diff --git a/app.py b/app.py index d02dde18..aec0977a 100644 --- a/app.py +++ b/app.py @@ -29,7 +29,19 @@ class UploadTarget(enum.Enum): PERSONAL_PROFILE = 'Personal Profile' LORA_LIaBRARY = 'LoRA Library' +# utils +def concatenate_images(images): + heights = [img.shape[0] for img in images] + max_width = sum([img.shape[1] for img in images]) + concatenated_image = np.zeros((max(heights), max_width, 3), dtype=np.uint8) + x_offset = 0 + for img in images: + concatenated_image[0:img.shape[0], x_offset:x_offset + img.shape[1], :] = img + x_offset += img.shape[1] + return concatenated_image + +# hooks def update_cloth(style_index): style = styles[style_index] prompts = [] @@ -44,10 +56,12 @@ def update_cloth(style_index): style['add_prompt_style']) multiplier_human = style['multiplier_human'] prompts.append(style['cloth_name']) - return gr.Radio.update(choices=prompts, - value=prompts[0], visible=True), gr.Textbox.update(value=example_prompt), gr.Slider.update(value=multiplier_human) - - + + + + return gr.Radio.update(choices=prompts, value=prompts[0], visible=True), \ + gr.Textbox.update(value=example_prompt), gr.Slider.update(value=multiplier_human) + def update_prompt(style_index, cloth_index): style = styles[style_index] if style_index == 0: @@ -71,18 +85,6 @@ def update_optional_styles(base_model_index): optional_styles = '\n'.join(style_list) return gr.Textbox.update(value=optional_styles) -def concatenate_images(images): - heights = [img.shape[0] for img in images] - max_width = sum([img.shape[1] for img in images]) - - concatenated_image = np.zeros((max(heights), max_width, 3), dtype=np.uint8) - x_offset = 0 - for img in images: - concatenated_image[0:img.shape[0], x_offset:x_offset + img.shape[1], :] = img - x_offset += img.shape[1] - return concatenated_image - - def train_lora_fn(base_model_path=None, revision=None, sub_path=None, output_img_dir=None, work_dir=None, ensemble=True, enhance_lora=False, photo_num=0): validation_prompt, _ = get_popular_prompts(output_img_dir) torch.cuda.empty_cache() @@ -163,7 +165,6 @@ def train_lora_fn(base_model_path=None, revision=None, sub_path=None, output_img f'--lora_text_encoder_alpha=32 ' f'--resume_from_checkpoint="fromfacecommon"') - def generate_pos_prompt(style_model, prompt_cloth): if style_model == styles[0]['name'] or style_model is None: pos_prompt = pos_prompt_with_cloth.format(prompt_cloth) @@ -175,14 +176,13 @@ def generate_pos_prompt(style_model, prompt_cloth): pos_prompt = pos_prompt_with_style.format(matched['add_prompt_style']) return pos_prompt - def launch_pipeline(uuid, pos_prompt, neg_prompt=None, base_model_index=None, user_model=None, num_images=1, - lora_file=None, + lora_choice=None, style_model=None, multiplier_style=0.25, multiplier_human=0.85, @@ -197,15 +197,16 @@ def launch_pipeline(uuid, # Check base model if base_model_index == None: - raise gr.Error('请选择基模型(Please select the base model)！') - + raise gr.Error('请选择基模型(Please select the base model)!') # Check output model if not user_model: - raise gr.Error('请选择产出模型(Please select the output model)！') - + raise gr.Error('请选择产出模型(Please select the output model)!') + # Check lora choice + if lora_choice == None: + raise gr.Error('请选择LORA模型(Please select the LORA model)!') # Check style model - if style_model == None: - raise gr.Error('请选择风格模型(Please select the style model)！') + if style_model == None and lora_choice == 'preset': + raise gr.Error('请选择风格模型(Please select the style model)!') base_model = base_models[base_model_index]['model_id'] revision = base_models[base_model_index]['revision'] @@ -215,7 +216,7 @@ def launch_pipeline(uuid, before_done_count = inference_done_count style_model = styles[style_model]['name'] - if lora_file is None or lora_file.name is None: + if lora_choice == 'preset': if style_model == styles[0]['name']: style_model_path = None else: @@ -228,9 +229,9 @@ def launch_pipeline(uuid, else: print(f'uuid: {uuid}') temp_lora_dir = f"/tmp/{uuid}/temp_lora" - file_name = os.path.basename(lora_file.name) - print(os.path.basename(lora_file.name).split('.')[-1], os.path.join(temp_lora_dir, file_name)) - if os.path.basename(lora_file.name).split('.')[-1] != 'safetensors' or not os.path.exists(os.path.join(temp_lora_dir, file_name)): + file_name = lora_choice + print(lora_choice.split('.')[-1], os.path.join(temp_lora_dir, file_name)) + if lora_choice.split('.')[-1] != 'safetensors' or not os.path.exists(os.path.join(temp_lora_dir, file_name)): raise ValueError(f'Invalid lora file: {lora_file.name}') style_model_path = os.path.join(temp_lora_dir, file_name) @@ -265,7 +266,7 @@ def launch_pipeline(uuid, train_file = os.path.join(lora_model_path, 'pytorch_lora_weights.bin') if not os.path.exists(train_file): - raise gr.Error('您还没有进行形象定制，请先进行训练。(Training is required before inference.)') + raise gr.Error('您还没有进行形象定制, 请先进行训练。(Training is required before inference.)') gen_portrait = GenPortrait(pose_model_path, pose_image, use_depth_control, pos_prompt, neg_prompt, style_model_path, multiplier_style, multiplier_human, use_main_model, @@ -282,7 +283,7 @@ def launch_pipeline(uuid, if not is_processing: cur_done_count = inference_done_count to_wait = before_queue_size - (cur_done_count - before_done_count) - yield ["排队等待资源中，前方还有{}个生成任务, 预计需要等待{}分钟...".format(to_wait, to_wait * 2.5), + yield ["排队等待资源中, 前方还有{}个生成任务, 预计需要等待{}分钟...".format(to_wait, to_wait * 2.5), None] else: yield ["生成中, 请耐心等待(Generating)...", None] @@ -292,14 +293,34 @@ def launch_pipeline(uuid, outputs_RGB = [] for out_tmp in outputs: outputs_RGB.append(cv2.cvtColor(out_tmp, cv2.COLOR_BGR2RGB)) - image_path = './lora_result.png' + + save_dir = os.path.join('/tmp', uuid, 'inference_result', base_model, user_model) + if lora_choice == 'preset': + save_dir = os.path.join(save_dir, 'style_' + style_model) + else: + save_dir = os.path.join(save_dir, 'lora_' + os.path.basename(lora_choice).split('.')[0]) + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + # use single to save outputs + if not os.path.exists(os.path.join(save_dir, 'single')): + os.makedirs(os.path.join(save_dir, 'single')) + for img in outputs: + # count the number of images in the folder + num = len(os.listdir(os.path.join(save_dir, 'single'))) + cv2.imwrite(os.path.join(save_dir, 'single', str(num) + '.png'), img) + if len(outputs) > 0: result = concatenate_images(outputs) + if not os.path.exists(os.path.join(save_dir, 'concat')): + os.makedirs(os.path.join(save_dir, 'concat')) + num = len(os.listdir(os.path.join(save_dir, 'concat'))) + image_path = os.path.join(save_dir, 'concat', str(num) + '.png') cv2.imwrite(image_path, result) - yield ["生成完毕(Generation done)！", outputs_RGB] + yield ["生成完毕(Generation done)!", outputs_RGB] else: - yield ["生成失败，请重试(Generation failed, please retry)！", outputs_RGB] + yield ["生成失败, 请重试(Generation failed, please retry)!", outputs_RGB] def launch_pipeline_inpaint(uuid, @@ -318,11 +339,11 @@ def launch_pipeline_inpaint(uuid, # Check base model if base_model_index == None: - raise gr.Error('请选择基模型(Please select the base model)！') + raise gr.Error('请选择基模型(Please select the base model)!') # Check output model if not user_model: - raise gr.Error('请选择产出模型(Please select the output model)！') + raise gr.Error('请选择产出模型(Please select the output model)!') if not uuid: if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio': @@ -360,7 +381,7 @@ def launch_pipeline_inpaint(uuid, if not is_processing: cur_done_count = inference_done_count to_wait = before_queue_size - (cur_done_count - before_done_count) - yield ["排队等待资源中，前方还有{}个生成任务, 预计需要等待{}分钟...".format(to_wait, to_wait * 2.5), + yield ["排队等待资源中, 前方还有{}个生成任务, 预计需要等待{}分钟...".format(to_wait, to_wait * 2.5), None] else: yield ["生成中, 请耐心等待(Generating)...", None] @@ -375,9 +396,9 @@ def launch_pipeline_inpaint(uuid, result = concatenate_images(outputs) cv2.imwrite(image_path, result) - yield ["生成完毕(Generation done)！", outputs_RGB] + yield ["生成完毕(Generation done)!", outputs_RGB] else: - yield ["生成失败，请重试(Generation failed, please retry)！", outputs_RGB] + yield ["生成失败, 请重试(Generation failed, please retry)!", outputs_RGB] class Trainer: @@ -399,11 +420,11 @@ def run( # Check Instance Valid if instance_images is None: - raise gr.Error('您需要上传训练图片(Please upload photos)！') + raise gr.Error('您需要上传训练图片(Please upload photos)!') # Check output model name if not output_model_name: - raise gr.Error('请指定产出模型的名称(Please specify the output model name)！') + raise gr.Error('请指定产出模型的名称(Please specify the output model name)!') # Limit input Image if len(instance_images) > 20: @@ -450,12 +471,12 @@ def run( enhance_lora=enhance_lora, photo_num=len(instance_images)) - message = f'训练已经完成！请切换至 [形象体验] 标签体验模型效果(Training done, please switch to the inference tab to generate photos.)' + message = f'训练已经完成!请切换至 [形象体验] 标签体验模型效果(Training done, please switch to the inference tab to generate photos.)' print(message) return message -def flash_model_list(uuid, base_model_index, lora_file:gr.File): +def flash_model_list(uuid, base_model_index, lora_choice:gr.Dropdown): if base_model_index is None: return gr.Radio.update(visible=False), gr.Dropdown.update(visible=False) @@ -470,8 +491,22 @@ def flash_model_list(uuid, base_model_index, lora_file:gr.File): folder_path = f"/tmp/{uuid}/{base_model_path}" folder_list = [] + lora_save_path = f"/tmp/{uuid}/temp_lora" + if not os.path.exists(lora_save_path): + lora_list = ['preset'] + else: + lora_list = sorted(os.listdir(lora_save_path)) + lora_list = ["preset"] + lora_list + if not os.path.exists(folder_path): - return gr.Radio.update(choices=[]),gr.Dropdown.update(choices=style_list) + if lora_choice == 'preset': + return gr.Radio.update(choices=[]), \ + gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True), gr.Radio.update(visible=True), \ + gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True) + else: + return gr.Radio.update(choices=[]), \ + gr.Dropdown.update(visible=False), gr.Radio.update(visible=False), \ + gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True) else: files = os.listdir(folder_path) for file in files: @@ -480,16 +515,21 @@ def flash_model_list(uuid, base_model_index, lora_file:gr.File): file_lora_path = f"{file_path}/pytorch_lora_weights.bin" if os.path.exists(file_lora_path): folder_list.append(file) - - if lora_file is not None and lora_file.name is not None: - return gr.Radio.update(choices=folder_list), gr.Dropdown.update(choices=style_list, visible=False) - return gr.Radio.update(choices=folder_list), gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True) + + if lora_choice == 'preset': + return gr.Radio.update(choices=folder_list), \ + gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True), gr.Radio.update(visible=True), \ + gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True) + else: + return gr.Radio.update(choices=folder_list), \ + gr.Dropdown.update(visible=False), gr.Radio.update(visible=False), \ + gr.Dropdown.update(choices=lora_list, visible=True), gr.File.update(visible=True) def update_output_model(uuid, base_model_index): # Check base model if base_model_index == None: - raise gr.Error('请选择基模型(Please select the base model)！') + raise gr.Error('请选择基模型(Please select the base model)!') base_model_path = base_models[base_model_index]['model_id'] @@ -526,16 +566,88 @@ def upload_lora_file(uuid, lora_file): uuid = 'qw' print("uuid: ", uuid) temp_lora_dir = f"/tmp/{uuid}/temp_lora" - os.makedirs(temp_lora_dir, exist_ok=True) + if not os.path.exists(temp_lora_dir): + os.makedirs(temp_lora_dir) shutil.copy(lora_file.name, temp_lora_dir) filename = os.path.basename(lora_file.name) newfilepath = os.path.join(temp_lora_dir, filename) print("newfilepath: ", newfilepath) - style_model = gr.Dropdown.update(visible=False) + + lora_list = sorted(os.listdir(temp_lora_dir)) + lora_list = ["preset"] + lora_list + + return gr.Dropdown.update(choices=lora_list, value=filename) + +def clear_lora_file(uuid, lora_file): + if not uuid: + if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio': + return "请登陆后使用! (Please login first)" + else: + uuid = 'qw' + + return gr.Dropdown.update(value="preset") + +def change_lora_choice(uuid, lora_choice, base_model_index): + if not uuid: + if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio': + return "请登陆后使用! (Please login first)" + else: + uuid = 'qw' + + style_list = base_models[base_model_index]['style_list'] + + if lora_choice == 'preset': + style_model = gr.Dropdown.update(choices=style_list, value=style_list[0], visible=True) + else: + style_model = gr.Dropdown.update(choices=style_list, value=style_list[0], visible=False) cloth_style = gr.Radio.update(visible=False) return style_model, cloth_style +def deal_history(uuid, base_model_index=None , user_model=None, lora_choice=None, style_model=None, deal_type="load"): + if not uuid: + if os.getenv("MODELSCOPE_ENVIRONMENT") == 'studio': + return "请登陆后使用! (Please login first)" + else: + uuid = 'qw' + + if base_model_index is None: + raise gr.Error('请选择基模型(Please select the base model)!') + if user_model is None: + raise gr.Error('请选择用户模型(Please select the user model)!') + if lora_choice is None: + raise gr.Error('请选择LoRa文件(Please select the LoRa file)!') + if style_model is None and lora_choice == 'preset': + raise gr.Error('请选择风格(Please select the style)!') + + base_model = base_models[base_model_index]['model_id'] + style_model = styles[style_model]['name'] + save_dir = os.path.join('/tmp', uuid, 'inference_result', base_model, user_model) + if lora_choice == 'preset': + save_dir = os.path.join(save_dir, 'style_' + style_model) + else: + save_dir = os.path.join(save_dir, 'lora_' + os.path.basename(lora_choice).split('.')[0]) + + if not os.path.exists(save_dir): + return gr.Gallery.update(value=[], visible=True), gr.Gallery.update(value=[], visible=True) + + if deal_type == "load": + single_dir = os.path.join(save_dir, 'single') + concat_dir = os.path.join(save_dir, 'concat') + single_imgs = [] + concat_imgs = [] + if os.path.exists(single_dir): + single_imgs = sorted(os.listdir(single_dir)) + single_imgs = [os.path.join(single_dir, img) for img in single_imgs] + if os.path.exists(concat_dir): + concat_imgs = sorted(os.listdir(concat_dir)) + concat_imgs = [os.path.join(concat_dir, img) for img in concat_imgs] + + return gr.Gallery.update(value=single_imgs, visible=True), gr.Gallery.update(value=concat_imgs, visible=True) + elif deal_type == "delete": + shutil.rmtree(save_dir) + return gr.Gallery.update(value=[], visible=True), gr.Gallery.update(value=[], visible=True) + def train_input(): trainer = Trainer() @@ -573,9 +685,9 @@ def train_input(): queue=False) gr.Markdown(''' - - Step 1. 上传计划训练的图片，3~10张头肩照（注意：请避免图片中出现多人脸、脸部遮挡等情况，否则可能导致效果异常） - - Step 2. 点击 [开始训练] ，启动形象定制化训练，约需15分钟，请耐心等待～ - - Step 3. 切换至 [形象体验] ，生成你的风格照片 + - Step 1. 上传计划训练的图片, 3~10张头肩照(注意: 请避免图片中出现多人脸、脸部遮挡等情况, 否则可能导致效果异常) + - Step 2. 点击 [开始训练] , 启动形象定制化训练, 约需15分钟, 请耐心等待～ + - Step 3. 切换至 [形象体验] , 生成你的风格照片 ''') gr.Markdown(''' - Step 1. Upload 3-10 headshot photos of yours (Note: avoid photos with multiple faces or face obstruction, which may lead to non-ideal result). @@ -585,30 +697,30 @@ def train_input(): with gr.Box(): with gr.Row(): - ensemble = gr.Checkbox(label='人物LoRA融合（Ensemble）', value=False) - enhance_lora = gr.Checkbox(label='LoRA增强（LoRA-Enhancement）', value=False) + ensemble = gr.Checkbox(label='人物LoRA融合(Ensemble)', value=False) + enhance_lora = gr.Checkbox(label='LoRA增强(LoRA-Enhancement)', value=False) gr.Markdown( ''' - - 人物LoRA融合（Ensemble）：选择训练中几个最佳人物LoRA融合。提升相似度或在艺术照生成模式下建议勾选 - Allow fusion of multiple LoRAs during training. Recommended for enhanced-similarity or using with Inpaint mode. - - LoRA增强（LoRA-Enhancement）：扩大LoRA规模，生成图片更贴近用户，至少5张以上多图训练或者艺术照生成模式建议勾选 - Boost scale of LoRA to enhance output resemblance with input. Recommended for training with more than 5 pics or using with Inpaint mode. + - 人物LoRA融合(Ensemble): 选择训练中几个最佳人物LoRA融合。提升相似度或在艺术照生成模式下建议勾选 - Allow fusion of multiple LoRAs during training. Recommended for enhanced-similarity or using with Inpaint mode. + - LoRA增强(LoRA-Enhancement): 扩大LoRA规模, 生成图片更贴近用户, 至少5张以上多图训练或者艺术照生成模式建议勾选 - Boost scale of LoRA to enhance output resemblance with input. Recommended for training with more than 5 pics or using with Inpaint mode. ''' ) - run_button = gr.Button('开始训练（等待上传图片加载显示出来再点，否则会报错）' + run_button = gr.Button('开始训练(等待上传图片加载显示出来再点, 否则会报错)' 'Start training (please wait until photo(s) fully uploaded, otherwise it may result in training failure)') with gr.Box(): gr.Markdown(''' - 请等待训练完成，请勿刷新或关闭页面。 + 请等待训练完成, 请勿刷新或关闭页面。 Please wait for the training to complete, do not refresh or close the page. ''') output_message = gr.Markdown() with gr.Box(): gr.Markdown(''' - 碰到抓狂的错误或者计算资源紧张的情况下，推荐直接在[NoteBook](https://modelscope.cn/my/mynotebook/preset)上进行体验。 + 碰到抓狂的错误或者计算资源紧张的情况下, 推荐直接在[NoteBook](https://modelscope.cn/my/mynotebook/preset)上进行体验。 - 安装方法请参考：https://github.com/modelscope/facechain . + 安装方法请参考: https://github.com/modelscope/facechain . If you are experiencing prolonged waiting time, you may try on [ModelScope NoteBook](https://modelscope.cn/my/mynotebook/preset) to prepare your dedicated environment. @@ -670,13 +782,16 @@ def inference_input(): with gr.Accordion("高级选项(Advanced Options)", open=False): # upload one lora file and show the name or path of the file - lora_file = gr.File( - value=None, - label="上传LoRA文件(Upload LoRA file)", - type="file", - file_types=[".safetensors"], - file_count="single", - ) + with gr.Box(): + lora_choice = gr.Dropdown(choices=["preset"], type="value", value="preset", label="LoRA文件(LoRA file)", visible=False) + lora_file = gr.File( + value=None, + label="上传LoRA文件(Upload LoRA file)", + type="file", + file_types=[".safetensors"], + file_count="single", + visible=False, + ) pos_prompt = gr.Textbox(label="提示语(Prompt)", lines=3, value=generate_pos_prompt(None, cloth_prompt[0]['prompt']), @@ -697,11 +812,18 @@ def inference_input(): num_images = gr.Number( label='生成图片数量(Number of photos)', value=6, precision=1, minimum=1, maximum=6) gr.Markdown(''' - 注意：最多支持生成6张图片!(You may generate a maximum of 6 photos at one time!) - 如果使用自定义LoRA文件，需要上传LoRA文件，否则默认使用风格模型的LoRA文件。(LoRA file is required if you are using custom LoRA file, otherwise the default LoRA file of the style model will be used.) + 注意: 最多支持生成6张图片!(You may generate a maximum of 6 photos at one time!) + 如果使用自定义LoRA文件, 需要上传LoRA文件, 否则默认使用风格模型的LoRA文件。(LoRA file is required if you are using custom LoRA file, otherwise the default LoRA file of the style model will be used.) + 使用自定义LoRA文件时, 需要手动再prompt中输入提示词, 否则可能不能正常触发LoRA文件中的风格。(You need to manually input the prompt when using custom LoRA file, otherwise the style in the LoRA file may not be triggered.) ''') - display_button = gr.Button('开始生成(Start!)') + with gr.Row(): + display_button = gr.Button('开始生成(Start!)') + with gr.Column(): + history_button = gr.Button('查看历史(Show history)') + load_history_text = gr.Text("load", visible=False) + delete_history_button = gr.Button('删除历史(Delete history)') + delete_history_text = gr.Text("delete", visible=False) with gr.Box(): infer_progress = gr.Textbox(label="生成进度(Progress)", value="当前无生成任务(No task)", interactive=False) @@ -709,29 +831,41 @@ def inference_input(): gr.Markdown('生成结果(Result)') output_images = gr.Gallery(label='Output', show_label=False).style(columns=3, rows=2, height=600, object_fit="contain") + + with gr.Accordion(label="历史生成结果(History)", open=False): + with gr.Row(): + single_history = gr.Gallery(label='单张图片(Single image history)') + batch_history = gr.Gallery(label='图片组(Batch image history)') + + lora_choice.change(fn=change_lora_choice, inputs=[uuid, lora_choice, base_model_index], outputs=[style_model, cloth_style], queue=False) + + lora_file.upload(fn=upload_lora_file, inputs=[uuid, lora_file], outputs=[lora_choice], queue=False) + lora_file.clear(fn=clear_lora_file, inputs=[uuid, lora_file], outputs=[lora_choice], queue=False) - lora_file.upload(fn=upload_lora_file, inputs=[uuid, lora_file], outputs=[style_model, cloth_style], queue=False) - lora_file.clear(fn=flash_model_list, inputs=[uuid, base_model_index], outputs=[user_model, style_model], queue=False) - style_model.change(update_cloth, style_model, [cloth_style, pos_prompt, multiplier_human], queue=False) cloth_style.change(update_prompt, [style_model, cloth_style], [pos_prompt, multiplier_style], queue=False) pose_image.change(update_pose_model, pose_image, [pose_model]) base_model_index.change(fn=flash_model_list, - inputs=[uuid, base_model_index, lora_file], - outputs=[user_model, style_model], + inputs=[uuid, base_model_index, lora_choice], + outputs=[user_model, style_model, cloth_style, lora_choice, lora_file], queue=False) update_button.click(fn=update_output_model, inputs=[uuid, base_model_index], outputs=[user_model], queue=False) display_button.click(fn=launch_pipeline, - inputs=[uuid, pos_prompt, neg_prompt, base_model_index, user_model, num_images, lora_file, style_model, multiplier_style, multiplier_human, + inputs=[uuid, pos_prompt, neg_prompt, base_model_index, user_model, num_images, lora_choice, style_model, multiplier_style, multiplier_human, pose_model, pose_image], outputs=[infer_progress, output_images]) + history_button.click(fn=deal_history, + inputs=[uuid, base_model_index, user_model, lora_choice, style_model, load_history_text], + outputs=[single_history, batch_history]) + delete_history_button.click(fn=deal_history, + inputs=[uuid, base_model_index, user_model, lora_choice, style_model, delete_history_text], + outputs=[single_history, batch_history]) return demo - def inference_inpaint(): """ Inpaint Tab with Ensemble-Lora + MultiControlnet, support preset_template