Patch line ui endings

1 files changed, 1814 insertions, 1814 deletions

diff --git a/modules/ui.py b/modules/ui.py
index a2b06aae..54d3293a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1814 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# 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 != 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 != None else 7860, cmd_opts.ngrok_region)
-
-
-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
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# 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'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
-paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\U0001f4c2'  # 📂
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
-    import csv
-    filenames = []
-    fullfns = []
-
-    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
-    class MyObject:
-        def __init__(self, d=None):
-            if d is not None:
-                for key, value in d.items():
-                    setattr(self, key, value)
-
-    data = json.loads(js_data)
-
-    p = MyObject(data)
-    path = opts.outdir_save
-    save_to_dirs = opts.use_save_to_dirs_for_ui
-    extension: str = opts.samples_format
-    start_index = 0
-
-    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
-        images = [images[index]]
-        start_index = index
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
-    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
-        at_start = file.tell() == 0
-        writer = csv.writer(file)
-        if at_start:
-            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        for image_index, filedata in enumerate(images, start_index):
-            image = image_from_url_text(filedata)
-
-            is_grid = image_index < p.index_of_first_image
-            i = 0 if is_grid else (image_index - p.index_of_first_image)
-
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
-            filename = os.path.relpath(fullfn, path)
-            filenames.append(filename)
-            fullfns.append(fullfn)
-            if txt_fullfn:
-                filenames.append(os.path.basename(txt_fullfn))
-                fullfns.append(txt_fullfn)
-
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
-    # Make Zip
-    if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
-
-        from zipfile import ZipFile
-        with ZipFile(zip_filepath, "w") as zip_file:
-            for i in range(len(fullfns)):
-                with open(fullfns[i], mode="rb") as f:
-                    zip_file.writestr(filenames[i], f.read())
-        fullfns.insert(0, zip_filepath)
-
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
-    if progress == 0:
-        return ""
-    else:
-        time_since_start = time.time() - shared.state.time_start
-        eta = (time_since_start/progress)
-        eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
-            if eta_relative > 3600:
-                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
-            elif eta_relative > 60:
-                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
-            else:
-                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
-        else:
-            return ""
-
-
-def check_progress_call(id_part):
-    if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False), gr_show(False)
-
-    progress = 0
-
-    if shared.state.job_count > 0:
-        progress += shared.state.job_no / shared.state.job_count
-    if shared.state.sampling_steps > 0:
-        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
-    if time_left != "":
-        shared.state.time_left_force_display = True
-
-    progress = min(progress, 1)
-
-    progressbar = ""
-    if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
-    image = gr_show(False)
-    preview_visibility = gr_show(False)
-
-    if opts.show_progress_every_n_steps != 0:
-        shared.state.set_current_image()
-        image = shared.state.current_image
-
-        if image is None:
-            image = gr.update(value=None)
-        else:
-            preview_visibility = gr_show(True)
-
-    if shared.state.textinfo is not None:
-        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
-    else:
-        textinfo_result = gr_show(False)
-
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
-    shared.state.job_count = -1
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-    shared.state.time_left_force_display = False
-
-    return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-def visit(x, func, path=""):
-    if hasattr(x, 'children'):
-        for c in x.children:
-            visit(c, func, path)
-    elif x.label is not None:
-        func(path + "/" + str(x.label), x)
-
-
-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(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
-    return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
-    return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with gr.Row(visible=False) as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
-    with gr.Row(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=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
-    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
-    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
-
-    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_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 as e:
-            if gen_info_string != '':
-                print("Error parsing JSON generation info:", file=sys.stderr)
-                print(gen_info_string, file=sys.stderr)
-
-        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:
-        _, 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]
-    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
-    style_class = ' class="red"' if (token_count > max_length) else ""
-    return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id="toprow"):
-        with gr.Column(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=2,
-                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-            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=2,
-                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-        with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-            paste = gr.Button(value=paste_symbol, elem_id="paste")
-            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
-            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_id="interrogate_col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1):
-            with gr.Row():
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                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():
-                with gr.Column(scale=1, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style.save_to_config = True
-
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style2.save_to_config = True
-
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
-    if textinfo is None:
-        textinfo = gr.HTML(visible=False)
-
-    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
-    check_progress.click(
-        fn=lambda: check_progress_call(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
-    check_progress_initial.click(
-        fn=lambda: check_progress_call_initial(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-
-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[key]
-    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 value
-
-
-def update_generation_info(args):
-    generation_info, html_info, img_index = args
-    try:
-        generation_info = json.loads(generation_info)
-        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
-            return html_info
-        return plaintext_to_html(generation_info["infotexts"][img_index])
-    except Exception:
-        pass
-    # if the json parse or anything else fails, just return the old html_info
-    return html_info
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
-def create_output_panel(tabname, outdir):
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
-    with gr.Column(variant='panel'):
-            with gr.Group():
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
-            generation_info = None
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "extras":
-                        save = gr.Button('Save', elem_id=f'save_{tabname}')
-
-                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
-                open_folder_button.click(
-                    fn=lambda: open_folder(opts.outdir_samples or outdir),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
-                        if tabname == 'txt2img' or tabname == 'img2img':
-                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-                            generation_info_button.click(
-                                fn=update_generation_info,
-                                _js="(x, y) => [x, y, selected_gallery_index()]",
-                                inputs=[generation_info, html_info],
-                                outputs=[html_info],
-                                preprocess=False
-                            )
-
-                        save.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                do_make_zip,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_info,
-                                html_info,
-                                html_info,
-                            ]
-                        )
-                else:
-                    html_info_x = gr.HTML()
-                    html_info = gr.HTML()
-                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
-    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, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_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="bytes", visible=False)
-
-        with gr.Row(elem_id='txt2img_progress_row'):
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="txt2img_progressbar")
-                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
-                with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
-            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),
-                _js="submit",
-                inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    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,
-                    firstphase_width,
-                    firstphase_height,
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ]
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-            )
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
-
-            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"),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
-    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, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
-        with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="img2img_progressbar")
-                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                setup_progressbar(progressbar, img2img_preview, 'img2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-
-                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
-                    with gr.TabItem('Inpaint', id='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=480)
-
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
-                    with gr.TabItem('Batch img2img', id='batch'):
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p 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.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
-            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
-                ]
-            )
-
-            mask_mode.change(
-                lambda mode, img: {
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_img_inpaint: gr_show(mode == 1),
-                    init_mask_inpaint: gr_show(mode == 1),
-                },
-                inputs=[mask_mode, init_img_with_mask],
-                outputs=[
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                ],
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_style,
-                    img2img_prompt_style2,
-                    init_img,
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    mask_mode,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            img2img_interrogate.click(
-                fn=interrogate,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
-
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
-            )
-
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            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_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
-                )
-
-            for button, (prompt, negative_prompt), (style1, style2), 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, style1, style2],
-                    outputs=[prompt, negative_prompt, style1, style2],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_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"),
-                (denoising_strength, "Denoising strength"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
-                with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
-                with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
-                with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
-                with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
-                with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
-        submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
-            _js="get_extras_tab_index",
-            inputs=[
-                dummy_component,
-                dummy_component,
-                extras_image,
-                image_batch,
-                extras_batch_input_dir,
-                extras_batch_output_dir,
-                show_extras_results,
-                gfpgan_visibility,
-                codeformer_visibility,
-                codeformer_weight,
-                upscaling_resize,
-                upscaling_resize_w,
-                upscaling_resize_h,
-                upscaling_crop,
-                extras_upscaler_1,
-                extras_upscaler_2,
-                extras_upscaler_2_visibility,
-                upscale_before_face_fix,
-            ],
-            outputs=[
-                result_images,
-                html_info_x,
-                html_info,
-            ]
-        )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-        extras_image.change(
-            fn=modules.extras.clear_cache,
-            inputs=[], outputs=[]
-        )
-
-    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)
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
-                with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                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)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
-                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
-            with gr.Column(variant='panel'):
-                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
-    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().style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="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')
-
-                with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    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'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    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"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="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')
-
-                with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
-                    with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
-                    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)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
-                    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)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
-                    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],
-                    )
-
-                with gr.Tab(label="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 gr.Row():
-                        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")
-                    with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
-                    with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column():
-                progressbar = gr.HTML(elem_id="ti_progressbar")
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
-                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
-        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
-            ],
-            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=[
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                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,
-            ],
-            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=[
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                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=[
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                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=[],
-        )
-
-    def create_setting_component(key, is_quicksettings=False):
-        def fun():
-            return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
-        info = opts.data_labels[key]
-        t = type(info.default)
-
-        args = info.component_args() if callable(info.component_args) else info.component_args
-
-        if info.component is not None:
-            comp = info.component
-        elif t == str:
-            comp = gr.Textbox
-        elif t == int:
-            comp = gr.Number
-        elif t == bool:
-            comp = gr.Checkbox
-        else:
-            raise Exception(f'bad options item type: {str(t)} for key {key}')
-
-        elem_id = "setting_"+key
-
-        if info.refresh is not None:
-            if is_quicksettings:
-                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-            else:
-                with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-        else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
-        return res
-
-    components = []
-    component_dict = {}
-
-    script_callbacks.ui_settings_callback()
-    opts.reorder()
-
-    def run_settings(*args):
-        changed = []
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp == dummy_component:
-                continue
-
-            if opts.set(key, value):
-                changed.append(key)
-
-        try:
-            opts.save(shared.config_filename)
-        except RuntimeError:
-            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
-    def run_settings_single(value, key):
-        if not opts.same_type(value, opts.data_labels[key].default):
-            return gr.update(visible=True), opts.dumpjson()
-
-        if not opts.set(key, value):
-            return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
-        opts.save(shared.config_filename)
-
-        return gr.update(value=value), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
-        result = gr.HTML()
-
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
-        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
-        quicksettings_list = []
-
-        cols_displayed = 0
-        items_displayed = 0
-        previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            for i, (k, item) in enumerate(opts.data_labels.items()):
-                section_must_be_skipped = item.section[0] is None
-
-                if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
-
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
-
-                        items_displayed = 0
-                        cols_displayed += 1
-
-                    previous_section = item.section
-
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
-                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
-                    quicksettings_list.append((i, k, item))
-                    components.append(dummy_component)
-                elif section_must_be_skipped:
-                    components.append(dummy_component)
-                else:
-                    component = create_setting_component(k)
-                    component_dict[k] = component
-                    components.append(component)
-                    items_displayed += 1
-
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='function(){}'
-        )
-
-        download_localization.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='download_localization'
-        )
-
-        def reload_scripts():
-            modules.scripts.reload_script_body_only()
-            reload_javascript()  # need to refresh the html page
-
-        reload_script_bodies.click(
-            fn=reload_scripts,
-            inputs=[],
-            outputs=[]
-        )
-
-        def request_restart():
-            shared.state.interrupt()
-            shared.state.need_restart = True
-
-        restart_gradio.click(
-            fn=request_restart,
-            _js='restart_reload',
-            inputs=[],
-            outputs=[],
-        )
-
-        if column is not None:
-            column.__exit__()
-
-    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", "ti"),
-    ]
-
-    css = ""
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        with open(cssfile, "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if not cmd_opts.no_progressbar_hiding:
-        css += css_hide_progressbar
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings_interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
-                component = create_setting_component(k, is_quicksettings=True)
-                component_dict[k] = component
-
-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
-                    interface.render()
-
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
-        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
-        settings_submit.click(
-            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-            inputs=components,
-            outputs=[text_settings, result],
-        )
-
-        for i, k, item in quicksettings_list:
-            component = component_dict[k]
-
-            component.change(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
-                outputs=[component, text_settings],
-            )
-
-        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
-        def get_settings_values():
-            return [getattr(opts, key) for key in component_keys]
-
-        demo.load(
-            fn=get_settings_values,
-            inputs=[],
-            outputs=[component_dict[k] for k in component_keys],
-        )
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                print("Error loading/saving model file:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
-            return results
-
-        modelmerger_merge.click(
-            fn=modelmerger,
-            inputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                save_as_safetensors,
-                custom_name,
-            ],
-            outputs=[
-                submit_result,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                component_dict['sd_model_checkpoint'],
-            ]
-        )
-
-    ui_config_file = cmd_opts.ui_config_file
-    ui_settings = {}
-    settings_count = len(ui_settings)
-    error_loading = False
-
-    try:
-        if os.path.exists(ui_config_file):
-            with open(ui_config_file, "r", encoding="utf8") as file:
-                ui_settings = json.load(file)
-    except Exception:
-        error_loading = True
-        print("Error loading settings:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-    def loadsave(path, x):
-        def apply_field(obj, field, condition=None, init_field=None):
-            key = path + "/" + field
-
-            if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
-
-            if getattr(obj, 'do_not_save_to_config', False):
-                return
-
-            saved_value = ui_settings.get(key, None)
-            if saved_value is None:
-                ui_settings[key] = getattr(obj, field)
-            elif condition and not condition(saved_value):
-                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
-            else:
-                setattr(obj, field, saved_value)
-                if init_field is not None:
-                    init_field(saved_value)
-
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-            apply_field(x, 'visible')
-
-        if type(x) == gr.Slider:
-            apply_field(x, 'value')
-            apply_field(x, 'minimum')
-            apply_field(x, 'maximum')
-            apply_field(x, 'step')
-
-        if type(x) == gr.Radio:
-            apply_field(x, 'value', lambda val: val in x.choices)
-
-        if type(x) == gr.Checkbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Textbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Number:
-            apply_field(x, 'value')
-
-        # Since there are many dropdowns that shouldn't be saved,
-        # we only mark dropdowns that should be saved.
-        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
-            apply_field(x, 'visible')
-
-    visit(txt2img_interface, loadsave, "txt2img")
-    visit(img2img_interface, loadsave, "img2img")
-    visit(extras_interface, loadsave, "extras")
-    visit(modelmerger_interface, loadsave, "modelmerger")
-
-    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
-        with open(ui_config_file, "w", encoding="utf8") as file:
-            json.dump(ui_settings, file, indent=4)
-
-    return demo
-
-
-def reload_javascript():
-    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-        javascript = f'<script>{jsfile.read()}</script>'
-
-    scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
-    for basedir, filename, path in scripts_list:
-        with open(path, "r", encoding="utf8") as jsfile:
-            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
-    if cmd_opts.theme is not None:
-        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
-    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
-    def template_response(*args, **kwargs):
-        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(
-            b'</head>', f'{javascript}</head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
-    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html

+import json

+import math

+import mimetypes

+import os

+import platform

+import random

+import subprocess as sp

+import sys

+import tempfile

+import time

+import traceback

+from functools import partial, reduce

+

+import gradio as gr

+import gradio.routes

+import gradio.utils

+import numpy as np

+from PIL import Image, PngImagePlugin

+

+

+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions

+from modules.paths import script_path

+

+from modules.shared import opts, cmd_opts, restricted_opts

+

+if cmd_opts.deepdanbooru:

+    from modules.deepbooru import get_deepbooru_tags

+

+import modules.codeformer_model

+import modules.generation_parameters_copypaste as parameters_copypaste

+import modules.gfpgan_model

+import modules.hypernetworks.ui

+import modules.ldsr_model

+import modules.scripts

+import modules.shared as shared

+import modules.styles

+import modules.textual_inversion.ui

+from modules import prompt_parser

+from modules.images import save_image

+from modules.sd_hijack import model_hijack

+from modules.sd_samplers import samplers, samplers_for_img2img

+import modules.textual_inversion.ui

+import modules.hypernetworks.ui

+from modules.generation_parameters_copypaste import image_from_url_text

+

+# 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 != 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 != None else 7860, cmd_opts.ngrok_region)

+

+

+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

+

+css_hide_progressbar = """

+.wrap .m-12 svg { display:none!important; }

+.wrap .m-12::before { content:"Loading..." }

+.wrap .z-20 svg { display:none!important; }

+.wrap .z-20::before { content:"Loading..." }

+.progress-bar { display:none!important; }

+.meta-text { display:none!important; }

+.meta-text-center { display:none!important; }

+"""

+

+# 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'  # ♻️

+art_symbol = '\U0001f3a8'  # 🎨

+paste_symbol = '\u2199\ufe0f'  # ↙

+folder_symbol = '\U0001f4c2'  # 📂

+refresh_symbol = '\U0001f504'  # 🔄

+save_style_symbol = '\U0001f4be'  # 💾

+apply_style_symbol = '\U0001f4cb'  # 📋

+

+

+def plaintext_to_html(text):

+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"

+    return text

+

+def send_gradio_gallery_to_image(x):

+    if len(x) == 0:

+        return None

+    return image_from_url_text(x[0])

+

+def save_files(js_data, images, do_make_zip, index):

+    import csv

+    filenames = []

+    fullfns = []

+

+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it

+    class MyObject:

+        def __init__(self, d=None):

+            if d is not None:

+                for key, value in d.items():

+                    setattr(self, key, value)

+

+    data = json.loads(js_data)

+

+    p = MyObject(data)

+    path = opts.outdir_save

+    save_to_dirs = opts.use_save_to_dirs_for_ui

+    extension: str = opts.samples_format

+    start_index = 0

+

+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only

+

+        images = [images[index]]

+        start_index = index

+

+    os.makedirs(opts.outdir_save, exist_ok=True)

+

+    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:

+        at_start = file.tell() == 0

+        writer = csv.writer(file)

+        if at_start:

+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])

+

+        for image_index, filedata in enumerate(images, start_index):

+            image = image_from_url_text(filedata)

+

+            is_grid = image_index < p.index_of_first_image

+            i = 0 if is_grid else (image_index - p.index_of_first_image)

+

+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)

+

+            filename = os.path.relpath(fullfn, path)

+            filenames.append(filename)

+            fullfns.append(fullfn)

+            if txt_fullfn:

+                filenames.append(os.path.basename(txt_fullfn))

+                fullfns.append(txt_fullfn)

+

+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])

+

+    # Make Zip

+    if do_make_zip:

+        zip_filepath = os.path.join(path, "images.zip")

+

+        from zipfile import ZipFile

+        with ZipFile(zip_filepath, "w") as zip_file:

+            for i in range(len(fullfns)):

+                with open(fullfns[i], mode="rb") as f:

+                    zip_file.writestr(filenames[i], f.read())

+        fullfns.insert(0, zip_filepath)

+

+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")

+

+def save_pil_to_file(pil_image, dir=None):

+    use_metadata = False

+    metadata = PngImagePlugin.PngInfo()

+    for key, value in pil_image.info.items():

+        if isinstance(key, str) and isinstance(value, str):

+            metadata.add_text(key, value)

+            use_metadata = True

+

+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)

+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))

+    return file_obj

+

+

+# override save to file function so that it also writes PNG info

+gr.processing_utils.save_pil_to_file = save_pil_to_file

+

+

+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):

+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):

+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats

+        if run_memmon:

+            shared.mem_mon.monitor()

+        t = time.perf_counter()

+

+        try:

+            res = list(func(*args, **kwargs))

+        except Exception as e:

+            # When printing out our debug argument list, do not print out more than a MB of text

+            max_debug_str_len = 131072 # (1024*1024)/8

+

+            print("Error completing request", file=sys.stderr)

+            argStr = f"Arguments: {str(args)} {str(kwargs)}"

+            print(argStr[:max_debug_str_len], file=sys.stderr)

+            if len(argStr) > max_debug_str_len:

+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)

+

+            print(traceback.format_exc(), file=sys.stderr)

+

+            shared.state.job = ""

+            shared.state.job_count = 0

+

+            if extra_outputs_array is None:

+                extra_outputs_array = [None, '']

+

+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]

+

+        shared.state.skipped = False

+        shared.state.interrupted = False

+        shared.state.job_count = 0

+

+        if not add_stats:

+            return tuple(res)

+

+        elapsed = time.perf_counter() - t

+        elapsed_m = int(elapsed // 60)

+        elapsed_s = elapsed % 60

+        elapsed_text = f"{elapsed_s:.2f}s"

+        if elapsed_m > 0:

+            elapsed_text = f"{elapsed_m}m "+elapsed_text

+

+        if run_memmon:

+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}

+            active_peak = mem_stats['active_peak']

+            reserved_peak = mem_stats['reserved_peak']

+            sys_peak = mem_stats['system_peak']

+            sys_total = mem_stats['total']

+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)

+

+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"

+        else:

+            vram_html = ''

+

+        # last item is always HTML

+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"

+

+        return tuple(res)

+

+    return f

+

+

+def calc_time_left(progress, threshold, label, force_display):

+    if progress == 0:

+        return ""

+    else:

+        time_since_start = time.time() - shared.state.time_start

+        eta = (time_since_start/progress)

+        eta_relative = eta-time_since_start

+        if (eta_relative > threshold and progress > 0.02) or force_display:

+            if eta_relative > 3600:

+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))

+            elif eta_relative > 60:

+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))

+            else:

+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))

+        else:

+            return ""

+

+

+def check_progress_call(id_part):

+    if shared.state.job_count == 0:

+        return "", gr_show(False), gr_show(False), gr_show(False)

+

+    progress = 0

+

+    if shared.state.job_count > 0:

+        progress += shared.state.job_no / shared.state.job_count

+    if shared.state.sampling_steps > 0:

+        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps

+

+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )

+    if time_left != "":

+        shared.state.time_left_force_display = True

+

+    progress = min(progress, 1)

+

+    progressbar = ""

+    if opts.show_progressbar:

+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""

+

+    image = gr_show(False)

+    preview_visibility = gr_show(False)

+

+    if opts.show_progress_every_n_steps != 0:

+        shared.state.set_current_image()

+        image = shared.state.current_image

+

+        if image is None:

+            image = gr.update(value=None)

+        else:

+            preview_visibility = gr_show(True)

+

+    if shared.state.textinfo is not None:

+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)

+    else:

+        textinfo_result = gr_show(False)

+

+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result

+

+

+def check_progress_call_initial(id_part):

+    shared.state.job_count = -1

+    shared.state.current_latent = None

+    shared.state.current_image = None

+    shared.state.textinfo = None

+    shared.state.time_start = time.time()

+    shared.state.time_left_force_display = False

+

+    return check_progress_call(id_part)

+

+

+def roll_artist(prompt):

+    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])

+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])

+

+    return prompt + ", " + artist.name if prompt != '' else artist.name

+

+

+def visit(x, func, path=""):

+    if hasattr(x, 'children'):

+        for c in x.children:

+            visit(c, func, path)

+    elif x.label is not None:

+        func(path + "/" + str(x.label), x)

+

+

+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(4)]

+

+

+def apply_styles(prompt, prompt_neg, style1_name, style2_name):

+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])

+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])

+

+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]

+

+

+def interrogate(image):

+    prompt = shared.interrogator.interrogate(image)

+

+    return gr_show(True) if prompt is None else prompt

+

+

+def interrogate_deepbooru(image):

+    prompt = get_deepbooru_tags(image)

+    return gr_show(True) if prompt is None else prompt

+

+

+def create_seed_inputs():

+    with gr.Row():

+        with gr.Box():

+            with gr.Row(elem_id='seed_row'):

+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)

+                seed.style(container=False)

+                random_seed = gr.Button(random_symbol, elem_id='random_seed')

+                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')

+

+        with gr.Box(elem_id='subseed_show_box'):

+            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)

+

+    # Components to show/hide based on the 'Extra' checkbox

+    seed_extras = []

+

+    with gr.Row(visible=False) as seed_extra_row_1:

+        seed_extras.append(seed_extra_row_1)

+        with gr.Box():

+            with gr.Row(elem_id='subseed_row'):

+                subseed = gr.Number(label='Variation seed', value=-1)

+                subseed.style(container=False)

+                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')

+                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')

+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)

+

+    with gr.Row(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=64, label="Resize seed from width", value=0)

+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)

+

+    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])

+    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])

+

+    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_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 as e:

+            if gen_info_string != '':

+                print("Error parsing JSON generation info:", file=sys.stderr)

+                print(gen_info_string, file=sys.stderr)

+

+        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:

+        _, 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]

+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])

+    style_class = ' class="red"' if (token_count > max_length) else ""

+    return f"<span {style_class}>{token_count}/{max_length}</span>"

+

+

+def create_toprow(is_img2img):

+    id_part = "img2img" if is_img2img else "txt2img"

+

+    with gr.Row(elem_id="toprow"):

+        with gr.Column(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=2,

+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"

+                        )

+

+            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=2,

+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"

+                        )

+

+        with gr.Column(scale=1, elem_id="roll_col"):

+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)

+            paste = gr.Button(value=paste_symbol, elem_id="paste")

+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")

+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")

+

+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")

+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")

+

+        button_interrogate = None

+        button_deepbooru = None

+        if is_img2img:

+            with gr.Column(scale=1, elem_id="interrogate_col"):

+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")

+

+                if cmd_opts.deepdanbooru:

+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

+

+        with gr.Column(scale=1):

+            with gr.Row():

+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")

+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")

+                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():

+                with gr.Column(scale=1, elem_id="style_pos_col"):

+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))

+                    prompt_style.save_to_config = True

+

+                with gr.Column(scale=1, elem_id="style_neg_col"):

+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))

+                    prompt_style2.save_to_config = True

+

+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button

+

+

+def setup_progressbar(progressbar, preview, id_part, textinfo=None):

+    if textinfo is None:

+        textinfo = gr.HTML(visible=False)

+

+    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)

+    check_progress.click(

+        fn=lambda: check_progress_call(id_part),

+        show_progress=False,

+        inputs=[],

+        outputs=[progressbar, preview, preview, textinfo],

+    )

+

+    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)

+    check_progress_initial.click(

+        fn=lambda: check_progress_call_initial(id_part),

+        show_progress=False,

+        inputs=[],

+        outputs=[progressbar, preview, preview, textinfo],

+    )

+

+

+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[key]

+    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 value

+

+

+def update_generation_info(args):

+    generation_info, html_info, img_index = args

+    try:

+        generation_info = json.loads(generation_info)

+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):

+            return html_info

+        return plaintext_to_html(generation_info["infotexts"][img_index])

+    except Exception:

+        pass

+    # if the json parse or anything else fails, just return the old html_info

+    return html_info

+

+

+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):

+    def refresh():

+        refresh_method()

+        args = refreshed_args() if callable(refreshed_args) else refreshed_args

+

+        for k, v in args.items():

+            setattr(refresh_component, k, v)

+

+        return gr.update(**(args or {}))

+

+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)

+    refresh_button.click(

+        fn=refresh,

+        inputs=[],

+        outputs=[refresh_component]

+    )

+    return refresh_button

+

+

+def create_output_panel(tabname, outdir):

+    def open_folder(f):

+        if not os.path.exists(f):

+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')

+            return

+        elif not os.path.isdir(f):

+            print(f"""

+WARNING

+An open_folder request was made with an argument that is not a folder.

+This could be an error or a malicious attempt to run code on your computer.

+Requested path was: {f}

+""", file=sys.stderr)

+            return

+

+        if not shared.cmd_opts.hide_ui_dir_config:

+            path = os.path.normpath(f)

+            if platform.system() == "Windows":

+                os.startfile(path)

+            elif platform.system() == "Darwin":

+                sp.Popen(["open", path])

+            else:

+                sp.Popen(["xdg-open", path])

+

+    with gr.Column(variant='panel'):

+            with gr.Group():

+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)

+

+            generation_info = None

+            with gr.Column():

+                with gr.Row():

+                    if tabname != "extras":

+                        save = gr.Button('Save', elem_id=f'save_{tabname}')

+

+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])

+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'

+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)

+

+                open_folder_button.click(

+                    fn=lambda: open_folder(opts.outdir_samples or outdir),

+                    inputs=[],

+                    outputs=[],

+                )

+

+                if tabname != "extras":

+                    with gr.Row():

+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)

+

+                    with gr.Row():

+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)

+

+                    with gr.Group():

+                        html_info = gr.HTML()

+                        generation_info = gr.Textbox(visible=False)

+                        if tabname == 'txt2img' or tabname == 'img2img':

+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")

+                            generation_info_button.click(

+                                fn=update_generation_info,

+                                _js="(x, y) => [x, y, selected_gallery_index()]",

+                                inputs=[generation_info, html_info],

+                                outputs=[html_info],

+                                preprocess=False

+                            )

+

+                        save.click(

+                            fn=wrap_gradio_call(save_files),

+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",

+                            inputs=[

+                                generation_info,

+                                result_gallery,

+                                do_make_zip,

+                                html_info,

+                            ],

+                            outputs=[

+                                download_files,

+                                html_info,

+                                html_info,

+                                html_info,

+                            ]

+                        )

+                else:

+                    html_info_x = gr.HTML()

+                    html_info = gr.HTML()

+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)

+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info

+

+

+def create_ui(wrap_gradio_gpu_call):

+    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, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_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="bytes", visible=False)

+

+        with gr.Row(elem_id='txt2img_progress_row'):

+            with gr.Column(scale=1):

+                pass

+

+            with gr.Column(scale=1):

+                progressbar = gr.HTML(elem_id="txt2img_progressbar")

+                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)

+                setup_progressbar(progressbar, txt2img_preview, 'txt2img')

+

+        with gr.Row().style(equal_height=False):

+            with gr.Column(variant='panel'):

+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)

+                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")

+

+                with gr.Group():

+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+

+                with gr.Row():

+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)

+                    tiling = gr.Checkbox(label='Tiling', value=False)

+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)

+

+                with gr.Row(visible=False) as hr_options:

+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)

+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)

+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)

+

+                with gr.Row(equal_height=True):

+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)

+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)

+

+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)

+

+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()

+

+                with gr.Group():

+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()

+

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

+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)

+

+            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),

+                _js="submit",

+                inputs=[

+                    txt2img_prompt,

+                    txt2img_negative_prompt,

+                    txt2img_prompt_style,

+                    txt2img_prompt_style2,

+                    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,

+                    firstphase_width,

+                    firstphase_height,

+                ] + custom_inputs,

+

+                outputs=[

+                    txt2img_gallery,

+                    generation_info,

+                    html_info

+                ],

+                show_progress=False,

+            )

+

+            txt2img_prompt.submit(**txt2img_args)

+            submit.click(**txt2img_args)

+

+            txt_prompt_img.change(

+                fn=modules.images.image_data,

+                inputs=[

+                    txt_prompt_img

+                ],

+                outputs=[

+                    txt2img_prompt,

+                    txt_prompt_img

+                ]

+            )

+

+            enable_hr.change(

+                fn=lambda x: gr_show(x),

+                inputs=[enable_hr],

+                outputs=[hr_options],

+            )

+

+            roll.click(

+                fn=roll_artist,

+                _js="update_txt2img_tokens",

+                inputs=[

+                    txt2img_prompt,

+                ],

+                outputs=[

+                    txt2img_prompt,

+                ]

+            )

+

+            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"),

+                (denoising_strength, "Denoising strength"),

+                (enable_hr, lambda d: "Denoising strength" in d),

+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

+                (firstphase_width, "First pass size-1"),

+                (firstphase_height, "First pass size-2"),

+                *modules.scripts.scripts_txt2img.infotext_fields

+            ]

+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)

+

+            txt2img_preview_params = [

+                txt2img_prompt,

+                txt2img_negative_prompt,

+                steps,

+                sampler_index,

+                cfg_scale,

+                seed,

+                width,

+                height,

+            ]

+

+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])

+

+    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, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)

+

+        with gr.Row(elem_id='img2img_progress_row'):

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

+

+            with gr.Column(scale=1):

+                pass

+

+            with gr.Column(scale=1):

+                progressbar = gr.HTML(elem_id="img2img_progressbar")

+                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)

+                setup_progressbar(progressbar, img2img_preview, 'img2img')

+

+        with gr.Row().style(equal_height=False):

+            with gr.Column(variant='panel'):

+

+                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:

+                    with gr.TabItem('img2img', id='img2img'):

+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)

+

+                    with gr.TabItem('Inpaint', id='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=480)

+

+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")

+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")

+

+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)

+

+                        with gr.Row():

+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")

+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")

+

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

+

+                        with gr.Row():

+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)

+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)

+

+                    with gr.TabItem('Batch img2img', id='batch'):

+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''

+                        gr.HTML(f"<p 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.{hidden}</p>")

+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)

+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)

+

+                with gr.Row():

+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")

+

+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)

+                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")

+

+                with gr.Group():

+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")

+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")

+

+                with gr.Row():

+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)

+                    tiling = gr.Checkbox(label='Tiling', value=False)

+

+                with gr.Row():

+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)

+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)

+

+                with gr.Group():

+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)

+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)

+

+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()

+

+                with gr.Group():

+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()

+

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

+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)

+

+            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

+                ]

+            )

+

+            mask_mode.change(

+                lambda mode, img: {

+                    init_img_with_mask: gr_show(mode == 0),

+                    init_img_inpaint: gr_show(mode == 1),

+                    init_mask_inpaint: gr_show(mode == 1),

+                },

+                inputs=[mask_mode, init_img_with_mask],

+                outputs=[

+                    init_img_with_mask,

+                    init_img_inpaint,

+                    init_mask_inpaint,

+                ],

+            )

+

+            img2img_args = dict(

+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),

+                _js="submit_img2img",

+                inputs=[

+                    dummy_component,

+                    img2img_prompt,

+                    img2img_negative_prompt,

+                    img2img_prompt_style,

+                    img2img_prompt_style2,

+                    init_img,

+                    init_img_with_mask,

+                    init_img_inpaint,

+                    init_mask_inpaint,

+                    mask_mode,

+                    steps,

+                    sampler_index,

+                    mask_blur,

+                    inpainting_fill,

+                    restore_faces,

+                    tiling,

+                    batch_count,

+                    batch_size,

+                    cfg_scale,

+                    denoising_strength,

+                    seed,

+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,

+                    height,

+                    width,

+                    resize_mode,

+                    inpaint_full_res,

+                    inpaint_full_res_padding,

+                    inpainting_mask_invert,

+                    img2img_batch_input_dir,

+                    img2img_batch_output_dir,

+                ] + custom_inputs,

+                outputs=[

+                    img2img_gallery,

+                    generation_info,

+                    html_info

+                ],

+                show_progress=False,

+            )

+

+            img2img_prompt.submit(**img2img_args)

+            submit.click(**img2img_args)

+

+            img2img_interrogate.click(

+                fn=interrogate,

+                inputs=[init_img],

+                outputs=[img2img_prompt],

+            )

+

+            if cmd_opts.deepdanbooru:

+                img2img_deepbooru.click(

+                    fn=interrogate_deepbooru,

+                    inputs=[init_img],

+                    outputs=[img2img_prompt],

+            )

+

+

+            roll.click(

+                fn=roll_artist,

+                _js="update_img2img_tokens",

+                inputs=[

+                    img2img_prompt,

+                ],

+                outputs=[

+                    img2img_prompt,

+                ]

+            )

+

+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]

+            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]

+            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_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],

+                )

+

+            for button, (prompt, negative_prompt), (style1, style2), 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, style1, style2],

+                    outputs=[prompt, negative_prompt, style1, style2],

+                )

+

+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])

+

+            img2img_paste_fields = [

+                (img2img_prompt, "Prompt"),

+                (img2img_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"),

+                (denoising_strength, "Denoising strength"),

+                *modules.scripts.scripts_img2img.infotext_fields

+            ]

+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)

+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)

+

+    modules.scripts.scripts_current = None

+

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

+        with gr.Row().style(equal_height=False):

+            with gr.Column(variant='panel'):

+                with gr.Tabs(elem_id="mode_extras"):

+                    with gr.TabItem('Single Image'):

+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")

+

+                    with gr.TabItem('Batch Process'):

+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")

+

+                    with gr.TabItem('Batch from Directory'):

+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")

+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")

+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)

+

+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')

+

+                with gr.Tabs(elem_id="extras_resize_mode"):

+                    with gr.TabItem('Scale by'):

+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)

+                    with gr.TabItem('Scale to'):

+                        with gr.Group():

+                            with gr.Row():

+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)

+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)

+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)

+

+                with gr.Group():

+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

+

+                with gr.Group():

+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")

+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)

+

+                with gr.Group():

+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)

+

+                with gr.Group():

+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)

+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)

+

+                with gr.Group():

+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)

+

+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)

+

+        submit.click(

+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),

+            _js="get_extras_tab_index",

+            inputs=[

+                dummy_component,

+                dummy_component,

+                extras_image,

+                image_batch,

+                extras_batch_input_dir,

+                extras_batch_output_dir,

+                show_extras_results,

+                gfpgan_visibility,

+                codeformer_visibility,

+                codeformer_weight,

+                upscaling_resize,

+                upscaling_resize_w,

+                upscaling_resize_h,

+                upscaling_crop,

+                extras_upscaler_1,

+                extras_upscaler_2,

+                extras_upscaler_2_visibility,

+                upscale_before_face_fix,

+            ],

+            outputs=[

+                result_images,

+                html_info_x,

+                html_info,

+            ]

+        )

+        parameters_copypaste.add_paste_fields("extras", extras_image, None)

+

+        extras_image.change(

+            fn=modules.extras.clear_cache,

+            inputs=[], outputs=[]

+        )

+

+    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)

+                html2 = gr.HTML()

+                with gr.Row():

+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])

+                parameters_copypaste.bind_buttons(buttons, image, generation_info)

+

+        image.change(

+            fn=wrap_gradio_call(modules.extras.run_pnginfo),

+            inputs=[image],

+            outputs=[html, generation_info, html2],

+        )

+

+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:

+        with gr.Row().style(equal_height=False):

+            with gr.Column(variant='panel'):

+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")

+

+                with gr.Row():

+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")

+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")

+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")

+                custom_name = gr.Textbox(label="Custom Name (Optional)")

+                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)

+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")

+                save_as_half = gr.Checkbox(value=False, label="Save as float16")

+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")

+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')

+

+            with gr.Column(variant='panel'):

+                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)

+

+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

+

+    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().style(equal_height=False):

+            with gr.Tabs(elem_id="train_tabs"):

+

+                with gr.Tab(label="Create embedding"):

+                    new_embedding_name = gr.Textbox(label="Name")

+                    initialization_text = gr.Textbox(label="Initialization text", value="*")

+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)

+                    overwrite_old_embedding = gr.Checkbox(value=False, label="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')

+

+                with gr.Tab(label="Create hypernetwork"):

+                    new_hypernetwork_name = gr.Textbox(label="Name")

+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

+                    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'")

+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)

+                    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"])

+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")

+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="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')

+

+                with gr.Tab(label="Preprocess images"):

+                    process_src = gr.Textbox(label='Source directory')

+                    process_dst = gr.Textbox(label='Destination directory')

+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])

+

+                    with gr.Row():

+                        process_flip = gr.Checkbox(label='Create flipped copies')

+                        process_split = gr.Checkbox(label='Split oversized images')

+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')

+                        process_caption = gr.Checkbox(label='Use BLIP for caption')

+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)

+

+                    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)

+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)

+

+                    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)

+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)

+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)

+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')

+

+                    with gr.Row():

+                        with gr.Column(scale=3):

+                            gr.HTML(value="")

+

+                        with gr.Column():

+                            with gr.Row():

+                                interrupt_preprocessing = gr.Button("Interrupt")

+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')

+

+                    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],

+                    )

+

+                with gr.Tab(label="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 gr.Row():

+                        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")

+                    with gr.Row():

+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])

+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")

+                    with gr.Row():

+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")

+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")

+

+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)

+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))

+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    steps = gr.Number(label='Max steps', value=100000, precision=0)

+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)

+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)

+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)

+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)

+

+                    with gr.Row():

+                        interrupt_training = gr.Button(value="Interrupt")

+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')

+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')

+

+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)

+

+                script_callbacks.ui_train_tabs_callback(params)

+

+            with gr.Column():

+                progressbar = gr.HTML(elem_id="ti_progressbar")

+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)

+

+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)

+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)

+                ti_progress = gr.HTML(elem_id="ti_progress", value="")

+                ti_outcome = gr.HTML(elem_id="ti_error", value="")

+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)

+

+        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

+            ],

+            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=[

+                process_src,

+                process_dst,

+                process_width,

+                process_height,

+                preprocess_txt_action,

+                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,

+            ],

+            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=[

+                train_embedding_name,

+                embedding_learn_rate,

+                batch_size,

+                dataset_directory,

+                log_directory,

+                training_width,

+                training_height,

+                steps,

+                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=[

+                train_hypernetwork_name,

+                hypernetwork_learn_rate,

+                batch_size,

+                dataset_directory,

+                log_directory,

+                training_width,

+                training_height,

+                steps,

+                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=[],

+        )

+

+    def create_setting_component(key, is_quicksettings=False):

+        def fun():

+            return opts.data[key] if key in opts.data else opts.data_labels[key].default

+

+        info = opts.data_labels[key]

+        t = type(info.default)

+

+        args = info.component_args() if callable(info.component_args) else info.component_args

+

+        if info.component is not None:

+            comp = info.component

+        elif t == str:

+            comp = gr.Textbox

+        elif t == int:

+            comp = gr.Number

+        elif t == bool:

+            comp = gr.Checkbox

+        else:

+            raise Exception(f'bad options item type: {str(t)} for key {key}')

+

+        elem_id = "setting_"+key

+

+        if info.refresh is not None:

+            if is_quicksettings:

+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)

+            else:

+                with gr.Row(variant="compact"):

+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)

+        else:

+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

+

+        return res

+

+    components = []

+    component_dict = {}

+

+    script_callbacks.ui_settings_callback()

+    opts.reorder()

+

+    def run_settings(*args):

+        changed = []

+

+        for key, value, comp in zip(opts.data_labels.keys(), args, components):

+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"

+

+        for key, value, comp in zip(opts.data_labels.keys(), args, components):

+            if comp == dummy_component:

+                continue

+

+            if opts.set(key, value):

+                changed.append(key)

+

+        try:

+            opts.save(shared.config_filename)

+        except RuntimeError:

+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'

+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'

+

+    def run_settings_single(value, key):

+        if not opts.same_type(value, opts.data_labels[key].default):

+            return gr.update(visible=True), opts.dumpjson()

+

+        if not opts.set(key, value):

+            return gr.update(value=getattr(opts, key)), opts.dumpjson()

+

+        opts.save(shared.config_filename)

+

+        return gr.update(value=value), opts.dumpjson()

+

+    with gr.Blocks(analytics_enabled=False) as settings_interface:

+        settings_submit = gr.Button(value="Apply settings", variant='primary')

+        result = gr.HTML()

+

+        settings_cols = 3

+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)

+

+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]

+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')

+

+        quicksettings_list = []

+

+        cols_displayed = 0

+        items_displayed = 0

+        previous_section = None

+        column = None

+        with gr.Row(elem_id="settings").style(equal_height=False):

+            for i, (k, item) in enumerate(opts.data_labels.items()):

+                section_must_be_skipped = item.section[0] is None

+

+                if previous_section != item.section and not section_must_be_skipped:

+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):

+                        if column is not None:

+                            column.__exit__()

+

+                        column = gr.Column(variant='panel')

+                        column.__enter__()

+

+                        items_displayed = 0

+                        cols_displayed += 1

+

+                    previous_section = item.section

+

+                    elem_id, text = item.section

+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))

+

+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:

+                    quicksettings_list.append((i, k, item))

+                    components.append(dummy_component)

+                elif section_must_be_skipped:

+                    components.append(dummy_component)

+                else:

+                    component = create_setting_component(k)

+                    component_dict[k] = component

+                    components.append(component)

+                    items_displayed += 1

+

+        with gr.Row():

+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")

+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")

+

+        with gr.Row():

+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')

+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')

+

+        request_notifications.click(

+            fn=lambda: None,

+            inputs=[],

+            outputs=[],

+            _js='function(){}'

+        )

+

+        download_localization.click(

+            fn=lambda: None,

+            inputs=[],

+            outputs=[],

+            _js='download_localization'

+        )

+

+        def reload_scripts():

+            modules.scripts.reload_script_body_only()

+            reload_javascript()  # need to refresh the html page

+

+        reload_script_bodies.click(

+            fn=reload_scripts,

+            inputs=[],

+            outputs=[]

+        )

+

+        def request_restart():

+            shared.state.interrupt()

+            shared.state.need_restart = True

+

+        restart_gradio.click(

+            fn=request_restart,

+            _js='restart_reload',

+            inputs=[],

+            outputs=[],

+        )

+

+        if column is not None:

+            column.__exit__()

+

+    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", "ti"),

+    ]

+

+    css = ""

+

+    for cssfile in modules.scripts.list_files_with_name("style.css"):

+        if not os.path.isfile(cssfile):

+            continue

+

+        with open(cssfile, "r", encoding="utf8") as file:

+            css += file.read() + "\n"

+

+    if os.path.exists(os.path.join(script_path, "user.css")):

+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:

+            css += file.read() + "\n"

+

+    if not cmd_opts.no_progressbar_hiding:

+        css += css_hide_progressbar

+

+    interfaces += script_callbacks.ui_tabs_callback()

+    interfaces += [(settings_interface, "Settings", "settings")]

+

+    extensions_interface = ui_extensions.create_ui()

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

+

+    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:

+        with gr.Row(elem_id="quicksettings"):

+            for i, k, item in quicksettings_list:

+                component = create_setting_component(k, is_quicksettings=True)

+                component_dict[k] = component

+

+        parameters_copypaste.integrate_settings_paste_fields(component_dict)

+        parameters_copypaste.run_bind()

+

+        with gr.Tabs(elem_id="tabs") as tabs:

+            for interface, label, ifid in interfaces:

+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):

+                    interface.render()

+

+        if os.path.exists(os.path.join(script_path, "notification.mp3")):

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

+

+        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)

+        settings_submit.click(

+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),

+            inputs=components,

+            outputs=[text_settings, result],

+        )

+

+        for i, k, item in quicksettings_list:

+            component = component_dict[k]

+

+            component.change(

+                fn=lambda value, k=k: run_settings_single(value, key=k),

+                inputs=[component],

+                outputs=[component, text_settings],

+            )

+

+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]

+

+        def get_settings_values():

+            return [getattr(opts, key) for key in component_keys]

+

+        demo.load(

+            fn=get_settings_values,

+            inputs=[],

+            outputs=[component_dict[k] for k in component_keys],

+        )

+

+        def modelmerger(*args):

+            try:

+                results = modules.extras.run_modelmerger(*args)

+            except Exception as e:

+                print("Error loading/saving model file:", file=sys.stderr)

+                print(traceback.format_exc(), file=sys.stderr)

+                modules.sd_models.list_models()  # to remove the potentially missing models from the list

+                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]

+            return results

+

+        modelmerger_merge.click(

+            fn=modelmerger,

+            inputs=[

+                primary_model_name,

+                secondary_model_name,

+                tertiary_model_name,

+                interp_method,

+                interp_amount,

+                save_as_half,

+                save_as_safetensors,

+                custom_name,

+            ],

+            outputs=[

+                submit_result,

+                primary_model_name,

+                secondary_model_name,

+                tertiary_model_name,

+                component_dict['sd_model_checkpoint'],

+            ]

+        )

+

+    ui_config_file = cmd_opts.ui_config_file

+    ui_settings = {}

+    settings_count = len(ui_settings)

+    error_loading = False

+

+    try:

+        if os.path.exists(ui_config_file):

+            with open(ui_config_file, "r", encoding="utf8") as file:

+                ui_settings = json.load(file)

+    except Exception:

+        error_loading = True

+        print("Error loading settings:", file=sys.stderr)

+        print(traceback.format_exc(), file=sys.stderr)

+

+    def loadsave(path, x):

+        def apply_field(obj, field, condition=None, init_field=None):

+            key = path + "/" + field

+

+            if getattr(obj, 'custom_script_source', None) is not None:

+              key = 'customscript/' + obj.custom_script_source + '/' + key

+

+            if getattr(obj, 'do_not_save_to_config', False):

+                return

+

+            saved_value = ui_settings.get(key, None)

+            if saved_value is None:

+                ui_settings[key] = getattr(obj, field)

+            elif condition and not condition(saved_value):

+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')

+            else:

+                setattr(obj, field, saved_value)

+                if init_field is not None:

+                    init_field(saved_value)

+

+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:

+            apply_field(x, 'visible')

+

+        if type(x) == gr.Slider:

+            apply_field(x, 'value')

+            apply_field(x, 'minimum')

+            apply_field(x, 'maximum')

+            apply_field(x, 'step')

+

+        if type(x) == gr.Radio:

+            apply_field(x, 'value', lambda val: val in x.choices)

+

+        if type(x) == gr.Checkbox:

+            apply_field(x, 'value')

+

+        if type(x) == gr.Textbox:

+            apply_field(x, 'value')

+

+        if type(x) == gr.Number:

+            apply_field(x, 'value')

+

+        # Since there are many dropdowns that shouldn't be saved,

+        # we only mark dropdowns that should be saved.

+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):

+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))

+            apply_field(x, 'visible')

+

+    visit(txt2img_interface, loadsave, "txt2img")

+    visit(img2img_interface, loadsave, "img2img")

+    visit(extras_interface, loadsave, "extras")

+    visit(modelmerger_interface, loadsave, "modelmerger")

+

+    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):

+        with open(ui_config_file, "w", encoding="utf8") as file:

+            json.dump(ui_settings, file, indent=4)

+

+    return demo

+

+

+def reload_javascript():

+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:

+        javascript = f'<script>{jsfile.read()}</script>'

+

+    scripts_list = modules.scripts.list_scripts("javascript", ".js")

+

+    for basedir, filename, path in scripts_list:

+        with open(path, "r", encoding="utf8") as jsfile:

+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"

+

+    if cmd_opts.theme is not None:

+        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"

+

+    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"

+

+    def template_response(*args, **kwargs):

+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)

+        res.body = res.body.replace(

+            b'</head>', f'{javascript}</head>'.encode("utf8"))

+        res.init_headers()

+        return res

+

+    gradio.routes.templates.TemplateResponse = template_response

+

+

+if not hasattr(shared, 'GradioTemplateResponseOriginal'):

+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse