import datetime import mimetypes import os import sys from functools import reduce import warnings from contextlib import ExitStack import gradio as gr import gradio.utils import numpy as np from PIL import Image, PngImagePlugin # noqa: F401 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call from modules import gradio_extensons, sd_schedulers # noqa: F401 from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow from modules.paths import script_path from modules.ui_common import create_refresh_button from modules.ui_gradio_extensions import reload_javascript from modules.shared import opts, cmd_opts import modules.infotext_utils as parameters_copypaste import modules.hypernetworks.ui as hypernetworks_ui import modules.textual_inversion.ui as textual_inversion_ui import modules.textual_inversion.textual_inversion as textual_inversion import modules.shared as shared from modules import prompt_parser from modules.sd_hijack import model_hijack from modules.infotext_utils import image_from_url_text, PasteField create_setting_component = ui_settings.create_setting_component warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning) warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning) # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') mimetypes.add_type('application/javascript', '.mjs') # Likewise, add explicit content-type header for certain missing image types mimetypes.add_type('image/webp', '.webp') mimetypes.add_type('image/avif', '.avif') if not cmd_opts.share and not cmd_opts.listen: # fix gradio phoning home gradio.utils.version_check = lambda: None gradio.utils.get_local_ip_address = lambda: '127.0.0.1' if cmd_opts.ngrok is not None: import modules.ngrok as ngrok print('ngrok authtoken detected, trying to connect...') ngrok.connect( cmd_opts.ngrok, cmd_opts.port if cmd_opts.port is not None else 7860, cmd_opts.ngrok_options ) def gr_show(visible=True): return {"visible": visible, "__type__": "update"} sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None # Using constants for these since the variation selector isn't visible. # Important that they exactly match script.js for tooltip to work. random_symbol = '\U0001f3b2\ufe0f' # 🎲️ reuse_symbol = '\u267b\ufe0f' # ♻️ paste_symbol = '\u2199\ufe0f' # ↙ refresh_symbol = '\U0001f504' # 🔄 save_style_symbol = '\U0001f4be' # 💾 apply_style_symbol = '\U0001f4cb' # 📋 clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️ extra_networks_symbol = '\U0001F3B4' # 🎴 switch_values_symbol = '\U000021C5' # ⇅ restore_progress_symbol = '\U0001F300' # 🌀 detect_image_size_symbol = '\U0001F4D0' # 📐 plaintext_to_html = ui_common.plaintext_to_html def send_gradio_gallery_to_image(x): if len(x) == 0: return None return image_from_url_text(x[0]) def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y): if not enable: return "" p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y) p.calculate_target_resolution() return f"from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}" 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 {width}x{height} to {target_width}x{target_height}" def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles): if mode in {0, 1, 3, 4}: return [interrogation_function(ii_singles[mode]), None] elif mode == 2: return [interrogation_function(ii_singles[mode]["image"]), None] elif mode == 5: assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" images = shared.listfiles(ii_input_dir) print(f"Will process {len(images)} images.") if ii_output_dir != "": os.makedirs(ii_output_dir, exist_ok=True) else: ii_output_dir = ii_input_dir for image in images: img = Image.open(image) filename = os.path.basename(image) left, _ = os.path.splitext(filename) print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8')) return [gr.update(), None] def interrogate(image): prompt = shared.interrogator.interrogate(image.convert("RGB")) return gr.update() if prompt is None else prompt def interrogate_deepbooru(image): prompt = deepbooru.model.tag(image) return gr.update() if prompt is None else prompt def connect_clear_prompt(button): """Given clear button, prompt, and token_counter objects, setup clear prompt button click event""" button.click( _js="clear_prompt", fn=None, inputs=[], outputs=[], ) def update_token_counter(text, steps, styles, *, is_positive=True): params = script_callbacks.BeforeTokenCounterParams(text, steps, styles, is_positive=is_positive) script_callbacks.before_token_counter_callback(params) text = params.prompt steps = params.steps styles = params.styles is_positive = params.is_positive if shared.opts.include_styles_into_token_counters: apply_styles = shared.prompt_styles.apply_styles_to_prompt if is_positive else shared.prompt_styles.apply_negative_styles_to_prompt text = apply_styles(text, styles) try: text, _ = extra_networks.parse_prompt(text) if is_positive: _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) else: prompt_flat_list = [text] prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) except Exception: # a parsing error can happen here during typing, and we don't want to bother the user with # messages related to it in console prompt_schedules = [[[steps, text]]] flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) prompts = [prompt_text for step, prompt_text in flat_prompts] token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0]) return f"{token_count}/{max_length}" def update_negative_prompt_token_counter(*args): return update_token_counter(*args, is_positive=False) def setup_progressbar(*args, **kwargs): pass def apply_setting(key, value): if value is None: return gr.update() if shared.cmd_opts.freeze_settings: return gr.update() # dont allow model to be swapped when model hash exists in prompt if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: return gr.update() if key == "sd_model_checkpoint": ckpt_info = sd_models.get_closet_checkpoint_match(value) if ckpt_info is not None: value = ckpt_info.title else: return gr.update() comp_args = opts.data_labels[key].component_args if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: return valtype = type(opts.data_labels[key].default) oldval = opts.data.get(key, None) opts.data[key] = valtype(value) if valtype != type(None) else value if oldval != value and opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() opts.save(shared.config_filename) return getattr(opts, key) def create_output_panel(tabname, outdir, toprow=None): return ui_common.create_output_panel(tabname, outdir, toprow) 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() settings = ui_settings.UiSettings() settings.register_settings() scripts.scripts_current = scripts.scripts_txt2img scripts.scripts_txt2img.initialize_scripts(is_img2img=False) with gr.Blocks(analytics_enabled=False) as txt2img_interface: toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box) dummy_component = gr.Label(visible=False) extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs", elem_classes=["extra-networks"]) extra_tabs.__enter__() with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False): with ExitStack() as stack: if shared.opts.txt2img_settings_accordion: stack.enter_context(gr.Accordion("Open for Settings", open=False)) stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings")) scripts.scripts_txt2img.prepare_ui() for category in ordered_ui_categories(): if category == "prompt": toprow.create_inline_toprow_prompts() elif category == "dimensions": with FormRow(): with gr.Column(elem_id="txt2img_column_size", scale=4): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width") height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height") with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height") if opts.dimensions_and_batch_together: with gr.Column(elem_id="txt2img_column_batch"): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") elif category == "cfg": with gr.Row(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale") elif category == "checkboxes": with FormRow(elem_classes="checkboxes-row", variant="compact"): pass elif category == "accordions": with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"): with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr: with enable_hr.extra(): hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0) with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"): hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps") denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"): hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x") hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y") with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container: hr_checkpoint_name = gr.Dropdown(label='Checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint") create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh") hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler") hr_scheduler = gr.Dropdown(label='Hires schedule type', elem_id="hr_scheduler", choices=["Use same scheduler"] + [x.label for x in sd_schedulers.schedulers], value="Use same scheduler") with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container: with gr.Column(scale=80): with gr.Row(): hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"]) with gr.Column(scale=80): with gr.Row(): hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"]) scripts.scripts_txt2img.setup_ui_for_section(category) elif category == "batch": if not opts.dimensions_and_batch_together: with FormRow(elem_id="txt2img_column_batch"): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") elif category == "override_settings": with FormRow(elem_id="txt2img_override_settings_row") as row: override_settings = create_override_settings_dropdown('txt2img', row) elif category == "scripts": with FormGroup(elem_id="txt2img_script_container"): custom_inputs = scripts.scripts_txt2img.setup_ui() if category not in {"accordions"}: scripts.scripts_txt2img.setup_ui_for_section(category) hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y] for component in hr_resolution_preview_inputs: event = component.release if isinstance(component, gr.Slider) else component.change event( fn=calc_resolution_hires, inputs=hr_resolution_preview_inputs, outputs=[hr_final_resolution], show_progress=False, ) event( None, _js="onCalcResolutionHires", inputs=hr_resolution_preview_inputs, outputs=[], show_progress=False, ) output_panel = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow) txt2img_inputs = [ dummy_component, toprow.prompt, toprow.negative_prompt, toprow.ui_styles.dropdown, batch_count, batch_size, cfg_scale, height, width, enable_hr, denoising_strength, hr_scale, hr_upscaler, hr_second_pass_steps, hr_resize_x, hr_resize_y, hr_checkpoint_name, hr_sampler_name, hr_scheduler, hr_prompt, hr_negative_prompt, override_settings, ] + custom_inputs txt2img_outputs = [ output_panel.gallery, output_panel.generation_info, output_panel.infotext, output_panel.html_log, ] txt2img_args = dict( fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']), _js="submit", inputs=txt2img_inputs, outputs=txt2img_outputs, show_progress=False, ) toprow.prompt.submit(**txt2img_args) toprow.submit.click(**txt2img_args) output_panel.button_upscale.click( fn=wrap_gradio_gpu_call(modules.txt2img.txt2img_upscale, extra_outputs=[None, '', '']), _js="submit_txt2img_upscale", inputs=txt2img_inputs[0:1] + [output_panel.gallery, dummy_component, output_panel.generation_info] + txt2img_inputs[1:], outputs=txt2img_outputs, show_progress=False, ) res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False) toprow.restore_progress_button.click( fn=progress.restore_progress, _js="restoreProgressTxt2img", inputs=[dummy_component], outputs=[ output_panel.gallery, output_panel.generation_info, output_panel.infotext, output_panel.html_log, ], show_progress=False, ) txt2img_paste_fields = [ PasteField(toprow.prompt, "Prompt", api="prompt"), PasteField(toprow.negative_prompt, "Negative prompt", api="negative_prompt"), PasteField(cfg_scale, "CFG scale", api="cfg_scale"), PasteField(width, "Size-1", api="width"), PasteField(height, "Size-2", api="height"), PasteField(batch_size, "Batch size", api="batch_size"), PasteField(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update(), api="styles"), PasteField(denoising_strength, "Denoising strength", api="denoising_strength"), PasteField(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d), api="enable_hr"), PasteField(hr_scale, "Hires upscale", api="hr_scale"), PasteField(hr_upscaler, "Hires upscaler", api="hr_upscaler"), PasteField(hr_second_pass_steps, "Hires steps", api="hr_second_pass_steps"), PasteField(hr_resize_x, "Hires resize-1", api="hr_resize_x"), PasteField(hr_resize_y, "Hires resize-2", api="hr_resize_y"), PasteField(hr_checkpoint_name, "Hires checkpoint", api="hr_checkpoint_name"), PasteField(hr_sampler_name, sd_samplers.get_hr_sampler_from_infotext, api="hr_sampler_name"), PasteField(hr_scheduler, sd_samplers.get_hr_scheduler_from_infotext, api="hr_scheduler"), PasteField(hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" or d.get("Hires schedule type", "Use same scheduler") != "Use same scheduler" else gr.update()), PasteField(hr_prompt, "Hires prompt", api="hr_prompt"), PasteField(hr_negative_prompt, "Hires negative prompt", api="hr_negative_prompt"), PasteField(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()), *scripts.scripts_txt2img.infotext_fields ] parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings) parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None, )) steps = scripts.scripts_txt2img.script('Sampler').steps txt2img_preview_params = [ toprow.prompt, toprow.negative_prompt, steps, scripts.scripts_txt2img.script('Sampler').sampler_name, cfg_scale, scripts.scripts_txt2img.script('Seed').seed, width, height, ] toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter]) toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter]) toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter]) toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter]) extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img') ui_extra_networks.setup_ui(extra_networks_ui, output_panel.gallery) extra_tabs.__exit__() scripts.scripts_current = scripts.scripts_img2img scripts.scripts_img2img.initialize_scripts(is_img2img=True) with gr.Blocks(analytics_enabled=False) as img2img_interface: toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box) extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs", elem_classes=["extra-networks"]) extra_tabs.__enter__() with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False): with ExitStack() as stack: if shared.opts.img2img_settings_accordion: stack.enter_context(gr.Accordion("Open for Settings", open=False)) stack.enter_context(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)) scripts.scripts_img2img.prepare_ui() for category in ordered_ui_categories(): if category == "prompt": toprow.create_inline_toprow_prompts() if category == "image": with gr.Tabs(elem_id="mode_img2img"): img2img_selected_tab = gr.Number(value=0, visible=False) with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img: init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height) add_copy_image_controls('img2img', init_img) with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch: sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color) add_copy_image_controls('sketch', sketch) with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint: init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color) add_copy_image_controls('inpaint', init_img_with_mask) with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color: inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color) inpaint_color_sketch_orig = gr.State(None) add_copy_image_controls('inpaint_sketch', inpaint_color_sketch) def update_orig(image, state): if image is not None: same_size = state is not None and state.size == image.size has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1)) edited = same_size and has_exact_match return image if not edited or state is None else state inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig) with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload: init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base") init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask") with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch: with gr.Tabs(elem_id="img2img_batch_source"): img2img_batch_source_type = gr.Textbox(visible=False, value="upload") with gr.TabItem('Upload', id='batch_upload', elem_id="img2img_batch_upload_tab") as tab_batch_upload: img2img_batch_upload = gr.Files(label="Files", interactive=True, elem_id="img2img_batch_upload") with gr.TabItem('From directory', id='batch_from_dir', elem_id="img2img_batch_from_dir_tab") as tab_batch_from_dir: hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' gr.HTML( "

Process images in a directory on the same machine where the server is running." + "
Use an empty output directory to save pictures normally instead of writing to the output directory." + f"
Add inpaint batch mask directory to enable inpaint batch processing." f"{hidden}

" ) 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") tab_batch_upload.select(fn=lambda: "upload", inputs=[], outputs=[img2img_batch_source_type]) tab_batch_from_dir.select(fn=lambda: "from dir", inputs=[], outputs=[img2img_batch_source_type]) with gr.Accordion("PNG info", open=False): img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", elem_id="img2img_batch_use_png_info") img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir") img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.") img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch] for i, tab in enumerate(img2img_tabs): tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab]) def copy_image(img): if isinstance(img, dict) and 'image' in img: return img['image'] return img for button, name, elem in copy_image_buttons: button.click( fn=copy_image, inputs=[elem], outputs=[copy_image_destinations[name]], ) button.click( fn=lambda: None, _js=f"switch_to_{name.replace(' ', '_')}", inputs=[], outputs=[], ) with FormRow(): resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize") elif category == "dimensions": with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): selected_scale_tab = gr.Number(value=0, visible=False) with gr.Tabs(): with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to: with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height") detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img") with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by: scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale") with FormRow(): scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview") gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider") button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to") on_change_args = dict( fn=resize_from_to_html, _js="currentImg2imgSourceResolution", inputs=[dummy_component, dummy_component, scale_by], outputs=scale_by_html, show_progress=False, ) scale_by.release(**on_change_args) button_update_resize_to.click(**on_change_args) tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab]) tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab]) if opts.dimensions_and_batch_together: with gr.Column(elem_id="img2img_column_batch"): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") elif category == "denoising": denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") elif category == "cfg": with gr.Row(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale") image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False) elif category == "checkboxes": with FormRow(elem_classes="checkboxes-row", variant="compact"): pass elif category == "accordions": with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"): scripts.scripts_img2img.setup_ui_for_section(category) elif category == "batch": if not opts.dimensions_and_batch_together: with FormRow(elem_id="img2img_column_batch"): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") elif category == "override_settings": with FormRow(elem_id="img2img_override_settings_row") as row: override_settings = create_override_settings_dropdown('img2img', row) elif category == "scripts": with FormGroup(elem_id="img2img_script_container"): custom_inputs = scripts.scripts_img2img.setup_ui() elif category == "inpaint": with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls: with FormRow(): mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur") mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha") with FormRow(): inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode") with FormRow(): inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill") with FormRow(): with gr.Column(): inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res") with gr.Column(scale=4): inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding") if category not in {"accordions"}: scripts.scripts_img2img.setup_ui_for_section(category) # 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) 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], ) output_panel = create_output_panel("img2img", opts.outdir_img2img_samples, toprow) img2img_args = dict( fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']), _js="submit_img2img", inputs=[ dummy_component, dummy_component, toprow.prompt, toprow.negative_prompt, toprow.ui_styles.dropdown, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur, mask_alpha, inpainting_fill, batch_count, batch_size, cfg_scale, image_cfg_scale, denoising_strength, selected_scale_tab, height, width, scale_by, resize_mode, inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, override_settings, img2img_batch_use_png_info, img2img_batch_png_info_props, img2img_batch_png_info_dir, img2img_batch_source_type, img2img_batch_upload, ] + custom_inputs, outputs=[ output_panel.gallery, output_panel.generation_info, output_panel.infotext, output_panel.html_log, ], show_progress=False, ) interrogate_args = dict( _js="get_img2img_tab_index", inputs=[ dummy_component, img2img_batch_input_dir, img2img_batch_output_dir, init_img, sketch, init_img_with_mask, inpaint_color_sketch, init_img_inpaint, ], outputs=[toprow.prompt, dummy_component], ) toprow.prompt.submit(**img2img_args) toprow.submit.click(**img2img_args) res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False) detect_image_size_btn.click( fn=lambda w, h, _: (w or gr.update(), h or gr.update()), _js="currentImg2imgSourceResolution", inputs=[dummy_component, dummy_component, dummy_component], outputs=[width, height], show_progress=False, ) toprow.restore_progress_button.click( fn=progress.restore_progress, _js="restoreProgressImg2img", inputs=[dummy_component], outputs=[ output_panel.gallery, output_panel.generation_info, output_panel.infotext, output_panel.html_log, ], show_progress=False, ) toprow.button_interrogate.click( fn=lambda *args: process_interrogate(interrogate, *args), **interrogate_args, ) toprow.button_deepbooru.click( fn=lambda *args: process_interrogate(interrogate_deepbooru, *args), **interrogate_args, ) steps = scripts.scripts_img2img.script('Sampler').steps toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter]) toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter]) toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter]) toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter]) img2img_paste_fields = [ (toprow.prompt, "Prompt"), (toprow.negative_prompt, "Negative prompt"), (cfg_scale, "CFG scale"), (image_cfg_scale, "Image CFG scale"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()), (denoising_strength, "Denoising strength"), (mask_blur, "Mask blur"), (inpainting_mask_invert, 'Mask mode'), (inpainting_fill, 'Masked content'), (inpaint_full_res, 'Inpaint area'), (inpaint_full_res_padding, 'Masked area padding'), *scripts.scripts_img2img.infotext_fields ] parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings) parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings) parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None, )) extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img') ui_extra_networks.setup_ui(extra_networks_ui_img2img, output_panel.gallery) extra_tabs.__exit__() scripts.scripts_current = None with gr.Blocks(analytics_enabled=False) as extras_interface: ui_postprocessing.create_ui() with gr.Blocks(analytics_enabled=False) as pnginfo_interface: with ResizeHandleRow(equal_height=False): with gr.Column(variant='panel'): image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") with gr.Column(variant='panel'): html = gr.HTML() generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info") html2 = gr.HTML() with gr.Row(): buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) for tabname, button in buttons.items(): parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image, )) image.change( fn=wrap_gradio_call(modules.extras.run_pnginfo), inputs=[image], outputs=[html, generation_info, html2], ) modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger() with gr.Blocks(analytics_enabled=False) as train_interface: with gr.Row(equal_height=False): gr.HTML(value="

See wiki for detailed explanation.

") with ResizeHandleRow(variant="compact", equal_height=False): with gr.Tabs(elem_id="train_tabs"): with gr.Tab(label="Create embedding", id="create_embedding"): new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure") new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func") new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'") overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") def get_textual_inversion_template_names(): return sorted(textual_inversion.textual_inversion_templates) with gr.Tab(label="Train", id="train"): gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") with FormRow(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks)) create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name") with FormRow(): embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate") with FormRow(): clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False) with FormRow(): batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size") gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step") dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory") with FormRow(): template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names()) create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file") training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize") steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") with FormRow(): create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every") save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every") use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight") save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding") preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img") shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags") tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out") latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method") with gr.Row(): train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding") interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training") train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork") params = script_callbacks.UiTrainTabParams(txt2img_preview_params) script_callbacks.ui_train_tabs_callback(params) with gr.Column(elem_id='ti_gallery_container'): ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4) gr.HTML(elem_id="ti_progress", value="") ti_outcome = gr.HTML(elem_id="ti_error", value="") create_embedding.click( fn=textual_inversion_ui.create_embedding, inputs=[ new_embedding_name, initialization_text, nvpt, overwrite_old_embedding, ], outputs=[ train_embedding_name, ti_output, ti_outcome, ] ) create_hypernetwork.click( fn=hypernetworks_ui.create_hypernetwork, inputs=[ new_hypernetwork_name, new_hypernetwork_sizes, overwrite_old_hypernetwork, new_hypernetwork_layer_structure, new_hypernetwork_activation_func, new_hypernetwork_initialization_option, new_hypernetwork_add_layer_norm, new_hypernetwork_use_dropout, new_hypernetwork_dropout_structure ], outputs=[ train_hypernetwork_name, ti_output, ti_outcome, ] ) train_embedding.click( fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ dummy_component, train_embedding_name, embedding_learn_rate, batch_size, gradient_step, dataset_directory, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, *txt2img_preview_params, ], outputs=[ ti_output, ti_outcome, ] ) train_hypernetwork.click( fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ dummy_component, train_hypernetwork_name, hypernetwork_learn_rate, batch_size, gradient_step, dataset_directory, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_file, preview_from_txt2img, *txt2img_preview_params, ], outputs=[ ti_output, ti_outcome, ] ) interrupt_training.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file) ui_settings_from_file = loadsave.ui_settings.copy() settings.create_ui(loadsave, dummy_component) interfaces = [ (txt2img_interface, "txt2img", "txt2img"), (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "train"), ] interfaces += script_callbacks.ui_tabs_callback() interfaces += [(settings.interface, "Settings", "settings")] extensions_interface = ui_extensions.create_ui() interfaces += [(extensions_interface, "Extensions", "extensions")] shared.tab_names = [] for _interface, label, _ifid in interfaces: shared.tab_names.append(label) with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo: settings.add_quicksettings() parameters_copypaste.connect_paste_params_buttons() with gr.Tabs(elem_id="tabs") as tabs: tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)} sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999)) for interface, label, ifid in sorted_interfaces: if label in shared.opts.hidden_tabs: continue with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"): interface.render() if ifid not in ["extensions", "settings"]: loadsave.add_block(interface, ifid) loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs) loadsave.setup_ui() if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio: gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) footer = shared.html("footer.html") footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API") gr.HTML(footer, elem_id="footer") settings.add_functionality(demo) update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit") settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale]) demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale]) modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint']) if ui_settings_from_file != loadsave.ui_settings: loadsave.dump_defaults() demo.ui_loadsave = loadsave return demo def versions_html(): import torch import launch python_version = ".".join([str(x) for x in sys.version_info[0:3]]) commit = launch.commit_hash() tag = launch.git_tag() if shared.xformers_available: import xformers xformers_version = xformers.__version__ else: xformers_version = "N/A" return f""" version: {tag}  •  python: {python_version}  •  torch: {getattr(torch, '__long_version__',torch.__version__)}  •  xformers: {xformers_version}  •  gradio: {gr.__version__}  •  checkpoint: N/A """ def setup_ui_api(app): from pydantic import BaseModel, Field class QuicksettingsHint(BaseModel): name: str = Field(title="Name of the quicksettings field") label: str = Field(title="Label of the quicksettings field") def quicksettings_hint(): return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()] app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint]) app.add_api_route("/internal/ping", lambda: {}, methods=["GET"]) app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"]) def download_sysinfo(attachment=False): from fastapi.responses import PlainTextResponse text = sysinfo.get() filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json" 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"]) import fastapi.staticfiles app.mount("/webui-assets", fastapi.staticfiles.StaticFiles(directory=launch_utils.repo_dir('stable-diffusion-webui-assets')), name="webui-assets")