Upload ltx_manager_helpers (4).py

ltx_manager_helpers (4).py

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+# ltx_manager_helpers.py
+# Copyright (C) 4 de Agosto de 2025  Carlos Rodrigues dos Santos
+# (Licenciamento e cabeçalhos permanecem os mesmos)
+
+import torch
+import gc
+import os
+import yaml
+import logging
+import huggingface_hub
+import time
+import threading
+import json
+
+from optimization import optimize_ltx_worker, can_optimize_fp8
+from hardware_manager import hardware_manager
+from inference import create_ltx_video_pipeline, calculate_padding
+from ltx_video.pipelines.pipeline_ltx_video import LatentConditioningItem
+
+logger = logging.getLogger(__name__)
+
+class LtxWorker:
+    """
+    Representa uma única instância da pipeline LTX-Video em um dispositivo específico.
+    Gerencia o carregamento do modelo para a CPU e a movimentação de/para a GPU.
+    """
+    def __init__(self, device_id, ltx_config_file):
+        # ... (código do LtxWorker __init__ permanece o mesmo) ...
+        self.cpu_device = torch.device('cpu')
+        self.device = torch.device(device_id if torch.cuda.is_available() else 'cpu')
+        logger.info(f"LTX Worker ({self.device}): Inicializando com config '{ltx_config_file}'...")
+        
+        with open(ltx_config_file, "r") as file:
+            self.config = yaml.safe_load(file)
+        
+        self.is_distilled = "distilled" in self.config.get("checkpoint_path", "")
+
+        models_dir = "downloaded_models_gradio"
+        
+        logger.info(f"LTX Worker ({self.device}): Carregando modelo para a CPU...")
+        model_path = os.path.join(models_dir, self.config["checkpoint_path"])
+        if not os.path.exists(model_path):
+             model_path = huggingface_hub.hf_hub_download(
+                repo_id="Lightricks/LTX-Video", filename=self.config["checkpoint_path"],
+                local_dir=models_dir, local_dir_use_symlinks=False
+            )
+        
+        self.pipeline = create_ltx_video_pipeline(
+            ckpt_path=model_path, precision=self.config["precision"],
+            text_encoder_model_name_or_path=self.config["text_encoder_model_name_or_path"],
+            sampler=self.config["sampler"], device='cpu'
+        )
+        logger.info(f"LTX Worker ({self.device}): Modelo pronto na CPU. É um modelo destilado? {self.is_distilled}")
+
+    def to_gpu(self):
+        """Move o pipeline para a GPU designada E OTIMIZA SE POSSÍVEL."""
+        if self.device.type == 'cpu': return
+        logger.info(f"LTX Worker: Movendo pipeline para a GPU {self.device}...")
+        self.pipeline.to(self.device)
+        
+        # A otimização agora ocorre aqui, uma única vez, quando o modelo vai para a GPU.
+        if self.device.type == 'cuda' and can_optimize_fp8():
+            logger.info(f"LTX Worker ({self.device}): GPU com suporte a FP8 detectada. Iniciando otimização...")
+            optimize_ltx_worker(self)
+            logger.info(f"LTX Worker ({self.device}): Otimização concluída.")
+        elif self.device.type == 'cuda':
+            logger.info(f"LTX Worker ({self.device}): Otimização FP8 não suportada ou desativada.")
+
+    def to_cpu(self):
+        """Move o pipeline de volta para a CPU e libera a memória da GPU."""
+        if self.device.type == 'cpu': return
+        logger.info(f"LTX Worker: Descarregando pipeline da GPU {self.device}...")
+        self.pipeline.to('cpu')
+        gc.collect()
+        if torch.cuda.is_available(): torch.cuda.empty_cache()
+
+    def generate_video_fragment_internal(self, **kwargs):
+        """Invoca a pipeline de geração."""
+        return self.pipeline(**kwargs).images
+
+class LtxPoolManager:
+    """
+    Gerencia um pool de LtxWorkers para otimizar o uso de múltiplas GPUs.
+    NOVO MODO "HOT START": Mantém todos os modelos carregados na VRAM para latência mínima.
+    """
+    def __init__(self, device_ids, ltx_config_file):
+        logger.info(f"LTX POOL MANAGER: Criando workers para os dispositivos: {device_ids}")
+        self.workers = [LtxWorker(dev_id, ltx_config_file) for dev_id in device_ids]
+        self.current_worker_index = 0
+        self.lock = threading.Lock()
+
+        # ######################################################################
+        # ##               MUDANÇA 1: PRÉ-AQUECIMENTO DAS GPUs                ##
+        # ######################################################################
+        if all(w.device.type == 'cuda' for w in self.workers):
+            logger.info("LTX POOL MANAGER: MODO HOT START ATIVADO. Pré-aquecendo todas as GPUs...")
+            for worker in self.workers:
+                worker.to_gpu()
+            logger.info("LTX POOL MANAGER: Todas as GPUs estão quentes e prontas.")
+        else:
+            logger.info("LTX POOL MANAGER: Operando em modo CPU ou misto. O pré-aquecimento de GPU foi ignorado.")
+        # ######################################################################
+
+    def _prepare_and_log_params(self, worker_to_use, **kwargs):
+        # ... (Esta função permanece exatamente a mesma) ...
+        target_device = worker_to_use.device
+        height, width = kwargs['height'], kwargs['width']
+        
+        conditioning_data = kwargs.get('conditioning_items_data', [])
+        final_conditioning_items = []
+        conditioning_log_details = []
+        for i, item in enumerate(conditioning_data):
+            if hasattr(item, 'latent_tensor'):
+                item.latent_tensor = item.latent_tensor.to(target_device)
+                final_conditioning_items.append(item)
+                conditioning_log_details.append(
+                    f"  - Item {i}: frame={item.media_frame_number}, strength={item.conditioning_strength:.2f}, shape={list(item.latent_tensor.shape)}"
+                )
+
+        first_pass_config = worker_to_use.config.get("first_pass", {})
+        padded_h, padded_w = ((height - 1) // 32 + 1) * 32, ((width - 1) // 32 + 1) * 32
+        padding_vals = calculate_padding(height, width, padded_h, padded_w)
+
+        pipeline_params = {
+            "height": padded_h, "width": padded_w, 
+            "num_frames": kwargs['video_total_frames'], "frame_rate": kwargs['video_fps'],
+            "generator": torch.Generator(device=target_device).manual_seed(int(kwargs.get('seed', time.time())) + kwargs['current_fragment_index']),
+            "conditioning_items": final_conditioning_items, 
+            "is_video": True, "vae_per_channel_normalize": True,
+            "decode_timestep": float(kwargs.get('decode_timestep', worker_to_use.config.get("decode_timestep", 0.05))),
+            "image_cond_noise_scale": float(kwargs.get('image_cond_noise_scale', 0.0)),
+            "prompt": kwargs['motion_prompt'],
+            "negative_prompt": kwargs.get('negative_prompt', "blurry, distorted, static, bad quality, artifacts"),
+            "guidance_scale": float(kwargs.get('guidance_scale', 2.0)),
+            "stg_scale": float(kwargs.get('stg_scale', 0.025)),
+            "rescaling_scale": float(kwargs.get('rescaling_scale', 0.15)),
+        }
+        
+        if worker_to_use.is_distilled:
+            pipeline_params["timesteps"] = first_pass_config.get("timesteps")
+            pipeline_params["num_inference_steps"] = len(pipeline_params["timesteps"]) if "timesteps" in first_pass_config else 20
+        else:
+            pipeline_params["num_inference_steps"] = int(kwargs.get('num_inference_steps', 20))
+        
+        log_friendly_params = pipeline_params.copy()
+        log_friendly_params.pop('generator', None)
+        log_friendly_params.pop('conditioning_items', None)
+        
+        logger.info("="*60)
+        logger.info(f"CHAMADA AO PIPELINE LTX NO DISPOSITIVO: {worker_to_use.device}")
+        logger.info(f"Modelo: {'Distilled' if worker_to_use.is_distilled else 'Base'}")
+        logger.info("-" * 20 + " PARÂMETROS DA PIPELINE " + "-" * 20)
+        logger.info(json.dumps(log_friendly_params, indent=2))
+        logger.info("-" * 20 + " ITENS DE CONDICIONAMENTO " + "-" * 19)
+        logger.info("\n".join(conditioning_log_details) if conditioning_log_details else "  - Nenhum")
+        logger.info("="*60)
+        
+        return pipeline_params, padding_vals
+    
+    def _execute_on_worker(self, execution_fn, **kwargs):
+        """
+        Função unificada para selecionar um worker e executar uma tarefa,
+        sem a lógica de carregar/descarregar.
+        """
+        worker_to_use = None
+        try:
+            with self.lock:
+                worker_to_use = self.workers[self.current_worker_index]
+                self.current_worker_index = (self.current_worker_index + 1) % len(self.workers)
+            
+            pipeline_params, padding_vals = self._prepare_and_log_params(worker_to_use, **kwargs)
+            
+            result = execution_fn(worker_to_use, pipeline_params, **kwargs)
+            
+            return result, padding_vals
+            
+        except Exception as e:
+            logger.error(f"LTX POOL MANAGER: Erro durante a execução em {worker_to_use.device if worker_to_use else 'N/A'}: {e}", exc_info=True)
+            raise e
+        finally:
+            # Apenas limpa o cache da GPU, não descarrega o modelo.
+            if worker_to_use and worker_to_use.device.type == 'cuda':
+                with torch.cuda.device(worker_to_use.device):
+                    gc.collect()
+                    torch.cuda.empty_cache()
+    
+    def generate_latent_fragment(self, **kwargs) -> (torch.Tensor, tuple):
+        """
+        Orquestra a geração de um novo fragmento de vídeo a partir do ruído.
+        """
+        def execution_logic(worker, params, **inner_kwargs):
+            params['output_type'] = "latent"
+            with torch.no_grad():
+                return worker.generate_video_fragment_internal(**params)
+
+        return self._execute_on_worker(execution_logic, **kwargs)
+
+    def refine_latents(self, upscaled_latents: torch.Tensor, **kwargs) -> (torch.Tensor, tuple):
+        """
+        Orquestra um passe de difusão curto em latentes já existentes para refinamento.
+        """
+        def execution_logic(worker, params, **inner_kwargs):
+            params['latents'] = upscaled_latents.to(worker.device, dtype=worker.pipeline.transformer.dtype)
+            params['strength'] = inner_kwargs.get('denoise_strength', 0.4)
+            params['num_inference_steps'] = int(inner_kwargs.get('refine_steps', 10))
+            params['output_type'] = "latent"
+            
+            logger.info("LTX POOL MANAGER: Iniciando passe de refinamento (denoise) em latentes de alta resolução.")
+            
+            with torch.no_grad():
+                return worker.generate_video_fragment_internal(**params)
+
+        return self._execute_on_worker(execution_logic, upscaled_latents=upscaled_latents, **kwargs)
+
+# --- Instanciação Singleton ---
+logger.info("Lendo config.yaml para inicializar o LTX Pool Manager...")
+with open("config.yaml", 'r') as f:
+    config = yaml.safe_load(f)
+ltx_gpus_required = config['specialists']['ltx']['gpus_required']
+ltx_device_ids = hardware_manager.allocate_gpus('LTX', ltx_gpus_required)
+ltx_config_path = config['specialists']['ltx']['config_file']
+ltx_manager_singleton = LtxPoolManager(device_ids=ltx_device_ids, ltx_config_file=ltx_config_path)
+logger.info("Especialista de Vídeo (LTX) pronto.")