""" 重构后的 Gradio Demo 主入口文件 整合了所有模块，支持中英文切换 """ import argparse import gc import json import logging import os import warnings import torch from loguru import logger from utils.i18n import DEFAULT_LANG, set_language from utils.model_utils import cleanup_memory, extract_op_name, get_model_configs from utils.ui_builder import build_ui, generate_unique_filename, get_auto_config_dict from lightx2v.utils.input_info import init_empty_input_info, update_input_info_from_dict from lightx2v.utils.set_config import get_default_config warnings.filterwarnings("ignore", category=UserWarning, module="huggingface_hub") warnings.filterwarnings("ignore", category=UserWarning, module="huggingface_hub.utils") logging.getLogger("httpx").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) os.environ["PROFILING_DEBUG_LEVEL"] = "2" os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" os.environ["DTYPE"] = "BF16" logger.add( "inference_logs.log", rotation="100 MB", encoding="utf-8", enqueue=True, backtrace=True, diagnose=True, ) global_runner = None current_config = None cur_dit_path = None def run_inference( prompt="", negative_prompt="", save_result_path="", infer_steps=4, num_frames=81, resolution="480p", seed=42, sample_shift=5, cfg_scale=1, fps=16, model_path_input=None, model_type_input="wan2.1", task_type_input="i2v", dit_path_input=None, high_noise_path_input=None, low_noise_path_input=None, t5_path_input=None, clip_path_input="", image_path=None, vae_path=None, qwen_image_dit_path_input=None, qwen_image_vae_path_input=None, qwen_image_scheduler_path_input=None, qwen25vl_encoder_path_input=None, z_image_dit_path_input=None, z_image_vae_path_input=None, z_image_scheduler_path_input=None, qwen3_encoder_path_input=None, aspect_ratio="1:1", use_lora=None, lora_path=None, lora_strength=None, high_noise_lora_path=None, low_noise_lora_path=None, high_noise_lora_strength=None, low_noise_lora_strength=None, ): cleanup_memory() auto_config = get_auto_config_dict(model_type=model_type_input, resolution=resolution, num_frames=num_frames, task_type=task_type_input) # 从 auto_config 中获取 offload 和 rope 相关配置 rope_chunk = auto_config["rope_chunk_val"] rope_chunk_size = auto_config["rope_chunk_size_val"] cpu_offload = auto_config["cpu_offload_val"] offload_granularity = auto_config["offload_granularity_val"] lazy_load = auto_config["lazy_load_val"] t5_cpu_offload = auto_config["t5_cpu_offload_val"] clip_cpu_offload = auto_config["clip_cpu_offload_val"] vae_cpu_offload = auto_config["vae_cpu_offload_val"] unload_modules = auto_config["unload_modules_val"] attention_type = auto_config["attention_type_val"] quant_op = auto_config["quant_op_val"] use_tiling_vae = auto_config["use_tiling_vae_val"] clean_cuda_cache = auto_config["clean_cuda_cache_val"] quant_op = extract_op_name(quant_op) attention_type = extract_op_name(attention_type) task = task_type_input is_image_output = task in ["i2i", "t2i"] save_result_path = generate_unique_filename(output_dir, is_image=is_image_output) if cfg_scale == 1: enable_cfg = False else: enable_cfg = True model_config = get_model_configs( model_type_input, model_path_input, dit_path_input, high_noise_path_input, low_noise_path_input, t5_path_input, clip_path_input, vae_path, qwen_image_dit_path_input, qwen_image_vae_path_input, qwen_image_scheduler_path_input, qwen25vl_encoder_path_input, z_image_dit_path_input, z_image_vae_path_input, z_image_scheduler_path_input, qwen3_encoder_path_input, quant_op, use_lora=use_lora, lora_path=lora_path, lora_strength=lora_strength, high_noise_lora_path=high_noise_lora_path, low_noise_lora_path=low_noise_lora_path, high_noise_lora_strength=high_noise_lora_strength, low_noise_lora_strength=low_noise_lora_strength, ) model_cls = model_config["model_cls"] model_path = model_config["model_path"] global global_runner, current_config, cur_dit_path logger.info(f"Auto-determined model_cls: {model_cls} (model type: {model_type_input})") if model_cls.startswith("wan2.2"): current_dit_path = f"{high_noise_path_input}|{low_noise_path_input}" if high_noise_path_input and low_noise_path_input else None else: current_dit_path = dit_path_input needs_reinit = lazy_load or unload_modules or global_runner is None or cur_dit_path != current_dit_path config_graio = { "infer_steps": infer_steps, "target_video_length": num_frames, "resolution": resolution, "resize_mode": "adaptive", "self_attn_1_type": attention_type, "cross_attn_1_type": attention_type, "cross_attn_2_type": attention_type, "attn_type": attention_type, "enable_cfg": enable_cfg, "sample_guide_scale": cfg_scale, "sample_shift": sample_shift, "fps": fps, "feature_caching": "NoCaching", "do_mm_calib": False, "parallel_attn_type": None, "parallel_vae": False, "max_area": False, "vae_stride": (4, 8, 8), "patch_size": (1, 2, 2), "lora_path": None, "strength_model": 1.0, "use_prompt_enhancer": False, "text_len": 512, "denoising_step_list": [1000, 750, 500, 250], "cpu_offload": True if "wan2.2" in model_cls else cpu_offload, "offload_granularity": ("phase" if "wan2.2" in model_cls else offload_granularity), "t5_cpu_offload": t5_cpu_offload, "clip_cpu_offload": clip_cpu_offload, "vae_cpu_offload": vae_cpu_offload, "use_tiling_vae": use_tiling_vae, "lazy_load": lazy_load, "rope_chunk": rope_chunk, "rope_chunk_size": rope_chunk_size, "clean_cuda_cache": clean_cuda_cache, "unload_modules": unload_modules, "seq_parallel": False, "warm_up_cpu_buffers": False, "boundary_step_index": 2, "boundary": 0.900, "use_image_encoder": False if "wan2.2" in model_cls else True, "rope_type": "torch", "t5_lazy_load": lazy_load, "bucket_shape": { "0.667": [[480, 832], [544, 960], [720, 960]], "1.500": [[832, 480], [960, 544], [960, 720]], "1.000": [[480, 480], [576, 576], [720, 720]], }, "aspect_ratio": aspect_ratio, } args = argparse.Namespace( model_cls=model_cls, seed=seed, task=task, model_path=model_path, prompt_enhancer=None, prompt=prompt, negative_prompt=negative_prompt, image_path=image_path, save_result_path=save_result_path, return_result_tensor=False, aspect_ratio=aspect_ratio, target_shape=[], ) input_info = init_empty_input_info(args.task) config = get_default_config() config.update({k: v for k, v in vars(args).items()}) config.update(config_graio) config.update(model_config) # 如果 model_config 中包含 lora_configs，设置 lora_dynamic_apply if config.get("lora_configs"): config["lora_dynamic_apply"] = True logger.info(f"Using model: {model_path}") logger.info(f"Inference config:\n{json.dumps(config, indent=4, ensure_ascii=False)}") # 初始化或重用 runner runner = global_runner if needs_reinit: if runner is not None: del runner torch.cuda.empty_cache() gc.collect() from lightx2v.infer import init_runner runner = init_runner(config) data = args.__dict__ update_input_info_from_dict(input_info, data) current_config = config cur_dit_path = current_dit_path if not lazy_load: global_runner = runner else: runner.config = config data = args.__dict__ update_input_info_from_dict(input_info, data) runner.run_pipeline(input_info) cleanup_memory() return save_result_path def main(lang=DEFAULT_LANG): """主函数""" set_language(lang) demo = build_ui( model_path=model_path, output_dir=output_dir, run_inference=run_inference, lang=lang, ) # 启动 Gradio 应用 demo.launch( share=True, server_port=args.server_port, server_name=args.server_name, inbrowser=True, allowed_paths=[output_dir], max_file_size="1gb", ) if __name__ == "__main__": parser = argparse.ArgumentParser(description="轻量级视频生成") parser.add_argument("--model_path", type=str, required=True, help="模型文件夹路径") parser.add_argument("--server_port", type=int, default=7862, help="服务器端口") parser.add_argument("--server_name", type=str, default="0.0.0.0", help="服务器IP") parser.add_argument("--output_dir", type=str, default="./outputs", help="输出视频保存目录") parser.add_argument("--lang", type=str, default="zh", choices=["zh", "en"], help="界面语言") args = parser.parse_args() global model_path, model_cls, output_dir model_path = args.model_path model_cls = "wan2.1" output_dir = args.output_dir main(lang=args.lang)