Delete managers/prompt_enhancer_manager.py

managers/prompt_enhancer_manager.py

@@ -1,136 +0,0 @@
-# engineers/deformes3D_thinker.py
-#
-# Copyright (C) 2025 Carlos Rodrigues dos Santos
-#
-# Version: 4.0.0 (Definitive)
-#
-# This is the definitive, robust implementation. It directly contains the prompt
-# enhancement logic copied from the LTX pipeline's utils. It accesses the
-# enhancement models loaded by the LTX Manager and performs the captioning
-# and LLM generation steps locally, ensuring full control and compatibility.
-
-import logging
-from PIL import Image
-import torch
-
-# Importa o singleton do LTX para ter acesso à sua pipeline e aos modelos nela
-from managers.ltx_manager import ltx_manager_singleton
-
-# Importa o prompt de sistema do LTX para garantir consistência
-from ltx_video.utils.prompt_enhance_utils import I2V_CINEMATIC_PROMPT
-
-logger = logging.getLogger(__name__)
-
-class Deformes3DThinker:
-    """
-    The tactical specialist that now directly implements the prompt enhancement
-    logic, using the models provided by the LTX pipeline.
-    """
-    
-    def __init__(self):
-        # Acessa a pipeline exposta para obter os modelos necessários
-        pipeline = ltx_manager_singleton.prompt_enhancement_pipeline
-        if not pipeline:
-            raise RuntimeError("Deformes3DThinker could not access the LTX pipeline.")
-        
-        # Armazena os modelos e processadores como atributos diretos
-        self.caption_model = pipeline.prompt_enhancer_image_caption_model
-        self.caption_processor = pipeline.prompt_enhancer_image_caption_processor
-        self.llm_model = pipeline.prompt_enhancer_llm_model
-        self.llm_tokenizer = pipeline.prompt_enhancer_llm_tokenizer
-        
-        # Verifica se os modelos foram realmente carregados
-        if not all([self.caption_model, self.caption_processor, self.llm_model, self.llm_tokenizer]):
-            logger.warning("Deformes3DThinker initialized, but one or more enhancement models were not loaded by the LTX pipeline. Fallback will be used.")
-        else:
-            logger.info("Deformes3DThinker initialized and successfully linked to LTX enhancement models.")
-
-    @torch.no_grad()
-    def get_enhanced_motion_prompt(self, global_prompt: str, story_history: str, 
-                                   past_keyframe_path: str, present_keyframe_path: str, future_keyframe_path: str,
-                                   past_scene_desc: str, present_scene_desc: str, future_scene_desc: str) -> str:
-        """
-        Generates a refined motion prompt by directly executing the enhancement pipeline logic.
-        """
-        # Verifica se os modelos estão disponíveis antes de tentar usá-los
-        if not all([self.caption_model, self.caption_processor, self.llm_model, self.llm_tokenizer]):
-            logger.warning("Enhancement models not available. Using fallback prompt.")
-            return f"A cinematic transition from '{present_scene_desc}' to '{future_scene_desc}'."
-
-        try:
-            present_image = Image.open(present_keyframe_path).convert("RGB")
-            
-            # --- INÍCIO DA LÓGICA COPIADA E ADAPTADA DO LTX ---
-            
-            # 1. Gerar a caption da imagem de referência (presente)
-            image_captions = self._generate_image_captions([present_image])
-            
-            # 2. Construir o prompt para o LLM
-            # Usamos a cena futura como o "prompt do usuário"
-            messages = [
-                {"role": "system", "content": I2V_CINEMATIC_PROMPT},
-                {"role": "user", "content": f"user_prompt: {future_scene_desc}\nimage_caption: {image_captions[0]}"},
-            ]
-
-            # 3. Gerar e decodificar o prompt final com o LLM
-            enhanced_prompt = self._generate_and_decode_prompts(messages)
-            
-            # --- FIM DA LÓGICA COPIADA E ADAPTADA ---
-
-            logger.info(f"Deformes3DThinker received enhanced prompt: '{enhanced_prompt}'")
-            return enhanced_prompt
-
-        except Exception as e:
-            logger.error(f"The Film Director (Deformes3D Thinker) failed during enhancement: {e}. Using fallback.", exc_info=True)
-            return f"A smooth, continuous cinematic transition from '{present_scene_desc}' to '{future_scene_desc}'."
-
-    def _generate_image_captions(self, images: list[Image.Image]) -> list[str]:
-        """
-        Lógica interna para gerar captions, copiada do LTX utils.
-        """
-        # O modelo Florence-2 do LTX não usa um system_prompt aqui, mas um task_prompt
-        task_prompt = "<MORE_DETAILED_CAPTION>"
-        inputs = self.caption_processor(
-            text=[task_prompt] * len(images), images=images, return_tensors="pt"
-        ).to(self.caption_model.device)
-
-        generated_ids = self.caption_model.generate(
-            input_ids=inputs["input_ids"],
-            pixel_values=inputs["pixel_values"],
-            max_new_tokens=1024,
-            num_beams=3,
-        )
-        
-        # Usa o post_process_generation para extrair a resposta limpa
-        generated_text = self.caption_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-        processed_result = self.caption_processor.post_process_generation(
-            generated_text, 
-            task=task_prompt, 
-            image_size=(images[0].width, images[0].height)
-        )
-        return [processed_result[task_prompt]]
-
-    def _generate_and_decode_prompts(self, messages: list[dict]) -> str:
-        """
-        Lógica interna para gerar prompt com o LLM, copiada do LTX utils.
-        """
-        text = self.llm_tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
-        model_inputs = self.llm_tokenizer([text], return_tensors="pt").to(self.llm_model.device)
-
-        output_ids = self.llm_model.generate(**model_inputs, max_new_tokens=256)
-        
-        input_ids_len = model_inputs.input_ids.shape[1]
-        decoded_prompts = self.llm_tokenizer.batch_decode(
-            output_ids[:, input_ids_len:], skip_special_tokens=True
-        )
-        return decoded_prompts[0].strip()
-
-# --- Singleton Instantiation ---
-try:
-    deformes3d_thinker_singleton = Deformes3DThinker()
-except Exception as e:
-    # A falha já terá sido logada dentro do __init__
-    deformes3d_thinker_singleton = None
-    raise e