webui.py

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

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):
    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()
    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

                # delete Image 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


reload_javascript()

title = 'AI Image'

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:
    currentTask = gr.State(worker.AsyncTask(args=[]))
    with gr.Row(elem_id="dConts"):
        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=True, autofocus=True, elem_classes='type_row', show_copy_button=True)

                    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(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')
                advanced_checkbox = gr.Checkbox(label='Advanced', value=False, 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)
                    with gr.TabItem(label='Image Prompt') as ip_tab:
                        ip_advanced = gr.Checkbox(label='Advanced', value=True, container=True)

                        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=True) 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=True)
                                        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)

                        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(elem_id="IoOFixed"):
                            with gr.Row(elem_id="IoOFixed2"):
                                inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
                                invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
                            
                            inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False, show_copy_button=True)
                            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)
                            example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False)

                        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.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')
                    with gr.TabItem(label='Metadata') as load_tab:
                        with gr.Column():
                            metadata_input_image = grh.Image(label='Drag any image generated', source='upload', type='filepath')
                            metadata_json = gr.JSON(label='Metadata')
                            metadata_import_button = gr.Button(value='Apply Metadata')

                        def trigger_metadata_preview(filepath):
                            parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)

                            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)

            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, show_copy_button=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(visible=False) as advanced_column:
            with gr.TabItem(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=1)

                output_format = gr.Radio(label='Output Format',
                                            choices=modules.flags.output_formats,
                                            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,
                                            show_copy_button=True)
                seed_random = gr.Checkbox(label='Random', value=True)
                image_seed = gr.Textbox(label='Seed', value=0, max_lines=1, visible=False, show_copy_button=True) # 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'<a href="file={get_current_html_path(output_format)}" target="_blank">\U0001F4DA History Log</a>')

                history_link = gr.HTML()
                shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)

            with gr.TabItem(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',
                                            show_copy_button=True)
                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, show_copy_button=True)

                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.TabItem(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_enabled = gr.Checkbox(label='Enable', value=True,
                                                    elem_classes=['lora_enable', 'min_check'], scale=1)
                            lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
                                                    choices=['None'] + modules.config.lora_filenames, value=n,
                                                    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=v,
                                                    elem_classes='lora_weight', scale=5)
                            lora_ctrls += [lora_enabled, 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.Column(visible=False):
                with gr.Tab(label='Advanced', elem_id="advancedAdvancedSettings"):
                    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.')
                    dev_mode = gr.Checkbox(label='Developer Debug Mode', value=True, container=True)

                    with gr.Column(visible=True) 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 Image\'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.')
                            disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results', 
                                                        value=modules.config.default_performance == 'Extreme Speed',
                                                        interactive=modules.config.default_performance != 'Extreme Speed',
                                                        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)

                            if not args_manager.args.disable_metadata:
                                save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=True,
                                                                    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=True)

                                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=True,
                                                                    info='See the results from preprocessors.')
                            skipping_cn_preprocessor = gr.Checkbox(label='Skip Preprocessors', value=True,
                                                                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=True)
                            mixing_image_prompt_and_inpaint = gr.Checkbox(label='Mixing Image Prompt and Inpaint',
                                                                        value=True)

                            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 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]

                    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 = [gr.update(choices=modules.config.model_filenames)]
                        results += [gr.update(choices=['None'] + modules.config.model_filenames)]
                        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

                    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 +
                                               [gr.update(interactive=x != 'Extreme Speed', value=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, 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)

        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 = [currentTask, generate_image_grid]
        ctrls += [
            prompt, negative_prompt, style_selections,
            performance_selection, aspect_ratios_selection, image_number, output_format, 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 += [disable_preview, disable_intermediate_results, disable_seed_increment]
        ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
        ctrls += [sampler_name, scheduler_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_metadata:
            ctrls += [save_metadata_to_images, metadata_scheme]

        ctrls += ip_ctrls

        state_is_generating = gr.State(False)

        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_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, base_model,
                             refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
                             generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls

        load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)

        def trigger_metadata_import(filepath, state_is_generating):
            parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
            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.parse_json(parameters)

            return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)


        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')

        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), ["Image V2", "Image Enhance", "Image Sharp"]
            if mode == flags.desc_type_anime:
                from extras.wd14tagger import default_interrogator as default_interrogator_anime
                return default_interrogator_anime(img), ["Image V2", "Image Masterpiece"]
            return mode, ["Image V2"]

        desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image],
                       outputs=[prompt, style_selections], show_progress=True, queue=False)


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]
)