"""模型组件构建模块 为不同的模型类型创建 UI 组件 这些函数需要在 Gradio 的 with 块内调用 """ import os import gradio as gr from utils.i18n import t from utils.model_choices import ( get_dit_choices, get_high_noise_choices, get_low_noise_choices, get_qwen3_encoder_choices, get_qwen25vl_encoder_choices, get_qwen_image_2512_dit_choices, get_qwen_image_2512_scheduler_choices, get_qwen_image_2512_vae_choices, get_qwen_image_dit_choices, get_qwen_image_scheduler_choices, get_qwen_image_vae_choices, get_z_image_turbo_dit_choices, get_z_image_turbo_scheduler_choices, get_z_image_turbo_vae_choices, ) from utils.model_handlers import ( download_model_handler, update_model_status, ) def get_lora_choices(model_path): """获取 LoRA 模型可选项，从 model_path/loras 目录检索 Args: model_path: 模型根路径 Returns: list: LoRA 文件列表，如果没有则返回 [""] """ loras_dir = os.path.join(model_path, "loras") if not os.path.exists(loras_dir): return [""] lora_files = [] # 支持常见的 LoRA 文件格式 lora_extensions = [".safetensors", ".pt", ".pth", ".ckpt"] for item in os.listdir(loras_dir): item_path = os.path.join(loras_dir, item) if os.path.isfile(item_path): # 检查是否是 LoRA 文件 if any(item.lower().endswith(ext) for ext in lora_extensions): lora_files.append(item) # 按文件名排序 lora_files.sort() return lora_files if lora_files else [""] def build_wan21_components(model_path, model_path_input, model_type_input, task_type_input, download_source_input, update_funcs, download_funcs, lang="zh"): """构建 wan2.1 模型相关组件 必须在 Gradio 的 with 块内调用 Returns: dict: 包含所有 wan2.1 相关组件的字典 """ # wan2.1：Diffusion模型 with gr.Column(elem_classes=["diffusion-model-group"]) as wan21_row: with gr.Row(): with gr.Column(scale=5): dit_choices_init = get_dit_choices(model_path, "wan2.1", "i2v") dit_path_input = gr.Dropdown( label="🎨 Diffusion模型", choices=dit_choices_init, value=dit_choices_init[0] if dit_choices_init else "", allow_custom_value=True, visible=True, ) with gr.Column(scale=1, min_width=150): # 初始化时检查模型状态，确定下载按钮的初始可见性 from utils.model_utils import check_model_exists, extract_model_name dit_btn_visible = False if dit_choices_init: first_choice = dit_choices_init[0] actual_name = extract_model_name(first_choice) dit_exists = check_model_exists(model_path, actual_name) dit_btn_visible = not dit_exists dit_download_btn = gr.Button("📥 下载", visible=dit_btn_visible, size="sm", variant="secondary") dit_download_status = gr.Markdown("", visible=False) lora_choices_init = get_lora_choices(model_path) with gr.Row(): with gr.Column(scale=1): use_lora = gr.Checkbox( label=t("use_lora", lang), value=False, ) lora_path_input = gr.Dropdown( label=t("lora", lang), choices=lora_choices_init, value=lora_choices_init[0] if lora_choices_init and lora_choices_init[0] else "", allow_custom_value=True, visible=False, info=t("lora_info", lang), ) lora_strength = gr.Slider( label=t("lora_strength", lang), minimum=0.0, maximum=10.0, step=0.1, value=1.0, visible=False, info=t("lora_strength_info", lang), ) # 绑定事件 update_dit_status = update_funcs["update_dit_status"] download_dit_model = download_funcs["download_dit_model"] dit_path_input.change( fn=lambda mp, mn, mt: update_dit_status(mp, mn, mt), inputs=[model_path_input, dit_path_input, model_type_input], outputs=[dit_download_btn], ) dit_download_btn.click( fn=download_dit_model, inputs=[model_path_input, dit_path_input, model_type_input, task_type_input, download_source_input], outputs=[dit_download_status, dit_download_btn, dit_path_input], ) # LoRA 开关变化时显示/隐藏相关组件 def on_use_lora_change(use_lora_val): return ( gr.update(visible=use_lora_val), # lora_path_input gr.update(visible=use_lora_val), # lora_strength ) use_lora.change( fn=on_use_lora_change, inputs=[use_lora], outputs=[lora_path_input, lora_strength], ) # 当 model_path 变化时更新 LoRA 选择 def update_lora_choices(model_path_val): lora_choices = get_lora_choices(model_path_val) return gr.update(choices=lora_choices, value=lora_choices[0] if lora_choices and lora_choices[0] else "") model_path_input.change( fn=update_lora_choices, inputs=[model_path_input], outputs=[lora_path_input], ) return { "wan21_row": wan21_row, "dit_path_input": dit_path_input, "dit_download_btn": dit_download_btn, "dit_download_status": dit_download_status, "use_lora": use_lora, "lora_path_input": lora_path_input, "lora_strength": lora_strength, } def build_wan22_components(model_path, model_path_input, task_type_input, download_source_input, update_funcs, download_funcs, lang="zh"): """构建 wan2.2 模型相关组件 必须在 Gradio 的 with 块内调用 Returns: dict: 包含所有 wan2.2 相关组件的字典 """ # wan2.2 专用：高噪模型 + 低噪模型 with gr.Row(visible=False, elem_classes=["wan22-row"]) as wan22_row: with gr.Column(scale=1): high_noise_choices_init = get_high_noise_choices(model_path, "wan2.2", "i2v") high_noise_path_input = gr.Dropdown( label="🔊 高噪模型", choices=high_noise_choices_init, value=high_noise_choices_init[0] if high_noise_choices_init else "", allow_custom_value=True, ) # 初始化时检查模型状态 from utils.model_utils import check_model_exists, extract_model_name high_noise_btn_visible = False if high_noise_choices_init: first_choice = high_noise_choices_init[0] actual_name = extract_model_name(first_choice) high_noise_exists = check_model_exists(model_path, actual_name) high_noise_btn_visible = not high_noise_exists high_noise_download_btn = gr.Button("📥 下载", visible=high_noise_btn_visible, size="sm", variant="secondary") high_noise_download_status = gr.Markdown("", visible=False) with gr.Column(scale=1): low_noise_choices_init = get_low_noise_choices(model_path, "wan2.2", "i2v") low_noise_path_input = gr.Dropdown( label="🔇 低噪模型", choices=low_noise_choices_init, value=low_noise_choices_init[0] if low_noise_choices_init else "", allow_custom_value=True, ) # 初始化时检查模型状态 low_noise_btn_visible = False if low_noise_choices_init: first_choice = low_noise_choices_init[0] actual_name = extract_model_name(first_choice) low_noise_exists = check_model_exists(model_path, actual_name) low_noise_btn_visible = not low_noise_exists low_noise_download_btn = gr.Button("📥 下载", visible=low_noise_btn_visible, size="sm", variant="secondary") low_noise_download_status = gr.Markdown("", visible=False) # LoRA 组件（Wan2.2 需要为 high_noise 和 low_noise 分别配置） lora_choices_init = get_lora_choices(model_path) with gr.Row(): with gr.Column(scale=1): use_lora = gr.Checkbox( label=t("use_lora", lang), value=False, ) # High Noise LoRA high_noise_lora_path_input = gr.Dropdown( label=t("high_noise_lora", lang), choices=lora_choices_init, value=lora_choices_init[0] if lora_choices_init and lora_choices_init[0] else "", allow_custom_value=True, visible=False, info=t("high_noise_lora_info", lang), ) high_noise_lora_strength = gr.Slider( label=t("high_noise_lora_strength", lang), minimum=0.0, maximum=10.0, step=0.1, value=1.0, visible=False, info=t("high_noise_lora_strength_info", lang), ) # Low Noise LoRA low_noise_lora_path_input = gr.Dropdown( label=t("low_noise_lora", lang), choices=lora_choices_init, value=lora_choices_init[0] if lora_choices_init and lora_choices_init[0] else "", allow_custom_value=True, visible=False, info=t("low_noise_lora_info", lang), ) low_noise_lora_strength = gr.Slider( label=t("low_noise_lora_strength", lang), minimum=0.0, maximum=10.0, step=0.1, value=1.0, visible=False, info=t("low_noise_lora_strength_info", lang), ) # 绑定事件 update_high_noise_status = update_funcs["update_high_noise_status"] update_low_noise_status = update_funcs["update_low_noise_status"] download_high_noise_model = download_funcs["download_high_noise_model"] download_low_noise_model = download_funcs["download_low_noise_model"] high_noise_path_input.change( fn=update_high_noise_status, inputs=[model_path_input, high_noise_path_input], outputs=[high_noise_download_btn], ) low_noise_path_input.change( fn=update_low_noise_status, inputs=[model_path_input, low_noise_path_input], outputs=[low_noise_download_btn], ) high_noise_download_btn.click( fn=download_high_noise_model, inputs=[model_path_input, high_noise_path_input, task_type_input, download_source_input], outputs=[high_noise_download_status, high_noise_download_btn, high_noise_path_input], ) low_noise_download_btn.click( fn=download_low_noise_model, inputs=[model_path_input, low_noise_path_input, task_type_input, download_source_input], outputs=[low_noise_download_status, low_noise_download_btn, low_noise_path_input], ) # LoRA 开关变化时显示/隐藏相关组件 def on_use_lora_change(use_lora_val): return ( gr.update(visible=use_lora_val), # high_noise_lora_path_input gr.update(visible=use_lora_val), # high_noise_lora_strength gr.update(visible=use_lora_val), # low_noise_lora_path_input gr.update(visible=use_lora_val), # low_noise_lora_strength ) use_lora.change( fn=on_use_lora_change, inputs=[use_lora], outputs=[high_noise_lora_path_input, high_noise_lora_strength, low_noise_lora_path_input, low_noise_lora_strength], ) # 当 model_path 变化时更新 LoRA 选择 def update_lora_choices(model_path_val): lora_choices = get_lora_choices(model_path_val) return ( gr.update(choices=lora_choices, value=lora_choices[0] if lora_choices and lora_choices[0] else ""), # high_noise_lora_path_input gr.update(choices=lora_choices, value=lora_choices[0] if lora_choices and lora_choices[0] else ""), # low_noise_lora_path_input ) model_path_input.change( fn=update_lora_choices, inputs=[model_path_input], outputs=[high_noise_lora_path_input, low_noise_lora_path_input], ) return { "wan22_row": wan22_row, "high_noise_path_input": high_noise_path_input, "high_noise_download_btn": high_noise_download_btn, "high_noise_download_status": high_noise_download_status, "low_noise_path_input": low_noise_path_input, "low_noise_download_btn": low_noise_download_btn, "low_noise_download_status": low_noise_download_status, "use_lora": use_lora, "high_noise_lora_path_input": high_noise_lora_path_input, "high_noise_lora_strength": high_noise_lora_strength, "low_noise_lora_path_input": low_noise_lora_path_input, "low_noise_lora_strength": low_noise_lora_strength, } def build_qwen_image_components(model_path, model_path_input, download_source_input, model_type_input, lang="zh"): """构建 Qwen-Image-Edit-2511/Qwen-Image-2512 模型相关组件（用于图片页面） 必须在 Gradio 的 with 块内调用 Args: model_path: 模型路径 model_path_input: 模型路径输入组件 download_source_input: 下载源输入组件 model_type_input: 模型类型输入组件（用于动态更新） lang: 语言代码，默认为 "zh" Returns: dict: 包含所有 Qwen-Image 相关组件的字典 """ # 根据模型类型选择函数 def get_dit_choices_func(model_path_val): model_type_val = model_type_input.value if hasattr(model_type_input, "value") else "Qwen-Image-Edit-2511" if model_type_val == "Qwen-Image-2512": return get_qwen_image_2512_dit_choices(model_path_val) else: return get_qwen_image_dit_choices(model_path_val) def get_vae_choices_func(model_path_val): model_type_val = model_type_input.value if hasattr(model_type_input, "value") else "Qwen-Image-Edit-2511" if model_type_val == "Qwen-Image-2512": return get_qwen_image_2512_vae_choices(model_path_val) else: return get_qwen_image_vae_choices(model_path_val) def get_scheduler_choices_func(model_path_val): model_type_val = model_type_input.value if hasattr(model_type_input, "value") else "Qwen-Image-Edit-2511" if model_type_val == "Qwen-Image-2512": return get_qwen_image_2512_scheduler_choices(model_path_val) else: return get_qwen_image_scheduler_choices(model_path_val) qwen_image_dit_choices_init = get_dit_choices_func(model_path) qwen_image_vae_choices_init = get_vae_choices_func(model_path) qwen_image_scheduler_choices_init = get_scheduler_choices_func(model_path) # 初始化时检查模型状态，确定下载按钮的初始可见性 def get_initial_download_btn_visibility(choices, model_path_val, model_type_val, base_category): if not choices: return False first_choice = choices[0] from utils.model_utils import check_model_exists, extract_model_name actual_name = extract_model_name(first_choice) exists = check_model_exists(model_path_val, actual_name) return not exists initial_model_type = model_type_input.value if hasattr(model_type_input, "value") else "Qwen-Image-Edit-2511" dit_btn_visible = get_initial_download_btn_visibility(qwen_image_dit_choices_init, model_path, initial_model_type, "dit") vae_btn_visible = get_initial_download_btn_visibility(qwen_image_vae_choices_init, model_path, initial_model_type, "vae") scheduler_btn_visible = get_initial_download_btn_visibility(qwen_image_scheduler_choices_init, model_path, initial_model_type, "scheduler") # Qwen-Image 模型配置 with gr.Column(elem_classes=["diffusion-model-group"]) as qwen_components_group: # Diffusion 模型 with gr.Row(): with gr.Column(scale=5): qwen_image_dit_path_input = gr.Dropdown( label=t("diffusion_model", lang), choices=qwen_image_dit_choices_init, value=qwen_image_dit_choices_init[0] if qwen_image_dit_choices_init else "", allow_custom_value=True, ) with gr.Column(scale=1, min_width=150): qwen_image_dit_download_btn = gr.Button(t("download", lang), visible=dit_btn_visible, size="sm", variant="secondary") qwen_image_dit_download_status = gr.Markdown("", visible=False) # VAE 和 Scheduler with gr.Row(): with gr.Column(scale=1): qwen_image_vae_path_input = gr.Dropdown( label=t("vae", lang), choices=qwen_image_vae_choices_init, value=qwen_image_vae_choices_init[0] if qwen_image_vae_choices_init else "", allow_custom_value=True, ) qwen_image_vae_download_btn = gr.Button(t("download", lang), visible=vae_btn_visible, size="sm", variant="secondary") qwen_image_vae_download_status = gr.Markdown("", visible=False) with gr.Column(scale=1): qwen_image_scheduler_path_input = gr.Dropdown( label=t("scheduler", lang), choices=qwen_image_scheduler_choices_init, value=qwen_image_scheduler_choices_init[0] if qwen_image_scheduler_choices_init else "", allow_custom_value=True, ) qwen_image_scheduler_download_btn = gr.Button(t("download", lang), visible=scheduler_btn_visible, size="sm", variant="secondary") qwen_image_scheduler_download_status = gr.Markdown("", visible=False) # Qwen25-VL 编码器 with gr.Row(): with gr.Column(scale=1): qwen25vl_encoder_choices_init = get_qwen25vl_encoder_choices(model_path) qwen25vl_encoder_path_input = gr.Dropdown( label=t("qwen25vl_encoder", lang), choices=qwen25vl_encoder_choices_init, value=qwen25vl_encoder_choices_init[0] if qwen25vl_encoder_choices_init else "", allow_custom_value=True, ) # 初始化时检查模型状态 from utils.model_utils import check_model_exists, extract_model_name qwen25vl_btn_visible = False if qwen25vl_encoder_choices_init: first_choice = qwen25vl_encoder_choices_init[0] actual_name = extract_model_name(first_choice) qwen25vl_exists = check_model_exists(model_path, actual_name) qwen25vl_btn_visible = not qwen25vl_exists qwen25vl_encoder_download_btn = gr.Button(t("download", lang), visible=qwen25vl_btn_visible, size="sm", variant="secondary") qwen25vl_encoder_download_status = gr.Markdown("", visible=False) # LoRA 组件 lora_choices_init = get_lora_choices(model_path) with gr.Row(): with gr.Column(scale=1): use_lora = gr.Checkbox( label=t("use_lora", lang), value=False, ) lora_path_input = gr.Dropdown( label=t("lora", lang), choices=lora_choices_init, value=lora_choices_init[0] if lora_choices_init and lora_choices_init[0] else "", allow_custom_value=True, visible=False, info=t("lora_info", lang), ) lora_strength = gr.Slider( label=t("lora_strength", lang), minimum=0.0, maximum=10.0, step=0.1, value=1.0, visible=False, info=t("lora_strength_info", lang), ) # LoRA 开关变化时显示/隐藏相关组件 def on_use_lora_change(use_lora_val): return ( gr.update(visible=use_lora_val), # lora_path_input gr.update(visible=use_lora_val), # lora_strength ) use_lora.change( fn=on_use_lora_change, inputs=[use_lora], outputs=[lora_path_input, lora_strength], ) # 当 model_path 变化时更新 LoRA 选择 def update_lora_choices(model_path_val): lora_choices = get_lora_choices(model_path_val) return gr.update(choices=lora_choices, value=lora_choices[0] if lora_choices and lora_choices[0] else "") model_path_input.change( fn=update_lora_choices, inputs=[model_path_input], outputs=[lora_path_input], ) # 模型类型变化时更新选择 def update_choices_on_model_type_change(model_type_val, model_path_val): if model_type_val == "Qwen-Image-2512": dit_choices = get_qwen_image_2512_dit_choices(model_path_val) vae_choices = get_qwen_image_2512_vae_choices(model_path_val) scheduler_choices = get_qwen_image_2512_scheduler_choices(model_path_val) else: dit_choices = get_qwen_image_dit_choices(model_path_val) vae_choices = get_qwen_image_vae_choices(model_path_val) scheduler_choices = get_qwen_image_scheduler_choices(model_path_val) return ( gr.update(choices=dit_choices, value=dit_choices[0] if dit_choices else ""), gr.update(choices=vae_choices, value=vae_choices[0] if vae_choices else ""), gr.update(choices=scheduler_choices, value=scheduler_choices[0] if scheduler_choices else ""), ) # 绑定下载按钮事件 def download_qwen_image_dit(model_path_val, model_name, model_type_val, download_source_val, progress=gr.Progress()): # 从模型名称中提取实际名称 from utils.model_utils import extract_model_name actual_name = extract_model_name(model_name) actual_name_lower = actual_name.lower() # 如果模型名称包含 "qwen_image_2512"，自动使用 qwen_image_2512_dit category # 否则根据 model_type_val 判断 if "qwen_image_2512" in actual_name_lower: category = "qwen_image_2512_dit" get_choices = get_qwen_image_2512_dit_choices model_type_val = "Qwen-Image-2512" # 确保使用正确的模型类型 elif model_type_val == "Qwen-Image-2512": category = "qwen_image_2512_dit" get_choices = get_qwen_image_2512_dit_choices else: category = "qwen_image_dit" get_choices = get_qwen_image_dit_choices return download_model_handler(model_path_val, model_name, category, download_source_val, get_choices_func=get_choices, model_type_val=model_type_val, progress=progress) def download_qwen_image_vae(model_path_val, model_name, download_source_val, progress=gr.Progress()): model_type_val = model_type_input.value if hasattr(model_type_input, "value") else "Qwen-Image-Edit-2511" category = "qwen_image_2512_vae" if model_type_val == "Qwen-Image-2512" else "qwen_image_vae" get_choices = get_qwen_image_2512_vae_choices if model_type_val == "Qwen-Image-2512" else get_qwen_image_vae_choices return download_model_handler(model_path_val, model_name, category, download_source_val, get_choices_func=get_choices, progress=progress) def download_qwen_image_scheduler(model_path_val, model_name, download_source_val, progress=gr.Progress()): model_type_val = model_type_input.value if hasattr(model_type_input, "value") else "Qwen-Image-Edit-2511" category = "qwen_image_2512_scheduler" if model_type_val == "Qwen-Image-2512" else "qwen_image_scheduler" get_choices = get_qwen_image_2512_scheduler_choices if model_type_val == "Qwen-Image-2512" else get_qwen_image_scheduler_choices return download_model_handler(model_path_val, model_name, category, download_source_val, get_choices_func=get_choices, progress=progress) def download_qwen25vl_encoder(model_path_val, model_name, download_source_val, progress=gr.Progress()): return download_model_handler(model_path_val, model_name, "qwen25vl_encoder", download_source_val, get_choices_func=get_qwen25vl_encoder_choices, progress=progress) # 绑定状态更新和下载事件 def get_model_category(model_type_val, base_category): if model_type_val == "Qwen-Image-2512": return f"qwen_image_2512_{base_category}" return f"qwen_image_{base_category}" def update_dit_status(model_path_val, model_name, model_type_val): category = get_model_category(model_type_val, "dit") return update_model_status(model_path_val, model_name, category) def update_vae_status(model_path_val, model_name, model_type_val): category = get_model_category(model_type_val, "vae") return update_model_status(model_path_val, model_name, category) def update_scheduler_status(model_path_val, model_name, model_type_val): category = get_model_category(model_type_val, "scheduler") return update_model_status(model_path_val, model_name, category) qwen_image_dit_path_input.change( fn=lambda mp, mn, mt: update_dit_status(mp, mn, mt), inputs=[model_path_input, qwen_image_dit_path_input, model_type_input], outputs=[qwen_image_dit_download_btn], ) qwen_image_vae_path_input.change( fn=lambda mp, mn, mt: update_vae_status(mp, mn, mt), inputs=[model_path_input, qwen_image_vae_path_input, model_type_input], outputs=[qwen_image_vae_download_btn], ) qwen_image_scheduler_path_input.change( fn=lambda mp, mn, mt: update_scheduler_status(mp, mn, mt), inputs=[model_path_input, qwen_image_scheduler_path_input, model_type_input], outputs=[qwen_image_scheduler_download_btn], ) qwen25vl_encoder_path_input.change( fn=lambda mp, mn: update_model_status(mp, mn, "qwen25vl_encoder"), inputs=[model_path_input, qwen25vl_encoder_path_input], outputs=[qwen25vl_encoder_download_btn], ) qwen_image_dit_download_btn.click( fn=download_qwen_image_dit, inputs=[model_path_input, qwen_image_dit_path_input, model_type_input, download_source_input], outputs=[qwen_image_dit_download_status, qwen_image_dit_download_btn, qwen_image_dit_path_input], ) qwen_image_vae_download_btn.click( fn=download_qwen_image_vae, inputs=[model_path_input, qwen_image_vae_path_input, download_source_input], outputs=[qwen_image_vae_download_status, qwen_image_vae_download_btn, qwen_image_vae_path_input], ) qwen_image_scheduler_download_btn.click( fn=download_qwen_image_scheduler, inputs=[model_path_input, qwen_image_scheduler_path_input, download_source_input], outputs=[qwen_image_scheduler_download_status, qwen_image_scheduler_download_btn, qwen_image_scheduler_path_input], ) qwen25vl_encoder_download_btn.click( fn=download_qwen25vl_encoder, inputs=[model_path_input, qwen25vl_encoder_path_input, download_source_input], outputs=[qwen25vl_encoder_download_status, qwen25vl_encoder_download_btn, qwen25vl_encoder_path_input], ) return { "qwen_image_dit_path_input": qwen_image_dit_path_input, "qwen_image_dit_download_btn": qwen_image_dit_download_btn, "qwen_image_dit_download_status": qwen_image_dit_download_status, "qwen_image_vae_path_input": qwen_image_vae_path_input, "qwen_image_vae_download_btn": qwen_image_vae_download_btn, "qwen_image_vae_download_status": qwen_image_vae_download_status, "qwen_image_scheduler_path_input": qwen_image_scheduler_path_input, "qwen_image_scheduler_download_btn": qwen_image_scheduler_download_btn, "qwen_image_scheduler_download_status": qwen_image_scheduler_download_status, "qwen25vl_encoder_path_input": qwen25vl_encoder_path_input, "qwen25vl_encoder_download_btn": qwen25vl_encoder_download_btn, "qwen25vl_encoder_download_status": qwen25vl_encoder_download_status, "use_lora": use_lora, "lora_path_input": lora_path_input, "lora_strength": lora_strength, "components_group": qwen_components_group, } def build_z_image_turbo_components(model_path, model_path_input, download_source_input, model_type_input, lang="zh"): """构建 Z-Image-Turbo 模型相关组件（用于图片页面） 必须在 Gradio 的 with 块内调用 Args: model_path: 模型路径 model_path_input: 模型路径输入组件 download_source_input: 下载源输入组件 model_type_input: 模型类型输入组件（用于动态更新） lang: 语言代码，默认为 "zh" Returns: dict: 包含所有 Z-Image-Turbo 相关组件的字典 """ # 初始化选择 z_image_turbo_dit_choices_init = get_z_image_turbo_dit_choices(model_path) z_image_turbo_vae_choices_init = get_z_image_turbo_vae_choices(model_path) z_image_turbo_scheduler_choices_init = get_z_image_turbo_scheduler_choices(model_path) qwen3_encoder_choices_init = get_qwen3_encoder_choices(model_path) # Z-Image-Turbo 模型配置 with gr.Column(elem_classes=["diffusion-model-group"], visible=False) as z_image_turbo_components_group: # Diffusion 模型 with gr.Row(): with gr.Column(scale=5): z_image_turbo_dit_path_input = gr.Dropdown( label=t("diffusion_model", lang), choices=z_image_turbo_dit_choices_init, value=z_image_turbo_dit_choices_init[0] if z_image_turbo_dit_choices_init else "", allow_custom_value=True, ) with gr.Column(scale=1, min_width=150): # 计算初始按钮可见性 import os initial_dit_btn_visible = False if z_image_turbo_dit_choices_init: initial_dit_value = z_image_turbo_dit_choices_init[0] from utils.model_utils import check_model_exists, extract_model_name actual_name = extract_model_name(initial_dit_value) initial_dit_btn_visible = not check_model_exists(model_path, actual_name) z_image_turbo_dit_download_btn = gr.Button(t("download", lang), visible=initial_dit_btn_visible, size="sm", variant="secondary") z_image_turbo_dit_download_status = gr.Markdown("", visible=False) # VAE 和 Scheduler with gr.Row(): with gr.Column(scale=1): z_image_turbo_vae_path_input = gr.Dropdown( label=t("vae", lang), choices=z_image_turbo_vae_choices_init, value=z_image_turbo_vae_choices_init[0] if z_image_turbo_vae_choices_init else "", allow_custom_value=True, ) # 计算初始按钮可见性 initial_vae_btn_visible = False if z_image_turbo_vae_choices_init: initial_vae_value = z_image_turbo_vae_choices_init[0] from utils.model_utils import check_model_exists, extract_model_name actual_name = extract_model_name(initial_vae_value) initial_vae_btn_visible = not check_model_exists(model_path, actual_name) z_image_turbo_vae_download_btn = gr.Button(t("download", lang), visible=initial_vae_btn_visible, size="sm", variant="secondary") z_image_turbo_vae_download_status = gr.Markdown("", visible=False) with gr.Column(scale=1): z_image_turbo_scheduler_path_input = gr.Dropdown( label=t("scheduler", lang), choices=z_image_turbo_scheduler_choices_init, value=z_image_turbo_scheduler_choices_init[0] if z_image_turbo_scheduler_choices_init else "", allow_custom_value=True, ) # 计算初始按钮可见性 initial_scheduler_btn_visible = False if z_image_turbo_scheduler_choices_init: initial_scheduler_value = z_image_turbo_scheduler_choices_init[0] from utils.model_utils import check_model_exists, extract_model_name actual_name = extract_model_name(initial_scheduler_value) initial_scheduler_btn_visible = not check_model_exists(model_path, actual_name) z_image_turbo_scheduler_download_btn = gr.Button(t("download", lang), visible=initial_scheduler_btn_visible, size="sm", variant="secondary") z_image_turbo_scheduler_download_status = gr.Markdown("", visible=False) # Qwen3 编码器 with gr.Row(): with gr.Column(scale=1): qwen3_encoder_path_input = gr.Dropdown( label=t("qwen3_encoder", lang), choices=qwen3_encoder_choices_init, value=qwen3_encoder_choices_init[0] if qwen3_encoder_choices_init else "", allow_custom_value=True, ) # 计算初始按钮可见性：检查整个仓库目录是否存在 import os initial_qwen3_btn_visible = False if qwen3_encoder_choices_init: repo_id = "JunHowie/Qwen3-4B-GPTQ-Int4" repo_name = repo_id.split("/")[-1] # "Qwen3-4B-GPTQ-Int4" repo_path = os.path.join(model_path, repo_name) initial_qwen3_btn_visible = not (os.path.exists(repo_path) and os.path.isdir(repo_path)) qwen3_encoder_download_btn = gr.Button(t("download", lang), visible=initial_qwen3_btn_visible, size="sm", variant="secondary") qwen3_encoder_download_status = gr.Markdown("", visible=False) # LoRA 组件 lora_choices_init = get_lora_choices(model_path) with gr.Row(): with gr.Column(scale=1): use_lora = gr.Checkbox( label=t("use_lora", lang), value=False, ) lora_path_input = gr.Dropdown( label=t("lora", lang), choices=lora_choices_init, value=lora_choices_init[0] if lora_choices_init and lora_choices_init[0] else "", allow_custom_value=True, visible=False, info=t("lora_info", lang), ) lora_strength = gr.Slider( label=t("lora_strength", lang), minimum=0.0, maximum=10.0, step=0.1, value=1.0, visible=False, info=t("lora_strength_info", lang), ) # 绑定下载按钮事件 def download_z_image_turbo_dit(model_path_val, model_name, download_source_val, progress=gr.Progress()): return download_model_handler(model_path_val, model_name, "z_image_turbo_dit", download_source_val, get_choices_func=get_z_image_turbo_dit_choices, progress=progress) def download_z_image_turbo_vae(model_path_val, model_name, download_source_val, progress=gr.Progress()): return download_model_handler(model_path_val, model_name, "z_image_turbo_vae", download_source_val, get_choices_func=get_z_image_turbo_vae_choices, progress=progress) def download_z_image_turbo_scheduler(model_path_val, model_name, download_source_val, progress=gr.Progress()): return download_model_handler(model_path_val, model_name, "z_image_turbo_scheduler", download_source_val, get_choices_func=get_z_image_turbo_scheduler_choices, progress=progress) def download_qwen3_encoder(model_path_val, model_name, download_source_val, progress=gr.Progress()): return download_model_handler(model_path_val, model_name, "qwen3_encoder", download_source_val, get_choices_func=get_qwen3_encoder_choices, progress=progress) # 绑定状态更新和下载事件 def update_dit_status(model_path_val, model_name): return update_model_status(model_path_val, model_name, "z_image_turbo_dit") def update_vae_status(model_path_val, model_name): return update_model_status(model_path_val, model_name, "z_image_turbo_vae") def update_scheduler_status(model_path_val, model_name): return update_model_status(model_path_val, model_name, "z_image_turbo_scheduler") def update_qwen3_encoder_status(model_path_val, model_name): return update_model_status(model_path_val, model_name, "qwen3_encoder") z_image_turbo_dit_path_input.change( fn=update_dit_status, inputs=[model_path_input, z_image_turbo_dit_path_input], outputs=[z_image_turbo_dit_download_btn], ) z_image_turbo_vae_path_input.change( fn=update_vae_status, inputs=[model_path_input, z_image_turbo_vae_path_input], outputs=[z_image_turbo_vae_download_btn], ) z_image_turbo_scheduler_path_input.change( fn=update_scheduler_status, inputs=[model_path_input, z_image_turbo_scheduler_path_input], outputs=[z_image_turbo_scheduler_download_btn], ) # Qwen3 编码器状态更新：检查整个仓库目录是否存在 def update_qwen3_encoder_status(model_path_val, model_name): return update_model_status(model_path_val, model_name, "qwen3_encoder") qwen3_encoder_path_input.change( fn=update_qwen3_encoder_status, inputs=[model_path_input, qwen3_encoder_path_input], outputs=[qwen3_encoder_download_btn], ) z_image_turbo_dit_download_btn.click( fn=download_z_image_turbo_dit, inputs=[model_path_input, z_image_turbo_dit_path_input, download_source_input], outputs=[z_image_turbo_dit_download_status, z_image_turbo_dit_download_btn, z_image_turbo_dit_path_input], ) z_image_turbo_vae_download_btn.click( fn=download_z_image_turbo_vae, inputs=[model_path_input, z_image_turbo_vae_path_input, download_source_input], outputs=[z_image_turbo_vae_download_status, z_image_turbo_vae_download_btn, z_image_turbo_vae_path_input], ) z_image_turbo_scheduler_download_btn.click( fn=download_z_image_turbo_scheduler, inputs=[model_path_input, z_image_turbo_scheduler_path_input, download_source_input], outputs=[z_image_turbo_scheduler_download_status, z_image_turbo_scheduler_download_btn, z_image_turbo_scheduler_path_input], ) qwen3_encoder_download_btn.click( fn=download_qwen3_encoder, inputs=[model_path_input, qwen3_encoder_path_input, download_source_input], outputs=[qwen3_encoder_download_status, qwen3_encoder_download_btn, qwen3_encoder_path_input], ) # LoRA 开关变化时显示/隐藏相关组件 def on_use_lora_change(use_lora_val): return ( gr.update(visible=use_lora_val), # lora_path_input gr.update(visible=use_lora_val), # lora_strength ) use_lora.change( fn=on_use_lora_change, inputs=[use_lora], outputs=[lora_path_input, lora_strength], ) # 当 model_path 变化时更新 LoRA 选择 def update_lora_choices(model_path_val): lora_choices = get_lora_choices(model_path_val) return gr.update(choices=lora_choices, value=lora_choices[0] if lora_choices and lora_choices[0] else "") model_path_input.change( fn=update_lora_choices, inputs=[model_path_input], outputs=[lora_path_input], ) return { "z_image_turbo_dit_path_input": z_image_turbo_dit_path_input, "z_image_turbo_dit_download_btn": z_image_turbo_dit_download_btn, "z_image_turbo_dit_download_status": z_image_turbo_dit_download_status, "z_image_turbo_vae_path_input": z_image_turbo_vae_path_input, "z_image_turbo_vae_download_btn": z_image_turbo_vae_download_btn, "z_image_turbo_vae_download_status": z_image_turbo_vae_download_status, "z_image_turbo_scheduler_path_input": z_image_turbo_scheduler_path_input, "z_image_turbo_scheduler_download_btn": z_image_turbo_scheduler_download_btn, "z_image_turbo_scheduler_download_status": z_image_turbo_scheduler_download_status, "qwen3_encoder_path_input": qwen3_encoder_path_input, "qwen3_encoder_download_btn": qwen3_encoder_download_btn, "qwen3_encoder_download_status": qwen3_encoder_download_status, "use_lora": use_lora, "lora_path_input": lora_path_input, "lora_strength": lora_strength, "components_group": z_image_turbo_components_group, }