import base64 import html import io import json import math import mimetypes import os import random import sys import tempfile import time import traceback import platform import subprocess as sp from functools import partial, reduce import numpy as np import torch from PIL import Image, PngImagePlugin import piexif import gradio as gr import gradio.utils import gradio.routes from modules import sd_hijack, sd_models, localization, script_callbacks from modules.paths import script_path from modules.shared import opts, cmd_opts, restricted_opts if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags import modules.shared as shared from modules.sd_samplers import samplers, samplers_for_img2img from modules.sd_hijack import model_hijack import modules.ldsr_model import modules.scripts import modules.gfpgan_model import modules.codeformer_model import modules.styles import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui import modules.images_history as img_his # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') if not cmd_opts.share and not cmd_opts.listen: # fix gradio phoning home gradio.utils.version_check = lambda: None gradio.utils.get_local_ip_address = lambda: '127.0.0.1' if cmd_opts.ngrok != None: import modules.ngrok as ngrok print('ngrok authtoken detected, trying to connect...') ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region) def gr_show(visible=True): return {"visible": visible, "__type__": "update"} sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None css_hide_progressbar = """ .wrap .m-12 svg { display:none!important; } .wrap .m-12::before { content:"Loading..." } .progress-bar { display:none!important; } .meta-text { display:none!important; } """ # Using constants for these since the variation selector isn't visible. # Important that they exactly match script.js for tooltip to work. random_symbol = '\U0001f3b2\ufe0f' # 🎲️ reuse_symbol = '\u267b\ufe0f' # ♻️ art_symbol = '\U0001f3a8' # 🎨 paste_symbol = '\u2199\ufe0f' # ↙ folder_symbol = '\U0001f4c2' # 📂 refresh_symbol = '\U0001f504' # 🔄 save_style_symbol = '\U0001f4be' # 💾 apply_style_symbol = '\U0001f4cb' # 📋 def plaintext_to_html(text): text = "

" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "

" return text def image_from_url_text(filedata): if type(filedata) == dict and filedata["is_file"]: filename = filedata["name"] tempdir = os.path.normpath(tempfile.gettempdir()) normfn = os.path.normpath(filename) assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory' return Image.open(filename) if type(filedata) == list: if len(filedata) == 0: return None filedata = filedata[0] if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] filedata = base64.decodebytes(filedata.encode('utf-8')) image = Image.open(io.BytesIO(filedata)) return image def send_gradio_gallery_to_image(x): if len(x) == 0: return None return image_from_url_text(x[0]) def save_files(js_data, images, do_make_zip, index): import csv filenames = [] fullfns = [] #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it class MyObject: def __init__(self, d=None): if d is not None: for key, value in d.items(): setattr(self, key, value) data = json.loads(js_data) p = MyObject(data) path = opts.outdir_save save_to_dirs = opts.use_save_to_dirs_for_ui extension: str = opts.samples_format start_index = 0 if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only images = [images[index]] start_index = index os.makedirs(opts.outdir_save, exist_ok=True) with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file: at_start = file.tell() == 0 writer = csv.writer(file) if at_start: writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"]) for image_index, filedata in enumerate(images, start_index): image = image_from_url_text(filedata) is_grid = image_index < p.index_of_first_image i = 0 if is_grid else (image_index - p.index_of_first_image) fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs) filename = os.path.relpath(fullfn, path) filenames.append(filename) fullfns.append(fullfn) if txt_fullfn: filenames.append(os.path.basename(txt_fullfn)) fullfns.append(txt_fullfn) writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]]) # Make Zip if do_make_zip: zip_filepath = os.path.join(path, "images.zip") from zipfile import ZipFile with ZipFile(zip_filepath, "w") as zip_file: for i in range(len(fullfns)): with open(fullfns[i], mode="rb") as f: zip_file.writestr(filenames[i], f.read()) fullfns.insert(0, zip_filepath) return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}") def save_pil_to_file(pil_image, dir=None): use_metadata = False metadata = PngImagePlugin.PngInfo() for key, value in pil_image.info.items(): if isinstance(key, str) and isinstance(value, str): metadata.add_text(key, value) use_metadata = True file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir) pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None)) return file_obj # override save to file function so that it also writes PNG info gr.processing_utils.save_pil_to_file = save_pil_to_file def wrap_gradio_call(func, extra_outputs=None): def f(*args, extra_outputs_array=extra_outputs, **kwargs): run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled if run_memmon: shared.mem_mon.monitor() t = time.perf_counter() try: res = list(func(*args, **kwargs)) except Exception as e: # When printing out our debug argument list, do not print out more than a MB of text max_debug_str_len = 131072 # (1024*1024)/8 print("Error completing request", file=sys.stderr) argStr = f"Arguments: {str(args)} {str(kwargs)}" print(argStr[:max_debug_str_len], file=sys.stderr) if len(argStr) > max_debug_str_len: print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) shared.state.job = "" shared.state.job_count = 0 if extra_outputs_array is None: extra_outputs_array = [None, ''] res = extra_outputs_array + [f"
{plaintext_to_html(type(e).__name__+': '+str(e))}
"] elapsed = time.perf_counter() - t elapsed_m = int(elapsed // 60) elapsed_s = elapsed % 60 elapsed_text = f"{elapsed_s:.2f}s" if (elapsed_m > 0): elapsed_text = f"{elapsed_m}m "+elapsed_text if run_memmon: mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()} active_peak = mem_stats['active_peak'] reserved_peak = mem_stats['reserved_peak'] sys_peak = mem_stats['system_peak'] sys_total = mem_stats['total'] sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2) vram_html = f"

Torch active/reserved: {active_peak}/{reserved_peak} MiB, Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)

" else: vram_html = '' # last item is always HTML res[-1] += f"

Time taken: {elapsed_text}

{vram_html}
" shared.state.skipped = False shared.state.interrupted = False shared.state.job_count = 0 return tuple(res) return f def calc_time_left(progress, threshold, label, force_display): if progress == 0: return "" else: time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) eta_relative = eta-time_since_start if (eta_relative > threshold and progress > 0.02) or force_display: if eta_relative > 3600: return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) elif eta_relative > 60: return label + time.strftime('%M:%S', time.gmtime(eta_relative)) else: return label + time.strftime('%Ss', time.gmtime(eta_relative)) else: return "" def check_progress_call(id_part): if shared.state.job_count == 0: return "", gr_show(False), gr_show(False), gr_show(False) progress = 0 if shared.state.job_count > 0: progress += shared.state.job_no / shared.state.job_count if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display ) if time_left != "": shared.state.time_left_force_display = True progress = min(progress, 1) progressbar = "" if opts.show_progressbar: progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) if opts.show_progress_every_n_steps > 0: if shared.parallel_processing_allowed: if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None: shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent) shared.state.current_image_sampling_step = shared.state.sampling_step image = shared.state.current_image if image is None: image = gr.update(value=None) else: preview_visibility = gr_show(True) if shared.state.textinfo is not None: textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) else: textinfo_result = gr_show(False) return f"

{progressbar}

", preview_visibility, image, textinfo_result def check_progress_call_initial(id_part): shared.state.job_count = -1 shared.state.current_latent = None shared.state.current_image = None shared.state.textinfo = None shared.state.time_start = time.time() shared.state.time_left_force_display = False return check_progress_call(id_part) def roll_artist(prompt): allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories]) artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats]) return prompt + ", " + artist.name if prompt != '' else artist.name def visit(x, func, path=""): if hasattr(x, 'children'): for c in x.children: visit(c, func, path) elif x.label is not None: func(path + "/" + str(x.label), x) def add_style(name: str, prompt: str, negative_prompt: str): if name is None: return [gr_show() for x in range(4)] style = modules.styles.PromptStyle(name, prompt, negative_prompt) shared.prompt_styles.styles[style.name] = style # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we # reserialize all styles every time we save them shared.prompt_styles.save_styles(shared.styles_filename) return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)] def apply_styles(prompt, prompt_neg, style1_name, style2_name): prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name]) prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name]) return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")] def interrogate(image): prompt = shared.interrogator.interrogate(image) return gr_show(True) if prompt is None else prompt def interrogate_deepbooru(image): prompt = get_deepbooru_tags(image) return gr_show(True) if prompt is None else prompt def create_seed_inputs(): with gr.Row(): with gr.Box(): with gr.Row(elem_id='seed_row'): seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1) seed.style(container=False) random_seed = gr.Button(random_symbol, elem_id='random_seed') reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed') with gr.Box(elem_id='subseed_show_box'): seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False) # Components to show/hide based on the 'Extra' checkbox seed_extras = [] with gr.Row(visible=False) as seed_extra_row_1: seed_extras.append(seed_extra_row_1) with gr.Box(): with gr.Row(elem_id='subseed_row'): subseed = gr.Number(label='Variation seed', value=-1) subseed.style(container=False) random_subseed = gr.Button(random_symbol, elem_id='random_subseed') reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed') subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01) with gr.Row(visible=False) as seed_extra_row_2: seed_extras.append(seed_extra_row_2) seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0) seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0) random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed]) random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed]) def change_visibility(show): return {comp: gr_show(show) for comp in seed_extras} seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras) return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed): """ Connects a 'reuse (sub)seed' button's click event so that it copies last used (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength was 0, i.e. no variation seed was used, it copies the normal seed value instead.""" def copy_seed(gen_info_string: str, index): res = -1 try: gen_info = json.loads(gen_info_string) index -= gen_info.get('index_of_first_image', 0) if is_subseed and gen_info.get('subseed_strength', 0) > 0: all_subseeds = gen_info.get('all_subseeds', [-1]) res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0] else: all_seeds = gen_info.get('all_seeds', [-1]) res = all_seeds[index if 0 <= index < len(all_seeds) else 0] except json.decoder.JSONDecodeError as e: if gen_info_string != '': print("Error parsing JSON generation info:", file=sys.stderr) print(gen_info_string, file=sys.stderr) return [res, gr_show(False)] reuse_seed.click( fn=copy_seed, _js="(x, y) => [x, selected_gallery_index()]", show_progress=False, inputs=[generation_info, dummy_component], outputs=[seed, dummy_component] ) def update_token_counter(text, steps): try: _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) except Exception: # a parsing error can happen here during typing, and we don't want to bother the user with # messages related to it in console prompt_schedules = [[[steps, text]]] flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) prompts = [prompt_text for step, prompt_text in flat_prompts] tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1]) style_class = ' class="red"' if (token_count > max_length) else "" return f"{token_count}/{max_length}" def create_toprow(is_img2img): id_part = "img2img" if is_img2img else "txt2img" with gr.Row(elem_id="toprow"): with gr.Column(scale=6): with gr.Row(): with gr.Column(scale=80): with gr.Row(): prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)" ) with gr.Row(): with gr.Column(scale=80): with gr.Row(): negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)" ) with gr.Column(scale=1, elem_id="roll_col"): roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0) paste = gr.Button(value=paste_symbol, elem_id="paste") save_style = gr.Button(value=save_style_symbol, elem_id="style_create") prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply") token_counter = gr.HTML(value="", elem_id=f"{id_part}_token_counter") token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") button_interrogate = None button_deepbooru = None if is_img2img: with gr.Column(scale=1, elem_id="interrogate_col"): button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate") if cmd_opts.deepdanbooru: button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") with gr.Column(scale=1): with gr.Row(): skip = gr.Button('Skip', elem_id=f"{id_part}_skip") interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt") submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') skip.click( fn=lambda: shared.state.skip(), inputs=[], outputs=[], ) interrupt.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) with gr.Row(): with gr.Column(scale=1, elem_id="style_pos_col"): prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys()))) prompt_style.save_to_config = True with gr.Column(scale=1, elem_id="style_neg_col"): prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys()))) prompt_style2.save_to_config = True return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button def setup_progressbar(progressbar, preview, id_part, textinfo=None): if textinfo is None: textinfo = gr.HTML(visible=False) check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False) check_progress.click( fn=lambda: check_progress_call(id_part), show_progress=False, inputs=[], outputs=[progressbar, preview, preview, textinfo], ) check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False) check_progress_initial.click( fn=lambda: check_progress_call_initial(id_part), show_progress=False, inputs=[], outputs=[progressbar, preview, preview, textinfo], ) def apply_setting(key, value): if value is None: return gr.update() # dont allow model to be swapped when model hash exists in prompt if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: return gr.update() if key == "sd_model_checkpoint": ckpt_info = sd_models.get_closet_checkpoint_match(value) if ckpt_info is not None: value = ckpt_info.title else: return gr.update() comp_args = opts.data_labels[key].component_args if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: return valtype = type(opts.data_labels[key].default) oldval = opts.data[key] opts.data[key] = valtype(value) if valtype != type(None) else value if oldval != value and opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() opts.save(shared.config_filename) return value def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): def refresh(): refresh_method() args = refreshed_args() if callable(refreshed_args) else refreshed_args for k, v in args.items(): setattr(refresh_component, k, v) return gr.update(**(args or {})) refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) refresh_button.click( fn=refresh, inputs=[], outputs=[refresh_component] ) return refresh_button def create_ui(wrap_gradio_gpu_call): import modules.img2img import modules.txt2img with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) with gr.Row(elem_id='txt2img_progress_row'): with gr.Column(scale=1): pass with gr.Column(scale=1): progressbar = gr.HTML(elem_id="txt2img_progressbar") txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) setup_progressbar(progressbar, txt2img_preview, 'txt2img') with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") with gr.Group(): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(equal_height=True): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() with gr.Group(): custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False) with gr.Column(variant='panel'): with gr.Group(): txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4) with gr.Column(): with gr.Row(): save = gr.Button('Save') send_to_img2img = gr.Button('Send to img2img') send_to_inpaint = gr.Button('Send to inpaint') send_to_extras = gr.Button('Send to extras') button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id) with gr.Row(): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) txt2img_args = dict( fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), _js="submit", inputs=[ txt2img_prompt, txt2img_negative_prompt, txt2img_prompt_style, txt2img_prompt_style2, steps, sampler_index, restore_faces, tiling, batch_count, batch_size, cfg_scale, seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, height, width, enable_hr, denoising_strength, firstphase_width, firstphase_height, ] + custom_inputs, outputs=[ txt2img_gallery, generation_info, html_info ], show_progress=False, ) txt2img_prompt.submit(**txt2img_args) submit.click(**txt2img_args) txt_prompt_img.change( fn=modules.images.image_data, inputs=[ txt_prompt_img ], outputs=[ txt2img_prompt, txt_prompt_img ] ) enable_hr.change( fn=lambda x: gr_show(x), inputs=[enable_hr], outputs=[hr_options], ) save.click( fn=wrap_gradio_call(save_files), _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", inputs=[ generation_info, txt2img_gallery, do_make_zip, html_info, ], outputs=[ download_files, html_info, html_info, html_info, ] ) roll.click( fn=roll_artist, _js="update_txt2img_tokens", inputs=[ txt2img_prompt, ], outputs=[ txt2img_prompt, ] ) txt2img_paste_fields = [ (txt2img_prompt, "Prompt"), (txt2img_negative_prompt, "Negative prompt"), (steps, "Steps"), (sampler_index, "Sampler"), (restore_faces, "Face restoration"), (cfg_scale, "CFG scale"), (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), (enable_hr, lambda d: "Denoising strength" in d), (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), (firstphase_width, "First pass size-1"), (firstphase_height, "First pass size-2"), *modules.scripts.scripts_txt2img.infotext_fields ] txt2img_preview_params = [ txt2img_prompt, txt2img_negative_prompt, steps, sampler_index, cfg_scale, seed, width, height, ] token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as img2img_interface: img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True) with gr.Row(elem_id='img2img_progress_row'): img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) with gr.Column(scale=1): pass with gr.Column(scale=1): progressbar = gr.HTML(elem_id="img2img_progressbar") img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) setup_progressbar(progressbar, img2img_preview, 'img2img') with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: with gr.TabItem('img2img', id='img2img'): init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480) with gr.TabItem('Inpaint', id='inpaint'): init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base") init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask") mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4) with gr.Row(): mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index") with gr.Row(): inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) with gr.TabItem('Batch img2img', id='batch'): hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' gr.HTML(f"

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.{hidden}

") img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs) img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs) with gr.Row(): resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize") steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index") with gr.Group(): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width") height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height") with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) with gr.Row(): batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) with gr.Group(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75) seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() with gr.Group(): custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True) with gr.Column(variant='panel'): with gr.Group(): img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4) with gr.Column(): with gr.Row(): save = gr.Button('Save') img2img_send_to_img2img = gr.Button('Send to img2img') img2img_send_to_inpaint = gr.Button('Send to inpaint') img2img_send_to_extras = gr.Button('Send to extras') button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id) with gr.Row(): do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) with gr.Row(): download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) with gr.Group(): html_info = gr.HTML() generation_info = gr.Textbox(visible=False) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) img2img_prompt_img.change( fn=modules.images.image_data, inputs=[ img2img_prompt_img ], outputs=[ img2img_prompt, img2img_prompt_img ] ) mask_mode.change( lambda mode, img: { init_img_with_mask: gr_show(mode == 0), init_img_inpaint: gr_show(mode == 1), init_mask_inpaint: gr_show(mode == 1), }, inputs=[mask_mode, init_img_with_mask], outputs=[ init_img_with_mask, init_img_inpaint, init_mask_inpaint, ], ) img2img_args = dict( fn=wrap_gradio_gpu_call(modules.img2img.img2img), _js="submit_img2img", inputs=[ dummy_component, img2img_prompt, img2img_negative_prompt, img2img_prompt_style, img2img_prompt_style2, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps, sampler_index, mask_blur, inpainting_fill, restore_faces, tiling, batch_count, batch_size, cfg_scale, denoising_strength, seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, height, width, resize_mode, inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, ] + custom_inputs, outputs=[ img2img_gallery, generation_info, html_info ], show_progress=False, ) img2img_prompt.submit(**img2img_args) submit.click(**img2img_args) img2img_interrogate.click( fn=interrogate, inputs=[init_img], outputs=[img2img_prompt], ) if cmd_opts.deepdanbooru: img2img_deepbooru.click( fn=interrogate_deepbooru, inputs=[init_img], outputs=[img2img_prompt], ) save.click( fn=wrap_gradio_call(save_files), _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", inputs=[ generation_info, img2img_gallery, do_make_zip, html_info, ], outputs=[ download_files, html_info, html_info, html_info, ] ) roll.click( fn=roll_artist, _js="update_img2img_tokens", inputs=[ img2img_prompt, ], outputs=[ img2img_prompt, ] ) prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): button.click( fn=add_style, _js="ask_for_style_name", # Have to pass empty dummy component here, because the JavaScript and Python function have to accept # the same number of parameters, but we only know the style-name after the JavaScript prompt inputs=[dummy_component, prompt, negative_prompt], outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], ) for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): button.click( fn=apply_styles, _js=js_func, inputs=[prompt, negative_prompt, style1, style2], outputs=[prompt, negative_prompt, style1, style2], ) img2img_paste_fields = [ (img2img_prompt, "Prompt"), (img2img_negative_prompt, "Negative prompt"), (steps, "Steps"), (sampler_index, "Sampler"), (restore_faces, "Face restoration"), (cfg_scale, "CFG scale"), (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), *modules.scripts.scripts_img2img.infotext_fields ] token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) with gr.Blocks(analytics_enabled=False) as extras_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): with gr.Tabs(elem_id="mode_extras"): with gr.TabItem('Single Image'): extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil") with gr.TabItem('Batch Process'): image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") with gr.TabItem('Batch from Directory'): extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running." ) extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path." ) show_extras_results = gr.Checkbox(label='Show result images', value=True) with gr.Tabs(elem_id="extras_resize_mode"): with gr.TabItem('Scale by'): upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2) with gr.TabItem('Scale to'): with gr.Group(): with gr.Row(): upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) with gr.Group(): extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") with gr.Group(): extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1) with gr.Group(): gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan) with gr.Group(): codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer) codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer) submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') with gr.Column(variant='panel'): result_images = gr.Gallery(label="Result", show_label=False) html_info_x = gr.HTML() html_info = gr.HTML() extras_send_to_img2img = gr.Button('Send to img2img') extras_send_to_inpaint = gr.Button('Send to inpaint') button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else '' open_extras_folder = gr.Button('Open output directory', elem_id=button_id) submit.click( fn=wrap_gradio_gpu_call(modules.extras.run_extras), _js="get_extras_tab_index", inputs=[ dummy_component, dummy_component, extras_image, image_batch, extras_batch_input_dir, extras_batch_output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, ], outputs=[ result_images, html_info_x, html_info, ] ) extras_send_to_img2img.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_img2img", inputs=[result_images], outputs=[init_img], ) extras_send_to_inpaint.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_inpaint", inputs=[result_images], outputs=[init_img_with_mask], ) with gr.Blocks(analytics_enabled=False) as pnginfo_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") with gr.Column(variant='panel'): html = gr.HTML() generation_info = gr.Textbox(visible=False) html2 = gr.HTML() with gr.Row(): pnginfo_send_to_txt2img = gr.Button('Send to txt2img') pnginfo_send_to_img2img = gr.Button('Send to img2img') image.change( fn=wrap_gradio_call(modules.extras.run_pnginfo), inputs=[image], outputs=[html, generation_info, html2], ) #images history images_history_switch_dict = { "fn": modules.generation_parameters_copypaste.connect_paste, "t2i": txt2img_paste_fields, "i2i": img2img_paste_fields } images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) with gr.Blocks() as modelmerger_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='panel'): gr.HTML(value="

A merger of the two checkpoints will be generated in your checkpoint directory.

") with gr.Row(): primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") custom_name = gr.Textbox(label="Custom Name (Optional)") interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3) interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method") save_as_half = gr.Checkbox(value=False, label="Save as float16") modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') with gr.Column(variant='panel'): submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() with gr.Blocks() as train_interface: with gr.Row().style(equal_height=False): gr.HTML(value="

See wiki for detailed explanation.

") with gr.Row().style(equal_height=False): with gr.Tabs(elem_id="train_tabs"): with gr.Tab(label="Create embedding"): new_embedding_name = gr.Textbox(label="Name") initialization_text = gr.Textbox(label="Initialization text", value="*") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"]) with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') with gr.Tab(label="Preprocess images"): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') process_split = gr.Checkbox(label='Split oversized images') process_caption = gr.Checkbox(label='Use BLIP for caption') process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) with gr.Row(visible=False) as process_split_extra_row: process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): run_preprocess = gr.Button(value="Preprocess", variant='primary') process_split.change( fn=lambda show: gr_show(show), inputs=[process_split], outputs=[process_split_extra_row], ) with gr.Tab(label="Train"): gr.HTML(value="

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

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") with gr.Row(): train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") with gr.Row(): embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005") hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001") batch_size = gr.Number(label='Batch size', value=1, precision=0) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False) with gr.Row(): interrupt_training = gr.Button(value="Interrupt") train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary') train_embedding = gr.Button(value="Train Embedding", variant='primary') with gr.Column(): progressbar = gr.HTML(elem_id="ti_progressbar") ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) ti_preview = gr.Image(elem_id='ti_preview', visible=False) ti_progress = gr.HTML(elem_id="ti_progress", value="") ti_outcome = gr.HTML(elem_id="ti_error", value="") setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) create_embedding.click( fn=modules.textual_inversion.ui.create_embedding, inputs=[ new_embedding_name, initialization_text, nvpt, overwrite_old_embedding, ], outputs=[ train_embedding_name, ti_output, ti_outcome, ] ) create_hypernetwork.click( fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ new_hypernetwork_name, new_hypernetwork_sizes, overwrite_old_hypernetwork, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, new_hypernetwork_activation_func, ], outputs=[ train_hypernetwork_name, ti_output, ti_outcome, ] ) run_preprocess.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, process_split_threshold, process_overlap_ratio, ], outputs=[ ti_output, ti_outcome, ], ) train_embedding.click( fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ train_embedding_name, embedding_learn_rate, batch_size, dataset_directory, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, *txt2img_preview_params, ], outputs=[ ti_output, ti_outcome, ] ) train_hypernetwork.click( fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ train_hypernetwork_name, hypernetwork_learn_rate, batch_size, dataset_directory, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, preview_from_txt2img, *txt2img_preview_params, ], outputs=[ ti_output, ti_outcome, ] ) interrupt_training.click( fn=lambda: shared.state.interrupt(), inputs=[], outputs=[], ) def create_setting_component(key, is_quicksettings=False): def fun(): return opts.data[key] if key in opts.data else opts.data_labels[key].default info = opts.data_labels[key] t = type(info.default) args = info.component_args() if callable(info.component_args) else info.component_args if info.component is not None: comp = info.component elif t == str: comp = gr.Textbox elif t == int: comp = gr.Number elif t == bool: comp = gr.Checkbox else: raise Exception(f'bad options item type: {str(t)} for key {key}') elem_id = "setting_"+key if info.refresh is not None: if is_quicksettings: res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {})) create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) else: with gr.Row(variant="compact"): res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {})) create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) else: res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {})) return res components = [] component_dict = {} def open_folder(f): if not os.path.exists(f): print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.') return elif not os.path.isdir(f): print(f""" WARNING An open_folder request was made with an argument that is not a folder. This could be an error or a malicious attempt to run code on your computer. Requested path was: {f} """, file=sys.stderr) return if not shared.cmd_opts.hide_ui_dir_config: path = os.path.normpath(f) if platform.system() == "Windows": os.startfile(path) elif platform.system() == "Darwin": sp.Popen(["open", path]) else: sp.Popen(["xdg-open", path]) def run_settings(*args): changed = 0 for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default): return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson() for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp == dummy_component: continue comp_args = opts.data_labels[key].component_args if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: continue if cmd_opts.hide_ui_dir_config and key in restricted_opts: continue oldval = opts.data.get(key, None) opts.data[key] = value if oldval != value: if opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() changed += 1 opts.save(shared.config_filename) return f'{changed} settings changed.', opts.dumpjson() def run_settings_single(value, key): if not opts.same_type(value, opts.data_labels[key].default): return gr.update(visible=True), opts.dumpjson() oldval = opts.data.get(key, None) if cmd_opts.hide_ui_dir_config and key in restricted_opts: return gr.update(value=oldval), opts.dumpjson() opts.data[key] = value if oldval != value: if opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() opts.save(shared.config_filename) return gr.update(value=value), opts.dumpjson() with gr.Blocks(analytics_enabled=False) as settings_interface: settings_submit = gr.Button(value="Apply settings", variant='primary') result = gr.HTML() settings_cols = 3 items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') quicksettings_list = [] cols_displayed = 0 items_displayed = 0 previous_section = None column = None with gr.Row(elem_id="settings").style(equal_height=False): for i, (k, item) in enumerate(opts.data_labels.items()): if previous_section != item.section: if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): if column is not None: column.__exit__() column = gr.Column(variant='panel') column.__enter__() items_displayed = 0 cols_displayed += 1 previous_section = item.section gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='

{}

'.format(item.section[1])) if k in quicksettings_names: quicksettings_list.append((i, k, item)) components.append(dummy_component) else: component = create_setting_component(k) component_dict[k] = component components.append(component) items_displayed += 1 with gr.Row(): request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") download_localization = gr.Button(value='Download localization template', elem_id="download_localization") with gr.Row(): reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') request_notifications.click( fn=lambda: None, inputs=[], outputs=[], _js='function(){}' ) download_localization.click( fn=lambda: None, inputs=[], outputs=[], _js='download_localization' ) def reload_scripts(): modules.scripts.reload_script_body_only() reload_javascript() # need to refresh the html page reload_script_bodies.click( fn=reload_scripts, inputs=[], outputs=[], _js='function(){}' ) def request_restart(): shared.state.interrupt() settings_interface.gradio_ref.do_restart = True restart_gradio.click( fn=request_restart, inputs=[], outputs=[], _js='function(){restart_reload()}' ) if column is not None: column.__exit__() interfaces = [ (txt2img_interface, "txt2img", "txt2img"), (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), (images_history, "Image Browser", "images_history"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), ] interfaces += script_callbacks.ui_tabs_callback() interfaces += [(settings_interface, "Settings", "settings")] with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file: css = file.read() if os.path.exists(os.path.join(script_path, "user.css")): with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: usercss = file.read() css += usercss if not cmd_opts.no_progressbar_hiding: css += css_hide_progressbar with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: with gr.Row(elem_id="quicksettings"): for i, k, item in quicksettings_list: component = create_setting_component(k, is_quicksettings=True) component_dict[k] = component settings_interface.gradio_ref = demo with gr.Tabs(elem_id="tabs") as tabs: for interface, label, ifid in interfaces: with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): interface.render() if os.path.exists(os.path.join(script_path, "notification.mp3")): audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) settings_submit.click( fn=run_settings, inputs=components, outputs=[result, text_settings], ) for i, k, item in quicksettings_list: component = component_dict[k] component.change( fn=lambda value, k=k: run_settings_single(value, key=k), inputs=[component], outputs=[component, text_settings], ) def modelmerger(*args): try: results = modules.extras.run_modelmerger(*args) except Exception as e: print("Error loading/saving model file:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) modules.sd_models.list_models() # to remove the potentially missing models from the list return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] return results modelmerger_merge.click( fn=modelmerger, inputs=[ primary_model_name, secondary_model_name, tertiary_model_name, interp_method, interp_amount, save_as_half, custom_name, ], outputs=[ submit_result, primary_model_name, secondary_model_name, tertiary_model_name, component_dict['sd_model_checkpoint'], ] ) paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2'] txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names] img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names] send_to_img2img.click( fn=lambda img, *args: (image_from_url_text(img),*args), _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]", inputs=[txt2img_gallery] + txt2img_fields, outputs=[init_img] + img2img_fields, ) send_to_inpaint.click( fn=lambda x, *args: (image_from_url_text(x), *args), _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]", inputs=[txt2img_gallery] + txt2img_fields, outputs=[init_img_with_mask] + img2img_fields, ) img2img_send_to_img2img.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_img2img", inputs=[img2img_gallery], outputs=[init_img], ) img2img_send_to_inpaint.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_inpaint", inputs=[img2img_gallery], outputs=[init_img_with_mask], ) send_to_extras.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_extras", inputs=[txt2img_gallery], outputs=[extras_image], ) open_txt2img_folder.click( fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples), inputs=[], outputs=[], ) open_img2img_folder.click( fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples), inputs=[], outputs=[], ) open_extras_folder.click( fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples), inputs=[], outputs=[], ) img2img_send_to_extras.click( fn=lambda x: image_from_url_text(x), _js="extract_image_from_gallery_extras", inputs=[img2img_gallery], outputs=[extras_image], ) settings_map = { 'sd_hypernetwork': 'Hypernet', 'CLIP_stop_at_last_layers': 'Clip skip', 'sd_model_checkpoint': 'Model hash', } settings_paste_fields = [ (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None))) for k, v in settings_map.items() ] modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt) modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt) modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img') modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img') ui_config_file = cmd_opts.ui_config_file ui_settings = {} settings_count = len(ui_settings) error_loading = False try: if os.path.exists(ui_config_file): with open(ui_config_file, "r", encoding="utf8") as file: ui_settings = json.load(file) except Exception: error_loading = True print("Error loading settings:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) def loadsave(path, x): def apply_field(obj, field, condition=None, init_field=None): key = path + "/" + field if getattr(obj,'custom_script_source',None) is not None: key = 'customscript/' + obj.custom_script_source + '/' + key if getattr(obj, 'do_not_save_to_config', False): return saved_value = ui_settings.get(key, None) if saved_value is None: ui_settings[key] = getattr(obj, field) elif condition and not condition(saved_value): print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') else: setattr(obj, field, saved_value) if init_field is not None: init_field(saved_value) if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: apply_field(x, 'visible') if type(x) == gr.Slider: apply_field(x, 'value') apply_field(x, 'minimum') apply_field(x, 'maximum') apply_field(x, 'step') if type(x) == gr.Radio: apply_field(x, 'value', lambda val: val in x.choices) if type(x) == gr.Checkbox: apply_field(x, 'value') if type(x) == gr.Textbox: apply_field(x, 'value') if type(x) == gr.Number: apply_field(x, 'value') # Since there are many dropdowns that shouldn't be saved, # we only mark dropdowns that should be saved. if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False): apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None)) apply_field(x, 'visible') visit(txt2img_interface, loadsave, "txt2img") visit(img2img_interface, loadsave, "img2img") visit(extras_interface, loadsave, "extras") visit(modelmerger_interface, loadsave, "modelmerger") if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): with open(ui_config_file, "w", encoding="utf8") as file: json.dump(ui_settings, file, indent=4) return demo def load_javascript(raw_response): with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: javascript = f'' jsdir = os.path.join(script_path, "javascript") for filename in sorted(os.listdir(jsdir)): with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: javascript += f"\n" if cmd_opts.theme is not None: javascript += f"\n\n" javascript += f"\n" def template_response(*args, **kwargs): res = raw_response(*args, **kwargs) res.body = res.body.replace( b'', f'{javascript}'.encode("utf8")) res.init_headers() return res gradio.routes.templates.TemplateResponse = template_response reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse) reload_javascript()