From ac7ecd2d847bf4e3a9503db0f2a291e32b82302c Mon Sep 17 00:00:00 2001 From: Tim Patton <38817597+pattontim@users.noreply.github.com> Date: Sat, 19 Nov 2022 14:49:22 -0500 Subject: [PATCH 1/6] Label and load SD .safetensors model files --- README.md | 1 + modules/modelloader.py | 1 + modules/sd_models.py | 24 ++++++++++++++++-------- requirements.txt | 1 + 4 files changed, 19 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 33508f31..ba9f3952 100644 --- a/README.md +++ b/README.md @@ -84,6 +84,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) +- Can use safetensors to safely load model files without python pickle ## Where are Aesthetic Gradients?!?! Aesthetic Gradients are now an extension. You can install it using git: diff --git a/modules/modelloader.py b/modules/modelloader.py index e4a6f8ac..7d2f0ade 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -82,6 +82,7 @@ def cleanup_models(): src_path = models_path dest_path = os.path.join(models_path, "Stable-diffusion") move_files(src_path, dest_path, ".ckpt") + move_files(src_path, dest_path, ".safetensors") src_path = os.path.join(root_path, "ESRGAN") dest_path = os.path.join(models_path, "ESRGAN") move_files(src_path, dest_path) diff --git a/modules/sd_models.py b/modules/sd_models.py index c59151e0..4ccdf30b 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -4,6 +4,7 @@ import sys import gc from collections import namedtuple import torch +from safetensors.torch import load_file import re from omegaconf import OmegaConf @@ -16,9 +17,10 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) -CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) +CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config', 'exttype']) checkpoints_list = {} checkpoints_loaded = collections.OrderedDict() +checkpoint_types = {'.ckpt':'pickle','.safetensors':'safetensors'} try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. @@ -45,7 +47,7 @@ def checkpoint_tiles(): def list_models(): checkpoints_list.clear() - model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"]) + model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt",".safetensors"]) def modeltitle(path, shorthash): abspath = os.path.abspath(path) @@ -60,15 +62,15 @@ def list_models(): if name.startswith("\\") or name.startswith("/"): name = name[1:] - shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] + shortname, ext = os.path.splitext(name.replace("/", "_").replace("\\", "_")) - return f'{name} [{shorthash}]', shortname + return f'{name} [{checkpoint_types[ext]}] [{shorthash}]', shortname cmd_ckpt = shared.cmd_opts.ckpt if os.path.exists(cmd_ckpt): h = model_hash(cmd_ckpt) title, short_model_name = modeltitle(cmd_ckpt, h) - checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config) + checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config, '') shared.opts.data['sd_model_checkpoint'] = title elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) @@ -76,12 +78,12 @@ def list_models(): h = model_hash(filename) title, short_model_name = modeltitle(filename, h) - basename, _ = os.path.splitext(filename) + basename, ext = os.path.splitext(filename) config = basename + ".yaml" if not os.path.exists(config): config = shared.cmd_opts.config - checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config) + checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config, ext) def get_closet_checkpoint_match(searchString): @@ -173,7 +175,13 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): # load from file print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") - pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) + if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'): + # safely load weights + # TODO: safetensors supports zero copy fast load to gpu, see issue #684 + pl_sd = load_file(checkpoint_file, device=shared.weight_load_location) + else: + pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) + if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") diff --git a/requirements.txt b/requirements.txt index 762db4f3..f7de9f70 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,3 +28,4 @@ kornia lark inflection GitPython +safetensors From 637815632f9f362c9959e53139d37e88ea9ace6f Mon Sep 17 00:00:00 2001 From: Tim Patton <38817597+pattontim@users.noreply.github.com> Date: Sun, 20 Nov 2022 13:36:05 -0500 Subject: [PATCH 2/6] Generalize SD torch load/save to implement safetensor merging compat --- modules/extras.py | 15 +- modules/sd_models.py | 25 +- modules/ui.py | 3626 +++++++++++++++++++++--------------------- 3 files changed, 1840 insertions(+), 1826 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index 71b93a06..820427de 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -249,7 +249,7 @@ def run_pnginfo(image): return '', geninfo, info -def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name): +def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, save_as_safetensors, custom_name): def weighted_sum(theta0, theta1, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) @@ -264,16 +264,16 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None) print(f"Loading {primary_model_info.filename}...") - primary_model = torch.load(primary_model_info.filename, map_location='cpu') + primary_model = sd_models.torch_load(primary_model_info.filename, primary_model_info, map_override='cpu') theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) print(f"Loading {secondary_model_info.filename}...") - secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') + secondary_model = sd_models.torch_load(secondary_model_info.filename, primary_model_info, map_override='cpu') theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) if teritary_model_info is not None: print(f"Loading {teritary_model_info.filename}...") - teritary_model = torch.load(teritary_model_info.filename, map_location='cpu') + teritary_model = sd_models.torch_load(teritary_model_info.filename, teritary_model_info, map_override='cpu') theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model) else: teritary_model = None @@ -314,12 +314,13 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path - filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' - filename = filename if custom_name == '' else (custom_name + '.ckpt') + output_exttype = '.safetensors' if save_as_safetensors else '.ckpt' + filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged' + output_exttype + filename = filename if custom_name == '' else (custom_name + output_exttype) output_modelname = os.path.join(ckpt_dir, filename) print(f"Saving to {output_modelname}...") - torch.save(primary_model, output_modelname) + sd_models.torch_save(primary_model, output_modelname) sd_models.list_models() diff --git a/modules/sd_models.py b/modules/sd_models.py index 4ccdf30b..2f8c2c48 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -4,7 +4,7 @@ import sys import gc from collections import namedtuple import torch -from safetensors.torch import load_file +from safetensors.torch import load_file, save_file import re from omegaconf import OmegaConf @@ -143,6 +143,22 @@ def transform_checkpoint_dict_key(k): return k +def torch_load(model_filename, model_info, map_override=None): + map_override=shared.weight_load_location if not map_override else map_override + if(checkpoint_types[model_info.exttype] == 'safetensors'): + # safely load weights + # TODO: safetensors supports zero copy fast load to gpu, see issue #684 + return load_file(model_filename, device=map_override) + else: + return torch.load(model_filename, map_location=map_override) + +def torch_save(model, output_filename): + basename, exttype = os.path.splitext(output_filename) + if(checkpoint_types[exttype] == 'safetensors'): + # [===== >] Reticulating brines... + save_file(model, output_filename, metadata={"format": "pt"}) + else: + torch.save(model, output_filename) def get_state_dict_from_checkpoint(pl_sd): if "state_dict" in pl_sd: @@ -175,12 +191,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): # load from file print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") - if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'): - # safely load weights - # TODO: safetensors supports zero copy fast load to gpu, see issue #684 - pl_sd = load_file(checkpoint_file, device=shared.weight_load_location) - else: - pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) + pl_sd = torch_load(checkpoint_file, checkpoint_info) if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") diff --git a/modules/ui.py b/modules/ui.py index a5953fce..a2b06aae 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1,1812 +1,1814 @@ -import html -import json -import math -import mimetypes -import os -import platform -import random -import subprocess as sp -import sys -import tempfile -import time -import traceback -from functools import partial, reduce - -import gradio as gr -import gradio.routes -import gradio.utils -import numpy as np -from PIL import Image, PngImagePlugin - - -from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions -from modules.paths import script_path - -from modules.shared import opts, cmd_opts, restricted_opts - -if cmd_opts.deepdanbooru: - from modules.deepbooru import get_deepbooru_tags - -import modules.codeformer_model -import modules.generation_parameters_copypaste as parameters_copypaste -import modules.gfpgan_model -import modules.hypernetworks.ui -import modules.ldsr_model -import modules.scripts -import modules.shared as shared -import modules.styles -import modules.textual_inversion.ui -from modules import prompt_parser -from modules.images import save_image -from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img -import modules.textual_inversion.ui -import modules.hypernetworks.ui -from modules.generation_parameters_copypaste import image_from_url_text - -# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI -mimetypes.init() -mimetypes.add_type('application/javascript', '.js') - -if not cmd_opts.share and not cmd_opts.listen: - # fix gradio phoning home - gradio.utils.version_check = lambda: None - gradio.utils.get_local_ip_address = lambda: '127.0.0.1' - -if cmd_opts.ngrok != None: - import modules.ngrok as ngrok - print('ngrok authtoken detected, trying to connect...') - ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region) - - -def gr_show(visible=True): - return {"visible": visible, "__type__": "update"} - - -sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" -sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None - -css_hide_progressbar = """ -.wrap .m-12 svg { display:none!important; } -.wrap .m-12::before { content:"Loading..." } -.wrap .z-20 svg { display:none!important; } -.wrap .z-20::before { content:"Loading..." } -.progress-bar { display:none!important; } -.meta-text { display:none!important; } -.meta-text-center { display:none!important; } -""" - -# Using constants for these since the variation selector isn't visible. -# Important that they exactly match script.js for tooltip to work. -random_symbol = '\U0001f3b2\ufe0f' # 🎲️ -reuse_symbol = '\u267b\ufe0f' # ♻️ -art_symbol = '\U0001f3a8' # 🎨 -paste_symbol = '\u2199\ufe0f' # ↙ -folder_symbol = '\U0001f4c2' # 📂 -refresh_symbol = '\U0001f504' # 🔄 -save_style_symbol = '\U0001f4be' # 💾 -apply_style_symbol = '\U0001f4cb' # 📋 - - -def plaintext_to_html(text): - text = "

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

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

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}
" - - 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: - shared.state.set_current_image() - image = shared.state.current_image - - if image is None: - image = gr.update(value=None) - else: - preview_visibility = gr_show(True) - - if shared.state.textinfo is not None: - textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) - else: - textinfo_result = gr_show(False) - - return f"

{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() - - if shared.cmd_opts.freeze_settings: - return gr.update() - - # dont allow model to be swapped when model hash exists in prompt - if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: - return gr.update() - - if key == "sd_model_checkpoint": - ckpt_info = sd_models.get_closet_checkpoint_match(value) - - if ckpt_info is not None: - value = ckpt_info.title - else: - return gr.update() - - comp_args = opts.data_labels[key].component_args - if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: - return - - valtype = type(opts.data_labels[key].default) - oldval = opts.data[key] - opts.data[key] = valtype(value) if valtype != type(None) else value - if oldval != value and opts.data_labels[key].onchange is not None: - opts.data_labels[key].onchange() - - opts.save(shared.config_filename) - return value - - -def update_generation_info(args): - generation_info, html_info, img_index = args - try: - generation_info = json.loads(generation_info) - if img_index < 0 or img_index >= len(generation_info["infotexts"]): - return html_info - return plaintext_to_html(generation_info["infotexts"][img_index]) - except Exception: - pass - # if the json parse or anything else fails, just return the old html_info - return html_info - - -def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): - def refresh(): - refresh_method() - args = refreshed_args() if callable(refreshed_args) else refreshed_args - - for k, v in args.items(): - setattr(refresh_component, k, v) - - return gr.update(**(args or {})) - - refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) - refresh_button.click( - fn=refresh, - inputs=[], - outputs=[refresh_component] - ) - return refresh_button - - -def create_output_panel(tabname, outdir): - def open_folder(f): - if not os.path.exists(f): - print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.') - return - elif not os.path.isdir(f): - print(f""" -WARNING -An open_folder request was made with an argument that is not a folder. -This could be an error or a malicious attempt to run code on your computer. -Requested path was: {f} -""", file=sys.stderr) - return - - if not shared.cmd_opts.hide_ui_dir_config: - path = os.path.normpath(f) - if platform.system() == "Windows": - os.startfile(path) - elif platform.system() == "Darwin": - sp.Popen(["open", path]) - else: - sp.Popen(["xdg-open", path]) - - with gr.Column(variant='panel'): - with gr.Group(): - result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4) - - generation_info = None - with gr.Column(): - with gr.Row(): - if tabname != "extras": - save = gr.Button('Save', elem_id=f'save_{tabname}') - - buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"]) - button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' - open_folder_button = gr.Button(folder_symbol, elem_id=button_id) - - open_folder_button.click( - fn=lambda: open_folder(opts.outdir_samples or outdir), - inputs=[], - outputs=[], - ) - - if tabname != "extras": - with gr.Row(): - do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) - - with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) - - with gr.Group(): - html_info = gr.HTML() - generation_info = gr.Textbox(visible=False) - if tabname == 'txt2img' or tabname == 'img2img': - generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button") - generation_info_button.click( - fn=update_generation_info, - _js="(x, y) => [x, y, selected_gallery_index()]", - inputs=[generation_info, html_info], - outputs=[html_info], - preprocess=False - ) - - save.click( - fn=wrap_gradio_call(save_files), - _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", - inputs=[ - generation_info, - result_gallery, - do_make_zip, - html_info, - ], - outputs=[ - download_files, - html_info, - html_info, - html_info, - ] - ) - else: - html_info_x = gr.HTML() - html_info = gr.HTML() - parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None) - return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info - - -def create_ui(wrap_gradio_gpu_call): - import modules.img2img - import modules.txt2img - - reload_javascript() - - parameters_copypaste.reset() - - modules.scripts.scripts_current = modules.scripts.scripts_txt2img - modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) - - with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) - dummy_component = gr.Label(visible=False) - txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) - - with gr.Row(elem_id='txt2img_progress_row'): - with gr.Column(scale=1): - pass - - with gr.Column(scale=1): - progressbar = gr.HTML(elem_id="txt2img_progressbar") - txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) - setup_progressbar(progressbar, txt2img_preview, 'txt2img') - - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) - sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") - - with gr.Group(): - width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - - with gr.Row(): - restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) - tiling = gr.Checkbox(label='Tiling', value=False) - enable_hr = gr.Checkbox(label='Highres. fix', value=False) - - with gr.Row(visible=False) as hr_options: - firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) - firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) - - with gr.Row(equal_height=True): - batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) - batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) - - cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) - - seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() - - with gr.Group(): - custom_inputs = modules.scripts.scripts_txt2img.setup_ui() - - txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples) - parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt) - - connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) - connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) - - txt2img_args = dict( - fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), - _js="submit", - inputs=[ - txt2img_prompt, - txt2img_negative_prompt, - txt2img_prompt_style, - txt2img_prompt_style2, - steps, - sampler_index, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - enable_hr, - denoising_strength, - firstphase_width, - firstphase_height, - ] + custom_inputs, - - outputs=[ - txt2img_gallery, - generation_info, - html_info - ], - show_progress=False, - ) - - txt2img_prompt.submit(**txt2img_args) - submit.click(**txt2img_args) - - txt_prompt_img.change( - fn=modules.images.image_data, - inputs=[ - txt_prompt_img - ], - outputs=[ - txt2img_prompt, - txt_prompt_img - ] - ) - - enable_hr.change( - fn=lambda x: gr_show(x), - inputs=[enable_hr], - outputs=[hr_options], - ) - - roll.click( - fn=roll_artist, - _js="update_txt2img_tokens", - inputs=[ - txt2img_prompt, - ], - outputs=[ - txt2img_prompt, - ] - ) - - txt2img_paste_fields = [ - (txt2img_prompt, "Prompt"), - (txt2img_negative_prompt, "Negative prompt"), - (steps, "Steps"), - (sampler_index, "Sampler"), - (restore_faces, "Face restoration"), - (cfg_scale, "CFG scale"), - (seed, "Seed"), - (width, "Size-1"), - (height, "Size-2"), - (batch_size, "Batch size"), - (subseed, "Variation seed"), - (subseed_strength, "Variation seed strength"), - (seed_resize_from_w, "Seed resize from-1"), - (seed_resize_from_h, "Seed resize from-2"), - (denoising_strength, "Denoising strength"), - (enable_hr, lambda d: "Denoising strength" in d), - (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), - (firstphase_width, "First pass size-1"), - (firstphase_height, "First pass size-2"), - *modules.scripts.scripts_txt2img.infotext_fields - ] - parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields) - - txt2img_preview_params = [ - txt2img_prompt, - txt2img_negative_prompt, - steps, - sampler_index, - cfg_scale, - seed, - width, - height, - ] - - token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) - - modules.scripts.scripts_current = modules.scripts.scripts_img2img - modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) - - with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True) - - with gr.Row(elem_id='img2img_progress_row'): - img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) - - with gr.Column(scale=1): - pass - - with gr.Column(scale=1): - progressbar = gr.HTML(elem_id="img2img_progressbar") - img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) - setup_progressbar(progressbar, img2img_preview, 'img2img') - - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - - with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: - with gr.TabItem('img2img', id='img2img'): - init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480) - - with gr.TabItem('Inpaint', id='inpaint'): - init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) - - init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base") - init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask") - - mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4) - - with gr.Row(): - mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") - inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") - - inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index") - - with gr.Row(): - inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) - inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) - - with gr.TabItem('Batch img2img', id='batch'): - hidden = '
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() - - img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples) - parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt) - - connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) - connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) - - img2img_prompt_img.change( - fn=modules.images.image_data, - inputs=[ - img2img_prompt_img - ], - outputs=[ - img2img_prompt, - img2img_prompt_img - ] - ) - - mask_mode.change( - lambda mode, img: { - init_img_with_mask: gr_show(mode == 0), - init_img_inpaint: gr_show(mode == 1), - init_mask_inpaint: gr_show(mode == 1), - }, - inputs=[mask_mode, init_img_with_mask], - outputs=[ - init_img_with_mask, - init_img_inpaint, - init_mask_inpaint, - ], - ) - - img2img_args = dict( - fn=wrap_gradio_gpu_call(modules.img2img.img2img), - _js="submit_img2img", - inputs=[ - dummy_component, - img2img_prompt, - img2img_negative_prompt, - img2img_prompt_style, - img2img_prompt_style2, - init_img, - init_img_with_mask, - init_img_inpaint, - init_mask_inpaint, - mask_mode, - steps, - sampler_index, - mask_blur, - inpainting_fill, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - denoising_strength, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - resize_mode, - inpaint_full_res, - inpaint_full_res_padding, - inpainting_mask_invert, - img2img_batch_input_dir, - img2img_batch_output_dir, - ] + custom_inputs, - outputs=[ - img2img_gallery, - generation_info, - html_info - ], - show_progress=False, - ) - - img2img_prompt.submit(**img2img_args) - submit.click(**img2img_args) - - img2img_interrogate.click( - fn=interrogate, - inputs=[init_img], - outputs=[img2img_prompt], - ) - - if cmd_opts.deepdanbooru: - img2img_deepbooru.click( - fn=interrogate_deepbooru, - inputs=[init_img], - outputs=[img2img_prompt], - ) - - - roll.click( - fn=roll_artist, - _js="update_img2img_tokens", - inputs=[ - img2img_prompt, - ], - outputs=[ - img2img_prompt, - ] - ) - - prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] - style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] - style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] - - for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): - button.click( - fn=add_style, - _js="ask_for_style_name", - # Have to pass empty dummy component here, because the JavaScript and Python function have to accept - # the same number of parameters, but we only know the style-name after the JavaScript prompt - inputs=[dummy_component, prompt, negative_prompt], - outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], - ) - - for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): - button.click( - fn=apply_styles, - _js=js_func, - inputs=[prompt, negative_prompt, style1, style2], - outputs=[prompt, negative_prompt, style1, style2], - ) - - token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) - - img2img_paste_fields = [ - (img2img_prompt, "Prompt"), - (img2img_negative_prompt, "Negative prompt"), - (steps, "Steps"), - (sampler_index, "Sampler"), - (restore_faces, "Face restoration"), - (cfg_scale, "CFG scale"), - (seed, "Seed"), - (width, "Size-1"), - (height, "Size-2"), - (batch_size, "Batch size"), - (subseed, "Variation seed"), - (subseed_strength, "Variation seed strength"), - (seed_resize_from_w, "Seed resize from-1"), - (seed_resize_from_h, "Seed resize from-2"), - (denoising_strength, "Denoising strength"), - *modules.scripts.scripts_img2img.infotext_fields - ] - parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) - parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields) - - modules.scripts.scripts_current = None - - with gr.Blocks(analytics_enabled=False) as extras_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - with gr.Tabs(elem_id="mode_extras"): - with gr.TabItem('Single Image'): - extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil") - - with gr.TabItem('Batch Process'): - image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") - - with gr.TabItem('Batch from Directory'): - extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.") - extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.") - show_extras_results = gr.Checkbox(label='Show result images', value=True) - - submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') - - with gr.Tabs(elem_id="extras_resize_mode"): - with gr.TabItem('Scale by'): - upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4) - with gr.TabItem('Scale to'): - with gr.Group(): - with gr.Row(): - upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) - upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) - upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) - - with gr.Group(): - extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") - - with gr.Group(): - extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") - extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1) - - with gr.Group(): - gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan) - - with gr.Group(): - codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer) - codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer) - - with gr.Group(): - upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False) - - result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples) - - submit.click( - fn=wrap_gradio_gpu_call(modules.extras.run_extras), - _js="get_extras_tab_index", - inputs=[ - dummy_component, - dummy_component, - extras_image, - image_batch, - extras_batch_input_dir, - extras_batch_output_dir, - show_extras_results, - gfpgan_visibility, - codeformer_visibility, - codeformer_weight, - upscaling_resize, - upscaling_resize_w, - upscaling_resize_h, - upscaling_crop, - extras_upscaler_1, - extras_upscaler_2, - extras_upscaler_2_visibility, - upscale_before_face_fix, - ], - outputs=[ - result_images, - html_info_x, - html_info, - ] - ) - parameters_copypaste.add_paste_fields("extras", extras_image, None) - - extras_image.change( - fn=modules.extras.clear_cache, - inputs=[], outputs=[] - ) - - with gr.Blocks(analytics_enabled=False) as pnginfo_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") - - with gr.Column(variant='panel'): - html = gr.HTML() - generation_info = gr.Textbox(visible=False) - html2 = gr.HTML() - with gr.Row(): - buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) - parameters_copypaste.bind_buttons(buttons, image, generation_info) - - image.change( - fn=wrap_gradio_call(modules.extras.run_pnginfo), - inputs=[image], - outputs=[html, generation_info, html2], - ) - - with gr.Blocks(analytics_enabled=False) as modelmerger_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - gr.HTML(value="

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(analytics_enabled=False) 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_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys) - new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) - new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") - new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") - overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') - - with gr.Tab(label="Preprocess images"): - process_src = gr.Textbox(label='Source directory') - process_dst = gr.Textbox(label='Destination directory') - process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) - - with gr.Row(): - process_flip = gr.Checkbox(label='Create flipped copies') - process_split = gr.Checkbox(label='Split oversized images') - process_focal_crop = gr.Checkbox(label='Auto focal point crop') - process_caption = gr.Checkbox(label='Use BLIP for caption') - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) - - with gr.Row(visible=False) as process_split_extra_row: - process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) - process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) - - with gr.Row(visible=False) as process_focal_crop_row: - process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_debug = gr.Checkbox(label='Create debug image') - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - with gr.Row(): - interrupt_preprocessing = gr.Button("Interrupt") - run_preprocess = gr.Button(value="Preprocess", variant='primary') - - process_split.change( - fn=lambda show: gr_show(show), - inputs=[process_split], - outputs=[process_split_extra_row], - ) - - process_focal_crop.change( - fn=lambda show: gr_show(show), - inputs=[process_focal_crop], - outputs=[process_focal_crop_row], - ) - - with gr.Tab(label="Train"): - gr.HTML(value="

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') - - params = script_callbacks.UiTrainTabParams(txt2img_preview_params) - - script_callbacks.ui_train_tabs_callback(params) - - with gr.Column(): - progressbar = gr.HTML(elem_id="ti_progressbar") - ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) - - ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) - ti_preview = gr.Image(elem_id='ti_preview', visible=False) - ti_progress = gr.HTML(elem_id="ti_progress", value="") - ti_outcome = gr.HTML(elem_id="ti_error", value="") - setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) - - create_embedding.click( - fn=modules.textual_inversion.ui.create_embedding, - inputs=[ - new_embedding_name, - initialization_text, - nvpt, - overwrite_old_embedding, - ], - outputs=[ - train_embedding_name, - ti_output, - ti_outcome, - ] - ) - - create_hypernetwork.click( - fn=modules.hypernetworks.ui.create_hypernetwork, - inputs=[ - new_hypernetwork_name, - new_hypernetwork_sizes, - overwrite_old_hypernetwork, - new_hypernetwork_layer_structure, - new_hypernetwork_activation_func, - new_hypernetwork_initialization_option, - new_hypernetwork_add_layer_norm, - new_hypernetwork_use_dropout - ], - outputs=[ - train_hypernetwork_name, - ti_output, - ti_outcome, - ] - ) - - run_preprocess.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - process_src, - process_dst, - process_width, - process_height, - preprocess_txt_action, - process_flip, - process_split, - process_caption, - process_caption_deepbooru, - process_split_threshold, - process_overlap_ratio, - process_focal_crop, - process_focal_crop_face_weight, - process_focal_crop_entropy_weight, - process_focal_crop_edges_weight, - process_focal_crop_debug, - ], - outputs=[ - ti_output, - ti_outcome, - ], - ) - - train_embedding.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - train_embedding_name, - embedding_learn_rate, - batch_size, - dataset_directory, - log_directory, - training_width, - training_height, - steps, - create_image_every, - save_embedding_every, - template_file, - save_image_with_stored_embedding, - preview_from_txt2img, - *txt2img_preview_params, - ], - outputs=[ - ti_output, - ti_outcome, - ] - ) - - train_hypernetwork.click( - fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - train_hypernetwork_name, - hypernetwork_learn_rate, - batch_size, - dataset_directory, - log_directory, - training_width, - training_height, - steps, - create_image_every, - save_embedding_every, - template_file, - preview_from_txt2img, - *txt2img_preview_params, - ], - outputs=[ - ti_output, - ti_outcome, - ] - ) - - interrupt_training.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - interrupt_preprocessing.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - def create_setting_component(key, is_quicksettings=False): - def fun(): - return opts.data[key] if key in opts.data else opts.data_labels[key].default - - info = opts.data_labels[key] - t = type(info.default) - - args = info.component_args() if callable(info.component_args) else info.component_args - - if info.component is not None: - comp = info.component - elif t == str: - comp = gr.Textbox - elif t == int: - comp = gr.Number - elif t == bool: - comp = gr.Checkbox - else: - raise Exception(f'bad options item type: {str(t)} for key {key}') - - elem_id = "setting_"+key - - if info.refresh is not None: - if is_quicksettings: - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) - else: - with gr.Row(variant="compact"): - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) - else: - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - - return res - - components = [] - component_dict = {} - - script_callbacks.ui_settings_callback() - opts.reorder() - - def run_settings(*args): - changed = [] - - for key, value, comp in zip(opts.data_labels.keys(), args, components): - assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" - - for key, value, comp in zip(opts.data_labels.keys(), args, components): - if comp == dummy_component: - continue - - if opts.set(key, value): - changed.append(key) - - try: - opts.save(shared.config_filename) - except RuntimeError: - return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.' - return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.' - - def run_settings_single(value, key): - if not opts.same_type(value, opts.data_labels[key].default): - return gr.update(visible=True), opts.dumpjson() - - if not opts.set(key, value): - return gr.update(value=getattr(opts, key)), opts.dumpjson() - - opts.save(shared.config_filename) - - return gr.update(value=value), opts.dumpjson() - - with gr.Blocks(analytics_enabled=False) as settings_interface: - settings_submit = gr.Button(value="Apply settings", variant='primary') - result = gr.HTML() - - settings_cols = 3 - items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) - - quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] - quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') - - quicksettings_list = [] - - cols_displayed = 0 - items_displayed = 0 - previous_section = None - column = None - with gr.Row(elem_id="settings").style(equal_height=False): - for i, (k, item) in enumerate(opts.data_labels.items()): - section_must_be_skipped = item.section[0] is None - - if previous_section != item.section and not section_must_be_skipped: - if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): - if column is not None: - column.__exit__() - - column = gr.Column(variant='panel') - column.__enter__() - - items_displayed = 0 - cols_displayed += 1 - - previous_section = item.section - - elem_id, text = item.section - gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='

{}

'.format(text)) - - if k in quicksettings_names and not shared.cmd_opts.freeze_settings: - quicksettings_list.append((i, k, item)) - components.append(dummy_component) - elif section_must_be_skipped: - components.append(dummy_component) - else: - component = create_setting_component(k) - component_dict[k] = component - components.append(component) - items_displayed += 1 - - with gr.Row(): - request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") - download_localization = gr.Button(value='Download localization template', elem_id="download_localization") - - with gr.Row(): - reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') - restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') - - request_notifications.click( - fn=lambda: None, - inputs=[], - outputs=[], - _js='function(){}' - ) - - download_localization.click( - fn=lambda: None, - inputs=[], - outputs=[], - _js='download_localization' - ) - - def reload_scripts(): - modules.scripts.reload_script_body_only() - reload_javascript() # need to refresh the html page - - reload_script_bodies.click( - fn=reload_scripts, - inputs=[], - outputs=[] - ) - - def request_restart(): - shared.state.interrupt() - shared.state.need_restart = True - - restart_gradio.click( - fn=request_restart, - _js='restart_reload', - inputs=[], - outputs=[], - ) - - if column is not None: - column.__exit__() - - interfaces = [ - (txt2img_interface, "txt2img", "txt2img"), - (img2img_interface, "img2img", "img2img"), - (extras_interface, "Extras", "extras"), - (pnginfo_interface, "PNG Info", "pnginfo"), - (modelmerger_interface, "Checkpoint Merger", "modelmerger"), - (train_interface, "Train", "ti"), - ] - - css = "" - - for cssfile in modules.scripts.list_files_with_name("style.css"): - if not os.path.isfile(cssfile): - continue - - with open(cssfile, "r", encoding="utf8") as file: - css += file.read() + "\n" - - if os.path.exists(os.path.join(script_path, "user.css")): - with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: - css += file.read() + "\n" - - if not cmd_opts.no_progressbar_hiding: - css += css_hide_progressbar - - interfaces += script_callbacks.ui_tabs_callback() - interfaces += [(settings_interface, "Settings", "settings")] - - extensions_interface = ui_extensions.create_ui() - interfaces += [(extensions_interface, "Extensions", "extensions")] - - with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: - with gr.Row(elem_id="quicksettings"): - for i, k, item in quicksettings_list: - component = create_setting_component(k, is_quicksettings=True) - component_dict[k] = component - - parameters_copypaste.integrate_settings_paste_fields(component_dict) - parameters_copypaste.run_bind() - - with gr.Tabs(elem_id="tabs") as tabs: - for interface, label, ifid in interfaces: - with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): - interface.render() - - if os.path.exists(os.path.join(script_path, "notification.mp3")): - audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) - - text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) - settings_submit.click( - fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), - inputs=components, - outputs=[text_settings, result], - ) - - for i, k, item in quicksettings_list: - component = component_dict[k] - - component.change( - fn=lambda value, k=k: run_settings_single(value, key=k), - inputs=[component], - outputs=[component, text_settings], - ) - - component_keys = [k for k in opts.data_labels.keys() if k in component_dict] - - def get_settings_values(): - return [getattr(opts, key) for key in component_keys] - - demo.load( - fn=get_settings_values, - inputs=[], - outputs=[component_dict[k] for k in component_keys], - ) - - def modelmerger(*args): - try: - results = modules.extras.run_modelmerger(*args) - except Exception as e: - print("Error loading/saving model file:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - modules.sd_models.list_models() # to remove the potentially missing models from the list - return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] - return results - - modelmerger_merge.click( - fn=modelmerger, - inputs=[ - primary_model_name, - secondary_model_name, - tertiary_model_name, - interp_method, - interp_amount, - save_as_half, - custom_name, - ], - outputs=[ - submit_result, - primary_model_name, - secondary_model_name, - tertiary_model_name, - component_dict['sd_model_checkpoint'], - ] - ) - - ui_config_file = cmd_opts.ui_config_file - ui_settings = {} - settings_count = len(ui_settings) - error_loading = False - - try: - if os.path.exists(ui_config_file): - with open(ui_config_file, "r", encoding="utf8") as file: - ui_settings = json.load(file) - except Exception: - error_loading = True - print("Error loading settings:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - def loadsave(path, x): - def apply_field(obj, field, condition=None, init_field=None): - key = path + "/" + field - - if getattr(obj, 'custom_script_source', None) is not None: - key = 'customscript/' + obj.custom_script_source + '/' + key - - if getattr(obj, 'do_not_save_to_config', False): - return - - saved_value = ui_settings.get(key, None) - if saved_value is None: - ui_settings[key] = getattr(obj, field) - elif condition and not condition(saved_value): - print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') - else: - setattr(obj, field, saved_value) - if init_field is not None: - init_field(saved_value) - - if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: - apply_field(x, 'visible') - - if type(x) == gr.Slider: - apply_field(x, 'value') - apply_field(x, 'minimum') - apply_field(x, 'maximum') - apply_field(x, 'step') - - if type(x) == gr.Radio: - apply_field(x, 'value', lambda val: val in x.choices) - - if type(x) == gr.Checkbox: - apply_field(x, 'value') - - if type(x) == gr.Textbox: - apply_field(x, 'value') - - if type(x) == gr.Number: - apply_field(x, 'value') - - # Since there are many dropdowns that shouldn't be saved, - # we only mark dropdowns that should be saved. - if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False): - apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None)) - apply_field(x, 'visible') - - visit(txt2img_interface, loadsave, "txt2img") - visit(img2img_interface, loadsave, "img2img") - visit(extras_interface, loadsave, "extras") - visit(modelmerger_interface, loadsave, "modelmerger") - - if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): - with open(ui_config_file, "w", encoding="utf8") as file: - json.dump(ui_settings, file, indent=4) - - return demo - - -def reload_javascript(): - with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: - javascript = f'' - - scripts_list = modules.scripts.list_scripts("javascript", ".js") - - for basedir, filename, path in scripts_list: - with open(path, "r", encoding="utf8") as jsfile: - javascript += f"\n" - - if cmd_opts.theme is not None: - javascript += f"\n\n" - - javascript += f"\n" - - def template_response(*args, **kwargs): - res = shared.GradioTemplateResponseOriginal(*args, **kwargs) - res.body = res.body.replace( - b'', f'{javascript}'.encode("utf8")) - res.init_headers() - return res - - gradio.routes.templates.TemplateResponse = template_response - - -if not hasattr(shared, 'GradioTemplateResponseOriginal'): - shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse +import html +import json +import math +import mimetypes +import os +import platform +import random +import subprocess as sp +import sys +import tempfile +import time +import traceback +from functools import partial, reduce + +import gradio as gr +import gradio.routes +import gradio.utils +import numpy as np +from PIL import Image, PngImagePlugin + + +from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions +from modules.paths import script_path + +from modules.shared import opts, cmd_opts, restricted_opts + +if cmd_opts.deepdanbooru: + from modules.deepbooru import get_deepbooru_tags + +import modules.codeformer_model +import modules.generation_parameters_copypaste as parameters_copypaste +import modules.gfpgan_model +import modules.hypernetworks.ui +import modules.ldsr_model +import modules.scripts +import modules.shared as shared +import modules.styles +import modules.textual_inversion.ui +from modules import prompt_parser +from modules.images import save_image +from modules.sd_hijack import model_hijack +from modules.sd_samplers import samplers, samplers_for_img2img +import modules.textual_inversion.ui +import modules.hypernetworks.ui +from modules.generation_parameters_copypaste import image_from_url_text + +# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI +mimetypes.init() +mimetypes.add_type('application/javascript', '.js') + +if not cmd_opts.share and not cmd_opts.listen: + # fix gradio phoning home + gradio.utils.version_check = lambda: None + gradio.utils.get_local_ip_address = lambda: '127.0.0.1' + +if cmd_opts.ngrok != None: + import modules.ngrok as ngrok + print('ngrok authtoken detected, trying to connect...') + ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region) + + +def gr_show(visible=True): + return {"visible": visible, "__type__": "update"} + + +sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" +sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None + +css_hide_progressbar = """ +.wrap .m-12 svg { display:none!important; } +.wrap .m-12::before { content:"Loading..." } +.wrap .z-20 svg { display:none!important; } +.wrap .z-20::before { content:"Loading..." } +.progress-bar { display:none!important; } +.meta-text { display:none!important; } +.meta-text-center { display:none!important; } +""" + +# Using constants for these since the variation selector isn't visible. +# Important that they exactly match script.js for tooltip to work. +random_symbol = '\U0001f3b2\ufe0f' # 🎲️ +reuse_symbol = '\u267b\ufe0f' # ♻️ +art_symbol = '\U0001f3a8' # 🎨 +paste_symbol = '\u2199\ufe0f' # ↙ +folder_symbol = '\U0001f4c2' # 📂 +refresh_symbol = '\U0001f504' # 🔄 +save_style_symbol = '\U0001f4be' # 💾 +apply_style_symbol = '\U0001f4cb' # 📋 + + +def plaintext_to_html(text): + text = "

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

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

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}
" + + 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: + shared.state.set_current_image() + image = shared.state.current_image + + if image is None: + image = gr.update(value=None) + else: + preview_visibility = gr_show(True) + + if shared.state.textinfo is not None: + textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) + else: + textinfo_result = gr_show(False) + + return f"

{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() + + if shared.cmd_opts.freeze_settings: + return gr.update() + + # dont allow model to be swapped when model hash exists in prompt + if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: + return gr.update() + + if key == "sd_model_checkpoint": + ckpt_info = sd_models.get_closet_checkpoint_match(value) + + if ckpt_info is not None: + value = ckpt_info.title + else: + return gr.update() + + comp_args = opts.data_labels[key].component_args + if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: + return + + valtype = type(opts.data_labels[key].default) + oldval = opts.data[key] + opts.data[key] = valtype(value) if valtype != type(None) else value + if oldval != value and opts.data_labels[key].onchange is not None: + opts.data_labels[key].onchange() + + opts.save(shared.config_filename) + return value + + +def update_generation_info(args): + generation_info, html_info, img_index = args + try: + generation_info = json.loads(generation_info) + if img_index < 0 or img_index >= len(generation_info["infotexts"]): + return html_info + return plaintext_to_html(generation_info["infotexts"][img_index]) + except Exception: + pass + # if the json parse or anything else fails, just return the old html_info + return html_info + + +def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): + def refresh(): + refresh_method() + args = refreshed_args() if callable(refreshed_args) else refreshed_args + + for k, v in args.items(): + setattr(refresh_component, k, v) + + return gr.update(**(args or {})) + + refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[refresh_component] + ) + return refresh_button + + +def create_output_panel(tabname, outdir): + def open_folder(f): + if not os.path.exists(f): + print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.') + return + elif not os.path.isdir(f): + print(f""" +WARNING +An open_folder request was made with an argument that is not a folder. +This could be an error or a malicious attempt to run code on your computer. +Requested path was: {f} +""", file=sys.stderr) + return + + if not shared.cmd_opts.hide_ui_dir_config: + path = os.path.normpath(f) + if platform.system() == "Windows": + os.startfile(path) + elif platform.system() == "Darwin": + sp.Popen(["open", path]) + else: + sp.Popen(["xdg-open", path]) + + with gr.Column(variant='panel'): + with gr.Group(): + result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4) + + generation_info = None + with gr.Column(): + with gr.Row(): + if tabname != "extras": + save = gr.Button('Save', elem_id=f'save_{tabname}') + + buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"]) + button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' + open_folder_button = gr.Button(folder_symbol, elem_id=button_id) + + open_folder_button.click( + fn=lambda: open_folder(opts.outdir_samples or outdir), + inputs=[], + outputs=[], + ) + + if tabname != "extras": + with gr.Row(): + do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) + + with gr.Row(): + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) + + with gr.Group(): + html_info = gr.HTML() + generation_info = gr.Textbox(visible=False) + if tabname == 'txt2img' or tabname == 'img2img': + generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button") + generation_info_button.click( + fn=update_generation_info, + _js="(x, y) => [x, y, selected_gallery_index()]", + inputs=[generation_info, html_info], + outputs=[html_info], + preprocess=False + ) + + save.click( + fn=wrap_gradio_call(save_files), + _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", + inputs=[ + generation_info, + result_gallery, + do_make_zip, + html_info, + ], + outputs=[ + download_files, + html_info, + html_info, + html_info, + ] + ) + else: + html_info_x = gr.HTML() + html_info = gr.HTML() + parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None) + return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info + + +def create_ui(wrap_gradio_gpu_call): + import modules.img2img + import modules.txt2img + + reload_javascript() + + parameters_copypaste.reset() + + modules.scripts.scripts_current = modules.scripts.scripts_txt2img + modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) + + with gr.Blocks(analytics_enabled=False) as txt2img_interface: + txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) + dummy_component = gr.Label(visible=False) + txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) + + with gr.Row(elem_id='txt2img_progress_row'): + with gr.Column(scale=1): + pass + + with gr.Column(scale=1): + progressbar = gr.HTML(elem_id="txt2img_progressbar") + txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) + setup_progressbar(progressbar, txt2img_preview, 'txt2img') + + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) + sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") + + with gr.Group(): + width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + + with gr.Row(): + restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) + tiling = gr.Checkbox(label='Tiling', value=False) + enable_hr = gr.Checkbox(label='Highres. fix', value=False) + + with gr.Row(visible=False) as hr_options: + firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) + firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) + + with gr.Row(equal_height=True): + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) + batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) + + cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) + + seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() + + with gr.Group(): + custom_inputs = modules.scripts.scripts_txt2img.setup_ui() + + txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples) + parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt) + + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) + connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + + txt2img_args = dict( + fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), + _js="submit", + inputs=[ + txt2img_prompt, + txt2img_negative_prompt, + txt2img_prompt_style, + txt2img_prompt_style2, + steps, + sampler_index, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + enable_hr, + denoising_strength, + firstphase_width, + firstphase_height, + ] + custom_inputs, + + outputs=[ + txt2img_gallery, + generation_info, + html_info + ], + show_progress=False, + ) + + txt2img_prompt.submit(**txt2img_args) + submit.click(**txt2img_args) + + txt_prompt_img.change( + fn=modules.images.image_data, + inputs=[ + txt_prompt_img + ], + outputs=[ + txt2img_prompt, + txt_prompt_img + ] + ) + + enable_hr.change( + fn=lambda x: gr_show(x), + inputs=[enable_hr], + outputs=[hr_options], + ) + + roll.click( + fn=roll_artist, + _js="update_txt2img_tokens", + inputs=[ + txt2img_prompt, + ], + outputs=[ + txt2img_prompt, + ] + ) + + txt2img_paste_fields = [ + (txt2img_prompt, "Prompt"), + (txt2img_negative_prompt, "Negative prompt"), + (steps, "Steps"), + (sampler_index, "Sampler"), + (restore_faces, "Face restoration"), + (cfg_scale, "CFG scale"), + (seed, "Seed"), + (width, "Size-1"), + (height, "Size-2"), + (batch_size, "Batch size"), + (subseed, "Variation seed"), + (subseed_strength, "Variation seed strength"), + (seed_resize_from_w, "Seed resize from-1"), + (seed_resize_from_h, "Seed resize from-2"), + (denoising_strength, "Denoising strength"), + (enable_hr, lambda d: "Denoising strength" in d), + (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), + (firstphase_width, "First pass size-1"), + (firstphase_height, "First pass size-2"), + *modules.scripts.scripts_txt2img.infotext_fields + ] + parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields) + + txt2img_preview_params = [ + txt2img_prompt, + txt2img_negative_prompt, + steps, + sampler_index, + cfg_scale, + seed, + width, + height, + ] + + token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) + + modules.scripts.scripts_current = modules.scripts.scripts_img2img + modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) + + with gr.Blocks(analytics_enabled=False) as img2img_interface: + img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True) + + with gr.Row(elem_id='img2img_progress_row'): + img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) + + with gr.Column(scale=1): + pass + + with gr.Column(scale=1): + progressbar = gr.HTML(elem_id="img2img_progressbar") + img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) + setup_progressbar(progressbar, img2img_preview, 'img2img') + + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + + with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: + with gr.TabItem('img2img', id='img2img'): + init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480) + + with gr.TabItem('Inpaint', id='inpaint'): + init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) + + init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base") + init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask") + + mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4) + + with gr.Row(): + mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") + inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") + + inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index") + + with gr.Row(): + inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) + inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) + + with gr.TabItem('Batch img2img', id='batch'): + hidden = '
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() + + img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples) + parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt) + + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) + connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + + img2img_prompt_img.change( + fn=modules.images.image_data, + inputs=[ + img2img_prompt_img + ], + outputs=[ + img2img_prompt, + img2img_prompt_img + ] + ) + + mask_mode.change( + lambda mode, img: { + init_img_with_mask: gr_show(mode == 0), + init_img_inpaint: gr_show(mode == 1), + init_mask_inpaint: gr_show(mode == 1), + }, + inputs=[mask_mode, init_img_with_mask], + outputs=[ + init_img_with_mask, + init_img_inpaint, + init_mask_inpaint, + ], + ) + + img2img_args = dict( + fn=wrap_gradio_gpu_call(modules.img2img.img2img), + _js="submit_img2img", + inputs=[ + dummy_component, + img2img_prompt, + img2img_negative_prompt, + img2img_prompt_style, + img2img_prompt_style2, + init_img, + init_img_with_mask, + init_img_inpaint, + init_mask_inpaint, + mask_mode, + steps, + sampler_index, + mask_blur, + inpainting_fill, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + denoising_strength, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + resize_mode, + inpaint_full_res, + inpaint_full_res_padding, + inpainting_mask_invert, + img2img_batch_input_dir, + img2img_batch_output_dir, + ] + custom_inputs, + outputs=[ + img2img_gallery, + generation_info, + html_info + ], + show_progress=False, + ) + + img2img_prompt.submit(**img2img_args) + submit.click(**img2img_args) + + img2img_interrogate.click( + fn=interrogate, + inputs=[init_img], + outputs=[img2img_prompt], + ) + + if cmd_opts.deepdanbooru: + img2img_deepbooru.click( + fn=interrogate_deepbooru, + inputs=[init_img], + outputs=[img2img_prompt], + ) + + + roll.click( + fn=roll_artist, + _js="update_img2img_tokens", + inputs=[ + img2img_prompt, + ], + outputs=[ + img2img_prompt, + ] + ) + + prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] + style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] + style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] + + for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): + button.click( + fn=add_style, + _js="ask_for_style_name", + # Have to pass empty dummy component here, because the JavaScript and Python function have to accept + # the same number of parameters, but we only know the style-name after the JavaScript prompt + inputs=[dummy_component, prompt, negative_prompt], + outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], + ) + + for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): + button.click( + fn=apply_styles, + _js=js_func, + inputs=[prompt, negative_prompt, style1, style2], + outputs=[prompt, negative_prompt, style1, style2], + ) + + token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) + + img2img_paste_fields = [ + (img2img_prompt, "Prompt"), + (img2img_negative_prompt, "Negative prompt"), + (steps, "Steps"), + (sampler_index, "Sampler"), + (restore_faces, "Face restoration"), + (cfg_scale, "CFG scale"), + (seed, "Seed"), + (width, "Size-1"), + (height, "Size-2"), + (batch_size, "Batch size"), + (subseed, "Variation seed"), + (subseed_strength, "Variation seed strength"), + (seed_resize_from_w, "Seed resize from-1"), + (seed_resize_from_h, "Seed resize from-2"), + (denoising_strength, "Denoising strength"), + *modules.scripts.scripts_img2img.infotext_fields + ] + parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) + parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields) + + modules.scripts.scripts_current = None + + with gr.Blocks(analytics_enabled=False) as extras_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + with gr.Tabs(elem_id="mode_extras"): + with gr.TabItem('Single Image'): + extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil") + + with gr.TabItem('Batch Process'): + image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") + + with gr.TabItem('Batch from Directory'): + extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.") + extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.") + show_extras_results = gr.Checkbox(label='Show result images', value=True) + + submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') + + with gr.Tabs(elem_id="extras_resize_mode"): + with gr.TabItem('Scale by'): + upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4) + with gr.TabItem('Scale to'): + with gr.Group(): + with gr.Row(): + upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) + upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) + upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) + + with gr.Group(): + extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") + + with gr.Group(): + extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") + extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1) + + with gr.Group(): + gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan) + + with gr.Group(): + codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer) + codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer) + + with gr.Group(): + upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False) + + result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples) + + submit.click( + fn=wrap_gradio_gpu_call(modules.extras.run_extras), + _js="get_extras_tab_index", + inputs=[ + dummy_component, + dummy_component, + extras_image, + image_batch, + extras_batch_input_dir, + extras_batch_output_dir, + show_extras_results, + gfpgan_visibility, + codeformer_visibility, + codeformer_weight, + upscaling_resize, + upscaling_resize_w, + upscaling_resize_h, + upscaling_crop, + extras_upscaler_1, + extras_upscaler_2, + extras_upscaler_2_visibility, + upscale_before_face_fix, + ], + outputs=[ + result_images, + html_info_x, + html_info, + ] + ) + parameters_copypaste.add_paste_fields("extras", extras_image, None) + + extras_image.change( + fn=modules.extras.clear_cache, + inputs=[], outputs=[] + ) + + with gr.Blocks(analytics_enabled=False) as pnginfo_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") + + with gr.Column(variant='panel'): + html = gr.HTML() + generation_info = gr.Textbox(visible=False) + html2 = gr.HTML() + with gr.Row(): + buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) + parameters_copypaste.bind_buttons(buttons, image, generation_info) + + image.change( + fn=wrap_gradio_call(modules.extras.run_pnginfo), + inputs=[image], + outputs=[html, generation_info, html2], + ) + + with gr.Blocks(analytics_enabled=False) as modelmerger_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + gr.HTML(value="

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") + save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format") + modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') + + with gr.Column(variant='panel'): + submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) + + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() + + with gr.Blocks(analytics_enabled=False) as train_interface: + with gr.Row().style(equal_height=False): + gr.HTML(value="

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_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys) + new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) + new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") + new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') + + with gr.Tab(label="Preprocess images"): + process_src = gr.Textbox(label='Source directory') + process_dst = gr.Textbox(label='Destination directory') + process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) + + with gr.Row(): + process_flip = gr.Checkbox(label='Create flipped copies') + process_split = gr.Checkbox(label='Split oversized images') + process_focal_crop = gr.Checkbox(label='Auto focal point crop') + process_caption = gr.Checkbox(label='Use BLIP for caption') + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) + + with gr.Row(visible=False) as process_split_extra_row: + process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) + process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) + + with gr.Row(visible=False) as process_focal_crop_row: + process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05) + process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05) + process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05) + process_focal_crop_debug = gr.Checkbox(label='Create debug image') + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + with gr.Row(): + interrupt_preprocessing = gr.Button("Interrupt") + run_preprocess = gr.Button(value="Preprocess", variant='primary') + + process_split.change( + fn=lambda show: gr_show(show), + inputs=[process_split], + outputs=[process_split_extra_row], + ) + + process_focal_crop.change( + fn=lambda show: gr_show(show), + inputs=[process_focal_crop], + outputs=[process_focal_crop_row], + ) + + with gr.Tab(label="Train"): + gr.HTML(value="

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') + + params = script_callbacks.UiTrainTabParams(txt2img_preview_params) + + script_callbacks.ui_train_tabs_callback(params) + + with gr.Column(): + progressbar = gr.HTML(elem_id="ti_progressbar") + ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) + + ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) + ti_preview = gr.Image(elem_id='ti_preview', visible=False) + ti_progress = gr.HTML(elem_id="ti_progress", value="") + ti_outcome = gr.HTML(elem_id="ti_error", value="") + setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) + + create_embedding.click( + fn=modules.textual_inversion.ui.create_embedding, + inputs=[ + new_embedding_name, + initialization_text, + nvpt, + overwrite_old_embedding, + ], + outputs=[ + train_embedding_name, + ti_output, + ti_outcome, + ] + ) + + create_hypernetwork.click( + fn=modules.hypernetworks.ui.create_hypernetwork, + inputs=[ + new_hypernetwork_name, + new_hypernetwork_sizes, + overwrite_old_hypernetwork, + new_hypernetwork_layer_structure, + new_hypernetwork_activation_func, + new_hypernetwork_initialization_option, + new_hypernetwork_add_layer_norm, + new_hypernetwork_use_dropout + ], + outputs=[ + train_hypernetwork_name, + ti_output, + ti_outcome, + ] + ) + + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + process_src, + process_dst, + process_width, + process_height, + preprocess_txt_action, + process_flip, + process_split, + process_caption, + process_caption_deepbooru, + process_split_threshold, + process_overlap_ratio, + process_focal_crop, + process_focal_crop_face_weight, + process_focal_crop_entropy_weight, + process_focal_crop_edges_weight, + process_focal_crop_debug, + ], + outputs=[ + ti_output, + ti_outcome, + ], + ) + + train_embedding.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + train_embedding_name, + embedding_learn_rate, + batch_size, + dataset_directory, + log_directory, + training_width, + training_height, + steps, + create_image_every, + save_embedding_every, + template_file, + save_image_with_stored_embedding, + preview_from_txt2img, + *txt2img_preview_params, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + train_hypernetwork.click( + fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + train_hypernetwork_name, + hypernetwork_learn_rate, + batch_size, + dataset_directory, + log_directory, + training_width, + training_height, + steps, + create_image_every, + save_embedding_every, + template_file, + preview_from_txt2img, + *txt2img_preview_params, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + interrupt_training.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + interrupt_preprocessing.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + def create_setting_component(key, is_quicksettings=False): + def fun(): + return opts.data[key] if key in opts.data else opts.data_labels[key].default + + info = opts.data_labels[key] + t = type(info.default) + + args = info.component_args() if callable(info.component_args) else info.component_args + + if info.component is not None: + comp = info.component + elif t == str: + comp = gr.Textbox + elif t == int: + comp = gr.Number + elif t == bool: + comp = gr.Checkbox + else: + raise Exception(f'bad options item type: {str(t)} for key {key}') + + elem_id = "setting_"+key + + if info.refresh is not None: + if is_quicksettings: + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + else: + with gr.Row(variant="compact"): + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + else: + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + + return res + + components = [] + component_dict = {} + + script_callbacks.ui_settings_callback() + opts.reorder() + + def run_settings(*args): + changed = [] + + for key, value, comp in zip(opts.data_labels.keys(), args, components): + assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" + + for key, value, comp in zip(opts.data_labels.keys(), args, components): + if comp == dummy_component: + continue + + if opts.set(key, value): + changed.append(key) + + try: + opts.save(shared.config_filename) + except RuntimeError: + return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.' + return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.' + + def run_settings_single(value, key): + if not opts.same_type(value, opts.data_labels[key].default): + return gr.update(visible=True), opts.dumpjson() + + if not opts.set(key, value): + return gr.update(value=getattr(opts, key)), opts.dumpjson() + + opts.save(shared.config_filename) + + return gr.update(value=value), opts.dumpjson() + + with gr.Blocks(analytics_enabled=False) as settings_interface: + settings_submit = gr.Button(value="Apply settings", variant='primary') + result = gr.HTML() + + settings_cols = 3 + items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) + + quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] + quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') + + quicksettings_list = [] + + cols_displayed = 0 + items_displayed = 0 + previous_section = None + column = None + with gr.Row(elem_id="settings").style(equal_height=False): + for i, (k, item) in enumerate(opts.data_labels.items()): + section_must_be_skipped = item.section[0] is None + + if previous_section != item.section and not section_must_be_skipped: + if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): + if column is not None: + column.__exit__() + + column = gr.Column(variant='panel') + column.__enter__() + + items_displayed = 0 + cols_displayed += 1 + + previous_section = item.section + + elem_id, text = item.section + gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='

{}

'.format(text)) + + if k in quicksettings_names and not shared.cmd_opts.freeze_settings: + quicksettings_list.append((i, k, item)) + components.append(dummy_component) + elif section_must_be_skipped: + components.append(dummy_component) + else: + component = create_setting_component(k) + component_dict[k] = component + components.append(component) + items_displayed += 1 + + with gr.Row(): + request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") + download_localization = gr.Button(value='Download localization template', elem_id="download_localization") + + with gr.Row(): + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') + restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') + + request_notifications.click( + fn=lambda: None, + inputs=[], + outputs=[], + _js='function(){}' + ) + + download_localization.click( + fn=lambda: None, + inputs=[], + outputs=[], + _js='download_localization' + ) + + def reload_scripts(): + modules.scripts.reload_script_body_only() + reload_javascript() # need to refresh the html page + + reload_script_bodies.click( + fn=reload_scripts, + inputs=[], + outputs=[] + ) + + def request_restart(): + shared.state.interrupt() + shared.state.need_restart = True + + restart_gradio.click( + fn=request_restart, + _js='restart_reload', + inputs=[], + outputs=[], + ) + + if column is not None: + column.__exit__() + + interfaces = [ + (txt2img_interface, "txt2img", "txt2img"), + (img2img_interface, "img2img", "img2img"), + (extras_interface, "Extras", "extras"), + (pnginfo_interface, "PNG Info", "pnginfo"), + (modelmerger_interface, "Checkpoint Merger", "modelmerger"), + (train_interface, "Train", "ti"), + ] + + css = "" + + for cssfile in modules.scripts.list_files_with_name("style.css"): + if not os.path.isfile(cssfile): + continue + + with open(cssfile, "r", encoding="utf8") as file: + css += file.read() + "\n" + + if os.path.exists(os.path.join(script_path, "user.css")): + with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: + css += file.read() + "\n" + + if not cmd_opts.no_progressbar_hiding: + css += css_hide_progressbar + + interfaces += script_callbacks.ui_tabs_callback() + interfaces += [(settings_interface, "Settings", "settings")] + + extensions_interface = ui_extensions.create_ui() + interfaces += [(extensions_interface, "Extensions", "extensions")] + + with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: + with gr.Row(elem_id="quicksettings"): + for i, k, item in quicksettings_list: + component = create_setting_component(k, is_quicksettings=True) + component_dict[k] = component + + parameters_copypaste.integrate_settings_paste_fields(component_dict) + parameters_copypaste.run_bind() + + with gr.Tabs(elem_id="tabs") as tabs: + for interface, label, ifid in interfaces: + with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): + interface.render() + + if os.path.exists(os.path.join(script_path, "notification.mp3")): + audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) + + text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) + settings_submit.click( + fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), + inputs=components, + outputs=[text_settings, result], + ) + + for i, k, item in quicksettings_list: + component = component_dict[k] + + component.change( + fn=lambda value, k=k: run_settings_single(value, key=k), + inputs=[component], + outputs=[component, text_settings], + ) + + component_keys = [k for k in opts.data_labels.keys() if k in component_dict] + + def get_settings_values(): + return [getattr(opts, key) for key in component_keys] + + demo.load( + fn=get_settings_values, + inputs=[], + outputs=[component_dict[k] for k in component_keys], + ) + + def modelmerger(*args): + try: + results = modules.extras.run_modelmerger(*args) + except Exception as e: + print("Error loading/saving model file:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + modules.sd_models.list_models() # to remove the potentially missing models from the list + return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] + return results + + modelmerger_merge.click( + fn=modelmerger, + inputs=[ + primary_model_name, + secondary_model_name, + tertiary_model_name, + interp_method, + interp_amount, + save_as_half, + save_as_safetensors, + custom_name, + ], + outputs=[ + submit_result, + primary_model_name, + secondary_model_name, + tertiary_model_name, + component_dict['sd_model_checkpoint'], + ] + ) + + ui_config_file = cmd_opts.ui_config_file + ui_settings = {} + settings_count = len(ui_settings) + error_loading = False + + try: + if os.path.exists(ui_config_file): + with open(ui_config_file, "r", encoding="utf8") as file: + ui_settings = json.load(file) + except Exception: + error_loading = True + print("Error loading settings:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + def loadsave(path, x): + def apply_field(obj, field, condition=None, init_field=None): + key = path + "/" + field + + if getattr(obj, 'custom_script_source', None) is not None: + key = 'customscript/' + obj.custom_script_source + '/' + key + + if getattr(obj, 'do_not_save_to_config', False): + return + + saved_value = ui_settings.get(key, None) + if saved_value is None: + ui_settings[key] = getattr(obj, field) + elif condition and not condition(saved_value): + print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') + else: + setattr(obj, field, saved_value) + if init_field is not None: + init_field(saved_value) + + if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: + apply_field(x, 'visible') + + if type(x) == gr.Slider: + apply_field(x, 'value') + apply_field(x, 'minimum') + apply_field(x, 'maximum') + apply_field(x, 'step') + + if type(x) == gr.Radio: + apply_field(x, 'value', lambda val: val in x.choices) + + if type(x) == gr.Checkbox: + apply_field(x, 'value') + + if type(x) == gr.Textbox: + apply_field(x, 'value') + + if type(x) == gr.Number: + apply_field(x, 'value') + + # Since there are many dropdowns that shouldn't be saved, + # we only mark dropdowns that should be saved. + if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False): + apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None)) + apply_field(x, 'visible') + + visit(txt2img_interface, loadsave, "txt2img") + visit(img2img_interface, loadsave, "img2img") + visit(extras_interface, loadsave, "extras") + visit(modelmerger_interface, loadsave, "modelmerger") + + if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): + with open(ui_config_file, "w", encoding="utf8") as file: + json.dump(ui_settings, file, indent=4) + + return demo + + +def reload_javascript(): + with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: + javascript = f'' + + scripts_list = modules.scripts.list_scripts("javascript", ".js") + + for basedir, filename, path in scripts_list: + with open(path, "r", encoding="utf8") as jsfile: + javascript += f"\n" + + if cmd_opts.theme is not None: + javascript += f"\n\n" + + javascript += f"\n" + + def template_response(*args, **kwargs): + res = shared.GradioTemplateResponseOriginal(*args, **kwargs) + res.body = res.body.replace( + b'', f'{javascript}'.encode("utf8")) + res.init_headers() + return res + + gradio.routes.templates.TemplateResponse = template_response + + +if not hasattr(shared, 'GradioTemplateResponseOriginal'): + shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse From 162fef394f8d80b54df7ede9e3b7ba65da23d3c5 Mon Sep 17 00:00:00 2001 From: Tim Patton <38817597+pattontim@users.noreply.github.com> Date: Mon, 21 Nov 2022 10:50:57 -0500 Subject: [PATCH 3/6] Patch line ui endings --- modules/ui.py | 3628 ++++++++++++++++++++++++------------------------- 1 file changed, 1814 insertions(+), 1814 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index a2b06aae..54d3293a 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1,1814 +1,1814 @@ -import html -import json -import math -import mimetypes -import os -import platform -import random -import subprocess as sp -import sys -import tempfile -import time -import traceback -from functools import partial, reduce - -import gradio as gr -import gradio.routes -import gradio.utils -import numpy as np -from PIL import Image, PngImagePlugin - - -from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions -from modules.paths import script_path - -from modules.shared import opts, cmd_opts, restricted_opts - -if cmd_opts.deepdanbooru: - from modules.deepbooru import get_deepbooru_tags - -import modules.codeformer_model -import modules.generation_parameters_copypaste as parameters_copypaste -import modules.gfpgan_model -import modules.hypernetworks.ui -import modules.ldsr_model -import modules.scripts -import modules.shared as shared -import modules.styles -import modules.textual_inversion.ui -from modules import prompt_parser -from modules.images import save_image -from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img -import modules.textual_inversion.ui -import modules.hypernetworks.ui -from modules.generation_parameters_copypaste import image_from_url_text - -# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI -mimetypes.init() -mimetypes.add_type('application/javascript', '.js') - -if not cmd_opts.share and not cmd_opts.listen: - # fix gradio phoning home - gradio.utils.version_check = lambda: None - gradio.utils.get_local_ip_address = lambda: '127.0.0.1' - -if cmd_opts.ngrok != None: - import modules.ngrok as ngrok - print('ngrok authtoken detected, trying to connect...') - ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region) - - -def gr_show(visible=True): - return {"visible": visible, "__type__": "update"} - - -sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" -sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None - -css_hide_progressbar = """ -.wrap .m-12 svg { display:none!important; } -.wrap .m-12::before { content:"Loading..." } -.wrap .z-20 svg { display:none!important; } -.wrap .z-20::before { content:"Loading..." } -.progress-bar { display:none!important; } -.meta-text { display:none!important; } -.meta-text-center { display:none!important; } -""" - -# Using constants for these since the variation selector isn't visible. -# Important that they exactly match script.js for tooltip to work. -random_symbol = '\U0001f3b2\ufe0f' # 🎲️ -reuse_symbol = '\u267b\ufe0f' # ♻️ -art_symbol = '\U0001f3a8' # 🎨 -paste_symbol = '\u2199\ufe0f' # ↙ -folder_symbol = '\U0001f4c2' # 📂 -refresh_symbol = '\U0001f504' # 🔄 -save_style_symbol = '\U0001f4be' # 💾 -apply_style_symbol = '\U0001f4cb' # 📋 - - -def plaintext_to_html(text): - text = "

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

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

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}
" - - 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: - shared.state.set_current_image() - image = shared.state.current_image - - if image is None: - image = gr.update(value=None) - else: - preview_visibility = gr_show(True) - - if shared.state.textinfo is not None: - textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) - else: - textinfo_result = gr_show(False) - - return f"

{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() - - if shared.cmd_opts.freeze_settings: - return gr.update() - - # dont allow model to be swapped when model hash exists in prompt - if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: - return gr.update() - - if key == "sd_model_checkpoint": - ckpt_info = sd_models.get_closet_checkpoint_match(value) - - if ckpt_info is not None: - value = ckpt_info.title - else: - return gr.update() - - comp_args = opts.data_labels[key].component_args - if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: - return - - valtype = type(opts.data_labels[key].default) - oldval = opts.data[key] - opts.data[key] = valtype(value) if valtype != type(None) else value - if oldval != value and opts.data_labels[key].onchange is not None: - opts.data_labels[key].onchange() - - opts.save(shared.config_filename) - return value - - -def update_generation_info(args): - generation_info, html_info, img_index = args - try: - generation_info = json.loads(generation_info) - if img_index < 0 or img_index >= len(generation_info["infotexts"]): - return html_info - return plaintext_to_html(generation_info["infotexts"][img_index]) - except Exception: - pass - # if the json parse or anything else fails, just return the old html_info - return html_info - - -def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): - def refresh(): - refresh_method() - args = refreshed_args() if callable(refreshed_args) else refreshed_args - - for k, v in args.items(): - setattr(refresh_component, k, v) - - return gr.update(**(args or {})) - - refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) - refresh_button.click( - fn=refresh, - inputs=[], - outputs=[refresh_component] - ) - return refresh_button - - -def create_output_panel(tabname, outdir): - def open_folder(f): - if not os.path.exists(f): - print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.') - return - elif not os.path.isdir(f): - print(f""" -WARNING -An open_folder request was made with an argument that is not a folder. -This could be an error or a malicious attempt to run code on your computer. -Requested path was: {f} -""", file=sys.stderr) - return - - if not shared.cmd_opts.hide_ui_dir_config: - path = os.path.normpath(f) - if platform.system() == "Windows": - os.startfile(path) - elif platform.system() == "Darwin": - sp.Popen(["open", path]) - else: - sp.Popen(["xdg-open", path]) - - with gr.Column(variant='panel'): - with gr.Group(): - result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4) - - generation_info = None - with gr.Column(): - with gr.Row(): - if tabname != "extras": - save = gr.Button('Save', elem_id=f'save_{tabname}') - - buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"]) - button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' - open_folder_button = gr.Button(folder_symbol, elem_id=button_id) - - open_folder_button.click( - fn=lambda: open_folder(opts.outdir_samples or outdir), - inputs=[], - outputs=[], - ) - - if tabname != "extras": - with gr.Row(): - do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) - - with gr.Row(): - download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) - - with gr.Group(): - html_info = gr.HTML() - generation_info = gr.Textbox(visible=False) - if tabname == 'txt2img' or tabname == 'img2img': - generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button") - generation_info_button.click( - fn=update_generation_info, - _js="(x, y) => [x, y, selected_gallery_index()]", - inputs=[generation_info, html_info], - outputs=[html_info], - preprocess=False - ) - - save.click( - fn=wrap_gradio_call(save_files), - _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", - inputs=[ - generation_info, - result_gallery, - do_make_zip, - html_info, - ], - outputs=[ - download_files, - html_info, - html_info, - html_info, - ] - ) - else: - html_info_x = gr.HTML() - html_info = gr.HTML() - parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None) - return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info - - -def create_ui(wrap_gradio_gpu_call): - import modules.img2img - import modules.txt2img - - reload_javascript() - - parameters_copypaste.reset() - - modules.scripts.scripts_current = modules.scripts.scripts_txt2img - modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) - - with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) - dummy_component = gr.Label(visible=False) - txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) - - with gr.Row(elem_id='txt2img_progress_row'): - with gr.Column(scale=1): - pass - - with gr.Column(scale=1): - progressbar = gr.HTML(elem_id="txt2img_progressbar") - txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) - setup_progressbar(progressbar, txt2img_preview, 'txt2img') - - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) - sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") - - with gr.Group(): - width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - - with gr.Row(): - restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) - tiling = gr.Checkbox(label='Tiling', value=False) - enable_hr = gr.Checkbox(label='Highres. fix', value=False) - - with gr.Row(visible=False) as hr_options: - firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) - firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) - - with gr.Row(equal_height=True): - batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) - batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) - - cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) - - seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() - - with gr.Group(): - custom_inputs = modules.scripts.scripts_txt2img.setup_ui() - - txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples) - parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt) - - connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) - connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) - - txt2img_args = dict( - fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), - _js="submit", - inputs=[ - txt2img_prompt, - txt2img_negative_prompt, - txt2img_prompt_style, - txt2img_prompt_style2, - steps, - sampler_index, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - enable_hr, - denoising_strength, - firstphase_width, - firstphase_height, - ] + custom_inputs, - - outputs=[ - txt2img_gallery, - generation_info, - html_info - ], - show_progress=False, - ) - - txt2img_prompt.submit(**txt2img_args) - submit.click(**txt2img_args) - - txt_prompt_img.change( - fn=modules.images.image_data, - inputs=[ - txt_prompt_img - ], - outputs=[ - txt2img_prompt, - txt_prompt_img - ] - ) - - enable_hr.change( - fn=lambda x: gr_show(x), - inputs=[enable_hr], - outputs=[hr_options], - ) - - roll.click( - fn=roll_artist, - _js="update_txt2img_tokens", - inputs=[ - txt2img_prompt, - ], - outputs=[ - txt2img_prompt, - ] - ) - - txt2img_paste_fields = [ - (txt2img_prompt, "Prompt"), - (txt2img_negative_prompt, "Negative prompt"), - (steps, "Steps"), - (sampler_index, "Sampler"), - (restore_faces, "Face restoration"), - (cfg_scale, "CFG scale"), - (seed, "Seed"), - (width, "Size-1"), - (height, "Size-2"), - (batch_size, "Batch size"), - (subseed, "Variation seed"), - (subseed_strength, "Variation seed strength"), - (seed_resize_from_w, "Seed resize from-1"), - (seed_resize_from_h, "Seed resize from-2"), - (denoising_strength, "Denoising strength"), - (enable_hr, lambda d: "Denoising strength" in d), - (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), - (firstphase_width, "First pass size-1"), - (firstphase_height, "First pass size-2"), - *modules.scripts.scripts_txt2img.infotext_fields - ] - parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields) - - txt2img_preview_params = [ - txt2img_prompt, - txt2img_negative_prompt, - steps, - sampler_index, - cfg_scale, - seed, - width, - height, - ] - - token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) - - modules.scripts.scripts_current = modules.scripts.scripts_img2img - modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) - - with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True) - - with gr.Row(elem_id='img2img_progress_row'): - img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) - - with gr.Column(scale=1): - pass - - with gr.Column(scale=1): - progressbar = gr.HTML(elem_id="img2img_progressbar") - img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) - setup_progressbar(progressbar, img2img_preview, 'img2img') - - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - - with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: - with gr.TabItem('img2img', id='img2img'): - init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480) - - with gr.TabItem('Inpaint', id='inpaint'): - init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) - - init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base") - init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask") - - mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4) - - with gr.Row(): - mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") - inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") - - inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index") - - with gr.Row(): - inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) - inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) - - with gr.TabItem('Batch img2img', id='batch'): - hidden = '
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() - - img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples) - parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt) - - connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) - connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) - - img2img_prompt_img.change( - fn=modules.images.image_data, - inputs=[ - img2img_prompt_img - ], - outputs=[ - img2img_prompt, - img2img_prompt_img - ] - ) - - mask_mode.change( - lambda mode, img: { - init_img_with_mask: gr_show(mode == 0), - init_img_inpaint: gr_show(mode == 1), - init_mask_inpaint: gr_show(mode == 1), - }, - inputs=[mask_mode, init_img_with_mask], - outputs=[ - init_img_with_mask, - init_img_inpaint, - init_mask_inpaint, - ], - ) - - img2img_args = dict( - fn=wrap_gradio_gpu_call(modules.img2img.img2img), - _js="submit_img2img", - inputs=[ - dummy_component, - img2img_prompt, - img2img_negative_prompt, - img2img_prompt_style, - img2img_prompt_style2, - init_img, - init_img_with_mask, - init_img_inpaint, - init_mask_inpaint, - mask_mode, - steps, - sampler_index, - mask_blur, - inpainting_fill, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - denoising_strength, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - resize_mode, - inpaint_full_res, - inpaint_full_res_padding, - inpainting_mask_invert, - img2img_batch_input_dir, - img2img_batch_output_dir, - ] + custom_inputs, - outputs=[ - img2img_gallery, - generation_info, - html_info - ], - show_progress=False, - ) - - img2img_prompt.submit(**img2img_args) - submit.click(**img2img_args) - - img2img_interrogate.click( - fn=interrogate, - inputs=[init_img], - outputs=[img2img_prompt], - ) - - if cmd_opts.deepdanbooru: - img2img_deepbooru.click( - fn=interrogate_deepbooru, - inputs=[init_img], - outputs=[img2img_prompt], - ) - - - roll.click( - fn=roll_artist, - _js="update_img2img_tokens", - inputs=[ - img2img_prompt, - ], - outputs=[ - img2img_prompt, - ] - ) - - prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] - style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] - style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] - - for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): - button.click( - fn=add_style, - _js="ask_for_style_name", - # Have to pass empty dummy component here, because the JavaScript and Python function have to accept - # the same number of parameters, but we only know the style-name after the JavaScript prompt - inputs=[dummy_component, prompt, negative_prompt], - outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], - ) - - for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): - button.click( - fn=apply_styles, - _js=js_func, - inputs=[prompt, negative_prompt, style1, style2], - outputs=[prompt, negative_prompt, style1, style2], - ) - - token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) - - img2img_paste_fields = [ - (img2img_prompt, "Prompt"), - (img2img_negative_prompt, "Negative prompt"), - (steps, "Steps"), - (sampler_index, "Sampler"), - (restore_faces, "Face restoration"), - (cfg_scale, "CFG scale"), - (seed, "Seed"), - (width, "Size-1"), - (height, "Size-2"), - (batch_size, "Batch size"), - (subseed, "Variation seed"), - (subseed_strength, "Variation seed strength"), - (seed_resize_from_w, "Seed resize from-1"), - (seed_resize_from_h, "Seed resize from-2"), - (denoising_strength, "Denoising strength"), - *modules.scripts.scripts_img2img.infotext_fields - ] - parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) - parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields) - - modules.scripts.scripts_current = None - - with gr.Blocks(analytics_enabled=False) as extras_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - with gr.Tabs(elem_id="mode_extras"): - with gr.TabItem('Single Image'): - extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil") - - with gr.TabItem('Batch Process'): - image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") - - with gr.TabItem('Batch from Directory'): - extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.") - extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.") - show_extras_results = gr.Checkbox(label='Show result images', value=True) - - submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') - - with gr.Tabs(elem_id="extras_resize_mode"): - with gr.TabItem('Scale by'): - upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4) - with gr.TabItem('Scale to'): - with gr.Group(): - with gr.Row(): - upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) - upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) - upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) - - with gr.Group(): - extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") - - with gr.Group(): - extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") - extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1) - - with gr.Group(): - gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan) - - with gr.Group(): - codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer) - codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer) - - with gr.Group(): - upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False) - - result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples) - - submit.click( - fn=wrap_gradio_gpu_call(modules.extras.run_extras), - _js="get_extras_tab_index", - inputs=[ - dummy_component, - dummy_component, - extras_image, - image_batch, - extras_batch_input_dir, - extras_batch_output_dir, - show_extras_results, - gfpgan_visibility, - codeformer_visibility, - codeformer_weight, - upscaling_resize, - upscaling_resize_w, - upscaling_resize_h, - upscaling_crop, - extras_upscaler_1, - extras_upscaler_2, - extras_upscaler_2_visibility, - upscale_before_face_fix, - ], - outputs=[ - result_images, - html_info_x, - html_info, - ] - ) - parameters_copypaste.add_paste_fields("extras", extras_image, None) - - extras_image.change( - fn=modules.extras.clear_cache, - inputs=[], outputs=[] - ) - - with gr.Blocks(analytics_enabled=False) as pnginfo_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") - - with gr.Column(variant='panel'): - html = gr.HTML() - generation_info = gr.Textbox(visible=False) - html2 = gr.HTML() - with gr.Row(): - buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) - parameters_copypaste.bind_buttons(buttons, image, generation_info) - - image.change( - fn=wrap_gradio_call(modules.extras.run_pnginfo), - inputs=[image], - outputs=[html, generation_info, html2], - ) - - with gr.Blocks(analytics_enabled=False) as modelmerger_interface: - with gr.Row().style(equal_height=False): - with gr.Column(variant='panel'): - gr.HTML(value="

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") - save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format") - modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') - - with gr.Column(variant='panel'): - submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) - - sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() - - with gr.Blocks(analytics_enabled=False) as train_interface: - with gr.Row().style(equal_height=False): - gr.HTML(value="

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_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys) - new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) - new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") - new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") - overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') - - with gr.Tab(label="Preprocess images"): - process_src = gr.Textbox(label='Source directory') - process_dst = gr.Textbox(label='Destination directory') - process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) - - with gr.Row(): - process_flip = gr.Checkbox(label='Create flipped copies') - process_split = gr.Checkbox(label='Split oversized images') - process_focal_crop = gr.Checkbox(label='Auto focal point crop') - process_caption = gr.Checkbox(label='Use BLIP for caption') - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) - - with gr.Row(visible=False) as process_split_extra_row: - process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) - process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) - - with gr.Row(visible=False) as process_focal_crop_row: - process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_debug = gr.Checkbox(label='Create debug image') - - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - with gr.Row(): - interrupt_preprocessing = gr.Button("Interrupt") - run_preprocess = gr.Button(value="Preprocess", variant='primary') - - process_split.change( - fn=lambda show: gr_show(show), - inputs=[process_split], - outputs=[process_split_extra_row], - ) - - process_focal_crop.change( - fn=lambda show: gr_show(show), - inputs=[process_focal_crop], - outputs=[process_focal_crop_row], - ) - - with gr.Tab(label="Train"): - gr.HTML(value="

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') - - params = script_callbacks.UiTrainTabParams(txt2img_preview_params) - - script_callbacks.ui_train_tabs_callback(params) - - with gr.Column(): - progressbar = gr.HTML(elem_id="ti_progressbar") - ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) - - ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) - ti_preview = gr.Image(elem_id='ti_preview', visible=False) - ti_progress = gr.HTML(elem_id="ti_progress", value="") - ti_outcome = gr.HTML(elem_id="ti_error", value="") - setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) - - create_embedding.click( - fn=modules.textual_inversion.ui.create_embedding, - inputs=[ - new_embedding_name, - initialization_text, - nvpt, - overwrite_old_embedding, - ], - outputs=[ - train_embedding_name, - ti_output, - ti_outcome, - ] - ) - - create_hypernetwork.click( - fn=modules.hypernetworks.ui.create_hypernetwork, - inputs=[ - new_hypernetwork_name, - new_hypernetwork_sizes, - overwrite_old_hypernetwork, - new_hypernetwork_layer_structure, - new_hypernetwork_activation_func, - new_hypernetwork_initialization_option, - new_hypernetwork_add_layer_norm, - new_hypernetwork_use_dropout - ], - outputs=[ - train_hypernetwork_name, - ti_output, - ti_outcome, - ] - ) - - run_preprocess.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - process_src, - process_dst, - process_width, - process_height, - preprocess_txt_action, - process_flip, - process_split, - process_caption, - process_caption_deepbooru, - process_split_threshold, - process_overlap_ratio, - process_focal_crop, - process_focal_crop_face_weight, - process_focal_crop_entropy_weight, - process_focal_crop_edges_weight, - process_focal_crop_debug, - ], - outputs=[ - ti_output, - ti_outcome, - ], - ) - - train_embedding.click( - fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - train_embedding_name, - embedding_learn_rate, - batch_size, - dataset_directory, - log_directory, - training_width, - training_height, - steps, - create_image_every, - save_embedding_every, - template_file, - save_image_with_stored_embedding, - preview_from_txt2img, - *txt2img_preview_params, - ], - outputs=[ - ti_output, - ti_outcome, - ] - ) - - train_hypernetwork.click( - fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), - _js="start_training_textual_inversion", - inputs=[ - train_hypernetwork_name, - hypernetwork_learn_rate, - batch_size, - dataset_directory, - log_directory, - training_width, - training_height, - steps, - create_image_every, - save_embedding_every, - template_file, - preview_from_txt2img, - *txt2img_preview_params, - ], - outputs=[ - ti_output, - ti_outcome, - ] - ) - - interrupt_training.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - interrupt_preprocessing.click( - fn=lambda: shared.state.interrupt(), - inputs=[], - outputs=[], - ) - - def create_setting_component(key, is_quicksettings=False): - def fun(): - return opts.data[key] if key in opts.data else opts.data_labels[key].default - - info = opts.data_labels[key] - t = type(info.default) - - args = info.component_args() if callable(info.component_args) else info.component_args - - if info.component is not None: - comp = info.component - elif t == str: - comp = gr.Textbox - elif t == int: - comp = gr.Number - elif t == bool: - comp = gr.Checkbox - else: - raise Exception(f'bad options item type: {str(t)} for key {key}') - - elem_id = "setting_"+key - - if info.refresh is not None: - if is_quicksettings: - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) - else: - with gr.Row(variant="compact"): - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) - else: - res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) - - return res - - components = [] - component_dict = {} - - script_callbacks.ui_settings_callback() - opts.reorder() - - def run_settings(*args): - changed = [] - - for key, value, comp in zip(opts.data_labels.keys(), args, components): - assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" - - for key, value, comp in zip(opts.data_labels.keys(), args, components): - if comp == dummy_component: - continue - - if opts.set(key, value): - changed.append(key) - - try: - opts.save(shared.config_filename) - except RuntimeError: - return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.' - return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.' - - def run_settings_single(value, key): - if not opts.same_type(value, opts.data_labels[key].default): - return gr.update(visible=True), opts.dumpjson() - - if not opts.set(key, value): - return gr.update(value=getattr(opts, key)), opts.dumpjson() - - opts.save(shared.config_filename) - - return gr.update(value=value), opts.dumpjson() - - with gr.Blocks(analytics_enabled=False) as settings_interface: - settings_submit = gr.Button(value="Apply settings", variant='primary') - result = gr.HTML() - - settings_cols = 3 - items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) - - quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] - quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') - - quicksettings_list = [] - - cols_displayed = 0 - items_displayed = 0 - previous_section = None - column = None - with gr.Row(elem_id="settings").style(equal_height=False): - for i, (k, item) in enumerate(opts.data_labels.items()): - section_must_be_skipped = item.section[0] is None - - if previous_section != item.section and not section_must_be_skipped: - if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): - if column is not None: - column.__exit__() - - column = gr.Column(variant='panel') - column.__enter__() - - items_displayed = 0 - cols_displayed += 1 - - previous_section = item.section - - elem_id, text = item.section - gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='

{}

'.format(text)) - - if k in quicksettings_names and not shared.cmd_opts.freeze_settings: - quicksettings_list.append((i, k, item)) - components.append(dummy_component) - elif section_must_be_skipped: - components.append(dummy_component) - else: - component = create_setting_component(k) - component_dict[k] = component - components.append(component) - items_displayed += 1 - - with gr.Row(): - request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") - download_localization = gr.Button(value='Download localization template', elem_id="download_localization") - - with gr.Row(): - reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') - restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') - - request_notifications.click( - fn=lambda: None, - inputs=[], - outputs=[], - _js='function(){}' - ) - - download_localization.click( - fn=lambda: None, - inputs=[], - outputs=[], - _js='download_localization' - ) - - def reload_scripts(): - modules.scripts.reload_script_body_only() - reload_javascript() # need to refresh the html page - - reload_script_bodies.click( - fn=reload_scripts, - inputs=[], - outputs=[] - ) - - def request_restart(): - shared.state.interrupt() - shared.state.need_restart = True - - restart_gradio.click( - fn=request_restart, - _js='restart_reload', - inputs=[], - outputs=[], - ) - - if column is not None: - column.__exit__() - - interfaces = [ - (txt2img_interface, "txt2img", "txt2img"), - (img2img_interface, "img2img", "img2img"), - (extras_interface, "Extras", "extras"), - (pnginfo_interface, "PNG Info", "pnginfo"), - (modelmerger_interface, "Checkpoint Merger", "modelmerger"), - (train_interface, "Train", "ti"), - ] - - css = "" - - for cssfile in modules.scripts.list_files_with_name("style.css"): - if not os.path.isfile(cssfile): - continue - - with open(cssfile, "r", encoding="utf8") as file: - css += file.read() + "\n" - - if os.path.exists(os.path.join(script_path, "user.css")): - with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: - css += file.read() + "\n" - - if not cmd_opts.no_progressbar_hiding: - css += css_hide_progressbar - - interfaces += script_callbacks.ui_tabs_callback() - interfaces += [(settings_interface, "Settings", "settings")] - - extensions_interface = ui_extensions.create_ui() - interfaces += [(extensions_interface, "Extensions", "extensions")] - - with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: - with gr.Row(elem_id="quicksettings"): - for i, k, item in quicksettings_list: - component = create_setting_component(k, is_quicksettings=True) - component_dict[k] = component - - parameters_copypaste.integrate_settings_paste_fields(component_dict) - parameters_copypaste.run_bind() - - with gr.Tabs(elem_id="tabs") as tabs: - for interface, label, ifid in interfaces: - with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): - interface.render() - - if os.path.exists(os.path.join(script_path, "notification.mp3")): - audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) - - text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) - settings_submit.click( - fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), - inputs=components, - outputs=[text_settings, result], - ) - - for i, k, item in quicksettings_list: - component = component_dict[k] - - component.change( - fn=lambda value, k=k: run_settings_single(value, key=k), - inputs=[component], - outputs=[component, text_settings], - ) - - component_keys = [k for k in opts.data_labels.keys() if k in component_dict] - - def get_settings_values(): - return [getattr(opts, key) for key in component_keys] - - demo.load( - fn=get_settings_values, - inputs=[], - outputs=[component_dict[k] for k in component_keys], - ) - - def modelmerger(*args): - try: - results = modules.extras.run_modelmerger(*args) - except Exception as e: - print("Error loading/saving model file:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - modules.sd_models.list_models() # to remove the potentially missing models from the list - return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] - return results - - modelmerger_merge.click( - fn=modelmerger, - inputs=[ - primary_model_name, - secondary_model_name, - tertiary_model_name, - interp_method, - interp_amount, - save_as_half, - save_as_safetensors, - custom_name, - ], - outputs=[ - submit_result, - primary_model_name, - secondary_model_name, - tertiary_model_name, - component_dict['sd_model_checkpoint'], - ] - ) - - ui_config_file = cmd_opts.ui_config_file - ui_settings = {} - settings_count = len(ui_settings) - error_loading = False - - try: - if os.path.exists(ui_config_file): - with open(ui_config_file, "r", encoding="utf8") as file: - ui_settings = json.load(file) - except Exception: - error_loading = True - print("Error loading settings:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - def loadsave(path, x): - def apply_field(obj, field, condition=None, init_field=None): - key = path + "/" + field - - if getattr(obj, 'custom_script_source', None) is not None: - key = 'customscript/' + obj.custom_script_source + '/' + key - - if getattr(obj, 'do_not_save_to_config', False): - return - - saved_value = ui_settings.get(key, None) - if saved_value is None: - ui_settings[key] = getattr(obj, field) - elif condition and not condition(saved_value): - print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') - else: - setattr(obj, field, saved_value) - if init_field is not None: - init_field(saved_value) - - if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: - apply_field(x, 'visible') - - if type(x) == gr.Slider: - apply_field(x, 'value') - apply_field(x, 'minimum') - apply_field(x, 'maximum') - apply_field(x, 'step') - - if type(x) == gr.Radio: - apply_field(x, 'value', lambda val: val in x.choices) - - if type(x) == gr.Checkbox: - apply_field(x, 'value') - - if type(x) == gr.Textbox: - apply_field(x, 'value') - - if type(x) == gr.Number: - apply_field(x, 'value') - - # Since there are many dropdowns that shouldn't be saved, - # we only mark dropdowns that should be saved. - if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False): - apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None)) - apply_field(x, 'visible') - - visit(txt2img_interface, loadsave, "txt2img") - visit(img2img_interface, loadsave, "img2img") - visit(extras_interface, loadsave, "extras") - visit(modelmerger_interface, loadsave, "modelmerger") - - if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): - with open(ui_config_file, "w", encoding="utf8") as file: - json.dump(ui_settings, file, indent=4) - - return demo - - -def reload_javascript(): - with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: - javascript = f'' - - scripts_list = modules.scripts.list_scripts("javascript", ".js") - - for basedir, filename, path in scripts_list: - with open(path, "r", encoding="utf8") as jsfile: - javascript += f"\n" - - if cmd_opts.theme is not None: - javascript += f"\n\n" - - javascript += f"\n" - - def template_response(*args, **kwargs): - res = shared.GradioTemplateResponseOriginal(*args, **kwargs) - res.body = res.body.replace( - b'', f'{javascript}'.encode("utf8")) - res.init_headers() - return res - - gradio.routes.templates.TemplateResponse = template_response - - -if not hasattr(shared, 'GradioTemplateResponseOriginal'): - shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse +import html +import json +import math +import mimetypes +import os +import platform +import random +import subprocess as sp +import sys +import tempfile +import time +import traceback +from functools import partial, reduce + +import gradio as gr +import gradio.routes +import gradio.utils +import numpy as np +from PIL import Image, PngImagePlugin + + +from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions +from modules.paths import script_path + +from modules.shared import opts, cmd_opts, restricted_opts + +if cmd_opts.deepdanbooru: + from modules.deepbooru import get_deepbooru_tags + +import modules.codeformer_model +import modules.generation_parameters_copypaste as parameters_copypaste +import modules.gfpgan_model +import modules.hypernetworks.ui +import modules.ldsr_model +import modules.scripts +import modules.shared as shared +import modules.styles +import modules.textual_inversion.ui +from modules import prompt_parser +from modules.images import save_image +from modules.sd_hijack import model_hijack +from modules.sd_samplers import samplers, samplers_for_img2img +import modules.textual_inversion.ui +import modules.hypernetworks.ui +from modules.generation_parameters_copypaste import image_from_url_text + +# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI +mimetypes.init() +mimetypes.add_type('application/javascript', '.js') + +if not cmd_opts.share and not cmd_opts.listen: + # fix gradio phoning home + gradio.utils.version_check = lambda: None + gradio.utils.get_local_ip_address = lambda: '127.0.0.1' + +if cmd_opts.ngrok != None: + import modules.ngrok as ngrok + print('ngrok authtoken detected, trying to connect...') + ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region) + + +def gr_show(visible=True): + return {"visible": visible, "__type__": "update"} + + +sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" +sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None + +css_hide_progressbar = """ +.wrap .m-12 svg { display:none!important; } +.wrap .m-12::before { content:"Loading..." } +.wrap .z-20 svg { display:none!important; } +.wrap .z-20::before { content:"Loading..." } +.progress-bar { display:none!important; } +.meta-text { display:none!important; } +.meta-text-center { display:none!important; } +""" + +# Using constants for these since the variation selector isn't visible. +# Important that they exactly match script.js for tooltip to work. +random_symbol = '\U0001f3b2\ufe0f' # 🎲️ +reuse_symbol = '\u267b\ufe0f' # ♻️ +art_symbol = '\U0001f3a8' # 🎨 +paste_symbol = '\u2199\ufe0f' # ↙ +folder_symbol = '\U0001f4c2' # 📂 +refresh_symbol = '\U0001f504' # 🔄 +save_style_symbol = '\U0001f4be' # 💾 +apply_style_symbol = '\U0001f4cb' # 📋 + + +def plaintext_to_html(text): + text = "

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

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

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}
" + + 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: + shared.state.set_current_image() + image = shared.state.current_image + + if image is None: + image = gr.update(value=None) + else: + preview_visibility = gr_show(True) + + if shared.state.textinfo is not None: + textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) + else: + textinfo_result = gr_show(False) + + return f"

{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() + + if shared.cmd_opts.freeze_settings: + return gr.update() + + # dont allow model to be swapped when model hash exists in prompt + if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: + return gr.update() + + if key == "sd_model_checkpoint": + ckpt_info = sd_models.get_closet_checkpoint_match(value) + + if ckpt_info is not None: + value = ckpt_info.title + else: + return gr.update() + + comp_args = opts.data_labels[key].component_args + if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: + return + + valtype = type(opts.data_labels[key].default) + oldval = opts.data[key] + opts.data[key] = valtype(value) if valtype != type(None) else value + if oldval != value and opts.data_labels[key].onchange is not None: + opts.data_labels[key].onchange() + + opts.save(shared.config_filename) + return value + + +def update_generation_info(args): + generation_info, html_info, img_index = args + try: + generation_info = json.loads(generation_info) + if img_index < 0 or img_index >= len(generation_info["infotexts"]): + return html_info + return plaintext_to_html(generation_info["infotexts"][img_index]) + except Exception: + pass + # if the json parse or anything else fails, just return the old html_info + return html_info + + +def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): + def refresh(): + refresh_method() + args = refreshed_args() if callable(refreshed_args) else refreshed_args + + for k, v in args.items(): + setattr(refresh_component, k, v) + + return gr.update(**(args or {})) + + refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[refresh_component] + ) + return refresh_button + + +def create_output_panel(tabname, outdir): + def open_folder(f): + if not os.path.exists(f): + print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.') + return + elif not os.path.isdir(f): + print(f""" +WARNING +An open_folder request was made with an argument that is not a folder. +This could be an error or a malicious attempt to run code on your computer. +Requested path was: {f} +""", file=sys.stderr) + return + + if not shared.cmd_opts.hide_ui_dir_config: + path = os.path.normpath(f) + if platform.system() == "Windows": + os.startfile(path) + elif platform.system() == "Darwin": + sp.Popen(["open", path]) + else: + sp.Popen(["xdg-open", path]) + + with gr.Column(variant='panel'): + with gr.Group(): + result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4) + + generation_info = None + with gr.Column(): + with gr.Row(): + if tabname != "extras": + save = gr.Button('Save', elem_id=f'save_{tabname}') + + buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"]) + button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder' + open_folder_button = gr.Button(folder_symbol, elem_id=button_id) + + open_folder_button.click( + fn=lambda: open_folder(opts.outdir_samples or outdir), + inputs=[], + outputs=[], + ) + + if tabname != "extras": + with gr.Row(): + do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False) + + with gr.Row(): + download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False) + + with gr.Group(): + html_info = gr.HTML() + generation_info = gr.Textbox(visible=False) + if tabname == 'txt2img' or tabname == 'img2img': + generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button") + generation_info_button.click( + fn=update_generation_info, + _js="(x, y) => [x, y, selected_gallery_index()]", + inputs=[generation_info, html_info], + outputs=[html_info], + preprocess=False + ) + + save.click( + fn=wrap_gradio_call(save_files), + _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]", + inputs=[ + generation_info, + result_gallery, + do_make_zip, + html_info, + ], + outputs=[ + download_files, + html_info, + html_info, + html_info, + ] + ) + else: + html_info_x = gr.HTML() + html_info = gr.HTML() + parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None) + return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info + + +def create_ui(wrap_gradio_gpu_call): + import modules.img2img + import modules.txt2img + + reload_javascript() + + parameters_copypaste.reset() + + modules.scripts.scripts_current = modules.scripts.scripts_txt2img + modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) + + with gr.Blocks(analytics_enabled=False) as txt2img_interface: + txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) + dummy_component = gr.Label(visible=False) + txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) + + with gr.Row(elem_id='txt2img_progress_row'): + with gr.Column(scale=1): + pass + + with gr.Column(scale=1): + progressbar = gr.HTML(elem_id="txt2img_progressbar") + txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) + setup_progressbar(progressbar, txt2img_preview, 'txt2img') + + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20) + sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") + + with gr.Group(): + width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + + with gr.Row(): + restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) + tiling = gr.Checkbox(label='Tiling', value=False) + enable_hr = gr.Checkbox(label='Highres. fix', value=False) + + with gr.Row(visible=False) as hr_options: + firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) + firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) + + with gr.Row(equal_height=True): + batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1) + batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1) + + cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0) + + seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() + + with gr.Group(): + custom_inputs = modules.scripts.scripts_txt2img.setup_ui() + + txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples) + parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt) + + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) + connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + + txt2img_args = dict( + fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), + _js="submit", + inputs=[ + txt2img_prompt, + txt2img_negative_prompt, + txt2img_prompt_style, + txt2img_prompt_style2, + steps, + sampler_index, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + enable_hr, + denoising_strength, + firstphase_width, + firstphase_height, + ] + custom_inputs, + + outputs=[ + txt2img_gallery, + generation_info, + html_info + ], + show_progress=False, + ) + + txt2img_prompt.submit(**txt2img_args) + submit.click(**txt2img_args) + + txt_prompt_img.change( + fn=modules.images.image_data, + inputs=[ + txt_prompt_img + ], + outputs=[ + txt2img_prompt, + txt_prompt_img + ] + ) + + enable_hr.change( + fn=lambda x: gr_show(x), + inputs=[enable_hr], + outputs=[hr_options], + ) + + roll.click( + fn=roll_artist, + _js="update_txt2img_tokens", + inputs=[ + txt2img_prompt, + ], + outputs=[ + txt2img_prompt, + ] + ) + + txt2img_paste_fields = [ + (txt2img_prompt, "Prompt"), + (txt2img_negative_prompt, "Negative prompt"), + (steps, "Steps"), + (sampler_index, "Sampler"), + (restore_faces, "Face restoration"), + (cfg_scale, "CFG scale"), + (seed, "Seed"), + (width, "Size-1"), + (height, "Size-2"), + (batch_size, "Batch size"), + (subseed, "Variation seed"), + (subseed_strength, "Variation seed strength"), + (seed_resize_from_w, "Seed resize from-1"), + (seed_resize_from_h, "Seed resize from-2"), + (denoising_strength, "Denoising strength"), + (enable_hr, lambda d: "Denoising strength" in d), + (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), + (firstphase_width, "First pass size-1"), + (firstphase_height, "First pass size-2"), + *modules.scripts.scripts_txt2img.infotext_fields + ] + parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields) + + txt2img_preview_params = [ + txt2img_prompt, + txt2img_negative_prompt, + steps, + sampler_index, + cfg_scale, + seed, + width, + height, + ] + + token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter]) + + modules.scripts.scripts_current = modules.scripts.scripts_img2img + modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) + + with gr.Blocks(analytics_enabled=False) as img2img_interface: + img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True) + + with gr.Row(elem_id='img2img_progress_row'): + img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) + + with gr.Column(scale=1): + pass + + with gr.Column(scale=1): + progressbar = gr.HTML(elem_id="img2img_progressbar") + img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) + setup_progressbar(progressbar, img2img_preview, 'img2img') + + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + + with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode: + with gr.TabItem('img2img', id='img2img'): + init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480) + + with gr.TabItem('Inpaint', id='inpaint'): + init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480) + + init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base") + init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask") + + mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4) + + with gr.Row(): + mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode") + inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index") + + inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index") + + with gr.Row(): + inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) + inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) + + with gr.TabItem('Batch img2img', id='batch'): + hidden = '
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() + + img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples) + parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt) + + connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) + connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True) + + img2img_prompt_img.change( + fn=modules.images.image_data, + inputs=[ + img2img_prompt_img + ], + outputs=[ + img2img_prompt, + img2img_prompt_img + ] + ) + + mask_mode.change( + lambda mode, img: { + init_img_with_mask: gr_show(mode == 0), + init_img_inpaint: gr_show(mode == 1), + init_mask_inpaint: gr_show(mode == 1), + }, + inputs=[mask_mode, init_img_with_mask], + outputs=[ + init_img_with_mask, + init_img_inpaint, + init_mask_inpaint, + ], + ) + + img2img_args = dict( + fn=wrap_gradio_gpu_call(modules.img2img.img2img), + _js="submit_img2img", + inputs=[ + dummy_component, + img2img_prompt, + img2img_negative_prompt, + img2img_prompt_style, + img2img_prompt_style2, + init_img, + init_img_with_mask, + init_img_inpaint, + init_mask_inpaint, + mask_mode, + steps, + sampler_index, + mask_blur, + inpainting_fill, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + denoising_strength, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + resize_mode, + inpaint_full_res, + inpaint_full_res_padding, + inpainting_mask_invert, + img2img_batch_input_dir, + img2img_batch_output_dir, + ] + custom_inputs, + outputs=[ + img2img_gallery, + generation_info, + html_info + ], + show_progress=False, + ) + + img2img_prompt.submit(**img2img_args) + submit.click(**img2img_args) + + img2img_interrogate.click( + fn=interrogate, + inputs=[init_img], + outputs=[img2img_prompt], + ) + + if cmd_opts.deepdanbooru: + img2img_deepbooru.click( + fn=interrogate_deepbooru, + inputs=[init_img], + outputs=[img2img_prompt], + ) + + + roll.click( + fn=roll_artist, + _js="update_img2img_tokens", + inputs=[ + img2img_prompt, + ], + outputs=[ + img2img_prompt, + ] + ) + + prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)] + style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)] + style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"] + + for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts): + button.click( + fn=add_style, + _js="ask_for_style_name", + # Have to pass empty dummy component here, because the JavaScript and Python function have to accept + # the same number of parameters, but we only know the style-name after the JavaScript prompt + inputs=[dummy_component, prompt, negative_prompt], + outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2], + ) + + for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs): + button.click( + fn=apply_styles, + _js=js_func, + inputs=[prompt, negative_prompt, style1, style2], + outputs=[prompt, negative_prompt, style1, style2], + ) + + token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) + + img2img_paste_fields = [ + (img2img_prompt, "Prompt"), + (img2img_negative_prompt, "Negative prompt"), + (steps, "Steps"), + (sampler_index, "Sampler"), + (restore_faces, "Face restoration"), + (cfg_scale, "CFG scale"), + (seed, "Seed"), + (width, "Size-1"), + (height, "Size-2"), + (batch_size, "Batch size"), + (subseed, "Variation seed"), + (subseed_strength, "Variation seed strength"), + (seed_resize_from_w, "Seed resize from-1"), + (seed_resize_from_h, "Seed resize from-2"), + (denoising_strength, "Denoising strength"), + *modules.scripts.scripts_img2img.infotext_fields + ] + parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) + parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields) + + modules.scripts.scripts_current = None + + with gr.Blocks(analytics_enabled=False) as extras_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + with gr.Tabs(elem_id="mode_extras"): + with gr.TabItem('Single Image'): + extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil") + + with gr.TabItem('Batch Process'): + image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file") + + with gr.TabItem('Batch from Directory'): + extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.") + extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.") + show_extras_results = gr.Checkbox(label='Show result images', value=True) + + submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') + + with gr.Tabs(elem_id="extras_resize_mode"): + with gr.TabItem('Scale by'): + upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4) + with gr.TabItem('Scale to'): + with gr.Group(): + with gr.Row(): + upscaling_resize_w = gr.Number(label="Width", value=512, precision=0) + upscaling_resize_h = gr.Number(label="Height", value=512, precision=0) + upscaling_crop = gr.Checkbox(label='Crop to fit', value=True) + + with gr.Group(): + extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") + + with gr.Group(): + extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") + extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1) + + with gr.Group(): + gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan) + + with gr.Group(): + codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer) + codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer) + + with gr.Group(): + upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False) + + result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples) + + submit.click( + fn=wrap_gradio_gpu_call(modules.extras.run_extras), + _js="get_extras_tab_index", + inputs=[ + dummy_component, + dummy_component, + extras_image, + image_batch, + extras_batch_input_dir, + extras_batch_output_dir, + show_extras_results, + gfpgan_visibility, + codeformer_visibility, + codeformer_weight, + upscaling_resize, + upscaling_resize_w, + upscaling_resize_h, + upscaling_crop, + extras_upscaler_1, + extras_upscaler_2, + extras_upscaler_2_visibility, + upscale_before_face_fix, + ], + outputs=[ + result_images, + html_info_x, + html_info, + ] + ) + parameters_copypaste.add_paste_fields("extras", extras_image, None) + + extras_image.change( + fn=modules.extras.clear_cache, + inputs=[], outputs=[] + ) + + with gr.Blocks(analytics_enabled=False) as pnginfo_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil") + + with gr.Column(variant='panel'): + html = gr.HTML() + generation_info = gr.Textbox(visible=False) + html2 = gr.HTML() + with gr.Row(): + buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"]) + parameters_copypaste.bind_buttons(buttons, image, generation_info) + + image.change( + fn=wrap_gradio_call(modules.extras.run_pnginfo), + inputs=[image], + outputs=[html, generation_info, html2], + ) + + with gr.Blocks(analytics_enabled=False) as modelmerger_interface: + with gr.Row().style(equal_height=False): + with gr.Column(variant='panel'): + gr.HTML(value="

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") + save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format") + modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') + + with gr.Column(variant='panel'): + submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) + + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() + + with gr.Blocks(analytics_enabled=False) as train_interface: + with gr.Row().style(equal_height=False): + gr.HTML(value="

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_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys) + new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) + new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") + new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') + + with gr.Tab(label="Preprocess images"): + process_src = gr.Textbox(label='Source directory') + process_dst = gr.Textbox(label='Destination directory') + process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) + process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) + + with gr.Row(): + process_flip = gr.Checkbox(label='Create flipped copies') + process_split = gr.Checkbox(label='Split oversized images') + process_focal_crop = gr.Checkbox(label='Auto focal point crop') + process_caption = gr.Checkbox(label='Use BLIP for caption') + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) + + with gr.Row(visible=False) as process_split_extra_row: + process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) + process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) + + with gr.Row(visible=False) as process_focal_crop_row: + process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05) + process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05) + process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05) + process_focal_crop_debug = gr.Checkbox(label='Create debug image') + + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + with gr.Row(): + interrupt_preprocessing = gr.Button("Interrupt") + run_preprocess = gr.Button(value="Preprocess", variant='primary') + + process_split.change( + fn=lambda show: gr_show(show), + inputs=[process_split], + outputs=[process_split_extra_row], + ) + + process_focal_crop.change( + fn=lambda show: gr_show(show), + inputs=[process_focal_crop], + outputs=[process_focal_crop_row], + ) + + with gr.Tab(label="Train"): + gr.HTML(value="

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') + + params = script_callbacks.UiTrainTabParams(txt2img_preview_params) + + script_callbacks.ui_train_tabs_callback(params) + + with gr.Column(): + progressbar = gr.HTML(elem_id="ti_progressbar") + ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) + + ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) + ti_preview = gr.Image(elem_id='ti_preview', visible=False) + ti_progress = gr.HTML(elem_id="ti_progress", value="") + ti_outcome = gr.HTML(elem_id="ti_error", value="") + setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) + + create_embedding.click( + fn=modules.textual_inversion.ui.create_embedding, + inputs=[ + new_embedding_name, + initialization_text, + nvpt, + overwrite_old_embedding, + ], + outputs=[ + train_embedding_name, + ti_output, + ti_outcome, + ] + ) + + create_hypernetwork.click( + fn=modules.hypernetworks.ui.create_hypernetwork, + inputs=[ + new_hypernetwork_name, + new_hypernetwork_sizes, + overwrite_old_hypernetwork, + new_hypernetwork_layer_structure, + new_hypernetwork_activation_func, + new_hypernetwork_initialization_option, + new_hypernetwork_add_layer_norm, + new_hypernetwork_use_dropout + ], + outputs=[ + train_hypernetwork_name, + ti_output, + ti_outcome, + ] + ) + + run_preprocess.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + process_src, + process_dst, + process_width, + process_height, + preprocess_txt_action, + process_flip, + process_split, + process_caption, + process_caption_deepbooru, + process_split_threshold, + process_overlap_ratio, + process_focal_crop, + process_focal_crop_face_weight, + process_focal_crop_entropy_weight, + process_focal_crop_edges_weight, + process_focal_crop_debug, + ], + outputs=[ + ti_output, + ti_outcome, + ], + ) + + train_embedding.click( + fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + train_embedding_name, + embedding_learn_rate, + batch_size, + dataset_directory, + log_directory, + training_width, + training_height, + steps, + create_image_every, + save_embedding_every, + template_file, + save_image_with_stored_embedding, + preview_from_txt2img, + *txt2img_preview_params, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + train_hypernetwork.click( + fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), + _js="start_training_textual_inversion", + inputs=[ + train_hypernetwork_name, + hypernetwork_learn_rate, + batch_size, + dataset_directory, + log_directory, + training_width, + training_height, + steps, + create_image_every, + save_embedding_every, + template_file, + preview_from_txt2img, + *txt2img_preview_params, + ], + outputs=[ + ti_output, + ti_outcome, + ] + ) + + interrupt_training.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + interrupt_preprocessing.click( + fn=lambda: shared.state.interrupt(), + inputs=[], + outputs=[], + ) + + def create_setting_component(key, is_quicksettings=False): + def fun(): + return opts.data[key] if key in opts.data else opts.data_labels[key].default + + info = opts.data_labels[key] + t = type(info.default) + + args = info.component_args() if callable(info.component_args) else info.component_args + + if info.component is not None: + comp = info.component + elif t == str: + comp = gr.Textbox + elif t == int: + comp = gr.Number + elif t == bool: + comp = gr.Checkbox + else: + raise Exception(f'bad options item type: {str(t)} for key {key}') + + elem_id = "setting_"+key + + if info.refresh is not None: + if is_quicksettings: + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + else: + with gr.Row(variant="compact"): + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) + else: + res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) + + return res + + components = [] + component_dict = {} + + script_callbacks.ui_settings_callback() + opts.reorder() + + def run_settings(*args): + changed = [] + + for key, value, comp in zip(opts.data_labels.keys(), args, components): + assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" + + for key, value, comp in zip(opts.data_labels.keys(), args, components): + if comp == dummy_component: + continue + + if opts.set(key, value): + changed.append(key) + + try: + opts.save(shared.config_filename) + except RuntimeError: + return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.' + return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.' + + def run_settings_single(value, key): + if not opts.same_type(value, opts.data_labels[key].default): + return gr.update(visible=True), opts.dumpjson() + + if not opts.set(key, value): + return gr.update(value=getattr(opts, key)), opts.dumpjson() + + opts.save(shared.config_filename) + + return gr.update(value=value), opts.dumpjson() + + with gr.Blocks(analytics_enabled=False) as settings_interface: + settings_submit = gr.Button(value="Apply settings", variant='primary') + result = gr.HTML() + + settings_cols = 3 + items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) + + quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] + quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') + + quicksettings_list = [] + + cols_displayed = 0 + items_displayed = 0 + previous_section = None + column = None + with gr.Row(elem_id="settings").style(equal_height=False): + for i, (k, item) in enumerate(opts.data_labels.items()): + section_must_be_skipped = item.section[0] is None + + if previous_section != item.section and not section_must_be_skipped: + if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): + if column is not None: + column.__exit__() + + column = gr.Column(variant='panel') + column.__enter__() + + items_displayed = 0 + cols_displayed += 1 + + previous_section = item.section + + elem_id, text = item.section + gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='

{}

'.format(text)) + + if k in quicksettings_names and not shared.cmd_opts.freeze_settings: + quicksettings_list.append((i, k, item)) + components.append(dummy_component) + elif section_must_be_skipped: + components.append(dummy_component) + else: + component = create_setting_component(k) + component_dict[k] = component + components.append(component) + items_displayed += 1 + + with gr.Row(): + request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications") + download_localization = gr.Button(value='Download localization template', elem_id="download_localization") + + with gr.Row(): + reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary') + restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary') + + request_notifications.click( + fn=lambda: None, + inputs=[], + outputs=[], + _js='function(){}' + ) + + download_localization.click( + fn=lambda: None, + inputs=[], + outputs=[], + _js='download_localization' + ) + + def reload_scripts(): + modules.scripts.reload_script_body_only() + reload_javascript() # need to refresh the html page + + reload_script_bodies.click( + fn=reload_scripts, + inputs=[], + outputs=[] + ) + + def request_restart(): + shared.state.interrupt() + shared.state.need_restart = True + + restart_gradio.click( + fn=request_restart, + _js='restart_reload', + inputs=[], + outputs=[], + ) + + if column is not None: + column.__exit__() + + interfaces = [ + (txt2img_interface, "txt2img", "txt2img"), + (img2img_interface, "img2img", "img2img"), + (extras_interface, "Extras", "extras"), + (pnginfo_interface, "PNG Info", "pnginfo"), + (modelmerger_interface, "Checkpoint Merger", "modelmerger"), + (train_interface, "Train", "ti"), + ] + + css = "" + + for cssfile in modules.scripts.list_files_with_name("style.css"): + if not os.path.isfile(cssfile): + continue + + with open(cssfile, "r", encoding="utf8") as file: + css += file.read() + "\n" + + if os.path.exists(os.path.join(script_path, "user.css")): + with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: + css += file.read() + "\n" + + if not cmd_opts.no_progressbar_hiding: + css += css_hide_progressbar + + interfaces += script_callbacks.ui_tabs_callback() + interfaces += [(settings_interface, "Settings", "settings")] + + extensions_interface = ui_extensions.create_ui() + interfaces += [(extensions_interface, "Extensions", "extensions")] + + with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo: + with gr.Row(elem_id="quicksettings"): + for i, k, item in quicksettings_list: + component = create_setting_component(k, is_quicksettings=True) + component_dict[k] = component + + parameters_copypaste.integrate_settings_paste_fields(component_dict) + parameters_copypaste.run_bind() + + with gr.Tabs(elem_id="tabs") as tabs: + for interface, label, ifid in interfaces: + with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid): + interface.render() + + if os.path.exists(os.path.join(script_path, "notification.mp3")): + audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) + + text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) + settings_submit.click( + fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), + inputs=components, + outputs=[text_settings, result], + ) + + for i, k, item in quicksettings_list: + component = component_dict[k] + + component.change( + fn=lambda value, k=k: run_settings_single(value, key=k), + inputs=[component], + outputs=[component, text_settings], + ) + + component_keys = [k for k in opts.data_labels.keys() if k in component_dict] + + def get_settings_values(): + return [getattr(opts, key) for key in component_keys] + + demo.load( + fn=get_settings_values, + inputs=[], + outputs=[component_dict[k] for k in component_keys], + ) + + def modelmerger(*args): + try: + results = modules.extras.run_modelmerger(*args) + except Exception as e: + print("Error loading/saving model file:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + modules.sd_models.list_models() # to remove the potentially missing models from the list + return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)] + return results + + modelmerger_merge.click( + fn=modelmerger, + inputs=[ + primary_model_name, + secondary_model_name, + tertiary_model_name, + interp_method, + interp_amount, + save_as_half, + save_as_safetensors, + custom_name, + ], + outputs=[ + submit_result, + primary_model_name, + secondary_model_name, + tertiary_model_name, + component_dict['sd_model_checkpoint'], + ] + ) + + ui_config_file = cmd_opts.ui_config_file + ui_settings = {} + settings_count = len(ui_settings) + error_loading = False + + try: + if os.path.exists(ui_config_file): + with open(ui_config_file, "r", encoding="utf8") as file: + ui_settings = json.load(file) + except Exception: + error_loading = True + print("Error loading settings:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + def loadsave(path, x): + def apply_field(obj, field, condition=None, init_field=None): + key = path + "/" + field + + if getattr(obj, 'custom_script_source', None) is not None: + key = 'customscript/' + obj.custom_script_source + '/' + key + + if getattr(obj, 'do_not_save_to_config', False): + return + + saved_value = ui_settings.get(key, None) + if saved_value is None: + ui_settings[key] = getattr(obj, field) + elif condition and not condition(saved_value): + print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') + else: + setattr(obj, field, saved_value) + if init_field is not None: + init_field(saved_value) + + if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible: + apply_field(x, 'visible') + + if type(x) == gr.Slider: + apply_field(x, 'value') + apply_field(x, 'minimum') + apply_field(x, 'maximum') + apply_field(x, 'step') + + if type(x) == gr.Radio: + apply_field(x, 'value', lambda val: val in x.choices) + + if type(x) == gr.Checkbox: + apply_field(x, 'value') + + if type(x) == gr.Textbox: + apply_field(x, 'value') + + if type(x) == gr.Number: + apply_field(x, 'value') + + # Since there are many dropdowns that shouldn't be saved, + # we only mark dropdowns that should be saved. + if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False): + apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None)) + apply_field(x, 'visible') + + visit(txt2img_interface, loadsave, "txt2img") + visit(img2img_interface, loadsave, "img2img") + visit(extras_interface, loadsave, "extras") + visit(modelmerger_interface, loadsave, "modelmerger") + + if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): + with open(ui_config_file, "w", encoding="utf8") as file: + json.dump(ui_settings, file, indent=4) + + return demo + + +def reload_javascript(): + with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: + javascript = f'' + + scripts_list = modules.scripts.list_scripts("javascript", ".js") + + for basedir, filename, path in scripts_list: + with open(path, "r", encoding="utf8") as jsfile: + javascript += f"\n" + + if cmd_opts.theme is not None: + javascript += f"\n\n" + + javascript += f"\n" + + def template_response(*args, **kwargs): + res = shared.GradioTemplateResponseOriginal(*args, **kwargs) + res.body = res.body.replace( + b'', f'{javascript}'.encode("utf8")) + res.init_headers() + return res + + gradio.routes.templates.TemplateResponse = template_response + + +if not hasattr(shared, 'GradioTemplateResponseOriginal'): + shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse From e134b74ce95773789f69d158d23e93b7fe9295dc Mon Sep 17 00:00:00 2001 From: Tim Patton <38817597+pattontim@users.noreply.github.com> Date: Mon, 21 Nov 2022 10:58:57 -0500 Subject: [PATCH 4/6] Ignore safetensor files --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index ee53044c..21fa26a7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ __pycache__ *.ckpt +*.safetensors *.pth /ESRGAN/* /SwinIR/* From 210cb4c128afdd65fa998229a97d0694154983ea Mon Sep 17 00:00:00 2001 From: Tim Patton <38817597+pattontim@users.noreply.github.com> Date: Mon, 21 Nov 2022 16:40:18 -0500 Subject: [PATCH 5/6] Use GPU for loading safetensors, disable export --- modules/sd_models.py | 5 +++-- modules/ui.py | 3 ++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 2f8c2c48..2bbb3bf5 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -147,8 +147,9 @@ def torch_load(model_filename, model_info, map_override=None): map_override=shared.weight_load_location if not map_override else map_override if(checkpoint_types[model_info.exttype] == 'safetensors'): # safely load weights - # TODO: safetensors supports zero copy fast load to gpu, see issue #684 - return load_file(model_filename, device=map_override) + # TODO: safetensors supports zero copy fast load to gpu, see issue #684. + # GPU only for now, see https://github.com/huggingface/safetensors/issues/95 + return load_file(model_filename, device='cuda') else: return torch.load(model_filename, map_location=map_override) diff --git a/modules/ui.py b/modules/ui.py index 54d3293a..c376a59d 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1187,7 +1187,8 @@ def create_ui(wrap_gradio_gpu_call): interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3) interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method") save_as_half = gr.Checkbox(value=False, label="Save as float16") - save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format") + # invisible until feature can be verified + save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format", visible=False) modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary') with gr.Column(variant='panel'): From ac90cf38c6b55d57d37923aa1fe86c7374e32d0b Mon Sep 17 00:00:00 2001 From: Tim Patton <38817597+pattontim@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:13:07 -0500 Subject: [PATCH 6/6] safetensors optional for now --- modules/sd_models.py | 9 ++++++++- requirements.txt | 1 - 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 2bbb3bf5..75f7ab09 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -4,7 +4,6 @@ import sys import gc from collections import namedtuple import torch -from safetensors.torch import load_file, save_file import re from omegaconf import OmegaConf @@ -149,6 +148,10 @@ def torch_load(model_filename, model_info, map_override=None): # safely load weights # TODO: safetensors supports zero copy fast load to gpu, see issue #684. # GPU only for now, see https://github.com/huggingface/safetensors/issues/95 + try: + from safetensors.torch import load_file + except ImportError as e: + raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}") return load_file(model_filename, device='cuda') else: return torch.load(model_filename, map_location=map_override) @@ -157,6 +160,10 @@ def torch_save(model, output_filename): basename, exttype = os.path.splitext(output_filename) if(checkpoint_types[exttype] == 'safetensors'): # [===== >] Reticulating brines... + try: + from safetensors.torch import save_file + except ImportError as e: + raise ImportError(f"Export as safetensors selected, yet it is not installed, use `pip install safetensors`: {e}") save_file(model, output_filename, metadata={"format": "pt"}) else: torch.save(model, output_filename) diff --git a/requirements.txt b/requirements.txt index f7de9f70..762db4f3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,4 +28,3 @@ kornia lark inflection GitPython -safetensors