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.advanced_parameters as advanced_parameters import modules.style_sorter as style_sorter import modules.meta_parser import args_manager import copy 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 def generate_clicked(*args): 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] execution_start_time = time.perf_counter() task = worker.AsyncTask(args=list(args)) 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': yield gr.update(visible=False), \ gr.update(visible=False), \ gr.update(visible=False), \ gr.update(visible=True, value=product) finished = True execution_time = time.perf_counter() - execution_start_time print(f'Total time: {execution_time:.2f} seconds') return 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, css=modules.html.css).queue() with shared.gradio_root: 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(elem_classes='type_row'): with gr.Column(scale=17): prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt', container=False, autofocus=True, elem_classes='type_row', lines=1024) 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) 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', visible=False) stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False) def stop_clicked(): import ldm_patched.modules.model_management as model_management shared.last_stop = 'stop' model_management.interrupt_current_processing() return [gr.update(interactive=False)] * 2 def skip_clicked(): import ldm_patched.modules.model_management as model_management shared.last_stop = 'skip' model_management.interrupt_current_processing() return stop_button.click(stop_clicked, outputs=[skip_button, stop_button], queue=False, show_progress=False, _js='cancelGenerateForever') skip_button.click(skip_clicked, 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') 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='Drag above image to here', source='upload', type='numpy') with gr.Column(): uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled) gr.HTML('\U0001F4D4 Document') 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(4): 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 Document') 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: inpaint_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') with gr.Row(): 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') inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.flags.inpaint_option_default, label='Method') 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 Document') example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) with gr.TabItem(label='Describe') as desc_tab: with gr.Row(): with gr.Column(): desc_input_image = grh.Image(label='Drag any image to here', source='upload', type='numpy') 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') gr.HTML('\U0001F4D4 Document') 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) with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column: with gr.Tab(label='Setting'): performance_selection = gr.Radio(label='Performance', choices=modules.flags.performance_selections, value=modules.config.default_performance) aspect_ratios_selection = gr.Radio(label='Aspect Ratios', choices=modules.config.available_aspect_ratios, value=modules.config.default_aspect_ratio, info='width × height', elem_classes='aspect_ratios') image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number) 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) if not args_manager.args.disable_image_log: gr.HTML(f'\U0001F4DA History Log') with gr.Tab(label='Style'): 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='Model'): 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, (n, v) in enumerate(modules.config.default_loras): with gr.Row(): lora_model = gr.Dropdown(label=f'LoRA {i + 1}', choices=['None'] + modules.config.lora_filenames, value=n) lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=v, elem_classes='lora_weight') lora_ctrls += [lora_model, lora_weight] with gr.Row(): model_refresh = 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 Document') 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='joint', 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).') 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) 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=-1, info='Set as negative number to disable. For developer debugging.') disable_preview = gr.Checkbox(label='Disable Preview', value=False, info='Disable preview during generation.') 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) 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') 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_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field] 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] adps = [disable_preview, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg, sampler_name, scheduler_name, generate_image_grid, overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength, overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint, debugging_cn_preprocessor, skipping_cn_preprocessor, controlnet_softness, canny_low_threshold, canny_high_threshold, refiner_swap_method] adps += freeu_ctrls adps += inpaint_ctrls 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 model_refresh_clicked(): modules.config.update_all_model_names() results = [] results += [gr.update(choices=modules.config.model_filenames), gr.update(choices=['None'] + modules.config.model_filenames)] for i in range(5): results += [gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()] return results model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls, queue=False, show_progress=False) performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 + [gr.update(visible=x != 'Extreme Speed')] * 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 ], queue=False, show_progress=False) 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) def inpaint_mode_change(mode): 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 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, modules.config.default_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, modules.config.default_inpaint_engine_version, 1.0, 0.618 ] inpaint_mode.input(inpaint_mode_change, inputs=inpaint_mode, 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) ctrls = [ prompt, negative_prompt, style_selections, performance_selection, aspect_ratios_selection, image_number, image_seed, 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] ctrls += ip_ctrls state_is_generating = gr.State(False) def parse_meta(raw_prompt_txt, is_generating): loaded_json = None try: if '{' in raw_prompt_txt: if '}' in raw_prompt_txt: if ':' in raw_prompt_txt: loaded_json = json.loads(raw_prompt_txt) assert isinstance(loaded_json, dict) except: loaded_json = None 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], outputs=[ advanced_checkbox, image_number, prompt, negative_prompt, style_selections, performance_selection, aspect_ratios_selection, overwrite_width, overwrite_height, sharpness, guidance_scale, adm_scaler_positive, adm_scaler_negative, adm_scaler_end, base_model, refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed, generate_button, load_parameter_button ] + lora_ctrls, 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(advanced_parameters.set_all_advanced_parameters, inputs=adps) \ .then(fn=generate_clicked, inputs=ctrls, 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=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed') 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) 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 and auth_enabled else None, blocked_paths=[constants.AUTH_FILENAME] )