Split history: merge

modules/ui_checkpoint_merger.py

+import datetime
+import json
+import mimetypes
+import os
+import sys
+from functools import reduce
+import warnings
+
+import gradio as gr
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin  # noqa: F401
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
+
+
+from modules.shared import opts, cmd_opts
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.textual_inversion import textual_inversion
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+import modules.extras
+
+create_setting_component = ui_settings.create_setting_component
+
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok is not None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(
+        cmd_opts.ngrok,
+        cmd_opts.port if cmd_opts.port is not None else 7860,
+        cmd_opts.ngrok_options
+        )
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+paste_symbol = '\u2199\ufe0f'  # ↙
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
+extra_networks_symbol = '\U0001F3B4'  # 🎴
+switch_values_symbol = '\U000021C5' # ⇅
+restore_progress_symbol = '\U0001F300' # 🌀
+detect_image_size_symbol = '\U0001F4D0'  # 📐
+up_down_symbol = '\u2195\ufe0f' # ↕️
+
+
+plaintext_to_html = ui_common.plaintext_to_html
+
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
+
+
+def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
+    from modules import processing, devices
+
+    if not enable:
+        return ""
+
+    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
+
+    with devices.autocast():
+        p.init([""], [0], [0])
+
+    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+
+
+def resize_from_to_html(width, height, scale_by):
+    target_width = int(width * scale_by)
+    target_height = int(height * scale_by)
+
+    if not target_width or not target_height:
+        return "no image selected"
+
+    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+
+
+def apply_styles(prompt, prompt_neg, styles):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
+
+
+def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
+    if mode in {0, 1, 3, 4}:
+        return [interrogation_function(ii_singles[mode]), None]
+    elif mode == 2:
+        return [interrogation_function(ii_singles[mode]["image"]), None]
+    elif mode == 5:
+        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+        images = shared.listfiles(ii_input_dir)
+        print(f"Will process {len(images)} images.")
+        if ii_output_dir != "":
+            os.makedirs(ii_output_dir, exist_ok=True)
+        else:
+            ii_output_dir = ii_input_dir
+
+        for image in images:
+            img = Image.open(image)
+            filename = os.path.basename(image)
+            left, _ = os.path.splitext(filename)
+            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
+
+        return [gr.update(), None]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image.convert("RGB"))
+    return gr.update() if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = deepbooru.model.tag(image)
+    return gr.update() if prompt is None else prompt
+
+
+def create_seed_inputs(target_interface):
+    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
+        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
+        seed.style(container=False)
+        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
+
+        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
+        subseed.style(container=False)
+        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
+        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
+
+    with FormRow(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
+
+    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
+    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+
+def connect_clear_prompt(button):
+    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
+    button.click(
+        _js="clear_prompt",
+        fn=None,
+        inputs=[],
+        outputs=[],
+    )
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError:
+            if gen_info_string:
+                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        text, _ = extra_networks.parse_prompt(text)
+
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
+    return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_classes="interrogate-col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row(elem_id=f"{id_part}_tools"):
+                paste = ToolButton(value=paste_symbol, elem_id="paste")
+                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
+                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
+                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+
+                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+                clear_prompt_button.click(
+                    fn=lambda *x: x,
+                    _js="confirm_clear_prompt",
+                    inputs=[prompt, negative_prompt],
+                    outputs=[prompt, negative_prompt],
+                )
+
+            with gr.Row(elem_id=f"{id_part}_styles_row"):
+                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
+                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+
+    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
+
+
+def setup_progressbar(*args, **kwargs):
+    pass
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data.get(key, None)
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return getattr(opts, key)
+
+
+def create_output_panel(tabname, outdir):
+    return ui_common.create_output_panel(tabname, outdir)
+
+
+def create_sampler_and_steps_selection(choices, tabname):
+    if opts.samplers_in_dropdown:
+        with FormRow(elem_id=f"sampler_selection_{tabname}"):
+            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+    else:
+        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+
+    return steps, sampler_index
+
+
+def ordered_ui_categories():
+    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
+
+    for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+        yield category
+
+
+def create_override_settings_dropdown(tabname, row):
+    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+    dropdown.change(
+        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
+        inputs=[dropdown],
+        outputs=[dropdown],
+    )
+
+    return dropdown
+
+
+def create_ui():
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
+
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
+
+        with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
+            from modules import ui_extra_networks
+            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='compact', elem_id="txt2img_settings"):
+                modules.scripts.scripts_txt2img.prepare_ui()
+
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
+
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="txt2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="txt2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "cfg":
+                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
+
+                    elif category == "hires_fix":
+                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+                                hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+                                with gr.Column(scale=80):
+                                    with gr.Row():
+                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+                                with gr.Column(scale=80):
+                                    with gr.Row():
+                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="txt2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "override_settings":
+                        with FormRow(elem_id="txt2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('txt2img', row)
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="txt2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+                    else:
+                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+
+            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+
+            for component in hr_resolution_preview_inputs:
+                event = component.release if isinstance(component, gr.Slider) else component.change
+
+                event(
+                    fn=calc_resolution_hires,
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[hr_final_resolution],
+                    show_progress=False,
+                )
+                event(
+                    None,
+                    _js="onCalcResolutionHires",
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[],
+                    show_progress=False,
+                )
+
+            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
+                _js="submit",
+                inputs=[
+                    dummy_component,
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_styles,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    hr_scale,
+                    hr_upscaler,
+                    hr_second_pass_steps,
+                    hr_resize_x,
+                    hr_resize_y,
+                    hr_sampler_index,
+                    hr_prompt,
+                    hr_negative_prompt,
+                    override_settings,
+
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressTxt2img",
+                inputs=[dummy_component],
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ],
+                show_progress=False,
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+                show_progress = False,
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (hr_scale, "Hires upscale"),
+                (hr_upscaler, "Hires upscaler"),
+                (hr_second_pass_steps, "Hires steps"),
+                (hr_resize_x, "Hires resize-1"),
+                (hr_resize_y, "Hires resize-2"),
+                (hr_sampler_index, "Hires sampler"),
+                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+                (hr_prompt, "Hires prompt"),
+                (hr_negative_prompt, "Hires negative prompt"),
+                (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+            ))
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+            ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
+
+        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
+
+        with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
+            from modules import ui_extra_networks
+            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
+
+        with FormRow().style(equal_height=False):
+            with gr.Column(variant='compact', elem_id="img2img_settings"):
+                copy_image_buttons = []
+                copy_image_destinations = {}
+
+                def add_copy_image_controls(tab_name, elem):
+                    with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+                        gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+                        for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+                            if name == tab_name:
+                                gr.Button(title, interactive=False)
+                                copy_image_destinations[name] = elem
+                                continue
+
+                            button = gr.Button(title)
+                            copy_image_buttons.append((button, name, elem))
+
+                with gr.Tabs(elem_id="mode_img2img"):
+                    img2img_selected_tab = gr.State(0)
+
+                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('img2img', init_img)
+
+                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('sketch', sketch)
+
+                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('inpaint', init_img_with_mask)
+
+                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        inpaint_color_sketch_orig = gr.State(None)
+                        add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
+
+                        def update_orig(image, state):
+                            if image is not None:
+                                same_size = state is not None and state.size == image.size
+                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                edited = same_size and has_exact_match
+                                return image if not edited or state is None else state
+
+                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
+
+                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(
+                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
+                            f"{hidden}</p>"
+                        )
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+                        with gr.Accordion("PNG info", open=False):
+                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+                    for i, tab in enumerate(img2img_tabs):
+                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+                def copy_image(img):
+                    if isinstance(img, dict) and 'image' in img:
+                        return img['image']
+
+                    return img
+
+                for button, name, elem in copy_image_buttons:
+                    button.click(
+                        fn=copy_image,
+                        inputs=[elem],
+                        outputs=[copy_image_destinations[name]],
+                    )
+                    button.click(
+                        fn=lambda: None,
+                        _js=f"switch_to_{name.replace(' ', '_')}",
+                        inputs=[],
+                        outputs=[],
+                    )
+
+                with FormRow():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+
+                modules.scripts.scripts_img2img.prepare_ui()
+
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
+
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                selected_scale_tab = gr.State(value=0)
+
+                                with gr.Tabs():
+                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+                                        with FormRow():
+                                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+
+                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+                                        with FormRow():
+                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+                                    on_change_args = dict(
+                                        fn=resize_from_to_html,
+                                        _js="currentImg2imgSourceResolution",
+                                        inputs=[dummy_component, dummy_component, scale_by],
+                                        outputs=scale_by_html,
+                                        show_progress=False,
+                                    )
+
+                                    scale_by.release(**on_change_args)
+                                    button_update_resize_to.click(**on_change_args)
+
+                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+                                    for component in [init_img, sketch]:
+                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="img2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "cfg":
+                        with FormGroup():
+                            with FormRow():
+                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="img2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "override_settings":
+                        with FormRow(elem_id="img2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('img2img', row)
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="img2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+                    elif category == "inpaint":
+                        with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+                            with FormRow():
+                                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+                                mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+                            with FormRow():
+                                inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+                            with FormRow():
+                                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+                            with FormRow():
+                                with gr.Column():
+                                    inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+                                with gr.Column(scale=4):
+                                    inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+                            def select_img2img_tab(tab):
+                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+                            for i, elem in enumerate(img2img_tabs):
+                                elem.select(
+                                    fn=lambda tab=i: select_img2img_tab(tab),
+                                    inputs=[],
+                                    outputs=[inpaint_controls, mask_alpha],
+                                )
+                    else:
+                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
+
+            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ],
+                show_progress=False,
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_styles,
+                    init_img,
+                    sketch,
+                    init_img_with_mask,
+                    inpaint_color_sketch,
+                    inpaint_color_sketch_orig,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    mask_alpha,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    image_cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    selected_scale_tab,
+                    height,
+                    width,
+                    scale_by,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                    img2img_batch_inpaint_mask_dir,
+                    override_settings,
+                    img2img_batch_use_png_info,
+                    img2img_batch_png_info_props,
+                    img2img_batch_png_info_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            interrogate_args = dict(
+                _js="get_img2img_tab_index",
+                inputs=[
+                    dummy_component,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                    init_img,
+                    sketch,
+                    init_img_with_mask,
+                    inpaint_color_sketch,
+                    init_img_inpaint,
+                ],
+                outputs=[img2img_prompt, dummy_component],
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
+
+            detect_image_size_btn.click(
+                fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+                _js="currentImg2imgSourceResolution",
+                inputs=[dummy_component, dummy_component, dummy_component],
+                outputs=[width, height],
+                show_progress=False,
+            )
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressImg2img",
+                inputs=[dummy_component],
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            img2img_interrogate.click(
+                fn=lambda *args: process_interrogate(interrogate, *args),
+                **interrogate_args,
+            )
+
+            img2img_deepbooru.click(
+                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+                **interrogate_args,
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
+                )
+
+            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, styles],
+                    outputs=[prompt, negative_prompt, styles],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (image_cfg_scale, "Image CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (denoising_strength, "Denoising strength"),
+                (mask_blur, "Mask blur"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+            ))
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        ui_postprocessing.create_ui()
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+                for tabname, button in buttons.items():
+                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+                    ))
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    def update_interp_description(value):
+        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+        interp_descriptions = {
+            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+        }
+        return interp_descriptions[value]
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='compact'):
+                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+                with FormRow(elem_id="modelmerger_models"):
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
+
+                with FormRow():
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
+
+                with FormRow():
+                    with gr.Column():
+                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+                    with gr.Column():
+                        with FormRow():
+                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+                with FormRow():
+                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+                with gr.Row():
+                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+                with gr.Group(elem_id="modelmerger_results_panel"):
+                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row(variant="compact").style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding", id="create_embedding"):
+                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+                    new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+                with gr.Tab(label="Preprocess images", id="preprocess_images"):
+                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
+
+                    with gr.Row():
+                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
+                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
+                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+
+                    with gr.Column(visible=False) as process_multicrop_col:
+                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
+                        with gr.Row():
+                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
+                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
+                        with gr.Row():
+                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
+                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
+                        with gr.Row():
+                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
+                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                    process_multicrop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_multicrop],
+                        outputs=[process_multicrop_col],
+                    )
+
+                def get_textual_inversion_template_names():
+                    return sorted(textual_inversion.textual_inversion_templates)
+
+                with gr.Tab(label="Train", id="train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with FormRow():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+
+                    with FormRow():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+                    with FormRow():
+                        clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+                        clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+                    with FormRow():
+                        batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+                        gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+                    with FormRow():
+                        template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+                        create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+                    with FormRow():
+                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+                    shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+                    tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+                    latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+                    with gr.Row():
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column(elem_id='ti_gallery_container'):
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout,
+                new_hypernetwork_dropout_structure
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_keep_original_size,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+                process_multicrop,
+                process_multicrop_mindim,
+                process_multicrop_maxdim,
+                process_multicrop_minarea,
+                process_multicrop_maxarea,
+                process_multicrop_objective,
+                process_multicrop_threshold,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                gradient_step,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                varsize,
+                steps,
+                clip_grad_mode,
+                clip_grad_value,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
+                use_weight,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                gradient_step,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                varsize,
+                steps,
+                clip_grad_mode,
+                clip_grad_value,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
+                use_weight,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+
+    settings = ui_settings.UiSettings()
+    settings.create_ui(loadsave, dummy_component)
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "train"),
+    ]
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings.interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    shared.tab_names = []
+    for _interface, label, _ifid in interfaces:
+        shared.tab_names.append(label)
+
+    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
+        settings.add_quicksettings()
+
+        parameters_copypaste.connect_paste_params_buttons()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
+            sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
+
+            for interface, label, ifid in sorted_interfaces:
+                if label in shared.opts.hidden_tabs:
+                    continue
+                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
+                    interface.render()
+
+            for interface, _label, ifid in interfaces:
+                if ifid in ["extensions", "settings"]:
+                    continue
+
+                loadsave.add_block(interface, ifid)
+
+            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+            loadsave.setup_ui()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        footer = shared.html("footer.html")
+        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
+        gr.HTML(footer, elem_id="footer")
+
+        settings.add_functionality(demo)
+
+        update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+        demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                errors.report("Error loading/saving model file", exc_info=True)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+            return results
+
+        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
+        modelmerger_merge.click(
+            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+            _js='modelmerger',
+            inputs=[
+                dummy_component,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                custom_name,
+                checkpoint_format,
+                config_source,
+                bake_in_vae,
+                discard_weights,
+                save_metadata,
+            ],
+            outputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                settings.component_dict['sd_model_checkpoint'],
+                modelmerger_result,
+            ]
+        )
+
+    loadsave.dump_defaults()
+    demo.ui_loadsave = loadsave
+
+    # Required as a workaround for change() event not triggering when loading values from ui-config.json
+    interp_description.value = update_interp_description(interp_method.value)
+
+    return demo
+
+
+def versions_html():
+    import torch
+    import launch
+
+    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
+    commit = launch.commit_hash()
+    tag = launch.git_tag()
+
+    if shared.xformers_available:
+        import xformers
+        xformers_version = xformers.__version__
+    else:
+        xformers_version = "N/A"
+
+    return f"""
+version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
+&#x2000;•&#x2000;
+python: <span title="{sys.version}">{python_version}</span>
+&#x2000;•&#x2000;
+torch: {getattr(torch, '__long_version__',torch.__version__)}
+&#x2000;•&#x2000;
+xformers: {xformers_version}
+&#x2000;•&#x2000;
+gradio: {gr.__version__}
+&#x2000;•&#x2000;
+checkpoint: <a id="sd_checkpoint_hash">N/A</a>
+"""
+
+
+def setup_ui_api(app):
+    from pydantic import BaseModel, Field
+    from typing import List
+
+    class QuicksettingsHint(BaseModel):
+        name: str = Field(title="Name of the quicksettings field")
+        label: str = Field(title="Label of the quicksettings field")
+
+    def quicksettings_hint():
+        return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
+
+    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
+
+    app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+    def download_sysinfo(attachment=False):
+        from fastapi.responses import PlainTextResponse
+
+        text = sysinfo.get()
+        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+