import datetime import mimetypes import os import sys from functools import reduce import warnings import gradio as gr import gradio.utils import numpy as np from PIL import Image, PngImagePlugin # noqa: F401 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call from modules import gradio_extensons # noqa: F401 from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow from modules.paths import script_path from modules.ui_common import create_refresh_button from modules.ui_gradio_extensions import reload_javascript from modules.shared import opts, cmd_opts import modules.generation_parameters_copypaste as parameters_copypaste import modules.hypernetworks.ui as hypernetworks_ui import modules.textual_inversion.ui as textual_inversion_ui import modules.textual_inversion.textual_inversion as textual_inversion import modules.shared as shared import modules.images from modules import prompt_parser from modules.sd_hijack import model_hijack from modules.generation_parameters_copypaste import image_from_url_text create_setting_component = ui_settings.create_setting_component warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning) warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning) # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') # Likewise, add explicit content-type header for certain missing image types mimetypes.add_type('image/webp', '.webp') if not cmd_opts.share and not cmd_opts.listen: # fix gradio phoning home gradio.utils.version_check = lambda: None gradio.utils.get_local_ip_address = lambda: '127.0.0.1' if cmd_opts.ngrok is not None: import modules.ngrok as ngrok print('ngrok authtoken detected, trying to connect...') ngrok.connect( cmd_opts.ngrok, cmd_opts.port if cmd_opts.port is not None else 7860, cmd_opts.ngrok_options ) def gr_show(visible=True): return {"visible": visible, "__type__": "update"} sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None # Using constants for these since the variation selector isn't visible. # Important that they exactly match script.js for tooltip to work. random_symbol = '\U0001f3b2\ufe0f' # 🎲️ reuse_symbol = '\u267b\ufe0f' # ♻️ paste_symbol = '\u2199\ufe0f' # ↙ refresh_symbol = '\U0001f504' # 🔄 save_style_symbol = '\U0001f4be' # 💾 apply_style_symbol = '\U0001f4cb' # 📋 clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️ extra_networks_symbol = '\U0001F3B4' # 🎴 switch_values_symbol = '\U000021C5' # ⇅ restore_progress_symbol = '\U0001F300' # 🌀 detect_image_size_symbol = '\U0001F4D0' # 📐 plaintext_to_html = ui_common.plaintext_to_html def send_gradio_gallery_to_image(x): if len(x) == 0: return None return image_from_url_text(x[0]) def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y): if not enable: return "" p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y) p.calculate_target_resolution() return f"from {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): 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"{token_count}/{max_length}" class Toprow: """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation""" def __init__(self, is_img2img): id_part = "img2img" if is_img2img else "txt2img" self.id_part = id_part with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"): with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6): with gr.Row(): with gr.Column(scale=80): with gr.Row(): self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"]) self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False) with gr.Row(): with gr.Column(scale=80): with gr.Row(): self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"]) self.button_interrogate = None self.button_deepbooru = None if is_img2img: with gr.Column(scale=1, elem_classes="interrogate-col"): self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"): with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"): self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt") self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip") self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') self.skip.click( fn=lambda: shared.state.skip(), inputs=[], outputs=[], ) self.interrupt.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) with gr.Row(elem_id=f"{id_part}_tools"): self.paste = ToolButton(value=paste_symbol, elem_id="paste") self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt") self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False) self.token_counter = gr.HTML(value="0/75", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"]) self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") self.negative_token_counter = gr.HTML(value="0/75", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"]) self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button") self.clear_prompt_button.click( fn=lambda *x: x, _js="confirm_clear_prompt", inputs=[self.prompt, self.negative_prompt], outputs=[self.prompt, self.negative_prompt], ) self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt) self.prompt_img.change( fn=modules.images.image_data, inputs=[self.prompt_img], outputs=[self.prompt, self.prompt_img], show_progress=False, ) def setup_progressbar(*args, **kwargs): pass def apply_setting(key, value): if value is None: return gr.update() if shared.cmd_opts.freeze_settings: return gr.update() # dont allow model to be swapped when model hash exists in prompt if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: return gr.update() if key == "sd_model_checkpoint": ckpt_info = sd_models.get_closet_checkpoint_match(value) if ckpt_info is not None: value = ckpt_info.title else: return gr.update() comp_args = opts.data_labels[key].component_args if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: return valtype = type(opts.data_labels[key].default) oldval = opts.data.get(key, None) opts.data[key] = valtype(value) if valtype != type(None) else value if oldval != value and opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() opts.save(shared.config_filename) return getattr(opts, key) def create_output_panel(tabname, outdir): return ui_common.create_output_panel(tabname, outdir) def create_sampler_and_steps_selection(choices, tabname): if opts.samplers_in_dropdown: with FormRow(elem_id=f"sampler_selection_{tabname}"): sampler_name = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0]) steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) else: with FormGroup(elem_id=f"sampler_selection_{tabname}"): steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) sampler_name = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0]) return steps, sampler_name def ordered_ui_categories(): user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)} for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)): yield category def create_override_settings_dropdown(tabname, row): dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True) dropdown.change( fn=lambda x: gr.Dropdown.update(visible=bool(x)), inputs=[dropdown], outputs=[dropdown], ) return dropdown def create_ui(): import modules.img2img import modules.txt2img reload_javascript() parameters_copypaste.reset() scripts.scripts_current = scripts.scripts_txt2img scripts.scripts_txt2img.initialize_scripts(is_img2img=False) with gr.Blocks(analytics_enabled=False) as txt2img_interface: toprow = Toprow(is_img2img=False) dummy_component = gr.Label(visible=False) extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs") extra_tabs.__enter__() with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False): with gr.Column(variant='compact', elem_id="txt2img_settings"): scripts.scripts_txt2img.prepare_ui() for category in ordered_ui_categories(): if category == "sampler": steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img") elif category == "dimensions": with FormRow(): with gr.Column(elem_id="txt2img_column_size", scale=4): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width") height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height") with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", 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": with gr.Row(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale") elif category == "checkboxes": with FormRow(elem_classes="checkboxes-row", variant="compact"): pass elif category == "accordions": with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"): with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr: with enable_hr.extra(): hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0) with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"): hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps") denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"): hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x") hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y") with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container: hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint") create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh") hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler") with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container: with gr.Column(scale=80): with gr.Row(): hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"]) with gr.Column(scale=80): with gr.Row(): hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"]) scripts.scripts_txt2img.setup_ui_for_section(category) elif category == "batch": if not opts.dimensions_and_batch_together: with FormRow(elem_id="txt2img_column_batch"): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count") batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size") elif category == "override_settings": with FormRow(elem_id="txt2img_override_settings_row") as row: override_settings = create_override_settings_dropdown('txt2img', row) elif category == "scripts": with FormGroup(elem_id="txt2img_script_container"): custom_inputs = scripts.scripts_txt2img.setup_ui() if category not in {"accordions"}: scripts.scripts_txt2img.setup_ui_for_section(category) hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y] for component in hr_resolution_preview_inputs: event = component.release if isinstance(component, gr.Slider) else component.change event( fn=calc_resolution_hires, inputs=hr_resolution_preview_inputs, outputs=[hr_final_resolution], show_progress=False, ) event( None, _js="onCalcResolutionHires", inputs=hr_resolution_preview_inputs, outputs=[], show_progress=False, ) txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples) txt2img_args = dict( fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']), _js="submit", inputs=[ dummy_component, toprow.prompt, toprow.negative_prompt, toprow.ui_styles.dropdown, steps, sampler_name, batch_count, batch_size, cfg_scale, height, width, enable_hr, denoising_strength, hr_scale, hr_upscaler, hr_second_pass_steps, hr_resize_x, hr_resize_y, hr_checkpoint_name, hr_sampler_name, hr_prompt, hr_negative_prompt, override_settings, ] + custom_inputs, outputs=[ txt2img_gallery, generation_info, html_info, html_log, ], show_progress=False, ) toprow.prompt.submit(**txt2img_args) toprow.submit.click(**txt2img_args) res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False) toprow.restore_progress_button.click( fn=progress.restore_progress, _js="restoreProgressTxt2img", inputs=[dummy_component], outputs=[ txt2img_gallery, generation_info, html_info, html_log, ], show_progress=False, ) txt2img_paste_fields = [ (toprow.prompt, "Prompt"), (toprow.negative_prompt, "Negative prompt"), (steps, "Steps"), (sampler_name, "Sampler"), (cfg_scale, "CFG scale"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()), (denoising_strength, "Denoising strength"), (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)), (hr_scale, "Hires upscale"), (hr_upscaler, "Hires upscaler"), (hr_second_pass_steps, "Hires steps"), (hr_resize_x, "Hires resize-1"), (hr_resize_y, "Hires resize-2"), (hr_checkpoint_name, "Hires checkpoint"), (hr_sampler_name, "Hires sampler"), (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()), (hr_prompt, "Hires prompt"), (hr_negative_prompt, "Hires negative prompt"), (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()), *scripts.scripts_txt2img.infotext_fields ] parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings) parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None, )) txt2img_preview_params = [ toprow.prompt, toprow.negative_prompt, steps, sampler_name, cfg_scale, scripts.scripts_txt2img.script('Seed').seed, width, height, ] toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter]) toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter]) extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img') ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery) extra_tabs.__exit__() scripts.scripts_current = scripts.scripts_img2img scripts.scripts_img2img.initialize_scripts(is_img2img=True) with gr.Blocks(analytics_enabled=False) as img2img_interface: toprow = Toprow(is_img2img=True) extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs") extra_tabs.__enter__() with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False): with gr.Column(variant='compact', elem_id="img2img_settings"): copy_image_buttons = [] copy_image_destinations = {} def add_copy_image_controls(tab_name, elem): with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"): gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}") for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']): if name == tab_name: gr.Button(title, interactive=False) copy_image_destinations[name] = elem continue button = gr.Button(title) copy_image_buttons.append((button, name, elem)) with gr.Tabs(elem_id="mode_img2img"): img2img_selected_tab = gr.State(0) with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img: init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height) add_copy_image_controls('img2img', init_img) with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch: sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color) add_copy_image_controls('sketch', sketch) with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint: init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color) add_copy_image_controls('inpaint', init_img_with_mask) with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color: inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color) inpaint_color_sketch_orig = gr.State(None) add_copy_image_controls('inpaint_sketch', inpaint_color_sketch) def update_orig(image, state): if image is not None: same_size = state is not None and state.size == image.size has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1)) edited = same_size and has_exact_match return image if not edited or state is None else state inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig) with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload: init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base") init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask") with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch: hidden = '
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") 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") scripts.scripts_img2img.prepare_ui() for category in ordered_ui_categories(): if category == "sampler": steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img") elif category == "dimensions": with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): selected_scale_tab = gr.State(value=0) with gr.Tabs(): with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to: with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn") 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 == "denoising": denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") elif category == "cfg": with gr.Row(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale") image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False) elif category == "checkboxes": with FormRow(elem_classes="checkboxes-row", variant="compact"): pass elif category == "accordions": with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"): scripts.scripts_img2img.setup_ui_for_section(category) elif category == "batch": if not opts.dimensions_and_batch_together: with FormRow(elem_id="img2img_column_batch"): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count") batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size") elif category == "override_settings": with FormRow(elem_id="img2img_override_settings_row") as row: override_settings = create_override_settings_dropdown('img2img', row) elif category == "scripts": with FormGroup(elem_id="img2img_script_container"): custom_inputs = scripts.scripts_img2img.setup_ui() elif category == "inpaint": with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls: with FormRow(): mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur") mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha") with FormRow(): inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode") with FormRow(): inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill") with FormRow(): with gr.Column(): inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res") with gr.Column(scale=4): inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding") def select_img2img_tab(tab): return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3), for i, elem in enumerate(img2img_tabs): elem.select( fn=lambda tab=i: select_img2img_tab(tab), inputs=[], outputs=[inpaint_controls, mask_alpha], ) if category not in {"accordions"}: scripts.scripts_img2img.setup_ui_for_section(category) img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples) img2img_args = dict( fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']), _js="submit_img2img", inputs=[ dummy_component, dummy_component, toprow.prompt, toprow.negative_prompt, toprow.ui_styles.dropdown, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps, sampler_name, mask_blur, mask_alpha, inpainting_fill, batch_count, batch_size, cfg_scale, image_cfg_scale, denoising_strength, selected_scale_tab, height, width, scale_by, resize_mode, inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, override_settings, img2img_batch_use_png_info, img2img_batch_png_info_props, img2img_batch_png_info_dir, ] + custom_inputs, outputs=[ img2img_gallery, generation_info, html_info, html_log, ], show_progress=False, ) interrogate_args = dict( _js="get_img2img_tab_index", inputs=[ dummy_component, img2img_batch_input_dir, img2img_batch_output_dir, init_img, sketch, init_img_with_mask, inpaint_color_sketch, init_img_inpaint, ], outputs=[toprow.prompt, dummy_component], ) toprow.prompt.submit(**img2img_args) toprow.submit.click(**img2img_args) res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False) detect_image_size_btn.click( fn=lambda w, h, _: (w or gr.update(), h or gr.update()), _js="currentImg2imgSourceResolution", inputs=[dummy_component, dummy_component, dummy_component], outputs=[width, height], show_progress=False, ) toprow.restore_progress_button.click( fn=progress.restore_progress, _js="restoreProgressImg2img", inputs=[dummy_component], outputs=[ img2img_gallery, generation_info, html_info, html_log, ], show_progress=False, ) toprow.button_interrogate.click( fn=lambda *args: process_interrogate(interrogate, *args), **interrogate_args, ) toprow.button_deepbooru.click( fn=lambda *args: process_interrogate(interrogate_deepbooru, *args), **interrogate_args, ) toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter]) toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter]) img2img_paste_fields = [ (toprow.prompt, "Prompt"), (toprow.negative_prompt, "Negative prompt"), (steps, "Steps"), (sampler_name, "Sampler"), (cfg_scale, "CFG scale"), (image_cfg_scale, "Image CFG scale"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()), (denoising_strength, "Denoising strength"), (mask_blur, "Mask blur"), *scripts.scripts_img2img.infotext_fields ] parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings) parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings) parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None, )) extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img') ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery) extra_tabs.__exit__() scripts.scripts_current = None with gr.Blocks(analytics_enabled=False) as extras_interface: ui_postprocessing.create_ui() with gr.Blocks(analytics_enabled=False) as pnginfo_interface: with gr.Row(equal_height=False): with gr.Column(variant='panel'): image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") with gr.Column(variant='panel'): html = gr.HTML() generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info") html2 = gr.HTML() with gr.Row(): buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) for tabname, button in buttons.items(): parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding( paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image, )) image.change( fn=wrap_gradio_call(modules.extras.run_pnginfo), inputs=[image], outputs=[html, generation_info, html2], ) modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger() with gr.Blocks(analytics_enabled=False) as train_interface: with gr.Row(equal_height=False): gr.HTML(value="

See wiki for detailed explanation.

") with gr.Row(variant="compact", equal_height=False): with gr.Tabs(elem_id="train_tabs"): with gr.Tab(label="Create embedding", id="create_embedding"): new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure") new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func") new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'") overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") with gr.Tab(label="Preprocess images", id="preprocess_images"): process_src = gr.Textbox(label='Source directory', elem_id="train_process_src") process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst") process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width") process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height") preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action") with gr.Row(): process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size") process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip") process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split") process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop") process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop") process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption") process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru") with gr.Row(visible=False) as process_split_extra_row: process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold") process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio") with gr.Row(visible=False) as process_focal_crop_row: process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight") process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight") process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight") process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") with gr.Column(visible=False) as process_multicrop_col: gr.Markdown('Each image is center-cropped with an automatically chosen width and height.') with gr.Row(): process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim") process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim") with gr.Row(): process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea") process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea") with gr.Row(): process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective") process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): with gr.Row(): interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing") run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess") process_split.change( fn=lambda show: gr_show(show), inputs=[process_split], outputs=[process_split_extra_row], ) process_focal_crop.change( fn=lambda show: gr_show(show), inputs=[process_focal_crop], outputs=[process_focal_crop_row], ) process_multicrop.change( fn=lambda show: gr_show(show), inputs=[process_multicrop], outputs=[process_multicrop_col], ) def get_textual_inversion_template_names(): return sorted(textual_inversion.textual_inversion_templates) with gr.Tab(label="Train", id="train"): gr.HTML(value="

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, ] ) run_preprocess.click( fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ dummy_component, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, process_split_threshold, process_overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold, ], outputs=[ ti_output, ti_outcome, ], ) train_embedding.click( fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ dummy_component, train_embedding_name, embedding_learn_rate, batch_size, gradient_step, dataset_directory, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, *txt2img_preview_params, ], outputs=[ ti_output, ti_outcome, ] ) train_hypernetwork.click( fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ dummy_component, train_hypernetwork_name, hypernetwork_learn_rate, batch_size, gradient_step, dataset_directory, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_file, preview_from_txt2img, *txt2img_preview_params, ], outputs=[ ti_output, ti_outcome, ] ) interrupt_training.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) interrupt_preprocessing.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file) settings = ui_settings.UiSettings() settings.create_ui(loadsave, dummy_component) interfaces = [ (txt2img_interface, "txt2img", "txt2img"), (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "train"), ] interfaces += script_callbacks.ui_tabs_callback() interfaces += [(settings.interface, "Settings", "settings")] extensions_interface = ui_extensions.create_ui() interfaces += [(extensions_interface, "Extensions", "extensions")] shared.tab_names = [] for _interface, label, _ifid in interfaces: shared.tab_names.append(label) with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo: settings.add_quicksettings() parameters_copypaste.connect_paste_params_buttons() with gr.Tabs(elem_id="tabs") as tabs: tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)} sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999)) for interface, label, ifid in sorted_interfaces: if label in shared.opts.hidden_tabs: continue with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"): interface.render() if ifid not in ["extensions", "settings"]: loadsave.add_block(interface, ifid) loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs) loadsave.setup_ui() if os.path.exists(os.path.join(script_path, "notification.mp3")) 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']) 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 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"])