gradio-theme-ui.py

import datetime
import json
import mimetypes
import os
import sys
from functools import reduce
import warnings

import gradio as gr
import gradio.utils
import numpy as np
from PIL import Image, PngImagePlugin  # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call

from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path
from modules.ui_common import create_refresh_button
from modules.ui_gradio_extensions import reload_javascript


from modules.shared import opts, cmd_opts

import modules.codeformer_model
import modules.generation_parameters_copypaste as parameters_copypaste
import modules.gfpgan_model
import modules.hypernetworks.ui
import modules.scripts
import modules.shared as shared
import modules.styles
import modules.textual_inversion.ui
from modules import prompt_parser
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.textual_inversion import textual_inversion
import modules.hypernetworks.ui
from modules.generation_parameters_copypaste import image_from_url_text
import modules.extras

create_setting_component = ui_settings.create_setting_component

warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')

if not cmd_opts.share and not cmd_opts.listen:
    # fix gradio phoning home
    gradio.utils.version_check = lambda: None
    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'

if cmd_opts.ngrok is not None:
    import modules.ngrok as ngrok
    print('ngrok authtoken detected, trying to connect...')
    ngrok.connect(
        cmd_opts.ngrok,
        cmd_opts.port if cmd_opts.port is not None else 7860,
        cmd_opts.ngrok_options
        )


def gr_show(visible=True):
    return {"visible": visible, "__type__": "update"}


sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None

# Using constants for these since the variation selector isn't visible.
# Important that they exactly match script.js for tooltip to work.
random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
reuse_symbol = '\u267b\ufe0f'  # ♻️
paste_symbol = '\u2199\ufe0f'  # ↙
refresh_symbol = '\U0001f504'  # 🔄
save_style_symbol = '\U0001f4be'  # 💾
apply_style_symbol = '\U0001f4cb'  # 📋
clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
extra_networks_symbol = '\U0001F3B4'  # 🎴
switch_values_symbol = '\U000021C5' # ⇅
restore_progress_symbol = '\U0001F300' # 🌀
detect_image_size_symbol = '\U0001F4D0'  # 📐
up_down_symbol = '\u2195\ufe0f' # ↕️


plaintext_to_html = ui_common.plaintext_to_html


def send_gradio_gallery_to_image(x):
    if len(x) == 0:
        return None
    return image_from_url_text(x[0])


def add_style(name: str, prompt: str, negative_prompt: str):
    if name is None:
        return [gr_show() for x in range(4)]

    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
    shared.prompt_styles.styles[style.name] = style
    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
    # reserialize all styles every time we save them
    shared.prompt_styles.save_styles(shared.styles_filename)

    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]


def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
    from modules import processing, devices

    if not enable:
        return ""

    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)

    with devices.autocast():
        p.init([""], [0], [0])

    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"


def resize_from_to_html(width, height, scale_by):
    target_width = int(width * scale_by)
    target_height = int(height * scale_by)

    if not target_width or not target_height:
        return "no image selected"

    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"


def apply_styles(prompt, prompt_neg, styles):
    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)

    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]


def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
    if mode in {0, 1, 3, 4}:
        return [interrogation_function(ii_singles[mode]), None]
    elif mode == 2:
        return [interrogation_function(ii_singles[mode]["image"]), None]
    elif mode == 5:
        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
        images = shared.listfiles(ii_input_dir)
        print(f"Will process {len(images)} images.")
        if ii_output_dir != "":
            os.makedirs(ii_output_dir, exist_ok=True)
        else:
            ii_output_dir = ii_input_dir

        for image in images:
            img = Image.open(image)
            filename = os.path.basename(image)
            left, _ = os.path.splitext(filename)
            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))

        return [gr.update(), None]


def interrogate(image):
    prompt = shared.interrogator.interrogate(image.convert("RGB"))
    return gr.update() if prompt is None else prompt


def interrogate_deepbooru(image):
    prompt = deepbooru.model.tag(image)
    return gr.update() if prompt is None else prompt


def create_seed_inputs(target_interface):
    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
        seed.style(container=False)
        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')

        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)

    # Components to show/hide based on the 'Extra' checkbox
    seed_extras = []

    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
        seed_extras.append(seed_extra_row_1)
        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
        subseed.style(container=False)
        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")

    with FormRow(visible=False) as seed_extra_row_2:
        seed_extras.append(seed_extra_row_2)
        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")

    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])

    def change_visibility(show):
        return {comp: gr_show(show) for comp in seed_extras}

    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)

    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox



def connect_clear_prompt(button):
    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
    button.click(
        _js="clear_prompt",
        fn=None,
        inputs=[],
        outputs=[],
    )


def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
    def copy_seed(gen_info_string: str, index):
        res = -1

        try:
            gen_info = json.loads(gen_info_string)
            index -= gen_info.get('index_of_first_image', 0)

            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
                all_subseeds = gen_info.get('all_subseeds', [-1])
                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
            else:
                all_seeds = gen_info.get('all_seeds', [-1])
                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]

        except json.decoder.JSONDecodeError:
            if gen_info_string:
                errors.report(f"Error parsing JSON generation info: {gen_info_string}")

        return [res, gr_show(False)]

    reuse_seed.click(
        fn=copy_seed,
        _js="(x, y) => [x, selected_gallery_index()]",
        show_progress=False,
        inputs=[generation_info, dummy_component],
        outputs=[seed, dummy_component]
    )


def update_token_counter(text, steps):
    try:
        text, _ = extra_networks.parse_prompt(text)

        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)

    except Exception:
        # a parsing error can happen here during typing, and we don't want to bother the user with
        # messages related to it in console
        prompt_schedules = [[[steps, text]]]

    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
    prompts = [prompt_text for step, prompt_text in flat_prompts]
    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
    return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"


def create_toprow(is_img2img):
    id_part = "img2img" if is_img2img else "txt2img"

    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
            with gr.Row():
                with gr.Column(scale=80):
                    with gr.Row():
                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])

            with gr.Row():
                with gr.Column(scale=80):
                    with gr.Row():
                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])

        button_interrogate = None
        button_deepbooru = None
        if is_img2img:
            with gr.Column(scale=1, elem_classes="interrogate-col"):
                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')

                skip.click(
                    fn=lambda: shared.state.skip(),
                    inputs=[],
                    outputs=[],
                )

                interrupt.click(
                    fn=lambda: shared.state.interrupt(),
                    inputs=[],
                    outputs=[],
                )

            with gr.Row(elem_id=f"{id_part}_tools"):
                paste = ToolButton(value=paste_symbol, elem_id="paste")
                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)

                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")

                clear_prompt_button.click(
                    fn=lambda *x: x,
                    _js="confirm_clear_prompt",
                    inputs=[prompt, negative_prompt],
                    outputs=[prompt, negative_prompt],
                )

            with gr.Row(elem_id=f"{id_part}_styles_row"):
                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")

    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button


def setup_progressbar(*args, **kwargs):
    pass


def apply_setting(key, value):
    if value is None:
        return gr.update()

    if shared.cmd_opts.freeze_settings:
        return gr.update()

    # dont allow model to be swapped when model hash exists in prompt
    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
        return gr.update()

    if key == "sd_model_checkpoint":
        ckpt_info = sd_models.get_closet_checkpoint_match(value)

        if ckpt_info is not None:
            value = ckpt_info.title
        else:
            return gr.update()

    comp_args = opts.data_labels[key].component_args
    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
        return

    valtype = type(opts.data_labels[key].default)
    oldval = opts.data.get(key, None)
    opts.data[key] = valtype(value) if valtype != type(None) else value
    if oldval != value and opts.data_labels[key].onchange is not None:
        opts.data_labels[key].onchange()

    opts.save(shared.config_filename)
    return getattr(opts, key)


def create_output_panel(tabname, outdir):
    return ui_common.create_output_panel(tabname, outdir)


def create_sampler_and_steps_selection(choices, tabname):
    if opts.samplers_in_dropdown:
        with FormRow(elem_id=f"sampler_selection_{tabname}"):
            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
    else:
        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")

    return steps, sampler_index


def ordered_ui_categories():
    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}

    for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
        yield category


def create_override_settings_dropdown(tabname, row):
    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)

    dropdown.change(
        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
        inputs=[dropdown],
        outputs=[dropdown],
    )

    return dropdown


def create_ui():
    import modules.img2img
    import modules.txt2img

    reload_javascript()

    parameters_copypaste.reset()

    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)

    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)

        dummy_component = gr.Label(visible=False)
        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)

        with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
            from modules import ui_extra_networks
            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')

        with gr.Row().style(equal_height=False):
            with gr.Column(variant='compact', elem_id="txt2img_settings"):
                modules.scripts.scripts_txt2img.prepare_ui()

                for category in ordered_ui_categories():
                    if category == "sampler":
                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")

                    elif category == "dimensions":
                        with FormRow():
                            with gr.Column(elem_id="txt2img_column_size", scale=4):
                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")

                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")

                            if opts.dimensions_and_batch_together:
                                with gr.Column(elem_id="txt2img_column_batch"):
                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")

                    elif category == "cfg":
                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")

                    elif category == "seed":
                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')

                    elif category == "checkboxes":
                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)

                    elif category == "hires_fix":
                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")

                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")

                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
                                hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")

                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
                                with gr.Column(scale=80):
                                    with gr.Row():
                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
                                with gr.Column(scale=80):
                                    with gr.Row():
                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])

                    elif category == "batch":
                        if not opts.dimensions_and_batch_together:
                            with FormRow(elem_id="txt2img_column_batch"):
                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")

                    elif category == "override_settings":
                        with FormRow(elem_id="txt2img_override_settings_row") as row:
                            override_settings = create_override_settings_dropdown('txt2img', row)

                    elif category == "scripts":
                        with FormGroup(elem_id="txt2img_script_container"):
                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()

                    else:
                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)

            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]

            for component in hr_resolution_preview_inputs:
                event = component.release if isinstance(component, gr.Slider) else component.change

                event(
                    fn=calc_resolution_hires,
                    inputs=hr_resolution_preview_inputs,
                    outputs=[hr_final_resolution],
                    show_progress=False,
                )
                event(
                    None,
                    _js="onCalcResolutionHires",
                    inputs=hr_resolution_preview_inputs,
                    outputs=[],
                    show_progress=False,
                )

            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)

            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)

            txt2img_args = dict(
                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
                _js="submit",
                inputs=[
                    dummy_component,
                    txt2img_prompt,
                    txt2img_negative_prompt,
                    txt2img_prompt_styles,
                    steps,
                    sampler_index,
                    restore_faces,
                    tiling,
                    batch_count,
                    batch_size,
                    cfg_scale,
                    seed,
                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
                    height,
                    width,
                    enable_hr,
                    denoising_strength,
                    hr_scale,
                    hr_upscaler,
                    hr_second_pass_steps,
                    hr_resize_x,
                    hr_resize_y,
                    hr_sampler_index,
                    hr_prompt,
                    hr_negative_prompt,
                    override_settings,

                ] + custom_inputs,

                outputs=[
                    txt2img_gallery,
                    generation_info,
                    html_info,
                    html_log,
                ],
                show_progress=False,
            )

            txt2img_prompt.submit(**txt2img_args)
            submit.click(**txt2img_args)

            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)

            restore_progress_button.click(
                fn=progress.restore_progress,
                _js="restoreProgressTxt2img",
                inputs=[dummy_component],
                outputs=[
                    txt2img_gallery,
                    generation_info,
                    html_info,
                    html_log,
                ],
                show_progress=False,
            )

            txt_prompt_img.change(
                fn=modules.images.image_data,
                inputs=[
                    txt_prompt_img
                ],
                outputs=[
                    txt2img_prompt,
                    txt_prompt_img
                ],
                show_progress=False,
            )

            enable_hr.change(
                fn=lambda x: gr_show(x),
                inputs=[enable_hr],
                outputs=[hr_options],
                show_progress = False,
            )

            txt2img_paste_fields = [
                (txt2img_prompt, "Prompt"),
                (txt2img_negative_prompt, "Negative prompt"),
                (steps, "Steps"),
                (sampler_index, "Sampler"),
                (restore_faces, "Face restoration"),
                (cfg_scale, "CFG scale"),
                (seed, "Seed"),
                (width, "Size-1"),
                (height, "Size-2"),
                (batch_size, "Batch size"),
                (subseed, "Variation seed"),
                (subseed_strength, "Variation seed strength"),
                (seed_resize_from_w, "Seed resize from-1"),
                (seed_resize_from_h, "Seed resize from-2"),
                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                (denoising_strength, "Denoising strength"),
                (enable_hr, lambda d: "Denoising strength" in d),
                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
                (hr_scale, "Hires upscale"),
                (hr_upscaler, "Hires upscaler"),
                (hr_second_pass_steps, "Hires steps"),
                (hr_resize_x, "Hires resize-1"),
                (hr_resize_y, "Hires resize-2"),
                (hr_sampler_index, "Hires sampler"),
                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
                (hr_prompt, "Hires prompt"),
                (hr_negative_prompt, "Hires negative prompt"),
                (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
                *modules.scripts.scripts_txt2img.infotext_fields
            ]
            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
            ))

            txt2img_preview_params = [
                txt2img_prompt,
                txt2img_negative_prompt,
                steps,
                sampler_index,
                cfg_scale,
                seed,
                width,
                height,
            ]

            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])

            ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)

    modules.scripts.scripts_current = modules.scripts.scripts_img2img
    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)

    with gr.Blocks(analytics_enabled=False) as img2img_interface:
        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)

        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)

        with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
            from modules import ui_extra_networks
            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')

        with FormRow().style(equal_height=False):
            with gr.Column(variant='compact', elem_id="img2img_settings"):
                copy_image_buttons = []
                copy_image_destinations = {}

                def add_copy_image_controls(tab_name, elem):
                    with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
                        gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")

                        for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
                            if name == tab_name:
                                gr.Button(title, interactive=False)
                                copy_image_destinations[name] = elem
                                continue

                            button = gr.Button(title)
                            copy_image_buttons.append((button, name, elem))

                with gr.Tabs(elem_id="mode_img2img"):
                    img2img_selected_tab = gr.State(0)

                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
                        add_copy_image_controls('img2img', init_img)

                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
                        add_copy_image_controls('sketch', sketch)

                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
                        add_copy_image_controls('inpaint', init_img_with_mask)

                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
                        inpaint_color_sketch_orig = gr.State(None)
                        add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)

                        def update_orig(image, state):
                            if image is not None:
                                same_size = state is not None and state.size == image.size
                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
                                edited = same_size and has_exact_match
                                return image if not edited or state is None else state

                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)

                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")

                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
                        gr.HTML(
                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
                            f"{hidden}</p>"
                        )
                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
                        with gr.Accordion("PNG info", open=False):
                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")

                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]

                    for i, tab in enumerate(img2img_tabs):
                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])

                def copy_image(img):
                    if isinstance(img, dict) and 'image' in img:
                        return img['image']

                    return img

                for button, name, elem in copy_image_buttons:
                    button.click(
                        fn=copy_image,
                        inputs=[elem],
                        outputs=[copy_image_destinations[name]],
                    )
                    button.click(
                        fn=lambda: None,
                        _js=f"switch_to_{name.replace(' ', '_')}",
                        inputs=[],
                        outputs=[],
                    )

                with FormRow():
                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")

                modules.scripts.scripts_img2img.prepare_ui()

                for category in ordered_ui_categories():
                    if category == "sampler":
                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")

                    elif category == "dimensions":
                        with FormRow():
                            with gr.Column(elem_id="img2img_column_size", scale=4):
                                selected_scale_tab = gr.State(value=0)

                                with gr.Tabs():
                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
                                        with FormRow():
                                            with gr.Column(elem_id="img2img_column_size", scale=4):
                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")

                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")

                                        with FormRow():
                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")

                                    on_change_args = dict(
                                        fn=resize_from_to_html,
                                        _js="currentImg2imgSourceResolution",
                                        inputs=[dummy_component, dummy_component, scale_by],
                                        outputs=scale_by_html,
                                        show_progress=False,
                                    )

                                    scale_by.release(**on_change_args)
                                    button_update_resize_to.click(**on_change_args)

                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
                                    for component in [init_img, sketch]:
                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)

                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])

                            if opts.dimensions_and_batch_together:
                                with gr.Column(elem_id="img2img_column_batch"):
                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")

                    elif category == "cfg":
                        with FormGroup():
                            with FormRow():
                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")

                    elif category == "seed":
                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')

                    elif category == "checkboxes":
                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")

                    elif category == "batch":
                        if not opts.dimensions_and_batch_together:
                            with FormRow(elem_id="img2img_column_batch"):
                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")

                    elif category == "override_settings":
                        with FormRow(elem_id="img2img_override_settings_row") as row:
                            override_settings = create_override_settings_dropdown('img2img', row)

                    elif category == "scripts":
                        with FormGroup(elem_id="img2img_script_container"):
                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()

                    elif category == "inpaint":
                        with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
                            with FormRow():
                                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
                                mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")

                            with FormRow():
                                inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")

                            with FormRow():
                                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")

                            with FormRow():
                                with gr.Column():
                                    inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")

                                with gr.Column(scale=4):
                                    inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")

                            def select_img2img_tab(tab):
                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),

                            for i, elem in enumerate(img2img_tabs):
                                elem.select(
                                    fn=lambda tab=i: select_img2img_tab(tab),
                                    inputs=[],
                                    outputs=[inpaint_controls, mask_alpha],
                                )
                    else:
                        modules.scripts.scripts_img2img.setup_ui_for_section(category)

            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)

            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)

            img2img_prompt_img.change(
                fn=modules.images.image_data,
                inputs=[
                    img2img_prompt_img
                ],
                outputs=[
                    img2img_prompt,
                    img2img_prompt_img
                ],
                show_progress=False,
            )

            img2img_args = dict(
                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                _js="submit_img2img",
                inputs=[
                    dummy_component,
                    dummy_component,
                    img2img_prompt,
                    img2img_negative_prompt,
                    img2img_prompt_styles,
                    init_img,
                    sketch,
                    init_img_with_mask,
                    inpaint_color_sketch,
                    inpaint_color_sketch_orig,
                    init_img_inpaint,
                    init_mask_inpaint,
                    steps,
                    sampler_index,
                    mask_blur,
                    mask_alpha,
                    inpainting_fill,
                    restore_faces,
                    tiling,
                    batch_count,
                    batch_size,
                    cfg_scale,
                    image_cfg_scale,
                    denoising_strength,
                    seed,
                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
                    selected_scale_tab,
                    height,
                    width,
                    scale_by,
                    resize_mode,
                    inpaint_full_res,
                    inpaint_full_res_padding,
                    inpainting_mask_invert,
                    img2img_batch_input_dir,
                    img2img_batch_output_dir,
                    img2img_batch_inpaint_mask_dir,
                    override_settings,
                    img2img_batch_use_png_info,
                    img2img_batch_png_info_props,
                    img2img_batch_png_info_dir,
                ] + custom_inputs,
                outputs=[
                    img2img_gallery,
                    generation_info,
                    html_info,
                    html_log,
                ],
                show_progress=False,
            )

            interrogate_args = dict(
                _js="get_img2img_tab_index",
                inputs=[
                    dummy_component,
                    img2img_batch_input_dir,
                    img2img_batch_output_dir,
                    init_img,
                    sketch,
                    init_img_with_mask,
                    inpaint_color_sketch,
                    init_img_inpaint,
                ],
                outputs=[img2img_prompt, dummy_component],
            )

            img2img_prompt.submit(**img2img_args)
            submit.click(**img2img_args)

            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)

            detect_image_size_btn.click(
                fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
                _js="currentImg2imgSourceResolution",
                inputs=[dummy_component, dummy_component, dummy_component],
                outputs=[width, height],
                show_progress=False,
            )

            restore_progress_button.click(
                fn=progress.restore_progress,
                _js="restoreProgressImg2img",
                inputs=[dummy_component],
                outputs=[
                    img2img_gallery,
                    generation_info,
                    html_info,
                    html_log,
                ],
                show_progress=False,
            )

            img2img_interrogate.click(
                fn=lambda *args: process_interrogate(interrogate, *args),
                **interrogate_args,
            )

            img2img_deepbooru.click(
                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
                **interrogate_args,
            )

            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]

            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
                button.click(
                    fn=add_style,
                    _js="ask_for_style_name",
                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
                    inputs=[dummy_component, prompt, negative_prompt],
                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
                )

            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
                button.click(
                    fn=apply_styles,
                    _js=js_func,
                    inputs=[prompt, negative_prompt, styles],
                    outputs=[prompt, negative_prompt, styles],
                )

            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])

            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)

            img2img_paste_fields = [
                (img2img_prompt, "Prompt"),
                (img2img_negative_prompt, "Negative prompt"),
                (steps, "Steps"),
                (sampler_index, "Sampler"),
                (restore_faces, "Face restoration"),
                (cfg_scale, "CFG scale"),
                (image_cfg_scale, "Image CFG scale"),
                (seed, "Seed"),
                (width, "Size-1"),
                (height, "Size-2"),
                (batch_size, "Batch size"),
                (subseed, "Variation seed"),
                (subseed_strength, "Variation seed strength"),
                (seed_resize_from_w, "Seed resize from-1"),
                (seed_resize_from_h, "Seed resize from-2"),
                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                (denoising_strength, "Denoising strength"),
                (mask_blur, "Mask blur"),
                *modules.scripts.scripts_img2img.infotext_fields
            ]
            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
            ))

    modules.scripts.scripts_current = None

    with gr.Blocks(analytics_enabled=False) as extras_interface:
        ui_postprocessing.create_ui()

    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
        with gr.Row().style(equal_height=False):
            with gr.Column(variant='panel'):
                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")

            with gr.Column(variant='panel'):
                html = gr.HTML()
                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
                html2 = gr.HTML()
                with gr.Row():
                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])

                for tabname, button in buttons.items():
                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
                    ))

        image.change(
            fn=wrap_gradio_call(modules.extras.run_pnginfo),
            inputs=[image],
            outputs=[html, generation_info, html2],
        )

    def update_interp_description(value):
        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
        interp_descriptions = {
            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
        }
        return interp_descriptions[value]

    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
        with gr.Row().style(equal_height=False):
            with gr.Column(variant='compact'):
                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")

                with FormRow(elem_id="modelmerger_models"):
                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")

                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")

                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")

                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])

                with FormRow():
                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")

                with FormRow():
                    with gr.Column():
                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")

                    with gr.Column():
                        with FormRow():
                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")

                with FormRow():
                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")

                with gr.Row():
                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')

            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
                with gr.Group(elem_id="modelmerger_results_panel"):
                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)

    with gr.Blocks(analytics_enabled=False) as train_interface:
        with gr.Row().style(equal_height=False):
            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")

        with gr.Row(variant="compact").style(equal_height=False):
            with gr.Tabs(elem_id="train_tabs"):

                with gr.Tab(label="Create embedding", id="create_embedding"):
                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")

                    with gr.Row():
                        with gr.Column(scale=3):
                            gr.HTML(value="")

                        with gr.Column():
                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")

                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
                    new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")

                    with gr.Row():
                        with gr.Column(scale=3):
                            gr.HTML(value="")

                        with gr.Column():
                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")

                with gr.Tab(label="Preprocess images", id="preprocess_images"):
                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")

                    with gr.Row():
                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")

                    with gr.Row(visible=False) as process_split_extra_row:
                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")

                    with gr.Row(visible=False) as process_focal_crop_row:
                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")

                    with gr.Column(visible=False) as process_multicrop_col:
                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
                        with gr.Row():
                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
                        with gr.Row():
                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
                        with gr.Row():
                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")

                    with gr.Row():
                        with gr.Column(scale=3):
                            gr.HTML(value="")

                        with gr.Column():
                            with gr.Row():
                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")

                    process_split.change(
                        fn=lambda show: gr_show(show),
                        inputs=[process_split],
                        outputs=[process_split_extra_row],
                    )

                    process_focal_crop.change(
                        fn=lambda show: gr_show(show),
                        inputs=[process_focal_crop],
                        outputs=[process_focal_crop_row],
                    )

                    process_multicrop.change(
                        fn=lambda show: gr_show(show),
                        inputs=[process_multicrop],
                        outputs=[process_multicrop_col],
                    )

                def get_textual_inversion_template_names():
                    return sorted(textual_inversion.textual_inversion_templates)

                with gr.Tab(label="Train", id="train"):
                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                    with FormRow():
                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")

                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")

                    with FormRow():
                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")

                    with FormRow():
                        clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
                        clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)

                    with FormRow():
                        batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
                        gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")

                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")

                    with FormRow():
                        template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
                        create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")

                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")

                    with FormRow():
                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")

                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")

                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")

                    shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
                    tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")

                    latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")

                    with gr.Row():
                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")

                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)

                script_callbacks.ui_train_tabs_callback(params)

            with gr.Column(elem_id='ti_gallery_container'):
                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
                gr.HTML(elem_id="ti_progress", value="")
                ti_outcome = gr.HTML(elem_id="ti_error", value="")

        create_embedding.click(
            fn=modules.textual_inversion.ui.create_embedding,
            inputs=[
                new_embedding_name,
                initialization_text,
                nvpt,
                overwrite_old_embedding,
            ],
            outputs=[
                train_embedding_name,
                ti_output,
                ti_outcome,
            ]
        )

        create_hypernetwork.click(
            fn=modules.hypernetworks.ui.create_hypernetwork,
            inputs=[
                new_hypernetwork_name,
                new_hypernetwork_sizes,
                overwrite_old_hypernetwork,
                new_hypernetwork_layer_structure,
                new_hypernetwork_activation_func,
                new_hypernetwork_initialization_option,
                new_hypernetwork_add_layer_norm,
                new_hypernetwork_use_dropout,
                new_hypernetwork_dropout_structure
            ],
            outputs=[
                train_hypernetwork_name,
                ti_output,
                ti_outcome,
            ]
        )

        run_preprocess.click(
            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
            _js="start_training_textual_inversion",
            inputs=[
                dummy_component,
                process_src,
                process_dst,
                process_width,
                process_height,
                preprocess_txt_action,
                process_keep_original_size,
                process_flip,
                process_split,
                process_caption,
                process_caption_deepbooru,
                process_split_threshold,
                process_overlap_ratio,
                process_focal_crop,
                process_focal_crop_face_weight,
                process_focal_crop_entropy_weight,
                process_focal_crop_edges_weight,
                process_focal_crop_debug,
                process_multicrop,
                process_multicrop_mindim,
                process_multicrop_maxdim,
                process_multicrop_minarea,
                process_multicrop_maxarea,
                process_multicrop_objective,
                process_multicrop_threshold,
            ],
            outputs=[
                ti_output,
                ti_outcome,
            ],
        )

        train_embedding.click(
            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
            _js="start_training_textual_inversion",
            inputs=[
                dummy_component,
                train_embedding_name,
                embedding_learn_rate,
                batch_size,
                gradient_step,
                dataset_directory,
                log_directory,
                training_width,
                training_height,
                varsize,
                steps,
                clip_grad_mode,
                clip_grad_value,
                shuffle_tags,
                tag_drop_out,
                latent_sampling_method,
                use_weight,
                create_image_every,
                save_embedding_every,
                template_file,
                save_image_with_stored_embedding,
                preview_from_txt2img,
                *txt2img_preview_params,
            ],
            outputs=[
                ti_output,
                ti_outcome,
            ]
        )

        train_hypernetwork.click(
            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
            _js="start_training_textual_inversion",
            inputs=[
                dummy_component,
                train_hypernetwork_name,
                hypernetwork_learn_rate,
                batch_size,
                gradient_step,
                dataset_directory,
                log_directory,
                training_width,
                training_height,
                varsize,
                steps,
                clip_grad_mode,
                clip_grad_value,
                shuffle_tags,
                tag_drop_out,
                latent_sampling_method,
                use_weight,
                create_image_every,
                save_embedding_every,
                template_file,
                preview_from_txt2img,
                *txt2img_preview_params,
            ],
            outputs=[
                ti_output,
                ti_outcome,
            ]
        )

        interrupt_training.click(
            fn=lambda: shared.state.interrupt(),
            inputs=[],
            outputs=[],
        )

        interrupt_preprocessing.click(
            fn=lambda: shared.state.interrupt(),
            inputs=[],
            outputs=[],
        )

    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)

    settings = ui_settings.UiSettings()
    settings.create_ui(loadsave, dummy_component)

    interfaces = [
        (txt2img_interface, "txt2img", "txt2img"),
        (img2img_interface, "img2img", "img2img"),
        (extras_interface, "Extras", "extras"),
        (pnginfo_interface, "PNG Info", "pnginfo"),
        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
        (train_interface, "Train", "train"),
    ]

    interfaces += script_callbacks.ui_tabs_callback()
    interfaces += [(settings.interface, "Settings", "settings")]

    extensions_interface = ui_extensions.create_ui()
    interfaces += [(extensions_interface, "Extensions", "extensions")]

    shared.tab_names = []
    for _interface, label, _ifid in interfaces:
        shared.tab_names.append(label)




















    with gr.Blocks(theme='gradio/base', analytics_enabled=False, title="Stable Diffusion") as demo:
        settings.add_quicksettings()

        parameters_copypaste.connect_paste_params_buttons()

        with gr.Tabs(elem_id="tabs") as tabs:
            tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
            sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))

            for interface, label, ifid in sorted_interfaces:
                if label in shared.opts.hidden_tabs:
                    continue
                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
                    interface.render()

            for interface, _label, ifid in interfaces:
                if ifid in ["extensions", "settings"]:
                    continue

                loadsave.add_block(interface, ifid)

            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)

            loadsave.setup_ui()

        if os.path.exists(os.path.join(script_path, "notification.mp3")):
            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)

        footer = shared.html("footer.html")
        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
        gr.HTML(footer, elem_id="footer")

        settings.add_functionality(demo)

        update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
        demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])

        def modelmerger(*args):
            try:
                results = modules.extras.run_modelmerger(*args)
            except Exception as e:
                errors.report("Error loading/saving model file", exc_info=True)
                modules.sd_models.list_models()  # to remove the potentially missing models from the list
                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
            return results

        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
        modelmerger_merge.click(
            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
            _js='modelmerger',
            inputs=[
                dummy_component,
                primary_model_name,
                secondary_model_name,
                tertiary_model_name,
                interp_method,
                interp_amount,
                save_as_half,
                custom_name,
                checkpoint_format,
                config_source,
                bake_in_vae,
                discard_weights,
                save_metadata,
            ],
            outputs=[
                primary_model_name,
                secondary_model_name,
                tertiary_model_name,
                settings.component_dict['sd_model_checkpoint'],
                modelmerger_result,
            ]
        )

    loadsave.dump_defaults()
    demo.ui_loadsave = loadsave

    # Required as a workaround for change() event not triggering when loading values from ui-config.json
    interp_description.value = update_interp_description(interp_method.value)

    return demo


def versions_html():
    import torch
    import launch

    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
    commit = launch.commit_hash()
    tag = launch.git_tag()

    if shared.xformers_available:
        import xformers
        xformers_version = xformers.__version__
    else:
        xformers_version = "N/A"

    return f"""
version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
&#x2000;•&#x2000;
python: <span title="{sys.version}">{python_version}</span>
&#x2000;•&#x2000;
torch: {getattr(torch, '__long_version__',torch.__version__)}
&#x2000;•&#x2000;
xformers: {xformers_version}
&#x2000;•&#x2000;
gradio: {gr.__version__}
&#x2000;•&#x2000;
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
"""


def setup_ui_api(app):
    from pydantic import BaseModel, Field
    from typing import List

    class QuicksettingsHint(BaseModel):
        name: str = Field(title="Name of the quicksettings field")
        label: str = Field(title="Label of the quicksettings field")

    def quicksettings_hint():
        return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]

    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])

    app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])

    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])

    def download_sysinfo(attachment=False):
        from fastapi.responses import PlainTextResponse

        text = sysinfo.get()
        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"

        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})

    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])