueError("`sigmas` and `timesteps` should have the same length") + + if num_inference_steps is not None: + if (sigmas is not None and len(sigmas) != num_inference_steps) or ( + timesteps is not None and len(timesteps) != num_inference_steps + ): + raise ValueError( + "`sigmas` and `timesteps` should have the same length as num_inference_steps, if `num_inference_steps` is provided" + ) + else: + num_inference_steps = len(sigmas) if sigmas is not None else len(timesteps) + + self.num_inference_steps = num_inference_steps + + # 1. Prepare default sigmas + is_timesteps_provided = timesteps is not None + + if is_timesteps_provided: + timesteps = np.array(timesteps).astype(np.float32) + + if sigmas is None: + if timesteps is None: + timesteps = np.linspace( + self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps + ) + sigmas = timesteps / self.config.num_train_timesteps + else: + sigmas = np.array(sigmas).astype(np.float32) + num_inference_steps = len(sigmas) + + # 2. Perform timestep shifting. Either no shifting is applied, or resolution-dependent shifting of + # "exponential" or "linear" type is applied + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) + else: + sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas) + + # 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value + if self.config.shift_terminal: + sigmas = self.stretch_shift_to_terminal(sigmas) + + # 4. If required, convert sigmas to one of karras, exponential, or beta sigma schedules + if self.config.use_karras_sigmas: + sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + elif self.config.use_exponential_sigmas: + sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + elif self.config.use_beta_sigmas: + sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps) + + # 5. Convert sigmas and timesteps to tensors and move to specified device + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) + if not is_timesteps_provided: + timesteps = sigmas * self.config.num_train_timesteps + else: + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32, device=device) + + # 6. Append the terminal sigma value. + # If a model requires inverted sigma schedule for denoising but timesteps without inversion, the + # `invert_sigmas` flag can be set to `True`. This case is only required in Mochi + if self.config.invert_sigmas: + sigmas = 1.0 - sigmas + timesteps = sigmas * self.config.num_train_timesteps + sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)]) + else: + sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) + + self.timesteps = timesteps + self.sigmas = sigmas + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + s_churn: float = 0.0, + s_tmin: float = 0.0, + s_tmax: float = float("inf"), + s_noise: float = 1.0, + generator: Optional[torch.Generator] = None, + per_token_timesteps: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[FlowMatchLCMSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + s_churn (`float`): + s_tmin (`float`): + s_tmax (`float`): + s_noise (`float`, defaults to 1.0): + Scaling factor for noise added to the sample. + generator (`torch.Generator`, *optional*): + A random number generator. + per_token_timesteps (`torch.Tensor`, *optional*): + The timesteps for each token in the sample. + return_dict (`bool`): + Whether or not to return a + [`~schedulers.scheduling_flow_match_lcm.FlowMatchLCMSchedulerOutput`] or tuple. + + Returns: + [`~schedulers.scheduling_flow_match_lcm.FlowMatchLCMSchedulerOutput`] or `tuple`: + If return_dict is `True`, + [`~schedulers.scheduling_flow_match_lcm.FlowMatchLCMSchedulerOutput`] is returned, + otherwise a tuple is returned where the first element is the sample tensor. + """ + + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `FlowMatchLCMScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if ( + self._scale_factors + and self._upscale_mode + and len(self.timesteps) != len(self._scale_factors) + 1 + ): + raise ValueError( + "`_scale_factors` should have the same length as `timesteps` - 1, if `_scale_factors` are set." + ) + + if self._init_size is None or self.step_index is None: + self._init_size = model_output.size()[2:] + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + sigma = self.sigmas[self.step_index] + sigma_next = self.sigmas[self.step_index + 1] + x0_pred = (sample - sigma * model_output) + + if self._scale_factors and self._upscale_mode: + if self._step_index < len(self._scale_factors): + size = [ + round(self._scale_factors[self._step_index] * size) + for size in self._init_size + ] + x0_pred = torch.nn.functional.interpolate( + x0_pred, + size=size, + mode=self._upscale_mode + ) + + noise = randn_tensor( + x0_pred.shape, generator=generator, device=x0_pred.device, dtype=x0_pred.dtype + ) + prev_sample = (1 - sigma_next) * x0_pred + sigma_next * noise + + # upon completion increase step index by one + self._step_index += 1 + if per_token_timesteps is None: + # Cast sample back to model compatible dtype + prev_sample = prev_sample.to(model_output.dtype) + + if not return_dict: + return (prev_sample,) + + return FlowMatchLCMSchedulerOutput(prev_sample=prev_sample) + + # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras + def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor: + """Constructs the noise schedule of Karras et al. (2022).""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + rho = 7.0 # 7.0 is the value used in the paper + ramp = np.linspace(0, 1, num_inference_steps) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return sigmas + + # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_exponential + def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor: + """Constructs an exponential noise schedule.""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + sigmas = np.exp(np.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps)) + return sigmas + + # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_beta + def _convert_to_beta( + self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6 + ) -> torch.Tensor: + """From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)""" + + # Hack to make sure that other schedulers which copy this function don't break + # TODO: Add this logic to the other schedulers + if hasattr(self.config, "sigma_min"): + sigma_min = self.config.sigma_min + else: + sigma_min = None + + if hasattr(self.config, "sigma_max"): + sigma_max = self.config.sigma_max + else: + sigma_max = None + + sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() + sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item() + + sigmas = np.array( + [ + sigma_min + (ppf * (sigma_max - sigma_min)) + for ppf in [ + scipy.stats.beta.ppf(timestep, alpha, beta) + for timestep in 1 - np.linspace(0, 1, num_inference_steps) + ] + ] + ) + return sigmas + + def _time_shift_exponential(self, mu, sigma, t): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + def _time_shift_linear(self, mu, sigma, t): + return mu / (mu + (1 / t - 1) ** sigma) + + def __len__(self): + return self.config.num_train_timesteps \ No newline at end of file diff --git a/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/wan_video_vae.py b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/wan_video_vae.py new file mode 100644 index 0000000000000000000000000000000000000000..6f7809c182bc70bc202383742e0592cce6780959 --- /dev/null +++ b/custom_nodes/ComfyUI_WanVideoWrapper/wanvideo/wan_video_vae.py @@ -0,0 +1,906 @@ +from einops import rearrange, repeat + +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +from comfy.utils import ProgressBar + +CACHE_T = 2 + + +def check_is_instance(model, module_class): + if isinstance(model, module_class): + return True + if hasattr(model, "module") and isinstance(model.module, module_class): + return True + return False + + +def block_causal_mask(x, block_size): + # params + b, n, s, _, device = *x.size(), x.device + assert s % block_size == 0 + num_blocks = s // block_size + + # build mask + mask = torch.zeros(b, n, s, s, dtype=torch.bool, device=device) + for i in range(num_blocks): + mask[:, :, + i * block_size:(i + 1) * block_size, :(i + 1) * block_size] = 1 + return mask + + +class CausalConv3d(nn.Conv3d): + """ + Causal 3d convolusion. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._padding = (self.padding[2], self.padding[2], self.padding[1], + self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + + def forward(self, x, cache_x=None): + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + x = F.pad(x, padding) + + return super().forward(x) + + +class RMS_norm(nn.Module): + + def __init__(self, dim, channel_first=True, images=True, bias=False): + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. + + def forward(self, x): + return F.normalize( + x, dim=(1 if self.channel_first else + -1)) * self.scale * self.gamma + self.bias + + +class Upsample(nn.Upsample): + + def forward(self, x): + """ + Fix bfloat16 support for nearest neighbor interpolation. + """ + return super().forward(x.float()).type_as(x) + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', + 'downsample3d') + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == 'upsample2d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + elif mode == 'upsample3d': + self.resample = nn.Sequential( + Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Conv2d(dim, dim // 2, 3, padding=1)) + self.time_conv = CausalConv3d(dim, + dim * 2, (3, 1, 1), + padding=(1, 0, 0)) + + elif mode == 'downsample2d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == 'downsample3d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + nn.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d(dim, + dim, (3, 1, 1), + stride=(2, 1, 1), + padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == 'upsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = 'Rep' + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] != 'Rep': + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] == 'Rep': + cache_x = torch.cat([ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2) + if feat_cache[idx] == 'Rep': + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.resample(x) + x = rearrange(x, '(b t) c h w -> b c t h w', t=t) + + if self.mode == 'downsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + cache_x = x[:, :, -1:, :, :].clone() + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + def init_weight(self, conv): + conv_weight = conv.weight + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + one_matrix = torch.eye(c1, c2) + init_matrix = one_matrix + nn.init.zeros_(conv_weight) + conv_weight.data[:, :, 1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + def init_weight2(self, conv): + conv_weight = conv.weight.data + nn.init.zeros_(conv_weight) + c1, c2, t, h, w = conv_weight.size() + init_matrix = torch.eye(c1 // 2, c2) + conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix + conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix + conv.weight.data.copy_(conv_weight) + nn.init.zeros_(conv.bias.data) + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1)) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ + if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + h = self.shortcut(x) + for layer in self.residual: + if check_is_instance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + h + + +class AttentionBlock(nn.Module): + """ + Causal self-attention with a single head. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = RMS_norm(dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + self.proj = nn.Conv2d(dim, dim, 1) + + # zero out the last layer params + nn.init.zeros_(self.proj.weight) + + def forward(self, x): + identity = x + b, c, t, h, w = x.size() + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.norm(x) + # compute query, key, value + q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute( + 0, 1, 3, 2).contiguous().chunk(3, dim=-1) + + # apply attention + x = F.scaled_dot_product_attention( + q, + k, + v, + #attn_mask=block_causal_mask(q, block_size=h * w) + ) + x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w) + + # output + x = self.proj(x) + x = rearrange(x, '(b t) c h w-> b c t h w', t=t) + return x + identity + + +class Encoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + downsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = 'downsample3d' if temperal_downsample[ + i] else 'downsample2d' + downsamples.append(Resample(out_dim, mode=mode)) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential(ResidualBlock(out_dim, out_dim, dropout), + AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout)) + + # output blocks + self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if check_is_instance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if check_is_instance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +class Decoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2**(len(dim_mult) - 2) + + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential(ResidualBlock(dims[0], dims[0], dropout), + AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout)) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i == 1 or i == 2 or i == 3: + in_dim = in_dim // 2 + for _ in range(num_res_blocks + 1): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + upsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # upsample block + if i != len(dim_mult) - 1: + mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' + upsamples.append(Resample(out_dim, mode=mode)) + scale *= 2.0 + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, 3, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## middle + for layer in self.middle: + if check_is_instance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if check_is_instance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if check_is_instance(m, CausalConv3d): + count += 1 + return count + + +class VideoVAE_(nn.Module): + + def __init__(self, + dim=96, + z_dim=16, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[False, True, True], + dropout=0.0,): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, + attn_scales, self.temperal_downsample, dropout) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, + attn_scales, self.temperal_upsample, dropout) + + def forward(self, x): + mu, log_var = self.encode(x) + z = self.reparameterize(mu, log_var) + x_recon = self.decode(z) + return x_recon, mu, log_var + + + #modification originally by @raindrop313 https://github.com/raindrop313/ComfyUI-WanVideoStartEndFrames + def encode_2(self, x, scale): + self.clear_cache() + ## cache + t = x.shape[2] + iter_ = 2 + (t - 2) // 4 + + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder(x[:, :, :1, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + elif i== iter_-1: + out_ = self.encoder(x[:, :, -1:, :, :], + feat_cache=[None] * self._enc_conv_num, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + else: + out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + out_head = out[:, :, :iter_ - 1, :, :] + out_tail = out[:, :, -1, :, :].unsqueeze(2) + mu, log_var = torch.cat([self.conv1(out_head), self.conv1(out_tail)], dim=2).chunk(2, dim=1) + if isinstance(scale[0], torch.Tensor): + scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale] + mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( + 1, self.z_dim, 1, 1, 1) + else: + scale = scale.to(dtype=mu.dtype, device=mu.device) + mu = (mu - scale[0]) * scale[1] + return mu + + + def encode(self, x, scale): + self.clear_cache() + ## cache + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + + for i in range(iter_): + self._enc_conv_idx = [0] + if i == 0: + out = self.encoder(x[:, :, :1, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + else: + out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=self._enc_feat_map, + feat_idx=self._enc_conv_idx) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + if isinstance(scale[0], torch.Tensor): + scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale] + mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view( + 1, self.z_dim, 1, 1, 1) + else: + scale = scale.to(dtype=mu.dtype, device=mu.device) + mu = (mu - scale[0]) * scale[1] + return mu + + + #modification originally by @raindrop313 https://github.com/raindrop313/ComfyUI-WanVideoStartEndFrames + def decode_2(self, z, scale): + self.clear_cache() + # z: [b,c,t,h,w] + if isinstance(scale[0], torch.Tensor): + scale = [s.to(dtype=z.dtype, device=z.device) for s in scale] + z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( + 1, self.z_dim, 1, 1, 1) + else: + scale = scale.to(dtype=z.dtype, device=z.device) + z = z / scale[1] + scale[0] + iter_ = z.shape[2] + z_head=z[:,:,:-1,:,:] + z_tail=z[:,:,-1,:,:].unsqueeze(2) + x = torch.cat([self.conv2(z_head), self.conv2(z_tail)], dim=2) + for i in range(iter_): + self._conv_idx = [0] + if i == 0: + out = self.decoder(x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + elif i==iter_-1: + out_ = self.decoder(x[:, :, -1, :, :].unsqueeze(2), + feat_cache=None, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) # may add tensor offload + else: + out_ = self.decoder(x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) # may add tensor offload + return out + + + + def decode(self, z, scale): + self.clear_cache() + # z: [b,c,t,h,w] + if isinstance(scale[0], torch.Tensor): + scale = [s.to(dtype=z.dtype, device=z.device) for s in scale] + z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view( + 1, self.z_dim, 1, 1, 1) + else: + scale = scale.to(dtype=z.dtype, device=z.device) + z = z / scale[1] + scale[0] + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + self._conv_idx = [0] + if i == 0: + out = self.decoder(x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + else: + out_ = self.decoder(x[:, :, i:i + 1, :, :], + feat_cache=self._feat_map, + feat_idx=self._conv_idx) + out = torch.cat([out, out_], 2) # may add tensor offload + return out + + def reparameterize(self, mu, log_var): + std = torch.exp(0.5 * log_var) + eps = torch.randn_like(std) + return eps * std + mu + + def sample(self, imgs, deterministic=False): + mu, log_var = self.encode(imgs) + if deterministic: + return mu + std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) + return mu + std * torch.randn_like(std) + + def clear_cache(self): + self._conv_num = count_conv3d(self.decoder) + self._conv_idx = [0] + self._feat_map = [None] * self._conv_num + # cache encode + self._enc_conv_num = count_conv3d(self.encoder) + self._enc_conv_idx = [0] + self._enc_feat_map = [None] * self._enc_conv_num + + +class WanVideoVAE(nn.Module): + + def __init__(self, z_dim=16, dtype=torch.float32): + super().__init__() + + self.dtype = dtype + + mean = [ + -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, + 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 + ] + std = [ + 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, + 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 + ] + self.mean = torch.tensor(mean) + self.std = torch.tensor(std) + self.scale = [self.mean, 1.0 / self.std] + + # init model + self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False) + self.upsampling_factor = 8 + + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if not left_bound: + x[:border_width] = (torch.arange(border_width) + 1) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,)) + return x + + + def build_mask(self, data, is_bound, border_width): + _, _, _, H, W = data.shape + h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0]) + w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1]) + + h = repeat(h, "H -> H W", H=H, W=W) + w = repeat(w, "W -> H W", H=H, W=W) + + mask = torch.stack([h, w]).min(dim=0).values + mask = rearrange(mask, "H W -> 1 1 1 H W") + return mask + + + def tiled_decode(self, hidden_states, device, tile_size, tile_stride): + _, _, T, H, W = hidden_states.shape + size_h, size_w = tile_size + stride_h, stride_w = tile_stride + + # Split tasks + tasks = [] + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + h_, w_ = h + size_h, w + size_w + tasks.append((h, h_, w, w_)) + + data_device = "cpu" + computation_device = device + + out_T = T * 4 - 3 + weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device) + values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device) + + pbar = ProgressBar(len(tasks)) + for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"): + hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.model.decode(hidden_states_batch, self.scale).to(data_device) + + mask = self.build_mask( + hidden_states_batch, + is_bound=(h==0, h_>=H, w==0, w_>=W), + border_width=((size_h - stride_h) * self.upsampling_factor, (size_w - stride_w) * self.upsampling_factor) + ).to(dtype=hidden_states.dtype, device=data_device) + + target_h = h * self.upsampling_factor + target_w = w * self.upsampling_factor + values[ + :, + :, + :, + target_h:target_h + hidden_states_batch.shape[3], + target_w:target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + :, + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + pbar.update(1) + values = values / weight + values = values.float().clamp_(-1, 1) + return values + + + def tiled_encode(self, video, device, tile_size, tile_stride): + _, _, T, H, W = video.shape + + if tile_size is None and tile_stride is None: + size_h, size_w = H //2, W // 2 + stride_h, stride_w = size_h // 2, size_w // 2 + else: + size_h, size_w = tile_size[0] * self.upsampling_factor, tile_size[1] * self.upsampling_factor + stride_h, stride_w = tile_stride[0] * self.upsampling_factor, tile_stride[1] * self.upsampling_factor + + # Split tasks + tasks = [] + for h in range(0, H, stride_h): + if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue + for w in range(0, W, stride_w): + if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue + h_, w_ = h + size_h, w + size_w + tasks.append((h, h_, w, w_)) + + data_device = device + computation_device = device + + out_T = (T + 3) // 4 + weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) + values = torch.zeros((1, 16, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device) + + pbar = ProgressBar(len(tasks)) + for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"): + hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device) + hidden_states_batch = self.model.encode(hidden_states_batch, self.scale).to(data_device) + + mask = self.build_mask( + hidden_states_batch, + is_bound=(h==0, h_>=H, w==0, w_>=W), + border_width=((size_h - stride_h) // self.upsampling_factor, (size_w - stride_w) // self.upsampling_factor) + ).to(dtype=video.dtype, device=data_device) + + target_h = h // self.upsampling_factor + target_w = w // self.upsampling_factor + values[ + :, + :, + :, + target_h:target_h + hidden_states_batch.shape[3], + target_w:target_w + hidden_states_batch.shape[4], + ] += hidden_states_batch * mask + weight[ + :, + :, + :, + target_h: target_h + hidden_states_batch.shape[3], + target_w: target_w + hidden_states_batch.shape[4], + ] += mask + pbar.update(1) + values = values / weight + values = values.float() + return values + + + def single_encode(self, video, device): + video = video.to(device) + x = self.model.encode(video, self.scale) + return x.float() + + + def single_decode(self, hidden_state, device): + hidden_state = hidden_state.to(device) + video = self.model.decode(hidden_state, self.scale) + return video.float().clamp_(-1, 1) + + def double_encode(self, video, device): + print('double_encode') + video = video.to(device) + x = self.model.encode_2(video, self.scale) + return x.float() + + def double_decode(self, hidden_state, device): + print('double_decode') + hidden_state = hidden_state.to(device) + video = self.model.decode_2(hidden_state, self.scale) + return video.float().clamp_(-1, 1) + + def encode(self, videos, device, tiled=False,end_=False, tile_size=None, tile_stride=None): + videos = [video.to("cpu") for video in videos] + hidden_states = [] + for video in videos: + video = video.unsqueeze(0) + if tiled: + hidden_state = self.tiled_encode(video, device, tile_size, tile_stride) + else: + if end_: + hidden_state = self.double_encode(video, device) + else: + hidden_state = self.single_encode(video, device) + hidden_state = hidden_state.squeeze(0) + hidden_states.append(hidden_state) + hidden_states = torch.stack(hidden_states) + return hidden_states + + + def decode(self, hidden_states, device, tiled=False, end_=False, tile_size=(34, 34), tile_stride=(18, 16)): + hidden_states = [hidden_state.to("cpu") for hidden_state in hidden_states] + videos = [] + for hidden_state in hidden_states: + hidden_state = hidden_state.unsqueeze(0) + if tiled: + video = self.tiled_decode(hidden_state, device, tile_size, tile_stride) + else: + if end_: + video = self.double_decode(hidden_state, device) + else: + video = self.single_decode(hidden_state, device) + video = video.squeeze(0) + videos.append(video) + return videos + + + @staticmethod + def state_dict_converter(): + return WanVideoVAEStateDictConverter() + + +class WanVideoVAEStateDictConverter: + + def __init__(self): + pass + + def from_civitai(self, state_dict): + state_dict_ = {} + if 'model_state' in state_dict: + state_dict = state_dict['model_state'] + for name in state_dict: + state_dict_['model.' + name] = state_dict[name] + return state_dict_ \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/.gitattributes b/custom_nodes/comfyui-tensorops/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..eba1110b5794582b53554bb1e4224b860d4e173f --- /dev/null +++ b/custom_nodes/comfyui-tensorops/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/.gitignore b/custom_nodes/comfyui-tensorops/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..6b1cf0c2a96617a9cb254dd7ccece8d1f7d154f0 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +config_.py \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/__init__.py b/custom_nodes/comfyui-tensorops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d998eae307a57377b852fc3fe321f91c8cd61d0 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/__init__.py @@ -0,0 +1,3 @@ +from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/comfyui-tensorops/nodes/__init__.py b/custom_nodes/comfyui-tensorops/nodes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4b2fbf8ca827d5e5451009f82b5110fe1654ac7a --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/__init__.py @@ -0,0 +1,54 @@ +from .channel_select import ChannelSelector +from .mask_image import MaskImage +from .save_surreal import SaveJsonToSurreal, SaveTextToSurreal +from .fetch_surreal import FetchJsonFromSurreal +from .foreground_mask import ForegroundMask +from .save_to_s3 import SaveImageToS3 +from .redis import SaveToRedis, FetchFromRedis +from .fal import FalDifferentialDiffusion, FalDiffusion +from .background_select import BackgroundSelect +from .layer_mask import GetLayerMask +from .stream import SendImageOnWebSocket, SendJsonOnWebSocket +from .separate_mask import SeparateMask +from .face_swap import FaceSwap + +NODE_CLASS_MAPPINGS = { + "ChannelSelector": ChannelSelector, + "MaskImage": MaskImage, + "SaveImageToS3": SaveImageToS3, + "SaveJsonToSurreal": SaveJsonToSurreal, + "SaveTextToSurreal": SaveTextToSurreal, + "FetchJsonFromSurreal": FetchJsonFromSurreal, + "ForegroundMask": ForegroundMask, + "SaveToRedis": SaveToRedis, + "FetchFromRedis": FetchFromRedis, + "FalDifferentialDiffusion": FalDifferentialDiffusion, + "FalDiffusion": FalDiffusion, + "BackgroundSelect": BackgroundSelect, + "GetLayerMask": GetLayerMask, + "SendImageOnWebSocket": SendImageOnWebSocket, + "SendJsonOnWebSocket": SendJsonOnWebSocket, + "SeparateMask": SeparateMask, + "FaceSwap": FaceSwap +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "ChannelSelector":"ChannelSelector", + "MaskImage": "MaskImage", + "SaveImageToS3": "SaveImageToS3", + "SaveJsonToSurreal": "SaveJsonToSurreal", + "SaveTextToSurreal": "SaveTextToSurreal", + "FetchJsonFromSurreal": "FetchJsonFromSurreal", + "ForegroundMask": "ForegroundMask", + "SaveToRedis": "SaveToRedis", + "FetchFromRedis": "FetchFromRedis", + "FalDifferentialDiffusion": "FalDifferentialDiffusion", + "FalDiffusion": "FalDiffusion", + "BackgroundSelect": "BackgroundSelect", + "GetLayerMask": "GetLayerMask", + "SendImageOnWebSocket": "SendImageOnWebSocket", + "SendJsonOnWebSocket": "SendJsonOnWebSocket", + "SeparateMask": "SeparateMask", + "FaceSwap": "FaceSwap" +} diff --git a/custom_nodes/comfyui-tensorops/nodes/background_select.py b/custom_nodes/comfyui-tensorops/nodes/background_select.py new file mode 100644 index 0000000000000000000000000000000000000000..c08c40f9ae02d4755e39a1d038a5c8d01aea83e5 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/background_select.py @@ -0,0 +1,71 @@ +import torch + + +def get_background_mask(tensor: torch.Tensor): + """ + Function to identify the background mask from a batch of masks in a PyTorch tensor. + + Args: + tensor (torch.Tensor): A tensor of shape (B, H, W, 1) where B is the batch size, H is the height, W is the width. + + Returns: + List of masks as torch.Tensor and the background mask as torch.Tensor. + """ + B, H, W = tensor.shape + + # Compute areas of each mask + areas = tensor.sum(dim=(1, 2)) # Shape: (B,) + + # Find the mask with the largest area + largest_idx = torch.argmax(areas) + background_mask = tensor[largest_idx] + + # Identify if the largest mask touches the borders + border_touched = ( + torch.any(background_mask[0, :]) or + torch.any(background_mask[-1, :]) or + torch.any(background_mask[:, 0]) or + torch.any(background_mask[:, -1]) + ) + + # If the largest mask doesn't touch the border, search for another one + if not border_touched: + for i in range(B): + if i != largest_idx: + mask = tensor[i] + border_touched = ( + torch.any(mask[0, :]) or + torch.any(mask[-1, :]) or + torch.any(mask[:, 0]) or + torch.any(mask[:, -1]) + ) + if border_touched: + background_mask = mask + break + + # Reshape the masks to match the original tensor shape + return background_mask + +class BackgroundSelect: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + }, + } + + RETURN_TYPES = ("MASK",) + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, mask: torch.Tensor): + # TODO loop through all masks + # identify the background mask + # return the background mask + background_mask = get_background_mask(mask) + return (background_mask,) + diff --git a/custom_nodes/comfyui-tensorops/nodes/channel_select.py b/custom_nodes/comfyui-tensorops/nodes/channel_select.py new file mode 100644 index 0000000000000000000000000000000000000000..f818b6a34367c057e4dc9ae4b959529699ec53de --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/channel_select.py @@ -0,0 +1,31 @@ +import torch + +class ChannelSelector: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "channel": ("INT", { + "default": 0, + "min": 0, #Minimum value + "max": 100, #Maximum value + "step": 1, #Slider's step + "display": "number" # Cosmetic only: display as "number" or "slider" + }) + }, + } + + RETURN_TYPES = ("IMAGE",) + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, image, channel): + # Select the specified channel and add a new dimension at position 0 + mask = image[channel].unsqueeze(0) + + return (mask,) + diff --git a/custom_nodes/comfyui-tensorops/nodes/config.py b/custom_nodes/comfyui-tensorops/nodes/config.py new file mode 100644 index 0000000000000000000000000000000000000000..0832a0dc8d61d5409b198b9a4d6b2f7a02c84744 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/config.py @@ -0,0 +1,13 @@ +import os +SURREAL_URL = "" +SURREAL_NAMESPACE = "" +SURREAL_USER = "" +SURREAL_PASSWORD = "" +DATALAKE_AWS_ENDPOINT_URL= "" +DATALAKE_AWS_ACCESS_KEY_ID= "" +DATALAKE_AWS_SECRET_ACCESS_KEY= "" +DATALAKE_AWS_REGION= "" +BUCKET = "" +REDIS_URL = "" +os.environ["FAL_KEY"] = "" +os.environ["REPLICATE_API_TOKEN"] = "" diff --git a/custom_nodes/comfyui-tensorops/nodes/face_swap.py b/custom_nodes/comfyui-tensorops/nodes/face_swap.py new file mode 100644 index 0000000000000000000000000000000000000000..a128bd175df83b005dfb63609e5705e81123bc31 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/face_swap.py @@ -0,0 +1,120 @@ +from typing import List +import numpy as np +import torchvision.transforms.functional as F +import PIL.Image +import torch +import replicate +import requests +import io +import random + +def pil_image_to_file(image: PIL.Image.Image) -> io.BytesIO: + + # Convert the Pillow image to a file-like object + image_file = io.BytesIO() + image.save(image_file, format="PNG") # Save image in PNG format or any other format you need + image_id = int(random.random() * 1e15) + image_file.name = f"{image_id}.png" # Optional: Set a name if needed + image_file.seek(0) # Reset the file pointer to the beginning + return image_file + +def get_image_from_url(url: str) -> PIL.Image.Image: + image_crop_bytes_rb = requests.get(url).content + image_crop_rb = PIL.Image.open(io.BytesIO(image_crop_bytes_rb)) + image_crop_rb = image_crop_rb.convert("RGBA") + return image_crop_rb + +def resize_with_aspect_ratio(image, new_width): + # Get original dimensions + original_width, original_height = image.size + + # Calculate the new height to maintain the aspect ratio + aspect_ratio = original_height / original_width + new_height = int(new_width * aspect_ratio) + + # Resize the image with the new width and calculated height + resized_image = image.resize((new_width, new_height), PIL.Image.LANCZOS) + + return resized_image + +class FaceSwap: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "face": ("IMAGE",), + "prompt": ("STRING", {"default": "", "multiline": True}), + "image_bbox": ("BBOX",), + "face_bbox": ("BBOX",), + "steps": ("INT", {"default": 10, "min": 10, "max": 25}), + "face_guidance": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 5.0, "step": 0.01}), + }, + } + + RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE") + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, image: torch.Tensor, face: torch.Tensor, prompt: str, image_bbox: torch.Tensor, face_bbox: torch.Tensor, steps: int, face_guidance: float): + image_bbox_array = sorted(image_bbox, key=lambda box: (box[2]-box[0]) * (box[3]-box[1]))[0] + face_bbox_array = sorted(face_bbox, key=lambda box: (box[2]-box[0]) * (box[3]-box[1]))[0] + print(f"Image bbox: {image_bbox_array}") + print(f"Face bbox: {face_bbox_array}") + full_image_array = image.squeeze(0).cpu().numpy() * 255.0 + full_image_pil = PIL.Image.fromarray(np.clip(full_image_array, 0, 255).astype(np.uint8)) + image_select_box = (int(image_bbox_array[0]*0.925), 0, min(int(image_bbox_array[2]*1.075), full_image_pil.size[0]), min(int(image_bbox_array[3]*1.25), full_image_pil.size[1])) + print(image_select_box) + image_pil = full_image_pil.crop((image_select_box[0], image_select_box[1], image_select_box[2], image_select_box[3])) + image_pil = resize_with_aspect_ratio(image_pil, 768) + face_array = face.squeeze(0).cpu().numpy() * 255.0 + face_pil = PIL.Image.fromarray(np.clip(face_array, 0, 255).astype(np.uint8)) + # input_data = { + # "seed": 42, + # "image": "https://replicate.delivery/pbxt/LrKLR1Mwa8tXAbwgij5vqQA4w9pEuFNJp30yaDGn1qdSOx95/Screenshot%202024-10-25%20at%2011.52.55%E2%80%AFAM.png", + # "model": "omni-zero", + # "prompt": "A person, comic", + # "style_image": "https://replicate.delivery/pbxt/LrKLQXFxHsTWCasb2usAjB6pW5i2lMmWWIhg7idRkpGXcKkg/Screenshot%202024-10-25%20at%201.53.46%E2%80%AFPM.png", + # "depth_strength": 0.5, + # "guidance_scale": 3, + # "identity_image": "https://replicate.delivery/pbxt/LrKLRAvXO8x7LMv8JD0RBDwp00BDy2e0PPbfI36QzpzTl6zw/WhatsApp%20Image%202024-10-25%20at%2013.59.51.jpeg", + # "image_strength": 0.15, + # "style_strength": 1, + # "negative_prompt": "blurry, out of focus", + # "number_of_steps": 10, + # "number_of_images": 1, + # "composition_image": "https://replicate.delivery/pbxt/LrKLQYhbCVjI9MvjgvtqBwB4c0iZrLFUKAkDG7n41kU0q1RJ/Screenshot%202024-10-25%20at%2011.52.55%E2%80%AFAM.png", + # "identity_strength": 1, + # "composition_strength": 1 + # } + output = replicate.run( + "okaris/omni-zero:036947f1e1961875eef47a561293978528bf3a847e79fedb20600c9ad25d0c59", + input={ + "seed": 42, + "image": pil_image_to_file(image_pil), + "model": "omni-zero", + "prompt": prompt, + "style_image": pil_image_to_file(image_pil), + "depth_strength": 0.5, + "guidance_scale": face_guidance, + "identity_image": pil_image_to_file(face_pil), + "image_strength": 0.10, + "style_strength": 1, + "negative_prompt": "blurry, out of focus, realism, photography", + "number_of_images": 1, + "composition_image": pil_image_to_file(image_pil), + "identity_strength": 1, + "number_of_steps": steps, + "composition_strength": 1 + } + ) + print(output) + out_image = get_image_from_url(output[0]) + out_image = out_image.resize((image_select_box[2] - image_select_box[0], image_select_box[3] - image_select_box[1])) + full_image_pil.paste(out_image, (image_select_box[0], image_select_box[1])) + out_image = F.to_tensor(full_image_pil).permute(1, 2, 0).unsqueeze(0) + out_image = torch.cat([out_image], dim=0) + return (out_image, torch.cat([F.to_tensor(image_pil).permute(1, 2, 0).unsqueeze(0)], dim=0), torch.cat([F.to_tensor(face_pil).permute(1, 2, 0).unsqueeze(0)], dim=0)) \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/fal.py b/custom_nodes/comfyui-tensorops/nodes/fal.py new file mode 100644 index 0000000000000000000000000000000000000000..63965de6c6309671c1574579fc594c99aa2f5fd0 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/fal.py @@ -0,0 +1,95 @@ +from PIL import Image +import torch +import requests +from io import BytesIO +import numpy as np +import fal_client + +class FalDiffusion: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ("STRING", {"multiline": False}), + "steps": ("INT",{"default": 2, "min": 1, "max": 8, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "external_tooling" + FUNCTION = "load" + + def load(self, prompt: str, steps: int): + # Fal handler + handler = fal_client.submit( + "fal-ai/flux/schnell", + arguments={ + "prompt": f"{prompt}", + "image_size": "square_hd", + "num_inference_steps": steps, + }, + ) + result = handler.get() + images = [] + for image in result['images']: + url = image['url'] + response = requests.get(url) + img = Image.open(BytesIO(response.content)) + img = np.array(img).astype(np.float32) / 255.0 + img = torch.from_numpy(img)[None,] + images.append(img) + return (torch.cat(images, dim=0),) + + +class FalDifferentialDiffusion: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "foreground_image": ("IMAGE", ), + "depth_image": ("IMAGE", ), + "foreground_prompt": ("STRING", {"multiline": False}), + "background_prompt": ("STRING", {"multiline": False}), + "strength": ("FLOAT",{"default": 1, "min": 0.01, "max": 3, "step": 0.01}), + "steps": ("INT",{"default": 14, "min": 1, "max": 32, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE",) + CATEGORY = "external_tooling" + FUNCTION = "load" + + def load(self, foreground_image: torch.Tensor, depth_image: torch.Tensor, foreground_prompt: str, background_prompt: str, strength: float, steps: int): + # Foreground Image + foreground_image_array = foreground_image.squeeze(0).cpu().numpy() * 255.0 + foreground_image_pil = Image.fromarray(np.clip(foreground_image_array, 0, 255).astype(np.uint8)) + foreground_output = BytesIO() + foreground_image_pil.save(foreground_output, format='PNG') + foreground_url = fal_client.upload(foreground_output.getvalue(), "image/png") + # Depth Image + depth_image_array = depth_image.squeeze(0).cpu().numpy() * 255.0 + depth_image_pil = Image.fromarray(np.clip(depth_image_array, 0, 255).astype(np.uint8)) + depth_output = BytesIO() + depth_image_pil.save(depth_output, format='PNG') + depth_url = fal_client.upload(depth_output.getvalue(), "image/png") + # Fal handler + handler = fal_client.submit( + "fal-ai/flux-differential-diffusion", + arguments={ + "prompt": f"{foreground_prompt}, {background_prompt}, 8k, unreal engine 5, hightly detailed, intricate detailed.", + "image_url": foreground_url, + "change_map_image_url": depth_url, + "strength": strength, + "num_inference_steps": steps, + }, + ) + result = handler.get() + images = [] + for image in result['images']: + url = image['url'] + response = requests.get(url) + img = Image.open(BytesIO(response.content)) + img = np.array(img).astype(np.float32) / 255.0 + img = torch.from_numpy(img)[None,] + images.append(img) + return (torch.cat(images, dim=0),) \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/fetch_surreal.py b/custom_nodes/comfyui-tensorops/nodes/fetch_surreal.py new file mode 100644 index 0000000000000000000000000000000000000000..dcbcd2564d0a7c56bc048dad80ef18ccf33430e6 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/fetch_surreal.py @@ -0,0 +1,30 @@ +from .surreal import surreal_connect + +SURREAL_TABLE = "processor" + +class FetchJsonFromSurreal: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "database": ("STRING", {"multiline": False}), + "id": ("STRING", {"multiline": False}), + "key": ("STRING", {"multiline": False}) + }, + } + + RETURN_TYPES = ("JSON",) + + FUNCTION = "main" + OUTPUT_NODE = True + CATEGORY = "database_ops" + + def main(self, database: str, id: str, key: str): + connection = surreal_connect(database) + query = f"SELECT {key} from {SURREAL_TABLE}:`{id}`" + results = connection.query(query).result + data = results[0][key] + print("results", data) + return [data] + diff --git a/custom_nodes/comfyui-tensorops/nodes/florence.py b/custom_nodes/comfyui-tensorops/nodes/florence.py new file mode 100644 index 0000000000000000000000000000000000000000..407bd9feadf5418fbf253456d1b109b056186f07 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/florence.py @@ -0,0 +1,459 @@ +import torch +import torchvision.transforms.functional as F +import io +import os +from typing import List +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from PIL import Image, ImageDraw, ImageColor, ImageFont +import random +import numpy as np +import re + +#workaround for unnecessary flash_attn requirement +from unittest.mock import patch +from transformers.dynamic_module_utils import get_imports + +def fixed_get_imports(filename: str | os.PathLike) -> list[str]: + if not str(filename).endswith("modeling_florence2.py"): + return get_imports(filename) + imports = get_imports(filename) + imports.remove("flash_attn") + return imports + + +import comfy.model_management as mm +from comfy.utils import ProgressBar +import folder_paths + +script_directory = os.path.dirname(os.path.abspath(__file__)) + +from transformers import AutoModelForCausalLM, AutoProcessor + +class DownloadAndLoadFlorence2Model: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ( + [ + 'microsoft/Florence-2-base', + 'microsoft/Florence-2-base-ft', + 'microsoft/Florence-2-large', + 'microsoft/Florence-2-large-ft', + 'HuggingFaceM4/Florence-2-DocVQA' + ], + { + "default": 'microsoft/Florence-2-base' + }), + "precision": ([ 'fp16','bf16','fp32'], + { + "default": 'fp16' + }), + "attention": ( + [ 'flash_attention_2', 'sdpa', 'eager'], + { + "default": 'sdpa' + }), + + }, + } + + RETURN_TYPES = ("FL2MODEL",) + RETURN_NAMES = ("florence2_model",) + FUNCTION = "loadmodel" + CATEGORY = "Florence2" + + def loadmodel(self, model, precision, attention): + device = mm.get_torch_device() + offload_device = mm.unet_offload_device() + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + + model_name = model.rsplit('/', 1)[-1] + model_path = os.path.join(folder_paths.models_dir, "LLM", model_name) + + if not os.path.exists(model_path): + print(f"Downloading Lumina model to: {model_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id=model, + local_dir=model_path, + local_dir_use_symlinks=False) + + print(f"using {attention} for attention") + with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports): #workaround for unnecessary flash_attn requirement + model = AutoModelForCausalLM.from_pretrained(model_path, attn_implementation=attention, device_map=device, torch_dtype=dtype,trust_remote_code=True) + processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) + + florence2_model = { + 'model': model, + 'processor': processor, + 'dtype': dtype + } + + return (florence2_model,) + +def calculate_bounding_box(width, height, flat_points) -> List[float]: + """ + Calculate the bounding box for a polygon. + + Args: + flat_points (list of int): Flat list of x, y coordinates defining the polygon points. + + Returns: + tuple: (min_x, min_y, max_x, max_y) defining the bounding box. + """ + if not flat_points or len(flat_points) % 2 != 0: + raise ValueError("The list of points must be non-empty and have an even number of elements") + + x_coords = flat_points[0::2] + y_coords = flat_points[1::2] + + min_x = min(x_coords) + max_x = max(x_coords) + min_y = min(y_coords) + max_y = max(y_coords) + + return [min_x / width, min_y / height, max_x / width, max_y / height] + +class Florence2Run: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE", ), + "florence2_model": ("FL2MODEL", ), + "text_input": ("STRING", {"default": "", "multiline": True}), + "task": ( + [ + 'region_caption', + 'dense_region_caption', + 'region_proposal', + 'caption', + 'detailed_caption', + 'more_detailed_caption', + 'caption_to_phrase_grounding', + 'referring_expression_segmentation', + 'ocr', + 'ocr_with_region', + 'docvqa' + ], + ), + "fill_mask": ("BOOLEAN", {"default": True}), + }, + "optional": { + "keep_model_loaded": ("BOOLEAN", {"default": False}), + "max_new_tokens": ("INT", {"default": 1024, "min": 1, "max": 4096}), + "num_beams": ("INT", {"default": 3, "min": 1, "max": 64}), + "do_sample": ("BOOLEAN", {"default": True}), + "output_mask_select": ("STRING", {"default": ""}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "JSON") + RETURN_NAMES =("image", "mask", "caption", "data") + FUNCTION = "encode" + CATEGORY = "Florence2" + + def encode(self, image, text_input, florence2_model, task, fill_mask, keep_model_loaded=False, + num_beams=3, max_new_tokens=1024, do_sample=True, output_mask_select=""): + device = mm.get_torch_device() + _, height, width, _ = image.shape + offload_device = mm.unet_offload_device() + annotated_image_tensor = None + mask_tensor = None + processor = florence2_model['processor'] + model = florence2_model['model'] + dtype = florence2_model['dtype'] + model.to(device) + + colormap = ['blue','orange','green','purple','brown','pink','olive','cyan','red', + 'lime','indigo','violet','aqua','magenta','gold','tan','skyblue'] + + prompts = { + 'region_caption': '', + 'dense_region_caption': '', + 'region_proposal': '', + 'caption': '', + 'detailed_caption': '', + 'more_detailed_caption': '', + 'caption_to_phrase_grounding': '', + 'referring_expression_segmentation': '', + 'ocr': '', + 'ocr_with_region': '', + 'docvqa': '' + } + task_prompt = prompts.get(task, '') + + if (task not in ['referring_expression_segmentation', 'caption_to_phrase_grounding', 'docvqa']) and text_input: + raise ValueError("Text input (prompt) is only supported for 'referring_expression_segmentation', 'caption_to_phrase_grounding', and 'docvqa'") + + if text_input != "": + prompt = task_prompt + " " + text_input + else: + prompt = task_prompt + + image = image.permute(0, 3, 1, 2) + + out = [] + out_masks = [] + out_results = [] + out_data = [] + pbar = ProgressBar(len(image)) + for img in image: + image_pil = F.to_pil_image(img) + inputs = processor(text=prompt, images=image_pil, return_tensors="pt", do_rescale=False).to(dtype).to(device) + + generated_ids = model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=max_new_tokens, + do_sample=do_sample, + num_beams=num_beams, + ) + + results = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + print(results) + # cleanup the special tokens from the final list + if task == 'ocr_with_region': + clean_results = str(results) + cleaned_string = re.sub(r'|<[^>]*>', '\n', clean_results) + clean_results = re.sub(r'\n+', '\n', cleaned_string) + else: + clean_results = str(results) + clean_results = clean_results.replace('    ', '') + clean_results = clean_results.replace('', '') + + #return single string if only one image for compatibility with nodes that can't handle string lists + if len(image) == 1: + out_results = clean_results + else: + out_results.append(clean_results) + + W, H = image_pil.size + + parsed_answer = processor.post_process_generation(results, task=task_prompt, image_size=(W, H)) + + if task == 'region_caption' or task == 'dense_region_caption' or task == 'caption_to_phrase_grounding' or task == 'region_proposal': + fig, ax = plt.subplots(figsize=(W / 100, H / 100), dpi=100) + fig.subplots_adjust(left=0, right=1, top=1, bottom=0) + ax.imshow(image_pil) + bboxes = parsed_answer[task_prompt]['bboxes'] + labels = parsed_answer[task_prompt]['labels'] + + mask_indexes = [] + # Determine mask indexes outside the loop + if output_mask_select != "": + mask_indexes = [n for n in output_mask_select.split(",")] + print(mask_indexes) + else: + mask_indexes = [str(i) for i in range(len(bboxes))] + + # Initialize mask_layer only if needed + if fill_mask: + mask_layer = Image.new('RGB', image_pil.size, (0, 0, 0)) + mask_draw = ImageDraw.Draw(mask_layer) + + for index, (bbox, label) in enumerate(zip(bboxes, labels)): + # Modify the label to include the index + indexed_label = f"{index}.{label}" + + if fill_mask: + if str(index) in mask_indexes: + print("match index:", str(index), "in mask_indexes:", mask_indexes) + mask_draw.rectangle([bbox[0], bbox[1], bbox[2], bbox[3]], fill=(255, 255, 255)) + if label in mask_indexes: + print("match label") + mask_draw.rectangle([bbox[0], bbox[1], bbox[2], bbox[3]], fill=(255, 255, 255)) + + # Create a Rectangle patch + rect = patches.Rectangle( + (bbox[0], bbox[1]), # (x,y) - lower left corner + bbox[2] - bbox[0], # Width + bbox[3] - bbox[1], # Height + linewidth=1, + edgecolor='r', + facecolor='none', + label=indexed_label + ) + # Calculate text width with a rough estimation + text_width = len(label) * 6 # Adjust multiplier based on your font size + text_height = 12 # Adjust based on your font size + + # Initial text position + text_x = bbox[0] + text_y = bbox[1] - text_height # Position text above the top-left of the bbox + + # Adjust text_x if text is going off the left or right edge + if text_x < 0: + text_x = 0 + elif text_x + text_width > W: + text_x = W - text_width + + # Adjust text_y if text is going off the top edge + if text_y < 0: + text_y = bbox[3] # Move text below the bottom-left of the bbox if it doesn't overlap with bbox + + # Add the rectangle to the plot + ax.add_patch(rect) + facecolor = random.choice(colormap) if len(image) == 1 else 'red' + # Add the label + plt.text( + text_x, + text_y, + indexed_label, + color='white', + fontsize=12, + bbox=dict(facecolor=facecolor, alpha=0.5) + ) + if fill_mask: + mask_tensor = F.to_tensor(mask_layer) + mask_tensor = mask_tensor.unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + mask_tensor = mask_tensor.mean(dim=0, keepdim=True) + mask_tensor = mask_tensor.repeat(1, 1, 1, 3) + mask_tensor = mask_tensor[:, :, :, 0] + out_masks.append(mask_tensor) + + # Remove axis and padding around the image + ax.axis('off') + ax.margins(0,0) + ax.get_xaxis().set_major_locator(plt.NullLocator()) + ax.get_yaxis().set_major_locator(plt.NullLocator()) + fig.canvas.draw() + buf = io.BytesIO() + plt.savefig(buf, format='png', pad_inches=0) + buf.seek(0) + annotated_image_pil = Image.open(buf) + + annotated_image_tensor = F.to_tensor(annotated_image_pil) + out_tensor = annotated_image_tensor[:3, :, :].unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + out.append(out_tensor) + + + pbar.update(1) + + plt.close(fig) + + elif task == 'referring_expression_segmentation': + # Create a new black image + mask_image = Image.new('RGB', (W, H), 'black') + mask_draw = ImageDraw.Draw(mask_image) + + predictions = parsed_answer[task_prompt] + + # Iterate over polygons and labels + for polygons, label in zip(predictions['polygons'], predictions['labels']): + color = random.choice(colormap) + for _polygon in polygons: + _polygon = np.array(_polygon).reshape(-1, 2) + # Clamp polygon points to image boundaries + _polygon = np.clip(_polygon, [0, 0], [W - 1, H - 1]) + if len(_polygon) < 3: + print('Invalid polygon:', _polygon) + continue + + _polygon = _polygon.reshape(-1).tolist() + + # Draw the polygon + if fill_mask: + overlay = Image.new('RGBA', image_pil.size, (255, 255, 255, 0)) + image_pil = image_pil.convert('RGBA') + draw = ImageDraw.Draw(overlay) + color_with_opacity = ImageColor.getrgb(color) + (180,) + draw.polygon(_polygon, outline=color, fill=color_with_opacity, width=3) + image_pil = Image.alpha_composite(image_pil, overlay) + else: + draw = ImageDraw.Draw(image_pil) + draw.polygon(_polygon, outline=color, width=3) + + #draw mask + mask_draw.polygon(_polygon, outline="white", fill="white") + + image_tensor = F.to_tensor(image_pil) + image_tensor = image_tensor[:3, :, :].unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + out.append(image_tensor) + + mask_tensor = F.to_tensor(mask_image) + mask_tensor = mask_tensor.unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + mask_tensor = mask_tensor.mean(dim=0, keepdim=True) + mask_tensor = mask_tensor.repeat(1, 1, 1, 3) + mask_tensor = mask_tensor[:, :, :, 0] + out_masks.append(mask_tensor) + pbar.update(1) + + elif task == 'ocr_with_region': + try: + font = ImageFont.load_default().font_variant(size=24) + except: + font = ImageFont.load_default() + predictions = parsed_answer[task_prompt] + scale = 1 + draw = ImageDraw.Draw(image_pil) + bboxes, labels = predictions['quad_boxes'], predictions['labels'] + + for box, label in zip(bboxes, labels): + bbox = calculate_bounding_box(width, height, box) + out_data.append({"label": label, "polygon": box, "box": bbox}) + color = random.choice(colormap) + new_box = (np.array(box) * scale).tolist() + draw.polygon(new_box, width=3, outline=color) + draw.text((new_box[0]+8, new_box[1]+2), + "{}".format(label), + align="right", + font=font, + fill=color) + + image_tensor = F.to_tensor(image_pil) + image_tensor = image_tensor[:3, :, :].unsqueeze(0).permute(0, 2, 3, 1).cpu().float() + out.append(image_tensor) + + elif task == 'docvqa': + if text_input == "": + raise ValueError("Text input (prompt) is required for 'docvqa'") + prompt = " " + text_input + + inputs = processor(text=prompt, images=image_pil, return_tensors="pt", do_rescale=False).to(dtype).to(device) + generated_ids = model.generate( + input_ids=inputs["input_ids"], + pixel_values=inputs["pixel_values"], + max_new_tokens=max_new_tokens, + do_sample=do_sample, + num_beams=num_beams, + ) + + results = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] + clean_results = results.replace('', '').replace('', '') + + if len(image) == 1: + out_results = clean_results + else: + out_results.append(clean_results) + + out.append(F.to_tensor(image_pil).unsqueeze(0).permute(0, 2, 3, 1).cpu().float()) + + pbar.update(1) + + if len(out) > 0: + out_tensor = torch.cat(out, dim=0) + else: + out_tensor = torch.zeros((1, 64,64, 3), dtype=torch.float32, device="cpu") + if len(out_masks) > 0: + out_mask_tensor = torch.cat(out_masks, dim=0) + else: + out_mask_tensor = torch.zeros((1,64,64), dtype=torch.float32, device="cpu") + + if not keep_model_loaded: + print("Offloading model...") + model.to(offload_device) + mm.soft_empty_cache() + + return (out_tensor, out_mask_tensor, out_results, out_data) + +NODE_CLASS_MAPPINGS = { + "DownloadAndLoadFlorence2Model": DownloadAndLoadFlorence2Model, + "Florence2Run": Florence2Run, +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DownloadAndLoadFlorence2Model": "DownloadAndLoadFlorence2Model", + "Florence2Run": "Florence2Run", +} \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/foreground_mask.py b/custom_nodes/comfyui-tensorops/nodes/foreground_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..4fe99cb5403493ac7a0224fb4620fe923b851335 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/foreground_mask.py @@ -0,0 +1,187 @@ +from typing import List +from PIL import Image, ImageDraw +import numpy as np +from collections import Counter +import torchvision.transforms.functional as F +import json + +def calculate_bounding_box(points) -> List[float]: + """ + Calculate the bounding box for a polygon. + + Args: + flat_points (list of int): Flat list of x, y coordinates defining the polygon points. + + Returns: + tuple: (min_x, min_y, max_x, max_y) defining the bounding box. + """ + if not points or len(points) % 2 != 0: + raise ValueError("The list of points must be non-empty and have an even number of elements") + + x_coords = points[0::2] + y_coords = points[1::2] + + min_x = min(x_coords) + max_x = max(x_coords) + min_y = min(y_coords) + max_y = max(y_coords) + + return [min_x, min_y, max_x, max_y] + + +def find_mode_color(image: Image.Image): + """ + Identify the most frequent (mode) color in a PIL image. + + Parameters: + image_path (str): The path to the input image. + + Returns: + tuple: The mode color in the image as an (R, G, B) tuple. + """ + # Convert image to RGB mode if it's not already + image = image.convert('RGB') + + # Get the list of pixels + pixels = list(image.getdata()) + + # Use Counter to count the frequency of each color + counter = Counter(pixels) + + # Find the most common color + mode_color = counter.most_common(1)[0][0] + + return mode_color + +def separate_foreground_background(image): + """ + Separate the Pillow image into foreground and background using the mode color and distance clustering. + + Parameters: + image_path (str): The path to the input image. + output_foreground (str): The path to save the foreground image. + output_background (str): The path to save the background image. + + Returns: + None + """ + # Convert image to RGBA mode to handle transparency + image = image.convert('RGBA') + pixels = np.array(image) + + # Calculate the Euclidean distance of each pixel to the mode color + background_color = find_mode_color(image) + print("Background color:", background_color) + mode_color_array = np.array(background_color) + distances = np.linalg.norm(pixels[:, :, :3] - mode_color_array, axis=2) + + # Determine the threshold distance for clustering + threshold_distance = np.mean(distances) + + print("Threshold distance:", threshold_distance) + # Create masks for foreground and background + foreground_mask = distances > threshold_distance + background_mask = distances <= threshold_distance + + # Create empty arrays for the new images + foreground_image = np.zeros_like(pixels) + background_image = np.zeros_like(pixels) + + # Copy the pixels to the new images based on the masks + foreground_image[foreground_mask] = pixels[foreground_mask] + background_image[background_mask] = pixels[background_mask] + + # Find the fg color + fg_color = find_mode_color(Image.fromarray(foreground_image, 'RGBA')) + + # Set foreground pixels with alpha == 255 to black + alpha_channel = foreground_image[:, :, 3] == 255 + foreground_image[alpha_channel, :3] = [255, 255, 255] + foreground_image[:, :, 3] = 255 + + # Convert back to PIL images + foreground_image = Image.fromarray(foreground_image, 'RGBA') + background_image = Image.fromarray(background_image, 'RGBA') + + # Invert Foreground As White + # foreground_image = ImageOps.invert(foreground_image.convert("RGB")) + + return foreground_image, fg_color + +def crop_polygon(image, points): + """ + Create a white mask on a black image of size width x height using a list of polygon points. + + Args: + points (list of tuples): List of (x, y) tuples defining the polygon points. + width (int): Width of the image. + height (int): Height of the image. + + Returns: + Image: Pillow Image object with the polygon mask. + """ + x_min, y_min, x_max, y_max = calculate_bounding_box(points) + image_crop = image.crop((x_min, y_min, x_max, y_max)) + return image_crop + +def mask_polygon(image, points): + """ + Crop a polygon from a Pillow image. + + Args: + image (PIL.Image): The input image. + flat_points (list of int): Flat list of x, y coordinates defining the polygon points. + + Returns: + PIL.Image: Cropped image of the polygon. + """ + if not points or len(points) % 2 != 0: + raise ValueError("The list of points must be non-empty and have an even number of elements") + + # Create a mask + mask = Image.new('L', image.size, 0) + draw = ImageDraw.Draw(mask) + new_box = (np.array(points) * 1.0).tolist() + draw.polygon(new_box, fill="white") + + # Apply the mask to the image + masked_image = Image.composite(image.convert("RGBA"), mask.convert("RGBA"), mask) + return masked_image + + +import torch + +class ForegroundMask: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "json_data": ("JSON",), + }, + } + + RETURN_TYPES = ("IMAGE",) + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, image: torch.Tensor, json_data: str): + print("items", json_data) + items = [item for item in json_data] + image = image.permute(0, 3, 1, 2) + image_pil = F.to_pil_image(image[0]) + full_image = Image.new("RGBA", image_pil.size, (0, 0, 0, 255)) + for item in items: + points = item["polygon"] + print("polygon", points) + masked_image = mask_polygon(image_pil, points) + masked_image_crop = crop_polygon(image_pil, points) + fg_image, fg_color = separate_foreground_background(masked_image_crop) + x_min, y_min, x_max, y_max = calculate_bounding_box(points) + full_image.paste(fg_image, (int(x_min), int(y_min))) + out_image = F.to_tensor(full_image) + return (out_image,) + diff --git a/custom_nodes/comfyui-tensorops/nodes/layer_mask.py b/custom_nodes/comfyui-tensorops/nodes/layer_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..20c6136ea2ead3f245d161909446f625fe62a6c5 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/layer_mask.py @@ -0,0 +1,129 @@ +import PIL +from typing import List +import numpy as np +import torchvision.transforms.functional as F +import torch + +def multiply_grayscale_images(image1, image2): + # Convert the images to NumPy arrays + image1_np = np.array(image1) + image2_np = np.array(image2) + + # Perform element-wise multiplication (ensure to use np.float32 to avoid overflow) + multiplied_image = image1_np.astype(np.float32) * image2_np.astype(np.float32) + + # Normalize the result to the range 0-255 (if needed) + multiplied_image = np.clip(multiplied_image, 0, 255) + + # Convert back to uint8 (8-bit grayscale image) + multiplied_image = multiplied_image.astype(np.uint8) + + # Convert back to an image and save the result + result_image = PIL.Image.fromarray(multiplied_image) + return result_image + +def create_color_masks(image: PIL.Image.Image): + # Load the image + image = image.convert("RGB") + image_np = np.array(image) # Convert to numpy array (Height x Width x 3) + # Find unique colors in the image + unique_colors = np.unique(image_np.reshape(-1, 3), axis=0) + output = [] + # Create masks for each color + for color in unique_colors: + if sum(color) == 0: + continue + mask = np.all(image_np == color, axis=-1) + color_str = '_'.join(map(str, color)) # Create a string representation of the color + output.append((color_str, mask)) + # Skip Background Mask Image + background_area = 0.0 + background_mask_index = -1 + for idx, (color_str, mask) in enumerate(output): + area = np.sum(mask > 0) / (mask.shape[0] * mask.shape[1]) + if area > background_area: + background_area = area + background_mask_index = idx + # Final Elements + elements = [] + for idx, (color_str, mask) in enumerate(output): + if idx == background_mask_index: + print(background_mask_index) + continue + mask_image = PIL.Image.fromarray(mask.astype(np.uint8) * 255) + elements.append((color_str, mask_image)) + # Final Background + final_background_mask_image = PIL.Image.new("L", (image.size[0], image.size[1]), 255) + draw = PIL.ImageDraw.Draw(final_background_mask_image) + for idx, (color_str, mask_image) in enumerate(elements): + final_background_mask_image = multiply_grayscale_images(final_background_mask_image, PIL.ImageOps.invert(mask_image)) + + return final_background_mask_image, elements + + +def create_text_masks(polygons, width, height): + # Loop over each polygon in the list + text_masks = [] + for i, polygon_coords in enumerate(polygons): + # Create a new grayscale image (L mode) with a black background (0) + mask = PIL.Image.new('L', (width, height), 0) + + # Create a drawing object + draw = PIL.ImageDraw.Draw(mask) + + # Convert the list of polygon coordinates into a format ImageDraw can use (list of tuples) + polygon_points = [(polygon_coords[j], polygon_coords[j + 1]) for j in range(0, len(polygon_coords), 2)] + + # Draw the polygon with white (255) fill + draw.polygon(polygon_points, fill=255) + text_masks.append(mask) + return text_masks + +class GetLayerMask: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "json_data": ("JSON",), + }, + } + + RETURN_TYPES = ("MASK", "MASK", "JSON") + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, image: torch.Tensor, json_data: str): + # Create PIL.Image + image = image.permute(0, 3, 1, 2) + image_pil = F.to_pil_image(image[0]) + # Create bg and elements + bg, elements = create_color_masks(image_pil) + # Create Text Masks + print("items", json_data) + items = [item for item in json_data] + text_polygon_list = [] + text_label_list = [] + text_masks = [] + + for item in items: + text_polygon_list.append(item["polygon"]) + text_label_list.append(item["label"]) + + for mask_image in create_text_masks(text_polygon_list, bg.size[0], bg.size[1]): + img = np.array(mask_image).astype(np.float32) / 255.0 + img = torch.from_numpy(img)[None,] + text_masks.append(img) + + output = [] + bg = np.array(bg).astype(np.float32) / 255.0 + bg = torch.from_numpy(bg)[None,] + output.append(bg) + for _, mask_image in elements: + img = np.array(mask_image).astype(np.float32) / 255.0 + img = torch.from_numpy(img)[None,] + output.append(img) + return (torch.cat(output, dim=0), torch.cat(text_masks, dim=0), text_label_list) diff --git a/custom_nodes/comfyui-tensorops/nodes/mask_image.py b/custom_nodes/comfyui-tensorops/nodes/mask_image.py new file mode 100644 index 0000000000000000000000000000000000000000..f6a578c7a0e1aa9b9eb2230dfa27f5b5f8748053 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/mask_image.py @@ -0,0 +1,28 @@ +import torch + +class MaskImage: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + }, + } + + RETURN_TYPES = ("IMAGE",) + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, image: torch.Tensor, mask: torch.Tensor): + mask = mask.unsqueeze(-1) + new_image = image * mask + print("MaskImage") + print("ImageShape", image.shape) + print("MaskShape", mask.shape) + print("NewImageShape", new_image.shape) + return (new_image,) + diff --git a/custom_nodes/comfyui-tensorops/nodes/redis.py b/custom_nodes/comfyui-tensorops/nodes/redis.py new file mode 100644 index 0000000000000000000000000000000000000000..a09cfc88f197625911af715b07b3f8358c369d87 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/redis.py @@ -0,0 +1,53 @@ + +from .config import REDIS_URL +import redis +import json + +class SaveToRedis: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False}), + "data": ("JSON",) + }, + } + + RETURN_TYPES = () + + FUNCTION = "main" + OUTPUT_NODE = True + CATEGORY = "database_ops" + + def main(self, key: str, data: dict): + connection = redis.Redis.from_url(REDIS_URL) + connection.set(key, json.dumps(data)) + connection.close() + return () + +class FetchFromRedis: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "key": ("STRING", {"multiline": False}) + }, + } + + RETURN_TYPES = ("JSON",) + + FUNCTION = "main" + OUTPUT_NODE = True + CATEGORY = "database_ops" + + def main(self, key: str): + connection = redis.Redis.from_url(REDIS_URL) + data = connection.get(key) + if data is None: + return {} + else: + data = json.loads(data) + return [data] + \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/redis_image.py b/custom_nodes/comfyui-tensorops/nodes/redis_image.py new file mode 100644 index 0000000000000000000000000000000000000000..7b4961899ebfb8ac8db4b4e771d004a2f600dab1 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/redis_image.py @@ -0,0 +1,41 @@ +from .config import REDIS_URL +import redis +import numpy as np +import base64 +from PIL import Image +from io import BytesIO + +def pil_image_to_base64(image): + # Create a BytesIO buffer to save the image + buffered = BytesIO() + + # Save the image in the buffer in PNG format (you can also use JPEG or others) + image.save(buffered, format="PNG") + + # Get the byte content of the image + img_bytes = buffered.getvalue() + + # Encode the image bytes to base64 + img_base64 = base64.b64encode(img_bytes).decode("utf-8") + + return img_base64 + +class SendImageToRedis: + @classmethod + def INPUT_TYPES(s): + return {"required": {"key": ("STRING", {"multiline": False}), "images": ("IMAGE",)}} + + RETURN_TYPES = () + FUNCTION = "send_images" + OUTPUT_NODE = True + CATEGORY = "tensorops" + + def send_images(self, key, images): + connection = redis.Redis.from_url(REDIS_URL) + connection.delete(key) + for tensor in images: + array = 255.0 * tensor.cpu().numpy() + image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8)) + connection.xadd(key, pil_image_to_base64(image)) + connection.close() + return () \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/sam2_nodes.py b/custom_nodes/comfyui-tensorops/nodes/sam2_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..7c6772520c46289e39ea6f4e39fd4411fdfbf438 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/sam2_nodes.py @@ -0,0 +1,743 @@ +import torch +from torch.functional import F +import os +import numpy as np +import json +import random + +from tqdm import tqdm +from contextlib import nullcontext + +from .load_model import load_model + +import comfy.model_management as mm +from comfy.utils import ProgressBar, common_upscale +import folder_paths + +script_directory = os.path.dirname(os.path.abspath(__file__)) + +class DownloadAndLoadSAM2Model: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model": ([ + 'sam2_hiera_base_plus.safetensors', + 'sam2_hiera_large.safetensors', + 'sam2_hiera_small.safetensors', + 'sam2_hiera_tiny.safetensors', + ],), + "segmentor": ( + ['single_image','video', 'automaskgenerator'], + ), + "device": (['cuda', 'cpu', 'mps'], ), + "precision": ([ 'fp16','bf16','fp32'], + { + "default": 'bf16' + }), + + }, + } + + RETURN_TYPES = ("SAM2MODEL",) + RETURN_NAMES = ("sam2_model",) + FUNCTION = "loadmodel" + CATEGORY = "SAM2" + + def loadmodel(self, model, segmentor, device, precision): + if precision != 'fp32' and device == 'cpu': + raise ValueError("fp16 and bf16 are not supported on cpu") + + if device == "cuda": + if torch.cuda.get_device_properties(0).major >= 8: + # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + device = {"cuda": torch.device("cuda"), "cpu": torch.device("cpu"), "mps": torch.device("mps")}[device] + + download_path = os.path.join(folder_paths.models_dir, "sam2") + model_path = os.path.join(download_path, model) + + if not os.path.exists(model_path): + print(f"Downloading SAM2 model to: {model_path}") + from huggingface_hub import snapshot_download + snapshot_download(repo_id="Kijai/sam2-safetensors", + allow_patterns=[f"*{model}*"], + local_dir=download_path, + local_dir_use_symlinks=False) + + model_mapping = { + "base": "sam2_hiera_b+.yaml", + "large": "sam2_hiera_l.yaml", + "small": "sam2_hiera_s.yaml", + "tiny": "sam2_hiera_t.yaml" + } + + model_cfg_path = next( + (os.path.join(script_directory, "sam2_configs", cfg) for key, cfg in model_mapping.items() if key in model), + None + ) + + model =load_model(model_path, model_cfg_path, segmentor, dtype, device) + + sam2_model = { + 'model': model, + 'dtype': dtype, + 'device': device, + 'segmentor' : segmentor + } + + return (sam2_model,) + + +class Florence2toCoordinates: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "data": ("JSON", ), + "index": ("STRING", {"default": "0"}), + "batch": ("BOOLEAN", {"default": False}), + }, + + } + + RETURN_TYPES = ("STRING", "BBOX") + RETURN_NAMES =("center_coordinates", "bboxes") + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, data, index, batch=False): + print(data) + try: + coordinates = coordinates.replace("'", '"') + coordinates = json.loads(coordinates) + except: + coordinates = data + print("Type of data:", type(data)) + print("Data:", data) + if len(data)==0: + return (json.dumps([{'x': 0, 'y': 0}]),) + center_points = [] + + if index.strip(): # Check if index is not empty + indexes = [int(i) for i in index.split(",")] + else: # If index is empty, use all indices from data[0] + indexes = list(range(len(data[0]))) + + print("Indexes:", indexes) + bboxes = [] + + if batch: + for idx in indexes: + if 0 <= idx < len(data[0]): + for i in range(len(data)): + bbox = data[i][idx] + min_x, min_y, max_x, max_y = bbox + center_x = int((min_x + max_x) / 2) + center_y = int((min_y + max_y) / 2) + center_points.append({"x": center_x, "y": center_y}) + bboxes.append(bbox) + else: + for idx in indexes: + if 0 <= idx < len(data[0]): + bbox = data[0][idx] + min_x, min_y, max_x, max_y = bbox + center_x = int((min_x + max_x) / 2) + center_y = int((min_y + max_y) / 2) + center_points.append({"x": center_x, "y": center_y}) + bboxes.append(bbox) + else: + raise ValueError(f"There's nothing in index: {idx}") + + coordinates = json.dumps(center_points) + print("Coordinates:", coordinates) + return (coordinates, bboxes) + +class Sam2Segmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "image": ("IMAGE", ), + "keep_model_loaded": ("BOOLEAN", {"default": True}), + }, + "optional": { + "coordinates_positive": ("STRING", {"forceInput": True}), + "coordinates_negative": ("STRING", {"forceInput": True}), + "bboxes": ("BBOX", ), + "individual_objects": ("BOOLEAN", {"default": False}), + "mask": ("MASK", ), + + }, + } + + RETURN_TYPES = ("MASK", ) + RETURN_NAMES =("mask", ) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, image, sam2_model, keep_model_loaded, coordinates_positive=None, coordinates_negative=None, + individual_objects=False, bboxes=None, mask=None): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + B, H, W, C = image.shape + + if mask is not None: + input_mask = mask.clone().unsqueeze(1) + input_mask = F.interpolate(input_mask, size=(256, 256), mode="bilinear") + input_mask = input_mask.squeeze(1) + + if segmentor == 'automaskgenerator': + raise ValueError("For automaskgenerator use Sam2AutoMaskSegmentation -node") + if segmentor == 'single_image' and B > 1: + print("Segmenting batch of images with single_image segmentor") + + if segmentor == 'video' and bboxes is not None: + raise ValueError("Video segmentor doesn't support bboxes") + + if segmentor == 'video': # video model needs images resized first thing + model_input_image_size = model.image_size + print("Resizing to model input image size: ", model_input_image_size) + image = common_upscale(image.movedim(-1,1), model_input_image_size, model_input_image_size, "bilinear", "disabled").movedim(1,-1) + + #handle point coordinates + if coordinates_positive is not None: + try: + coordinates_positive = json.loads(coordinates_positive.replace("'", '"')) + coordinates_positive = [(coord['x'], coord['y']) for coord in coordinates_positive] + if coordinates_negative is not None: + coordinates_negative = json.loads(coordinates_negative.replace("'", '"')) + coordinates_negative = [(coord['x'], coord['y']) for coord in coordinates_negative] + except: + pass + + if not individual_objects: + positive_point_coords = np.atleast_2d(np.array(coordinates_positive)) + else: + positive_point_coords = np.array([np.atleast_2d(coord) for coord in coordinates_positive]) + + if coordinates_negative is not None: + negative_point_coords = np.array(coordinates_negative) + # Ensure both positive and negative coords are lists of 2D arrays if individual_objects is True + if individual_objects: + assert negative_point_coords.shape[0] <= positive_point_coords.shape[0], "Can't have more negative than positive points in individual_objects mode" + if negative_point_coords.ndim == 2: + negative_point_coords = negative_point_coords[:, np.newaxis, :] + # Extend negative coordinates to match the number of positive coordinates + while negative_point_coords.shape[0] < positive_point_coords.shape[0]: + negative_point_coords = np.concatenate((negative_point_coords, negative_point_coords[:1, :, :]), axis=0) + final_coords = np.concatenate((positive_point_coords, negative_point_coords), axis=1) + else: + final_coords = np.concatenate((positive_point_coords, negative_point_coords), axis=0) + else: + final_coords = positive_point_coords + + # Handle possible bboxes + if bboxes is not None: + boxes_np_batch = [] + for bbox_list in bboxes: + boxes_np = [] + for bbox in bbox_list: + boxes_np.append(bbox) + boxes_np = np.array(boxes_np) + boxes_np_batch.append(boxes_np) + if individual_objects: + final_box = np.array(boxes_np_batch) + else: + final_box = np.array(boxes_np) + final_labels = None + + #handle labels + if coordinates_positive is not None: + if not individual_objects: + positive_point_labels = np.ones(len(positive_point_coords)) + else: + positive_labels = [] + for point in positive_point_coords: + positive_labels.append(np.array([1])) # 1) + positive_point_labels = np.stack(positive_labels, axis=0) + + if coordinates_negative is not None: + if not individual_objects: + negative_point_labels = np.zeros(len(negative_point_coords)) # 0 = negative + final_labels = np.concatenate((positive_point_labels, negative_point_labels), axis=0) + else: + negative_labels = [] + for point in positive_point_coords: + negative_labels.append(np.array([0])) # 1) + negative_point_labels = np.stack(negative_labels, axis=0) + #combine labels + final_labels = np.concatenate((positive_point_labels, negative_point_labels), axis=1) + else: + final_labels = positive_point_labels + print("combined labels: ", final_labels) + print("combined labels shape: ", final_labels.shape) + + mask_list = [] + try: + model.to(device) + except: + model.model.to(device) + + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + if segmentor == 'single_image': + image_np = (image.contiguous() * 255).byte().numpy() + comfy_pbar = ProgressBar(len(image_np)) + tqdm_pbar = tqdm(total=len(image_np), desc="Processing Images") + for i in range(len(image_np)): + model.set_image(image_np[i]) + if bboxes is None: + input_box = None + else: + if len(image_np) > 1: + input_box = final_box[i] + input_box = final_box + + out_masks, scores, logits = model.predict( + point_coords=final_coords if coordinates_positive is not None else None, + point_labels=final_labels if coordinates_positive is not None else None, + box=input_box, + multimask_output=True if not individual_objects else False, + mask_input = input_mask[i].unsqueeze(0) if mask is not None else None, + ) + + if out_masks.ndim == 3: + sorted_ind = np.argsort(scores)[::-1] + out_masks = out_masks[sorted_ind][0] #choose only the best result for now + scores = scores[sorted_ind] + logits = logits[sorted_ind] + mask_list.append(np.expand_dims(out_masks, axis=0)) + else: + _, _, H, W = out_masks.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=bool) + for out_mask in out_masks: + combined_mask = np.logical_or(combined_mask, out_mask) + combined_mask = combined_mask.astype(np.uint8) + mask_list.append(combined_mask) + comfy_pbar.update(1) + tqdm_pbar.update(1) + + elif segmentor == 'video': + mask_list = [] + if hasattr(self, 'inference_state'): + model.reset_state(self.inference_state) + self.inference_state = model.init_state(image.permute(0, 3, 1, 2).contiguous(), H, W, device=device) + + if individual_objects: + for i, (coord, label) in enumerate(zip(final_coords, final_labels)): + _, out_obj_ids, out_mask_logits = model.add_new_points( + inference_state=self.inference_state, + frame_idx=0, + obj_id=i, + points=final_coords[i], + labels=final_labels[i], + ) + else: + _, out_obj_ids, out_mask_logits = model.add_new_points( + inference_state=self.inference_state, + frame_idx=0, + obj_id=1, + points=final_coords, + labels=final_labels, + ) + + pbar = ProgressBar(B) + video_segments = {} + for out_frame_idx, out_obj_ids, out_mask_logits in model.propagate_in_video(self.inference_state): + video_segments[out_frame_idx] = { + out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() + for i, out_obj_id in enumerate(out_obj_ids) + } + pbar.update(1) + if individual_objects: + _, _, H, W = out_mask_logits.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=np.uint8) + for i, out_obj_id in enumerate(out_obj_ids): + out_mask = (out_mask_logits[i] > 0.0).cpu().numpy() + combined_mask = np.logical_or(combined_mask, out_mask) + video_segments[out_frame_idx] = combined_mask + + if individual_objects: + for frame_idx, combined_mask in video_segments.items(): + mask_list.append(combined_mask) + else: + for frame_idx, obj_masks in video_segments.items(): + for out_obj_id, out_mask in obj_masks.items(): + mask_list.append(out_mask) + + if not keep_model_loaded: + try: + model.to(offload_device) + except: + model.model.to(offload_device) + + out_list = [] + for mask in mask_list: + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.permute(1, 2, 0) + mask_tensor = mask_tensor[:, :, 0] + out_list.append(mask_tensor) + mask_tensor = torch.stack(out_list, dim=0).cpu().float() + return (mask_tensor,) + +class Sam2VideoSegmentationAddPoints: + @classmethod + def IS_CHANGED(s): # TODO: smarter reset? + return "" + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "coordinates_positive": ("STRING", {"forceInput": True}), + "frame_index": ("INT", {"default": 0}), + "object_index": ("INT", {"default": 0}), + }, + "optional": { + "image": ("IMAGE", ), + "coordinates_negative": ("STRING", {"forceInput": True}), + "prev_inference_state": ("SAM2INFERENCESTATE", ), + }, + } + + RETURN_TYPES = ("SAM2MODEL", "SAM2INFERENCESTATE", ) + RETURN_NAMES =("sam2_model", "inference_state", ) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, sam2_model, coordinates_positive, frame_index, object_index, image=None, coordinates_negative=None, prev_inference_state=None): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + + + if segmentor != 'video': + raise ValueError("Loaded model is not SAM2Video") + if image is not None: + B, H, W, C = image.shape + model_input_image_size = model.image_size + print("Resizing to model input image size: ", model_input_image_size) + image = common_upscale(image.movedim(-1,1), model_input_image_size, model_input_image_size, "bilinear", "disabled").movedim(1,-1) + + try: + coordinates_positive = json.loads(coordinates_positive.replace("'", '"')) + coordinates_positive = [(coord['x'], coord['y']) for coord in coordinates_positive] + if coordinates_negative is not None: + coordinates_negative = json.loads(coordinates_negative.replace("'", '"')) + coordinates_negative = [(coord['x'], coord['y']) for coord in coordinates_negative] + except: + pass + + positive_point_coords = np.array(coordinates_positive) + positive_point_labels = [1] * len(positive_point_coords) # 1 = positive + positive_point_labels = np.array(positive_point_labels) + print("positive coordinates: ", positive_point_coords) + + if coordinates_negative is not None: + negative_point_coords = np.array(coordinates_negative) + negative_point_labels = [0] * len(negative_point_coords) # 0 = negative + negative_point_labels = np.array(negative_point_labels) + print("negative coordinates: ", negative_point_coords) + + # Combine coordinates and labels + else: + negative_point_coords = np.empty((0, 2)) + negative_point_labels = np.array([]) + # Ensure both positive and negative coordinates are 2D arrays + positive_point_coords = np.atleast_2d(positive_point_coords) + negative_point_coords = np.atleast_2d(negative_point_coords) + + # Ensure both positive and negative labels are 1D arrays + positive_point_labels = np.atleast_1d(positive_point_labels) + negative_point_labels = np.atleast_1d(negative_point_labels) + + combined_coords = np.concatenate((positive_point_coords, negative_point_coords), axis=0) + combined_labels = np.concatenate((positive_point_labels, negative_point_labels), axis=0) + + model.to(device) + + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(model.device), dtype=dtype) if autocast_condition else nullcontext(): + if prev_inference_state is None: + print("Initializing inference state") + if hasattr(self, 'inference_state'): + model.reset_state(self.inference_state) + self.inference_state = model.init_state(image.permute(0, 3, 1, 2).contiguous(), H, W, device=device) + else: + print("Using previous inference state") + B = prev_inference_state['num_frames'] + self.inference_state = prev_inference_state['inference_state'] + _, out_obj_ids, out_mask_logits = model.add_new_points( + inference_state=self.inference_state, + frame_idx=frame_index, + obj_id=object_index, + points=combined_coords, + labels=combined_labels, + ) + inference_state = { + "inference_state": self.inference_state, + "num_frames": B, + } + sam2_model = { + 'model': model, + 'dtype': dtype, + 'device': device, + 'segmentor' : segmentor + } + return (sam2_model, inference_state,) + +class Sam2VideoSegmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "inference_state": ("SAM2INFERENCESTATE", ), + "keep_model_loaded": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("MASK", ) + RETURN_NAMES =("mask", ) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, sam2_model, inference_state, keep_model_loaded): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + inference_state = inference_state["inference_state"] + B = inference_state["num_frames"] + + if segmentor != 'video': + raise ValueError("Loaded model is not SAM2Video") + + model.to(device) + + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + + #if hasattr(self, 'inference_state'): + # model.reset_state(self.inference_state) + + pbar = ProgressBar(B) + video_segments = {} + for out_frame_idx, out_obj_ids, out_mask_logits in model.propagate_in_video(inference_state): + print("out_mask_logits",out_mask_logits.shape) + _, _, H, W = out_mask_logits.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=np.uint8) + for i, out_obj_id in enumerate(out_obj_ids): + out_mask = (out_mask_logits[i] > 0.0).cpu().numpy() + combined_mask = np.logical_or(combined_mask, out_mask) + video_segments[out_frame_idx] = combined_mask + pbar.update(1) + + mask_list = [] + # Collect the combined masks + for frame_idx, combined_mask in video_segments.items(): + mask_list.append(combined_mask) + print(f"Total masks collected: {len(mask_list)}") + + if not keep_model_loaded: + model.to(offload_device) + + out_list = [] + for mask in mask_list: + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.permute(1, 2, 0) + mask_tensor = mask_tensor[:, :, 0] + out_list.append(mask_tensor) + mask_tensor = torch.stack(out_list, dim=0).cpu().float() + return (mask_tensor,) + + +def get_background_mask(tensor: torch.Tensor): + """ + Function to identify the background mask from a batch of masks in a PyTorch tensor. + + Args: + tensor (torch.Tensor): A tensor of shape (B, H, W, 1) where B is the batch size, H is the height, W is the width. + + Returns: + List of masks as torch.Tensor and the background mask as torch.Tensor. + """ + B, H, W = tensor.shape + + # Compute areas of each mask + areas = tensor.sum(dim=(1, 2)) # Shape: (B,) + + # Find the mask with the largest area + largest_idx = torch.argmax(areas) + background_mask = tensor[largest_idx] + + # Identify if the largest mask touches the borders + border_touched = ( + torch.any(background_mask[0, :]) or + torch.any(background_mask[-1, :]) or + torch.any(background_mask[:, 0]) or + torch.any(background_mask[:, -1]) + ) + + # If the largest mask doesn't touch the border, search for another one + if not border_touched: + for i in range(B): + if i != largest_idx: + mask = tensor[i] + border_touched = ( + torch.any(mask[0, :]) or + torch.any(mask[-1, :]) or + torch.any(mask[:, 0]) or + torch.any(mask[:, -1]) + ) + if border_touched: + background_mask = mask + break + + # Reshape the masks to match the original tensor shape + return background_mask + + +class Sam2AutoSegmentation: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sam2_model": ("SAM2MODEL", ), + "image": ("IMAGE", ), + "points_per_side": ("INT", {"default": 32}), + "points_per_batch": ("INT", {"default": 64}), + "pred_iou_thresh": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.01}), + "stability_score_thresh": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01}), + "stability_score_offset": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "mask_threshold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_n_layers": ("INT", {"default": 0}), + "box_nms_thresh": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_nms_thresh": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_overlap_ratio": ("FLOAT", {"default": 0.34, "min": 0.0, "max": 1.0, "step": 0.01}), + "crop_n_points_downscale_factor": ("INT", {"default": 1}), + "min_mask_region_area": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "use_m2m": ("BOOLEAN", {"default": False}), + "keep_model_loaded": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("MASK", "MASK", "IMAGE", "BBOX",) + RETURN_NAMES =("mask", "background_mask", "segmented_image", "bbox" ,) + FUNCTION = "segment" + CATEGORY = "SAM2" + + def segment(self, image, sam2_model, points_per_side, points_per_batch, pred_iou_thresh, stability_score_thresh, + stability_score_offset, crop_n_layers, box_nms_thresh, crop_n_points_downscale_factor, min_mask_region_area, + use_m2m, mask_threshold, crop_nms_thresh, crop_overlap_ratio, keep_model_loaded): + offload_device = mm.unet_offload_device() + model = sam2_model["model"] + device = sam2_model["device"] + dtype = sam2_model["dtype"] + segmentor = sam2_model["segmentor"] + + if segmentor != 'automaskgenerator': + raise ValueError("Loaded model is not SAM2AutomaticMaskGenerator") + + model.points_per_side=points_per_side + model.points_per_batch=points_per_batch + model.pred_iou_thresh=pred_iou_thresh + model.stability_score_thresh=stability_score_thresh + model.stability_score_offset=stability_score_offset + model.crop_n_layers=crop_n_layers + model.box_nms_thresh=box_nms_thresh + model.crop_n_points_downscale_factor=crop_n_points_downscale_factor + model.crop_nms_thresh=crop_nms_thresh + model.crop_overlap_ratio=crop_overlap_ratio + model.min_mask_region_area=min_mask_region_area + model.use_m2m=use_m2m + model.mask_threshold=mask_threshold + + model.predictor.model.to(device) + + B, H, W, C = image.shape + image_np = (image.contiguous() * 255).byte().numpy() + + out_list = [] + segment_out_list = [] + mask_list=[] + background_list = [] + pbar = ProgressBar(B) + autocast_condition = not mm.is_device_mps(device) + with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): + for img_np in image_np: + result_dict = model.generate(img_np) + mask_list = [item['segmentation'] for item in result_dict] + bbox_list = [item['bbox'] for item in result_dict] + + # Generate random colors for each mask + num_masks = len(mask_list) + colors = [tuple(random.choices(range(256), k=3)) for _ in range(num_masks)] + + # Create a blank image to overlay masks + overlay_image = np.zeros((H, W, 3), dtype=np.uint8) + + # Create a combined mask initialized to zeros + combined_mask = np.zeros((H, W), dtype=np.uint8) + + # Select Background Mask + background_mask = get_background_mask(torch.from_numpy(np.stack(mask_list, axis=0))) + print(f"Background Mask", background_mask.shape) + # Iterate through masks and color them + for mask, color in zip(mask_list, colors): + + # Combine masks using logical OR + combined_mask = np.logical_or(combined_mask, mask).astype(np.uint8) + + # Convert mask to numpy array + mask_np = mask.astype(np.uint8) + + # Color the mask + colored_mask = np.zeros_like(overlay_image) + for i in range(3): # Apply color channel-wise + colored_mask[:, :, i] = mask_np * color[i] + + # Blend the colored mask with the overlay image + overlay_image = np.where(colored_mask > 0, colored_mask, overlay_image) + out_list.append(torch.from_numpy(combined_mask)) + background_list.append(background_mask) + segment_out_list.append(overlay_image) + pbar.update(1) + + stacked_array = np.stack(segment_out_list, axis=0) + segment_image_tensor = torch.from_numpy(stacked_array).float() / 255 + + if not keep_model_loaded: + model.predictor.model.to(offload_device) + + mask_tensor = torch.stack(out_list, dim=0) + return (mask_tensor.cpu().float(), torch.stack(background_list, axis=0).cpu().float(), segment_image_tensor.cpu().float(), bbox_list) + +NODE_CLASS_MAPPINGS = { + "DownloadAndLoadSAM2Model": DownloadAndLoadSAM2Model, + "Sam2Segmentation": Sam2Segmentation, + "Florence2toCoordinates": Florence2toCoordinates, + "Sam2AutoSegmentation": Sam2AutoSegmentation, + "Sam2VideoSegmentationAddPoints": Sam2VideoSegmentationAddPoints, + "Sam2VideoSegmentation": Sam2VideoSegmentation +} +NODE_DISPLAY_NAME_MAPPINGS = { + "DownloadAndLoadSAM2Model": "(Down)Load SAM2Model", + "Sam2Segmentation": "Sam2Segmentation", + "Florence2toCoordinates": "Florence2 Coordinates", + "Sam2AutoSegmentation": "Sam2AutoSegmentation", + "Sam2VideoSegmentationAddPoints": "Sam2VideoSegmentationAddPoints", + "Sam2VideoSegmentation": "Sam2VideoSegmentation" +} \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/save_surreal.py b/custom_nodes/comfyui-tensorops/nodes/save_surreal.py new file mode 100644 index 0000000000000000000000000000000000000000..401b7b95cdc822cd5d2cd8979a864d50c0e0d89d --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/save_surreal.py @@ -0,0 +1,53 @@ +from .surreal import surreal_connect + +SURREAL_TABLE = "processor" + +class SaveJsonToSurreal: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "database": ("STRING", {"multiline": False}), + "json": ("JSON",), + "id": ("STRING", {"multiline": False}), + "key": ("STRING", {"multiline": False}) + }, + } + + RETURN_TYPES = () + + FUNCTION = "main" + OUTPUT_NODE = True + CATEGORY = "database_ops" + + def main(self, database: str, id: str, key: str, json: str): + connection = surreal_connect(database) + query = f"UPDATE {SURREAL_TABLE}:`{id}` CONTENT {{{key}: {json}}};" + connection.query(query) + return () + +class SaveTextToSurreal: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "database": ("STRING", {"multiline": False}), + "text": ("STRING",{"forceInput": True}), + "id": ("STRING", {"multiline": False}), + "key": ("STRING", {"multiline": False}) + }, + } + + RETURN_TYPES = () + + FUNCTION = "main" + OUTPUT_NODE = True + CATEGORY = "database_ops" + + def main(self, database: str, id: str, key: str, text: str): + connection = surreal_connect(database) + query = f"UPDATE {SURREAL_TABLE}:`{id}` CONTENT {{{key}: '{text}'}};" + connection.query(query) + return () \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/save_to_s3.py b/custom_nodes/comfyui-tensorops/nodes/save_to_s3.py new file mode 100644 index 0000000000000000000000000000000000000000..0ed55ca2d9c85522e70e31fd85bad9317f94e7d4 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/save_to_s3.py @@ -0,0 +1,73 @@ +from .config import DATALAKE_AWS_ACCESS_KEY_ID, DATALAKE_AWS_ENDPOINT_URL, DATALAKE_AWS_SECRET_ACCESS_KEY, BUCKET +import boto3 +from botocore.client import Config +import torch +import PIL +import PIL.Image +import io +import logging +import numpy as np + +logger = logging.getLogger(__name__) + +S3_RESOURCE = None + +def init_s3(): + global S3_RESOURCE + if not S3_RESOURCE: + S3_ENDPOINT_URL = DATALAKE_AWS_ENDPOINT_URL + AWS_ACCESS_KEY_ID = DATALAKE_AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY = DATALAKE_AWS_SECRET_ACCESS_KEY + # Initialize the S3 client + S3_RESOURCE = boto3.resource( + "s3", + endpoint_url=S3_ENDPOINT_URL, + aws_access_key_id=AWS_ACCESS_KEY_ID, + aws_secret_access_key=AWS_SECRET_ACCESS_KEY, + config=Config(signature_version="s3v4"), + ) + +def store_image(key: str, image: PIL.Image.Image): + global S3_RESOURCE + init_s3() + try: + s3_bucket = S3_RESOURCE.Bucket(BUCKET) + file_content = io.BytesIO() + image.save(file_content, format="webp") + file_content.seek(0) + logger.info(f"StoreImage: {key} in {BUCKET}") + s3_bucket.put_object(Key=key, Body=file_content) + except Exception as e: + logger.error(f"StoreContentError {key}: {e}") + +class SaveImageToS3: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "database": ("STRING", {"multiline": False}), + "key": ("STRING", {"multiline": False}), + "image": ("IMAGE",), + }, + } + + RETURN_TYPES = () + + FUNCTION = "main" + OUTPUT_NODE = True + CATEGORY = "database_ops" + + def main(self, database: str, key: str, image: torch.Tensor): + B = image.shape[0] + for i in range(B): + im = image[i] + img_array = im.squeeze(0).cpu().numpy() * 255.0 + img_pil = PIL.Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8)) + if i > 0: + final_key = f"{database}/{key}-{i}.webp" + else: + final_key = f"{database}/{key}.webp" + print("final_key", final_key) + store_image(final_key, img_pil) + return () \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/separate_mask.py b/custom_nodes/comfyui-tensorops/nodes/separate_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..88af2a287e35df14fd7670ef885a29ac468bc04f --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/separate_mask.py @@ -0,0 +1,138 @@ +from typing import List +import numpy as np +import torchvision.transforms.functional as F +import PIL.Image +import PIL.ImageChops +from typing import List, Tuple +import torch +from scipy.ndimage import label, find_objects + + +def find_bounding_boxes(image: PIL.Image.Image) -> List[Tuple[int, int, int, int]]: + """ + Find the smallest non-overlapping bounding boxes that contain all white values in a grayscale image. + + Parameters: + image_path (str): Path to the grayscale image. + + Returns: + list: A list of tuples representing the bounding box coordinates (x_min, y_min, x_max, y_max). + """ + # Load image and convert to numpy array + image = image.convert("L") + image_array = np.array(image) + + # Create a binary mask of white pixels + binary_mask = image_array > 0 + + # Label connected components + labeled_array, num_features = label(binary_mask) + + # Find bounding boxes for each labeled region + bounding_boxes = [] + slices = find_objects(labeled_array) + + for slice_tuple in slices: + if slice_tuple is not None: + y_min, y_max = slice_tuple[0].start, slice_tuple[0].stop + x_min, x_max = slice_tuple[1].start, slice_tuple[1].stop + bounding_boxes.append((x_min, y_min, x_max, y_max)) + + return bounding_boxes + + +def get_padded_image(src_image: PIL.Image.Image, desired_width: int, desired_height: int) -> PIL.Image.Image: + # Get the dimensions of the original masked image + original_width, original_height = src_image.size + + # Create a new image with the desired dimensions and a transparent background + padded_image = PIL.Image.new("RGBA", (desired_width, desired_height), (0, 0, 0, 0)) + + # Calculate the position to paste the masked image (center it in the new padded image) + x_offset = (desired_width - original_width) // 2 + y_offset = (desired_height - original_height) // 2 + + # Paste the original masked image onto the padded image + padded_image.paste(src_image, (x_offset, y_offset), src_image) + return padded_image + +def select_element( + src_image: PIL.Image.Image, + mask_image: PIL.Image.Image, + bboxes +) -> List[PIL.Image.Image]: + """ + Select an element from an element image and place it on a background image using a mask. + + Parameters: + mask_image (PIL.Image.Image): A binary mask image. + background_image (PIL.Image.Image): A background image. + element_image (PIL.Image.Image): An element image. + + Returns: + PIL.Image.Image: The composite image with the element placed on the background. + """ + mask_image = mask_image.convert("L") + data = [] + cropped_rgbs = [] + area_min_threshold = 1000 + fixed_width = max(box[2]-box[1] for box in bboxes) + fixed_height = max(box[3]-box[1] for box in bboxes) + fixed_width = int(fixed_width) + fixed_height = int(fixed_height) + for bbox in bboxes: + print(bbox) + x_min, y_min, x_max, y_max = bbox + x_min = int(x_min) + y_min = int(y_min) + x_max = int(x_max) + y_max = int(y_max) + # Crop the bounding box area from the mask and RGBA images + mask_crop = mask_image.crop((x_min, y_min, x_max, y_max)) + rgba_crop = src_image.crop((x_min, y_min, x_max, y_max)) + + # Apply the mask to the alpha channel of the RGBA image + r, g, b, a = rgba_crop.split() + a = PIL.Image.composite(a, mask_crop, mask_crop) + masked_image = PIL.Image.merge("RGBA", (r, g, b, a)) + + if masked_image.size[0] * masked_image.size[1] > area_min_threshold: + masked_output = PIL.Image.new("RGBA", src_image.size, (255, 255, 255, 255)) + masked_output.paste(masked_image, (x_min, y_min)) + masked_output = masked_output.split()[3] + data.append(masked_output) + fixed_size_image = get_padded_image(PIL.Image.merge("RGBA", (r, g, b, PIL.ImageChops.invert(a))), fixed_width, fixed_height) + cropped_rgbs.append(fixed_size_image) + return data, cropped_rgbs + + +class SeparateMask: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "bboxes": ("BBOX",), + }, + } + + RETURN_TYPES = ("MASK", "IMAGE") + + FUNCTION = "main" + + CATEGORY = "tensorops" + + def main(self, image: torch.Tensor, mask: torch.Tensor, bboxes): + img_array = image.squeeze(0).cpu().numpy() * 255.0 + mask_array = mask.squeeze(0).cpu().numpy() * 255.0 + mask_pil = PIL.Image.fromarray(np.clip(mask_array, 0, 255).astype(np.uint8)) + img_pil = PIL.Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8)).convert("RGBA") + masks, images = select_element(img_pil, mask_pil, bboxes) + masks_items = [F.to_tensor(item) for item in masks] + images_items = [F.to_tensor(item).permute(1, 2, 0).unsqueeze(0) for item in images] + out_mask = torch.cat(masks_items, dim=0) + out_image = torch.cat(images_items, dim=0) + print(out_image.shape) + return (out_mask, out_image,) diff --git a/custom_nodes/comfyui-tensorops/nodes/stream.py b/custom_nodes/comfyui-tensorops/nodes/stream.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c23aa4b9456dcb6ebde8056b1f3af8642222ad --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/stream.py @@ -0,0 +1,46 @@ +from __future__ import annotations +from PIL import Image +import numpy as np +from server import PromptServer, BinaryEventTypes + +class SendImageOnWebSocket: + @classmethod + def INPUT_TYPES(s): + return {"required": {"event": ("STRING", {"multiline": False}), "images": ("IMAGE",)}} + + RETURN_TYPES = () + FUNCTION = "send_images" + OUTPUT_NODE = True + CATEGORY = "tensorops" + + def send_images(self, event, images): + for tensor in images: + array = 255.0 * tensor.cpu().numpy() + image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8)) + server = PromptServer.instance + server.send_sync( + BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, + ["PNG", image, event], + server.client_id, + ) + return () + + +class SendJsonOnWebSocket: + @classmethod + def INPUT_TYPES(s): + return {"required": {"event": ("STRING", {"multiline": False}), "json": ("JSON",)}} + + RETURN_TYPES = () + FUNCTION = "send_json" + OUTPUT_NODE = True + CATEGORY = "tensorops" + + def send_json(self, event, json): + server = PromptServer.instance + server.send_sync( + event, + json, + server.client_id, + ) + return () \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/nodes/surreal.py b/custom_nodes/comfyui-tensorops/nodes/surreal.py new file mode 100644 index 0000000000000000000000000000000000000000..7ebf14e4575239a6ccbe34ce216f1531ee94f13f --- /dev/null +++ b/custom_nodes/comfyui-tensorops/nodes/surreal.py @@ -0,0 +1,7 @@ +from surrealist import Surreal +from .config import SURREAL_URL, SURREAL_NAMESPACE, SURREAL_USER, SURREAL_PASSWORD + +def surreal_connect(database: str): + surreal_client = Surreal(SURREAL_URL, namespace=SURREAL_NAMESPACE, database=database, credentials=(SURREAL_USER, SURREAL_PASSWORD), use_http=True, timeout=10) + surreal_connection = surreal_client.connect() + return surreal_connection \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/pyproject.toml b/custom_nodes/comfyui-tensorops/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..ba593b64025bd06b61332c03ea8b9e54cd77c6c2 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "comfyui-tensorops" +description = "Nodes to perform tensor operations in ComfyUI" +version = "1.0.0" +license = "MIT" +dependencies = ["transformers>=4.38.0"] + +[project.urls] +Repository = "https://github.com/un-seen/comfyui-tensorops" +# Used by Comfy Registry https://comfyregistry.org + +[tool.comfy] +PublisherId = "shkr" +DisplayName = "comfyui-tensorops" +Icon = "" \ No newline at end of file diff --git a/custom_nodes/comfyui-tensorops/requirements.txt b/custom_nodes/comfyui-tensorops/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3eb931a61f9cc67a77195091e6285fdccb3f726 --- /dev/null +++ b/custom_nodes/comfyui-tensorops/requirements.txt @@ -0,0 +1,8 @@ +tqdm +numpy +surrealist +boto3==1.34.86 +redis +fal-client +scipy +replicate \ No newline at end of file diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example new file mode 100644 index 0000000000000000000000000000000000000000..29ab2aa72319354b147b7dd79e1c3179e54d3d06 --- /dev/null +++ b/custom_nodes/example_node.py.example @@ -0,0 +1,155 @@ +class Example: + """ + A example node + + Class methods + ------------- + INPUT_TYPES (dict): + Tell the main program input parameters of nodes. + IS_CHANGED: + optional method to control when the node is re executed. + + Attributes + ---------- + RETURN_TYPES (`tuple`): + The type of each element in the output tuple. + RETURN_NAMES (`tuple`): + Optional: The name of each output in the output tuple. + FUNCTION (`str`): + The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() + OUTPUT_NODE ([`bool`]): + If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example. + The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected. + Assumed to be False if not present. + CATEGORY (`str`): + The category the node should appear in the UI. + DEPRECATED (`bool`): + Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain + functional in existing workflows that use them. + EXPERIMENTAL (`bool`): + Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to + significant changes or removal in future versions. Use with caution in production workflows. + execute(s) -> tuple || None: + The entry point method. The name of this method must be the same as the value of property `FUNCTION`. + For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + """ + Return a dictionary which contains config for all input fields. + Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT". + Input types "INT", "STRING" or "FLOAT" are special values for fields on the node. + The type can be a list for selection. + + Returns: `dict`: + - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required` + - Value input_fields (`dict`): Contains input fields config: + * Key field_name (`string`): Name of a entry-point method's argument + * Value field_config (`tuple`): + + First value is a string indicate the type of field or a list for selection. + + Second value is a config for type "INT", "STRING" or "FLOAT". + """ + return { + "required": { + "image": ("IMAGE",), + "int_field": ("INT", { + "default": 0, + "min": 0, #Minimum value + "max": 4096, #Maximum value + "step": 64, #Slider's step + "display": "number", # Cosmetic only: display as "number" or "slider" + "lazy": True # Will only be evaluated if check_lazy_status requires it + }), + "float_field": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.01, + "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. + "display": "number", + "lazy": True + }), + "print_to_screen": (["enable", "disable"],), + "string_field": ("STRING", { + "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node + "default": "Hello World!", + "lazy": True + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + #RETURN_NAMES = ("image_output_name",) + + FUNCTION = "test" + + #OUTPUT_NODE = False + + CATEGORY = "Example" + + def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen): + """ + Return a list of input names that need to be evaluated. + + This function will be called if there are any lazy inputs which have not yet been + evaluated. As long as you return at least one field which has not yet been evaluated + (and more exist), this function will be called again once the value of the requested + field is available. + + Any evaluated inputs will be passed as arguments to this function. Any unevaluated + inputs will have the value None. + """ + if print_to_screen == "enable": + return ["int_field", "float_field", "string_field"] + else: + return [] + + def test(self, image, string_field, int_field, float_field, print_to_screen): + if print_to_screen == "enable": + print(f"""Your input contains: + string_field aka input text: {string_field} + int_field: {int_field} + float_field: {float_field} + """) + #do some processing on the image, in this example I just invert it + image = 1.0 - image + return (image,) + + """ + The node will always be re executed if any of the inputs change but + this method can be used to force the node to execute again even when the inputs don't change. + You can make this node return a number or a string. This value will be compared to the one returned the last time the node was + executed, if it is different the node will be executed again. + This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash + changes between executions the LoadImage node is executed again. + """ + #@classmethod + #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen): + # return "" + +# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension +# WEB_DIRECTORY = "./somejs" + + +# Add custom API routes, using router +from aiohttp import web +from server import PromptServer + +@PromptServer.instance.routes.get("/hello") +async def get_hello(request): + return web.json_response("hello") + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "Example": Example +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "Example": "Example Node" +} diff --git a/custom_nodes/websocket_image_save.py b/custom_nodes/websocket_image_save.py new file mode 100644 index 0000000000000000000000000000000000000000..15f87f9f56175f33df18c6142f9e13c4503b1186 --- /dev/null +++ b/custom_nodes/websocket_image_save.py @@ -0,0 +1,44 @@ +from PIL import Image +import numpy as np +import comfy.utils +import time + +#You can use this node to save full size images through the websocket, the +#images will be sent in exactly the same format as the image previews: as +#binary images on the websocket with a 8 byte header indicating the type +#of binary message (first 4 bytes) and the image format (next 4 bytes). + +#Note that no metadata will be put in the images saved with this node. + +class SaveImageWebsocket: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ),} + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "api/image" + + def save_images(self, images): + pbar = comfy.utils.ProgressBar(images.shape[0]) + step = 0 + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) + step += 1 + + return {} + + @classmethod + def IS_CHANGED(s, images): + return time.time() + +NODE_CLASS_MAPPINGS = { + "SaveImageWebsocket": SaveImageWebsocket, +} diff --git a/execution.py b/execution.py new file mode 100644 index 0000000000000000000000000000000000000000..f6006fa123748a6571ee5bdb52884113fc18d9d7 --- /dev/null +++ b/execution.py @@ -0,0 +1,1047 @@ +import copy +import heapq +import inspect +import logging +import sys +import threading +import time +import traceback +from enum import Enum +from typing import List, Literal, NamedTuple, Optional + +import torch + +import comfy.model_management +import nodes +from comfy_execution.caching import ( + CacheKeySetID, + CacheKeySetInputSignature, + DependencyAwareCache, + HierarchicalCache, + LRUCache, +) +from comfy_execution.graph import ( + DynamicPrompt, + ExecutionBlocker, + ExecutionList, + get_input_info, +) +from comfy_execution.graph_utils import GraphBuilder, is_link +from comfy_execution.validation import validate_node_input + + +class ExecutionResult(Enum): + SUCCESS = 0 + FAILURE = 1 + PENDING = 2 + +class DuplicateNodeError(Exception): + pass + +class IsChangedCache: + def __init__(self, dynprompt, outputs_cache): + self.dynprompt = dynprompt + self.outputs_cache = outputs_cache + self.is_changed = {} + + def get(self, node_id): + if node_id in self.is_changed: + return self.is_changed[node_id] + + node = self.dynprompt.get_node(node_id) + class_type = node["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if not hasattr(class_def, "IS_CHANGED"): + self.is_changed[node_id] = False + return self.is_changed[node_id] + + if "is_changed" in node: + self.is_changed[node_id] = node["is_changed"] + return self.is_changed[node_id] + + # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED + input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None) + try: + is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED") + node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed] + except Exception as e: + logging.warning("WARNING: {}".format(e)) + node["is_changed"] = float("NaN") + finally: + self.is_changed[node_id] = node["is_changed"] + return self.is_changed[node_id] + + +class CacheType(Enum): + CLASSIC = 0 + LRU = 1 + DEPENDENCY_AWARE = 2 + + +class CacheSet: + def __init__(self, cache_type=None, cache_size=None): + if cache_type == CacheType.DEPENDENCY_AWARE: + self.init_dependency_aware_cache() + logging.info("Disabling intermediate node cache.") + elif cache_type == CacheType.LRU: + if cache_size is None: + cache_size = 0 + self.init_lru_cache(cache_size) + logging.info("Using LRU cache") + else: + self.init_classic_cache() + + self.all = [self.outputs, self.ui, self.objects] + + # Performs like the old cache -- dump data ASAP + def init_classic_cache(self): + self.outputs = HierarchicalCache(CacheKeySetInputSignature) + self.ui = HierarchicalCache(CacheKeySetInputSignature) + self.objects = HierarchicalCache(CacheKeySetID) + + def init_lru_cache(self, cache_size): + self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size) + self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size) + self.objects = HierarchicalCache(CacheKeySetID) + + # only hold cached items while the decendents have not executed + def init_dependency_aware_cache(self): + self.outputs = DependencyAwareCache(CacheKeySetInputSignature) + self.ui = DependencyAwareCache(CacheKeySetInputSignature) + self.objects = DependencyAwareCache(CacheKeySetID) + + def recursive_debug_dump(self): + result = { + "outputs": self.outputs.recursive_debug_dump(), + "ui": self.ui.recursive_debug_dump(), + } + return result + +def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}): + valid_inputs = class_def.INPUT_TYPES() + input_data_all = {} + missing_keys = {} + for x in inputs: + input_data = inputs[x] + _, input_category, input_info = get_input_info(class_def, x, valid_inputs) + def mark_missing(): + missing_keys[x] = True + input_data_all[x] = (None,) + if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)): + input_unique_id = input_data[0] + output_index = input_data[1] + if outputs is None: + mark_missing() + continue # This might be a lazily-evaluated input + cached_output = outputs.get(input_unique_id) + if cached_output is None: + mark_missing() + continue + if output_index >= len(cached_output): + mark_missing() + continue + obj = cached_output[output_index] + input_data_all[x] = obj + elif input_category is not None: + input_data_all[x] = [input_data] + + if "hidden" in valid_inputs: + h = valid_inputs["hidden"] + for x in h: + if h[x] == "PROMPT": + input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] + if h[x] == "DYNPROMPT": + input_data_all[x] = [dynprompt] + if h[x] == "EXTRA_PNGINFO": + input_data_all[x] = [extra_data.get('extra_pnginfo', None)] + if h[x] == "UNIQUE_ID": + input_data_all[x] = [unique_id] + if h[x] == "AUTH_TOKEN_COMFY_ORG": + input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)] + if h[x] == "API_KEY_COMFY_ORG": + input_data_all[x] = [extra_data.get("api_key_comfy_org", None)] + return input_data_all, missing_keys + +map_node_over_list = None #Don't hook this please + +def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None): + # check if node wants the lists + input_is_list = getattr(obj, "INPUT_IS_LIST", False) + + if len(input_data_all) == 0: + max_len_input = 0 + else: + max_len_input = max(len(x) for x in input_data_all.values()) + + # get a slice of inputs, repeat last input when list isn't long enough + def slice_dict(d, i): + return {k: v[i if len(v) > i else -1] for k, v in d.items()} + + results = [] + def process_inputs(inputs, index=None, input_is_list=False): + if allow_interrupt: + nodes.before_node_execution() + execution_block = None + for k, v in inputs.items(): + if input_is_list: + for e in v: + if isinstance(e, ExecutionBlocker): + v = e + break + if isinstance(v, ExecutionBlocker): + execution_block = execution_block_cb(v) if execution_block_cb else v + break + if execution_block is None: + if pre_execute_cb is not None and index is not None: + pre_execute_cb(index) + results.append(getattr(obj, func)(**inputs)) + else: + results.append(execution_block) + + if input_is_list: + process_inputs(input_data_all, 0, input_is_list=input_is_list) + elif max_len_input == 0: + process_inputs({}) + else: + for i in range(max_len_input): + input_dict = slice_dict(input_data_all, i) + process_inputs(input_dict, i) + return results + +def merge_result_data(results, obj): + # check which outputs need concatenating + output = [] + output_is_list = [False] * len(results[0]) + if hasattr(obj, "OUTPUT_IS_LIST"): + output_is_list = obj.OUTPUT_IS_LIST + + # merge node execution results + for i, is_list in zip(range(len(results[0])), output_is_list): + if is_list: + value = [] + for o in results: + if isinstance(o[i], ExecutionBlocker): + value.append(o[i]) + else: + value.extend(o[i]) + output.append(value) + else: + output.append([o[i] for o in results]) + return output + +def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None): + results = [] + uis = [] + subgraph_results = [] + return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) + has_subgraph = False + for i in range(len(return_values)): + r = return_values[i] + if isinstance(r, dict): + if 'ui' in r: + uis.append(r['ui']) + if 'expand' in r: + # Perform an expansion, but do not append results + has_subgraph = True + new_graph = r['expand'] + result = r.get("result", None) + if isinstance(result, ExecutionBlocker): + result = tuple([result] * len(obj.RETURN_TYPES)) + subgraph_results.append((new_graph, result)) + elif 'result' in r: + result = r.get("result", None) + if isinstance(result, ExecutionBlocker): + result = tuple([result] * len(obj.RETURN_TYPES)) + results.append(result) + subgraph_results.append((None, result)) + else: + if isinstance(r, ExecutionBlocker): + r = tuple([r] * len(obj.RETURN_TYPES)) + results.append(r) + subgraph_results.append((None, r)) + + if has_subgraph: + output = subgraph_results + elif len(results) > 0: + output = merge_result_data(results, obj) + else: + output = [] + ui = dict() + if len(uis) > 0: + ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} + return output, ui, has_subgraph + +def format_value(x): + if x is None: + return None + elif isinstance(x, (int, float, bool, str)): + return x + else: + return str(x) + +def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results): + unique_id = current_item + real_node_id = dynprompt.get_real_node_id(unique_id) + display_node_id = dynprompt.get_display_node_id(unique_id) + parent_node_id = dynprompt.get_parent_node_id(unique_id) + inputs = dynprompt.get_node(unique_id)['inputs'] + class_type = dynprompt.get_node(unique_id)['class_type'] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if caches.outputs.get(unique_id) is not None: + if server.client_id is not None: + cached_output = caches.ui.get(unique_id) or {} + server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id) + return (ExecutionResult.SUCCESS, None, None) + + input_data_all = None + try: + if unique_id in pending_subgraph_results: + cached_results = pending_subgraph_results[unique_id] + resolved_outputs = [] + for is_subgraph, result in cached_results: + if not is_subgraph: + resolved_outputs.append(result) + else: + resolved_output = [] + for r in result: + if is_link(r): + source_node, source_output = r[0], r[1] + node_output = caches.outputs.get(source_node)[source_output] + for o in node_output: + resolved_output.append(o) + + else: + resolved_output.append(r) + resolved_outputs.append(tuple(resolved_output)) + output_data = merge_result_data(resolved_outputs, class_def) + output_ui = [] + has_subgraph = False + else: + input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data) + if server.client_id is not None: + server.last_node_id = display_node_id + server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id) + + obj = caches.objects.get(unique_id) + if obj is None: + obj = class_def() + caches.objects.set(unique_id, obj) + + if hasattr(obj, "check_lazy_status"): + required_inputs = _map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True) + required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], [])) + required_inputs = [x for x in required_inputs if isinstance(x,str) and ( + x not in input_data_all or x in missing_keys + )] + if len(required_inputs) > 0: + for i in required_inputs: + execution_list.make_input_strong_link(unique_id, i) + return (ExecutionResult.PENDING, None, None) + + def execution_block_cb(block): + if block.message is not None: + mes = { + "prompt_id": prompt_id, + "node_id": unique_id, + "node_type": class_type, + "executed": list(executed), + + "exception_message": f"Execution Blocked: {block.message}", + "exception_type": "ExecutionBlocked", + "traceback": [], + "current_inputs": [], + "current_outputs": [], + } + server.send_sync("execution_error", mes, server.client_id) + return ExecutionBlocker(None) + else: + return block + def pre_execute_cb(call_index): + GraphBuilder.set_default_prefix(unique_id, call_index, 0) + output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) + if len(output_ui) > 0: + caches.ui.set(unique_id, { + "meta": { + "node_id": unique_id, + "display_node": display_node_id, + "parent_node": parent_node_id, + "real_node_id": real_node_id, + }, + "output": output_ui + }) + if server.client_id is not None: + server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) + if has_subgraph: + cached_outputs = [] + new_node_ids = [] + new_output_ids = [] + new_output_links = [] + for i in range(len(output_data)): + new_graph, node_outputs = output_data[i] + if new_graph is None: + cached_outputs.append((False, node_outputs)) + else: + # Check for conflicts + for node_id in new_graph.keys(): + if dynprompt.has_node(node_id): + raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.") + for node_id, node_info in new_graph.items(): + new_node_ids.append(node_id) + display_id = node_info.get("override_display_id", unique_id) + dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id) + # Figure out if the newly created node is an output node + class_type = node_info["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: + new_output_ids.append(node_id) + for i in range(len(node_outputs)): + if is_link(node_outputs[i]): + from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1] + new_output_links.append((from_node_id, from_socket)) + cached_outputs.append((True, node_outputs)) + new_node_ids = set(new_node_ids) + for cache in caches.all: + cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused() + for node_id in new_output_ids: + execution_list.add_node(node_id) + for link in new_output_links: + execution_list.add_strong_link(link[0], link[1], unique_id) + pending_subgraph_results[unique_id] = cached_outputs + return (ExecutionResult.PENDING, None, None) + caches.outputs.set(unique_id, output_data) + except comfy.model_management.InterruptProcessingException as iex: + logging.info("Processing interrupted") + + # skip formatting inputs/outputs + error_details = { + "node_id": real_node_id, + } + + return (ExecutionResult.FAILURE, error_details, iex) + except Exception as ex: + typ, _, tb = sys.exc_info() + exception_type = full_type_name(typ) + input_data_formatted = {} + if input_data_all is not None: + input_data_formatted = {} + for name, inputs in input_data_all.items(): + input_data_formatted[name] = [format_value(x) for x in inputs] + + logging.error(f"!!! Exception during processing !!! {ex}") + logging.error(traceback.format_exc()) + tips = "" + + if isinstance(ex, comfy.model_management.OOM_EXCEPTION): + tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number." + logging.error("Got an OOM, unloading all loaded models.") + comfy.model_management.unload_all_models() + + error_details = { + "node_id": real_node_id, + "exception_message": "{}\n{}".format(ex, tips), + "exception_type": exception_type, + "traceback": traceback.format_tb(tb), + "current_inputs": input_data_formatted + } + + return (ExecutionResult.FAILURE, error_details, ex) + + executed.add(unique_id) + + return (ExecutionResult.SUCCESS, None, None) + +class PromptExecutor: + def __init__(self, server, cache_type=False, cache_size=None): + self.cache_size = cache_size + self.cache_type = cache_type + self.server = server + self.reset() + + def reset(self): + self.caches = CacheSet(cache_type=self.cache_type, cache_size=self.cache_size) + self.status_messages = [] + self.success = True + + def add_message(self, event, data: dict, broadcast: bool): + data = { + **data, + "timestamp": int(time.time() * 1000), + } + self.status_messages.append((event, data)) + if self.server.client_id is not None or broadcast: + self.server.send_sync(event, data, self.server.client_id) + + def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex): + node_id = error["node_id"] + class_type = prompt[node_id]["class_type"] + + # First, send back the status to the frontend depending + # on the exception type + if isinstance(ex, comfy.model_management.InterruptProcessingException): + mes = { + "prompt_id": prompt_id, + "node_id": node_id, + "node_type": class_type, + "executed": list(executed), + } + self.add_message("execution_interrupted", mes, broadcast=True) + else: + mes = { + "prompt_id": prompt_id, + "node_id": node_id, + "node_type": class_type, + "executed": list(executed), + "exception_message": error["exception_message"], + "exception_type": error["exception_type"], + "traceback": error["traceback"], + "current_inputs": error["current_inputs"], + "current_outputs": list(current_outputs), + } + self.add_message("execution_error", mes, broadcast=False) + + def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): + nodes.interrupt_processing(False) + + if "client_id" in extra_data: + self.server.client_id = extra_data["client_id"] + else: + self.server.client_id = None + + self.status_messages = [] + self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False) + + with torch.inference_mode(): + dynamic_prompt = DynamicPrompt(prompt) + is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs) + for cache in self.caches.all: + cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) + cache.clean_unused() + + cached_nodes = [] + for node_id in prompt: + if self.caches.outputs.get(node_id) is not None: + cached_nodes.append(node_id) + + comfy.model_management.cleanup_models_gc() + self.add_message("execution_cached", + { "nodes": cached_nodes, "prompt_id": prompt_id}, + broadcast=False) + pending_subgraph_results = {} + executed = set() + execution_list = ExecutionList(dynamic_prompt, self.caches.outputs) + current_outputs = self.caches.outputs.all_node_ids() + for node_id in list(execute_outputs): + execution_list.add_node(node_id) + + while not execution_list.is_empty(): + node_id, error, ex = execution_list.stage_node_execution() + if error is not None: + self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) + break + + result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results) + self.success = result != ExecutionResult.FAILURE + if result == ExecutionResult.FAILURE: + self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) + break + elif result == ExecutionResult.PENDING: + execution_list.unstage_node_execution() + else: # result == ExecutionResult.SUCCESS: + execution_list.complete_node_execution() + else: + # Only execute when the while-loop ends without break + self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False) + + ui_outputs = {} + meta_outputs = {} + all_node_ids = self.caches.ui.all_node_ids() + for node_id in all_node_ids: + ui_info = self.caches.ui.get(node_id) + if ui_info is not None: + ui_outputs[node_id] = ui_info["output"] + meta_outputs[node_id] = ui_info["meta"] + self.history_result = { + "outputs": ui_outputs, + "meta": meta_outputs, + } + self.server.last_node_id = None + if comfy.model_management.DISABLE_SMART_MEMORY: + comfy.model_management.unload_all_models() + + +def validate_inputs(prompt, item, validated): + unique_id = item + if unique_id in validated: + return validated[unique_id] + + inputs = prompt[unique_id]['inputs'] + class_type = prompt[unique_id]['class_type'] + obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] + + class_inputs = obj_class.INPUT_TYPES() + valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{}))) + + errors = [] + valid = True + + validate_function_inputs = [] + validate_has_kwargs = False + if hasattr(obj_class, "VALIDATE_INPUTS"): + argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS) + validate_function_inputs = argspec.args + validate_has_kwargs = argspec.varkw is not None + received_types = {} + + for x in valid_inputs: + input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs) + assert extra_info is not None + if x not in inputs: + if input_category == "required": + error = { + "type": "required_input_missing", + "message": "Required input is missing", + "details": f"{x}", + "extra_info": { + "input_name": x + } + } + errors.append(error) + continue + + val = inputs[x] + info = (input_type, extra_info) + if isinstance(val, list): + if len(val) != 2: + error = { + "type": "bad_linked_input", + "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]", + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val + } + } + errors.append(error) + continue + + o_id = val[0] + o_class_type = prompt[o_id]['class_type'] + r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES + received_type = r[val[1]] + received_types[x] = received_type + if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type): + details = f"{x}, received_type({received_type}) mismatch input_type({input_type})" + error = { + "type": "return_type_mismatch", + "message": "Return type mismatch between linked nodes", + "details": details, + "extra_info": { + "input_name": x, + "input_config": info, + "received_type": received_type, + "linked_node": val + } + } + errors.append(error) + continue + try: + r = validate_inputs(prompt, o_id, validated) + if r[0] is False: + # `r` will be set in `validated[o_id]` already + valid = False + continue + except Exception as ex: + typ, _, tb = sys.exc_info() + valid = False + exception_type = full_type_name(typ) + reasons = [{ + "type": "exception_during_inner_validation", + "message": "Exception when validating inner node", + "details": str(ex), + "extra_info": { + "input_name": x, + "input_config": info, + "exception_message": str(ex), + "exception_type": exception_type, + "traceback": traceback.format_tb(tb), + "linked_node": val + } + }] + validated[o_id] = (False, reasons, o_id) + continue + else: + try: + # Unwraps values wrapped in __value__ key. This is used to pass + # list widget value to execution, as by default list value is + # reserved to represent the connection between nodes. + if isinstance(val, dict) and "__value__" in val: + val = val["__value__"] + inputs[x] = val + + if input_type == "INT": + val = int(val) + inputs[x] = val + if input_type == "FLOAT": + val = float(val) + inputs[x] = val + if input_type == "STRING": + val = str(val) + inputs[x] = val + if input_type == "BOOLEAN": + val = bool(val) + inputs[x] = val + except Exception as ex: + error = { + "type": "invalid_input_type", + "message": f"Failed to convert an input value to a {input_type} value", + "details": f"{x}, {val}, {ex}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + "exception_message": str(ex) + } + } + errors.append(error) + continue + + if x not in validate_function_inputs and not validate_has_kwargs: + if "min" in extra_info and val < extra_info["min"]: + error = { + "type": "value_smaller_than_min", + "message": "Value {} smaller than min of {}".format(val, extra_info["min"]), + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + if "max" in extra_info and val > extra_info["max"]: + error = { + "type": "value_bigger_than_max", + "message": "Value {} bigger than max of {}".format(val, extra_info["max"]), + "details": f"{x}", + "extra_info": { + "input_name": x, + "input_config": info, + "received_value": val, + } + } + errors.append(error) + continue + + if isinstance(input_type, list): + combo_options = input_type + if val not in combo_options: + input_config = info + list_info = "" + + # Don't send back gigantic lists like if they're lots of + # scanned model filepaths + if len(combo_options) > 20: + list_info = f"(list of length {len(combo_options)})" + input_config = None + else: + list_info = str(combo_options) + + error = { + "type": "value_not_in_list", + "message": "Value not in list", + "details": f"{x}: '{val}' not in {list_info}", + "extra_info": { + "input_name": x, + "input_config": input_config, + "received_value": val, + } + } + errors.append(error) + continue + + if len(validate_function_inputs) > 0 or validate_has_kwargs: + input_data_all, _ = get_input_data(inputs, obj_class, unique_id) + input_filtered = {} + for x in input_data_all: + if x in validate_function_inputs or validate_has_kwargs: + input_filtered[x] = input_data_all[x] + if 'input_types' in validate_function_inputs: + input_filtered['input_types'] = [received_types] + + #ret = obj_class.VALIDATE_INPUTS(**input_filtered) + ret = _map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS") + for x in input_filtered: + for i, r in enumerate(ret): + if r is not True and not isinstance(r, ExecutionBlocker): + details = f"{x}" + if r is not False: + details += f" - {str(r)}" + + error = { + "type": "custom_validation_failed", + "message": "Custom validation failed for node", + "details": details, + "extra_info": { + "input_name": x, + } + } + errors.append(error) + continue + + if len(errors) > 0 or valid is not True: + ret = (False, errors, unique_id) + else: + ret = (True, [], unique_id) + + validated[unique_id] = ret + return ret + +def full_type_name(klass): + module = klass.__module__ + if module == 'builtins': + return klass.__qualname__ + return module + '.' + klass.__qualname__ + +def validate_prompt(prompt): + outputs = set() + for x in prompt: + if 'class_type' not in prompt[x]: + error = { + "type": "invalid_prompt", + "message": "Cannot execute because a node is missing the class_type property.", + "details": f"Node ID '#{x}'", + "extra_info": {} + } + return (False, error, [], {}) + + class_type = prompt[x]['class_type'] + class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) + if class_ is None: + error = { + "type": "invalid_prompt", + "message": f"Cannot execute because node {class_type} does not exist.", + "details": f"Node ID '#{x}'", + "extra_info": {} + } + return (False, error, [], {}) + + if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: + outputs.add(x) + + if len(outputs) == 0: + error = { + "type": "prompt_no_outputs", + "message": "Prompt has no outputs", + "details": "", + "extra_info": {} + } + return (False, error, [], {}) + + good_outputs = set() + errors = [] + node_errors = {} + validated = {} + for o in outputs: + valid = False + reasons = [] + try: + m = validate_inputs(prompt, o, validated) + valid = m[0] + reasons = m[1] + except Exception as ex: + typ, _, tb = sys.exc_info() + valid = False + exception_type = full_type_name(typ) + reasons = [{ + "type": "exception_during_validation", + "message": "Exception when validating node", + "details": str(ex), + "extra_info": { + "exception_type": exception_type, + "traceback": traceback.format_tb(tb) + } + }] + validated[o] = (False, reasons, o) + + if valid is True: + good_outputs.add(o) + else: + logging.error(f"Failed to validate prompt for output {o}:") + if len(reasons) > 0: + logging.error("* (prompt):") + for reason in reasons: + logging.error(f" - {reason['message']}: {reason['details']}") + errors += [(o, reasons)] + for node_id, result in validated.items(): + valid = result[0] + reasons = result[1] + # If a node upstream has errors, the nodes downstream will also + # be reported as invalid, but there will be no errors attached. + # So don't return those nodes as having errors in the response. + if valid is not True and len(reasons) > 0: + if node_id not in node_errors: + class_type = prompt[node_id]['class_type'] + node_errors[node_id] = { + "errors": reasons, + "dependent_outputs": [], + "class_type": class_type + } + logging.error(f"* {class_type} {node_id}:") + for reason in reasons: + logging.error(f" - {reason['message']}: {reason['details']}") + node_errors[node_id]["dependent_outputs"].append(o) + logging.error("Output will be ignored") + + if len(good_outputs) == 0: + errors_list = [] + for o, errors in errors: + for error in errors: + errors_list.append(f"{error['message']}: {error['details']}") + errors_list = "\n".join(errors_list) + + error = { + "type": "prompt_outputs_failed_validation", + "message": "Prompt outputs failed validation", + "details": errors_list, + "extra_info": {} + } + + return (False, error, list(good_outputs), node_errors) + + return (True, None, list(good_outputs), node_errors) + +MAXIMUM_HISTORY_SIZE = 10000 + +class PromptQueue: + def __init__(self, server): + self.server = server + self.mutex = threading.RLock() + self.not_empty = threading.Condition(self.mutex) + self.task_counter = 0 + self.queue = [] + self.currently_running = {} + self.history = {} + self.flags = {} + + def put(self, item): + with self.mutex: + heapq.heappush(self.queue, item) + self.server.queue_updated() + self.not_empty.notify() + + def get(self, timeout=None): + with self.not_empty: + while len(self.queue) == 0: + self.not_empty.wait(timeout=timeout) + if timeout is not None and len(self.queue) == 0: + return None + item = heapq.heappop(self.queue) + i = self.task_counter + self.currently_running[i] = copy.deepcopy(item) + self.task_counter += 1 + self.server.queue_updated() + return (item, i) + + class ExecutionStatus(NamedTuple): + status_str: Literal['success', 'error'] + completed: bool + messages: List[str] + + def task_done(self, item_id, history_result, + status: Optional['PromptQueue.ExecutionStatus']): + with self.mutex: + prompt = self.currently_running.pop(item_id) + if len(self.history) > MAXIMUM_HISTORY_SIZE: + self.history.pop(next(iter(self.history))) + + status_dict: Optional[dict] = None + if status is not None: + status_dict = copy.deepcopy(status._asdict()) + + self.history[prompt[1]] = { + "prompt": prompt, + "outputs": {}, + 'status': status_dict, + } + self.history[prompt[1]].update(history_result) + self.server.queue_updated() + + # Note: slow + def get_current_queue(self): + with self.mutex: + out = [] + for x in self.currently_running.values(): + out += [x] + return (out, copy.deepcopy(self.queue)) + + # read-safe as long as queue items are immutable + def get_current_queue_volatile(self): + with self.mutex: + running = [x for x in self.currently_running.values()] + queued = copy.copy(self.queue) + return (running, queued) + + def get_tasks_remaining(self): + with self.mutex: + return len(self.queue) + len(self.currently_running) + + def wipe_queue(self): + with self.mutex: + self.queue = [] + self.server.queue_updated() + + def delete_queue_item(self, function): + with self.mutex: + for x in range(len(self.queue)): + if function(self.queue[x]): + if len(self.queue) == 1: + self.wipe_queue() + else: + self.queue.pop(x) + heapq.heapify(self.queue) + self.server.queue_updated() + return True + return False + + def get_history(self, prompt_id=None, max_items=None, offset=-1): + with self.mutex: + if prompt_id is None: + out = {} + i = 0 + if offset < 0 and max_items is not None: + offset = len(self.history) - max_items + for k in self.history: + if i >= offset: + out[k] = self.history[k] + if max_items is not None and len(out) >= max_items: + break + i += 1 + return out + elif prompt_id in self.history: + return {prompt_id: copy.deepcopy(self.history[prompt_id])} + else: + return {} + + def wipe_history(self): + with self.mutex: + self.history = {} + + def delete_history_item(self, id_to_delete): + with self.mutex: + self.history.pop(id_to_delete, None) + + def set_flag(self, name, data): + with self.mutex: + self.flags[name] = data + self.not_empty.notify() + + def get_flags(self, reset=True): + with self.mutex: + if reset: + ret = self.flags + self.flags = {} + return ret + else: + return self.flags.copy() diff --git a/extra_model_paths.yaml.example b/extra_model_paths.yaml.example new file mode 100644 index 0000000000000000000000000000000000000000..b55913a5a9bcbd46b490e7f560b6632e5c780881 --- /dev/null +++ b/extra_model_paths.yaml.example @@ -0,0 +1,47 @@ +#Rename this to extra_model_paths.yaml and ComfyUI will load it + + +#config for a1111 ui +#all you have to do is change the base_path to where yours is installed +a111: + base_path: path/to/stable-diffusion-webui/ + + checkpoints: models/Stable-diffusion + configs: models/Stable-diffusion + vae: models/VAE + loras: | + models/Lora + models/LyCORIS + upscale_models: | + models/ESRGAN + models/RealESRGAN + models/SwinIR + embeddings: embeddings + hypernetworks: models/hypernetworks + controlnet: models/ControlNet + +#config for comfyui +#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc. + +#comfyui: +# base_path: path/to/comfyui/ +# # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads +# #is_default: true +# checkpoints: models/checkpoints/ +# clip: models/clip/ +# clip_vision: models/clip_vision/ +# configs: models/configs/ +# controlnet: models/controlnet/ +# diffusion_models: | +# models/diffusion_models +# models/unet +# embeddings: models/embeddings/ +# loras: models/loras/ +# upscale_models: models/upscale_models/ +# vae: models/vae/ + +#other_ui: +# base_path: path/to/ui +# checkpoints: models/checkpoints +# gligen: models/gligen +# custom_nodes: path/custom_nodes diff --git a/folder_paths.py b/folder_paths.py new file mode 100644 index 0000000000000000000000000000000000000000..9ec952940cdda022439f9dcd91c0d858b7801bf4 --- /dev/null +++ b/folder_paths.py @@ -0,0 +1,425 @@ +from __future__ import annotations + +import os +import time +import mimetypes +import logging +from typing import Literal, List +from collections.abc import Collection + +from comfy.cli_args import args + +supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'} + +folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {} + +# --base-directory - Resets all default paths configured in folder_paths with a new base path +if args.base_directory: + base_path = os.path.abspath(args.base_directory) +else: + base_path = os.path.dirname(os.path.realpath(__file__)) + +models_dir = os.path.join(base_path, "models") +folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) +folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) + +folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) +folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) +folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions) +folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions) +folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) +folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) +folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) +folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) +folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) + +folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) +folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) + +folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) + +folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set()) + +folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) + +folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) + +folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) + +output_directory = os.path.join(base_path, "output") +temp_directory = os.path.join(base_path, "temp") +input_directory = os.path.join(base_path, "input") +user_directory = os.path.join(base_path, "user") + +filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {} + +class CacheHelper: + """ + Helper class for managing file list cache data. + """ + def __init__(self): + self.cache: dict[str, tuple[list[str], dict[str, float], float]] = {} + self.active = False + + def get(self, key: str, default=None) -> tuple[list[str], dict[str, float], float]: + if not self.active: + return default + return self.cache.get(key, default) + + def set(self, key: str, value: tuple[list[str], dict[str, float], float]) -> None: + if self.active: + self.cache[key] = value + + def clear(self): + self.cache.clear() + + def __enter__(self): + self.active = True + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.active = False + self.clear() + +cache_helper = CacheHelper() + +extension_mimetypes_cache = { + "webp" : "image", + "fbx" : "model", +} + +def map_legacy(folder_name: str) -> str: + legacy = {"unet": "diffusion_models", + "clip": "text_encoders"} + return legacy.get(folder_name, folder_name) + +if not os.path.exists(input_directory): + try: + os.makedirs(input_directory) + except: + logging.error("Failed to create input directory") + +def set_output_directory(output_dir: str) -> None: + global output_directory + output_directory = output_dir + +def set_temp_directory(temp_dir: str) -> None: + global temp_directory + temp_directory = temp_dir + +def set_input_directory(input_dir: str) -> None: + global input_directory + input_directory = input_dir + +def get_output_directory() -> str: + global output_directory + return output_directory + +def get_temp_directory() -> str: + global temp_directory + return temp_directory + +def get_input_directory() -> str: + global input_directory + return input_directory + +def get_user_directory() -> str: + return user_directory + +def set_user_directory(user_dir: str) -> None: + global user_directory + user_directory = user_dir + + +#NOTE: used in http server so don't put folders that should not be accessed remotely +def get_directory_by_type(type_name: str) -> str | None: + if type_name == "output": + return get_output_directory() + if type_name == "temp": + return get_temp_directory() + if type_name == "input": + return get_input_directory() + return None + +def filter_files_content_types(files: list[str], content_types: List[Literal["image", "video", "audio", "model"]]) -> list[str]: + """ + Example: + files = os.listdir(folder_paths.get_input_directory()) + videos = filter_files_content_types(files, ["video"]) + + Note: + - 'model' in MIME context refers to 3D models, not files containing trained weights and parameters + """ + global extension_mimetypes_cache + result = [] + for file in files: + extension = file.split('.')[-1] + if extension not in extension_mimetypes_cache: + mime_type, _ = mimetypes.guess_type(file, strict=False) + if not mime_type: + continue + content_type = mime_type.split('/')[0] + extension_mimetypes_cache[extension] = content_type + else: + content_type = extension_mimetypes_cache[extension] + + if content_type in content_types: + result.append(file) + return result + +# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format +# otherwise use default_path as base_dir +def annotated_filepath(name: str) -> tuple[str, str | None]: + if name.endswith("[output]"): + base_dir = get_output_directory() + name = name[:-9] + elif name.endswith("[input]"): + base_dir = get_input_directory() + name = name[:-8] + elif name.endswith("[temp]"): + base_dir = get_temp_directory() + name = name[:-7] + else: + return name, None + + return name, base_dir + + +def get_annotated_filepath(name: str, default_dir: str | None=None) -> str: + name, base_dir = annotated_filepath(name) + + if base_dir is None: + if default_dir is not None: + base_dir = default_dir + else: + base_dir = get_input_directory() # fallback path + + return os.path.join(base_dir, name) + + +def exists_annotated_filepath(name) -> bool: + name, base_dir = annotated_filepath(name) + + if base_dir is None: + base_dir = get_input_directory() # fallback path + + filepath = os.path.join(base_dir, name) + return os.path.exists(filepath) + + +def add_model_folder_path(folder_name: str, full_folder_path: str, is_default: bool = False) -> None: + global folder_names_and_paths + folder_name = map_legacy(folder_name) + if folder_name in folder_names_and_paths: + paths, _exts = folder_names_and_paths[folder_name] + if full_folder_path in paths: + if is_default and paths[0] != full_folder_path: + # If the path to the folder is not the first in the list, move it to the beginning. + paths.remove(full_folder_path) + paths.insert(0, full_folder_path) + else: + if is_default: + paths.insert(0, full_folder_path) + else: + paths.append(full_folder_path) + else: + folder_names_and_paths[folder_name] = ([full_folder_path], set()) + +def get_folder_paths(folder_name: str) -> list[str]: + folder_name = map_legacy(folder_name) + return folder_names_and_paths[folder_name][0][:] + +def recursive_search(directory: str, excluded_dir_names: list[str] | None=None) -> tuple[list[str], dict[str, float]]: + if not os.path.isdir(directory): + return [], {} + + if excluded_dir_names is None: + excluded_dir_names = [] + + result = [] + dirs = {} + + # Attempt to add the initial directory to dirs with error handling + try: + dirs[directory] = os.path.getmtime(directory) + except FileNotFoundError: + logging.warning(f"Warning: Unable to access {directory}. Skipping this path.") + + logging.debug("recursive file list on directory {}".format(directory)) + dirpath: str + subdirs: list[str] + filenames: list[str] + + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + for file_name in filenames: + try: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + except: + logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.") + continue + + for d in subdirs: + path: str = os.path.join(dirpath, d) + try: + dirs[path] = os.path.getmtime(path) + except FileNotFoundError: + logging.warning(f"Warning: Unable to access {path}. Skipping this path.") + continue + logging.debug("found {} files".format(len(result))) + return result, dirs + +def filter_files_extensions(files: Collection[str], extensions: Collection[str]) -> list[str]: + return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) + + + +def get_full_path(folder_name: str, filename: str) -> str | None: + """ + Get the full path of a file in a folder, has to be a file + """ + global folder_names_and_paths + folder_name = map_legacy(folder_name) + if folder_name not in folder_names_and_paths: + return None + folders = folder_names_and_paths[folder_name] + filename = os.path.relpath(os.path.join("/", filename), "/") + for x in folders[0]: + full_path = os.path.join(x, filename) + if os.path.isfile(full_path): + return full_path + elif os.path.islink(full_path): + logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path)) + + return None + + +def get_full_path_or_raise(folder_name: str, filename: str) -> str: + """ + Get the full path of a file in a folder, has to be a file + """ + full_path = get_full_path(folder_name, filename) + if full_path is None: + raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.") + return full_path + + +def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]: + folder_name = map_legacy(folder_name) + global folder_names_and_paths + output_list = set() + folders = folder_names_and_paths[folder_name] + output_folders = {} + for x in folders[0]: + files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) + output_list.update(filter_files_extensions(files, folders[1])) + output_folders = {**output_folders, **folders_all} + + return sorted(list(output_list)), output_folders, time.perf_counter() + +def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None: + strong_cache = cache_helper.get(folder_name) + if strong_cache is not None: + return strong_cache + + global filename_list_cache + global folder_names_and_paths + folder_name = map_legacy(folder_name) + if folder_name not in filename_list_cache: + return None + out = filename_list_cache[folder_name] + + for x in out[1]: + time_modified = out[1][x] + folder = x + if os.path.getmtime(folder) != time_modified: + return None + + folders = folder_names_and_paths[folder_name] + for x in folders[0]: + if os.path.isdir(x): + if x not in out[1]: + return None + + return out + +def get_filename_list(folder_name: str) -> list[str]: + folder_name = map_legacy(folder_name) + out = cached_filename_list_(folder_name) + if out is None: + out = get_filename_list_(folder_name) + global filename_list_cache + filename_list_cache[folder_name] = out + cache_helper.set(folder_name, out) + return list(out[0]) + +def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]: + def map_filename(filename: str) -> tuple[int, str]: + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[:prefix_len + 1] + try: + digits = int(filename[prefix_len + 1:].split('_')[0]) + except: + digits = 0 + return digits, prefix + + def compute_vars(input: str, image_width: int, image_height: int) -> str: + input = input.replace("%width%", str(image_width)) + input = input.replace("%height%", str(image_height)) + now = time.localtime() + input = input.replace("%year%", str(now.tm_year)) + input = input.replace("%month%", str(now.tm_mon).zfill(2)) + input = input.replace("%day%", str(now.tm_mday).zfill(2)) + input = input.replace("%hour%", str(now.tm_hour).zfill(2)) + input = input.replace("%minute%", str(now.tm_min).zfill(2)) + input = input.replace("%second%", str(now.tm_sec).zfill(2)) + return input + + if "%" in filename_prefix: + filename_prefix = compute_vars(filename_prefix, image_width, image_height) + + subfolder = os.path.dirname(os.path.normpath(filename_prefix)) + filename = os.path.basename(os.path.normpath(filename_prefix)) + + full_output_folder = os.path.join(output_dir, subfolder) + + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: + err = "**** ERROR: Saving image outside the output folder is not allowed." + \ + "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ + "\n output_dir: " + output_dir + \ + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + logging.error(err) + raise Exception(err) + + try: + counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 + except ValueError: + counter = 1 + except FileNotFoundError: + os.makedirs(full_output_folder, exist_ok=True) + counter = 1 + return full_output_folder, filename, counter, subfolder, filename_prefix + +def get_input_subfolders() -> list[str]: + """Returns a list of all subfolder paths in the input directory, recursively. + + Returns: + List of folder paths relative to the input directory, excluding the root directory + """ + input_dir = get_input_directory() + folders = [] + + try: + if not os.path.exists(input_dir): + return [] + + for root, dirs, _ in os.walk(input_dir): + rel_path = os.path.relpath(root, input_dir) + if rel_path != ".": # Only include non-root directories + # Normalize path separators to forward slashes + folders.append(rel_path.replace(os.sep, '/')) + + return sorted(folders) + except FileNotFoundError: + return [] diff --git a/hook_breaker_ac10a0.py b/hook_breaker_ac10a0.py new file mode 100644 index 0000000000000000000000000000000000000000..c3e1c063382760535cee82e8bfcbc85d17e2cce4 --- /dev/null +++ b/hook_breaker_ac10a0.py @@ -0,0 +1,17 @@ +# Prevent custom nodes from hooking anything important +import comfy.model_management + +HOOK_BREAK = [(comfy.model_management, "cast_to")] + + +SAVED_FUNCTIONS = [] + + +def save_functions(): + for f in HOOK_BREAK: + SAVED_FUNCTIONS.append((f[0], f[1], getattr(f[0], f[1]))) + + +def restore_functions(): + for f in SAVED_FUNCTIONS: + setattr(f[0], f[1], f[2]) diff --git a/input/009c.mp4 b/input/009c.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..bc050ecc7391f1cff783a2253b3496afcd57302b --- /dev/null +++ b/input/009c.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7d7f9a8cd3fed299f6b2ee8530c1cb912f652a7e4d6c9ed1f0d1078e7074f9f +size 791753 diff --git a/input/dasha.mp4 b/input/dasha.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..6637facfde24ea5378370b2fe1f57db2a10d2d6d --- /dev/null +++ b/input/dasha.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:598f2dd35a5ebcd959b4326a2651b9458844674beee1c5614529e71547fb47a5 +size 3120064 diff --git a/latent_preview.py b/latent_preview.py new file mode 100644 index 0000000000000000000000000000000000000000..95d3cb7338eafc63ce060b43528c6017d8d55953 --- /dev/null +++ b/latent_preview.py @@ -0,0 +1,108 @@ +import torch +from PIL import Image +from comfy.cli_args import args, LatentPreviewMethod +from comfy.taesd.taesd import TAESD +import comfy.model_management +import folder_paths +import comfy.utils +import logging + +MAX_PREVIEW_RESOLUTION = args.preview_size + +def preview_to_image(latent_image): + latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1 + .mul(0xFF) # to 0..255 + ) + if comfy.model_management.directml_enabled: + latents_ubyte = latents_ubyte.to(dtype=torch.uint8) + latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) + + return Image.fromarray(latents_ubyte.numpy()) + +class LatentPreviewer: + def decode_latent_to_preview(self, x0): + pass + + def decode_latent_to_preview_image(self, preview_format, x0): + preview_image = self.decode_latent_to_preview(x0) + return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) + +class TAESDPreviewerImpl(LatentPreviewer): + def __init__(self, taesd): + self.taesd = taesd + + def decode_latent_to_preview(self, x0): + x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2) + return preview_to_image(x_sample) + + +class Latent2RGBPreviewer(LatentPreviewer): + def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None): + self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1) + self.latent_rgb_factors_bias = None + if latent_rgb_factors_bias is not None: + self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu") + + def decode_latent_to_preview(self, x0): + self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) + if self.latent_rgb_factors_bias is not None: + self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device) + + if x0.ndim == 5: + x0 = x0[0, :, 0] + else: + x0 = x0[0] + + latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias) + # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors + + return preview_to_image(latent_image) + + +def get_previewer(device, latent_format): + previewer = None + method = args.preview_method + if method != LatentPreviewMethod.NoPreviews: + # TODO previewer methods + taesd_decoder_path = None + if latent_format.taesd_decoder_name is not None: + taesd_decoder_path = next( + (fn for fn in folder_paths.get_filename_list("vae_approx") + if fn.startswith(latent_format.taesd_decoder_name)), + "" + ) + taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path) + + if method == LatentPreviewMethod.Auto: + method = LatentPreviewMethod.Latent2RGB + + if method == LatentPreviewMethod.TAESD: + if taesd_decoder_path: + taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device) + previewer = TAESDPreviewerImpl(taesd) + else: + logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) + + if previewer is None: + if latent_format.latent_rgb_factors is not None: + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias) + return previewer + +def prepare_callback(model, steps, x0_output_dict=None): + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = get_previewer(model.load_device, model.model.latent_format) + + pbar = comfy.utils.ProgressBar(steps) + def callback(step, x0, x, total_steps): + if x0_output_dict is not None: + x0_output_dict["x0"] = x0 + + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + return callback + diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..d488c0f4c12b5904620e362a7a0b8ced9358ff48 --- /dev/null +++ b/main.py @@ -0,0 +1,335 @@ +import comfy.options +comfy.options.enable_args_parsing() + +import os +import importlib.util +import folder_paths +import time +from comfy.cli_args import args +from app.logger import setup_logger +import itertools +import utils.extra_config +import logging +import sys + +if __name__ == "__main__": + #NOTE: These do not do anything on core ComfyUI, they are for custom nodes. + os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' + os.environ['DO_NOT_TRACK'] = '1' + +setup_logger(log_level=args.verbose, use_stdout=args.log_stdout) + +def apply_custom_paths(): + # extra model paths + extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml") + if os.path.isfile(extra_model_paths_config_path): + utils.extra_config.load_extra_path_config(extra_model_paths_config_path) + + if args.extra_model_paths_config: + for config_path in itertools.chain(*args.extra_model_paths_config): + utils.extra_config.load_extra_path_config(config_path) + + # --output-directory, --input-directory, --user-directory + if args.output_directory: + output_dir = os.path.abspath(args.output_directory) + logging.info(f"Setting output directory to: {output_dir}") + folder_paths.set_output_directory(output_dir) + + # These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes + folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints")) + folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip")) + folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae")) + folder_paths.add_model_folder_path("diffusion_models", + os.path.join(folder_paths.get_output_directory(), "diffusion_models")) + folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras")) + + if args.input_directory: + input_dir = os.path.abspath(args.input_directory) + logging.info(f"Setting input directory to: {input_dir}") + folder_paths.set_input_directory(input_dir) + + if args.user_directory: + user_dir = os.path.abspath(args.user_directory) + logging.info(f"Setting user directory to: {user_dir}") + folder_paths.set_user_directory(user_dir) + + +def execute_prestartup_script(): + if args.disable_all_custom_nodes and len(args.whitelist_custom_nodes) == 0: + return + + def execute_script(script_path): + module_name = os.path.splitext(script_path)[0] + try: + spec = importlib.util.spec_from_file_location(module_name, script_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return True + except Exception as e: + logging.error(f"Failed to execute startup-script: {script_path} / {e}") + return False + + node_paths = folder_paths.get_folder_paths("custom_nodes") + for custom_node_path in node_paths: + possible_modules = os.listdir(custom_node_path) + node_prestartup_times = [] + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__": + continue + + script_path = os.path.join(module_path, "prestartup_script.py") + if os.path.exists(script_path): + if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes: + logging.info(f"Prestartup Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes") + continue + time_before = time.perf_counter() + success = execute_script(script_path) + node_prestartup_times.append((time.perf_counter() - time_before, module_path, success)) + if len(node_prestartup_times) > 0: + logging.info("\nPrestartup times for custom nodes:") + for n in sorted(node_prestartup_times): + if n[2]: + import_message = "" + else: + import_message = " (PRESTARTUP FAILED)" + logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) + logging.info("") + +apply_custom_paths() +execute_prestartup_script() + + +# Main code +import asyncio +import shutil +import threading +import gc + + +if os.name == "nt": + logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) + +if __name__ == "__main__": + if args.cuda_device is not None: + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) + os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device) + logging.info("Set cuda device to: {}".format(args.cuda_device)) + + if args.oneapi_device_selector is not None: + os.environ['ONEAPI_DEVICE_SELECTOR'] = args.oneapi_device_selector + logging.info("Set oneapi device selector to: {}".format(args.oneapi_device_selector)) + + if args.deterministic: + if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ: + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8" + + import cuda_malloc + +import comfy.utils + +import execution +import server +from server import BinaryEventTypes +import nodes +import comfy.model_management +import comfyui_version +import app.logger +import hook_breaker_ac10a0 + +def cuda_malloc_warning(): + device = comfy.model_management.get_torch_device() + device_name = comfy.model_management.get_torch_device_name(device) + cuda_malloc_warning = False + if "cudaMallocAsync" in device_name: + for b in cuda_malloc.blacklist: + if b in device_name: + cuda_malloc_warning = True + if cuda_malloc_warning: + logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") + + +def prompt_worker(q, server_instance): + current_time: float = 0.0 + cache_type = execution.CacheType.CLASSIC + if args.cache_lru > 0: + cache_type = execution.CacheType.LRU + elif args.cache_none: + cache_type = execution.CacheType.DEPENDENCY_AWARE + + e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru) + last_gc_collect = 0 + need_gc = False + gc_collect_interval = 10.0 + + while True: + timeout = 1000.0 + if need_gc: + timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0) + + queue_item = q.get(timeout=timeout) + if queue_item is not None: + item, item_id = queue_item + execution_start_time = time.perf_counter() + prompt_id = item[1] + server_instance.last_prompt_id = prompt_id + + e.execute(item[2], prompt_id, item[3], item[4]) + need_gc = True + q.task_done(item_id, + e.history_result, + status=execution.PromptQueue.ExecutionStatus( + status_str='success' if e.success else 'error', + completed=e.success, + messages=e.status_messages)) + if server_instance.client_id is not None: + server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id) + + current_time = time.perf_counter() + execution_time = current_time - execution_start_time + + # Log Time in a more readable way after 10 minutes + if execution_time > 600: + execution_time = time.strftime("%H:%M:%S", time.gmtime(execution_time)) + logging.info(f"Prompt executed in {execution_time}") + else: + logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) + + flags = q.get_flags() + free_memory = flags.get("free_memory", False) + + if flags.get("unload_models", free_memory): + comfy.model_management.unload_all_models() + need_gc = True + last_gc_collect = 0 + + if free_memory: + e.reset() + need_gc = True + last_gc_collect = 0 + + if need_gc: + current_time = time.perf_counter() + if (current_time - last_gc_collect) > gc_collect_interval: + gc.collect() + comfy.model_management.soft_empty_cache() + last_gc_collect = current_time + need_gc = False + hook_breaker_ac10a0.restore_functions() + + +async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None): + addresses = [] + for addr in address.split(","): + addresses.append((addr, port)) + await asyncio.gather( + server_instance.start_multi_address(addresses, call_on_start, verbose), server_instance.publish_loop() + ) + + +def hijack_progress(server_instance): + def hook(value, total, preview_image): + comfy.model_management.throw_exception_if_processing_interrupted() + progress = {"value": value, "max": total, "prompt_id": server_instance.last_prompt_id, "node": server_instance.last_node_id} + + server_instance.send_sync("progress", progress, server_instance.client_id) + if preview_image is not None: + server_instance.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server_instance.client_id) + + comfy.utils.set_progress_bar_global_hook(hook) + + +def cleanup_temp(): + temp_dir = folder_paths.get_temp_directory() + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir, ignore_errors=True) + + +def setup_database(): + try: + from app.database.db import init_db, dependencies_available + if dependencies_available(): + init_db() + except Exception as e: + logging.error(f"Failed to initialize database. Please ensure you have installed the latest requirements. If the error persists, please report this as in future the database will be required: {e}") + + +def start_comfyui(asyncio_loop=None): + """ + Starts the ComfyUI server using the provided asyncio event loop or creates a new one. + Returns the event loop, server instance, and a function to start the server asynchronously. + """ + if args.temp_directory: + temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp") + logging.info(f"Setting temp directory to: {temp_dir}") + folder_paths.set_temp_directory(temp_dir) + cleanup_temp() + + if args.windows_standalone_build: + try: + import new_updater + new_updater.update_windows_updater() + except: + pass + + if not asyncio_loop: + asyncio_loop = asyncio.new_event_loop() + asyncio.set_event_loop(asyncio_loop) + prompt_server = server.PromptServer(asyncio_loop) + + hook_breaker_ac10a0.save_functions() + nodes.init_extra_nodes( + init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0, + init_api_nodes=not args.disable_api_nodes + ) + hook_breaker_ac10a0.restore_functions() + + cuda_malloc_warning() + setup_database() + + prompt_server.add_routes() + hijack_progress(prompt_server) + + threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start() + + if args.quick_test_for_ci: + exit(0) + + os.makedirs(folder_paths.get_temp_directory(), exist_ok=True) + call_on_start = None + if args.auto_launch: + def startup_server(scheme, address, port): + import webbrowser + if os.name == 'nt' and address == '0.0.0.0': + address = '127.0.0.1' + if ':' in address: + address = "[{}]".format(address) + webbrowser.open(f"{scheme}://{address}:{port}") + call_on_start = startup_server + + async def start_all(): + await prompt_server.setup() + await run(prompt_server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start) + + # Returning these so that other code can integrate with the ComfyUI loop and server + return asyncio_loop, prompt_server, start_all + + +if __name__ == "__main__": + # Running directly, just start ComfyUI. + logging.info("Python version: {}".format(sys.version)) + logging.info("ComfyUI version: {}".format(comfyui_version.__version__)) + + if sys.version_info.major == 3 and sys.version_info.minor < 10: + logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.") + + event_loop, _, start_all_func = start_comfyui() + try: + x = start_all_func() + app.logger.print_startup_warnings() + event_loop.run_until_complete(x) + except KeyboardInterrupt: + logging.info("\nStopped server") + + cleanup_temp() diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip/put_clip_or_text_encoder_models_here b/models/clip/put_clip_or_text_encoder_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip_vision/put_clip_vision_models_here b/models/clip_vision/put_clip_vision_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6 --- /dev/null +++ b/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_clip_skip_2_fp16.yaml b/models/configs/v1-inference_clip_skip_2_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7eca31c7b5e571c2b1348e94ed9d69978ebd2d52 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2_fp16.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_fp16.yaml b/models/configs/v1-inference_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..147f42b17b835cc839338156f99e8f971df5c1aa --- /dev/null +++ b/models/configs/v1-inference_fp16.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inpainting-inference.yaml b/models/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45f3f82d461cd8c6109f26ec3b1da75366eda0b0 --- /dev/null +++ b/models/configs/v1-inpainting-inference.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ec8dfbfefe94ae8522c93017668fea78d580acf --- /dev/null +++ b/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5c9b9cb29ca162ade44a7c922f59e75d7d57813 --- /dev/null +++ b/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference.yaml b/models/configs/v2-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c --- /dev/null +++ b/models/configs/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference_fp32.yaml b/models/configs/v2-inference_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d03231f3f2c2e8ef8fbe0d781e5f3d65409ef3a --- /dev/null +++ b/models/configs/v2-inference_fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inpainting-inference.yaml b/models/configs/v2-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32a9471d71b828c51bcbbabfe34c5f6c8282c803 --- /dev/null +++ b/models/configs/v2-inpainting-inference.yaml @@ -0,0 +1,158 @@ +model: + base_learning_rate: 5.0e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + +lightning: + find_unused_parameters: True + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + metrics_over_trainsteps_checkpoint: + params: + every_n_train_steps: 10000 + + image_logger: + target: main.ImageLogger + params: + enable_autocast: False + disabled: False + batch_frequency: 1000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + ddim_steps: 50 # todo check these out for depth2img, + ddim_eta: 0.0 # todo check these out for depth2img, + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/models/controlnet/put_controlnets_and_t2i_here b/models/controlnet/put_controlnets_and_t2i_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/diffusers/put_diffusers_models_here b/models/diffusers/put_diffusers_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/diffusion_models/put_diffusion_model_files_here b/models/diffusion_models/put_diffusion_model_files_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/embeddings/put_embeddings_or_textual_inversion_concepts_here b/models/embeddings/put_embeddings_or_textual_inversion_concepts_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/gligen/put_gligen_models_here b/models/gligen/put_gligen_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/hypernetworks/put_hypernetworks_here b/models/hypernetworks/put_hypernetworks_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/loras/put_loras_here b/models/loras/put_loras_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/photomaker/put_photomaker_models_here b/models/photomaker/put_photomaker_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/style_models/put_t2i_style_model_here b/models/style_models/put_t2i_style_model_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/text_encoders/put_text_encoder_files_here b/models/text_encoders/put_text_encoder_files_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/unet/put_unet_files_here b/models/unet/put_unet_files_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/upscale_models/put_esrgan_and_other_upscale_models_here b/models/upscale_models/put_esrgan_and_other_upscale_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae/put_vae_here b/models/vae/put_vae_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/new_updater.py b/new_updater.py new file mode 100644 index 0000000000000000000000000000000000000000..9a203acdd71f62b3b1122cb879c75344f5859c5d --- /dev/null +++ b/new_updater.py @@ -0,0 +1,35 @@ +import os +import shutil + +base_path = os.path.dirname(os.path.realpath(__file__)) + + +def update_windows_updater(): + top_path = os.path.dirname(base_path) + updater_path = os.path.join(base_path, ".ci/update_windows/update.py") + bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") + + dest_updater_path = os.path.join(top_path, "update/update.py") + dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") + dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") + + try: + with open(dest_bat_path, 'rb') as f: + contents = f.read() + except: + return + + if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): + return + + shutil.copy(updater_path, dest_updater_path) + try: + with open(dest_bat_deps_path, 'rb') as f: + contents = f.read() + contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') + with open(dest_bat_deps_path, 'wb') as f: + f.write(contents) + except: + pass + shutil.copy(bat_path, dest_bat_path) + print("Updated the windows standalone package updater.") # noqa: T201 diff --git a/node_helpers.py b/node_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..4ff960ef8afeadd935b6e48a9a683b7ef6970b2f --- /dev/null +++ b/node_helpers.py @@ -0,0 +1,60 @@ +import hashlib +import torch + +from comfy.cli_args import args + +from PIL import ImageFile, UnidentifiedImageError + +def conditioning_set_values(conditioning, values={}, append=False): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + for k in values: + val = values[k] + if append: + old_val = n[1].get(k, None) + if old_val is not None: + val = old_val + val + + n[1][k] = val + c.append(n) + + return c + +def pillow(fn, arg): + prev_value = None + try: + x = fn(arg) + except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416 + prev_value = ImageFile.LOAD_TRUNCATED_IMAGES + ImageFile.LOAD_TRUNCATED_IMAGES = True + x = fn(arg) + finally: + if prev_value is not None: + ImageFile.LOAD_TRUNCATED_IMAGES = prev_value + return x + +def hasher(): + hashfuncs = { + "md5": hashlib.md5, + "sha1": hashlib.sha1, + "sha256": hashlib.sha256, + "sha512": hashlib.sha512 + } + return hashfuncs[args.default_hashing_function] + +def string_to_torch_dtype(string): + if string == "fp32": + return torch.float32 + if string == "fp16": + return torch.float16 + if string == "bf16": + return torch.bfloat16 + +def image_alpha_fix(destination, source): + if destination.shape[-1] < source.shape[-1]: + source = source[...,:destination.shape[-1]] + elif destination.shape[-1] > source.shape[-1]: + destination = torch.nn.functional.pad(destination, (0, 1)) + destination[..., -1] = 1.0 + return destination, source diff --git a/nodes.py b/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..99411a1fe1b044abdc0534bad4690053c4134224 --- /dev/null +++ b/nodes.py @@ -0,0 +1,2361 @@ +from __future__ import annotations +import torch + +import os +import sys +import json +import hashlib +import traceback +import math +import time +import random +import logging + +from PIL import Image, ImageOps, ImageSequence +from PIL.PngImagePlugin import PngInfo + +import numpy as np +import safetensors.torch + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + +import comfy.diffusers_load +import comfy.samplers +import comfy.sample +import comfy.sd +import comfy.utils +import comfy.controlnet +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator + +import comfy.clip_vision + +import comfy.model_management +from comfy.cli_args import args + +import importlib + +import folder_paths +import latent_preview +import node_helpers + +def before_node_execution(): + comfy.model_management.throw_exception_if_processing_interrupted() + +def interrupt_processing(value=True): + comfy.model_management.interrupt_current_processing(value) + +MAX_RESOLUTION=16384 + +class CLIPTextEncode(ComfyNodeABC): + @classmethod + def INPUT_TYPES(s) -> InputTypeDict: + return { + "required": { + "text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}), + "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}) + } + } + RETURN_TYPES = (IO.CONDITIONING,) + OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." + + def encode(self, clip, text): + if clip is None: + raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") + tokens = clip.tokenize(text) + return (clip.encode_from_tokens_scheduled(tokens), ) + + +class ConditioningCombine: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "combine" + + CATEGORY = "conditioning" + + def combine(self, conditioning_1, conditioning_2): + return (conditioning_1 + conditioning_2, ) + +class ConditioningAverage : + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), + "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "addWeighted" + + CATEGORY = "conditioning" + + def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): + out = [] + + if len(conditioning_from) > 1: + logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + pooled_output_from = conditioning_from[0][1].get("pooled_output", None) + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) + t0 = cond_from[:,:t1.shape[1]] + if t0.shape[1] < t1.shape[1]: + t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) + + tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) + t_to = conditioning_to[i][1].copy() + if pooled_output_from is not None and pooled_output_to is not None: + t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) + elif pooled_output_from is not None: + t_to["pooled_output"] = pooled_output_from + + n = [tw, t_to] + out.append(n) + return (out, ) + +class ConditioningConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning_to": ("CONDITIONING",), + "conditioning_from": ("CONDITIONING",), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "concat" + + CATEGORY = "conditioning" + + def concat(self, conditioning_to, conditioning_from): + out = [] + + if len(conditioning_from) > 1: + logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from),1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + return (out, ) + +class ConditioningSetArea: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + +class ConditioningSetAreaPercentage: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + +class ConditioningSetAreaStrength: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, strength): + c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) + return (c, ) + + +class ConditioningSetMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, mask, set_cond_area, strength): + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask.shape) < 3: + mask = mask.unsqueeze(0) + + c = node_helpers.conditioning_set_values(conditioning, {"mask": mask, + "set_area_to_bounds": set_area_to_bounds, + "mask_strength": strength}) + return (c, ) + +class ConditioningZeroOut: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "zero_out" + + CATEGORY = "advanced/conditioning" + + def zero_out(self, conditioning): + c = [] + for t in conditioning: + d = t[1].copy() + pooled_output = d.get("pooled_output", None) + if pooled_output is not None: + d["pooled_output"] = torch.zeros_like(pooled_output) + conditioning_lyrics = d.get("conditioning_lyrics", None) + if conditioning_lyrics is not None: + d["conditioning_lyrics"] = torch.zeros_like(conditioning_lyrics) + n = [torch.zeros_like(t[0]), d] + c.append(n) + return (c, ) + +class ConditioningSetTimestepRange: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "set_range" + + CATEGORY = "advanced/conditioning" + + def set_range(self, conditioning, start, end): + c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, + "end_percent": end}) + return (c, ) + +class VAEDecode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "samples": ("LATENT", {"tooltip": "The latent to be decoded."}), + "vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}) + } + } + RETURN_TYPES = ("IMAGE",) + OUTPUT_TOOLTIPS = ("The decoded image.",) + FUNCTION = "decode" + + CATEGORY = "latent" + DESCRIPTION = "Decodes latent images back into pixel space images." + + def decode(self, vae, samples): + images = vae.decode(samples["samples"]) + if len(images.shape) == 5: #Combine batches + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + return (images, ) + +class VAEDecodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32}), + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), + "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time."}), + "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "_for_testing" + + def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8): + if tile_size < overlap * 4: + overlap = tile_size // 4 + if temporal_size < temporal_overlap * 2: + temporal_overlap = temporal_overlap // 2 + temporal_compression = vae.temporal_compression_decode() + if temporal_compression is not None: + temporal_size = max(2, temporal_size // temporal_compression) + temporal_overlap = max(1, min(temporal_size // 2, temporal_overlap // temporal_compression)) + else: + temporal_size = None + temporal_overlap = None + + compression = vae.spacial_compression_decode() + images = vae.decode_tiled(samples["samples"], tile_x=tile_size // compression, tile_y=tile_size // compression, overlap=overlap // compression, tile_t=temporal_size, overlap_t=temporal_overlap) + if len(images.shape) == 5: #Combine batches + images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) + return (images, ) + +class VAEEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent" + + def encode(self, vae, pixels): + t = vae.encode(pixels[:,:,:,:3]) + return ({"samples":t}, ) + +class VAEEncodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), + "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), + "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}), + "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "_for_testing" + + def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8): + t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap) + return ({"samples": t}, ) + +class VAEEncodeForInpaint: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/inpaint" + + def encode(self, vae, pixels, mask, grow_mask_by=6): + x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio + y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + pixels = pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2 + y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + #grow mask by a few pixels to keep things seamless in latent space + if grow_mask_by == 0: + mask_erosion = mask + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + t = vae.encode(pixels) + + return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) + + +class InpaintModelConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + "mask": ("MASK", ), + "noise_mask": ("BOOLEAN", {"default": True, "tooltip": "Add a noise mask to the latent so sampling will only happen within the mask. Might improve results or completely break things depending on the model."}), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/inpaint" + + def encode(self, positive, negative, pixels, vae, mask, noise_mask=True): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + orig_pixels = pixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + + out_latent = {} + + out_latent["samples"] = orig_latent + if noise_mask: + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, + "concat_mask": mask}) + out.append(c) + return (out[0], out[1], out_latent) + + +class SaveLatent: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), + "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = () + FUNCTION = "save" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + + # support save metadata for latent sharing + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = None + if not args.disable_metadata: + metadata = {"prompt": prompt_info} + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + file = f"{filename}_{counter:05}_.latent" + + results: list[FileLocator] = [] + results.append({ + "filename": file, + "subfolder": subfolder, + "type": "output" + }) + + file = os.path.join(full_output_folder, file) + + output = {} + output["latent_tensor"] = samples["samples"].contiguous() + output["latent_format_version_0"] = torch.tensor([]) + + comfy.utils.save_torch_file(output, file, metadata=metadata) + return { "ui": { "latents": results } } + + +class LoadLatent: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] + return {"required": {"latent": [sorted(files), ]}, } + + CATEGORY = "_for_testing" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "load" + + def load(self, latent): + latent_path = folder_paths.get_annotated_filepath(latent) + latent = safetensors.torch.load_file(latent_path, device="cpu") + multiplier = 1.0 + if "latent_format_version_0" not in latent: + multiplier = 1.0 / 0.18215 + samples = {"samples": latent["latent_tensor"].float() * multiplier} + return (samples, ) + + @classmethod + def IS_CHANGED(s, latent): + image_path = folder_paths.get_annotated_filepath(latent) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, latent): + if not folder_paths.exists_annotated_filepath(latent): + return "Invalid latent file: {}".format(latent) + return True + + +class CheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders" + DEPRECATED = True + + def load_checkpoint(self, config_name, ckpt_name): + config_path = folder_paths.get_full_path("configs", config_name) + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + +class CheckpointLoaderSimple: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}), + } + } + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + OUTPUT_TOOLTIPS = ("The model used for denoising latents.", + "The CLIP model used for encoding text prompts.", + "The VAE model used for encoding and decoding images to and from latent space.") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents." + + def load_checkpoint(self, ckpt_name): + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out[:3] + +class DiffusersLoader: + @classmethod + def INPUT_TYPES(cls): + paths = [] + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + for root, subdir, files in os.walk(search_path, followlinks=True): + if "model_index.json" in files: + paths.append(os.path.relpath(root, start=search_path)) + + return {"required": {"model_path": (paths,), }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders/deprecated" + + def load_checkpoint(self, model_path, output_vae=True, output_clip=True): + for search_path in folder_paths.get_folder_paths("diffusers"): + if os.path.exists(search_path): + path = os.path.join(search_path, model_path) + if os.path.exists(path): + model_path = path + break + + return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) + + +class unCLIPCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) + return out + +class CLIPSetLastLayer: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP", ), + "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "set_last_layer" + + CATEGORY = "conditioning" + + def set_last_layer(self, clip, stop_at_clip_layer): + clip = clip.clone() + clip.clip_layer(stop_at_clip_layer) + return (clip,) + +class LoraLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), + "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}), + "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}), + } + } + + RETURN_TYPES = ("MODEL", "CLIP") + OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.") + FUNCTION = "load_lora" + + CATEGORY = "loaders" + DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together." + + def load_lora(self, model, clip, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + lora_path = folder_paths.get_full_path_or_raise("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + self.loaded_lora = None + + if lora is None: + lora = comfy.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora) + +class LoraLoaderModelOnly(LoraLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (folder_paths.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lora_model_only" + + def load_lora_model_only(self, model, lora_name, strength_model): + return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) + +class VAELoader: + @staticmethod + def vae_list(): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + sd3_taesd_enc = False + sd3_taesd_dec = False + f1_taesd_enc = False + f1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + elif v.startswith("taesd3_decoder."): + sd3_taesd_dec = True + elif v.startswith("taesd3_encoder."): + sd3_taesd_enc = True + elif v.startswith("taef1_encoder."): + f1_taesd_dec = True + elif v.startswith("taef1_decoder."): + f1_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + if sd3_taesd_dec and sd3_taesd_enc: + vaes.append("taesd3") + if f1_taesd_dec and f1_taesd_enc: + vaes.append("taef1") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesd3": + sd["vae_scale"] = torch.tensor(1.5305) + sd["vae_shift"] = torch.tensor(0.0609) + elif name == "taef1": + sd["vae_scale"] = torch.tensor(0.3611) + sd["vae_shift"] = torch.tensor(0.1159) + return sd + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae_name": (s.vae_list(), )}} + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + + CATEGORY = "loaders" + + #TODO: scale factor? + def load_vae(self, vae_name): + if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: + sd = self.load_taesd(vae_name) + else: + vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + vae.throw_exception_if_invalid() + return (vae,) + +class ControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, control_net_name): + controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path) + if controlnet is None: + raise RuntimeError("ERROR: controlnet file is invalid and does not contain a valid controlnet model.") + return (controlnet,) + +class DiffControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, model, control_net_name): + controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path, model) + return (controlnet,) + + +class ControlNetApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_controlnet" + + DEPRECATED = True + CATEGORY = "conditioning/controlnet" + + def apply_controlnet(self, conditioning, control_net, image, strength): + if strength == 0: + return (conditioning, ) + + c = [] + control_hint = image.movedim(-1,1) + for t in conditioning: + n = [t[0], t[1].copy()] + c_net = control_net.copy().set_cond_hint(control_hint, strength) + if 'control' in t[1]: + c_net.set_previous_controlnet(t[1]['control']) + n[1]['control'] = c_net + n[1]['control_apply_to_uncond'] = True + c.append(n) + return (c, ) + + +class ControlNetApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }, + "optional": {"vae": ("VAE", ), + } + } + + RETURN_TYPES = ("CONDITIONING","CONDITIONING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning/controlnet" + + def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]): + if strength == 0: + return (positive, negative) + + control_hint = image.movedim(-1,1) + cnets = {} + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1]) + + +class UNETLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + + CATEGORY = "advanced/loaders" + + def load_unet(self, unet_name, weight_dtype): + model_options = {} + if weight_dtype == "fp8_e4m3fn": + model_options["dtype"] = torch.float8_e4m3fn + elif weight_dtype == "fp8_e4m3fn_fast": + model_options["dtype"] = torch.float8_e4m3fn + model_options["fp8_optimizations"] = True + elif weight_dtype == "fp8_e5m2": + model_options["dtype"] = torch.float8_e5m2 + + unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name) + model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options) + return (model,) + +class CLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2"], ), + }, + "optional": { + "device": (["default", "cpu"], {"advanced": True}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B" + + def load_clip(self, clip_name, type="stable_diffusion", device="default"): + clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) + + model_options = {} + if device == "cpu": + model_options["load_device"] = model_options["offload_device"] = torch.device("cpu") + + clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options) + return (clip,) + +class DualCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), + "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), + "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream"], ), + }, + "optional": { + "device": (["default", "cpu"], {"advanced": True}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama" + + def load_clip(self, clip_name1, clip_name2, type, device="default"): + clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) + + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) + clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) + + model_options = {} + if device == "cpu": + model_options["load_device"] = model_options["offload_device"] = torch.device("cpu") + + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options) + return (clip,) + +class CLIPVisionLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), + }} + RETURN_TYPES = ("CLIP_VISION",) + FUNCTION = "load_clip" + + CATEGORY = "loaders" + + def load_clip(self, clip_name): + clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name) + clip_vision = comfy.clip_vision.load(clip_path) + if clip_vision is None: + raise RuntimeError("ERROR: clip vision file is invalid and does not contain a valid vision model.") + return (clip_vision,) + +class CLIPVisionEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "image": ("IMAGE",), + "crop": (["center", "none"],) + }} + RETURN_TYPES = ("CLIP_VISION_OUTPUT",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip_vision, image, crop): + crop_image = True + if crop != "center": + crop_image = False + output = clip_vision.encode_image(image, crop=crop_image) + return (output,) + +class StyleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} + + RETURN_TYPES = ("STYLE_MODEL",) + FUNCTION = "load_style_model" + + CATEGORY = "loaders" + + def load_style_model(self, style_model_name): + style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name) + style_model = comfy.sd.load_style_model(style_model_path) + return (style_model,) + + +class StyleModelApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "style_model": ("STYLE_MODEL", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), + "strength_type": (["multiply", "attn_bias"], ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_stylemodel" + + CATEGORY = "conditioning/style_model" + + def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type): + cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + if strength_type == "multiply": + cond *= strength + + n = cond.shape[1] + c_out = [] + for t in conditioning: + (txt, keys) = t + keys = keys.copy() + # even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it + if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0): + # math.log raises an error if the argument is zero + # torch.log returns -inf, which is what we want + attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0])) + # get the size of the mask image + mask_ref_size = keys.get("attention_mask_img_shape", (1, 1)) + n_ref = mask_ref_size[0] * mask_ref_size[1] + n_txt = txt.shape[1] + # grab the existing mask + mask = keys.get("attention_mask", None) + # create a default mask if it doesn't exist + if mask is None: + mask = torch.zeros((txt.shape[0], n_txt + n_ref, n_txt + n_ref), dtype=torch.float16) + # convert the mask dtype, because it might be boolean + # we want it to be interpreted as a bias + if mask.dtype == torch.bool: + # log(True) = log(1) = 0 + # log(False) = log(0) = -inf + mask = torch.log(mask.to(dtype=torch.float16)) + # now we make the mask bigger to add space for our new tokens + new_mask = torch.zeros((txt.shape[0], n_txt + n + n_ref, n_txt + n + n_ref), dtype=torch.float16) + # copy over the old mask, in quandrants + new_mask[:, :n_txt, :n_txt] = mask[:, :n_txt, :n_txt] + new_mask[:, :n_txt, n_txt+n:] = mask[:, :n_txt, n_txt:] + new_mask[:, n_txt+n:, :n_txt] = mask[:, n_txt:, :n_txt] + new_mask[:, n_txt+n:, n_txt+n:] = mask[:, n_txt:, n_txt:] + # now fill in the attention bias to our redux tokens + new_mask[:, :n_txt, n_txt:n_txt+n] = attn_bias + new_mask[:, n_txt+n:, n_txt:n_txt+n] = attn_bias + keys["attention_mask"] = new_mask.to(txt.device) + keys["attention_mask_img_shape"] = mask_ref_size + + c_out.append([torch.cat((txt, cond), dim=1), keys]) + + return (c_out,) + +class unCLIPConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_adm" + + CATEGORY = "conditioning" + + def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): + if strength == 0: + return (conditioning, ) + + c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True) + return (c, ) + +class GLIGENLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} + + RETURN_TYPES = ("GLIGEN",) + FUNCTION = "load_gligen" + + CATEGORY = "loaders" + + def load_gligen(self, gligen_name): + gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name) + gligen = comfy.sd.load_gligen(gligen_path) + return (gligen,) + +class GLIGENTextBoxApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), + "clip": ("CLIP", ), + "gligen_textbox_model": ("GLIGEN", ), + "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), + "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning/gligen" + + def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): + c = [] + cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected") + for t in conditioning_to: + n = [t[0], t[1].copy()] + position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] + prev = [] + if "gligen" in n[1]: + prev = n[1]['gligen'][2] + + n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) + c.append(n) + return (c, ) + +class EmptyLatentImage: + def __init__(self): + self.device = comfy.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the latent images in pixels."}), + "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of the latent images in pixels."}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) + } + } + RETURN_TYPES = ("LATENT",) + OUTPUT_TOOLTIPS = ("The empty latent image batch.",) + FUNCTION = "generate" + + CATEGORY = "latent" + DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling." + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) + return ({"samples":latent}, ) + + +class LatentFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "frombatch" + + CATEGORY = "latent/batch" + + def frombatch(self, samples, batch_index, length): + s = samples.copy() + s_in = samples["samples"] + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s["samples"] = s_in[batch_index:batch_index + length].clone() + if "noise_mask" in samples: + masks = samples["noise_mask"] + if masks.shape[0] == 1: + s["noise_mask"] = masks.clone() + else: + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = masks[batch_index:batch_index + length].clone() + if "batch_index" not in s: + s["batch_index"] = [x for x in range(batch_index, batch_index+length)] + else: + s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] + return (s,) + +class RepeatLatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "repeat" + + CATEGORY = "latent/batch" + + def repeat(self, samples, amount): + s = samples.copy() + s_in = samples["samples"] + + s["samples"] = s_in.repeat((amount, 1,1,1)) + if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: + masks = samples["noise_mask"] + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) + if "batch_index" in s: + offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 + s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] + return (s,) + +class LatentUpscale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = samples + else: + s = samples.copy() + + if width == 0: + height = max(64, height) + width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2])) + elif height == 0: + width = max(64, width) + height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1])) + else: + width = max(64, width) + height = max(64, height) + + s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) + return (s,) + +class LatentUpscaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, scale_by): + s = samples.copy() + width = round(samples["samples"].shape[-1] * scale_by) + height = round(samples["samples"].shape[-2] * scale_by) + s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") + return (s,) + +class LatentRotate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "rotate" + + CATEGORY = "latent/transform" + + def rotate(self, samples, rotation): + s = samples.copy() + rotate_by = 0 + if rotation.startswith("90"): + rotate_by = 1 + elif rotation.startswith("180"): + rotate_by = 2 + elif rotation.startswith("270"): + rotate_by = 3 + + s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) + return (s,) + +class LatentFlip: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "flip" + + CATEGORY = "latent/transform" + + def flip(self, samples, flip_method): + s = samples.copy() + if flip_method.startswith("x"): + s["samples"] = torch.flip(samples["samples"], dims=[2]) + elif flip_method.startswith("y"): + s["samples"] = torch.flip(samples["samples"], dims=[3]) + + return (s,) + +class LatentComposite: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples_to": ("LATENT",), + "samples_from": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): + x = x // 8 + y = y // 8 + feather = feather // 8 + samples_out = samples_to.copy() + s = samples_to["samples"].clone() + samples_to = samples_to["samples"] + samples_from = samples_from["samples"] + if feather == 0: + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + else: + samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + mask = torch.ones_like(samples_from) + for t in range(feather): + if y != 0: + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + + if y + samples_from.shape[2] < samples_to.shape[2]: + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + if x != 0: + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + if x + samples_from.shape[3] < samples_to.shape[3]: + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + rev_mask = torch.ones_like(mask) - mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask + samples_out["samples"] = s + return (samples_out,) + +class LatentBlend: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples1": ("LATENT",), + "samples2": ("LATENT",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0, + "max": 1, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "blend" + + CATEGORY = "_for_testing" + + def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): + + samples_out = samples1.copy() + samples1 = samples1["samples"] + samples2 = samples2["samples"] + + if samples1.shape != samples2.shape: + samples2.permute(0, 3, 1, 2) + samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') + samples2.permute(0, 2, 3, 1) + + samples_blended = self.blend_mode(samples1, samples2, blend_mode) + samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) + samples_out["samples"] = samples_blended + return (samples_out,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + +class LatentCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "crop" + + CATEGORY = "latent/transform" + + def crop(self, samples, width, height, x, y): + s = samples.copy() + samples = samples['samples'] + x = x // 8 + y = y // 8 + + #enfonce minimum size of 64 + if x > (samples.shape[3] - 8): + x = samples.shape[3] - 8 + if y > (samples.shape[2] - 8): + y = samples.shape[2] - 8 + + new_height = height // 8 + new_width = width // 8 + to_x = new_width + x + to_y = new_height + y + s['samples'] = samples[:,:,y:to_y, x:to_x] + return (s,) + +class SetLatentNoiseMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "mask": ("MASK",), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "set_mask" + + CATEGORY = "latent/inpaint" + + def set_mask(self, samples, mask): + s = samples.copy() + s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + return (s,) + +def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): + latent_image = latent["samples"] + latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + callback = latent_preview.prepare_callback(model, steps) + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + out = latent.copy() + out["samples"] = samples + return (out, ) + +class KSampler: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}), + "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}), + "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}), + "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}), + } + } + + RETURN_TYPES = ("LATENT",) + OUTPUT_TOOLTIPS = ("The denoised latent.",) + FUNCTION = "sample" + + CATEGORY = "sampling" + DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image." + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): + return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) + +class KSamplerAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": (["enable", "disable"], ), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "return_with_leftover_noise": (["disable", "enable"], ), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): + force_full_denoise = True + if return_with_leftover_noise == "enable": + force_full_denoise = False + disable_noise = False + if add_noise == "disable": + disable_noise = True + return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) + +class SaveImage: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ("IMAGE", {"tooltip": "The images to save."}), + "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) + }, + "hidden": { + "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" + }, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image" + DESCRIPTION = "Saves the input images to your ComfyUI output directory." + + def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + for (batch_number, image) in enumerate(images): + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results } } + +class PreviewImage(SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadImage: + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + files = folder_paths.filter_files_content_types(files, ["image"]) + return {"required": + {"image": (sorted(files), {"image_upload": True})}, + } + + CATEGORY = "image" + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + def load_image(self, image): + image_path = folder_paths.get_annotated_filepath(image) + + img = node_helpers.pillow(Image.open, image_path) + + output_images = [] + output_masks = [] + w, h = None, None + + excluded_formats = ['MPO'] + + for i in ImageSequence.Iterator(img): + i = node_helpers.pillow(ImageOps.exif_transpose, i) + + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + image = i.convert("RGB") + + if len(output_images) == 0: + w = image.size[0] + h = image.size[1] + + if image.size[0] != w or image.size[1] != h: + continue + + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + elif i.mode == 'P' and 'transparency' in i.info: + mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1 and img.format not in excluded_formats: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + + return (output_image, output_mask) + + @classmethod + def IS_CHANGED(s, image): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class LoadImageMask: + _color_channels = ["alpha", "red", "green", "blue"] + @classmethod + def INPUT_TYPES(s): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True}), + "channel": (s._color_channels, ), } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "load_image" + def load_image(self, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + i = node_helpers.pillow(Image.open, image_path) + i = node_helpers.pillow(ImageOps.exif_transpose, i) + if i.getbands() != ("R", "G", "B", "A"): + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + i = i.convert("RGBA") + mask = None + c = channel[0].upper() + if c in i.getbands(): + mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 + mask = torch.from_numpy(mask) + if c == 'A': + mask = 1. - mask + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + return (mask.unsqueeze(0),) + + @classmethod + def IS_CHANGED(s, image, channel): + image_path = folder_paths.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + + +class LoadImageOutput(LoadImage): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("COMBO", { + "image_upload": True, + "image_folder": "output", + "remote": { + "route": "/internal/files/output", + "refresh_button": True, + "control_after_refresh": "first", + }, + }), + } + } + + DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration." + EXPERIMENTAL = True + FUNCTION = "load_image" + + +class ImageScale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = image + else: + samples = image.movedim(-1,1) + + if width == 0: + width = max(1, round(samples.shape[3] * height / samples.shape[2])) + elif height == 0: + height = max(1, round(samples.shape[2] * width / samples.shape[3])) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) + s = s.movedim(1,-1) + return (s,) + +class ImageScaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, scale_by): + samples = image.movedim(-1,1) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +class ImageInvert: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "invert" + + CATEGORY = "image" + + def invert(self, image): + s = 1.0 - image + return (s,) + +class ImageBatch: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "batch" + + CATEGORY = "image" + + def batch(self, image1, image2): + if image1.shape[1:] != image2.shape[1:]: + image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) + s = torch.cat((image1, image2), dim=0) + return (s,) + +class EmptyImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + + CATEGORY = "image" + + def generate(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return (torch.cat((r, g, b), dim=-1), ) + +class ImagePadForOutpaint: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, left, top, right, bottom, feathering): + d1, d2, d3, d4 = image.size() + + new_image = torch.ones( + (d1, d2 + top + bottom, d3 + left + right, d4), + dtype=torch.float32, + ) * 0.5 + + new_image[:, top:top + d2, left:left + d3, :] = image + + mask = torch.ones( + (d2 + top + bottom, d3 + left + right), + dtype=torch.float32, + ) + + t = torch.zeros( + (d2, d3), + dtype=torch.float32 + ) + + if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: + + for i in range(d2): + for j in range(d3): + dt = i if top != 0 else d2 + db = d2 - i if bottom != 0 else d2 + + dl = j if left != 0 else d3 + dr = d3 - j if right != 0 else d3 + + d = min(dt, db, dl, dr) + + if d >= feathering: + continue + + v = (feathering - d) / feathering + + t[i, j] = v * v + + mask[top:top + d2, left:left + d3] = t + + return (new_image, mask.unsqueeze(0)) + + +NODE_CLASS_MAPPINGS = { + "KSampler": KSampler, + "CheckpointLoaderSimple": CheckpointLoaderSimple, + "CLIPTextEncode": CLIPTextEncode, + "CLIPSetLastLayer": CLIPSetLastLayer, + "VAEDecode": VAEDecode, + "VAEEncode": VAEEncode, + "VAEEncodeForInpaint": VAEEncodeForInpaint, + "VAELoader": VAELoader, + "EmptyLatentImage": EmptyLatentImage, + "LatentUpscale": LatentUpscale, + "LatentUpscaleBy": LatentUpscaleBy, + "LatentFromBatch": LatentFromBatch, + "RepeatLatentBatch": RepeatLatentBatch, + "SaveImage": SaveImage, + "PreviewImage": PreviewImage, + "LoadImage": LoadImage, + "LoadImageMask": LoadImageMask, + "LoadImageOutput": LoadImageOutput, + "ImageScale": ImageScale, + "ImageScaleBy": ImageScaleBy, + "ImageInvert": ImageInvert, + "ImageBatch": ImageBatch, + "ImagePadForOutpaint": ImagePadForOutpaint, + "EmptyImage": EmptyImage, + "ConditioningAverage": ConditioningAverage , + "ConditioningCombine": ConditioningCombine, + "ConditioningConcat": ConditioningConcat, + "ConditioningSetArea": ConditioningSetArea, + "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, + "ConditioningSetAreaStrength": ConditioningSetAreaStrength, + "ConditioningSetMask": ConditioningSetMask, + "KSamplerAdvanced": KSamplerAdvanced, + "SetLatentNoiseMask": SetLatentNoiseMask, + "LatentComposite": LatentComposite, + "LatentBlend": LatentBlend, + "LatentRotate": LatentRotate, + "LatentFlip": LatentFlip, + "LatentCrop": LatentCrop, + "LoraLoader": LoraLoader, + "CLIPLoader": CLIPLoader, + "UNETLoader": UNETLoader, + "DualCLIPLoader": DualCLIPLoader, + "CLIPVisionEncode": CLIPVisionEncode, + "StyleModelApply": StyleModelApply, + "unCLIPConditioning": unCLIPConditioning, + "ControlNetApply": ControlNetApply, + "ControlNetApplyAdvanced": ControlNetApplyAdvanced, + "ControlNetLoader": ControlNetLoader, + "DiffControlNetLoader": DiffControlNetLoader, + "StyleModelLoader": StyleModelLoader, + "CLIPVisionLoader": CLIPVisionLoader, + "VAEDecodeTiled": VAEDecodeTiled, + "VAEEncodeTiled": VAEEncodeTiled, + "unCLIPCheckpointLoader": unCLIPCheckpointLoader, + "GLIGENLoader": GLIGENLoader, + "GLIGENTextBoxApply": GLIGENTextBoxApply, + "InpaintModelConditioning": InpaintModelConditioning, + + "CheckpointLoader": CheckpointLoader, + "DiffusersLoader": DiffusersLoader, + + "LoadLatent": LoadLatent, + "SaveLatent": SaveLatent, + + "ConditioningZeroOut": ConditioningZeroOut, + "ConditioningSetTimestepRange": ConditioningSetTimestepRange, + "LoraLoaderModelOnly": LoraLoaderModelOnly, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "KSampler": "KSampler", + "KSamplerAdvanced": "KSampler (Advanced)", + # Loaders + "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", + "CheckpointLoaderSimple": "Load Checkpoint", + "VAELoader": "Load VAE", + "LoraLoader": "Load LoRA", + "CLIPLoader": "Load CLIP", + "ControlNetLoader": "Load ControlNet Model", + "DiffControlNetLoader": "Load ControlNet Model (diff)", + "StyleModelLoader": "Load Style Model", + "CLIPVisionLoader": "Load CLIP Vision", + "UpscaleModelLoader": "Load Upscale Model", + "UNETLoader": "Load Diffusion Model", + # Conditioning + "CLIPVisionEncode": "CLIP Vision Encode", + "StyleModelApply": "Apply Style Model", + "CLIPTextEncode": "CLIP Text Encode (Prompt)", + "CLIPSetLastLayer": "CLIP Set Last Layer", + "ConditioningCombine": "Conditioning (Combine)", + "ConditioningAverage ": "Conditioning (Average)", + "ConditioningConcat": "Conditioning (Concat)", + "ConditioningSetArea": "Conditioning (Set Area)", + "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetMask": "Conditioning (Set Mask)", + "ControlNetApply": "Apply ControlNet (OLD)", + "ControlNetApplyAdvanced": "Apply ControlNet", + # Latent + "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", + "SetLatentNoiseMask": "Set Latent Noise Mask", + "VAEDecode": "VAE Decode", + "VAEEncode": "VAE Encode", + "LatentRotate": "Rotate Latent", + "LatentFlip": "Flip Latent", + "LatentCrop": "Crop Latent", + "EmptyLatentImage": "Empty Latent Image", + "LatentUpscale": "Upscale Latent", + "LatentUpscaleBy": "Upscale Latent By", + "LatentComposite": "Latent Composite", + "LatentBlend": "Latent Blend", + "LatentFromBatch" : "Latent From Batch", + "RepeatLatentBatch": "Repeat Latent Batch", + # Image + "SaveImage": "Save Image", + "PreviewImage": "Preview Image", + "LoadImage": "Load Image", + "LoadImageMask": "Load Image (as Mask)", + "LoadImageOutput": "Load Image (from Outputs)", + "ImageScale": "Upscale Image", + "ImageScaleBy": "Upscale Image By", + "ImageUpscaleWithModel": "Upscale Image (using Model)", + "ImageInvert": "Invert Image", + "ImagePadForOutpaint": "Pad Image for Outpainting", + "ImageBatch": "Batch Images", + "ImageCrop": "Image Crop", + "ImageStitch": "Image Stitch", + "ImageBlend": "Image Blend", + "ImageBlur": "Image Blur", + "ImageQuantize": "Image Quantize", + "ImageSharpen": "Image Sharpen", + "ImageScaleToTotalPixels": "Scale Image to Total Pixels", + "GetImageSize": "Get Image Size", + # _for_testing + "VAEDecodeTiled": "VAE Decode (Tiled)", + "VAEEncodeTiled": "VAE Encode (Tiled)", +} + +EXTENSION_WEB_DIRS = {} + +# Dictionary of successfully loaded module names and associated directories. +LOADED_MODULE_DIRS = {} + + +def get_module_name(module_path: str) -> str: + """ + Returns the module name based on the given module path. + Examples: + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "my_custom_node" + get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes + Args: + module_path (str): The path of the module. + Returns: + str: The module name. + """ + base_path = os.path.basename(module_path) + if os.path.isfile(module_path): + base_path = os.path.splitext(base_path)[0] + return base_path + + +def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool: + module_name = get_module_name(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] + sys_module_name = module_name + elif os.path.isdir(module_path): + sys_module_name = module_path.replace(".", "_x_") + + try: + logging.debug("Trying to load custom node {}".format(module_path)) + if os.path.isfile(module_path): + module_spec = importlib.util.spec_from_file_location(sys_module_name, module_path) + module_dir = os.path.split(module_path)[0] + else: + module_spec = importlib.util.spec_from_file_location(sys_module_name, os.path.join(module_path, "__init__.py")) + module_dir = module_path + + module = importlib.util.module_from_spec(module_spec) + sys.modules[sys_module_name] = module + module_spec.loader.exec_module(module) + + LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir) + + try: + from comfy_config import config_parser + + project_config = config_parser.extract_node_configuration(module_path) + + web_dir_name = project_config.tool_comfy.web + + if web_dir_name: + web_dir_path = os.path.join(module_path, web_dir_name) + + if os.path.isdir(web_dir_path): + project_name = project_config.project.name + + EXTENSION_WEB_DIRS[project_name] = web_dir_path + + logging.info("Automatically register web folder {} for {}".format(web_dir_name, project_name)) + except Exception as e: + logging.warning(f"Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': {e}") + + if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: + web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) + if os.path.isdir(web_dir): + EXTENSION_WEB_DIRS[module_name] = web_dir + + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + for name, node_cls in module.NODE_CLASS_MAPPINGS.items(): + if name not in ignore: + NODE_CLASS_MAPPINGS[name] = node_cls + node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path)) + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + return True + else: + logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + return False + except Exception as e: + logging.warning(traceback.format_exc()) + logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") + return False + +def init_external_custom_nodes(): + """ + Initializes the external custom nodes. + + This function loads custom nodes from the specified folder paths and imports them into the application. + It measures the import times for each custom node and logs the results. + + Returns: + None + """ + base_node_names = set(NODE_CLASS_MAPPINGS.keys()) + node_paths = folder_paths.get_folder_paths("custom_nodes") + node_import_times = [] + for custom_node_path in node_paths: + possible_modules = os.listdir(os.path.realpath(custom_node_path)) + if "__pycache__" in possible_modules: + possible_modules.remove("__pycache__") + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue + if module_path.endswith(".disabled"): continue + if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes: + logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes") + continue + time_before = time.perf_counter() + success = load_custom_node(module_path, base_node_names, module_parent="custom_nodes") + node_import_times.append((time.perf_counter() - time_before, module_path, success)) + + if len(node_import_times) > 0: + logging.info("\nImport times for custom nodes:") + for n in sorted(node_import_times): + if n[2]: + import_message = "" + else: + import_message = " (IMPORT FAILED)" + logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) + logging.info("") + +def init_builtin_extra_nodes(): + """ + Initializes the built-in extra nodes in ComfyUI. + + This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI. + If any of the extra node files fail to import, a warning message is logged. + + Returns: + None + """ + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") + extras_files = [ + "nodes_latent.py", + "nodes_hypernetwork.py", + "nodes_upscale_model.py", + "nodes_post_processing.py", + "nodes_mask.py", + "nodes_compositing.py", + "nodes_rebatch.py", + "nodes_model_merging.py", + "nodes_tomesd.py", + "nodes_clip_sdxl.py", + "nodes_canny.py", + "nodes_freelunch.py", + "nodes_custom_sampler.py", + "nodes_hypertile.py", + "nodes_model_advanced.py", + "nodes_model_downscale.py", + "nodes_images.py", + "nodes_video_model.py", + "nodes_train.py", + "nodes_sag.py", + "nodes_perpneg.py", + "nodes_stable3d.py", + "nodes_sdupscale.py", + "nodes_photomaker.py", + "nodes_pixart.py", + "nodes_cond.py", + "nodes_morphology.py", + "nodes_stable_cascade.py", + "nodes_differential_diffusion.py", + "nodes_ip2p.py", + "nodes_model_merging_model_specific.py", + "nodes_pag.py", + "nodes_align_your_steps.py", + "nodes_attention_multiply.py", + "nodes_advanced_samplers.py", + "nodes_webcam.py", + "nodes_audio.py", + "nodes_sd3.py", + "nodes_gits.py", + "nodes_controlnet.py", + "nodes_hunyuan.py", + "nodes_flux.py", + "nodes_lora_extract.py", + "nodes_torch_compile.py", + "nodes_mochi.py", + "nodes_slg.py", + "nodes_mahiro.py", + "nodes_lt.py", + "nodes_hooks.py", + "nodes_load_3d.py", + "nodes_cosmos.py", + "nodes_video.py", + "nodes_lumina2.py", + "nodes_wan.py", + "nodes_lotus.py", + "nodes_hunyuan3d.py", + "nodes_primitive.py", + "nodes_cfg.py", + "nodes_optimalsteps.py", + "nodes_hidream.py", + "nodes_fresca.py", + "nodes_apg.py", + "nodes_preview_any.py", + "nodes_ace.py", + "nodes_string.py", + "nodes_camera_trajectory.py", + "nodes_edit_model.py", + ] + + import_failed = [] + for node_file in extras_files: + if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): + import_failed.append(node_file) + + return import_failed + + +def init_builtin_api_nodes(): + api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") + api_nodes_files = [ + "nodes_ideogram.py", + "nodes_openai.py", + "nodes_minimax.py", + "nodes_veo2.py", + "nodes_kling.py", + "nodes_bfl.py", + "nodes_luma.py", + "nodes_recraft.py", + "nodes_pixverse.py", + "nodes_stability.py", + "nodes_pika.py", + "nodes_runway.py", + "nodes_tripo.py", + "nodes_rodin.py", + "nodes_gemini.py", + ] + + if not load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"): + return api_nodes_files + + import_failed = [] + for node_file in api_nodes_files: + if not load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"): + import_failed.append(node_file) + + return import_failed + + +def init_extra_nodes(init_custom_nodes=True, init_api_nodes=True): + import_failed = init_builtin_extra_nodes() + + import_failed_api = [] + if init_api_nodes: + import_failed_api = init_builtin_api_nodes() + + if init_custom_nodes: + init_external_custom_nodes() + else: + logging.info("Skipping loading of custom nodes") + + if len(import_failed_api) > 0: + logging.warning("WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed_api: + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") + if args.windows_standalone_build: + logging.warning("Please run the update script: update/update_comfyui.bat") + else: + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("") + + if len(import_failed) > 0: + logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed: + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") + if args.windows_standalone_build: + logging.warning("Please run the update script: update/update_comfyui.bat") + else: + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("") + + return import_failed diff --git a/notebooks/comfyui_colab.ipynb b/notebooks/comfyui_colab.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5560b5ff9c3757c7531da25211f1dc496ce3eef3 --- /dev/null +++ b/notebooks/comfyui_colab.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb" + }, + "outputs": [], + "source": [ + "#@title Environment Setup\n", + "\n", + "\n", + "OPTIONS = {}\n", + "\n", + "USE_GOOGLE_DRIVE = False #@param {type:\"boolean\"}\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "WORKSPACE = 'ComfyUI'\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "\n", + "if OPTIONS['USE_GOOGLE_DRIVE']:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + " \n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['UPDATE_COMFY_UI']:\n", + " !echo -= Updating ComfyUI =-\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd" + }, + "outputs": [], + "source": [ + "# Checkpoints\n", + "\n", + "### SDXL\n", + "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", + "\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", + "\n", + "# SDXL ReVision\n", + "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", + "\n", + "# SD1.5\n", + "!wget -c https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "# SD2\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# unCLIP models\n", + "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", + "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", + "\n", + "\n", + "# Loras\n", + "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", + "\n", + "\n", + "# T2I-Adapter\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", + "\n", + "# ControlNet SDXL\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "\n", + "# GLIGEN\n", + "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", + "\n", + "\n", + "# ESRGAN upscale model\n", + "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with cloudflared (Recommended Way)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with localtunnel\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!npm install -g localtunnel\n", + "\n", + "import threading\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh" + }, + "outputs": [], + "source": [ + "import threading\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..9d0f90032d48f59028cbbf43ccba426e3c292c58 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "ComfyUI" +version = "0.3.43" +readme = "README.md" +license = { file = "LICENSE" } +requires-python = ">=3.9" + +[project.urls] +homepage = "https://www.comfy.org/" +repository = "https://github.com/comfyanonymous/ComfyUI" +documentation = "https://docs.comfy.org/" + +[tool.ruff] +lint.select = [ + "N805", # invalid-first-argument-name-for-method + "S307", # suspicious-eval-usage + "S102", # exec + "T", # print-usage + "W", + # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names. + # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f + "F", +] +exclude = ["*.ipynb"] diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000000000000000000000000000000000000..a224d8cbb5511cf6be0c0cf75ae92268339e11d6 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,9 @@ +[pytest] +markers = + inference: mark as inference test (deselect with '-m "not inference"') + execution: mark as execution test (deselect with '-m "not execution"') +testpaths = + tests + tests-unit +addopts = -s +pythonpath = . diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b5364f15ba47c9bf09136a6fc81af3cd36c4c12d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,37 @@ +comfyui-frontend-package==1.23.4 +comfyui-workflow-templates==0.1.30 +comfyui-embedded-docs==0.2.3 +opencv-python +imageio-ffmpeg +torchvision==0.23.0 +torchsde +numpy>=1.25.0 +einops +transformers==4.49.0 +tokenizers>=0.13.3 +sentencepiece>=0.2.0 +safetensors>=0.4.2 +aiohttp>=3.11.8 +yarl>=1.18.0 +pyyaml +huggingface_hub +Pillow +pillow>=10.3.0 +scipy +tqdm +psutil +alembic +SQLAlchemy +ollama +surrealist +sageattention +color-matcher +matplotlib +mss +ftfy +accelerate>=1.2.1 +diffusers>=0.33.0 +peft>=0.17.0 +protobuf +pyloudnorm +gguf>=0.17.1 \ No newline at end of file diff --git a/server.py b/server.py new file mode 100644 index 0000000000000000000000000000000000000000..878b5eeb1eb537c68be1708a06d6b688bec2503d --- /dev/null +++ b/server.py @@ -0,0 +1,899 @@ +import os +import sys +import asyncio +import traceback + +import nodes +import folder_paths +import execution +import uuid +import urllib +import json +import glob +import struct +import ssl +import socket +import ipaddress +from PIL import Image, ImageOps +from PIL.PngImagePlugin import PngInfo +from io import BytesIO + +import aiohttp +from aiohttp import web +import logging + +import mimetypes +from comfy.cli_args import args +import comfy.utils +import comfy.model_management +import node_helpers +from comfyui_version import __version__ +from app.frontend_management import FrontendManager + +from app.user_manager import UserManager +from app.model_manager import ModelFileManager +from app.custom_node_manager import CustomNodeManager +from typing import Optional, Union +from api_server.routes.internal.internal_routes import InternalRoutes + +class BinaryEventTypes: + PREVIEW_IMAGE = 1 + UNENCODED_PREVIEW_IMAGE = 2 + TEXT = 3 + +async def send_socket_catch_exception(function, message): + try: + await function(message) + except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err: + logging.warning("send error: {}".format(err)) + +@web.middleware +async def cache_control(request: web.Request, handler): + response: web.Response = await handler(request) + if request.path.endswith('.js') or request.path.endswith('.css') or request.path.endswith('index.json'): + response.headers.setdefault('Cache-Control', 'no-cache') + return response + + +@web.middleware +async def compress_body(request: web.Request, handler): + accept_encoding = request.headers.get("Accept-Encoding", "") + response: web.Response = await handler(request) + if not isinstance(response, web.Response): + return response + if response.content_type not in ["application/json", "text/plain"]: + return response + if response.body and "gzip" in accept_encoding: + response.enable_compression() + return response + + +def create_cors_middleware(allowed_origin: str): + @web.middleware + async def cors_middleware(request: web.Request, handler): + if request.method == "OPTIONS": + # Pre-flight request. Reply successfully: + response = web.Response() + else: + response = await handler(request) + + response.headers['Access-Control-Allow-Origin'] = allowed_origin + response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS' + response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' + response.headers['Access-Control-Allow-Credentials'] = 'true' + return response + + return cors_middleware + +def is_loopback(host): + if host is None: + return False + try: + if ipaddress.ip_address(host).is_loopback: + return True + else: + return False + except: + pass + + loopback = False + for family in (socket.AF_INET, socket.AF_INET6): + try: + r = socket.getaddrinfo(host, None, family, socket.SOCK_STREAM) + for family, _, _, _, sockaddr in r: + if not ipaddress.ip_address(sockaddr[0]).is_loopback: + return loopback + else: + loopback = True + except socket.gaierror: + pass + + return loopback + + +def create_origin_only_middleware(): + @web.middleware + async def origin_only_middleware(request: web.Request, handler): + #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason. + #in that case the Host and Origin hostnames won't match + #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime + if 'Host' in request.headers and 'Origin' in request.headers: + host = request.headers['Host'] + origin = request.headers['Origin'] + host_domain = host.lower() + parsed = urllib.parse.urlparse(origin) + origin_domain = parsed.netloc.lower() + host_domain_parsed = urllib.parse.urlsplit('//' + host_domain) + + #limit the check to when the host domain is localhost, this makes it slightly less safe but should still prevent the exploit + loopback = is_loopback(host_domain_parsed.hostname) + + if parsed.port is None: #if origin doesn't have a port strip it from the host to handle weird browsers, same for host + host_domain = host_domain_parsed.hostname + if host_domain_parsed.port is None: + origin_domain = parsed.hostname + + if loopback and host_domain is not None and origin_domain is not None and len(host_domain) > 0 and len(origin_domain) > 0: + if host_domain != origin_domain: + logging.warning("WARNING: request with non matching host and origin {} != {}, returning 403".format(host_domain, origin_domain)) + return web.Response(status=403) + + if request.method == "OPTIONS": + response = web.Response() + else: + response = await handler(request) + + return response + + return origin_only_middleware + +class PromptServer(): + def __init__(self, loop): + PromptServer.instance = self + + mimetypes.init() + mimetypes.add_type('application/javascript; charset=utf-8', '.js') + mimetypes.add_type('image/webp', '.webp') + + self.user_manager = UserManager() + self.model_file_manager = ModelFileManager() + self.custom_node_manager = CustomNodeManager() + self.internal_routes = InternalRoutes(self) + self.supports = ["custom_nodes_from_web"] + self.prompt_queue = execution.PromptQueue(self) + self.loop = loop + self.messages = asyncio.Queue() + self.client_session:Optional[aiohttp.ClientSession] = None + self.number = 0 + + middlewares = [cache_control] + if args.enable_compress_response_body: + middlewares.append(compress_body) + + if args.enable_cors_header: + middlewares.append(create_cors_middleware(args.enable_cors_header)) + else: + middlewares.append(create_origin_only_middleware()) + + max_upload_size = round(args.max_upload_size * 1024 * 1024) + self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) + self.sockets = dict() + self.web_root = ( + FrontendManager.init_frontend(args.front_end_version) + if args.front_end_root is None + else args.front_end_root + ) + logging.info(f"[Prompt Server] web root: {self.web_root}") + routes = web.RouteTableDef() + self.routes = routes + self.last_node_id = None + self.client_id = None + + self.on_prompt_handlers = [] + + @routes.get('/ws') + async def websocket_handler(request): + ws = web.WebSocketResponse() + await ws.prepare(request) + sid = request.rel_url.query.get('clientId', '') + if sid: + # Reusing existing session, remove old + self.sockets.pop(sid, None) + else: + sid = uuid.uuid4().hex + + self.sockets[sid] = ws + + try: + # Send initial state to the new client + await self.send("status", { "status": self.get_queue_info(), 'sid': sid }, sid) + # On reconnect if we are the currently executing client send the current node + if self.client_id == sid and self.last_node_id is not None: + await self.send("executing", { "node": self.last_node_id }, sid) + + async for msg in ws: + if msg.type == aiohttp.WSMsgType.ERROR: + logging.warning('ws connection closed with exception %s' % ws.exception()) + finally: + self.sockets.pop(sid, None) + return ws + + @routes.get("/") + async def get_root(request): + response = web.FileResponse(os.path.join(self.web_root, "index.html")) + response.headers['Cache-Control'] = 'no-cache' + response.headers["Pragma"] = "no-cache" + response.headers["Expires"] = "0" + return response + + @routes.get("/embeddings") + def get_embeddings(request): + embeddings = folder_paths.get_filename_list("embeddings") + return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) + + @routes.get("/models") + def list_model_types(request): + model_types = list(folder_paths.folder_names_and_paths.keys()) + + return web.json_response(model_types) + + @routes.get("/models/{folder}") + async def get_models(request): + folder = request.match_info.get("folder", None) + if not folder in folder_paths.folder_names_and_paths: + return web.Response(status=404) + files = folder_paths.get_filename_list(folder) + return web.json_response(files) + + @routes.get("/extensions") + async def get_extensions(request): + files = glob.glob(os.path.join( + glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True) + + extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)) + + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): + files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True) + extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote( + name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files))) + + return web.json_response(extensions) + + def get_dir_by_type(dir_type): + if dir_type is None: + dir_type = "input" + + if dir_type == "input": + type_dir = folder_paths.get_input_directory() + elif dir_type == "temp": + type_dir = folder_paths.get_temp_directory() + elif dir_type == "output": + type_dir = folder_paths.get_output_directory() + + return type_dir, dir_type + + def compare_image_hash(filepath, image): + hasher = node_helpers.hasher() + + # function to compare hashes of two images to see if it already exists, fix to #3465 + if os.path.exists(filepath): + a = hasher() + b = hasher() + with open(filepath, "rb") as f: + a.update(f.read()) + b.update(image.file.read()) + image.file.seek(0) + return a.hexdigest() == b.hexdigest() + return False + + def image_upload(post, image_save_function=None): + image = post.get("image") + overwrite = post.get("overwrite") + image_is_duplicate = False + + image_upload_type = post.get("type") + upload_dir, image_upload_type = get_dir_by_type(image_upload_type) + + if image and image.file: + filename = image.filename + if not filename: + return web.Response(status=400) + + subfolder = post.get("subfolder", "") + full_output_folder = os.path.join(upload_dir, os.path.normpath(subfolder)) + filepath = os.path.abspath(os.path.join(full_output_folder, filename)) + + if os.path.commonpath((upload_dir, filepath)) != upload_dir: + return web.Response(status=400) + + if not os.path.exists(full_output_folder): + os.makedirs(full_output_folder) + + split = os.path.splitext(filename) + + if overwrite is not None and (overwrite == "true" or overwrite == "1"): + pass + else: + i = 1 + while os.path.exists(filepath): + if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465 + image_is_duplicate = True + break + filename = f"{split[0]} ({i}){split[1]}" + filepath = os.path.join(full_output_folder, filename) + i += 1 + + if not image_is_duplicate: + if image_save_function is not None: + image_save_function(image, post, filepath) + else: + with open(filepath, "wb") as f: + f.write(image.file.read()) + + return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) + else: + return web.Response(status=400) + + @routes.post("/upload/image") + async def upload_image(request): + post = await request.post() + return image_upload(post) + + + @routes.post("/upload/mask") + async def upload_mask(request): + post = await request.post() + + def image_save_function(image, post, filepath): + original_ref = json.loads(post.get("original_ref")) + filename, output_dir = folder_paths.annotated_filepath(original_ref['filename']) + + if not filename: + return web.Response(status=400) + + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + type = original_ref.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) + + if output_dir is None: + return web.Response(status=400) + + if original_ref.get("subfolder", "") != "": + full_output_dir = os.path.join(output_dir, original_ref["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + file = os.path.join(output_dir, filename) + + if os.path.isfile(file): + with Image.open(file) as original_pil: + metadata = PngInfo() + if hasattr(original_pil,'text'): + for key in original_pil.text: + metadata.add_text(key, original_pil.text[key]) + original_pil = original_pil.convert('RGBA') + mask_pil = Image.open(image.file).convert('RGBA') + + # alpha copy + new_alpha = mask_pil.getchannel('A') + original_pil.putalpha(new_alpha) + original_pil.save(filepath, compress_level=4, pnginfo=metadata) + + return image_upload(post, image_save_function) + + @routes.get("/view") + async def view_image(request): + if "filename" in request.rel_url.query: + filename = request.rel_url.query["filename"] + filename, output_dir = folder_paths.annotated_filepath(filename) + + if not filename: + return web.Response(status=400) + + # validation for security: prevent accessing arbitrary path + if filename[0] == '/' or '..' in filename: + return web.Response(status=400) + + if output_dir is None: + type = request.rel_url.query.get("type", "output") + output_dir = folder_paths.get_directory_by_type(type) + + if output_dir is None: + return web.Response(status=400) + + if "subfolder" in request.rel_url.query: + full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) + if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: + return web.Response(status=403) + output_dir = full_output_dir + + filename = os.path.basename(filename) + file = os.path.join(output_dir, filename) + + if os.path.isfile(file): + if 'preview' in request.rel_url.query: + with Image.open(file) as img: + preview_info = request.rel_url.query['preview'].split(';') + image_format = preview_info[0] + if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''): + image_format = 'webp' + + quality = 90 + if preview_info[-1].isdigit(): + quality = int(preview_info[-1]) + + buffer = BytesIO() + if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb': + img = img.convert("RGB") + img.save(buffer, format=image_format, quality=quality) + buffer.seek(0) + + return web.Response(body=buffer.read(), content_type=f'image/{image_format}', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + + if 'channel' not in request.rel_url.query: + channel = 'rgba' + else: + channel = request.rel_url.query["channel"] + + if channel == 'rgb': + with Image.open(file) as img: + if img.mode == "RGBA": + r, g, b, a = img.split() + new_img = Image.merge('RGB', (r, g, b)) + else: + new_img = img.convert("RGB") + + buffer = BytesIO() + new_img.save(buffer, format='PNG') + buffer.seek(0) + + return web.Response(body=buffer.read(), content_type='image/png', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + + elif channel == 'a': + with Image.open(file) as img: + if img.mode == "RGBA": + _, _, _, a = img.split() + else: + a = Image.new('L', img.size, 255) + + # alpha img + alpha_img = Image.new('RGBA', img.size) + alpha_img.putalpha(a) + alpha_buffer = BytesIO() + alpha_img.save(alpha_buffer, format='PNG') + alpha_buffer.seek(0) + + return web.Response(body=alpha_buffer.read(), content_type='image/png', + headers={"Content-Disposition": f"filename=\"{filename}\""}) + else: + # Get content type from mimetype, defaulting to 'application/octet-stream' + content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream' + + # For security, force certain mimetypes to download instead of display + if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}: + content_type = 'application/octet-stream' # Forces download + + return web.FileResponse( + file, + headers={ + "Content-Disposition": f"filename=\"{filename}\"", + "Content-Type": content_type + } + ) + + return web.Response(status=404) + + @routes.get("/view_metadata/{folder_name}") + async def view_metadata(request): + folder_name = request.match_info.get("folder_name", None) + if folder_name is None: + return web.Response(status=404) + if not "filename" in request.rel_url.query: + return web.Response(status=404) + + filename = request.rel_url.query["filename"] + if not filename.endswith(".safetensors"): + return web.Response(status=404) + + safetensors_path = folder_paths.get_full_path(folder_name, filename) + if safetensors_path is None: + return web.Response(status=404) + out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024) + if out is None: + return web.Response(status=404) + dt = json.loads(out) + if not "__metadata__" in dt: + return web.Response(status=404) + return web.json_response(dt["__metadata__"]) + + @routes.get("/system_stats") + async def system_stats(request): + device = comfy.model_management.get_torch_device() + device_name = comfy.model_management.get_torch_device_name(device) + cpu_device = comfy.model_management.torch.device("cpu") + ram_total = comfy.model_management.get_total_memory(cpu_device) + ram_free = comfy.model_management.get_free_memory(cpu_device) + vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True) + vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True) + + system_stats = { + "system": { + "os": os.name, + "ram_total": ram_total, + "ram_free": ram_free, + "comfyui_version": __version__, + "python_version": sys.version, + "pytorch_version": comfy.model_management.torch_version, + "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded", + "argv": sys.argv + }, + "devices": [ + { + "name": device_name, + "type": device.type, + "index": device.index, + "vram_total": vram_total, + "vram_free": vram_free, + "torch_vram_total": torch_vram_total, + "torch_vram_free": torch_vram_free, + } + ] + } + return web.json_response(system_stats) + + @routes.get("/prompt") + async def get_prompt(request): + return web.json_response(self.get_queue_info()) + + def node_info(node_class): + obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] + info = {} + info['input'] = obj_class.INPUT_TYPES() + info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()} + info['output'] = obj_class.RETURN_TYPES + info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES) + info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output'] + info['name'] = node_class + info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class + info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else '' + info['python_module'] = getattr(obj_class, "RELATIVE_PYTHON_MODULE", "nodes") + info['category'] = 'sd' + if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True: + info['output_node'] = True + else: + info['output_node'] = False + + if hasattr(obj_class, 'CATEGORY'): + info['category'] = obj_class.CATEGORY + + if hasattr(obj_class, 'OUTPUT_TOOLTIPS'): + info['output_tooltips'] = obj_class.OUTPUT_TOOLTIPS + + if getattr(obj_class, "DEPRECATED", False): + info['deprecated'] = True + if getattr(obj_class, "EXPERIMENTAL", False): + info['experimental'] = True + + if hasattr(obj_class, 'API_NODE'): + info['api_node'] = obj_class.API_NODE + return info + + @routes.get("/object_info") + async def get_object_info(request): + with folder_paths.cache_helper: + out = {} + for x in nodes.NODE_CLASS_MAPPINGS: + try: + out[x] = node_info(x) + except Exception: + logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.") + logging.error(traceback.format_exc()) + return web.json_response(out) + + @routes.get("/object_info/{node_class}") + async def get_object_info_node(request): + node_class = request.match_info.get("node_class", None) + out = {} + if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS): + out[node_class] = node_info(node_class) + return web.json_response(out) + + @routes.get("/history") + async def get_history(request): + max_items = request.rel_url.query.get("max_items", None) + if max_items is not None: + max_items = int(max_items) + return web.json_response(self.prompt_queue.get_history(max_items=max_items)) + + @routes.get("/history/{prompt_id}") + async def get_history_prompt_id(request): + prompt_id = request.match_info.get("prompt_id", None) + return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id)) + + @routes.get("/queue") + async def get_queue(request): + queue_info = {} + current_queue = self.prompt_queue.get_current_queue_volatile() + queue_info['queue_running'] = current_queue[0] + queue_info['queue_pending'] = current_queue[1] + return web.json_response(queue_info) + + @routes.post("/prompt") + async def post_prompt(request): + logging.info("got prompt") + json_data = await request.json() + json_data = self.trigger_on_prompt(json_data) + + if "number" in json_data: + number = float(json_data['number']) + else: + number = self.number + if "front" in json_data: + if json_data['front']: + number = -number + + self.number += 1 + + if "prompt" in json_data: + prompt = json_data["prompt"] + valid = execution.validate_prompt(prompt) + extra_data = {} + if "extra_data" in json_data: + extra_data = json_data["extra_data"] + + if "client_id" in json_data: + extra_data["client_id"] = json_data["client_id"] + if valid[0]: + prompt_id = str(uuid.uuid4()) + outputs_to_execute = valid[2] + self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) + response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} + return web.json_response(response) + else: + logging.warning("invalid prompt: {}".format(valid[1])) + return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) + else: + error = { + "type": "no_prompt", + "message": "No prompt provided", + "details": "No prompt provided", + "extra_info": {} + } + return web.json_response({"error": error, "node_errors": {}}, status=400) + + @routes.post("/queue") + async def post_queue(request): + json_data = await request.json() + if "clear" in json_data: + if json_data["clear"]: + self.prompt_queue.wipe_queue() + if "delete" in json_data: + to_delete = json_data['delete'] + for id_to_delete in to_delete: + delete_func = lambda a: a[1] == id_to_delete + self.prompt_queue.delete_queue_item(delete_func) + + return web.Response(status=200) + + @routes.post("/interrupt") + async def post_interrupt(request): + nodes.interrupt_processing() + return web.Response(status=200) + + @routes.post("/free") + async def post_free(request): + json_data = await request.json() + unload_models = json_data.get("unload_models", False) + free_memory = json_data.get("free_memory", False) + if unload_models: + self.prompt_queue.set_flag("unload_models", unload_models) + if free_memory: + self.prompt_queue.set_flag("free_memory", free_memory) + return web.Response(status=200) + + @routes.post("/history") + async def post_history(request): + json_data = await request.json() + if "clear" in json_data: + if json_data["clear"]: + self.prompt_queue.wipe_history() + if "delete" in json_data: + to_delete = json_data['delete'] + for id_to_delete in to_delete: + self.prompt_queue.delete_history_item(id_to_delete) + + return web.Response(status=200) + + async def setup(self): + timeout = aiohttp.ClientTimeout(total=None) # no timeout + self.client_session = aiohttp.ClientSession(timeout=timeout) + + def add_routes(self): + self.user_manager.add_routes(self.routes) + self.model_file_manager.add_routes(self.routes) + self.custom_node_manager.add_routes(self.routes, self.app, nodes.LOADED_MODULE_DIRS.items()) + self.app.add_subapp('/internal', self.internal_routes.get_app()) + + # Prefix every route with /api for easier matching for delegation. + # This is very useful for frontend dev server, which need to forward + # everything except serving of static files. + # Currently both the old endpoints without prefix and new endpoints with + # prefix are supported. + api_routes = web.RouteTableDef() + for route in self.routes: + # Custom nodes might add extra static routes. Only process non-static + # routes to add /api prefix. + if isinstance(route, web.RouteDef): + api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) + self.app.add_routes(api_routes) + self.app.add_routes(self.routes) + + # Add routes from web extensions. + for name, dir in nodes.EXTENSION_WEB_DIRS.items(): + self.app.add_routes([web.static('/extensions/' + name, dir)]) + + workflow_templates_path = FrontendManager.templates_path() + if workflow_templates_path: + self.app.add_routes([ + web.static('/templates', workflow_templates_path) + ]) + + # Serve embedded documentation from the package + embedded_docs_path = FrontendManager.embedded_docs_path() + if embedded_docs_path: + self.app.add_routes([ + web.static('/docs', embedded_docs_path) + ]) + + self.app.add_routes([ + web.static('/', self.web_root), + ]) + + def get_queue_info(self): + prompt_info = {} + exec_info = {} + exec_info['queue_remaining'] = self.prompt_queue.get_tasks_remaining() + prompt_info['exec_info'] = exec_info + return prompt_info + + async def send(self, event, data, sid=None): + if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE: + await self.send_image(data, sid=sid) + elif isinstance(data, (bytes, bytearray)): + await self.send_bytes(event, data, sid) + else: + await self.send_json(event, data, sid) + + def encode_bytes(self, event, data): + if not isinstance(event, int): + raise RuntimeError(f"Binary event types must be integers, got {event}") + + packed = struct.pack(">I", event) + message = bytearray(packed) + message.extend(data) + return message + + async def send_image(self, image_data, sid=None): + image_type = image_data[0] + image = image_data[1] + max_size = image_data[2] + if max_size is not None: + if hasattr(Image, 'Resampling'): + resampling = Image.Resampling.BILINEAR + else: + resampling = Image.Resampling.LANCZOS + + image = ImageOps.contain(image, (max_size, max_size), resampling) + type_num = 1 + if image_type == "JPEG": + type_num = 1 + elif image_type == "PNG": + type_num = 2 + + bytesIO = BytesIO() + header = struct.pack(">I", type_num) + bytesIO.write(header) + image.save(bytesIO, format=image_type, quality=95, compress_level=1) + preview_bytes = bytesIO.getvalue() + await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid) + + async def send_bytes(self, event, data, sid=None): + message = self.encode_bytes(event, data) + + if sid is None: + sockets = list(self.sockets.values()) + for ws in sockets: + await send_socket_catch_exception(ws.send_bytes, message) + elif sid in self.sockets: + await send_socket_catch_exception(self.sockets[sid].send_bytes, message) + + async def send_json(self, event, data, sid=None): + message = {"type": event, "data": data} + + if sid is None: + sockets = list(self.sockets.values()) + for ws in sockets: + await send_socket_catch_exception(ws.send_json, message) + elif sid in self.sockets: + await send_socket_catch_exception(self.sockets[sid].send_json, message) + + def send_sync(self, event, data, sid=None): + self.loop.call_soon_threadsafe( + self.messages.put_nowait, (event, data, sid)) + + def queue_updated(self): + self.send_sync("status", { "status": self.get_queue_info() }) + + async def publish_loop(self): + while True: + msg = await self.messages.get() + await self.send(*msg) + + async def start(self, address, port, verbose=True, call_on_start=None): + await self.start_multi_address([(address, port)], call_on_start=call_on_start) + + async def start_multi_address(self, addresses, call_on_start=None, verbose=True): + runner = web.AppRunner(self.app, access_log=None) + await runner.setup() + ssl_ctx = None + scheme = "http" + if args.tls_keyfile and args.tls_certfile: + ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) + ssl_ctx.load_cert_chain(certfile=args.tls_certfile, + keyfile=args.tls_keyfile) + scheme = "https" + + if verbose: + logging.info("Starting server\n") + for addr in addresses: + address = addr[0] + port = addr[1] + site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx) + await site.start() + + if not hasattr(self, 'address'): + self.address = address #TODO: remove this + self.port = port + + if ':' in address: + address_print = "[{}]".format(address) + else: + address_print = address + + if verbose: + logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address_print, port)) + + if call_on_start is not None: + call_on_start(scheme, self.address, self.port) + + def add_on_prompt_handler(self, handler): + self.on_prompt_handlers.append(handler) + + def trigger_on_prompt(self, json_data): + for handler in self.on_prompt_handlers: + try: + json_data = handler(json_data) + except Exception: + logging.warning("[ERROR] An error occurred during the on_prompt_handler processing") + logging.warning(traceback.format_exc()) + + return json_data + + def send_progress_text( + self, text: Union[bytes, bytearray, str], node_id: str, sid=None + ): + if isinstance(text, str): + text = text.encode("utf-8") + node_id_bytes = str(node_id).encode("utf-8") + + # Pack the node_id length as a 4-byte unsigned integer, followed by the node_id bytes + message = struct.pack(">I", len(node_id_bytes)) + node_id_bytes + text + + self.send_sync(BinaryEventTypes.TEXT, message, sid) diff --git a/user/comfyui.db b/user/comfyui.db new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/user/default/comfy.settings.json b/user/default/comfy.settings.json new file mode 100644 index 0000000000000000000000000000000000000000..b43b431d609bfe8f9f1a3d4f6c8e05db4b430a02 --- /dev/null +++ b/user/default/comfy.settings.json @@ -0,0 +1,4 @@ +{ + "Comfy.TutorialCompleted": true, + "Comfy.LinkRenderMode": 2 +} \ No newline at end of file diff --git a/user/default/was-node-suite/whitelist-dirs.list b/user/default/was-node-suite/whitelist-dirs.list new file mode 100644 index 0000000000000000000000000000000000000000..8fe190fb9fab475e5dbae2637347c29e5fdb116b --- /dev/null +++ b/user/default/was-node-suite/whitelist-dirs.list @@ -0,0 +1,3 @@ +/home/qingyan/code/ComfyUI-0.3.43/input +/home/qingyan/code/ComfyUI-0.3.43/output +/home/qingyan/code/ComfyUI-0.3.43/temp diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8a867aca652f588717fa3fd7f65b8e4c2777e692 --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,19 @@ +def get_path_after_pexel(path_str): + # 查找'pexel'第一次出现的位置 + pexel_index = path_str.find('pexel') + if pexel_index == -1: + # 如果没有找到'pexel'，返回空字符串或根据需求处理 + return "" + + # 从'pexel'出现的位置之后开始查找第一个'/' + slash_index = path_str.find('/', pexel_index) + if slash_index == -1: + # 如果在'pexel'之后没有找到'/'，返回空字符串或根据需求处理 + return "" + + # 返回'/'后面的部分 + return path_str[slash_index + 1:] + +# # 测试示例 +# test_path = "/robby/share/Editing/qingyan/data/pexels-video-msl-human10w-101frames/social/interview/5971451_cottonbro_studio.mp4" +# print(get_path_after_pexels(test_path)) # 输出: social/interview/5971451_cottonbro_studio.mp4 \ No newline at end of file diff --git a/utils/extra_config.py b/utils/extra_config.py new file mode 100644 index 0000000000000000000000000000000000000000..a0fcda9e8900ee2c67d79a1e588ff973f9ea7ea6 --- /dev/null +++ b/utils/extra_config.py @@ -0,0 +1,34 @@ +import os +import yaml +import folder_paths +import logging + +def load_extra_path_config(yaml_path): + with open(yaml_path, 'r', encoding='utf-8') as stream: + config = yaml.safe_load(stream) + yaml_dir = os.path.dirname(os.path.abspath(yaml_path)) + for c in config: + conf = config[c] + if conf is None: + continue + base_path = None + if "base_path" in conf: + base_path = conf.pop("base_path") + base_path = os.path.expandvars(os.path.expanduser(base_path)) + if not os.path.isabs(base_path): + base_path = os.path.abspath(os.path.join(yaml_dir, base_path)) + is_default = False + if "is_default" in conf: + is_default = conf.pop("is_default") + for x in conf: + for y in conf[x].split("\n"): + if len(y) == 0: + continue + full_path = y + if base_path: + full_path = os.path.join(base_path, full_path) + elif not os.path.isabs(full_path): + full_path = os.path.abspath(os.path.join(yaml_dir, y)) + normalized_path = os.path.normpath(full_path) + logging.info("Adding extra search path {} {}".format(x, normalized_path)) + folder_paths.add_model_folder_path(x, normalized_path, is_default) diff --git a/utils/install_util.py b/utils/install_util.py new file mode 100644 index 0000000000000000000000000000000000000000..0f59bcf9193570a36641bd4e0c70c80d05be1a0f --- /dev/null +++ b/utils/install_util.py @@ -0,0 +1,18 @@ +from pathlib import Path +import sys + +# The path to the requirements.txt file +requirements_path = Path(__file__).parents[1] / "requirements.txt" + + +def get_missing_requirements_message(): + """The warning message to display when a package is missing.""" + + extra = "" + if sys.flags.no_user_site: + extra = "-s " + return f""" +Please install the updated requirements.txt file by running: +{sys.executable} {extra}-m pip install -r {requirements_path} +If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem. +""".strip() diff --git a/utils/json_util.py b/utils/json_util.py new file mode 100644 index 0000000000000000000000000000000000000000..da45af4f74f386973d3f1968d88b083ca5906d81 --- /dev/null +++ b/utils/json_util.py @@ -0,0 +1,26 @@ +def merge_json_recursive(base, update): + """Recursively merge two JSON-like objects. + - Dictionaries are merged recursively + - Lists are concatenated + - Other types are overwritten by the update value + + Args: + base: Base JSON-like object + update: Update JSON-like object to merge into base + + Returns: + Merged JSON-like object + """ + if not isinstance(base, dict) or not isinstance(update, dict): + if isinstance(base, list) and isinstance(update, list): + return base + update + return update + + merged = base.copy() + for key, value in update.items(): + if key in merged: + merged[key] = merge_json_recursive(merged[key], value) + else: + merged[key] = value + + return merged