Update inferencer.py

inferencer.py

@@ -1,312 +1,312 @@
-# Copyright 2025 Bytedance Ltd. and/or its affiliates.
-# SPDX-License-Identifier: Apache-2.0
-
-from copy import deepcopy
-from typing import List, Dict, Tuple, Optional, Union, Any
-import matplotlib.pyplot as plt
-
-from PIL import Image
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn.attention.flex_attention import create_block_mask
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_utils import PreTrainedModel
-
-from data.data_utils import pil_img2rgb
-from modeling.bagel.qwen2_navit import NaiveCache
-
-
-
-VLM_THINK_SYSTEM_PROMPT = '''You should first think about the reasoning process in the mind and then provide the user with the answer. 
-The reasoning process is enclosed within <think> </think> tags, i.e. <think> reasoning process here </think> answer here'''
-
-GEN_THINK_SYSTEM_PROMPT = '''You should first think about the planning process in the mind and then generate the image. 
-The planning process is enclosed within <think> </think> tags, i.e. <think> planning process here </think> image here'''
-
-
-class InterleaveInferencer:
-    def __init__(self, model, vae_model, tokenizer, vae_transform, vit_transform, new_token_ids):
-        self.model = model
-        self.vae_model = vae_model
-        self.tokenizer = tokenizer
-        self.vae_transform = vae_transform
-        self.vit_transform = vit_transform
-        self.new_token_ids = new_token_ids
-        
-    def init_gen_context(self): 
-        gen_context = {
-            'kv_lens': [0],
-            'ropes': [0],
-            'past_key_values': NaiveCache(self.model.config.llm_config.num_hidden_layers),
-        }
-        return gen_context
-
-    @torch.no_grad()
-    def update_context_text(self, text, gen_context):
-        # used for interleave data, currently only support 1 data inference, 
-
-        past_key_values = gen_context['past_key_values']
-        kv_lens = gen_context['kv_lens']
-        ropes = gen_context['ropes']
-        generation_input, kv_lens, ropes = self.model.prepare_prompts(
-            curr_kvlens=kv_lens,
-            curr_rope=ropes, 
-            prompts=[text],
-            tokenizer=self.tokenizer, 
-            new_token_ids=self.new_token_ids,
-        )
-
-        past_key_values = self.model.forward_cache_update_text(past_key_values, **generation_input)        
-        gen_context['kv_lens'] = kv_lens
-        gen_context['ropes'] = ropes
-        gen_context['past_key_values'] = past_key_values
-        
-        return gen_context
-
-    @torch.no_grad()
-    def update_context_image(self, image, gen_context, vae=True, vit=True):
-        # used for interleave data, currently only support 1 data inference, 
-
-        assert vae or vit
-        past_key_values = gen_context['past_key_values']
-        kv_lens = gen_context['kv_lens']
-        ropes =  gen_context['ropes']
-
-        if vae:
-            ## update vae
-            generation_input, kv_lens, ropes = self.model.prepare_vae_images(
-                curr_kvlens=kv_lens,
-                curr_rope=ropes, 
-                images=[image],
-                transforms=self.vae_transform, 
-                new_token_ids=self.new_token_ids,
-            )
-            past_key_values = self.model.forward_cache_update_vae(self.vae_model, past_key_values, **generation_input)
-        
-        if vit:
-            ## update vit
-            generation_input, kv_lens, ropes = self.model.prepare_vit_images(
-                curr_kvlens=kv_lens,
-                curr_rope=ropes, 
-                images=[image],
-                transforms=self.vit_transform, 
-                new_token_ids=self.new_token_ids,
-            )
-            past_key_values = self.model.forward_cache_update_vit(past_key_values, **generation_input)
-
-        gen_context['kv_lens'] = kv_lens
-        gen_context['ropes'] = ropes
-        gen_context['past_key_values'] = past_key_values
-        
-        return gen_context
-
-    @torch.no_grad()
-    def gen_image(
-        self, 
-        image_shape, 
-        gen_context, 
-        cfg_text_scale=4.0,
-        cfg_img_scale=1.5,
-
-        cfg_text_precontext=None, 
-        cfg_img_precontext=None, 
-        cfg_interval=(0.4, 1.0),
-        cfg_renorm_min=0.0,
-        cfg_renorm_type="global",
-        
-        num_timesteps=50, 
-        timestep_shift=3.0
-    ):
-        # print(cfg_renorm_type)
-        past_key_values = gen_context['past_key_values']
-        kv_lens = gen_context['kv_lens']
-        ropes = gen_context['ropes']
-        generation_input = self.model.prepare_vae_latent(
-            curr_kvlens=kv_lens,
-            curr_rope=ropes, 
-            image_sizes=[image_shape], 
-            new_token_ids=self.new_token_ids,
-        ) 
-        
-        # text cfg
-        cfg_text_past_key_values = cfg_text_precontext['past_key_values']
-        kv_lens_cfg = cfg_text_precontext['kv_lens']
-        ropes_cfg = cfg_text_precontext['ropes']
-        generation_input_cfg_text = self.model.prepare_vae_latent_cfg(
-            curr_kvlens=kv_lens_cfg,
-            curr_rope=ropes_cfg, 
-            image_sizes=[image_shape], 
-        )
-
-        # img cfg
-        cfg_img_past_key_values = cfg_img_precontext['past_key_values']
-        kv_lens_cfg = cfg_img_precontext['kv_lens']
-        ropes_cfg = cfg_img_precontext['ropes']
-        generation_input_cfg_img = self.model.prepare_vae_latent_cfg(
-            curr_kvlens=kv_lens_cfg,
-            curr_rope=ropes_cfg, 
-            image_sizes=[image_shape], 
-        )
-
-        unpacked_latent = self.model.generate_image(
-            past_key_values=past_key_values,
-            cfg_text_past_key_values=cfg_text_past_key_values,
-            cfg_img_past_key_values=cfg_img_past_key_values,
-            num_timesteps=num_timesteps,
-            cfg_text_scale=cfg_text_scale,
-            cfg_img_scale=cfg_img_scale,
-            cfg_interval=cfg_interval,
-            cfg_renorm_min=cfg_renorm_min,
-            cfg_renorm_type=cfg_renorm_type,
-            timestep_shift=timestep_shift,
-            **generation_input,
-            cfg_text_packed_position_ids=generation_input_cfg_text['cfg_packed_position_ids'],
-            cfg_text_packed_query_indexes=generation_input_cfg_text['cfg_packed_query_indexes'],
-            cfg_text_key_values_lens=generation_input_cfg_text['cfg_key_values_lens'],
-            cfg_text_packed_key_value_indexes=generation_input_cfg_text['cfg_packed_key_value_indexes'],
-            cfg_img_packed_position_ids=generation_input_cfg_img['cfg_packed_position_ids'],
-            cfg_img_packed_query_indexes=generation_input_cfg_img['cfg_packed_query_indexes'],
-            cfg_img_key_values_lens=generation_input_cfg_img['cfg_key_values_lens'],
-            cfg_img_packed_key_value_indexes=generation_input_cfg_img['cfg_packed_key_value_indexes'],
-        )
-
-        image = self.decode_image(unpacked_latent[0], image_shape)
-        return image
-
-        
-    def decode_image(self, latent, image_shape):
-        H, W = image_shape
-        h, w = H // self.model.latent_downsample, W // self.model.latent_downsample
-
-        latent = latent.reshape(1, h, w, self.model.latent_patch_size, self.model.latent_patch_size, self.model.latent_channel)
-        latent = torch.einsum("nhwpqc->nchpwq", latent)
-        latent = latent.reshape(1, self.model.latent_channel, h * self.model.latent_patch_size, w * self.model.latent_patch_size)
-        image = self.vae_model.decode(latent)
-        image = (image * 0.5 + 0.5).clamp(0, 1)[0].permute(1, 2, 0) * 255
-        image = Image.fromarray((image).to(torch.uint8).cpu().numpy())
-
-        return image
-
-    @torch.no_grad()
-    def gen_text(self, gen_context, max_length: int = 500, do_sample: bool = True, temperature: float = 1.0):
-        gen_context = deepcopy(gen_context)
-        past_key_values = gen_context['past_key_values']
-        kv_lens = gen_context['kv_lens']
-        ropes = gen_context['ropes']
-
-        generation_input = self.model.prepare_start_tokens(kv_lens, ropes, self.new_token_ids)
-        for unpacked_latent in self.model.generate_text(
-            past_key_values=past_key_values,
-            max_length=max_length,
-            do_sample=do_sample,
-            temperature=temperature,
-            end_token_id=self.new_token_ids['eos_token_id'],
-            **generation_input,
-        ):
-            output = self.tokenizer.decode(unpacked_latent[:,0])
-            # output = output.split('<|im_end|>')[0].split('<|im_start|>')[1]
-            yield output
-        
-    @torch.no_grad()
-    def interleave_inference(
-        self,
-        input_lists: List[Union[str, Image.Image]],
-        think=False,
-        understanding_output=False,
-        max_think_token_n=1000,
-        do_sample=False, # for gen_text
-        temperature=0.3, # for gen_text
-        # gen_image kargs
-        cfg_text_scale=3.0,
-        cfg_img_scale=1.5,
-        cfg_interval=[0.4, 1.0],
-        timestep_shift=3.0,
-        num_timesteps=50,
-        cfg_renorm_min=0.0,
-        cfg_renorm_type="global",
-        image_shapes=(1024, 1024), # Default, can be overridden by actual input image
-    ):
-        gen_context = self.init_gen_context()
-        cfg_text_context = self.init_gen_context()
-        cfg_img_context = self.init_gen_context()
-
-        current_image_shapes = image_shapes
-
-        # Use torch.cuda.amp.autocast if available, otherwise a simple context manager
-        # For simplicity, assuming it's handled externally or not strictly needed for this snippet
-        # with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
-
-        if think:
-            system_prompt = VLM_THINK_SYSTEM_PROMPT if understanding_output else GEN_THINK_SYSTEM_PROMPT
-            gen_context = self.update_context_text(system_prompt, gen_context)
-            cfg_text_context = self.update_context_text(system_prompt, cfg_text_context)
-            cfg_img_context = self.update_context_text(system_prompt, cfg_img_context)
-
-        for input_term in input_lists:
-            if isinstance(input_term, str):
-                gen_context = self.update_context_text(input_term, gen_context)
-                cfg_text_context = self.update_context_text(input_term, cfg_text_context)
-                cfg_img_context = self.update_context_text(input_term, cfg_img_context)
-            elif isinstance(input_term, Image.Image):
-                current_image_shapes = input_term.size[::-1] # H, W
-                use_vae_for_input_image = not understanding_output
-                gen_context = self.update_context_image(input_term, gen_context, vae=use_vae_for_input_image, vit=True)
-                cfg_text_context = self.update_context_image(input_term, cfg_text_context, vae=use_vae_for_input_image, vit=True)
-                # cfg_img_context does not typically see input images
-            else:
-                raise ValueError(f"Unsupported input type: {type(input_term)}")
-
-        if understanding_output: # Generate text
-            yield from self.gen_text(gen_context, max_length=max_think_token_n, do_sample=do_sample, temperature=temperature)
-        else: # Generate image
-            if think:
-                thought_text_parts = []
-                for part in self.gen_text(gen_context, max_length=max_think_token_n, do_sample=do_sample, temperature=temperature):
-                    yield part # Stream the thought
-                    thought_text_parts.append(part)
-                full_thought_text = "".join(thought_text_parts)
-                if full_thought_text: # Only update if thought was generated
-                    gen_context = self.update_context_text(full_thought_text, gen_context)
-                    cfg_text_context = self.update_context_text(full_thought_text, cfg_text_context)
-            
-            img = self.gen_image(
-                image_shape=current_image_shapes, 
-                gen_context=gen_context, 
-                cfg_text_precontext=cfg_text_context, 
-                cfg_img_precontext=cfg_img_context,
-                cfg_text_scale=cfg_text_scale, 
-                cfg_img_scale=cfg_img_scale, 
-                cfg_interval=cfg_interval, 
-                timestep_shift=timestep_shift, 
-                num_timesteps=num_timesteps,
-                cfg_renorm_min=cfg_renorm_min,
-                cfg_renorm_type=cfg_renorm_type,
-            )
-            yield img
-
-    def __call__(
-        self, 
-        image: Optional[Image.Image] = None, 
-        text: Optional[str] = None, 
-        **kargs 
-    ) -> Any:
-        input_list = []
-        if image is not None:
-            input_list.append(image)
-        if text is not None:
-            input_list.append(text)
-        
-        if not input_list and not kargs.get('force_empty_input', False): # allow forcing for special cases if needed
-            return
-
-        # Intelligent setting of 'understanding_output' if not provided by caller
-        # This helps app.py's simpler calls like inferencer(text=...) to correctly produce text.
-        if 'understanding_output' not in kargs:
-            if text is not None and image is None: # Primarily text input
-                kargs['understanding_output'] = True
-            elif image is not None and text is None: # Primarily image input, assume image-to-text (captioning/VQA)
-                kargs['understanding_output'] = True
-            # If both text and image, or neither, rely on caller or default (False for image gen)
-
-        yield from self.interleave_inference(input_list, **kargs)
+# Copyright 2025 Bytedance Ltd. and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+
+from copy import deepcopy
+from typing import List, Dict, Tuple, Optional, Union, Any
+import matplotlib.pyplot as plt
+
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.attention.flex_attention import create_block_mask
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_utils import PreTrainedModel
+
+from data.data_utils import pil_img2rgb
+from modeling.bagel.qwen2_navit import NaiveCache
+
+
+
+VLM_THINK_SYSTEM_PROMPT = '''You should first think about the reasoning process in the mind and then provide the user with the answer. 
+The reasoning process is enclosed within <think> </think> tags, i.e. <think> reasoning process here </think> answer here'''
+
+GEN_THINK_SYSTEM_PROMPT = '''You should first think about the planning process in the mind and then generate the image. 
+The planning process is enclosed within <think> </think> tags, i.e. <think> planning process here </think> image here'''
+
+
+class InterleaveInferencer:
+    def __init__(self, model, vae_model, tokenizer, vae_transform, vit_transform, new_token_ids):
+        self.model = model
+        self.vae_model = vae_model
+        self.tokenizer = tokenizer
+        self.vae_transform = vae_transform
+        self.vit_transform = vit_transform
+        self.new_token_ids = new_token_ids
+        
+    def init_gen_context(self): 
+        gen_context = {
+            'kv_lens': [0],
+            'ropes': [0],
+            'past_key_values': NaiveCache(self.model.config.llm_config.num_hidden_layers),
+        }
+        return gen_context
+
+    @torch.no_grad()
+    def update_context_text(self, text, gen_context):
+        # used for interleave data, currently only support 1 data inference, 
+
+        past_key_values = gen_context['past_key_values']
+        kv_lens = gen_context['kv_lens']
+        ropes = gen_context['ropes']
+        generation_input, kv_lens, ropes = self.model.prepare_prompts(
+            curr_kvlens=kv_lens,
+            curr_rope=ropes, 
+            prompts=[text],
+            tokenizer=self.tokenizer, 
+            new_token_ids=self.new_token_ids,
+        )
+
+        past_key_values = self.model.forward_cache_update_text(past_key_values, **generation_input)        
+        gen_context['kv_lens'] = kv_lens
+        gen_context['ropes'] = ropes
+        gen_context['past_key_values'] = past_key_values
+        
+        return gen_context
+
+    @torch.no_grad()
+    def update_context_image(self, image, gen_context, vae=True, vit=True):
+        # used for interleave data, currently only support 1 data inference, 
+
+        assert vae or vit
+        past_key_values = gen_context['past_key_values']
+        kv_lens = gen_context['kv_lens']
+        ropes =  gen_context['ropes']
+
+        if vae:
+            ## update vae
+            generation_input, kv_lens, ropes = self.model.prepare_vae_images(
+                curr_kvlens=kv_lens,
+                curr_rope=ropes, 
+                images=[image],
+                transforms=self.vae_transform, 
+                new_token_ids=self.new_token_ids,
+            )
+            past_key_values = self.model.forward_cache_update_vae(self.vae_model, past_key_values, **generation_input)
+        
+        if vit:
+            ## update vit
+            generation_input, kv_lens, ropes = self.model.prepare_vit_images(
+                curr_kvlens=kv_lens,
+                curr_rope=ropes, 
+                images=[image],
+                transforms=self.vit_transform, 
+                new_token_ids=self.new_token_ids,
+            )
+            past_key_values = self.model.forward_cache_update_vit(past_key_values, **generation_input)
+
+        gen_context['kv_lens'] = kv_lens
+        gen_context['ropes'] = ropes
+        gen_context['past_key_values'] = past_key_values
+        
+        return gen_context
+
+    @torch.no_grad()
+    def gen_image(
+        self, 
+        image_shape, 
+        gen_context, 
+        cfg_text_scale=4.0,
+        cfg_img_scale=1.5,
+
+        cfg_text_precontext=None, 
+        cfg_img_precontext=None, 
+        cfg_interval=(0.4, 1.0),
+        cfg_renorm_min=0.0,
+        cfg_renorm_type="global",
+        
+        num_timesteps=50, 
+        timestep_shift=3.0
+    ):
+        # print(cfg_renorm_type)
+        past_key_values = gen_context['past_key_values']
+        kv_lens = gen_context['kv_lens']
+        ropes = gen_context['ropes']
+        generation_input = self.model.prepare_vae_latent(
+            curr_kvlens=kv_lens,
+            curr_rope=ropes, 
+            image_sizes=[image_shape], 
+            new_token_ids=self.new_token_ids,
+        ) 
+        
+        # text cfg
+        cfg_text_past_key_values = cfg_text_precontext['past_key_values']
+        kv_lens_cfg = cfg_text_precontext['kv_lens']
+        ropes_cfg = cfg_text_precontext['ropes']
+        generation_input_cfg_text = self.model.prepare_vae_latent_cfg(
+            curr_kvlens=kv_lens_cfg,
+            curr_rope=ropes_cfg, 
+            image_sizes=[image_shape], 
+        )
+
+        # img cfg
+        cfg_img_past_key_values = cfg_img_precontext['past_key_values']
+        kv_lens_cfg = cfg_img_precontext['kv_lens']
+        ropes_cfg = cfg_img_precontext['ropes']
+        generation_input_cfg_img = self.model.prepare_vae_latent_cfg(
+            curr_kvlens=kv_lens_cfg,
+            curr_rope=ropes_cfg, 
+            image_sizes=[image_shape], 
+        )
+
+        unpacked_latent = self.model.generate_image(
+            past_key_values=past_key_values,
+            cfg_text_past_key_values=cfg_text_past_key_values,
+            cfg_img_past_key_values=cfg_img_past_key_values,
+            num_timesteps=num_timesteps,
+            cfg_text_scale=cfg_text_scale,
+            cfg_img_scale=cfg_img_scale,
+            cfg_interval=cfg_interval,
+            cfg_renorm_min=cfg_renorm_min,
+            cfg_renorm_type=cfg_renorm_type,
+            timestep_shift=timestep_shift,
+            **generation_input,
+            cfg_text_packed_position_ids=generation_input_cfg_text['cfg_packed_position_ids'],
+            cfg_text_packed_query_indexes=generation_input_cfg_text['cfg_packed_query_indexes'],
+            cfg_text_key_values_lens=generation_input_cfg_text['cfg_key_values_lens'],
+            cfg_text_packed_key_value_indexes=generation_input_cfg_text['cfg_packed_key_value_indexes'],
+            cfg_img_packed_position_ids=generation_input_cfg_img['cfg_packed_position_ids'],
+            cfg_img_packed_query_indexes=generation_input_cfg_img['cfg_packed_query_indexes'],
+            cfg_img_key_values_lens=generation_input_cfg_img['cfg_key_values_lens'],
+            cfg_img_packed_key_value_indexes=generation_input_cfg_img['cfg_packed_key_value_indexes'],
+        )
+
+        image = self.decode_image(unpacked_latent[0], image_shape)
+        return image
+
+        
+    def decode_image(self, latent, image_shape):
+        H, W = image_shape
+        h, w = H // self.model.latent_downsample, W // self.model.latent_downsample
+
+        latent = latent.reshape(1, h, w, self.model.latent_patch_size, self.model.latent_patch_size, self.model.latent_channel)
+        latent = torch.einsum("nhwpqc->nchpwq", latent)
+        latent = latent.reshape(1, self.model.latent_channel, h * self.model.latent_patch_size, w * self.model.latent_patch_size)
+        image = self.vae_model.decode(latent)
+        image = (image * 0.5 + 0.5).clamp(0, 1)[0].permute(1, 2, 0) * 255
+        image = Image.fromarray((image).to(torch.uint8).cpu().numpy())
+
+        return image
+
+    @torch.no_grad()
+    def gen_text(self, gen_context, max_length: int = 500, do_sample: bool = True, temperature: float = 1.0):
+        gen_context = deepcopy(gen_context)
+        past_key_values = gen_context['past_key_values']
+        kv_lens = gen_context['kv_lens']
+        ropes = gen_context['ropes']
+
+        generation_input = self.model.prepare_start_tokens(kv_lens, ropes, self.new_token_ids)
+        for unpacked_latent in self.model.generate_text(
+            past_key_values=past_key_values,
+            max_length=max_length,
+            do_sample=do_sample,
+            temperature=temperature,
+            end_token_id=self.new_token_ids['eos_token_id'],
+            **generation_input,
+        ):
+            output = self.tokenizer.decode(unpacked_latent)
+            # output = output.split('<|im_end|>')[0].split('<|im_start|>')[1]
+            yield output
+        
+    @torch.no_grad()
+    def interleave_inference(
+        self,
+        input_lists: List[Union[str, Image.Image]],
+        think=False,
+        understanding_output=False,
+        max_think_token_n=1000,
+        do_sample=False, # for gen_text
+        temperature=0.3, # for gen_text
+        # gen_image kargs
+        cfg_text_scale=3.0,
+        cfg_img_scale=1.5,
+        cfg_interval=[0.4, 1.0],
+        timestep_shift=3.0,
+        num_timesteps=50,
+        cfg_renorm_min=0.0,
+        cfg_renorm_type="global",
+        image_shapes=(1024, 1024), # Default, can be overridden by actual input image
+    ):
+        gen_context = self.init_gen_context()
+        cfg_text_context = self.init_gen_context()
+        cfg_img_context = self.init_gen_context()
+
+        current_image_shapes = image_shapes
+
+        # Use torch.cuda.amp.autocast if available, otherwise a simple context manager
+        # For simplicity, assuming it's handled externally or not strictly needed for this snippet
+        # with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
+
+        if think:
+            system_prompt = VLM_THINK_SYSTEM_PROMPT if understanding_output else GEN_THINK_SYSTEM_PROMPT
+            gen_context = self.update_context_text(system_prompt, gen_context)
+            cfg_text_context = self.update_context_text(system_prompt, cfg_text_context)
+            cfg_img_context = self.update_context_text(system_prompt, cfg_img_context)
+
+        for input_term in input_lists:
+            if isinstance(input_term, str):
+                gen_context = self.update_context_text(input_term, gen_context)
+                cfg_text_context = self.update_context_text(input_term, cfg_text_context)
+                cfg_img_context = self.update_context_text(input_term, cfg_img_context)
+            elif isinstance(input_term, Image.Image):
+                current_image_shapes = input_term.size[::-1] # H, W
+                use_vae_for_input_image = not understanding_output
+                gen_context = self.update_context_image(input_term, gen_context, vae=use_vae_for_input_image, vit=True)
+                cfg_text_context = self.update_context_image(input_term, cfg_text_context, vae=use_vae_for_input_image, vit=True)
+                # cfg_img_context does not typically see input images
+            else:
+                raise ValueError(f"Unsupported input type: {type(input_term)}")
+
+        if understanding_output: # Generate text
+            yield from self.gen_text(gen_context, max_length=max_think_token_n, do_sample=do_sample, temperature=temperature)
+        else: # Generate image
+            if think:
+                thought_text_parts = []
+                for part in self.gen_text(gen_context, max_length=max_think_token_n, do_sample=do_sample, temperature=temperature):
+                    yield part # Stream the thought
+                    thought_text_parts.append(part)
+                full_thought_text = "".join(thought_text_parts)
+                if full_thought_text: # Only update if thought was generated
+                    gen_context = self.update_context_text(full_thought_text, gen_context)
+                    cfg_text_context = self.update_context_text(full_thought_text, cfg_text_context)
+            
+            img = self.gen_image(
+                image_shape=current_image_shapes, 
+                gen_context=gen_context, 
+                cfg_text_precontext=cfg_text_context, 
+                cfg_img_precontext=cfg_img_context,
+                cfg_text_scale=cfg_text_scale, 
+                cfg_img_scale=cfg_img_scale, 
+                cfg_interval=cfg_interval, 
+                timestep_shift=timestep_shift, 
+                num_timesteps=num_timesteps,
+                cfg_renorm_min=cfg_renorm_min,
+                cfg_renorm_type=cfg_renorm_type,
+            )
+            yield img
+
+    def __call__(
+        self, 
+        image: Optional[Image.Image] = None, 
+        text: Optional[str] = None, 
+        **kargs 
+    ) -> Any:
+        input_list = []
+        if image is not None:
+            input_list.append(image)
+        if text is not None:
+            input_list.append(text)
+        
+        if not input_list and not kargs.get('force_empty_input', False): # allow forcing for special cases if needed
+            return
+
+        # Intelligent setting of 'understanding_output' if not provided by caller
+        # This helps app.py's simpler calls like inferencer(text=...) to correctly produce text.
+        if 'understanding_output' not in kargs:
+            if text is not None and image is None: # Primarily text input
+                kargs['understanding_output'] = True
+            elif image is not None and text is None: # Primarily image input, assume image-to-text (captioning/VQA)
+                kargs['understanding_output'] = True
+            # If both text and image, or neither, rely on caller or default (False for image gen)
+
+        yield from self.interleave_inference(input_list, **kargs)