import gradio as gr import torch import numpy as np import tempfile import os import yaml import json import threading from pathlib import Path # Importações de Hugging Face from huggingface_hub import snapshot_download, HfFolder from transformers import T5EncoderModel, T5TokenizerFast from diffusers import LTXLatentUpsamplePipeline from diffusers.models import AutoencoderKLLTXVideo, LTXVideoTransformer3DModel from diffusers.schedulers import FlowMatchEulerDiscreteScheduler # Nossa pipeline customizada e utilitários from pipeline_ltx_condition_control import LTXConditionPipeline, LTXVideoCondition from diffusers.utils import export_to_video from PIL import Image, ImageOps import imageio # --- Configuração de Logging e Avisos --- import warnings import logging warnings.filterwarnings("ignore", category="UserWarning") warnings.filterwarnings("ignore", category="FutureWarning") warnings.filterwarnings("ignore", message=".*") from huggingface_hub import logging as hf_logging hf_logging.set_verbosity_error() # --- Classe de Serviço para Carregamento e Gerenciamento dos Modelos --- class VideoGenerationService: """ Encapsula o carregamento e a configuração das pipelines de IA. Carrega os componentes de forma explícita e modular a partir de um arquivo de configuração. """ def __init__(self, config_path: Path): print("=== [Serviço de Geração de Vídeo] Inicializando... ===") if not torch.cuda.is_available(): raise RuntimeError("CUDA é necessário para rodar este serviço.") self.device = "cuda" self.torch_dtype = torch.bfloat16 print(f"[Init] Dispositivo: {self.device}, DType: {self.torch_dtype}") with open(config_path, "r") as f: self.cfg = yaml.safe_load(f) print(f"[Init] Configuração carregada de: {config_path}") print(json.dumps(self.cfg, indent=2)) # Parâmetros do YAML self.base_repo = self.cfg.get("base_repo") self.checkpoint_path = self.cfg.get("checkpoint_path") self.upscaler_repo = self.cfg.get("spatial_upscaler_model_path") self._initialize() print("=== [Serviço de Geração de Vídeo] Inicialização concluída. ===") def _initialize(self): print(f"=== [Init] Baixando snapshot do repositório base: {self.base_repo} ===") local_repo_path = snapshot_download( repo_id=self.base_repo, token=os.getenv("HF_TOKEN") or HfFolder.get_token(), resume_download=True ) print("[Init] Carregando componentes da pipeline a partir de arquivos locais...") self.vae = AutoencoderKLLTXVideo.from_pretrained(local_repo_path, subfolder="vae", torch_dtype=self.torch_dtype) self.text_encoder = T5EncoderModel.from_pretrained(local_repo_path, subfolder="text_encoder", torch_dtype=self.torch_dtype) self.tokenizer = T5TokenizerFast.from_pretrained(local_repo_path, subfolder="tokenizer") self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(local_repo_path, subfolder="scheduler") # Causa do erro anterior: desativar explicitamente o dynamic shifting para compatibilidade if hasattr(self.scheduler.config, 'use_dynamic_shifting') and self.scheduler.config.use_dynamic_shifting: print("[Init] Desativando 'use_dynamic_shifting' no scheduler.") self.scheduler.config.use_dynamic_shifting = False print(f"[Init] Carregando pesos do Transformer de: {self.checkpoint_path}") self.transformer = LTXVideoTransformer3DModel.from_pretrained( local_repo_path, subfolder="transformer", weight_name=self.checkpoint_path, torch_dtype=self.torch_dtype ) print("[Init] Montando a LTXConditionPipeline...") self.pipeline = LTXConditionPipeline( vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, scheduler=self.scheduler, transformer=self.transformer ) self.pipeline.to(self.device) self.pipeline.vae.enable_tiling() print(f"[Init] Carregando o upsampler espacial de: {self.upscaler_repo}") self.upsampler = LTXLatentUpsamplePipeline.from_pretrained( self.upscaler_repo, vae=self.vae, torch_dtype=self.torch_dtype ) self.upsampler.to(self.device) # --- Inicialização da Aplicação --- CONFIG_PATH = Path("ltx_config.yaml") if not CONFIG_PATH.exists(): raise FileNotFoundError(f"Arquivo de configuração '{CONFIG_PATH}' não encontrado. Crie-o antes de executar a aplicação.") # Instancia o serviço que carrega e mantém os modelos service = VideoGenerationService(config_path=CONFIG_PATH) pipeline = service.pipeline pipe_upsample = service.upsampler FPS = 24 # --- Lógica Principal da Geração de Vídeo --- def round_to_nearest_resolution_acceptable_by_vae(height, width, vae_temporal_compression_ratio): height = height - (height % vae_temporal_compression_ratio) width = width - (width % vae_temporal_compression_ratio) return height, width def prepare_and_generate_video( condition_image_1, condition_strength_1, condition_frame_index_1, condition_image_2, condition_strength_2, condition_frame_index_2, prompt, duration, negative_prompt, height, width, guidance_scale, seed, randomize_seed, progress=gr.Progress(track_tqdm=True) ): try: conditions_data = [ (condition_image_1, condition_strength_1, condition_frame_index_1), (condition_image_2, condition_strength_2, condition_frame_index_2) ] if randomize_seed: seed = random.randint(0, 2**32 - 1) num_frames = int(duration * FPS) + 1 temporal_compression = pipeline.vae_temporal_compression_ratio num_frames = ((num_frames - 1) // temporal_compression) * temporal_compression + 1 # Etapa 1: Preparar condições para baixa resolução downscale_factor = 2 / 3 downscaled_height = int(height * downscale_factor) downscaled_width = int(width * downscale_factor) downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae( downscaled_height, downscaled_width, pipeline.vae_temporal_compression_ratio ) conditions_low_res = [] for image, strength, frame_index in conditions_data: if image is not None: processed_image = ImageOps.fit(image, (downscaled_width, downscaled_height), Image.LANCZOS) conditions_low_res.append(LTXVideoCondition( image=processed_image, strength=strength, frame_index=int(frame_index) )) pipeline_args_low_res = {"conditions": conditions_low_res} if conditions_low_res else {} latents = pipeline( prompt=prompt, negative_prompt=negative_prompt, width=downscaled_width, height=downscaled_height, num_frames=num_frames, generator=torch.Generator().manual_seed(seed), output_type="latent", **pipeline_args_low_res ).frames # Etapa 2: Upscale upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2 upscaled_latents = pipe_upsample(latents=latents, output_type="latent").frames # Etapa 3: Preparar condições para alta resolução (para manter frames imutáveis) conditions_high_res = [] for image, strength, frame_index in conditions_data: if image is not None: processed_image_high_res = ImageOps.fit(image, (upscaled_width, upscaled_height), Image.LANCZOS) conditions_high_res.append(LTXVideoCondition( image=processed_image_high_res, strength=strength, frame_index=int(frame_index) )) pipeline_args_high_res = {"conditions": conditions_high_res} if conditions_high_res else {} final_video_frames_np = pipeline( prompt=prompt, negative_prompt=negative_prompt, width=upscaled_width, height=upscaled_height, num_frames=num_frames, denoise_strength=0.999, latents=upscaled_latents, generator=torch.Generator(device="cuda").manual_seed(seed), output_type="np", **pipeline_args_high_res ).frames[0] # Etapa 4: Exportação video_uint8_frames = [(frame * 255).astype(np.uint8) for frame in final_video_frames_np] output_filename = "output.mp4" with imageio.get_writer(output_filename, fps=FPS, quality=8, macro_block_size=1) as writer: for frame_idx, frame_data in enumerate(video_uint8_frames): progress((frame_idx + 1) / len(video_uint8_frames), desc="Codificando frames do vídeo...") writer.append_data(frame_data) return output_filename, seed except Exception as e: print(f"Ocorreu um erro: {e}") import traceback traceback.print_exc() return None, seed # --- Interface Gráfica com Gradio --- with gr.Blocks(theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Lexend Deca"), "sans-serif"]), delete_cache=(60, 900)) as demo: gr.Markdown("# Geração de Vídeo com LTX\n**Crie vídeos a partir de texto e imagens de condição.**") with gr.Row(): with gr.Column(scale=1): prompt = gr.Textbox(label="Prompt", placeholder="Descreva o vídeo que você quer gerar...", lines=3, value="O Coringa dançando em um quarto escuro, iluminação dramática.") with gr.Accordion("Imagem de Condição 1", open=True): condition_image_1 = gr.Image(label="Imagem 1", type="pil") with gr.Row(): condition_strength_1 = gr.Slider(label="Peso", minimum=0.0, maximum=1.0, step=0.05, value=1.0) condition_frame_index_1 = gr.Number(label="Frame", value=0, precision=0) with gr.Accordion("Imagem de Condição 2", open=False): condition_image_2 = gr.Image(label="Imagem 2", type="pil") with gr.Row(): condition_strength_2 = gr.Slider(label="Peso", minimum=0.0, maximum=1.0, step=0.05, value=1.0) condition_frame_index_2 = gr.Number(label="Frame", value=0, precision=0) duration = gr.Slider(label="Duração (s)", minimum=1.0, maximum=10.0, step=0.5, value=2) with gr.Accordion("Configurações Avançadas", open=False): negative_prompt = gr.Textbox(label="Prompt Negativo", lines=2, value="pior qualidade, embaçado, tremido, distorcido") with gr.Row(): height = gr.Slider(label="Altura", minimum=256, maximum=1536, step=32, value=768) width = gr.Slider(label="Largura", minimum=256, maximum=1536, step=32, value=1152) with gr.Row(): guidance_scale = gr.Slider(label="Guidance", minimum=1.0, maximum=5.0, step=0.1, value=1.0) randomize_seed = gr.Checkbox(label="Seed Aleatória", value=True) seed = gr.Number(label="Seed", value=0, precision=0) generate_btn = gr.Button("Gerar Vídeo", variant="primary", size="lg") with gr.Column(scale=1): output_video = gr.Video(label="Vídeo Gerado", height=400) generated_seed = gr.Number(label="Seed Utilizada", interactive=False) generate_btn.click( fn=prepare_and_generate_video, inputs=[ condition_image_1, condition_strength_1, condition_frame_index_1, condition_image_2, condition_strength_2, condition_frame_index_2, prompt, duration, negative_prompt, height, width, guidance_scale, seed, randomize_seed, ], outputs=[output_video, generated_seed] ) if __name__ == "__main__": demo.queue().launch(server_name="0.0.0.0", server_port=7860, debug=True, show_error=True)