stable-diffusion-webui/modules/ui.py

import datetime
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 gradio_extensons  # noqa: F401
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
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.generation_parameters_copypaste as parameters_copypaste
import modules.hypernetworks.ui as hypernetworks_ui
import modules.textual_inversion.ui as textual_inversion_ui
import modules.textual_inversion.textual_inversion as textual_inversion
import modules.shared as shared
import modules.images
from modules import prompt_parser
from modules.sd_hijack import model_hijack
from modules.generation_parameters_copypaste import image_from_url_text

create_setting_component = ui_settings.create_setting_component

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

# 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')

# Likewise, add explicit content-type header for certain missing image types
mimetypes.add_type('image/webp', '.webp')

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'  # 📐


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 calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
    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)
    p.calculate_target_resolution()

    return f"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 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 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 update_token_counter(text, steps, *, is_positive=True):
    try:
        text, _ = extra_networks.parse_prompt(text)

        if is_positive:
            _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
        else:
            prompt_flat_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 update_negative_prompt_token_counter(text, steps):
    return update_token_counter(text, steps, is_positive=False)


class Toprow:
    """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""

    def __init__(self, is_img2img):
        id_part = "img2img" if is_img2img else "txt2img"
        self.id_part = id_part

        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():
                            self.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"])
                            self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)

                with gr.Row():
                    with gr.Column(scale=80):
                        with gr.Row():
                            self.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"])

            self.button_interrogate = None
            self.button_deepbooru = None
            if is_img2img:
                with gr.Column(scale=1, elem_classes="interrogate-col"):
                    self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
                    self.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"):
                    self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
                    self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
                    self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')

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

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

                with gr.Row(elem_id=f"{id_part}_tools"):
                    self.paste = ToolButton(value=paste_symbol, elem_id="paste", tooltip="Read generation parameters from prompt or last generation if prompt is empty into user interface.")
                    self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt", tooltip="Clear prompt")
                    self.apply_styles = ToolButton(value=ui_prompt_styles.styles_materialize_symbol, elem_id=f"{id_part}_style_apply", tooltip="Apply all selected styles to prompts.")
                    self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False, tooltip="Restore progress")

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

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

                self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
                self.ui_styles.setup_apply_button(self.apply_styles)

        self.prompt_img.change(
            fn=modules.images.image_data,
            inputs=[self.prompt_img],
            outputs=[self.prompt, self.prompt_img],
            show_progress=False,
        )


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_name = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0])
            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_name = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0])

    return steps, sampler_name


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()

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

    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
        toprow = Toprow(is_img2img=False)

        dummy_component = gr.Label(visible=False)

        extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
        extra_tabs.__enter__()

        with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
            with gr.Column(variant='compact', elem_id="txt2img_settings"):
                scripts.scripts_txt2img.prepare_ui()

                for category in ordered_ui_categories():
                    if category == "sampler":
                        steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "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", tooltip="Switch width/height")

                            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":
                        with gr.Row():
                            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 == "checkboxes":
                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
                            pass

                    elif category == "accordions":
                        with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
                            with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
                                with enable_hr.extra():
                                    hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)

                                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_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
                                    create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")

                                    hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")

                                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"])

                            scripts.scripts_txt2img.setup_ui_for_section(category)

                    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 = scripts.scripts_txt2img.setup_ui()

                    if category not in {"accordions"}:
                        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)

            txt2img_args = dict(
                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
                _js="submit",
                inputs=[
                    dummy_component,
                    toprow.prompt,
                    toprow.negative_prompt,
                    toprow.ui_styles.dropdown,
                    steps,
                    sampler_name,
                    batch_count,
                    batch_size,
                    cfg_scale,
                    height,
                    width,
                    enable_hr,
                    denoising_strength,
                    hr_scale,
                    hr_upscaler,
                    hr_second_pass_steps,
                    hr_resize_x,
                    hr_resize_y,
                    hr_checkpoint_name,
                    hr_sampler_name,
                    hr_prompt,
                    hr_negative_prompt,
                    override_settings,

                ] + custom_inputs,

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

            toprow.prompt.submit(**txt2img_args)
            toprow.submit.click(**txt2img_args)

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

            toprow.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,
            )

            txt2img_paste_fields = [
                (toprow.prompt, "Prompt"),
                (toprow.negative_prompt, "Negative prompt"),
                (steps, "Steps"),
                (sampler_name, "Sampler"),
                (cfg_scale, "CFG scale"),
                (width, "Size-1"),
                (height, "Size-2"),
                (batch_size, "Batch size"),
                (toprow.ui_styles.dropdown, 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 and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" 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_checkpoint_name, "Hires checkpoint"),
                (hr_sampler_name, "Hires sampler"),
                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" 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()),
                *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=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
            ))

            txt2img_preview_params = [
                toprow.prompt,
                toprow.negative_prompt,
                steps,
                sampler_name,
                cfg_scale,
                scripts.scripts_txt2img.script('Seed').seed,
                width,
                height,
            ]

            toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
            toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])

        extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
        ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)

        extra_tabs.__exit__()

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

    with gr.Blocks(analytics_enabled=False) as img2img_interface:
        toprow = Toprow(is_img2img=True)

        extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
        extra_tabs.__enter__()

        with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(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", 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="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
                        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", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
                        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="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
                        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", image_mode="RGBA", 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", "Model hash"], 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")

                scripts.scripts_img2img.prepare_ui()

                for category in ordered_ui_categories():
                    if category == "sampler":
                        steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "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", tooltip="Switch width/height")
                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")

                                    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 == "denoising":
                        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 == "cfg":
                        with gr.Row():
                            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)

                    elif category == "checkboxes":
                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
                            pass

                    elif category == "accordions":
                        with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
                            scripts.scripts_img2img.setup_ui_for_section(category)

                    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 = 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],
                                )

                    if category not in {"accordions"}:
                        scripts.scripts_img2img.setup_ui_for_section(category)

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

            img2img_args = dict(
                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                _js="submit_img2img",
                inputs=[
                    dummy_component,
                    dummy_component,
                    toprow.prompt,
                    toprow.negative_prompt,
                    toprow.ui_styles.dropdown,
                    init_img,
                    sketch,
                    init_img_with_mask,
                    inpaint_color_sketch,
                    inpaint_color_sketch_orig,
                    init_img_inpaint,
                    init_mask_inpaint,
                    steps,
                    sampler_name,
                    mask_blur,
                    mask_alpha,
                    inpainting_fill,
                    batch_count,
                    batch_size,
                    cfg_scale,
                    image_cfg_scale,
                    denoising_strength,
                    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=[toprow.prompt, dummy_component],
            )

            toprow.prompt.submit(**img2img_args)
            toprow.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,
            )

            toprow.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,
            )

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

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

            toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
            toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])

            img2img_paste_fields = [
                (toprow.prompt, "Prompt"),
                (toprow.negative_prompt, "Negative prompt"),
                (steps, "Steps"),
                (sampler_name, "Sampler"),
                (cfg_scale, "CFG scale"),
                (image_cfg_scale, "Image CFG scale"),
                (width, "Size-1"),
                (height, "Size-2"),
                (batch_size, "Batch size"),
                (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                (denoising_strength, "Denoising strength"),
                (mask_blur, "Mask blur"),
                *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=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
            ))

        extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
        ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)

        extra_tabs.__exit__()

    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(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],
        )

    modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()

    with gr.Blocks(analytics_enabled=False) as train_interface:
        with gr.Row(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", 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=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', columns=4)
                gr.HTML(elem_id="ti_progress", value="")
                ti_outcome = gr.HTML(elem_id="ti_error", value="")

        create_embedding.click(
            fn=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=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(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(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(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_ui.blocks, "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=shared.gradio_theme, 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()

                if ifid not in ["extensions", "settings"]:
                    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])

        modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])

    loadsave.dump_defaults()
    demo.ui_loadsave = loadsave

    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

    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"])