# ltx_manager_helpers.py # Copyright (C) 4 de Agosto de 2025 Carlos Rodrigues dos Santos # # ORIGINAL SOURCE: LTX-Video by Lightricks Ltd. & other open-source projects. # Licensed under the Apache License, Version 2.0 # https://github.com/Lightricks/LTX-Video # # MODIFICATIONS FOR ADUC-SDR_Video: # This file is part of ADUC-SDR_Video, a derivative work based on LTX-Video. # It has been modified to manage pools of LTX workers, handle GPU memory, # and prepare parameters for the ADUC-SDR orchestration framework. # All modifications are also licensed under the Apache License, Version 2.0. 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 from ltx_video.models.autoencoders.vae_encode import vae_decode logger = logging.getLogger(__name__) class LtxWorker: def __init__(self, device_id, ltx_config_file): 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}") if self.device.type == 'cuda' and can_optimize_fp8(): logger.info(f"LTX Worker ({self.device}): GPU com suporte a FP8 detectada. Iniciando otimização...") self.pipeline.to(self.device) optimize_ltx_worker(self) self.pipeline.to(self.cpu_device) logger.info(f"LTX Worker ({self.device}): Otimização concluída. Modelo pronto.") elif self.device.type == 'cuda': logger.info(f"LTX Worker ({self.device}): Otimização FP8 não suportada ou desativada. Usando modelo padrão.") def to_gpu(self): if self.device.type == 'cpu': return logger.info(f"LTX Worker: Movendo pipeline para a GPU {self.device}...") self.pipeline.to(self.device) def to_cpu(self): 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): return self.pipeline(**kwargs).images class LtxPoolManager: 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() self.last_cleanup_thread = None def _cleanup_worker_thread(self, worker): logger.info(f"LTX CLEANUP THREAD: Iniciando limpeza de {worker.device} em background...") worker.to_cpu() def _prepare_and_log_params(self, worker_to_use, **kwargs): target_device = worker_to_use.device height, width = kwargs['height'], kwargs['width'] conditioning_data = kwargs.get('conditioning_items_data', []) final_conditioning_items = [] # --- LOG ADICIONADO: Detalhes dos tensores de condicionamento --- 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))), "decode_noise_scale": float(kwargs.get('decode_noise_scale', worker_to_use.config.get("decode_noise_scale", 0.025))), "image_cond_noise_scale": float(kwargs.get('image_cond_noise_scale', 0.0)), "stochastic_sampling": bool(kwargs.get('stochastic_sampling', worker_to_use.config.get("stochastic_sampling", False))), "prompt": kwargs['motion_prompt'], "negative_prompt": kwargs.get('negative_prompt', "blurry, distorted, static, bad quality, artifacts"), "guidance_scale": float(kwargs.get('guidance_scale', 1.0)), "stg_scale": float(kwargs.get('stg_scale', 0.0)), "rescaling_scale": float(kwargs.get('rescaling_scale', 1.0)), } 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 8 else: pipeline_params["num_inference_steps"] = int(kwargs.get('num_inference_steps', 7)) # --- LOG ADICIONADO: Exibição completa dos parâmetros da pipeline --- 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)) logger.info("="*60) # --- FIM DO LOG ADICIONADO --- return pipeline_params, padding_vals def generate_latent_fragment(self, **kwargs) -> (torch.Tensor, tuple): worker_to_use = None progress = kwargs.get('progress') try: with self.lock: if self.last_cleanup_thread and self.last_cleanup_thread.is_alive(): self.last_cleanup_thread.join() worker_to_use = self.workers[self.current_worker_index] previous_worker_index = (self.current_worker_index - 1 + len(self.workers)) % len(self.workers) worker_to_cleanup = self.workers[previous_worker_index] cleanup_thread = threading.Thread(target=self._cleanup_worker_thread, args=(worker_to_cleanup,)) cleanup_thread.start() self.last_cleanup_thread = cleanup_thread worker_to_use.to_gpu() 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) pipeline_params['output_type'] = "latent" if progress: progress(0.1, desc=f"[Especialista LTX em {worker_to_use.device}] Gerando latentes...") with torch.no_grad(): result_tensor = worker_to_use.generate_video_fragment_internal(**pipeline_params) return result_tensor, padding_vals except Exception as e: logger.error(f"LTX POOL MANAGER: Erro durante a geração de latentes: {e}", exc_info=True) raise e finally: if worker_to_use: logger.info(f"LTX POOL MANAGER: Executando limpeza final para {worker_to_use.device}...") worker_to_use.to_cpu() 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.")