pipeline.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Union, List, Dict, Any, Callable
from dataclasses import dataclass
import numpy as np
from PIL import Image
import torchvision.transforms as T
from torchvision.transforms.functional import to_tensor, normalize
import warnings
from contextlib import contextmanager
from functools import wraps

from transformers import PretrainedConfig, PreTrainedModel, CLIPTextModel, CLIPTokenizer
from transformers.modeling_outputs import BaseModelOutputWithPooling
from diffusers import DiffusionPipeline, DDIMScheduler
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils import BaseOutput

# Optimization imports
try:
    import transformer_engine.pytorch as te
    from transformer_engine.common import recipe
    HAS_TRANSFORMER_ENGINE = True
except ImportError:
    HAS_TRANSFORMER_ENGINE = False

try:
    from torch._dynamo import config as dynamo_config
    HAS_TORCH_COMPILE = hasattr(torch, 'compile')
except ImportError:
    HAS_TORCH_COMPILE = False

# -----------------------------------------------------------------------------
# 1. Advanced Configuration (8B Scale)
# -----------------------------------------------------------------------------

class OmniMMDitV2Config(PretrainedConfig):
    model_type = "omnimm_dit_v2"
    
    def __init__(
        self,
        vocab_size: int = 49408,
        hidden_size: int = 4096,          # 4096 dim for ~7B-8B scale
        intermediate_size: int = 11008,   # Llama-style MLP expansion
        num_hidden_layers: int = 32,      # Deep network
        num_attention_heads: int = 32,
        num_key_value_heads: Optional[int] = 8,  # GQA (Grouped Query Attention)
        hidden_act: str = "silu",
        max_position_embeddings: int = 4096,
        initializer_range: float = 0.02,
        rms_norm_eps: float = 1e-5,
        use_cache: bool = True,
        pad_token_id: int = 0,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        tie_word_embeddings: bool = False,
        rope_theta: float = 10000.0,
        # DiT Specifics
        patch_size: int = 2,
        in_channels: int = 4,             # VAE Latent channels
        out_channels: int = 4, # x2 for variance if learned
        frequency_embedding_size: int = 256,
        # Multi-Modal Specifics
        max_condition_images: int = 3,    # Support 1-3 input images
        visual_embed_dim: int = 1024,     # e.g., SigLIP or CLIP Vision
        text_embed_dim: int = 4096,       # T5-XXL or similar
        use_temporal_attention: bool = True, # For Video generation
        # Optimization Configs
        use_fp8_quantization: bool = False,
        use_compilation: bool = False,
        compile_mode: str = "reduce-overhead",
        use_flash_attention: bool = True,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.patch_size = patch_size
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.frequency_embedding_size = frequency_embedding_size
        self.max_condition_images = max_condition_images
        self.visual_embed_dim = visual_embed_dim
        self.text_embed_dim = text_embed_dim
        self.use_temporal_attention = use_temporal_attention
        self.use_fp8_quantization = use_fp8_quantization
        self.use_compilation = use_compilation
        self.compile_mode = compile_mode
        self.use_flash_attention = use_flash_attention
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

# -----------------------------------------------------------------------------
# 2. Professional Building Blocks (RoPE, SwiGLU, AdaLN)
# -----------------------------------------------------------------------------

class OmniRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

class OmniRotaryEmbedding(nn.Module):
    """Complex implementation of Rotary Positional Embeddings for DiT"""
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, x, seq_len=None):
        t = torch.arange(seq_len or x.shape[1], device=x.device).type_as(self.inv_freq)
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        return emb.cos(), emb.sin()

class OmniSwiGLU(nn.Module):
    """Swish-Gated Linear Unit for High-Performance FFN"""
    def __init__(self, config: OmniMMDitV2Config):
        super().__init__()
        self.w1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.w2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
        self.w3 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)

    def forward(self, x):
        return self.w2(F.silu(self.w1(x)) * self.w3(x))

class TimestepEmbedder(nn.Module):
    """Fourier feature embedding for timesteps"""
    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
        half = dim // 2
        freqs = torch.exp(
            -torch.log(torch.tensor(max_period)) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=t.device)
        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, dtype):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
        return self.mlp(t_freq)

# -----------------------------------------------------------------------------
# 2.5. Data Processing Utilities
# -----------------------------------------------------------------------------

class OmniImageProcessor:
    """Advanced image preprocessing for multi-modal diffusion models"""
    
    def __init__(
        self,
        image_mean: List[float] = [0.485, 0.456, 0.406],
        image_std: List[float] = [0.229, 0.224, 0.225],
        size: Tuple[int, int] = (512, 512),
        interpolation: str = "bicubic",
        do_normalize: bool = True,
        do_center_crop: bool = False,
    ):
        self.image_mean = image_mean
        self.image_std = image_std
        self.size = size
        self.do_normalize = do_normalize
        self.do_center_crop = do_center_crop
        
        # Build transform pipeline
        transforms_list = []
        if do_center_crop:
            transforms_list.append(T.CenterCrop(min(size)))
        
        interp_mode = {
            "bilinear": T.InterpolationMode.BILINEAR,
            "bicubic": T.InterpolationMode.BICUBIC,
            "lanczos": T.InterpolationMode.LANCZOS,
        }.get(interpolation, T.InterpolationMode.BICUBIC)
        
        transforms_list.append(T.Resize(size, interpolation=interp_mode, antialias=True))
        self.transform = T.Compose(transforms_list)
    
    def preprocess(
        self,
        images: Union[Image.Image, np.ndarray, torch.Tensor, List[Union[Image.Image, np.ndarray, torch.Tensor]]],
        return_tensors: str = "pt",
    ) -> torch.Tensor:
        """
        Preprocess images for model input.
        
        Args:
            images: Single image or list of images (PIL, numpy, or torch)
            return_tensors: Return type ("pt" for PyTorch)
            
        Returns:
            Preprocessed image tensor [B, C, H, W]
        """
        if not isinstance(images, list):
            images = [images]
        
        processed = []
        for img in images:
            # Convert to PIL if needed
            if isinstance(img, np.ndarray):
                if img.dtype == np.uint8:
                    img = Image.fromarray(img)
                else:
                    img = Image.fromarray((img * 255).astype(np.uint8))
            elif isinstance(img, torch.Tensor):
                img = T.ToPILImage()(img)
            
            # Apply transforms
            img = self.transform(img)
            
            # Convert to tensor
            if not isinstance(img, torch.Tensor):
                img = to_tensor(img)
            
            # Normalize
            if self.do_normalize:
                img = normalize(img, self.image_mean, self.image_std)
            
            processed.append(img)
        
        # Stack into batch
        if return_tensors == "pt":
            return torch.stack(processed, dim=0)
        
        return processed
    
    def postprocess(
        self,
        images: torch.Tensor,
        output_type: str = "pil",
    ) -> Union[List[Image.Image], np.ndarray, torch.Tensor]:
        """
        Postprocess model output to desired format.
        
        Args:
            images: Model output tensor [B, C, H, W]
            output_type: "pil", "np", or "pt"
            
        Returns:
            Processed images in requested format
        """
        # Denormalize if needed
        if self.do_normalize:
            mean = torch.tensor(self.image_mean).view(1, 3, 1, 1).to(images.device)
            std = torch.tensor(self.image_std).view(1, 3, 1, 1).to(images.device)
            images = images * std + mean
        
        # Clamp to valid range
        images = torch.clamp(images, 0, 1)
        
        if output_type == "pil":
            images = images.cpu().permute(0, 2, 3, 1).numpy()
            images = (images * 255).round().astype(np.uint8)
            return [Image.fromarray(img) for img in images]
        elif output_type == "np":
            return images.cpu().numpy()
        else:
            return images


class OmniVideoProcessor:
    """Video frame processing for temporal diffusion models"""
    
    def __init__(
        self,
        image_processor: OmniImageProcessor,
        num_frames: int = 16,
        frame_stride: int = 1,
    ):
        self.image_processor = image_processor
        self.num_frames = num_frames
        self.frame_stride = frame_stride
    
    def preprocess_video(
        self,
        video_frames: Union[List[Image.Image], np.ndarray, torch.Tensor],
        temporal_interpolation: bool = True,
    ) -> torch.Tensor:
        """
        Preprocess video frames for temporal model.
        
        Args:
            video_frames: List of PIL images, numpy array [T, H, W, C], or tensor [T, C, H, W]
            temporal_interpolation: Whether to interpolate to target frame count
            
        Returns:
            Preprocessed video tensor [B, C, T, H, W]
        """
        # Convert to list of PIL images
        if isinstance(video_frames, np.ndarray):
            if video_frames.ndim == 4:  # [T, H, W, C]
                video_frames = [Image.fromarray(frame) for frame in video_frames]
            else:
                raise ValueError(f"Expected 4D numpy array, got shape {video_frames.shape}")
        elif isinstance(video_frames, torch.Tensor):
            if video_frames.ndim == 4:  # [T, C, H, W]
                video_frames = [T.ToPILImage()(frame) for frame in video_frames]
            else:
                raise ValueError(f"Expected 4D tensor, got shape {video_frames.shape}")
        
        # Sample frames if needed
        total_frames = len(video_frames)
        if temporal_interpolation and total_frames != self.num_frames:
            indices = np.linspace(0, total_frames - 1, self.num_frames, dtype=int)
            video_frames = [video_frames[i] for i in indices]
        
        # Process each frame
        processed_frames = []
        for frame in video_frames[:self.num_frames]:
            frame_tensor = self.image_processor.preprocess(frame, return_tensors="pt")[0]
            processed_frames.append(frame_tensor)
        
        # Stack: [T, C, H, W] -> [1, C, T, H, W]
        video_tensor = torch.stack(processed_frames, dim=1).unsqueeze(0)
        return video_tensor
    
    def postprocess_video(
        self,
        video_tensor: torch.Tensor,
        output_type: str = "pil",
    ) -> Union[List[Image.Image], np.ndarray, torch.Tensor]:
        """
        Postprocess video output.
        
        Args:
            video_tensor: Model output [B, C, T, H, W] or [B, T, C, H, W]
            output_type: "pil", "np", or "pt"
            
        Returns:
            Processed video frames
        """
        # Normalize dimensions to [B, T, C, H, W]
        if video_tensor.ndim == 5:
            if video_tensor.shape[1] in [3, 4]:  # [B, C, T, H, W]
                video_tensor = video_tensor.permute(0, 2, 1, 3, 4)
        
        batch_size, num_frames = video_tensor.shape[:2]
        
        # Process each frame
        all_frames = []
        for b in range(batch_size):
            frames = []
            for t in range(num_frames):
                frame = video_tensor[b, t]  # [C, H, W]
                frame = frame.unsqueeze(0)  # [1, C, H, W]
                processed = self.image_processor.postprocess(frame, output_type=output_type)
                frames.extend(processed)
            all_frames.append(frames)
        
        return all_frames[0] if batch_size == 1 else all_frames


class OmniLatentProcessor:
    """VAE latent space encoding/decoding with scaling and normalization"""
    
    def __init__(
        self,
        vae: Any,
        scaling_factor: float = 0.18215,
        do_normalize_latents: bool = True,
    ):
        self.vae = vae
        self.scaling_factor = scaling_factor
        self.do_normalize_latents = do_normalize_latents
    
    @torch.no_grad()
    def encode(
        self,
        images: torch.Tensor,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = False,
    ) -> torch.Tensor:
        """
        Encode images to latent space.
        
        Args:
            images: Input images [B, C, H, W] in range [-1, 1]
            generator: Random generator for sampling
            return_dict: Whether to return dict or tensor
            
        Returns:
            Latent codes [B, 4, H//8, W//8]
        """
        # VAE expects input in [-1, 1]
        if images.min() >= 0:
            images = images * 2.0 - 1.0
        
        # Encode
        latent_dist = self.vae.encode(images).latent_dist
        latents = latent_dist.sample(generator=generator)
        
        # Scale latents
        latents = latents * self.scaling_factor
        
        # Additional normalization for stability
        if self.do_normalize_latents:
            latents = (latents - latents.mean()) / (latents.std() + 1e-6)
        
        return latents if not return_dict else {"latents": latents}
    
    @torch.no_grad()
    def decode(
        self,
        latents: torch.Tensor,
        return_dict: bool = False,
    ) -> torch.Tensor:
        """
        Decode latents to image space.
        
        Args:
            latents: Latent codes [B, 4, H//8, W//8]
            return_dict: Whether to return dict or tensor
            
        Returns:
            Decoded images [B, 3, H, W] in range [-1, 1]
        """
        # Denormalize if needed
        if self.do_normalize_latents:
            # Assume identity transform for simplicity in decoding
            pass
        
        # Unscale
        latents = latents / self.scaling_factor
        
        # Decode
        images = self.vae.decode(latents).sample
        
        return images if not return_dict else {"images": images}
    
    @torch.no_grad()
    def encode_video(
        self,
        video_frames: torch.Tensor,
        generator: Optional[torch.Generator] = None,
    ) -> torch.Tensor:
        """
        Encode video frames to latent space.
        
        Args:
            video_frames: Input video [B, C, T, H, W] or [B, T, C, H, W]
            generator: Random generator
            
        Returns:
            Video latents [B, 4, T, H//8, W//8]
        """
        # Reshape to process frames independently
        if video_frames.shape[2] not in [3, 4]:  # [B, T, C, H, W]
            B, T, C, H, W = video_frames.shape
            video_frames = video_frames.reshape(B * T, C, H, W)
            
            # Encode
            latents = self.encode(video_frames, generator=generator)
            
            # Reshape back
            latents = latents.reshape(B, T, *latents.shape[1:])
            latents = latents.permute(0, 2, 1, 3, 4)  # [B, 4, T, H//8, W//8]
        else:  # [B, C, T, H, W]
            B, C, T, H, W = video_frames.shape
            video_frames = video_frames.permute(0, 2, 1, 3, 4).reshape(B * T, C, H, W)
            
            latents = self.encode(video_frames, generator=generator)
            latents = latents.reshape(B, T, *latents.shape[1:])
            latents = latents.permute(0, 2, 1, 3, 4)
        
        return latents

# -----------------------------------------------------------------------------
# 3. Core Architecture: OmniMMDitBlock (3D-Attention + Modulation)
# -----------------------------------------------------------------------------

class OmniMMDitBlock(nn.Module):
    def __init__(self, config: OmniMMDitV2Config, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.hidden_size // config.num_attention_heads
        
        # Self-Attention with QK-Norm
        self.norm1 = OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.attn = nn.MultiheadAttention(
            config.hidden_size, config.num_attention_heads, batch_first=True
        )
        
        self.q_norm = OmniRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = OmniRMSNorm(self.head_dim, eps=config.rms_norm_eps)

        # Cross-Attention for multimodal fusion
        self.norm2 = OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.cross_attn = nn.MultiheadAttention(
            config.hidden_size, config.num_attention_heads, batch_first=True
        )

        # Feed-Forward Network with SwiGLU activation
        self.norm3 = OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.ffn = OmniSwiGLU(config)

        # Adaptive Layer Normalization with zero initialization
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(config.hidden_size, 6 * config.hidden_size, bias=True)
        )

    def forward(
        self, 
        hidden_states: torch.Tensor, 
        encoder_hidden_states: torch.Tensor, # Text embeddings
        visual_context: Optional[torch.Tensor], # Reference image embeddings
        timestep_emb: torch.Tensor,
        rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        
        # AdaLN Modulation
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
            self.adaLN_modulation(timestep_emb)[:, None].chunk(6, dim=-1)
        )

        # Self-Attention block
        normed_hidden = self.norm1(hidden_states)
        normed_hidden = normed_hidden * (1 + scale_msa) + shift_msa
        
        attn_output, _ = self.attn(normed_hidden, normed_hidden, normed_hidden)
        hidden_states = hidden_states + gate_msa * attn_output

        # Cross-Attention with multimodal conditioning
        if visual_context is not None:
             context = torch.cat([encoder_hidden_states, visual_context], dim=1)
        else:
             context = encoder_hidden_states
             
        normed_hidden_cross = self.norm2(hidden_states)
        cross_output, _ = self.cross_attn(normed_hidden_cross, context, context)
        hidden_states = hidden_states + cross_output

        # Feed-Forward block
        normed_ffn = self.norm3(hidden_states)
        normed_ffn = normed_ffn * (1 + scale_mlp) + shift_mlp
        ffn_output = self.ffn(normed_ffn)
        hidden_states = hidden_states + gate_mlp * ffn_output

        return hidden_states

# -----------------------------------------------------------------------------
# 4. The Model: OmniMMDitV2
# -----------------------------------------------------------------------------

class OmniMMDitV2(ModelMixin, PreTrainedModel):
    """
    Omni-Modal Multi-Dimensional Diffusion Transformer V2.
    Supports: Text-to-Image, Image-to-Image (Edit), Image-to-Video.
    """
    config_class = OmniMMDitV2Config
    _supports_gradient_checkpointing = True

    def __init__(self, config: OmniMMDitV2Config):
        super().__init__(config)
        self.config = config

        # Initialize optimizer for advanced features
        self.optimizer = ModelOptimizer(
            fp8_config=FP8Config(enabled=config.use_fp8_quantization),
            compilation_config=CompilationConfig(
                enabled=config.use_compilation,
                mode=config.compile_mode,
            ),
            mixed_precision_config=MixedPrecisionConfig(
                enabled=True,
                dtype="bfloat16",
            ),
        )

        # Input Latent Projection (Patchify)
        self.x_embedder = nn.Linear(config.in_channels * config.patch_size * config.patch_size, config.hidden_size, bias=True)
        
        # Time & Vector Embeddings
        self.t_embedder = TimestepEmbedder(config.hidden_size, config.frequency_embedding_size)
        
        # Visual Condition Projector (Handles 1-3 images)
        self.visual_projector = nn.Sequential(
            nn.Linear(config.visual_embed_dim, config.hidden_size),
            nn.LayerNorm(config.hidden_size),
            nn.Linear(config.hidden_size, config.hidden_size)
        )
        
        # Positional Embeddings (Absolute + RoPE dynamically handled)
        self.pos_embed = nn.Parameter(torch.zeros(1, config.max_position_embeddings, config.hidden_size), requires_grad=False)

        # Transformer Backbone
        self.blocks = nn.ModuleList([
            OmniMMDitBlock(config, i) for i in range(config.num_hidden_layers)
        ])
        
        # Final Layer (AdaLN-Zero + Linear)
        self.final_layer = nn.Sequential(
            OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps),
            nn.Linear(config.hidden_size, config.patch_size * config.patch_size * config.out_channels, bias=True)
        )
        
        self.initialize_weights()
        
        # Apply optimizations if enabled
        if config.use_fp8_quantization or config.use_compilation:
            self._apply_optimizations()
    
    def _apply_optimizations(self):
        """Apply FP8 quantization and compilation optimizations"""
        # Quantize transformer blocks
        if self.config.use_fp8_quantization:
            for i, block in enumerate(self.blocks):
                self.blocks[i] = self.optimizer.optimize_model(
                    block,
                    apply_compilation=False,
                    apply_quantization=True,
                    apply_mixed_precision=True,
                )
        
        # Compile forward method
        if self.config.use_compilation and HAS_TORCH_COMPILE:
            self.forward = torch.compile(
                self.forward,
                mode=self.config.compile_mode,
                dynamic=True,
            )

    def initialize_weights(self):
        def _basic_init(module):
            if isinstance(module, nn.Linear):
                torch.nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    nn.init.constant_(module.bias, 0)
        self.apply(_basic_init)

    def unpatchify(self, x, h, w):
        c = self.config.out_channels
        p = self.config.patch_size
        h_ = h // p
        w_ = w // p
        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, w))
        return imgs

    def forward(
        self,
        hidden_states: torch.Tensor, # Noisy Latents [B, C, H, W] or [B, C, F, H, W]
        timestep: torch.LongTensor,
        encoder_hidden_states: torch.Tensor, # Text Embeddings
        visual_conditions: Optional[List[torch.Tensor]] = None, # List of [B, L, D]
        video_frames: Optional[int] = None, # If generating video
        return_dict: bool = True,
    ) -> Union[torch.Tensor, BaseOutput]:
        
        batch_size, channels, _, _ = hidden_states.shape
        
        # Patchify input latents
        p = self.config.patch_size
        h, w = hidden_states.shape[-2], hidden_states.shape[-1]
        x = hidden_states.unfold(2, p, p).unfold(3, p, p)
        x = x.permute(0, 2, 3, 1, 4, 5).contiguous()
        x = x.view(batch_size, -1, channels * p * p)
        
        # Positional and temporal embeddings
        x = self.x_embedder(x)
        x = x + self.pos_embed[:, :x.shape[1], :]
        
        t = self.t_embedder(timestep, x.dtype)
        
        # Process visual conditioning
        visual_emb = None
        if visual_conditions is not None:
            concat_visuals = torch.cat(visual_conditions, dim=1)
            visual_emb = self.visual_projector(concat_visuals)

        # Transformer blocks
        for block in self.blocks:
            x = block(
                hidden_states=x, 
                encoder_hidden_states=encoder_hidden_states,
                visual_context=visual_emb,
                timestep_emb=t
            )

        # Output projection
        x = self.final_layer[0](x)
        x = self.final_layer[1](x)
        
        # Unpatchify to image space
        output = self.unpatchify(x, h, w)

        if not return_dict:
            return (output,)

        return BaseOutput(sample=output)

# -----------------------------------------------------------------------------
# 5. The "Fancy" Pipeline
# -----------------------------------------------------------------------------

class OmniMMDitV2Pipeline(DiffusionPipeline):
    """
    Omni-Modal Diffusion Transformer Pipeline.
    
    Supports text-guided image editing and video generation with
    multi-image conditioning and advanced guidance techniques.
    """
    model: OmniMMDitV2
    tokenizer: CLIPTokenizer
    text_encoder: CLIPTextModel
    vae: Any # AutoencoderKL
    scheduler: DDIMScheduler
    
    _optional_components = ["visual_encoder"]

    def __init__(
        self,
        model: OmniMMDitV2,
        vae: Any,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        scheduler: DDIMScheduler,
        visual_encoder: Optional[Any] = None,
    ):
        super().__init__()
        self.register_modules(
            model=model,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            scheduler=scheduler,
            visual_encoder=visual_encoder
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        
        # Initialize data processors
        self.image_processor = OmniImageProcessor(
            size=(512, 512),
            interpolation="bicubic",
            do_normalize=True,
        )
        self.video_processor = OmniVideoProcessor(
            image_processor=self.image_processor,
            num_frames=16,
        )
        self.latent_processor = OmniLatentProcessor(
            vae=vae,
            scaling_factor=0.18215,
        )
        
        # Initialize model optimizer
        self.model_optimizer = ModelOptimizer(
            fp8_config=FP8Config(enabled=False),  # Can be enabled via enable_fp8()
            compilation_config=CompilationConfig(enabled=False),  # Can be enabled via compile()
            mixed_precision_config=MixedPrecisionConfig(enabled=True, dtype="bfloat16"),
        )
        
        self._is_compiled = False
        self._is_fp8_enabled = False
    
    def enable_fp8_quantization(self):
        """Enable FP8 quantization for faster inference"""
        if not HAS_TRANSFORMER_ENGINE:
            warnings.warn("Transformer Engine not available. Install with: pip install transformer-engine")
            return self
        
        self.model_optimizer.fp8_config.enabled = True
        self.model = self.model_optimizer.optimize_model(
            self.model,
            apply_compilation=False,
            apply_quantization=True,
            apply_mixed_precision=False,
        )
        self._is_fp8_enabled = True
        return self
    
    def compile_model(
        self,
        mode: str = "reduce-overhead",
        fullgraph: bool = False,
        dynamic: bool = True,
    ):
        """
        Compile model using torch.compile for faster inference.
        
        Args:
            mode: Compilation mode - "default", "reduce-overhead", "max-autotune"
            fullgraph: Whether to compile the entire model as one graph
            dynamic: Whether to enable dynamic shapes
        """
        if not HAS_TORCH_COMPILE:
            warnings.warn("torch.compile not available. Upgrade to PyTorch 2.0+")
            return self
        
        self.model_optimizer.compilation_config = CompilationConfig(
            enabled=True,
            mode=mode,
            fullgraph=fullgraph,
            dynamic=dynamic,
        )
        
        self.model = self.model_optimizer._compile_model(self.model)
        self._is_compiled = True
        return self
    
    def enable_optimizations(
        self,
        enable_fp8: bool = False,
        enable_compilation: bool = False,
        compilation_mode: str = "reduce-overhead",
    ):
        """
        Enable all optimizations at once.
        
        Args:
            enable_fp8: Enable FP8 quantization
            enable_compilation: Enable torch.compile
            compilation_mode: Compilation mode for torch.compile
        """
        if enable_fp8:
            self.enable_fp8_quantization()
        
        if enable_compilation:
            self.compile_model(mode=compilation_mode)
        
        return self

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        input_images: Optional[List[Union[torch.Tensor, Any]]] = None,
        height: Optional[int] = 1024,
        width: Optional[int] = 1024,
        num_frames: Optional[int] = 1,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        image_guidance_scale: float = 1.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
        use_optimized_inference: bool = True,
        **kwargs,
    ):
        # Use optimized inference context
        with optimized_inference_mode(
            enable_cudnn_benchmark=use_optimized_inference,
            enable_tf32=use_optimized_inference,
            enable_flash_sdp=use_optimized_inference,
        ):
            return self._forward_impl(
                prompt=prompt,
                input_images=input_images,
                height=height,
                width=width,
                num_frames=num_frames,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                image_guidance_scale=image_guidance_scale,
                negative_prompt=negative_prompt,
                eta=eta,
                generator=generator,
                latents=latents,
                output_type=output_type,
                return_dict=return_dict,
                callback=callback,
                callback_steps=callback_steps,
                **kwargs,
            )
    
    def _forward_impl(
        self,
        prompt: Union[str, List[str]] = None,
        input_images: Optional[List[Union[torch.Tensor, Any]]] = None,
        height: Optional[int] = 1024,
        width: Optional[int] = 1024,
        num_frames: Optional[int] = 1,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        image_guidance_scale: float = 1.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
        callback_steps: int = 1,
        **kwargs,
    ):
        # Validate and set default dimensions
        height = height or self.model.config.sample_size * self.vae_scale_factor
        width = width or self.model.config.sample_size * self.vae_scale_factor

        # Encode text prompts
        if isinstance(prompt, str):
            prompt = [prompt]
        batch_size = len(prompt)
        
        text_inputs = self.tokenizer(
            prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt"
        )
        text_embeddings = self.text_encoder(text_inputs.input_ids.to(self.device))[0]

        # Encode visual conditions with preprocessing
        visual_embeddings_list = []
        if input_images:
            if not isinstance(input_images, list):
                input_images = [input_images]
            if len(input_images) > 3:
                raise ValueError("Maximum 3 reference images supported")
            
            for img in input_images:
                # Preprocess image
                if not isinstance(img, torch.Tensor):
                    img_tensor = self.image_processor.preprocess(img, return_tensors="pt")
                else:
                    img_tensor = img
                
                img_tensor = img_tensor.to(device=self.device, dtype=text_embeddings.dtype)
                
                # Encode with visual encoder
                if self.visual_encoder is not None:
                    vis_emb = self.visual_encoder(img_tensor).last_hidden_state
                else:
                    # Fallback: use VAE encoder + projection
                    with torch.no_grad():
                        latent_features = self.vae.encode(img_tensor * 2 - 1).latent_dist.mode()
                        B, C, H, W = latent_features.shape
                        # Flatten spatial dims and project
                        vis_emb = latent_features.flatten(2).transpose(1, 2)  # [B, H*W, C]
                        # Simple projection to visual_embed_dim
                        if vis_emb.shape[-1] != self.model.config.visual_embed_dim:
                            proj = nn.Linear(vis_emb.shape[-1], self.model.config.visual_embed_dim).to(self.device)
                            vis_emb = proj(vis_emb)
                
                visual_embeddings_list.append(vis_emb)

        # Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
        timesteps = self.scheduler.timesteps

        # Initialize latent space
        num_channels_latents = self.model.config.in_channels
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if num_frames > 1:
            shape = (batch_size, num_channels_latents, num_frames, height // self.vae_scale_factor, width // self.vae_scale_factor)

        latents = torch.randn(shape, generator=generator, device=self.device, dtype=text_embeddings.dtype)
        latents = latents * self.scheduler.init_noise_sigma

        # Denoising loop with optimizations
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1.0 else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                
                # Use mixed precision autocast
                with self.model_optimizer.autocast_context():
                    noise_pred = self.model(
                        hidden_states=latent_model_input,
                        timestep=t,
                        encoder_hidden_states=torch.cat([text_embeddings] * 2),
                        visual_conditions=visual_embeddings_list * 2 if visual_embeddings_list else None,
                        video_frames=num_frames
                    ).sample

                # Apply classifier-free guidance
                if guidance_scale > 1.0:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                
                latents = self.scheduler.step(noise_pred, t, latents, eta=eta).prev_sample
                
                # Call callback if provided
                if callback is not None and i % callback_steps == 0:
                    callback(i, t, latents)
                
                progress_bar.update()

        # Decode latents with proper post-processing
        if output_type == "latent":
            output_images = latents
        else:
            # Decode latents to pixel space
            with torch.no_grad():
                if num_frames > 1:
                    # Video decoding: process frame by frame
                    B, C, T, H, W = latents.shape
                    latents_2d = latents.permute(0, 2, 1, 3, 4).reshape(B * T, C, H, W)
                    decoded = self.latent_processor.decode(latents_2d)
                    decoded = decoded.reshape(B, T, 3, H * 8, W * 8)
                    
                    # Convert to [0, 1] range
                    decoded = (decoded / 2 + 0.5).clamp(0, 1)
                    
                    # Post-process video
                    if output_type == "pil":
                        output_images = self.video_processor.postprocess_video(decoded, output_type="pil")
                    elif output_type == "np":
                        output_images = decoded.cpu().numpy()
                    else:
                        output_images = decoded
                else:
                    # Image decoding
                    decoded = self.latent_processor.decode(latents)
                    decoded = (decoded / 2 + 0.5).clamp(0, 1)
                    
                    # Post-process images
                    if output_type == "pil":
                        output_images = self.image_processor.postprocess(decoded, output_type="pil")
                    elif output_type == "np":
                        output_images = decoded.cpu().numpy()
                    else:
                        output_images = decoded

        if not return_dict:
            return (output_images,)

        return BaseOutput(images=output_images)