naked

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('<a href="https://github.com/lllyasviel/Fooocus/discussions/390" target="_blank">\U0001F4D4 Document</a>')
                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). <a href="https://github.com/lllyasviel/Fooocus/discussions/557" target="_blank">\U0001F4D4 Document</a>')

                    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():
                        inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas')
                        inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', height=500, visible=False)

                    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 <a href="https://github.com/lllyasviel/Fooocus/discussions/414" target="_blank">\U0001F4D4 Document</a>')
                    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('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
        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'<a href="file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')

        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('<a href="https://github.com/lllyasviel/Fooocus/discussions/117" target="_blank">\U0001F4D4 Document</a>')
            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_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 process before any mask invert)')
                    inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
                    invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
                    
                    inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine,
                                     inpaint_strength, inpaint_respective_field,
                                     inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate]

                    inpaint_mask_upload_checkbox.change(lambda x: gr.update(visible=x),
                                                       inputs=inpaint_mask_upload_checkbox,
                                                       outputs=inpaint_mask_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]

            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, inpaint_mask_image]
    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] )