import gradio as gr import random import os import json import time import shared import modules.config import fooocus_version import modules.html import modules.async_worker as worker import modules.constants as constants import modules.flags as flags import modules.gradio_hijack as grh import modules.style_sorter as style_sorter import modules.meta_parser import args_manager import copy import launch from extras.inpaint_mask import SAMOptions from modules.sdxl_styles import legal_style_names from modules.private_logger import get_current_html_path from modules.ui_gradio_extensions import reload_javascript from modules.auth import auth_enabled, check_auth from modules.util import is_json def get_task(*args): args = list(args) args.pop(0) return worker.AsyncTask(args=args) def generate_clicked(task: worker.AsyncTask): import ldm_patched.modules.model_management as model_management with model_management.interrupt_processing_mutex: model_management.interrupt_processing = False # outputs=[progress_html, progress_window, progress_gallery, gallery] if len(task.args) == 0: return execution_start_time = time.perf_counter() finished = False yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \ gr.update(visible=True, value=None), \ gr.update(visible=False, value=None), \ gr.update(visible=False) worker.async_tasks.append(task) while not finished: time.sleep(0.01) if len(task.yields) > 0: flag, product = task.yields.pop(0) if flag == 'preview': # help bad internet connection by skipping duplicated preview if len(task.yields) > 0: # if we have the next item if task.yields[0][0] == 'preview': # if the next item is also a preview # print('Skipped one preview for better internet connection.') continue percentage, title, image = product yield gr.update(visible=True, value=modules.html.make_progress_html(percentage, title)), \ gr.update(visible=True, value=image) if image is not None else gr.update(), \ gr.update(), \ gr.update(visible=False) if flag == 'results': yield gr.update(visible=True), \ gr.update(visible=True), \ gr.update(visible=True, value=product), \ gr.update(visible=False) if flag == 'finish': if not args_manager.args.disable_enhance_output_sorting: product = sort_enhance_images(product, task) yield gr.update(visible=False), \ gr.update(visible=False), \ gr.update(visible=False), \ gr.update(visible=True, value=product) finished = True # delete Fooocus temp images, only keep gradio temp images if args_manager.args.disable_image_log: for filepath in product: if isinstance(filepath, str) and os.path.exists(filepath): os.remove(filepath) execution_time = time.perf_counter() - execution_start_time print(f'Total time: {execution_time:.2f} seconds') return def sort_enhance_images(images, task): if not task.should_enhance or len(images) <= task.images_to_enhance_count: return images sorted_images = [] walk_index = task.images_to_enhance_count for index, enhanced_img in enumerate(images[:task.images_to_enhance_count]): sorted_images.append(enhanced_img) if index not in task.enhance_stats: continue target_index = walk_index + task.enhance_stats[index] if walk_index < len(images) and target_index <= len(images): sorted_images += images[walk_index:target_index] walk_index += task.enhance_stats[index] return sorted_images def inpaint_mode_change(mode, inpaint_engine_version): assert mode in modules.flags.inpaint_options # inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, # inpaint_disable_initial_latent, inpaint_engine, # inpaint_strength, inpaint_respective_field if mode == modules.flags.inpaint_option_detail: return [ gr.update(visible=True), gr.update(visible=False, value=[]), gr.Dataset.update(visible=True, samples=modules.config.example_inpaint_prompts), False, 'None', 0.5, 0.0 ] if inpaint_engine_version == 'empty': inpaint_engine_version = modules.config.default_inpaint_engine_version if mode == modules.flags.inpaint_option_modify: return [ gr.update(visible=True), gr.update(visible=False, value=[]), gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts), True, inpaint_engine_version, 1.0, 0.0 ] return [ gr.update(visible=False, value=''), gr.update(visible=True), gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts), False, inpaint_engine_version, 1.0, 0.618 ] reload_javascript() title = f'Fooocus {fooocus_version.version}' if isinstance(args_manager.args.preset, str): title += ' ' + args_manager.args.preset shared.gradio_root = gr.Blocks(title=title).queue() with shared.gradio_root: currentTask = gr.State(worker.AsyncTask(args=[])) inpaint_engine_state = gr.State('empty') with gr.Row(): with gr.Column(scale=2): with gr.Row(): progress_window = grh.Image(label='Preview', show_label=True, visible=False, height=768, elem_classes=['main_view']) progress_gallery = gr.Gallery(label='Finished Images', show_label=True, object_fit='contain', height=768, visible=False, elem_classes=['main_view', 'image_gallery']) progress_html = gr.HTML(value=modules.html.make_progress_html(32, 'Progress 32%'), visible=False, elem_id='progress-bar', elem_classes='progress-bar') gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768, elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'], elem_id='final_gallery') with gr.Row(): with gr.Column(scale=17): prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt', autofocus=True, lines=3) default_prompt = modules.config.default_prompt if isinstance(default_prompt, str) and default_prompt != '': shared.gradio_root.load(lambda: default_prompt, outputs=prompt) with gr.Column(scale=3, min_width=0): generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True) reset_button = gr.Button(label="Reconnect", value="Reconnect", elem_classes='type_row', elem_id='reset_button', visible=False) load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False) skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', elem_id='skip_button', visible=False) stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False) def stop_clicked(currentTask): import ldm_patched.modules.model_management as model_management currentTask.last_stop = 'stop' if (currentTask.processing): model_management.interrupt_current_processing() return currentTask def skip_clicked(currentTask): import ldm_patched.modules.model_management as model_management currentTask.last_stop = 'skip' if (currentTask.processing): model_management.interrupt_current_processing() return currentTask stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever') skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False) with gr.Row(elem_classes='advanced_check_row'): input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check') enhance_checkbox = gr.Checkbox(label='Enhance', value=modules.config.default_enhance_checkbox, container=False, elem_classes='min_check') advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check') with gr.Row(visible=False) as image_input_panel: with gr.Tabs(): with gr.TabItem(label='Upscale or Variation') as uov_tab: with gr.Row(): with gr.Column(): uov_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False) with gr.Column(): uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled) gr.HTML('\U0001F4D4 Documentation') with gr.TabItem(label='Image Prompt') as ip_tab: with gr.Row(): ip_images = [] ip_types = [] ip_stops = [] ip_weights = [] ip_ctrls = [] ip_ad_cols = [] for _ in range(flags.controlnet_image_count): with gr.Column(): ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300) ip_images.append(ip_image) ip_ctrls.append(ip_image) with gr.Column(visible=False) as ad_col: with gr.Row(): default_end, default_weight = flags.default_parameters[flags.default_ip] ip_stop = gr.Slider(label='Stop At', minimum=0.0, maximum=1.0, step=0.001, value=default_end) ip_stops.append(ip_stop) ip_ctrls.append(ip_stop) ip_weight = gr.Slider(label='Weight', minimum=0.0, maximum=2.0, step=0.001, value=default_weight) ip_weights.append(ip_weight) ip_ctrls.append(ip_weight) ip_type = gr.Radio(label='Type', choices=flags.ip_list, value=flags.default_ip, container=False) ip_types.append(ip_type) ip_ctrls.append(ip_type) ip_type.change(lambda x: flags.default_parameters[x], inputs=[ip_type], outputs=[ip_stop, ip_weight], queue=False, show_progress=False) ip_ad_cols.append(ad_col) ip_advanced = gr.Checkbox(label='Advanced', value=False, container=False) gr.HTML('* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1). \U0001F4D4 Documentation') def ip_advance_checked(x): return [gr.update(visible=x)] * len(ip_ad_cols) + \ [flags.default_ip] * len(ip_types) + \ [flags.default_parameters[flags.default_ip][0]] * len(ip_stops) + \ [flags.default_parameters[flags.default_ip][1]] * len(ip_weights) ip_advanced.change(ip_advance_checked, inputs=ip_advanced, outputs=ip_ad_cols + ip_types + ip_stops + ip_weights, queue=False, show_progress=False) with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab: with gr.Row(): with gr.Column(): inpaint_input_image = grh.Image(label='Image', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas', show_label=False) inpaint_advanced_masking_checkbox = gr.Checkbox(label='Enable Advanced Masking Features', value=False) inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.config.default_inpaint_method, label='Method') inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False) outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction') example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False) gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Documentation') example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) with gr.Column(visible=False) as inpaint_mask_generation_col: inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", mask_opacity=1, elem_id='inpaint_mask_canvas') invert_mask_checkbox = gr.Checkbox(label='Invert Mask When Generating', value=False) inpaint_mask_model = gr.Dropdown(label='Mask generation model', choices=flags.inpaint_mask_models, value=modules.config.default_inpaint_mask_model) inpaint_mask_cloth_category = gr.Dropdown(label='Cloth category', choices=flags.inpaint_mask_cloth_category, value=modules.config.default_inpaint_mask_cloth_category, visible=False) inpaint_mask_dino_prompt_text = gr.Textbox(label='Detection prompt', value='', visible=False, info='Use singular whenever possible', placeholder='Describe what you want to detect.') example_inpaint_mask_dino_prompt_text = gr.Dataset( samples=modules.config.example_enhance_detection_prompts, label='Detection Prompt Quick List', components=[inpaint_mask_dino_prompt_text], visible=modules.config.default_inpaint_mask_model == 'sam') example_inpaint_mask_dino_prompt_text.click(lambda x: x[0], inputs=example_inpaint_mask_dino_prompt_text, outputs=inpaint_mask_dino_prompt_text, show_progress=False, queue=False) with gr.Accordion("Advanced options", visible=False, open=False) as inpaint_mask_advanced_options: inpaint_mask_sam_model = gr.Dropdown(label='SAM model', choices=flags.inpaint_mask_sam_model, value=modules.config.default_inpaint_mask_sam_model) inpaint_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05) inpaint_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05) inpaint_mask_sam_max_detections = gr.Slider(label="Maximum number of detections", info="Set to 0 to detect all", minimum=0, maximum=10, value=modules.config.default_sam_max_detections, step=1, interactive=True) generate_mask_button = gr.Button(value='Generate mask from image') def generate_mask(image, mask_model, cloth_category, dino_prompt_text, sam_model, box_threshold, text_threshold, sam_max_detections, dino_erode_or_dilate, dino_debug): from extras.inpaint_mask import generate_mask_from_image extras = {} sam_options = None if mask_model == 'u2net_cloth_seg': extras['cloth_category'] = cloth_category elif mask_model == 'sam': sam_options = SAMOptions( dino_prompt=dino_prompt_text, dino_box_threshold=box_threshold, dino_text_threshold=text_threshold, dino_erode_or_dilate=dino_erode_or_dilate, dino_debug=dino_debug, max_detections=sam_max_detections, model_type=sam_model ) mask, _, _, _ = generate_mask_from_image(image, mask_model, extras, sam_options) return mask inpaint_mask_model.change(lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] + [gr.update(visible=x == 'sam')] * 2 + [gr.Dataset.update(visible=x == 'sam', samples=modules.config.example_enhance_detection_prompts)], inputs=inpaint_mask_model, outputs=[inpaint_mask_cloth_category, inpaint_mask_dino_prompt_text, inpaint_mask_advanced_options, example_inpaint_mask_dino_prompt_text], queue=False, show_progress=False) with gr.TabItem(label='Describe') as desc_tab: with gr.Row(): with gr.Column(): desc_input_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False) with gr.Column(): desc_method = gr.Radio( label='Content Type', choices=[flags.desc_type_photo, flags.desc_type_anime], value=flags.desc_type_photo) desc_btn = gr.Button(value='Describe this Image into Prompt') desc_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='desc_image_size', visible=False) gr.HTML('\U0001F4D4 Documentation') def trigger_show_image_properties(image): value = modules.util.get_image_size_info(image, modules.flags.sdxl_aspect_ratios) return gr.update(value=value, visible=True) desc_input_image.upload(trigger_show_image_properties, inputs=desc_input_image, outputs=desc_image_size, show_progress=False, queue=False) with gr.TabItem(label='Enhance') as enhance_tab: with gr.Row(): with gr.Column(): enhance_input_image = grh.Image(label='Use with Enhance, skips image generation', source='upload', type='numpy') gr.HTML('\U0001F4D4 Documentation') with gr.TabItem(label='Metadata') as metadata_tab: with gr.Column(): metadata_input_image = grh.Image(label='For images created by Fooocus', source='upload', type='pil') metadata_json = gr.JSON(label='Metadata') metadata_import_button = gr.Button(value='Apply Metadata') def trigger_metadata_preview(file): parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file) results = {} if parameters is not None: results['parameters'] = parameters if isinstance(metadata_scheme, flags.MetadataScheme): results['metadata_scheme'] = metadata_scheme.value return results metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image, outputs=metadata_json, queue=False, show_progress=True) with gr.Row(visible=modules.config.default_enhance_checkbox) as enhance_input_panel: with gr.Tabs(): with gr.TabItem(label='Upscale or Variation'): with gr.Row(): with gr.Column(): enhance_uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=modules.config.default_enhance_uov_method) enhance_uov_processing_order = gr.Radio(label='Order of Processing', info='Use before to enhance small details and after to enhance large areas.', choices=flags.enhancement_uov_processing_order, value=modules.config.default_enhance_uov_processing_order) enhance_uov_prompt_type = gr.Radio(label='Prompt', info='Choose which prompt to use for Upscale or Variation.', choices=flags.enhancement_uov_prompt_types, value=modules.config.default_enhance_uov_prompt_type, visible=modules.config.default_enhance_uov_processing_order == flags.enhancement_uov_after) enhance_uov_processing_order.change(lambda x: gr.update(visible=x == flags.enhancement_uov_after), inputs=enhance_uov_processing_order, outputs=enhance_uov_prompt_type, queue=False, show_progress=False) gr.HTML('\U0001F4D4 Documentation') enhance_ctrls = [] enhance_inpaint_mode_ctrls = [] enhance_inpaint_engine_ctrls = [] enhance_inpaint_update_ctrls = [] for index in range(modules.config.default_enhance_tabs): with gr.TabItem(label=f'#{index + 1}') as enhance_tab_item: enhance_enabled = gr.Checkbox(label='Enable', value=False, elem_classes='min_check', container=False) enhance_mask_dino_prompt_text = gr.Textbox(label='Detection prompt', info='Use singular whenever possible', placeholder='Describe what you want to detect.', interactive=True, visible=modules.config.default_enhance_inpaint_mask_model == 'sam') example_enhance_mask_dino_prompt_text = gr.Dataset( samples=modules.config.example_enhance_detection_prompts, label='Detection Prompt Quick List', components=[enhance_mask_dino_prompt_text], visible=modules.config.default_enhance_inpaint_mask_model == 'sam') example_enhance_mask_dino_prompt_text.click(lambda x: x[0], inputs=example_enhance_mask_dino_prompt_text, outputs=enhance_mask_dino_prompt_text, show_progress=False, queue=False) enhance_prompt = gr.Textbox(label="Enhancement positive prompt", placeholder="Uses original prompt instead if empty.", elem_id='enhance_prompt') enhance_negative_prompt = gr.Textbox(label="Enhancement negative prompt", placeholder="Uses original negative prompt instead if empty.", elem_id='enhance_negative_prompt') with gr.Accordion("Detection", open=False): enhance_mask_model = gr.Dropdown(label='Mask generation model', choices=flags.inpaint_mask_models, value=modules.config.default_enhance_inpaint_mask_model) enhance_mask_cloth_category = gr.Dropdown(label='Cloth category', choices=flags.inpaint_mask_cloth_category, value=modules.config.default_inpaint_mask_cloth_category, visible=modules.config.default_enhance_inpaint_mask_model == 'u2net_cloth_seg', interactive=True) with gr.Accordion("SAM Options", visible=modules.config.default_enhance_inpaint_mask_model == 'sam', open=False) as sam_options: enhance_mask_sam_model = gr.Dropdown(label='SAM model', choices=flags.inpaint_mask_sam_model, value=modules.config.default_inpaint_mask_sam_model, interactive=True) enhance_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05, interactive=True) enhance_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05, interactive=True) enhance_mask_sam_max_detections = gr.Slider(label="Maximum number of detections", info="Set to 0 to detect all", minimum=0, maximum=10, value=modules.config.default_sam_max_detections, step=1, interactive=True) with gr.Accordion("Inpaint", visible=True, open=False): enhance_inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.config.default_inpaint_method, label='Method', interactive=True) enhance_inpaint_disable_initial_latent = gr.Checkbox( label='Disable initial latent in inpaint', value=False) enhance_inpaint_engine = gr.Dropdown(label='Inpaint Engine', value=modules.config.default_inpaint_engine_version, choices=flags.inpaint_engine_versions, info='Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.') enhance_inpaint_strength = gr.Slider(label='Inpaint Denoising Strength', minimum=0.0, maximum=1.0, step=0.001, value=1.0, info='Same as the denoising strength in A1111 inpaint. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') enhance_inpaint_respective_field = gr.Slider(label='Inpaint Respective Field', minimum=0.0, maximum=1.0, step=0.001, value=0.618, info='The area to inpaint. ' 'Value 0 is same as "Only Masked" in A1111. ' 'Value 1 is same as "Whole Image" in A1111. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') enhance_inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate', minimum=-64, maximum=64, step=1, value=0, info='Positive value will make white area in the mask larger, ' 'negative value will make white area smaller. ' '(default is 0, always processed before any mask invert)') enhance_mask_invert = gr.Checkbox(label='Invert Mask', value=False) gr.HTML('\U0001F4D4 Documentation') enhance_ctrls += [ enhance_enabled, enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert ] enhance_inpaint_mode_ctrls += [enhance_inpaint_mode] enhance_inpaint_engine_ctrls += [enhance_inpaint_engine] enhance_inpaint_update_ctrls += [[ enhance_inpaint_mode, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field ]] enhance_inpaint_mode.change(inpaint_mode_change, inputs=[enhance_inpaint_mode, inpaint_engine_state], outputs=[ inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field ], show_progress=False, queue=False) enhance_mask_model.change( lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] + [gr.update(visible=x == 'sam')] * 2 + [gr.Dataset.update(visible=x == 'sam', samples=modules.config.example_enhance_detection_prompts)], inputs=enhance_mask_model, outputs=[enhance_mask_cloth_category, enhance_mask_dino_prompt_text, sam_options, example_enhance_mask_dino_prompt_text], queue=False, show_progress=False) switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}" down_js = "() => {viewer_to_bottom();}" input_image_checkbox.change(lambda x: gr.update(visible=x), inputs=input_image_checkbox, outputs=image_input_panel, queue=False, show_progress=False, _js=switch_js) ip_advanced.change(lambda: None, queue=False, show_progress=False, _js=down_js) current_tab = gr.Textbox(value='uov', visible=False) uov_tab.select(lambda: 'uov', outputs=current_tab, queue=False, _js=down_js, show_progress=False) inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False) ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False) desc_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False) enhance_tab.select(lambda: 'enhance', outputs=current_tab, queue=False, _js=down_js, show_progress=False) metadata_tab.select(lambda: 'metadata', outputs=current_tab, queue=False, _js=down_js, show_progress=False) enhance_checkbox.change(lambda x: gr.update(visible=x), inputs=enhance_checkbox, outputs=enhance_input_panel, queue=False, show_progress=False, _js=switch_js) with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column: with gr.Tab(label='Settings'): if not args_manager.args.disable_preset_selection: preset_selection = gr.Dropdown(label='Preset', choices=modules.config.available_presets, value=args_manager.args.preset if args_manager.args.preset else "initial", interactive=True) performance_selection = gr.Radio(label='Performance', choices=flags.Performance.values(), value=modules.config.default_performance, elem_classes=['performance_selection']) with gr.Accordion(label='Aspect Ratios', open=False, elem_id='aspect_ratios_accordion') as aspect_ratios_accordion: aspect_ratios_selection = gr.Radio(label='Aspect Ratios', show_label=False, choices=modules.config.available_aspect_ratios_labels, value=modules.config.default_aspect_ratio, info='width × height', elem_classes='aspect_ratios') aspect_ratios_selection.change(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}') shared.gradio_root.load(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}') image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number) output_format = gr.Radio(label='Output Format', choices=flags.OutputFormat.list(), value=modules.config.default_output_format) negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.", info='Describing what you do not want to see.', lines=2, elem_id='negative_prompt', value=modules.config.default_prompt_negative) seed_random = gr.Checkbox(label='Random', value=True) image_seed = gr.Textbox(label='Seed', value=0, max_lines=1, visible=False) # workaround for https://github.com/gradio-app/gradio/issues/5354 def random_checked(r): return gr.update(visible=not r) def refresh_seed(r, seed_string): if r: return random.randint(constants.MIN_SEED, constants.MAX_SEED) else: try: seed_value = int(seed_string) if constants.MIN_SEED <= seed_value <= constants.MAX_SEED: return seed_value except ValueError: pass return random.randint(constants.MIN_SEED, constants.MAX_SEED) seed_random.change(random_checked, inputs=[seed_random], outputs=[image_seed], queue=False, show_progress=False) def update_history_link(): if args_manager.args.disable_image_log: return gr.update(value='') return gr.update(value=f'\U0001F4DA History Log') history_link = gr.HTML() shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False) with gr.Tab(label='Styles', elem_classes=['style_selections_tab']): style_sorter.try_load_sorted_styles( style_names=legal_style_names, default_selected=modules.config.default_styles) style_search_bar = gr.Textbox(show_label=False, container=False, placeholder="\U0001F50E Type here to search styles ...", value="", label='Search Styles') style_selections = gr.CheckboxGroup(show_label=False, container=False, choices=copy.deepcopy(style_sorter.all_styles), value=copy.deepcopy(modules.config.default_styles), label='Selected Styles', elem_classes=['style_selections']) gradio_receiver_style_selections = gr.Textbox(elem_id='gradio_receiver_style_selections', visible=False) shared.gradio_root.load(lambda: gr.update(choices=copy.deepcopy(style_sorter.all_styles)), outputs=style_selections) style_search_bar.change(style_sorter.search_styles, inputs=[style_selections, style_search_bar], outputs=style_selections, queue=False, show_progress=False).then( lambda: None, _js='()=>{refresh_style_localization();}') gradio_receiver_style_selections.input(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False).then( lambda: None, _js='()=>{refresh_style_localization();}') with gr.Tab(label='Models'): with gr.Group(): with gr.Row(): base_model = gr.Dropdown(label='Base Model (SDXL only)', choices=modules.config.model_filenames, value=modules.config.default_base_model_name, show_label=True) refiner_model = gr.Dropdown(label='Refiner (SDXL or SD 1.5)', choices=['None'] + modules.config.model_filenames, value=modules.config.default_refiner_model_name, show_label=True) refiner_switch = gr.Slider(label='Refiner Switch At', minimum=0.1, maximum=1.0, step=0.0001, info='Use 0.4 for SD1.5 realistic models; ' 'or 0.667 for SD1.5 anime models; ' 'or 0.8 for XL-refiners; ' 'or any value for switching two SDXL models.', value=modules.config.default_refiner_switch, visible=modules.config.default_refiner_model_name != 'None') refiner_model.change(lambda x: gr.update(visible=x != 'None'), inputs=refiner_model, outputs=refiner_switch, show_progress=False, queue=False) with gr.Group(): lora_ctrls = [] for i, (enabled, filename, weight) in enumerate(modules.config.default_loras): with gr.Row(): lora_enabled = gr.Checkbox(label='Enable', value=enabled, elem_classes=['lora_enable', 'min_check'], scale=1) lora_model = gr.Dropdown(label=f'LoRA {i + 1}', choices=['None'] + modules.config.lora_filenames, value=filename, elem_classes='lora_model', scale=5) lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight, maximum=modules.config.default_loras_max_weight, step=0.01, value=weight, elem_classes='lora_weight', scale=5) lora_ctrls += [lora_enabled, lora_model, lora_weight] with gr.Row(): refresh_files = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button') with gr.Tab(label='Advanced'): guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01, value=modules.config.default_cfg_scale, info='Higher value means style is cleaner, vivider, and more artistic.') sharpness = gr.Slider(label='Image Sharpness', minimum=0.0, maximum=30.0, step=0.001, value=modules.config.default_sample_sharpness, info='Higher value means image and texture are sharper.') gr.HTML('\U0001F4D4 Documentation') dev_mode = gr.Checkbox(label='Developer Debug Mode', value=False, container=False) with gr.Column(visible=False) as dev_tools: with gr.Tab(label='Debug Tools'): adm_scaler_positive = gr.Slider(label='Positive ADM Guidance Scaler', minimum=0.1, maximum=3.0, step=0.001, value=1.5, info='The scaler multiplied to positive ADM (use 1.0 to disable). ') adm_scaler_negative = gr.Slider(label='Negative ADM Guidance Scaler', minimum=0.1, maximum=3.0, step=0.001, value=0.8, info='The scaler multiplied to negative ADM (use 1.0 to disable). ') adm_scaler_end = gr.Slider(label='ADM Guidance End At Step', minimum=0.0, maximum=1.0, step=0.001, value=0.3, info='When to end the guidance from positive/negative ADM. ') refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method, choices=['joint', 'separate', 'vae']) adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01, value=modules.config.default_cfg_tsnr, info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR ' '(effective when real CFG > mimicked CFG).') clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=flags.clip_skip_max, step=1, value=modules.config.default_clip_skip, info='Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).') sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list, value=modules.config.default_sampler) scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list, value=modules.config.default_scheduler) vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames, value=modules.config.default_vae, show_label=True) generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch', info='(Experimental) This may cause performance problems on some computers and certain internet conditions.', value=False) overwrite_step = gr.Slider(label='Forced Overwrite of Sampling Step', minimum=-1, maximum=200, step=1, value=modules.config.default_overwrite_step, info='Set as -1 to disable. For developer debugging.') overwrite_switch = gr.Slider(label='Forced Overwrite of Refiner Switch Step', minimum=-1, maximum=200, step=1, value=modules.config.default_overwrite_switch, info='Set as -1 to disable. For developer debugging.') overwrite_width = gr.Slider(label='Forced Overwrite of Generating Width', minimum=-1, maximum=2048, step=1, value=-1, info='Set as -1 to disable. For developer debugging. ' 'Results will be worse for non-standard numbers that SDXL is not trained on.') overwrite_height = gr.Slider(label='Forced Overwrite of Generating Height', minimum=-1, maximum=2048, step=1, value=-1, info='Set as -1 to disable. For developer debugging. ' 'Results will be worse for non-standard numbers that SDXL is not trained on.') overwrite_vary_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Vary"', minimum=-1, maximum=1.0, step=0.001, value=-1, info='Set as negative number to disable. For developer debugging.') overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Upscale"', minimum=-1, maximum=1.0, step=0.001, value=modules.config.default_overwrite_upscale, info='Set as negative number to disable. For developer debugging.') disable_preview = gr.Checkbox(label='Disable Preview', value=modules.config.default_black_out_nsfw, interactive=not modules.config.default_black_out_nsfw, info='Disable preview during generation.') disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results', value=flags.Performance.has_restricted_features(modules.config.default_performance), info='Disable intermediate results during generation, only show final gallery.') disable_seed_increment = gr.Checkbox(label='Disable seed increment', info='Disable automatic seed increment when image number is > 1.', value=False) read_wildcards_in_order = gr.Checkbox(label="Read wildcards in order", value=False) black_out_nsfw = gr.Checkbox(label='Black Out NSFW', value=modules.config.default_black_out_nsfw, interactive=not modules.config.default_black_out_nsfw, info='Use black image if NSFW is detected.') black_out_nsfw.change(lambda x: gr.update(value=x, interactive=not x), inputs=black_out_nsfw, outputs=disable_preview, queue=False, show_progress=False) if not args_manager.args.disable_image_log: save_final_enhanced_image_only = gr.Checkbox(label='Save only final enhanced image', value=modules.config.default_save_only_final_enhanced_image) if not args_manager.args.disable_metadata: save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images, info='Adds parameters to generated images allowing manual regeneration.') metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme, info='Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.', visible=modules.config.default_save_metadata_to_images) save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme], queue=False, show_progress=False) with gr.Tab(label='Control'): debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False, info='See the results from preprocessors.') skipping_cn_preprocessor = gr.Checkbox(label='Skip Preprocessors', value=False, info='Do not preprocess images. (Inputs are already canny/depth/cropped-face/etc.)') mixing_image_prompt_and_vary_upscale = gr.Checkbox(label='Mixing Image Prompt and Vary/Upscale', value=False) mixing_image_prompt_and_inpaint = gr.Checkbox(label='Mixing Image Prompt and Inpaint', value=False) controlnet_softness = gr.Slider(label='Softness of ControlNet', minimum=0.0, maximum=1.0, step=0.001, value=0.25, info='Similar to the Control Mode in A1111 (use 0.0 to disable). ') with gr.Tab(label='Canny'): canny_low_threshold = gr.Slider(label='Canny Low Threshold', minimum=1, maximum=255, step=1, value=64) canny_high_threshold = gr.Slider(label='Canny High Threshold', minimum=1, maximum=255, step=1, value=128) with gr.Tab(label='Inpaint'): debugging_inpaint_preprocessor = gr.Checkbox(label='Debug Inpaint Preprocessing', value=False) debugging_enhance_masks_checkbox = gr.Checkbox(label='Debug Enhance Masks', value=False, info='Show enhance masks in preview and final results') debugging_dino = gr.Checkbox(label='Debug GroundingDINO', value=False, info='Use GroundingDINO boxes instead of more detailed SAM masks') inpaint_disable_initial_latent = gr.Checkbox(label='Disable initial latent in inpaint', value=False) inpaint_engine = gr.Dropdown(label='Inpaint Engine', value=modules.config.default_inpaint_engine_version, choices=flags.inpaint_engine_versions, info='Version of Fooocus inpaint model. If set, use performance Quality or Speed (no performance LoRAs) for best results.') inpaint_strength = gr.Slider(label='Inpaint Denoising Strength', minimum=0.0, maximum=1.0, step=0.001, value=1.0, info='Same as the denoising strength in A1111 inpaint. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') inpaint_respective_field = gr.Slider(label='Inpaint Respective Field', minimum=0.0, maximum=1.0, step=0.001, value=0.618, info='The area to inpaint. ' 'Value 0 is same as "Only Masked" in A1111. ' 'Value 1 is same as "Whole Image" in A1111. ' 'Only used in inpaint, not used in outpaint. ' '(Outpaint always use 1.0)') inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate', minimum=-64, maximum=64, step=1, value=0, info='Positive value will make white area in the mask larger, ' 'negative value will make white area smaller. ' '(default is 0, always processed before any mask invert)') dino_erode_or_dilate = gr.Slider(label='GroundingDINO Box Erode or Dilate', minimum=-64, maximum=64, step=1, value=0, info='Positive value will make white area in the mask larger, ' 'negative value will make white area smaller. ' '(default is 0, processed before SAM)') inpaint_mask_color = gr.ColorPicker(label='Inpaint brush color', value='#FFFFFF', elem_id='inpaint_brush_color') inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, inpaint_advanced_masking_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate] inpaint_advanced_masking_checkbox.change(lambda x: [gr.update(visible=x)] * 2, inputs=inpaint_advanced_masking_checkbox, outputs=[inpaint_mask_image, inpaint_mask_generation_col], queue=False, show_progress=False) inpaint_mask_color.change(lambda x: gr.update(brush_color=x), inputs=inpaint_mask_color, outputs=inpaint_input_image, queue=False, show_progress=False) with gr.Tab(label='FreeU'): freeu_enabled = gr.Checkbox(label='Enabled', value=False) freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01) freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02) freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99) freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95) freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2] def dev_mode_checked(r): return gr.update(visible=r) dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools], queue=False, show_progress=False) def refresh_files_clicked(): modules.config.update_files() results = [gr.update(choices=modules.config.model_filenames)] results += [gr.update(choices=['None'] + modules.config.model_filenames)] results += [gr.update(choices=[flags.default_vae] + modules.config.vae_filenames)] if not args_manager.args.disable_preset_selection: results += [gr.update(choices=modules.config.available_presets)] for i in range(modules.config.default_max_lora_number): results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()] return results refresh_files_output = [base_model, refiner_model, vae_name] if not args_manager.args.disable_preset_selection: refresh_files_output += [preset_selection] refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls, queue=False, show_progress=False) state_is_generating = gr.State(False) load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections, performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection, overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, clip_skip, base_model, refiner_model, refiner_switch, sampler_name, scheduler_name, vae_name, seed_random, image_seed, inpaint_engine, inpaint_engine_state, inpaint_mode] + enhance_inpaint_mode_ctrls + [generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls if not args_manager.args.disable_preset_selection: def preset_selection_change(preset, is_generating, inpaint_mode): preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {} preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content) default_model = preset_prepared.get('base_model') previous_default_models = preset_prepared.get('previous_default_models', []) checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {}) embeddings_downloads = preset_prepared.get('embeddings_downloads', {}) lora_downloads = preset_prepared.get('lora_downloads', {}) vae_downloads = preset_prepared.get('vae_downloads', {}) preset_prepared['base_model'], preset_prepared['checkpoint_downloads'] = launch.download_models( default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads, vae_downloads) if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '': del preset_prepared['prompt'] return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating, inpaint_mode) def inpaint_engine_state_change(inpaint_engine_version, *args): if inpaint_engine_version == 'empty': inpaint_engine_version = modules.config.default_inpaint_engine_version result = [] for inpaint_mode in args: if inpaint_mode != modules.flags.inpaint_option_detail: result.append(gr.update(value=inpaint_engine_version)) else: result.append(gr.update()) return result preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating, inpaint_mode], outputs=load_data_outputs, queue=False, show_progress=True) \ .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \ .then(lambda: None, _js='()=>{refresh_style_localization();}') \ .then(inpaint_engine_state_change, inputs=[inpaint_engine_state] + enhance_inpaint_mode_ctrls, outputs=enhance_inpaint_engine_ctrls, queue=False, show_progress=False) performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 + [gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 + [gr.update(value=flags.Performance.has_restricted_features(x))] * 1, inputs=performance_selection, outputs=[ guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive, adm_scaler_negative, refiner_switch, refiner_model, sampler_name, scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results ], queue=False, show_progress=False) output_format.input(lambda x: gr.update(output_format=x), inputs=output_format) advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column, queue=False, show_progress=False) \ .then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False) inpaint_mode.change(inpaint_mode_change, inputs=[inpaint_mode, inpaint_engine_state], outputs=[ inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field ], show_progress=False, queue=False) # load configured default_inpaint_method default_inpaint_ctrls = [inpaint_mode, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field] for mode, disable_initial_latent, engine, strength, respective_field in [default_inpaint_ctrls] + enhance_inpaint_update_ctrls: shared.gradio_root.load(inpaint_mode_change, inputs=[mode, inpaint_engine_state], outputs=[ inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, disable_initial_latent, engine, strength, respective_field ], show_progress=False, queue=False) generate_mask_button.click(fn=generate_mask, inputs=[inpaint_input_image, inpaint_mask_model, inpaint_mask_cloth_category, inpaint_mask_dino_prompt_text, inpaint_mask_sam_model, inpaint_mask_box_threshold, inpaint_mask_text_threshold, inpaint_mask_sam_max_detections, dino_erode_or_dilate, debugging_dino], outputs=inpaint_mask_image, show_progress=True, queue=True) ctrls = [currentTask, generate_image_grid] ctrls += [ prompt, negative_prompt, style_selections, performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, read_wildcards_in_order, sharpness, guidance_scale ] ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls ctrls += [input_image_checkbox, current_tab] ctrls += [uov_method, uov_input_image] ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image] ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment, black_out_nsfw] ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, clip_skip] ctrls += [sampler_name, scheduler_name, vae_name] ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength] ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint] ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold] ctrls += [refiner_swap_method, controlnet_softness] ctrls += freeu_ctrls ctrls += inpaint_ctrls if not args_manager.args.disable_image_log: ctrls += [save_final_enhanced_image_only] if not args_manager.args.disable_metadata: ctrls += [save_metadata_to_images, metadata_scheme] ctrls += ip_ctrls ctrls += [debugging_dino, dino_erode_or_dilate, debugging_enhance_masks_checkbox, enhance_input_image, enhance_checkbox, enhance_uov_method, enhance_uov_processing_order, enhance_uov_prompt_type] ctrls += enhance_ctrls def parse_meta(raw_prompt_txt, is_generating): loaded_json = None if is_json(raw_prompt_txt): loaded_json = json.loads(raw_prompt_txt) if loaded_json is None: if is_generating: return gr.update(), gr.update(), gr.update() else: return gr.update(), gr.update(visible=True), gr.update(visible=False) return json.dumps(loaded_json), gr.update(visible=False), gr.update(visible=True) prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False) load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating, inpaint_mode], outputs=load_data_outputs, queue=False, show_progress=False) def trigger_metadata_import(file, state_is_generating): parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file) if parameters is None: print('Could not find metadata in the image!') parsed_parameters = {} else: metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) parsed_parameters = metadata_parser.to_json(parameters) return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating, inpaint_mode) metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \ .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True), outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \ .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \ .then(fn=get_task, inputs=ctrls, outputs=currentTask) \ .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \ .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False), outputs=[generate_button, stop_button, skip_button, state_is_generating]) \ .then(fn=update_history_link, outputs=history_link) \ .then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed') reset_button.click(lambda: [worker.AsyncTask(args=[]), False, gr.update(visible=True, interactive=True)] + [gr.update(visible=False)] * 6 + [gr.update(visible=True, value=[])], outputs=[currentTask, state_is_generating, generate_button, reset_button, stop_button, skip_button, progress_html, progress_window, progress_gallery, gallery], queue=False) for notification_file in ['notification.ogg', 'notification.mp3']: if os.path.exists(notification_file): gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False) break def trigger_describe(mode, img): if mode == flags.desc_type_photo: from extras.interrogate import default_interrogator as default_interrogator_photo return default_interrogator_photo(img), ["Fooocus V2", "Fooocus Enhance", "Fooocus Sharp"] if mode == flags.desc_type_anime: from extras.wd14tagger import default_interrogator as default_interrogator_anime return default_interrogator_anime(img), ["Fooocus V2", "Fooocus Masterpiece"] return mode, ["Fooocus V2"] desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image], outputs=[prompt, style_selections], show_progress=True, queue=True) if args_manager.args.enable_auto_describe_image: def trigger_auto_describe(mode, img, prompt): # keep prompt if not empty if prompt == '': return trigger_describe(mode, img) return gr.update(), gr.update() uov_input_image.upload(trigger_auto_describe, inputs=[desc_method, uov_input_image, prompt], outputs=[prompt, style_selections], show_progress=True, queue=True) enhance_input_image.upload(lambda: gr.update(value=True), outputs=enhance_checkbox, queue=False, show_progress=False) \ .then(trigger_auto_describe, inputs=[desc_method, enhance_input_image, prompt], outputs=[prompt, style_selections], show_progress=True, queue=True) def dump_default_english_config(): from modules.localization import dump_english_config dump_english_config(grh.all_components) # dump_default_english_config() shared.gradio_root.launch( inbrowser=args_manager.args.in_browser, server_name=args_manager.args.listen, server_port=args_manager.args.port, share=args_manager.args.share, auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None, allowed_paths=[modules.config.path_outputs], blocked_paths=[constants.AUTH_FILENAME] )