import os import random from datetime import datetime import gradio as gr from utils.i18n import DEFAULT_LANG, t from utils.model_components import ( build_qwen_image_components, build_z_image_turbo_components, ) from utils.model_utils import HF_AVAILABLE, MS_AVAILABLE, 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""" if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") extension = ".png" if is_image else ".mp4" filename = f"{timestamp}{extension}" return os.path.join(output_dir, filename) def build_image_page( model_path, output_dir, run_inference, lang=DEFAULT_LANG, ): # 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_image", 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) # 任务类型选择（前端显示大写，传递值小写） task_type_input = gr.Radio( label=t("task_type", lang), choices=[("I2I", "i2i"), ("T2I", "t2i")], value="i2i", info=t("task_type_info", lang), elem_classes=["horizontal-radio"], ) # 模型类型选择（根据任务类型动态更新） model_type_input = gr.Radio( label=t("model_type", lang), choices=["Qwen-Image-Edit-2511"], value="Qwen-Image-Edit-2511", info=t("model_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"], ) # 构建 Qwen 模型组件（默认显示） qwen_image_components = build_qwen_image_components(model_path, model_path_input, download_source_input, model_type_input, lang) qwen_image_dit_path_input = qwen_image_components["qwen_image_dit_path_input"] qwen_image_vae_path_input = qwen_image_components["qwen_image_vae_path_input"] qwen_image_scheduler_path_input = qwen_image_components["qwen_image_scheduler_path_input"] qwen25vl_encoder_path_input = qwen_image_components["qwen25vl_encoder_path_input"] qwen_image_dit_download_btn = qwen_image_components["qwen_image_dit_download_btn"] qwen_image_vae_download_btn = qwen_image_components["qwen_image_vae_download_btn"] qwen_image_scheduler_download_btn = qwen_image_components["qwen_image_scheduler_download_btn"] qwen25vl_encoder_download_btn = qwen_image_components["qwen25vl_encoder_download_btn"] qwen_image_use_lora_input = qwen_image_components["use_lora"] qwen_image_lora_path_input = qwen_image_components["lora_path_input"] qwen_image_lora_strength_input = qwen_image_components["lora_strength"] # 构建 Z-Image-Turbo 模型组件（默认隐藏） z_image_turbo_components = build_z_image_turbo_components(model_path, model_path_input, download_source_input, model_type_input, lang) z_image_turbo_dit_path_input = z_image_turbo_components["z_image_turbo_dit_path_input"] z_image_turbo_vae_path_input = z_image_turbo_components["z_image_turbo_vae_path_input"] z_image_turbo_scheduler_path_input = z_image_turbo_components["z_image_turbo_scheduler_path_input"] qwen3_encoder_path_input = z_image_turbo_components["qwen3_encoder_path_input"] z_image_turbo_dit_download_btn = z_image_turbo_components["z_image_turbo_dit_download_btn"] z_image_turbo_vae_download_btn = z_image_turbo_components["z_image_turbo_vae_download_btn"] z_image_turbo_scheduler_download_btn = z_image_turbo_components["z_image_turbo_scheduler_download_btn"] qwen3_encoder_download_btn = z_image_turbo_components["qwen3_encoder_download_btn"] z_image_turbo_use_lora_input = z_image_turbo_components["use_lora"] z_image_turbo_lora_path_input = z_image_turbo_components["lora_path_input"] z_image_turbo_lora_strength_input = z_image_turbo_components["lora_strength"] # 获取组件容器以便控制显示/隐藏 qwen_components_group = qwen_image_components.get("components_group", None) z_image_turbo_components_group = z_image_turbo_components.get("components_group", None) with gr.Accordion(t("input_params", lang), open=True, elem_classes=["input-params"]): image_files = gr.File( label=t("input_image", lang), file_count="multiple", file_types=["image"], height=150, interactive=True, ) image_gallery = gr.Gallery( label=t("image_preview", lang), columns=4, rows=2, height=200, object_fit="contain", show_label=True, ) image_path = gr.Textbox(label=t("image_path", lang), visible=False) aspect_ratio = gr.Dropdown( label=t("aspect_ratio", lang), choices=["1:1", "16:9", "9:16", "4:3", "3:4"], value="1:1", visible=True, info=t("aspect_ratio_info", lang), ) # 任务类型变化处理函数 def on_task_type_change(task_type_val): if task_type_val == "t2i": # t2i 模式：显示 Qwen-Image-2512 和 Z-Image-Turbo，隐藏图片输入和宽高比 return ( gr.update(choices=["Z-Image-Turbo", "Qwen-Image-2512"], value="Z-Image-Turbo"), # model_type_input gr.update(visible=False), # image_files gr.update(visible=False), # image_gallery gr.update(value=""), # image_path gr.update(visible=False), # aspect_ratio ) else: # i2i 模式：显示 Qwen-Image-Edit-2511，显示图片输入和宽高比 return ( gr.update(choices=["Qwen-Image-Edit-2511"], value="Qwen-Image-Edit-2511"), # model_type_input gr.update(visible=True), # image_files gr.update(visible=True), # image_gallery gr.update(), # image_path (保持不变) gr.update(visible=True), # aspect_ratio ) # 绑定任务类型变化事件 task_type_input.change( fn=on_task_type_change, inputs=[task_type_input], outputs=[model_type_input, image_files, image_gallery, image_path, aspect_ratio], ) # 模型类型变化处理函数 def on_model_type_change(model_type_val, model_path_val): # 控制组件组显示/隐藏 show_qwen = model_type_val in ["Qwen-Image-2512", "Qwen-Image-Edit-2511"] show_z_image_turbo = model_type_val == "Z-Image-Turbo" # 导入更新函数 from utils.model_handlers import update_model_status from utils.model_utils import extract_model_name # 更新模型选择 if model_type_val == "Qwen-Image-2512": from utils.model_choices import get_qwen_image_2512_dit_choices, get_qwen_image_2512_scheduler_choices, get_qwen_image_2512_vae_choices 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) # 更新下载按钮状态 from utils.model_choices import get_qwen25vl_encoder_choices qwen25vl_encoder_choices = get_qwen25vl_encoder_choices(model_path_val) dit_btn_update = update_model_status(model_path_val, extract_model_name(dit_choices[0]) if dit_choices else "", "qwen_image_2512_dit") vae_btn_update = update_model_status(model_path_val, extract_model_name(vae_choices[0]) if vae_choices else "", "qwen_image_2512_vae") scheduler_btn_update = update_model_status(model_path_val, extract_model_name(scheduler_choices[0]) if scheduler_choices else "", "qwen_image_2512_scheduler") qwen25vl_btn_update = update_model_status(model_path_val, extract_model_name(qwen25vl_encoder_choices[0]) if qwen25vl_encoder_choices else "", "qwen25vl_encoder") return ( gr.update(visible=show_qwen), # qwen_components_group gr.update(visible=show_z_image_turbo), # z_image_turbo_components_group gr.update(choices=dit_choices, value=dit_choices[0] if dit_choices else ""), # qwen_image_dit_path_input gr.update(choices=vae_choices, value=vae_choices[0] if vae_choices else ""), # qwen_image_vae_path_input gr.update(choices=scheduler_choices, value=scheduler_choices[0] if scheduler_choices else ""), # qwen_image_scheduler_path_input gr.update(), # z_image_turbo_dit_path_input (保持不变) gr.update(), # z_image_turbo_vae_path_input (保持不变) gr.update(), # z_image_turbo_scheduler_path_input (保持不变) gr.update(), # qwen3_encoder_path_input (保持不变) gr.update(), # aspect_ratio (保持不变) dit_btn_update, # qwen_image_dit_download_btn vae_btn_update, # qwen_image_vae_download_btn scheduler_btn_update, # qwen_image_scheduler_download_btn qwen25vl_btn_update, # qwen25vl_encoder_download_btn gr.update(), # z_image_turbo_dit_download_btn gr.update(), # z_image_turbo_vae_download_btn gr.update(), # z_image_turbo_scheduler_download_btn gr.update(), # qwen3_encoder_download_btn ) elif model_type_val == "Z-Image-Turbo": from utils.model_choices import get_qwen3_encoder_choices, get_z_image_turbo_dit_choices, get_z_image_turbo_scheduler_choices, get_z_image_turbo_vae_choices dit_choices = get_z_image_turbo_dit_choices(model_path_val) vae_choices = get_z_image_turbo_vae_choices(model_path_val) scheduler_choices = get_z_image_turbo_scheduler_choices(model_path_val) qwen3_encoder_choices = get_qwen3_encoder_choices(model_path_val) # 更新下载按钮状态 dit_btn_update = update_model_status(model_path_val, extract_model_name(dit_choices[0]) if dit_choices else "", "z_image_turbo_dit") vae_btn_update = update_model_status(model_path_val, extract_model_name(vae_choices[0]) if vae_choices else "", "z_image_turbo_vae") scheduler_btn_update = update_model_status(model_path_val, extract_model_name(scheduler_choices[0]) if scheduler_choices else "", "z_image_turbo_scheduler") qwen3_btn_update = update_model_status(model_path_val, extract_model_name(qwen3_encoder_choices[0]) if qwen3_encoder_choices else "", "qwen3_encoder") return ( gr.update(visible=show_qwen), # qwen_components_group gr.update(visible=show_z_image_turbo), # z_image_turbo_components_group gr.update(), # qwen_image_dit_path_input (保持不变) gr.update(), # qwen_image_vae_path_input (保持不变) gr.update(), # qwen_image_scheduler_path_input (保持不变) gr.update(choices=dit_choices, value=dit_choices[0] if dit_choices else ""), # z_image_turbo_dit_path_input gr.update(choices=vae_choices, value=vae_choices[0] if vae_choices else ""), # z_image_turbo_vae_path_input gr.update(choices=scheduler_choices, value=scheduler_choices[0] if scheduler_choices else ""), # z_image_turbo_scheduler_path_input gr.update(choices=qwen3_encoder_choices, value=qwen3_encoder_choices[0] if qwen3_encoder_choices else ""), # qwen3_encoder_path_input gr.update(visible=True), # aspect_ratio gr.update(), # qwen_image_dit_download_btn gr.update(), # qwen_image_vae_download_btn gr.update(), # qwen_image_scheduler_download_btn gr.update(), # qwen25vl_encoder_download_btn dit_btn_update, # z_image_turbo_dit_download_btn vae_btn_update, # z_image_turbo_vae_download_btn scheduler_btn_update, # z_image_turbo_scheduler_download_btn qwen3_btn_update, # qwen3_encoder_download_btn ) else: # Qwen-Image-Edit-2511 from utils.model_choices import get_qwen_image_dit_choices, get_qwen_image_scheduler_choices, get_qwen_image_vae_choices 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) # 更新下载按钮状态 from utils.model_choices import get_qwen25vl_encoder_choices qwen25vl_encoder_choices = get_qwen25vl_encoder_choices(model_path_val) dit_btn_update = update_model_status(model_path_val, extract_model_name(dit_choices[0]) if dit_choices else "", "qwen_image_dit") vae_btn_update = update_model_status(model_path_val, extract_model_name(vae_choices[0]) if vae_choices else "", "qwen_image_vae") scheduler_btn_update = update_model_status(model_path_val, extract_model_name(scheduler_choices[0]) if scheduler_choices else "", "qwen_image_scheduler") qwen25vl_btn_update = update_model_status(model_path_val, extract_model_name(qwen25vl_encoder_choices[0]) if qwen25vl_encoder_choices else "", "qwen25vl_encoder") return ( gr.update(visible=show_qwen), # qwen_components_group gr.update(visible=show_z_image_turbo), # z_image_turbo_components_group gr.update(choices=dit_choices, value=dit_choices[0] if dit_choices else ""), # qwen_image_dit_path_input gr.update(choices=vae_choices, value=vae_choices[0] if vae_choices else ""), # qwen_image_vae_path_input gr.update(choices=scheduler_choices, value=scheduler_choices[0] if scheduler_choices else ""), # qwen_image_scheduler_path_input gr.update(), # z_image_turbo_dit_path_input (保持不变) gr.update(), # z_image_turbo_vae_path_input (保持不变) gr.update(), # z_image_turbo_scheduler_path_input (保持不变) gr.update(), # qwen3_encoder_path_input (保持不变) gr.update(), # aspect_ratio dit_btn_update, # qwen_image_dit_download_btn vae_btn_update, # qwen_image_vae_download_btn scheduler_btn_update, # qwen_image_scheduler_download_btn qwen25vl_btn_update, # qwen25vl_encoder_download_btn gr.update(), # z_image_turbo_dit_download_btn gr.update(), # z_image_turbo_vae_download_btn gr.update(), # z_image_turbo_scheduler_download_btn gr.update(), # qwen3_encoder_download_btn ) # 绑定模型类型变化事件 model_type_input.change( fn=on_model_type_change, inputs=[model_type_input, model_path_input], outputs=[ qwen_components_group, z_image_turbo_components_group, qwen_image_dit_path_input, qwen_image_vae_path_input, qwen_image_scheduler_path_input, z_image_turbo_dit_path_input, z_image_turbo_vae_path_input, z_image_turbo_scheduler_path_input, qwen3_encoder_path_input, aspect_ratio, qwen_image_dit_download_btn, qwen_image_vae_download_btn, qwen_image_scheduler_download_btn, qwen25vl_encoder_download_btn, z_image_turbo_dit_download_btn, z_image_turbo_vae_download_btn, z_image_turbo_scheduler_download_btn, qwen3_encoder_download_btn, ], ) def update_image_path_and_gallery(files): if files is None or len(files) == 0: return "", [] paths = [f.name if hasattr(f, "name") else f for f in files] return ",".join(paths), paths image_files.change( fn=update_image_path_and_gallery, inputs=[image_files], outputs=[image_path, image_gallery], ) with gr.Row(): prompt = gr.Textbox( label=t("prompt", lang), lines=3, placeholder=t("prompt_placeholder", lang), max_lines=5, ) negative_prompt = gr.Textbox( label=t("negative_prompt", lang), lines=3, placeholder=t("negative_prompt_placeholder", lang), max_lines=5, value="", ) with gr.Row(): seed = gr.Slider( label=t("random_seed", lang), minimum=0, maximum=MAX_NUMPY_SEED, step=1, value=generate_random_seed(), ) infer_steps = gr.Slider( label=t("infer_steps", lang), minimum=1, maximum=100, step=1, value=8, info=t("infer_steps_image_info", lang), ) # aspect_ratio 已在上面定义，这里不需要重复定义 cfg_scale = gr.Slider( label=t("cfg_scale", lang), minimum=1, maximum=10, step=1, value=1, info=t("cfg_scale_info", lang), ) # 模型类型变化时更新 cfg_scale 和 infer_steps（Z-Image-Turbo 默认值） def on_model_type_change_for_defaults(model_type_val): if model_type_val == "Z-Image-Turbo": return ( gr.update(value=9), # infer_steps gr.update(value=1), # cfg_scale ) else: return ( gr.update(), # infer_steps (保持不变) gr.update(), # cfg_scale (保持不变) ) # 绑定模型类型变化事件，更新默认值 model_type_input.change( fn=on_model_type_change_for_defaults, inputs=[model_type_input], outputs=[infer_steps, cfg_scale], ) save_result_path = gr.Textbox( label=t("output_image_path", lang), value=generate_unique_filename(output_dir, is_image=True), info=t("output_image_path_info", lang), visible=False, ) # 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_image = gr.Image( label=t("output_image", lang), height=600, show_label=False, ) infer_btn = gr.Button( t("generate_image", lang), variant="primary", size="lg", elem_classes=["generate-btn"], ) def run_inference_wrapper( prompt_val, negative_prompt_val, save_result_path_val, infer_steps_val, seed_val, cfg_scale_val, model_path_val, model_type_val, task_type_val, image_path_val, qwen_image_dit_path_val, qwen_image_vae_path_val, qwen_image_scheduler_path_val, qwen25vl_encoder_path_val, z_image_turbo_dit_path_val, z_image_turbo_vae_path_val, z_image_turbo_scheduler_path_val, qwen3_encoder_path_val, aspect_ratio_val, qwen_image_use_lora_val, qwen_image_lora_path_val, qwen_image_lora_strength_val, z_image_turbo_use_lora_val, z_image_turbo_lora_path_val, z_image_turbo_lora_strength_val, ): # 根据模型类型传递不同的参数 if model_type_val == "Z-Image-Turbo": result = run_inference( prompt=prompt_val, negative_prompt=negative_prompt_val, save_result_path=save_result_path_val, infer_steps=infer_steps_val, seed=seed_val, cfg_scale=cfg_scale_val, model_path_input=model_path_val, model_type_input=model_type_val, task_type_input=task_type_val, image_path=None, # Z-Image-Turbo 只支持 t2i，不需要图片 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=z_image_turbo_dit_path_val, z_image_vae_path_input=z_image_turbo_vae_path_val, z_image_scheduler_path_input=z_image_turbo_scheduler_path_val, qwen3_encoder_path_input=qwen3_encoder_path_val, aspect_ratio=aspect_ratio_val, # Z-Image-Turbo 需要 aspect_ratio use_lora=z_image_turbo_use_lora_val, lora_path=z_image_turbo_lora_path_val, lora_strength=z_image_turbo_lora_strength_val, ) else: result = run_inference( prompt=prompt_val, negative_prompt=negative_prompt_val, save_result_path=save_result_path_val, infer_steps=infer_steps_val, seed=seed_val, cfg_scale=cfg_scale_val, model_path_input=model_path_val, model_type_input=model_type_val, task_type_input=task_type_val, image_path=image_path_val, qwen_image_dit_path_input=qwen_image_dit_path_val, qwen_image_vae_path_input=qwen_image_vae_path_val, qwen_image_scheduler_path_input=qwen_image_scheduler_path_val, qwen25vl_encoder_path_input=qwen25vl_encoder_path_val, 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=aspect_ratio_val, use_lora=qwen_image_use_lora_val, lora_path=qwen_image_lora_path_val, lora_strength=qwen_image_lora_strength_val, ) return gr.update(value=result) infer_btn.click( fn=run_inference_wrapper, inputs=[ prompt, negative_prompt, save_result_path, infer_steps, seed, cfg_scale, model_path_input, model_type_input, task_type_input, image_path, qwen_image_dit_path_input, qwen_image_vae_path_input, qwen_image_scheduler_path_input, qwen25vl_encoder_path_input, z_image_turbo_dit_path_input, z_image_turbo_vae_path_input, z_image_turbo_scheduler_path_input, qwen3_encoder_path_input, aspect_ratio, qwen_image_use_lora_input, qwen_image_lora_path_input, qwen_image_lora_strength_input, z_image_turbo_use_lora_input, z_image_turbo_lora_path_input, z_image_turbo_lora_strength_input, ], outputs=[output_image], )