import random import gradio as gr from utils.i18n import DEFAULT_LANG, t from utils.model_choices import ( get_clip_model_choices, get_dit_choices, get_high_noise_choices, get_low_noise_choices, get_t5_model_choices, get_vae_choices, ) from utils.model_components import build_wan21_components, build_wan22_components from utils.model_handlers import TOKENIZER_CONFIG, create_download_wrappers, create_update_status_wrappers from utils.model_utils import HF_AVAILABLE, MS_AVAILABLE, check_model_exists, extract_model_name, is_fp8_supported_gpu MAX_NUMPY_SEED = 2**32 - 1 def generate_random_seed(): """生成随机种子""" return random.randint(0, MAX_NUMPY_SEED) def generate_unique_filename(output_dir, is_image=False): """生成唯一文件名""" import os from datetime import datetime timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") ext = ".png" if is_image else ".mp4" filename = f"{timestamp}{ext}" return os.path.join(output_dir, filename) def build_video_page( model_path, output_dir, run_inference, lang=DEFAULT_LANG, ): """构建视频生成页面 - 重构简化版""" # 创建 update 和 download 包装函数 update_funcs = create_update_status_wrappers() get_choices_funcs = { "get_dit_choices": get_dit_choices, "get_high_noise_choices": get_high_noise_choices, "get_low_noise_choices": get_low_noise_choices, "get_t5_model_choices": get_t5_model_choices, "get_clip_model_choices": get_clip_model_choices, "get_vae_choices": get_vae_choices, } download_funcs = create_download_wrappers(get_choices_funcs) # 判断模型是否是 distill 版本 def is_distill_model(model_type, dit_path, high_noise_path): if model_type == "Wan2.1": check_name = dit_path.lower() if dit_path else "" else: check_name = high_noise_path.lower() if high_noise_path else "" return "4step" in check_name # 主布局：左右分栏 with gr.Row(): # 左侧：配置和输入区域 with gr.Column(scale=5): # 模型配置区域 with gr.Accordion(t("model_config", lang), open=True, elem_classes=["model-config"]): gr.Markdown(t("model_config_hint", lang)) # FP8 支持提示 if not is_fp8_supported_gpu(): gr.Markdown(t("fp8_not_supported", lang)) model_path_input = gr.Textbox(value=model_path, visible=False) # 模型类型 + 任务类型 + 下载源 with gr.Row(): model_type_input = gr.Radio( label=t("model_type", lang), choices=["Wan2.1", "Wan2.2"], value="Wan2.1", info=t("model_type_info", lang), elem_classes=["horizontal-radio"], ) task_type_input = gr.Radio( label=t("task_type", lang), choices=[("I2V", "i2v"), ("T2V", "t2v")], value="i2v", info=t("task_type_info", lang), elem_classes=["horizontal-radio"], ) download_source_input = gr.Radio( label=t("download_source", lang), choices=(["huggingface", "modelscope"] if (HF_AVAILABLE and MS_AVAILABLE) else (["huggingface"] if HF_AVAILABLE else ["modelscope"] if MS_AVAILABLE else [])), value=("modelscope" if MS_AVAILABLE else ("huggingface" if HF_AVAILABLE else None)), info=t("download_source_info", lang), visible=HF_AVAILABLE or MS_AVAILABLE, elem_classes=["horizontal-radio"], ) # wan2.1 和 wan2.2 组件 wan21_components = build_wan21_components(model_path, model_path_input, model_type_input, task_type_input, download_source_input, update_funcs, download_funcs, lang) wan21_row = wan21_components["wan21_row"] dit_path_input = wan21_components["dit_path_input"] dit_download_btn = wan21_components["dit_download_btn"] wan21_use_lora_input = wan21_components["use_lora"] wan21_lora_path_input = wan21_components["lora_path_input"] wan21_lora_strength_input = wan21_components["lora_strength"] wan22_components = build_wan22_components(model_path, model_path_input, task_type_input, download_source_input, update_funcs, download_funcs, lang) wan22_row = wan22_components["wan22_row"] high_noise_path_input = wan22_components["high_noise_path_input"] low_noise_path_input = wan22_components["low_noise_path_input"] high_noise_download_btn = wan22_components["high_noise_download_btn"] low_noise_download_btn = wan22_components["low_noise_download_btn"] wan22_use_lora_input = wan22_components["use_lora"] wan22_high_noise_lora_path_input = wan22_components["high_noise_lora_path_input"] wan22_high_noise_lora_strength_input = wan22_components["high_noise_lora_strength"] wan22_low_noise_lora_path_input = wan22_components["low_noise_lora_path_input"] wan22_low_noise_lora_strength_input = wan22_components["low_noise_lora_strength"] # 文本编码器（模型 + Tokenizer） with gr.Row() as t5_row: with gr.Column(scale=1): t5_model_choices_init = get_t5_model_choices(model_path) t5_path_input = gr.Dropdown( label=t("text_encoder", lang), choices=t5_model_choices_init, value=t5_model_choices_init[0] if t5_model_choices_init else "", allow_custom_value=True, ) # 初始化时检查 T5 模型状态 t5_btn_visible = False if t5_model_choices_init: first_choice = t5_model_choices_init[0] actual_name = extract_model_name(first_choice) t5_exists = check_model_exists(model_path, actual_name) t5_btn_visible = not t5_exists t5_download_btn = gr.Button(t("download", lang), visible=t5_btn_visible, size="sm", variant="secondary") t5_download_status = gr.Markdown("", visible=False) with gr.Column(scale=1): # 初始化时检查 T5 Tokenizer 状态 t5_tokenizer_config = TOKENIZER_CONFIG.get("t5") t5_tokenizer_name = t5_tokenizer_config["name"] if t5_tokenizer_config else "google" t5_tokenizer_exists = check_model_exists(model_path, t5_tokenizer_name) if t5_tokenizer_config else False t5_tokenizer_status_text = f"{t5_tokenizer_name} ✅" if t5_tokenizer_exists else f"{t5_tokenizer_name} ❌" t5_tokenizer_btn_visible = not t5_tokenizer_exists t5_tokenizer_hint = gr.Dropdown( label=t("text_encoder_tokenizer", lang), choices=["google ✅", "google ❌"], value=t5_tokenizer_status_text, interactive=False, ) t5_tokenizer_download_btn = gr.Button(t("download", lang), visible=t5_tokenizer_btn_visible, size="sm", variant="secondary") t5_tokenizer_download_status = gr.Markdown("", visible=False) # 图像编码器（模型 + Tokenizer，条件显示） with gr.Row(visible=True) as clip_row: with gr.Column(scale=1): clip_model_choices_init = get_clip_model_choices(model_path) clip_path_input = gr.Dropdown( label=t("image_encoder", lang), choices=clip_model_choices_init, value=clip_model_choices_init[0] if clip_model_choices_init else "", allow_custom_value=True, ) # 初始化时检查 CLIP 模型状态 clip_btn_visible = False if clip_model_choices_init: first_choice = clip_model_choices_init[0] actual_name = extract_model_name(first_choice) clip_exists = check_model_exists(model_path, actual_name) clip_btn_visible = not clip_exists clip_download_btn = gr.Button(t("download", lang), visible=clip_btn_visible, size="sm", variant="secondary") clip_download_status = gr.Markdown("", visible=False) with gr.Column(scale=1): # 初始化时检查 CLIP Tokenizer 状态 clip_tokenizer_config = TOKENIZER_CONFIG.get("clip") clip_tokenizer_name = clip_tokenizer_config["name"] if clip_tokenizer_config else "xlm-roberta-large" clip_tokenizer_exists = check_model_exists(model_path, clip_tokenizer_name) if clip_tokenizer_config else False clip_tokenizer_status_text = f"{clip_tokenizer_name} ✅" if clip_tokenizer_exists else f"{clip_tokenizer_name} ❌" clip_tokenizer_btn_visible = not clip_tokenizer_exists clip_tokenizer_hint = gr.Dropdown( label=t("image_encoder_tokenizer", lang), choices=["xlm-roberta-large ✅", "xlm-roberta-large ❌"], value=clip_tokenizer_status_text, interactive=False, ) clip_tokenizer_download_btn = gr.Button(t("download", lang), visible=clip_tokenizer_btn_visible, size="sm", variant="secondary") clip_tokenizer_download_status = gr.Markdown("", visible=False) # VAE with gr.Row() as vae_row: vae_choices_init = get_vae_choices(model_path) vae_path_input = gr.Dropdown( label=t("vae", lang), choices=vae_choices_init, value=(vae_choices_init[0] if vae_choices_init else ""), allow_custom_value=True, interactive=True, ) # 初始化时检查 VAE 状态 vae_btn_visible = False if vae_choices_init: first_choice = vae_choices_init[0] actual_name = extract_model_name(first_choice) vae_exists = check_model_exists(model_path, actual_name) vae_btn_visible = not vae_exists vae_download_btn = gr.Button(t("download", lang), visible=vae_btn_visible, size="sm", variant="secondary") vae_download_status = gr.Markdown("", visible=False) # 使用预创建的包装函数 update_dit_status = update_funcs["update_dit_status"] update_high_noise_status = update_funcs["update_high_noise_status"] update_low_noise_status = update_funcs["update_low_noise_status"] update_t5_model_status = update_funcs["update_t5_model_status"] update_t5_tokenizer_status = update_funcs["update_t5_tokenizer_status"] update_clip_model_status = update_funcs["update_clip_model_status"] update_clip_tokenizer_status = update_funcs["update_clip_tokenizer_status"] update_vae_status = update_funcs["update_vae_status"] download_t5_model = download_funcs["download_t5_model"] download_t5_tokenizer = download_funcs["download_t5_tokenizer"] download_clip_model = download_funcs["download_clip_model"] download_clip_tokenizer = download_funcs["download_clip_tokenizer"] download_vae = download_funcs["download_vae"] # T5 模型路径变化时，只更新 T5 模型状态（不更新 Tokenizer） t5_path_input.change( fn=update_t5_model_status, inputs=[model_path_input, t5_path_input], outputs=[t5_download_btn], ) # CLIP 模型路径变化时，只更新 CLIP 模型状态（不更新 Tokenizer） clip_path_input.change( fn=update_clip_model_status, inputs=[model_path_input, clip_path_input], outputs=[clip_download_btn], ) # Tokenizer 独立检测（当 model_path 变化时，同时更新所有 Tokenizer） def update_all_tokenizers(model_path_val): """更新所有 Tokenizer 状态""" t5_tokenizer_result = update_t5_tokenizer_status(model_path_val) clip_tokenizer_result = update_clip_tokenizer_status(model_path_val) return ( t5_tokenizer_result[0], # t5_tokenizer_hint t5_tokenizer_result[1], # t5_tokenizer_download_btn clip_tokenizer_result[0], # clip_tokenizer_hint clip_tokenizer_result[1], # clip_tokenizer_download_btn ) # 绑定 model_path 变化事件，更新所有 Tokenizer 状态 model_path_input.change( fn=update_all_tokenizers, inputs=[model_path_input], outputs=[ t5_tokenizer_hint, t5_tokenizer_download_btn, clip_tokenizer_hint, clip_tokenizer_download_btn, ], ) vae_path_input.change( fn=update_vae_status, inputs=[model_path_input, vae_path_input], outputs=[vae_download_btn], ) # 绑定下载按钮事件 t5_download_btn.click( fn=download_t5_model, inputs=[model_path_input, t5_path_input, download_source_input], outputs=[t5_download_status, t5_download_btn, t5_path_input], ) t5_tokenizer_download_btn.click( fn=download_t5_tokenizer, inputs=[model_path_input, download_source_input], outputs=[ t5_tokenizer_download_status, t5_tokenizer_hint, t5_tokenizer_download_btn, ], ) clip_download_btn.click( fn=download_clip_model, inputs=[model_path_input, clip_path_input, download_source_input], outputs=[clip_download_status, clip_download_btn, clip_path_input], ) clip_tokenizer_download_btn.click( fn=download_clip_tokenizer, inputs=[model_path_input, download_source_input], outputs=[ clip_tokenizer_download_status, clip_tokenizer_hint, clip_tokenizer_download_btn, ], ) vae_download_btn.click( fn=download_vae, inputs=[ model_path_input, vae_path_input, download_source_input, ], outputs=[ vae_download_status, vae_download_btn, vae_path_input, ], ) # 统一的模型组件更新函数 def update_model_components(model_type, task_type, model_path_val): """统一更新所有模型组件""" show_clip = model_type == "Wan2.1" and task_type == "i2v" show_image_input = task_type == "i2v" is_wan21 = model_type == "Wan2.1" # 获取模型选项 t5_choices = get_t5_model_choices(model_path_val) vae_choices = get_vae_choices(model_path_val) clip_choices = get_clip_model_choices(model_path_val) if show_clip else [] # 更新 Tokenizer 状态 t5_tokenizer_result = update_t5_tokenizer_status(model_path_val) clip_tokenizer_result = update_clip_tokenizer_status(model_path_val) # 更新模型下载按钮状态 t5_btn_update = update_t5_model_status(model_path_val, t5_choices[0] if t5_choices else "") clip_btn_update = update_clip_model_status(model_path_val, clip_choices[0] if clip_choices else "") if show_clip else gr.update() vae_btn_update = update_vae_status(model_path_val, vae_choices[0] if vae_choices else "") if is_wan21: dit_choices = get_dit_choices(model_path_val, "wan2.1", task_type) # 更新 DIT 下载按钮状态 from utils.model_utils import extract_model_name dit_btn_update = update_dit_status(model_path_val, extract_model_name(dit_choices[0]) if dit_choices else "", "wan2.1") if dit_choices else gr.update(visible=False) return ( gr.update(visible=True), # wan21_row gr.update(visible=False), # wan22_row gr.update(choices=dit_choices, value=dit_choices[0] if dit_choices else "", visible=True), # dit_path_input gr.update(), # high_noise_path_input gr.update(), # low_noise_path_input gr.update(visible=show_clip), # clip_row gr.update(visible=True), # vae_row gr.update(visible=True), # t5_row gr.update(choices=t5_choices, value=t5_choices[0] if t5_choices else ""), # t5_path_input gr.update(choices=clip_choices, value=clip_choices[0] if clip_choices else ""), # clip_path_input gr.update(choices=vae_choices, value=vae_choices[0] if vae_choices else ""), # vae_path_input gr.update(visible=show_image_input), # image_input_row gr.update(label=t("output_video_path", lang)), # save_result_path t5_tokenizer_result[0], # t5_tokenizer_hint t5_tokenizer_result[1], # t5_tokenizer_download_btn clip_tokenizer_result[0], # clip_tokenizer_hint clip_tokenizer_result[1], # clip_tokenizer_download_btn t5_btn_update, # t5_download_btn clip_btn_update, # clip_download_btn vae_btn_update, # vae_download_btn dit_btn_update, # dit_download_btn gr.update(), # high_noise_download_btn gr.update(), # low_noise_download_btn ) else: # wan2.2 high_noise_choices = get_high_noise_choices(model_path_val, "wan2.2", task_type) low_noise_choices = get_low_noise_choices(model_path_val, "wan2.2", task_type) # 更新 high_noise 和 low_noise 下载按钮状态 from utils.model_utils import extract_model_name high_noise_btn_update = ( update_high_noise_status(model_path_val, extract_model_name(high_noise_choices[0]) if high_noise_choices else "") if high_noise_choices else gr.update(visible=False) ) low_noise_btn_update = ( update_low_noise_status(model_path_val, extract_model_name(low_noise_choices[0]) if low_noise_choices else "") if low_noise_choices else gr.update(visible=False) ) return ( gr.update(visible=False), # wan21_row gr.update(visible=True), # wan22_row gr.update(visible=False), # dit_path_input gr.update(choices=high_noise_choices, value=high_noise_choices[0] if high_noise_choices else ""), # high_noise_path_input gr.update(choices=low_noise_choices, value=low_noise_choices[0] if low_noise_choices else ""), # low_noise_path_input gr.update(visible=show_clip), # clip_row gr.update(visible=True), # vae_row gr.update(visible=True), # t5_row gr.update(choices=t5_choices, value=t5_choices[0] if t5_choices else ""), # t5_path_input gr.update(choices=clip_choices, value=clip_choices[0] if clip_choices else ""), # clip_path_input gr.update(choices=vae_choices, value=vae_choices[0] if vae_choices else ""), # vae_path_input gr.update(visible=show_image_input), # image_input_row gr.update(label=t("output_video_path", lang)), # save_result_path t5_tokenizer_result[0], # t5_tokenizer_hint t5_tokenizer_result[1], # t5_tokenizer_download_btn clip_tokenizer_result[0], # clip_tokenizer_hint clip_tokenizer_result[1], # clip_tokenizer_download_btn t5_btn_update, # t5_download_btn clip_btn_update, # clip_download_btn vae_btn_update, # vae_download_btn gr.update(), # dit_download_btn high_noise_btn_update, # high_noise_download_btn low_noise_btn_update, # low_noise_download_btn ) # 输入参数区域 with gr.Accordion(t("input_params", lang), open=True, elem_classes=["input-params"]): # 图片输入（i2v 时显示） with gr.Column(visible=True) as image_input_row: image_files = gr.File( label=t("input_images", lang), file_count="multiple", file_types=["image"], height=150, interactive=True, ) # 图片预览 Gallery image_gallery = gr.Gallery( label=t("uploaded_images_preview", lang), columns=4, rows=2, height=200, object_fit="contain", show_label=True, ) # 将多个文件路径转换为逗号分隔的字符串 image_path = gr.Textbox( label=t("image_path", lang), visible=False, ) def update_image_path_and_gallery(files): if files is None or len(files) == 0: return "", [] # 提取文件路径 paths = [f.name if hasattr(f, "name") else f for f in files] # 返回逗号分隔的路径和图片列表用于 Gallery 显示 return ",".join(paths), paths image_files.change( fn=update_image_path_and_gallery, inputs=[image_files], outputs=[image_path, image_gallery], ) with gr.Row(): with gr.Column(): prompt = gr.Textbox( label=t("prompt", lang), lines=3, placeholder=t("prompt_placeholder", lang), max_lines=5, ) with gr.Column(): negative_prompt = gr.Textbox( label=t("negative_prompt", lang), lines=3, placeholder=t("negative_prompt_placeholder", lang), max_lines=5, value="镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", ) with gr.Column(visible=True): resolution = gr.Dropdown( choices=["480p", "540p", "720p"], value="480p", label=t("max_resolution", lang), info=t("max_resolution_info", lang), ) with gr.Column(scale=9): seed = gr.Slider( label=t("random_seed", lang), minimum=0, maximum=MAX_NUMPY_SEED, step=1, value=generate_random_seed(), ) with gr.Column(): default_dit = get_dit_choices(model_path, "wan2.1", "i2v")[0] if get_dit_choices(model_path, "wan2.1", "i2v") else "" default_high_noise = get_high_noise_choices(model_path, "wan2.2", "i2v")[0] if get_high_noise_choices(model_path, "wan2.2", "i2v") else "" default_is_distill = is_distill_model("wan2.1", default_dit, default_high_noise) if default_is_distill: infer_steps = gr.Slider( label=t("infer_steps", lang), minimum=1, maximum=100, step=1, value=4, info=t("infer_steps_distill", lang), ) else: infer_steps = gr.Slider( label=t("infer_steps", lang), minimum=1, maximum=100, step=1, value=40, ) # 根据模型类别设置默认CFG default_cfg_scale = 1 if default_is_distill else 5 with gr.Row(): sample_shift = gr.Slider( label=t("sample_shift", lang), value=5, minimum=0, maximum=10, step=1, info=t("sample_shift_info", lang), ) cfg_scale = gr.Slider( label=t("cfg_scale", lang), minimum=1, maximum=10, step=1, value=default_cfg_scale, info=t("cfg_scale_info", lang), ) # 统一的模型参数更新函数（推理步数和CFG） def update_model_params(model_type, dit_path, high_noise_path): is_distill = is_distill_model(model_type, dit_path, high_noise_path) return ( gr.update(value=4 if is_distill else 40), # infer_steps gr.update(value=1 if is_distill else 5), # cfg_scale ) # 监听模型路径和类型变化 inputs = [model_type_input, dit_path_input, high_noise_path_input] for trigger in [dit_path_input, high_noise_path_input, model_type_input]: trigger.change( fn=update_model_params, inputs=inputs, outputs=[infer_steps, cfg_scale], ) with gr.Row(visible=True): fps = gr.Slider( label=t("fps", lang), minimum=8, maximum=30, step=1, value=16, info=t("fps_info", lang), ) video_duration = gr.Slider( label=t("video_duration", lang), minimum=1.0, maximum=10.0, step=0.1, value=5.0, info=t("video_duration_info", lang), ) save_result_path = gr.Textbox( label=t("output_video_path", lang), value=generate_unique_filename(output_dir), info=t("output_video_path_info", lang), visible=False, # 隐藏输出路径，自动生成 ) # 模型类型和任务类型切换都使用同一个函数（在 image_input_row 和 save_result_path 定义后绑定） model_type_input.change( fn=update_model_components, inputs=[model_type_input, task_type_input, model_path_input], outputs=[ wan21_row, wan22_row, dit_path_input, high_noise_path_input, low_noise_path_input, clip_row, vae_row, t5_row, t5_path_input, clip_path_input, vae_path_input, image_input_row, save_result_path, t5_tokenizer_hint, t5_tokenizer_download_btn, clip_tokenizer_hint, clip_tokenizer_download_btn, t5_download_btn, clip_download_btn, vae_download_btn, dit_download_btn, high_noise_download_btn, low_noise_download_btn, ], ) task_type_input.change( fn=update_model_components, inputs=[model_type_input, task_type_input, model_path_input], outputs=[ wan21_row, wan22_row, dit_path_input, high_noise_path_input, low_noise_path_input, clip_row, vae_row, t5_row, t5_path_input, clip_path_input, vae_path_input, image_input_row, save_result_path, t5_tokenizer_hint, t5_tokenizer_download_btn, clip_tokenizer_hint, clip_tokenizer_download_btn, t5_download_btn, clip_download_btn, vae_download_btn, dit_download_btn, high_noise_download_btn, low_noise_download_btn, ], ) # 右侧：输出区域 with gr.Column(scale=4): with gr.Accordion(t("output_result", lang), open=True, elem_classes=["output-video"]): output_video = gr.Video( label="", height=600, autoplay=True, show_label=False, visible=True, ) infer_btn = gr.Button( t("generate_video", lang), variant="primary", size="lg", elem_classes=["generate-btn"], ) # 包装推理函数，视频页面只返回视频，只传递必要的参数 def run_inference_wrapper( prompt_val, negative_prompt_val, save_result_path_val, infer_steps_val, video_duration_val, resolution_val, seed_val, sample_shift_val, cfg_scale_val, fps_val, model_path_val, model_type_val, task_type_val, dit_path_val, high_noise_path_val, low_noise_path_val, t5_path_val, clip_path_val, vae_path_val, image_path_val, wan21_use_lora_val, wan21_lora_path_val, wan21_lora_strength_val, wan22_use_lora_val, wan22_high_noise_lora_path_val, wan22_high_noise_lora_strength_val, wan22_low_noise_lora_path_val, wan22_low_noise_lora_strength_val, ): # 视频页面不需要 qwen_image 相关参数，使用 None # offload 和 rope 相关参数已在 run_inference 中通过 get_auto_config_dict 自动决定 # 将视频时长（秒）转换为帧数 num_frames_val = int(video_duration_val * fps_val) + 1 # 提取所有路径参数中的模型名，移除状态符号（✅/❌） from utils.model_utils import extract_model_name dit_path_val = extract_model_name(dit_path_val) if dit_path_val else None high_noise_path_val = extract_model_name(high_noise_path_val) if high_noise_path_val else None low_noise_path_val = extract_model_name(low_noise_path_val) if low_noise_path_val else None t5_path_val = extract_model_name(t5_path_val) if t5_path_val else None clip_path_val = extract_model_name(clip_path_val) if clip_path_val else "" vae_path_val = extract_model_name(vae_path_val) if vae_path_val else None if model_type_val == "Wan2.1": use_lora = wan21_use_lora_val lora_path = wan21_lora_path_val lora_strength = wan21_lora_strength_val low_noise_lora_path = None low_noise_lora_strength = None high_noise_lora_path = None high_noise_lora_strength = None else: use_lora = wan22_use_lora_val high_noise_lora_path = wan22_high_noise_lora_path_val high_noise_lora_strength = wan22_high_noise_lora_strength_val low_noise_lora_path = wan22_low_noise_lora_path_val low_noise_lora_strength = wan22_low_noise_lora_strength_val lora_path = None lora_strength = None result = run_inference( prompt=prompt_val, negative_prompt=negative_prompt_val, save_result_path=save_result_path_val, infer_steps=infer_steps_val, num_frames=num_frames_val, resolution=resolution_val, seed=seed_val, sample_shift=sample_shift_val, cfg_scale=cfg_scale_val, # enable_cfg 会根据 cfg_scale 在 run_inference 内部自动设置 fps=fps_val, model_path_input=model_path_val, model_type_input=model_type_val, task_type_input=task_type_val, dit_path_input=dit_path_val, high_noise_path_input=high_noise_path_val, low_noise_path_input=low_noise_path_val, t5_path_input=t5_path_val, clip_path_input=clip_path_val, vae_path=vae_path_val, image_path=image_path_val, use_lora=use_lora, lora_path=lora_path, lora_strength=lora_strength, high_noise_lora_path=high_noise_lora_path, high_noise_lora_strength=high_noise_lora_strength, low_noise_lora_path=low_noise_lora_path, low_noise_lora_strength=low_noise_lora_strength, ) return gr.update(value=result) infer_btn.click( fn=run_inference_wrapper, inputs=[ prompt, negative_prompt, save_result_path, infer_steps, video_duration, resolution, seed, sample_shift, cfg_scale, fps, model_path_input, model_type_input, task_type_input, dit_path_input, high_noise_path_input, low_noise_path_input, t5_path_input, clip_path_input, vae_path_input, image_path, wan21_use_lora_input, wan21_lora_path_input, wan21_lora_strength_input, wan22_use_lora_input, wan22_high_noise_lora_path_input, wan22_high_noise_lora_strength_input, wan22_low_noise_lora_path_input, wan22_low_noise_lora_strength_input, ], outputs=[output_video], )